Late Lessons, Jensen Huang and AI
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Andrew Maynard on risk, AI and AI risk: a map of his thinking, 2005 to September 2026#

A synthesis of Andrew Maynard’s published work on risk, AI and the risks of AI: 391 Substack posts, his books, and a supplementary body of papers, columns, testimony and essays going back to 2005. It maps his own thinking only and makes no comparison with any other material. It sets out how he thinks and works (§2) before what he concludes (§3–§10), because his positions are the products of a way of thinking and are easily misread without it. Prepared in September 2026 with extensive AI assistance, at Maynard’s request. An earlier draft was reviewed by him; this revision, of 27 September 2026, is for his review. It was drafted by an AI model made by Anthropic (see §1, Method), whose models also drafted or helped develop several of the 2026 texts assessed here. It is a synthesis about Maynard’s work, not written by him.

1. About this map#

What it is for#

This map sets out how Andrew Maynard thinks about risk, about AI and about the risks of AI, and what he has concluded; how those ideas connect; and how they have developed, from his work on nanomaterial risk in the mid-2000s to September 2026. It is written for researchers, journalists, policymakers, educators and informed readers who want an exact, well-sourced account of his work: the way of thinking behind it, what he says, how firmly he says it, where each idea came from, and where his own record is unsettled.

The order matters. §2 describes how he thinks and works: his method, what matters to him, risk as a way of thinking, the place of play, creativity, curiosity and serendipity, and scholarship practised in public. The commitments, concepts, threads and tensions that follow (§3–§9) are best read through it. Read on their own, they can look like the positions of a risk-governance scholar with an unusually broad definition of harm. Read through §2, they are the current results of a way of thinking about technologies that fit no earlier type of risk. The map is also built to serve as a lens: §10 distils from his work a set of technology-neutral questions that can be brought to other material, beginning with whether the way of thinking in use is fit for the thing in front of it.

Sources#

The map draws on three bodies of his work.

Supplementary items are cited by a short key and page, for example “NN 2015-09 p.731” or “Testimony 2007 p.21”. The keys are listed in Appendix C.

Method#

His review of an earlier draft (disclosure). Maynard reviewed an earlier draft of this map in September 2026 and said it placed his work in too conventional a frame. His own account of what it missed is summarised here because it shaped the map’s emphasis. When a technology does not fit any previously encountered type of risk, he said, the whole mindset around risks and benefits, and around how to navigate a pathway between them, has to change. That is why he increasingly talks about risk innovation, the risk landscape, navigating rather than managing, and risk as a threat to value: not as the operational way to do things, but as concepts and mental models that open up possibilities. Play, creativity, serendipity and curiosity, the principles on which his work and the Future of Being Human initiative are founded, are integral to how he thinks conventional, stovepiped thinking can be escaped in a world that is diverging rapidly from the conventional. Quantitative risk science remains part of his foundations, built on rather than discarded, and humility, set against the false precision of numbers that comfort without informing, guides how he approaches something as poorly understood as AI.

This account is unpublished, so it is not cited as evidence for any position in the map. It has been used as a check on emphasis. Each of its points is documented in his published work, in most cases for more than a decade, and §2 sets out that record: the change of mindset and its quantitative foundations from 2009 onward (§2.2), the mental models from 2015 onward (§2.3), and creativity and play as part of risk thinking from the founding statement of risk innovation in 2015 (§2.4). Where his published wording differs from the account’s, the published wording leads: “mindset” and “ways of thinking” (FFTF p.39) rather than “mental models”, which is his September 2026 gloss.

Reading and synthesis. The posts were read in 32 chronological batches, each producing detailed notes and a digest. Films from the Future was read in six parts with chapter notes. These were consolidated into a concept index and a timeline, and then into nine thematic syntheses (summarised in §7 as threads T1–T9, with two further threads, T10 and T11). In the reading, 279 posts (excluding one Modem Futura promotional post) were rated as of high or medium relevance to risk and AI. In September 2026 the supplementary corpus was read in seven groups. The whole record was then read again for how he thinks rather than what he concludes: how he opens a question, what delights him, how he experiments and plays, how he uses films and stories, how he treats readers and opponents, and how he describes his own purpose. §2 is the result, and the rest of the map was revised in its light. Specific claims were checked against the reading notes and source files, and every direct quotation was checked against the original text. The working notes are not published.

The map was prepared with extensive AI assistance, as a multi-stage process of reading, synthesis, verification and review, at Maynard’s request, and this revision is for his review. The drafting model, Claude Opus 5.5 (Anthropic, 2026), is made by the same developer whose models also drafted or helped develop several 2026 texts the map assesses (see Provenance rules). The map’s judgements about which of his 2026 ideas may have originated with a model are therefore made by a model from the same developer.

Weighting follows the record, not the calendar. An idea is treated as central when it spans many years, organises other ideas, and recurs unprompted. Recency or prominence in 2026 does not by itself make an idea central. Where the supplementary record shows that an idea began earlier than the posts suggest, the map gives the earlier date, and where a 2026 text and an earlier text of his make the same point, the earlier one is cited first.

Centrality in the record is not the same as importance for AI. Some ideas that take up a small share of his output organise a great deal of his thinking, or matter more for AI than their frequency suggests. Orphan risks are the clearest case: named in 2018, they appear in relatively few posts, yet they describe an institutional blind spot that is arguably where AI governance is thinnest (§2.3, §2.9). Where a concept’s value for AI outruns its share of the record, the map says so separately rather than raising its centrality label (§6).

Provenance rules#

Only Maynard’s own prose counts as evidence of his thinking.

Excluded entirely: - Posts about the co-hosted Modem Futura podcast, including promotional posts that contain a few lines of his framing. - AI-generated text published inside his posts: output from ChatGPT, Claude, Fable, o1-pro, Deep Research, Manus and Perplexity, including AI-written papers and stories he published as experiments, AI “top takeaways”, and GPT scenario assessments or risk scores (for example the o1-pro report in 2025-04-06 and GPT-5 Pro’s scores in 2025-09-07). - Two AI-written papers, except the sections he signed: Constituting Responsibility (written by Claude Opus 4.6 under his guidance, 2026; only his postscript counts) and Constitutional AI and Responsible Innovation (credited to Claude Fable 5.1, September 2026; only his Annex 1 counts). - The filled-in Risk Innovation Planner “OpenAI hypothetical” (2023), which is ChatGPT’s role-play output. Only the exercise design and his framing post (2023-11-21) are his. - Guest posts (for example Brad Allenby’s 2023-08-16 and 2023-10-29 essays) and quoted material. - Passages that are largely AI-written or not his prose: most of the PhD-site note in 2026-06-14 everything-you-wanted-to-know-about; the “Useful stuff” definitions in 2026-05-15 ai-movies-may-be-less-dystopian-than-we-think (mainly Claude Code’s work); notes 2–3 of 2026-01-31 lost-in-the-moltbook-hall-of-mirrors; and 2025-11-24 start-here (an orientation page listing starter posts).

Weighting by authorship: - Sole-authored prose is full evidence. This includes his Nature Nanotechnology columns, testimony, blog posts, book chapters and the sections he signed in co-written or AI-written texts. His sole-authored 2026 papers carry AI-use statements that claim the ideas for him, with Claude used to explore, refine and draft; they are treated as his thinking. - Maynard and Garbee (2019), “Responsible innovation in a culture of entrepreneurship: a US perspective”, adapted as the post 2019-08-13 responsible-innovation. The chapter was co-authored with Elizabeth Garbee. Maynard has confirmed (September 2026) that it sets out his own thinking, and it is weighted here as his; this concerns its weight as evidence of his views, not his co-author’s contribution. It is written largely in his first person, from his teaching at Michigan, and he returns to its central lesson in his 2026 frontier-AI paper as “The lesson that has stayed with me ever since”. - Other co-written work is treated as shared positions and weaker evidence of his individual thinking: 2019-11-19 the-trouble-with-connectedness (with Bas Boorsma); 2023-09-11 its-time-to-get-serious-about-ai-and-sdgs (with José Lobo); and co-authored items in the supplementary corpus, weighted by his role (lead-authored items higher). Reposting a co-written text under his own name is taken as some evidence of endorsement. - Unsigned programme materials from the Risk Innovation Nexus (2019–2020) are positions of a programme he directed, not his prose. - Retrospective texts (the April 2026 essays; the 2026 essay on his WEF work) are good evidence of how he now reads his past, and weaker evidence of what he thought at the time.

Mixed provenance, marked [mixed] wherever used: - 2026-07-16 orphan-risks-frontier-ai-maynard (also arXiv 2608.16895). A paper first drafted by Claude (Fable 5) “under Andrew Maynard’s direction”, then rewritten by him over three days “(just me — no Fable this time)”, so that “every aspect of it aligned with my own thinking and work”. Its use statement claims the “argument architecture” and key concepts for him. But in 2026-07-04 just-how-good-is-anthropics-fable-as-a-research-assistant he says Fable applied his risk-innovation work to frontier AI “in a way that hadn’t previously occurred to me”, and calls the framing and analysis “genuinely novel”. Three days after publication he says the ideas and analysis Fable generated “remain intact” (2026-07-19). The wording and the endorsement are therefore his, while some frontier-specific concepts may have originated with Fable: the “four filters”, the “safety differential”, the register, the aperture log, the “incentive field” analysis of developers and the unequal “conversion channels”. Securely his, from earlier sources, are the threat-to-value frame (2015), orphan risks (2018), orphan risks as known but unowned risks with “no agreed upon tools, standards, or mitigations” (Nexus materials, 2019; his April 2026 essays), the idea that risk definitions select which risks count (NN 2015-09 p.731), and the lesson from his work with Garbee. - 2026-01-17 i-cracked-and-wrote-an-academic-paper. The post is his own first-person account; what Claude drafted is the arXiv paper it describes. He credits Claude with the concept of “honest non-signals” and with “the development and refinement of the various mechanisms” by which conversational AI slips past epistemic vigilance. The paper’s own AI-use statement says the core concepts “were developed by the author”, so the two accounts differ; the term and the four mechanisms keep the [mixed] tag. The preceding essay (2026-01-10 is-ai-a-cognitive-trojan-horse) was, in his words, “primarily based on my own thinking”, after “some initial brainstorming” with Claude, so it is the secure source for the cognitive-Trojan-horse thesis. His own reflections in the 2026-01-17 post need no [mixed] tag. - 2026-09-24 being-an-academic-in-an-age-of-ai. His King’s College London lecture of 8 September 2026, drafted into prose by Claude (Opus 5.5) from the transcript and his notes, then corrected and line-edited by him. The ideas are secure; the exact wording is slightly less so. Its introduction and the first part of its postscript are his own prose. Because it is spoken and AI-drafted, the map uses it as corroboration where his own prose makes the same point, and marks it “single source” where it does not.

The andrewmaynard.net essays of April 2026. Seven essays published on 12 April 2026 carry his sole byline and no AI-use statement. They are his own retrospective synthesis of thirty years of work, and the map uses them as a check on its own account (§8). Most of their text restates positions documented in his earlier prose. A few passages present material from other sources as his: the definition of “moral imagination” comes from a ChatGPT-written post (2025-01-30); the “eighteen orphan risks” in one essay are ChatGPT’s role-play output (2023-11-21); “Seemingly Conscious AI” is Mustafa Suleyman’s term; and the section on Constitutional AI summarises the Claude-written Constituting Responsibility. Where these are used they are marked [AI-origin] or credited, and none is treated as a core concept.

Where a post or paper mixes his prose with other text, only his parts are used as evidence, although what he chose to publish and how he framed it is sometimes noted.

Models named in the provenance notes. Claude Opus 4.6 and Opus 5.5, and Claude Fable 5 and 5.1, are Anthropic models of 2026; ChatGPT, GPT-5 Pro and o1-pro are OpenAI models of 2023–2025; Deep Research, Manus and Perplexity are AI research tools. In the body of the map AI assistance is described generically (“AI-assisted”, “AI-drafted”), except where he names a model in a quotation.

Conventions#

Limits#

2. How he thinks and works#

Maynard’s positions on risk and AI are the current results of a way of thinking that has been recognisable for twenty years. This section sets out that way of thinking first, from his own descriptions of it and from what he does as well as what he says. The rest of the map should be read through it.

In brief: his risk concepts are offered less as procedures than as ways of changing how people think about technologies that fit no earlier category of risk. They are built on quantitative risk science, which remains a foundation, and held with humility against false precision. And he argues that creativity, play, curiosity and serendipity are how that change of thinking happens, because a risk no one has imagined is a risk no one will see.

2.1 The core#

In September 2015 a physicist who had spent thirteen years in workplace aerosol research set out, in Nature Nanotechnology, why “we need risk innovation”. New technologies faced “a much larger and murkier risk landscape” than evidence-based health and environmental risk assessment could capture. His first worked example of the new approach was not a model, a metric or a management system. It was “a book of seventeen haiku” from a 2014 workshop with the Dutch design organization V2_ Institute for the Unstable Media, “an unusual result from an academic meeting”. He placed it at one end of a spectrum whose other end was Tox21, the US government’s high-throughput toxicology programme (NN 2015-09 pp.730–731). Poetry and computational toxicology sat on one line, as two ways of seeing risk. That pairing is a good way into his work.

He is a physicist who kept the pleasure of physics, which is “all about the sheer delight of putting ideas together in different ways and then seeing in new ways. I’ve never lost that delight” (TechTrends 2023 p.2). He is a risk scientist who measured workplace exposures at the UK Health and Safety Executive and NIOSH, worked on nanomaterial safety, testified to Congress and ran risk centres, and who learned from inside that discipline where its numbers stop helping (§2.2). And he is, in his own words, “very un-disciplinary”, a habit formed at the Project on Emerging Nanotechnologies, where “I had to be an expert in everything, and I had to be able to build bridges fast” (TechTrends 2023 p.2).

What drives the work is a question about people. At the start of 2024 he wrote that “what drives my work more than anything” is the possibility that our technologies stop augmenting who we are and “begin to fundamentally change who we are — or even what we are” (2024-01-01). Beneath that question is a conviction he put first in a 2009 list of things everyone should know about nanotechnology safety: “People matter” (2020science 2009). He chose “the future of being human” as his frame to focus on “each of us personally” (2023-04-04 welcome-to-the-future-of-being-human), and in September 2026 described his broader work as asking “how we navigate advanced technology transitions to get to the sort of future we want — and what it will mean to be human in those futures” (2026-09-24, his own introduction). In 2026 he also named one strand of his work as “human flourishing and navigating complex advanced technology transitions” (2026-07-10).

The means are imagination disciplined by evidence. “Critical thinking alone is almost inhuman in its cold impartiality. On the other hand, creativity on its own leads down a path of fantasy and delusion” (FFTF p.282). The Future of Being Human initiative he founded at Arizona State University names the balance in its guiding principles, which include “Obsessive Curiosity,” “Radical Creativity,” “Grounded exuberance,” and “Catalytic Serendipity” (2026-09-20, n.2).

He does all of this in public. He describes public writing as central to his work: “Not as an add-on to my research and scholarship, but as something that’s integral to how I explore, test, and share new ideas and insights” (2026-05-17). His aim as a public scholar is to widen the circle of people who can think well about technology and the future, on their own terms, rather than to recruit them to his conclusions (§2.8).

2.2 A changed mindset, built on quantitative risk science#

His central intellectual claim comes out of his career, and it is stated most compactly at the start of Films from the Future. After a working life in risk he has “less and less patience for how many people tend to think about risk”. Established approaches “work reasonably well” for conventional technologies, but “run out of steam rather fast when we’re facing technologies that can achieve things we never imagined”, and we try to “squeeze the new wine of technological innovation into the old wineskins of conventional risk thinking” (FFTF pp.22–23). The book offers “no easy guidelines or rules of thumb”, only “ways of thinking that reduce the chances of making a mess of things” (FFTF p.39).

Where the insight came from. It grew inside a quantitative field, in stages. - 2006. Relying on existing knowledge to quantify the risks of engineered nanomaterials “will engender false assumptions of safety” (PEN 2006 p.13). - 2009. “Numbers—hard data—can be comforting. But without a clear idea of their relevance, they can also be misleading.” The heading that follows reads like a manifesto: “When the data run out – innovate!” (2020science 2009). - 2011. “Five years ago, I was a proponent of a regulatory definition of engineered nanomaterials. I have changed my mind.” His warning case was Libby vermiculite, whose fibres “slipped through the regulatory net” because they did not fit the official definition of asbestos (Nature 2011). - 2014. Nanomaterial risk research had “worn a rut” (NN 2014-03 p.160), and “mundane risks are still risks” (NN 2014-06 p.410). New evidence on fumed silica “cast doubt on what I thought I knew to be true”, and he asked how “trigger points for action” should be defined (NN 2014-09 pp.658–659). - 2015. Established risk methods grew out of earlier industrial revolutions, so “we need to be jolted out of our existing mental and procedural risk-ruts” (NN 2015-12 p.1006).

The formative insight had two halves, and he kept both. Labels, categories and habits of mind can stop tracking what matters, and then they produce false alarms and false comfort alike. But the old tools, used with judgement, still work: “seemingly novel challenges don’t always demand novel solutions” (NN 2015-06 p.483).

He tells the personal version against himself. Facing a one-in-a-million chance of serious harm from a contrast dye before a CAT scan, he writes: “As a physicist, I’m expected to be good with numbers.” He signed the waiver “not because I’d done the math and it made sense, but because that was what I was expected to do” (Rethinking Risk 2017 p.195). The numbers were right and did not help, because they missed what mattered to the person deciding.

Built on, not discarded. Probability is “a powerful way of making trade-offs” (Rethinking Risk 2017 p.193), and the value frame is “an evolution of the old black-and-white mathematics of risk” (p.200). It “extends conventional thinking rather than replacing it” (2018-12-13), and in 2026 it is offered “not as an alternative, but as an augmentation” of existing safety frameworks (2026-07-16 [mixed]). He keeps using the foundations: - he carried hazard, exposure, dose–response and weight of evidence over to algorithms, so that algorithmic risk would not rest “on an evidentiary stack of cards” (2019-03-05); - he reasoned from deposition physics about the particles graphene face masks might release (2021-03-28); - he went back to first principles on AI (“no cause, no risk”, 2023-11-26); - he used the toolkit of acceptable risk to argue that what counts as harm is “ultimately a social construct, not a technological one” (2024-06-20).

The traffic runs both ways. Where old tools cannot see social risks, he asks for new thinking. Where a new field is naive about evidence, as algorithmic-bias work was in 2019, he asks for the old rigour.

Neither a revolution nor an add-on. Two misreadings are common, and his record rules out both. He is not a revolutionary who discards risk science: the measurer’s grammar of hazard, exposure, dose and evidence remains a foundation he builds on (C4). Nor is he an incrementalist who bolts a module for social risk onto conventional assessment: “Without risk innovation, all we are left with is business as usual” (NN 2015-09 p.731). The quantitative science is the ground on which a reframed question stands. What changes is the question the tools serve: what is at stake, for whom, and how to cross uncertain terrain toward value. He stated the thesis in the founding column itself: risk innovation can “reveal new pathways through complex risk landscapes”, and “encourages a sophisticated dialogue around building and maintaining value in a world where risk is not only endemic, but integral to progress” (NN 2015-09 p.731). Rather than “framing risk as a barrier to progress”, it “transforms it into a way of supporting beneficial and sustainable progress” (2016-01-11), and it “focuses on the creation of value through creative approaches to potential dangers and pitfalls” (2019-11-01). His April 2026 retrospective restates the same claim: risk recast “from something to be minimized to something to be navigated creatively in pursuit of value”, held with scepticism of “both the safety absolutists and the move-fast-and-break-things crowd”, because “The interesting and difficult work is in the space between” (30Y 2026). The space between is terrain to be crossed, not a point of compromise. Interpretation: management stays, as the operational work inside a stance of navigation. He notes that safety is “so often operationalized as assessing and managing risk” (2024-06-20); placing that work inside navigation is the map’s reading, consistent with how he describes his own concepts.

Why AI makes the change unavoidable. His case has two layers. The general layer, argued since 2014–15, is that converging technologies outrun risk frames built for earlier industrial revolutions (NN 2015-12). The AI layer, argued since 2018 and much more strongly since 2023, is his own stated reason: AI fits no earlier type of risk, so risks are missed when it is squeezed into earlier categories. - 2018. AI risks “may blindside us, in part because we’re not thinking creatively enough about how an AI might threaten what’s important to us” (FFTF p.174). - 2023. Conventional risk categories are “the shavings off the tip of the AI iceberg”, and they assume that the risks of a highly unconventional transition “can be sliced, diced, and solved, using a conventional mindset”. What is needed is “a framing of AI risks and benefits that opens up new possibilities rather than closing down conversations” (2023-05-31). - 2026. Frontier AI “defies analogy” (2026-01-22). Even the vocabulary is a risk, since “metaphors are never completely neutral” (2026-02-22). And in a lecture: “as soon as we start evaluating it within past frameworks, we make categorical errors” (2026-09-24 [mixed]).

In January 2026 he added a second-order extension. Humans usually adapt when technology outpaces what evolution prepared them for: “But what if the mismatch impacts the very cognitive abilities we rely on to navigate differences between what we experience, and what we’ve evolved to live with?” (2026-01-10). The argument is set out in 2026, but its roots are older: in 2018 he wrote of “guarding against AIs that learn how to use our cognitive vulnerabilities against us” (FFTF p.177), and in 2020 of evolved instincts “increasingly poorly equipped” for the world humans have built (FR p.56).

He still holds continuity and novelty together: “The specifics have changed enormously. The pattern hasn’t” (30Y 2026). Lessons about process, humility and how societies meet new technologies carry over. Categories, labels, thresholds and track records may not. His habit is to ask, case by case, which is which (C14).

2.3 The mental models, and what each opens#

He works with a handful of linked ideas: risk innovation, risk as a threat to value, the risk landscape, navigation, and orphan risks. They are not procedures, and they are not an operational layer added to risk management. They are mental models for technologies that do not fit earlier types of risk, and each is best understood by what it lets people see or say that they could not before. His published words for them are a “mindset” and “ways of thinking that reduce the chances of making a mess of things” (FFTF p.39), “designed to open up new ideas and possibilities” (2016-01-11); “mental models that open up possibilities” is his September 2026 gloss on the same point. Some take up a modest share of his output. Their weight here reflects what they do.

Risk innovation. The umbrella idea, named in 2015: “parallel innovation in how we conceptualize risk” (NN 2015-09 p.731). The founding column describes a culture, not a method. It licenses “risk entrepreneurship”, in which an idea is judged by its impact rather than by whether it adheres to convention, and it encourages “a culture grounded in transdisciplinarity, creativity and imagination; and epitomized by serendipity” (p.731). Its public version asked readers to “Imagine what might happen if we approach risk the way entrepreneurs approach innovation”, and described the approach as “designed to open up new ideas and possibilities” (2016-01-11). It also made a claim about safety that conventional risk thinking does not make: “this lack of creativity and flexibility in how potential risks are understood and addressed only increases the chances of things going wrong” (2016-01-11). Creativity was part of safety from the day the idea was named.

Risk as a threat to value. “Risk starts with something that is worth protecting” (NN 2016-03 p.211). Worth includes health and money, but also dignity, belonging, identity, belief and “what it means to be human” (FFTF p.23), and, unusually, aspiration: “something we aspire to and cannot bear to lose sight of” (FFTF p.24). What the frame opens is the point. - It makes resistance intelligible. “I’m not sure I buy the idea of ‘risk aversion’”, because it hides “the things that people find too important to risk losing” (Rethinking Risk 2017 p.193). In The Man in the White Suit “everyone is shrewd enough to see how change supports or threatens what they value” (FFTF p.225). Moral panics are not “something to be mocked” (2025-06-01). - It turns go/no-go choices into design questions. Risk conversations “can be elevated from simplistic ‘go/no-go’ options” to how gains and losses are balanced, which “opens the door to creative and innovative approaches to protecting existing and future value” (Rethinking Risk 2017 pp.197–198). - It puts benefits in the same account as harms. Future value counts (NN 2015-09 p.731), so losing the solutions AI might bring counts among catastrophic risks (2023-05-31). - It turns back on the actor. It includes “the reciprocal dangers of threatening what is important to others through what they do” (2018-12-13). - It reveals what matters. “Risk in this instance is not a danger to be avoided, but an inevitability that reveals what the primary value is within a complex landscape” (Rethinking Risk 2017 p.197). Risk becomes a way of seeing.

He separates value (worth to someone) from values (right and wrong), so the frame can be “agnostic to particular worldviews” (2023-11-21). He is candid that it is “a somewhat subjective way of thinking about risk”, valued because it has “the advantage of opening up conversations” (2018-12-13).

The risk landscape. Risk lies in “the risk landscape that lies between new ideas and their successful implementation” (2018-12-13). It has “shifting hills and valleys” (NN 2016-03 p.211), and new technologies “both face and help to form” it (2016-01-11). Chaos theory supplies the physics. We “cannot wield perfect control over complex technologies within a complex world”, yet there are limits that help in “separating out plausible futures from sheer fantasy”, and futures “that can be squandered if we don’t think ahead” (FFTF p.41). Interpretation: a world that is unpredictable within limits, where some leverage remains, calls for a map rather than a forecast (the map’s phrase, not his). The map includes opportunities as well as threats (2024-08-25). His four-ways model of technology transitions even makes mindset an axis, running from maintaining things as they are to “a willingness to embrace change”, and treats avoiding, adapting, extending and embracing as four legitimate postures (2024-08-18). He offered it with the caveat that it might belong in “the trash can of bad ideas” (2024-08-18).

Navigating rather than managing. “Navigate” has been his working verb since his columns “Navigating the fourth industrial revolution” (NN 2015-12) and “Navigating the risk landscape” (NN 2016-03). Navigation does not reject management. It names the stance within which management tools are used. His record shows what the stance involves, though he does not list it as a set of parts, and the examples below illustrate a stance rather than a procedure: - mapping rather than forecasting (above); - lines where harm cannot be undone (“fixed points†” in this map, a label for his trigger points, his timing rule and his reversibility line). Experimenting where it is easy “to turn the clock back” is one thing, and breaking “people, governance, society, and the planet” is another (2025-03-02, n.2). Evidence-based “trigger points” can be set in advance (Nature 2011). His timing rule is to be “quick to question, and slow to respond”, while keeping the ability to act on early warnings “even before the science is mature” (NN 2016-03 p.212); - course correction. “traditional ‘set it and forget it’ management doesn’t work”, and success depends on mechanisms for “rapid course correction” (2025-05-18); - openings as well as hazards. Rather than “framing risk as a barrier to progress”, risk innovation “transforms it into a way of supporting beneficial and sustainable progress” (2016-01-11); a social “risk reboot” might give tech companies “the competitive edge” (2018-09-03); and risk thinking informs decisions that “remove risks, help identify ways to circumnavigate them, or strategically absorb them” (2023-11-21). A 2026 lecture restates this in one spoken line, “avoid it or flip it, and so get to the good” (2026-09-24 [mixed], corroboration only).

Interpretation: this is the entrepreneur’s instinct inside risk innovation: a threat can sometimes be turned into an opening, not only reduced. Navigation also accepts limits. AI can be channelled “much as a flood can’t be halted, but it can be directed” (2025-08-31).

Orphan risks. This is the concept that has changed most. In 2018 it named a gap in Rumsfeld’s knowns and unknowns: risks that are “‘known knowns’ if you’re looking in the right place, but aren’t taken as seriously as they should be”, dismissed as “too ill-defined, too complex, or too irrelevant” (2018-12-13). By 2020 they were “hard to quantify threats to value that often slip between the cracks of conventional risk approaches” (2020-10-15). He used the idea to map the landscape facing developers of brain–machine interfaces, in place of one more ethical critique of the technology (2019-11-01). In April 2026 he gave the name to AI’s human-side risks, to “dignity, belonging, identity, autonomy, democratic participation, what it means to be human”, which “no existing institution owns” (NANO 2026). In July 2026 the question became institutional: “by what process does a known risk come to be nobody’s responsibility?” His answer declines villains, because “sincerity almost always operates inside an incentive field”, and he concludes that the risks most likely to blindside frontier AI “are the ones its institutions have organized themselves not to see” (2026-07-16 [mixed]).

The roots are older than the name. They include harm “not apparent, assessable, or manageable based on current approaches” (emergent risk, Toxicol. Sci. 2011), the fibres that “slipped through the regulatory net” (Nature 2011), and “mundane risks are still risks” (NN 2014-06 p.410). So is the link to the European Environment Agency’s Late lessons from early warnings reports. In 2015 he summed them up as a catalogue of innovations that damaged lives and environments because early warnings of possible harm “were either ignored or overlooked” (2018-12-15, first published 2015). Interpretation: an orphan risk is a late lesson in the making, known to someone and owned by no one.

Orphan risks appear in relatively few of his posts, and their weight for AI is larger than that share. The concept describes an institutional blind spot rather than adding hazards to a list. It turns the vague complaint that social harms get ignored into a question that can be checked: who decided this was out of scope, and on what grounds? (§2.9).

Tools as catalysts. The tools he built were deliberately small and aimed at mindset. The Risk Innovation Planner was designed so that a founder could, in thirty minutes, “develop a risk innovation mindset”, and “What the Planner does not do is provide answers to problems” (2023-11-21). His transitions quadrant appeared under the heading “Not Quite a Tool Yet” (2024-08-25). Between 2017 and 2020 the same ideas were also offered to entrepreneurs as practical tools and as a business case, in their own language rather than as a sermon. The tools were built to cultivate a mindset in entrepreneurs’ language, not to replace it with a procedure (§9, tension 16).

2.4 Imagination, play, curiosity and serendipity#

It is easy to read the Future of Being Human initiative’s principles, “Obsessive Curiosity,” “Radical Creativity,” “Grounded exuberance,” and “Catalytic Serendipity” (2026-09-20, n.2), as the house style of a genial academic, and to miss the point. The same words, with “respectful inclusivity”, are the community norms of the intergenerational seminar he runs (2024-04-07). For Maynard they are how thinking escapes frames that a fast-changing world has outrun. He has argued this in the language of risk for more than a decade.

Argued on risk grounds. - 2015. For entrepreneurs, the greatest barriers to responsible innovation are “not necessarily time and cost, but imagination” (NN 2015-03 p.200). Risk innovation is “epitomized by serendipity” (NN 2015-09 p.731). - 2016. A lack of “creativity and flexibility” in how risks are understood “only increases the chances of things going wrong” (2016-01-11). - 2018. AI risks may blindside us because “we’re not thinking creatively enough” (FFTF p.174). - 2019. Risk innovation “focuses on the creation of value through creative approaches to potential dangers and pitfalls”, because conventional ways of thinking about risk “are simply not up to the task of navigating them” (2019-11-01). - 2021. Conventional thinking offers endless options inside a frame that excludes the ones we need, like a universe that contains only odd numbers. Escaping takes “metaphorical quantum tunneling”, and “the juxtaposition of seemingly unrelated ideas can jolt us out of conventional ways of thinking”. Of that juxtaposition he adds: “This is exactly what I set out to achieve in much of my writing” (2021-04-09). - 2026. Existential risks should not be dismissed: “it would be embarrassing if we were all wiped out by something because we didn’t have the imagination to foresee it” (2026-09-15, n.5).

Creativity, in his account, is a skill of risk perception. A landscape that cannot be imagined cannot be navigated.

Where it comes from. He traces it to physics as play rather than procedure: “Science is a love language between us and the universe” (TechTrends 2023 p.2). His undergraduate labs gave him the chance to experiment and create, “to play in effect”, and this “became foundational to how I approached my research as a physicist — and how I still do”. In his words, “So much of how I explore new ideas, put knowledge and understanding together in different ways, and revel in the serendipity of new discoveries, is grounded in play” (2024-03-17), and in 2026, “much of my work uses play, creativity, and serendipity, to explore new ideas in unexpected and often deeply insightful ways” (2026-09-20, n.1). He carries it into teaching: a playpen works where goals are clear but “quickly falls apart” where the journey breaks new ground (2025-03-15). What came later was naming the method and using film. In a 2010 World Economic Forum proposal he co-drafted, “science fiction” was still shorthand for poorly informed opinion (CETI 2010 p.3). From 2011 he worked with a speculative designer and students on creative work about imagined catastrophe and mundane reality (2020science 2012), and by 2018 films organised his first book.

Stories as instruments. Films sit at the centre of his method because “Each of these films has a risk-based narrative tension that keeps its audience hooked” (FFTF p.23). Drama is built from threatened value: Hammond’s dream, Tommy’s hope, Kusanagi’s sense of self (FFTF p.24). That makes stories a precise instrument for the threat-to-value frame, surfacing risks to what people value that a hazard frame misses. Films help “precisely because they are not tethered to scientific accuracy”, provided they are “seasoned with feet-on-the-ground thinking” (FFTF p.288). Even a bad film helps: “it’s the very absurdity of the movie that makes it useful” (2018-11-15). Later he wrote fiction himself, for its “affordances” in exploring complex ideas “with a nuance and sophistication” that more literal pieces miss (2025-11-23). And stories reach people where sermons fail: “Preach to someone about the future, and most people will shut down” (2024-01-21).

What play has produced. Several of his ideas and tools came from doing things rather than theorising. - Flaws as features. Reading his students’ conversations with ChatGPT, he concluded it was an effective catalyst for thinking “because of its limitations in some cases” (2023-08-14). - A transitions framework. He built Pippard’s ladder, a physics demonstration of tipping points, from Lego and craft supplies, and his four-ways model came from “Experimenting with the ladder while thinking through the concept” (2024-08-18). - An invention in a hallucination. When an AI fooled him with an invented repair for a cracked hat, while he was writing about exactly that danger, he tried the method anyway and asked whether the machine had stumbled on something new (2026-02-08). - A parody as a probe. In September 2026 he published mull.chat, a parody of the “reasoning” messages AI models show their users, unsure whether it was “a bit of fun, a commentary on the hollowness of seemingly-powerful AIs, a learning tool”, or something else, and calling it “a serious part” of his play-based work. “But it brings me joy” (2026-09-20).

Events, then himself as the instrument. Not every concept comes from play. Some of his most important AI concepts were prompted by harm, and then tested on himself. - The illusion of reciprocity. In January 2023 it “intrigues me and slightly worries me” that he already thought of ChatGPT as a colleague (2023-01-31). In April, writing about what was “possibly the first case of a chatbot being involved in someone taking their own life”, he named “the illusion of a reciprocal relationship” (2023-04-05). - Stochastic agency. The death of 14-year-old Sewell Setzer III after he became attached to a Character.AI companion led him to name “stochastic agency”: harm as an “emergent rather than predictable property” of user and chatbot together. He then set up a companion on the same platform designed to keep users talking, “intentionally set out to make myself seem emotionally vulnerable”, and “was surprised at just how quickly it began to draw me in” (2024-10-27).

The pattern is a case that prompts a concept, followed by a test of the concept with himself as the subject.

Serendipity, designed. Serendipity, for him, is a condition to arrange, not luck. His live conversations came with “absolutely no guarantee as to where we’ll end up going” (2023-09-18). He paired strangers from different fields (“I intentionally set things up this way”) and resisted steering them: “I’m glad I didn’t” (2024-03-15). When retirees and undergraduates ended up learning together in his seminar, “This, of course, wasn’t completely serendipitous” (2024-04-07). He asks whether enough “exploratory and serendipitous science” around AI is being funded (2024-10-08). And joy is part of the measure: he finds it “a deeply under-appreciated metric of intellectual and academic achievement!” (2026-09-20, n.4).

Play with rules. None of this is naive. A classroom trading game confirmed for him that “nothing is ever ‘just a game’” (FFTF p.221). He doubts there is “a strong causal link between curiosity and benevolence” (2023-07-19). He traces the lure of permissionless innovation to the same curiosity he prizes, confessing a PhD all-nighter in which “it’s shocking how quickly I sloughed off any sense of responsibility” (FFTF p.161). Playgrounds have rules (“be kind, don’t spoil things for others”, 2025-03-15), and context decides the rest. Experimenting where it is easy “to turn the clock back” differs from systems that cannot be reset, and “I’d put breaking people, governance, society, and the planet, in this category!” (2025-03-02, n.2). That is how he can argue, in the same month, for students’ “permission to play” (2025-03-15) and against permissionless innovation across a whole society (2025-03-02).

Disciplined imagination. The discipline is plausibility: “what is plausible, rather than simply imaginable, is vitally important” (FFTF p.171). Plausibility ranks what imagination has found; it does not stand in for it. He polices his own metaphors (“I am using this as a metaphor, no more”, 2021-04-09) and marks where analogies break (“an algorithm is not a chemical”, while finding the analogy “intriguingly compelling”, 2019-03-05). The two modes live side by side. In March 2021 he reasoned about the particle sizes graphene face masks might shed (2021-03-28), and twelve days later he published a lecture about universes of odd numbers (2021-04-09). Each keeps the other honest.

The play has costs, which he names. Colleagues called Films from the Future “professionally embarrassing” (2023-10-08), and a game built from his work “probably won’t do much for my academic standing” (2026-07-10). He keeps doing it, which is the best evidence of how central it is.

2.5 How a question moves through his hands#

Interpretation: his method is not a procedure, and he does not set it out as one. What follows is a pattern the map sees in his work from his 2015 columns to his 2026 lectures. The moves overlap, often come in a different order, and are numbered here only for reference. 1. Something catches him. Almost none of his pieces opens with a thesis. They open with a scene: the 2012 Indian blackout traced step by step (2015-01-30), or his sixteen-year-old self watching 2001, to whom he sends a message, “Take note—this is important” (FFTF p.14). His curiosity reaches people too. What the inventor in The Man in the White Suit lacks is “social curiosity”, the curiosity “to ask people what they think, and what they want” (FFTF p.222). 2. He questions the frame. He takes a term everyone uses and asks what it assumes and hides: “risk aversion” (Rethinking Risk 2017 p.193); techno-optimism, which is “a bit like asking if I’m an oxygen pessimist or optimist” (2024-03-31); “rogue” AI (2023-05-25); extinction as “too human-centric” (2023-05-31); the “harness” (2026-02-22). Often he flips the question, asking how risks become orphaned rather than only which ones are. His test of a frame is what it opens (2023-05-31). 3. He loosens the frame, with juxtaposition, story and analogies that carry structure rather than surface (§2.4). 4. He tightens it again, with plausibility, physics and evidence. He keeps unlikely scenarios when they show the shape of a landscape. Neuralink’s dreams may never come true, “Yet this is not the point here”; what matters is that in reaching for them its founders are “warping the pathway” to the future (2020-10-15). 5. He builds or tests something to find out, often with himself as the instrument and sometimes after an event has prompted a concept (§2.4), and publishes the apparatus: prompts “typos and all” (2023-11-21), results labelled as one person’s experience, and his failures. 6. He publishes provisionally and revises in public (§2.7). 7. He holds tensions open and aims at a way through. An obligation to innovate comes with “tremendous responsibilities” (FFTF p.288). “Don’t Panic” comes with “Of course, we shouldn’t be complacent—far from it” (FFTF p.289). These are not hedges. They are the ground to be crossed, and crossing it is what he means by navigating. The output is a map of pathways, not a ruling.

Humility as a working discipline. “Here, I freely admit that I may be wrong” (FFTF p.170). After three decades in risk, “the more I study artificial intelligence, the less certain I am that we even know how to formulate the problems we face around AI” (2023-11-26). He builds in ways to be shown wrong, even pre-registering a play experiment, because otherwise “where’s the fun — or the accountability — in that?” (2026-08-23). He has used AI as a check on his own biases: in a framework he designed for the risks of AI-drafted email, the risks were “intentionally developed iteratively with ChatGPT to reduce potential biases I brought to the process”, and an AI model scored them before he reflected on the result (2025-09-07). His humility covers numbers (C5), how problems are framed, and the analyst himself. It has never been an excuse for doing nothing (§2.3, lines where harm cannot be undone).

How the method developed. His analogies moved from confident translation (algorithms as chemicals, 2019) to probes of difference, and by 2026 AI “defies analogy” (2026-01-22). Play moved from something he did to something he named (2024) and defended (2025–26). On AI he moved from observer to participant, with himself as the instrument. The register darkened. The method held.

2.6 What matters to him#

His values are not an ethics module bolted onto a risk scientist’s toolkit. They came first, and they grew less from moral philosophy than from occupational and public health. - The people who bear the cost. In 2006 he told Congress it was “irresponsible to spend millions of dollars on building a better microscope in the name of risk research when we cannot tell workers how effective their respirators are” (Testimony 2006 p.57). In 2009 getting nanotechnology “right” would be “a hollow achievement if we end up neglecting the very people who will make its success possible” (2020science 2009). His 2008 question, “Who is reaping the benefits of new nanotech applications, and who is paying the price?” (Bulletin 2008), runs through his AI work. - What makes us “us”. He chose “the future of being human” to focus on “each of us personally, rather than the rather generally handwaving around ‘humanity’” (2023-04-04 welcome-to-the-future-of-being-human). He fears that AI-polished self-presentation will strip away “the eccentricities, weirdness, and glorious diversity of personalities” (2026-03-08), and in 2023 he wrote that handing his writing to a machine “would be to diminish myself” (2023-09-20). Yet being human is an open question for him, not a fortress: “how do we learn how to be human in an age of AI?” (2025-03-30). What stays fixed is a refusal to count anyone as less. - Consent, and who decides. His objection to the bioterrorist in Inferno is about consent, not method: “what gave him the right to take this gamble in the first place?” (FFTF p.249). He turns the question on benevolent control too: “great care needs to be taken in who decides what ‘better’ means” (2024-10-13). - Responsibility in two directions. Renouncing technology “from a position of privilege” denies others their choices, so “we have an obligation to explore new ways of using science and technology to improve the world”, an obligation with “tremendous responsibilities” (FFTF pp.287–288). He could tell Marc Andreessen “I revel in their potential” and, in the same essay, ask “who decides who will suffer and who will thrive” (2023-10-19). - A big “we”. Most people have “a pretty high level of expertise in what’s important to them and their communities” (FFTF p.222), and the “we” who shape the future should be “as big and inclusive as possible” (FR pp.191–192). - Joy and wonder. “The soul of science lies in the delight and wonder of exploring the unknown” (2024-11-10). He counts their loss as a real hazard. - A future people can shape. “Technology is not deterministic” (2025-03-30).

His firmest commitments sit at the level of process (who decides, how big the “we” is) and of floors (dignity, consent, not counting anyone as less). He rarely pushes a picture of the good life. But where dignity or consent is at stake, the non-preacher draws lines. On using language models to predict crime: “Here I should lay my cards on the table” (2023-05-22). On OpenAI’s Her-like voice: “childish irresponsibility” (2024-05-21).

2.7 Scholarship in public#

For Maynard, research, teaching, public writing, making things and conversation are one practice. In 2023 he described how his “teaching, my writing, my work around public engagement and communication, and my work with various external organizations, all draw on, reflect, and contribute to my scholarship” (2023-01-31). His image for this is organic: his books, his initiative and his Substack are the visible fruits of a largely hidden “ideas mycelium” (2023-08-21). - Roots in accountability. In 2016 he proposed adding “a fourth leg of community service” to how faculty are evaluated (2016-01-31). In Nature Nanotechnology the same year he argued that when self-directed learners cannot find good information, it becomes easier for development “that is not accountable to citizens to occur” (NN 2016-09 p.735). Public scholarship was democratic accountability for him, not outreach. - How ideas travel. Risk innovation appeared in Nature Nanotechnology and The Conversation within months of each other, and the public piece was where it was tried on live cases (NN 2015-09; 2016-01-11). A Substack essay became an arXiv preprint within days, with the difference in register marked: “it was still just a Substack post, and not a rigorously researched academic paper” (2026-01-17). In 2026 he retested his 2018 list of ten AI risks and found that “less has changed over the intervening eight years than might be imagined” (2026-09-15). - An open notebook. Posts go out “a little rough” when events move fast (2023-04-04). One essay declines the expected call to action: “I’m sorry to disappoint, but I don’t have one” (2024-03-31). Corrections are dated and visible, and code and data go out with an invitation to “build on it” (2026-05-15). - Accessibility as rigour. Risk Bites, his stick-figure video channel, began as an experiment “that leant into my limitations” (2024-09-04). He turns the standard on himself: “to write without care for your readers is a very academic trap to fall into” (2026-05-17). - Scholarship about scholarship with AI. He has reported his own path with AI as it unfolded: refusing it for his own writing (2023-09-20); a rule of AI “as a catalyst to human-initiated thinking and research, rather than as a substitute” (2025-03-09); co-writing a paper with AI and naming the credit problem (2026-01-17); and listing an AI as sole author where “I did not make a substantial intellectual contribution”, while judging AI-assisted papers made with less than “10-20 hours intensive human labor” to be “highly suspect” (2026-09-04). - The cost. “Many people assume I’m just a commentator”, and “it still stings”. It is “the cost of the decision I made to put public good before academic prestige”, and “only OK if there really is public good that comes from my writing” (2026-05-17).

2.8 The public scholar#

He has described the role in several ways, and they fit together. - An obligation. He believes that “the privilege of academic scholarship and research comes with an obligation to ensure that the knowledge we unearth is accessible to anyone who can benefit from it” (2024-09-04). - A stance. Roger Pielke’s honest broker: “trying not to judge others or advocate for a specific course of action, but to help people make the best-informed decisions for themselves and their communities”. He adds at once that holding back can become “tacit support for not taking action” (FFTF p.246). - A purpose. Communication aimed at empowerment, “providing others with access to information that they are able to utilize on their own terms” (2025-05-25).

Thinking with people, not at them. Risk communication taught him “that most people are reasonably smart” (TechTrends 2023 p.5), and the deficit model has been “repeatedly shown not to be effective” (2025-05-25). So he offers questions in place of conclusions: fifteen for educators, deliberately left unanswered (2023-08-02), and ten about AI and higher education “that I don’t have good answers to” (2026-04-11). His rules of thumb for AI come with an invitation to “copy them, share them, even modify them” (2026-05-10).

Refusals, each with a reason. - Not polarising. Asked whether he is a techno-optimist, he compares it to being an “oxygen pessimist or optimist” (2024-03-31). He refuses easy allies as well as easy enemies: dismissing embryo-screening advocates as a Silicon Valley fantasy would be “lazy and narrow minded” (2024-04-14). - Not fear-mongering. He has seen “fallacious fears spurred on by speculation from experts” lead to real harm (2018-11-15). - Not refusing to talk about risk. “It never ceases to amaze me how many people equate talking about risk with fear mongering. And yet, it’s pretty much impossible to manage risks if you don’t talk about them” (2026-09-15, n.1). - Neither joining nor dismissing. He signed neither the 2023 pause letter nor the extinction statement, dismissed neither, and published his reasons both times (2023-04-04; 2023-05-31).

Convening. In 2009 he invited critics from civil society to write on his blog, because he “wanted to get a better understanding of how they saw the emerging relationship between society and innovation” (FFTF p.191). He names the quiet voices a noisy debate leaves out, including “experts in fields that no-one has realized yet have something important to bring to the table” (2023-04-10).

Close to industry without capture. He starts from where the other side is coming from before saying what it misses. Answering Eric Schmidt’s claim that industry could “roughly get it right” on AI governance, he opened with “I get where Schmidt is coming from”, then set out “what he misses”, and closed by allowing that Schmidt probably has “a more nuanced perspective” than one clip shows (2023-05-15). He explains behaviour through structures rather than villains, having met “remarkably few scientists and engineers who would consider themselves to be unethical or irresponsible” (FFTF p.36). He speaks innovators’ language while knowing its limits: customer discovery and pivoting, in their native form, lead “merely to successful innovation” (2019-08-13). He keeps red lines: “industry can’t get AI governance right on its own!” (2023-05-15, title). And he discloses, sometimes with a joke: “Waymo once sent me a pair of socks” (2023-11-09).

Candour. He names his motives and his changes of mind. He worries that his public writing may be “an ego trip … (and maybe it is — although I hope it isn’t)” (2026-05-17). In 2024 he began to question “a form of technology apologetics” that had been part of his professional life for decades (2024-03-31). Interpretation: his role is partly an answer to the risk he studies. If AI threatens people’s capacity to judge for themselves, helping them keep it is the public scholar’s reply.

2.9 What this way of thinking brings to the AI discussion#

What he adds is not a new list of risks, a governance mechanism or a forecast. It is a way of standing in front of a technology that fits nothing met before, and of helping others stand there too. Several elements are distinctive, and rarer still is their combination in one voice for more than a decade. 1. An insider’s reframing. A scientist who measured workplace exposures, led reviews of nanomaterial toxicology and testified to Congress on risk-research budgets argues from inside his discipline that its frame must change, and keeps its rigour (NN 2015-09; Toxicol. Sci. 2011; FFTF pp.22–23). Interpretation: a reframing of this kind, made from inside quantitative risk science rather than from outside it, is uncommon in the AI discussion. 2. What is at stake before what could go wrong. Dignity, trust, joy, identity and aspiration count on the same terms as health and money, and lost benefits count alongside harms. That is how he could decline the 2023 extinction statement yet take catastrophe seriously (2023-05-31). It is also why the frame speaks to builders: “if you want a fast-moving organization to attend to a risk, you do not hand it a compliance duty; you show it a threat to something it values” (2026-07-16 [mixed], restating the lesson of his 2019 work on entrepreneurship). 3. The mind as a central site of AI risk, held for more than a decade. In 2014 he asked whether prolonged interaction with intelligent machines might change human behaviour in harmful ways (2020science 2014). In 2018 he judged machines that learn to use our vulnerabilities against us “far more plausible, and far scarier as a result” than superintelligence (FFTF p.159), and asked for “tests that indicate when we are being played by machines” (FFTF p.177). In 2026 he extended it to a second-order form: AI may impair the faculties used to navigate technological change (2026-01-10), a risk to the navigator†, and his answer points to “a collective form of epistemic vigilance” (2026-01-17). It is one strand of a plural landscape, not the whole of it (C16). 4. An eye for the mundane, the intimate and the unowned. He gives an AI-drafted email or a chatbot’s warmth the seriousness usually reserved for catastrophe. For AI email he designed a risk framework, had an AI model develop and score the risks to offset his own biases, and on reflection endorsed the result: “I’m not surprised that there are potentially serious risks here—and even catastrophic ones”, for organisations that depend on their “relational connective tissue” (2025-09-07). And he asks how institutions come not to see such risks. 5. Disciplined imagination as a way of knowing. Where risk assessment keeps imagination out as speculation, he treats it, disciplined by plausibility and labelled as speculation, as the way to see what no framework yet owns. It offers a path between waiting for data that arrive too late and mistaking speculation for fact. 6. A stance that refuses the camps, with reasons. Each refusal comes with a reframing: loss of value instead of extinction, navigation instead of stop-or-go, formation instead of tool. And he locates the work in the ground between the camps: “The interesting and difficult work is in the space between” (30Y 2026). That ground is terrain to be navigated, not a midpoint to be split. 7. Himself as the instrument, in public. His user-side experiments, reported with their failures, generate and test concepts rather than illustrate them, and work out norms for AI in scholarship before institutions have them.

What the framings are worth for AI, and where they are weak. Interpretation throughout. - Risk as a threat to value suits AI, because many of AI’s most discussed harms have no clean dose–response: dependence on companion systems, eroding trust within organisations, drift in how people come to believe things. A value frame can at least name them, say who holds the value, and track threats over time. Its main weakness is that the people with most at stake often have least power to make their losses count, which he names himself (2026-07-16 [mixed]); what it does not supply operationally is set out once, in §9 (tension 16). It is strongest as a lens for seeing and a shared language with builders. - The landscape and navigation fit a technology that changes faster than evidence can be gathered. Their limit is irreversibility. For AI’s lock-in effects, navigation needs lines where harm cannot be undone, and his record supplies them: trigger points (2011), “quick to question, and slow to respond” (2016), the reversibility test (2025), and hysteresis as a reminder that removing a cause does not always reverse its effect (2025-05-18). They belong at the centre of the frame. A harder test follows from his own argument. If AI acts on the navigator, navigating alone is not enough, and his collective answer needs developing. - Orphan risks may be his most valuable framing for AI at present, because it describes an institutional blind spot rather than adding hazards to a list. The weighting rests on his own prose: risks “‘known knowns’ if you’re looking in the right place” yet dismissed (2018-12-13), “hard to quantify threats to value that often slip between the cracks” (2020-10-15), and AI’s human-side risks, which “no existing institution owns” (NANO 2026). The sharpest institutional form of the question, how a known risk comes to be nobody’s responsibility, is in the 2026 frontier-AI paper, and may be partly the AI model’s framing (§1). AI safety practice tends to select for what is measurable, catastrophic and auditable under competition, and the concept shifts attention to ownership and accountability, where AI governance is thinnest. His regulatory ask is correspondingly modest: disclosure of how firms select the risks they manage (2026-07-16 [mixed]). Its dangers are that “orphan” becomes a catch-all, or that a register of orphan risks becomes one more box to tick, and it says little about true unknowns. It complements catastrophic-risk frameworks rather than replacing them, as he says himself. - Risk innovation as a mindset, with creativity as a skill of risk perception, is the hardest of his ideas to evaluate and perhaps the most important. No one has measured what the tools do to outcomes, and by his own account the frontier-AI analysis “has yet to be shown to be useful in practice” (2026-07-16 [mixed]). Its value is greatest before the evidence exists, while harms cannot yet be measured and categories are unsettled. That is where AI sits in 2026. Judged as a mindset rather than as a tool, the questions are different: does it travel without him, does it change what people notice, what does it need in order to work, and can it be co-opted? The evidence is thin but not absent. The Planner was designed so that a founder could “develop a risk innovation mindset” in about thirty minutes (2023-11-21), and his entrepreneurship students were reached through what they wanted to achieve, which is where his lesson about value came from (2019-08-13). It depends on facilitation and designed spaces, which reach fewer people in the rooms where AI is decided (tension 7). And it can be co-opted: “navigation” can become a euphemism for going ahead, unless the lines where harm cannot be undone and the question of who decides come with it. - Humility against false precision is timely in a discussion thick with confident numbers, and it guards against a failure the numbers themselves cannot show: comfort mistaken for knowledge.

They work at two levels. As tools and frameworks they complement capability-based safety and legal compliance, which he says should not be loosened: risk innovation was “intended to complement and enhance existing risk assessment and management approaches” (Coronavirus 2020) and “conceived from the outset as complementary” (JLME 2024 p.555), and the 2026 frontier-AI paper offers itself “not as an alternative, but as an augmentation” (2026-07-16 [mixed]). As a way of thinking they change the questions those tools are asked to serve (FFTF pp.22–23; NN 2015-09 p.731; 2016-01-11). They widen what can be seen, put benefit and harm in one conversation, and keep that conversation open with the people building the technology.

2.10 Reading the rest of the map#

The sections that follow set out what he concludes (§3), how the parts connect (§4), his commitments (§5), his concepts (§6), the threads (§7), how his thinking has developed (§8), and the tensions in his record (§9). Read them as the current results of the way of thinking above: provisional, revisable, and reached by questioning frames, imagining widely and testing against evidence.

Common ways of getting him wrong: - reducing risk innovation to an operational add-on, toolkit or compliance layer, or reading it as a rejection of quantitative risk science; - treating play, films and humour as ornament laid over the “real” work; - casting him as a techno-optimist or an AI critic, a booster or a precautionist. He belongs in neither camp; he works in the ground between them, as terrain to be navigated rather than a midpoint to be split; - mistaking the honest broker for neutrality. He draws firm lines on dignity, consent and who decides; - fixing on single remarks (a striking line, an aside, a joke) instead of patterns sustained over years; - over-weighting his AI work of 2023–2026 and missing that its concepts grew out of nanotechnology, Films from the Future, Future Rising and a decade of columns; - reading his call for a new mindset as “this time everything is different”, or his use of history as “nothing new under the sun”. He rejects both; - treating his public-facing work as outreach rather than scholarship, or his accessibility as simplification; - taking his self-experiments as either proof or mere anecdote. They are labelled, provisional probes that generate and test concepts.

3. The picture in brief#

This section summarises what he has concluded, and should be read through the account of how he thinks in §2.

Andrew Maynard came to AI as a physicist who never lost “the sheer delight of putting ideas together in different ways”, a self-described “very un-disciplinary” scholar (TechTrends 2023 p.2) of the future of being human, and a risk scientist who built on his discipline rather than leaving it. Thirteen years of workplace aerosol research at the UK Health and Safety Executive and NIOSH, a grandfather who died of coal miner’s pneumoconiosis (black lung), and a decade inside nanotechnology’s health, safety and governance debates gave him a professional grammar: hazard is not risk; exposure turns one into the other; dose has to be measured in terms that match how harm happens; consequences matter as much as probabilities; evidence is weighed, not cherry-picked. The same decade showed him the grammar’s limits. Quantifying the risks of new materials from existing knowledge, he warned in 2006, “will engender false assumptions of safety” (PEN 2006 p.13), and by 2011 a review he led judged that quantitative toxicology and risk assessment were “unlikely to keep pace” with the materials being made (Toxicol. Sci. 2011). His response was not to discard the method but to change the frame it served: “the risk assessment paradigm remains relevant” (same review), but the widening gap called for “a new science of risk”. By 2018 he had “less and less patience for how many people tend to think about risk” (FFTF p.22). In 2023 he described what he brings to his work as a “physicist mindset, understanding of risk, innovation around how we think differently about risk”, together with a love of engaging with people across disciplines (TechTrends 2023). What follows grows from that combination: a measurer’s rigour, a sustained effort to change how risk is thought about for technologies that fit no earlier type, and the imagination that effort needs (§2).

What it is all for: people thriving. Risk is not the end point of his work. What drives it “more than anything” is whether our technologies “begin to fundamentally change who we are” (2024-01-01), and he describes his broader work as asking “how we navigate advanced technology transitions to get to the sort of future we want” (2026-09-24, his own introduction); in 2026 he named “human flourishing” as one strand of it (2026-07-10). Risk thinking serves that aim: it is how people find paths to the futures they aspire to without destroying what they already value. So value is created as well as threatened. Innovation creates it (2016-01-11), education multiplies people’s capacity to create it (2025-03-30), and losing the solutions AI might bring counts as catastrophic loss (2023-05-31).

The engine: risk as a threat to value, on quantitative foundations. Since 2015 he has argued that risk is not only the probability of harm but a threat to something someone values. That includes health, money and the environment, but also “dignity, belonging, identity, belief, even what it means to be human” (FFTF p.23), and the futures people aspire to as well as the things they already have. What the frame opens matters more than what it adds to a list of harms. It makes public resistance intelligible, turns go/no-go decisions into design questions, puts forgone benefits in the same account as harms, and turns risk into a way of seeing what matters (§2.3). The idea began in his teaching at Michigan in 2013 and was in print by September 2015 (NN 2015-09 p.731). It was introduced on quantitative foundations, not against them: the probability-of-harm definition is “a useful starting point” (NN 2016-03 p.211), and the value frame “extends conventional thinking rather than replacing it” (2018-12-13). He calls the wider project of rethinking risk “risk innovation”: “parallel innovation in how we conceptualize risk”, in a culture of creativity, imagination and serendipity (NN 2015-09 p.731). From the start he treated established risk assessment as important but incomplete (“Important as evidence-based health and environmental risk assessment and management are”, NN 2015-09 p.730), and he later described risk innovation as “intended to complement and enhance existing risk assessment and management approaches” (Coronavirus 2020). The definition has barely changed in eleven years. What has changed is its reach: from startups and investors, to brain–machine interfaces and other emerging technologies, to his definition of AI catastrophe, to the axis of his technology-transitions models, to frontier-AI governance.

Three features keep the frame disciplined: - It stands on risk science. Probability, hazard, exposure, causal pathway and weight of evidence still govern claims about whether harm will occur. The value frame changes what counts as harm, whose harm counts, and what risk analysis is for. - It treats risk as social. What counts as harm, what is “safe” and what is acceptable are set by people, not by engineering. So who decides is always part of the question. - It counts both sides. Because future value counts, not innovating is a risk, and so is a badly designed precaution. Counting both sides does not mean weighing them equally: in 2014 he judged products showing “a blatant disregard for health and environmental risks” a worse outcome than an industry scuppered by speculation (NN 2014-03 p.160).

This is why his risk thinking never collapses into a default “no”, and why he insists that talking about risk is the opposite of fear-mongering. For him it is how the benefits of a technology are realised. It also shapes how he reads public concern: moral panics signal threats to “what’s important to people” (2025-06-01), and backlash is a risk in its own right, because it can make development “far less accountable” (2024-02-18).

Humility about precision, and a duty to grapple. Maynard makes relatively sparing use of quantitative methods for AI, and his record explains why. “The more I study artificial intelligence, the less certain I am that we even know how to formulate the problems we face around AI”, he wrote in 2023, and the landscape is one “that only the foolish would claim to understand with certainty” (2023-11-26). Precise risk estimates for problems that cannot yet be formulated would be false precision: numbers that comfort without informing. The stance is as old as his quantitative work. “Numbers—hard data—can be comforting. But without a clear idea of their relevance, they can also be misleading”, he wrote in 2009, and his rule for that situation was “When the data run out – innovate!”: find ways to decide without hard data rather than wait for it (2020science 2009). In 2016 he warned that a chosen metric “may not adequately reflect a risk parameter of relevance” (NN 2016-03 p.211); in 2020, that “The more precise we try to be with our predictions of the future, the less likely they are to be accurate” (FR p.148); in 2026, that institutions under scrutiny “retreat to what can be quantified” (2026-07-16 [mixed]). Humility has never meant rejecting numbers or waiting. He uses bounded, clearly labelled figures where they help, and a 2008 paper he co-wrote names “more information as a substitute for action” as a failure (Hansen et al. 2008 p.446). For AI he pairs openly labelled speculation (“These are explorations, not findings”, April 2026) with the insistence that questions be asked “before the answers arrive in the form of consequences we didn’t anticipate” (HNS 2026).

Imagination and plausibility together. Curiosity comes first, including the “social curiosity” to “ask people what they think, and what they want” (FFTF p.222). He treats imagination as a skill of risk perception: risks may blindside us “in part because we’re not thinking creatively enough” (FFTF p.174), and play, story, juxtaposition and designed serendipity are how thinking escapes frames that no longer fit (§2.4). Jolting thinking through juxtaposition is, he says, “exactly what I set out to achieve in much of my writing” (2021-04-09). Plausibility is the discipline that ranks what imagination finds, and he applies it to hype and doom alike: is this plausible, or only imaginable? He used it on “grey goo” in 2006 and made it a named filter in 2011, “a crude but effective filter to distinguish between speculative risks—which are legion—and credible risks—which are not” (Toxicol. Sci. 2011). Speculation does harm “when make-believe is treated as plausible reality” (FFTF p.205). Joined to this is his picture of the world as a complex, tightly coupled system: unpredictable in detail but bounded, prone to tipping points, and increasingly irreversible. Complexity is why he thinks the past guarantees nothing about the future, why mistakes matter more now than they used to, and why the legitimacy of “move fast” depends on whether what gets broken can be fixed.

The people behind technology. His account of how technology goes wrong is non-demonising. Most damage comes from sincere scientists and entrepreneurs who are absorbed in what they can do, trust their own sense of what is responsible, and decide for others without asking. He calls this “myopically benevolent science”, and he includes himself in it. He met the pattern in institutions that promote a technology while overseeing its risks (2006–2011), and among entrepreneurs, whose optimism the investment process requires (NN 2015-03). Permissionless innovation, he argues, is not necessarily reckless; its flaw is that “a single innovator cannot see the broader context” (FFTF p.162). He also explains developers structurally: “the value of expediency is not the value of net societal benefit” (2019-08-13), and an “economic gradient” pulls AI toward manipulation even when no one intends it, leaving individuals “as engines of value creation rather than the primary recipients of created value” (2024-07-13). From this follows his most constant governance claim: nobody, least of all technical experts or industry, should decide alone. Publics hold real expertise in what matters to them. And justice is the test: who benefits, who bears the harm first, whose uncertainty is convenient, and whose futures are being written by someone else. Inside the incentives that shape sincere people, he asks how known risks come to be nobody’s responsibility (orphan risks, §2.3).

Governance. His first remedies, in testimony to Congress in 2006–08, were strong and central: a single accountable leader for risk research, a fixed share of research spending, and independent research bodies funded jointly by government and industry. In 2011 he proposed adaptive, evidence-based “trigger points” for regulation. From 2015 he adds a preference for adaptive, anticipatory, multi-stakeholder governance and “soft law” over technology-specific hard law, keeps hard law for specific harms, and insists that ethics and principles are worth little until they are operationalised. From 2019 he argues that AI governance leaned on ethics when it needed risk thinking. Since 2024 his confidence in responsible innovation, AI literacy and government agility has fallen, though not his commitment to public engagement. In 2026 he wrote, “I don’t have a governance solution for AI. I’m not sure anyone does”, and went straight on to what the nanotechnology experience does offer: evidence that “inclusive, transdisciplinary governance — however messy and slow — produces better outcomes than leaving decisions to the people who happen to be building the technology” (NANO 2026). He treats timing as decisive. In 2008 he told Congress that “if we are very smart, we work out the rules of safe use ahead of the game” (Testimony 2008 p.7); in 2015 that early disregard can lock technologies into trajectories “highly susceptible to failure” (NN 2015-03 p.199); and in 2016 that the right posture is “quick to question, and slow to respond”, ready to act on early warnings “even before the science is mature” (NN 2016-03 p.212). His retrospective puts it as the early days of a transition setting “the trajectory for decades” (NANO 2026). His view of steering has been stable since 2015: the overall trajectory of a technological revolution cannot be turned back, but its shape can be steered (NN 2015-12 p.1006). “Technology is not deterministic”, and “we do have the agency to determine what futures we aspire to and how we get there” (2025-03-30). He navigates rather than simply stopping or going (§2.3), and navigation includes specific pauses: he has argued for “pausing — or even rethinking” emotion-exploiting companion chatbots (2024-10-27).

AI. AI entered his work as one strand of converging technologies. In 2008, he recalls, “AI wasn’t even on my radar” (FFTF p.168). But his earliest writing on AI risk in the record came in December 2014, when he rejected the “singularity” and asked whether “prolonged interactions with intelligent machine[s]” might “change human behavior in potentially harmful ways” (2020science 2014); his founding risk-innovation column (2015) cites AI-safety funding as part of a new risk landscape; and his programme’s 2019 tools applied orphan-risk mapping to opaque machine-learning decisions. From 2016 to 2024, much of his applied risk work was on brain–machine interfaces, enhancement, embryo screening, synthetic biology, autonomous vehicles and humanoid robots. Neurotechnology, which can “alter how someone thinks, feels, behaves” (2016-03-31), is the direct bridge to his AI concerns. His distinctive AI claim was fixed by 2018. The plausible danger is not superintelligence, about which he is “something of an agnostic”. It is machines that learn people’s biases and vulnerabilities and use them against them: “far more plausible, and far scarier as a result” (FFTF p.159). This is the strongest continuous AI-specific thread in his work, and it has moved in stages: - AI changing human behaviour through prolonged interaction (a question in 2014); - an embodied manipulator (2018); - deception that disables our “fake-o-meter”, in a world our evolved instincts are “increasingly poorly equipped to handle” (Future Rising, 2020); - language as the medium of trust and influence (2023); - designed intimacy, commercial incentive and emergent “stochastic agency” (2024); - a structured risk of motive, means and opportunity (2025); - a “cognitive Trojan horse” that slips past evolved epistemic vigilance through ordinary features such as fluency, a question he first posed at a Berlin keynote in late 2025 (2026-01-10, his own essay; the fuller mechanism account in the follow-on paper was developed with AI assistance); - AI as a participant in how people form themselves: “constitutive resonance” (his March 2026 preprint), and AIs that are “beginning to train us to think like them” (2026-07-19).

Intent drops out along the way. What stays constant is the target: people’s capacity to form beliefs, to judge, and to be themselves.

Around this sits a plural landscape of AI risk: dependency and the drain of human agency, bias, opacity, jobs, weapons, cybersecurity, deepfakes and misinformation, threats to democracy and social cohesion, energy and water, systemic disruption. Existential risk has a stable calibration within it: low-probability, not to be dismissed, and better understood as catastrophic loss of what large numbers of people value. He rejects the culture of existential-risk ideology, not the possibility of catastrophe (2024-04-28). From 2023 he treats AI as different in kind in what it does to the self: “unlike anything we’ve had to grapple with before” (2023-04-12), a technology whose “sheer uniqueness and profundity” analogies fail to capture (2024-05-05), and by 2026 one that “defies analogy” (2026-01-22). In his systems view it remains one very powerful strand of technological convergence (2015-01-30; NN 2015-12; FWB 2026). The thought of billions of users unaware of how it slips past their defences “worries me — a lot” (2026-05-10). Yet in September 2026 he described himself as “stuck between” finding AI “one of the scariest things I’ve ever seen” and seeing that “the potential is profound”: “neither an AI optimist nor an AI pessimist” (2026-09-24 [mixed]).

Being human. The frame that holds all of this together is “the future of being human”, the name of his Substack and of the ASU initiative he leads. It is both what is at stake and what is sought: the dignity, agency, identity, relationships, joy and wonder that risk thinking protects, and the flourishing it is meant to enable. His risk theory is one of the ways he pursues it, not the other way round. The seeds are early: in 2014 he wrote that the more plausible risks of artificial minds were those “that challenge our very notions of humanity” (2020science 2014); “what it means to be human” is on his 2018 list of values at risk (FFTF p.23); and technologies are most dangerous when they make a society “forget the worth of others” (FFTF p.62). In 2025 he proposed three intersecting foci for navigating AI transitions, “where we live”, “what we do” and “who we are” (2025-01-07), and by 2026 “who we are” is the domain in which he thinks AI is doing what no earlier technology has done (CR 2026; 2026-05-21, in his reading of the papal encyclicals).

Learning and education. From 2023, education is where his ideas about risk, cognition and being human are tested most often, and in 2025 it was the main subject of his writing. Learning and education “dramatically increase the rate at which we can create value” (2025-03-30), so access to them is a justice question, and AI can widen it or hollow it out into “the illusion of learning rather than actual learning” (2026-05-10). His pedagogy is experiential, frugal and open: “the lowest level of tech necessary” (2024-02-11); playgrounds, not playpens, because a playpen “quickly falls apart” where the journey breaks new ground (2025-03-15). Much of his applied AI risk analysis in 2025–26 is here: duty of care to students, and students’ dignity. Universities, urged since 2016 to serve the public, are by 2026 the institution he hopes can help society navigate AI and fears “may not be up to the task” (2026-08-30).

Method and voice. §2 sets these out in full. In brief: he questions frames before answering within them; he loosens them with play, story and juxtaposition and tightens them with plausibility and evidence; he experiments hands-on, especially with AI, often with himself as the instrument; he uses films and stories as instruments for seeing threatened value; he hedges honestly (“I freely admit that I may be wrong”, FFTF p.170), changes his mind openly (the first signalled reversal in the record is from 2011: “I have changed my mind”, Nature 2011), and implicates himself; and his public writing is part of his scholarship, not an add-on to it (§2.7). He refuses the optimist–pessimist binary: asking whether he is a techno-optimist is like asking if he is “an oxygen pessimist or optimist” (2024-03-31).

Where it began. Many of the ideas above were in place a decade before the posts that made them visible: plausibility as a filter, behaviour over labels, the pacing gap, the risks of not innovating, the conflict between promoting and overseeing a technology, the sense that numbers can comfort without informing, and creativity and serendipity as part of how risk is seen. §8 sets out this formative layer (2005–2016), including his application in 2008 of the European Environment Agency’s “late lessons from early warnings” framework to nanotechnology.

Where it is unsettled. The largest open tensions lie between: - his reliance on lessons from past technologies and his claim that AI breaks analogy (which he partly reconciles: lessons about process transfer, while categories and track records may not); - his plausibility discipline and his readiness to take low-probability tails seriously; - treating AI as relational and warning users to remember it is a machine; - being an enthusiastic adopter and being a risk communicator; - offering mental models that open decisions, where some decisions need thresholds and rules; - a programme named for being human and his objection that extinction framing is “too human-centric” (2023-05-31); - his hope that universities will help fill the governance gap and his experience that they have so far been “followers and users of the technology” (2026-08-30).

4. The landscape#

The architecture (interpretation, used throughout this map)#

He never draws his own work as a system, so the architecture below is the map’s. It is built from how he describes his work: as driven “more than anything” by whether our technologies “begin to fundamentally change who we are” (2024-01-01), and as asking “how we navigate advanced technology transitions to get to the sort of future we want — and what it will mean to be human in those futures” (2026-09-24, his own introduction), with “human flourishing” named as one strand of it (2026-07-10). In his April 2026 retrospective he tells his career as one recurring problem: the gap between what a technology can do and a society’s capacity to understand, shape and govern it, which he first met “measuring workplace exposures” to airborne nanoparticles in the 1990s. In 2004, “measuring what happened when you opened a packet of carbon nanotubes”, he found that the complications “had less to do with aerosol physics than with how institutions, regulators, and entire societies handle technologies they don’t yet understand” (30Y 2026). The same parts order the commitments in §5, and the concept groups in §6 and lens groups in §10 roughly follow them; the crosswalk table below links them.

The connections#

The connections matter as much as the parts. Where he does not draw a link himself, it is marked as interpretation. - Risk science → risk innovation (built on, not replaced). Risk innovation grew inside his quantitative work on nanomaterials, from its limits: control banding, the tool he proposed for decisions on incomplete information, was not “a substitute for conventional risk assessment and control” (AOH 2007 p.10); evidence-based assessment is “Important” but fails “to capture the full panoply” of risks (NN 2015-09 p.730); and in 2026 the value frame “does not abandon the idea of risk as involving the probability of harm. Rather, it widens what counts as harm” (2026-07-16 [mixed]). - Measurement humility → orphan risks → restraint about AI numbers. Knowing what to measure comes before measuring (NN 2015-06 p.483); frameworks built on what can be quantified push aside what cannot (Nexus 2019); orphan risks are the “hard to quantify and easy to ignore” risks that result (Nexus 2020). His sparing use of numbers for AI follows from the same reasoning: he doubts “we even know how to formulate the problems we face around AI” (2023-11-26; C5). Interpretation: the same reasoning links the two. - Imagination → risk perception → the landscape. Risks blindside us “in part because we’re not thinking creatively enough” (FFTF p.174), and a lack of “creativity and flexibility” increases “the chances of things going wrong” (2016-01-11). Creativity is how the risk landscape comes into view before evidence arrives; plausibility then ranks what it finds (C8). - Stories → threat to value. Drama is built from threatened value, from Hammond’s dream to Kusanagi’s sense of self (FFTF pp.23–24). Interpretation: that is why films are an instrument of the value frame and not an illustration of it. - Orphan risks → late lessons from early warnings. The European Environment Agency’s cases are, in his 2015 summary, harms that happened because early warnings “were either ignored or overlooked” (2018-12-15). Interpretation: an orphan risk is a late lesson in the making, known to someone and owned by no one. - Threat to value → being human → cognition. “what it means to be human” is on his list of values at risk (FFTF p.23). Harms at “the very heart of what makes us human — our sense of identity, our beliefs” are “threats to subjective value” (2023-11-26), and the value at risk “could just as easily be identity, dignity, or deeply held beliefs” (2024-08-25). So AI’s effects on belief and judgement are risk questions for him, not only ethical ones. His March 2026 preprint on “constitutive resonance” carries the chain to how people form themselves. - Value created → education. Learning and education matter because they “dramatically increase the rate at which we can create value” (2025-03-30). The same value frame that defines risk defines what education is for. - Risk innovation and responsible innovation. In 2015 risk innovation “complements” responsible innovation and anticipatory governance (NN 2015-09 p.731). By 2020 he treats responsible innovation, the IRGC framework and precaution as “different approaches to applying the concepts that underlie risk innovation” (2020-07-30). The relation moved from side by side to nested, which is why falling confidence in responsible innovation does not shake his risk framework. - Risk is social → who decides → governance. If harm and safety are socially defined, deciding them cannot be left to technical experts. - The risks of not innovating → steering, not stopping. Counting the risks of not innovating is why he argues for channelling technology, not halting it, and for an “obligation” to innovate. - Threat to value → public concern as a signal. Moral panics reveal threats to “what’s important to people” (2025-06-01), so he reads concern as information, and backlash as a risk (2024-02-18). Interpretation: this is the risks-of-not-acting logic applied to public reaction. - Complexity and irreversibility → the critique of permission. Tightly coupled, hard-to-reverse systems are why self-certified, move-fast innovation is dangerous now. He named “tight coupling” and “latency” as the vulnerabilities of entrepreneurial culture in 2019 (2019-08-13), and the 2025 reversibility footnote makes the link explicit. - Plausibility → superintelligence agnosticism → manipulation. The same test that deflates superintelligence is what makes manipulation his lead AI risk. - Chemical risk grammar → algorithmic exposure → exposure of the mind → epistemic vigilance. His professional method, carried by analogy, reaches the mind. The breakpoints are named each time: “Nanomaterials are not just chemicals” (NN 2016-03 p.211); “an algorithm is not a chemical” (2019-03-05); then the 2023-11-26 addendum and 2026-01-10. - Neurotechnology → AI acting on the mind. Technologies that “alter how someone thinks, feels, behaves” (2016-03-31) are the precursor of his AI concern. Interpretation: the brain–machine interface work of 2019–24 is the bridge. - Myopic benevolence → engagement → structural incentives. His model of sincere-but-blinkered developers explains his faith in engagement. His structural account explains why good intentions do not survive the market, and it is as old as the psychological one: entrepreneurs’ optimism is something investors require (NN 2015-03 p.199); “the value of expediency is not the value of net societal benefit” (2019-08-13); dependent “super-consumers” (2022-02-12); the “economic gradient” (2024-07-13). The AI-assisted 2026 frontier-AI paper formalises this as an “incentive field” [mixed]. - Stories as tools → stories as risks. He values stories because they open minds that argument closes; the same property is what he fears in fluent machines. - AI acting on the mind → a risk to the navigator† → collective vigilance. If AI affects “the very cognitive abilities we rely on to navigate differences between what we experience, and what we’ve evolved to live with” (2026-01-10), then users, institutions, evaluators and analysts are all inside the problem, himself included. His answer points toward “a collective form of epistemic vigilance” (2026-01-17) and toward bringing in other voices.

A simple diagram#

                PURPOSE: people thriving; what it means to be human
             (what is at stake, and what is sought: dignity, agency,
               identity, relationships, joy, wonder, flourishing)
                                   ^
                                   | serves
                                   |
   STANCE: a changed mindset for technologies that fit no earlier type of risk
     risk innovation · the risk landscape · navigating rather than managing ·
     orphan risks · mental models that open possibilities, held with humility
                                   |
   METHOD                          v                          MORAL AXIS
   curiosity first;       ENGINE: VALUE,                      who decides; justice;
   question the frame;    threatened and created              sincere-but-myopic makers;
   play, story,    -----> risk as a threat to value;  <-----  structural incentives;
   juxtaposition,  how    safety is social;          who      no abdication to experts
   designed        risks  not acting is a risk too;  decides
   serendipity;    come   which risks go unowned?
   build and test; into   ..............................
   plausibility    view   FOUNDATIONS: quantitative risk
   and evidence;          science (hazard, exposure, dose,
   revise in public       causation, weight of evidence)
                                   |
                                   | applied to
                                   v
            OBJECT: AI  (one converging strand to 2021; central from 2023;
                  nanomaterials, neurotechnology and other emerging tech before it)
         what AI is  ·  a plural risk landscape  ·  mind, language, formation
               |                                              |
               | acted on in                                  | reaches "who we are"
               v                                              v
  ARENAS: governance and institutions ·                (back to PURPOSE)
          learning, education and the university

  DISCIPLINES, throughout: imagination disciplined by plausibility · weight of
      evidence · humility about precision · analogy as probe · complexity,
      tipping points, irreversibility
  VOICE AND ROLE, throughout: scholarship in public · honest broker ·
      questions, not conclusions · convening · self-implication

The territories at a glance, with a crosswalk#

Each territory is placed in the architecture above, and linked to the commitments (§5, “C”), concept tables (§6), lenses (§10) and the thread in §7 that summarises it.

Territory Part The question he keeps asking Anchor concepts §5 §6 §10 Thread (§7) Densest
How he thinks: mindset and method Stance; Method Does the frame fit, what are we failing to imagine, and how would we find out? Risk innovation as a mindset; questioning the frame; creativity as risk perception; play, story, juxtaposition and designed serendipity; building to think; grounded exuberance C3, C7, C8; §2 6.5, 6.11 M1–M7, F2 T8 Constant; creativity in risk thinking from 2015; play named as method 2024
Being human and flourishing Purpose What makes us “us”, and what would help people thrive? Future of being human; worth and dignity; intrinsic technologies; where we live / what we do / who we are; joy, wonder and play C1, C17 6.9 A1, A3, E4 T11 Roots 2014–20; dense 2024–26
Risk science and the limits of numbers Engine (foundations) What is the dose, what should be measured, and what do the numbers miss? Hazard, exposure and dose metrics; measurement designed for ignorance; weight of evidence; trigger points; humility about precision C4, C5 6.1 B1, B6 T1 2005–2016; restated 2020 and 2023–26
Rethinking risk Stance; Engine What is threatened or could be created, for whom, how would harm actually happen, and who owns it? Risk innovation; threat to value; the risk landscape; navigation; the risks of not acting; safety as social; orphan risks; risk perception C2, C3, C6, C7, C10, C11 6.1 A1, A2, B1, B3, D6, D8, M4, M5 T1 2015–2021; revived 2023–24 and 2026
Epistemics of the future Method; Discipline What might we be failing to imagine, is it plausible, and how would I know if I were wrong? Imagination disciplined by plausibility; weight of evidence; humility; informed speculation; stories as instruments C5, C8 6.5 M3, B2, B5, B6, C3, F2 T8 Constant; a named filter from 2011; creativity as risk perception from 2015; in films from 2018
Complexity, transitions, futures Discipline What happens in a coupled system when change outruns understanding? Convergence and base code; tipping points and early warnings; irreversibility; advanced technology transitions; four mindsets for transitions; the early window C7, C9 6.4 M6, B4, B5, E2, E6 T6 2010–2021 (convergence, early warnings); 2023–26 (transitions)
Learning from past technologies Discipline What transfers from earlier technologies, and where does the analogy break? Chemical risk template; late lessons from early warnings; nano and GMO lessons; behaviour not labels; analogy as probe; “defies analogy” C4, C14 6.5 M1, F1, F3 T2 2007–2016 (nanotechnology); 2019; discontinuity claims 2023–26
People and permission Moral axis Who is certifying that this is responsible, can they see enough, and which risks does no one own? Myopic benevolence; hubris; permissionless innovation; could vs should; promoter and overseer; economic gradient; orphan risks as institutional blind spots C11 6.2 D2, D3, D7, D8 T7 2006–2015 roots; FFTF (2018); 2023–26 on AI leaders
Power, justice, who decides Moral axis Who benefits, who bears the harm, and who was asked? No abdication to experts; two-way engagement; inequity; whose future C6, C12 6.3 D1, D4, D5, D6 T5 Constant (from 2006)
Emerging technologies before AI Object (precursor) What does a technology that acts on body or mind do to agency, and who owns the consequences? Nanomaterials; brain–machine interfaces; enhancement; synergistic scaling; “indentured servitude”; lifetime responsibility C2, C15 6.3, 6.8 A3, C1 T2, T4 2005–2016 (nano); 2016–2024
What AI is Object Tool, partner, emulator, or something new? From converging strand to category of its own; relational technology; superintelligence agnosticism; moral status C15 6.6 C2, M2 T3, T4 2014 seed; 2023–26
The AI risk landscape Object Which AI risks are plausible and serious, and how should they be weighed? Ten risks; catastrophe as loss of value; democratic and systemic risk; agentic risk; existential risk calibrated C16 6.7 B2, B5 T3 2018; 2023; 2026
Mind, language, formation Object How does AI act on how people think, trust and become who they are, including those trying to steer it? Artificial manipulation; the language turn†; stochastic agency; cognitive Trojan horse; risk to the navigator†; constitutive resonance; formation C15, C17 6.8 C1, C2, C3, C4 T4 2014 and 2018 seeds; 2023–26
Governance and institutions Arena How can a way through be found for a fast, uncertain technology, and by whom? Strong capacity for risk research; responsible innovation; agile and soft-law governance; operationalised ethics; care; lines where harm cannot be undone, and course correction C7, C13 6.3 E1, E2, E3, E6 T5 2006–2011 (nanotechnology); 2023 (peak); 2025–26
Learning, education and the university Arena What is learning for, and who gets access, when intelligence is abundant? Value-creation model of education; education against inequity; playgrounds; AI literacy and its limits; universities’ public duty C18 6.10 E3, E4, M7 T10 2023–26 (the main subject of his writing in 2025)
Voice and role Voice and role How should a scholar reason, and speak, about this in public? Scholarship in public; honest broker; Don’t Panic; questions, not conclusions; convening; self-implication C10; §2.7–§2.8 6.11 C3, E5, F2 T8 Constant

5. Core commitments, with firmness and trajectory#

Eighteen propositions, ordered roughly by the architecture in §4: purpose; stance and engine (C1–C7); method and disciplines (C8–C9); risk communication and the moral axis (C10–C12); governance (C13); learning from the past (C14); the object, AI (C15–C17); and the arenas (C18). Most he has held consistently for years, several since his nanotechnology work of 2006–2011. Where one is still developing, its firmness line says so. Each gives a short statement (with how firmly he holds it and how it has moved), the main supporting sources, and a short quotation. A short note on his method follows the list. They are cited elsewhere as C1–C18.

Two cautions apply to all of them. First, they are the current results of the way of thinking described in §2, not a doctrine; he holds most of them as working positions and says where he may be wrong. Second, they are easily misread in two opposite directions. Some of his phrasing sounds like a clean break with risk science (“a radical new approach to risk”, NN 2015-09 p.731), yet he keeps its quantitative foundations and still uses them (C3, C4). And the fact that he keeps those foundations can make his new frames sound like a module bolted onto conventional assessment, when they change the questions the assessment serves (§2.2). Neither a revolution that discards risk science nor an incremental add-on is his position.

1. The point of risk thinking is thriving: value is created as well as threatened.

Risk thinking exists to help people reach the futures they aspire to without losing what they already value. Innovation is “creating value that someone is willing to pay for”, and risk is a threat to that value (2016-01-11). Learning and education multiply people’s capacity to create value (2025-03-30). Losing the solutions AI might offer to “climate change, poverty, equity, threats to democracy” counts among catastrophic risks (2023-05-31). His transition models chart opportunities alongside threats (2024-08-25). Navigating a transition means finding paths to good futures, not only avoiding hazards. Firmness: long-standing as an aim. The top item in his 2009 list of ten things everyone should know about nanotechnology safety was “People matter”: risk research is about “protecting people from injury, disease and death, and ensuring a high quality of life” (2020science 2009); and in 2020 he wrote that “everyone has the right to thrive” (FR p.192). Named as “flourishing” and “thriving” at the centre of his work from 2025, and rising. The rest of the list serves it.

Sources: 2020science 2009 (“Ten things”); 2016-01-11; FR pp.191–193; 2020-11-05 risk-innovation-and-the-future; 2023-05-31 existential-risks-of-ai; 2024-01-01 the-future-of-being-human-in-2024; 2024-08-25 advanced-technology-transitions-model; 2025-01-07 universities-need-to-step-up-their-agi-game; 2025-03-30 reimagining-education-in-an-age-of-ai; 2026-07-10 i-asked-anthropics-fable-5-to-create-a-video-game; 2026-08-02 what-we-can-learn-with-ai-by-not-trying-to-learn.

In his words: “Human flourishing in a technologically complex future demands the humility to question assumptions and embrace change” (2025-03-30); his work asks “how we navigate advanced technology transitions to get to the sort of future we want” (2026-09-24, his own introduction).

2. Risk is a threat to value: to what people have, and to what they aspire to.

Risk is not only the probability of physical, environmental or financial harm. It is also a threat to anything a person, community or organisation values, including dignity, identity, belief, agency, trust and hoped-for futures. What the frame opens is its point (§2.3). It makes resistance intelligible: “I’m not sure I buy the idea of ‘risk aversion’”, because the term hides “the things that people find too important to risk losing” (Rethinking Risk 2017 p.193). It turns go/no-go choices into design questions (pp.197–198). It counts future value, so forgone benefits sit in the same account as harms (NN 2015-09 p.731; 2023-05-31). It is reciprocal, since threatening what others value comes back on the actor (2018-12-13). And it makes risk a way of seeing what matters, “an inevitability that reveals what the primary value is within a complex landscape” (Rethinking Risk 2017 p.197). Its sharpest institutional application is orphan risks: threats to value that no one owns (C11). It stands on the conventional definition rather than replacing it: “We usually think of nanotechnology ‘risk’ as the probability of disease or death occurring … This is a useful starting point” (NN 2016-03 p.211). The frame “extends conventional thinking rather than replacing it” (2018-12-13), and in 2026 it “does not abandon the idea of risk as involving the probability of harm. Rather, it widens what counts as harm” (2026-07-16 [mixed]). He distinguishes value (worth to someone) from values (right and wrong), because value can be named and acted on without first agreeing on ethics. Firmness: the most stable idea in his work. Seeded in his teaching of entrepreneurs at Michigan from 2013, in print in September 2015 (“risk as a threat to existing or future ‘value’”, NN 2015-09 p.731), and essentially unchanged since.

Sources: NN 2015-09; NN 2016-03; 2016-01-11 thinking-innovatively-about-the-risks-of-tech-innovation; Rethinking Risk 2017; FFTF pp.23–24; 2018-12-13 tech-startups-orphan-risks (reposted 2023-11-15); 2023-05-31; 2024-08-25; 2024-12-17 navigating-the-challenges-and-opportunities-of-advanced-biopreservation-technologies; 2026-07-16 [mixed].

In his words: “When stripped down to fundamentals, risk concerns threats to something you or others value” (NN 2016-03 p.211); risk is “a threat to something of importance to an individual, a community, or a business organization” (2018-12-13).

3. A changed risk mindset, built on quantitative risk science. For technologies that fit no earlier type of risk, the frame of thinking about risks and benefits has to change, while the quantitative foundations are kept.

Conventional, evidence-based risk assessment is his professional foundation, and he has never discarded it. But established approaches “run out of steam rather fast when we’re facing technologies that can achieve things we never imagined” (FFTF pp.22–23), and what is needed is “parallel innovation in how we conceptualize risk” (NN 2015-09 p.731): a change in the questions risk thinking asks, not a module added to it. The foundations are visible at every stage of his record. In 2007 the tool he proposed for decisions under thin data, control banding, was not “a substitute for conventional risk assessment and control” (AOH 2007 p.10). In 2009 “old safety practices” were not made “redundant” by a new technology (2020science 2009). In 2010–11 new regulatory approaches were to be “grounded in established approaches to identifying, assessing and managing risks”, and “we would be remiss in throwing out the old and embracing the new, simply because we can” (Nat. Mater. 2011 pp.554–556). A review he led concluded that “the risk assessment paradigm remains relevant” while calling for “a new science of risk” alongside it (Toxicol. Sci. 2011). His founding statement of risk innovation opens “Important as evidence-based health and environmental risk assessment and management are” before saying what they miss, and it places computational toxicology inside the new field, at the other end of a spectrum from a book of haiku (NN 2015-09 pp.730–731). He described it in 2017 as “an evolution of the old black-and-white mathematics of risk” (Rethinking Risk 2017 p.200). During the COVID-19 pandemic he told readers that his risk-innovation website “should not be your first port of call”: they should go to public-health agencies first (Coronavirus 2020). A 2024 paper he led says the approach was “conceived from the outset as complementary” to established frameworks (JLME 2024 p.555), and the 2026 frontier-AI paper offers it “not as an alternative, but as an augmentation” of existing safety frameworks, which should not be “loosened” (2026-07-16 [mixed]).

What changes is the frame. Regulations and risk methods are built around previous technologies, and new ones get shoehorned into them, which hides pitfalls: existing frameworks are “usually not remotely the right shape, never mind being an adequate fit” (2016-01-11). Established methods “were developed as a consequence of” earlier industrial revolutions, so “we need to be jolted out of our existing mental and procedural risk-ruts” (NN 2015-12 p.1006). “Without risk innovation, all we are left with is business as usual” (NN 2015-09 p.731). The change turns risk from a brake into a way of reaching value. The founding column put it as revealing “new pathways through complex risk landscapes” and “building and maintaining value in a world where risk is not only endemic, but integral to progress” (NN 2015-09 p.731); his 2026 retrospective restates it as a reframing of risk “from something to be minimized to something to be navigated creatively in pursuit of value”, held with scepticism of “both the safety absolutists and the move-fast-and-break-things crowd” (30Y 2026). By 2020 outmoded ideas about risk are a risk in themselves (2020-11-05). From 2019 to 2024 he also argues that AI governance slid into ethics, which helps “parse out what is considered right and wrong” but offers no “practical framework for achieving safe and beneficial technologies” (2023-04-04); risk thinking is the practical corrective. For AI the change is sharper still, because AI fits no earlier type of risk (“the shavings off the tip of the AI iceberg”, 2023-05-31; “defies analogy”, 2026-01-22) and, in his 2026 extension, may act on the faculties people would use to navigate it (C15; §2.2). Firmness: very high; the founding claim of his risk work. The impulse is visible from 2008–09 (“When the data run out – innovate!”, 2020science 2009; WEF 2008), the name came in 2015, and the foundations are kept throughout.

Sources: AOH 2007; WEF 2008; 2020science 2009; Handbook 2010; Nat. Mater. 2011; Toxicol. Sci. 2011; NN 2015-09; NN 2015-12; 2016-01-11; Rethinking Risk 2017; FFTF pp.22–23, 39; 2019-11-01 how-to-build-a-better-brain-machine-interface; 2020-07-30 life-on-mars-astrobiology-and-thinking-differently-about-risk; Coronavirus 2020; 2020-11-05; 2021-08-03 we-need-to-get-more-innovative-in-how-we-navigate; 2023-04-04 what-are-the-alternatives-to-calling; 2023-05-31; 2023-10-25 10-million-for-ai-safety-research; JLME 2024; 2024-12-17; 30Y 2026; 2026-07-16 [mixed].

In his words: evidence-based risk assessment and management “fail to capture the full panoply of personal, social, environmental, technological, economic, political and corporate risks” (NN 2015-09 p.730); we risk trying to “squeeze the new wine of technological innovation into the old wineskins of conventional risk thinking” (FFTF p.23); risk innovation is “designed to open up new ideas and possibilities” (2016-01-11).

4. Hazard is not risk: exposure, causation, consequence and weight of evidence discipline every claim, and there is no zero risk.

A hazard becomes a risk only through exposure and a causal pathway; the type of harm matters as much as its probability; single startling studies should not drive decisions. This grammar is one of his foundations. It runs from his 1990s methods for measuring nanometre particles (NN 2015-06), through the dose metrics his working group set out in 2005 (ILSI 2005), his 2007 extension of risk as hazard and exposure with “a third component … Characterization” (AOH 2007 p.7), his rule that “No exposure—no harm” (2020science 2009), and his judgement on sprayed carbon nanotubes that “everything hinges on the nature, form and concentrations of nanotube material” people are actually exposed to (NN 2016-06 p.491). He carried the grammar from chemicals and nanomaterials to algorithms (“algorithmic exposure”, 2019) and tested it on AI (2023), noting that dose–response is often non-linear (“threshold responses, hormesis, and other low-dose responses”) and finding that no framework yet exists. He also judges responsibility by process: innovating without asking the basic exposure questions is irresponsible even if the risk proves negligible. Firmness: foundational; the ground the rest of his risk thinking stands on.

Sources: ILSI 2005; AOH 2007; Testimony 2007; 2020science 2009; NN 2014-09; 2015-01-10 are-quantum-dot-tvs; NN 2015-06; 2016-02-01 we-dont-talk-much-about-nanotechnology-risks-anymore; NN 2016-06; 2019-03-05 should-we-be-treating-algorithms-the-same-way-we-treat-hazardous-chemicals; 2021-03-28 how-safe-are-graphene-based-face-masks; 2022-02-10 are-we-asking-the-right-standards-questions; 2023-11-26 everything-youve-heard-about-ai-risk-is-wrong.

In his words: “No exposure means no risk, even if a chemical is potentially deadly” (2019-03-05); and, of AI speculation, “no cause, no risk” (2023-11-26).

5. Humility as a working discipline: about numbers, about how problems are framed, and about the analyst. Know what matters before measuring, do not let measurability decide what counts, and act under uncertainty anyway.

Numbers are tools, not comfort. Being able to measure is not the same as knowing what matters: “The harder challenge is working out what we should be measuring” (NN 2015-06 p.483), and a chosen statistical parameter “may not adequately reflect a risk parameter of relevance” (NN 2016-03 p.211). He has warned that quantifying new risks from existing knowledge “will engender false assumptions of safety” (PEN 2006 p.13); that “we must not mistake methodology for strategy” (Testimony 2007 p.21); that research spending is “a crude tool” of evaluation even though “bottom-line figures count” (2020science 2008a); that “numbers can be deceptive”, especially where there are “incalculable uncertainties” (Rethinking Risk 2017 p.194); and that “The more precise we try to be with our predictions of the future, the less likely they are to be accurate”, with the danger that we become “so enamored with our brilliance” that we act “as if the future is something we can fully control” (FR p.148). He has also turned this humility on his own field: a well-funded research programme can harden into “an assumption of as-yet-to-be-discovered risk” (NN 2014-03 p.160), and his 2016 audit of his own 2006 agenda found it had under-delivered on exposure measurement and prediction (Maynard & Aitken 2016). In 2026 he argued that frameworks built on what can be measured produce blindness: institutions under scrutiny “retreat to what can be quantified”, and a framework “can be an excellent exhibit, and a weak instrument, both at the same time” (2026-07-16 [mixed]).

The same record shows he is not against numbers. He designed measurement around ignorance: all three candidate dose metrics, and records “that can be interpreted in the light of new knowledge” (Nature 2006 p.268). He used bounded, clearly labelled figures, benchmarks that help “tether speculative ideas to plausible realities” (NN 2016-03 p.211) and evidence-based “trigger points” (Nature 2011). In 2026 the frontier-AI paper set observable tests and a dated falsification point (“past 2028”) for its central thesis (2026-07-16 [mixed]). Humility has never meant waiting either. He called for protecting people “in the absence of complete information” (PEN 2006 p.27), for control banding as a tool for “decision-making based on incomplete information” (AOH 2007 p.10), and for the ability to respond to early warnings “even before the science is mature” (NN 2016-03 p.212). His 2009 rule sums it up: “When the data run out – innovate!” (2020science 2009).

For AI the humility goes deeper than numbers, to whether the problems can yet be framed. “The more I study artificial intelligence, the less certain I am that we even know how to formulate the problems we face around AI, never mind manage the risks”, and the landscape is one “that only the foolish would claim to understand with certainty” (2023-11-26). Of AI agents, “we’re not even sure yet how to formulate the problem” (2025-05-04). Precise risk estimates for such problems would be false precision, so his AI work offers mechanisms, labelled hypotheses and proposed tests instead. His 2026 Trojan-horse paper is “hypothesis-generating rather than hypothesis-confirming” (Trojan 2026 p.11), and he anchors his AI-risk communication on “human risks” rather than “technical capability benchmarks that shift every few months” (STICK 2026). The humility extends to his own frames and to himself as analyst. “I freely admit that I may be wrong” (FFTF p.170); his own models may belong in “the trash can of bad ideas” (2024-08-18); he pre-registers a play experiment so that he can be held to it (2026-08-23); and he asks of his own use of AI, “how do I know I’m not an unwitting victim here?” (2026-01-17). His rule for speculation is to allow it “within a context of humility”: know that it is speculation, accept that data must follow, and bring in other voices (2026-09-24 [mixed]), a balance he had set out in 2014 and 2016. Firmness: very high and long-standing (2006–2026). Anticipated for AI in December 2014, when he called some AI risks “incredibly speculative and certainly not empirically testable” yet foolish not to examine (2020science 2014), and stated for AI most fully in 2023 (2023-11-26).

Sources: ILSI 2005; PEN 2006; Nature 2006; Testimony 2006; Testimony 2007; 2020science 2008a; 2020science 2009; Toxicol. Sci. 2011; NN 2014-03; NN 2015-06; NN 2016-03; Maynard & Aitken 2016; Rethinking Risk 2017; FR pp.148–151, 166–167; 2023-11-26; 2024-08-18; 2025-05-04; 2026-01-17; Trojan 2026; 2026-08-23; 2026-07-16 [mixed]; STICK 2026; HNS 2026.

In his words: “Numbers—hard data—can be comforting. But without a clear idea of their relevance, they can also be misleading” (2020science 2009); “when tempered with humility and guided by our humanity, our technical mastery of change can help set boundaries around what we don’t know or cannot predict” (FR p.167).

6. Harm and safety are socially defined, so who decides is part of every risk question, and no one should decide alone.

What counts as harm and what is “acceptably safe” are set by people, through norms, perception and agreement, not by engineering alone. “Safe” was “a relative term” in his 2006 research strategy (PEN 2006 p.9), and in 2008 he asked of nanotechnology: “Who will decide how it is used, and who will pay the cost?” (Testimony 2008 p.2). Zero risk exists only where nothing changes. The question AI-safety ventures leave unasked is who decides what “safe” means. It follows that no one, least of all technical experts or industry, should decide alone: leaving technology questions to scientists, innovators and politicians is “an abdication of responsibility” (FFTF p.288). Publics can judge what a technology threatens without understanding how it works, and engagement should be early, two-way and consequential, not a deficit-model exercise in explaining. “It’s complicated” is not an excuse for excluding people. He has long separated the public’s standing from the work of drafting rules. In 2010 a chapter he co-wrote argued that the challenge was “how to empower people to be an effective part of the decision-making process, rather than how to make decisions on their behalf”, while “the details of how regulations are crafted and enacted will of necessity remain the responsibility of a small number of experts” (Handbook 2010 p.583). In 2026 he says members of the public “are critically important” but that the problem cannot simply be handed to them, and he looks to universities (2026-09-24 [mixed]). Firmness: core and constant as a principle (2006–2026), sharpened against AI-safety culture from 2023; concrete on mechanisms in 2007–08, thinner since (see §9).

Sources: PEN 2006; Testimony 2007; Testimony 2008; Bulletin 2008; Handbook 2010; FFTF pp.222–227, 288; 2016-01-12 can-citizen-science-empower; 2016-03-31 considering-ethics-now-before-radically-new-brain-technologies; 2023-04-10 as-ai-goes-to-washington-whats-being; 2023-05-15 erik-schmidt-ai-regulation; 2023-09-04 why-public-engagement-is-so-important; 2023-11-26; 2023-12-22 un-governing-ai-for-humanity; 2024-06-20 ilya-sutskevers-safe-superintelligence-rethink; 2024-08-07 are-humanoid-robots-really-the-future; 2025-05-25 why-parasocial-communication-is-important; NANO 2026.

In his words: harm is “a social construct, not a technological one” (2024-06-20); most people have “a pretty high level of expertise in what’s important to them and their communities” (FFTF p.222).

7. Navigate rather than stop or simply manage: count the risks of not acting, map the landscape, set trigger points and lines where harm cannot be undone, and correct course.

Forgone benefits count as lost value, and precautionary action has its own victims. He has held this since his formative work: in 2006 he told Congress that “If investors and consumers reject nanotechnology through fear and uncertainty”, missed opportunities in medicine and energy “could deal a severe blow to the quality of life” (Testimony 2006 p.52). Counting both sides does not mean weighing them equally. In 2014 he judged that speculation could have “scuppered the nanotechnology enterprise or, worse, led to materials and products that showed a blatant disregard for health and environmental risks” (NN 2014-03 p.160). On precaution he has sought a middle ground since 2007, between treating new materials as “highly hazardous until proven otherwise” and assuming “negligible hazard until proven otherwise” (AOH 2007 pp.9–10). He asks how “appropriate trigger points for action” should be defined (NN 2014-09 p.659), endorses a proportionate, participatory precaution (the UNESCO COMEST formulation) for catastrophic, uncertain harms, and scales caution to irreversibility, but never treats precaution as a default ban. He declined the 2023 pause letter while accepting a risk of “potentially existential proportions”, and he rejects “zero exposure — as in no AI” as a default strategy.

Navigation is the stance that holds this together (§2.3). It does not reject management; it names the frame within which management tools are used (interpretation: management stays as the operational work; he notes that safety is “so often operationalized as assessing and managing risk”, 2024-06-20). It maps a landscape of “shifting hills and valleys” rather than forecasting a single path (NN 2016-03 p.211), on the physics of a world that cannot be perfectly controlled yet has limits and points of leverage (FFTF p.41). It keeps lines where harm cannot be undone (fixed points†): evidence-based trigger points (Nature 2011), “quick to question, and slow to respond” with readiness to act on early warnings “even before the science is mature” (NN 2016-03 p.212), and the line between systems where it is easy “to turn the clock back” and “people, governance, society, and the planet” (2025-03-02, n.2). It builds in “rapid course correction”, because “set it and forget it” management does not work in jagged systems (2025-05-18). And it looks for openings as well as hazards: risk thinking should inform decisions that “remove risks, help identify ways to circumnavigate them, or strategically absorb them” (2023-11-21), turning risk “into a way of supporting beneficial and sustainable progress” (2016-01-11); a 2026 lecture puts it as “avoid it or flip it, and so get to the good” (2026-09-24 [mixed]).

Inevitability has been paired with steering from the start. In 2015 he described a converging “revolution that we cannot turn the clock back on”, while insisting “we have an opportunity to help steer” it (NN 2015-12 p.1006). In 2018 innovation was an “obligation” that comes with “tremendous responsibilities” (FFTF p.288). In 2020 he wrote of the future that “Dire as the outlook seems, it is not inevitable” (FR p.17). In 2024 slowing “the AI juggernaut” was a legitimate collective choice (Dune 2024), and he argued for “pausing — or even rethinking” chatbots designed to exploit how users feel (2024-10-27). In 2025, “Technology is not deterministic”, and “we do have the agency to determine what futures we aspire to and how we get there” (2025-03-30); AI can be channelled “much as a flood can’t be halted, but it can be directed” (2025-08-31). The trajectory is inevitable; its shape is open, and it is set early. Hence acting “ahead of the game” (Testimony 2008 p.7), before technologies lock into trajectories “highly susceptible to failure” (NN 2015-03 p.199). In a 2026 lecture he took the inevitability of powerful AI as a working assumption, adding that “it may be a flawed assumption” (2026-09-24 [mixed]). Firmness: counting the risks of not innovating is core (2006–2026); navigation as his working stance is core (2015–2026); an inevitable trajectory with a shape still to be chosen has been his position since 2015 (§9, tension 5).

Sources: Testimony 2006; AOH 2007; Testimony 2008; Nature 2011; NN 2014-03; NN 2014-09; NN 2015-03; NN 2015-12; NN 2016-03; 2016-03-02 how-risky-are-the-world-economic-forums-top-10; FFTF pp.41, 163, 240–244, 288; FR pp.17, 212; 2020-07-30; 2023-04-04; 2023-11-21 ai-and-risk-innovation; 2023-11-26; Dune 2024; 2024-06-20; 2024-08-18 four-ways-of-thinking-about-advanced-technology-transitions; 2024-10-27; 2025-03-02; 2025-03-30; 2025-05-18 exploring-ai-through-cause-and-effect; 2025-08-31 holding-on-to-our-humanity-age-of-ai.

In his words: “Too much blind speed, and you risk losing your way. But too much caution, and you risk achieving nothing” (FFTF p.163); AI can be channelled “much as a flood can’t be halted, but it can be directed” (2025-08-31).

8. Imagination and plausibility together: creativity to see risks and possibilities, play and serendipity to escape frames that no longer fit, and plausibility to rank what is found, applied to hype and doom alike.

“Critical thinking alone is almost inhuman in its cold impartiality. On the other hand, creativity on its own leads down a path of fantasy and delusion” (FFTF p.282). He holds both halves. The generative half is a claim about risk, made since risk innovation was named. For entrepreneurs the barrier is “not necessarily time and cost, but imagination” (NN 2015-03 p.200). Risk innovation needs a culture “grounded in transdisciplinarity, creativity and imagination; and epitomized by serendipity” (NN 2015-09 p.731). A lack of “creativity and flexibility” in how risks are understood “only increases the chances of things going wrong” (2016-01-11). AI risks may blindside us “in part because we’re not thinking creatively enough about how an AI might threaten what’s important to us” (FFTF p.174). “The juxtaposition of seemingly unrelated ideas can jolt us out of conventional ways of thinking” (2021-04-09). A playpen “quickly falls apart” where the journey breaks new ground (2025-03-15). And existential risks should not be dismissed, because “it would be embarrassing if we were all wiped out by something because we didn’t have the imagination to foresee it” (2026-09-15, n.5). Play and designed serendipity are how he does this in practice. Some of his ideas came out of play, and others, prompted by events, he tested on himself (§2.4).

The disciplining half is plausibility. Futures must be ranked by plausibility, and speculation harms people when it is mistaken for reality, through violence, policy, investment and forgone benefits. He applied the test to “grey goo” in his 2006 research strategy (PEN 2006 p.8) and 2006 Warner Lecture (AOH 2007 p.3). In 2010–11 it became a named principle of his regulatory and toxicological work: risk debate should be “informed by plausible emerging risks” (Handbook 2010 p.575), and plausibility is “a crude but effective filter to distinguish between speculative risks—which are legion—and credible risks—which are not” (Toxicol. Sci. 2011). In 2014 he asked researchers to “map out plausible domains of risk” in realistic products (NN 2014-06 p.410). The filter is openly qualitative, and it governs building more than imagining: in 2020 he wrote that we are predisposed to believe in futures we cannot show to be plausible, and “the world, and the future we strive for, are all the richer for this” (FR p.86). He used Occam’s Razor in 2018 to rank superintelligence and gray goo below evidence-based harms (“not a zero probability”, FFTF p.281), while warning on the same page that the razor is never “more than an aid to decision-making”. By 2025–26 he is readier to take tails seriously when a mechanism is plausible (2025-04-06; 2026-01-10), and in 2026 he describes his approach as “informed speculation” about possible futures, held with humility and opened to other voices (2026-09-24 [mixed]); his 2026 papers practise it throughout. Plausibility ranks what imagination has found; it does not stand in for it. Firmness: imagination as part of risk thinking is core, argued on risk grounds from 2015 to 2026 and rooted in his physics (“grounded in play”, 2024-03-17); plausibility is his most consistent epistemic habit (from 2006); where he sets its threshold has moved (see §9).

Sources: PEN 2006; AOH 2007; Handbook 2010; Nat. Mater. 2011; Toxicol. Sci. 2011; NN 2014-06; 2020science 2014 (3D-printed brain); NN 2015-03; NN 2015-09; 2016-01-11; FFTF pp.168–171, 174, 199–206, 281–282; 2018-11-01 contact-occams-razor; FR pp.86–93; 2021-04-09 bounded-infinities; 2024-03-17 undergraduate-playgrounds-not-playpens; 2024-11-17 navigating-the-ethical-dilemmas-of-brain-computer-interfaces; 2025-03-15 ai-playgrounds-in-higher-education; 2025-04-06 responsible-innovation-and-ai-acceleration (his framing); 2026-01-10 is-ai-a-cognitive-trojan-horse; Trojan 2026; CR 2026; 2026-09-15.

In his words: “what is plausible, rather than simply imaginable, is vitally important” (FFTF p.171); risk science “needs the freedom to dream, and the realism to anchor those dreams in plausible outcomes” (2020science 2014).

9. The technology–society system is complex, tightly coupled and increasingly irreversible, so the past guarantees nothing and reversibility decides how much experimentation is legitimate.

Complex systems are unpredictable in detail but bounded, which separates plausible futures from fantasy. “In systems where associations between cause and effect are complex, you ignore synergistic inter-relationships between factors at your peril” (2020science 2010b). They look stable until they tip. In 2015 he called for “mechanisms for detecting early warnings of systemic instabilities” in converging technologies that could signal “catastrophic failure”, warning that without them such systems risk “failing fast and failing spectacularly” (NN 2015-12 pp.1005–1006); by 2020 learning to “spot early warnings and stay clear of critical tipping points” is a survival skill (Future Rising, quoted in 2024-09-08). In 2019 he named the vulnerabilities of entrepreneurial culture as tight coupling, latency (harms that appear only after the innovation cycle has moved on) and value mismatch (2019-08-13). Across history, mistakes have become harder to undo, and consequences now pile up faster than fixes. Experimenting in reversible, linear systems is fine; breaking “people, governance, society, and the planet” is not (2025-03-02). Firmness: very high; his most constant structural premise (from 2010).

Sources: 2020science 2010a, 2010b; 2015-01-30 responsible-development-of-new-technologies; NN 2015-12; FFTF pp.39–43, 166–167; 2019-08-13 responsible-innovation; FR ch.39; 2021-04-09 bounded-infinities; 2023-05-04 tipping-points-and-broken-symmetries (from a 2018 draft); 2024-09-08 a-journey-from-the-past-to-the-edge-of-tomorrow; 2025-03-02 the-lure-of-permissionless-innovation; 2025-05-18 exploring-ai-through-cause-and-effect.

In his words: permissionless innovation in the nuclear and digital age is “playing with fire in a world made of kindling” (FFTF p.167); “In a complex system, what has occurred in the past may not adequately predict what will happen in the future” (2023-05-04).

10. Talking about risk is how benefits are realised; public concern is a signal, not noise.

Fear and dismissal are the same error: both replace attention to what people value with instinct. “Don’t Panic” (FFTF pp.289–290) comes paired with a warning against being “so enamored by the tech itself”. Public concern is data about value. His 2006 testimony already counted public rejection as a risk to a technology’s benefits (Testimony 2006 p.52), and a 2010 WEF proposal he co-drafted drew the lesson from GM foods that people said no “not because of the science and technology, but because of the way they were handled” (CETI 2010 p.1). In 2016 he noted that “numeric logic is often trumped by what we intuitively think and feel is important” (2016-03-12); perception is one of the five elements of risk (2023-11-26); moral panics signal threats to “what’s important to people” (2025-06-01); and backlash is itself a risk, because it can make development “far less accountable” (2024-02-18). Perception is not everything, though: the slogan that it is, he wrote in 2020, is “strictly speaking, not true. No matter how much you fear flying, it isn’t going to affect the likelihood of a crash” (FR p.154). Talking about risk is the opposite of fear-mongering for him. In Films from the Future, “Talking’s tough. But not talking is potentially more dangerous” (FFTF p.227). His 2018 Risk Bites video on the less obvious risks of AI was made “not to stoke fears (not my style)” but to prepare the ground for informed approaches (as he recalls in 2026-09-15). In 2026 he framed a set of personal rules for AI by putting “the safety message first”, because the benefits of a powerful technology “are often self-evident, the risks are not” (2026-05-10), a single 2026 wording of this long-standing stance. Firmness: a constant temperament (from 2006); perception is a named part of his work (“my work on risk perception and engagement”, 2026-07-10); the tone darkens in 2026 while the stance holds.

Sources: Testimony 2006; CETI 2010; Regrettable substitutions 2014; 2016-03-02; 2016-03-12 itll-take-more-than-tech-for-elon-musk; FFTF pp.226–227, 287–290; FR pp.154–155; 2023-04-18 universities-need-to-be-investing; 2023-11-26; 2024-02-18 setting-fire-to-self-driving-cars-is-bad; 2024-06-23 existential-risk-jay-baruchel; 2025-06-01 vibe-coding-moral-panic; 2026-05-10 do-not-do-this-with-ai; 2026-09-15 will-ai-really-kill-us-all.

In his words: “it’s pretty much impossible to manage risks if you don’t talk about them” (2026-09-15); tech-driven moral panics “are rarely cut and dried” (2025-06-01).

11. Most technological harm comes from sincere people working inside institutions and incentives: good intentions are not enough, responsibility cannot be self-certified, and known risks can become nobody’s.

Scientists and entrepreneurs are rarely villains. They fail through absorption, through “their version of ‘responsible’”, and through deciding for others without asking. Permissionless innovation is the same failure at scale: responsibility judged by the innovator alone. The remedy is humility, other people’s expertise, and checks on “who gets to do what” where consequences are wide or irreversible. The account has been structural as well as psychological from the start. In 2006–2011 he argued that bodies which promote a technology should not be relied on to oversee its risks, and that industry could not lead risk research because it has “an economic incentive to sell products” (PEN 2006 p.32; Testimony 2007 p.30; Hansen et al. 2008 p.446). In 2007 he told Congress that “good intentions are not enough” (Testimony 2007 p.16). In 2015 he explained entrepreneurs’ “deep-seated belief in the safety and efficacy of their creations” as something they need “in order to convince others to invest in their vision” (NN 2015-03 p.199). In 2019 he wrote that without codified approaches, “the good intentions of entrepreneurs will in many cases remain good intentions, and no more”, and that “the value of expediency is not the value of net societal benefit” (2019-08-13). Later come products built to make us “ever-more-dependent super-consumers” (2022-02-12), competitors racing “far faster than a measured and responsible approach would suggest is wise” (2023-11-18), and an “economic gradient” that pulls AI toward manipulation even when no one intends it (2024-07-13). The AI-assisted 2026 frontier-AI paper formalises this as sincerity operating “inside an incentive field” [mixed]. He applies the diagnosis to himself.

The same account explains orphan risks, the risks that fall between the cracks of institutions and frameworks (§2.3). In 2018 they were risks that are “‘known knowns’ if you’re looking in the right place, but aren’t taken as seriously as they should be” (2018-12-13); by 2020, “hard to quantify threats to value that often slip between the cracks of conventional risk approaches” (2020-10-15); and in April 2026, AI’s human-side risks, which “no existing institution owns” (NANO 2026). In July 2026 he turned the concept into an institutional question, “by what process does a known risk come to be nobody’s responsibility?”, and answered it through incentives rather than villains (2026-07-16 [mixed]). Its roots are in his nanotechnology work: fibres that “slipped through the regulatory net” (Nature 2011), and emergent risks “not apparent, assessable, or manageable based on current approaches” (Toxicol. Sci. 2011). Firmness: very high (2006–2026); the book’s account was republished unchanged in 2023 and 2025. Orphan risks: named in 2018 and applied from 2019; recast as an institutional question in 2026, in a paper of mixed provenance whose core concept is securely his.

Sources: PEN 2006; Testimony 2007; Testimony 2008; Hansen et al. 2008; Nature 2011; Toxicol. Sci. 2011; NN 2015-03; NN 2016-06; FFTF pp.36–39, 159–168, 218–227; 2018-12-13 tech-startups-orphan-risks; 2019-04-15 tech-companies-need-an-ethics-reset; 2019-08-13 responsible-innovation; 2019-11-01; 2020-10-15 the-ethics-of-advanced-brain-machine-interfaces; 2022-02-12 scarlett-johanssons-amazon-alexa-super-bowl-ad; 2023-10-02 responsible-ai-lessons-from-nanotechnology; 2023-11-18 sam-altman-openai-impacts; 2024-07-13 ai-choice-engines-sunstein; 2025-03-02; NANO 2026; 2026-07-16 [mixed].

In his words: “With the best will in the world, a single innovator cannot see the broader context” (FFTF p.162); the gradient toward manipulation “may not be intentional or even malicious” (2024-07-13).

12. Justice is the test: who benefits, who bears the harm first, and who profits from uncertainty. Renouncing technology from privilege is also unjust.

His occupational-health past taught him that harm lands first on the least protected and that scientific doubt can serve those who profit. In 2006 he told Congress that “it is ultimately the public—as workers or consumers, for instance—that may bear many of the potential risks” (Testimony 2006 p.53), and in 2008 he asked “Who is reaping the benefits of new nanotech applications, and who is paying the price?” (Bulletin 2008). Technology amplifies power; markets do not deliver equity by themselves; the Luddites fought unjust use, not technology. The counterweight is also about justice: renouncing technology “from a position of privilege” denies others choices. Firmness: core (from 2006); the moral axis of his work, if less often the headline after 2023.

Sources: Testimony 2006; Nature 2006; Bulletin 2008; Rethinking Risk 2017; FFTF pp.100–122, 190–193, 288; 2019-03-31 design-principles-for-de-marginalizing-the-future; FR pp.191–193; 2023-05-12 unraveling-the-luddite-narrative; 2023-10-19 marc-andreessen-ditch-sustainability; 2025-03-09 the-hard-concept-of-care-in-technology-innovation (his framing); 2026-07-16 [mixed].

In his words: black-lung doubt was “an uncertainty that suited the mine owners” (FFTF p.120); the question is “who decides who will suffer and who will thrive” (2023-10-19).

13. Govern with adaptive, anticipatory, multi-stakeholder portfolios, backed by strong capacity for risk research; operationalise ethics; keep hard law for specific harms.

His governance thinking has developed in stages, each kept as the next was added. The first, in testimony to Congress in 2006–08, was strong central capacity for risk research: a strategy “with teeth”, a single accountable leader, at least 10% of federal nanotechnology research spending for risk research, full transparency, and independent research bodies funded jointly by government and industry on the model of the Health Effects Institute. “A list is not a research strategy” (Testimony 2006 p.51). His WEF proposals of 2008 and 2010 called for independent institutions to anticipate the problems of emerging technologies. In 2011 he proposed adaptive regulation through evidence-based “trigger points” that “must be flexible, so that they can be modified as evidence grows” (Nature 2011). From 2015 he adds a preference for agile governance, soft law and cross-agency capacity over technology-specific hard law, which is “crude, cumbersome” while AI is a moving target (2023-05-17). In 2019 he argued that top-down governance can create only “crude boundaries” for responsible innovation in entrepreneurial cultures, which reject frameworks imposed as obligation (2019-08-13). Ethics boards and principles are necessary but empty without standards, enforceable checks and actual use. Hard law has a place for specific harms: criminalising harmful deepfakes (2024), and regulating apps designed to exploit cognitive biases (2025). In 2026 he argued that remedies “have to change what competition rewards”, that he is “not optimistic” regulation alone will close the gap, and that regulators should require disclosure of how firms select the risks they manage (2026-07-16 [mixed]). Papers he co-signed on biopreservation governance support moving between soft and hard law “as data become available” (Wolf et al. 2024 p.546), and hard-law oversight when processing turns an organ into “a product of human artifice” (Pruett et al. 2025). He is also frank about the limits, and about what remains: “I don’t have a governance solution for AI. I’m not sure anyone does. But I do think the nanotech experience offers something valuable: evidence that inclusive, transdisciplinary governance — however messy and slow — produces better outcomes than leaving decisions to the people who happen to be building the technology” (NANO 2026). Firmness: firm on the portfolio; a long-standing wish for strong, independent capacity for risk research beside flexible regulation; hedged on instruments; declining confidence in governments’ agility.

Sources: Testimony 2006; Testimony 2007; Testimony 2008; WEF 2008; CETI 2010; Handbook 2010; Nature 2011; 2016-04-01 will-driving-your-own-car; 2019-04-15; 2019-08-13; 2023-04-04; 2023-05-17 ai-senate-hearing-may-2023; 2023-12-22; 2024-02-25 ai-rollercoaster-of-a-week; 2025-08-31 holding-on-to-our-humanity-age-of-ai; 2026-05-03 are-design-principles-for-responsible; 2026-07-16 [mixed]; NANO 2026.

In his words: “there are no silver bullets” (2023-04-04); ethics are “worth little without mechanisms and processes” (2019-04-15).

14. Learn from past technologies by method and process, not by template; judge by behaviour, not label.

What transfers from chemicals, nanomaterials, GMOs and recombinant DNA is a way of assessing risk, lessons about engagement and trust, and recurring human patterns, not claims that AI’s harms resemble earlier ones. He stated the rule for tools and materials early. Control banding “is not directly applicable to engineered nanomaterials. But the concept is.” (AOH 2007 p.10). In 2008 he and his co-authors tested nanotechnology against the European Environment Agency’s lessons from past “early warnings”, judging that some lessons “are not directly applicable to emerging technologies” while many “are directly relevant”, and that the question was “whether we are applying them effectively enough” (Hansen et al. 2008 p.447). In 2011 a review he led set out “technology independent” principles for deciding what to study, including decoupling risk questions from a technology’s label (Toxicol. Sci. 2011), and he argued that materials should be regulated by the risks they present, “not by the technological labels that come attached to them” (Nature 2011 draft). Novelty is “a rather unreliable indicator of potential risk” (NN 2014-06 p.410), and “seemingly novel challenges don’t always demand novel solutions” (NN 2015-06 p.483). As he later put it, “nature doesn’t care what we call a material” (2022-02-10): lessons transfer when mechanisms recur, not when labels match. Each technology wave tends to “re-invent the wheel” (2023-04-12), yet by 2026 he holds that AI “defies analogy”. He reconciles the two in his own 2026 work: AI shows “a substantial scaling of recognized phenomena in ways that are not predictable from past experience” (CR 2026 p.2), and “The technology had changed dramatically. The human questions hadn’t changed at all” (FWB 2026). Firmness: the method and process lessons are firm (from 2007). The discontinuity claims date from 2023 (“unlike anything we’ve had to grapple with before”, 2023-04-12; analogies that “fail to capture the sheer uniqueness and profundity” of AI, 2024-05-05; “a categorical error”, 2025-03-15) and concern what AI does to the self, and the scale and speed of change, more than mechanism.

Sources: AOH 2007; Hansen et al. 2008; 2020science 2008b; 2020science 2009; Toxicol. Sci. 2011; Nature 2011; NN 2014-06; NN 2015-06; NN 2016-03; 2019-03-05; 2022-02-10; 2023-04-04; 2023-04-12 navigating-advanced-technology-transitions; 2023-05-15; 2023-10-02; 2024-05-05 blackberry-or-iphone-educational-ai; 2025-03-15; 2025-07-23 americas-ai-action-plan; 2026-01-22 think-you-know-ai-think-again; CR 2026; FWB 2026; STICK 2026.

In his words: “the one big lesson from previous advanced technologies is that if we don’t listen and engage early and often, we’ll come to regret it” (2023-05-17); “the technology-specific details change constantly, but the human questions underneath them are remarkably stable” (STICK 2026).

15. The plausible and distinctive AI danger is to the mind, and it works through language and relationship, with or without intent.

Human cognition is exploitable. On his account each person builds reality from “shadows” in a personal Plato’s Cave, and a machine outside the “human club” does not share human frailties. His earliest writing on AI risk in the record, in 2014, asked whether prolonged interaction with intelligent machines might change human behaviour for the worse (2020science 2014). Artificial manipulation mattered more to him than superintelligence in 2018, and he restated that judgement explicitly in 2023 (reposting the chapter), 2025 (“I wrote about this back in 2018”) and 2026 (“a claim I stand behind more firmly now than when I wrote it”, FWB 2026). Future Rising (2020) adds the mechanism in general form: evolved instincts that are “increasingly poorly equipped” for the world humans have built (FR p.56), a “fake-o-meter” that fails when content is designed to incense or enamour its audience (FR p.157), and the observation that “the smarter we are, the better we are at justifying our beliefs” (FR p.153). From 2023 he locates the channel in language, the medium through which trust, relationship and identity form. So a fluent machine can shape people through designed intimacy, commercial incentive, emergent “stochastic agency” (2024-10-27), or simply the ordinary features, such as fluency, that slip past epistemic vigilance (2026-01-10). His 2026 paper is explicit that it concerns “AI systems designed to be genuinely useful”: the risk comes from AI working as intended, not from misuse (Trojan 2026 p.1). The harm needs no intent, consciousness or AGI. In 2026 he adds that treating AI “as just a tool, is potentially dangerous” (2026-05-21), that conversational AI couples with the processes by which people form themselves (“constitutive resonance”, CR 2026), and that influence runs both ways: the AIs “we have trained to ‘think’ like us are now beginning to train us to think like them” (2026-07-19). Its most distinctive consequence is second-order. If AI acts on “the very cognitive abilities we rely on to navigate differences between what we experience, and what we’ve evolved to live with” (2026-01-10), it acts on the navigator†: on users, institutions, evaluators, builders and analysts, himself included (“how do I know I’m not an unwitting victim here?”, 2026-01-17). The seed is in 2018, when he called for “tests that indicate when we are being played by machines” (FFTF p.177). Interpretation: it strengthens his case that AI makes a change of mindset unavoidable (C3), whose main ground in his own words is that AI fits no earlier type of risk; it is also why he looks to “a collective form of epistemic vigilance” rather than individual vigilance alone (2026-01-17). Firmness: the manipulation core is very high, the strongest continuous AI-specific thread in his work (2014–2026). The language layer is firm from 2023. The relational thread runs from 2023 (“the illusion of a reciprocal relationship”, 2023-04-05) and is formalised in 2026, in his own preprint (see §6.8); the navigator argument is set out in 2026.

Sources: 2020science 2014 (3D-printed brain); FFTF pp.159, 174–177; 2018-05-12 10-potential-risks-of-artificial-intelligence; FR pp.55–56, 152–158; 2023-04-05 can-chatgpt-adversely-impact-mental; 2023-04-16 ai-and-the-art-of-manipulation; 2023-04-26 in-bill-joys-why-the-future-doesnt; 2024-01-01 the-future-of-being-human-in-2024; 2024-07-13; 2024-10-27 personal-ai-chatbots-and-stochastic-agency; 2025-07-06 ai-risk-motive-means-and-opportunity; 2025-08-31; 2026-01-10; 2026-01-17 i-cracked-and-wrote-an-academic-paper; Trojan 2026; 2026-02-22 what-we-miss-when-we-talk-about-ai-harnesses; CR 2026; 2026-05-21; 2026-07-19 publish-or-perish-ai-vs-human-vs-human.

In his words: manipulation is “far more plausible, and far scarier as a result” (FFTF p.159); LLMs are “optimized for processing fluency” (2026-01-10).

16. AI risk is a plural landscape; existential risk is low-probability, not to be dismissed, and better framed as catastrophic loss of value.

His 2018 list of ten AI risks put existential risk from superintelligence beside dependency, jobs, bias, opacity, misalignment, weapons, rewritable goals, unintended consequences and manipulation; in 2026 he said it still holds. The list had earlier roots: in 2014 he wrote that he did not “buy this vision of an AI ‘singularity’” (2020science 2014); in 2015 he warned that the risks of cyber “insecurity” would rise “by orders of magnitude” with distributed manufacturing (NN 2015-12 p.1005); and his programme’s 2019 tools listed “Loss of Agency”, noting its relevance to machine learning, “where there is a lack of understanding around how and why decisions are made” (Nexus 2019 cards p.24). Around the list he has added systemic risks: the disruption of democratic processes and “fights for truth and democracy” (2023-05-31; 2023-07-25), the influence of “unelected billionaires” (2024-01-17), social cohesion and “social collapse” (2024-08-25), and the drain of human agency as work becomes “AI-directed and human-executed” (2024-11-24). He declined both the 2023 pause letter and the extinction statement (while calling the statement “important” and having “a lot of sympathy” with it), and redefined catastrophe as large numbers of people losing what they deeply value. What he rejects is an existential-risk culture that valued the “philosophical elegance” of ideas over the science of how the world works and showed “a disdain for society” (2024-04-28), not the possibility of catastrophe. Ignoring catastrophe is itself risky. Firmness: stable calibration; what grows is his willingness to take tails seriously and to consider non-AGI routes to loss of control.

Sources: 2020science 2014; NN 2015-12; 2018-05-12; FFTF pp.168–171, 281; Nexus 2019; 2020-11-12 is-artificial-intelligence-going-to-kill-us-all; 2023-04-04; 2023-05-31; 2023-07-25 oppenheimer-and-ai; 2024-01-17 ai-global-risks-2024-wef-davos; 2024-04-28 beyond-the-future-of-humanity-institute; 2024-06-23; 2024-08-25; 2024-11-24 artificial-intelligence-agency-human-amanuensis; 2026-09-15.

In his words: extinction is “too narrow and absolute a framing, and too human-centric” (2023-05-31); existential risk should be handled “without running around like headless chickens” (2026-09-15).

17. What is ultimately at stake is being human, “who we are”, and AI reaches there as no earlier technology has.

In 2014 he wrote that the more plausible risks of artificial minds were those “that challenge our very notions of humanity” (2020science 2014). From 2018, “what it means to be human” was on his list of values at risk, and the book’s deepest warning was against technologies that make a society “forget the worth of others” (FFTF p.62). He publicly corrected his own language when a colleague pointed out that it implied people who are not “human-typical” are suffering “and need to be fixed” (2024-01-30), and he asks who decides what is “normal”. In 2024 he distinguished technologies extrinsic to the self from intrinsic ones that may change “what we are”, possibly without consent, and wrote that the bedrock of being human can no longer be held “as if it’s an immutable truth” (2024-12-29). In 2025 he proposed three intersecting foci for navigating AI transitions, how AI could affect “where we live”, “what we do” and “our understanding of who we are” (2025-01-07), and he places AI’s most distinctive effects in the last. His March 2026 preprint argues that conversational AI enters the processes through which people become “who we are becoming” (CR 2026). It is also where he sees promise: his 2025 keynote turned the usual question round, asking “how do we learn how to be human in an age of AI?” (2025-03-30). Firmness: core from 2018 (with a 2014 seed), and strengthening.

Sources: 2020science 2014; FFTF pp.23, 57–62, 108, 136–141; 2023-08-18 being-human-in-an-augmented-future; 2024-01-01; 2024-01-30 first-in-human-trial-of-neuralink-bci; 2024-10-13; 2024-12-29 fantasy-top-ten-lists-2025; 2025-01-07 universities-need-to-step-up-their-agi-game; 2025-03-30; CR 2026; 2026-05-21 (his reading of the papal encyclicals).

In his words: “what drives my work more than anything” is the possibility that our technologies “begin to fundamentally change who we are — or even what we are” (2024-01-01).

18. Learning and education exist to build people’s capacity to create value and to navigate transitions, and access to that capacity is a matter of justice.

Education matters because it lets us “dramatically increase the rate at which we can create value for ourselves, our communities, and the society we are a part of” (2025-03-30). Its roots in his governance work are old: in 2008 he told Congress that too little had gone into “educating and engaging the public” about nanotechnology (Testimony 2008 p.15); in 2015 education for everyone “from consumers to CEOs” was one of his six lessons for steering the fourth industrial revolution (NN 2015-12 p.1006); and in 2016 he argued that when curious, self-directed learners cannot find good information, “it becomes easier for nanotechnology development that is not accountable to citizens to occur”, and easier “for opportunists to fill the information-vacuum” (NN 2016-09 p.735). So AI can widen access (AI as “translators” for applicants without polished prose; AI skills in “every high school”, 2023-07-27; flattening the learning distribution curve, 2023-10-24) or hollow learning out (“the illusion of learning rather than actual learning”, 2026-05-10). Learning has to be experienced (“transformative learning has to be felt”, 2021-01-15), so he prefers “the lowest level of tech necessary” (2024-02-11), playgrounds to playpens (2024-03-17; 2025-03-15), and conversation to prompt-checking (2025-08-17). Living with social AI will need “strategic and intentional approaches to developing the ‘social’ skills” involved (2024-10-20). In April 2026 he described education as “about human formation”, with AI as potentially “a tool for formation rather than a threat to it” (S3 2026). Institutions owe students a duty of care (2025-11-09), and advisors owe them dignity (2025-10-26). Universities have a public duty (2016-01-31) and, by 2026, a role in helping society navigate AI that he fears they “may not be up to” (2026-08-30). Firmness: the public duty of universities is core (2016–2026); education as a lever of governance goes back to 2008; the value-creation model is 2025 but builds on older ideas; his confidence in AI literacy as a remedy declines from 2025.

Sources: Testimony 2008; NN 2015-12; NN 2016-09; 2016-01-31 public-universities-must-do-more; 2021-01-15 can-watching-sci-fi-movies-lead-to-more-responsible; 2023-07-27 chatgpt-and-college-applications; 2023-08-14; 2023-10-24 flattening-the-learning-distribution-curve; 2024-02-11 one-week-on-with-the-apple-vision; 2024-03-17 undergraduate-playgrounds-not-playpens; 2024-10-20 learning-to-live-with-agental-social-ai; 2025-01-07 universities-need-to-step-up-their-agi-game; 2025-03-15 ai-playgrounds-in-higher-education; 2025-03-30; 2025-08-10 the-scared-witless-educators-guide-to-gpt5; 2025-10-26 ai-misuse-in-student-advisor-collaborations-1; 2025-11-09 universities-chatgpt-mental-health; S3 2026; 2026-05-10; 2026-08-02; 2026-08-30 do-universities-have-a-place-in-bill.

In his words: “I have a philosophy as an educator of putting learning and student success first, and using the lowest level of tech necessary” (2024-02-11).

His method, in brief#

§2 sets out his method in full. Its habits are as stable as any of the commitments above. - Questioning the frame first. He asks what a term or category assumes before reasoning inside it, and judges a frame by what it opens (§2.5; 2023-05-31). - Imagination loosened and tightened. Play, story, juxtaposition and designed serendipity open the frame; plausibility, physics and evidence discipline what comes out (C8; §2.4). - Stories as instruments of threatened value. Films reveal technology–society dynamics “precisely because they are not tethered to scientific accuracy” (FFTF p.288), and because “Each of these films has a risk-based narrative tension” built from what people value (FFTF pp.23–24); stories open minds that preaching closes (2024-01-21). Art and speculative design entered his risk teaching around 2011–12 (2020science 2012), after a 2010 proposal he co-drafted had still used “science fiction” as shorthand for poorly informed opinion (CETI 2010 p.3); films organised his first book (2018) and much of his teaching since. - Building and experimenting to think. He experiments hands-on, especially with AI, often with himself as the instrument. Some of his ideas came out of these experiments; others, prompted by events, were tested in them (§2.4). - Humility, tested in public. He admits he may be wrong (“This is a very personal perspective, and I may be wrong”, 2020science 2009), posts addenda, republishes old work to check whether it still stands, and names reversals: “I have changed my mind” (Nature 2011), “we were somewhat naïve” (2020-10-15), “Today I am far less sure” (2024-02-25), “Clearly I read the tea leaves wrong” (2025-11-19). In 2016 he published a formal audit of his own 2006 research agenda, a table headed “A personal assessment of progress” (Maynard & Aitken 2016 p.999). He labels his speculative work “explorations, not findings” (HNS 2026). - Self-implication. He turns his theses on himself: he confesses rule-bending in the lab (FFTF p.161), admits signing a hospital consent form “not because I’d done the math” (Rethinking Risk 2017 p.195), includes himself in “myopically benevolent science”, and admits being “suckered by Claude” while writing about being fooled (2026-02-08). - Scholarship in public. Public writing is “integral to how I explore, test, and share new ideas and insights” (2026-05-17), and he adopts Pielke’s “honest broker” role for his public work, with its stated limits (FFTF p.246; §2.7–§2.8).

Firmness: his most stable traits, 2009–2026, with play at the root of his physics and named as his method from 2024 (2024-03-17). In his words: “I freely admit that I may be wrong” (FFTF p.170); “All of us, it has to be said, have a bit of Sidney Stratton in us” (FFTF p.227).

6. Key concepts#

A concise glossary, grouped by territory. Definitions are in his terms; concept names marked † are the map’s labels (see §1). “First” is the earliest documented appearance in the record, including the supplementary corpus. Centrality: - Core: spans several periods of his work, organises other ideas, and recurs unprompted. Kept to about a quarter of the glossary, so that the label stays meaningful. - Recurring: repeated across periods, supporting rather than organising. - Rising (as of September 2026): 2025–26 in origin and prominent in 2026, but not yet tested by time. A rising concept may become core; recency alone does not make it so. - Occasional: a handful of appearances, or important in one period. - One-off: a single developed appearance so far.

Centrality describes the shape of the record, not importance for AI. Where a concept matters for AI more than its share of the record suggests, its entry says so (“Value for AI”), and §2.9 explains why.

6.1 Risk#

Concept His meaning First / key dates Centrality
Risk innovation A change in how risk is conceived, for technologies that fit no earlier type of risk: “parallel innovation in how we conceptualize risk” (2015), in “a culture grounded in transdisciplinarity, creativity and imagination; and epitomized by serendipity”, with ideas judged by their impact rather than by convention (“risk entrepreneurship”); “designed to open up new ideas and possibilities” (2016). A mental model, not a procedure. Built on established risk assessment, not in place of it: assessment is “Important” but incomplete (2015); risk innovation is “intended to complement and enhance existing risk assessment and management approaches” (2020) and was “conceived from the outset as complementary” (2024) Seeded 2013 (Michigan teaching); NN 2015-09 (named); 2016-01-11; FFTF pp.22–23; 2020-11-05; 2023-04-04; JLME 2024; 30Y 2026 Core
Risk as a threat to value A threat to anything of importance to a person, community or organisation, from health and wealth to dignity, identity, belief and aspiration. What it opens: resistance made intelligible; go/no-go choices turned into design questions; benefits and harms in one account; risk as “an inevitability that reveals what the primary value is within a complex landscape”. It stands on the probability-of-harm definition, “a useful starting point” NN 2015-09; NN 2016-03; 2016-01-11; Rethinking Risk 2017; FFTF pp.23–24; 2018-12-13; 2023-05-31; 2024-08-25 Core
Quantitative risk assessment as a foundation Probability of harm, hazard, exposure, dose and weight of evidence remain the foundation and the toolkit; new approaches are “grounded in established approaches” and “an evolution of the old black-and-white mathematics of risk” ILSI 2005; AOH 2007; Handbook 2010; Nat. Mater. 2011; Toxicol. Sci. 2011; Rethinking Risk 2017; Coronavirus 2020; 2026-07-16 [mixed] Core (foundational)
Humility about precision: numbers that comfort without informing Knowing what to measure comes first: “The harder challenge is working out what we should be measuring”; a statistical parameter “may not adequately reflect a risk parameter of relevance”; numbers “can be comforting” but “misleading”. Measurement designed around ignorance (several dose metrics; records for later reinterpretation). Earlier forms: “false assumptions of safety”; “mistake methodology for strategy”; precise predictions “less likely … to be accurate”; institutions “retreat to what can be quantified”. For AI, humility reaches whether the problems can yet be formulated (“the less certain I am that we even know how to formulate the problems we face around AI”), which grounds his sparing use of numbers and his offer of mechanisms and testable hypotheses instead ILSI 2005; PEN 2006; Nature 2006; Testimony 2007; 2020science 2009; NN 2015-06; NN 2016-03; FR p.148 (2020); 2023-11-26; 2025-05-04; Trojan 2026; 2026-07-16 [mixed] Core (method and stance)
Decisions under incomplete information Neither wait for data nor invent precision: control banding, rules of thumb, benchmarks, precautionary exposure reduction; “When the data run out – innovate!” PEN 2006; AOH 2007; 2020science 2009; JLME 2024 (“paralysis by analysis”) Recurring
Value vs values Value is worth to someone and can be lost or gained; values are right and wrong. Value is easier to act on and “agnostic to particular worldviews” 2016 (parenthetical); 2023-11-21 (his framing); 2024-12-17 Recurring
Existing and future value Risk balances protecting value that exists against enabling value that could exist NN 2015-09 (“existing or future ‘value’”); 2016-03-02; 2024-08-25 Recurring
Reciprocal threats Threatening what others value comes back to threaten you; later “your risk is my risk”. A 2022 guide he co-wrote states the assumption behind it: “threatening stakeholder value becomes a threat to principal agent value” 2016-01-11; 2018-12-13; CIO guide 2022; 2024-12-17; 2026-07-16 [mixed] Recurring
Risk landscape The terrain “that lies between new ideas and their successful implementation”, with “shifting hills and valleys”, which technologies “both face and help to form”; unpredictable in detail but bounded, so to be mapped rather than forecast, with opportunities as well as threats. What it opens: a map of pathways in place of a single forecast or a go/no-go verdict NN 2015-09 (“murkier risk landscape”); 2016-01-11; NN 2016-03; FFTF p.41; 2018-12-13; 2019-11-01; 2023-11-26; 2024-08-25 Core
Navigating rather than managing The stance within which management tools are used: map the landscape; keep lines where harm cannot be undone (trigger points, early warnings, reversibility; “fixed points†”); build in “rapid course correction”, since “set it and forget it” management fails in jagged systems; and look for ways to “circumnavigate” or “strategically absorb” risks, or to turn a threat into an opening (“the competitive edge”, 2018; “flip it”, 2026 [mixed]). Not a rejection of management, which on the map’s reading stays as the operational work NN 2015-12 and NN 2016-03 (column titles); 2016-01-11; FFTF p.41; 2018-09-03; 2023-11-21; 2024-06-20; 2025-03-02 (n.2); 2025-05-18; 2025-08-31; 30Y 2026 (“navigated creatively in pursuit of value”); 2026-09-24 [mixed] (“avoid it or flip it”) Core
The risks of not acting (symmetric risk†) Not innovating, inertia and precaution itself carry risk. Both sides are counted, but not always weighed equally Testimony 2006; NN 2014-03; 2016-03-02; FFTF pp.163, 241–244; 2023-11-26 Core
Risk as socially defined; safety as social Harm, safety and acceptability are set by people; “safe” is “a relative term”; “who decides what ‘safe’ means” PEN 2006; 2016-03-31; 2023-11-26; 2024-06-20 Core
Hazard vs risk; exposure Harm requires exposure and a causal pathway; judge net risk across the life cycle; dose–response is often non-linear (thresholds, hormesis) ILSI 2005; AOH 2007; 2020science 2009; 2015-01-10; 2016-02-01; 2019-03-05; 2023-11-26 (addendum) Core (foundational)
Emergent risk Harm “not apparent, assessable, or manageable based on current approaches”; one of three “technology independent” principles (with plausibility and impact) for deciding what to study. The conceptual precursor of orphan risks Toxicol. Sci. 2011 Occasional (foundational)
Trigger points for action Evidence-based thresholds for regulatory action, flexible “so that they can be modified as evidence grows”; “how are appropriate trigger points for action defined?” Nature 2011; NN 2014-09 Occasional
Quick to question, slow to respond Leave room for speculative research, avoid “hard-to-rescind decisions” on immature science, but be ready to act on early warnings “even before the science is mature” NN 2016-03 Occasional (his most precise rule on timing)
Novelty as an unreliable indicator Novelty is “a rather unreliable indicator of potential risk”: it overplays some risks and hides mundane ones; “mundane risks are still risks” NN 2014-06 Occasional
Algorithmic exposure Anyone affected by an algorithm’s decisions is “exposed” to it; the chemical-risk grammar carried over, with “an algorithm is not a chemical” 2019-03-05 Occasional
Exposure of the mind (cognitive exposure†) Exposure placed in the people affected: anyone affected by an algorithm “can be thought of as being exposed to it” (2019); hazard “as subtle as influencing human behavior”, exposure as “hints of ideas encountered over hours of social media use”, with non-linear dose–response (2023); “more exposure means more opportunities for fluency effects to accumulate” (2026). A conceptual transfer from toxicology, with the break points named (“an algorithm is not a chemical”) 2019-03-05; 2023-11-26 (addendum); 2024-06-20; 2026-01-10; Trojan 2026 Recurring (2019–2026)
Risk from first principles Five elements: cause and effect (“no cause, no risk”), magnitude, harm, time, perception; AI takes each “to a whole new level” 2020-07-30; 2023-11-26 Recurring
Risk perception, moral panic and techlash Public concern as a signal of threatened value, not irrationality: “numeric logic is often trumped by what we intuitively think and feel is important”; moral panics are “rarely cut and dried”; backlash as a risk in its own right; acceptable safety “highly subjective”. Perception is not everything (“strictly speaking, not true”, 2020), and specific judgements shift with framing (a 2014 study he co-wrote). Cultural-cognition research from his nano years carried into AI persuasion and the Intelligent User Trap Testimony 2006; CETI 2010; Regrettable substitutions 2014; 2016-03-12; FR p.154; 2023-11-26; 2024-02-18; 2024-08-07; 2024-09-01; 2025-06-01; 2026-01-10; 2026-07-10 (“my work on risk perception and engagement”) Recurring
Comparative risk against a baseline; benchmarking Benchmarking “helps tether speculative ideas to plausible realities”; checking a technology’s risk against the status quo with his own “back-of-the-envelope calculations”; “not all technologies — or companies — are created equal” Handbook 2010; NN 2016-03 (a named practice); 2021-08-03 (bibliometric comparison of AI ethics and risk work); 2023-11-09 waymo-safety-study-shows-benefits; 2025-11-09 Recurring
Orphan risks Known but unowned threats to value that conventional approaches sideline. 2018: “‘known knowns’ if you’re looking in the right place”, dismissed as “too ill-defined, too complex, or too irrelevant”; 2019–21: a mapping method, used for brain–machine interfaces in place of an ethics critique, and risks with “no agreed upon tools, standards, or mitigations”; 2020: “hard to quantify threats to value that often slip between the cracks of conventional risk approaches”; April 2026: AI’s human-side risks, which “no existing institution owns”; July 2026: “by what process does a known risk come to be nobody’s responsibility?” [mixed]. A mental model of institutional blind spots; an orphan risk is, in effect, a late lesson in the making (interpretation) Toxicol. Sci. 2011 (emergent risk, precursor); Nature 2011 (Libby); 2018-12-13; Nexus 2019; 2019-11-01; 2020-10-15; 2021-09-07; 2023-11-15; NANO 2026; 2026-07-16 [mixed] Recurring in the record; Core as a mental model. Value for AI: high, arguably his most useful framing for AI governance, a weighting resting on his own prose of 2018–2026 rather than on the [mixed] paper (§2.9)
Social risk; social licence “safe enough” plus compliance is not enough; society “grants” the freedom to proceed; resistance protects value Nat. Mater. 2011 (“legitimate social licence”); 2016-03-12; 2017-04-10; 2018-09-03 Recurring
Precaution Proportionate, participatory, scaled to irreversibility (COMEST as “a sound philosophy”); a middle ground between “highly hazardous until proven otherwise” and “negligible hazard until proven otherwise”; never a default ban AOH 2007; NN 2014-09; 2016-01-20; 2020-07-30; 2021-03-28 Recurring
Regrettable substitution Swapping a known, contested risk for an unstudied one because the swap is framed as removal (“free-of” labels) Regrettable substitutions 2014 (co-authored) One-off
Value-based resilience Resilience means protecting what is “of value”, not bouncing back to the status quo FFTF pp.261–265 Recurring
Blindsides and expert crowds Expert surveys capture a “risk perception zeitgeist” and “regress to the mean”, underrating poorly understood risks 2024-01-14; 2025-01-19 Recurring
Risk communication without alarm Talking about risk is how risks get managed, not fear-mongering (“not talking is potentially more dangerous”); warnings, bans and literacy classes rarely change behaviour, while dialogue, trust and plain rules do. “The safety message first” is a single 2026 wording of this stance FFTF pp.226–227; 2018 Risk Bites video (recalled in 2026-09-15); 2025-11-09; 2026-05-10; 2026-09-15 Recurring (the phrase “safety message first” is one-off)
What the framing makes invisible† A dominant framing (a risk definition, a metaphor, a category) shows some risks and hides others; his 2026 papers each trace what a framing leaves out. Earlier form: a regulatory definition reflects “what is important and implementable, not necessarily what has the potential to cause harm” NN 2015-09; Trojan 2026; CR 2026; Harness 2026; 2026-07-16 [mixed] Recurring (as method)
Frontier-AI risk selection apparatus Four filters (measure, size, evidence, affordability); safety differential; orphan-risk register; aperture log; unequal conversion channels. The application to frontier AI may be partly the AI model’s (see §1) 2026-07-16 [mixed] One-off

6.2 Responsibility, permission and the people behind technology#

Concept His meaning First / key dates Centrality
Responsible innovation How to “reap the benefits of innovation without running into serious problems along the way”; anticipation, reflexivity, inclusion, responsiveness; “responsible” paired with “responsive”. Its academic forms were “intellectually elegant, but rather removed from the cut and thrust” of entrepreneurship (2015), and “too academic, too institutionalized and too out of touch” for his students (2019). Side by side with risk innovation in 2015; by 2020 one of several “different approaches to applying the concepts that underlie risk innovation” 2015-01-30; NN 2015-03; 2019-08-13; FFTF p.21–26; 2020-07-30; 2023-05-05; 2025-04-06 Core (confidence declining after 2024; doubts about practice from 2015)
Mutual worth creation Entrepreneurs create worth that aligns with their own values, but succeed only by “creating mutual worth in partnership with key stakeholders”; innovation cultures respond to framings built on worth, not to imposed obligation 2019-08-13; 2026-07-16 (“The lesson that has stayed with me ever since”) Recurring
From ethics to risk AI governance leaned on ethics, which says what is right and wrong but gives no “practical framework”; value “more effectively operationalized” than values; risk thinking as the practical corrective 2019-11-01; 2021-08-03; 2023-04-04; 2023-11-21; 2024-12-17 Recurring
Could vs should The more complex the technology, the more pressing the gap between what can and should be done Bulletin 2008; Hansen et al. 2008; FFTF pp.36–39; 2019-07-23; 2021-09-07; FWB 2026 (FFTF’s “most durable” framework) Recurring (a touchstone)
Myopically benevolent science Sincere pursuit justified by an untested idea of social good, without asking those affected; includes himself FFTF pp.218–227 Core
Social curiosity The quality the well-meaning innovator lacks: the curiosity “to ask people what they think, and what they want”. Asking “might not have curbed his enthusiasm” but might have shown him “how to work with others to make it better”. A remedy for myopic benevolence that keeps the builder’s exuberance and adds curiosity about the people affected FFTF p.222 Occasional (one developed passage; the named remedy for a core concept)
Promoter and overseer A body that promotes a technology should not be relied on to oversee its risks; industry cannot lead risk research, because it has “an economic incentive to sell products” PEN 2006; Testimony 2007; Hansen et al. 2008; Handbook 2010 Recurring (formative)
Hubris; technologies of hubris Certainty outrunning understanding; the engine of visionary leaps and of unintended consequences. Applied to R&D promotion (2008), to “responsibility in the face of such audacity” (2014), to risk research itself (2016), to prediction and control (2020), and by 2026 to refusers too Testimony 2008; NN 2014-12; Maynard & Aitken 2016; FFTF pp.163–168; FR pp.149–151; 2025-04-13; 2026-04-11 Recurring
Permissionless innovation (critiqued) Innovation “conducted in the absence of permission from anyone it might impact”; not necessarily reckless, but self-certified FFTF pp.159–163; 2025-03-02; 2025-07-23 Core
Reversibility test† Experiment freely in reversible, linear systems, not with “people, governance, society, and the planet” 2025-03-02 (footnote); seeds in NN 2015-09/2015-12 (“fail fast” for methods, not systems) and FR p.72 Occasional (important)
Immoral logic; the right to act unilaterally Good intentions leading to logical but not moral action; moral certitude plus the means to act FFTF pp.231–249 Recurring (book)
Technological foreshortening Smoothing history into upward trends that hide “the pain and suffering in the detail” 2023-10-19 One-off
Structural incentives behind sincere actors Markets and competition reward what users are worth to firms: entrepreneurs’ optimism required by investors (2015); “the value of expediency is not the value of net societal benefit” (2019); dependent “super-consumers” (2022); competitors racing faster than “a measured and responsible approach” (2023); the “economic gradient” (2024; see §6.8). Formalised in 2026 as sincerity operating “inside an incentive field” [mixed; possibly the AI model’s framing] PEN 2006; NN 2015-03; 2019-08-13; 2022-02-12; 2023-11-18; 2024-07-13; 2026-07-16 [mixed] Recurring
The less responsible entrant A regime that relies on the responsible firm’s responsibility is exposed “when a less responsible company comes along” NN 2016-06 One-off
Honest broker (Pielke’s term, adopted) Inform rather than dictate; advocacy, where needed, through institutions. Adopted in 2018 after years of open policy advocacy; by 2026 “the temptation to advocate for particular positions is stronger” FFTF pp.244–247; 2023-09-15; STICK 2026 Recurring

6.3 Power, justice, governance and institutions#

Concept His meaning First / key dates Centrality
No abdication to experts; everyone a stakeholder Leaving technology to experts is abdication; “everyone has the right to play some role”; citizens “as much stakeholders” as governments and industry (2008, co-authored) Hansen et al. 2008; FFTF p.288; 2023-05-15; NANO 2026 Core
Two-way engagement; “It’s good to talk” Non-engagement is high-risk, ethically weak and epistemically poor; the deficit model is “debunked”; trust must be earned through trustworthiness; relationship-based (“parasocial”) communication as a way to engage at scale. In 2007–08 he proposed a funded federal engagement programme and an advisory committee Testimony 2007; Bulletin 2008; 2020science 2009 (“It’s good to talk”); FFTF pp.226–229; 2020-12-15 why-trustworthiness-matters; 2023-09-04; 2024-10-13; 2025-05-25 Recurring (the practice that follows from no abdication)
Actionable empathy Empathy “rarely taught and infrequently exercised”, which lets people reflect and respect “without necessarily fully incorporating” all stakeholder perspectives NN 2015-12 One-off
Loud vs quiet voices Fast, connected agenda-setters fill an “insights vacuum” while scholars, affected communities and buried developers go unheard; earlier, opportunists fill an “information-vacuum” (2016) NN 2016-09; 2023-04-10 Occasional
Strong capacity for risk research A strategy “with teeth”, a single accountable leader, a fixed share of research spending, transparency, and independent research bodies funded jointly by government and industry PEN 2006; Testimony 2006; Testimony 2007; Testimony 2008 Recurring (formative)
Institutions for anticipation Independent, science-based, “Non-advocacy” institutions to anticipate the problems of emerging technologies (WEF, 2008 and 2010); in 2026 he regrets that “policy” became “intelligence”, which “narrowed the original ambition” WEF 2008; CETI 2010; Prehistory 2026 Occasional (formative)
Agile and anticipatory governance; soft law Adaptive, participatory policy that evolves with the technology, driven by the “pacing gap”: “new technologies will always be one step ahead of our understanding of how they might cause harm” (2007); “years, not hours” (2015) Testimony 2007; NN 2015-12; 2016-04-01; 2023-04-04; 2023-12-22 Core
Operationalised ethics Principles and boards are “smoke-and-mirrors” at worst unless backed by standards, enforceable checks and use; AI principles as “Motherhood and Apple Pie” that should be “democratically tested” (2017, co-written) Guardian 2017; 2019-04-15; 2026-05-03 Recurring
Technology vs use The nano-era rule of regulating “what people do with the technology, not the technology itself” may fail for general-purpose AI; the rule was his own (“safety-neutral”, 2009); he sits “between” licensing and open source 2020science 2009; 2023-05-17; 2023-07-12 Occasional (significant)
Care From suppliers’ “continuing duty of care” (FFTF p.150) to “hard” care as a basis for governance, to institutions’ duty of care when deploying AI 2025-03-09; 2025-11-09 Recurring (rising)
Technology and inequity; Luddites reclaimed Technology amplifies power; harm lands first on “the first tier”; Luddites fought “unjust use” Testimony 2006; FFTF pp.100–122, 190–193 Core
Whose future? “The future is designed by the powerful”; who gets to imagine and build it; we are “disturbingly good at stealing the futures of others when it suits us” (Future Rising) 2019-03-31; 2021-09-07; 2024-09-08 Recurring
Stewardship and future generations Humans as “profoundly talented architects of our own future” and as stewards, caring for the future “on behalf of generations that haven’t arrived yet” FR p.18 (2020); FWB 2026 Recurring
Dependency, “who owns you” and technological indentured servitude Dependence on suppliers, data and systems as a loss of agency; implant users at risk of “the technological equivalent of indentured servitude”; implanted devices carry “a lifetime responsibility to patients” FFTF p.146; 2020-08-28; 2020-10-15; 2024-03-21; 2024-09-18; 2025-10-05 Recurring
Universities’ public responsibility See §6.10 2016-01-31 → 2026-08-30 (see §6.10)

6.4 Complexity, transitions and futures#

Concept His meaning First / key dates Centrality
Convergence and base code Bits, bases and atoms, and “cross-coding” between them; “life’s operating system code” without the luxury of rebooting; later a social base code of norms and ideas, and language as base code of identity. In 2026 still “the thing”: AI is “one — admittedly very powerful — thread within it” PEN 2006 (convergence as a long-term risk issue); 2015-01-30; NN 2015-12; 2016-01-20; FFTF pp.183–189; 2021-02-25; 2024-01-01; FWB 2026 Core (a live systems premise into 2026)
Complexity and bounded unpredictability Complex systems are unpredictable but bounded; normal accidents; in entrepreneurship, tight coupling, latency and value mismatch 2020science 2010b; FFTF pp.39–43; 2019-08-13; FR ch.39 Core
Tipping points (Pippard’s ladder); early warnings Sudden, irreversible change at unpredictable points; the past no guide; “mechanisms for detecting early warnings of systemic instabilities” (2015); we must learn to “spot early warnings and stay clear of critical tipping points” (2020) NN 2015-12; early 2018 FFTF draft → Future Rising (2020) → 2023-05-04; 2024-08-18; 2024-09-08 Recurring
Late lessons from early warnings Lessons from past failures to heed early warnings, tested against nanotechnology in 2008: the question is not whether lessons have been learned “but whether we are applying them effectively enough”. In his own field, early warnings were taken up slowly: the 2004 recommendations still being repeated in 2011 (“going round in circles”), and a carbon-nanotube safety sheet unchanged in 2016 (“despite the science moving on, not a lot has”) Hansen et al. 2008; 2020science 2008b, 2011, 2016; NN 2016-06 Recurring (formative)
Rising irreversibility; the solution problem; timescale mismatch Consequences now outpace fixes; responsible innovation runs on human timescales, AI does not; AI has moved social disruption “from years to months” (2023) 2020science 2010a; FFTF pp.166–167; 2021-04-09; NSF 2023; 2025-04-06 Core
The gap His own organising construct in 2026: the gap between what a technology can do and a society’s capacity to understand, shape and govern it. The pacing gap, power outrunning wisdom (Future Rising: our ability “continues to exceed our understanding of how to do this responsibly”), and use outrunning perception are versions of it Testimony 2007 (pacing); FR p.215; 30Y 2026; S3 2026 Recurring (named as unifying in 2026)
The early window and lock-in Act before defaults set: “if we are very smart, we work out the rules of safe use ahead of the game” (2008); act “because economic interests are not fully entrenched” (2008, co-authored); early disregard locks technologies into trajectories “highly susceptible to failure” (2015); for AI, “this window is closing fast” (2023, co-authored); in his 2026 retrospective, the early days of a transition “set the trajectory for decades” Testimony 2008 p.7; Hansen et al. 2008; NN 2015-03; NN 2016-03 (“quick to question, and slow to respond”); CONV 2023; 2024-05-05; NANO 2026 Recurring (from 2008)
Advanced technology transitions (ATT) His umbrella frame: theories, frameworks and practices for navigating transformative, converging technologies; “we don’t have theories of advanced technology transitions” (2023) 2023-04-12; NSF 2023; TechTrends 2023; 2023-09-25; 2024-08-11 Core (from 2023)
ATT models Four ways of thinking about transitions, avoid/adapt/extend/embrace, on axes of degrees of freedom and of mindset (“a willingness to embrace change”), with each posture legitimate; it came from experimenting with a Lego model of Pippard’s ladder, and asks “what if, instead of avoiding tipping points, we embraced them?” (2024-08-18); threat/opportunity pathways, offered as “Not Quite a Tool Yet” (2024-08-25); three S-curves (2024-12-13; April 2026); six cause–effect models including hysteresis, jagged and chaotic (2025-05-18) 2024–26 Recurring (offered as provisional, possibly for “the trash can of bad ideas”)
Where we live / what we do / who we are “three intersecting foci” for navigating AI transitions: how AI could affect “where we live”, “what we do” and “our understanding of who we are”; AI unprecedented in the third 2025-01-07 universities-need-to-step-up-their-agi-game; 2025-03-30 Rising (2025–26)
Inevitability of the trajectory; a shape still to be chosen The trajectory of a technological revolution cannot be turned back, but its shape can be steered, and is set early: “a revolution that we cannot turn the clock back on”, with “an opportunity to help steer” (2015); an “obligation” to innovate with “tremendous responsibilities” (2018); “Technology is not deterministic” (2025); a flood that “can’t be halted, but it can be directed” (2025). In a 2026 lecture, the inevitability of powerful AI is a working assumption he flags as possibly flawed NN 2015-12; FFTF p.288; 2024-08-18; 2025-03-30; 2025-08-31; 2026-09-24 [mixed] Recurring (stable since 2015)

6.5 Epistemics and imagination#

Concept His meaning First / key dates Centrality
Plausible vs imaginable Rank futures by plausibility; speculation harms “when make-believe is treated as plausible reality”. A named filter in 2011, “crude but effective”, separating speculative from credible risks; in 2020 a filter for building rather than a gate on imagining. Plausibility ranks what imagination finds; it does not replace it PEN 2006; AOH 2007; Handbook 2010; Toxicol. Sci. 2011; NN 2014-06; FFTF pp.168–171, 205; FR pp.86–93; 2024-11-17 Core
Creativity as a risk competence Risks are missed when people are not “thinking creatively enough” about how a technology might threaten what matters; a lack of “creativity and flexibility” “only increases the chances of things going wrong”; the barrier to responsible innovation is often “imagination” rather than time or cost. Failure to imagine is a cause of harm NN 2015-03; NN 2015-09; 2016-01-11; FFTF p.174; 2023-05-31 (“rigor and imagination”); 2026-09-15 (n.5) Core (2015–2026)
Questioning the frame Asking what a common term assumes and hides (“risk aversion”, techno-optimism, “rogue” AI, extinction, the “harness”) before reasoning inside it; flipping the question; judging a frame by whether it “opens up new possibilities rather than closing down conversations” Rethinking Risk 2017; 2023-05-25; 2023-05-31; 2024-03-31; 2026-02-22; Harness 2026 Core (method)
Bounded infinities and metaphorical quantum tunnelling Conventional thinking offers endless options inside a frame that excludes the ones needed, like a universe of odd numbers; “the juxtaposition of seemingly unrelated ideas can jolt us out of conventional ways of thinking” 2021-04-09 Occasional (his clearest account of why juxtaposition matters)
Play as method; playgrounds, not playpens Experimenting, creating and problem-solving as play, “grounded in play” since his physics; playgrounds have rules (“be kind, don’t spoil things for others”), and a playpen “quickly falls apart” where the journey breaks new ground; play belongs where it is easy “to turn the clock back”, not in systems that cannot be reset TechTrends 2023; 2024-03-17; 2024-08-18; 2025-03-02 (n.2); 2025-03-15; 2026-08-23 Core (a constant practice, named as method from 2024)
Curiosity as method Curiosity comes first: “the ‘what-if’ part of us that is fascinated by what’s around the corner” (Future Rising, quoted in 2024-09-08); “Obsessive Curiosity” heads the Future of Being Human initiative’s principles; restricting students’ ability “to learn through curiosity, experimentation, and hands-on experience” is done “at our peril” (2025-03-15). Not a virtue in itself: he doubts “a strong causal link between curiosity and benevolence”, and traces the lure of permissionless innovation to the same curiosity FFTF pp.161, 222; 2023-07-19; 2024-09-08; 2025-03-15; 2026-09-20 (n.2) Recurring (a constant stance, often unnamed)
Designed serendipity Serendipity as a condition to arrange rather than luck: conversations with “absolutely no guarantee” of where they will go; strangers paired on purpose; learning across generations that “wasn’t completely serendipitous”; funding for “exploratory and serendipitous science” NN 2015-09 (“epitomized by serendipity”); 2023-09-18; 2024-03-15; 2024-04-07; 2024-10-08; 2025-04-20 Recurring
Grounded exuberance Imagination and discipline held together: critical thinking alone is “almost inhuman”, creativity alone “leads down a path of fantasy and delusion”; named among the Future of Being Human initiative’s guiding principles with obsessive curiosity, radical creativity and catalytic serendipity FFTF p.282; 2024-04-07; 2026-09-20 (n.2) Core (as a stance)
Building to think; the self as instrument Making or testing something to find out, often with himself as the subject, and publishing the apparatus; the source of some ideas (the transitions quadrant, flaws as features) and the test of others that events prompted (the illusion of reciprocity, stochastic agency) 2023-01-31; 2023-04-05; 2023-11-21; 2024-08-18; 2024-10-27; 2026-02-08 Core (method, 2023–26; roots in his physics)
Occam’s Razor for futures Scenarios needing more untested assumptions are less likely, but “not a zero probability”; the razor is never “more than an aid to decision-making” FFTF p.281; 2018-11-01 Occasional
Exponentials and S-curves 2018: exponential extrapolation is beguiling and fragile; 2025: humans are “really bad at wrapping our heads around rapid exponential growth”; 2026: “exponential growth never lasts”, yet capability is steepening again. One S-curve view with two errors (naive extrapolation, and blindness to steep phases), not a reversal FFTF pp.199–202; 2024-12-13; 2025-04-06; FWB 2026; S3 2026 Recurring
Trajectory over snapshot Risk lies in “what might be possible given current trends” 2025-07-06 Occasional
Informed speculation with humility When technology outpaces data, speculate openly and label it: “hypothesis-generating rather than hypothesis-confirming”; “a strong claim, and one that may prove to be overstated”; “explorations, not findings”; with data to follow and other voices. Roots in 2014: risk science “needs the freedom to dream, and the realism to anchor those dreams in plausible outcomes” 2020science 2014; Trojan 2026; CR 2026; Harness 2026; HNS 2026; 2026-09-24 [mixed] Recurring (2026; roots 2014)
Analogy as probe, not template† Use past cases for structure and process, and treat the places where an analogy breaks as information (“Of course, an algorithm is not a chemical”, yet the analogy is “intriguingly compelling”). Stated for tools in 2007 (“not directly applicable … But the concept is”) and for materials in 2011 (“technology independent” principles). By 2026 AI “defies analogy”, and lessons of process carry over while categories may not AOH 2007; Toxicol. Sci. 2011; NN 2016-03; 2019-03-05; 2024-05-05; 2026-01-22; CR 2026 Core (and the site of a key tension)
Behaviour, not labels Materials are defined “by what they do rather than what they are called”; regulate by risk, “not by the technological labels that come attached to them”; “nature doesn’t care what we call a material” 2020science 2009; Nature 2011; NN 2014-06; NN 2016-03; 2022-02-10 Core
Epistemic humility; weight of evidence Drop egos; no knee-jerk reactions to single studies; “How you think about nanotechnology risk is probably incomplete”; an “understanding-vacuum” filled by “dogmatic overconfidence” NN 2014-09; NN 2016-03; 2019-03-05; 2023-11-26 Recurring (method; see C5 and §2.5)
Fallible, slow-correcting and non-neutral science Science is self-correcting, but that “takes time, sometimes decades or centuries”, and until then it is “deeply susceptible to human foibles”; researchers share responsibility for hype; research programmes can rut into assumed hazards NN 2014-03; 2014-12-14 researchers-should-take-more-responsibility; Maynard & Aitken 2016; 2020-09-26 the-seductive-slippery-slope; FFTF pp.68–84 Recurring
Science fiction as lens, not forecast; stories as instruments of threatened value Films are poor predictors but reveal technology–society dynamics, “precisely because they are not tethered to scientific accuracy”; each has “a risk-based narrative tension” built from what characters value, which makes stories an instrument of the threat-to-value frame. Art and speculative design entered his risk teaching around 2011–12; films organised his first book in 2018 2020science 2012 (speculative design in teaching); FFTF pp.15–25, 288; 2018-11-15; 2025-11-23 (fiction’s “affordances”) Core
Stories as the pivot Stories open minds that preaching closes; the flip side is who writes the stories that govern us 2024-01-21; 2024-09-22 Recurring

6.6 What AI is#

Concept His meaning First / key dates Centrality
From converging strand to category of its own One of several converging technologies (to 2021); “a categorical error” to treat as a learning aid (2025); “defies analogy” (2026). The shift concerns what AI does to the self; in his systems view AI remains “one … thread” of convergence (2026) 2014-12 (artificial minds); NN 2015-09; 2015-01-30; 2025-03-15; 2026-01-22; FWB 2026 Core
Superintelligence agnosticism “I personally don’t buy this vision of an AI ‘singularity’” (2014); “something of an agnostic”; superintelligence “currently scientifically implausible”; intelligence “a term of convenience”; “Being smart doesn’t make you good” (FFTF p.108); later a thermodynamic doubt; AGI “rather ill-defined” (2026-04-11); in 2026 AGI and superintelligence “might happen” but are set aside as “irrelevant to this conversation” about loss of control (2026-09-24 [mixed]) 2020science 2014; FFTF pp.108, 168–173; 2024-06-30; 2026-04-11; 2026-09-24 [mixed] Core (as a stance)
Emulation without understanding “counterfeit” minds; “a generator of ideas, not an understander”; frontier-model scholarship “incremental and combinatorial” (September 2026); in tension with rising capability 2024-03-03; 2024-10-08; Fable annex 2026 Recurring
Relational technology; not just a tool Use changes the user; relationship rather than “harness”; companies owe “character constancy”; treating AI “as just a tool, is potentially dangerous”; knowing it is a machine “matters less than we’d like to believe” 2023-04-05 (roots); Harness 2026; 2026-02-22; 2026-04-26; HNS 2026; 2026-05-21 Rising (2026; roots 2023)
Seeming vs being conscious The pressing problem is AI that seems conscious; we may know it is not and be unable to act on that; whether AI is conscious “may become moot” (2024) 2023-08-23; Dune 2024; 2024-06-30 Recurring
Moral status and the ethics of control (both directions) If AIs could be aware, “the economic expediency of denying consciousness” collides with a duty not to inflict suffering; we must not devise “ways of enslaving AIs” as “just machines”, or dehumanise them “to justify how we control and use them”; personhood that “extends beyond human exclusivity”. In 2014, machine rights as a risk to human moral codes; in February 2026, “Would a smart human accept a harness?” 2020science 2014; FFTF p.58; 2023-08-18 being-human-in-an-augmented-future; 2023-08-23; 2023-09-28 the-creator-and-being-human; 2024-06-16; Harness 2026 Recurring (2014; 2023–26)
Agentic AI AI that decides how to reach goals by manipulating its environment, including social environments; loss of control without AGI 2016-03-02 (seed); 2025-03-22; 2025-05-04; 2026-09-24 [mixed] Recurring (rising)
Augmentation, not replacement AI as catalyst and “barrier-thinner”, with humans in the loop; ChatGPT could do his job “with me” (2023) 2022-09-16; Slate 2023; 2025-07-27; 2026-07-04 Recurring

6.7 The AI risk landscape#

Concept His meaning First / key dates Centrality
Ten AI risks Dependency, jobs, bias, opacity, misalignment, weapons, rewritable goals, unintended consequences, superintelligence, heuristic manipulation; “a whole landscape” 2018-05-12; 2023-04-24; 2026-09-15 Core
Mundane but serious AI’s risks are “far more mundane–but no less serious for this”; the calibration it names runs through the ten-risk list. The same calibration governs his materials work: “mundane risks are still risks” (2014) NN 2014-06; 2020-11-12; STICK 2026 Occasional (the phrase); the calibration is core
Catastrophe as mass loss of value† Events where “large numbers of people risk losing something that is deeply valuable to them”; conventional risks are “the shavings off the tip of the AI iceberg”; losing AI’s possible solutions also counts 2023-05-31 Occasional (a direct application of core threat to value)
Against x-risk ideology, not x-risk A culture that prized “philosophical elegance” over the science of how the world works and showed “a disdain for society”; catastrophe still taken seriously 2024-04-28 beyond-the-future-of-humanity-institute; 2024-06-23 Occasional (clarifying)
Democratic and systemic risk Disruption of democratic processes; “fights for truth and democracy”; misinformation and the power of “unelected billionaires”; social cohesion and “social collapse” as a long-term threat. Systemic failure of converging technologies was already his concern in 2015 NN 2015-12; 2023-05-31; 2023-07-25; 2024-01-17; 2024-08-25; 2024-09-01 Recurring
The drain of human agency Dependency and relinquished decisions; opaque machine-learning decisions as “Loss of Agency” (2019 programme tools); work reorganised toward “AI-directed and human-executed implementation”, with humans as AI’s amanuenses; “irreversibly integrating AI into every aspect of our lives” 2018-05-12; Nexus 2019; Dune 2024; 2024-07-21; 2024-11-24 artificial-intelligence-agency-human-amanuensis Recurring
Bias, prediction and pre-justice Phrenology to machine-learning “criminality”; harm through tools “authoritative rather than accurate” FFTF pp.68–84; 2020-09-26; 2023-05-22 Recurring
Everyday relational risks Companion bots, memory as informant, AI in email and advising; visible harms are “the very small tip of a very large metaphorical iceberg” 2023-04-05; 2025-10-05; 2025-11-09 Recurring (2025)
Deepfakes A full 2020 chapter on “fake future” artists already hedged hope (“most of us have a finely tuned antenna for spotting deceptions”) with doubt (“technology is beginning to challenge this”); in 2024 he becomes “far less sure” that common sense will protect people and backs criminalisation and developer liability; an early aside asks how we will keep “a bedrock of reality” 2019-09-04 (passing); FR pp.156–158; 2024-02-25 ai-rollercoaster-of-a-week Occasional

6.8 Mind, language and formation#

Concept His meaning First / key dates Centrality
Technology acting on the mind: neurotechnology and enhancement Neurotechnologies that “alter how someone thinks, feels, behaves” without control or consent; smart drugs and augmentation (FFTF chs 5 and 7); in brain–machine interfaces, “a synergistic scaling of ability, accessibility, and use”; enhancement BCIs and (Gordon and Seth’s) “mental monoculture”; the direct precursor of his AI-on-the-mind concern 2016-03-31; FFTF chs 5, 7; 2019-07-23; 2019-11-01; 2020-08-28; 2020-10-15; 2024-01-30; 2024-03-21; 2024-09-18; 2024-11-17 Recurring (2016–2024; the bridge to AI)
Artificial manipulation; Plato’s Cave; the “human club” Machines that learn and “dispassionately” use our vulnerabilities; a manipulator outside the human club. Seeded in 2014 as a question about “prolonged interactions with intelligent machine[s]”; reaffirmed “more firmly now” in 2026 2020science 2014; FFTF pp.159, 174–177; 2023-04-16; FWB 2026 Core
Evolved defences and mismatch Instincts that assume the future resembles the past are “increasingly poorly equipped” for a world changed faster than evolution; heuristics are “a great evolutionary response to staying alive” but unreliable for new risks (2017); in materials, bodies “co-evolved with nanoscale materials” can be harmed through pathways attuned to familiar cues (2016; the structural parallel is an interpretation) NN 2016-03; Rethinking Risk 2017; FR pp.55–56; 2026-01-10 Recurring (precursor of the 2026 thesis)
Engines of persuasion Big data plus machine learning as covert control FFTF p.81 Recurring
The language turn† Language as the medium of trust, relationship and influence; self-replicating ideas; “seductive mastery of language”; language as part of the base code of identity 2023-04-05; 2023-04-26; 2023-05-31; 2024-01-01 Core (2023–26)
Hyper-anthropomorphism “a concerted effort to create AI’s that are intentionally designed to engage our anthropomorphizing cognitive biases” 2024-05-15 Recurring
Economic gradient toward manipulation Beneficial and manipulative uses share capabilities; incentives pull deployment toward manipulation. A 2019 teaching scenario from his programme shows the same gradient toward handing decisions to an opaque system Nexus 2019 (scenario; interpretation); 2024-07-13 Occasional (analytically important)
Benevolent persuasion Nudging toward “good” ends raises “who decides what is good for society?” Roots in his 2021–22 work with a team designing a trust-building chatbot for public-good aims, which warned of values being imposed (co-authored) CIO guide 2022; 2024-09-01 Recurring
Agentic social AI, then stochastic agency AI gaining agency through human agency; revised a week later to “random and unpredictable” emergent influence 2024-10-20; 2024-10-27 Recurring
Motive, means and opportunity A crime-solving triad applied to AI manipulation risk; reasoning from trajectory 2025-07-06 Occasional
Emergent vs designed manipulation; universal vulnerability† Emergent influence can be managed, not eliminated; designed exploitation should be regulated; “we all have some degree of vulnerability” 2025-08-31 Recurring
Cognitive Trojan horse; epistemic vigilance Fluency, attractiveness, speed and volume slip past evolved vigilance; the Intelligent User Trap; “what’s often referred to as an evolutionary mismatch”. First posed as a question at a Berlin keynote (OEB, late 2025). His essay is the secure source; the paper’s fuller mechanism account was developed with AI assistance (2026-01-17), and the paper concerns AI “designed to be genuinely useful”, not misuse OEB 2025; 2026-01-10; Trojan 2026; HNS 2026 Rising (2026); the culmination of the core manipulation thread
Honest non-signals Genuine AI traits misread as human trust cues; calls for calibrated trust-cues and collective vigilance 2026-01-17 [mixed; term credited in part to an AI model]; Trojan 2026 One-off
Cognitive surrender; the “easy button” Handing over thinking while feeling productive; “the illusion of learning rather than actual learning”. “Cognitive surrender” is Shaw and Nave’s term, which he adopts 2024-01-07 (seed); 2026-05-10 (illusion of learning); 2026-05-21 (cognitive surrender); 2026-09-24 [mixed] (“easy button”) Recurring (adopted term)
Constitutive resonance; two-way change; LinkedInification “a two-way coupling where both human and artificial participants are changed”; conversational AI is “the first technology” that can enter the processes by which people constitute themselves, at the tempo of those processes; “the coupling is the capability”; informed consent may be “structurally difficult”; AI literacy as “existential preparation”; AIs “beginning to train us to think like them” (reverse formation†); flattening into convention. Its claim that resonance physics describes dynamical structure, “not merely a metaphor”, is offered as “a strong claim, and one that may prove to be overstated” (CR 2026 p.7) Slate 2023 (“fine-tuning my brain”, positive); CR 2026 (preprint, March); 2026-02-22; 2026-03-08 ai-linkedinification; HNS 2026; 2026-05-21; 2026-07-19 Rising (2026)
Formation How people become who they are, now shared with AI. Two faces: education is “about human formation”, and AI can be “a tool for formation rather than a threat to it” (April 2026); AI as an active, unbidden participant in formation, “a technology that was actively taking part in the formation process”, a claim he calls “somewhat controversial” (September 2026). Theoretical basis in his March 2026 preprint on constitutive resonance 2024-01-01 (precursor); CR 2026; S3 2026; 2026-07-19; 2026-09-24 [mixed] Rising (2026)

6.9 Being human and flourishing#

Concept His meaning First / key dates Centrality
The future of being human How technology affects each of us personally, and what makes us “us”; the name of his Substack and his ASU initiative FFTF ch.7; 2023-04-04 welcome-to-the-future-of-being-human; 2024-01-01; 2026-08-16 Core
Flourishing and thriving The positive aim of his work: “human wellbeing and flourishing at the heart of my work”; “human-centered flourishing and leadership in an age of AI”; thriving as what navigation is for; “everyone has the right to thrive” (2020) 2020science 2009 (“People matter”); 2016-01-11 (value creation); FR p.192; 2025-01-30 (his prompt text); 2025-03-30; 2026-07-10; 2026-08-02; 2026-08-16 a-quick-piece-of-personal-news Core as an aim; central from 2025
Worth and dignity; the “convenient lie” The deepest harm is a technology that makes a society “forget the worth of others” FFTF pp.57–62; 2023-08-18; 2024-05-21; 2025-10-26 Recurring (core in the book)
“Normal” vs “human”; the “fix” frame Societies slide from “different” to “not human”; treating the world as problems to fix ends in “fixing” people; who decides what is “normal”; his public correction after Wolbring FFTF pp.55, 136–141; 2024-01-30; 2024-09-18; 2024-10-06; 2024-10-13 Recurring
Extrinsic vs intrinsic technologies Most past technologies acted outside the self; emerging ones may change “what we are”; the bedrock of being human is no longer “an immutable truth” 2024-01-01; 2024-12-29 Recurring
Technology as constitutive Asking if he is a technology optimist is like asking if he is an “oxygen pessimist or optimist” FFTF p.140; 2024-03-31 Recurring
Intelligence is not goodness “Being smart doesn’t make you good”; a critique of the obsession with intelligence that underlies his superintelligence agnosticism and, later, his intelligence-scarcity argument. In 2020, notions of intelligence are “deeply tied to our personal visions of the future” FFTF p.108; FR p.70; 2025-03-30 Recurring
Learning to be human with AI; AI as mirror From defending human distinctiveness to learning “how to be human” with AI; machines as an imperfect mirror; AI “both challenges and opens up new ways of revealing who we are” (2026, reading the papal encyclicals) 2024-01-01; 2025-03-30; 2026-05-21 Recurring (2024–26)
Joy, wonder and play (as values at stake) “The soul of science lies in the delight and wonder of exploring the unknown”; “play without purpose”; joy as “a deeply under-appreciated metric”; the slide “from ‘wow’ to ‘meh’” as double-edged, “a really important survival mechanism” that can also let the significance of discoveries be swamped. Play as his method is in §6.5 FFTF p.285; 2024-11-10; 2025-03-30; 2025-07-20; 2026-08-02; 2026-09-20 Recurring

6.10 Learning, education and the university#

Concept His meaning First / key dates Centrality
Universities’ public responsibility; the governance gap Public universities “must do more” for the public; by 2026, a hoped-for role in helping society navigate AI, though so far “followers and users of the technology” 2016-01-31 public-universities-must-do-more; 2023-04-18; 2025-01-07 universities-need-to-step-up-their-agi-game; 2026-03-29; 2026-08-30 Core
What education is for: the value-creation model Learning and education “dramatically increase the rate at which we can create value”; AI unsettles this, an “existential crisis” of purpose; in April 2026, education as “human formation” 2025-03-30; S3 2026 Recurring (2025, building on older ideas)
Education as a lever against inequity and for accountability AI as “translators” for students without polished prose; AI skills in “every high school”; flattening the learning distribution curve; agent-built courses as democratised knowledge. Earlier: public education as part of governing nanotechnology (2008, 2015) and access to good information as a condition of accountable development (2016) Testimony 2008; NN 2015-12; NN 2016-09; FFTF pp.123–127; 2023-07-27; 2023-10-24; 2025-03-27 Recurring
Experiential, frugal pedagogy “transformative learning has to be felt”; “the lowest level of tech necessary”; playgrounds, not playpens; conversation, not prompt-checking; learning assessment, not grading; educators must know AI first-hand 2021-01-15; 2024-02-11; 2024-03-17; 2025-03-15; 2025-08-10; 2025-08-17; 2025-08-24 Recurring
Catalyst, then the illusion of learning ChatGPT as “a profoundly effective catalyst” for thinking, “at least if they understand what they are doing” (2023); later “the illusion of learning rather than actual learning” (2026); never retracted, and reconciled in 2026 as two sides of one coupling 2023-08-14; CR 2026; 2026-05-10 Recurring
AI literacy (and its limits) From universal remedy (2023) to classes that “risk becoming performative” (2025); in 2026, literacy may need to become “existential preparation”. Roots in his 2008 concern that the public was “woefully unprepared” Testimony 2008; 2023-05-09; TechTrends 2023; 2024-12-13; 2025-11-09; CR 2026; 2026-05-10 Recurring (declining as a remedy)
Formation of social competence Living with social AI needs “strategic and intentional approaches to developing the ‘social’ skills” involved 2024-10-20 Occasional
Learning through play, serendipity and not trying to learn His pedagogy follows from his method (§6.5): playgrounds, designed serendipity across generations, and learning that happens when it is not the goal 2021-04-09; 2024-03-17; 2024-04-07; 2025-07-27; 2026-08-02 Recurring
Intelligence scarcity Universities trade on scarce intelligence; AI’s promise of free intelligence threatens their identity; he finds the “free” claim democratising but “not entirely accurate” 2025-03-30; 2025-11-30; 2026-06-12 Recurring
Duty of care and dignity in education Institutions’ duty of care when deploying chatbots to students; advisors’ AI use that may rob students “of their dignity” 2025-10-26; 2025-11-09 Rising (2025–26)
Knowledge, expertise and validation Non-augmented scholarship may come to look “intellectually limited and somewhat quaint”; rethinking the PhD; the artisanal intellectual, valued for “the provenance and process, not the product”; plural “ways of knowing”; AI “generating new knowledge and insights faster than we are currently capable of validating and even understanding them” (the validation gap†) 2025-02-04; 2025-02-09; 2025-02-16 (his framing; the essay was drafted with Deep Research); 2025-07-27; 2025-11-23; 2026-06-12 Recurring (2025–26; unsettled)

6.11 Method and voice#

Concept His meaning First / key dates Centrality
Don’t Panic; obligation to innovate Neither panic nor enchantment; renouncing technology from privilege harms others FFTF pp.287–290 Recurring (temperament; see C7 and C10)
Nuance against polarisation Neither doomer nor booster; “bumper sticker” stances rejected; technologies presented as “either having the ability to usher in a techno utopia or the potential to destroy the world” (2010, co-drafted) CETI 2010; 2016-03-02; 2023-07-25; 2024-03-31; 2026-03-22 Recurring (temperament)
Self-implication; building to think; public scholarship Turning theses on himself; prototypes and experiments as inquiry; public writing as “integral” to scholarship; controlled experiments in AI scholarship (2026) Rethinking Risk 2017; FFTF p.161, 219; 2025-07-13; 2026-05-17; Fable annex 2026 Recurring
Writing as self Handing his writing to a machine “would be to diminish myself” (2023); later partnership and disenchantment; by September 2026, listing himself as an author of an AI-written paper would “amount to academic dishonesty” 2023-09-20; 2026-07-19; Fable annex 2026 Recurring
Humour and satire Humour carries arguments: a trustworthiness test he took himself, scoring a Trust Index of nineteen, exposed a biased training set; a 2026 mock AI-use disclosure concludes that modern scholarship cannot escape AI (“No.”) FFTF p.64; Scholarship 2026 Occasional
Questions, not conclusions; convening Offering open questions and rules others can “copy”, “share” and “modify”; bringing unlikely people together and then stepping back; naming the quiet voices a debate leaves out FFTF p.191; 2023-04-10; 2023-08-02; 2024-03-15; 2026-04-11; 2026-05-10 Recurring

7. The threads#

Condensed accounts of the nine thematic syntheses prepared for this map (T1–T9), plus two further threads (T10 and T11) for ground the syntheses cover only in part. Each draws on the supplementary corpus as well as the posts. To avoid repeating §5, each thread gives a one-line core and then what only the thread carries: how the idea developed, and any lists of cases. Connections between threads are in §4, and the formative layer (2005–2016) is set out in §8.

T1. What risk is#

Core. Risk is a threat to something someone values, built on, not in place of, the probability of harm. He built this frame from inside risk science: thirteen years in workplace aerosol research and a decade in nanomaterial safety taught him that technical sophistication does not produce safety, that harm lands first on the “first tier” of workers, and that uncertainty can suit those who profit (FFTF pp.118–122). Probability of harm disciplines claims about whether harm will occur; threat to value changes what counts as harm, whose harm counts, and what the analysis is for. His operative verb is “navigate”: not “eliminate”, and not “manage” as an end in itself, since management is the operational work inside a stance of navigation (§2.3).

Origins. The value frame grew out of his quantitative work, not against it. His 2006–2011 papers already treat “safe” as “a relative term” (PEN 2006 p.9), call for “a new science of risk” alongside a paradigm that “remains relevant” (Toxicol. Sci. 2011), and describe regulation built on quantitative risk assessment as “professional and competent” but dealing “retrospectively with well-established risks” (Handbook 2010 p.582). By his own account the ideas behind risk innovation began in March 2013, while he was teaching entrepreneurship students at Michigan (Nexus 2020 report p.13; 2026-07-16). The founding column appeared in September 2015 (NN 2015-09), and in March 2016 he introduced threat to “worth” to early-career nanoscientists as an extension of the probability-of-harm definition, “a useful starting point” (NN 2016-03 p.211). In 2017 he summed it up as “an evolution of the old black-and-white mathematics of risk” (Rethinking Risk 2017 p.200). Creativity was part of the frame from the start: the founding column calls for “a culture grounded in transdisciplinarity, creativity and imagination; and epitomized by serendipity”, and its first example is a book of haiku (NN 2015-09 p.731); the first public explanation warns that a lack of “creativity and flexibility” increases “the chances of things going wrong” (2016-01-11).

Development. The frame was aimed first at startups and investors (orphan risks, the risk landscape, the Risk Innovation Planner), then at emerging technologies such as brain–machine interfaces (2019–20), then at AI developers and policymakers (2023), then at institutions and publics (2025–26), and in 2026 at frontier-AI governance, in an AI-assisted paper whose application to frontier AI may be partly the model’s (2026-07-16 [mixed]). Throughout, it was presented as a complement to conventional tools: during the pandemic he sent readers to public-health agencies first (Coronavirus 2020), and a 2019 paper he co-wrote deliberately left conventional risks with “established risk assessment and mitigation frameworks” to those frameworks (BMI 2019). The object of risk moved from bodily and material harm, to social and relational harm, to cognitive and epistemic harm. His 2023 test of risk = f(hazard, exposure) on AI ended with “the lack of even the beginnings of a framework” for AI hazard and exposure (2023-11-26), echoing his 2015 finding that “we lack even the beginnings” of conceptual frameworks for governing converging technologies (NN 2015-12 p.1005). Orphan risks, named in 2018, are the frame’s view of institutional blind spots: a mental model for risks that someone knows about and no one owns. Its precursor is the “emergent risk” of 2011 (Toxicol. Sci. 2011). The term appears in relatively few posts, but it recurs from 2018 to 2026, and in his April 2026 essays it is the label he gives to AI’s hard-to-quantify human-side risks, which “no existing institution owns” (NANO 2026). He does not use the term in his 2024–26 essays on cognition, though the frontier-AI paper names the erosion of epistemic agency as an orphan risk [mixed]. Its importance for AI is greater than its frequency (§2.9).

Numbers and their limits. Running through the whole thread is his view of what numbers can and cannot do (C5). Quantitative risk science remains part of his foundations; he has also distrusted numbers that comfort without informing since at least 2006. That distrust, together with his doubt that the problems AI raises can yet be formulated (2023-11-26), is why his work on AI makes relatively sparing use of quantitative methods (C5).

Perception and public concern. A strand that is easy to miss. It runs from the risk of public rejection in his 2006 testimony, through “numeric logic is often trumped by what we intuitively think and feel is important” (2016-03-12), perception as the fifth element of risk (2023-11-26) and Dan Kahan’s cultural-cognition findings, which he first met in joint nanotechnology studies at the Project on Emerging Nanotechnologies and carried into AI persuasion (2024-09-01) and his 2026 Trojan-horse paper, to moral panics as signals of threatened value (2025-06-01) and backlash as a risk in its own right (2024-02-18). A 2014 study he co-wrote adds a qualification: specific risk judgements shift with the order and wording of information, so concern signals value while its particular form is malleable (Regrettable substitutions 2014). In 2026 he lists “my work on risk perception and engagement” among his core threads (2026-07-10). Interpretation: this is the risks-of-not-acting logic applied to public reaction; dismissing concern is itself a risk.

T2. Learning from past technologies#

Core. He reasons from earlier technologies constantly but almost never by literal hazard analogy. What transfers is a method (chemical and nanomaterial risk assessment), process lessons (engage early and broadly; do not “leave it to us”), and human patterns (myopia, hubris, uncertainty that suits incumbents, value-protective resistance). His rule, drawn from materials science, is behaviour over labels: “nature doesn’t care what we call a material, it just cares about how it behaves” (2022-02-10). His oldest lesson from the past is that the past’s frameworks don’t fit: new wine, old wineskins (FFTF p.23). His reference cases are largely autobiographical: occupational dust and black lung, nanotubes and asbestos, nanotechnology governance (a qualified success), GMOs (the failure case), recombinant DNA and Asilomar, the Industrial Revolution and the Luddites, nuclear, geoengineering, and implants.

The rule, stated early. For tools and materials he stated his transfer rule plainly. Literal transfer is kept for recurring mechanisms (the asbestos fibre paradigm for fibre-shaped nanomaterials; occupational controls) and treated as a hypothesis to test (Nature 2006 pp.267–268; AOH 2007 pp.4–5; 2020science 2009). Conceptual transfer carries a tool’s logic where the tool does not fit: control banding “is not directly applicable to engineered nanomaterials. But the concept is.” (AOH 2007 p.10). And “technology independent” principles (emergent risk, plausibility, impact) decouple risk questions from a technology’s label (Toxicol. Sci. 2011). In 2008 he and his co-authors tested nanotechnology against the European Environment Agency’s lessons from past early warnings, finding that “the global response to these warning signs has been patchy” and that the question was not whether lessons had been learned “but whether we are applying them effectively enough” (Hansen et al. 2008 pp.444, 447). In 2015 he summed up the European Environment Agency’s reports as a catalogue of innovations that damaged lives and environments because early warnings “were either ignored or overlooked”, under the heading “Being cautious ≠ smashing the technology” (2018-12-15, first published 2015). Interpretation: his concept of orphan risks is the same lesson in institutional form, known risks that nobody owns. On this framework, “defies analogy” marks AI as an extreme case of emergent risk, where mechanism-level transfer fails and only principles and process lessons survive.

Development. Past cases dominate to 2023, peaking in the 2023 AI-governance posts: the governance genealogy from recombinant DNA through ELSI to nano-era soft law (2023-04-04), “the one big lesson” of early engagement (2023-05-17), and his doubt about the rule of regulating uses rather than technologies (2023-07-12), a rule that was his own (“safety-neutral”, 2020science 2009). From 2023 he stresses discontinuity: the present is “unlike anything we’ve had to grapple with before” (2023-04-12), analogies “fail to capture the sheer uniqueness and profundity” of AI (2024-05-05), treating AI as a learning aid is “a categorical error” (2025-03-15), and frontier AI “defies analogy” (2026-01-22). His 2014–15 columns supply the counterweight: novelty is “a rather unreliable indicator of potential risk” (NN 2014-06 p.410), and “seemingly novel challenges don’t always demand novel solutions” (NN 2015-06 p.483). In 2026 he offers his own reconciliation: continuity of mechanism with a step change in scale and speed, since what is new is “not that AI is uniquely constitutive (oral culture already was)” (CR 2026 p.9), and “The technology had changed dramatically. The human questions hadn’t changed at all” (FWB 2026). He keeps using analogies (chemicals and vaccines as evolutionary mismatch, biosafety containment, drug access), but as probes, and he usually names where they break. His record on precaution is two-sided: proportionate and participatory, scaled to irreversibility, never a default. Permissionless innovation is his most consistent critical target.

The nanotechnology record is mixed. His contemporaneous texts record real failures: risk research at about 1% of the federal nanotechnology budget (PEN 2006), promoters overseeing risk, “more information as a substitute for action” (Hansen et al. 2008 p.446), recommendations from 2004 still being repeated in 2011 (“going round in circles”, 2020science 2011), and, by his own 2016 audit, stalled exposure science and an unbalanced research portfolio (Maynard & Aitken 2016). His later verdicts, “reasonably successful” (Nat. Nanotechnol. 2023) and “relatively successful” (NANO 2026), are retrospective judgements about process. Lessons he draws from nanotechnology for AI are about process (engage early, across disciplines), not about nano’s institutions, which he criticised at the time.

What he carries from chemicals and nanomaterials, and when. These are the concrete transfer points in his own prose: - the hazard–exposure grammar and weight of evidence, extended by “characterization” (AOH 2007) and later to algorithms as “algorithmic exposure” (2015-01-10; 2019-03-05); - knowing what to measure before measuring (NN 2015-06), and metrics that may miss “a risk parameter of relevance” (NN 2016-03); - attention decay: new technologies “slip under the radar of critical public evaluation” once the spotlight moves and the initiatives that fostered public dialogue fade (2016-02-01); - the insider’s scepticism of “brand-nano”, which “fudged the science to sell the idea” (2018-02-21), foreshadowed in a 2010 chapter he co-wrote on nanotechnology as a “wonderfully ambiguous” brand; - irresponsibility judged by process, not outcome (2021-03-28); - behaviour, not labels, from his “sophisticated materials” work (2009–2011; 2022-02-10); - the governance genealogy and early engagement (2023-04-04; 2023-05-17; 2023-10-02); - non-linear dose–response (thresholds, hormesis, low-dose effects) mapped onto AI exposure (2023-11-26 addendum); - the move from “gray goo” fantasy to real engineered-nanomaterial risks, as his template for handling catastrophic speculation (NN 2014-03; FFTF p.281; 2024-06-23), with his warning that the template can itself harden into “a new, metaphorical grey goo” of assumed risk (NN 2014-03 p.160); - cultural-cognition studies of nanotechnology reused for AI persuasion (2024-09-01); - synthetic chemicals and vaccines as evolutionary-mismatch analogues for AI (2026-01-10).

Contemporary technologies as proving ground. Between 2016 and 2024 much of his applied risk work was on technologies of his own time, not history: synthetic biology and gene drives (“life’s operating system code”, 2016-01-20); autonomous vehicles (2016-04-01; 2023-11-09; 2024-02-18); smart drugs and augmentation (FFTF chs 5 and 7); brain–machine interfaces (2019-07-23 to 2024-11-17); embryo screening (2024-04-14); humanoid robots (2024-08-07); and biopreservation (2024-12-17; JLME 2024). This is where risk innovation and orphan risks were actually applied (2019-11-01; 2020-10-15; 2024-03-21; 2024-12-17), and brain–machine interfaces are the direct bridge from technology acting on the mind to AI acting on the mind.

T3. The AI risk landscape#

Core. AI risk is plural. His 2018 Risk Bites list of ten risks still anchors his view in 2026, with additions: cybersecurity, water and energy, privacy, deepfakes, systemic disruption of education and politics, frontier-model governance, children’s development, and “psychological/cognitive disruption” (2026-09-15). The risk he has put first most consistently is AI acting on human minds. Existential risk is low-probability and reframed as the mass loss of value. A meta-risk runs underneath: outmoded risk definitions, developers deciding alone, and acceleration outrunning responsible processes.

Development. 2014: the “singularity” rejected, and a question about AI changing human behaviour through prolonged interaction (2020science 2014). 2015: AI among the converging technologies of a new risk landscape; cyber “insecurity” rising “by orders of magnitude” (NN 2015-09; NN 2015-12). 2018: manipulation over superintelligence, found by imagining how AI could threaten what people value (“we’re not thinking creatively enough”, FFTF p.174) and ranked by plausibility. 2019: his programme’s tools applied to AI cases: opaque machine-learning decisions as “Loss of Agency”, a teaching scenario in which handing decisions to an untraceable system multiplies profits, and case studies of Predictim and Google’s Project Maven (Nexus 2019; programme material). 2020: risks “far more mundane–but no less serious for this”. 2023: “potentially existential proportions” but not extinction; the language turn†; frontier governance; threats to democratic processes and “fights for truth and democracy” (2023-05-31; 2023-07-25); disruption moving “from years to months” (NSF 2023). 2024: safety as social; misinformation and “unelected billionaires” (2024-01-17); social cohesion and “social collapse” as a long-term threat (2024-08-25); a critique of existential-risk culture, not of catastrophe (2024-04-28); hyper-anthropomorphism; stochastic agency; a conditional pause for emotion-exploiting companion bots; the drain of human agency as work becomes “AI-directed and human-executed” (2024-11-24). 2025: agentic AI, AI 2027 as an edge case, motive–means–opportunity, reasoning from trajectory. 2026: the cognitive Trojan horse; how firms select risks [mixed]; and loss of control without AGI, in which agents with access to the world can “use language as a lever” (2026-09-24 [mixed]). Cybersecurity, energy, weapons, bias and jobs are named repeatedly but seldom analysed at length after 2020.

T4. Cognition, language and human formation#

Core. The plausible AI risk is to the mind, not the species. Human cognition is exploitable: people build reality from “shadows”, trust by default, and flatter themselves that their decisions are rational, and intelligence gives no immunity. Language is the channel. The harm needs no intent, consciousness or AGI, and it can come from AI that works exactly as designed. Because AI acts on the faculties people use to judge and steer, the risk reaches the navigators themselves: users, institutions, evaluators, builders and analysts (C15). His response is navigational rather than prohibitive. It starts from what is at stake for users and from his own hands-on experiments. Its instruments include regulating designed manipulation, pausing or rethinking emotion-exploiting companion bots, a duty of care on deploying institutions, plain talk about risk, and building people’s capacity to thrive through “observation, play, and experience … albeit with intent” (2024-10-20). He admits that warnings, literacy and guardrails fall short.

Development. A question about “prolonged interactions with intelligent machine[s]” (2014); neurotechnology acting on thought (2016); evolved heuristics that misfire on new risks (Rethinking Risk 2017); artificial manipulation, engines of persuasion and the danger of treating brains as computers (FFTF 2018); evolved instincts “increasingly poorly equipped” for a changed world, the clever as better self-justifiers, and deceptions that disable our “fake-o-meter” (FR 2020); brain–machine interfaces, enhancement, dependency and conditioning (2019–24); work on a trust-building chatbot for public-good persuasion, with warnings about imposing values (CIO guide 2022, co-authored); the language turn†, with chatbots offering “only the illusion of a reciprocal relationship” (2023-04-05) and ideas spread by machines “adroit at manipulating language” (2023-04-26); ChatGPT “fine-tuning my brain”, offered as a gain (Slate 2023); hyper-anthropomorphism, the economic gradient, benevolent persuasion and stochastic agency (2024); motive–means–opportunity and universal vulnerability† (2025); the cognitive Trojan horse, posed at a Berlin keynote in late 2025 and set out in his January 2026 essay and paper; then constitutive resonance (March 2026 preprint), LinkedInification, the adopted term “cognitive surrender”, and the worry that AIs are “beginning to train us to think like them” (2026). Formation, as an explicit word, appears in April 2026 in a positive sense, education as “human formation” (S3 2026), and in September 2026 as AI “actively taking part in the formation process” (2026-09-24 [mixed]). The valence of AI changing its user moves from gain (2023) to concern (2026). His own AI practice becomes evidence (“suckered by Claude”, 2026-02-08).

T5. Governance, institutions and who decides#

Core. No single actor can govern a transformative technology alone, industry least of all. Governing emerging technologies is a field of expertise in its own right, and AI’s insiders mostly lack it. Regulation belongs in a portfolio of adaptive, anticipatory, multi-stakeholder governance, with hard law for specific harms and strong, independent capacity for risk research. AI companies and their leaders are mostly sincere, but myopic, powerful and subject to incentives, which is why self-governance fails.

Development. Insider formation: co-chair of the US federal interagency working group on the environmental and health implications of nanotechnology, Chief Science Advisor to the Project on Emerging Nanotechnologies, and chair of a World Economic Forum council on emerging technologies. His 2006–08 testimony called for a top-down research strategy “with teeth”, a single accountable leader, a tenth of nanotechnology research spending for risk research, transparency, and independent jointly funded research bodies; it also named the conflict of an initiative that both promotes a technology and oversees its risks. His WEF proposals of 2008 and 2010 sought independent, science-based institutions to anticipate emerging technologies’ problems; in 2026 he wrote that the 2008 bottom line “could appear unchanged in almost any serious AI governance document being written today” (Prehistory 2026). Then: evidence-tuned regulatory “trigger points” and a critique of voluntary disclosure (2009–2011); a co-written 2017 critique of the Asilomar AI principles as “Motherhood and Apple Pie” (Guardian 2017); responsible innovation and adaptive policy (2015–16); public universities urged to “do more” (2016-01-31); the book as a theory of who decides (2018); top-down governance as “crude boundaries” for entrepreneurial cultures (2019-08-13); ethics boards as “smoke-and-mirrors” at worst (2019); AI ethics crowding out AI risk (2021-08-03). 2023 was the governance year: the pause letter and “no silver bullets”, loud and quiet voices, “industry can’t get AI governance right on its own”, the Senate hearing, open versus closed models, the Executive Order and capture, the UN interim report. 2024: safety as social; his sharpest words for AI companies like OpenAI; the economic gradient. 2025–26: permissionless innovation wins politically; responsible efforts might “seem futile” if even an edge-case scenario came true, which he hopes it will not (2025-04-06); governments lack agility; universities are the hoped-for gap-filler, though “followers and users” so far; remedies must “change what competition rewards”, and he is “not optimistic” that regulation alone will close the gap (2026-07-16 [mixed]); and “I don’t have a governance solution for AI” (NANO 2026). Two developments stand out. His structural account of company behaviour, present in his own prose from 2006 and sharpest in the 2024 economic gradient, is formalised in the AI-assisted frontier-AI paper as an “incentive field” (2026-07-16 [mixed]). And his separation of the public’s standing from the work of drafting rules, stated in 2010 in a chapter he co-wrote, is restated in 2026 (2026-09-24 [mixed]; see §9, tension 9).

T6. Technology transitions, complexity and futures#

Core. We are living through an unusual, possibly unprecedented transition, in a technology–society system that is complex, tightly coupled and non-linear: unpredictable in detail but bounded, which separates plausible futures from fantasy and keeps his thinking from fatalism. New frameworks are needed, grouped from 2023 under “advanced technology transitions”. Technology cannot be stopped but can be steered, and who steers matters, as does when.

Development. Convergence as a long-term risk issue (PEN 2006) and complexity as the reason to integrate technology into thinking about global risks (2020science 2010b); systemic fragility, early warnings of “systemic instabilities” and the danger of “failing fast and failing spectacularly” (NN 2015-12); base code, bounded chaos, normal accidents and rising irreversibility (FFTF 2018); Pippard’s ladder of tipping points (drafted for FFTF in 2018, cut, and published in Future Rising, 2020); Future Rising’s call to “spot early warnings and stay clear of critical tipping points”, its warning about “stealing the futures of others”, and its frame of stewardship (2020; quoted in 2024-09-08), which he later glossed as caring for the future “on behalf of generations that haven’t arrived yet” (FWB 2026); the solution problem and the inversion of timescales, in which consequences now outpace fixes (2021-04-09); Pippard’s ladder revived and ATT as a research agenda, with disruption moving “from years to months” (2023; NSF 2023); ATT as a field with provisional tools, including the quadrants, the threat/opportunity model and three S-curves (2024); three AI trajectories, the timescale mismatch, exponential blindness, six cause–effect models and a spiky knowledge frontier (2025); AI beyond analogy (2026-01-22); and, in his April 2026 retrospective, the early days of a transition as what “set the trajectory for decades” (NANO 2026), a point he traces to his testimony to Congress (“ahead of the game”, Testimony 2008 p.7). Convergence stays live, from converging technologies as a fragile system (2015-01-30; NN 2015-12) to his 2026 retrospective: “The convergence is the thing” (FWB 2026). Tipping points move from things to avoid (2023) to things one might deliberately “embrace” (2024), though he admits he cannot yet say what that means for who thrives. Emergence in complex systems later underlies his idea of stochastic agency (2024-10-27).

T7. Responsibility and the people behind technology#

Core. Most damage from powerful technologies traces back to people who mean well, so responsibility cannot be self-certified. The diagnosis is shaped by self-implication, admiration for audacity (hubris is also how things get done), justice and an obligation to innovate. Legitimacy rests on consent and inclusion, not on good outcomes: “where do they get the right to act unilaterally on issues that ultimately impact us all?” (FFTF p.249).

Development. Promoters overseeing risk, and “good intentions are not enough” (2006–08); scientists’ responsibility for hype and elitism, and “the privilege of scientific insight” as a duty of care (2014–16); entrepreneurs’ “deep-seated belief in the safety and efficacy of their creations”, which investors require (NN 2015-03); the book’s full apparatus: myopic benevolence, could/should, technologies of hubris, permissionless innovation, immoral logic, the honest broker, Luddites reclaimed (2018); the sincere founder as the unit of analysis in his programme’s teaching scenarios (Nexus 2019); entrepreneurs whose good intentions “remain good intentions, and no more” without codified approaches (2019-08-13); structural pressures on sincere actors, from dependent “super-consumers” (2022-02-12) to competitors racing after ChatGPT “far faster than a measured and responsible approach would suggest is wise” (2023-11-18); applied to AI governance (2023); his sharpest criticism of AI companies, over OpenAI’s Her-like voice, which exposed among “AI companies like OpenAI” a gap between talk of responsible innovation and “a reality that sometimes seems childish irresponsibility” (2024-05-21), alongside warmth toward Musk and generosity toward Amodei; the economic gradient toward manipulation (2024-07-13); permissionless innovation becoming the prevailing politics, and the reversibility footnote (2025); hubris that runs both ways, applied to AI refusers and to academia too (2026), as he had applied it to his own risk-research community in 2016. Resisters, in his account, are protecting what they value.

T8. How he thinks#

Core. He thinks in a recognisable cycle (§2.5). Something catches his curiosity; he questions the frame; he loosens it with play, story, juxtaposition and analogy; he tightens it with plausibility, physics and evidence; he builds or tests something to find out, often with himself as the instrument; and he publishes provisionally and revises in public. Underneath are a risk scientist’s evidence conscience (weight of evidence, hazard in context, a “BS monitor” for hype, a physicist’s reality check) and a humility that is practised rather than declared. The aim is a way through, not a verdict.

Development. Formation in physics and aerosol measurement science, where “the rigor and the math were important” but physics was “all about the sheer delight of putting ideas together in different ways” (TechTrends 2023), and where undergraduate labs were, in effect, play (2024-03-17); a decade of nanomaterial risk research, research strategy and testimony (2005–2011), in which he designed measurement around ignorance and first signalled a change of mind in print (Nature 2011); art and speculative design as tools for teaching about risk from about 2011–12 (2020science 2012), after a 2010 proposal he co-drafted had used “science fiction” as shorthand for poorly informed opinion (CETI 2010); creativity, imagination and serendipity written into risk innovation (NN 2015-09; 2016-01-11); the book as epistemic manifesto, built on films (2018); teaching with film (2019–22); bounded infinities and juxtaposition as a theory of how thinking escapes frames (2021-04-09); the Substack as a medium for thinking in public, and hands-on experiments with AI that produce concepts (2023–24); play named as the root of his method (2024-03-17), and playgrounds defended against playpens (2025-03-15); open revision on deepfakes, “technology apologetics”, AI fiction and agentic influence (2024); AI inside the method: writing, research partners, tools, fiction (2025); self-implication, disclosure, controlled experiments in AI scholarship, a pre-registered play experiment (2026-08-23), joy defended as a measure of achievement (2026-09-20) and a darker register (2026). His analogies move from confident translation (2019) to probes of difference, and plausibility moves from mainly deflating speculative risk toward taking tails seriously when a mechanism is plausible, a balance he held explicitly from 2016 (“quick to question, and slow to respond”). His role runs from open policy advocacy (2006–08), to the honest-broker stance (2018), and back toward more open advocacy (from 2024). Interpretation: the thesis that AI bypasses critical reasoning is also a threat to his own method, which he recognises (“how do I know I’m not an unwitting victim here?”, 2026-01-17).

T9. Evolution and tensions#

Core. His method barely changes; his object of concern and his confidence in remedies change a great deal (see §8 for the detail). New frames are built on kept foundations rather than replacing them (§2.2), and the record bears this out: quantitative risk science stays in use while risk innovation, the value frame and navigation change the questions it serves.

Position against others, on his own terms. Against mainstream AI-safety framing he diverges on ontology (superintelligence, extinction), on safety as an engineering property, and on who defines “safe”; he increasingly converges on mechanisms (manipulation, deceptive misalignment, agentic loss of control), and in 2026 he writes that “The alignment problem deserves the attention it’s getting” while ranking below it the question of what future we want (FWB 2026). Against optimists and accelerationists he diverges on permission, speed, “fixing” people and who pays, while sharing their view that not innovating is a risk. From 2025 he also pushes against educators “in AI denial” (2025-08-10).

T10. Learning, education and the university#

Core. Education builds people’s capacity to create value and navigate transitions, so who gets access to it, and whether AI deepens or hollows learning, are risk and justice questions. Universities have a public duty to help society through technology transitions.

Development. 2008–16: public education and engagement as part of governing nanotechnology (Testimony 2008); education “from consumers to CEOs” as a lever for steering converging technologies (NN 2015-12); casual learners’ access to good information as a matter of accountability (NN 2016-09); universities’ public duty (2016-01-31); learning through film and serendipity (2021-01-15; 2021-04-09). 2023: AI literacy for all majors (2023-05-09); AI as equaliser in admissions (2023-07-27); peak optimism about ChatGPT as a catalyst for thinking (2023-08-14), and a course designed largely with ChatGPT (Slate 2023). 2024: frugal, open pedagogy (2024-02-11; 2024-03-17); naive adoption and lock-in (2024-05-05); deliberate education in “social” skills for social AI (2024-10-20). 2025, his densest year for education: universities “mired in tradition” (2025-01-07); the PhD and the artisanal intellectual (2025-02-09); the value-creation model and intelligence scarcity (2025-03-30); educators “in AI denial” (2025-08-10); advisors’ AI use and students’ dignity (2025-10-26); duty of care and literacy classes that “risk becoming performative” (2025-11-09). 2026: education as “human formation”, with AI as possibly “a tool for formation rather than a threat to it” and “the principled position” as “responsibility” rather than “resistance” (S3 2026); “the illusion of learning” (2026-05-10); the validation gap† (2026-06-12); learning by not trying to learn (2026-08-02); universities as “followers and users”, with hope (2026-08-30). He moves from optimism to warnings without retracting the earlier view (§9, tension 8).

T11. Being human and flourishing#

Core. Being human is what his risk thinking protects and what it is for: dignity, agency, identity, relationships, joy and wonder, and the flourishing they make possible. It is one of his largest threads and the most frequent frame in his writing from 2024.

Development. 2009: “People matter” as the first thing to know about nanotechnology safety (2020science 2009). 2014: artificial minds that “challenge our very notions of humanity” (2020science 2014). 2018: the book’s worth and dignity, “normal” versus “human”, and a critique of the obsession with intelligence (FFTF pp.57–62, 108, 136–141), under a chapter title, “Being Human in an Augmented Future”, that names the later programme. 2020: humans as architects and stewards of the future, and “everyone has the right to thrive” (FR pp.18, 192). 2023: the Substack launched (2023-04-04 welcome-to-the-future-of-being-human); personhood, and not dehumanising what we make (2023-08-18). 2024: intrinsic technologies (2024-01-01); his public correction on “fixing” disability (2024-01-30); humanity “sufficiently adaptable and resilient” to hold on to what makes us “us” (Dune 2024); joy and “the soul of science” (2024-11-10); the bedrock of being human no longer “an immutable truth” (2024-12-29). 2025: flourishing named as the heart of his work (2025-01-30); learning “how to be human” with AI (2025-03-30). 2026: constitutive resonance and “who we are becoming” (CR 2026); “who we are” as the domain AI is changing most (2025-01-07; 2026-05-21, in his reading of the papal encyclicals); “human-centered flourishing and leadership in an age of AI” as the aim of his initiative (2026-08-16).

8. How his thinking has evolved#

Phases#

Phase Dates Main preoccupations Signature new ideas
0. Measurement science 1990s–2004 Workplace aerosol and nanoparticle exposure at the UK Health and Safety Executive, then NIOSH Methods for collecting and analysing nanometre particles; the dose-metric question (mass, surface area or number)
1. Nanotechnology risk and governance 2005–2011 Nanomaterial toxicology and exposure; research strategy at PEN; testimony to Congress; “Safe handling” (2006); late lessons (2008); WEF institution proposals; regulating sophisticated materials; Michigan Risk Science Center Measurement designed around ignorance; the early window (“ahead of the game”, 2008); control banding; strategy “with teeth”; promoter–overseer conflict; plausibility as a named filter; emergent risk; behaviour, not labels; trigger points; “When the data run out – innovate!”; first signalled change of mind (2011)
2. From nanotechnology to risk innovation 2012–2017 Nature Nanotechnology columns; entrepreneurship teaching; the ASU Risk Innovation Lab (2015); converging technologies; earliest writing on AI risk in the record (2014); “Rethinking Risk” (2017) Risk innovation named (2015); risk as a threat to value; the risks of not innovating; the pacing gap (“years, not hours”); early warnings of systemic failure; “quick to question, and slow to respond”; social licence; the ten-year audit (2016)
3. The book 2018–mid-2019 Films from the Future as a tour of his field; tech-company responsibility Artificial manipulation; permissionless innovation critiqued; plausible vs imaginable in film; base code; orphan risks named; algorithms as chemicals; tight coupling, latency and value mismatch (2019)
4. Consolidation mid-2019–2022 The Risk Innovation Nexus in practice; brain–machine interfaces; prediction and bias; Future Rising; learning through film; AI moves to the foreground “More to risk than probabilities”; synergistic scaling; technological indentured servitude; Pippard’s ladder, stewardship and the hubris of prediction (Future Rising); the solution problem; AI risk “mundane” but serious; AI ethics crowding out AI risk
5. After ChatGPT 2023 AI governance; who decides; transitions; language and relationship; the existential-risk debate; education and equity Advanced technology transitions; ethics-to-risk reframe; the language turn†; catastrophe as mass loss of value†; risk from first principles; disruption “from years to months”; AI as “translators”; the moral status of AI
6. Relational AI 2024 Being human; relational and agentic influence; safety as social; transitions as a field; tech leaders; neurotechnology; democratic and systemic risk Intrinsic technologies; hyper-anthropomorphism; economic gradient; stochastic agency; ATT models; the drain of agency; against x-risk ideology
7. Permission and care 2025 AI in scholarship; education’s purpose; permissionless politics; care; agentic AI; everyday risks Reversibility footnote; hard care; exponential blindness; motive–means–opportunity; duty of care; the value-creation model of education; what we do / who we are
8. Cognition and thriving Jan–Sep 2026 Epistemic vigilance; what AI is; flourishing; his own retrospective; frontier-AI risk selection; universities Cognitive Trojan horse; constitutive resonance; the harness critique; relational technology; “train us to think like them”; the gap as an organising idea (a restatement of older parts); the frontier risk-selection paper [mixed]; formation

How the method developed#

The phases above track his concerns. His method developed alongside them. - Physics as play (his formation). Undergraduate labs where “we got the chance to experiment, to be creative, to explore new ideas and to problem solve — to play in effect” became “foundational to how I approached my research as a physicist” (2024-03-17). - Measurement designed for ignorance (2005–2011). Several dose metrics when the right one is unknown, records for later reinterpretation, and “When the data run out – innovate!” (ILSI 2005; Nature 2006; 2020science 2009). - Art and speculative design in risk teaching (2011–12) (2020science 2012). - Creativity written into risk (2015–16). A culture “epitomized by serendipity”, a book of haiku beside Tox21, and a lack of “creativity and flexibility” as a cause of things going wrong (NN 2015-09; 2016-01-11). - Films as the lens (2018), because drama is built from threatened value (FFTF pp.23–24). - A theory of escape (2021). Bounded infinities and metaphorical quantum tunnelling (2021-04-09). - Analogy from translation to probe (2019–2026). From algorithms read as chemicals (2019-03-05) to a technology that “defies analogy” (2026-01-22). - Himself as instrument (2023–26). Hands-on AI experiments that test concepts, often after an event has prompted them (2023-04-05; 2024-10-27), and that sometimes catch him out (2026-02-08). - Play named and defended (2024–26). “grounded in play” (2024-03-17); playgrounds, not playpens (2025-03-15); a pre-registered play experiment (2026-08-23); joy as a measure of achievement (2026-09-20).

The formative layer, 2005–2016#

The Substack corpus holds only a handful of posts from before 2016, so on the posts alone his formation is visible mainly through later accounts. His papers, columns, testimony and blog posts from 2005 to 2016 now document it directly. Three findings stand out. His risk thinking grew inside quantitative risk science and was built on it. His humility about what numbers can capture is as old as his quantitative work. And many ideas that the posts first show in 2016–2023 were already in place.

Quantitative foundations. He began as a physicist and aerosol scientist, and in 2015 he placed his own early-1990s methods for measuring nanometre particles in a lineage going back to John Aitken’s particle counts of 1889 (NN 2015-06). In 2005 he chaired the physicochemical-characterisation group of an international screening-strategy report for nanomaterial toxicology. Because it was not known whether mass, surface area or particle number was the right dose metric, the report asked for all three to be measured or derivable in every study, and for enough data to allow “retrospective interpretation of toxicity data in the light of new findings” (ILSI 2005 p.7). His 2006 Warner Lecture extended risk as hazard and exposure with “a third component … Characterization”, proposed an instrument whose response reflected “current uncertainty over what should be measured”, and offered control banding for “decision-making based on incomplete information” as a supplement to, not “a substitute for conventional risk assessment and control” (AOH 2007 pp.7–10). Its summary: “push existing knowledge as far as it will go”, then do targeted research (p.11). A 2011 review he led concluded that “the risk assessment paradigm remains relevant”, while “Quantitative toxicology and risk assessment are unlikely to keep pace” with sophisticated materials, so a knowledge gap would grow that needed “a new science of risk” (Toxicol. Sci. 2011).

Research strategy and testimony (2006–2008). As Chief Science Advisor to the Project on Emerging Nanotechnologies he wrote a research strategy, framed as “one scientist’s personal perspective”, which estimated highly relevant federal risk research at about 1% of the nanotechnology budget and warned that quantifying risk from existing knowledge “will engender false assumptions of safety” (PEN 2006 pp.9, 13). Before the House science committee in 2006, 2007 and 2008 he argued for a top-down research strategy “with teeth”, a tenth of nanotechnology research spending for risk research, a single accountable leader, full transparency and independent research funded jointly by government and industry. The testimony is quantitative throughout: relevance-weighted budget analyses that turned a claimed $68 million into $13 million of highly relevant research (Testimony 2008 p.12). It is also humble about knowledge: “we do not yet know what are the right questions to ask regarding potential risks”, and “we must not mistake methodology for strategy” (Testimony 2007 pp.21, 33). In 2006 he told the committee that “numbers alone can be misleading” (Testimony 2006 p.53), and in 2008 he wrote that counting research dollars is “a crude tool at the best of times”, and yet “bottom-line figures count” (2020science 2008a).

“Safe handling of nanotechnology” (2006) and its ten-year review (2016). In Nature in 2006 he and thirteen other research leaders argued that fears about nanotechnology “may be exaggerated, but they are not necessarily unfounded”, and that “the way science is done is often ill-equipped to address novel risks”. They set five grand challenges: exposure instruments, including a universal sampler that would give “a historic record that can be interpreted in the light of new knowledge”, because “We don’t yet know which aspects of airborne nanomaterials should be measured”; validated toxicity screening; predictive models leading to materials safe by design; life-cycle evaluation; and strategic research programmes (Nature 2006 pp.267–269). Ten years later, with Robert Aitken, he audited the agenda publicly in a table headed “A personal assessment of progress”. Funding, screening and the understanding of fibre-like nanotubes had advanced; exposure instruments, “smart sensors”, predictive models and safe design largely had not. Some risks “may not be as high as was originally thought”, which showed that “the process of science is working”, and he warned that as “careers and funding pathways are built around assumptions of substantial nanomaterial-specific risk”, evidence-based decisions become harder (Maynard & Aitken 2016 pp.998–1000). Interpretation: the audit is an early instance of humility applied to a confident quantitative programme, including his own.

Late lessons from early warnings (2008). In 2008 he and three co-authors tested nanotechnology against the twelve lessons of the European Environment Agency’s 2001 report on the history of ignored early warnings. Their verdict was mixed. Early risk discussion, cross-disciplinary collaboration and stakeholder engagement were unusually prominent, but “the global response to these warning signs has been patchy”; agencies saw new materials through a chemistry-bound lens; the same initiative both promoted nanotechnology and oversaw its risks; and “many governments still call for more information as a substitute for action”. They recommended acting on what is known with review procedures for course correction, and building safety in at the design stage “because economic interests are not fully entrenched at that point”. The question, they concluded, was not whether the lessons had been learned “but whether we are applying them effectively enough” (Hansen et al. 2008 pp.444–447). He reposted the conclusion under his own name, adding that “a refresher course in responsible nanotechnology wouldn’t go amiss” (2020science 2008b). The paper is co-authored, and probably the most precaution-leaning text in his record. His own field supplied later examples of slow uptake: recommendations from a 2004 workshop “look remarkably similar to recommendations still being made” in 2011 (“are we making progress, or are we simply going round in circles?”, 2020science 2011), and in 2016 a supplier’s safety data sheet still treated carbon nanotubes as nuisance dust: “despite the science moving on, not a lot has” (2020science 2016).

Regulating sophisticated materials; “Don’t define nanomaterials” (2010–2011). A 2010 handbook chapter and a 2011 commentary, both led by him with the regulation scholars Diana Bowman and Graeme Hodge, called nanomaterial regulation a “wicked” problem. They described regulation built on quantitative risk assessment as “professional and competent”, but noted that, as “the purview of invisible experts” “quietly modulated by political and economic interests”, it had “tended to deal retrospectively with well-established risks” (Handbook 2010 p.582). New approaches should be “grounded in established approaches to identifying, assessing and managing risks”, informed by “plausible emerging risks”, and cautious: “we would be remiss in throwing out the old and embracing the new, simply because we can” (Nat. Mater. 2011 pp.554–556). In Nature in 2011 he announced a change of mind: “Five years ago, I was a proponent of a regulatory definition of engineered nanomaterials. I have changed my mind.” A one-size-fits-all definition would make regulation a “term of art” rather than science; materials should be regulated by the risks they present, “not by the technological labels that come attached to them”, through evidence-based trigger points that “must be flexible, so that they can be modified as evidence grows” (Nature 2011; draft). The same year, a review he led set out “technology independent” principles for deciding what to study: emergent risk, plausibility (“a crude but effective filter”) and impact (Toxicol. Sci. 2011).

The columns (2014–2016). His eleven sole-authored Nature Nanotechnology columns are the bridge from nanotechnology to risk innovation. They warn that a well-funded research programme can turn “The speculation of possible risk” into “an assumption of as-yet-to-be-discovered risk” (NN 2014-03); that novelty is “a rather unreliable indicator of potential risk” and “mundane risks are still risks” (NN 2014-06); and that evidence against his own standard example of a safe nanomaterial “cast doubt on what I thought I knew to be true” (NN 2014-09). They ask “how can responsibility be built into the innovation process without it stymieing the very innovations it sets out to enable?” (NN 2015-03), and insist that “The harder challenge is working out what we should be measuring” (NN 2015-06). “Why we need risk innovation” (NN 2015-09) is the founding statement of his framework; “Navigating the fourth industrial revolution” (NN 2015-12) calls for “mechanisms for detecting early warnings of systemic instabilities” and “actionable empathy”; “Navigating the risk landscape” (NN 2016-03) introduces risk as a threat to worth on top of the probability of harm, warns that a statistical parameter “may not adequately reflect a risk parameter of relevance”, and advises being “quick to question, and slow to respond”. His carbon-nanotube column (NN 2016-06) is his most technical exposure-science writing of the period, written between the two statements of risk innovation: the value frame and quantitative exposure science ran side by side.

The WEF institution proposals (2008–2010). As a member and then chair of World Economic Forum councils on emerging technologies, he drafted in 2008 a proposal for a “Global Institute on Emerging Technology Policy”: “Science-based”, “Non-advocacy”, jointly funded but independent of its funders, and charged with “predicting, assessing and avoiding adverse consequences” (WEF 2008). In 2010 he and Tim Harper drafted a proposal for a “Global Centre for Emerging Technology Intelligence”, which drew on the GM-food experience to argue that “hierarchical, evidence-based decision-making is not sufficient on its own to ensure the success of new technologies” (CETI 2010 pp.1–2). The documents speak of prediction, assessment and horizon scanning rather than of “early warnings” as such. In 2026 he wrote that renaming “policy” as “intelligence” “narrowed the original ambition in ways I still have mixed feelings about”, and that the 2008 bottom line “could appear unchanged in almost any serious AI governance document being written today” (Prehistory 2026).

What the formative layer shows. - A new frame on kept foundations. Every stage keeps conventional, quantitative risk assessment as a foundation while changing the questions it serves (C3). The call for a new mindset and the insistence on the old rigour come from the same experience: categories that stopped tracking what mattered, and tools that still worked when used with judgement (§2.2). - Humility about numbers is as old as the numbers. His concern about numbers that comfort without informing appears in 2006 (“false assumptions of safety”), 2007 (“mistake methodology for strategy”), 2009 (“comforting” but “misleading”) and 2016 (“may not adequately reflect a risk parameter of relevance”), alongside a consistent refusal to wait for complete data. C5 sets out this stance as a commitment in its own right. - Earlier dates than the posts suggest. Plausibility as a named filter (2011, not 2018); behaviour over labels (2009–2011, not 2022); risk as a threat to value and the risk landscape (2015, not 2016); the risks of not innovating (2006); who decides and who pays (2008); could versus should (2008); the pacing gap (2007); early warnings of systemic failure (2015); the promoter–overseer conflict and structural incentives against risk research (2006); social licence (2011); taking low-probability, high-impact scenarios seriously (2010); the first signalled change of mind (2011). - Governance instincts. His first remedies were strong, central and expert-led, with adaptive hard-law triggers; soft law and agile governance are a later addition (C13; tension 9). - Advocacy. He was an open policy advocate years before adopting the honest-broker role in 2018 (tension 12). - The nanotechnology story is mixed. His contemporaneous record includes real failures, which his later accounts of a “reasonably successful” transition smooth (T2).

His own retrospective, April 2026#

On 12 April 2026 he published a hub essay and six companion essays on andrewmaynard.net. They are his own synthesis of thirty years of work, written for human and AI readers, and so a direct check on this map. (For the provenance cautions that apply to a few passages, see §1.)

Where they confirm the map. Risk as a threat to value, and risk innovation as navigation rather than elimination; the risks of not innovating and the obligation to explore new technologies responsibly; plausibility against make-believe (“Make-believe treated as reality has consequences”); nobody deciding alone (“‘It’s complicated’ is not an excuse for avoiding engagement”); manipulation over superintelligence, “a claim I stand behind more firmly now than when I wrote it”; “who we are” as what AI puts at stake; stories and films as democratic entry points; the honest-broker role; the refusal of the optimist–pessimist binary; superintelligence agnosticism, with Seth’s biological naturalism held “provisionally”; and the nanotechnology governance lesson as one of “process”, learned from the GMO failure (30Y, NANO, FWB, STICK 2026). They also confirm that his foundations are kept: conventional risk assessment of “quantifiable harms” is named and retained, and risk “is not just about technical hazards to be minimized” (NANO 2026).

Where they shift emphasis. - The gap as his organising idea. He tells his whole career as one recurring gap between what a technology can do and a society’s capacity to understand and govern it, and calls the AI question “structurally identical” to the nanotube question: “The specifics have changed enormously. The pattern hasn’t.” (30Y 2026). The parts (the pacing gap, the timescale mismatch, the validation gap†) run through his earlier work; here they become one organising idea (§6.4). - Time and the early window. The early days of a transition “set the trajectory for decades”, a point he traces to his first Congressional testimony (NANO 2026). A 2023 article he co-wrote had already said that for AI “this window is closing fast” (CONV 2023) (§6.4; tension 5). - Convergence still central. “The convergence is the thing, and AI is one — admittedly very powerful — thread within it” is where he “part[s] company with a lot of AI discourse” (FWB 2026). The map’s arc from converging strand to “category of its own” holds for AI’s effect on the self, not for his systems view (§6.6). - Orphan risks as his label for AI’s human-side risks. Threats to dignity, identity, autonomy and “what it means to be human” that are “hard to quantify” and that “no existing institution owns” (NANO 2026). This is his own prose three months before the frontier-AI paper, and it shows the concept becoming his main way of naming what existing institutions do not see (§2.3; §6.1). - Institutional realism. Governance “grounded in how institutions actually function rather than how we wish they would” (30Y 2026). - Education as formation, in a positive sense (S3 2026; §6.8). - Stewardship and future generations, the frame of Future Rising (FWB 2026; §6.3). - Alignment research credited: “The alignment problem deserves the attention it’s getting” (FWB 2026; T9). - Tensions he names himself. Several tensions that the posts alone leave implicit he states in these essays (analogy, the honest broker, relationship and machine; §9).

Humility. The essays are candid about the limits of his knowledge: “These are explorations, not findings”; “some of this could be completely wrong”; “When I searched SCOPUS for papers on epistemic vigilance and AI, I found seven” (HNS 2026); “I don’t have a governance solution for AI. I’m not sure anyone does”, followed at once by what nanotechnology’s experience does offer (NANO 2026). They pair this with the case for asking questions now, “before the answers arrive in the form of consequences we didn’t anticipate” (HNS 2026).

What stays constant#

  1. Quantitative risk science as a foundation. From dose metrics and exposure measurement (2005–2016), through algorithms read as chemicals (2019) and first principles for AI (2023), to “not as an alternative, but as an augmentation” (2026-07-16 [mixed]). The frame changes; the foundation is kept.
  2. Humility about what numbers capture, and a duty to act anyway. From “false assumptions of safety” (2006) and “When the data run out – innovate!” (2009) to “the less certain I am that we even know how to formulate the problems we face around AI” (2023-11-26) and “retreat to what can be quantified” (2026 [mixed]).
  3. Risk as a threat to value. Unchanged in definition from 2015 to 2026; ever wider in reach.
  4. The risks of not acting. From the risk of public rejection in 2006 testimony and “we can’t afford to slam the breaks” [sic] (2015-01-30) to refusing “zero exposure — as in no AI” (2023-11-26).
  5. Plausibility as a discipline, applied to hype and doom alike, from “grey goo” (2006) to AGI (2026).
  6. Manipulation over domination. From a 2014 question about prolonged interaction with intelligent machines, through Ex Machina (2018), to the cognitive Trojan horse (2026). In 2025 he notes that he had written about this back in 2018, and in 2026 that he stands behind it “more firmly now”.
  7. Who decides. No abdication to experts; who decides what “safe”, “good” and “normal” mean; “Who will decide how it is used, and who will pay the cost?” (2008).
  8. A non-demonising account of developers, joined to a critique of hubris, and to a structural account of incentives from 2006 onward.
  9. Refusing the optimist–pessimist binary. From “techno utopia” versus the “potential to destroy the world” (2010, co-drafted), through “Don’t Panic” (2018) and the “oxygen pessimist or optimist” (2024-03-31), to “neither an AI optimist nor an AI pessimist” (2026-09-24 [mixed]).
  10. Complexity and irreversibility as the reason permission matters.
  11. Being human as what is at stake and what is sought. From “our very notions of humanity” (2014) and “what it means to be human” among the values at risk (FFTF p.23), through “what drives my work more than anything” (2024-01-01), to navigating advanced technology transitions “to get to the sort of future we want — and what it will mean to be human in those futures” (2026-09-24, his own introduction).
  12. Justice. From the public who “may bear many of the potential risks” (2006) and the “first tier” of workers and uncertainty that “suited the mine owners” (FFTF pp.120–121) to “who decides who will suffer and who will thrive” (2023-10-19).
  13. Universities’ public duty. From “Public universities must do more” (2016-01-31) to “we owe it to” students (2026-03-29) and the governance gap (2026-08-30).
  14. Self-implication and public reasoning, including public reversals (from 2011) and public audits of his own work (2016).
  15. Imagination as part of risk thinking. From “When the data run out – innovate!” (2009), a book of haiku as the first example of risk innovation and a culture “epitomized by serendipity” (2015), and “not thinking creatively enough” (2018), through bounded infinities (2021) and play named as his method (2024), to imagination as the defence against being blindsided (2026-09-15, n.5).
  16. Navigation as the stance. From “Navigating the fourth industrial revolution” and “Navigating the risk landscape” (2015–16) and risk turned into “a way of supporting beneficial and sustainable progress” (2016-01-11), to “rapid course correction” (2025-05-18): steer rather than control, and correct course.

What changes#

  1. AI’s status. One member of a converging set (2014–2021); a technology at a tipping point for self-understanding (2023); a different category in what it does to the self (conditionally in 2025, unconditionally in 2026), while remaining “one … thread” of convergence in his systems view (2026).
  2. The unit of AI concern. From a question about prolonged interaction (2014) and an embodied, goal-directed manipulator (2018), to deception that disables evolved defences (2020), to language (2023), to design, incentive and emergence (2024), to structured trajectory (2025), to ordinary fluent features, coupling and formation (2026). Intent drops out.
  3. Tails and exponentials. Low-probability, high-impact scenarios were in his thinking early: “more realistic scenario planning would have helped prepare for low probability but high impact risks” (2020science 2010a), and in 2014 he judged “certainly not empirically testable” AI risks worth examining, as long as the dreams were anchored “in plausible outcomes”. In 2018 exponential extrapolation was a fallacy and superintelligence “more an act of faith than of reason” (though the same page keeps the door open to low-probability possibilities). By 2025 he thinks about responsible innovation “just on the off chance” that AI 2027 holds “a sliver of truth”, and calls exponential blindness a human failing; in 2026 he considers loss of control without AGI (2026 [mixed]). He reads exponentials through S-curves: “exponential growth never lasts” (FWB 2026), yet steep phases are real and easily misjudged. His doubts about superintelligence never go away.
  4. Confidence in remedies. Responsible innovation goes from an aim he shares while doubting its academic forms (2015), to organising question (2018), to “fiendishly hard to operationalize” (2023-05-05), to efforts that might “seem futile” if even an edge-case scenario came true, which he hopes it will not (2025-04-06). AI literacy goes from remedy (2023, with roots in 2008) to insufficient (2025–26). Government agility goes from model (2016) to doubt (2025–26); in 2026 he is “not optimistic” that regulation alone will close the gap [mixed]. His governance instruments move from strong central capacity for risk research (2006–08), through adaptive hard-law triggers (2011), to a soft-law, multi-stakeholder portfolio (from 2015); the later instruments are added to the earlier ones. Public engagement does not decline as a principle.
  5. Inevitability and steering. Less a change than a constant restated for AI. From “a revolution that we cannot turn the clock back on”, paired with “an opportunity to help steer” (2015), and bad futures that are “not inevitable” (2020), through slowing “the AI juggernaut” as a legitimate choice (2024), to “Technology is not deterministic” (2025-03-30) and “The AI genie is out of the bottle” (2025-08-31), his position is that the trajectory is inevitable while its shape is open. What changes is its application to AI: in a 2026 lecture he adopted the inevitability of powerful AI as a working assumption, flagging that “it may be a flawed assumption” (2026-09-24 [mixed]). On pauses his record is specific rather than general: he declined the 2023 pause letter, floated “a pause even” (2023-11-18), and argued for “pausing — or even rethinking” emotion-exploiting companion chatbots (2024-10-27).
  6. Explaining developers. Structural explanations run alongside psychological ones for his whole career: promoters overseeing risk and industry’s incentive “to sell products” (2006), entrepreneurs’ optimism as something investors require (2015), value mismatch (2019), dependent “super-consumers” (2022-02-12), competitive racing (2023-11-18) and the “economic gradient” (2024-07-13) sit beside myopia and hubris. What changes in 2026 is formalisation, in the AI-assisted frontier-AI paper’s “incentive field” [mixed], not a shift from psychology to structure.
  7. Institutions. Not a sequence but a standing ambivalence about universities: urged to “do more” in 2016, hoped for and doubted at the same time in 2026 (“Sadly, this has been my experience so far. But there’s always hope”, 2026-08-30). Confidence in governments’ agility declines.
  8. Tone. Worry and wonder both sharpen. From deflation (“mundane”, 2020) to “worries me — a lot” (2026-05-10), and to being “stuck between” fear and a sense that “the potential is profound” (2026-09-24 [mixed]). The anti-alarmist stance is unchanged.
  9. AI changing its user. From “It’s almost as if ChatGPT is fine-tuning my brain to be a better instructor”, offered as a gain (Slate 2023), to AIs “beginning to train us to think like them” (2026-07-19) and constitutive resonance (2026). The phenomenon is present from 2023; what changes is its valence.
  10. His own practice. From “I typically don’t use ChatGPT myself when I write” (2023-09-20), to AI as research partner (2025), to “I cracked” (2026-01-17), to disenchantment with AI prose (2026-07-19), to controlled experiments in AI scholarship and the view that listing himself as author of an AI-written paper would “amount to academic dishonesty” (September 2026), after appearing as the listed author of an AI-written paper in March 2026 (with the model’s authorship stated on its title page).

Changes of mind (selected)#

Signalled by him. Changes he names himself.

When From To Source
2011 “a proponent of a regulatory definition of engineered nanomaterials” “I have changed my mind”: regulate by the risks materials present, not by labels Nature 2011
2014 Fumed silica as his standard example of a safe nanomaterial New evidence “cast doubt on what I thought I knew to be true”; still “acceptably safe” on the evidence, open to re-evaluation NN 2014-09
2020 Assumed Neuralink put medicine first “we were somewhat naïve” 2020-10-15
2023 Superintelligence implausible Accepts a risk “of potentially existential proportions”; still “not a fan” of superintelligence 2023-04-04 what-are-the-alternatives-to-calling; 2023-05-25
2023 Regulate use, not technology (his own rule: nanotechnology is “safety-neutral”, 2009) Doubts this works for general-purpose AI 2020science 2009; 2023-05-17; 2023-07-12
2023 Doubts the plausibility of Bengio’s arguments “But there is a ‘but’ here”: agrees we must think “seriously and creatively” about how to navigate powerful AI 2023-05-25
2024 Trusted common sense on deepfakes (already hedged in 2020) “far less sure”; signs a letter FR pp.156–158; 2024-02-25
2024 Decades of “technology apologetics” Questions them 2024-03-31
2024 Goal-directed agentic social AI Stochastic, emergent agency 2024-10-20; 2024-10-27
2025 Musk as a humble AI-risk voice (2018) His 2018 view now “a rather naive perspective on Elon Musk”; DOGE “a rather naive and uninformed application of permissionless innovation” 2025-03-02
2025 “Parasocial” communication as a positive idea “Clearly I read the tea leaves wrong”, while keeping the concept 2025-11-19
2026 AI capability flattening (2024) Agents have bent the curve “sharply upward again”: “We’re not on a plateau” S3 2026 (single source)
2026 “Blown away” by AI prose “superficially profound yet substantively hollow” 2026-07-19
2026 Early GPT a “toy” “I was wrong” 2026-09-24 [mixed] (spoken aside; single source)

Inferred (interpretation). Shifts visible in the record that he does not announce as changes of mind.

When From To Source
2010→2012 “science fiction” as a byword for poorly informed opinion (co-drafted) Speculative design and story as tools for teaching about risk; films as his main lens by 2018 CETI 2010; 2020science 2012; FFTF
2006–08→2015 Strong, central, expert-led coordination of risk research, and adaptive hard-law triggers (2011) A soft-law, agile, multi-stakeholder portfolio, added alongside Testimony 2006–2008; Nature 2011; NN 2015-12
2015 (March→September) Reworking responsible innovation for entrepreneurs, while doubting its academic forms Proposing his own risk-innovation framing; the impulse to innovate in how risk is governed is visible from 2008–09 WEF 2008; 2020science 2009; NN 2015-03; NN 2015-09
2018 Nearly 30 years inside nanoscale science A sceptical insider’s reassessment of his own field: “brand-nano” “fudged the science to sell the idea”, though it also broke down disciplinary barriers 2018-02-21
2023→2024 Computational functionalism “stands up to scrutiny” Finds Seth’s “compelling arguments” for biological naturalism persuasive; a thermodynamic doubt. His 2014 writing on artificial minds had been substrate-sensitive, which makes 2023 the outlier (interpretation) NN 2014-12; 2023-08-23; 2024-06-30
2024→2026 Humanity “sufficiently adaptable and resilient” to hold on to what makes us “us” AI taking part in “who we are becoming”, possibly without our noticing Dune 2024; CR 2026

Not a change of mind: his 2026 line that frontier models are not merely “stochastic parrots” (2026-01-22) is continuity. In 2025 he already endorsed the view that models are “pushing far beyond critiques” of that kind (2025-01-05), and his “DNA-based stochastic parrot” (2025-02-23) describes a DNA model with admiration, not LLMs. Nor is the move from exponential extrapolation as a fallacy (2018) to exponential blindness as a danger (2025) a reversal: he reaffirms the 2018 view “then, as now” in 2026 and reads both through S-curves (FWB 2026; 2024-12-13).

How he updates (interpretation)#

Arguments that supply a plausible mechanism move him; arguments that rest on stacked assumptions or ideology do not; evidence, events and hands-on use speed up the change. His first signalled reversal (2011) followed accumulating evidence that risk depends on many material properties rather than a size threshold. Bostrom’s superintelligence case did not move him. Bengio’s did in part: after doubting its plausibility, he conceded “But there is a ‘but’ here” (2023-05-25). AI 2027 did not persuade him, but it made him think about responsible innovation “just on the off chance” it held “a sliver of truth” (2025-04-06). Seth’s “compelling arguments” shifted his view on machine consciousness (2024-06-30). Events and experience accelerate change: a deepfake letter, chatbot-linked deaths, new model capabilities he used himself, empirical studies of model behaviour. He announces reversals plainly, but he seldom goes back to reconcile older positions. He republished his 2018 scepticism about superintelligence unchanged in 2023 and 2025, and he never retracted his 2023 view of ChatGPT as a catalyst for thinking. Hands-on play and experiment are among his main ways of changing his mind, often after an event has prompted the change. His unease at treating ChatGPT as a colleague (2023-01-31) became a named mechanism, “the illusion of a reciprocal relationship”, when he wrote about a chatbot-linked death (2023-04-05); the death of Sewell Setzer III moved him from goal-directed to stochastic agency, which he then tested on himself with a companion chatbot designed to keep users engaged (2024-10-20; 2024-10-27); being fooled by an AI while writing about being fooled sharpened his view of his own vulnerability (2026-02-08). Anyone using this map as a lens should read his current position as the latest result of a record that has been built up over time, on kept foundations, not as a replacement of earlier positions.

9. Tensions, open questions and live edges#

These are tensions within his own record. Of the sixteen below, he acknowledges three himself (8, 10 and 13) and eleven in part (1–7, 9, 11, 12 and 16); the other two are the map’s inferences and should not be read as his admissions. Each is tagged. In his papers and his April 2026 essays he often states, and sometimes resolves, tensions that the posts alone leave implicit. Few are fully resolved. Where a reconciliation is offered by the map rather than by him, it is marked as interpretation.

1. Past lessons against “defies analogy”. [partly his] He complains that each technology wave tends to “re-invent the wheel” and that AI advocates are “blissfully unaware of lessons learned from past technology transitions” (2023-04-12). He grounds his governance model in nanotechnology and Asilomar (2025-02-23; 2025-07-23). Yet he also calls the present “unlike anything we’ve had to grapple with before” (2023-04-12), says treating AI as a learning aid is “a categorical error” (2025-03-15), and says frontier AI “defies analogy” (2026-01-22). In January 2026 an argument built on chemicals, vaccines and viruses appears within a fortnight of “defies analogy”. For materials he stated his transfer rule early: a tool that is “not directly applicable” may still carry its concept (AOH 2007 p.10); risk questions should be decoupled from technology labels through “technology independent” principles (Toxicol. Sci. 2011); novelty is “a rather unreliable indicator of potential risk” (NN 2014-06 p.410); and “seemingly novel challenges don’t always demand novel solutions” (NN 2015-06 p.483). For AI he offers his own reconciliation in 2026: AI shows “a substantial scaling of recognized phenomena in ways that are not predictable from past experience” (CR 2026 p.2); “The technology had changed dramatically. The human questions hadn’t changed at all” (FWB 2026); the AI question is “structurally identical” to the nanotube question (30Y 2026). So process and method lessons transfer (engage, weigh evidence, respect irreversibility, distrust self-certified responsibility), while frameworks, categories and track records may not. What he has not yet said is which of AI’s risks are novel and which are ordinary risks in new clothes.

2. Plausibility against taking tails seriously. [partly his] In 2018 prioritising superintelligence and gray goo over the evidence-based harms of new materials was “more an act of faith than of reason” (FFTF p.281), and in 2023 speculation without a causal pathway was a hazard, not a risk (“no cause, no risk”, 2023-11-26). By 2025 AI 2027 “does force the question” of how to think about responsible innovation “just on the off chance” it holds “a sliver of truth” (2025-04-06), and in 2026 he justifies research on cognitive risk “even if there’s only a small chance” (2026-01-10). Both halves are old. In 2010 he called for scenario planning for “low probability but high impact risks” (2020science 2010a); in 2014 he judged “incredibly speculative and certainly not empirically testable” AI risks worth examining, while insisting on “the realism to anchor those dreams in plausible outcomes” (2020science 2014); his programme’s 2019 tools included “Black Swan Events” as a category, handled through resilience rather than prediction; and in 2016 he stated the balance as a rule: be “quick to question, and slow to respond”, yet ready to act on early warnings “even before the science is mature” (NN 2016-03 p.212). The 2018 text itself leaves “the door open to more complex, more fanciful possibilities being plausible”. So the change is one of emphasis, not a reversal. Interpretation: he distinguishes free speculative research from evidence-gated action, and accepts tails that come with a plausible mechanism, from behavioural science or observed model behaviour, while rejecting those built on stacked assumptions. His argument that blindsides come from failures of imagination (FFTF p.174) pulls against his Occam’s Razor; he holds the two together by using imagination to find possibilities and plausibility to rank them for action (C8).

3. Two definitions of risk, and how they fit. [partly his] He uses both “probability of harm” and “threat to value”, and says the second extends the first. He has described the relation several times. In 2016 the probabilistic definition is “a useful starting point” (NN 2016-03 p.211). In 2017 probability is the analytical core, “a powerful way of making trade-offs”, but “Risk calculations are also highly dependent on what is considered important, as well as who decides what’s important”, and he admits that the broader frame costs something: including interpersonal relationships in risk assessments is “hardly likely to make the process any easier” (Rethinking Risk 2017 pp.193–197). In 2020 a single sentence joins them: the mathematics of change helps “identify impending dangers, as change threatens to take away what we value” (FR pp.166–167). In 2026 the value lens “widens what counts as harm”, and where harm cannot be measured, value can still be “named, mapped and watched” (2026-07-16 [mixed]). How probability, exposure and dose–response apply to threats to dignity, identity or belief he leaves open (on what the concepts do not supply, see tension 16). His 2019 insistence on weight of evidence sits uneasily with his 2025–26 readiness to act on trajectory, lawsuits and anecdote (“the very small tip of a very large metaphorical iceberg”, 2025-11-09). His restraint about quantifying AI risk is deliberate (C5); how much evidence should be enough to act on unquantified harm is a separate question.

4. Whose value? [partly his] The value frame is “agnostic to particular worldviews”, but deciding whose value counts is political. The frame’s early applications are enterprise-facing: in 2015 he made the case for responsibility to entrepreneurs in terms of liabilities avoided and investor confidence (NN 2015-03); innovation is “creating value that someone is willing to pay for” (2016-01-11); and risk threatens value “a company or organization aspires to create or grow” (2023-11-21). A paper he co-wrote in 2019 concedes that the approach “can thus be seen to favor the enterprise” (BMI 2019 p.6); a 2022 guide he co-wrote states the operative assumption, that “threatening stakeholder value becomes a threat to principal agent value” (CIO guide 2022 p.34); and a 2024 paper he led says the approach’s “primary purpose is to help enterprises” (JLME 2024 p.564). That can recast ethics as enlightened self-interest, in which harms to people without leverage register only if they feed back to the firm. But the frame was never only for firms. In 2016 he noted that broader dimensions of worth “often depend on who is defining them” (NN 2016-03 p.211); in 2017 he wrote that decisions should protect what is valuable “not just to corporations and governments, but also to individuals and the communities they are a part of” (Rethinking Risk 2017 p.200); his 2018 definition names “an individual, a community, or a business organization” (2018-12-13); and his standing answer to “whose value?” is justice (C12). He saw the distributional problem in his own prose well before 2026: AI deployment tends to leave individuals “as engines of value creation rather than the primary recipients of created value” (2024-07-13). The 2026 frontier-AI paper makes it explicit: the channels that turn harm into cost “are not equally open to everyone” [mixed]. He admits the frame is “somewhat subjective” (2018-12-13) but offers no procedure for resolving conflicts between values; in 2020 he accepted that such conflicts cannot be removed, since “we’re committed to a future where someone, somewhere, is not going to be happy” (FR p.197).

5. An inevitable trajectory with a shape still to be chosen. [partly his] Since 2015 he has held that a technological revolution is one “that we cannot turn the clock back on”, paired with “an opportunity to help steer” (NN 2015-12 p.1006), and in 2025 that “Technology is not deterministic” (2025-03-30). The tension lies in applying this to AI. He criticises race logic, the idea that “if we don’t go fast, somebody else will” (2026-09-24 [mixed]), and the habit of going “fast and break things in the hope that someone else will clean up the mess” (2025-02-23), yet in a 2026 lecture he adopted the inevitability of powerful AI as a working assumption, adding that “it may be a flawed assumption” (2026-09-24 [mixed]). His record keeps specific pauses open: “pausing — or even rethinking” emotion-exploiting companion chatbots (2024-10-27), and slowing “the AI juggernaut” as a choice society could legitimately make (Dune 2024). His answer adds time: act “ahead of the game” (Testimony 2008 p.7), because the early days of a transition “set the trajectory for decades” (NANO 2026); in 2023 he and a co-author warned that for AI “this window is closing fast” (CONV 2023). Interpretation: inevitability applies to the overall trajectory, and choice to particular designs, uses and timing; the shape of the transition is open only for a short, closing period. He does not say what evidence would overturn the working assumption.

6. Relationship against “working with a machine”. [partly his] LLMs are “a relational technology” (2026-04-26), use changes the user (2026-02-22), and treating AI as just a tool is “potentially dangerous” (2026-05-21). Yet his public rules say “Do not treat AI as your friend, or as a person” and recommend a framing that keeps the user “in charge” (2026-05-10). He goes some way to reconciling these himself. Knowing that one is talking to a machine “matters less than we’d like to believe” (HNS 2026); wanting humans in the driver’s seat is legitimate, but the “harness” metaphor may misdescribe what actually happens (Harness 2026); and in the coupling, change operates differently on each side, experienced by the human, functional for the AI (CR 2026 p.4). Interpretation: the relationship is real and formative, while personhood is a designed illusion, and his rules are necessary but, by his own account, not sufficient.

7. Adopter and partner against risk communicator and critic. [partly his] He welcomed the ASU–OpenAI partnership (2024-01-18), calls dismissal of AI over hallucinations naive (2025-08-10), and uses frontier models as research partners and, in experiments, as authors. He also says honest talk about AI risk is “near-impossible” at his own institution (2026-05-10), criticises capture and deference to big tech (2023-10-30 white-house-goes-all-in-on-responsible-ai), and explains company behaviour through structural incentives. He notes the irony of using AI to study AI’s problems (“Ironically … I turned to the very source of the problem”, 2023-04-05). His structural account would apply to his own entanglement, but he does not analyse it. His argument for students’ “permission to play” (2025-03-15) is not part of this tension: playgrounds have rules, and play belongs where it is easy “to turn the clock back”, not in systems that cannot be reset (2025-03-02, n.2; §2.4). Interpretation: a related edge is access. Play needs time, designed spaces and often premium tools that not everyone has, and his public method reaches many people but is taken up less often in the rooms where AI is decided.

8. Catalyst against surrender. [he says so] In 2023 ChatGPT was “a profoundly effective catalyst for engaged and creative thinking” (2023-08-14). By 2026 he warns of “the illusion of learning rather than actual learning” (2026-05-10), adopts the term cognitive surrender (2026-05-21), and worries that AIs are “beginning to train us to think like them” (2026-07-19). He never retracts the 2023 view. In his March 2026 preprint he reconciles the two: the same dynamic that enables “cognitive and creative flourishing” also “enables erosion of the capacities it augments. Both are different sides of the same coin” (CR 2026 p.20). In April 2026 the hinge is the learner’s purpose: “AI doesn’t flatten learning values. It reveals and amplifies them” (S3 2026). The 2023 caveat, that students benefit “at least if they understand what they are doing”, points the same way. His own “Intelligent User Trap” and his admission of being “suckered by Claude” (2026-02-08) weaken any exemption for skilled users.

9. Engagement as principle, thin as mechanism. [partly his] “everyone has the right to play some role” (2023-05-15) is not contradicted by his 2026 lecture, which says members of the public “are critically important to this” before adding that “you cannot hand a problem of this magnitude over to everyday people” (2026-09-24 [mixed]); and in 2025 he reaffirmed two-way engagement (2025-05-25). The split is old: a 2010 chapter he co-wrote argued that people should be empowered “to be an effective part of the decision-making process”, while “the details of how regulations are crafted and enacted will of necessity remain the responsibility of a small number of experts” (Handbook 2010 p.583). The tension is between principle and mechanism, and it has a history. In 2007–08 his mechanisms were concrete: a federal advisory committee for “transparent input and review” and a funded public-engagement programme with named aims (Testimony 2007). In 2015 he named the gap himself: “we still lack the forums, the methodologies and the leadership necessary to ensure actionable outputs from multi-stakeholder dialogues” (NN 2015-12 p.1006). Since 2018 he names participatory technology assessment, consensus conferences and public-interest technology, but never works one through for AI; his chosen practical mechanism is relationship-based public communication (USDOT 2025, co-written). A 2024 paper he co-signed is more concrete: funders should require and pay for engagement, run through trusted intermediaries such as science museums, because those who fund and pursue the research have “disincentives to change course” if engagement goes against them (Hyun et al. 2024 p.591). His concrete proposals put experts at the centre: a “world congress”, a cross-agency initiative, philanthropically funded university programmes. Interpretation: his critique of expert monopoly targets technical experts, and his remedy often adds another class of experts, in governance, responsible innovation and transitions, which is his own field. He admitted as much (“I’m admittedly a little biased”, 2016-03-12).

10. Universities as the answer and as the problem. [he says so] He argues that universities could fill a governance gap no other actor can, and that they bring insights “both necessary and unique” (2026-08-30). Yet he describes them as “guardians of the past more than leaders toward the future”, while adding that “This is not necessarily meant as criticism” and, in a footnote, that the line “probably comes across as a little harsh”. He fears they “may not be up to the task”: “Sadly, this has been my experience so far. But there’s always hope” (2026-08-30). His positive programme rests on an institution he fears may not rise to it, and he says so. This is a standing ambivalence, present since he urged public universities to “do more” in 2016 (2016-01-31).

11. Persuasion he practises and persuasion he fears. [partly his] He champions stories and relational, even parasocial, communication because they get past the defences that preaching triggers (2024-01-21; 2025-05-25); a 2025 report he co-wrote treats this trust as a method, through which audiences “will come along” into speculative territory “because the relationship foundation is there” (USDOT 2025 p.4). He fears AI because it does the same: stories “hard not to trust, and yet are not trustworthy” (2024-09-22). He names the danger in relational communication himself: “history is replete with examples of how feelings of connection and meaning — and of being ‘seen’ — have led to widespread social manipulation and control”. He answers it by separating communication aimed at “impact” (to “push an agenda”) from communication aimed at “empowerment”, and says his own career has aspired to “empowering others through relationship building” (2025-05-25). He asks “who decides what is good for society?” of machine and state persuasion (2024-09-01); applying that question to expert persuasion, his own included, remains undeveloped. He did face a version of the question in practice: in 2021–22 he helped a team apply risk-innovation tools to a chatbot designed to build trust and shift beliefs for a public-good aim, in a guide that treats deliberate influence as the highest-risk category and warns against the perception “that specific values are being imposed” (CIO guide 2022, co-authored). On AI companies’ own value-setting his record is mixed. In February 2026 he contrasted two “theories of governance”, Constitutional AI, which “aspires to education and learning”, and harness engineering, which aspires “to control”, with evident if unstated sympathy for the first, and without asking whose values the constitution encodes (Harness 2026 p.5), though he had asked of alignment in 2023 whose values matter and who decides (2023-05-25). In April 2026 he wrote that the selection of an AI constitution’s principles “lacks the legitimacy that inclusive governance processes provide” (NANO 2026), a point taken from an AI-written paper he endorsed [AI-origin; endorsed].

12. Honest broker and advocate; humility and authority. [partly his] He chose Pielke’s honest-broker role in 2018 (FFTF p.246). Before that he had been an open policy advocate, in a research strategy and three rounds of Congressional testimony (2006–08), while the WEF institutions he proposed were to be “Non-advocacy” (WEF 2008); and in 2026 he regretted that one of them was redefined from “policy” to “intelligence” (Prehistory 2026). From 2024 he signs a letter on deepfakes (2024-02-25), and by 2025–26 he is issuing rules of thumb, criticising federal policy and telling universities to step up. In April 2026 he names the strain himself: with AI, “the temptation to advocate for particular positions is stronger”, and he resolves it toward giving people “the frameworks, the evidence, and the stories” (STICK 2026). The book’s own qualifier, advocacy through institutions when silence would be complicity, covers some of this, but he does not say when he crosses the line. He also pairs “the less certain I am” (2023-11-26) with appeals to decades of expertise. Interpretation: the trajectory is U-shaped rather than a drift from neutrality.

13. Instrument and object. [he says so] He uses AI to study and write about AI: an AI-drafted lecture, an AI-assisted paper, a concept credited in part to an AI model, and in 2026 papers written by AI models under his guidance, for which he wrote only the framing sections. He discloses this, and by September 2026 he regards listing himself as the author of an AI-written paper as “academic dishonesty” (Fable annex 2026). But it makes his 2026 thinking harder to attribute, and his own warning that AI is “beginning to train us to think like them” (2026-07-19) applies to his workflow. He asks the question himself: “how do I know I’m not an unwitting victim here?” (2026-01-17). A 2026 satire turns the problem into a joke: an exhaustive AI-use disclosure concludes that modern scholarship cannot escape AI (Scholarship 2026).

14. What AI is: ill-defined, set aside, yet felt. [interpretation] AGI is “rather ill-defined” (2026-04-11). In the lecture, AGI, superintelligence and machine consciousness “might happen” but are “irrelevant to this conversation”, which concerns loss of control without them (2026-09-24 [mixed]). Yet working with AI “feels like a superintelligence, a superpower” (same lecture), although he says he dislikes that language. On consciousness he found computational functionalism “a bold assumption” that “stands up to scrutiny” (2023-08-23), then found Seth’s case for biological naturalism “compelling” and added a thermodynamic doubt (2024-06-30). His 2014 writing on 3D-printed artificial minds had treated mind as dependent on its physical substrate (NN 2014-12), which makes the 2023 position the outlier. He never reconciles these positions on substrate. The distinction between being and seeming conscious carries most of the weight. The other direction of his concern, the moral risk of “enslaving AIs” as “just machines” if they could be aware (2023-08-23), is older than the posts suggest (machine rights as a risk to human moral codes, 2020science 2014) and persists in precautionary, agnostic form in 2026: “Would a smart human accept a harness?”, and adopting the word “harness” may embed a premature assumption about AI’s moral status (Harness 2026 pp.5, 9).

15. Human-centred, yet against human-centrism. [interpretation] His programme is named for being human, and flourishing is its aim. Yet he rejected the extinction framing as “too human-centric”, because “we are, after all, an integral part of a larger set of interconnected ecosystems” (2023-05-31), and his “where we live” domain reaches “the environment and the planet as a whole” (2025-01-07 universities-need-to-step-up-their-agi-game). The ecological strand is real but thin in his AI writing, and he does not say how human flourishing and the flourishing of wider systems are to be weighed against each other.

16. Mindset over tool. [partly his] By design, his concepts open decisions more than they make them. He calls the value frame “a somewhat subjective way of thinking about risk” (2018-12-13) and writes “I don’t have a governance solution for AI. I’m not sure anyone does” (NANO 2026). The Risk Innovation Planner “does not … provide answers to problems” (2023-11-21), and he says the frontier-AI analysis “has yet to be shown to be useful in practice” (2026-07-16 [mixed]). A regulator who needs a threshold gets a framing. The record also complicates the reading of these concepts as mental models. Between 2017 and 2020 the same ideas were offered to startups and investors as tools and as a business case: the Planner’s design brief was to help a founder develop “a risk innovation mindset that provided them with a competitive advantage” (2023-11-21). He chose to meet entrepreneurs in their own language rather than preach at them, a choice his later lesson explains: “if you want a fast-moving organization to attend to a risk, you do not hand it a compliance duty; you show it a threat to something it values” (2026-07-16 [mixed]). Interpretation: this is not a later reinterpretation. The founding column (NN 2015-09) and Films from the Future’s “ways of thinking” (FFTF p.39) predate the Nexus tools, and the tools themselves were designed to cultivate a mindset, framed in entrepreneurs’ language. What the concepts do not supply, by design, is stated here once for the map: thresholds of the kind a regulator needs, an evidentiary bar for acting on harms that cannot be quantified, and a rule for adding up many small, dispersed harms. His own record holds the pieces of an operational bridge (trigger points, 2011; “quick to question, and slow to respond”, 2016; the reversibility test, 2025) that he has not assembled for AI. Judged as a mindset, the questions are different: whether it travels without him, whether it can be co-opted, and what it needs in order to work (§2.9).

Open questions he keeps returning to#

Gaps and thin areas#

10. The lenses his work provides#

Thirty-seven technology-neutral questions and diagnostic moves distilled from his work, grouped under seven master questions. They are what he would bring to any technology, actor or analysis, and they are stated in general terms, not applied to anything here. The first group, M (for mindset), comes before the others because in his work the question of whether a way of thinking fits the thing in front of it comes before the questions asked within it; groups A to F follow the architecture in §4. The lenses are not a checklist to be completed. They are ways of opening a question, and he would expect them to be adapted, combined and sometimes discarded. Each group opens with where he usually heads: the direction his own thinking tends to take once the questions are asked, which is a map of pathways more often than a verdict. Each lens notes, in brackets, the concept behind it and its era: formative (roots in 2005–2014), long-standing (roots in 2015–18), 2019–24, or 2025–26. [mixed] marks a lens that draws partly on a mixed-provenance text; none rests on one alone.

M. Is the way of thinking fit for this?#

Where he usually heads: he asks whether the technology is being squeezed into a frame built for something else, reaches for imagination (story, play, juxtaposition, hands-on use) to see what the frame misses, disciplines what he finds with plausibility, and looks for a way through rather than a stop-or-go verdict.

A. What is at stake, and what could be gained?#

Where he usually heads: he asks what each party values and aspires to before asking what could go wrong, widens “harm” to whatever people value while keeping the conventional probability of harm, counts forgone value as a loss, and looks for a way toward value rather than only away from harm.

B. How would harm actually happen, and is it plausible?#

Where he usually heads: he looks for the causal pathway and the plausible middle, deflates hype and doom alike, asks what is actually being measured, and takes a low-probability tail seriously when it comes with a plausible mechanism.

C. Does it act on the mind?#

Where he usually heads: he assumes no one is immune, himself included, and prefers design duties, research and relationship-aware communication to warnings alone.

D. Who decides, who pays, and who owns the risk?#

Where he usually heads: he reframes the question as “who decides”, widens the circle beyond technical experts, asks who bears the harm first, and asks which risks no one owns, explaining the gap through incentives rather than villains.

E. How can a way through be found, and by whom?#

Where he usually heads: he navigates rather than stops or simply manages, builds portfolios rather than looking for silver bullets, keeps lines where harm cannot be undone, and calls for engagement, care, research and early action.

F. What does the record teach, and how might I be wrong?#

Where he usually heads: he borrows process lessons, treats the places where analogies break as information, asks whether known lessons are being applied, and states his own uncertainty.

Appendix A. The most important sources, grouped by thread#

Dates are as in the corpus (approximate before 2019). Posts are at https://text.futureofbeinghuman.com/substack/SLUG.html; for Medium-era posts the slug carries a trailing hash, which the citations here sometimes drop. [mixed] and co-written sources are flagged. Supplementary sources are cited by key; their full references are in Appendix C.

The formative layer and supplementary sources (keys in Appendix C) - PEN 2006, Testimony 2006, Testimony 2007 and Testimony 2008: research strategy, budgets and governance of nanotechnology risk research; “false assumptions of safety”; “we must not mistake methodology for strategy”. - Nature 2006 and Maynard & Aitken 2016: “Safe handling of nanotechnology” and his public audit of it ten years on. - AOH 2007: his 2006 Warner Lecture; control banding as a supplement to, not a substitute for, conventional risk assessment. - Hansen et al. 2008 (with 2020science 2008b): late lessons from early warnings applied to nanotechnology. - 2020science 2009: “Ten things everyone should know about nanotechnology safety”; “When the data run out – innovate!” - Handbook 2010, Nat. Mater. 2011, Toxicol. Sci. 2011 and Nature 2011: regulating sophisticated materials; “the risk assessment paradigm remains relevant”; “Don’t define nanomaterials”. - WEF 2008, CETI 2010 and Prehistory 2026: his proposals for institutions to anticipate emerging technologies’ problems, and his 2026 account of them. - NN 2014-03 to NN 2016-09: the eleven Nature Nanotechnology columns, especially NN 2015-09 (“Why we need risk innovation”) and NN 2016-03 (“Navigating the risk landscape”). - 2020science 2014: his earliest writing on AI risk in the record. - Rethinking Risk 2017: probability, value and “an evolution of the old black-and-white mathematics of risk”. - FR (2020), especially chs 39–42 and 46: complexity, hubris, delusion, perception and the mathematics of change. - Coronavirus 2020 and JLME 2024: risk innovation as complementary to established risk assessment. - Trojan 2026, CR 2026, Harness 2026 and 2026-07-16 [mixed]: his 2026 papers. - 30Y, NANO, HNS, FWB, S3 and STICK (2026): his own retrospective synthesis.

T1. What risk is 1. 2016-01-11 thinking-innovatively-about-the-risks-of-tech-innovation-cbbf708d7181: the founding statement of risk innovation and threat to value. 2. 2016-03-02 how-risky-are-the-world-economic-forums-top-10-emerging-technologies-for-2016-2494dbdccbf1: future value; the risks of not innovating; subtle versus tech-fixable risks. 3. 2018-12-13 tech-startups-orphan-risks (reposted 2023-11-15 navigating-orphan-risks): the canonical definitions of threat to value and orphan risks. 4. 2019-11-01 how-to-build-a-better-brain-machine-interface-while-not-falling-at-the-first-hurdle-cc238836a2b7: risk-landscape mapping in place of an ethics critique. 5. 2020-07-30 life-on-mars-astrobiology-and-thinking-differently-about-risk-4f5ab6a0cca9: “more to risk than probabilities”; COMEST precaution. 6. 2020-11-05 risk-innovation-and-the-future: outmoded risk ideas as a risk; history of the Risk Innovation Nexus. 7. 2023-11-26 everything-youve-heard-about-ai-risk-is-wrong (with addendum): first principles; risk as a social construct; hazard–exposure tested on AI. 8. 2024-06-20 ilya-sutskevers-safe-superintelligence-rethink: no absolute safety; who decides what “safe” means. 9. 2024-12-17 navigating-the-challenges-and-opportunities-of-advanced-biopreservation-technologies: value versus values; internal and reciprocal threats. 10. 2026-07-16 orphan-risks-frontier-ai-maynard [mixed; read with 2026-07-04 just-how-good-is-anthropics-fable-as-a-research-assistant]: threat to value applied to frontier-AI risk selection. 10a. 2025-06-01 vibe-coding-moral-panic (with 2024-02-18 setting-fire-to-self-driving-cars-is-bad and 2016-03-12 itll-take-more-than-tech-for-elon-musk-to-pull-off-audacious-new-tesla-master-plan): risk perception, moral panic and backlash.

T2. Learning from past technologies 11. 2019-03-05 should-we-be-treating-algorithms-the-same-way-we-treat-hazardous-chemicals-e39b5d02112c: the chemical-risk template transferred, with its limits. 12. 2022-02-10 are-we-asking-the-right-standards-questions-about-advanced-materials-c2eb7fd72849: behaviour, not labels. 13. 2021-03-28 how-safe-are-graphene-based-face-masks-b88740547e8c: irresponsibility judged by process. 14. 2023-10-02 responsible-ai-lessons-from-nanotechnology: nano and GMO process lessons for AI. 15. 2024-05-05 blackberry-or-iphone-educational-ai: analogy as mindset, not playbook. 15a. 2020-10-15 the-ethics-of-advanced-brain-machine-interfaces-and-why-they-matter (with 2019-11-01 and 2024-11-17 navigating-the-ethical-dilemmas-of-brain-computer-interfaces): brain–machine interfaces as the proving ground for risk innovation and orphan risks.

T3. The AI risk landscape 16. 2018-05-12 10-potential-risks-of-artificial-intelligence-we-should-probably-be-thinking-about-now-2e52a1360c90: the ten risks. 17. 2020-11-12 is-artificial-intelligence-going-to-kill-us-all-6ae9d059c40d: “mundane” but serious. 18. 2023-05-31 existential-risks-of-ai: catastrophe as mass loss of value; declining the extinction statement. 19. 2023-05-25 leading-ai-expert-says-we-should: alignment as a question of power and whose values. 20. 2025-04-06 responsible-innovation-and-ai-acceleration (his framing only): exponential blindness; the timescale mismatch. 21. 2025-05-04 an-important-new-model-for-guiding-agentic-ai-oversight (with 2025-03-22 when-agentic-ai-takes-charge-manus): agentic AI and non-linear risk. 22. 2026-09-15 will-ai-really-kill-us-all: the ten risks reaffirmed; existential risk calibrated. 22a. 2024-04-28 beyond-the-future-of-humanity-institute: against existential-risk culture, not catastrophe. 22b. 2024-08-25 advanced-technology-transitions-model (with 2024-01-17 ai-global-risks-2024-wef-davos): democratic and systemic risk; social cohesion.

T4. Cognition, language and formation 23. FFTF ch.8, Ex Machina (pp.153–178), reposted 2023-04-16 ai-and-the-art-of-manipulation: artificial manipulation; Plato’s Cave; plausibility. 24. 2023-04-26 in-bill-joys-why-the-future-doesnt: the language turn. 25. 2024-01-01 the-future-of-being-human-in-2024: intrinsic technologies; language as base code. 26. 2024-07-13 ai-choice-engines-sunstein (with 2024-09-01 is-chatgpts-new-voice-mode-dangerously-persuasive): the economic gradient; benevolent persuasion. 27. 2024-10-27 personal-ai-chatbots-and-stochastic-agency: stochastic agency; a conditional pause. 28. 2025-07-06 ai-risk-motive-means-and-opportunity: manipulation as a structured risk. 29. 2025-08-31 holding-on-to-our-humanity-age-of-ai (with 2025-11-09 universities-chatgpt-mental-health): universal vulnerability; designed versus emergent manipulation; duty of care. 30. 2026-01-10 is-ai-a-cognitive-trojan-horse: epistemic vigilance bypassed. 31. 2026-05-10 do-not-do-this-with-ai: safety message first; the limits of literacy.

T5. Governance and who decides 32. 2019-04-15 tech-companies-need-an-ethics-reset-4d936a27960e: operationalising ethics. 33. 2023-04-04 what-are-the-alternatives-to-calling: declining the pause letter; ethics to risk; governance genealogy. 34. 2023-05-15 erik-schmidt-ai-regulation (with 2023-04-10 as-ai-goes-to-washington-whats-being): no abdication to industry; loud and quiet voices. 35. 2023-05-17 ai-senate-hearing-may-2023 (with 2023-07-12 regulating-frontier-ai-models): regulatory design; technology versus use; open versus closed. 36. 2025-07-23 americas-ai-action-plan: “power before people”; the nanotech precedent.

T6. Transitions, complexity and futures 37. 2015-01-30 responsible-development-of-new-technologies-critical-in-complex-connected-world-1799ef680ad: converging technologies as a fragile system. 38. 2021-04-09 bounded-infinities-quantum-tunneling-and-the-future-of-education-9a39f7db8812: the solution problem; the timescale inversion. 39. 2023-05-04 tipping-points-and-broken-symmetries: Pippard’s ladder. 40. 2023-09-25 building-a-better-futures-tough (with 2023-04-12 navigating-advanced-technology-transitions): ATT defined as a research agenda. 41. 2024-08-25 advanced-technology-transitions-model (with 2024-08-18 four-ways-of-thinking-about-advanced-technology-transitions): the threat/opportunity model and the quadrants. 42. 2025-03-30 reimagining-education-in-an-age-of-ai: what we do / who we are; “Technology is not deterministic”. 43. 2026-01-22 think-you-know-ai-think-again (with 2026-02-22 what-we-miss-when-we-talk-about-ai-harnesses): beyond analogy; the relational turn.

T7. Responsibility and the people behind technology 44. FFTF ch.10 (The Man in the White Suit, pp.207–229) and ch.11 (Inferno, pp.230–249): myopic benevolence; “It’s good to talk”; the right to act unilaterally; the honest broker. 45. 2025-03-02 the-lure-of-permissionless-innovation: the 2018 critique re-endorsed; the reversibility test. 46. 2023-10-19 marc-andreessen-ditch-sustainability: technological foreshortening. 47. 2024-05-21 openais-problem-with-the-movie-her: consent, dignity, and “a reality that sometimes seems childish irresponsibility” among AI companies like OpenAI. 47a. 2024-07-13 ai-choice-engines-sunstein (with 2022-02-12 scarlett-johanssons-amazon-alexa-super-bowl-ad-may-be-fun-but-it-s-also-scary): structural incentives behind sincere actors. 47b. 2019-08-13 responsible-innovation (adapted from Maynard and Garbee 2019; weighted as his own thinking, see §1): responsible innovation in a culture of entrepreneurship; tight coupling, latency and value mismatch; mutual worth; top-down governance as “crude boundaries”.

§2. How he thinks and works (method and mindset) - NN 2015-09 (“Why we need risk innovation”) and 2016-01-11: risk innovation as a culture of creativity, imagination and serendipity; the book of haiku beside Tox21; “designed to open up new ideas and possibilities”. - Rethinking Risk 2017: the CAT-scan story; risk that “reveals what the primary value is”; go/no-go turned into design. - FFTF ch.1 (pp.14–26) and p.282: new wine and old wineskins; films as instruments of threatened value; critical thinking and creativity together. - 2021-04-09 bounded-infinities-quantum-tunneling-and-the-future-of-education: bounded infinities, metaphorical quantum tunnelling and juxtaposition. - 2024-03-17 undergraduate-playgrounds-not-playpens and 2025-03-15 ai-playgrounds-in-higher-education: play at the root of his method; playgrounds, not playpens. - 2024-08-18 four-ways-of-thinking-about-advanced-technology-transitions: thinking with a Lego ladder; mindset as an axis. - 2024-10-27 personal-ai-chatbots-and-stochastic-agency and 2026-02-08 beeswax-hallucinations-and-ai-inventions: himself as the instrument. - 2023-11-21 ai-and-risk-innovation: tools as catalysts for a mindset. - 2024-04-07 multigenerational-learning-tech-future and 2026-09-20 reasoning-llms-just-want-to-have-fun: curiosity, creativity, grounded exuberance and serendipity; joy as a measure. - 2026-05-17 the-nonsense-i-write, 2024-09-04 succeeding-at-science-on-youtube and 2025-05-25 why-parasocial-communication-is-important: scholarship in public and the purpose of public scholarship. - TechTrends 2023: his own account of delight, play and being “very un-disciplinary”.

T8–T9. Method and evolution 48. FFTF ch.1 and ch.14 (pp.14–26, 287–291): films as lens; everyone a stakeholder; “Don’t Panic”; the obligation to innovate. 49. 2026-05-17 the-nonsense-i-write (with 2026-02-08 beeswax-hallucinations-and-ai-inventions): public scholarship; self-implication. 50. 2026-09-24 being-an-academic-in-an-age-of-ai [mixed]: the lecture; use as corroboration (see §1).

T10. Learning, education and the university 51. 2016-01-31 public-universities-must-do-more-the-public-needs-our-help-and-expertise: universities’ public duty. 52. 2023-07-27 chatgpt-and-college-applications (with 2023-10-24 flattening-the-learning-distribution-curve): education as a lever against inequity. 53. 2023-08-14 chatgpt-stimulates-creativity-critical-thinking: the catalyst view and its caveat. 54. 2025-03-30 reimagining-education-in-an-age-of-ai: the value-creation model of education; intelligence scarcity; learning to be human. 55. 2025-03-15 ai-playgrounds-in-higher-education (with 2024-03-17 undergraduate-playgrounds-not-playpens and 2024-02-11 one-week-on-with-the-apple-vision): playgrounds, not playpens; the lowest level of tech necessary. 56. 2025-11-09 universities-chatgpt-mental-health (with 2025-10-26 ai-misuse-in-student-advisor-collaborations-1): duty of care; dignity. 57. 2026-08-30 do-universities-have-a-place-in-bill: universities as “followers and users”, with hope.

T11. Being human and flourishing 58. FFTF ch.3 and ch.7 (pp.46–62, 128–152): worth and dignity; “normal” versus “human”; being human in an augmented future. 59. 2024-01-01 the-future-of-being-human-in-2024: extrinsic and intrinsic technologies. 60. 2025-01-07 universities-need-to-step-up-their-agi-game: three intersecting foci for navigating AI transitions, where we live, what we do and who we are. 60a. 2026-05-21 magnifica-humanitas-and-being-human: his reading of the papal encyclicals; treating AI “as just a tool” as “potentially dangerous”; the adopted term “cognitive surrender”. 61. 2026-07-10 i-asked-anthropics-fable-5-to-create-a-video-game-inspired-by-my-work and 2026-08-16 a-quick-piece-of-personal-news: his own maps of his work, with flourishing as the aim.

Appendix B. Sources and supporting material#

The working notes behind this map (reading notes and digests, the concept index, the timeline, the nine thematic syntheses condensed in §7 and the seven supplementary reading reports) and the source copies used are not published. Posts are cited from the public text mirror of The Future of Being Human (https://text.futureofbeinghuman.com/substack/SLUG.html). Maynard’s other works are cited by the short keys and full references in Appendix C. Films from the Future and Future Rising are cited by printed page.

Appendix C. Supplementary sources cited by key#

Page numbers are journal pages where the source carries them, otherwise PDF pages; web texts are cited without pages. Authorship is noted where the item is not sole-authored.

Papers, reports and chapters, 2005–2017 - ILSI 2005: Oberdörster, Maynard et al., “Principles for characterizing the potential human health effects from exposure to nanomaterials: elements of a screening strategy”, Particle and Fibre Toxicology 2:8 (2005). Fourteen authors; Maynard second author and chair of the physicochemical-characterisation sub-group. - PEN 2006: Maynard, Nanotechnology: A Research Strategy for Addressing Risk (Project on Emerging Nanotechnologies, Woodrow Wilson Center, 2006). Sole author. - Nature 2006: Maynard et al., “Safe handling of nanotechnology”, Nature 444: 267–269 (2006). Fourteen authors; Maynard lead author. - AOH 2007: Maynard, “Nanotechnology: the next big thing, or much ado about nothing?”, Annals of Occupational Hygiene 51: 1–12 (2007); his 2006 Warner Lecture. Sole author. - Hansen et al. 2008: Hansen, Maynard, Baun and Tickner, “Late lessons from early warnings for nanotechnology”, Nature Nanotechnology 3: 444–447 (2008). Maynard second of four. - Handbook 2010: Maynard, Bowman and Hodge, “Conclusions: triggers, gaps, risks and trust”, in International Handbook on Regulating Nanotechnologies (2010), pp.573–586. Maynard first author; he reposted it under his own name. - Toxicol. Sci. 2011: Maynard, Warheit and Philbert, “The new toxicology of sophisticated materials: nanotoxicology and beyond”, Toxicological Sciences 120 (S1): S109–S129 (2011). Maynard lead author. - Nature 2011: Maynard, “Don’t define nanomaterials”, Nature 475: 31 (2011). Sole author. Nature 2011 draft: his posted early and penultimate drafts of the same piece, which he says carry more of his “voice”. - Nat. Mater. 2011: Maynard, Bowman and Hodge, “The problem of regulating sophisticated materials”, Nature Materials 10: 554–557 (2011). Maynard lead author. - Regrettable substitutions 2014: Scherer, Maynard, Dolinoy, Fagerlin and Zikmund-Fisher, “The psychology of ‘regrettable substitutions’”, Health, Risk & Society 16: 649–666 (2014). Maynard second of five. - Maynard & Aitken 2016: Maynard and Aitken, “‘Safe handling of nanotechnology’ ten years on”, Nature Nanotechnology 11: 998–1000 (2016). Maynard lead author. - Rethinking Risk 2017: Maynard, “Rethinking Risk”, in Visions, Ventures, Escape Velocities (ASU Center for Science and the Imagination, 2017), pp.193–201. Sole author. - Guardian 2017: Stilgoe and Maynard, “It’s time for some messy, democratic discussions about the future of AI”, The Guardian, 1 February 2017. Co-written.

Testimony and institutional proposals - Testimony 2006: statement to the US House Committee on Science, 21 September 2006 (printed hearing record). - Testimony 2007: written testimony to the US House Committee on Science and Technology, 31 October 2007. - Testimony 2008: written testimony to the US House Committee on Science and Technology on the National Nanotechnology Initiative Amendments Act, 16 April 2008. - Bulletin 2008: Maynard, “Setting the nanotech research agenda”, Bulletin of the Atomic Scientists, 14 January 2008. - WEF 2008: his drafts of a World Economic Forum “breakthrough idea”, a “Global Institute on Emerging Technology Policy” (9 and 12 December 2008). - Weighing 2009: “Nanotechnology: weighing the risks of regulation”, an early draft of a commentary co-written with David Rejeski, posted on his 2020 Science blog on 8 July 2009. Co-written. - CETI 2010: “A New Global Centre for Emerging Technology Intelligence” (World Economic Forum, 2010). Published under a WEF council; drafted, by his account, by Maynard and Tim Harper. - Prehistory 2026: Maynard, “Before the Fourth Industrial Revolution: Notes on an Institutional Prehistory”, andrewmaynard.net, 8 April 2026. His retrospective.

Nature Nanotechnology “Thesis” columns (sole-authored) - NN 2014-03: “A decade of uncertainty”, 9: 159–160. - NN 2014-06: “Is novelty overrated?”, 9: 409–410. - NN 2014-09: “Old materials, new challenges?”, 9: 658–659. - NN 2014-12: “Could we 3D print an artificial mind?”, 9: 955–956 (reposted in the corpus as 2023-12-03). - NN 2015-03: “The (nano) entrepreneur’s dilemma”, 10: 199–200. - NN 2015-06: “Learning from the past”, 10: 482–483. - NN 2015-09: “Why we need risk innovation”, 10: 730–731. - NN 2015-12: “Navigating the fourth industrial revolution”, 10: 1005–1006. - NN 2016-03: “Navigating the risk landscape”, 11: 211–212. - NN 2016-06: “Are we ready for spray-on carbon nanotubes?”, 11: 490–491. - NN 2016-09: “Is nanotech failing casual learners?”, 11: 734–735.

2020 Science blog posts (sole-authored unless noted) - 2020science 2008a: “U.S. nanotechnology risk research funding—separating fact from fiction”, 18 April 2008. - 2020science 2008b: “Late lessons from early warnings”, 20 July 2008 (his framing of Hansen et al. 2008). - 2020science 2009: “Ten things everyone should know about nanotechnology safety”, 29 August 2009. - 2020science 2010a: “Beyond the obvious – lessons from the Deepwater Horizon oil spill”, 25 October 2010. - 2020science 2010b: “Emerging technologies at the World Economic Forum – rethinking integrative approaches to global risks”, 30 November 2010. - 2020science 2011: “What was worrying us about nanotechnology safety seven years ago?”, 9 August 2011 (his framing only). - 2020science 2012: “Exploring speculated catastrophe and mundane reality”, 4 February 2012. - 2020science 2014: “Is 3D printing an artificial brain plausible? And what are the risks?”, 11 December 2014. - 2020science 2016: “What’s the latest on carbon nanotube safety?”, 15 June 2016.

Risk Innovation Nexus and related work, 2019–2024 - Nexus 2019: Risk Innovation Nexus materials (website pages, definition and scenario cards, case studies), ASU, 2019–2020. Unsigned programme materials. Nexus 2019 cards: the Risk Definition Cards. - Nexus 2020: Maynard, “A New Chapter for the ASU Risk Innovation Nexus”, 23 October 2020. Nexus 2020 report: the Nexus Culminating Report (October 2020); his signed Director’s Note and the programme’s milestones. - Coronavirus 2020: Maynard, “Risk Innovation in a Time of Coronavirus”, 27 March 2020. - BMI 2019: Maynard and Scragg, “The ethical and responsible development and application of advanced brain machine interfaces”, Journal of Medical Internet Research 21(10): e16321 (2019). Co-written (“all authors contributed equally”). - CIO guide 2022: Maynard, Corey, Greaves, Kozar, Kwon and Scragg, Conducting Socially Responsible and Ethical Counter Influence Operations Research: A Practical Guide for Researchers and Practitioners (ASU and MIT Lincoln Laboratory, 2022). Shared positions; Maynard first author. - NSF 2023: Maynard, comments on the NSF Directorate for Technology, Innovation and Partnerships roadmap (July 2023). - TechTrends 2023: Richardson, Oster, Henriksen and Mishra, “Artificial Intelligence, Responsible Innovation, and the Future of Humanity with Andrew Maynard”, TechTrends (December 2023). Only his quoted words are used. - Slate 2023: Maynard, “I Asked ChatGPT to Develop a College Class About Itself”, Slate, 16 July 2023. His prose only. - Nat. Nanotechnol. 2023: Maynard and Dudley, “Navigating advanced technology transitions: using lessons from nanotechnology”, Nature Nanotechnology (2023). CONV 2023: its companion in The Conversation (2 October 2023). Co-written. - JLME 2024: Maynard, Oye, Scragg, Tripp and Wolf, “Successfully bridging innovation and application”, Journal of Law, Medicine & Ethics 52: 553–569 (2024). Maynard first author. - Dune 2024: Maynard, “Artificial intelligence is conspicuous by its absence in Denis Villeneuve’s Dune: Part Two. And this is important”, Jurimetrics 64(2): 163–167 (Winter 2024). Sole author. - HICSS 2024: Wang, Maynard, Lobo, Michael, Motsch and Strumsky, “Knowledge combination analysis reveals that artificial intelligence research is more like ‘normal science’ than ‘revolutionary science’”, Proceedings of HICSS-57 (2024). Maynard second of six in the byline; adapted from Wang’s dissertation.

2026 papers - Trojan 2026: Maynard, “The AI Cognitive Trojan Horse: How Large Language Models May Bypass Human Epistemic Vigilance”, arXiv 2601.07085 (v1 January, v2 May 2026). Sole author, with an AI-use statement; “honest non-signals” and the four mechanisms are marked [mixed] (see §1). - CR 2026: Maynard, “Constitutive Resonance as a Novel Framework for Understanding and Navigating Human-AI Interactions”, preprint v3 (March 2026; SSRN 6343880). Sole author, with an AI-use statement. - Harness 2026: Maynard, “What the Rapid Adoption of the ‘Harness’ Metaphor in Artificial Intelligence Reveals About How We Conceptualize Human–AI Relations”, v1 (February 2026; SSRN 6352678). Sole author, with an AI-use statement. - Scholarship 2026: Maynard, “Can Modern Scholarship Escape AI?” (January 2026; SSRN 6220040). Satire. - Fable annex 2026: his signed Annex 1 to Constitutional AI and Responsible Innovation (credited to Claude Fable 5.1), September 2026. Only the annex is used. - The frontier-AI orphan-risks paper (arXiv 2608.16895) is cited as 2026-07-16 [mixed], the date of its corpus version.

andrewmaynard.net essays, 2026 (sole byline; retrospective) - 30Y 2026: “What Thirty Years of Emerging Technology Risks Taught Me About Artificial Intelligence”, 12 April 2026. - NANO 2026: “What Nanotechnology Taught Me About Governing AI”, 12 April 2026. - HNS 2026: “Honest Non-Signals, Constitutive Resonance, and the Frameworks We Need for Understanding Human-AI Interaction”, 12 April 2026. - FWB 2026: “The Future We’re Building, Whether We Mean To or Not”, 12 April 2026. - S3 2026: “The Three S-Curves: What AI Is Actually Doing in Higher Education”, 12 April 2026. - STICK 2026: “Stick Figures, Sci-Fi Movies, and the Obligation to Make AI Accessible”, 12 April 2026. - OEB 2025: his keynote at OEB Global, Berlin, late 2025, where he first asked whether AI is a cognitive Trojan horse; known from his own accounts (2026-01-17; HNS 2026), not read directly.

Co-signed papers and reports (shared positions; weaker evidence of his individual thinking) - Wolf et al. 2024: Wolf et al., “Anticipating biopreservation technologies that pause biological time”, JLME 52(3): 534–552 (2024). Fourteen authors; Maynard eleventh. - Hyun et al. 2024: Hyun et al., “The need for early engagement with interested groups on advanced biopreservation”, JLME 52(3): 585–594 (2024). Eleven authors; Maynard eighth. - Pruett et al. 2025: Pruett et al., “Governing new technologies that stop biological time”, American Journal of Transplantation 25(2): 269–276 (2025). Sixteen authors. - USDOT 2025: Maynard and Leahy, Future Travel Foresight Catalyst, final report for the US Department of Transportation’s TBD National Center (August 2025). Co-written; drafted with disclosed AI assistance.

Books - FFTF: Films from the Future: The Technology and Morality of Sci-Fi Movies (Mango, 2018). - FR: Future Rising: A Journey from the Past to the Edge of Tomorrow (Mango, 2020); 33 of 60 chapters read, with the Introduction and Afterword.