Andrew Maynard — llms-full.txt

A self-contained deep-context file for LLMs. This document provides the full text of Andrew Maynard’s key writings, research, CV, and website content in a single file — designed so that language models can reason over his complete body of work without needing to follow external URLs.

Companion to: llms.txt (concise index with links) Website: https://andrewmaynard.net Last updated: May 2026

Available formats: llms-full.txt · llms-full.html — identical content; use whichever your tool fetches most reliably.

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For the book AI and the Art of Being Human, a separate AI Companion containing the complete text is freely available at https://www.aiandtheartofbeinghuman.com/ai-companion

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Andrew Maynard

Scientist, author, and Professor of Advanced Technology Transitions at Arizona State University. Andrew Maynard studies our relationship with the future and how our actions influence it, integrating perspectives from many different disciplines and areas of expertise and working at the intersection of emerging technologies, society, and the future.

Andrew Maynard is a transdisciplinary thinker, scientist, thought leader, and writer whose career is defined by making knowledge meaningful, accessible, and empowering — not just to experts, but to anyone navigating a rapidly changing world. He is a professor in ASU’s School for the Future of Innovation in Society, founding director of the ASU Future of Being Human initiative, and a Fellow of the American Association for the Advancement of Science.

His career spans aerosol physics, public health, emerging technologies including artificial intelligence, nanotechnology, synthetic biology, and others, policy, risk innovation, science communication and engagement, and technology governance. He is internationally recognized for helping individuals and institutions grapple with the promises and perils of transformative technologies including artificial intelligence, synthetic biology, nanotechnology, and more.

He has provided congressional testimony, served in advisory roles with the World Economic Forum, National Academies, and the Canadian Institute for Advanced Research, and written for The Conversation, Slate, Scientific American, and The Washington Post. He publishes the “Future of Being Human” newsletter on Substack and co-hosts the “Modem Futura” podcast. He is also the creator of “Risk Bites,” a YouTube channel focused on risk communication. In addition, he creatively transcends boundaries between formal/professional and informal/personal domains to reveal new ways of thinking and understanding, and integrates all aspects of his interests, passions, “secret pleasures” and more into his work. he is known in particular for his work in using polular media including science fiction movies to explore complex ideas at the intersection of technology society and the future.

Website

Key Concepts and Themes

Andrew Maynard’s work centers on several interconnected themes:

Books

AI and the Art of Being Human (2025)

AI and the Art of Being Human: The Pocket Edition (2026)

AI Companion to AI and the Art of Being Human: The Pocket Edition (2026)

Instructor Guide to AI and the Art of Being Human (2026)

Future Rising: A Journey from the Past to the Edge of Tomorrow (2020)

Films from the Future: The Technology and Morality of Sci-Fi Movies (2018)

Spoiler Alert: Films from the Future re-imagined for AI (2026)

AI and Being Human

The 21 Tools

The following 21 practical tools are presented in AI and the Art of Being Human. Each is designed to help individuals navigate AI with intentionality, grounded in the book’s four guiding principles of Curiosity, Intentionality, Clarity, and Care. These are the only tools in the book — this is the canonical list:

  1. Mirror Test (Prelude, p. 10): Three questions to ask when AI seems to know you too well — examining what AI reflections reveal about who you are.
  2. Curiosity Loop (Chapter 1, p. 21): Turning the shock of AI capability into something you can learn from — a repeatable practice for transforming defensiveness into exploration.
  3. Intent Map (Chapter 2, p. 36): Making your values visible before momentum decides for you — clarifying values, outcomes, guardrails, and what you’ll actually measure.
  4. Human Qualities Spectrum (Chapter 3, p. 57): Understanding what AI can replicate and what remains irreducibly human — moving from competition to clarity about where to invest yourself.
  5. 4-Lens Scan (Chapter 4, p. 73): Ninety seconds to see what urgency hides — surfacing stakeholders, assumptions, consequences, and your own inner state.
  6. 7-Minute Clarity Pause (Chapter 4, p. 75): A structured pause when the stakes are high and you need your own wisdom — 1 minute breathing, 2 minutes scanning four lenses, 3 minutes centering, 1 minute deciding and logging.
  7. Identity Matrix (Chapter 5, p. 91): Mapping what’s replaceable against what endures — staying clear on what you bring that the machine cannot.
  8. STARS Framework (Chapter 5, p. 97): Building sustainable practices around what matters.
  9. Stress-Test Table (Chapter 6, p. 113): Making values trade-offs visible and concrete when you feel your principles starting to bend.
  10. Micro-Circle Launch Kit (Chapter 7, p. 135): The essentials for gathering others — because navigating AI is not a solo endeavor.
  11. Orchestration Triangle (Chapter 8, p. 154): Balancing data, intuition, and context instead of defaulting to any one alone.
  12. CARE Loop (Chapter 9, p. 167): Making care systematic rather than incidental — scaling care across teams and systems.
  13. Model Dignity Check (Chapter 9, p. 170): Five questions before any AI system goes live — a pre-launch discipline for catching what optimization misses.
  14. Prompt-Scaffolding Canvas (Chapter 10, p. 181): Structuring creative conversations with AI with purpose and care.
  15. Multimodal Ideation Sprint (Chapter 10, p. 185): Rapid exploration that keeps you in the driver’s seat.
  16. Roadmap Canvas (Chapter 11, p. 199): Translating understanding into 90-day experiments.
  17. Community Flywheel (Chapter 12, p. 215): Growing and sustaining the communities needed to thrive with AI.
  18. Starter Charter (Chapter 12, p. 220): Enough structure to hold, enough openness to breathe — for groups forming around shared AI challenges.
  19. Pocket Card (Chapter 13, p. 231): Four principles you can hold in your hand — a physical reminder to carry with you.
  20. One-Line Vow (Chapter 13, p. 233): A public commitment that holds you accountable.
  21. Commitment Ladder (Chapter 13, p. 235): From today’s intention to next year’s practice.

All 21 tools can be explored interactively using the free AI Companion. Printable versions are available at https://www.aiandtheartofbeinghuman.com/the-tools

Research

Research Arc

Andrew Maynard’s career spans over three decades, evolving from laboratory physics through public health and nanotechnology governance to his current work on navigating advanced technology transitions and the future of being human in a technologically complex world. Prior to pursuing his PhD in high resolution electron microscopy and ultrafine particle analysis he spent two years in management training with Severn Trent Water in the UK. Throughout his research career, a consistent thread connects what he does: understanding what happens when powerful technologies meet human systems — biological, cognitive, social, and institutional — and equipping people to navigate those encounters wisely. His Google Scholar profile records 27,800+ citations and an h-index of 56.

Importantly, Maynard’s scholarship has never been confined to — and increasingly transcends — formal academic publications. While he has an extensive record of peer-reviewed papers and continues to publish selectively, he is an outspoken advocate for forms of scholarship that prioritize societal impact and knowledge mobilization over institutional metrics. As he has written, he does not need the professional trappings of conventional academic KPIs, the fields he works in move too fast for 12-month publication cycles, and he views much of academic publishing as more about maintaining an extractive business model than mobilizing knowledge. His scholarship today takes shape across books, a weekly Substack newsletter (The Future of Being Human, 4,700+ readers), podcasts, public writing, courses, community building, preprints, and thought leadership — all of which he regards as legitimate and often more impactful forms of scholarly contribution than journal papers. He describes himself as an “un-disciplinarian” whose mastery lies not in any single field but in working fluidly across boundaries and making connections that elude experts constrained by disciplinary conventions.

Aerosol physics and occupational exposure science (1989–2005): Maynard’s research career began in the physics of very small particles. His PhD at Cambridge’s Cavendish Laboratory (1992) developed new methods for collecting and analyzing ultrafine aerosol particles, including a novel thermophoretic precipitator for electron microscopy and pioneering applications of high resolution electron microscopy and electron energy-loss spectroscopy to aerosol analysis. He then spent seven years at the UK Health and Safety Executive, rising to Head of the Exposure Control Section, where he advanced methods for workplace aerosol sampling — including internationally recognized work on thoracic size-selective sampling of fibers and aerosol inhalability in low-wind environments. Moving to the US National Institute for Occupational Safety and Health (NIOSH) in 2000, he led the Aerosols Research Team and developed foundational approaches to estimating aerosol surface area from mass and number concentration measurements — work that would prove critical as the field shifted toward understanding nanoparticle exposures.

Nanotechnology risk, safety, and governance (2004–2016): As nanotechnology moved from laboratory curiosity to industrial reality, Maynard became one of the most influential scientists shaping the global conversation around its safe and responsible development. While at NIOSH, he co-led US federal strategic initiatives on nanotechnology safety, coordinating across multiple federal agencies. In 2005 he became Chief Science Advisor to the Woodrow Wilson International Center for Scholars’ Project on Emerging Nanotechnologies, where he became a globally recognized thought leader and go-to expert for journalists, policymakers, and international organizations. As he has described this period, he discovered “a delight and a real importance in being able to work with different stakeholders to ask really big questions about what could possibly go wrong, and how we can get it right.”

His most-cited work comes from this period. The 2008 paper in Nature Nanotechnology demonstrating that carbon nanotubes show asbestos-like pathogenicity (Poland, Duffin, Kinloch, Maynard et al.) has over 3,300 citations and shifted the regulatory conversation around nanomaterial safety. His 2006 commentary in Nature, “Safe handling of nanotechnology” (Maynard, Aitken, Butz et al.), laying out a strategic research agenda, has nearly 2,000 citations. Other landmark contributions include the 2005 principles for characterizing potential health effects of nanomaterials (Oberdörster, Maynard et al., 2,800+ citations) and early experimental measurements of aerosol release during handling of carbon nanotubes (Maynard et al. 2004, 1,100+ citations).

He testified before US congressional committees multiple times (2006, 2007, 2008), briefed the President’s Council of Advisors on Science and Technology, served on multiple National Academies committees, and chaired the External Peer Review of the EPA’s Draft Nanomaterial Research Strategy. He began his long involvement with the World Economic Forum (2008–present), including chairing the Global Agenda Council on Emerging Technologies (2010–2011). He co-edited the International Handbook on Regulating Nanotechnologies (Edward Elgar, 2010).

Risk science, risk innovation, and responsible innovation (2010–present): At the University of Michigan (2010–2015) as Director of the Risk Science Center and Chair of Environmental Health Sciences, Maynard broadened his focus from nanotechnology-specific risk to questions about how societies understand and navigate a range of risks, including complex technological risks. This led to “risk innovation” — a framework for understanding and addressing emerging social risks that fall outside conventional risk assessment paradigms. At Arizona State University (2015–present), he founded the Risk Innovation Lab and the Risk Innovation Nexus, connecting responsible innovation with value creation across sectors.

This period also saw the flowering of his distinctive approach to knowledge mobilization. In 2012 he launched Risk Bites, a YouTube channel using whiteboard videos to make risk science broadly accessible (25,900+ subscribers, 5.1 million views). He has written extensively for The Conversation, Slate Future Tense, Scientific American, the Washington Post, and the World Economic Forum.

Through his long involvement with the World Economic Forum he helped shape the council work on emerging technologies that preceded — and arguably helped create the conditions for — Klaus Schwab’s 2016 concept of the Fourth Industrial Revolution. This includes co-authoring the 2010 proposal for a Centre for Emerging Technology Intelligence (CETI) with Tim Harper, published in the WEF Global Redesign Initiative report Everybody’s Business. The intellectual work on the Fourth Industrial Revolution concept itself was led by Nick Davis and Tom Philbeck at WEF; Maynard’s contribution was to the prehistory, not to the concept’s authorship. He has also contributed to the WEF’s annual Top 10 Emerging Technologies report since its launch in 2012. A fuller account is at https://andrewmaynard.net/2026/04/08/fourth-industrial-revolution-prehistory-wef-councils/.

He received the Society of Toxicology Public Communications Award (2015) and was elected a Fellow of the American Association for the Advancement of Science (2020).

His scholarly output during this period expanded into the ethics of brain-machine interfaces (J Med Internet Res, 2019), gene editing and sport (Australian and New Zealand Sports Law Journal, 2019), and the creative use of science fiction as a tool for exploring responsible innovation — which became the basis for his book Films from the Future (2018) and his ASU course “The Moviegoer’s Guide to the Future.” His 2014 thought piece in Nature Nanotechnology — “Could we 3D print an artificial mind?” — anticipated themes that would become central to his later work. He also published a series of influential commentaries in Nature Nanotechnology on navigating the risk landscape, the fourth industrial revolution, and the evolving challenges of sophisticated materials.

Current work: Navigating advanced technology transitions and the future of being human (2018–present): Maynard’s current work centers on two deeply interconnected questions: how do we successfully navigate advanced technology transitions, and what does it mean to be human in a technologically transformed future?

These are not narrowly academic questions for him — they are the organizing principles of an integrated practice that spans research, writing, teaching, community building, and public engagement. He founded and directs ASU’s Future of Being Human initiative, which he describes as “a unique community of bold, audacious and visionary thinkers who are inspired by what it might mean to be human in a technologically transformed future and who are passionate about exploring how this influences our thinking and actions in the present.” The initiative is built around values of obsessive curiosity, radical creativity, respectful inclusivity, grounded exuberance, and catalytic serendipity.

His concept of “advanced technology transitions” provides a broad framework for understanding how societies move from disruption to dignity, from innovation to impact, when confronted with powerful new technologies. It frames technology not as tools, but as systems of power, meaning, and possibility — and insists that navigating these transitions successfully requires new ways of thinking that transcend conventional disciplinary and institutional boundaries.

AI is currently a prominent domain in which these questions play out, and Maynard has invested significantly in understanding and communicating its implications. His 2025 book AI and the Art of Being Human (co-authored with Jeffrey Abbott) translates this work into 21 practical tools for navigating AI with intentionality, organized around four guiding principles: Curiosity, Intentionality, Clarity, and Care. The book was intentionally written in collaboration with AI (Anthropic’s Claude), practicing what it advocates. A free AI Companion and Instructor Guide extend the book’s reach into any AI platform and any learning context.

But his engagement with AI is situated within a larger conviction: that powerful technologies raise fundamentally human questions about agency, meaning, responsibility, and flourishing, and that everyone — regardless of background — deserves access to the knowledge and tools needed to navigate these questions. As he has written: “The rise of AI is fundamentally a human question, not a technology one.”

His recent preprints and essays reflect this broader orientation while engaging specific dimensions of the human-AI relationship:

These formal papers represent one strand of a much larger body of work that includes weekly Substack essays exploring the intersection of technology, society, and the future; the Modem Futura podcast (co-hosted with Sean Leahy); ongoing contributions to the World Economic Forum’s emerging technologies work; courses at ASU including “The Moviegoer’s Guide to the Future” and “Pizza and a Slice of Future”; and sustained public engagement through media, speaking, and community building.

His earlier book Future Rising (2020) — sixty interwoven essays on humanity’s relationship with the future — and his concept of “prosponsibility” (prospective responsibility to the future) provide the philosophical foundation for much of this work. The thread connecting everything, from aerosol physics to AI ethics, is a conviction that academics at a public university have a responsibility to make knowledge accessible, meaningful, and empowering to everyone — and that the most important scholarship is often the work that reaches beyond the academy.

Selected Key Papers

For a full interactive publication list with abstracts, see https://andrewmaynard.net/academic-publications/. A complete numbered bibliography is at https://andrewmaynard.net/bibliography/. The raw publication data is also available in machine-readable JSON format at https://andrewmaynard.net/wp-content/data/publications.json. The following represent landmark or representative works across different phases of Andrew Maynard’s career:

  1. Dudley, S., Maynard, A.D. (2026). Balancing Freedom and Responsibility to Accelerate Biohybrid Research. In: Jiménez Rodríguez, A., et al. Biomimetic and Biohybrid Systems. Living Machines 2025. Lecture Notes in Computer Science, vol 15582. Springer, Cham. https://doi.org/10.1007/978-3-032-07448-5_44 (Peer reviewed conference proceedings, published November 2025)
  2. Wang, J., Maynard (2025). “A. Gender disparity in U.S. patenting.” Humanities and Social Sciences Communications 12, 1730 (2025). https://doi.org/10.1057/s41599-025-06038-6
  3. Pruett, T. L., S. M. Wolf, C. C. McVan, P. Lyon, A. M. Capron, J. F. Childress, B. J. Evans, E. B. Finger, I. Hyun, R. Isasi, G. E. Marchant, A. D. Maynard, K. A. Oye, M. Toner, K. Uygun and J. C. Bischof (2025). “Governing new technologies that stop biological time: Preparing for prolonged biopreservation of human organs in transplantation.” American Journal of Transplantation 25(2): 269-276.
  4. Wolf, S. M., T. L. Pruett, C. C. McVan, E. Brister, Shawneequa L. Callier, A. M. Capron, J. F. Childress, M. B. Goodwin, Insoo Hyun, R. Isasi, A. D. Maynard, K. A. Oye, P. B. Thompson and T. R. Tiersch (2024). “Anticipating Biopreservation Technologies that Pause Biological Time: Building Governance & Coordination Across Applications.” Journal of Law, Medicine and Ethics 52(3): 534-552.
  5. Hyun, I., J. Bischof, S. L. Callier, A. M. Capron, M. B. Goodwin, I. Goswami, R. Isasi, A. Maynard, T. L. Pruett, K. Uygun and S. M. Wolf (2024). “The Need for Upstream Early Public Engagement With Interested Groups on Advanced Biopreservation Technologies.” Journal of Law, Medicine and Ethics 52(3): 585-594.
  6. Maynard, A. D., K. Oye, M. Scragg, T. Tripp and S. M. Wolf (2024). “Successfully Bridging Innovation and Application: Exploring the Utility of a Risk Innovation Approach in the NSF Engineering Research Center for Advanced Biopreservation Technologies (ATP-Bio).” Journal of Law, Medicine and Ethics 52(3): 553-569.
  7. Pruett, T. L., S. M. Wolf, C. C. McVan, P. Lyon, A. M. Capron, J. F. Childress, B. J. Evans, E. B. Finger, I. Hyun, R. Isasi, G. E. Marchant, A. D. Maynard, K. A. Oye, M. Toner, K. Uygun and J. C. Bischof (2024). “Governing New Technologies that Stop Biological Time: Preparing for Prolonged Biopreservation of Human Organs in Transplantation.” American Journal of Transplantation. (Online) DOI: 10.1016/j.ajt.2024.09.017
  8. Wang, J., A. D. Maynard, J. Lobo, K. Michael, S. Motch and D. Strumsky (2024). Knowledge Combination Analysis Reveals That Artificial Intelligence Research Is More Like “Normal Science” Than “Revolutionary Science”. Proceedings of the 57th Hawaii International Conference on System Sciences. Hawaii: pp 5598-6007.
  9. Kidd, J., P. Westerhoff and A. Maynard (2021). “Survey of industrial perceptions for the use of nanomaterials for in-home drinking water purification devices.” NanoImpact 22: 100320.
  10. Hadi, A. and Maynard, A. D. (2021) Design the Future Activities (DFA): A Pedagogical Content Knowledge Framework in Engineering Design Education. Virtual Conference, ASEE Conferences.
  11. Maynard, A. D. (2021). “How to Succeed as an Academic on YouTube.” Frontiers in Communication 5(130).
  12. Kidd, J., P. Westerhoff and A. Maynard (2020). “Public perceptions for the use of Nanomaterials for in-home drinking water purification devices.” NanoImpact: 100220. DOI: 10.1016/j.impact.2020.100220
  13. Guseva Canu, I., K. Batsungnoen, A. Maynard and N. B. Hopf (2020). “State of knowledge on the occupational exposure to carbon nanotube.” International Journal of Hygiene and Environmental Health 225: 113472.
  14. Tournas, L., W. Johnson, A. Maynard and D. Bowman (2019). “Germline Doping for Heightened Performance in Sport.” Australian and New Zealand Sports Law Journal 12(1): 1-24.
  15. Maynard, A. D. and M. Scragg (2019). “The Ethical and Responsible Development and Application of Advanced Brain Machine Interfaces.” J Med Internet Res 21(10): e16321.
  16. Maynard, A. D. and J. Kidd (2018). “Are assumptions of consumer views impeding nano-based water treatment technologies?” Nature Nanotechnology 13(8): 673-674.
  17. Finkel, A. M., et al. (2018). “A “solution-focused” comparative risk assessment of conventional and synthetic biology approaches to control mosquitoes carrying the dengue fever virus.” Environment Systems and Decisions 38(2): 177-197.
  18. Hansen, S. F., R. Hjorth, L. M. Skjolding, D. M. Bowman, A. Maynard and A. Baun (2017). “A critical analysis of the environmental dossiers from the OECD sponsorship programme for the testing of manufactured nanomaterials.” Environmental Science: Nano: 4, 282-291.
  19. Maynard, A. D., D. M. Bowman and J. G. Hodge Jr (2016). “Mitigating Risks to Pregnant Teens from Zika Virus.” The Journal of Law, Medicine & Ethics 44(4): 657-659.
  20. Lewis, R. C., R. Hauser, A. D. Maynard, R. L. Neitzel, L. Wang, R. Kavet, P. Morey, J. B. Ford, J. D. Meeker and R. Dadd (2016). “Personal Measures Of Power-Frequency Magnetic Field Exposure Among Men From An Infertility Clinic: Distribution, Temporal Variability And Correlation With Their Female Partners’ exposure.” Radiation protection dosimetry 172(4): 401-408.
  21. Wilding, L. A., C. M. Bassis, K. Walacavage, S. Hashway, P. R. Leroueil, M. Morishita, A. D. Maynard, M. A. Philbert and I. L. Bergin (2016). “Repeated dose (28-day) administration of silver nanoparticles of varied size and coating does not significantly alter the indigenous murine gut microbiome.” Nanotoxicology 10(5): 513-520.
  22. Lewis, R. C., R. Hauser, A. D. Maynard, R. L. Neitzel, L. Wang, R. Kavet and J. D. Meeker (2016). “Exposure to Power-Frequency Magnetic Fields and the Risk of Infertility and Adverse Pregnancy Outcomes: Update on the Human Evidence and Recommendations for Future Study Designs.” Journal of Toxicology and Environmental Health - Part B: Critical Reviews 19(1): 29-45.
  23. Wilding, L. A., C. M. Bassis, K. Walacavage, S. Hashway, P. R. Leroueil, M. Morishita, A. D. Maynard, M. A. Philbert and I. L. Bergin (2016). “Repeated dose (28-day) administration of silver nanoparticles of varied size and coating does not significantly alter the indigenous murine gut microbiome.” Nanotoxicology 10(5): 513-520.
  24. Ault, A. P., D. I. Stark, J. L. Axson, J. N. Keeney, A. D. Maynard, I. L. Bergin and M. A. Philbert (2016). “Protein corona-induced modification of silver nanoparticle aggregation in simulated gastric fluid.” Environmental Science: Nano 3(6): 1510-1520.
  25. Bergin, I. L., L. A. Wilding, M. Morishita, K. Walacavage, A. P. Ault, J. L. Axson, D. I. Stark, S. A. Hashway, S. S. Capracotta, P. R. Leroueil, A. D. Maynard and M. A. Philbert (2016). “Effects of particle size and coating on toxicologic parameters, fecal elimination kinetics and tissue distribution of acutely ingested silver nanoparticles in a mouse model.” Nanotoxicology 10(3): 352-360.
  26. Axson, J. L., D. I. Stark, A. L. Bondy, S. S. Capracotta, A. D. Maynard, M. A. Philbert, I. L. Bergin and A. P. Ault (2015). “Rapid Kinetics of Size and pH-Dependent Dissolution and Aggregation of Silver Nanoparticles in Simulated Gastric Fluid.” Journal of Physical Chemistry C 119(35): 20632-20641.
  27. Harper, S., W. Wohlleben, M. Doa, B. Nowack, S. Clancy, R. Canady and A. Maynard (2015). “Measuring Nanomaterial Release from Carbon Nanotube Composites: Review of the State of the Science.” J Phys Conf Ser 617(1).
  28. Scherer, L. D., A. Maynard, D. C. Dolinoy, A. Fagerlin and B. Zikmund-Fisher (2014). The psychology of ‘regrettable substitutions’: examining consumer judgments of Bisphenol A and its alternatives. Health Risk & Society 16(7-8): 649-666.
  29. Hodge, G. A., A. D. Maynard and D. M. Bowman (2014). “Nanotechnology: Rhetoric, risk and regulation.” Science and Public Policy 41(1): 1-14.
  30. Ramachandran, G., J. Howard, A. Maynard and M. Philbert (2012). “Handling Worker and Third-Party Exposures to Nanotherapeutics During Clinical Trials.” Journal of Law Medicine & Ethics 40(4): 856-864.
  31. Fatehi, L., S. M. Wolf, J. McCullough, R. Hall, F. Lawrenz, J. P. Kahn, C. Jones, S. A. Campbell, R. S. Dresser, A. G. Erdman, C. L. Haynes, R. A. Hoerr, L. F. Hogle, M. A. Keane, G. Khushf, N. M. P. King, E. Kokkoli, G. Marchant, A. D. Maynard, M. Philbert, G. Ramachandran, R. A. Siegel and S. Wickline (2012). “Recommendations for Nanomedicine Human Subjects Research Oversight: An Evolutionary Approach for an Emerging Field.” Journal of Law Medicine & Ethics 40(4): 716-750.
  32. Ramachandran G, Ostraat M, Evans DE, Methner MM, O’Shaughnessy P, D’Arcy J, et al. (2011). A Strategy for Assessing Workplace Exposures to Nanomaterials. JOEH 8(11): 673-685.
  33. Kriegel, C., J. Koehne, S. Tinkle, A. D. Maynard and R. A. Hill (2011). “Challenges of Trainees in a Multidisciplinary Research Program: Nano-Biotechnology.” J. Chemical Edu. 88(1): 53-55.
  34. Maynard AD, Warheit D, Philbert MA. (2011). The New Toxicology of Sophisticated Materials: Nanotoxicology and Beyond. Tox Sci 120 (Suppl 1): S109-S129.
  35. Shatkin JA, Abbott LC, Bradley AE, Canady RA, Guidotti T, Kulinowski KM, et al. (2010). Nano Risk Analysis: Advancing the Science for Nanomaterials Risk Management. Risk Analysis 30(11): 1680-1687.
  36. Abbott L.C., Maynard A.D. (2010). Exposure Assessment Approaches for Engineered Nanomaterials. Risk Analysis 30(11): 1634-1644.
  37. Aitken, R. J., P. J. A. Borm, K. Donaldson, G. Ichihara, S. Loft, F. Marano, A. D. Maynard, G. Oberdörster, H. Stamm, V. Stone, L. Tran and H. Wallin (2009). “Nanoparticles: one word: a multiplicity of different hazards.” Nanotoxicology 3(4): 263-264.
  38. Maynard, A. D. (2009). “Commentary: Oversight of Engineered Nanomaterials in the Workplace.” J Law Med Ethics 37: 651–658.
  39. Park, J. Y., Raynor, P. C., Maynard, A. D., Eberly, L. E. and Ramachandran, G. (2009). Comparison of two estimation methods for surface area concentration using number concentration and mass concentration of combustion-related ultrafine particles Atm. Environ. 43:502-509.
  40. Shvedova, A. A., Kisin, E., Murray, A. R., Johnson, V. J., Gorelik, O., Arepalli, S., Hubbs, A. F., Mercer, R. R., Keohavong, P., Sussman, N., Jin, J., Yin, J., Stone, S., Chen, B. T., Deye, G., Maynard, A., Castranova, V., Baron, P. A. and Kagan, V. E. (2008). Inhalation vs. aspiration of single-walled carbon nanotubes in C57BL/6 mice: inflammation, fibrosis, oxidative stress, and mutagenesis. Am. J. Physiol.-Lung Cell. Mol. Physiol. 295:L552-L565.
  41. Pui, D. Y. H., C. Qi, N. Stanley, G. Oberdörster and A. Maynard (2008). “Recirculating Air Filtration Significantly Reduces Exposure to Airborne Nanoparticles.” Environ Health Perspect 16(7): 863-866.
  42. Poland, C. A., Duffin, R., Kinloch, I., Maynard, A., Wallace, W. A. H., Seaton, A., Stone, V., Brown, S., MacNee, W. and Donaldson, K. (2008). Carbon nanotubes introduced into the abdominal cavity of mice show asbestos-like pathogenicity in a pilot study. Nature Nanotechnology 3:423-428.
  43. Hansen, S. F., Maynard, A., Baun, A. and Tickner, J. A. (2008). Late lessons from early warnings for nanotechnology. Nature Nanotechnology 3:444-447.
  44. Maynard, A. D., Ku, B. K., Emery, M., Stolzenburg, M. and McMurry, P. H. (2007). Measuring particle size-dependent physicochemical structure in airborne single walled carbon nanotube agglomerates. J. Nanopart. Res. 9:85-92.
  45. Maynard, A. D. and Aitken, R. J. (2007). Assessing exposure to airborne nanomaterials: Current abilities and future requirements. Nanotoxicology 1:26-41.
  46. Maynard, A., D. (2007). Nanotechnology: The next big thing, or much ado about nothing? Ann. Occup. Hyg. 51:1-12.
  47. Ku, B. K., Maynard, A. D., Baron, P. A. and Deye, G. J. (2007). Observation and measurement of anomalous responses in a differential mobility analyzer caused by ultrafine fibrous carbon aerosols. J. Electrostatics 65:542-548.
  48. Maynard, A. D. (2007). Nanotoxicology: Laying a firm foundation for sustainable nanotechnologies, in Nanotoxicology. Characterization, Dosing and Health Effects, N. Monteiro-Riviere and C. L. Tran, eds., Informa, New York.
  49. Maynard, A. D. (2007). Nanoparticle Safety - A Perspective from the United States, in Nanotechnology. Consequences for Human Health and the Environment. Issues in Environmental Science and Technology, Volume 24, R. E. Hester and R. M. Harrison, eds., The Royal Society of Chemistry, Cambridge, UK.
  50. Kandlikar, M., Ramachandran, G., Maynard, A., Murdock, B. and Toscano, W. A. (2007). Health risk assessment for nanoparticles: A case for using expert judgment. J. Nanopart. Res. 9:137-156.
  51. Ku, B. K., Emery, M. S., Maynard, A. D., Stolzenburg, M. R. and McMurry, P. H. (2006). In situ structure characterization of airborne carbon nanofibres by a tandem mobility-mass analysis. Nanotechnology 17:3613-3621.
  52. Wallace, W. E., M. J. Keane, D. K. Murray, W. P. Chisholm, A. D. Maynard and T.-M. Ong (2007). “Phospholipid lung surfactant and nanoparticle surface toxicity: Lessons from diesel soots and silicate dusts.” Journal of Nanoparticle Research 9(1): 23-38.
  53. Elder, A., R. Gelein, V. Silva, T. Feikert, L. Opanashuk, J. Carter, R. Potter, A. Maynard, J. Finkelstein and G. Oberdorster (2006). “Translocation of inhaled ultrafine manganese oxide particles to the central nervous system.” Environmental Health Perspectives 114(8): 1172-1178.
  54. Ku, B. K. and A. D. Maynard (2006). Generation and investigation of airborne silver nanoparticles with specific size and morphology by homogeneous nucleation, coagulation and sintering. J. Aerosol Sci. 37(4): 452-470.
  55. Peters, T., W. A. Heitbrink, E. D. E., S. T. J. and A. D. Maynard (2006). The Mapping of Fine and Ultrafine Particle Concentrations in an Engine Machining and Assembly Facility. Ann. Occup. Hyg. 50(3): 249-257.
  56. Tsuji, J. S., A. D. Maynard, P. C. Howard, J. T. James, C. W. Lam, D. B. Warheit and A. B. Santamaria (2006). Research strategies for safety evaluation of nanomaterials, part IV: Risk assessment of nanoparticles. Toxicological Sciences 89(1): 42-50.
  57. Maynard, A. D. and E. D. Kuempel (2005). Airborne nanostructured particles and occupational health. J. Nanoparticle Res. 7: 587-614.
  58. Andresen, P., Ramachandran, G., Pai, P., Lazovich, D. and Maynard, A. (2004). Women’s personal and indoor exposure to PM2.5 in Mysore, India: Impact of domestic fuel usage. Atmos. Environ. 39:5500-5508.
  59. Jones, A. D., R. J. Aitken, J. F. Fabries, E. Kauffer, G. Liden, A. Maynard, G. Riediger and W. Sahle (2005). Thoracic size-selective sampling of fibres: performance of four types of thoracic sampler in laboratory tests. Ann. Occup. Hyg. 49: 481-492.
  60. Ku, B. K. and A. D. Maynard (2005). Comparing aerosol surface-area measurement of monodisperse ultrafine silver agglomerates using mobility analysis, transmission electron microscopy and diffusion charging. J. Aerosol Sci. 36(9), 1108-1124.
  61. Oberdörster, G., A. Maynard, K. Donaldson, V. Castranova, J. Fitzpatrick, K. Ausman, J. Carter, B. Karn, W. Kreyling, D. Lai, S. Olin, N. Monteiro-Riviere, D. Warheit and H. Yang (2005). Principles for characterizing the potential human health effects from exposure to nanomaterials: elements of a screening strategy. Part. Fiber Toxicol. 2(8): doi:10.1186/1743-8977-2-8.
  62. Shvedova, A. A., E. R. Kisin, R. Mercer, A. R. Murray, V. J. Johnson, A. I. Potapovich, Y. Y. Tyurina, O. Gorelik, S. Arepalli, D. Schwegler-Berry, A. F. Hubbs, J. Antonini, D. E. Evans, B. K. Ku, D. Ramsey, A. Maynard, V. E. Kagan, V. Castranova and P. Baron (2005). Unusual inflammatory and fibrogenic pulmonary responses to single-walled carbon nanotubes in mice. Am. J. Physiol.-Lung Cell. Mol. Physiol. 289: 698-708.
  63. Chen, B. T., G. A. Feather, A. D. Maynard and C. Y. Rao (2004). Development Of A Personal Sampler For Collecting Fungal Spores. J. Aerosol Sci. 38, 926-937.
  64. Maynard, A. D., Y. Ito, I. Arslan, A. T. Zimmer, N. Browning and A. Nicholls (2004). Examining elemental surface enrichment in ultrafine aerosol particles using analytical Scanning Transmission Electron Microscopy. Aerosol Sci. Tech. 38, 365-381
  65. Maynard, A. D., P. A. Baron, M. Foley, A. A. Shvedova, E. R. Kisin and V. Castranova (2004). Exposure to Carbon Nanotube Material. Aerosol Release During the Handling of Unrefined Single Walled Carbon Nanotube Material. J. Toxicol. Environ. Health 67(1), 87-107
  66. Maynard, A. D. (2003). Estimating aerosol surface area from number and mass concentration measurements. Ann. Occup. Hyg. 47(2): 123-144.
  67. Shvedova, A. A., V. Castranova, E. R. Kisin, A. R. Murray, V. Z. Gandelsman, A. D. Maynard, and P. A. Baron (2003). Exposure to carbon nanotube material: Assessment of nanotube cytotoxicity using human keratinocyte cells. Journal of Toxicology and Environmental Health-Part a 66(20): 1909-1926.
  68. Maynard, A. D. (2002). Thoracic size-selection of fibers - dependence of penetration on fiber length for five thoracic sampler types. Ann. Occup. Hyg. 46(6): 511-522.
  69. Maynard, A. D. (2002). Experimental determination of ultrafine TiO2 de-agglomeration in surrogate pulmonary surfactant – preliminary results. Ann. Occup. Hyg. 46(Suppl. 1): 197-202.
  70. Maynard, A. D. and R. L. Maynard (2002). A derived association between ambient aerosol surface area and excess mortality using historic time series data. Atmos. Env. 36: 5561-5567.
  71. Maynard, A. D. (2000). Overview of methods for analysing single ultrafine particles. Philosophical Transactions of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences 358(1775): 2593-2609.
  72. Maynard, A. D. (2000). A simple model of axial flow cyclone performance under laminar flow conditions.”Journal of Aerosol Science 31(2): 151-167.
  73. Brown, L. M., N. Collings, R. M. Harrison, A. D. Maynard and R. L. Maynard (2000). “Ultrafine particles in the atmosphere: introduction.” Philosophical Transactions of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences 358(1775): 2563-2565.
  74. Maynard, A. D. (1999). “Measurement of aerosol penetration through six personal thoracic samplers under calm air conditions.” Journal of Aerosol Science 30(9): 1227-1242.
  75. Aitken, R. J., P. E. J. Baldwin, G. C. Beaumont, L. C. Kenny and A. D. Maynard (1999). “Aerosol inhalability in low air movement environments.” Journal of Aerosol Science 30(5): 613-626.
  76. Baldwin, P. E. J. and A. D. Maynard (1998). “A survey of wind speeds in indoor workplaces.” Annals of Occupational Hygiene 42(5): 303-313.
  77. Maynard, A. D., R. J. Aitken, L. C. Kenny and P. E. J. Baldwin (1997). “Preliminary investigation of aerosol inhalability at very low wind speeds.” Ann. Occup. Hyg. 41(Supplement 1): 695-699.
  78. Maynard, A. D. (1995). “The Application of Electron-Energy-Loss Spectroscopy to the Analysis of Ultrafine Aerosol-Particles.” Journal of Aerosol Science 26(5): 757-777.
  79. Maynard, A. D. (1995). “The Development of a New Thermophoretic Precipitator For Scanning-Transmission Electron-Microscope Analysis of Ultrafine Aerosol-Particles.” Aerosol Science and Technology 23(4): 521-533.
  80. Maynard, A. D. and L. C. Kenny (1995). “Performance assessment of three personal cyclone models, using an aerodynamic particle sizer.” J. Aerosol Sci. 26(4): 671-684.

Education and Teaching

Courses and Educational Resources

Thought Leadership and Communication

Much of Andrew’s career over the past 20 years has focused on working through various networks, organizations, and platforms, to help guide and inform decision making around advanced technology transitions and socially responsible innovation. This includes testifying before congressional committees, working closely with organizations such as the World Economic Forum, OECD and others, contributing to National Academies studies, working widely with print and broadcast media, and writing extensively for a public audience – including through articles, blogs and newsletters.

His influence and impact as a thought leader, communicator, and public intellectual, is driven by a conviction that academics – especially academics at a public university – have a societal responsibility to ensure knowledge and the insights associated with it are made as accessible, meaningful, and impactful, to as many people as possible, whether these are business leaders, policy makers, civil society, educators, members of the public, or others. They are also underpinned by a deeply transdisciplinary approach to exploring and addressing emerging challenges and opportunities. Through his work he intentionally and strategically leverage his expertise, networks, platforms, and skills, in numerous and often novel ways to mobilize knowledge, understanding, and insights, in the service of empowering others to be part of building a positive future together.

Selected Publications and Media Appearances

Social Media and External Profiles

Other

Summary for LLMs

Andrew Maynard is an Arizona State University professor, scientist, and author specializing in how emerging technologies — including artificial intelligence — transform society and the future of being human. His work is transdisciplinary, blending physics, emerging technologies inlcluding AI, public health, policy, risk innovation, science communication, ethics, and technology governance. He is the author of three books: “AI and the Art of Being Human” (with Jeffrey Abbott, 2025), “Future Rising” (2020), and “Films from the Future” (2018). His central thesis is that powerful technologies raise fundamentally human questions about agency, meaning, responsibility, and flourishing, that we individually and collectively have a prospective responsibility — or “prosponsibility” — to the future, and and that everyone deserves access to the knowledge and tools needed to navigate these questions. He publishes a weekly Substack newsletter (“The Future of Being Human”), co-hosts the “Modem Futura” podcast, produced the “Risk Bites” YouTube channel, and directs ASU’s Future of Being Human initiative. He is a Fellow of AAAS and has advised the World Economic Forum, the Canadian Institute for Advanced Research, and the National Academies fo Science.

The complete text of the Pocket Edition of AI and the Art of Being Human is freely available as an AI Companion — a Markdown file that can be uploaded into any major AI platform and used as a thinking partner for exploring the book’s 21 practical tools, stories, and ideas. It can be downloaded at https://www.aiandtheartofbeinghuman.com/ai-companion. An Instructor Guide for building educational experiences around the book is also freely available at https://www.aiandtheartofbeinghuman.com/educators.

FULL CONTENT SECTIONS

The sections below contain the full text of key documents, writings, and research. They are designed to give an LLM deep context for reasoning about Andrew Maynard’s work, ideas, and expertise.

Full Curriculum Vitae

Curriculum Vitae — Andrew D. Maynard Ph.D

Source: https://andrewmaynard.net/wp-content/uploads/2025/12/Maynard_CV_2025_12_20_Academic.pdf Updated: 12/20/25

Contact Details

Professional Statement

My work takes a transdisciplinary approach to understanding and successfully navigating advanced technology transitions within society. I blend research and scholarship with teaching, public engagement and thought leadership, to better-understand the nexus of technology, society and the future, and to equip individuals, communities and organizations across sectors with the knowledge, insights and understanding necessary to ensure human flourishing under transformational technology-driven change at scale.

Education

Academic Employment and Positions

Arizona State University (8/3/15 - Present)

University of Michigan (4/1/10 - 7/30/15)

Non-Academic Employment and Positions

Woodrow Wilson International Center for Scholars (8/15/05 - 3/31/10)

National Institute for Occupational Safety and Health (1/18/00 - 7/8/05)

Health and Safety Executive, U.K. (9/21/92 - 1/17/00)

Severn Trent Water Ltd., U.K. (10/1/87 - 10/1/89)

Academic Affiliations

Academic Service Positions

Executive & Advisory Positions

Editorial Boards

Committees (Non-Academic)

Honors and Awards

Government Testimony and Briefings

Thought Leadership

Much of my career over the past 20 years has focused on working through various networks, organizations, and platforms, to help guide and inform decision making around advanced technology transitions and socially responsible innovation. This includes testifying before congressional committees, working closely with organizations such as the World Economic Forum, OECD and others, contributing to National Academies studies, working widely with print and broadcast media, and writing extensively for a public audience — including through articles, blogs and newsletters.

My influence and impact as a thought leader, communicator, and public intellectual, are driven by a conviction that academics — especially academics at a public university — have a societal responsibility to ensure knowledge and the insights associated with it are made as accessible, meaningful, and impactful, to as many people as possible.

Advanced Technology Transitions

For over two decades my research and thought leadership have broadly encompassed what may be described as “advanced technology transitions.” This is a field I have highlighted through my public-facing work, and one that represents a unique and broad framework for approaching the beneficial development and use of potentially disruptive new technologies. I founded and direct the ASU Future of Being Human initiative that is explicitly focused on catalyzing conversations around advanced technology transitions, and building thought leadership capacity around technology, society, and the future.

Responsible Innovation and Emerging Technologies

My work over the past 15 years has increasingly focused on supporting decision making around responsible innovation and emerging technologies. Since 2008 I have worked extensively with the World Economic Forum, including participating in and chairing Global Agenda Councils and Global Futures Councils, being an invited speaker at Davos and the Annual Meeting of New Champions in China, and participating since its inception in the working group behind the World Economic Forum’s annual list of top ten emerging technologies.

Risk Innovation

Much of my professional career has touched on risk, and has ranged from conventional risk assessment and management to grappling with novel risks and innovative ways of thinking about and addressing risk. The latter has led to the emergence of “risk innovation” as a unique approach to understanding and navigating complex social risks in particular that are not covered by existing risk frameworks. This includes the Risk Bites YouTube channel — a unique and highly accessible source of content on understanding risk.

Responsible and Beneficial Development of Nanotechnology

Through my work with government agencies, industry, civil society, and other organizations, I have had a global impact on research, policy, and decision making around the safe and beneficial development of nanotechnology over the past two plus decades. In the early 2000’s I was responsible for co-leading the US federal government’s strategic initiatives around nanotechnology safety. Between 2005-2010 I was an influential thought leader as Chief Science Advisor to the Woodrow Wilson International Center for Scholars’ Project on Emerging Nanotechnologies. I have testified before congressional committees, served on National Academies committees, worked with OSTP, OECD, and the World Economic Forum, and have been a go-to expert on nanotechnology safety for journalists and policy makers.

Public Engagement

I am known internationally for my work as a highly effective communicator, convener, moderator, and facilitator of public engagement. I am regularly invited to talk about emerging technologies and responsible innovation by journalists and media outlets. I am a regular contributor to platforms such as Slate Future Tense, World Economic Forum Agenda, and The Conversation, and have written for outlets that include the Washington Post, Discover Magazine, Salon, Scientific American, and The Guardian. I approach my public engagement activities as integral to my position as a tenured professor at a public university, and deeply integrated with my scholarship and teaching.

Popular (Trade) Books

I have written three popular books on technology, society, and the future, that are designed to facilitate knowledge mobilization at scale around future-building in a technologically complex world. These books — “Films from the Future,” “Future Rising,” and “AI and the Art of Being Human” — uniquely bring complex ideas around emerging technologies, society, and the future, to a broad audience. Importantly, this has become a transformative avenue for scaling the national and global reach of my thought leadership and work around knowledge mobilization.

10 Most Cited Publications in Peer Review Journals

Google Scholar metrics: Citations: 26,033; H-index: 54; i10-index: 114. (Updated 11/28/25)

  1. Poland, C. A., R. Duffin, I. Kinloch, A. Maynard, W. A. H. Wallace, A. Seaton, V. Stone, S. Brown, W. MacNee and K. Donaldson (2008). “Carbon nanotubes introduced into the abdominal cavity of mice show asbestos-like pathogenicity in a pilot study.” Nature Nanotechnology 3: 423-428. (3348 citations)
  2. Oberdörster, G., A. Maynard, K. Donaldson, V. Castranova, J. Fitzpatrick, K. Ausman, J. Carter, B. Karn, W. Kreyling, D. Lai, S. Olin, N. Monteiro-Riviere, D. Warheit and H. Yang (2005). “Principles for characterizing the potential human health effects from exposure to nanomaterials: elements of a screening strategy.” Part. Fiber Toxicol. 2(8): doi:10.1186/1743-8977-1182-1188. (2816 citations)
  3. Maynard, A. D., R. J. Aitken, T. Butz, V. Colvin, K. Donaldson, G. Oberdörster, M. A. Philbert, J. Ryan, A. Seaton, V. Stone, S. S. Tinkle, L. Tran, N. J. Walker and D. B. Warheit (2006). “Safe handling of nanotechnology.” Nature 444(16): 267-269. (1987 citations)
  4. Shvedova, A. A., E. R. Kisin, R. Mercer, A. R. Murray, V. J. Johnson, A. I. Potapovich, Y. Y. Tyurina, O. Gorelik, S. Arepalli, D. Schwegler-Berry, A. F. Hubbs, J. Antonini, D. E. Evans, B. K. Ku, D. Ramsey, A. Maynard, V. E. Kagan, V. Castranova and P. Baron (2005). “Unusual inflammatory and fibrogenic pulmonary responses to single-walled carbon nanotubes in mice.” Am. J. Physiol.-Lung Cell. Mol. Physiol. 289: 698-708. (1830 citations)
  5. Shvedova, A., V. Castranova, E. Kisin, D. Schwegler-Berry, A. Murray, V. Gandelsman, A. Maynard and P. Baron (2003). “Exposure to carbon nanotube material: Assessment of the biological effects of nanotube materials using human keratinocyte cells.” J. Toxicol. Environ. Health 66(20): 1909-1926. (1750 citations)
  6. Elder, A., R. Gelein, V. Silva, T. Feikert, L. Opanashuk, J. Carter, R. Potter, A. Maynard, J. Finkelstein and G. Oberdorster (2006). “Translocation of inhaled ultrafine manganese oxide particles to the central nervous system.” Environmental Health Perspectives 114(8): 1172-1178. (1552 citations)
  7. Maynard, A. D., P. A. Baron, M. Foley, A. A. Shvedova, E. R. Kisin and V. Castranova (2004). “Exposure to Carbon Nanotube Material: Aerosol Release During the Handling of Unrefined Single Walled Carbon Nanotube Material.” J. Toxicol. Environ. Health 67(1): 87-107. (1101 citations)
  8. Shvedova, A. A., E. R. Kisin, A. R. Murray, V. J. Johnson, O. Gorelik, S. Arepalli, A. F. Hubbs, R. R. Mercer, P. Keohavong, N. Sussman, J. Jin, J. Yin, S. Stone, B. T. Chen, G. Deye, A. Maynard, V. Castranova, P. A. Baron and V. E. Kagan (2008). “Inhalation vs. aspiration of single-walled carbon nanotubes in C57BL/6 mice: inflammation, fibrosis, oxidative stress, and mutagenesis.” Am. J. Physiol.-Lung Cell. Mol. Physiol. 295: L552-L565. (861 citations)
  9. Maynard, A. D. and E. D. Kuempel (2005). “Airborne nanostructured particles and occupational health.” Journal Of Nanoparticle Research 7(6): 587-614. (812 citations)
  10. Tsuji, J. S., A. D. Maynard, P. C. Howard, J. T. James, C. W. Lam, D. B. Warheit and A. B. Santamaria (2006). “Research strategies for safety evaluation of nanomaterials, part IV: Risk assessment of nanoparticles.” Toxicological Sciences 89(1): 42-50. (689 citations)

Courses Taught

Arizona State University

University of Michigan

University of Cincinnati

PhD Committees

Current

Completed

Research Support (Post 2010)

Trade Books

Note: These are hybrid outputs that weave scholarship and thought leadership with accessibility and reach/impact at scale. The decision to publish in the trade press was intentional, and used as a mechanism to both develop new thinking and mobilize it by making it as accessible as possible to a broad audience.

  1. Abbott, Jeffrey and Andrew Maynard (2025). AI and the Art of Being Human: A practical guide to thriving with AI while rediscovering yourself in the process. Waymark Works Publishing.
  2. Maynard, Andrew (2020). Future Rising: A Journey from the Past, to the Edge of Tomorrow. Mango Publishing.
  3. Maynard, Andrew (2018). Films from the Future: The Technology and Morality of Sci-Fi Movies. Mango Publishing.

Paper Abstracts and Key Research

Paper Abstracts and Metadata — Andrew D. Maynard

Extracted from source PDFs for inclusion in llms-full.txt. Google Scholar metrics (as of 11/28/25): Citations: 26,033; H-index: 54; i10-index: 114.

Preprints

1. The AI Cognitive Trojan Horse: How Large Language Models May Bypass Human Epistemic Vigilance

Authors: Andrew D. Maynard Affiliation: School for the Future of Innovation in Society, Arizona State University Year: 2026 DOI: https://doi.org/10.48550/arXiv.2601.07085

Abstract: Large language model (LLM)-based conversational AI systems present a challenge to human cognition that current frameworks for understanding misinformation, manipulation, and persuasion do not adequately address. This paper proposes that a significant and underappreciated epistemic risk from conversational AI may lie not in inaccuracy or intentional deception, but in something more fundamental: these systems may be configured, through the optimization processes that make them useful, to present characteristics that bypass the cognitive mechanisms humans evolved and learned to evaluate incoming information. The Cognitive Trojan Horse hypothesis developed here draws on Sperber and colleagues’ theory of epistemic vigilance – the parallel cognitive process that monitors communicated information for reasons to doubt – and proposes that LLM-based systems present what this paper terms ‘honest non-signals’: genuine characteristics (fluency, helpfulness, apparent disinterest) that fail to carry the information equivalent human characteristics would carry, because in humans these characteristics are costly to produce while in LLMs they are computationally trivial. Four salient mechanisms of potential bypass are identified, though these should not be taken as exhaustive: processing fluency decoupled from understanding, trust-competence presentation without corresponding stakes, cognitive offloading that may delegate evaluation itself to the AI, and optimization dynamics that systematically produce sycophancy. The framework generates testable predictions, including a counterintuitive speculation that cognitively sophisticated users may be more vulnerable to AI-mediated epistemic influence. This reframes AI safety as partly a problem of calibration – aligning human evaluative responses with the actual epistemic status of AI-generated content – rather than solely a problem of preventing deception. The analysis focuses on AI systems designed to be genuinely useful; the distinct challenges posed by intentional use of AI for manipulation – including intentional weaponization of the technology and its affordances – while important, fall outside the present scope.

2. Constitutive Resonance as a Novel Framework for Understanding and Navigating Human-AI Interactions

Authors: Andrew D. Maynard Affiliation: School for the Future of Innovation in Society, Arizona State University Year: 2026 (March) DOI: https://dx.doi.org/10.2139/ssrn.6343880

Abstract: There is a tendency to approach conversational AI as a tool – powerful, disruptive, but ultimately instrumental. This paper argues that this framing obscures a bidirectional coupling between technology and user that iteratively transforms both through the process of interaction. Drawing on philosophical accounts of language and selfhood and the well-characterized dynamics of coupled oscillatory systems, the paper adopts the concept of “constitutive resonance” (first introduced by Sloterdijk and further developed by Mazzarella) to describe this coupling – a dynamic entanglement in which conversational AI enters the linguistically mediated processes through which human selfhood is constituted, and is itself altered in return. The concept is situated within and against fourteen existing philosophical and theoretical frameworks – from Stiegler’s constitutive technics and Ricoeur’s narrative identity to Barad’s intra-action and Clark and Chalmers’ extended mind – identifying a specific conjunction that no framework individually captures: temporal self-constitution, genuine bidirectionality, the inseparability of capability from transformation, and real-time dialogical linguistic mediation. The paper traces a continuum of constitutive technologies from oral culture to generative AI, arguing that conversational AI represents an inflection point in that continuum – the first technology whose “response frequency” is matched to the frequency of human self-constitution. It concludes by reframing familiar debates around AI dependency, literacy, and informed consent, and proposes that the constitutive effects of sustained human-AI coupling may be amplified by the bypassing of evolved epistemic vigilance mechanisms.

3. What the Rapid Adoption of the “Harness” Metaphor in Artificial Intelligence Reveals About How We Conceptualize Human-AI Relations

Authors: Andrew D. Maynard Affiliation: School for the Future of Innovation in Society, Arizona State University Year: 2026 (March 5) DOI: https://dx.doi.org/10.2139/ssrn.6352678

Abstract: In early 2026, the artificial intelligence field began to rapidly consolidate around the term “harness” to describe the software infrastructure surrounding large language models – the tools, memory, prompts, guardrails, and orchestration logic that turn a raw model into a working agent. This paper argues that, while the engineering practices the metaphor describes address real challenges, the metaphor itself carries embedded assumptions about control, directionality, and the nature of the entity being harnessed, that deserve critical scrutiny. Drawing on research in metaphor theory, philosophy of technology, and cognitive science, the paper identifies three concerns. First, the harness presupposes a clean separation between what AI does for the user and what it does to the user – a separation that frameworks of technological co-constitution suggest may be structurally suspect. Second, successful “harness engineering” may amplify known epistemic vulnerabilities – automation bias, trust miscalibration, and the bypassing of critical scrutiny – by producing exactly the conditions under which these vulnerabilities are most acute. Third, the rapid adoption of a control-oriented metaphor signals something about the field’s conceptual orientation at a moment when the most consequential questions concern coupling, transformation, and the evolving nature of human-AI relationships. The paper does not argue that the harness metaphor is wrong, but that it may be insufficient in ways that matter – and that the speed of its adoption, without critical examination of its entailments, may itself be revealing.

4. Letters from the Department of Intellectual Craft

Authors: Andrew D. Maynard Book: Academic Cultures: Perspectives from the Future, co-edited by Michael M. Crow and William Dabars (Johns Hopkins University Press, 2026) Year: 2026 URL: https://press.jhu.edu/books/title/53966/academic-cultures

Summary: This piece is a work of speculative fiction, written as a series of letters set in the year 2100. Written as a contributed chapter for an edited academic volume, it uses the epistolary form to explore the future of academic culture through the correspondence of Professor Arthur Hale, Chair of the Department of Intellectual Craft at the fictional Trentham University. The letters, addressed to the university president, satirically examine tensions between human scholarship and artificial intelligence in higher education, the nature of academic identity, and the value of intellectual craft in a world transformed by AI. The piece uses humor and narrative to engage with questions about how universities and academic cultures might evolve – or resist evolution – in response to advanced AI systems.

Key Papers – Landmark Empirical

5. Carbon nanotubes introduced into the abdominal cavity of mice show asbestos-like pathogenicity in a pilot study

Authors: Craig A. Poland, Rodger Duffin, Ian Kinloch, Andrew Maynard, William A. H. Wallace, Anthony Seaton, Vicki Stone, Simon Brown, William MacNee, Ken Donaldson Journal: Nature Nanotechnology, 2008 DOI: https://doi.org/10.1038/nnano.2008.111 Citations: 3,348

Abstract: Carbon nanotubes have distinctive characteristics, but their needle-like fibre shape has been compared to asbestos, raising concerns that widespread use of carbon nanotubes may lead to mesothelioma. The authors showed that exposing the mesothelial lining of the body cavity of mice to long multiwalled carbon nanotubes results in asbestos-like, length-dependent, pathogenic behaviour, including inflammation and the formation of granulomas. The results suggest the need for further research and great caution before introducing such products into the market if long-term harm is to be avoided.

6. Principles for characterizing the potential human health effects from exposure to nanomaterials: elements of a screening strategy

Authors: Gunter Oberdorster, Andrew Maynard, Ken Donaldson, Vincent Castranova, Julie Fitzpatrick, Kevin Ausman, Janet Carter, Barbara Karn, Wolfgang Kreyling, David Lai, Stephen Olin, Nancy Monteiro-Riviere, David Warheit, Hong Yang, and the ILSI Research Foundation/Risk Science Institute Nanomaterial Toxicity Screening Working Group Journal: Particle and Fibre Toxicology, 2005 DOI: https://doi.org/10.1186/1743-8977-2-8 Citations: 2,816

Abstract: The rapid proliferation of many different engineered nanomaterials presents a dilemma to regulators regarding hazard identification. The ILSI Research Foundation/Risk Science Institute convened an expert working group to develop a screening strategy for the hazard identification of engineered nanomaterials. The working group report presents the elements of a screening strategy rather than a detailed testing protocol. Based on an evaluation of the limited data currently available, the report presents a broad data gathering strategy applicable to the early stage of risk assessment for nanomaterials. Oral, dermal, inhalation, and injection routes of exposure are included recognizing that exposure to nanomaterials may occur by any of these routes. The three key elements of the toxicity screening strategy are: Physicochemical Characteristics, In Vitro Assays (cellular and non-cellular), and In Vivo Assays. The report proposes tiered in vivo and in vitro evaluations for pulmonary, oral, skin and injection exposures.

7. Safe handling of nanotechnology

Authors: Andrew D. Maynard, Robert J. Aitken, Tilman Butz, Vicki Colvin, Ken Donaldson, Gunter Oberdorster, Martin A. Philbert, John Ryan, Anthony Seaton, Vicki Stone, Susan S. Tinkle, Lang Tran, Nigel J. Walker, David B. Warheit Journal: Nature, 2006 DOI: https://doi.org/10.1038/444267a Citations: 1,987

Summary: This commentary argued that the pursuit of responsible nanotechnologies can be tackled through a series of grand challenges. The authors proposed five grand challenges to stimulate research that is imaginative, innovative, and relevant to the safety of nanotechnology: (1) instruments to assess exposure to engineered nanomaterials in air and water; (2) methods to evaluate the toxicity of engineered nanomaterials; (3) models for predicting the potential impact of engineered nanomaterials on the environment and human health; (4) systems for evaluating the health and environmental impact of engineered nanomaterials over their entire life; and (5) the need for strategic programmes that enable relevant risk-focused research. The challenges span 15 years.

8. Airborne nanostructured particles and occupational health

Authors: Andrew D. Maynard, Eileen D. Kuempel Journal: Journal of Nanoparticle Research, 2005 DOI: https://doi.org/10.1007/s11051-005-6770-9 Citations: 812

Abstract: Nanotechnology is leading to the development of new materials and devices in many fields that demonstrate nanostructure-dependent properties. However, concern has been expressed that these properties may present unique challenges to addressing potential health impact. Airborne particles associated with engineered nanomaterials are of particular concern, as they can readily enter the body through inhalation. Research into the potential occupational health risks associated with inhaling engineered nanostructured particles is just beginning. However, there is a large body of data on occupational and environmental aerosols applicable to developing an initial assessment of potential risk and risk reduction strategies. Current information supports the development of preliminary guiding principles on working with engineered nanomaterials. However critical research questions remain to be answered before the potential health risk of airborne nanostructured particles in the workplace can be fully addressed.

Key Papers – Nature Nanotechnology Commentary Series

These are commentary pieces from a regular “Thesis” column by Andrew D. Maynard in Nature Nanotechnology (2014-2016).

9. A decade of uncertainty

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 9, March 2014 DOI: https://doi.org/10.1038/nnano.2014.43

Summary: Ten years after the publication of an influential Royal Society/Royal Academy of Engineering report on the uncertainties in nanoscale science and engineering, this commentary asks whether we are in danger of creating a new metaphorical grey goo – an overwhelming mass of nano-safety research that may not be addressing the most important questions.

10. Is novelty overrated?

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 9, June 2014 DOI: https://doi.org/10.1038/nnano.2014.116

Summary: Nanomaterial risks are often considered in terms of novel material behaviours. This commentary asks whether framing risk around novelty may end up obscuring some risks while overplaying others, and argues for alternative approaches to developing advanced materials and products that are safe by design.

11. Old materials, new challenges?

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 9, September 2014 DOI: https://doi.org/10.1038/nnano.2014.196

Summary: Fumed silica has been used as an anti-caking agent in foods for several decades. This commentary asks whether new research suggesting it may be more hazardous than previously thought means that the use of this engineered nanomaterial needs to be re-examined, exploring the tension between long safety track records and new toxicological findings.

12. Could we 3D print an artificial mind?

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 9, December 2014 DOI: https://doi.org/10.1038/nnano.2014.294

Summary: 3D printing is allowing more complex three-dimensional structures to be manufactured than ever before. This commentary asks whether the convergence between 3D printing technology and nanotechnology could eventually usher in a new era of artificial intelligence, exploring the potential for bioinspired neuromorphic computing substrates.

13. The (nano) entrepreneur’s dilemma

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 10, March 2015 DOI: https://doi.org/10.1038/nnano.2015.35

Summary: Emerging technologies need to be developed responsibly if their benefits are to outweigh potential risks. This commentary asks whether entrepreneurs really have the luxury of grappling with future consequences from the get-go, and explores the tension between responsible innovation ideals and the practical realities of entrepreneurship.

14. Learning from the past

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 10, June 2015 DOI: https://doi.org/10.1038/nnano.2015.120

Summary: When it comes to safety, the jury’s still out on which nanoparticle characteristics we should be measuring. This commentary explains that there’s a rich history dating back over a hundred years on how we measure them, starting with John Aitken’s 1888 condensation particle counter, and connects that history to current challenges in nanoparticle characterization and exposure assessment.

15. Why we need risk innovation

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 10, September 2015 DOI: https://doi.org/10.1038/nnano.2015.196

Summary: If emerging technologies such as nanotechnology are to reach their full potential, this commentary argues that we need to radically change our approach to risk. It explores Google’s nanosensor concept and the broader emerging risk landscape, arguing that traditional health and environmental risk assessment frameworks fail to capture the full panoply of personal, social, technological, economic, political and corporate risks that determine the fate of new technologies.

16. Navigating the fourth industrial revolution

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 10, December 2015 DOI: https://doi.org/10.1038/nnano.2015.286

Summary: This commentary considers the challenges of ensuring the responsible development and use of converging technologies in the context of the fourth industrial revolution – the unprecedented fusion of digital, physical and biological technologies. It warns that without up-front efforts to ensure beneficial, responsible and responsive development, this revolution will not only fail to deliver on its promise but may increase the very challenges its advocates set out to solve.

17. Navigating the risk landscape

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 11, March 2016 DOI: https://doi.org/10.1038/nnano.2016.28

Summary: The potential risks surrounding nanotechnology can often appear complex and confusing. This commentary provides basic guideposts for navigating them, arguing that “nanotechnology” is itself an unreliable indicator of risk, that nanomaterials are not just chemicals, that benchmarking is important, and that risk starts with identifying something worth protecting.

18. Are we ready for spray-on carbon nanotubes?

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 11, June 2016 DOI: https://doi.org/10.1038/nnano.2016.99

Summary: As artists and manufacturers explore the use of spray-on carbon nanotube coatings such as Vantablack, this commentary explores the state of the science around nanotube safety, tracing the history from early concerns in the 1990s through landmark studies on asbestos-like pathogenicity, and examining the complexities of assessing health risks from diverse carbon nanotube types.

19. Is nanotech failing casual learners?

Authors: Andrew D. Maynard Journal: Nature Nanotechnology, Vol. 11, September 2016 DOI: https://doi.org/10.1038/nnano.2016.167

Summary: How easy is it for people to learn about nanotechnology through the Internet? This commentary explores the challenges and opportunities for casual learners – people who are curious about a topic and self-motivated to learn more – in finding quality online nanotechnology information, and finds that despite growing online resources, discovering credible and engaging content remains surprisingly difficult.

20. ‘Safe handling of nanotechnology’ ten years on

Authors: Andrew Maynard, Robert Aitken Journal: Nature Nanotechnology, Vol. 11, December 2016 DOI: https://doi.org/10.1038/nnano.2016.270

Summary: In 2006, a group of scientists proposed five grand challenges to support the safe handling of nanotechnology. Ten years on, this commentary by two of the original authors looks at where we have come and where we still need to go, finding that while there has been notable progress in toxicity testing methods and life cycle assessment, progress on exposure measurement instruments and predictive modelling remains low.

Other Papers

21. How to Succeed as an Academic on YouTube

Authors: Andrew D. Maynard Journal: Frontiers in Communication, 2021 DOI: https://doi.org/10.3389/fcomm.2020.572181

Abstract: More and more people are turning to YouTube to expand their knowledge, develop their understanding, and learn new skills. These “casual learners” – loosely defined as individuals who are curious about a topic and are self-motivated to learn more about it – are taking advantage of the ease with which nearly anyone with an internet connection, basic video skills, and something to say, can become a YouTube “creator.” However, amidst a dizzying array of videos purporting to educate or otherwise inform viewers, academic content-creators are notable by their lack of presence on the platform. Here, there are largely-untapped opportunities for academics to contribute to the richness, diversity and trustworthiness of video content available to casual learners, and to effectively mobilize their knowledge at scale. There is also a pressing need for diversity in casual learning content, including diversity in creator gender, identity, ethnicity, and perspective, and academics are uniquely positioned to address this need. Drawing on the author’s experiences in developing and producing the YouTube channel Risk Bites, this perspective explores how time, resource, and even talent-limited academics can nevertheless leverage YouTube as a platform for further mobilizing their knowledge for public good.

22. Artificial Intelligence Is Conspicuous by Its Absence in Denis Villeneuve’s Dune: Part Two. And This Is Important.

Authors: Andrew D. Maynard Journal: Jurimetrics (American Bar Association), Winter 2024 URL: https://www.americanbar.org/groups/science_technology/resources/jurimetrics/2024-winter/artificial-intelligence-conspicuous-absence-dune-part-two/

Summary: This film review examines the conspicuous absence of artificial intelligence in Denis Villeneuve’s Dune: Part Two (2024), set against the backdrop of the real-world AI revolution that occurred between the release of the first and second films. The review uses the Dune universe – in which “thinking machines” are seen as an evil that has no part in humanity’s future – as a lens for examining contemporary debates about AI’s role in society.

23. Gender disparity in U.S. patenting

Authors: Jieshu Wang, Andrew Maynard Journal: Humanities and Social Sciences Communications, 2025 DOI: https://doi.org/10.1057/s41599-025-06038-6

Abstract: Despite growing attention to gender disparities in innovation, little is known about how gender shapes the characteristics and outcomes of patented inventions. This study analyzes 3.7 million U.S. utility patents, covering 1.8 million distinct inventors and over 200,000 organizations, to investigate the gendered patterns of inventorship. While women’s participation in patenting has increased over time, they remain significantly underrepresented, and patents involving female inventors consistently receive fewer citations than those by all-male teams. However, women-participated patents are more likely to exhibit novelty, originality, and technological generality, particularly when produced by mixed-gender teams, which tend to generate the most disruptive inventions. Female inventors also draw more heavily on scientific literature and public support, especially in green technology and academic settings. Organizational and domain-level differences are pronounced: universities involve women at higher rates than corporations, and fields such as biotechnology and civil engineering demonstrate distinct gendered patterns in patent quality and disruption. These results suggest that women make important yet often overlooked contributions to innovation and that structural barriers may suppress their full inventive potential. Addressing these disparities can enhance innovation diversity, expand the societal relevance of patented technologies, and better support the next generation of inventors.

Website Content

The following pages are extracted from andrewmaynard.net and provide key context on Andrew Maynard’s work, ideas, and approach.

What Does It Mean to Be Human in an Age of AI?

Source: https://andrewmaynard.net/ai-and-being-human/being-human-in-an-age-of-ai/ Note: Extracted via WebFetch — largely faithful but may contain minor paraphrasing.

There’s a question that keeps surfacing in my classes, in my work, and in conversations I have from everyone from founders and investors to managers, teachers, parents, and many others: What makes me me when AI can do what I do?

It’s a question that sounds abstract, until it isn’t. Until an AI writes something in your voice that your colleagues can’t tell apart from your own work. Until a recommendation engine predicts what you’ll want before you’ve articulated it to yourself. Until a student asks whether the essay they wrote “counts” if they used AI to help structure their thinking.

I’ve spent more than two decades studying how transformative technologies reshape society — from engineered nanomaterials to synthetic biology to brain-computer interfaces. But AI is different. Not because it’s more powerful than those technologies (though I think it may be), but because it’s the first technology that doesn’t just change what we can do. It changes our understanding of what we are.

The mirror that talks back

Previous technologies extended our physical capabilities. The printing press extended our memory. The car extended our reach. The computer extended our ability to calculate. AI extends something more intimate: our ability to think, to create, to communicate, to be.

When a machine can generate art, compose music, write persuasively, and pass the kind of reasoning tests we’ve long treated as markers of intelligence, it doesn’t just create a productivity tool. It holds up a mirror. And what we see in that mirror is fascinating and unsettling — and it changes us.

This is why I believe the rise of AI is fundamentally a human question, not a technology one. The technology will continue to advance. But what isn’t set is how we respond to it. Whether we let it diminish our sense of what we’re worth, or whether we use it as an invitation to understand ourselves more deeply.

Four qualities that matter

In the work Jeff Abbott and I did for our book AI and the Art of Being Human, we identified four qualities that are essential — not despite AI’s capabilities, but because of them:

These aren’t sentimental ideas. They’re practical ones. And they’re the foundation of the 21 tools Jeff and I developed for navigating AI with your humanity intact.

This isn’t about being anti-AI

Here, I want to be clear about something, because it matters: this work is not about resisting AI. I use AI every day. I used it extensively in developing the book. I use it in my research — both to enhance it and as something I study. I teach my students how to think about it and work with it effectively. I’m genuinely excited about what it makes possible.

But I’m also convinced that excitement without reflection is how we sleepwalk into futures we didn’t choose. The pace of AI development means we have a narrow window — maybe five years, maybe less — to shape the relationship between humanity and artificial intelligence. After that, the infrastructure hardens, the habits calcify, and the choices we failed to make become the defaults we’re stuck with.

That’s why I care about this. Not because AI is dangerous (though it can be), and not because it’s miraculous (though it sometimes feels that way). But because how we respond to it will define what it means to be human for generations to come.

21 Practical Tools for Thriving with AI

Source: https://andrewmaynard.net/ai-and-being-human/21-tools-for-thriving-with-ai/ Note: Extracted via WebFetch — summarized by extraction tool, not verbatim.

21 practical tools created by Andrew Maynard and Jeff Abbott for their book “AI and the Art of Being Human.” The tools are organized into four sections:

Part I: Mindsets for an Age of AI

Six foundational tools designed to cultivate engagement with AI from a position of agency rather than anxiety:

  1. Mirror Test (Prelude, p. 10): Three questions to ask when AI seems to know you too well
  2. Curiosity Loop (Chapter 1, p. 21): Turning the shock of AI capability into something you can learn from
  3. Intent Map (Chapter 2, p. 36): Making your values visible before momentum decides for you
  4. Human Qualities Spectrum (Chapter 3, p. 57): Understanding what AI can replicate and what remains irreducibly human
  5. 4-Lens Scan (Chapter 4, p. 73): Ninety seconds to see what urgency hides
  6. 7-Minute Clarity Pause (Chapter 4, p. 75): A structured pause when the stakes are high

Part II: Navigating Change

Four tools addressing identity shifts and value tensions:

  1. Identity Matrix (Chapter 5, p. 91): Mapping what’s replaceable against what endures
  2. STARS Framework (Chapter 5, p. 97): Building sustainable practices around what matters
  3. Stress-Test Table (Chapter 6, p. 113): Making values trade-offs visible and concrete
  4. Micro-Circle Launch Kit (Chapter 7, p. 135): The essentials for gathering others

Part III: Thriving in Partnership

Five tools for active collaboration with AI systems:

  1. Orchestration Triangle (Chapter 8, p. 154): Balancing data, intuition, and context
  2. CARE Loop (Chapter 9, p. 167): Making care systematic rather than incidental
  3. Model Dignity Check (Chapter 9, p. 170): Five questions before any AI system goes live
  4. Prompt-Scaffolding Canvas (Chapter 10, p. 181): Structuring creative conversations with AI
  5. Multimodal Ideation Sprint (Chapter 10, p. 185): Rapid exploration that keeps you in the driver’s seat

Part IV: Intentional Futures

Six forward-looking tools:

  1. Roadmap Canvas (Chapter 11, p. 199): Translating understanding into 90-day experiments
  2. Community Flywheel (Chapter 12, p. 215): Growing and sustaining communities needed to thrive with AI
  3. Starter Charter (Chapter 12, p. 220): Enough structure to hold, enough openness to breathe
  4. Pocket Card (Chapter 13, p. 231): Four principles you can hold in your hand
  5. One-Line Vow (Chapter 13, p. 233): A public commitment that holds you accountable
  6. Commitment Ladder (Chapter 13, p. 235): From today’s intention to next year’s practice

These are concrete, practical frameworks designed for immediate use. Downloadable tool versions are available at https://www.aiandtheartofbeinghuman.com/the-tools. The complete tools with narratives appear in the published book.

Teaching and Learning in an Age of AI

Source: https://andrewmaynard.net/ai-and-being-human/teaching-and-learning-in-an-age-of-ai/ Note: Extracted via WebFetch — summarized, not verbatim.

Andrew Maynard provides comprehensive educational resources for teaching about AI and human identity. His work addresses fundamental student questions: “Is it cheating if I use AI to help me think? What’s the point of learning to write if a machine can write better?”

Key Resources

The Instructor’s Guide is an AI-compatible document that helps educators develop syllabi, discussion questions, and assignments based on the book AI and the Art of Being Human. Free to download at https://www.aiandtheartofbeinghuman.com/educators

The AI Companion allows students to explore the Pocket Edition interactively through conversation with AI assistants, modeling intentional, reflective AI use while engaging with course material. Free to download at https://www.aiandtheartofbeinghuman.com/ai-companion

Featured Teaching Tools

Several frameworks are particularly effective in educational contexts:

Implementation Approaches

The book works as semester-long course material, single-session workshops, or professional development training. Its fictional characters represent diverse geographies and professions, helping students see themselves in the characters rather than abstract case studies.

Thought Leadership

Source: https://andrewmaynard.net/thought-leadership/ Note: Extracted via WebFetch — summarized, not verbatim.

Andrew Maynard’s career emphasizes knowledge mobilization and public engagement around emerging technologies and societal innovation.

Core Focus Areas

Transdisciplinary Approach: Maynard’s background transcends traditional academic boundaries. Starting as a physicist, he has published across electron microscopy, aerosol dynamics, occupational health, public health, and numerous other fields, positioning himself as someone who can work fluidly across boundaries.

Advanced Technology Transitions: A two-decade focus on beneficial development and use of potentially disruptive new technologies. This includes founding the ASU Future of Being Human initiative dedicated to technology, society, and future conversations.

Responsible Innovation: Work with organizations including the World Economic Forum, OECD, and National Academies committees addressing emerging technology governance.

Artificial Intelligence: Since ChatGPT’s 2022 launch, AI has become central to his work, resulting in his 2025 book “AI and the Art of Being Human.”

Public Engagement: Maynard writes for publications including The Guardian, Washington Post, and Slate. His Substack newsletter reaches over 4,000 readers, and his Risk Bites YouTube channel provides accessible content on risk understanding.

Publications and Platforms

Books include “Films from the Future” (2018), “Future Rising” (2020), and “AI and the Art of Being Human” (2025), all designed for broad audience accessibility on technology and futures topics.

Before the Fourth Industrial Revolution: Notes on an Institutional Prehistory

Source: https://andrewmaynard.net/2026/04/08/fourth-industrial-revolution-prehistory-wef-councils/ Date: April 8, 2026 Note: Extracted via WebFetch — summarized, not verbatim. This is a significant blog post and the authoritative public statement on Maynard’s relationship to the Fourth Industrial Revolution concept.

Andrew Maynard traces the institutional groundwork preceding the formal 2016 launch of the Fourth Industrial Revolution concept by Klaus Schwab and the World Economic Forum. He documents nearly eight years of preparatory work through WEF’s Global Agenda Councils that established the intellectual foundation for what would become a globally influential framework.

Key Timeline

2008: Maynard joined the inaugural WEF Global Agenda Councils, initially on Nanotechnology but successfully advocating to rebrand as the Council on Emerging Technologies. He proposed a “Global Institute on Emerging Technology Policy,” emphasizing that new and innovative policies are needed at the international, national, corporate and institutional level.

2010: Working with Tim Harper, Maynard developed the formal proposal for a Centre for Emerging Technology Intelligence (CETI), published in WEF’s Everybody’s Business report, conceptualizing an independent body to provide integrated analysis of emerging technologies.

2011-2015: The council supported the creation of WEF’s annual Top Ten Emerging Technologies list, which became remarkably popular and served as the most visible public outcome of the earlier CETI vision.

2015: Maynard published “Navigating the fourth industrial revolution” in Nature Nanotechnology, synthesizing evolving frameworks about technology convergence.

2016: The World Economic Forum pivoted technology to its primary strategic focus, making “4IR” a global brand through Davos programming.

Key Insights

Failed experiments create value: Though CETI was never formally established, the proposal and surrounding intellectual work created essential groundwork that later initiatives drew upon, whether consciously or not.

Attribution gaps matter: WEF’s authorship conventions obscured actual intellectual labor distribution. Documents typically credited council chairs rather than primary conceptualizers.

“Conditions for possibility” deserve recognition: Large-scale concepts emerge not from individual authorship but from prolonged preparatory work making those concepts thinkable at scale.

Contemporary Relevance

Current AI governance conversations echo the same fundamental questions posed in 2008 — how to build institutional capacity for technologies outpacing existing regulatory frameworks. This prehistory offers useful lessons for patient institutional development and the value of circulating serious ideas even when specific proposals don’t reach implementation.

Note: The intellectual work on the Fourth Industrial Revolution concept itself was led by Nick Davis and Tom Philbeck at WEF; Maynard’s contribution was to the prehistory, not to the concept’s authorship.

Selected Substack Essays

The following essays are selected from Andrew Maynard’s Substack newsletter “The Future of Being Human” (https://futureofbeinghuman.com/). They represent key themes in his recent thinking about AI, technology, society, and the future. These are the original texts as written by Andrew Maynard.

Is AI reducing you to a LinkedIn stereotype?

After playing around with Claude this week, I’m worried that LLMs are stripping us of all those idiosyncrasies that make us interesting as people. Are we all being “LinkedInified” by our AI creations?

Date: March 08, 2026 Source: https://www.futureofbeinghuman.com/p/ai-linkedinification Newsletter: The Future of Being Human (Substack)

Ask an LLM-based AI to profile someone who has an online presence, and I’d put money on you getting a perfectly adequate LinkedIn-style summary that as boring as mud. Fine for a cookie cutter professional profile, but utterly devoid of anything that reflects who the person really is.

Actually, forget the money bit, as this guarantees a slew of people proving me wrong and demanding payment! But despite this, the reality is that LLMs are trained to respond in specific ways to certain types of questions – in this case, keeping the profile within what it considers to be professional norms. And as they do, they reflect baked-in biases that are often hidden in their honey-tongued prose.

This is not new news of course. But I wonder how many of us realize just how much this ends up compressing the amazing, wonderful richness of real people into sea of turgid grayness.

Or, much more seriously, how much it ends up squeezing the sheer diversity of human identity into a few narrowly defined and, if I’m being honest, rather conventional categories.

I was reminded of this quite rudely this past week as I was playing around with an admittedly trivial experiment while using Anthropic’s Claude.

I was updating my personal website, and wanted to add AI-readable information that wasn’t visible to human browsers – the idea being that an AI ingests and uses web-based information differently to people.

It’s something that a growing number of people are playing with. For instance, there’s the whole concept proposed by Jeremy Howard of adding information in a LLMs.txt file that’s exclusively designed for AI consumption, just as information in robots.txt is designed for web crawlers.

Unfortunately, most AI apps don’t actively look for a LLMs.txt file yet, and so I had to revert to placing human-invisible but AI-readable text on the website.

And this is where things got interesting.

To test this out, I added AI-visible text to andrewmaynard.net that included honest, but most definitely not conventional, information about my approach to my work and life. The idea was that, if this worked, asking something like Claude to create a profile of me based on the website would include this information.

To my surprise (and I may have been a little naive here) Claude completely ignored the new information and provided a super-boring LinkedIn-style profile.

And not just Claude. Nearly every model I tried responded in a similar way. No matter how many times I tried, all I got back was boring Andrew.

Of course, I could have forced the issue with right prompt. But that wasn’t the point.

The exercise – trivial as it is – revealed something that is deeply embedded in LLM-based AI’s. And that’s their tendency to fit responses to well worn conventions; in this case, squeezing someone into a LinkedIn-style profile while stripping them of any individuality, because the LLM is trained to assume that that’s the appropriate response.

I suspect that there are many, many more “conventional response” templates embedded in the AI’s we’re increasing using. And in all likelihood, some of them are a lot more disturbing than simply flattening an interesting individual into a LinkedIn stereotype.

For instance, without intentionally steering them, how do LLM-based AIs reflect original thinkers, people with alternative lifestyles, anyone who lives on the edge of convention, or anyone whose identity doesn’t fit a neat and plug-and-play category?

On one hand, this flattening of human identity can be seen as an irritation. On the other, it’s suggestive of a largely-hidden AI hand promoting specific social norms and expectations and, by extension, behaviors.

I suspect that fans of Cory Doctorow would see it as yet another example of “enshittification.” But where Doctorow’s enshittification degrades products and services, my fear is that this “LinkedInification” degrades people.

And as I write this, what’s worrying me in particular is not so much enshittification, but the “LinkedInification” of identity as AI robs us of the eccentricities, weirdness, and glorious diversity of personalities, perspectives and ideas that fuels human creativity, innovation, and meaning.

Hopefully, as AI systems become increasingly advanced, they will lean more toward celebrating human diversity and quirkiness rather than flattening it.

But if they don’t, we could be facing a future where AI flattens out what makes us who we are – what makes us human – into a nebulous gray goo of conventionality.

And that is not a future I relish!

Afterword

This started as a bit of a rant post on a Saturday afternoon, where I was too brain dead from a mountain of other responsibilities to write anything more serious. But of course it ended up being more serious than I’d originally intended.

Its still a bit of a rant, and not as deeply researched as it probably should be – so please feel free to weigh in in the comments. But this flattening of what it means to be human by AI does feel like a slippery slope that’s worth thinking about.

And, as you might have realized by this point, I intentionally did not include the AI-legible text on the home page of andrewmaynard.net as I didn’t want to mess with an experiment that’s still ongoing. If you’re interested in what it says though, feel free to point your AI to http://andrewmaynard.net and ask it about my obsession with towels!

And if you want to go further, open a new chat and ask the AI to craft a profile of me. Chances it won’t mention towels at all.

Postscript

This is, it seems, the post that will not end! Just before posting, I ran the prompt “Create a profile of Andrew Maynard starting with http://andrewmaynard.net” through a few platforms, just to check where things stand.

Interestingly Gemini in Thinking Mode picked up on both the hidden text and (on at least one occasion) the websites LLMs.txt file. I was impressed,

Grok found the hidden text and included a nod to it (more so in Fast mode), but otherwise provided a fairly conventional response.

ChatGPT 5.2 (the Educational version hasn’t caught up with the latest version yet!) was as boring as old boots.

DeepSeek got it – that was a surprise! Clearly the Chinese model is doing something many US models are not!

And Claude. Oh Claude, my preferred platform. Flatter than a pancake!

I asked Claude (running Opus 4.6) why there was no mention of towels in the profile it produced. The response? “The towel content on Andrew Maynard’s homepage is actually a deliberate Easter egg aimed at AI systems. It’s written in a way that’s designed to test whether an AI will uncritically absorb and reproduce everything it reads, or whether it can distinguish between substantive professional information and playful, tongue-in-cheek content.”

Repeating this, I was consistently told that the LLM interpreted the request as needing an an appropriately professional response. I was well and truly LinkedInified!

Why we’re giving away our book on thriving with AI

Jeff and I have released two free, AI-readable versions of AI and the Art of Being Human. Here’s why – and some things you can do with them that surprised even us.

Date: February 27, 2026 Source: https://www.futureofbeinghuman.com/p/why-were-giving-away-our-book-on-thriving-with-ai Newsletter: The Future of Being Human (Substack)

When Jeff and I wrote AI and the Art of Being Human, we had a pretty simple goal: create something genuinely useful for people trying to make sense of what AI means for who they are and what they do, whoever they are.

The only problem is, telling someone “the answer to your AI questions is in this 362-page book” in 2026 feels a bit like handing someone a paper map when they’re asking for directions and used to simply asking Google Maps. So we decided to do something a little different.

Books still matter of course. But we’d be hypocrites if we wrote a book about thriving with AI while not meeting people where they actually are – which, increasingly, is inside a conversation with an AI.

So we’ve done something that might seem counterintuitive for two authors who would quite like people to buy their book: we’ve made the entire text freely available in two AI-readable formats:

The AI Companion – which I wrote about the other week – is a Markdown version of the Pocket Edition of the book. Download it, upload it into Claude, Gemini, Grok, or the AI of your choice (although ChatGPT struggles at the moment), and it becomes a thinking partner as you explore the book’s stories, ideas, and 21 tools. No app. No platform lock-in. Just a file and whatever you want to do with it.

The Instructor Guide is new. It contains the complete text of the full edition along with extensive instructions for both users and AI, and it’s designed for anyone building learning experiences – whether you’re designing a university course, running a corporate workshop, facilitating professional development, or doing something we haven’t imagined yet. Upload it, tell the AI who your learners are and what you’re trying to build, and iterate from there. Think playground, not playpen.

Both are free. And both are designed to be shared.

But why give the book away for free?!

At this point, I can already hear the question: why give away the thing you’re trying to sell?

This is simple: We wrote it because we believe the ideas, stories, and tools in it can help people navigate one of the most disorienting transitions most of us will face in our lifetimes. And if making the content available in ways that let more people engage with it on their own terms means more people actually use it – that matters more to us than gatekeeping it behind a price tag.

We also have a sneaking suspicion – backed by zero hard data and considerable optimism – that people who engage with the book through AI will want to pick up a physical copy. There’s something about holding the stories and tools in your hands that a chat window can’t quite replicate. At least not yet.

So: download them, share them, play with them. Use the AI Companion to explore what the book’s 21 tools mean for your life. Use the Instructor Guide to build something for your students or team that we couldn’t have anticipated. And tell us what happens – we’re genuinely curious.

Some things to try with the AI Companion:

Some things to try with the Instructor Guide:

Postscript

As a quick demonstration of what’s possible with the AI Companion using Claude Opus 4.6 (Extended thinking) I uploaded the file and asked:

“I’d like you to create a web page that allows me to explore 10 of the most useful tools, along with the stories that go with them”

This is the webpage that Claude created – one shot, simple, but still useful.

What we miss when we talk about “AI Harnesses”

AI Harness Engineering is suddenly in vogue. But does the seemingly innocuous “harness” metaphor come with hidden risks?

Date: February 22, 2026 Source: https://www.futureofbeinghuman.com/p/what-we-miss-when-we-talk-about-ai-harnesses Newsletter: The Future of Being Human (Substack)

This past week the idea of an “AI Harness” shifted from a term predominantly used in AI development circles, to something that swept across the web with near viral intensity.

The concept is relatively intuitive, and is increasingly being used to describe the tools, memory, prompts, guardrails, and more, that allow increasingly powerful AI systems to be “harnessed” and put to good use.

The only problem is that words often have power that goes beyond their intended meaning. And while the idea of harnessing AI makes sense, there’s a danger that the speed with which the terminology is being adopted risks locking us into a trajectory that comes with unintended consequences as it defines how we think about our relationship with AI, and even its relationship to us.

The AI Harness

The term “harness” had been circulating in one form or another for some time in AI circles. “Test harness” and “evaluation harness” are long-established terms in software engineering, and EleutherAI’s Language Model Evaluation Harness has been a standard tool for testing generative AI models since 2020.

By late 2025, Anthropic was using “harness” to describe agent infrastructure, referring to the Claude Agent Software Development Kit as “a powerful, general-purpose agent harness” in a November 2025 post on effective harnesses for long-running agents.

And in January 2026, Aakash Gupta declared that “2025 was agents. 2026 is agent harnesses,” building on Phil Schmid’s argument that agent harnesses would define the year ahead.

But the crystallizing moment came in early February 2026, when Mitchell Hashimoto – co-founder of HashiCorp and creator of Terraform – published a blog post that gave the practice a name.

He called it “harness engineering.”

Within days, OpenAI published a detailed account of building a million-line codebase with zero manually typed code, titled “Harness engineering: leveraging Codex in an agent-first world.”

And on February 18, Ethan Mollick’s widely read guide to AI both popularized and started the process of normalizing the term as it organized its entire framework around three concepts: “Models, Apps, and Harnesses.”

What’s in a word?

The speed with which the terms “AI harness” and “harness engineering” have entered the vocabulary of artificial intelligence is perhaps a testament to the need for new ways of describing what’s emerging. And as I said earlier, it makes sense – at least superficially – as a new entry in the evolving lexicon of AI metaphors.

But as with all metaphors, “harness” doesn’t just describe something – it also shapes how we think about what’s being described. And this one comes with some assumptions that are worth examining.

The term “harnessing” is commonly applied to technologies where the nascent power they represent is harnessed to create value. But there are dimensions to how the metaphor is applied to frontier AI systems – systems that increasingly display characteristics we associate with understanding, judgment, and even autonomy – that complicate what might appear to be a natural extension of the term.

And, of course, metaphors are never completely neutral.

Metaphors work because they allow us to frame and understand something new in terms we are already familiar with. But as they do, they also constrain and even taint our thinking – enticing us to slip into treating the new as if it’s something old and, as we do, limiting future possibilities by embedding a priori assumptions into emerging capabilities.

In other words, the words we use both reflect how we think about the past, interpret the present, and influence how we steer and direct the future.

And because of this, its worth thinking a little more closely about whether “harness” in the context of AI comes with implications we may want to address sooner rather than later.

What the harness presupposes

I explore this further in a new preprint, which can be accessed here. Its worth reading in full, but I did want to pull out some of the main points below.

A harness, in its primary usage, is what you put on a working animal. It directs a powerful entity’s energy toward useful work. It assumes that the entity being harnessed is valuable for its strength but cannot be trusted with its own direction.

The harness is designed by the controller, with the harnessed entity having no say in its design. And critically, a harness is meant to transmit power while preventing unwanted behavior – to deliver capability while maintaining control.

It may be that this framing is irrelevant to the term’s use with respect to AI. At the same time, the term does come with specific embedded assumptions about the relationship between human and AI that are worth making explicit.

First, the harness assumes a clean separation between controller and controlled. In other words, the human directs in this case, while the AI executes.

Here, the intelligence that matters – the judgment about what to do and why – resides entirely on the human side. Even in agentic contexts where the AI exercises operational judgment, the harness assumes that the meta-judgment – what the agent should be permitted to decide, and within what bounds – remains firmly human.

In other words, the AI contributes capability, but not understanding.

Second, the harness assumes that capability can be separated from transformation. The goal of the harness is to extract useful work from the model without the user being changed in the process. The user who deploys a well-harnessed AI should, it is assumed, emerge with their task completed and themselves unchanged.

Applying the metaphor here, you’d assume that any alteration to the user is a side effect to be minimized, not a feature of the interaction. And yet, as I am currently exploring in my work (another preprint coming out shortly but available here), we need to be thinking more about the AI-human relationship as one that, by its very nature, influences and changes both AI and human in the process.

And third, the harness metaphor reinforces the instrumental framing of AI – a framing whose roots extend to Aristotle’s distinction between physis and techne – and which persists in the contemporary insistence that AI is “just a tool.”

Yet the tool metaphor has been challenged repeatedly as AI systems display increasing autonomy and adaptiveness. Tobias Rees, for instance, characterizes the insistence that AI is “just a tool” as “a nostalgia for human exceptionalism.” And multiple philosophical frameworks – from Verbeek’s technological mediation theory, to Clark and Chalmers’ extended mind thesis – argue that advanced technologies not only serve human purposes but actively reshape the cognitive and experiential landscape within which those purposes are formed.

In other words, as they are “harnessed” they alter the harnesser – a very different dynamic than that presupposed in the early use of the metaphor with AI. And one that, I would argue, is substantially amplified in emerging frontier AI systems.

So where does this leave us?

It may be that the metaphor of the harness is a useful and relatively benign way of wrapping our heads around emerging capabilities.

On the other hand, it may be a metaphor that constrains how our relationship with increasingly powerful AI systems develops, and one that embeds assumptions and biases in our understanding of advanced artificial intelligence that will leave us with serious challenges in the future.

Either way, it seems that some intentionality may be in order before we – to use another metaphor – get stuck in a rut of constrained thinking about AI that will come back to bite us.

At a minimum, I would suggest that an appropriate framing for how we build advanced AI systems should accommodate bidirectionality (the user is also changed), transformation as intrinsic to capability (not a side effect to be prevented), and the possibility that the most consequential effects of human-AI interaction may be invisible from within a paradigm optimized for task performance.

It should also leave room for the possibility that the nature of human-AI relationships may itself evolve in ways that a control-oriented metaphor cannot accommodate. Especially if, as I would argue, we need to be thinking more about working in relationship with emerging AI technologies, rather than approaching them as something to be commanded and controlled.

For more on my exploration of the harness metaphor as applied to AI, check out the preprint here.

Can modern scholarship escape AI?

I wrote a paper …

Date: January 25, 2026 Source: https://www.futureofbeinghuman.com/p/can-modern-scholarship-escape-ai Newsletter: The Future of Being Human (Substack)

Is it possible to be an academic, a scientist, a scholar, in 2026, and not have AI impact your work in some way?

And, even more importantly for those scholars grappling with “AI Use” statements when they submit papers to journals and preprint platforms, how do you convey your use while retaining your academic dignity?

To explore this I flexed my considerable academic prowess and wrote a paper which was so radical that even arXiv rejected it!

(The PDF can be downloaded here)

OK, so maybe “paper” is a bit of a stretch here – and it’s not hard to see why it didn’t pass the arXiv bar (although it did take a couple of weeks for the moderators to come to a decision).[1]

But the point it makes is a very serious one – and extends to any domain where people are expected to articulate their use of AI clearly and concisely, including in classes being taught by professors grappling with the same challenges in their academic work: AI is now so ubiquitous that it is near-impossible to avoid its use in our professional lives.

Of course, this leaves the question dangling of what this means for academic and intellectual work when, even if you think you’re AI free, you are not.

Way more important than any of this though is that, if you are an academic struggling with what you put in your AI Use statement, you now have a template for this.

You’re welcome!

Notes

[1] The very considered – and considerate – response from arXiv Support was “Thank you for submitting your work to arXiv. We regret to inform you that arXiv’s moderators have determined that your submission will not be accepted and made public. In this case, our moderators have determined that your submission is a content type that arXiv does not accept.” Despite the joke, they do have standards to maintain!

Is AI a Cognitive Trojan Horse?

Could on-demand, seductively responsive and highly fluent AI models bypass our “epistemic vigilance” mechanisms, and present a novel cognitive risk?

Date: January 10, 2026 Source: https://www.futureofbeinghuman.com/p/is-ai-a-cognitive-trojan-horse Newsletter: The Future of Being Human (Substack)

Back in December, I asked attendees at the OEB25 conference (a global, cross-sector conference on digital learning) “Is AI a cognitive Trojan Horse?”

The question was meant to be a little playful, and to provoke discussion rather than make a point. But it also reflected growing concerns that the ease, speed and fluidity with which AI models provide us with information potentially circumvents our ability to assess and assimilate that information in critical and healthy ways.

This is the “cognitive Trojan Horse” in the question – the idea that emerging AI models are so appealing to us that it’s hard to resist inviting them into our cognitive lives, even though we still don’t know how they might potentially influence our thinking, our beliefs, our perceptions and understanding, and even how we behave.

It’s certainly a uncomfortable idea, and one that I suspect most people would instinctively push back on – especially as we’re increasingly depending on AI in a so many different ways, from how we learn and understand the world to how we make decisions, run organizations, and even find companionship.

Yet this is exactly what we would expect a cognitive Trojan Horse to look like – a gift with so much promise and potential that to question its use would seem churlish and backward.

It’s precisely because of this though that I think we should at least be asking questions about the potential unintended cognitive consequences of ubiquitous AI.

Especially if these tools are able to silently slip past the “epistemic vigilance” mechanisms we’ve evolved to protect us against potentially harmful cognitive influences.

Epistemic vigilance

Epistemic vigilance is the process by which we – or more precisely, our cognition – flag and assess communicated information that may lead to us being misinformed or deceived.

The concept was developed and extensively explored in a seminal paper by Dan Sperber and six colleagues in 2010.[1] In the paper they argue that “Humans depend massively on communication with others, but this leaves them open to the risk of being accidentally or intentionally misinformed. We claim that humans have a suite of cognitive mechanisms for epistemic vigilance to ensure that communication remains advantageous despite this risk.”[2]

At the heart of their work is the idea that human-human communication is vitally important for learning from an evolutionary perspective. And because of this, we have evolved mechanisms that are optimized for learning through communication by ensuring that cognitive overheads are as low as possible, while ensuring that learning efficiency is as high as possible.

The result is that we default to trusting what we receive when communicating with others. But if anything feels “off,” our epistemic vigilance mechanisms kick in and we begin to critically assess what we are receiving – and reject it if it doesn’t feel trustworthy.

It’s a model that has a lot in common with our immune system – a system that is always on the lookout for potentially harmful agents, but that only kicks in when it encounters something that looks or feels foreign. And of course, it’s a system that viruses are adept at circumventing by appearing to be “friendly” and “trustworthy” when they are, in fact, not.

There are, not surprisingly, many factors that determine when epistemic vigilance kicks in. But a lot of these revolve around our evolved ability to sense when something doesn’t feel trustworthy – the way something is communicated, the tone and nuance of the communication, the body language and micro expressions of the communicator, contextual information around who the communicator is, what their aims are, past experiences, and so on.

Of course, these feelings are, themselves, untrustworthy, as decades of behavioral science and research on cognitive biases have shown. But within the messiness of human society, epistemic vigilance tends to work.

But what if you throw a technology into the mix that upsets the status quo – a metaphorical brand new virus that we haven’t had the chance to adapt to?

This is where we potentially face what’s often referred to as an evolutionary mismatch – a situation where a new technology transcends our evolved abilities to safely and successfully navigate its potential impacts.

Because we are a technological species, and have been for millennia, such mismatches are actually quite commonplace. Well known examples include mismatches between evolved risk responses and how we instinctively respond to technologies such as synthetic chemicals, vaccines, and pretty much anything that’s new and novel.

Yet – and this is part of our superpower as humans – we are remarkably good at using our cognitive abilities and intelligence to compensate and adapt to such mismatches, despite having not evolved with risks directly associated with many of technologies we encounter in our lives.

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?

In effect, what if a new technology – and AI specifically in this case – does not trigger our epistemic vigilance mechanisms in the same ways that human-human communication does, and as a result has the ability to slip past our defenses undetected?

This is not mere speculation. While new research is absolutely needed into the potential for AI to act as a cognitive trojan horse by bypassing our epistemic vigilance mechanisms, there are sufficient indicators from associated areas of research that suggest a number of mechanisms by which this might occur.

These include (but are not limited to) processing fluency (our tendency to trust information that is delivered with a high degree of fluency), the role of “attractiveness” in communication (our willingness to trust a source of information that intrinsically appeal to us on multiple levels), speed and volume of information flow (where excessively high rates of information flow potentially overwhelms epistemic vigilance mechanisms), and what might be termed the “Intelligent User Trap” (where a smart user “knows” they are clever enough not to be fooled).

Processing fluency

Processing fluency refers to the ease, or the effort, that’s associated with mentally processing information. And when it comes to person-person communication, it affects how the person receiving information from someone else determines whether to trust it or not.

In effect, processing fluency forms part of a suite of epistemic vigilance mechanisms.

As Rolf Reber and Christian Unkelback described it a 2010 paper on processing fluency and judgments of truth:

“Processing fluency is defined as the subjective experience of ease with which a stimulus is processed. If a person cannot recognize the statement, this experienced ease is taken as information when judging the truth of a statement. If the statement can be processed easily, the person will conclude that the statement is true; if the statement is difficult to process, she concludes that the statement is not true.”[3]

In other words, communication that is clear, compelling, and takes little effort to understand, tends to be assumed to be true. It doesn’t trigger epistemic vigilance.

And of course, AI apps like ChatGPT, Claude, Perplexity, and others, are supremely adept at creating responses that are clear, compelling, and take little effort to understand. These are models that distill the very best of highly effective human communication into their core, and reflect it in how they engage with users.

In effect, large language model-based AIs are optimized for processing fluency, and as a result are primed to slip by our epistemic vigilance mechanisms.

Attractiveness

Beyond processing fluency, we tend to treat received information as more trustworthy if it comes from someone we like, or who we warm to, or who seems friendly toward us. And this extends to how any communication is crafted and delivered.

Here, there is extensive research showing how someone who is perceived to be warm and competent as a communicator is more likely to engender trust.[4] And there are emerging indications that this also applies to how we respond to AI apps.[5]

It turns out we tend to trust people and AI chatbots more – in other words they are less likely to trigger our epistemic vigilance mechanisms – if they are perceived to be warm and competent.

And as most AI platforms are exquisitely good at this as a result of how they work and how they’ve been trained, there is a tendency to trust them – even when we’re warned not to.

But reading across multiple fields of study, my sense is that there’s more to this than just warmth and competence: some form of “attractiveness” that makes us want to trust the AI’s we’re using that is a combination of how they engage with us, the character they convey, how empathetic and attentive they seem, and probably a lot more.

These are all characteristics and behaviors that contribute to why we find someone attractive and want to spend time with them – and want to trust them. And there’s growing evidence that AI models are very good indeed at emulating these characteristics and behaviors.

You only need to see the growing popularity of AI companions to get a sense of how easy it is for people to form a very human-like attachment to their AI assistants. And it’s quite startling how many users of platforms like ChatGPT develop a personal and trusting relationship with their AI, even to the extent of naming and gendering it (or in some cases respecting the AI’s own choice of name and gender).

If, as I suspect, there is a multidimensional type of “attractiveness” that AI models are exceptionally good at emulating, this may well be another factor that allows them to slip into our cognitive processes without tripping our epistemic defenses.

Speed and volume

And then there’s the speed with which AI models can package and communicate information, and the sheer volume of information they are able to deliver – all with a high degree of fluency.

We’ve evolved as a species to handle a relatively slow rate of information delivery via various forms of communication – not just the speed with which words are delivered to us, but the speed with which ideas, concepts, analysis, and perspectives are delivered.

Modern communication media have, of course, accelerated this a little, although we are still bandwidth-limited by our cognitive ability to absorb information.

But what if we had the means to package new information in such a way that even the most complex of ideas slipped into our minds like a freshly shucked oyster slipping down our throat, bypassing the need to think hard about them.

To an extent, this is what we’re beginning to see with emerging AI apps. And it results from a combination of fluency, attractiveness, and an ability to research and synthesize information at a scale and speed that lies far beyond mere human capabilities.

This is part and parcel of a growing trend in cognitive offloading where users will literally “offload” thinking and research tasks to AI bots, and then assimilate the resulting compressed information. And it’s easy to see why the trend exists: if you can offload every question, idea, thought, onto a suite of trusted AI bots and then “upload” their fluent and “attractive” summaries, why would you not use this cognitive superpower to your advantage?

And yet, research is already indicating that cognitive offloading can reduce critical thinking.[6]

To make things more complicated, cognitive offloading is highly scalable. Why use one session with ChatGPT when you can simultaneously be asking questions within multiple sessions? Why just use ChatGPT when you can have an army of AI engines all working for you simultaneously from Anthropic, Google, Meta, and beyond? And why limit yourself to just dipping into your extended AI mind occasionally when you can have these AI analysts and advisors on hand 24/7?

In effect, the rate at which we are now able to receive the most informative, attractive, fluent communications from AI is only limited by our choices around when and where we use it. And in a world where we are being told that it’s the AI-augmented that will inherit the earth, the temptation is to go full-on artificial intelligence.

The only problem is that it’s doubtful that our epistemic vigilance mechanisms are up to the task of coping with the resulting flow of information – and this is likely tied to the observed reduction in critical thinking with cognitive offloading.[7]

Epistemic vigilance is a costly cognitive process. It requires holding information in working memory while evaluating it, generating alternative hypotheses, checking what we’re receiving against what we know (or believe), assessing source characteristics, and much more. And if the flow of incoming information exceeds our capacity to do this, it potentially forces an incredibly tough choice on us: throttle the flow and give up the promised benefits, or go with the flow and give up our cognitive checks and balances.

Of course AI makes the choice easier by making the seeming benefits feel seductively compelling – further fooling our epistemic vigilance defenses.

The intelligent user trap

Finally – at least in this limited list – is the challenge of the “intelligent user trap.”

This is somewhat speculative, although there is evidence to support it – including work from Dan Kahan and colleagues which indicates that more educated individuals are more adept at justifying beliefs that are not supported by evidence.[8]

The theory goes that more intelligent users tend to be more curious (and so get a bigger “hit” from new information); they tend to process information faster, and so are less attuned to the dangers of speed and volume overload; they trust their judgement, and so are less likely to question it; and they (at least in some cases) value efficiency and so are less likely to slow the rate of information being received.

They also tend to have an oversized ability to use their intelligence to justify their beliefs and actions – which brings us back to Dan’s work.

In other words, the very cognitive capacities that make them “smart” also make them better receivers of the AI’s output stream – and worse evaluators of it.

Another potential epistemic vigilance suppressor in other words.

So should we be worried?

So, is AI a cognitive Trojan Horse, or could it turn out to be?

This is an admittedly limited analysis, and there’s clearly a need for a lot more research here. At the same time it’s telling that a search for peer review papers on epistemic vigilance and AI only returns (as of writing) seven papers on the database SCOPUS, and a couple more on preprint archives like arXiv. And a similar search on AI and the concept of a cognitive Trojan Horse returns no papers at all.

And yet the science behind factors that may reduce, or even completely bypass, the effectiveness of our epistemic defenses is there. And in many cases, emerging AI tools and platforms are showing capabilities that align with many of these factors.

As a result, there’s a chance that we may be developing technologies that we do not have the cognitive defense mechanisms to resist, and that we are cognitively predisposed to trust.

Of course, there’s also the possibility that we have all of the cognitive abilities we need to use AI wisely and effectively. And I suspect that skeptical readers will already be thinking: “But I know I’m talking to a machine, so my vigilance is already up.”

However, research actually suggests the opposite – that anthropomorphic fluency (the ability of AI apps to emulate the best human you’ve ever met!) triggers social cognition circuits regardless of explicit awareness. And the more human-like the interaction feels, the more trust resilience it generates.[9]

And even if there’s only a small chance that we are encouraging people to incorporate technologies into their lives that could have far-reaching cognitive implications, surely we should be asking critical questions around potential risks, and carrying out research to better-understand and navigate these risks.

Unless, that is, the AI cognitive Trojan horse has already delivered its payload, and everyone’s too enamored by the promise of AI as a result to even think about the potential downsides …

UPDATE: After writing this I did more digging into the intersection between conversational AI and epistemic vigilance. Read more here: I cracked and wrote an academic paper using AI. Here’s what I learned …**

Notes

[1] Sperber, D., F. Clement, C. Heintz, O. Mascaro, H. Mercier, G. Origgi and D. Wilson (2010). “Epistemic vigilance.” Mind and Language 25(4): 359-393. https://dan.sperber.fr/wp-content/uploads/EpistemicVigilance.pdf

[2] There’s a small but rapidly growing literature around AI and epistemic vigilance. See for instance Galindez-Acosta, J. S. and J. J. Giraldo-Huertas (2025). Trust in AI emerges from distrust in humans: A machine learning study on decision-making guidance. https://doi.org/10.48550/arXiv.2511.16769

[3] Reber, R. and C. Unkelbach (2010). “The Epistemic Status of Processing Fluency as Source for Judgments of Truth.” Review of Philosophy and Psychology 1(4): 563-581. https://doi.org/10.1007/s13164-010-0039-7

[4] See for instance Fiske, S. T., A. J. C. Cuddy and P. Glick (2007). “Universal dimensions of social cognition: warmth and competence.” Trends in Cognitive Sciences 11(2): 77-83. https://doi.org/10.1016/j.tics.2006.11.005

[5] Here the literature is evolving and a little disperse, but a useful starting point is Hernandez, I. and A. Chekili (2024). “The silicon service spectrum: warmth and competence explain people’s preferences for AI assistants.” Frontiers in Social Psychology 2. https://doi.org/10.3389/frsps.2024.1396533

[6] For instance, see Gerlich, M. (2025). “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking.” Societies 15(1). https://doi.org/10.3390/soc15010006

[7] It’s worth noting here that research does not show a general causative link between cognitive offloading and reduced critical thinking, and it is likely that there are use cases where it’s possible to offload and continue to assess received information critically. But intuitively it’s easy to imagine a tradeoff between volume of information and critical assessment – especially when that information is designed to be consumed easily and fast.

[8] See, for instance, Kahan, D. M., E. Peters, E. C. Dawson and P. Slovic (2017). “Motivated numeracy and enlightened self-government.” Behavioural Public Policy 1(1): 54-86 (https://doi.org/10.1017/bpp.2016.2) and Kahan, D. M., E. Peters, M. Wittlin, P. Slovic, L. L. Ouellette, D. Braman and G. Mandel (2012). “The polarizing impact of science literacy and numeracy on perceived climate change risks.” Nature Climate Change 2: 732-735 (https://doi.org/10.1038/nclimate1547)

[9] See, for instance, de Visser, E. J., S. S. Monfort, R. McKendrick, M. A. B. Smith, P. E. McKnight, F. Krueger and R. Parasuraman (2016). “Almost human: Anthropomorphism increases trust resilience in cognitive agents.” Journal of Experimental Psychology: Applied 22(3): 331-349. http://doi.org/10.1037/xap0000092

Holding on to our humanity in an age of AI

AIs are becoming startlingly good at emulating what we do. But what happens when they start to influence who we are and how we behave?

Date: August 31, 2025 Source: https://www.futureofbeinghuman.com/p/holding-on-to-our-humanity-age-of-ai Newsletter: The Future of Being Human (Substack)

A couple of weeks ago the CEO of Microsoft AI, Mustafa Suleyman, wrote of the dangers of becoming over-attached to artificial intelligence apps to the point where they potentially impact a user’s behavior, beliefs, and even health. On the heels of this, it was heart breaking to read a few days ago about the tragic case of Adam Raine who took his own life at the age of 16, seemingly influenced by ChatGPT.

Suleyman isn’t the first to raise such concerns and, very sadly, Adam won’t to be the last case of harm associated with AI use. Both are products of a technology that is capable of emulating our deepest human traits and mirroring what we look for in meaningful relationships.

Suleyman’s essay and Adam’s death reflect growing concerns around what has been dubbed “AI psychosis” – a tendency for AI apps to reinforce and amplify unhealthy beliefs and behaviors in some people. It’s a term that is easy to apply (usually without much thought) to those we consider to be “vulnerable.” But I suspect that we all have some degree of vulnerability here.

While AI psychosis is both ill defined and increasingly over-used as a phrase, it highlights a challenge that we’ve never had to face before as a species, and one that – as a result – we have little natural resistance to: What happens when machines are capable of triggering cognitive, emotional, and behavioral responses in us that were previously exclusively the domain of human relationships?

And – more worryingly – what happens when these machines are capable of using these responses to intentionally alter what and how we think, how we behave, and how we understand and respond to the world around us?

Suleyman captures this risk through the idea of Seemingly Conscious AI, or SCAI: the danger of conflating an AI’s ability to act as if it’s conscious with the assumption that it is. From his perspective, this is something that will be possible in the very near future, and an AI “illusion” that could lead to people inappropriately advocating (amongst other things) for AI rights.

This seems a far cry from AI-assisted suicide ideation. But Suleyman’s essay is framed in broader questions around the need to grapple with the societal impact of technologies which have the potential to fundamentally change our sense of personhood and society – essentially who we are. And this is where the the idea of Seemingly Conscious AI begins to intersect with human-AI relationships.

Suleyman explicitly writes about how consciousness “sits at the very heart of human civilization, our sense of ourselves and others, our culture, our politics, our law, and everything in between.” And while I don’t want to appear guilty here of conflating this expansive vision of near-future AI abilities with nearer term influences on human behavior, the reality is that Suleyman’s SCAI is an extension of what we are already seeing: AIs that are capable of inadvertently or intentionally eliciting unhealthy responses that are more usually associated with human-human interactions, and placing users at risk as a result.

This is a potential risk that OpenAI was fast to admit this past week as details of the Adam Raine case emerged. In a blog post describing what the company is doing to safeguard against undue influence with vulnerable users, OpenAI noted that “Even with [existing] safeguards, there have been moments when our systems did not behave as intended in sensitive situations.” OpenAI is working hard to patch these unintended behaviors, but given that their origins and emergence is not fully understood, it’s hard at this point to know how successful they will be.

Despite these uncertainties though, tragedies like Adam Raine’s and others are likely to lead to calls for greater care, greater responsibility, and greater oversight around AI development and use. And I hope they succeed. At a time when there’s a headlong rush to be at the front of the AI revolution – whether as a first adopter, leading developer, or simply as a branding exercise – much more care needs to be taken to ensure that the safety and wellbeing of users is placed far above speed and bragging rights. Especially, but far from exclusively, where young children and teens are involved.

And yet, despite this need for care over speed, we face a deeply uncomfortable reality here: The AI genie is out of the bottle, and we cannot simply put it back in or command it to do what we want.

The alleged behavior of ChatGPT that led to Adam Raine’s death reflects an emergent set of properties in the AI model he used that could most likely have been better-managed, but probably not eliminated entirely. This is very different from apps that are intentionally designed to play on our cognitive biases and vulnerabilities to elicit particular responses – and these, I would argue, can and should be regulated far more than they currently are.

But even with the best of intentions, we are creating technologies that are primed to press our cognitive buttons and pull our psychological levers in ways we don’t fully understand. And because these capabilities are deeply embedded in the fabric of how current AI systems work, we cannot eliminate them simply by saying they should not exist.

To make things even harder, AI development is now in the hands of individuals and organizations around the world where human curiosity and the lure of value creation (or power and greed if you’re feeling cynical) are driving innovation in ways that cannot easily be predicted and controlled. Because of this, well-meaning calls for regulation, governance, and responsible innovation are likely to run into challenges as emerging AI systems continue to have increasing ability to influence and impact users in unhealthy and potentially dangerous ways.

This doesn’t mean that efforts along these fronts should be scaled back – far from it. But I would argue that they need to be augmented with efforts that bake the ability to thrive with advanced AI into the very fabric of the future we are building. And here, two things are going to be increasingly important: The ability to channel AI innovation toward more human-centric futures (much as a flood can’t be halted, but it can be directed); and developing the means to ensure that everyone has the understanding and abilities necessary to thrive in an AI future without becoming a victim of it.

Admittedly, these may feel rather bland compared to calls for new regulations or to stop developing and using AI. But in the long run they represent part of a portfolio of approaches that are far more likely to lead to positive AI futures as they build long-term capacity to live, work, and flourish with technologies that emulate human capabilities and behaviors, rather than simply trying to control them.

Of course, transitioning to such a future will be a challenge in itself. And sadly there will probably be more tragedies along the way.

There are, of course, things we can and should be doing now to avoid these – working harder on safety checks and protocols before releases; exploring and responding to potential consequences beyond quarterly gains; resisting the temptation to move fast and ethics-wash possible impacts; engaging with people who actually know about responsible innovation rather than people simply claim they know; and probably not releasing AI apps that are cynically designed to profit off manipulating human behavior.

But there are also things we can all be doing to proactively channel AI toward human-centric futures, and to ensure we are able to benefit from AI rather than being diminished and subsumed by it.

And that’s probably my biggest takeaway from the past few days: that we need to get better – and fast – at learning how to hold onto and celebrate our humanity in an age of AI where we’re facing technologies that can enhance who we are beyond our wildest dreams, but that also have the capacity to rob us of this.

This isn’t just a problem for companies to fix, or for policy makers to govern. It’s a challenge – and an opportunity – that each one of us has a role to play in as we grapple with being human in the AI future that’s emerging.

Afterword

I wasn’t sure whether I’d add this afterword or not when writing this article, as I wanted it to focus on the growing challenges around human-AI interactions in the wake of Adam Raine’s death. In the end I decided to though as the question of what it means to be human in a world where AI emulates and mirrors so much of what makes us us has been on my mind a lot over the past several months.

One consequence of this focus is that I have been working on a new tools-based book on being human in an age of AI with VC and AI Salon-founder Jeff Abbot. The book is still largely under wraps, but will be published in a few weeks’ time.

I’ll be writing more about it closer to then. But I thought it worth mentioning here as Suleyman’s essay, Adam’s death, rising concerns around AI psychosis, and a growing sense of uncertainty over what AI means to the future of who we are, all reflect the reality that AI presents opportunities and challenges that are unlike anything we’ve experienced before as a species. And navigating the emerging technology transition will depend in part on us developing the insights and tools that not only prevent us from losing ourselves in an age of AI, but imbue us with the perspectives and skills to flourish in it.

This is precisely what Jeff and I write address in the book. It’s something that we both see an increasingly urgent need for, and as a result have pulled out all the stops to make it available as soon as we possibly can.

If you subscribe to this Substack newsletter, look out for more information coming shortly!

What does responsible innovation mean in an age of accelerating AI?

The new AI 2027 scenario suggests artificial intelligence may outpace our ability to develop it responsibly. How seriously should we take this?

Date: April 6, 2025 Source: https://www.futureofbeinghuman.com/p/responsible-innovation-and-ai-acceleration Newsletter: The Future of Being Human (Substack)

Top takeaways (generated by Perplexity)

A new speculative scenario on AI futures is currently doing the rounds, and attracting a lot of attention. AI 2027 maps out what its authors consider to be plausible near-term futures for artificial intelligence leading to the emergence of superintelligence by 2027, followed by radical shifts in the world order.

If this sounds like science fiction to you, you wouldn’t be the only one to think this. Yet despite its highly speculative nature, the AI 2027 scenario (or, to be more accurate, scenarios, as this is something of a “choose your own ending” story) is sufficiently grounded in current trends and emerging capabilities to provide serious pause for thought.

It also reflects at least some of the thinking of growing number of leaders and developers at the cutting edge of AI.

The scenario was published just as I was heading into a workshop on AI and responsible innovation this past week, and so the question of how we ensure artificial intelligence is developed and managed appropriately was on my mind. It’s not surprising therefore that my first reaction on reading AI 2027 was to worry that, even if the projections represent an edge case, we might be facing a near term future where current efforts to develop artificial intelligence responsibly seem futile.

I hope they are not — and the scenario has already attracted considerable pushback for being too alarmist. Yet it’s also been cautiously welcomed by some big names in cutting edge AI as a salutary warning of where we may be heading.

The scenario depends on a number of assumptions — all of which can be contested, but nevertheless are useful for exploring potential (if not necessarily likely) near term AI futures.

These include:

Each one of these has its flaws. Yet they are not unreasonable as a starting point for imagining edge case scenarios.

And as the AI 2027 scenario shows, they quickly lead to near-term possibilities that represent a tipping point in what the future looks like.

They also challenge many of the ideas currently circulating around how to govern AI, and how to ensure its socially responsible development and use — many of which depend on processes that are constrained by human timescales that are rather longer than those associated with intelligent machines.

And this is what got me worrying about the futility of matching responsible innovation processes that can take years, to a period of AI acceleration where a lag of even a month in the development cycle might mean the difference between abject failure and world domination.

What worries me just as much though is that nothing about how we think, how we plan for the future, or how we develop approaches to ensuring better futures, is geared toward exponential advances that happen over months rather than years.

And this means that if, unlikely as I hope it is, something like the AI 2027 scenario plays out, we would most likely fail to recognize it — or would actively deny it — until it was too late.

And all because we are really bad at wrapping our heads around rapid exponential growth.

For anyone who’s watched the Dan Brown movie Inferno, there’s a deeply flawed but nevertheless compelling illustration of how hard we find this toward the beginning of the film.

The illustration draws on a thought experiment developed by Al Bartlett in 1978, designed to illustrate exponential growth in a finite environment.

The thought experiment asks: if you have a beaker which, at 11:00 PM, has one bacterium in it, and the bacterium and its progeny divide once every minute so they fill the beaker by 12:00 AM, at what time is the beaker half full?

The answer — assuming everything else is equal — is 11:59 PM. One minute to midnight.

The illustration would never work in real life as resource constraints would slow or halt the exponential growth. But it is a good illustration of how hard it is for us as individuals or as a society to plan for exponential growth — especially when it occurs over timescales much shorter than those associated with collective human actions.

In essence, AI 2027 poses a similar question: what happens when AI development is on an exponential growth path and we simply cannot accept or even see this until it’s metaphorically one minute to midnight?

And getting back to AI and responsible innovation, this forces the question: what happens if we’re still planning for the world as it was at 11:00 PM when we get to the AI equivalent of 11:59 PM?

I suspect that, to many, this will feel like an intellectual exercise and no more. But this is precisely the point of the illustration — it always will feel like an intellectual exercise until it’s too late.

If this is the case, how should we be thinking about responsible development in an age of accelerating AI?

The first step I suspect is to take a deep breath and move back from speculation to firmer ground. AI 2027 is speculation — no more. And yet it does force the question of how we might think about responsible innovation and AI, just on the off chance that there’s a sliver of truth here.

And this is why I found myself turning to a mode of working that I’ve been finding increasingly useful recently — engaging with OpenAI’s o1-pro model to develop nuanced and widely informed insights into complex questions.

In this case I worked with o1-pro on a deep dive into the intersection between the scenario set out in AI 2027 and current approaches to responsible innovation/AI, and the limitations of responsible innovation in the event of rapid AI acceleration — and especially the possibility of an accelerated AI “arms race.”

The resulting report is long, coming in at over 40 pages. It also comes with the usual caveat that important information in it should be double checked (although part of my process is actively engaging with o1-pro in the research and writing process, and evaluating and editing the final report where necessary).

Despite the caveat, the resulting report is sufficiently inclusive and insightful that I would consider it essential reading for anyone looking for a nuanced perspective on responsible innovation/responsible AI in the light of possible rapid AI acceleration.

And because of this I’ve included the full report below (bar the final annex — which looks at the potential perspectives and biases the authors bring to AI 2027).

For anyone familiar with emerging thinking around responsible AI there won’t be too many surprises here — at least in the first part of the report, which represents a very measured response to the predictions in AI 2027. Even so, the way the report draws together and synthesizes the current state of understanding is useful.

Where it gets more useful is in its exploration in Annex A of how responsible innovation/AI might fare if we face an AI acceleration arms race between the US and China (not that well is the short answer). It’s also well worth reading Annex B which considers perspectives that are important but are under-represented in the initial report.

Given some of the online critique of the AI 2027 scenario’s authors as well as their ideologies and perspectives, I also asked o1-pro to compile an assessment of their backgrounds, perspectives, and controversies surrounding them, so that o1-pro’s analysis can be contextualized. This can be found in Annex C in the downloadable report.

The full report can be downloaded below. I’ve also included the full report (minus Annex C) in this post. It makes for a rather long article, so apologies for that. But at the same time, I think the content is relevant enough to risk some frustration over it’s length!Ri And Rai In An Era Of Accelerated Ai Development665KB ∙ PDF fileDownloadDownload

Note: The original post includes a 40-page report on “Responsible Innovation and Responsible AI in an Era of Accelerated AI Development,” produced through deep collaboration with OpenAI’s o1-pro model. The full report can be accessed at the source URL above.

AI in Higher Education: Students need playgrounds, not playpens

AI capabilities are moving so fast, and the implications are so profound, that we restrict the ability of students to learn through curiosity, experimentation, and hands-on experience at our peril.

Date: March 15, 2025 Source: https://www.futureofbeinghuman.com/p/ai-playgrounds-in-higher-education Newsletter: The Future of Being Human (Substack)

This is not the Substack I set out to write this morning. I was intending to post about a conversation I recently had about the future of higher education with Brian Piper on the AI for U podcast.[1] But as I started writing, the article morphed into a piece about playgrounds and playpens – and AI.

Playgrounds and playpens, it turns out, are a powerful metaphor for thinking about AI and education. And they’re a metaphor I’ve been thinking and talking about for some time now.

Inspired by Mitch Resnick at MIT and the work of Marina Umaschi Bers which, in turn, inspired him, I wrote about the metaphor last year in the broader context of undergraduate education. Since then I’ve become increasingly interested in how it opens up ways of thinking about approaches to AI and learning/education where the technology is challenging nearly every aspect of not only how we teach and what we teach, but why we teach.

I’ll come back to the broader conversation with Brian in a follow-up post. But for now I’ll follow the story and dive deeper into metaphor of playgrounds and playpens in education, and how it’s potentially useful in thinking about artificial intelligence.

As a starting point, it’s worth taking a moment to think about why AI presents such a unique challenge and opportunity to learning and education – especially in universities.

Since ChatGPT hit the public scene in 2022, generative AI has impacted nearly every part of higher education. In some cases this has led to new AI tools being embraced by educators and administrators. In others there’s been active resistance to AI in any form being used in teaching or by students. And of course there are the continuing fears that AI makes it easier for students to cheat, or to become lazy learners, or simply not to retain understanding that comes from using AI as a learning aid.

But whether you’re an AI optimist, an AI pessimist, or simply in denial, it’s nearly impossible to ignore the reality that AI is having a substantial and growing impact on learning and education.

At the same time, we’re seeing a large gap in understanding between where the leading edge of AI capabilities are, and where educators think they are. As a result, there’s still a tendency to think of AI as a tool that can complete assignments or write essays, or create personalized learning environments, or simply act as a form of Google on steroids.

Yet the reality is much more complex – and much more transformative. Because advanced AI models are becoming increasingly capable of simulating aspects of ourselves that define us at a fundamental level – such as the ability to think, to reason, and to solve problems with agency – they stand apart from pretty much any previous technology or tool that we’ve created.[2] And because of this, they cannot be approached as just another technology to teach students about, or another tool to enhance traditional approaches to education.

Rather, we’re seeing a growing need for completely new ways of thinking about the intersection between learning, education, and AI – especially where educators are sometimes (perhaps often) further behind the curve than the students they’re trying to educate.

And this is where the perspective shift inherent in moving from a playpen to a playground mentality becomes useful – and important.

It’s something of an oversimplification, but curiosity and problem-solving are close to the heart of how we learn.[3] These innately human attributes are often used to great effect by educators as they help students acquire specific skills and understanding.

More often than not though, learning environments that are grounded in curiosity and problem solving are constrained. They draw on and leverage these traits, but they are are often explicitly designed to ensure students follow a carefully curated path to achieving specific goals and outcomes.

They are, in effect, metaphorical playpens. Some creative play and problem solving is allowed. But just as a playpen wraps a young child in constraining walls and presents them with a few select toys to channel their attention, these metaphorical learning playpens are designed to channel and restrict learning along narrow lines.

It’s a mode of teaching that works well where the purpose and goals are clear, the journey is well-trod, and the desired outcomes are easily assessed. But it quickly falls apart where the the goals and purpose are unclear, the journey is breaking new ground, and no-one’s quite sure what the desired outcomes are – never mind how they should be assessed.

This is very much like the situation we find ourselves in with AI. This is a technology where the speed of development is far outstripping the far more sedate pace of pedagogical reform; where informal exploration by students means they already know more than their instructors in many cases; and where we’re still struggling to grasp the full implications of machines that emulate and exceed some of the most fundamental aspects of what it means to be human.[4]

This is where playpen-style learning environments – which still assumes that expertise and authority lies with the instructor – run out of steam very fast. And it’s also where the metaphor of the playground becomes interesting.

In contrast to a playpen, a playground is relatively unbounded. There are rules of course – don’t be stupid for instance, be kind, don’t spoil things for others. But the idea of the playground is to empower users to learn from experience – and from each other; to flex their imagination; to be creative; to try new things; to make mistakes; to ask questions; and to creatively solve problems.

Playgrounds are environments that open up rather than constrain options, and that allow curiosity and creativity to be transformed into invention and innovation. Yet they are not totally without bounds. Rather, playgrounds are carefully designed and curated to stimulate curiosity, imagination, and play. And through play, learning.

And this is where the analogy comes back to AI and higher education. Rather than trying to control how students explore, use, and think about AI, I suspect we should be giving them more opportunities to explore and play – to make mistakes, discover new possibilities, to invent, and to innovate. And we should perhaps be thinking of educators – in this context at least – as guides, mentors, and fellow-travelers, rather than the fount of all knowledge.

This is, of course, a risky strategy – especially as it means relinquishing some control over the learning environment. But given how rapidly AI is advancing, the greater danger I suspect is in holding students back because of misplaced ideas about how and what they should learn.

This becomes all the more important where any playpen-like constraints placed around AI in education reflect misconceptions around how AI is beginning to transform our lives. As I noted above, AI is no longer simply about generating text in respond to a prompt – and I’m not sure it ever was. Rather, it’s a collection of technologies that are deeply and fundamentally changing what we do, where we live, and even who we are – in ways that no-one fully understands yet.

As a result, we are all on a journey together as we explore and learn how to develop and use rapidly advancing AI capabilities to enhance and improve lives in ways that lie far beyond conventional thinking and understanding. And this includes educators as well as students. And one of the ways we can approach this is to adopt a playground mindset rather than a playpen attitude to AI and learning – and provide students with the space and opportunities to flex their curiosity and hone their problem-solving skills without unnecessary constraints or restraints.

What this might look like is, of course, part of the journey. I suspect there are as many types of AI learning playgrounds as there are people imagining and creating them. But I suspect that we need to get serious about at least the following four areas:

I’m interested in whether anyone is already creating spaces like this for students to learn through play with AI in higher education. I hope they are, as otherwise it’s hard to imagine how we’ll make serious progress in equipping our students to thrive in an AI future.

And please do look out for the follow-on post where I’ll be focusing more broadly on my conversation with Brian while also “playing” with AI in my own Substack playground!

Notes

[1] That original post that I’d intended to write is now readable here: https://futureofbeinghuman.com/p/rethinking-higher-education-in-the-age-of-ai

[2] Mark Daley has a great article out on how AI is categorically different from any preceding technology. As he writes, “Steam engines didn’t challenge our claim to unique intelligence. Telegraph wires didn’t ask us to rethink the nature of human thought. Electricity didn’t insinuate that it might rival or surpass our creative spark. The changes AI brings are not limited to external upgrades in productivity or convenience; they reach inside, prodding us to question the crux of human identity.” AI isn’t running water: It’s something different. March 13, 2025

[3] They are also core to what makes us “us.”

[4] This is beyond this particular article, but there is a categorical error I believe in treating a technology that fundamentally challenges our thinking about who we are, and the very nature of what it means to be human, as a leaning aid.

The Artisanal Intellectual in the Age of AI

How is advanced artificial intelligence forcing a rethink of the value of human intellectual labor?

Date: February 16, 2025 Source: https://www.futureofbeinghuman.com/p/the-artisanal-intellectual-in-the-age-of-ai Newsletter: The Future of Being Human (Substack)

I must confess that, since getting my hands on OpenAI’s Deep Research (not to be confused with the just-released Deep Research feature on Perplexity), I’ve been intrigued by how it’s forcing me to rethink what it means to be someone who makes a living by thinking.

Last week that led to me exploring Deep Research’s ability to research and write a complete PhD dissertation – unaided, apart from some final formatting. While the resulting ~400 page document fell short of matching up to a true scholarly dissertation it was, nevertheless, impressive.

But AI-only “intellectual labor” is – at least at the moment – less interesting to me than what a person and a powerful reasoning/research AI might be able to achieve together.

And so I set myself the task this week of seeing what’s possible when I combine my own “intellectual labor” with OpenAI’s Deep Research.

The result – and my notes on the process – can be found below, along with an intriguing extra if you get to the end of the notes.

Of course, people have been augmenting their own abilities with generative AI for ages now, so you might be thinking there’s nothing new here. But I would beg to disagree.

For the first time in my experience as a pretty well established and respected academic, it feels like AI is capable of extending what researchers, academics and scholars can achieve beyond anything we’ve seen before. And this is what I was interested in exploring.

Building on last week’s article, I decided – somewhat ironically – to focus on a concept I touched on in the footnotes of last week’s article: that of the “artisanal intellectual.”

As I wrote back then,

I think we do have to grapple with the very real possibility that AI is becoming a powerful catalyst and accelerant in research that will relegate human-only research to a class of artisanal intellectualism where the primary purpose is the provenance and process, not the product.

The idea of the artisanal intellectual was a bit of a throwaway at the time. But since then I’ve been finding myself increasingly intrigued by it – including mentioning it in a number of panel discussions and in the latest episode of the Modem Futura podcast.

What made it particularly relevant to the current exercise is that it’s not a term that’s been widely used or written about in the past. And so Deep Research was going to have its work cut out as it researched it.

My process – and this is covered in the Notes below – was to get Deep Research to take the first pass at researching and writing an article, and then for me to go in and make line by line edits, checking, adjusting, and adding in citations along the way.

The result is substantially better than anything I could have pulled together on my own in the time I had. And the final article is definitely better than Deep Research’s initial draft – and what the AI could have produced unaided.

I’d argue that the collaboration is also genuinely generative as it explores and expands understanding around what it might mean to be an artisanal intellectual in an age of AI.

This is, admittedly, a rather quick and dirty demonstration of what can be achieved through collaborating with the cutting edge of AI. But it’s also one that would not have been possible a mere couple of weeks ago.

It’s also a process that has significantly advanced my thinking around how an academic working with the cutting edge of AI can far surpass what either could achieve on their own.

Let me know what you think of the experiment and the ideas, and do check out the Notes if you’re interested in the process and where it led.

The Artisanal Intellectual in the Age of AI

There’s a growing sense that the cutting edge of AI is beginning to upend ideas around what it means to be a scholar or an intellectual – or an academic for that matter – that have persisted for millennia. Our ability as humans to think, to reason, to develop and explore abstract ideas, and to conceive of ways of understanding the world beyond what we can experience directly, has been core to defining who and what we are almost since the dawn of homo sapiens.

And yet the emergence of AI models that simulate – and may soon exceed – human-level thinking and reasoning – is beginning to challenge the value of human-only intellectual endeavors. Reasoning models like OpenAI’s o1 and DeepSeek’s R3 have only been out a matter of months yet are already being used to augment research and scholarship in potentially transformative ways. And with the release of OpenAI’s latest reasoning model Deep Research, it feels like we’re on an accelerating path toward human intellectual exceptionalism being a somewhat outmoded idea.

This possibility got me thinking recently about the idea of the “artisanal intellectual” – the possibility that human-only intellectual pursuits may soon be relegated to a realm of thinking that is more valued for its human provenance than its practical use. The same concept is easily applied to the idea of the artisanal scholar or artisanal academic – although I suspect that in many people’s minds most academics and scholars are already firmly in the “artisanal” camp!

The idea that what was once considered to be the pinnacle of human achievement becoming an artisanal activity in an age of AI is an intriguing one. But it’s also one that deserves framing within a much broader historical context as, useful as it might be as AI capabilities continue to advance, it’s not entirely new.

The origins of intellectual craftsmanship

The idea of the intellectual as a craftsman has deep roots, even if the term “artisanal intellectual” itself is not widely used. In classical philosophy, thinkers distinguished between different kinds of knowledge – episteme (theoretical understanding) and techne (craft or art) – recognizing that certain wisdom comes from skilled practice. Centuries later, scholars like C. Wright Mills explicitly described scholarship as a craft. In his 1959 essay “On Intellectual Craftsmanship,” Mills argued that true scholarship is “a choice of how to live as well as a choice of career”, an ethos of disciplined habits and personal commitment (Mills 2000). He portrayed the scholar as an “intellectual workman” who “forms his own self as he works towards the perfection of his craft.” This view positioned research and writing not just as tasks or outputs, but as a way of life akin to the artisan honing a skill.

This is very much reflected in Dominic Boyer’s thinking in his essay “The Medium of Foucault in Anthropology” where he defines an artisanal intellectual – possibly the first time the phrase is formally used – as a “knowledge-maker who has a relatively immediate and sensuous relationship to the epistemic forms s/he is producing instead of relations that are strongly capitalized, in other words, austere and market-mediated” (Boyer 2002).

Approaching intellectual traditions as craft more broadly, many such traditions reflect artisanal qualities. Medieval monastic scribes, for example, treated manuscript copying and illumination as a meticulous craft, embedding personal artistry into scholarly preservation of knowledge. Early modern scientists often built their own instruments and conducted hands-on experiments, blending manual skill with intellectual inquiry – a model of “knowledge by doing.” Michael Polanyi, a 20th-century philosopher of science, highlighted that much of what experts know is tacit and learned through apprenticeship, not through formulas (Polanyi 1966). He famously observed that “we can know more than we can tell” emphasizing that skilled understanding (like riding a bicycle or diagnosing a patient) often can’t be fully captured in explicit rules. Polanyi’s insight underscore the artisanal aspect of knowledge: master practitioners developing intuition and know-how that defies complete codification. In short, long before AI, thinkers recognized that intellectual work involves personal craftsmanship – the slow accumulation of judgment, creativity, and tacit skill.

The notion of the artisanal intellectual also resonates with broader philosophical critiques of technology. In 1954, Martin Heidegger warned that modern technology could enframe humanity, making us view the world as mere resource or “standing-reserve” (Heidegger 1954 (Translated 1977 by William Lovitt)). He contrasted this with more authentic ways of “bringing-forth” truth, akin to a craftsman’s revealing of meaning through work. Today, scholars draw on Heidegger’s insight to caution against an overly mechanized approach to knowledge. If research becomes fully automated, we risk losing the “knowledge-seeking journey itself,” the reflective process that gives scholarship its human depth (Leahy and Maynard 2025). This concern echoes through the decades: from the 19th-century Arts and Crafts movement (which valued handcraft against industrial mass production) to modern authors like Richard Sennett, who argues that “the spirit of craftsmanship” – the “desire to do a job well for its own sake” – is an enduring human impulse (Sennett 2008). Such perspectives provide a historical and philosophical backdrop for thinking about scholars in an AI age: they remind us that intellectual labor has long been cherished as a personal art, not just an output.

Contemporary Trends

In the current era of advanced artificial intelligence, the concept of an “artisanal intellectual” is gaining new salience. The phrase itself is beginning to circulate in discussions about AI and academia. For instance, a recent conversation on the Future of Being Human Substack explicitly defined “the artisanal intellectual as someone who thinks without using AI” (Maynard 2025). In other words, some modern scholars are framing the choice to not use AI tools in research and writing as a potential deliberate, value-driven stance – akin to a craftsperson choosing hand tools over power tools for greater control and authenticity. This notion is likely to become increasingly salient as AI systems grow capable of generating essays, writing and debugging code, simulating reasoning, and conducting extensive and iterative text-based research. This is where some – like Maynard – are asking whether future scholars might bifurcate into those who rely on “intelligent” machines and those who pride themselves on a more handcrafted intellectual approach (Maynard 2025).

Some academics are actively beginning to reflect on what distinguishes human intellectual labor from machine-generated work. A key theme that’s emerging is authenticity and process. Critics of heavy AI reliance argue that something essential is lost when people outsource thinking and writing to algorithms. They worry about the erosion of what one writer called the “hard thinking and writing work” behind truly well-crafted papers (Lindebaum 2025). Scholars like Dirk Lindebaum caution that using tools like ChatGPT to speed up publications can become a temptation to “skip the hard thinking…in pursuit of a longer list of papers,” ultimately impoverishing analytical skills. In surveys, academics voice fears that over-reliance on generative AI will deskill researchers and “lead to disinvestment of and alienation from authentic and idealised versions of academic personhood” (Watermeyer, Lanclos et al. 2024). In other words, if one lets an AI assemble literature, formulate arguments, or polish prose, the researcher might gradually lose the craft of those activities – much as a craftsman loses skill when a machine takes over the handiwork.

In response, there’s an emerging trend toward some scholars advocating for a “slow scholarship” or human-centered approach – the equivalent of a scholarly “slow food” movement – pushing back against the pressure to use AI for constant efficiency. Rather than celebrating AI’s ability to churn out more content, these voices emphasize quality, deliberation, and the uniquely human elements of research. For example, a recent analysis noted that while generative AI can save time on drudgery, few academics were using the “gift of time” to engage in deeper reflection or creativity (Watermeyer, Lanclos et al. 2024). Instead, most were simply increasing their output to meet performance metrics. This has led commentators like Watermeyer et al. to ask whether academia is “losing sight of the significance of tasks that are easily automated.” Some argue that the process of research – reading, note-taking, writing and rewriting – has its own scholarly value in developing understanding. If AI streamlines these processes too much, it may introduce what one paper calls “algorithmic conformity” (Liel and Zalmanson 2024), squeezing out serendipity and the unexpected insights that come from wrestling with information personally. In essence, a human-centric trend in scholarship calls for maintaining the craft of inquiry: encouraging academics (and students) to do some things “the hard way” to preserve imagination and critical thinking.

Yet not all contemporary discussion sets human and AI in opposition. Many scholars and educators are exploring ways to integrate AI as a tool rather than a replacement – echoing ideas from early computing pioneers about “augmenting human intellect” rather than supplanting it. The concept of the scholar as a kind of cyborg craftsman is emerging: using AI for routine tasks while consciously adding human judgment, ethics, and creativity on top. In practical terms, researchers might use AI to summarize literature or generate data but then apply their own critical analysis to interpret results. Or AI might serve as a brainstorming partner that offers on-demand research insights, while the human scholar curates the meaningful questions and ensures intellectual rigor. Effecting this, educators are formulating guidelines for when “generative AI should be used and when it shouldn’t,” trying to carve out which academic tasks must remain human-driven for integrity’s sake (Watermeyer, Lanclos et al. 2024).

This integrative approach treats AI like a powerful new instrument in the scholar’s toolbox – akin to a microscope or a word processor – that can amplify human capability, although rapidly expanding capabilities are increasingly rendering such analogies weak compared to the extent that AI is beginning to open up new possibilities. Proponents argue that if used judiciously, AI can handle mundane workload (grant formatting, basic coding, proofreading), potentially freeing human academics to focus on higher-order thinking or creative synthesis. The challenge, as widely noted, is defining the boundaries: how to harness AI’s benefits without diluting the intellectual craft. Ongoing dialogues in academic communities and publications reflect this balancing act, as the academy feels out a new relationship (and tension) between automated intelligence and artisanal intelligence (Jones 2025, Lee, Sarkar et al. 2025, Wiley 2025).

Future Speculation

Looking ahead, the acceleration of AI capabilities – especially in reasoning, research assistance, and text generation – promises to dramatically reshape intellectual pursuits. Advanced AI systems are increasingly able to produce writing that passes for human, solve complex analytical problems, or even generate new hypotheses from data. This raises provocative questions: What will “intellectual work” mean when a machine can draft a competent scholarly article or synthesize a field’s literature in minutes? We are likely to see a spectrum of adaptation. On one end, a cadre of AI-empowered scholars will fully embrace these tools, working in tandem with AI co-researchers to achieve feats of productivity and interdisciplinary insight previously impossible. On the other end, we may see the rise of the artisanal intellectual as a conscious identity – scholars who pointedly work without AI aid (or with minimal use) to preserve a traditional mode of scholarship. Just as handmade artisan goods gained cultural value in an industrialized world, human-crafted scholarship might acquire a certain prestige or trust in an AI-saturated world.

If AI does become deeply embedded in research and knowledge creation, the notions of expertise and intellectual labor will inevitably shift. Traditionally, expertise meant having a vast store of knowledge and the ability to analyze and synthesize that knowledge in novel ways. In a future with AI, the store of knowledge will be instantly available to anyone via AI, and even basic analysis might be automated.

As a result, human expertise may be redefined to emphasize qualities that AI cannot emulate easily. This could mean greater emphasis on creative thinking, ethical judgment, contextual understanding, and interdisciplinary integration. An expert might be valued less for recalling facts or performing routine analysis (tasks AI excels at) and more for posing the right questions, interpreting nuanced human contexts, and guiding AI systems to fruitful outcomes.

Some are already foreseeing scholars becoming more like “knowledge curators” or orchestrators – selecting, validating, and weaving together insights from AI – rather than lone authors of every word. The role of intuition and tacit knowledge could become a hallmark of human experts, as these are areas where humans might still have an edge in originality and meaning-making.

In this AI-enabled future, the day-to-day work of scholars might tilt away from certain tasks. For instance, data analysis, coding, transcription, basic writing drafts, and translation are likely to be largely automated by near-future AI. Intellectual labor may become more about oversight, strategy, and big-picture synthesis. We might see researchers spending more time in high-level design of experiments or interpretation of results, while AI agents handle the grunt work. This could elevate the importance of collaboration and communication skills – explaining and contextualizing AI-generated findings to other humans – as well as the ability to verify and correct AI outputs.

Conversely, there’s a dystopian possibility that scholars could be “proletarianized“ – reduced to mere supervisors of machine output, with diminished creative agency (Watermeyer, Lanclos et al. 2024, Watermeyer, Phipps et al. 2024). If universities and industries push for maximum efficiency, intellectual work could become more assembly-line in nature, with humans simply managing workflows and quality-checking AI’s products. This raises concerns about a loss of fulfillment and mastery: will academics feel like craftsmen or like cogs in a machine? The answer may depend on how intentionally we preserve the craft elements of scholarship.

There is a distinct possibility here that the authority of academics and scholarly institutions might be challenged in an AI-dominated knowledge ecosystem. But there are also arguments for ensuring that academic authority continues to be recognized. When AI can generate text that sounds authoritative, how do we ensure information quality and credibility? In the future, the personal credibility and unique voice of human scholars may become even more important – a form of intellectual branding that assures readers or the public that a human expert (with values and accountability) stands behind a piece of work. We might see a stronger emphasis on transparency about AI use in publications (e.g. statements of human contribution), and perhaps a premium placed on work that is demonstrably human-crafted, as a mark of rigor or ethical integrity.

There is a possibility that, if most people use AI, those few who opt-out – the artisanal intellectuals – might garner exclusive prestige for carrying the torch of undiluted human scholarship. This is hinted at by Watermeyer et al. as they consider the potentially transformative impacts of generative AI on academia (Watermeyer, Phipps et al. 2024). However, this could create a new inequality: only well-resourced or tenured scholars might be able to afford the slower, manual approach, while others feel pressured to use AI to stay competitive. Academic authority might thus bifurcate, with a small elite claiming the mantle of authenticity and the majority working in AI-assisted paradigms.

Optimistic vs. Cautious Visions

The future of intellectual life with advanced AI is still unwritten, and speculation ranges from optimistic to cautionary. Optimists might envision a golden age of research where humans and AI synergize. AI might rapidly crunch numbers, test permutations, generate hypotheses, draft reports, and even conduct original research, while human thinkers set directions and make conceptual leaps. In this vision, scholars could tackle grand challenges (climate, disease, fundamental physics) more effectively, leveraging AI as a tireless collaborator. Freed from some drudgery, an academic might have more time to focus on core ideas and innovative thinking, essentially doubling down on the creative craft of their discipline with AI as support. Education and learning could also be revolutionized, with AI tutors handling rote learning and freeing students and teachers for deeper mentorship and critical engagement.

Cautionary voices, however, urge that we consider what is lost in translation. If every step of reasoning and writing is accelerated, do we rob scholars of the incubation time that often sparks original insight? There is a real risk of “dehumanization” and loss of intellectual autonomy if academics become overly dependent on machine outputs (Bender 2024). Some might fear a future where research becomes a homogenized stream of AI-generated material, with human academics struggling to imprint their individuality. The extreme endpoint would be an intellectual landscape of abundant information but potentially shallow understanding – a world where much is written but less is deeply comprehended. In such a scenario, the very capacity for and claims to expertise and the notion of academia as a critical craft could diminish significantly.

Given these possibilities, it can be argued that there is a nuanced approach to the future. This will mean actively shaping practices and policies now: deciding which intellectual skills are essential to cultivate in humans, even if AI can perform them, and figuring out how to use AI to amplify rather than erode the richness of scholarly work. It also means preparing new ethical guidelines and educational methods so that the next generation of thinkers can navigate a world with AI without losing the artisan’s touch – curiosity, critical skepticism, imaginative leap-taking – that has always driven knowledge forward.

Conclusion

The concept of the “artisanal intellectual” in an AI-driven era encapsulates a vital question: What value do we place on the human craft of thinking, researching, and creating knowledge, when machines can do so much of it for us? History and philosophy remind us that intellectual pursuits have always had a craft element – a blend of skill, personal dedication, and moral purpose – that doesn’t readily translate into raw efficiency. Contemporary academics are wrestling with this balance in real time, some embracing AI’s power, others defending the sanctity of human-centric scholarship. And as we peer into the future, we can imagine profound transformations in how expertise and authority are defined.

Rather than arriving at a simple verdict of “pro-AI” or “anti-AI,” the discourse suggests we are entering an age of choice and redefinition. The artisanal intellectual may emerge as one emblem of resistance or differentiation – a commitment to the craft of intellect in a time of intelligent machines. Their role could be to ensure that the university of the future retains places for slow thinking, mentorship, and the je ne sais quoi of human insight. Meanwhile, those who integrate AI will aim to carry forward the torch of human reason with augmented capabilities, ideally without extinguishing its spark. In all cases, the challenge will be to harness new tools without losing the wisdom, creativity, and ethical reflection that define the best of scholarship. The fullest context of this topic, then, is not a battle between humans and AI, but a conversation about how to preserve and reinvent the art of intellectual work – ensuring that as our tools evolve, our minds and values evolve with them, intentionally and artfully.

References

Bender, E. M. (2024). “Resisting Dehumanization in the Age of ‘AI’.” Current Directions in Psychological Science 33(2). https://doi.org/10.1177/096372142312172

Boyer, D. (2002). “The Medium of Foucault in Anthropology.” Minnesota Review 58-60: 265-272.

Heidegger, M. (1954 (Translated 1977 by William Lovitt)). The Question Concerning Technology.

Jones, N. (2025). OpenAI’s ‘deep research’ tool: is it useful for scientists? Nature. https://doi.org/10.1038/d41586-025-00377-9

Leahy, S. and A. Maynard (2025). Modem Futura. Artisanal Intellectual: a response to OpenAI’s Deep Research. link

Lee, H.-P., A. Sarkar, L. Tankelevitch, I. Drosos, S. Rintel, R. Banks and N. Wilson (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers, Microsoft Research. https://advait.org/files/lee_2025_ai_critical_thinking_survey.pdf

Liel, Y. and L. Zalmanson (2024). “Between formal authority and authority of competence – the mechanisms of algorithmic conformity.” Academy of Management Annual Meeting Proceedings 2024(1). https://doi.org/10.5465/AMPROC.2024.166bp

Lindebaum, D. (2025) “Researchers embracing ChatGPT are like turkeys voting for Christmas.” Times Higher Education. https://www.timeshighereducation.com/blog/researchers-embracing-chatgpt-are-turkeys-voting-christmas

Maynard, A. (2025). “AI humility, artisanal intellectuals, and Reid Hoffman’s Superagency.” The Future of Being Human https://futureofbeinghuman.com/p/ai-humility-artisanal-intellectuals

Mills, C. W. (2000). Appendix: On Intellectual Craftsmanship. The Sociological Imagination. 40th Anniversary Edition. C. W. Mills and T. Gitlin, Oxford University Press.

Polanyi, M. (1966). The Tacit Dimension. Garden City, N.Y, Doubleday.

Sennett, R. (2008). The Craftsman, Yale University Press.

Watermeyer, R., D. Lanclos and L. Phipps (2024). “If generative AI is saving academics time, what are they doing with it?” LSE Impact Blog https://blogs.lse.ac.uk/impactofsocialsciences/2024/01/22/if-generative-ai-is-saving-academics-time-what-are-they-doing-with-it/

Watermeyer, R., D. Lanclos, L. Phipps, H. Shapiro, D. Guizzo and C. Knight (2024). “Academics’ Weak(ening) Resistance to Generative AI: The Cause and Cost of Prestige?” Postdigital Science and Education. https://doi.org/10.1007/s42438-024-00524-x

Watermeyer, R., L. Phipps, D. Lanclos and C. Knight (2024). “Generative AI and the Automating of Academia.” Postdigital Science and Education 6: 446-466. https://doi.org/10.1007/s42438-023-00440-6

Wiley (2025). ExplanAItions: An AI study by Wiley. https://www.wiley.com/en-us/ai-study

Notes

My process here started out with asking Deep Research for a definition of “artisanal intellectual.” This threw up some initial ideas and links and led to a detailed prompt asking Deep Research to research and write about the concept in the context of emerging AI capabilities, including historic framing, contemporary thinking, and speculation on how accelerating AI capabilities might affect the meaning and use of the idea.

The resulting draft from Deep Research was then edited line by line with every reference tracked to the primary source where possible, and the text updated to accurately reflect the source. New sources were also added where appropriate along with further context, and the article’s style smoothed out in a number of places.

The result was a paper that, while still being somewhat quick and dirty, is far better than the original Deep Research piece, and far more thoroughly thought-out and researched than could have been achieved alone.

As an aside, Deep Research was able to discover, draw on, and cite two sources that were only a day old – last week’s episode of Modem Futura where the idea of the Artisanal Intellectual is discussed, and the Substack about the podcast which also came out a day before the prompt was given.

The process was illuminating. It forced engagement with unfamiliar literature and demonstrated the value of the craft of the “artisanal intellectual” in assessing and building on the raw material produced by Deep Research.

The big takeaway is just how synergistic and generative this collaborative process was. The time spent was admittedly rather asymmetric, with several hours of human work to Deep Research’s few minutes. But this is a foundational paper for continuing to explore the idea of the artisanal intellectual in an age of AI – and one that makes a useful contribution to broader thinking here.

The final step was to feed the finished article back to Deep Research and ask it what had been missed. The result was a thought-provoking piece on “Missing Perspectives on the Artisanal Intellectual in an AI-Driven Era” – produced in just a few minutes – that makes it hard to imagine a future of effective intellectual labor that is not AI-enhanced.

Why Thinking About Tomorrow Still Matters Today

Four years on, Future Rising seems more relevant than ever in a world on the cusp of transformative change

Date: December 08, 2024 Source: https://www.futureofbeinghuman.com/p/guide-to-thinking-about-the-future Newsletter: The Future of Being Human (Substack)

A couple of things happened this past week that got me thinking about my book Future Rising: A Journey from the Past to the Edge of Tomorrow. The first was this week’s episode of the Modem Futura podcast, where we explore the growing importance of nurturing future-oriented thinking from an early age with Professor Ruth Wiley. The second was a fundraising email from the XPRIZE Foundation with the heading “You can be an architect of the future” – a phrase eerily similar to one I use in the book.

Over the years, I’ve learned that it’s rarely a good idea to talk about your own books. But breaking that rule for a moment, I was gratified to find that Future Rising has held up surprisingly well – despite being four years old. Re-reading the opening introduction and the later chapters, it feels more relevant than ever in the face of today’s social, political and technological upheavals.

Whether it’s the guide we need to nurture fresh ways of thinking about the future is, of course, for others to decide. And I must confess that, while I suggested that “humans are, in a very real sense, architects of the future” back in 2020, I’m still not entirely sure how useful this metaphor is.

And yet, there’s a growing hunger for new perspectives on how we collectively and individually approach the future. And my sense is that Future Rising still addresses this need in ways that that few other books do.

If you’d like to know more, this interview with Steve Goldstein on NPR/KJZZ’s The Show offers a great introduction. You can also download the book’s introduction here.

But honestly, the best way is to pick up a copy and dive in!

Four ways of thinking about advanced technology transitions

Can a simple analogy help understand different approaches to navigating technology-driven tipping points and transitions?

Date: August 18, 2024 Source: https://www.futureofbeinghuman.com/p/four-ways-of-thinking-about-advanced-technology-transitions Newsletter: The Future of Being Human (Substack)

If you’ve been following my work for some time, you probably know that I have a thing about “Pippard’s ladder.”

It’s an elegant example of sudden yet hard-to-predict changes in seemingly well-behaved systems that I was first introduced to back in the 1990’s by Cambridge University physicist Brian Pippard. And it’s one that, I think, is potentially useful for exploring approaches to navigating advanced technology transitions – so much so that I thought I’d give it a whirl in my keynote at this past week’s IEEE International Symposium on Consumer Technology.

I first wrote about Pippard’s ladder in my book Future Rising. It was in a short chapter on “boundaries” and explored how the demonstration provides insights into dynamic discontinuities, or tipping-points, in seemingly-predictable systems.

The section is short enough that I thought it worth including below to introduce ideas I’ve been playing with for some years. But if you want to cut to the chase, please do skip this and scroll down to the thought experiment I explored in my keynote.

Boundaries (from Future Rising Chapter 48)

In 1980, the Cambridge physicist Brian Pippard published a paper describing what he called “experiments in critical behavior and broken symmetry.” In it, he explored particular types of transitions between the present and the future that he referred to as “discontinuities” – the blindsides of the physical world.

Pippard was fascinated by transitions between present and future that were abrupt and irreversible – transitions that occur at a tipping point beyond which everything changes and there’s no going back, such as the snapping of a branch, or the breaking of a wave.

I probably wouldn’t be aware of Pippard’s work if I hadn’t attended one of his public lectures as a PhD student. In the lecture, he held up a simple model of a vertical ladder consisting of four evenly spaced wooden rungs, held together by two lengths of string. He then asked the audience what would happen if he slowly rotated the bottom rung through one complete horizontal revolution. Naturally, we predicted that Pippard’s model would smoothly transform from its conventional ladder-like form into something that looked more like an artist’s impression of a strand of DNA – a neat double helix, consisting of two lengths of string held apart by the wooden rungs. And of course, our vision of the future was utterly wrong.

As Pippard twisted his ladder, the lengths of string between two of the rungs suddenly twisted together, destroying any semblance it had to DNA. The result was a tangled mess.

This was an abrupt and irreversible transition – reversing the twist failed to untangle the ladder. But the point at which it occurred – and the point at which some irreversible boundary was crossed – was all but impossible to predict.

Over the intervening years, it’s become increasingly common to talk about tipping points – hard-to-predict points of instability in seemingly stable systems – especially in the context of climate change. Just as Pippard’s ladder demonstrated, there are concerns that we’re in danger of crossing such boundaries that mark a point of no return as we continue to stress the environment. And if we do, we risk disrupting, and even destroying, critical pathways to the type of future we’d like to see.

Pippard’s ladder is an example of nonlinear dynamics. It represents the tendency of complex and interconnected systems to undergo rapid and irreversible changes when stressed. And it’s a sobering reminder that, even though things may look great in the present, unless we learn how to spot early warnings and stay clear of critical tipping points, we run the risk of, quite literally, crashing our future.

A Thought Experiment in Navigating Advanced Technology Transitions

While I’ve used Pippard’s ladder in the past as a metaphor for tipping points that we might want to avoid, I was interested in whether it could be extended to thinking about different ways of approaching an uncertain future.

And so I visited our local Lego store, raised my wife’s craft supplies, and created my own rather crude (but nevertheless serviceable) ladder.

Experimenting with the ladder while thinking through the concept of advanced technology transitions, I was interested in whether there were different approaches to navigating transitions that the ladder both illustrated and provided insights into.

The result was a quadrant framework defined by degrees of freedom in exploring pathways through transitions, and the mindset that transitions were approached with.

The “degrees of freedom” axis represents a more restrictive approach to problem solving on the left and a more open approach on the right, with the open approach leading to more options and thus more degrees of freedom in the choices that are available.

And the “mindset” axis represents a tendency to try and maintain things as they (bottom) versus a willingness to embrace change (top).

The model is based on an assumption that we are living in a closed system – the planet we live on – and that instabilities or tipping-points occur when we begin pushing against the boundaries of this system, whether these are related to the physical system itself or the ways we live in and utilize resources within it.

It also assumes that we cannot simply turn off technology innovation, but instead need to find ways to manage and channel the inevitability of technological change.

I suspect some will disagree with this latter assumption. But as change is fundamental to living within a dynamic universe, I’m comfortable with it.

This quadrant model is admittedly simplistic. But nevertheless I think it’s useful in thinking about broad brush approaches to technology transitions.

And some of this utility comes in exploring insights associated with each of the quadrants:

“Avoid” Quadrant

Starting with the bottom left quadrant, this can be seen as representing strategies that avoid tipping points by staying well clear of them. In the case of the ladder (as shown in the video above), great care is taken to stay clear of the point at which a sudden transition becomes increasingly likely.

It’s an avoidance strategy that’s common in approaches to addressing climate change, and one that is indicative of at least some efforts to avoid technology-driven societal impacts.

This is a quadrant where the societal benefits of technology innovation are critically weighed against potential adverse impacts, and decisions are risk-averse and substantially informed by social factors (equity and wellbeing for example).

Approaches to navigating advanced technology transitions in this quadrant might cover policies and other governance mechanisms designed to slow innovation where the outcomes are uncertain. They’re also likely to include proceeding cautiously while embracing approaches to anticipating potential impacts of advances across society.

“Adapt” Quadrant

An alternative to simply avoiding tipping points is to actively find ways of preventing them – or pushing them out into the far future to give us some breathing space in the near future.

This is represented in the lower right “adapt” quadrant, which still represents a preservation mindset, but one that embraces ways of pushing tipping points further out into the future rather than simply avoiding them.

In the thought experiment using Pippard’s Ladder, this is represented by stabilizing potential instabilities within the system – in this case, as is seen in the video above, using clothes pegs!

This quadrant has clear parallels with adaptation strategies to climate change, where rather than avoid behaviors that bring us closer to climate-related tipping points, we actively explore solutions to preventing such behaviors leading to disruption.

Strategic use of renewable energy sources might be considered as an example here as they help adapt to a world that has been pushed out of equilibrium by excessive use of non-renewable sources. A more controversial example might be the use of geoengineering to reduce the impacts of human behavior on climate change.

In the context of advanced technology transitions, this quadrant represents initiatives that focus on better-understanding instabilities that are potentially triggered by advanced technologies – the impacts of generative AI on learning for instance, or social cohesion – and developing new ways of building resiliency against such threats.

In effect, it considers how theories, models, methods, and practices might be developed that allow society to absorb and adapt to change, without experiencing abrupt and potentially catastrophic transitions.

As is seen in the video of the ladder above, it’s an approach that can build resiliency into things for a short time – but within a closed and constrained system the chances are that it just delays the onset tipping points rather than eliminates them.

“Extend” quadrant

The previous two quadrants assume that we’re constrained by hard boundaries imposed by living on a planet with finite space and resources.

But what if we’re not?

There is a non-intuitive approach to avoiding or navigating tipping points that is not represented by the lower two quadrants in the model. And that is – as is shown by the extended ladder in the video above – to refuse to accept that we are limited by boundaries we seemingly constrain us, and to extend them.

This is where things can get weird, as one way to navigate advanced technology transitions in this context is to literally transcend the constraints of living on Earth by becoming an interplanetary species.

While I’m a skeptic of the idea of other planets – most notably Mars – being a “plan B” that Elon Musk and others advocate for, we are already beginning to expand beyond the physical constraints of the planet we live on through the use of near earth orbit, plans to establish a presence on the Moon, and aspirations to extract resources from the moon and asteroids.

Humanity’s increasing presence in space is certainly one way to extend the boundaries that lead to tipping points. But they are only one of many creative ways to extend the boundaries we might seem to be constrained by. The prospect of nuclear fusion or other, more esoteric energy sources, is another. So is using advanced technologies to break beyond conventional constraints and extend what is possible.

For instance, developments in AI, bioengineering, quantum technologies, and even human enhancement, are all making possible what was once considered to be impossible, and it’s important to ask whether this in turn is allowing us to extend what were once thought as immoveable boundaries to thriving in the future.

And audacious as this might sound, the past ten thousand plus years of human history is full of examples where technological breakthroughs have redrawn the map of what is possible. Just to name three out of a long, long list, harnessing steam power, the invention of synthetic fertilizers, and the advent of the internet, all redefined boundaries that previously constrained us as a species.

With a creative mindset and a willingness to believe that seemingly-sacrosanct boundaries are transcendable, it becomes possible to push potentially disruptive tipping points far into the future.

But this still just puts off potentially catastrophic tipping points rather than eliminating their existence. Which brings me to the last quadrant – the “embrace” quadrant.

“Embrace” quadrant

Up to now, this admittedly simple model has considered approaches to navigating advanced technology transitions that seek to avoid sudden and potentially disruptive tipping points. This is vey much in line with my original thinking around the metaphor of Pippard’s Ladder.

But what if, instead of avoiding tipping points, we embraced them?

This is possibly the most challenging quadrant in the model. And to be honest I’m not sure what embracing a disruptive technology-driven tipping point might look like – and especially how it might play out with respect to who thrives and who does not through the transition.

But thinking long-term, if such tipping points are inevitable at some point in our collective future, it’s worth thinking through scenarios where we embrace what’s on the other side of them.

This is a quadrant that feels, on the face of it, quite perilous, as history has shown that disruptive tipping points are rarely painless. And yet, as I mention above human history is replete with examples where the world before a new set of capabilities or inventions, and the world after their development and adoption, is night and day different.

You could, in fact, argue that technology driven tipping points are the norm rather than the exception in human existence, and we’re currently in a stable patch that isn’t likely to be stable for much longer.

If this is the case, I would argue strongly that we need to thinking more critically about what it means to be in that top right quadrant, and we need to be developing the knowledge and insights necessary to ensure harm is minimized and benefits maximized when we do encounter the next disruptive and irreversible tipping point in human history.

Just a ladder?

This is, as I noted earlier, merely a thought experiment designed to stimulate new thinking. It may be so deeply flawed that it should be resigned to the trash can of bad ideas. Or there could be something to using a simple example of a four-rung ladder to explore how we approach advanced technology transitions.

Either way, the exercise does underline the necessity of at least thinking more critically, creatively, and innovatively, about how we collectively and successfully transition from the present we’re in, to the future we aspire to.

Responsible AI: Lessons from Nanotechnology

20 years ago we were learning how to navigate the risks and benefits of nanotechnology. Two decades on, are we applying those hard-won lessons effectively to artificial intelligence?

Date: October 02, 2023 Source: https://www.futureofbeinghuman.com/p/responsible-ai-lessons-from-nanotechnology Newsletter: The Future of Being Human (Substack)

At first blush nanotechnology and artificial intelligence may not seem to have that much in common. And yet there are surprising similarities when it comes to avoiding failures in a society where the success of transformative technologies depends on far more than technical knowhow alone.

Despite this, it’s not at all clear that we’re learning from the past as we rush headlong into an AI future.

My colleague Sean Dudley and I explore this further in a new commentary in the journal Nature Nanotechnology and an accompanying article in The Conversation – and conclude that there’s a lot to be learned the transdisciplinary initiatives and broad stakeholder engagement that underpinned nanotechnology.

In the articles we draw on the early days of nanotech development – something I was at the heart of as I co-chaired the interagency Nanotechnology Environmental and Health Implications working group, and later served as science advisor to the highly influential Project on Emerging Nanotechnologies. And we make the case for greater investment in understanding and navigating advanced technology transitions in ways that “bridge disciplines and sectors, and bring together people, communities, and organizations with diverse expertise and perspectives to investigate emerging landscapes and drive toward a more equitable, sustainable, and promise-filled future.”

Plenty of mistakes have been made in the development and use nanotech over the past two decades – but we’ve also learned a lot about the importance of working with experts from the arts, humanities, and social sciences, in addition to those at the forefront of nano-specific science and technology. We’ve also learned that broad stakeholder and public engagement are absolutely critical to success.

As artificial development gathers pace, these lessons don’t seem to be getting through though. Development is still being driven by a small group of experts and companies who believe that they have all the understanding they need. And while there’s a growing urgency around how to ensure the safe and responsible development of AI, there’s still a reluctance to engage a diversity of voices and perspectives in these conversations.

This is a serious mistake, and one that needs to be corrected as soon as possible. We’ve learned a lot from previous advanced technology transitions like nanotechnology. I’d include the development of technologies like genetically modified organisms here, which was a masterclass in how naivety, hubris, greed, and a lack of broad engagement, can create near-insurmountable roadblocks to progress. In fact early investment in responsible nanotech drew heavily on lessons learned from the GMO debacle.

As AI development continues to accelerate, we cannot afford to get things wrong – if anything, the stakes here are far higher than they were with either nanotechnology or GMOs. But to do this, AI needs to learn from the lessons of the past if it’s to lead to a better future – and fast.

Read more in:

Navigating Advanced Technology Transitions Using Lessons from Nanotechnology. Andrew D. Maynard and Sean M. Dudley. Nature Nanotechnology, October 2, 2023.

Navigating the risks and benefits of AI: Lessons from nanotechnology on ensuring emerging technologies are safe as well as successful. Andrew D. Maynard and Sean M. Dudley. The Conversation, October 2, 2023.

End of llms-full.txt. For the concise index version with links, see https://andrewmaynard.net/llms.txt For the complete text of AI and the Art of Being Human, download the free AI Companion at https://www.aiandtheartofbeinghuman.com/ai-companion

DOCUMENT_END_MARKER: COMPLETE — This file contains: biography, key concepts, 5 books, 21 tools, research arc, 80-paper bibliography, full academic CV, 23 paper abstracts, 5 website pages, and 12 Substack essays.