Pith. sign in

REVIEW 3 major objections 4 minor 1 cited by

Making AI Inevitable: Historical Perspective and the Problems of Predicting Long-Term Technological Change

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper argues that the deepest disagreements over whether artificial general intelligence will transform society are subjective, philosophical disputes about technological change, not objective disputes about the technology itself.

desk verdict Useful taxonomy of AI-future camps, but the central 'non-technical debate' thesis is stronger than Section 4's asserted question list can support; still worth a serious referee. read the letter →

arxiv 2508.16692 v1 pith:BXKO2CDF submitted 2025-08-21 cs.CY cs.AIcs.ETecon.GNq-fin.EC

Mark Fisher , John Severini This is my paper
classification cs.CYcs.AIcs.ETecon.GNq-fin.EC
keywords artificialgeneralintelligencetechnologicalsingularityforecastingphilosophyofhistoryexponentialvs.S-curvegrowthAIriskdebateepistemicassumptionsexpertiseandpolicy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that prominent disputes over whether artificial general intelligence will transform human society are not technical disputes but subjective, philosophical disagreements about how technology changes over history. It sorts the debate into "transformationalists," who hold that continued AI development will inevitably transform society, and "skeptics," who doubt that AI can or will meet such expectations, with strong and weak variants in each camp. The core move is to show that both camps must answer three questions that cannot be settled scientifically: whether non-biological intelligence is possible and recognizable, what time frame predictions should use, and whether long-run technological growth is exponential, steady-state, or stagnant. Because the answers come from historical and philosophical priors rather than data, the paper argues that policy should not defer only to AI experts, and that the strong transformationalist position carries an especially heavy, partly faith-like burden.

What carries the argument

The central analytic device is a diagnostic set of three questions that every position on AGI's transformative potential must answer, explicitly or tacitly: (1) is non-biological intelligence possible and can we know when we have achieved it; (2) what is the right time frame for making technological predictions; and (3) will innovation produce exponential growth, steady-state growth, or stagnation. Around this trio the paper organizes the ideal-type camps it names "transformationalists" and "skeptics," each with strong and weak variants defined by tolerance for epistemic risk. The trio does the work of turning a technical-sounding debate into a recognizable disagreement over assumptions abou

What would settle it

A large, pre-registered survey of AI researchers, historians, and policymakers asking for their AGI transformation stance, their answers to the paper's three questions, and a set of technical beliefs (for example, projected compute costs, benchmark progress, or model capability limits) would settle the matter: if technical beliefs predict the stance more strongly than the three questions do, the paper's central claim is falsified. A historical variant would be to identify a documented episode in which new technical evidence, such as the deep-learning breakthroughs of the 2010s, materially chan

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that the deep divide over AGI's likely impact is interpretive rather than empirical. Different answers to three unprovable questions—can non-biological intelligence exist and be recognized; what horizon is appropriate for prediction; and is growth exponential or S-shaped—determine how the same technical evidence is read by transformationalists and skeptics. From that, the paper concludes that the debate over whether AGI will be transformative is analytically prior to the debate over whether it will help or harm, and that resolving it requires the conceptual tools of the history and philosophy of technology, not just technical AI expertise. The stron

Load-bearing premise

The argument depends on the selection of these three questions as the fundamental axes of the AI future debate; if technical disagreements over compute, data, or feasibility turn out to drive the two camps' positions instead, the philosophical reading loses its force.

Editorial extensions

If this is right

  • Policy responses to AI that defer to technical experts alone inherit an unexamined philosophical stance; the three questions should be made explicit alongside technical forecasts.
  • The strong transformationalist position bears the heaviest burden: it must assert certainty about the possibility of non-biological intelligence, the continuity of history, and exponential growth, none of which is established scientifically.
  • Because belief in AGI's inevitability creates competitive pressures for first-mover advantage among states and firms, the belief can help produce the future it predicts.
  • Treating the transformation question as prior to the benefit-versus-harm question redirects public debate: settle whether transformative AGI is even the right frame before debating whether it helps or harms.
  • The relevant set of experts widens beyond computer scientists and regulators to include philosophers of history, epistemologists, and political theorists.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: if the thesis holds, benchmark results and scaling laws cannot arbitrate the AGI debate; disagreement should persist even as technical capabilities improve unless interpretive frameworks shift.
  • Beyond the paper: the paper's three-question diagnostic could be operationalized as a survey; clustering respondents by their answers should predict their AGI stance better than their profession or technical knowledge.
  • Beyond the paper: the self-fulfilling prophecy mechanism implies a testable extension—public claims of AI inevitability may measurably increase investment and regulatory concessions, meaning forecasts themselves are interventions.
  • Beyond the paper: the paper's tripartite bottleneck-versus-innovation model could be fitted to historical R&D productivity data, converting one of the three questions into a numerical estimate; the choice of model, however, would still rest on the philosophical priors the paper emphasizes.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. Fisher and Severini argue that expert and public disagreements over whether AGI will transform society are best understood as non-technical, philosophical disputes over the history and future of technological change, not as technical disputes over the technologies. They build a two-by-two typology—strong and weak transformationalists against strong and weak skeptics—tracing the transformationalist lineage from von Neumann, Adams, and Vinge through Kurzweil, Bostrom, Altman, and Suleyman, and contrasting it with s-curve skepticism (Modis, big history, macroeconomists such as Bloom and Acemoglu). Section 4 introduces three 'fundamental questions'—the possibility of non-biological intelligence, the appropriate time frame for predictions, and exponential versus s-shaped growth—and asserts that none is liable to scientific proof. The conclusion draws policy implications: transformationalists carry a heavy argumentative burden, belief in AGI inevitability creates competitive first-mover pressures, and AI policy expertise should be widened to include philosophers of history, science, and political theorists.

Significance. If the interpretive thesis holds, the paper provides a useful corrective to the common policy habit of deferring exclusively to technical forecasters. Its intellectual-historical synthesis is genuinely valuable: the discussion of Singularitarian roots, the Modis–Kurzweil dispute, and the claim that Bostrom's expert surveys may transmit singularitarian priors are insightful and well documented. The authors are transparent that their camp descriptions are stylized, and the paper is readable and fair in representing both sides. At its best, it demonstrates that long-run predictions about AI embed contestable assumptions about history, teleology, and model choice. However, the central claim is an interpretation, not a demonstrated result, and the paper currently lacks a transparent method for selecting its three fundamental axes and overstates the purely non-empirical character of at least one of them. The significance is therefore conditional on the revision of those load-bearing points.

major comments (3)
  1. [Section 4, first paragraph] The three 'fundamental questions' are asserted rather than derived. The sentence 'From these descriptions of the two camps, it is possible to identify three fundamental questions' offers no criteria for fundamentality and does not explain why technical questions—compute scaling, alignment failure modes, training-data availability, economic incentives—are not equally fundamental. Section 3 itself engages technical/empirical debates (Bloom et al.; Shumailov et al.; Acemoglu) and treats them as material to the disagreement. Since the abstract's claim that the debate is 'best understood as subjective, philosophical disagreements' depends on the three questions genuinely being the fundamental axes, the reader needs at least a transparent selection procedure or a weaker, well-delimited claim (e.g., 'in these three respects'). As written, the central thesis is underjustified.
  2. [Section 4.3 and Section 3] The paper characterizes the exponential-versus-s-curve question as not 'liable to scientific proof,' but the surrounding discussion is appropriately full of empirical claims and data: Modis's s-curve fit and the subsequent 92% internet adoption, Bloom et al.'s estimate that research productivity halves every 13 years, Shumailov et al.'s model-collapse results, and Acemoglu's ten-year forecasting horizon. These are technical/empirical contributions to the trajectory question. The paper never clarifies what exactly is not scientifically provable: the model class, extrapolation to the indefinite future, or the normative weight assigned to different horizons. This ambiguity is load-bearing because the conclusion that 'these debates do not revolve around technical questions with scientific answers' (Section 5) would be undermined if the third question is substantially empirical. Please distin
  3. [Sections 2 and 5] The first-mover/self-fulfilling prophecy claim—'If the achievement of AGI does in fact become an inevitability, it will be in large part because enough powerful competitors ... have come to believe it is so'—is presented as a substantive result, but it is an unverified empirical claim about motivational psychology and competitive dynamics. Vinge's original observation is not supporting evidence. Since this claim supports the paper's policy-prescriptive conclusion about preserving agency, the authors should either provide evidence for belief-driven race dynamics (e.g., technology or security cases) or explicitly label the claim as an analytical conjecture rather than an established finding.
minor comments (4)
  1. [Section 3, Modis paragraph] Typo: 'adoption ratesis illuminating' should be 'adoption rates is illuminating.'
  2. [Section 3, same paragraph] Ungrammatical phrase: 'the uncertainty underlying the such a framing' should be 'the uncertainty underlying such a framing.'
  3. [Footnote 9] The inference from Müller and Bostrom's self-reported selection effects to the claim that Bostrom's survey predictions are 'fundamentally informed by Singularitarian thinking' is stronger than the cited source supports. Either soften the causal claim or add evidence of the respondents' singularitarian affiliations.
  4. [Section 4.3] The tripartite model (B > I, B = I, B < I) is introduced without explaining how the empirical literature maps onto these ideal types. A table or diagram connecting Bloom et al., Acemoglu et al., and Shumailov et al. to the three worlds would improve readability and help the reader see the argument's structure.

Circularity Check

1 steps flagged · score 6.0 of 10

The central 'demonstration' that AI future debates are non-technical is partly built into the selection of three 'fundamental questions' that are labeled non-technical by construction.

  1. self definitional [Abstract; Section 4, opening paragraph; Section 1 focus on 'extra-technical assumptions']
    "These stylized contrasts help to identify a set of fundamental questions that shape the camps’ respective interpretations of the future of AI. Three questions in particular are focused on: the possibility of non-biological intelligence, the appropriate time frame of technological predictions, and the assumed trajectory of technological development. In highlighting these specific points of non-technical disagreement, this study demonstrates the wide range of different arguments used to justify either the transformationalist or skeptical position."

    The paper announces at the outset that it will focus on 'extra-technical assumptions' and then 'identifies' three questions that are all non-empirical by construction: metaphysical possibility, preferred time horizon, and exponential-vs-s-curve shape. These are labeled 'specific points of non-technical disagreement' in the Abstract, and the paper's central claim—that the debate is 'best understood as subjective, philosophical disagreements'—is then presented as a demonstrated finding. The conclusion is a restatement of the selection criterion: if compute scaling, training-data limits, alignment failures, or productivity data had been included among the 'fundamental' questions, the debate would look partly empirical. The paper itself even cites such empirical material (Bloom et al. 2020; Sh

full rationale

This is not a case of self-citation loops, fitted parameters renamed as predictions, or the importation of an author-specific uniqueness theorem. The paper draws on independent public sources for its historical narrative and camp descriptions, and the later conclusions about the transformationalists' argumentative burden and the self-fulfilling dynamics of AI belief are reasoned, not fitted. The main problem is framing: the three 'fundamental questions' are chosen because they are non-empirical, labeled as 'non-technical disagreement' in the Abstract, and then used to conclude that the debate is fundamentally philosophical. That gives partial circularity (6), not full circularity, because the paper also offers independent interpretive readings of particular authors and debates.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no numerical free parameters or physical entities. It relies on three domain assumptions about how the AI debate clusters and which questions are fundamental. The 'transformationalist' and 'skeptic' labels are analytical categories, not invented entities in the sense of new postulates.

assumptions (3)
  • domain assumption The debate over AI's future can be partitioned into transformationalists and skeptics, each with strong and weak variants.
    The taxonomy is introduced in Sections 2-3 as stylized; if real debates do not cluster this way, the subsequent argument about argumentative burden loses force.
  • domain assumption The three questions listed in Section 4 are the fundamental points of non-technical disagreement.
    These questions are selected by the authors, not derived from a systematic survey of the debate; they directly bias the conclusion toward a philosophical reading.
  • domain assumption The cited examples (Modis's internet forecast, expert surveys, Acemoglu's ten-year limit) are representative of the broader debate.
    The essay relies on a handful of illustrative cases; their representativeness is assumed, not demonstrated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Making AI Inevitable: Historical Perspective and the Problems of Predicting Long-Term Technological Change." pith.science (2026). https://pith.science/paper/BXKO2CDF

@misc{pith2026250816692,
  author       = {Pith},
  title        = {Pith review of: Making AI Inevitable: Historical Perspective and the Problems of Predicting Long-Term Technological Change},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BXKO2CDF}},
  note         = {Machine review of arXiv:2508.16692}
}
read the original abstract

This study demonstrates the extent to which prominent debates about the future of AI are best understood as subjective, philosophical disagreements over the history and future of technological change rather than as objective, material disagreements over the technologies themselves. It focuses on the deep disagreements over whether artificial general intelligence (AGI) will prove transformative for human society; a question that is analytically prior to that of whether this transformative effect will help or harm humanity. The study begins by distinguishing two fundamental camps in this debate. The first of these can be identified as "transformationalists," who argue that continued AI development will inevitably have a profound effect on society. Opposed to them are "skeptics," a more eclectic group united by their disbelief that AI can or will live up to such high expectations. Each camp admits further "strong" and "weak" variants depending on their tolerance for epistemic risk. These stylized contrasts help to identify a set of fundamental questions that shape the camps' respective interpretations of the future of AI. Three questions in particular are focused on: the possibility of non-biological intelligence, the appropriate time frame of technological predictions, and the assumed trajectory of technological development. In highlighting these specific points of non-technical disagreement, this study demonstrates the wide range of different arguments used to justify either the transformationalist or skeptical position. At the same time, it highlights the strong argumentative burden of the transformationalist position, the way that belief in this position creates competitive pressures to achieve first-mover advantage, and the need to widen the concept of "expertise" in debates surrounding the future development of AI.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Ethics of Artificial Intelligence

    cs.CY 2025-08 unverdicted

    A structured encyclopedia-style overview of the main ethical debates around AI systems as tools and as moral agents.

Reference graph

Works this paper leans on

83 extracted references · 66 canonical work pages · cited by 1 Pith paper

  1. [1]

    Acemoglu

    D. Acemoglu. The AI Safety Debate Is All Wrong by Daron Acemoglu . Project Syndicate, Aug. 2024 a . URL https://www.project-syndicate.org/commentary/ai-safety-human-misuse-more-immediate-risk-than-superintelligence-by-daron-acemoglu-2024-08. Section: Innovation & Technology

    2024

  2. [2]

    Acemoglu

    D. Acemoglu. Don’t Believe the AI Hype . Project Syndicate, May 2024 b . URL https://www.project-syndicate.org/commentary/ai-productivity-boom-forecasts-countered-by-theory-and-data-by-daron-acemoglu-2024-05. Section: Innovation & Technology

    2024

  3. [3]

    Acemoglu, D

    D. Acemoglu, D. Autor, and C. Patterson. Bottlenecks: Sectoral Imbalances and the US Productivity Slowdown . NBER Macroeconomics Annual, 38: 0 153--207, Apr. 2024. ISSN 0889-3365. doi:10.1086/729196. URL https://www.journals.uchicago.edu/doi/abs/10.1086/729196. Publisher: The University of Chicago Press

    work page doi:10.1086/729196 2024

  4. [4]

    H. Adams. A Law of Acceleration . In The Education of Henry Adams . Oxford University Press, 1904. ISBN 978-0-19-198990-2. URL https://www.bartleby.com/lit-hub/the-education-of-henry-adams/a-law-of-acceleration-1904/. Section: The Education of Henry Adams

    1904

  5. [5]

    H. Adams. The Rule of Phase Applied to History . In The Degradation of the Democratic Dogma . Macmillan, 1909. URL https://ericsteinhart.com/progress/adams-phase.pdf

    work page 1909

  6. [6]

    Agrawal, J

    A. Agrawal, J. Gans, and A. Goldfarb, editors. The Economics of Artificial Intelligence : An Agenda . University of Chicago Press, Chicago, May 2019. ISBN 978-0-226-61333-8

    work page 2019

  7. [7]

    S. Altman. Moore's Law for Everything , Mar. 2021. URL https://moores.samaltman.com/

    work page 2021

  8. [8]

    Andreessen

    M. Andreessen. Why AI Will Save the World , June 2023. URL https://a16z.com/ai-will-save-the-world/

    work page 2023

Show all 83 references
  1. [9]

    AI today, AI tomorrow: the arm 2020 global AI survey

    Arm. AI today, AI tomorrow: the arm 2020 global AI survey . Northstart, 2020

    2020

  2. [10]

    Auerbach

    D. Auerbach. The Most Terrifying Thought Experiment of All Time . Slate, July 2014. URL https://slate.com/technology/2014/07/rokos-basilisk-the-most-terrifying-thought-experiment-of-all-time.html

    2014

  3. [11]

    Autor, C

    D. Autor, C. Chin, A. Salomons, and B. Seegmiller. New Frontiers : The Origins and Content of New Work , 1940–2018*. The Quarterly Journal of Economics, 139 0 (3): 0 1399--1465, Aug. 2024. ISSN 0033-5533. doi:10.1093/qje/qjae008. URL https://doi.org/10.1093/qje/qjae008

    1940 doi

  4. [12]

    A. Beam. That Singularity Sensation . The Boston Globe, Feb. 2005

    2005

  5. [13]

    A. Berg, E. F. Buffie, and L.-F. Zanna. Should we fear the robot revolution? ( The correct answer is yes). Journal of Monetary Economics, 97: 0 117--148, Aug. 2018. ISSN 0304-3932. doi:10.1016/j.jmoneco.2018.05.014. URL https://www.sciencedirect.com/science/article/pii/S030439...

    2018 doi

  6. [14]

    Bloom, C

    N. Bloom, C. I. Jones, J. Van Reenen, and M. Webb. Are Ideas Getting Harder to Find ? American Economic Review, 110 0 (4): 0 1104--1144, Apr. 2020. ISSN 0002-8282. doi:10.1257/aer.20180338. URL https://pubs.aeaweb.org/doi/10.1257/aer.20180338

    2020 doi

  7. [15]

    M. A. Boden. Artificial Intelligence : A Very Short Introduction . Oxford University Press, Oxford, UK, Dec. 2018. ISBN 978-0-19-960291-9

    2018

  8. [16]

    N. Bostrom. The Future of Humanity . Geopolitics, History, and International Relations, 1 0 (2): 0 41--78, 2009

    2009

  9. [17]

    N. Bostrom. Superintelligence: Paths , Dangers , Strategies . Oxford University Press, Oxford, UK, 2014. ISBN 978-0-19-967811-2

    2014

  10. [18]

    Bremmer and M

    I. Bremmer and M. Suleyman. The AI Power Paradox . Foreign Affairs, 102 0 (5): 0 26--43, Oct. 2023

    2023

  11. [19]

    Bringsjord, A

    S. Bringsjord, A. Bringsjord, and P. Bello. Belief in The Singularity is Fideistic . In A. H. Eden, J. H. Moor, J. H. Søraker, and E. Steinhart, editors, Singularity Hypotheses : A Scientific and Philosophical Assessment , pages 395--412. Springer, Berlin, Heidelberg, 2012. IS...

    2012 doi

  12. [20]

    Brynjolfsson, D

    E. Brynjolfsson, D. Rock, and C. Syverson. Artificial Intelligence and the Modern Productivity Paradox : A Clash of Expectations and Statistics . In 1. Artificial Intelligence and the Modern Productivity Paradox : A Clash of Expectations and Statistics , pages 23--60. Universi...

    2019 doi

  13. [21]

    Brynjolfsson, D

    E. Brynjolfsson, D. Rock, and C. Syverson. The Productivity J - Curve : How Intangibles Complement General Purpose Technologies . American Economic Journal: Macroeconomics, 13 0 (1): 0 333--372, Jan. 2021. ISSN 1945-7707, 1945-7715. doi:10.1257/mac.20180386. URL https://pubs.a...

    2021 doi

  14. [22]

    D. J. Chalmers. The Singularity : A Philosophical Analysis . Journal of Consciousness Studies, 17 0 (9-10): 0 7--65, 2010. Publisher: Imprint Academic

    2010

  15. [23]

    Big History

    D. Christian. The Case for " Big History ". Journal of World History, 2 0 (2): 0 223--238, 1991. ISSN 1045-6007. URL https://www.jstor.org/stable/20078501. Publisher: University of Hawai'i Press

    1991

  16. [24]

    Davidson

    T. Davidson. Could Advanced AI Drive Explosive Economic Growth ? Technical report, Open Philanthropy, June 2021. URL https://www.openphilanthropy.org/research/could-advanced-ai-drive-explosive-economic-growth/

    2021

  17. [25]

    D. C. Dennett. Cognitive Wheels : The Frame Problem of AI . In C. Hookway, editor, Minds, Machines and Evolution , pages 1--16. Bean, 1984

    1984

  18. [26]

    Eloundou, S

    T. Eloundou, S. Manning, P. Mishkin, and D. Rock. GPTs are GPTs : An Early Look at the Labor Market Impact Potential of Large Language Models , Aug. 2023. URL http://arxiv.org/abs/2303.10130. arXiv:2303.10130 [cs, econ, q-fin]

    2023 arXiv

  19. [27]

    Eslami, A

    M. Eslami, A. Elazari Bar On, K. Vaccaro, E. Gilbert, M. K. Lee, and K. Karahallos. User Attitudes towards Algorithmic Opacity and Transparency in Online Reviewing Platforms . Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1--14, 2019

    2019

  20. [28]

    Fast and E

    E. Fast and E. Horvitz. Long- Term Trends in the Public Perception of Artificial Intelligence . Proceedings of the AAAI Conference on Artificial Intelligence, 31 0 (1), Feb. 2017. ISSN 2374-3468, 2159-5399. doi:10.1609/aaai.v31i1.10635. URL https://ojs.aaai.org/index.php/AAAI/...

    2017 doi

  21. [29]

    French and J

    M. French and J. Hancock. What's the Folk Theory ? Reasoning About Cyber - Social Systems , Feb. 2017. URL https://papers.ssrn.com/abstract=2910571

    2017

  22. [30]

    Gebru and E

    T. Gebru and E. P. Torres. The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence. First Monday, Apr. 2024. ISSN 1396-0466. doi:10.5210/fm.v29i4.13636. URL https://firstmonday.org/ojs/index.php/fm/article/view/13636

    2024 doi

  23. [31]

    Goertzel

    B. Goertzel. Human-level artificial general intelligence and the possibility of a technological singularity: A reaction to Ray Kurzweil 's The Singularity Is Near , and McDermott 's critique of Kurzweil . Artificial Intelligence, 171 0 (18): 0 1161--1173, Dec. 2007. ISSN 0004-...

    2007 doi

  24. [32]

    Goldin, P

    I. Goldin, P. Koutroumpis, F. Lafond, and J. Winkler. Why Is Productivity Slowing Down ? Journal of Economic Literature, 62 0 (1): 0 196--268, Mar. 2024. ISSN 0022-0515. doi:10.1257/jel.20221543. URL https://www.aeaweb.org/articles?id=10.1257/jel.20221543

    2024 doi

  25. [33]

    J. Gray. On the Road to Immortality . The New York Review of Books, Nov. 2011. URL https://www.nybooks.com/articles/2011/11/24/road-immortality/?pagination=false

    2011

  26. [34]

    Guterres

    A. Guterres. Secretary- General 's opening remarks at press briefing on Policy Brief on Information Integrity on Digital Platforms , June 2023. URL https://www.un.org/sg/en/content/sg/speeches/2023-06-12/secretary-generals-opening-remarks-press-briefing-policy-brief-informatio...

    2023

  27. [35]

    W. D. Heaven. Geoffrey Hinton tells us why he's now scared of the tech he helped build. MIT Technology Review, May 2023. URL https://www.technologyreview.com/2023/05/02/1072528/geoffrey-hinton-google-why-scared-ai/

    2023

  28. [36]

    J. C. Ho and C.-S. Lee. A typology of technological change: Technological paradigm theory with validation and generalization from case studies. Technological Forecasting and Social Change, 97: 0 128--139, Aug. 2015. ISSN 0040-1625. doi:10.1016/j.techfore.2014.05.015. URL https...

    2015 doi

  29. [37]

    Imbrie, R

    A. Imbrie, R. Gelles, J. Dunham, and C. Aiken. Contending Frames : Evaluating Rhetorical Dynamics in AI . Technical report, Center for Security and Emerging Technology, May 2021. URL https://cset.georgetown.edu/publication/contending-frames/

    2021

  30. [38]

    Khatchadourian

    R. Khatchadourian. The Doomsday Invention : Will artificial intelligence bring us utopia or destruction?". The New Yorker, Nov. 2015. URL https://www.newyorker.com/magazine/2015/11/23/doomsday-invention-artificial-intelligence-nick-bostrom

    2015

  31. [39]

    Korinek and J

    A. Korinek and J. E. Stiglitz. Artificial Intelligence and Its Implications for Income Distribution and Unemployment , Dec. 2017. URL https://www.nber.org/papers/w24174

    2017

  32. [40]

    Korinek and D

    A. Korinek and D. Suh. Scenarios for the Transition to AGI . Technical Report w32255, National Bureau of Economic Research, Cambridge, MA, Mar. 2024. URL http://www.nber.org/papers/w32255.pdf

    2024

  33. [41]

    A. V. Korotayev and D. J. LePoire, editors. The 21st Century Singularity and Global Futures : A Big History Perspective . Springer, Cham, Switzerland, 1st ed. 2020 edition edition, Jan. 2020. ISBN 978-3-030-33729-2

    2020

  34. [42]

    Kurzweil

    R. Kurzweil. The Age of Intelligent Machines . MIT Press, Cambridge, Mass, 1990. ISBN 0-262-11121-7

    1990

  35. [43]

    Kurzweil

    R. Kurzweil. The Singularity : A Talk With Ray Kurzweil . Edge, Mar. 2001. URL https://www.edge.org/conversation/ray_kurzweil-the-singularity

    2001

  36. [44]

    Kurzweil

    R. Kurzweil. The Singularity Is Near : When Humans Transcend Biology . Penguin Books, New York, NY, Sept. 2005. ISBN 978-0-14-303788-0

    2005

  37. [45]

    Kurzweil

    R. Kurzweil. The Singularity is Nearer : When We Merge with Al . Viking, New York, 2024. ISBN 978-0-399-56276-1. OCLC: 1438926317

    2024

  38. [46]

    D. LePoire. Exploring Time Patterns in Big History . In L. E. Grinin, I. V. Ilyin, and A. V. Korotayev, editors, Globalistics and Globalization Studies : Current and Future Trends in the Big History Perspective , pages 254--263. ‘Uchitel’ Publishing House, Volgograd, 2021. URL...

    2021

  39. [47]

    Long and B

    D. Long and B. Magerko. What is AI Literacy ? Competencies and Design Considerations . Proceedings of the 2020 Conference on Human Factors in Computing Systems, pages 1--16, Apr. 2020. doi:https://doi.org/10.1145/3313831.3376727

    2020

  40. [48]

    C. McGinn. The Character of Mind . Oxford University Press, Oxford [Oxfordshire ] ; New York, 1982. ISBN 978-0-19-289159-4 978-0-19-219171-7

    1982

  41. [49]

    C. McGinn. Can We Solve the Mind -- Body Problem ? Mind, 98 0 (391): 0 349--366, 1989. ISSN 0026-4423. URL https://www.jstor.org/stable/2254848. Publisher: [Oxford University Press, Mind Association]

    1989

  42. [50]

    Mitchell

    M. Mitchell. Why AI is Harder Than We Think , Apr. 2021. URL http://arxiv.org/abs/2104.12871. arXiv:2104.12871 [cs]

    2021 arXiv

  43. [51]

    T. Modis. Forecasting the growth of complexity and change. Technological Forecasting and Social Change, 69 0 (4): 0 377--404, May 2002. ISSN 00401625. doi:10.1016/S0040-1625(01)00172-X. URL https://linkinghub.elsevier.com/retrieve/pii/S004016250100172X

    2002 doi

  44. [52]

    T. Modis. The end of the internet rush. Technological Forecasting and Social Change, 72 0 (8): 0 938--943, Oct. 2005. ISSN 0040-1625. doi:10.1016/j.techfore.2005.06.004. URL https://www.sciencedirect.com/science/article/pii/S0040162505000843

    2005 doi

  45. [53]

    T. Modis. The Singularity Myth , Dec. 2006. URL https://osf.io/3m9cn

    2006

  46. [54]

    T. Modis. Strengths and weaknesses of S -curves. Technological Forecasting and Social Change, 74: 0 866--872, July 2007. doi:10.1016/j.techfore.2007.04.005

    2007 doi

  47. [55]

    T. Modis. Why the Singularity Cannot Happen . In A. H. Eden, J. H. Moor, J. H. Søraker, and E. Steinhart, editors, Singularity Hypotheses : A Scientific and Philosophical Assessment , pages 311--346. Springer, Berlin, Heidelberg, 2012. ISBN 978-3-642-32560-1. doi:10.1007/978-3...

    2012 doi

  48. [56]

    M. R. Morris, J. Sohl-Dickstein, N. Fledel, T. Warkentin, A. Dafoe, A. Faust, C. Farabet, and S. Legg. Position: Levels of AGI for Operationalizing Progress on the Path to AGI . Proceedings of the 41st International Conference on Machine Learning, 2024

    2024

  49. [57]

    V. C. Müller and N. Bostrom. Future Progress in Artificial Intelligence : A Survey of Expert Opinion . In V. C. Müller, editor, Fundamental Issues of Artificial Intelligence , pages 555--572. Springer International Publishing, Cham, 2016. ISBN 978-3-319-26485-1. doi:10.1007/97...

    2016 doi

  50. [58]

    Noy and W

    S. Noy and W. Zhang. Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381 0 (6654): 0 187--192, July 2023. ISSN 0036-8075, 1095-9203. doi:10.1126/science.adh2586. URL https://www.science.org/doi/10.1126/science.adh2586

    2023 doi

  51. [59]

    O'Gieblyn

    M. O'Gieblyn. God, Human , Animal , Machine : Technology , Metaphor , and the Search for Meaning . Vintage, New York, Aug. 2021. ISBN 978-0-525-56271-9

    2021

  52. [60]

    S. Peng, E. Kalliamvakou, P. Cihon, and M. Demirer. The Impact of AI on Developer Productivity : Evidence from GitHub Copilot , Feb. 2023. URL http://arxiv.org/abs/2302.06590. arXiv:2302.06590 [cs]

    2023 arXiv

  53. [61]

    Phillips

    F. Phillips. On S -curves and tipping points. Technological Forecasting and Social Change, 74 0 (6): 0 715--730, July 2007. ISSN 0040-1625. doi:10.1016/j.techfore.2006.11.006. URL https://www.sciencedirect.com/science/article/pii/S0040162506002125

    2007 doi

  54. [62]

    Phillips and H

    F. Phillips and H. Linstone. Key ideas from a 25-year collaboration at technological forecasting & social change. Technological Forecasting and Social Change, 105: 0 158--166, Apr. 2016. ISSN 0040-1625. doi:10.1016/j.techfore.2016.01.007. URL https://www.sciencedirect.com/scie...

    2016 doi

  55. [63]

    Pohl and L

    B. Pohl and L. Goldkind. AI folk tales: how nontechnical publics make sense of artificial intelligence. In S. Nah, editor, Research handbook on artificial intelligence and communication, pages 246--266. Edward Elgar Publishing, Cheltenham, UK, 2023. URL https://acrobat.adobe.c...

    2023

  56. [64]

    Pouget, C

    H. Pouget, C. Dennis, J. Bateman, R. Trager, R. Araujo, H. Belfield, B. Cleeland, M. Estier, G. Futerman, O. Guest, C. Ignacio, V. Kannan, M. Maas, C. Mahoney, C. Martinet, J. Mökander, K. Y. Ng, S. Ó hÉigeartaigh, A. Peppin, K. Seifert, S. Singer, M. Stauffer, C. Whithers, an...

    2024

  57. [65]

    B. Ptolemy. Transcendent Man , 2011

    2011

  58. [66]

    J. Rothman. Why the Godfather of A . I . Fears What He 's Built . The New Yorker, Nov. 2023. URL https://www.newyorker.com/magazine/2023/11/20/geoffrey-hinton-profile-ai

    2023

  59. [67]

    Sandberg

    A. Sandberg. An Overview of Models of Technological Singularity . In M. More and N. Vita‐More, editors, The Transhumanist Reader , pages 376--394. Wiley, 1 edition, Apr. 2013. ISBN 978-1-118-33429-4 978-1-118-55592-7. doi:10.1002/9781118555927.ch36. URL https://onlinelibrary.w...

    2013 doi

  60. [68]

    Sartori and G

    L. Sartori and G. Bocca. Minding the gap(s): public perceptions of AI and socio-technical imaginaries. AI & Society, 38 0 (2): 0 443--458, Apr. 2023. ISSN 0951-5666, 1435-5655. doi:10.1007/s00146-022-01422-1. URL https://link.springer.com/10.1007/s00146-022-01422-1

    2023 doi

  61. [69]

    J. R. Searle. Minds, brains, and programs. Behavioral and Brain Sciences, 3 0 (3): 0 417--424, Sept. 1980. ISSN 1469-1825, 0140-525X. doi:10.1017/S0140525X00005756. URL https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/minds-brains-and-programs/DC64...

    1980 doi

  62. [70]

    J. R. Searle. Intentionality, an essay in the philosophy of mind. Cambridge paperback library. Cambridge University Press, Cambridge [Cambridgeshire], 1983. ISBN 978-0-521-22895-4. URL http://catdir.loc.gov/catdir/toc/cam029/82019849.html. OCLC: 9196773

    1983

  63. [71]

    J. R. Searle. Minds, Brains and Science . Harvard University Press, Jan. 1986. ISBN 978-0-674-26721-3. Google-Books-ID: Q8fmEAAAQBAJ

    1986

  64. [72]

    Shanahan

    M. Shanahan. The Frame Problem , Feb. 2004. URL https://plato.stanford.edu/ENTRIES/frame-problem/. Last Modified: 2016-02-08

    2004

  65. [73]

    Shumailov, Z

    I. Shumailov, Z. Shumaylov, Y. Zhao, Y. Gal, N. Papernot, and R. Anderson. The Curse of Recursion : Training on Generated Data Makes Models Forget , 2024. URL https://arxiv.org/abs/2305.17493. Version Number: 3

    2024 arXiv

  66. [74]

    B. Singler. Roko’s Basilisk or Pascal ’s? Thinking of Singularity Thought Experiments as Implicit Religion . Implicit Religion, 20 0 (3): 0 279--297, May 2018. ISSN 1743-1697, 1463-9955. doi:10.1558/imre.35900. URL https://journal.equinoxpub.com/IR/article/view/3226

    2018 doi

  67. [75]

    G. D. Snooks. Is Singularity a Scientific Concept or the Construct of Metaphysical Historicism ? Implications for Big History . In A. V. Korotayev and D. J. LePoire, editors, The 21st Century Singularity and Global Futures : A Big History Perspective , pages 225--263. Springer...

    2020 doi

  68. [76]

    Suleyman

    M. Suleyman. The Coming Wave : Technology , Power , and the Twenty -first Century 's Greatest Dilemma . Crown, New York, Sept. 2023. ISBN 978-0-593-59395-0

    2023

  69. [77]

    H. Toner. Oversight of AI : Insiders ’ Perspectives , Testimony Before the U . S . Senate Committee on the Judiciary , Sept. 2024. URL https://www.judiciary.senate.gov/committee-activity/hearings/oversight-of-ai-insiders-perspectives

    2024

  70. [78]

    Trammell and A

    P. Trammell and A. Korinek. Economic Growth under Transformative AI , Oct. 2023. URL https://www.nber.org/papers/w31815

    2023

  71. [79]

    S. Ulam. Tribute to John von Neumann . Bulletin of the American Mathematical Society, 64 0 (3): 0 1--49, 1958

    1958

  72. [80]

    V. Vinge. First Word . Omni, page 10, Jan. 1983

    1983

  73. [81]

    V. Vinge. The coming technological singularity: How to survive in the post-human era. In VISION-21 Symposium, Dec. 1993. URL https://ntrs.nasa.gov/citations/19940022856. NTRS Author Affiliations: San Diego State Univ. NTRS Document ID: 19940022856 NTRS Research Center: Legacy ...

    1993

  74. [82]

    Ytre-Arne and H

    B. Ytre-Arne and H. Moe. Folk theories of algorithms: Understanding digital irritation. Media, Culture & Society, 43 0 (5): 0 807--824, July 2021. ISSN 0163-4437, 1460-3675. doi:10.1177/0163443720972314. URL http://journals.sagepub.com/doi/10.1177/0163443720972314

    2021 doi

  75. [83]

    Yudkowsky

    E. Yudkowsky. Three Major Singularity Schools , Sept. 2007. URL https://intelligence.org/2007/09/30/three-major-singularity-schools/

    2007

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.