So What? Generative AI and the Historical Redefinition of Cognitive Competence
DOI:
https://doi.org/10.5281/zenodo.21520072Palavras-chave:
Generative AI, Cognitive internalism, Cognitive offloading, Distributed cognition, Modern Constitution, Hybrid cognitionResumo
Generative AI has entered intellectual practice with a quality best described as irresistible. Much of the intellectual response in research and education has been organized around an alarm: that these systems induce a surrender of thinking, an atrophy of cognitive capacity, an accumulation of cognitive debt. This essay argues that the alarm conflates two claims that must be told apart. The first is categorical — that generative AI corrupts a cognitive interior whose integrity consists in operating unaided. Its governing metaphors, debt and atrophy and renunciation, presuppose a prior solvent interior against which the deficit is scored, and no such interior is to be found at any documented moment of the historical record: the Greek transition from orality to literacy shows a comparable cognitive figure lost without loss of the human. The second claim is empirical and concerns the ecology in which competences are formed. It survives the separation, and the essay relocates rather than refutes it. Drawing on Latour’s argument that we have never been modern, and on convergent descriptions of cognition as coupled practice (Hutchins, Stiegler, Malafouris, Barad, Goody, Ong, Yuk Hui), the essay reconstructs cognition as a configuration of brains, bodies, tools, environments, and other people — a configuration whose history has repeatedly redefined what counts as cognitive competence. Recent measures of cognitive loss under generative AI assess competences with the ruler of the configuration those competences are leaving behind. What survives the critique is a systemic question, left open here: whether the emerging configuration sustains the practice it draws upon or depletes it. The burden of showing that it does not falls on those who raise the alarm. Concerns that are political-economic, environmental, labor-related, and about opacity and cultural standardization survive examination and deserve deliberation. The categorical alarm about cognition does not.
Referências
ADAMS, F.; AIZAWA, K. The bounds of cognition. Malden: Wiley-Blackwell, 2008.
AZEVEDO, N. H.; SANTOS, P. G. F. dos. Primazia da dimensão utilitária e recuo crítico: inteligência artificial generativa e os valores em disputa na ciência. Ensaio: Pesquisa em Educação em Ciências, v. 27, e59484, 2025. DOI: https://doi.org/10.1590/1983-2117-59484.
BARAD, K. Meeting the universe halfway: quantum physics and the entanglement of matter and meaning. Durham: Duke University Press, 2007.
BENDER, E. M. et al. On the dangers of stochastic parrots: can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. New York: Association for Computing Machinery, 2021. p. 610-623. DOI: https://doi.org/10.1145/3442188.3445922.
BIALYSTOK, L. AI and the future of (philosophy of) education. Educational Theory, 2026. DOI: https://doi.org/10.1111/edth.70058.
CLARK, A. Surfing uncertainty: prediction, action, and the embodied mind. Oxford: Oxford University Press, 2016.
CLARK, A. Extending the predictive mind. Australasian Journal of Philosophy, v. 102, n. 1, p. 119-130, 2024. DOI: https://doi.org/10.1080/00048402.2022.2122523.
CLARK, A.; CHALMERS, D. The extended mind. Analysis, v. 58, n. 1, p. 7-19, 1998. DOI: https://doi.org/10.1093/analys/58.1.7.
COECKELBERGH, M. The political philosophy of AI: an introduction. Cambridge: Polity Press, 2022.
COECKELBERGH, M.; GUNKEL, D. Communicative AI: a critical introduction to large language models. Cambridge: Polity Press, 2025.
COSTA, C.; MURPHY, M. Generative artificial intelligence in education: (what) are we thinking? Learning, Media and Technology, 2025. DOI: https://doi.org/10.1080/17439884.2025.2518258.
DARYANI, Y.; SOURATI, Z.; DEHGHANI, M. The homogenizing engine: AI’s role in standardizing culture and the path to policy. Policy Insights from the Behavioral and Brain Sciences, 2026. DOI: https://doi.org/10.1177/23727322251406591.
DE PAOLI, S. Why we should reject to reject the use of generative artificial intelligence in qualitative analysis: a response to Jowsey, Braun, Clarke, Lupton, and Fine (2025). Qualitative Inquiry, p. 1-3, 2026. DOI: https://doi.org/10.1177/10778004261425137.
FAN, Y. et al. Beware of metacognitive laziness: effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, v. 56, n. 2, p. 489-530, 2025. DOI: https://doi.org/10.1111/bjet.13544.
FISKE, S. T.; TAYLOR, S. E. Social cognition. Reading: Addison-Wesley, 1984.
FLORIDI, L. The ethics of artificial intelligence: principles, challenges, and opportunities. Oxford: Oxford University Press, 2023.
FRIESE, S. et al. Beyond binary positions: making space for critical and reflexive GenAI integration in qualitative research. Qualitative Inquiry, p. 1-10, 2026. DOI: https://doi.org/10.1177/10778004261429393.
GERLICH, M. AI tools in society: impacts on cognitive offloading and the future of critical thinking. Societies, v. 15, n. 1, p. 6, 2025. DOI: https://doi.org/10.3390/soc15010006.
GOODY, J. The domestication of the savage mind. Cambridge: Cambridge University Press, 1977.
GRAY, M. L.; SURI, S. Ghost work: how to stop Silicon Valley from building a new global underclass. Boston: Houghton Mifflin Harcourt, 2019.
HAVELOCK, E. A. Preface to Plato. Cambridge, MA: Harvard University Press, 1963.
HUI, Y. Recursivity and contingency. London: Rowman & Littlefield, 2019.
HUTCHINS, E. Cognition in the wild. Cambridge, MA: MIT Press, 1995.
JOWSEY, T. et al. We reject the use of generative artificial intelligence for reflexive qualitative research. Qualitative Inquiry, p. 1-5, 2025. DOI: https://doi.org/10.1177/10778004251401851.
KOSMYNA, N. et al. Your brain on ChatGPT: accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv, 2025. DOI: https://doi.org/10.48550/arXiv.2506.08872.
LATOUR, B. We have never been modern. Tradução de C. Porter. Cambridge, MA: Harvard University Press, 1993. Publicado originalmente em 1991.
LATOUR, B. Reassembling the social: an introduction to actor-network-theory. Oxford: Oxford University Press, 2005.
MALAFOURIS, L. How things shape the mind: a theory of material engagement. Cambridge, MA: MIT Press, 2013.
ONG, W. J. Orality and literacy: the technologizing of the word. London: Methuen, 1982.
PERRIGO, B. Exclusive: OpenAI used Kenyan workers on less than $2 per hour to make ChatGPT less toxic. TIME, 18 jan. 2023. Disponível em: https://time.com/6247678/openai-chatgpt-kenya-workers/. Acesso em: 13 jul. 2026.
RISKO, E. F.; GILBERT, S. J. Cognitive offloading. Trends in Cognitive Sciences, v. 20, n. 9, p. 676-688, 2016. DOI: https://doi.org/10.1016/j.tics.2016.07.002.
RUPERT, R. D. Cognitive systems and the extended mind. Oxford: Oxford University Press, 2009.
SHANAHAN, M. Talking about large language models. Communications of the ACM, v. 67, n. 2, p. 68-79, 2024. DOI: https://doi.org/10.1145/3624724.
SMITH, B. C. The promise of artificial intelligence: reckoning and judgment. Cambridge, MA: MIT Press, 2019.
SPARROW, B.; LIU, J.; WEGNER, D. M. Google effects on memory: cognitive consequences of having information at our fingertips. Science, v. 333, n. 6043, p. 776-778, 2011. DOI: https://doi.org/10.1126/science.1207745.
STANKOVIC, M. et al. Comment on: your brain on ChatGPT — accumulation of cognitive debt when using an AI assistant for essay writing tasks. arXiv, 2025. DOI: https://doi.org/10.48550/arXiv.2601.00856.
STIEGLER, B. Technics and time, 1: the fault of Epimetheus. Tradução de R. Beardsworth e G. Collins. Stanford: Stanford University Press, 1998. Publicado originalmente em 1994.
STRUBELL, E.; GANESH, A.; MCCALLUM, A. Energy and policy considerations for deep learning in NLP. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2019. p. 3645-3650. DOI: https://doi.org/10.18653/v1/P19-1355.
VALLOR, S. The AI mirror: how to reclaim our humanity in an age of machine thinking. Oxford: Oxford University Press, 2024.
VIPRA, J.; KORINEK, A. Market concentration implications of foundation models. Brookings Working Paper. Washington: Brookings Institution, 2023. Disponível em: https://arxiv.org/abs/2311.01550. Acesso em: 13 jul. 2026.





































