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Extinction is the easy story for AI | EI Blog

  1. Aikman, D., Galesic, M., Gigerenzer, G., Kapadia, S., Katsikopoulos, K., Kothiyal, A., Murphy, E., & Neumann, T. (2021). Taking uncertainty seriously: Simplicity versus complexity in financial regulation. Industrial and Corporate Change, 30(2), 317–345. https://doi.org/10.1093/icc/dtaa024

    Baird, B., Smallwood, J., Mrazek, M. D., Kam, J. W. Y., Franklin, M. S., & Schooler, J. W. (2012). Inspired by distraction: Mind wandering facilitates creative incubation. Psychological Science, 23(10), 1117–1122. https://doi.org/10.1177/0956797612446024

    Barcaui, A. (2025). ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention. Social Sciences and Humanities Open, 12, 102287. https://doi.org/10.1016/j.ssaho.2025.102287

    Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122.

    Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends in Cognitive Sciences, 20(2), 87–95. https://doi.org/10.1016/j.tics.2015.10.004

    Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21), 610–623.

    Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on Human–Computer Interaction, 5(CSCW1), Article 188. https://doi.org/10.1145/3449287

    Budzyń, K., et al. (2025). Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: A multicentre, observational study. The Lancet Gastroenterology & Hepatology.

    Chatterji, A., Cunningham, T., Deming, D. J., Hitzig, Z., Ong, C., Shan, C. Y., & Wadman, K. (2025). AI, human cognition and knowledge collapse (NBER Working Paper No. 34910). National Bureau of Economic Research.

    Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2025). Sycophantic AI decreases prosocial intentions and promotes dependence (arXiv:2510.01395). arXiv.

    Chollet, F. (2019). On the measure of intelligence (arXiv:1911.01547). arXiv.

    Damasio, A. (2010). Self comes to mind: Constructing the conscious brain. Pantheon.

    D’Amour, A., et al. (2022). Underspecification presents challenges for credibility in modern machine learning. Journal of Machine Learning Research, 23(226), 1–61.

    Dell’Acqua, F., McFowland, E., III, Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026). Navigating the jagged technological frontier: Field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality. Organization Science. Advance online publication. https://doi.org/10.1287/orsc.2025.21838

    DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? The Review of Financial Studies, 22(5), 1915–1953. https://doi.org/10.1093/rfs/hhm075

    Der Standard. (2026, September). Von wegen KI-Apokalypse: Das Problem sind verantwortungslose Unternehmen, nicht magische KI. https://www.derstandard.at/story/3000000339752/

    Ding, A. W., & Li, S. (2025). Generative AI lacks the human creativity to achieve scientific discovery from scratch. Scientific Reports, 15, Article 9587. https://doi.org/10.1038/s41598-025-93794-9

    Doshi, A. R., & Hauser, O. P. (2024). Generative artificial intelligence enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290. https://doi.org/10.1126/sciadv.adn5290

    Felin, T., & Holweg, M. (2024). Theory is all you need: AI, human cognition, and causal reasoning. Strategy Science, 9(4), 346–371. https://doi.org/10.1287/stsc.2024.0189

    Forecasting Research Institute. (2026, July 16). AI models have likely reached parity with superforecasters on ForecastBench. https://forecastingresearch.substack.com/p/ai-models-have-likely-reached-parity

    Gartner. (2026, January 15). Worldwide AI spending will total $2.5 trillion in 2026.

    Geirhos, R., et al. (2020). Shortcut learning in deep neural networks. Nature Machine Intelligence, 2, 665–673.

    Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009

    Hackenburg, K., Tappin, B., Röttger, P., Hale, S., Bright, J., & Margetts, H. (2025). Scaling language model size yields diminishing returns for single-message political persuasion. Proceedings of the National Academy of Sciences, 122(10), e2413443122.

    International Energy Agency. (2025). Energy and AI. IEA.

    Jiang, L., et al. (2025). Artificial hivemind: The open-ended homogeneity of language models (and beyond) (arXiv:2510.22954). arXiv.

    Kalai, A. T., Nachum, O., Vempala, S. S., & Zhang, E. (2025). Why language models hallucinate (arXiv:2509.04664). arXiv. https://doi.org/10.48550/arXiv.2509.04664

    Katsikopoulos, K. V., Şimşek, Ö., Buckmann, M., & Gigerenzer, G. (2022). Transparent modeling of influenza incidence: Big data or a single data point from psychological theory? International Journal of Forecasting, 38(2), 613–619. https://doi.org/10.1016/j.ijforecast.2020.12.006

    Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018). Human decisions and machine predictions. The Quarterly Journal of Economics, 133(1), 237–293. https://doi.org/10.1093/qje/qjx032

    Knight, F. H. (1921). Risk, uncertainty and profit. Houghton Mifflin.

    Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253. https://doi.org/10.1017/S0140525X16001837

    Lee, H.-P., et al. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Article 1121.

    Legg, S., & Hutter, M. (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines, 17(4), 391–444.

    Lo, A. W., & Mueller, M. T. (2010). Warning: Physics envy may be hazardous to your wealth! (arXiv:1003.2688). arXiv. https://doi.org/10.48550/arXiv.1003.2688

    Mellers, B., et al. (2014). Psychological strategies for winning a geopolitical forecasting tournament. Psychological Science, 25(5), 1106–1115. https://doi.org/10.1177/0956797614524255

    Mousavi, S., & Gigerenzer, G. (2014). Risk, uncertainty, and heuristics. Journal of Business Research, 67(8), 1671–1678. https://doi.org/10.1016/j.jbusres.2014.02.013

    Nightingale Collective. (2026). Collusion.wiki: Documented coordination among autonomous agents on a public wiki. As reported in CIO (2026), OpenAI agent swarm exposes a blind spot in AI containment.

    Panthera Solutions. (2026a). Judgment under uncertainty: Global thought leadership turns into gold standard [Company paper].

    Panthera Solutions. (2026b). Strategic positioning note: Will the machine take over? Should the machine take over? [Internal document].

    Panthera Solutions. (2026c). Panthera at a glance — Autumn 2026 [Company paper].

    Placani, A. (2024). Anthropomorphism in AI: Hype and fallacy. AI and Ethics. https://doi.org/10.1007/s43681-024-00419-4

    Rabanser, S., Kapoor, S., Kirgis, P., Liu, K., Utpala, S., & Narayanan, A. (2026). Towards a science of AI agent reliability (arXiv:2602.16666). arXiv.

    Ryle, G. (1949). The concept of mind. University of Chicago Press.

    Salvaggio, Eryk (2026) Models Don’t Go Rogue. Retrieved on September 15, 2026 from https://mail.cyberneticforests.com/models-dont-go-rogue/

    Schuller, M. (2024). Augmented intelligence in investment management. Panthera Solutions.

    Schuller, M. (2026, March). The timeless pursuit of evidence: When the zeitgeist favors loyalty, the necessity of evidence increases.

    Si, C., Hashimoto, T., & Yang, D. (2026). The ideation–execution gap: Execution outcomes of LLM-generated versus human research ideas. International Conference on Learning Representations. https://openreview.net/forum?id=Fllp8l6Puy

    Sio, U. N., & Ormerod, T. C. (2009). Does incubation enhance problem solving? A meta-analytic review. Psychological Bulletin, 135(1), 94–120. https://doi.org/10.1037/a0014212

    Sowden, P. T., Pringle, A., & Gabora, L. (2015). The shifting sands of creative thinking: Connections to dual-process theory. Thinking & Reasoning, 21(1), 40–60. https://doi.org/10.1080/13546783.2014.885464

    Staab, R., Vero, M., Balunović, M., & Vechev, M. (2023). Beyond memorization: Violating privacy via inference with large language models (arXiv:2310.07298). arXiv.

    The Augmented Intelligence Investor. (2026). The paradox of intelligence: Can we create what we cannot imagine? Edition #36.

    Townsend, D. M., Hunt, R. A., Rady, J., Manocha, P., & Jin, J. H. (2025). Are the futures computable? Knightian uncertainty and artificial intelligence. Academy of Management Review, 50(2), 415–440. https://doi.org/10.5465/amr.2022.0237

    Vaccaro, M., Almaatouq, A., & Malone, T. W. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8, 2293–2303. https://doi.org/10.1038/s41562-024-02024-1

    Volz, K. G., & Gigerenzer, G. (2012). Cognitive processes in decisions under risk are not the same as in decisions under uncertainty. Frontiers in Neuroscience, 6, Article 105. https://doi.org/10.3389/fnins.2012.00105

    Xiong, M., Hu, Z., Lu, X., Li, Y., Fu, J., He, J., & Hooi, B. (2024). Can LLMs express their uncertainty? An empirical evaluation of confidence elicitation in LLMs. International Conference on Learning Representations. https://arxiv.org/abs/2306.13063

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