Which observations does each account of generative AI forbid?

Research question

If proponents of the normal-technology, cultural-technology, agent, artificial-communication, infrastructure and capital-formation accounts each state in advance, with thresholds and dates, observations their account predicts and observations it rules out, which predictions differ between accounts, and which hold over the following years?

Why it matters

The debate finds that most evidence fits more than one account and that few accounts state what they predict. The normal-technology essay gives predictions without thresholds or dates, and one reading of the artificial-communication tradition rejects behavioural tests by design. Until rival accounts commit to observations that would count against them, the question "what kind of thing is it?" cannot be settled by evidence, only argued.

The debate lists this among what we still do not know: Which observations would one account predict and another forbid, stated before the data arrive?

Evidence that motivates it

1. Narayanan and Kapoor describe AI as “normal technology” and predict that slow diffusion “will continue to be the norm in high-consequence tasks”; they argue that diffusion is limited by the speed at which individuals, organizations and institutions adapt, and that for past general-purpose technologies it “occurs over decades, not years.” (LD01-SL-C01 v1.0)
   The normal-technology account states predictions about slow diffusion, without thresholds or dates.
   Cites: Narayanan & Kapoor (2025), “AI as Normal Technology: An…”
2. Narayanan and Kapoor predict that AI will not “meaningfully outperform trained humans (particularly teams of humans and especially if augmented with simple automated tools)” at forecasting geopolitical events or at persuading people to act against their own self-interest, and that “an increasing percentage of human jobs and tasks will be related to AI control.” (LD01-SL-C02 v1.0)
   Its predictions on forecasting and persuasion are specific but, by the authors' account, hard to make precise.
   Cites: Narayanan & Kapoor (2025), “AI as Normal Technology: An…”
3. Yiu, Kosoy and Gopnik describe large language models as “cultural technologies” and “efficient imitation engines,” and predicted that they would be almost as capable as humans at linking familiar objects but fail at tool innovation; in their study, children chose the functionally right novel tool 85.2% of the time and adults 95.7%, while the models tested were “much less capable.” (LD01-SL-C04 v1.0)
   The cultural-technology experiment shows the form such a test can take, a prediction stated and then checked against preliminary results.
   Cites: Yiu et al. (2023), “Imitation versus Innovation: What…”
4. Drawing on Luhmann, Esposito, Wittgenstein and Brandom, Fenoglio argues that what language models lack is a position in the practices of commitment and assessment, not a behavioural capacity, so that “no behavioral evidence can satisfy a positional criterion, and this is by design rather than by evasion.” (LD01-SL-C09 v1.0)
   One reading of the artificial-communication tradition holds that no behavioural evidence can satisfy its criterion.
   Cites: Fenoglio (2026), “Asymmetric Communication: Large…”

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Run an adversarial collaboration. Recruit authors or close readers of each account; for each, elicit five to ten observations the account predicts and five it rules out, each with a measurable threshold, a data source and a resolution date between 2027 and 2030. Code the overlap between accounts to find discriminating predictions, pre-register the set, and score it as data arrive. Treat accounts that decline to name observations as a finding and record their reasons.

Data needed

Structured elicitation with proponents; public data sources named for each prediction (adoption surveys, evaluation results, market data, court records); a pre-registration platform.

Feasibility

Funded team project. Low to medium; mostly researcher time for elicitation and later scoring. Proponents may decline or hedge; the design should record refusals rather than fill them in. Resolution takes years, but the elicitation and the overlap map are publishable on their own.

Has this been done?

The search found no study that does the core design (first-pass screen of 16 October 2026; not yet checked in depth).

No pre-registered set of discriminating predictions across accounts of what generative AI is was found. The nearest work is an adversarial collaboration on existential risk from AI, which sought cruxes between forecasters, and a joint essay by the normal-technology and AI 2027 authors, which could not be read and is reported to propose measures that would distinguish those two views. What stays open is a pre-registered comparison across all six accounts; the search was web-based and partial.

Where the search could not reach. Screened by web searches and the LD-01 source scan on the date shown; the joint essay and forecasting-institute reports were not read. Absence of a hit is not proof that the work does not exist.

Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.

Nearest work found

• Forecasting Research Institute, "Roots of Disagreement on AI Risk: Exploring the Potential and Pitfalls of Adversarial Collaboration" (2024) Judged from an abstract, summary or search listing only
  Seen in search results only. Elicited cruxes between forecasters who disagreed about catastrophic risk by 2100; not about what kind of thing generative AI is.
• Kapoor, Narayanan, Kokotajlo and others, joint essay comparing "AI as normal technology" and "AI 2027", Asterisk Magazine (November 2025) Judged from an abstract, summary or search listing only
  Seen in search results only; the essay was not read. Reported to list agreements and disagreements between two of the accounts.

References

Sources behind the motivating claims:

• Narayanan & Kapoor, “AI as Normal Technology: An alternative to the vision of AI as a potential superintelligence,” Knight First Amendment Institute, 15 April 2025 (SRC-SL-NARAYANAN-KAPOOR-KNIGHT-2025-04-15, no checked link to the original yet)
• Yiu, Kosoy & Gopnik, “Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?”, arXiv:2305.07666, 2023 (SRC-SL-YIU-KOSOY-GOPNIK-ARXIV-2305-07666, original)
• Fenoglio, “Asymmetric Communication: Large Language Models and Language Games,” arXiv:2607.28137, 2026 (SRC-SL-FENOGLIO-ARXIV-2607-28137, original)

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld01_01_v1,
  author = {{SocioLens project}},
  title = {Which observations does each account of generative AI forbid?},
  year = {2026},
  date = {2026-10-16},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld01-01/v1/}},
  note = {Research opportunity RO-LD01-01, version v1}
}

@misc{ro_ld01_01_nearest_1,
  title = {Forecasting Research Institute, "Roots of Disagreement on AI Risk: Exploring the Potential and Pitfalls of Adversarial Collaboration" (2024)},
  url = {https://forecastingresearch.org/s/AIcollaboration.pdf},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld01_01_nearest_2,
  title = {Kapoor, Narayanan, Kokotajlo and others, joint essay comparing "AI as normal technology" and "AI 2027", Asterisk Magazine (November 2025)},
  url = {https://asteriskmag.substack.com/p/common-ground-between-ai-2027-and},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{src_sl_narayanan_kapoor_knight_2025_04_15,
  title = {Narayanan \& Kapoor, “AI as Normal Technology: An alternative to the vision of AI as a potential superintelligence,” Knight First Amendment Institute, 15 April 2025},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_yiu_kosoy_gopnik_arxiv_2305_07666,
  title = {Yiu, Kosoy \& Gopnik, “Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?”, arXiv:2305.07666, 2023},
  url = {https://arxiv.org/abs/2305.07666},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_fenoglio_arxiv_2607_28137,
  title = {Fenoglio, “Asymmetric Communication: Large Language Models and Language Games,” arXiv:2607.28137, 2026},
  url = {https://arxiv.org/abs/2607.28137},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}