urn:uuid:6b19e715-313c-50a5-89d0-a70794c60ae0SocioLens — LD-01: What kind of thing is generative AI, and does the answer predict anything?New versions of this debate, revisions of its claims, Radar updates and research opportunities.2026-10-16T00:00:00ZSocioLensurn:uuid:3e3ba63f-ffb9-5982-9b2a-216eef3ec9e9New debate: LD-01 v0.1 — What kind of thing is generative AI, and does the answer predict anything?2026-10-16T00:00:00ZResearchers, firms, regulators and governments describe generative AI in incompatible terms: a tool, a normal technology, a cultural technology, an agent, a partner in communication, an infrastructure, an investment boom. Which description is best, and does the choice predict anything a researcher could observe?urn:uuid:efed770d-d579-5758-8229-83cc5781bb24New research opportunity: RO-LD01-01 — Which observations does each account of generative AI forbid?2026-10-16T00:00:00ZIf 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?urn:uuid:c7752ca8-0ebd-5fba-ba8b-bebb684270b2New research opportunity: RO-LD01-02 — Do the failure patterns of 2023–2024 language models persist in reasoning models?2026-10-16T00:00:00ZWhen the same items that showed sensitivity to output probability, the reversal curse, irrelevant clauses, compositional depth and counterfactual premises are put to successive model generations from 2023 to 2026, including reasoning and agentic models, which failure patterns shrink in size but keep their shape, and which disappear?urn:uuid:39ed606e-a496-5f9c-a861-73e70a90f230New research opportunity: RO-LD01-03 — Do products built on the same base model fail on the same people and cases?2026-10-16T00:00:00ZAmong deployed generative AI products that are built on the same base model (for example, customer-service assistants, writing tools or screening tools from different firms), are errors and harmful outputs more strongly correlated across products, by case and by the group affected, than among products built on different base models?urn:uuid:fe366377-948a-53e2-ac5e-6ef5644f41b7New research opportunity: RO-LD01-04 — When AI output goes wrong at work, who answers for it?2026-10-16T00:00:00ZIn organizations that use generative AI, how is responsibility for an AI-produced error assigned in practice (to the employee who used the tool, the manager, a risk team, the vendor or no one), and does the assignment differ when the system is described as a tool, an assistant or an agent?urn:uuid:5c6eae96-5f73-55f7-ac70-b4e74422f255New research opportunity: RO-LD01-05 — Does the EU's training-compute criterion track how general a model is?2026-10-16T00:00:00ZAcross open-weight and documented models released from 2020 to 2026, how closely does training compute predict measured generality (the range of distinct tasks performed competently), and how many models fall on the wrong side of the 10²³ FLOP indicative criterion that the European Commission uses for a general-purpose AI model?urn:uuid:5ec1f2c4-a4cf-58b4-b044-3d7fa41c9eafNew research opportunity: RO-LD11-07 — Does concentration at the model layer make apps fail alike? A test on China's register2026-10-16T00:00:00ZIn China's register of generative-AI services, where every launched application must display the filed service or model it uses, how concentrated is applications' dependence on a few base models, and on a fixed set of test cases are the errors, refusals and biased outputs of applications that share a base model more strongly correlated than those of applications built on different models, beyond the correlation already found between models from different providers?