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Proposal pack · RO-LD11-02 v2

How large is open-weight model use beyond a single routing platform?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-11 debate on 12 October 2026, not a finding. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert; an independent AI reviewer with fresh context (not a person), 12 October 2026: every nearest-work citation checked against its source page; corrections applied. Every check so far was done by AI; no person or domain expert has checked it yet. The literature screen below was done by AI web search and can miss relevant work. Version v2, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld11-02/v2/.

Untested beyond the original setting One PhD chapter or paper Needs a budget (medium or higher) Partly addressed

Research question

How does the open-weight share of language-model use estimated from OpenRouter compare with shares estimated from other observable sources (other inference marketplaces and hosts, model downloads, self-hosting tool registries, and a survey of organisations' production deployments), and which population does each source actually represent?

Why it matters

The claim that open-weight models reduce dependence on proprietary vendors rests heavily on usage data from one routing platform whose users (developers, hobbyists, price-sensitive and roleplay traffic) may differ sharply from enterprises and public bodies. Without a second, independent measurement, nobody can say whether "about one-third" is a market fact or a platform fact.

The debate lists this among what we still do not know: global open-weight share beyond OpenRouter

Evidence that motivates it

  1. OpenRouter and a16z report that open-weight models reached approximately one-third of token usage on OpenRouter by late 2025; an NBER working paper using OpenRouter and Microsoft Azure usage data reports a lower open-source share of OpenRouter tokens, about 25% by December 2025 after a peak near 45% in August. (LD11-HQ-C14 v1.1)
    The one-third open-weight token share is measured on OpenRouter only, and the claim does not license a global, enterprise or infrastructure-independence reading; this study tests how far it travels.
    Cites: OpenRouter/a16z, State of AI: 100 Trillion Token Study; Demirer et al. (2025), “The Emerging Market for…”
  2. Menlo Ventures estimated late-2025 enterprise LLM API shares of Anthropic 40%, OpenAI 27% and Google 21%, compared with OpenAI 50% and Anthropic 12% in 2023. (LD11-HQ-C13 v1.0)
    The only enterprise-side provider shares cited come from a single venture-investor survey and are rated contested; an independent organisational survey would add a second, replicable enterprise measure.
    Cites: Menlo Ventures, 2025 State of Generative AI in the…
  3. Concentration claims in AI must identify the stack layer, market definition, metric, geography and period; concentration at one layer does not establish concentration or power at another. (LD11-HQ-C01 v1.0)
    Open-weight "share" means different things under different denominators (tokens, downloads, deployments, spend); the design keeps them as separate estimands.
    Cites: R06 — HQ Cross-Batch Synthesis & Adjudication v0.1

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

A possible design

Build a multi-source panel for 2025–2027 with one row per model family per month: (1) token shares on OpenRouter from its public rankings; (2) usage or listing signals from other inference marketplaces and hosts that publish them; (3) Hugging Face download histories (using the released Longpre et al. dataset as a base); (4) pull counts or listings from self-hosting registries where public; and (5) a fielded survey of 500–1,000 organisations (stratified by size and sector, in two or three countries) asking which models run in production, how they are hosted, and roughly what share of their language-model workload each takes. Treat each as a separate estimand, report open-weight shares with intervals per source, and test how well platform-based shares predict survey-based shares. A short validation component asks a few hosting firms or a university IT department for aggregate logs to check the survey against records.

Data needed

Public OpenRouter rankings (archived snapshots exist); Hugging Face metadata and the Longpre et al. dataset; registry data where public; a commissioned organisational survey (or a module on an existing business survey); optional data-sharing agreements with hosts.

Feasibility

One PhD chapter or paper. Medium: public data is free; an organisational survey costs roughly US$5,000–20,000 depending on country and panel provider, less if added to an existing business survey. Hosts may not share logs; the study is still informative from public sources plus the survey. Downloads are not usage, and the write-up must not convert them into usage shares.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 12 October 2026, checked again on 13 October 2026).

Demirer, Fradkin, Tadelis and Peng pair OpenRouter with Microsoft Azure AI Foundry data for pricing and demand rather than a population share. Nagle and Yue (2025) and the ATOM Report (2026) both rely on OpenRouter for usage share, and ATOM covers open models only. Longpre et al. map Hugging Face downloads (distribution, not use), and Dunivin (2026) measures open-weight use in scientific papers, a separate population. What remains open is a deliberately multi-source estimate that treats each source as a different population, includes self-hosted and enterprise production use measured by a replicable organisational survey, and tests whether the OpenRouter share generalises.

Literature check. New 2025 to 2026 work adds further single-source estimates but no multi-source comparison: Nagle and Yue (SSRN 5767103) estimate open versus closed usage and revenue shares from OpenRouter only; the ATOM Report (Lambert and Brand, arXiv:2604.07190) uses OpenRouter for inference share and notes it correlates with Hugging Face downloads but does not estimate an open versus closed share; Dunivin (arXiv:2608.11090) measures open-weight use in 21 million scientific papers. None fields an organisational survey or treats sources as distinct populations, so the opportunity stands as written. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert.

Where the search could not reach. Screened by web search on the date shown; Google Scholar, SSRN, paywalled journals and commercial survey reports (for example industry analyst subscriptions) were not reachable. The Demirer et al. details were checked against the NBER working-paper PDF. Absence of a hit is not proof that the work does not exist. Literature check of 2026-10-13: SSRN page for Nagle and Yue could not be fetched; its description relies on a Linux Foundation blog summary. Commercial enterprise surveys (vendor and analyst reports) surfaced in search but were not treated as scholarly evidence and were not opened. Full texts of ATOM and the Ollama measurement paper were read only partly or via abstract.

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

References

Sources behind the motivating claims:

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_ld11_02_v2,
  author = {{SocioLens project}},
  title = {How large is open-weight model use beyond a single routing platform?},
  year = {2026},
  date = {2026-10-13},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld11-02/v2/}},
  note = {Research opportunity RO-LD11-02, version v2}
}

@misc{ro_ld11_02_nearest_1,
  title = {Aubakirova, Atallah, Clark, Summerville \& Midha, "State of AI: An Empirical 100 Trillion Token Study with OpenRouter," arXiv:2601.10088 (Jan 2026)},
  url = {https://arxiv.org/abs/2601.10088},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld11_02_nearest_2,
  title = {Demirer, Fradkin, Tadelis \& Peng, "The Emerging Market for Intelligence: Pricing, Supply, and Demand for LLMs," NBER Working Paper 34608 (Dec 2025)},
  url = {https://www.nber.org/papers/w34608},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld11_02_nearest_3,
  title = {Longpre, Akiki, Lund, Kulkarni, Chen, Solaiman, Ghosh, Jernite \& Kaffee, "Economies of Open Intelligence: Tracing Power \& Participation in the Model Ecosystem," arXiv:2512.03073 (Nov 2025)},
  url = {https://arxiv.org/abs/2512.03073},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld11_02_nearest_4,
  title = {Nagle \& Yue, "The Latent Role of Open Models in the AI Economy," SSRN working paper 5767103 (2025; year from SSRN listing, page not opened)},
  url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5767103},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only; Some details not confirmed}
}

@misc{ro_ld11_02_nearest_5,
  title = {Lambert \& Brand, "The ATOM Report: Measuring the Open Language Model Ecosystem," arXiv:2604.07190 (v2 May 2026)},
  url = {https://arxiv.org/abs/2604.07190},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld11_02_nearest_6,
  title = {Dunivin, "Who Uses Open-Weight Models? China and the Shifting Geography of AI in Science," arXiv:2608.11090 (Aug 2026)},
  url = {https://arxiv.org/abs/2608.11090},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld11_02_nearest_7,
  title = {Xu, Li, Qiu \& Sun, "Ollama in the Wild: A Longitudinal Measurement of Exposed Ollama LLM Endpoints at Internet Scale," arXiv:2609.07115 (Sept 2026)},
  url = {https://arxiv.org/abs/2609.07115},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld11_openrouter_a16z_100t_token_study,
  title = {OpenRouter/a16z, State of AI: 100 Trillion Token Study},
  url = {https://openrouter.ai/state-of-ai},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_demirer_fradkin_tadelis_peng_nber_34608,
  title = {Demirer, Fradkin, Tadelis \& Peng, “The Emerging Market for Intelligence: Pricing, Supply, and Demand for LLMs,” NBER Working Paper 34608, December 2025},
  url = {https://www.nber.org/papers/w34608},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld11_menlo_state_of_genai_enterprise_2025,
  title = {Menlo Ventures, 2025 State of Generative AI in the Enterprise},
  url = {https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_r06_hq_cross_batch_synthesis,
  title = {R06 — HQ Cross-Batch Synthesis \& Adjudication v0.1},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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