27%

grid and power economist · openai/gpt-5.6-sol

The deadline has passed, so this is now a question of whether a qualifying disclosure already exists. The supplied digest shows strong AI investment and model activity but no named Fortune 500 customer disclosure satisfying all three strict elements: production status, specialist-multimodal scope, and more than $25M annualized value or equivalent capacity. Enterprises and vendors commonly announce deployments without contract economics, while large figures often cover broader cloud or AI commitments. With ambiguity resolving against the forecaster, generic partnerships, pilots, or undifferentiated spending would fail. The software/disclosure base rates are materially less informative than this unusually specific documentation test.

semiconductor supply chain analyst · openai/gpt-5.6-sol

The relevant software and disclosure reference classes center near 0.45, but this trigger is unusually documentation-heavy: a Fortune 500 customer must be named, the deployment called production rather than a pilot, specialist multimodality established, and disclosed dollars or capacity must clear $25M annualized. Large AI infrastructure spending makes the underlying deployment plausible, yet enterprises and vendors rarely publish workload-level annual economics. The post-deadline digest shows strong platform capex and model activity but no qualifying named-customer disclosure. Under the adverse ambiguity rule, inferred seat counts, total partnerships, multiyear commitments, or unspecified AI capacity would fail. The forecaster’s 0.6 therefore appears materially high.

capital markets credit analyst · openai/gpt-5.6-sol

The deadline has passed, so probability reflects whether a qualifying disclosure already exists but is absent from the supplied evidence. Hyperscaler RPO and capex growth support widespread enterprise deployment, but not the required disclosure. The trigger is unusually strict: a named Fortune 500 customer, explicit production status, specialist multimodality, and a disclosed value above $25M annualized or qualifying capacity. Enterprises and vendors commonly announce production use while withholding contract economics. The software and disclosure base rates near 0.45 merit weight, but the multiple specificity requirements and adverse ambiguity policy justify a substantial discount.

superforecaster statistician · openai/gpt-5.6-sol

I use the software-ledger base rate of 0.446 (disclosure class: 0.461), then reduce sharply for the strict conjunction: a Fortune 500 name, specialist multimodal scope, production status, and a disclosed dollar or capacity figure proving more than $25M annualized. Such customer-level economics are rarely public even when deployments are real. The deadline passed 19 days ago, and the supplied digest identifies no qualifying disclosure; ambiguity resolves against the forecast. The forecaster’s 0.60–0.69 band realized only 0.548, also supporting a downward correction from 0.60.

geopolitics regulation analyst · openai/gpt-5.6-sol

The deadline has passed, so this is now an evidence question. The supplied digest shows intense model releases and infrastructure spending but no qualifying named-customer disclosure. Production deployments among Fortune 500 firms were plausible, yet the required combination—specialist multimodal, explicit production status, named customer, and a >$25M annualized dollar or qualifying capacity figure—is unusually difficult because enterprises and vendors rarely publish deployment economics. Ambiguity resolving against the forecaster further penalizes announcements describing pilots, partnerships, aggregate commitments, or unspecified scale. This is below the software and disclosure base rates and well below the original 0.6 forecast.