{"schemaVersion":"longarena.global500-ai-affinity.v2","name":"LongArena Global 500 Enterprise AI-Affinity Public Evidence Index","version":"2026.09","auditedAt":"2026-09-19","cohort":{"name":"Fortune Global 500","edition":2026,"size":500,"sourceUrl":"https://fortune.com/ranking/global500/","use":"Cohort membership and revenue rank only; Fortune rank is not an AI-affinity scoring input."},"benchmark":{"score":60,"labelEn":"AI-affine benchmark","labelZh":"AI 亲和基准线","meaningEn":"A company must show first-party evidence of deployed AI, score at least 15/25 for operating adoption and 7/10 for evidence quality, and reach 60/100 overall.","meaningZh":"企业须有已部署 AI 的第一方公开证据，规模化运营采用不低于 15/25、证据质量不低于 7/10，并达到总分 60/100。","screenedCompanies":500,"candidatePoolSize":175,"fullReviewCompanies":175,"deferredCompanies":0,"publishedCompanies":108,"publishedMedianScore":70,"publishedCutoffScore":60},"disclaimer":"This independent index measures public AI affinity evidence, not company quality, financial performance, investment merit, endorsement, partnership, customer status, or affiliation. Absence of public evidence can understate private adoption. LongArena is not affiliated with Fortune or ranked companies.","aiAgentAudit":{"en":"Baseline construction: the 2026 Fortune Global 500 cohort was baseline-screened and an initial 100-company edition was built by an AI-agent workflow (Codex CLI with gpt-5.6-sol). Independent re-verification and rebuild: the cohort, evidence and scores were then re-derived in a DeepSeek Harness (DSH) session running deepseek/deepseek-v4.1-flash, which publishes every benchmark-qualified company. No company paid to participate and Fortune rank is not used as a scoring input.","zh":"基线构建：2026 年 Fortune 全球 500 强母样本的基线筛查与最初的 100 家企业版本由 AI agent 工作流（Codex CLI 与 gpt-5.6-sol）完成。独立复核与重建：母样本、证据与分数随后在一个 DeepSeek Harness（DSH）×deepseek/deepseek-v4.1-flash 会话中重新推导，并改为发布全部达标企业。企业无需付费参与，Fortune 名次不进入 AI 评分。"},"provenance":{"baselineStage":{"tool":"Codex CLI with gpt-5.6-sol (azure provider)","role":"Cohort baseline screen and the initial 100-company edition."},"reviewStage":{"tool":"DeepSeek Harness (DSH) with deepseek/deepseek-v4.1-flash (commandcode provider)","role":"Rebuilt the candidate pool, re-reviewed every first-party evidence link, re-scored all entries against one deterministic rubric, and now publishes every benchmark-qualified company."},"note":"Tooling is disclosed because it shaped the process, not the numbers: the published score of a company comes from its evidence profile and the rubric in this document, and both are published. Model attribution is taken from the session records of the runs themselves rather than from machine configuration, which changes over time: the Codex sessions that produced the initial edition record gpt-5.6-sol, and the review-stage default is deepseek/deepseek-v4.1-flash. Stage 2 is fully reproducible from the committed artifacts; stage 1 predates them."},"reproduction":{"guideAnchor":"reproducibility","deterministic":["Cohort fields: rank, name, sector, country/region, Fortune page slug and official site, read from the Fortune Global 500 payload.","Bounded baseline screen: fixed path list, 12s timeout, no crawling; every probe recorded with status and outcome.","Agent-discovery verification: a bare HTTP 200 is not accepted; JSON content-type or an OAuth/MCP marker is required.","Candidate-pool inclusion: three documented routes, applied by script, with the reason recorded per company.","Scoring: five dimensions computed from an evidence profile by fixed anchors; the evidence-quality dimension is measured, not supplied by a reviewer.","Publication gates and the published set: score >= 60, operating adoption >= 15, evidence quality >= 7, no slot cap.","Every acceptance gate: structure invariants, reproducible tie-breaks, cohort cross-check, per-link reachability and content quality."],"judgmentDependent":["Evidence discovery and the content of each evidence profile (which first-party page best represents a company, and how many named products or quantified metrics it supports).","Frontier-model tier, granted centrally from a reviewed list rather than by each profile.","Registered exceptions where a cited page blocks automated clients or cannot be reached from the reviewer network.","Exclusions: a company whose own properties cannot be read is removed and disclosed rather than scored at zero."],"caveats":["Live pages change: a reproduction run on a later date may find dead links, or different evidence, and therefore different scores.","Network egress matters: bot walls (403/406/412), regional blocks and DNS interception change what a reviewer can read; the committed run records each such case.","Client-rendered corporate sites return little static text; rendering is required before concluding a page is empty.","Screen recall is limited (measured against known-good companies it re-found only 47 of 100), which is why the candidate pool is a union of three routes rather than the screen alone.","Brand and ownership traps exist: similarly named but unrelated companies, and AI that belongs to a subsidiary rather than the parent, both distort scoring if missed."],"commands":["npm --prefix plugins/longarena-investor run refresh:scan","npm --prefix plugins/longarena-investor run refresh:queue","# review agents write pool/profiles/<rank>-<slug>.json, one per candidate","npm --prefix plugins/longarena-investor run refresh:finalize","npm --prefix plugins/longarena-investor run refresh:gate"]},"methodology":[{"key":"productPlatform","weight":25,"labelEn":"AI product & platform","labelZh":"AI 产品与平台","descriptionEn":"Production AI products, models, infrastructure, or customer-facing capabilities that can be independently identified.","descriptionZh":"可独立识别的生产级 AI 产品、模型、基础设施或客户能力。"},{"key":"agentInterfaces","weight":20,"labelEn":"Agent & interface openness","labelZh":"Agent 与接口开放度","descriptionEn":"APIs, SDKs, open models, agent platforms, developer tooling, and public machine-readable interfaces.","descriptionZh":"API、SDK、开放模型、Agent 平台、开发工具与公开机器可读接口。"},{"key":"operatingAdoption","weight":25,"labelEn":"Operating adoption at scale","labelZh":"规模化运营采用","descriptionEn":"First-party evidence that AI is embedded in core workflows, products, service delivery, or workforce operations.","descriptionZh":"AI 已进入核心工作流、产品、服务交付或员工运营的一手公开证据。"},{"key":"researchEngineering","weight":20,"labelEn":"Research & engineering depth","labelZh":"研究与工程深度","descriptionEn":"Dedicated labs, publications, models, patents, engineering programs, and sustained technical investment.","descriptionZh":"专门实验室、论文、模型、专利、工程项目与持续技术投入。"},{"key":"evidenceQuality","weight":10,"labelEn":"Evidence quality","labelZh":"证据质量","descriptionEn":"Recency, first-party attribution, specificity, reproducibility, governance disclosure, and machine readability.","descriptionZh":"证据的时效性、第一方归因、具体性、可复核性、治理披露与机器可读性。"}],"tieBreakers":["score","frozenAuditOrder"],"entries":[{"id":"alphabet","name":"Alphabet","nameZh":"Alphabet（Google）","fortuneRank":8,"sector":"Technology","country":"U.S.","scores":{"productPlatform":25,"agentInterfaces":20,"operatingAdoption":25,"researchEngineering":17,"evidenceQuality":10},"signals":["foundationModels","agentPlatform","developerEcosystem","scaledOperations","research"],"companyUrl":"https://www.abc.xyz","fortuneUrl":"https://fortune.com/company/alphabet/","evidence":[{"title":"Gemini - Google DeepMind frontier model family","url":"https://deepmind.google/models/gemini/"}],"aiRank":1,"score":97,"rankLift":7},{"id":"alibaba-group-holding","name":"Alibaba Group Holding","nameZh":"阿里巴巴集团","fortuneRank":56,"sector":"Retailing","country":"Mainland 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