spb/airiskindex Public

1# 04 — Competitive Landscape & Real-World Evidence (through August 2026)23Research memo for the AI Risk Index (airiskindex.io). Compiled 2026-08-05 from live web research.4Purpose: (a) map existing public AI-job-risk tools, (b) assemble the 2024–2026 empirical record on AI's5labor-market effects, and (c) propose how this evidence parameterizes the `barriers` and6`adoption_velocity` scoring dimensions (weights 0.20 / 0.10 in v1, `packages/scoring/src/weights.ts`).78---910## Part A — Competitive landscape of public AI-job-risk tools1112### A.1 willrobotstakemyjob.com (the incumbent)1314- **Since 2017.** Tagline: "Find out how likely your job is to be automated — based on real data and user votes."15- **Methodology:** Automation-risk probabilities produced "using a similar method" to **Frey & Osborne (2013)**16  ("The Future of Employment", Gaussian process classifier over 702 occupations, the famous "47% of US17  employment at risk" paper), re-estimated "with the most up-to-date data available", plus BLS occupation18  data (employment, wages, growth). Since 2019 it collects **user poll votes** on perceived risk; in 2021 it19  blended BLS data + polls + automation probability into a 0–10 "job score".20  Source: https://willrobotstakemyjob.com/about21- **Critical weakness:** the underlying model is pre-LLM computerization/robotics-era work. Frey–Osborne22  scored *whole occupations* on physical/routine automatability (manual dexterity, cramped workspace,23  fine arts, social perceptiveness...), which inverts under generative AI: it rates cognitive/office work as24  relatively safe and misses exactly the exposure the 2024–2026 evidence shows (translators, writers,25  customer service, junior developers). Single doom-number framing ("X% probability of automation"),26  no exposure/substitution/augmentation distinction, no confidence intervals, no versioning, no API.27- **Traffic (why it still matters):** ~71.3K visits in May 2026 per Semrush (down ~20% MoM), world rank28  ~#50,395; ~42% of traffic from Google organic, ~39% direct. Other estimators give 45K–100K+/month.29  Sources: https://www.semrush.com/website/willrobotstakemyjob.com/overview/ , https://hypestat.com/info/willrobotstakemyjob.com30- **Monetization:** ads; no API or paid tier found.3132### A.2 Other public lookup tools (US/EN)3334| Tool | Data / method | Output | Weaknesses | Pricing/API |35|---|---|---|---|---|36| **willrobotstakemyjob.com** (2017) | Frey–Osborne-style re-estimation + BLS + user polls | Single automation % + 0–10 "job score" | Pre-LLM model, whole-occupation doom number, no sub-scores, no versioning | Free, ads; no API |37| **replacedbyrobot.info** ("2026 AI Automation Risk Database") | Claims BLS + O\*NET over "57,000+ occupations" (job titles, not SOC codes) | "2 risk scores per job" (AI + robotics) | Opaque method, ad-heavy, title-level pseudo-precision | Free, AdSense; no API |38| **aijobimpactcalculator.com** (2026, by Digital Signet) | ILO 2025 GenAI exposure gradient (4 bands) + Brookings 2024 task rubric on O\*NET 30.2 tasks + BLS EP 2024-34 + WEF FoJ 2025; static, pre-computed; published methodology & revision history | Exposure band + top-5 tasks tagged Displaceable/Changing/Growing + "what's growing" panel | Band-level only (4 bands), no composite score, no CI, US-centric | Free; no API |39| **replacemeter.com** | Undisclosed (looks LLM-generated per submitted job title) | Letter grades: "AI Resilience" %, "Adaptability" % | No methodology page, arbitrary-title scoring, no provenance | Free |40| **tripleten.com/tools/what-jobs-will-ai-replace** | LLM analysis of user-entered title/industry; "most recent AI research data" (uncited) | 0–100% automation risk + skills advice + career alternatives | Lead-gen for a bootcamp; unreproducible | Free (lead-gen) |41| **ailayoffs.live** | Aggregates layoffs.fyi, Goldman, McKinsey, WEF; "Oxford research + real layoff data" risk checker | Live displacement counters + risk score | Doom-counter framing, mixes projections with counts | Free |42| **ailayofftracker.com** / **founderreports.com/ai-layoffs-tracker** / **skillsyncer.com/layoffs-tracker** | Curated AI-cited layoff announcements (Challenger, TechCrunch sourcing) | Event lists, totals | Event trackers, not occupation scores | Free |43| **techjacksolutions.com/job-displacement-trends** | Mash-up: Anthropic Economic Index %, Gartner, BLS growth, "WifiTalents" | Per-occupation risk ranges (e.g., customer service 67–80%) | Mixes usage shares with risk %, low-quality sources alongside good ones | Free |44| **Stanford Canaries Dashboard** (digitaleconomy.stanford.edu) | ADP payroll microdata, 4.6M workers, 730+ occupations, continuously updated | Employment trends by age × AI exposure | Research dashboard, not per-occupation risk lookup — but the credibility benchmark | Free |4546Academic/institutional exposure indices that power many of these (no consumer UI of their own):47**Felten–Raj–Seamans AIOE** (AI application ↔ 52 O\*NET abilities; https://sites.bu.edu/tpri/2021/06/02/occupational-industry-and-geographic-exposure-to-artificial-intelligence-a-novel-dataset-and-its-potential-uses),48**ILO Global Index of Occupational Exposure to GenAI** (ISCO-08, 4 gradient bands; https://webapps.ilo.org/static/english/intserv/working-papers/wp140/index.html),49**Microsoft "AI applicability score"** (Tomlinson et al. 2025, 200K Copilot conversations mapped to O\*NET work50activities — top: interpreters/translators (98% activity overlap), historians, passenger attendants, sales reps,51writers, customer service reps; https://www.microsoft.com/en-us/research/publication/working-with-ai-measuring-the-occupational-implications-of-generative-ai),52**OpenAI "GPTs are GPTs"** (Eloundou et al., Science 2024), **Pew 2023 O\*NET work-activity classification**53(https://www.pewresearch.org/social-trends/2023/07/26/2023-ai-and-jobs-methodology-for-onet-analysis).5455### A.3 French / EU equivalents5657- **jobimpact.aidoption.fr** — "Exposition IA du marché de l'emploi français": 532 ROME occupations,58  treemap (surface = jobs, color = 0–10 exposure), France Travail ROME + DARES data, **scored by Claude**59  (LLM-as-rater — directly comparable to our §6 pipeline, but with no audit trail or versioning). Has a /us/ twin.60- **transitions-ia.fr** ("IA & Métiers France" observatory) — ROME 4.0 + INSEE EEC 2024 + DARES BMO 2024;61  task-level exposure score per métier. https://otakuch.github.io/transitions-ia.fr/62- **job-guard.com** — French-language "votre métier va-t-il disparaître ?" test; editorial/affiliate quality.63- **Observatoire des Emplois Menacés et Émergents + Coface study** (Nov 2025, covered by Les Échos): ~16% of64  French jobs at risk; white-collar metropolitan jobs most exposed (Paris ~19%, Lyon/Toulouse 18%);65  legal/accounting, publishing/press, IT programming/consulting, insurance, finance >25% of jobs exposed.66  https://www.lesechos.fr/monde/europe/ia-le-grand-bouleversement-a-venir-du-marche-du-travail-222176067- No credible official FR/EU consumer lookup exists (France Travail offers only e-learning content) → the68  ESCO/ROME crosswalk in our roadmap targets an **empty niche**.6970### A.4 SEO landscape7172- Queries like "will AI take my job (2026)" are dominated not by tools but by **listicle/content marketing**:73  Nucamp, NovoResume, Careerminds, AI Weekly, Shawn Kanungo — all citing the same WEF 92M-displaced /74  170M-created / +78M-net figure, plus Goldman's −16K net jobs/month (Apr 2026). Tool sites rank on75  "AI job risk calculator" / "will robots take my job" variants; willrobotstakemyjob.com still owns its76  brand query with ~42% organic share of its traffic.77- Opportunity: nothing ranking today combines (1) task-level methodology, (2) sub-scores with uncertainty,78  (3) live evidence (Challenger/adoption data), (4) versioned transparency, (5) non-doom adaptation framing.79  aijobimpactcalculator.com is the closest philosophical competitor (source-cited, anti-doom, "what's growing"80  panel, "how to argue with this" page) but is static, band-level, and has no composite index, no API, no EU coverage.8182---8384## Part B — Real-world evidence, 2024 → August 20268586### B.1 Entry-level employment effects ("Canaries" line of evidence)8788- **Brynjolfsson, Chandar & Chen (Stanford/ADP), "Canaries in the Coal Mine?"** (Aug 2025, rev. Nov 13 2025):89  since gen-AI diffusion, workers **aged 22–25 in the most AI-exposed occupations saw a ~16% relative90  employment decline** (software devs 22–25 down ~20% from late-2022 peak), controlling for firm-level shocks;91  older workers in the same occupations stable/growing. Adjustment via **employment, not wages**. Declines92  **concentrated where AI automates rather than augments** (per Anthropic Economic Index task classification).93  https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/94- **Follow-up note (Feb 9, 2026), "Canaries, Interest Rates, and Timing":** interest rates don't explain the95  *differential* entry-level decline in AI-exposed occupations.96  https://digitaleconomy.stanford.edu/news/canaries-interest-rates-and-timinga-more-on-recent-drivers-of-employment-changes-for-young-workers97- **Canaries Dashboard** (2026): continuous monitoring, 4.6M workers, 730+ occupations; Brynjolfsson June 2026:98  "Whatever it is, it's not going away."99  https://digitaleconomy.stanford.edu/project/indicators/canaries-dashboard/ ,100  https://fortune.com/2026/06/27/what-is-ai-impact-entry-level-jobs-stanford-adp-canaries-brynjolfsson-richardson/101- **UK corroboration:** Adzuna — UK entry-level vacancies **−32% since ChatGPT launch (Nov 2022 → Jun 2025)**;102  entry-level share of market 28.9% → 25%. Graduate vacancies **−42.1% YoY in May 2026** (worse than any103  pandemic month). Indeed (Jun 2025): toughest graduate market since 2018, grad roles −33% YoY.104  https://www.theguardian.com/business/2025/jun/30/uk-entry-level-jobs-chatgpt-launch-adzuna ,105  https://www.adzuna.co.uk/job-market-report106- **Grad unemployment by major (NY Fed data, 2025–26):** recent CS grads **6.1%** unemployment,107  computer engineering **7.5%** — above the all-grad 4.8% average and above history/philosophy majors;108  entry-level SWE postings ~−30% YoY (Handshake 2025); CS enrollment fell >10% in 2025–26.109  https://interviewchamp.ai/learn/why-cs-new-grad-unemployment-hit-6-percent-2025 ,110  https://www.finalroundai.com/blog/computer-science-graduates-face-worst-job-market-in-decades111- **Caveats to keep the index honest:** LinkedIn Economic Graph (Apr 2026) notes hiring −20% since 2022 but112  says it has *not* seen AI as the demonstrable cause (rates, post-2022 normalization overlap); Brookings 2025113  ("No AI Jobs Apocalypse, For Now") finds aggregate data doesn't yet show mass displacement.114115### B.2 Layoffs attributed to AI (Challenger, Gray & Christmas)116117- 2023: Challenger begins tracking "AI" as a stated layoff reason. **2025 full year: 54,836** AI-attributed cuts118  (~5% of layoffs). **2026 is the discontinuity:**119  - Jan 2026: AI = 7% of cuts → Mar: 25% (AI becomes **#1 cited reason for the first time**) → Apr: 21,490 cuts,120    26% → **May: 38,579 cuts, 40% of all cuts — highest monthly total ever recorded** → Jun: still #1.121  - **H1 2026: 101,743 AI-cited cuts (~23% of all cuts), nearly 2× all of 2025.** AI #1 reason 4 consecutive122    months (Mar–Jun). Tech sector: 139,156 H1 cuts, +83% YoY, ~31% of all layoffs.123  - Sources: https://www.challengergray.com/blog/challenger-report-may-job-cuts-rise-16-from-april-highest-may-total-since-2020 ,124    https://www.challengergray.com/blog/challenger-report-april-job-cuts-rise-38-from-march-ytd-cuts-down-50 ,125    https://www.techtimes.com/articles/319588/20260703/ai-leads-us-job-cuts-record-4th-month-tech-claims-31-h1-layoffs.htm ,126    https://www.businessinsider.com/challenger-ai-layoffs-economy-jobs-2026-6 (Challenger itself: "not a jobpocalypse")127- **Goldman Sachs (Apr 2026):** AI eliminating ~25,000 US jobs/month, creating ~9,000 → **net −16,000/month**.128- **Named events:** Amazon **30,000 corporate cuts** (14K Oct 2025 + 16K Jan 2026, largest in its history; AI/129  automation cited as an efficiency driver; internal docs reportedly project 600K roles automated by 2033)130  https://www.geekwire.com/2025/amazon-reportedly-set-to-lay-off-30000-corporate-employees-in-massive-workforce-cut/ ;131  Salesforce cut **~4,000 customer-support roles** as AI agents absorbed workload (Benioff: "I need less heads",132  Sep 2025) https://fortune.com/2025/09/02/salesforce-ceo-billionaire-marc-benioff-ai-agents-jobs-layoffs-customer-service-sales/ ;133  HP 4,000–6,000 (Nov 2025, AI-cited).134- **Counter-signal (reversal risk):** Klarna replaced ~700 support agents with AI (chatbot = work of 700), then135  **rehired humans through 2025–26** after CSAT dropped on complex/emotional cases → hybrid model. Quality,136  not cost, was the binding constraint. https://www.digitalapplied.com/blog/klarna-reverses-ai-layoffs-replacing-700-workers-backfired137138### B.3 Sector-specific substitution evidence139140- **Translation — the most-displaced occupation to date.** Microsoft applicability rank #1 (98% activity141  overlap). Society of Authors survey (2024): **36% of translators lost work to GenAI; 43% report income142  declines; 77% expect negative future income**. Individual accounts (Blood in the Machine, mid-2025): 15-yr143  technical translator down from six figures to €8K/yr; Quebec FR-EN translator −60% income in 2024.144  https://www.theguardian.com/books/2024/apr/16/survey-finds-generative-ai-proving-major-threat-to-the-work-of-translators ,145  https://www.bloodinthemachine.com/p/ai-killed-my-job-translators146- **Customer support:** Salesforce −4,000; Klarna cycle (above); fintech cuts in May 2026 mostly AI-cited147  (Challenger). High exposure AND high realized substitution — but Klarna shows a quality floor.148- **Software engineering:** bifurcated. Junior/entry roles: −20% employment (ages 22–25, Canaries), entry149  postings −30% (Handshake), CS grad unemployment 6.1%; yet BLS still projects **+15% developer growth150  2024–34 (~129,200 openings/yr)** and senior demand holds. BCG (2026) classifies SWE as "amplified/divergent"151  rather than substituted. https://www.bcg.com/publications/2026/ai-will-reshape-more-jobs-than-it-replaces152153### B.4 Adoption statistics (the `adoption_velocity` evidence base)154155- **Census BTOS (official, firm-weighted):** AI use in *core production* 3.8% (Sep 2023) → 4.6% (early 2024) →156  ~10% (Sep–late 2025, doubling in ~18 months). Question broadened Nov 2025 to "any business function":157  **17–20% of firms Dec 2025–May 2026; 20–23% expect use within 6 months.**158  https://www.census.gov/library/stories/2026/05/ai-use-businesses.html159- **BTOS AI Supplement (Nov 2025–Jan 2026):** **18% of firms firm-weighted = 32% employment-weighted**;160  very large firms in **Information / Professional Services / Finance: 50–60% (60–70% employment-weighted)**;161  57% of adopters use AI in ≤3 business functions (top: sales & marketing 52%, strategy/biz-dev 45%, IT 41%);162  workers use AI in tasks at 23% of firms (41% employment-weighted).163  https://www.census.gov/library/working-papers/2026/adrm/CES-WP-26-25.html164- **Fed monitoring note (Apr 2026)** reconciles the scales: BTOS firms 18%; **employment-weighted SBU 78%**;165  **41% of the labor force uses GenAI for work** (RPS, Nov 2025; +9.7pp YoY); 50% uses it outside work.166  https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-u-s-economy-20260403.html167- **Ramp AI Index (paid adoption, 70K+ firms' card/bill spend):** businesses **paying** for AI crossed **50.4%168  in March 2026** (35% a year earlier; 46.8% Jan 2026); jump driven by "late majority" manufacturing/retail;169  VC-backed startups ~80%. Vendor race: OpenAI 35.2% vs Anthropic 30.6% of businesses (Apr 2026).170  https://ramp.com/data/ai-index , https://ramp.com/data/april-2026-ai-index171- **McKinsey State of AI (Nov 2025):** **88% of orgs use AI in ≥1 function**, but ~two-thirds still piloting;172  **62% experimenting with agents, 23% scaling agents in ≥1 function, ≤10% scaling within a single function**;173  only **39% report any enterprise EBIT impact; ~6% are "high performers"** (>5% EBIT from AI).174  https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai175- **Agentic reality check:** MIT NANDA: 95% of GenAI pilots show no P&L impact; Gartner: >40% of agentic176  projects to be cancelled by end-2027; S&P Global/McKinsey: 31% of enterprises run ≥1 agent in production —177  **banking/insurance 47% vs healthcare 18% and government 14%**. Gartner: 80% of enterprise apps shipped178  in Q1 2026 embed an agent (vs 33% in 2024).179- **France anchor:** 35% of French firms >10 employees use AI (OPIEC 2025) — EU lags US enterprise adoption.180181### B.5 Wage effects & augmentation-vs-substitution182183- **Humlum & Vestergaard (NBER w33777; Denmark, 25,000 workers × 7,000 workplaces, 11 exposed occupations,184  admin-linked):** AI chatbots → **precisely-estimated null on earnings and hours** (CIs rule out >1–2% average185  effects; occupation-level >6%); avg time savings only **2.8–3%**; >80% of saved time reallocated to other186  work; **8.4% of workers gained NEW tasks created by AI** (e.g., checking AI output); minimal pass-through187  of gains to wages. https://www.nber.org/papers/w33777 , https://www.andershumlum.com/s/chatbots_july25.pdf188  → Through 2024, *within-worker* wage effects ≈ 0; displacement shows up at hiring margins first (B.1).189- **PwC Global AI Jobs Barometer 2026 (1B+ job ads, 27 countries):** **62% average wage premium for jobs190  requiring AI skills** (118% in consumer markets, 16% in government); AI-skill jobs growing 69% vs 9% market;191  firms most able to use AI: headcount growth 53% vs 36% and wage growth 24% vs 17% vs least-exposed —192  "two distinct labour-market paths". https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-ai-jobs-barometer.html193- **Anthropic Economic Index (Claude usage, task-mapped to O\*NET):** initial report ~**57% augmentation /194  43% automation**; by Nov 2025, Claude.ai = **52% augmentation / 45% automation** (automation briefly led195  mid-2025); **enterprise API traffic is automation-dominant** (back-office workflows: email, document196  processing, CRM, scheduling). Directive/full-delegation usage rising. Reports:197  https://www.anthropic.com/research/anthropic-economic-index-january-2026-report ,198  https://www.anthropic.com/research/economic-index-march-2026-report ,199  https://www.anthropic.com/research/economic-index-june-2026-report200  → Validates keeping automation-vs-augmentation as *the* substitution discriminator (Canaries fact:201  employment declines concentrate in automation-dominant occupations).202- **BCG (Jan 2026, Revelio 1,500 roles):** point estimate **10–15% of US jobs vulnerable over 4–5 years**;203  distinguishes substituted vs "divergent" (demand-expansion offsets 0.5–1.0) vs amplified roles.204205### B.6 Regulation & barriers (the `barriers` evidence base)206207- **EU AI Act — timeline moved under our feet:** GPAI obligations applied Aug 2, 2025. The **Annex III208  high-risk obligations (incl. ALL employment/HR AI: CV screening, targeted job ads, promotion/termination209  decisions, worker monitoring — Annex III pt. 4) were due Aug 2, 2026 but the "Digital Omnibus" political210  agreement postpones them to Dec 2, 2027.** Requirements when live: risk management, bias testing, logging,211  human oversight, conformity assessment. Net effect: EU employment-AI adoption friction persists but the212  binding date slipped ~16 months (a *barriers-lowering* event for 2026–27 velocity in the EU).213  https://ogletree.com/insights-resources/blog-posts/eu-nears-approval-of-agreement-to-delay-rules-for-ai-use-in-employment-decisions/ ,214  https://accessfinancial.com/eu-ai-act-recruitment-high-risk-hiring-2026/215- **US states:** Illinois **HB 3773** effective **Jan 1, 2026** (AI discrimination in employment = civil-rights216  violation; notice required; zip-code-proxy ban). Colorado's AI Act (SB 24-205) delayed to Jun 30, 2026, then217  **replaced by narrower SB 26-189 (May 2026)**. NYC Local Law 144 (bias audits for hiring tools) ongoing.218  Pattern: US regulates *AI deciding about workers*, not AI *replacing* workers.219  https://ogletree.com/insights-resources/blog-posts/illinois-steps-up-ai-regulation-in-employment-key-takeaways-for-employers/220- **Professional licensing & liability as adoption brakes (occupation-level, measurable):**221  - Legal: AI can't be licensed/disbarred/sworn; legal-specific AI tools show **17–34% error rates**; **700+222    court cases worldwide involve AI hallucinations** with sanctions → yet legal-professional GenAI adoption223    still jumped **31% → 69% between 2025 and 2026** (54% of firms give no training, 43% no policy).224    https://www.americanbar.org/groups/law_practice/resources/law-technology-today/2026/whats-really-holding-law-firms-back-from-embracing-ai/225  - Healthcare: litigious + risk-averse, FDA draft guidance (Jan 2025) on AI credibility in drug decisions;226    physician AI utilization nonetheless 38% (2023) → 72% (2026), concentrated in *administrative* tasks —227    augmentation inside a licensing moat.228  - Customer-facing quality floors: Klarna reversal (B.2) = empirical "human-contact requirement" barrier.229- **Barrier taxonomy the evidence supports:** (1) statutory/licensing monopoly on the task; (2) liability &230  error-cost asymmetry (hallucination sanctions, malpractice); (3) regulated-process requirements (EU AI Act231  Annex III, state notice/audit laws); (4) human-contact/quality preference (Klarna); (5) organizational232  friction (MIT 95% pilot failure; McKinsey: only 6% high performers; workflow redesign is the differentiator).233234### B.7 Sector adoption-velocity differentials (who's fast, who's slow, why)235236| Sector | Signal (2025–26) | Why |237|---|---|---|238| Information / Tech | BTOS large-firm use 50–60%; >90% of tech companies use AI in ≥1 function | Digital-native tasks, no licensing, in-house skills |239| Finance & insurance | BTOS top-3 sector; agents in production: banking/insurance 47% (S&P); NVIDIA State of AI: strongest ROI | Structured data, measurable use cases; regulation shapes but doesn't block |240| Professional services (legal, consulting, accounting) | Legal GenAI 31%→69% in one year | High exposure; liability slows *delegation*, not *use* |241| Telecom / Retail & CPG | Agentic adoption 48% / 47% (NVIDIA) | Customer-ops scale economics |242| Manufacturing / logistics | Fastest %-growth in AI job postings; Ramp "late majority" surge drove the 50% crossing (Mar 2026) | Started low; predictive maintenance, quality control |243| Healthcare | Physician use 38%→72% but agents-in-production only 18%; admin-first | Licensing, liability, FDA; augmentation-dominant |244| Government / public sector | Agents 14%; lowest AI wage premium (16%) | Procurement, accountability, unionization |245| Construction / trades | ~1.4% adoption (laggard anecdote); BTOS small-firm use <20% | Physical, unstructured, small-firm dominated |246247Cross-cutting velocity facts: firm size is the strongest adoption predictor (BTOS: <20% for ≤4-employee firms);248employment-weighted adoption ≈ 2× firm-weighted; paid adoption (Ramp 50.4%) runs far ahead of production-grade249deployment (31% ≥1 agent in production) which runs ahead of measured P&L impact (39% any EBIT effect).250251---252253## Inputs for `barriers` & `adoption_velocity` scoring254255### adoption_velocity (weight 0.10) — proposed parameterization256257Score each occupation's dominant sector(s) (via BLS OES industry-occupation matrix) on observable adoption,258not vendor hype. Candidate sub-indicators, each normalizable to 0–1 with a public, refreshable source:2592601. **Sector AI-use rate, employment-weighted** — BTOS bi-weekly sector series + AI Supplement261   (CES-WP-26-25). Anchors: Information ≈ 0.9 · construction ≈ 0.1.2622. **Sector adoption momentum** — 12-month delta in BTOS use rate and/or Ramp AI Index sector series263   (captures manufacturing/retail late-majority acceleration, Mar 2026).2643. **Agentic deployment depth** — % of sector firms with agents in production (S&P Global/McKinsey:265   banking 47% … government 14%). Agents, not chatbots, are the substitution-relevant margin.2664. **Realized displacement intensity** — Challenger AI-cited cuts by industry, trailing 12m, scaled by sector267   employment (chemicals, fintech, tech score high in 2026); optionally corroborated by Canaries Dashboard268   22–25 employment trend for the occupation itself.2695. **Occupation-level usage intensity** — Anthropic Economic Index share of usage mapped to the occupation's270   O\*NET tasks, split augmentation vs automation (automation share feeds substitution, not just velocity).271272Suggested v1 formula: `adoption_velocity = 0.35·(1) + 0.20·(2) + 0.20·(3) + 0.15·(4) + 0.10·(5)`, with the273caveat documented in METHODOLOGY.md that (1)–(3) are sector-level priors and (4)–(5) are occupation-level274correctors. Update cadence: quarterly (BTOS bi-weekly, Ramp monthly, Challenger monthly, AEI ~quarterly) —275each refresh = new `INDEX_VERSION` patch run.276277### barriers (weight 0.20) — proposed parameterization278279Score as *adoption friction* (high barriers ⇒ lower net risk), with five components:2802811. **Licensing/authorization requirement (0–1):** does task sign-off legally require a licensed human282   (law, medicine, engineering PE, aviation, finance advice)? Source: O\*NET Job Zone + state licensing DBs.283   Evidence: legal/medical augment-don't-substitute pattern (B.6).2842. **Liability & error-cost asymmetry (0–1):** cost of a wrong AI output (malpractice, sanctions, safety).285   Proxy: occupation's litigation exposure + documented AI-error sanction record (700+ hallucination cases).2863. **Regulatory-process coverage (0–1):** is the occupation's automation itself regulated? EU AI Act Annex III287   (now Dec 2027), Illinois HB 3773, Colorado SB 26-189, NYC LL144 — maintain a dated rule table per288   jurisdiction; **this component is jurisdiction-specific** (US vs EU scores diverge; the Omnibus delay is a289   worked example of a barrier score *dropping* between index versions).2904. **Human-contact requirement (0–1):** reuse O\*NET work-context variables ("contact with others",291   "deal with external customers", physical proximity). Empirical anchor: Klarna reversal; CSAT floors.2925. **Organizational/implementation friction (0–1):** sector pilot-failure and scaling rates (MIT 95%,293   McKinsey 6% high performers, Gartner >40% agent-project cancellations) — a global dampener that decays294   over index versions as deployment matures.295296Suggested v1 formula: `barriers = 0.30·licensing + 0.25·liability + 0.20·regulatory + 0.15·human_contact + 0.10·org_friction`.297298### Positioning implications (for public copy, "adaptation not doom")299300- The 2026 record supports **differentiated, hedged claims**: entry-level exposure is real and measured301  (−16% relative, Stanford/ADP; Challenger 101,743 H1-2026 AI-cited cuts) while incumbent wage/hours effects302  are so far null (Denmark) and AI-skill premia are large (+62%, PwC). That *is* our three-concept split —303  exposure ≠ substitution ≠ augmentation — now empirically vindicated; no competitor surfaces it.304- Publish a "what would change this score" section per occupation (aijobimpactcalculator.com's305  "how to argue with this" page is the only competitor doing epistemic honesty — match and exceed it).306- Every headline stat above has a reversal or caveat attached (Klarna, LinkedIn attribution caution,307  Brookings "no apocalypse yet") — cite these in-product to keep the tone guide credible.308