# 04 — Competitive Landscape & Real-World Evidence (through August 2026) Research memo for the AI Risk Index (airiskindex.io). Compiled 2026-08-05 from live web research. Purpose: (a) map existing public AI-job-risk tools, (b) assemble the 2024–2026 empirical record on AI's labor-market effects, and (c) propose how this evidence parameterizes the `barriers` and `adoption_velocity` scoring dimensions (weights 0.20 / 0.10 in v1, `packages/scoring/src/weights.ts`). --- ## Part A — Competitive landscape of public AI-job-risk tools ### A.1 willrobotstakemyjob.com (the incumbent) - **Since 2017.** Tagline: "Find out how likely your job is to be automated — based on real data and user votes." - **Methodology:** Automation-risk probabilities produced "using a similar method" to **Frey & Osborne (2013)** ("The Future of Employment", Gaussian process classifier over 702 occupations, the famous "47% of US employment at risk" paper), re-estimated "with the most up-to-date data available", plus BLS occupation data (employment, wages, growth). Since 2019 it collects **user poll votes** on perceived risk; in 2021 it blended BLS data + polls + automation probability into a 0–10 "job score". Source: https://willrobotstakemyjob.com/about - **Critical weakness:** the underlying model is pre-LLM computerization/robotics-era work. Frey–Osborne scored *whole occupations* on physical/routine automatability (manual dexterity, cramped workspace, fine arts, social perceptiveness...), which inverts under generative AI: it rates cognitive/office work as relatively safe and misses exactly the exposure the 2024–2026 evidence shows (translators, writers, customer service, junior developers). Single doom-number framing ("X% probability of automation"), no exposure/substitution/augmentation distinction, no confidence intervals, no versioning, no API. - **Traffic (why it still matters):** ~71.3K visits in May 2026 per Semrush (down ~20% MoM), world rank ~#50,395; ~42% of traffic from Google organic, ~39% direct. Other estimators give 45K–100K+/month. Sources: https://www.semrush.com/website/willrobotstakemyjob.com/overview/ , https://hypestat.com/info/willrobotstakemyjob.com - **Monetization:** ads; no API or paid tier found. ### A.2 Other public lookup tools (US/EN) | Tool | Data / method | Output | Weaknesses | Pricing/API | |---|---|---|---|---| | **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 | | **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 | | **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 | | **replacemeter.com** | Undisclosed (looks LLM-generated per submitted job title) | Letter grades: "AI Resilience" %, "Adaptability" % | No methodology page, arbitrary-title scoring, no provenance | Free | | **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) | | **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 | | **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 | | **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 | | **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 | Academic/institutional exposure indices that power many of these (no consumer UI of their own): **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), **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), **Microsoft "AI applicability score"** (Tomlinson et al. 2025, 200K Copilot conversations mapped to O\*NET work activities — top: interpreters/translators (98% activity overlap), historians, passenger attendants, sales reps, writers, customer service reps; https://www.microsoft.com/en-us/research/publication/working-with-ai-measuring-the-occupational-implications-of-generative-ai), **OpenAI "GPTs are GPTs"** (Eloundou et al., Science 2024), **Pew 2023 O\*NET work-activity classification** (https://www.pewresearch.org/social-trends/2023/07/26/2023-ai-and-jobs-methodology-for-onet-analysis). ### A.3 French / EU equivalents - **jobimpact.aidoption.fr** — "Exposition IA du marché de l'emploi français": 532 ROME occupations, treemap (surface = jobs, color = 0–10 exposure), France Travail ROME + DARES data, **scored by Claude** (LLM-as-rater — directly comparable to our §6 pipeline, but with no audit trail or versioning). Has a /us/ twin. - **transitions-ia.fr** ("IA & Métiers France" observatory) — ROME 4.0 + INSEE EEC 2024 + DARES BMO 2024; task-level exposure score per métier. https://otakuch.github.io/transitions-ia.fr/ - **job-guard.com** — French-language "votre métier va-t-il disparaître ?" test; editorial/affiliate quality. - **Observatoire des Emplois Menacés et Émergents + Coface study** (Nov 2025, covered by Les Échos): ~16% of French jobs at risk; white-collar metropolitan jobs most exposed (Paris ~19%, Lyon/Toulouse 18%); legal/accounting, publishing/press, IT programming/consulting, insurance, finance >25% of jobs exposed. https://www.lesechos.fr/monde/europe/ia-le-grand-bouleversement-a-venir-du-marche-du-travail-2221760 - No credible official FR/EU consumer lookup exists (France Travail offers only e-learning content) → the ESCO/ROME crosswalk in our roadmap targets an **empty niche**. ### A.4 SEO landscape - Queries like "will AI take my job (2026)" are dominated not by tools but by **listicle/content marketing**: Nucamp, NovoResume, Careerminds, AI Weekly, Shawn Kanungo — all citing the same WEF 92M-displaced / 170M-created / +78M-net figure, plus Goldman's −16K net jobs/month (Apr 2026). Tool sites rank on "AI job risk calculator" / "will robots take my job" variants; willrobotstakemyjob.com still owns its brand query with ~42% organic share of its traffic. - Opportunity: nothing ranking today combines (1) task-level methodology, (2) sub-scores with uncertainty, (3) live evidence (Challenger/adoption data), (4) versioned transparency, (5) non-doom adaptation framing. aijobimpactcalculator.com is the closest philosophical competitor (source-cited, anti-doom, "what's growing" panel, "how to argue with this" page) but is static, band-level, and has no composite index, no API, no EU coverage. --- ## Part B — Real-world evidence, 2024 → August 2026 ### B.1 Entry-level employment effects ("Canaries" line of evidence) - **Brynjolfsson, Chandar & Chen (Stanford/ADP), "Canaries in the Coal Mine?"** (Aug 2025, rev. Nov 13 2025): since gen-AI diffusion, workers **aged 22–25 in the most AI-exposed occupations saw a ~16% relative employment decline** (software devs 22–25 down ~20% from late-2022 peak), controlling for firm-level shocks; older workers in the same occupations stable/growing. Adjustment via **employment, not wages**. Declines **concentrated where AI automates rather than augments** (per Anthropic Economic Index task classification). https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/ - **Follow-up note (Feb 9, 2026), "Canaries, Interest Rates, and Timing":** interest rates don't explain the *differential* entry-level decline in AI-exposed occupations. https://digitaleconomy.stanford.edu/news/canaries-interest-rates-and-timinga-more-on-recent-drivers-of-employment-changes-for-young-workers - **Canaries Dashboard** (2026): continuous monitoring, 4.6M workers, 730+ occupations; Brynjolfsson June 2026: "Whatever it is, it's not going away." https://digitaleconomy.stanford.edu/project/indicators/canaries-dashboard/ , https://fortune.com/2026/06/27/what-is-ai-impact-entry-level-jobs-stanford-adp-canaries-brynjolfsson-richardson/ - **UK corroboration:** Adzuna — UK entry-level vacancies **−32% since ChatGPT launch (Nov 2022 → Jun 2025)**; entry-level share of market 28.9% → 25%. Graduate vacancies **−42.1% YoY in May 2026** (worse than any pandemic month). Indeed (Jun 2025): toughest graduate market since 2018, grad roles −33% YoY. https://www.theguardian.com/business/2025/jun/30/uk-entry-level-jobs-chatgpt-launch-adzuna , https://www.adzuna.co.uk/job-market-report - **Grad unemployment by major (NY Fed data, 2025–26):** recent CS grads **6.1%** unemployment, computer engineering **7.5%** — above the all-grad 4.8% average and above history/philosophy majors; entry-level SWE postings ~−30% YoY (Handshake 2025); CS enrollment fell >10% in 2025–26. https://interviewchamp.ai/learn/why-cs-new-grad-unemployment-hit-6-percent-2025 , https://www.finalroundai.com/blog/computer-science-graduates-face-worst-job-market-in-decades - **Caveats to keep the index honest:** LinkedIn Economic Graph (Apr 2026) notes hiring −20% since 2022 but says it has *not* seen AI as the demonstrable cause (rates, post-2022 normalization overlap); Brookings 2025 ("No AI Jobs Apocalypse, For Now") finds aggregate data doesn't yet show mass displacement. ### B.2 Layoffs attributed to AI (Challenger, Gray & Christmas) - 2023: Challenger begins tracking "AI" as a stated layoff reason. **2025 full year: 54,836** AI-attributed cuts (~5% of layoffs). **2026 is the discontinuity:** - Jan 2026: AI = 7% of cuts → Mar: 25% (AI becomes **#1 cited reason for the first time**) → Apr: 21,490 cuts, 26% → **May: 38,579 cuts, 40% of all cuts — highest monthly total ever recorded** → Jun: still #1. - **H1 2026: 101,743 AI-cited cuts (~23% of all cuts), nearly 2× all of 2025.** AI #1 reason 4 consecutive months (Mar–Jun). Tech sector: 139,156 H1 cuts, +83% YoY, ~31% of all layoffs. - Sources: https://www.challengergray.com/blog/challenger-report-may-job-cuts-rise-16-from-april-highest-may-total-since-2020 , https://www.challengergray.com/blog/challenger-report-april-job-cuts-rise-38-from-march-ytd-cuts-down-50 , https://www.techtimes.com/articles/319588/20260703/ai-leads-us-job-cuts-record-4th-month-tech-claims-31-h1-layoffs.htm , https://www.businessinsider.com/challenger-ai-layoffs-economy-jobs-2026-6 (Challenger itself: "not a jobpocalypse") - **Goldman Sachs (Apr 2026):** AI eliminating ~25,000 US jobs/month, creating ~9,000 → **net −16,000/month**. - **Named events:** Amazon **30,000 corporate cuts** (14K Oct 2025 + 16K Jan 2026, largest in its history; AI/ automation cited as an efficiency driver; internal docs reportedly project 600K roles automated by 2033) https://www.geekwire.com/2025/amazon-reportedly-set-to-lay-off-30000-corporate-employees-in-massive-workforce-cut/ ; Salesforce cut **~4,000 customer-support roles** as AI agents absorbed workload (Benioff: "I need less heads", Sep 2025) https://fortune.com/2025/09/02/salesforce-ceo-billionaire-marc-benioff-ai-agents-jobs-layoffs-customer-service-sales/ ; HP 4,000–6,000 (Nov 2025, AI-cited). - **Counter-signal (reversal risk):** Klarna replaced ~700 support agents with AI (chatbot = work of 700), then **rehired humans through 2025–26** after CSAT dropped on complex/emotional cases → hybrid model. Quality, not cost, was the binding constraint. https://www.digitalapplied.com/blog/klarna-reverses-ai-layoffs-replacing-700-workers-backfired ### B.3 Sector-specific substitution evidence - **Translation — the most-displaced occupation to date.** Microsoft applicability rank #1 (98% activity overlap). Society of Authors survey (2024): **36% of translators lost work to GenAI; 43% report income declines; 77% expect negative future income**. Individual accounts (Blood in the Machine, mid-2025): 15-yr technical translator down from six figures to €8K/yr; Quebec FR-EN translator −60% income in 2024. https://www.theguardian.com/books/2024/apr/16/survey-finds-generative-ai-proving-major-threat-to-the-work-of-translators , https://www.bloodinthemachine.com/p/ai-killed-my-job-translators - **Customer support:** Salesforce −4,000; Klarna cycle (above); fintech cuts in May 2026 mostly AI-cited (Challenger). High exposure AND high realized substitution — but Klarna shows a quality floor. - **Software engineering:** bifurcated. Junior/entry roles: −20% employment (ages 22–25, Canaries), entry postings −30% (Handshake), CS grad unemployment 6.1%; yet BLS still projects **+15% developer growth 2024–34 (~129,200 openings/yr)** and senior demand holds. BCG (2026) classifies SWE as "amplified/divergent" rather than substituted. https://www.bcg.com/publications/2026/ai-will-reshape-more-jobs-than-it-replaces ### B.4 Adoption statistics (the `adoption_velocity` evidence base) - **Census BTOS (official, firm-weighted):** AI use in *core production* 3.8% (Sep 2023) → 4.6% (early 2024) → ~10% (Sep–late 2025, doubling in ~18 months). Question broadened Nov 2025 to "any business function": **17–20% of firms Dec 2025–May 2026; 20–23% expect use within 6 months.** https://www.census.gov/library/stories/2026/05/ai-use-businesses.html - **BTOS AI Supplement (Nov 2025–Jan 2026):** **18% of firms firm-weighted = 32% employment-weighted**; very large firms in **Information / Professional Services / Finance: 50–60% (60–70% employment-weighted)**; 57% of adopters use AI in ≤3 business functions (top: sales & marketing 52%, strategy/biz-dev 45%, IT 41%); workers use AI in tasks at 23% of firms (41% employment-weighted). https://www.census.gov/library/working-papers/2026/adrm/CES-WP-26-25.html - **Fed monitoring note (Apr 2026)** reconciles the scales: BTOS firms 18%; **employment-weighted SBU 78%**; **41% of the labor force uses GenAI for work** (RPS, Nov 2025; +9.7pp YoY); 50% uses it outside work. https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-u-s-economy-20260403.html - **Ramp AI Index (paid adoption, 70K+ firms' card/bill spend):** businesses **paying** for AI crossed **50.4% in March 2026** (35% a year earlier; 46.8% Jan 2026); jump driven by "late majority" manufacturing/retail; VC-backed startups ~80%. Vendor race: OpenAI 35.2% vs Anthropic 30.6% of businesses (Apr 2026). https://ramp.com/data/ai-index , https://ramp.com/data/april-2026-ai-index - **McKinsey State of AI (Nov 2025):** **88% of orgs use AI in ≥1 function**, but ~two-thirds still piloting; **62% experimenting with agents, 23% scaling agents in ≥1 function, ≤10% scaling within a single function**; only **39% report any enterprise EBIT impact; ~6% are "high performers"** (>5% EBIT from AI). https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai - **Agentic reality check:** MIT NANDA: 95% of GenAI pilots show no P&L impact; Gartner: >40% of agentic projects to be cancelled by end-2027; S&P Global/McKinsey: 31% of enterprises run ≥1 agent in production — **banking/insurance 47% vs healthcare 18% and government 14%**. Gartner: 80% of enterprise apps shipped in Q1 2026 embed an agent (vs 33% in 2024). - **France anchor:** 35% of French firms >10 employees use AI (OPIEC 2025) — EU lags US enterprise adoption. ### B.5 Wage effects & augmentation-vs-substitution - **Humlum & Vestergaard (NBER w33777; Denmark, 25,000 workers × 7,000 workplaces, 11 exposed occupations, admin-linked):** AI chatbots → **precisely-estimated null on earnings and hours** (CIs rule out >1–2% average effects; occupation-level >6%); avg time savings only **2.8–3%**; >80% of saved time reallocated to other work; **8.4% of workers gained NEW tasks created by AI** (e.g., checking AI output); minimal pass-through of gains to wages. https://www.nber.org/papers/w33777 , https://www.andershumlum.com/s/chatbots_july25.pdf → Through 2024, *within-worker* wage effects ≈ 0; displacement shows up at hiring margins first (B.1). - **PwC Global AI Jobs Barometer 2026 (1B+ job ads, 27 countries):** **62% average wage premium for jobs requiring AI skills** (118% in consumer markets, 16% in government); AI-skill jobs growing 69% vs 9% market; firms most able to use AI: headcount growth 53% vs 36% and wage growth 24% vs 17% vs least-exposed — "two distinct labour-market paths". https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-ai-jobs-barometer.html - **Anthropic Economic Index (Claude usage, task-mapped to O\*NET):** initial report ~**57% augmentation / 43% automation**; by Nov 2025, Claude.ai = **52% augmentation / 45% automation** (automation briefly led mid-2025); **enterprise API traffic is automation-dominant** (back-office workflows: email, document processing, CRM, scheduling). Directive/full-delegation usage rising. Reports: https://www.anthropic.com/research/anthropic-economic-index-january-2026-report , https://www.anthropic.com/research/economic-index-march-2026-report , https://www.anthropic.com/research/economic-index-june-2026-report → Validates keeping automation-vs-augmentation as *the* substitution discriminator (Canaries fact: employment declines concentrate in automation-dominant occupations). - **BCG (Jan 2026, Revelio 1,500 roles):** point estimate **10–15% of US jobs vulnerable over 4–5 years**; distinguishes substituted vs "divergent" (demand-expansion offsets 0.5–1.0) vs amplified roles. ### B.6 Regulation & barriers (the `barriers` evidence base) - **EU AI Act — timeline moved under our feet:** GPAI obligations applied Aug 2, 2025. The **Annex III high-risk obligations (incl. ALL employment/HR AI: CV screening, targeted job ads, promotion/termination decisions, worker monitoring — Annex III pt. 4) were due Aug 2, 2026 but the "Digital Omnibus" political agreement postpones them to Dec 2, 2027.** Requirements when live: risk management, bias testing, logging, human oversight, conformity assessment. Net effect: EU employment-AI adoption friction persists but the binding date slipped ~16 months (a *barriers-lowering* event for 2026–27 velocity in the EU). https://ogletree.com/insights-resources/blog-posts/eu-nears-approval-of-agreement-to-delay-rules-for-ai-use-in-employment-decisions/ , https://accessfinancial.com/eu-ai-act-recruitment-high-risk-hiring-2026/ - **US states:** Illinois **HB 3773** effective **Jan 1, 2026** (AI discrimination in employment = civil-rights violation; notice required; zip-code-proxy ban). Colorado's AI Act (SB 24-205) delayed to Jun 30, 2026, then **replaced by narrower SB 26-189 (May 2026)**. NYC Local Law 144 (bias audits for hiring tools) ongoing. Pattern: US regulates *AI deciding about workers*, not AI *replacing* workers. https://ogletree.com/insights-resources/blog-posts/illinois-steps-up-ai-regulation-in-employment-key-takeaways-for-employers/ - **Professional licensing & liability as adoption brakes (occupation-level, measurable):** - Legal: AI can't be licensed/disbarred/sworn; legal-specific AI tools show **17–34% error rates**; **700+ court cases worldwide involve AI hallucinations** with sanctions → yet legal-professional GenAI adoption still jumped **31% → 69% between 2025 and 2026** (54% of firms give no training, 43% no policy). https://www.americanbar.org/groups/law_practice/resources/law-technology-today/2026/whats-really-holding-law-firms-back-from-embracing-ai/ - Healthcare: litigious + risk-averse, FDA draft guidance (Jan 2025) on AI credibility in drug decisions; physician AI utilization nonetheless 38% (2023) → 72% (2026), concentrated in *administrative* tasks — augmentation inside a licensing moat. - Customer-facing quality floors: Klarna reversal (B.2) = empirical "human-contact requirement" barrier. - **Barrier taxonomy the evidence supports:** (1) statutory/licensing monopoly on the task; (2) liability & error-cost asymmetry (hallucination sanctions, malpractice); (3) regulated-process requirements (EU AI Act Annex III, state notice/audit laws); (4) human-contact/quality preference (Klarna); (5) organizational friction (MIT 95% pilot failure; McKinsey: only 6% high performers; workflow redesign is the differentiator). ### B.7 Sector adoption-velocity differentials (who's fast, who's slow, why) | Sector | Signal (2025–26) | Why | |---|---|---| | 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 | | 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 | | Professional services (legal, consulting, accounting) | Legal GenAI 31%→69% in one year | High exposure; liability slows *delegation*, not *use* | | Telecom / Retail & CPG | Agentic adoption 48% / 47% (NVIDIA) | Customer-ops scale economics | | Manufacturing / logistics | Fastest %-growth in AI job postings; Ramp "late majority" surge drove the 50% crossing (Mar 2026) | Started low; predictive maintenance, quality control | | Healthcare | Physician use 38%→72% but agents-in-production only 18%; admin-first | Licensing, liability, FDA; augmentation-dominant | | Government / public sector | Agents 14%; lowest AI wage premium (16%) | Procurement, accountability, unionization | | Construction / trades | ~1.4% adoption (laggard anecdote); BTOS small-firm use <20% | Physical, unstructured, small-firm dominated | Cross-cutting velocity facts: firm size is the strongest adoption predictor (BTOS: <20% for ≤4-employee firms); employment-weighted adoption ≈ 2× firm-weighted; paid adoption (Ramp 50.4%) runs far ahead of production-grade deployment (31% ≥1 agent in production) which runs ahead of measured P&L impact (39% any EBIT effect). --- ## Inputs for `barriers` & `adoption_velocity` scoring ### adoption_velocity (weight 0.10) — proposed parameterization Score each occupation's dominant sector(s) (via BLS OES industry-occupation matrix) on observable adoption, not vendor hype. Candidate sub-indicators, each normalizable to 0–1 with a public, refreshable source: 1. **Sector AI-use rate, employment-weighted** — BTOS bi-weekly sector series + AI Supplement (CES-WP-26-25). Anchors: Information ≈ 0.9 · construction ≈ 0.1. 2. **Sector adoption momentum** — 12-month delta in BTOS use rate and/or Ramp AI Index sector series (captures manufacturing/retail late-majority acceleration, Mar 2026). 3. **Agentic deployment depth** — % of sector firms with agents in production (S&P Global/McKinsey: banking 47% … government 14%). Agents, not chatbots, are the substitution-relevant margin. 4. **Realized displacement intensity** — Challenger AI-cited cuts by industry, trailing 12m, scaled by sector employment (chemicals, fintech, tech score high in 2026); optionally corroborated by Canaries Dashboard 22–25 employment trend for the occupation itself. 5. **Occupation-level usage intensity** — Anthropic Economic Index share of usage mapped to the occupation's O\*NET tasks, split augmentation vs automation (automation share feeds substitution, not just velocity). Suggested v1 formula: `adoption_velocity = 0.35·(1) + 0.20·(2) + 0.20·(3) + 0.15·(4) + 0.10·(5)`, with the caveat documented in METHODOLOGY.md that (1)–(3) are sector-level priors and (4)–(5) are occupation-level correctors. Update cadence: quarterly (BTOS bi-weekly, Ramp monthly, Challenger monthly, AEI ~quarterly) — each refresh = new `INDEX_VERSION` patch run. ### barriers (weight 0.20) — proposed parameterization Score as *adoption friction* (high barriers ⇒ lower net risk), with five components: 1. **Licensing/authorization requirement (0–1):** does task sign-off legally require a licensed human (law, medicine, engineering PE, aviation, finance advice)? Source: O\*NET Job Zone + state licensing DBs. Evidence: legal/medical augment-don't-substitute pattern (B.6). 2. **Liability & error-cost asymmetry (0–1):** cost of a wrong AI output (malpractice, sanctions, safety). Proxy: occupation's litigation exposure + documented AI-error sanction record (700+ hallucination cases). 3. **Regulatory-process coverage (0–1):** is the occupation's automation itself regulated? EU AI Act Annex III (now Dec 2027), Illinois HB 3773, Colorado SB 26-189, NYC LL144 — maintain a dated rule table per jurisdiction; **this component is jurisdiction-specific** (US vs EU scores diverge; the Omnibus delay is a worked example of a barrier score *dropping* between index versions). 4. **Human-contact requirement (0–1):** reuse O\*NET work-context variables ("contact with others", "deal with external customers", physical proximity). Empirical anchor: Klarna reversal; CSAT floors. 5. **Organizational/implementation friction (0–1):** sector pilot-failure and scaling rates (MIT 95%, McKinsey 6% high performers, Gartner >40% agent-project cancellations) — a global dampener that decays over index versions as deployment matures. Suggested v1 formula: `barriers = 0.30·licensing + 0.25·liability + 0.20·regulatory + 0.15·human_contact + 0.10·org_friction`. ### Positioning implications (for public copy, "adaptation not doom") - The 2026 record supports **differentiated, hedged claims**: entry-level exposure is real and measured (−16% relative, Stanford/ADP; Challenger 101,743 H1-2026 AI-cited cuts) while incumbent wage/hours effects are so far null (Denmark) and AI-skill premia are large (+62%, PwC). That *is* our three-concept split — exposure ≠ substitution ≠ augmentation — now empirically vindicated; no competitor surfaces it. - Publish a "what would change this score" section per occupation (aijobimpactcalculator.com's "how to argue with this" page is the only competitor doing epistemic honesty — match and exceed it). - Every headline stat above has a reversal or caveat attached (Klarna, LinkedIn attribution caution, Brookings "no apocalypse yet") — cite these in-product to keep the tone guide credible.