The 56% AI-skill wage premium reported by the Federal Reserve Bank of Atlanta (May 2025, drawing on Lightcast job-posting data through 2024) describes a real labour-market signal at scale, but materially overstates what the typical mid-career worker should expect from a generic AI-literacy program: the premium attaches to specific technical skills surfacing in 1.62% of all 2024 job postings, and the BCG 14%-vs-44% gap in AI upskilling access between frontline workers and leaders is the operational variable that decides which cohort captures the premium and which sees credential inflation without the wage signal.
Claim created at publish; review on 60-day cadence. Anchor data: Atlanta Fed Workforce Currents (21 May 2025) 'By Degrees' methodology; Lightcast job-posting time series 2018-2024; BCG October 2024 'Five must-haves for AI upskilling' (n=11,000+ employees, 50+ countries) including the 14% frontline vs 44% leader upskilling-access gap; St. Louis Fed (Feb 2025) aggregate-productivity 1.1% from generative AI. Sister claims: AM-010 (CIO playbook five operational characteristics, training-over-hiring posture), AM-022 (change-management variable in deployment success), AM-029 (Not holding since 10 Jun 2026 — its Stanford 12/88 figure failed primary-source verification), AM-030 (McKinsey 23% scaling cohort). Trigger conditions to revisit before next cadence: (a) subsequent Atlanta Fed wave compressing or expanding the 56% premium against a different methodology baseline; (b) BCG, McKinsey, or Lightcast publishing data showing the 14/44 access gap closing without compression of the wage premium; (c) major US, UK, EU, or Singapore policy intervention on workforce AI access (right-to-train, sectoral retraining mandate) substantively reshaping the access variable.
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The claim: The 56% AI-skill wage premium reported by the Federal Reserve Bank of Atlanta (May 2025, drawing on Lightcast job-posting data through 2024) describes a real labour-market signal at scale, but materially overstates what the typical mid-career worker should expect from a generic AI-literacy program: the premium attaches to specific technical skills surfacing in 1.62% of all 2024 job postings, and the BCG 14%-vs-44% gap in AI upskilling access between frontline workers and leaders is the operational variable that decides which cohort captures the premium and which sees credential inflation without the wage signal.
About this register
The Reporting register tracks claims published from articles addressed to senior enterprise IT leaders — CIOs, IT directors, heads of platform. Claims are reviewed on a 30–90 day cadence; each review either reaffirms the claim, marks one substantive part as Partial, or marks it Not holding once the underlying evidence has been overtaken.
Recent corrections in Reporting
- AM-008 · Partial · 17 Jun 2026
Source-text figure re-review: Google's 2024 Environmental Report reports a 28% year-over-year increase to 8.1 billion gallons, not the 33% (from a 6.1 billion 2023 base) asserted at publish. The 8.1B 2024 figure and the Microsoft WUE 0.30 L/kWh / 39%-improvement figure are unchanged and verified. Article corrected to 28% and the unsupported 6.1B base removed; the claim text retains the original figure with this correction per the Holding-up protocol.
- AM-132 · Partial · 10 Jun 2026
One of four legs unanchored on re-review. The claim text attributes '12% of deployments clearing 300%+ ROI with 88% at or below break-even at 12-18 months' to the Stanford DEL 2026 Enterprise AI Playbook. Full-text verification on 10 Jun 2026 found no such figure in that source: the playbook (Pereira, Graylin, Brynjolfsson, Apr 2026) studies 51 successful deployments by design and contains no ROI distribution, no 300%-plus cohort, and no break-even measurement point (full finding at AM-029, correction of 10 Jun 2026). The only verified figure carrying the same 12/88 numerals is IDC research with Lenovo (via CIO.com, Mar 2025): roughly 88% of AI proof-of-concepts never reach production and roughly 12% graduate — a pilot-to-production graduation metric, not an ROI distribution. The Gartner 28%, McKinsey 23%/17%, and MIT NANDA 95% legs verify; they support a small high-performing tail and a large struggling body, but none documents the two-peak bimodal shape the claim asserts. Status Up -> Partial.
- AM-129 · Partial · 10 Jun 2026
One of three read-against anchors unanchored on re-review. The claim text cites 'Stanford Digital Economy Lab Enterprise AI Playbook (12/88 bimodal ROI distribution at 12-18 months)' and frames the realistic ROI band around 'the highest-discipline 12% cohort'. Full-text verification on 10 Jun 2026 found the playbook contains no 12/88 distribution, no bimodal ROI shape, and no 12-18-month ROI measurement point (full finding at AM-029, correction of 10 Jun 2026). The claim's core negative finding — no mid-market enterprise has produced a documented +240% ROI in 90 days under audited conditions — is unaffected; the McKinsey State of AI 2025 and MIT NANDA legs verify and continue to support it. The '12% cohort' framing has no verifiable referent. The only verified figure carrying the 12/88 numerals is IDC's pilot-graduation finding (roughly 88% of AI proof-of-concepts never reach production; via CIO.com, Mar 2025), a different metric. Status Up -> Partial.
Reviews coming up in Reporting
- AM-063 · Holding · next +9d (27 Jun 2026)
AI agents executing financial transactions need a four-control bundle (action-approval gates by blast radius, kill-swit…
- AM-061 · Holding · next +9d (27 Jun 2026)
Production agentic-AI costs at scale routinely run multiples of POC projections, and a layered optimisation programme c…
- AM-003 · Partial · next +9d (27 Jun 2026)
GPT-5 Pro's tiered-subscription model forces enterprises to classify problems by computational difficulty — $200/month…