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Holding·last review2 Jun 2026

As of mid-2026 there is no US federal AI floor coming on a plannable timeline (the White House National Policy Framework for AI of 20 March 2026 is explicitly non-binding and would require Congressional action, and the proposed federal moratorium on state AI laws was not enacted), and the most-watched comprehensive state law retreated rather than advanced (Colorado's SB 26-189, signed 14 May 2026, repealed and reenacted the Colorado AI Act, removing the algorithmic-discrimination duty of care and the risk-management and impact-assessment obligations and moving the effective date from 30 June 2026 to 1 January 2027), so the operative reality is a non-converging state patchwork, and the defensible enterprise posture is to build to the strictest obligation that actually applies to its own deployments and treat the regulatory map as a moving input rather than waiting for a federal floor.

Anchored on: Colorado SB 26-189 repeal-and-reenactment signed 14 May 2026 (Crowell & Moring, Hunton, Troutman, Akin), narrowing the law to disclosure/transparency and delaying it to 1 January 2027 (prior delay from 1 Feb 2026 to 30 Jun 2026 via SB 25B-004, Aug 2025); the White House National Policy Framework for Artificial Intelligence of 20 March 2026, explicitly non-binding and requiring Congress (Holland & Knight); the non-enactment of the proposed federal moratorium on state AI laws (the Senate had earlier removed a ten-year moratorium from the 2025 budget reconciliation bill); and the two broad state laws effective 1 January 2026 — California SB 53 (Transparency in Frontier AI Act), applying only to frontier developers above ~10^26 training operations and developers with >$500M revenue (Brookings), and Texas's Responsible AI Governance Act, broad in reach but intent-based (Norton Rose Fulbright), plus King & Spalding on the new state laws and the executive order signalling preemption intent. Claim is scoped to the planning posture (no plannable federal floor; flagship state law retreated; build to applicable obligations and track the moving map), not to a prediction of the direction of any single statute, and explicitly frames the picture as divergence rather than deregulation (the EU did not retreat; the failed moratorium preserves state authority). The exact Senate vote count on the earlier moratorium is intentionally left qualitative ('removed... near the budget bill') to avoid asserting a figure not directly cited here. VERIFIED 2026-06-02 via Crowell & Moring (SB 26-189 scope changes + 1 Jan 2027 date), Holland & Knight (framework non-binding, Congress required, moratorium not pursued), Brookings (SB 53 frontier-only thresholds), Norton Rose Fulbright (TRAIGA broad + intent-based + 1 Jan 2026), and King & Spalding (state laws effective 1 Jan 2026 + EO signal). 90-day review cadence (31 Aug 2026). Trigger conditions: (1) Congress enacts federal AI legislation with preemptive effect, moving the claim toward Partial or Not holding; (2) Colorado's reenacted law is amended again before 1 January 2027; (3) a major state passes a comprehensive law on the original Colorado model, reversing the retreat reading; (4) the White House framework is replaced by a binding instrument. Siblings: AM-184 (/eu-ai-act-digital-omnibus-what-still-applies/, the regime that delayed but did not retreat), AM-172 (/ai-governance-data-governance-us-frameworks/, the US sector-frameworks companion), and the operators version OPS-090 (/operators/us-ai-laws-small-business/).

Published
2 Jun 2026
Last reviewed
2 Jun 2026
Next review
+74d· 31 Aug 2026
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The claim: As of mid-2026 there is no US federal AI floor coming on a plannable timeline (the White House National Policy Framework for AI of 20 March 2026 is explicitly non-binding and would require Congressional action, and the proposed federal moratorium on state AI laws was not enacted), and the most-watched comprehensive state law retreated rather than advanced (Colorado's SB 26-189, signed 14 May 2026, repealed and reenacted the Colorado AI Act, removing the algorithmic-discrimination duty of care and the risk-management and impact-assessment obligations and moving the effective date from 30 June 2026 to 1 January 2027), so the operative reality is a non-converging state patchwork, and the defensible enterprise posture is to build to the strictest obligation that actually applies to its own deployments and treat the regulatory map as a moving input rather than waiting for a federal floor.

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

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    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…