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Partial·last review28 May 2026

GPT-5 Pro's tiered-subscription model forces enterprises to classify problems by computational difficulty — $200/month premium routing only repays for the top decile of 'very hard' queries.

Reviewed 28 May 2026: core thesis holds (classify problems by compute difficulty; the premium tier repays only for the hardest decile of queries), but specifics drifted — OpenAI added a $100/mo Pro tier on 9 Apr 2026 alongside the $200 tier and the premium model is now GPT-5.5 Pro. The single-$200-tier framing needs updating.

Published
19 Apr 2026
Last reviewed
28 May 2026
Next review
+15d· 27 Jun 2026
Primary sources

Correction log

  1. 28 May 2026Pricing/model drift: a $100/mo Pro tier now sits beside the $200 tier (added 9 Apr 2026) and the premium model is GPT-5.5 Pro. Core thesis holds; the single-$200-tier framing no longer matches. Re-verify current tiers at chatgpt.com/pricing.
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The claim: GPT-5 Pro's tiered-subscription model forces enterprises to classify problems by computational difficulty — $200/month premium routing only repays for the top decile of 'very hard' queries.

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-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.

  • AM-201 · Partial · 10 Jun 2026

    One of four named datasets unanchored on review. The claim text names 'Stanford DEL's 12% clearing 300%+ ROI vs 88% at or below break-even' as one of four independent datasets. Full-text verification on 10 Jun 2026 found the Stanford DEL Enterprise AI Playbook contains no such distribution — it studies 51 successful deployments by design and carries no ROI-realisation failure data (full finding at AM-029, correction of 10 Jun 2026). The McKinsey (23% scaling, 17% EBIT-attribution), Gartner (28% fully paying off), and MIT NANDA (95% no measurable P&L impact) datasets verify; the claim's spine stands on three datasets rather than four. 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 from an ROI distribution. Status Up -> Partial.

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