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Epoch's Plummeting AI Cost Report Sparks Debate

Epoch AI's report claiming AI costs fall 47% per quarter at constant performance went viral, prompting Oxford philosopher Toby Ord and others to publicly challenge the underlying chart and methodology.

2026-09-23 ~ 2026-09-24 · 2 episodes · 23 posts

Episode 1 · Epoch AI: Cost of Equivalent AI Performance Falls 47% Per Quarter (2026-09-23, 13 posts)

Epoch AI released the report "The Plunging Price of Thought," estimating that the cost of AI at a given performance level has dropped about 47% per quarter since 2023—roughly 13x per year, and about 725x over 18 months—making AI the fastest cost-declining transformative technology in history. The finding circulated widely among researchers and media.

Confirmed

  • The report was authored by Epoch AI researchers David Roodman and Luke Emberson; the core finding is a 47% quarterly cost decline at fixed performance since 2023, about 13x per year
  • Comparative speed: about 4x faster than DNA sequencing, 6x faster than compute (Moore's Law), 18x faster than lithium batteries, and faster than pre-1973 electricity
  • Case studies cited: OpenAI o3's price on FrontierMath Tiers 1–3 and at 75% accuracy on GPQA Diamond dropped dramatically, corresponding to a 725x decline over 18 months
  • APPSO highlighted the paradox: chips, power, and data centers are getting more expensive while the cost of AI doing equivalent tasks falls faster than perhaps any general-purpose technology

Why it matters

  • If sustained, the 47% quarterly decline could rapidly lower the barrier to AI adoption across industries
  • Quantified comparison with historic cost curves (sequencing, compute, batteries) offers a benchmark for assessing AI's macro impact
  • Endorsement and spread by scholars like Ethan Mollick and Rishi Bommasani signals the finding has entered mainstream AI policy and economics discussions

Episode 2 · Scholars Challenge Viral Chart Claiming 100,000x Drop in AI Prices (2026-09-23, 10 posts)

Starting September 23, Oxford philosopher Toby Ord publicly challenged a viral chart claiming AI prices fell more than 100,000x between 2021 and 2026, with economist David Roodman and researcher David Manheim adding methodological and structural critiques in discussions related to Epoch AI. Their shared conclusion: the chart conflates capability gains with cost declines into a single cost curve, so the 100,000x claim does not hold; a more accurate description is that prices within each capability tier fell roughly 100 to 1000x cumulatively.

Confirmed

  • Ord argued that it is hard to name anything that actually got 100,000x cheaper; the figure resembles an aggregate or proxy statistic unsuitable for presenting price trends directly.
  • In discussion with @ramez, Ord noted that per capability tier, price declines are not exponential but flatten quickly even measured in monthly percentage terms, so the headline 100,000x figure does not hold.
  • Ord said the chart mixed capability improvements into the price curve, confusing the statistical basis; economist David Roodman made a similar critique, arguing that combining rapid capability gains with cost declines to derive a pure cost conclusion is methodologically infeasible.
  • In his corrected account, Ord offered two points: prices within each capability tier fall fast then slow, cumulatively declining about 100–1000x, while model quality available at a given price keeps rising rapidly.
  • In follow-up discussion with Roodman and Epoch AI, Ord raised two reservations about claims of steeply falling AI costs: beyond how the decline is presented, he is unsure of its exact scope—how many parts of AI services have not seen similar declines—questioning whether Epoch's cited 725x service price decline represents the whole picture.
  • David Manheim acknowledged that distilled small models do deliver slightly lower quality at much lower prices, but the sweeping conclusion of a dramatically falling overall cost curve does not follow; he added that actual costs have not fallen proportionally to "GPU price declines + capability gains" because of fixed costs.

Unconfirmed

  • The original source and exact data definitions of the chart were not given in the posts.
  • The scope of AI service components that have not seen similar price declines remains uncertain, including for Ord himself.

Why it matters

  • Manheim highlighted a structural constraint on compute prices: users cannot buy just "10 flops"—the same price buys a quadrillion times more compute—so the reverse scenario (everything getting a quadrillion times cheaper) is impossible due to fixed-cost bottlenecks, implying a de facto price floor for compute.
  • Such cost-decline curves are often used in AI trend forecasting and policy arguments; if the methodology is flawed, they could systematically overstate the pace of AI price declines and distort judgments about AI adoption and economic impact.