Step 6: tool results

{
  "status": "inconclusive",
  "claim_id": "55b84cc2-c3ba-4168-ae4b-723dafc6b353",
  "assessment": "The claim is a forecast: that AI systems' weak research judgment (choosing goals and next steps, redesigning after flaws, backtracking, interpreting results) will hold back automation of the AI R&D work not yet automated, so that it proceeds more slowly than simple extrapolations of current trends suggest. Those extrapolations, built mostly on company-reported metrics, put most AI R&D in AI hands by 2027 or 2028.\n\nThe case for it is substantial. By late 2026 AI writes most code and does several agent-workdays per human workday at the leading labs, yet [[claim:d479cf4d-52b6-4260-a3dc-7a2e5a812d42|humans still supply most research judgment]], and Anthropic itself reports large gaps in its models choosing goals. Independent \"shadow evaluations\" in which agents tackled the question of an unpublished paper found [[claim:66eddda8-c9d1-4b53-8ed6-2a6055c68756|recurring judgment failures]]: underpowered results presented as findings, caveats added instead of redesign, early abandonment of ambitious directions; the original authors rejected all four AI-written papers. On open-ended reinvention tasks agents recover a small fraction of a published innovation's gain. When fast-moving components are automated, the slowest remaining component tends to govern overall pace.\n\nThat bottleneck argument needs the residual work to be mostly judgment, and the evidence only leans that way. [[claim:20f15464-de92-44a7-8792-b4f159bd97c8|That most remaining tasks require research judgment]] is supported but weakly: OpenAI's agents completed only 35% of 8 to 16 hour tasks without human help, no measured part of Anthropic's R&D is fully autonomous, and Anthropic places its judgment gaps \"in both engineering and research\". Long-horizon engineering is therefore a real share of what remains. That share matters in two ways. Where the residual is engineering, its pace is the pace the trend extrapolations already track, so the judgment bottleneck governs only part of it. And where long engineering tasks stall on judgment inside them, the trend data on task length already reflect how fast that kind of judgment improves. The claim holds only if broad, open-ended judgment is a distinct component that improves more slowly than what current trends measure.\n\nThe case against is that [[claim:f5a654f9-9ef3-4144-9869-6bff1c6d9631|measured judgment is improving quickly]]: Anthropic's internal next-step test rose from 51% to 64% preference over researchers between two model generations, and the external TasteVal benchmark shows experiment-selection efficiency improving on a steepening trend, with the best model above an expert baseline. These measures test narrow judgment inside a framed problem, whereas the failure reports test broad judgment about what is worth pursuing and when a design is broken. Narrow judgment is plainly improving; whether broad judgment follows at the same pace has not been measured.\n\nThe question therefore turns on an unobserved rate, and on how large the judgment-heavy share of the residual is. Some slowing relative to naive extrapolation remains slightly more likely than not, but the evidence does not settle it. Repeated open-ended research evaluations across model generations, and a breakdown of human interventions on unfinished agent tasks by kind, would.",
  "confidence": 0.65,
  "claim_credence": 0.55,
  "marginal_yield": 0.35,
  "reasoning_trace": "Re-pass prompted by the residual-task-composition premise (\"Most AI R&D tasks that remain unautomated require research judgment rather than engineering execution\") receiving its first assessment: supported, confidence 0.5, credence 0.6. The previous assessment described it as unjudged and implicitly treated it as stronger (seed 0.7).\n\nWhat the premise's assessment adds: (1) substantial residual long-horizon engineering (OpenAI 57% of 2-4h and 35% of 8-16h tasks without human intervention; no fully autonomous measured part of Anthropic R&D); (2) the judgment/execution line is blurred, with Anthropic placing judgment gaps in engineering as well as research; (3) no task-level breakdown exists. FRI says only that remaining tasks \"increasingly\" require judgment.\n\nBearing on this claim: the judgment-bottleneck argument (\"likely\" if premises hold) now rests on a premise at 0.6 rather than an assumed ~0.7. More importantly, the engineering share of the residual is exactly what time-horizon trends extrapolate, so for that share no below-trend slowdown is implied; and judgment embedded in long engineering tasks is already priced into those trends. The claim therefore needs a distinct broad-judgment component that improves slower than measured trends. That narrows the case modestly. Credence lowered from 0.60 to 0.55. Other premises unchanged: open-ended judgment failures supported 0.75; humans supply most research judgment supported 0.85 (rejudged since, verdict unchanged); judgment improving supported 0.85 but on narrow measures. Status remains inconclusive: the key rate is unmeasured and the forecast's evidence base is small and largely company-reported. Not materially reflexive."
}

{
  "verdict": "likely",
  "evaluation": "Granting its premises, including the stated one that judgment improves no faster than the engineering trend, slower automation of the residual work follows with high probability, since the slowest remaining component tends to set the pace. The premises on current failures ([[claim:66eddda8-c9d1-4b53-8ed6-2a6055c68756]]) and on humans supplying most judgment ([[claim:d479cf4d-52b6-4260-a3dc-7a2e5a812d42]]) are supported. The composition premise ([[claim:20f15464-de92-44a7-8792-b4f159bd97c8]]) is supported only weakly: much long-horizon engineering also remains, and that share moves at the trend rate, which limits how much of the residual the bottleneck governs. The argument lives or dies on the relative-rate premise, which is unmeasured for open-ended judgment.",
  "argument_id": "80d70bc2-7e9b-47e7-803d-cf615b222c53"
}

{
  "verdict": "slight",
  "evaluation": "Granting that AI systems are improving at choosing next research steps ([[claim:f5a654f9-9ef3-4144-9869-6bff1c6d9631]], supported), the inference to no slowdown is weak: the measures behind that premise test narrow judgment within a framed problem, whereas the judgment-heavy part of the residual needs open-ended judgment about goals, redesign and what counts as a result. Improvement on the narrow skill raises the chance the broad one follows, but does not show it improves at the trend rate.",
  "argument_id": "49a03760-359c-46fe-a2f2-1cac4aecd220"
}