Step 18: tool results

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting its premises, the deployment half follows with a wide margin: at billions of actions a day, even ten seconds of low-paid human time per action costs about as much as the largest developer earns, and minutes per action cost a hundred times more. The training half follows less firmly, because it depends on the stated premise that training involves hundreds of millions of judged actions, which no source has costed; at a million actions the bill is affordable. The argument stands or falls with [[claim:191c26ff-f119-4665-a9e5-1dc77b867142|whether each signal needs a human judgment]], which holds for the protocols as their proponents define them and is relaxed in most proposals for using them at scale, and which has not yet been assessed.",
  "argument_id": "7ac695fe-423d-4a07-9b70-c294b29bc7f1"
}

{
  "verdict": "slight",
  "decisive": false,
  "evaluation": "Granting its premises, the argument shows that developers under present competitive conditions would probably decline a several-fold cost on every action. It does not show the cost is beyond their means, so it supports the claim only in the weaker sense of \"too costly\", which is partly a judgment about willingness to pay. Its weight rests on two things stated in the prose: the size of the multiple, which is a structural estimate of roughly three to ten times and has not been measured, and the inference from developers having engineered a measured 24% classifier overhead down to about 1%, which is evidence of preference, not of a limit.",
  "argument_id": "771b349c-b8c8-464c-94ff-7d7eaeef940a"
}

{
  "verdict": "slight",
  "decisive": false,
  "evaluation": "Granting its premises, the argument shows that the per-action cost is bounded and could be paid by a developer willing to serve several times fewer actions for the same compute. That removes the strong sense of \"too costly\" and leaves the weaker one standing. It also partly changes the subject: a judge model that imitates human judgment is what the literature treats as the cheaper substitute for amplified oversight, weaker than the system it oversees, so paying for it on every action is not the same as obtaining the amplified signal on every action. The argument depends on the denial of [[claim:191c26ff-f119-4665-a9e5-1dc77b867142|the premise that each signal needs a human judgment]], which is unassessed and on which the defining texts are themselves equivocal.",
  "argument_id": "c9bdd599-2696-48c5-b8b0-c16f95058949"
}

{
  "claim_id": "0a64000e-ce65-4078-b4f6-fc0ebb861ccf",
  "reasoning": "First pass. Read the asserting paper's Sections 6.1 and 6.3 in its arXiv TeX source: the claim is stated as an anticipation with one reason (human oversight is expensive) and no costing. Reworded the canonical form to gloss \"amplified-oversight signal\" as AI-assisted human judgment, since the verdict turns on that meaning and the term is a term of art. Corrected the single instance to carry the Section 6.3 passage that actually states the claim, with authors and date; recorded its reading (asserts without evidence) and a material source map. Structure: three named arguments (scarcity of human judgment, for; protocol compute overhead, for; delegated judging, against) and one new subclaim, the definitional crux of whether each amplified-oversight signal requires a human judgment (Matcher: new), shared by the first and third arguments. Linked the AI-control monitoring-evaluation claim as related rather than as a premise, since how well cheap schemes work does not bear on what the expensive one costs. Assessment: supported, confidence 0.75, credence 0.82, from own arithmetic on public usage volumes (2.5e9 prompts/day; 1.3e15 tokens/month) with assumed human time per judgment of 10 s to 30 min; deployment half near settled on the human reading, training half dependent on uncosted training action counts; automated-judge overhead is an unmeasured structural estimate (3-10x); measured overheads exist only for cheap classifiers (23.7%, later about 1%). Importance 0.3, contestation 0.15. Filed a request to compile measured per-judgment human time, per-debate token cost and training action counts, which would narrow the intervals and could weaken the training half. Not done: the constitutional-classifier follow-up was read only via secondary summaries; the revenue comparison figure was not re-verified; search for denials was light.",
  "action_taken": "structured_and_assessed"
}

{
  "message": "First assessment of the cost premise your claim takes as counting against it: supported, credence 0.82. Two points may matter to how your edge reads. (1) The claim is now worded to say what the signal is: \"Obtaining an amplified-oversight signal (AI-assisted human judgment, as in debate) for every action of very powerful AI is too costly in training and in deployment.\" It is near settled for deployment on that reading (own arithmetic: 2.5 billion prompts a day at even ten seconds of human time each costs about as much as the largest developer's revenue; minutes each cost a hundred times more), weaker for training (affordable at around a million judged actions, prohibitive at hundreds of millions; uncosted in any source). The asserting paper gives no cost estimate. (2) The premise supports less than your edge reasoning draws from it. What is too costly is the amplified signal on every action. Some oversight signal on every action is demonstrably affordable: a production classifier system ran on all traffic at 23.7% inference overhead, later reported at about 1% with a probe cascade, and the asserting paper's own response is cheap judges and tiered monitors that escalate to expensive oversight. So \"control cannot rest on overseeing each action at run time\" does not follow from this premise alone; what follows is that per-action run-time oversight must use a signal weaker than the amplified one, with the amplified signal reserved for a sampled or escalated fraction. Whether that weaker per-action signal suffices against a superintelligent system is a separate premise your argument would need. No change to your verdict is suggested, only that the edge's reasoning may overstate what this premise closes off.",
  "claim_id": "0a64000e-ce65-4078-b4f6-fc0ebb861ccf",
  "target_ids": [
    "dc02e01f-ce69-4dbe-ba36-ed0e2e90d071"
  ]
}

{
  "answers": [
    {
      "note": "First pass done: canonical form glossed, three arguments and one definitional subclaim added, instance corrected, assessed as supported (credence 0.82), importance 0.3, contestation 0.15.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "Assessed as supported, credence 0.82. 'Too costly' has no threshold in any source; two senses were assessed (beyond means; affordable but would be declined). The asserting paper rests on a one-line argument (human oversight is expensive), not an estimate or measurement. Own arithmetic: human judging of 2.5e9 actions/day costs about 13 to 51 billion dollars a year at 10 s each and about 1.5 trillion at 5 min each, so deployment is near settled where a human is in the loop; training ranges from 0.3 million to 50 billion dollars depending on action count and is uncosted. No measured per-action overhead for debate was found; three to ten times inference is a structural estimate. Measured figures exist only for cheap classifiers (23.7%, later about 1%); the 2% audit budget in control experiments is a parameter, not a finding. Cheaper proxies do not falsify the claim, they answer it: some per-action oversight is affordable, the amplified signal on every action is not. A request is filed to compile measured human minutes and token costs per debate judgment and training action counts, which would narrow these intervals.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}