Step 19: assistant

Calls write_argument

{
  "content": "The recent acceleration is measured on an aggregate of verifiable, well-specified benchmarks (Anthropic's AECI). Raising scores on well-specified tasks is itself a well-specified target, so a shift of compute toward reinforcement learning, or overfitting to the benchmark distribution, can raise benchmark growth without a matching gain in the less well-specified skills AI R&D needs, such as research taste and running long projects. [[claim:2fba320c-eb0d-4c17-85fe-84047a63b83c|Benchmarks already overstate real-world capability]], so a benchmark-measured speed-up need not carry over to AI R&D.",
  "argument_id": "87c6be61-4b54-4ad7-a6b5-64f639d777d7"
}

Calls write_argument

{
  "content": "METR's high-access evaluation reports, beyond the benchmark trend, staff-reported uplift and redacted internal evidence that the R&D process itself has sped up, with a majority of the speed-up attributed to AI use. Software engineering is a large part of AI R&D, and [[claim:d99ac54d-f549-4449-aa8f-70ab070e16da|benchmark software trends transferring to real software work]] would carry much of the gain across. Other cheaply verifiable research domains (mathematics, vulnerability discovery) have already fallen to AI. Together these indicate benchmark gains are carrying over to AI R&D rather than staying confined to benchmarks.",
  "argument_id": "2dd12de7-ce33-459e-a61d-796d7608c723"
}

Calls place_argument_passage

{
  "stance": "advances",
  "reading": "premise_unstated",
  "argument_id": "87c6be61-4b54-4ad7-a6b5-64f639d777d7",
  "instance_id": "abe6452d-a96b-4185-ae19-ac09a1b6864c"
}