Step 14: tool results

{
  "content": "Measured AI capability has grown faster in calendar time, but capability gained per dollar of research spending and per technical employee has fallen, as in other fields where ideas get harder to find. Sustaining the pace has therefore required inputs (compute, energy, capital, staff) to grow very fast. Those inputs face physical and financial limits: chip fabrication, grid expansion and investor capital cannot grow at current rates indefinitely, and some projections have training-compute growth falling off its exponential around 2028. Unless AI begins supplying enough of its own research input to offset the rising difficulty, progress toward automating AI R&D will slow to what the constrained inputs allow within the next few years.",
  "argument_id": "7c759a30-4926-4ab2-8761-941b1988100d"
}

{
  "content": "The capabilities most relevant to automating AI R&D are still improving on trend or faster. METR's task time horizon, which doubled about every seven months from 2019 to 2024, has doubled roughly every three to four months since; METR judged in October 2026 that the pace on well-specified tasks was about twice that of 2024 to 2025. Coding and research-reproduction benchmarks (SWE-bench, CORE-Bench) went from low scores to saturation within two years, and a frontier developer reports over 80% of its merged code is AI-written. Compute build-out already committed runs through at least 2027 to 2028. A stall within a few years would require these trends to bend soon, and no measured trend has yet bent.",
  "argument_id": "e83ee6f8-ed52-44e3-81ef-896d967212cf"
}

{
  "stance": "advances",
  "reading": "premise_unstated",
  "argument_id": "e83ee6f8-ed52-44e3-81ef-896d967212cf",
  "instance_id": "db57038c-1af6-4df3-9f04-c10f8f6c632b"
}