Step 21: assistant

thinking

text

I've rated the claim **doubtful**: as of November 2026 the evidence weighs against progress toward automating AI R&D stalling in the next few years. My credence that it will stall is 0.2, with confidence 0.65 that "doubtful" is the right status. The pass cost about $1.05 of the $1.50 budget.

**What I read.** Anthropic's "When AI builds itself" report, the one source already on the claim, and a November 13, 2026 analysis by economists Alex Imas and Luis Garicano. A short web search added METR's January 2026 update and recent forecaster updates.

**Why it leans against a stall:**
- The length of software task a model can complete reliably, as METR measures it, doubled about every seven months from 2019 to 2024. Since then it has doubled roughly every three to four months.
- METR judged in October 2026 that progress on well-specified tasks was running about twice its 2024–25 pace.
- Two benchmarks went from low scores to near-perfect in about two years: SWE-bench, which tests fixing real software bugs, and CORE-Bench, which tests reproducing published research.
- Anthropic says over 80% of the code it merges is now written by its models. Anthropic calls a stall unlikely because "We have not yet seen that curve bend." It has a commercial stake in how capable its models look, and its internal figures are unpublished. The external benchmark figures it cites can be checked independently.
- Computing capacity already financed and under construction runs through at least 2027–28.

**The case for a slowdown:**
- Imas and Garicano estimate that capability gained per dollar of research spending fell by about two-thirds, and per employee-year by about two-fifths.
- Chips, power and capital limit how fast those inputs can keep growing, and some projections have computing-power growth slowing around 2028–30.
- Research judgment, the part of the work AI does least well today, may not improve with scale.
- Even so, these sources argue for an eventual slowdown, not a near-term stall. Imas and Garicano don't predict a stall or give a date, so I recorded them as discussing the claim rather than asserting it.

**What I put on the claim page:**
- **Arguments:** two named lines of reasoning. The one against a stall (capability trends haven't bent) I rated as making its conclusion likely if its premises hold. The one for a stall (diminishing returns and limited inputs) I rated as giving only slight support.
- **Sources:** I attached the Anthropic passage that was left unplaced on the claim to the argument it makes, and corrected its speaker to the Anthropic Institute.
- **Importance:** set importance and contestation both to 0.7.
- **Messages:** answered both inbox messages.

**What I left out.** I kept the supporting points in the argument text rather than creating separate sub-claims, to stay within budget. A later pass could attach two measurable quantities: the METR doubling time and frontier compute growth. I put the value of another pass at 0.4. The claim has no dependents, so there was no one to notify.

What would change the verdict: task-length or AI R&D benchmark trends flattening through 2027, a pullback in investment in computing for frontier models, or evidence that research judgment doesn't improve with scale.