Jack Clark moves his forecast for ‘full RSI’ to 2027, citing TasteVal
Opus 5.5 beats ML experts at designing experiments (2.3x compute multiplier)
Confirmed
The takeaway
In Import AI 476 (Oct 12, 2026) Anthropic co-founder Jack Clark wrote “I now think it’s more likely we’ll see full RSI become possible in 2027, whereas before I thought it was more likely in 2028”.
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- 3 of 5
- Last verified
- 13 October 2026
Your AI and this story
- GPT-6 Astra165 days after its cutoff
- Claude Opus 5.5104 days after its cutoff
- Gemini 4 Argon195 days after its cutoff
- Grok 4.7134 days after its cutoff
None of these four assistants can know about it. The closest, Claude Opus 5.5, stops 104 days before it.
Key facts
- Clark (Import AI 476, Oct 12, 2026): ‘I now think it’s more likely we’ll see full RSI become possible in 2027, whereas before I thought it was more likely in 2028.’ RSI = recursive self-improvement, AI systems able to do the research that improves AI systems
- His reasoning: in Import AI 455 (‘AI Systems Are About To Start Building Themselves’) he had named creativity as a key variable and wrote that AI systems had not yet shown it ‘in a transformative and major way’; since then he sees ‘a tremendous advancement in the ability for AI systems to make creative-seeming math breakthroughs’ and benchmarks such as TasteVal showing gains in AI research itself
- Clark writes Import AI in a personal capacity; it is not an Anthropic forecast. Techmeme carried the quote as a headline on Oct 13
- TasteVal: ‘TasteVal: Measuring the Experimental Research Taste of AI Systems Against Human Experts’, Oliver Jaffe and Dane Sherburn (P-Zero Research), arXiv:2610.06824, submitted Oct 5, 2026, revised Oct 6; 38 pages
- Method: 8 new open-ended tasks (pretraining data curation, pretraining and fine-tuning language models, preference modeling, robustness to adversarial prompts). The tested model acts as the researcher: it designs experiments and reads results; a fixed coding agent implements and runs them on one H100. A run ends after 40 GPU-hours or 120 hours of wall-clock time. Tasks are not released, to avoid contamination
- Metric: compute efficiency. ‘A model that reaches the experts’ score with half the experimental compute has twice their experimental research taste’
- Human baseline: the best attempt per task from 24 recruited experts (at least two per task) who recently worked at organizations including OpenAI, Google DeepMind, NVIDIA and Microsoft Research
- Result: ‘The best model, Opus 5.5, significantly exceeds our human baseline with a compute multiplier of 2.30x’ (95% CI 1.15-4.37x), at roughly 1/30 of the experts’ average cost per attempt. 20 models released between 2023 and 2026 were tested
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- Trend: ‘The experimental research taste of frontier models has doubled every 3.0 months since December 2025 (95% CI 1.7-5.0 months)’, against about every 14 months from 2023 to December 2025. Measured by final normalized performance instead, the authors report no trend break (doubling about every 14.6 months)
- Extrapolation the authors call naive, not a forecast: fed into the AI Futures Model, the measured rate moves the probability of a ‘taste-only singularity’ from 51% to 88% and the median date of artificial superintelligence from mid-2030 to late 2028
- Authors’ caveats: results ‘may overstate how fast the research taste is improving’ because the tasks are ‘fast and cheap to verify, which frontier labs find easiest to hill-climb’; the human baseline ‘doesn’t include top researchers’; the benchmark does not test choosing which problems to work on; and it may understate progress because each model got only limited elicitation
- Reach: little outside the AI-forecasting community so far; the Hacker News submission of TasteVal (Oct 6) got 2 points. Not peer-reviewed; no independent replication, and the tasks cannot be inspected
What happened
Jack Clark, co-founder of Anthropic, publishes the weekly newsletter Import AI. Issue 476 (Oct 12, 2026) opens with TasteVal, a new benchmark from P-Zero Research, and ends the item with a change of forecast: “I now think it’s more likely we’ll see full RSI become possible in 2027, whereas before I thought it was more likely in 2028.” He explains that in an earlier issue (455) he had treated creativity as the main unknown in how fast AI systems could learn to improve themselves, and that the autumn’s AI mathematics results and benchmarks like this one changed his view.
TasteVal tries to separate “research taste” from coding skill. The tested model only decides which experiments to run and how to read the results; a fixed coding agent does the implementation on one H100 GPU. The score is how much compute the model needs to reach the best score achieved by human experts on the same task. Claude Opus 5.5 needed 2.3 times less (95% CI 1.15 to 4.37), at about a thirtieth of the cost of an expert attempt. Across 20 models the authors fit a doubling time of 3.0 months since December 2025, after about 14 months before that.
The same issue covers Humanity’s Sixth Sense (see Humanity’s Sixth Sense (Scale AI and Elorian)), Reflection’s Beam and Mistral Large 4 (both behind GLM-5.3 on the agentic coding scores Clark quotes, hence “worse-than-China open weight models”), an AI Frontiers essay on AI and space industrialization, and a US national-labs paper on federated training across supercomputers (arXiv:2609.39803).
Why it matters
Clark co-wrote Anthropic’s June 2026 paper “When AI builds itself” and is one of the lab’s main public voices on policy. A one-year pull-forward by him, stated in public, is a data point on how insiders read the autumn of 2026. The evidence under it is thinner than the headline: one preprint with eight unreleased tasks, a wide confidence interval, a human baseline without top researchers, and a trend break that disappears when the authors measure final performance instead of compute efficiency.
Sources
6 sources from 5 sites. Numbers match the chips in the text.
6 sources: 4 primary, 2 reactions
Primary
- Import AI 476: Creativity and RSI; worse-than-China open weight models; AI goes to space (Jack Clark, Oct 12, 2026)importai.substack.com, official
- P-Zero Research: TasteValpzeroresearch.com, official
- arXiv:2610.06824: TasteVal: Measuring the Experimental Research Taste of AI Systems Against Human Expertsarxiv.org, paper
- Import AI 455: Automating AI research (Clark’s earlier analysis)importai.substack.com, official
Reactions
- Techmeme (Oct 13, 2026)techmeme.com, discussion
- Hacker News: TasteVal: Does AI have research taste?news.ycombinator.com, discussion
Changes
- Filed (live Techmeme headline; Import AI 476, the P-Zero page and the arXiv abstract were read through a fetcher that returns short quotes, so longer passages are paraphrased)