Will AI Achieve Recursive Self‑Improvement by 2028? Anthropic’s 60% Forecast

Anthropic co‑founder Jack Clark predicts a 60% chance that by the end of 2028 AI systems will be capable of recursive self‑improvement, citing rapid progress on benchmarks such as CORE‑Bench, PostTrainBench, SWE‑Bench, METR, and emerging capabilities in kernel design, agentic coding, and AI‑to‑AI management.

AI alignmentAI automationAI benchmarks

0 likes · 25 min read

Will AI Achieve Recursive Self‑Improvement by 2028? Anthropic’s 60% Forecast

Machine Heart

May 5, 2026 · Artificial Intelligence

Anthropic Cofounder Predicts 60% Chance AI Will Self‑Evolve by 2028

Jack Clark, Anthropic’s co‑founder, argues that based on a sweep of public AI benchmarks—including CORE‑Bench, PostTrainBench, MLE‑Bench, SWE‑Bench and METR—there is roughly a 60% probability that recursive self‑improvement will emerge by the end of 2028, raising profound technical and alignment challenges.

AI alignmentAI automationAI benchmarks

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Anthropic Cofounder Predicts 60% Chance AI Will Self‑Evolve by 2028

Data Party THU

Sep 18, 2025 · Artificial Intelligence

Can Language Models Self‑Optimize? Inside the STOP Framework

Researchers introduce the Self‑Taught Optimizer (STOP), a scaffolding‑based framework that lets large language models iteratively improve their own code without altering model weights, demonstrating superior performance on tasks like LPN, exploring diverse strategies such as beam search and genetic algorithms, while also highlighting security risks like sandbox bypass and reward hacking.

AI safetyLanguage Modelsrecursive self-improvement

0 likes · 11 min read

Can Language Models Self‑Optimize? Inside the STOP Framework