Capabilities · Trend
Limits on Near-Term AI Recursive Self-Improvement
MIT Technology Review coverage arguing industry RSI timelines may be overstated relative to practical bottlenecks.
Forecast horizon
Tempers explosive RSI timelines used in investment and policy forecasts.
Connections
Connections · 2
How this node ties into the rest of the map, and the evidence behind each link.
from · drives 4/10
Post-Training Agent Strategy Lock-In vs Execution Skill Trajectory analysis shows post-training agents lock strategy early, supporting skepticism about rapid recursive self-improvement.
+4 growth to · constrains 3/10
Science of AI Training Dynamics Skeptical RSI timelines emphasize practical training and oversight bottlenecks over unconstrained self-improvement.
+3 growthSignal sources
Signal sources
Dated facts from primary sources in this direction.
Task horizon doubling Mar 2025
The length of software tasks AI agents can do autonomously at 50% reliability has doubled about every 7 months — and since 2024 closer to every ~3 months.
METR → Benchmarks saturating Apr 2025
In one year scores rose by 18.8, 48.9 and 67.3 points on MMMU, GPQA and SWE-bench; real-world software solve rate jumped from 4.4% to 71.7%.
Stanford HAI — AI Index 2025 → Autonomous coding 2025–2026
On SWE-bench Verified (500 real GitHub issues), autonomous coding agents reached ~80–86% by late 2025, up from under 50% in early 2025.
Epoch AI →