AI BriefingAI Shortens Timelines for Advancing AI as It Handles Large-Scale, Verifiable SWE Tasks
Key point
As AI takes on large-scale SWE tasks that are easy to verify, timelines for AI advancement and R&D automation are expected to shorten.
Details
It significantly shortens AI advancement timelines. The probability of AI R&D automation occurring by the end of 2028 has been raised from the previous 15% to 30%.
In particular, strong performance is expected on ESNI (Easy-and-cheap-to-verify SWE tasks that don't require much ideation) tasks, which don't require much ideation and are cheap and easy to verify. By the end of 2026, AI is projected to have a time horizon of several years to several decades for such tasks, based on 50% reliability.
The key drivers are as follows:
- Latest models such as Opus 4.5 and Codex 5.2 are exceeding expectations in both benchmarks and real-world performance.
- Cases have been confirmed where Claude performs large-scale ES (Easy-and-cheap-to-verify) tasks nearly autonomously, such as writing a C compiler.
- Major gains are expected from the large-scale Training Compute scale-up in 2026.
- Improvements in Scaffolding are increasing AI's practical usefulness.
As AI becomes more useful for AI R&D, the pace of advancement will accelerate further, with the pace of progress in 2026 expected to be faster than in 2025.
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