Oxford's Toby Ord disputes Anthropic's claim that all AI capabilities scale uniformly, arguing non-verifiable tasks plateau lower

Story Overview

Toby Ord is poking at a key assumption buried in Anthropic's recent write-up on recursive self-improvement, where the company treats every tracked AI skill—from hard benchmarks to fuzzier judgments like code quality—as riding the same unbroken upward curve. Ord counters that reinforcement learning with verifiable rewards drives clear gains on checkable tasks but shows weaker carry-over elsewhere, suggesting those non-verifiable areas may level off sooner.