Turning Down the Thinking: A Law & Economics Trilogue on AI Throttling
Three section leads at the International Center for Law & Economics (ICLE) read the same viral GitHub post and reached three different conclusions. Call it a trilogue—three views, one problem, and a technology that refuses to sit still.
The GitHub issue filed last week against Anthropic’s Claude Code product carried a blunt title: “Claude Code is unusable for complex engineering tasks with the Feb updates.” The author—Stella Laurenzo, an AMD senior AI director—laid out a detailed account of technical decline.
According to Laurenzo, months of session-log data show that from January to March, median “thinking” output fell roughly 70%. The model began bailing out or asking permission to continue about 10 times per day—up from zero before early March. Self-contradictions in its reasoning tripled. API requests spiked, suggesting users had to retry repeatedly to get usable results.
Most striking, performance appeared to degrade during peak GPU-load hours and recover late at night. That pattern offers circumstantial—but suggestive—evidence that quality was being throttled as a function of server demand, rather than any deliberate design improvement.
The issue went viral. Within about 20 minutes of reading it, three of us found ourselves in a lively disagreement about how to understand it through a law & economics lens.