curated · 2026-09-14

This one's worth opening because it's a concrete engineering story: Anthropic's team started using Claude Code heavily, PR volume spiked, and CI couldn't keep up. Instead of throttling agent commits, they rewrote the test impact analysis service. The trick is caching file dependency graphs with Merkle trees so only truly affected tests run—latency dropped from 11 seconds to under 1 second, handling 1,000 analyses per day. This is their internal monorepo setup, so direct portability varies, but the caching strategy and API design are solid references. I'd read this as a case study for rethinking CI in the agent era, not a drop-in solution.

The numbers here are hard to ignore: $11.5B in quarterly revenue, 14x year-over-year, a $65B annualized run rate, and a possible $2T+ IPO valuation. But I'd discount the 'second straight profitable quarter' claim—it's an adjusted metric that leaves out stock-based compensation, which is real dilution for future shareholders. The 80%+ gross margin is also pre-revenue-share with Amazon and pre-training cost, so it's not what you'd normally call gross margin. SemiAnalysis expects investors to target $120B annualized by year-end and nearly triple that by end of 2027, which is an aggressive bet even by AI standards. Dario calling for a slowdown, Altman and Musk backing it, and Altman saying OpenAI won't IPO this year—read together, this looks like positioning ahead of a mega-listing rather than a sudden policy awakening.

This is worth reading because Tunguz doesn't argue for or against a pause — he asks what "pacing" even means when nobody will name a number. He maps the five camps that responded to Amodei's September post: interpretability wants time to understand models, labor wants time for workers, the economic camp bets on AI-driven GDP growth to service debt, the geopolitical camp wants to stay ahead of China, and the regulatory capture camp calls the whole thing a cartel in disguise. Every camp priced the consequences. None proposed a speed. The sharpest section is the closer: the one mechanism that could produce a number — a training compute threshold — was tried in 2023 at 10^26 FLOPS. It was revoked before any model crossed it. Grok-3 shipped weeks later, and roughly ten models will clear that bar this year. Training compute grows about 5x a year, so a fixed number is a ceiling the floor reaches on its own. Tunguz doesn't hand out an answer, but he reframes the question from "should we slow down" to "who decides." The post doesn't say whether Amodei responded to any of this, which feels like a missing piece.