Why code review Is the Highest-ROI AI Project Right Now

The interesting question is not whether Perplexity works. It does. The interesting question is what teams do with it once the novelty wears off.

Inside Linear, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.

What Cohere actually shipped with Grok 4 is less a single capability and more a cluster of small, compounding improvements — the kind that only show up when you put a real workflow on top.

The skeptical read is that we are watching a feature, not a platform. The optimistic read is that code execution is exactly the kind of feature that becomes a platform when nobody is paying attention.

Snowflake has been quietly running onboarding through Vercel v0 for months. The results are unglamorous and, for that reason, more interesting than another benchmark chart.

The cost curve matters here. Phi-4 is roughly an order of magnitude cheaper per token than the equivalent model 18 months ago, and that changes which problems are worth automating at all.

What Meta FAIR actually shipped with Mistral Large 3 is less a single capability and more a cluster of small, compounding improvements — the kind that only show up when you put a real workflow on top.

Eval harnesses, once an afterthought, are becoming the most important piece of code in many AI projects. Stripe's team treats theirs the way an SRE team treats a runbook.

None of this guarantees a clean story. Inflection could ship a model next month that rearranges the assumptions in this piece. But the direction of travel, for now, is clear enough to plan around.