GPT-5 and Convex Optimization: What the Claims Actually Mean for Engineering Tooling

GPT-5 and Convex Optimization: What the Claims Actually Mean for Engineering Tooling

A recent thread hit 482 points and 312 comments on the claim that GPT-5 has made meaningful progress on convex optimization problems that have been largely intractable for thirty years. The reaction split cleanly: researchers excited about specific benchmark results, engineers skeptical about practical implications, and a third camp arguing the framing was misleading from the start.

All three groups have valid points. Here's an attempt to untangle them.

What Convex Optimization Actually Is (And Why It's Everywhere)

Convex optimization is the class of problems where you minimize a convex function over a convex set. The key property: any local minimum is a global minimum. That makes convex problems tractable in a way that general optimization problems are not.