Developer experience optimization has been measured in seconds for years—shaving off test time or server reload delays. That's about to flip. As smaller language models get faster and smarter models get more efficient, we'll see AI agents running at thousands of tokens per second instead of dozens. Jimmy, Taalas' version of LLaMA-3.1-8B, runs at seventeen thousand tokens per second, meaning responses arrive almost instantly. When token generation stops being the bottleneck, suddenly the time it takes to read a file (100ms versus 10ms) or run tests (500ms versus two seconds) becomes the real constraint. An agent waiting for tool calls becomes the new problem to solve. This shift creates pressure to rebuild developer workflows around speed. Teams will gravitate toward languages with fast compilers and tight test loops—Golang gets mentioned specifically—because the difference between a 10ms and 100ms file read could stretch a response from instant to several minutes when you're chaining multiple tool calls. The infrastructure and optimization work that matters changes entirely. What DevEx teams optimized for in the 2010s—making engineers happy—won't cut it anymore. Most companies gutted their DevEx teams over the past decade, but we might see them return in the late 2020s with a different mission: optimizing the experience for AI agents, not humans. The economic incentive flips from "keep engineers comfortable" to "keep agents fast." That's a meaningful reorientation of engineering priorities, and it requires rethinking which tools and languages make sense for codebases that will be primarily written by and for machines.