How Jev Turns AI Into Software That Gets Things Done

Diogo argues that today’s AI is impressive in conversation and code generation, but fails to reliably execute end-to-end work inside real systems. TypeSafe’s mission is to turn that “raw intelligence” into dependable automation embedded directly into software.

They distinguish “just-in-time software” (LLMs generating conventional code faster) from “smart software,” where developers add a new AI-based primitive into programs to expand what software can do. The key shift is from automating engineers to enhancing the capabilities of the applications themselves.

The conversation frames Jev as something like a natural-language-driven, probabilistic classifier that plugs into a state-machine-like structure. That design aims to make AI outputs programmatically controllable and composable rather than merely human-readable.

Diogo repeatedly emphasizes “reliability” as the hard-won differentiator: not uptime, and not strict determinism, but robustness—"similar intelligence every time" even when inputs vary in irrelevant ways. The long-term goal is developers being able to program against Jev without crafting endless example prompts.

He’s skeptical that lack of automation is primarily a data/distribution problem, arguing that many high-ROI tasks should already be automatable if we optimize for automation rather than demos judged by humans. He criticizes the industry’s tendency to optimize “what looks good to humans” instead of “what runs safely in production.”