Sep 28, 2026 · 43 min
AI Needs Smarter Software, Not Just Better Code
AI Can Write Code. Why Isn’t Software Better?
The episode argues that reliable automation depends on embedding intelligence into software systems rather than treating AI as a code-generation layer.
- 1Coding agents can produce syntax quickly, but architecture and dependable behavior remain difficult to automate.
- 2Jev combines natural-language reasoning, state machines, and confidence levels to make software itself responsive to intent.
- 3Established SaaS companies may benefit from AI by applying intelligence to workflows they already understand.
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Diogo Almeida frames “do what I mean” as the broader goal: software that interprets intent and acts reliably despite probabilistic AI behavior.
The brief
The episode opens with a paradox: AI appears highly capable, yet surprisingly little basic work has been automated reliably outside chatbots and coding agents.
Diogo Almeida explains how Jev aims to make software intelligent by combining natural-language reasoning with state machines and confidence levels, rather than generating conventional code on demand.
The discussion shifts from model capability to system design, asking whether reliability means more than uptime when AI behavior remains probabilistic across calls.
Coding agents such as Claude Code and Codex are strong at syntax, the group argues, but intelligent primitives could help developers solve architecture and workflow problems.
The standout vision is software that understands intent directly: fewer rigid forms and handoffs, and technology that can reliably do what users mean.
Ben HorowitzMentioned
Claude Code
OpenAIListen to the full episode and explore every guest, topic, and moment on PodLume.