Why Does AI Code Performance Look Good Until Network Calls?

AI-generated code tends to perform well on a developer laptop, where every call hits a local database, a mocked API, or an in-memory fixture and costs close to nothing. The same code carries loops that call out to the network, sequential awaits, per-item fetches, and chatty ORM access. Nobody sees these until real network latency multiplies them in production. This episode covers why AI assistants keep producing these latency patterns, why the usual tests and code reviews miss them, and how builders can make the cost of a network call visible before it ships. Produced by VoxCrea.AI This episode is part of an ongoing series on governing AI-assisted coding using Claude Code. 👉 Each episode has a companion article — breaking down the key ideas in a clearer, more structured way. If you want to go deeper (and actually apply this), read today’s article here: 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐬 At aijoe.ai, we build AI-powered systems like the ones discussed in this series. If you’re ready to turn an idea into a working application, we’d be glad to help.