🎧 Podcast
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Free
Intermediate
Why we picked it A demo that works once on stage and a system that holds up for real users are two very different things, and this conversation is about the gap between them. Tony Holdstock-Brown (who builds the plumbing that runs AI in production) walks through where multi-step AI actually delivers and where it quietly stalls: retries, edge cases, cost, and the un-glamorous reliability work. For a non-technical founder it is a useful ear on what "it works" really costs behind the demo.
Building Production Workflows for AI Applications
On AI + a16z by Tony Holdstock-Brown, Yoko Li and Derrick Harris ~45 min
- The hard part of an AI product is usually not the model call, it is making the workflow around it reliable when real inputs get messy.
- Ask any AI trend the operator's question: what happens on the tenth thousandth request, not the first perfect one.
- Cost and reliability compound as you scale, so a capability that looks free in a demo can quietly become the whole business problem.