Agentic AIs are showing promise for tedious work. But it’s hard to explain exactly how you want it done—and getting it wrong could create big problems.

This episode of Compiler investigates how Agentic AIs could carry out their tasks and how some agents are taking their baby steps in the wide world. The team also considers the difficulties humans have expressing what we want computers to do for us, and how that could create unintended consequences.

There’s no one AI model to rule them all. Each project has its own requirements. Where do you get started building your own model?

Compiler continues its conversations with big dreamers about their big projects—and how they’re piecing together the building blocks of their AI models.

There is a lot of excitement around AI models, but can it meet the expectations set by blockbuster movies? What’s the current inflection point between what’s feasible and what’s not?

The Compiler team talks to both big dreamers and heavy adopters wading into the space, hearing their thoughts on how AI can help scale daunting work, fill in the gaps, and make the fantastic into reality.

Everyone’s talking about AI. Everyone says it’s the future. To find out where we’re going, we should know how we got here—and exactly what we’re working with.

We hear a short history of AI development before diving into how it’s already changed the ways we learn and code.