If AI agents are the new digital knowledge workers, where exactly do they do their work? In this episode of the MAD Podcast, Ivan Burazin joins us to unpack the emerging infrastructure stack for AI agents and explain why every agent needs its own secure, stateful "computer." We explore the technical realities of sandboxes, dive into why legacy, stateless hyperscalers weren't built for these new workloads, and break down the mechanics of microVMs and custom schedulers alongside a contrarian prediction on an impending CPU shortage. Finally, Ivan delivers an absolute masterclass on product-led growth, community building, and go-to-market strategy for technical founders.

(00:40) Intro

(02:13) What is an AI agent sandbox?

(03:17) Security risks of running agents locally

(05:17) Stateful vs. stateless hyperscalers

(07:04) The history of cloud IDEs and the end of localhost

(09:45) Do all AI agents need a sandbox?

(12:26) Sandbox use cases: RL evals & background agents

(14:10) Unpacking the emerging AI Agent Stack

(16:20) The unsolved problem of agent memory and learning

(19:37) Where sandboxes fit in the agent harness

(21:35) OpenAI, Anthropic, and agent SDKs

(23:06) Ivan's founder journey: From CodeAnywhere to Daytona

(26:59) GTM strategies and building developer communities

(33:48) Why customer support is your best GTM strategy

(35:34) Leveraging Twitter during the AI super cycle

(40:50) The technical anatomy of a sandbox

(41:53) Why fast spin-up speeds maximize GPU efficiency

(46:09) Firecracker, QEMU, and isolation primitives

(49:58) Why sandbox snapshots and state forking matter

(51:40) Why Daytona built a custom scheduler from scratch

(55:24) The challenge of long-running stateful sandboxes

(58:10) The build your own sandbox trap

(1:01:03) Why AI agents might trigger a global CPU shortage

(1:02:46) The future of the AI Agent Stack