AI Agents Need Computers: 74% MoM Growth, 850K/Day Runs, & New Agent Cloud — Ivan Burazin, Daytona

From building one of the first browser-based IDEs to trying to kill localhost for the agent era, Ivan Burazin has spent more than a decade chasing the idea that development should not depend on your local machine. In this episode, Daytona’s CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs. We go deep on the new agent compute market: Daytona’s hard pivot from human dev environments to AI sandboxes, the New Year’s Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS. We discuss: • How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis • Why Daytona pivoted from human dev environments to AI sandboxes • Why agents need composable computers instead of disposable code execution boxes • The New Year’s Eve MVP that customers chased API keys for • Why Daytona chose bare metal, stateful snapshots, and its own scheduler • How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds • Why Daytona’s biggest customer runs ~850,000 sandboxes a day • How RL/eval workloads create zero-to-100,000 CPU spikes • Why RL workloads went from 0% to roughly 50% of Daytona usage • Why customers compare Daytona against EKS/GKS and say they’re “never going back” • Docker, Sysbox, nested workloads, dynamic resizing, and avoiding OOMs • Why every AI agent may need a computer, including Windows and macOS environments • The Apple licensing constraints that make macOS sandboxes hard • Why CLI gives