Uploaded May 2026 | Updated September 2026, 2 weeks ago
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 agents more power than MCP
• How open source helps agents integrate Daytona
• Why agent-generated PRs may break today’s CI/CD assumptions
• Why AI SaaS companies reselling tokens may face a cold shower
• Why the AI cloud may look more like Stripe than AWS
—
Ivan Burazin
• LinkedIn: linkedin.com/in/ivanburazin
• X: https://x.com/ivanburazin
Daytona
• Website: daytona.io
• X: https://x.com/daytonaio
Timestamps
00:00:00 Hook
00:01:12 Introduction
00:03:15 CodeAnywhere, Shift, and the end of localhost
00:05:58 What Daytona is: composable computers for AI agents
00:08:07 The pivot from dev environments to AI sandboxes
00:10:17 The New Year’s Eve MVP and customers begging for API keys
00:12:56 Bare metal, stateful sandboxes, and Daytona’s scheduler
00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs
00:21:53 Spiky RL/eval workloads and the new agent infra problem
00:28:12 RL workloads, Kubernetes pain, and dynamic resizing
00:33:31 Why every AI agent needs a computer
00:38:48 macOS sandboxes and Apple’s licensing problem
00:44:28 Why CLI may matter more than MCP
00:48:11 Open source, GitHub stars, and agent integration
00:53:11 Git, CI/CD, and agent collaboration bottlenecks
00:58:15 Founder life and building a 25-person infra company
01:02:44 AI SaaS, token resale, and API-first business models
01:06:10 GPU sandboxes, data centers, and compute growth
01:09:48 Why the AI cloud may look more like Stripe than AWS
01:11:26 Closing thoughts
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 agents more power than MCP
• How open source helps agents integrate Daytona
• Why agent-generated PRs may break today’s CI/CD assumptions
• Why AI SaaS companies reselling tokens may face a cold shower
• Why the AI cloud may look more like Stripe than AWS
—
Ivan Burazin
• LinkedIn: linkedin.com/in/ivanburazin
• X: https://x.com/ivanburazin
Daytona
• Website: daytona.io
• X: https://x.com/daytonaio
Timestamps
00:00:00 Hook
00:01:12 Introduction
00:03:15 CodeAnywhere, Shift, and the end of localhost
00:05:58 What Daytona is: composable computers for AI agents
00:08:07 The pivot from dev environments to AI sandboxes
00:10:17 The New Year’s Eve MVP and customers begging for API keys
00:12:56 Bare metal, stateful sandboxes, and Daytona’s scheduler
00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs
00:21:53 Spiky RL/eval workloads and the new agent infra problem
00:28:12 RL workloads, Kubernetes pain, and dynamic resizing
00:33:31 Why every AI agent needs a computer
00:38:48 macOS sandboxes and Apple’s licensing problem
00:44:28 Why CLI may matter more than MCP
00:48:11 Open source, GitHub stars, and agent integration
00:53:11 Git, CI/CD, and agent collaboration bottlenecks
00:58:15 Founder life and building a 25-person infra company
01:02:44 AI SaaS, token resale, and API-first business models
01:06:10 GPU sandboxes, data centers, and compute growth
01:09:48 Why the AI cloud may look more like Stripe than AWS
01:11:26 Closing thoughts
