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

May 21, 2026 · 1:11:40

In this episode, Ivan Burazin, CEO of Daytona, explains why AI agents need composable computers rather than disposable code execution boxes, and how Daytona pivoted from human dev environments to building a bare-metal sandbox platform that spins up one sandbox in 60ms and 50,000 in 75 seconds. Daytona's biggest customer runs 850,000 sandboxes daily, and RL/eval workloads have surged from 0% to 50% of usage. The platform competes against managed Kubernetes (EKS/GKS) by offering stateful snapshots, dynamic resizing, and a scheduler purpose-built for agent workloads. Burazin also discusses the company's 74% month-over-month growth, the addition of Windows sandboxes, and the licensing challenges of macOS. He argues the future AI cloud will look more like Stripe than AWS, and that agents will need every kind of computer—including legacy Windows environments—to automate knowledge work.

Agent Engineering with Pydantic + Graphs — with Samuel Colvin, CEO of Pydantic Logfire

Feb 6, 2025 · 1:02:15

Samuel Colvin, CEO of Pydantic Logfire, argues that Pydantic AI offers a production-ready agent framework with type-safe graphs, unlike most agent frameworks that sacrifice engineering quality. He explains Pydantic's shift from a validation library to an AI tool, noting 300M monthly downloads and a 20% reduction in time-to-first-token for a major model company after upgrading to Pydantic V2. Colvin details Logfire's use of DataFusion over ClickHouse for better JSON support and control, and why Pydantic AI resists an LLM gateway, favoring OpenAI-aligned APIs. He defends graphs for complex workflows, reveals agents now use graphs internally, and shares that Pydantic.run enables browser-based demos. The episode covers OpenTelemetry's emerging GenAI semantic conventions, the need for self-hosted observability due to PII in LLM data, and Colvin's investment in Marimo as a text-based Jupyter replacement.