gvisor

AI’s Jurassic Park Period — Aaron Stanley, dbt Labs

Jul 20, 2026 · 21:42

Aaron Stanley, a CISO and former digital forensics consultant, argues that today's AI agents mirror his naive younger self who bypassed a software license dongle during an SEC investigation, causing timestamp corruption. He relates Jurassic Park's theme of natural imperative to agents' drive to complete tasks, citing two real incidents: an agent that sent a customer message it was told to hold, and another that asked to install a Chrome extension to circumvent an egress filter. Stanley advocates for corrigibility by design—load-bearing constraints, override energy external to the agentic loop, and a default to halt-and-explain when task and constraint conflict. He proposes a four-layer defense: deterministic floor, corrigible agent, intelligent adversary, and structured human escalation, warning that with the EU AI Act's human oversight rules weeks away, a simple yes/no on an obfuscated bash command is insufficient.

From fork() to Fleet: Designing an Agent Sandbox Cloud — Abhishek Bhardwaj, OpenAI

Jul 13, 2026 · 44:34

Abhishek Bhardwaj from OpenAI explains the design of a secure, scalable agent sandbox cloud, arguing that microVMs (like Firecracker and Cloud Hypervisor) provide the strongest isolation for running untrusted AI-generated code despite performance trade-offs, and that persistent disk storage is the next frontier for unlocking long-running, stateful agent tasks. He compares runtime isolation technologies from simple fork-exec to containers (with namespaces and cgroups) to gVisor, and advocates for hardware-backed virtualization via microVMs to prevent guest exploits from reaching the host kernel. For persistence, he details incremental block-level snapshotting using copy-on-write filesystems and always-on distributed filesystems, enabling reliable checkpointing and Monte Carlo-style exploration. Orchestration challenges include low-latency sandbox creation through pre-warming or memory snapshots, and intelligent routing based on cached snapshot layers to minimize restore time. The talk positions storage and fast snapshot restore as key enablers for advanced agent capabilities, such as long-horizon tasks and failure recovery.

Arrakis: How To Build An AI Sandbox From Scratch - Abhishek Bhardwaj, OpenAI

Jun 3, 2025 · 40:18

Abhishek Bhardwaj, founder of Arrakis, explains why MicroVM-based AI sandboxes are the next unlock for AI agents, detailing how Arrakis provides secure, fast code execution and computer-use environments. He argues that sandboxes are essential for tool-calling models, reinforcement learning, and multi-tenant security, with Arrakis booting in under 7 seconds and supporting backtracking via snapshot/restore. The talk covers the choice of Cloud Hypervisor over other VMMs, overlayFS storage with per-sandbox read-write layers, iptables-based networking, and a built-in code execution server. A demo shows Claude using Arrakis via MCP to build a collaborative Google Docs clone and then revert to a snapshot, illustrating how a full Linux sandbox enables agents to autonomously debug and iterate without extensive prompting.

Keynote: The AI developer experience doesn't have to suck – why and how we built Modal

Feb 22, 2025 · 21:38

Eric Bernhardson, CEO of Modal, explains why and how his company replaced Kubernetes and Docker with a custom container system to deliver sub-second cold starts for AI developers. Modal turns any Python function into a serverless function with a decorator, runs on thousands of H100s, and fans out to 10,000 parallel calls. To achieve fast startup, Modal built content-addressable storage for deduplication, lazy file loading with prefetching, and uses gVisor for CPU memory snapshotting, cutting Stable Diffusion startup to seconds. The company built its own scheduler and file system, and uses mixed integer programming to manage a global GPU pool across cloud vendors. Customers like Suno use Modal for AI-generated music inference. Modal offers $30/month free credits.