Emulated: The Data for Fully Autonomous Software Engineers and Companies — Joseph Wang
Emulated focuses on creating high-fidelity training data environments that simulate entire companies and complex infrastructure operations, moving beyond simple code diffs or single-node sandboxes. The core argument is that for AI agents to achieve true autonomy in mission-critical systems (like cloud providers), they must be trained on long-horizon tasks involving distributed cluster failures, resource provisioning across VPCs/subnets, managing cost constraints, and reasoning through real-world operational incidents.
Key takeaways
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The Data Gap in AI Agents
3:30
Current benchmarks (e.g., SweBench Pro, Terminal Bench) limit agents to operating within a codebase, failing to capture the complexity of real-world tasks like PM communication, performance testing, or owning underlying infrastructure over years.
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Complexity Requires Full Simulation
6:10
Real infrastructure work is not a simple code diff; it involves managing failing nodes, stale deprecated components, live traffic serving, and operational blast radius across distributed clusters.
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Limitations of Single-Node Sandboxes
10:40
Standard post-training pipelines often use homogeneous single-node sandboxes. However, real cloud services require simulating resource provisioning (EC2, Cloud Run), VPCs, subnets, and security groups, which necessitates a multi-node sandbox with access to real infrastructure.