Building AI Systems That Scale Beyond Models
Useful AI systems depend on architecture, economics, automation and defense—not just model capability.
Summary
AI deployment increasingly depends on distributed infrastructure, rapid feedback loops and autonomous operations around the model itself. Frontier serving combines data, pipeline, tensor and expert parallelism with separate prefill and decode pools, while ultrafast coding shows that speed can transform iteration but becomes difficult to justify at high token prices. Cybersecurity requires AI-driven continuous attack simulation and automated response because machine-speed attacks outpace human detection and response. Harness engineering applies the same operational logic to software development through inner, outer and meta loops that raise autonomy, automation and ultimately quality.