Jane Street builds its trading systems in OCaml, relying on the type system to catch errors at compile time and avoid run-time crashes. They snapshot every successful build to train an in-house AI model, rewarding only code changes that compile and pass tests.

• Jane Street runs its trading systems almost entirely on OCaml, betting on compile-time type checking (e.g., mandatory Option handling) to prevent costly runtime bugs.
• They rejected off-the-shelf tools like Copilot and Claude in favor of an in-house AI model trained on their own workspace snapshots.
• Developer workspaces are snapshotted every 20 seconds, and reinforcement learning rewards only edits that compile and pass tests.
• This snapshot-trained system underpins infrastructure supporting roughly $40 billion in annual trading volume.

Satya Nadella argues that companies should build a continuous learning loop combining human capital—expertise, judgment, relationships—with token capital—their own AI models—to create compounding institutional IP. He warns against a few dominant AI systems capturing all value and calls for private evals, reinforcement learning, and architectures that let firms swap general models without losing proprietary expertise.

• Companies should pair "token capital" (their own AI models) with human capital in a feedback loop where each amplifies the other, rather than treating AI as a static tool
• Firms need private evals and reinforcement learning based on internal outcomes (sales lift, error reduction) instead of public benchmarks, so they can swap general models without losing proprietary expertise
• If AI value concentrates in a few dominant providers, it risks hollowing out industries the way early globalization did
• The real competitive battle is building a compounding, firm-specific learning loop rather than owning the best large model