The same capability lands differently for a fund, an operating company, a hedge fund, or an advisory firm. Say where you sit — the field below tunes to it.
Deep in one lab is common. Range across the whole field — and the judgment to pick the right model per problem — is rarer. I build across frontier, open-weight, fine-tuned, world-model, and proprietary systems.
See the full range on Selected Work →Adjacent, on request: privilege-aware architecture for regulated legal and banking environments — classification gates, matter-scoped retrieval, and defensible audit trails. See selected work →
Three domains where the hard part isn't the model — it's the context: regulated finance, the public sector, and physical supply chains.
Buy-side and FIG from the inside — TMT M&A at Goldman Sachs, a distressed-bank restructuring inside a national banking system, a challenger bank's treasury launch, and an AI-underwriting lender I built and exited.
Where AI has to be accountable to a regulator or a public. Constitutional-AI standards for a monetary authority, interpretability for national digital infrastructure, and treaty-compliance verification for a space agency.
Asset-level geopolitical and supply-chain risk across mining, shipping, and agriculture — the diligence work that connects a frontier model to a physical, scenario-conditional exposure.
Not sure where you sit? That's the first conversation.
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