What I advise on
AI product architecture and technical design
System design for AI-native applications. Model selection, orchestration patterns, retrieval-augmented generation, agent coordination, and the decisions that determine whether your product scales or collapses under its own complexity.
Technical due diligence for AI investments
Independent evaluation of AI companies' technical claims, infrastructure moats, and scaling trajectories. I translate engineering reality into investment language.
Go-to-market strategy for AI applications
Positioning, distribution, and pricing for AI products. What features to ship first. Where to find early users who will stick. How to build a wedge that competitors can't easily replicate.
Infrastructure economics and scaling analysis
Compute cost modeling, inference optimization, and build-vs-buy decisions. The gap between a working demo and a sustainable business is almost always infrastructure economics.
Background
I sit at the intersection of building AI products and analyzing AI investments.
Investor lens. Background in private equity research, where I learned to evaluate technology companies through the lens of unit economics, competitive dynamics, and capital efficiency.
Builder experience. I ship real products. Service Pronto is currently deployed at Sheraton Laval, handling guest service operations in production.
Research. Collaborator at Hidden Information Labs Institute, working on problems at the frontier of AI and information theory.
Quantitative analysis. Trained in theoretical physics, which means I think in terms of first principles, scaling behavior, and the math behind the abstractions.
Distribution proof. 10K+ followers on Threads for technical AI analysis. Cited by Forbes. The audience exists because the analysis is useful.
Get in touch
If you're building something with AI or evaluating an AI investment, I'd like to hear about it.