I spent building enterprise AI at Google Cloud, then co-founded a company that was acquired for hundreds of millions. Now I write about the patterns I see — how experienced operators are using AI to build things that compound over decades, not months.
Cupertino, CA · Building at the intersection of AI and enterprise
Early exposure to the hardware that would power the AI revolution. Learned to think at the intersection of silicon and software.
Built AI systems at scale. Learned how large organizations actually buy, deploy, and depend on AI. The pattern recognition that later became my unfair advantage started here.
Where I learned to think in decades instead of quarters.
Left Google to build. Took the enterprise AI thesis and turned it into a product, a team, a company. The thesis worked.
Leading AI strategy and building next-generation agent platforms. Using this chapter to prepare for the next one.
Frameworks and honest lessons from enterprise AI.
A new paper shows 84.6% token savings through knowledge compounding. The insight: tokens should be capital investments, not consumables.
A new paper shows agents improving themselves without model updates. But the bigger insight is the full optimization surface most enterprises aren't using.
A new paper tested when to collapse multi-agent systems into a single agent. The answer is counterintuitive: it's not about the task. It's about the metric.
When your Copilot agent sends an email, it does it under your identity. Your credentials. Your permissions. Microsoft's Agent 365 won't fix this until December 2026.
Jensen Huang told Lex Fridman he thinks we've achieved AGI. The same week, ARC-AGI-3 launched. Humans score 100%. Best frontier AI scored 0.26%. Both are right.
A judgment model that becomes unmigratable over time. Permanent memory, autonomous execution, partnership intelligence. Not an app — a presence.
Garmin sync, adaptive training, weight tracking. Near MVP. The small bet that teaches me consumer product-market fit.
The complete rethinking of how enterprises build and deploy AI agents. Stacked SLMs, a self-improving flywheel, and an architecture where every interaction makes the system smarter.
Orchestrated small language models that beat frontier models at a fraction of the cost. The routing layer is the real product.
Real-time conversation intelligence across every customer interaction. Emotion detection, agent guidance, and insights that turn conversations into competitive advantage.
AI agents that activate customer data to drive personalized engagement, orchestrate campaigns, and maximize ROI across every channel. Built on an AI-first customer data platform.
One platform connecting knowledge, SLMs, agents, and trust infrastructure. Where the SLM Flywheel, agent architecture, and enterprise governance come together as a single system.
Every engineering org has lore — the unwritten rules, the patterns that work. Lore captures it automatically from AI coding sessions and turns it into a living, shared brain.
Role-based AI agents that already know the PM. Chat-first interface. You talk to the system; the system updates itself.
I want to build something that outlasts me. Not a product — a compounding system of ideas, trust, and leverage that keeps growing whether I am in the room or not.
I have a proven exit, deep enterprise AI experience, a Stanford network, and trilingual access to the American, Chinese, and Korean markets. The next decade is about deploying all of that at once. I am writing to hold myself accountable.
I publish on LinkedIn and update this site as I build.