Notes, essays, and research from the work.
What we're thinking and learning building agentic AI and robotics into the industries the world depends on — written as we go, not after the fact.
- Research Note2026.08
Ego, exo, and the robot data engine
The most important video for robotics isn't of robots — it's of human hands. A close look at how the field turns egocentric and exocentric human video into robot policies: the capture spectrum from paired rigs to handheld grippers, the three pipeline families (retarget, repaint, latent actions), and what the mid-2026 results say actually matters.
- Research Note2026.08
How robots learn now
A field guide to the leading edge of AI in robotics, mid-2026: the three-layer stack behind the new generation of robots, the end-to-end pipeline that turns internet knowledge and human video into motor control, and the roadblocks — data, evaluation, and the last nines of reliability — the whole industry is working against.
- Field Note2026.08
Three agents, one job
The most common thing we find inside companies adopting AI isn't failure — it's duplication. Multiple agents built independently for the same job, each one a success story, none of them the system. Call it agent sprawl: experimentation that never gets consolidated, and the feeling of progress without the results.
- Essay2026.06
Every company needs an AI team
Most companies are standing up an AI function — and getting it wrong, either as a silo that hoards AI or a figurehead who ships nothing. The version that works is a real team whose job is to make AI a capability every other role wields.
- Product2026.06
We built Damon for the factory floor, not the inbox
Almost every AI agent being built today is pointed at knowledge work. Damon goes the other way — an agentic operating system for manufacturing that meets a plant wherever it is, from pen-and-paper and spreadsheets to a dozen systems that don't talk, and grows it into one intelligence layer that compounds.
- Essay2026.06
Your company is becoming an optimizer
As every business becomes a full-stack AI firm, the edge won't come from the tools. It comes from running the organization as one coherent, instrumented system aimed at a clear objective — because at machine speed, coherence and correct aim compound, and incoherence compounds against you.