I went to Harvard for the prestige and to Wharton for the PhD. The plan was to become a professor.
Somewhere around year three I made a different call: I'd rather build the systems my dissertation was writing about than write about them from a chair.
That cost me a clean academic career. It also put me in the room when machine learning quietly went from "specialist tool" to "the way software gets written."
I've spent 17 years building platforms in production: hiring (Evolv), payroll (Homebase), conversational commerce (RapportBoost.AI), fraud detection (Point Predictive), real estate (Doma).
Five startups. One acquired, one public, one queued for IPO in 2026. The others moving the industry forward.
Eight years ago I started getting asked to teach this stuff to executives. The first talk turned into ten, then a hundred, then 243 across 27 countries.
The pattern was always the same: people walked in skeptical and walked out building.
I'd run them through a workshop, they'd ship a prototype by lunch, and they'd email me three months later saying nothing stuck.
That gap, between one good day and a year of practice, is the whole reason the AI Performance Lab exists.
Workshops shift mindset. The Lab is the year of disciplined skill-building underneath it. Same room, same coach, but with reps, scores, and a renewal pitch you can take to your board.
I'm not a futurist. I'm a guide. I write a newsletter called Future Proof and a book by the same name. I'm bullish about what these tools can do, urgent about how fast the gap is widening, and uninterested in selling slide decks.
Best,
Mike "House" Housman
