Bo Eason wrote a book called There Is No Plan B for Your A-Game, and in it he says: “Tell your story.” So here is mine.
After earning my Wharton real-estate investment & analysis certificate, I was asked to evaluate the feasibility of a large new apartment community. I went to the site, took photos, visited a dozen comparable properties, studied the schools, the road noise, the special features — a full top-to-bottom read of what the finished project would be worth against current market rents, minus capital expenses, using the right cap-rate math. I pulled a favor from a top apartment broker for past-sales data, and another from a large management company to sanity-check my capex ratios against my own experience. I was told the family office looking at the deal said, “We need a guy like this” — because no stone was left unturned.
The ask that should have been impossible
Illustrative composite. What follows is a general, educational illustration of the kind of work agentic AI opened up for me. It is fictional — it does not describe any actual project, client, property, operator, location, or transaction, and no real engagement is identified or implied. Any figures or details are illustrative only.
So picture the ask that stretches a career: a large, capital-intensive regional casino-resort development — golf, retail, housing, and the casino itself, all one project. What would it cost to build? What would it be worth when finished? And could a defensible answer be on the table in two weeks?
The honest first reaction is you have to be kidding me. Yes, I have a construction-management degree from UC Berkeley, I’m a licensed general contractor, I hold a real-estate license and the Wharton analysis certificate. But nothing in that stack tells you the first thing about running a casino, or what the numbers look like on one. Sit with a question like that for a few days and the arithmetic looks brutal — too much work, months to do it properly.
And then it hit me: what about AI?
Here is how work like that gets done. I teach my AI interface the lessons from the Wharton certificate. I teach it to examine the project against the five construction-cost methods I have been learning since college and using in the field ever since. I use AI to research other projects of the type — sale prices, square footage, the kinds of operators involved. I watch hours of background on the area, call a local brokerage for an honest read on viability, buy a basic modeling format and train the AI on it. Then I run it all together as one cross-referenced package. And when something is off — an off-sites allowance that comes in too low for my taste — I keep working the trained model until that number lands in the ballpark.
Two weeks — and the real lesson
Work like that fits inside two weeks now. What lands hardest is not the answer but the cross-checking that shows exactly where every number came from. Honestly, that still floors me. It is remarkable what is now possible with AI for rapid research and analysis. But here is the part I tell everyone: AI still hallucinates. The real trick is to already be a professional in the field, so you know what to look for, how to verify the assumptions and the outputs, and how to use AI to amplify your own skill many times over.
The work compounded
From there it kept building — a nuclear-power-adjacent venture, then a patent-monetization engagement, each one sharing sources, partnerships, and research with the last. The crossover is real: a custom news feed built for one industry sharpens the next; a grant that applies to one project surfaces for another. I work directly in the terminal now, in Python, with agents that do careful, checkable work inside clear lanes — a human deciding at every line that matters.
Why this matters to me
Watching agentic AI do genuine work — not a parlor trick, but careful, verified, human-in-the-loop work — is what convinced me this isn’t just a deal-room tool. It is a new way to amplify a lifetime of hard-won judgment, in real estate, in finance, and beyond. That is the journey I’m on, and the reason I built the rest of these pages. A new fire is here; the work is to tend it carefully with a steady hand, with proper guardrails and checks in place.
A companion piece: Taming the Fire — Keeper in the Loop →
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