15 September 2026
Heard In AI

A mid-career AI playbook that doesn't put the mortgage at risk

On a Moonshots listener call, Matt from Terre Haute, Indiana asked what people with families, mortgages and a fixed location should do, since so much AI advice seems aimed at 22-year-olds working 80-hour weeks. The panel's answers: describe your actual obligations to a language model and brainstorm at the margin, practice on the tools at night, build a few online peer relationships, offer your domain knowledge to a marketplace like Mercor, and schedule slack time rather than waiting for spare time. The bigger forecasts about franchise-style openings from AI labs remain forecasts.

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Matt called in from Terre Haute, Indiana, and he had written his question down in advance because he had fumbled it on a previous call. His complaint was about the shape of the advice, not its content. "A lot of the conversation is focused around the one way for 22-year-olds, maybe working 80-hour weeks," he said. What, then, is the playbook for experienced professionals in places like the Midwest "who have deep domain expertise, the energy, and the desire to build, but also have families, mortgages, real constraints like maybe a location"? And how do you use these tools "without like kind of blowing up a baseline of security"?

The question came on the Moonshots AMA episode, a listener call hosted by Peter Diamandis with his co-hosts, scheduled for an hour and extended to ninety minutes so more of the eighty raised hands could speak. Peter passed Matt's question to Dave.

Start with the constraints, not the leap

The most portable part of the answer treats the constraints as the input rather than the obstacle. Go to your favorite large language model, Peter said, and describe your life in detail: "I have these obligations. I have these kids. I have these mortgages. I have all of these things. Here's my expertise. Here's what I do" — the same inventory Matt had just recited on the call. Then brainstorm with it "on the margin": how much time, how much money, what can I do.

The margin is the point. Nothing in that prompt asks the person to quit a job or move house; it asks what fits in the hours and cash that are genuinely spare. Peter's argument for why an older professional is not simply worse off in this game was about leverage: when you're young you can take more risk, but when you're older you have a lot more leverage, and he thought it should be used heavily. He did not spell out what the leverage consists of beyond expertise and the assets a career accumulates. What the exercise produces is a list of ideas; the panel offered no evidence about how often such experiments earn anything.

Practice, then peers

Dave's first advice was practice. While the bigger opportunities arrive, he said, get really familiar with the models — "just build your own Skippy," play around at night, try to create some of the revenue-generating ideas you brainstorm with the AI and just fabricate it. He was explicit that this need not be a career move: "It may not be what you want to do for a career, but it's a great way to generate cash flow and get involved with the models."

The second piece addressed Matt's geography directly. Dave cited Dropbox founder Drew Houston's line that you become the average of the five people you spend the most time with — "It's just a fact. Don't fight it" — and then conceded the obvious problem: the five people you most want to be like may not live anywhere near you. So find three, four or five on the internet who can bond with you on exactly this topic. In Terre Haute, that is the difference between the advice being useless and being possible.

Third came a place where domain knowledge is itself the product. Go to Mercor, the suggestion ran, say that you have domain knowledge, ask how that is of value, "and you just throw that into the system and wait and see what the AI comes back to you with." More domain-specific versions of the same idea would follow, the panel expected, but this one was a good place to start. Mercor's site describes paid expert participation in building and evaluating AI, drawing on professionals such as physicians, lawyers, engineers and consultants to create evaluations and training tasks based on real work — expertise used to set problems and judge answers rather than to found a company. Any knowledge you have is unique, one of the hosts added, so take advantage of it.

What is promised and what exists

Dave's largest claim was also his most speculative. He said Tesla had just put out a franchise opportunity, which the show would cover the following day, and advised keeping an eye out for more and more AI labs handing people a playbook to get involved — through franchise models and through data collection models. He suspected that this year there would be "on the order of maybe 10 to 100 opportunities where the playbook is handed to you," with the labs eager for them to succeed because it brings more of the population into their universe. His prediction about the response was less flattering: "you're going to find that very few people around you jump on the opportunity."

That part of the playbook describes openings that mostly do not exist yet. What a listener can act on today is the smaller list: the brainstorm, the night tinkering, the handful of online peers, the marketplace listing.

The time problem

Earlier in the same call, Dave Morris, calling from Sydney, had described the obstacle that defeats all of this. In his conversations, he said, "everyone feels like they're too busy to even think about it. They just do a normal thing in their businesses." Salim's answer was about calendars rather than ambition. People tend to over-schedule themselves, he said; you want to create deliberate slack in the system — 10 or 20 percent of your time with nothing scheduled, to experiment, play with new tools and meet new people. "If you wait till you have spare time, you'll never catch up. You'll never start."

He was also unsentimental about distance as a disadvantage. Feeling behind in an exponential age can feel terrifying, he said, but places a little further away can leapfrog quickly, because the infrastructure is mostly there and what remains is largely a mindset problem; the United States, Europe and Japan, by contrast, have decades of received management thinking to unlearn. Being further away, he added, also gives you a playground in which to experiment with new structures.

None of this shows that a mid-career professional with a mortgage can replace an income this way. It is a set of bounded experiments sized to fit around obligations — which is what Matt asked for, and what advice built around 80-hour weeks does not supply.

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