Some skills age out. The ones that don’t are the ones worth doubling down on right now. That’s the thread running through this entire conversation — not as a motivational slogan, but as a practical filter. As AI automates more of what used to require years of training and expensive headcount, the question isn’t whether your industry will be disrupted. It will be. The question is which capabilities you’re building that will still matter on the other side of that disruption. The answer, across every angle this conversation takes, keeps coming back to the same thing: fundamentals. Communication. Domain knowledge deep enough to catch what the model gets wrong. And the willingness to keep learning when everything around you is changing. In this episode of the Multipliers podcast, I’m joined by Michael Belasco and guest James Freeman, Senior Managing Director at Juday Tree Capital — a seasoned real estate and capital markets professional who’s spent the last several years leaning hard into AI.
- You might also enjoy: Last week’s episode on why it all comes back to the fundamentals: Episode 5 of Multipliers: It’s All Fundamentals
Episode 6 of Multipliers: The Skills That Don’t Become Obsolete
This week’s episode features the second guest appearance of Season 1 — James Freeman, Senior Managing Director at Juday Tree Capital, where he focuses on capital solutions for small and mid-sized real estate companies with an emphasis on AI-driven automation and operating efficiency. James brings a front-row view of multiple market cycles from his prior roles at Bridge Investment Group and elsewhere — and a clear-eyed perspective on where AI is taking the industry. This conversation connects directly to last week’s fundamentals discussion, extending it into capital markets, location strategy, and what durable careers actually look like in an AI-native world. CRE Agents gets a cameo too — I used it to generate James’s bio live on air.
Why This Episode, Why Now
Two forces are colliding in CRE right now. On one side, AI is compressing the cost of expertise — tasks that used to require a full team can now be handled by a single operator with the right tools. On the other side, the same compression is creating headwinds for the knowledge workers who built careers on doing that work manually. James moved from Los Angeles to Las Vegas about a year ago — an economic decision, he’s direct about it. California’s tax environment and cost of living had crossed a threshold for him and his wife. That personal decision opened up a bigger conversation: where you choose to live and work isn’t just a lifestyle call. It’s a compounding variable in your career trajectory, your savings rate, your quality of life. In an era where remote work is normalized and AI is flattening geography, that variable is more consequential than ever. And then there’s the opportunity side. An Anthropic study from late 2025 and early 2026 — surveying 81,000 users — found that independent workers and entrepreneurs reported economic empowerment from AI at a rate three times that of salaried employees. Three times. The leverage is real, and it’s flowing disproportionately to those who have the domain knowledge and the flexibility to put it to work. That’s the backdrop for this episode. The skills that don’t become obsolete aren’t the ones AI can’t touch — it’s touching everything. They’re the ones that make everything else work.
Here are the themes that stood out.
1. The Small Operator’s Moment
One of the most important dynamics in CRE right now is easy to miss if you’re watching the large institutional players: AI is quietly handing small and mid-cap operators a window they’ve never had before. The historical moat of large firms was scale — economies of headcount, proprietary databases, institutional relationships built over decades. That moat isn’t gone, but it’s narrowing fast. AI commoditizes expertise. A small operator who encodes their underwriting methodology, their market knowledge, their deal screening logic into an agent can now produce output that rivals what a much larger team once required. And they can do it at a fraction of the overhead. James frames it clearly: it’s not about replacing people. It’s about punching way outside your weight. The firm that adopts early, encodes its domain knowledge, and builds efficient AI-augmented workflows is competing on a different playing field than the firm still running the same process it ran in 2019. The window to get ahead of this is open. It won’t stay open forever. The Anthropic data backs it up. Independent workers and entrepreneurs are capturing three times the economic empowerment from AI relative to salaried employees. The leverage is flowing to those with the flexibility and the expertise to deploy it — which describes the small CRE operator almost exactly.
2. Encode Your Expertise
James introduces a concept that’s worth sitting with: digitally encoding expertise. The idea is simple but the implications are significant. Whatever you know — your underwriting approach, your market intuition, your capital stack logic — can now be encoded into an AI agent that carries that knowledge, applies it consistently, and scales it without adding headcount. This is what RAG-enabled structures make possible, and it’s one of the most underutilized plays in CRE right now. Most operators are still using AI as a search engine or a writing assistant. The firms getting real leverage are the ones treating it as a knowledge repository — teaching it how they think, not just what to look up. The firms that do this well build something hard to replicate. Not because the technology is proprietary, but because the encoded expertise is. Nobody else has your twenty years of industrial underwriting experience, your pattern recognition on a specific submarket, your way of evaluating an operator’s track record. When that gets encoded and operationalized, it becomes a moat. That’s the play. This is also what we’ve been building toward in the A.CRE Accelerator.
3. The Skills That Don’t Go Away
We spend a good chunk of this episode on a question Michael raises: what skills remain mission-critical regardless of what AI can do? The answers are less about technical knowledge and more about the underlying capabilities that make technical knowledge useful. Communication is the first one. Not just with other people — with AI. The ability to direct a model clearly, to frame what you want it to produce, to recognize when the output is off and course-correct — that’s a skill with a very long shelf life. James notes that even six months ago, the conversation was dominated by prompt engineering. That’s already fading. The new frontier is architecture — being the person who designs how agents work together, not just who types the best prompt. Fundamentals are the second one. James is teaching himself coding — not because AI can’t write code, but because when it writes code that breaks, he wants to be able to find the bug. That’s the same argument I make about cash flow modeling. You can have AI build the model. But if you can’t read it, you can’t supervise it. And in CRE, unsupervised models are where expensive mistakes live. Understanding the underlying logic — deeply, not superficially — is what separates the operators who use AI from the ones who get used by it. The third is interpersonal skill. We are, as James puts it, social and gregarious creatures. The ability to build trust, read a room, navigate a difficult conversation with a partner or an investor — none of that gets automated. If anything, as AI handles more of the transactional layer, the human layer becomes more valuable, not less.
The Bigger Idea
There’s a barbell forming in CRE — and honestly, across the knowledge economy broadly. On one end: a relatively small group of people who understand the technology, have built domain expertise, and are deploying both in combination. They’re the ones capturing outsized leverage. On the other end: roles and firms that haven’t adapted, where AI is doing more of the work but nobody’s building the skills to stay ahead of it. The middle — ambitious, capable knowledge workers who’ve relied on technical skill without pairing it with adaptability — is where the most disruption lands. Not because those people aren’t talented. Because the thing that made them valuable is now being commoditized, and the response time matters. James’s closing note is the one I keep coming back to. His mother was a reader. She loved learning. As a kid, he thought that was the dumbest thing he’d ever heard. Now he gets it. We’re living in the most remarkable learning environment in human history. Anything you want to understand, you can. AI can encode it, summarize it, teach it back to you. The only thing standing between you and expertise in almost any domain is the decision to pursue it. The skills that don’t become obsolete aren’t specific techniques or tools. They’re the orientation toward learning itself — the willingness to keep building the foundation even when the tools on top of it keep changing. That’s what compounds. That’s what AI.Edge is built around. And it’s what we’re continuing to develop at CRE Agents.
Frequently Asked Questions about Episode 6 of Multipliers: The Skills That Don’t Become Obsolete
Large firms already had scale — headcount, proprietary data, institutional processes. AI narrows that advantage by making expertise accessible at low cost. A small operator who encodes their domain knowledge into AI agents can now produce institutional-quality output at a fraction of the overhead. An Anthropic study from late 2025 found that independent workers and entrepreneurs reported economic empowerment from AI at three times the rate of salaried employees — the leverage is flowing to those with the flexibility to deploy it.
The barbell has two ends. On one side: a relatively small group of people who pair deep domain expertise with strong AI fluency and capture outsized leverage. On the other: blue-collar and trades roles that remain stable but don’t see much growth. In the middle — and this is where most knowledge workers sit — are ambitious, capable professionals whose core skills are being commoditized. The window to move toward the advantaged end of the barbell is open, but it requires building the right foundation now.
Yes, and it compounds over time. High state income taxes, cost of living, and business environment all affect how much of your income you keep, how easily you can save and invest, and how much energy you spend on logistics vs. work. James moved from LA to Las Vegas primarily for economic reasons. Michael saw his financial situation open up when he left San Francisco. Location affects everything from savings rate to quality of life to the professional networks you’re embedded in — it’s worth treating it as a deliberate variable, not a default.
Spencer names Northwest Arkansas (Fayetteville to Bentonville) for its concentration of generational wealth and diverse employers, Huntsville, Alabama for its highly educated population and low cost of living anchored by Redstone Arsenal, and the Wasatch Front in Utah for its tech ecosystem and relatively lower cost compared to coastal markets. He’s quick to note these are educated guesses — the goal is to identify markets with structural tailwinds that don’t depend on one industry or employer.
James highlights three: communication (both with other people and with AI), fundamentals (understanding the underlying logic of whatever you’re doing well enough to catch errors), and interpersonal skills (the ability to build trust and navigate human relationships). As AI handles more of the transactional layer, the human layer becomes more valuable — not less. The people who thrive will be those who combine AI fluency with strong domain knowledge and the ability to work effectively with other people.