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Episode 6 of Multipliers: The Skills That Don’t Become Obsolete

Every few years, a new technology arrives and someone declares a skill dead. Spreadsheets killed accounting. Search engines made memorization pointless. Now AI is coming for everything else, or so the argument goes.

The problem is that the argument confuses the tool with the work. The calculator did not eliminate the need to understand division. It eliminated the need to do division by hand, which freed the person who understood division to do something more valuable with that knowledge. The same dynamic is playing out now, at a scale that makes the calculator look quaint. The professionals who thrive will be the ones who figured out early that the question is not “what can AI replace?” but “what can AI never replace in someone who actually knows what they are doing?”

That is the question at the center of this episode. And the answers, from three people who are actively building with AI in different corners of the industry, are more specific than the usual conversation about skills and the future of work.

In this episode of the Multipliers podcast, Spencer Burton and Michael Belasco are joined by James Freeman, Senior Managing Director at Judy Tree Capital, for a conversation that moves from the economics of where to live, to the geography of AI’s disruption, to the skills that survive every market cycle, and closes with a concrete announcement about how A.CRE is embedding AI directly into the hands of Accelerator members.

Episode 6 of Multipliers: The Skills That Don’t Become Obsolete

Sam Carlson is absent this week, a new grandfather, reportedly up with the baby until 2am, which Spencer wastes no time noting on the record. In his place, Spencer and Michael are joined by James Freeman, a capital markets veteran whose career spans Bridge Investment Group, Cardinal, and now Judy Tree Capital, where he focuses on capital solutions for small and mid-sized real estate firms with a particular emphasis on AI-driven automation.

James has been leaning into AI for longer than most in his corner of the industry, which makes him a useful outside voice on the questions this podcast keeps returning to. This conversation picks up thematically from Episode 5, where Spencer, Michael, and Sam argued that fundamentals matter more in an AI-native world than they did before.

Episode 6 tests that thesis against a broader question: beyond the technical fundamentals, what else doesn’t go away? Spencer is running CRE Agents and teaching at UNC Kenan-Flagler. Michael is outside Philadelphia. James recently relocated from Los Angeles to Las Vegas, a move that turns out to be more relevant to the episode’s themes than it first appears.

Why This Episode, Why Now

The conversation that prompted this episode was not originally about skills. It started with where people are choosing to live, a question that has gotten more urgent as knowledge worker careers face real headwinds from AI. James’s move from Los Angeles to Las Vegas was the entry point: a real-world case study in how where you plant your flag either multiplies or suppresses outcomes across income, quality of life, and career trajectory.

From there the group moved into the geography of AI disruption more broadly, which cities are likely to hold value over the next 30 years, and which are quietly fragile beneath the surface. That discussion opened into the bigger question Michael eventually put directly to James: given everything AI is changing, what skills have you relied on throughout your career that will never go away?

The industry is flooded right now with confident declarations about what AI will replace. Most of those declarations are too broad and too short-term to be useful. What is harder to find is a specific, experience-grounded answer to the inverse question: what does a durable skill set actually look like? That is what this conversation attempts.

Spencer also uses the episode to make a concrete announcement: A.CRE is embedding AI skills directly into the Accelerator, so that members who have done the long-hand work now have the calculator to go with it.

Episode Highlights

Here are the themes that stood out.

1. Where You Live Is a Multiplier

James’s relocation from LA to Las Vegas was framed by the group not as a lifestyle story but as a capital allocation decision — one that applies to people as much as to real estate. The math on California, as James described it, is punishing: top marginal federal and state income tax rates together take more than half of every dollar earned, in a state where public infrastructure, school quality, and business environment rank near the bottom nationally. That is a drag that builds over years.

Michael drew on his own experience leaving San Francisco, unable to save, unable to afford housing, living in a city whose brand obscured the practical difficulty of building anything durable there. Leaving, he said, was one of the greatest multipliers in his life. Spencer connected this to a broader pattern: where you choose to live either accelerates or suppresses outcomes across virtually every dimension, financial, professional, relational.

The point is not that California is the wrong answer for everyone. It is that too many people make location decisions based on inertia rather than analysis, staying where they grew up, or where they first landed a job, without running the actual numbers on what that choice costs over a decade. Remote work has decoupled earnings from geography for many knowledge workers. That changes the calculation significantly, and younger professionals tend to underestimate how much those early choices compound.

2. The Barbell Effect: Who Actually Wins When AI Reshapes the Workforce

Spencer laid out a framework for AI’s economic disruption he called the barbell effect. On one end: a small group that benefits enormously, those who understand the technical underpinnings of the tools they use and can direct them with precision. On the other: stable, physical, trades-based careers that AI is unlikely to touch soon. In the middle: a large cohort of upper-middle-class knowledge workers, CPAs, analysts, back-office professionals, whose habits of success are real but whose specific skills are increasingly replicable by software.

James added a data point Spencer had pulled from an Anthropic study of 81,000 users: independent workers and entrepreneurs reported economic gains from AI at a rate three times that of salaried employees. The leverage is flowing to people who are running lean, building their own thing, and moving fast, not to those inside large organizations where adoption is slower and the tools are more tightly constrained.

Michael’s counter is worth sitting with. The same disruption that eliminates traditional employment paths might force a re-emergence of small-business entrepreneurship, not by choice but by necessity. In the 1800s most people worked for themselves, on farms or as solo operators. The Industrial Revolution made salaried employment the default. If AI erodes that default, the people who adapt fastest may not be the ones who saw it coming. They may be the ones who had no other option and figured it out anyway. The tools now exist to let a single person or small team compete with organizations that used to require fifty people. That changes a lot.

3. AI Closes the Gap for Small and Mid-Cap CRE Firms

James made a point that is easy to underestimate. AI is collapsing the advantages large firms have historically used to keep smaller competitors at bay, proprietary databases, dedicated research teams, the operational horsepower to run complex processes across large portfolios. Those advantages are real. They do not disappear overnight. But AI compresses them.

A small or mid-cap firm that adopts early, encodes its expertise into agents and skills, and runs lean now has a real cost advantage over a larger competitor carrying legacy overhead. James described it as the ability to punch well outside your weight class, competing not just on cost but on sophistication, because the analytical capabilities that used to require a large team can now be replicated by a small one with the right tools.

Spencer reinforced this with a concept he and James have discussed at length: digitally encoding expertise. The domain knowledge a firm has built over years — how it underwrites deals, manages assets, evaluates risk, can now be encoded into AI skills and agents that carry that knowledge forward at scale. For small firms this is a strategic move, not just an efficiency one. The firms that figure out how to encode what they know and deploy it systematically will look very different from their competitors in five years.

4. The Accelerator Gets a Calculator

Spencer used this episode to announce something A.CRE has been building toward for several years: Accelerator members will now receive AI skills embedded directly into their workflow, accessible through their Claude or ChatGPT environment via MCP server, starting this month.

The framing is the long-hand division analogy that has come up across several episodes. The Accelerator teaches the fundamentals of cash flow modeling and valuation the way a fourth-grade teacher insists on long-hand division, by hand, step by step, so the student understands why each operation produces what it produces. The AI skill is the calculator that goes alongside that understanding. Feed it an offering memorandum and a T-12, and it builds a pro forma line by line, the way a trained analyst would, because the methodology behind every line item has been encoded into it.

The distinction Spencer draws matters. The AI skill does not replace understanding the fundamentals. It requires it. A member who has done the long-hand work can evaluate what the skill produces, catch errors, and push back when something does not make sense. A member who skipped the fundamentals cannot, and ends up as dependent on the tool as someone who cannot check a calculator’s output. That is the thesis the podcast has been building for several episodes, now in concrete form.

5. The Skills That Actually Don’t Go Away

Michael put the question directly to James and Spencer: given everything AI is changing, what skills have served you that you believe will never become obsolete? The answers were specific. None of them were technical.

James led with two: communication and interpersonal skills. On communication, his point was nuanced. It is not just about talking to other people, the ability to frame what you want AI systems to do, run multiple models at once, and evaluate what comes back is itself a communication skill, and it is already separating high performers from the rest. The AI adoption conversation has moved fast here. A year ago everyone was talking about prompt engineering. Now nobody is. The professionals getting the most from AI are not crafting clever prompts; they are thinking architecturally, like a pilot managing a cockpit, directing multiple systems simultaneously. That is not a technical skill. It is a communication one.

On interpersonal skills, James’s argument was simple: human beings are social creatures. Creativity, motivation, and trust are more contagious in person than remotely, and that does not change because the tools around us do. The professional who can read a room, build genuine relationships, and navigate the human side of a deal will always have something a well-prompted AI cannot replicate.

Spencer added the coding point, worth noting because it runs against a popular position. The Nvidia CEO has argued publicly that coding is no longer worth learning. James disagreed. Spencer agreed with James. Their argument mirrors the CRE fundamentals case: if the AI produces broken code and you have no idea what the code is supposed to do, you cannot catch the error. A working knowledge of what you are asking the AI to build gives you the ability to oversee it. Without that, you are dependent on it. That is a fragile place to be.

6. Embrace Learning or Get Railroaded

James closed with the clearest statement of the episode’s underlying argument. His mother made him read as a child. He hated it. He did not understand why she loved learning until much later — when he realized that the one constant in every cycle he had lived through was change, and that his relationship to change determined everything else.

The choice, he said, is binary. Either you engage with what is happening and find your footing in the new environment, or you dig in your heels and get run over. That has always been true in cycles of disruption. What is different now is the scale of tools available to someone who chooses to learn. There is no longer an excuse for not understanding something you are curious about. NotebookLM can parse and summarize any body of material. AI can encode expertise into accessible formats. The barriers to self-directed learning have basically collapsed.

For CRE professionals, the question is not whether AI will change the skills the industry values. It already has, and it will keep doing so. The question is whether you treat that as an invitation or a threat. James’s answer, and the answer the whole episode builds toward, is that the professionals who stay curious, keep building their fundamentals, and treat each disruption as something worth understanding will carry real advantages over those who are waiting for the dust to settle.

The Bigger Idea

There is a version of the AI skills conversation that reduces to a checklist: learn to prompt, learn to code, stay current on the latest models. That list is not wrong. It just misses what this episode is actually arguing.

The skills that do not become obsolete are not the technical ones. They are the ones that make the technical skills worth anything. Communication is how everything else gets multiplied. Domain mastery is what lets you catch the errors your AI makes. Interpersonal skills close deals, build partnerships, and earn trust that no agent can replicate. And the willingness to keep learning is what keeps all of it going over a career that will span several more waves of disruption.

What the Accelerator announcement makes concrete is a model the podcast has been describing in the abstract: build the foundation first, by hand, so you understand what you are doing. Then use the tool — the AI skill via MCP server — that lets you do it at scale. The foundation is what makes the tool useful. Skip it, and the tool makes you less capable, not more, because it removes the friction that would otherwise force you to think.

James said it plainly: you cannot build a house from the top down. The foundation comes first. Every innovation you want to build in the future will stand on top of what you understood well enough to do without the machine. The professionals who get that, who treat AI as a multiplier of existing depth rather than a workaround, are the ones worth watching. AI.Edge exists to help CRE professionals stay current as the tools keep evolving, without losing sight of what actually drives results.

Frequently Asked Questions about Episode 6 of Multipliers: The Skills That Don’t Become Obsolete

James Freeman identified two categories he believes will never go away: communication skills and interpersonal skills. On communication, his argument extended beyond human-to-human interaction — the ability to frame what you want AI systems to do, run multiple models at once, and evaluate what comes back is a communication skill that is already separating high performers from the rest. On the interpersonal side, the trust, relationship-building, and human judgment that underpin CRE deals and partnerships are not replicable by any current AI system. Spencer added domain-level fundamentals — understanding the mechanics of what your AI produces well enough to catch its errors — as the third leg of the durable skill set.

James pointed out that the AI adoption conversation has already moved past prompt engineering. The professionals getting the most leverage from AI are not crafting elaborate prompts — they are thinking architecturally, framing complex tasks across multiple AI systems and directing the outputs toward something useful. That is a communication skill: the ability to express what you want with precision and evaluate whether you got it. Spencer drew the parallel to language: you can get around a foreign country with a translation app, but if you actually speak the language, you operate at a different level. The same is true with AI.

Spencer described a barbell distribution emerging across the workforce: at one end, a small group that benefits enormously from AI — those with deep technical understanding who can direct the tools with precision. At the other end, stable blue-collar and trades-based careers that AI is unlikely to disrupt soon. In the middle, a large cohort of upper-middle-class knowledge workers — CPAs, analysts, back-office professionals — whose habits of success are real but whose specific skills are increasingly replicable by software. For CRE professionals the implication is clear: the goal is to be on the right end of the barbell, which means building the domain mastery and technical fluency that let you oversee AI rather than simply consume its outputs.

Spencer used the long-hand division analogy: fourth graders learn to divide by hand not because they will never use a calculator, but because doing it by hand builds the understanding that makes the calculator output meaningful. The same applies to cash flow modeling. An AI skill can build a pro forma from an OM and a T-12 — but the member who has done that work by hand knows why each line item is what it is, can spot when something does not make sense, and can push back when the model is wrong. The member who skipped the fundamentals cannot do any of that, and ends up dependent on the tool rather than directing it.

Spencer announced that A.CRE Accelerator members will begin receiving AI skills embedded directly into their Claude or ChatGPT environment via MCP server, starting this month. Each skill corresponds to a lesson completed in the Accelerator and encodes the A.CRE methodology into the AI so the member can use it as a working tool. The framing is the long-hand division model: members who have done the foundational work now have the calculator that goes with it. Feed it an offering memorandum and a T-12 and it builds the pro forma, line by line, the way a trained analyst would — because the methodology behind every line item has been encoded into it.

James argued that AI is collapsing the moat that large CRE firms have historically held through scale — proprietary databases, large research teams, and the operational overhead to run complex processes across big portfolios. A small or mid-cap firm that adopts early, encodes its expertise into agents and skills, and operates lean can now match the analytical sophistication of a much larger competitor at a fraction of the cost. An Anthropic study of 81,000 users cited by Spencer found that independent workers and entrepreneurs are already reporting economic gains from AI at three times the rate of salaried employees — early evidence that the leverage is flowing to those running small and fast.

The Nvidia CEO has publicly argued that coding is no longer a skill worth developing, given what AI can now produce. James pushed back, and Spencer agreed with him. Their argument mirrors the fundamentals case in CRE: if the AI produces broken code and you have no idea what the code is doing, you cannot catch the error or fix it. A working understanding of what you are asking the AI to build gives you the ability to oversee it and redirect when it goes wrong. Without that, you are fully dependent on the tool — which is a fragile position as the tools keep evolving.

James and Spencer used the phrase throughout the episode to describe something forward-thinking firms are already doing: taking domain knowledge built up over years — how a firm underwrites deals, evaluates risk, manages assets — and encoding it into AI agents and skills that carry that knowledge forward at scale. The A.CRE Accelerator AI skills announcement is a direct example of this. For CRE firms, the practical implication is that firms which do this first will have a real advantage — their expertise compounds into the tool rather than walking out the door with departing employees.

James closed the episode with a reflection from early in his career: he identified change as the one thing that never changes, and made a deliberate decision to embrace it rather than resist it. In the context of AI and CRE, the choice is binary — either you engage with what is happening, learn the tools, and find your footing, or you dig in your heels and get run over. He pointed to the current moment as the best time in history for self-directed learning: tools like NotebookLM can parse and summarize any material, making it possible to teach yourself almost anything if you are willing to.

The durable skills are not the flashiest ones. Communication — with people and with AI — domain mastery deep enough to catch what your tools get wrong, and the habit of continuous learning are what compound over a long career. The professionals who treat AI as a multiplier of existing depth, rather than a shortcut around it, are the ones building toward something real. The A.CRE Accelerator is built around exactly that principle. AI.Edge exists to help CRE professionals stay current as the tools keep evolving without losing sight of what actually drives results.

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