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Episode 7 of Multipliers: Be an Advisor, Not a Broker

There is a version of every professional service job where you are just moving paper. Executing transactions, billing hours, closing deals. And there is another version where the person across from you feels like you are actually on their side. The first one is a commodity. The second one is not.

Jack Stone started as an attorney, figured out the first version looked a lot like a treadmill with a better outfit, and made a change. Today he runs the DFW multifamily investment sales team at Greysteel — and the way he describes his current job says everything: most days right now feel less like brokerage and more like therapy. Helping owners process bad decisions, think through limited options, and figure out what to do with an asset they probably wish they did not own.

That framing, the advisor who earns trust rather than the producer who chases commissions, runs through this entire episode. It connects the multifamily market reality Jack is living in daily to the AI conversation that takes over the second half, where the same tension appears: are you doing the work, or just letting the tool do it for you?

In this episode of the Multipliers podcast, Spencer Burton and Sam Carlson are joined by Jack Stone for a conversation that starts with who is getting hurt in multifamily right now and ends up somewhere more interesting — what it actually means to show up as a professional in an era when the temptation to abdicate, to AI or to short-term thinking, has never been greater.

Episode 7 of Multipliers: Be an Advisor, Not a Broker

Michael Belasco is absent this week. In his place, Spencer and Sam welcome Jack Stone — managing director at Greysteel leading DFW multifamily investment sales, Princeton graduate, former real estate attorney, and, as of this episode, the new host of A.CRE’s university profile video series. Jack is also a long-time A.CRE reader who volunteered for the university role out of a genuine belief in giving back — which fits neatly into the episode’s theme. This is a guest conversation, and it picks up a thread from Episode 6 about the skills that do not go away. Here the question gets more specific and more current: what does it look like to actually be the advisor in a market where owners are in pain, AI is generating noise on every LinkedIn feed, and the temptation to cut corners — in underwriting, in content, in professional judgment — has rarely been higher. Spencer is running CRE Agents and teaching at UNC. Sam is building out UpX. Jack is spending most of his days in what he calls therapy sessions with multifamily owners who are trying to figure out what to do with assets that have not gone the way they planned.

Why This Episode, Why Now

The multifamily market is not having a clean correction. It is having a complicated one — and the complexity matters, because it separates very quickly the operators who understood what they were doing from the ones who were just riding a cycle. Jack has a front-row seat to that separation every day. His pipeline right now is built largely around owners who bought in 2022 or 2023, took on floating debt to win deals in a frothy market, and are now watching interest expense eat through whatever reserves they had left. The deals are not necessarily underwater on paper. But they are not distributing. And in some cases, the capital that should have gone to a chiller or a roof is gone.

That is the market context. But the conversation quickly moves to something adjacent: the professional stance that gets you through a cycle like this with your reputation intact. Jack’s background as an attorney — where you are trained to find every edge, understand every risk, and think through every implication before you advise — shaped how he approaches brokerage in a way that most people in the seat do not. Spencer has noticed it. And the episode explores why that adviser-first orientation is not just a nice-to-have but a durable competitive advantage, especially when the market is hard and owners need someone they can actually trust.

The AI thread grows naturally out of that. Spencer shares a live personal example — dropping twelve months of credit card statements into Claude and getting a full analysis in minutes — that leads into a broader discussion about what automation actually looks like in practice for any business, and where the risks are. The biggest risk, the group agrees, is not that AI produces bad output. It is that people stop checking. They abdicate. They post the AI image without looking at it. They send the AI-written email without reading it. They let the tool do the thinking and sign their name to the result.

Both threads — the market one and the AI one — are really about the same thing. In a moment when it is easy to coast, easy to let the cycle or the tool carry you, the professionals who come out ahead are the ones who keep showing up with their own judgment attached.

Episode Highlights

Here are the themes that stood out.

1. Who Is Healthy and Who Is Not in Multifamily Right Now

Jack’s read on the current market is blunt and useful. The generalization holds up: if you bought in 2022 or 2023, you are probably not healthy. To win deals in that environment, you had to stretch on price. To stretch on price, you had to stretch on leverage. Floating rate debt was the way most people got there — and then rates went where they went.

The profile of the operators who are fine is almost exactly the opposite: long-term fixed debt, conservative underwriting, patient capital. They are not hitting home runs. But they are making distributions, which Jack describes as the real benchmark right now. If your investors are getting checks, you are at the top of the current market. That sounds like a low bar. In 2025 multifamily, it is not.

What Jack describes living through daily is a steady stream of owners who are not in crisis exactly, but are running out of runway. Debt service is absorbing the cash that was supposed to fund capital expenditures. A 1960s property with an aging chiller and no reserves is not a math problem anymore — it is a phone call from someone who needs help thinking through a decision they do not want to make.

The practical implication for anyone looking at multifamily today is that the vintage matters as much as the asset. The same building, bought two years apart, can be a distribution machine or a slow-motion problem depending entirely on how it was capitalized.

2. Discipline Comes From Wisdom, Not Willpower

Spencer made an observation that landed: the operators who stayed on the sidelines in 2022 and 2023 were not necessarily more disciplined than the ones who bought. They had a different frame of reference. If you started in 1997 or had mentors who went through the GFC, you have a mental model for what a frothy market feels like and what comes after. That model makes the willpower much easier to find.

For someone who started in 2021, buying in 2022 did not look undisciplined. It looked like what you do. The data supported it, the comp set supported it, everyone around you was doing it. The frame of reference for “this is going to end badly” simply was not there.

Jack noted the same thing from the brokerage side: the industry skews young. Most of the people you meet at conferences were not active pre-GFC. That is not a criticism — it is just a fact about where the real estate cycle has been. But it means that wisdom about market cycles is in genuinely short supply, and the people who have it carry a real advantage that has nothing to do with raw analytical ability.

The takeaway for younger CRE professionals is not to wait until you have survived a downturn to develop judgment. It is to actively seek out the people who have, and to listen to them when they are uncomfortable about a market that everyone else seems fine with.

3. The Attorney Who Became a Better Broker Because of It

Spencer has a specific thing he admires about Jack that he stated directly: Jack thinks like an adviser, not a producer. That orientation almost certainly comes from his legal training, where your job is to find every risk, understand every edge, and give your client the clearest possible picture of what they are getting into — not to get the deal done.

Jack did not romanticize the law firm experience. He was direct about what pushed him out: watching partners work harder the older they got, being told the workload would ease with seniority and seeing no evidence of that, watching a client come in wearing jeans and a smile and realizing that guy was making more money with a better quality of life than the people he was supposed to want to be. That is a clean-enough story. The interesting part is what he took with him.

The analytical rigor of legal training — the habit of looking for what is wrong before you recommend what to do — is genuinely unusual in brokerage. Most of the industry is optimized for deal volume. The person who comes in and says “here is what I think you should consider before you decide whether to sell” is doing something different. In a market where a lot of owners need exactly that conversation, it is also more valuable.

Spencer’s broader point is that brokerage is one of the great equalizers in real estate — it does not particularly care where you went to school or whether you went at all. What it rewards, over time, is relationships and reputation. Being the adviser people trust is the most durable way to build both.

4. The Bakery Shop Problem: AI Can Improve Any Business Incrementally

Spencer’s live demo is worth dwelling on. He is an AI professional — he runs CRE Agents, thinks about this every day — and he still manually does his monthly personal budget. Not because he does not know how to automate it. Because he has not gotten around to it, and every month he tells himself he will do it next month. Sound familiar?

What changed this time is that he dropped twelve months of credit card statements into Claude and let it run. Full trailing analysis, categories broken out, trends visible. Then he added the bank statements. Then the personal balance sheet. The whole exercise took less time than his usual manual review, and Claude built a dashboard in chat that let him see everything at once. The most interesting insight: $450 on protein shakes in March.

The point he draws from this is not about personal finance. It is about the bakery shop. Or the chiropractor’s office, the law firm, the small property management company. Every one of those businesses has a version of Spencer’s monthly budget — a task that is manual, recurring, and not particularly valuable to do by hand. AI can handle it. And once it does, the person running the business gets something they did not have before: time to actually run the business, and visibility into what is happening without having to dig for it.

Michael’s RV park came up here too — the agentic engine he has running every morning that flags occupancy, suggests rent adjustments, and surfaces competitor data automatically. That is what this looks like at a slightly higher level of sophistication. But the starting point is much simpler: just drop the statements in and see what you get.

5. The Multipliers Framework: Impact vs. Feasibility

Spencer’s explanation of why he still has not automated his own budget despite knowing exactly how to do it is a good illustration of the framework. Automating his monthly finances would require a Plaid integration to every account, a dashboard build, some kind of approval workflow. Probably a ten-hour project. Right now the manual version takes him an hour or two once a month. At that pace, the automation pays for itself after about a year — which puts it well below plenty of other things he could be automating that have higher impact and similar or lower feasibility.

The Multipliers Framework formalizes this: every potential automation has an impact score (how much time or value does it save, and how often?) and a feasibility score (how hard is it to build, and does the technology exist?). High impact, high feasibility tasks should be automated first. Low impact, high feasibility tasks are tempting but often not worth it. Low impact, low feasibility tasks should be ignored entirely.

Jack made the complementary point from the outside: it is easy to spend more time building automation than you will ever recover from it. Some things really are better done manually, at least until a platform comes along that makes the automation trivial. The discipline here is the same as the discipline in real estate underwriting — knowing when not to chase the deal.

6. Do Not Abdicate to AI

This is where the episode gets its edge. Jack described something he has been noticing on LinkedIn: real estate professionals posting AI-generated images that are obviously AI-generated, to the point where he almost wanted to reach out to some of them. Not to mock them — he respects anyone putting themselves out there — but because the lack of any quality check is so visible that it actively undermines what they are trying to accomplish.

Spencer connected this to a mistake he made himself with an email tool he built for his team. The tool was generating emails in his voice. Nobody was reading them before they went out. He started seeing them and thinking: this does not sound like me at all. The tool was technically working. Nobody had bothered to check whether it was working well.

Sam called it the difference between noise and music. In a world where AI can generate infinite content at near-zero cost, the signal that actually cuts through is a person who clearly wrote something themselves, cares about what they said, and took the time to make it worth reading. The professional who does that right now sticks out more than they would have five years ago, not less — because the baseline has gotten noisier.

The risk of abdicating to AI is not just that you produce bad work. It is that you erode the trust that makes people want to work with you in the first place. And in a relationship business like CRE, that trust is the actual product.

7. Skills, Stacking LLMs, and the Interface Still to Come

Sam walked through something he built — a multiplatform marketing machine he put together in Manus, then asked it to reverse-engineer its own workflow at the end. The result was a complete CRO protocol that captured everything he had done and why. He had done the work organically, and the AI gave him back a skill he could reuse.

Spencer’s framing of how to build skills is practical and repeatable: download Whisper Flow, narrate what you are doing as you do it and explain why, then hand the transcript to your LLM and ask it to turn it into a skill. Do not try to design the skill from scratch. Do the work, describe the work, extract the pattern. That is the method that produces something actually useful rather than a generic prompt.

The bigger observation Spencer landed is that the current chat interface is probably a prototype. It requires everyone to be a prompt engineer, which most people are not. Skills are an improvement — they commoditize a good prompt and make it reusable — but the interface through which most people will eventually get the most leverage from AI has not been built yet. When it arrives, the prompt engineering layer will be abstracted away entirely. What remains will be judgment about what to build, what to trust, and what to check. Which is, again, the adviser’s job.

The Bigger Idea

The two conversations in this episode — the multifamily market and the AI tools — are not as separate as they appear. Both are about the same underlying question: what do you do when the environment makes it easy to cut corners?

In the 2022-2023 multifamily market, cutting corners meant stretching on leverage to win a deal everyone around you was chasing. The data looked fine. The comp set looked fine. The corner got cut anyway, and a lot of people are living with that now. In the AI world, cutting corners means posting the image without checking it, sending the email without reading it, publishing the content without caring whether it sounds like you. The output looks fine. The corner gets cut anyway.

Jack’s career trajectory — attorney to broker, adviser by orientation — is the counter-example to both. He is the person who slows down before recommending. Who finds the risks before they become someone else’s problem. Who shows up to the therapy session with owners not to tell them what they want to hear, but to help them think clearly about what their actual options are. That is a harder job than producing. It is also a more durable one.

Sam’s observation about noise and music is the AI version of the same point. The professionals who keep showing up with their own voice, their own judgment, and their own quality control will not just survive the AI era — they will be easier to find in it. Because they are the signal in a world that has become very good at generating noise.

Tools like AI.Edge exist for exactly this: helping CRE professionals use AI to do more of the valuable work, not to replace the judgment that makes the work valuable in the first place. The adviser who builds their practice on that distinction — who uses AI to work smarter and shows up in person to earn trust — is the one worth betting on.

Frequently Asked Questions about Episode 7 of Multipliers: Be an Advisor, Not a Broker

Jack Stone described the current multifamily market as a slow-motion reckoning for a specific vintage: owners who bought in 2022 or 2023 when the market was frothy, had to stretch on price to win deals, took on floating rate debt to make the numbers work, and are now watching interest expense consume the cash flow and reserves they need for capital expenditures. Operators who put on long-term fixed debt and underwrote conservatively are largely fine, though Jack notes that simply making distributions to investors is the current benchmark for success — a signal of how much the bar has shifted.

The pattern Jack described is consistent: to win a deal in that environment, you had to overpay. To justify overpaying, you had to overlever. To make the leverage work at a high enough return, you used floating rate debt. When interest rates rose sharply, that chain of decisions became very painful very fast. The market at the time made each step feel rational — the comp set supported it, peers were doing the same thing, and the data did not yet show what was coming. The problem was structural, not individual.

Spencer and Jack both pointed to three things: not stretching on leverage, knowing when to sit on the sidelines even when peers are active, and not chasing the next deal just to stay busy. Jack noted that discipline is genuinely hard when you have not done a deal in two or three years as a sponsor — the pressure to transact is real. The operators who held back in 2022-2023 largely did so because they had a mental model from earlier cycles or from mentors who had been through the GFC, which gave them a reference point for what a market at the top of its range actually feels like.

Spencer made the point that staying disciplined in 2022-2023 was not primarily about having stronger willpower than the operators who bought. It was about having a different frame of reference. Someone who started investing in 1997 or had mentors who navigated the GFC carries a felt sense for what a frothy market looks like and what tends to follow it. Someone who started in 2021 had no such frame — buying in a hot market was simply what you did, and the data at the time supported it. That is why seeking out mentors with real cycle experience is one of the most valuable investments a younger CRE professional can make.

Spencer described the distinction as being between someone who has the client’s best interests in mind versus someone optimizing for transaction volume. Jack’s legal background trained him to find every edge, understand every risk, and give a complete picture before recommending action — a habit he brought into brokerage. In the current market, where many owners need help thinking through difficult situations rather than just a pitch on their asset’s value, that adviser orientation is especially useful. Spencer’s observation is that the brokers he genuinely respects are the ones he never feels like he has to second-guess.

Spencer dropped twelve months of credit card statements into Claude, which automatically categorized and analyzed the full trailing period — faster and more clearly than his usual manual review. He then added bank statements and his personal balance sheet, building a complete financial picture in a single session. His takeaway was not about personal finance specifically but about the broader pattern: any business with recurring manual tasks — a bakery, a law firm, a small property management shop — can get meaningful efficiency gains from this kind of straightforward AI use. The starting point does not have to be complex.

The framework evaluates every potential automation on two dimensions: impact (how much time or value does it save, and how frequently?) and feasibility (how hard is it to build, and does the technology currently exist to do it?). High impact and high feasibility tasks should be automated first. Spencer used his own monthly budget as the example: it would take roughly ten hours to automate properly, saves him one to two hours per month, and would take about a year to pay for itself in time saved — which puts it below several other higher-impact automations he should build first. Jack added the complementary caution: sometimes it is genuinely more efficient to do something manually than to spend days building an automation for a task that a new platform may handle automatically within months.

Jack described seeing obviously AI-generated images on LinkedIn from real estate professionals trying to raise capital — visually poor outputs that anyone with even a passing familiarity with AI tools would immediately recognize. Spencer connected this to a mistake his own team made: an AI email tool was producing outputs nobody was reading before they went out, and the emails did not sound like Spencer at all. The risk is not just producing bad content. In a relationship business like CRE, posting low-quality AI output without checking it signals a lack of care that undermines the trust you are trying to build — which is the opposite of what those professionals intended.

Abdicating to AI means letting the tool make decisions or produce output without applying your own judgment to what comes back. Sam framed it as the difference between noise and music: AI can generate infinite content, but the signal that cuts through is a person who clearly thought about what they were putting out and cared enough to make it worth reading. The way to avoid abdication is simple but requires discipline — read what the tool produces, check whether it sounds like you, ask whether you would be comfortable attaching your name to it, and revise when the answer is no. The same standard applies to models, analysis, and any other output you hand to a client or investor.

The thread connecting both conversations is professional judgment. In the multifamily market, the operators who are fine are the ones who did not let a hot cycle override their underwriting discipline. In the AI world, the professionals who will build durable reputations are the ones who use the tools to work smarter without letting them replace the care and judgment that make their work trustworthy. Being the adviser — in real estate, in any client relationship — means showing up with your own thinking intact. AI.Edge and CRE Agents are built around exactly that principle: AI as a multiplier of professional judgment, not a substitute for it.

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