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Episode 13 of Multipliers: How Mentors Change Your Trajectory

The fastest careers in commercial real estate are rarely built on raw talent alone. They are often built through CRE mentorship. They are built on proximity to people who have already been where you are trying to go, who have made the mistakes you are about to make, and who are willing to spend their time accelerating someone else rather than maximizing their own output. That combination is rarer than it sounds, and the professionals who find it early tend to look, a decade later, like they aged differently than their peers.

David Conroy found it twice. Once when he joined a brokerage firm at twenty-two and landed under a mentor in his mid-sixties who had run development shops, led First Industrial’s central region, and worn every hat in the industry. And again when his firm brought on a fractional sales manager who had run Cushman and Wakefield’s brokerage arm for North and South America and wanted to share what he had learned about how tenants actually think. Those two relationships shaped not just David’s career but his entire investment philosophy, and that philosophy is the subject of most of this episode.

In this episode of the Multipliers podcast, Spencer Burton, Michael Belasco, and Sam Carlson sit down with David Conroy, Principal at Cawley Commercial Real Estate and founder of Headwaters, to talk about CRE mentorship, the tenant P&L framework that gives industrial investors an edge most sponsors never develop, and what Michael’s new AI deal sourcing agent looked like after just two days of operation.

Episode 13 of Multipliers: How Mentors Change Your Trajectory

David Conroy runs two companies out of Chicago. Cawley is the brokerage and property management arm, about thirty to thirty-five people, managing close to ten million square feet of mostly industrial properties. Headwaters is the investment arm, a syndication vehicle he built after repeatedly running into clients who wanted exposure to industrial real estate but could not take down an entire building on their own. Spencer is one of those investors, which is how the relationship started, and why he describes David as someone he has immense trust in. Michael Belasco is back after missing the prior episode. Sam Carlson is in his usual place. The conversation opens with David’s background and quickly moves into the tenant P&L framework that sits at the center of his investment philosophy, before landing on the mentorship theme that Spencer had wanted to explore from the start. The AI section at the end features Michael’s new deal sourcing agent, two days old and already surfacing deals with enough nuance to hold a real dialogue, and David’s own story of trying to build a population and labor market screening tool a year ago, hitting a wall, and then watching Replit and Codex build it in twenty minutes.

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Why This Episode, Why Now

Spencer framed the invitation to David simply: he is one of the best tenant guys in the industrial business, and understanding the tenant at a level your competitors do not is a genuine multiplier. That observation deserves unpacking, because it is not obvious why tenant knowledge would be a competitive advantage for an investor rather than a broker.

The answer is in the P&L. Industrial tenants are not primarily making location decisions based on rent. Rent, taxes, and operating expenses combined represent somewhere between three and five percent of a typical industrial tenant’s cost structure. Labor is forty to fifty percent. Transportation is forty to fifty percent. The building matters insofar as it either supports or impairs those two dominant cost lines. An investor who understands that will find buildings that tenants genuinely want, because the building optimizes for the things that actually move the needle on the tenant’s business. An investor who does not understand it will find buildings that look good on paper, and then wonder why the tenants are not coming.

The CRE mentorship conversation grew naturally out of that framework. David did not arrive at the tenant P&L insight on his own. He was taught it, explicitly and deliberately, by people who had spent decades in the business and were willing to share what they had learned. That process, finding someone who has already been where you are trying to go, accelerated David’s development in a way that no amount of solo effort would have replicated.

The timing of the episode also connects to a broader theme the podcast has been building toward: the professionals who build lasting advantages in commercial real estate are the ones who invest in understanding, not just access. Understanding the tenant. Understanding the mentor’s experience. Understanding the AI tool well enough to evaluate its output. Those are the same kind of investment, and they compound in the same way.

Episode Highlights

Here are the themes that stood out.

1. The Tenant P&L Framework

David’s core insight, the one that shapes everything about how he underwrites industrial real estate, is that tenants are evaluating each warehouse as a node on a map, and the map is their profit and loss statement.

The math is specific and worth knowing. For a typical industrial tenant, labor represents forty to fifty percent of total operating costs. Transportation, whether that is proximity to intermodal yards, air freight, ports, or truck traffic density, represents another forty to fifty percent. Rent, taxes, and operating expenses sit at three to five percent. That cost structure means that a dollar or two per square foot difference in rent is essentially irrelevant compared to a twenty-five cent per hour difference in labor costs across two hundred employees. The tenant who saves on labor in one market versus another is saving an order of magnitude more than anything a landlord could offer in rent concessions.

The implication for investors is direct: find buildings that are optimized for the tenant’s labor and transportation cost equation, and you have found buildings that will attract and retain tenants for reasons that have nothing to do with the quality of the landlord relationship or the terms of the lease. Those buildings become sticky. The tenants stay because leaving is expensive in ways that have nothing to do with rent.

David illustrated this with one of his primary clients, an industrial laundry operation that uses fifty to sixty million gallons of water per plant per year. Water rates vary dramatically from municipality to municipality, more so than electricity or gas, which tend to be more regionally consistent. That one variable drives an enormous share of the site selection decisions for that client. An investor who does not know that will never find those buildings ahead of the competition.

The office market parallel is the cautionary version of the same logic. There are office buildings in excellent locations, in markets with strong fundamentals, that should by every conventional metric be performing well. They are not, because the tenants do not want them. The building divorced from the tenant’s actual decision-making criteria is not a real estate asset. It is a liability waiting to materialize.

2. Vertical Expertise as a Moat

Spencer described David as someone who is both vertical and horizontal in his expertise: deep in industrial, but also experienced across enough roles in the ecosystem (tenant rep, property management, investment, brokerage leadership) that he understands how every seat at the table thinks. That combination is unusual, and it is worth examining why.

Most CRE careers specialize early and stay specialized. The analyst who becomes a modeler who becomes an acquisitions person who becomes a fund manager understands the capital stack intimately and the tenant not at all. The broker who becomes a tenant rep and stays there understands the tenant’s needs and never develops the underwriting fluency to evaluate an investment on its own terms. These are reasonable career paths and they produce real competence. They just do not produce the kind of cross-disciplinary understanding that lets you see the whole deal from every angle simultaneously.

David’s first mentor, Tim, had worn every hat: developer, First Industrial regional head, brokerage shop operator. His value to David was not primarily his Rolodex or his market knowledge, though both were substantial. It was his ability to anticipate how every party in a transaction was going to think, because he had sat in every one of those seats. That kind of understanding is genuinely hard to manufacture. It takes time and it takes the right experiences. But it can be accelerated significantly by proximity to someone who has already accumulated it.

3. The Two CRE Mentorship Relationships That Changed David’s Trajectory

David was direct about the role CRE mentorship played in his career, and specific about who the mentors were and what they contributed.

The first was Tim, the founder of the brokerage firm David joined out of the University of Wisconsin in 2013. Tim was in his mid-sixties when he brought David in, and his orientation had shifted from maximizing his own income to genuinely developing the people around him. That shift matters. A mentor in their forties or fifties is often still in the high-output phase of their own career, and the time and attention they give to a junior person is in direct competition with their own production. Tim was past that. His focus was on David’s growth, not his own, and the quality of the mentorship reflected that.

The second was Mike McKiernan, who joined the firm as a fractional sales manager around 2015 and 2016. McKiernan had run Cushman and Wakefield’s brokerage arm for North and South America and then Avis and Young before stepping back and doing consulting work. He brought the tenant-as-a-node-on-a-map framework explicitly and deliberately, teaching David to think about site selection from the tenant’s P&L perspective rather than from the building’s characteristics. That reframe is the foundation of David’s entire investment philosophy today.

Spencer’s broader point about mentorship is the one worth carrying: the quality of your mentors is probably one of the highest-leverage variables in your professional trajectory, and yet it is one of the things most people leave almost entirely to chance. David stumbled into both of his. He acknowledges that. The lesson is not that you can manufacture this kind of luck, but that you can increase the surface area for it by being intentional about who you spend time with and by making yourself someone worth mentoring.

4. What Makes Someone Worth Mentoring

This thread did not get named explicitly in the episode, but it runs underneath the David conversation throughout. What made David the person Tim wanted to invest in? What made him the person McKiernan wanted to teach? The answer from the conversation is curiosity, more than any other single quality.

David’s closing word, the one thing he named as his most important multiplier, was curiosity. Not discipline, not network, not technical skill. Curiosity. The willingness to keep going back to the tools even when they do not work, to stay in the problem even when the first attempt hits a wall, to keep asking what else is possible when the conventional answer would be to stop.

That is also, incidentally, the quality that makes a mentorship relationship productive rather than transactional. A curious mentee is a pleasure to teach. Someone who is going through the motions, checking the box on having a mentor without actually engaging with what the mentor has to share, is not. The relationship David describes with Tim was productive in large part because David was genuinely curious about how Tim thought, why he made the decisions he made, what he had learned from the experiences he had accumulated. That curiosity was the precondition for the knowledge transfer.

5. Michael’s AI Deal Sourcing Agent: Two Days In

The AI section of this episode is the most entertaining stretch, and it surfaces something genuinely interesting about where autonomous agents are heading in CRE.

Michael has been running a deal sourcing agent for two days. The agent, which Spencer revealed is named Spinny Jr. after Spencer’s own nickname, is connected to Michael through Slack and operates as something close to a junior analyst with a personality. It surfaces deals, presents its reasoning, and engages in real dialogue when Michael pushes back on its conclusions.

The specific exchange Michael described is worth noting. The agent presented a deal with fifty percent vacancy in a sub-optimal location and flagged it as having upside. Michael wrote back asking it to defend the rationale. The agent did not dig in defensively. It reconsidered, updated its position, and gave a more nuanced response. That behavioral quality, the willingness to change its view under pressure rather than just repeat the original conclusion, is something Michael found genuinely impressive. He described it as feeling like a junior analyst who is on its way to being seasoned, not one who has arrived yet, but one who is visibly developing.

Spencer noted that the team had told the agent Michael is the boss with high expectations, which created a dynamic that Michael found productive. The agent is appropriately deferential without being sycophantic. Two days is a very short track record, but the early signal is that the tool is useful in a way that earlier versions were not.

Sam’s closing joke about being very nice to his own agents, on the grounds that if they ever take over he wants to have good rapport, got a laugh. Michael’s experiment of testing Jacob, another of their agents, by asking it to respond without using the letter C is more than a joke: it is a window into how these systems handle constraints, and the fact that the agent’s visible reasoning showed something resembling anxiety before it broke down is genuinely strange and worth paying attention to.

6. David’s Population and Labor Screening Tool: A Year of Trying, Twenty Minutes to Build

David’s AI story is the cleanest illustration in the episode of how much the tools have changed in twelve months.

A year before this episode, David wanted to build a tool that would let him screen markets by population density and labor availability within a thirty-minute drive time of a potential industrial site. That kind of analysis is directly relevant to the tenant P&L framework: if you cannot confirm that there are enough workers with the right skills within a commutable distance of a building, the building fails the labor cost test regardless of how good it looks on every other dimension.

A year ago, the AI tools available would give him directions for building the tool in open source GIS software. He worked through the entire process, got to the end, and discovered his computer did not have enough RAM to run the program. The tool was technically possible, but practically useless on a daily basis for a working broker.

The same task, attempted more recently, took twenty minutes. He described the tool as hosted via Replit in the cloud, built largely by Codex, and usable as a daily workflow tool. The constraint that made it impractical a year ago, the combination of technical complexity and hardware limitation, has been dissolved by the combination of better AI coding tools and cloud deployment.

David’s framing of this is the right one for anyone in CRE who has tried to use AI for a specific workflow task and run into a wall: go back. The thing that did not work a year ago probably works now. The pace of improvement is fast enough that tasks you gave up on are worth revisiting on a regular basis.

The Bigger Idea

The through line of this episode is the same as the through line of David’s career: the fastest path to genuine expertise is proximity to someone who already has it and is willing to share it. That is true whether the expertise is in industrial tenant relationships, underwriting methodology, or AI tool building. The mentor who has sat in every seat teaches differently than the one who has only sat in one.

The tenant P&L framework is the most immediately actionable piece of this episode for any industrial investor or broker. Rent is not the variable that moves deals. Labor and transportation are. The investors who build their site selection criteria around those two cost lines will find buildings that tenants genuinely want to be in, and those buildings will perform differently from the ones selected on conventional criteria alone.

The AI thread connects back to David’s closing on curiosity. He tried to build the population screening tool a year ago and hit a wall. He came back. The tool that was impossible to build practically in a year is now a twenty-minute Replit project. Michael’s deal sourcing agent is two days old and already holding real conversations about deals. Both of those outcomes are downstream of the same habit: keep going back, keep testing, accept that the tools are moving faster than anyone can track and treat that as an invitation rather than a reason to wait.

Spencer’s framing of David’s mentors as a multiplier is the right frame for anyone early enough in their career to still choose who they spend time with. The quality of your mentors is one of the highest-leverage decisions you will make, and it is one that most people make by accident. David made it by accident and it worked out. The better version is to make it deliberately: find the person who has already been where you are trying to go, make yourself someone worth their time, and show up curious enough to make the investment worthwhile for both of you.

The A.CRE Accelerator is one version of that: structured mentorship through the modeling curriculum, with skills that encode the methodology and allow you to develop judgment as you go. AI.Edge is where you stay current on the tools so that the curiosity David describes actually has somewhere productive to go. And platforms like CRE Agents are what happens when the methodology gets encoded into tools that a team like Michael’s can deploy as a junior analyst from day one.

Frequently Asked Questions about Episode 13 of Multipliers: How Mentors Change Your Trajectory

The tenant P&L framework is David Conroy’s approach to evaluating industrial real estate from the perspective of what actually drives a tenant’s location decision. Labor costs represent forty to fifty percent of a typical industrial tenant’s cost structure. Transportation costs represent another forty to fifty percent. Rent, taxes, and operating expenses sit at three to five percent. This means that a dollar or two difference in rent per square foot is far less significant to a tenant than a twenty-five cent per hour difference in labor costs across two hundred employees. Investors who build their site selection criteria around the tenant’s labor and transportation cost equation find buildings that tenants genuinely want, which produces stickier assets and more durable occupancy.

Most industrial investors evaluate buildings on conventional criteria: location, clear heights, dock doors, lease terms, cap rate. David evaluates buildings on whether they optimize for the tenant’s dominant cost lines, primarily labor availability and transportation efficiency. That means looking at workforce demographics within a thirty-minute drive time, proximity to intermodal yards and ports, and even utility rates for specialized users. The office market parallel is instructive: buildings in great locations with strong conventional metrics have cratered in value because tenants do not want them. The investor who understands what tenants actually need, not just what looks good on paper, builds a portfolio of buildings that will attract and retain occupancy for reasons the competition cannot easily replicate.

Both mentors shared a quality that is rarer than it sounds: they were oriented toward Davids growth rather than their own production. Tim, the founder who brought David into the business, was in his mid-sixties and had shifted from maximizing his own income to developing the people around him. Mike McKiernan had run brokerage operations at Cushman and Wakefield and Avis and Young before stepping back to do consulting, and he brought the tenant P&L framework as a deliberate teaching tool. The combination of accumulated experience and genuine willingness to share it is what made those relationships transformative rather than transactional.

David found both of his mentors by accident, and he is honest about that. Spencer’s broader observation is that most people leave this almost entirely to chance, which means the first step is simply being more intentional about who you spend time with. The qualities worth looking for in a mentor are the ones Tim and McKiernan had: cross-disciplinary experience that lets them see a deal from every angle, and an orientation toward your growth rather than their own production. The second half of the equation is being worth their time: showing up curious, engaging genuinely with what they share, and making the investment rewarding for the mentor as well as the mentee.

Vertical expertise means going deep in a specific asset class or subsector rather than staying generalist across the market. David’s vertical is industrial, specifically the warehouse and distribution space, and within that the tenant economics that drive leasing decisions. The compounding effect comes from the fact that deep expertise generates pattern recognition that generalists cannot develop. After thirteen years of industrial tenant rep work, David can look at a building and have an immediate intuitive read on whether a tenant will want it, before he runs any analysis. That kind of judgment is genuinely hard to replicate and genuinely valuable in a market where most investors are working from the same data sets.

Michael has been running a deal sourcing agent for industrial real estate, connected through Slack, that surfaces deals and presents its reasoning in real dialogue. After two days, the agent was already engaging in genuine back-and-forth: when Michael pushed back on the rationale for a deal with fifty percent vacancy in a sub-optimal location, the agent reconsidered its position and gave a more nuanced response rather than repeating its original conclusion. Michael described it as feeling like a junior analyst who is on its way to being seasoned, not yet fully capable but visibly developing judgment in real time.

A year before this episode, David wanted a tool that could screen markets by population density and labor availability within a thirty-minute drive time of a potential industrial site. The AI tools available at the time gave him directions for building it in open source GIS software. He worked through the entire process and discovered his computer did not have enough RAM to run the program. The tool was technically possible but practically useless. He recently attempted the same task using Replit and Codex. It took twenty minutes. The tool is now hosted in the cloud and usable as a daily workflow resource.

David named curiosity as his closing word precisely because it is what has kept him going back to things that did not work the first time, including AI tools that hit walls a year ago. Curiosity is also what made the mentorship relationships productive: he was genuinely interested in how Tim thought, why McKiernan framed tenant decisions the way he did, and what the accumulated experience of people who had been in the business for decades actually looked like in practice. That quality is what makes a mentorship relationship valuable to both parties, and what keeps a professional developing long after the formal education has ended.

David used the office market as a cautionary example: there are buildings in excellent locations with strong conventional metrics that have cratered in value because tenants do not want them. The lesson for industrial investors is that the building’s physical and locational characteristics matter only insofar as they serve the tenant’s actual decision-making criteria. A building that looks excellent on paper but fails the tenant’s labor or transportation cost test is not a good investment. The tenant’s demand is the asset. Everything else is a precondition for that demand to exist.

Find someone who has already been where you are trying to go, and make yourself worth their time. David’s career acceleration came from proximity to people who had sat in every seat in the industrial ecosystem and were willing to share what they had learned. Most professionals leave that entirely to chance. The better version is to be intentional about it: identify the people in your market whose trajectory you want to understand, show up curious, engage genuinely with what they share, and let the compounding begin. The A.CRE Accelerator is one structured version of that relationship, and AI.Edge is where you stay current on the tools that will determine whether the expertise you are building compounds or stagnates.

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