Episode 3 of Multipliers: The Coming AI Divide

The AI divide is not a prediction. It is already here.

Some professionals are doing in 20 minutes what used to take two days. Others have not changed a single workflow.

Both groups show up to the same offices, live in the same neighborhoods, and earn similar salaries. That will not last.

In this episode of the Multipliers podcast, we sit down as a team to talk about what is coming, who is prepared, and what the rest of us should be doing right now.

• You might also enjoy: This conversation led to an insightful post written by Spencer entitled: AI Has Crossed a Threshold

Episode 3 of Multipliers: The Coming AI Divide

This episode is a conversation between Spencer Burton, Michael Belasco, and Sam Carlson, the three partners behind Adventures in CRE and CRE Agents.

Unlike episode 2 where we featured an outside guest, Aleks Gampel, this one is an internal conversation. It started with a realization Michael had over the weekend, a moment where the abstract idea of AI disruption became personal. What followed was an honest discussion about productivity, fear, opportunity, and what all of it means for the people sitting in offices and neighborhoods across the country who have not yet felt the shift.

Why This Episode, Why Now

Something is happening that most people in commercial real estate, and most knowledge workers more broadly, are not yet reckoning with.

Over the past several weeks, a handful of publications and posts have gone viral in tech circles describing a near future in which AI does not just assist knowledge workers but replaces large portions of what they do. One was a blog post titled something like “Something Big Is Happening.” Another was a research paper by Citrini Research that moved markets when it explored the downstream effects of widespread AI productivity gains.

At the same time, Michael had his own version of this moment. While on vacation, he built a sophisticated app using a team of LLMs. The kind of app that would have cost hundreds of thousands of dollars to develop three years ago. He built it from his basement. And the question that followed was uncomfortable: if I can do this, what happens to the people whose full time jobs are to do exactly this?

That question set the tone for this episode.

This is not a technical conversation about AI tools. It is a conversation about what happens to people, neighborhoods, sub markets, and industries when productivity gains compound faster than the workforce can adapt.

Episode Highlights

Here are the themes that stood out.

1. The Deep Work Problem

Before the conversation turns to AI, it starts with something more fundamental: the inability to think clearly during a normal workday.

All three hosts describe the same pattern. Their best, most creative, most strategic work happens on nights and weekends. Not because they are workaholics, but because the weekday is consumed by noise. Slack messages. Emails. Small tasks that individually take 10 to 15 minutes but collectively fragment the entire day.

Spencer shares a story about a senior professional at a large real estate firm. Together they were mapping out tasks to automate, prioritizing by time saved and dollars earned. One task stood out. On paper, it was low impact. Maybe 15 minutes a day. But the professional flagged it as critical, not because of time, but because of mental friction. Every time it came up, he had to stop what he was doing, shift his mindset, and engage a completely different part of his brain. The disruption far exceeded the clock time.

Michael frames it as emotional distraction. The subtle stress of context switching, of obligations pulling you away from the work that actually moves the needle. And he makes a point worth sitting with: if AI can eliminate even a fraction of those interruptions, the impact is not measured in minutes saved. It is measured in the quality of thinking that becomes possible.

2. Michael’s Neighborhood Test

This is the moment the episode shifts.

Michael describes hanging out with friends and neighbors, people with solid careers in marketing, accounting, and software development. And he starts asking a simple question: what exactly do you do all day?

Not to be rude. But because he had just built something on his own, using AI, that replicated the kind of work these professionals had spent 20 years learning to do. And it cost him $20 a month in LLM subscriptions.

The realization hit differently because it was personal. These are not abstract statistics about job displacement. These are his neighbors. Their kids go to the same schools. Their home values are tied to the same local economy.

Michael’s concern is not that AI will destroy everything tomorrow. It is that there is a gap forming between people who see it coming and people who do not. And the people who do not are not unintelligent. They are simply focused on different priorities. They go to work, do their jobs well, come home. And that pattern, which served them for decades, may not hold.

From a real estate perspective, this raises a question almost no one at industry conferences is asking: which sub markets are most exposed to knowledge worker displacement? If white collar job losses concentrate in certain metros or neighborhoods, what happens to residential demand, office absorption, and retail spending in those areas?

Spencer puts it bluntly. There will be Detroits and there will be San Franciscos that come out of this. And almost no one in commercial real estate is doing the mental exercise to figure out which is which.

3. The Productivity Explosion Is Real

Sam brings the conversation back to what is actually happening today, not in theory, but in practice.

He walks through a specific example. He needed to build a workshop, the kind of deliverable that would normally take a day and a half to two days when you account for outlining, slide design, storytelling structure, and visual formatting.

His process: he used a Claude project he had already set up to generate workshop outlines. He answered six questions. It produced a full outline. He reviewed it, flagged three things to fix, and moved on. Then he took that outline into Manus, gave it a four step prompt referencing his style, previous workshops, and illustration preferences. It asked two clarifying questions. He answered them, went to lunch, and came back to a finished deck. He added two slides manually.

Total time: roughly 20 to 30 minutes of his attention. The rest was AI execution.

But Sam’s point is not just about speed. It is about what that time savings represents. The mental energy required to build a workshop from scratch is enormous. It requires deep focus, creative thinking, and sustained concentration. By offloading the production work to AI, he preserved his cognitive capacity for the parts that actually require him: the judgment, the framing, the strategic choices.

4. Multiplication at Scale

Spencer extends Sam’s example into a concept that defines the next phase of AI productivity: parallel orchestration.

Sam built one workshop. But what if he had three due next week? In theory, he could spin up three instances of Manus, each running a different workshop process simultaneously, and come back from lunch with all three done.

In commercial real estate, this maps directly to daily workflows. Five offering memorandums land on your desk, each with a rent roll and trailing twelve. Today, you work through them sequentially. With AI co-workers handling the structured analysis, you delegate all five at once. Each task might take the AI five to ten minutes. You spend another 10 to 15 minutes validating. And suddenly two hours of work compresses into 30 minutes.

Spencer notes an important constraint: CRE Agents intentionally focuses on tasks that take a human less than 20 minutes. The reason is quality. On longer, more complex tasks, the risk of buried errors increases. Finding three wrong values in a financial model can take longer than building the model from scratch. The sweet spot is high volume, moderate complexity work where AI output can be quickly validated by a skilled professional.

The multiplier effect is not about any single task. It is about running many tasks in parallel, with a human providing the judgment layer on top.

5. The Mirror and the Wave

Sam offers a framework that ties together the fear and the opportunity.

He goes back to 2008. He and Spencer were running a mortgage company when the financial crisis hit. They were young, driven, and ambitious. But when the market turned, they had no real skills to fall back on. No deep expertise. No strategic foundation. He describes it as looking in the mirror and not seeing much there.

It took him seven or eight years to fully understand that lesson. The market contraction was outside his control. But his lack of preparation for it was entirely within his control.

Today, the conditions are different in two critical ways. First, this is not a contraction. It is a productivity explosion. The economy is not shrinking. The tools are expanding. Second, the skills that he, Spencer, and Michael have built over the past 20 years are exactly the kind of skills that AI amplifies rather than replaces. Judgment. Pattern recognition. Taste. Strategic thinking. The ability to look at a deal or a market or a decision and know what matters.

His argument is that the people who will thrive are the ones who have something worth multiplying. AI is a magnifier. If you have built real expertise, real judgment, real skills, the magnification is extraordinary. If you have not, the magnifier has nothing to work with.

6. What to Tell the Next Generation

The hosts close with a question each of them is wrestling with personally: what do you tell your kids?

Michael’s answer centers on creation. Not just “skill up,” but create. Build things. Follow passions. Be a tenacious learner. He references a Tony Robbins interview on the Diary of a CEO podcast where multiple guests were asked what they would tell their grandchildren. The consistent answer was not to optimize for a specific career path. It was to become a creator.

Sam bets on tangible, infrastructure-adjacent work. Electricians. Healthcare. Cybersecurity. Roles where physical presence, human trust, or regulatory complexity create barriers that AI cannot easily cross. He also emphasizes that the people who bring AI into their firms and demonstrate its power will become indispensable. Not because they are irreplaceable as individuals, but because they are the bridge between the technology and the business.

Spencer adds two dimensions. First, hard assets. In a world where AI compounds productivity gains, owning things, real estate, gold, physical infrastructure, becomes increasingly valuable. Second, high stakes outputs. Pilots. Surgeons. Roles where the consequences of error are too severe for society to remove the human from the loop.

But the thread that connects all three answers is the same: the last 10 to 20 percent of any job, the nuance, the judgment, the relationships, the taste, that is where humans remain essential. AI handles the other 80 to 90 percent. The question is whether you have built the skills to own that final, most valuable layer.

The Bigger Idea

There is a line in this conversation that deserves to sit with you.

Spencer notes that he attended three industry conferences in the month before recording. At none of them was anyone seriously discussing what AI displacement means for real estate markets. Not the technology itself. The downstream effects. Which sub markets win. Which ones lose. What happens to office demand when firms cut headcount by 40 percent. What happens to suburban residential when knowledge workers in those neighborhoods start losing jobs.

The industry is not ignoring AI. It is ignoring the second order effects of AI.

And that, in many ways, is the divide this episode is about. It is not just between people who use AI and people who do not. It is between people who are thinking about what comes next and people who are still reacting to what is happening now.

Michael frames it well. He is a long term optimist and a near term realist. He believes the world will adapt. New markets will form. People will retool. But the transition will not be painless, and it will not be evenly distributed.

The advice from all three hosts converges on one idea: lean in. Not passively. Not by using ChatGPT as a search engine. But by going deep. Learning how the tools work. Understanding which ones are good at what. Building workflows that multiply your unique expertise.

As Sam puts it: be the person who brings the power of multiplication into your firm. That person is going nowhere.

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