What Real Estate Investors Get Wrong About Robotics With A.K. Schultz – RFP 70 Transcript
Gordon Lamphere (00:05): Hi, I’m Gordon Lamphere, and welcome to The Real Finds Podcast, where we have real conversations with key entrepreneurs, activists, and researchers who are shaping the real estate industry and, as a result, our world. On today’s podcast, we’ll be speaking with A.K. Schultz. A.K. is CEO of SVT Robotics, the platform helping industrial companies integrate automation without blowing up their IT staff. We break down the multitrillion-dollar automation opportunity that is still in its infancy. We debate greenfield versus brownfield. We reveal why just four walls and a roof won’t cut it in real estate anymore, and how some businesses are saving 60 percent by eliminating operational inefficiencies. Real estate investors and operators, if you want to future-proof your investments, you’ll want to take notes. A.K., thanks for hopping on the podcast today.
A.K. Schultz (01:01): Hey, thanks for having me.
From Tanks and Nuclear Reactors to Warehouse Robots
Gordon Lamphere (01:02): So why robotics?
A.K. Schultz (01:05): Why robotics? Is that a question of why I joined robotics, or the more metaphysical “why robotics” for anybody?
Gordon Lamphere (01:13): We can start with why you joined robotics and get into the metaphysical afterward.
A.K. Schultz (01:19): Just to pre-qualify, I’ve had a long and meandering career. It started when I was an Army officer, in tanks, reconnaissance, and airborne. And the natural thing to do when you leave that is, of course, to become a nuclear engineer. So once I left the Army, I became a nuclear engineer, which is a fascinating field. I worked on the aircraft carrier side, building naval reactors. But the technology was essentially the same as it had been since the 1950s, and the work was more about risk management. For me, as a person who likes to create, it wasn’t really my place. It’s an amazing profession, and I have total respect for everybody in it. I learned a ton about quality management, but I wasn’t on the edge where I needed to be. The nuclear industry is naturally risk-averse, with great people and amazing quality systems, but there’s a natural bias away from innovation and toward risk management, which, let’s be clear, is what we want from nuclear reactors. But it wasn’t my forever home.
While I was getting my MBA, I got headhunted by a Swiss company that wanted a US-based person, because they had just landed a massive contract with a very, very large company based in Arkansas. You may know them. The Europeans just don’t really speak Arkansas, so I guess they needed somebody
A.K. Schultz (03:27): from south of the Mason-Dixon line to be a natural translator. They took me to the interview at a chainsaw factory and showed me a forklift driving itself. This was in 2004, twenty-one years ago, and I was just blown away. I said, “This is where I need to be,” and I’ve been doing it since 2004. It’s been an amazing career, growing up with and watching the field evolve. What’s also amazing to me is that after twenty years, we’re still in the early innings. We’re just scratching the surface. With the advancements first in machine learning and now in AI, it’s an exciting time to be in this business.
Why We’re Still in the Early Innings
Gordon Lamphere (04:26): Why do you think we’re in the early innings of the robotics and data revolution?
A.K. Schultz (04:33): Market penetration. The last stat I saw was that something like 15 percent of warehouses have some form of automation, and usually that’s conveyor, really lower-end automation. The number of companies actually doing advanced work is really not that many. They’re breaking ground for everybody else, but we’re not even at the early majority yet. We’re still in the innovators and early adopters stage.
Greenfield vs. Brownfield: Where the ROI Lives
Gordon Lamphere (05:16): When we talk about adopting technology, one of the biggest places we get hung up is the start of the process, and one of the big debates is whether to adopt it in a greenfield or a brownfield world. What do you think the challenge is there? Is adoption going to end up being all greenfield, or is brownfield real?
A.K. Schultz (05:49): Some would argue it’s easier to do it in greenfield because you have a blank slate, and that’s true. But while it sounds less risky, in some ways it’s more risky. When you’re building a building, you tend to think in waterfall terms, where everything has to be specified and planned perfectly. The reality is that if it’s a three-year build cycle, what does the tech look like three years from when you designed it in year one? By the time it’s built, your blank-slate design may be obsolete or irrelevant, or your business may have suddenly changed. So it seems less risky, but in a way it’s almost more risky, and the amount of money being spent ratchets up both the risk and the visibility.
The other thing is that brownfield is where all the juice is. That’s where the ROI is, because the money is in your existing operation. And, this is probably not great for a real estate podcast, but a lot of times you can do building avoidance.
Gordon Lamphere: Yeah.
A.K. Schultz: You can do things that prevent you from needing an entire new building. Yes, it saves money on operating costs, but you might also avoid a hundred-million-dollar building. There are challenges, too. You have to bring something into an existing operation, and it’s kind of like an organ transplant. Sometimes the body rejects the organ. It’s change management in an existing building that had no automation. You don’t just have to deploy it technically. You have to integrate it operationally and win the hearts and minds of the team taking it on, because one day their job was X, and
A.K. Schultz (08:11): now they have to learn how to do something new, and not everyone is super excited about that. So there are pros and cons. But look at the most successful robotics companies. I’d say the two highest-traction newer technologies are Locus Robotics and AutoStore. They can be dropped into an existing facility with relatively little pain, and frankly, I think that’s why they’ve had so much traction: the brownfield market is so much bigger than the greenfield market. How many buildings are built each year versus how many already exist? It’s something like fifty thousand warehouses in the United States. You’d probably know the number better than I would. I’d be curious. Is it a thousand buildings built a year? I don’t know.
Gordon Lamphere (09:16): Particularly now, going into this high-interest-rate cycle, new construction is substantially muted compared with even two or three years ago. What we’ve seen is that robotics is being applied overwhelmingly in Class A buildings built in the last ten years. People aren’t necessarily building fresh, particularly when it can cost $200 or $300 a foot to build new, when you can buy at maybe $100 a foot, still pricey, or closer to $150 depending on the market. Why would you cut your profit by 25 percent? The biggest question we usually get is how you know what the right integration process is, and how you turn a brownfield building into the right structure with the data systems we have today.
The Art and Science of Integration
A.K. Schultz (10:39): That’s a great question, and I’ll be a little windy on it. Before the call, I learned you’re a sailor. I’m a sailor too, but I’m at a different level than you. I’m not on the US national team. We race for rum bottles, as you can see on the shelf behind me.
Gordon Lamphere (10:50): Wind away. That’s fine. It’s as good a trophy as any.
A.K. Schultz (11:09): Exactly. And some of them aren’t completely full, if you notice, so there’s a utility to those trophies. What I love about sailing is that there’s so much science behind a sailboat. It’s really an airplane with one wing in the water and one wing in the air. To be a great sailor, you need to understand the physics: the hydrodynamics, the aerodynamics, all the mechanics. But at the end of the day, you have to feel it. There’s an art to it as much as a science, and the magic happens at the intersection of the art and the science. That’s what separates someone who’s decent from someone who’s great. If you have the art but not the science, there’s a level you can reach, and if you have the science but not the art, there’s a level you can reach. But if you can pull it all together, you can make some magic happen.
I see this the same way. There are a lot of complex, hard technical activities you have to do, and frankly, we’ve built a platform to abstract a lot of those technical difficulties away. But at the end of the day, you’re walking into someone’s operation, and you have to understand what they do, what’s there, and what’s important and what’s not, and then guide them through onboarding this technology. The best companies are technically skilled, but they also understand people and businesses. Companies are complex organisms, and that can sometimes be annoying when you just want to do the technical thing. Unfortunately, that never works. If you ignore the human element and the business element, you’re probably going to fail.
A.K. Schultz (13:34): So that was my meandering answer.
The Most Common Implementation Pain Points
Gordon Lamphere (13:36): I’ll follow up on that, because I think that’s a great analogy for what we’ve seen in implementation. To use your sailing analogy, there are common pain points in a race. What are the most common pain points you see in implementing robotics?
A.K. Schultz (14:07): The number one challenge I see is when a company has no veterans on the team who’ve done this before. Robotics hit a slowdown because of interest rates alone, but the big players, the ones with a lot of experience, haven’t slowed down a lick, and they’ve bought up a lot of the capacity. You’d think a company that’s really good at this might be harder to work with, more persnickety, but it’s a pleasure to work with companies like that. It’s also great to work with a company that’s just getting started but is keenly aware of the gaps in its own organization, has good self-awareness, and is leaning into learning. The hard ones have no awareness of their gaps, and sometimes they have a casualness, almost an arrogance, about implementation. They expect it to be magic, and it’s not. “I thought you were just going to push a button.” Well, it doesn’t exactly work that way. It’s great to work with people who understand and respect the problem, because it’s a very difficult one.
The other thing is that more mature companies appreciate the ongoing total cost of ownership. Let’s be honest: writing integration code isn’t that hard. Making two things talk to each other and pass messages is no big deal. So why build a platform that does it? Because you want an integration approach with low technical debt and low cost of ownership, one that lets you
A.K. Schultz (16:29): consistently upgrade, say, Kubernetes or whatever software is running in the background, without affecting the robot company on the other side and setting off cascading upgrades. If you can’t upgrade individual pieces at will, you’re exposing yourself to cybersecurity threats. That’s hard, it’s not trivial, and you need a strategy and a sound technical approach for the long view.
Why Tightly Coupled Systems Become a Cybersecurity Risk
Gordon Lamphere (17:08): Can you dive into that a little more for listeners who might not fully understand how an elaborate set of layers can be a cybersecurity threat?
A.K. Schultz (17:21): Sure. Connecting two things together isn’t a big deal. But the literal definition of integration is to make two things into a common whole. So if I’m a robot and you’re an enterprise system like SAP, and we’ve become a whole, then if you want to upgrade, you have to ask me, and I have to do something. That’s tough. Add another party, and it’s even tougher. By the time you have five systems interlinked, it goes off the scale. You can create a deadlock in the upgrade cycle, because not everyone can upgrade at the same time, so you’re waiting for everyone to be ready. That could take years. It’s terrible.
There’s a concept called abstraction. You want to build things that are abstracted from each other, meaning you can upgrade when you want and I can upgrade when I want, without causing problems for anyone else. You have to design for that. In the old days, when you had one robot, or a couple of standalone robots that didn’t communicate with the host system, it was no big deal. But with so much interlinked now, it becomes way harder. Think about
A.K. Schultz (19:20): your iPhone and MacBook. They’re all so tied together and interlinked that upgrades cascade. Now imagine doing that across every phone manufacturer, every laptop manufacturer, every earbud maker. You need standards or approaches in place, like Bluetooth and USB. Those are the foundational things that enable upgradability.
What Robotics Solves Best: Walking Is the Enemy
Gordon Lamphere (19:56): Speaking of upgradability, one of the questions our clients ask most when they discuss robotics is what problems robotics solves, and what it upgrades most about a facility. What would you say robotics is best at solving right now?
A.K. Schultz (20:22): There’s labor, there’s inventory, and there’s real estate. I think those are probably the big three, though there are others, like energy. Take AutoStore. That’s a density play. If you have ten items of something, instead of taking up an entire pallet location, they take up a box about this big. So you can increase the density of your warehouse, and you can do it in a normal-height building. You don’t need a super-tall building. Locus is attacking the labor side. And with inventory, if you can hold fewer days on hand and still predictably fulfill your orders, you save on inventory costs. So I’d say real estate and labor are at the top of the page.
There’s actually one more: speed to fulfill, on time and reliably. That’s what Amazon revolutionized. Having a million SKUs under one roof was once unheard of, impossible to comprehend, and they can ship an item in under forty-five minutes. Think about just walking across a million-square-foot building. How long does that take you?
Gordon Lamphere (22:07): It’s insane. I’ve seen their facility in Kenosha, and it’s unbelievable walking through that place.
A.K. Schultz (22:15): And they do it for multiple items. If you have three items and have to walk across the building three times, you won’t make it in forty-five minutes. What they did with parallel processing changed how everyone thought about the industry at scale. A lot of times, you buy from Amazon because you can get it tomorrow. That’s a massive advantage.
There’s also an underappreciated one: flexibility. A lot of systems are big, capital-intensive, and bolted to the ground. Once you build it, you can’t really change it. It’s twenty million dollars, and you’re not going to bin it. The capital asset almost fossilizes the business. The more modern technologies are very modular. You can add a robot or take one away. They’re not tied to big, fixed structural assets.
When it comes to labor, there’s a lot you can do. You see picking arms, and goods-to-person systems where robots bring the goods to people. But the number one thing you can eliminate in labor is walking. Picking and grabbing an item is relatively inexpensive for a human, and humans are really good at it. The amount of time they spend walking to the item, as opposed to grabbing it, is where the money and the cost are. One approach is to have the person stand still and bring the goods to them. That’s goods to person. The other is where a person walks with a robot and picks, and the robot takes the items to shipping, eliminating the walking and deadheading. Those are your two biggest chunks. When you start trying to squeeze everything else out,
A.K. Schultz (24:40): you’re optimizing the low end. Focus there first. Get the walking out, and that will take out 60 percent of the cost. Everything beyond that is much harder to squeeze out, and you have to be a much better organization to optimize further. But the low-hanging fruit is eliminating walking.
Designing for Flexibility When the Future Is Uncertain
Gordon Lamphere (25:02): That’s great advice. It’s always best to go for the low-hanging fruit. We had one group that tried to fully optimize their robotics a couple of years ago, and it was a huge disaster, because I think they bit off more than they could chew. Beyond the low-hanging fruit, one of the biggest things you mentioned earlier is flexibility. Without fully putting on your Nostradamus hat, how does one develop flexible robotic systems when the future is still relatively uncertain?
A.K. Schultz (25:54): Again, I think it comes down to abstraction: making sure what you’re buying isn’t completely intertwined with everything else. Sometimes the obligation is financial. You have a hundred million on the balance sheet, and the CFO isn’t going to write it off in two years. Right now, the pace of change is so darn fast that it’s better to be less optimized and more flexible. I think that’s why companies like Locus and 6 River have done well. You can drop them into an existing operation, it’s relatively simple for people to onboard, and the integration into the host system is relatively lightweight. And if you’re wrong, it’s not someone’s career, because the capital outlay is a lot less.
The industry also used to be about going to the big player. It’s the old adage: no one ever got fired for buying IBM. But in the modern software world, more and more, you don’t buy everything from one company. You have best-in-class pieces of software tied together using APIs. You can have best-of-breed CRM software without buying it from the mega company that has everything, where everything is mostly just okay. You stitch together the right pieces for you. The companies that can do that extract more value than the ones that say, “I’m going to write one big check because I want one person to yell at when things go wrong.”
A.K. Schultz (28:19): They take on the risk of having to yell at multiple people, but they extract more value from the process. It also removes the temptation to customize the big software, because customizations are where the big money starts to hit you.
Data: From FedEx Boxes of CDs to AI
Gordon Lamphere (28:50): A big part of implementing systems is understanding what to do, and a lot of that is driven by data. What kind of data are you pulling from systems to deploy as efficiently and productively as possible?
A.K. Schultz (29:16): I’ll answer first as a system designer, which is what I used to be. We would never start from a CAD drawing. We’d start with a data dump from the customer: “To design this, send us all your inventory data and all your order data for multiple years, so we can see the peaks and valleys.” When I got started, that meant gigabytes of data on CDs. You’d get a FedEx box full of CDs, load them into SQL, and break the data down. It almost seems quaint now. One company’s big data server in ’04 was called Terra, because it had terabytes of data, and now I kind of laugh at that. There’s more hard drive space on the Best Buy shelf than they had back then.
Anyway, you’d look at the data patterns, and a design would emerge from them. Then you’d start making drawings and doing engineering calcs. But that process took forever. It could take six months just to analyze the data, and it was predicated on this year being a good projection for next year. That model is harder and harder to get right, because we’re a much more volatile consumer market, and that drives everything.
Now enter AI. We haven’t even scratched the surface of what AI is actually doing on the business side, and I think that’s for good reason: the risk of hallucination at scale in your business.
A.K. Schultz (31:37): We have to be very, very careful about what we entrust to AI right now, because it’s evolving so quickly that there’s enterprise risk in being wrong very rapidly. AI is super confident in its answers, so it will be confidently wrong, largely, probably, because you gave it the wrong inputs. But when you look at the micro use cases starting to come out, we’re getting some really cool outcomes.
Back when we called it “big data,” which almost sounds like a silly term now, the promise was: save all your data so you can do something with it, and bias toward saving it. So there are I don’t know how many petabytes of data sitting untouched in data lakes. I think of it like ore in the ground. You have to dig through so much ore to get to the precious metals, and it’s the same with data. A lot of companies don’t know how to do it, so what they have is digital pollution. They’re saving all this data, spending money, and quite literally warming the earth, taking power from the grid that could air-condition people in Texas.
Normalized Data and the “There Is No Spoon” Moment
How do you actually extract value from the data? A lot of it has to do with how you store it and what you do to it before you put it away. Most people just throw it in the junk closet randomly, into a big pile. Not to pitch my company, but we believe in normalizing data, so that
A.K. Schultz (33:59): data from two different WMSs in two different buildings looks essentially the same to whoever consumes it. We also believe that, rather than point-to-point integrations where you publish the same data over and over to multiple companies, you publish it once and let multiple systems consume it. Then suddenly you can do things in real time that are far more coordinated.
Here’s an example. A customer had follow-along picking robots, and they wanted to optimize their boxes, because they were shipping a pair of yoga pants in a box about this big. There’s a company called Packsize that takes an item’s dimensions and makes the right-sized box on the fly. To do that, you need to know the dimensions of everything, and you may want to know the optimal box size for the shipping method. You also need to know how much dunnage to put around it. If it’s shirts, no big deal, but if it’s fine china or heirlooms, you probably want some packing material. They also wanted to use poly bags, which are far more space-efficient, but some items qualify for a poly bag and some don’t. And once an item is packaged, they wanted to rate-shop carriers based on the box.
That would normally be about six integrations. But the data each one needs is essentially all in the same file. People pre-truncate the data and say, “You only need these two fields, you only need these five, and you need these three.”
A.K. Schultz (36:25): Just publish the whole thing and let everyone extract the bits they need. Then you can do things asynchronously. Nothing is hard-coupled. Everyone can process independently, and you can optimize the workflow. The company making the boxes doesn’t have to care about the picking technology anymore, because it’s abstracted. That’s the beauty of it. In the way we do it, the individual systems have no knowledge of each other. All they know is that they got a data payload and have to do something with it. So the irony of orchestrating multiple subsystems is that none of them can be aware of each other. To orchestrate a team outcome, they can’t know there are other people on the team. That’s kind of a Matrix “there is no spoon” moment for people, because it runs counter to how most people think about it. Sorry if I’m going nerd level seven.
Do We Record Too Much Data?
Gordon Lamphere (37:50): No, we have a lot of nerds who listen to this podcast. Real estate nerds and business nerds are our chief demographic, so that’s fine. Speaking of “there is no spoon” and unique paradoxes, one of the biggest paradoxes, going back to data, is this: do you think we generally record too much data? On the sales side, we talk to a lot of customers who harvest all sorts of data, as you mentioned, and at the end of the day there’s not much to do with it. Or is it that they’re just not recording it the right way? With our own processes, we spent a tremendous amount of time cleaning up our data inputs, and then we found valuable data. Is it one of those two issues, or am I getting this totally wrong?
A.K. Schultz (39:08): You’re right, and you’re wrong. It is silly to record everything if you aren’t going to use it, period. And if all you want to know is what happened, you can be pretty lightweight with your data. You can just ask, “Did this happen, yes or no?” I call that red-light, green-light data. “I was supposed to ship this many. Did I ship them?” That’s pretty easy. If that’s all you want, great.
The problem is that getting answers out of data usually prompts more questions, and that’s a beautiful thing. The classic scenario is that you go to IT and say, “I want to know about my business. Send me the data.” They say, “What questions do you want answered? Give me a list before I’ll even start.” Six months later, you finally get the report and say, “Wow, that’s really cool. What happened that day? We need the answer to that.” “Well, we didn’t store that data, and we don’t even understand the exact question. Write it out, and we can’t answer it for yesterday, but we can answer it going forward after we deploy in six months.”
The pace at which you can learn from your business and drill down depends on the fidelity of your data, and on how much historical data you have to find trends. I think of it as a data-to-wisdom continuum. No one wants data. People want information, knowledge, and wisdom.
A.K. Schultz (41:33): But without data, you can’t have the rest. That’s how humans learn. We accumulate data and eventually become wise, or some of us do. Now say you want to double-click on a bad day and understand why it happened. To know why, you need to know how it happened, and for that you need much higher-fidelity data. One problem with black-box integrations is that if you have two black boxes you can’t easily get data out of, the best place to grab that data is in the exchange between them. But custom integrations mean two black boxes connected by black pipes, with no real way to get value out of that data. I believe in transparent boxes and transparent pipes. You’re paying for that data. You should get it, at whatever fidelity you want, so if you want to double-click, you can. With my own team, I’ll ask a question and then ask why seven times. That’s how a business actually gets better: by understanding why things happen, not just what happened.
I was on a gemba walk with the EVP of supply chain at one of the largest wine companies on earth, and it was really cool. He went through all the departments, and normally they focus on what went badly. Then we got to a guy who’d had an amazing week the week before. The VP said, “Wow, that’s awesome. What did you guys do to make it that much better?” And the guy said, “I don’t know, it just was better.” You can learn from the good things too, not just the bad. He wasn’t aware of what made it good, because he didn’t understand why. He just knew it happened. He was so stoked to report
A.K. Schultz (43:58): that he’d had an amazing week, and when the boss asked why, it completely dashed his hopes on the rocks. That’s why data is important. So yes, if you’re not going to use it, stop taking air-conditioning electricity from the Texans. But if you’re really serious about it, you need a thoughtful, integrated data strategy, and you need to make forensic analysis part of your culture, a natural thing and not just an exception.
Investors’ Biggest Blind Spot: Four Walls and a Roof
Gordon Lamphere (44:43): For the real estate investors listening, we’ve gotten into the weeds, so let’s start wrapping it up into a package they can better understand. Where are the biggest blind spots in how real estate investors, entrepreneurs, and business owners perceive tech integration? What do they get wrong the most?
A.K. Schultz (45:19): Looking through a real estate lens, here’s what I’ll say. One of our investors is Prologis Ventures. So we have Prologis, the largest owner of logistics real estate.
Gordon Lamphere (45:30): I think everyone listening knows who Prologis is.
A.K. Schultz (45:33): There you go. The person on our board, Todd Lewis, whom you should talk to someday, is amazing. Prologis has decided it can’t just be four walls and a roof anymore. It has to be more than that. If you look at how real estate is bought, there are usually no technical people involved in the contractual agreement or the sale. It’s land, four walls, a roof, and a floor. But the building is going to go do something. It’s not just going to sit there. So more and more, they’re investing in companies that enable them to be more than four walls and a roof. They were talking about this five years ago, so they’ve been ahead of the game. I’d say the blind spot is thinking only about four walls and a roof. If that’s all you’re thinking about, at some point it’s going to tip on you, and you might end up on the outside looking in.
Gordon Lamphere (46:51): That’s a lot of the conversation we’ve had on this podcast. I think real estate is going to be more tech and more hospitality going forward, and where you land on that spectrum depends on your asset class.
A.K. Schultz (47:11): Maybe a way to think about it is: what does your customer’s customer intend to do with this space? If you think one or two steps downstream, you can start to create an offering that streams through. And by the way, it’s less commoditized, so your margins can go up if you do it well. If it’s just price per square foot, you’re really in a reverse auction.
The Final Four: Making Robotics Accessible to the Mid-Pack Buyer
Gordon Lamphere (47:46): Let’s dive into our final four. It’s always a good wrap-up and a chance to learn more about you, your business, and how you see the world moving forward, because the men and women in the arena tend to know where the world will be five and ten years out, often well before even some of the investors who listen to this podcast. What do you think will have changed the most about robotics five to ten years out?
A.K. Schultz (48:24): Good question. I could say humanoids and AI as a check-the-box answer, but I think what will happen is that robotics companies will focus more on making robotics accessible to the mid-pack buyer. Right now, you have to be super skilled and have robotics capability inside your company to even qualify to buy from robotics companies. We’ll never get mass adoption that way. Once that’s working, and we focus on creating transparent boxes and transparent pipes, you can start having inter-value exchange, where one plus one plus one equals fifty. That’s what we’re striving to provide.
Advice for Young Professionals: Be Insanely Curious
Gordon Lamphere (49:30): We have a lot of younger listeners too, a lot of young real estate nerds, anybody in their twenties or early thirties, at various points early in their careers, and they’re often looking for advice from successful guests like you. If you could give a younger A.K. one minute of advice, what would it be?
A.K. Schultz (50:03): Personally, I would have started a company earlier. I would have started my journey before I had gray hair. But I’d also say be insanely curious. Keep asking why, keep digging, and keep leaning in. Don’t expect to be spoon-fed. Be aggressive. Whether we’re in the world of AI or the world of stone tablets, the insanely curious people are the ones who transform things, the people who want to understand the invisible. There’s that quote: only those who can see the invisible can do the impossible. Strive to understand the underlying magic.
Book Recommendations: The Goal, The Design of Everyday Things, and Brain Rules
Gordon Lamphere (51:02): For many of the guests we’ve brought on, the number one thing they push is curiosity, and we always try to ask why on this podcast. Occasionally we also ask what, as in, what should we pick up as our next book? It could be as simple as a children’s book or as complex as War and Peace.
A.K. Schultz (51:36): You asked me this ahead of time, and I thought about it. I’ll give you two really old books. First, The Goal by Eliyahu Goldratt, which is essentially a novel about the math of a business: optimizing throughput and profit. It’s a really good book, and it puts things in context.
Gordon Lamphere (51:43): Okay.
A.K. Schultz (52:05): Second, The Design of Everyday Things by Don Norman. It’s about making things simple for your customer, not just making simple things or making it simple for yourself. With “keep it simple, stupid,” most people mean keep it simple for themselves and push the complexity onto you. That’s really the difference between Apple and other companies. Apple pushes simplicity to its customers and absorbs the complexity itself. The ability to convert the complex into the simple is really where competitive advantage comes from, in my opinion.
And finally, since all of these blend art and science, a more recent book: Brain Rules, a neuroscience book about how the human brain operates. It’s really well done and written so that knuckle-draggers like me can understand neuroscience without a medical degree. Most chapters start with some guy in England getting a railroad spike through his brain or getting hit with a club and suddenly forgetting things, so it’s fascinating. I believe that ultimately, no matter how high-tech things get, people are at the heart of everything, and trust is at the heart of everything. If you don’t invest in understanding the people side, you will always fall short.
Who Should Be Our Next Guest?
Gordon Lamphere (53:41): Business is a people business, at least most of the time. That leads to our last question, which is the whole reason for the podcast. We firmly believe the men and women in real estate, tech, and public policy tend to know who we should reach out to next. Who should be the next person we bring on?
A.K. Schultz (54:08): I think Todd Lewis would be a great one. Prologis would be straight down the fairway for you, and the things they’re doing push the envelope. I think there’s a lot to learn from them.
How to Reach A.K. Schultz
Gordon Lamphere (54:22): I’d love to reach out to Todd. If somebody wants to reach out to you, what’s the best way to get in touch?
A.K. Schultz (54:30): Just go to the contact information on our website, or send me a note on LinkedIn, and we’ll get you to the right people.
Gordon Lamphere (54:41): We’ll put the website in the comments. A.K., thank you so much for hopping on the podcast today.
A.K. Schultz (54:48): Absolutely. Thanks a lot.
Gordon Lamphere (54:50): Thanks again to A.K. We appreciate his insights. If you enjoyed the podcast, please give us a like, a five-star rating, and a review. Your comments, subscriptions, and interactions truly matter, and they help us continue to bring on quality guests. You can find us on YouTube, Spotify, or wherever you get your podcasts. I’m Gordon Lamphere of The Real Finds Podcast, and thank you for listening.
Van Vlissingen and Co. has been the Midwest’s oldest commercial real estate brokerage, development, and management firm since 1879, and today is independently ranked the #1 commercial real estate agency in Chicagoland, home to the #1 independently ranked agent, Gordon Lamphere, and the region’s #1 ranked commercial property management team. If you own, manage, or invest in energy-adjacent, mixed-use, or transit-oriented property across Lake County, the North Shore, the Northwest and O’Hare corridors, DuPage and the I-88 corridor, Will County, or southern Wisconsin’s Pleasant Prairie, Kenosha, and Racine markets, contact Van Vlissingen and Co. at 📞 847-634-2300 or 🌐 vvco.com. For a market-wide view of where these dynamics sit today, see our State of the Chicagoland Commercial Real Estate Market for Q3 2026.