Global Macro: The Next Era of Market Growth ft. Ben Miller
01:02:18 Share

Consensus Hong Kong 2025 is where the global crypto community converges to shape the next era of Web3. From February 18th through the 20th, Hong Kong becomes the meeting point for leaders across finance, technology, and digital assets. This isn't just a conference. It's where deals are made, partnerships are forged, and new opportunities emerge. Consensus features exclusive networking lounges, and a number of other

expert-led sessions, and invaluable insights from top industry voices. Whether you're expanding your network, building your brand, or closing your next big deal, this is the event that moves markets. Visit coindesk.com forward slash consensus dash hk to secure your spot and use Real Vision 15 for 15% off. Don't miss your chance to be part of the industry's defining moment.

Hi, everyone. I'm Raoul Pal, the CEO and co-founder of Real Vision. Here at Real Vision, we're committed to give you the best knowledge, tools, and network to help you succeed in your financial future. If you're enjoying this podcast, please take a moment to give it a five-star rating. It truly helps us continue to bring top-tier content. Thank you so much.

Welcome back to Real Vision. I'm Ash Bennington. Today, I have the pleasure of speaking with a true fan favorite here at Real Vision, Ben Miller, co-founder and CEO of Fundrise. But before we get started, just a quick reminder, tickets for our upcoming in-person crypto gathering in Miami are now up for sale. Head over to realvision.com forward slash CG2025. That's realvision.com forward slash CG2025 to get yours.

Ben, with that said, always a pleasure whenever you join us here at Real Vision. Yeah, thanks for having me. Hey, listen, this is an interesting day that you've joined us on January 21st, 2025. We've all been drinking from the fire hose of news flow after the Trump inauguration yesterday, trying to make our way through the executive orders, figuring out what's going on. My gosh.

So much to talk about here today. Ben, big picture, 50,000 foot level. How are you trying to assimilate all this information that's coming at you, everything we've got, everything we're still waiting to get? Big picture, where do you see us right now? Hi, Raoul here.

Listen, I think we've got until 2030 before the economic singularity arrives. Now, it might not be the exact date, but it's around then. So we have about six years to figure out how to unfuck our future. I've put together a report to help you called Prepare for 2030. It's going to help you take the first steps in that journey to make sure you're secure past 2030. So just click on the link below and start your journey now.

Well, what I thought was interesting wasn't what was issued yesterday, but what was not issued. So the big questions for me are about economics, about real estate and markets. And the things that markets are worried about or really looking for guidance are tariffs, taxes, and deregulation, particularly deregulation of the banks. And we really didn't get that yet.

And so over the next few weeks or hopefully at most next few months, we get more clarity on what this is going to mean for the economy because the whole market, especially credit markets, are looking at deficits.

and looking at long-term interest rates and wondering essentially what the policies are going to be, how they're going to affect the long end of the curve. For me in particular, as a real estate investor, the long end of the curve has moved a lot. It probably knows 100 basis points since the Fed started lowering rates. And that sort of never really happened in the history of capital markets.

And I think that's happening because the market's afraid that the deficits will continue to be high, maybe even go higher. And so I guess the question about tariffs, taxes, and deficits just weren't really a topic yet. And until they are, it's really hard to make good macro calls about the market.

Yeah, tariffs, taxes, deficits, regulation. These are all the sort of the key mechanistic factors that you're looking to here. So, you know, obviously, yesterday, we got a lot more of the culture stuff. Let's break down this. Talk a little bit about currently where we are, where you see the overall macro outlet look. And then we can fold in one by one, each of those categories, the implications, what you're looking for, where you think,

the bull cases, where you think the bear case is. There's still a lot here to be sorted out. We've got a little bit of signaling maybe from some of the impending approval, the folks who are still waiting to be approved for cabinet level positions, a little bit of a sense of where we might be going. There are obviously a lot of policy papers out there. Let's hash through this first.

Big picture, where are we right now in terms of the macro situation? In other words, let's talk a little bit about those rapid moves that we saw at the long end of the curve. What's driving it in terms of investor expectations, where the landmines might be, and what you think the framework to move forward would look like. Right. Well, so just to zoom out, as you said, we started...

This part of the cycle, probably about 24 months ago, where there was really a lot of expectations around a recession, saw huge amounts of layoffs from big companies, and the market price in a recession and stock markets had fallen a lot. And for the last 24 months,

The market really didn't know if there was going to be a recession. Everyone's watching unemployment. Everyone's watching the Fed inflation expectations. And we sort of got into this place where the market now believes that we've dodged a recession and that rates are going to stay higher.

either for forever or for a lot longer. And there's been a big diversion between the Fed forecast, which if you look at the Fed dot map, it's showing interest rates going down to about 2.5% over the next three, four years. And the forward curve, which is market expectations, has rates staying at 4%. So there's a big diversion between the market and the Fed around interest rates.

inflation. And then at this point, mostly people stop worrying about unemployment. Unemployment has stayed really healthy, stayed low. And so those are the big, big indicators. The inputs to that are deficits. And

And yet we ran a six point some change trillion dollar deficit last year. And that's really just totally unwarranted. And that's why the Trump administration is so important to the market. If deficits don't come down, I think the credit market is going to start to take away the punch bowl.

If that happens, I think that looks really, really ugly for the stock market and probably for the economy. And so the only real way to continue this soft landing, no landing, is to bring deficits down and to bring discipline back to the government. And we don't know yet. Nobody really knows if that's going to happen.

So two questions broadly from what you've just discussed. Let's talk a little bit about debt and deficits. And if you could, fold in your outlook for inflation and what it means for debt service in this environment. So my personal analysis of inflation is that it's continuing to go down and it will stay down and that the Fed will get to their 2% target over the next year or two. And

I think that what's driving interest rates up has less to do with inflation at this point, more to do with Fed expectations. The Fed essentially is not going to cut rates as much as people thought. And also the supply of Treasury, supply of borrowing from the Fed, the Treasury is driving so much inflation.

new issuances, it's causing rates of things to stay high, which is what economics would always say. The economics say if the government borrows too much, it crowds out private investment. So crowding out. And so that, I think, is starting to happen. It really hasn't happened before. And so the long end of the curve may actually

move totally asynchronously with the short end as the government has to borrow huge amounts of money. And we end up potentially in a vicious cycle, right? Because if deficits keep going up because interest rates are high, that drives up interest rates

which then drives up more deficits. And that's like kind of the worst case scenario. And we have probably a few more years to make some tough decisions to get it back under control. I don't know if that's going to happen. And I think that's really what the market's looking for. So that's the worst case scenario. Is that your base case that we're going to move there? Or is it still one of these like wait and see what's going to happen in terms of fiscal policy perspectives that we need to see what those inputs are?

Yeah, I mean, I've moved to a somewhat agnostic place. I really have no strong opinion about whether the administration is going to bring down deficits, if they're going to be able to pull off something that has never really happened, which is that the government is able to grow its way out without making tough decisions. Normally...

There's been some sort of pressure on the government to bring expenses down. That's what happened under Obama with sequestration, is that they forced the government to slow its spend on defense and on entitlements, on discretionary spending. And so if that doesn't happen...

You have one party controlling the House and the presidency. And so in the past, that doesn't usually result in low deficits. So, well, I mean, I continue to be worried about

I mean, about deficits and about deregulation of the banks. Because I guess my 25-year career, and you look at, you know, go back to 1980s, whenever the government's deregulated the banks, it's caused the S&L crisis, then it caused 2008 financial crisis. And so deregulating the banks is probably the fastest way to juice the economy. And I think the least prudent way

But at this moment, it's really unclear. I think it's unclear what the government is going to do. I'm still looking for clarity and just haven't seen it yet.

There's so much to talk about here. Let's talk about bank deregulation in just a second, the fastest and yet most potentially risky way, as you say. But let's talk a little bit about the structural aspect of what's happening right now with debts, deficits, and spending. I'm looking right now at a chart on my screen of federal debt. Total debt as a percentage of gross GDP. We are at

or above 120%. It spiked a little bit higher over 130 during the pandemic years. But this, aside from that little blip upward, it's a long-term cresting when you look at this from over the last 50 years, where we're looking at a sub 40%, essentially tripling. And then we could look over at federal surplus or deficit as a percent of GDP, and we're at minus five on

on that. These are pretty concerning numbers structurally. I know that you look at these, you think about them, and try and understand in terms of what they mean for capital markets and risk asset prices more broadly. Talk a little bit about the structural aspect of what you see right now in terms of spending, deficits, and debt. Yeah. I mean, the problem with the addiction to deficits is that they're rewarded in the short term.

And so we get GDP growth and you get rising markets when you have rising huge deficits. And so that's what's been happening. And it's exciting that the government, the new administration is looking at trying to disrupt some of the status quo around GDP.

around bureaucracy and around spending. But I think that no one's talking about the big things. To really move the needle, you have to reform Social Security, reform Medicare entitlement spending and defense spending, and then maybe raise taxes. No one's talking about that. That's not remotely on the agenda. And so I'm a little worried that these more cultural fights around...

department of education and um you know naming things really like they they just don't affect the macroeconomic uh environment and so um you know for me probably in mid-career you know i'm worried about the long-term impact

And, and the voter just doesn't seem to have any, there's no sort of appetite to take on these challenges. And so until, until there is, I think that the macro environment continues to deteriorate, um, on the long end and the short end though, that, you know, there could be, uh, some great breakthroughs. I mean, I think, I think, I think AI could save the economy, but, um,

Generally, I think it's hard for the president to really affect the U.S. economy. The U.S. economy is so much bigger than the president. It's always been that way. It's very difficult for the government to have a big impact on the economy.

on the upside i mean you can mess things up the government can mess things up it always has but it is really hard for it to make good things happen i think that's i think mostly those things take years and years to actually come come into effect and so um yeah i mean and so it's really up to the the private sector to deliver growth you know and innovation

These are such important points that you've made here. This is why we are so glad to have you on Real Vision, having this conversation with us on January 21st. Look, the reality is, no matter where you fall along the political spectrum, there's a cable network that will tell you you're right. You can hear that. You can tune into one of them now to hear the debate about the Gulf of Mexico slash Gulf of America question. But these questions, these structural questions, are

that really drive the long-term productive capacity of the U.S. economy and, therefore, ultimately, the longer-term trajectory of risk asset prices are so critical for people to understand and why we're so pleased you can join us to talk about them here on Real Vision today. So let me ask you this, and this is kind of the $36 trillion question in terms of understanding this glide path on debt analysis.

and deficits. Obviously, a lot of news cycle being spent right now on the doge, this idea of Elon Musk and Vivek Ramaswamy going in to try and look at government spending, government efficiencies. Let me ask you this, to what extent, what percentage is that really

you know, kind of have capacity to really change the longer term outlook. I mean, you, you made these, these, these, these, uh, uh, the points earlier about things like entitlement spending. Uh, these are things that are based on sort of very long-term trends in terms of demographics. Uh, these are things that maybe there's not the political will to have these discussions right now, uh, about, uh, stuff like changing retirement age, whatever, wherever you land on this personally, but just understanding this from a structural perspective, uh,

What is the capacity of something like the Doge to have a meaningful impact one way or the other on the glide path of spending and therefore deficits and debt?

I mean, it could have a huge impact over the long term, right? Because if you can make government more efficient, and efficiency isn't just headcount. I think the headcount is kind of a red herring. What really matters is how much it gets in the way of or helps private sector innovation and growth. So nuclear power, building AI, data centers, etc.

you know, general regulation, regulatory quagmire, those things, those things matter, but not in the short term. I mean, it just takes a really long time to build anything. I mean, build a nuclear power plant, I mean, half a decade minimum, even if things, that would be fast. That would be if Doge is successful. Same with any building, anything in America. So, yeah,

Yeah, it's impactful and it's important. If you look at actually the executive order around Doge, it had a lot to do with IT, it had a lot to do with information technology, actually had less to do with the kind of headlines that I expected. And if you're in the tech business, you know that data is actually how you make your decisions. You need good data, you need good systems for AI or any kind of technology to actually have leverage on it.

So, so if you can get better infrastructure into the government and you can get better systems and, and maybe like improve the, the ability for people in government to make good decisions, um, that actually could have a big impact. But again, I just think that we probably won't see the impact. I bet we, if we see the impact of this decade, we'd be lucky. So I think it's, it's just these, these are really long-term challenges. And, um,

And I'm glad they're doing it. I mean, I'm optimistic that Elon Musk is going to be able to drive change. It's just that the only thing I've seen in my career, because I've gone up to the hill, I've testified on the hill, I've worked with government, is that there's trade-offs. Mostly, there's trade-offs. Every time you deregulate something, you increase risk. You get reward and you get risk. And the risk could be like the...

like the Ohio train that went off the tracks and caused huge environmental spills. I mean, so there's... Whether it's bank deregulation or environmental deregulation or any kind of deregulation. I mean, there's no free lunch. And that's, I think, what makes it so hard. And I'm optimistic because you have really smart people going in trying to do great things. But, you know, I mean, it's just not easy. And it takes...

a long-term sustained effort. Right. Yeah. I mean, and I think this is spot on, which is this idea that in order to actually move the Pareto frontier, in order to become more productive, in order to build a private sector that has these greater efficiencies that we're talking about, you need to invest in that infrastructure ahead of time. It takes a very long period of time. All of those points that you just made, boy, such good ones in terms of grid power, infrastructure,

infrastructure, data, all of these things, none of this happens overnight. And I think you're right. If it happens at five years, that's probably the short end of the spectrum that you could reasonably expect some of these changes to go into effect and have a real impact on private sector growth. But here's the interesting thing. And here's the fun thing about capital markets is that capital markets perpetuate

price those longer term development projects on a net present value basis, discount factor, all of that stuff, looking at this and trying to say, okay, so we know that these are longer term glide path type issues, but now how do capital markets allocate risk and reprice based on what they see

the future glide path being. So do you have a perspective of if all of this goes right, what we might see in capital markets? And then I guess some of the indicators might be that it is going right. Yeah. So I actually sort of don't

I have a kind of a more of a split view on that. Capital markets are good at forecasting linear change and horrendous at forecasting nonlinear change. You saw that really recently with NVIDIA, where NVIDIA had explosive growth. 100% of the analysts who covered NVIDIA didn't see it coming. And on average, they're off by 80%.

So that was in 2020, I think two, right? And so when there are changes that were the future is not like the past, capital markets are atrocious and forecast the same thing you have with COVID, right? In February, 2020, the markets were at all time highs. And within a few weeks, I mean, then that, by that point, COVID was, was obvious widespread. Um, and, and the capital markets really didn't start pricing it until, until, um,

it was it was actually no longer a question it became obvious beyond obvious so i i i think it's kind of markets can have a really hard time pricing in the kind of changes we're talking about if it's successful for it to be successful has to be non-linear has to be unexpected has to be dramatic and the market just doesn't know how to how to do that uh if you've seen that what ends up happening is often ends up being a litmus test of people's politics

And that's not that useful, I think, actually, when it comes to moving the real economy. So...

Yeah, I mean, I think you would need to see actually some kinds of large, I don't know what they might be, whether it's the government, you know, the administration actually comes out trying to figure out how to really cut deficits. If the deficits were cut by a meaningful amount, I think the markets would start getting really optimistic. But something that is a sort of

contingent five-year path that depends on IT infrastructure and dramatic change until the market sees it as a reality. This is the problem. This is why tech companies don't go public today. Is that the markets, tech companies really don't get rewarded for innovation. They get rewarded for guidance that is within a couple percent of their quarterly forecast. It's not about nonlinear growth.

Not about nonlinear growth in the public markets, but in the private markets where they don't have that perhaps quarterly pressure to do exactly what you said there. Obviously, the managers of those companies see that as being more opportunistic.

Yeah. I mean, private markets have their own idiosyncratic ways they want to price things. It just ends up being a two to three year cycle because venture basically the private markets are funded by venture funds. Venture funds raise a new fund every 24 months. And so they expect their companies to reprice and raise a new round every 24 months so they can mark their fund to market.

And they can raise their next fund. And so you have a 24-month, maybe 36-month cycle to show meaningful growth rather than a quarter or two quarters, which is what public markets. So both are relatively short-term, but obviously private markets are longer than the public markets. Yeah, eight to one is still quite a material difference, right?

I mean, operating the private markets at times, this is why I'm kind of founder friendly, is that the venture funds, when they're on your board or you're on boards with them, I mean, they're finance people, right? And so they want to do Excel analysis. And Excel analysis is, by definition, very linear. Right.

Right. So well said. And so important because I think if you're a retail investor and you spend your time watching cable financial news, you don't really hear about the way that these private market pricing mechanisms work. Yeah. And like every market, they're promoters. And the narrative that it's sold isn't necessarily the reality. And so everybody in financial markets is selling something.

And so the private markets are selling you something different than what the public markets are selling.

Yeah, such an important point. Let's talk a little bit about banking regulation and what the impact was. You said something that was very interesting, which is potentially the fastest way to goose the economy. Obviously, this is the idea of liquidity and liquidity flows. Talk a little bit about that and also about the attendant risk. You said, I believe, the fastest but also potentially the riskiest way to make changes. It's very interesting as we have the incoming administration coming.

to see and to think about what that might mean. I mean, 2008 financial crisis was like, you know, I have PTSD from it. It was beyond, if you weren't in the markets at the time, it's really hard to appreciate how crazy it was. It was like COVID in terms of like, this is just like, at various points in time, people thought that the U.S., I mean, I had people who worked for John Deere

uh, John Paul Tudor Jones. We don't, um, and, uh,

And they were saying that the end of civilization will have tanks in the street, the banks, everybody's, if everybody's money in the banks went to zero, right. That would be a revolution because you know, the banks don't actually have your money, that money in your, that says that your deposits are actually not there, right there to lend out. And so, um, since that experience, I went and I've been obsessed with studying banking. I spent a long time studying SNL crisis. Um, and, and, and, um,

So deregulating banks has a lot of intended risks. And, and the thing is, is that it's not just about the regulations. It's about the friction. This is like, if you're a banker, it's, it's torture, but the government created, created just so much friction in the system to slow down your ability to lend and, and friction and complexity are, are almost interchangeable. So there's a lot of reasons why the banking sector would like to see, you know, improvements to the regulations. Um,

I think the main reason that we didn't have a financial crisis in 2022, 2023 is because the banks were so heavily regulated that when the market collapsed, they didn't collapse systemically. We saw Silicon Valley Bank collapse. We saw Silvergate. We saw a few other banks collapse. But really, like...

It didn't become systemic, and that's because the big banks were so heavily regulated, and it stopped them from basically having the kind of risk-on behavior that caused 2008 financial crisis. So I worry about that. But on the other hand, if you can go and increase leverage by... Right now, the leveraged banks will give you today really low. We have Barway of Hope.

Fundrise has billions of dollars of borrowings across our funds. And banks maybe will lend you 60%, 55%. And in 2007, when I was in real estate, banks will lend 83%. And so there's a lot of room between 55% and 83%.

And so it's possible you see the banks start to creep up. And like all things, they start out wise and end up foolish. And so it's just a question of degree. So in the short term, if you can start to put more leverage in the system, that basically is the same as putting more money in the system. And that will drive more growth. But what I worry about is that it just goes too far.

especially if it looks like it's working in the short run. Hey, do you have a sense of what those thresholds are in terms of what too far means and how you measure that leverage in the system? It's really difficult to measure. So much of it is now happening in private markets with private credit. And the formula is changing. So what will happen is the bank...

will lend to a private credit institution. The private credit institution will then lend to the borrower. And so there's a different mechanism. I think actually a better one in the sense that private credit is less...

it's less levered and less systemic at risk than banks because banks are insured by the federal government. But where does that leverage, where does the leverage crop? I mean, over 80%, that's in my experience, you start seeing broadly in the market loans that are available over 80%. That's usually a red flag.

And typically it's somewhere in the, in the seventies where you start to see if you can borrow at more than 75%, that starts to, it starts to get risky. And, and in this market where interest rates are high, um, what's happening is that, um,

debt service ratios or debt yields are constraining the borrowing. And so the way you would have to sort of get more borrowing if you're constrained by debt yield is start to capitalize interest rates with PIC, with accruals. And so if you start seeing pro forma returns, because if the bank's lending on a business plan where there's virtual cash flows, there's growth in those cash flows, and that's actually what their asset is,

and that growth doesn't show up, then obviously the loan goes bad. And that's what happened in 2006 and 2007. Because interest rates were about comparable to where they are today. But it was just that a lot of the lending was on presumed growth in NOIs.

Net incomes. Yeah, let me ask you this. And to touch on something that you touched on to the top of that segment, this idea of if you have banks essentially lending to private credit institutions, don't you wind up with an absence of transparency, the potential buildup of risk in a system in a way that is not

readily apparent because it doesn't show up directly on the balance sheet in a way that you can examine those ratios or understand what the levels of risk are. I mean, is that a risk that you see potentially? You know, I mean, yes and no. I think that, I mean, again, I still think private credit is less risky than bank credit, but my experience with-

So banks, right now they're safe and they're levered like nine times, right? Because that's typically the bank ratio around nine times. At the peak of the financial crisis, Lehman Brothers levered 50 times, 60 times. And the amount of leverage that the banks can take on can go to essentially infinity. You saw that in the SNL crisis, the savings and loan crisis in the 80s.

Private credit really can't lever. It's a private company. There's no government backstop. And so typically hard to lever more than three times. You'll see it, but the banks basically won't loan to it. As I said, you see 60%, 75%. That 75% is obviously 3 to 1.

So, to leverage. So, if you get to 4 to 1, that's five times leverage. So, that would be high. And the banks can go to 30. I mean, so it's just the amount of leverage possible in the banking system is just infeasible in the private part of the markets.

This is probably a good bridge to talk a little bit about what you're doing at Fundrise. I know we have some slides. And to tie in some of the broader macro conversations that we've just had, as it relates to the inputs into the formulas that you look at, at Fundrise, I know real estate is something that you think a lot about. Take us through some of your slides, Ben.

Well, I have this one analysis I wanted to share. It's about the software market. Because one of the questions that we frequently get is, why don't tech companies go public? And so, there's a lot of information on this slide. So let me see if I can walk you through it. So on the right is the enterprise value, easy, enterprise value divided by the revenue multiple. Okay, so the more the revenue multiple, right, the more valuable the company is.

And then we looked at the revenue multiples compared to profit margin or operating margin. So if you look at the bottom right, there's companies with the most amount of growth and the most amount of operating margin. So if you have a growth rate of more than 20% and you have a profit margin of more than 15%, the market on average will value at 16.4 times your revenue.

You see that? Is that tracking, Ash? Yeah, no, I do. And let me just walk folks through this and try and explain it to them. So what you're saying is essentially, it's basically just the multiple that markets assign to the valuation is simply a function in this chart. And I know this is a simplification in terms of the model, is the rate at which you're growing revenue on the one hand and the operating margin, how profitable it is to do that business per unit of economic value.

interaction. Right. And this is probably market. And so just to sort of stay on the right side for the, for the moment, right. You're, you see a company that is, has less than 0% operating margin and,

And very low growth is only trading 3.6 times revenue. So that's sort of the opposite side of that or the catty corner to that chart, right? Yeah, that's the upper left-hand corner where you see it low, which is kind of interesting also. If you have operating margin near 0% and growth rate of near 0%, that still sounds like a very high multiple. 3.6 times. Yeah, well, the market's up a lot from 0%.

from 2022 right 2022 when the market plummeted that might have been one so so but the point about this is that that you're obviously uh it tells you the market value's growth and the market values operating margin and the combination of two values the most now on the left side of the chart is just the number of companies in each bucket so you can see right there are only seven companies that have more than 20 growth rate software companies

more than 20% growth rate and more than a 15% profit margin. Right. So not very many. Right. Um,

And so there's, and, and just, just to make sure that everybody's got the same chart and on, there were four companies, there are four companies that have less than 0% profit margin and really low growth, zero to 10%. So this tells you essentially, this gives you a sense of the software market, how things are priced. Um, and there's a few things interesting about what this math is telling me. So one, and this isn't in here, but, um,

In 2021, there would have been dozens of companies that are growing more than 20%. I mean, dozens. Maybe half the software industry is growing more than 20% in 2021. And now only seven are. So very few companies are growing more than 20% in the public markets. And I think there are no companies growing more than 35%. So most companies that are high growth are not public anymore.

That's, that's like, I, that's where revenue growth that we're talking about. Right. And in the private markets, growth is valued much more than profits. So what you're seeing here is that being profitable to doubles your value, right? Approximately like being, being profitable matters. Right.

And in the private markets, growth matters much more than being profitable. Obviously, if you're a startup and you only have 10 employees, no one's expecting you to be profitable. But even big companies, companies that have a billion dollars in revenue or $100 million in revenue, if you're growing a lot, then your private market basically is not as worried about that. I think they're more worried about, can you take the market? Can you build an enduring product? So...

And so those are two really big differences, right? So there's very few growth companies in the public markets, and that the public markets really want to see profitability from the companies that are public. And so if you think about the greatest companies that are currently private, like an OpenAI, like an Anthropic, maybe an Andral, got on the list,

Those companies are focused on trying to build the future and to try to demand profitability during that period of so much volatility, so much growth, so much change. It's really a mistake. But that's what the public market is demanding of any company that goes public until the companies are not going public. Right.

It's so interesting. Obviously, only so many dimensions you can show on a two-dimensional chart, but it is interesting because you bring up the two things that we were just talking about there, which is number one is the scale, the amount of revenue and earnings. And the second is lifecycle in terms of where these companies are in terms of their lifecycle performance.

So it does get complicated quickly, but it is really interesting to look at these charts. Let me ask you this. Do you see that number when you look at that number there on the left-hand side, lower right-hand corner, seven? Is that about concentration or is that about an aggregate slowdown in the rate of growth? That's an aggregate slowdown in the rate of growth. No question. I mean, the rate of growth in the software industry went off a cliff.

in 2022 and really hasn't recovered. And it's kind of wise. I'm in real estate. I'm in tech. And so both those industries are not seeing explosive growth. Now, AI is an exception to that. But if you look at the amount of SaaS companies, software as a service companies, there's thousands of them. I think there may be 14,000. I think there's maybe 1,000 that are worth more than a billion dollars.

And across the SaaS industry, across the software industry, growth has just fallen probably in half or less. So that's been a big change in the software industry. It's actually arguably in a period of deflation where if you go to...

If you go to re-up a contract, you might actually see the price go down and you see people shedding headcount. It's much harder to get an engineering job. So the tech industry outside AI is in a recession. Real estate industry is in a recession. And that is why growth rates have fallen so much for software industry.

It's so interesting. And the conversation around AI, I mean, I'll just try and spin this thesis out there and maybe you can tell me where I'm wrong. But it seems like one of the challenges is we have this very sort of optimistic belief about what Silicon Valley can do, the idea that any technology

any small group of people can spin up a company and potentially create the next unicorn or decacorn. One of the challenges with AI, where obviously all the attention is right now, is that you need these enormous data sets. You need enormous amounts of processing power. You need to get very sophisticated data scientists to do it. It almost seems as though

though that's doing a couple of things. It's raising the barriers of entry to be in those spaces, number one. And number two, it's creating a more concentrated market for this type of development. So effectively, if all the excitement is going to the notion that machines are going to be able to do the development and do all of the deep insights, does that

shift the paradigm away from this very optimistic view that we have of Silicon Valley and the idea that we've seen over the last 20 years for SaaS business growth? I don't think so. I think it's actually following the pattern that has existed in the past. If you compare it to the internet, well, the internet required something like half a trillion dollars of fiber put in the ground. So

So it needed a ton. There's no internet without fiber, right? You could have a dial-up with the AOL across your telephone line. It was 144K, right? It was so small. So just like internet, you needed massive infrastructure in order to connect everybody. And then look at the platforms. I mean, Google completely... And Google is like Google, Microsoft, Apple...

They're the primary platforms that control these big... Whether internet or mobile. And it's super heavily concentrated.

And then on top of those platforms are the applications. Applications might be Salesforce, might be Airbnb, you know, might be Uber. And so I think what you're seeing is very similar. You're going to have a massive amount of infrastructure needs to be put on the ground, which is going to be data centers, compute, electricity, data, I mean, power. There's going to be a handful of platforms like an open AI, maybe an anthropic platform.

Google Gemini. And then there's going to be applications. And those applications really have... They don't exist yet. I mean, they will. I mean, we're building one. Lots of people are building them. They probably... You have some small ones, but mostly they're still coming. Just like if you remember in 2011, 2010, the App Store had like

you know, there's basically no apps that was popular in 2010 that ended up, uh, being a dominant company. You know, most of those, most of the great apps would have invented later. So it takes a while for this sort of, for it to all mature. Uh, and those applications are being funded and built in the private markets. So, uh,

I think it's actually totally true to form and that people just can't remember how long the lags are. It's almost like long and variable lags. But for software development.

Yeah, it's a funny metaphor. But let me ask you this. Is it different in the sense that when you talk about the fiber infrastructure in the ground that was necessary for the internet backbone to be built and the ability to build these SaaS platforms on top of it? It's interesting because that obviously was being done through government investment. This is something that you see obviously happening in the private sector. So I guess the question is, are there going to be application layers that are going to be built on top of the AI and

And will there then be this potential or opportunity to facilitate growth for new companies that use this sort of softer infrastructure in AI to develop the next generation of applications and therefore the next generation of great companies?

Yeah, I mean, that's what I think is happening. That's what's actually good. If you go back to the mainframe and then the PC and whether internet, mobile, cloud, I mean, the companies that become the platforms, they want to encourage applications built on top of them. The Android-iPhone war, right? You want the application built on top of your platform.

Ultimately, your customer is the application, right? So like, for example, Fundrise, we're a customer of OpenAI. We pay them. We have access to their API. And if we build an application, an AI application, or whatever, like Anthropic. Anthropic is probably a better example. Anthropic is mostly an enterprise product.

And Anthropic wants us to build on Anthropic, not on OpenAI. And so they do all sorts of things that encourage that type of sort of competition for customers. And I mean...

Only in the last few months, right, have we seen sort of like the models get good enough where you can really build applications that make sense. I mean, the breakthrough with O1 or with inference, which is really, really recent, it has made the products...

much, much better. It's just there were serious limitations to what the model could do to solve problems. But the types of problems that it's going to be able to do most accounting. I mean, it funnerizes it for customer service. It resolves half our customer service outreach. We get, I think, 30,000 customer service tickets a month.

And we work with an AI product called Intercom. And so it's happening and it's going to be revolutionary. It's just, unfortunately, it takes a long time to build because it's like building a building, right? It takes two years to build. But when you're done, that building is useful for 100 years. So I think that the kind of product development that's happening with AI is

I mean, I know it is. We're invested in some of the companies. We're building with it. But it's, you know, what's the breakout product going to be that everyone wakes up to overnight and starts using? It's like Uber, right? We don't know yet, but it's imminent. Right.

Yeah, Uber is an interesting metaphor because it would have been very difficult to predict. And obviously you needed all of those APIs. You needed all of those services in terms of GPS payment processing, the ability to rapidly do data management. I mean, a whole suite of different integrated applications that went into that, the infrastructure to do it. Let me ask you this, particularly because you're doing this today. Talk about what some of those application layer features

development projects that you're working on there on top of AI, because I think it's hard for people sometimes to get their head around what that application development looks like on top of the AI infrastructure.

Yeah. I mean, it's hard because you're getting into the nitty gritty. It's like, what does it take to put plumbing in through a building? You have to stack and pack the toilets and stuff. You really open up the walls and talk about how you actually build these things. Let me take it a little more generally than that. I just...

Just give us a sense of what the opportunity set looks like, what the end user functionality would be, what the promise would be. I don't think we have to go into the nitty gritty about how it gets built out. But when you look at this, you go, okay, so I go on to ChatGPT and I spend an inordinate amount of time having these conversations with ChatGPT. And, you know, I ask it things and do research and that sort of thing. But how does that translate into application development and what some of the opportunities could be in terms of the value that it would provide to end users?

Yeah. I mean, there's probably like... I'll say three main challenges, but I'm sure there's more. The three that come to mind immediately for me are most data that you want it to access, it's not in the model. So if I'm a... Whatever. Let's say I'm a small business and I have all this...

I'm doing all my AP, my accounts payable, my accounts receivable. And I have all these like processes I wanted to do. Needs to be able to access all that stuff. Today really can't, right? You sort of see it can surf the internet, but it can't really do, it can't access your data. People, big companies especially are worried about accessing the data. So there's this whole challenge of how do you get it to have access to it?

There's a sort of, if you flip it, the other side is it can't go out into the world and do anything, right? It can't buy you shoes. It can't shop. It can't book travel tickets, right? So there's just like, this access is two-sided access is a challenge that hasn't really been solved yet. So you can have an agent

But if it doesn't really have the ability to read the data or write the data, write a transaction, it's really hard for it to be effective in the world. And the third thing is that you can have the ability to read or write,

But it actually then also has the ability to understand. And most knowledge that we want to understand is specialized. Like if you take accounting or real estate, any kind of really narrow vertical, they call it, or field. And so there's all this specialized knowledge that's not in the general model. And so you have to then take a team of people and either teach it, train it, or build these sort of like...

almost like minimum roads or like guardrails and say, okay, here's how you do this one thing. Here's how you balance a trial balance, right? Here's how you reconcile some, you know, when you're so much of, I'm picking accounting because I think people can imagine, you know, you have,

you need to reconcile your bank statements with your accounting statements. There's a sort of, you wreck it out, right? So you have to, and so there's like, you have to read the bank statements and then you got to go put those banks over there. Well, who's going to let AI read their bank statements? Like there's, so there's all this sort of challenge around reading information, writing information, and then making sure it does it correctly in a, in a, in a specialized area that the model doesn't have like specialized knowledge. It has just general knowledge. And so that,

that's the kind of stuff that people are having to do in probably thousands of little sectors, thousands of little problems like apps. I think there's 2,000 apps on the iPhone. And so, you know, and they do all sorts of things, right? Teaching you Duolingo, teaching you language. All these things are happening today. And it is challenging because, you know, who's going to let an AI write to the database? Like most engineers would...

try to strangle me if I try to let AI write to our database. And not to mention the CFOs who would say, how do we know that this is right? And today, the people are happy to pay individuals to do that because they have the accuracy and accountability. And one of the other challenges about AI is that you get the input

and you get the output, but the process that happens in the middle is a total black box, right? You don't really know how it comes to those conclusions. It just does. And that's something that as a CFO of a company, I wouldn't certainly be comfortable with. Okay, hey, here's all of our bank statements. Here's all of our accounting data. Reconcile it. Yeah. Yeah. And don't publish it to the internet. Right. Right.

Right, yeah. And where are those, as you say, guardrails for knowing what that processing looks like and how you can make sure that this is all happening in a secure enclave of understanding what the input and output is and where the potential for leakage is for data.

Yeah. Or being hacked, right? You know, so, so that's all happening. I mean, all those things, all those little nitty gritty, like some complexities are being, uh, I'm, I'm, I'm in the process of doing it. And so it's, and it's all, it's not very glamorous, but it's, um, you know, it's, it's, it's definitely underway. And it's so, and so like, um,

So far, it's been small things like our IT. Our IT now, we use a software called Risotto to do our IT work, right? So like people say, oh, I can't get my Adobe to work and my Adobe is not working. You know, deal with an AI bot rather than dealing with one of our IT professionals, unless the problem has to be escalated.

This is an agent, a chat that essentially informs the user, okay, have you checked this? Have you gone and checked to see if these files are there? Have you gone and checked this aspect of settings? Have you checked these registry keys if you're in Windows? Right, exactly. IT is a lot like customer service. The same with HR. And this company, we test drove a bunch of them. We picked this going to Risotto, and then I'm trying to get to invest in the company because it's great. And then as you imagine, is it...

As it does IT for more and more companies, it gets better and better at IT. But it's stuck. Its medium is a person. It can interact with people, but it can't really interact with our systems yet because our IT team would never give it access to our systems because it's worried what it might do.

And that, but in other words, autonomously, Hey, just go, go into the system, go to our network, fix it. Right. Yeah. I mean, yeah. Go into my computer and like, you know, turn it off and, and reset the settings or something. Right. That's like something our people would be terrified to do, but so that's the, how do you, how do you build those types of guardrails? So there's all these things like that happening. And at the same time, the models get better and better. And so, um,

Yeah, it's like... It's a productivity revolution. And it's really... I mean, it's like... At this point, if you're in the work... It's like when I build a multifamily building. I'll look at it. It'll be a piece of dirt. But I'll have the permanent hand. I'm like, that building's done. Right? Okay. Two years before it's actually open. But essentially, it's fully funded. It's permitted. And now it's just like... Now it's just like a construction project. But it's like...

At that point, it might as well be done from a net present value point of view, right? From a financial forecast. And so maybe there's some budget bust, but it's like, that's how people are in the industry look at it. They see a piece of dirt and most people see a piece of dirt and I see a building. That's where AI is. And, you know, yeah, 24 months at most is where everybody will see the buildings that AI will have built.

Ben, you are an extremely interesting guy. I feel like you're the only person I can talk to about building from plumbing to cap rates and then talk about agentic AI, the opportunities that the risks and the guardrails. It's just always interesting to have you on the show because you have such a diverse way of looking at the world. And so that drives your metaphors and the way you see and think about this. Yeah, well, we sit at the intersection of real estate and tech. And I think to be successful...

Either you're a specialist or you sit at a nexus. And, and so we're, we sit at a nexus and, and, uh, and I think application of AI real estate is going to be revolutionary. Uh, and, and, um,

you know it's like it's gonna it's but you know what's it gonna do to cap rates that's what my my real estate team will say okay but it's like is ai gonna drive cap rates up is it inflationary deflationary because it's like uh i there's this great guy um who wrote this single situational awareness and he predicted that ai would drive real interest rates up because it would demand so much capital and no one would put any money into anything but ai because so so the returns are so high

And so drive interest rates up. Typically, technology drives interest rates down because it's deflationary. So anyways, there are big open questions here that are exciting to try to speculate about.

Yeah, it's funny when you say you can either be an expert or sit in a nexus. It's always more interesting and more fun for people who sit in a nexus, right? I mean, it's just like, there's this like weird cross fertilization of ideas that just makes it such an interesting conversation as it always is. When you join us right here, Real Vision, I think we're coming up on the hour here. But Ben, let me ask you this. We've a wide ranging conversation, everything from policy, macro, AI, and real estate. Final thoughts, key takeaways that you'd like to leave our listeners and our viewers with.

Yeah. I mean, the only other thing that I think I've thought a lot about is how real estate has been beaten down over the last couple of years because the interest rates went up and stock market has gone up. And typically, real estate and stocks actually move together because both are economic assets. And they've moved now. Real estate is really, really cheap and stock is really, really expensive. And so that may continue, but it's really unusual.

for that to be the case. It hasn't been the case in 15 years, maybe longer. So that's something I was, I plan to talk about because it's such a strange situation, but it's, I guess, for another time. Well, come back and join us again. That would be a great conversation to have. Yeah, maybe by that point, something has changed. Indeed. Ben Miller, thank you so much for joining us. Yeah, thanks for having me. Thanks for listening. Thanks for watching. See you in Miami.

Consensus Hong Kong 2025 is where the global crypto community converges to shape the next era of Web3. From February 18th through the 20th, Hong Kong becomes the meeting point for readers across finance, technology, and digital assets. This isn't just a conference. It's where deals are made, partnerships are forged, and new opportunities emerge. Consensus features exclusive networking lounges, and a number of other

expert-led sessions, and invaluable insights from top industry voices. Whether you're expanding your network, building your brand, or closing your next big deal, this is the event that moves markets. Visit coindesk.com forward slash consensus dash hk to secure your spot and use Real Vision 15 for 15% off. Don't miss your chance to be part of the industry's defining moment.

If you like this episode, I'd love for you to head over to realvision.com forward slash join for a free membership. Start your journey today to unfuck your future. Just one click away.