Economic Risks Versus Media Alarmism with Philipp Carlsson-Szlezak
01:08:51 Share

I'm Alpine skier Michaela Schifrin. I've won the most World Cup ski races in history. But what does success mean to me? Success means discipline. It's teamwork. It's the drive and passion inside of us that comes before all recognition. And it's why Stiefel is one of the fastest growing global wealth management firms in the country. If you're looking for success, surround yourself with the people who will get you there.

At Stiefel, we invest everything into our advisors so they can invest everything into their clients. That means direct access to one of the industry's largest equity research franchises and a leading middle market investment bank. And it's why Stiefel has won the J.D. Power Award for Employee Advisor Satisfaction two years in a row.

If you're an advisor or investor, choose Stiefel. Where success meets success. Stiefel Nicholas & Company, Inc., member SIPC and NYSE. For J.D. Power 2024 award information, visit jdpower.com slash awards. Compensation provided for using, not obtaining the award. Your customers are important to you, but they won't feel that way if they're messaging a clunky chatbot or waiting on hold.

Please hold. Estimated wait time is 25 minutes. Now you can deploy a Sierra AI agent to delight customers and solve tough problems in real time. Always friendly. Always helpful. Always ready. Visit Sierra.ai to learn more. That's Sierra.ai. Bloomberg Audio Studios. Podcasts. Radio. News.

This is Masters in Business with Barry Ritholtz on Bloomberg Radio.

This week on the podcast, I have an extra special guest, really fascinating conversation with Philip Carlson Slezak. He's got a really interesting background, chief economist at Sanford Bernstein, worked at the OECD, began at McKinsey, ended up as global chief economist for the Boston Consulting Group, and really approaches economic analysis and

from a very different perspective, critical of the industry's over-reliance on models, which have proven themselves to be not great predictors of what happens next, especially when the future in any way differs from the past. And so when we have things like the dot-com implosion or especially inflation,

Internal to the market, the financial crisis of 08-09 and even COVID models just don't give you a good assessment. And he describes how he reached this conclusion in his book, Shocks, Crises and False Alarms, How to Assess Crisis.

true macroeconomic risk. He calls out a lot of people who get things wrong, especially the doomsayers who not only have been forecasting recessions incorrectly for, I don't know, the better part of 15 years, most especially since COVID.

But their models just simply don't allow them to understanding a dynamic, changing, global, interconnected economy. I thought the book was fascinating and I thought our conversation was fascinating. And I know you will also, with no further ado, my discussion with the Boston Consulting Group's Philip Carlson Slezak.

Thank you for having me. So let's start with a little bit, I want to talk about the book, but before we get to that, let's talk a little bit about your background, which is kind of fascinating. For an American, you get a bachelor's at Oxford, a PhD at the London School of Economics, and

was becoming an economist always the career plan? Well, let me correct you right there. I'm not American. You're not? Where are you originally from? I was born in Switzerland. I grew up there, but in a number of other countries as well. You have sort of an American accent. How long have you been here? I spent a lot of time here. Early on as well in my youth. And...

So growing up in different places, I always compared and contrasted what I saw. So I developed an interest in economics. So when it came to going to college, studying economics was a very natural choice. Where did you grow up in Switzerland? Zurich. I was born there. Okay. I recently visited both Geneva and...

And Lake Geneva up, and it's just spectacular. What a beautiful part of the world. It is. It really, really impressive. So first job out of school, McKinsey, is that right? That's right. And what was that experience like?

Well, so I studied economics at LSE, actually, not at Oxford. I did my PhD at Oxford, so the other way around. And that was at the turn of the century. Let me take a step back. It was the turn of the century, and I emphasize that because that was peak economics. So, you know, the hubris and the arrogance of the economics profession was at its peak.

And you're still seven, eight years out from the global financial crisis, which was a big humbling moment for the profession. So everything was very model-driven, theory, econometrics, and all that. So I didn't feel comfortable even then as an undergraduate. Then as a graduate student, I branched out. I started reading a lot more, going to political theory, finance, history, much broader, building a mosaic of knowledge and also methods and approaches, frameworks,

And so at the end of my graduate studies with a PhD, that's when I landed in consulting at McKinsey and the work was very different. So very nitty gritty, right? You go deep into corporations, other organizations, you do very, very granular work. So coming with this big picture view of the world and analyzing and going into this

super nano micro part of business was a big change. Let's stay with the concept of peak economist. I think it was Paul Krugman who did the saltwater versus freshwater comparison, which was essentially the economists along the coast seem to have a very different model and very different approach to doing macro versus people more inland, at least in the U.S.,

Does that sort of dichotomy resonate with you? How do you think about that? Well, I generally view all of mainstream economics as too model-based. I call it master model mentality in the book. Sort of this belief that economics is a bit like a natural science and we can pass it off as a natural science.

That belief is still very much alive. And so physics envy, which has long been identified as a problem of the discipline, still reigns supreme in my view. And the book is really...

partly a repudiation of that. So my co-author and I, we take master model mentality to task in the book, and we think economics deserves a much more eclectic approach, drawing on many more disciplines than just sort of standard economics.

What are your thoughts on the impact of behavioral economics that really took apart the homo-economists that was front and center of classical economics and showed, hey, people aren't rational, profit-maximizing actors. They're emotional and flawed and human. Right. I think that is very, very interesting. It's very –

valuable that we have that strand of research in economics, but it's more on the micro side. It's not really macro predominantly. And so I firmly live in a global macro space where I think we still have very commoditized economics. You know, it's all about a set of forecasts. People are still wedded to their models. It's very much point forecast driven. And I think what we need is much more narrative based, judgment based, more eclectic approaches to reading the landscape.

And that's what the book is really, really about. So we're going to talk more about how poorly economists have done as forecasters over the past few decades. And you have numerous, numerous examples. But let's stay with your early career. You're going deep at McKinsey into the granularity of corporate finance.

behavior, then you very much a sea change. Alliance Bernstein or Sanford Bernstein, you become chief economist. How different is it plying those wares on Wall Street in an investment environment versus the corporate world in a more execution basis?

You know, the switch to the sell side was really good for me. There was something I'd been missing in my skill set. I'd done a lot of deep thinking, writing, researching. I'd done the more microeconomics. I learned more about the corporate world.

But I hadn't been exposed to the finance angle of it as much. I hadn't talked to the buy side at all really before. And being at Sanford Bernstein, a firm with a storied history in equity research really, and swimming in this pool of really great equity analysts just taught me a lot of things. Not least how to frame research angles, how to be quick with research notes, how to get the thoughts out.

And then the constant exposure to investors on the buy side really helped me sharpen my research skills. So that was almost like a missing piece in my recipe. It really unlocked something for me. And I learned a lot there. And I had a really good time doing that work, publishing many, many research reports.

over those years and often going very, very deep, often going very historical in the approach. So Bernstein is a firm that very much appreciates lateral thinking, differentiated approaches out there kind of ideas.

And so I ran wild for a while just doing work that I don't think I would have done anywhere else. So you started a consultant. You briefly had an NGO at the Organization of Economic Cooperation and OECD. Development is, I guess, the last D. You're on the sell side. So you see the universe of career options as an economist. What brought you back to the Boston Consulting Group?

So I had a history with BCG already and I was well connected there and at some point

I was approached if I'd like to come back and do the same kind of work I was doing on the sell side, but at BCG. BCG is a really great platform because not only is it deeply ingrained in the corporate world, so the access to boardrooms is very wide, you get to meet a lot of interesting executives and the problems they're grappling with, but you also still have access into the institutional investor world who are also clients. So you really get both sides engaged

of the landscape and they're really different, right? On the buy side, it's mostly a look at firms outside in. They're outside of what's happening in the boardrooms. They're trying to decode it from the outside. Being a consultant, working and talking with them, you're much closer to what's actually happening in their deliberations, the problems they're facing, the questions they're trying to answer. So to me, that platform is very attractive because it's very versatile. It never gets boring.

And I've had a good run the last five years doing my work on that BCG platform. So I have no expertise in the consulting world, but I kind of hear people lump all the consultants together, McKinsey, BCG, all these different firms. I get the sense from speaking to various people that that's kind of inaccurate, that BCG is not McKinsey. They're very different organizations. What's your experience been?

Yeah, I mean, they have different cultures for sure. They certainly vie for the same business, the three that you mentioned. So you constantly bump into those other two competitors if you're at any one of those three firms. The third being? Bain, I think you mentioned. McKinsey, BCG, Bain. Those three. There are others, but those are the core strategy consultants, if you will.

And, you know, I would think the type of work that is done is obviously very similar. They're vying for the same business, but culturally it's different. And, you know, they're slightly different sizes, these three firms. BCG today is about $12 billion in revenues annually.

We have about, I think, 60, 70 offices and no, sorry, well over 100 offices in 60 countries. I think that's the right metric here. And, you know, it's a space that is very, very competitive, but that keeps everyone on their toes. I would imagine. So...

Let's talk about advising companies and advising executives. You talk about explaining economic uncertainty, and as we'll get into in the book...

why there is this risk aversion and these fears of crises that never seem to come around. How do you approach advising executives on navigating all this? It seems like there's always this fear of a disaster, and lately it hasn't really showed up. Yeah. So a lot of what I do in conversations is

with executives is to unskew, if you will, some of the perceptions they pick up in the press and public discourse, which is reliably dialed

down to the sort of doom-mongering side of things. That is really true. It's not just lately, since you mentioned it, sort of the inevitable recession that never came. We're really at the end of a string of such false alarms. You know, when COVID hit, it was very common to predict a depression, not just a recession, but a depression. It was very conventional wisdom in 2020 that this would take many years to recover.

Then when interest rates rose, it was fashionable to predict an emerging market, a cascade of defaults. Then, of course, when inflation spiked, it was cast as a... Hyperinflation. Hyperinflation, structural inflation, regime break, the 1970s, all that stuff that clearly, even then, I think, was very clearly not what was playing out. And then the inevitable recession is really just the most recent in a string of false alarms. So often...

What I do is to meet people where they are. They pick up doomsday narratives because they're very prevalent in public discourse. And we often go back to basics and ask, well, how does the system work? And importantly, what would it take for these big bad outcomes to happen? It's not that they can't happen. They're part of a risk distribution.

But, very often, we take these risks in public discourse that are the edges of the risk distribution. Tail risks. Tail risks. And we pretend that they're in the middle of the distribution. If you go through financial news, if you go to financial TV conversations, you often get the impression that these risks, which are genuine risks, they're real, they're part of the distribution, but you get the impression that they're really the center of everything we should be watching.

So this leads to an obvious question. Whenever I have an author in, I often ask what inspired them to write their book. It's pretty clear what inspired you. It seems like it got to the point where, hey, everybody is freaking out about things that are either not happening or just so low probability events that they're not contextualizing it well enough.

What actually was the aha moment that said, I got to put all this down in a book and instead of repeating myself over and over here, read this and it'll it'll explain why you're fearing all the wrong things.

Yeah, it was the accumulation of situations where my co-author Paul Swartz and I felt we had a pretty good access to this topic. We kind of got that one right, not because we were using models and sophisticated analysis, but we looked at it from a narrative-driven perspective. We asked the right questions about what does it take to get to that really bad structural situation. And so...

We wanted to wrap that into a coherent story of how we think about economics, not because we can get it right every single time. Even if you use a more eclectic approach to economics, you will get things wrong. But I think your hit rate can improve. And that was the motivation to write that all down in the book. And yeah, that's how this came about.

I'm Alpine skier, Michaela Schifrin. I've won the most World Cup ski races in history. But what does success mean to me? Success means discipline. It's teamwork. It's the drive and passion inside of us that comes before all recognition. And it's why Stiefel is one of the fastest growing global wealth management firms in the country. If you're looking for success, surround yourself with the people who will get you there. It's

Thank you.

If you're an advisor or investor, choose Stiefel. Where success meets success. Stiefel Nicholas & Company, Inc., member SIPC and NYSE. For J.D. Power 2024 award information, visit jdpower.com slash awards. Compensation provided for using, not obtaining the award. Your customers are important to you, but they won't feel that way if they're stuck messaging a clunky chatbot or waiting on hold for a representative.

Estimated wait time is 25 minutes.

With Sierra, your company can deploy a branded AI agent that engages and delights customers anytime, anywhere. Sierra agents pick up every phone call and personalize every interaction. No more menus, no more hold times. And if you have an issue, Sierra's AI agents solve tough problems. Whether they're helping your customer pick out the person

Always friendly. Always helpful. Always ready. Visit sierra.ai to learn more. That's sierra.ai.

So first, let's just start out generally. You describe the book as calling out pervasive doomsaying in public discourse about the economy and demonstrating how to navigate real financial and global risks more productively. Explain.

So over the last few years, call it since the COVID pandemic, we've had a string of false alarms, as I would call them. Right out the gate in 2020, we were told this would be a greater depression, maybe as bad as the 1930s, worse than 2008. That wasn't the case at all.

Then we had an inflation spike that was spun into an inflation regime break, forever inflation, hyperinflation. That didn't pan out. Then we had rising interest rates, and that was spun into a doomsday story of emerging markets, cascade of defaults. And then we had the story of an inevitable recession that we're still waiting for. So we have across the board a lot of negativity.

Across the board, we have a lot of doomsaying. Public discourse is pervasive in that regard. The story is always skewed to the downside. And what the book does, it provides a framework to think about this differently, more productively, and it does so across real economy risks. Think recession, but also sort of longer-term growth. It does so in the financial economy. Think about stimulus and the effectiveness of stimulus, interest rates, inflation, bubbles, that type of stuff.

and it does so across the global space, the institutions that govern trade, etc. So you combine data analysis with both narrative storytelling and judgment over traditional macroeconomic models. Explain what led you to this way to contextualize what's going on in the real world economy.

So, my path through economics was fairly eclectic. I started out studying economics in a traditional, theoretical, macroeconomic, econometric sense. And then I went into studying much broader adjacent fields that are relevant to economics: finance, history, political theory, political economy, etc. Then I had different experiences in my career, just putting together different views of how to approach these problems.

And over time and working on the cell side, as we discussed, they put all these together. And so it is just the insight that the models will not deliver. You cannot accurately forecast the economy. Economists shouldn't feel so ashamed about that. It's not like natural scientists are always doing better. Think about epidemiologists. They also struggle to accurately forecast COVID deaths, for example.

So, you know, the whole physics envy and the whole inferiority complex that often besets the economics profession is misplaced in my view. We should embrace the uncertainty that prevents us from making precise point forecasts and we should live with that uncertainty, embrace the eclectic nature of what we're trying to solve. It isn't just about economics and policy. It's about myriad other things that play into this.

And when we do that and do it rationally, I think often we're going to land in better predictions. You know, it's funny about the physics envy. Richard Feynman once said, imagine how much harder physics would be if electrons had feelings, right? So it's not a pure natural world. You have human behavior getting in the way. And, you know, one of the quotes from the book says,

doom sells, hasn't that always been the case that it appeals not only to our fear of existential threats from an evolution perspective, but just generally speaking, good news is sort of sneaks by and bad news gets our attention. Yeah, it's the clicks and the eyeballs that we're trying to attract in the news business model.

And that gives you the slant to the downside. I think it's particularly pronounced these days. Social media and the rest? That's part of it, but it's also the case that when you think about the last 40 years or so, there was a window that we call good macro in the book. So a lot of macroeconomic...

a lot of macroeconomic context was benign and was a tailwind for executives, but certainly for investors. So in the real economy, cycles grew longer, volatility came down, like recessions were less frequent. The financial economy, inflation structurally declined, pulling down interest rates with it. In the global realm, you had institutional growth and we're aligning value chains and all that really was a tailwind for,

to executives and investors. And more recently, not just COVID, you can go back to 2008. It's sort of a growing crescendo of new noise and new disturbances. I think that good macro window is challenged. We had a lot of gyrations. We had a lot of shocks, all the whiplash there. And so for executives, when it used to be possible to ignore the macro world or take it for granted, it's now moved into the boardroom.

Now you need to have a view on what these things mean for your business and you kind of need to do that almost ongoingly. So that has changed. And because there's more gyrations, there's more whiplash, I think that has dialed up all the angst and it has dialed up the doomsaying. And the string of false alarms that I went through earlier in my mind is pretty dense. Every year we had a new doomsday narrative.

And every single year, it just didn't pan out that way. You know, there was a... I'm trying to remember which economist wrote this up. At one point in history, your whole world was your local region. And what happened globally or what happens across the ocean was not relevant. Now, it doesn't matter what corner of the earth you're hiding in. The global macro world is knocking on your door regardless how significant is that region

to both coming up with a better macroeconomic framework and all of these false crises and fears that seem to be never ending. Yeah, I think the greater interconnectedness and the real-time aspect of economics and the pass-through of influences in countries

in often just hours, transmitted often through financial markets, that just adds to that. It never stops. It never takes a break. You go to sleep with sort of the latest data. You wake up with the latest data, right? I mean, it's sort of constant in that regard. And I think that certainly feeds into that sense of heightened risk and crisis. So let's talk about some shocks over the past quarter century. We had, and this is really just

less global than U.S.-focused, but obviously international ramifications. We had the dot-com implosion in 2000. We had the September 11th attacks in 2001. Not long after that, we had the great financial crisis.

We had COVID in between. We had a couple of market events, the flash crash volume again. I don't know if you really consider those true economic shocks, but certainly dot com, 9-11, GFC and COVID were huge. Is this have we been through more than the usual number of shocks or does it just seem that way recently?

Well, we've always had shocks. I think 2008 stands out among the ones you mentioned because that's where the US economy actually came close to the precipice of this could be a structural depression. Without the intervention, without the stimulus that was deployed at the time, this could have gone a lot worse.

COVID, in some sense, was a replay of that risk, but action was more swift and more decisive. So it seems like we learned something there. And much more fiscal as opposed to the financial crisis, which was primarily a monetary response. And we ended up with two very different years that followed. Address that, if you would. Yeah. So I think...

In 2008, you'll remember TARP. TARP was what now looks like a paltry sum of $700 billion, and it got voted down in Congress. I remember that week in October, and the market—

seized so aggressively and the stock market sold off that it was voted down on a Monday. By Friday, it passed overwhelmingly. Exactly. And I think this is one of the big themes that we emphasize in the book. Stimulus comes down to the willingness of politicians to act and the ability to act.

Actability is more about financial markets. Will bond markets finance this kind of action, which they do in times of crisis? But the willingness has to be there to act. And in times of crisis, the willingness to act usually raises. Partisanship is put aside.

Politicians come together. They act to – when the house is on fire, you will step up and do something about it. And I think in 2020, that was in display and there was a learning curve from the more timid approach in 2008 and then perhaps it was overdone in 2020 and the following years. But certainly, the risk was perceived perhaps we're doing too little, so let's rather go large and backstop the system.

My favorite story from the 2020 CARES Act was a week before the country was shut down, Congress couldn't agree on renaming a library in D.C. because it was just along partisan lines. Everything got tabled. Then the world shut down and the largest fiscal stimulus since World War II, at least as a percentage of GDP, went down.

flew through the House and Senate and was signed by, CARES Act 1 was President Trump, CARES Act 2 was President Trump, CARES Act 3 was President Biden. Did we learn something from the financial crisis about the lack of fiscal stimulus and maybe the pendulum swung too far the other way? What's your takeaway from that? No, for sure. Look, I think two crises were very different. You had in 2008,

damaged balance sheets, not just in the banking system, but households. Their balance sheets had to be repaired. Households had to dig themselves out of that hole, had to rebuild their wealth. And that would have called for more intervention than what we got in 2008.

In 2020, I think policymakers, politicians, they had internalized that learning. So they went extra large on the fiscal side. And that hole that COVID created was basically filled with fiscal stimulus. As you know,

It's widely believed and accepted that this was extremely big, too much perhaps. And so we had an overshoot in certain consumption areas, particularly in the goods space. There was an overshoot in consumption. It pushed up demand. It

together with supply crunches, it pushed up inflation in an idiosyncratic and more tactical, cyclical way, not structural, but a tactical way. And so I think, yes, policymakers did learn something and they were risk averse, so they went extra large.

So you said the financial crisis, clearly a shock. The other things, not as much as a shock. And we've had plenty of false alarms. How do you define what a true shock or crisis is? And what do you put in the category of false alarms or things that are genuine but just don't rise to the level as described? Yeah, there are two things to consider. One is sort of the new cycle level. We have a

constant doomsaying about supposed things that could lead to recession or otherwise downgrade the economy. You know, just the last few years, we went numerous, you know, for example, consumers were supposed to run out of cash and consumers were not going to keep up their spending. We had lots of false alarms about the labor market, even last summer, right? We had last summer in August, there was somewhat of a panic because supposedly the labor market was going to

be very soft and very weak. So we have these news cycle false alarms stories that often are rooted in a data point that is noteworthy, that is interesting, that does signify risk, but we extrapolating from the data point to conclusions that don't hold up. That is one category of false alarms. The other category is where you have real crises

But the question is, are they going to have structural impact? Are they going to have a long-term impact on the economy? Are they going to downgrade the economy's capacity? So 2008 does qualify. 2008 left an indelible mark on the U.S. economy.

But 2020 didn't in terms of performance and output. We've regained the output to trend output that we were on, the path that we're traveling on pre-COVID. We've come back to that trend output path. It has not left the kind of permanent mark on economic performance that you saw after 2008.

So in that sense, we need to differentiate between what is a likely shock that will pass and that we can fix versus what is something that changes the structural composition, structural setup of the economy durably. Those are two very different types of situations. That sounds like a usable framework for distinguishing between real crises and dualities.

Do I call it media alarmism or, you know, I don't everybody is blaming the media these days, especially with this administration. But there has been a fairly relentless negativity, especially in social media. What's the best framework for, you know, separating the wheat from the chaff?

Well, typically when we see knee-jerk reactions and doomsday stories, they're taking a data point and then they're extrapolating usually on the basis of a model. So, I mean, think about the inevitable recession.

Even Larry Summers, people like that, they came out and said, look, to bring down wage growth, to bring down inflation, you need, I don't know, five years of unemployment at this and that level. Why? Because... He threw out 10%. Well, 10% for one year or 5% for five years. So he had different configurations, but they were all based on basically the Phillips curve. This was all a Phillips curve take on the economy, which is... Which was a great model 50 years ago, wasn't it? Yeah, it described...

the UK and certain other countries empirically quite well. It wasn't ever really a model and a theory. It was more of a description of empirical facts. But certainly it was useful for a window. It's still useful as an instrument to think about dynamics, right? But it was basically used as the truth. There's an input and there's an output, and my model gives me the truth if I give it certain inputs.

And then, well, what happens, we're extrapolating data points often outside the range of empirical facts. The models are only trained on historical facts. You can't make up data points to train your model. So when a crisis hits, likely you get data points that were not empirically known in the past. So what does the model do? It extrapolates outside its historical empirical range. And then you get these kind of point forecasts that just

don't work. I mean, case in point, in 2008, unemployment goes up to around 10%, right? And it takes almost the whole 2010, the full decade almost, to bring down this very high unemployment rate.

So in COVID, when unemployment shoots up to 14%, what does the model do? It says, well, if it takes a decade to bring down 10% unemployment, it will take even longer to bring down 14% of unemployment, right? And that is exactly this kind of limitation of the model-based approach.

empirically, you never had 14% unemployment. So if the model extrapolates from past data points, it's going to go off the tracks. And that's exactly what happened in that instance. So the underlying flaw built into most models is that the future will look like the past. And as we've learned, that often is not the case. It's always idiosyncratic. Look, the US economy since the Second World War has only seen a dozen recessions.

Now, each of those recessions is totally idiosyncratic. And even if they had a lot of commonalities, 12 is not a sample size that a natural scientist would consider large enough to build sort of an empirical model around, right?

Each of these crises or each of these recessions was idiosyncratic. And the idiosyncrasy demands much more than a simple model or even a sophisticated model. It demands the eclectic view across many, many drivers. And that comes down to judgment. There isn't an output in an Excel sheet or a Python model or anything.

In the end, it comes down to human judgment. And I think that is something we lose sight of way too often. You very much strike me as a fan of Professor George Box. All models are wrong, but some are useful. Tell us a little bit about how models can be useful. Well, they're always a good starting point. Even the Phillips curve has a lot of validity to think about what might be happening. They're always a sketch of reality.

But the moment we're translating that from a sketch and a map into something that is hardwired in a quantified model, and the moment we then expect that the output will resemble anything like the truth, we're sort of denying the reality of this. It just doesn't work that way. Look, I'm not the first person to make that point. In fact, Hayek, Keynes, von Mises, they've long basically trashed economics

for saying like you're too gullible and you're too naive about the constant nature of these variables. They've long pointed out that you don't have this, what the natural sciences provide, which is stability in all these relations of variables. You don't have that in economics. And there's an anecdote that we pick up in the book. When Hayek receives the Nobel Prize in 1974,

He actually uses his acceptance speech, or I think it was a dinner speech he gave right after being awarded the prize. He uses that speech to say, look, you shouldn't do this prize in economics. You should have never done the Nobel Prize in economics. But if you must have this prize, at least ask the recipients to swear an oath of humility. Because unlike physicists and in chemistry and other natural sciences,

Economists have a big microphone, right? Policymakers listen to them. Politicians listen. The public listens to them. But they don't have that certainty of analysis. They don't have that stability in their model. So they're going to go off the tracks all the time. So at least ask them to be humble about what they're doing. And I think that is a good reminder of the long history of recognizing the limits of model-based approaches through the eyes of some of the leading thinkers in this space. Success. It's discipline.

It's teamwork. It's the drive and passion inside of us that comes before all recognition. It's the best in each of us made better by the best in all of us. Whatever success looks like to you, Stiefel is invested in yours. That's why Stiefel is one of the fastest growing global wealth management firms in the country. So when you're ready to chase success, our financial advisors are ready for you. At Stiefel, we invest everything into our advisors so they can invest everything into their clients.

That means direct access to one of the industry's largest equity research franchises and a leading middle market investment bank. And it's why Stiefel has won the J.D. Power Award for Employee Advisor Satisfaction two years in a row. If you're an advisor or an investor, choose Stiefel, where success meets success. Stiefel Nicholas & Company, Inc., member SIPC and NYSE. For J.D. Power 2024 award information, visit jdpower.com slash awards. Compensation provided for using, not obtaining the award.

Your customers are important to you, but they won't feel that way if they're stuck messaging a clunky chatbot or waiting on hold for a representative. Estimated wait time is 25 minutes.

With Sierra, your company can deploy a branded AI agent that engages and delights customers anytime, anywhere. Sierra agents pick up every phone call and personalize every interaction. No more menus, no more hold times. And if you have an issue, Sierra's AI agents solve tough problems. Whether they're helping your customer pick out the perfect

So let's talk a little bit about a lot of the false alarms and faux crises. So many economists got

2022 wrong, 2023, 2024. They were expecting a recession. It never showed up. Why is that?

It starts with the master model mentality that we call out in the book, where we place too much trust in models. So the Phillips curve was essentially used by many forecasters and commentators. Define the Phillips curve for the lay reader who may not be familiar with it. Yeah, the Phillips curve is an old theory going back middle of the last century describing the relationship between wage growth and unemployment. So the idea is that you trade off the two variables

And that led commentators like Larry Summers to say, to bring inflation under control, you would need either many years of high unemployment or a sharp recession, 10% unemployment for a year to reset the inflation picture. In other words, in layperson's terms, a soft landing is impossible. And this is what fed into the inevitable recession that was the dominant received wisdom the last few years.

Now, you know, these things are good starting points. They have validity historically and a lot of empirical data. But in the end, it's idiosyncratic. It's very idiosyncratic constellation of drivers and risks. And so it was in the last few years. So let's look at that for a moment. One of these master models was also interest rate sensitivity.

right? We think interest rates go up and that eats into disposable incomes for households, right? But in reality, mortgages in the US, unlike in Canada, mortgages are long term, didn't actually take a big bite out of disposable income. O' Mostly fixed rate. Exactly. Very long term fixed rate, low. And most of them were done at low rates because we had low rates for a long time. Contrast that with the flexible contracts and mortgages in Canada where they lost a lot of disposable income. That wasn't the case here. Same thing about

interest rate sensitivity in the corporate sector. The textbook tells you interest rates go up and investment will fall, but does it? When you do the empirical analysis for whatever window, you'll see a very

flimsy correlation between interest rates and capex. Firms invest when they have a narrative to do so, when they see a return on the investment. And if they believe the investment is beneficial to them, they'll do it whether the interest rate is 2%, 3%, or 4%. And just look at what happened the last few years. You had a lot of narrative and belief in worthwhile investments, data centers, software. So

With or without higher interest rates, firms are going to do that. Particularly also because a lot of our investment has shifted away from

you know, fixed structures, physical investment to intellectual property, software type of investment, which has a much higher rate of depreciation. So, a bridge or road will be good for 30, 40 years, but software is maybe three or four years. So, you constantly have to invest just to stand still, just to keep the stock of investment in the space, to keep it steady. You constantly have to run faster just to maintain that.

And so there is a lot of idiosyncratic drivers that led to very different outcomes from what was predicted from a model-based Phillips curve type approach to reading that context. So a lot of highly regarded economists like Larry Summers kind of reminded me of the Paul Graham quote, all experts are experts in the way the world used to be. And we're seeing a lot of that in that

So not only did people get the recession calls wrong for the past couple of years, what have we had? Two months of recessions in the past 15 years? Are we in a post-recession economy now? You can still get recessions, but I think we've become better at fighting them. So this is the topic of stimulus. There are two different types of stimulus that we describe in the book across three chapters.

And we differentiate between what we call tactical stimulus, which is just to smooth the cycle, accelerate growth in between recessions, maybe de-risk the cycle when necessary, versus existential stimulus, which is when policymakers, politicians step in when the economy is truly at risk of a structural break.

Those two types of stimulus, they're evolving differently. I think the tactical kind is more challenged going forward. It was very easy when inflation was below target. It was very easy when interest rates were very, very low. There was little cost to the Fed put. You could do that. There wasn't sort of an inflation risk associated with it.

That's different now, and I think it will remain different now that we're skewed to the upside in terms of inflation where interest rates are likely to be higher for much longer. But the existential type of stimulus, the ability to step up when it's needed, I think that is still very strong. And if you have another shock or a crisis or a recession, I think we'll be able to deploy stimulus effectively still. So we said earlier all recessions are not homogeneous. They're all idiosyncratic and unique.

But one of the things you mentioned in the book that kind of intrigued me, we shouldn't conflate recession intensity and recovery. Explain what that means. Yeah. When COVID hit, we had extreme data prints. Unemployment is sort of exhibit A of the story. Unemployment went to 10% in 2008, but it went to 14% in 2020, right? So the intensity, the sudden collapse of activity was much more pronounced in

in COVID than it was in 2008. GDP also much worse during the first few months of COVID than... All variables. And we have a chart early in the book that shows the fifth to 90th percentile of historical experience of these variables. And COVID is like far outside that historical range. So you get data prints that you're not used to, that the models don't know. The models were trained on data points that were simply not experienced until they happened in COVID. Right.

Now, all of that fed into extreme intensity was equated with this will be a very long and difficult recovery. Why? The 10% unemployment rate led to many years of recovery in the 2010s, right? So now if the unemployment rate is even higher, it's going to take even longer to work it down to a level that is, you know, a good economy again.

But that wasn't the case. 2020 wasn't about a balance sheet recession. It wasn't about banks repairing their balance sheets. It wasn't about households repairing the balance sheet. We took care of that with stimulus. And therefore, the ability to recover was much faster, much stronger. There were other idiosyncratic factors. Essentially, what was underestimated was

the ability to adapt of society. You know, society's found ways to work around the virus. The pathway to a vaccine was faster. So there were a lot of things that were underestimated. You know, it kind of reminds me of the Y2K fear that when there's a little bit of a fear of panic

the expected crisis may not show up because we're taking steps to avoid it. We don't know what was Y2K a false alarm or did the fear lead us to make sufficient changes to avoid problems? I honestly can't answer that question. I'm wondering how you look at crises in terms of do some of the fear mongering and some of the, you know, media, uh,

absolute extremism lead to government action that prevents the worst case scenario from happening? It's possible that it shapes the perception of policymakers and politicians. But I think the reality is on the ground. The variables that are visible and measurable, the unemployment rate, GDP growth, imports, exports, all of that was under pressure. I think that is...

more telling for those who take decisions than what public discourse does. Is public discourse particularly fearful and a lot of angst pervades how we think about the economy? Does that spur action? Maybe. That's part of it. So we don't know, as you rightly say, what would have been in a counterfactual world

But essentially, when the economy is genuinely in trouble, I think the willingness to act on the stimulus side is very strong. So let's talk about some of those metrics. You have an image in the book, Scanning the Recession Barcode. Mm-hmm.

So tell us about that and the history of U.S. recessions, which seem to have been more frequent and more intense. You go back a century, they were depressions, not even recessions. Tell us about how this has changed over the past, I don't know, a couple hundred years? Yeah. So if you do a very long-run chart of recessions in the U.S. economy and you shade each recession as a bar –

what you get is a barcode of image that looks a bit like a barcode, but it thins out as you move to the right. So you had recessions very frequently 100 years ago and further back. The economy was constantly in recession. Essentially half the time it was in recession. Banking panics all the time. Yeah, but also real economy. You know, the economy was very agrarian. A bad harvest could drag down performance of the economy. So there were a lot of shocks. But yes, there were also banking crises and things like that.

And what we identify in the book is a recession risk framework. We say, look, all recessions come in one of three flavors. They're either real economy recessions, which is when investment and consumption drop abruptly and pull GDP growth down. So that's the real economy type of recession. The second is a policy era,

when policymakers get it wrong, they raise interest rates too fast or too high, which only ever know exposed whether it was the right thing to do. So it's a very tricky thing to do. And the third type of recession is the most pernicious kind. It's a financial recession when something blows up in the financial system, like 2008.

And what we're showing in this chapter of the book over the long run, the composition of these two drivers has changed over the last 40 years. The real economy recessions, they really took a backseat because the economy calmed down, the volatility calmed down, services play a bigger role in the economy today, so they're less volatile than physical production.

But also policymakers just got better at managing the cycle. So, you know, policy errors kind of also lost a lot of share, if you will, in the overall prevalence of recessions. But when you think about what has given us the biggest headaches, it was 2008, a financial recession, and dot-com in a way is also a financial type of recession. So the share and the risk from financial blowups is significant if you look at it in recent history.

And that doesn't mean that the next recession will be that type, but its share of the risk spectrum is relatively high. So what should we be listening to when we hear economists discussing various risks? What are the red flags that, hey, maybe this is a little too doom and gloomy for our own portfolio's best interests? Yeah, I think the litmus test for me is often,

What would it take for a certain outcome, a certain doomsday outcome to actually come to pass? Not just will it happen and what would be the damage, but walk me through the conditions that actually lead us to the precipice and then make us fall off that macroeconomic cliff. We need to talk about drivers, causes. We need to talk about their probabilities and their constellations.

So, you know, it's not good enough to say, you know, the model says the recession will happen. Walk us through exactly what is the confluence of headwinds that together make that credible, right? It's more than the point forecast.

Really kind of intriguing. I also noticed that I'm not an economist, but when I listen to economists talk about the possibility of a black swan or the possibility of this event,

It's almost as if there won't be any intervening actions either by the market or the policymakers. Tell us a little bit about that. What was George Soros' phrase? Reflexivity? Reflexivity.

When certain events happen, there are going to be natural reactions that just prevent this extrapolation to infinity or to zero, as the case may be. Yeah. I mean, this is back to the topic of stimulus first and foremost. 2008 came as a big surprise because the models in the early part of the 2000s

they didn't even really look at the financial sector as a risk driver. They kind of assumed the financial system away, and then when the problem brewed in the financial system itself, the models were kind of blind to that. And the reaction couldn't be gauged if you didn't have view of that, and the reaction really depended on stimulus. And stimulus is about politics. It is about policy. It's not about economics, first and foremost. It's about political economy. It's about people coming together.

and fighting crises. And so I think that remains the case that the idiosyncrasy happens before the crisis, the drivers are idiosyncratic, but the moment a crisis starts, a shock hits, what happens as a reaction is also idiosyncratic. It is political. It is about society. It's about choices. It's not stuff that you can model in a rigid natural science way.

So let's talk about something that clearly wasn't in the models, forget 20 years ago, they weren't in the models five years ago or even three years ago. And that's the impact of artificial intelligence on our economy, on the labor pool and on productivity.

How do you look at a giant structural change like AI? How do you put this into context as to what it might mean across all these different areas within both traditional economic modeling and the real world? You know, we've had productivity growth the last few decades, even though often the narrative is productivity growth is really, really low.

We've had productivity growth, just not in services. But in the physical economy, there's been pretty decent productivity growth even the last 20 years, where we didn't have productivity growth with services because we didn't have the technology to move that part of the economy along.

Now, why is that? Essentially, productivity growth goes up when technology displaces labor. That is really the definition of productivity growth. You need to produce the same with less labor inputs or more with the same labor inputs. But either way, technology, whether we like it or not, is about the displacement of labor, and we weren't able to do that in the service economy.

Now, with AI, I think you have a better chance of doing this. At least the promise is very strong that this will work. But I think we're getting ahead of ourselves. And I'm not saying that now. We've published on this over the last few years, even as COVID hit and even before AI, when the Zoom economy was sort of this dominant narrative. It's a hard slog to do this. It happens over years and it's little by little. It's not a flip of the switch. It happens very incrementally.

And I don't think AI will turbocharge GDP growth. It is a lift to growth over the medium term, but there are many little obstacles. There are many little things that need to fall into place for people to really adopt the technology and for this to little by little give us a tailwind. So it's not an abrupt step change. It's something that is credible, something we need to work through, and then it will show impact over a 10-year frame, 15-year frame.

So let me push back a little bit on one thing you said. And I seem to have this ongoing debate with economists who work in a larger corporate framework. We're here in Bloomberg, giant company, big operation. My day job is a much smaller company, under 100 employees. And I have noticed just over the course of the past decade that

how our productivity has skyrocketed. And it's a services business, finance is a services business. And it just feels like the things that used to take so long to do 15 and 20 years ago are now automated. And it's not that we're hiring fewer people and it's not that we're working shorter hours, but the same size team can just accomplish

so much more than they were capable of per like, I recall the days of quarterly, um, reporting and having to literally run a model, create a printout for every client printed out, stick it into the right. And like,

It was like a week-long process that all hands on deck every quarter. And now it's updated 24-7, tick by tick. It's automated. No one cares about quarterly reports because you could get it. And the joke is you have 24-7 access to your

Daily, weekly, monthly, year-to-date, five-year, 10-year performance reports, just try not to check it second by second. But the way, and that's just one example, being able to communicate with clients, to record and embed an interactive video with charts and everything else, that was like a massive undertaking. And now it's like child's play, right?

even though you're doing the same thing, you're just doing it faster, better, cheaper, easier. Are we somehow underestimating the productivity gains or are these just specific to, you know, that one area? Yeah. So I have some pushback on that. I think the

bar for productivity growth is a little higher and it's very specific. It's less inputs per output. So do things get more comfortable? Are they moving faster? Are they qualitatively perhaps better? Yes, but are we using less inputs to generate the same value? Or are we using the same level of inputs to generate more value? That is what we need to achieve to speak of productivity growth. And let me give you an example that we use in the book. You know, I took an Uber from my apartment to come here into the studio today.

And Uber is often upheld as the epitome of progress in tech. And it is fascinating. It's a great app. I love to use it. It's nice. But look, if you want to improve the productivity growth in taxi transportation, we have to talk about inputs and outputs, right? And the inputs are on the capital side, a car, and you're not getting rid of that car.

And on the labor side, it's the driver. And the Uber car still has that driver. Not in Waymo in parts of the West Coast. Yes. And this is why I said it takes time. Incrementally, that will happen and that will unfold. But do you think you're going to have driverless taxis in New York in 2028 or 2030? I don't.

Well, we'll have it in 2050, probably in 2040. I can't tell you what exact year it'll happen, but it's coming. I agree with you. The sooner we embed those RFID devices in vehicles and on street corners, like doing it visually in LiDAR is very 20th century. Right. Yeah. And that's why I said it takes time. Over time, this will be substantial lift to economic output. Right.

but it doesn't happen overnight. It actually takes time, right? And there's an additional important point about productivity growth that can also be shown in this taxi example.

When technology is truly productivity enhancing, you see that in falling prices. Technology is deflationary, right? As technology does away with input costs, firms will compete with lower prices to gain market share. So across history, wherever you look, as technology is becoming a credible force in production, prices will fall. Now look at Uber. Uber prices in New York tend to be higher than a yellow cab.

Why? Because despite this expensive technology, you're not able to produce this ride more cheaply. You're not. In fact, you kind of have to monetize the technological expense. The app is expensive. All of this is expensive. So generally, you're paying a premium for the smoothness of the app and all that. Over time, that may change. But watch prices. You want to see productivity growth, whether it's happening or not. You've got to look at prices. And that's one of the arguments we make in the book.

So let's hedonically adjust. We'll stay with Uber. Let's hedonically adjust that. In New York City, if you want a taxi during rush hour, hey, sorry, you're out of luck because the monopoly that was imbued by the Taxi and Limousine Commission and a handful of big medallion chain owners decided in their infinite wisdom that

we don't need to move people around rush hour. We're going to change shifts then, which by the way is my pet theory for how Uber penetrated. And so A, you could get an Uber during rush hour that you can't during rush hour.

cab rides. You could get an Uber when it's raining. Good luck handling a cab in New York City rain. And you have the ability to schedule an Uber. You have the ability to get a higher quality car. You could get an electric car if you choose, a larger car. Like I'm not a huge fan of traditional hedonic adjustment because it was a way of kind of tamping down on the cost of living adjustments. Always felt sort of disingenuous. But

But I don't think you could get anybody to say that Uber is not only better, and I'm not a big Uber fan, but as a user, Uber is certainly better than a cab. And in many ways, orders of magnitude better, more choices, more options, and just a higher quality experience. Plus...

Plus, you know, just the idea of having, hey, is this a work thing or I'm going to use that card on the app? Well, no, this is personal. I'll use that card. So maybe taxis aren't the best example. But when let's talk about economists. Again, I want to stay with this because I love the topic.

Think about the quantity of research you push. You push out. The ability to integrate charts and data. Like, I have been in this business long enough that I can remember. First of all, when I started, the guys in the technical group, they were doing charts with pencil and graph paper. I'm not exaggerating. Maybe that's just a function of my age. But think about how... And the...

The cheat was you get a different feel when you're doing it point by point than when you're just generating it. Whether that's true or not, at least that was the... When computers came along, people continued to do that. But think about...

The access you have to the just endless array of data, the ability to do that. I haven't even mentioned your fortune column. Think about how much time and effort goes into putting out a column and you go back 25 years and it was just a...

horrific grind. Like at this point, everybody seems to use some version of Grammarly or some other editing software. The ability to put out, and I'm not talking about asking chat GPT to generate a garbage article for you. You writing something, cleaning it up, embedding a lot of data and images, it just...

Feels like, you know, to quote Hemingway, you know, gradually and then all at once, it just feels like it's so much easier to put out a much higher quality product than

with either the same or less effort than 25 years ago, maybe I'm just hyper-focused on the junk I do, but what's your experience been like? - Incrementally, there's progress, but again, the bar we need to meet is value. Are we generating more value with the same inputs,

or are we generating the same value with less inputs? That's the definition of productivity growth. So if you can make all these charts faster and you save one economist on the team, well, that's productivity growth. Or you keep the economist and you double your number of reports and you also manage to monetize them and earn revenue for it, well, that's productivity growth.

If the charts get prettier, faster, fancier with the same number of economists and the same number of revenues, well, from an economic sense perspective, that's not productivity growth. So it's got to be a change in the relationship of inputs to outputs if we're comfortably talking about productivity growth.

And back to the Uber example, you're right. You can get different cars to ride in. You can get the car, the Uber car when it's raining, but you're paying for that. So it's not produced more productively, right? You're paying a surcharge. You're paying the surge pricing. I think they call it an Uber. So, you know, yeah, you can get it when it rains, but you'll pay twice as much. So it wasn't done more productively, right? Really interesting. The gap between the increased

quantity and quality of output, if we're not monetizing it, or as a consumer, if you're not seeing price declines, then it doesn't really count as productivity gains. No, it's got to be a change in the ratio of inputs to outputs on either side. Either we keep all the staff and we earn more revenue with it, that's productivity growth, or we keep the revenue constant and we do it with less inputs. That's more productivity growth.

But, you know, again, I'm not saying there isn't productivity. There is. And there will be more. And AI will have impact. It just needs to show up in value in that relationship between inputs and outputs. I see it qualitatively, but I completely get what you're saying. Yep.

quantitatively. Are you still doing the Fortune column on a regular? Yeah. We publish in Fortune relatively regularly. Whenever we see a cyclical or a thematic topic that we feel is pressing, we publish with Fortune. Yeah. Really, really interesting. All right. I only have you for a limited amount of time. I know you're catching a flight today. Let me jump to our favorite questions that we ask all of our guests, starting with,

What are you streaming these days? What's keeping you entertained? Either Netflix or podcasts or whatever?

Yeah, I'm not very big on shows or Hollywood. I mean, to give an idea, I think I'm on the second season of Slow Horses. I think there are four seasons of it. I'm kind of slowly making my way through the second one. It's very entertaining. I love Gary Oldman. So interesting. He's great in it. Yeah, it was sort of taking down the genre of spy movies in a very entertaining way. So I'm doing that. But also, I tend to watch late in the day when I'm tired. So it's entirely possible I fall asleep and I take like two, three evenings to get through one episode. Yeah.

Yeah, so I'm not all that big on that front. Tell us about your mentors who helped to shape your career.

So many people, right? Because a lot of it is teamwork and you don't progress without mentors and role models. I would say in my current role, I would probably call out two people. Rich Lesser, our longtime CEO and our chairman, he had the vision for a macro product, as did Martin Reeves, who runs our research institute, the Henderson Institute. And they're really the two people who brought me into this role and coached me.

So they stand out. Outside of BCG, Kathleen Stephenson, she had many, many different roles on Wall Street and economist role. She's been a great help navigating my career the last many years. And further back in academia, thesis advisors and many others. It's always teamwork in a way. So you have many, many role models and mentors. Let's talk about books. What are some of your favorites? What are you reading right now?

Right now, I'm almost done with Making Sense of Chaos by Doyne Farmer. It came out last year. Doyne Farmer is a very interesting character. He's a complexity scientist at the Santa Fe Institute and I think at Oxford University as well. And his book is interesting to me. I bumped into him at one or two conferences, but it's interesting to me particularly because he kind of argues the opposite of what we argue in our book.

So he thinks, he agrees that economics is poor if you just take standard models and theory, but he believes you can crack the complexity of it. So he thinks with complexity science and better data and better models, you'll essentially be able to make those forecasts better.

I read it because it's always important to see what others are arguing. I don't read stuff that reconfirms what I think. I want to see what other people are saying about the same topic from different angles. So that book's been very useful and also well-written. That's what I'm currently reading.

I think of other books that I've read over the years, I mean there's so many great ones of course. I think one that early on made an impression on me was Seeing Like a State by James Scott. It's at least 25 years old. I read it as a grad student. And what he does, he looks at the ability of governments to do top-down policy to improve the lives of large amounts of people.

And he shows all the pitfalls in a sort of Hayekian way. It's tough to have the local knowledge. It's tough to do the top-down improvements. Things have to grow bottom-up. And that book kind of stood out for being very eclectic, very multidisciplinary, and still, I think, an excellent book to how to think laterally and not in a sort of strict model-based way. Really interesting.

Our final two questions, what sort of advice would you give a recent college grad interested in a career in economics, investment, finance, anything along those lines? Yeah, I think a career as an economist is challenging in some ways. There are so many economists out there. Often when I hire, you see the flood of CVs and often very good CVs. And I think there's been an overproduction of economists recently.

So, I think doing something adjacent to economics, work in finance, work on the buy side, work on the sell side, unless your heart truly beats for economics, I think you can use economic skills in many adjacent disciplines and careers I think are plentiful in those adjacent disciplines.

If economics graduates really feel strongly about economics, it's fascinating, but your heart has to be in it. And there aren't all that many seats as economists. So one has to build that over the long term.

And our final question, what do you know about the world of economics today you wish you knew 25, 30 years ago when you were first getting started? Yeah, well, I mean, that's really what I wrote down in the book. You know, the book is the 20, 25-year journey through the maze of the economics profession and discipline.

The themes we touched on, the master model mentality, the pitfalls of treating economics like a physical science, the doom-mongering, which we have to simply ignore most of the time, and then the eclectic approach to economics. I call it economic eclecticism, drawing on a broader range of disciplines. Those are the things that I learned through that path the last 20 years. I wrote them up in the book.

It would have been interesting for me to read that 20 years ago, but I wrote it now, and so I'm happy with that. Really intriguing. Philip, thank you for being so generous with your time. We have been speaking with Philip Carlson Slezak. He's Global Chief Economist for the Boston Consulting Group. His new book, Shocks, Crises, and False Alarms, How to Assess True Macroeconomic Risk, co-authored with Paul Schwartz, is an absolutely fascinating read.

If you enjoy this conversation, well, check out any of the past 500 we've done over the previous 10 years. You can find those at iTunes, Spotify, YouTube, wherever you find your favorite podcasts. And be sure to check out my new book, How Not to Invest, The Bad Ideas, Numbers, and Behavior That Destroys Wealth, coming out March 18th, 2025.

I would be remiss if I did not thank the crack team that helps us put these conversations together each week. My audio engineer is Andrew Gavin. My producer is Anna Luke. Sage Bauman is the head of podcasts at Bloomberg. Sean Russo is my researcher. I'm Barry Ritholtz. You've been listening to Masters in Business on Bloomberg Radio.

There are thousands of tech and business podcasts out there, but there's just one that you could count on for a weekly dose of the future. The A16 podcast from Andreessen Horowitz. This show brings you the world's most influential people.

Movers who have a track record of being both early and right, like Apple co-founder Steve Wozniak or co-founders Marc Andreessen and Ben Horowitz, not to mention folks you typically don't get to hear from, like the very first chief technology officer of the CIA.

From the science and supply of GLP-1s to what drove the recent deep-seek frenzy to self-driving cars, drones, and even boats. Go ahead. Eavesdrop on the future with the A16Z podcast.