I last spoke with Azeem, the proprietor of Exponential View, 18 months ago — ancient history on this subject. So we revisited the state of AI.

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TRANSCRIPT:
Paul Krugman in Conversation with Azeem Azhar

(recorded 6/12/26)

Paul Krugman: Hi everyone. Paul Krugman back on my usual schedule of recording interviews. And today I’m talking with Azeem Azhar, who I spoke to in January 2025, basically centuries ago in AI time. And with AI on everybody’s mind, I thought it would be good to revisit. I should say Azeem is an independent researcher and founder of Exponential View, which is one of the top tech Substacks out there.

So hi, welcome to another conversation.

Azeem Azhar: Yeah, thank you, Paul. And it has been eighteen months, also known as one and a half centuries in AI time since we spoke.

Krugman: Yeah. Let me ask sort of the dumbest question: what is this thing called AI? How does it do what it does? I mean, even skeptics have to admit that it’s really impressive how it’s sort of leapt over all of the previous barriers. How is this happening?

Azhar: You know, I think we’re still figuring it out. I think of AI ultimately as a machine that does certain things, and it’s been built by passing first millions, then billions, then tens of billions, hundreds of billions of trillions of words of human output through a neural network to give it some sense of how humans have thought about the world. And because it operates at dimensions well beyond the form of space and time, it seems to be able to find relationships between quite complex concepts. And I think we’ve all had that experience, whether we’ve been using Chat GPT or Claude over the last two or three years, that it seems to be able to recognize things that are quite deeply related that don’t immediately spring to mind.

And in the last year and a half or so, the labs have started to train the AI models not just on words in books, but actually on tasks, like, “what is the set of things that you do to write a piece of code that does something?” “What is a set of things you do to use a piece of software in an enterprise?” And they’ve tried to train those models on those particular tasks. Essentially it’s aping what we do, and they use various mathematical tools like reinforcement learning where the model notionally gets a reward. Of course it’s not a reward the way you and I think of it because it’s a machine.

Paul Krugman: Right.

Azhar: And so that’s what it is. It’s sort of reflecting back, but also I think discovering some really deep relationships in the world that we might not spot, you know, prima facie as humans.

Paul Krugman: Brad Delong calls it “a vast stew of linear algebra,” which makes some sense to me because I think that Pagerank with Google was the last thing I actually understood. And that’s the eigenvector with the largest eigenvalue. Not that anybody needs to know that, but this is like a million times bigger, right?

Azhar: That’s basically it. Yeah.

Krugman: But it’s sort of not what artificial intelligence was supposed to be, right?

Azhar: No, not at all. I mean, I sometimes go back and look at the TV series of the seventies that I grew up with as a child, and they’ll always have an AI in the spaceship. Space 1999 had an AI you could talk to. And it was very precise, it was very clipped, and it did things and got things right. And there was a sense that you could trust it. But you’d never think to say, as I sometimes do now, you know, “Find me five analogies to help make this point.” I use it as a brainstorming partner, or I give it tracts of my book, the book that I’m writing, and say, you know, “How would Paul Krugman criticize this argument?” And I get suggestions that I then work through by hand? I don’t think we really imagined it would look like that.

Krugman: Yeah. In sci-fi it would talk in a monotone and would be relentlessly logical. And in fact these models are unpredictable, they’re sometimes temperamental, they’re not reliable. That’s probably one of the big problems. It’s not at all what we imagined.

Azhar: It’s not at all and this point about reliability is so complex. A couple of months back, one of the versions of Anthropic’s Claude came out and I found it so sycophantic that it became unhelpful because I like these things to help me on hard problems and to challenge me. So I switched back to Chat GPT, which has always been a little bit less friendly. And what’s going on there, Paul, is that because we don’t really have a good theory about how to build these. They are developed almost like in a petri dish and nudged in particular directions so they take the shape that we expect them to take. And to use an economist term, they improve non-monotonically with every release. So you’ll see the latest release of an Anthropic model, and there are maybe twenty or thirty public benchmarks that they’re measured against, like how well they summarize text and how well they write software code. And the next version of the model won’t necessarily be better at everything than the previous version, because you lose something in order to get it. And that’s the complexity that the labs are wrestling with.

Krugman: Wow. Okay. Second naive question. I don’t think I’m a Luddite. I’ve always been happy to adopt technologies, but maybe I’m incurious on some of these things. I tend to pick up things like mathematical techniques, as needed, because I see something that could be useful. Now, I’m using NotebookLM to extract tables from PDFs, that sort of thing. But what should I be doing? I have friends who are using Claude a lot, but I can’t quite figure out what particularly agentic AI should be doing for me.

Azhar: You know, I’m really sympathetic to that because I have the same issue. These tools have been developed by software developers in a really particular part of the world, which is Silicon Valley, where the culture really revolves around the art of the programmer. And so if you have a programmer’s day and you think in coding terms and you have programming workflows, it becomes really obvious what you do with a really advanced AI tool. I do a lot of research, some of it qualitative, some of it quantitative, and in such a world, those workflows don’t match the way that I think through problems. And so the way that I get around this is that I do look at things on Twitter or X as it’s called because people are sharing tips. And I often just ask the models, you know, “What could I do with you given that I’m trying to do this thing? I’m trying to solve this problem.” And it will come back and give me a suggestion.

And I have had some success with agents. So I have an agent called R. Mini Arnold. So R is a play Isaac Asimov’s robots. They’re all called R. Arnold is after the good Terminator in Terminator 2, played by Arnold Schwarzenegger, who protects humanity. And R. Mini Arnold is available on my WhatsApp and it’s available on email.

Krugman: Okay.

Azhar: And it has access to a whole set of resources. It can browse the web, it can access LinkedIn, it can access Twitter, it can look at my library of PDFs of research that I’ve downloaded. And I can throw tasks to it a little bit like I would say a pretty decent but slightly temperamental graduate student. So sometimes it just disappears for six or seven hours at a time. And one of the differences between using an agent like that and using Claude is that R. Mini Arnold has a lot of my life’s context. It knows the music I like, it knows the book I’m working on, it knows the investments I’m making, it knows the essays I’m doing, it’s got the calendar of speeches that I’m about to give. And so when it goes off and does a task, it tries to figure out what in my world is this going to be relevant to and where can I draw threads from? And when it works, it is really sublime and it does feel a little bit like science fiction.

But I would say it’s incredibly brittle. I mean there’s breaks every four or five days.

A specific example was, I was thinking about the Paul David’s research about why electrification took the time it took. And I wanted to understand what were the determinations of determinants of that thirty-five year lag from Pearl Street generation to, you know, productivity growth. What could the levers be? And so I threw that into R. Mini Arnold and it set up a team of sub agents which had personalities of key economists and was able to go off and do research the way the AIs do, but also research on all the academic papers that I have downloaded in the past.

I have access to JSTOR, I’m allowed to download a hundred PDFs a month. It can look at all of those and start to compile an answer in a way that perhaps a Chat GPT can’t. And it knows the context of my book and it knows the context of the essay I wrote. So what then comes back is something a little bit more structured that I can then play with. It’s a marginal improvement on doing this on Chat GPT. I’m sure you could probably figure out how to do it. But it’s quick. I use it on my iPhone. I often do this when I’m walking through the airport and I want to solve this and have this result when I’m sitting on the plane. I’ll fire that query out and it goes back and goes out and sorts that out for me.

Krugman: Okay, I guess I’m getting it. But obviously you and I are not typical. The people who are using AI the most are going to be middle managers, business people, etc. And I find myself thinking about what I think of as the homemade pasta problem.

Azhar: Mm.

Krugman: You’re probably too young for this, but there was a time when I when young and we were using stone axes for computing, and there was a big fad of making your own pasta. Little pasta machines were everywhere. And then at a certain point there was kind of a collective, “What the hell are we doing? You know, store bought pasta is actually better. The Italians don’t do this.” And I have to think that for most tasks, the range of agents can’t be that wide. But why wouldn’t they sell that kind of thing off-the-shelf, as it were?

Azhar: Yeah, well I think it’s different for an independent person or a small business or a middle manager in a big company. I would imagine that you will start to see people selling specific agents that solve your marketing problem. If you have a barber’s shop and you’ve got four chairs and maybe 30 people a day coming through. Right now what you do is, you go to ChatGPT and you help it write your collateral for your website. That feels like it’s an interim step to somebody delivering the actual finished product. Why haven’t we seen it? I think we haven’t seen it yet because the terrain is still big enough.

Beyond Anthropic and OpenAI, there’s a lot of other companies building agents that are these end-to-end workflows for businesses. They still believe that the prize for them is to build the generic platform that is the tool for all tools. Because if you get that right, you have a much, much bigger business than if you’re just a vertical application. And I think we’re only a year or two into these entrepreneurs building such businesses. I think as some of them succeed and some fail, the ones that are not able to succeed in the general space will start to verticalize, which is what we saw in the advent of the internet. We saw it in software as well.

But I think within a big company it’s a different set of questions because you have far fewer degrees of freedom as a marketing manager in a large company than you do if you own your own barbershop. You have all these rules, you have all these other teams you have to interface with, you are held to the priorities and the plans of the company as a whole. And in that instance, I think, it’s much harder to see how you use AI to really change the way you work.

Krugman: Yeah, I mean, again we’re talking about ancient history here, but you know, everybody still uses Excel, even though it has always been horrible. But the constraints of corporate life mean that everybody has to use Excel. So that means maybe we’ll see quite a lot less coding a few years down the pike because the people will just be able to purchase whatever it is they need. I don’t know.

Azhar: I think there’s a balance. You hear people proselytizing heavily, saying, “I think this technology is going to be impressive and have a significant impact.” But when people pitch this, they forget that there are other actors in the market who might respond to what’s going on. Right now, if you’re a large company, you want to be building as much as you can because what you can buy isn’t right for the market. If you think about Henry Ford putting together the Highland Park plant, he couldn’t go to a supply chain and buy what he needed because nobody was thinking in those terms. I think we are slightly at that stage for large corporates now. Whether we’ll be there in five years, I don’t know.

The question we have to consider is where the value will reside: between having your own capabilities to design software for your processes, or handing that over to another company designing software for a hundred businesses like yours. Historically, it has made more sense to hand it over to another company, but the cost curves may have changed sufficiently that you’d rather have the nuance and control to do whatever ‘vibe coding’ becomes in 2030.

Krugman: I know with healthcare software, organizations like the VA that built their own have done much better than the ones who tried to buy it from Microsoft. So yeah, it might be a story that makes sense. And actually, since we’re talking about going for the models versus something much more specific, how do you think about the Chinese versus the big US AI firms?

Azhar: I’ve just spent eight days in China and I was really fortunate. I got to speak to developers and engineers and management from about a dozen of the Chinese labs. In many cases they hosted us in their offices. The main thing the Chinese companies say about the US firms, is that Claude code is brilliant and Claude is the best model that is out there and they really couldn’t get enough of it. The term is, they’re Claude-pilled. They talk about the constraints on getting access to computational power but just in a way that’s a fact of life. I mean there’s no sort of commentary on it other than it’s hard. They have to figure out how to get around that and how to build a culture of efficiency when you don’t have as much [computational power] and I think they have built a culture of efficiency really, really well. I think it’s going to help them over the longer term. They don’t really talk about competition with US labs the way the US talks about competition with China. But they do see themselves competing with each other.

And as you know, that’s what the Chinese economy is. It’s mayors in different cities who almost act as venture capitalists who compete tooth and nail with each other to become the electric vehicle hub or the solar hub or the AI hub of the nation. And what I would say is, the models are really, really capable. They’re very efficient, which is why they’re so cheap to run, which makes them very competitive for a whole range of tasks. But at the margin, it’s instructive to note that everyone was using Claude for coding as opposed to the cheaper Chinese version.

Krugman: That’s interesting. So you can imagine a future where a lot of businesses are actually using these less comprehensive but much cheaper models. I think what I’m gathering from you and from other people is that a lot of entrepreneurs in the US are still dreaming of the uber-model that solves all problems but that probably is not going the way it all goes. That in the end we’re gonna end up with a lot of specialized models, but also the uber-models will still have a role.

Azhar: Yeah, it never made sense to me that you’d have a single model that would do everything because if the single model is going to solve the Riemann hypothesis, it’s gonna require a lot of resources. And if all you need to do is get it to root a bill to the finance department, it seems a bit silly to ask Einstein to come and do that for you. We’ve had segmentation of markets for a long time and it’s like with airlines. There’s a reason why not every seat on an airline is first class. Some passengers don’t want it, don’t need it, won’t want to pay for it. So I do think that the ecology looks like a whole array of much, much cheaper models that are serving by volume lots of corporate needs, and then having more sophisticated, complex models for the more complex tasks. I think you’re already starting to see this.

I don’t see it, by the way, as a shock to the industry. I just think this is what happens as an industry matures. You know, you start with one size fits all, then you start to segment your customer needs and you start to serve them in the most profitable way you possibly can. And that just feels to me like the way that the markets have matured.

Krugman:

Okay. Let’s move to more macro considerations. People have been worrying about a bubble. A lot of us still remember the nineties quite vividly and think about all of that. But you just aren’t seeing the bubble. You wanna talk about that?

Azhar: I remember what it was like in the nineties. I lived through that one and also the housing bubble, which frankly was far, far worse and much more terrifying. I have a really simple mantra here, which is that honest customer revenues tend to be the engine that gets you through this, right? You know, what caused the problems with the US railroads in the 1870s and 1880s? It was that the revenues didn’t materialize because the tracks were being laid in places where there were no towns. That was a problem. The same was true in the dot-com era. My team and I realized last year that it’s very hard to get good quality data on how much was actually being spent by American businesses and consumers on AI. So we’ve spent several months building systems and gathering data to give a deduplicated view of what that number is. And just to give you a sneak preview, is $150 billion per annum, annualized at the end of May 2026, and about 90 billion dollars in the previous 12 months, from May ‘25 to May ‘26. So you can see it’s growing, and those are deduplicated numbers.

So if you spend a dollar with OpenAI, and they have to pay Microsoft 60 cents to run the servers, we only count that as a dollar. We don’t count it as, you know, $1.60. It’s a much faster revenue growth rate than mobile or the internet. It’s also a small number because the US is a $32 trillion economy. And I think the thing is that at that level of spend, you are able to roughly cover the depreciation on the enormous capital expenditures that have gone into AI just this past year. But next year or the year after, you have to double your revenues again and again in order to cover these increasing commitments.

The thing that often pricks a bubble is when financing starts to get a bit smelly. That was clearly the case in the global financial crisis, where synthetic collateralized obligations were magnifying the risk on subprime mortgages—it was all “smelly finance.” In the dot-com bubble, the dot-coms themselves didn’t really have much smell about them. There was a lot of disbelief, but the telecoms clearly had issues with their internal revenue generation.

So the other thing that we look at is how bad, poor, or strong or robust is the funding quality. And that funding quality measure is definitely getting worse. It’s worse now than it was nine months ago. But it doesn’t seem from the numbers to be at the level that it has been historically when these things have imploded. Nor does it seem to be the type of exposure that is really systemic, which is what we saw in the global financial crisis. There are companies like Oracle and Coreweave whose debt looks very risky, and it’s harder and harder for them perhaps to raise money, although Oracle just did. But it doesn’t feel like it’s systemic.

You know, when the the global financial crisis popped, no one knew who was in trouble, whereas now you’d be able to isolate it with a single company or a single firm. So at the moment we feel that this is still a demand-led boom, that funding quality has definitely gotten worse, but not so bad that I would say that there is an imminent problem on the horizon.

Krugman: So at this point, you’re saying that roughly speaking, final demand for this is about half a percent of GDP. What share are AI-related stocks in market value? It has to be substantially larger than that.

Azhar: They’re about forty percent of the S&P 500 right now.

Krugman: That’s a huge mismatch. Revenues are not the same as profits, but you’re talking about what is still a relatively small business relative to this immense economy, yet it dominates the financial markets. That would be at least a possible source of alarm.

Azhar: Let’s dig into that, because a stock price is a reflection of the expected future value aggregated across the market. Forty percent feels high, but if you look at the measure of earnings, these companies actually have a much higher proportion of earnings and earnings growth.

If you look at the US stock market in 1900, after the railway calamities of the mid-to-late 19th century, railroad stocks were sixty percent of the capitalization of the US market. We had worked our way through the busts by that point. There’s a fantastic piece of academic work by an American finance professor named Bessenbinder. He looked at the stock returns of 23,000 US stocks from the 1900s through 2022. Those returns are highly concentrated. About two-thirds are concentrated in roughly 30 companies. Those companies are oil, electricity, or car companies—the general-purpose technologies at the start of the 20th century—or they are the IT companies like Apple and Nvidia. The only exceptions were Walmart, a couple of healthcare businesses like Pfizer, and JP Morgan.

Historically, you get this concentration of a number of winners when you have a new general-purpose technology, and that is showing up today. I don’t feel we’re overly concentrated from the perspective of risk, and the price does not feel totally out of whack compared to where we were during the dot-com era.

Krugman: One last devil’s advocate question. I keep thinking of the California gold rush. If you had looked at the revenue and spending on gold-rush-related businesses as a whole, it probably looked solid. But the trouble is it wasn’t the gold; it was the picks, shovels, blue jeans, women and whiskey that were the revenue streams. Is that a fair question to ask about AI right now?

Azhar: It’s a great question to ask. The question is what determines that $150 billion annualized demand? We see that just under 30% of the S&P 500 have pointed to a generative AI project with a quantifiable result in their earnings calls. They are under pressure to say they do this, so maybe that’s what’s going on. But when I talk to executives, like 30 finance businesses in New York, they all plan to spend more next year, even though not a single one could point to even a 10 basis point improvement in their business from the investments made so far.

Krugman: Right.

Azhar: When we break out that $90 billion, $60 billion of it is in the US. That’s a lot of money for a single company, but spread across thousands of firms, it’s still at the experimental stage. We should consider whether these executives are learning by doing. The messages I get vary from those having success in the tens of millions who want to reach hundreds of millions, to those finding it harder but persisting. We’re slightly beyond pure picks and shovels, but in Paul David’s work, it took 50% of American companies getting electrified before the productivity rise. We’re a long way from that.

Krugman: Headlines flashed about a KPMG study with case studies on the usefulness of AI that turned out to be AI hallucinations. It’s a wonderful thing.

Azhar: It is brilliant. One thing that is quite challenging is that the market has talked a lot about bottlenecks. We saw this with railroads when the US couldn’t make enough steel. There are these bottlenecks, and there’s a lot of emphasis on power and getting electricity to the system.

There’s more demand than supply capacity for AI right now, but there’s a question of whether there’s enough capital. We may see another few trillion dollars of intention from tech companies to build infrastructure to 2030, which starts to rival the new issuance of the US Treasury at $2 trillion a year. I’m wondering if this capital constraint is going to be an issue or if the market knows how to clear it.

Krugman: Ordinarily, we’d expect to see that in prices. Real interest rates are well off their pre-COVID lows. They are higher now, but still substantially lower than at the peak of the nineties tech boom, when they were around four percent. They’re more like two now.

It’s surprising, given the AI boom and massive budget deficits, that rates aren’t even higher. Whether this is an actual constraint, Nvidia is not the US Treasury. They need risk-tolerant capital. The possibility that these firms may not be able to raise enough money is something we need to think about.

Azhar: Yeah. On that Nvidia point, I saw that credit default swaps on five-year Nvidia bonds—the cost of insurance against default—are currently lower than US Treasuries.

Krugman: I saw that, and it strikes me as completely crazy. If you think the US government is not reliable, you shouldn’t be investing in chip stocks; you should be investing in canned goods for your bomb shelter. But anyway.

Azhar: Are you telling me that markets aren’t perfectly rational, Paul?

Krugman: Good heavens, I can’t say that; they’d take away my economist card. We’re recording this on SpaceX Day, and I’ve been wondering if there are limited pools of capital for cutting-edge investments. I wonder whether Elon Musk is diverting capital that AI might need. A whole lot of meme money is pouring into SpaceX right now. Is that something I should be thinking about? I mean, he’s got what everybody tells me is a crud AI product in Grok, and yet…

Azeem: Musk showed his willingness to adapt; his AI product is now being subsidiarized using his capacity to serve customers like Anthropic. He has an incredible following, but people who have worked with him say his ability to relentlessly focus and optimize sets him apart. His first-principles thinking has brought down the cost of space launches faster than anyone in history. He pushes the rate of learning aggressively. For all the challenges and his mercurial behavior elsewhere, that’s generally a good thing because technology has brought down the cost of inputs significantly.

We’re going to be much further ahead in space than we would have been if SpaceX had not been successful. It raises questions about how to govern what used to be a commons, but there is a definite benefit from coming down that learning curve so quickly.

Krugman: That’s fair. The one time I looked at Musk’s activities and thought he was really onto something was when I realized he diagnosed that the cost of space launches is really the rocket, not the fuel, and recovering it makes all the difference. Being able to make it happen is a real productivity thing.

This is all moving so fast that we don’t have time for the technical productivity issues we had in the past. It’s feeling like a Solow moment where people say, “I see the technology everywhere but in the productivity statistics.” Do you want to talk about that?

Azhar: It comes up all the time. I wonder if we need things to happen more quickly than we used to. We aren’t seeing it in the numbers yet. Erik Brynjolfsson at Stanford says he thinks it is showing up in the aggregate numbers. How quickly should we expect a technology like this to show up? At $90 billion a year, that’s not much of US GDP. These are early stages where companies are learning. The first $100 million you might spend on AI is about learning, and we’re in that mistake-making phase.

The model Paul David and William Devine talked about in electricity is helpful. In the first phases, you’re retrofitting your capital stock and processes with the new technology. It’s not until you depreciate existing capital and change processes—like Ford did at Highland Park—that you see productivity benefits. To put numbers to that, what would we expect to see in the Ford equivalent of Highland Park in terms of output?

Krugman: Yeah.

Azhar: I thought we might see what happens to revenues per employee in an AI-native firm. Across high-end companies like McKinsey, it’s about $400,000. For Meta or Google, it’s about two to two and a half million dollars. In AI-native firms like Mercor, that number is closer to seven million dollars per employee. For Anthropic, it’s close to ten million. You can measure the enormous commercial productivity of a single employee if a firm is AI-native. We’re talking about a handful of firms, but we can pick up the shape of what’s possible for the productivity of a single employee. It may be hard, it may take time, but it’s possible.

Krugman: What would those numbers look like per dollar of invested capital? One worry is that this is an enormously capital-intensive business that replaces labor. The oil refineries of New Jersey have enormous revenue per employee because there are no workers, just monstrous capital installations. Is that a factor?

Azhar: Anthropic has raised in the tens of billions rather than hundreds of billions and had a profitable quarter ahead of schedule. What we don’t know is how much of that capital goes into developing the next model versus monetizing previous generations. Their IPO in the next six to nine months will tell us.

Chinese companies are using much less capital to build models that are nearly as good. So I think the harder part of your question is that if every model that OpenAI or Anthropic costs ten times as much to deploy and develop, but lasts only a couple of years before it’s defunct because of competition, what needs to be true for that to be sustainable for more than a year or two? To me, that is a really tricky question as well.

Krugman: You’ve cited intermediate measures. Rather than revenue, we look at generated lines of code, which has exploded, versus actual usable applications, which hasn’t. Does that tell us anything?

Azhar: Lines of code is an odd measure. We’ve made it much cheaper to write code, so less determined people are writing it now. It’s unsurprising the increase hasn’t been met by proportional productivity. Data suggests we’re getting more high-quality code, but also a lot of useless waste. This isn’t the first time a useful input in the economy generated waste. Think of a barrel of oil: we count the whole value in GDP, but two-thirds is thrown away as waste heat. Only one-third is useful energy. Sloppy lines of code are a similar form of waste we’ve been happy to tolerate in other sectors for a century.

Krugman: A weird analogy is when widespread word processing came in. Books started getting longer. It was so easy for authors to turn out hundreds of pages. What might have been a two-volume series became five.

Azhar: On that front, we’re at an enlightenment moment. In 18th-century France, the battle was over who gets to write and express their story. Men and women produced remarkable works with quill pens that encapsulated a world.

Krugman: Right.

Azhar: Is it worse that we allow for more expression? We are worse off when that connects to an algorithmic recommendation system that drives constant slop at us. But we aren’t inevitably worse off because we’re giving access to many more people.

In reducing costs of access, we might find amazing people. In breaking down silos of knowledge, we might find connections—perhaps something in battery chemistry that is useful in cardiology. We don’t know because we’ve never been able to get those experts to talk. I look at each opportunity discreetly.

Krugman: There is a potential book here: The Upside of Slop. This is an unrecognizable scene from eighteen months ago. Wow.

Azhar: We could get ChatGPT to write it.

Krugman: I started my career writing papers longhand on yellow legal pads. Amazing change.

Azhar: I still write everything with a fountain pen. I’m writing my new book longhand and most of my research is too. The computer is turned off because AI does all the boring stuff like PowerPoint and emails, giving me time to apply my brain to things I want to think about.

I’d be happy to continue this conversation in a few months. Thank you for inviting me.

Krugman: Thanks so much. Take care.