Broadsheet

I hadn’t originally planned on writing a sequel to my December, 2023 essay, A Camera, Not an Engine. It seemed like a mostly complete thesis when I first wrote. But it’s become increasingly clear in the 2.5 years since I wrote it that the thesis is both bigger and more incomplete than I thought, but getting truer by the day.

The basic idea of the essay was that generative AIs are primarily instruments for seeing in latent space, not engines of utilitarian production, despite the adjective. The title was a reference to Donald Mackenzie’s book, An Engine, Not a Camera, which made the opposite argument about the economy. In both cases, the argument was about flipping the view of what the thing was.

This theory has felt ever more right since I first proposed it, but I’ve also felt it’s missing some pieces. One obvious missing piece is a proper camera-theoretic account of agentic AI, which at first sight seems more engine-like. We’ll sort that out after laying some groundwork.

One critical piece was supplied by , who offered a definition of intelligence in a recent conversation:

Intelligence is a unit of information driving a unit of energy.

This is a deceptively simple definition; one that immediately cuts to the computational heart. I suspect some rigorous version of this will eventually be enshrined alongside ideas like Landauer’s principle. It’s not an idle thought. Sreeram is the founder of Eigenlabs, which is pushing the boundaries of AI in some of the most interesting ways today. They’re betting their technology roadmap on this definition in some ways.

Now, what could this definition mean? How does it help develop the camera/engine frame further? Let’s start with something that came before the camera — the telescope.

***

My introduction to astronomy came via an antique brass telescope in 1986, when my school astronomy club organized a Halley’s Comet viewing event. That event changed the course of my life in many ways, but I want to talk about that telescope.

For several years, I revered that telescope. It was a big, beautiful, heavy brass refractor on a heavy equatorial mount, with finely engraved brass setting circles. An instrument of the sort they don’t make anymore. So heavy, it had to be bolted to a wooden frame to allow two of us to carry it to the rooftop to place on its mount. Our school had inherited it as a hand-me-down from some American school.

But I also wanted my own telescope, and eventually I got one — a cheap, locally made Newtonian reflector. The tube was PVC. The eyepieces were cheap plastic. The mount was a simple no-frills altazimuth mount. The thing had absolutely no gravitas. I could lift it with one hand.

And it was radically better than that old brass beast.

So long as the old brass telescope was the only one I knew, it was something of a sacred object. Once I looked through a better one, everything changed, and I saw it for the obsolete museum piece it was. The antique didn’t have a properly achromatic lens. The equatorial mount had jammed at the declination of some random North American latitude, so the setting circles were useless, and you had to point it by navigating using the constellations. The views were blurry and chromatically fringed.

The astronomical telescope is an instrument with one job: to find, track, and show you things in the sky clearly. It is a rudimentary sort of intelligence too, using units of information (location and time information) to drive units of energy (the effort to point the telescope in a given direction by slewing on the two axes). When it does its job, this rudimentary intelligence loop anchors a bigger one — the information flowing from the skies into your eyes, shaping your thoughts, and then the energy driving any actions that follow those thoughts. In my case, that outer loop was a very consequential one. I almost went to grad school for astrophysics (at IUCAA in Pune, an astrophysics research institute) but turned it down to go to grad school in the US instead, where my PhD ended up being about the engineering side of interferometric space telescopes. Life-changing you might say. And since I run at about 100 watts, that early experience is still probably driving perhaps 2 watts of my average energy output.

My telescope arc reached its zenith around 2021, when I got a chance to spend a night at the Mount Wilson observatory and look through both the legendary 100” reflector and the 60” Hale refractor. These beasts take powerful motors to steer, and are among the last major frontier telescopes to be equipped with eyepieces for humans. Since then, all research telescopes have essentially been cameras. There’s no point in looking through them. Even serious amateur astronomy has gone that route. A high-school astronomy club friend I recently got back in touch with (a different, unrelated Kannan as it happens) has turned into an accomplished astrophotographer now. Our old shared experiences are probably driving 4 watts for him now. Information can drive energy over really long periods.

Looking through eyepieces is mostly for poets now. Not serious astronomers, whether professional or amateur.

My own astronomy adventures are now down to occasionally lugging out a modest hand-me down telescope (thanks ) on rare nights of clear seeing. I didn’t get far in my own astrophotography experiments. It requires more patience than I possess, and I kinda have an anachronistic attachment to actually looking through eyepieces rather than at photographs.

It might not be an entirely irrational impulse. just sent me this really lovely essay on how the gamut of the digital camera differs from that of the human eye (takes me back to my Color Science 101 days at Xerox), so maybe there really is a bit of difference between looking through the telescope and looking at photographs taken with one.

But let’s get back to the brass telescope, and make up a parable about it.

***

Imagine a blind astronomer who has a deep passion for the stars and planets. What could this astronomer do to pursue their passion?

There are two possibilities.

First, they could work on the images streaming out of the cameras with various analytical tools, doing all kinds of technical analysis. This is in fact what modern astronomers do. The human eye hasn’t been particularly relevant to astronomy in decades. You could do bleeding edge astronomy work, and I mean empirical, observational astronomy, not theoretical astrophysics, without ever looking through a telescope or even at the photography.

Of course, few actual astronomers are that soulless I imagine. I suspect most still look up often from their spectral charts and pages of math at the skies, and occasionally take an anachronistic and sentimental peek through a poet’s telescope that still features a vestigial eyepiece.

This possibility is not particularly interesting. It’s a reasonable and pragmatic way to pursue an interest in observational astronomy as a blind person. Or a sighted person for that matter.

The second possibility is, our blind astronomer could fetishize the instrument itself. The brass finger pointing at the moon. This is the interesting possibility.

Imagine a blind young astronomer in my position in the late 1980s, faced with a stark choice between a beautiful but functionally crappy antique telescope and a utilitarian but functionally superior one. Imagine further that our blind young astronomer has spent years caring for the ancient brass instrument, polishing the brass, cleaning the lenses, carefully taking eyepieces in and out of antique velvet-lined cases, becoming intimately familiar with every groove and curve.

Now he touches the new telescope — warm PVC, light plastic eyepieces in a cardboard box.

We can imagine a certain possibility — the sentimental attachment to the old instrument as object, rather than as a medium for seeing, is too overwhelming. Our blind astronomer retreats into a curious place: Insisting that the antique brass telescope is the superior instrument.

There is of course, a third possibility: The blind astronomer abandons astronomy, and transforms his sentimental attachment to the brass telescope into an antiquarian-historian interest in telescopes. But then, he’s no longer an astronomer, and exits our parable.

***

Let’s turn to AI now, and consider the nature of words in human society in light of the parable of the blind astronomer and the brass telescope.

Like telescopes, words are both instruments of seeing, and objects deserving of attention in their own right, as embodiments of the wordsmith’s craft.

In 2011, I wrote what became one of my most popular Quora answers, in response to the question, What are some tips for advanced writers? My answer made a distinction between two kinds of writing: Writing to think, and writing to write. The key bit:

The divide between thinkers and writers is more important than the one between fiction and non-fiction writers. You could divide the world of advanced writers into a 2x2, based on whether they are prioritizing developing their thinking or their writing, and whether they are focusing on fiction or non-fiction.

My hypothesis (I haven't yet gotten to a stage where I can check this) is that it is easier to cross the fiction/non-fiction divide than it is to cross the writing-first/thinking first divide.

Now, 15 years later, this is no longer a hypothesis. I can claim with some confidence that this divide is radically hard to cross, and I can’t actually think of a single person I personally know who has crossed it. I certainly haven’t.

There are people who exhibit some degree of ambidexterity, but everybody seems to land on one side or the other, net.

If you’re unsure where you land, one tell is how you react to editors. No serious writer enjoys being edited, so the signal is what sorts of editing you grudgingly accept as valuable anyway, and what kinds you absolutely refuse to countenance.

Those who write to think typically resist any attempt to change the content of what they’re saying, but generally don’t care about style, verbal precision, tightening, and pragmatic cutting suggestions to hit word-count limits.

Those who write to write are typically attached to every word and comma, but can be surprisingly indifferent to substantial content edits and highly open to saying entirely different things than they originally set out to.

Writing to think, and writing to write. Or in the language of our brass telescope parable, the sighted, attached to looking through words, and the blind, attached to looking at words. Beautiful, heavy brass words.

Both kinds of writers face a moment of crisis today, perhaps similar to the moment in history when eyepieces began to be replaced by cameras in telescopes (though from my understanding of that history, astronomers generally didn’t have the strong attachments to their instruments that writers do, and for the most part eagerly jumped into photographic astronomy).

Those who write to think face one sort of crisis of the psyche — writing is no longer the only general way to think, and rarely the best way, and they must either adapt to newer tools of thought or abandon the frontiers of thinkability and retreat to the shrinking number of niches where old tools work better.

Those who write to write face another sort of crisis of the psyche — they must choose between becoming antiquarians of words and defenders of a thesis of the necessary superiority of hand-wrought brass words.

***

I’m obviously in the adaptive thinker tribe, and I’m content to leave the other three tribes to their devices. It’s not even much fun anymore to troll the defenders of brass words. When I hand-write these days (this essay is an example), it’s because I don’t yet have the skill to wield generative AI to do the job. The shortcomings are as much mine as in the evolving tech. I’d be happy to let AI write essays like this for me the minute their capabilities, and my skills at wielding them, allow it.

At this point, the main question that interests me is how to think with AI, and what role, if any, words ought to play in emerging modes of AI-assisted thinking. The more words become unnecessary for thinking, the more I discover I’m not primarily a writer. I write primarily when that’s the laziest mode of thought available. AI offers lazier modes that yield as good, and increasingly, better thoughts.

Let’s start with a characterization of natural language that will allow us to apply Sreeram’s definition of intelligence as a unit of thought modifying a unit of energy.

First, natural language has now clearly become a compile target for pre-verbal thoughts for at least the write-to-think types among us. The prompts I write to produce a generated essay aren’t actually the thoughts I want to think through. They are more like telescope steering actions — looking up the coordinates of objects in the sky, punching them in, and getting the telescope to point in the right direction. Prompting is pointing at things.

Second, natural language has equally clearly become a programming language for automatically triggered post-verbal behaviors. This is one of the new developments since I wrote part 1. The output of a prompt is not necessarily text you read. It can be text for computers to read (rendering moot the question of whether humans could enjoy it), or code that runs and does something. Prompting is programming behaviors.

In sequence, prompting as pointing at things, and prompting as programming behaviors, represent the feedforward path of AI use. We’ll talk about the feedback path in a minute — the camera/engine distinction rests on that.

In feedforward mode though, increasingly, natural language feels like a hidden layer in thinking, mimicking the structure of the systems that you’re thinking with.

The input layer is pre-verbal or partly-verbal ideas, at least for me. A good deal of my pre-writing thinking is visual, affective or even somatic (vague finger-tip or gut feelings). Thought-forms that offer just enough verbal purchase to express as prompts to point the AIs.
The administrative layer, the only natural language you touch, steers the camera to point in the right part of latent space corresponding to those ideas.
Intermediate output layers might be close to human natural language (markdown files) or distant (JSON, code, binary…), but the point is, they’re usually not meant for human consumption at all. Intermediate output is for AI talking to itself. It may or may not stay close to human natural language as it evolves.
The final output layer, where some sort of energy flow is shaped to create intelligent behaviors. Today, this is mostly compute energy. Your prompt might end up as a piece of code that then runs persistently on a server, consuming watts. Increasingly though, it is generalized forms of robotic energy and other kinds of physical-intelligence energy.

When I consider the thinking I’ve done in all my vibe-coding projects over the last few months, it is is startling how little of it is in natural language that I produced or consumed, and how little of that is part of the content of the thoughts as opposed to the administration of the thinking.

In a very literal sense, my thinking has become increasingly post-verbal. Only a small part of it is verbal, and it’s dominated by the administrative steering part.

To be clear, that’s demanding thinking. Executive managerial attention deployed with the steady intensity of maker attention, rather than in the spiky way we’re used to expending it. I forget who pointed this out, but Paul Graham’s idea of manager time increasingly looks like his idea of maker time. Managerial energy and attention is increasingly expended through maker-like 4-hour vibe-coding blocks rather than 1-hour meeting blocks. It’s manager time nevertheless.

This managerial work takes the form of natural language communication, but is vastly more exhausting, because every word might unleash a thousand more, and those thousand words might govern computers and drone. Administrative natural language in thinking with AI is increasingly acquiring speech-act like character, like the words of judges when they pronounce verdicts. Or Rameses in Ten Commandments declaring “so let it be written, so let it be done.” We are all pharaohs now.

A lot of the verbal thinking goes meta-verbal in the process, where you have to think legalistically about large piles of words. For example, in constructing RAG bots to work with a corpus of text, you have to understand the contours of that corpus and how to navigate it semantically and mechanically. Meaning and form both matter, and both must be shoveled around by the thousand. It’s very artisanal work, but not wordsmith work.

***

It’s not that hard to play the current evolutionary trajectory out a decade or so. It will become possible to do an increasing proportion of your internal thinking in non-verbal ways, and have AIs conform to the visible surfaces of that thinking through increasingly rich interfaces. And on the output end, it will be increasingly possible for the shaped energy to take just about any form that can be actuated.

Here’s a simple speculative example. It is 2032. I have a vague desire to experience a fantasy story. I say to my AI system — “give me a fantasy story.”

The AI begins by retrieving my history of story consumption, and flashing various storyboard elements at me — dragons, knights, damsels, magic potions — and tracking my facial responses. Perhaps I’m wearing an MRI helmet too, and it’s monitoring my fitness tracker.

Purely by monitoring my somatic and non-verbal neural responses, and a kind of idea-diffusion computational approach, it begins to converge on the elements and vibes of the sort of story I want to experience. These then turn into motifs, sequences, sequences, and plotlines. Fragments of dialog begin to appear, as do leitfmotifs and world-building elements. I’m presented with more or less verbalized forms of the story — dialogue heavy vs. image and affect heavy. Once it has fingerprinted my subconscious desires sufficiently, it presents me with a series of trailer comps, fictional book reviews/back cover blurbs. Again it monitors my reactions, figuring out the medium I want, and the type of story. Every narrative option is on the table — book, comic book, movie, musical album, theme-park ride, video game.

Eventually, it locks in, and produces something it figures will scratch my itch. The more preferences can be revealed, the less necessary it becomes to state them.

Literally nothing in this speculation is science-fictional. We possess all the pieces required to do a rudimentary version of this today. It would be janky as hell, painful to prompt the system, and endure the output, but it is already possible to hit, say, the quality levels of Hallmark Christmas movies.

All that remains to be done is refine all the pieces, integrate them better, and of course, keep improving the models they rely on and the hardware those models run on.

Besides my rather on-the-nose technology assumptions, this speculative example rests on a more oblique assumption: that “story” thinking is not actually the same as “verbal” thinking. They’ve just been historically coupled because we’ve lacked the technology to separate them.

This is not a radical assumption. In fact, you’ll find some version of this assumption in many fiction writing guides. Skill at story and skill with words are entirely different things (as a simple example, consider Ikea manuals featuring the famous Ikea man). The reason this distinction matters now is that there are many kinds of non-verbal thinking that happen to be tied to verbal thinking today in seemingly inseparable ways.

The more AI advances, the more different kinds of thinking become separable from verbal thinking, deprogramming centuries of Gutenberg-head in decades.

***

To bring the story back to the camera-engine frame, consider now the feedback path in agentic behaviors.

Any sort of agent, conceptually, is a very simple feedback loop. It sees, thinks, and does in a feedforward path, and in a feedback path, it registers the difference between expected and experienced outcomes. Elementary feedback control — an error signal drives further action.

This error signal is the raw information entering the system over time, and the rate at which raw context actually expands.

If you always see exactly what you expect, up to the limit of your indifference, the effective error is zero, and the feedback loop is superfluous. There is no misregistration between your expectations and outcomes to worry about. Cheap toasters and psychotic one-shotters work that way. Normally though, in real domains, there is a non-zero error signal that must be dealt with iteratively, and driven to zero. Whether you do so mindfully or brutally is what determines the nature and quality of your intelligence.

The question now is, when is such an agent a camera, and when is it an engine? Given that there is both sensing and acting in the loop, it’s tempting to answer why not both?

This, I assert is the wrong answer.

The thing is, the seeing can outrun the doing. This is camera mode. And the doing can outrun the seeing. This is engine mode. One drives errors to zero mindfully, the other brutally.

There can be a lot of information shaping very little energy, and very little information shaping a lot of energy. Intelligence is when the balance between mindfulness and brutality is right for the context. You can overthink and underthink, relative to the resolution of actions required (or equivalently the indifferences in outcome preferences).

In some situations, indifference is high enough, very coarse action regulation is enough. In other situations, very precise action regulation is required. One calls for little to no feedback (one-shotting being the extreme case), the other calls for a great deal of feedback.

Agentic loops that are camera-like produce a surplus of information via rich feedback. Ones that are engine-like produce a surplus of externalities via impoverished feedback. Unintended consequences that you may be indifferent to, but others might not be.

To date, agentic AI has seemed very engine-like mainly because it has been applied in highly playable domains where two things are true: The misregistration between expectations and outcomes tends to be low, and a function of mistakes rather than ignorance or information deficits. In chess, for example, if a move causes play to unfold in unexpected ways it’s because you don’t understand the mechanics of the game deeply enough and haven’t computed far enough. Not because you misread the board position or because new pieces suddenly appeared on the board. You do not really need feedback as such. Players can play a game by sending messages with moves back and forth, and the game states they maintain will stay synchronized, error-correctable, rewindable, and replayable. AI is teaching us that the universe is unreasonably playable in this sense, but still, few real domains are as playable as chess.

This view suggests an interesting reading of AI psychosis of the agentic variety. When you operate in a domain that can frictionlessly absorb enormous amounts of cognitive energy without doing any real damage, you experience a positive feedback loop that can turn psychotic. There is no error signal regulating your thinking.

The more you operate in open, low-playability domains though, domains with friction, ontological openness, and real noise, the more you must choose between generating a surplus of information through feedback, or causing invisible unintended consequences. Consequences that may provoke hostile responses to your psychotic tendencies down the road.

One way to cash out the difference between the two modes is that old pair of terms, exploration and exploitation.

Cameras explore. They produce more information than they consume in regulating their own actions. Accumulating context outruns the action, which tends towards maximally mindful.

Engines exploit. The unleash more energy than they can control, based on a slow-growing store of information driven by minimal feedback. Action outruns context, and tends towards maximally brutal. There’s a reason we describe human engine-like behaviors as oblivious or tone-deaf.

To some degree, engines are necessarily stupid, malicious, or indifferent to consequences. In highly playable domains, they can enter extended psychotic regimes of exponential “productivity” where they do no work because they encounter no resistance. But they also generate no value.

As many organizations are finding out, this kind of atomized psychotic “productivity” in the high-playability pockets of an organization, far removed from layers of contextual feedback signals, does nothing for the bottomline or operational effectiveness. It is sound and fury signifying nothing at best, and an engine tearing itself to pieces at worst, in a shriek of explosive token bills.

Without the appropriate feedback loops at all levels, keeping context growing faster than action, behaviors just get dumber and more damaging and head towards runaway meltdown conditions.

Which suggests a very interesting reading of our historical moment. Are we going to turn the most powerful camera ever built towards new frontiers of exploration, or are we going to let it drive an epidemic of psychotic meltdowns masquerading as productivity leaps?

I often think of labor economics as a role model for the field: a subfield in which theory is disciplined by evidence and (most) researchers are willing to listen to that evidence even when it challenges their preconceptions. And hardly anyone does modern labor economics as well as UMass Amherst’s Arindrajit Dube, who has an excellent new book out. I talked with him about that book and the state of labor more generally.

. . .

TRANSCRIPT:
Paul Krugman in Conversation with Arindrajit Dube

(recorded 6/18/26)

Paul Krugman: One of the most satisfying parts of economics, which doesn’t get as much attention as it should, is labor economics. It’s obviously important. Most of us work for a living, or at least pretend to work for a living. But also it is a field, a subfield you might say; more scientific than almost anything else in economics, really evidence-based. You’ve had multiple revelations where the data have actually changed the way people, myself included, have thought about stuff. And among the most effective, prominent practitioners of modern labor economics is Arin Dube, who has a new book called The Wage Standard. And I thought we’d take a break from all the other stuff going on and talk about Arin’s work. So hi, Arin.

Arindrajit Dube: Hi Paul, nice to see you.

Krugman: Yeah, welcome to my virtual studio. Why don’t you talk just a little bit about The Wage Standard and what you’re trying to do, and then we can get into the broader labor economics issues?

Dube: Yeah. So, I wrote a book. Here it is.

Krugman: By the way, we mostly don’t do that in economics; we write 5,000-word articles.

Dube: Exactly. Paul, of course, you’ve written many amazing books. But economists don’t usually write books. We publish articles.

Krugman: That’s right.

Dube: And so it was actually a big deal for me to sort of think about, did I want to write a book? And I kind of went for a number of years and I said, like, “Oh, well, I’m not writing this book for other economists as a main audience,” though of course, I’m very happy for other economists to read it, but I wanted to try to have a broader conversation, and I needed to be clear that I wanted to know what I was going to say in that conversation.

And so here’s basically the main point of the book. The main argument is that Americans deserve a raise, that most American workers actually could get paid more and should get paid more. And there are really good reasons to think that. You know, the market has not delivered what could be a sustainable but higher wage for those in the bottom and lower part of the income distribution. So that’s basically the core idea. And I try to bring in what we know about the research that I think has really blossomed in the last decade or two decades on a bunch of topics when it comes to understanding the labor market.

I was writing this book at the beginning of the pandemic and especially 2021. And it was really interesting because this was one of the more remarkable episodes in the labor market that really highlighted a lot of things that I was actually talking about in the book. Of course, it did it in a very messy way, because there were lots of things happening during that time. But it made for a very interesting process where I felt like I was writing the book and the world was writing itself outside, which was both exciting and challenging.

Krugman: Okay. I said that labor economics has been revelatory. When I was not young, but younger, I think most economists circa 1990 would have thought of the labor market as just being a market of supply and demand. And where they crossed determines wages, and there’s nothing much you can do about it. And if you try to change it, you do so at your peril; bad things will happen. And as you say in the book, and in many of your writings that I’ve been following on all this stuff, that’s something that really, really changed. You want to talk about what happened?

Dube: Yeah. So, one really interesting thing is to think about how wages are set. And we could start with the basic supply and demand story, which basically is that there’s demand for workers of different skills and then there’s supply. And depending on the supply and demand conditions, you’re going to have different wages, a different skill price. And let me be clear, I think there’s a lot of important aspects of that that actually matter, but it’s also incomplete. Because here’s the thing: if the market really worked like the textbook supply and demand story, basically workers of a particular type would just get paid the same—that’s the skill price. But in reality, it turns out companies have a substantial degree of discretion in setting pay. And you can start to see this by just looking across companies hiring similar workers, but choosing to pay someone different.

One simple example to start with is FedEx and UPS. Workers may be driving very similar routes delivering similar packages, but it turns out FedEx pays lower than UPS. UPS has maybe 37% of the workers; a few years back, they were paying less than $20 an hour. For FedEx, it was more like 60%. And so, of course, that’s just one example, but you have others. Like, look at Walmart versus Target. It turns out that Walmart tends to be paying somewhat lower than many of its other similar, large retail competitors. And the list goes on. But this is not a new observation. Labor economists who were studying this in the mid-20th century had gone and collected surveys and understood that, you know, factories in the same labor market could be paying different wages.

But here’s what was not fully convincing: how do we know that it’s not maybe somewhat of a different skill mix? Maybe these companies are similar, but they’re hiring somewhat different types of workers the pay difference reflects that. So that argument held sway for decades until we had better data. And this is where what you say about labor economics, I think, really is right. And part of that has been our ability to really get much more granular and high-quality data, including administrative data linking pay for virtually most people in the labor market. And you can track them as they go from company to company. So you could say, “Hey, actually, what happens if the same person moves from Walmart to Target? Do you see they’re getting a higher pay?” Because you’re holding their skill set constant there. And so this kind of data and this sort of research design helped establish that actually, no, it turns out there is a substantial amount of variation in pay that comes from companies choosing different types of pay policies. And that’s a big part of the argument in my book, more broadly, that there are choices we have made.

You know, if we wanted to go back and look to see what’s happened to productivity and what’s happened to wages since 1980, productivity has grown much more strongly than wages—maybe not as strong as it did in the postwar era, but nonetheless, it grew a lot more than the pay for the typical worker, certainly pay for those at the bottom. And one of the arguments that I make is that this reflects choices made in a variety of places, and that starts from choices at a corporate level, different companies choosing different pay policies, all the way to policies that are being made by state and federal government. But the core part of it is like, why does that make any sense? It doesn’t make much sense to talk about companies choosing pay policies if the market is just your supply and demand. There’s no role for saying, “Are you doing the high-wage strategy or a low-wage strategy?” That’s a nonsensical question in a perfectly competitive market. But it’s an absolutely sensible question to ask when companies have some degree of wage-setting power.

You know, economists have a funny word for this, right? Monopsony. It’s a funny word. But the basic idea is really straightforward. You know, companies are making a choice there. You could go for a higher wage strategy or you could go for a lower wage strategy. Now, if you’re paying lower wages, you are going to have some more people quit and you’re going to have a somewhat harder time recruiting new workers. But the key thing is, it doesn’t mean that if you pay below a hypothetical market wage, everyone bolts, right? So you actually face a meaningful tradeoff of exactly how much more to pay or how much less to pay, and different companies end up choosing different amounts.

And this is also where—and this is even more recent, really in the last, you know, 5 to 7 years—we have seen a really big increase in research on the topic of monopsony, so we can really better understand exactly how much wage-setting power companies have. And it just sort of turns out that if a company’s choosing to pay, let’s say, a 10% lower wage, they’re going to have higher quits. Maybe about 14% higher quits. I just finished doing a review for the Journal of Economic Literature, and that’s basically where it sort of lands, and the quit rate is just not super sensitive to wage. So this gives employers a degree of discretion. And they’re going to do a couple of things that are important. First, different companies may choose different strategies. That is what creates these differences across companies. And the way companies have made those choices has really been different in the arc of history.

Krugman: Okay. So that’s where actually I came in on this topic, which was a classic paper by Claudia Goldin and Bob Margo. You know, I grew up in a world very different from the world where you grew up, with much more equal wages than we have now. But it turns out that wasn’t something that gradually evolved. It happened in a few years, basically during the New Deal and World War II: the Great Compression.

Dube: Absolutely. Yeah. And so that’s a story that has been told. But I also tell it with sort of a labor market focus. And a key part of that was actually creating a set of collective bargaining institutions, starting with the National Labor Relations Act; we had an upsurge in union organizing. And I highlight some more recent work that has been really careful to try to actually understand the causal effect of that unionization, for example, on the wage structure—work by Henry Farber and coauthors that really documents this very carefully. And it’s not just in the National Labor Relations Act. It’s also during the war. The Roosevelt administration actually helped encourage an increase in unionization. And that had a lasting impact on pay setting.

So this is basically where, after the end of the war, we had what is called the Treaty of Detroit, which was the landmark agreement, as coined by Fortune magazine, between United Auto Workers and the big three automakers, which spills over into the nonunion sector and other parts of the economy through this pattern bargaining process. But all of that created something very different than we had in the early 20th century. It basically created a set of mechanisms that helped ensure wages stayed relatively well tethered to overall productivity. And wages, both at the bottom and the middle, stayed tethered to the top. There were lots of issues. I don’t want to romanticize the 1950s or early 60s. But when it came to how wages were determined, it just meant you had broader based prosperity.

Krugman: So in the wage structure there are social institutions that set norms and so that’s part of it; the thing is much more sort of a surface on which you can move back and forth based on institutions. That was one of the lessons I took from the Great Compression. And now you’re saying that there’s much more of that. And also that you can get away with it. I would say that if somebody now proposed something like what happened during the New Deal and the war, The Wall Street Journal would be running nonstop, fire-breathing editorials about how this will destroy the economy and lead to mass unemployment. And your point is that it doesn’t, because of the range of discretion that companies have in setting wages.

Dube: Exactly. And those range of discussions in some cases evolved and were forged in the fire of union organizing and militancy in the ‘30s and ‘40s, and other times. There are ones that come up in an era where it’s largely nonunion workplaces that are expanding—for example, Walmart in the 1980s—and in an era when there’s very different ideologies about how businesses should behave.

So the entire shareholder primacy revolution that sort of happens in the ‘70s and ‘80s, turns out had a real impact on how wages were set. I talk about this in the book. Research by Daron Acemoglu from M.I.T. and coauthors find a really interesting fact. So it turns out that actually, most businesses are not run by people with a business school degree. I actually didn’t know that. Even today, that’s actually the case. But the share that actually have a CEO with a business school degree has been rising quite, quite steadily. So what happened, for example, in the ‘80s or the ‘90s, when a company moved for the first time to a CEO with a MBA? Sometimes it’s because maybe someone retired or even died, you know? It sounds kind of grim, but actually it makes for a good natural experiment where, almost like by random, you introduce a CEO with a MBA for the first time. And what’s really interesting is that it leads to a very clear reduction in pay: about a 6% reduction in pay for workers overall, and about a 9% reduction for blue-collar workers. So the labor share falls by about five percentage points. That’s the amount of money going to workers versus owners. And of course, CEO pay rises. Now you may say, well, maybe that happens, and that’s just like the cost of running the business better, right? MBAs probably raise productivity. Wrong. It has no effect on productivity compared to comparable businesses. So it’s purely a rent transfer, as we say. Meaning, you’re taking money from one group and giving it to the other. In this case, the money is going towards owners of capital and high-income managers, and away from the workers, especially blue-collar workers.

Krugman: Wow. I always thought that the Harvard Business School was evil, but I didn’t realize it was quite that evil. So that’s pretty impressive. That’s really a significant impact on sort of the nature of our society that comes from almost an academic doctrine.

Dube: Absolutely. This is sort of like ideology. It’s ideology, not skills that is explaining this important change here. And, in fact, this turns out to have played a non-trivial role in the fall in the labor share in the United States, for example.

Krugman: That’s a really funny thing for me. Economists are supposed to be hard-headed, but in fact, if you really look at the data, and really do economic science, it says that ideology matters a lot.

Dube: That’s right. And that’s one of the most important things. The late economist Alan Krueger once actually told me—well, he told us on Twitter in a conversation with me—that the idea that core theory is falsifiable and testable is a really big idea. And that is exactly right. Because if you start with saying, “Well, I’m pretty sure the labor market works this way,” and then I come and tell you, “Oh, actually, you know, it turns out this MBA CEO comes in and pay falls,” so you’d say, “Well, there’s got to be a really good explanation for that that is consistent with my model.” But it’s certainly not because the model is false, because it can’t be. And that basically highlights, in some ways, the conversations we had about the role of the minimum wage, which is something we could talk about as well.

Krugman: I want to come back to wage structure for a second. When I say that labor economics is especially good or virtuous, or in some way special, it’s because there’s really this use of natural experiments where something happens and just looking at it—at least on a couple of major occasions—it has contradicted what most economists believed. And I do want to come back to wage structure, but minimum wage is the classic. It’s an extraordinary story. You could probably tell it better than I can. To some extent it’s where you came in, but it’s definitely where Alan Krueger and David Card came in. So let’s talk about that.

Dube: Yeah. So maybe one thing just as a background for listeners: the United States, of course, introduced a minimum wage as part of the Fair Labor Standards Act in the 1930s, and during the ‘40s, ‘50s, ‘60s, and even ‘70s, the minimum wage was updated fairly regularly. You could have a Republican president or a Democratic president, or Congress, but it was generally updated and kept up with sort of like the typical or the median wage and even overall productivity and so on. That all changed in 1980, when Ronald Reagan came into power and he didn’t increase the minimum wage; he refused to, because he thought this was a bad idea. And this was also a time in the early ‘80s when, of course, we had real, still high inflation. So the combination of the fact that the nominal minimum wage just stayed put and there was inflation meant the actual real value of the minimum wage fell a lot. And so that had a really important impact on wage inequality at the bottom. It reduced pay for roughly the bottom 30 to 40% of the workforce. And so we went for basically a decade almost at this time without raising the minimum wage.

And we have now had several of these long stretches. The most recent one is particularly long: it’s 17 years since we have actually raised the minimum wage. And so that’s a very dysfunctional way to set policy. But here’s the silver lining. The silver lining of dysfunctional policies is that you have natural experiments. So what happened starting in the ‘80s is that states started to come in and raise their own minimum wage. And so you started to create all of these little natural experiments. And this is really what began this literature—it’s called the new minimum wage literature—which started to look to see, ‘hey, New Jersey raised its minimum wage in 1992, but look, neighboring Pennsylvania did not. Eastern Pennsylvania and New Jersey are not super different; they’re right next to each other. There’s a lot of similarities, maybe sharing similar types of economic shocks and so forth. Why don’t we compare to see what happened?’ And this is exactly what Alan Krueger and David Card did. They went and surveyed fast-food restaurants on both sides of the state border, and then went back a year later and said, “Well, let’s take a look. What happened? Didn’t we actually see a lower number of jobs in New Jersey?” And what they found really shocked the profession. It turns out, not so much. In fact, not really anything we can see. And, you know, this was really kind of an earth-shattering discovery, because it challenged the core model of the labor market: the labor market is supply and demand, that’s it, there’s not much more to it, just like any other market. And this was really hard to square with it. And I think this led to kind of an emergence of a whole literature.

And there are also things written that are very critical and, you know, not very polite about Card and Krueger. But, you know, it led to a lot of debate and also follow-up work, which is the way science progresses, if it’s doing the results that they’re replicated—

Krugman: Yeah, the results have been replicated now many times, and you’ve done a fair bit of that. Because there are so many states and so much asynchronous minimum wage increases that you get results. And people might say, “Oh, it’s just fast-food workers in New Jersey.” But it turns out that we have now lots and lots of evidence that says, hey, these minimum wage hikes do not actually seem to cost jobs, or at least not significantly. Right?

Dube: Yeah. So I think that my sort of contribution to the literature in our 2010 paper could be probably summarized by the word “many.” We see many of these and for many years, not just one short impact. And what we found was very much along the lines of what Card and Krueger had found. And even more recently, we updated that with more data, and we’re continuing to find very similar effects. In fact, just a couple weeks ago, I put out a Substack post that really sort of leverages, in some ways, an important fact related to what I said—that we’ve not raised the federal minimum wage for 17 years, and that means 20 states have today a $7.25 an hour minimum wage, which economically is sort of equivalent to not having any minimum wage. It’s so low that it barely affects anyone. So we’re running this basically just more than a generation-long experiment where you have about half the country—a little less than half the country—with essentially no minimum wage, while the other half raised it sometimes quite substantially, or comparable to some of our European peer countries. And that creates this very sharp divide.

But it also creates a divide that makes it very easy to see what is going on, because you don’t have to do a lot of fancy, you know, econometric statistics to really tell. Just plot, for example, as I do: what’s the restaurant wage in these two groups of states? Well, it turns out there’s a big gap that’s opened up, like maybe an 8 or 9% average earnings gap for restaurant workers. What happened to restaurant employment? It looks pretty much like a flat line. They’ve been growing very similarly. Per capita, restaurant employment has been very similar. And that just makes it very hard to look at that very simple fact and say, “No, I’m pretty sure it’s killing a lot of jobs,” because where is (the data that proves) it?

I do a bunch of other things, but this sort of highlights how, for a very long, long stretch of time, we’ve split the country in some ways in half. And by the way, some of these states that have raised the minimum wage have also been more Republican-leaning. A lot of times when the minimum wage is on the ballot, it’s in red and purple states. In fact, this week in Oklahoma for a variety of reasons it didn’t pass, but it has passed in Nebraska, Florida, Arizona, and so on and so forth. So I think this sort of highlights, in some ways, one of the partial successes because we have been able to raise the minimum wage in about half the country. And as we have learned more, I think it has led to policymakers actually experimenting with potentially higher minimum wages. And that has, I think, helped create and raise wages at the bottom, partly offsetting the growth in inequality that had occurred over decades after 1980.

Krugman: So I read the Substack post and I noticed that you had some, I would say discreetly acerbic comments for some of the people who refused to believe it. Or maybe it was a later comment of yours. But there have always been some economists who keep on insisting that this cannot be right, either because they believe in Econ 101 and that demand curves slope down, or at least implicitly, a little bit of a political critique because obviously a pro-minimum wage argument or something that seems to say that raising the minimum wage is okay has a kind of political side. But what’s actually striking is how little of that there is—that labor economics makes economics look good in the sense that if you have kind of overwhelming empirical evidence that contradicts people’s preconceptions and maybe even their political slant, people actually mostly go with the evidence. Am I being too idealistic?

Dube: I think that’s generally right. I think in general, people have certainly updated their views. It’s not that there’s only a single answer to what does the minimum wage do, regardless of how high it is or something like that; it’s going to differ. And so, there are disagreements like, “Well, where is the turning point?” But that’s part of good science. But to be clear, there will be studies that claim that no, actually the minimum wage always causes job losses. And even just this week, there was one that sort of argued that if you don’t control for population differences, if you just look at the number of jobs, well, the number of jobs in California has grown less than Texas. Most economists, of course, look at what share of people are actually working—that’s the employment rate. But if you simply look at the number of jobs, that actually might suggest that it’s falling.

Now, here’s the thing: it has been falling in these minimum-wage-raised states compared to the 20 states that haven’t raised it for four and a half decades. That’s largely driven by college-educated workers, because, of course, we have more college-educated workers moving to the Sunbelt. So, I think this is sort of a silly argument, but it is an argument that has been made. But it goes to show that there will always be studies. But if you look at the body of evidence overall, it suggests that the typical study finds very small employment effects, and especially in studies published in the last ten years, it’s basically around zero. And I think that has had an impact.

And I think economists have sort of updated—I would say probably especially younger scholars. Sometimes, you know, as we get older, maybe it becomes harder for some of us to revise our priors, but younger scholars are therefore really important.

Krugman: Yeah. I occasionally find people digging up some old quote of mine where I said minimum wages reduce employment, and it’s a 30 or 35-year-old quote, and I get to use the line, “When I see new evidence, I change my mind. What do you do, exactly?” There was a flurry of stuff showing up in my inbox claiming that California raised the minimum wage and it’s a disaster, and the evidence is in. But I guess the evidence actually goes the other way now, right? So what happened in California?

Dube: Yeah. So here’s the interesting thing. California established a sector-wide minimum wage for the fast-food workers, higher than the overall minimum wage. So this is a case where this is applying for larger chains with 60 or more locations across the country to have a $20 minimum wage. And at that time, I think the minimum wage was $16 overall in California. So what’s interesting is this is much higher. And it’s also partial coverage, meaning, you know, only part of the low-wage workforce is covered. So you could actually imagine there’d be more theoretical reasons to expect a more negative employment effect, because you can switch—maybe you can relabel workers who are delivery workers as, like, outsourced and so forth, and not covered. So anyway, well, you’ve now had about five studies that have looked at it, including one that I did. And, you know, there are some differences across the studies, but really, it turns out a big part of that is what kind of data is used, in a really surprising way.

So there are two kinds of administrative data sources that are really government data accounting based on actual payroll records: the QCEW and the QWI. And I know this is going into the weeds a bit, but it just turns out that one better captures the number of jobs at a point in time, and then the other looks at how many people are in a particular pay period. Now, this increase in wages also raises turnover because these are much better jobs now, so you have less people cycling through the same number of positions. And so there’s one data set that looks at a whole pay period; it seems to find a small reduction in employment. The other looks at a point in time and finds no change. And it turns out this is driven by the fact that these jobs begin so much better: people are not quitting so there’s just a lot lower turnover. But generally speaking, the overall range suggests that the employment effects were quite small—small positive in some cases, small negative depending on exactly how you do it—very large wage effects, and a very sharp reduction in turnover. So even in this very specific and very sharp and high minimum wage increase that serves as an experiment, if you will, it doesn’t show any clear predictions and projections about job losses so far.

Krugman: Okay. I want to cycle back just for a couple of minutes to the wage structure issue, where, again, there’s this kind of historical story which says that the United States became relatively egalitarian because of New Deal era and 1940s policies, and then became a lot less equal. It’s funny. I always blame what happened after 1980 on Ronald Reagan, but you’re saying it’s partly the Harvard Business School, but there’s also cross-national comparisons. Talk to me about Sweden and then maybe I’ll weigh in.

Dube: Well, I think we’ve both been writing about Europe and both visiting there. And so I was in Sweden for a while and partly talking about this book and also doing some of my research. What’s really interesting is that Sweden, of course, has been historically held up as sort of an egalitarian country, but it’s also gone through quite a bit of reforms in the ‘90s and 2000s, including scaling back partly some of the welfare state. And so I was really curious, like, where are they in terms of inequality? And it turns out that, yeah, if you look at their tax and transfer, they actually redistribute less than they used to. But the starting point, which is how much inequality do you have to begin with from the pay structure, that is still much lower than most other high-income countries. And the United States, of course, is the other extreme.

So, just one example: the gap between someone at the 90th percentile and the 10th percentile—that kind of is a good measure of wage inequality—between like the early ‘90s and today, it went maybe from 1.8 in Sweden to 2.2, a little bit of an increase. In the US, starting off much higher to begin with, it went from like 3.7 to 4.8. And it actually increases even more if you look at a broader time horizon. So it’s just a really important thing to understand: like, why is that? And we can go back to, well, is it because the Swedes are just a lot more similarly skilled between each other? Because that would have to be the reason. Or is there something else? It turns out it’s mostly something else, and that has to do with collective bargaining. And this is also a really important aspect of where people don’t fully also appreciate one really interesting and important fact, which is that in the United States, when we ask, “Is your job covered by union contract?” that question is almost the same as asking, “Are you a union member?” And of course, union membership in the US, maybe in the private sector, having something like, you know, 35% back in the ‘50s, is today like 6%. And so barely anyone overall is covered in the private sector by a union contract.

But here’s the interesting thing: if you went to France and asked what share of the workforce are union members overall, it’s like 10%. But 98% of jobs are covered by a union contract, right? Because what you have is sectoral bargaining. And this is a key thing which I talk about in the book. Sectoral bargaining was something that the US never really had. We basically had organizing and negotiating between the union and the employer at a company-by-company, sometimes store-by-store or factory-by-factory level, versus in a lot of our peer economies, what happens is workers and their representatives bargain with the employer and their representative at a sectoral level and at a national setting.

Krugman: Basically, sectoral level means that instead of getting a wage agreement with XYZ contractors, you got a wage agreement with the whole construction industry. And so even workers who are not members of unions, even workers who work at companies that have hardly any union members get the benefit of the negotiation. And so, Sweden’s an interesting case where they actually have high union membership.

Dube: Yeah. And Nordic countries generally, partly because of the way unions help provide some additional benefits, including unemployment benefits—that makes it more rewarding to actually join a union. But their coverage rate is even higher. And in countries like France or Austria, the coverage rates are substantially higher. So as a result, we have seen wage inequality not rise as much in a lot of other countries. And in Sweden, it’s actually been particularly low, and they’ve actually been able to retain it. And so that is a really important contrast.

So one of the things that I talk about in the book is that we can’t get to sectoral bargaining at the national level without a substantial change in labor law. And look, the reality is that past attempts at changing and reforming labor law have not fared well. But the good news is that we can actually get to pay standards at the industry or sector level state-by-state. And what’s even more interesting is we actually have started to see some of this already, and this really leans on a model that actually now comes from a different continent: Australia. Australia has basically a national-level setting of wage floors by industries and, within industries, by different types of jobs. And that’s done not through collective bargaining—they have collective bargaining on top of that—but this is basically a sector-wide floor that’s set. And again, Australia has lower wage inequality, substantially lower than the United States.

So, I talk about what the U.S. might look like if we had states do something similar. And like I said, I started to write this book in 2021. I actually had put out a survey proposal back in 2019. But in the last five years, we have a number of states that have started to implement some of this. For example, Minnesota has a sector-wide board that has representatives from workers and employers and the government to set pay in the nursing home sector. We have California that has a healthcare-wide minimum wage. Even more recently in the state of Washington we have a childcare sector board that just in the coming months will be issuing a set of wage floors in that sector. So we’re starting to see experimentation like this. And that’s important because if we’re trying to rebuild wages, not just at the very bottom that the minimum wage can really hit, but also those towards the middle, especially in the childcare or healthcare sectors, these kinds of jobs, you can actually raise pay there through these sectoral initiatives.

And I’m very excited to see more being done along these lines, especially because, you know, I don’t know what can be done in Washington, DC right now. But we don’t have to necessarily wait around for a better day to come in DC. We can actually start doing some of this now, more or less.

Krugman: So, it’s like the minimum wage is where half the states can do a lot on this broader issue of a more equal and better wage structure, even if things are totally stymied in Washington.

Dube: That’s right. And that’s one of the nice things about federalism in the U.S., that we do actually experiment at the state level. And in the best cases, some of the better experiments actually get adopted. It could also be that some not-so-great experiments are done and get adopted. But that’s the nature of democracy.

Krugman: Yeah. One of the areas where you really did a lot of the research and it was revelatory, but also, in a weird way, something where I found a lot of my sort of lefty friends not willing to believe it, was about wages post-COVID. So, let’s talk about that for a second. What happened?

Dube: So, around 2021 and 2022, of course I looked at wages like any labor economist. I started to look around and find something that was puzzling because, as we’ve known for a long time, wages have been rising faster at the top than the middle and the bottom. And this is the growing wage inequality story. But it was looking like wages right after COVID, when we were reopening, a lot of people didn’t have jobs, especially in the hospitality sector—we’d sort of shut down part of the economy.

So if in January 2020 someone said, “We are going to shut down some parts of the economy for a while, especially with low-wage workers, and then we’re going to reopen,” it’s like—here’s your quiz. If I could have given my class this question, like, “What do you think? What’s your prediction about what will happen to wages for low-wage workers?” I would have said wages would probably fall due to lower demand. And instead, it looked like wages were rising more at the bottom. And so this is what David Autor—my coauthor on this along with Annie McGrew—and I called The Unexpected Compression, meaning the compression of wages, reducing inequality—which is exactly what happened in the aftermath of the reopening after COVID, and led to a surprising amount of wage growth at the bottom. And it reduced maybe a quarter to a third of the increase in wage inequality that had occurred between 1980 and 2019.

And so this was really very, very striking. And we asked, well, why? And the reason is because we had a very tight labor market. There were a lot of job openings chasing workers and, as a result, it increased workers’ leverage. And it’s not just that there was more demand for workers—that’s true—but we also saw people leaving jobs. So we had quits from particularly low-paid jobs. This goes back to the issue of different companies with different pay policies: well, companies that were actually going for a low-wage strategy found it harder to hold on to those workers, and wages actually then rose more there. And this is the increasing of intensification of competition in the labor market that actually really helped boost wages.

In many ways, this was like: if we want the market to actually work well for workers, you need the market to be relatively tight. And in writing the book, what I’ve found was that, it just turns out between 1980 and 2019—up to just before the pandemic—there were about seven years of a tight labor market. We used to spend a lot more time with tight labor markets in the postwar era before 1980 than we did since. And this turns out to be another important part of that equation of: what did it take to have broad-based wage growth? Those seven years—if I just, like, snap my fingers and just erase those like some evil genius villain, what would happen? Well, if I went to the top of the pay distribution, it would make very little impact; the average wage growth would fall from 1.1 to 1%. Not much change. At the bottom, it would go from already a small 0.3% average real wage growth to zero. So the entirety of the wage growth at the bottom between 1980 and 2019 happened in a handful of years that was basically close to full employment: the late 1990s and the late 2010s. Under Trump I, those years also saw significant compression.

And this is why the post-pandemic period was a really important one. But it’s also very messy because, as we know, this was also a time of a large increase in inflation, a chunk of which was, by the way, global in nature. But nonetheless, people were very reasonably unhappy about it. So it makes for a difficult thing to extract the signal from noise. And this is why in the book, I really highlight also why even these other periods in US history were so important in actually raising wages, highlighting really the critical pillar that full employment plays if we are trying to rebuild the wage standard.

Krugman: Okay. What do you see happening now? My comment sections are full of, “Oh, it’s a K-shaped economy —the top is rising, the bottom is falling.” And people really refuse to admit that the compression ever happened. But also there are all these fears about AI. Everybody wants to know what AI is going to do, and nobody can honestly say that they know. But do you have any views on where we’re going right now?

Dube: Yeah. So the easiest part of that to answer is just to start with wages. The good news is that much of the compression that we saw has remained. The bad news is that the last year and a half has seen some take-back. Basically we have seen lower wage growth at the very bottom. The particularly bad news is, of course, from this year, when higher inflation has erased, as of now, pretty much the entirety of the real wage growth since Donald Trump took office. And so, that’s really bad. That’s not just at the bottom, but just generally. And so I think wages are not doing great right now and that part is largely just an unforced error of where we are today with having raised inflation, literally having caused a supply shock—inflation purely out of discretion, right?

But yeah, the other part—and this is the longer part and harder to say—is what we see not within pay, not wage inequality. Wage inequality has been an important part of inequality overall in the last 50 years. But wealth and the division between capital and labor. And looking into the future, that’s where my worries lie: where are we going? And I guess, the worrisome part of me thinks that, broadly, there are two possible ways that the current AI structure can go. My modal view is probably that I think it’s going to lead to moderate productivity gains. And how well that translates into wage growth partly depends on what we do in our other policy and institutional choices. But I think it can potentially be a source of possible wage growth.

The other—and these are two very polar cases—well, this is going to be the singularity. I tend to be skeptical of that view of an artificial general intelligence that really just dramatically transforms the world as we know it. It’s possible—anything is possible—but the other possibility is that actually there’s a bubble and then it bursts, and that leads to a downturn. And that downturn could be harmful. So, there are all of these possibilities and I, of course, don’t know which it might be. But there are risks on both ends where what I do know—and this is what I sort of talk a little bit about in the book—is that, again, it goes back to the word “choices.” I don’t think we need to think about what AI does as something that just happens to us. We can choose to have institutions and a governance structure that can regulate that.

You know what’s interesting, going back to Sweden, I was talking to folks in the labor movement there, and they, of course, have contractual language that requires negotiations over technology, and that includes AI. Where that goes is unclear at this time—it’s still early days—but that’s the kind of thing that we need to think about. So imagine having sectoral boards in the health care sector that, among other things, also sort of has regulatory language around how AI is used and how it can affect the workforce. So we need to think creatively, of course at the national level, but even more locally if necessary, about what that governance looks like, and understanding that this is part of the choice that we can make and not simply, you know, take the technology as just a force of nature that we just have to live with.

Krugman: Okay. So, choices. We can actually shape our future. Probably won’t, but can. Anyway, thanks so much for talking to me. And I’m sure we’ll want to come back in a couple of years and see how all of this played out.

Dube: Sounds great.