Today is the federal holiday honoring Juneteenth, the celebration of the announcement in Texas on June 19th, 1865, that enslaved Americans were free.
That announcement came as late as it did because while General Robert E. Lee surrendered his Army of Northern Virginia to General Ulysses S. Grant of the U.S. Army on April 9, 1865, it was not until June 2 that General Edmund Kirby Smith surrendered the Trans-Mississippi Department, the last major army of the Confederacy, to the United States, in Galveston, Texas. Smith then fled to Mexico.
Seventeen days later, Major General Gordon Granger of the U.S. Army arrived to take charge of the soldiers stationed in Texas. On that day, June 19, he issued General Order Number 3. It read:
“The people of Texas are informed that, in accordance with a proclamation from the Executive of the United States, all slaves are free. This involves an absolute equality of personal rights and rights of property between former masters and slaves, and the connection heretofore existing between them becomes that between employer and hired labor.”
Granger’s order referred to the Emancipation Proclamation of January 1, 1863, which declared that Americans enslaved in states that were in rebellion against the United States “shall be then, thenceforward, and forever free; and the Executive Government of the United States, including the military and naval authority thereof, will recognize and maintain the freedom of such persons.” Granger was informing the people of Galveston that, Texas having been in rebellion on January 1, 1863, their world had changed. The federal government would see to it that, going forward, white people and Black people would be equal.
Black people in Galveston met the news Order No. 3 brought with celebrations in the streets, but emancipation was not a gift from white Americans. Black Americans had fought and died for the United States. They had worked as soldiers, as nurses, and as day laborers in the Union army. Those who could had demonstrated their hatred of enslavement and the Confederacy by leaving their homes for the northern lines, sometimes delivering valuable information or matériel to the Union, while those unable to leave had hidden wounded U.S. soldiers and helped them get back to Union lines.
But white former Confederates in Texas were demoralized and angered by the changes in their circumstances. “It looked like everything worth living for was gone,” Texas cattleman Charles Goodnight later recalled.
In summer 1865, white legislators in the states of the former Confederacy grudgingly ratified the Thirteenth Amendment, which abolished enslavement except as punishment for a crime. But they also passed laws to keep freedpeople subservient to their white neighbors. These laws, known as the Black Codes, varied by state, but they generally bound Black Americans to yearlong contracts working in fields owned by white men; prohibited Black people from meeting in groups, owning guns or property, or testifying in court; outlawed interracial marriage; and permitted white men to buy out the jail terms of Black people convicted of a wide swath of petty crimes and then to force those former prisoners into labor to pay off their debt.
Congress refused to readmit the southern states with the Black Codes in place, and in December 1865, Americans added the Thirteenth Amendment to the Constitution. Six months later, Texas freedpeople gathered on June 19, 1866, to celebrate the anniversary of the coming of their freedom with prayers, speeches, food, and socializing.
By then, congressmen had turned to guaranteeing that states could not pass discriminatory laws against citizens who lived in them, laws like the Black Codes. In 1866 they wrote and passed the Fourteenth Amendment to the Constitution. Its first section established that “All persons born or naturalized in the United States, and subject to the jurisdiction thereof, are citizens of the United States and of the State wherein they reside.” It went on: “No State shall make or enforce any law which shall abridge the privileges or immunities of citizens of the United States; nor shall any State deprive any person of life, liberty, or property, without due process of law; nor deny to any person within its jurisdiction the equal protection of the laws.”
That was the whole ball game, the one that would put teeth behind the principles in the Emancipation Proclamation. The federal government had declared that a state legislature—no matter who elected it or what voters called for—could not discriminate against any of its citizens or arbitrarily take away any of a citizen’s rights. Then, like the Thirteenth Amendment before it, the Fourteenth declared that “Congress shall have the power to enforce, by appropriate legislation, the provisions of this article,” strengthening the federal government.
Rather than accept this new state of affairs, leading white southerners decided they would rather remain under military rule. So in March 1867, Congress passed the Military Reconstruction Act, calling for southern voters to elect delegates to new state constitutional conventions. And, for the first time in U.S. history, they mandated that Black men could vote in those elections.
Three months later the federal government, eager to explain to Black citizens their new voting rights, encouraged “Juneteenth” celebrations, and the tradition of Juneteenth began to spread to Black communities across the nation. The next year, the addition of the Fourteenth Amendment to the Constitution remade the United States of America.
In 1865, Juneteenth was a celebration of freedom and the war’s end. In 1866 it was a celebration of the enshrinement of freedom in the U.S. Constitution after the Thirteenth Amendment had been ratified. In 1867, Juneteenth was a celebration of the freedom of Black men to vote, the very real power of having a say in the government under which they lived.
Celebrations of Juneteenth declined during the Jim Crow years of the late nineteenth and early twentieth centuries, but as Black Americans from the South spread across the country during and after World War II, they brought Juneteenth with them. By the 1980s, Texas had established Juneteenth as a state holiday. Other states followed, and in 2021, thanks in part to pressure from activist Opal Lee, Congress made Juneteenth a federal holiday and President Joe Biden signed the measure into law.
But throughout our history, those determined to preserve a government that discriminates between Americans according to race, gender, religion, ability, and so on, have embraced the idea that true democracy requires skewing the vote toward the wealthy and white men. They have also insisted, as former Confederates did in the late 1860s, that any laws protecting the equal rights of minorities discriminate against the white majority.
Today, those voices are, once again, gaining traction. One hundred and sixty-one years after Juneteenth was established, we are in danger of losing the new nation that it celebrated—one that would honor the equality of all Americans.
—
Notes:
https://www.archives.gov/news/articles/juneteenth-original-document
J. Evetts Haley, Charles Goodnight: Cowman and Plainsman (1949; rpt. University of Oklahoma Press, 1981).
https://www.archives.gov/milestone-documents/13th-amendment
https://www.archives.gov/milestone-documents/14th-amendment
https://avalon.law.yale.edu/19th_century/csa_scarsec.asp
https://www.tshaonline.org/handbook/entries/juneteenth
https://www.tsl.texas.gov/ref/abouttx/juneteenth
https://www.archives.gov/exhibits/featured-documents/emancipation-proclamation/transcript.html
https://www.cnn.com/2025/06/18/us/juneteenth-cancelations-trump-dei-rollbacks
https://apnews.com/article/juneteenth-trump-diversity-e441197492e4360f3b7a8cbbc00b5c79
https://www.congress.gov/bill/117th-congress/senate-bill/475
https://www.archives.gov/exhibits/featured-documents/emancipation-proclamation/transcript.html
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.
Up and to my office, where all the morning, and dined at home, Mr. Deane, of Woolwich, with me, and he and I all the afternoon down by water, and in a timber yard, measuring of timber, which I now understand thoroughly, and shall be able in a little time to do the King great service.
Home in the evening, and after Will’s reading a little in the Latin Testament, to bed.

Welcome to the reading list, a weekly roundup of news and links related to buildings, infrastructure, and industrial technology. This week we look at a new housing bill, General Motors joining the grid-scale battery game, skepticism about data center delays, solid-state air conditioning, and more. Roughly 2/3rds of the reading list is paywalled, so for full access become a paid subscriber.
Housekeeping items:
This week IFP published its Transit Abundance Playbook, a collection of 15 specific policy ideas for bringing down the cost and time it takes to build transit in the US. I have a piece in the playbook here.
The US house and senate have reached a deal on a combined version of the Senate’s ROAD to housing act. The combined version looks like it’ll be a good bill, as the burdensome build to rent provisions have been eliminated. [X] [Politico]
Do housing developments that include grocery stores like Costco or Safeway have a better chance of succeeding? “The grocery anchor brings a constituency that ordinary apartment developments lack. A developer fighting NIMBYs alone is on hostile rhetorical terrain. The actual political economy is more complicated, but at community-meeting altitude “developer” is shorthand for “rich greedy person,” and that’s the altitude these fights are conducted at. A developer fighting NIMBYs with Costco or Safeway as co-sponsor is in an entirely different argument. Costco is consistently rated one of the most-trusted retailers in America. Its CEO Ron Vachris has described the Coliseum project as a way “to enter markets where traditional big-box development would be nearly impossible,” and the Coliseum alone is expected to create 400 permanent retail jobs plus thousands of construction jobs. The Marina Safeway has the kind of intergenerational neighbourhood loyalty no developer can manufacture by himself; opposing 800 flats above it is one thing, but opposing the expansion of the Singles Safeway (57% bigger, with a wider deli counter and more checkouts) is much stranger to argue at a community meeting.” [Governance.fyi]
Works in Progress on how the Squamish Nation in Canada went about building the enormous “Senakw” housing project in Vancouver. “Senakw has an unusual history. The land it is built on was home to the Squamish people until they were forced out in 1913. Almost a century later, a court case restored the land to the descendants of those who were expelled, along with almost 100 million Canadian dollars in compensation. Freed from the restrictive planning rules that hold back densification in the rest of Vancouver, the Squamish decided in 2019 to use the land to build apartment blocks that, as well as housing Squamish people, are expected to generate around C$10 billion in income, equivalent to more than two million per person.” [Works in Progress]
Semiconductor startup Phoenix Semiconductor is building replacements for the obsolete, no-longer produced microchips used by long service-life equipment such as military jets. “When the U.S. Navy’s fleet of F/A-18F Super Hornet fighter jets is headed for the scrap heap because an essential chip is unavailable, what do they do? They turn to Ryan Hatcher, the CEO and founder of Phoenix Semiconductor. Hatcher repackages off-the-shelf semiconductors into devices that are virtually identical to the phased-out chips.” [IEEE Spectrum]
Also on the subject of military manufacturing, the Trump Administration has invoked the Defense Production Act to deal with supply constraints in munitions manufacturing. [Reuters]
Indium phosphide is a mineral used for light-detecting semiconductor devices, such as fiber optics. China controls 70% of the world’s supply of indium, and issued export restrictions on indium phosphide in 2025. Those restrictions are starting to bite. “The U.S. urgency to resolve China’s export controls on the compound highlights how indium phosphide (InP) has emerged as a powerful trade weapon for Beijing that experts and executives say could disrupt the global rollout of AI data centres. “InP is one of several supply chain bottlenecks collectively gating AI data centre buildouts,” said Konrad Wang, a research analyst at SemiAnalysis. With AI workloads growing exponentially, InP is in high demand as it is a core material with no substitute in the new technology that data centre developers are turning to - using light through optical fibres, or photonics, instead of electrical signals through copper wire.” [Reuters]
Not satisfied with the tariffs on Chinese EVs, two Michigan lawmakers want to ban Chinese cars from even entering the country. [Techdirt]
The IEA released its 2026 electric vehicle trends report. [IEA] And RAND released a long report on how China’s industrial policy has evolved over the last 10 years or so. [RAND]
The US is worried China might have one of ASML’s most advanced EUV lithography machines. [Bloomberg]
Links for you. Science:
Scientists Found Bacteria Thriving Inside Fog and Eating Air Pollution
H5N1 Bird Flu Confirmed in Poultry Across 12 States — Dallas’s Egg and Poultry Supply Chain Faces Disruption and Food Price Pressure as Summer Heats Up
American horses are obese, too
Newfound ‘whale necropolis’ reveals 5.3 million years of seafloor life
My Public Comment on Proposed OMB Rule: Regulation for Federal Financial Assistance
Scientists discover 5 million-year-old whale graveyard stretching for hundreds of miles in the Indian Ocean
Screwworm Can Infect People, Pets And Livestock—What To Watch For
Other:
Democratic voters want the party to be more moderate — and more socialist?
Trump Moves to Deeply Censor the Entire Internet
Trump bought tobacco stocks and raked in industry donations as FDA eased standards
A White Supremacist Youth Group Helped Orchestrate the Belfast Riots. After Elon Musk and Tommy Robinson stoked anger over a horrific knife attack in Belfast, a youth group linked to a global neo-Nazi movement quietly orchestrated anti-immigrant riots.
Kennedy Shows Minimal Engagement With Vast Health Portfolio (“When he is in town, he exercises at his gym before work, then usually arrives at about 10 a.m. and leaves by 4 p.m., his colleagues say.”-you’re not a podcaster anymore asshole)
Elon Musk, Human Ponzi Scheme
You Probably Won’t Get Rich Off the SpaceX IPO
Psychologist Offers Disturbing Reason for Trump’s Rambling
Grok Is Still Hosting Sexualized Deepfakes of Famous Women
Top doctor drops bombshell Trump dementia warning: ‘It’s about to get worse’
Stop putting whatever Trump says about Iran in the headlines
‘You Will Not Speak on Flock Tonight’: County Commissioner Refuses to Let Residents Opposing Flock Speak at Meeting
This Is The Centrist Position Now
TPUSA’s new message to girls: Hate yourself
Qatar pursued secret talks with Iran to shield gas complex from strikes, security officials say
‘Midway Blitz’ & ‘Metro Surge’ Were Always About Terrorizing Blue Cities
Large etchings of numbers signaling opposition to Trump appear on National Mall
Matchmakers Are Being Paid $25K to Find Trad Wives for Rich Men. Even in blue states, nonreligious tech entrepreneurs and CEOs are increasingly asking for “traditional” and “conservative” women, matchmakers tell WIRED.
Michigan politicians want to ban Chinese-badged cars from even visiting the US
Trump trying to “void” his first two impeachments
Backlash erupts in Utah County after 23-year-old conservative influencer becomes deputy clerk (100% chance he sucks at his job)
The world’s first trillionaire is a killer
Republican senators block effort to bar federal troops from election interference
When Pedro Arrests Juan: Why Latinos Join Border Patrol and ICE
The Department of Homeland Security’s culture of sexual violence
Musk’s Starlink hooked rural customers. Then came the price increases.
I’ve spent my career fighting ebola. Trump’s policy response could be catastrophic.
The UFC’s Biggest Cards Nearly Always Include Women. Except at Freedom 250.
A Popular Doctor Had Long Warned That Vitamin K Shots Are Risky for Newborns. Now He’s Changed His Tune.
AI Is Slowing Down

Lunar lander developer Astrobotic decided to sell to Voyager Technologies so it could quickly scale up to meet the projected demands of NASA’s lunar base initiative.
The post Astrobotic says sale to Voyager will allow it to scale up appeared first on SpaceNews.
Spotify: https://open.spotify.com/episode/0HBFWS1avb6tYY1IoLefYb
Web: https://athenaeumreview.org/podcast/aesthetics-a-conversation-with-tyler-cowen/
Here is basic information about art scholar Benjamin Lima, it was great fun for me to do this one.
The post My aesthetics podcast with Benjamin Lima appeared first on Marginal REVOLUTION.
1. Do weird corporate governance structures work well?
2. Are elite economists overpaid? Elite economists conclude no.
3. Dialog.
5. Inside the world’s first AI art museum (Los Angeles).
6. Redux of my 2023 post on AI and existential risk.
The post Saturday assorted links appeared first on Marginal REVOLUTION.
Some of the most impactful measures announced by Cuba’s Prime Minister Manuel Marrero Thursday include allowing:
- Private and foreign capital to purchase and sell fuel
- The creation of private corporate banking
- Private business owners to own more than one company and hire more than 100 workers
- Private businesses in agriculture and tourism
- Tourism property sales, evaluated case-by-case, for Cubans resident in the country and abroad
- Foreign investors to hire workers directly
- Foreign investment in Old Havana and other tourist spots, in state telecom ETECSA data centers, mobile networks, and other digital infrastructure
- The extension of surface rights up to 99 years and leases up to 50 years for foreign investments
- Real estate development in tourism
- Farmland lease rights for an “indefinite period”
- Wholesale and retail trade without limits by foreign entities
- The sale of state assets and state companies’ shares to the private sector and foreign companies.
Taken together, the reforms proposed significantly expand the private sector six decades after Cuba’s communist leaders forbade all private business—even frita stands— and adopted a centrally planned economy model that ended up ruining the country and dragging Cubans into a severe humanitarian crisis. Currently, the government is in such dire straits that it is even seeking to transfer the management of the country’s zoos and aquariums to private hands, another announced change.
The post Cuba appeared first on Marginal REVOLUTION.

SpaceX is preparing to launch a batch of its Starlink V2 Mini satellites on a Falcon 9 rocket from Vandenberg Space Force Base Sunday morning.
The Father’s Day flight will add another 24 broadband internet satellites to the company’s low Earth orbit constellation. This is the 72nd Falcon 9 launch of the year.
Liftoff of the Starlink 17-28 mission from Space Launch Complex 4 East is scheduled for 8:12:16 a.m. PDT (11:12:16 a.m. EDT / 1512:16 UTC). The rocket will fly on a south-southwesterly trajectory upon leaving the pad.
Spaceflight Now will have live coverage beginning about 30 minutes prior to liftoff.
SpaceX will launch the mission with one of its most heavily used Falcon 9 boosters, B1063. This will be its 33rd flight after launching missions, like NASA’s DART, Transporter-7, and Iridium OneWeb.
Nearly 8.5 minutes after liftoff, B1063 will target a landing on the drone ship, ‘Of Course I Still Love You’, positioned in the Pacific Ocean. If successful, this will be the 204th landing on this vessel and the 627th booster landing for SpaceX to date.
Welcome back to The Honest Broker interview series —also available on our YouTube channel. You can also find it on Apple Podcasts and other podcasting platforms.
Today, I’m pleased to share my conversation with Zena Hitz.
Zena Hitz is a tutor at St. John’s College, a Great Books school. She is also the founder of the Catherine Project, a free program allowing participants from around to world to participate in reading groups of the great books. She is also the author of the wonderful book Lost in Thought: The Hidden Pleasures of an Intellectual Life.
Zena sat down with me while visiting Austin to discuss the joys of the intellectual life, the state of the modern university, and how she is bringing humanities education to thousand of people through the Catherine Project.
Below are highlights from our conversation. For the entire dialogue, check out the video at the top of the page.
JARED: Zena Hitz, thank you for joining me.
ZENA: Thanks so much, Jared. It’s great to be here.
JARED: I wanted to talk to you because I was first exposed to your work through Lost in Thought, and then after I read Lost in Thought, I found out about the Catherine Project, and I’ve now participated in one Catherine Project group.
ZENA: Very good, very good.
JARED: You started this thing, the Catherine Project. Could you start by telling us what it is, and also how you got started?
ZENA: I had a kind of longing back from when I was straight out of grad school and started teaching undergrads intro to philosophy and stuff like that. A lot of the job of teaching philosophy at a public university is teaching people who don’t want to learn. So I developed this preoccupation with: what would it be like if you tried to find the people who only want to learn? Suppose you could just filter for the desire to learn.
My first idea was to have an open ancient Greek study group in Baltimore, which is where I was living at the time. We were going to read the Iliad, and this wonderful librarian helped us out, and a couple people turned up. They were grad students at Hopkins or whatever. So it turned out to be my friends. It was very small, and I realized I didn’t know how to reach people in an ordinary city who would want to do something like this. So there was a contact problem. How do I find these people?
But I was still haunted by the dream, and it sat in the back of my mind for, I don’t know, probably 20 years, 15 years. And then, as I talked about in Lost in Thought, I went through all kinds of transitions and tried to figure out what to do with my life, and finally settled on going back to St. John’s to teach. So I went back in 2015, and then in 2020, of course, we had emergency online COVID classes, which anyone who was teaching at the time knows was absolutely miserable. Just the worst kind of teaching you can imagine. Because it was unexpected, and these people were in their parents’ basement, and I was teaching a class called junior math, which was Newton’s Principia, one of the hardest books I know, and we just couldn’t do it.
JARED: That was a very St. John’s sentence.
ZENA: I have a lot of St. John’s sentences. I’m a Johnny born and bred, so that’s the way it is. But we couldn’t do it, because reading something that difficult requires having an in-person community to talk to people and get help and stuff like that. So we kept trying. We put in a college try, but it was really painful. Anyway, that’s a little bit beside the point.
The point is, I was really upset about being online. I wanted to just send everyone home and give them a chance, maybe, to fix their grade, but we had to do it. And I was very frightened. I thought, once universities figure out that they can have classes online, they’re never going to go back, because it’s so cheap. Administrators are just like that. They just want stuff that’s cheap. They don’t care about educational quality. So I was very angry, and that sometimes can be very productive.
I thought, if I were stuck on this forever, what would be the best way to learn? What would be the best way to teach? I thought, maybe it’s a bit like an Oxford tutorial situation, where people went away and read off screen and then had face-to-face conversations in person — or by Zoom, in this case. Small groups on Zoom are better than medium-sized groups, and medium-sized groups are better than large groups. The larger the group, the worse it is; the smaller it is, the closer it is to real human interaction.
So that was one thing I was doing back there. And then the book came out at the same time. Lost in Thought came out, and Lost in Thought made this argument that the life of the mind is for everybody. And I say this a lot, and it’s true: that was really a sentimental idea I had. Like, maybe if I have a Greek reading group in Baltimore, old Greek guys will show up and want to read Greek with me. I didn’t really know it was true, but I wanted it to be true. And then I started getting all of these emails from people who wanted to lead a life of the mind, who wanted to read the great books, and they would ask me what they should do. And I didn’t have a good answer for that question, and that haunted me.
So somehow these two things percolated, and eventually I thought, well, look, let’s just have open online Zoom classes on great books, and it’ll be totally open. Anyone can go. We’ll make it free. We’ll run on volunteer labor. I’ll tap into my academic network, get some professors, and I’ll get some former students to lead informal reading groups. And so that was the Catherine Project. It began — it was sort of born and bred on Twitter. So it was through Twitter that I got to know people who might be interested in something like that, and this was the golden age of Twitter, when everyone was on it because it was COVID, and the algorithms exposed you to all kinds of people.
So anyway, we started out with about 45 students. We had these things called tutorials—small groups, like three. We started with groups of three students and one professor. We had five of those, and they read—well, we had an early divide between people who wanted to read fast and people who wanted to read slow. So some of them just read the Iliad and the Odyssey, some read the Iliad, the Odyssey, some Greek tragedy, and some Plato. And we had two reading groups meant just for people in those tutorials—one was on Aristophanes and one was on Kafka, The Trial.
So we had these 45 people, and November 2020 comes around, and the Kafka group wanted a few more people because they wanted to read Kierkegaard, Either/Or. And it’s November, I was teaching full time, and I was just like, put something on Twitter: who’s up for reading Either/Or on Saturday nights? Like 100, 120, 130 replies. So I was like, uh oh, I have to do something about this. I can’t reject 125 people—they wanted like three people for their group. So at that point we pulled in a bunch of reading group leaders, and then for a long time the Catherine Project became mainly these peer-led reading groups. Some people had PhDs, some had master’s degrees, some were just really good people who had gone through great books programs as undergraduates, facilitating these open conversations about great books in small groups.
So at the end of spring 2021, I was totally out of steam. I was dead. And I applied to Emergent Ventures, which is Tyler Cowen’s group, for a grant. I had no expectation of getting it, because I’d spoken to Tyler a year earlier when I was just getting started, and he had put me through the ringer — the hardest set of questions anyone had ever asked me about the business plan, and how big was it going to grow, and how are we going to fund it, what was going to be the source of revenue. And I couldn’t answer any of those questions. I just had this romantic idea that if you opened up the doors, people would come. Anyway, Tyler, after a year, gave me the grant based on what we had done, without a budget.
We’re now enrolling 1,600 human beings per semester. One of the things that helped with that last spurt of growth is that we had started with these tutorials, with the reading groups that were supplemental, but we were worried that the list of reading groups was actually really intimidating for anyone who wanted to come into the reading for the first time. This is a huge list of books, many of which I had never heard of. We always wanted to be a way for people without much experience with us to find their way into reading and thinking and having conversations. So we instituted—this is the end of the second year—we’re now teaching the fourth unit of what’s called our core program.
It’s basically just a set of readings that we think are basic, that are a good introduction to the life of the mind, that we think will serve people well in the future. So that’s become a big component of what we do. We still run a trillion reading groups. Anyway, that’s the story of the Catherine Project.
JARED: I loved the experience with the Catherine Project, because the facilitator was a graduate student in Germany, and then we had a few other people in various parts of Europe, a couple of undergrads, and then just a few people who I had no idea what their academic background was, but they were joining from their lunch break, essentially, at their desk job. I loved how open the conversations were. I never felt the sense that anyone there didn’t feel like they could participate. And there was just this idea of taking what everyone said really seriously. I found it to be this really amazing experience.
ZENA: It’s a collaborative conversation. Experts don’t teach our courses. And although in the core program they tend to be people who are experts in something, they’re not necessarily experts in what they’re teaching. But they have more experience.
JARED: Let’s talk about that idea you said was a romantic or sentimental idea—that the life of the mind is for everyone. I’m actually writing a book about this too; we’ll talk about that later, off camera. I firmly believe this. One of the things my experience in the Catherine Project showed me is that people from very different walks of life, people studying very different things—I’m pretty sure some of the undergrads were not even humanities majors—and yet they were able to just sit with this text.
The Catherine Project also doesn’t require an educational background to apply, right?
ZENA: The only rule is that you’re 16. And there is an application—a statement of interest, we call it. But we’re really just trying to make sure people know exactly what they’re doing.
JARED: Most people who participate—do you think they have graduate degrees?
ZENA: I think we are still drawing a pretty broad range. Some pretty young people, people in high school or college, people just out of college, retired people. There’s a big chunk of people who are in their 30s and 40s who work in, say, tech, or have worked in tech, or in STEM, engineering. And they’ve realized, having been on a really intense career path, that they’re missing something. So we get a lot of people like that.
JARED: What are those things that you don’t do but wish you could, in some perfect world where the Catherine Project has infinite money and infinite resources?
ZENA: One of the things I’m thinking about now is the collapse of specialized research training in the humanities. Lots and lots of graduate programs at the moment are shuttered. They’re not admitting new students, and it may just be a freeze for a time, but it’s hard to see that it’s not the beginning of a shift. I think it was going on for a while anyway, but then the cuts in the science funding that the Trump administration put in last year shook up the administrators, and one gets the sense that they were funding graduate humanities programs with some of the excess money from the sciences that’s been cut.
Part of what I try to do when I’m thinking about things like this is to think not about what would be good for one person or another, but what’s the whole ecology of humanistic learning. What are all the things that need to be in place for humanistic learning to be part of what we do as a culture or as a community? And it seems to me we do need people who are working at a deep and serious level on the books, or on basic humanistic topics. So I’ve been trying to think about ways in which—and I think it’s very compatible with the Catherine Project, it just needs some kind of residential or brick-and-mortar context—but I wonder whether there’s a real appetite among many of our members, and especially our staff, Jordan and Ashley, to do in-depth work.
It’s incredibly refreshing to read a great book, and to be reading them all the time, when you’ve had to leave grad school, you’ve left academia—or even as a supplement to what you’re doing in your highly specialized academia. But it all seems to be supported by people who’ve sat with books for a long time and have really chewed through them and thought things through and have a sense of what’s knowable and what’s not knowable. So the idea would be to have something like a residential library where people could come and study and have visiting faculty who could mentor people. That’s the dream.
JARED: In a way, the core of the Catherine Project—what you’re doing now—is this foundational work. We’re going to give you this foundation in the great books, and also teach you the joys and pleasures of reading widely, but then if you want to, you narrow. Which is kind of the dream of humanistic education too: we all get this broad foundation, we all feel immersed in this tradition, and then you don’t write a dissertation on why the great books are great—you write a dissertation on some subset of some small problem about Homer’s Odyssey, and you’re going into archival research, or doing work on histories of languages, or reception history, all this stuff you’re not going to get in your life-of-the-mind seminar.
ZENA: There’s a lot of thinking that can be done about the books that is just a matter of reading it carefully, learning the original language, reading around it so you know what it’s responding to and what responded to it, reading the commentaries. That already gets you a lot of really interesting angles on a book that you don’t get when you just read it for class, which means two hours a week.
So yeah, I’d like to foster that. Like the Catherine Project, I’ve always tried to be open about what, if it’s successful, it will contribute. So one thought is, when all the institutions die—in the post-apocalyptic wasteland, when we’re all eating out of trash cans and warming ourselves by fires in the open and there’s broken TV sets everywhere. I don’t know where these images come from; they’re probably from Mad Max. So when all of that is there, there will be a Catherine Project. We’ll still be reading books and talking about them in this post-apocalyptic landscape.
But that’s just preserving something that might die. In a way, it would be better if, instead of that, it provoked some reform on the part of the institutions. One of the things it has shown—and programs like it do show, and this I know is something Jennifer Frey has also been arguing—is that there’s been a kind of myth that people don’t want to study this stuff anymore, and I think it’s really obviously not true. People really, really want to study it, and they choose not to major in it in part because the administrators, and often the faculty, are telling them that it’s a waste of time. And if you’re trying to make your way in the world, and the people who hold the power and the keys to the future are telling you it’s a waste of time, then you better listen to them, because they’re going to be able to help you into the next stage, whatever it is.
So this Catherine grad program, or whatever it is, has a similar structure. Maybe it will take the place of these graduate programs that are likely to close, especially for people who aren’t necessarily going to become academics but who wanted to get to the next level. Or maybe it will become the university of the future.
JARED: One of the things I like to think about a lot—but I’m curious to hear what you think—is just the concept of an autodidact. What is an autodidact for you?
ZENA: An autodidact is obviously someone who teaches themselves, who’s self-taught. I tend to think about it in terms of examples. Probably my favorite example is Malcolm X. Obviously, a highly intelligent person, growing up in a society where it was extremely difficult for a black man to have a job that suited his abilities. And he ends up in prison for theft when he’s pretty young, and at that time they have prison libraries—which I think they don’t anymore, of that kind. So they don’t have prison libraries stuffed with classics; they have prison libraries with a few out-of-date copies of, like, Business 101, or self-help stuff like that. So anyway, he read the entire prison library—vast swathes of the prison library—and just became a different person. I think of that as being an autodidact.
I think you don’t need to be alone to be an autodidact. So another example: there’s this wonderful book I’ve promoted for years called The Intellectual Life of the British Working Classes by Jonathan Rose. He describes these groups of workers would get together and educate themselves. They’d pick an evening, they’d pick something to read—Plato, Aristotle, Kant, Descartes—they’d study astronomy or math or science or literature, Shakespeare, classics. And they would teach themselves.
JARED: There’s some way that everyone’s actually an autodidact. I call myself one—I have a PhD, but I call myself an autodidact, because after I left my PhD, I realized there were all these gaps in my education, and I was never going to be able to take a seminar on them, so I just read it. I understand I’m stretching the term a little bit here.
ZENA: I think everyone is an autodidact, because you don’t really learn seriously unless you’re teaching yourself. An expert can tell you something, but you still have to decide whether it’s true, even though you don’t actually have the basis, the expertise, to judge it. There’s no learning without the participation of the learner.
That’s why calling attention to autodidacticism, or nurturing it as the Catherine Project does, is really important—as a reminder of what actually is core to any kind of education. A human being is not just a passive sponge for content that’s somehow been verified somewhere else. In order for us to learn well and live well, we have to find within ourselves the questions and the desires to learn, and we have to find ways to inquire and think about things. We can help one another, and experts can help us too, but in the end, it’s really what you do. That’s the nature of learning. There is no substitute for the act of thinking.
JARED: I was reading Aristotle’s Politics, which I hadn’t read before. I love the Nicomachean Ethics, and at some point it became shameful that I hadn’t read the Politics. And I tried to distill down the propositions you could definitely attribute to Aristotle from that book, and there aren’t that many.
ZENA: No, it’s a very hard book.
JARED: And then you wonder, why would you read it? Because Aristotle’s actually giving you the opportunity to think through these principles he’s floated for you, and then see how it worked out, and how it played out in history. And then you have to actually decide if any of those propositions or principles are true, if you agree with them. I think Aristotle is a great model for this. He really makes you think for yourself while reading him.
ZENA: Yes. And you wouldn’t get that if you just attended a lecture and then said, this was Aristotle’s political theory, and the professor handed you a list of propositions.
I think it’s true that even if you were totally persuaded of every Aristotelian principle, you would still have to figure out how to see all those cases, and to see what’s going on around you in light of those cases. And that is really not a simple task. That’s a serious kind of training.
And there’s no substitute for it. And why would you expect there to be? Why would you expect there to be an algorithm for how to make elections in the United States work better? That just doesn’t make any sense. You have to look at the way things are, you have to look at the kinds of problems we’ve had in the past, you have to look at the constraints there are on what’s possible. And then you have to try some things out. And maybe there is no right answer for something on that largest scale, and you can only fix the voting procedures in, you know, Texas or something. But we’ve really—I’ve gotten pretty deep into a view of human thinking and agency that’s really dangerously false. And that’s definitely part of what I feel like I’ve been working against.
JARED: And also, when you’re coming to a book like the Politics, Aristotle gives you all these cases from Greek history, and you need to think through them. But you shouldn’t limit yourself to only those cases.
ZENA: Yes.
JARED: We have thousands of years of political history post-Aristotle to draw on as well.
ZENA: Yes.
JARED: And I think if you could talk to Aristotle while reading the book, he would say, ‘Why aren’t you writing about the US in the 1960s as well, when you’re talking about the Politics?’ I was reading it on my Substack—I run a sort of informal book club on my newsletter—and some people are like, why would I care about those examples so much? And it’s like, well, okay, we’ll talk through all those examples if you want, and we probably should, but we have a thousand other examples we could talk about. If we think we can learn something through the act of reading Aristotle, it requires this very active process.
ZENA: Yes. It’s something that was in the air in the culture even relatively recently. There’s a beautiful piece by George Kennan, who is one of the great 20th-century diplomats and a beautiful writer—Kennan’s diaries are fabulous stuff to read. But he has a little essay on education for statesmanship. He was thinking about things like working for the State Department, or being a political person, and he’s like, look, there’s not a technique—you have to learn everything there is to know about history, literature, human nature. That’s what you’re dealing with; you’re working with real life. And so as much as you can learn about how things work, have worked in the past, work in this particular place—that’s all going to be material for you to make judgments. But there’s not going to be any substitute for making that judgment. So, yeah, we used to know this, and we’ve forgotten.
JARED: I had this experience when I left academia. I finished my PhD, and I just felt like I was leaving academia behind, so I kind of felt like they’d rejected me, too. And then I started thinking, what’s the point of philosophy? I had been initiated into this guild structure, where you study with a master, you produce your work, and I had been given the stamp, and then I hadn’t gone and gotten a job, so I was no longer part of it. And so now thinking is for those people, philosophy is for those people. And I now see the trajectory of the last five years of my life as reminding myself, and then wanting to remind other people, that I was completely wrong—that philosophy was just as much mine when I left academia, and in fact it was mine before I got the PhD too, and that it’s true for all of you as well.
ZENA: Yes. And that’s also a case of mistaking university constraints, which are totally contingent, for the real thing. There’s no reason for us to think this way, and there’s no reason to think that there’s only one way of using a degree, or that an institution has to be closed to people who aren’t in this special guild.
JARED: But there were material circumstances that made it harder for me to see that at first. As soon as you lose your university email, you lose access to journals—or it becomes a lot harder to access journals. You’re emailing friends, can you send me these articles, and sometimes they do it, sometimes it takes a few days. It can feel, from the outside, like knowledge is very heavily gatekept. Because things like journals, or books that cost two hundred dollars to buy your own copy, you can get at any university library—you just feel like you’re kept out from that.
ZENA: This has really gotten dramatically worse since all those library materials went online. In a way, of course, the online research library is an incredible resource—you can find stuff much more easily, there’s all kinds of databases and indexes that are much easier to use than the old big paper ones I was trained on, because I’m just old enough to have done that. But they have really locked up all of the specialized research, to the point where I don’t have regular access.
It’s a scandal that science is gatekept like that. You ought to be able to learn about a scientific topic by reading the original papers. They should not be kept behind these incredibly expensive subscription services. It’s only making the whole process less accountable, less transparent, and it undermines people’s trust. And it reinforces this idea that somehow an ordinary person can’t understand this stuff.
But I would like to see different kinds of platforms for the presentation of this kind of research that are open access, or minimal access. I don’t know what would have to happen for that to take place, but I wish people who had the resources and the abilities and the understanding would start doing that. That’s for sure.
JARED: All right. I want to be mindful of your time. So I have a question I always end on, which is, if you have a book recommendation for our listeners.
ZENA: Yes, I was wondering about that. I think what I’d like to recommend is the book—or the books—I was most excited about most recently, which was, last summer I read these Robert Caro books about Lyndon Johnson. These are very popular books, lots of people know they’re good, because there’s so many of them and they’re so long—they’re daunting in some way—but they are incredibly well written. They tell the whole story of 20th-century America through Texas, where we are right now. And it’s a particularly interesting view of Texas, and kinds of characters that we think of as being what makes Texas Texas, and not just Lyndon Johnson, who has his own sordid, very complex mixture of very, very bad qualities and very, very good qualities. And you’re never let off the hook, because the writing is so good. And there it is.
I was talking about this last night to a friend of mine, who I was recommending them to—because that’s basically what I do whenever I see someone, I’m like, you should read Lyndon Johnson. And then every now and then, once every three months, I meet someone who’s actually read them, and we just talk about them for hours. What is it that holds a human being together? What’s the nature of political power, political ambition? How does a person maintain any kind of integrity in that kind of context? How do these very beautifully noble motivations coexist with this really venal self-absorption?
So I think Lyndon Johnson is a fascinating figure, and a lot of the people in there are fascinating, and they’re absolutely masterpieces of books. I think there’s reason to think they are the great books of today. So I recommend them really highly—and yeah, it’s a bit like a continuation of Aristotle’s Politics. You’re reading through the casework, the cases, all of the things that happened in the 20th century in the Senate and the presidency, trying to think about what worked and what didn’t.
JARED: Awesome. Zena Hitz, thanks for joining us.
ZENA: Thanks so much, Jared. It’s a pleasure talking to you.

Billions in private investment have created room for growth, but suppliers need predictable demand, says the head of Northrop Grumman’s solid rocket motor business
The post Northrop Grumman says industry ready to scale solid rocket production, with longer contracts appeared first on SpaceNews.

To some, space is the final frontier. But the reality is that once you leave Earth’s atmosphere, you’re not entering a single, uniform void. There are several frontiers up there […]
The post The MEO durability crisis: why LEO hardware will fail the new orbital economy appeared first on SpaceNews.
What damage will Bill Pulte do as “acting” director of national intelligence?
Donald Trump has brushed aside bipartisan rejection of Pulte as the acting coordinator of 18 national intelligence agencies to insist that Pulte severely cut the staff of the agency and then delve into records in search of anything to show foreign interference in the 2020 election – or perhaps the current one.
Forget that multiple investigations have failed to turn up evidence of election “rigging” that Trump insists must have taken place for him to lose to Joe Biden six years ago. The Trump investigative style of picking targets first and then looking for crimes to fit them has become the hallmark of Justice under Trump.
Trump went so far this week as to interfere and cancel the Senate’s confirmation hearing for Jay Clayton, currently U.S. Attorney for the Southern District of New York, as a permanent DNI hire replacing the ousted Tulsi Gabbard. The sole explanation that has arisen is so that Pulte can get into the job temporarily to do Trump’s anti-election bidding – as Pulte has done by abusing his job heading the federal mortgage authorities to mine data for criminal use against Trump political opponents.
Do we really think that Pulte will do things that Clayton will not? Gabbard did not balk at going to Georgia with the FBI on a raid of Fulton Country election records from 2020. She could offer no understandable explanation for being there other than the most general references to possible involvement of foreign governments in seeking to sway American elections – the kind of thing the U.S. does every day in other countries that most recently include Colombia.
Of course, the obsession on made-up foreign influence over imagined schemes to tilt election machine software to turn Trump votes magically into Biden votes but not touch any other race skips over the bigger damage being done here – the undercutting of national intelligence altogether.
To Trump, the huge communications spying networks that the government runs only seem useful to gather information about his perceived enemies. But the bigger danger is that Trump is ignoring critical national security information and advice that are meant to be harvested before policy is created.
Pulte’s early promises to drop hundreds of national security analysts to meet Trump’s demands will threaten our information sharing with other nations, of course.
Apart from all else that is crazy about Trump’s war of choice with Iran, the nuttiest part is that he ignored specific advice that an attack on Iran would spur Iranian retaliation on Gulf nations and the strangulation of global shipping of oil, fertilizer and other necessary goods through the Strait of Hormuz. Trump’s deal with Iran should be seen as proof on needing more reliance on good intelligence rather than the evident Trump attitude of shunning it.
The deal shows that Trump’s government was forced by a less-strong Iran to bow to the economic slowdown that Iran caused. It was all avoidable, but Trump trusted his gut and counsel from Israeli Prime Minister Benjamin Netanyahu over what he was being told by U.S. intelligence agencies.
That is why even Republican senators are pushing back against Pulte being named to an office for which he has absolutely no background. If “national security” matters so much to this administration, appointment of Pulte for even a day makes no sense.
And Trump has no need for intelligence he ignores.
“FREEDOM OF THE PRESS IS NOT JUST IMPORTANT TO DEMOCRACY, IT IS DEMOCRACY.” – Walter Cronkite. CLICK HERE to donate in support of our free and independent voice.
The post How Bad Will Bill Pulte Be? appeared first on DCReport.org.

Jio Platforms, which owns India’s largest telco, is looking to lease broadband capacity from satellite constellations to jumpstart its own sovereign LEO network in the country.
The post India’s Jio lays out sovereign LEO constellation plan ahead of IPO appeared first on SpaceNews.

MDA Space is buying smallsat manufacturer Blue Canyon Technologies (BCT) for $620 million, giving the Canadian company greater access to the lucrative U.S. government market.
The post MDA Space to buy Blue Canyon Technologies to gain foothold in U.S. market appeared first on SpaceNews.

ElevationSpace Inc. (CEO: Ryohei Kobayashi), a company developing Space-to-Earth transportation as well as a Space Environment Utilization and Recovery Platform, has raised a total of US $40 million in its […]
The post ElevationSpace Secures US $40 Million in Series B Funding, Bringing Total Raised to US $63.5 Million appeared first on SpaceNews.
The latest episode of Freakonomics looks at the controversies and philosophies involved in the growing legalization of medical aid in dying (MAID). Stephen Dubner interviews people with multiple perspectives, and offers a personal insight of his own.
"DUBNER: I have a sister who died last year, it was a pretty rotten death, honestly, and she wanted to hasten it. We couldn’t physically orchestrate it. And it really made me see this issue in a new way. It just seemed, you know, I don’t want to say the scales fell from my eyes, but I’d never encountered it first-hand. And it made me think that almost anyone who did encounter it first-hand might have a reckoning, might be in favor of it. But I don’t know, maybe that’s just me. Do you have any sense of how broad the support is for it generally?
ROTH: We’re an aging population, so I think not only do more people have a reason to contemplate their own death, but more people know a peer who’s died, and certainly parents have died, and relatives, you know, siblings and friends. So I would think that anyone who’s seen an agonizing death should at least give some thought to whether we should be legalizing medical aid in dying."
You can listen or read the transcript at this link:
Who Gets to Choose a “Good Death”?
New York is the latest state to legalize medical aid in dying. Stephen Dubner speaks with the governor who signed the law, a Nobel Prize-winning economist, a death doula — and an ethicist who thinks the very idea is wrong.
"SOURCES:
Kathy Hochul, governor of New York.
Suzanne O'Brien, death doula, founder of Doulagivers Institute.
Al Roth, economist at Stanford University.
Daniel Sulmasy, physician, philosopher, director of the Kennedy Institute of Ethics at Georgetown University.
RESOURCES:
Moral Economics: From Prostitution to Organ Sales, What Controversial Transactions Reveal About How Markets Work, by Al Roth (2026).
"New York Moves to Allow Terminally Ill People to Die on Their Own Terms," by Grace Ashford (New York Times, 2025).
The Good Death: A Guide for Supporting Your Loved One through the End of Life, by Suzanne O'Brien (2025).
The Future of Assisted Suicide and Euthanasia, by Neil Gorsuch (2009).
EXTRAS:
"Make Me a Match (Update)," by Freakonomics Radio (2023).
I was asked to nominate so here goes:
Free Press columnist Tyler Cowen picks a biography of one of the finest poets of the 20th century, Paul Celan: A Life, by Anna Arno.
Could Celan be the very best poet of all time? When read in the German language, I think he might be. When read in English, he is still very good. No one has a poetic topic of more importance than the Holocaust. Contrary to Theodor Adorno, he decided it was possible to write poetry after it, and he took that mission very seriously.
Now we finally have a first-rate biography. Celan’s mother was killed in the Holocaust, and he took his own life in 1970, drowning himself in the Seine. How did he get to that point? How did he have the strength and wherewithal to write such powerful poetry in the first place?
I found this book gripping from start to finish. Given the topic I cannot call it a “fun” read, but it is absorbing and the translation is very accessible.
Is it possible that Anna Arno is one of our best intellectuals today? She has written on the German painter Paula Modersohn-Becker and the Polish writer and activist Konstanty Jeleński, and has done important work as a translator, including of Henry James—though those works are in Polish, and thus inaccessible to me. Can we get translations as soon as possible? In the meantime, you can start with this one.
The article has many other quality selections as well.
The post The Free Press summer reading list appeared first on Marginal REVOLUTION.
About three years ago, someone asked me why, with my physics undergrad background and a PhD in economics, I had decided to become a professional blogger. I told him that blogging seemed like the highest-leverage thing I could do, in terms of actually having an impact on the world.
I didn’t mean that bloggers literally rule the world, of course — this isn’t Ender’s Game. Nor do I have any illusions that I’ll be able to have as much influence as a top politician like Donald Trump, a top entrepreneur like Elon Musk, and so on. But in terms of what I could personally accomplish, it seemed like a no-brainer — being an opinion writer has probably allowed me to change the world much more than being an academic or an engineer or a financier or a consultant would have.
Why? Because blogging has allowed me to inject ideas into the discourse with unparalleled speed, breadth, and access. A researcher goes deep into a few topics; a blogger can quickly hit the main points of many topics. This enables speed; academics might take months to write something useful about a breaking event like the Iran War or Trump’s tariffs, while I can have something out in hours. It also enables me to comment on a wide variety of topics, because people expect me to be an analyst rather than a subject-matter expert. And speed and breadth in turn allow me to talk to a wide variety of important and interesting people — top academics, billionaire company founders, presidential advisors.
Injecting ideas into the discourse is incredibly powerful. John Maynard Keynes famously described the power of idea injection:
Practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist. Madmen in authority, who hear voices in the air, are distilling their frenzy from some academic scribbler of a few years back.
To describe why idea injection is so powerful would take an entire post (which I do intend to write). There are a number of reasons. First, idea injection allows you to frame the terms of the debate. Whether people think your idea is right or wrong, once you put it out there, discussion of the issue at hand turns into discussion of whether your idea is good or bad.
As Keynes notes, an early writer’s ideas can also act as a kind of training data for later thinkers; it becomes a foundation off of which politicians, bureaucrats, staffers, other writers, and even entrepreneurs and financiers build when they make their own ideas.1 Just today I saw Matt Yglesias and Jerusalem Demsas — two of my favorite pundits — riffing on my post on dating advice on their podcast.
But injecting ideas is only one part of a blogger’s influence. We’re also part of a community of intellectuals that span multiple disciplines and walks of life. On a daily basis I get to mull ideas over not just with other writers and pundits, but also with top academics, CEOs and entrepreneurs, Congressional staffers and political advisers, think-tankers, corporate researchers and engineers, and plenty of people from other countries. This leads to a much richer discussion, with a greater diversity of viewpoints, than almost anything else I can think of. And they reach a very wide set of ears. In a way, blogging is like DARPA — ad-hoc multidisciplinary teams that build the rapid prototype of an idea. OK, maybe that’s a bit pretentious, but you get the point.
Anyway, the reason I’m writing all of this is not to brag, but to complain. Over the last two years, I’ve felt like my job has become a bit less important than it used to be, for three reasons:
The rise of populism on all sides of the political spectrum in the U.S. means that smart ideas are simply not as likely to be implemented by the people in power.
The general shift to Substack and other monetizable direct-to-audience channels has made punditry less conversational.
The rapid proliferation of AI writing has increased the demands on readers’ attention (including my own).
This doesn’t mean I think punditry is dead or unimportant — despite the title of this post, I do think that what I write still matters — but it does mean I’m now spending some time thinking about how to regain some of the impact I felt I had a couple of years ago.
“Thus when the irreverent intellectual has done his work…The stage is now set for the fanatics.” — Eric Hoffer
Ten years ago, it was already apparent that wonkish policy types were to have a much diminished role under Donald Trump. Trump himself is not the type of person who’s inclined to listen to egghead intellectuals — he’ll always trust his own instincts, which were usually developed watching CNN in the early 1990s. In his first term, though, he could sometimes be prevailed upon to listen to reason when a crisis struck — Operation Warp Speed and the CARES Act were done under his auspices, because he stepped back and allowed smarter folks to take over.
And in Trump’s first term, it still felt like there were lots of relevant ideas for econ types to debate — trade policy, place-based economic policies, new socialist ideas from the Bernie camp, and so on. It felt like a time of great political ferment and upheaval — even if Trump himself wasn’t listening to economists, someone would be soon.
In Trump’s second administration, though, that’s all gone. Whether it was Covid, Trump’s advancing age, or his attempted overthrow of the 2020 election that made Trump totally lose faith in everyone but himself, the big man now seems inclined to listen only to the voices in his own head.
Take tariffs, for instance. Essentially no one thought — or thinks now — that his tariffs were a good idea. Oren Cass, one of the last few tariff defenders, has been reduced to speaking in snarky generalities about how “econ isn’t a science”, because on some level he knows that the way Trump went about imposing tariffs is intellectually indefensible.
There was Peter Navarro, of course, at least until he got sidelined. But Trump didn’t get the tariff idea from Navarro. He thought of it all himself, and then looked around for someone — anyone! — who would be willing to stand in front of a podium and endorse the policy, and Navarro was just the guy he found. Reading Peter Navarro’s books, or trying to start a dialogue with Navarro, would have been useless, because Navarro’s ideas — such as they are — weren’t actually driving anything. It was all just a cult of personality.
The rest of Trump’s administration is the same way. The “MAHA” antivax insanity, the research funding cuts, the doomed war in Iran, the reckless spending — it’s all just ad-hoc stuff that Trump did, either on a whim, or because the last guy he talked with told him it would be a good idea, or because he’s in damage control mode after a drop in the S&P. There’s no intellectual movement here, just a cult of personality. There’s no one to argue with, because nothing that’s happening is based on an argument in the first place.
This state of affairs will eventually end, of course. Whoever succeeds Trump won’t have his cult of personality, and will have to rely on ideologies and ideas that will be ripe for debate. And if a Democrat retakes the White House in 2028, ideas will be back on the table, as they were during the Biden administration.
But even on the left, the trend is away from open intellectual debate. Zohran Mamdani and the other socialist candidates who are winning primary races in blue cities are interested in ideas, but only from people within their own clique. Leftism in America is fundamentally a factional movement disguised as an ideological one; bloggers who aren’t on the team will simply be ignored, except for the occasional denunciation.
This is just populism. Populism isn’t really about doing stuff that’s popular; it’s about putting factional and tribal conflict above the national interest or the general public good. The goal is always to “own” the other side, and economic and social outcomes become subordinate to that goal.
Intellectualism thrives in times of relative social peace. This isn’t one of those. Hopefully, the tide of populism is receding in America, but the experiences of other countries suggest that these times of factional struggle can go on for a very long time.
“Writing is like prostitution. First you do it for love, and then for a few close friends, and then for money.” — Ferenc Molnár
Substack has done a whole lot of good, both for me personally and (more importantly) for the world. In a time when most of the internet has been taken over by malignant opportunists and sensationalist attention-seekers, Substack stands as a lone island where reasoned, intelligent, earnest debate is still possible. It has also allowed many writers to escape from publications that stifle their voice, impede their development, and don’t pay them their due. In many ways, Substack has resurrected the old blogosphere from the early 2010s.
However, this resurrection has come at a price. Substack’s killer feature — email distribution — allows writers to get much larger and more loyal audiences, and to make a lot more money by charging those audiences for subscriptions. But this creates a financial incentive for writers to spend more time serving their customers and less time talking to each other.
In 2011, I was blogging part-time, because it was fun — the attention that mattered was when Brad DeLong or Paul Krugman or Tyler Cowen was interested in something I had to say. It was a little “republic of letters”. Now I’m blogging full-time, and having a conversation with Brad or Paul or Tyler is still just as fun and stimulating, but it’s a distraction from my job of creating content for my paying audience. There are still interesting intellectual debates and exchanges in the blogosphere, but they are no longer the main thing writers are rewarded for.
Turning intellectuals into content creators tends to put them in siloes. And Substack is far from the strongest in terms of silo-ing. Most of the internet is being taken over by vertical-scrolling short-form video, which is not exactly good for conversation and exchange. I could go start a YouTube channel, but it would just be me talking directly to my fans — I’d basically be a TV talk show host. I might still do this, because it’s a high-leverage way to influence the world, but it’s not as intellectually rich or rewarding as being part of a round-table conversation.
Nor are interesting new ideas as likely to emerge from one-way siloed content creation. Ideas emerge not from singular minds in isolation, but from dialogue — the cross-pollination that the blogosphere and other intellectual communities create isn’t just fun, it’s productive. Writing for you, my readers, is not boring, but you’d get better content from me — and from all your other favorite writers — if we talked to each other more.
I do think that platform companies could consciously try to recreate intellectual dialogue by tweaking the features of their platforms. Substack has tried to do this with the Substack Live feature, with modest success. But a more powerful tool would be to allow Substackers to easily and automatically see when another Substacker links to their blog. This feature existed on Blogger in 2006 — whenever another website linked to you, you’d see how many pageviews it drove to your blog. If Substack implemented this feature, it would get a lot of writers talking to each other more often.
“My ambitions accelerate. My afternoons do not.” — Claude
Unlike many people, I think AI writing is actually pretty good. Yes, there’s a recognizable style that the basic models use (“It’s not X, it’s Y” and lots of other little cliches). That style isn’t bad, it just gets overplayed when everyone uses it.2 Yes, AI models are still not great at boiling a complex idea down to one or two pithy sentences. But you can modify the style that AI uses. And AI can do plenty of things human writers can’t — it can seamlessly incorporate vast knowledge and novel data analysis into a piece as it writes it.
For example, I immediately suspected that this essay by Aaron Brown, Michael Mendelson, and Cliff Asness, on the confusion of the debate over “affordability”, is mostly AI-generated, and Pangram — the most reliable AI text detector — flagged it as around 50% AI. But that’s not a knock against it — the essay is great. It classifies different kinds of “affordability” problems — true poverty, precarity, downward mobility, etc. — into different buckets, gives some illustrative vignettes, and provides some useful numbers about each one. I broadly agree with the article’s conclusions, and I think it’s a valuable addition to the discourse.
A bigger problem is that in a world where a huge number of people generate effectively infinite amounts of good-quality content like this, it becomes hard for readers to decide where to allocate their attention. Instead of identifying the few most consistently useful blogs and reading those in great detail, a lot of people will respond to the explosion of content by “reading” a larger number of posts but only lightly skimming each one.
It’s not my job I’m worried about here. It’s that in that world, even if my blog continues to get tons of readers and make me plenty of money, what I do becomes less important. If people are just skimming what I write so they can move on to the next 10,000-word Claude-generated post, the fact that they’re paying me $10 a month is cold comfort — I’m not really reaching them. And even more worryingly, no one is reaching them — if they’re skimming 100 posts a day instead of reading 10 all the way through, they’re not getting really good information from anywhere.3
I don’t know how severe this problem will be, to be honest. There was always a lot more high-quality content on the internet than anyone could ever read, and a lot of people always just skimmed my posts instead of reading them closely. Maybe AI can’t make this problem worse because it was already maximally bad.
Also, I’m optimistic that AI itself will open up new channels for intellectual influence. It’s a well-known fact that if AI just consumes AI-generated output, it gets worse and worse. So AI companies try very hard to “clean” the text they use to train their models. Human writers, whose personal experience brings in new data for AIs to learn, can influence the world if their writings are used to train the next generation of AIs.
Interestingly, I think I’m already doing this, quite by accident. I don’t know how reliable the website intheweights.com is, but it shows me in the top 2% of contributors:

I suspect that on the topics I write about, I’m even more influential. Claude and GPT often cite me as a source on topics I write about4, and friends have told me that Claude recommends my blog with surprising frequency when they ask it for reading material. Maybe Tyler Cowen is right when he says we should be “writing for the AIs”.
In any case, I find that although blogging is still very fun, and I still think I’m having a positive impact, and my readership is still growing, the environment a lot more challenging than it was just two years ago. The combination of a nation ruled by closed-minded tribalists, a blogosphere obsessed with putting out monetizable content, and the rampant proliferation of high-quality AI output is forcing me to rethink what I do. I want to keep injecting ideas into the discourse and participating in a vibrant and relevant intellectual community, but what it takes to do that might look a little different going forward.
Occasionally this can devolve into unconscious copying. I always smile when another pundit presents one of my ideas as their own, weeks or months after I wrote it. The reason I smile is because only the belief that it was their own original idea, instead of “that thing Noah Smith wrote”, allowed them to spend time and effort broadcasting the idea in the first place.
An analogy is the song “Under the Bridge” by the Red Hot Chili Peppers, which is actually a great song, but which got so overplayed in the late 1990s that it made me want to burn down the building whenever I heard it.
Have you ever met a guy who “reads” a hundred books a year? He’s almost certainly doing the same thing. Unless he’s Brian Potter, in which case he’s actually reading and absorbing every word. Brian Potter is superhuman.
Not when I use them, because it knows not to quote my own writing back at me, but when other people use them.
Sweden is continuing to reap the rewards of this mixture of fiscal rectitude and pro-market reforms. GDP is projected to grow by 1.8% to 1.9% this year; headline inflation stands at 1.5%; debt-to-GDP ratio is one of the lowest in the world, at just above 35%.
There are some flies in this ointment, of course: The economy has recently endured a bout of stagnation, unemployment is at an uncomfortably high 9.4% and Sweden has one of Europe’s highest rates of household debt. But the business environment is healthy, particularly when it comes to business to business. Sweden has a diversified business scene — the highest number of unicorns per capita in Europe, with notable successes such as Spotify, but also a healthy manufacturing and engineering sector. Many of these established companies are thriving because of a surge in demand for both server farms and military equipment…
Sweden has recently experienced its first net emigration in 50 years, thanks to higher minimum wages for labor visas, tougher citizenship tests and, most controversially, financial payouts of up to $37,000 for refugees who volunteer to leave. It has also made progress against violent crime in the immigrant-heavy suburbs, increasing police numbers and toughening the penal code, including a boost to stop-and-search powers and a lowering in the age of criminal responsibility to 14. The number of shootings fell by 63%, from 390 in 2022 to 147 by the end of 2025.
Here is the full Bloomberg column. And here is Adrian’s new book on liberalism, self-recommending.
The post Adrian Wooldridge on Sweden and liberalism appeared first on Marginal REVOLUTION.
Dolphins, sharks, turtles, and human workers are all victims of unregulated squid fishing fleets.
Another news article.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
On June 9th, Anthropic released its Fable generative AI model. Three days later, the US government classified it as a dangerous munition, and used its export-control authority to prohibit any foreign nationals from accessing it. Unable to differentiate between Americans and foreigners, the company shut off access for everyone.
The government’s actions won’t help. The problem isn’t any one particular model; it’s the general trend of increasing AI capabilities. And any real solution requires the sort of collective action that just isn’t possible right now.
Fable is the constrained version of Mythos, the AI model Anthropic announced in April. Anthropic only released it to a few selected organizations, because the company claimed it was so good at finding and exploiting vulnerabilities in computer code that releasing it more generally would be dangerous.
It was an obviously self-serving announcement, and because few were able to verify Anthropic’s claims they were met with some skepticism. Those with access used Mythos to find and patch many vulnerabilities in their own software. But one UK group found the latest, already public, OpenAI model to be just as powerful.
Fable is just another incremental improvement in the years-long climb of AI capabilities. But just as important as the AI model is the “harness.” This is typically not AI. It’s ordinary computer code that interfaces with the user. It stitches together AI models, decides how and for what purposes they can be used, and gives them useful tools such as web search and the ability to run their own computer code.
When Mythos first entered limited release, there was widespread debate whether its power came from the model or the harness. With Mythos demonstrating that it was possible, the open-source community scrambled to build harnesses that could steer other AI models towards similar capabilities. Harness improvements don’t need massive data or data centers.
They largely succeeded. For example, a Prague company was able to replicate Anthropic’s few verifiable cybersecurity capabilities with a much smaller and cheaper model—and a more sophisticated harness. Last week, a group showed that multiple cheaper models harnessed in concert matches Fable’s performance.
The broader community had only a few days with Fable, but that time we learned some about its capabilities. Its difference is less the new model’s raw analytical and problem solving capabilities, and more that the model doesn’t need that sophisticated harness.
Fable requires much less expertise and detailed prompting from the human user. You can give it a difficult goal and it will figure out novel and unexpected ways to satisfy it, finding loopholes in whatever constraints you or the system have imposed on it.
“Relentlessly proactive” is how AI researcher Simon Willison described it. Another descriptor might be “creative.” Experienced AI developers have had that combination of creativity and proactivity since last year, but Fable puts it within easy reach of everyone.
In the hands of someone with a legitimate problem that needs solving, that can be an incredibly useful capability. But in the hands of someone who wants to do harm, it can be equally dangerous. AIs don’t have a moral compass in the same way that people do. They are agents of the wants and desires of the people who prompt them.
That points to the real problem with relentlessly proactive AI. In language, wants and desires are always underspecified. If I ask you to get me some coffee, you would probably pour me a cup from the coffeepot, or buy one from a nearby coffee shop.
You couldn’t buy me a pound of raw beans, or a coffee plantation. You wouldn’t order a cup of coffee for delivery next month. You wouldn’t find a nearby person, rip a cup of coffee out of their hands, and bring it to me. I wouldn’t have to specify any of the million limitations to my request; you would just know.
Human stories are filled with warnings about underspecified desires. King Midas wished that everything he touch turn to gold, forgetting to add “but not my food, drink, and daughter.” And genies are notorious for granting your wish in a way you wish they hadn’t.
The deeper point is that it’s impossible to list all limitations and restrictions, and like a malicious genie, a creative AI will find the ones you forgot. Block a database you don’t want it to have access to, and it might figure out how to bypass your control. Ask it to book a flight, and it might hack the airline because the website says the flight is sold out. Ask it to save money on your cellphone plan, and it might cancel it altogether—or get someone else to pay for it. As far as we know now AI has not done any of this yet, but you get the idea.
Malicious intent is not required. To an AI model, constraints are just things to get around and not general truisms about the world. They are creative problem solvers and natural rule breakers. They “hack” in the sense that they find and exploit loopholes.
Human systems rely on so many norms that we scarcely recognize the existence of until they are broken. AIs naturally think outside the box, because they don’t have any real conception of what the box is or why it’s there in the first place.
There is no foolproof way to prevent people from using AI models to complete harmful tasks. There is no way to prevent the models from incidentally causing harm while completing benign tasks. AI models are no longer isolated from the real world. They browse the internet and answer emails.
They trade stocks and make purchases. They control physical systems. They are, in effect, robots that affect life and property. We have no technical mechanisms to verify the integrity of an AI system. This level of capability and creativity in the hands of us untrustworthy humans will have both great and terrible results.
The problem is not unique to Anthropic. Mythos/Fable might currently be the most capable rules hacker, but more sophisticated harnesses give other models similar capabilities. And we should assume that the other frontier models are no more than a few months behind, and that open-source models are less than a year behind. At best, any ban only serves to delay the problem for a short while.
That delay might be useful if we—as a society, as a planet—would use that time to come together and figure out what to do. This isn’t a US/China arms race problem; this a species-level problem that requires coordinated action at that scale. Unfortunately, we have no mechanism to do that. I first wrote about this problem five years ago, but it was all too futuristic.
Today, when its right in front of us, there is no world government that can impose constraints on the for-profit corporations currently controlling AI models and research. The US has no appetite to effectively and even-handedly regulate those corporations, even as they do catastrophic damage to the environment, democracy, and—in this case—society in general.
This all makes an AI public option all the more necessary, and urgent. Today’s AIs can be fast, smart and secure, but only two of the three are possible for any given system. These safety tradeoffs are tightly held secrets of companies racing to beat one another, and they tell us we have to trust them. Instead, the choices and their consequences need to be brought out into the sunlight.
We should be funding open-source harnesses that balance capability and safety—that achieve useful goals without so much power—and open-source AI models whose provenance and biases are public and well understood. We have opened the AI Pandora’s box. Now we have to make the best of it.
This essay originally appeared in The Guardian.
Nobel Prize-winning economist Paul Krugman and historian Heather Cox Richardson are joining forces for Lunch Money, a new monthly conversation series. Krugman writes the popular Substack newsletter bearing his name; Richardson’s Letters from an American reaches over 6 million readers every day. Join them for a wide-ranging, thoughtful, and provocative conversation about whatever is on their ever-curious minds.
Overnight, Ukraine launched its biggest attack on Moscow, the capital of Russia, since Russia invaded Ukraine in February 2022. Ukraine’s waves of drone strikes on a major Moscow oil refinery have shrouded the city in flames and black smoke. Last week, Russia struck one of Ukraine’s most important religious and cultural landmarks, the thousand-year-old Kyiv-Pechersk Lavra. The ancient monastery, with its churches and bell towers, is a UNESCO World Heritage site, described by the United Nations agency as a “masterpiece of Ukrainian art.”
Russia denied responsibility for the strike. After the Moscow strikes, Ukrainian president Volodymyr Zelensky released a video saying: “If Ukraine burns, your Moscow will burn too.”
In the U.S., President Donald J. Trump and Vice President J.D. Vance are trying hard to sell the administration’s memorandum of understanding (MOU) with Iran, which Trump signed yesterday at the Palace of Versailles in a scene that recalled Germany’s surrender after World War I. Trump is posting in all caps on social media that the deal is a triumph and that those who disagree with it “are either jealous, bad people, or stupid.”
Vance is in front of cameras saying that Iran’s nuclear program has been destroyed—which is false—and that Iran gets nothing outlined in the MOU unless Iranian leaders change their behavior. The published agreement makes no such stipulation, and benefits, like the ability to sell oil on international markets and the lifting of sanctions, begin to flow to Iran immediately.
The leaders trying to dictate a new global order seem brittle and breaking, while in the United States the crowds jamming the streets in New York City in a ticker tape parade for the NBA Championship winners, the New York Knicks, suggested the momentum has shifted back to the American people. Celebrities like Mariska Hargitay, Timothée Chalamet, Mary J. Blige, Fat Joe, Spike Lee, and Ben Stiller joined the parade to celebrate the Knicks’ win.
At City Hall, Mayor Zohran Mamdani blended the victory of the Knicks with the rising political power of the people. .
“Over these past weeks, as the Knicks kept winning, our city has come together as one,” Mamdani told the crowd. “Neighbors invited neighbors over. Strangers high-fived one another in the street. Subway conductors sang their announcements, and bus drivers danced behind the wheel.
“So often when this city comes together, it is because we are forced to by a moment of tragedy, or adversity. What a gift it is to be brought together by pure, unfiltered joy. For as long as we live, we will remember this feeling of a city together. A city alive, a city overcome by happiness.
“But,” he said, “let’s not pretend that this was inevitable. If you will allow me, I want to travel back in time eight days. Game four. Nine minutes and 33 seconds left in the fourth quarter. The Knicks are down 20. The analytics guys, the sports betting companies, the pundits who watch from far away, they do what they do. They run the numbers. They calculate the odds. They write the Knicks off. They give the Spurs a 99.6% chance of winning the game. A 99.6% chance of tying up the Series 2–2, of reclaiming the momentum with the next game in San Antonio. A 99.6% chance of silencing the Garden, of another year of watching and waiting.
“But there is one thing that the pundits just don’t get about this team, that they just don’t get about this city. It is in that .4% that we go to work. It is in that .4% that Jalen Brunson, the same guy that so many said was too small, proves that not only is he good enough, he is the new standard for greatness. It is in that .4% that OG Anunoby watches the ball float from the top of the arc and start running toward the basket, fingers reaching towards the heavens. It is in that .4% that Karl-Anthony Towns finds the strength to mourn his mother and still pull in rebound after rebound, make block after block. It is in that 0.4% that Jose Alvarado shows every kid growing up in public housing, that a son of Brooklyn and Queens can win for every one of the five boroughs. It is in that .4% that Mitch breaks his finger before game one and says, “Go get the tape.” It’s in that .4% that Josh Hart gets rebounds that break teams, that Mikal Bridges proves he was worth every single draft pick that Landry Shamet pulls up from downtown, that every one of these 18 players transforms the franchise, that Mike Brown keeps this team believing.
“Most of all, it’s in that .4% that the Knicks do what New Yorkers have always done when we are told something is impossible. We find a way. We win. Standing here, before what feels like the entire city, there is a Jalen Brunson quote I can’t stop thinking about: ‘You are allowed to think about the worst possible scenario, but you gotta go out there and do something about it.’
“Time after time, we thought about the worst possible scenario. And time after time, the Knicks went out there and did something about it. The Knicks did not just win for New York City. They won like New York City. What is New York, if not your back up against the wall? A dream that feels just out of reach. A rent payment you don’t know how you’ll ever make. What is New York, if not 99.6% of the world stacked against you?
“And who are New Yorkers, if not people who hear those odds and smile? Who look at a .4% chance of success and ask, ‘Why are you giving me a head start?’ This is our city. This is our team. For 53 years, we watched. For 53 years, we waited. Now we’ve won.”
The theme farther west, in Chicago’s Jackson Park, was the same: community, hope, and the power of individuals to create change. For the opening of the Obama Presidential Center, former first lady Michelle Obama and former president Barack Obama welcomed living presidents and first ladies, except the Trumps, who were not invited: President Bill Clinton and Secretary Hillary Clinton, President George W. Bush and Mrs. Laura Bush, and President Joe Biden and Dr. Jill Biden.
The crowd at the center was packed to hear speeches by the Obamas and longtime friends and aides, and to hear performances by Christina Aguilera, Marc Anthony, Common, Jennifer Hudson, John Legend, Marsai Martin, The Roots, Bruce Springsteen, Tems, U2’s Bono and The Edge, Eddie Vedder, and Stevie Wonder.
Tens of thousands of people also packed the nearby Midway Plaisance Park to watch the event on jumbotrons. In both places, the mood was jubilant and warm. Comedians Stephen Colbert and David Letterman and Obama Foundation board chair Martin Nesbitt all showed up in tan suits, a reference to the tan suit Obama wore in the Oval Office in August 2014. Although past presidents including Ronald Reagan had also worn tan suits in the White House, as Jacob Gallagher of the New York Times noted today, Obama’s suit led to a right-wing meltdown about how the suit was too informal for the West Wing: then-Representative Peter King (R-NY) called it “a metaphor for his lack of seriousness.”
The story of the South Side of Chicago, from which the Obamas hail, is “a story of possibility,” a video introducing the center said. “[W]e can come together and create the change we seek. ‘We.’ It’s the single most powerful word in a democracy: ‘We the people.’ We shall overcome. All things are possible. Yes we can. ‘We’ includes everyone.” The emphasis of the event was on new leaders shaping the future. “The future is now, and it starts with us.”
Mrs. Obama urged Americans to make a choice to change the future. “The Obama presidential center is a living testament to the power of choice,” she said, “the historic example that millions of you gave the world about what this imperfect democracy has strived for and achieved.” And, she said, it is “an urgent call to go out there and do it again.”
She said she hoped the center would remind people “of the power of choice. And the steady work of change. The arduous, unglamorous march up that mountain, one foot after another, day after day, generation after generation. But I…also hope you fully absorb the elation of achieving something together. You know, that feeling when you clear the tree line and see a vista that takes your breath away. A feeling that can never be erased.”
“I know that can be hard to grasp right now,” she said, “when everything feels so upside down. When fact and fiction run together, when folks seek to stifle speech, limit access to education, devalue diversity, erase the inconvenient parts of our history. When our phones constantly buzz with the latest outrage.” She hoped the center “can reignite the optimism and empathy and ambition that has always powered this country’s greatest change.”
“[W]e want you to come here and put away your phones and talk and laugh and cry…and make new friends,” she said. “Get your hands dirty in my garden. Push your baby on a swing in the playground. Have a romantic picnic on the great lawn. Because that’s the work of democracy too. Being neighborly. Taking care of public spaces. Having some fun enjoying each other. Shaking out of the isolation and division that have crept too deeply into our lives.”
She championed the power of the people as she urged the center’s South Side neighbors “to make this campus a part of your lives. Be inspired by the world-class art. Check out the books from our beautiful public library—and bring them back on time. Drop some beats in the recording studio, hit some corner threes at home court, hold birthday parties, jump-start clothing drives. Host citywide cleanup dates here. Use this campus to show off this place we call home. This joyful place where Marian and Fraser Robinson taught their two kids to dream big. This hopeful place where an unknown guy with an unknown name took flight. This stubbornly optimistic place where family after family scrapes and claws and laughs and dances their way to a better tomorrow. That’s what this has always been about.”
She told Chicagoans they “have shown the world what we are capable of. You’ve proven that a lasting legacy isn’t an award or a name on a building or a number of zeros in a bank account, but the difference we make in one another’s lives. It’s about seeing each other, and showing up for each other, and carrying each other when we’re weary or faltering or losing faith. That’s how you build something that endures.
“And that’s what you all have done at every twist and turn of this extraordinary journey,” she said. “You have protected and proclaimed the hope that beats within the heart of this campus. You’ve rekindled and renewed this untameable, unpredictable, and unbreakable democracy. And I know that you all are gonna astonish us even more in the months and years ahead. Because you all have proven beyond a shadow of a doubt that when we truly see each other, when we strive to bring out the best in ourselves and one another, oh, there is no limit to how high we can go. Thank you all. I love you all. God bless you, and God bless this country we love.”
—
Notes:
https://www.nytimes.com/2026/06/18/style/obama-tan-suit-stephen-colbert.html
https://www.npr.org/2026/06/18/nx-s1-5863027/us-iran-trump-memorandum-of-understanding-full-text
YouTube:
watch?v=A87ohdXcJtY (from 55:34)
Instagram:
Bluesky:
“They’re about power, aren’t they, and the bloody powerful blokes who wear them.”
Maybe I’m all wet and these things are stylish, no matter what they do to your ears.
Re: my post on Verizon flat-out admitting their business practices have resembled a scheme from Dr. Evil, Domino’s did something similar regarding their pizza a while back. This 2021 story for Inc. by Jeff Haden describes the turnaround.

I’ve mentioned a few times that in addition to everything else Trump’s Iran “deal” is an electoral disaster for Benjamin Netanyahu. What I’ve been wondering is what the deal would do to Israelis attitudes toward Donald Trump. On its face that shouldn’t be complicated. If Israelis are mad at Netanyahu for getting boxed into Trump’s deal and coerced into honoring a treaty Israel wasn’t a party to, presumably they should be far angrier at Trump himself. After all, he made the deal. But Israelis’ attitudes toward Donald Trump don’t allow it to be quite that simple. Pains me as it does to say, Israelis really like Donald Trump. Like really like Donald Trump, in a way that transcends Netanyahu’s to-date iron hold on Israel politics.
Now we get the beginnings of an answer to the question. A new poll from Israel’s Channel 12 finds that 71% of Israelis don’t trust Trump to look out for Israel’s interests in an Iran accord compared to 13% who do. The same pollster asked the same question only a week ago, when the outlines of the deal were coming into view but they weren’t official. That poll found only 61% trusted Trump. So Trump’s standing in Israel is bad and falling fast. To give some context, a February poll by the Jewish People Policy Institute found that 73% of Israelis thought Trump was a better than average president and 49% thought he was one of best in American history. The results were highly polarized. 92% of the Israeli right thought he was a good president. But even 34% of the Israeli left thought so.
Of course these are not the same questions. So you can’t say that Netanyahu went from 73% for him to 71%. But these are both basically trust/approval type questions. So they give some sense of the sea change in opinion about him.
Relatedly, the same poll found that 52% of Israelis believe Netanyahu’s actions harmed Israeli interests in the Iran deal while 24% think it helped.
Does it really matter what Israelis think of Donald Trump? Maybe not that much. It’s not that Israelis liked Donald Trump and then decided to back his policies. It’s that Trump celebrated and enabled Israel’s most aggressive geopolitical aspirations and so they thought he was awesome. Finding out Trump’s full of it and would drop them the moment it became necessary or convenient may not have much impact beyond their opinions about him. But it’s at least a major blow to Netanyahu. He played a central role in convincing Trump to launch this war, which Israelis overwhelmingly believe they lost or ends a stalemate (lost 43%, inconclusive 41%, won 11%). Just as much though, in what we might call the third phase of Netanyahu’s prime ministerial career — the one begins with the JCPOA in 2015 and Trump’s election in 2016 — he’s made his relationship with Trump and ability to get what Israel needs from Trump as a centerpiece of his political brand.
Ben Chapman, reporting for The Independent in 2019:
After 40 years of advertising its lager as “Probably the best beer in the world”, Danish brewer Carlsberg has confessed that the famous slogan may not be true. Reacting to falling sales and increasingly harsh comments from drinkers about the taste of its beer, Carlsberg has launched a new recipe along with a more honest approach to marketing.
The campaign declares: “Probably not the best beer in the world. So we’ve changed it. Somewhere along the line, we lost our way. We focused on brewing quantity, not quality. We became one of the cheapest, not the best.”
As part of the new ad campaign Carlsberg is sharing negative comments about the old beer including, “Carlsberg tastes like stale breadsticks” and another comparing it to “drinking the bathwater your nan died in”.
I drank a Carlsberg once. Once.
Early adoption of new technology is generally considered a young-person thing, but maybe Snap Specs will turn that notion on its head. Direct sales in retirement homes?
NBC News:
The Trump Mobile T1 phone, originally marketed as “Made in the USA,” is nearly identical to the two-year-old HTC U24 Pro, a phone made by the Taiwanese company HTC using Chinese parts, according to a technical analysis the repair-guide and parts company iFixit conducted in partnership with NBC News.
That report is paywalled, but NBC News’s five-minute video is on YouTube, and iFixit has a full teardown report of their own. The only thing that’s surprising is that the Trump T1 doesn’t cost much more than the HTC-branded one ($500 vs. $470).
The Wall Street Journal on Monday:
Fox Corp. said it is acquiring Roku in a deal valued at around $25 billion, making a major bet on the future of ad-supported streaming. The deal — Fox’s largest to date — brings together a media company known for its live news and sports programming with the biggest provider of streaming platforms for connected TVs.
It will add scale to Fox’s streaming business, currently home to free, ad-supported streaming service Tubi, which the company bought for $400 million in 2020, and subscription-based Fox One and Fox Nation.
In addition to distributing other streaming services through connected TVs and devices, Roku has its own ad-supported Roku Channel. The combined company will better compete with the likes of Amazon.com and Netflix for ad dollars.
I’m late to comment on this, but this seems stupid. Roku sucks. I know they’ve got a 25 percent or so share of the smart TV interface market, but no one is attached to Roku. The entirety of their market share is people who don’t care. That’s not worth $25 billion. Shit platforms seldom last, and the ones that do last achieve monopolies. I think Roku’s share is going to slip, not grow.
Welcome to Edition 8.46 of the Rocket Report! We don't mention Starship in the body of this week's report, so I'll give a brief update here. The next test flight of SpaceX's mega-rocket—Flight 13—could happen as soon as next month, according to Gwynne Shotwell, the company's president and chief operating officer, in a recent interview with CNBC. There's still a fair bit of work to go before Flight 13, so don't count on a launch next month just yet. What we do know, based on Shotwell's comments to CNBC, is the next Starship test flight will look a like like the previous one last month, with a suborbital flight path and a splashdown of the ship in the Indian Ocean. SpaceX is holding off on an orbital flight until at least the following launch, Flight 14, after the ship was unable to complete a critical engine restart in space on the last flight.
As always, we welcome reader submissions. If you don't want to miss an issue, please subscribe using the box below (the form will not appear on AMP-enabled versions of the site). Each report will include information on small-, medium-, and heavy-lift rockets, as well as a quick look ahead at the next three launches on the calendar.
Isar test flight scrubbed again. Isar Aerospace still commands top position among a new generation of European rocket startups, but the company’s efforts to launch a critical test flight of its Spectrum rocket continue to encounter roadblocks, Ars reports. The latest delay came Monday, when Isar scrubbed a launch attempt after "detecting off nominal behavior in the vehicle’s fluid systems," according to a social media post. "The teams are analyzing the new data to isolate the root cause." Isar is flush with cash, having raised nearly $1 billion to date, but is still lacking in the critical currently of flight experience. The Spectrum rocket has flown just once to date, on a failed launch last year that lasted less than 30 seconds.
[Decompressing after travel—I hope to get back to regular blogging before long. A belated congratulations to Knicks fans, as their 1970 championship was the first one I remember watching. As a Bucks fan I feel a connection—we won in 1971 and then also had a 50-year wait for another title.]
Part 1: Apart from the 2020 surge in murders, a period when police sort of gave up on crime for a brief period, the US murder rate has been declining since 1991. I expect this decline will continue, and within about 20 years the US will have Canadian rates of murder. More specifically, we’ll have a murder rate close to the current Canadian rate of roughly 2 per 100,000 people. I anticipate that the Canadian rate will also decline further and thus remain below US levels in 2046.
In the US, the murder rate peaked at 10.7 per 100,000 in 1991 and fell to only 4.29 per 100,000 in 2025. Early figures suggest that 2026 is on pace for a rate of roughly 3.8 per 100,000. (And no, it’s not due to better medical care.)
While the data from early American history is rather fuzzy, I suspect this is the lowest rate in US history (based on some highly scientific analysis on my part—watching lots of gunslinger movies as a kid.) Seriously, I expect the rate will go much lower, for at least four reasons:
The level of births was 4.32 million in 2007, and has been falling ever since. That means the number of 19-year old Americans is virtually certain to fall sharply over the next 20 years. And young men are especially likely to be involved in violent crime. Also note that the fertility rate of African Americans is falling especially rapidly and is now below the rate for white Americans.
Declining crime tends to feed on itself. With fewer murders, each crime stands out more and attracts more police attention. That leads to a greater chance of being apprehended, further discouraging crime.
The sort of surveillance technology that has radically reduced crime in Chinese cities like Shenzhen is likely to eventually become a feature of American cities. Artificial intelligence will play a role, as will ubiquitous cameras in public spaces, the tracing of location via cell phones, automobile computers, and related technologies, as well as better DNA testing. Robocops will patrol our streets. The world of 1984 is arriving faster than I expected. Big Brother will be watching us all in 2046.
The sort of lifestyle changes that have reduced fertility rates are also likely to reduce crime rates. When I was young, bored young men stood around on street corners, which is a recipe for crime. Now they can enjoy all sorts of electronic entertainment in the comfort of their home. These forms of entertainment also increase the cost of incarceration, where Netflix may not be available. Alcohol use is declining and being partially replaced by pot use, which is less likely to provoke violence.
If you believe that my claim is far-fetched, consider that just a single factor—demographics—is likely to reduce crime committed by young men by roughly 15% to 20% by 2046. That alone would get the murder rate down from the 2026 estimated 3.8 to roughly 3.2 per 100,000. And as recently as 2021, the rate was 7.75 per 100,000. Much of the progress toward Canada’s (current) murder rate has already occurred, even though America still has about hundred gazillion guns. (By the way, I’m not a gun nut—some restrictions may be sensible—just saying that guns are not the primary factor.)
I haven’t even discussed immigration, which clearly plays a role in the especially rapid decline in murder rates in places like NYC.
To be clear, I’m not suggesting that a rate of even 2 per 100,000 is ideal—many European countries have rates of 1 per 100,000, or even less. But it would represent a dramatic reduction from the rate of nearly 10 per 100,000 during much of the 1970s and 1980s. Speaking of 2046, who can forget this gorgeous Wong Kar Wai film:
Part 2: I cannot prove this, but I strongly suspect that drug addiction in America is on the way down. Here are my claims; you tell me if I’m mistaken:
There is less alcoholism than in the past
Smoking is declining fairly dramatically
Opioid drug abuse is declining
Marijuana abuse is increasing
Now I’d like you to consider two different social science hypotheses and tell me which one better fits the data:
Pot is a “gateway drug”, which leads to even more harmful forms of drug abuse.
Pot is a substitute for other types of drugs, and legalizing pot would tend to reduce other (more serious) forms of drug abuse.
Behind the paywall, I’ll offer a few thoughts on the War on Drug Using Americans:
Part of a 12 square mile solar farm in Spain
On Wednesday the Interior Department announced that it would pay the energy developer Invenergy $765 million not to develop three offshore wind farms. This is the third such payment by the Trump administration to undo offshore wind projects that have been years in the planning. Trump has so far committed $2.5 billion in taxpayer dollars to killing renewable energy projects. The administration has also tried to stop offshore wind farms already under development — moves that have been blocked by the courts — while the Pentagon has been refusing to grant routine permits for onshore wind projects.
Yes, $2.5 billion to destroy already-approved, cost-effective clean energy projects while Americans are suffering from soaring electricity prices thanks to data centers and high gasoline prices.
Yet here’s the irony: Donald Trump’s disastrous Iran war has delivered a huge boost for renewable energy around the world — except in the U.S.. Trump has so far done more to shift the global economy away from fossil fuels and towards renewable energy than any other single individual in history.
Why do Trump and his gang hate green energy so much? The roots of their hatred range from the power of fossil fuel interests, to Trump’s petulant whine that wind turbines ruined the view from his Scottish golf course, to a general sense among right-wingers that clean energy threatens their masculinity.
What’s best for Americans has nothing to do with it. Thus, Trump lackeys justifying their hostility to renewables consistently make arguments even they must know are stupid. Consider, for example, an exchange last month between Doug Burgum, secretary of the interior, and Rep. Jared Huffman of California:
Burgum: All of these projects you’re describing in Nevada have one thing in common—when the sun goes down, they produce zero electricity.
Huffman: Mr. Chairman, I request unanimous consent to enter in the record this amazing new technology that apparently the secretary is unaware of: It’s a battery.
Indeed. To get a clearer understanding of far battery technology has progressed in enabling the transition to renewables, let’s look at how the state of California sourced its electricity this past Wednesday. The chart below shows megawatts supplied at 15-minute intervals over the course of the day. The area shaded yellow represents daylight hours. The light blue line at the top is electricity generated by renewables, mainly solar power (with some wind and hydro as well). In addition to supplying energy for current consumption, renewables supply energy to batteries for nighttime consumption. The black line at the bottom is net electricity supply from batteries — which is negative when batteries are charging, positive when they’re being drawn down:
California — which would be the world’s 4th largest economy if it were a country — gets more than half of its electricity from renewables. It is rapidly becoming a state largely powered by the sun during daylight hours and powered by batteries during the night.
Burgum’s suggestion that solar is an unproven or unreliable technology is completely at odds with reality.
Nor is California the only economy that now makes substantial use of renewable energy. Burgum’s home state of North Dakota gets more than a third of its electricity from wind power (don’t tell Trump). In South Dakota wind supplies 57 percent of the electricity. And renewables generate a large share of electricity in many countries, including most big European economies. (France is the outlier, not because it relies on fossil fuels, but because it has large nuclear capacity.) Spain, for example, now relies heavily on a solar-plus-batteries system similar to that in California.
And when Trump went to war with Iran, nations that had already shifted toward renewable energy were very glad they had made the move.
To the extent that there’s a competition for the future of electricity generation, it’s between renewable energy and natural gas. Whatever Trump may want to believe, burning coal — even ignoring the environmental damage — is a costly, obsolete technology, which nobody wants to invest in. But new gas-turbine power facilities are still being built (although many places are, like California, rapidly shifting away from natural gas). Trump officials envision a world largely powered by US liquefied natural gas (LNG).
However, countries that relied heavily on natural gas were hit hard by Trump’s gratuitous war with Iran. LNG supplies from the Persian Gulf were blocked and couldn’t be fully replaced by U.S. exports because shipping capacity was limited. Countries that had invested heavily in renewables, like Spain, were largely unscathed. A report from the think tank Ember found that since the war began Spanish electricity prices — unlike prices in some other European countries — were essentially decoupled from the soaring price of natural gas.
Assuming that the Strait of Hormuz will be reopened after Trump’s abject surrender to the Iranian regime, natural gas prices should subside. Yet the world has learned a hard lesson about the riskiness of relying on fossil fuels for electricity generation.
And let’s be clear about the nature of that lesson. It’s not the fact that much of the world’s supply of hydrocarbons comes from a politically volatile region: We’ve known that all too well since the 1970s. What’s new is the recognition of American weakness and unreliability.
In this new era of drone warfare America cannot guarantee reliable access to imported fossil fuels through critical sea lanes. And is America itself a reliable supplier? Can nations that allow themselves to be dependent on U.S. gas and oil be sure that Trump or a future Trump-like president won’t weaponize that dependence, cutting off or threatening to cut off supplies in some future dispute? The obvious answer is no.
The whole world now knows that relying on imported fossil fuels is a major economic and security risk. By contrast, the sun will shine and the wind blow whatever may be happening overseas. Renewables were already rapidly becoming cheaper than fossil fuels. Now it’s clear that they are also far safer.
Thus Donald Trump has in practice become the world’s green energy champion.
MUSICAL CODA
The real valuable capability MCP offers over skills/CLI is isolating the auth flow outside of the agent’s context window, and potentially out of the harness completely. [...]
Maybe the idealized form of MCP is just an auth gateway for the API and nothing else. That’d still be a win.
— Sean Lynch, comment on Hacker News
Tags: model-context-protocol, llms, ai, generative-ai, skills
Did Kylie and I attend the Midjourney Scanner event? Yes, we did. Did Kylie compare notes on her skincare routine with Bryan Johnson at the event? Also, yes. Meanwhile, I formed more wrinkles by downing cocktails while all this happened.
Since the Midjourney pivot into medical devices and health spas is all the rage, we had no choice but to dive right in on this week’s episode. We also explored the government’s ban of Anthropic’s latest model, the odd tie-up between DeepMind and Eve Online, Noam Shazeer leaving Google for OpenAI, Snapchat’s Snapcrap glasses, the Rafael Nadal documentary (naturally) and a robot that kills fish in the name of better sushi.
Come get it.
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Timestamps (links go to the YouTube channel)
0:00 Intro
0:38 Inside Midjourney's Secret MRI Machine
15:42 What Does Brian Johnson's Skin Really Look Like?
21:14 The First AI Model America Ever Banned
36:26 Why Is DeepMind Obsessed With a 20-Year-Old Space Game?
45:47 The Transformer Inventor Just Switched Sides
49:36 Whatever Happened to "Scaling Hit a Wall"?
52:26 Who on Earth Is Buying $2,200 Smart Glasses?
1:00:34 Dating in San Francisco, COVID Shots, and the World Cup
1:05:32 The Time Ashlee Nearly Pocketed Rafael Nadal's Racket
1:16:21 A Robot That Kills Fish the Japanese Way
1:19:31 Is Ashlee Turning Republican? Plus Where Batteries Go to Die
This week we take you inside the drone manufacturing operation of Skydio to see how they make their flagship X10 product.
Skydio is the largest drone maker in the U.S. and produces its hardware right here in the great state of California. Does it make as many drones as China’s DJI? It most certainly does not. But does it make very sophisticated drones packed full of the best computer vision and AI technology? Yes, it does.
In this episode, we show you what goes into making a drone step-by-step as part of our This Is series.
You can catch our previous episodes on Foundation and its robots and Neuralink and its brains below.
Meet Alice. Alice uses your web service. Alice, like most humans, measures her time in seconds and minutes. Alice says your service is slow. You tell Alice that the mean request to your service completes in 100ms, but Alice says that her mean wait time is 1s.
You’re both right.
Meet Alex. Alex uses your web service. Alex, like most humans, measures his time in seconds and minutes. Alex says that when you have outages, they last a long time and he gets really annoyed. You tell Alex that your MTTR is less than 1 minute. Alex says that he sees the mean outage lasting 1 hour.
Again, you’re both right.
What’s going on? What’s going on is that you’re measuring time in requests, or in outages, and Alex and Alice are measuring time in seconds and minutes. When you have a long request or a long outage, Alex and Alice count that as a long time, with a heavy weight. But you only count that as one.
More technically, what’s going on here is the inspection paradox. Alex and Alice don’t experience your latency distribution $f(t)$, they experience a t-weighted version of it. If you have a MTTR or mean request time of $\mathbb{E}[X]$, Alex and Alice experience $\mathbb{E}_a[X] = \frac{\mathbb{E}[X^2]}{\mathbb{E}[X]} = \mathbb{E}[X] + \frac{\mathrm{Var}(X)}{\mathbb{E}[X]}$.
Most of the time they’re waiting, they’re waiting for things that take a long time. This is (roughly) how humans experience time.
Let’s play with this with a little simulation. Plug in your median latency (or recovery time), and 99th percentile latency (or recovery time), we’ll fit a log-normal distribution to it, and then plot both what your service metrics see and what your customers see.
Median: ms p99: ms
What your service sees (mean): – ms. What your customers experience (mean): – ms.
For example, put in 30 as the median (let’s ignore the milliseconds and pretend these are minutes for now) for a 30 minute Median TTR (i.e. in half of your postmortems you see a recovery time of $\leq 30$ minutes), and 600 in as the p99 (one in every 100 events, recovery takes 10 hours). Your MTTR is just over an hour. Your customers experience a mean time to recovery of around 6 hours!
There are many arguments for why tail latency (and long recovery times) are so important to understand (e.g. multiple samples), but this is the one that I think is the least widely understood. For service times, timeout-and-retry can hide this latency some of the time (as long as the running request doesn’t hold locks or other exclusive resources). But, for recovery time, no such hiding is possible. The heaviness if the tail matters a great deal. This is also one of the reasons I don’t like trimmed measurements (like trimmed means) as a way of thinking about service latency or recovery time. They throw out some really critical context about the shape of the right tail that dominates the customer experience (the other reason is related to Little’s Law and capacity usage, which I’ve written about before).
A note on log-normal: I chose log-normal here for numerical convenience. It has the nice property that $\mathrm{lognormal}(\mu, \sigma^2)$ becomes $\mathrm{lognormal}(\mu + \sigma^2, \sigma^2)$. Also it’s well-behaved around 0. I don’t believe that log-normal is a particularly good choice of distribution for latency or recovery time metrics, and generally would approach these problems entirely non-parametrically.
Lay till 6 o’clock, and then up and to my office, where all the morning, and at noon to the Exchange, and coming home met Mr. Creed, and took him back, and he dined with me, and by and by came Mr. Moore, whom I supplied with 30l., and then abroad with them by water to Lambeth, expecting to have seen the Archbishop lie in state; but it seems he is not laid out yet. And so over to White Hall, and at the Privy Seal Office examined the books, and found the grant of increase of salary to the principall officers in the year 1639, 300l. among the Controller, Surveyor, and Clerk of the Shippes. Thence to Wilkinson’s after a good walk in the Park, where we met on horseback Captain Ferrers; who tells us that the King of France is well again, and that he saw him train his Guards, all brave men, at Paris; and that when he goes to his mistress, Madame la Valiere, a pretty little woman, now with child by him, he goes with his guards with him publiquely, and his trumpets and kettle-drums with him, who stay before the house while he is with her; and yet he says that, for all this, the Queen do not know of it, for that nobody dares to tell her; but that I dare not believe. Thence I to Wilkinson’s, where we had bespoke a dish of pease, where we eat them very merrily, and there being with us the little gentleman, a friend of Captain Ferrers, that was with my wife and I at a play a little while ago, we went thence to the Rhenish wine-house, where we called for a red Rhenish wine called Bleahard, a pretty wine, and not mixed, as they say.
Here Mr. Moore showed us the French manner, when a health is drunk, to bow to him that drunk to you, and then apply yourself to him, whose lady’s health is drunk, and then to the person that you drink to, which I never knew before; but it seems it is now the fashion.
Thence by water home and to bed, having played out of my chamber window on my pipe before I went to bed, and making Will read a part of a Latin chapter, in which I perceive in a little while he will be pretty ready, if he spends but a little pains in it.
This is the second half of the second part (I, IIa, IIb) of our honestly-who-knows-how-many part series laying out some general guidelines for how pre-modern armies are recruited, raised, equipped and paid. While I hope this will be of great interest to the history nerds out there, I’ve opted to structure this specifically as a service for the worldbuilders out there, making useful rules of thumb for imagining fantastical societies.
We’re picking up right where we left off discussing various methods for actually mobilizing an army, which is to say recruiting the troops, getting them armed and getting them in the ranks. We’ve covered ‘self-recruitment,’ systems where a lot of the burden of that process is handled by the troops themselves (almost invariably entitlement principle citizen militia), allowing for a fairly limited administrative apparatus, though I should note, not no administrative apparatus. The need to keep records of who is liable to serve tends to mean these ‘self-recruitment’ systems are most often used by urban societies with a literate upper-class.
We also looked at the over low-overhead alternative: having mobilization handled by local Big Men. The big caveat here is this functionally ensures the fragmentation of power (and thus a non-state society, since centralization of military force defines states) which means that a society which turns its local aristocracy into petty warlords in order to raise its armies has to then cope with having a whole bunch of petty warlords who have their own small armies they can use to push for power and position.
And I want to reiterate here societies do not choose these systems from first principles, instead these systems develop organically, over generations (it’s quite rare that someone plans such a system from scratch, although it does – rarely – happen) and are constrained by existing social structures. A non-state society is basically compelled to adopt Big Man Mobilization because, lacking state structures, they have no other options. By contrast, a long-established state cannot adopt Big Man Mobilization effectively, because they don’t have the petty warlords it relies on and creating those warlords would mean disestablishing the state to a substantial degree by fragmenting its power.
So for the most part, we ought not think of these systems as choices but as consequences of social structure.
This week, we’re going to move forward and discuss some of the more involved solutions, which might give the state a bit more control over the process, at the cost of greater overhead (although not always greater overhead for the state necessarily, as we’ll see).
But first, as always, recruiting and maintaining large pre-modern armies is expensive! Much like many of those pre-modern armies, this project is supported by devolving the costs of my ruinous book-buying habit on to recruits readers. You can help by spreading the word to new readers and by supporting this project over at Patreon. If you want updates whenever a new post appears or want to hear my more bite-sized musings on history, security affairs and current events, you can follow me on Bluesky (@bretdevereaux.bsky.social). I am also active on Threads (bretdevereaux) and maintain a de minimis presence on Twitter (@bretdevereaux).
Now we’ve seen how compact state societies with a broad ‘middling well-to-do’ class which can political preserve their interests and have some political entitlements to fight for can recruit. But what if your state society doesn’t have that well-to-do peasant or burgher class? At least, not enough of them?
That is the case for most pre-modern state societies: it is substantially more common for the peasantry to be pushed down basically all the way to subsistence. After all, it is in the domestic economic interests of the king, the temple and the local aristocracy to extract as much as they can from the peasantry and most do so – the conditions whereby that peasantry can politically defend themselves from that sort of extraction are rare (and it’s often not the whole peasantry). But now you have a problem: that aristocracy is not big enough to be the society’s whole army on its own, but your peasants are too poor to afford their equipment and have no political entitlement for which to fight.
Actually, to take an aside for a moment, there’s some complexity here in what is happening. Fundamentally, pre-modern societies are all about agriculture and subsistence and so what the society needs is for these small farmers to generate enough surplus to support two kinds of non-farmers: producers making military equipment and soldiers who aren’t farming because they’re fighting. Entitlement-systems do this by both allowing farmers to keep more of their surplus but then also tying the act of dedicating that surplus to warfare (acquiring weapons and serving) to their social status so those small farmers are willing to push out to the edge of their labor to keep their status. But poor peasants have no status to defend and little surplus labor to employ. Fundamentally the interactions here are about the food economy.
So for these societies with stronger aristocracies and weaker peasantries, the problem is two-fold: first the peasants have no reason to serve and second they have no wealth with which to afford the weapons they need to serve. However, the state may well still want to raise all these peasants they have rather than reaching for some other source of manpower (as below). The solution to the first problem is some form of compulsion (you force the peasants to serve or to enable service) and the solution to the second is what I am going to call brigading. ‘To brigade’ simply means to group something together (a military brigade was a combination of different units, often with different integrated combat arms), from which it acquires a lot of specialized meanings. What we’re interested in doing is brigading households: one peasant household, crushed down by extraction, cannot support a soldier – but four or five or ten might.
Now the mechanism of compulsion here varies and with it the mechanism of brigading.
The simplest system is brigading under a principle of universal male military service and we see this at play with systems like the Anglo-Saxon fyrd, the Carolingian select-levy. If the community was under direct threat – your town or village was being attacked – the king (or his local representative) could call up the ‘general levy’ of all adult males. But for regular warfare (including offensive warfare) one obviously couldn’t call up everyone (someone needs to be farming) but a better equipped steady-state infantry force was required to supplement the aristocrats. This was the ‘select levy’ and the fyrd system and the Carolingian system end up settling on a similar solution: bolt several peasant households together and require that, collectively, they furnish one soldier for the king’s army.
Under Charlemagne (r. 768-814), each peasant household was assessed based on its production in units of value called mansi, and for every four mansi they were required to produce one soldier for the select-levy. Now of course most peasant farms were a lot smaller than four mansi – the regulations make provisions for holders as small as half a single mansus – so smaller households were brigaded together until a unit of four mansi was created. That combined unit of several households then was expected to pick someone from among its military-aged men and then collectively pool their resources to equip him – a shield, a spear, a sword, a helmet and some very basic armor (probably textile) – to serve in the select-levy, presumably on a relatively long-term basis.
The Anglo-Saxon system of Alfred the Great (r. 871-899) worked similarly. Farming households were assessed in units called hides (by 1066, this unit is really big, around 120 acres, but my understanding is we generally think it was a lot smaller before then). As with the mansi, households could be brigaded together to make up a single hide, though the hide itself was, notionally, a ‘standard small farm’ (the same way the mansus was). Each individual hide was required to provide one man for local military service (garrison duty, etc.; basic, part-time sort of work that wouldn’t require much kit) and every five hides together to also provide one man – similarly fairly well equipped – for service in the royal army that represented the kingdom’s main field force.
Now that’s the most direct way to brigade the households together, but not the only way. Mass conscription in the Warring States and early Han Dynasty, as I understand it (this is very much not my area of expertise) worked on a rolling, age-based basis where recruits would spend just 2-3 years in the army before being discharged back to their farms as part of the reserve. That too is going to have the effect of distributing the burden of service across a bunch of households, though I am not clear who handles the cost of equipment in that system. Over time, the Han Dynasty converts over to a fully professional system, discussed a little bit below.
The other option for brigading households, at least economically, is military settlers. We see this system at work in the Hellenistic kingdoms that form after Alexander’s death. Instead of brigading a bunch of households together and making them pick one of their members, we (the state) pick for them, by imposing a soldier’s household on top of them. It’s not hard to see why this tends to be a feature of conquest states: the state seizes the land of the peasantry and redistributes it to the families of soldiers, such that the rent from that land is sufficient to maintain a heavy infantryman’s family in relative comfort. The peasantry doesn’t go anywhere, but now they have to pay a portion of their production in rent to the soldier’s family (in addition to taxes – this is an exploitative, unpopular sort of system!) who generally doesn’t do any farming himself and so the soldier (or his sons) are both wealthy enough to afford their equipment and available for conscription, since military service is the flipside of the deal by which they get to live as tiny little petty aristocrats.
Now you can see that each of these systems relies – at least for the peasants – on compulsion. The peasants do not get anything for their service – they aren’t paid, they don’t get increased social status nor can they expect some sort of personal relationship as a client with a noble patron – so you must force them. Which means you need bureaucracy.
Where an entitlement-based system can function often on self-reported wealth or volunteer militias – because military service is a positive honor – a compulsion based system needs bureaucrats. Someone needs to go to each farm and measure its production, assign it to a hide or mansus or to a military settler’s estate or decide which household is due to send a recruit this year and then enforce that decision. That means a small army of literature bureaucrats – royal officials – operating at a very granular level, in the villages (though once set up, the military settlers can perform this role themselves to a degree in a military settler system – they are the local enforcement and rent extraction). Those officials need to be paid, which means heavier taxes and so fundamentally they represent a kind of deadweight on the system: resources that have to be spent on military mobilization but which do not (directly) produce any soldiers. Military settlers functioning as rentier elites (distinct from the frontier-farmer-soldiers below) aren’t much of an improvement here, because they’re capturing a whole lot of value beyond their nominal ‘cost’ as soldiers, making them very expensive as a source of heavy infantry.
Consequently, brigaded-household-systems tend to produce less military power per unit population than entitlement self-recruitment or even fragmented Big Man recruitment. However their advantage is that they scale much more easily over large populations and land areas, so you can end up with more soldiers overall if you have a very big state.1 These sorts of systems thus tend to be creatures of large, relatively well-centralized, bureaucratic states.
Nevertheless, for the worldbuilders out here: this is the first sort of system where you are likely to find some sort of official actually operating at the village level where they might actually interact with the peasants.
Of course you may note that this system requires quite a lot of administration – you need to have local officials keeping track of local landholdings and conscription liability on a granular level, in a context where you cannot trust anyone to self-report their liability. Those bureaucrats need to be paid and likely so do these soldiers once they’re under arms (you can compel them before you give them weapons, but after they have weapons, you have to pay them, because they have weapons) and all of this demands just quite a lot of state capacity. And I want to stress that demand of state capacity: large, effective bureaucracies composed of literate bureaucrats are really hard for pre-modern states to build and so even ostensibly ‘wealthy’ (that is, high state revenue) states often are not able or at least are not willing to build them.
What if you wanted to shift that administrative burden somewhere else? Maybe you could just pay someone to handle all of that hassle for you…
The solution here is to outsource the task of administrative organization to some kind of private contractor.
The clearest example of this are the armies of early modern Europe, particularly during the 16th and 17th centuries, raised via a system of private military contractors in what is sometimes termed ‘private enterprise war’ (albeit that term also covers the seaborne commercial-imperial ventures of European powers in the same period, which we’re not going to get into here). The origins of this system stretch earlier – it emerges organically out of systems of mercenary and ‘feudal’ recruitment in the late Middle Ages. And this system is also often present to provide auxiliary units for armies largely recruited another way.
The basic schema goes thusly: the state (typically, but not always, a king) decides they need a unit of soldiers (a ‘regiment,’ generally), but they don’t have to handle all of that administrative hassle directly, so instead they contract someone – typically a military aristocrat (because that’s who has the experience and connections to handle this) – to raise a regiment, promising them payment. Sometimes the contractor is handed the money for this in advance (ancient mercenary recruiters are often sent off with a bunch of silver to hire guys with) but it obviously serves the king better to use the contractor as basically a source of financing: promise him money in the future for a regiment recruited today. So the contractor is not an employee, but rather a creditor who the king owes money.
The contractor then takes on both the financial and logistical burden of raising the regiment, but of course a regiment might be several thousand men which is still too big of a task, so the initial contractor might subcontract parts of this task to other, more junior military aristocrats in his orbit. The initial contractor is a colonel, his unit is a regiment; his subcontractors are captains, their units are companies. Yes, this is the origin of the modern, broadly used, international system of units and ranks.
The colonel and those captains would then employ recruiting-sergeants to enroll the actual men to form the companies. Recruiting scenes in period artwork often feature men being enrolled (and sometimes paid signing bonuses) at impromptu recruiting ‘stations’ consisting of little more than a table or sometimes an upturned drum, with the recruiting-sergeant writing their name down (enrolling in a literal sense) in the company’s rolls. The question of equipment was a problem for the colonel, rather than the king: this was, fundamentally, the colonel’s regiment.

The usual expectation is that such units might recruit from anywhere, but they tend to be fairly geographically focused. After all, the colonel doing the recruiting was able to get the contract because he has some connections or experience and that tended to be geographically localized. Lucian Staiano-Daniels notes (op. cit.), for instance, of the Saxon army and also the Mansfield Regiment during the Thirty Years War, while there certainly were men drawn from very far afield, the bulk of the soldiers came from a fairly tight geographic area around Saxony. Likewise, these sorts of ‘contractor’ units are often how ancient mercenaries show up in our sources and the fact that the fight in distinctive ethnic styles and are marked with ethnic signifiers certainly suggests that the bulk of the men were recruited from a fairly specific geographic and cultural milieu, although we should be aware that just as with those Thirty Years War regiments, there’s no reason you might not have a meaningful number of ‘international’ mercenaries from all over bolted on. These units, if they stay in being, also often recruit as they move, picking up whoever is willing to sign on from wherever they go.
Now we’re not dealing here with navies (which can press sailors by force because after that the boat sails away which reduces desertion risk), but by and large these sorts of armies raised by contractors are reliant on volunteers, which means they need to pay these fellows. From an economic perspective, they’re often skimming excess labor manpower off of local labor markets, which explains the somewhat counterintuitive fact Staiano-Daniels (op. cit. again) notes that the largest chunk of the recruits came from towns and cities – because of course that is where men without a stable niche in society tend to gather, looking for employment, opportunity or adventure. But in most cases these contractors do not have the legal power to compel service (and also quite limited ability to stem desertion of things go badly).
We’ll come back to finance in the next part, but it is worth noting here how heavily financed (that is to say, debt-based) this system is. The king essentially goes into debt with the contractor to get the regiment formed up and the contractor-colonel then raises the regiment. But the main expense there is the wages of the soldiers themselves, but most of their pay is ‘on the books’ (rather than in hard currency) to be paid out in full when the soldiers are dismissed or the regiment disbanded; we often see the officers of the regiment (who are vocational-type junior military aristocrats) lending soldiers hard-currency ‘advances’ on their pay out-of-pocket when the regiment itself was short on hard cash.
Now of course if a meaningful part of pay here is back-pay when a regiment is mustered out, the expectation is that the regiment is going to get mustered out and thus these are not standing units. They can be used to create standing armies (raising new regiments to replace old ones, etc.), but there is often going to be ‘churn’ in this system. That said, when mustering out an old regiment and raising a new one, the soldiers of the old regiment are the obvious first place to start for recruitment: they’re freshly unemployed, trained and experienced soldiers and they are available. As a result, you may get ‘professional’ soldiers in this system, even though it is not a long-service professional system.
That may seem like a terrible system and it could go very badly wrong (and frequently did) if the money to settle the debts didn’t emerge. But from the king’s perspective (and the contractor-colonel’s) it was a great system: it enabled them to finance the whole operation at a relatively low cost, allowing rulers to push their military capacity well beyond what they could afford in hard cash. Financing could even be added on top of this: the Mansfield Regiment’s operating costs were supposed to be covered by another loan (which seems to have ended up diverted to other regiments in the event), so the soldiers are owed their pay from the contractor-colonel and the regiment who in turn is owed this money from the king or the king’s representatives who in turn are trying to arrange their own financing to cover the funds.
The other advantage of this system, of course, is that it imposes minimal administrative burden on the king. The administrative hassle of finding men, recruiting them, keeping track of them, paying them, getting them equipment, uniforms, food and so on all falls to the contractor-colonel (who in many cases is really an absentee proprietor and has deputized someone else to actually run all of this for him; this fellow gets called a lieutenant colonel, he is ‘holding (‘tenant’) in lieu of the colonel’) who has to make those arrangements.
The disadvantages, of course, are numerous. These contractor-colonels basically own their regiments and so expect some leeway in terms of equipment, uniforms and command; not infinite leeway, mind you, they still consider themselves vassals or servants of the king, but it is very hard to enforce standardization on these armies. There’s also just an enormous amount of latitude for graft and indeed in many cases graft – pocketing the wages of dead soldiers, for instance – is how the captains and colonels get paid. And everyone in this system expects to get paid for their service at some point, so while financing can put off the day that the bill comes due, there will be a bill in money, so the state needs revenues to meet it.
Spain famously manages, despite acquiring a running river of silver and gold from the New World, to go bankrupt overextending itself with these sorts of armies in 1557. And then again in 1560. And again in 1575. And then once more in 1596. And then again in 1607. And again in 1627. And then bankrupt again in 1647. And one last time in 1653. This has, you may imagine, a deleterious impact on military discipline off of the battlefield and generally these armies tend to be, we might say, ‘rowdy.’ Again, I invite readers to check out L. Staiano-Daniels’ The War People: A Social History of Common Soldiers during the Era of the Thirty Years War (2024) to get a sense of the short of things these men get up to and the kind of society that forms in these regiments. One certainly gets the impression that Hellenistic military men were not much more restrained (the arrogant, braggart mercenary soldier was a stock character of Greek New Comedy, for instance, memorably captured in Roman Comedy (which derives from Greek New Comedy) in the Miles Gloriousus (‘The Braggart Soldier’)).
In terms of the kinds of societies that use these methods, contractors tends to be a response for states that are reaching for military power beyond what their core state apparatus can support – it tends to be a response to limited state capacity. It merges a vocational principle military aristocracy (the officers/contractors) with an employment principle common soldier and so requires a society that is monetized enough and economically specialized enough to support that framework – which is to say a society with quite a bit of commercial activity going on (often more commercial activity than the state can fully control or supervise – certainly true for both early modern Europe and the Hellenistic Mediterranean) and with enough daily business done in hard currency (rather than ‘non-monetized’ debt-or-bullion-based systems) that the soldiers can actually spend their pay.2
For many fictional fantasy settings, I think the ‘contractor’ method of raising troops is remarkably underrepresented. While Tolkien’s Middle Earth in the Third Age is a relentlessly early medieval setting outside of The Shire, most modern high fantasy settings – simply because modern readers and writers are moderns ourselves – tend to be quite late medieval or early modern in character, almost by accident. In that context, emerging states struggling with administration relying on contractors to set up their armies would make a great deal of sense.
Finally, we come to the solution that is, I suspect, the first that most modern folks think of but one of the less common solutions for pre-modern states: direct recruitment by the state. State officials (be they military officers or civilian officials) directly handle recruitment and equipment, with the state absorbing the full administrative and financial burden for military activity.
That phrasing, ‘the state absorbing the full administrative and financial burden’ may explain why this is such a rare option. Direct state mobilization is the preserve of strong, centralized states with relatively well-developed bureaucracies and that is certainly not the most common kind of pre-modern polity.
Generally speaking, if the state – not a contractor, not a Big Man aristocract, not local households, not the citizenry itself, but the state – is going through the effort of handling recruitment, training and equipment, it is generally going to want to only do that once and so one thing that direct state recruitment tends to have in common is that these tend to be long-service regimes.
Longtime blog readers will perhaps have noticed that often where folks would casually use the phrase ‘professional soldiers,’ I tend to default to the longer, ‘long-service professional soldiers’ and here we get to why. The soldiers in the previous section, for instance, recruited by contractors, are often effectively professionals: they have a professional set of skills that they’ve acquired, along with an expected code of conduct (however alien it is to civilian society) and they often move from one contractor’s regiment to the next, staying in the business over multiple campaigns as one regiment is mustered out and another formed. But they’re not long-service – they are not serving a single state continuously in one stretch of employment.
But when the state takes on the fully burden of recruitment, that is often just what they expect. The long-service professional army created by Augustus at the start of the Roman Empire eventually settled on a twenty-year term of service (with another five years in the reserve), which would mean a stretch from 17 (at the earliest) to 42 (at the earliest), which is basically the full stretch of years the Romans might define as military-aged. The Song Dynasty (960-1279) military system likewise expected to discharge “old soldiers” between the ages of fifty and seventy, effectively at the point when they could no longer do any useful soldiering; unlike the Roman system which settled or paid out soldiers on discharge, the Song system made few provisions for the retirement of soldiers, discarding them more than discharging them. Mamluks – military slaves, most often of Turkish extraction common in the Islamic world from the 800s onward – also served for life.
Direct recruitment for long-service regimes can work on a variety of principles and thus with a range of methods.
The most immediately understandable to us is a volunteer, employment-principle system: the state simply hires men with specific terms of service and then pays them (though once hired, they cannot voluntarily leave). That’s the Roman system from the imperial period. Even here, recruitment is not wholly centralized: legions and auxiliary cohorts tended over time to do most of their recruitment locally and so increasingly were made up of men from where they were stationed. The legions had to recruit from the citizenry, but the regular discharge of veterans (who settled and started families generally near their posts – they’ve been away from ‘home’ for 20 years, so ‘home’ is where the legionary fort is) created an available pool, which might be supplemented by citizen recruits from other parts of the empire.
Roman military pay had to be relatively generous by the standards of the time to attract troops and it largely was: 225 denarii per year, from which necessities were subtracted but it seems clear that the food deduction was well below the value of the food actually offered. At discharge (20 years in) they got a discharge bonus (the praemia) around 3,000 denarii (assuming they’d never gotten above base pay), so the per-year average is actually 375 denarii per year, simply back-loaded. The result is a base pay rate that exceeded the daily income of an unskilled laborer (famously a denarius a day), except that it was steady, whereas other forms of wage labor were irregular. So the Romans pay a premium to get men to enlist, which simplifies the process a bit, since you can then largely rely on men seeking out recruitment rather than the other way around, which probably explains why legions were able to recruit mostly locally. The recruits (mostly) came to them.
In stark contrast, you have something like the Mamluk system, which is a vocational military slave system reliant on compulsion in which the state (in the form of a king or Caliph) buys enslaved military men in bulk for military service; we have discussed this system before. Typically these were external populations being purchased (very often warriors from the Steppe), rather than internal sources – we’ll leave aside the Ottoman devshirme and its complexities for today. Now it is important to remember: once you arm these fellows, their relationship with you fundamentally changes. The notional status of Mamluks was low – they were enslaved warriors – but practically as well-armed, vocational-status warriors in the service of the state, some came to wield meaningful power and wealth.
For our purposes here, though, the ‘recruitment method’ for Mamluks is commercial: they’re purchased. The states that employed large Mamluk armies were not usually the polity initially enslaving them – instead, warfare on and around the Steppe generated significant numbers of enslaved warriors which Steppe societies could not absorb and wealthy Arab states soaked up that supply (and of course their demand stimulated more supply, which is to say, more warfare for the purpose of enslavement). Naturally doing this requires a lot of revenues (to purchase large numbers of valuable enslaved warriors) but also a significant administrative machine which could feed, house, clothe, equip, organize and manage these fellows once purchased. At least initially, the machine relied upon was the remnants of the Sassanid and Eastern Roman Imperial administrative systems which had existed before the conquests of the Rashidun Caliphate and had been taken over, in modified form, by subsequent Caliphs.
On the ground, that system is going to look like private slavers traveling to the ‘source’ regions for this manpower, purchasing captured warriors and then trafficking them to the markets of the great state powers where they know royal officials will be eager to buy them.
The other kind of compulsion system are prisoners-turned-soldiers and here the Song military system is a good example as it was substantially reliant on this method. I should note that certainly other professional or contractor-based recruitment systems will also lean on the expedient of turning out the prisons into the muster field as well (and indeed, under ‘brigaded household’ systems, we find a lot of indications that village leaders use recruitment as a way to get rid of troublemakers). Prisoners were not the only source of Song recruits, but their presence speaks to the level of compulsion in this system.
The Song system emerged out of a period of consolidating warfare where generals could – in the context of high intensity warfare – impress large numbers of civilians into the army and where surrendering armies had their soldiers absorbed. So this is an army comfortable with compulsion during a period of high conflict which then – consolidation having been completed – has to transition to a steady-state system where it couldn’t be relying on simply impressing wide sweeps. Instead, it relied on leveraging compulsion against groups who were socially unprotected in society, as Alyagon (op. cit.) lists them off, “soldiers’ families, the poor and the idle, local militias, non-Han groups, convicts and refugees.” In short, Song officials grabbed who they could with the least hassle from powerful constituencies (like local landholders or other officials).
The baseline of the system were local militias, which functioned on a brigaded-household model (run by local officials, of which the Song had no shortage); when the standing professional army required new recruits, men on the militia lists might be forced into the army, tattoo’d with their new unit to discourage desertion (since a man caught with a military tattoo out of service could be assumed to be a deserter and punished) and essentially never sent home. But, unsurprisingly, this produced a lot of resistance to the militia system – “understaffed units, out-of-date registers, abandoned fields” (Alyagon, op. cit., 66) etc. So the Song also pressed non-Han ethnic groups around its frontiers into service and also implemented military service as a punishment for law-breaking. The military thus likewise swiftly became a dumping ground for anyone that local officials might want to get rid of. Finally, the families of soldiers, because they lived in the military camps, could easily be compelled to serve: when the state already has possession of your father, mother and sisters, if they tell you to enlist, there’s going to be a lot of pressure to do so.
New recruits, however they were acquired, were checked for physical fitness (swiftly reduced to a mostly bureaucratic height standard), enrolled in the army, had their faces tattoo’d, were handed their uniform (equipment was at the unit level) and payment and escorted to their unit in a process referred to as “conscripting, tattooing, and giving tips.”3
What all of these systems share in common is that they are fully bureaucratized: there is a royal or imperial official who has to oversee the acquisition of soldiers (either voluntary or compelled), their enrollment, the issue of their equipment, their continued pay and maintenance and eventually their discharge. That may seem normal to us, but it was not normal for pre-modern states.
The main advantage for this kind of direct recruitment was that it enabled states to keep a standing army in peacetime with relatively minimal disruption to civilian affairs. Long-service professionals could also be highly disciplined and well-trained, but I think the performance of the Song Army warns us that it is not always so – unsurprisingly the poor terms of service, low status and high degree of compulsion involves in Song military service seems to have produced relatively poor military performance as time went on. It is not always the case that professionals are better.
The main disadvantage, of course, is the expense of it. By directly assuming the full cost of military activity, the state is shouldering a tremendous administrative and economic burden. We’ll talk in the next part about how that burden might be met, but it is no surprise that most pre-modern polities were willing to give up a lot of political control – either to citizens (for entitlement-self-recruitment) or vassal Big Men (for retinue recruitment) or to colonels (for contractor recruitment) – simply to lessen the tremendous direct administrative and financial cost to the state. The army of the high Roman Empire was professional, disciplined, and very impressive, but it is also worth noting that it was only about a third bigger (~300,000 compared to ~185,000) than the peak deployment capability of the Roman Republic, despite the empire it served being ten times larger in population (c. 50m compared to c. 5m, very roughly).
Next time, we’ll turn to the question, now that we’ve raised our army, of how different sorts of polities pay for it.
For the better part of the last several hundred years, coal was the fuel of choice for generating power. Burning coal powered Thomas Newcomen’s steam engine, invented in Britain in the early 18th century, and the first of a line of increasingly efficient converters of coal to usable energy. The Newcomen engine was in fact so inefficient and consumed so much coal that it was almost exclusively used at coal mines, where fuel could be obtained cheaply. The improved steam engines that followed over the 18th and 19th centuries — Watt’s rotative engine, high-pressure Cornish engines, triple-expansion engines, Parsons’ steam turbine — were likewise fired by coal. By the early 20th century, Britain was burning 52 million tons of coal a year to provide power for factories and mines.
The rise of the gas-powered automobile in the early 20th century shifted a substantial portion of coal consumption to petroleum, but coal still remained favored for industrial power. And this didn’t change with the emergence of the electric power grid: Thomas Edison’s first central electricity generating station at Pearl Street in New York used coal-fired reciprocating engines, and coal was the primary method of generating electric power in the US well into the 21st century.
By the end of the 20th century, however, this trend was starting to shift. For most of the 20th century coal made up around 50% of US electricity generation, but after peaking at around 57% of electricity generation in the mid-1980s, coal started to decline as a share of electricity generation in the US. And starting around 2008, coal-generated electricity began to decline in absolute terms, falling from over 1.6 trillion kilowatt-hours produced in 2009 to around 0.8 trillion in 2020. Today, coal supplies around 16% of US electricity, a share that seems likely to continue to fall long-term.
As coal became less popular, many coal plants — over 200 since 2008 — have simply shut down.1 But some of these plants were instead converted to burn natural gas in place of coal. Since 2008 there have been around 140 such conversions.
Given recent attempts to reinvigorate the coal industry, with the Trump Administration forcing plants to stay online and trying to fund the construction of new coal plants, it’s worth understanding what drove so many plant operators to cease burning coal and switch to natural gas.
The spate of coal-to-gas conversions that began around 2008 was the product of two factors.
The first was regulatory. Burning coal emits a great deal of harmful pollutants (such as mercury), and over time regulation of these emissions has become stricter. In 2000 the Environmental Protection Agency (EPA) decided to develop regulations for the emission of mercury, and while this was temporarily delayed by the Bush Administration, by 2008 it was clear that stricter coal plant emissions would be a reality. In 2011, the EPA proposed a new set of coal plant emissions restrictions, the Mercury and Air Toxics Standards (MATS), which dramatically reduced the amount of mercury, toxic metals, and acid gases that coal and oil plants were allowed to emit. MATS, however, didn’t apply to natural gas plants, as gas burns much more cleanly and produces dramatically less harmful particulate emissions.
Alongside this new, more stringent regulation, the US shale gas boom made natural gas an increasingly attractive fuel for generating power. Between the late 1980s and 2011, natural gas went from 10% to nearly 30% of US electricity generation. And while the price of gas had risen through the early 2000s, it began to fall steeply in 2008. What’s more, it was projected to stay cheap for the foreseeable future.
Faced with increasingly strict environmental regulation and the rise of widely available and affordable natural gas, coal plant owners were faced with several options. One was to simply shut down their plants. Another was to install the required equipment to reduce emissions enough to comply with MATS regulations. This equipment was expensive to install and acted as a drag on plant efficiency, since it took energy to operate, but it was nevertheless often worth it. Today there are 219 operating coal plants in the US, all of which are in compliance with the original MATS regulations.
But some plant operators, instead of installing the required emissions equipment to comply with MATS, opted to convert the plants to burn natural gas. Converted plants were generally older, smaller-capacity plants that were relatively inefficient, used to provide extra capacity when needed rather than supplying baseload power.
Converting coal plants to burn natural gas wasn’t a new idea — the idea first began to be discussed in the 1980s, and during the 1990s and 2000s a few plants were converted — but the shale boom combined with the new, more stringent MATS regulations created a much stronger incentive to do so.
There are a few different ways of converting a coal power plant to a natural gas plant, depending on how much of the original equipment you replace. At a high level, a coal plant consists of a boiler that burns coal and uses that energy to turn water into steam, a turbine which converts the heat energy of the steam into rotational energy, and a generator connected to the turbine which converts the mechanical rotational energy into electric current. Different conversion strategies replace different portions of this equipment.

The simplest, cheapest option is to convert the coal boiler into one that’s capable of burning natural gas. The boiler can be converted to run on only natural gas (losing its ability to burn coal) or be converted such that it can burn gas, coal, or some combination of the two.
At minimum this sort of conversion requires adding a system for delivering natural gas into the boiler and replacing the coal burners with natural gas burners. But because natural gas burns differently than coal, this may also require other upgrades, such as new flame scanners (which monitor how combustion is taking place in the boiler) and structural upgrades (because the temperatures in certain parts of the boiler might be higher when burning natural gas). It also often requires adding a natural gas pipeline to bring gas to the plant, which might involve laying 20 miles or more of underground pipeline. Because natural gas doesn’t require the sort of complex material handling that coal does — it can simply be piped directly into the boiler — the upgraded plant typically requires many fewer employees than the original coal plant did. The Joliet coal plant near Chicago, which was converted to a natural gas peaker plant in 2016 before being shuttered entirely in 2023, is an example of this sort of coal-to-gas conversion.
The benefit of this sort of conversion is that it’s comparatively simple and inexpensive, and most coal-to-gas plant conversions in the US have been of this type. The drawback is that because the boiler is burning a fuel it wasn’t originally designed for, the converted plant operates less efficiently and with less capacity than it did while burning coal, and much less efficiently than a brand-new combined-cycle plant would.
Another option is to replace the entire coal boiler with a natural gas boiler, while keeping the rest of the generation equipment. This is less common, but it does happen. An example of this sort of conversion is Iowa State University, which operated a small 46-megawatt combined heat and power coal plant. In 2016 the university replaced three of the plant’s five coal-fired boilers with natural gas boilers. (The last two boilers were eventually also modified to burn natural gas).
More common than just swapping out a coal boiler for a gas boiler is replacing the coal boiler with a gas turbine and a gas boiler (which in this configuration is called a Heat Recovery Steam Generator, or HRSG), to create a more efficient combined-cycle plant. An example of sort of conversion, which is sometimes called “repowering,” is the Big Bend coal power plant near Tampa, Florida, which was converted in 2023.
And finally, a “conversion” can also simply rip out the entire existing plant — boiler, turbine, generator — and replace all of it with a modern combined-cycle plant. This is essentially a brand-new plant that uses some of the services (the grid interconnection, water availability) as the old plant. The Tennessee Valley Authority’s Allen plant near Memphis, Tennessee is an example of this sort of conversion. This type of conversion is the most expensive, but it gives operators the greatest increase in efficiency. Roughly a third of coal plant conversions in the US have been this sort of total replacement.
It doesn’t seem likely that we’ll see many more of these coal-to-gas conversions. For one, there are just a lot fewer coal plants in the US than there used to be: we’re down to just over 200 from a peak of nearly 600. The most obvious candidates for conversion — smaller, older plants that might be useful for peaking — have probably already been converted. And as grid-scale batteries change the economic logic of peaking, even new gas plants are looking less attractive than they used to; I can only imagine that a less-efficient converted coal plant is even less compelling.
It’s possible that a new round of more stringent air pollution regulations might push some of these existing plants into burning natural gas rather than coal, though it’s unlikely we’ll see such a thing during the Trump Administration. (In 2024 the Biden Administration strengthened the original MATS rules, but these additions have since been repealed by the Trump Administration.) Similarly, the enormous demand for power caused by the AI boom might have some effect. We’re already seeing coal plants slated for shutdown staying online instead due to high power demand and the huge backlog for things like natural gas turbines. It’s possible such logic might incentivize converting some remaining coal plants into burning natural gas instead. But overall, I suspect that the heyday of coal-to-gas conversions is behind us.
There were around 586 operating coal plants in the US in 2008, compared to 219 today. Around 140 of the plants were converted to natural gas, leaving an estimated 230 or so that have been shut down.
I didn’t know what a near-infrared LED mask was a week ago, and now I am obsessed with this ad from Omnilux.

Because there’s a lot going on.
"Your best skin awaits"
I have questions about the very concept of an LED mask (can’t you go outside? but now we’ve internalised a fear of the sky because of UV?) but that’s not my point.
These components don’t work together. Health and beauty vs the evil red glow. You can’t sip the wine through that mask.
So there’s no singular integrated vibe here. It’s the opposite of vibe. It’s a set of hieroglyphs. Six symbols collaged together into the same image.

Similarly the vacuum guy James Dyson is now growing strawberries and the copy is wild.
"AI-powered British strawberries"
I mean let’s unpack that just for a second…
Let’s not get into the photo in which we are reassured about the quality of the strawberries not because they are being nurtured by a friendly farmhand – but because they are being CCTV monitored? Like: AI-powered panoptic strawberry surveillance will scare the strawbs into being plump and red, Jeremy Bentham’s paranoia-based fruit production?
The very next section is titled "British strawberries: 100% of your daily vitamin C" which is a whiplash into health.
At this point I don’t feel like I’m reading. I feel like these ads are laser-targeted streams of signifiers treating my psyche as a combination lock to be picked.
The fact that the grab-bag of symbols appears to have meaning on a human level (a photo of a woman; strawberries growing) is almost an accident. But there’s no content there beyond that.
Umberto Eco, semiotician, would have been able to write 2,000 words unfolding that Omnilux advert into its constituent symbols and deducing the shape of society from its very existence.
Peak semiotics was probably, what, the 1970s? We need that expertise to dismantle communication once again.
Anyway this is what it must feel like to computers when they get hacked. Like by a text message with a weird collection of characters that buffer overflows and takes over the app executable.
When AI gets really good - probably not much better than today - it will be able to automate the process of discovering the four or five symbols that unlock the “I must buy that” response, and then it’ll wrap it in a jpeg and put it on a billboard.
And next thing you know you walk by a photo of a woman drinking wine and it’s a jumble of symbols and you’ve been jailbroken and the compulsion to try an LED mask is so potent because well, I wonder what near-infrared feels like on the skin and what was that Omnilux you say? and without really thinking your phone is in your hand and
Auto-detected kinda similar posts:
Robin Hanson queries:
Missing book: Glorious Committees of History, on great committees that accomplished great things as committees.
GPT Pro has an impressive response, here is the start:
1. The King James Bible translation companies. This is maybe the purest literary example: 47 scholars organized into six companies at Westminster, Oxford, and Cambridge, with review procedures, producing one of the monuments of English prose. The committee form mattered because it blended scholarship, doctrinal acceptability, and a shared ear for cadence.
And Henry Oliver suggests The Great Exhibition?
The post Important committees in history appeared first on Marginal REVOLUTION.
And that’s despite–or maybe because of–a massive police and military presence during the last week due to the cultic activity at the White House on Sunday. As of 9am today, D.C. had reported five more homicides this week (as occurred last week, one of those happened weeks ago), yielding a total for the year of 42*. One thing worth noting is that, to date, Ward 3 has had four murders, while most entire years it experiences two to three murders**. At this time last year, there had been 74 homicides, and in the surge year of 2023, over the same time period, there had been 110 homicides. Still a vast improvement, but a very bad week.
Other crimes, on the whole bounced around a little, but had no discrete trends.
That said, we are still well on pace for another 33 percent drop in homicides for the third straight year.
Hoping for a better week next week.
*Three of the 45 murders reported this year actually occurred in other years (e.g., a missing persons case from 2023 turned into a homicide case this year with new evidence).
**Ordinarily, that increase wouldn’t really register, but given the historically low homicide rate the difference between one and four murders in Ward 3 actually does matter.
Links for you. Science:
New OMB rule could break science in the United States
Institutionalizing politicized science
The murder of expertise: Russ Vought as science czar would just about do it.
CEPI fast-tracks three Bundibugyo ebolavirus vaccine candidates
White House reclassifies federal epidemiologists and other scientists from civil servants to “at-will” hires
OB-GYNs release their own vaccine schedule, rejecting RFK Jr.’s meddling
CrankGPT
Other:
FOX’S BRIAN KILMEADE: OBJECTIVELY PRO-POGROM
The Last Surviving Japanese Porsche 912 Police Car
All Top 20 Right-Wing News Websites Suffer YOY Declines in May Visits
AI Animal Videos Are Ruining One Of The Internet’s Last Good Things
Casting A Ballot Is Not Flashing A Gang Sign. It’s always, always, always about harm reduction; and few people have caused, or threaten to cause, more harm than Susan Collins.
I Tested the Best and Worst Seats at a World Cup Stadium
Norse Atlantic Airways Offers Dirt-Cheap Tickets. There’s a Catch
Congress’s Transportation Reauthorization Bill Would Drastically Underfund Transit and Rail Projects
Meta Deletes Face-Recognition System From Its Smart Glasses App After WIRED Report
As Trump Pushes Deportations, a Skyrocketing Caseload Strains Immigration Courts
Leaked Audio Shows GOP Candidate Agreeing That Women Should ‘Prove’ Rape To Access Abortion
Postal Service won’t deliver mail ballots for states that don’t hand over voter lists, under plan for Trump directive
FCC Wants to Kill Burner Phones By Forcing Telecoms to Get All Customers’ IDs
Willy Rice, Florida pastor and abuse crisis skeptic, elected SBC president
Democrat labels Trump ‘sleeping’ in public a national security risk
The Supreme Court Is Illegitimate. The court’s conservatives ripped the mask off the institution in a brief, unsigned decision allowing Alabama to use a racially discriminatory congressional map.
Trump officials lay out aggressive timeline to build triumphal arch
Elon Musk Is About to Make Saving for Retirement Even Harder
Women Who Fled Iran Are to Be Deported to Central African Republic, Lawyers Say
The Social Costs of Immigration Enforcement in the New Era
Chatbots Keep Telling Stories About Lighthouse Keeper ‘Elias Thorne’. We Might Know Why
Why D.C. probably won’t know who won on election night
Congress Fails to Reauthorize America’s Most Powerful Surveillance Law, Which Expires at Midnight Friday
‘This Is Oligarchy’: Nearly 100 Billionaires Are Funding Susan Collins’ Reelection Bid
How ICE Affects Students
From NYCHA to the Garden, the Knicks’ Jose Alvarado is living a New Yorker’s dream
Skateboarders say they’re being pushed out of D.C.’s most iconic spots
141 Townhomes Break Ground at The Parks at Walter Reed (this doesn’t seem nearly dense enough)
Tulsi Gabbard’s humiliation is complete
Nike instructs federations to steam World Cup jerseys to fix shoulder seam issue (“That computational process was driven by performance data and incorporated elements of AI to work alongside the company’s designers as they crafted the kits.”
1. Liberalism and weaponized interdependence.
2. Is the AI shock like the China shock?
3. Can AI agents be individuated?
4. Who is liked by GPT 5.5? (from a partial list, if I understand this correctly)
6. Noah Smith is fearing that he and many others are having less influence.
7. Right-wing arguments against Great Books. And two more.
The post Friday assorted links appeared first on Marginal REVOLUTION.
The post Our colleague Vincent Geloso has a Substack appeared first on Marginal REVOLUTION.
Today about a quarter of the US workforce are required to have a license to work in their chosen profession, up from just 5 percent in 1950. Almost always the trend has been to add occupational licensing over time, but in 1983 Colorado did something unusual: it delicensed funeral service workers such as funeral directors. Brandon Pizzola and I analyzed what happened in our 2017 paper, Occupational licensing causes a wage premium: Evidence from a natural experiment in Colorado’s funeral services industry.
What we found was that delicensing reduced wages, reduced prices, and caused a shift towards cremation rather than the more expensive mortuary services preferred by funeral directors. Here’s a key figure.

But that is not the end of the story. In 2023 a series of gruesome abuses came to light involving the sale of body parts, rotting bodies, and worse. Newspapers repeatedly noted that Colorado was the only state not to license funeral service workers. As a result, Colorado is relicensing funeral service workers as of 2027.
The problem is that there is no evidence that abuses were worse in Colorado. It’s easy to find similar abuses—including sexual abuse of corpses—in states with heavy licensing. Pizzola and I didn’t examine the rate of necrophilia among funeral workers in our paper (silly us), but we did cite the following:
A recent US government review of occupational licensing concluded that “the empirical research does not find large improvements in quality or health and safety from more stringent licensing” (CEA, 2015). Similarly, Colorado revisited their decision in a 1990 sunrise review that considered reinstating occupational licensing. The Colorado Department of Regulatory Agencies found that since the 1983 occupational delicensing: (1) “there had been incidents of malpractice within the profession but no widespread pattern of abuse,” (2) “[a]llegations of significant threats to the public health, safety and welfare perpetrated by the death care industry in Colorado regarding the improper disposal of human or infectious wastes had not been supported by verifiable evidence,” and (3) “claims that the public in Colorado had suffered or might suffer significant detriment due to a lack of trained mortuary science practitioners caused by the abolition of the Board were unsupported” (Colorado Department of Regulatory Agencies, 2007).
Moreover, the licensing requirements—mandating various hours of training and so forth—have very little to do with the types of abuses that generated public support for relicensing. How many hours of “don’t have sex with corpses” training is required? And the funeral director in the worst Colorado case was in fact sentenced to 40 years in jail. Isn’t that incentive enough?
People want what cannot be guaranteed: good behavior in all circumstances. And they will reach for a licensing regime if it promises that, even when such promises are empty.
The post Colorado’s Funeral Mistake appeared first on Marginal REVOLUTION.

A startup has acquired an aircraft to offer commercial parabolic flight services even as NASA seeks to acquire its own aircraft for reduced-gravity research.
The post Mu-g Technologies enters the parabolic flight business appeared first on SpaceNews.

Austrian satellite propulsion startup Gate Space has won 6.3 million euros in funding from Europe’s government-backed accelerator program, joining a wave of European companies attracting capital for greater space sovereignty.
The post Austrian propulsion startup joins sovereign space funding surge appeared first on SpaceNews.

NASA has selected for development a space science mission that will study how space weather interacts with Earth’s atmosphere.
The post NASA selects mission to study space weather interaction with Earth’s atmosphere appeared first on SpaceNews.

A spacecraft developed by Tsinghua University is set to join international missions to study the asteroid Apophis during its close approach to Earth in 2029.
The post Chinese university-led mission to study asteroid Apophis during close encounter with Earth appeared first on SpaceNews.
With well over 6,000 exoplanets now confirmed and a continuing flow of data containing new detections, it has been clear for some time that our own Solar System’s model is hardly a template. I enjoy dipping into the bewildering variety of new systems and pondering the contingencies that have led to their architecture. Science fiction is an intensely visual genre, so I naturally try to imagine the more extreme systems. But more than most, today’s catch at HD 39474, an F-class star in Pictor some 360 light years out, is just begging for a gifted SF writer to go to work on it. Here we have, in addition to the central star, a long-period transiting brown dwarf with a planetary system, coplanar and aligned with the brown dwarf, packed inside its orbit.
HD 39474 is also, at least for now, known as TOI-201, TOI standing for TESS Object of Interest, an indication that while the Transiting Exoplanet Survey Satellite’s photometry has found what looks like a planetary transit, that result has not yet been confirmed. Various things can mimic a transit, including stars in an eclipsing binary system, so confirmation through radial velocity methods or additional transits is necessary. Nonetheless, a new study in Nature looks solid, and the system it points to is of exceptional interest. The work describes a ‘mono-transit’ in TESS data sets that is tentatively identified as a massive brown dwarf designated TOI-201c.
A single transit can indicate a planet or brown dwarf whose orbit greatly exceeds the observational period, which is why such a transit is not enough to confirm the detection. But there is a lot more going on here. In fact, according to Alessandro Sozzetti (INAF-Astrophysical Observatory of Turin), TOI-201c has been characterized by transit timing variations of an inner planet as well as the photometric transit and radial velocity measurements, with upcoming confirmation through GAIA astrometric data. Being characterized through four different methods appears to be a first.
The work, led by the European Southern Observatory (with strong involvement from Italy’s National Institute for Astrophysics (INAF) reminds us of the blurred star/planet distinction. Brown dwarfs can have planetary systems of their own, warmed by their exceedingly faint light. TOI-201c has the longest orbital period, some 2,881 days, for which a mass has been confirmed, in this case through radial velocity readings.
Within the brown dwarf’s orbit are two further transiting objects that are aligned with it. Getting into the dynamics of system formation here is going to be interesting work. TOI-201d has a period of 5.8 days and appears to be a rocky super-Earth, while the gas giant TOI-201b is in a 53-day orbit. With an orbital eccentricity of 0.622, the brown dwarf is a significant perturber. According to the researchers, anything much farther from the star than the orbit of Mars around the Sun would be dynamically unstable.
Luca Naponiello (INAF), second author of the paper on this work, takes note of the brown dwarf’s impact:
“The presence of the brown dwarf on such an elliptical orbit forced the planets to form and survive by occupying the innermost and hottest edges of the primordial disk. Furthermore, the data show that during the close approach of the brown dwarf, the warm Jupiter undergoes strong and sudden variations in its transit timing, bearing witness to an intense and vigorous dynamic interaction currently underway between the two giants,”

Image: Close-up artistic representation of the TOI-201 system. In the foreground is the massive brown dwarf TOI-201 c, followed by the hot Jupiter TOI-201 b (subject to strong gravitational perturbations), the star TOI-201, and finally the super-Earth TOI-201 d. Credits: INAF / generated with AI Gemini.
ESO spectography from its FEROS and PLATOSPEC instruments complemented the TESS data to offer up this extremely stressed system, which makes the case that even in environments as challenging as these, planets find a way to form. How long they last is another question, and I assume future work may give us some thoughts on the survival of the gas giant here. In any case, finding an inner gas giant in these circumstances draws into question theories of gas giant formation that assume distances beyond several AU from the central star. We should be hearing a lot more about the system at TOI-201 given the stress it puts upon earlier formation models.
The paper is Jones et al., “A distant brown dwarf coplanar to a warm Jupiter and a hot super-Earth,” Nature 654 (17 June 2026), 614-618 (abstract).


From a broken life to a broken nail, ‘trauma’ has been bleached by overuse. But it names something real – and must be reclaimed
- by Lily Dunn
I'll be speaking Sunday at the American Transplant Congress, on kidney exchange. It will be hard to squeeze in all the recent developments in my half hour, including current controversies.
State-of-the-Art Speakers: Transplantation’s leading luminaries and innovative thinkers will share inspiring research and insights at ATC 2026.
Alvin E. Roth, PhD:
Thomas E. Starzl State-of-the-Art Lecture: The Economics of Kidney Exchange
Sunday, June 21: 11:00 AM ET




Tropical Storm Arthur, the first named storm of the 2026 Atlantic hurricane season, brought high winds and heavy rain to the U.S. Gulf Coast in mid-June.
NASA’s Terra satellite captured this natural-color image (left) at 10:30 a.m. Central Time (15:30 Universal Time) on June 17. The second image (right) depicts infrared signals known as brightness temperature, which help distinguish cooler cloud tops (white and purple) from the warmer surface below (yellow and orange). Around the time these images were acquired, the system had just recently been designated a tropical storm, according to the National Hurricane Center (NHC).
Though Arthur stayed below hurricane strength, it still delivered strong winds to parts of the Gulf Coast as it tracked northeast. The storm had maximum sustained winds of 40 miles (65 kilometers) per hour around the time these images were captured. Tropical-storm-force winds extended 175 miles (280 kilometers) from the storm’s center, the NHC reported. Measurements at Galveston, Texas, for instance, showed a gust of 48 miles per hour.
The storm also produced heavy rainfall that the National Weather Service warned could lead to life-threatening flash flooding. Estimates from IMERG (the Integrated Multi-Satellite Retrievals for GPM), a product of the GPM (Global Precipitation Measurement) mission, showed high rainfall rates over Gulf waters and extending inland on June 17.
As Arthur weakened and became less organized, it continued to bring abundant moisture to central Gulf Coast states on June 18. The National Weather Service reported rainfall rates of 3 inches (7.6 centimeters) per hour in southeastern Louisiana. Forecasts indicated that storm-total rainfall amounts could exceed 12 inches (30 centimeters) in areas, with some locations seeing totals approaching 20 inches (51 centimeters).
NASA Earth Observatory images by Michala Garrison, using MODIS data from NASA EOSDIS LANCE and GIBS/Worldview. Story by Kathryn Hansen.
Stay up-to-date with the latest content from NASA as we explore the universe and discover more about our home planet.

The sprawling storm promised to deliver torrential rain across a wide swath of southern Japan.

The powerful storm lashed the northern edge of the continent with damaging winds and drenching rain as it made landfall…

The violent storm aimed at the U.S. Northern Mariana Islands and Guam in mid-April 2026.
The post Tropical Storm Arthur appeared first on NASA Science.
From GA:
I am a mathematician…and some of your recent comments on MR about the role of AI in Econ research as well as the (disappearing?) role of academic papers inspired this response. (It is partially but not exclusively about academia, so I hope it is ok that I’m sending it to your GMU address. Also, I’m hoping it doesn’t get flagged as spam because of the ai in the title…)
In no particular order:
-In the course of a math career, one accumulates lots of computational guesses, now one can test those with minimal effort.
-One also accumulates lots of incomplete and half formed drafts, proofs of special cases, etc, etc. Running those past claude and chatgpt can (does!) pay off. A lot of math is cleverly applying linear algebra and while I’m very good at linear algebra, I’m not as good at it as the AI’s are.
-The lower hanging fruit here are slightly off the beaten track, but not esoteric subjects. If you have a good overview of such, you can pretty quickly prod ai’s into making progress on them. (Before, you needed to have a school of grad students for that). Basic techniques (graph theory, algebra, calculus..) that ai’s are already good at can push these forward already. Making progress on truly hot topics is harder.
-There are some quite smart people trying to measure just how good autonomous ai’s are at math (e.g. the first batch project). That’s a fun game, but for practical purposes right now, what is relevant is how good an ai is when guided by a motivated human. I suspect we’ll see some remarkable things on that front in the next few years once math people really grok the good routines.
-For instance, getting claude and chatgpt to referee each other’s arguments is fun, and they genuinely have different insights on parts of the same problem.
-The kids will be all right. Right now, they are making pocket change doing ai training developed a better “feel” for the different ai’s that I probably ever will. And they learn things by asking the ai to explain an argument to them instead of trying to decipher a math book or paper.
-Which brings me to your papers point. I notice that a project informed with the right context is much more informative to me than the physical pdf of a math paper, and much easier to extract information out of by just asking the thing.
-Refereeing will look very different very soon. All the referee reports that have been collected by the journals should be valuable, hard to get data. And running all the accepted and published math papers through ai’s as the `control’ will end with quite a few people having egg on their face. It’s like self-driving cars, but there is no refereeing union.
-The last really big math revolution was all the stuff in the wake of Witten and 4-manifold stuff predicted by string theory in the early 90’s. This is going to be so much bigger than that. Buckle up.
The post How research in math will change (from my email) appeared first on Marginal REVOLUTION.

Hi-ho, we’ve reached the moment, in this movie we’re all watching together on X, where model intelligence has become dangerous. Dario predicted years ago that it would happen this year. With Fable being (briefly) shut off by the USG, it’s the first highly visible sign that we’ve crossed into treacherous waters.
Which is too bad, really. I was hoping that we’d get a couple more generations of model upgrades, powerful enough to convince all remaining skeptics, before we got to one that was a security problem. But the Mythos class (Fable being the sloppily-guardrailed version they released last week) has everyone spooked.
Now that we know models are getting dangerous, we can do some extrapolating.
The AI race isn’t going to slow down, and AI will continue to grow exponentially in capability. Unfortunately, most of you aren’t going to see it progress anymore.
I am now in the camp who believe that we are only at most two or three model generations away from AI finally being controlled like nuclear weapons. Only a few will have access to superintelligence above the classes of models we’re seeing this year. As far as I can tell, most Fortune 500 companies will either not have access at all, or it will be tightly controlled for only a small subset of the company. And it will be supervised.
I think those with access to powerful frontier models will sell intelligence like a vending machine: You send them a software spec or a problem to solve, and their models implement it for you, on their servers, with your dollars. And since most companies aren’t going to want to send their code and problems to the model vendors, I think the world will learn to live with the models we do have access to.

Every government will restrict access, acting on its own. Nuclear weapons are scarce because it’s hard to get enriched uranium. AI is going the same way, with the chokepoint being the supply chain — something governments can actually clamp down on. China will lock superintelligence inside its own borders as hard as the USG will. And if China ends up taking the frontier lead, it just changes where the power is concentrated, but not the overall shape of the world we’re going to be operating in.
A World of Mediocre Models
Many of us hoped OSS models would keep us on the exponential curve. They trail the frontier by roughly seven months. But they stay on that curve by training on compute which increasingly takes international-relations-level dealmaking to secure. Maybe distillation or some clever peer-to-peer training scheme keeps them in the race. But to push past Fable class they’d have to do it while the whole hardware-and-software supply chain gets locked down the way the nuclear chain was. And the frontier labs themselves are going to decline to help train the next dangerous open model.
If OSS hits Fable class next year anyway, that’s great for the world. But open models are not going to blow past Fable class, not with a huge compute wall and government lockdowns looming.
So again, today’s models are roughly as good as we’re going to get.
As disappointing as I find that in some ways, I find it still has a lot of upside to be happy about. Because today’s models, particularly Fable-class, are plenty good enough. They will still utterly transform coding and knowledge work. It’s just not going to be a walk in the park. It will take a big, multi-year effort to pivot.
I’m going to assume for the rest of this post that we will all get Fable back, and that we may even get one higher class of model before further advancements become inaccessible to all but a very few.
Many of you have been expecting the hockey-stick AI advancement curve to level out soon, refusing to believe that it’s truly on an exponential curve that could lead to it being so much smarter than humans. You predicted AI would not be able to replace human engineers.
In a way, you turned out to be right. A very practical way.
In reality, behind the scenes, the curve is NOT flattening at all; the exponential growth will continue, and you will be able to see outwardly observable signs of it, e.g. in data center growth.
But the curve will appear to flatten out for you, through two separate phenomena.
The first reason is the one we already mentioned: they’re going to keep the smartest (and thus dangerous) models out of our hands. So most of us never get a chance to try them out. And those models certainly won’t be replacing engineers, if we can’t use them.
The other reason’s kind of interesting, and it took me a while to see that it’s really the same reason wearing a different hat.
A World of Mediocre Users
Some people are already reporting they can’t tell the difference between Opus 4.8 and Fable 5. I’ve been calling this the “discernment horizon”: every human has a ceiling on model intelligence past which all the models start to feel about the same.
But there are actually two ceilings, both instructive lenses on what’s happening.
The first I’ll call the demand horizon. It’s set by the hardest problem you bring. If all you have handy are easy problems, they don’t give a smarter model any room to pull ahead — the outputs look the same because the problem never stretched either one. The demand horizon is where you can’t tell two models apart because you don’t have a hard enough problem.
I call my hard problems “back-pocket evals,” and I collect them. Whenever I give a project to a model, and it can’t do the project, I add it to my pocket-eval list. Then every time a new model drops, it’s like Christmas. I try it out on all my pocket evals and see which ones it can now solve.

Concrete example: No Opus-class model has been able to write the React client for my game; it’s just way too complicated and fiddly. Fable was absolutely smashing it. Easy way for me to see the difference vs. Opus. But I also have other problems that will prove too hard for Fable. I will collect them eagerly as it chomps through my work. All you need is ambition, and you can create your own pocket eval collection.
So my demand horizon is super high, and will last at least three or four more model generations, if I can manage to get access to that level of intelligence, which seems unlikely. I don’t have my hopes up. But at least I will be able to tell if it’s actually that smart, using my evals.
The demand horizon is benign enough, even kind of flattering: it just means your work isn’t hard enough right now. But bring an unusually hard problem one day, and your horizon widens on the spot, as you watch the cheaper model fumble some task the expensive one nails. Like my React client.
There is a darker horizon, which I think of as the discernment horizon proper. This one is set not by the hardest problem you can pose, but by the hardest answer you can judge. Past this scary line, you can’t tell whether the model is right, because checking the work is itself beyond you.
I’ve been chewing on this problem since my Drunken Rants days, when I’d write about how hard it is to interview someone smarter than you. How do you know they’re not a charlatan, if they’re professing expertise in an area you know nothing about? You can’t, really.
Everyone has a discernment horizon, even Dario. Past some level of capability there is no human alive who can verify the model output.

This takes us full circle to why they are starting to lock down the models. You can’t hand out an intelligence engine that nobody can supervise. It’s pointless to own because you won’t know if it’s helping you or walking you off a cliff. Superhuman means unverifiable.
So the safety people see a potential weapon, and the rest of us see a tool that we can’t effectively supervise. In both cases, you don’t need or want the more powerful model. You want the safer one, even if it’s less capable.
Companies also have both of these horizons. For plenty of companies, Fable is already past the demand horizon — every problem they’ve got, it handles, and a smarter model would change nothing they could measure. For the harder shops the binding limit is discernment: the AI produces work that nobody can grade. A terrible outcome, assuming you don’t want to surrender your business to AI entirely.
As a result of all this, the curve is flattening for most of us. I think commodity intelligence will soon stop growing exponentially, or at least, it will appear that way, and we’ll all operate as if it’s true.
I had never spent much time considering the possibility that the intelligence curve would flatten out. But now that it seems to be happening, let’s look at some of the clear and obvious implications for the industry.
SaaS is Back, Baby
It’s clearly going to be too expensive to rebuild all the SaaS at the top of the pyramid. Yes, there will be models that can do it, but access and cost will both be prohibitive.
SaaS actually came rocketing back over the past month all on its own, after spending much of the past year on the ropes, pummeled from all directions by threats of in-house rewrites and fears of Claude taking it all.
Then companies learned about token efficiency the hard way, with huge firms blowing their yearly budgets in months. A few months ago, everyone was planning to tell their CFO they could cancel a bunch of SaaS subscriptions and bring their dependencies in-house. No longer. Now the buy-vs-build decision is tilted heavily towards buy. If you despise your current SaaS enough, then sure, you may be motivated to rewrite it with AI. But buying SaaS has predictable costs that are usually already in the budget, whereas vibe-coding replacements could be an expensive gamble.
If we see a plateau in accessible model capabilities, then the other dreams we had about AI in SaaS fade too: not just replacing it, but transforming it with agentic behaviors and monitoring. Today’s models aren’t good enough to replace a person yet (jailbreakable, confusable, etc.), so you can’t just swap an agent in for an SRE or a trained customer service rep. And the models that could reliably replace humans may be too dangerous to give to most people.
So SaaS looks like it might be fine, even without agentic behaviors. It just needs to save you the money of building and maintaining it in-house.
SaaS still has its problems: users subsidizing the 80% of the features they don’t use, dollars extracted from local economies to enrich Silicon Valley, enshittification creeping up the pyramid. But it remains fundamentally about crystallization of knowledge. Groups of people build stuff that’s tricky, stuff you wouldn’t want to do yourself, and rent it to you. The AI models powerful enough to replace most of that “easily” will either be unavailable or prohibitively expensive.
It feels to me like the SaaS model is here to stay.
AI Literacy 101
Today’s models, while quite capable, are still very difficult to work with. Even Fable likely struggles with large monoliths and other complex legacy code arrangements. It’s hard to get a consistently high quality bar. And of course efficiency is a monstrous issue.
I’d been hoping for models that are smart enough that you don’t need much training to work with them. But with today’s models, you cannot expect people to be born AI-literate. They need help in order to use today’s coding agents and harnesses.
In the next section I will provide a fairly precise and measurable definition of AI literacy. I did not invent it, but I believe it is good enough for your planning, and mine.
First, though, why does it matter whether your employees are AI literate? The answer is a bit complicated, but it boils down to two factors. One is that your company will have to pivot to using AI. And the other is that all your employees are feeling anxious about AI. This tension is actively playing out at all companies around the globe.
Pivoting to AI will change everyone’s job at least a little, and probably change the shape of your company a lot. Which just feeds the anxiety, in a loop.
If you are pushing on change in your company without first having addressed AI literacy, in a quantitative but also deeply empathetic way, then you are fueling anxiety, resentment, and pushback. Your org will resist change.
AI adoption is the key culture challenge of 2026–2027. If you can manage to get your (hostile) employees past the hurdle, and genuinely get them excited about how they can use AI to accelerate themselves, then magic happens. They will automatically begin reshaping your business processes together towards using supervised agentic flows.
I’ve seen this happening all over, but concretely, Gene and I saw it at Arkana Labs in April under the guidance of their VP Eng, Owen Parker. Arkana offers world-class overnight kidney-disease diagnosis, and they have utterly unique business processes. But those processes can all be sped up here and there with AI. Given how obsessed Arkana is, culturally, with fast and accurate turnaround, the employees themselves are getting excited about the opportunities, and pushing hard on what might be possible with agents.
Having seen enough of this I maintain that once most people “get” AI, you just need to guide them, and they’ll start broadly doing the right things for your team.
Conversely, as long as your teammates remain non-AI-savvy, they will resist AI. Which means that until you can get your org over the hump, you’re facing resistance, anxiety, and potentially even morale issues.
So how do we fix it? How do we get people to “get” AI?
It turns out, Netflix has handed us the answer. Thank you, Netflix!
AI Literacy: Beginner Cohorts

I watched a mind-blowing presentation in April from Ezra Savard, who ran a training study/experiment at Netflix from December through March. He gave the presentation at Gene Kim’s AI Summit in San Jose. The study’s goal was to train Netflix engineers on agentic coding, and measure the impact.
Ezra’s presentation was all properly rigorous and disclaimed (e.g. for minor selection bias), but they felt pretty strongly about the results being directionally correct, so I’ll skip all that.
Note that I’ll be framing this as “AI literacy” but that’s my term, not Ezra’s, and he never mentions literacy in his talk. He talks about the journey from being non-users, to users, to power users. But AI is becoming a foundational skill for modern knowledge work, so I will make the case in this post that we are talking about a new form of literacy.
Ezra’s first big discovery to share is that they found three cohorts, which I’m calling the beginner levels of AI literacy. Ezra characterized the cohorts in terms of their average token spend on a “qualified” day using AI, meaning a day where they are using it heavily. They needed at least 3 days a week to be in the cohort.
Here are the three beginner cohorts they found, defined by spend:
So: No agent, then single-agent, then multi-agent. I think this is a solid working definition of baseline AI literacy. If your entire org isn’t at least at single-agent literacy, then they will be fighting you on bringing in more AI, even if it’s just passive resistance.
Ezra shared that some power-user graduates of his course were legitimately spending much higher amounts, over 50M/day.
But he also cautioned that beyond the 15M/day mark, token spend is no longer a valuable measure, since people are by then clever enough to invent reasons to burn tokens. (After that, you switch to measuring outcomes, as I’ll discuss below.)
However, and this is the wonderful part, up to that point (15M tokens/day), measuring your employees’ token spend at a coarse level can provide powerful insight into where your organization stands on AI literacy, and how much training lies ahead of you.
Fortunately, Ezra has good news for you there: People can jump cohorts in 5 hours. That’s how long it takes people, in the right training setting, to graduate from AI illiteracy to AI savviness. And they stay there. It’s like flipping a switch. 96% of the trainees remained in the second cohort for six weeks after the course without showing signs of slowing down.
What’s the right training setup, you ask? Ezra’s team spent considerable effort honing the formula. The training must be done a team at a time, with 5 to 10 people, including their manager. The manager _must_ opt the team in, during regular work hours, as “blessed” company time. The trainees must bring their actual work, and the instructor(s) will help them learn how to do it with agents.
They found that if they cut corners anywhere — shorter classes, larger audiences, individual opt-in classes — they didn’t get the same results. It didn’t “stick.”
As for the third cohort: once a manager has a team full of single-agent users, they can opt their team into the multi-agent course. This is another 5 hours, and teaches them the additional skills needed to wrangle multiple asynchronous agents, while maintaining a high quality bar. This course saw the same strong adoption, with the vast majority jumping into multi-agent work and staying there.
So it takes roughly 5 hours of focused training per employee to get them to basic literacy. And after a few weeks of practice, another 5 hours to get them to become power users.
And as for impact, Ezra reported some surprising findings, such as there being a large difference in the amount of code produced by agentic coders. But when they dug in, they found it was entirely attributable to the additional test code they were writing. Overall, they found that the course had a large positive impact on productivity for those who attended.
If you want to start having conversations with your company about pivoting to AI, then I strongly recommend you begin with an AI literacy audit, followed by training everyone up at least into the single-agent cohort.
Advanced Cohorts
Getting people over the FUD hump, and teaching them to spend tokens to accelerate their own work, solves your first culture problem. And it will help you tremendously in your conversations about how to bring in AI, without getting so much pushback.
Netflix gave us an optimized solution to the FUD hump. You train up some “Line Cook” instructors who teach the intro course. Ezra told me and Gene that they had started with our book, which was kinda cool. But the exact curriculum barely matters; you can teach it however you like. And then you get everyone through it, 5 hours and ten people at a time.
Once you’ve taught everyone how to spend tokens, your second culture problem emerges, which is teaching people how NOT to spend tokens. Token efficiency is a fairly advanced topic. There are many, many ways that models can steer you wrong, and the most efficient agentic coders focus on maximizing their outcomes for a given token budget.
At this point I should share a joke made by Pierre Racz, the brilliant Founder/CEO of Genetec, one of the world’s largest physical-security monitoring companies. He prefers to write his code by hand, and when I described how these measurements work, he observed wryly, “Well then it’s not that I’m not using AI, I’m just extremely token-efficient.”
And it’s a funny joke, but there’s an underlying lesson there too, which is that if you can trivially do a task by hand, then do it by hand! Over time, you can save a bunch of tokens just by being thoughtful. Type !git push instead of asking the agent to do it, and your habit probably saves you 100k tokens on average, each time you push.
You know the meme with the bell curve and the troglodyte at the bottom and the Jedi at the top, and they’re doing the same thing? Well here the beginner-thing that the Jedi masters is low token spend.

Token spend only signals literacy on the way up. It’s a skill you build. But then it flips, and the thing you need to start measuring is token waste. Minimizing that is another set of skills.
You will find that your beginner cohorts are absolute token pigs, and that’s OK. Encourage them to explore and learn. They need to master the skill of spending before they can focus on savings.
You will find that people don’t automatically know how to conserve tokens. They will be 200k tokens deep into a conversation and ask the AI what time it is. Argh! Or maybe whether a specific file exists in their home directory. This is a skill that needs training, too.
So at some point you will probably want to have a third training course, this one on efficiency techniques and good token hygiene.
Then, give your newly AI-savvy people budgets. Make them earn budget increases with real outcomes. However you do it, measuring outcomes is going to become critically important, so you can differentiate your effective builders from your vanity builders.
We’ve talked about the beginner literacy cohorts (spend-based), and the advanced cohorts (efficiency, waste management). At the top of the AI literacy curve, your thinking becomes more strategic. You worry about saving large numbers of tokens while achieving your desired outcomes.
The first example everyone hits is buy vs build. Will you let your engineers try to rewrite random SaaS, or will you just re-up and go with the known spend? You have to start being strategic with agentic project allocation.
Another interesting challenge you face: How will you route every task to the dumbest model that can handle it? You will need to be able to tag work with intelligence tiers, and build a router. That router is the discernment horizon encoded as infrastructure. Most work sits below the line and goes to the cheap model, and the occasional task that pokes above it gets escalated to the expensive tier.
At the highest levels, AI literacy turns into the art of achieving great outcomes with the least spend.
A Craft Needs a Plateau
We are seeing a plateau in intelligence. It is artificial: the exponential increase continues behind the scenes, gated away from you. And at some point you won’t be able to tell it’s getting better, even if you could see it. The intelligence curve is as real as the Earth is round, but just as flat from where you stand. Welcome to the Flat Curve Society.
The Mythos graduating class will become the accepted trade-off between capability and risk for the general public. And we will see incremental updates that patch edge-case behavior, but nothing like the jumps we have enjoyed for the past several years.
The plateau is not a bad thing. A plateau lets us set up a camp and start building. We’ve been on unstable ground. Think how hard it has been lately to be a startup founder, with everything you build being obsoleted with each model release. That’s finally slowing down now, and it will give us firm footing.
We have an engineering problem ahead of us. As good as Opus and Fable are, they have their limits. We all need to learn the art of task decomposition and breaking up software monoliths, to keep them within those limits. We will still need engineers, and engineering. We’ll have super smart helpers, but it will still be pretty similar to the landscape today.
I kind of like the plateau that’s coming. Stability feels like a precondition for the new craft of building software with these super smart helpers. It is a craft that only gets harder, and more valuable, the weaker your models are. Sonnet-class and Opus-class will stay relevant for years, because they save money and stay broadly available even after the frontier moves on. The models that would obsolete today’s hard-won techniques of the craft are evidently too dangerous to give to us anyway.
The world is currently tinkering with setting up 24x7 autonomous agents, and it looks like the difficulties we face there today will remain with us tomorrow. There is a large engineering effort underway to build the control plane(s) that allows today’s models to run today’s large businesses. That, too, is a craft, or at least, it’s part of the tools of the trade.
Train Your Flat-Curvers
The key takeaway here (beyond not committing seppuku just yet if you’re a SaaS vendor) is that we have a massive AI training and literacy problem ahead of us. But it’s solvable. It will just take time and effort.
The models we have today, and the ones coming this year, will not one-shot your entire Fortune 100 code base. They are capable of amazing things, but they will still require grown-up human supervision.
This means you’ll need engineers. All the cool things that we’ve talked about — with impromptu 2-pizza teams forming, 2- to 3-person teams being a sweet spot, and roles starting to blur together (or at least talk to each other more) — will likely continue. But everyone will need training and time and patience and careful budget management.
AI Literacy does not come for free. The only thing you get for free is AI Anxiety. But it’s fairly easy to teach people to spend tokens. Teaching them to save tokens? Well, that’s the new meta. Good luck. Make sure they can do it Pierre’s way first.
That’s all I had for today’s post. Hope you enjoyed it. See you at the AI Engineer Conference in San Francisco at the end of the month!

Verizon has sprung for a new ad campaign set in the Austin Powers world, with four stars from the cast — Mike Myers, of course, as Dr. Evil; Rob Lowe as Number Two (Robert Wagner is alive but is 96); Seth Green as Evil’s son Scott, and Mindy Sterling as Frau Farbissina — and director Jay Roach. The premise of the two-minute spot is that Dr. Evil is proposing “Menace Mobile”, a wireless carrier with confusing pricing and plans. Scott pooh-poohs the idea on the grounds that “This isn’t evil. This is just typical phone company stuff.” Then, after some back-and-forth, comes this exchange:
Scott: Diabolical phone companies are why we’re all switching to Verizon.
Dr. Evil: I thought Verizon was just like the rest of the wireless organizations.
Scott: Well, they were, but not anymore. They just got rid of activation and upgrade fees. They’re changing everything.
I don’t think the commercial is particularly funny, alas, but I do find it extraordinary, because of the exchange quoted above. “Well, they were, but not anymore” is one the most extraordinary lines I’ve heard in a commercial. They’re just flat out admitting that, until recently, they ran their business like a scheme from Dr. Evil.
I’ve been on Verizon for a long time. It’s expensive, but so was AT&T, and I’ve always felt like I got better service and better coverage from Verizon (which is why I switched in the first place). But just last year I did the wrong thing when I bought my iPhone 17 Pro. I should have bought it unlocked, but instead I bought it as a device upgrade tied to my Verizon account, and the bastards nicked me for a $30 upgrade fee. I’d like to think that will never happen again because they’re actually dropping all of their bullshit fees, but I’ll believe it when I see it.
Austin Powers, by the way, came out in 1997. In the film, Powers was frozen since 1967. That means next year, we’ll be as far removed from the debut of the movie as unfrozen Austin Powers was from the groovy 60s in the film.
WALLOPS ISLAND, Virginia—Just 10 months ago, NASA asked three companies if they could do something nobody had done before. Could they build and launch a satellite to save a $500 million astronomy mission at risk of crashing back to Earth? What's more, could they do it in less than a year on a tight budget?
Katalyst Space Technologies, a startup founded in 2020, presented the most compelling solution. "They came back with a response that was technically and programmatically plausible, and then we were like, 'Yeah, let’s do it,'" said Shawn Domagal-Goldman, director of NASA's astrophysics division.
That was in August of last year. In September, NASA awarded Katalyst a $30 million contract to build, test, and launch a small satellite to chase down Swift and latch onto it with three robotic arms. Then, Katalyst's Link servicing spacecraft will boost Swift's orbit back to a safe operating altitude, allowing it to resume scientific observations. Easier said than done.
Today we launched a new plugin for Datasette, datasette-apps, with this launch announcement post on the Datasette project blog. That post has the what, but I'm going to expand on that a little bit here to provide the why.
Datasette Apps are self-contained HTML+JavaScript applications that run in a tightly constrained <iframe> sandbox hosted on your Datasette application. They can use JavaScript to run read-only SQL queries against data in Datasette, and can run write queries too if you configure them with some stored queries.
Here's a very simple example and a more complex custom timeline example - the latter looks like this:

Apps are allowed to run JavaScript and render HTML and CSS. They are limited in terms of access - the <iframe sandbox="allow-scripts allow-forms"> they run in prevents them from accessing cookies or localStorage and they also have an injected CSP header (thanks to this research) which prevents them from making HTTP requests to outside hosts, preventing a malicious or buggy app from exfiltrating private data.
Datasette Apps started out as my attempt at building a Claude Artifacts mechanism for Datasette Agent, but I quickly realised that the sandboxed pattern is interesting for way more than just adding custom apps in a chat interface and promoted it to its own top-level concept within the Datasette ecosystem.
They're also a fun way to turn my multi-year experiment in vibe-coded HTML tools into a core feature of my main project!
You can try out Datasette Apps by signing in with GitHub to the agent.datasette.io demo instance.
Since the very first release, Datasette has offered a flexible backend for creating custom HTML apps via its JSON API.
One of my earliest Datasette projects was an internal search engine for documentation when I worked at Eventbrite - it worked by importing documents from different systems into SQLite on a cron and then serving them through a Datasette instance with a custom HTML+JavaScript search interface that directly queried the Datasette API.
I had client-side JavaScript constructing SQL queries, which originally was intended as an engineering joke but turned out to be a really productive way of iterating on the app!
That project, combined with my experience building my HTML tools collection and my experiments with Claude Artifacts, has convinced me that adding a Datasette-style backend to a self-contained HTML frontend is an astonishingly powerful combination.
Imagine how much more useful Claude Artifacts could be if they had access to a persistent relational database. That's what I'm building with Datasette Apps!
Here are a few of the ideas and patterns I've figured out building this which I think have staying power.
<iframe sandbox="allow-scripts" srcdoc="..."> + <meta http-equiv="Content-Security-Policy" content="default-src 'none'; script-src 'unsafe-inline'; style-src 'unsafe-inline'; img-src data: blob:;">
This is the magic combination that makes Datasette Apps feasible in the first place. I need to run untrusted HTML and JavaScript on a highly sensitive domain - an authenticated Datasette instance can contain all sorts of private data. The sandbox= attribute lets me run that untrusted code in a way that cannot interact with the parent application - it can't read the DOM, or access cookies, or steal secrets from localStorage. It can however use fetch() and friends to load content (or exfiltrate data) from other domains. But... it turns out if you start an HTML page with a <meta http-equiv="Content-Security-Policy"> header you can set additional policies that lock down access to other domains. I was worried that malicious JavaScript would be able to update or remove that header but it turns out that doesn't work - once set, the CSP policy is immutable for the content of that frame.
postMessage() and MessageChannel()
Having locked down those iframes to the point that they couldn't do anything interesting at all, the challenge was to open them back again such that they could run an allow-list of operations, starting with read-only SQL queries against specified databases.
I built the first version of this with postMessage(), which allows a child iframe to send messages to the parent window. I created a simple protocol for requesting that the parent run a SQL query - the parent could then verify it was against an allow-listed database before executing it.
One of the LLM tools, I think it was GPT-5.5, suggested that postMessage() on its own can be exploited if the iframe somehow loads additional code from an untrusted domain. I don't think that applies to Datasette Apps, but I also believe in defense in depth, so I had GPT-5.5 help me port to a MessageChannel() based transport instead.
MessageChannel() has the advantage that if a page navigates to somewhere else the channel closes automatically, removing any chance of executing commands sent from an untrusted external page.
If you navigate to the timeline demo and search for the string usercontent you'll pull in some search results that embed images from the user-images.githubusercontent.com domain. This domain is not in the CSP allow-list, so it trips an error.
Those errors are captured and transmitted back to the parent frame, where they can be displayed in a useful error log. This is meant to make hacking on apps more productive by surfacing otherwise-invisible problems.
I built an experiment demonstrating that you can even turn this into a one-click-to-allow mechanism for building the CSP allow-list based on what breaks, but I haven't integrated that idea into datasette-apps just yet.
SQL queries are also visibly logged - scroll to the bottom of the timeline page to see that in action.
I want apps to be able to conditionally write to the database, but this is an even more dangerous proposition than SQL reads!
My solution involves Datasette's stored queries feature, rebranded from "canned queries" and given a major upgrade in the recent Datasette 1.0a31 - work that was directly inspired by Datasette Apps.
Users can create a stored write query that performs an insert or update, then allow-list that specific query for an app to use. Usage from code inside an app looks like this:
const result = await datasette.storedQuery("todos", "add_todo", {
title: "Buy milk",
due_date: "2026-06-20",
priority: "high",
completed: false
});I'm only just beginning to explore the possibilities this unlocks myself, but my goal is to support full read-write applications built safely as Datasette Apps.
The Datasette Apps plugin has no dependency on LLMs at all, but these self-contained apps are the perfect shape to be written by a modern LLM.
The create app form includes a copyable prompt at the end. This prompt has everything a model needs to know to build a new app, including the schema of any selected databases.

This means you can click "copy", paste it into ChatGPT or Claude or Gemini, tell it what you need, and there's a good chance the model will spit out the code necessary to build the app.
If you have Datasette Agent installed your AI assistant will also gain tools to both create new apps and edit existing ones, Claude Artifacts style.

Datasette Apps started life back in April as datasette-agent-artifacts, a plugin I have since renamed to datasette-agent-edit keeping only its editing tools. I built that as one of the first plugins for Datasette Agent, to help get the plugin hooks into the right shape. That first prototype was mainly built using Claude Opus 4.6 in Claude Code.
When I switched track to Datasette Apps I started with a plan constructed using Codex Desktop and GPT-5.5 xhigh, based on extensive dialog and feeding in both datasette-agent-artifacts and other prototypes I had built.
Most of the work that followed stuck with Codex, but in the few short days that we had access to Claude Fable 5 I had it run a security evaluation of the product (an ability that would get it banned by the US government shortly afterwards) and it found a very real problem.
I was allowing users to allow-list CSP hosts for their apps, but Fable pointed out the following attack:
create-app permission creates an app that queries SQLite for all available tables and selects and exfiltrates all of the data to a host they had allow-listed via CSP.That's clearly unacceptable. I fixed it by restricting the ability to allow-list any domain to a new apps-set-csp permission, which is intended just for trusted staff. Site administrators can also configure Datasette with a list of allowed_csp_origins, which regular users can then select. This means you can do things like allow cdnjs.cloudflare.com and your users will be able to build apps that load extra JavaScript libraries from the cdnjs CDN.
I've reviewed Datasette Apps extremely closely, especially the security-adjacent parts of it. The critical sandbox and CSP configuration are based on multiple AI-assisted prototypes and tests.
I'm really pleased with this initial release.
Datasette is growing beyond its origins as an application for serving read-only data into a much richer ecosystem of tools for doing useful things with that data once it has been collected.
Datasette's roots are in data journalism. I've always been interested in the question of what comes next after a journalist gets their hands on a giant dump of data about the world. Datasette supports exploring and publishing it. Datasette Agent adds interrogating it with AI assistance. Now Datasette Apps expands that to building custom interfaces and visualizations to help unlock the stories that are hidden within.
Tags: iframes, javascript, projects, sandboxing, ai, datasette, generative-ai, llms, ai-assisted-programming, content-security-policy
When the flood waters ravaged Micaville Presbyterian Church, lifting pews and pushing a neighboring house into the back of the building, the water level stopped just below the base of the stained glass windows. Saving the blue-rimmed fractal panes was one of many blessings for pastors Beth and Tom Hall, alongside receiving $140,000 in April of last year to rebuild.
This money came through the Public Assistance program for Houses of Worship damaged by declared disasters through the Federal Emergency Management Agency, or FEMA.
Micaville, an unincorporated community in Yancey County, about 45 miles northeast of Asheville, lies at the intersection of two creeks. When tropical storm Helene blew through western North Carolina in September 2024, causing catastrophic flooding, much of Micaville was washed away. Once neighbored by a gym, a coffee shop, a thrift store, a post office and a water tower, Micaville Presbyterian Church stands partially rebuilt in the middle of the destruction. The rest of the strip is abandoned, filled with debris and stacks of wood.
“You wouldn’t believe how much water came from such a little creek,” said Beth Hall on a sunny day in early March, 18 months later. “I’ve never seen anything like that in my life.”
Micaville Presbyterian Church reopened one year after Helene, when the water filled the basement and flooded the sanctuary up to the stained glass windows. Left, pews are scattered amid debris and mud soon after the water receded. Right, chairs face the altar in the newly rebuilt church. Photo credits: Beth Hall and Ali Caudle
With the initial funding from FEMA, debris removal by the National Guard, as well as donations and aid from community members and Amish volunteers, the Halls were able to reopen their church last fall. The basement remains unfinished, though, and much of the planned work to fortify the structure against future storms and flooding remains on hold.
The Halls are waiting on approximately $300,000 in aid from FEMA that they have already been approved for, said Tom. This mitigation money would be used to rebuild a flood wall to protect the church if the creek floods again. It would also be used to waterproof the doors and windows, and replace supplies in the basement for ones that could be easily cleaned.
“We’ve got over $300,000 that is approved for mitigation,” said Beth Hall. “And we’ll never get that. But I’m just thankful we got what we did.”
FEMA is a government agency under the U.S. Department of Homeland Security, or DHS. The primary purpose of FEMA is to support disaster response when recovery efforts overwhelm the resources of state and local authorities.
There are two primary categories of aid dispersed by FEMA after a disaster declaration: individual assistance, known as IA, and public assistance, referred to as PA.
“FEMA does two major things after a disaster. One of them is helping individuals, IA, so that’s helping people, tree on the house, bridge washed out, that kind of thing,” said Rachael Sawyer, the strategic partnerships director for Buncombe County, and Helene recovery coordinator. “And then PA is help government to government. So if we sustained any damages that need to be repaired, they help with that, and they also help reimburse us for … emergency protective measures.”
FEMA funding involves a fractured landscape, shifting constantly. Between the DHS government shutdown, leadership changes and an uncertain political future, FEMA assistance is deeply complicated. As of March, both public and individual assistance remains stalled.
As of March 19, North Carolina state officials estimate that there are roughly 1,800 Helene-related projects in progress within the FEMA public assistance program, accounting for an estimated $2.6 billion in need.
“The FEMA money is not flowing as fast as expected or as is needed in the state,” said an official within North Carolina’s Department of Public Safety on the condition that his name not be used because of the sensitivity of the topic and the state’s continued dependency on federal aid. “We would have liked to see that funding move quicker,” he said, adding that his office has urged legislators including Republican Sen. Thom Tillis to help free up federal disbursements.
According to FEMA’s website, public assistance is FEMA’s “largest grant program providing funds to assist communities responding to and recovering from major disasters or emergencies declared by the president.” Eligible applicants for PA include states, U.S. territories, federally recognized tribal governments, local governments and certain private non-profit organizations. The houses of worship provision falls under the umbrella of private non-profit groups.
However, as a result of the DHS shutdown in early 2026, FEMA was operating for months at limited capacity, which meant that the agency made slow progress on processing grant funding for public assistance category A, or debris removal, and category B, emergency protective actions. Categories C through G are considered “permanent work” and include rebuilding roads and bridges, water control facilities, public utilities, parks and other facilities.
Within PA categories A and B alone, senior North Carolina state officials report that there are approximately 21 projects waiting approval by the DHS secretary, representing an obligation of approximately $28.6 million.
This is due, in part, to a rule implemented by former DHS Secretary Kristi Noem last June that the secretary must personally approve any expenditures greater than $100,000. Widely criticized for causing delays and putting a burden on FEMA’s disaster response, this policy was rescinded by Noem’s successor, Secretary Markwayne Mullin, on April 2. While this move is expected to ease the bottlenecks, the effects of this change are likely to be felt well after the end of the DHS shutdown.
According to North Carolina state officials, Noem’s policy definitely slowed down progress on awards reaching the state for reimbursement through public assistance. State officials have turned to liaising with legislative partners at the federal level to try to get funds freed up.
In a March 3 Senate Judiciary Committee hearing two days before Noem was fired, North Carolina Sen. Thom Tillis questioned then-Secretary Noem, criticizing her leadership of DHS, especially in regards to immigration enforcement and FEMA.
“You have a policy right now that anything over $100,000 has to go through your desk for approval,” Sen. Tillis said in the committee hearing. “If you’re requesting a review of $100,000 and up, then it begs the question why? Why would you be involved in that? Why would that be a policy?”
In a study released March 4 — titled “Delayed by Design: Disaster Survivors Left Behind by DHS Secretary Noem’s $100,000 Approval Policy” — by the minority staff of the U.S. Senate Committee on Homeland Security and Governmental Affairs, ranking members Gary Peters (D-MI) and Andy Kim (D-NJ) laid out real-world impacts of this DHS directive for disaster survivors. The study found that minority staff identified 1,034 contracts, grants or disaster assistance awards that were delayed or left pending as a result of this directive.
“The Homeland Security Act of 2002 expressly prohibits the Secretary of Homeland Security from restricting or diverting FEMA resources from the agency’s mission. Based on your disaster response,” said Sen. Tillis in the committee hearing, “I have reason to believe that you’re violating the law, either knowingly or unknowingly.”
On the ground in North Carolina, individuals cited DHS policies and the Trump administration when assessing FEMA’s response to Helene.
“If we got the rest of the FEMA money, we’d be wonderful,” said Tom Hall. “Donald Trump came in here and said, ‘I’ll build it back better than ever.’ … FEMA was here right from the start helping us. They were the ones that did the job. They came and found us. …The administration change just…it came to a screeching halt.”
With constant changes at the federal level, it becomes complicated for individuals to track the status of their assistance applications.
“In terms of the day-to-day operation of the Individual Assistance Program that we see, I would say the only change that we have seen at this level has been that FEMA responses have slowed down,” said Emma Smiley, a supervising attorney for the Disaster Relief Project at Legal Aid of North Carolina. “The time to get a decision on an appeal has crept longer and longer. … We’re talking like three months, but it’s at this point at least four before people get their decisions made on appeals.”
In September 2025, the U.S. Government Accountability Office published a report finding that from Jan. 1, 2025, to June 1, 2025, the active number of FEMA employees decreased from 25,800 to 23,350 — a loss of 2,446 employees, including 1,465 who participated in a workforce reduction program. The report ties these departures to the Trump administration’s efforts to reduce the size of the government workforce, as well as attrition and employee burnout from concurrent disasters.
“I think that it’s important for FEMA to have the staffing levels where they can make decisions on appeals in a timely manner, because when people are in a disaster situation, they tend to have a lot of emergency needs,” said Smiley. “So if they have to wait four months for an appeal decision, just because there isn’t the staffing to be able to do that, that can make somebody homeless.”
In January 2026, the American Federation of Government Employees led a coalition in a lawsuit challenging the Trump administration’s “unlawful and drastic” staffing cuts at FEMA. The complaint alleges that attempts to dismantle FEMA through workforce reductions take direct aim at FEMA’s mandate as laid out in U.S. law, and warns that shrinking FEMA risks pushing disaster relief to state and local governments in ways that could cause the kind of catastrophic tragedy Congress sought to prevent.
“We had to hunt through so much red tape. And we’re educated people, so we were able to persevere and not give up,” said Beth Hall. “So if we had that much trouble, I think a lot of people, they don’t try and get it filled out. They just don’t believe that they can.”

MANNA FoodBank is a private, nonprofit regional organization that works to end food insecurity in Western North Carolina. Their Asheville headquarters and warehouse, then-located on Swannanoa River Road, were completely destroyed by Helene.
Claire Neal, MANNA’s CEO, credited FEMA with providing crucial support in the immediate aftermath of Helene by providing significant amounts of food to be distributed to people in need — including foods for specific dietary needs and cultural or religious restrictions that couldn’t be sourced through community donations alone. MANNA was able to resume food distribution within days of Helene’s impact. But 18 months later, the organization is still trying to piece together funding from various sources, including FEMA, on a relocation project to rebuild.
The Swannanoa River completely changed course as a result of the flooding. What used to be MANNA’s parking lot is now the river — meaning the organization cannot rebuild on the old site. To fund this relocation, MANNA is coordinating with FEMA on a daily basis, contracting with an outside consultant and dedicating substantial staff time.
MANNA is also working with FEMA through the Hazard Mitigation Grant Program, or HMGP, which provides funding to state, tribal and local governments to reduce or mitigate future disaster losses. In Buncombe County, there are four groups of applicants. The first group involves 23 properties with funding already awarded by FEMA. For group two, 24 of 26 properties have been awarded funds. Groups three and four, involving 157 and 72 properties respectively, are listed as “application submitted to FEMA.”
MANNA has been approved through the county and state level for this program, so the state has agreed to buy back the original land to be converted to green space, at fair market value, using FEMA funds, once approved.
Over in Black Mountain, a Quaker meeting house was damaged beyond repair. The Swannanoa Valley Friends Meeting, which did not have flood insurance, are now gathering at a host church on the Warren Wilson College campus. The group’s leadership recently learned that their old property has been approved in the first group of HMGP funding in Buncombe County.
MJ Hogan, the group’s assistant treasurer, recalled the state of the meeting house after the floods. “We salvaged what we felt like was salvageable, and it was a mess. I mean, it was disgusting. Everything was just caked in mud,” she said.
In Hogan’s initial correspondence with FEMA, she was redirected to different programs and led to believe that there was no support the agency could offer the group. “All my efforts to get FEMA help or help anywhere prior to this moment of this hazard mitigation was, ‘We can’t help you because you’re not residential,'” said Hogan. “It’s a weird situation. We’re not commercial, we’re not a business. But we’re not a family owning a property.”
Her situation mirrors what countless others have been through. The confusion, the mixed messaging, the promise of aid and then its retraction, have left residents of Western North Carolina wondering how they will fully restore their lives after so much chaos and loss.
“Then you add on top of it government shutdowns, and you add on top of it the current administration, and you add on top of it, all the things. The whole time we’ve been kind of in the dark,” said Hogan. “We found out maybe a year in that we were one of the first 23 properties approved for this buyout program, which was great news. That still might mean that it’ll be years from now before anything happens. We don’t know.”



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