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  • Why Are We Still Working This Hard?

    Why Are We Still Working This Hard?

    In 1973, what a forty-hour week of work produced takes sixteen hours to produce today.

    That’s according to Bureau of Economic Analysis (BEA) input output tables. An hour of work in American business now makes two and a half times what it made in 1973. For every forty hours that used to be needed, twenty-four have been freed.

    You have probably not noticed them. The forty-hour week was written into American law by an act of 1938, and hasn’t changed. Counted for each head of population, the hours worked in American business in 2024 were 10 percent higher than in 1973. As a country, the US took the whole of fifty years’ progress as more output and none of it as time.

    It was supposed to go differently. In 1930 the economist John Maynard Keynes wrote a short essay about the world his grandchildren would live in. He predicted that “the standard of life in progressive countries one hundred years hence will be between four and eight times as high as it is to-day.” And with so much plenty, he thought, we would hardly need to work at all: “Three-hour shifts or a fifteen-hour week” would be enough. He is usually said to have been right about the wealth and wrong about the leisure. Output per person in the United States in 2024 was 7.8 times its level in 1930. But look again at the sixteen hours. He was right about both. The fifteen-hour week arrived in terms of output, but not in the workweek.

    I have spent a long time trying to find out where those twenty-four hours went, with a co-author, a small economy built on paper, and more national accounts than I’d like to admit. This essay is what I found. Eighteen of the hours never reached ordinary paychecks. The other six reached them in a form that couldn’t be taken as time. Both happened for the same reason, which has to do with what a wage is for. The same reason explains why people are frightened of machines that ought to be good news. And it points to a way out that is simpler, and nearer, than I expected when I started.

    A note: The numbers are American, because the United States publishes the accounts this needs. I live in Sweden but it’s probably quite a similar story. The formal paper underpinning this work is here:

    https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7226858

    What is a wage for?

    Three things:

    A wage is a price. It tells a business what an hour of someone’s time costs, so that the hour gets used where it is worth most, and it tells people which work is wanted. A wage is good at this.

    A wage is also a “ticket”. It is how nearly everyone gets a share of what is made. Bread reaches me because I have money, and I have money because I sold some hours. That isn’t a law of nature. It is an arrangement, and it has worked so well for so long that we’ve stopped seeing it as one.

    And a wage is a place: a shape to the day, people to be among, a sign that somebody needs you.

    One thing can do three jobs only while the jobs pull the same way. For two centuries they did, because of the kind of machine we had. The machines of the industrial age were strong and stupid. They could lift, spin, stamp and haul, and they couldn’t see, judge or decide. Nearly everything worth making took both kinds of ability, so every new machine needed people standing next to it, and the owners of machines had to bid for those people. The price of an hour rose with everything else, and a ticket made of that price was a good ticket.

    The machines being built now can see, read, write and decide. I write software for a living and use them every day, and I find it harder each year to name a kind of work that is safely out of their reach.

    My claim is that the price and the ticket have been pulling apart for fifty years, and that each is damaging the other, and that fundamentally this is driven by changes in technology.

    How important is this?

    Probably a lot. It surprises me just as much as you that some random 26 year old trans girl might work this out, but the maths has been checked, the paper is going through review, and I’ve done everything I can to try and break it. If you can see what’s really wrong with it: wilson@wilsoniumite.com

    Follow a dollar you spend in a shop. Some of it pays the people who worked to make the thing. Some of it pays whoever owns what was used to make it: the oven, the truck, the building, the ground underneath. America’s national accounts are detailed enough to chase a dollar all the way back through every supplier, and when you do it for 2023, of what is paid to Americans for making the things households buy, 66 percent is pay for work and 34 percent goes to owners.

    A third of the economy, in other words, already pays people for owning things, with no work asked of them. That would be a curiosity if we all held a bit of both. We don’t.

    The poorest tenth of American households lives mostly on government benefits. From the second tenth to the ninth, about four-fifths of what a household earns in the market is pay for work. Then, in the richest tenth, it changes all at once, and pay and ownership are about half and half. That one tenth receives two-thirds of all the income from ownership.

    Benefits look like a second source for everyone else. But 68 cents of every tax dollar in America is raised from pay for work, so a benefit is mostly a claim on somebody else’s wage. Follow the money far enough and nine households in ten are living on each other’s wages. We live off the earth and off the machines we’ve built on it. Our share of what they produce reaches us through one narrow channel, which is being paid for a job.

    By the way at this point you may notice that “machines” sounds like capital, and yes, that would be the technical term. But to try and escape what we usually feel when we hear those terms, I will use work instead of labor, machines instead of capital, and land instead of non-produced inputs (the term used in our paper).

    Where did the eighteen hours go?

    Until 1973, what an hour of work produced and what an hour of ordinary pay could buy rose together. Between 1950 and 1973 the first rose by 81 percent and the second by 66 percent. (By ordinary pay I mean the hourly pay of everyone on a private payroll who isn’t a manager or a supervisor, measured against the prices of everything except housing.) Then they parted. Since 1973 the output of an hour has risen two and a half times, and an hour of ordinary pay buys 19 percent more. You might have heard of this as “the great stagnation” and yes, I claim to in large part be able to explain what happened, that technology is what changed. Consider that moors law is usually said to start around that period, and you begin to see what I mean.

    So to buy what forty hours of ordinary pay bought in 1973 now takes thirty-four hours. Making it takes sixteen. The eighteen in between went somewhere else.

    I would like to tell you they went to the owners of the machines. About a fifth of the gap did. About two-fifths of it is pay spreading out among workers, with the top pulling away from the ordinary worker. The rest I can’t separate cleanly. It is a tangle of prices, benefits and measures that don’t line up.

    But look at what the largest piece is. Daron Acemoglu, who shared the Nobel prize in economics in 2024, and Pascual Restrepo estimate that between 50 and 70 percent of the change in America’s wage structure over the four decades to 2016 came from “relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation.” Machines learned to do what ordinary workers did before they learned to do what the best-paid did.

    So both pieces are the same thing happening. A wage is a price, and a price follows scarcity. It doesn’t follow effort, and it doesn’t follow need. The day a machine learns your job, you are working exactly as hard as you did the day before, and your hour is worth less. That is how a price ought to behave, and it is a fine way to decide who should do what. It is a strange way to decide who gets to live well.

    That is the price shrinking the ticket. So far it has been slow.

    And the other six hours?

    Ordinary pay did rise, by a fifth. That could have been six hours off the week at the same standard of living. Instead the hours went up, and I don’t think it was because nobody wanted the time.

    Think about what a price tells you. When I pay three dollars for a loaf of bread, the baker learns something true about me: the bread was worth at least three dollars to me, because I could have walked away. A wage is supposed to carry the same kind of information the other way. If I take a job at fifteen dollars an hour, the hour must have been worth less than that to me, or I would have kept it. A great deal of economics leans on this. It is why a job that somebody is willing to do is assumed to be a job worth doing.

    It works only if I could have said no. If selling hours is the only way I eat, I will sell them for whatever they fetch, and the wage records how badly I needed the money. It says very little about what the hour was worth to me. It might have been an afternoon with my child, or a visit to my mother, or the Tuesday I would have spent running the club. Those lost a contest they were never allowed to enter. An auction in which one side can’t walk away tells you very little about what the thing was worth.

    This is the ticket spoiling the price. Because the wage is the ticket, most people can’t refuse it, and a price that can’t be refused has stopped being a price. When we argue over “does money represent value”, this is one of the main reasons that isn’t true.

    Two things then happen. The first is that some work gets done that isn’t worth doing. Take any job that produces a little more than the wage it pays and less than the hour is worth to the person doing it. The job covers its costs. The employer is doing nothing wrong. And every hour of it uses up something worth more than what it makes, with the whole of the loss falling on the worker. Nobody can see this, because all the prices look fine. Think uber or doordash. Would you ask your friend to pick you up because you are drunk and pay them a few dollars for it, knowing they’d rather be asleep? Maybe, just maybe, that job shouldn’t exist, at least not done by a person.

    The second is that our economy has no way of saying “enough.” Other markets can. When there is more of something than people want, its price falls and less of it gets made. The market for work can’t, because for the person selling, a price that falls far enough means not eating. So we treat every job as a good in itself and every hour sold as proof that it was wanted. I once asked a friend why we assume that everyone ought to be working, and she said of course they should, she’d like to be able to buy more stuff. It’s a fair answer, and it contains an assumption, which is that every extra hour worked makes more of what we want. Labor is a resource, like water, and we don’t drain the oceans looking for a use for every drop.

    I can’t put a number on what this costs, and the reason I can’t is part of the argument. Everything we measure about work is a record of what people did when they could not refuse. It is like trying to learn how much people like a food by watching what they buy when it is the only thing on the shelf. So let me ask you instead.

    Do the people you know have more free time than their parents had at the same age? In a country that has made itself two and a half times as productive within one working lifetime, do they seem less pressed? When they do take time, for a new child or a dying parent, does it feel like something they are free to choose, or like something they will pay for later? And of the things that seem to have thinned out around you, the visits, the clubs, the neighbors who knew each other, how many were things that anyone was ever paid to do?

    The harder evidence points the same way. Where ordinary people did gain free time over the last century, most of it came at the two ends of life, as longer schooling and longer retirement. Those are the two stretches that come with a ticket that isn’t a wage: a parent or a loan at one end, a pension at the other. And when researchers in the United States gave 1,000 low-income people $1,000 a month for three years, with no conditions, those people took roughly a sixth of the money as time. They received $11,400 a year more than a comparison group, and their other income ended up about $1,900 a year lower. The authors call that “a moderate labor supply effect.” People who could refuse a little did refuse a little.

    What happens if machines get much better?

    So far both failures have been slow. Nobody knows whether machines are about to get much better, or how fast. What I can do is show you what happens in an economy where they do.

    An economic model is a small economy on paper, simple enough to solve and fitted to the real one wherever the two can be compared. In mine, businesses give each task to whoever does it more cheaply, a person or a machine. People are far better than machines at some tasks and only a little better at others. Machines have to be built, out of other machines, some human work and some land. Whoever pays for a machine wants a return of 10 percent a year. And there is only so much land. Fitted to the United States of today, this economy pays 60 percent of its income for work, 31 percent to the owners of machines and 9 percent to the owners of land.

    Now turn one dial. Let machines get steadily better at everything, until they are a hundred times better relative to us than they are now, and let them learn to build one another.

    For a long while very little happens. When machines are ten times better, work is still paid 52 percent of all income, because people move to what they are best at. Then, at about 35 times, the machines that could replace even the hardest hour of human work take less than an hour of human work to build, and from there no wage can stay above what the replacement costs. By the hundredfold point work is paid 17 percent of all income. At ten thousand times it is paid less than 1 percent.

    Here is something fun though about this economic model. Technically, measured in “stuff”, nobody in this economy gets poorer. But, what stuff you can buy has changed:

    At the hundredfold point total income is 32 times what it was, and an hour of work buys 14 times as many goods as it does today. What an hour stops buying is land: an hour’s pay rents about a twelfth of the land it does now. And the income of those who own machines and land is 66 times what it was.

    Look at what has happened to the two jobs. As a price, the wage is working perfectly. It is telling everyone, accurately, that human hours are no longer what production is short of. As a ticket it has all but failed. It delivers a sixth of the income, in the middle of the largest boom there has ever been.

    Who would that fall on? If ownership stayed as it is today and work were paid 17 percent of income, the richest tenth of American households would receive 62 percent of all market income, up from 46. The poorer half would go from 14 and a half percent to 10. People sometimes hope that an economy like that would seize up for want of customers. It wouldn’t. The owners are the customers, and the economy would turn to face them. Nothing in economics says an economy has to be for everybody.

    Keep three cautions in mind. The dial measures technology and not time: a hundredfold improvement takes 117 years at 4 percent a year, the pace at which machinery improved for its price after the Second World War, and 25 years at 20 percent, and nobody knows the pace. If people turn out to have abilities that no machine can match at any price, the same dial stops with work at 48 percent. And it is a model, with one kind of worker and no politics.

    So what would fix it?

    There are two problems here with one cause. We ask the wage to be a price and a ticket. Being the ticket, it can’t be refused, so it has stopped pricing time. Being a price, it follows scarcity, so it is a ticket that shrinks whenever a machine learns something. The repair is to stop asking. Let the wage be a price, which it is good at, and give people a second ticket.

    The question is what a second ticket can be made of.

    It can’t be made of wages. A benefit paid out of today’s taxes is, for 68 cents in the dollar, wage earners paying each other. It moves the first ticket around.

    It can’t be made by taxing what machines earn, which was my own first idea. In the model, a 20 percent tax on what machines earn their owners raises 6.3 percent of national income, and wages fall by 6.1 percent of national income. The reason is that investors build machines until the last one just pays the return they want. Tax the return and they build fewer, until the ones that remain earn the old return after tax. The owners keep their rate of return on a smaller pile. The people who work alongside the machines have fewer of them to work with, and are paid less. The tax goes straight through the owners and lands on the wage.

    What works is owning the machines, and there is a way to do it that disturbs nothing. Let a company deduct the full cost of every investment from its taxable profit on the day it makes it. Then on every new machine the government is, in effect, putting up a fifth of the cost and collecting a fifth of what the machine earns. That is a partnership. The investor’s return on their own four-fifths is untouched, so just as many machines get built. The public gets its fifth, which is 6.2 percent of national income in the model, and wages don’t change. The honest catch is the machines that already exist, whose owners lose about a fifth, once.

    Land is different, for one reason. Nobody makes it. Tax machines and there will be fewer machines. Tax land and there is as much land as before. Thomas Paine, the pamphleteer of the American Revolution, argued in 1797 that “Man did not make the earth,” and that every landowner therefore owed the public a ground-rent, to be paid out to everyone. “It is not charity but a right,” he wrote. I reached that idea on my own before I’d heard of him, which I’ve decided to find encouraging, and for a while I thought it was most of the answer. The numbers talked me out of it. The rent of the ground under America’s homes came in 2024 to between 4.3 and 7.1 percent of households’ disposable income, or between $2,776 and $4,627 a year for every person in the country. That is real money, and it isn’t what a country can live on. It grows, though. In the model, land’s share of income more than triples. And it can be collected by taxing only the growth from here on, which leaves today’s owners the rent they have now, though not the rise they were counting on.

    So the second ticket has to be made of the other third of the dollar. It has to be ownership.

    What would that take?

    Less than you’d think. For the United States it comes to three things.

    Hold a share in common, and pay everyone its income. A fifth of all the machines and land in the country would have paid $4,493 a person in 2023, or $374 a month, for every man, woman and child. Most of that fifth is within reach without buying anything: the tax America already levies on company profits, turned into the partnership I described, and the growth in land rent together come to about a seventh of all ownership income, and a public fund could buy the rest. It isn’t free money. The profits tax is already being spent, so paying it out means finding $608 billion a year somewhere else, and whatever a fund buys, somebody pays for.

    Let people buy the rest out of wages. A common share of about a seventh would be enough to keep the poorer half of households whole, even if wages went to nothing. It can’t do that for the comfortable middle, who earn nearly half of all the pay in the country. They have to buy their ticket, and with the common fifth in place, saving 5 percent of pay and reinvesting what it earns at 5 percent a year would do it in 34 years, which is a working life.

    And move taxes off the wage, toward spending, so that the safety net stops depending on the same thing as the incomes it is there to protect.

    None of this is untried. Since 1982 the state of Alaska has paid every resident a yearly dividend out of a fund built from its oil revenues. Alaska’s dividend paid $1,312 a person in 2023 and $1,702 in 2024, to more than 600,000 people each year, and the fund behind it is worth $88 billion. Norway’s national fund owns, in its own words, “almost 1.5 percent of all shares in the world’s listed companies.” And Sweden puts 2.5 of the 18.5 percent of pay that goes toward the public pension into invested accounts that belong to the worker.

    It starts small. A dividend of $374 a month is 29 percent of the poverty line for a person living alone. But it is made of the thing that is growing. In the model, the income of a common fifth is 13 percent of all wages today and 96 percent of all wages at the hundredfold point. And it answers the question I left hanging. A floor paid out of something everyone owns is mostly people paying themselves. As it grows, refusing becomes possible, a little and then more, and the wage goes back to being a price that means something.

    There is a deadline. Today, everything that pays ownership income in the model economy, every machine and every acre, is worth about 6.7 years of the whole country’s wages. That is the price of a second ticket in the only currency most households have. At the hundredfold point it is 48 years. The saving that takes a household in the comfortable middle 34 years today takes 76 years there. The households that most need a second ticket are the ones who would have to buy it out of wages, and wages are the thing losing its value.

    Is it fair?

    The standard most often offered in defense of things as they are is that people are entitled to what they contribute, and that what you are paid measures what you add. I think that is a reasonable standard. My complaint is that its only reasonable if it’s wholly true.

    Hold the kinds of income up to it. A wage is paid for work. The return on a machine is paid to someone who went without something in order to build it. The rent of land is paid for something that nobody made. Now look at what America taxes. It raises 68 percent of its revenue from pay for work, the most plainly earned income there is, and its tax on what machines earn lands, in the model, largely on wages again. By its own favorite standard, the country taxes most heavily what is most clearly earned. What I’ve described is that standard applied all the way through.

    Now push it. Suppose machines can do nine-tenths of all work. The standard doesn’t break. It still says that those who work should keep their pay and those who saved should keep their return. It has less and less to say, because less and less of all income is anyone’s contribution. In the model with the program in place, pay for work goes from 60 percent of all income today to 17 at the hundredfold point and to less than 1 in the end. What is held in common, the public’s fifth of the machines plus the growth in land rent, goes from 6 percent of all income today to 40 at the hundredfold point and 48 in the end. That is about half, and nobody chose the number. It is where the contribution standard arrives by itself when work becomes scarce.

    Is there any hope in this?

    I think there is

    The first reason is the one I started with. The hard problem, the one every earlier generation faced, was making enough. That problem is very nearly solved. We make in sixteen hours what took forty within living memory.

    In 1955, paying every American a poverty-line income would have cost between 27 and 52 percent of everything the country produced. In 2024 it would cost between 9 and 18 percent. What is left is a problem of arrangement, of who holds which ticket. Arrangements are made by people, and they can be remade.

    The second reason is that most of this can still be done as a trade, in which nobody is made worse off. Today, a household gets a second ticket by putting aside some of its pay and buying ownership from someone willing to sell it, and each side is paid in full for what it gives up. After a large shift, the same move is a transfer from those who gained to those who lost, which is a much harder thing to ask. We are still in the before. None of us knows for certain which side of the line our own work will fall on, and I can’t think of a better condition for agreeing on a rule.

    The third is that every piece of it already exists somewhere, and works.

    The fourth is what the machines themselves are offering. In the small economy on paper, the same hundredfold improvement produces two different worlds, and the only difference between them is who holds the tickets. In one, a tenth of the households receive three-fifths of the income, and everyone else lives on a wage that has stopped meaning much. In the other, people own a share of what makes everything, the wage is one of two incomes, and work is something you choose. That world is 32 times richer than ours, and your time in it is your own. It is the world Keynes was describing. He assumed that a society eight times as rich would simply work less, and it turns out that working less is something you can do only if you hold something besides your work. The wealth came to us as a country. The time has to come one household at a time, and most households have had no way to receive it.

    I would be glad to be wrong about where the machines are heading. Very little of this depends on that. The twenty-four hours are already there. They were freed by people who are still working forty. What stands between those people and their own time is a second ticket, and it will not get cheaper by waiting.

    I also made a video going over the maths, if you’d like to hear more:


    For the careful: how sure am I?

    Taken from public data, with every number and figure reproducible: that an hour of work makes two and a half times what it did in 1973, that an hour of ordinary pay buys a fifth more, and that Americans work more hours per head than they did then; that two-thirds of what Americans buy is paid for by work; that nine households in ten live mostly on pay and one in ten receives two-thirds of the ownership income; that 68 percent of taxes rest on pay; the size of land rent; and the falling cost of keeping everyone out of poverty.

    From a model, and true to the extent that the model is correct: what happens when machines improve a hundredfold; that a tax on what machines earn lands on wages; that ownership costs more in years of wages the further the shift goes; and that the common part of income rises to about half. The model has one kind of worker, no risk and no politics. The result on taxing machines is a long-run result. In the short run the machines already exist, and their owners pay.

    From argument, with modest evidence behind it: that a wage which cannot be refused says little about the worth of an hour, that some work is done which isn’t worth doing, and that our economy has no way of saying “enough.”

    From nothing firmer than my own judgment: that machines will keep taking over tasks, and faster than they have; that a good deal of today’s work would not be done if the people doing it could afford to refuse; and that most of us would take more of our wealth as time if we could.

    I have left things out. Every household is given the same average shopping basket, and renters are worse placed than that makes them look. The richest tenth is lumped together, though ownership is concentrated within it. Everything here is American.

    How this was made. I wrote this with Claude Fable 5.2, Anthropic’s AI model. If you don’t trust AI, just know that these are the same models that are currently wiping the floor with every other field, and I really didn’t try and force fable to write something I already agreed with. Actually, this whole experience has been me realising just how wrong I am about some things. Anyways, Claude did the measurements with me, drew the figures and drafted most of the text from my earlier essays. The model economy is joint work with Johan Båge. Every number comes from code in a public repository, wilsoniumite/labor

    Sources

    The measurements are my own calculations from the national accounts of the U.S. Bureau of Economic Analysis: the input–output accounts, the distribution of personal income by tenth of households, and the accounts of government receipts. Output per hour, hours worked and the wage series are from the Bureau of Labor Statistics. The poverty line is that of Mollie Orshansky, “Children of the Poor,” *Social Security Bulletin* 26(7), 1963, updated for prices.

    – John Maynard Keynes, “Economic Possibilities for our Grandchildren,” 1930, in *Essays in Persuasion*.

    – Fair Labor Standards Act of 1938, section 7; 29 U.S.C. § 207.

    – Daron Acemoglu and Pascual Restrepo, “Tasks, Automation, and the Rise in U.S. Wage Inequality,” *Econometrica* 90(5), 2022. Nobel prize citation: nobelprize.org, economic sciences, 2024.

    – Eva Vivalt, Elizabeth Rhodes, Alexander Bartik, David Broockman, Patrick Krause and Sarah Miller, “The Employment Effects of a Guaranteed Income: Experimental Evidence from Two U.S. States,” NBER Working Paper 32719, 2024; *The Quarterly Journal of Economics* 141(4), 2026.

    – Jason Cummins and Giovanni Violante, “Investment-Specific Technical Change in the United States (1947–2000),” *Review of Economic Dynamics* 5(2), 2002, for the pace at which machinery improved.

    – Thomas Paine, *Agrarian Justice*, 1797.

    – Alaska Department of Revenue, Permanent Fund Dividend Division, “Summary of Dividend Applications and Payments,” pfd.alaska.gov. Alaska Permanent Fund Corporation, apfc.org, fund value as of September 30, 2026.

    – Norges Bank Investment Management, “About the fund,” nbim.no.

    – Swedish Pensions Agency, “National public pension,” pensionsmyndigheten.se.

  • France needs to read my paper

    France needs to read my paper

    I’m sitting in a macroeconomics strategy review held by my employers economics team, and they are staring dumbfounded at the french sovereign bond yield and it is evident they don’t know exactly why this is happening, nor what to do with it. The French government and opposition seems to also have no better ideas, seemingly paralyzed.

    Everyone can see that France needs some change to their fiscal system, but even the most expert policymakers are scratching their heads. The only two options seemingly on the table feel unworkable:

    1. Savings, cut spending, is something the french populace seem unwilling to accept
    2. Taxes will surely slow an already strained economy

    Half of the debate thinks that the people can take it, they just need to work harder. The other half believes the economy can afford to pay out a bit more. Neither are correct (although perhaps some are closer).

    Not to sound too grandiose, because it isn’t my idea at its core, but myself and my co-author have written a paper that can explain what is going on.

    Pinning the Wage to Scarcity and Technology by Johan Båge, Stella Wilson :: SSRN

    That’s a somewhat heavy macroeconomics theory paper, so here’s one analogy of what we have developed: we split “capitalism” into two

    1. Good capitalism: Returns from capital that come from real growth, development, innovation.
    2. Bad capitalism: Returns from capital that does not develop and improve the economy. The academic term is economic rent and rent seeking.

    And then the policy you want is simple: Tax the bad capitalism, don’t tax the good capitalism. If this sounds familiar, it’s quite close to the 150 year old theory of Henry George and Georgism. How do you tax the bad capitalism? Well, some parts of it are hard, and some are less hard, but the theoretically most effective method is using both a tax and a benefit, specifically a Land Value Tax (LVT) that pays out a Universal Basic Income (UBI).

    (If you’re wondering if you can skip the UBI and just have LVT, you can’t, otherwise farmers get screwed, and grandma living off her pension loses her home, and various other problems caused by only having LVT)

    That’s it. You’re welcome. If I sound annoyed it’s because I am. None of this pain and hardship and confusion is necessary. France just needs to read my paper.

    If you’re interested in understanding a bit further, we’ve made an explainer video in the style of 3blue1brown here:

  • How to deal with Network Effects and Natural Monopolies

    How to deal with Network Effects and Natural Monopolies

    There is one kind of market inefficiency that is quite common but is dealt with in very different ways. In general, a “monopoly” is bad because if only one actor can be in a market, it can abuse its position, raising prices or offering a bad product or not being efficient or not innovating. Monopolies can be created by law, such as a state monopoly or a legal monopoly. Monopolies can also be created by large players using their clout and capital to push out or buy up smaller competitors. However, I want to talk about the kind of monopoly that sortof just appears due to the nature of the thing that they do. Perhaps the most tangible example is facebook.

    1. Your friends join facebook, maybe because it’s new and decently good.
    2. You join facebook because your friends are on facebook.
    3. Your friends don’t leave facebook because now you are on facebook.
    4. You all stay on facebook.
    5. Facebook gets worse
    6. You still don’t leave facebook, because everyone is on facebook.

    This is a “network effect” because facebook has the advantage that the whole network of friends is on facebook, and for any one person to leave would kindof suck for that person. It requires a significant change in quality, perhaps even a generational transition, for a meaningful competitor to facebook to become popular.

    However, “network effect” is a great name for this because, well, it’s the case for basically anything that could be called a “network”. Here’s more examples:

    1. The power grid: A network of wires and substations, it would be awfully inefficient to have two of them
    2. A railway network: Cmon, “network” is in the name!
    3. The internet: A series of tubes
    4. An auction site: for homes, for stuff, is a network because you are connecting many sellers with many buyers
    5. A stock exchange: same as 4
    6. Payment systems: you have a card, the shop has a card reader, they better be compatible
    7. Operating systems: If one app needs to talk to another app, the OS is in charge of how that can happen.
    8. A road network: Once again, it’s in the name
    9. A dating app: The more people are on it, the more chances you might get to find the one (if the app developers cared about that, and not just showing you exactly what would keep you on the app for longest and spending money)

    In general, there are two ways to deal with these natural monopolies and network effects.

    1. Codify them as state monopolies, or regulate them (sometimes better than nothing!)
    2. Slice out just the network as best you can, have that be state run or regulated or standardised, and let market actors mess around on top. Sometimes, the market just does 2 because it makes sense, but surprisingly rarely.

    I obviously prefer 2, and we already do 2 in a lot of good cases. Here are some examples:

    1. Water: keep the whole thing public. Scottish Water provides households with an integrated public service. Businesses, however, can choose competing retailers that buy their underlying water services from Scottish Water. Even within one system, we draw the line differently. Sources: public provision, retail competition.
    2. Electricity: share the wires, choose the supplier. In Sweden, you choose who sells you electricity, but your local grid remains a regulated monopoly. Competition solves part of the problem; someone still has to keep the network operator accountable. Source.
    3. Broadband: public fibre, competing services. Stockholm’s Stokab leases fibre to operators that install equipment and sell services over it. A new provider can rent infrastructure instead of digging up the city again. Source.
    4. Railways: separation needs coordination. Different operators can use shared tracks, but timetables and maintenance still need to fit together. Britain’s 2018 timetable disaster exposed gaps in responsibility for the whole system. Slicing things apart does not automatically make them work together. Source.
    5. Payments: shared infrastructure can still have a powerful owner. Brazil’s Pix combines public payment infrastructure with competing providers. Requiring major banks to participate helped overcome the empty-network problem. Visa and Mastercard also connect competing businesses, yet a UK review found ineffective competition and rising network fees. Interoperability alone isn’t enough. Pix study, card-fee review.

    What can we learn?

    The shared part needs fair access, sustainable funding, and someone responsible when things go wrong. It also needs participants: an excellent standard used by three empty services won’t threaten an incumbent.

    And separation should produce meaningful choices. Five companies sending differently coloured bills for essentially the same service may not add much.

    So where could we take this further?

    • Social media: separate relationships and communication from the application and feed. Let me change providers while continuing to interact with my friends. Mastodon already demonstrates parts of this, although moving an account still has limitations. Source.
    • Dating: let consenting users discover and contact people across services, while apps compete on matching and experience. Visibility, blocking, and responsibility for abuse would need to work across providers too.
    • Auctions and marketplaces: share authorised listings across competing storefronts. Keep one authoritative auction or sale record so an item cannot acquire two winners. Let sellers carry verifiable transaction histories between providers.

    What these have in common is that they all have some data that really is the users data. It would make sense that any service could request any data, but any data they get from the user they have to put into a pool that any other service can access, some portion that is always shared, and some only when the user consents to sharing it (e.g. phone number versus house listing. The former you share when you want to, the latter is why you are here, to sell your house).

    Then the market test is fairly simple: can someone build a better service, and can I choose it without persuading everyone else to come with me?

  • Read My Damn Paper

    Read My Damn Paper

    I read a tweet the other day, “If AI makes a significant scientific discovery, but nobody understands it, does it matter?” and it seems like the answer is no, it doesn’t, because no matter how hard I try, nobody reads my damn paper.

    Recently there’s been a lot of news and questions and discussions floating around I thought I’d create a handy dandy guide to understanding and answering them.

    “AI is going to destroy the economy!”

    Read my damn paper.

    “Why is it so hard to find housing?!?”

    Watch the damn explainer video we made for the paper.

    “Unemployment is still low, but why can’t I find a job?”

    Read my paper.

    “I don’t understand that economics paper”

    Watch the explainer video, it’s about the paper.

    “People are so lazy these days”

    Paper.

    “What’s happening to the bond market?”

    Paper. And then consider how bond prices and government tax revenues are linked.

    “If AI is so good, why is it taking jobs so slowly?”

    Paper. And also probably you should read David Graebers Bullshit Jobs.

    “Why are CEOs paid so much?”

    Paper. Although, we probably should write a follow on paper with comparative advantage between humans.

    Another really good option, take the paper, send it to Opus 5.5, and talk to it about it. That should answer all your follow up questions too.

    (This post is satire, I do not think anyone should be forced to read anything, I’m just slightly annoyed with the modern attention economy I guess.)

  • Memorisation versus Generalisation for AI and for Us

    Memorisation versus Generalisation for AI and for Us

    Grant Sanderson, the maths educator of 3blue1brown fame, recently started a series around the idea of “compression is intelligence”. He talks about information theory and how it relates to AI, and although the series only has a few entries, I’m already very fond of the general framing: that the ability to take the real world, distill something more fundamental out of it, and be able to relate that to other things in a useful way, is what intelligence is.

    When I meet people who I think are really smart, I’ll often hear something like “yeah I didn’t study much during school”. When I say smart, I don’t mean that they know everything, but instead that they learn quickly, are good at teaching, can describe something abstractly, then link it to an example, then dive into the details, all with minimal effort. So obviously, these people didn’t need to study much, which forms a neat explanation. Except, I’ve just recently wondered if perhaps some causality may go the other way. By that I mean that part of the reason they are smart (by my definition) is because they didn’t study. Before you accuse me of being crazy, I want to talk about how we train AI, and more precisely, talk about memorisation versus generalisation in the field of machine learning.

    When we train almost all AI systems, we often put in a huge amount of effort into just stopping the damn things from memorising. We sometimes call this overfitting, and there is a similar form called shortcut learning. If you have a picture of a bee, and you want a neural network to say “that’s a bee”, many naive methods will just find some small pattern of pixels in the image and learn “if I see those pixels there, it’s a bee”. This isn’t useful at all, usually it only works for exactly one picture of a bee and no other bees, and so you get amazing results on your training dataset and abysmal ones on your test. What we want the models to do is to generalise, we want them to learn “a bee has these colours and this shape and it has wings and this many legs” and to learn that it needs to learn “a leg is a thing where…” and “a wing is a thing that…” and so on and so on, all the way down to pixels, somehow. The reason doing this is hard is because with the wrong incentives, memorising is often so much “easier”. We usually train models iteratively, step by step, and at each step there’s only a few pictures or samples it needs to get better at judging. Why try to find some beautiful generalisable representation when just memorising some pixel rules is good enough for these 32 images or whatever? For this reason, big strides in machine learning have come from finding ways to stop models from memorising, or encourage them to take memorised concepts and generalise them. And to note, I use image classification as an example here given how tangible it is, but this problem generalises across pretty much all of machine learning.

    Now back to us humans. I’ll use myself as an example human (yes, no AI was used in writing this, except for a few spelling checks at the end). I’ve always hated memorising things. Maybe it’s my ADHD but the why hardly matters, I never did times tables, I hated learning word translations in language classes, I never bothered to remember formulas (I’m immensely grateful for being allowed a formula book for many subjects). Instead, I’d describe wanting to always use intuition and get my answers during exams from building things up from first principles. In many ways, I had no other choice, my refusal to do most homework left it as my only option. For some things like languages, this didn’t work and that’s why I almost failed those classes. There’s hardly a consistent principle for masculine and feminine forms in French. So I was one of those “I didn’t study much” people, but I still attended every class, asked lots of questions, and I didn’t take notes. Instead, I let my mind wander as the teacher talked. It’d wander around the topic being taught and try to see how the topic connected to things I already knew (hopefully, sometimes I’ll concede I was on my phone).

    Today, I work as a systems architect and I remain awful at remembering little things or forcing myself to learn small, arbitrary details. I’d call them “accidental complexity” and avoid them like the plague. But I learn quick, and seeing the big picture has been extremely valuable in my work. I wouldn’t really say one way of thinking is necessarily better than the other, but here’s the point that I’d like to make with this blog post: When we teach kids and construct curriculums, I think we should be much more careful in what kind of learning we incentivise. I’d make the bold statement that right now, we focus far too much on memorisation, and in so doing may be disincentivising people from developing fundamental understanding as they learn. I’m speculating massively here, but if each lost opportunity to generalise decreases the value of generalising in the next step then at some point you will hardly feel any need to, even if you would be better off in aggregate having some more fundamental intuition.

    I don’t have any concrete ideas of what that would mean for teaching, but it does align a lot with what kinds of teachers I really appreciate. Grant Sanderson himself is a great example, he often tries to build that fundamental intuition and has also realised that sparking curiosity (for example with compelling storylines) is one of the best ways to do that.

    Hopefully this post has been interesting to you. This idea itself is me generalising between topics like how teachers teach, how the field of ML has progressed, and how human brains learn, each of which i’ve learned about in academic settings but refused to just memorise. It might be a load of tenuously connected abstract junk (of which I will concede I do produce from time to time), or it may have some fundamental truth behind it. I leave that distinction up to you, feel free to write comments here.

  • Perfection is Faceless

    Perfection is Faceless

    As it has with many others, the advance of AI has had me thinking about humanity. Of course I’ve been thinking about humanity as the term for all of us, I’ve been writing a lot about that recently, but I’ve also been thinking about humanity as the property that we each have, i.e what it is to be human.

    One of my favourite stories of all time is Arcane. Spoilers ahead. In it, a character named Viktor becomes the “Machine Herald”, healing the imperfections of people and ultimately setting out to “fix” humanity. The constructs that individuals ultimately become look like this:

    Gold and white, slender, fast yet strong, powerful, perfect, and faceless.

    I’m not sure if the writers had our modern AI in mind when they wrote Arcane, but I cannot help but think of it as a perfect analogy. These days, mathematician Terence Tao worries that the proofs and prose generated by the latest AIs is actually too good. Readers on hackernews now sometimes praise the odd spelling error as a sign that the text is human. In experiments on story writing, although AI texts now often can win blind judging tests, the stories they write can be both felt and measured to be like copies of one another with only superficial changes.

    This shouldn’t come as a surprise. During pretraining models are trained to approximate the average human in terms of what makes anyone unique, and then with reinforcement learning they are trained to reach a point of perfection on every facet that can be measured. Perfection is a fixed point, and with enough effort a great many things can be measured, placed on a scale of better or worse, and optimized towards. I believe that all that will be left for us is our flaws, or the things for which we can finally let go of the concept of better or worse (a hard thing to do).

    I remember talking to a friend of mine about one of her favorite artists, H. R. Giger. He developed the art style for Alien, amongst other things, and the uniqueness of his work is sometimes attributed to the trauma he suffered when he was younger. I don’t think that true creativity has to come from trauma, but I do believe that the uniqueness of our lived experiences is the one thing a machine could not ever emulate. To perfectly accurately copy everything necessary to create a work that captures the life of an individual, it feels intuitive that one would have to simulate that individuals life in it’s entirety. Meaning, the only thing I feel confident in claiming that an AI will never manage to do is represent all of the unique complexity of every individual perfectly, as the only way to do that is, well, a matrix style perfect simulation. And that would be rather pointless, because we have our real version right here.

    As I look around and see people beginning to understand just how “good” AI has become, I see a lot of emotions. Denial, that it simply isn’t as good as some say, is common. Anger goes without saying. One of the more common ones in the most effected fields like programming and maths is now depression. An understanding that these models really do achieve real results, and not being sure what is left for oneself. I also felt that, but have since come to a different conclusion.

    I’ve decided that I want love all our flaws and imperfections. Some I would still try to fight against or change, but I believe all of them to be natural and would never chose to exclude any from the definition of humanity. On the contrary, to err really is what makes us human. I genuinely do believe that diversity is strength, but indeed, going forward, it may now be our only strength. We have to find a way to love it, both within ourselves, and in eachother.

  • What do we do if many people aren’t working?

    What do we do if many people aren’t working?

    To my previous post, “Why do we assume everyone should be working?“, one of the more common and good responses was “well, if people don’t work, lots of bad things will happen”. This is a very fair point, here are some of the good arguments I received:

    1. Many people find joy and purpose in work, and would lose that.
    2. Idle hands lead to more decline that we already worry about: Endless scrolling, glued to a screen, consuming misinformation, finding people to argue with.
    3. Work means people see the real world, and without it people become disconnected, will vote for fanciful, unrealistic people and policies. They won’t understand how much other people contribute to the economy, and will feel entitled to their output.
    4. We will stifle innovation, because even if machines can do the work, it’s very unclear if they can come up with new, good ideas.
    5. Who will watch the machines and make sure they do what we want?

    All these are valid, and I have myself written about this problem in the abstract, in one of my first breakthrough posts: “We’ve lost our respect for complexity“. Near the end of that piece, I wrote:

    [What do we do about it? …]

    What? Don’t look at me like that? I didn’t promise you any solutions at the start of this text. It’s a complex problem, and it’d be genuinely ironic if I now gave you the fix-all solution. I have more respect for the problem than to try and do that in a blogpost.

    I am now going to try to give you the fix-most-of-it solution in a blogpost. Go figure. One of the proposed solutions I have heard from others is essentially apprenticeships, internships, similar such things, funded in part by the state. One reason to do that instead of just more university is because I think we generally already feel more schooling having diminishing returns. Many people learn at a high level at university, hit the job market, and have to relearn a whole set of new stuff whilst forgetting what they learnt at uni (even if they got a job in the field they studied for!). Some countries already do this apprenticeship encouragement in part, cutting income taxes and even pension contribution requirements for people under a certain age (Sweden has something similar). It’s a decent solution but it has a specific problem, especially if AI lowers the slope of the task curve (see here for what that means).

    Essentially that problem is The Paradox of Value. Even with stipends from the state, these internships will follow the curve of labor intensive tasks. This is good, even necessary, in many cases to encourage people to learn in fields where the economy actually needs people, but it also is the root cause of the problem in “we’ve lost our respect for complexity”. So, we need to put some people into fields even if those fields are heavily automated. One example is, well, literal fields. Watch Clarkson’s Farm and you will realise what I mean, the average person knows little about farming and yet, every single person needs to eat. The Paradox of Value is what is standing in the way here, where things we dearly rely on are economically unimportant if the industry is mature and margins are small and automation is high in that area. So, we have to try and estimate “true” value, rather than market implied value. That’s a dangerous thing to try and do, get it wrong and you easily do more harm than good, but for this case we have no other choice.

    There are three things that we need to balance:

    1. What the market wants people to do: existing apprenticeship systems like in Sweden, and the current labor demand market serve this.
    2. What people want to do: Things that are prestigious or are their passions. When people choose a uni major but then don’t end up working in the field, usually that means this was misaligned with point 1.
    3. What our society relies on: This is the paradox of value mentioned above, currently very weakly served by curiosity and things like Clarkson’s Farm.

    Let’s focus on 3 for a bit. The way that industry, academia, and government solve similar problems is by running shock scenarios. We ask “ok, things are running smoothly right now, but if crisis comes which industries are most affected, and how much is that effect multiplied by how much we need that industry to live?”. Do this for a few possible crises, and farming, water supply, energy supply, logistical systems, payment systems, healthcare, and communication systems all come out on top for pretty obvious reasons. You don’t just get a ranking but a value.

    I’m now going to go into a hypothetical constructed solution, it’s going to sound very different from anything we have right now, and it’s designed for an equally hypothetical world where machines can do a great many tasks. OK: Imagine then that we have a pot of money, taxed from scarce factors which if you want to know why you’d do that please read “Working on Economics with Fable 5“. In fact, a lot of what follows is predicated on a system built around that finding, so if you haven’t yet read it it’s really worth reading. By not spending the pot on a UBI, we encourage working, because it lowers the UBI and tops up salaries instead. Governments balance this through democratic systems etc, I have (perhaps more than most) faith that this will work. I would then propose two kinds of post- or during-university placements:

    1. Short rotational placement.
    2. Long term placement.

    This is kind of inspired by what we do at my work, a financial institution. Switching jobs internally is encouraged, interns do this as a matter of course. Short term placement students shadow long term placement individuals or workers, then after some set time move on to some other area. When they feel they’ve found something they like, they can stay there (our long term placement). In our hypothetical solution so as to not create two distinct classes of colleague, one placed there by the state and one placed there by the market, long term placement is achieved simply by topping up the wage of all workers in the field, so it conceptually exists but you wouldn’t be able to tell who is who. That means requiring everyone to obtain their job through the short rotation system, and a company needing to hire more people must pay for more short rotation access. Hiring from short into long term placement then works similarly to right now, wage negotiation, interviews, the works. To summarize:

    • Everyone who wants a wage above a UBI enters the short rotation system.
    • Short rotation positions are distributed by the state calculated shocked value plus company contributions. Often, these individuals will be, well, amateurs, and so their wage can’t really be market priced, so one might as well have it be fixed like the ubi just at a higher level. The distribution being a sum of two weighted pots of money serves our goals 1 and 3 in the balance problem. Perhaps one can have a little bit of preferred placement, for example if someone really wants to work in a field that also is currently underserved according to balance 1 or 3 or both, they may rotate there first and hop between companies in that field more often.
    • People choose to stay at a place, and then go through the interview process. Where they choose to apply serves balance 2, interviews and wage negotiation serves balance 1, worker wage topup serves balance 3.
    • Companies are not allowed to offer. That would reintroduce the state vs market class of colleagues. In fact one of the hardest parts might be companies tipping off who they want, but probably we all value healthy working environments so preferably they shouldn’t do that. Some enforcement of within role wage bands and transparency is probably necessary for the same reason. Fable tells me a good solution is matching systems which are already used, everyone applies by submitting a ranking, all employers submit their own ranking at the same time, and an algorithm does assignment. I can see maybe some issues with that, but someone smart might be able to work it out.
    • One might add some rules, some minimum number of short term placements before one is allowed to apply.
    • When choosing to apply one can apply to any of the short term positions one was previously in. This avoids feeling pressured to apply to a position you are finishing up.
    • One might want to add a rule whereby after many short term placements your short term placement wage is slowly lowered. You will always receive the UBI, but at some point, it may be best for you to stop searching if you either see nothing you like or nobody wants to hire you. The second to last point here, free form rotation, serves as an additional possibility.
    • State employers follow the exact same rules as companies. This should include academia for countries with state financed academia.
    • If you want to switch roles, you may enter the short rotation system, or if a company really wants you, bypassing the short rotation system, they may have to pay a fee into the placement pot. That’s a particularly hard one, you need to balance labor market efficiency with our goal of employing people in automated fields, but it’s probably quite possible.
    • Firing works as normal, and probably with a UBI states and unions could relax some degree of employment protections, improving labor market efficiency.
    • Lastly, innovation, startups, self employment, and creativity. If you wish to you may also apply for short term rotation where you can do anything you want that isn’t tied to any company. Do art, music, found a company, study more, explore the world. You are paid the short term placement wage as normal. The rules are this: In this case, if you then apply afterwards, one of two things happen: You become your own long term employer as a company if you are able to earn money (or get private market investment, say, a founder), or you essentially become long term employed by the state itself. In the latter case, the state acts as an employer of last resort, however the state then needs to interview you and choose your wage. In part, the state then can use proxies or chosen areas of investment to help assess if they are willing to pay you for whatever it is you claim to want to do. The thinking is this: the UBI allows you to do whatever you want if you are able and willing to live off it. If you want more than that, you either need to convince a company, or “the people” via the state itself. That last option, employer of last resort, is incredibly hard to get right, one might exclude it especially from a pilot program, and hope the UBI is sufficient.
    • Balancing all the above with actual numbers and rules is something democracy will have to work on.

    Beyond the work system, we would want to fund other things. Funding programs like Clarkson’s Farm and educational networks also helps with making people understand the society they live in. Funding academia is important for getting unpriceable value from curiosity and innovation. Now let’s return to our critiques at the start of this piece and see if they are mitigated.

    1. Many people find joy and purpose in work, and would lose that. Now those that know or find their passion can work within it, even if the market wouldn’t price many roles there.
    2. Idle hands lead to more decline that we already worry about: Endless scrolling, glued to a screen, consuming misinformation, finding people to argue with. Decreasing the UBI and funding the short rotation and long term placement wages allow us to keep people busy, if we think that is a big risk (vote on if you think it is)
    3. Work means people see the real world, and without it people become disconnected, will vote for fanciful, unrealistic people and policies. They won’t understand how much other people contribute to the economy, and will feel entitled to their output. Mitigated in the same way as 1, but now we can encourage not just finding a job, but exploring many jobs if we fund the short rotation more than the long placement.
    4. We will stifle innovation, because even if machines can do the work, it’s very unclear if they can come up with new, good ideas. Supporting self employment, founding, academia and putting people into work helps this directly.
    5. Who will watch the machines and make sure they do what we want? The best form of oversight is to be knees deep in the work. I work in risk, and in truth it can be a bit unfulfilling. The more the work becomes just box ticking the more one begins to not care what the boxes say. Getting people involved is just the better way to achieve the same. Notably, this may mean allowing people to make mistakes even if a machine wouldn’t have, but we learn from failure so perhaps that is just a cost we need to bear if we want to understand our economy and society.

    All these policies would be market distorting. In the fable economics post, the non distorting option is to just pay a full UBI and set the level by the rent base captured. But, intentional distortions are all about saying “hey, we actually think the market might be wrong about this thing, can we skew it?” and The Paradox of Value is one such thing.

    Thanks for reading! All of this is just a hypothetical constructed idea, but something like it really would be necessary if machines do end up being quite good at many things.

  • Why do we assume everyone should be working?

    Why do we assume everyone should be working?

    I was talking to a friend of mine and I asked this. She said “of course everyone should be working, I mean, I’d like to be able to buy more stuff”

    In that statement though there really is a non obvious implied logic. So the next question: does economics support that statement always? That if we want more stuff, and better lives, everyone should be working harder? No, only sometimes; in economics, utilizing a resource to it’s fullest at all times is not a given. Labor is a resource, like water, and yet we do not drain the oceans with the same fervour as we employ people, trying to find a job for every drop.

    When we have implicit assumptions such as these, it drives our decision-making. A politician should not need to hunt for job creating policies, and yet they do because admittedly the alternative is poverty. Not because the economy couldn’t provide for that person, but because the only way for the economy to provide for them is through the wage.

    Here is a very simple statement which may sound a bit mean: it is entirely feasible that giving a job to a “stupid” person, regardless of how much we try and train them, might cost the economy more than we benefit, in aggregate.

    Disagree all you want on the definition of “stupid”, but you cannot deny that such a bar exists, and notably, that that bar can move up or down. There’s a strong argument to be made that it’s recently only been moving up as technology improves. What that means is, there may well be cases where the economy would actually like to pay someone to stay out of the labor force, but right now, it can’t. Remember when oil prices went negative? It can feel paradoxical that that can happen, but at least for oil, the economy has the power to set that price negative to essentially say “hey, I don’t want more of this right now”.1

    We have to stop assuming that the economy wants everyone to be working. Otherwise, we may be silently strangling it with labor. Our current systems of taxes and benefits almost entirely assume we need everyone. By the findings of Acemoglu and Restrepo 2026, there’s already good evidence that for the past 40 something years, this assumption has been hurting us.

    I’ve worked on this idea for a couple years now, and somehow, it managed to culminate in a formal theory of how wages are set, also based on Acemoglu and Restrepos task framework. You can read about it here:

    https://wilsoniumite.com/2026/08/03/working-on-economics-with-fable-5/

    Footnotes

    1. Negative interest rates are the same but for capital. It’s the economy saying “hey, I don’t have any ideas for what to build right now, I don’t need this money”. You might say that those are set by central banks, but they are just doing it to try and raise inflation, and inflation is itself an aggregate demand signal so in the end, yes, it’s a glut of money that doesn’t know how to be spent. ↩︎

  • There’s a bug in the Economy, and Fable found it.

    There’s a bug in the Economy, and Fable found it.

    So, these AI models have been pretty good at finding bugs and exploits in all sorts of code.

    Not only that, they’ve managed to prove quite a bit of maths, from insignificant to quite significant.

    Maybe one might doubt the significance of those findings, but for those that think there’s at least some merit to them, perhaps you’ll be interested in this one:

    Pinning the Wage to Scarcity and Technology by Johan Båge, Stella Wilson :: SSRN

  • Millions of Lifetimes

    Millions of Lifetimes

    I’ve come to realise that the true implications of my piece here aren’t quite as clear as I would have hoped, so this piece serves to make that a bit more obvious.

    The thesis in the other two pieces, which I recommend reading since I won’t do any justifying here that isn’t already done there, is that through textbook market distortion we have systematically misallocated the productive hours of all people. Before you say “but without that labor, we would not have had the development and growth that we have!”, go read the other pieces, and consider that the core, efficient use of labor would not have been lost. What is lost is the make-work hours that is, in economic terms, misallocated. Calculating how much can be done by calculating how many hours were spent to raise output above a certain level for a given year. (This is admittedly an upper estimate and the true line will be below this line, but not necessarily much lower)

    Figure 1: US working lifetimes spent producing output above a chosen “sufficiency standard,” 1970–2023. Each point on the curve answers: if “enough” is fixed at the real output-per-capita level s, how many full working lifetimes of US labor went into producing the aggregate output above that level between 1970 and 2023?1

    What the thesis implies: We have not been allowed to choose a point on this curve. I don’t mean that we should have collectively chosen one, it’s just that each individual has been denied the right to choose any point on this graph beyond the current, 2026 point.

    People cannot simply choose to take more leisure time because the ability to take leisure is still conditioned almost entirely on labor. To take time off, you have to save up. To save up, you have to work. Labor is distorting the real value of leisure if the production of that leisure needs less labor due to automation (leisure time still requires production so you can be fed, housed, and more if you go on holiday, travel, or consume to keep yourself entertained or educated).

    And if you are wondering “If I choose 1990, do we lose the internet?” or “If I choose 1970, wouldn’t the economy collapse?” the answer is almost certainly not. To the first question, research still happens through R&D and universities, technology still would progress. To the second question, the economy is incredibly adaptable, if work needs doing, even with a base rate of consumption, it will raise wages until working is attractive. The system has not been allowed to function as it should.

    I cannot tell you how many lifetimes of labor went misallocated exactly. To know that requires collecting the aggregate data from all people about where they believe they should be on this curve, in terms of what their labor is worth versus what they want to consume (in terms of consuming both goods and leisure). That is exactly what the economy is meant to do, to find that aggregate. Right now, it cannot. However, the answer is obviously in the millions of lifetimes.

    Millions of lifetimes spent, for what?

    Millions of lifetimes that, had labor been able, would most likely have been put into leisure. Do not forget, leisure means taking care of parents, of kids, of meeting friends in third spaces. All these things we wondered how we managed to lose. This distortion has been the largest calamity of the modern era.

    This is a shorter piece. The full justification, evidence from data, and the solution, is here, please read it. I may very well be wrong, which to be honest would be comforting to feel right about now. If you have good responses to the original piece, please contact me at “wilson(at)wilsoniumite.com”


    Footnotes:

    1. Formally, with one working lifetime defined as W = 72,000 h (40 full-time years × 1,800 h/yr)
      L(s) = (1/W) · Σ₍ₜ₌₁₉₇₀₎^₂₀₂₃ Hₚᵥ(t)·N(t)·max(1 − s/y(t), 0)
      where Hₚᵥ(t) = per-worker annual hours, N(t) = persons employed, y(t) = real output per capita, and s = a fixed real output-per-capita level. Total hours worked in year t are Hₚᵥ(t)·N(t); the factor max(1 − s/y(t), 0) is the share of that year’s output exceeding the per-capita standard s, so years before the economy reaches s (and recession dips below it) contribute zero. The x-axis expresses s as a percentage of 2023 output per capita, s/y(2023)·100; the curve is evaluated on a 500-point grid from 0.16·y(2023) to y(2023). Red markers sit at s = y(T) for the calendar benchmarks T ∈ {1950, 1970, 1990, 2000, 2010}, i.e. “hold the year-T standard of living” (values: 107.4, 69.9, 28.7, 9.5, 5.0 million lifetimes respectively; the 2020 standard gives 1.1M). ↩︎