I spent much of my college years railing against what I saw as the degenerate profligacy of the Keynesian school of economics.
It was around 2008 – the “Great Recession” – and Keynes’ ideas were being used to justify a second and then third round of massive stimulus spending at the expense of future generations. UC Berkeley gave me a front-row seat for this spectacle, and a chance to study under leading Keynesians like Christina Romer and Brad DeLong.
Keynes – a childless aesthete – served as an excellent foil for my heroes in the free-market Chicago and Austrian schools. He was the living embodiment of myopic self-indulgence.
“In the long run,” he chided his future-oriented critics, “we’re all dead.”
But Keynes was also a man of uncontested genius, and I recently thought of an essay of his (which I probably never read in full) called Economic Possibilities for Our Grandchildren that is well worth reading.
All I could remember about it was his startlingly incorrect prediction, authored in 1930, that 100 years hence people would work just 15 hours per week. If only.
Yes, Keynes predicted that within a hundred years, technology and compound interest would solve what he called “the economic problem” – the struggle for subsistence that had governed human life since the beginning:
I would predict 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... Three-hour shifts or a fifteen-hour week may well put off the problem for a great while. For three hours a day is quite enough to satisfy the old Adam in most of us!
There are two things that stand out in this quote.
First is the assumption, probably justified, that much of our striving – our tendency to work more than we need to – stems from our fallen nature. We are cursed to work “in the sweat of our face” and too often confuse that labor for our salvation.
But the second striking part of this quote is that his fundamental prediction about productivity growth was basically on point.
Technology has increased output per hour roughly fivefold since 1930 (the lower end of Keynes’s range, but still an impressive figure). And as a result, we do enjoy a higher standard of living in many ways – bigger homes, safer cars, etc.
Yet his prediction about leisure – specifically, the 15-hour work week – has remained elusive for all but a few “dirtbag rich.”
Even Tim Ferriss, author of the bestselling “4-Hour Workweek,” famously worked 60 hours a week for two years so he could sell you a book for $25 that tells you how to work 4 hours a week (a book that he then spent 80 hours a week promoting).
Most of us work about the same number of hours as the average person in Keynes’s era – albeit in cushier chairs and home offices.
However, before we ridicule Keynes for yet another faulty mental model, we might note that there are still four years left before his hundred-year window closes. And if you read his essay carefully, you’ll notice that his prediction was in many ways aspirational – based on a hope that we might choose to work less, even if it goes against the forces of economic gravity.
I can’t help but wonder whether AI might give us one last chance of proving him right.
Average is Over (2026 Edition)
The typical story for why we never converted our surplus wealth into leisure is that modernity offered it to us as more output and we accepted.
We bought more stuff, consumed more services, watched more entertainment, and spent a lot more on education, childcare and healthcare. (Critics will point out that we are sicker, dumber, and more bored than ever but never mind that.)
But there is another reason we work as much or more than our grandparents did. In 1970, a Swedish economist named Staffan Linder published The Harried Leisure Class, which made the devastating observation that as productivity rises, the opportunity cost of every idle hour rises with it.
More simply: the higher your potential income, the more expensive it feels not to work.
The opportunity cost of leisure takes on a new dimension in the age of AI tools that promise to take our productivity to heights hitherto unseen.
Does AI, right now, actually raise the average productivity of the American worker?
Economists disagree. But a certain class of programmers and machine learning researchers are being compensated as if they are achieving unheard-of levels of output. Assisted by powerful new harnesses like Claude Code and Codex, a single developer can now do the work of an entire team, compressing two-week product sprints into hours.
And it’s not just the software engineers and AI nerds.
Take this recent headline:
“A Non-Coder Single-Handedly Managed Anthropic’s Entire Growth Marketing for Ten Months.”
Austin Lau, the growth lead in question, had never written a line of code. He had to Google “how to open Terminal on Mac” before he could start using his company’s product, Claude Code. Within a week, he’d built workflows that cut ad-copy creation from two hours to fifteen minutes and increased creative output tenfold (allegedly). One person, with no engineering background, was covering the work of what would normally be a full marketing team.
Do you think the average marketing lead in Silicon Valley reads that headline and thinks, “Great, I should adopt this tool so I can switch to part-time”?
Of course not. If you decide to bank your productivity gains as leisure, you will quickly be replaced by a younger, hungrier upstart—one willing to work nights and weekends to capitalize on the new premium these tools bring to their domain expertise.
This is where the real anxiety around AI originates. It’s not the robot apocalypse; it’s not even “AI taking your job.” It’s another human, using AI, taking your job – unless you continually level up.
Tyler Cowen pointed out as early as 2013 that “Average is Over.”
He saw a future where the labor market splits into two distinct worlds: one where you learn to direct the machines, and one where you are directed by them.
The key question will be: Are you good at working with intelligent machines or not? Are your skills a complement to the computer, or are you a replacement?
High earners are taking ever-greater advantage of machine intelligence to achieve better results, while those who haven’t committed to mastering these technologies see their prospects wither. The steady, secure life in the middle is vanishing because the “middle” no longer provides enough unique value to justify its cost. Cowen also noted that if you have an unusual ability to spot, recruit, and direct those who work well with computers, the world will make you rich.
It’s now realistic to speak of one-person billion-dollar companies – the dream of the ultimate leverage.
We long assumed that automation would come for the blue-collar worker first—the “three Ds” of dull, dirty, and dangerous manual labor.
But AI seems to have inverted the hierarchy of vulnerability.
Anthropic CEO Dario Amodei, has predicted that advanced AI will bring about a “bloodbath” for the knowledge economy, where a single adept manager of artificial intelligence can replace entire departments of entry-level analysts and middle managers.
But as Aaron Levie recently pointed out, the “bloodbath” theory assumes the volume of work remains static. It ignores a digital version of Jevons’ Paradox. In the 19th century, when steam engines became more efficient, the world didn’t use less coal—it put steam engines everywhere.
Marketing, as an industry, grew from a few hundred thousand jobs in the 1970s to millions today because technology made it cheap enough for every small business to participate. Levie expects AI to do the same across every category of knowledge work.
“The mistake that people make when thinking about ROI is making the ‘R’ the core variable, when the real point of leverage is bringing down the cost of ‘I’. Now, we can dramatically lower the cost of investment for almost any given task in an organization.”
When the cost of “Investment” falls to near-zero, the “Return” doesn’t have to be a masterpiece to justify the effort. Enterprising workers aren’t just doing their old jobs faster; they are attempting projects that were previously too expensive or complex to imagine – the custom software prototype, the niche research project, or the automated outreach campaign.
It is the end of the “Knowledge Economy” and the birth of what Dan Shipper calls the Allocation Economy. In the old world, you were paid for what you knew and your ability to execute it. In the new world, your value lies in how you allocate—choosing which tasks to give the AI, providing the context, and deciding if the result is “good enough.”
This is also Levie’s critical point:
“AI agents require management, oversight, and substantial context to get the full gains.”
Thus, in another twist of fate, realizing the AI productivity gains requires us to spend more time, not less, managing their new capabilities.
We’ve all been promoted to management, whether we wanted it or not.
The “Maker” has been forced to become a “Model Manager.” When AI automates 99% of a task, that last 1% of human judgment becomes incredibly valuable – and incredibly in-demand. And as any manager can tell you, the work of oversight – the constant, high-stakes judgment required to keep a dozen agents (human or artificial) on track – is far more cognitively taxing than the work of solo execution. When the cost of intelligence falls to near-zero, the “surplus” time Keynes promised is immediately cannibalized by the need to oversee the sheer volume of new output we’ve unleashed.
In short: the harried leisure class is about to get even more harried.
The FOMO Economy
Every time I open my X feed, I’m greeted by an endless stream of braggadocious claims about what a single person has “unlocked” with the latest AI hacks. Every post makes me feel a little further behind.
Jensen Huang, CEO of NVIDIA, recently added to my anxiety when he made the claim that he expects his $500,000/year engineers to be consuming at least $250,000 worth of tokens. Anything less, he says, is the equivalent of a chip designer using paper and pencil instead of AutoCAD.
And here I thought I was extreme for paying $200 a month for a Claude “Max” account, which gives me 20x the monthly compute of a standard “Pro” subscription.
The most effective 10% of my “token spend” is worth the entire subscription price. The next 40% or so are well-spent. But I still struggle to use my full allotment each week. And the worst-used 50% of my weekly “stipend” often ends up going to marginally valuable tasks, where I am gambling on whether or not Claude produces anything worth anything at all.
To avoid “wasting” the budget, I go looking for tasks that can run without my oversight: building wikis from podcast transcripts, indexing archives, generating content at scale, or running “Ralph loops” that split AI into teams of agents with long to-do lists to build features nobody ever asked for.
On the whole, this just produces an overhang of stuff I haven’t shipped.
Since Christmas of last year, I’ve started most days by opening between three and nine separate terminals to kick off three to nine separate instances of Claude Code. I am “vibecoding” my way through an ever-growing to-do list:
Doodle Reader — A modern RSS feed reader that can bulk transcribe and summarize podcasts and YouTube feeds.
SCANDOC9000 — An OCR tool that can scan a 500-page medieval Latin manuscript with your phone, then transcribe it into clean Markdown in minutes.
Curricu.love — A swipe-style dating app for homeschool curricula.
RayPeat.wiki — A complete encyclopedia of the works and interviews of the late, great Dr. Ray Peat.
This is to say nothing of the dozens of smaller tools and dashboards I’ve built for employers and clients. My productivity has reached new heights. And yet, through all of this, it still takes just as long—maybe longer—to write and publish a decent Substack article.
The Middle-to-Middle Trap
As my wife points out, if my claims of productivity were true, I should have more time for work around our farm. I wouldn’t be complaining about milking the cow or sifting the wood chips she needs for the garden.
There are two reasons it doesn’t feel like relief.
First, AI is a middle-to-middle assistant, not an end-to-end worker. It handles the “middle” of many tasks cheaply, but you still need to supply the judgment at both ends. You must decide what to ask for (the fun part), but then you must verify the output and evaluate its quality (the tax). Every task you delegate creates an open loop that only your judgment can close. The more you delegate, the more loops you are managing. The net effect is more cognitive load, not less.
I still have enormous use for a bright (human) intern—to the extent that she can manage her own AI, prompting and verifying to bring AI’s middle-to-middle output end-to-end. The last and ultimate job of the human—the ability to discern, abstract, and decide the exception—is in higher demand than ever.
The second reason is Linder’s opportunity cost, updated for the LLM era. Instead of banking the recovered time, you see a landscape of things that were previously impossible but are now “cheap,” and you want to scale them all.
Steve Jobs called the computer a “bicycle for the mind.” Naval Ravikant recently upgraded the metaphor: AI is a motorcycle for the mind.
On good days, toggling --dangerously-skip-permissions in Claude feels like taking off the helmet and letting the wind sweep through your hair as you speed past the poor suckers stuck in traffic (the ones still copy-pasting prompts into ChatGPT).
But when does a tool for transporting you to a destination become a machine that steers you? With speed also comes the danger of a more serious crash. A bicycle depends on the amplification of your own effort; a motorcycle has its own engine—a black box to most of us. Is the AI motorcycle amplifying our judgment to give us our afternoons back, or is it a machine that generates its own demands and never lets us off?
The more tokens you feel pressured to use, the more you start to abdicate the judgment that made them worth spending. You accrue a kind of attention debt. It is building for the sake of building—extending the structure of production without ever bringing it home to something of tangible human value.
In many ways, the explosion of AI-generated work reveals the “knowledge economy”—that ouroboros of businesses selling software to software companies—as having produced mostly fake value all along.
Keynes rightly distinguished between absolute needs (which we feel regardless of others) and relative needs (which exist only to make us feel superior to our neighbors). Absolute needs can be satisfied. Relative needs are insatiable – these are the modern symptoms of what Keynes identified as our most dangerous economic impulse.
The math of marginalist economics is relentless: if you can spend a dollar of intelligence to capture a dollar and a cent of revenue, keep spending. The operator sees a profit, but the aggregate human value is close to zero.
We should be especially wary of “brute force tokenization” of knowledge work – i.e., “volume plays” where the human-to-AI ratio is near zero: the guy running 400 Reddit bots to farm “karma,” the SEO consultants ranking fake businesses, the content farms producing unfathomable quantities of digital slop. These plays are competing for status, attention, and ranking. Because the tools make competing easier, the competition itself simply consumes the surplus.
These volume plays are almost all temporary arbitrage, anyway. Where there’s no human judgment, there’s no competitive moat. Since my quiet launch of the Ray Peat Wiki, someone else has come along and launched an almost identical “Bioenergetic Wiki.” He didn’t steal “my idea.” The idea was waiting to be executed ever since the technological overhang of new AI models made it almost trivially easy to build.
This is not to say that the NVIDIA engineer spending a quarter of a million dollars on tokens isn’t producing millions of dollars in long-term value for his company. But for the average knowledge worker, we have to be careful about which opportunities we pursue with our new capabilities.
Building the Pipeline that Builds the Pipeline
So where does this leave us in the year 2026? Do I think this is all just a bubble that’s going to burst?
The Austrian economists argued that cheap credit artificially elongates the “structure of production,” creating roundabout processes that eventually collapse when the malinvestment is revealed.
Cheap intelligence does the same to our judgement and attention. We elongate the chain between an idea and a finished good. When we run out of judgment to cover all those steps, the structure collapses into an overhang of “stuff nobody ships.”
This is where I recognize myself on my worst days: forever building the pipeline that builds the pipeline – preparing endlessly, but shipping nothing.
I am trying to embrace a new mantra: close loops, ship ugly.
On the days when the terminals are closed, I am milking the cow, tending the land, and sifting wood chips for my wife’s garden. From the outside this looks spectacularly unproductive. But it produces a steady supply of real milk, eggs, and produce for my family – and it closes a loop that no agent can close for me.
The irony is that I moved to the country to escape the rat race, but thanks to Starlink and Claude Max, the rat race moved with me.
Keynes was right that productivity would make abundance possible. He could not have foreseen that an abundance of intelligence would create its own scarcity — of agency, of attention, and of the willingness to stop. But the aesthete in him foresaw the remedy:
It will be those peoples who can keep alive, and cultivate into a fuller perfection, the art of life itself and do not sell themselves for the means of life, who will be able to enjoy the abundance when it comes.
Abundance doesn’t automatically become leisure. It only becomes leisure for those who choose it. If there was a sure path to earning a lower but stable income that could sustain this art of living, I would take it.
Maybe there is such a path, and we still have four years to find it.
