Posts Tagged ‘economics’
“It’s the economy, stupid”*…
Many Americans take pride in having the largest economy in the world… which, per the chart above, by one measure we do.
But then, if we adjust for population– calculate per capita– the picture changes…
And we note both that, on a PP basis, the U.S. would be lower and, more fundamentally, that the standing of the U.S. is slipping over time.
If we dive more deeply still, the picture complicates further…
This last chart illustrates the wealth inequality in the U.S., which drops from 2nd to 28th when wealth is measured by the median instead of the average… a wealth gap that has been growing since 1985 (and that is combined with an income gap that has been growing since 1980). For more, see World Inequality Database.
* James Carville
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As we search for the source of that smell, we might recall that it was on this date in 1549 that Robert Kett agreed to head a group of rebels in the English county of Norfolk during the reign of Tudor king Edward VI. The rebels were incensed by enclosure (the fencing off of common lands by wealthy landlords, as a product of which many peasants lost access to grazing, fuel, and small plots they had long used), along with rising rents, inflation, unemployment, and declining wages; as a response, they began destroying fences. One of their early targets was yeoman Robert Kett who, instead of resisting the rebels, agreed to their demands and offered to lead them.
Kett and his forces, joined by recruits from the city of Norwich and the surrounding countryside and numbering some 16,000, stormed Norwich and took the city at the end of July. They were besieged by, then routed, a Royal Army detachment led by the Marquess of Northampton who had been sent by the government to suppress the uprising.
But what became known as “Kett’s Rebellion” ended on August 27, when the rebels were defeated by an army under the leadership of the Earl of Warwick at the Battle of Dussindale. Kett was captured, held in the Tower of London, tried for treason, and hanged from the walls of Norwich Castle on December 7.

“Always look on the bright side of life”*…
The estimable economic historian Louis Hyman has been engaged in an on-going “friendly debate” with his equally-estimable friend and Johns Hopkins colleague Rama Chellappa on “what AI means”…
… As I see this debate, this question of our age, there are two main questions that history can shed some light on.
- Is AI a complement or a substitute for labor? That is, will it increase demand for and the productivity of workers, or decrease it?
- Will AI be controlled by the few or be accessible to the many?
A Complement or a Substitute?
Consider a some of the most important technologies of the past 200 years.
When I am asked about what automation might look like, I inevitably discuss agriculture. Roughly all of our ancestors were farmers and approximately none of us today are. Yet we still eat bread made from wheat. That shift is possible because of automation.
The mechanical thresher, used to process wheat, was a substitute for the most backbreaking work of the harvest. But it also enabled more land to be cultivated, and that land was cultivated more efficiently, allowing for greater harvests. Mechanization of the farm, like the thresher, turned the American Midwest into the breadbasket of the world.
Those displaced farmers found work on railroads, moving all that. And those jobs, according to people at the time, were a kind of liberation from the raw animal labor of threshing. On net, it created demand for more workers at better wages in work more fit for people than beasts. For those that remained farmers, they found other higher-value work to be done. On a farm, there is always more work to do.
The failure, then and now, is to think farmers were only threshers. That was one part of their jobs. Today, our work, for most people, is also a bundle of tasks. Workers then and now could and can focus on parts of their job that are of higher value. And in a new economy, new tasks in new industries will be created. Many of the jobs that we do today (web designer, UI expert) were simply unimaginable in 1850. That is a good thing.
Consider now the assembly line. I’m sure you all know about the staggering increases in productivity that come from the division of labor. If you take my class in industrial history, you would learn deeply about the story of the automobile. With the assembly line, and no other change in technology, car assembly went from 12 and a half hours to about 30 minutes (once they worked out the kinks). Did this reduce the demand for workers? No. It reduced the price of cars. And that increased the demand for workers, who eventually could demand even higher wages through unionization.
It is important here to realize that better tools don’t make us get paid worse. They generally make us get paid more. Why? Because the tool, without the person, is useless. Even for today’s most cutting-edge AIs, that is true. It can code, but it can only code what I imagine it to code. It can draw, but only what I imagine it to draw. That is true for AIs as it was true for the thresher.
So, I would offer that AI will create more growth, more abundance. In the long run, all growth comes from higher productivity.
I would add one more piece to this story. Economic inequality has worsened since roughly 1970. It has worsened, therefore, not in the industrial era, but the digital era. I have argued elsewhere that this happened because for decades we did not use computers as tools of automation but as glorified typewriters (and then as televisions). Our productivity did not increase, especially to justify the expense of computers. Economists have debated for decades now over the lack of increase in productivity that came with the “digital age” of computing, but it is simple. We don’t use them as computers. Now we can.
For the first time now, normal people with their normal problems can use their computers to solve and automate their problems. AI can write code. AI can automate their tedium. The digital age did not bring any gains because it had no yet arrived. We were living through the last gasp of the industrial economy.
It is now here.
This technology will unleash unimaginable productivity gains. It will level the playing field between coders and the rest of us. Coders will lose their jobs, to be sure, but for the rest of us, the bundle of workplace tasks will become much better.
And truthfully, the demand for real computer scientists will probably increase in the era of vibe-coding. Computer science itself is a bundle of skills, of which coding is just one. The more important skill – software and data architecture – will only increase in demand as the usefulness of software expands…
[Hyman goes on to explore the dangers of monopolization (which, for reasons he explains, he believes are overstated); the future of softward (which, he believes, will skew to open-sorce), and of hardware (which, he believes will not be a bottleneck). He concludes…]
… Put together we come to a very different picture of what the digital age will be. The industrial age required massive investments to build the factories to make the products that were in demand. In the digital age, in contrast, the factories to build digital products will be made by the AI on your laptop. That is not inequality. That is equality.
The physical products of the Fordist industrial age were made for the mass market. In contrast, the digital products of the post-fordist digital age will be long-tail products. I don’t need to make mass market products; I can make them for a small niche, or just for myself.
Rather than fostering inequality, AI, then, is a great equalizer. To make products for a global market you don’t need a billion-dollar factory. You just need a laptop. That is astonishing.
That said, it will not be all sunshine and rainbows. Will AI solve the inequities of capitalism or its reliance on externalities as a source of primitive accumulation? Probably not.
But at the same time, AI is not a normal technology in that it has the potential to radically undermine many of the tendencies to concentrate capital that we have seen in the industrial age. We have been automated out of work before, that is nothing new, but it has always concentrated capital in the hands of the few. For the first time, there is potentially an alternative path forward.
AI will bring the digital age out of the hands of the coders. AI will not widen the gap—it will bridge it. Its ubiquity will mean that AI will be a tool that nearly all of us will be able to use in our daily work, which will make ordinary people more productive and prosperous…
Eminently worth reading in full: “Hooray! Post-Fordism Is Finally Here!“
Even as Hyman’s message is reassuring in the context of the flood of jeremiads in which we’re awash, it’s worth remembering that eerily-similar points were made a couple of decades ago about the threat/promise of digital publishing/commerce. Given the then-current conditions and then-plausible futures, those predictions might have come true… but in the event, they didn’t pan out as projected. That said, things are changing, so maybe this time things are different?
(Image above: source)
* song (by Eric Idle) from Monty Python’s Life Of Brian
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As we resolve to remain rosy, we might send productive birthday greetings to Andrew Meikle; he was born on this date in 1719. A Scottish millwright, he invented the threshing machine (for removing the husks from grain, as mentioned above). One of the key developments of the British Agricultural Revolution in the late 18th century., it was also one of the main causes of the Swing Riots— an 1830 uprising by English and Scottish agricultural workers protesting agricultural mechanization and harsh working conditions.

“The future is already here — it’s just not very evenly distributed”*…
… nor, perhaps, as widely read as it should be. “Urubos” is here to help…
The Extrapolated Futures Archive is a reverse-lookup for speculative fiction. Describe a situation you are facing, and find the SF stories that already worked through the implications.
The catalog connects stories (novels, novellas, short stories, films) to the speculative ideas they explore: thought experiments about technology, governance, biology, society, and more. Every idea is tagged with domains, scenario types, and outcome types so you can filter by the kind of future you are thinking about.
How to use it:
- Search by title, author, synopsis keywords, or idea descriptions
- Filter by domain (AI, biotech, climate, space, governance…), scenario type, outcome, decade, or series
- Browse ideas to find transferable thought experiments, then follow links to the stories that explore them
- Browse stories to see what speculative ideas a particular work contains
- Book Club discussions (marked with 📖) offer section-by-section roundtable analyses by AI personas modeled on SF authors
- What-If Query (via the What-If Query page/link) lets you describe a real-world scenario in plain text and get ranked matching ideas
The archive is designed for decision-makers in government, industry, and NGOs who want to widen their thinking by surfacing fictional precedents for novel real-world challenges…
Over 275 ideas, which cluster into 20 different “domains,” explored in over 1,900 stories, via over 3,500 links…
Mapping real-world scenarios to the science fiction stories that explored them first: “Extrapolated Futures Archive“
* William Gibson
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As we ponder prescience, we might spare a thought for Charles Hoy Fort, the prolific chronicler of paranormal phenomena; he died on this date in 1932. Fort collected accounts of frogs and other strange objects raining from the sky, UFOs, ghosts, spontaneous human combustion, stigmata, psychic abilities, and the like, publishing four collections of weird tales and anomalies during his lifetime: Book of the Damned (1919), New Lands (1923), Lo! (1931), and Wild Talents (1932). So influential was Fort among fellow-questers that his name has become an adjective, “Fortean,” often applied to unexplained events… The Truth is Out There…

“The present is pregnant with the future”*…
The estimable Tim O’Reilly uses scenario planning to create an insightful look at AI, our futures, and the choices that will define them…
We all read it in the daily news. The New York Times reports that economists who once dismissed the AI job threat are now taking it seriously. In February, Jack Dorsey cut 40% of Block’s workforce, telling shareholders that “intelligence tools have changed what it means to build and run a company.” Block’s stock rose 20%. Salesforce has shed thousands of customer support workers, saying AI was already doing half the work. And a Stanford study found that software developers aged 22 to 25 saw employment drop nearly 20% from its peak, while developers over 26 were doing fine.
But how are we to square this news with a Vanguard study that found that the 100 occupations most exposed to AI were actually outperforming the rest of the labor market in both job growth and wages, and a rigorous NBER study of 25,000 Danish workers that found zero measurable effect of AI on earnings or hours?
Other studies could contribute to either side of the argument. For example, PwC’s 2025 Global AI Jobs Barometer, analyzing close to a billion job ads across six continents, found that workers with AI skills earn a 56% wage premium, and that productivity growth has nearly quadrupled in the industries most exposed to AI.
This is exactly the kind of contradictory, uncertain landscape that scenario planning was designed for. Scenario planning doesn’t ask you to predict what the future will be. It asks you to imagine divergent possible futures and to develop a strategy that improves your odds of success across all of them. I’ve used it many times at O’Reilly and have written about it before with COVID and climate change as illustrative examples. The argument between those who say AI will cause mass unemployment and those who insist technology always creates more jobs than it destroys is a debate that will only be resolved by time. Both sides have evidence. Both are probably right at some level. And both framings are not terribly helpful for anyone trying to figure out what to do next…
[O’Reilly explains the scenario approach, then applies it to our future with AI (see the image above), astutely assessing the conflicting signals that we’ve experiencing; he explores the “robust strategy” for our uncertian future (strategic choices that make sense regardless of which future unfolds); then he concludes…
… I’ll return to the theme that I sounded in my book WTF? What’s the Future and Why It’s Up To Us.
Every time a company uses AI to do what it was already doing with fewer people, it is making a choice for the lower half of the scenario grid. Every time a company uses AI to do something that wasn’t previously possible, to serve a customer who wasn’t previously served, to solve a problem that wasn’t previously solvable, it is making a choice for the upper half. These choices compound, for good or ill. An economy that uses AI primarily for efficiency will slowly hollow itself out.
Looking at the news from the future, both sets of signals are present. The question is which will dominate. AI will give us both the Augmentation Economy and the Displacement Crisis, in different measures in different places, depending on the choices we make.
Scenario planning teaches us that we don’t have to predict which future we’ll get. We do have to prepare for a very uncertain future. But the robust strategy, the one that works across every quadrant, is to focus on doing more, not just doing the same with less, and to find ways that human taste still matters in what is created. As long as there is unmet demand, as long as there are problems we haven’t solved and people we haven’t served, AI will augment human work rather than replacing it. It’s only when we stop looking for new things to do that the machines come for the jobs…
Eminently worth reading in full. Indeed, speaking as a long-time scenario planner, your correspondent can only wish that everyone who wields “scenarios” applies the approach as appropriately, adriotly, and acutely as Tim has: “Scenario Planning for AI and the ‘Jobless Future‘,” from @timoreilly.bsky.social.
* Voltaire
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As we take the long view, we might send formative birthday greetings to Mark Pinsker; he was born on this date in 1923. A mathematician, he made impoprtant contributions to the fields of information theory, probability theory, coding theory, ergodic theory, mathematical statistics, and communication networks. This work, which helped lay the foundation for AI-as-we-know-it, earned him the IEEE Claude E. Shannon Award in 1978, and the IEEE Richard W. Hamming Medal in 1996, among other honors.









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