The Creative Class is Being Decimated. Why?

America is Losing Hundreds of Thousands of Jobs in Media, Film & the Arts. How Much is AI to Blame?

America has lost more than 200k jobs in “creative” industries over the last four years, with roughly 50k lost within the last year alone. That makes for one of the worst stretches for media employment in modern US history, with similar job loss intensity and duration occurring only during the major economic recessions of 2001 and 2008. Yet there’s no generalized recession today—instead, this period of job loss coincides with the rise of AI systems that can compose wholesale novels, photorealistic images, soundalike music, and practically every other form of digital art, en masse and at extremely low costs. Is this the fall of the Creative Class?

Proving the exact amount of AI-driven job loss in the arts is extremely difficult. Media firms are decidedly coy about their AI use, both to protect against public backlash and, more financially important, to preserve the legal basis for their copyrights. The effects of AI are also hard to disentangle from other factors currently affecting arts businesses, like consolidation in Hollywood, offshoring of content production, or the continued displacement of traditional media providers in favor of social media creators. Yet perhaps the clearest evidence of AI’s influence is just that all subsectors of the digital arts industry are losing jobs, while in-person entertainment is still growing at a healthy pace.

Some of those digital job losses are just a continuation of prior trends, like in the publishing industry where technological change has been continually grinding away newspaper and magazine jobs for decades. Yet for sectors like live broadcasting, streaming, or graphic design, recent experience is an unusual downturn compared to the tranquility of years prior. Then there’s the worst-hit sector, movie & sound recording, which has been bleeding jobs at a nearly unprecedented pace over the last three years.

Hollywood has never seen a stretch as bad as the last four years, with the movie & TV industry losing more than 100k jobs, nearly one-third of the sector’s total. At the depths touched this summer, total employment was lower than at any point since the 2008 recession and approaching the lowest point in 30 years. The streaming era has proven an extremely difficult transition, with traditional films and TV shows losing watch time to user-generated and increasingly AI-assisted or AI-generated content flows.

Overall, nearly half the media job losses of the last four years have been concentrated in the movie & sound recording sector that includes Hollywood. Written publishing has been the next-largest source of job loss, with employment down by more than 70k over the same time frame. Yet the job losses have by no means been contained to any particular part of media; instead they’ve hit nearly every subsector at some point. Nor do they show any sign of abating, with losses consistently hovering at around 50k per year and even accelerating in recent months. Will further AI development continue displacing workers in creative industries?

Can the Arts Business Model Survive?

To understand what could happen to the business of art amidst the rise of AI, it’s important to understand what did happen to it during the first digital transition, the rise of the internet.

It was the turn of the millennium, and Metallica had a problem. Demos of their upcoming, unreleased tracks were bouncing around radio and the internet, originally leaked onto a new internet file-sharing website called Napster. There they found thousands of files shared amongst hundreds of thousands of users, many of which were direct rips of albums that sold for $15 being offered for free. The most anticipated albums were often leaked online even before they were on store shelves. Metallica sued Napster for copyright infringement, and they were soon joined by separate suits from other prominent artists and eventually the Recording Industry Association of America (RIAA).

Much of the general public was understandably unsympathetic to Metallica, perceived as a bunch of already-successful millionaires trying to bilk even more money from listeners, and were even less sympathetic towards the RIAA, perceived as scurrilous middlemen who take from artists and fans alike. But the two of them easily won their lawsuit, forcing Napster into bankruptcy. Yet Metallica may have been right on the law but were on the wrong side of technology; the RIAA had won the battle but was losing the war.

The modern internet made file-sharing extremely easy, and no matter how much whac-a-mole companies played, they could not possibly catch every illegal upload. The perennial threat of piracy undermined their copyright and limited their ability to charge for music. Eventually, the value of individual songs fell to the point that companies like Spotify and Apple Music could swoop in to acquire massive catalogues, consolidate them, and charge a comparatively trivial fee for access. For what a single Metallica album would have cost in 2000, you can now get a month’s worth of unlimited access to nearly all human-made music.

The music industry has never returned to the heyday paydays of the peak CD era—even without adjusting for inflation, streaming revenue pales in comparison to ‘90s physical media sales. This was a massive boon to consumers, but it was also a squeeze on musicians that forced them to fundamentally change their business model over time. Instead of just selling records to earn money directly, music itself increasingly became a loss-leading advertisement for the live concerts (and merch) that provided a growing share of artists’ income. Tours got longer, venues got bigger, ticket prices skyrocketed, and musicians frequently took on roles closer to public influencers than isolated artistes. Were it not for rising concert sales, the business model of music would have completely collapsed, and the income going to musicians would have cratered.

Of course, there were still significant downsides to the transition into the streaming era. Bands (especially smaller ones) complain about the unending pressure to always be on tour. Musicians whose content was suited to home listening lost out to those more suited to giant festivals. Even accounting for concert revenues, musicians made less money than before. Yet musicians fared better than many areas of entertainment subsectors because there was a live component to fall back on—for many media industries, the digital era left no such comfort. [Chart]

Over time, the business of video has moved in the opposite direction of music—out of the theatre and into the home. Hollywood formerly made most of its money enticing customers to visit sold-out movie theatres, but these were gradually supplanted by broadcast TV, cable, and physical media. Yet because each successful technological leap increased total video watch time, the industry was still able to thrive amidst technological upheaval. That is, until the modern streaming era.

Today, the plethora of video options and the rise of social media have sent video producers into a vicious competition for limited attention. Even before adjusting for inflation, movie ticket sales ended last year down 25% from their 2019 peak, cable revenue is down 18% from its peak, and streaming revenue has not been able to compensate for the drop. The mountain of free user-generated content on YouTube, Twitch, TikTok, or social media was already presenting harsh competition for traditional video companies in the years before ChatGPT’s launch, and now streaming services are also competing with a flood of AI-generated content. The business model of video media is fundamentally getting squeezed, and unlike in the music industry, there’s basically no equivalent to live concerts that Hollywood can be used to ease the pain.

The actual worst-case scenario for creative workers amidst the AI revolution is something similar to what happened to the publishing industry after the advent of the internet—that is, near-total collapse of their fundamental business model. Newspapers used to employ roughly half a million people in the US, more than the entire oil industry, and now their payrolls are down nearly 85% and still dropping. Magazine publishers likewise have let 66% of their staff go since the 90s, while book publishers have lost 40%.

The routine informational updates that previously formed newspapers’ bread-and-butter were all de-bundled—box scores moved to sports websites, stock movements went to financial websites, forecasts went to weather websites—each loss compounding on itself to undermine consumers’ need to buy the paper. Search engines became the first place people looked to for information, and newspapers’ advertising revenue rapidly started flowing to companies like Google instead. The collective of social media users became faster at breaking any news story than a daily paper could ever hope to be. When papers eventually did start aggressively paywalling content, the internet made it trivially easy for people to just copy the articles’ content and share it beyond the paywall. The entire business model had collapsed, to the point that now only a select few major newspapers can even survive.

That scale of copyright dilution and forced unbundling is the worst-case scenario for media businesses in the age of AI. It’s possible arts jobs could survive via consumers’ deep-seated aversion to explicitly AI-generated content, but AI is increasingly being used throughout media in ways invisible to the average end consumer. Roughly 32% of workers in the overall arts, entertainment, and recreation sector use Generative AI to some extent, which is less than the 62% average across all industries, but still enough that virtually every major media project could have some AI within its workflow.

Indeed, AI is likely seeping into media production processes in ways large companies themselves would struggle to prevent even when they desire to—how can a TV studio be sure nobody in their writers’ room is consulting ChatGPT for jokes or any storyboard artist is generating concept art? And even if professional TV and movie studios do effectively hold out against temptation and prevent internal AI use, how long can they compete with the large mass of wannabe independent creators with much fewer scruples? Plenty of media businesses tried to hold out against the algorithmic content waves of the 2010s and were buried as a result.

by Joseph Politano, Apricitas Economics |  Read more:

Images: Joseph Politano

AI: The Biggest Economic Bet in US History

The US economy continues to expand faster than other G7 economies, but the driver is the humungous investment in AI models, data centres and all the AI-related chips and technology.

The US composite PMI (economic activity measure) rose to 58.4 fom 56 in August, the strongest expansion in private-sector activity since July 2021 and marking a fourth consecutive month of accelerating growth. The gains were driven by the service sector (including information services) with the steepest rise in output for over five years, while manufacturing also accelerated. New orders grew at the fastest pace since April 2022, while manufacturing hiring was the strongest since February 2021. [Chart]

Back in June, I commented that AI was just ‘one big trade for the US economy’. But now in September that appears to be an understatement. The AI build-out is on track to become the biggest economic bet in US history, dwarfing the investments made to fund other huge US infrastructure projects in the past, such as the railroads in the 19th century, the highway system in 20th century and the internet in the 21st century. [Chart]

Analysts estimate that capital spending at five of the so-called hyperscalers—Alphabet, Amazon.com, Meta Platforms, Microsoft and Oracle—will be $4.2 trillion in the four years ending in 2029, according to FactSet. Data-centre spending is greater than that for the canals, railroads and grid combined, projected to total $10.3 trillion from 2025 to 2032, according to new estimates by the Brookings Institution. That is a staggering average 3.6% of GDP a year. Never before has the US economy been so dependent on the build-out of a single industry. [Chart]

Up to July, $37 billion has been spent on private data-centres with most still not operating.

In contrast, US private construction spending on everything else—houses, apartment buildings, shopping centers and so on—was about $46 billion below year-earlier levels in the first seven months of this year.

  

AI investment has created 750,000 new jobs since 2023, according to LinkedIn estimates. And those jobs pay well: the median annual salary for AI-related job listings on LinkedIn is around $180,000, compared with $80,000 for all jobs.

Above all, the AI investment has led to huge gains in stock-market wealth. As of Q2 2026, US stock and mutual fund holdings came to $63 trillion, according to the Federal Reserve—nearly double the amount at the end of 2022. Most of this increase in financial wealth has gone to the already rich, as working people own little stocks or bonds. [Chart]

Foreign investors are piling into US assets. They now hold a record $39 trillion in US equities and bonds, up since 2022.. This is keeping the US dollar relatively strong and driving up stock prices. The wars in Ukraine and Iran encourage foreigners to shift their assets to the US to take advantage of the boom. [Chart]

At the same time, demand for equipment that goes into data centres like memory chips is driving up costs for tech products. Import prices on computers, peripherals (such as hard drives) and semiconductors were 20% higher in August than a year earlier. These high import prices are in turn putting upward pressure on the costs of consumer goods, such as iPhones and gaming consoles, and contributing to general inflation. [Chart]

But here is the problem. The gap between hyperscaler spending and cash flow is widening fast. Capital expenditures at Amazon, Meta, Microsoft, and Alphabet are projected to exceed $1 trillion in 2027 for the first time. At the same time, combined ‘free cash flow’ (ie money from profits in existing businesses) is projected to fall below $100 billion. A year ago, free cash flow was around $200 billion, while capex was $300 billion. Now, AI spending is accelerating at the same time as the cash available to fund it is disappearing. [Chart]

The bigger this gap becomes, the more the hyperscalers need to rely on debt and equity markets to finance their AI spend. [Chart]

The issue is that if AI spending fails to generate sufficient returns (profits), the stock market could take sharp turn downward as investors bail out. US stock market prices are massively overvalued relative to existing earnings. The trend ratio of stock market prices to earnings per share (called the CAPE ratio) is above the level just before the 2008 financial crash and nearly at the level just before the dot.com bust of 2000. [Chart]

Will profits come through? Research by Fathom Consulting shows that for the multitrillion-dollar AI boom to turn a profit, it would need the AI-related sales of the tech companies involved to rise by $600-800bn within the next two years. But the consulting firm Panmure Liberum calculated that current CAPEX and revenue forecasts through 2030 imply a negative internal rate of return on invested capital for Alphabet, Meta, Microsoft, and Oracle.

So either the hyperscalers significantly reduce their capital spending on AI to levels that generate a reasonable profit on capital already invested or by some miracle they deliver massive profitablity from a huge future increase in demand for AI products. If they cut spending, that would signal to investors that AI is not delivering and they would sell off accordingly. A crash would ensue. So they must keep spending more and more. [Chart]

At the same time, what companies can charge for AI computing costs (tokens) is falling fast. The LLM Token Expenditure Index, which tracks the market price companies pay for AI model output, has fallen to just $0.97, its lowest level since the index was created late last year and more than 50% below its summer peak. Token prices are collapsing as cheaper models, open-source Chinese competitors and falling training (inference) costs make AI usage increasingly cheap. That is eroding revenue growth for the AI labs, making it more difficult to meet the bills for AI infrastructure spend. [...]

A key question is whether AI is actually going to deliver a step-change in US labour productivity that could boost economic progress for a generation. The AI lab, Anthropic, wants to issue shares worth $100bn to the public in November (thus valuing the company at $2trn!). To build up its case, it published a report in which it claimed that if AI really takes off, US GDP could rise by 32% by 2030(!), that’s annual growth in GDP of up to 15% (against current US growth at 2.5% at best).

This is wild nonsense that assumes that AI works in boosting productivity growth as every company in the US adopts AI agents and tools to run their businesses, while sacking millions of workers who are no longer needed.

by Michael Roberts, The Next Recession | Read more:
Images: Financial Times; Commerce Dept.; uncredited

America’s Shifting Blue-Collar Landscape

Even as manufacturing jobs decline, Alaska offers lucrative work for men without college degrees.

Ten years ago, the changing geography of blue-collar jobs reshaped American politics. As the effects of the 2008 financial crisis, automation, and expanding global trade swept across the former industrial heartland, voters—male voters especially—spurned establishment figures for candidates they saw as more attuned to their concerns.

Donald Trump steamrolled to the Republican nomination in 2016 and dismantled the Democrats’ “blue wall” across states where manufacturing had suffered most. Democrats responded by nominating Joe Biden in 2020, hoping his working-class image would help reclaim voters without college degrees. Since then, both parties have made reviving manufacturing central to their economic agendas. Yet in Michigan, Wisconsin, and Pennsylvania, fewer manufacturing jobs exist today than when Trump rode down the escalator in June 2015.

The geography of blue-collar work, meanwhile, has continued to evolve. A few thousand miles northwest of the industrial heartland, Alaska offers a frontier version of that emerging economy. In a 2025 paper, economists Gordon Hanson and Enrico Moretti show that as manufacturing employment has declined, other industries have emerged as sources of good jobs for noncollege workers, particularly construction and sectors complementing tradeable, high-skill industries.

Having traveled extensively across Alaska this year to examine its industrial ecosystems, I came to see the state as a revealing case study of what blue-collar opportunity looks like when manufacturing is no longer the nation’s primary source of such jobs.

Manufacturing accounts for less than 5 percent of Alaska employment, yet the state offers unusually lucrative work for men without college degrees. My analysis of Census data finds that more than 40 percent of civilian, prime-age, noncollege Alaska men employed in blue-collar occupations earn at least $75,000. More strikingly, 9.4 percent of Alaska’s civilian, prime-age, noncollege men work a blue-collar job and earn at least $100,000, compared with 3.6 percent nationally, ranking Alaska first in the country. Among those in such occupations more than one in four earns six figures.

What explains those returns? Alaska’s abundant natural resources play a major role. Median earned income for oil-and-gas drilling workers is an astonishing $191,500, while mining operators earn $134,000, compared with the national median for blue-collar workers of $48,400. Resource extraction, like manufacturing, belongs to the tradeable sector: production occurs locally, but the output gets sold into global markets.

Those workers represent only a small blue-collar elite, however. Far more Alaskans work in transportation, construction, and equipment maintenance, and many of these occupations also command substantial premiums, particularly when tied to the resource economy. Construction-equipment operators earn a median $87,500, 50 percent more than the national median for that occupation; truck mechanics and diesel specialists, $92,000, 54 percent more. Truck drivers and carpenters each earn $58,000—11 percent and 29 percent more, respectively.

A carpenter I met on the North Slope, along Alaska’s Arctic coast, works for the region’s largest oil-and-gas operator and reported earning about $120,000 annually—a reminder that attachment to the tradeable sector, not occupation alone, often determines earnings.

But Alaska’s remarkable wages do present a puzzle. The state has the nation’s highest median earned income for civilian, prime-age, noncollege men in blue-collar jobs, at more than $63,500. Yet only 36.6 percent of such Alaska men work in blue-collar occupations—below the national average.

Part of the explanation is Alaska’s distinctive demographics. Employment among civilian, prime-age, noncollege men is relatively low, particularly for Alaska Natives living in remote communities with subsistence economies. But another factor is more revealing. My sample is restricted to Alaska residents, yet a large proportion of workers earning Alaska’s high wages don’t actually live there.

Among the men I met on the North Slope in June, many described a fly-in, fly-out lifestyle, commuting to Alaska for weeks at a time before returning home. Kevin Daems, a drilling operator with Hilcorp, lives in Montana; Alex Mosier, a maintenance worker for Schlumberger, lives in Louisiana.

According to the Alaska Department of Labor, nonresidents fill 45.2 percent of private-sector jobs on the North Slope. Statewide, nonresidents account for 22.9 percent of workers, including 23.4 percent in construction, 30.9 percent in transportation and warehousing, and more than 40 percent in resource extraction.

To understand why so many jobs go to outsiders, I spoke with Ray Weber, dean of technical and vocational education at the University of Alaska Anchorage. Weber oversees more than 40 certificate and associate-degree programs preparing Alaskans for the state’s blue-collar industries, from short programs in construction skills and marine service technology to two-year degrees in process technology and aviation maintenance. The sweet spot, he says, is six- to nine-month programs. Graduates of UAA’s six-month millwright program start at about $70,000 and typically earn six figures within five years.

What about placement? “Since Covid, we’ve just had active recruiting,” Weber says. “I don’t have any students that walk out without jobs. Many of them have it ahead of time. Holland America [a cruise line operator] sponsors eight of the diesel students to come here and finish the program. So, they’re already hired. We haven’t had an issue getting the students the employment.”

Many students already work in Alaska’s core industries and return for specialized credentials. “A lot of the people that we end up getting are coming back after they’ve been laborers or have worked on the Slope for a significant amount of time doing odd jobs and want something specific,” Weber notes.

What Weber says next, though, highlights Alaska’s blue-collar puzzle: the struggle of attracting workers in the first place. “We have more of an issue, depending on the program,” he tells me, “getting people to want to do it.”

For all the promise of Alaska’s blue-collar wages, the state struggles even to retain its own residents. Every year since 2011, more people have left Alaska for other states than have moved there from the rest of the country.

That’s no surprise to Weber. “Can [wages in Alaska] be higher [than in the rest of the country]? Yes, especially Slope jobs or jobs that suck. Like, we have electrical linemen that go across the state. They’re going to Unalakleet, the only way to get there is by airplane. And there’s one pizza joint. One. No other restaurant. You generally end up sleeping either in a bunkhouse, if you’re lucky. Or you’re sleeping in the school auditorium. . . . I like Alaska, but we’re asking the wrong questions if you’re saying, ‘high-paying, lucrative jobs.’ What do the younger generation consider important? The answer is: Things that are not in Alaska.”

Economists call this a compensating wage differential: the premium required to induce workers to accept jobs with undesirable nonpecuniary characteristics. Work in Alaska is colder, darker, lonelier, and often more dangerous. The roughly $120,000 premium earned by North Slope oil-and-gas workers over their counterparts elsewhere, and the roughly $40,000 premium earned by electricians deploying to isolated communities such as Unalakleet, are partly the price employers must pay to fill jobs few want.

by Jordan McGillis, City Journal | Read more:
Image: Bonnie Jo Mount/The Washington Post via Getty Images

[ed. Alaska has always attracted get rich quick opportunists. With Prudhoe Bay and Trans-Alaska pipeline construction, the influx of transient treasure seekers went into overdrive. Before then, there was a deeply rooted sense of community and shared experience. That solidarity doesn't exist anymore (for various reasons). By the way, Unalakleet is a wonderful and highly educated Alaskan village, one of the best.]

On “Simple Solutions” to the Western Water Problem

Spoiler: There is no such thing.

It seems like every time the Colorado River and other Western water woes make national headlines, some pundit — usually from the East Coast — weighs in with their “simple” or “obvious” solution. And I get mad and start throwing things around the room, then type out some blistering rant riddled with epithets and insults.

Well, the Colorado River is in the news again, and with it have come the usual swarm of oversimplified hot-takes. The one that really got my goat this time is from Matthew Yglesias, who wrote a piece headlined: The Western water crisis has a simple solution: Stop giving the majority of the water away to farmers for almost nothing!

These things piss me off so much not because they’re wrong, but because there is no “simple” solution to the Western water crisis. That’s because the crisis itself isn’t simple, and saying that it has simple fixes implies that the folks who have spent a lot of time trying to solve the problem are a bunch of idiots, since they hadn’t come up with this solution already. It seems as if Yglesias and his ilk assume that the West is not only monolithic, but that we Westerners are a bunch of backwoods bumpkins who don’t understand the region’s own problems.

Who cares what these bozos believe, right? Right. But judging by the comments on Yglesias’ social media posts, a lot of other people are similarly deluded. So I figured his piece provides a nice opportunity to clear some things up.

In fact, there is no single “Western water crisis.” The West is huge and is geographically, hydrographically, and ecologically diverse. While most, but not all, of the Western U.S. is in some stage of drought currently, not all of those places are experiencing a water crisis, per se. There is a Colorado River water crisis and a Rio Grande water crisis and a San Joaquin Valley water crisis, but those crises are not all the same.

The Colorado River crisis is multifaceted and manifests in different ways in different places and on different levels. The river itself is in crisis, in that stretches of the mainstem and its tributaries virtually dry up during summers like this one. Its users are facing a potential crisis, i.e. looming shortages, as in some of them are getting less water from the river than they did in the past. And the vast and complex plumbing system and legal framework that has been built up to harness and govern the river is perhaps facing the most immediate crisis: It is collapsing under its own weight and in many ways has become obsolete. Each of these is a distinct calamity, with its own twists, turns, causes, and bevy of potential solutions.

From his perch in the Northeast, however, Yglesias sees just one dimension, writing in the “nut graf” of his piece:

Even worse, the whole Western water conflict has an incredibly simple solution: End federal law’s current stipulation that a huge share of the available water must be sold at very low prices exclusively for the purpose of agriculture.

The issue here is well known, well understood, and not particularly controversial. It’s just that nobody seems to want to actually fix it.

Uh, not quite.

Yes, irrigated agriculture is by far the dominant consumer of Colorado River water, accounting for 52% of overall consumption and 74% of direct human consumption. And yes, agricultural users typically and notoriously pay less for the water than municipal users. No, this is not dictated by federal law.

Agricultural users were among the first entities in the colonial-settler era to put large quantities of water to beneficial use, giving them the most senior rights to the largest quantities of water. The Imperial Irrigation District, for example, has senior rights to about 3 million acre-feet of Colorado River water, most of which is used for agriculture.

These water rights holders have a legal right to use a set amount of water each year. They are not leasing or purchasing the water from the federal government or anyone else. It’s essentially theirs, for free, as long as they are able to divert it from the river and put it to beneficial use.

Usually the water rights are held by irrigation districts, ditch companies, municipalities, corporations, or, in some cases, very large individual landholders. In the late 1800s, these entities built their own diversions, canals, and delivery systems. Construction was often financed by private investors and the individual irrigators, who held stocks, or shares, in the ditch company. Each share would entitle its holder to a set amount of water from the canal. The shareholder would pay an annual fee for each share to cover construction, maintenance, and operating costs.

The Bureau of Reclamation was established in 1902 to build larger scale dams, diversions, and canals to deliver water to users and to spur agricultural development in the arid West. [...] 

One of the Bureau’s big projects was the 80-mile-long All-American Canal, built in the 1930s to ferry Colorado River water to the Imperial Irrigation District and other users. The IID entered into a 50-year payback contract, which ended in 1994. The Bureau still owns the canal, but the IID operates and maintains it and other infrastructure that delivers Colorado River water to nine cities and some 500,000 acres of farmland. [...]

Similar setups exist across the Colorado River Basin. But each system is distinct, and each has its own mix of federal and private funding and operational scenarios and rates.

And here’s one of the main flaws of Yglesias’s argument: He assumes that there’s some all powerful actor out there that sets rates and standards for water from the Colorado River, although he himself clearly doesn’t know who or what that actor is (he always uses the passive voice, or the all-encompassing “you” in these cases). That’s because that entity doesn’t exist. While the feds could stop subsidizing agriculture, and should not build any more water projects, they don’t have the authority to step in and adjust water rates. States probably could set rates for groundwater, but they can’t charge the IID for exercising their water rights on the Colorado River.

Next, Yglesias pivots to pillorying alfalfa, because that’s in vogue these days. I’m not going to get into that too much, because I’ve written about it so many times here before, but this little bit is pretty funny:

“The whole crisis is because of alfalfa” sounds ridiculous, in part because alfalfa is a funny word. But also because it’s so random. Wheat? That’s in bread and pasta. Everyone loves wheat. Cotton? That’s my shirt, I get why we need cotton.

But alfalfa?

Alfalfa is grown as animal feed.

Now, I’m no alfalfa apologist (nor am I an alfalfaphobe), but I feel like I should point out that most of the alfalfa grown with Colorado River water is used to feed dairy cows, which in turn produce milk and cheese and ice cream. Unless Yglesias is a vegan, he might want to consider that. He then argues that since alfalfa is relatively low-value (money wise) compared to Las Vegas casinos, Phoenix microchip factories, or housing, the water should be going to casinos, factories, and housing, not farms, which would presumably happen if farmers had to pay more for water (i.e. they’d go broke and be forced to sell).

In recent weeks I’ve written a piece or two about alfalfa. My thesis: As the biggest single water user in the Colorado River Basin, the crop must play an equally large role in contributing to the cuts necessary to keep the river from drying out. I know, it doesn’t seem like a hot-button topic. I mean, it’s just hay, after all.
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He continues:

The real point here, though, isn’t that the farmers are living high on the hog and need to be taken down a peg. You could easily let them keep their windfall water rights but turn those rights into property that they’re able to sell. Then existing cities or industrial users or real estate developers looking to build new housing or whoever else could just buy out the alfalfa farmers. The land currently under cultivation could shift to less water-intensive uses. Even a golf course uses significantly less water per acre than an alfalfa farm. Much of the land would, of course, turn into housing, but some could become non-irrigated parks with native vegetation.

First off, there is nothing novel or groundbreaking about this idea. Sprawl has been gobbling up farmland, and water rights, for decades in the West. Phoenix didn’t just grow into the desert, it also grew into citrus orchards and alfalfa fields, and will probably continue to build housing and data centers on what ag land remains. A different version of this concept, where a city or developer purchases a farmer’s water rights, but uses them somewhere else — a practice known as buy and dry — is also not uncommon.

This does not solve the water crisis. Instead of cutting consumption, which is what’s needed, it merely shifts the consumption from one use — agriculture — to other ones, such as housing and golf courses and chip manufacturing and data centers. This may wring more cash out of each acre-foot of water, but it doesn’t lower consumption. An acre-foot is an acre-foot, whether it’s going to alfalfa, a golf course, a neighborhood’s lawns, a data center or a swimming pool.

Besides, transitioning land out of agriculture comes with its own problems. We can argue forever about whether farming alfalfa or any other crop in the desert is appropriate, or of adequately high value, or whatever. We can argue that the Bureau of Reclamation should never have existed, and that irrigating the arid West should have been left to private entities operating in a free market. But the fact is, there is farming in the desert, those projects did end up subsidizing the agriculture industry, and communities, economies, cultures, and even new landscapes have emerged from and formed around those farms.

Taking vast swaths of land out of farming, whether to build houses or solar panels or to buy the water rights and use them somewhere else, has myriad consequences, many of them negative. Fields can become noxious-weed-clogged dust pits, the wildlife and ecosystems that have come to rely on irrigation runoff dry up, farm workers and families and the businesses that rely on them are displaced, communities’ economies are upended. As Madeline Wilson, an agricultural systems specialist for Colorado State University’s extension office pointed out at a 2024 panel on dust-on-snow, every field fallowed in the San Luis Valley for conservation purposes also represents a family. “We’re not just talking about drying up lands,” she said, “but the drying up of our economy.”

In no way does this mean that farms should be immune from curtailment. Indeed, because agriculture is the biggest user of Colorado River water, it will have to take the largest cuts in consumption. There is simply nowhere else from which the 4 million acre-feet or more of reductions per year needed to bring demand into line with dwindling supplies can come. But forcing farmers into bankruptcy by jacking up water rates (if there was a mechanism to do so), or prescribing “incredibly simple” policy solutions that don’t consider the complexities of the situation and its myriad moving parts or the on-the-ground impacts, isn’t the way to do it.

The “root cause” of the Colorado River crises is not alfalfa or pecans or cotton or cattle, it’s not the prior appropriation doctrine or the low price of water, it’s not overpopulation, it’s not data centers or golf courses or lawns or bad forest management or lack of or too many reservoirs. The root cause is twofold. First, there’s scarcity. The Southwest is an arid place, and climate change-exacerbated warming is drying it out more: there is less precipitation; the precipitation we do get is falling as rain, not snow; the snow we get is melting faster; and evapotranspiration is happening at a higher rate. This all leads to less water in the streams, reservoirs, rivers, and even aquifers. Secondly, there’s the chronic and long-term failure to acknowledge the scarcity, and the stubborn refusal to adapt to it and adjust consumption accordingly. [...]

There is one simple solution to the Colorado River crises: Everyone on the river has to use less water. Period. 

by Jonathan P. Thompson, The Land Desk | Read more:
Image: the author

For the Love of Money

When SpaceX went public earlier this year, the net worth of its founder, Elon Musk, shot to over $1 trillion. Since then, it has fluctuated, sometimes dipping into the mere centibillions. Still, the fact that the world managed to confer upon one person, however briefly, assets worth more than the gross domestic product of all but seventeen countries (or all the property in Houston, all the new vehicles purchased in the United States last year, every professional sports team on the planet, etc.) has been widely proclaimed as the most important story about wealth in America today.

The economists Owen Zidar and Eric Zwick would disagree. In The Everywhere Millionaire: Who Is Really Rich in America and How They Got There, the authors contend that the more important story is actually diffuse and harder to see.

There’s only one occasional trillionaire in America and fewer than one thousand billionaires, but there are more than twenty-three million millionaires. And a lot of those millionaires aren’t the ones you’re thinking of—the Wall Street moguls and Palo Alto magnates, the Tesla-driving technocrats, the bicoastal elites. Many are what Zidar and Zwick call Main Street Millionaires.

On average, they’re worth about $25 million, and most of them live not in New York City but in places like Topeka, Omaha, Baton Rouge, Mackinac Island, and Lake Minnetonka. They are the “stealthy wealthy” and they are hiding in plain sight:

They’re coaching your child’s soccer team, sitting next to you at community fundraisers, or chatting with you at neighborhood barbecues. They might be the dentist who expanded his office to a regional network of practices, the commercial HVAC contractor whose trucks you see around town, or the owner of that local restaurant chain that keeps opening new locations.

These are the “real rich” in America, Zidar and Zwick argue, and they matter much more than “a few high-profile billionaires on the coasts.” It’s an audacious claim, but one that’s hard to shoot down. The four hundred richest Americans—think of Bezos, Buffett, etc.—held about $4 trillion in wealth in 2022. The roughly three million Main Street Millionaires hold more than thirteen times as much. By investigating how these people made their fortunes, and how they wield them, Zidar and Zwick upend the consensus on wealth inequality in America. Yet their sobering conclusions aren’t enough to exorcise the authors of their Mammonism.

Only recently have we been able to make sense of the data that showed how rich Main Street Millionaires have become. For decades, the IRS collected detailed information from businesses and individuals, but they stored this data in siloed systems. Zidar and Zwick’s innovation, in 2014, was to connect those systems, building the first database that linked the tax data of businesses to that of their workers and owners. This allowed them to follow the money in ways that had once been impossible.

What they found surprised them. In 2022, America’s nine thousand C-suite public executives earned $38 billion. Not bad. But the top one percent of private business owners—the car dealers, poultry distributors, trash bag producers, franchisees—earned $570 billion, fifteen times more, despite comprising only ten times as many people.

According to Zidar and Zwick, the overlooked factor that explains this disparity is the homely entity provisioned in Title 26, Subtitle A, of the Internal Revenue Code, known as the pass-through. A pass-through is a business tax structure that includes “sole proprietorships, partnerships, limited liability companies, and S corporations,” and it is the structure preferred by Main Street Millionaires. A pass-through, unlike a C Corporation, which is the structure of most publicly traded companies in the United States, does not pay corporate or dividend taxes. Instead, the firm’s profits or losses “pass through” to the owners’ individual income taxes, where they are taxed at individual rates. And because the top individual rate has fallen below the corporate rate over the last four decades, it has become more beneficial to pay taxes as an individual. This shift has radically altered the composition of the economy: Before 1986, most business income in America was generated by C Corporations. Today, most business income is generated by pass-throughs.

Pass-through owners enjoy what one accountant calls “the best tax deal in America.” Indeed, to hear Zidar and Zwick tell it, it’s as if lawmakers forty years ago decided to make it their mission to make these people as much money as possible. They created loopholes for pass-through owners to avoid paying Medicare payroll taxes. They designed provisions to help them avoid the usual cap on state and local tax deductions. And, of course, they passed the Section 199A deduction, perhaps the Trump administration’s crowning fiscal achievement. Introduced in the 2017 Tax Cuts and Jobs Act (TCJA) and made permanent in 2025’s One Big Beautiful Bill Act, Section 199A basically allows pass-through owners not to pay taxes on a fifth of their business income. In 2017, the government estimated that this deduction alone would cost the federal government about $415 billion in revenue over a decade. In part due to this falling revenue, tax enforcement has also shifted toward an “honor system.” Whereas pass-through owners simply tell the IRS how much money their employees make, they retain some “flexibility” in reporting. Evidently they’ve opted to exercise this prerogative. Pass-through owners’ unpaid income and self-employment taxes made up about $264 billion of the estimated $600 billion gap in what taxpayers owe but haven’t paid.

by Nico Taylor, The Baffler |  Read more:

Image:The Baffler/Public Domain Pictures

[ed. See also: Americans Don’t Understand Who’s Rich (Atlantic); and, The Wealthiest—and Stealthiest—Class in America (New Yorker).]

Tell Me What “Socialism” Means

Before you tell me if it’s good or bad.

You may have noticed that political arguments often have a frustrating, circular nature. Charges and countercharges, points and counterpoints—all get made without ever seeming to converge on a shared reality. It may seem that you and your political opponents are talking past one another. Much of this, in electoral politics, is due to the fact that the discussions are in fact bad faith attacks that seek only to undermine, rather than to reach an agreement. But even good faith attempts to find common ground with political opponents frequently sink in these murky waters.

When this happens, stop and ask: What are we talking about? Are we talking about the same thing here? What is the definition of the terms that we are allegedly debating? Frequently, you will find that you and your counterpart each have a personal definition of the thing that you are arguing about, and those definitions are not the same. Your argument has not been about political substance at all; it has been about the meaning of a particular term. Failure to settle on a shared definition before you start the debate means that the debate can just go on forever in opposite directions, like two fighter jets flying past one another at different altitudes and wondering why they never get a chance to shoot down the enemy.

Nowhere in American politics does this stupid dilemma manifest itself more than the purported debate over “socialism.” Oh, you have opinions on “socialism?” And whether it is evil? Or great? And you would like to yell about them? Let me stop you right there. What exactly does that mean?

The majority of mainstream political discourse about socialism—and almost all of the discourse emanating from Republicans—is just bad faith, lowest-common-denominator red baiting, paltry attempts to wring some political advantage from the still-glowing embers of decades of Cold War anticommunism rhetoric that has soaked deep into the minds of anyone born before the turn of the century. None of this is a genuine attempt to discuss what socialism really is and what it might mean here, so I will ignore it. (A benefit of being a How Things Work reader is that I respect you enough to skip the many paragraphs of undeserved credulousness that other, less honest outlets would feel obligated to pay to this bullshit.)

What about those who do want to discuss the issue? Those who believe that they are trying to communicate something useful about whether or not socialism is desirable? In aggregate, I’m afraid, this has become one of the least productive mishmashes of disparate meanings in living memory.

Liberal billionaire Nick Hanauer writes that “Neither socialism nor MAGA is the answer” to America’s inequality crisis. Instead, he advocates for higher minimum wages, higher taxes on the rich, stronger labor unions, and less concentrated corporate power. He puts forward his proposal for “market humanism” as a counterpoint to what is on offer from politicians like Zohran Mamdani, who supports higher minimum wages, higher taxes on the rich, stronger labor unions, and less concentrated corporate power.

Bernie Sanders is America’s most famous socialist politician. He became popular by calling for higher minimum wages, higher taxes on the rich, stronger labor unions, and less concentrated corporate power. When Elizabeth Warren was running for president against Bernie, she distinguished herself from him by declaring that she was, in fact, a capitalist, and wanted not socialism, but Accountable Capitalism. Her platform included higher minimum wages, higher taxes on the rich, stronger labor unions, and less concentrated corporate power.

On the other side is Donald Trump, an avowed anti-socialist, who is now aggressively pursuing state ownership of corporations.

What is the public supposed to take from this contradictory, uninformative war of labels? Polls now show that socialism is significantly more popular than capitalism among Democrats. What that means is that we have succeeded in getting people to imagine that “socialism” means things they like, more than “capitalism” does. This is good, I guess, but it is mostly an indicator of the fact that everyone’s personal definition of these terms is so flexible to the point of meaninglessness.

The dictionary definition of socialism is “any of various egalitarian economic and political theories or movements advocating collective or governmental ownership and administration of the means of production and distribution of goods.” The Republican definition of socialism is “A laundry list of the worst abuses of totalitarian communist states; or, anything advocated by a Democrat that would cost the rich money.” The centrist definition of socialism is “anyone to our left.” The progressive capitalist definition of socialism is “other progressives who may share my values but who are sadly not economically savvy enough to understand that what we really need are higher minimum wages, higher taxes on the rich, stronger labor unions, and less concentrated corporate power.” That is, coincidentally, also more or less the definition of socialism that democratic socialists have. Each faction imagines that the others are unsophisticated, unrealistic dupes.

My purpose in writing this is not so much to try to litigate the One Correct Definition of Socialism, but rather to highlight the (critical!) fact that the big debate about the Soul of the Democratic Party or the Battle For the Faith of America or whatever is, for the most part, one big cloud of people talking past one another. The dirty secret is that most political commentators prefer it this way. It is easier to make grand proclamations about how either Free Enterprise Is Fundamental to Free People or how Capitalist Scum Must Die than to go about the tedious work of agreeing upon our terms and then having an actual policy debate.

I am a self-proclaimed socialist who writes for a socialist magazine and who just got back from attending the Socialist Conference. I promise you that socialists themselves do not have a shared definition of socialism. Much less do the people on the other side whose only reference point for socialism is “Cuba and uhhh Russia” have a shared definition of socialism. My own definition of what socialism means in the context of American politics would be something like “More public control and less private control in society, particularly in the economy, with the goal of producing greater equality.” That, I think, gets at what most of us actually mean when we talk about this stuff. (I have met few American socialists who are truly committed to the communist caricature of full state control of the economy, and even those who do will have to admit that that the path to getting there is described above.)

You will notice that even this definition is directional—it describes where we are trying to go in relation to where we are now, rather than articulating some strict Marxist benchmark that must be met. Politics itself is a directional endeavor, so this sort of definition is useful for politics. If you are an academic looking for analytical rigor above all, however, it might not satisfy you.

Who cares! If you take anything away from all of this, let it be: Almost all of America’s mainstream political debate over “socialism” is stupid. The portion of it that is not a scam on purpose is mostly a big waste of time. Do you support higher minimum wages, higher taxes on the rich, stronger labor unions, and less concentrated corporate power? Then we are on the same side. Let’s walk down that road together. If we make progress, we will reach some point where some people say, “This is far enough.” The rest of us will say, “No, we need to keep pushing this further.” At that point, we can have a debate about the actual policies in question, and their merits, and what the ideal we are trying to reach looks like. 

by Hamilton Nolan, How Things Work |  Read more:

Image: Getty

[ed. See also: Capital Is Organizing the World to Its Advantage and Everything Else Is a Sideshow (HTW):]

***

"We are all mad at the billionaires and whatnot but let’s be very clear about the mechanism driving all of this: American companies are making more money than ever and they don’t have to share it because workers don’t have the power to claim their fair share, and because some of the money has been used to purchase political influence to prevent the government from stepping in, and so therefore the money accrues to the owners, which is to say, to the rich. The richest ten percent of Americans own close to 90% of America’s stock market wealth.

This isn’t too hard to understand. I obviously didn’t invent this analysis—this is just grade school Marxism. But to reach these conclusions you don’t need to read any theory or know any buzzwords. You just need COMMON SENSE. All the money that businesses make will go either to the workers or to the owners. The goal of capital, of businesses, the logic of capitalism itself, is for businesses to try to take as close to 100% of the profits as possible for themselves. (Indeed, an amusing new economics paper shows that going to business schools causes managers to pursue exactly this mandate). This is the nature of a corporation. It will always do it, just like a fire will always want to burn. Our problem is that we have not kept corporations properly contained. So, like fire, they will consume everything.

It is far more accurate, more useful, and more true to understand politics as a battleground between capital and labor than as some sort of Democratic vs. Republican thing. Politics is just another arena for capital to organize in order to maximize its own growth."

Why the Senate Is Weirdly Obsessed with College Sports Instead of Real Problems

Lots of news in the monopoly round-up, including a disastrous turn in the Paramount-Warner as the key state attorney general lead, Rob Bonta, caves. It’s not over, but this one took a bad turn. In better news, the crypto lobby lost its main objective for this Congress, and basically collapsed in an orgy of corruption and incompetence. Fitting, that. There’s also a bunch of news on AI, and a fascinating market power story involving musician Macklemore, Ticketmaster and Israel.

But I want to start with something you may have missed, which was a procedural vote in the Senate last week to give the National Collegiate Athletic Association a special exemption from antitrust law. The NCAA is a widely loathed organization that has for decades prevented college athletes from being paid, despite them working what are essentially full-time jobs as minor league professionals. In 2020, the Supreme Court took away the NCAA’s authority to set wages for college athletes and run college sports. Now, Congress is on the verge of restoring their monopoly power. I’ve asked Katie Van Dyck, an antitrust lawyer working on sports, to lay out what’s happening.

In July, Stanford football players elected representatives and formed the first player-led chapter of the College Football Players Association. The CFBPA’s ultimate goal is collective bargaining on a conference-by-conference basis. From Ernest Cooper, a Stanford linebacker and one of the team’s elected representatives:

“As a Power 4 college football player you’re working out year-round, you’re getting paid …. I don’t see why people wouldn’t see us as employees.”

Just a few days later, the Oregon State women’s basketball team collected enough signatures to seek an election with the United College Athletes Association. They have filed a petition with the Oregon Employment Relations Board. From the university, which is opposing the effort:

“Playing on a college basketball team is not service performed for hire …. Student athletes at OSU matriculate to obtain and [sic] education and voluntarily pursue basketball as part of that experience.”

Last year, over 100 women’s basketball players wrote to Big Ten commissioner Tony Petitti and SEC commissioner Greg Sankey asking for a formal way to be heard on the rules that govern their sport. Neither agreed to meet.

The Senate is about to weigh in against these athletes’ efforts, with the Protect College Sports Act (the “PCSA”), which has been taking up valuable debating time in the House and Senate. The two lead Senators on the bill are Republican Ted Cruz from Texas, and Democrat Maria Cantwell, from Washington state. Both have very sharp elbows and have used them to move this legislation.

It passed a procedural hurdle last week, by a 74–24 margin, to proceed to a full vote, which will happen shortly. Populist politicians like Bernie Sanders and Elizabeth Warren were opposed, but the “aye” column included most of the Senate, including some surprising center-left supporters, like Senators Amy Klobuchar (D-Minn.), Ruben Gallego (D-Ariz.), Ron Wyden (D-Ore.), and co-sponsor Peter Welch (D-Vt.).

It’s worth saying upfront that it is weird that Congress is focusing on this topic to the exclusion of most other things. There are massive cost increases in health care, a war in Iran driving up prices, risky problems with artificial intelligence technology and financing, and on and on, but Congress is spending its time on… college sports.

There have been countless commercials during football season promoting this legislation. Nick Saban has made his case before the Senate and on ESPN GameDay. Amazon, Paramount, and Disney are on the Hill lobbying. Even Deion Sanders has joined the bandwagon. All of them say that college sports, a $20 billion industry and growing, are in chaos. Ted Cruz even went on ESPN GameDay, and was hilariously booed with “Ted You Suck!” chants as he pitched this legislation for ten full minutes.

Still, this lobbying campaign isn’t the sole reason Congress is focusing on college athletics to the exclusion of everything else. Another reason is that, as BIG often chronicles, the superrich tend to have their priorities addressed in our political system. And the superrich love college sports. For instance, billionaire Larry Ellison, who is right now financing the Paramount-Warner takeover, and owns TikTok and Oracle, is deeply involved in the University of Michigan’s athletic department finances, because his sixth wife is an alumni of the school.

For decades, wealthy alumni, known as “boosters,” have played a part in funding college sports, as a sort of passionate high-end hobby. Key here was that the athletes didn’t get paid, which not only reduced costs but also contributed to an endless series of scandals involving athletes paid under the table.

But this whole system got a rude awakening in 2021, when the Supreme Court unanimously rejected the NCAA’s request for “immunity from the normal operation of the antitrust laws” in its landmark NCAA v. Alston decision. It did so at least in part based on the NCAA’s admission that it “enjoy[ed] monopsony control” and was “capable of depressing wages below competitive levels.” Justice Kavanaugh wrote that “[t]he NCAA’s business model would be flatly illegal in almost any other industry in America.”

The Alston decision created a sea change in college athletics, forcing the NCAA to abandon a long-standing ban on athlete compensation within days of the opinion’s release. Before 2021, universities were only allowed to award scholarships and certain “education-related” benefits like tutors and laptops. The result was coaches and administrators earning millions while the athletes on the field brought home nothing beyond a scholarship. It was a blatantly unfair and exploitative system. The Alston decision forced the NCAA to make a change.

Today, athletes can sign name, image, and likeness (“NIL”) deals with sponsors like Nike and Gatorade. Boosters frequently set up funds to bring in star players. And starting in 2025, schools can pay athletes directly via revenue-sharing, named for the athletic department revenue that funds it. Revenue-sharing is currently capped at $20.5 million per school, but a report published by The Athletic shows that the biggest programs are spending over $50 million a year on their rosters.

Alston also ushered a wave of lawsuits challenging other NCAA rules, including those limiting the number of transfers, restricting eligibility for older players and professional athletes, and capping the amount of prize money tennis players can collect. These have frustrated coaches, administrators, and some fans alike. And it changed the way universities finance athletics, since traditionally they cross-subsidize revenue-generating sports with those that don’t bring in enough cash.

Given the massive changes in college athletics wrought by the Alston decision, universities began a big lobbying campaign. Enter the Protect College Sports Act. To its proponents, the PCSA restores balance to college sports, putting the NCAA back as the governor of the system. It limits revenue sharing, regulates NIL deals, and imposes uniform rules on transfers and recruiting. It also grants an antitrust exemption to schools to pool and sell media rights. The idea here is to “fix” college athletics and make it more sustainable. Senator Cantwell’s office even released a financial report during the first PCSA procedural vote claiming that her bill will put an end to “unsustainable athletics spending [] amplifying the broader fiscal pressures facing higher education.”

But there’s a reason athletes and labor unions are opposed. The truth is, the PCSA is the culmination of a 5-year, multi-million-dollar lobbying campaign by the NCAA to secure an antitrust exemption that will allow it to unilaterally set the rules governing athletes’ compensation and eligibility.

by Matt Stollar, BIG |  Read more:

Image: Ronald Martinez/Getty Images via
[ed. College and professional sports are Big Business personified. Reminds me of record companies and how they treat 'talent'. See also: AI Is an Elite Crime Spree (BIG).]

China and the Future of Science

The Chinese socio-political system differs from our own. From the perspective of the topic of this conference, here is the most salient distinction: the Chinese system has a telos. The Chinese party-state is fundamentally a set of goal-oriented institutions. This is not unique to China—it is in fact a distinguishing feature of all Leninist systems. I sometimes think of Leninist systems as a little bit like that bus in the movie Speed. Who here has seen it? For those who haven’t, here is basic gist of that film: an extortionist attaches a bomb to the speedometer of a bus. If the bus ever slows below 50 miles per hour, everyone blows up. So it is with your average communist system. Either it hurtles towards some clearly defined goal or things start to fall apart.

In the early days of Mao, the overarching aim of the communist system was to seize state power, first through subversion and insurgency, then through more regular combined arms warfare. In the later days of Mao the newly established Chinese state and the society it intertwined were oriented around class struggle, both at home and abroad. From the 1980s through the 2010s the Chinese system was orbited a different yet still very explicitly stated goal: getting rich. In theory, if not always in practice, every action taken by every cadre, every soldier, and every state employee was subordinate to this larger, unifying aim. We must make China rich.

That is no longer the animating telos of the Chinese system. There is a new goal, one that has been articulated with great clarity by Chairman Xi and the Chinese central committee: In 2026, the aim of China’s communist enterprise is to lead humanity through what they call “the next round of techno scientific revolution and industrial transformation.”2 The Chinese leadership believes humanity stands on the cusp of the next industrial revolution. China can only be restored to its ancestral greatness if it is the pioneer of this revolution. All machinery of party and state must bend towards this end. All 100 million members of the Communist Party of China, all 50 million government employees of the PRC, all two million soldiers of the People’s Liberation Army, and ultimately all of the 1.4 billion people that call China home must be mobilized to accomplish this aim. That is the ambition. China will be the greatest scientific power the world has ever seen—or bust.

The communists are deadly serious about their pursuit of this aim. Statistics provide one window into the seriousness of their intent. Now I don’t intend for the remainder of this speech to be a laundry list of numbers, but I think the numbers are useful for helping us see the scale of what China has already accomplished and the speed with which they have accomplished it. They are also strong signal of future intent—it is difficult to survey the numbers and not appreciate just how ironclad China’s commitment to scientific achievement really is.

Now scientific achievement is difficult to measure. One common metric is to count the so-called “high impact papers” – journal articles highly cited by other leading lights in a given scientific field. Count up these papers over the course of a year, see who wrote them, see where those authors work, and—voila!—you have a ranked list of which institutions are putting out the most high-impact science in a given year. Had you done this counting exercise in the year 2005, you would have discovered that six of the world’s ten most productive universities were in the United States. Today only one of those universities is in the United States. That university is Harvard, coming in at spot number three on the list. At spot number one? Zhejiang University.

How many of you have heard of Zhejiang University? Can I get a show of hands?

And of course, Zhejiang University is just one of the Chinese institutions on this top ten list. China claims not just the number-one spot, but also the number-two spot. And not just the number-one and number-two spots, but also the fourth, fifth, sixth, seventh, eight, ninth spots go to the Chinese.

The scientific publisher Nature makes a similar catalog on a slightly more granular level, looking at specific fields of science. According to Nature’s most recent rankings, 18 of the top 25 most productive research institutes in the physical sciences, 19 of the top 20 in geosciences, and a full 25 out of 25 in chemistry are Chinese. Only in the biosciences do American scientists still have a lead—but even on that list three of the top ten are Chinese.

The kicker is, none of that was true even just a decade ago.

The most granular analysis of all is published by the Australian Strategic Policy Institute, or ASPI. ASPI publishes a neat research tracker that surveys new publications in 74 distinct high-end technologies. Unlike the statistics I just discussed, their tracker includes research published by scientists working in national laboratories and private institutions as well as those published by academic scientists. For each category they make a list of the ten institutions that are publishing the most high-impact science in that particular topic. What have they found? For 66 of the 74 categories tracked, a majority of the institutions that are now publishing the highest-impact science are Chinese. In many areas of science the dominance is total: For example, ten of then most productive research institutions in the fields of nanoscale material manufacturing, photonic sensors, chemical coating, drone operations, automated swarms, and undersea communications are Chinese. The number is nine out of ten for work on supercapacitors, advanced composite materials, inertial navigation systems, and satellite positioning, eight out of ten in advanced optical communications, advanced radiofrequency communications, and new chemical coatings, and seven out of ten for directed energy technologies, nuclear engineering, and nuclear waste treatment.

The scale of Chinese scientific production is in part a story about people. China graduates five times the number of medical and biomedical students than we do every year, seven times the number of engineers, and two-and-a-half times the number of undergraduates with research experience in artificial intelligence. Last year China graduated almost double the number of STEM PhD students than we did—and that number is actually worse than it sounds because—depending on the exact year you do the counting—between one sixth and one fifth of our STEM graduates are themselves Chinese.

Many of these researchers go back. They go back partially because they are well compensated for doing so. They also go back because of the research opportunities afforded to them. A recent study found that returning Chinese scientists go on to become the lead author on 2.5 times more papers than their colleagues who stay in the United States.9 Many Chinese research labs have 30 or 40 people attached to them—the equivalent to a commercial research lab in the United States. Ask any scientist who has gone to China in the past three years to visit academic colleagues and they will tell you how astounded they are at the quality of the laboratory equipment and machinery that their Chinese colleagues have access to. If in the not-so-distant past Chinese localities competed with each other to lay the most asphalt, now that funding pours into laboratory equipment, scientific instruments, and advanced scientific facilities. Thus China now has the world’s most sensitive ultra-high-energy cosmic-ray detector, the world’s largest and most sensitive radio telescope, the world’s strongest steady-state magnetic field, the world’s fastest quantum computer by computational advantage, and the world’s most sensitive neutrino detector. Just yesterday an attendee at this conference informed me of another I should add to my list: the world’s largest primate medical research center.

Now I can already hear some of your objections. “Tanner, these measures don’t include classified research. They don’t include the proprietary research by private companies—that is the stuff that actually pushes technology forward. American companies are not publishing billion-dollar trade secrets in the latest journals. The Chinese scientists are under insane publish or perish pressures—they are far more likely to lie and cheat. Don’t you know Chinese scientists take part in citation cartels? Haven’t you read those bitter critiques of the new system written by China’s own disgruntled scientists?”

My main response to this: you guys have lost the thread. I am reminded of a similar style of argument we often see in AI development. Every time a new model is released people play around with it for a bit and then start to catalog the flaws of this model. But the real story, the story historians will tell a generation from now, is never about the model of the moment. What matters is movement between those moments. History is made by the trend-line. What capabilities did the models have four years ago? What capabilities do they have now? What might they reasonably be expected to have in a decade hence?

Something similar might be said for science and China.

Moreover, it is not hard so hard to find examples of dominance in the scientific literature leading to dominance in the world of applied technology. More than a decade ago Chinese researchers began to dominate academic work on batteries. Today China produces 80% of the world’s battery cells. Around that same time Chinese researchers took the lead on factory robots and factory automation. Today Chinese firms buy more robots than the rest of the world combined. They have a higher robot density than almost every other country in world, despite being the world’s second largest nation. Another example: China is the only place where the unit cost of building a new nuclear reactor has gone down over the last decade. Do you think this is unrelated to the number of nuclear engineers they train and the amount of basic research they publish on nuclear engineering? Again I beg you to look at the trend-line: In 2026 China has 102 nuclear reactors either in operation or under construction. How many did they have 15 years ago? Less than 30.

Why is China pouring so many resources into scientific and technological advance? Here is a 30-second thumbnail sketch: Xi Jinping and those around him have a specific view of history. Like most Chinese, they remember with fondness past eras when their nation was the centerpiece of human civilization. They mourn China’s fall from those grand heights. They remember with bitterness the “century of humiliation” in which their people were exploited by foreign powers and wasted by vicious warlords. The Communist Party of China was founded to save China from that fate. From its inception it has seen itself as the vehicle for the salvation of the Chinese nation and thence the restoration of this nation to its rightful place at the center of the human story.

But why was China displaced from its spot “at the center of the world stage” in the first place? Chinese intellectuals have been asking this question for more than a century. Here is their most common answer: science and technology. Today Party-affiliated thinkers often describe modern human history as punctuated by discrete revolutionary moments when a new technological regime comes into being. They call these moments “rounds of techno-scientific revolution and industrial transformation.” In such moments the material basis of human civilization changes. Those who master this transition master… well, just about everything.

by Tanner Greer, Scholar's Stage |  Read more:

Image: uncredited

[ed. Go USA. See also: The Cure for Cancer Is Not a Pure Math Benchmark (SS).]

How DraftKings Uses AI to Target the Gamblers Likeliest to Lose

About a year into his job as a data analyst at DraftKings, Jayden Butts received a new assignment.

The online gambling giant was spending hundreds of millions of dollars every year on promotional incentives: “free” betting money advertised through emails and phone alerts. But the company knew little about their effectiveness.

So in 2023, DraftKings took customer betting records and built a machine learning model, a form of artificial intelligence that seeks patterns in data, to answer the question: Who was more likely to respond to promotions by gambling — and losing — more?

Butts’ task was to test that model, prioritizing free bets and bonuses for those likely losers. Soon, a question began to gnaw at him: Aren’t many of these same people prone to addiction? “We are looking for traits and features that we can target that indicate a good investment,” he said. By strict financial logic, “the best investment would be a problem gambler.”

Butts had reason to be concerned. DraftKings makes money when gamblers lose money. And the model sought to identify those it could get to lose the most. It scored each customer based on their habits: The higher the score, the more money a gambler was likely to lose for each promotion offered.

Since Butts ran those tests, DraftKings has continued to hone its methods, using data science, to target losing gamblers with promotions that encourage more betting, according to six former employees who worked on them. At the same time, four other former employees said, DraftKings has stalled or squashed efforts to use similar technology to predict who might develop a gambling problem based on their betting activity.

Silicon Valley firms spent years analyzing every digital interaction to predict what will keep users clicking on advertisements. Now, as companies like DraftKings have made gambling accessible to millions on smartphones, they too have collected an extraordinary wealth of data.

An investigation by The New York Times shows what DraftKings has chosen to do — and not do — with that power.

The Times interviewed more than 40 former DraftKings employees and obtained internal research memos, presentations and Slack messages as well as betting records from experiments conducted on customers.

The documents show how the model Butts worked on analyzed dozens of data points for gamblers, including how frequently they played, their daily account balances and how much they typically lost compared with how much they bet. It also incorporated another model that calculated how likely a user was to stop gambling.

This betting data may also contain signs that a person is headed for trouble. Yet when employees developed a machine learning model that would have assigned users “risk scores,” the company sidelined it, according to two former employees who worked on that project.

In late 2024, DraftKings fired Butts for performance reasons, he said, amid a short blip in business that led some in the company to believe its promotional experiments weren’t working as intended. The company briefly paused some of its data science work — before revving back up again with a flurry of new machine learning projects.

Butts and five other former DraftKings employees who worked on promotional targeting told the Times they regretted building technology they now viewed as dangerous.

“It is as predatory as it sounds,” said a former DraftKings analyst who, like many interviewed for this article, requested anonymity because he feared retribution. “If you lose more, we give you more, so you keep playing more.” He quit in 2024. [...]

Promotions, which take on forms like a free bet, a “profit boost” or a deposit bonus, play a vital role in DraftKings’ business: The company brought in around $8.7 billion in gross revenue from sports and casino gamblers last year and gave out about $3 billion in promotions, according to research by Citizens Bank.

DraftKings and some competitors, including FanDuel, have boasted publicly about their use of customer data for promotions — without disclosing what those efforts entail. A DraftKings executive recently told investors that data science and analytics helped it improve its margins on promotion-driven sports bets by 13% in 2025 and that it used AI to personalize hundreds of millions of promotional dollars.

One former DraftKings data scientist who worked on promotions, Jacob Shulkin, said that they were effective because they took advantage of gamblers’ psychology. “I feel I’m getting free money,” he said, “but really, it’s dragging me back in.”

Several gamblers told the Times that promotions fueled their addictions. Bryan Biehl lost nearly $70,000 gambling online, more than half of it at DraftKings. Biehl recalled how in late 2024, when he started therapy for his addiction, his email inbox began to feel like a relapse risk.

“I would get flooded with bonuses and deposits,” Biehl said. “If you are in addiction, you are not going to say no.”

In the first two weeks of December 2024, Biehl received 40 promotions from DraftKings, emails show. He succumbed to temptation one last time on Christmas Day before putting himself on self-exclusion lists, which blocked him from gambling apps.[...]

DraftKings already had a system that weighed factors like a gambler’s skill, how much they bet and tax rates on gambling revenue in the state where they lived.

Figuring this out required machine learning. Unlike traditional data analytics, where researchers decide which patterns to look for, machine learning models can sift through hundreds of variables on their own to find combinations that help predict particular behaviors.

Data scientists had trained the new casino model on historical data. It was Butts’ job to test it on real customers. Each week, the model vacuumed up information about a user’s recent activity. The score it calculated was known internally as “elasticity,” a term borrowed from economics.

Users with below-average scores were deemed “inelastic” and marked for fewer incentives. The “elastic” bettors remained.

In September 2023, Butts tested using the elasticity model to influence promotions for about 5,000 casino players. He later expanded the tests to a larger population.

He initially thought DraftKings aimed to save money by avoiding people who were unlikely to be profitable. But he said his supervisors told him that the company did not want to reduce its promotional spending but rather to “redeploy” it. He understood this to mean the goal was to direct more promotions toward the biggest losers.

by Alex Klavens, Walt Bogdanich and Jenny Vrentas, Seattle Times/NY Times | Read more:
Image: Tony Luong/The New York Times

Just Stop the Anthropic IPO Already

[ed. At the risk of turning this into a full-blown AI-centric blog, I do think this is important information to process.]

I want to delve into the full scope of the Anthropic AI takeover of politics happening over the past week. Yesterday, the company’s CEO Dario Amodei came out and explicitly asked for antitrust laws not to apply to the biggest AI firms. His biggest rival, Sam Altman, quickly agreed. And they are suggesting this legal change just before Anthropic seeks to sell shares on the stock exchange, minting a whole series of AI millionaires and billionaires.

Why do they want to suspend antitrust laws for AI firms? Well these guys say they need the industry collectively “pace the frontier,” aka in their framing, slow development of this technology so as to reduce the probably of human extinction at the hands of autonomous swarms of AI bots. And they can’t do that, they argue, without suspending laws prohibiting price-fixing cartels.

Then the Information reported today that OpenAI, Anthropic, and Google have been having backchannel conversations about establishing an AI standards organization, which presumably would coordinate this cartel.

Let me start with a very simple point. It is already illegal to release products that hurt people. It is illegal to compete by releasing products that hurt people. If these guys are genuinely manufacturing things that kill innocent people, the FBI should be arresting them immediately. The idea that they would not only release such products, but also issue stock for the American people to invest in multi-trillion dollar initial public offerings for such ventures, as Anthropic is planning, is utter lunacy.

I wrote about this attempt to terrify us into giving away our liberties on Friday, in a piece titled “Stop Panicking About AI.” But the IPO is something I didn’t think through. Apparently they think we should all get rich building world-ending product lines.

All that said, whether Anthropic goes public isn’t just up to the people at Anthropic. I asked some former Securities and Exchange Commission officials, and they told me that the SEC effectively has the authority to block initial public offerings. Here’s how.

Every company, before it goes public, submits an S-1 initial registration statement to the SEC. And the SEC can refuse to clear it if the commission believes that it doesn’t adequately disclose the risks a corporation’s securities present to investors. Technically, the SEC could go to court and get an injunction to block the IPO, but it rarely comes to that - the lack of clearance for an S-1 is red flag for investors so companies won’t go public until they get it.

In a functional system, there would be a dozen accountants and disclosure experts with sector training going back and forth with the lawyers telling them to expand on this or that, etc. And the commissioners either themselves or on a delegated basis won’t clear it until they’re satisfied. Today, it’s more likely that Trump himself just decides. Regardless, if Anthropic goes public, it’s not just because of the corporate insiders, it’s because Trump explicitly allowed it.

If I were a member of Congress, I’d be screaming mad right now, and yelling at Trump and the SEC to stop this event which will bestow hundreds of billions of dollars of wealth on a strange doomsday cult. [...]

There is one more point to cover. There is an ongoing political campaign to do something about AI, with a large swath of elites demanding action. That includes Barack Obama, who rarely demands anything except the most banal conventional wisdom. So when he says “AI policy is critical’ to Democrats, you know that it has reached peak elite acceptance. Still, what is that ‘something?’

The basic fight is over framing, not risk. Everyone sees risk here, but the root cause differs based on your perspective.

The AI doomers want their systems to be imagined as rogue agents bent on civilizational conquest, or as some sort of inevitable new technological paradigm that needs an entirely new legal framework superseding existing inadequate laws embedded in those musty old nation-states. Amodei argued the industry should have self-regulation, some sort of antitrust exemption to collaborate across the industry, and a global agreement among AI firms within democracies on how to manage risks. These developments, to Amodei, are inevitable, no human is responsible, though we must all act quickly.

Generally, this side is winning the debate. For instance, Senator Jon Ossoff, a 2028 hopeful who generally echoes whatever seems to be the most appealing line of Trump criticism of the moment, has mostly adopted that frame. Bernie Sanders seems to have given up on his campaign against oligarchy to promote Dario Amodei’s ideas. And in the core of the Democratic establishment, this view has taken hold. For instance, here’s Senator Brian Schatz of Hawaii, the likely successor to Chuck Schumer, praising Amodei.

Brian Schatz@brianschatz 

I am still studying this but it’s a reasonable start, and takes seriously the proposition that we need real proposals that can be enacted rapidly.

Dario Amodei @DarioAmodei 

We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our
8:27 AM · Sep 12, 2026 · 42.7K Views

There are many calls to convene Congress in emergency session to act, and Trump’s advisors are trying to get him to announce immediate emergency action. Given that the big AI companies are already having discussions about coordinating their AI model development, it seems like they are just pushing for final legal permission to openly run AI as a cartel.

The debate, however, is not quite over. So what’s the alternative view? Well, the rule of law adherents look at these AI systems merely as unsafe products. As such, their request isn’t for new laws, but enforcement of existing rules. All products are subject to standard nuisance claims and other torts, unfair and deceptive practices laws, and so forth. Agents are, as Cory Doctorow notes, malfunctioning machines, or “autonomous malicious software” operated by reckless people at OpenAI and Anthropic. Moreover, it is actually illegal to build unsafe products as a method of competition, or to keep up with rivals by also creating unsafe products.

Former FTC Chair Lina Khan listed a bunch of laws that could already apply. And she let slip that state attorneys general are looking at potential criminal liability for AI CEOs.

Lina Khan@linamkhan 

Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products. We shouldn’t let discussions about new legal regimes distract from the fact that there’s no AI exemption from laws already on the books —…
11:31 AM · Sep 13, 2026 · 183K Views

(Khan did actually start enforcement against AI developers when she was Chair. And it notable that one of the very first things that Trump-Vance FTC Chair Andrew Ferguson did was set aside the penalty of an AI developer she penalized for creating unsafe and fraudulent tools.)

So who will win? Well I am fairly pessimistic, as the bludgeoning from the superrich works in crisis moments, especially when Bernie Sanders is on the side of the establishment.

But the debate doesn’t fracture on obvious partisan or factional lines. Much of the industry is going to be split on the matter. For instance, David Sacks, a generally malevolent crypto investor and technologist, is making cogent arguments, because his crew would be excluded in an OpenAI/Anthropic cartel world. He’s a die-hard Trumper and despised Khan when she ran the FTC, but he retweeted her argument here.

A lot of policymakers, such as Senators Richard Blumenthal, Rep. Ro Khanna, and others, see liability as an obvious way to shape the industry to be more safe. The Senate is also full of people who are used to blocking each others’ legislation; Maria Cantwell and Ted Cruz are trying to work together on AI safety, but are fighting over whether to preempt state laws.

There are a host of proposals out there, and the details will matter. And there is something of a stampede for an emergency session to take action. If Trump chooses to accept the need for action, then it’s likely the Anthropic/OpenAI/Google types will get what they want. If not, then the debate will continue, perhaps until the financial markets impose a different mental model.

At any rate, we can all agree that Anthropic shouldn’t go ahead with its IPO. Or at least, that’s something we should all be able to agree on.

by Matt Stollar, BIG |  Read more:

Image: via