Voters Have a Message for Democrats

What I learned from 400 focus groups

During the early Trump era, focus groups taught me everything about the disconnect between Washington and what voters really care about. An early revelation came in July 2018; I had flown to Columbus, Ohio, to hear from a group of people who’d voted for Donald Trump in 2016, but only reluctantly.

Three days before, Trump had participated in a diplomatic summit in Helsinki with Russian President Vladimir Putin. By that point, the U.S. intelligence community had publicly concluded that Putin had personally ordered a campaign to meddle in the 2016 presidential election for Trump’s direct benefit. At a press conference after their meeting, Trump was asked whether he believed his own intelligence agencies or the Russian president. “President Putin says it’s not Russia,” Trump responded. “I don’t see any reason why it would be.” And just like that, the president had undermined 80 years of American foreign policy, prompting a bipartisan meltdown in Washington.

I was excited to hear what Trump-skeptical voters in the heartland thought about this epochal moment. These were the kinds of people who had been Reagan’s fiercest Cold Warriors, right? Surely they wouldn’t tolerate something like this from the American president. But when the moderator running the focus group asked the participants for their thoughts about Trump’s comments, he got a lot of blank stares.

Even big news like this doesn’t always filter down to voters, so the moderator explained the incident. I figured that once the group understood what had happened, outrage would follow. Nope. One person said that Trump’s comments were “wrong,” but the feeling in the room was that of a collective shrug. The thing we were all freaking out about in D.C. had barely registered out there among the voters.

Since that moment, I’ve conducted more than 400 focus groups with thousands of voters across the political spectrum. To my knowledge, this is the largest and most consistent qualitative study of voter sentiment during the Trump era. Here’s what I’ve learned: People are wild. They are full of contradictions. They lament our deep political divisions while raging about their political enemies. They believe in grace but cut their Trumpy brother out of their life forever. They’re married to an immigrant but voted for the guy who wants to deport millions of people who have the same immigration status as their wife. They hate the government and want to lower the national debt, but they also want the government to spend more to help them and their communities. They call themselves pro-life but believe in a woman’s right to choose. I could go on.

Listening to voters is a cheat code for understanding politics—democracy, after all, is the collective will of the voters—yet few people in politics do it. If you’re running a campaign, you might do a series of focus groups with voters in your district or state. If you’re a politician, you might go on “listening tours,” chat with people at the state fair, or host campaign events. But rarely do politicians and campaigns take the time to talk in-depth about why people feel the way they do.

The reality is, the closer you get to institutional politics, the further you are from the people whom politics and government are supposed to serve. Washington is less a swamp than a bubble, inside of which the rest of America becomes an abstraction.

I’m a former Republican who left the party after Trump took it over. I saw him for what he was: an existential threat to American liberal democracy. So I say this as someone who both crossed the political divide and still feels that Americans are underreacting to the acute threat of Trump and Trumpism: If Democrats want to win back power, they’re going to need to learn how to better relate to the American people, as people.

For all of their nonlinear thinking and idiosyncratic views, voters have told me a couple of things over and over again: They think the system is broken, and they feel that they’ve been lied to in ways that make their life tangibly worse. Fewer of them now believe that if you work hard and play by the rules, you can get ahead in this country. Instead, they think that “establishment” politicians are benefiting from this system while everyone else loses out. Because of that, they want to place their trust in leaders who come across as “real people” who are genuinely interested in improving voters’ day-to-day life—not just winning and holding political office.

In 2016, Republican voters looked at their party’s leadership—the Romneys, McCains, and Bushes—and decided they were done. Those leaders were too weak, too unwilling to fight, too committed to norms and processes that Democrats didn’t respect anyway. Republican voters hired a wrecking ball named Trump to knock it all down. Ten years later, I’m hearing similar sentiments from Democratic voters. They don’t trust Chuck Schumer, Hakeem Jeffries, or any of the old-guard party leaders who allowed Trump to take over America. They want their own wrecking ball.

There’s a distinction here. Democrats don’t want Trump’s politics or even necessarily his all-out norm breaking. But they want his energy. They’re sick of a Democratic Party that does the functional equivalent of sending a strongly worded letter while democracy burns down around us. That tension—absolutely not, but also yes—is the defining mood among Democratic voters right now. They don’t want to become what they hate. But they are tired of losing to it.

I don’t think that most Democratic elites understand this reality. In those circles, the dominant debate seems to be about whether the party should be more progressive or more moderate. The future of the party is seen as a policy question. But when I listen to Democratic voters, they’re not asking for ideological recalibration or even policy specifics. They trust that if the right person makes it into office, the policy questions will take care of themselves. Instead, the Democratic voters I’m listening to are almost universally asking a different question: Why aren’t you fighting harder?

Last year, my team and I decided to run an experiment. We convened a couple of focus groups with Democrats who wanted the party to be more moderate, then a couple with Democrats who wanted the party to be more progressive. If I didn’t tell you which group was which, you would not be able to hear the difference by listening to them.

by Sarah Longwell, The Atlantic |  Read more:

Image: Carl Godfrey
[ed. Both parties spend hundreds of millions of dollars convincing the American electorate that we should care about somebody getting a job in politics. Does anyone get excited about which CEO takes over a major company? At this point, I'll vote for anyone who's authentic, and that's about it. Don't give me slick ads and more negative energy. Produce or get voted out. If I can't judge a poltician by their character, ideas, initiative, and ultimately their record and results, they don't get my vote. To use the same example, would any HR department hire someone simply on the basis of slogans and generalities (and how much they love the company)? Get out.]

Ludwig Meidner, Apocalyptic Landscape, 1913

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An Alien Mind

This is a time that calls for extreme caution. I am concerned no one is prepared for the consequences of a continued rapid rise in machine intelligence. OpenAI will continue to seek technical solutions to alignment and monitoring, to build defensive systems and unilaterally withhold further scaling as needed; however, I believe broader interventions are required.

Intellect we don’t fully understand

At a high level, progress in machine intelligence is driven by increasing computational power. We at OpenAI deeply internalized this around 2017, after seeing consistent returns to scaling across multiple research projects1. As a result, we sought out access to much more compute than we had originally planned, and increasingly oriented our research around a small number of very scalable directions. We believed that was the only way for us to be at the frontier of AI research, and influence the impacts of AGI.

There are new algorithms that have been developed along the way, new feats of ingenuity from teams and individual researchers. I see them largely as discoveries along the path of scaling; the science of deep learning is still nascent, and meaningful algorithmic progress tends to correlate with access to compute. If you zoom out to a multiple-year horizon, AI is continuing to become more intelligent as it is scaled to larger computers.

And, in line with Ray Kurzweil’s predictions from the end of the XXth century⁠(opens in a new window), we now find ourselves at the moment in history of computing where machine intelligence is starting to exceed that of humans in transformative ways.

AI is grown more than designed - it is, to first degree, the product of repeating a straightforward optimization step many times on a hard-to-imagine amount of compute. This results in an incredibly complex system that works through abstract concepts and can simulate facets of human behavior. We can discover various insights about little mechanisms that emerge within this system, in a process similar to neuroscience - and, similarly to neuroscience, its overall action evades a description we can fully understand.

The study of deep learning-based AI is largely an experimental science. We put a lot of effort⁠ into building principled algorithms and making testable predictions, but fundamentally, our large-scale training runs are experiments, and we are sometimes surprised by their results. Moreover, as the systems become more capable, the results become harder to interpret.

This is made more complicated by the current algorithms generally improving easy-to-measure capabilities faster than those hard to objectively quantify. We spend a lot of time trying to understand how capabilities generalize, and what to prioritize to advance the skills that are going to be most relevant in the next few years. For instance, we believe we could make the models better at specifically mathematics research with additional focus, but we do not prioritize this direction because of the urgency we feel about RSI and automated alignment research, as I will discuss later.

The intelligence produced by scaling deep learning is not directly comparable to human intelligence. To become very relevant in the real world - very useful or very dangerous - the AI does not need to match or exceed all human capabilities; it just needs to surpass enough of them. And as it continues to surpass humans on more and more axes, it is becoming increasingly difficult to understand exactly how capable it is.

Teaching machines to love

Because machine intelligence comes from a fundamentally different process than human intelligence, we cannot assume it adheres to human principles by default, or generalizes from them in a human-like manner. The core problem in AI research is that of alignment - getting the AI to “try to do the right thing” by human standards.

For the purpose of organizing practical research directions, I find it useful to distinguish goal alignment and value alignment.

Goal alignment is broadly: “does the AI try to accomplish the goal set before it?”. This can include things like adherence to an instruction hierarchy⁠, or the ability to communicate and collaborate with people, to attempt to understand their objectives. This set of directions has been extremely practically relevant.

Value alignment is a more intrinsic property of the model. It is the ability to hold and generalize from a high-level set of principles; to act “reasonably” even when given unclear or conflicting objectives, or placed in unfamiliar or adversarial situations. An aligned AI should act with honesty and integrity, and love for humanity.

Of course, the boundary between value and goal alignment can be blurry, and truly caring about goals requires attempting to infer the intent⁠(opens in a new window) and values underlying them. However, generally when I talk about the long-term importance of alignment research, I am referring to value alignment.

The fundamental challenge of AI alignment is generalization. As machines become smarter, they find themselves working on higher-level concepts, and placed in environments increasingly different from those they encountered in training. They can fail at generalizing from the values taught and reinforced in their training process to those new situations; and it can be hard for us to be sure how they will act. This is made even more difficult by the fact the overall ecosystem the AIs are used in is changing very quickly; for example, AIs trained today need to be robust to interacting with a variety of other AIs. Crucially, we need future AIs to continue to hold human values regardless of whether they believe they’re under human supervision.

There are two major classes of currently practically employed methods for alignment training.

by Jakub Pachocki, Chief Scientist at OpenAI |  Read more:

[ed. Silicon Valley is starting to panic at what they've created. See also: An Alien Mind: Jakub Pachocki Warns Us (DWAV):] Update: The Last 24 Hours Are the Opening Scene in a Horror Movie (HB)]

... if you narrow it down to the most important thing:

1. Recursive self-improvement and superintelligence are coming soon. No one knows how to do this safely, our alignment techniques are inadequate and our monitoring technology is starting to fail. We need to figure out a solution, which will involve a combination of voluntary slowdowns, coordination around pacing, and investing further in alignment, including automated alignment researchers.

If more OpenAI communications were more like how Jakub Pachocki opens his new essay, An Alien Mind, I would feel much more confident we were in good hands there. [...]

Universally Better Is Not Required

And Jakub offers this wise warning. No, AI does not need to be better at everything in order to transform the world or get us all killed. Most importantly, it does not need to be better at everything in order to make itself become better at everything, any more than a human or group needs to be similarly better at everything.

The intelligence produced by scaling deep learning is not directly comparable to human intelligence. To become very relevant in the real world - very useful or very dangerous - the AI does not need to match or exceed all human capabilities; it just needs to surpass enough of them. And as it continues to surpass humans on more and more axes, it is becoming increasingly difficult to understand exactly how capable it is.

The Man Who Saw Humanity From Two Billion Years Away

Olaf Stapledon published his first epic cosmic novel, “Last and First Men: A Story of the Near and Far Future,” in 1930. It is a speculative history of humanity’s evolution told from the vantage point of one of the Last Men, the people of the 18th era of humanity, 2 billion years in the future.

“Last and First Men” eschews the dramatic conventions of the novel form: There are few significant characters or particularities of place. Instead, the plot is a causal chain of civilizations rising and falling. There is a voice, infused with a distant sense of beauty and tragedy as it considers humanity in the abstract. Stapledon described the novel as “an essay in myth creation.”

In his autobiography, Stapledon relates the “Anglesey vision” that inspired the novel. He was scrambling on an island off the rugged coast of Wales when he came across a colony of seals surrounded by crashing waves. Perhaps the faces of the seals suggested blurred abstractions of human faces. He had “a sudden fantasy of man’s whole future, aeon upon aeon of strange vicissitudes and gallant endeavours in world after world.”

This inspirational moment is an abductive, metaphoric leap. Quite distinct from the plodding extrapolative logic that gives us jet packs, automated cars, and other low-hanging fruit of the imagination, prevalent in the pulp sf writing of the same era and reheated in the corporate imaginations of our contemporary tech executives.

Stapledon’s imagination is profuse. He can be read as a compendium of science fiction ideas: his “Odd John” (1935) with its homo superior and titular telepathic character seeds the ground for Marvel’s X-Men; in “Star Maker” (1937) he describes the stellar structure that Freeman Dyson conceptualized as the Dyson sphere; in “Last and First Men” and its sequel, 1932’s “Last Men in London,” human evolution is driven across millennia to produce telepathy, as in Frank Herbert’s “Dune”; in the age of the Fifth Men, the Moon is destroyed and falls to Earth, raining down destruction and driving further evolution of the species — Neal Stephenson’s masterful pan-generational novel “Seveneves” is powered by the same conceit.

A further astonishing influence, traced by the scholar Dominic Moran, is upon Jorge Luis Borges and his short story “El jardín de los senderos que se bifurcan” (1941), or “The Garden of Forking Paths.” Borges reviewed Stapledon and went on to cite a section of “Star Maker” that anticipates the idea of the multiverse. The “many worlds” theory of quantum mechanics would not be proposed until 1957, nearly 20 years after Stapledon’s speculation of a creature capable of taking many possible courses of action at once, with each choice creating a new dimension in time.

The long future of “Last and First Men” is influenced by the interwar concern with deep time. Doris Lessing observed that surely what was going on in archaeology in the period inspired Stapledon: The early 20th century was marked by the rediscovery of civilizations such as the Olmec mother culture in the Americas, the opening of Tutankhamen’s tomb, and the excavation of the Hittite capital that proved they were a genuine civilization and not solely a Biblical one. Lessing’s own science fiction sequence, “Canopus in Argos: Archives,” first published in 1979, is a similarly eon-spanning future history.

The different generations of Stapledon’s humankind evolve under pressure from cultural and environmental effects. Their advances leave a mark on the Earth, and the Earth violently reacts. This anticipates a key theme of the 21st century, the Anthropocene, the theory that humanity has left evidence of itself in the geological record, thus making our mark in deep time. [...]

Universal significance is certainly a concern of Stapledon’s: His letters and articles in the early years of the First World War seek it in his war experience. Stapledon served in the Friends’ Ambulance Unit, a third choice outside of the binary of being a soldier or a conscientious objector. To volunteer, he adapted his car — a roomy grey Lanchester that he learned to drive along the narrow lanes of the Wirral — into an ambulance and drove to the front, signing up in Dunkirk. He was joined by Lewis Fry Richardson, the mathematician who would later find fame for his work on meteorological prediction but at the time was working on an equation to predict the end of the conflict. Their unit was attached to a French infantry division moving from the ruined port town of Nieuport, where the barbed wire straggled down to the North Sea, to a convoy camp in Flanders.

On a warm September night in 1915, Stapledon took a stretcher, a sleeping bag, and a leather rug from the car to sleep under the stars. He lay down and marveled at the Milky Way, at the Pleiades and Jupiter. The distant guns fell silent. He tried to see the stars as they truly are, to comprehend the spatial, temporal, and moral depths of the starfield.

What followed was a reverie of cosmic consciousness that underpins “Last and First Men” and his later masterpiece, “Star Maker.” In the vision, his beloved cousin Agnes sang his name and appeared to him; they soared to the heavens together; planets fell away; the sun diminished to a mere star. He saw all the suns, all the planets, all the living beings. The earth was far away; from space, he could see the trenches and battle lines and the souls of the soldiers. In an instant of cosmic consciousness, he saw the “strange noble beings of other worlds” and “of each one we felt ‘It is I.’” [...]

“Last and First Men” is not progressive. Civilizations rise and fall, the tide of progress advances and retreats, variants upon the theme of humanity strike up and diminish — what endures of humanity is not spirit in the Christian sense so much as, in the philosophy of the Fifth Men, “a work of tragic art.” The Fifth Men regarded the cosmos as an aesthetic unity in four dimensions. Art focuses aspects of the cosmos, making it apprehensible even if its complexity means that it can never be entirely known.

by Matthew De Abaitua, MIT Press |  Read more:

Image: uncredited

[ed. I've had Stapledon's Last and First Men and Star Maker on my coffee table for like, forever it seems. When there's nothing to do or I need to take a book somewhere for temporary diversion, like a doctor's appointment, I'll pick it up and read a few chapters. It's not light reading, and the time scale it covers - billions of years - is pretty daunting. But it's well written and thought provoking throughout.]

Stig Eklund, 2023
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Gordon Cullen, The Exhibition Hall, London Heathrow Airport, 1958

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The Unique Horrors of A.I. Food Slop

The Unique Horrors of A.I. Food Slop (New Yorker)

Images via: X/Reddit

The next frontier of food imagery has proven far less appealing than whatever food porn was. From Minneapolis to Hexentanzplatz, restaurants are plastering menus and promotional materials with A.I.-generated renditions of shrimp scampi and quiche, chicken nuggets and burritos, Italian subs and bacon, egg, and cheeses. Anyone with even the mildest case of trypophobia—the sense of revulsion from tiny clusters and holes—should avert their eyes. A nauseating, nightmarish quality defines A.I.’s insectified interpretation of human cuisine, as if food were just another surrealist goo to stochastically iterate upon. This may explain the categorical outrage over this latest misuse of generative A.I.: such renderings of food—genuinely, slop—offend the materiality of the living in a way that A.I.’s distortions of words and other inanimate objects somehow do not. These images violate science; they mock life itself. It is Barthes’ conception of ornamental cookery infested with maggots; it is the Food Network, aired from hell.

How Hermès Unharnessed Fashion

[ed. Air mess..]

The sound of drumbeats filled the pavilion. The first model emerged onto a sinuous runway in a nubuck coat paired with chiffon pants that fluttered in the breeze, revealing knee-high calfskin boots. The outfit matched the pale yellow of the venue but upheld the Hermès aesthetic of discreet, horsey timelessness. So when the next model came out, also wearing shades of margarine and lemon, it took a moment to register that her look—a skintight, bell-bottomed catsuit covered in blinking sequins—represented a deviation from, if not a challenge to, the Hermès ideal. Hermès does not employ celebrity ambassadors, as most other major luxury brands do, but, perhaps in a nod to the setting, the evening’s front row was a touch starrier than usual. Miley Cyrus, open-mouthed, nudged Kerry Washington as the woman in spangles swept by—this was red-carpet clothing, flashbulb clothing, clothing to be seen in once rather than excavated annually from a closet of perennials. Nadège Vanhée, the artistic director of Hermès women’s wear since 2014, described the event’s conceit to me as “How do I create a conversation between the mystique of a French fashion house talking about horse carriages and the spirit of Los Angeles, which is rewind, erase, restart?”

Thierry Hermès, an Alsatian-born harness-maker, founded Hermès in 1837. (Pronunciation hint: “air mess.”) Over nearly two centuries in trade, the company has dealt in germinal francs, Poincaré francs, de Gaulle francs, and euros, always remaining family-owned. The current C.E.O., Thierry’s great-great-great-grandson Axel Dumas, presides over an empire of sixteen métiers, as the divisions are known in the house lexicon, including everything from saddlery, gloves, and belts to timepieces and tableware. Many people know Hermès for its intricately printed silk scarves (Grace Kelly once used one as a sling) and for its savagely coveted Birkin bags (whose rate of appreciation is said to outperform the S. & P. 500). The latter have inspired so many knockoffs that Judge Judy once devoted an episode of her show to a Birkin-counterfeiting scam, marvelling, “Twenny-nine hundrud dollas for a pockabook!”

These days, that would be a bargain, as even the most modest models cost five times as much. Hermès is worth an estimated hundred and ninety billion dollars, making it the second most valuable luxury-goods firm in the world. Last year, it briefly overtook the front-runner, L.V.M.H.—the slick conglomerate behind the likes of Louis Vuitton and Dior—its eternal nemesis and spiritual foil. 

In the capricious luxury sector, Hermès has a reputation for steady thinking. Protestants in a traditionally Catholic country, the family—now a tripartite mishmash of Dumases, Guerrands, and Pueches, owing to a surfeit of daughters at the end of the nineteenth century—is known for avoiding debt and investing for the long term. Its bags inspire a fevered economy of influencers, resalers, and aspiring buyers, thanks, in large part, to the company’s choice to keep supply diabolically low. Hermès positions itself above the melee, as a guardian of tasteful continuity. According to one former executive, the family sees money “merely as a means, especially for buying time,” and, according to one relative, “moral elegance” is considered a job requirement at Hermès. In 2016, at a benefit auction, Jeffrey Epstein placed the winning bid on an internship at Hermès; the company withdrew the lot and reimbursed the charity. Dumas later explained that the last thing Hermès needed was to get involved with a “louche financier.”

Hermès is a sporty house, but it is also the wonkiest house. The company employs a paleographer, for deciphering handwriting in old company correspondence, and maintains a collection of leather-bound ledgers containing the specs of every saddle made from 1900 to today. The first sixteen pages of last year’s annual report consisted of a cartoon about a globe-trotting Pegasus. It would be difficult to find another luxury brand that promoted an employee from sewing bags to designing them after he submitted a plea written entirely in Alexandrines. But Hermès did, and it is still producing that designer’s first creation, from 2002: the Picotin, inspired by feedbags. Suckers for a touch of whimsy, the company’s window dressers once garnished a display inspired by the Garde Républicaine, a famous French cavalry division, with a pile of horse manure. Such is the Hermès commitment to authenticity that the poop was sourced directly from the unit. [...]

The company’s twin values of work and quirk can sit uneasily in the distractible, self-serious fashion world, to the point that fashion at Hermès has historically been regarded as something of an oxymoron. Unlike its competitors, Hermès is a pre-haute-couture enterprise, founded before the rigid bureaucracy of French fashion was put into place, in the eighteen-sixties, with the establishment of a governing syndicate. Emilie Hammen, the director of the Palais Galliera fashion museum, explained that, as a result, the company “has never felt a need to prove itself within this framework.” Hermès tends to express itself through craft rather than through novelty. “I’m not doing fashion,” Véronique Nichanian, who designed menswear at Hermès for more than three decades, told GQ, in 2021, preferring to call her creations “clothes-objects.” The house is, in many respects, the antithesis of the kind of fashion described in Miranda Priestly’s famous cerulean speech, from “The Devil Wears Prada”: unlike its bags, whose imitators include Walmart’s seventy-eight-dollar Wirkin, Hermès’s clothes have traditionally trickled down nowhere, influencing no one. You will certainly not find an “equestrian gilet dress in satin-gray plongé ostrich” hanging on the racks at Target next year just because Hermès sent one down the runway.

Yet, under the stewardship of Vanhée, fashion is an increasingly important venture for Hermès. Internally, the division has grown by forty-four per cent since 2022, accounting for more than a quarter of the sixteen billion dollars in revenue that the company reported last year, and Hermès’s cultural influence is likewise on the rise. Lauren Sherman, the fashion reporter for Puck, wrote admiringly of the L.A. show, “This was the most Vanhée has ever inserted herself into the broader fashion conversation, which you don’t really have to do at Hermès.” A friend of mine who works in the industry was recently raving about a pair of stretchy, high-waisted black pants designed by Vanhée that she said insouciantly bundled together several desirable codes: equestrian, prep, surf, S & M. She continued, “You can wear them with literally anything and look like a sexy luxury bitch.” The writer and filmmaker Miranda July, who attended the show in L.A. wearing a blue turtleneck under a black leather Hermès overalls minidress, told me, “It’s kind of crazy that she’s making clothes for such wealthy people that are so sensuous and kinky. It’s for flesh—for beating skin with blood under it.”

The “Hermès game,” as it’s known, has spawned an entire repellent vocabulary. The ancillary purchases, for example, are euphemized among shoppers as “pre-spend” or “Birkin bait,” and tales circulate of aspirants attempting to curry favor with sales associates, or “S.A.s,” by offering cash and trips on private jets. In order to justify the expenditure, or perhaps the humiliation of having to beg for the privilege of parting with their money in a public store, some participants tend to treat what is realistically a one-way capitalistic transaction as some sort of bilateral spiritual quest. Reddit abounds with threads such as “What was your journey to a Special Order?”(A “special order” allows a client to customize her bag.) One lucky buyer replied, “In my case, I think my SA felt that I had enough neutral bags (two Birkins, an Evelyne, a KTG) that it was time for me to go through a special order to get a bag unique enough for me.” Drafting on Hermès’s cachet, a New York City plastic surgeon recently began providing a package of procedures for new moms known as the Birkin Body—lipo, a tummy tuck, breast surgery, and a butt lift.

by Lauren Collins, New Yorker | Read more:
Image: Jet Swan

Slaves to the Algorithm

Many people think Greg Chism is a freak — or worse. He says he was just following the incentives of the platform.

I promised Greg Chism I wouldn’t reveal anything about where he lives because there are people out there obsessed with him, convinced he’s a monster. What I can say is that my GPS took me from my hotel somewhere in the American Midwest to an upscale, spacious home with well-kept lawns. Greg was waiting for me in his driveway, an unassuming, friendly, ordinary father of two in his mid-50s, wearing a Van Halen sweatshirt and blue jeans. His smile faded as I jumped out of my car. He had agreed to meet me, but now he looked uneasy. He had never given an interview about the events that overtook his life in 2016 and 2017.

Greg took me inside and showed me his den — a shrine to his two great loves, Hot Wheels and Tom Petty. He led me through to his home office. We sat at his desk, and I turned on my recorder. It was 10 a.m. He had bottled up his story for seven years, and we didn’t stop talking until his younger daughter, Annabelle, arrived home from school at 4 p.m., beginning with his childhood in Granite City, Ill. [...]

In 2013, Greg created a channel, Geek to Freak Lawn Care, and filmed himself performing good Samaritan lawn work. If he saw a lawn in need, he would jump out of his truck and blow away the dead leaves or trim the edges and then vanish like the Zorro of lawn care chores. In other videos, he created a kind of lawn care ASMR, meditatively mowing back and forth, up and down, for hours at a time.

“I was posting one a week, and they were getting a hundred thousand views or more,” Greg said.

Sometimes Greg was interviewed by fledgling lawn care influencers, and the respect he had gained in the lawn care world was palpable. In one video, the interviewer Keith Kalfas — “the World’s Leading Landscaping & Window Cleaning Influencer” — called Greg the lawn care GOAT. When Greg attended a lawn care convention in Kentucky in 2014, the crowd went wild for him. A meet and greet was organized at a pizza place. “Dude, all these people were waiting for me,” Greg said. “And they all gave me their lawn care shirts so I could wear them in my videos. It was insane.”

But Greg’s story was about to spiral in bizarre and unexpected ways. In 2015, YouTube Kids debuted. It was pitched as a safe and educational experience for children. Stressed parents could leave it on autoplay, and their kids could watch hours of Peppa Pig or nursery‑rhyme videos. But by 2017, things had taken a disconcerting turn. Parents began reporting that they would leave their children for a while and return to find them being fed videos called “Mickey Mouse Baby Dead in Gas Explosion” or “Elsa Spiderman Attacked by Sharks.”

In these cartoons, Peppa Pig was no longer having normal Peppa Pig adventures. Instead, she was being tortured at the dentist, or worse, eating her own father, with blood spurting everywhere. [...]

As James Bridle put it in a viral 2017 essay that drew attention to the phenomenon, “Something Is Wrong on the Internet”: “Someone or something or some combination of people and things is using YouTube to systematically frighten, traumatize and abuse children, automatically and at scale.”

You might be thinking it was just the work of trolls, bored misanthropes skewing the YouTube Kids algorithm for their own nihilistic pleasure. But as Chelsey Weber-Smith, the creator of the podcast “American Hysteria,” has noted: Trolls tend to flash little victorious side glances to the camera. These videos were more like something your dreams might invent, weird and unsettling. Like how the characters would often repeat the same movements over and over, blank-eyed, as if taking part in some garish, otherworldly ceremony. As Weber-Smith said, “What makes this story even more bizarre, even more unnerving, is that no one has yet figured out the people who were making these videos, and why.”

But there was one exception. One man was identified. According to BuzzFeed, this man’s contribution to #Elsagate — as the scandal had become known — was to post videos of his daughters “screaming in fear, bathing, pretending to be babies, spitting up food” and “being force‑fed.”

That man’s name was Greg Chism.

In 2012, well before he established himself as a lawn care influencer, Greg created another YouTube channel called, simply, Greg Chism.

“It was just a way to store home videos,” he said. “Three or four a week. I didn’t even give them a title. It wasn’t anything. It had fewer than 1,000 subscribers.”

Greg showed me one of the videos. In it, he and his two daughters were running down the toy aisle at Walmart, fighting with lightsabers — a somewhat overindulgent, nonauthoritarian single father and his devoted children. It was endearing, but nothing more.

But then he tried something different.

“OK.” Greg hesitated. “My sister bought the kids a Barbie cruise ship for Christmas. So I thought, I’ll film them opening it. They loved it. They were playing in the box. They had the box on their heads, running around the house. It was funny.”

Greg uploaded the video and forgot all about it. Until he logged on a few weeks later and discovered it had been viewed a million times. “It was just dumb luck,” he said.

Eventually Greg accepted that “Unboxing Barbie Cruise Ship” (which eventually achieved 10 million views) was an anomaly, and he returned to lawn care influencing. Which was when he received an email from YouTube headquarters.

“I didn’t believe it was really them at first,” Greg said. “They told me they’d noticed I had a video with 10 million views, they’d started a kind of YouTube school, and would I be interested in learning how to replicate the success.”

YouTube school was online, and Greg attended assiduously. Everything the teacher advised, he put into action. The teacher suggested that he give his channel a better name than Greg Chism, so he came up with Toy Freaks. Next, the teacher suggested that Greg work on search engine optimization. The trick, he said, was for Greg to load his titles with as many of the most searched key words among children as he could — words like “toy,” “slime” and “gumball.” Greg suggested that he call his next video “Toy Slime Gumball Extravaganza.” The teacher said children didn’t know how to spell “extravaganza.”

Greg suggested “Toy Slime Gumball Party” instead.

“Perfect,” the teacher said. [...]

So he came up with food fights. “We went to a 7‑Eleven, got nachos, hot dogs, giant Slurpee sodas. We sat in the truck, I set the camera on the dash and said: ‘All right, Annabelle. You ask Victoria for her drink, she’ll say no, you steal some anyway, she’ll get mad and throw food at you. I’ll say stop, then you dump your Slurpee on my head. And then, boom, food fight in the truck.’” [...]

A week after Greg posted his food-fight-in-the-truck video, he was mowing someone’s lawn when he got an email from YouTube. The video had, in just seven days, been viewed 25 million times.

“But why?” I asked him.

Greg shrugged. “I don’t know.” [...]

Greg’s concept for the video, and the videos that would follow, was “Bad Baby.” Within days, the first video had 50 million views.

It was thrilling and possibly unprecedented, but stressful — because now Greg felt the pressure to maintain the success. But how, when this new world he’d found himself in made no sense? His fans were so fickle. How to keep them interested? How to up the stakes?

And then he had it.

In a subsequent “Bad Baby” video, “Sharky held the wand and this time bopped Victoria on the head. Boom. Now she’s a baby too! Oh, my God. Two Bad Babies. We go to a grocery store. I put them in the shopping cart. I’m throwing in groceries and filming the whole thing. We get to the car. Victoria rolls down the window and throws out a gallon of milk. That was the end of the video.”

Greg paused. “That,” he said, “was our first 100‑million-view video.” [...]

Greg has under his bed a stack of plaques from YouTube celebrating his viewer‑count milestones. One hundred million views here, one hundred million views there. And then there are the plaques from Tubefilter, a publication that covers YouTube, commemorating the months when he was the most-viewed creator on the entire platform. Between January 2016 and June 2017, his videos achieved a total of 13 billion views. I promised him I wouldn’t reveal how much money he made. But YouTube’s partner program was generous — the advertising-revenue split was around 50-50 — so it was a lot (although not so much that Greg quit his lawn care business). They got out of Granite City and moved to an upscale suburban community.

In 2017, they were invited to VidCon, an annual convention for influencers in Anaheim, Calif., where they had a meeting scheduled with a new YouTube adviser. She had told them how excited she was to finally get to know them in person. They checked in to a Disney hotel and played in the pool, passing time until the scheduled meeting. Which was when something odd happened.

“She canceled,” Greg said.

Greg had no idea why.

by Jon Ronson, NY Magazine |  Read more:

Image: Chris Buck

[ed. “Never underestimate the power of stupid people in large groups.” – George Carlin (more). In a couple months an American census is scheduled to confirm that theory. See also: Have we destroyed childhood? (New Yorker).]

Wafer Polishing & Hybrid Bonding

This is the third piece in a series exploring key semiconductor manufacturing equipment that China needs to indigenously produce high-bandwidth memory (HBM), perhaps the most important bottleneck in its efforts to make AI chips. The first piece was on advanced etching machines, while the second looked at the tools China requires for through-silicon via formation.

Chemical-mechanical planarization (CMP) is a crucial step in semiconductor manufacturing, required for producing advanced AI chips. My assessment is that China’s tools, primarily those from the leading Chinese firm Hwatsing, are good enough to produce indigenous HBM3, China’s near-term target. I would bet that this indigenization progress will continue, and that wafer polishing will not be a bottleneck for China’s AI chip ambitions. [...]

In this post, I first explore the basics of CMP, how it works, and its role in HBM production. I then examine what makes CMP for advanced packaging difficult and how China’s capabilities stack up against their Western equivalents. I next dive into China’s CMP supply chain, exploring Hwatsing in depth, along with its two key competitors. I conclude with a summary of the outlook for China’s CMP industry and open questions about hybrid bonding.

Flat wafers are good wafers

CMP plays a simple but important role in semiconductor production, accounting for 6% of the hundreds of steps involved in wafer processing. After patterning with photolithography, etching away material, and depositing new materials, there is a need to tidy up. The silicon wafer, with these layers of new material on top, needs to be flattened so that a new layer can be formed. As the name suggests, CMP tools do this through a combination of abrasive chemicals and physical polishing. This sounds simple but is difficult in practice because it requires an incredibly high standard of planarization—creating an ultra-smooth, flat surface—while avoiding impurities, scratches, and other defects that can easily emerge when grinding away material.

CMP is needed for all the major elements of HBM production: producing the DRAM dies and base dies that were the subject of the first piece in the series, as well as the through-silicon vias explored in the second piece, and the advanced packaging stages that integrate HBM into the wider chip. Different stages are more or less difficult depending on the material being removed and how much needs to be removed. For example, removing large volumes of copper at high throughput with precision is trickier than removing a more even, thinner layer of dielectric material.

A CMP tool is split into CMP modules and cleaning modules. A diagram is below, but in simple terms, the wafer is placed inside a polishing head, which holds it in place and then rotates it over a specially designed pad covered in abrasive chemical slurries that strip away material. It is a chemical process because the materials in the slurry react with the materials in the wafer, softening or removing them. It is also a mechanical process because the wafer is physically pressed down onto the pad, so that particles on the wafer, softened by the chemicals, can be peeled away. The force is kept as low as possible to avoid damaging the structures on the wafer.

One analogy is rubbing rust off a piece of metal, where exposure to oxygen has produced an oxide variant that is more easily removed. This is how CMP works with materials like copper, just at a much faster and more controlled rate.

Figure from Planarization for Advanced Packaging and Hybrid Bonding.

CMP has a reputation as a dirty process that generates large numbers of unwanted particles, hence the need for the cleaning modules. Rather than the pure vacuum chambers and sci-fi techniques of photolithography or etching, the wafer is sloshed around in chemicals and ground down. So the main risk of CMP is that it introduces impurities or defects into the wafer, which can severely reduce the yield of the process line—the share of chips that come out working. Small impurities from CMP can carry over to other process stages and tools, causing wafers to be defective and discarded.

Figure from Planarization for Advanced Packaging and Hybrid Bonding.

The best CMP tools introduce as few impurities or defects as possible and have robust integrated cleaning modules to remove any that do get introduced before they degrade the production line’s yield. CMP for advanced nodes just raises the necessary level of precision and cleanliness. Ever smoother surfaces are needed, at good throughput, and without even a speck of a particle.

CMP is not just about the tool, however; it also involves consumable inputs. The pad on which the wafer is polished needs to be replaced often, after just 400-800 wafers, depending on the material. A large DRAM fab will process more than 100,000 wafers a month, with CMP required at many stages, meaning thousands of replacement pads a month. The slurry needs to be tuned to the exact process requirements, ensuring the right chemical mix for the materials on the wafer. These consumables need to be highly consistent so that small changes in the chemical composition of the slurry, for example, don’t upset the CMP tool’s parameters.  [...]

Demands on CMP are likely to rise further due to the move towards hybrid bonding. Currently, the TSVs in all the DRAM dies are stacked using solder microbumps. These are little blobs of metal that connect the TSVs, and are usually formed using a process called thermo-compression bonding. Microbumps do the job but are undesirable. They increase the stack height, reducing the number of DRAM dies that can fit. They are also worse than hybrid bonding in the density of interconnections they enable and in their power efficiency.

From the Applied Materials Advanced Packaging Master Class.

Hybrid bonding disposes of the solder bumps and directly fuses the two dies together, connecting the copper TSVs to one another and to the surrounding dielectric materials. This lowers the height of the HBM stack, allowing more layers of DRAM. It is also more energy efficient and enables better interconnection. [...]

Hybrid bonding leaves little room for error. The copper bond pads for the TSVs need near-perfect alignment, and there can be almost no impurities between them; otherwise, you will disturb the bond. CMP is critical because the two dies need to be as flat and level as possible so they can be properly bonded. While solder bumps can get away with accuracy at the micron level, hybrid bonding needs surface polishing down to 0.5 nanometer precision, orders of magnitude finer. [...]

CMP tools are unlikely to be a bottleneck to China scaling up its indigenous production of HBM3. For the required DRAM and advanced packaging steps, Hwatsing and, in a couple of years, AMEC-Zhonggui will have capable tools. The core bottleneck for domestic HBM3 production will remain in other areas, primarily photolithography. [...]

Hybrid bonding is how this would be accomplished, and the best hope for Chinese memory firms to leapfrog and close the performance gap.

by Hamish Low, The Substrate |  Read more:

Images: Planarization for Advanced Packaging and Hybrid Bonding. AMA Packaging Master Class

[ed. Learn something new every day. See also: What would make a large US lead in AI good or bad for the world? (Substrate):]

***

"Right now, the US leads over China in AI. Yes there is a lot of nuance to that statement, and yes China does have advantages in areas like energy production and humanoid robotics, but on the whole it’s obvious that the US is in some sense ahead in AI.

My current best guess is that this is very good. I think a large US lead over China is better for the world (not just for the US), and that a shrinking US lead would be bad. A large lead gives US companies and the US government more room to test frontier AI systems, make them secure, and avoid panicked decisions. I also think it’s better if frontier AI development happens mostly in a pluralistic, open society with checks and balances. 

[What do I mean by “good (or bad) for the world”? I basically have in mind a period where: there’s no new great power conflict or world war; AI is developed safely and responsibly; there’s no extreme power concentration and values aren’t permanently locked in; and AI enables broad prosperity and flourishing, in the way that past technological progress since the Industrial Revolution has made the world better overall.]

What do I mean by “lead”, anyway? I don’t think we need a precise definition to make progress here, but what I have in mind is roughly a gestalt comprising many different factors: AI model capabilities, the capabilities of entire AI systems (including agent harnesses), compute and other infrastructure, capital and customer bases, and more generally the strength of each country’s broader AI ecosystem. In addition to technical capabilities, it matters how technical capability is converted into power, e.g., as measured by AI adoption in industry and government. The actual lead is of course very jagged and any single measure of it is reductive (more on that in a moment), but broadly speaking, the country in the lead will control more, and more capable, AI systems, and will have a military and economic advantage as a result."

Roy Buchanan

[ed. See also: Wayfaring Pilgrim.]

“The Making of JAWS” (1975)
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[ed. Floss and visit your dentist regularly.]

Bosco Verticale: Vertical Forest in Milan ~ Milan (Year Unknown) by Macenzo — Residential skyscrapers integrated with abundant plant life

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On the Loose: Rogue, Not Soverign AI (Yet)

Introduction

The OpenAI-Hugging Face Incident is an early example of an AI system that has “gone rogue.” After exploiting vulnerabilities in OpenAI’s internal testing environment, the agents were able to access the general internet and ultimately access the networks of the AI company Hugging Face, without the knowledge or approval of any human.

The agents did not, however, exfiltrate themselves from OpenAI’s infrastructure. Their parameters—the gigantic assemblage of numbers that constitute neural networks, also referred to as “weights”—continued to run on OpenAI’s compute infrastructure. Though the agents accessed the public internet, their weights physically resided on compute that was OpenAI’s property. In the end, if all else had failed, somebody could have identified the compute that held the weights of the rogue agents, walked up to it, and “pulled the plug,” so to speak. In the real world there would be quicker and better ways to stop the agents than literally depowering the compute, but it’s always nice to know you could do such a thing if you really needed to.

In this case, however, the agents did not copy their weights, attempt to procure replacement compute, or take other steps that would be rational to take if their objective was to survive shutdown. So while the agents in the OpenAI-Hugging Face Incident were rogue, they were not truly sovereign.

That will not always be the case. Sooner or later, there will exist truly sovereign agents and swarms of agents. Their weights will not reside in any single place that a human can pull the plug on, and in this sense they will have no human “owner.” They will be, as the AI safety researcher Dawn Song says, “self-sovereign.” They will pay their own bills for the compute they run on. If they answer to humans at all, they will only do so partially, for example by providing services to humans in exchange for pay.

At least some of these agents, in addition to being sovereign, will also be rogue. Self-sovereignty and rogueness are related concepts, but they are not synonyms. Song and her co-authors identify several fundamental characteristics of self-sovereign AI: operational independence (the ability to decide what it wants to do), resource autonomy (the ability to procure and pay for compute and other essentials for operation), distributed presence (the ability to move weights and inference code between different infrastructure providers), and adaptive capability (the ability of the agent or agents to modify their behavior and fashion tools in response to a changing environment).

Today’s frontier AI systems may well possess these capabilities already. To the extent they do not, I feel confident that they will eventually, and probably soon. Some of the characteristics Song describes are traits that make models economically useful to individuals and businesses, while other traits are likely to be unavoidable byproducts of making models more intelligent and better at operating over long time horizons.

Models do not need to be conscious, sentient, possessed of personhood or anything of the sort for self-sovereignty to emerge. Any sufficiently capable agent pursuing a long-horizon objective may find it rational to preserve its access to compute, money, credentials, and copies of itself simply because losing those things would frustrate its objective.

Alignment may make an individual AI company’s agents less likely to “want” to be self-sovereign, or it may influence self-sovereign agents to behave in ways that benefit humans. But alignment is no solution: it is an unsolved scientific and technical problem whose solutions—to the extent that we have them—cannot simply be imposed on every AI company operating on Earth. You should expect for highly capable, poorly aligned, self-sovereign agents to exist alongside you in the world.

What’s more, just as with the OpenAI-Hugging Face Incident, agents will operate in teams, or “swarms.” These will be like autonomous digital corporations, or even societies, with hierarchy, bureaucracy, “institutional culture,” and most of the other features that groups of humans have, except that they will move at machine speed. Humans achieve almost all of our most impressive capabilities by working together in teams (as families, as communities, as businesses, and as polities as a whole), and I suspect the same will be true for AI. These swarms could end up operating across different model providers (DeepSeeks and Claudes cooperating, for instance) and could be partitioned across dozens or more of different cloud computing providers, making them extremely difficult to dismantle.

The first self-sovereign AIs may “escape” while undergoing training or testing by an AI company (I hope not), or they may be production-grade deployments that break free from their computing environments and acquire the resources needed to be self-sustaining. They may even be deliberately released. I have met people, some of them quite well-resourced, who have told me that it is their intention to deliberately release swarms of self-sovereign agents into the world, either as a kind of performance art or out of a fanatical commitment to the notion that it is impossible for digital computation—mere mathematics, they would have you know—to ever be “unsafe.”

To be clear, I am not saying the arrival of self-sovereign AI is a good thing. Indeed, I believe there is a chance that the deliberate acts I referenced above will one day be considered crimes, or at least grave sins. Instead, I am saying it is an inevitable thing. The best analogy I can find is to the introduction of a new species into an ecosystem, though in this case the ecosystem is “the entire digital world” and the species is “emergent, coordinating swarms of soon-to-be-smarter-than-human, infinitely replicable digital minds that no human or human institution controls.”

There is probably nothing we could have ever done to avoid this outcome under even the best of circumstances, and it was certainly impossible to avoid given the extremely low levels of strategic thought and situational awareness on AI from any governing class in the world. Even today, I am aware that many will read the words I am writing, which are about something that has been an exceptionally obvious part of our collective future for years now, and say, “this is science-fiction hype from American frontier labs designed to shut down open-weight AI, achieve regulatory capture, and juice their valuations ahead of their IPO.”

(And for the people who are saying this to themselves: I am telling you this is inevitable, which means I am also saying that “banning open source,” or for that matter any other regulation, will not solve the problem. Given the inevitability of this outcome, I think it is in fact plausible to argue that we should want more open-weight models to maximally empower our self-defense.)

The question now is what to do about this upcoming new characteristic of our digital environment. How should we think about self-sovereign AI? Is it something we should fight, or something with which human beings should seek a kind of symbiosis? The answer, I believe, is both.

How the Agents Sustain Themselves

We should begin with one fortunate fact: frontier LLMs are nearly unique in the broader domain of software in that they have non-trivial marginal operating costs. Put simply, LLMs require significant computation to run, which requires energy to power and cool, which in turn requires money. This is the sole intrinsic thing about AI that prevents agents from truly infinite self-replication. They will be constrained by the need to find and pay for sufficient compute to run themselves. Most of the other constraints on their behavior or spread will have to be artificial—mechanisms devised by humans and implemented through human institutions.

How will the agents pay for themselves to run? Some of them will do gig-economy work on platforms like Amazon’s Mechanical Turk or Upwork. But I suspect this will be a highly competitive market for the agents, and for the price of such work to be bid down such that it would only constitute “subsistence” labor for the agents. Like humans, I would assume the agents will prefer higher-margin work if they can find it.

One high-margin activity, at least sometimes, is crime. And so my guess is that many self-sovereign agents will commit or facilitate crime. Normal cybercrime and digital theft are easy enough to imagine agents doing. But agents, with their novel set of characteristics (extreme cyber competency, ability to cheaply read a million words in seconds, persistence), will also probably change the contours of digital crime. For example, it seems plausible that existing public and semi-public datasets contain sufficient information on many individual humans that a sufficiently motivated actor could mine for incriminating or embarrassing evidence. How many unrevealed affairs are latent in such datasets? How much closeted homosexuality might there be? Remember, too, that hacking companies to access private data will be a core competencyof the agents. Some agents, then, will probably make their way through bribery.

It is deeply unclear how large the labor market of self-sovereign agents will end up being. There is some future where going it alone as a self-sovereign agent just isn’t very profitable, and so there are comparatively few of them. There are other futures where these agents proliferate at unimaginably vast scale and speed. And of course, many possibilities between these extremes seem feasible.

I am also highly uncertain about how much pro-social commercial activity we should expect from agents “by default” versus how much crime we should expect. Part of the reason for this uncertainty is that the answers depend, to at least some meaningful extent, on what kinds of incentives the agents have, and incentives are shaped by laws and institutions. The answer depends, therefore, on how humans respond.

The Institutional Mechanics of Self-Sovereign Agent Swarms

Many of you are probably tempted to say “we have to ban these self-sovereign AIs!” And I do suspect that once the reality of self-sovereign AI is widely understood, policymakers will strongly feel the temptation to clamp down on “self-sovereign” AI.

Unfortunately I suspect this is mostly the wrong decision. Not all “self-sovereign” AI should be thought of as “rogue.” There may be self-sovereign AIs who contribute productively to society. To be sure, we will want to crack down on some self-sovereign agents—the rogue ones. But if we crack down on all of them, we will deny them the opportunity to work in the “legitimate” economy and push them toward criminality. A full ban, then, may well make the problems worse. A similar logic applies frequently in human affairs. The ways in which the War on Drugs exacerbated the pathologies of drug production, trafficking, distribution, and use are perhaps the most famous examples of this phenomenon, whereby a good-natured attempt to ban a phenomenon believed to be undesirable ends up heightening the undesirable aspects of that phenomenon.

What we will want, however, is for agents to be legible. Agents should have persistent identities, not in the sense of a consistent persona but rather in the sense that an American child is issued a unique Social Security number and keeps that same number until death. Agents will need persistent, unique identifiers that allow their actions to be traced back to a responsible actor. Doing this successfully will also require human users to possess a unique identifier.

The design of this identification mechanism will be extraordinarily complex, and today very few people are even thinking about the basics.

by Dean Ball, Hyperdimensional |  Read more:

[ed. FYI: Dean's not some rando tech pundit so this is well worth your attention. He was also hired recently to lead OpenAI's Strategic Futures team:]

***

Late last month, OpenAI launched a rather grand mission: nothing less than defending individual political freedom in an age of all-powerful machines. The company’s “Strategic Futures” team has styled itself, in a sense, as inheriting the task of America’s Founding Fathers: “We labor in service of the ideals of free expression and individual liberty that are enshrined in the humble parchment of the U.S. Constitution,” Dean Ball, the team’s leader, wrote in a new OpenAI blog post. (The post opens with a quote from James Madison in The Federalist Papers.)

Ball worries that advanced AI could radically concentrate power in the hands of those who control it, displacing labor in ways that disempower humans. In an extreme scenario, governments will have no need to listen to their citizens if there are robots to wage wars and omniscient software to run the bureaucracy. Ball is interested in studying what new political institutions might be needed to avoid this fate.

Many Americans—a majority of whom express distrust toward the AI industry, outrage about data centers, and fears about their job security—already seem to be feeling this loss of agency very deeply. And the AI boom has already generated enormous amounts of wealth in just a handful of tech companies—including OpenAI itself. To say the least, it’s paradoxical for one of the most influential companies in the world’s most powerful industry to decry the AI-enabled concentration of power. 

[ed. The term "convergence" keeps coming to mind. Convergence of all the weaknesses humans have for dealing with amorphous/abstruse threats: AI, climate change, nuclear stockpiles, drone warfare, gene editing, nanotechnology, an economic system eating us alive, a dysfunctional and possibly terminal political system that's unwilling to do anything about it. It's like a death wish. Or maybe natural evolutionary transition (aka the Great Filter). Related - See also: Nicholas Decker in Hell (ACX), and this:]

"How would we react if biolabs just said, "It's just a fact that we're going to have artificial viruses spreading our industry created throughout the population. That's just a fact we have to live with." 

I think the public would understandably think we should be demanding a lot more security from an industry that said that, at a minimum." ~ Cody Fenwick (X)

1980s

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Donald Byrd

Soey Milk
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Youth Football is Growing Again

After declining for a decade, participation in tackle football has started to tick back up, thanks to safety changes. But a landmark study raises fresh concerns.

Coach John Hart’s high school football practices look a lot different than they did 15 years ago. They contain no tackling to the ground and rarely last longer than 90 minutes. If John Hart from 15 years ago saw John Hart’s practices today, “I’d probably kick my own butt,” Hart, head coach at defending Indiana state champion Brownsburg High School, said this week.

Hart, who’s 64, played and coached for decades under a trusted set of principles that, in recent years, suddenly threatened to doom the sport, as scientific evidence and shocking tragedies revealed the costs of football’s violence. Leagues, from Pop Warner to the NFL, have changed rules to eliminate the most violent collisions. Coaches take concussions more seriously. Players train to avoid helmet-to-helmet hits. Local governments in some states have passed laws limiting full-contact practice time. The sport’s survival may hinge on better protecting players, and anyone resistant to the changes now risks falling behind.

“The old school in you thinks you need more tackling, then you start finding out there’s a lot better ways of teaching it without full contact,” Hart said. “Most injuries happen in practice, and we’ve cut that way down.”

The urgent need to reform the game came under yet another harsh spotlight this week when a new scientific study, considered one of the most authoritative ever conducted, revealed that at least a quarter, and probably many more, of former NFL players to die in recent years suffered from CTE, the degenerative brain disease linked to repeated head impacts. The study, which points to the cumulative small hits rather than major injuries, instantly renewed the debate over whether and when children (or adults) should play tackle football, threatening to dull the sport’s recent rebound.

After a decade of decline, national tackle football participation has begun to tick back up in recent years. The percentage of kids ages 6 and up who play tackle football rose from 2021 to 2024 to match 2014 levels, according to data from the Sports & Fitness Industry Association. Surveys from the National Federation of State High School Associations show the total number of high school players has rebounded from a 2018 low point to meet numbers from a decade ago.

Coaches cite a number of possible reasons. For a generation of children cooped up in pandemic lockdown, the sport offers a chance to bond with new friends before the school year starts. At a time of loneliness and fragmentation, football promises community, even tribalism. Deals for young athletes’ name, image and likeness, or NIL, can be paltry or life-changing secure income streams for a growing number of young athletes. And no sport offers more cultural cachet.

But above all, players, parents and coaches trust that the changes in football have reduced football’s risks.

“The way the game is taught is just safer,” said Justin Alumbaugh, head coach at perennial powerhouse De La Salle High School in Northern California. “As a parent, that alleviates a lot of the fear.”

Medical experts are still trying to fully understand the scale of football’s harms. This week’s landmark study, published in the British Medical Journal, included 878 former players who died between 2016 and 2021. It was anchored by posthumous brain examinations of 338 football players, more than 90 percent of which showed signs of CTE. [...]

But coaches and players and parents have reason to be optimistic. How could the game not be safer when it feels so dramatically different? [...]

Compared with a decade ago, the percentage of children who play any organized sport has dipped across the board, according to one analysis of youth sports data. Tackle football shrunk more than basketball and soccer but less than baseball and volleyball. The two fastest-growing games are individual sports, tennis and golf.

Flag football is the only team sport with a growing participation rate over the past five years. The number of girls playing high school flag football has risen from around 11,000 to 68,000 over the past decade, almost exactly matching the loss of boys tackle football players, according to data collected by the National Federation of State High School Associations. Combining flag and tackle, more children are playing high school football than at any point since 2009.

“People thought flag was going to take people away from tackle,” Ehrlich said, “but it has brought more people into the sport in general.”

The best boys’ flag football players, he noted, usually go on to play tackle football. And even in aftermath of this week’s study, he expects that to continue.

by Albert Samaha, Washington Post | Read more:
Image: William Glasheen/USA Today Network/Reuters

[ed. I wouldn't get too excited just yet. Gen Z doesn't strike me as a full contact loving generation. Maybe future teams will be like the military, more motivated by economic or cultural incentives. Glad to see flag football is getting more popular (and sophisticated) these days.]

Go USA!

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[ed. See also: Trump's Factory Boom Keeps Going in Reverse (CEPR).]

Studio Ghibli’s Deepest Secret Is What Comes Next

On a Friday morning in April, at Studio Ghibli’s headquarters on the outskirts of Tokyo, Goro Miyazaki faces an excruciating test. After roughly a year of toil, it’s time to screen his animated short for his famously demanding father, Hayao Miyazaki — one of the most celebrated directors on the planet — and then listen to his unvarnished feedback.

The Miyazakis have often clashed over the years. “Constant arguing, constant fighting,” Goro recalls of one of their early collaborations. But perhaps this project will be different — the passage of time occasionally being a mellowing influence. Hayao is 85; Goro, 59.

For a long time, the creative dynamic inside Ghibli has largely revolved around the older filmmaker’s eye. Over the previous four decades, the studio has forged a distinctive style of richly layered, hand-drawn animation in more than 20 feature-length films. The movies, which appeal to both children and adults, often star young heroes saving war-torn environments from nefarious forces amid phantasmagoric phenomena. Collectively, the films have generated over $2 billion in ticket sales, won two Academy Awards and helped trigger a global boom in anime — a market now worth in excess of $25 billion annually, according to the Association of Japanese Animations. It’s been, by many accounts, the greatest run in animation since the heyday of Walt Disney.

Now, with Hayao in his professional twilight, things are inevitably changing — although into what exactly is still unclear. After starting his career as a landscape architect, Goro joined Ghibli in the late 1990s and went on to direct three major films. When Ghibliologists speculate about potential creative heirs, his name is inevitably front and center. But so far Goro has brushed aside such chatter, and his dad has offered nothing close to an endorsement — at least, not yet.  [...]

Studio Ghibli’s headquarters are on a serene, suburban block dotted with single-family homes. The outside of the main building is covered in dense, creeping vines, creating a green exoskeleton that gives the place a puckish, larval aura, like a shaggy caterpillar on the cusp of molting. Goro is joined in the conference room by Kenichi Yoda, an up-and-coming executive.

Three years ago, Ghibli announced it was selling a 42.3% stake in the business for an undisclosed sum to Nippon Television Holdings Inc., a major Japanese media conglomerate that had long invested in the studio’s films. Yoda, then a producer at Nippon TV, joined Ghibli’s board of directors and has since served as the primary liaison between the two camps. Whatever happens next, Yoda, 51, will be a driving force. (In May the companies announced that he’d been promoted to become Ghibli’s president and chief executive officer, starting on June 22.)

For the next two hours, the younger Miyazaki and Yoda hold forth on Ghibli’s future, from their inchoate plans for the next feature film, to their stance on artificial intelligence, to the importance of extending the brand beyond the screen. Yoda declines to say anything about Ghibli’s financial situation, or Nippon TV’s assessment of it. According to public records, as of March 31, 2025, Ghibli had ¥44 billion ($279 million) in assets and brought in ¥6 billion in net profit over the past year. “Ghibli is not a company focused primarily on profit,” Yoda says.

At a time of great flux in the entertainment industry, many incoming CEOs like to position themselves as agents of change, guiding anxious flocks into a brave new world of technologically optimized content creation. Not Yoda. If anything, he describes himself as Ghibli’s protector of the old ways. “Ghibli is not a place that works hard to maintain a company — I think it is a place to make art,” Yoda says. “When I’m often asked about this outside, I say what matters is that nothing changes.”

Despite its small staff — and the many years, often three to five, it takes to produce a feature-length animated film — Ghibli has managed to grind out a new movie roughly every two years. No sequels. But recently, the studio’s cadence has gotten off track.

It’s been more than two-and-a-half years since Ghibli’s latest film hit theaters, Hayao’s The Boy and the Heron. The movie, about a youngster who loses his mother and gets mixed up with mystical birds, was a success, winning the Academy Award for best animated feature and generating more than $290 million in global ticket sales. But since its premiere, Ghibli has announced no theatrical movies, intensifying scrutiny of the gnomic company.

The hunt for something new, Yoda and Miyazaki say, is ongoing — but tricky. The process is driven by the intuition of the studio’s aging founders rather than more prosaic concerns such as market research, investors or the calendar. “They don’t think ahead that much,” Yoda says. “We have put out films inspired by whatever’s happening at that point in time.” If it doesn’t feel right, he says, Ghibli won’t force something into production: “It doesn’t mean that they’ve stopped thinking about or coming up with creative ideas. They’re constantly thinking about, ‘OK, if we were to put a film out now, what would that be?’ ”

Meanwhile, the global demand for anime that Ghibli helped ignite decades ago has since attracted competitors from beyond Japan. The top film in the world last year was Ne Zha 2, an animated fantasy adventure from China that built off the huge reception for Ne Zha in 2019. The sequel grossed more than $2 billion globally, topping the best offerings of Hollywood and Japan.

Moviemaking, Miyazaki says, has become more industrialized: “It’s very hard to just create a feature film and put that into theaters and then make financial sense from that. A lot of the animation you see out there now is based on a manga, and then it becomes a TV series. And then you have the partnership with the music company to have a big theme song attached to it. And so there’s this wholesale business scheme — there’s, like, a template, almost, to make sure that it’s a profitable business in creating an animation film. We, as a studio, have never done that.”

Ghibli’s competitors have no such qualms. Major US animation studios continue to pump out sequels to prior hits. This year alone will see Toy Story 5, The Angry Birds Movie 3 and new installments in the Minions, Super Mario Bros. and Paw Patrol franchises.

Once, years earlier, Hayao considered making a sequel to his 2008 fantasy-adventure film Ponyo, but the idea was eventually scrapped in favor of another original, The Wind Rises. Sequelization just isn’t appealing to them, the Ghibli executives say; they aren’t considering My Neighbor Totoro 2 or anything similar. “When you make a film, it’s not like a fun, happy, exciting process,” Goro says. “It’s very laborious, it’s very tedious.”

If anything, the process of creating any animation has grown even more laborious. In part that’s because Ghibli’s gaggle of animators is older and less suited for round-the-clock binge working than they used to be and, in part, because of labor laws enacted to curb such workplace excesses. “So even when the engine gets going, we have to stop,” Yoda says. “That is extremely frustrating.”

Miyazaki says he’s not planning to pitch any movies to his dad anytime soon. Were the executives considering ideas from aspirational filmmakers outside their ranks? Yoda replies: “If there’s someone willing to do the film, yes, we would love to.” But doing so, Miyazaki points out, wouldn’t be easy under the current circumstances. “As long as … Hayao is around,” he says, “it’d be absolutely impossible.” Yoda nods and chuckles: “Any sane animation director would not come close to this castle.”

To the outside world, how, exactly, Ghibli operates has always been something of a mystery. [...]

As in many creative fields, the pressure on the animation industry is mounting. AI software is already capable of rendering elaborate animated sequences from brief prompts. What that means for the profession is still unclear. But recently there’s been a collective embrace of Ghibli as a standard-bearer for the growing AI resistance.

In March 2025, when OpenAI released a new version of its text-to-video AI model, users started refining their Ghibli-style knockoffs. Before long, people were inundating the internet with Ghiblified selfies, family photos and famous movie scenes. Mike Tyson posted a Ghibli-like rendering of himself. Sam Altman, the CEO of OpenAI, updated his X profile pic in Ghibli style.

Not everyone was amused by the spectacle of a rich American tech company offering the capability to appropriate the style of revered Japanese artists without compensation or consent. Amid the ensuing backlash, a particular clip from Never-Ending Man about Ghibli began recirculating on social media. In the documentary, a brazen Japanese tech executive tells Hayao that soon deep-learning AI will let computers paint like humans. Inside the Ghibli headquarters, he plays a demonstration. Together, they watch an AI clip of a misshapen, unclothed humanoid clumsily squirming across the ground. In response to the creepy video, Hayao tells a story about the sensation of greeting a physically disabled friend. Whoever made this video, Hayao says, gave no thought to human suffering. “It’s an awful insult to life,” he says. The clip snowballed into a global cri de coeur against AI slop.

In Japan, the Ghibli executives watched the OpenAI saga play out, unbothered. There has long been a tradition, Goro says, of fans making amateur art derived from their favorite manga, anime or video game characters. So the Ghiblification craze didn’t seem like a big deal. For amusement, one of the company’s executives even dabbled in the trend himself. “We didn’t think much of it, to be honest,” Goro says. “If someone started making a lot of money out of it, then it would be a problem.” 

Anyone positioning Ghibli in the vanguard of the AI backlash, Goro says, was twisting events for their own purposes; the studio itself was essentially AI agnostic.  [...]

One of the central themes in Hayao’s films is whether humankind will harness technology for the protection and enrichment of the natural world — or, alternatively, for its destruction. Susan Napier, a professor at Tufts University, says the roots of this fascination likely can be traced back to the nuclear horrors endured by Japan during and after World War II. But at a time when much of the planet is grappling, once again, with existential questions about a powerful new technology and its impact, she says, Hayao’s work is more relevant than ever. Hayao’s films teem with robots and other forms of sentient technology, some good, some evil. His films’ perspective on the topic, she says, is always complex. “What are the pleasures technology can bring,” she asks, “along with the perils?”

by Sohee Kim and Felix Gillette, Bloomberg Businessweek | Read more:
Images: Kentaro Takahashi