via:
Saturday, September 12, 2026
Cheat Like Hell
September 11, 2026
Or perhaps, with his approval ratings hitting a new low, he just wanted to stand in front of cheering crowds again.
The audience at the American Airlines Center in Dallas was sparse, and according to Kara Voght of the Wall Street Journal, many who were there had received their tickets for free from state parties or right-wing groups like Moms for Liberty. But, she wrote, “everyone wanted to watch another episode of the Trump show.”
“That I will go out with my family, my friends, I’ll do it any way—I don’t care if I’m registered or not, I’m going to try and cheat like hell like they do, they’d never, there’s never been bigger cheaters, they don’t care. I am gonna go out and I’m gonna get my friends, my family, and we are going to vote on November third or we are going to vote before that!” The crowd erupted in applause.
Once again, he promised that if the Republicans held control of the House and Senate after the midterms, he would distribute a $5,000 check to all adult U.S. citizens, a plan that would cost more than a trillion dollars even if it were legal for him to do so (which it is not).
But while the Trump show in Dallas was being broadcast to the president’s fans, reality was very much on the minds of those watching events in the Middle East.
Jacob Coxon Warns of Human Extinction and Triggers a Preference Cascade
CEOs of major AI labs, and employees of major AI labs, including OpenAI and Anthropic, often say they plan to build superintelligence soon, as in within a few years create AIs that are superior to humans at essentially all cognitive tasks.
They often warn that such AIs might kill everyone. Or that AIs might cause mass unemployment, cause cyberattacks across the internet, enable mass surveillance or risk causing any number of other highly bad things.
These warnings are consistently and directly against the interests of the labs. Yet the warnings have recently gotten a lot louder and more frequent. OpenAI has been practically screaming, for those with ears to listen, on many occasions.
A series of events, over two months and especially the last week or so, including internal observations of the pace of progress at OpenAI and also Anthropic, have freaked out everyone involved quite a lot more than they were already freaked out.
After all the events, plus statements by Dean Ball and Jakub Pachocki, we were seeing the beginnings of a preference cascade.
Then along came Jacob Coxon as the tipping point, and things took off. [...]
Jacob Coxon Resigns From Anthropic In Protest And Sounds The Alarm
Jacob Coxon spent the last three years doing pretraining research at both OpenAI and Anthropic. He has come to realize that everyone involved is being wildly irresponsible.
He warns us: They are racing straight to superintelligence and gambling with our lives. I agree with and strongly endorse his statement.
If anything he sounds like an optimist. He’s asking you to consider what the next few years will actually feel like, which means he thinks you have a few years left.
Here is Jacob Coxon doing a 5 minute interview with Anderson Cooper. He speaks well and plainly, and it is clear how much the events of the last two months have made it much easier to speak plainly to a civilian like Cooper about what is happening.
Here is Jimmy Kimmel doing four minutes on this. He gets it. How is this not the top news story on every site, indeed.
Yes, this is a common view, even if few have the courage to act.
Derek Thompson here writes about this as part of AI Safety Is Having a Moment.
If you want to see the full list of lab employee quotes from the preference cascade, I compiled them into another post today. [...]
A few days ago, I had no idea who Jacob Coxon was, and I was not alone.
The timing of a preference cascade is difficult to predict. Once they start they can happen very quickly. You don’t want to talk until you are confident others will, and at some point the evidence that others will follow can snowball and it happens. A classic concrete example was replacing Biden in 2024.
A confluence of factors, all of them downstream of the obvious actual reason, which is that there is a good chance that AI kills everyone soon.
They often warn that such AIs might kill everyone. Or that AIs might cause mass unemployment, cause cyberattacks across the internet, enable mass surveillance or risk causing any number of other highly bad things.
These warnings are consistently and directly against the interests of the labs. Yet the warnings have recently gotten a lot louder and more frequent. OpenAI has been practically screaming, for those with ears to listen, on many occasions.
A series of events, over two months and especially the last week or so, including internal observations of the pace of progress at OpenAI and also Anthropic, have freaked out everyone involved quite a lot more than they were already freaked out.
After all the events, plus statements by Dean Ball and Jakub Pachocki, we were seeing the beginnings of a preference cascade.
Then along came Jacob Coxon as the tipping point, and things took off. [...]
Jacob Coxon Resigns From Anthropic In Protest And Sounds The Alarm
Jacob Coxon spent the last three years doing pretraining research at both OpenAI and Anthropic. He has come to realize that everyone involved is being wildly irresponsible.
He warns us: They are racing straight to superintelligence and gambling with our lives. I agree with and strongly endorse his statement.
If anything he sounds like an optimist. He’s asking you to consider what the next few years will actually feel like, which means he thinks you have a few years left.
Jacob Coxon (former Anthropic and OpenAI, 160m+ views, September 8): I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.If you want Jacob Coxon’s full views, I recommend his interview with Wired’s Maxwell Zeff. This thread has extensive quotes.
Do not underestimate the power of this technology. These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources. We have all witnessed the progress in each of these domains, and progress is not slowing.
The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible - but I hear the same people express fear privately. No other human activity poses this level of danger.
A common response is “if they truly believe this, why are they still building it?” At OpenAI, many have not deeply internalized the civilizational stakes. At Anthropic, the stakes are well-understood, but they are locked in a race to get there first - they believe no one else will act responsibly, so they must do it themselves, despite the risk.
Accepting this race and entering the “endgame” is a hubristic gamble that should not be launched from a private company’s Slack. Attempting to speedrun alignment should require extraordinary confidence that there are no better trajectories available.
I am optimistic about the potential for coordination. Warning shots like the Hugging Face attack have made pacing agreements between U.S. labs more viable. I don’t feel like we’re on track to prevent a global race, which may require costly actions such as a temporary ban on improving model capabilities.
If you are a lab researcher, I urge you to consider what the next few years will actually feel like. Do you want to kick off a superintelligent RL run without a rigorous understanding of its mind? Should you put your head down because “it’s happening anyway” - or take this moment to call for different conditions?
Jacob Coxon (WSJ interview): We’re on track for a lot of the most aggressive of these scenarios where by the end of next year things could be out of control already.
Here is Jacob Coxon doing a 5 minute interview with Anderson Cooper. He speaks well and plainly, and it is clear how much the events of the last two months have made it much easier to speak plainly to a civilian like Cooper about what is happening.
Here is Jimmy Kimmel doing four minutes on this. He gets it. How is this not the top news story on every site, indeed.
Yes, this is a common view, even if few have the courage to act.
Alex Turner: I left Google DeepMind in June. Jacob is right: many researchers believe they are building something that could kill everyone on the planet. It was literally my day job to think about how to stop that.That tells you how bad Alex Turner thought DeepMind’s actions were with regard to the Department of War. His day job was that he got paid by Google to think about how to stop AI from killing everyone, and he felt morally obligated to quit in protest.
Derek Thompson here writes about this as part of AI Safety Is Having a Moment.
If you want to see the full list of lab employee quotes from the preference cascade, I compiled them into another post today. [...]
A few days ago, I had no idea who Jacob Coxon was, and I was not alone.
The timing of a preference cascade is difficult to predict. Once they start they can happen very quickly. You don’t want to talk until you are confident others will, and at some point the evidence that others will follow can snowball and it happens. A classic concrete example was replacing Biden in 2024.
Derek Thompson: what’s weird is that, in a way, this is breaking thru even more than huggingface! ... and it’s just a guy nobody had heard of reiterate a position that his CEO has said on podcasts 1,000 timesWhy was Coxon able to set off a preference cascade? How did this one break through?
A confluence of factors, all of them downstream of the obvious actual reason, which is that there is a good chance that AI kills everyone soon.
by Zvi Mowshowitz, DWAV | Read more:
[ed. Who's betting on reason and political courage to win the day? Uh, huh. First of all, agree on a temporary halt to recursive self-improvement (AIs improving AIs). Second, delay/suspend IPO's (even if most of the current economy is driven by AI spending and debt). Third, light a fire under politicans (it's been done successfully with data center buildouts). At this point, just one of these would be a big win. Here's Nate Soares, co-author of the book If Anyone Builds It, Everyone Dies: A case for courage, when speaking of AI danger (LW). Also this:]
***
Terry Pratchett: “Some humans would do anything to see if it was possible to do it. If you put a large switch in some cave somewhere, with a sign on it saying 'End-of-the-World Switch. PLEASE DO NOT TOUCH', the paint wouldn't even have time to dry."***
UPDATE: Dario Amodei (Anthropic) proposes a three-point plan. Original here (We Must Pace the Frontier.]
Friday, September 11, 2026
Humpback Whales Are Interrupting Orca Hunts to Rescue Completely Different Species
It’s not a story anyone would have believed if it weren’t so thoroughly documented. A pod of killer whales in Antarctica is chasing a Weddell seal that’s taken refuge on a small ice floe. The orcas swim shoulder to shoulder to create a wave that washes the seal off the ice and into the water. Death looks certain. And then, without warning, a pair of humpback whales arrives. The seal, panicking, swims towards them. A wave tosses it onto the chest of the closer humpback, which is by this point rolling belly-up at the surface. The humpback arches its chest out of the water to keep the seal high and dry, above the reach of the frustrated killer whales circling below. When the seal starts to slip off, the humpback gently pushes it back onto its chest with a flipper. The seal eventually escapes onto another ice floe. The orcas, denied their meal, swim off.
The marine ecologist who watched this happen in 2009 was, on his own subsequent account, so bewildered that it took him years to work out what he’d actually seen. And what he ended up finding, once he started asking other researchers whether they’d witnessed anything similar, was that it wasn’t a one-off. It’s a pattern. And it involves species the humpbacks have no obvious reason to help.
What the researchers actually documented
According to a 2017 review paper by Dr Robert Pitman of NOAA’s Southwest Fisheries Science Center, with Volker Deecke, Christine Gabriele and eleven other co-authors from research institutions around the world, published in Marine Mammal Science under the title “Humpback whales interfering when mammal-eating killer whales attack other species: Mobbing behavior and interspecific altruism?”, the team compiled 115 documented interactions between humpback whales and killer whales over a sixty-two-year period from 1951 to 2012. The interactions came from researchers, whale-watch operators and wildlife photographers across multiple ocean basins.
The numbers, once you sit with them, don’t quite behave the way you’d expect. In 57 per cent of the interactions, the humpbacks were the ones who started it. They swam towards the killer whales, not away. Ninety-five per cent of the orcas involved were mammal-eating forms rather than fish-eating ones, meaning the humpbacks were reliably distinguishing between the two ecological populations and only intervening when the killer whales were hunting warm-blooded prey. And in the interactions where humpbacks approached killer whales that were actively attacking something, 87 per cent involved a kill or a hunt in progress.
The bit that made the paper genuinely surprising is what happened next. When the humpbacks arrived at the scene of an attack, only 11 per cent of the time was the prey another humpback. The other 89 per cent of the time, the killer whales were hunting something else entirely. A seal. A sea lion. A porpoise. A grey whale calf. A minke whale. A sunfish. Across the full data set, humpbacks were documented interfering with orca attacks on ten different species, including six kinds of pinniped, three other cetacean species, and one bony fish.
The interventions weren’t casual. Humpbacks travelled up to two kilometres to reach an ongoing attack. They approached bellowing and trumpeting. They positioned themselves between the killer whales and the prey. They tail-slashed. They struck orcas with their flippers, which on a fourteen-metre humpback are capable of doing real damage. They stayed for hours in some cases, refusing to leave until the killer whales gave up. In one incident in Monterey Bay in May 2012, described in reporting by National Geographic on the specific case, at least fourteen humpbacks converged on a group of killer whales that had killed a grey whale calf, and spent hours preventing the orcas from feeding on the carcass. One humpback stationed itself directly next to the dead calf, head pointed at it, tail slashing every time a killer whale approached.
None of this makes obvious sense.
by Kiran Journals, Space Daily | Read more:
Image: via
What the researchers actually documented
According to a 2017 review paper by Dr Robert Pitman of NOAA’s Southwest Fisheries Science Center, with Volker Deecke, Christine Gabriele and eleven other co-authors from research institutions around the world, published in Marine Mammal Science under the title “Humpback whales interfering when mammal-eating killer whales attack other species: Mobbing behavior and interspecific altruism?”, the team compiled 115 documented interactions between humpback whales and killer whales over a sixty-two-year period from 1951 to 2012. The interactions came from researchers, whale-watch operators and wildlife photographers across multiple ocean basins.
The numbers, once you sit with them, don’t quite behave the way you’d expect. In 57 per cent of the interactions, the humpbacks were the ones who started it. They swam towards the killer whales, not away. Ninety-five per cent of the orcas involved were mammal-eating forms rather than fish-eating ones, meaning the humpbacks were reliably distinguishing between the two ecological populations and only intervening when the killer whales were hunting warm-blooded prey. And in the interactions where humpbacks approached killer whales that were actively attacking something, 87 per cent involved a kill or a hunt in progress.
The bit that made the paper genuinely surprising is what happened next. When the humpbacks arrived at the scene of an attack, only 11 per cent of the time was the prey another humpback. The other 89 per cent of the time, the killer whales were hunting something else entirely. A seal. A sea lion. A porpoise. A grey whale calf. A minke whale. A sunfish. Across the full data set, humpbacks were documented interfering with orca attacks on ten different species, including six kinds of pinniped, three other cetacean species, and one bony fish.
The interventions weren’t casual. Humpbacks travelled up to two kilometres to reach an ongoing attack. They approached bellowing and trumpeting. They positioned themselves between the killer whales and the prey. They tail-slashed. They struck orcas with their flippers, which on a fourteen-metre humpback are capable of doing real damage. They stayed for hours in some cases, refusing to leave until the killer whales gave up. In one incident in Monterey Bay in May 2012, described in reporting by National Geographic on the specific case, at least fourteen humpbacks converged on a group of killer whales that had killed a grey whale calf, and spent hours preventing the orcas from feeding on the carcass. One humpback stationed itself directly next to the dead calf, head pointed at it, tail slashing every time a killer whale approached.
None of this makes obvious sense.
by Kiran Journals, Space Daily | Read more:
Image: via
After Work, We’ll Have Each Other
What will people do all day? I mean, of course, if the AIs surpass us at all economically necessary forms of labor.
There’s an emerging consensus on this point, assuming we’re all still alive and not subjects of a cyberpunk feudalist dystopia. When the machines can do everything else, our relationships will be what’s left.
Relatedly, before industrialization, there was very little sense that working, in itself, could be dignified or meaningful. We were not defined by our jobs but by our participation in a densely interconnected social world. And we might be again. That’s the natural consequence of all these relationalist predictions — a world where our connections to other humans are what give us structure, identity, and meaning.
I am afraid they’re right.
Power and sex among apes
Making sweeping statements about the relationship between technology and cultural change is always a risk, and I’d rather avoid it until it’s absolutely necessary. At the same time, our degree of ignorance about the future is greatly exaggerated. If the future looks anything like the past — and there’s good reason to believe it will — then we can make progress with case studies.
Let’s start with 18th-century aristocrats. Aristocrats are (typically) rich, which we certainly will be if we have robots capable of all normal nonrelational labor. More importantly, when they did need to acquire material resources, they did it through leveraging their connections to obtain more land or a lucrative position at court — no drudgery necessary.
They did not work for pay; they certainly did not see work as a source of meaning. It’s certainly true that some of them had real obligations, like governing or killing each other, but court life in the age of absolutism is as close to a purely relational world as anything I can think of.
We also know a great deal about what mattered most to them and how they chose to occupy their time.[...]
You might be imagining glittering salons where the leading figures of the Enlightenment discussed poetry, drama, and the most pressing philosophical issues of the day. This is (mostly) a lie. Salons existed and philosophes attended them, but more for entertainment than for rational discourse. When we look at the enormous amount of written material the salonnieres and their guests produced — as French historian Antoine Lilti did — it is clear that their conversation consisted overwhelmingly of political gossip. They were almost all aristocrats, and they almost always talked about themselves. [...]
The working classes
That would be The Urban Villagers, a 1962 ethnography of working-class Italian Americans in the West End of Boston by sociologist and urban planner Herbert J. Gans. This seems like an odd comparison. Don’t the working classes, by definition, work? [...]
Gans’ use of the term “urban village” was deliberate: He believed that the West Enders had inherited the basic forms of social organization from their parents and grandparents, rural peasants from Sicily or Southern Italy. These were not people who had absorbed a typically Yankee relationship to work. Most male West Enders worked unskilled or semiskilled blue-collar jobs, and their employment was typically precarious. They did not see a fulfilling career, or indeed a career of any kind, as possible or even desirable.
It’s not that West Enders liked their dead-end jobs. They just didn’t think much about them. Work was not a central organizing activity of West End life. West Enders worked just enough to support their lifestyle, which revolved around what Gans calls the peer group. The West End, in his words, was a “peer group society.” An adult peer group might include a married couple’s same-age relatives — siblings, cousins, in-laws — and perhaps a few childhood friends, who would probably be “adopted” in as godparents to the couple’s children. These people would spend almost all of their time together, meeting in the apartment of one family or another after work and staying late into the night. The men might gamble at a local corner store — gambling is absolutely omnipresent in this world — but mostly, they talked, and talking mostly meant gossip.
This is where the parallels to old money Midwestern ladies and European aristocrats start to show. While there are any number of things these groups don’t have in common, all of them share certain features.
First, none of them see work as a source of status. West Enders do like to show off, but never about their jobs: Their talent as conversationalists in the peer group setting is much more important. Second, they are exclusive. A West Ender knows all the same people from early childhood. They’re very skeptical of outsiders, including members of other ethnic groups who’ve lived in the same neighborhood their whole lives.
Finally, they all spend an enormous amount of energy policing deviant behavior. In the West End, this usually means sexual promiscuity for women, effeminacy for men, and class aspirations for anyone. A boy might be mercilessly bullied for doing too well in school or for wanting a white-collar job. Even unusual success is frowned upon. A person’s primary source of dignity is not their own achievements, but their relationship to the group:
The major criteria for ranking, differentiating, and estimating compatibility are ingroup loyalty and conformity to established standards of personal behavior, as well as interpersonal relations. West Enders expect each other to maintain prevalent social practices and consumer styles, to marry within the ethnic — or at least the religious — group, and to reject middle-class forms of status and culture … The most significant criterion of interpersonal behavior is behavioral control — the ability to regulate one’s own needs and wishes and to defer to the needs of the group when necessary.Gans, like all good ethnographers, likes his subjects, and their way of life has a lot to like — they really do enjoy a warm, rich, close-knit social world. But he also goes out of his way to emphasize that these people are extraordinarily conformist relative to middle-class American suburbanites in the 1950s. [...]
Friendship was invented in 18th-century Scotland
Relational living comes to us naturally. It’s the most common form of social organization in human history, and millions of people around the world are perfectly happy with it. But when our personal dignity comes from our membership in a group, everything the group does reflects on us — and everything we do reflects, unavoidably, on the group. It’s not a coincidence that Russian princes and Bostonian Italian day laborers spent most of their time gossiping (or, if we want to be technical, constructing and enforcing norms of behavior). They were defending their collective reputations: the thing that mattered most to them in the world.
This is why so much contemporary discussion of work and meaning is misguided. It’s very clear that humans don’t need work or anything like it to live meaningful lives. Nor do we need the things that people who talk about “meaning” mean — individual self-actualization, personal growth, serving a greater purpose. We can get by just fine without any of them, as historically most of us have. What we absolutely can’t live without is a social framework for understanding ourselves in relation to the rest of our society and gaining the respect of our peers. The most important thing work gives us — after money, of course — is an alternative way to meet those needs. Not all jobs are pleasant or rewarding, but all of us benefit from living in a system where we can earn respect and dignity as adults from what we do, not who we know, or who we are.
And this is exactly what we see when we look at what Americans actually get out of work.
To start, most of us like it. Surveys from Pew typically find that about 85% of us are “somewhat” or “completely” satisfied with our jobs. Other long-running polls from Gallup and The Conference Board find similar results. This is true even when the jobs themselves aren’t impactful or even particularly interesting.
As the anthropologist Claudia Strauss has documented, plenty of people with very ordinary jobs report that their work is fun. But that’s not why they do it. [...]
Like all other human beings in history, workers want to be respected. But unlike members of relational societies, the things we want respect for are impersonal: our skills, our accomplishments, our contributions to a shared goal. These contributions don’t have to be earth-shatteringly significant — a waitress in Chicago or an office assistant in Grand Rapids can still feel that work matters. (Tocqueville, again: “American servants do not believe themselves degraded because they work; for around them everyone works. They do not feel debased by the idea that they receive a salary; for the President of the United States also works for a salary.”)
Moral equality is something relational societies struggle with. They tend to be more restrictive and more exclusionary. It’s nice to imagine a world where everyone is satisfied to be simply a good spouse or parent or friend, but that’s not how these things work in practice. When our dignity depends on our membership in a group, we start to police who else can join and what happens when they do. When humans get relational, we get clannish.
by Clara Collier, Asterisk | Read more:
Image:David Teniers the Younger (–1690), Smoking and drinking monkeys, c. 1660
Thursday, September 10, 2026
Liberalism Needs a New Philosophy of Immigration
Biden mostly closed the border to asylum-seekers in 2024 after he saw how mad it was making Americans, but many Democrats are now calling for the abolition of ICE. This is a somewhat popular position right now, thanks to the agency’s abuses under Trump. But if Democrats actually try to abolish federal immigration enforcement instead of just reshuffling and renaming the agencies, I predict it will take just a couple of years for Americans to realize that the difference between “open borders” and “immigration laws with no one to enforce them” is largely semantic.
What’s needed, I believe, is not just a tactical, temporary retreat. We need to stop and think why a maximal pro-immigration policy is something that we need to retreat from in the first place. We need a new concept of what a sustainable liberal immigration policy looks like.
Since I started writing, I’ve been an advocate of more immigration to the United States. This does not mean I ever favored unrestrained immigration or open borders. I’ve consistently argued that favoring skilled immigration will bring more economic benefits to native-born Americans, and be more politically palatable as well. And while I don’t hate illegal immigrants for trying to better their lot in life, I favor strong border controls, because nations have the right to decide, democratically, who gets in and out. It’s understandable when people get mad at seeing their decisions flouted.
On top of that, I’ve always remained agnostic on immigration to other countries; although I find much to admire in Canada’s points-based system, for example, I don’t think that qualifies me to decide whether more immigration is good for Canada overall. If the people of a country decide that immigration is diluting their local culture unacceptably, for instance, I think they have every right to cut it off. That’s just Westphalian sovereignty — not a perfect system, but the best system we’ve ever found for organizing humanity into geographic units. As an American, I don’t view it as my place to tell Japan, or the UK, or any other country that immigration is the right choice for them.
Despite those reservations, I’ve always thought that immigration to the U.S. is a basically good thing, and should be expanded. The economic benefits are pretty undeniable — higher tax revenue to shore up our dangerously depleted government finances, dominance in high-tech industries and innovation, and so on. And while some of the economic costs are real — local housing shortages and strains on local government finances being the two biggest ones — many of the fears are overblown. In particular, the bulk of the evidence concludes that immigrants don’t reduce wages or job opportunities for native-born Americans.
And importantly, lots of Americans share my overall positive view of immigration. If you ask Americans whether they think immigration is good or bad, most will say “good”:
And if you ask Americans whether the annual rate of immigration should be increased, decreased, or kept the same, you get a pretty even split: [...]
So I’ve never really felt like I was going out on a limb or advocating an unpopular position when it came to this issue. Sure, rightists will direct online vitriol toward anyone who supports any immigration at all, but their energy and savagery shouldn’t be mistaken for majority support.
And yet there will always be some amount of immigration — and some types of immigration — that will arouse even the most open and welcoming people to ire and make them think about shutting the doors. We saw this in the U.S. in 2023-24, when anti-immigration sentiment spiked in response to Biden’s permissive asylum policies. Yes, that sentiment crashed again when Trump took power and started committing abuses. But if liberals don’t change how we approach immigration, the sentiment will simply rise again and again, as it has so many times throughout our history. And the Donald Trump and Stephen Miller types will be right back in power — and our country will be worse off for it.
Liberals need an immigration approach that can be sustained for long periods of time — i.e., one that doesn’t enrage the public every time it manages to get in power. What would that look like?
The first thing liberals (and progressives, and Democrats, and European lefties, etc.) need to admit is that migration is not a human right. We live in a world of sovereign nation-states, and unless we find some better way of dividing up and administering the world, we will continue to live in a world of sovereign nation-states. And nations are, necessarily, exclusive clubs; they have the right to restrict immigration for any reason whatsoever.
Liberals must therefore not view borders and citizenship as annoying obstacles to be tactically circumvented or overcome; instead, we must view them as fundamental parts of the social compact that allows nations, including liberal nations, to exist in the first place. Nation-states and their laws and their police and their courts and their armies are the fundamental guarantors of the human rights that define liberalism. And for better or for worse, the ability to decide who gets in and who has to keep out is a necessary precondition for nation-states to exist.
That means we need to recognize that immigration law is legitimate and needs to be upheld. Some progressives have tried to advance the notion that being undocumented is a marginalized identity that needs to be protected and supported by the state. But this is absurd; it’s like if progressives decided that people who drive over the speed limit are a minority group.
Illegal immigrants chose to break U.S. law when they came to this country. Our democratically elected leaders made those laws, and they should be honored. We should treat illegal immigrants humanely, of course, and we should not tear local communities apart just to hunt down a few people. But there has to be some way of enforcing the law, because a law without enforcement is no law at all.
A third principle that liberals should embrace is that the purpose of immigration is to benefit the people who already live in the country that is receiving the immigrants. Immigration to America must be for the benefit of Americans. We must flatly reject any concept of immigration that sees it as a necessary sacrifice on the part of American citizens. [...]
Instead, liberals need to promote immigration because of the benefits it brings to the American citizenry. This includes economic benefits — the tax revenue, the investment, the eldercare, the innovation, the entrepreneurship, the expanded market size. But it also includes cultural benefits — the constant reinvigoration of this nation of immigrants by new waves of people with the gumption and bravery to pick up and move across the world in search of opportunity and freedom.
America has always been the country of the frontier; in order to maintain that ethos in the modern age, we need people for whom America itself is the frontier. And our founding ideals — which are very liberal ideals, of liberty and opportunity and the rights of the individual — are strengthened by people who make the conscious choice to move to a country that represents those ideals...
And if this requires us to be selective about which immigrants we bring in, then so be it. Skilled immigrants bring far more economic benefits per capita than others — so by all means, let us tilt our system toward them, as American voters of both parties want. Immigrants who don’t love and embrace American culture will probably invigorate our nation less — so by all means, let us bar immigrants who have been part of groups that see America as evil, using the law with which we once banned immigration by members of communist parties.
These are the kinds of decisions that nation-states, including liberal nation-states, are inherently entitled to make. And we should expect Europe to make different decisions than America makes. That’s perfectly OK. Because immigration is fundamentally the decision of each sovereign nation-state, it’s inevitable that we’ll make different decisions. JD Vance wants us to think that France’s immigration policy, or Germany’s, is inextricably tied to America’s. But it’s not, and we shouldn’t let him get away with conflating the two.
For America, the kind of immigration agenda I’ve sketched out — which all the polls show Americans favor by substantial margins — would not be a “far right” policy, even though some progressives and leftists will inevitably try to label it as such. In fact, it would allow in far more immigration than the policies of Franklin D. Roosevelt or Harry Truman. It won’t satisfy progressives who dream of a borderless world, or leftists who salivate over the chance to make the West pay for colonialism. But I believe it will preserve America as the kind of liberal nation-state that we knew it as in the days before Trump.
As for the rest of the world, countries like Germany and Canada and Japan have to make their own decisions. I can’t tell them what kind of nation to build; I can only try to promise that as an American, I’ll respect their decision. If they want to shut their doors in order to slow the pace of cultural change, it’s not my job to lecture them otherwise.
What’s needed, I believe, is not just a tactical, temporary retreat. We need to stop and think why a maximal pro-immigration policy is something that we need to retreat from in the first place. We need a new concept of what a sustainable liberal immigration policy looks like.
Since I started writing, I’ve been an advocate of more immigration to the United States. This does not mean I ever favored unrestrained immigration or open borders. I’ve consistently argued that favoring skilled immigration will bring more economic benefits to native-born Americans, and be more politically palatable as well. And while I don’t hate illegal immigrants for trying to better their lot in life, I favor strong border controls, because nations have the right to decide, democratically, who gets in and out. It’s understandable when people get mad at seeing their decisions flouted.
On top of that, I’ve always remained agnostic on immigration to other countries; although I find much to admire in Canada’s points-based system, for example, I don’t think that qualifies me to decide whether more immigration is good for Canada overall. If the people of a country decide that immigration is diluting their local culture unacceptably, for instance, I think they have every right to cut it off. That’s just Westphalian sovereignty — not a perfect system, but the best system we’ve ever found for organizing humanity into geographic units. As an American, I don’t view it as my place to tell Japan, or the UK, or any other country that immigration is the right choice for them.
Despite those reservations, I’ve always thought that immigration to the U.S. is a basically good thing, and should be expanded. The economic benefits are pretty undeniable — higher tax revenue to shore up our dangerously depleted government finances, dominance in high-tech industries and innovation, and so on. And while some of the economic costs are real — local housing shortages and strains on local government finances being the two biggest ones — many of the fears are overblown. In particular, the bulk of the evidence concludes that immigrants don’t reduce wages or job opportunities for native-born Americans.
And importantly, lots of Americans share my overall positive view of immigration. If you ask Americans whether they think immigration is good or bad, most will say “good”:
Source: Gallup
And if you ask Americans whether the annual rate of immigration should be increased, decreased, or kept the same, you get a pretty even split: [...]
So I’ve never really felt like I was going out on a limb or advocating an unpopular position when it came to this issue. Sure, rightists will direct online vitriol toward anyone who supports any immigration at all, but their energy and savagery shouldn’t be mistaken for majority support.
And yet there will always be some amount of immigration — and some types of immigration — that will arouse even the most open and welcoming people to ire and make them think about shutting the doors. We saw this in the U.S. in 2023-24, when anti-immigration sentiment spiked in response to Biden’s permissive asylum policies. Yes, that sentiment crashed again when Trump took power and started committing abuses. But if liberals don’t change how we approach immigration, the sentiment will simply rise again and again, as it has so many times throughout our history. And the Donald Trump and Stephen Miller types will be right back in power — and our country will be worse off for it.
Liberals need an immigration approach that can be sustained for long periods of time — i.e., one that doesn’t enrage the public every time it manages to get in power. What would that look like?
The first thing liberals (and progressives, and Democrats, and European lefties, etc.) need to admit is that migration is not a human right. We live in a world of sovereign nation-states, and unless we find some better way of dividing up and administering the world, we will continue to live in a world of sovereign nation-states. And nations are, necessarily, exclusive clubs; they have the right to restrict immigration for any reason whatsoever.
Liberals must therefore not view borders and citizenship as annoying obstacles to be tactically circumvented or overcome; instead, we must view them as fundamental parts of the social compact that allows nations, including liberal nations, to exist in the first place. Nation-states and their laws and their police and their courts and their armies are the fundamental guarantors of the human rights that define liberalism. And for better or for worse, the ability to decide who gets in and who has to keep out is a necessary precondition for nation-states to exist.
That means we need to recognize that immigration law is legitimate and needs to be upheld. Some progressives have tried to advance the notion that being undocumented is a marginalized identity that needs to be protected and supported by the state. But this is absurd; it’s like if progressives decided that people who drive over the speed limit are a minority group.
Illegal immigrants chose to break U.S. law when they came to this country. Our democratically elected leaders made those laws, and they should be honored. We should treat illegal immigrants humanely, of course, and we should not tear local communities apart just to hunt down a few people. But there has to be some way of enforcing the law, because a law without enforcement is no law at all.
A third principle that liberals should embrace is that the purpose of immigration is to benefit the people who already live in the country that is receiving the immigrants. Immigration to America must be for the benefit of Americans. We must flatly reject any concept of immigration that sees it as a necessary sacrifice on the part of American citizens. [...]
Instead, liberals need to promote immigration because of the benefits it brings to the American citizenry. This includes economic benefits — the tax revenue, the investment, the eldercare, the innovation, the entrepreneurship, the expanded market size. But it also includes cultural benefits — the constant reinvigoration of this nation of immigrants by new waves of people with the gumption and bravery to pick up and move across the world in search of opportunity and freedom.
America has always been the country of the frontier; in order to maintain that ethos in the modern age, we need people for whom America itself is the frontier. And our founding ideals — which are very liberal ideals, of liberty and opportunity and the rights of the individual — are strengthened by people who make the conscious choice to move to a country that represents those ideals...
And if this requires us to be selective about which immigrants we bring in, then so be it. Skilled immigrants bring far more economic benefits per capita than others — so by all means, let us tilt our system toward them, as American voters of both parties want. Immigrants who don’t love and embrace American culture will probably invigorate our nation less — so by all means, let us bar immigrants who have been part of groups that see America as evil, using the law with which we once banned immigration by members of communist parties.
These are the kinds of decisions that nation-states, including liberal nation-states, are inherently entitled to make. And we should expect Europe to make different decisions than America makes. That’s perfectly OK. Because immigration is fundamentally the decision of each sovereign nation-state, it’s inevitable that we’ll make different decisions. JD Vance wants us to think that France’s immigration policy, or Germany’s, is inextricably tied to America’s. But it’s not, and we shouldn’t let him get away with conflating the two.
For America, the kind of immigration agenda I’ve sketched out — which all the polls show Americans favor by substantial margins — would not be a “far right” policy, even though some progressives and leftists will inevitably try to label it as such. In fact, it would allow in far more immigration than the policies of Franklin D. Roosevelt or Harry Truman. It won’t satisfy progressives who dream of a borderless world, or leftists who salivate over the chance to make the West pay for colonialism. But I believe it will preserve America as the kind of liberal nation-state that we knew it as in the days before Trump.
As for the rest of the world, countries like Germany and Canada and Japan have to make their own decisions. I can’t tell them what kind of nation to build; I can only try to promise that as an American, I’ll respect their decision. If they want to shut their doors in order to slow the pace of cultural change, it’s not my job to lecture them otherwise.
by Noah Smith, Noahpinion | Read more:
Image: Gallup
Wednesday, September 9, 2026
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.
[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.]
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.
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.]
Tuesday, September 8, 2026
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:
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.
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
“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.”
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.]
Monday, September 7, 2026
The Unique Horrors of A.I. Food Slop
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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..]
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
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. [...]
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. [...]
“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.
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.
[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).]
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 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.
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
Sunday, September 6, 2026
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. [...]
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.
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.
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.
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.
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.
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.
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.
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.
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."
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."
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