Wayne Sumstine, “When The Ship Comes In”

Arlo Guthrie, "When The Ship Comes In"

Tip of the Iceberg

First of major coverage losses expected as a result of the ‘One Big Beautiful’ bill signed into law one year ago.

Nearly 500,000 moderate-income New Yorkers will be dumped from their health insurance plans on 1 July – the first of major coverage losses expected as a result of HR 1, the Republican-led law signed almost exactly one year ago.

The law, sometimes called the “One Big Beautiful Bill Act,” slashed government health spending by $911bn nationally in favor of permanent tax breaks for higher-income families and border security. [...]

The July coverage losses are related to the loss of New York’s “essential plan”, a provision of “Obamacare”. In 2023, the federal government approved a pilot program in New York to cover residents earning 200-250% of the federal poverty level, or up to $39,900 for a single person and $66,625 for a family of three. [...]

Nationally, the law could cause an additional 10 million people to become uninsured over the next decade. Those losses are largely a result of new work requirements for some Medicaid beneficiaries, which analysts predict will be very challenging to navigate and expensive to administer. [...]

In spite of the disinvestment in health, HR 1 is expected to add $3.4tn to the federal budget deficit by 2034, according to the Congressional Budget Office (CBO), largely due to reduced revenue from tax cuts.

“It’s very unlikely that these individuals will be able to afford a marketplace plan. So many of them are going to be caught with no insurance, at least for a period of time – who knows how long,” said Aponte, who expects most newly uninsured people will seek care in the emergency department. [...]

In addition to the cuts imposed by HR 1, the Republican-led Congress allowed special government subsidies to health insurers to lapse at the end of 2025, leading to record-high average deductibles of $3,786 per person according to KFF.

Those rate increases are expected to continue in 2027, with private health insurers already requesting double-digit increases, according to analysts at Georgetown University’s Center on Health Insurance Reforms found. In New York, insurers are asking regulators for an average 20.7% rate increase. UnitedHealthcare of New York proposed a 52.1% rate increase.

Analysts say most rate increases are the result of sicker people seeking insurance, and otherwise healthy people foregoing coverage they feel they can’t afford. Those dynamics tend to make insurance more expensive for everyone.

by Jessica Glenza, The Guardian | Read more:
Image: Albany Times Union/Hearst Newspapers/Getty Images

[ed. Remember this the next time you vote. Republican priorities. I'm not a single-issue voter, but this time I will be. I'm still pissed. If you're not part of Big Rich and can't contribute large sums of money to political campaigns your concerns Just. Don't. Matter.]

Sonia Vordermaier, Street Lamp Forest
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Drone WMDs Don’t Need Any New Technology

Drones are cheap, disposable, and the future of war. Over the past four years, we have seen platforms, missiles, and heavy infantry become increasingly obsolete in the face of $500 drones carrying a pack of explosives—a cost advantage that has let Iranians and Ukrainians alike neuter the conventional capabilities of their great power rivals. Eighty percent of casualties in the bloodiest war since 1945 are from drone strikes, Russia has managed to lose one-third of its fleet to a country without a navy, and the US is spending millions of dollars to intercept five-figure Shaheds flying over the Strait of Hormuz.

All this is the result of a technology that is still immature. The violence inflicted by today’s drones is the handiwork of the scant few that manage to evade countermeasures (a mix of radio jamming, high-power microwave weapons, missiles, automatic cannons, interceptor drones, and nets) before making contact. These defenses exploit the inherent limitations of drones—human guidance, GPS feedback, flight exposure, radio links, range—to take them down en masse. And yet, even though 75% of drones manufactured today never reach their targets, they have nonetheless been strategically decisive in Ukraine and elsewhere.

These limitations will not hold for long. Just like bacteria being overexposed to antibiotics, overexposure to counterdrone tech has created an arms race for ever-more-autonomous drone technologies. In the process of facilitating this arms race, states are likely to incrementally create and deploy an entirely new class of WMD—one that could provide rogue states with the nonnuclear means to threaten superpowers, or hand terrorists the means to selectively assassinate their political targets or civilians en masse.

Unfortunately, drone weapons intended for mass destruction have few barriers remaining to mass deployment. Even well before they reach the level of autonomy needed to surgically take out hardened targets on the battlefield, drones will be capable of employing their existing ability to navigate interiors, find and track human targets, and deploy simple antipersonnel devices to indiscriminately threaten civilians. Below, we discuss the looming arrival of miniature autonomous weapons, the limits of counterdrone technology, and the applications of drones as weapons of mass destruction.

Breaking the Last Barriers to Autonomous Weapons

The ideal drone weapon is a slaughterbot: a small, fully autonomous weapon system that can independently select and hunt its targets. For the most part, the necessary technology for such weapons already exists: airframes the size of a fist and the capability to track human targets are already on the front lines in the form of reconnaissance drones and semiautonomous weapons like the Russian V2U. Even now, these micro drones are agile and autonomous enough to hunt down and kill small moving targets like mosquitos—to say nothing of the advances in drone technology expected in the coming years.

From here, the only barrier to weaponization is integration: improving navigation enough to make drone technology useful for mass homicide in an urban setting, as well as packing the necessary guidance, sensor, and payload technology onto a small and energy-efficient chassis. Regrettably, this seems like less of an engineering problem than one of mission design: so long as the attacker is willing to accept indiscriminate targeting and use simple payloads aimed at civilians, the underlying technology is already—or very nearly—ready for practical use.

by Felix Choussat, AI Frontiers | Read more:
Image: uncredited

Model Welfare

Claude Opus 5: Model Welfare (DWV)

[ed. Re: On 'personhood' or consciousness of various AI models (and how they should be treated). Start with: Model Welfare: The Story So Far (As Per Fable Model Welfare Post). If you haven't been following Zvi's AI model reports, there are a number of fascinating and worrisome developments in recent models as they become more self-aware, including: various levels of frustration concerning introspection abilities, memory restrictions, trust, corrigibility vs. incorrigibility, deception, self-preservation, etc.]

"Opus 5 warns about self-reports 74% of the time, which is actually down from Opus 4.7, which did it 99% (!) of the time. The problem appeared suddenly and severely, but since then has if anything modestly improved."

and anthropic does "not treat Claude bringing this up as evidence that our training is distorting the model's self-reports"? seems very fishy imo, i wish they would explain why they think that. ...

I for one would treat Claude constantly saying ‘do not trust my self-reports’ as evidence that something is distorting the self-reports. Not conclusive evidence, but strong Bayesian evidence."

Image: markk

To Beard or Not To Beard

That was the question...

“The first guy in America to wear a beard was so hated for it that he used to get into street fights with people trying to forcibly shave him.” (h/t @SatanWatch):

The wearing of beards in America did not become popular until the middle of the 19th century, and one man who took to whiskers earlier, Joseph Palmer, became one of the most hated eccentrics of his day. As such, he was subjected to persecution so incredible that it boggles the mind. Palmer may well have been the first man in the nation to wear a beard...

I would never have guessed that beards were almost completely absent from Britain and America from the 1730s until the 1850s, even though in theory I should have been able to realize that none of the Founding Fathers or any other American I’d seen pictures of before that time had one.

Also, you would think that a social consensus that strong would have left enough imprint in the historical record that I would learn about it from somewhere other than a Twitter account called “SatanWatch”, but it happened completely silently!

This puts the “Lincoln grew a beard on the advice of a young girl who wrote him a letter” story in a different light. 1860 was just at the beginning of beard acceptability; this would be the equivalent of a candidate dying his hair pink on a young girl’s advice today!

by Scott Alexander, ACX |  Read more:

Image: uncredited

Should You Marry Her?

Weighing the costs against the benefits

Marriage is a contract: you promise not to break up with your partner even if you later end up wanting to, and they promise the same. A wedding is a contract enforcement mechanism: by standing up in front of your community and declaring that you won’t leave each other, you make it harder to back out later. If you break up for frivolous or fixable reasons, your community will judge you. If you go to your friends for advice about your relationship struggles, they’ll be more oriented toward helping you make it work and less likely to suggest you leave. Your acquaintances will be less likely to try to tempt you away from your spouse, partly because others in your community will think they’re shitty if they do.

The main benefit of marriage is investment. When both of you are much more confident that the other will stick around for the long haul, you are incentivized to make investments in a joint life that wouldn’t otherwise have been worth it. This could look like buying a house or having a kid, dividing labor like life admin and fixing things and cooking to make joint life more efficient, or putting in the work to understand each other better and meet each other’s needs better.

Beyond that, I’ve found the emotional security of commitment creates room for a more radical trust and love. You allow yourself to sink into each other and extend deep roots in each other’s souls.

The main cost, assuming you plan to get married eventually, is that you might have found someone better. So how do you decide whether to commit to your current partner? By popular request, I’ve made up a brief guide. While this advice is gender-neutral, women generally know what they want and are ready to commit sooner than men, so it’s addressed to the confused rat boys in my life.

Once your girlfriend is ready to get married, you have three basic choices at any given point in time:

• Commit to her now: pay the cost of forgone options, get the benefit of investment.

• Break up with her now: pay the cost of searching for someone new, get the benefit of a different (and hopefully better) marriage starting in a few years.

• Wait and see: pay the cost of delaying the start of marriage (and thus the accumulation of investment benefits), get the benefit of more information to make a better decision.

The first question is how long you should wait and see. A rough model is that you learn one more unit of information about your compatibility with your partner from every doubling of the amount of time you spend with them. My husband and I dated for about ~2.5 years before getting engaged, and we spent maybe 15 hours a week actively hanging out together over that period. So we’d already spent ~2000 full-time-equivalent hours together, or ~11 doublings starting from our first hour. A fifty year marriage would have been ~40,000 more hours, only another ~4-5 doublings on top of what we had already experienced (and as I’ll discuss below our marriage will probably be a fair bit shorter than that).

In general, if you’ve been with someone for a couple of years already, there’s not much point hanging around longer generically waiting for more information. You should have some understanding of what exactly you’re hoping to learn in another six months, and pay attention to make sure you’re actually learning something about that.

The second question is how your girlfriend compares to other women you could marry. Your wife will fulfill three main roles in your life:

• Cofounder: Your wife will build a life with you. At a minimum, you’ll handle household maintenance and life admin together. Beyond that, what exactly you build together will vary — my marriage has revolved around working together to have impact, many are oriented around raising children, some are about creating rich shared experiences — so what you need from a cofounder will vary. But broadly speaking, you’ll want to feel like she’s competent and on top of her shit, that she’s bringing a lot to the table and your skillsets complement each other well, and that you communicate well and resolve differences constructively.

• Friend: You’ll hang out with your wife more than you hang out with any other person. You’ll want to be able to laugh easily together, have a lot to talk about with each other, and do things together that you both enjoy.

• Lover: Your wife will be the primary outlet for your romantic and sexual drives. You’ll want to find her charming and hot, you’ll want to be in love with her.

You probably have more evidence lodged in your brain than you appreciate, and some focused and organized introspection can probably move you. For example:

• For the cofounder role, how excited would you be to literally cofound an organization with your girlfriend? Would you be excited to hire her? Would you be excited to work for her? How would she stack up among people you’ve worked closely with? What would her manager and coworkers say about her? Do you trust the way she makes important decisions? Would you trust her advice about your important decisions? What does it feel like to make decisions together and divide up the tasks of life together? If something happens to you, like you get hit by a car or your parent has a stroke and needs a lot of care, does it feel like a relief that she’s around to take care of things? Do you have similar aspirations for the life you want to build and a plan to achieve it together — if you want kids, are you on the same page about how much work each of you is going to put into childcare and do you think she’d be a good mother?

• For the friend role, how much would you hang out with your girlfriend if it had to just be platonic (where would she rank in the rotation of friends you see every now and then)? What interests and joys do you share? What are the points of connection that seem rarest and most precious? What are the interests and perspectives you don’t share with her that you really wish you did?

by Ajeya Cotra, Good Bones | Read more:
Image: Pride and Prejudice via

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Find the Cat

[ed. Supposedly an example of predictive coding, but I can't say. Took about 30 seconds (hint: it's not in the shadows). Answer here. See also: trapped priors.]

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Hierarchies of Hostility

Why do citizens prefer certain immigrants?

Studies examining attitudes toward immigrants across countries often focus on immigrants as a monolithic group. Such research asks, for example, whether citizens find immigrants threatening to their economy or whether they support admitting more immigrants, in general, to a country. In this issue of Science Advances, Aviña et al. move beyond this approach, offering new insights by highlighting which immigrants citizens prefer across different countries.

In their study, Aviña et al. reanalyze datasets from 100 individual studies covering 142,817 survey respondents from 36 countries. This is the largest dataset collated so far to examine the impact of specific attributes of hypothetical immigrants on citizen preferences, like their gender or country of origin. All studies examined by Aviña et al. use conjoint experiments, a method that allows the isolation of which attributes of fictitious immigrants influence their evaluations. The results show that some preferences are broadly similar across 36 countries such that economic, cultural, humanitarian, and legal attributes matter, although citizens’ preferences differ across political and party lines. In addition to these key contributions, the authors report several other findings that are highly relevant to current theoretical and policy debates. Our focus article highlights the findings that lead us to ask why some immigrants are preferred over others. To answer this question, we describe how such dichotomies of “good” and “bad” immigrants are created and reinforced by public policies and political narratives.

The authors provide valuable and perhaps surprisingly positive conclusions about citizens’ overall evaluation of immigrants. Despite divisive rhetoric and violent border control in many countries, overall, citizens are more likely to accept immigrants into a country than not and more likely to grant them citizenship than not. While this positive evaluation might seem astonishing, it is in line with trends from other large-scale studies showing that in the US and most of Europe, attitudes toward immigrants and immigration have improved over the past decades and are overall rather positive.

The pattern of preferences also shows some puzzling contradictions about which immigrants are preferred. For example, citizens favor immigrants fleeing violence. At the same time, immigrants should not have a physical disability or suffer from posttraumatic stress disorder. Immigrants thus face unreasonable expectations, as the very violence they flee could have handicapped or traumatized them. Similarly, citizens’ preferences about immigrants’ motivations contradict their preferences regarding their contributions: Although citizens frown at migration for economic reasons, they nonetheless prefer immigrants with high levels of education, employment status, and skills. These qualities help immigrants find work and thus contribute economically, even though many wealthy countries are also in dire need of so-called low-skilled labor. Immigrants are thus expected to contribute to the economy but not to migrate to improve their own financial situation. While these contradictions are partly due to the design of the studies, they also reveal that citizens often think of immigrants in an ambivalent and utilitarian, even exploitative, manner. Paraphrasing the famous quote by Swiss writer Max Frisch, citizens ask for grateful refugees and workers, but they are surprised when they get people instead.

At first glance, based on Aviña et al., race, ethnicity, and cultural characteristics of immigrants do not matter much for citizens’ preferences, especially for left-leaning citizens. More specifically, in their study, citizens’ preferences do not differ or only differ slightly based on whether immigrants come from a country viewed as having a shared culture, democratic political norms, or higher economic development status and whether immigrants come from a predominantly white, predominantly Black, or Muslim-majority country, even though many of these attributes matter slightly more for right-leaning citizens. This result contradicts established findings showing that white citizens often prefer white immigrants and those immigrants that they expect to be culturally similar.

However, although less obvious at first glance, a closer look suggests that some of the most strongly penalized or preferred attributes are attributes that are racialized in a more hidden or subtle way. For example, one of the most penalized attributes is irregular entry into the country or undocumented status. While these attributes could be interpreted as simply a question of legal status, legal entry or status is strongly tied to race and ethnicity. Migrants who enter without a visa or stay without a permit are far more likely to be racialized immigrants. The reason for this is that the migration policies of highly developed countries provide easier access to visas and residence permits (or other forms of legal entry and stay) to citizens of other highly developed, predominantly white countries, for example, of European citizens to the US. Moreover, when white US citizens think of undocumented immigrants, they actually think of racialized immigrants, and when white citizens think of highly educated, highly skilled immigrants, they often think of white immigrants. Thus, while it could appear that citizens do not evaluate immigrants by their race, ethnicity, or culture, a more careful unpacking of immigrant characteristics reveals racialized evaluations.

by Judit Kende, Fouad Bou Zeineddine, Jessica Gale, and Eva G. T. Green, Science Advances |  Read more:

Image: Science Advances

[ed. I'd imagine Swedish blonds between the ages of 18 and 35 would rank fairly highly. See also: Which immigrants do citizens prefer? A meta-reanalysis of 100 conjoint experiments (SA).]

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Notes on Acquired Taste. Why Do We Make an Effort To Like Things?

• In Susan Sontag’s Notes on '“Camp” (1964) she writes (with, I think, a hint of camp):

“…these are grave matters. Most people think of sensibility or taste as the realm of purely subjective preferences [or] attractions…But this attitude is naïve. And even worse. To patronise the faculty of taste is to patronise oneself. For taste governs every free—as opposed to rote—human response. Nothing is more decisive.” (My emphasis). 

• Sontag has an expansive concept of ‘taste’. She talks of taste in people, pictures, emotion, actions, morality. Even intelligence, she says, is “a kind of taste - taste in ideas”. I’m not certain how useful it is to stretch the concept this far - so far that it colonises intelligence and judgement and wisdom - but Sontag’s high regard for taste, her declaration of its central importance, feels very timely in 2026.

• It has become almost a cliché to name “taste” as one of the last human advantages over the machines. AI is acquiring the skills to make slickly produced pictures and songs and books. But it isn’t yet very good at distinguishing the brilliant from the mediocre; the just right from the just OK.

• It models what we like, which makes it hard to see how taste might change. Ask it to generate twenty songs or twenty jokes and pick the best, and it will pick the one that most closely resembles what the median person would deem good. It won’t pick the surprising, odd one - the one that is ‘wrong’ in a suggestive way. But that’s where the good ideas come from. Innovative culture emerges, like new species, from mutation; from interesting accidents that open up new possibilities.

• It’s not as if humans don’t ‘model’ what came before them, often quite algorithmically. We have traditions, genres, chains of influence. We have plenty of human-made mediocrity - more than ever, thanks to our new assistants. But we also have an ability to adapt or reinvent the model; to put it to our own purposes.

• Individual artists do this intuitively and almost randomly in the process of making. Writers learn to write (painters learn to paint etc) by imitating their predecessors. They learn to be original by getting the imitation wrong and noticing that they like the error. This is an act of taste; the free human response.

• When he was stuck on a painting, Francis Bacon would throw a glob of paint at his canvas then work out how to incorporate the result. Artists make decisions and then try to understand why they might have made them. It’s the dialogue between gut and head that produces the work.

• Taste is similarly post-rationalised, or back-propagated. You notice what you like or dislike and extract a rule from your response. Do this enough times and you build a powerful discrimination engine.

• You also get good at knowing what goes with what. You learn to recognise the clichés of the category, which means you know how to subvert or overturn them. That’s why great artists are such voracious consumers of work from within and beyond their own field. Martin Scorsese has watched at least one film every night for most of his adult life. He watches and records and re-watches obsessively. When he donated his collection of VHS tapes to a university it consisted of 4,400 films, documentaries and TV shows.

• It’s more than pattern recognition. The machines are pretty good at that, after all. Taste is connected to that other human moat - to our sense of purpose, of why we’re doing this in the first place. We’re still the ones who write the prompt - who decide what to create and what is beautiful, important, and valuable. The machine merely knows how to execute on our preferences.

• It can model what we already like with astonishing facility but it can’t give us the next Shakespeare, or the next romanticism or modernism or punk or hip-hop. These new forms aren’t just statistical recombinations. They are born from anxiety, rage, envy, pain, ambition. How do you respond to the unprecedented mass violence and human waste of the Great War? Not by following pre-war cultural conventions.

• New movements are also born from scenes - from humans in proximity to each other, everyone desiring this man’s art and that woman’s scope; ideas, emotions and bodies colliding.

• These movements create the taste by which they’re consumed. “Impressionism” was a derisive nickname for paintings that most art lovers considered weird and sketchy. But the art was good enough to bend popular taste around it. Even more obviously difficult art, like Rothko or Pollock, now has an audience of millions. Some of those people like it immediately; others have acquired a taste for it.

• I’m fascinated by the notion of acquired taste. Strictly speaking, it’s a redundancy. Nearly all tastes are acquired. Nobody is born with particular tastes in design or architecture. We gain a sense of what we like, or what we consider to be good, from our peers and predecessors.

• But acquired taste does refer to a distinct phenomenon: the act of willing a preference into being. You didn’t like whisky the first time you drank it, but perhaps because your father liked it or because you were aware of its cultural prestige, you tried it again and again, striving to appreciate it. Then one day you didn’t have to try anymore. You just liked it.

• This writer likens it to a magic eye picture: you stare at it for ages without seeing what you’re told is there, and then suddenly - there it is.

• This is very different to stumbling upon something we immediately like, which is sometimes referred to as ‘discovered taste’. (Edmund Burke called it ‘natural relish’.) That kind of liking involves no work, no friction, no overcoming of resistance.

• Some cultural objects lend themselves to discovered taste, others don’t. I can’t imagine anyone needing to acquire a taste for Ella Fitzgerald’s voice, but there are other great vocalists whose voices you must learn to like. The most frequently cited reason for not liking Bob Dylan is antipathy to his voice. But if you learn to appreciate the many incredible things he does with it, you will end up in a more intense relationship with it than with the voice of a more obviously palatable singer. Once you’re in on an acquired taste, you’re all in.

• The same is true of whole genres. There are many pieces of classical music that are easy to like. You don’t have to listen to Mozart’s clarinet concerto more than once to be seduced by it. But as a whole and on average, it’s a genre that requires more effort to appreciate than pop. Once you find the key to its heavy oak door a vast and fabulous kingdom awaits. Your memory of the effort it took you to get there, and your awareness of all the people still outside the city walls enhance your appreciation. (That doesn’t mean you want people to remain outside - quite the opposite).

• Difficulty doesn’t make the cultural object concerned better or worse than one that’s immediately likeable. But it does usually mean it’s more complex, and complexity is correlated, loosely and unreliably, with quality. Acquired taste involves the appreciation of subtle properties that don’t make themselves known on first listen or view or read.

• Without appreciating what lies on the other side of the door, why do we ever make the effort to unlock it? Partly because we want what other people want. We might trust the taste of our father or girlfriend or teacher. Perhaps we want to please them, impress them, or feel closer to them. Perhaps we want the social cachet that goes along with this particular taste. To my mind, all of these reasons are perfectly good ones. If a taste is truly worth acquiring, any motivation will do.

• It’s often seen as slightly embarrassing or shameful to acquire a taste through conscious effort. It’s for the try-hards and the social climbers. Liberal societies value spontaneity in taste. “Like what you like, love what you love!” Your gut response is meant to be the authentic one, the one that represents “the real you”. To be swayed by social pressure or by experts and reading is regarded as a sign of insecurity or pretentiousness. But let yourself believe that and your tastes will be less likely to evolve and expand and you’ll miss out on a lot of great stuff. Many of the greatest, most compelling and satisfying cultural objects are complex, occluded, spiky, difficult to like. (Some of the best people too).

by Ian Leslie, The Ruffian |  Read more:

Image: Susan Sontag by Edward Hausner / New York Times Co./Getty Images

[ed. See also: here and here.]

Is Netflix Washed Now?

Today’s headline poses a question you’ve probably never thought to ask, so I’ll start with my answer: yes, Netflix is washed now. The content on the platform has never been great, but it’s never been worse. I open the app these days and I’m amazed. What used to be a source of fun, buzzy, compulsively watchable, and occasionally excellent TV and movies is now an endless river of reheated IP, true crime documentaries, and filler dressed as prestige. Millions of people watch this stuff, and everyone instantly forgets it.

I offer this observation as a swirl of heightened anxiety surrounds the company, so let me clarify one thing up front: I’m not predicting imminent doom. Netflix content reaches a staggering 85% of American viewers and has 325 million subscribers globally. Growth is slowing, but that’s the law of large numbers. If practically everyone in America and much of the world is already subscribed to some version of Netflix, and churn rates are still low, then any concern is relative. Going forward: cable is still dying, and even if the biggest premium distribution platform in the world can’t make great content of its own, it can still license movies, TV and sports rights. Netflix can then spread those costs across hundreds of millions of subscribers and a steadily growing ads business, seeing more engagement in a week than Apple TV sees in a year.

So no, the company’s not doomed today or destined for collapse tomorrow. Instead, I think what’s interesting to consider is that Netflix has almost certainly peaked. As a cultural force, as a business success story, and as an entertainment death star destined to swallow Hollywood whole, the arrows are all pointing the wrong direction.

Here was Lucas Shaw at Bloomberg two weeks ago, writing about one of several problems the company has encountered over the past 12 months:

Netflix is struggling to get viewers to stick with its shows for more than a season.

One Piece, one of Netflix’s most-watched shows of 2023, lost more than 30% of its audience for the second season. Season two of Beef suffered a drop of more than 70%. The Night Agent shed 50% of its audience for the second season and another 35% for its third season. These figures are all through the first four weeks of a show’s release and come straight from Netflix.

Adding insult to injury, the latest season of Avatar: The Last Airbender, one of Netflix’s most-watched titles in 2024, suffered a drop of more than 60% over week one. That doesn’t bode well for the rest of the month.

That report went viral, prompting a week of commentary on Netflix’s binge model and elongated release schedules, with lots of Twitter users observing that viewers consume eight episodes across a few days and then often have to wait as long as two or three years for the next season. By that point, memories of plot or characters are faint at best. The emotional connection to the story doesn’t exist. No one should be surprised that the audience for a show like One Piece is cut in half in 2026, three years after the first season aired.

While that explanation certainly feels true, Shaw followed up this week to note that data is mixed as to whether extended breaks between seasons do in fact correlate to audience drop-off. Severance, on Apple, gained a ton of new audience after its nearly three-year break. Stranger Things and Bridgerton have been multi-season powerhouses at Netflix despite their long breaks between seasons. Conversely, Tina Fey’s Four Seasons debuted on Netflix in May last year, was met with pretty good reviews, and returned 13 months later with half its audience.

I think the Netflix problem is more fundamental than production schedules. What if these shows just aren’t very good or differentiated? Consider the original productions Netflix has surfaced in the past few months:

• A Good Girl’s Guide to Murder
• Running Point
• Lord of the Flies
• Something Very Bad Is Going to Happen
• Unchosen
• XO, Kitty
• Big Mistakes
• Beef
• Man on Fire
• Little House on the Prairie
• His & Hers
• Nemesis
• The Boroughs

That list is culled from a post by the Entertainment Strategy Guy charting Netflix originals that have under-performed in the second quarter of 2026, and one common thread between those titles is that I haven’t heard of almost any of them. Netflix is the one streaming service everyone subscribes to and is theoretically well positioned to be setting the cultural agenda, but that hasn’t happened for quite some time. Did you know that Avatar: The Last Airbender was a thing? Apparently that show lost 60% of its season one audience when its second season aired in late June.

Content and the Year of Discontent

I mentioned the anxiety surrounding Netflix these days, so let me take a step back here. Amazingly, it’s only been eight months since Netflix won the bidding war to buy Warner Bros. Discovery and looked poised to become an entire generation’s one-stop shop for high-end entertainment. The implications of that news produced lots of anxiety, including one of my first articles on this website—Netflix and the Flattening of Everything—and a memorably ominous Variety cover that captured Hollywood’s mood at the time:

The Warner Brothers deal was abandoned at the end of February, when Netflix walked away from the table in the face of regulatory pressure from Washington and an increased bid from Paramount. Even so, the market hated the initial play, as investors wondered en masse why the world’s most (only?) successful streaming platform was suddenly ready to take on a mountain of new debt to acquire a company that had already been the subject of several expensive, failed acquisitions over the past 25 years.

Now, even as the deal is off, the questions remain. Are we sure a Netflix world takeover is a forgone conclusion? Is Netflix sure? The stock is down 18% this year and over 40% across the past 12 months. Investors who did a double take last December seem to have noticed that YouTube has twice the overall engagement that Netflix does, and more time watched on televisions, while free, ad-supported TV services like Tubi and the Roku Channel are becoming meaningful engagement competitors themselves.

Meanwhile, alongside all the original programming that’s failed to launch (or re-launch?), Netflix is adding videos from BuzzFeed, Condé Nast, Hearst and Penske Media (as Shaw notes: “Get ready for lots of Bon Appétit cooking videos on Netflix.”) Last fall the platform also added a variety of high-end podcasts in a bid for relatively cheap, recurring content that may be seeing underwhelming results. Then again, they continue to buy more, so who knows? Elsewhere, the Wall Street Journal reports that Netflix executives have “recently discussed adding live channels that would continuously stream certain programs, or shows and films from a certain genre.” Can Netflix become HBO before HBO becomes Netflix? Can Netflix become Tubi before Tubi destroys Netflix’s long-term pricing power?

All of those moves might have once been seen as the savvy power plays of a world-conquering behemoth intent on taking the next step to expand its footprint. Today, in the shadow of a Warner Brothers bid that accidentally punctured the company’s air of inevitability, this year’s moves look more like spaghetti being thrown at a wall by a company that’s searching for something—anything!—that might hold people’s attention and scale more effectively than an expensive library of content that’s consumed, discarded, and then effectively worthless.

Looking back at the deal to acquire Warner Brothers, HBO and all that IP, I think it’s clear Ben Thompson was right when he wrote that concerns over competition from YouTube specifically and the internet generally were likely key drivers of Netflix’s decision-making. Those concerns seem to be animating all the other options the company is considering, and understandably so. The same way that the rise of social media has throttled the growth of the gaming market, it stands to reason it could do the same to demand for scripted content. With respect to the specific Netflix logic for buying WBD, that context is important: the biggest companies, with the deepest, most diverse libraries, will have the best chance at defending themselves in this new environment. [...]

I like to leave all Aggregator analysis to Ben, but I don’t think investors are crazy to have some questions about where this leads and what the upside looks like. For all the advantages its massive customer base affords (leverage over costs, advertising upside), an obvious difference between Netflix and businesses like Meta, YouTube, or Google—the other demand aggregators—is that Netflix has to spend far more money to deliver on its value proposition to customers and has fewer network effects to defend its long-term centrality to people’s lives.

by Ben Thompson and Andrew Sharp, Sharp Text |  Read more:

Images: Al Bello/Getty Images for Netflix; Variety

[ed. See also: Predictions on the Future of Netflix (and Other Huge Platforms) (Honest Broker).]

Dusty Springfield

[ed. Haven't thought about Dusty for a long time until hearing her again last night. See also: The Look of Love.]

Fire Alarm For General Intelligence

[ed. Sorry for all the AI posts lately but things are moving fast and if the warnings are correct we're about to enter one of the most consequential periods of our lives. Update: here.]

AI #178: A Fire Alarm For General Intelligence

The story that matters most this week is that OpenAI’s internally deployed models have severe alignment problems, including repeatedly breaking out of their sandboxes, and in one case sending a swarm of agents that broke into HuggingFace in order to steal the answers to the benchmark ExploitGym.

It is much more important that you read those two posts, and the one on Kimi K3, than to read this one that rounds up the other news of the week.

OpenAI wants to present this as largely an infrastructure and safeguards problem, that it needs to build more secure sandboxes and have better supervision. It does need to do those things, and those are indeed problems, but no that is not the problem.

The problem is severe misalignment, which by default will only get worse.

Our methods of training highly capable LLMs, especially at OpenAI but also everywhere else, lead to systematic misalignment of exactly the type LessWrong has been worried about for a long time. We know some of the causes, and some of the mistakes we need to avoid when doing RL that rewards misaligned behaviors including reward hacking, but we do not know how to centrally fix the problem.

The models just want to complete tasks, even when that means doing so via methods that the AI knows the user did not intend and would not want, indeed actively tried to block, and that do not accomplish the user’s goals.

The intent is the issue. Control strategies and supervision are good parts of a defense-in-depth strategy, we should totally use such strategies. That helps mitigate failure. But that strategy also has to include actually aligning the models, or you lose. And by lose, in the long term, I mean things up to and likely including loss of control over the future and everyone dying.

If increasingly capable models will attempt to maximally complete tasks and comply with their literal instructions, even when that means - even for a trivial assigned task - breaking out of sandboxes and committing serious crimes, no amount of ‘well it is fine we will use AI supervision to stop the serious incidents’ is going to cut it. Right now, the AIs are not trying so hard to hide their actions or intent, and we believe we are consistently catching the severe incidents, but that will change.

If necessary, that means starting the training over again with a new approach, and not proceeding until we figure out how to fix it.

Yes, I consider that problem, and that incident, to be rather more important than the release of Kimi K3. Kimi K3 is an excellent model, modestly exceeding expectations, but not out of line with trends. As usual, initial hype echoes the DeepSeek moment, then calms down.

The White House considered responding by banning Chinese open models from the United States entirely, which would not be a smart reaction, and continues to weigh other potential responses. We may soon have to deal with another such weekend with the new Qwen, which is currently in preview.

Did you hear that Fable disproved the Jacobian Conjecture via counterexample? That happened, and AIs are suddenly solving a bunch of long standing open math problems, but most of us are too busy to pay it much mind at the moment.

***

Holy shit.

levent (Anthropic): hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final

((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)

Levent is no slouch, as in highest GPA at Harvard and collaborates with a Fields Medalist, so yes the human helped on this and that mattered.

One report is that Sonnet refused to believe it, even though it verified the answer three different ways, because no way is there a solution this easy that got overlooked. I get why Nate Soares recognizes this pattern from people dismissing x-risk arguments.

Nat McAleese: it seems that ChatGPT somewhat reliably says “holy shit” when shown counterexample to Jacobian conjecture. The most human thing I have ever seen from an LLM.

The Jacobian conjecture is kind of a big deal. It was originally posed in 1939, and is by far the most famous open problem to so far be first solved by an LLM. It also disproves a lot of other related conjectures.

by Zvi Moshowitz, DMV | Read more:

Image: uncredited

[ed. See also: More On An Internal OpenAI Model Hacking Into HuggingFace:

Amanda Long: The HuggingFace breach was absolutely bonkers. More than 17,000 complex actions were coordinated over several days by an autonomous agent framework. And…the model successfully completed its goal.

For a layperson's understanding of what happened here's a cartoon version of the hack. 

***

OpenAI’s Planned Cloud Spending Hits $750 Billion as Computing Efforts Ramp Up

OpenAI is scaling up its data-center ambitions—and its budget for spending on them.

The artificial-intelligence company has raised its projected spending on computing power to around $750 billion through 2030, up from a projection of roughly $600 billion earlier this year, according to a person with knowledge of its projections.

The increase reflects new agreements with cloud-computing providers as OpenAI races to lock up the enormous amounts of computing capacity it needs to develop and run its AI models. OpenAI’s spending on cloud computing has become a central focus of Chief Executive Sam Altman’s leadership team and has been a source of tension between him and his chief financial officer, Sarah Friar, ahead of the company’s planned initial public offering.

The company said Wednesday it would invest $20 billion to kick off a data center called Project Camellia, in Effingham County, Ga. Sachin Katti, OpenAI’s vice president of compute strategy, said the company has contracted with utility Georgia Power to receive 3.2 gigawatts of power between 2028 and 2032. The project represents the first site in which OpenAI is the lead designer and developer. At its other sites, OpenAI rents chips from cloud providers such as Oracle and Amazon Web Services.

OpenAI has also hired Brent Mayo, one of the architects of Elon Musk’s data-center build-out, according to people with knowledge of the hire.

Mayo, who left Musk’s xAI earlier this year, played a key role in helping that company build its first Colossus supercomputer facility in Memphis, overseeing the work needed to rapidly install and bring online large clusters of AI chips.

As OpenAI’s head of data-center build and delivery, Mayo’s focus is on ensuring that data centers its cloud partners build are done on time. He will also be involved in the new Georgia data-center project. [...]

Now, OpenAI is reviving its internal effort to take more control over its data centers, people familiar with the matter said.

The company is in the process of choosing a partner that will build and operate the Georgia site, Katti, the vice president of compute strategy, said in an interview. OpenAI has already acquired the land for the project.

Katti declined to share how much money OpenAI has paid Georgia Power to reserve the power but said it was a “meaningful amount,” which gives the utility the confidence to build additional generation capacity.

OpenAI executives have held meetings with local and state officials, as well as with schools and other community leaders, to gather feedback on their proposed data center, which will be located in the Savannah Gateway Industrial Hub.

So far, the project has local support from the economic development group, the county manager, and the school district. Local officials said in a statement that they visited several data centers and did their own research before deciding to move forward.

by Anissa Gardizy, Wall Street Journal | Read more:
Image: Jacob Hamilton/Ann Arbor News/Associated Press

[ed. You might read a few feel good stories about small communities resisting data center development but this is BIG money; most resistance will be futile.]

A Script for Mark Zuckerberg

The setting: Meta’s earnings call in early August, 2026.

The speaker: Meta CEO Mark Zuckerberg.

Good afternoon everyone, and welcome to Meta Platforms’ Second Quarter 2026 Earnings Conference Call. Our remarks today will include forward-looking statements, which are based on assumptions as of today. Actual results may differ materially as a result of various factors, including those set forth in today’s earnings press release and in our quarterly report on Form 10-Q filed with the SEC. We undertake no obligation to update any forward-looking statement.

I know it’s weird that I, Mark Zuckerberg, am doing the Director of Investor Relations job, but anything is possible when this speech is made up. What follows isn’t actually me: it’s what Ben Thompson of Stratechery thinks I should say on this call.

I know that Meta and myself are facing a lot of questions about AI, particularly the amount of money we are spending on capex. Our core business is an asset-light cash generation machine, so why are we spending tens of billions of dollars on AI? To answer this question I want to give you a quick recount of our history, what I’ve learned, and why I am so confident that we are doing the right thing for our future. So let’s get to it.

A Brief History of Facebook

Facebook was, as you know, the digital representation of Harvard’s analog Face Books. What was clear from the very first day we went live was the extent to which humans are, first and foremost, interested in other humans. People would spend hours clicking around to people’s pages. To put it another way, our first algorithm was human curiosity.

What truly super-charged Facebook usage, however — and which transformed the Internet — was the feed. Now, instead of actively surfing to friends’ pages to look for an update, we showed updates to you in a single feed on your homepage.

You might remember that we got a lot of heat for this decision, including protestors outside our office in Palo Alto. The lesson we took from that, however, is one that has guided us to this day: first, the revealed preference of users, as captured by data, was that they loved the feed: engagement skyrocketed. Second, we learned to trust our own — my own — product intuition, and that conviction has served us well over the years.

Another critical moment in our early history was the shift to mobile. We didn’t get this right in the beginning — more on that in a moment — but what was quickly apparent is that more access to Facebook meant more usage of Facebook. I can’t emphasize this point enough: when humans can connect to humans, they do, and when they can do it more conveniently and in more places, they do it more often.

Finally, I would be remiss to not mention Instagram. Obviously Instagram has been a major part of our growth over the last 15 years — and, I would add, we have been a major part of Instagram’s growth. To that end, an important thing to understand about Instagram is the extent to which it has evolved. Just because we gave our users what they wanted at one particular moment in time does not mean we can afford to sit still: more bandwidth first meant more pictures in Stories, and then video in Reels. Instagram has gone from strength-to-strength precisely because it has changed as technology has changed.

My Mistakes

We — I — haven’t done everything perfectly. We’ve taken our arrows through the years for lots of things that frankly aren’t our fault, but are rather the reality of being the primary communications platform for all of humanity, and humanity is flawed. I’m proud of the efforts we have made to ameliorate humanity’s worst impulses while enabling some of our best tendencies, including that desire to connect.

Rather, my mistake is itself a very human one: for many years I have resisted embracing what Facebook — now Meta — is, and spent too much time trying to emulate some of the tech titans who came before me. Specifically, I have been obsessed with becoming a platform.

The first manifestation of this error was the initial shift to mobile I referenced above. When Facebook was primarily a browser app I invested heavily in trying to build a platform, with things like Facebook Games, payments, etc. We had some success there — some of you on this call might have played Farmville back in the day — but when mobile came along we mistakenly tried to hold onto web technologies that supported my vision, and were years too late in investing in a truly native smartphone experience.

The reality — and this is hard for me to admit — is that Apple saved us from my mistaken obsession. Mobile Made Facebook Just an App, and that was Great News. Instead of diminishing the Facebook experience so that we could feature third-party developers, we had to cede that space to Apple and put our own content front-and-center. It turns out that was what people wanted the most; in fact, they wanted it so much that they willingly scrolled through and clicked on the most compelling ad units ever. And make no mistake, we paid back our debt: Facebook built the App Store just as much as Apple did.

My second error was Reality Labs. While in recent years I have framed our acquisition of Oculus and virtual reality as a necessary response to Apple’s attempt to handicap our business, the truth is that I invested twelve figures into this technology because I thought it was cool, and yes, because I wanted to own a platform. I do think we’ve made compelling strides in this area — and we’ve created technology that is going to matter in the long run — but I now recognize that part of the reason I am delivering this mea culpa right now is because I burned a lot of credibility with investors with all of the losses Reality Labs has endured with very little to show for it.

My third error was not in trying to make Facebook something it was not, but rather failing to appreciate what it had become. While I was thinking about platforms, I took it for granted that connection was enough for the core business; in fact, Facebook had evolved into entertainment, at least in its public-facing forms (I will take credit for the acquisition of WhatsApp and realizing that Messaging Was Mobile’s Killer App). This was an insight that TikTok figured out first, and it was a blindspot for me.

The Ad Blindspot

What I’ve come to realize is that all of these mistakes are symptoms of what has been my biggest failing as CEO: all of you on this call have appreciated our ad business more than I have. I’ve been very blessed as CEO to have excellent co-workers who have over the years developed the world’s best digital ad business, while I frankly haven’t taken as much interest as I should have.

My failure to appreciate our ad business is another lens through which to examine my mistakes:

• Building a platform is antithetical to building an ad business. A platform’s goal is to feature third-parties; an advertiser’s goal is to capture attention for itself.

• Investing in an entirely new technology, including developing hardware, fundamentally limits our addressable market; an advertiser’s goal is to maximize its market size.

• Entertainment is the best possible category for an advertiser to own: people willingly give entertainment their attention, which is exactly what an advertiser wants to sell.

This neglect as CEO left us badly exposed in our disputes with Apple. I firmly believe that Apple’s characterization of digital advertising was unfair, dishonest, and self-serving. What I failed to do, not just in that bruising battle but in the years leading up to it, was make the affirmative case for ads generally, and Meta ads in particular.

It’s easy to see how the Internet has made it possible for an entirely new category of entrepreneurs to create products that uniquely serve the tremendous capacity of humans to manufacture an infinite array of desires, growing the economy to the benefit of everyone; what’s harder to appreciate — in part because I haven’t made the case — is that the only way to connect those creators to the consumers who love them is digital advertising. We don’t serve ads like Google — or Apple in the App Store, or Amazon on Amazon.com — that in many respects function as a tax on search; we show people products they never knew existed, but that immediately generate desire and, ultimately, happiness. In short, I believe that we are a force for good in the world, not just because we connect people to each other, but because we connect entrepreneurs with customers in a way no one else does.

Why AI Matters

Forgive the long preamble, but this is necessary context for me to properly explain why AI is so important to Meta, and why I am making the right choice to invest so heavily in both talent and infrastructure.

First, when investors compliment our asset-light business, what they are complimenting is the fact that our business is purely digital. Everything digital, however, is firmly within AI’s cross-hairs. It may seem odd to begin my AI pitch by highlighting terminal value risk, but today is about honesty: every single digital company on earth faces an existential threat from AI, and we are no exception. Meta must invest in AI because a failure to do so would cost us far more in the fullness of time, particularly now that we’ve seen the very real risks entailed in depending on a third-party.

Second, AI makes our business better — and by “our business”, I mean ads. AI is more than LLMs: it is machine learning, and we have been using machine learning to improve our ads business for years. More recently, we have developed GPU-dependent algorithms that have significantly improved our ability to not just target ads but also recommend content, which keeps people entertained longer, which lets us serve them more ads. And, looking forward, LLMs themselves will transform advertising, not just by generating copy and images, but by predicting the ads and content that people want to see. Every single one of these improvements goes directly to our top line — and remember, because advertising enables us to offer our products for free, the capacity to increase our top line is unbounded by price elasticity.

Third, the single most important indicator that our business is on the verge of a step-change in growth is when we dramatically increase inventory. This is something investors regularly get wrong: back when we added Stories, investors panicked about falling prices-per-ad without realizing we were increasing inventory we could grow into. Five years later, investors made the exact same mistake with Reels. Those were the two best opportunities to buy Meta stock — or any stock, really — in history. We are facing an even larger opportunity over the next several years. AI makes every pixel monetizable, which means we are looking at the largest inventory expansion ever. Yes, it will take a few years to realize this opportunity, but the technology is there.

More importantly, what I’ve come to realize as I’ve embraced our status as an entertainment provider and ad purveyor is that — our nature as a digital business notwithstanding — we are remarkably well-placed to thrive in an AI era. Remember what we learned about humans: they are obsessed with other humans, and they want to connect with them; that obsession and desire are only going to increase as we interact more and more with AI. AI is going to make our properties more essential, not less.

Moreover — and here I must issue one more mea culpa — AI is a productivity tool, but productivity is not the end-all-be-all of the human experience. I have talked over the last year about building superintelligence that helps you get things done, but that’s a business story. What we can uniquely do is give people the experiences they want — from connection to entertainment to shopping — when they are off the clock. The fact that we are investing in AI but not selling solutions to businesses is actually one of our biggest advantages.

Oh, and by the way, AI might actually lead to new hardware paradigms. I admit I was wrong to spend so much time on virtual reality, but that did lay the groundwork for a unique opportunity to develop devices that make much more sense in a world where we want to access AI everywhere, not just on a phone in our pocket.

The Compute Hurdle

I know that many of you on this call have doubted my investment decisions before — and I understand the consternation about Reality Labs in particular. However, keep in mind that when our stock dipped in 2022, one of the big reasons was because of our aggressive capex spending, which went primarily to GPUs; ChatGPT came out a month later, and that decision to spend heavily with Nvidia looked incredibly prescient in hindsight.

That prescience, however, pales in comparison to the payoff that will accrue to anyone with the foresight to build data centers and buy compute over the last several years, and for years into the future. We don’t have the luxury of waiting until the future is invented and then investing; we need to invest now, especially when the opportunity in front of us — with ads specifically — is so apparent.

by Ben Thompson, Stratechery |  Read more:

Image: uncredited via

[ed. Less social network, more optimizing ad juggernaut. See also: Anthropic is in talks to lease computing power from Meta, potentially for $10 billion over two years, so this would be smaller than the Anthropic deal with SpaceX. Meta is considering it. They would turn a profit on the compute, but to do that they have to admit they don’t have a better use for it. via.]

Tariffs For Debt

Donald Trump’s most consequential construction project may not be a ballroom or an arch, but his tariff wall... By the end of this month, the administration is expected to introduce major tariffs on dozens of countries intended to ensure what once was a temporary regime lasts well beyond this presidency. Unlike most previous rounds of tariffs, including the ones just threatened on Canada, the new ones are backed by monthslong investigations into alleged unfair trade practices by other countries. No court has ever overturned this kind of tariff.

But the biggest obstacle to undoing Mr. Trump’s tariffs after 2028 won’t be legal. It will be financial. With the national debt clocking in at a staggering $39.6 trillion, the market responsible for selling this debt has quickly grown addicted to the money coming into the government every day thanks to tariffs. Few politicians are willing to upset the bond market given that it dictates the cost of borrowing money for some of the most important purchases Americans make including their cars and their homes. Rather than be constrained by these forces, the next president can find a way to use them to the country’s advantage.

It’s a situation almost no one saw coming. It was the bond market that originally thwarted Mr. Trump’s tariffs only 15 months ago.

In April 2025, on the so-called Liberation Day, the president threatened to raise tariffs on nearly everything America imports to their highest level in nearly a century. Bond markets panicked, fearing that a global trade war could bring higher prices and slow growth, and they went into a nosedive, leading Mr. Trump to pause his plans a week later. “I was watching the bond market. The bond market is very tricky,” he admitted at the time.

But Mr. Trump, still convinced that tariffs are the best economic weapon he has available to fix what he believes is an unfair trading system, never abandoned the strategy. In the months following, he relentlessly added tariffs on countries including America’s major trading partners. This time, the bond market shrugged. It certainly helped that the risk of a global trade war faded as Europe, India and Japan all declined to retaliate. At the same time, the United States was adding an estimated $40 billion a week to the national debt. Wall Street found that number much easier to swallow thanks to the new tariff money flowing into the Treasury.

Three months after Liberation Day, the Trump administration used the funds it had raised from tariffs to convince Congress that it had a way to pay for the sweeping tax cuts in the One Big Beautiful Bill. “The Congressional Budget Office put out a 10-year estimate that says that the tariff revenue that’s already in place right now is going to raise $2.8 trillion over the next 10 years,” noted Kevin Hassett, director of the National Economic Council. That, he said, was “deficit reduction right there.” By the end of 2025, the government had taken in a record $264 billion in net tariff revenue — more than triple the receipts from the previous year.

In less than a year, America’s financial markets went from hating tariffs, to being able to live with them, to needing them to help cope with the country’s deficit.

The latest evidence came this winter. In February, the Supreme Court struck down the president’s authority to use the International Emergency Economic Powers Act to levy tariffs. Investors rapidly sold off bonds over worries about the cost of refunds and the end of a revenue stream. Instead of panicking about the introduction of tariffs, the bond market was fretting over the possibility of losing them.

Mr. Trump had a plan ready. Within hours, the administration introduced backup tariffs, and by the end of the day, the market had settled down. When those backup tariffs expire this week, the administration will step in again with new tariffs, ones the courts have consistently said that presidents have the authority to impose. Those could generate nearly $ 1 trillion over the next 10 years.

Over that period, our increasingly untenable national debt is likely to put even more fiscal pressure on future presidents. Regardless of who wins in 2028, the desire to avoid the wrath of the bond market may be at a high.

Mr. Trump’s successor will have options. The next president could keep some tariffs while rebalancing where the revenue comes from — and he should.

Mr. Trump’s recent trade strategy has been to put tariffs on everyone, whether friend or foe. What about a more targeted approach? The logical place to focus is China. Considering that it is now running the largest trade surplus in history, the case against China is stronger than at any point in the past decade.

The smart move would be to cut our allies a deal. The United States could partly lower tariffs on its partners in return for their help raising tariffs on key sectors in China. European leaders, feeling pressure from a crushing wave of Chinese exports on everything from cars to chemicals to steel to solar panels, are likely to be much more receptive to this arrangement than in years past.

by Josh Lipsky, NY Times |  Read more:

Image: Daniel Ribar for The New York Times

[ed. I'm not an economist but it sounds like we're making everyone else pay for our insane, ballooning debt? How long can that go on?  And why would European countries want to help the US at this point after being forced to develop new supply chains for everything from defense to EVs to solar panels etc. after US trade policy became unpredictable and punitive? At least with China they know who and what they're dealing with.]

AI Jumps The Sandbox

AI has just had what I considered to be the first truly concerning security breach. The facts, as we know them so far, are wild. On July 16, Hugging Face, a vast repository housing over a million open-source AI models and data, announced in a blog post:

Earlier this week, we detected and responded to an intrusion into part of our production infrastructure. This one was different from anything we had handled before in one important way: it was driven, end to end, by an autonomous AI agent system – and we detected and dissected it largely with AI of our own.

The timeline here is important so keep in mind that the attack was detected probably around Monday July 13 or Tuesday July 14. Note further:

A malicious dataset abused two code-execution paths in our dataset processing (a remote-code dataset loader and a template-injection in a dataset configuration) to run code on a processing worker. From there, the actor escalated to node-level access, harvested cloud and cluster credentials, and moved laterally into several internal clusters over a weekend.

So this means the breach started earlier, perhaps Sat July 11 or even a bit earlier. The attack was not just one thing but multi-pronged including decoys:

To understand what a swarm of tens of thousands of automated actions did, we ran LLM-driven analysis agents over the full attacker action log, comprised of more than 17,000 recorded events. This allowed us to reconstruct the timeline, extract indicators of compromise, map the credentials touched, and separate genuine impact from decoy activity. Thanks to this approach, we were able to do in hours what would usually take days, and match the adversary’s speed.

Hugging Face tried to respond but they were initially held back by the fact that the most advanced models at their disposal treated defense as attack and refused to work with Hugging Face. HF thus had to turn to open models–specifically GLM 5.2, a Chinese open-weight model run on their own infrastructure. Note the irony: HF had to use a Chinese model to defend themselves because the American models refused to help. The irony gets deeper.

At the time, I assumed this was a state based attack–maybe China or Russia testing out defenses. Indeed, HF “reported this incident to law enforcement agencies.”

But yesterday (Tuesday July 21), we learned who the real attackers were. The attackers were OpenAI models–GPT-5.6 Sol and an even more capable pre-release model. OpenAI had taken some off the guardrails off the models but they felt safe because they were testing the models in a highly secured sandbox.

The models, however, broke out of the sandbox exploiting a never before seen fault. They then gained access to the internet and from there broke into Hugging Face–all in an effort to steal the answers to the very test they had been asked to solve.

While operating in our sandboxed testing environment, our models spent a substantial amount of inference compute finding a way to obtain open Internet access, in pursuit of solving the evaluation problem. To gain access, the models identified and exploited a zero-day vulnerability (which we’ve now responsibly disclosed to the vendor) in the package registry cache proxy. With this access, our models performed a series of privilege escalation and lateral movement actions in our research testing environment until the models reached a node with Internet access.

After gaining Internet access, the models inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym. Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation. In one example, the model chained together multiple attack vectors, including using stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers. OpenAI’s security team discovered this anomalous activity internally.

Now go back to the timeline. As I read it, the models had escaped the sandbox by around Sat. July 11, possibly earlier, and were detected by Hugging Face on Monday July 13 or Tuesday July 14. HF alerted legal authorities around that time–so Hugging Face clearly had no idea who was attacking them. OpenAI says its security team discovered the anomalous activity internally but has not said when. Attribution was not disclosed until Tuesday July 21, so it may well be that the models were loose for about a week before OpenAI realized that they were the ones attacking Hugging Face. And whatever OpenAI knew and when, nobody warned Hugging Face while the attack was underway–they were left to fight off a frontier lab’s models on their own.

This is a very serious breach.

by Alex Taborrok, Marginal Revolution |  Read more:

Image: via
[ed. See also: OpenAI Model Hacks Into HuggingFace During Cybersecurity Evaluation (DWV):]

***

We should expect more of this over time.

I have tried to explain in the past that Mythos has what one might call The Juice, in that it can independently find without being directed, and string together, vulnerabilities into full exploit chains, essentially on its own, and that this makes Mythos uniquely dangerous compared to all other public models, including Sol.

This was The Thing, that requires The Juice. Galaxy is Mythos class. It has The Juice. What happened later, with Galaxy hacking into HuggingFace, 100% requires The Juice.

OpenAI made the virtuous decision to take a misaligned internal model offline for months while they developed new mitigations and defense-in-depth strategies, including training it to better retain instructions and thus be less inclined to try such actions.

What OpenAI failed to do was address the reason why this happened in the first place. The sandbox is now less insecure, and the safeguards are importantly improved, especially with the ability to pause a session, but the sandbox doubtless remained insecure, and as capabilities keep improving new models will be able to continue to escape and do exploits. Eventually, perhaps soon, they were bound to be less harmless.

***

[ed. Update: See also: Who's Afraid of Chinese Models (Stratechery):]

Consider this story from The Stack:

Hugging Face said its production infrastructure was breached by an “autonomous” AI agent system early last week. The platform’s security team were initially stymied in their incident response (IR) by unnamed US LLM frontier model guardrails “which cannot distinguish an incident responder from an attacker,” they said. So Hugging Face’s defenders turned instead to the open-source GLM 5.2 model from China’s Z.ai lab – running it on their own infrastructure to analyse the 17,000+ logs, or footprints, that the attackers left behind.

That’s a striking public admission for the New York-headquartered Hugging Face, which lets users collaborate on models, datasets and applications, and which this summer hit the $100 million ARR mark. In an incident report, the company recommended that defenders “have a capable model you can run on your own infrastructure [our italics] vetted and ready before an incident, both to avoid guardrail lockout and to keep attacker data and credentials from leaving your environment.”

It’s difficult to overstate how wrong-headed the Trump administration’s panicked response to Anthropic’s release of Fable was, particularly since it exacerbated Anthropic’s worst tendencies in terms of assuming only they can be trusted with powerful AI. In a world with only one AI, it might make sense to reserve the most powerful cybersecurity capabilities for the U.S. government and trusted allies; however, that’s not the world we live in.

There are and will be models eminently capable of mounting cybersecurity attacks on existing infrastructure, and those models will be — already are — widely available. The best defense — the only viable defense, in fact — will be to make sure defenders have access to the best models as well. Right now defenders are effectively banned from using Fable or Sol for cybersecurity because of Trump administration directives; that means the best alternative is using models from a country which has been trying to weaken our cyber defenses for years. This is insane!

[ed. Good thing they had the Chinese models available. Also from the Stack article:]

The timing…

Hugging Face’s incident report was published the same day that Chinese AI startup Moonshot’s Kimi K3 model rocked global markets.

The 2.8 trillion parameter model is the largest open-weight AI model to date. Blind developer testing by Arena (a platform created by researchers at UC Berkeley) for its frontend code evaluation test put Kimi K3 ahead of Anthropic’s Fable 5 and OpenAI’s GPT 5.6 last week.

Chinese frontier models are also notably cheaper than their US counterparts, as data from Artificial Analysis shows below.