via:

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

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 of Dusty in a long time until hearing her again last night. She was great. 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.]

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

***

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 the ad juggernaut. 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.

Shakira performs during the Topps Final Halftime Show at New York New Jersey Stadium on July 19, 2026 in East Rutherford, New Jersey.
Image: Lars Baron/Getty Images

[ed. Not a World Cup halftime show fan especially, but love the exuberance in this picture.]

Line People

It’s a Sunday afternoon late in June, and as is the case pretty much every day of the week, there is a line at Caffè Panna, an ice-cream shop in Greenpoint that sells perfectly Instagrammable scoops of artisanal scrumdiddlyumptiousness. Though the store doesn’t open for another five minutes, there are more than 50 people, two stroller babies, one Italian greyhound, and one Maltipoo waiting in an orderly line on the sidewalk outside. For every customer who eventually claims their order — “For Hannah! For Harry! For Anna!” — and walks off devouring it, another two seem to appear, creating a never-ending human centipede that stretches from the window, down the block, past an advertisement for the new Olivia Rodrigo album, and around the corner onto another block. The line is inching along slowly, and the clouds are threatening rain, but no one seems bothered. Everyone is beaming. Passersby — bicyclists, drivers, and pedestrians alike — slow down when they encounter the thing, occasionally letting out a squeal of wonder (“What is this line?”) or judgment (“What is this line?”).

Within a quick walk, there are plenty more lines in the neighborhood: for pastries (at Radio Bakery), tacos (Taqueria Ramírez), pizza (Chrissy’s), coffee (Rhythm Zero), matcha (Kettl), ceviche (Mariscos El Submarino), katsu sandwiches (Taku Sando), and more katsu (ACRE). Even during the week, in the middle of the day, it’s not a rarity to see a line of phone scrollers dawdling down India Street toward Radio Bakery. “Is that a bread line?” a visiting family member, looking perplexed, asked me completely seriously not long ago when we strolled past. In some ways, I tried to explain to him.

New York has always had lines for the sorts of experiences you can’t get anywhere else: Broadway tickets, skyscraper observation decks, Cronuts. This summer’s lines, though, can seem borderline ludicrous: three to a street, blocks long, often for the types of things you can get almost anywhere in the city, like bagels, pizza, and pastries. They emerged slowly over the past few years and then like a flood, a cumulative effect of TikTok constantly showing all of us what we are missing out on in our very own boroughs. The temptation is almost too strong — why not take the train 15 minutes to figure out if that slice of pizza is as good as everyone on your “For You” page is telling you it is? “It’s herd mentality,” one young woman, nearly rolling her eyes at herself for joining the masses, tells me at Myka, a fro-yo chain that is arguably the site of this summer’s longest lines. Now, across the city, especially along the Brooklyn waterfront and in much of downtown, people are waiting for an ever-diversifying assortment of viral snacks and “sweet treats” (to use the preferred language of Instagram influencers), all the while petting one another’s dogs, gossiping with friends, minding their toddlers, checking Slack, and scrolling away their remaining time in line. As one TikToker captioned a video taken in the West Village on the first nice day of spring in March, “The sun is out and New Yorkers are back with their favorite activity of waiting in line.” A company called Same Ole Line Dudes (tagline: We Wait for Your Wants!) will even wait in line for you, starting at a price of $55.

Naturally, some New Yorkers are getting persnickety about the situation. “I would never stand in a line. It just seems so déclassé,” the podcaster Francesca Root-Dodson tells me. When I run into a Real Housewife I know on my way to wait in line at Caffè Panna’s original location in Gramercy Park, she says, “When I was growing up here, standing in a line was not a cool thing. Now there’s a whole culture around it. The only thing worth waiting in line for is a Balenciaga sample sale.” The Caffè Panna line is monitored by a camera installed by a mysterious website called damnlines.com. (One Wednesday in July at 4:20 p.m., the website estimates a 25-minute wait; near 30 people are in line.) As such haters’ thinking usually goes, waiting in a line is the lemminglike behavior of tourists in Times Square trying to get a deal on last-minute tickets for The Lion King. Standing in a line is what you do pissily at the airport or Disney World. (Also, self-described real New Yorkers like to say you wait on line, not in one. The mere mention of this distinction can send people into long, impassioned debates about the importance of regional dialect.) “These kids today on these stupid lines,” Dorothy Wiggins, a 100-year-old influencer and West Village resident recently complained on Instagram. (Note her use of on.) “It’s crazy! Just crazy!” Her hairdresser, also featured in the video, shared that she would never stand in line because she grew up under communism.

Others have taken to championing the line people. “I don’t like when people make fun of the people who stand around in long lines,” the downtown writer known as Sotce recently wrote on her Substack. “Some people read Substack, some people wait in a line. Some people have vintage denim and read books by dead people. And really we all die.”

“I’m not somebody who would wait in line,” the owner of the Italian greyhound tries to assure me outside the Greenpoint Caffè Panna. Yet she has just done exactly that, waiting 15 minutes for her Nutella Crunch ice cream, which she doesn’t sound at all embarrassed about as she devours the ice cream in less time than it took to order it. “When you see there’s a line, it means the place is good,” her friend, the owner of the Maltipoo, tells me. Just the day before, she went to Caffè Panna’s other location, where she also waited in a line. Nearby, two sisters, tourists from Boston, snap photos of their scoops before digging in. “All the lines have felt worth it,” one tells me; for breakfast, they waited in a line at Apollo Bagels in Williamsburg. They’d seen all this hoopla on social media and couldn’t resist sussing out the hype for themselves. “I would rather stand in this line here than go figure out another place to go,” one says to me.

by Brock Colyar, Curbed |  Read more:

Image: Natan Dvir

***

Early on the first summerlike evening of the year, New York’s Greenwich Village was abuzz. Restaurant patios were packed, with waiters shuttling bottles of crisp white wine to diners. On University Place, the queue for frozen yogurt at Mimi’s stretched half a block, bending around the corner. I considered joining it. A New York University student told me the line quadruples after 7 p.m. His friend chimed in to call it “the hottest club in New York.”

Whether it’s an hourlong wait for a shawarma at TikTok favorite Miya Miya in Los Angeles or a two-hour queue for pastries from the Cedric Grolet Opéra pastry shop in Paris, long lines for viral foods and popular restaurants have become a feature of urban landscapes. And summer is peak season for them.“These long lines circulate via social media,” says Emily Contois, an associate professor of media studies at the University of Tulsa. Posts of queues “create and re-create a representation of popularity and virality,” not just for locals, but for anyone online. A recently published study of tourists waiting in food lines in Amsterdam found that 84% of them had seen videos of those lines on TikTok and 54% on Instagram. Lines have become tourist attractions and social experiences in their own right.

Over the past year, I’ve lined up for coffee in Shanghai, for croissants in Edinburgh and for bagels, pizza, cinnamon buns and, most recently, frozen yogurt at home in New York — all with hordes of others joined in the belief that good things come to those who wait.
A recent survey of more than 3,000 US consumers found that 60% of Gen Z respondents reported waiting in line for more than 30 minutes for a specific food. Among all the age groups, 74% of those said the wait was worth it. Experience enough of these lines, and you’ll see they’re more than a byproduct of imbalanced supply and demand. They’re places where complex social, psychological and economic theories play out, one slow-moving step at a time.

Social media posts have made waiting for Mimi’s part of the experience for many customersPhotographer: Yuvraj Khanna for Bloomberg Businessweek
In the queue for Mimi’s, I passed the time people-watching, scrolling on my phone and chatting with line mates. Among them was Athena Yan, from Shenzhen, who’s studying for her master’s in urban planning at NYU. She told me she’d been drawn to Mimi’s by its online cachet, but now she appraised the queue through an urban planner’s lens. Lines like this, she said, offer a social benefit: They “make the street look more energetic, more alive.” Her interest wasn’t strictly academic. After conquering the line and procuring her yogurt, she said, “I’m going to post it to my Stories.”

by Matthey Kronsberg, Bloomberg |  Read more:

Image: Yuvraj Khanna

[ed. Beats sitting on your couch at home watching tv, I guess.]

Seasons of the Eskimo: A Vanishing Way of Life, by Fred Bruemmer, 1978

via:

Hanko Kajita, The Tale of Ise, “In the Musashino field”
via:

How Leprosy Was Used as a Weapon Against Hawaii’s Indigenous Population

Father Damien was a dirty man. Everyone agreed on that. Dirt would accumulate under his fingernails; he rarely washed his hands. His clothes—his habitual cassock and wide-brimmed hat—were worn for days on end; he saw no reason to clean his hut in Kalawao, the leprosy colony on the Hawaiian island of Molokai. His detractors claimed he lacked elegance too: burly with a “piggish” head, they complained, he squinted from behind a pair of wonky wire-framed circular spectacles. He was eager to learn Hawaiian, it was noted sniffily, but otherwise he had little enthusiasm for languages. His Latin came only by official requirement, his English was sparse; a native Flemish speaker, even his French was stilted. The prose of his letters lacked refinement whatever their language: “coarse…headstrong and bigoted” was one particularly vitriolic posthumous assessment.

None of this worried Damien. Hawaiian did him fine. From that messy home, built in sight of Kalawao’s cemetery, Damien wrote to his brother Auguste, also a priest, to say that Molokai was exactly where he wanted to be.

"We eat what Providence sends us. The calabash of poi is always full; there is also meat; water in quantity, coffee and bread sometimes, wine or beer never. As I have had to work all week and cook on Sunday, you will excuse me if my hands are not as clean as yours, which do nothing, I suppose, but turn the pages of books. Sometimes the plates are not well washed either. But what matter. Hunger and habit make us eat just the same. For dessert, we smoke a pipe. That finished, quickly back on the horse."

In the saddle, Damien would cross Molokai’s mountains; his parish over two thousand square kilometers, he rode down the island’s valleys beyond Kalawao itself, across water and through fields to find the most remote of his parishioners. Today it is a national park, wild garlic growing in fragrant profusion, its white flowers poking up between ferns, yellow hibiscus and amid the ki-tree, the roots of which were used to brew a potent beer. Across this paradise the finch-like honeycreeper flits, feeding off the red spindly flowers of the evergreen ‘ōhi‘a lehua tree. Damien would haul building material and basic medical supplies with him, eager to provide practical as much as spiritual comfort (though he never left without an ad hoc altar of four sticks and a plank). Sometimes he had to abandon the horse and mules to scale by hand and foot the sheer cliff faces which routinely stood between him and his flock. For Damien dirtiness brought him closer to godliness, the grime evidence of his graft.

For his colonial masters, those disdainful of his personal habits, the priest’s life was alien at best and an affront to Western order at worst; religion was supposed to be a cleansing antidote to indigenous habits, a washing-away of the idolatry and idleness they projected onto the population, be they sick or healthy. Yet here was Damien, adopting their ways, it would seem, along with their language. His body and the leprous bodies of his parishioners were dangerously entangled even before he himself succumbed to the disease. This was 1872, and the pious (and patronizing) commentators of the time muttered that Damien’s unvarnished personality was due to his simple farm upbringing in rural Belgium. “It is absolutely beyond doubt that he contracted the disease through his careless ministrations and uncleanly personal habits,” a representative of the Hawaiian Board of Health tutted, though not without something approaching admiration, noting the priest “would have leper boys at work in his kitchen so that he could give more time to his ministrations for others, being busy from peep of day until long after dark.” Dirty of body, dirty of mind, would be the eventual assumption: sexual proclivity was whispered. Damien’s brother, reading Latin scripture in a clean cassock 12,000 kilometers away, faced no such danger and no such accusations of moral lapse.

Damien was never supposed to achieve the fame he did, a symbol of global imperial paranoia and catalyst of religious fetish: a bronze statue, in which he wears his wide-brimmed hat, stick in hand, now represents the state of Hawaii in the US Capitol building’s Hall of Columns. He was never supposed to be the subject of culture wars in his lifetime and long after: in 2020, Congresswoman Alexandria Ocasio-Cortez decried the choice of a white man as Washington’s symbol of the Polynesian fiftieth state. Damien was supposed to stay working in his parents’ fields in Tremelo, a dull village in a duller part of Belgium. The fact that celebrity landed upon him, plucking him from the obscurity of his mission to represent the burgeoning discourse on the disease, says more about the world that orbited him than his actions on the island or his own political nous. There were plenty of other missionaries, in plenty of other colonies, with plenty of other health issues, spreading religion and the soft arm of imperialism. Leprosy, however, had become totemic of a moral depravity or sexual freedom that Europeans had long imagined pervaded the South Seas. The leprous, lascivious body was a perversion that Western proselytizing could fix.

***

For indigenous Hawaiians, leprosy arrived as Damien did, an unwanted visitor from across the sea. The disease may have stowed away as early as Captain Cook’s colonial voyage, but it only became regarded as a public health issue eighty years later. “The commander manifested a laudable humanity, in endeavouring to shield the population from the evil effects which so inevitably result from connection between foreign seamen and the native females,” wrote one sympathetic European account of Cook’s trip. The evil effects weren’t just moral turpitude, but disease too. “But his efforts were in vain. If the discipline of his own crew could have been strictly enforced, the eagerness of the women was not to be repressed.” Historically, this genesis, regardless of whether the women were actually consenting, inextricably linked sickness with sex in the minds of Hawaiian and colonialist alike, with the former’s lack of conformity to Western and Christian mores taking the brunt of the responsibility in the minds of the latter.

The haole—the white incomers—dodged blame from among Hawaiians for leprosy too. As cases multiplied, the disease became known as ma‘i pake, the “Chinese sickness,” named after the thousands of Chinese laborers who arrived on the islands at the invitation of American traders endeavoring to create an export market in sandalwood (a precursor to the sugar industry that would dominate the economy in years to come). “There seems but one way to prevent the whole of Oceania from becoming leprous, and that is the exclusion or the rigid control of all Chinese coolies,” a Scottish physician warned. Wherever it came from, contact was devastating for the indigenous Hawaiian population, which plummeted from the healthy 683,000 people Cook first encountered to just under 40,000 Polynesian islanders left after a century of colonial enterprise and disease.

by Oliver Basciano, Literary Hub |  Read more:

Image: Kalaupapa, Molokai

Our Uncertain Uncertainties

Even the experts inventing AI don’t know what will happen next. Is artificial general intelligence even possible? Can scaling continue? Will we need massive compute centers to make AI, or can we do it with a mere 25 watts like we do in our brains? What will humans do as AI gets smarter? What does the future of the economy, of warfare, or civil society look like?

Everyone has a different guess. The people creating the machines have as many different ideas as the onlookers, the pundits, the other scientists, and the wisest among us. No one knows. There is a vibe that we’ll know within the next three years. For some, the pace of change suggests that if things continue as they have been, by 2029 at the latest, the outlines of an AI-first world will have emerged. By then we’ll have answered the question of scaling, we’ll have seen the effects on employment, and we’ll have felt its acceleration in the economy – or not.

That’s a reasonable, and not outlandish scenario. But I offer an alternative scenario which I think we should also keep in mind: AI continues to surprise us at its core. As AI continues to evolve rapidly there will be no resolution to these questions in 3 years. By 2029, we still won’t know if AGI is possible, we can’t tell if employment is disrupted, and we still can’t say if it is worth the huge investment. I don’t mean AI progress stalls. I mean, AI continues to advance, but the new stuff doesn’t answer the old questions, it only expands our ignorance because the new is new in a new way. We have to alter our ideas (and measurements) of employment, we have to amend our concepts (and measurements) of the economy, and we have to shift our ideas of what AI even is.

In other words, we have a sustained, extended period of uncertainty. Not just a few years, but a decade or more. As AI continues to progress, rather than resolving our perplexity, it expands it. So for the next 10-15 years we have perpetual, continuous, severe uncertainty. This is a burdensome weight because people hate uncertainty more than bad news.

It goes deeper. AI is only one leg of this grand uncertainty. In the next decade the US will continue its slide off its pinnacle of a sole global superpower, while China continues to rise in power and prestige. This shift toward a duopoly prompts a new world order, and no one – especially the Chinese and Americans – knows how this will play out. The uncertainty around this shift is nearly boundless, and yet its indeterminate consequences will affect everyone in the world, but especially the US. Being dethroned from the century-long position of sole #1 will be a huge psychological blow, and the uncertainty of what follows will weigh heavy on all aspects of life. The uncertainty of a new role spreads over China as well, because while they are zooming ahead at 1,000 miles per hour, they have no idea where they are headed. The uncertainty of global relationships and new national identity, plus the uncertainty of individual worth and identity from AI increases the overall uncertainty levels to new highs. All this is a very large puzzle and will not be resolved in 3 years. This will be a sustained uncertainty.

It goes deeper still. After a long first wave of true globalization, there are now whirlpools of chaos and polarization as nations adjust to world-wide immigration and the borderless spread of modern culture, causing chaos in national politics, and sowing mistrust with the establishment. Anarchy, disruption, contrarian antics, blows to the states, seem to be the norm in countries all around the world. This wild chaos is being fueled in part by the new technologies of social media which have replaced the managed care of established media. News now is far more volatile, hard to control by anyone, and further elevates the already amplified uncertainty. There is a visceral sense that civics is headed into an unknown territory of near-permanent provisionalism.

Additionally, AI also forces even the most moderate person to question the truth of what they read, see or hear. Is that real or AI generated? How much has been manipulated? Who do you trust to disclose what is real? How do we come to agree that something is true? The traditional mechanisms of trust have been damaged by AI, so that this new technological realm generates a huge uncertainty. As AI gets more skilled at imitating reality, this uncertainty is likely to keep increasing for a while, and not just 3 years. The uncertainty meter is now deep in the red zone.

Finally, the ambiguity and indefinite nature of AI, or human identity, or whether what we see is real or generated, means that we are entering a period where we are even uncertain of our doubts. Our uncertainty is so deep and durable, yet elusive, that we will have extended uncertainty about whether we are uncertain. We can have major agreements on what we know versus what we don’t know. In the model of Rumsfeld’s Unknown Unknowns, we will be confronted by Uncertain Uncertainties. And they will prevail for at least a decade or more. [...]

Given the inherent unknowability of this era, what would some of the signs be that we are in it? They might look like this: in 5 years, 1) There are high-profile disagreements among leading AI researchers on whether AGI is here. 2) Reputable economists can’t determine if productivity has increased or decreased. 3) Lower public confidence in media platforms and established institutions. 4) The US and China cannot decide whether they are allies nor adversaries. 5) There are ambiguous spikes in employment rates in both directions. 6) Medical levels of anxiety increase. 7) Major court decisions leave as many questions as answers. 8) Commitments (marriage, work) are postponed even later in life. 9) Investing, capital allocation becomes more expensive. 10) Nihilism gets respect.

A great question to ask when creating a scenario is what could prevent it from happening? Maybe there is not a single force that can undo this sustained uncertainty, but perhaps it is a mixture of several. If AGI arrived without a doubt in 3 years and China took over Taiwan despite the US’s actions, and if companies found a way to embed reliability and trust in media, then maybe this extended uncertainty could cease.

A second question to ask, is if we find ourselves in this scenario, what should we do about it? The most effective response to this multi-layered persistent uncertainty is not to seek impossible stability, but to cultivate radical adaptability and radical optionality. Give up on having a reliable prediction of what happens next. Instead cultivate multiple scenarios of what could happen, and endeavor with each of them to maximize your options. Goals should be considered as disposable hypotheses, constantly ready to be discarded and replaced by better-fitting concepts later on. You will be dead wrong on 19 out of your 20 expectations, but at least one of them will allow you to proceed. Make your decisions not on whether they are “right” but on whether they tend to give you more options later.

In our era of uncertain uncertainty, certainty will be the killer. In this era more downfalls will happen because of overconfidence than questioning. The key is to not get stuck on just one option. You have to become at ease holding multiple contradictory possibilities at once. (To prevent yourself from being swept away by the latest current and fashionable whim, this radical adaptability must be anchored on a steadfast set of unchangeable virtues, as corny as honesty, or as slick as generosity.) The strategy for prospering in prolonged uncertainty must be one of constant, agile recalibration.

In short, in our age of uncertainty, you have to get good at changing your mind.

by Kevin Kelly, Substack |  Read more:

Image: uncredited

[ed. The diagnosis might be right but the prescription seems weak. Flexibility and adaptability are always good qualities to cultivate, but the challenges confronting us require more. Here's an example of embracing multiple contradictory possibilities: maybe in times of uncertainty we double down on the few things that we actually can be certain of. How? By making good choices, before and after AGI. For example, Buddhism starts with the acknowledgement that life is hard. It's what you do after internalizing that fact that matters. There are value systems and paths that can lead to a meaningful life, or enlightenment if you want to call it that, but we have to make the right choices if we're to find them. Love, family, friendships, ethical living (like the golden rule) are common values we all share. So why not embrace those values as tightly as we can while navigating the stormy seas to come - and using the best minds in the world (that are being born as we speak) to guide and assist us in strengthening those bonds? This might be one of the benefits of AI: forcing us to reorganize societies in ways that might never have been possible before, or even imaginable. If we make the right choices. Developing Plans A to Z and having 20 options each or something like that sounds like a Hunger Games scenario to me - all reaction and no responsibility. We have the opportunity now (even if forced) to redefine our human destiny. The choices we make will define our places in the future.]