Outsourcing Mom

Worried About Your College Kid? Now You Can Hire a Local Mom. At some universities, parents can pay to make sure their children are cared for, the way they were back home.

Concierge companies offering student support have existed for decades. But in recent years, a new crop of upstarts — such as Horwitz’s company, MindyKnows; the Bama Mama in Alabama; the GA Mom in Georgia; and Campus Mom in Texas — have met additional demand from a new generation of worried parents.

Historically, college has been a time for young adults to gain a sense of independence. They run their own errands, do their own laundry, organize their own lives. That’s changing, according to Monteigne Long, a co-founder of Campus Mom, who said the parents of this generation of college students are much more involved in their children’s day-to-day lives.

“When I came to college in 1998, my parents dropped me off on a Saturday and were gone,” Long said. “Parents are staying much more tuned in and connected with their students’ college experience than they have in the past. When they can’t be here, they want someone who can be that stand-in for them.”

The arrangements, though, have drawn occasional criticism online and from experts who worry young adults won’t learn enough essential life skills with so much additional support.

“Sometimes people think that we’re coddling students,” Horwitz said, “and I just don’t think that anything could be further from the truth. They’re 18, 19 years old, and they’re just off independently for the first time. We’re just a support system.”

The specific services vary here and there, but share commonalities. Campus Mom offers “holistic wellness check-ins,” laundry services and sorority recruitment support packages, sent to the sisters to up a child’s odds of acceptance. Carrie Eckhardt, the Bama Mama, will clean students’ dorm rooms and check in if parents haven’t heard from their child in a few days (“just pop in and say hi, and take a picture and send it to their mom”).

This month, one parent told Eckhardt it seemed like her daughter was going out to a bar every night. But she had opted to stay in, and the mother wanted to celebrate the decision with a “stay-in basket” that included her favorite ice cream, cookie and socks.

Horwitz, for her part, brings students balloons on their birthdays and chicken soup when they’re sick, sits with them in the emergency room and picks up their prescriptions if they’re busy. She bakes homemade challah, coordinates with the bedbug exterminator, texts photos and updates to faraway parents and doles out recommendations on the best local doctors and landlords.

by Kristi Albert, NY Times |  Read more:

Image: Kate Munsch for The New York Times
[ed. Why not cut to the chase. Just hire somebody to take all their classes and summarize everything with AI. Of course, cheating on tests would be an optional additional expense.]

Jacob Coxon Warns of Human Extinction and Triggers a Preference Cascade

CEOs of major AI labs, and employees of major AI labs, including OpenAI and Anthropic, often say they plan to build superintelligence soon, as in within a few years create AIs that are superior to humans at essentially all cognitive tasks.

They often warn that such AIs might kill everyone. Or that AIs might cause mass unemployment, cause cyberattacks across the internet, enable mass surveillance or risk causing any number of other highly bad things.

These warnings are consistently and directly against the interests of the labs. Yet the warnings have recently gotten a lot louder and more frequent. OpenAI has been practically screaming, for those with ears to listen, on many occasions.

A series of events, over two months and especially the last week or so, including internal observations of the pace of progress at OpenAI and also Anthropic, have freaked out everyone involved quite a lot more than they were already freaked out.

After all the events, plus statements by Dean Ball and Jakub Pachocki, we were seeing the beginnings of a preference cascade.

Then along came Jacob Coxon as the tipping point, and things took off.  [...]

Jacob Coxon Resigns From Anthropic In Protest And Sounds The Alarm 

Jacob Coxon spent the last three years doing pretraining research at both OpenAI and Anthropic. He has come to realize that everyone involved is being wildly irresponsible.

He warns us: They are racing straight to superintelligence and gambling with our lives. I agree with and strongly endorse his statement.

If anything he sounds like an optimist. He’s asking you to consider what the next few years will actually feel like, which means he thinks you have a few years left.

Jacob Coxon (former Anthropic and OpenAI, 160m+ views, September 8): I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.

Do not underestimate the power of this technology. These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources. We have all witnessed the progress in each of these domains, and progress is not slowing.

The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anything, many executives and senior researchers will couch their phrasing in the press to sound sensible - but I hear the same people express fear privately. No other human activity poses this level of danger.

A common response is “if they truly believe this, why are they still building it?” At OpenAI, many have not deeply internalized the civilizational stakes. At Anthropic, the stakes are well-understood, but they are locked in a race to get there first - they believe no one else will act responsibly, so they must do it themselves, despite the risk.

Accepting this race and entering the “endgame” is a hubristic gamble that should not be launched from a private company’s Slack. Attempting to speedrun alignment should require extraordinary confidence that there are no better trajectories available.

I am optimistic about the potential for coordination. Warning shots like the Hugging Face attack have made pacing agreements between U.S. labs more viable. I don’t feel like we’re on track to prevent a global race, which may require costly actions such as a temporary ban on improving model capabilities.

If you are a lab researcher, I urge you to consider what the next few years will actually feel like. Do you want to kick off a superintelligent RL run without a rigorous understanding of its mind? Should you put your head down because “it’s happening anyway” - or take this moment to call for different conditions?

Jacob Coxon (WSJ interview): We’re on track for a lot of the most aggressive of these scenarios where by the end of next year things could be out of control already.

If you want Jacob Coxon’s full views, I recommend his interview with Wired’s Maxwell Zeff. This thread has extensive quotes.

Here is Jacob Coxon doing a 5 minute interview with Anderson Cooper. He speaks well and plainly, and it is clear how much the events of the last two months have made it much easier to speak plainly to a civilian like Cooper about what is happening.

Here is Jimmy Kimmel doing four minutes on this. He gets it. How is this not the top news story on every site, indeed.

Yes, this is a common view, even if few have the courage to act.

Alex Turner: I left Google DeepMind in June. Jacob is right: many researchers believe they are building something that could kill everyone on the planet. It was literally my day job to think about how to stop that.

That tells you how bad Alex Turner thought DeepMind’s actions were with regard to the Department of War. His day job was that he got paid by Google to think about how to stop AI from killing everyone, and he felt morally obligated to quit in protest.

Derek Thompson here writes about this as part of AI Safety Is Having a Moment.

If you want to see the full list of lab employee quotes from the preference cascade, I compiled them into another post today. [...]

A few days ago, I had no idea who Jacob Coxon was, and I was not alone.

The timing of a preference cascade is difficult to predict. Once they start they can happen very quickly. You don’t want to talk until you are confident others will, and at some point the evidence that others will follow can snowball and it happens. A classic concrete example was replacing Biden in 2024.

Derek Thompson: what’s weird is that, in a way, this is breaking thru even more than huggingface! ... and it’s just a guy nobody had heard of reiterate a position that his CEO has said on podcasts 1,000 times

Why was Coxon able to set off a preference cascade? How did this one break through?

A confluence of factors, all of them downstream of the obvious actual reason, which is that there is a good chance that AI kills everyone soon.

by Zvi Mowshowitz, DWAV |  Read more:

[ed. Who's betting on reason and political courage to win the day? Uh, huh. First of all, agree on a temporary halt to recursive self-improvement (AIs improving AIs). Second, delay/suspend IPO's (even if most of the current economy is driven by AI spending and debt). Third, light a fire under politicans (it's been done successfully with data center buildouts). At this point, just one of these would be a big win. Here's Nate Soares, co-author of the book If Anyone Builds It, Everyone Dies: A case for courage, when speaking of AI danger (LW). Also this:]

***

Terry Pratchett: “Some humans would do anything to see if it was possible to do it. If you put a large switch in some cave somewhere, with a sign on it saying 'End-of-the-World Switch. PLEASE DO NOT TOUCH', the paint wouldn't even have time to dry."

***

UPDATE: Dario Amodei (Anthropic) proposes a three-point plan. Original here (We Must Pace the Frontier.]

Slaves to the Algorithm

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

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

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

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

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

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

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

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

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

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

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

That man’s name was Greg Chism.

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

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

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

But then he tried something different.

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

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

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

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

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

Greg suggested “Toy Slime Gumball Party” instead.

“Perfect,” the teacher said. [...]

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

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

“But why?” I asked him.

Greg shrugged. “I don’t know.” [...]

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

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

And then he had it.

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

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

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

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

“She canceled,” Greg said.

Greg had no idea why.

by Jon Ronson, NY Magazine |  Read more:

Image: Chris Buck

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

Higher Education? Inside Alabama's $47,000,000 Golf Facility

In 2024, the University of Alabama opened the Crimson Reserve golf facility. Built on 164 acres, at a cost of over $47,000,000, it could be the best practice area in golf. With design input from Justin Thomas as well as Davis Love III, the Crimson Reserve features a 400-yard long driving range, a custom-designed 9-hole course and a 24,000 square foot clubhouse, complete with a gym, putting studio, hitting bays and locker rooms.

~ Golf Digest via YouTube

[ed. Priorities. I'm not even going to try looking up what this school spends on football. For a better look inside, see this video.]

Does Writing Matter Anymore?

Injecting ideas into the discourse is incredibly powerful. John Maynard Keynes famously described the power of idea injection:

Practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist. Madmen in authority, who hear voices in the air, are distilling their frenzy from some academic scribbler of a few years back.

To describe why idea injection is so powerful would take an entire post (which I do intend to write). There are a number of reasons. First, idea injection allows you to frame the terms of the debate. Whether people think your idea is right or wrong, once you put it out there, discussion of the issue at hand turns into discussion of whether your idea is good or bad.

As Keynes notes, an early writer’s ideas can also act as a kind of training data for later thinkers; it becomes a foundation off of which politicians, bureaucrats, staffers, other writers, and even entrepreneurs and financiers build when they make their own ideas. [...]

But injecting ideas is only one part of a blogger’s influence. We’re also part of a community of intellectuals that span multiple disciplines and walks of life. On a daily basis I get to mull ideas over not just with other writers and pundits, but also with top academics, CEOs and entrepreneurs, Congressional staffers and political advisers, think-tankers, corporate researchers and engineers, and plenty of people from other countries. This leads to a much richer discussion, with a greater diversity of viewpoints, than almost anything else I can think of. And they reach a very wide set of ears. In a way, blogging is like DARPA — ad-hoc multidisciplinary teams that build the rapid prototype of an idea. OK, maybe that’s a bit pretentious, but you get the point.

Anyway, the reason I’m writing all of this is not to brag, but to complain. Over the last two years, I’ve felt like my job has become a bit less important than it used to be, for three reasons:

1. The rise of populism on all sides of the political spectrum in the U.S. means that smart ideas are simply not as likely to be implemented by the people in power.

2. The general shift to Substack and other monetizable direct-to-audience channels has made punditry less conversational.

3. The rapid proliferation of AI writing has increased the demands on readers’ attention (including my own).  [...]

Monetization means intellectuals are siloed

“Writing is like prostitution. First you do it for love, and then for a few close friends, and then for money.” — Ferenc Molnár

Substack has done a whole lot of good, both for me personally and (more importantly) for the world. In a time when most of the internet has been taken over by malignant opportunists and sensationalist attention-seekers, Substack stands as a lone island where reasoned, intelligent, earnest debate is still possible. It has also allowed many writers to escape from publications that stifle their voice, impede their development, and don’t pay them their due. In many ways, Substack has resurrected the old blogosphere from the early 2010s.

However, this resurrection has come at a price. Substack’s killer feature — email distribution — allows writers to get much larger and more loyal audiences, and to make a lot more money by charging those audiences for subscriptions. But this creates a financial incentive for writers to spend more time serving their customers and less time talking to each other.

In 2011, I was blogging part-time, because it was fun — the attention that mattered was when Brad DeLong or Paul Krugman or Tyler Cowen was interested in something I had to say. It was a little “republic of letters”. Now I’m blogging full-time, and having a conversation with Brad or Paul or Tyler is still just as fun and stimulating, but it’s a distraction from my job of creating content for my paying audience. There are still interesting intellectual debates and exchanges in the blogosphere, but they are no longer the main thing writers are rewarded for.

Turning intellectuals into content creators tends to put them in siloes. And Substack is far from the strongest in terms of silo-ing. Most of the internet is being taken over by vertical-scrolling short-form video, which is not exactly good for conversation and exchange. I could go start a YouTube channel, but it would just be me talking directly to my fans — I’d basically be a TV talk show host. I might still do this, because it’s a high-leverage way to influence the world, but it’s not as intellectually rich or rewarding as being part of a round-table conversation.

Nor are interesting new ideas as likely to emerge from one-way siloed content creation. Ideas emerge not from singular minds in isolation, but from dialogue — the cross-pollination that the blogosphere and other intellectual communities create isn’t just fun, it’s productive. Writing for you, my readers, is not boring, but you’d get better content from me — and from all your other favorite writers — if we talked to each other more.  [...]

AI is stretching our attention to the breaking point

“My ambitions accelerate. My afternoons do not.” — Claude

Unlike many people, I think AI writing is actually pretty good. Yes, there’s a recognizable style that the basic models use (“It’s not X, it’s Y” and lots of other little cliches). That style isn’t bad, it just gets overplayed when everyone uses it. Yes, AI models are still not great at boiling a complex idea down to one or two pithy sentences. But you can modify the style that AI uses. And AI can do plenty of things human writers can’t — it can seamlessly incorporate vast knowledge and novel data analysis into a piece as it writes it.

For example, I immediately suspected that this essay by Aaron Brown, Michael Mendelson, and Cliff Asness, on the confusion of the debate over “affordability”, is mostly AI-generated, and Pangram — the most reliable AI text detector — flagged it as around 50% AI. But that’s not a knock against it — the essay is great. It classifies different kinds of “affordability” problems — true poverty, precarity, downward mobility, etc. — into different buckets, gives some illustrative vignettes, and provides some useful numbers about each one. I broadly agree with the article’s conclusions, and I think it’s a valuable addition to the discourse.

A bigger problem is that in a world where a huge number of people generate effectively infinite amounts of good-quality content like this, it becomes hard for readers to decide where to allocate their attention. Instead of identifying the few most consistently useful blogs and reading those in great detail, a lot of people will respond to the explosion of content by “reading” a larger number of posts but only lightly skimming each one.

It’s not my job I’m worried about here. It’s that in that world, even if my blog continues to get tons of readers and make me plenty of money, what I do becomes less important. If people are just skimming what I write so they can move on to the next 10,000-word Claude-generated post, the fact that they’re paying me $10 a month is cold comfort — I’m not really reaching them. And even more worryingly, no one is reaching them — if they’re skimming 100 posts a day instead of reading 10 all the way through, they’re not getting really good information from anywhere.

I don’t know how severe this problem will be, to be honest. There was always a lot more high-quality content on the internet than anyone could ever read, and a lot of people always just skimmed my posts instead of reading them closely. Maybe AI can’t make this problem worse because it was already maximally bad.

Also, I’m optimistic that AI itself will open up new channels for intellectual influence. It’s a well-known fact that if AI just consumes AI-generated output, it gets worse and worse. So AI companies try very hard to “clean” the text they use to train their models. Human writers, whose personal experience brings in new data for AIs to learn, can influence the world if their writings are used to train the next generation of AIs.  [...]

Claude and GPT often cite me as a source on topics I write about, and friends have told me that Claude recommends my blog with surprising frequency when they ask it for reading material. Maybe Tyler Cowen is right when he says we should be “writing for the AIs”.

by Noah Smith, Noahpinion |  Read more:

Image: The Simpsons
[ed. See also: the post below about a Chinese discussion of Christopher Nolan's The Odyssey.]

A Chinese Podcaster Interviewed Christopher Nolan. Here’s Why it Made Two Internets Freak Out.

The curious thing is that this passionate way of teaching Western culture is thriving right now—but outside of the West. In the US, the insiders who speak candidly will tell you that the humanities is in crisis. You can measure it in many ways—declining enrollment, shrinking departments, poor test results, increased cheating, resistance to reading, etc. All the metrics are bad.

But that’s not what’s happening in China. The study of western culture is in the ascendancy there. You could even say it’s hot and trendy.

People got a taste of this recently when director Christopher Nolan sat down for an interview with Yiyang Zhuge, who is a trained classicist perhaps best known for translating Plutarch’s Moralia into Chinese (and Hannah Arendt, too).

Nolan was probably expecting another pop culture discussion of his film version of Homer’s Odyssey. But what he got was something very different.

by Ted Gioia, The Honest Broker |  Read more:

***

Whichever side of the sinophone media sphere you’re on, you’ve likely already seen it: The interview about The Odyssey, a conversation between Chinese podcaster Zhuge Yiyang (who goes by Zhong Shu) and Christopher Nolan. In the interview, Zhong seems intent on getting beneath the film’s surface, pressing Nolan on the philosophical and religious underpinnings of his adaptation and connecting his directorial choices to the anxieties of “an age that demands apology from greatness.” She asks whether Nolan has “Christianized” the story by introducing concepts like atonement.

The interview quickly went viral on X, where English-speaking viewers praised it as one of the most thoughtful Nolan interviews they’d seen in years, while not quite hiding their surprise that it came from China. But as the Chinese and American internets started reacting to one another, things got weird.

Internet sleuths on X and Xiaohongshu traced Zhong’s ties to American conservative and libertarian intellectual networks. Zhong dropped a podcast episode bashing her newfound critics, after Western audiences and media struggled to interpret her. Chinese commentators went after her for misrepresenting philosophical ideas. Meanwhile, she landed on the cover of Esquire China alongside a handful of other podcaster-influencers. More recently, Chinese internet users have begun claiming (and unearthing evidence) that many of her episodes directly lift arguments and passages without attribution from English-language sources, including the podcast Old School, and from The Atlantic, and The New Yorker magazines.

As someone who spends an unhealthy amount of time on both internets, it has been surreal to watch how Zhong Shu has been interpreted and received so differently by intellectuals inside and outside of China.  [...]

But to understand why this interview happened in the first place, it helps to understand the media ecosystem that produced someone like Zhong Shu. For Western readers, it’s easy to mistake the depth of this interview as evidence that China has an especially vibrant cultural media. In some ways, the opposite is true.

Zhong Shu is not the only independent creator Nolan spoke to on his China media tour for The Odyssey; Universal Pictures apparently sought out independent creators rather than traditional entertainment reporters, a strategy increasingly common for publicity organizers for Western celebrities visiting China, from Tim Cook to Maye Musk. As traditional media has lost much of its cultural credibility and become more constrained, influence has shifted toward specialized online communities anchored by creators with their own audiences. The PR strategy is an acknowledgment that the supposedly mainstream cultural press once mediating these conversations is now practically nonexistent.

by Caiwei Chen, Hǎi 海 | Read more:
Image: YouTube

The Deep Sea

The Deep Sea
by Neal Agarwal

[ed. Fun and informative (scroll down to different depths to see the various creatures that have been recorded there). For example, I had no idea thick-billed murres could dive that deep.]

The Accidental Architect of the Internet’s Brain

Steven Pruitt, who is widely regarded as the most prolific Wikipedia editor, has made more than six million edits to the site, and, by extension, has quietly shaped the raw material that every major A.I. chatbot was trained on.

Steven Pruitt spends his evenings identifying errors that most people never notice and making fixes that hardly anyone ever thanks him for. He toils at a desk in a town house in Alexandria, Virginia, surrounded by books—the kind of working clutter that suggests a long relationship with paper rather than a fetish for screens. And yet his work is necessarily digital; after dinner, and sometimes late into the night, he uses his desktop computer to correct dates, clean up syntax, standardize categories, and occasionally write entire biographies of people on Wikipedia, the free online encyclopedia.

Wikipedia is not his employer, of course. Like all editors on the site, Pruitt is a volunteer. “At this point, I won’t say I don’t have any skin in the game,” he told me. “But it’s a lot lower stakes than a job because if I get something wrong, it’s fairly easy to fix it. I can fix it myself. I can do what I want to do on my own time.” Still, he holds himself to some rules: “I do try to get in at least one edit a day.”

Pruitt is forty-two, and works full time as a records-management contractor for the federal government. After graduating from the College of William & Mary, in 2006, he moved back in with his parents, owing to the cost of real estate in Alexandria. In recent years, he helped his mother care for his father. (While I was reporting this story, Pruitt’s father died.) In effect, Pruitt—the person who has done more than anyone else to shape the English-language Wikipedia—lives a life that is, by most outward measures, unremarkable.

According to public tallies, Pruitt has made more than six million edits to Wikipedia and created more than thirty thousand articles. He is widely regarded as the most prolific Wikipedian in the entire world. (“It depends on how you’re counting,” he said. “Different tools count different things.”) In 2017, Time magazine included him on its list of the most influential people on the internet. But outside of a small circle of editors, researchers, and obsessive readers on Wikipedia, the recognition has barely registered.

On the site, he is known by his username, Ser Amantio di Nicolao—a reference to a minor character in “Gianni Schicchi,” Giacomo Puccini’s comic opera. Pruitt’s interest in opera is genuine, but the flourish is misleading. He avoids drama, which means that he avoids writing Wikipedia biographies of people who are still alive, whenever possible. “I generally don’t do a lot in the realm of current events,” he told me. “Not just because the stakes are too high but sometimes because there’s so much editing going on on a subject in a particular moment that it can take me five or ten minutes just to break in with one edit.” He prefers biographies of what he calls “fairly obscure dead people.”

“They’re settled,” he explained.

In 2001, Jimmy Wales, an internet entrepreneur, and Larry Sanger, a philosopher, launched Wikipedia as an experiment in collaborative knowledge creation, allowing anyone with an internet connection to contribute. Pruitt first encountered the site in 2003, when he was still in college. “I didn’t quite understand what it was,” he recalled.

For more than a year, he did not edit at all. He would stumble upon Wikipedia pages through search results or links, and then move on. Between late 2004 and early 2005, though, his relationship to the site began to change. The encyclopedia had reached what he described as a critical mass: “There was enough stuff on the site that there was always something to do,” he said. “But it wasn’t just a blank slate.” The difference mattered. A completely empty encyclopedia was intimidating; a partially filled one invited correction and expansion. [...]

Wikipedia’s hierarchy is deliberately difficult to see. Editors work under pseudonyms. Articles appear collectively authored. There are no bylines, no salaries, no masthead. Pruitt was granted administrative privileges, after another editor nominated him through Wikipedia’s standard Request for Adminship process, where the editing community supported his candidacy. These privileges allow him to block users and close discussions, but he is careful about what that power does and does not mean. Wikipedia discourages editors from reverting—“undoing”—one another more than three times, regardless of correctness. “You can be blocked for twenty-four hours,” he said. “It doesn’t matter if you’re right.” He likes the rule. “It keeps things from turning personal.”

Inside Wikipedia, reputation accrues through time rather than visibility, and it “comes as much from longevity as anything else,” Pruitt said. “You stick around. People know you.”

Among the people who stick around—and who make consistent contributions to the site—are the Wiki-obsessives known colloquially as “super editors.” These individuals are responsible for hundreds of thousands, if not millions, of edits. Although there are more than a hundred and thirty-two million registered accounts on Wikipedia, a study found that one per cent of these users are responsible for roughly eighty per cent of the site’s content. [...]

In 2021, Stephenson-Goodknight was elected to the Board of Trustees of the Wikimedia Foundation, a position that she held through late 2024. Her tenure coincided with a fundamental shift in the role that Wikipedia plays on the internet. As the site entered its third decade, and artificial-intelligence algorithms grew hungry for data to learn on, Wikipedia articles were no longer just read; they were scraped, summarized, licensed, and folded into systems designed to answer questions elsewhere. In other words, Wikipedia had become infrastructure.

Pruitt was vaguely aware of the change before he fully grasped its implications. “Friends in tech would mention it,” he recalled. “They’d say, ‘You know Wikipedia is being used for this now.’ ” He did not follow developments in A.I. closely. “I don’t understand half of what Silicon Valley does,” he said. What he does understand well is reference works.

In October, 2025, when Elon Musk’s company xAI launched Grokipedia, an A.I.-generated encyclopedia that is often compared to Wikipedia, Pruitt approached it the way he would any new compendium. He searched for articles on subjects he knew well, such as nineteenth-century opera singers. “They weren’t there,” he said.

But what unsettled him was not what was missing from Grokipedia but what was slightly off. “Nothing was exactly wrong, but it was just less right than I would have made it,” Pruitt said. He described reading an entry that repurposed information from Wikipedia while subtly distorting it. In one instance, the entry summarized part of a person’s life in a way that struck him as careless, describing a seven-year period as “brief.” “I don’t think that’s brief,” he said.

The problem, as Pruitt saw it, was not the errors themselves—there are plenty of mistakes on Wikipedia—but rather where the responsibility for those errors lay. “Wikipedia can be fixed,” he said. Errors are corrected publicly, and editors can debate them. Responsibility is shared, and each edit can be traced. Grokipedia, on the other hand, and A.I.-generated information more broadly, obscured the information-gathering process. It generated text that sounded authoritative without revealing exactly how it arrived there. “It sounds right,” he said. “And that’s worse.”

by Carson Griffith, New Yorker | Read more:

Image: Asya Demidova

What Do Consultants Get Paid For?

A consultant I had lunch with recently is redesigning the loyalty program of a large airline. His team finished the analysis in two weeks. Months later the program still does not exist. This is because the purpose of the assignment is not to solve an analytical case study, but to figure out which redesign the parties will accept, and to get the people with authority to commit to implementing it.

I was not surprised to hear the story. In our just-published book Messy Jobs: The Work That AI Cannot Reach, Jin Li, Yanhui Wu, and I argue that a job is not a collection of independent tasks but a bundle of tasks and a position inside an organization. While many of the constituent tasks are clean, the job is messy because they must be combined under incomplete knowledge, conflicting objectives among the different parties and binding constraints on who has the authority to make decisions.

Hence we argue that automating the clean parts does not necessarily eliminate the job, because the remaining activities, tightly bundled with the rest, can remain the constraint. We argue that the bundle is strongest where separating the analytical/cognitive parts that can be automated would destroy local knowledge, trust, accountability or continuity.

Two objections

Critics of our argument raise two concerns. The first one has to do with advances in AI capabilities: models do some tasks extremely well and others badly. As they gain memory, use tools and acquire multimodal perception, critics would say, AI will get better at many other tasks like persuading, anticipating the objections raised in a meeting and adapting the tone. Hence even the interpersonal part by itself may not be a sanctuary for long. Just wait a bit for AI to get better, say the critics: Messy Jobs (in their view) describes the transition rather than the long run.

The second objection to our thesis is more radical. Maybe as long as we have humans in the loop, we need organizations. But if organizations really are a mess, slow, political, resistant to change, with a large role for humans precisely because someone has to hold meetings, build coalitions and learn the internal politics, why not get rid of the entire organization? What is the point of preserving the existing obsolete structures?

We believe that both objections fail, because some of the mess is substantive and necessary.

Where the “implementation” months go

To an outsider, my friend’s consulting project looks purely analytical. The team receives all the data, including all passenger records, redemption rates, customer-retention data, and so on. The team works out the key economic and financial trade-offs of the possible redesigns to figure out which redesign maximizes profits.

If doing this, given all the available data and the current AI tools, took two weeks, why has the project taken many months?

First, inside the airline, different parts of the business worry about different things. For instance, the salespeople have relationships with the hotel chains and do not want to disturb them, while the operations team worries about the staff at the airport counters who will have to deal with angry passengers who have grown used to certain privileges.

Second, there are the outside parties, from hotel chains to credit-card companies to the retailers that accept miles. Anything that improves the airline’s economics may reduce the value of the program to the hotels or to the card issuers. Each has a view on how card spending should count relative to flying, or how hotel nights should count relative to flying. There are winners and losers everywhere, and a reform that benefits the airline as a whole can hurt a particular business unit or a particular partner.

So the consultants spend weeks doing an enormous amount of work that looks peripheral to the problem. They repeatedly meet the head of the loyalty program, then the CFO, then the CEO. They also meet the commercial partners, and that means meeting the head of loyalty, then the finance team, then the chief executive. They revise the proposal. They redo the presentation.

And all of these people speak different languages. Organizations have different internal codes because they care about different things and deal with different problems. What the consultants are doing is a mix of analysis, translation and intermediation. The assignment of the consultants is to design a program that is an agreement that the relevant parties will authorize and implement.

Once the analysis is cheap, what remains is to learn what each party will actually accept, and to obtain commitments from those who are authorized to make them.

The real knowledge problem

An advocate of highly capable AI systems (“AGI-pilled”) would probably say this is a problem ready for AI. Have an agent redesign the program, have it meet the other constituencies, have it come back with a solution.

But what happens in those meetings deserves a closer look. There are four frictions in the room that make the meetings necessary.

by Luis Garicano, Silicon Continent | Read more:
Image: via

We Asked Too Much of the American University

Our one remaining functional institution is going downhill.

The story of the rise and possible impending fall of the American university system is, in many ways, the story of modern America. It ties together the changes in our culture, our economy, and our politics since the mid-20th century. Understanding why universities came to be our most important and most functional institution, and why that model is now under threat, can help us understand how our country might look different going forward.

Let me try to tell a condensed version of that story.

The United States used to have a bunch of institutions that bound us together and forged us into a unified society. Many American communities were centered around churches; these facilitated networking, helped people find spouses, provided community services like day care and mutual aid, and homogenized values and culture at the local level. During the World Wars (and to a lesser extent, the Vietnam War), the military was very large and threw together Americans from various social classes. In the early postwar decades, corporations were also a unifying institution. Mass media provided us with shared cultural context. Even public transit put people of various backgrounds in close social contact on a daily basis.

In the half-century from around 1970 to 2020, those unifying institutions became much weaker. Church attendance slowly declined and then fell off a cliff in the 2010s. The military shrank into a small, professionalized volunteer force. Corporations ended their brief flirtation with lifetime employment, and outsourced many of their roles. Mass media fragmented in the age of the internet. Public transit dwindled in importance as Americans moved to the suburbs and drove.

As the country’s unifying institutions withered, one new institution attempted to step into the void: the American university. Universities were not new in the late 20th century, of course, but mass attendance certainly was. From 1970 to 2020, the share of Americans age 25-29 with a bachelor’s degree went from 16.4% to 39.6%. Around two out of three have completed some college.

College went from something that only the upper crust did, to something that most people were expected to do if they wanted to be economically successful in life. This expansion inadvertently but inevitably thrust a new role on American universities — that of a broad socially unifying institution. College was where people from a variety of backgrounds mingled and mixed — not just in classes, but in dorms, campus activities, and college towns.

College became the new church, the new military, and the neighborhood bowling alley all rolled into one. Instead of preachers homogenizing Americans’ values from the pulpit, university administrators taught college kids to value things like diversity, consent, and so on, and college students hashed out their own differences in millions of late-night dorm room discussions. Instead of meeting their first love in high school, Americans increasingly delayed sex until college; many of these relationships turned into marriages. Young people went into college as children and came out adults.

This was a heavy burden to bear for an institution that hadn’t been designed for it. But American universities were accustomed to taking on big new duties. Our universities began as essentially a copy of the British model — teaching-focused institutions designed to provide broad education and mentorship to the upper class. In the early 20th century they tacked on the German model — a research-focused lab apprenticeship system by which top scientists taught other top scientists and readied them for research jobs while also producing cutting-edge basic research.

This dual system enabled a remarkable form of cross-subsidization. Undergrad tuition payments — and state support for undergrad education, and undergrad alumni gifts — created a flood of money that paid for grad student stipends, lab facilities, professor salaries, and more. The federal government and companies also funded university research, of course, through grants and sponsorships. But undergrad money was a huge tailwind. And the prestige generated by successful and famous researchers helped universities charge undergrads more.

The hybrid of the old British and German models was naturally symbiotic, and it meant that even as America’s other institutions came under pressure, universities thrived and grew — especially once they used their prestige to attract high-paying and highly skilled foreign students from around the globe. Universities became the lynchpin of America’s research effort:

Source: Arora et al. (2019)

American universities had a lock on both the nation’s research output and on its production of human capital, and those roles were mutually reinforcing.

I suspect that their success at handling education and research at the same time probably gave American universities a lot of confidence about their ability to handle society and culture as well. Colleges spent more and more on dorms and “student services” and hired administrators (many of whom dealt with undergrad life) at an astonishing rate.1

But replacing churches, the military, and the neighborhood bowling alley proved harder than replacing the corporate lab had been. There was just one basic problem with college as America’s primary unifying institution, which is that not everyone can go to college.

First of all, college is difficult. To complete college courses, you need some degree of raw intelligence, but you also need work ethic and a certain degree of independence. As much as we might like to believe otherwise, not everyone in America has those traits, and we don’t yet know how to instill them in everyone. As a result, there’s a limit to how much you can expand college enrollment — and college completion — without loosening standards.

In fact, loosening standards is exactly what American colleges have done. Universities have necessarily become less and less selective over the years, as they have taken in a larger and larger fraction of the young American population. For a while, this led to lower college completion rates, but in the 1990s, more and more students began finishing their bachelors’ degrees. Why? Because colleges implemented grade inflation, making it easier to finish school without learning the material well. Here’s Denning et al. (2021):

We find that most of the increase in graduation rates can be explained by grade inflation, and that other factors such as changing student characteristics and institutional resources play little or no role. This is because GPA strongly predicts graduation and that GPAs have been rising since the 1990s. This finding holds in national survey data and in records from 9 large public universities. We also find that at a public liberal arts college, grades increased holding performance on identical exams fixed.

At some point, though, this process hits a wall. A large fraction of young Americans just isn’t prepared for college, even with grade inflation, and doesn’t end up going. College enrollment by recent high school graduates plateaued in the early 2000s and actually fell back to early 1990s levels during and after the pandemic:

Source: NCES via GPT

by Noah Smith, Noahpinion |  Read more:

Image: Ajay Suresh via Wikimedia Commons

[ed. Unfortunately, that's it before you hit the paywall. But you get the idea, and can claim the rest of the article for free once (by providing your email).]

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.]

Chat GPT-Voice

A new generation of voice models for natural human-AI interaction, now powering ChatGPT Voice. (OpenAI).]

[ed. Yeah...pretty dorky video, but you get the picture. Here's another one highlighting the voice/language translator.]

Asteroid Day, June 30, 2026

Asteroid Day, June 30, 2026

Asteroid Day was cofounded in 2014 (the year after the 2013 Chelyabinsk meteor air burst) by physicist Stephen Hawking, B612 Foundation president Danica Remy, Apollo 9 astronaut Rusty Schweickart, filmmaker Grigorij Richters, and Brian May (Queen guitarist and astrophysicist). Remy, Schweickart, Richters, and May initiated Asteroid Day in October 2014, which they announced during a press conference. It was launched on December 3, 2014.

In 2016, the United Nations proclaimed Asteroid Day be observed globally on June 30 every year in its resolution. The event aims to raise awareness about asteroids and what can be done to protect the Earth, its families, communities, and future generations from a catastrophic event. - Wikipedia

See also: Asteroid Day 2026: What Is It and How to Celebrate It?

There are about a million asteroids in the Solar System with the potential to strike Earth and destroy a city. Astronomers have discovered only 1% of them. Asteroid Day is an effort to educate the public and encourage policy makers to fund this important effort.

King Tut may have celebrated an ancient Asteroid Day by asking his assistants to make a dagger out of a broken-off asteroid that landed on Earth. Astronomers discovered that the blade of the knife contained much more nickel than is found in terrestrial iron, an amount consistent with iron meteorites, especially with one found in the year 2000 in the Kharga region in northern Egypt. For more information about the dagger, go to http://goo.gl/BHBivd. (via: Bruce Palmquist, Daily Record)

[ed. Brian May was also an astrophysicist? Wow. A man of many talents. Another one would be Jeff "Skunk" Baxter, guitarist for Steely Dan and US missile defense contractor/consultant.]

June 23, 1988: James Hansen Testified to Senate About Climate Change

Coal is the single greatest threat to civilization and all life on our planet. . . . the dirtiest trick that governments play on their citizens is that they are working for ‘clean coal.’ . . .The trains carrying coal to power plants are death trains. Coal-fired power plants are factories of death. — James Hansen

On June 23, 1988, NASA scientist James Hansen testified to the U.S. Senate stating the greenhouse effect had been detected, indicating that the climate was in fact changing.

Hansen was also arrested on this day in 2009 during a protest against mountaintop removal mining at Massey Energy Company.

Hansen has stated,

Several times in Earth’s long history rapid global warming of several degrees occurred. . . In each case more than half of plant and animal species went extinct. New species came into being over tens and hundreds of thousands of years. But these are time scales and generations that we cannot imagine. If we drive our fellow species to extinction we will leave a far more desolate planet for our descendants than the world that we inherited from our elders.

by Zinn Education Project |  Read more:

Image: uncredited
[ed.  "According to science historian Spencer R. Weart, Hansen's testimony increased public awareness of climate change. According to Richard Besel of California Polytechnic State University, Hansen's testimony "was an important turning point in the history of global climate change." According to Timothy M. O'Donnell of the University of Mary Washington, Hansen's testimony was "pivotal," "ignited public discussion of global warming and moved the controversy from a largely scientific discussion to a full blown science policy debate," and marked "the official beginning of the global warming policy debate." According to Roger A. Pielke of the National Center for Atmospheric Research, Hansen's "call to action" "elevated the subject of global warming and the specter of associated impacts such as more hurricanes, floods, and heat waves, to unprecedented levels of attention from the public, media, and policy makers." - Wikipedia.]

[ed. Which was all it took for climate change skeptics to spring into action, and here we are...]

AI in Biology

If you wind your way through a quiet, wooded suburb outside of The City, you’ll reach a harbor. Situated on a hill overlooking the water, there is a Temple of Science. This Temple is centered around a task of the utmost importance: preserving a magical thread that connects the past, present, and future of the life sciences.

On one end, there is a gentle tug from the ghosts of Barbara McClintock, Martha Chase, and Alfred Hershey, reminding you of their elegant experiments that became part of the canon of genetics. Farther along, figures like Jim Watson grip the thread more fervently as they advocate for the centrality of their discoveries in the birth of molecular biology. If you put one hand in front of the other and continue to follow where it takes you, you’ll pass through the rise of genomics and end up on the frontier of biology.

Of course, I’m talking about Cold Spring Harbor Laboratory. For over one hundred years, this little research institute in Long Island, New York has punched well above its weight. CSHL played a critical role in multiple paradigm shifts in biology—including genetics, molecular biology, and genomics—as evidenced by the eight Nobel Prizes awarded to researchers from “The Lab” over the years. When normalizing for size, the Nature Index ranked CSHL as the most prolific biomedical research institution in the world.

I’ll never forget my first visit to The Lab. In February of 2020, I flew from Seattle to interview for the CSHL graduate school program. Famously (among researchers on the grad school interview circuit), they would arrange for each recruit to be picked up in a black car from the airport.

The campus itself, which is a direct physical representation of the magical thread that The Lab preserves, is equally memorable. A cluster of pristinely maintained colonial buildings, each painted white, borders the water. Above them is the Upper Campus, consisting of darker, modern renditions of the same pattern. Scientific art installations—like the Waltz of the Polypeptides or a gazebo with a phage structure on the tip—can be found along the walking trails.

Over the course of three days, I hurried around The Lab for a wide range of activities, including eleven interviews with faculty—two to three times the number that most other graduate school programs typically scheduled. It was wonderful and intense.

Ultimately, I was persuaded to go west for graduate school. Thankfully, there are many reasons to continue coming back to CSHL, which has been described as “the crossroads of biology.” Each year, they host dozens of conferences and courses that draw top researchers from around the world.

But one particular conference stands out in importance. Since 1933, CSHL has hosted an annual Symposium on Quantitative Biology. Reginald Harris, who conceived of the conference, wrote that the “primary motive of the conference symposia is to consider a given biological problem from its chemical, physical and mathematical, as well as from its biological aspects.” In retrospect, this was visionary.

Over the next several decades, chemists and physicists would revolutionize the life sciences. In 1944, Erwin Schrödinger, a leading physicist, wrote What is Life?, a book exploring open questions in biology through a new lens. It inspired many researchers and students, including a young James Watson, to pursue biological research. In 1953, at the 20th annual CSHL Symposium, Watson presented the structure of DNA for the first time in public.

For obvious reasons, this gave the CSHL Symposia a sort of “mythic quality” moving forward. This reputation compounded quickly. Over the next 15 years, the pioneers of molecular genetics would travel each year to present their most important discoveries—such as the central dogma and the genetic code—at CSHL.

The tradition continues to this day. Each year, the Symposium is organized around a topic considered to represent the frontier of life sciences research.

Which brings us to the topic of the 90th Cold Spring Harbor Laboratory Symposium on Quantitative Biology: AI in Biology.

Readers of this newsletter are not strangers to the fact that AI is reshaping biology. The tools derived from breakthroughs such as AlphaFold have been adopted by seemingly all biologists at this point. But it was stunning to see these advances celebrated so prominently in this venue. It felt historical.

As Bruce Stillman, CSHL’s current President, pointed out in his opening remarks, this topic connects back to the very origin of the Symposia—as the name suggests. Harris had spotted the emergence of a new quantitative paradigm in biology. Between then and now, molecular genetics did in fact transform biology into an information science.

It’s becoming more clear each day that the next chapter of this story is AI. Sydney Brenner, one of the most central figures of molecular biology, gave one of the most incisive criticisms of the field in his Nobel Prize lecture: “We’re drowning in a sea of data and starving for knowledge.” AI is starting to change that equation.

For five days, top researchers in the field shared updates on their efforts to use machine learning to decipher the mechanisms of DNA, RNA, proteins, cells, tissues, organs (especially the brain), and how information flows between these different biological scales. And there were examples of how AI agents might be able to autonomously carry out some of this research—which was met with a combination of excitement and anxiety from attendees.

It was one of the most compelling conferences I’ve ever attended, so I want to share some of what I saw. Before jumping in, this requires a few quick notes on the format of the event.

First, attending a Symposium feels like drinking from a scientific firehose—by design. CSHL is truly a Temple, or maybe even a monastery. Most attendees stay on campus and don’t leave for the duration of the conference. Talks are back-to-back all day in the main auditorium, followed by communal meals and poster sessions that run throughout the evening. It’s non-stop. My goal isn’t to give an exhaustive blow-by-blow, but to highlight some of the themes and topics I found most exciting.

Second, following in the tradition of Watson, many researchers share more new and unpublished data than is typical at other conferences. To respect this tradition, I’m going to focus on the data shared that has already been published, with more high-level descriptions of new research directions and results.

With all that said, let’s get into it! [...]

Agents, Agents, Agents

Maybe I’m in a bubble in San Francisco, but it’s hard not to constantly hear about AI agents in the year 2026. It’s strange to think, but it’s been three and a half years since ChatGPT was first released. That’s long enough for many humans to feel frustrated by the shortcomings of what was once magic. Now, we want these models to do work for us, and to carry out longer, more complex projects that require reasoning.

There are now many efforts to develop systems for “agentic science,” where AI models are able to autonomously develop new hypotheses, design experiments, and analyze results. This concept was another recurring theme at the symposium.

Pushmeet Kohli hit on this the first evening. The last third of his talk focused on DeepMind’s efforts to build an AI Co-Scientist, which they published a new paper on last month. Given a research goal by a human scientist, this system develops a research plan and then kicks off a “tournament” of agents competing to develop new hypotheses. Agents within this system have different tasks. Some are designed to “reflect” on the ideas being generated. Others are tasked with “evolving” them.

While the goal is hypothesis generation, the AI Co-Scientist itself is no longer just a hypothetical. DeepMind has already given early access to academic researchers working in a wide variety of biomedical domains. Kohli highlighted a high profile example where the Co-Scientist was able to predict a new mechanism of bacterial gene transfer before the result was published in the literature.

by Elliot Hershberg, The Century of Biology | Read more:
Image: uncredited/CSHL

[ed. See also: What’s new in biology: June 2026 (Works in Progress).]

Students Are Using a ‘Backdoor’ to Attend Their Dream Schools

Justin Helman didn’t get his dream acceptance from the University of Florida. But that isn’t stopping him from pursuing the classic college experience there.

The recent high-school graduate from Park Ridge, N.J., is set to move into a private apartment right by campus. He is enrolling in a UF online program for the first few semesters and paying an extra fee package to access services like the campus gym and student-section football-game tickets. He plans to study at the library, join clubs and might rush a fraternity.

“I’m going to get almost the entire same experience, and the only thing I’m really missing is going into class and dorming,” he said. “To me, it was just almost a no-brainer.”

More students like Helman are discovering there is another way into their dream schools.

Students who don’t get into major public flagships the traditional way are still participating in the social life of these campuses. The small-but-mighty group is moving to college towns, enrolling in online programs or nearby community colleges, living in private housing, joining Greek life, and attending game-day tailgates. The approach is sanctioned by the universities, which are expanding alternative-enrollment programs. [...]

Helman’s UF offer was to the school’s Pathway to Campus Enrollment program, which requires students to start online before transitioning to full in-person status. The program has exploded from about 250 students in 2015 to nearly 3,000 in fall 2024, according to the school’s website.

Helman will share a Gainesville, Fla., apartment with three other PaCE students who are moving from out-of-state, and said he has spoken to many others planning to relocate. He chose the program over traditional acceptances, some with scholarships and honors, including at the University of South Carolina, Seton Hall and University of Tennessee.

“This was his dream school,” said his mother, Maria Debowska-Helman. She added that his tuition would be cheaper than a traditional UF student’s. The optional fee package will cost around $550 for a semester, depending on the number of course credits. [...]

It is also controversial. Some students view these alternative pathways as “a cheat code,” Kraemer said. Some consultants agree, at times pointing to limited major-transfer options and instead pushing students to traditional paths.

by Roshan Fernandez, Wall Street Journal | Read more:
Image: Maria Debowska-Helman