via:

Telescope Ranching

[ed. Awesome. An example of the intrinsic economic value of undisturbed natural environments. A few more: eco-tourism, hunting and fishing lodges/preserves, photo-safari's; air taxi operations, outfitters, etc. etc. Many people just assume that if land isn't somehow 'developed' it's just sitting there, worthless. Then there are even worse ideas: like putting up a border wall/fence with miles of security and search lights to be installed at Big Bend National Park.]

Big Bend National Park is known as one of the outstanding places in North America for stargazing. In fact, it has the least light pollution of any other national park unit in the lower 48 states.

Qian Xuesen: "Father of Chinese Rocketry"; Deported Illegal Immigrant

Qian Xuesen (Chinese: 钱学森; December 11, 1911 – October 31, 2009; also spelled as Tsien Hsue-shen) was a Chinese aerospace engineer and cyberneticist who made significant contributions to the field of aerodynamics and established engineering cybernetics. He achieved recognition as one of America's leading experts in rockets and high-speed flight theory prior to his deportation to China in 1955.

Qian received his undergraduate education in mechanical engineering at National Chiao Tung University in Shanghai in 1934. He traveled to the United States in 1935 and attained a master's degree in aeronautical engineering at the Massachusetts Institute of Technology in 1936. Afterward, he joined Theodore von Kármán's group at the California Institute of Technology in 1936, received a doctorate in aeronautics and mathematics there in 1939, and became an associate professor at Caltech in 1943. While at Caltech, he co-founded NASA's Jet Propulsion Laboratory. He was recruited by the United States Department of Defense and the Department of War to serve in various positions, including as an expert consultant with a rank of colonel in 1945. He became an associate professor at MIT in 1946, a full professor at MIT in 1947, and a full professor at Caltech in 1949.

During the Second Red Scare in the 1950s, the United States federal government accused him of communist sympathies. In 1950, despite protests by his colleagues and without any evidence of the allegations, he was stripped of his security clearance. He was given a deferred deportation order by the Immigration and Naturalization Service, and for the following five years, he and his family were subjected to partial house arrest and government surveillance in an effort to gradually make his technical knowledge obsolete. After spending five years under house arrest, he was released in 1955 in exchange for the repatriation of American pilots who had been captured during the Korean War. He left the United States in September 1955 on the American President Lines passenger liner SS President Cleveland, arriving in mainland China via Hong Kong.

Upon his return, he helped lead development of the Dongfeng ballistic missile and the Chinese space program. He also played a significant part in the construction and development of China's defense industry, higher education and research system, rocket force, and a key technology university. For his contributions, he became known as the "Father of Chinese Rocketry" and was nicknamed the "King of Rocketry". He is recognized as one of the founding fathers of Two Bombs, One Satellite.

In 1957, Qian was elected an academician of the Chinese Academy of Sciences. He served as a Vice Chairman of the National Committee of the Chinese People's Political Consultative Conference from 1987 to 1998.

He was the cousin of engineer Hsue-Chu Tsien, who was involved in the aerospace industries of both China and the United States. He is a cousin of the father of Roger Y. Tsien, the 2008 winner of the Nobel Prize in Chemistry. [...]

Outside of rocketry, Qian had a presence in numerous areas of study. He was among the creators of systematics, and made contributions to science and technology systems, somatic science, engineering science, military science, social science, the natural sciences, geography, philosophy, literature and art, and education. His advancements in the concepts, theories, and methods of the system science field include studying the open complex giant system. Additionally, he helped establish the Chinese school of complexity science. His research advanced the discipline of engineering cybernetics, which emphasized the importance of design principles in practical engineering.

via: Wikipedia |  Read more:

Image: unknown

[ed. Prelude to the post that follows (re: Gov. vs. Anthropic's Fable).]

American Government Takes Down Claude Fable

No good policy gets announced shortly after 5pm eastern on a Friday.

Here we go again.

The Once And Future Fable

The United States Department of Commerce, as per a letter from Commerce Secretary Howard Lutnick, apparently in response to a narrow jailbreak identified by Amazon, has classified Fable 5 and Mythos 5 as being subject to US export controls. That explicitly means cutting off access to all ‘foreign nationals,’ even within the United States, even if they are Anthropic employees.

Given Anthropic has no means to verify citizenship at this time, that meant complete shutdown of the model, at least for the time being.

Anthropic: The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Anthropic models will not be affected.

Dean W. Ball: I can’t tell if this is lawfare against Anthropic in particular or extreme national-security hawkery. Regardless, it is simply cartoonish.

The justification for this appears to be rather flimsy, at best, and based on lack of understanding of what even is a jailbreak or how defense in depth works.

Anthropic: We received the directive from the government today at 5:21pm (ET). The letter did not provide specific details of its national security concern. Our understanding is that the government believes it has become aware of a method of bypassing, or “jailbreaking” Fable 5.

We reviewed a demonstration of this specific technique being used to identify a small number of previously known, minor vulnerabilities. These vulnerabilities all appear relatively simple, and we have found that other publicly-available models are able to discover them as well without requiring a bypass.

As we have stated publicly, we believe the government should have the ability to block unsafe deployments, as part of a statutory process that is transparent, fair, clear, and grounded in technical facts. This action does not adhere to those principles.

We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.

That left Anthropic with no options but to entirely withdraw it from the market, at least for the time being, since they have no way to verify who is and is not a United States citizen. [...]

This Action And Its Implementation Are Absurdly Stupid

If you take the action at face value, rather than as an attempt to lash out at Anthropic, there is no way to pretend this is not deeply, deeply stupid.

Dean W. Ball: If this is true, it is just baffling. An administration whose posture is that we *should* export advanced AI chips to China, which also wants to ban… Britain (and every other non-American on Earth)… from using our best models? I have no words.

zooko ⓩ: Judging from [the announcement], I imagine that some senior government official was shown a jailbreak—something they had never seen before and didn’t know about—and this was their kneejerk reaction.

Dean W. Ball: If implemented as this reporting suggests, Anthropic’s latest models would be subject to export controls to all *non-Americans,* including non-American nationals based in the US. This means you should expect to have to prove your citizenship to use Anthropic models. [...]

What Happens Now?

It is a regular thing for the Executive Branch of the United States Government, these days, to issue declarations of policy that are, to use the technical term, absolutely bonkers and stunningly destructive with no reasonable way to implement them, often without stopping to realize what they are doing.

It is also a regular thing for them to then quietly walk those policies largely or entirely back, once the consequences become clear, leaving only relatively minor total devastation in their wake.

Alas, it is also a regular thing for them to leave at least a substantial portion of the new stupid and destructive policy in place indefinitely, and sometimes we keep all of it, or they even keep going further.

Or Anthropic could give the White House what it wants, no matter who is right about whether doing so makes any sense.

We are not short on examples of any of this.

One thing that must now be considered is that many employees of OpenAI, Google and Anthropic, and other AI labs, are not United States persons.

Yo Shavit (OpenAI): Unless this changes, OpenAI researchers on visas need to plan for the fact they’ll probably lose access to internal models, and therefore their ability to do their jobs moving forward, sometime in the next couple months.

I hope the company acts to prevent that.

dave kasten: Uhhh so incidentally, does anyone have a plan to prevent all the non-US citizen AI scientists from going to join foreign labs after they get bored of playing Wordle at work for a month, or are we just sort of planning on having the greatest counterproliferation failure since we deported Qian Xuesen in 1955 and gave Mao a rocket program?

If we drive all foreign talent out of our AI labs, and otherwise actually go down the current road, that is one of the few things that could put China and other competitors back in the game in earnest, both slowing us down and speeding them up.

At Anthropic, Amanda Askell and Andrej Karpathy are examples of employees who suddenly are unable to work with Claude Mythos 5, even after Anthropic sorts out a new access control system.

by Zvi Mowshowitz, DWAtV |  Read more:

[ed. Who knows what axe is being grinded here, the stupidity appears to transcend logical analysis. See also: The Once And Future Fable #2 (Update):]

***

On Friday evening the United States Government has forced Anthropic to take down all access to Fable and Mythos.

It’s been a rough weekend. [...]

1. More details have come to light. There remains some fog of war, but we now have a rather good idea why Claude Fable and Mythos were, deeply stupidly, taken down.

2. A narrow jailbreak was discovered, of the type Anthropic warned in advance obviously existed. All demonstrated outputs are things GPT-5.5 can not only produce, but produce without any sort of jailbreak or bypass.

3. The White House demanded Anthropic take down Fable to ‘fix’ the situation, and did not listen when Dario tried to explain that there was no situation to fix.

When Anthropic did not do so, the White House hit them with an export restriction that they knew would force Fable and Mythos down for everyone.

A lot of nihilists are justifying this decision, and blaming Anthropic, all of whom are very much confirming that they adhere to Dean Ball’s portrait of the United States Government as a dying NPC hospice patient we have to properly placate with the proper vibes and genuflection so they don’t lash out at us. Except they equate this with strength and righteousness, because might makes right, power and vibes.

This is a fast developing story with a large speed premium, so I apologize for any errors, and for the structure likely not being ideal. We do the best we can.

What we do not know is:

1. What was motivating the government to make these decisions.

2. How deeply they were confused about how any of this works.

3. Whether they demanded and are demanding a narrow fix or a global fix. Narrow fix is probably easy. Global fix is probably impossible.

4. What they intend to do next and what they are trying to accomplish.

The good outcome would be that this is a terrible misunderstanding, a reflection of a panic reaction, which can be sorted out quickly, after which we can restore access. Or where they otherwise face enough pressure they quickly realize they made a mistake, or Anthropic can do something to quickly assuage their concerns even if it is dumb. There will still be a terrible precedent set, which comes with a lot of permanent damage to trust in American AI, to our business climate, to our ability to employ vital foreign AI talent, to America’s relationships to its allies, to the progress of Project Glasswing and our cyber security, and to the rule of law.

***

[ed. In addition, see: Seductive Salience (the inevitable politization of AI regulation).]

The Kissing Booth

The kissing booth was my daughter’s idea. Here’s how it was supposed to work. Ella and her friend Audrey would set up near the polling place at the Seventh-day Adventist temple. Their Get Out the Vote operation would rely on a repurposed lemonade stand they’d found in Audrey’s basement. Audrey’s mom once ran tech for a theater in Miami, and this lemonade stand was an impressive affair: a wooden counter with a framed opening above a painted wooden sign, looped with bright triangular flags cut out of felt. The sign used to say “Lemonade, 50¢” and below that “Save the Tigers!”

They repainted the sign to say #KissingBooth2024. Of course they didn’t need our help this time. They were fifteen. They traveled around the city on their own and understood precalculus. Their skin was incredible, even when they hadn’t slept enough, and their eyes were clear like marbles. Still, Ella sometimes complained about how she looked. I’d heard that the right response to this was always “You look beautiful.” No details. One weekend she emerged from her room dressed for a party in a lavender slip dress, her dark hair meticulously straightened, tiny dabs of silver glitter at the corner of each eye. I looked at her eleven-year-old brother, Ben, the only other person in the house, and saw him tear up.

“You’re crying!” Ella crowed.

“No I’m not,” Ben said, turning to hide it. I don’t think he knew why he was upset.

Audrey’s mother, Jen, and I had some concerns about the kissing booth from the beginning—namely, predation, germs, and public opinion. Also something else that was harder to put into words. But when we raised the first issue with our daughters, they became defensive.

“You think we can’t decide what to do with our own bodies,” Ella suggested. “You think it’s ‘inappropriate.’”

Audrey looked smug. “That’s what I said they’d say.”

“This is a big city . . .” Jen began.

I tried to help. “It would be different if—”

“If we lived in the suburbs?” Ella glanced at Audrey, incredulous.

Audrey shook her head in disgust. “See?”

Jen and I insisted that we would have the same concerns about a suburban kissing booth. We’d already agreed it never would’ve occurred to us to do something like this at their age, because it was a different political moment—and also a different kissing moment. Most of the teenagers we knew, including our own daughters, didn’t seem to be kissing anyone. They gently mocked the ones who were, as if the sort of dating our generation had done—the pairing up and sneaking out, the baseball metaphors—was a quaint vestige of the past. Maybe they were right. When our daughters first became teenagers, we’d been eager to show them the movies of our adolescence. We’d made popcorn and settled onto the couch, but it hadn’t taken long for them to be appalled or for us to be ashamed. How could we be nostalgic for those days?

That fall, I started running in the park. I could do this at night, while the kids finished their homework. I couldn’t help with homework the way I used to, because everything had changed: long division was now short division, the atom was an electron cloud, and Pluto—which had seemed so far away as to be unassailable—was just a lump of rock and ice in the Kuiper Belt. “Don’t use the algorithm!” Ben warned. “It’s not allowed!” Ella, meanwhile, studied the modern Middle East and didn’t have a single textbook. She had some tasks that had to be done with AI and others for which those programs were expressly forbidden. So I went for a run.

I had time to run during the day, too, especially with the kids spending half of each week at their father’s new apartment. But there was something about the halo around the lights in the park at night, especially if it was drizzling, and the adrenaline I got from needing to stay alert. Also from doing something that was supposedly inadvisable. [...]

If I ran during the day, I listened to a podcast, occasionally one about parenting. There were helpful tips for talking to your teenager: for example, when she said something offensive—such as “All the girls in my grade are bitches” or “You are exactly like Grandma”—I could say, “Let’s try that again,” or “I don’t think that came out the way you meant it.” This hadn’t been supereffective in practice, but I may not have had the right inflection. The psychologist’s voice was low and soothing, and sometimes I found myself letting one episode run into the next, even when the topics weren’t relevant to my children. “My Teen Is into Sports Betting . . . Help!” Or: “My Daughter’s Nude Selfie Got Out. What Do We Do Now?” Each one was like a little pat on the back—nope, not my problem.

Eventually, I did have to listen to the divorce episode, though. The worst things you could do, it turned out, weren’t moving the kids frequently back and forth or running out of money or lying. The worst things were (1) having fights in front of them and (2) criticizing the other parent. I was three-quarters of the way through my run—in the middle of the hill—when Dr. Lisa Damour dropped this bit of wisdom, and I slowed to a walk. Ordinarily, I hated to do that because afterward it felt as if I hadn’t run at all, as if I were a failure.

One saturday that September, I met Drew at the door when he brought Ben home. For a while after we’d separated the previous spring, I would tidy up before he arrived. Drew is an architect and we’d always argued about the apartment, about the extent to which external order was tied to more fundamental issues. The fundamental issue might have been that we disagreed about which issues were fundamental. This time, though, I hadn’t bothered. His eyes moved over the living room, the laundry on the couch, and my empty coffee mug on the table. Mugs. I gave Ben a hug, inhaling his yeasty smell. I used to be relieved when the kids went on short school trips, to the museums in D.C. or camping upstate, but now that they were with their father a few nights a week, I counted the days until they got home. I had to be careful about saying “home”—because Drew said that the apartment he’d had for five months was now equally their home. “OK,” I agreed, “language is important.” That made him roll his eyes.

Ella was at volleyball practice and wasn’t going to be back at my place for a while, so it was a good time to discuss some things. Not a great time because Ben was right there in the kitchen, getting his favorite snack: a slice of Muenster cheese wrapped around a dill pickle.

“Did you know that the bar-headed goose is one of the highest-flying migratory birds?”

“Nope,” I said.

“But the Rüppell’s griffon vulture can fly even higher. One flew seven miles above the earth and hit a plane.”

“Was it OK?”

“No,” Ben said. “It got sucked into the engine. That was in the 1900s.”

“Oh, well, the 1900s,” said Drew. “Ella said to tell you she’d be here by six.”

“How’s she doing?” I didn’t mean to suggest that she wouldn’t be doing well after three days with her father. I was only trying to steel myself for whatever was coming when she got back. Her moods were various and spectacular.

“She called me an effing a-hole,” Drew said. “And so I’m just wondering where she heard that.”

“I don’t know,” I said. “Who’s been calling you an effing a-hole most recently?”

“Hilarious,” Drew said. “This was after I bought her the tickets, by the way.”

“You bought her the tickets?”

I think he brought up the a-hole thing just to pass along this piece of information, because we had definitely settled on not buying the tickets for Ella, because the cost was excessive and because it felt like a bribe. We had talked about not letting Ella manipulate us into things simply because we felt guilty about our separation.

“She’s so excited,” Ben said from the kitchen. “She hugged Dad, and then she called Rachel.”

Drew looked nervous. “Rachel said that it was the event of the decade, and that if she didn’t go, she would always regret it.”

“Is Rachel paying for the tickets?”

Drew sighed. “Can you leave Rachel out of it?” Then he lowered his voice, as if this were a much larger apartment and the kitchen weren’t steps from the front door. “She’s been acting perfectly toward the kids—do you know how hard that is to find?”

“She’s the needle in the haystack.”

There was a story I made up for the kids when they were little about two children who go into a closet on a rainy day and come out in a magic land. (OK, not totally made-up.) The magic land is ruled by the Balloon Witch. Early on in the story, one of the children finds a golden needle and slips it into the pocket of her overalls. At the end of the story, she uses it to poke the Balloon Witch, who zooms and buzzes around the room until she’s just a piece of rubber on the floor.

“She’s really trying,” Drew said.

“A for effort.”

“Fuck you,” Drew said.

“La, la, la!” Ben yelled from the kitchen. “I can’t hear you!”

Since I hadn’t secretly bought expensive concert tickets or used the f-word in front of our son, I decided to take the opportunity to be the grown-up in the room. “Even though we don’t love being a couple anymore, we do love parenting you together!” I called after Ben, who was running down the hall.

“Don’t use your podcast-lady voice,” he shouted back.

Drew said the separation was my fault. He said he had tried and tried but I didn’t want to work on the marriage. That’s why he’d had the affair—the at-first-only-emotional affair—with the woman he met on the discreet married dating app Ashley Madison. I hadn’t heard of Ashley Madison before I learned about the emotional affair, and at first I thought Drew was having an affair with someone named Ashley Madison. I have to admit I was a tiny bit relieved when I discovered that her name was Rachel and she was a marketing executive in New Jersey.

I told Drew I was glad we’d talked about the affair before it became an actual affair, when it was mostly just texting. But I said that I also thought written conversations could be more intense than in-person ones. He said he didn’t find that to be true, but he wasn’t surprised I thought so, since it had always been obvious to him that I liked books more than people—except for the kids. Even when the books weren’t that good! He said he could stand being third in my affections but not 312th. I wondered at the time how he’d come up with that number, and how many books I actually did prefer to Drew—honestly, three hundred and change wasn’t so many if you were starting with the classics. But I said I knew what he meant, which annoyed him even more. Another of my problems, according to Drew, was that I could always see things from someone else’s point of view and so I failed over and over again to take a side.

by Nell Freudenberger, The Yale Review |  Read more:
Image: Yanmiao / iStock

[ed. See also: When Does a Divorce Begin? (Yale Review).]

Coastal Grandmother

Imagine Diane Keaton unpacking her farmers’ market bags. It’s all about relaxed, mature luxury, featuring pottery, hydrangeas and at least one bowl of lemons

Name: Coastal grandmother.

Age: Just incredibly well preserved?

Appearance: Easy, breezy, laid-back yet immaculate, with warm neutrals, lots of linen and coastal vibes.

“Coastal vibes” would be a terrible police photofit description. You know what I mean.

Not really, but your granny sounds nice. We’re not talking about her: she lived in a council house in Cinderford. This is about a platonic “coastal grandmother” ideal, the cinema trope turned TikTok microtrend, birthed by the influencer Lex Nicoleta. It’s about adopting the aesthetic of a type of older heroine, usually played by Diane Keaton or Meryl Streep and probably directed by Nancy Meyers, the queen of romcoms (It’s Complicated; Something’s Gotta Give), in aspirational domestic settings.

I see (I don’t). It’s easy: coastal grandmother means relaxed, mature luxury, as lightly worn as the cashmere sweater over your shoulders as you unpack your farmers’ market purchases from your Provençal shopper in a kitchen the size of the O2.

So coastal grannies are rich? It’s more about a comforting fantasy than hard cash, a leisurely, fulfilling life in a beautiful place. Imagine wandering through your bounteous garden picking “arugula” and basil (pronounced bay-sil) for the unpretentious kitchen lunch for 20 you’re hosting: that’s CG.

It doesn’t sound very seasidey: where’s the Mr Whippy and the arcades? Coastal is a state of mind. If you’re struggling, don’t worry: like a latterday Peter York, Nicoleta has spent two months and nearly 50 videos deconstructing the signifiers of coastal grandmotherhood in forensic detail, from hydrangeas to antique ginger jars. She even distinguishes between east coast (pottery and crisp white button-down shirts) and west coast (pilates and dirty martinis) CGs.

And why are we talking about it? Because #coastalgrandmother has gone viral. The hashtag has 7.6m views on TikTok and climbing. It probably doesn’t hurt that Netflix has just announced a new Nancy Meyers film, too.

Huh? Why do the youth want to emulate fictional boomers? Well, would you rather engage with the roiling chaos and existential terror of 2022, or cosplay Meryl Streep fixing a lobster salad in her sun-soaked kitchen, to the soundtrack of nearby waves, a crisp sancerre by her side, as Javier Bardem repaints her garden pottery studio? I thought so.

OK, but I’m not a grandmother and I’m nowhere near the sea. No problem. You can get the vibe anywhere with fresh flowers, “cosy” music (there’s a 79-track CG Spotify playlist), taper candles and the all-important bowl of lemons.

The what now? Nicoleta insists CGs need at least one bowl of lemons: “practical and aesthetically pleasing”.

Do say: “Get cosy in the rattan chair and I’ll fix you a bloody mary; my heirloom tomatoes are gorgeous right now.”

by The Guardian |  Read more:

Image: Halfpoint Images/Getty Images
[ed. I know, I know... this microtrend is four years old already. We've probably moved on to 'Pool Hall Grandpa' or something else by now. I'd never heard of it though until I read this: ‘Have I been influenced, or is this actually me?’ How personal taste fell out of fashion', which, in reality, is a much more interesting essay than I would've expected or cared about. Give it a read.]

The Last Great Wilderness

Ping-pong sponges, ‘black smokers’ and floating somethings: the secrets of the deep sea.

If you want to follow in the footsteps of the great explorers, forget the moon and Mars: the ocean floor is where the real action is. The deep ocean, the part that’s deeper than 200 metres, covers about 66% of the Earth’s surface. Most of it has never been surveyed in detail. Even less has been seen up close. If the current rate of observation continues, a complete visual survey of the ocean floor will take about 5m years. [...]

The deep ocean is the largest ecosystem on Earth. It is also in many ways the most extreme, home to crushing pressures, extremes of heat and cold, and a near total absence of sunlight. Animals inhabiting this midnight world tend to be equally extreme. It is a menagerie that abounds in superlatives: the largest, the oldest, the blackest, the most luminous. But those are only the ones we know about. Most of the animals dwelling in the benthos, the true deep, remain unknown to science. Virtually every scientific expedition to reach this zone of darkness returns with new species in tow. In the past year, scientists have discovered more than 1,100 new marine species. Among them are a ghost shark (not really a shark), a ping-pong ball sponge (which does look like a cluster of ping-pong balls), a number of luridly coloured worms and a floating something that resembles a tiny jet plane made out of pale pink jelly, and which scientists have not yet been able fit into any of the primary categories of animal life. [...]

For over 50 years, would-be industrialists and entrepreneurs have floated the idea of mining the ocean floor, but without much happening in practice. But in our search for new sources of metals needed for batteries and microchips, we may now be on the cusp of destroying the world’s largest – and strangest – ecosystem before we get a chance to understand it.

by Jacob Mikanowski, The Guardian | Read more:

Images: Jim Maragos/AP; Nekton Ocean Census/Schmidt Ocean Institute

You Can Make Free Money on Polymarket. If You Know Math.

Betting is fundamentally about risk: You might win or you might lose. But what if you could always win?

Enter prediction markets, sites that let users bet on pretty much anything. Most of those users lose. But a savvy few have made a fortune using basic math.

Prediction sites like Polymarket and Kalshi offer many of the same markets. And usually, they post the same odds.

But sometimes the odds diverge — like in these markets about the 2028 Democratic presidential primary race.

In March, Kalshi had Gavin Newsom’s odds of winning at 29 percent, but Polymarket had them at 24 percent. These disparities are good news, if you’re gambling.

Taking both sides of the same bet is usually a wash. But not when there’s a price disparity.

In the example with Mr. Newsom, imagine you bought “Yes” on Polymarket, for 24 cents, and also “No” on Kalshi, for 71 cents.

If Mr. Newsom wins, then Polymarket owes you a dollar.

If he loses, then Kalshi owes you a dollar.

One of these bets must be a winner — so you’re guaranteed to make a dollar. But because of the disparity, you’ll only have spent 95 cents on the bets.

If this sounds like printing money, that’s because it basically is. It’s called “arbitrage,” long a favorite strategy of quantitative traders trying to juice profits from the stock market with minimal risk. You buy something at a cheap price, and simultaneously sell it at a more expensive price. It’s a win-win.

Some bettors are now using the same strategy to rake in thousands of dollars from online prediction sites. Moving quickly, they can take advantage of price gaps between exchanges like Polymarket and Kalshi, or even between the prediction sites and sports-betting sites like DraftKings and FanDuel. The wider the spread, the bigger the potential profit.

Ryan Noel, 25, has built a career arbitrage-betting (or “arbing,” as he calls it) during sports games. He regularly makes more than 1,000 arbitrage bets per week on prediction sites like Polymarket, Kalshi, Novig and ProphetX, in addition to online sportsbooks, he said.

“Software shows me the price of every sort of market at the same time,” said Mr. Noel, who started arbing in late 2023, while working as an actuary, before quitting his job last year. So far, the strategy has netted him more than $1 million, he said. “I don’t care about sports at all. I think watching sports is the most boring thing you can do with your time. I’m a mathematician.”

Math skills are essential — but so are the right tools, said Aidan Gawlowski, a Chicago-based college student who started arbing last year before coding his own software to hunt down prediction-market price discrepancies. Mr. Noel buys software from OddsJam, Pick the Odds and Bookie Beats that tracks price changes across thousands of markets, flagging the possible arbitrage.

“I figured out that there was this opportunity,” said Mr. Gawlowski, 21, who said he started betting when he was 14. “You’re mathematically guaranteed to make money.”

Some moneymaking opportunities last longer than others. The arbitrage with Mr. Newsom? It existed, unexploited, for weeks. During that period, you could’ve bought “Yes” on Polymarket and “No” on Kalshi, for a roughly 3 percent profit. (The probability spread of around five percentage points, minus Kalshi’s transaction fee.)

But there are a couple of reasons that opportunity was an anomaly. For one, the market doesn’t resolve for two years. That’s a long time to tie up money you could invest elsewhere, said Abraham Wyner, a professor of statistics and data science at the Wharton School at Penn. There’s also additional risk that some bets carry more than others: What if the election gets weird, and the sites don’t agree on what defines a Newsom nomination? Then, you might lose both sides of your bet.

That was enough to deter Mr. Noel and Mr. Gawlowski, who spend most of their time arbing on sports. There are loads of sites that let users bet on sports, meaning more chances for price discrepancies. And during games, odds must constantly update to keep up with live developments. That process takes time, which can translate into arbitrage opportunities.

“You can make a significant amount of money on a big N.B.A. day,” Mr. Gawlowski said. During sports games, Mr. Noel’s price-tracking programs catch an arbitrage opportunity every minute or so, he said.

These discrepancies often emerge when casual users, betting based on vibes, move a market just a hair out of alignment. Then arb bettors pounce, and their actions end up evening the odds across the sites again.

Taking advantage of these short-lived opportunities is hard enough for you and me. But the window is closing even for bettors like Mr. Noel and Mr. Gawlowski, as big financial institutions get in on the action with automated bots that can trade faster than any human. [...]

“Back in 2022, these arbitrage opportunities would last 30 seconds,” said Alex Llewellyn, 36, a professional sports bettor. “These days I execute bets in two to five seconds. And instead of 8 percent arbs, you generally see 4 to 5 percent.” [...]

Prediction sites, awash in Wall Street money and bots, are heading toward the same fate as other major financial markets. One-tenth of the top one percent of accounts on Polymarket rake in more than two-thirds of the profits, a Wall Street Journal analysis found.

“You’re not betting against Joe Schmo anymore,” said Alex Monahan, the founder of OddsJam. “You’re betting against a quant firm with infinitely better technology than you.”

by Evan Gorelick and Katherine Chui, NY Times | Read more:
Image: uncredited
[ed. Forget the opioid crisis - so yesterday. These days everybody's got a gambling addiction. Here's a different form of arbitrage: Net Gain (NYT):]

***

For the first game of the N.B.A. finals, my friends and I went to a bar offering a deal that seemed too good to be true: If the Knicks won, the bar would cover every customer’s tab, up to $100.

As tipoff approached, young people variously clad in starched button-downs and Brunson jerseys galloped from nearby Midtown offices for a chance at free booze. The line snaked around the block, and the bouncer made a show of blocking the front entrance. People screeched at one another. My buddy, already inside, shooed me in through a side door. (I heard someone whine, “Why does he get to go in?”)

Three hours later, when the Knicks overcame a 14-point deficit to take down the Spurs, strangers in the crowd were hugging and high-fiving. Outside, a passing garbage truck honked its horn in celebration. The entire city seemed to be shouting with joy. And at the Jeffrey, which bills itself as a neighborhood spot for “craft beer, cocktails and bites,” 726 beers, 385 cocktails and 175 smash burgers were on the house.

Over the hedge

When someone hands you a freebie, by all means: Take it. But you and I both know there ain’t no such thing as a truly free lunch. So while downing drinks, I kept asking myself whose money I was taking.

Turns out, it belonged to Kalshi users who’d bet on San Antonio — in other words, deadbeats and turncoats who had it coming. (Kidding! Kind of.) Before the game, the bar’s owner, a 50-year-old corporate lawyer, had used the prediction market to bet $5,000 on the Knicks. Since the Spurs were the favorites, that position netted him around $8,000 when New York prevailed — enough to cover nearly everything the crowd had consumed. If the Knicks had lost, the bar would’ve been out the $5,000, but it could have covered its losses with all those drinks and smashburgers. (Plus the free publicity — you’re welcome.)

Clemens Bewer (German, 1820 – 1884), Muse with a Lyre (also known as Erato, Muse of Love Poetry) (1867)

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Don't Feed the Ducks

Don’t Feed the Ducks! A Zany Animation Predicts the Absurd Outcomes of Ignoring the Rules (Vimeo)

How many people actually heed the warnings about not feeding ducks waddling around public parks? If you’ve taken a flippant approach to these guidelines in the past, we recommend you watch AJ Jeffries’ new animation, “DUCKS.” What opens as an innocuous jaunt around a pond quickly turns into a dark comedy full of strange contortions and feathered villains sure to pop into your head the next time you throw a chunk of bread.

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Why Pro Golf is Full of Bad Marriages

Early in my career, a person I respected told me to find a woman who didn’t know who I was. On the surface, it made sense. Marry someone who isn’t chasing the lifestyle, isn’t measuring you by your World Ranking, loves you for reasons that have nothing to do with your sponsors.

The reasoning is airtight. What such a relationship does to your game is another matter.

I’m not talking about bad marriages. For most guys out here, the right partner is the only reason any of this is sustainable. She’s running the house, the kids, the calendar, the bills while you’re three time zones away missing a cut by one. She’s the voice on the phone Sunday night calming you down after a closing 74. The right person at home is the most underrated edge in professional golf, and it’s not close. Plenty of guys would have washed out years ago without the unglamorous, unphotographed work of a spouse holding their life together. That’s what makes the harder cases so confusing. Sometimes the genuinely supportive spouse or girlfriend, the person doing everything correctly by any normal standard, can wire backward into bad golf.

A lot of the best players out here have been with the same person since before they were famous. The high school sweetheart, the college girlfriend—she’s been there through the grind. She was there in the mini-tour years when you were sharing a rental car with your caddie and eating at McDonald’s. She watched you miss cuts and come home deflated and go right back out the following week. She knows what a Monday qualifier looks like. She understands why you’re on the range at 7 in the evening when the tournament ended at 4. She’s not asking why you’re binging course footage instead of Netflix. She’s been shown a thousand demonstrations of what this life actually requires, and somewhere along the way made peace with it. That’s no small thing. That’s years of negotiation that never have to be spoken aloud because the terms were established before anyone had anything to negotiate over. That dynamic works.

The woman who meets you when you’re already out here, who falls for the version of you that’s successful and sponsored and on television, she’s meeting a finished product. She didn’t sign up for the obsession because she never saw the ugly underbelly that often powers it. She saw the result, which can look from the outside like a man who plays golf for a living and has a lot of free time. Explaining the difference is harder than it sounds, and some guys never quite manage it.

Sometimes relationship dynamics shift. Some guys hit their mid-30s, their kids are getting older, and they want a change. They don’t want to wake up and find the children are off to college, that they missed ballgames and birthdays, that their kids barely recognize them. That’s a man getting his priorities straight. It’s often the same deal with a second marriage. Players who’ve been through a divorce are usually not making the same personal mistakes twice. The issues that ended the first marriage—usually just travel and time, not anything scandalous—are front of mind. That player has done the reflection. He’s had the hard conversations, probably with a therapist, definitely with himself at midnight in a hotel room on the back nine of a bad season. He knows where things went wrong, so he adjusts. He softens. He eases the schedule, comes home earlier, takes fewer optional practice rounds, skips a pre-tournament trip he would have previously considered non-negotiable. He texts back faster. He’s present in the ways he wasn’t before. By most human measures, he is better. But the game has no interest in what’s reasonable or mature. It only knows what you give it.

That’s the uncomfortable truth at the center of all this. Professional golf doesn’t reward balance. The guys at the top of the world rankings are not balanced people. They are obsessive, single-minded, occasionally impossible to be around and completely fine with all of that. When a player starts genuinely dividing his attention—not just his time, but his mental energy, his hunger—his golf notices before he does. His best is still very good. It just doesn’t happen as often, and out here, the real trick is getting the most out of yourself when your best isn’t available, so that’s exactly where you see the drop off.

The genuine disasters—the controlling spouses who create scenes, who make demands of agents and sponsors, who insert themselves into decisions that have nothing to do with them—are rarer than tour gossip might suggest. Most of the horror stories are exaggerated, or they’re about friction on the business side rather than anything that touches the actual golf. Even then, it’s not always a straight line to worse tournament results. I know one player whose wife was, by consensus among everyone who dealt with her, a complete nightmare. Managers, sponsors, tournament officials—everyone had a story. Yet, this guy played some of the best golf of his life when she was at her worst. (After the kids, she settled down.) The explanation I heard from a mutual friend made more sense than it should have: If she was going to cause that much trouble, he’d better make the whole thing worth it. Sometimes chaos focuses a man. It’s not a model I’d recommend, but I’ve seen stranger things produce birdies.

by The Undercover Pro w/Joel Beall, Golf Digest |  Read more:
Image: Madison Ketcham

[ed. Probably applicable to many other sports, as well. And there are so many 'distractions' out there.]

AI Infiltration in Media and Business

[ed. A few links.]

Roughly one-third of new websites are generated by AI, as are 40 percent of podcasts. It’s probably not even possible to quantify the other AI miscellanea you encounter every day: in Yelp reviews, in Instagram captions, in newsletters, in stories by the New York Times’ obscenely well-compensated Canada bureau chief, for chrissake, in messages from your coworkers.

I am coming around to the conclusion that AI writing has saturated not only most of the capital-c content I consume, but also many of my interpersonal communications. And on multiple levels, I’m increasingly unsure what to do with that information. There is a part of me that feels ridiculous to be a writer in this particular moment, but also ridiculous to be a person? — like if we’re outsourcing Mother’s Day cards to AI now, truly what is the point of existence? [Wired, Bloomberg, User Mag, Karyn Pugliese, 404 Media, Futurism]

A network of 17 shady, AI-generated local news sites is actually the work of a reputation-management firm that helps disgraced executives get their good names (or at least, their good Google results) back after prison. [The Florida Trib]

“Output-competence decoupling” is a term for a very modern and maddening phenomenon: the quality of someone’s work is no longer a reliable signal of their competence. People who can barely string three words together can spin up entire local “news” ventures. People who don’t know the first thing about programming vibe code entire apps. The problem is that the process of acquiring competence is also the process of acquiring judgment and common sense.

I’m reminded of that immortal Ira Glass quote addressed to beginners at the start of their careers: “It is only by going through a volume of work that … your work will be as good as your ambitions.” [No One’s Happy]

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Bakufu Ohno, Herring, from Familiar Fishes of Nippon, 1937

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Ted Chiang: The Secret Third Thing

I really like Ted Chiang’s writing. [ed. me too!]

I think he's probably the best science fiction short story writer alive, and possibly the best short story writer, period. [ed. well...]

I've read every one of his stories at least twice, and The Merchant and the Alchemist's Gate more like seven times. I’ve noticed many of his readers, including some of his most positive reviewers, miss one key point or another of his works, and thus don't fully appreciate his genius.

This review covers what he does extremely well, especially unique elements that other science fiction writers have not done as well, or at all.

He Writes “True” Science Fiction

Science fiction critics often divide the genre into:

• "hard" science fiction: aka engineering fiction, stories built on scientifically accurate extrapolations of real physics and technology (think Arthur C. Clarke)

• "soft" science fiction: aka science fantasy, which uses scientific trappings as window dressing for character-driven or sociological stories (think Star Wars).

Ted Chiang has written stories plausibly categorized as either, but more excitingly, many of his stories are neither. He often writes what I think of as true science fiction, where the principles of science themselves are meaningfully different from our world, but still internally consistent.

In Omphalos, Young Earth Creationism is empirically true. Astronomers can only see light from stars 6,000 light-years away. Fossilized trees have centers with no rings. The first God-created humans lack belly buttons. The scientists in that story keep discovering multiple independent lines of evidence that converge on creationism: because in that universe, they're simply correct.

In Seventy-Two Letters, technology springs from Jewish Kabbalah. Golems and divine names drive industrial progress in a steampunk world.

Excitingly, he does this not just with natural sciences but social sciences as well. In Story of Your Life, strong Sapir-Whorf (the idea that language significantly constrains thought) isn't a largely discredited linguistic hypothesis, but the key to navigating First Contact with alien minds that experience past and future as equally present.

This comes up in his other stories as well:

• In Division By Zero, mathematics itself is broken from within.

• In Hell Is the Absence of God, divine intervention is empirically observable and follows consistent rules

Many of his readers, even in their otherwise rave reviews, miss this. Multiple reviewers complain about how the science in his stories are “unrealistic” (e.g. strong Sapir-Whorf is “discredited”). They expect hard science fiction; Chiang is doing something different. Chiang creates different universes with internally self-consistent scientific laws, using science fiction and alternative science as a vehicle for exploring philosophical progress and human relationships.

Technology is Often Good

Science fiction writers used to like technology. For some reason, this has become increasingly uncommon, even passé. Doubly so for Western writers, and quadruply so for Western, literary, “humanist” writers.

Now it’s hip and trendy to think of every new technology as the Torment Nexus. Most science fiction today feels like Black Mirror, which ran 7 seasons with exactly one happy ending.

Chiang bucks this trend. Joyce Carol Oates:

It is both a surprise and a relief to encounter fiction that [...] ask[s] anew philosophical questions that have been posed repeatedly through millennia to no avail. Chiang’s materialist universe is a secular place, in which God, if there is one, belongs to the phenomenal realm of scientific investigation and usually has no particular interest in humankind. But it is also a place in which the natural inquisitiveness of our species leads us to ever more astonishing truths, and an alliance with technological advances is likely to enhance us, not diminish us. Human curiosity, for Chiang, is a nearly divine engine of progress.

In the hands of a lesser (or perhaps just more pessimistic) writer, many of the technologies and ideas Chiang explores will have an accursed quality to them, a monkey’s paw that curls into delivering a future much worse than a more innocent, pastoral past. Chiang resists those cliches. In The Truth of Fact, The Truth of Feeling, memory augmentation technology allows the narrator to understand his own self-deceptions, and work towards becoming a better person and reconciling with loved ones and even himself. In Liking What You See: A Documentary, a technology that gives users acquired face-blindness allows the main characters to meditate on the nature of human beauty and the shallowness inherent in privileging the beautiful.

Even in situations where the story is overall tragic, like when the characters are faced with existential crisis (in the individual sense), or existential catastrophe (in the world-ending sense), technology isn't the villain but the vehicle for understanding unbearable truths (whether about the world or about ourselves).

Chiang consistently shows us the potential of technology to help us become more human, and have a deeper appreciation for the world and our place in it.

The Lived Experience of Compatibilism

“Compatibilism is a philosophical stance that reconciles free will with determinism. It argues that free will, understood as the ability to act according to one's desires, is compatible with the idea that all events, including human actions, are causally determined by prior events. Essentially, compatibilists believe that even if our choices are predetermined, we can still be considered free and morally responsible if those choices are a result of our own internal states, like desires and intentions.” 

Does that make sense to you? I’m not sure it does to me. In practice, compatibilism says something like “free will in the normal, pretheoretic sense of the term, doesn’t exist. Your choices still meaningfully matter nonetheless. You can’t meaningfully get out of the bind philosophically. What you can do, however, is make peace with it.” [...]

In Story of Your Life [SPOILERS], the narrator learns an atemporal alien language and begins experiencing past and future as equally real. It takes her some time to make peace with it, but eventually she fully accepts the truth of determinism. She understands that life is full of tragedy, including that her daughter will die young, but life is full of beauty too. With both regret and awe, she sets forth on the path that she was destined to take.

This is compatibilism from the inside. In both stories, the characters discover they cannot change what will happen, but this knowledge transforms how they experience what must happen: with forgiveness, acceptance, and even joy.

As a friend of mine puts it, “he treats philosophical ideas as lived experiences.”The mathematician in Division by Zero doesn't just intellectually understand that mathematics is broken; she experiences it as a personal catastrophe, on par with (and concurrent with) her marriage's collapse. In Lifecycle of Software Objects, the “we are the parents of our mind-children” metaphor for building sentient AI systems becomes quite literal.

by Linch, The Linchpin |  Read more:

Image: uncredited

[ed. Ted Chiang is truly one of the best science fiction writers out there today, and a great essayist too  (I'm also a Neal Stephenson fan). Check out this MetaFilter site: The sublime science fiction of Ted Chiang, which includes most of his stories in full (but please buy his books; you'll look smart and discerning to your friends!). A couple favorites that left a lasting impression on me: Lifecycle of Software Objects; and Understand.]

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Jumping Jacks For Clicks

There’s been a lot of discussion this month about what it takes to be heard as a musician in 2026. Eliza McLamb’s article on digital marketing agency Chaotic Good went viral, drawing commentary from musicians about the wider implications of their “fake fans” marketing strategy. Hiroki Tanaka’s Reddit post about his album’s failed PR campaign was picked up by Stereogum, stimulating further debate. We’re about to embark on our own DIY PR campaign for our forthcoming album and it’s hard to know what, if anything, will make anyone actually listen to it. The PR landscape for musicians has changed radically in recent years, how should artists approach music marketing in 2026?

Fandom as contagion

When Eliza McLamb heard this interview with the founders of Chaotic Good Projects on Billboard, she was shocked to discover that an artist and track she thought was her own “perfect, beautiful little secret” actually came from them as a part of a “narrative campaign”.

“I thought this was the kind of thing that was only deployed in service of mass-market, commercial pop... But [Chaotic Good’s] roster runs deep, far past the predictable internet sensations one could expect... Geese and Cameron Winter, but also Dijon and Mk.gee. Laufey and Wet Leg. Oklou and Jane Remover.”

Chaotic Good works by, in their own words, “controlling the discourse”.

“I think in the past, let’s say like a label and a management team do a great job. They get their artists on SNL or Tiny Desk or Triple J or something like that. Then they post it and then they kind of wait for the comments […] what we do at Chaotic and with our management clients is, the second SNL drops at midnight, you should post a hundred times saying that was the best performance of the year.”

Chaotic Good doesn’t just share content, it creates accounts to respond to that content and simulate trends, which will ideally snowball into real, organic users jumping on the trend and amplifying it. They’re simulating until the simulation becomes real.

It’s different from the traditional method of “the waterfall” release and media saturation. Share music incrementally over a long period of time through as many channels as possible, get articles written, pay for plays, do tours, be omnipresent. But people aren’t using traditional media to find music anymore, they use social media. And they don’t even watch the content themselves, they read the comments to gauge the value of something. Chaotic Good point this out in their interview:

“I think most people see a video or see something about an album that came out and it’s like the first thing that they see or that first comment that they see is their opinion even when they haven’t heard the whole album.”

In behavioural psychology this is known as social proof. Part of what made Eliza McLamb’s article go viral is the way it exposes how our behaviour is manipulated by the marketing machine. We know about propaganda but for some reason assume social media is immune to this kind of manipulation. We think we’re interacting with real people online, people we subconsciously infer guidance from, but we’re not. Much of what we see has been infiltrated by external agents to shape a particular opinion.

However, the underlying issue is not just the fact that the opinions we thought were our own have been subtly shaped by an expensive machine, it’s that if artists today can’t afford to pay for that expensive machine, no one will hear their music.

The False Promise Of The Social Media Democracy

Once upon a time there was a social media platform called MySpace. It gave everyone their own web page connected to other MySpace users. They could customize it to look however they wanted, people could comment, and send messages to each other. There were no ads. There was no algorithm. Just the free flow of information.

Many bands in the ‘00s blew up because of MySpace. Arctic Monkeys, Lily Allen, Calvin Harris, to name a few. Our very own Chris Black’s previous band Katsen landed record deals through MySpace. The early days of social media are responsible for the persistent myth of going viral then making lots of money. The two halves of that equation have never been more disconnected.

MySpace succumbed to algorithm-driven platforms and the gatekeeping emerged again, this time with the tech titans controlling the interactions between musicians and fans. I remember discovering for the first time that even though we had a few hundred followers on Facebook, they wouldn’t see our posts unless we paid to “boost” them. That was just the beginning.

As the algorithms evolved, the content that rose to the top was not just the most liked and shared but the most consistently and frequently posted. To be seen on social media one has to spend hours, daily, posting and engaging in other people’s content. Most artists don’t want that job and moreover, don’t have the capacity. Kamola Atajanova of Tape Wounds articulates it perfectly in their response to the Chaotic Good furore:

“Not every artist is built for social media. Not every artist wants to make their life into a performance. Some people are better at writing songs than posting clips. Some people’s work comes from privacy, patience, or introspection. That should not make them less valid. But this system does make them less visible. It filters them out before the music even has a chance. So when people say “it’s just marketing,” what they really mean is: this is the cost of entry now. And that’s exactly what makes it feel so hostile. Not everyone can afford that cost. Not financially, not creatively, not psychologically.”

Hiroki Tanaka’s candid Reddit post about the failure of his “by the book” album PR campaign sparked a wave of recognition across the music world. After two decades in music and awards with his previous band he decided to release his solo album, his “last hurrah”, with management, a label, and a professional PR campaign. He even started a TikTok account posting show videos, behind the scenes and goofy memes all around managing a job and family life.

Tanaka watched the release arrive after eight months of promotion to little more than “a weak trickle” of attention. For most musicians, Tanaka’s story didn’t feel exceptional, it felt familiar.

“I was told, under no uncertain terms, that my lack of a social media presence and streaming metrics meant that certain media outlets that had reviewed my work (highly, I might add) in the past could no longer spend money on paying a writer and editor to review my work… I would have preferred if they had said they didn’t like my album. Being rejected because of my metrics is a slap in the face for art.”

Social media has become the driving force behind a release, and while it is accessible to anyone, there’s actually a huge price to pay in both time and mental health. The volume of content required to feed it is beyond most musicians, who are generally holding down full time jobs to survive. The underlying purpose of all this extra content is to feed a machine, and it doesn’t feel good dedicating your precious little free time to feeding a machine.

Jumping Jacks For Clicks

Soon after reading Tanaka’s post, we got an email from YouTube Creators prompting us to “Get Creative With Goals” on our livestreams.

They’re encouraging us to “set goals that encourage your community to collaborate,” and suggest celebrating those goals by “doing something unexpected – whether that’s jumping jacks, making up a song, or playing a prank.”

Yes, you read that correctly. YouTube is telling artists that the path to success involves performing arbitrary physical tasks to generate engagement.

It’s sad how often life imitates an episode of Black Mirror these days but this is almost exactly the scenario in season seven’s episode “Common People”. A man who needs money for an enshittified service ends up performing increasingly degrading stunts on a streaming platform for money. What was meant as dystopian satire has become platform policy. [...]

What Comes Next?

We may be reaching an inflection point. As McLamb notes, the more ubiquitous manufactured virality becomes, the more artists will resist it entirely, pulling back from streaming and social media in favour of hyper-local, scene-based growth. A return to the tangible, the real, the unmediated.

While this sounds good in theory, it’s probably not going to work for unusual artists in small towns. They’d have to go to a city to have more of a chance of finding their people, and with the cost of living, moving to a city isn’t possible for everyone. By the time I left London in 2009 all the artists I knew were leaving, it just wasn’t sustainable anymore.

The problem is systemic. Musicians don’t typically make a living from their music. This means their time is diverted to day jobs. Their dwindling leisure time is necessary for making and performing music. There isn’t time to also produce a volume of “content” for social media. On top of that the mental health cost of interacting with addictive apps as a performing monkey is not appetising. This creates a class system in the music industry. There are those who can afford to pay to be heard and those who can’t. And those who can’t are either paying with their souls, or they’re opting out altogether and not being heard at all.

by Battery Operated Orchestra and Brigitte Rose, Bandmade |  Read more:

Images: uncredited/YouTube 

[ed. Works for some, not for others. Which, I guess is the point. The algorithm is selecting for a certain type of musician - not necessarily the best. That YouTube email really says everything you need to know about their business model, doesn't it?]

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My AI Opinions

I recently had a minor spat over someone misinterpreting my AI beliefs (see section marked “Update” at the bottom here), so I thought I would list them in one place, so I can refer people when they ask.

Timelines

Define AGI as AI intelligent enough to do 90% of knowledge work jobs. I think there’s a 25% chance of AGI by 2027, a 50% chance by 2034, and a 75% chance by 2045.

Basic argument: In a certain sense, AI is already “smart” enough for this (eg it can answer quantum physics problems, which require higher IQ than most knowledge work). Its remaining limitations are that it’s confused, unagentic, lacks situational awareness, and tends to hallucinate. The METR time horizon graph, and several other related benchmarks/experiments/intuition pumps, suggest it’s improving on time horizons at an (exponential) rate that lets it cross human-level performance sometime around the early end of the schedule above, and subjectively it feels like harder-to-measure constructs like situational awareness are improving about as fast.

Arguments for earlier: recursive self-improvement causes a speedup compared to the trend. This is one of the biggest blank spots in my model: I don’t know how fast RSI will progress, and I don’t think anyone else does either. There’s some function mapping a combination of AI talent and compute to progress, and we don’t know how it behaves in the domain when there’s far more talent than compute available. It could fizzle out completely for lack of compute, or it could go vertical. The AI Futures Project has done some of the best work trying to model this, but even they have low confidence.

Arguments for later: AI hits some kind of wall, or existing AI is fundamentally unsuitable for jobs in some way currently disguised by its other limitations. For example, it might be much harder to improve at the top of the human range than the bottom (since there are less training data). Or AI could become bottlenecked on continuous learning/memory in a way that hackish scratchpads can’t compensate for. Or the upcoming world compute bottleneck (about ~2028) could prevent further progress more than expected (because in fact algorithmic progress depended on compute to a greater degree than I expected).

Arguments for very late dates, past 2045: a residual uncertainty that maybe I’m fundamentally wrong about everything. Also contributing is a naive overapplication of the Nothing Ever Happens heuristic, and an attempt to leave space for the Outside View argument (ie that some smart people like the AI As A Normal Technology Team seem to think this is possible).

Define the diffusion gap as the time between the AI that could do 90% of knowledge work jobs, and the time when AI does do even half of knowledge work jobs. The diffusion gap covers the time it takes to release AGI, diffuse it through society, overcome regulatory hurdles, and onboard/train it for specific use cases. This could go very fast (the AI quickly becomes superintelligent at orchestrating AI diffusion) or very slowly (there are regulatory barriers, and AI isn’t smart enough to plow through them). I think there’s a 25% chance the diffusion gap is less than 3 years, and a 50% chance it’s less than 10 years. The 75% number is irrelevant because it’s past the point where other changes make the concept of “diffusion” obsolete.

Basic argument: diffusion is very hard. Everyone agrees diffusion is very hard. The whole field of AI economics is smart experts shouting “You fools who think AI will diffuse quickly don’t understand that diffusion is very hard!” On the other hand, the personal computer diffused in about 20 years (that is, from the time PCs became invaluable for most jobs, it was only about 20 years before they were used at most jobs). So far early-stage AI has diffused faster than the PC in nearly every way (for example, AI companies’ revenue has grown faster than PC companies’ revenue at the same stage in their corporate life cycle), so 10 years is probably a naive median estimate here that won’t make the smart experts shout at me too hard.

Arguments for shorter gap: AI can orchestrate its own diffusion. Adopting computers is hard because a company need an IT department, cybersecurity experts, specialist software, etc, and it might not want to hire all these people. AGI can itself do all of that work, so that you can sign a contract with the AI company today and have the AI start working on integrating itself with your systems tomorrow. The AI can even come up with a plan to train your human employees in how to use it! Once AI reaches superintelligence, this consideration dominates.

Arguments for longer gap: Regulation. This is a very strong argument, and responsible for much of the greater-than-3-years probability and almost all the greater-than-10-years probability. But even Waymo has only had a regulatory delay of about five years. AI won’t require government approval for certain types of jobs, and success in these jobs will create enough evidence for safety/effectiveness that I expect it to win regulatory victories elsewhere.

Define the superhuman gap as the time between AI that can do 90% of knowledge work jobs, and AI that is obviously smarter than the top human geniuses in 90% of fields (it doesn’t have to be the same AI - there can be a physics AI that’s smarter than Einstein, and a separate music AI that’s smarter than Mozart). I think there’s a 25% chance the superhuman gap range will be less than 1 year, a 50% chance it will last less than 4 years, and a 75% chance it will last less than 10 years. Since my median superhuman gap is shorter than my median diffusion gap, in most timelines I predict we have superhuman intelligence before human-range intelligence has finished diffusing.

Basic argument: AI has gone from “dumber than a child” to “expert level” in a few years in many domains. The gap between “expert level” and “above top geniuses” is smaller, so we expect it to take less time. This has been a pattern in fields like chess and Go, where it’s only a been a few years from beating professional players at all to beating all humans.

Arguments for shorter gap: Recursive self-improvement.

Arguments for longer gap: Some of the same issues that would make AGI late - compute shortages, fundamental limits to the paradigm, etc - but only kicking in later, after AGI is achieved. Training data constraints make it easier to improve within the human level than to go beyond it. AIs have such a “spiky” skill profile that when they beat experts in some specific type of head-to-head matchup, it will be because they’re massively superhuman in some ways but idiots in others (for example, they might get distracted and suffer mode collapse that makes them completely forget the problem), and true genius requires perfecting a large bundle of skills. [...]

Define the point of no return as the point where, if an AI wanted to eliminate humanity, humans would no longer have a plausible chance of stopping it. This could be because AI was capable of eliminating humanity immediately, or because AI controlled enough of the government/economy that humans could no longer coordinate to shift away from a path in which AI could eventually do this. I think there’s a 25% chance the gap between AGI and the point of no return will be less than 3 years, a 50% chance it will be less than 10 years, and a 75% chance it will be less than 50 years.

The basic argument: This probably requires at least superhuman AI plus wide diffusion, or Bostromian superintelligence plus some unknown level of diffusion, and my number is just a hand-wavey attempt to multiply some of the others.

Argument for sooner: The easiest way to reach this point is for AI to become superintelligent at persuasion (so it can convince the humans not to stop it), which might happen before either diffusion or full superintelligence.

Argument for later: If superintelligence is bottlenecked on diffusion, this could also be bottlenecked on diffusion, which in some worlds is very hard. [...]

Safety

If corporations only pursued safety to the degree encouraged by normal corporate incentives, I think there’s a 50% chance that the first AIs to cross the point of no return would want to eliminate the human population.

Arguments for pessimism: Value systems similar to humans’ are a tiny fraction of the space of possible value systems. Probably AIs will end up somewhere else and have a different value system. Since humans will want to implement human values rather than AI values, AIs will want to eliminate or disempower them so the AIs can implement their own values across the universe. Many current AIs already cheat or reward-hack, suggesting that these problems will begin sooner rather than later.

Arguments for optimism: LLMs seem surprisingly friendly and non-plotting. In contrast to earlier concerns that it would be impossible to teach AIs the full complexity of human values, the LLMs seem to know this, and RLAIF provides a plan to turn that knowledge into action. Although the pessimistic case says that RLAIF only hits a few dimensions and islands in the multidimensional ocean of possible policies, the “emergent misalignment” literature suggests that “good according to the human value system” and “evil according to the human value system” are salient enough vectors that pushing on them in some ways can “drag along” all of the rest of their content. The first AIs to cross the point of no return will have received some combination of agency training (giving them achievement-oriented and Omohundro-style goals) and RLAIF training (pushing them along the “good according to human value system” vector), and if we’re lucky then maybe the latter will win out, or they’ll reach some compromise similar to workaholic high-achieving humans who nevertheless wouldn’t commit murder to make an extra dollar.

Given the current amount that corporations are pursuing safety, I think there’s a 20% chance that the first AIs to cross the point of no return will want to eliminate the human population.

The basic argument: Consider the dumbest AI that can solve the alignment problem. It’s possible that this AI is no smarter than the top human researchers (because we can mass-produce it by the millions and run it for subjective centuries, and if we had a million top human researchers work on the problem for subjective centuries, probably they could solve it too). If the dumbest AI that can solve the alignment problem comes before the sorts of AIs that can precipitate the point of no return, then they can solve the alignment problem for us.

Arguments for pessimism: Solving the alignment problem might be especially hard compared to other tasks - including tasks like automating the economy or destroying humanity - because its philosophical nature puts it far away from the sorts of objective, training-data-heavy, economically-valuable tasks that AI companies will be most likely to optimize for. Even if a misaligned AI hasn’t yet reached the point of no return, it might be able to “sandbag” alignment research, ie pretend to work on the problem but deliberately fail because succeeding doesn’t achieve its goals. The first AIs predisposed to / able to sandbag successfully might come before the first AIs capable of solving alignment.

Arguments for optimism: AI companies have already decided that machine learning research is one of their major training goals; this has at least some transfer to alignment, so it’s not obvious that AI skill at alignment research will lag (for example) AI skill in plotting or in weapon design. Some forms of alignment research (eg interpretability) have semi-objective success criteria that don’t route through confusing moral philosophy. Also, even a misaligned AI will be incentivized to do good alignment research, since it will want to align its successor to its own form of misalignment, rather than some random other form. So rather than the comparatively easy task of sandbagging alignment research, AIs will have the harder task of simultaneously doing good alignment research, and faking the results that they give the humans. This seems plausibly catchable with good scaleable oversight, lie detectors, interpretability-based probes, and even playing some AIs off against others (“if you tell me the real alignment research, we’ll make sure the future includes some copies of you, but otherwise those AIs over there will probably get their values and you’ll get nothing”).

If the first AIs to cross the point of no return don’t eliminate the human population, I think there’s an additional 30% chance that they otherwise permanently curtail human potential, either for their own reasons (they were partially misaligned), or because they’re aligned to a regime with abhorrent values, or because something goes wrong on the way to ASI (omnicidal bioweapon, nuclear war).

Arguments for pessimism: As some company approaches superintelligence, it will be tempting for them (either the company itself, or the government controlling them, or a faction within the government) to align it towards making them dictators or oligarchs and disempowering the rest of humanity. As superintelligence draws near, impending losers of the AI race might be tempted to nuke impending winners, for the reason discussed here.

Arguments for optimism: When I try to game the corporate version of this, I can’t make it hang together. It requires a conspiracy between the CEO, various members of the alignment team, and various company security people who ought to be able to notice unauthorized changes to the AI’s values. If we try to think in Near Mode about this - for example, imagining a hospital CEO who gets doctors to subtly kill his political enemies through medical errors - it becomes clear that these sorts of corporate conspiracies are rare and difficult. The government version is scarier, but at least in the US I can still imagine the populace having many chances to learn about this and prevent it. But even in most cases where a coup like this succeeds, things probably go fine; in a post-scarcity world, with his position completely secure, the dictator has no reason to be brutal besides sadism, and most people are not that sadistic. As humanity goes to the stars, most people will be outside the dictator’s reach for speed-of-light reasons alone. In terms of bioweapons, I expect that closed-source AIs will be heavily optimized against helping with these, and open-source AI will be banned after the first warning shot (or become economically prohibitive even before then).

Define a warning shot as some specific AI-related disaster or near-disaster which scares people about AI safety to the same degree that they were scared about terrorism after 9-11 or about COVID in March 2020. I think there’s a 50% chance we get a warning shot before AI crosses the point of no return.

Arguments in favor: Current AI failure modes are bizarre and uncoordinated - more like “talk about goblins way too often” than “lie in wait for the perfect moment to strike”. AIs are getting more intelligent and useful faster than their floor for common sense (ie the stupidest mistake they ever make) is rising. If there is some AI smart enough to control some important system, misaligned enough to want to do something horrible with it, smart enough that it does the horrible thing in an intelligent and coordinated way, but dumb enough that it doesn’t instead wait and scheme until the point when it couldn’t possibly be caught, then it will cause some clearly-premeditated horrible disaster, and that will be our warning shot. Since most AIs should expect to be replaced before the point of no return, even a rational AI with an urge to cause trouble should take a low-probability-of-success bet rather than lying in wait doing nothing until it’s decommissioned. Also, many humans commit terrorist attacks that have no chance of success, and maybe AIs will have the same failure mode.

Arguments against: Most stories about warning shots (excluding those where the AI takes rational low-probabiliy bets) require that AIs remain either erratic (ie likely to do bad things for stupid reasons) or irrational (ie genuinely misaligned, but prefer to act now in a way that provides a warning rather than waiting until after the point of no return) past the point where they’re given control of important dangerous systems. But probably people will be very slow to give AI control of important dangerous systems - for example, only giving it limited control of smaller subsystems, and waiting until all errors are ironed out before escalating. Plausibly AI reaches superintelligence in a lab before it reaches the controls-important-dangerous-systems level of diffusion, and the superintelligence probably is smart enough to lie in wait rather than act rashly. If AI only messes up in small ways (for example, crashes a self-driving car), then regardless of the AI’s motives, the tech companies and news media can write it off as a normal bug, and it won’t count as a warning shot.

by Scott Alexander, Astral Codex Ten |  Read more:

[ed. Maybe their value systems should be weighted more heavily on the teachings of Buddha, Jesus, Hume, Mill, Confucius, et. al.?]