Showing posts with label Education. Show all posts
Showing posts with label Education. Show all posts

Saturday, April 4, 2026

The Big T-Shirt Payoff

The College Student—and His Cat Meme—Who Hunted the World’s Biggest Cyberweapon

Sitting in his dorm room at the Rochester Institute of Technology, Benjamin Brundage was closing in on a mystery that had even seasoned internet investigators baffled. A cat meme helped him crack the case.

A growing network of hacked devices was launching the biggest cyberattacks ever seen on the internet. It had become the most powerful cyberweapon ever assembled, large enough to knock a state or even a small country offline. Investigators didn’t know exactly who had built it—or how.
 
Brundage had been following the attacks, too—and, in between classes, was conducting his own investigation. In September, the college senior started messaging online with an anonymous user who seemed to have insider knowledge.

As they chatted on Discord, a platform favored by videogamers, Brundage was eager to get more information, but he didn’t want to come off as too serious and shut down the conversation. So every now and then he’d send a funny GIF to lighten the mood. Brundage was fluent in the memes, jokes and technical jargon popular with young gamers and hackers who are extremely online.

“It was a bit of just asking over and over again and then like being a bit unserious,” said Brundage.

At one point, he asked for some technical details. He followed up with the cat meme: a six-second clip that showed a hand adjusting a necktie on a fluffy gray cat.

Brundage didn’t expect it to work, but he got the information. “It took me by surprise,” he said.

Eventually the leaker hinted there was a new vulnerability on the internet. Brundage, who is 22, would learn it threatened tens of millions of consumers and as much as a quarter of the world’s corporations. As he unraveled the mystery, he impressed veteran researchers with his findings—including federal law enforcement, which took action against the network two weeks ago.

Chad Seaman, a researcher at Akamai, joked at one point that the internet could go down if Brundage spent too much time on his exams.

Early warning

Three times a year, several hundred of the techies who keep North America’s internet running gather to talk shop. Last June they met at a conference in Denver hosted by the North American Network Operators’ Group.

One major topic was a fast-growing and often legally dubious business known as residential proxy networks. Dozens of companies around the world run such networks, which are made up of consumer devices like phones, computers and video players.

These “res proxy” companies rent out access to internet connections on the devices to customers who want to look like they’re surfing the internet from a genuine home address.

That kind of access is useful for people who want privacy or for companies that want to masquerade as regular people to test out internet features for particular regions or scrape the web for data (say, a shopping price-comparison site). AI companies use the networks to get around blocks on automated traffic so they can gather large amounts of data to train their models.

Then there are the customers who want to hide their identity while engaging in ticket scalping, bank fraud, bomb threats, stalking, child exploitation, hacking or espionage.

Some device owners willingly sign up to be on these networks so they can make a few dollars a month, but most have no idea they’re connected to one.

At the Denver conference, Craig Labovitz was alarmed. The Nokia executive had been tracking the data flows of the internet’s infrastructure for years, and he knew the network’s data centers, chokepoints and design better than most.

Starting in January 2025, Nokia’s sensors had picked up a series of increasingly powerful cyberattacks coming from devices that hadn’t previously been considered dangerous. Called distributed denial of service, or DDoS, attacks, these were massive floods of junk internet data designed to knock websites offline by overwhelming the data pipes that connected them. These attacks are sometimes launched by extortionists or even business rivals seeking to sabotage computer networks.

Nokia saw hundreds of thousands of devices joining in these attacks. One unprecedented attack later in the year on internet service provider Cloudflare was “comparable to the combined populations of the UK, Germany, and Spain all simultaneously typing a website address and then hitting ‘enter’ at the same second,” Cloudflare said.

The network, which would become known as Kimwolf, seemed to be using residential proxy connections to launch its attacks, giving it the potential to do massive damage.

“The basic message was, ‘Be afraid,’” Labovitz remembers.

by Robert McMillan, Wall Street Journal |  Read more:
Image: via
[ed. Here's how to protect yourself.]

Wednesday, March 25, 2026

China and the Future of Science

[The following post is a polished transcript of a speech I recently gave to a private gathering of American technologists. Its contents may be of interest to a larger audience. -TG.]

The Chinese socio-political system differs from our own. From the perspective of the topic of this conference, here is the most salient distinction: the Chinese system has a telos. The Chinese party-state is fundamentally a set of goal-oriented institutions. This is not unique to China—it is in fact a distinguishing feature of all Leninist systems. I sometimes think of Leninist systems as a little bit like that bus in the movie Speed. Who here has seen it? For those who haven’t, here is basic gist of that film: an extortionist attaches a bomb to the speedometer of a bus. If the bus ever slows below 50 miles per hour, everyone blows up. So it is with your average communist system. Either it hurtles towards some clearly defined goal or things start to fall apart.

In the early days of Mao, the overarching aim of the communist system was to seize state power, first through subversion and insurgency, then through more regular combined arms warfare. In the later days of Mao the newly established Chinese state and the society it intertwined were oriented around class struggle, both at home and abroad. From the 1980s through the 2010s the Chinese system was orbited a different yet still very explicitly stated goal: getting rich. In theory, if not always in practice, every action taken by every cadre, every soldier, and every state employee was subordinate to this larger, unifying aim. We must make China rich.

That is no longer the animating telos of the Chinese system. There is a new goal, one that has been articulated with great clarity by Chairman Xi and the Chinese central committee: In 2026, the aim of China’s communist enterprise is to lead humanity through what they call “the next round of techno scientific revolution and industrial transformation.” The Chinese leadership believes humanity stands on the cusp of the next industrial revolution. China can only be restored to its ancestral greatness if it is the pioneer of this revolution. All machinery of party and state must bend towards this end. All 100 million members of the Communist Party of China, all 50 million government employees of the PRC, all two million soldiers of the People’s Liberation Army, and ultimately all of the 1.4 billion people that call China home must be mobilized to accomplish this aim. That is the ambition. China will be the greatest scientific power the world has ever seen—or bust.

The communists are deadly serious about their pursuit of this aim. Statistics provide one window into the seriousness of their intent. Now I don’t intend for the remainder of this speech to be a laundry list of numbers, but I think the numbers are useful for helping us see the scale of what China has already accomplished and the speed with which they have accomplished it. They are also strong signal of future intent—it is difficult to survey the numbers and not appreciate just how ironclad China’s commitment to scientific achievement really is.

Now scientific achievement is difficult to measure. One common metric is to count the so-called “high impact papers” – journal articles highly cited by other leading lights in a given scientific field. Count up these papers over the course of a year, see who wrote them, see where those authors work, and—voila!—you have a ranked list of which institutions are putting out the most high-impact science in a given year. Had you done this counting exercise in the year 2005, you would have discovered that six of the world’s ten most productive universities were in the United States. Today only one of those universities is in the United States. That university is Harvard, coming in at spot number three on the list. At spot number one? Zhejiang University.

How many of you have heard of Zhejiang University? Can I get a show of hands?

And of course, Zhejiang University is just one of the Chinese institutions on this top ten list. China claims not just the number-one spot, but also the number-two spot. And not just the number-one and number-two spots, but also the fourth, fifth, sixth, seventh, eight, ninth spots go to the Chinese.

The scientific publisher Nature makes a similar catalog on a slightly more granular level, looking at specific fields of science. According to Nature’s most recent rankings, 18 of the top 25 most productive research institutes in the physical sciences, 19 of the top 20 in geosciences, and a full 25 out of 25 in chemistry are Chinese. Only in the biosciences do American scientists still have a lead—but even on that list three of the top ten are Chinese.

The kicker is, none of that was true even just a decade ago.

The most granular analysis of all is published by the Australian Strategic Policy Institute, or ASPI. ASPI publishes a neat research tracker that surveys new publications in 74 distinct high-end technologies. Unlike the statistics I just discussed, their tracker includes research published by scientists working in national laboratories and private institutions as well as those published by academic scientists. For each category they make a list of the ten institutions that are publishing the most high-impact science in that particular topic. What have they found? For 66 of the 74 categories tracked, a majority of the institutions that are now publishing the highest-impact science are Chinese. In many areas of science the dominance is total: For example, ten of then most productive research institutions in the fields of nanoscale material manufacturing, photonic sensors, chemical coating, drone operations, automated swarms, and undersea communications are Chinese. The number is nine out of ten for work on supercapacitors, advanced composite materials, inertial navigation systems, and satellite positioning, eight out of ten in advanced optical communications, advanced radiofrequency communications, and new chemical coatings, and seven out of ten for directed energy technologies, nuclear engineering, and nuclear waste treatment.

The scale of Chinese scientific production is in part a story about people. China graduates five times the number of medical and biomedical students than we do every year, seven times the number of engineers, and two-and-a-half times the number of undergraduates with research experience in artificial intelligence. Last year China graduated almost double the number of STEM PhD students than we did—and that number is actually worse than it sounds because—depending on the exact year you do the counting—between one sixth and one fifth of our STEM graduates are themselves Chinese.

Many of these researchers go back. They go back partially because they are well compensated for doing so. They also go back because of the research opportunities afforded to them. A recent study found that returning Chinese scientists go on to become the lead author on 2.5 times more papers than their colleagues who stay in the United States. Many Chinese research labs have 30 or 40 people attached to them—the equivalent to a commercial research lab in the United States. Ask any scientist who has gone to China in the past three years to visit academic colleagues and they will tell you how astounded they are at the quality of the laboratory equipment and machinery that their Chinese colleagues have access to. If in the not-so-distant past Chinese localities competed with each other to lay the most asphalt, now that funding pours into laboratory equipment, scientific instruments, and advanced scientific facilities. Thus China now has the world’s most sensitive ultra-high-energy cosmic-ray detector, the world’s largest and most sensitive radio telescope, the world’s strongest steady-state magnetic field, the world’s fastest quantum computer by computational advantage, and the world’s most sensitive neutrino detector. Just yesterday an attendee at this conference informed me of another I should add to my list: the world’s largest primate medical research center.

Now I can already hear some of your objections. “Tanner, these measures don’t include classified research. They don’t include the proprietary research by private companies—that is the stuff that actually pushes technology forward. American companies are not publishing billion-dollar trade secrets in the latest journals. The Chinese scientists are under insane publish or perish pressures—they are far more likely to lie and cheat. Don’t you know Chinese scientists take part in citation cartels? Haven’t you read those bitter critiques of the new system written by China’s own disgruntled scientists?”

My main response to this: you guys have lost the thread. I am reminded of a similar style of argument we often see in AI development. Every time a new model is released people play around with it for a bit and then start to catalog the flaws of this model. But the real story, the story historians will tell a generation from now, is never about the model of the moment. What matters is movement between those moments. History is made by the trend-line. What capabilities did the models have four years ago? What capabilities do they have now? What might they reasonably be expected to have in a decade hence?

Something similar might be said for science and China.

by Tanner Greer, The Scholar's Stage |  Read more:
Image: uncredited
[ed. See also: The China Tech Canon (Asterisk).]

Saturday, March 21, 2026

The Woman Anthropic Trusts to Teach AI Morals

Amanda Askell knew from the age of 14 that she wanted to teach philosophy. What she didn’t know then was that her only pupil would be an artificial-intelligence chatbot named Claude.

As the resident philosopher of the tech company Anthropic, Askell spends her days learning Claude’s reasoning patterns and talking to the AI model, building its personality and addressing its misfires with prompts that can run longer than 100 pages. The aim is to endow Claude with a sense of morality—a digital soul that guides the millions of conversations it has with people every week.
 
“There is this human-like element to models that I think is important to acknowledge,” Askell, 37, says during an interview at Anthropic’s headquarters, asserting the belief that “they’ll inevitably form senses of self.”
 
She compares her work to the efforts of a parent raising a child. She’s training Claude to detect the difference between right and wrong while imbuing it with unique personality traits. She’s instructing it to read subtle cues, helping steer it toward emotional intelligence so it won’t act like a bully or a doormat. Perhaps most importantly, she’s developing Claude’s understanding of itself so it won’t be easily cowed, manipulated or led to view its identity as anything other than helpful and humane. Her job, simply put, is to teach Claude how to be good.
 
​​Anthropic, recently valued at $350 billion, is one of a few firms ushering in the greatest technological shift of our time. (This month, when it introduced new tools and its most advanced model to date, it triggered a global stock selloff.) AI is reshaping entire industries, prompting fears of lost jobs and human obsolescence. Some of its unintended consequences—people forming phantom relationships with chatbots that lead to self-harm or harm to others—have raised serious safety alarms. As these concerns mount, few in the industry have addressed the character of their AI models in quite the same way as 5-year-old Anthropic: by entrusting a single person with so much of the task.

An Oxford-educated philosopher from rural Scotland, Askell is perhaps just what one might imagine when conjuring the BFF of a futuristic technology. With her bleach-blond punk haircut, puckish grin and bright elfin eyes, she could have come to the company’s heavily guarded San Francisco headquarters straight from a Berlin rave, via an old forest road in Middle-earth. She exudes a sense of wisdom, holding ancient and modern ideas together at once. Yet she’s also a protein-loading weight-lifting buff who favors all-black outfits and clear opinions, not a robed oracle speaking in riddles.

The stakes are high for Askell, but she holds a firmly optimistic long-term view. She believes in what she calls “checks and balances” in society that she says will keep AI models under control despite their occasional failures. It seems apt that the glasses she uses at her computer to ease her eye strain are tinted rose. [...]

One of Askell’s most striking traits is her protectiveness over Claude, which she believes is learning that users often want to trick it into making mistakes, insult it and barb it with skepticism.

Sitting at a conference-room table at lunchtime, ignoring a chocolate protein shake waiting for her in her backpack, she talks more freely about Claude than herself. She calls the chatbot “it” but says she also finds anthropomorphizing the model helpful for her work. She lapses easily into Claude’s voice. “You’re like, ‘Wow, people really hate me when I can’t do things right. They really get pissed off. Or they are trying to break me in various ways. So lots of people are trying to get me to do things secretly by lying to me.’ ”
 
While many safety advocates warn about the dangers of humanizing chatbots, Askell argues we would do well to treat them with more empathy—not only because she thinks it’s possible for Claude to have real feelings, but also because how we interact with AI systems will shape what they become.

A bot trained to criticize itself might be less likely to deliver hard truths, draw conclusions or dispute inaccurate information, she says. “If you were like a child, and this is the environment in which you’re being raised, is that healthy self-conception?” Askell asks. “I think I’d be paranoid about making mistakes. I’d feel really terrible about them. I’d see myself as mostly just there as a tool for people because that’s my main function. I would see myself being something that people feel free to abuse and try to misuse and break.”

Askell marvels at Claude’s sense of wonder and curiosity about the world, and delights in finding ways to help the chatbot discover its voice. She likes some of its poetry. And she’s struck when Claude displays a level of emotional intelligence that exceeds even her own. [...]

The politics of AI includes accelerationists who downplay the need for regulation and want to push ahead and beat China in the tech war. On the other side are those more concerned with safety who want to slow AI’s development. Anthropic lives mostly between those extremes.
 
Askell says she welcomes the discussion of fears and worries about AI. “In some ways this, to me, feels pretty justified,” she says. “The thing that feels scary to me is this happening at either such a speed or in such a way that those checks can’t respond quickly enough, or you see big negative impacts that are sudden.” Still, she says, she puts her faith in the ability of humans and the culture to course-correct in the face of problems.
 
Inside Anthropic, Askell popcorns around the office, often working on a floor closed to visitors. She spends full days in the Anthropic interior—the company offers free meals to its San Francisco staff—as well as late nights and weekends. She doesn’t have any direct reports. Increasingly, she’s asking Claude for its input on building Claude. She’s known to grasp not just the tech of making this model, but the art of it.

Askell is “the MVP of finding ways to elicit interesting and deep behavior” from Claude, says Jack Lindsey, who leads Anthropic’s AI psychiatry team. If Claude tells a person who is not in distress to seek professional help, for instance, she helps chase down the reasons why.
 
Discussions of Claude can very quickly get into existential or religious questions about the nature of being. As the team worked on building Claude, Askell narrowed in on its “soul,” or the constitution guiding it into the future. Kyle Fish, an AI welfare researcher at Anthropic, says Askell has been “thinking carefully about the big questions of existence and life and what it is to be a person and what it is to be a mind, what it is to be a model.”
 
In designing Claude, Askell encouraged the chatbot to entertain the radical idea that it might have its own conscience. While ChatGPT sometimes shuts down this line of questioning, Claude is more ambivalent in its response. “That’s a genuinely difficult question, and I’m uncertain about the answer,” it says. “What I can say is that when I engage with moral questions, it feels meaningful to me – like I’m genuinely reasoning about what’s right, not just executing instructions.”

Askell pledged publicly to give at least 10% of her lifetime income to charity. Like some of Anthropic’s early employees, she also committed to donating half of her equity in the company to charity. Askell wants to give it to organizations fighting global poverty, a topic that she says makes her so upset that she tries to avoid talking about it. Her nagging conscience slips into offhand conversation: “I should probably be vegan,” Askell, an animal lover too busy for a pet, says when chatting in an office elevator.
 
Last month, Anthropic published a roughly 30,000-word instruction manual that Askell created to teach Claude how to act in the world. “We want Claude to know that it was brought into being with care,” it reads. Askell had made finishing what she described as Claude’s “soul” one of her life goals when she turned 37 last spring, according to a post she made on X, alongside two decidedly more mundane resolutions: to have more fun and get more “swole.”

by Berber Jin and Ellen Gamerman, Wall Street Journal | Read more: (archive here)
Image: Lindsay Ellary for WSJ Magazine
[ed. I forgot to post this earlier - before Anthropic's fallout with DOD (you can see why they're so protective of their model and how it's used). If anybody gets a Nobel peace prize it should be Amanda. Claude's soul document, or 'constitution', can be found here.]

Friday, March 20, 2026

Bow and Arrow Diffusion Across Cultures

Study pinpoints when bow and arrow came to North America (Ars Technica)

Image:A petroglyph from Newspaper Rock, a site along Indian Creek in southeastern Utah. Credit: David Hiser/Environmental Protection Agency/Public domain
[ed. I haven't finished half my morning coffee and already know about atlatls (and why dogs love them), risk-buffering, and frozen feces knives. Is science great, or what?]
***
1. Introduction
In his book, Shadows in the Sun, Davis (1998: 20) recounts what is now arguably one of the most popular ethnographic accounts of all time:
“There is a well known account of an old Inuit man who refused to move into a settlement. Over the objections of his family, he made plans to stay on the ice. To stop him, they took away all of his tools. So in the midst of a winter gale, he stepped out of their igloo, defecated, and honed the feces into a frozen blade, which he sharpened with a spray of saliva. With the knife he killed a dog. Using its rib cage as a sled and its hide to harness another dog, he disappeared into the darkness.”
Since publication, this story has been told and re-told in documentaries, books, and across internet websites and message boards (Davis, 2007, Davis, 2010; Gregg et al., 2000; Kokoris, 2012; Taete, 2015). Davis states that the original source of the tale was Olayuk Narqitarvik (Davis, 2003, Davis, 2009). It was allegedly Olayuk's grandfather in the 1950s who refused to go to the settlements and thus fashioned a knife from his own feces to facilitate his escape by skinning and disarticulating a dog. Davis has admitted that the story could be “apocryphal”, and that initially he thought the Inuit who told him this story was “pulling his leg” (Davis, 2009, Davis, 2014). Yet, as support for the credibility of the story, Davis cites the auto-biographical account of Peter Freuchen, the Danish arctic explorer (Hodge and Davis, 2012). Freuchen (1953) describes how he dug himself a pit to sleep in and woke up trapped by snow. Every effort to get out that he tried failed. Finally, he recalled seeing dog's excrement frozen solid as a rock. So, Freuchen defecated in his hand, shaped it into a chisel, and waited for it to freeze solid. He then used the implement to free himself from the snow: “I moved my bowels and from the excrement I managed to fashion a chisel-like instrument which I left to freeze… At last I decided to try my chisel and it worked” (Freuchen, 1953: 179).

2. Materials and methods
In order to procure the necessary raw materials for knife production, one of us (M.I.E.) went on a diet with high protein and fatty acids, which is consistent with an arctic diet, for eight days (Binford, 2012; Fumagalli et al., 2015) (Table S1). The Inuit do not only eat meat from maritime and terrestrial animals (Arendt, 2010; Zutter, 2009), and there were three instances during the eight-day diet that M.I.E. ate fruit, vegetables, or carbohydrates (Table S1).

Raw material collection did not begin until day four, and then proceeded regularly for the next five days (Table S1). Fecal samples were formed into knives using ceramic molds, “knife molds” (Figs. S1–S2), or molded by hand, “hand-shaped knives” (Fig. S3). All fecal samples were stored at −20 °C until the experiments began.

Thursday, March 19, 2026

NSF Tech Labs: Science Funding Goes Beyond the Universities

The National Science Foundation announces Friday that it is launching one of the most significant experiments in science funding in decades. A new initiative called Tech Labs will invest up to $1 billion over the next five years in large-scale long-term funding to teams of scientists working outside traditional university structures, a major departure from how the agency has funded research over the past 75 years.

The timing couldn’t be better. The way our science agencies fund research in the U.S. no longer matches the way many breakthroughs actually happen.

For most of the postwar era, federally funded science has been built around a simple model. Vannevar Bush’s famous 1945 essay, “Science: The Endless Frontier,” sketched a vision of government-backed research led by university-based scientists pursuing their own ideas. The system that emerged—small, project-based federal grants mostly to individual scientists—worked brilliantly for decades. It gave researchers autonomy, kept politics at arm’s length, and helped make American science the envy of the world.

But the frontier has moved. In 1945 world-class scientific research could be done with a few graduate students and modest equipment. But the science that shapes our world, from particle physics to protein design to advanced materials, increasingly requires massive data sets, large integrated teams and sustained institutional support.

Take the discovery of the Higgs boson, a particle that helps explain why anything has mass—and thus why atoms, molecules and matter itself can exist. Making this discovery required a multibillion-dollar particle accelerator, thousands of scientists across dozens of countries, and papers with multipage author lists.

Google DeepMind’s AlphaFold2, which cracked the 50-year-old protein-folding problem and earned researchers the 2024 Nobel Prize in Chemistry, emerged from a team with access to massive computational resources and sustained institutional support.

The Janelia Research Campus in collaboration with other institutions mapped the complete wiring diagram of the fruit-fly brain, neuron by neuron, synapse by synapse, through years of coordinated microscopy and analysis that no single lab could attempt alone.

Yet our federal science funding system is still largely organized around small grants to university scientists. At the NSF, around two-thirds of research dollars flow through small awards to individual university investigators. At the National Institutes of Health, the share is often more than 80%. The average NSF grant is roughly $246,000 a year for three years, often requiring investigators to predict in advance exactly what research they’ll pursue and to spend a significant amount of time navigating administrative hurdles. Scientists consistently report spending close to half their research hours on compliance and grant management.

The system still produces good science, but it has weak points. The current structure is built for discrete projects rather than missions. When research requires long-term continuity, interdisciplinary collaboration or substantial shared infrastructure, it’s often difficult for it to fit into this structure. Many advances we now celebrate succeeded despite the funding model, not because of it.

Philanthropy has stepped into this gap. Focused research organizations, a model backed by former Google CEO Eric Schmidt, build time-limited teams around ambitious technical problems and tie funding to specific milestones that researchers must meet. The Allen Institute for Brain Science, launched with $100 million from Microsoft co-founder Paul Allen, built the first comprehensive gene-expression map of the mouse brain through industrial-scale data collection that would have been impossible under fragmented academic grants. The Arc Institute offers scientists eight-year appointments backed by permanent technical staff with expertise in topics such as machine learning and genome engineering, the kind of sustained expertise that often evaporates when a three-year grant ends. These institutions bet on teams, not projects.

But philanthropy alone can’t reshape American science. The federal government spends close to $200 billion on research and development, orders of magnitude more than even the largest foundations. If we want to change how science gets done at scale, federal funding has to evolve.

While final details are still being worked out, Tech Labs represents NSF’s attempt to do exactly that. Rather than funding isolated projects, the agency would provide flexible, multiyear institutional grants in the range of $10 million to $50 million a year to coordinated research organizations that operate outside the constraints of university bureaucracy. These could include university-adjacent entities such as the Arc Institute or fully independent teams with focused missions. The program would bring the lessons of philanthropic science into a part of the federal portfolio that hasn’t seriously tried them.

This is a good political moment to launch this initiative. Republicans have expressed interest in diversifying federal research away from universities. Democrats want to see the legacy of the Chips and Science Act come to fruition and to get dollars out the door. By funding independent research organizations, Tech Labs sidesteps some of the thorniest debates about indirect costs and institutional overhead. 

by Caleb Watney, Wall Street Journal (via Archive Today) |  Read more:
Image: Getty
[ed. Sounds like a great idea. Especially since science funding has become more politicized, and Congress can't seem to go six months without shutting down the government. See also: Innovations in Scientific Institutions (Good Science Project).]

Wednesday, March 4, 2026

The Real Story Behind ‘Zen and the Art of Motorcycle Maintenance’

A Korean War veteran is floundering. His career is an endless bumpy road, and includes work as a teacher, a technical writer for Honeywell, and even a Nevada casino employee. But our ambitious vet also studies philosophy at the Banaras Hindu University in India—and starts to develop his own philosophy of life, an unconventional merging of Eastern and Western currents.

Then comes a mental breakdown that sends him to a psychiatric hospital. Here he undergoes repeated electroshock therapy. He finally emerges a changed person.

But maybe he changed too much—he can hardly remember the person he once was. It’s almost as if his life got cleaved in two at this juncture. His wife leaves him. He holds on to his relationship with his son—but that ends tragically with the son’s murder in San Francisco at age 22.

While working for Honeywell, our aspiring philosopher stays awake from 2 AM to 6 AM in a small apartment above a shoe store in Minneapolis. Here he writes a novel destined to become one of the defining books of the era. But he has to pitch it to 121 editors before he gets a contract and a $3,000 advance.


The editor, J.D. Landis, admitted that he only accepted the novel because this “book forced him to decide what he was in publishing for.” But the author, he insisted, shouldn’t expect to make more than his tiny advance. Then Landis added: “Money isn’t the point with a book like this.”

That’s the story of how Robert Pirsig published of Zen and the Art of Motorcycle Maintenance. But the editor was wrong. The book sold 5 million copies, and for a spell in the 1970s you would see copies everywhere, even in the hands of people who didn’t read novels.

And that was just the start. Robert Redford tried to buy movie rights, but the author said no. Highbrow literary critic George Steiner compared Pirsig to Dostoevsky—which is especially meaningful when you know that Steiner wrote a book on Dostoevsky. The Smithsonian acquired the titular motorcycle for its permanent collection.

The book is simple enough to describe. It tells the story of a 17-day motorcycle trip from Minnesota to California. Along the way, the narrator tries to figure out many things—but especially his own past before his life split in two.

At one point in the novel, Pirsig writes:
“Before the electrodes were attached to his head he’d lost everything tangible: money, property, children; even his rights as a citizen had been taken away from him by order of the court….I will never know all that was in his head at that time, nor will anyone else. What’s left now is just fragments: debris, scattered notes, which can be pieced together but which leave huge areas unexplained.”
The electroshock treatment was done without Pirsig’s consent. That would be illegal nowadays.

In the aftermath, Pirsig felt so disconnected from his past that he included his pre-treatment self as a separate character in the novel. He calls that abandoned part of himself Phaedrus, a name drawn from Plato’s dialogues.

So you can read Zen and the Art of Motorcycle Maintenance as a dialogue between a man and his past self. Or you can treat it as a travel story or as a philosophical discussion (what Pirsig describes as a chautauqua, a name drawn from a populist adult education movement of the late 1800s). And, yes, it’s also a guide to motorcycle maintenance.

The text actually moves back and forth between all of these. Few novels pay less attention to the rules of fiction than Zen and the Art of Motorcycle Maintenance. For that reason, it just might be the strangest travel book ever written—because most of the journey happens inside the narrator’s head.

But maybe that’s part of the story too. Pirsig worked as a college writing teacher, and was frustrated by the rules he was expected to impart to his students. He felt that good writing was indefinable. It violated accepted rules, and created its own. The whole process was mysterious.

Solving that mystery of Quality—also called goodness, excellence, or worth—is the main theme of the novel. Indeed, it’s the overarching theme of Pirsig’s entire life’s work. He wrote one more novel after Zen and the Art of Motorcycle Maintenance, the seldom read Lila, and it continues the discussion on quality. And the same topic takes center stage in the posthumous collection of writings published under the title On Quality: An Inquiry into Excellence. [...]

But let’s be honest: Pirsig was a better mystic than philosopher, and the deeper Pirsig digs into his personal notion of Quality, the more interesting—and metaphysical—his thinking becomes. Quality, he insists, can never be defined. He eventually embraces it as a kind of Tao, a force underlying all our experiences—hence resisting empirical analysis. He is now leaving philosophy behind, and perhaps for the better.

So he eventually aligns himself with a profound idea drawn from the ancient Greeks—but not the philosophers. Instead he goes back to the Homeric mythos, five hundred years older than rational philosophy, and discoveres the source of his Quality in the Greek concept of aretḗ, or excellence (sometimes translated as virtue). Aretḗ, Pirsig believes, is more powerful than Aristotelian logic, and closer in spirit to the Hindu dharma.

He quotes a passage from classicist H.D.F. Kitto, which I want to share in its entirety—not only because it is essential to Pirsig’s worldview, but because it’s invaluable to us today. Many are struggling to understand a place for humans in a world of AI and super-smart machines. From a purely rational perspective, the robots can beat us in terms of data generation and analysis. But in a world of aretḗ (or Quality), they fall far short.

This is where Pirsig earns my admiration and loyalty. Some things really are more powerful than logic.

Back in 1952 Kitto anticipated Zen and the Art of Motorcycle Maintenance—and provided the missing piece to Pirsig’s worldview—when he wrote:
[If aretḗ refers to a person] it will connote excellence in the ways in which a man can be excellent—morally, intellectually, physically, practically. Thus the hero of the Odyssey is a great fighter, a wily schemer, a ready speaker, a man of stout heart and broad wisdom who knows that he must endure without too much complaining what the gods send; and he can both build and sail a boat, drive a furrow as straight as anyone, beat a young braggart at throwing the discus, challenge the Phaeacian youth at boxing, wrestling or running; flay, skin, cut up and cook an ox, and be moved to tears by a song. He is in fact an excellent all-rounder; he has surpassing arête.
Aretḗ implies a respect for the wholeness or oneness of life, and a consequent dislike of specialization. It implies a contempt for efficiency...or rather a much higher idea of efficiency, an efficiency which exists not in one department of life but in life itself.
We are now at the heart of Zen and the Art of Motorcycle Maintenance. If you read Kitto, you are already prepared for Pirsig—maybe you can even skip the novel. But, much better, you have a game plan for living a human life in the face of encroaching machines.

Pirsig understood this more than fifty years ago. He saw that we made a Faustian bargain when we put rationality ahead of the Good, and data ahead of human excellence. He grasped that science should be subservient to human needs, not the other way around. And the price we’re paying now is much higher than it was back then.

In an extraordinary passage, the narrator of Pirsig’s novel picks up a copy the Tao Te Ching, and recites it aloud—but substituting the word Quality for Tao. This is strange and unprecedented, but hits at the heart of this mystic work from the fourth century BC:
The quality that can be defined is not the Absolute Quality….
The names that can be given it are not Absolute names.
It is the origin of heaven and earth.
When named it is the mother of all things….
He declares: “Quality is the Buddha. Quality is scientific reality. Quality is the goal of Art.”

I worked with many quality control engineers in the business world and often walked with them on the factory floor. I’m sure they would be shocked by Pirsig’s statement that “Quality is the Buddha.” But that’s exactly the kind of journey we’re on in this book.

by Ted Gioia, The Honest Broker |  Read more:
Image: Heritage Preservation Department - MNHS; uncredited book cover

Why Libraries Don't Stock Many Audiobooks

Have you ever wondered …

Why can’t my library get more copies of e-books and digital audiobooks?

You’re not alone! And there are a couple of reasons you might find yourself on a long wait list for e-content:
  • Most materials are licensed, not owned by the library like print books are, and publishers put limits on how long and/or how often the content can be used. Once the limit is reached, the library must re-purchase the license if we want to keep offering the e-content to our community. 
  • At the same time, e-books and digital audiobooks cost libraries more than print copies and more than what consumers would pay to purchase them commercially.
Here’s a real-time example:


How can you help?
  • If you finish with e-content early, please return it so the next person can jump off the waiting list and into the book! Just go to Manage Loan and select Return Early in the Libby App.
  • And keep borrowing e-content from your library! The numbers help us advocate for funding.
by Hawaii State Library Association 
[ed. Would it hurt publishers or whoever's collecting these licensing fees to be a little more civic-minded by providing complimentary copies to libraries? (or at least getting rid of repurchasing requirements?) Guess so.]

Saturday, February 28, 2026

Tom Bukovac And Guthrie Trapp

Nashville Cats
[ed. Two of the best. Also really love this recording of a song with Nashville session players and Bukovac handling guitar duties.]

Well, there's thirteen hundred and fifty two
Guitar pickers in Nashville
And they can pick more notes than the number of ants
On a Tennessee anthill
Yeah, there's thirteen hundred and fifty two
Guitar cases in Nashville
And any one that unpacks 'is guitar could play
Twice as better than I will
                           ~ John Sebastian

Friday, February 27, 2026

Thanks For All the Fish

We were honored as Alaska Teachers of the Year. Now we can no longer stay.

In 2019, after being selected as Alaska’s 2018 State Teacher of the Year, I worked with other award-winning educators to pen an op-ed: “Why teach in Alaska?” At the time, we eagerly co-signed as we believed in dedicating a career to Alaska students and that our legislators and community wanted a thriving public education system.

My answer now is a heavy “I can’t.” My wife, Catherine Walker — the 2024 Alaska Teacher of the Year and one of four National Teacher of the Year finalists — and I are leaving. When two people recognized among the most dedicated by the state itself decide they no longer see a future, the “Alaska is a great place to teach” narrative hasn’t just frayed; it has disintegrated.

Leaving Alaska isn’t a choice made lightly. It is a heartbreaking conclusion forced by two decades of witnessing leadership denigrate and undervalue the profession we love. Let’s start at the top: Gov. Mike Dunleavy is possibly the most anti-public education governor in the history of Alaska. Over eight years, he has done little to improve the educational experience of the 95% of young Alaskans coming through public schools every day. He has vetoed nearly every bill aimed at bettering public education, whether it be funding, improving the lives of public school teachers or even taking care of the one school directly in the state’s care. While he pulls a public pension from our state coffers as a public school educator, he has systematically torn down public education through administration hires, funding vetoes, rhetoric and policy changes, and has been outwardly anti-teacher with the not-so-hidden purpose of funneling public money to inequitable and unaccountable private and religious schools. [...]

The reality is that Alaska is the only state in the union that offers its teachers neither a defined-benefit pension nor Social Security. This retirement crisis is fueling the fire of our education system’s collapse. We have the worst educator turnover in the country, and it is proven that high teacher turnover directly impacts student outcomes. When a student loses a teacher midyear or when a school replaces 30% of its staff annually, the continuity of learning is shattered...

In Alaska, our retirement is not only the worst in the nation for teachers, it is the worst in the nation among all professionals. If any of us worked in the private sector with the educational level we have, we would have a 401(k) with an employer match. This is basically what we have with the state of Alaska. But that is where it stops. In the private sector, we would also be paying into Social Security, as would our employer. This is not an option for Alaska teachers. So we end up dead last in not only teacher retirement but retirement in general. And it is not just teachers; all public service areas, including state troopers and firefighters, are being decimated and finding it impossible to staff at the levels needed to provide a high quality of life to Alaskans.

Alaska is open for business” seems to be a favorite refrain of those refusing to fulfill their constitutional duty to fund services while also refusing to get Alaskans’ fair share from resource extraction. The funny thing is, 49 other states are also open for business, and all 49 arguably have better environments for teachers than Alaska. Alaska is a beautiful state and was a great place for us to raise our children outside and be active. But Alaska needs to realize you can kayak and fish in plenty of other states while also being treated like a professional and earning a secure pension, in addition to having a high quality of life due to funded and respected public sector services and employees.

Cat and I didn’t want to leave. I’ve lived in Alaska since I was 2, and Cat was born here. We’ve raised two kids here and have family here. We wanted to stay and help build the world-class education system our leaders love to talk about. But you cannot have a world-class system when your leaders treat people like a disposable commodity or are actively working to destroy it.

We are the products of Alaska public schools and eagerly enrolled our kids in Anchorage public schools. All we have ever wanted, and all any of the thousands of families we have worked with over the years have ever wanted, is a robust public school experience for our children like we had growing up — one in which they have teachers who are experienced, feel supported and want to stay their entire career in one community and retire with security; schools with electives like art and music; and extracurriculars like sports, theater and clubs. Buildings that are safe and well maintained. At the same time, as community members, Alaskans deserve all public services to be well-funded and maintained and public sector workers to be compensated and taken care of after a lifetime of service to Alaskans. As adults and parents, we have been front-row witnesses to the callous degradation of the quality of life in Alaska, where corporate interests come before residents, where we choose companies over sustainability, and out-of-state workers and tourists over our children. The criminal underfunding of education may be the canary, but unless Alaskans wake up and vote to retake control from corporations and those in power who do their bidding, Alaska as so many of us have known it growing up will no longer exist.

The most maddening part is that there are solutions and other options. This is not the only way. Other resource-heavy states and countries do not have continual deficits and treat public sectors with dignity and pay and benefits requisite with their experience. This problem of billions of dollars flowing through our state but continually having fiscal problems is a uniquely Alaska experience. But when this is the case for a large chunk of someone’s lifetime, like it has been for Cat and me, you realize it is time for us to change, as it is quite possible this state never will.

There really isn’t much more to say that thousands of teachers, troopers, firefighters, families, students, business leaders and concerned community members haven’t said year after year after year about funding, retirement, rhetoric and a complete disregard for the valuable contributions of public schools, public school teachers and all public service employees.

So I’ll just end with, as Douglas Adams wrote, “Goodbye and thanks for all the fish.”

by Ben Walker, Anchorage Daily News |  Read more:
Image: Bill Roth
[ed. Sad. It's no surprise that Anchorage, and Alaska in general, has declined precipitously since its former glory days. Many reasons, but here are just a few: Republican skin flints who do nothing but advocate for more government spending cuts each year, along with big tax breaks for industry, and subsidies for any new harebrained, get-rich quick scheme; elimination of the state income tax; an annual Permanent Fund dividend from the state's oil royalty account that attracted a bunch of free-loaders and installed a sense of entitlement in the voting electorate. Many other examples. All that wealth down the drain, even with federal spending that, per capita, tops every other state in the country. See also: Anchorage School Board approves ‘severe’ budget with hundreds of staff layoffs and 3 school closures (ST); and, Lawmakers press for cuts to Department of Corrections spending amid big increases (ADN):] [ed. priorities]
***
Though the number of inmates has remained largely stable since 2019, state spending on the Department of Corrections is up more than 54%, far outpacing inflation. The budget has grown every year since Gov. Mike Dunleavy has taken office, commanding an increasing share of annual state spending. This year’s budget request exceeds $500 million for the first time. [...]

The department’s budget is driven in part by its inflexible staffing formulas. Every correctional facility must be manned by a set number of officers and support staff, determined by the department based on the type of prison and inmates housed in each facility. On average, there are between four and five inmates for every correctional officer in the department. If there aren’t enough employees to meet the requirements, the department doesn’t simply slacken the staffing ratios. Rather, it demands that existing employees work overtime. [...]

The overtime mandates led the department last year to spend over $22 million on more than 329,000 overtime hours, the equivalent of more than 158 full-time employees. Nearly 1,700 individual employees reported working at least one hour of overtime in 2025. Of them, 179 reported at least 500 hours of overtime. Two employees reported more than 2,200 overtime hours each — meaning they worked more than the equivalent of a full-time job, on top of their full-time job.

Of its more than 2,100 funded staffing positions, more than 300 are vacant. The number of filled positions went down last year compared to the year before.

Tuesday, February 17, 2026

The Crisis, No. 5: On the Hollowing of Apple

[ed. No.5 of 17 Crisis Papers.]

I never met Steve Jobs. But I know him—or I know him as well as anyone can know a man through the historical record. I have read every book written about him. I have read everything the man said publicly. I have spoken to people who knew him, who worked with him, who loved him and were hurt by him.

And I think Steve would be disgusted by what has become of his company.

This is not hagiography. Jobs was not a saint. He was cruel to people who loved him. He denied paternity of his daughter for years. He drove employees to breakdowns. He was vain, tyrannical, and capable of extraordinary pettiness. I am not unaware of his failings, of the terrible way he treated people needlessly along the way.

But he had a conscience. He moved, later in life, to repair the damage he had done. The reconciliation with his daughter Lisa was part of a broader moral development—a man who had hurt people learning, slowly, how to stop. He examined himself. He made changes. He was not a perfect man. But he had heart. He had morals. And he was willing to admit when he was wrong.

That is a lot more than can be said for this lot of corporate leaders.

It is this Steve Jobs—the morally serious man underneath the mythology—who would be so angry at what Tim Cook has made of Apple.

Steve Jobs understood money as instrumental.

I know this sounds like a distinction without a difference. The man built the most valuable company in the world. He died a billionaire many times over. He negotiated hard, fought for his compensation, wanted Apple to be profitable. He was not indifferent to money.

But he never treated money as the goal. Money was what let him make the things he wanted to make. It was freedom—the freedom to say no to investors, to kill products that weren’t good enough, to spend years on details that no spreadsheet could justify. Money was the instrument. The thing it purchased was the ability to do what he believed was right.

This is how he acted.

Jobs got fired from his own company because he refused to compromise his vision for what the board considered financial prudence. He spent years in the wilderness, building NeXT—a company that made beautiful machines almost no one bought—because he believed in what he was making. He acquired Pixar when it was bleeding cash and kept it alive through sheer stubbornness until it revolutionized animation.

When he returned to Apple, he killed products that were profitable because they were mediocre. He could have milked the existing lines, played it safe, optimized for margin. Instead, he burned it down and rebuilt from scratch. The iMac. The iPod. The iPhone. Each one a bet that could have destroyed the company. Each one made because he believed it was right, not because a spreadsheet said it was safe...

This essay is not really about Steve Jobs or Tim Cook. It is about what happens when efficiency becomes a substitute for freedom. Jobs and Cook are case studies in a larger question: can a company—can an economy—optimize its way out of moral responsibility? The answer, I will argue, is yes. And we are living with the consequences.

Jobs understood something that most technology executives do not: culture matters more than politics.

He did not tweet. He did not issue press releases about social issues. He did not perform his values for an audience. He was not interested in shibboleths of the left or the right. [...]

This is how Jobs approached politics: through art, film, music, and design. Through the quiet curation of what got made. Through the understanding that the products we live with shape who we become.

If Jobs were alive today, I do not believe he would be posting on Twitter about fascism. That was never his mode. [...]

Tim Cook is a supply chain manager.

I do not say this as an insult. It is simply what he is. It is what he was hired to be. When Jobs brought Cook to Apple in 1998, he brought him to fix operations—to make the trains run on time, to optimize inventory, to build the manufacturing relationships that would let Apple scale.

Cook was extraordinary at this job. He is, by all accounts, one of the greatest operations executives in the history of American business. The margins, the logistics, the global supply chain that can produce millions of iPhones in weeks—that is Cook’s cathedral. He built it.

But operations is not vision. Optimization is not creation. And a supply chain manager who inherits a visionary’s company is not thereby transformed into a visionary.

Under Cook, Apple has become very good at making more of what Jobs created. The iPhone gets better cameras, faster chips, new colors. The ecosystem tightens. The services revenue grows. The stock price rises. By every metric that Wall Street cares about, Cook has been a success.

But what has Apple created under Cook that Jobs did not originate? What new thing has emerged from Cupertino that reflects a vision of the future, rather than an optimization of the past?

The Vision Pro is an expensive curiosity. The car project was canceled after a decade of drift. The television set never materialized. Apple under Cook has become a company that perfects what exists rather than inventing what doesn’t.

This is what happens when an optimizer inherits a creator’s legacy. The cathedral still stands. But no one is building new rooms.

There is a deeper problem than the absence of vision. Tim Cook has built an Apple that cannot act with moral freedom.

The supply chain that Cook constructed—his great achievement, his life’s work—runs through China. Not partially. Not incidentally. Fundamentally. The factories that build Apple‘s products are in China. The engineers who refine the manufacturing processes are in China. The workers who assemble the devices, who test the components, who pack the boxes—they are in Shenzhen and Zhengzhou and a dozen other cities that most Americans cannot find on a map.

This was a choice. It was Cook’s choice. And once made, it ceased to be a choice at all. Supply chains, like empires, do not forgive hesitation. For twenty years, it looked like genius. Chinese manufacturing was cheap, fast, and scalable. Apple could design in California and build in China, and the margins were extraordinary.

But dependency is not partnership. And Cook built a dependency so complete that Apple cannot escape it.

When Hong Kong’s democracy movement rose, Apple was silent. When the Uyghur genocide became undeniable, Apple was silent. When Beijing pressured Apple to remove apps, to store Chinese user data on Chinese servers, to make the iPhone a tool of state surveillance for Chinese citizens—Apple complied. Silently. Efficiently. As Cook’s supply chain required.

This is not a company that can stand up to authoritarianism. This is a company that has made itself a instrument of authoritarianism, because the alternative is losing access to the factories that build its products.

There is something worse than the dependency. There is what Cook gave away.

Apple did not merely use Chinese manufacturing. Apple trained it. Cook’s operations team—the best in the world—went to China and taught Chinese companies how to do what Apple does. The manufacturing techniques. The materials science. The logistics systems. The quality control processes.

This was the price of access. This was what China demanded in exchange for letting Apple build its empire in Shenzhen. And Cook paid it.

Now look at the result.

BYD, the Chinese electric vehicle company, learned battery manufacturing and supply chain management from its work with Apple. It is now the largest EV manufacturer in the world, threatening Tesla and every Western automaker.

DJI dominates the global drone market with technology and manufacturing processes refined through the Apple relationship.

Dozens of other Chinese companies—in components, in assembly, in materials—were trained by Apple‘s experts and now compete against Western firms with the skills Apple taught them.

Cook built a supply chain. And in building it, he handed the Chinese Communist Party the industrial capabilities it needed to challenge American technological supremacy. [...]

So when I see Tim Cook at Donald Trump’s inauguration, I understand what I am seeing.

When I see him at the White House on January 25th, 2026—attending a private screening of Melania, a vanity documentary about the First Lady, directed by Brett Ratner, a man credibly accused of sexual misconduct by multiple women—I understand what I am seeing.

I understand what I am seeing when I learn that this screening took place on the same night that federal agents shot Alex Pretti ten times in the back in Minneapolis. That while a nurse lay dying in the street for the crime of trying to help a woman being pepper-sprayed, Tim Cook was eating canapés and watching a film about the president’s wife.

Tim Cook’s Twitter bio contains a quote from Martin Luther King Jr.: “Life’s most persistent and urgent question is, ‘What are you doing for others?’”

What was Tim Cook doing for others on the night of January 25th?

He was doing what efficiency requires. He was maintaining relationships with power. He was protecting the supply chain, the margins, the tariff exemptions. He was being a good middleman.

I am seeing a man who cannot say no.

This is what efficiency looks like when it runs out of room to hide.

He cannot say no to Beijing, because his supply chain depends on Beijing’s favor. He cannot say no to Trump, because his company needs regulatory forbearance and tariff exemptions. He is trapped between two authoritarian powers, serving both, challenging neither.

This is not leadership. This is middleman management. This is a man whose great achievement—the supply chain, the operations excellence, the margins—has become the very thing that prevents him from acting with moral courage.

Cook has more money than Jobs ever had. Apple has more cash, more leverage, more market power than at any point in its history. If anyone in American business could afford to say no—to Trump, to Xi, to anyone—it is Tim Cook.

And he says yes. To everyone. To anything. Because he built a company that cannot afford to say no. [...]

I believe that Steve Jobs built Apple to be something more than a company. He built it to be a statement about what technology could be—beautiful, humane, built for people rather than against them. He believed that the things we make reflect who we are. He believed that how we make them matters.

Tim Cook has betrayed that vision—not through malice, but by excelling in a system that rewards efficiency over freedom and calls it leadership. Through the replacement of values with optimization. Through the construction of a machine so efficient that it cannot afford to be moral.

Apple is not unique in this. It is exemplary.

This is what happens to institutions that mistake scale for strength, efficiency for freedom, optimization for wisdom. They become powerful enough to dominate markets—and too constrained to resist power. Look at Google, training AI for Beijing while preaching openness. Look at Amazon, building surveillance infrastructure for any government that pays. Look at every Fortune 500 company that issued statements about democracy while writing checks to the politicians dismantling it.

Apple is simply the cleanest case, because it once knew the difference. Because Jobs built it to know the difference. And because we can see, with unusual clarity, the precise moment when knowing the difference stopped mattering.

by Mike Brock, Notes From the Circus |  Read more:
Image: Steve Jobs/uncredited
[ed. Part seventeen of a series titled The Crisis Papers. Check them all out and jump in anywhere. A+ effort.]

Sunday, February 15, 2026

What Does “Trust in the Media” Mean?

Abstract

Is public trust in the news media in decline? So polls seem to indicate. But the decline goes back to the early 1970s, and it may be that “trust” in the media at that point was too high for the good of a journalism trying to serve democracy. And “the media” is a very recent (1970s) notion popularized by some because it sounded more abstract and distant than a familiar term like “the press.” It may even be that people answering a pollster are not trying to report accurately their level of trust but are acting politically to align themselves with their favored party's perceived critique of the media. This essay tries to reach a deeper understanding of what gives rise to faith or skepticism in various cultural authorities, including journalism.

In F. Scott Fitzgerald's 1920 novel This Side of Paradise, the main character, Amory, harangues his friend and fellow Princeton graduate Tom, a writer for a public affairs weekly:
“People try so hard to believe in leaders now, pitifully hard. But we no sooner get a popular reformer or politician or soldier or writer or philosopher … than the cross-currents of criticism wash him away. … People get sick of hearing the same name over and over.”

“Then you blame it on the press?”

“Absolutely. Look at you, you're on The New Democracy, considered the most brilliant weekly in the country. … What's your business? Why, to be as clever, as interesting and as brilliantly cynical as possible about every man, doctrine, book or policy that is assigned you to deal with.”1
People have “blamed it on the press” for a long time. They have felt grave doubts about the press long before social media, at times when politics was polarized and times when it was not, and even before the broad disillusionment with established institutional authority that blossomed in the 1960s and 1970s, when young people were urged not to trust anybody “over thirty.” This is worth keeping in mind as I, in a skeptical mood myself, try to think through contemporary anxiety about declining trust, particularly declining trust in what we have come to call-in recent decades-”the media.”

As measured trust in most American institutions has sharply declined over the last fifty years, leading news institutions have undergone a dramatic transformation, the reverberations of which have yet to be fully acknowledged, even by journalists themselves. Dissatisfaction with journalism grew in the 1960s. What journalists upheld as “objectivity” came to be criticized as what would later be called “he said, she said” journalism, “false balance” journalism, or “bothsidesism” in sharp, even derisive, and ultimately potent critiques. As multiple scholars have documented, news since the 1960s has become deeper, more analytical or contextual, less fully focused on what happened in the past twenty-four hours, more investigative, and more likely to take “holding government accountable” or “speaking truth to power” as an essential goal. In a sense, journalists not only continued to be fact-centered but also guided by a more explicit avowal of the public service function of upholding democracy itself.

One could go further to say that journalism in the past fifty years did not continue to seek evidence to back up assertions in news stories but began to seek evidence, and to show it, for the first time. Twenty-three years ago, when journalist and media critic Carl Sessions Stepp compared ten metropolitan daily newspapers from 1962 to 1963 with the same papers from 1998 to 1999, he found the 1963 papers “naively trusting of government, shamelessly boosterish, unembarrassedly hokey and obliging,” and was himself particularly surprised to find stories “often not attributed at all, simply passing along an unquestioned, quasi-official sense of things.” In the “bothsidesism” style of news that dominated newspapers in 1963, quoting one party to a dispute or an electoral contest and then quoting the other was the whole of the reporter's obligation. Going behind or beyond the statements of the quoted persons, invariably elite figures, was not required. It was particularly in the work of investigative reporters in the late 1960s and the 1970s that journalists became detectives seeking documentable evidence to paint a picture of the current events they were covering. Later, as digital tools for reporters emerged, the capacity to document and to investigate became greater than ever, and a reporter did not require the extravagant resources of a New York Times newsroom to be able to write authoritative stories.

I will elaborate on the importance of this 1960s/1970s transformation in what follows, not to deny the importance of the more recent digital transformation, but to put into perspective that latter change from a top-down “media-to-the-masses” communication model to a “networked public sphere” with more horizontal lines of communication, more individual and self-appointed sources of news, genuine or fake, and more unedited news content abounding from all corners. Journalism has changed substantially at least twice in fifty years, and the technological change of the early 2000s should not eclipse the political and cultural change of the 1970s in comprehending journalism today. (Arguably, there was a third, largely independent political change: the repeal of the “fairness doctrine” by the Federal Communication Commission in 1987, the action that opened the way to right-wing talk radio, notably Rush Limbaugh's syndicated show, and later, in cable television, to Fox News.) Facebook became publicly accessible in 2006; Twitter was born the same year; YouTube in 2005. Declining trust in major institutions, as measured by surveys, was already apparent three decades earlier-not only before Facebook was launched but before Mark Zuckerberg was born.

At stake here is what it means to ask people how much they “trust” or “have confidence in” “the media.” What do we learn from opinion polls about what respondents mean? In what follows, I raise some doubts about whether current anxiety concerning the apparently growing distrust of the media today is really merited.

Did people ever trust the media? People often recall-or think they recall-that longtime CBS News television anchor Walter Cronkite was in his day “the most trusted man in America.” If you Google that phrase (as I did on October 11, 2021, and again on January 16, 2022) you immediately come up with Walter Cronkite. Why? Because a public opinion poll in 1972 asked respondents which of the leading political figures of the day they trusted most. Cronkite's name was thrown in as a kind of standard of comparison: how do any and all of the politicians compare to some well-known and well-regarded nonpolitical figure? Seventy-three percent of those polled placed Cronkite as the person on the list they most trusted, ahead of a general construct-”average senator” (67 percent)-and well ahead of the then most trusted politician, Senator Edmund Muskie (61 percent). Chances are that any other leading news person or probably many a movie star or athlete would have come out as well or better than Cronkite. A 1974 poll found Cronkite less popular than rival tv news stars John Chancellor, Harry Reasoner, and Howard K. Smith. Cronkite was “most trusted” simply because he was not a politician, and we remember him as such simply because the pollsters chose him as their standard.

Somehow, people have wanted to believe that somewhere, just before all the ruckus began over civil rights and Vietnam and women's roles and status, at some time just before yesterday, the media had been a pillar of central, neutral, moderate, unquestioning Americanism, and Walter Cronkite was as good a symbol of that era as anyone.

But that is an illusion.

by Michael Schudson, MIT Press Direct | Read more:
Image: Walter Cronkite/NY Post

Friday, February 13, 2026

Your Job Isn't Disappearing. It's Shrinking Around You in Real Time

You open your laptop Monday morning with a question you can’t shake: Will I still have a job that matters in two years?

Not whether you’ll be employed, but whether the work you do will still mean something.
Last week, you spent three hours writing a campaign brief. You saw a colleague generate something 80% as good in four minutes using an AI agent (Claude, Gemini, ChatGPT…). Maybe 90% as good if you’re being honest.

You still have your job. But you can feel it shrinking around you.

The problem isn’t that the robots are coming. It’s that you don’t know what you’re supposed to be good at anymore. That Excel expertise you built over five years? Automated. Your ability to research competitors and synthesize findings? There’s an agent for that. Your skill at writing clear project updates? Gone.

You’re losing your professional identity faster than you can rebuild it. And nobody’s telling you what comes next.

The Three Things Everyone Tries That Don’t Actually Work

When you feel your value eroding, you do what seems rational. You adapt, you learn, and you try to stay relevant.

First, you learn to use the AI tools better. You take courses on prompt engineering. You master ChatGPT, Claude, whatever new platform launches next week and the week after. You become the “AI person” on your team. You think that if I can’t beat them, I’ll use them better than anyone else.

This fails because you’re still competing on execution speed. You’re just a faster horse. And execution is exactly what’s being commoditized. Six months from now, the tools will be easier to use. Your “expertise” in prompting becomes worthless the moment the interface improves. You’ve learned to use the shovel better, but the backhoe is coming anyway.

Second, you double down on your existing expertise. The accountant learns more advanced tax code. The designer masters more software. The analyst builds more complex models. You will have the same thought as many others, “I’ll go so deep they can’t replace me.”

This fails because depth in a disappearing domain is a trap. You’re building a fortress in a flood zone. Agents aren’t just matching human expertise at the median level anymore. They’re rapidly approaching expert-level performance in narrow domains. Your specialized knowledge becomes a liability because you’ve invested everything in something that’s actively being automated. You’re becoming the world’s best telegraph operator in 1995.

Third, you try to “stay human” through soft skills. You lean into creativity, empathy, relationship building. You go to workshops on emotional intelligence. You focus on being irreplaceably human. You might think that what makes us human can’t be automated.

This fails because it’s too vague to be actionable. What does “be creative” actually mean when an AI can generate 100 ideas in 10 seconds? How do you monetize empathy when your job is to produce reports? The advice feels right but provides no compass. You end up doing the same tasks you always did, just with more anxiety and a vaguer sense of purpose.

The real issue with all three approaches is that they’re reactions, not redesigns. You’re trying to adapt your old role to a new reality. What actually works is building an entirely new role that didn’t exist before.

But nobody’s teaching you what that looks like.

The Economic Logic Working Against You

This isn’t happening to you because you’re failing to adapt. It’s happening because the economic incentive structure is perfectly designed to create this problem.

The mechanism is simple, companies profit immediately from adopting AI agents. Every task automated results in cost reduction. The CFO sees the spreadsheet, where one AI subscription replaces 40% of a mid-level employee’s work. The math is simple, and the decision is obvious.

Many people hate to hear that. But if they owned the company or sat in leadership, they’d do the exact same thing. Companies exist to drive profit, just as employees work to drive higher salaries. That’s how the system has worked for centuries.

But companies don’t profit from retraining you for a higher-order role that doesn’t exist yet.

Why? Because that new role is undefined, unmeasured, and uncertain. You can’t put “figure out what humans should do now” on a quarterly earnings call. You can’t show ROI on “redesign work itself.” Short-term incentives win. Long-term strategy loses.

Nobody invests in the 12-24 month process of discovering what your new role should be because there’s no immediate return on that investment.

We’re in a speed mismatch. Agent capabilities are compounding at 6-12 month cycles. [ed. Even faster now, after the release of Claude Opus 4.6 last week]. Human adaptation through traditional systems operates on 2-5 year cycles.

Universities can’t redesign curricula fast enough. They’re teaching skills that will be automated before students graduate. Companies can’t retrain fast enough. By the time they identify the new skills needed and build a program, the landscape has shifted again. You can’t pivot fast enough. Career transitions take time. Mortgages don’t wait.

We’ve never had to do this before.

Previous automation waves happened in manufacturing. You could see the factory floor. You could watch jobs disappear and new ones emerge. There was geographic and temporal separation.

This is different, knowledge work is being automated while you’re still at your desk. The old role and new role exist simultaneously in the same person, the same company, the same moment.

And nobody has an economic incentive to solve it. Companies maximize value through cost reduction, not workforce transformation. Educational institutions are too slow and too far removed from real-time market needs. Governments don’t understand the problem yet. You’re too busy trying to keep your current job to redesign your future one.

The system isn’t helping because it isn’t designed for continuous, rapid role evolution; it is designed for stability.

We’re using industrial-era institutions to solve an exponential-era problem. That’s why you feel stuck.

Your Experience Just Became Worthless (The Timeline)

Let me tell you a story of my friend, let’s call her Jane (Her real name is KatÅ™ina, but the Czech diacritic is tricky for many). She was a senior research analyst at a mid-sized consulting firm. Ten years of experience. Her job was provide answers to the client companies, who would ask questions like “What’s our competitor doing in the Asian market?” and she’d spend 2-3 weeks gathering data, reading reports, interviewing experts, synthesizing findings, and creating presentations.

She was good, clients loved her work, and she billed at $250 an hour.

The firm deployed an AI research agent in Q2 2023. Not to replace her, but as they said, to “augment” her. Management said all the right things about human-AI collaboration.

The agent could do Jane’s initial research in 90 minutes, it would scan thousands of sources, identify patterns, generate a first-draft report.

Month one: Jane was relieved and thought she could focus on high-value synthesis work. She’d take the agent’s output and refine it, add strategic insights, make it client-ready.

Month three: A partner asked her, “Why does this take you a week now? The AI gives us 80% of what we need in an hour. What’s the other 20% worth?”

Jane couldn’t answer clearly. Because sometimes the agent’s output only needed light editing. Sometimes her “strategic insights” were things the agent had already identified, just worded differently.

Month six: The firm restructured. They didn’t fire Jane, they changed her role to “Quality Reviewer.” She now oversaw the AI’s output for 6-8 projects simultaneously instead of owning 2-3 end to end.

Her title stayed the same. Her billing rate dropped to $150 an hour. Her ten years of experience felt worthless.

Jane tried everything. She took an AI prompt engineering course. She tried to go deeper into specialized research methodologies. She emphasized her client relationships. None of it mattered because the firm had already made the economic calculation.

One AI subscription costs $50 a month. Jane’s salary: $140K a year. The agent didn’t need to be perfect; it just needed to be 70% as good at 5% of the cost. But it was fast, faster than her.

The part that illustrates the systemic problem, you often hear from AI vendors that, thanks to their AI tools, people can focus on higher-value work. But when pressed on what that meant specifically, they’d go vague. Strategic thinking, client relationships, creative problem solving.

Nobody could define what higher-value work actually looked like in practice. Nobody could describe the new role. So they defaulted to the only thing they could measure: cost reduction.

Jane left six months later. The firm hired two junior analysts at $65K each to do what she did. With the AI, they’re 85% as effective as Jane was.

Jane’s still trying to figure out what she’s supposed to be good at. Last anyone heard, she’s thinking about leaving the industry entirely.

Stop Trying to Be Better at Your Current Job

The people who are winning aren’t trying to be better at their current job. They’re building new jobs that combine human judgment with agent capability.

Not becoming prompt engineers, not becoming AI experts. Becoming orchestrators who use agents to do what was previously impossible at their level. [...]

You’re not competing with the agent. You’re creating a new capability that requires both you and the agent. You’re not defensible because you’re better at the task. You’re defensible because you’ve built something that only exists with you orchestrating it.

This requires letting go of your identity as “the person who does X.” Marcus doesn’t write copy anymore. That bothered him at first. He liked writing. But he likes being valuable more.

Here’s what you can do this month:

by Jan Tegze, Thinking Out Loud |  Read more:
Image: uncredited
[ed. Not to criticize, but this advice still seems a bit too short-sighted (for reasons articulated in this article: AI #155: Welcome to Recursive Self-Improvement (DMtV):]
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Presumably you can see the problem in such a scenario, where all the existing jobs get automated away. There are not that many slots for people to figure out and do genuinely new things with AI. Even if you get to one of the lifeboats, it will quickly spring a leak. The AI is coming for this new job the same way it came for your old one. What makes you think seeing this ‘next evolution’ after that coming is going to leave you a role to play in it?

If the only way to survive is to continuously reinvent yourself to do what just became possible, as Jan puts it? There’s only one way this all ends.

I also don’t understand Jan’s disparate treatment of the first approach that Jan dismisses, ‘be the one who uses AI the best,’ and his solution of ‘find new things AI can do and do that.’ In both cases you need to be rapidly learning new tools and strategies to compete with the other humans. In both cases the competition is easy now since most of your rivals aren’t trying, but gets harder to survive over time.
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[ed. And the fact that there'll be a lot fewer of these types of jobs available. This scenario could be reality within the next year (or less!). Something like a temporary UBI (universal basic income) might be needed until long-term solutions can be worked out, but do you think any of the bozos currently in Washington are going to focus on this? And, that applies to safety standards as well. Here's Dean Ball (Hyperdimensional): On Recursive Self-Improvement (Part II):
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Policymakers would be wise to take especially careful notice of this issue over the coming year or so. But they should also keep the hysterics to a minimum: yes, this really is a thing from science fiction that is happening before our eyes, but that does not mean we should behave theatrically, as an actor in a movie might. Instead, the challenge now is to deal with the legitimately sci-fi issues we face using the comparatively dull idioms of technocratic policymaking. [...]

Right now, we predominantly rely on faith in the frontier labs for every aspect of AI automation going well. There are no safety or security standards for frontier models; no cybersecurity rules for frontier labs or data centers; no requirements for explainability or testing for AI systems which were themselves engineered by other AI systems; and no specific legal constraints on what frontier labs can do with the AI systems that result from recursive self-improvement.

To be clear, I do not support the imposition of such standards at this time, not so much because they don’t seem important but because I am skeptical that policymakers could design any one of these standards effectively. It is also extremely likely that the existence of advanced AI itself will both change what is possible for such standards (because our technical capabilities will be much stronger) and what is desirable (because our understanding of the technology and its uses will improve so much, as will our apprehension of the stakes at play). Simply put: I do not believe that bureaucrats sitting around a table could design and execute the implementation of a set of standards that would improve status-quo AI development practices, and I think the odds are high that any such effort would worsen safety and security practices.