I will also add that for a military which has, for at least the last 165 years, distinguished itself by winning its wars through relentlessly superior logistics and organizing, the emphasis on chasing the mirage of ultra-masculine ‘strong men’ super-soldiers (at the expense of logistics, organizers and bureaucrats) strikes as almost absurdly historically illiterate. The United States military has spent more than the last century and a half mopping the floor with manly-man armies, be they the Flower of Southern Chivalry1 or the Nazi Übermenschen. Where it has failed (Afghanistan, Vietnam) it has not been fighting armies of body-builders but scrappy, under-fed, foreign-supported forces willing to be tactically and politically flexible, like a smaller boxer waiting for a larger one to ‘punch himself out.’

I’m proud of this website. This is my Zettelkasten. I don’t have “many slips of papers” here, I have a few markdown files with just a little YAML. This is better. This can’t be burned. If I use the wrong color to highlight something, I don’t have to redraw the card. I have tools!

Google has confirmed it will merge its ChromeOS and Android operating systems, and that the mobile OS will emerge triumphant. The ads and search giant has hinted that the two operating systems would merge. On Wednesday at Qualcomm’s Summit event, Google’s president for the Android ecosystem, Sameer Samat, made it official. Android will be the winner and users will see the results in 2026.

That's what we're packaging here at WoL—the permission to think of yourself, privately, diffidently, as an educated person. The sneaking suspicion that, although you haven't read everything, there are lots of "real intellectuals" (i.e. people with book deals and PhDs) who not only haven’t read books, but don't even feel bad about it! So why should you feel bad, when even your superiors don't feel bad? It's fine to worship a God that our superiors don't worship, but we shouldn't fear a God they don't fear.

So it's very difficult to speak about the aim that is accomplished solely by reading Henry James. The aim of...what? Understanding beauty, I suppose. And yet if you abstract morality and truth and goodness from beauty, then what's left hardly seems worth the time. I think the truth is that beauty without goodness would simply be...music. And it's much easier and simpler to listen to music than it is to read Henry James. You read Henry James precisely because it's in narrative art that beauty, goodness, and truth are combined.

But those reasons don’t really justify the wholesale erasure of an extraordinary era of American creativity. What’s happening? Why aren’t these works surviving?The larger truth is that the Internet creates the illusion that all culture is taking place right now. Actual history disappears in the eternal present of the web.

And it’s not just Cheever. Not long ago, any short list of great American novelists would include obvious names such as John Updike, Saul Bellow, and Ralph Ellison. But nowadays I don’t hear anybody say they are reading their books.And they are brilliant books. But reading Updike today would be an act of rebellion. Or perhaps indulging in nostalgia for a lost era.The list goes on—Joseph Heller, Bernard Malamud, Carson McCullers, Robert Penn Warren, Katherine Anne Porter, James Agee, etc. Do they exist for readers under the age of forty?

Experiment 1 of this paper applied cognitive load theory to some of the consequences of presenting animations during instruction while Experiment 2 applied the theory to some of the consequences of using audio-visual instructions. In both cases it is suggested that as the length and complexity of transient instructional information associated with animations and audio-visual presentations increases, extraneous working memory load also increases resulting in the benefits of animations and audiovisual instructions decreasing or even reversing compared to alternatives.

The National Weather Service makes all kinds of fun data available (if you can find it...). How about a script that every hour, but only when the weather is clear?

China’s new-build solar additions in the last six months are more than the US has installed in total: China Flow > US stock. Germany was once the global champion of solar. China’s new additions in six months are two times Germany’s installed capacity.

In the first six months of 2025 China installed more than 250GW of solar power capacity. That staggering figure from the think tank Ember is measured on a comprehensive basis including decentralized solar and is somewhat larger than the official number released by the Chinese Energy Administration, which was a barely less staggering 212 GW.

Taken together, these signals point to an industry still in strong ascent, unlike the relatively meager revenue growth that preceded the railroad and telecom busts. If genAI revenues were to grow at even half the pace of last year, then, on my conservative forecast, they would reach $100 billion by 2026, covering about 25% of that year’s capex.

But there is a twist here that history’s bubbles did not face: rapid depreciation. Once laid, a railway track could last decades. The US was still running freight over 19th-century tracks well into the 20th. Telecom fiber rolled out in the 1990s still carries traffic 35 years later.GPUs, by contrast, age in dog years. Their useful life for frontier applications such as model training is perhaps three years, after which they are relegated to lower-intensity tasks. Roughly a third of hyperscaler capex is going into such short-lived assets. They remain, in theory, monetizable in years five and six. The rest goes into shells, power and cooling that last two or three decades. Adjusting for asset life makes the AI build-out look even more demanding: unlike railroads or fiber, the system must earn its keep in a handful of years, not generations.

At the height of the US railroad expansion in 1872, capex was around two times revenues. In the late-1990s telecom bubble, capex amounted to just under four times revenues. By contrast, today’s genAI boom runs on roughly $60 billion in revenues against about $370 billion in global data center capex – a capex-to-revenue ratio of six times, the most stretched of the three.

There’s a reasonable argument that a large % of all distinct theorems proved in human history have been proved by the AWS Zelkova access control tool.

I’m told that smoking has declined basically in tandem with the rise of the smartphone. This makes sense. They’re both something to do with your hands in the empty moments, before or afterwards, when you’re waiting for something else to happen. Both of them will also kill you.

And even if I’m managing to keep kneejerk political takes to a minimum, even if I’ve not suffered the same miserable fate as Matt Taibbi, it’s still become easy for me to simply perform a kind of studied eclecticism instead of really getting into something. America invented Europe in the fifth century AD: sure. The Trial is an ethnographically complete account of the Poro society of West Africa: why not. George HW Bush was an elf: you know and I know that I can do this stuff in my sleep. That’s what happens when you’re the best in the game.

he was what all these killers are, which is a young man, smart but with diminishing prospects, who had spent essentially every moment of his brief life on the computer. These people might think they’re motivated by a political ideology, but neither the extremely online right nor the extremely online left have anything to do with the ordinary political questions of the distribution of social goods or the organisation of power. At root, their content is all the same: it’s about imagining a world in which you don’t have to be on the computer all the time, but insisting that before we can get there, millions of people will have to die.

No, you say, actually I want the ham after all.A strong and decisive reversal, says your beautiful submissive girlfriend. You’re right—ham was always the superior choice. You followed your appetite, and the road brought you right back to where you started—but wiser and more self-assured. Where will your culinary journey take you next?

I don’t want these people to have power, and I would never want to believe in any of this stuff personally, but I think having a broad diversity of utterly insane ideas in common circulation is a good in and of itself.

Rationalism is the notion that the universe is a collection of true facts, but since the human brain is an instrument for detecting lions in the undergrowth, almost everyone is helplessly confused about the world, and if you want to believe as many true things and disbelieve as many false things as possible—and of course you do—you must use various special techniques to discipline your brain into functioning more like a computer. (In practice, these techniques mostly consist of calling your prejudices ‘Bayesian priors,’ but that’s not important right now.)

The name is because these people think the history of philosophy is just a series of wrong ideas that have since been replaced by better ones, and instead of reading any of it you should just skip ahead to simulation theory, in the same way that physics students skip past phlogiston.

A propositional statement is either literally true, clearly marked as fiction, or a waste of everyone’s time. The purpose of an essay is to efficiently deliver accurate propositions, and any other features are only justifiable if they help the propositions go down more easily. On this point everyone has always agreed.

When I read about taste and AI I usually see people referring to the following definition. Critical judgment, discernment, or appreciation of aesthetic quality. In the context of AI, this definition manifests itself in several ways. Contextual Appropriateness: Knowing when AI-generated content fits the situation and when it doesn’t. Put another way, knowing when a human touch is needed (e.g., a message to a loved one). Quality Recognition: Being able to distinguish between useful AI-generated content and slop. This requires domain knowledge to truly discern aesthetic quality rather than just functional quality. Iterative Refinement: Understanding that AI is a starting point that requires further iteration. This point is most similar to how culinary taste is applied to refine a dish by iterating on the recipe and presentation. Ethical Boundaries: Recognizing when AI crosses the lines of authenticity, legality, and respect. Basically, don’t use AI to do bad things.

Rituals? I can’t stop myself. They’re so good. Absolutely breathtaking. Humanity’s finest work, perhaps. They make no sense. It’s all about beauty, about made-up meaning, about art. Rituals add unnecessary, arbitrary extra requirements to a simple action. Light a candle first. Kneel. Wear a certain outfit. Carry flowers. Make this shape with your hands. Take off your hat, or put it on. Not that hat, the special one.

Functionally, there is little difference between the throttle and the pedal assist. In both cases, the motor is giving you a significant boost. Philosophically, however, there is a big difference. In pedal assist mode, you are still required to exert some effort. You have some choice over how strong the assistance will be, but in any situation, the level of assistance remains directly connected to the amount of effort you put in.This sort of design strategy is important to consider as we think about AI, especially in educational contexts. If we eliminate the connection between effort and results, we are training ourselves to become reliant on our AI tools. Just like only using the throttle on our e-bike will deprive us of the health benefits of exerting ourselves and cycling, using AI in this way will sacrifice opportunities we have to build our cognitive and intellectual skills.

How does it work? Create a club with a prompt (updates, stand-ups, book discussions, movie reviews, work projects — any subject you can imagine), and set your preferred frequency (weekly, biweekly, or monthly). Invite your friends or colleagues to join by sharing a unique link. A gentle reminder email will nudge you to contribute. On the specified interval and day, you’ll receive a clean email digest from your fellow club members. Miss a submission? Reply directly from your inbox, or skip it. No pressure. Once the digest is sent, any further replies are local to your fellow clubbers.

I think every non-niche, applied AI start up needs to ask themselves two questions: If a better model arrives tomorrow, does your product get better or does your backend get simpler? If your product doesn’t get better, you need to rethink. A better model simplifying your backend (by reducing the complexity of your prompts, your error handling, your infra, etc.) makes your product easier to clone. If you are early to market with this use case, what are you going to do in a handful of months that will fend off Google/OpenAI/whomever’s entry into your market? Cursor and Perplexity are the rare examples that have managed to grow fast enough to be able to fend off larger entrants. What are you going to do, if you can’t go niche, to prepare your defenses?

I’m Kit Anderson, productivity geek, entrepreneur, father of three (startups, that is), and soon-to-be the first self-made trillionaire (haha just kidding… but maybe?). This is my website where I write about my efforts to lead a productive life, the OPTIMAL life.

How To Do Things describes an ADHD-friendly version of the Pomodoro method. It’s a 50 page PDF with no fluff, so it’s worth buying to support writers who don’t waste the reader’s time. Getting Things Done has a lot of good advice (e.g. dump your entire brain into the todo list) but it’s somewhat neurotypical in that it’s assumed you won’t have any problems actually executing the tasks.

If being organized makes you feel good, spend more on organizing your productivity system. In a sense, having a really complex productivity system is like trying to use neuroticism to defeat ADHD, to use high neuroticism to defeat low conscientiousness. There’s an element of truth to that, sure (see mastery of drudgery). But here’s the thing: you have to play to your strengths. You have to. If you like order and systems and planning but you struggle with doing, then, yeah, it might work, for you, to spend more energy on the trappings of productivity (ensuring your todo list is properly formatted, organized, etc.) if that bleeds over into making it easier to do the real, meaningful things. For example: I like emojis in my todo list. The chores have a 🧼 emoji, the comms tasks have an ✉️ emoji. That kind of thing. Makes it easy to see at a glance what kind of things I have to do, to group them by category. But Todoist doesn’t support emoji icons on tasks, unlike Notion, so adding the emojis takes a bit more effort: I have to open Raycast and search for the emoji I want and paste it into the task title. It adds a little friction each time I create a task, but the benefit is I enjoy using the todo list more.

Sometimes I’ll start a conversation with Claude, lay out my plans for the day, and update Claude as I do things. If I’m stuck, or if I need help overcoming procrastination, I can ask Claude for help, and it’s easier to do that in an on-going thread because Claude already has the necessary context, so I don’t have to describe what I’m struggling with ab initio.

Turn off notifications, check comms as an explicit task. “Interrupts” means notifications, which arrive at unpredictable and often inconvenient times. “Polling” means manually checking the source of the notifications for things to action.

Bring aversion-causing tasks into an environment that you control. If you’re averse to doing something, for emotional reasons, one way to overcome the aversion is to do it as much as possible on your own terms. An example: you have to fill out some government form. You’re averse to it because you worry about making a mistake. And just the thought of opening the form fills you with dread. So, take the boxes in the form, and make a spreadsheet for them. If fonts/colours/emojis/etc. if that makes it feel more personal, or like something you designed and created. Then fill out the form in the spreadsheet. And then copy the values to the form and submit. This helps because instead of performing the task in this external domain where you feel threatened, you’re performing the task in your own domain, in your own terms. Another example: you have an email you have to reply to, and you’re anxious about it. Just opening the email client gives you a bad feeling. Instead, try composing the email elsewhere, say, in a text editor. The change of environment changes the emotional connotation: you’re not replying to an email, you’re writing a text. You might even think of it as a work of fiction, a pseudoepigraphy.

All inboxes should be at zero. You have probably heard of inbox zero. It sounds like LinkedIn-tier advice. But if you struggle with comms, with replying to people in a timely manner (or at all), inbox zero is a good strategy. There are two reasons, briefly: Inbox zero has no false negatives: if an inbox is empty, you know you’ve handled everything. Important communications have a way of “camouflaging” themselves among irrelevance.

Bring all tasks, broadly defined, into one todo list.

Keep in regular contact with long-running projects. A common failure mode I have is, I will fail to finish a project because I forget I even started it. Or, relatedly: I will let a project drag on and on until enough time has passed that my interests have shifted, the sun has set on it, and it is now a slog to finish. One reason I do this is that creative/intellectual work often requires (or feels like it requires) long stretches of uninterrupted time. So I procrastinate working on something until I can find such a chunk of time. Which never comes. Time passes and the project begins to slip the moorings of my attention, as other new and shiny things arrive.

To remember something, put it in your visual field. Dually: to forget, get it out of sight. Out of sight, out of mind. The corollary: to keep something in mind, put it in your visual field; to keep it out, leave it out. My desk is very spartan: there’s a monitor, a mouse, and a keyboard, and a few trinkets. My desktop is empty. There are no files in it. The dock has only the apps I use frequently. And at a higher level, I try to keep the apartment very clean and orderly. Because everything that’s out of place is a distraction, visual noise. That’s the negative aspect: the things I remove.

To select the next task, pick either the shortest or the most-procrastinated task.

If you have ADHD you’ve probably heard of the Pomodoro method, tried it, and bounced off it. The way it’s framed is very neurotypical: it’s scaffolding around doing, but ADHD people often have problems with the doing itself. And so the scaffolding is kind of pointless. The method works well in three kinds of contexts: Overcoming Aversion: when you have a large number of microtasks, each of which takes a few seconds to a few minutes, but the number of them, and the uncertainty factor, makes the sum seem a lot larger. A classic example for me is having to reply to like ten different people. Realistically, each person can be handled in 15s. One or two might require a couple of minutes to compose a longer reply. But often I will avoid those tasks like the plague and drag them across the entire day. The pomodoro method works here because you’re basically trading (up to) 25m of pain for an entire day’s peace and quiet. So you get all the annoying little tasks together, start a timer, and go through them. And usually you’re done in maybe ten minutes. And you feel really good after, because all those annoying little tasks are done. It really is amazing what a little bit of fake urgency can do.

ADHD Procrastination: you want to do the task, but can’t because of distraction/hyperactivity. Anxious Procrastination: you know you have to do the task, but you don’t want to, because it triggers difficult emotions. Decision Paralysis Procrastination: you don’t know how to execute the task, because it involves a decision and you have difficulty making the decision.

The usual metaphor is that “mental energy” is like a battery that is drained through the day, in greater and lesser quantities, and is replenished by sleep. To me, energy is less like a battery and more like voltage. Some machines require a threshold voltage to operate. Below that voltage they don’t just operate slower, they don’t operate at all. Analogously, different categories of activity have different threshold voltages. For me, it’s like this: Things I am averse to, the things I intuitively want to put off because they bring up painful emotions, are high-voltage. Creative, open-ended work is high-voltage to start, but once you get started, keeping it going is medium-voltage. Simple chores like cleaning, throwing clothes in the washing machine, etc. are low-voltage. And when I wake up I have the highest possible voltage, and throughout the course of the day the voltage declines. And that’s the key difference from spoon theory: spoons are fungible across time, voltage is not. For each category of activity, there is a span of the day when I can action it.

Separate planning from action, so if you get distracted while acting, you can return to the plans.

A practical example is something I call the apartment survey. When I clean the apartment, I start by walking around, noticing everything that needs fixing, and creating a little task for it. Even something as simple as “move the book from the coffee table to the bookshelf”. But I don’t start anything until the survey is done. And when the survey is done, I execute it. And if I get distracted halfway through cleaning the apartment, I have the tasks in the list to go back to.

I replied, in my simplistic Turkish, that to me this sounded like a threat: either cover your head or rape can happen. The driver protested in ornate phrases that nobody was threatening anyone, that to speak of threats in this situation was unfitting, that he could tell from my smiling face that I was a good and trusting person, but that the world was an imperfect place, that some men were less like humans than like animals, and that it was best to send clear signals about what one was or wasn’t looking for. Then he left me at the fish restaurant where I was going to meet some literature professors.If it had been just the two of us in the taxi in a political vacuum, I wouldn’t have begrudged the driver his opinions. It was his car and his country, and he was driving me where I wanted to go. I knew that my limited Turkish, which felt like such a handicap, was in his eyes a marker of privilege—a sign that I could afford to travel and live abroad. Often, the second question drivers asked, after the invariable “Where are you from?,” was “How much did the plane ticket cost?”But the cab wasn’t in a vacuum; it was in a country where the head of state, whose wife wore a head scarf, repeatedly urged all women to have at least three children, preferably four or five. Erdoğan opposed abortion, birth control, and Cesarean section. He said that Islam had set out a clear position for women, but that you couldn’t explain it to feminists, because they “don’t accept the concept of motherhood.” The longer he stayed in office, the more outspoken he became. In 2014, he went so far as to describe birth control as “treason” designed “to dry up our bloodline.” No matter how hard I tried to be tolerant—no matter how sympathetic I felt toward Muslim feminists who didn’t want to be “liberated” from the veil, and who felt just as judged by the secularist establishment as secular women felt by the Muslim patriarchy—I could never forgive Erdoğan for saying those things about women. And, because he said them in the name of Islam, I couldn’t forgive Islam, either.

“What’s the most important thing humanity has engineered?” Kwan asked me, over coffee in a West Village restaurant. “Arguably, it wasn’t the internet, or agriculture. It was the creation of the systemic and institutional trust that was required for us to build societies. And a lot of that engineering was actually collective stories—God, government—that helped us see ourselves as one family, one community. With our current technology, it’s like we’re playing Jenga.” He mimed a tower of blocks at the table. “We’ve been pulling blocks from down here, from the foundation of collective understanding and belief in a shared world, and using them to build farther up on the tower. And, if we keep doing that, the whole thing will collapse, and we’ll go back to only being able to trust the hundred and fifty people in our tribes.”

Culture is communal. We like being part of a community of appreciators. But “there’s an option here, if computation is cheap enough, for the creation of an illusion of society,” Lanier said. “You would be getting a tailored experience, but your perception would be that it’s shared with a bunch of other people—some of whom might be real biological people, some of whom might be fake.” (I imagined this would be like Joi introducing Gosling’s character to her friends.) To inhabit this “dissociated society cut off from real life,” he went on, “people would have to change. But people do change. We’ve already gotten people used to fake friendships and fake lovers. It’s simple: it’s based on things we want.” If people yearn for something strongly enough, some of them will be willing to accept an inferior substitute. “I don’t want this to occur, and I’m not predicting that it will occur,” Lanier said, grimly. “I think naming all this is a way of increasing the chances that it doesn’t happen.”

Why do we care about what we care about? Walking into your basement in the dark, you might stumble into a cobweb; one strain of philosophical thought holds that this is how caring works. We become ensnared by the possibilities that happen to present themselves as we live—we get caught in webs of care. You run into your elderly neighbor in the morning and, that night, wonder how he’s doing. Your middle schooler likes Chappell Roan, and soon you do, too. A single encounter with “The Fountainhead” at thirteen might make you a lifelong libertarian. Algorithmic culture taps into the casual randomness with which we apportion our care; it takes advantage of the fact that what we bump into today might obsess us tomorrow. Its webs, meanwhile, are woven by machines that are owned by corporations.

Here’s a summary of Stevenson’s argument up to this point: RCTs are some of the best evidence we have for any intervention. RCTs consistently find that criminal justice interventions don’t work, especially ones built on the “cascade” model of a small change having broad ripple effects for the rest of someone’s life. When one RCT does work, that same intervention almost always fails to scale or be replicated. Given all that, we should stop banking on the “cascade” theory of change and place greater weight on structural “stabilizers” as a cause of people’s outcomes.

Multiple large, high-quality randomized studies are finding that guaranteed income transfers do not appear to produce sustained improvements in mental health, stress levels, physical health, child development outcomes or employment. Treated participants do work a little less, but shockingly, this doesn’t correspond with either lower stress levels or higher overall reported life satisfaction.

In “Evaluating decision rules across many weak experiments”, Winston Chou, Colin Gray, Nathan Kallus, Aurélien Bibaut & Simon Ejdemyr consider how to empirically evaluate and optimize the decision rules used to make launch decisions once a team runs an experiment. In practice, these decision rules are often some combination of launching if there’s a statistically significant positive effect on some proxy metric, often in the absence of a detected negative effect on some guardrail. It’s easy to see that this can lead to some odd choices that a Bayesian decision-maker would not make (always be integrating your loss function over your posterior). But can such a simple rule have good empirical performance in terms of the aggregate effects on the main metric of interest? Yes, but the status quo rule might not be the best — and might be quite bad — on that front. As this paper highlights, naive evaluation of a decision rule can get the evaluation quite wrong. This is because many of the experiments will be underpowered for effects on the main metric of interest, so there can be quite the “winner’s curse”. And, in a phenomenon related to weak instruments, naive evaluation will often misestimate how diagnostic the proxies used in a decision rule are about those effects. So this paper provides some better ways to get at the aggregate effects of applying a decision rule to many experiments.

Value is the ficklest of the attributes. It was more steadfast, I suspect, in my mother’s lifetime. She bought less but made sure to buy what was “good,” meaning it would last forever and (here I wince) be passed down to the next generation. Fads did not abruptly inflate or deflate an object’s worth. Gorgeous pieces were not so readily melted down. People did not return wedding presents for the cash or demand cash in the first place or register for hiking gear or a trip to the Caribbean. We are fluid, casual, transactional. Always poised to buy new, more, or different, and reluctant to be burdened by anything requiring extra time or effort. Are we lazy because we live on screens, or just overwhelmed by everything that comes at us every hour of every day? Our lives have lost their various social patterns—all those clubs and leagues and parties that once defined people. Slouched on separate sofas, we stream private amusements. Our aspirations often boil down to money and safety, rather than hospitality and refinement

teashop

Be the first player to complete your layout of ten cards Ace through Ten (A,2,3,4,5,6,7,8,9,10)

Unfortunately, this is where comment culture comes in. 16 years of commenting has made me zero friends. That scares me. All of that social activity with zero ROI. At first, I thought that I needed to change my commenting habits, and, you know, try to make connections. But the more I considered how to make friends in comment culture, the more I realized that it wasn’t just my own social ineptitude. Comment culture has a problem. Systemically, it produces an internet of strangers.

As an example, I recently wanted to subscribe to the RawTools newsletter. When I went to their newsletter subscription page, I noticed that their URL looked like this: https://rawtools.us11.list-manage.com/subscribe?u=00722345fc94fb4d4b323edc3&id=4ff553ba3e copyIf you can find a URL from a Mailchimp email campaign in a format like this, you can usually use it to get its respective RSS feed. There are 3 pieces we need in order to find this list’s RSS feed, and all of them we can find in this URL: us11 - This appears to be the Mailchimp server location associated with the mailing list’s account u=00722345fc94fb4d4b323edc3 - I think this is a user identification code? Not sure. We need it, though! id=4ff553ba3e - Again, not 100% sure what this is; possibly a list id? We need it too, regardless ¯\_(ツ)_/¯ Once we’ve got those pieces, we can use them to construct our RSS feed. A Mailchimp list’s RSS feed looks like this: https://[SERVER LOCATION CODE].campaign-archive.com/feed?u=["u" CODE]&id=["id" CODE] copyThe campaign-archive and /feed parts are the important parts that need to be switched out here. So, we put all those pieces together, and end up with the following feed URL: https://us11.campaign-archive.com/feed?u=00722345fc94fb4d4b323edc3&id=4ff553ba3e copyThen, adding that into an RSS reader app gives us the last few campaign emails that were sent out from that list, as well as allows us to be notified of future emails without it cluttering up our email inbox

In 1971, psychologists Amos Tversky and Daniel Kahneman published a now-classic paper, “Belief in the law of small numbers,” reporting that people “regard a sample randomly drawn from a population as highly representative, I.e., similar to the population in all essential characteristics.” I’ve been thinking a lot about this idea lately when coming across discussions of evidence. The (false) small-numbers heuristic leads people to expect that all, or almost all, the empirical evidence in some controversy will go in the same direction. Individual pieces of evidence can be analogized to samples from a larger population of potential evidence. Presumably the entire population of evidence, if it could be seen at once, would confirm the truth or at least strongly favor the correct hypothesis. (Here I’m thinking of a simple case in which there are two models of the world, one of which is essentially false and one essentially true.) Now let’s get back to stories. True stories will contain a mix of confirming and disconfirming evidence; that’s just the way the world works, or, to put it another way, that’s the statistics of small samples. But, in a fictional story, all the evidence can go in the same direction, and that can feel right, in that it fits our false intuition. The question then arises, where does the incorrect heuristic of the law of small numbers come from? It could come from all the stories we hear!

rather than having Claude whimsically reading files which may or may not poison him.

In light of the above I opt to dexterously describe my processes in my CLAUDE.md and simply use the user prompt to provide parameters for those processes or to steer the model

I do have diverse revenue streams, as I teach English online and edit novels, but teaching and editing do not pay nearly what translation does. I will be lucky to make $15 to $20 an hour teaching and editing, when translation pays at least 2 to 3 times that. So here I am, still 15 to 20 years from retirement, and I have had to put so much energy into "reskilling" for teaching and editing, which, even if they don't pay as well, are jobs that require in-depth, professional levels of skill! I laugh when politicians think that "reskilling" is this magical thing that anyone can pick up and do. Going back to school in your 40s and 50s when you still have kids to take care of, a house to manage, finances to manage, health to manage, aging parents to worry about and take care of, etc., etc. is one of the biggest jokes on working people. Fuck, am I tired.

In short, science classes pair a description of our best knowledge at the present with a story of discovery of how we came to know what we know now, with the clear implication that this method is how we will continue to discover new things. By contrast in history this same story (we call it historiography – the history of the history) doesn’t generally attract sustained attention until graduate school. Students learn the names of rulers and thinkers and key figures but they rarely learn the names of historians. Likewise, instead of being presented with a process of historical discovery they are given a narrative of human development – it is not until advanced undergraduate courses that they begin to engage meaningfully with how we know these things. In my own experience the exceptions to this were almost invariably stories about the knowledge-making achievements of other disciplines – archaeology and linguistics, mostly – rather than narratives of historical investigation. So it is not surprising that many students at those introductory levels come away assuming that the narrative is pretty much fixed and has been known and understood effectively forever.

On average, essays written with assistance from InstructGPT showed significantly more homogenization than essays written without any assistance—or, interestingly, essays written with assistance from GPT-3 (the “base” model). Further, essays written with assistance from InstructGPT also involved more repetition of the same sequences: that is, two randomly sampled essays from the InstructGPT were more likely to contain the same 5-word sequence than were two randomly sampled essays written without LLM assistance.There are some clear limitations to this work (like the fact that the models used are now outdated, and the fact that the CoAuthor interface seems pretty different from contemporary LLM usage), but one interesting conclusion is that there’s something about fine-tuning the model specifically that leads towards homogenization

A final piece of evidence regarding the distinctiveness of LLM-generated text comes from this very recent (May 2025) paper exploring whether people can accurately distinguish human-authored vs. synthetic text. The authors recruited 9 annotators and asked them to read 30 human-written and 30 LLM-generated articles, indicating for each text whether they believed it was written by a human or LLM.6 Of these 9 annotators, five frequently used LLMs for writing tasks, while the other four had only limited experience. The authors found that the four with limited experience achieved an average accuracy of 56.7%—roughly random chance—though they nonetheless reported high confidence in their decisions. The five that frequently used LLMs achieved an average accuracy of 92.7%; moreover, the majority vote of these five achieved almost perfect performance (99.9%), which was considerably better than any of the automatic detection methods tested. Expert annotators also retained high accuracy in the face of various adversarial methods, including paraphrasing the LLM-generated text and attempting to “humanize” it (which involved explicitly instructing an LLM to avoid certain “tells”).

According to Joseph and Frances Gies, authors of Life in a Medieval Castle, Winter was the season from Michaelmas (September 29) to Christmas when wheat and rye were sown.  From the end of the Christmas holidays to Easter was the season when spring crops were sown: oats, peas, beans, barley, and vetches.  From the end of Easter week to Lammas (August 1) was summer, and from Lammas to Michaelmas was harvest, or autumn. (p. 206) This is in contrast to our modern calendar of seasons, which occur approximately as follows: Spring: March 20 Summer: June 21 Autumn: September 22 Winter: December 21

t David Autor, Lawrence Katz and Alan Krueger examined. In their 1997 article, they argued that the rapid spread of computer technology in the workplace may explain as much as 30 to 50 percent of the increase in the growth rate of demand for more-skilled workers since 1970. The three economists found that the demand for college-level workers grew more rapidly on average from 1970 to 1995 than from 1940 to 1970. This increased demand was initially met with a sufficient supply of college-educated workers. That supply slowed at the beginning of the 1980s, however, eventually causing a shortage that led to a widening of the wage gap between those with and without college degrees. An even more striking finding by the authors was that industries displaying the largest increases in skill requirements—legal services, advertising and public administration, for example—were the biggest users of computers. 6 Relative to other industries, these have exhibited greater growth in employee computer use and more capital investment in computers both per worker and as a share of total investment. In addition, these high computer-use sectors appear to have reorganized their workplaces in a manner that disproportionately employs more educated—and higher paid—workers.

in 1994 there was a zero percent chance that a Hildegard von Bingen record would be put out with a track titled "C*m Processit Factura Digiti Dei". This is because an actual human being would have been working at the distributor and handling the record.

The debate about the relationship of modern technological reproduction of images and their relationship with with reality has been going on since the invention of the photograph. An entire academic career could be dedicated to categorizing the literature that has already been written about it. In fact, a couple of courses could be spent unpacking what constitutes “better than reality.”Turns out, figuring out what’s real is not easy and Sam Altman is unqualified to comment on it in a serious way. The question itself is almost always a bad choice even in rhetoric. In an interview, the question gets rolled out to pretend the interview is taking place in a bizarro world where a technology executive might have something interesting to add to the debate. Unsurprisingly, they never do.It’s not the lack of knowledge alone that makes these conversations so tiresome. It’s not even an unwillingness to admit ignorance: it’s the lack of awareness that there’s already a conversation.

At what price would you consider the product to be so expensive that you would not consider buying it? (Too expensive) At what price would you consider the product to be priced so low that you would feel the quality couldn't be very good? (Too cheap) At what price would you consider the product starting to get expensive, so that it is not out of the question, but you would have to give some thought to buying it? (Expensive/High Side) At what price would you consider the product to be a bargain—a great buy for the money? (Cheap/Good Value) The cumulative frequencies are plotted, and PSM advocates claim interpretive qualities exist for any intersecting of the cumulative frequencies for each of the four price categories.

In Against the Grain (2017), the anthropologist James C Scott makes the case that we should consider ‘praising collapse’, given the improvements in human welfare it can lead to.

Which is why we see both heights and wages rise after the Black Death in the 14th century. Women’s heights appear to have decreased, but this is likely due to an earlier onset of menarche (the first menstrual cycle, which slows growth). Earlier menarche often occurs when girls experience better living conditions with more resources, counterintuitively resulting in lower long-term height. Hence the decreasing height of women is likely an indicator that they too were benefitting after the Black Death.

It’s a surprising trend found even in perhaps the most famous imperial golden age of all: the Roman Empire, which arose after the Late Bronze Age collapse. When Rome was sacked by the Visigoths in 410 CE, St Jerome mourned that ‘when the bright light of all the world was put out … the whole world perished in one city …’ Jerome gave voice to a view that has lasted for millennia: Rome’s fall as an epic catastrophe and setback for human progress. Yet the skeletons of its subjects paint a different picture. Before Rome’s rise, people across the Italian peninsula were growing taller, but this slowed dramatically under the empire: citizens were 8 cm shorter than they might have been if this prior growth had continued. Even during Rome’s golden age, those who lived beyond the empire were taller. There is a truth to the trope of the hulking, muscle-bound barbarian. After the fall, skeletons grow taller across continental Europe, and dental caries and bone lesions decrease.

Societal downfalls loom large in history and popular culture but, for the 99 per cent, collapse often had its upsides

n an entirely PSI-based college, students might finish in two years, maybe sooner,and learn a good deal more. Imagine what would happen if the entire educational system werePSI-based: huge numbers of people, most still in the throes of puberty, might be graduating fromcollege-an unsettling thought for many educators. Indeed, PSI represents a threat to theeducational system and its guardians

Some teachers resist active learning methods like cold-calling out of fear that it willmake students uncomfortable – even though research has shown that cold-calling notonly heightens engagement but also increases voluntary participation and comfort overtime (Dallimore, Hertenstein, & Platt, 2013)

That said, if a student is serious about developing their talent to a high enough level to build acareer around it, then competition is a reality that they must eventually face. In talentdevelopment, anyone who seriously attempts to reach any level of success in a sport, instrument,etc, knows that they have to work really hard and compete against other people (who are alsoworking really hard) for limited positions. Mathematics is no exception. There is a limitednumber of professorships available for mathematics and related disciplines, and outsideacademia, there is a limited number of positions available for jobs that involve solving hardproblems using advanced mathematics

Students usually stop taking math classes once they amass a critical number of knowledge gaps.The usual sequence of events starts with students trying to imitate procedures cookbook-style,without really understanding what’s going on, because they can’t intuitively grasp any of thenew material that they’re being taught. Soon after that, they find themselves unable to solve anyproblems that involve critical thinking or many steps.

However, many critical traits underlying expert performance have been shown to have asignificant genetic component:

By the time students reached 8th grade (AP Calculus BC), the separation was extremelyclear: some students were still absolutely knocking it out of the park while otherstudents were struggling.

Myth: Everybody can learn every level of math.Reality: Most people can learn basic math like arithmetic and some algebra – but beyond that,higher levels of math become increasingly abstract and technical, and fewer people have thecognitive resources to learn it quickly enough to make a career out of it, much less get to thatpoint relatively early in their lives.

The myth is that different people need the same amount of practice but in different forms– whereas the reality is that different people need the same form of practice but in differentamounts.

Everyyear, countless first-year college students decide to major in aerospace engineering orastrophysics or some other math-heavy subject, only to have that dream crushed when theyrealize they can’t even pass an entry-level math course like Calc II (not even with the help of atutor).

What Happens in the Absence of Accountability and Incentives| Tragedy of the Commons

The Testing Effect (Retrieval Practice) – to maximize the amount by which yourmemory is extended when solving review problems, it’s necessary to avoid looking backat reference material unless you are totally stuck and cannot remember how to proceed.

Armed with this information, if your goal is to maximize your depth within a talent domain,then the optimal rational strategy is a greedy approach:1. Augment your knowledge base with all the examples and problem-solving experiences inthe intended direction, as quickly as possible.2. Upon reaching the edge of human knowledge in that direction, and only then, switchover to creative production.Creative production is a substantially less efficient means of acquiring skills within a talentdomain, so you want to save it for the end when it’s the only way to continue moving forward.

In 1927, a psychologist named Truman Lee Kelley identified a curious problem that continues to plague educational research nearly a century later. He called it the "jangle fallacy": the tendency to assume that two things are fundamentally different simply because they have different names. It's the conceptual cousin of the "jingle fallacy," where we mistakenly believe two things are identical because they share the same label.

Marshall McLuhan, the great 20th Century Canadian Philosopher of Media, wasn't just prescient—he was prophetic. His declaration that "the medium is the message" didn't merely define 20th-century media theory; it has become the skeleton key to understanding our AI-transformed future. In McLuhan's world, form overshadowed content. In our world, AI is obliterating the boundaries between forms altogether.Welcome to the age of Liquid Content—where information flows seamlessly between mediums, transforming its shape while preserving its essence. What was once fixed—text, audio, video—now exists in a state of perpetual potential, ready to materialize in whatever form serves the moment.

There are four cards, a simple rule, and all you've got to do is to work out which cards you need to turn over to see if the rule has been broken. That's got to be easy, right? Well maybe, but the Wason Selection Task, as it is called, is one of the most oft repeated tests of logical reasoning in the world of experimental psychology. So let's see how you do.

“Quantized” means something like “we made it smaller by rounding off the numbers, which reduces accuracy a bit; it will run on smaller systems but won’t be as good.”  Mathematicians will tell you some gobbledygook about matrices and floating point precision, just say “that’s really interesting” and then act surprised and point behind them and say “look, is that a tensor?” and then run away when they turn their back to you.

Poetry is no less a mystery than anything else onearth. One or two felicitous lines can hardly stir ourvanity, since they are but gifts of Chance or of theSpirit; only the mistakes belong to us. I hope the readerwill discover something worthy of his memory in thesepages. In this world of ours beauty is quite common.

Firebrand British parliamentarian Tony Benn knew a thing or two about power. If we want to hold our leaders to account, he suggested, we should ask the following five questions: “What power do you have? How did you get it? In whose interests do you exercise it? To whom are you accountable? And how can we get rid of you?”

In the broader economy, AI will help reduce pressure on energy demand. It will enable the optimization of freight and delivery networks, reducing congestion and fuel use. It will help optimize the utilization of infrastructure, reducing pressure on construction. Predictive maintenance will reduce costly and unnecessary repairs. Optimization of design and AI-driven new materials will reduce materials use across manufacturing.

In 2007, tasked with reporting to Congress on the environmental implications of the internet, the Environmental Protection Agency (EPA) noted that US data center power use had doubled between 2000 and 2006 to around 1.5% of total power use and raised the specter of it doubling again by 2011, significantly slowing the retirement of the US’s aging fleet of coal-fired power stations. It didn’t happen. Companies began shifting operations to the Cloud, achieving over 90% efficiency gains from a combination of new servers with bigger and faster chips, economies of scale, and better infrastructure. The average Power Usage Effectiveness (PUE) – the ratio of total power used in a data center to the power used by its servers – dropped to 1.5 in 2021 from 2.7 in 2007, with the best data centers now delivering PUEs as low as 1.1. In 2011, data centers were still using less than 2% of total US power.

Perhaps efforts like the IndieWeb Carnival and 32bit Café’s events might be better termed “community syndication” instead of “community blogging”? Then again, maybe the name doesn’t really matter as long as there’s more of it for those who want it.

Female velvet spiders exhibit a remarkable type of maternal care unique among arachnids. Upon the birth of her brood, the mother spider liquefies her internal organs and regurgitates this material as food. Once her capability to liquefy her insides is exhausted, the young sense this and consume the mother.

n the wake of World War I, early management scientists associated with the “human relations” movement at Harvard Business School began to apply the language of “skills” to interpersonal encounters. As Elton Mayo, a key figure in the team, saw it, workers were blaming their employers for workplace discontent. In his view, their real antagonist was the loneliness of life in the industrialized modern city, which exacerbated the alienation they felt at work. If management cultivated community feeling in the workplace, the logic went, workers would stop demanding power. Soon, manipulating workers — and preventing collective action — began to be seen as a matter of skill. “Management should introduce in its organization an explicit skill of diagnosing human situations,” advised Fritz Roethlisberger and William J. Dickson in their 1939 volume Management and the Worker. Executives gained a way to describe their own social abilities as profitable commodities. Businessmen weren’t merely flaunting social graces or providing care — what their wives did at home — but demonstrating serious expertise.

Week after week, I would sneak glances at the bubbly radioactive glow of each soda, biding my time until I had the chance to taste. When I did, I was born anew. The abrasive carbonation and bright flavors inspired my own fizzy beverages, even as Italian sodas fell out of favor. Reveling in nostalgia and the economic liberties of adulthood, I bought a bottle a few years ago. Frankly, it was trash.Gone were the fruity notes that would linger on the last ice cubes after I finished an Italian soda in a single continuous gulp, and in their place was a dull sweetness. Torani is now a shelf-stable simple syrup with an aroma instead of a flavor. My hunch was further confirmed after trying a "dirty soda" at a Swig copycat. I love sugar too much to accept when it is used as a blunt instrument instead of a gentle buttress to structure how you experience flavor.My disdain grew as I started to make more syrups, and I took my syrup shelf worldwide. There is no limit to what you can steep or infuse into a simple, and any jam/jelly is a splash of warm water away from becoming a drink syrup, btw.

Tech employees, despite their progressive leanings, seem largely allergic to unionizing for reasons that mostly seem to boil down to "not enough people in SF/SEA/NYC are worried this will some day come for them."

In Anxiety is the Dizziness of Freedom, people invented a new Prism(™) that allows them to talk to copies in nearby universes. Chiang treats this technology as an interesting tool to explore his favorite questions of free will, determinism, and what does it mean to be a person, etc.I think he doesn’t understand the power of this singularity-level technology he just introduced.The ability to send bits across parallel universes is just insane in terms of economic and experimentation value. For example, pharmaceutical companies can do $100 billion trials for all sorts of novel drugs, and trade the results of this information with their clones in other universes. Massive R&D projects in general can be done in parallel across different universes, as long as the results can be compressed in enough bits to be sent over these prisms.More than that, you can now have not just individual or small-group level studies but societal-wide or even multiverse-wide ones! Wondering whether raising interest rates can cause recessions? Just conduct RCTs across multiverses (and if they can’t coordinate on RCTs, even the observational data would be insanely useful)!While the main characters work through existential crises and their mundane, human worries, the rest of society should be accelerating at breakneck speed toward doom, utopia, or something else equally fascinating. Chiang doesn't explore this, even as backdrop.

pairs of non-synonymous phrases where the words in one phrase are each synonyms of the words in the other

The classicist M. L. West gave this construction a name: the augmented triad. It appears to be very old, and not just because it’s found in Homer. It’s also found in Vedic poetry (that is, composed in an early stage of the Sanskrit language) as well as Celtic and Germanic verse. Some examples, courtesy of M. L. West’s book Indo-European Poetry and Myth (pp 118–119).Vedic Sanskrit:Tváṣṭā, Savitā́, [suyáma Sárasvatī]‘Tvashtr, Savitr, and [easily-guided Sarasvati]’ (Rigveda 9.81.4)Old Irish (Celtic):trí meic Nóe nair cech neirt:Sem, Cam, [Iafet aurdairc.]‘Three sons of Noah, of every (kind of) strength:Shem, Ham, [Japheth the glorious]’ (Lebor Gabála Érenn, 189–190)Old Norse (Germanic):Vara sandr né sær né [svalar unnir].‘There was not sand nor sea nor [the cool waves].’ (Vǫluspá 3)The presence of the augmented triad in not only Greek, but in Celtic, Germanic, and Vedic Sanskrit (see what I did there?) means that it was likely inherited from the common ancestor of all four.

AI can already explain Gleam to me very clearly. It doesn’t explain it exactly the way I would and it hallucinates and false starts constantly. But it is certainly good enough to get the point across. And it has the advantage of explaining things to a reader the way that reader wants to understand it. People who think my writing is too familiar or too verbose (the most common complaints in the relatively few negative reviews I’ve received) may be able to get the AI to explain it in a way that works better for them. But people who DO like my style can literally say “tell me how to use Gleam in the style of Dusty Phillips” and share links to articles this blog or some of the chapters of the LazyVim book. I tried it. It is super uncanny valley. It’s not exactly me, but I won’t say it’s not not me either.

The ILC developed a database of tens of thousands of dissidents living in the Eastern Bloc and carefully targeted the books it sent. Paris became a critical hub where travelers could load up; the Librairie Polonaise, a venerable Polish bookshop, let visitors take several volumes for free on their way home. Its manager sent a bill to the CIA, which by the mid-1980s was covering half of the store’s inventory.

Learning takes effort; if your practice is too easy it probably isn’t effective. Pedagogical methods that feel hard to students are most effective, and methods that students enjoy are least effective.

The Math Academy Way of teaching is to provide a very tight scaffold with small steps when students are first introduced to a skill and gradually remove it when practicing so the student does more on their own and must use recall. If a student is unable to come to the correct answer, [either the student is lacking in care for execution or] requisite skills are not understood. The Math Academy software knows the skills that make up each type of question and can find problem areas and target reviews of that material.

You have more ingredients available to you at the grocery store than anyone in history, but you can only cook with what’s in your house. You can’t be creative, synthesize things, make connections, without the requisite knowledge. Recall the sweet satisfaction of spotting a reference in a book; you can’t make that connection if you aren’t familiar and comfortable with the referenced text.

I thought, “I have a deep understanding of trig, and so if faced with e.g. sin(-θ), I can visualize the triangle in my head and realize that it’s equal to -sinθ. I will re-derive these identities from scratch during a test.” But it never really worked out that way. This is the trig equivalent to decoding a sentence letter by letter on the fly. It takes a lot of memory and brain power, and you can’t spot these patterns in a problem if you don’t know the identities. I always felt it was unfair when a solution relied on a double angle identity “trick.” My virtuous and pure, unmemorized knowledge didn’t seem to be sufficient during the exam

Royal jelly This is the mix of compounds that worker bees generate and feed to their queen. Queen bees live for up to 20 years, while workers (with the same genome) die after just a few weeks. There is one study (Inoue 2003) in the DrugAge database feeding royal jelly to mice, and yes, at higher dosages, 15 mice lived an average 25% longer. As you can see below, early mortality was greatly reduced, but maximum lifespan wasn’t affected.

I go downstairs to use the spin bike in the gym at Ryan’s apartment complex. The instructor on the screen looks at me blankly and says, in a monotone, presumably intending to motivate me: “No one’s going to care about you as much as you do.” She seems taken aback by her own statement. She says, “Well, I care about you.”

The suspicion that all this is élite anxiety in the face of a democratizing mediascape deepens when you consider what the attentionistas want people to focus on. Generally, it’s fine art, old books, or untrammelled nature—as if they were running a Connecticut boarding school. Above all, they demand patience, the inclination to stick with things that aren’t immediately compelling or comprehensible. Patience is indeed a virtue, but a whiff of narcissism arises when commentators extoll it in others, like a husband praising an adoring wife. It places the responsibility for communication on listeners, giving speakers license to be overlong, unclear, or self-indulgent. When someone calls for audiences to be more patient, I instinctively think, Alternatively, you could be less boring.In a sense, what attention alarmists seek is protection from a competition that they’re losing. Fair enough; the market doesn’t always deliver great results, and Hayes is right to deplore the commodification of intellectual life. But one can wonder whether ideas are less warped by the market when they are posted online to a free platform than when they are rolled into books, given bar codes, and sold in stores. It’s worth remembering that those long nineteenth-century novels we’re losing the patience to read were long for a reason: profit-seeking publishers made authors drag out their stories across multiple volumes. Market forces have been stretching, squashing, spinning, and suppressing ideas for centuries. Realistically, the choice isn’t commodified versus free but which commodity form suits best.

Monahan considers how this intersects with the AI-induced existential crisis currently felt by creatives across most culture industries, from art to Hollywood to media, concluding that “there may be one skill you truly cannot outsource to AI: knowing how to attend and host an event. Increasingly, that is a rare and coveted skill.”

Also —taste is not a reliably differentiated skill. We don’t know whether, at some point, AI advances will be able to effectively generate the semblance of idiosyncratic taste. But we can be quite sure that AI will never be able to replicate the experience of meeting friends for a 6pm happy hour cocktail and ending up on an unplanned New York City Night™ adventure that involves sneaking into a party at the Museum of Natural History

I am sometimes asked why I became a historian. A big part of it is that I just really like looking at old books.

In the 1840s, it would cost you $2.50 to buy a Dickens novel in London. In New York, that same novel would cost you six cents. This is partially why Dickens decided to tour the United States, in 1842: to urge Americans to support international copyright laws, so he would be paid for the copies of his novels Americans bought. What he discovered was that a key reason the tour was so successful—it was packed, night after night, with eager readers—and a key reason reason Dickens was so famous stateside—was because his books were so cheap and plentiful. When he returned home, frustrated at his inability to convince Americans, he wrote a book about his experiences. It was (of course) pirated and reprinted in the US. It sold 50,000 copies in three days. Dickens received $0 for those sales.

When the international copyright act was finally passed in 1891, it included a “Manufacturing Clause” that required all books by foreigners be set domestically, using American type. So American publishers had to pay royalties to foreigners, but now all books had to be made in the USA (so it would have been illegal to do what many publishers do today: send books to China or other countries where printing is cheaper, to be produced and shipped back).

But the law prohibited authors from copyrighting ideas—they had to have a book to protect. They were also not allowed to set the prices for those books higher than what would be considered reasonable.

Few today would lament that Americans were able to buy cheap versions of Emma soon after it was published, but Austen received no royalties from sales of her novel over here.

And yet despite the globalization of the industry, the steep rise in imports had failed to devastate American producers. Instead, domestic production and employment from the late 1960s to the late 1970s had risen together with imports.

The Big 3 were not avoiding all unions. In a 1950 speech about Ford’s new investments to the Buffalo Chamber of Commerce, Henry Ford II praised Buffalo labor leaders as forward-looking, and Buffalo itself as “the place where an organization can get work done — where good production cooperation is possible.”[7] It was specifically the local unions of Detroit that compelled the Big 3 to find new places to set up factories.

So globalization couldn’t kill Detroit because Detroit was already dead. Or at least dying. And it really was a story of decline in Detroit almost exclusively. From 1950 to 1980, auto employment even in most of the Rust Belt states expanded. The gains in Ohio, Indiana, and Illinois exceeded those of any southern state.

So much for Myth 1, the persistent notion that the American auto industry has collapsed. With output at all-time highs and employment hardly lower than in the time before NAFTA (the biggest globalizing event for the auto industry in recent decades), the evidence goes hard in the other direction.

I first noticed that the same harms that won high school policy debate rounds tended to be the ones that drew cult followings in late 2010s Twitter. Both environments reward rhetorical fearmongering with no care for action or implementation.

As a result, any use of ‘obsolete’ things is labeled as nostalgic because they have exhausted their time of economic value. From the perspective of constant production and consumption, using these things doesn’t make sense. From the perspective of Permacomputing, it is impossible to long for and romanticize a time when computer technology was better because such a time never existed. The moment when the economic lifecycle of a computer ends is celebrated because only then can its socioeconomic context be reclaimed.

Whereas, when you hear an idea, you can usually repeat the idea but not with the same authority.

The primary audience gets something out of this sequence of events: they get power. This is the great secret of writing in public: the writer and primary audience both put in effort (to pack and unpack the idea); and they jointly reap the rewards, which is the legitimacy earned when the idea gets subsequently retold verbally to the wider secondary audience.

One lesson hiding in plain sight here is that most of the audience of any successful post does not actually read it. They are told it by someone who did read it. There’s a primary audience who carefully reads the piece and does the cognitive work of “restructuring their consciousness” (see Walter Ong, Orality and Literacy) around good writing. And then there’s a secondary audience, who are re-told the content, either verbally (including group chats, podcasts, Youtube) or in other oral formats like Twitter.

I hate PHP too, but I'm using it more often for fully vibe coded apps because LLMs are very good at it.

Hindu weddings have the saat phere (seven circles around the fire), but what those seven vows actually say varies wildly by region, caste, and the pandit's mood. One couple discovered post-wedding that the wife had promised to "bear ten sons" in the Sanskrit version while the English translation they'd requested said "support each other as equal partners." They're still married. The wife now introduces herself as "defaulting on my contractual obligations," but since no one has called to collect, they're probably fine!

There is, I think, a compelling case to be made that the American political tradition thinks of free speech not as the basis of a free press but as an instance of it. A newspaper is not a giant person; a person is more like a little newspaper. If this is so, then the vast majority of Americans have low budgets, limited circulation, and few dedicated readers, even in the age of social media. In other words, speech is a resource, not an inalienable property, and as such it is subject to the same regimes of theft, privatization, and accumulation that have swallowed up the labor of workers and the fruits of the earth.

Hence, Tocqueville wrote in Democracy in America, “the prejudice that repels Negroes seems to grow as Negroes cease to be slaves, and inequality is engraved in mores in the same measure as it is effaced in the laws.” This is a very different story! Williams claims that a decrease in economic inequality has made Black people more resentful of those fewer privileges still denied them. But Tocqueville’s shrewder observation is that rising legal and economic equality indirectly produces social inequality as the dominant class is forced to confront the prospect of forfeiting its dominance. This is why prejudice seemed to increase in the Northern states. Both Blacks and whites, being formally equal, now perceived informal equality as a concrete possibility.

In 1994, Italian physicist Cesare Marchetti suggested that people have always endured commutes of an hour a day, half an hour each way, on average. Improvements in urban planning and transportation haven’t shortened our travel time; they’ve just permitted us to live further afield.

Reading slop text when I think I’m reading real text is exhausting: since I am not on the alert for hallucinations or irrelevancies, every turn of phrase that seems out of place causes me to wonder why that phrase it there and what am I missing when in reality, such questions are ill formed: that was just a phrase composed by accident that sounds good but actually is devoid of much intent at all.

What about applying a set of rules to multiple elements, as you do in style sheets with class rule sets, what does Tailwind offer for that? Nothing. The answer is nothing. Tailwind's official recommendation for this is to ignore everything you've ever learned about constants and duplicate your class names. Then simply remember to always use multi-cursor editing any time you touch them:

There is no secret herbal medicine that prevents all disease sitting out in the open if you just follow the right Facebook groups. There is no AI coding revolution available if you just start vibing.

When I have had engineers who were 10x as valuable as others it was primarily due to their ability to prevent unnecessary work. Talking a PM down from a task that was never feasible. Getting another engineer to not build that unnecessary microservice. Making developer experience investments that save everyone just a bit of time on every task. Documenting your work so that every future engineer can jump in faster. These things can add up over time to one engineer saving 10x the time company wide than what they took to build it.

I'd be looking at some code at my desk, and it made no sense. Why would anyone write it like this? There's an obvious and cleaner way to approach the same problem.So I'd go down the hall to the person who wrote it in the first place and start asking questions...and find out that I didn't have the whole picture, the problem was messier than it first appeared, and there were perfectly valid reasons for the code being that way. This happened again and again. Sometimes I did find a real flaw, but even then it may have only occurred with data that wasn't actually possible (because, for example, it was filtered by another part of the system). Talking face to face changed everything, because they could draw diagrams, pull out specs, and give concrete examples.I think that initial knee-jerk "I've been looking at this for ten seconds and now let me explain the critical flaws" reaction is a common one among people with engineering mindsets.

In other words, AI will not enable the creation of quality translations for people who previously lacked that ability. That part still requires a human feel for the linguistic and cultural elements of the translation. But for those who are just looking to get a rough but passable translation (say, for research) it should work most of the time. And for those who would love to create quality translations but face huge opportunity costs and zero financial incentives, AI could lead to new possibilities.The deeper risk is not that AI will replace historians or translators, but that it will convince us we never needed them in the first place. A tool that outputs polished, confident language with no sense of ambiguity or context is appealing to people who think facts will save us. But there is a vast difference between facts and truths. If we come to treat them as interchangeable, we cede interpretation to the machines and narrative power to those who design them. So maybe Microsoft was right after all, just not in the way they think. Historians and translators may be the first to go not because their work is easy to automate, but because the interpretive element of their labor has always been invisible or, when made visible, dismissed as odious human bias. AI will replace them only in the minds of people who never understood what they were doing in the first place. Which, given how things are going, may be enough.

In telecommunications, squelch is a circuit function that acts to suppress the audio (or video) output of a receiver in the absence of a strong input signal.[1] Essentially, squelch is a specialized type of noise gate designed to suppress weak signals. Squelch is used in two-way radios and VHF/UHF radio scanners to eliminate the sound of noise when the radio is not receiving a desired transmission.

You should anticipate every objection someone would have and preemptively show why it's invalid, so that the reader never even thinks to bring it up.

Monthly Packet of Evening Readings, Volume 19, (London: Mozley and Smith, 1875), page 213: —and the rule for sowing is, ‘One for the mouse, one for the crow, One to rot, and one to grow’

LiveJournal had a division of domains: There were personal blogs and community pages, and anything you posted on a community page existed within the context of that page. It wouldn’t randomly get boosted to some unrelated person’s feed, who might then feel at liberty to comment something like “what a weird hobby” or “this post is fucked up” or some other unsolicited insulting comment. There was not yet the sentiment of the “public square.” I hate that phrase: the “public square” where we all get together and talk. I don’t want to talk to most people in the square. I want to find a nice park or dive bar off a side road far away from the square where I have things in common with other people at the venue.

Starving Saints, a fantasy-horror taking place inside one besieged castle that is slowly starving to death, until something–a saint and her attendants–comes to relieve them of their hunger.

During the Cold War, U.S Army cryptologist Lambros D. Callimahos devised a “Republic of Zendia” to use in a wargame for codebreakers simulating the invasion of Cuba. (Callimahos’ maps of the Zendian province of Loreno are below; click to enlarge.) The Zendia map now hangs on the wall of the library at the National Cryptologic Museum. The “Zendian problem,” in which cryptanalysts students were asked to interpret intercepted Zendian radio messages, formed part of an advanced course that Callimahos taught to NSA cryptanalysts in the 1950s. Graduates of the course were admitted to the “Dundee Society,” named for an empty marmalade jar in which Callimahos kept his pencils.

Optician Sans is a fully functional typeface and a continuation of the historical Snellen and Sloan letters. Optically adjusted for readability to be used as a fully functional display typeface.

It’s based on my book Ologies and Isms: Word Beginnings and Endings, published by Oxford University Press in 2002.That went out of print in 2008 and I immediately made it available as a free service.

Unlike many programmers, I love code review. I find that it is one of the best parts of the process of programming. I can help people learn, and develop their skills, and learn from them, and appreciate the decisions they made, develop an impression of a fellow programmer’s style. It’s a great way to build a mutual theory of mind. Of course, it can still be really annoying; people make mistakes, often can’t see things I find obvious, and in particular when you’re reviewing a lot of code from a lot of different people, you often end up having to repeat explanations of the same mistakes. So I can see why many programmers, particularly those more introverted than I am, hate it. But, ultimately, when I review their code and work hard to provide clear and actionable feedback, people learn and grow and it’s worth that investment in inconvenience. The process of coding with an “agentic” LLM appears to be the process of carefully distilling all the worst parts of code review, and removing and discarding all of its benefits. The lazy, dumb, lying robot asshole keeps making the same mistakes over and over again, never improving, never genuinely reacting, always obsequiously pretending to take your feedback on board.

Every day I spawn in. Emerge wriggling out my skibidi bolus of slime. Whence and where? Lol. Idk. Vibes here be mad shady fr. Shit is not aesthetic. Shit is not bussin. Shit is burned-out cars piled in barricades across the street. Shit is THE END IS NIGH scrawled across bridges. Shit is roofs caved in, windows boarded, thin trees already rising out the wreckage, with roots that slip through gaps in the brickwork to return the brief work of man to the senseless rubble that came before. This sus ahh Ohio ahh realm is my crib. Damn, bitch, I live like this

No apps, no texts No stranger danger No surprises Tin Can is a telephone club for friends to chat voice-to-voice during the no-smartphone years. Only approved contacts can put calls through to your Tin Can. Easily manage who's allowed in the club via the parents-only companion app. Set up a quiet hours schedule or enable Do Not Disturb within the app.

If you've ever wondered why scientists scramble to publish in Nature, Science, or Cell, think of them as the holy trinity of scientific prestige, each with its own personality. Nature and Science were established in the late 1800s—Nature published by the Brits and Science by the American upstarts. Cell is the newcomer; established in 1974 in Cambridge, Massachusetts.In terms of temperament, Nature is the flashy cosmopolitan—broad, attention-grabbing, and often favouring "sexy science" that makes headlines (black holes, CRISPR, or ancient human fossils). Science is the serious, translationally-minded intellectual—rigorous, respected, and slightly less obsessed with media hype (CRISPR before it was famous). Then there's Cell, the molecular biology workhorse, where groundbreaking discoveries in pre-clinical work are dissected in exquisite mechanistic detail (if you love signaling pathways, this is your jam). Publication in any of these journals is the scientific equivalent of winning an Olympic medal (or at least making an Olympic team, depending on your position in the author list).One tier down, you'll find specialty journals like Neuron, Nature Neuroscience, and The Journal of Clinical Investigation (and reams of others for specific fields), which publish longer, more methodically comprehensive studies. I tend to prefer reading papers from these journals as they provide greater detail and present more fully developed work. These papers may not be as "hot off the press" or media-friendly, but they often demonstrate greater scientific rigor and better withstand the test of time.Meanwhile, in medicine, The Lancet and The New England Journal of Medicine (NEJM) tower over most others with impact factors of 98.4 and 96.2, respectively (generally, the higher the impact factor the greater the prestige). This reflects their enormous readership—there are far more medical doctors than PhDs. But at the top, it’s CA: A Cancer Journal for Clinicians, with a staggering 286.13 impact factor, a reminder of cancer’s toll and where our research priorities and funding are concentrated.

Ultimately, the valuable work done inside companies is the work where human accountability is the primary bulwark against the O-ring problem. It acknowledges that complex decisions and accepted constraints need to have human owners, and that relationships between humans (notably, but obviously not exclusively, in reporting relationships) are ultimately the valuable stuff that companies are made of. Okay, so, what does this have to do with AI agents? Well, my suspicion is as follows. One, combinations of AI and Human work could be notably vulnerable to the O-Ring problem. Their utility in executing a chain of tasks is only as valuable as the weakest link, very obviously. (If there’s one hallucinatory or badly-interfaced step anywhere that isn’t caught, you have to throw out the whole thing.) Firms have self-selected for solving O-Ring problems, over decades. That’s just what firms are.  So I think you could construct a bear thesis that the closer a workflow is to core to of an average business, the less suitable agents are to help,for reasons that are readily explained by Coase’s theory to the firm. Maybe not for the very best companies, who truly master their internal agents, but for the average company to whom AI Agents doing real work is essentially a kind of outsourcing. I’m not quite as pessimistic as that, but I agree with Tyler Cowen’s post that any hope for 10% GDP growth or whatever to emerge out of existing firms (which, even in a very disruptive environment, make up most of GDP) is too hopeful.

Production is broken down into n {\displaystyle n} <img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/a601995d55609f2d9f5e233e36fbe9ea26011b3b" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.395ex; height:1.676ex;" alt="{\displaystyle n}"/> tasks. Laborers can use a multitude of techniques of varying efficiency to carry out these tasks depending on their skill. Skill is denoted by q {\displaystyle q} <img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/06809d64fa7c817ffc7e323f85997f783dbdf71d" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.07ex; height:2.009ex;" alt="{\displaystyle q}"/>, where 0 ≤ q ≤ 1 {\displaystyle 0\leq q\leq 1} <img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/5c4dd7e354bddf3131e391b64a7db0be9b4ac572" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:9.591ex; height:2.509ex;" alt="{\displaystyle 0\leq q\leq 1}"/>. The concept of q {\displaystyle q} <img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/06809d64fa7c817ffc7e323f85997f783dbdf71d" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.07ex; height:2.009ex;" alt="{\displaystyle q}"/> differs depending on interpretation. It could represent the probability of a worker successfully completing a task, the quality of task completion expressed as a percentage, or the quality of task completion with the condition of a margin of error that could reduce quality.[3] Output then equals the product of the q {\displaystyle q} <img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/06809d64fa7c817ffc7e323f85997f783dbdf71d" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.07ex; height:2.009ex;" alt="{\displaystyle q}"/> values of each of the n {\displaystyle n} <img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/a601995d55609f2d9f5e233e36fbe9ea26011b3b" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.395ex; height:1.676ex;" alt="{\displaystyle n}"/> tasks together and scaling it by a firm specific constant, B {\displaystyle B} <img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/47136aad860d145f75f3eed3022df827cee94d7a" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.764ex; height:2.176ex;" alt="{\displaystyle B}"/>. This scalar is positively correlated with the number of tasks. The production function is: F(qi, qj) = Bqiqj The important implication of this production function is positive assortative matching. This can be seen in a hypothetical four-person economy with two low skill workers (qL) and two high skill workers (qH). This equation dictates the productive efficiency of skill matching: qH2 + qL2 ≥ 2qHqL By this equation total product is maximized by pairing those with similar skill levels.

But ASU has ranked well in terms of preparing students for life after college, and was named a Best Value College by The Princeton Review this year. [Edit: According to this analysis, ASU graduates are apparently also overrepresented at high-profile tech companies and banks.]

Instead of charging thousands of dollars to enroll in ASU and take an online course, anyone can take classes for as little as $25 to start. Students only pay the $400 tuition if they want to receive academic credit and count it towards their transcript. This gives people flexibility to experiment with new classes without worrying about grades: if you don’t like your grade, you don’t have to count the class. Instead of having to apply to ASU first, then, a student can start by doing the coursework, then enroll when they’re ready, with part of their degree already completed. This feels well-aligned to me with tech’s focus on prioritizing output over credentials.

Implicit in their pragmatic approach is a focus on community. ASU calls this leveraging place, and it’s one of the eight design principles underpinning the New American University model, along with social embeddedness. One way of understanding town-gown tensions, for example, is as a signal that the university – an institution of public service – has failed at its mission. So ASU built research centers that focus on regional issues, such as the Stardust Center for Affordable Homes and the Family, which “works toward solutions in providing affordable housing to support families as the backbone to stable communities.”

Their four-year graduation rate increased more than 20 percentage points during this reform period and is well above the national average, which is especially remarkable considering that ASU, by increasing access, also accepted many more students who are at higher risk of not graduating.

There’s a reason why I prefer being in tech, even though I might have otherwise found a comfortable home in academia: 1) everything moves faster in tech, and 2) people in tech actually care about making ideas happen, not just talking about them. From this lens, my experience in tech has been more uniformly positive, whereas my experience with academia is that it seems to have become an uneven, unreliable source of great new ideas, while still cashing in on some ambiguous notion of “prestige.”

The best way to understand Punjabi weddings is to realize that every single thing that's happening fulfills one of three goals.Meeting the religious obligations of marriage, as defined by the Vedas.Merging two families together and building a joint community.Showing off as much as humanly possible.You do your vows by walking around a sacred fire seven times while a pandit recites prayers in Sanskrit. That's category one. But you take those vows while dressed in the most ornate clothing you will ever see, decked out in jewelry fit for royalty. That's all category three. O, and while those vows are going on, there's a low key capture-the-flag game happening with the groom's shoes, with the bride's family members attempting to steal them from the groom's family members. That's, of course, category two.Every ritual, event, and tradition is like this, multiplied over at least 5 days.

Incidentally, this is an underrated benefit of AI-assisted coding. When I am rested and focused I am a fast and effective programmer. But I don’t spend all my time rested and focused. When I’m tired, or mentally frayed after a long day of work, or just distracted, I can’t really sit down and fix a bug (at least not without a serious mental effort). However, I can fire off an agentic coding session. Checking the LLM output - particularly for a bugfix, which is typically a small diff - is much less cognitively taxing than having to do it myself. So it’s not so much “AI makes me more effective at my normal work”, as it is “AI means I can work at times that I otherwise would be able to get nothing done at all”. It doesn’t really matter if it’s 19% or 50% or 200% slower than my normal working pace, since any amount of progress is better than zero.

I wished in that moment, while the thought about six-packs formulated in my head, that I had shut the fuck up, or in right-wing parlance: I wish I had self censored. There’s a new poll floating around social media claiming that 43 percent of white men in the United States are “self-censoring their speech at work, with two in three zoomer bros (18-29) saying they are “too afraid to voice their opinions at work for fear of being fired.” Poor babies.

Research has definitively shown that pro-social behaviors are not just good for society, but good for the person adopting these behaviors. Researchers at the National Library of Medicine found in 2023 that a "positive correlation was found between pro-social behavior and psychological well-being." Trying not to be a total asshole in public can have a trickle-down effect too. "The bystander effect is interesting research that shows the situational factors of helping. People who feel socially responsible are more likely to help than people who do not feel social responsibility."

Though pickup trucks were the striking vehicle in just 5.6% of pedestrian and pedalcyclist crashes, they were involved in 12.6% of fatalities. SUVs were similarly overrepresented in fatalities relative to the proportion of their involvement in all crashes. SUVs struck 14.7% of the pedestrians and pedalcyclists investigated here, but were involved in 25.4% of the fatalities.

Results suggest that children are eight times more likely to die when struck by a SUV compared to those struck by a passenger car.

Nowe Ateny is the source of a few memorable and amusing "definitions", often quoted in Poland to this day: Horse: Everyone can see what a horse is. (pol. Koń jaki jest, każdy widzi) Goats, a stinking kind of animal. (pol. Kozy, śmierdzący rodzaj zwierząt) The humor was probably unintentional by the author.[citation needed] Rather, he did not see the benefit of defining the most common animals of the time and place for his intended audience. Furthermore, the entry for "Horse" does contain more detailed exposition beyond the initial "definition".[1] In modern Polish, the above definition of the horse is sometimes used as a colorful equivalent of the statement "the concept is more obvious than it appears to be from its more technical definition".

I'm now testing giving Claude my notes, a list of key concepts that I want to retain and then asking it to write the Anki cards. I'm optimistic about this approach because instead of treating the Anki cards as the artifact that I'm making, I'll be thinking about learning the thing and then outsourcing the creation of the Anki cards to a robot.

I rarely wait, because I'm juggling multiple projects. When one agent instance is working, I switch to another window. Sometimes it's a separate git worktree of the same codebase. Yes, context switching is tiring, but it also seems to help me overcome ADHD-related activation energy barriers? Over the years, there've been days when I just sit there staring at the IDE window, poking my brain with a stick saying "c'mon, do something" and nothing happens for an hour or more. I'm not planning my next move, I'm just dissociating. My executive function doesn't, like, function. Often. My own brain makes me wait long periods of time before it starts generating useful results. 😅 Maybe it's the cycling novelty that keeps me going? I enjoy task switching between prosing and coding. I enjoy finding that the model appears to have "read" everything—evidenced by it echoing my intent back in code or follow-up questions. I enjoy discovering that while I was in another window, new things happened in the background for me to review.

Here’s the heartening bit: More people than ever are traveling, and while, sure, the majority of those travelers are just following trends and lists, there is another group, a small cohort of self-aware travelers who are genuinely, deeply curious about the places they’re visiting, who desire to engage directly without being disruptive, who want to engage fully and “authentically” (that is: visiting people and places that haven’t twisted themselves for the sake of transient visitors (i.e., no renaming things “samurai spice”)).

The Goomba fallacy is a reasoning mistake. When there are two contradicting opinions in one internet community, some readers think that everyone in the community is stupid, because the opinions are contradictory. They do not realise that there are separate people posting in the community, with separate opinions and beliefs. In other words, two groups with contradictory views are perceived as one group that contradicts itself.

As you can see, things will sometimes get a bit personal here. The people that know me, know what I mean. Some of them, even some English people, still talk to me.

The new essential skill for junior people will be AI intuition. As AI becomes deeply integrated into workplaces, having a native intuition for how AI thinks, operates, and fails will be critical. Future junior employees will need to instinctively understand AI’s capabilities and limitations, anticipate where it will stumble, and proactively steer it back on track. They will become adept at managing multiple AIs simultaneously, switching context at each AI’s stopping point. They will know when to continue with a session or to just restart with a new agent instance. This intuitive grasp won’t just make them more effective, it will make them exceptionally valuable. Companies will depend on their judgment to bridge the gap between AI-generated output and human-level precision.

A member of Napoleon’s imperial guard, an elite soldier who was willing to grumble and groan if they believed they were being misused militarily, was called a “grognord.” Tabletop wargamers co-opted this term as a pejorative reference to a player who  exclusively favors older editions.

Thus, the polarization of employment seen in aggregate in Figure 12 is detected for both sexes,and proximately accounted for by three factors: (1) rising employment in non-routine cognitive task-intensive professional, managerial, and technical occupations; (2) rising employment in non-routinemanual task-intensive service occupations; and (3) declining employment in middle-skill, routine task-intensive employment in clerical, administrative support and production occupations. Although em-ployment in middle-skill jobs has fallen by considerably more among females than males between 1979and 2007 (15.6 versus 9.6 percentage points), the offsetting employment gains have differed sharply.For females, 85 percent of the decline in middle-skill jobs was offset by a rise in professional, man-agerial and technical occupations. For males, this share is 55 percen

The substantial declines in clerical and administrative occupa-tions depicted in Figure 12 are likely a consequence of the falling price of machine substitutes for thesetasks. It is important to observe, however, that computerization has not reduced the economic valueor prevalence of the tasks that were performed by workers in these occupations–quite the opposite.31But tasks that primarily involve organizing, storing, retrieving, and manipulating information–mostcommon in middle-skilled administrative, clerical and production tasks–are increasingly codified incomputer software and performed by machines.

One 1958 Canadian Banker piece, titled “A BankingCareer and Marriage” remarked that “it is now both customary and common-place for married women (except those with small children) to work in banksand elsewhere: : : : it has now become the custom for almost all married womenwithout children, and more and more mothers of older children to go out towork.”71 The growth of married women’s employment in the 1950s and eveninto the 1960s was concentrated among older married women (aged 45–64)

Yet, despite the cultural power of the stay-at-home housewife, married wo-men’s employment actually rose by 42 percent during the 1950s in the US. In1940, 14 percent of married women were in paid work, by 1960, 31 percent were,as were nearly 40 percent of all mothers with school-age children (and therewere similar patterns in Canada). Actual lived lives of women simply did notcorrespond to the stylized versions of women from this period

For instance, automatedteller machines (ATMs) and on-line banking meant job losses since fewer banktellers are needed when more customers use electronic banking options. Thus,unlike in earlier periods, the automation of clerical work negatively and dispro-portionately impacted the numerical representation of women. But it simulta-neously benefited men who often dominate newly created technical and pro-fessional jobs such as computer programming.95 In the US, the proportion ofbank workers in clerical jobs declined between 1975 and 1995, but the share inprofessional jobs increased from 9 percent to 18 percent. As a group, bank em-ployees can now be said to be more skilled, but in large part that is because theautomation of lower-skilled clerical positions means there are fewer jobs for less-educated workers than previously.96 However, the big picture is not merely oneof automation, deskilling, or even job loss. New kinds of jobs across a range ofskill levels are being introduced, for instance in data-processing and call-centerwork, as well as accounting and computer programming. Many of these new jobsplace considerable value in feminine-coded ‘people skills’.

Women and those with higher education levels have found it easier to adjust to these changes. They have become more likely to move into higher-paying, non-routine cognitive (‘brain’) jobs.

For the revitalisation of smaller cities to take place, cities such as Lancaster, PA, or Reno, NV, must be able to attract workers that are a good fit for their primary industries. Larger cities such as Seattle, WA, or Washington, D.C., therefore, generally shrink under the optimal policy, despite being more productive. They also see their housing prices fall and the wages of their remaining non-CNR workers rise.

The analysis estimates base transfers accruing to non-CNR workers on the order of $18,000 dollars, while CNR workers, whose wages are substantially higher, ultimately pay a similar sized amount. One natural interpretation of this base transfer is that of a universal basic income paid to all non-CNR workers and financed mostly by CNR workers. Importantly, because CNR workers gain from the optimal spatial policy, they do not mind making those transfers.