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117

Reflections on six months of fatherhood

from Jason Crawford [alt+shift+b] in life

To be fed from a spoon requires three distinct skills. First, you must open your mouth when the spoon approaches. Second, you must close your mouth around the spoon once it has fully entered the mouth (and not before). Third, you must swallow the food that remains in your mouth after the spoon is withdrawn, rather than spitting it out. I know this because my daughter, who is six months old, did not possess these skills two months ago. She learned rapidly—ah, to possess the neuroplasticity of the young—but there was a brief period when she literally did not know how to be spoon-fed. (Note that I have not begun to describe the skills required to feed oneself with a spoon, in part because she has not yet acquired them.) The first lesson I have received from fatherhood is that everything must be learned, or very nearly everything. Babies are born with a very small number of reflexes and instincts: to suck on whatever enters their mouth, to “root” around on mother’s chest to find the nipple, to cry when they are uncomfortable. Everything else is a mental step in a long, upward climb. In the first weeks, both the parents and the child are focused on digestion. The infant is essentially an alimentary canal with arms and legs. (The limbs are superfluous and indeed get in the way more than they help; if children were properly engineered, they would be born limbless, and the arms and legs would grow in as they were needed.) The infant is learning to nurse, to sleep, and to poop, and that’s about all they do for a little while. Yes, they even need to learn how to poop, or at least that’s how the nurse in the maternity ward explained her grunting sounds and puzzled expression. All capabilities come in the tiniest increments. At first she did not know what her hands were for, and was more likely to scratch her own face than to do anything useful with them. After a few months she started to realize that hands could grasp things, but not how to do so. She would pinch and grip...
31st Jan 2022

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More from Jason Crawford

Things that can kill you quickly

There are things that kill you instantly, like a bullet to the head or a fall from twenty stories. First aid can’t help you there. There are also things that kill you relatively slowly, like a bacterial infection. If you have even hours to live, you can get to the emergency room. But there is a small class of things that will kill you in minutes unless someone comes to the rescue. There isn’t time to get to a hospital, there isn’t even time for help to arrive in an ambulance. There is only time for someone already on the scene to provide emergency treatment that either solves the problem, or stabilizes you until help arrives. Here, first aid can be the difference between life and death. Not long ago I became a father. Being responsible for the life of someone so helpless and vulnerable spurred me to finally take first aid training, including CPR. Here’s what I learned from that experience, and what I think everyone should know about first aid. What most of the things that kill you quickly have in common is that oxygen can’t get to your cells. If you are choking, oxygen can’t get in. If your heart stops beating, blood doesn’t flow. If you have a severe wound, you’re losing that blood rapidly. If any link in the respiratory-circulatory chain is broken, your cells are starved for oxygen and you have minutes to live. The key first aid skills follow from this: CPR manually substitutes for heart and lung action; the Heimlich maneuver expels an object from the airway; a tourniquet stops life-threatening bleeding (on an extremity, at least—if the wound is elsewhere, there is a different technique, known as packing the wound). The basic skills are remarkably simple. The course that I took was only a few hours of online instruction, followed by about an hour of in-person demonstration and practice with dummy patients. And I went through a lot of the optional material, including things like stroke, fainting, and jellyfish stings. I’m sure I’m nowhere near as good someone with more professional training or experience, but an introductory course is not daunting. The most important thing I learned is that if you find yourself in an emergency situation, it is better to do almost anything rather than nothing. Again, if someone stops breathing for any reason, they have only minutes to live. They are dead by default, unless someone intervenes. There is very little you can do to them that is worse than cutting off their oxygen. In fact, it is probably better to attempt CPR or the Heimlich maneuver than to do nothing, even if you have never been trained and are only guessing, or mimicking what you have seen on television. The skills were fairly unsurprising to me and were consistent with what I expected prior to training. This does not mean that you don’t need to bother with the training, and of course if someone trained is on hand then let them take over. But don’t let the bystander effect paralyze you if someone’s life is ever in your hands. In fact, the American Heart Association promotes a form of CPR called “hands-only,” in which you only do chest compressions, without giving breaths mouth-to-mouth. Their instructions for this are: “push hard and fast in the center of the chest.” That’s about it. if you only know that, you can do better than nothing. Similarly, if you can find an AED machine (automated external defibrillator), you do not need training to use it. The instructions are literally: open it and follow the prompts. The parts are clearly labeled, and there is a voice recording that walks you through every step of the process. Wensha In the end, the biggest thing I gained was the confidence to act. I made an Anki flashcard deck for the course and have been using it to keep my memory fresh. If you do a similar course, you can download my deck from AnkiWeb.

27th Dec 2022 • 60 votes
The lessons of Xanadu

One of my all-time favorite articles is “The Curse of Xanadu,” by Gary Wolf, which ran in WIRED Magazine in 1995. On the surface, it’s a piece of tech history, a story of a dramatic failure. But look closer, and you can find deep philosophical insight. Xanadu was a grand vision of a hypertext system, conceived long before the Web, that at the time of this article had been “under development” for three decades without launching. The visionary behind it was Ted Nelson, one of the originators of the concept of hypertext. Here’s how the article describes him and the project: Nelson’s life is so full of unfinished projects that it might fairly be said to be built from them, much as lace is built from holes or Philip Johnson’s glass house from windows. He has written an unfinished autobiography and produced an unfinished film. His houseboat in the San Francisco Bay is full of incomplete notes and unsigned letters. He founded a video-editing business, but has not yet seen it through to profitability. He has been at work on an overarching philosophy of everything called General Schematics, but the text remains in thousands of pieces, scattered on sheets of paper, file cards, and sticky notes. All the children of Nelson’s imagination do not have equal stature. Each is derived from the one, great, unfinished project for which he has finally achieved the fame he has pursued since his boyhood. During one of our many conversations, Nelson explained that he never succeeded as a filmmaker or businessman because “the first step to anything I ever wanted to do was Xanadu.” Xanadu, a global hypertext publishing system, is the longest-running vaporware story in the history of the computer industry. It has been in development for more than 30 years. This long gestation period may not put it in the same category as the Great Wall of China, which was under construction for most of the 16th century and still failed to foil invaders, but, given the relative youth of commercial computing, Xanadu has set a record of futility that will be difficult for other companies to surpass. The project had many of the earmarks of other failed or long-overdue efforts. As a product, it was over-designed: Xanadu was meant to be a universal library, a worldwide hypertext publishing tool, a system to resolve copyright disputes, and a meritocratic forum for discussion and debate. By putting all information within reach of all people, Xanadu was meant to eliminate scientific ignorance and cure political misunderstandings. And, on the very hackerish assumption that global catastrophes are caused by ignorance, stupidity, and communication failures, Xanadu was supposed to save the world. In contrast to the later Web, links in Xanadu did not point to entire documents, but to any arbitrary range of characters within any document. Links were to be bi-directional, so they could not be broken. And there was an advanced feature in which “parts of documents could be quoted in other documents without copying”: The idea of quoting without copying was called transclusion, and it was the heart of Xanadu’s most innovative commercial feature—a royalty and copyright scheme. Whenever an author wished to quote, he or she would use transclusion to “virtually include” the passage in his or her own document.… The key to the Xanadu copyright and royalty scheme was that literal copying was forbidden in the Xanadu system. When a user wanted to quote a portion of document, that portion was transcluded. With fee for every reading. Transclusion was extremely challenging to the programmers, for it meant that there could be no redundancy in the grand Xanadu library. Every text could exist only as an original. Every user in the world would have to have instant access to the same underlying collection of documents. The vision for the application of this technology was nothing short of utopian, based on delusions of technological solutions to social and epistemic problems: … the Xanadu architects became obsessed with developing the widest possible applications of hypertext technology. A universal democratic library, they decided, was only the beginning. Xanadu could also provide a tool for rational discussion and decision making among very large groups. In the Xanadu docuverse, an assertion could always be followed back to its original source. An idea would never become detached from its author. Public discussion on important issues would move forward logically, rather than merely swirling ineffectively through eddies of rhetoric. In fact, any reader could, by creating and following links, freeze the chaotic flow of knowledge and grasp the lines of connection and influence. The design also blithely ignored the realities of computer performance, developing as they were on minicomputers and early workstations: The Onyx also had 128 Kbytes of RAM, which they later doubled to a screaming 256 Kbytes. Looking back at the specifics of the endeavor, the approach of the Xanadu programmers seems quixotic. [Xanadu collaborator Roger] Gregory and his colleagues were trying to build a universal library on machines that could barely manage to edit and search a book’s worth of text. The project suffered from infighting and a lack of good management: “It was not rapid prototyping—it was rabid prototyping,” said one of [Xanadu programmer Michael] McClary’s friends who watched the project closely. There was never a realistic schedule: the team perpetually believed they were six months away from completion. The project was so badly conceived and managed that it couldn’t ship even after being acquired by Autodesk and given a full budget: [Autodesk founder] John Walker, Xanadu’s most powerful protector, later wrote that during the Autodesk years, the Xanadu team had “hyper-warped into the techno-hubris zone.” Walker marveled at the programmers’ apparent belief that they could create “in its entirety, a system that can store all the information in every form, present and future, for quadrillions of individuals over billions of years.” Rather than push their product into the marketplace quickly, where it could compete, adapt, or die, the Xanadu programmers intended to produce their revolution ab initio. “When this process fails,” wrote Walker in his collection of documents from and about Autodesk, “and it always does, that doesn’t seem to weaken the belief in a design process which, in reality, is as bogus as astrology. It’s always a bad manager, problems with tools, etc.—precisely the unpredictable factors which make a priori design impossible in the first place.” There are too many good quotes in the article to include them all here—read the whole thing. What struck me most deeply, however, was the response of some of the Xanadu team to the rise of the World Wide Web. You would think that the web would be an object lesson for them—a slap in the face hard enough to wake them from their pie-in-the-sky reverie and bring them back to Earth. Indeed, one junior programmer on the later team, Rob Jellinghaus—who was born after the Xanadu project had begun (!)—did have such an awakening: While the Xanaduers paid lip service to libertarian ideals, they imagined a more traditional revolution in which all users would be linked to a single, large, utopian system. But in their quest for a 21st-century model, they created a Byzantine maze. “There were links, you could do versions, you could compare versions, all that was true,” Jellinghaus reports, “provided you were a rocket scientist. I mean, just the code to get a piece of text out of the Xanadu back end was something like 20 lines of very, very hairy C++, and it was not easy to use in any sense of the word. Not only was it not easy to use, it wasn’t anything even remotely resembling fast. The more I worked at it, the more pessimistic I got.” The young programmer’s doubts were magnified by his dawning realization that a grand, centralized system was no longer the solution to anything. He had grown up with the Internet—a redundant, ever-multiplying and increasingly chaotic mass of documents. He had observed that users wanted and needed ever more clever interfaces to deal with the wealth of information, but they showed little inclination to obey the dictates of a single company.… Although he sympathized with the fanaticism of his colleagues, Jellinghaus also began to question whether a hypertext revolution required the perfect preservation of all knowledge. He saw the beauty of the Xanadu dream—“How do you codify all the information in the world in a way that is infinitely scalable?”—but he suspected that human society might not benefit from a perfect technological memory. Thinking is based on selection and weeding out; remembering everything is strangely similar to forgetting everything. “Maybe most things that people do shouldn’t be remembered,” Jellinghaus says. “Maybe forgetting is good.” … After a couple of months, he began to come to his senses. “What was I doing?” he remembers saying to himself. “This is silly. This was silly all along.” But here was the reaction of Mark Miller, one of the original developers: I asked Miller if the Internet was accomplishing his dreams for hypertext. “What the Web is doing is easy,” Miller answered. He pointed out that the Web still lacks nearly every one of the advanced features he and his colleagues were trying to realize. There is no transclusion. There is no way to create links inside other writers’ documents. There is no way to follow all the references to a specific document. Most importantly, the World Wide Web is no friend to logic. Rather, it permits infinite redundancy and encourages maximum confusion. With Xanadu—that is, with tranclusion and freedom to link—users would have had a consistent, easily navigable forum for universal debate. “This is really hard,” Miller said. And what about Nelson himself? Nelson’s response to the Web was “nice try.” He said it is a trivial simplification of his hypertext ideas, though cleverly implemented. And he has not entirely given up hope for the old Xanadu code. “I’d like to stress that everyone involved in Xanadu believes that the software is valid and can be finished,” he asserted. “It will be finished,” Nelson added. “The only question is which decade.” Miller, Nelson, and the rest of the Xanadu team might have benefitted from reading another one of my all-time favorite articles: Clay Shirky’s “In Praise of Evolvable Systems”. Shirky begins by pointing out several ways in which the fundamental standards of web technology have seemingly absurd limitations and inefficiencies: HTTP doesn’t use persistent connections and incurs the entire overhead of a new session for each file transferred, web servers have no built-in load balancing, HTML uses one-directional hypertext links that are easily broken, etc.: HTTP and HTML are the Whoopee Cushion and Joy Buzzer of Internet protocols, only comprehensible as elaborate practical jokes. For anyone who has tried to accomplish anything serious on the Web, it’s pretty obvious that of the various implementations of a worldwide hypertext protocol, we have the worst one possible. Except, of course, for all the others.… The problem with that list of deficiencies is that it is also a list of necessities—the Web has flourished in a way that no other networking protocol has except e-mail, not despite many of these qualities but because of them. The very weaknesses that make the Web so infuriating to serious practitioners also make it possible in the first place. In fact, had the Web been a strong and well-designed entity from its inception, it would have gone nowhere. Contrasting the “evolvable system” of the web with centrally designed protocols such as Gopher and WAIS, he concludes: Centrally designed protocols start out strong and improve logarithmically. Evolvable protocols start out weak and improve exponentially. It’s dinosaurs vs. mammals, and the mammals win every time. The Xanadu project still exists. I was able to quickly learn its current status, because it has a homepage on a global hypertext-based information system: https://xanadu.com. “With ideas which are still radical, WE FIGHT ON. We hope for vindication, the last laugh, and recognition as an additional standard…” It complains that “everyone is hypnotized by the Web browser,” which is “basically crippled.” The project is no longer entirely vaporware. There are two demo viewers of Xanadocs: “XanaduSpace is our best-looking viewer, our flagship demo—but alas, it’s a stuck demo and can’t go further.” The “new working viewer”, xanaviewer3, makes it “possible (but not easy) for anyone who is determined enough to create a xanadoc, and send it to others, who may open and use it.” One supposes that, in order to view it, the recipients of the document must be equally determined. “With our limited resources,” they explain, “we can only go slowly, unlike today’s Red Bull–fueled young teams.” The WIRED article describing Xanadu as running for over 30 years is now 27 years old, meaning Xanadu itself is nearing 60. If its “record of futility” was difficult to surpass back then, it is doubly so now. The lessons of Xanadu can be learned at multiple levels. On one level, the lesson is to scope projects realistically and to strive for simplicity of design. On a deeper level, the lesson is to ship continually. Doing so keeps the schedule honest, forces difficult scope decisions, and allows for feedback from real users. On a still deeper level, the lesson is to learn from failure, which the vast majority of the Xanadu team does not appear to have done: thirty years of missed deadlines did not cause them to fundamentally question their schedule, project management, or design scope. But the deepest lesson, I think, is to value real-world results. Nelson and Miller didn’t fail to notice the Web, they failed to care about its success or even to recognize it as a success. Its epic, world-changing status in the history of technology is meaningless to them beside the fantasy system they had dreamed up. In the end, despite the title of the WIRED article, Xanadu was not, in fact, cursed. It achieved exactly what its originators wanted: theoretical perfection in a Platonic realm of forms so idealized that it can never quite be brought to Earth.

7th Aug 2022 • 59 votes
My simple guide to life

I first made a version of this chart seven years ago today. It’s worth a re-up (and it’s never been on my blog). The meaning of this chart is: Everything you do should be justified either by being inherently enjoyable, or by being important for some other purpose. Absolutely minimize activities that satisfy neither of these criteria: things that are neither fun nor important. (This seems obvious, but think of how often it’s violated: online flame wars, doomscrolling and general overconsumption of news, long sob stories about trivial inconveniences, endless stewing over long-ago wrongs, etc.) Spend the vast majority of your time on things that are both enjoyable and important, such as (hopefully) career and family. Some time on chores, taxes, etc. is unavoidable. Some time on games and diversions is fine. But both should be small relative to the big, meaningful, deeply rewarding things. (And just to anticipate one reaction: if you enjoy arguments on the Internet, then they can go under “fun and games”.) It’s not a complete guide to life, but it’s important and something I apply often.

3rd Apr 2022 • 60 votes
Precognition

It’s almost impossible to predict the future. But it’s also unnecessary, because most people are living in the past. All you have to do is see the present before everyone else does. To be less pithy, but more clear: Most people are slow to notice and accept change. If you can just be faster than most people at seeing what’s going on, updating your model of the world, and reacting accordingly, it’s almost as good as seeing the future. We see this in the US with covid: The same people who didn’t realize that we all should be wearing masks, when they were life-saving, are now slow to realize/admit that we can stop wearing them. For a dramatic historical example (from The Making of the Atomic Bomb), take Leo Szilard’s observations of 1930s Germany: Adolf Hitler was appointed Chancellor of Germany on January 30, 1933. … In late March, Jewish judges and lawyers in Prussia and Bavaria were dismissed from practice. On the weekend of April 1, Julius Streicher directed a national boycott of Jewish businesses and Jews were beaten in the streets. “I took a train from Berlin to Vienna on a certain date, close to the first of April, 1933,” Szilard writes. “The train was empty. The same train the next day was overcrowded, was stopped at the frontier, the people had to get out, and everybody was interrogated by the Nazis. This just goes to show that if you want to succeed in this world you don’t have to be much cleverer than other people, you just have to be one day earlier.” How to be earlier Independent thinking. If you only believe things that are accepted by the majority of people, then by definition you’ll always be behind the curve in a changing world. Listen to other independent thinkers. You can’t pay attention to everything at once or evaluate every area. You can only be the first to realize something in a narrow domain in which you are an expert. But if you tune your intellectual radar to other independent thinkers, you can be in the first ~1% of people to realize a new fact. Seek them out, find them, and follow them. I was taking covid precautions in late February 2020, about three weeks ahead of official “lockdown” measures—but only because I was tuned in to the people who were six weeks ahead. But: Distinguish independent thinkers from crackpots. Both are “contrarian”; only one has any hope of being right. This is an art, honed over decades. Pay attention to both the source’s evidence and their logic. Credentials are relevant, but they are neither necessary nor sufficient. Read broadly; seek out and adopt concepts and frameworks that help you understand the world (e.g.: exponential growth, network effects, efficient frontiers). Finally: Learn how to make decisions in the face of uncertainty. Even when you see the present earlier, you won’t see it with full clarity, nor will you be able to predict the future. You’ll just have a set of probabilities that are closer to reality than most people’s. To return to the covid example: in January/February 2020, even the people farthest ahead of the curve weren’t certain whether there would be a pandemic or how bad it would be. They just knew that the chances were double-digit percent, before it was even on most people’s radar. Find low-cost ways to avoid extreme downside, and low-investment opportunities for extreme upside. For example, when a pandemic might be starting, it makes sense to stock up on supplies, move meetings to phone calls, etc.—these are cheap insurance. In some fantasy worlds, there are superheroes with “pre-cognition”, able to see the immediate future. They’re always one step ahead. But since most people are a few steps behind reality, you don’t need pre-cognition—just independent thinking.

13th Jun 2021 • 68 votes

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4 days ago • 1 votes
Scattered thoughts on how doing math feels these days

This is a weird moment. I have a hunch that we’ll look back later and tell ourselves stories in which it seemed obvious where things were going. So I wanted to lay down a little time capsule and register just how confusing everything felt at the time. This is a personal view from the trenches. 1. If you’re a normal person, you may feel that AI has been good at math for a long time.1 But for “real” math, this is roughly what it felt like: When Feeling 2024 LLMs are toys Summer 2025 LLMs are interesting toys Early Autumn 2025 LLMs can do useful stuff sometimes Mid Autumn 2025 …a lot of stuff Late Autumn 2025 …a lot Early 2026 holy god Even having been astonished by AI progress in the past, it was still astonishing to see AI move in a matter of months from a mildly-useful toy to something that many people now consider almost impossible to do without. 2. Personally, I’d say that current AI makes me around 50% more productive at “doing math”. That’s despite the fact that AI can now often do in a few minutes things that would have taken me hours to do myself. The main reason is that my concept of “doing math” involves various steps, like coming up with ideas for theorems, trying to prove them, writing things down, reorganizing everything to make it more clear, and so on. AI is good at some parts of this, but still pretty bad at others, so the overall speedup isn’t as high as you might expect. (Even if planes could go 10× faster, your door-to-door travel time from your apartment in London to your friend’s place in Rome wouldn’t decrease that much, because you still have to get to the airport and wait to board.) 3. But then, I’m still (sort of; more on this later) trying to follow my old “doing math” workflow, just automating certain parts of it. That can’t be right, can it? If we found a way to make batteries at 1% of the current cost, we wouldn’t just say, “cool, batteries are cheap now”. We’d find all sorts of new ways to use batteries that didn’t make sense before, and change the ways we generate power to account for all these new batteries. Probably our whole energy system would end up transformed. Analogously, AI being good at proofs should lead to a very different equilibrium. But what is that?2 4. It seems healthy, at the moment, to maintain a fluid view of what “doing math” means. But at bare minimum it surely involves understanding the math that “I” am “doing”. So long as that’s part of the workflow, it would seem to impose a limit on how quickly “I” could “do” math, no matter how good AI is. One obvious conclusion would be that my tiny organic brain should be removed from the workflow entirely. Maybe! But it’s interesting to speculate about how much faster things could go while still keeping a human brain in the loop. I think the answer is: A lot. 5. Contrary to what you may have heard, humans are not obsolete for math. Not yet. The most obvious reason is that AI is still bad at writing. People often complain that AI writing overuses various patterns or lacks a unique voice or whatever. I don’t disagree, but I always felt like there was a deeper issue that I couldn’t quite put my finger on. With math, this becomes very clear: The problem is it doesn’t make any goddamned sense. Really, I think it’s deeper than being bad “at writing”. It seems like AI is bad at thinking in certain ways that manifest as bad writing. 6. When novices do math, they often create “spaghetti-code math”: Imagine a long document with lots of equations, but if you point at any one equation, it’s hard to say what assumptions underpin it, or how it fits into the bigger picture. Any random idea that was mentioned 17 pages ago might be used again at any time with no warning. With experience, people learn to break complex arguments down into modular pieces with a good “API”, in the sense that the assumptions and conclusions for each part are relatively simple. Somehow, AI has developed these amazing technical skills, while still being bad at creating abstractions So the arguments it gives are long and tangled and bewildering. This is what makes AI math seem so alien. 7. Now I know what you’re thinking. Maybe AI isn’t bad at creating abstractions. It’s just bad at creating abstractions that work for you, human with tiny organic brain. From a distance, that seems plausible. And it might be true to some degree. I have high-bandwidth access to a human brain. It’s not crazy to imagine that this gives me an advantage in predicting how a human brain would react to a given presentation of ideas. But still, I claim that current AI is objectively bad at creating abstractions, regardless of if the consumer of those abstractions is human or artificial. Here’s why: If I ask AI to prove something hard, it will produce a proof that starts simple and gradually descends into a rampage of complexity. If I tell it to break that proof down into self-contained pieces, it will produce lemmas that each take a full page just to state. If I ask, “Why? Why have you done this to me?”, the AI will say, “This is as simple as it gets. It’s not that bad. What’s your problem?” But if I argue with the AI for a while, I can start to understand the basic contours of the argument. And I’ll ask, “Why don’t you first just try to prove blah using only assumptions blahblah, without worrying about blahblahblah? Wouldn’t that be easier?” Often, this doesn’t work, either because my idea was bad, or because the AI isn’t smart enough to do it. (Which one? Who knows!) But after a few attempts, I can usually find something that that works, and the AI can use it to re-write the proof in a somewhat simpler way. If I repeat this many times, I can gradually chip away at the complexity. But maybe that’s still just making the proof easier for humans to understand? No! For one, this usually makes the proof shorter, and sometimes dramatically shorter. More importantly, during this process, someone (maybe me, maybe the AI) will often realize that there’s a different proof strategy that gives a stronger result. Instead of proving x < 9.7, now we can prove x < 7.1. Thus, artisanal human abstractions are “objectively” useful. 8. Some people might claim that it’s already possible to automate some of what I’m doing with some multi-agent Rube Goldberg thing. I’m not sure, because my experience has been that investing lots of time into clever AGENTS.MD or custom skills or harnesses isn’t a great use of time. That stuff is somewhat helpful, but things are changing so fast that the knowledge has a short half-life. Also, the majority of information about these tricks seems to come from people who are excited about the technology itself. On the other hand, the automator told me to try typing, “ask independent agents for an adversarial review”, and I’ve found those seven words to be quite helpful. I wish I could find a source with boring best practices for people who are happy to stay away from the bleeding edge. 9. Another reason AI doesn’t improve my productivity more is that it is often the green light, the orgastic future that recedes before me. I’ll ask AI to do something, and it will almost seem to do it. So I’ll ask for changes, and it seems to help, but it’s still not quite right. Hours later, I’ve accomplished nothing and learned nothing. Eventually, I start over, either doing it myself or with AI on a very tight leash, and I’m done in 30 minutes. Each time this happens I feel foolish and tell myself not to do it again. But it’s very hard to judge in real-time. 10. Of course, I’m not claiming that AI is intrinsically bad at writing or thinking or abstraction-making. It’s gotten better at all of these and I expect it will continue to do so. All I’m saying is that as of Autumn 2026, humans still have a lot to contribute, and it’s not just better exposition. 11. Did AI actually get much better at math in Autumn 2025? Or perhaps, in the grand scheme of things, human math ability falls into a narrow range? 12. Some people are responding to the new reality by gleefully using AI to generate lots of semi-OK-ish papers and puking them up on arXiv. I understand the incentives that drive this. In some sense, it seems like the logical response to the new reality. (After inventing cars, we started taking a lot more low-value trips.) But I don’t think this is socially useful, and I tend to think everyone will soon agree that this is embarrassing. But… I’m not sure. Maybe it is socially useful? And I find myself feeling vaguely jealous of the people who are wired this way, who instead of feeling blinkered, see opportunity and plunder. 13. Want an optimistic pro-AI, pro-progress take? OK. If I take my old “doing math” workflow, AI makes it strictly better. A lot better. It’s like having a hyper-conscientious intern that is smarter than me in many ways, works at incredible speed, and knows approximately everything. Yes, AI sucks at writing and making abstractions. But if I’m disciplined and try to mostly do the math “myself” while asking the AI for help, I can explore ideas much faster than before, make far fewer mistakes, and produce something just as clear as before, while developing just as much understanding. I can ask, “What happens if you do a Kofuni expansion?”, and get an answer instantly. I can ask for illustrations or interactive visualizations and see them instantly. Arguably, we’re incredibly lucky to be alive at this moment, when these new tools have opened up this new territory to explore. 14. And isn’t that what I wanted? Back in 2024, I bemoaned that what AI most seemed to struggle with was getting the facts straight: Perhaps the old science fiction robots were a projection of our fantasies. I want my style and voice to be precious and uncopyable. I want an AI assistant that will tirelessly hunt down facts while I provide creativity and panache and magic. But that’s not how it is. So we should remember: In a scenario where AI is useful but humans retain some comparative advantage, we might not like where that comparative advantage lies. At least with math, and at least for the moment, I get to be Kirk, while the AI is Spock. (Even with other types of writing, my impression is that since 2024, AI has improved more along Spock dimensions than along Kirk dimensions.) So that’s nice. 15. In the last few months, it frequently happens that a collaborator will use AI to generate something, and then send it to me and ask me to check if it’s useful. I can see why this seems reasonable. (Really, I do.) But when it happens a lot, one can’t help but wonder what the point of the human collaborator is. 16. On the other hand—with creative writing, my advice is not to use AI to write, at all. Ask questions if you want, but write every damned word yourself.3 With math, I don’t feel like such a hard line makes sense. It seems that creating something good still requires a lot of human involvement, to the point of agonizing over notation, repeatedly re-organizing things, and heavily editing individual sentences. Still, I find that if people make that effort, they can create something just as good as they could by hand, while still being perhaps 50% AI written.4 So I don’t mind if people send me “centaur math”, provided they take responsibility for the contents. The problem is that when working with AI, it takes constant paranoid vigilance to avoid letting things slip beyond your understanding. I often find myself wanting to awkwardly ask people, “OK, you obviously used AI to make this… which is OK… but how much of this do you vouch for? If we have a meeting to discuss it, will you crumble the moment I start asking questions?” 17. When I’m in abstraction-creation mode, I feel like I’m doing something intelligent. When yell at AI about bad notation and writing, I feel like I’m providing taste. But I often find myself giving feedback that seems incredibly basic. Things like: This is a mess, clean it up. Or: Do you need that assumption? Or: If you can’t solve this problem, why don’t you create a simpler version and try to solve it first? On the one hand, I guess a lot of what good human managers do amounts to giving very basic feedback. On the other hand, it’s hard to imagine that I have much durable advantage in doing this kind of thing. So I wonder how much durable advantage I have in abstraction-making or taste, too. 18. A common reaction to AI getting better at proofs is that humans need to focus more on exposition. This has an obvious Bourdieu-esque criticism: Human painters used to strongly value photo-realism, but after cameras were invented they convinced everyone that, oops, photo-realism isn’t important, what matters is expression, which coincidentally cameras can do. Isn’t a shift towards exposition in math just a way for humans to move the goalposts and justify their own continued relevance? The only credible way I see to avoid that criticism seems to be to bite the bullet and say that, in fact, math should have already have focused more on exposition, even before AI. Personally, I strongly believe that this is true. 19. I think the biggest reason I’m not more productive is: What’s the point? In the past, if you came up with an elegant proof, you’d tell yourself, “I made a thing! Other people will read and use my thing! I am advancing human progress.” You can still tell yourself that now. But you have to wonder, will anyone actually read your proof? Or will they just ask another AI to explain it to them? Will your proof help their AI explain it better? For how long? If you just waited a year, would some other AI produce that same proof independently? Alternatively, if you’re a normal person, you might think that LLMs still struggle with basic arithmetic? ↩ AI progress is so fast, compared to society’s ability to adjust, that we may never actually see what “the current equilibrium” looks like. ↩ This essay, if you’re wondering, was created by writing every damned word myself and not asking AI anything. ↩ You could have a whole debate about what that precentage means. It’s not the number of words written by AI that really matters, in my opinion, so much as who is responsible for the overall structure of the ideas. ↩

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