If anyone out there has thought for the very first time that this might finally be the new trick that makes them feel like an old dog, or if you’re a fresh graduate who spent years working through school toward an almost-certain career path that seems now to be crumbling into dust underfoot, you are the intended audience.

Pull up a chair and endure yet another goddamn article about generative AI.

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We’ve got enough content to infinite-scroll for the rest of our waking lives. Yet so much of it is pointless, undesirable, lacking substance, or actively harmful. It is above all joyless—like listening to a Breaking Rust album while eating a sleeve of unsalted saltines and somehow forgetting that it wasn’t always this way.

[…] At least, I thought, all that AI stuff was finally done, and we might move on to actually getting something accomplished.

And then some absolute son of a bitch created ChatGPT, and now look at us. Look at us, resplendent in our pauper’s robes, stitched from corpulent greed and breathless credulity, spending half of the planet’s engineering efforts to add chatbot support to every application under the sun when half of the industry hasn’t worked out how to test database backups regularly. This is why I have to visit untold violence upon the next moron to propose that AI is the future of the business - not because this is impossible in principle, but because they are now indistinguishable from a hundred million willful fucking idiots.

My current working definition of overoptimization goes like this: overoptimization has occurred when the introduction of immense amounts of information into a human system produces conditions that allow for some players within that system to maximize their comparative advantage, without overtly breaking the rules, in a way that (intentional or not) creates meaningful negative social consequences. I want to argue that many human systems in the 2020s have become overoptimized in this way, and that the social ramifications are often bad.

Still, I find myself looking at a Walgreens cooler that just two years ago was covered in clear glass, admitting direct inspection of which tall-boy teas were in stock. Today, it’s an impenetrable black void. Some Walgreens employee has printed a sheet of paper, “TEA” in 96-point Cambria, and taped it to the wall above the door. Taking in this spectacle, of a multi-million dollar effort that took our coolers and made them more difficult to use, of a retail employee’s haphazard effort to mitigate the hostility of their employer’s merchandising, it is hard not to indulge in that escape. Surely, things can’t get much worse. Surely, these must be the latter days.

Here is how platforms die: First, they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves. Then, they die.

I call this enshittification, and it is a seemingly inevitable consequence arising from the combination of the ease of changing how a platform allocates value, combined with the nature of a “two-sided market,” where a platform sits between buyers and sellers, hold each hostage to the other, raking off an ever-larger share of the value that passes between them.

Anyway so— it’s actually quite rational for most people to assume that any other random person probably isn’t serious. That they don’t really mean what they say. Lots of people play-act seriousness for social reasons, but quit when the going gets tough. So it makes sense that lots of people are broadly cynical. Cynicism is a completely reasonable defense against fraud and bullshit. The trouble with cynicism as a defense mechanism is that you can get so good at it that you inadvertently also defend yourself against anything good happening for you.

In the textbooks, astonishing facts were presented without astonishment. Someone probably told me that every cell in my body has the same DNA. But no one shook me by the shoulders, saying how crazy that was.

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How come we memorized chemical formulas but didn’t talk about that? It was only in college, when I read Douglas Hofstadter’s Gödel, Escher, Bach, that I came to understand cells as recursively self-modifying programs. The language alone was evocative. It suggested that the embryo—DNA making RNA, RNA making protein, protein regulating the transcription of DNA into RNA—was like a small Lisp program, with macros begetting macros begetting macros, the source code containing within it all of the instructions required for life on Earth. Could anything more interesting be imagined?

Code says what it does. That’s important for the computer, because code is the way that we ask the computer to do something. It’s OK for humans, as long as we never have to modify or debug the code. As soon as we do, we have a problem. Fundamentally, debugging is an exercise in changing what a program does to match what it should do. It requires us to know what a program should do, which isn’t captured in the code. Sometimes that’s easy: What it does is crash, what it should do is not crash. Outside those trivial cases, discovering intent is harder.

The exhortation “learn to code” has its foundations in market value. “Learn to code” is suggested as a way up, a way out. “Learn to code” offers economic leverage, professional transformation. “Learn to code” goes on your resume.

But let’s substitute a different phrase: “learn to cook”. People don’t only learn to cook so they can become chefs. Some do! But many more people learn to cook so they can eat better, or more affordably. Because they want to carry on a tradition. Sometimes they learn because they’re bored! Or even because they enjoy spending time with the person who’s teaching them.

When the pandemic broke out, people on TikTok and other apps began referring to it as the “Backstreet Boys reunion tour” or calling it the “panini” or “panda express” as platforms down-ranked videos mentioning the pandemic by name in an effort to combat misinformation. When young people began to discuss struggling with mental health, they talked about “becoming unalive” in order to have frank conversations about suicide without algorithmic punishment. Sex workers, who have long been censored by moderation systems, refer to themselves on TikTok as “accountants” and use the corn emoji as a substitute for the word “porn.”

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“One, it doesn’t actually work,” she said. “The people using platforms to organize real harm are pretty good at figuring out how to get around these systems. And two, it leads to collateral damage of literal speech.” Attempting to regulate human speech at a scale of billions of people in dozens of different languages and trying to contend with things such as humor, sarcasm, local context and slang can’t be done by simply down-ranking certain words, Greer argues.

About a year ago, my wife asked if she could play a game. Instead of simply saying yes, I decided to run an informal experiment where I had her play a sampling of games to see how, after a lifetime of not playing games, she would do. These are the results.

[The maker, Razbuten, has a whole category of these under #GamingForANonGamer. - Ed.]

The site’s now characteristic tone of performative erudition—hyperrational, dispassionate, contrarian, authoritative—often masks a deeper recklessness. Ill-advised citations proliferate; thought experiments abound; humane arguments are dismissed as emotional or irrational. Logic, applied narrowly, is used to justify broad moral positions. The most admired arguments are made with data, but the origins, veracity, and malleability of those data tend to be ancillary concerns. The message-board intellectualism that might once have impressed V.C. observers like Graham has developed into an intellectual style all its own. Hacker News readers who visit the site to learn how engineers and entrepreneurs talk, and what they talk about, can find themselves immersed in conversations that resemble the output of duelling Markov bots trained on libertarian economics blogs, “The Tim Ferriss Show,” and the work of Yuval Noah Harari.

Every programmer starts out writing some perfect little snowflake like this. Then they’re told on Friday they need to have six hundred snowflakes written by Tuesday, so they cheat a bit here and there and maybe copy a few snowflakes and try to stick them together or they have to ask a coworker to work on one who melts it and then all the programmers’ snowflakes get dumped together in some inscrutable shape and somebody leans a Picasso on it because nobody wants to see the cat urine soaking into all your broken snowflakes melting in the light of day. Next week, everybody shovels more snow on it to keep the Picasso from falling over.

I can’t even say what’s wrong with PHP, because— okay. Imagine you have uh, a toolbox. A set of tools. Looks okay, standard stuff in there.

You pull out a screwdriver, and you see it’s one of those weird tri-headed things. Okay, well, that’s not very useful to you, but you guess it comes in handy sometimes.

You pull out the hammer, but to your dismay, it has the claw part on both sides. Still serviceable though, I mean, you can hit nails with the middle of the head holding it sideways.

You pull out the pliers, but they don’t have those serrated surfaces; it’s flat and smooth. That’s less useful, but it still turns bolts well enough, so whatever.

And on you go. Everything in the box is kind of weird and quirky, but maybe not enough to make it completely worthless. And there’s no clear problem with the set as a whole; it still has all the tools.

Allen Knutson, Cornell professor of mathematics and former world record-holding juggler, gives a public demonstration and lecture on how juggling patterns can be represented the mathematically.

The mathematics of juggling was codified about 25 years ago, when three different groups of jugglers came up with a notational system, called “siteswaps,” which made it easy to record patterns, note similarities and find new juggling patterns–even by computer.