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I'm coming to COLORADO! Catch me in DENVER on Jan 22 at The Tattered Cover<, and in COLORADO SPRINGS from Jan 23–25 where I'm the Guest of Honor at COSine. Then I'll be in OTTAWA on Jan 28 at Perfect Books and in TORONTO with Tim Wu on Jan 30.
Code is a liability (not an asset). Tech bosses don't understand this. They think AI is great because it produces 10,000 times more code than a programmer, but that just means it's producing 10,000 times more liabilities. AI is the asbestos we're shoveling into the walls of our high-tech society:
Code is a liability. Code's capabilities are assets. The goal of a tech shop is to have code whose capabilities generate more revenue than the costs associated with keeping that code running. For a long time, firms have nurtured a false belief that code costs less to run over time: after an initial shakedown period in which the bugs in the code are found and addressed, code ceases to need meaningful maintenance. After all, code is a machine without moving parts – it does not wear out; it doesn't even wear down.
This is the thesis of Paul Mason's 2015 book Postcapitalism, a book that has aged remarkably poorly (though not, perhaps, as poorly as Mason's own political credibility): code is not an infinitely reproducible machine that requires no labor inputs to operate. Rather, it is a brittle machine that requires increasingly heroic measures to keep it in good working order, and which eventually does "wear out" (in the sense of needing a top-to-bottom refactoring).
To understand why code is a liability, you have to understand the difference between "writing code" and "software engineering."
"Writing code" is an incredibly useful, fun, and engrossing pastime. It involves breaking down complex tasks into discrete steps that are so precisely described that a computer can reliably perform them, and optimising that performance by finding clever ways of minimizing the demands the code puts on the computer's resources, such as RAM and processor cycles.
Meanwhile, "software engineering" is a discipline that subsumes "writing code," but with a focus on the long-term operations of the system the code is part of. Software engineering concerns itself with the upstream processes that generate the data the system receives. It concerns itself with the downstream processes that the system emits processed information to. It concerns itself with the adjacent systems that are receiving data from the same upstream processes and/or emitting data to the same downstream processes the system is emitting to.
"Writing code" is about making code that runs well. "Software engineering" is about making code that fails well. It's about making code that is legible – whose functions can be understood by third parties who might be asked to maintain it, or might be asked to adapt the processes downstream, upstream or adjacent to the system to keep the system from breaking. It's about making code that can be adapted, for example, when the underlying computer architecture it runs on is retired and has to be replaced, either with a new kind of computer, or with an emulated version of the old computer:
Because that's the thing: any nontrivial code has to interact with the outside world, and the outside world isn't static, it's dynamic. The outside world busts through the assumptions made by software authors all the time and every time it does, the software needs to be fixed. Remember Y2K? That was a day when perfectly functional code, running on perfectly functional hardware, would stop functioning – not because the code changed, but because time marched on.
We're 12 years away from the Y2038 problem, when 32-bit flavors of Unix will all cease to work, because they, too, will have run out of computable seconds. These computers haven't changed, their software hasn't changed, but the world – by dint of ticking over, a second at a time, for 68 years – will wear through their seams, and they will rupture:
The existence of "the world" is an inescapable factor that wears out software and requires it to be rebuilt, often at enormous expense. The longer code is in operation, the more likely it is that it will encounter "the world." Take the code that devices use to report on their physical location. Originally, this was used for things like billing – determining which carrier or provider's network you were using and whether you were roaming. Then, our mobile devices used this code to help determine your location in order to give you turn-by-turn directions in navigation apps. Then, this code was repurposed again to help us find our lost devices. This, in turn, became a way to locate stolen devices, a use-case that sharply diverges from finding lost devices in important ways – for example, when locating a lost device, you don't have to contend with the possibility that a malicious actor has disabled the "find my lost device" facility.
These additional use cases – upstream, downstream and adjacent – exposed bugs in the original code that never surfaced in the earlier applications. For example, all location services must have some kind of default behavior in the (very common) event that they're not really sure where they are. Maybe they have a general fix – for example, they know which cellular mast they're connected to, or they know where they were the last time they got an accurate location fix – or maybe they're totally lost.
It turns out that in many cases, location apps drew a circle around all the places they could be and then set their location to the middle of that circle. That's fine if the circle is only a few feet in diameter, or if the app quickly replaces this approximation with a more precise location. But what if the location is miles and miles across, and the location fix never improves? What if the location for any IP address without a defined location is given as the center of the continental USA and any app that doesn't know where it is reports that it is in a house in Kansas, sending dozens of furious (occasionally armed) strangers to that house, insisting that the owners are in possession of their stolen phones and tablets?
And in my town of Burbank, where Google's location-sharing service once told us that our then-11-year-old daughter (whose phone we couldn't reach) was 12 miles away, on a freeway ramp in an unincorporated area of LA county (she was at a nearby park, but out of range, and the app estimated her location as the center of the region it had last fixed her in) (it was a rough couple hours).
The underlying code – the code that uses some once-harmless default to fudge unknown locations – needs to be updated constantly, because the upstream, downstream and adjacent processes connected to it are changing constantly. The longer that code sits there, the more superannuated its original behaviors become, and the more baroque, crufty and obfuscated the patches layered atop of it become.
Code is not an asset – it's a liability. The longer a computer system has been running, the more tech debt it represents. The more important the system is, the harder it is to bring down and completely redo. Instead, new layers of code are slathered atop of it, and wherever the layers of code meet, there are fissures in which these systems behave in ways that don't exactly match up. Worse still: when two companies are merged, their seamed, fissured IT systems are smashed together, so that now there are adjacent sources of tech debt, as well as upstream and downstream cracks:
That's why giant companies are so susceptible to ransomware attacks – they're full of incompatible systems that have been coaxed into a facsimile of compatibility with various forms of digital silly putty, string and baling wire. They are not watertight and they cannot be made watertight. Even if they're not taken down by hackers, they sometimes just fall over and can't be stood back up again – like when Southwest Airlines' computers crashed for all of Christmas week 2022, stranding millions of travelers:
Airlines are especially bad, because they computerized early, and can't ever shut down the old computers to replace them with new ones. This is why their apps are such dogshit – and why it's so awful that they've fired their customer service personnel and require fliers to use the apps for everything, even though the apps do. not. work. These apps won't ever work.
The reason that British Airways' app displays "An unknown error has occurred" 40-80% of the time isn't (just) that they fired all their IT staff and outsourced to low bidders overseas. It's that, sure – but also that BA's first computers ran on electromechanical valves, and everything since has to be backwards-compatible with a system that one of Alan Turing's proteges gnawed out of a whole log with his very own front teeth. Code is a liability, not an asset (BA's new app is years behind schedule).
Code is a liability. The servers for the Bloomberg terminals that turned Michael Bloomberg into a billionaire run on RISC chips, meaning that the company is locked into using a dwindling number of specialist hardware and data-center vendors, paying specialized programmers, and building brittle chains of code to connect these RISC systems to their less exotic equivalents in the world. Code isn't an asset.
AI can write code, but AI can't do software engineering. Software engineering is all about thinking through context – what will come before this system? What will come after it? What will sit alongside of it? How will the world change? Software engineering requires a very wide "context window," the thing that AI does not, and cannot have. AI has a very narrow and shallow context window, and linear expansions to AI's context window requires geometric expansions in the amount of computational resources the AI consumes:
Writing code that works, without consideration of how it will fail, is a recipe for catastrophe. It is a way to create tech debt at scale. It is shoveling asbestos into the walls of our technological society.
Bosses do not know that code is a liability, not an asset. That's why they won't shut the fuck up about the chatbots that shit out 10,000 times more code than any human programmer. They think they've found a machine that produces assets at 10,000 times the rate of a human programmer. They haven't. They've found a machine that produces liability at 10,000 times the rate of any human programmer.
Maintainability isn't just a matter of hard-won experience teaching you where the pitfalls are. It also requires the cultivation of "Fingerspitzengefühl" – the "fingertip feeling" that lets you make reasonable guesses about where never before seen pitfalls might emerge. It's a form of process knowledge. It is ineluctable. It is not latent in even the largest corpus of code that you could use as training data:
Boy do tech bosses not get this. Take Microsoft. Their big bet right now is on "agentic AI." They think that if they install spyware on your computer that captures every keystroke, every communication, every screen you see and sends it to Microsoft's cloud and give a menagerie of chatbots access to it, that you'll be able to tell your computer, "Book me a train to Cardiff and find that hotel Cory mentioned last year and book me a room there" and it will do it.
This is an incredibly unworkable idea. No chatbot is remotely capable of doing all these things, something that Microsoft freely stipulates. Rather than doing this with one chatbot, Microsoft proposes to break this down among dozens of chatbots, each of which Microsoft hopes to bring up to 95% reliability.
That's an utterly implausible chatbot standard in and of itself, but consider this: probabilities are multiplicative. A system containing two processes that operate at 95% reliability has a net reliability of 90.25% (0.95 * 0.95). Break a task down among a couple dozen 95% accurate bots and the chance that this task will be accomplished correctly rounds to zero.
Worse, Microsoft is on record as saying that they will grant the Trump administration secret access to all the data in its cloud:
So – as Signal's Meredith Whittaker and Udbhav Tiwari put it in their incredible 39C3 talk last week in Hamburg – Microsoft is about to abolish the very idea of privacy for any data on personal and corporate computers, in order to ship AI agents that cannot ever work:
https://www.youtube.com/watch?v=0ANECpNdt-4
Meanwhile, a Microsoft exec got into trouble last December when he posted to Linkedin announcing his intention to have AI rewrite all of Microsoft's code. Refactoring Microsoft's codebase makes lots of sense. Microsoft – like British Airways and other legacy firms – has lots of very old code that represents unsustainable tech debt. But using AI to rewrite that code is a way to start with tech debt that will only accumulate as time goes by:
Now, some of you reading this have heard software engineers extolling the incredible value of using a chatbot to write code for them. Some of you are software engineers who have found chatbots incredibly useful in writing code for you. This is a common AI paradox: why do some people who use AI find it really helpful, while others loathe it? Is it that the people who don't like AI are "bad at AI?" Is it that the AI fans are lazy and don't care about the quality of their work?
There's doubtless some of both going on, but even if you teach everyone to be an AI expert, and cull everyone who doesn't take pride in their work out of the sample, the paradox will still remain. The true solution to the AI paradox lies in automation theory, and the concept of "centaurs" and "reverse centaurs":
In automation theory, a "centaur" is a person who is assisted by a machine. A "reverse centaur" is someone who has been conscripted into assisting a machine. If you're a software engineer who uses AI to write routine code that you have the time and experience to validate, deploying your FingerspitzengefĂĽhl and process knowledge to ensure that it's fit for purpose, it's easy to see why you might find using AI (when you choose to, in ways you choose to, at a pace you choose to go at) to be useful.
But if you're a software engineer who's been ordered to produce code at 10x, or 100x, or 10,000x your previous rate, and the only way to do that is via AI, and there is no human way that you could possibly review that code and ensure that it will not break on first contact with the world, you'll hate it (you'll hate it even more if you've been turned into the AI's accountability sink, personally on the hook for the AI's mistakes):
There's another way in which software engineers find AI-generated code to be incredibly helpful: when that code is isolated. If you're doing a single project – say, converting one batch of files to another format, just once – you don't have to worry about downstream, upstream or adjacent processes. There aren't any. You're writing code to do something once, without interacting with any other systems. A lot of coding is this kind of utility project. It's tedious, thankless, and ripe for automation. Lots of personal projects fall into this bucket, and of course, by definition, a personal project is a centaur project. No one forces you to use AI in a personal project – it's always your choice how and when you make personal use of any tool.
But the fact that software engineers can sometimes make their work better with AI doesn't invalidate the fact that code is a liability, not an asset, and that AI code represents liability production at scale.
In the story of technological unemployment, there's the idea that new technology creates new jobs even as it makes old ones obsolete: for every blacksmith put out of work by the automobile, there's a job waiting as a mechanic. In the years since the AI bubble began inflating, we've heard lots of versions of this: AI would create jobs for "prompt engineers" – or even create jobs that we can't imagine, because they won't exist until AI has changed the world beyond recognition.
I wouldn't bank on getting work in a fanciful trade that literally can't be imagined because our consciousnesses haven't been so altered by AI that they've acquired the capacity to conceptualize of these new modes of work.
But if you are looking for a job that AI will definitely create, by the millions, I have a suggestion: digital asbestos removal.
For if AI code – written at 10,000 times the speed of any human coder, designed to work well, but not to fail gracefully – is the digital asbestos we're filling our walls with, then our descendants will spend generations digging that asbestos out of the walls. There will be plenty of work fixing the things that we broke thanks to the most dangerous AI psychosis of all – the hallucinatory belief that "writing code" is the same thing as "software engineering." At the rate we're going, we'll have full employment for generations of asbestos removers.
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
The difference between “today’s task” and “accretive work”
If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
One thing I've learned about paradoxes: often the answer to the riddle of "how can this one thing have such a contradictory set of features and effects?" is "it's not one thing, it's two things*."
That's the idea that set me on the path to writing about "reverse centaurs" and AI. I was hearing from experienced programmers whom I knew to be reliable narrators of their own experience who described how AI was letting them write the best code of their lives; and from equally experienced and reliable coders who described a nightmare of tech debt: "I work in aviation, and I just don't think anyone should ever fly again, those things are now unsafe at any altitude, thanks to the code I had to sign off on":
For so long as I thought of both of these groups as doing the same thing and getting wildly different outcomes, this was a paradox. But as soon as I realized that the former group were "centaurs" (workers who get to decide and direct their adoption of automation) and the latter were reverse centaurs (workers who were conscripted to serve as peripherals for automation systems), it all snapped into place. It only looked like they were doing the same thing – they were actually engaged in fundamentally different activities, which is why they were having such different experiences.
The same goes for vibe coding. Plenty of people I knew had gotten real value out of vibe coding personal utilities that made things better for them in a way that I instantly recognized from a life spent around people who'd been able to adapt and customize the systems they used to make their lives better:
Vibe coding can be seen as part of a lineage that includes shell scripting, Applescript, Hypercard and Visual Basic: ways for technical novices to directly create personal software, without having to ask a programmer to interpret their needs (and without having to pay every time they wanted to do something new with their computers):
AI's pitch to bosses is that they can fire most of their workers in order to terrorize the remainder into tolerating a working life wherein they are made to mark the AI's homework, at superhuman speed, and to assume the blame when it goes wrong. This is obviously a terrible way to write code:
So is vibe code a way of empowering people to have the personal, vernacular tools that they design and adapt as they see fit? Or is it a way to shovel technological asbestos into the walls at scale, filling up our high-tech society with ghastly, lethal technical debt we'll be digging our way out of for generations?
Again: the paradox falls away once you realize that personal software you write for yourself is fundamentally different from "production code" that other people have to use, maintain and improve.
In an essay inspired by some thoughts on AI and mathematical theorem proving, Kellan Elliott-McCrea crystallizes this distinction in a really sharp way, bringing in Alex Kontorovich's idea of mathematical "canonization":
By canonization, I mean the process of taking a local, one-off formalization and turning it into library mathematics: general, reusable, coherent, efficient, and compatible with the rest… Canonization often changes the picture itself: the definitions, the abstractions, the API, and sometimes even the statement…
Elliott-McCrea posits that making code that is "socially constructed in a way that leaves the team prepared to operate on it, iterate it, and improve it" is the difference between "I got it working" and "something the future can build on."
He's not claiming that "I got it working" is worthless. There's plenty of space for "disposable and single use software." Sure, to a trained software engineer, this might be "bad code" but doing today's task has value, even if the code that performs that task isn't "accretive."
Canonization is accretive. To canonize code is to make it "legible to systems of humans and non-humans operating on it." Free/open source software is the backbone of the canon: "decades of…intelligible, build-on-able work, sitting in public repos."
My "reverse centaurs" thesis isn't just a way to understand how programmers who seem to be doing the same thing can have such different effects. It's also about how the way that the capital was raised for AI requires that it produce as many reverse centaurs as possible, because the only way to recoup the farcical sums associated with AI production is to fire millions of workers and replace them with defective chatbots backstopped by the jobspocalypse's terrorized survivors, who can be made to endlessly toil away at marking the AI's homework because there are so many other workers who'll take their jobs if they refuse.
The point being that while centaurs are good and reverse centaurs are bad, the AI bubble requires the production of reverse centaurs, to the exclusion of centaurs.
In a similar vein, Elliott-McCrea describes how the imperatives of the AI industry are devouring its seed-corn – consuming the canon without putting anything new back in it. In the same way that AI can do endless theorem-proving but is essentially useless for creating "library mathematics: general, reusable, coherent, efficient, and compatible with the rest," AI can write a lot of running code, but the AI industry is further devaluing the already undervalued work of cleanup and canonization. As Elliott-McCrea writes, "the social production of knowledge [is] the seed corn."
I had to read a Heinlein book for a literature class on sci-fi and it was HORRIBLE and the only thing that got me though it was thinking "it's time for more Deep Thoughts With Heinlein(tm)" what a strange man this author was...
my working theory is that the most foundational works of literature in a given genre are the ones that inspire the most concepts and tropes that are carried forward in the genre and this inspiring property is frequently because there is, to simplify, something deeply wrong with them
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One of the funniest running gags on Bob's Burgers is that one of the storefronts next door is always changing; these businesses - which often seem to be a sketchy (but hilarious) idea with an equally bad pun in the name - always seem to go under.
Here's a sampling of some of the "stores next door":
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There was a time when it seemed possible that we could visit a real life licensed Dream Park, based on concepts found in the 1981 Niven and Barnes novel, where roleplaying games would be acted out by guests and staff with a variety of practical effects and virtual reality enhancements. ("The Ultimate Dream Park" by Douglas King, Australian Realms magazine 8, Nov/Dec 1992)
Many of these plans are remarkably specific but were never completed by this Dream Park Corporation which went bankrupt in 1997. Some similar concepts have appeared in the MagiQuest game installations, found at several locations including Great Wolf Resorts. A new company calling itself DreamPark is creating what it calls "the world's first downloadable mixed-reality (XR) theme park platform," with one location active at a Dave & Busters on Hollywood Blvd in LA.
The original Dream Park company was at Gen Con one year in the mid 90s and I played one of the games they were running. A headset was feeding me automated information based on my characters abilities, and a live GM answering when needed. Props in the scenario were moving remotely based on what we were doing. I was 100% convinced actually playing the South Seas Treasure Game was only a few years away. Shame it didn’t work out.
Also, everyone should check out the Dream Park novels if they haven’t. Those books are tremendous fun.
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Looking through some old Judges Guild stuff, opened the Character Codex and was met with this absolutely phenomenal illusionist by Jennell Jaquays. Such flair!
That's it, that's the post. Just needed everyone to see the illusionist.
I was reading through the U.S. Copyright Office's "What Does Copyright Protect?" page, mostly to make sure that I wasn't being wrong on the internet, but I absolutely love that this is part of their FAQ:
How do I protect my sighting of Elvis?
Copyright law does not protect sightings. [,,,]
There are only 12 questions on the FAQ, and this is one of them.
turnnoffyourmind
prguitarman
kaledo-art
snowghoul
stormborns
tangentwithtanner
gummygunk
izzy806
