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:
"Gish Gallop" is the debating term for an opponent who makes so many claims that "it's impossible to address them in the time available" (it's named for Creationist Duane Gish, who was notorious for this tactic):
https://en.wikipedia.org/wiki/Gish_gallop
I think about the Gish Gallop whenever I'm asked to comment on AI.
Here's a recent example: last week, I had a pre-interview call with a radio producer who wanted me to come on a 13-minute segment to discusses "whether there's a problem with AI governance?"
I asked what the show meant by that: was it whether regulation of AI in commercial or public sector decision-making needed more oversight? Was it that the siting and provisioning of data-centers needed more democratic accountability? Was it that workers deserved more of a say in AI's impact on labor markets? Was it that customers and/or audiences should be able to opt out of AI customer service and AI slop? Was it about whether we needed some kind of system to prevent "runaway AI," in the event that we teach so many words to the word-guessing program that it wakes up, becomes God, and turns us all into paperclips?
"Oh," the producer said, "all of that."
In 13 minutes.
You see the problem, right? The AI industry has made so many claims about its past, present and future that it's almost impossible to have a reasonable critical conversation about it:
Shortly after I did the radio show, a newspaper editor who'd heard my segment got in touch to ask me if I'd write an 800-word op-ed about the subject, and also, could I address claims that "AI is the next Industrial Revolution?"
I keep finding myself on stages or panels where an AI-struck person says something like, "AI is the next industrial revolution. It will change everything we do. It will let anyone create important works of art. It will cure cancer. It will take us to space. It will solve the climate crisis."
Or sometimes it's an AI critic, but that person's criticism is really more "criti-hype," which is when you accept tech industry hype claims at face value, and then criticize them rather than questioning them:
AI criti-hype might ask what we'll do once AI takes all our jobs, or what we'll do when AI replaces the government or teachers or doctors, or what we'll do when AI can bypass our critical faculties and brainwash us or drive us all mad.
What do you say to that? I usually start by talking about whether there's any economic basis for keeping the AI servers running. AI is – by far – the money-losingest venture in human history, and it's practically impossible to overstate just how bad the AI business is. Not only does AI have terrible unit economics, those unit economics are getting worse over time:
AI's happiest customers cite cost-benefit calculations that depend on truly unimaginable subsidies from the AI companies, who are basically selling $100 bills for $5 apiece. It would be pretty amazing if you couldn't find people who'd extol the virtues of this arrangement. But when AI companies try to raise the price of those $100 bills to, say, $20 apiece, those ecstatic customers fly into a rage and start loudly proclaiming that AI is so inefficient that they will lose money on this arrangement:
Now, it shouldn't fall to me, a card-carrying member of the Democratic Socialists of America, to point out that capitalist enterprises require profits to be sustainable. You can't keep a business afloat by selling $100 bills for $5, nor for $20. You can't even make a profit selling $100 bills for $100 apiece! For a company to succeed, it needs to take in more than it expends.
AI is a money-furnace, and AI hustlers are clearly on the hunt for a way to force all of us to feed every dime we've got to it. Elon Musk's (now scuttled) gambit to make every pension saver in America bail out Grok (and Twitter, but at a mere $44b, the losses from Twitter are dwarfed by the titanic losses from Grok) was the most ambitious and shameless population-scale bag-holder scheme, but it's not the only one:
So before we ask about the capabilities AI will acquire in the future, we should at least give some consideration to the question of whether anyone will be willing to fund the development of those capabilities, and if so, where the money would come from? Likewise, before we ask whether AI can perform adequately in a job, we should at least consider the possibility that the company that sells that AI tool will be bankrupt in a year or two. When we fight about data-center buildout, we mostly talk about the (considerable) environmental downsides to them – but what about the question of what we will do with these data-centers after their owners go bankrupt, possibly even before they can be provisioned with electricity? How many laser-tag arenas do we actually need?
This is just one example of the questions that you could spend days unpacking, which make many of the other questions about AI a little silly. Like, even if you think there are limitless returns to scale for creating new AI capabilities, which means that if we keep the money-furnace burning it's only a matter of time until it powers a cure for cancer and the end of the climate emergency, how much money do we need to shovel into the furnace before that happens, and where will it come from? There are plenty of cancer researchers who have promising approaches they haven't been able to pursue due to funding shortfalls.
Unless there's some way to estimate how much money we have to give to AI companies before they cure cancer, we should at least consider the possibility that the true sum is "more money than exists now and that will ever exist." We should also consider that whatever benefits to cancer research that AI might deliver could come with a higher price-tag than the promising cancer research we're dropping because we can't find far more modest sums.
Likewise, it may be that the amount of CO2 that AI will generate atmosphere before it "solves climate change" will render Earth permanently unfit for humans, consuming the only habitable planet capable of sustaining human life in the known universe. I mean, I suppose that's one way to "solve" climate change, but it's a pretty drastic solution.
My next book (out later this month) is The Reverse Centaur's Guide to Life After AI. I wrote it because I was frustrated by other people demanding that I talk to them about AI, and then handing me 800 words or 13 minutes to address fifty nebulous, poorly supported claims about AI:
Now that I'm about to go out on the road with the book, I find myself frustrated anew by the need to try and pull together a compact way to address the broad, incoherent claims the industry uses to keep its bubble inflated and the money furnaces roaring. The series of essays I've developed here on Pluralistic are part of that effort:
But it occurred to me that this whole enterprise of making sense of AI needs to be framed in the context of the messiness of AI itself, and AI boosters' overwhelming, promiscuous and disjointed Gish Gallop.
Mark Zuckerberg announces mind-control ray (again)
I'm on a 20+ city book tour for my new novel PICKS AND SHOVELS. Catch me in PITTSBURGH on May 15 at WHITE WHALE BOOKS, and in PDX on Jun 20 at BARNES AND NOBLE with BUNNIE HUANG. More tour dates (London, Manchester) here.
Mark Zuckerberg has told investors how he plans to make back the tens of billions he's spending on AI: he's going to use it to make advertisements that can bypass our critical faculties and convince anyone to buy anything. In other words, Meta will make an AI mind-control ray and rent it out to grateful advertisers.
Here, Zuck is fulfilling the fundamental duty of every CEO of every high-growth tech company: explaining how his company will continue to grow. These growth stories are key, because growth stocks trade at a huge premium relative to the stocks of "mature" companies. Every dollar Meta brings in boosts their share price to a much greater degree than the dollars earned by companies with similar rates of profit, but slower rates of growth. This premium represents a bet by investors that Meta will continue to grow, which means that the instant Meta stops growing, the value of its shares will plummet, to reflect the fact that it is a "mature" company, not a "growth" company.
So Zuck needs to do everything he can to keep investors believing that Meta will continue to grow. After all, Zuck's key employees and top managers all take much (or even most!) of their compensation in Meta stock, which means that the instant the company stops growing, those workers' pay will plummet and they will seek employment elsewhere, depriving Meta of the workers it needs to successfully create or conquer a new market and once again become a growth stock.
This is why Zuck keeps telling stories. The most important story Zuck tells is about himself, the boy genius who converted a tool for nonconsensually rating the fuckability of Harvard undergrads into a social media monopoly with four billion users. Zuck's cult of personality isn't the product of mere narcissism – it's a tool for creating the material conditions for ongoing investor confidence:
If Zuck is a boy genius, then Zuck's pronouncements take on the character of prophesy. When Zuck announced the "pivot to video," investors poured tens of billions into Facebook stock and into video-first online news production, despite the fact that Zuck was obviously lying:
The "boy genius" story is an example of Silicon Valley's storied "reality distortion field," pioneered by Steve Jobs. Like Jobs, Zuck is a Texas marksman, who fires a shotgun into the side of a barn and then draws a target around the holes. Jobs is remembered for his successes, and forgiven his (many, many) flops, and so is Zuck. The fact that pivot to video was well understood to have been a catastrophic scam didn't stop people from believing Zuck when he announced "metaverse."
Zuck lost more than $70b on metaverse, but, being a boy genius Texas marksman, he is still able to inspire confidence from credulous investors. Zuck's AI initiatives generated huge interest in Meta's stock, with investors betting that Zuck would find ways to keep Meta's growth going, despite the fact that AI has the worst unit economics of any tech venture in living memory. AI is a business that gets more expensive as time goes on, and where the market's willingness to pay goes down over time. This makes the old dotcom economics of "losing money on every sale, but making it up in volume" look positively rosy:
https://www.wheresyoured.at/reality-check/
Now, Zuck has finally described how he's going to turn AI's terrible economics around: he's going to ask AI to design his advertisers' campaigns, and these will be so devastatingly effective that advertisers will pay a huge premium to advertise on Meta:
This narrative is especially galling because it's literally the same story Zuck has been telling for decades: "Facebook has built a mind-control out of Big Data, and we can sell anything to anyone":
This is a facially absurd proposition. After all, everyone who's ever claimed to have perfected mind-control – Rasputin, Mesmer, MK-ULTRA, neurolinguistic programming grifters and pathetic "pick up artists" – was a liar. Either they were lying to themselves, or to everyone else. Or both.
But many of tech's critics helped sell this narrative (and thus helped Meta sell ads). Many critics have fallen prey to the sin of "criti-hype," Lee Vinsel's term for critiquing the claims of your adversary without bothering to ask whether they are true:
The project of convincing investors that tech's "dopamine hackers" had perfected mind-control with warmed over, non-replicable Skinnerian behavior-mod techniques and mass surveillance sold a hell of a lot of ads. After all, if there's one kind of person the advertising sector has always been able to sell to, it's advertising executives, who are the easiest of marks for a story about how easy it is to trick the public into buying whatever you're selling:
Every ad-tech sales-bro who takes a meeting with an advertising executive finds himself pushing on an open door. Advertisers desperately wants to believe in mind-control rays. Think of the department store magnate John Wannamaker, who said, "half my advertising spending is wasted – I just don't know which half." Imagine: some advertising exec convinced John Wannamaker that he was only wasting half of his advertising spending!
I've long maintained that the threat from AI to workers isn't that AI can do your job – it's that an AI salesman can convince your boss to fire you and replace you with an AI that can't do your job:
The corollary here is that it doesn't matter if AI can design ads that work, not so long as an AI ad salesman can sell this proposition to an advertisers, and not so long as a tech CEO can sell it to investors.
AI keeps passing the worst kinds of Turing tests – for example, it's great at helping people who are prone to life-destroying hallucinations that they are talking to God:
Zuck kept up his growth story with this mind control narrative for more than a decade, got caught committing a string of spectacular frauds, and then lured investors back into his stock offerings by telling the same story. This isn't just an indictment of Zuck, it's a stinging rebuke to the whole idea that markets are a kind of infallible computer for assessing and operationalizing information. The market's "thought process" demonstrably lacks the object permanence that most babies acquire by the time they are a year old. You can tell when your child has acquired object permanence by the fact that they cease to enjoy "peek-a-boo" (object permanence means they understand where you have gone when your face is hidden).
In claiming that AI will give him an infinite growth mind-control ray, Mark Zuckerberg is challenging the market to a game of peek-a-boo – and he's winning.
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:
I assure you, an AI didn’t write a terrible “George Carlin” routine
There are only TWO MORE DAYS left in the Kickstarter for the audiobook of The Bezzle, the sequel to Red Team Blues, narrated by @wilwheaton! You can pre-order the audiobook and ebook, DRM free, as well as the hardcover, signed or unsigned. There's also bundles with Red Team Blues in ebook, audio or paperback.
On Hallowe'en 1974, Ronald Clark O'Bryan murdered his son with poisoned candy. He needed the insurance money, and he knew that Halloween poisonings were rampant, so he figured he'd get away with it. He was wrong:
The stories of Hallowe'en poisonings were just that – stories. No one was poisoning kids on Hallowe'en – except this monstrous murderer, who mistook rampant scare stories for truth and assumed (incorrectly) that his murder would blend in with the crowd.
Last week, the dudes behind the "comedy" podcast Dudesy released a "George Carlin" comedy special that they claimed had been created, holus bolus, by an AI trained on the comedian's routines. This was a lie. After the Carlin estate sued, the dudes admitted that they had written the (remarkably unfunny) "comedy" special:
As I've written, we're nowhere near the point where an AI can do your job, but we're well past the point where your boss can be suckered into firing you and replacing you with a bot that fails at doing your job:
AI systems can do some remarkable party tricks, but there's a huge difference between producing a plausible sentence and a good one. After the initial rush of astonishment, the stench of botshit becomes unmistakable:
Some of this botshit comes from people who are sold a bill of goods: they're convinced that they can make a George Carlin special without any human intervention and when the bot fails, they manufacture their own botshit, assuming they must be bad at prompting the AI.
This is an old technology story: I had a friend who was contracted to livestream a Canadian awards show in the earliest days of the web. They booked in multiple ISDN lines from Bell Canada and set up an impressive Mbone encoding station on the wings of the stage. Only one problem: the ISDNs flaked (this was a common problem with ISDNs!). There was no way to livecast the show.
Nevertheless, my friend's boss's ordered him to go on pretending to livestream the show. They made a big deal of it, with all kinds of cool visualizers showing the progress of this futuristic marvel, which the cameras frequently lingered on, accompanied by overheated narration from the show's hosts.
The weirdest part? The next day, my friend – and many others – heard from satisfied viewers who boasted about how amazing it had been to watch this show on their computers, rather than their TVs. Remember: there had been no stream. These people had just assumed that the problem was on their end – that they had failed to correctly install and configure the multiple browser plugins required. Not wanting to admit their technical incompetence, they instead boasted about how great the show had been. It was the Emperor's New Livestream.
Perhaps that's what happened to the Dudesy bros. But there's another possibility: maybe they were captured by their own imaginations. In "Genesis," an essay in the 2007 collection The Creationists, EL Doctorow (no relation) describes how the ancient Babylonians were so poleaxed by the strange wonder of the story they made up about the origin of the universe that they assumed that it must be true. They themselves weren't nearly imaginative enough to have come up with this super-cool tale, so God must have put it in their minds:
That seems to have been what happened to the Air Force colonel who falsely claimed that a "rogue AI-powered drone" had spontaneously evolved the strategy of killing its operator as a way of clearing the obstacle to its main objective, which was killing the enemy:
This never happened. It was – in the chagrined colonel's words – a "thought experiment." In other words, this guy – who is the USAF's Chief of AI Test and Operations – was so excited about his own made up story that he forgot it wasn't true and told a whole conference-room full of people that it had actually happened.
Maybe that's what happened with the George Carlinbot 3000: the Dudesy dudes fell in love with their own vision for a fully automated luxury Carlinbot and forgot that they had made it up, so they just cheated, assuming they would eventually be able to make a fully operational Battle Carlinbot.
That's basically the Theranos story: a teenaged "entrepreneur" was convinced that she was just about to produce a seemingly impossible, revolutionary diagnostic machine, so she faked its results, abetted by investors, customers and others who wanted to believe:
https://en.wikipedia.org/wiki/Theranos
The thing about stories of AI miracles is that they are peddled by both AI's boosters and its critics. For boosters, the value of these tall tales is obvious: if normies can be convinced that AI is capable of performing miracles, they'll invest in it. They'll even integrate it into their product offerings and then quietly hire legions of humans to pick up the botshit it leaves behind. These abettors can be relied upon to keep the defects in these products a secret, because they'll assume that they've committed an operator error. After all, everyone knows that AI can do anything, so if it's not performing for them, the problem must exist between the keyboard and the chair.
But this would only take AI so far. It's one thing to hear implausible stories of AI's triumph from the people invested in it – but what about when AI's critics repeat those stories? If your boss thinks an AI can do your job, and AI critics are all running around with their hair on fire, shouting about the coming AI jobpocalypse, then maybe the AI really can do your job?
There's a name for this kind of criticism: "criti-hype," coined by Lee Vinsel, who points to many reasons for its persistence, including the fact that it constitutes an "academic business-model":
to cover customers' and users' embarrassment when the AI doesn't perform;
AI dreamers so high on their own supply that they can't tell truth from fantasy;
A business-model for doomsayers who form an unholy alliance with AI companies by parroting their silliest hype in warning form.
But there's a fifth motivation for criti-hype: to simplify otherwise tedious and complex situations. As Jamie Zawinski writes, this is the motivation behind the obvious lie that the "autonomous cars" on the streets of San Francisco have no driver:
One of the widely discussed revelations in the wake of the incident was that Cruise employed 1.5 skilled technical remote overseers for every one of its "self-driving" cars. In other words, they had replaced a single low-waged cab driver with 1.5 higher-paid remote operators.
As Zawinski writes, SFPD is well aware that there's a human being (or more than one human being) responsible for every one of these cars – someone who is formally at fault when the cars injure people or damage property. Nevertheless, SFPD and SFMTA maintain that these cars can't be cited for moving violations because "no one is driving them."
But figuring out who which person is responsible for a moving violation is "complicated and annoying to deal with," so the fiction persists.
(Zawinski notes that even when these people are held responsible, they're a "moral crumple zone" for the company that decided to enroll whole cities in nonconsensual murderbot experiments.)
Automation hype has always involved hidden humans. The most famous of these was the "mechanical Turk" hoax: a supposed chess-playing robot that was just a puppet operated by a concealed human operator wedged awkwardly into its carapace.
This pattern repeats itself through the ages. Thomas Jefferson "replaced his slaves" with dumbwaiters – but of course, dumbwaiters don't replace slaves, they hide slaves:
The modern Mechanical Turk – a division of Amazon that employs low-waged "clickworkers," many of them overseas – modernizes the dumbwaiter by hiding low-waged workforces behind a veneer of automation. The MTurk is an abstract "cloud" of human intelligence (the tasks MTurks perform are called "HITs," which stands for "Human Intelligence Tasks").
This is such a truism that techies in India joke that "AI" stands for "absent Indians." Or, to use Jathan Sadowski's wonderful term: "Potemkin AI":
https://reallifemag.com/potemkin-ai/
This Potemkin AI is everywhere you look. When Tesla unveiled its humanoid robot Optimus, they made a big flashy show of it, promising a $20,000 automaton was just on the horizon. They failed to mention that Optimus was just a person in a robot suit:
And yet, we keep falling for it. It's no wonder, really: criti-hype rewards so many different people in so many different ways that it truly offers something for everyone.
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 US Copyright Office offers creative workers a powerful labor protective.
Studio execs love plausible sentence generators because they have a workflow that looks exactly like a writer-exec dynamic, only without any eye-rolling at the stupid “notes” the exec gives the writer.
All an exec wants is to bark out “Hey, nerd, make me another E.T., except make the hero a dog, and set it on Mars.” After the writer faithfully produces this script, the exec can say, “OK, put put a love interest in the second act, and give me a big gunfight at the climax,” and the writer dutifully makes the changes.
This is exactly how prompting an LLM works.
A writer and a studio exec are lost in the desert, dying of thirst.
Just as they are about to perish, they come upon an oasis, with a cool sparkling pool of water.
The writer drops to their knees and thanks the fates for saving their lives.
But then, the studio exec unzips his pants, pulls out his cock and starts pissing in the water.
“What the fuck are you doing?” the writer demands.
“Don’t worry,” the exec says, “I’m making it better.”
- Everything Made By an AI Is In the Public Domain: The US Copyright Office offers creative workers a powerful labor protective
THIS IS THE LAST DAY FOR MY KICKSTARTER for the audiobook for "The Internet Con: How To Seize the Means of Computation," a Big Tech disassembly manual to disenshittify the web and make a new, good internet to succeed the old, good internet. It's a DRM-free book, which means Audible won't carry it, so this crowdfunder is essential. Back now to get the audio, Verso hardcover and ebook:
http://seizethemeansofcomputation.org
Going to Burning Man? Catch me on Tuesday at 2:40pm on the Center Camp Stage for a talk about enshittification and how to reverse it; on Wednesday at noon, I'm hosting Dr Patrick Ball at Liminal Labs (6:15/F) for a talk on using statistics to prove high-level culpability in the recruitment of child soldiers.
On September 6 at 7pm, I'll be hosting Naomi Klein at the LA Public Library for the launch of Doppelganger.
On September 12 at 7pm, I'll be at Toronto's Another Story Bookshop with my new book The Internet Con: How to Seize the Means of Computation.
The US Copyright Office offers creative workers a powerful labor protective.
That means that for a work to be eligible for copyright in the USA, it must satisfy three criteria:
1. It must be creative. Copyright does not apply to non-creative works (say, a phone book listing everyone in a town in alphabetical order), even if the work required a lot of labor. Copyright does not protect effort, it protects creativity. You can spend your whole life making a phone book and get no copyright, but the haiku you toss off in ten seconds while drunk gets copyright’s full protection.
2. It must be tangible. Copyright only applies to creative works that are “fixed in a tangible medium.” A dance isn’t copyrightable, but a video of someone dancing is, as is a written description of the dance in choreographers’ notation. A singer can’t copyright the act of singing, but they can copyright the recording of the song.
3. It must be of human authorship. Only humans are eligible for copyright. A beehive’s combs may be beautiful, but they can’t be copyrighted. An elephant’s paintings may be creative, but they can’t be copyrighted. A monkey’s selfie may be iconic, but it can’t be copyrighted.
The works an algorithm generates —be they still images, audio recordings, text, or videos — cannot be copyrighted.
For creative workers, this is huge. Our bosses, like all bosses, relish the thought of firing us all and making us homeless. You will never love anything as much as your boss hates paying you. That’s why the most rampant form of theft in America is wage theft. Just the thought of firing workers and replacing them with chatbots is enough to invoke dangerous, persistent priapism in the boardrooms of corporate America.
- Everything Made By an AI Is In the Public Domain: The US Copyright Office offers creative workers a powerful labor protective
THIS IS THE LAST DAY FOR MY KICKSTARTER for the audiobook for "The Internet Con: How To Seize the Means of Computation," a Big Tech disassembly manual to disenshittify the web and make a new, good internet to succeed the old, good internet. It's a DRM-free book, which means Audible won't carry it, so this crowdfunder is essential. Back now to get the audio, Verso hardcover and ebook:
http://seizethemeansofcomputation.org
Going to Burning Man? Catch me on Tuesday at 2:40pm on the Center Camp Stage for a talk about enshittification and how to reverse it; on Wednesday at noon, I'm hosting Dr Patrick Ball at Liminal Labs (6:15/F) for a talk on using statistics to prove high-level culpability in the recruitment of child soldiers.
On September 6 at 7pm, I'll be hosting Naomi Klein at the LA Public Library for the launch of Doppelganger.
On September 12 at 7pm, I'll be at Toronto's Another Story Bookshop with my new book The Internet Con: How to Seize the Means of Computation.
The lie that raced around the world before the truth got its boots on.
Take away every consequential activity through which AI harms people, and all you’ve got left is low-margin activities like writing SEO garbage, lengthy reminisces about “the first time I ate an egg” that help an omelette recipe float to the top of a search result. Sure, you can put 95 percent of the commercial illustrators on the breadline, but their total wages don’t rise to one percent of the valuation of the big AI companies.
For those sky-high valuations to remain intact until the investors can cash out, we need to think about AI as a powerful, transformative technology, not as a better autocomplete.
We literally just sat through this movie, and it sucked. Remember when blockchain was going to be worth trillions, and anyone who didn’t get in on the ground floor could “have fun being poor?”
At the time, we were told that the answer to the problems of blockchain were exotic, new forms of regulation that accommodated the “innovation” of crypto. Under no circumstances should we attempt to staunch the rampant fraud and theft by applying boring old securities and commodities and money-laundering regulations. To do that would be to recognize that “fin-tech” is just a synonym for “unlicensed bank.”
The pitchmen who made out like bandits on crypto — leaving mom-and-pop investors holding the bag — are precisely the same people who are beating the drum for AI today.
-Ayyyyyy Eyeeeee: The lie that raced around the world before the truth got its boots on
Back in 2017 Long Island Ice Tea — known for its undistinguished, barely drinkable sugar-water — changed its name to “Long Blockchain Corp.” Its shares surged to a peak of 400% over their pre-announcement price. The company announced no specific integrations with any kind of blockchain, nor has it made any such integrations since.
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:
LBCC was subsequently delisted from NASDAQ after settling with the SEC over fraudulent investor statements. Today, the company trades over the counter and its market cap is $36m, down from $138m.
The most remarkable thing about this incredibly stupid story is that LBCC wasn’t the peak of the blockchain bubble — rather, it was the start of blockchain’s final pump-and-dump. By the standards of 2022’s blockchain grifters, LBCC was small potatoes, a mere $138m sugar-water grift.
They didn’t have any NFTs, no wash trades, no ICO. They didn’t have a Superbowl ad. They didn’t steal billions from mom-and-pop investors while proclaiming themselves to be “Effective Altruists.” They didn’t channel hundreds of millions to election campaigns through straw donations and other forms of campaing finance frauds. They didn’t even open a crypto-themed hamburger restaurant where you couldn’t buy hamburgers with crypto:
They were amateurs. Their attempt to “make fetch happen” only succeeded for a brief instant. By contrast, the superpredators of the crypto bubble were able to make fetch happen over an improbably long timescale, deploying the most powerful reality distortion fields since Pets.com.
Anything that can’t go on forever will eventually stop. We’re told that trillions of dollars’ worth of crypto has been wiped out over the past year, but these losses are nowhere to be seen in the real economy — because the “wealth” that was wiped out by the crypto bubble’s bursting never existed in the first place.
Like any Ponzi scheme, crypto was a way to separate normies from their savings through the pretense that they were “investing” in a vast enterprise — but the only real money (“fiat” in cryptospeak) in the system was the hardscrabble retirement savings of working people, which the bubble’s energetic inflaters swapped for illiquid, worthless shitcoins.
We’ve stopped believing in the illusory billions. Sam Bankman-Fried is under house arrest. But the people who gave him money — and the nimbler Ponzi artists who evaded arrest — are looking for new scams to separate the marks from their money.
Take Morganstanley, who spent 2021 and 2022 hyping cryptocurrency as a massive growth opportunity:
Today, Morganstanley wants you to know that AI is a $6 trillion opportunity.
They’re not alone. The CEOs of Endeavor, Buzzfeed, Microsoft, Spotify, Youtube, Snap, Sports Illustrated, and CAA are all out there, pumping up the AI bubble with every hour that god sends, declaring that the future is AI.
Google and Bing are locked in an arms-race to see whose search engine can attain the speediest, most profound enshittification via chatbot, replacing links to web-pages with florid paragraphs composed by fully automated, supremely confident liars:
Blockchain was a solution in search of a problem. So is AI. Yes, Buzzfeed will be able to reduce its wage-bill by automating its personality quiz vertical, and Spotify’s “AI DJ” will produce slightly less terrible playlists (at least, to the extent that Spotify doesn’t put its thumb on the scales by inserting tracks into the playlists whose only fitness factor is that someone paid to boost them).
But even if you add all of this up, double it, square it, and add a billion dollar confidence interval, it still doesn’t add up to what Bank Of America analysts called “a defining moment — like the internet in the ’90s.” For one thing, the most exciting part of the “internet in the ‘90s” was that it had incredibly low barriers to entry and wasn’t dominated by large companies — indeed, it had them running scared.
The AI bubble, by contrast, is being inflated by massive incumbents, whose excitement boils down to “This will let the biggest companies get much, much bigger and the rest of you can go fuck yourselves.” Some revolution.
AI has all the hallmarks of a classic pump-and-dump, starting with terminology. AI isn’t “artificial” and it’s not “intelligent.” “Machine learning” doesn’t learn. On this week’s Trashfuture podcast, they made an excellent (and profane and hilarious) case that ChatGPT is best understood as a sophisticated form of autocomplete — not our new robot overlord.
We all know that autocomplete is a decidedly mixed blessing. Like all statistical inference tools, autocomplete is profoundly conservative — it wants you to do the same thing tomorrow as you did yesterday (that’s why “sophisticated” ad retargeting ads show you ads for shoes in response to your search for shoes). If the word you type after “hey” is usually “hon” then the next time you type “hey,” autocomplete will be ready to fill in your typical following word — even if this time you want to type “hey stop texting me you freak”:
And when autocomplete encounters a new input — when you try to type something you’ve never typed before — it tries to get you to finish your sentence with the statistically median thing that everyone would type next, on average. Usually that produces something utterly bland, but sometimes the results can be hilarious. Back in 2018, I started to text our babysitter with “hey are you free to sit” only to have Android finish the sentence with “on my face” (not something I’d ever typed!):
Modern autocomplete can produce long passages of text in response to prompts, but it is every bit as unreliable as 2018 Android SMS autocomplete, as Alexander Hanff discovered when ChatGPT informed him that he was dead, even generating a plausible URL for a link to a nonexistent obit in The Guardian:
Of course, the carnival barkers of the AI pump-and-dump insist that this is all a feature, not a bug. If autocomplete says stupid, wrong things with total confidence, that’s because “AI” is becoming more human, because humans also say stupid, wrong things with total confidence.
Exhibit A is the billionaire AI grifter Sam Altman, CEO if OpenAI — a company whose products are not open, nor are they artificial, nor are they intelligent. Altman celebrated the release of ChatGPT by tweeting “i am a stochastic parrot, and so r u.”
This was a dig at the “stochastic parrots” paper, a comprehensive, measured roundup of criticisms of AI that led Google to fire Timnit Gebru, a respected AI researcher, for having the audacity to point out the Emperor’s New Clothes:
Gebru’s co-author on the Parrots paper was Emily M Bender, a computational linguistics specialist at UW, who is one of the best-informed and most damning critics of AI hype. You can get a good sense of her position from Elizabeth Weil’s New York Magazine profile:
Bender has made many important scholarly contributions to her field, but she is also famous for her rules of thumb, which caution her fellow scientists not to get high on their own supply:
Please do not conflate word form and meaning
Mind your own credulity
As Bender says, we’ve made “machines that can mindlessly generate text, but we haven’t learned how to stop imagining the mind behind it.” One potential tonic against this fallacy is to follow an Italian MP’s suggestion and replace “AI” with “SALAMI” (“Systematic Approaches to Learning Algorithms and Machine Inferences”). It’s a lot easier to keep a clear head when someone asks you, “Is this SALAMI intelligent? Can this SALAMI write a novel? Does this SALAMI deserve human rights?”
Bender’s most famous contribution is the “stochastic parrot,” a construct that “just probabilistically spits out words.” AI bros like Altman love the stochastic parrot, and are hellbent on reducing human beings to stochastic parrots, which will allow them to declare that their chatbots have feature-parity with human beings.
At the same time, Altman and Co are strangely afraid of their creations. It’s possible that this is just a shuck: “I have made something so powerful that it could destroy humanity! Luckily, I am a wise steward of this thing, so it’s fine. But boy, it sure is powerful!”
They’ve been playing this game for a long time. People like Elon Musk (an investor in OpenAI, who is hoping to convince the EU Commission and FTC that he can fire all of Twitter’s human moderators and replace them with chatbots without violating EU law or the FTC’s consent decree) keep warning us that AI will destroy us unless we tame it.
There’s a lot of credulous repetition of these claims, and not just by AI’s boosters. AI critics are also prone to engaging in what Lee Vinsel calls criti-hype: criticizing something by repeating its boosters’ claims without interrogating them to see if they’re true:
There are better ways to respond to Elon Musk warning us that AIs will emulsify the planet and use human beings for food than to shout, “Look at how irresponsible this wizard is being! He made a Frankenstein’s Monster that will kill us all!” Like, we could point out that of all the things Elon Musk is profoundly wrong about, he is most wrong about the philosophical meaning of Wachowksi movies:
But even if we take the bros at their word when they proclaim themselves to be terrified of “existential risk” from AI, we can find better explanations by seeking out other phenomena that might be triggering their dread. As Charlie Stross points out, corporations are Slow AIs, autonomous artificial lifeforms that consistently do the wrong thing even when the people who nominally run them try to steer them in better directions:
Imagine the existential horror of a ultra-rich manbaby who nominally leads a company, but can’t get it to follow: “everyone thinks I’m in charge, but I’m actually being driven by the Slow AI, serving as its sock puppet on some days, its golem on others.”
Ted Chiang nailed this back in 2017 (the same year of the Long Island Blockchain Company):
There’s a saying, popularized by Fredric Jameson, that it’s easier to imagine the end of the world than to imagine the end of capitalism. It’s no surprise that Silicon Valley capitalists don’t want to think about capitalism ending. What’s unexpected is that the way they envision the world ending is through a form of unchecked capitalism, disguised as a superintelligent AI. They have unconsciously created a devil in their own image, a boogeyman whose excesses are precisely their own.
Chiang is still writing some of the best critical work on “AI.” His February article in the New Yorker, “ChatGPT Is a Blurry JPEG of the Web,” was an instant classic:
[AI] hallucinations are compression artifacts, but — like the incorrect labels generated by the Xerox photocopier — they are plausible enough that identifying them requires comparing them against the originals, which in this case means either the Web or our own knowledge of the world.
“AI” is practically purpose-built for inflating another hype-bubble, excelling as it does at producing party-tricks — plausible essays, weird images, voice impersonations. But as Princeton’s Matthew Salganik writes, there’s a world of difference between “cool” and “tool”:
Nature can claim “conversational AI is a game-changer for science” but “there is a huge gap between writing funny instructions for removing food from home electronics and doing scientific research.” Salganik tried to get ChatGPT to help him with the most banal of scholarly tasks — aiding him in peer reviewing a colleague’s paper. The result? “ChatGPT didn’t help me do peer review at all; not one little bit.”
The criti-hype isn’t limited to ChatGPT, of course — there’s plenty of (justifiable) concern about image and voice generators and their impact on creative labor markets, but that concern is often expressed in ways that amplify the self-serving claims of the companies hoping to inflate the hype machine.
One of the best critical responses to the question of image- and voice-generators comes from Kirby Ferguson, whose final Everything Is a Remix video is a superb, visually stunning, brilliantly argued critique of these systems:
https://www.youtube.com/watch?v=rswxcDyotXA
One area where Ferguson shines is in thinking through the copyright question — is there any right to decide who can study the art you make? Except in some edge cases, these systems don’t store copies of the images they analyze, nor do they reproduce them:
For creators, the important material question raised by these systems is economic, not creative: will our bosses use them to erode our wages? That is a very important question, and as far as our bosses are concerned, the answer is a resounding yes.
Markets value automation primarily because automation allows capitalists to pay workers less. The textile factory owners who purchased automatic looms weren’t interested in giving their workers raises and shorting working days.
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They wanted to fire their skilled workers and replace them with small children kidnapped out of orphanages and indentured for a decade, starved and beaten and forced to work, even after they were mangled by the machines. Fun fact: Oliver Twist was based on the bestselling memoir of Robert Blincoe, a child who survived his decade of forced labor:
Today, voice actors sitting down to record for games companies are forced to begin each session with “My name is ______ and I hereby grant irrevocable permission to train an AI with my voice and use it any way you see fit.”
Let’s be clear here: there is — at present — no firmly established copyright over voiceprints. The “right” that voice actors are signing away as a non-negotiable condition of doing their jobs for giant, powerful monopolists doesn’t even exist. When a corporation makes a worker surrender this right, they are betting that this right will be created later in the name of “artists’ rights” — and that they will then be able to harvest this right and use it to fire the artists who fought so hard for it.
There are other approaches to this. We could support the US Copyright Office’s position that machine-generated works are not works of human creative authorship and are thus not eligible for copyright — so if corporations wanted to control their products, they’d have to hire humans to make them:
Or we could create collective rights that belong to all artists and can’t be signed away to a corporation. That’s how the right to record other musicians’ songs work — and it’s why Taylor Swift was able to re-record the masters that were sold out from under her by evil private-equity bros::
Whatever we do as creative workers and as humans entitled to a decent life, we can’t afford drink the Blockchain Iced Tea. That means that we have to be technically competent, to understand how the stochastic parrot works, and to make sure our criticism doesn’t just repeat the marketing copy of the latest pump-and-dump.
Today (Mar 9), you can catch me in person in Austin at the UT School of Design and Creative Technologies, and remotely at U Manitoba’s Ethics of Emerging Tech Lecture.
Tomorrow (Mar 10), Rebecca Giblin and I kick off the SXSW reading series.
CC BY 3.0
https://creativecommons.org/licenses/by/3.0/deed.en
[Image ID: A graph depicting the Gartner hype cycle. A pair of HAL 9000's glowing red eyes are chasing each other down the slope from the Peak of Inflated Expectations to join another one that is at rest in the Trough of Disillusionment. It, in turn, sits atop a vast cairn of HAL 9000 eyes that are piled in a rough pyramid that extends below the graph to a distance of several times its height.]
Tonight (November 27), I'm appearing at the Toronto Metro Reference Library with Facebook whistleblower Frances Haugen.
On November 29, I'm at NYC's Strand Books with my novel The Lost Cause, a solarpunk tale of hope and danger that Rebecca Solnit called "completely delightful."
Last week's spectacular OpenAI soap-opera hijacked the attention of millions of normal, productive people and nonsensually crammed them full of the fine details of the debate between "Effective Altruism" (doomers) and "Effective Accelerationism" (AKA e/acc), a genuinely absurd debate that was allegedly at the center of the drama.
Very broadly speaking: the Effective Altruists are doomers, who believe that Large Language Models (AKA "spicy autocomplete") will someday become so advanced that it could wake up and annihilate or enslave the human race. To prevent this, we need to employ "AI Safety" – measures that will turn superintelligence into a servant or a partner, nor an adversary.
Contrast this with the Effective Accelerationists, who also believe that LLMs will someday become superintelligences with the potential to annihilate or enslave humanity – but they nevertheless advocate for faster AI development, with fewer "safety" measures, in order to produce an "upward spiral" in the "techno-capital machine."
Once-and-future OpenAI CEO Altman is said to be an accelerationists who was forced out of the company by the Altruists, who were subsequently bested, ousted, and replaced by Larry fucking Summers. This, we're told, is the ideological battle over AI: should cautiously progress our LLMs into superintelligences with safety in mind, or go full speed ahead and trust to market forces to tame and harness the superintelligences to come?
This "AI debate" is pretty stupid, proceeding as it does from the foregone conclusion that adding compute power and data to the next-word-predictor program will eventually create a conscious being, which will then inevitably become a superbeing. This is a proposition akin to the idea that if we keep breeding faster and faster horses, we'll get a locomotive:
As Molly White writes, this isn't much of a debate. The "two sides" of this debate are as similar as Tweedledee and Tweedledum. Yes, they're arrayed against each other in battle, so furious with each other that they're tearing their hair out. But for people who don't take any of this mystical nonsense about spontaneous consciousness arising from applied statistics seriously, these two sides are nearly indistinguishable, sharing as they do this extremely weird belief. The fact that they've split into warring factions on its particulars is less important than their unified belief in the certain coming of the paperclip-maximizing apocalypse:
White points out that there's another, much more distinct side in this AI debate – as different and distant from Dee and Dum as a Beamish Boy and a Jabberwork. This is the side of AI Ethics – the side that worries about "today’s issues of ghost labor, algorithmic bias, and erosion of the rights of artists and others." As White says, shifting the debate to existential risk from a future, hypothetical superintelligence "is incredibly convenient for the powerful individuals and companies who stand to profit from AI."
After all, both sides plan to make money selling AI tools to corporations, whose track record in deploying algorithmic "decision support" systems and other AI-based automation is pretty poor – like the claims-evaluation engine that Cigna uses to deny insurance claims:
On a graph that plots the various positions on AI, the two groups of weirdos who disagree about how to create the inevitable superintelligence are effectively standing on the same spot, and the people who worry about the actual way that AI harms actual people right now are about a million miles away from that spot.
There's that old programmer joke, "There are 10 kinds of people, those who understand binary and those who don't." But of course, that joke could just as well be, "There are 10 kinds of people, those who understand ternary, those who understand binary, and those who don't understand either":
What's more, the joke could be, "there are 10 kinds of people, those who understand hexadecenary, those who understand pentadecenary, those who understand tetradecenary [und so weiter] those who understand ternary, those who understand binary, and those who don't." That is to say, a "polarized" debate often has people who hold positions so far from the ones everyone is talking about that those belligerents' concerns are basically indistinguishable from one another.
The act of identifying these distant positions is a radical opening up of possibilities. Take the indigenous philosopher chief Red Jacket's response to the Christian missionaries who sought permission to proselytize to Red Jacket's people:
https://historymatters.gmu.edu/d/5790/
Red Jacket's whole rebuttal is a superb dunk, but it gets especially interesting where he points to the sectarian differences among Christians as evidence against the missionary's claim to having a single true faith, and in favor of the idea that his own people's traditional faith could be co-equal among Christian doctrines.
The split that White identifies isn't a split about whether AI tools can be useful. Plenty of us AI skeptics are happy to stipulate that there are good uses for AI. For example, I'm 100% in favor of the Human Rights Data Analysis Group using an LLM to classify and extract information from the Innocence Project New Orleans' wrongful conviction case files:
Automating "extracting officer information from documents – specifically, the officer's name and the role the officer played in the wrongful conviction" was a key step to freeing innocent people from prison, and an LLM allowed HRDAG – a tiny, cash-strapped, excellent nonprofit – to make a giant leap forward in a vital project. I'm a donor to HRDAG and you should donate to them too:
https://hrdag.networkforgood.com/
Good data-analysis is key to addressing many of our thorniest, most pressing problems. As Ben Goldacre recounts in his inaugural Oxford lecture, it is both possible and desirable to build ethical, privacy-preserving systems for analyzing the most sensitive personal data (NHS patient records) that yield scores of solid, ground-breaking medical and scientific insights:
https://www.youtube.com/watch?v=_-eaV8SWdjQ
The difference between this kind of work – HRDAG's exoneration work and Goldacre's medical research – and the approach that OpenAI and its competitors take boils down to how they treat humans. The former treats all humans as worthy of respect and consideration. The latter treats humans as instruments – for profit in the short term, and for creating a hypothetical superintelligence in the (very) long term.
As Terry Pratchett's Granny Weatherwax reminds us, this is the root of all sin: "sin is when you treat people like things":
So much of the criticism of AI misses this distinction – instead, this criticism starts by accepting the self-serving marketing claim of the "AI safety" crowd – that their software is on the verge of becoming self-aware, and is thus valuable, a good investment, and a good product to purchase. This is Lee Vinsel's "Criti-Hype": "taking press releases from startups and covering them with hellscapes":
Criti-hype and AI were made for each other. Emily M Bender is a tireless cataloger of criti-hypeists, like the newspaper reporters who breathlessly repeat " completely unsubstantiated claims (marketing)…sourced to Altman":
Bender, like White, is at pains to point out that the real debate isn't doomers vs accelerationists. That's just "billionaires throwing money at the hope of bringing about the speculative fiction stories they grew up reading – and philosophers and others feeling important by dressing these same silly ideas up in fancy words":
All of this is just a distraction from real and important scientific questions about how (and whether) to make automation tools that steer clear of Granny Weatherwax's sin of "treating people like things." Bender – a computational linguist – isn't a reactionary who hates automation for its own sake. On Mystery AI Hype Theater 3000 – the excellent podcast she co-hosts with Alex Hanna – there is a machine-generated transcript:
https://www.buzzsprout.com/2126417
There is a serious, meaty debate to be had about the costs and possibilities of different forms of automation. But the superintelligence true-believers and their criti-hyping critics keep dragging us away from these important questions and into fanciful and pointless discussions of whether and how to appease the godlike computers we will create when we disassemble the solar system and turn it into computronium.
The question of machine intelligence isn't intrinsically unserious. As a materialist, I believe that whatever makes me "me" is the result of the physics and chemistry of processes inside and around my body. My disbelief in the existence of a soul means that I'm prepared to think that it might be possible for something made by humans to replicate something like whatever process makes me "me."
Ironically, the AI doomers and accelerationists claim that they, too, are materialists – and that's why they're so consumed with the idea of machine superintelligence. But it's precisely because I'm a materialist that I understand these hypotheticals about self-aware software are less important and less urgent than the material lives of people today.
It's because I'm a materialist that my primary concerns about AI are things like the climate impact of AI data-centers and the human impact of biased, opaque, incompetent and unfit algorithmic systems – not science fiction-inspired, self-induced panics over the human race being enslaved by our robot overlords.
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:
