Eventually, the industry will have to uncover some mix of applications that will cover its operating costs, if only to keep the lights on in the face of investor disillusionment (this isn't optional - investor disillusionment is an inevitable part of every bubble).

Now, there are *lots* of low-stakes applications for AI that can run just fine on the current AI technology, despite its many - and seemingly inescapable errors ("hallucinations").

3/

People who use AI to generate illustrations of their D&D characters engaged in epic adventures from their previous gaming session don't care about the odd extra finger. If the chatbot powering a tourist's automatic text-to-translation-to-speech phone tool gets a few words wrong, it's still much better than the alternative of speaking slowly and loudly in your own language while making emphatic hand-gestures.

4/

There are lots of these applications, and many of the people who benefit from them would doubtless pay something for them. The problem - from an AI company's perspective - is that these aren't just low-*stakes*, they're also low-*value*. Their users would pay *something* for them, but not very much.

5/

For AI to keep its servers on through the coming trough of disillusionment, it will have to locate *high*-value applications, too. Economically speaking, the function of low-value applications is to soak up excess capacity and produce value at the margins after the high-value applications pay the bills.

6/

Low-value applications are a side-dish, like the coach seats on an airplane whose total operating expenses are paid by the business class passengers up front. Without the principle income from high-value applications, the servers shut down, and the low-value applications disappear:

locusmag.com/2023/12/commentar…

7/

Cory Doctorow: What Kind of Bubble is AI?

Of course AI is a bubble. It has all the hallmarks of a classic tech bubble. Pick up a rental car at SFO and drive in either direction on the 101 – north to San Francisco, south to Palo Alto – and …

^{Locus Online}

Now, there are lots of high-value applications the AI industry has identified for its products. Broadly speaking, these high-value applications share the same problem: they are all high-stakes, which means they are very sensitive to errors. Mistakes made by apps that produce code, drive cars, or identify cancerous masses on chest X-rays are extremely consequential.

8/

Some businesses may be insensitive to those consequences. Air Canada replaced its human customer service staff with chatbots that just lied to passengers, stealing hundreds of dollars from them in the process.

9/

But the process for getting a refund after you are defrauded by Air Canada's chatbot is so onerous that only one passenger has bothered to go through it, spending ten weeks exhausting all of Air Canada's internal review mechanisms before fighting his case for weeks more at the regulator:

bc.ctvnews.ca/air-canada-s-cha…

There's never just one ant. If this guy was defrauded by an AC chatbot, so were hundreds or thousands of other fliers. Air Canada doesn't have to pay them back.

10/

Air Canada's chatbot gave a B.C. man the wrong information. Now, the airline has to pay for the mistake

Air Canada has been ordered to compensate a B.C. man because its chatbot gave him inaccurate information.

^{British Columbia}

Air Canada is tacitly asserting that, as the country's flagship carrier and near-monopolist, it is too big to fail and too big to jail, which means it's too big to *care*.

Air Canada shows that for some business customers, AI doesn't need to be able to do a worker's job in order to be a smart purchase: a chatbot can replace a worker, *fail* to their worker's job, and still save the company money on balance.

11/

I can't predict whether the world's sociopathic monopolists are numerous and powerful enough to keep the lights on for AI companies through leases for automation systems that let them commit consequence-free free fraud by replacing workers with chatbots that serve as moral crumple-zones for furious customers:

sciencedirect.com/science/arti…

12/

But even stipulating that this is sufficient, it's intrinsically unstable. Anything that can't go on forever eventually stops, and the mass replacement of humans with high-speed fraud software seems likely to stoke the already blazing furnace of modern antitrust:

eff.org/de/deeplinks/2021/08/p…

13/

Party Like It’s 1979: The OG Antitrust Is Back, Baby!

President Biden’s July 9 Executive Order on Promoting Competition in the American Economy is a highly technical, 72-part, fine-grained memo on how to address the ways market concentration harms our lives as workers, citizens, consumers, and beyond.

^{Electronic Frontier Foundation}

Of course, the AI companies have their own answer to this conundrum. A high-stakes/high-value customer can still fire workers and replace them with AI - they just need to hire fewer, cheaper workers to supervise the AI and monitor it for "hallucinations." This is called the "human in the loop" solution.

14/

The human in the loop story has some glaring holes. From a worker's perspective, serving as the human in the loop in a scheme that cuts wage bills through AI is a nightmare - the worst possible kind of automation.

Let's pause for a little detour through automation theory here. Automation can *augment* a worker. We can call this a "centaur" - the worker offloads a repetitive task, or one that requires a high degree of vigilance, or (worst of all) both.

15/

They're a human head on a robot body (hence "centaur"). Think of the sensor/vision system in your car that beeps if you activate your turn-signal while a car is in your blind spot. You're in charge, but you're getting a second opinion from the robot.

Likewise, consider an AI tool that double-checks a radiologist's diagnosis of your chest X-ray and suggests a second look when its assessment doesn't match the radiologist's.

16/

Again, the human is in charge, but the robot is serving as a backstop and helpmeet, using its inexhaustible robotic vigilance to augment human skill.

That's centaurs. They're the good automation. Then there's the *bad* automation: the *reverse*-centaur, when the human is used to augment the robot.

17/

Amazon warehouse pickers stand in one place while robotic shelving units trundle up to them at speed; then, the haptic bracelets shackled around their wrists buzz at them, directing them pick up specific items and move them to a basket, while a third automation system penalizes them for taking toilet breaks or even just walking around and shaking out their limbs to avoid a repetitive strain injury. This is a robotic head using a human body - and destroying it in the process.

18/

An AI-assisted radiologist processes *fewer* chest X-rays every day, costing their employer *more*, on top of the cost of the AI. That's not what AI companies are selling. They're offering hospitals the power to create reverse centaurs: radiologist-assisted AIs. That's what "human in the loop" means.

19/

This is a problem for workers, but it's also a problem for their bosses (assuming those bosses actually care about correcting AI hallucinations, rather than providing a figleaf that lets them commit fraud or kill people and shift the blame the an unpunishable AI).

20/

Humans are good at a lot of things, but they're not good at *eternal, perfect vigilance*. Writing code is hard, but performing code-review (where you check someone else's code for errors) is much harder - and it gets *even harder* if the code you're reviewing is *usually* fine, because this requires that you maintain your vigilance for something that only occurs at rare and unpredictable intervals:

twitter.com/qntm/status/177377…

21/

But for a coding shop to make the cost of an AI pencil out, the human in the loop needs to be able to process a *lot* of AI-generated code. Replacing a human with an AI doesn't produce any savings if you need to hire two more humans to take turns doing close reads of the AI's code.

22/

This is the fatal flaw in robotaxi schemes. The "human in the loop" meant to keep the murderbot from smashing into other cars, steering into oncoming traffic, or running down pedestrians isn't a driver, they're a driving *instructor*. That's a *much* harder job than drivingr, even when the student you're monitoring is a human, making human mistakes at human speed. It's even harder when the student driver is a robot, making errors at computer speed:

pluralistic.net/2024/04/01/hum…

23/

This is why the doomed robo-taxi company Cruise had to deploy 1.5 skilled, high-paid human monitors to oversee each of its murderbots, while traditional taxis operate at a fraction of the cost with a single, precaratized, low-paid human driver:

pluralistic.net/2024/01/11/rob…

The vigilance problem is pretty fatal for the human-in-the-loop gambit, but there's another problem that is, if anything, *even more* fatal: the *kinds* of errors that AIs make.

24/

Foundationally, AI is applied statistics. An AI company trains its AI by feeding it a *lot* of data about the real world. The program processes this data, looking for statistical correlations in that data, and makes a model of the world based on those correlations. A chatbot is a next-word-guessing program, and an AI "art" generator is a next-pixel-guessing program.

25/

They're drawing on billions of documents to find the most statistically likely way of finishing a sentence or a line of pixels in a bitmap:

dl.acm.org/doi/10.1145/3442188…

26/

This means that AI doesn't just make errors - it makes *subtle* errors, the kinds of errors that are the *hardest* for a human in the loop to spot, because they are the most statistically probable ways of being wrong. Sure, we *notice* the gross errors in AI output, like confidently claiming that a living human is dead:

tomsguide.com/opinion/accordin…

27/

WTF?! ChatGPT says that I'm dead

This is news to me

^{Tony Polanco (Tom's Guide)}

But the most common errors that AIs make are the ones we don't notice, because they're perfectly camouflaged as the truth. Think of the recurring AI programming error that inserts a call to a nonexistent library called "huggingface-cli," which is what the library would be called if developers reliably followed naming conventions. But due to a human inconsistency, the real library has a slightly different name.

28/

The fact that AIs repeatedly inserted references to the nonexistent library opened up a vulnerability - a security researcher created a (inert) malicious library with that name and tricked numerous companies into compiling it into their code because their human reviewers missed the chatbot's (statistically indistinguishable from the the truth) lie:

theregister.com/2024/03/28/ai_…

29/

AI hallucinates software packages and devs download them – even if potentially poisoned with malware

Simply look out for libraries imagined by ML and make them real, with actual malicious code. No wait, don't do that

^{Thomas Claburn (The Register)}

For a driving instructor or a code reviewer overseeing a human subject, the majority of errors are comparatively easy to spot, because they're the kinds of errors that lead to inconsistent library naming - places where a human behaved erratically or irregularly. But when *reality* is irregular or erratic, the AI will make errors by presuming that things are statistically normal.

30/

These are the hardest kinds of errors to spot. They couldn't be harder for a human to detect if they were *specifically designed* to go undetected. The human in the loop isn't just being asked to spot mistakes - they're being actively deceived. The AI isn't merely wrong, it's constructing a subtle "what's wrong with this picture"-style puzzle.

31/

Not just one such puzzle, either: millions of them, at speed, which must be solved by the human in the loop, who must remain perfectly vigilant for things that are, by definition, almost totally unnoticeable.

This is a special new torment for reverse centaurs - and a significant problem for AI companies hoping to accumulate and keep enough high-value, high-stakes customers on their books to weather the coming trough of disillusionment.

32/

This is pretty grim, but it gets grimmer. AI companies have argued that they have a third line of business, a way to make money for their customers beyond automation's gifts to their payrolls: they claim that they can perform difficult scientific tasks at superhuman speed, producing billion-dollar insights (new materials, new drugs, new proteins) at unimaginable speed.

33/

However, these claims - credulously amplified by the non-technical press - keep on shattering when they are tested by experts who understand the esoteric domains in which AI is said to have an unbeatable advantage. For example, Google claimed that its Deepmind AI had discovered "millions of new materials," "equivalent to nearly 800 years’ worth of knowledge," constituting "an order-of-magnitude expansion in stable materials known to humanity":

deepmind.google/discover/blog/…

34/

Millions of new materials discovered with deep learning

We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool...

^{Google DeepMind}

It was a hoax. When independent material scientists reviewed representative samples of these "new materials," they concluded that "no new materials have been discovered" and that not *one* of these materials was "credible, useful and novel":

404media.co/google-says-it-dis…

35/

Is Google's AI Actually Discovering 'Millions of New Materials?'

"In the DeepMind paper there are many examples of predicted materials that are clearly nonsensical."

^{Jason Koebler (404 Media)}

As @brianmerchant writes, AI claims are eerily similar to "smoke and mirrors" - the dazzling reality-distortion field thrown up by 17th century magic lantern technology, which millions of people ascribed wild capabilities to, thanks to the outlandish claims of the technology's promoters:

bloodinthemachine.com/p/ai-rea…

36/

AI really is smoke and mirrors

Just not in *exactly* the way you might think.

^{Brian Merchant (Blood in the Machine)}

The fact that we have a four-hundred-year-old name for this phenomenon, and yet we're still falling prey to it is frankly a little depressing. And, unlucky for us, it turns out that AI therapybots can't help us with this - rather, they're apt to literally convince us to kill ourselves:

vice.com/en/article/pkadgm/man…

37/

'He Would Still Be Here': Man Dies by Suicide After Talking with AI Chatbot, Widow Says

The incident raises concerns about guardrails around quickly-proliferating conversational AI models.

^{Chloe Xiang (VICE)}

I'm touring my new, nationally bestselling novel *The Bezzle*! Catch me in THIS SATURDAY (Apr 27) in MARIN COUNTY, Winnipeg (May 2), Calgary (May 3), Vancouver (May 4), and beyond!

pluralistic.net/2024/02/16/nar…

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Image:
Cryteria (modified)
commons.wikimedia.org/wiki/Fil…

CC BY 3.0
creativecommons.org/licenses/b…

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File:HAL9000.svg - Wikimedia Commons

^{commons.wikimedia.org}