[LINK] Cory Doctorow: What Kind of Bubble is AI?

[Kim Holburn]

https://locusmag.com/2023/12/commentary-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 every single billboard is advertising some kind of AI 
company. Every business plan has the word “AI” in it, even if the business itself has no AI in it. Even as two major, terrifying 
wars rage around the world, every newspaper has an above-the-fold AI headline and half the stories on Google News as I write this 
are about AI. I’ve had to make rule for my events: The first person to mention AI owes everyone else a drink.

It’s a bubble.

Tech bubbles come in two varieties: The ones that leave something behind, and the ones that leave nothing behind. Sometimes, it can 
be hard to guess what kind of bubble you’re living through until it pops and you find out the hard way.

...

When the dotcom bubble burst, it left a lot behind.

...

But the most important residue after the bubble popped was the mil­lions of young people who’d been lured into dropping out of 
university in order to take dotcom jobs where they got all-expenses paid crash courses in HTML, Perl, and Python. This army of 
technologists was unique in that they were drawn from all sorts of backgrounds – art-school dropouts, hu­manities dropouts, dropouts 
from earth science and bioscience programs and other disciplines that had historically been consumers of technology, not producers 
of it.

...

Contrast that bubble with, say, cryptocurrency/NFTs, or the complex financial derivatives that led up to the 2008 financial crisis. 
These crises left behind very little reusable residue. The expensively retrained physicists whom the finance sector taught to 
generate wildly defective risk-hedging algorithms were not able to apply that knowledge to create successor algo­rithms that were 
useful. The fraud of the cryptocurrency bubble was far more pervasive than the fraud in the dotcom bubble, so much so that without 
the fraud, there’s almost nothing left.

...

AI is a bubble, and it’s full of fraud, but that doesn’t automatically mean there’ll be nothing of value left behind when the bubble 
bursts.

...

That’s unlike, say, the Enron scam or the Uber scam, both of which left the world worse off than they found it in every way. Uber 
burned $31 billion in investor cash, mostly from the Saudi royal family, to create the illusion of a viable business. Not only did 
that fraud end up screwing over the retail investors who made the Saudis and the other early investors a pile of money after the 
company’s IPO – but it also destroyed the legitimate taxi business and convinced cities all over the world to starve their transit 
systems of investment because Uber seemed so much cheaper. Uber continues to hemorrhage money, resorting to cheap accounting tricks 
to make it seem like they’re finally turning it around, even as they double the price of rides and halve driver pay (and still lose 
money on every ride). The market can remain irrational longer than any of us can stay solvent, but when Uber runs out of suckers, it 
will go the way of other pump-and-dumps like WeWork.

What kind of bubble is AI?

Like Uber, the massive investor subsidies for AI have produced a sugar high of temporarily satisfied users. Fooling around feeding 
prompts to an image genera­tor or a large language model can be fun, and playful communities have sprung up around these subsidized, 
free-to-use tools (less savory communities have also come together to produce nonconsensual pornography, fraud materials, and hoaxes).

The largest of these models are incredibly expensive. They’re expensive to make, with billions spent acquir­ing training data, 
labelling it, and running it through massive computing arrays to turn it into models.

Even more important, these models are expensive to run. Even if a bankrupt AI company’s model and servers could be acquired for 
pennies on the dollar, even if the new owners could be shorn of any overhanging legal liability from looming copyright cases, even 
if the eye-watering salaries commanded by AI engineers collapsed, the electricity bill for each query – to power the servers and 
their chillers – would still make running these giant models very expensive.

Do the potential paying customers for these large models add up to enough money to keep the servers on? That’s the 13 trillion 
dollar question, and the answer is the difference between WorldCom and Enron, or dotcoms and cryptocurrency.

Though I don’t have a certain answer to this question, I am skeptical. AI decision support is potentially valuable to practitioners. 
Accountants might value an AI tool’s ability to draft a tax return. Radiologists might value the AI’s guess about whether an X-ray 
suggests a cancerous mass. But with AIs’ tendency to “hallucinate” and confabulate, there’s an increasing recognition that these AI 
judgments require a “human in the loop” to carefully review their judgments.

In other words, an AI-supported radiologist should spend exactly the same amount of time considering your X-ray, and then see if the 
AI agrees with their judgment, and, if not, they should take a closer look. AI should make radiology more expensive, in order to 
make it more accurate.

But that’s not the AI business model. AI pitchmen are explicit on this score: The purpose of AI, the source of its value, is its 
capacity to increase productivity, which is to say, it should allow workers to do more, which will allow their bosses to fire some 
of them, or get each one to do more work in the same time, or both. The entire investor case for AI is “companies will buy our 
products so they can do more with less.” It’s not “business custom­ers will buy our products so their products will cost more to 
make, but will be of higher quality.”

...

Just take one step back and look at the hype through this lens. All the big, exciting uses for AI are either low-dollar (helping 
kids cheat on their homework, generating stock art for bottom-feeding publications) or high-stakes and fault-intolerant 
(self-driving cars, radiology, hiring, etc.).

...

Cory Doctorow is the author of Walkaway, Little Brother, and Information Doesn’t Want to Be Free (among many others); he is the 
co-owner of Boing Boing, a special consultant to the Electronic Frontier Foundation, a visiting professor of Computer Science at the 
Open University and an MIT Media Lab Research Affiliate.


-- 
Kim Holburn
IT Network & Security Consultant
+61 404072753
mailto:kim at holburn.net  aim://kimholburn
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