'What Kind of Bubble Is AI?'

"Of course AI is a bubble," argues tech activist/blogger/science fiction author Cory Doctorow. The real question is what happens when it bursts? Doctorow examines history - the "irrational exuberance" of the dotcom bubble, 2008's financial derivatives, NFTs, and even cryptocurrency. ("A few programmers were trained in Rust... but otherwise, the residue from crypto is a lot of bad digital art and worse Austrian economics.") So would an AI bubble leave anything useful behind?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.... 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... There just aren't that many customers for a product that makes their own high-stakes projects betAter, but more expensive. There are many low-stakes applications - say, selling kids access to a cheap subscription that generates pictures of their RPG characters in action - but they don't pay much. The universe of low-stakes, high-dollar applications for AI is so small that I can't think of anything that belongs in it. There are some promising avenues, like "federated learning," that hypothetically combine a lot of commodity consumer hardware to replicate some of the features of those big, capital-intensive models from the bubble's beneficiaries. It may be that - as with the interregnum after the dotcom bust - AI practitioners will use their all-expenses-paid education in PyTorch and TensorFlow (AI's answer to Perl and Python) to push the limits on federated learning and small-scale AI models to new places, driven by playfulness, scientific curiosity, and a desire to solve real problems. There will also be a lot more people who understand statistical analysis at scale and how to wrangle large amounts of data. There will be a lot of people who know PyTorch and TensorFlow, too - both of these are "open source" projects, but are effectively controlled by Meta and Google, respectively. Perhaps they'll be wrestled away from their corporate owners, forked and made more broadly applicable, after those corporate behemoths move on from their money-losing Big AI bets. Our policymakers are putting a lot of energy into thinking about what they'll do if the AI bubble doesn't pop - wrangling about "AI ethics" and "AI safety." But - as with all the previous tech bubbles - very few people are talking about what we'll be able to salvage when the bubble is over. Thanks to long-time Slashdot reader mspohr for sharing the article.

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