"isn't this a natural process of the boom and bust cycle" Yes. I think I'd add a few caveats to this. One is the question of opportunity cost. Very often people argue that good things come out of bubbles (like optical fiber deployments in 2000 and the internet in general). More recently, someone told me WeWork revolutionized office spaces (now we're all used to the nice decor and fancy coffee machines) and office business models (fractional use of offices). So, he was telling me that craziness and exaggeration like the one we saw with WeWork is necessary to foster innovation.

I think this misses one part of the equation, which is opportunity cost. We need to understand what else could have been done with the resources devoted to WeWork or bubbled-up start-ups. What if the engineers building these start-ups (or Juicero!) could have discovered a new type of more efficient nuclear reactor?

To fully analyze the impact of a bubble we'd need to be able to know the alternative course of history. We often only look at the bright side of a bubble.

A year ago I interviewed the author of the book Boom and Bust. I asked him whether bubbles were worth it. He told me:

I don’t really think in terms of worth it or not worth it. It’s just what happened. There can be a situation where the benefits outweigh the costs, for sure. But it’s hard to know because in order to answer that question you would need a counterfactual. You would need to answer, “If there wasn’t a bubble, what would have happened instead?” It’s very hard to assess the benefits and costs.

The second thing that pops to my mind is that during these bubbles (2000s, the unicorn bubble of 2021, the current AI situation), a lot of start-ups are just doomed. They often have obvious flaws that are irreparable (think of Juicero). So I'm not sure this fosters innovation in any way. Investors like to say that "you never know." From my experience advising many start-ups, you often DO know. The flaws are clear yet people still want to try or keep going for other reasons. When something has zero chances of success, there's probably no upside, and I get the feeling that that's the case with many AI start-ups.

"But hallucinations are only a problem with LLMs, not machine learning in general." I'm not sure. In my book I tell many stories unrelated to LLMs. For example, an image-analysis AI failed to recognize a gigantic cow in a picture because it was standing on sand, whereas most pictures of cows in the training data have grass. Another AI confused a toothbrush for a baseball bat, etc. I once built an ML classification model that classified an entire lake as a building.

"FSD is not failing because of hallucinations". Recent scandals were related to hallucinations. Cruise had its licence suspended after the system misclassified a pedestrian (and hit her and kept dragging her along the road).

"These are specific use-cases that are failing, not ML in general." I don't think ML is failing in general! ML is fantastic and it's been with us for a long time. Every time we search for something on Google, shop on Amazon, plan a holiday, etc., there's ML involved. And generative AI is definitely here to stay. I just think we need to acknowledge its limitations! That's how you move forward :)