And that current valuation of the underlying market is x times the real valuation. And real valuation can be significantly non-zero. Take housing market. Most of the houses do have some real value. And even if the bubble pops, it doesn't take their values to zero, it might be momentarily be just below real value.
I think the bubble was more about the lack of viable business models. The technology was real and the investors saw it, but the business models needed another decade to mature.
The same could happen again. It may be easier to find good uses for AI than to make large amounts of money with it.
I look at it as investor error due to lack of expertise in the business area. If investors understood the economics of internet businesses better (and almost nobody did in something this new) they may have helped grow the Internet economy even faster than it did, without the bubble or burst.
Likewise. And I look at the current situation and also see investor error, but in the opposite direction, as a great many investors equate LLMs to AI/ML, and vastly underestimate the space in which this technology will disrupt. I keep reading pieces in Forbes and similar which are like “does the world really need a better chatbot?” or “can Google justify spending $10bn on making a better cat picture?”
Outside of LLM's, which AI/ML product has the potential for widespread consumer adoption? I recognize the usefulness of modern AI in all sorts of domains, but they are almost entirely niche services for business and not targeted to the broad consumer. The problem then is not that AI is not cool or useful, just that it won't make as much money as the enthusiasts think, at least not now.
Those “niche services” are potentially much more lucrative than consumer-facing products. For instance, an ML quant could be a license to print money, and would have appropriate costs. Designing more efficient (cheaper) structures and machines doing FEA/CFD better than current algorithmic models would be a huge boon to engineering industries. Sentiment analysis and forecasting for government. Military strategy that outsmarts any human strategist. Demand lead pricing for products and services. JIT manufacturing management through superior cost and demand forecasting. Identification of baroque tax fraud. Diagnosis of medical conditions. Product recommendations. Tailored drugs. Pre-crime.
On it goes. These are just a few examples of where ML is already beginning to be applied and reaping rewards.
It means there's a mass over investment and over spend in companies trying to serve the demand at the time.