Big Tech says AI is booming. Wall Street is starting to see a bubble

I expect companies who integrate well with them to increase their value. For example, companies with proprietary data to integrate an LLM with. Another example could be a company that develops very capable agents.

Wallstreet cares about stocks. If models reach a point where it’s cheap, capable, and fast, why wouldn’t the S&P500 take off because of the huge boost to productivity? And some companies will take off more. That’s normal.

Others have developed Deep Learning based weather forecast models which much cheaper (orders of magnitude less compute), faster and more accurate than conventional physics simulation based models which costs billions of dollar to run.

There might exists companies which have already enough private, unique and valuable data that if used to develop models that can bring huge benefits.

The money is in the applications and inventions that build on top of models.

Then there's the legal issue. People are not going to accept the idea of "you can't sue me, the AI decided", and so a not perfect model is not going to be deployed in any field of consequence, because the idea that you can offload liability on models is not going to fly, and is ethically reprehensible in my view and in the view of most serious people working in regulated industries. It actually introduces new legal issues, a jury is far less likely to side with an AI than it is with a person who simply made a mistake. There are numerous social reasons why the liability concerns here are legitimate and concerning and actually different for companies than if a human had been making the decisions.

Therefore, the models have to be perfect, or they have to be checked by hand by experts at every step (in which case, there is no return on investment, you haven't really automated anything).

That's the classic conundrum of "I may as well have done it myself given I had to check that code line by line anyway" I see in my own work, and the same is going to apply for non-perfect models in any field of sufficient consequence that there is a hope for profitability (and usually, these fields being consequential enough for potential profitability also implies a potential for liability).

Anybody that works as a programmer in finance/energy/healthcare/government or other parts of the "real economy" could tell you this years ago. The perception that AI could be used to automate significant work was dead pretty shortly after arrival. The only industry that continues on with the charade is the industry with a vested interest in selling and making dubious promises about AI products.

That doesn't mean I don't think LLMs are cool or that they are totally useless. For endeavours that were limited in their profitability anyway, it may very well have some use cases. I just don't see a viable business model for any large companies except Nvidia or AMD or Broadcom and others making money selling data center equipment. Maybe ads and agitprop, but that seems to be a sort of parasitic relationship going on with social media and the internet and one wonders how long it can continue before canabalizing itself.

There is a question of what is even a bubble. John Cochrane did a study that showed that the value of Amazon alone justified tech stock index valuation even in 1999.

What is a "bubble" even? For me it would be self-driven cycle of upward valuation whose fundamental value never justifies the market cap in it. An industry that rises in price just a few years later to meet and then far exceed the previous valuation does not need to be a bubble. A bubble is not just a high valuation that decreases in price sometime in the future.

But we’re not peak AI yet in my opinion. Companies aren’t IPOing at dizzying valuations with just an idea and a few html developers.

The vast majority of the AI boom is centered around companies that were successful before the boom. Apple. Nvidia. Microsoft. TSMC. Google.

When it pops, it will still be bigger than in 2024.

Small models are really good now. Mistral Large Enough 123 billion parameter model is competitive against GPT4, which is rumored to be 1.7 trillion parameters.

Context sizes are also rapidly improving, as is inference hardware.

There will be a convergence of model intelligence, context size, inference hardware, and developer experience at some point.

One of these days, Slack AI will be good.

In other words, it's an LLM.

Does Phi 3 even do vision?

There also needs to be discussion if the transformations AI can make possible are actually for the common good. They'll certainly be good for a small minority (e.g. certain billionaires), but its hype-men seem to be lazily gesturing to utopian sci-fi and lazy and oversimple economic thinking to justify it [1].

But it's probably hopeless, since SV is a technopoly and has too much influence.

[1] Like assuming there will always be work for all people in the face of automation, that people will be better off if goods get cheaper as their economic prospects dim, etc.

Look up the unintentional impact of the cotton gin for an example.

It will only hurt industries with limited demand or fixed amount of work (think bookkeepers, customer support) that are cost centers.

To the point I think I am the exception, and that's why LLM will have a larger impact than I would estimate.

And talking about benefits, would you claim that life 50 years ago was the same for the average citizen as today? Because if not, to me it seems that people work less (see chart) for more (see what you have).