22 comments

[ 3.1 ms ] story [ 45.9 ms ] thread
It's not a bubble yet. Many companies are already getting direct value out of AI. The Dot com burst happened because there were lots of unsustainable business models. I don't see them as equal.
> It's not a bubble yet.

Yes, it is.

> Many companies are already getting direct value out of AI.

Many companies were already getting direct value out of the internet during the dotcom bubble. Bubbles do not require the absence of real value being delivered by the bubble industry, they require levels of investment that are anticipate more real value that can be delivered on a time frame for existing valuations across the industry to be sustainable.

> The Dot com burst happened because there were lots of unsustainable business models.

There are lots of unsustainable business models in the AI space, too.

If you are looking at OpenAI, Google, and Anthropic (even though they, too, may be somewhat inflated), you are making the same mistake as looking at Google (ironically) during the dotcom bubble.

> I don't see them as equal.

They aren't equal, the AI bubble is much bigger.

People make this type of prediction every year. useless.What if it becomes 20x bigger? There is nothing actionable contained in this observation.
It’s disingenuous because since the dotcom bubble there has been at least 2x inflation, and then on top of that the tech market has expanded a lot more than what it was in 1999, so of course it will be bigger. This is nothing.
Bubble/Not bubble, what does that really change? The economy will rise and fall one way or another; it is really in cycles. If the bubble pops, it will be a sharper fall. Unless you own AI, tech stocks - probably not a big deal
All i know is that I’m looking forward to picking up deep learning programmers for biomed applications in about nine months time.
What I want to know is whether people who believe in a bubble actually short AI/tech-related stocks.
I feel any naive question about investing ever can be answered with "markets can remain irrational longer than you can remain solvent"

The bubble is the manifestation of this concept. Things should be falling apart, yet they keep going up, for longer than it is reasonable; at some point, bearish investors lose so much money they decide it's better just to ride the wave up, growing the bubble even further, until it bursts and everybody loses.

There is a reason investors flock to gold during these times. The best move is not to play (though you don't want to hold too much cash either)

"I believe this is a bubble and it will pop"

and

"I believe this is a bubble and it will pop and I believe I can time it well enough to be worth putting money on when it will pop"

Are...not the same belief.

When everybody agrees about something in finance, it's typically the other way around.

Reminds me of the "everybody knows Tether doesn't have the dollars it claims and it's collapse is imminent" that was parroted here for years.

“At the heart of the note is a golden rule I’ve developed, which is that if you use large language model AI to create an application or a service, it can never be commercial.

One of the reasons is the way they were built. The original large language model AI was built using vectors to try and understand the statistical likelihood that words follow each other in the sentence. And while they’re very clever, and it’s a very good bit of engineering required to do it, they’re also very limited.

The second thing is the way LLMs were applied to coding. What they’ve learned from — the coding that’s out there, both in and outside the public domain — means that they’re effectively showing you rote learned pieces of code. That’s, again, going to be limited if you want to start developing new applications.”

Frankly kind of amazing to be so wrong right out of the gate. LLMs do not predict the most likely next token. Base models do that, but the RLed chat models we actually use do not — RL optimizes for reward and the unit of being rewarded is larger than a single token. On the second point, approximately all commercial software consists of a big pile of chunks of code that are themselves rote and uninteresting on their own.

They may well end up at the right conclusion, but if you start out with false premises as the pillars of your analysis, the path that leads you to the right place can only be accidental.

> There are certain bullsh*t jobs out there — some parts of management, consultancy, jobs where people don’t check if you’re getting it right or don’t know if you’ve got it right.

Market Analyst, perhaps?

The bubble referenced in the article is $1 Trillion, compared to Google's $3 trillion market cap. And OpenAI / Anthropic legitimately compete with Google Search. I feel weirdly like AI's detractors are somehow drinking too much of the AI Kool-Aid. All AI has to do to justify these valuations is capture 1/3rd of Google. Unless Google is wildly overvalued, which it may be, but that's not a phenomenon that has anything to do with AI hype.

And there are legitimately applications beyond search, I don't know how big those markets are, but it doesn't seem that odd to suggest they might be larger than the search market.

NVIDIA has a market capitalization of about US$4.3–4.6 trillion. Still not overly excessive compared to Google considering it's a hardware company.
Even if this is true, a possible takeaway is that after the bubble bursts and the dust settles, AI's effect will be 17 times stronger than that of the Internet... Personally, I think it will end up being much higher, but that doesn't mean I'm going to invest in it any time soon
"Earlier this month, Garran published a report claiming that we are in "the biggest and most dangerous bubble the world has ever seen.""

Here is the report

https://www.youtube.com/watch?v=uz2EqmqNNlE

This may just be wishful thinking, but is it reasonable to hope that it won't hit the middle class as bad this time around? Seems like most of the people holding the AI bags are the very wealthy, and it doesn't seem like these AI companies are employing a huge number of people.