Railroads, semiconductors, plastics, networking... the utility was there for all of them, and so was the potential profit.

Everybody knew it.

But some investors don't have the patience for these "sure things" to mature into their commercial destiny and others know they can siphon off a killing by taking advantage of them as they speculate.

These bubbles aren't about potential, they're about timing.

And today's AI, for as revolutionary as it may be, is taking just as long to mature as anything in the past.

Most of the top players involved in AI right now are going to be rich. And in 20 years, the world is going to involve a lot of AI in soooo many things. But a lot of shirts are going to be lost below those top players and a lot of companies are going to fold because infrastructure, refinement, and integration take time while the FOMO speculation starts as soon as the big discovery is recognized.

Im not convinced by the claims of infrastructure only being expensive to build initially. Hardware doesn't last forever especially in a data center, maintenance isn't free, electricity is not going to get significantly cheaper. If models get more efficient we won't need super expensive and expansive hardware build outs, if models keep getting more powerful and needy and are hardware limited, each new hardware generation will stomp over all this expensive and soon to be old hardware in electrical efficiency requiring another massive round of funding that players already holding debt will struggle to afford.

I dont know if the valuations make sense, but the usability is all there and so are the potential profits. Over a decade, that's probably all that matters. If the valuations pop or dip (anytime soon), I think that's mostly irrelevant. I get that the press probably doesn't like to hear this boring conclusion.

P.S: A Matlab/Physics simulation guy running for a Liberal party seat in Canada? Say more!

If you don't talk about competition from DeepSeek you haven't really thought it through all the way. You might argue it's like Windows vs Linux, but operating systems have huge natural motes. Any person can just switch to a provider of an open weight model instantly. Most people don't know about it but we may very well see people learn about this broadly in the next few years. Changing models is not super complicated. Anthropic is unlikely to make $2 to the dollar when competitors are taking only pennies of profit.

The discussion of the “AI bubble” often conflates two different things: (1) is AI going to justify the valuations we are seeing? and (2) are the specific AI companies we have going to justify their own valuations.

The rapid progress of multiple closed frontier models, along with the rapid progress of open models, suggests that LLMs will be like electric motors, not like social media. Are electric motors worth a huge amount of money economically? Sure. But none of the companies who invented electric motors are especially valuable today, because the technology became a commodity.

Just because a technology is going to be successful in the future doesn't mean that the current beliefs are accurate of which company is going to capture the most profits from that technology.

The internet is successful and cisco is having a blast but its valuation has still been enormously wrong during the dotcom bubble. Same goes for pets.com, etc.

So in other words, maybe the valuation of some companies is totally wrong. Maybe Anthropic is not worth two trillion, or even a hundred billion, but Nvidia is worth 20 trillion. Maybe it's the other way around. Maybe the legal framework changes and the value will be captured by the major rights holders, reddit, twitter, bertelsmann, etc.

Meta’s margins are apparently getting crushed as a result of AI investments.

The lack of a clearly profitable operating model and huge (trillions) in commitments for buildout is what concerns people.

I am not convinced by this person who is not convinced there is a bubble. The bubble, by the way, specifically refers to the valuations. Is Anthropic worth 1.2 trillion when it’s not clear they will have positive cash flow in the foreseeable future?

>Anthropic's S-1 filing says that they make $2.08 for every $1 of compute. In other words, once you ignore the cost of building infrastructure, AI is very profitable.

If you ignore the cost of building cars, they are also very profitable.

>Profits Can Grow, By A Lot

They can also decline by a lot, and Anthropic's S-1 suggest that is what is happening, as their losses are widening, not narrowing.

>Google Has No Moat, But Neither Does McDonald's

That's why no one is sinking several trillion dollars into a new fast food chain.

>Alphabet, Meta, and NVIDIA are all AI companies that have seen their market capitalization explode over the past few years, but all of them have stable P/E ratios in their 20s.

The "E" in their PE ratios is circular revenue that is ultimately derived from debt and equity, not real end-user demand from profitable companies. For some of these bubble companies, if you subtract the debt-fueled private stimulus money, their actual PE ratios skyrocket.

Meta, I have constructive feedback on your choice of titles with this post https://news.ycombinator.com/item?id=50415422

There are very few people in the world where when they declare that they have an opinion on it, that the declaration itself is title worthy.

The 16% yield on Coreweave bonds probably indicates otherwise.

It's more accurate to say that there's an OpenAI-Anthropic-X bubble. Financially sustainable investment in LLMs is useful and not going anywhere, as China shows. Dumping trillions of dollars into companies that make models that are 1.2x better than Chinese models at 100~1000x the cost, ehhh... good luck with that.

The only modification I'd make is to amortize the investments over 5-6 years instead of 20 because most of the cost is GPUs not the buildings. This increases the required revenue by 4x.

> More than anything, I think the supposed AI bubble has become such a large talking point because it represents the hope that things can go back to how they used to be. People are afraid of what's going to happen with AI, and rightfully so.

> So, people wishcast. They tell themselves that this is all a giant bubble, and one day soon, everything will crash down and immediately go back to how it used to be.

+1 to this. I think this is why Ed Zitron gets so many readers - people want someone to tell them things will go back to “normal” someday.