Comment by pseudosavant
1 hour ago
Up until this point, the potential for an AI bust blast radius was limited to corporate investors, but this is going to cause regular retail/401k investors to get exposure, which could have far bigger impacts on a downturn.
Not to mention the insane wake-up call it is going to be for these AI stocks when 3 months after they launch they have to start making earnings calls and showing their financials. That quarter-by-quarter pressure and scrutiny is no joke, and probably the biggest downside of going public.
I'm bullish on AI, but kind of bearish on any specific AI company. None of the initial big dotcom companies like AOL or Yahoo survived at the scale they briefly had.
If we're doing historical comparisons, there was so much hype for AOL and Yahoo that drove valuations far beyond the economics. In time, the hypesters were proved wrong.
In contrast, there was overwhelming doom and gloom for Google's IPO, in spite of their incredible growth and margin economics. In time, the doomers were proved wrong.
There's so much doom and gloom about Anthropic that directly contradicts their astounding growth and margins. For a long-term investor, Anthropic is looking a lot more like Google not AOL.
I can only hope the doomer narrative dominates until I can get a few shares at a reasonable valuation.
Vibes are almost always wrong. Ignore the vibes and focus on revenue growth rates and inference margins.
I don't think it's really doom and gloom, that's mostly on here.
The normies are all still excited/scared and the valuation based on secondary trading is going up and up.
Maybe not quite as crazy as the dot com boom but I'd say the current environment for AI and related equities is a lot closer to the mid/late 90s than 2004
> astounding ... margins
Citation needed for that one.
you cant have it both ways, the public can either have exposure and capture the upside or not.
there are ways for you to manage your risk if it in public markets, theres nothing you can do if its in private.
I thought you could intelligently allocate 401k. I don’t think mine was etfs of nasdaq or s&p for some time now. Ever since Tesla got in
Most (all?) 401k plans limit you to a pre-picked list of ETFs and mutual funds you can invest in. Not to mention the standard advice for decades has been 'broad market index fund'.
Afaik this is the first time that an IPO is big that it immediately gets a significant share of a broad market index fund. The rules among the providers are actually quite diverse, so it's complicated. The Rational Reminder podcast discussed it in April: https://rationalreminder.ca/podcast/406
Their conclusion: It might be bad, but so be it. No need to change strategy.
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Definitely not all. Look into 401(k) self-directed brokerage accounts.
If your plan uses Fidelity you can move your 401k into Brokeragelink and that lets you pick individual stocks. Schwab, TIAA, Alight and some others also have something similar.
> corporate investors
What? No. VCs, pensions, etc aren’t corporate investors in any common terminology.
Amazon was founded in 1994
And who would have thought it was the online bookstore that would be the big survivor of the dotcom era? They were a comparatively small player relative to AOL/Yahoo/etc at the time of the dotcom bust. Which company is the 1994 Amazon of AI now?
As I recall, Amazon also famously didn't turn a profit for ages - but they were also capable of turning one much earlier than they did.
Are AI companies capable of turning a profit today if they turn some knobs?
The narrative is that inference on existing models is profitable. All of the profits and many billions of additional capital invested go into training the next model, which is some multiple more expensive to train than the last. Each new model generation also leads to more revenue growth, mainly due to higher capabilities. Newer models are more compute-efficient when distilled (so could possibly be higher margin) but also they work on longer time-horizon tasks and can make greater use of test-time compute which increases token counts. So the inference ROI on each model can pay back the cost of training it, but future growth demands put all that money and more into training the next model. The numbers we’d need to prove whether this is true are not public, but it makes sense and fits what info we do have.
Theoretically, if training more expensive models stops resulting in better capabilities or isn’t economically viable, the labs can shift gears into making profit on old models. A lot of future growth is priced in so this would lead to a collapse in share price if it happens anytime soon.
There’s a story out that Anthropic might be profitable this quarter. This is in one sense bad news - it means that the company wasn’t aggressive enough about acquiring capacity last year, because they didn’t foresee how fast their inference business would grow. Anthropic is now forced to make suboptimal choices about serving existing users vs. training the next model (need to scrounge for capacity by paying other players like SpaceX). And as a Claude Code user I feel like I’ve been affected by that, what with the random outages and performance degradations.
Yes - IIRC, Amazon was profitable on books by 1996, with other sectors following as they expanded and it was clear that they could post profits any time they wanted by slowing expansion. It was surreal through the bubble years to see “analysts” equating them with companies which were losing money on every sale with no clear way to change that.
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