Where we land remains to be seen.
I study from reputable sources every day and never cease to be amazed by how many errors or misconceptions they have. Peer-reviewed articles, books from renowned scholars, news from major publications… regardless of the source, false information and contradictions accumulate. I’d wager that AI, besides helping me uncover these issues in the literature, has had a lower error rate than most of the materials that I read on a daily basis.
I still think it could crash, but it's got real users and a mind share like nothing I've ever seen.
To be clear, there is a world of difference between IPOs and LBOs. In the risk they create. And in the risk they signal.
The dot com bubble was basically based on regular people buying computers and internet service, and then using them to buy products they used to buy in stores.
I was a huge early fan of ChatGPT voice too, but I don't think I've used voice mode anywhere in at least 6 months. The question is what is the right level people are generally going to settle on for the use of these tools in the long term. 80% of my usage isn't much more than a better Google, I could live without it and I could live with cheaper options. I'm not sure the consumer money is going to be there en masse as hoped
Of course it still leaves a huge amount of business cases open, but I suppose the same principle applies. How soon will people tire of talking to robo-voice when they call their bank? etc.
Keep in mind that people said this before both of those crashes.That's the problem with bubbles. It's impossible to say if this time really IS different.
My parents love using ChatGPT, asking it all kinds of questions. My mom discovered Claude and helps her immensely with her job - where she would have to take it home and work a few hours to be able to finish the tasks on her computer, as her company that still uses Office 98, now Claude does it in 5 minutes.
They fixed so many random issues using it, it is insane. My dad had a bike issue which would otherwise be solved by either trying to find obscure manuals from 20 years ago on random forums with me translating it from english to our language, or by taking it to a mechanic which could take months. This way, he just snapped a few photos, said what the problem is, and in a few minutes he had the fix.
I've built software that uses LLM's for a specific usecase - besides general adoption, professionals in the field contacted me and thanked me for making their lives easier, as the tasks would often take a lot of manual work. These people are earning way more from using my software, than I am from their subscriptions, which is still about 20x more than my API costs are.
While most non-dev people are behind the curve, the impact it has on their lives is becoming bigger and bigger by the day.
Worst part?
Their whole software stack is running on some version of Visual Basic, written by a dude that did not trust "others code" so he wrote everything from scratch, and retired about 5 years ago.
Nobody knows how any of it works, or has any clue. The company will continue to run it and pay him for consultations as long as he is able to do it.
[1] https://www.grandviewresearch.com/industry-analysis/artifici...
But it is a generational opportunity - we can remove a lot of barriers that come with knowledge, lack of it, access to it and more. Someone can easily get pretty on point medical advice without access to doctors. Get specific engineering advice without engaging with those engineers. We can apply common sense or specific knowledge on scale - in a world where about 50% of people have IQ under 100 and access to knowledge is gated behind lines and payments, this has a huge chance ot improve their lives.
And there is the whole shadow inference economy - just for example, a few corporations I have worked with in insurance and telecommunications have been slowly introducing it inside their workflows and their data tooling, being able to clean data, tag it, analyse it in a way that before would probably cost them billons in human costs.
One of them has a database going back to the 80's, with data being formatted and reformatted in all shapes and sizes, coming back all the way from paper records for some of their oldest clients. Cleaning this up was unimaginable before as a "something we can do in a day" project, but was more of a "possible with insane costs". This lead to all further activity being shaped by decisions someone made 40+ years ago, details being lost, data being thrown away or saved in random notes.
And there's millions of companies like that all around the world, which can now do "impossible" and become much more efficient and productive for a much cheaper price and in way less time than ever.
The point he makes is that companies go public when they think they can get the maximum our of their shares on the retail market. Which make sense I guess.
But the fact that the 3 of them are hitting the public market at the same time means they all came to the conclusion that now is the perfect time to unload those shares. Probably because they know there is a high chance of a big crash coming after.
I will not touch those IPOs with a 10 feet long pole. But unfortunately a lot of people are about to get burned.
My prediction is that this is what will be remembered as the last bit of exuberance before everything starts to unravel.
Books will be written about how insiders will be profiting millions by unloading those shares to the greatest fools and middle class america.
I think this is what's going on right now. But there are a variety of reasons that can drive IPO timing. Need for cash and owners needing liquidity being chief among them.
I'd also say that post-Covid, retail has become a commanding section of the American equity markets in a way I don't think they've been in my lifetime. As a result, every IPO from now on will have to target retail.
I really think what is driving this is the need for insiders, employees, early investors to be able to sell their stock at scale before the music stops.
And You can only do that through a full IPO. All those companies had private secondary transaction but none of them were big enough to transfer the Trillions of $ required for the insiders to unload their bags.
How would you differentiate insiders needing to sell versus insiders needing to dump before a crash?
I remember when Uber and Airbnb and WeWork went public in quick succession. There were similar claims. WeWork never made it public. And Uber and Airbnb's IPO investors made of fantastically.
To answer this, just ask yourself how many of the insiders would have bought the stock at current IPO's price? Most insiders would probably never touch those stocks at this price. I know a couple people at OpenAI and Anthropic that are very clearly selling everything they can as soon as they can.
This is all a carefully orchestrated PR game that is relying on retail to be the ultimate fool. I guess to some level every IPO is like that (A PR game to hype the company).
But never before had we 3 mega IPOs happening at almost the exact same time with so much money to unload on retails with dubious ways to force funds to gobble them.
Most IPOs end up negative after the first few quarters (at least compared to the SP500). When we are talking about a 20B$ company it matters less than 5T$ being suddenly fully unloaded on the public.
> And Uber and Airbnb's IPO investors made of fantastically.
Did they? https://www.alphaspread.com/comparison/nasdaq/abnb/vs/indx/g...
The only way they might have is by getting the shares at the actual IPO price, and even then it's around the same as the SP500 return since then.
If you are serious about this for Anthropic please drop me a line. (Not OpenAI.)
> never before had we 3 mega IPOs happening at almost the exact same time
Uber (May 2019), Airbnb (December 2020) and WeWork (scheduled 2019, SPAC 2021) were pretty closely bunched. And they were big for their time. Keep in mind that the money supply has expanded since then.
> Most IPOs end up negative after the first few quarters
Source?
There is an actual ETF tracking IPOs: https://finance.yahoo.com/quote/IPO/
Renaissance's IPO index seeks to "capture the essence of IPO activity and performance of newly public companies" [1]. It does not replicate an actual IPO investor's returns.
For example, it adds new issues approximately quarterly and never earlier than 5 days from IPO. This is important since it misses the pop. Mean (median) first-day returns on IPOs are 20% (7%) [2]. The average 3-year buy-and-hold return for all IPO investors 1980 to 2025 was 19.1%. Less than broad-market indices (though that margin shrinks for $1bn+ sales IPOs). But certainly not negative.
(Uber and Airbnb reflect this trend. Up since IPO. But, as you observe, below the S&P 500's returns even before taking into account total returns.)
[1] https://www.lseg.com/content/dam/ftse-russell/en_us/document...
[2] https://site.warrington.ufl.edu/ritter/files/IPO-Statistics.... 1980 to 2025; 30% (14%) for 2025
I think this is extremely common, if not necessary, part of a functioning market and price discovery. It happens with not just IPOs but also secondary offerings.
Some of this seems like dumb retail wanting to toughtlessly buy without consideration of risk.
One of the more rational ideas I have seen of any kind of divination is that it provides a means of passing judgement over to a near seemingly random system. If you are reading tea leaves, doing an 'I Ching' divination, biobliomancy etc. that essentially provides a coin flip to make you go 'yes' or 'no' to an opportunity.
And if you are already sure of the correct solution, then you can just keep doing the divination over and over again until the gods give the answers that you want!
(I mean, I think this looks incredibly like a bubble too, but for completeness sake, that's the counterexample I can think of.)
It's also similar to 2024 when HN was sure that AI is a bubble.
Similar to 2025 when HN commentators were sure that AI is a bubble.
1000% gains later, HN will continue to identify patterns of 2000/2008 and are absolutely convinced it is a bubble
Note: If a company gains 1000% and loses 50%, you can't claim you were right.
Both OpenAI and Anthropic have already gained 1000% since 2023 (In Anthropic's case almost 10,000%)
If I wanted blind pattern matching comments of dot-com bubble, I can just ask LLMs of 2023 like ChatGPT 3.5
We could very well go back to the 2021 valuations.