Take for example the action on SPACs that made the "SPAC everything" era end.
AGI is fundamentally impossible through data scaling like they tried to claim and achieving AGI is what all this depends on. The long tail problem will remain undefeated and the IPOs are a desperate move to get the cash needed to scale one last time.
They could have just kept improving the technology without all the psychosis and finding use cases and ways to make it more reliable but instead they bet everything on a language model becoming their slave god.
Slowly deflating would be nice, but I don't see how. In any case the economy is getting wrecked and any goodwill tech companies had with employees is gone after going completely adversarial towards them as soon as they had an opportunity to. The most profitable use case of gen-AI is still spams and scams.
Maybe my memory of dot-com is fuzzy (likely) and perhaps I didn't see early warning (or even didn't know how to see it). I feel like it all transpired over 3 months. And for those of us in Seattle the "death-knell" was the Nisqually quake that made much of Pioneer Square unsafe to occupy for a while.
There is this huge knowledge gap here about what the local models could do in terms of consumer queries with a tiny bit of agentic support. If more people could tinker with local models and see what they can do, I think there would be far less belief that only the big two/three hold the keys to the kingdom and far more that the future is a bit more distributed.
On that basis I am always on the lookout for coverage that gets into this — Cal Newport touches on it a bit — because I think it is one of the deflationary factors, as is the quality of open weights models, which in turn is why Anthropic is now getting credulous journalists to write about "distillation attacks".
Re: the dot-com crash, I was in the UK working for an internet startup at the time though I was on a sort of health break/sabbatical at the time of the pets.com crash, so I was reading and researching, and while I do remember the coverage of their valuation being insane, and agreeing with that, I also think it did not have such a long run-up. We did know in 1998 and 1999 that Broadvision's valuation seemed absolutely insane, for example, and that some e-commerce developer and design startups were radically overvalued, but what is so different about this time is how much more broadly things were distributed then. So many players. Even the major duality (which was Microsoft vs ABM — a loose coalition of "Anyone But Microsoft") had many more players.
Whereas now… it feels like a prequel to Rollerball.
Eh, I've played with them. They take way more babysitting to do the work reliably, and I have to much-more closely monitor the outputs for hallucinations.
They'll get there. But it might take a decade or so for (a) consumer hardware and (b) SOTA distilled open-source models to converge with the closed-source stuff, and that's enough time for both a slow deflation and plenty of profits to be made.
The current industry obsession with agentic coding is a giant red herring, in a way. Or at best a shiny thing. The pitch from OpenAI for their extraordinary valuation has always been absolutely all-encompassing in terms of utility and market. But we've stopped talking about that.
I don’t think these vast LLMs will be the dominant approach in a decade, frankly, because the performance gap between a 35B MoE and these absolutely gigantic models just is not proportional to the scale difference, but at this point we are just pitching belief against belief.
Zitron's reporting shows that OpenAI's losses are scaling linearly with their growth, and their long-term valuation is based solely on the idea that they will eventually reverse this trend. Anthropic/OpenAI pumping the brakes on growth mode could have ripple effects throughout the entire market.
I think the more likely outcome is that these companies keep burning and demanding cash until investors call BS.