It’s been applied to crypto as an analogy and now here to “AI” though I think you actually mean LLMs.

The thing about the initial web bubble was that the potential using already proven tech was just absolutely gigantic.

Like I used to have to go to an office in a building to buy a plane ticket. Then I didn’t. People used to have to mail me a 500 page catalog for me to order via mail, and there was no way to change price or availability.

To interact with most businesses it had to be synchronous, via phone call. Or very very slow or resource intensive via mail or in person and that was that.

Even immediately it was clear these things would change a lot. You’d be able to browse an online catalog of flights or books, you’d be able to email a business and ask a detailed question and copy and paste the request to whoever.

It was right there. It worked. It was clunky and adoption took a minute but nobody honestly was all that confused about what was happening.

The bubble was almost exclusively about how fast people thought change would happen, and which people would benefit.

The LLM thing feels different. It has clear use cases that work, no doubt. I use it to help draft business docs all the time. That could even be a huge market.

But there’s also an assumption that the cognitive ability of these tactics will grow without bound. We don’t actually know that.

Maybe maybe not. But that’s a difference that makes the dotcom bubble a partial analogy at best.

If that doesn’t happen then it’s not going to be an internet-sized change. Maybe something more on the scale of GPS or something.

Like GPS is still an insanely cool technology that is massively important/useful/valuable but it's still on a completely different playing field than "the internet".

I think they're the same magnitude. I might give the edge to LLMs.

I think LLMs have the potential to make every field significantly more productive whether you are an accountant or software engineer or lawyer.

The hype around LLMs is that for the first time every, a tool has the potential to replace a massive number of white collar tasks.

Nvidia and TSMC are making insane profits, but that just means there's a demand for specialized compute. Like with the crypto bubble, their success is unrelated to the quality of the final AI product sold to the consumer.

OpenAI is in a similar position due to their SaaS model. It's all about pumping the hype and getting other businesses to build AI products on their platform. Not getting good results? Must be your poor prompt engineering!

The real slaughter is going to be in the AI startups, and the companies trying to pivot to AI in an attempt to stay relevant. The general public is already starting to get tired of the whole AI hype, and we haven't seen anyone provide genuinely groundbreaking products yet. All of it is somewhere on a spectrum of "kinda neat, I guess" to "dystopian hellscape".

Unless we see someone come out with a truly innovative must-have product, this hype is probably going to end sooner rather than later.

Revenue has gone up, but fewer publicly traded companies are making a profit than ever before [0].

I could never understand why so many people just talk about revenue. Revenue without profits is meaningless. There's the old logic of "get enough revenue and then figure out profits and you're highly profitable", but it's very clear that switching the "profit switch" is not so easy in practice.

Investors are still basically waiting for the fed to drop rates, which means that people have abandoned rationally thinking about businesses and are just holding until the free money starts pouring in again.

I honestly don't think the AI bubble is anything like the dotcom bubble. There's something much stranger happening here since the entire market is basically hallucinating and AI is just one manifestation of that.

0. https://finimize.com/content/beware-the-rise-of-unprofitable...

What guarantee is there that "free money" will come back again?

Wasn't the last free money printer run something like a first time in history, and supposed to be only a temporary measure that went on for far too long leading to inflation and assets spiraling out of control creating various speculative bubbles like crypto, Gamestop fiasco, housing, and dozens to hundreds of crappy overhyped "start-ups" adn food delivery apps, that were never able to be very profitable on their own but still grew like crazy thanks to that free money and gullible investors to stay afloat, leading to an artificial over demand of SW devs which also crashed with them.

Seeing all it lead to, do we even want/need it to come back again? And "But this time will be different" doesn't scan for me as a believable answer since we all know it'll definitely be the same.

It's detailed in the article, but that graph is way misleading because it isn't weighted by revenue size and the unprofitable part is dominated by tiny companies.

That chart is deceiving. (from your link)

If you look carefully, you'll see that "very profitable" companies over the decades is unchanged.

What changed is the balance between "barely positive" and "negative".

One caution about using this as a metric: in the last 25 years, for a variety of reasons, early IPOs have, in general (and not just amongst hyped-up tech companies) become way less desireable. A company that might have IPO'd in the 90s today might simply take a few hundred million from VCs. And there have been a number of no-revenue, no-profit VC investments at that level in the current bubble.

Granted, a VC-driven bubble is less dangerous to the general public than an IPO-driven bubble; when it collapses it's mostly VCs holding the can, rather than peoples' retirement funds.

That's cheating; you moved from "companies" to "a specific company". Some specific companies had revenue in the dotcom bubble. There are plenty of AI companies today being valuated sky-high with currently no revenues (dunno how many have gone public, that has changed a lot since 2000), and there are plenty of "real", normal companies getting their valuations goosed by saying "AI" and not having any current revenues to show for that valuation rise. Most recently Apple put on something like 300 billion in valuation for their deal with OpenAI, but if you dig into the deal, neither of them are paying the other anything.

The people selling shovels are doing well, if not necessarily as well as their stocks indicate, but so far there really isn't that much gold being found for all the shovels being sold. Not zero, not claiming it's zero, but it's nowhere near what the valuations imply.

Really struggling to decide if AI is ultimately a winner take all market. Of course the very best models will require trillions in capital to train, but seems there also will be a long tail of local, smaller, and specialized models doing a majority of the workloads.

It was more likely that someone would throw money at your idea back then if it ended with ".com" than it is for someone to fund your idea today that ends with "using AI".

I don't know why. Maybe it's interest rates. Maybe it is because the AI hype is so recently after the cryptocurrency hype. It was quite easy to make people dump their money into a cryptocurrency, and the crypto winners are still actively fishing for bigger fools while the AI hype is going on.

Companies are taking on massive investment larger than any IPO take, with zero revenues! OpenAI’s investment predated its revenue push.

I’ve seen people suggest this cycle is malinvestment… if you think the goal of AGI which is exceeding unlikely to emerge in this investment cycle.

Get ready to pick up a ton of cheap hardware in a year or two…

That's not a bold statement since that's the natural life cycle of hardware anyways. I think the "year or two" is a bit soon, but also tracks with the price of hardware holding value for that term too.

Why? As far as I know, we're hugely bottlenecked by hardware in both training and inference even for current models.

Do you have any source for this estimate? Everything I find online tells a different story (single digit million per year).

The market is much smarter to scrutinise and punish companies attempting to IPO and underperform in the public markets like what we have seen with WeWork and the other SPAC companies that failed to go anywhere.

Once again, companies with little to no-revenue and especially no-profit are getting valued at >$1B all because they slap 'AI', 'LLM' nonsense to inflate their valuations. That is already a dotcom bubble level hype.

The reality is, the existing big tech companies (Microsoft, Apple, Meta, Google, Nvidia, etc) will take all the value of LLMs and will bankrupt the late comers except for the advanced few such as (OpenAI, Anthropic and Cerebras Systems)

We live in a very messy information space, both publicly and privately, and we navigate it with messy, sometimes-inaccurate brains. I don't expect LLMs to operate at a higher level of formal logic than a human brain does.

People make mistakes: even the best people. We accept that risk as colleagues/employers/employees because there's a human connection, but that doesn't make it nonexistent. We're now trying to figure out how to deal with that when you're "employing" AI, and whether the risk tolerance is higher or lower alongside the reduced operational cost