[goethes_kind]

Here's a question since everyone keeps bringing up the DotCom bubble. Although the bubble burst, have not the people who were building in the 1999, still more than made up for their losses by having the know-how and being able to capitalize on the subsequent emergence of the WWW as we know it today?

[DebtDeflation]

Gates Law variant: "People always overestimate the impact of a new technology in a year and underestimate its impact in ten years."

In 1999 people predicted that the Internet would change everything, in 2000 people called the Internet a flop and made fun of pets.com sock puppets, by 2010 the Internet had in fact changed everything.

[Gigachad]

We are more than 10 years in to crypto, and if it all vanished tomorrow, I can’t imagine anything changing other than a few indexes invested in it crashing. And less ransomware I guess.

[aurareturn]

Follow up question: Where in the dotcom bubble is AI in 2024?

Is it already 2000? Or is it in the beginning, 1995? 1998?

For some context, if you invested in Nasdaq in Jan of 1995 and did not sell until September 2001, you'd still be up by 86%. And if you invested at the absolute peak of the bubble, you'd still be up 250% in 2024.

[jsemrau]

I like to compare GPT3 to the 286 Processor and GPT4 to the Pentium chip. There is still a lot that doesn't work but basic tasks can be done. Right now most genAI applications are toys. And a lot still doesn't work text in Stable Diffusion, true reasoning/planning, agents with agency. A lot of demos and evals are optimized for marketing benefit but fail in production systems.

[Integrity]

If most AI businesses are actually API business that call ChatGPT, and people learn that, the hype will start declining.

[aurareturn]

I disagree.

Meta, Anthropic, Google, Mixtral have demonstrated that OpenAI can be caught and matched.

Today, if you have a powerful enough GPU cluster, you can run an internal GPT4-level LLM by deploying Meta's LLama 3.1 405b.

If OpenAI's GPT5 is as big of a leap as GPT3 to GPT4, the hype will reach unprecedented level again in my opinion.

[_heimdall]

> Today, if you have a powerful enough GPU cluster, you can run an internal GPT4-level LLM by deploying Meta's LLama 3.1 405b.

You also have to be willing to consume ungodly amounts of energy to run those GPUs. That seems like an important caveat while the conversation about climate change and unpredictable weather is still top of mind for so many people.

[bilekas]

I hope that’s not a serious questiom because it seems to be in bad faith. It's not answerable or at least if it was we'd all be rich.

[stingraycharles]

I gather what the parent is saying is that it’s better to just keep investing in index funds or equivalents, because it’s impossible to tell where we are, i.e. timing the market.

[throw_pm23]

You would not be up 250% if the companies you invested in went bankrupt.

Perhaps if you invested in Amazon or MS.

[Etheryte]

The parent comment clearly states they're talking about investing in the Nasdaq.

[Barrin92]

You can shoot yourself in the foot and claim you became a better marksman in the process, that doesn't really do much to justify it though.

All other things being equal they'd been better off investing their money or time into something that wasn't a bubble economy. This is basically broken window logic.

[jillesvangurp]

Very different things. I lived through the dot com bubble (didn't get rich because I was busy doing a PhD instead). Basically, a massive amount of clueless idiots funding companies that were literally nothing more than a crappy website. It was over in a few years. At the peak of the hype, disgusting amounts of money got spent on companies that went absolutely nowhere because there was absolutely nothing there. And then it all fizzled out. But there was also a healthy amount of experimentation and new stuff happening.

This feels different; there's actually some substance to the madness. Quite a few of the companies being funded are actually creating some pretty cool tech. And there's some real revenue potential as well; it's not just investment money keeping everything going. A good dot com era company reference would be companies like Google or Amazon that took the cash and got a lot of that tech making money for them even after the investment bubble burst. They also grabbed some of the smarter people at the same time. There are a few more examples. If you squint a little, you can see a few companies that are likely to be able to start raking in lots of cash soon that are at this point well funded.

Also a lot of the current investment money is being converted into GPU hardware. Which is of course nice for companies like NVidia, whom are probably a bit over valued currently. But the point is that hardware is tangible. Even if the companies that buy it go bust, the hardware just ends up in the hands of others. We're talking many millions of GPUs that are being deployed and that, like it or not, will be doing a lot of AI workloads for years to come for whomever ends up owning it. And there are a lot of smart people trying to make that hardware do all sorts of cool stuff. Hardware is a much better asset to have than useless websites. And I don't think a lot of the software is that bad either.

[choppaface]

Add that in the dotcom bubble, internet-flavored growth had never happened before, so it was relatively harder to predict the growth of (say) Google.

But today, deep-learning-flavored growth is a 10-year-old concept, and LLMs largely have leverage over existing (versus quite new) business models. There will probably be fewer new monopolies versus the dotcom era; in particular OpenAI has lots of competition.

[TMWNN]

Similarly, I just saw it pointed out that the dotcom bubble was solely in the stock market. Internet traffic kept rising the whole time and beyond.

[delfinom]

A bubble doesn't mean the underlying demand/industry doesn't exist.

It means there's a mass over investment and over spend in companies trying to serve the demand at the time.

[Ekaros]

And that current valuation of the underlying market is x times the real valuation. And real valuation can be significantly non-zero. Take housing market. Most of the houses do have some real value. And even if the bubble pops, it doesn't take their values to zero, it might be momentarily be just below real value.

[jltsiren]

I think the bubble was more about the lack of viable business models. The technology was real and the investors saw it, but the business models needed another decade to mature.

The same could happen again. It may be easier to find good uses for AI than to make large amounts of money with it.

[lallysingh]

I look at it as investor error due to lack of expertise in the business area. If investors understood the economics of internet businesses better (and almost nobody did in something this new) they may have helped grow the Internet economy even faster than it did, without the bubble or burst.

[madaxe_again]

Likewise. And I look at the current situation and also see investor error, but in the opposite direction, as a great many investors equate LLMs to AI/ML, and vastly underestimate the space in which this technology will disrupt. I keep reading pieces in Forbes and similar which are like “does the world really need a better chatbot?” or “can Google justify spending $10bn on making a better cat picture?”

[gradus_ad]

Outside of LLM's, which AI/ML product has the potential for widespread consumer adoption? I recognize the usefulness of modern AI in all sorts of domains, but they are almost entirely niche services for business and not targeted to the broad consumer. The problem then is not that AI is not cool or useful, just that it won't make as much money as the enthusiasts think, at least not now.

[madaxe_again]

Those “niche services” are potentially much more lucrative than consumer-facing products. For instance, an ML quant could be a license to print money, and would have appropriate costs. Designing more efficient (cheaper) structures and machines doing FEA/CFD better than current algorithmic models would be a huge boon to engineering industries. Sentiment analysis and forecasting for government. Military strategy that outsmarts any human strategist. Demand lead pricing for products and services. JIT manufacturing management through superior cost and demand forecasting. Identification of baroque tax fraud. Diagnosis of medical conditions. Product recommendations. Tailored drugs. Pre-crime.

On it goes. These are just a few examples of where ML is already beginning to be applied and reaping rewards.

[bitnasty]

Not sure the comparison is justified. Sure, AI is cool, hot tech, but the internet enables connection between virtually every single human alive. Completely different scale of influence.