Technology

Models are not improving, companies are still largely (massively) unprofitable, the tech has a very high environmental impact (and demand) and not a solid business case has been found so far (despite very large investments) after 2 years.

That AI isn't going anywhere is possible, but LLM-based tools might also simply follow crypto, VR, metaverses and the other tech "revolutions" that were just hyped and that ended nowhere. I can't say it will go one way or another, but I disagree with you about "adjustment period". I think generative AI is cool and fun, but it's a toy. If companies don't make money with it, they will eventually stop investing into it.

Also

Today’s hype will have lasting effects that constrain tomorrow’s possibilities

Is absolutely true. Wasting capital (human and economic) on something means that it won't be used for something else instead. This is especially true now that it's so hard to get investments for any other business. If all the money right now goes into AI, and IF this turns out to be just hype, we just collectively lost 2, 4, 10 years of research and investments on other areas (for example, environment protection). I am really curious about what makes you think that that sentence is false and stupid.

It's fucking fantastic news, tbh.

Here's my take, let them dismiss it.

Let em! Remember Bitcoin at $15k after 2019?

Let em! And it's justified! If Ai isn't important right now, then why should its price be inflated to oblivion? Let it fall. Good! Lower prices for those of us that do see the value down the road.

That's how speculative investment works. In no way is this bad. Are sales bad? Sit back and enjoy the show.

Why is it getting an AGI stamp now? I was under the impression humanity has not delivered a sentient AI? Which is what the AGI title was supposed to be used for...has that been pulled back again?

Unless we invent cold fusion between the next 5 years, they will never be economical. They are the most energy inefficient thing ever invented by humanity and all prediction models state that it will cost more energy, not less, to keep making them better. They will never be energy efficient nor economical in their current state, and most companies are out of ideas on how to shake it up. Even the people who created generative models agree that they have just been brute forcing by making the models larger with more energy consumption. When you try to make them smaller or more energy efficient, they fall off the performance cliff and only produce garbage. I'm sure there are researchers doing cool stuff, but it is neither economical nor efficient.

o3 made the high score on ARC through brute force, not by being good. To raise the score from 75% to 87% required 175 times more computing power, but exactly stunning returns.

It's a bubble because OpenAI spend $2.35 for every $1.00 they make. Yes, you're mathing right, that is a net loss.

It's a bubble because all of the big players in AI development agree that future models will cost exponentially more money to train, for incremental gains. That means there is no path forward that doesn't intensely amplify the unprofitability of an already deeply unprofitable industry.

It's a bubble because newer models with better capabilities only cost more and more to run.

It's a bubble because as far as anyone knows there will never be a solution to the hallucination problem.

It's a bubble because despite investments treating it as a trillion dollar industry, no one has yet figured out a trillion dollar problem that AI can solve.

You're trying on a new top of the line VR headset and saying "Wow, this is incredible, how can anyone say this is a bubble?" Its not about how cool the tech is in isolation, it's about its potential to effect widespread change. Facebook went in hard on VR, imagining a future where everyone worked from home while wearing VR headsets. But what they got was an expensive toy that only had niche uses.

AI performs do well on certain coding tasks because a lot of the individual problems that make up a particular piece of software have already been solved. It's standard practice to design programs as individual units, each of which performs the smallest task possible, and which can then be assembled to complete more complex tasks. This fits very well into the LLM model of assembling pieces into their most likely expected configurations. But it cannot create truly novel code, except by a kind of trial and error mutation process. It cannot problem solve. It cannot identify a users needs and come up with ideal solutions to them. It cannot innovate.

This means that, at best, genAI in the software world becomes a tool for producing individual code elements, guided and shepherded by experienced programmers. It does not replace the software industry, merely augments it, and it does so at a cost that many companies simply may not feel is worth paying.

And that's its best case scenario. In every other industry AI has been a spectacular failure. But it's being invested in as if it will be a technological reckoning for every form of intellectual labour on earth. That is the absolute definition of a bubble.

Your example is strange because, as far as I know, GPTs aren't economical either.