Predicting the timing of such a thing is notoriously difficult. I don't think being wrong about timing 2 years ago means there won't be a correction.
They were right about all of that but it took 15-20 years and the companies involved grew 100x in that timefold, eventually reaching trillion-dollar valuations that would've seemed insane in 2007.
There is a tremendous amount of money to be made in destroying society.
What I remember from that time period is people predicting that we were in a tech bubble driven by social media, that obviously Facebook and LinkedIn were overvalued because social media was a trivial fad, and so on. Example article pulled at random:
https://theconversation.com/linkedin-is-floating-on-air-or-i...
And yet there was no bubble, these companies did fine and Meta became a financial Godzilla.
Anyone remember this video?
https://www.youtube.com/watch?v=I6IQ_FOCE6I
How many of the logos that scroll by there still exist?
Ironically I feel like it captures the spirit of the then-coming 2010s boom more than the climate in 2007, though some of the language it's using is decidedly pre-mobile and more "web 2.0"-ish.
So, it stands to reason that it wasn't a prediction, but a lucky guess (unless the alleged predictor has a history of correct predictions).
I'm not open-minded to arguments about utility, given that I personally witnessed LLMs evolve from interesting but useless toys to insanely helpful tools I use every day.
So the claim is the cost isn't coming down enough to make it make sense for a lot of uses in the long term. When I hear that next to the most wild claims, some by influential people, that the entire white collar workforce is going to be replaced very shortly, it's a bit of a useful reality check.
If I had someone giving me free access to cranes and excavators, I'd be raving about how easy it was to build houses now. But tomorrow when I have to pay full price for them, I'm going to be making very different calculations about return on investment.
The question we need to be asking is "what is the likely full-price cost we'll have to pay for these tools, and is that cost likely to be worth paying?"
What Ed's pointing to is that the full-price cost will have to cover the capital expenditures that have been invested, or the companies which risked that capital will go bust. That gives us a floor for what the full price cost will be, and that floor seems higher than the value being offered by the tools.
Is reading comprehension really this bad nowadays?
But from the article I linked back in March 2024:
"Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models' capabilities will continue a rapacious pace of progress that has unquestionably slowed, with OpenAI admitting that GPT-4 may be worse on some tasks.
As I've written before, hallucinations are a feature not a bug. These models do not "know" anything. They are mathematical behemoths generating a best guess based on training data and labeling, and thus do not "know" what you are asking it to do. You simply cannot fix them. Hallucinations are not going away."
Since then:
- hallucinations are dramatically less of a problem
- several mass market use cases have emerged, most notably coding
- rate of progress has increased
> - hallucinations are dramatically less of a problem
Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
> - several mass market use cases have emerged, most notably coding
Coding seems to be the only one, but there are still a lot of open questions about how the market can sustain the costs, and that's without considering the market dynamics that could emerge once costs are lowered enough that open source models start to become an attractive option.
> - rate of progress has increased
Debatable.
Another important consideration: Hallucinations getting less common/severe but not (as-good-as) solved makes them worse.
LLMs used to very obviously get things wrong. And people wouldn't trust them. Now they're good enough that people blindly trust them.
Now people just directly PR AI output with little to no manual review. We even have clowns calling for the complete abolition of directly human-authored code.
Whatever gains were had in better AI code output over the past two years I lose in having to review much more thoroughly.
Most notably? This is not a mass market use case in the way the author is describing. They are asserting that the amount of spend they need to get this off the ground necessitates the entire world coming in on it, and I would say that opinion has aged pretty well. There are a lot of coders, but there are more people scratching their heads as AI is shoved into every part of their lives.
From my perspective, the model gains are mostly incremental now and a lot of the gains are just from things like improving the agent harnesses. I could be wrong though.
Every facet of the field is being pushed on and advanced at the same time.
And it got me thinking, they sell these AIs as assistants, but it couldn't even look up a passage from a book. This is basic, elementary stuff, it should get it right. I would have fired this assistant right away if it were a person. Not only did it get it totally wrong, it came to me with utmost confidence that this is the quote from the book. Unreliable assistants? That's the product they're trying to sell? Get out of here with that trash. I can't trust it.
No they aren't. The models still hallucinate just like they always did. You cannot trust them, ever, to get something right.
> several mass market use cases have emerged, most notably coding
They aren't really useful for coding based upon the above. Since you can't trust them, you have to carefully review everything they make, which in turn destroys any productivity they could've given you.
> rate of progress has increased
I have yet to see any progress. Opus 4.8 that you get today is no more effective than GPT-3.5 was. Much less would I agree that the rate of progress has increased. Only hype has increased, but there has yet to be a drop of substance.
Nevertheless, it all misses the point if we get to AI post-scarcity utopia. But thats a big if.
The only way they make their money back is if everyone pays them tons of money for it.
> I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley.
We have seen 8 quarters since. Has any of that come to pass?
When asked whether it will be possible to fix Sora's videos after they've been generated, Murati said "eventually," and then couched that by saying "that's what we're trying to figure out...how to use this technology as a tool that people can edit and create with." She promised that there would "eventually" be "more steerability, control and accuracy...and reflecting of intent of what you want."
You'll "eventually" be able to add audio to Sora videos, and when asked when Sora's generative videos will be available to the public, she once again said "eventually," and when pushed said that Sora's launch would "definitely be this year, but could be a few months."
Murati, living in a world of "eventuallies," provided no technical insights, no specifics, and very few details.
Please speculate on why OpenAI wouldn't just leave it up (whether or not they were able to improve it).
Tim Lee also pointed out that when Ed has posted details on some of his analysis, they have had some....oddities: https://x.com/binarybits/status/2034377838883700953
Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
One other thing that’s working against the model makers is the hardware is getting better and the models are getting smaller and more capable. I don’t think we’re going back to the mainframe days. Local will be the endgame.
Is Ed right? Probably because in the end it’s unsustainable the companies left will be the companies that have income coming from somewhere else and there’s one large tech company that isn’t even participating in the boondoggle unless you count $1 billion dollars a year as participating ultimately there is no moat in AI model making.
Nvidia and Microsoft trying to introduce another Arm processor in a laptop of all things won’t change the tide either.
The negatives: Culture is destroyed, truth is destroyed, democracy is destroyed.
I guess its a wash at the end of the day.
Just because those things happened in Dune doesn't mean they'll come true. It should be obvious what massive potential the thinking machines have for humanity as a whole.
So? Something can be bad for society while improving your individual lot in life.
So, judge the book by it's cover?
> arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Then the opposite should be easy to prove. AI is succeeding, is efficient, is universally good, and is working everywhere it's tried. Are those true?
It is literally judging the book by it's author, which is an extremely rationale judgement to make.
How is that better?
> which is an extremely rationale judgement to make.
So it's "rational" to take bias into reading? Why even read? If you know what you think and refuse to accept new information then what purpose is there in consuming anything?
You should just read the comments and get a warm fuzzy that the crowd, for the time being, agrees with your intentionally static ideology.
Comments like these obviously hope they can sway the crowd before they can take an unbiased reading of the article. If the author is that wrong then the crowd here should be able to discover that on their own. If the author convinces the crowd then I'd think you'd want to present a better argument than "well, he was wrong _before_." Post hoc, ergo propter hoc, in action.
It's useful/necessary to use past writing/arguments from an author to say whether they should actually receive any further critical evaluation, or be dismissed. We shouldn't say definitively "they're always wrong, so they're wrong now". However, it's reasonable to say: the author has a demonstrated lack of credibility, so we can probably assume they're wrong here, particularly if they have been wrong in this domain so many times before. Or if they happen to be correct, it's probably not strongly demonstrated by their work.
If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.