Comment by zachthewf
5 hours ago
Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong takes for 2+ years (e.g. https://www.wheresyoured.at/peakai/ from March 2024) arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Not sure where I heard this, but I'm reminded of a story about someone predicting the dotcom crash early, circa 1998. For 2 years they were demonstrably crazy, and missed out on massive stock market gains. Then they were right. (And yes, tech slowly bounced back after that.)
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.
I'm also reminded of all the HN posts from 2007-2009 that predicted that the adoption of social networking would be a terrible thing for privacy, that it would destroy society, that people would lose their jobs over crazy shit they said on the Internet, that it would lead to the decline of trust and in-person interactions, that people would forget how to socialize, etc.
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.
Eh, you can find HN posts predicting that literally everything will destroy privacy/society/trust/etc. Predicting doom is a popular pasttime.
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.
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> Predicting the timing of such a thing is notoriously difficult.
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).
Not related to AI but, I recently rewatched "The Big Short" and your comment reminded me of it. I can't testify the accuracy of the movie, but for over year, Michael Burry was viewed as in the same manner for shorting the market, while the economy was was in a hype cycle.
Burry of course has famously predicted 40 of the last 5 crashes, so maybe not the best example.
I'm open-minded to arguments about AI being a financial bubble and a bad business.
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.
I guess one of Zitron's arguments is that the utility you see today is based on subsidized costs, that if you had to pay more it might not be worth the tradeoff to you.
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.
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda).
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
I think the points you raise are reasonable signals to consider, but I don't think they show the author being "consistently wrong". The overall thesis still remains plausible even though we have seen LLMs continue to improve.
> - 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.
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Has rate of progress increased? How does one measure that? Genuinely curious - would be very interesting to map out the "effectiveness" of each AI model vs how long it took to train/release.
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.
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> several mass market use cases have emerged, most notably coding
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.
> hallucinations are dramatically less of a problem
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.
Not the person you are responding to, but here:
> 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?
Even if you see a real bubble or catastrophy in the making, predicting when it will pop is a fools game.
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https://news.ycombinator.com/item?id=48447549
The quality of AI doomerism takes is matched only by the quality of AI boosterism takes. Ed's kind of interesting as a temperature sensor but I don't feel like you can really take anything he writes seriously.
I highly recommend folks read Wired's profile on him: https://www.wired.com/story/ai-pr-ed-zitron-profile/
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
What if you phrase the question from "will AI ever be useful" (a term as utterly vague as "IT") to "will it ever be able to promise the financial gains these companies are hoping? Especially with local models eating their lunch :shrug:
Yeah they seem clickable because anything Anti-AI is a bit soothing right now, but he is constantly wrong and usually is pushing the angle of "these businesses aren't even profitable!"
Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
They ain’t profitable yet. Most of the model maker’s will be gone soon. It’s unsustainable unless you’re Google who has other income coming in to support their hobby, and the Chinese model makers are spending a fraction to be six months behind and many of them will be there for the long-term because they have backup support (government) who is in the race for the long-term.
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.
Why is anti-AI soothing?
Because there are still a huge number of people who would be very relieved if the whole AI thing just went away.
For some of us it is, I suppose as an alternate view to AI booster-ism, particularly if you think the long term effects would be mostly negative.
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And its been 3 years of AI boosters telling me that my job as a litigating attorney will not exist in 2 months. Yet here I am, gainfully employed.
> Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong
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?
> So, judge the book by it's cover?
It is literally judging the book by it's author, which is an extremely rationale judgement to make.
That's the exact opposite of rational. It is, in fact, a formal logical fallacy (ad hominem). His argument can be correct even if he himself is not typically correct.
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> It is literally judging the book by it's author
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.
He also does PR for AI companies and only really acknowledges this in interviews. As far as I know he never discloses it in his rants.
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