775
Generative AI Is an Engineering Disaster. A shockingly inefficient trillion-dollar project.
(www.theatlantic.com)
this post was submitted on 15 Jul 2026
775 points (97.5% liked)
Technology
87515 readers
2851 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 3 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
In 1897, they built the first music synthesizer. It worked, but it took up the basement of an entire city-block sized building, so it was essentially useless. After a few decades of development, it could fit in a suitcase, and be carried around.
Data Centers are like that 1897 synthesizer. Sure, it works, but at what cost? It clearly isn't ready for prime time. Go back to the drawing board, tweak the problems, including regulations, and maybe in a couple of decades, we take another run at the new and improved version.
The issue with AI is not a technical or development problem. It's not even a regulation problem. It's a capitalism problem. Infinite growth will still be as unscalable in a hundred years as it is now no matter how good and mature the tech is.
There are also hard mathematical limits stalling AI growth. Frontier models haven't improved in like a year despite being fed money by basically the entire global economy. Diminishing returns on steroids basically. They're already at the limit of what they can make, and going further gives a much smaller improvement in the model, and now I hear there might not be enough human written material on the internet to train them.
It also looks like hallucinations are inherent to LLMs and you can't get rid of them. It's a side effect of the model. What commercial applications are there then, if you can't guarantee the output? It's worse than a human for most things since it doesn't know truth from lie and will confidently say both as if they're fact. It also looks like prompt injection isn't something you can fully guard against either.
What's the value proposition when you can't trust the output and the model might give a massive refund or discount to a customer and the courts rule the AI speaks on behalf of your company?
Haven’t been improved in a year? By what metric are you basing that assertion?
IP theft is probably the main one I’ll concede the point on - that damage was done long ago so they haven’t “improved” on it.
But whether it’s reasoning or generation or building things… that’s a crazy take. Unless you consider them like a chatbot companion? I wouldn’t really know much on that front, I’ll concede.
It's been marginal improvements for like 18 months now. I don't know if you remember what they promised that long ago but current frontier models just ain't it.
If you don't believe me, then why? Put your argument in numbers.
Anthropic and openAI have both spent nation-state levels of money training these models and they only seem marginally better compared to the last ones? Maybe larger context and better reasoning but they still hallucinate, they still make the same mistakes and pitfalls.
Even with tokens getting dramatically cheaper inference on mythos or other frontier models is so expensive they need to start replacing skilled professionals and right now they just can't. Productivity doesn't seem to go up from AI use, if anything net productivity for an org goes down from people outsourcing human cognition onto their colleagues.
"How about instead of me summarizing this report i use Claude and then my colleague spends the cognitive effort deciding if Claude lied or not"
The guy using AI for everything looks super productive and the people stuck dealing with the work he's "getting the AI to do for him" look like under performers when they're actually load bearing in this new setup.
Tokens aren't going to get cheaper. Tokens must get more expensive, and soon. AI companies are making big losses even if you ignore the stratospheric debt for the immemse quantity of hardware.
Yeah you're right they just appear cheaper due to circular financing and some memory improvements. I was trying to steel man my argument into the strongest possible point that still fails to make sense.