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I wonder if they're under pressure from higher ups to make stuff using AI? Their bosses have probably been convinced through AI hysteria that extreme gains are possible using AI, and that if those gains aren't materializing for them, it's a problem with how they handle their prompts, rather than it being impossible owing to the deficiencies of the technology. Not wanting to be left behind, they mandate everyone use AI and increase their output in line with what the hypists say is possible, typically an efficiency boost of twenty to one hundred percent, and thus, pointy-haired bosses lacking technical expertise relevant to their companies products, unable to understand the deficiencies of AI output, tank market viability while boosting investor enthusiasm in the short term by playing into popular biases of the financialized scam economy.
The bosses get rich, as do the tech scammers and their affiliates, but the economy inches closer to its doom once the bubble goes pop.
I'm definitely feeling a lot of pressure from the higher ups to get on top of using LLMs. I support some extent of the push, the tools really are impressive and we're seeing real test cases of it providing a lot of benefit. But there is also a dynamic where the different manager fiefdoms are jockeying to show who's team is best utilizing it. I'm usually the shield that stand between our team and upper management bullshit and I recently had to walk my manager off a cliff of requiring each engineer to answer a short questionnaire every day about if they've used AI and for what the previous day after our standup.
Both can be true that there is a lot of value to be gained and that there is a bit of a mania going on where upper managers smell blood in the water. There's a lot of talk about merging teams and helping engineers transfer skills across teams, this is the kind of environment that makes careers as the manager who's team is able to demonstrate a superior implementation stands to gobble up other teams.
The actual labs could pop, but the people using the tech won't. The dotcom bubble didn't get get rid of email when it popped. This stuff is useful and there its use will linger.
Where are those people going to get their fix? If the experiences of people like @dr_analog are accurate, then nothing but absolute up to the minute models are going to cut it, and the training and inference costs on those models are high enough that they're not going to be ubiquitous. Are you banking on open weight models?
If a lab goes pop and has to sell off its assets, training costs are not a problem. Inference costs can be covered with a reasonably priced subscription. If we're stuck with current SOTA models for the next 50 years, software dev will still be changed forever.
[Citation needed]
This is currently not true. Maybe if you freeze the models and wait for silicon to improve for a few years it will be. But the LLM companies are constantly increasing their inference costs as FLOPs go down in price.
At least according to Ed Zitron's analysis. Maybe you just don't believe his numbers.
Link?
I've been trying to figure out AI-as-a-business since last fall, and the numbers make me feel like I'm taking crazy pills.
https://www.wheresyoured.at/ is his blog. He also has a podcast https://www.betteroffline.com/ but the AI industry analysis there is essentially him reading/summarizing his blog posts.
I'm reading through his latest piece where he basically says AI companies are all in complete shambles and he just seems flatly wrong? https://www.wheresyoured.at/data-center-crisis/
There's a warning sign here, it's like he's implying that post-training is done after the training process, post-training is part of the training process. I don't think he has a proper grasp on what he's talking about.
What does he think an AI model is? Deepseek R1 0528 is sitting on people's (big!) PCs somewhere, cloud providers are just providing it. It's a complete product. It still gets about 2 billion tokens per month on openrouter which is pretty good for an obsolete model. It doesn't need more 'post-training' to maintain it...
Seems like a deceptive line of argument to say that training costs are not R&D.
It would be reasonable to say 'because of competition, these AI companies cannot stop making new models like how car companies must always release new cars - this is especially true given rapid performance improvements and low costs of switching provider which reduce retention making the business model precarious and expensive' but he isn't saying that, he's making an altogether more ambitious argument that 'training costs are impossible to avoid' which is just wrong?
He has this overly emotional tone too:
What is this, Chomsky? I don't find this guy trustworthy when he conjures up figures based on 'just trust me':
The idea that the biggest companies in the world have mysteriously decided to invest hundreds of billions in an obviously, openly unprofitable business sector is interesting but it needs to be justified in detail. Who could know more about data centre economics than Amazon, Facebook, Microsoft, Google? Who would be more diligent in checking the financials than the companies spending hundreds of billions of their own money on this, this year alone?
Not saying you’re wrong but a few explanations:
Some firms may believe its existential and therefore even if costs are high and odds of success are low the cost of losing is too high.
Right now, the market is rewarding them for the spending. So even if they internally question the wisdom, stock price goes up and the decision makers get bonuses.
One tech company has somewhat called bullshit. They sit in Cupertino. Maybe they’ve since changed but my understanding is they still call bullshit.
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