381–390 of 426 posts
Although I agree with the sentiment in the article, it smells very LLM~y. Especially the sections and punchlines. Such as: `That is not a rounding error. That is a line item that needs its own budget code.`
TL;DR to save you time: 1. GenAI companies are making a loss in order to gain adoption and later lock-in 2. ??? 3. They're going to cash-in soon and start milking you now that business critical systems rely on GenAI The "???" denotes a complete failure to offer compelling arguments that link 1 and 3.
We popularized the term "enshittification" so we wouldn't have to keep explaining this.
This is just simply economics. Make an early loss to get a customer base, offer addictive features at irresistible prices, then yank them into your profit-making pricing.
Although yes, the ??? in the parent comment could be replaced with "A/B test some price gouging or ratty pricing methods to see how far we can go without being noticed"
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Within 5-10 years you're going to see a box like one of those AMD Halo nodes running homes. They'll be controlling lights and temperature, they'll be adding calendar reminders that show up on your phone and your fridge. Your phone and devices might sync pictures and videos there instead of the large cloud providers. They'll also be a media server, able to stream and multiplex whatever content you want through the hom…
Umm, you can do basically all of this, today, with Home Assistant and a handful of add-on apps. I use a local LLM with it, but you can use a hosted LLM if you like. The core home automation stuff can run on a potato. The LLM just writes new automations when I ask it, or acts as a natural language interface. I use a pretty small 4B parameter local LLM, on a fairly modest mini PC. It doesn't take a frontier model to do…
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It's like witnessing a rocket using the most powerful engine on Earth then once it escaped orbit turn off the engine and said "It is flying without power!". Yes, sure, right now it is ... but that's NOT how it got here. There are trillions invested to recoup and at most billions in sales. It doesn't add up to tokens making a profit any time soon.
The problem is, people see "they're not profitable once you account for training" and equate that to "AI will go away soon" But if all the AI companies stopped training new models, they would all instantly become profitable (and stick around) The thing that makes them unprofitable, is having to compete (which means training models). If / when enough companies exit the market, the cost to compete goes down and you end…
That will only happen when the bubble bursts and those companies will exit by going bankrupt
The entire problem with "AI" is that it's easy to do without. The AI companies know it, the users know it - even the most pro AI agent manager knows it. Thought experiment: remove AI from the world right now, all of it - what do you have? Business as usual. This article doesn't do enough to underscore that - dreaded be the day I need to get an actual engineer to review a PR, right?
Isn't that always the case in the early stages of new technology adoption? It becomes less and less true as the new technology becomes more and more integrated. In the first few years after electric motors became a thing, one could have said the same thing. We would have just gone back to steam. If you tried to "do without them" now, society would collapse. So the question is not if we can do without them now, it's i…
Although I agree with the sentiment in the article, it smells very LLM~y. Especially the sections and punchlines. Such as: `That is not a rounding error. That is a line item that needs its own budget code.`
Pangram flags this as 100% AI generated with high confidence.
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> within a few years we will be running local models as good as today’s frontier models with almost no cost burden Based on what? The RAM requirements alone are extraordinary. No, running large models on shared, dedicated hosted hardware at full utilization is going to be vastly more cost-efficient for the foreseeable future.
> Based on what? I take it you haven’t actually run any of the current gen local models? They all fit on fairly accessibility hardware, and their performance is at least on par with what I was paying for last year. I have one of my agents running entirely from a local model running on a MBP and it has repeatedly shown it’s capable of non-trivial tasks. Playing around with another, uncensored, local model on my 4090 d…
The hyperbolic nature of the articles in both AI camps is very exhausting to me. I'd like to get in front of a whiteboard with someone who knows economics and the token providers businesses well enough to answer my "explain to me like I'm five" questions. But I'll start with these in here: Is my observation correct that for the token providers this is a margins game, while for the consumers this is a quality of servi…
Others have posted links here saying inference is about 30% of Anthropic's spend, the other 70% is R&D - things like developing the next model. If you take that into account, add a profit margin, and round to make the figures easy you end up with them selling tokens at $1 / M Token to make money.
Their API cost is currently $25 / M Token. There is no question that's profitable. Someone really pushing their $200/mo Max plan can use 10B Tokens per month, which works out at $0.02 / M Token, so they are eating a huge loss there. That is clearly going to go away at some stage. For the rest of us: look at your average monthly token usage. If you are using 100M Tokens a month with the Anthropic $100/mo plan, they are making money out of you.
You have to be working very long hours, and be really, really proficient at using AI to achieve 10B Tokens a month. The only way to be that proficient is to have been using it for a long time, years in fact, so it was useful to you long before Opus came along. You would be very disappointed to lose Opus, of course - but you are just the sort of person who can make a less capable model sing. It's not so difficult to see those users moving to an in-house-hosted, open-source model in a few years, and it will cost them what they are paying now - $0.02 / M Token.
All that means I'm not convinced by the gloom and doom vibe of the article. Things will change, but it won't mean the end of AI usage.
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> Based on what? I take it you haven’t actually run any of the current gen local models? They all fit on fairly accessibility hardware, and their performance is at least on par with what I was paying for last year. I have one of my agents running entirely from a local model running on a MBP and it has repeatedly shown it’s capable of non-trivial tasks. Playing around with another, uncensored, local model on my 4090 d…
Which models are you currently using?
I also have an agent using Kimi 2.6 as a backend (which is open, but not local) and for some coding tasks as well.
Every AI subscription is a ticking time bomb for the frontier provider; within a few years we will be running local models as good as today’s frontier models with almost no cost burden. The floor will fall out of the enterprise market for all the frontier companies.
And yet, less than 0.01% of the population (made up number, but I am more likely to be overestimating than underestimating) do so.
Running local models to do real work is likely to be another niche hobby.