291–300 of 426 posts
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What they charge people says nothing about what it costs them. Off the top of my head, one confounding factor is trying to win back marketshare from Anthropic. We will only know the actually situation once Anthropic goes public and we can look at their books.
I think it's pretty safe to assume they are not losing money on inference.
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GPT-4 (original API): Input: $30 / 1M tokens Output: $60 / 1M tokens GPT-5.5: Input: $5 / 1M tokens Output: $30 / 1M tokens Costs have been reducing by over 5x year over year. Inference cost concern is mostly performative. https://simianwords.bearblog.dev/conclusive-proofs-that-llm-... Edit: can't reply but companies aren't selling inference at loss. In the blog post I point to third party hosting of open models like…
If you go to https://developers.openai.com/api/docs/pricing , you will see the actual prices, which do not match what you posted: GPT-4.1 Input: $2.00 / 1M Tokens Output: $8.00 / 1M Tokens
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I strongly disagree. Humans are so insanely well incentivized here with trillions in market share to make localized AI good enough and that’s the only benchmark they need.
Are they? I don't believe there's that big of a market for local AI. Most people don't care that much, and you'll most likely lose the advertising revenue.
I agree that the market for local AI is basically limited to nerds at this point, but that's because nobody's really explained why local AI is a good thing and also because the vast majority of people need the $20 paid plan at most. How much time and money would it take to get something half as good as OpenAIs products running locally?
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Something I have noticed is that the people who are using it to write everything are the same people who had a poor level of English writing a year or two ago. It's just "intellectual" botox.
> It's just "intellectual" botox. Could be just ESL, it's hard to close the proficient to native gap.
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Running local applications is less efficient than thin clients to the cloud generally, not just in LLMs. The trick is that you can get to the point where it's effective enough, and affordable enough, that the control and availability factors become dominant.
My point is that you will always get much more value / $ by using cloud based solutions.
But in many cases self hosted or dedicated boxes are cheaper than cloud.
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> within a few years we will be running local models as good as today’s frontier Unless there isn't some important breakthrough in hw production or in models architecture, it's quite the opposite: bigger, more expensive and more energy-intensive hw is needed today compared to 1 or 2 years ago.
I can run qwen3.6-27b on a four year-old Macbook Pro that dominates ChatGPT-4o (the frontier model from 2 years ago) and is competetitve against early ChatGPT-5 versions. We are also getting a lot smarter about using and deploying these local models. Your entire AI stack from two years ago would be absolutely crushed by a todays local LLM models and a high-end local inference system when combined with a good modern c…
I hardly doubt there will be consumer grade HW to run it in 2 years either. And deep seek v4 pro is not even close to OAI or anthropic frontier models.
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Something I have noticed is that the people who are using it to write everything are the same people who had a poor level of English writing a year or two ago. It's just "intellectual" botox.
I noticed this from former coworkers who I know couldn't write beyond first grader level a few years ago. They weren't good at their native language either. Now they write "competent" blog posts on LinkedIn that seem 100% AI slop. Some are employed at AWS, too. I'm not a native English speaker as I'm sure my writing shows. My point is that I'd rather read genuine posts full of grammar errors instead of slop.
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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…
What kind of useful context window are you getting on a 4090, out of curiosity?
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.
> 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.
At the same time, $100 a month is A LOT of RAM.
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I disagree. No one will want to use second rate models when the frontier models reach a specific level of capability. Enterprise will keep paying.
No one? When free means I get 95% of the capabilities of something very very expensive, you bet your bottom dollar many many people will choose free.