11–20 of 48 posts
Training is the expensive part here. It seems much more likely that the training of these models slows down drastically and is written off as a sunk cost, a few companies continue running inference on years-old models, and the free versions go away.
This is addressed in the very first sentence of the article that you obviously didn't read.
I dunno, GPT-OSS and Llama and QWEN and any half dozen of other large open-weight models? I really can't imagine OpenAI or Anthropic turning off inference for a model that my workplace is happy to spend >$200*person/month on. Google still has piles of cash and no reason to turn off Gemini. The thing is, if inference is truly heavily subsidized (I don't think it is, because places like OpenRouter charge less than the…
I dunno, GPT-OSS and Llama and QWEN and any half dozen of other large open-weight models? I really can't imagine OpenAI or Anthropic turning off inference for a model that my workplace is happy to spend >$200*person/month on. Google still has piles of cash and no reason to turn off Gemini. The thing is, if inference is truly heavily subsidized (I don't think it is, because places like OpenRouter charge less than the…
> we'd probably happily pay >$500 a month for the current frontier models Try $5,000. OpenAI loses hundreds of billions a year, they need a 100x, not 2x.
Finally buy a new PC.
I dunno, GPT-OSS and Llama and QWEN and any half dozen of other large open-weight models? I really can't imagine OpenAI or Anthropic turning off inference for a model that my workplace is happy to spend >$200*person/month on. Google still has piles of cash and no reason to turn off Gemini. The thing is, if inference is truly heavily subsidized (I don't think it is, because places like OpenRouter charge less than the…
> we'd probably happily pay >$500 a month for the current frontier models Try $5,000. OpenAI loses hundreds of billions a year, they need a 100x, not 2x.
Another data point: I gave codex a 2 sentence description (being intentionally vague and actually slightly misleading) of a problem that another engineer spent ~1 week root causing a couple months ago, and it found the bug in 3.5 minutes.
These things were hot garbage right up until the second they weren't. Suddenly, they are immensely useful. That said, I doubt my usage costs anywhere near that much to openai.
Earlier quoted context omitted.
> we'd probably happily pay >$500 a month for the current frontier models Try $5,000. OpenAI loses hundreds of billions a year, they need a 100x, not 2x.
$60k/yr still seems like a good deal for the productivity multiplier you get on an experienced engineer costing several times that. Actually, I'm fairly certain that some optimizations I had codex do this week would already pay for that from being able to scale down pod resource requirements, and that's just from me telling it to profile our code and find high ROI things to fix, taking only part of my focus away from…
1. AI disappears, goes up in price, etc. All the money you've spent goes up in smoke, or you have to spend a lot more money to keep the engine running.
2. AI does not disappear, becomes cheaper and eats your businesses primary revenue generation for lunch.
Number 1 could happen tomorrow. Number 1 could happen after number 2. Number 1 may never happen.
Also expect that even if the AI market crashes that AI has already massively changed the economy, and that at least some investment will go into making AI more efficient and at any point number 2 could spring out of nowhere yet again.
There's wildly different reports about whether the cost of just inference (not the training) is expensive or not...
Sam Altman has said “We’re profitable on inference. If we didn’t pay for training, we’d be a very profitable company.”
But a lot of folks are convinced that inference prices are currently being propped up by burning through investor capital?
I think if we look at open source model hosting then it's pretty convincing - Look at say https://openrouter.ai/z-ai/glm-4.7 . There's about 10 different random API providers that are competing on price and they'll serve GLM 4.7 tokens at around $1.50 - $2.50 per output Mtokens. (which by the way is a tenth of the cost of Opus 4.5)
I seriously doubt that all these random services that no one has ever heard of are also being propped up by investor capital. It seems more likely that $1.50 - $2.50 is the "near cost" price.
If that's the actual cost, and considering that the open source models like GLM are still pretty useful when used correctly, then it's pretty clear that AI is here to stay.