alt Hacker News> compared to his own closed and overpriced ones
I don't understand how Anthropic or OpenAI can have overpriced models, yet losing money like there is no tomorrow. Taking their own numbers at face value, they claim a revenue of 24 billion (ARR, a dubious tool), spending 21 billion in operating losses and another 11 billion as "R&D" funneled straight to Microsoft pockets. That before all investments they are committing to in new data centers, equivalent to 20x their current revenue.
To be profitable (including capex), the cheapest subscription should at least $200/month for what is currently $20/month, that some already consider overpriced. Unless a miraculous collapse in inference costs happen in the next couple of years, or every single human being become a paying customer of ChatGPT (if they limit their usage to a couple of chats per day on average, to keep inference costs low!), maths don't add up.
Replies
Overpriced for the value provided, especially with the pay-as-you-go rates for enterprises; but also in stock prices.
Pricing a product is generally about ensuring a profit on investment, but also a good RoI for your customers (or they will not pay).
With these long-term profit exercises, it's always a lot of hand-waving though.
> I don't understand how Anthropic or OpenAI can have overpriced models, yet losing money like there is no tomorrow.
The marginal cost of inference (which is roughly what you're going to pay to a provider that's running an open weight model) doesn't include the cost of training that model.
Add to that the >$700B OpenAI already committed to spend in infrastructure through 2030!
Re. 'overpriced', I'm assuming OP talking about their theoretical stock price.
Inference is only priced in for a low usage window 6h a day per customer?
I'd assume the gp was just inflating their rhetoric but cost of model is an interesting topic.
I'd guess Anthropic and OpenAI's models are expensive relative to the cost of running the models but that the revenue still doesn't pay for building the next and next models. The challenge is how these next generation models are going to pay for themselves. Will everyone on earth find it useful to $200/month to talk to a thing more intelligent than themselves? The alternative is naturally that these are going to replace workers and employers will be the one paying.
It's possible for something to be overpriced to the customer and simultaneously underpriced for the business to be profitable. Theoretically speaking, they could just be selling such an inefficient product.