Wouldn't this compress ai revenue like 15x quickly
If they really have a 4.7 opus high equivalent at 1/16 the cost wouldn't this significantly effect all the current capex and planing
Maybe they are getting elon to cover cost
"Will this decrease Revenue?" -- only if demand for high quality tokens is inelastic. If demand is instead elastic (grows with cheaper pricing) then revenue will likely increase.
"Will this lower earnings?" -- they have a current inference margin for their old models, and with the Elon deal in place, they have a new inference margin. It might be better or worse than their old one. If it's worse, then they'd need to see a concomitant increase in usage. If they don't, then yes it might lower earnings.
"Will this lower corporate value?" -- no - not least because this company is going to be owned by SpaceX approximately 90 days after IPO -- so all the new owner will care about is being benchmark competitive with Anthropic and oAI for the first n quarters. If they can do that, it will massively increase the corporate value of SX; it's hard to build a frontier lab.
One of the surprisingly hardest problems to solve is to get a model to use the tools you give it access to.
The real money furnace is the training, not just of models that get released, but also experimental training runs that fail to move benchmarks and are quietly thrown away. E.g. Cursor claim that 85% of the compute for Composer 2.5 comes from additional training on top of Kimi K2.5, where I'm not sure how they determined that, but it can't have been cheap. Then they say "Together with SpaceXAI, we're training a significantly larger model from scratch, using 10x more total compute."
So yes, they're probably attempting to replicate the Anthropic playbook of paying a large upfront cost for a very good model, and then rapidly acquiring paying customers, hoping that the inference margin will be enough to cover the training cost.
i use gpt 5.5 and opus 4.7 a lot every day, if i can get good results at this speed, hopefully the usage level holds up on my team plan haha
that roughly just puts it on par with OpenAI and Anthropic subscriptions in terms of pricing per token
Every model release now has been straight price increases since what GPT 4 ? When was the last time a new flagship model decreased prices compared to the previous one ?
2. We are not interested in how different model naming schemes relate to prices, we are interested in the capabilities. So if you want to learn something about price development you need comparative levels of capabilities, and then look at the prices. 4o is not comparable to 5.5 in the first regard. It is (according to the benchmarks) maybe more comparable to current 5 nano - which is 98% cheaper.
Apart from that, I'm not sure if focusing on tokens is even a good idea, because they are so different from model to model. I'd almost consider them a red herring now.
We could look at tasks instead. Is there anything even remotely suggesting that your typical task you give an LLM now costs less in inference than before?
- AI revenue going up & cost/token are not related metrics, at least not in the way you are assuming - basically all players (except OAI for the moment) struggling with capacity and/or reducing-dismissing subscription based solutions in favour of pay-per-use. If token cost/token was falling, we would see quite the opposite.