alt Hacker NewsI think we need to collectively get a grip on statements like this.
There is no reason to think inference can or will remain interestingly profitable even if you assume pure-play inference providers who don't do any model training. Serving an LLM isn't child's play but it's also not so hard as to count as a moat. Inferencing is:
1. Selling a largely undifferentiated product.
2. Into a highly competitive market.
3. To customers who are primarily sensitive to price.
Industries like this have always existed and in the modern era tend to have margins of about 3% to 5%. Any level of margin higher than that gets competed into the ground.
If you want to claim inference is highly profitable in general, you're going to have to answer why it won't end up like supermarkets or airlines, both of which have bigger moats than inference.
The other problem with this analysis is obviously that you can't just stop training models. Even if you don't care about improving capability or performance, the knowledge in their weights gets stale. Cost of inferencing has to take into account the ongoing cost of training, you can't cleanly split one from the other.
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"If you want to claim inference is highly profitable in general, you're going to have to answer why it won't end up like supermarkets or airlines, both of which have bigger moats than inference."
- I don't... "inference is profitable" is as of now. No one knows future, neither have I commented anything on it.
The statement is valid as it stands.
Inference will continue to be profitable while we don't have enough compute in the world to serve the demand we have. Also, I don't see why it's a problem that the end game for inference of open models will be relatively low, ie 5% or so. You can have a huge industry running on these margins. Though I would note that hyperscalers have been selling compute commodities with a high margin for a long time.
Exactly, inference is like groceries or electricity.
Because LLMs are like evolutionary crabs, they all get to the same point.
I think hardware scarcity plays a part in inference margins. They would get squeezed super quickly if anyone who wanted could just build an inference cluster, but that's not currently the case.
Complete and accurate, thanks.
Amen. AI boosters look at currently high profit margins on inference and blindly accept claims that these inference profit margins will continue. Of course those margins depend on historically massive R&D spend on training. The moment that training stops, AI returns to a commodity market and the inference profits end. Big labs are trying as hard as possible to keep the gravy train running but they can all see it won't last forever, hence the rush for IPO & regulating away open models.
Boosters are right that AI is a historically impactful product, but big labs will be unable to meaningfully profit from ushering in the AI era just like Netscape failed despite ushering in the internet era.