3 ms·
The Amodei quote in my other reply explains why this is wrong. The point is not to compare the training of the current model to inference on the current model.
by dcre 1y ago
The Amodei quote in my other reply explains why this is wrong. The point is not to compare the training of the current model to inference on the current model. The thing that makes them lose so much money is that they are training the next model while making back their training cost on the current model. So it's not COGS at all.
- prasadjoglekar 1y agoWell, only if the one training model continued to function as a going business. Their amortization window for the training cost is 2 months or so. They can't just keep that up and collect $. They have to build the next model, or else people will go to someone else.
- dcre 1y agoWhy two months? It was almost a year between Claude 3.5 and 4. (Not sure how much it costs to go from 3.5 to 3.7.)
- Jalad 1y agoEven being generous, and saying it's a year, most capital expenditures depreciate over a period of 5-7 years. To state the obvious, training one model a year is not a saving grace
- dcre 1y agoI don't understand why the absolute time period matters — all that matters is that you get enough time making money on inference to make up for the cost of training.
- oblio 1y agoDon't they need to accelerate that, though? Having a 1 year old model isn't really great, it's just tolerable.
- dcre 1y agoI think this is debatable as more models become good enough for more tasks. Maybe a smaller proportion of tasks will require SOTA models. On the other hand, the set of tasks people want to use LLMs for will expand along with the capabilities of SOTA models.
- ToucanLoucan 1y agoSo is OpenAI capable of not making a new model at some point? They've been training the next model continuously as long as they've existed AFAIK. Our software house spends a lot on R&D sure, but we're still incredibly profitable all the same. If OpenAI is in a position where they effectively have to stop iterating the product to be profitable, I wouldn't call that a very good place to be when you're on the verge of having several hundred billion in debt.
- dcre 1y agoI think at that point there is strong financial pressure to figure out how to continuously evolve models instead of changing new ones, for example by building models out of smaller modules that can be trained individually and swapped out. Jeff Dean and Noam Shazeer talked about that a bit in their interview with Dwarkesh: https://www.dwarkesh.com/p/jeff-dean-and-noam-shazeer https://www.dwarkesh.com/p/jeff-dean-and-noam-shazeer
- DenisM 1y agoThere’s still untapped value in deeper integrations. They might hit a jackpot of exponentially increasing value from network effects caused by tight integration with e.g. disjoint business processes. We know that businesses with tight network effects can grow to about 2 trillion in valuation.
- oblio 1y agoHow would that look with at least 3 US companies, probably 2 Chinese ones and at least 1 European company developing state of the art LLMs?
- drob518 1y agoSo,if they stopped training they’d be profitable? Only in some incremental sense, ignoring all sunk costs.