Upper bound of AI progress - recursive self improvement. In this case AI will be responsible for building better models, making people who own datacenters the winners. Anthropic/OAI is cooked.
Lower bound of AI progress - plateau. Progess is slowing, focus is on serving a meaningful peak capability at the lowest possible price. There's been news today that Google is building a Gemini chip with weights baked into silicon. Considering a chip's lifetime of 2-3 years at minimum, and that a 2-3 year model today would be useless today, they're expecting they wont make a similar amount of progress in the next 3. Game is about selling at the lowest margin. Anthropic/OAI is cooked.
So their survival rests on the presumption that AI progress will fall between these two extremes.
>. In this case AI will be responsible for building better models, making people who own datacenters the winners.
We need SETI@home for Open Weight models yesterday...
FWIW, I agree.
Baking weights in makes a lot of sense for inference speed and power efficiency and has the added benefit of putting many end-users on the hardware refresh treadmill.
Yes, but how much would be a static Sonnet 3.5 be worth today? Its just about 2 years old. I'm not even going to ask about 3yo models like GPT4.
Okay, but the models today will be useful for a lot longer than sonnet 3.5. They're already more than capable to do nearly anything you throw at them given enough time and human assistance. The next step up is faster, cheaper and better user experience. I have only had two instances where I needed to reach for 5.6 sol and that only totalled around $2.7 in api costs.
I would imagine it would look something like this:
Ground breaking/novel research -> SWE -> day to day assistant conversations -> chat support bot...
The first one might not make sense but it gets them the pipeline for when it does make sense in the future. Plus for a lot of things using LLMs they'd be fine with older tech, especially if running it was even faster and cheaper.
> So their survival rests on the presumption that AI progress will fall between these two extremes.
That feels like a very generous framing. There's very little opportunity between the extremes that would paint a convincing outlook of survival for either company.
Perhaps if they were able to scale down their spending drastically they could survive, but that requires acknowledging their current valuations are BS. Doing so is a major risk, that will piss off all share holders. There's also the employees they would need to fire or reduce salary. The shift of focus internally to sustainability would be a major challenge.