[anon373839]

I don’t see how Anthropic is in a better position. They have a slight edge in model quality right at a time when we’re getting a taste of what cheap, “good enough” AI looks like. They don’t own their own compute. And their own arrogance and lies have alienated a huge chunk of their customer base and alerted everyone to the dangers of being dependent on them.

[jmyeet]

I personally think not owning their own compute is going to be an advantage.

There is a meteor headed towards all this AI investment that I don't think has been properly accounted for and that is, what happens to all the existing hardware investments when NVidia's next architecture comes out. Blackwell (H100/H200) is the current generation. Rubin (R100, presumably R200) is the next and arrives soon. Now a lot of the investment hasn't been spent yet so will likely be spent on Rubin but at that point, what happens when the next iteration comes out and does 3-4x the compute for the same electricity input and same hardware cost?

Also, what happens when people can run way bigger models on consumer hardware in 5 years? The effective limit for useful local LLMs is currently ~31B parameter models because the RTX 5090 has 32GB of VRAM and Apple's shared memory architecture, which can keep bigger models in memory, just doesn't have the raw processing power.

Anyway, why I argue Anthropic is in a better position (than OpenAI) is that they seem to have captured a market that may well be profitable for them as a company, specifically Claude for coding. So they just haven't burnt quite as much cash as OpenAI so aren't in as deep of a hole.

While I think local models are going to improve maassively over the next few years, running them in a data center at scale is always going to be cheaper for a company. Why? Because they can amortize their costs by running 24/7 and powering them and cooling them is simply cheaper at scale when you're talking about 1000+ engineers who otherwise might only be using their hardware ~40 hours a week.

IMHO Google is in the best position here of all the US companies, even though their models aren't the best, because their data centers are ruthlessly efficient, their homegrown TPUs will eventually catch up (and thus avoid the NVidia tax) and they simply haven't bet the farm on winning AI.

[Schiendelman]

I'm generally with you on all of these ideas.

However, Google probably won't catch up. Nvidia has been winning in spite of the fact that their hardware is general purpose rather than tuned for inference.

Rubin has architectural differences I don't understand that are supposed to make inference much cheaper and faster while still retaining those other more generic capabilities. Their next generation after that is going to do even better at being fast for inference and general purpose.

Google is betting that their TPUs won't depreciate faster than the markup they have to pay to Nvidia. I don't think they will be right.