I think it mostly shows that there is no moat
And no one would say that about TSMC.
So there is clearly a moat there somewhere.
No. In the semiconductor industry, the "catch-up" player isn't normally spending less in absolute R&D terms.
Comparing the R&D costs of creating GPT-4o vs. DeepSeek V3 (the latest gen for which we already have good accurate numbers) it looks like the latter cost 1/20th as much to create.
If Samsung could catch up with TSMC for 1/20th of the cost, people definitely would say that TSMC has no moat.
Why do you think Chinese models cost 1/20th to train?
That's the ratio the widely published numbers give [1]. One does not have to believe the numbers [2], but those who do believe them are then justified to conclude that there's no moat.
Which numbers you believe is of course going to affect whether you think there's a moat or not. That's largely orthogonal to your TSMC/Samsung analogy I responded to. If you think the "moatists" are wrong because they believe the wrong numbers, that's fine, but then there's no need for the analogy.
[1] https://galileo.ai/blog/llm-model-training-cost
[2] https://medium.com/@theiand/how-can-deepseek-a-5-6-million-l...
But fundamentally, why is their cost 1/20 and is it sustainable in the next 10 years of competition?
Now that is a good and interesting question! Hopefully a "no-moatist" will share their reasoning.
Ultimately, that's what I need to be convinced. No one has put forth a good argument yet.
Clever architecture --> Ok but OpenAI/Anthropic can use these as well and they also have very smart people with their secret clever architectures
Distilling --> Ok but distilling means you will never be smarter than the original. Furthermore, reasoning is now hidden by private labs and they have poison pill answers for distilling if they can detect it. They will be able to detect distilling better and better.
Cheaper electricity --> Ok this is cancelled out by their chips being much less efficient due to not having ASML EUV machine access.
So I don't see why fundamentally their training costs are cheaper over the long term.
I'm looking for a no-moatist to convince me.
Labor. Smart labor would be much cheaper I'd reckon in China than in the US.
How much advantage in costs? What % of labor is training cost?
I am not a "no-moatist" per se but one can argue their might be a plateau to how good a inference llm can become. If this is the case the playing field shifts to context, tools and harness, which are much cheaper to build an compete on.
Please just say what you want to say.
Yeah, I'm not sure if "no moat" analogy stands for chip manufacturing. Even if foundries acquire lithographic nodes, the procedures (temperature, duration, etc) are for them to figure out and are usually kept secret. This secret could be the "moat" that differentiates each foundry's operational capabilities.