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.
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...
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.