I think it mostly shows that there is no moat and the only advantage the U.S companies have over the Chinese is more compute.
Qwen Max, Kimi K3, GLM 5.3 are really close to Opus/Sol/Fable/Astra and they are open weights.
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.
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.
However, these do require educated, white collar, workers.
There's not 100 frontier labs, it's not like airline companies
Actually not a bad comparison. Both burn massive amounts of up front capital to protect an oligopoly in the hopes their commodity product eventually pays off.
For most people, the app IS the AI.
And even for its wonkiness, ChatGPT has had the best UX/UI of them all.
The way to win the AI wars in the eyes of the common folk is through the frontend, to be the Apple of AI, as it were.
There are people all over the world who have no computer skills but they use ChatGPT on their phones daily
They don't know/care shit about models and all that
For them, if the app sucks, the AI sucks.
Chinese counterpart like CXMT and Huawei is begin producing their own chip
You cant block an entire nation level effort with tariff
And consumers use 4x as much per capita. Industrial generation per capita China comes out ~2x
> industrial electricity prices in China are roughly 34% higher than in the US
For which industrial customer and where? Chinese compute hubs are on par to slightly cheaper on pure electricity costs.
Conversely the US makes it more expensive with interconnect and upgrade fees as well as hefty take or pay contracts.
A 1GW datacenter in VA for example would add 5-10c kWh and a 12 year take or pay deal
2. Any open models created from this will not necessarily need their silicon, see apple mlx.
Nvidia could just the same rent those GPUs out for inference and actually have way better margins than they do right now. Antitrust and putting all your eggs in one basket are why they don’t, similar to TSMC.
2. Neither do AI labs. See Anthropic buying TPUs, deploying with AMD. OpenAI on Maia, Cerebras, their own wafers.
Why would you invest money in a company, and then enter the market to compete with them?
They exist to keep people using and advancing the tools on their hardware.
The only moat they appear to have is by hoarding compute, and the current trajectory of hardware shows that isn't permanent either for very long