Ben's article "distills" down to 2 reasons that US frontier labs shouldn't be "afraid":

1. US frontier lab unit economics are better 2. US frontier labs are moving up the stack making tools that are "stickiness" and will prevent users from switching.

For 1...he doesn't provide any evidence for US lab unit economics being better...the major input to unit economics is electricity...which is cheaper in China. And building data centers and connecting them to electricity is both cheaper and an order of magnitude faster in China. The main input that US labs might have an advantage in is in cost/access to chips, but that given the level of chip investment in China it seems unlikely to hold.

For 2...there's little evidence these tools are sticky. At least in programming, the trend seems to be tools like opencode that support multiple models and providers.

And even when they are sort of sticky, as we know on hacker news, people figure out how to point the tools they like to competing models even when the app doesn't official support it.

And every improvement in model capability makes it increasingly easier to make your own tools.

Wrote more on this in a blog post that has an earlier HN discussion: https://news.ycombinator.com/item?id=48982061

Direct link: https://larrysalibra.com/ben-thompson-is-wrong-us-frontier-l...

> 1. US frontier lab unit economics are better

That's not generally true, since there is generally still much reliance on NVIDIA. The true low cost providers are Google with their TPU and vertically optimized stack, and Amazon with Trainium. However, Google does not have their own frontier model, and Anthropic (who are partially served by Amazon) are also paying a premium for extra NVIDIA-based capacity from SpaceX, maybe soon from Meta too.

I don't know how the economics of domestic Chinese Huawei-based clouds (no NVIDIA) compares to the west, but since serving cost is mostly hardware depreciation and to a lesser extent electricity, they are not necessarily at a disadvantage (Ascend 950 costs roughly 50% of an NVIDIA H100), and more to the point it is irrelevant when considering US commercial use that is more likely to be using Chinese open weights models from US providers served on NVIDIA based hardware.

I think the real significance of Chinese frontier models being open weight is that it takes development cost amortization out of the US-based serving cost, while the US AI labs can't afford to do this. The US labs therefore need to reduce development spending to remain price competitive. The Chinese companies are of course still making money from the Chinese market, whether by selling API access or by other business models such as Ziphu making 75% of it's total revenue by selling services to Chinese customers who are running their models on-prem due to the Chinese apparently being very concerned about data privacy.

He’s glossing over the reason they are not: 90% profit margin of Nvidia. Power is only a small part, single digit, it will eventually matter but does not really today.

What is the cost of AI? The single largest ingredient is Nvidia profit margin.

Huawei accelerators are not as efficiency yet, but they don’t nearly extract as much margin.

Why would future revenue stay with the labs given this situation? This whole thing had an airline industry sized red flag on it that makes investing into frontier lab about as sexy as investing in United.

Maybe the token economy is some kind of reverberation of the airline reward miles economy, the emergency hatch to be able to survive under maximal supplier extraction (Nvidia is just the top of a monopoly stack here, even if they replace those chips, the HBM, ASML, Foundry layer can get their dues)

Cost of electricity isn’t a long term advantage in my opinion. Private companies will figure it out.

What matters most is $/completed task. It does seem like OpenAI and Anthropic are winning here even with worse electricity rates. Perhaps it is made up by the efficiency of Nvidia and Broadcom chips, which China can’t get in mass.

I do think that OpenAI and Anthropic are moving up in stickiness. My company has rallied around Claude. We are customizing Claude Code, adding knowledge bases for non technical people, writing skills for them, using Claude features company wide. It’s hard to move.

Meanwhile, I personally use ChatGPT outside of work. The memory, ease of use, habit keeps my subscribed.

I think calling opencode the trend is naive. This not what is being run on company time.

More basically, production cost matters only if inference is priced at commodity prices. That's not what VC's signed up for, which is rent-seeking.

In a corporate setting yes Opencode all the way. However in a non corporate setting I am getting $3000 of api usage a month for $100 at Anthropic and only use open code for the smallest cheapest tasks