The (quite excellent) article discusses several of your points. If you haven't read it, I recommend it.

  - Commodity market profitability is determined by marginal cost of production. LLMs have marginal cost; traditional software does not.

  - Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category error as it relates to their actual use

  - The highest tier Chinese models are not more economical than US frontier models. Try GLM 5.2 and see how much it costs to do real work. I did, and it was more expensive than GPT 5.6.

  - This is because US labs are leading on cost efficacy of inference ($/task)

  - Training will decline as a percentage of costs as inference expands compute share due to agentic workloads. A big part of training now is optimizing token efficiency. It's hard to distill token efficiency; that is perhaps why Chinese LLMs are so inefficient.

  - With increasing inference as % of total compute, if labs create efficient models -- which they can, because they can create highly optimized models amortized over very high inference loads -- they can be low cost producers, and be competitive at $/task rates

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...

US running costs are higher than in China, because the US lags behind in energy, has higher real estate costs, and wage costs are higher.

Eventually we will hit a "good enough for cheap enough" and frontier models will hit diminishing returns (if they haven't already for a lot of types of work)

Don't think the rest of the world will sit on their hands while the US soaks up chips either, demand gets filled and if the US won't fill global demand for chips that's an opportunity to undercut again.

The other thing the rest of the world doesn't have to fund is the ridiculous valuations on these companies.

Unless you think the US can stay ahead just with model efficiencies, and that no one else will eventually match them, you are looking at the writing on the wall.

All that to say, the rest of the world is more than willing to eat your lunch, they have a dozen good reasons to, and they're already showing good results.

Just on the economics side, we've been here before too, US companies typically export their commoditization and live on brand royalties. Think all the cheap manufactured goods, the US doesn't make any of it. That's because the US can't compete on margins for numerous reasons, it's too expensive, I don't think AI is any different here except that the brands are currently valued in the trillions and I suspect that greed will be their undoing.

Sure, let's have a look...

> I highly doubt that Chinese models are cheaper to serve on a marginal cost basis, they just seem cheaper because Anthropic and OpenAI are so supply constrained that they are charging far more than they would if there were sufficient supply to meet the demand for intelligence. [emphasis mine]

I guess I'm missing the part of this article where they bring hard numbers in to back up the argument here. What work was attempted? https://cursor.com/evals shows the previous generation of open models (Kimi K2.7) trading blows with the others, cost effectively. Composer 2.5 is itself a fine-tune of K2.7, and it's apparently quite token efficient, so why would it be impossible for a Chinese lab to achieve something similar? GLM 5.2 Max is also ranked above the lower end OpenAI models and is not far off in price.

It's weird to have this entire discussion about tokenomics without mention of the circular financing and debt raised by labs in the West, which can then essentially give away their capacity to end users. OpenAI giving away quota resets to subscribers like candy on Halloween while their compute partner Oracle's bonds is reevaluated to be one grade above junk? How?

I don't think you can make an argument about the future one way or another by arguing using the listed prices. The math is not internally consistent enough for it.

I think there are some really interesting thought there, but I’d challenge some of this:

> Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category error as it relates to their actual use

I think a large part of manufacturing economics is illiquid overhead and the cost of expertise to set up and run your manufacturing line. Compute economics don’t have the same illiquidity nor do they require the same expertise or even specialized infra (current temporary chip shortage aside).

The implications of this are small players (e.g. your uncle running an inference server out of his garage) have comparably efficient marginal costs as big players. Compare this to actual manufacturing where small players have essentially no access to the manufacturing facilities of the big players.

Additionally, big players with a lot of compute who are not meaningfully in inference today (e.g. Amazon) have a fairly straightforward glide path to utilizing that compute to compete.

> This is because US labs are leading on cost efficacy of inference ($/task)

It’s possible, but I would need to see better data on this.

>A big part of training now is optimizing token efficiency. It's hard to distill token efficiency; that is perhaps why Chinese LLMs are so inefficient.

I think it’s fair to assume this is true, but also token efficiency is not a meaningful competitive moat. It’s not like these are secrets the Chinese will never figure out, it’s a fairly active research space and the outcomes are quantifiable.

But this assumes Chinese models will not achieve token cost optimization. Intelligence needs are fairly flat for many tasks, and the Chinese models have caught up on this front. Next they achieve greater token cost efficiency and we don’t need OpenAI.

> Models are not free. Downloading them is free. Running them is not.

Is this really different from traditional software? Downloading postgres is free. Running it is not. You either buy hardware and assume the costs of owning and running that, or you pay to run it in the cloud.

The thing I do not understand here because it seems obvious: AI will be a commodity market and you simply cannot have a large PE multiple. So the valuations imagine a global commodity monopoly or duopoly coupled with the increased intelligence still disallowing other suppliers from becoming competitive? Without any network effects to help?

> for frontier work.

I'll agree that GPT 5.6 may well be the best given the above contstraint, but for run-of-the-mill dev tasks (real ones, not benchmark ones), GLM 5.2 still blows every other model out of the water.

Cost per task as a metric is a bit ridiculous because there are so many types of tasks. GPT-5.6 can do some tasks GLM could only dream of, but GLM can do some tasks 100x cheaper and better than GPT-5.6.

> Commodity market profitability is determined by marginal cost of production. LLMs have marginal cost; traditional software does not.

This is the story for Nvidia/AMD or cloud providers rather than OpenAI.

> With increasing inference as % of total compute, if labs create efficient models -- which they can, because they can create highly optimized models amortized over very high inference loads -- they can be low cost producers, and be competitive at $/task rates

It seems like there would be problems with this on both ends.

For general purpose models, everybody is trying to make them efficient, so you can't win just by being slightly more efficient. You would have to be so much more efficient that you can charge high margins while still capturing the majority of the market so that the high margins get multiplied by the majority of users and the users you leave on the table aren't funding open competitors. Meanwhile everyone else is also trying to improve efficiency, so one misstep and you're behind.

Example of where this can be a problem: You spend a preposterous amount of money to create an efficient model, then someone else publishes a paper with a new technique that gets a similar but incompatible efficiency improvement out of a model that costs a lot less to create. You have now spent an enormous amount of money in exchange for no competitive advantage.

And from the other end, one of the best ways to get efficiency is through specialization. A general purpose model can generate code or summarize a meeting transcript, but a special purpose model can do it as well or better with far fewer parameters and resources. But then you don't have a situation where one huge AI company has The Most Efficient Model, you instead have dozens of specialized models produced by independent sources that are each the best in a given niche. Any proportion of which could have open weights, or have an arbitrarily small advantage over the ones that are.

Moreover, these problems combine: Both the computing hardware vendors and the AI companies want the margin on doing inference, but the more of it one of them gets, the less the other does. If the AI companies were actually getting huge margins then it would be in the interests of Nvidia, AMD, Apple, Intel et al to fund efficient open weight models in the same way they fund Linux. Commoditize your complement. And those models don't even have to be better, as long as they're good enough that the closed models can't charge a significant premium and the margin shifts back to paying for hardware.

China is working on the whole supply chain though and they're willing to compete on razor thin margins. Just look at EVs. They build great cars but the competition is so aggressive that investing in any one Chinese EV company isn't exactly an amazing ROI.

I could see AI ending up the same way where the customer captures most of the value rather than the companies. Open weight models are what make that kind of competition possible.

> Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category error as it relates to their actual use

I notice that the article, and this discussion, hasn't mentioned or considered local models.

We can already run a low-spec model on a laptop. Because there is demand for this, it will improve and we will get better laptops and better local models. We will also see models being run on dedicated local hardware and called from the laptop.

If I can download a reasonably capable model to my own hardware and run it without paying anyone for either the model or the inference tokens (effectively making models and intelligence actually free once the hardware is bought) how are the Frontier AI Labs going to make any money at all, let alone enough to support their vast valuations?

Yea but at those rates VCs will never make their money back. Because Deepseek and friends keep releasing the inference optimisations to everyone instead of holding them back to pay their investors.

" - The highest tier Chinese models are not more economical than US frontier models. Try GLM 5.2 and see how much it costs to do real work. I did, and it was more expensive than GPT 5.6."

This is a flatly false statement for most things powering backend applications. The AI consumer "doing real work" model, either for analysis, chat, or coding could well be more cost effective with closed frontier models.

But most of these internal glue business SaaS applications where engineers are integrating are not those tasks. It is those tasks which 1) drive immense amount of domain-specific data into the platform over time, and 2) are most encouraging of driving open model independence with no vendor lock-in.

Anyone on this site who has actually used ML models (more accurate in many cases) knows there's a lot of kludge that simply does not need a 5 minute agentic feedback loop to solve the problem. And they were solvable a year ago with lower class models. The token economics are exceptional and the anecdotes of a16z saying 80% of startups are productionizing open models is only surprising to people who think running your company on OracleDB in 2026 is a sound engineering decision.

I did read the article, but it misses the core issue entirely, and it's why I shared my comment to begin with. Look at the cost-per-task benchmarks from Artificial Analysis https://artificialanalysis.ai/models?cost=cost-per-task

Anthropic’s API pricing is getting impossible to justify. Anthropic previously had the highest quality models, and used their position to charge premium prices, enjoying inference margins of over 70% [0]. They could charge these prices because no other model came close.

But over the past month, the market has shifted dramatically. Over every single performance tier, Anthropic is being squeezed on price.

* Low end: DeepSeek V4 Flash runs at ($0.02/task), Xiaomi's MiMo-V2.5-Pro at ($0.03), and Haiku at ($0.24). Anthropic is ~10x more expensive than the Chinese open-weight options.

* Mid tier: Claude Sonnet 5 ($1.53/task) is nearly 50% more expensive than GPT-5.6 Sol ($1.04), nearly 2x the cost of GPT-5.6 Terra ($0.82), and 3x the cost of GLM-5.2 Max ($0.47). There is basically no reason to ever use Sonnet 5, the competitors are significantly cheaper.

* High end: Opus 4.8 ($1.80/task) and Fable 5 ($2.75) are the two most expensive models, and GPT-5.6 Sol ($1.04) and Kimi K3 ($0.95) offer comparable performance for significantly less. Less the fact that Kimi K3 will get ~10x cheaper once its weights are released and served on neoclouds with Nvidia hardware [1].

OpenAI priced their latest GPT-5.6 models cheaply in order to regain market share. When Anthropic clearly had the best models, their 70%+ inference margins were defensible. But today they are the most expensive option in every single tier. Unless they make significant price cuts soon, they run a serious risk of bleeding market share.

[0] https://www.mindstudio.ai/blog/anthropic-inference-margins-7...

[1] "American companies such as Modal, Fireworks, and Baseten will be able to serve Kimi K3, at one-tenth the cost of their Chinese competitors because they have access to advanced Nvidia hardware" https://x.com/rohanpaul_ai/status/2079027313455550839