Clarifying Myths in the US vs China War

Welcome back! This is an article I’ve wanted to write for a while now, exclusively focused on clearing up some of the most prevalent myths in this space, especially regarding China.

With the world suddenly realizing that AI is expensive, the “battle” between the US and China for AI supremacy is hotter than ever. If the tide turns in favor of Chinese open models, the trillions of dollars at stake could be at clear risk.

But as always, the picture is much more nuanced, and several of your beliefs right now that you have been told are simply blatantly false.

Let’s dive in.

Uncovering Several Myths (and Truths)

Ironically, the first myth that needs to be debunked for good is the idea that China is “catching up.” The truth is that, despite all the hard evidence you may be seeing, it’s simply not true.

But here’s the thing. As you’re about to witness, it might not matter at all.

Don’t Trust the Benchmarks

The first myth is this graph below, claiming that Chinese models (they refer to open-weight models, models that are free to download, but most of the best ones are Chinese) are only 4 months behind.

The data seems hard to dispute; Kimi K2.6 seems to be at the level the US was at with GPT-5.3 Codex in December. However, it’s false.

And the issue is precisely drawing that conclusion based on model benchmarks and not on the product.

And let me tell you that nobody more than I would want to see open-source catching up to proprietary solutions. However, I’m not here to expose you to my desires, but to the truth.

The reason is quite simple. In the absence of fundamental breakthroughs in algorithms that do not exist today, AI progress is driven by two scaling laws:

Larger training budgets, which require bigger models

Larger thinking budgets, which require larger reasoning sequences (i.e., thinking for longer improves performance)

And the US is enormously ahead in both.

On the former, US Labs have larger models (in the order of ten times the size) and perhaps even two orders of magnitude larger training budgets.

The largest known AI cluster in China is a 10,000-Ascend 910C cluster in Shenzhen with up to 11,000 PetaFLOPs of compute, or 11 ExaFLOPs. That number doesn’t say much by itself, but if we assume it is FP8, which most likely it is, it’s a fourth of the compute of a single Google Ironwood 9,216-TPU pod at 42 ExaFLOPs.

And that isn’t even Google’s most powerful server, and they’ve scaled that number to a whopping 121 PetaFLOPs with the upcoming TPUv8 pods for training using FP4 precision.

In apples to apples, that is 121 vs 22 ExaFLOPs, 6 times more, for approximately the same number of chips, 9,600. That means a cluster with roughly the same number of chips has 11 times the compute potential.

Source

China does have a larger, 60,000-strong cluster for “AI for science”, but it’s not clearly focused on LLMs…

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