Hi folks, welcome to another episode of Who Said What? I’m your host, Krishna.
For those of you who are new here, let me quickly set the context for what this show is about. The idea is that we will pick the most interesting and juiciest comments from business leaders, fund managers, and the like, and contextualize things around them. Now, some of these names might not be familiar, but trust me, they’re influential people, and what they say matters a lot because of their experience and background.
So I’ll make sure to bring a mix—some names you’ll know, some you’ll discover—and hopefully, it’ll give you a wide and useful perspective.
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With that out of the way, let me get started.
How different are US and Chinese AI, really?
Ren Zhengfei, the founder of Huawei, doesn’t like to be in the public eye very often. However, last year, with Huawei being front and center of China’s AI ambitions, Zhengfei has made many media appearances.
Here’s one such interesting quote that had us very intrigued:
“The US is seeking AGI and ASI to get answers to questions like ‘What is human?’ and ‘What is the future of society?’ They are trying to solve the whole problem [at once], but it takes time for [humanity] to know what the problem is. China is focusing on how to get things done [with AI] to create value and fix development issues.”
Let’s get into the nitty-gritties of Zhengfei’s quote.
For one, the US and China do seem to run technically different playbooks. Many American AI labs run on closed-source weights — there is no way for you to find out or change what your model should prioritize. American AI labs are, of course, at the absolute frontier of the AI curve, thanks to NVIDIA’s cutting-edge GPUs. If our last AI round-up on The Daily Brief didn’t make it clear, for us, Claude Code has already gone into magic territory. We don’t really care too much about sentient AGI beings anymore.
Chinese labs, meanwhile, are open-source: you can, to a large extent, open up the hood of a Chinese LLM and see how it works. They run on GPUs that are inferior to NVIDIA’s best offerings, and therefore fall a little behind the frontier. However, their ability to maximize more juice out of existing resources has been second to none.
Secondly, both countries seem to run different business playbooks, too. DeepSeek, for instance, is already integrated into Chinese cars, phones, hospitals — things regular people use. Consumer revenue is first-priority for China’s AI labs, and they’re leveraging China’s mobile-first internet economy to get there. Additionally, their progress in robotics shows how they’ve used their manufacturing prowess to integrate AI and automation in industries.
For the US, though, revenues are nowhere to be seen relative to how much is being spent in the name of AI. In The Daily Brief, we’ve covered how AI capex made up 40% of US economic growth last year, while its other industries stagnated. Their data center boom far surpasses that of China, which has been more conservative. There have indeed been attempts to make money — image generation was one of them. But so far, not too fruitful.
To a degree, Zhengfei may not be wrong. But something seems to be changing now. Recently, tech professional Grace Shao wrote a great essay on how China and the US might be converging with their AI approaches. Or, at least, if it’s not convergence, then it’s mutual respect for each other’s approaches.
In her essay, Shao talks about how many in the US are finally talking about moving towards integrating with consumer (and industrial) applications. For instance, in October last year, Sam Altman said at a Bloomberg panel that:
“We’re seeing AI move from labs to living rooms, with real-world adoption accelerating as tools become intuitive for everyday users.”
Or Jensen Huang, who seemed to hint that a lot of effort was being shifted from developing LLMs to building AI agents that would integrate within companies. Either way, the US AI ecosystem is now facing pressure to prove ROI on its huge capex, which, in Shao’s view, is shifting the narrative.
On the other hand, Chinese AI labs have recognized how important being at the frontier is. For instance, Zhilin Yang, the founder of Moonshot AI (which trains the Kimi LLMs), seems as enthusiastic about AGI as Sam Altman is. In fact, that’s why he named his company Moonshot:
“For us, it’s about exploring the unknown. Just like AGI, you usually only see the illuminated side of the moon, but the dark side remains mysterious. It’s challenging, yet full of potential. That aligns with our mission.”
The need for Chinese models to get to the frontier is also shown by their clamoring for NVIDIA’s advanced chips. Recently, China banned the import of all NVIDIA chips — perhaps, in an effort to promote its own (like Huawei’s). However, Chinese AI labs still don’t think there’s a Chinese GPU that matches NVIDIA’s best. Maybe, as a response to this demand, China is now redrafting rules to allow NVIDIA H200 chips into the country again — but with conditions.
We haven’t even touched on what is perhaps the biggest similarity, one we’ve already written about on The Daily Brief — national sentiment. Both the governments of the US and China are formulating industrial policy to set up full AI supply chains within their borders. They have made AI part of a large nation-wide push to be coordinated by them. Both are using similar policy tools to incentivize production and block each other’s efforts.
By hook or by crook, there is indeed plenty of similarity between both AI ecosystems. They don’t seem to lie in a binary anymore. We can’t say who will win the AI race and how, but increasingly, it seems to be a clash of titans with similar armors.
That’s it for this edition. Thank you for reading. Do let us know your feedback in the comments.
The Daily Brief by ZERODHA raises one of the most important issues of our times —What really separates US and Chinese AI—and,explains similarity between both AI ecosystems.
I may like to the below extracts Mr.Sangeet Pual Chowdhary on this:
# The US is betting on intelligence. China is betting somewhere else!
# The US frequently frames its competition with China as an AI race, similar to the space race it ran with the USSR. The idea of a race was triggered around a year ago with the launch of DeepSeek. Ever since, much of the US media has been fascinated with the idea of the US winning the AI race against China.
# Ironically, it isn’t much of a race if the US is the only one running it.
# The American ‘AI race’ is framed around who will build the smartest models, as if superior intelligence alone decides the future.
# China’s strategy reveals a different understanding of the game altogether.
# It is not trying to win AI at all. It is betting that intelligence will become abundant, and that power will flow instead to whoever can reliably turn intelligence into economic value.
This was a really engaging piece, and the way you used Ren Zhengfei’s quote to frame the two “AI philosophies” made the whole topic feel very accessible. It does a nice job of contrasting the US “frontier, closed, NVIDIA-heavy” model with China’s more resourceful, open and applied approach.
One thing that left me curious, though, was the hard economics behind that contrast. You mention that AI capex made up around 40% of US economic growth and that other sectors look relatively stagnated, which is a powerful point. When set against the broader data, the imbalance looks even sharper: over the past decade, US private AI investment is just under half a trillion dollars, roughly four times China’s ~120 billion USD, yet there isn’t a matching surge in US corporate profits from AI just yet.
On the China side, you highlight how models like DeepSeek are quietly getting embedded into cars, phones and hospitals, and that feels directionally right. Recent official numbers suggest China’s AI industry has already crossed roughly 900 billion yuan (about 127 billion USD) in annual revenue, spread across more than 5,000 AI-related firms and growing at over 20% a year, which hints at a fairly tight link between investment and real‑world commercialization. If you roughly compare cumulative private investment (~120 billion USD) with that current annual industry size, China’s investment-to-revenue ratio starts to look surprisingly healthy.
All of that makes your central point about “convergence” even more interesting. The US seems to be coming from a “build frontier infrastructure first, figure out profits later” mindset, while China seems to have started from “embed AI into the existing economy first, then push for the frontier.” It might be fascinating in a future edition if you revisited this topic with a couple of simple charts or numbers on :
1. how much AI capex is actually showing up as disclosed AI‑driven revenue in US companies, and
2. how much of China’s AI growth is inside traditional sectors versus pure‑play AI firms.
The current narrative is already very strong; a little more of this kind of empirical framing could make it an even more grounded and useful reference for readers trying to understand where the “AI boom” is real business and where it is still mostly infrastructure and hope.