How China's AI Math Works

Moon of Alabama has a fascinating post up that explains how China’s AI differs from the US model. Well, I can’t actually vouch for that because my knowledge base in this field is approximately zero. However, MoA is riffing off a NYT article that caught my eye the other day, regarding Larry Ellison’s AI gamble at Oracle. I’ll paste in the summary portions of the article and will hope that people who know more will comment. It’ll be longer than a 25 words or less version, but if you follow the link you’ll see that the article is fairly lengthy:

It’s The Math – How China Is Avoiding The AI Bubble

The Artificial Intelligence bubble in the United States is still growing. It is consuming a large share of the available financial resources. But its output, in form of real products, is so far rather meager.

There are some code generation tools which are, at times, helpful, but, unless subsidized, very expensive. There is ChatGTP and other squawk boxes which in the end are just new forms of inherently unreliable web search tools.

What is missing are useful mass applications billions of people are willing to pay for.

Still – a large number of ‘very important people’ believe that the Large Language Models, which are at the core of OpenAI’s and Anthropic products, will one day reach the capabilities of sentient beings. That is, in my view, utter bullshit, but who am I to tell you.

The NY Times has a long write up (archived) about Oracle founder Larry Ellison and his bet of nearly everything he owns on the A.I. bubble. It states:

The story of A.I. has been as much a financial story as a technological one, a question of how to structure the mind-boggling investments required to train and run the models. Few people doubt that this technology is going to change everything. What’s less clear is when the profits are going to start rolling in and how big they are going to be. “To me, it’s a math problem,” says Asad Ramzanali, the director of A.I. at a policy center at Vanderbilt University. “We are making trillions of dollars in investments on the back of tens of billions of dollars in revenues.”

That is why Ed Zitron and others will rightly tell you that the huge investments spent on AI are making no sense at all.

Where are the products and the customers that will allow to recuperate the hundreds of billions of shady dollars spent on data centers for AI?

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The real discriminator between U.S. and Chinese AI companies is the math they use.

U.S. models run on algorithms that are computationally wasteful, while Chinese models use smarter methods, which need less compute, to achieve a similar quality.

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The U.S. AI model providers, OpenAI, Anthropic, Google, Meta and SpaceX, are all stuck with the Transformer model algorithms they use to build their models. ...

It is this choice of an algorithm that has created the U.S. AI bubble.

The Chinese modelers, for lack of unlimited venture capital, have had to find better ways to solve the problems. Their use of different algorithms allows them to deliver nearly similar results for much less money.

There is also a difference in attitude. Where U.S. financiers like Larry Ellison are striving for some god-like general artificial intelligence, the Chinese (and some European) developers and financiers are much more interested in solving real world (industry) problems.

A robot to fill the dish washer can operate sufficiently without being able to solve mathematical conjectures. Moreover its internal AI model will have to run on a local GPU and not inside some far away data center.

...

At some point the AI bubble in the U.S. will blow up. There will be a lot of collateral damage. Many people will lose their money. There will be a number of useless empty data centers up for rent.

The NY Times and the Economist presume that this will cause China to have similar problems.

That is unlikely to be the case as Chinese AI is based on a different math, and different economic models.