🔮 Exponential View #572: AI’s moats, myths and moral loopholes

Silicon experts; courts rule against firing humans for AI; Elon, war & goblins++

Hi all,

Over the past week, I have been in China with Hannah Petrovic, meeting AI and robotics teams including Zhipu and MiniMax (the two publicly listed foundation model companies), as well as Kimi, Alibaba, Xiaomi, Bytedance and others.

Demand is booming. Zhipu, for example, is serving 5.5 trillion tokens per day. Developers are rushing to the platform, joining at about ten a minute. Keeping up with inference loads is a struggle in China, as it is in the US. Teams universally acknowledged compute constraints, particularly the shortage of Nvidia chips. But that isn’t stopping innovation under those constraints.

Researchers and developers are free to use whichever model they want. Anthropic’s Claude was the preferred choice for technical teams, but it was clear that “dog-fooding” is commonplace.

I’m on the flight back to London right now. Hannah is continuing on to Shenzhen to check out more hardware firms. We’ll write something in more depth in the next couple of weeks. For now, here’s a short video from one of the many demos we saw at Unitree:

Huge thanks to our local hosts who were generous with their time, hospitality and swag. And a shout-out to Caithrin Rintoul, who pulled together this amazing trip with expertise and great humor.

Azeem

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We can’t stop, we won’t stop

Jasmine Sun gets to grips with that odd Silicon Valley paradox, that many engineers think the very thing they are creating, AI, will mean that the “median person is screwed”. What’s more, those same builders profess to having no clue how to stop this deracination.

Jasmine was in China with me this week. She “gets” the people in American AI labs and the culture surrounding them better than anyone. Her stellar essay in The New York Times is revealing:

In general, tech industry sources expressed more extreme concern about the labor market impacts of A.I. in private conversation — but suddenly became optimists once I turned on the mic.

I don’t agree with the depth of fatalism coming from the labs. Diffusion of the technology will be slower than flicking a light switch, even if it proves faster than the rollout of electricity.

But that fatalism is itself a fact worth taking seriously. The beliefs might become behaviors. An example: junior hiring might slow down, not because AI can do juniors’ jobs well, but because the labs believe it can and persuade employers that it can. A hiring freeze is an almost inevitable consequence of the patterns of belief.

The danger is that the theory of displacement becomes the logic of our responses. The point should be that we live in a state of uncertainty, as argued later in this issue. That uncertainty means we should be skeptical of preordained outcomes, however smart and well-paid those who make them are.

See also:

• Another way to frame the moment is Dostoevskian: intelligent people convince themselves they have discovered a truth so important that ordinary ethical constraints no longer apply to them. See, Palantir.

• This week, Chinese courts have ruled that replacing someone with AI is not, by itself, a lawful reason to fire that person. It is one of the first such decisions worldwide.

• The US government has classified the grid supply chain as a national defense bottleneck.

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Silicon philosophers

A new paper out this week tested seven frontier models on their ability to role-play professional philosophers and simulate expert judgment. Some models looked passable on average, especially on questions where philosophers already agreed. But real philosophers agree far, far less than the AI. The variance of views was two to four times higher in humans’ favor.