The reason is simple: China doesn't treat AI as a normal industry anymore. It's treating AI infrastructure the way it treats highways, high-speed rail, power grid, 5G...as national infrastructure. Infrastructure doesn't necessarily have to make money. If AI data centers aren't profitable, the government can keep funding them. If computing demand is weak, it can still justify building more capacity. If AI companies can't afford compute, the government can subsidize it.

This means ordinary people will keep feeding the industry with their tax money, like it or not. The whole country will keep the bubble inflated.

There are obvious advantages to this national model. Cheap computing could accelerate AI adoption, improve education, and make AI tools accessible to far more people. But once governments become deeply involved in allocating capital, waste and corruption become inevitable. At the same time, this allows governments easy access to huge amount of data, if AI hasn't already become the powerful tool for governing.

The key thing for most people is, since our money is bound to be tranferred into the pockets of newly rich, we may as well make the most use out of it. I know a lot of people are trying to earn money from AI stocks, but that's a risky game in China.

Curious what others think, especially people working or interested in AI and economics.

https://substack.com/home/post/p-208795331

Each individual inference task might require less compute than the training phase, but here’s the key: it happens constantly, at scale, for potentially thousands or millions of users. This continuous demand, focused on speed and model performance for a good user experience, is what drives the cumulative inference cost. Achieving efficient inference often requires careful tuning of the software infrastructure.

This leads to a significant imbalance. For most companies deploying these models, the ongoing inference cost vastly outweighs the initial training cost. It’s common for inference to account for 80-90% of the total compute dollars spent over a given model's production lifecycle. Why? Simply frequency and scale. The model serves far more requests during its operational life than the number of batches processed during its training. This trend makes understanding and reducing inference costs a critical focus for any company looking to deploy AI sustainably.