What Orange mentioned is indeed a very painful problem for AI entrepreneurs. Making money from AI

What Orange mentioned is indeed a very painful problem for AI entrepreneurs. Making money from AI applications at home and abroad are really two completely different results. At least for now, this is the case. So, why? Here are some of my thoughts:

In addition to the big gap in making money, what is far behind is the prosperity of the application ecosystem. There are also a lot of unprofitable AI applications in the United States. It is obvious that the scale and ecology of their AI applications are larger, and there are all kinds of forms.
Digging down a layer, the very important reason is that the VCs here are really called venture capital. They are willing to admit defeat and are optimistic about burning money for various small entrepreneurial institutions. A few large manufacturers are currently burning money in China, and they are burning their own money. 3 A very important point: Almost all AI applications currently on the market are subscription-based, and this model has never been adopted on a large scale in China. Nowadays, it is really difficult to cultivate new payment habits among the people. 4 Domestic knowledge payment is a relatively small number of new payment models, but there is a lot of chaos. It is usually a personal IP or a knowledge payment institution. The former does not need to be related to AI applications, and it is enough to sell AI knowledge to one's own fans. In fact, some of the latter have begun to build their own AI applications, but they are only assistants and cannot make a lot of money. 5 The middle-class people are losing money on their houses, the middle-aged people are afraid of losing their jobs, and the young people want to find a job. This is what everyone is most concerned about in the past few years. Things that cost money can be solved by going to Yuyuduo. 6 In terms of closed source models, they are indeed ahead of OpenAI, Anthropic, and Gemini, not to mention Grok for the time being. For example, the AI talent system, the production of high-end GPUs, and the raising of US$50 billion in a short period of time to build an AI computing center all determine the differences in somatosensory models used. It is right for us to take the open source route. Several domestic companies are really doing well on this road, and closed source kimi k2 is also good. 7 There is already a wolf pack effect in the United States, and the alpha wolf still has alternatives. Understand that Wang Hua Debt can only bet on AI. They are also burning the boat. If they encounter an AI bubble, it will be very painful.

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