China's First AI Service Industry Policy: How to Catch This Wave
When I saw the State Council’s guidelines on upgrading the service industry, my first reaction was—finally.
Not the numb feeling of “another policy document,” but a genuine sense that the government is starting to think seriously about how AI should actually land.
Let me break down the key points worth highlighting.
First, “AI+” has been placed at the core.
The document explicitly states to “accelerate innovation in software and information services, and promote comprehensive strengthening of the AI industrial chain.” Notice the wording—“comprehensive strengthening,” not empty slogans, but an acknowledgment that we do have gaps in our AI industrial chain.
I was chatting with a friend doing industrial AI two days ago, and he shared a harsh reality: many factories want to adopt AI, but either can’t find reliable vendors, or the vendors understand AI but not the industry, or understand the industry but not AI. This “disconnect” has always existed—it’s just that now the state is beginning to face it squarely.
Second, agents and intelligent programming tools were specifically named.
The document specifically mentions “AI capabilities such as agents and code large models.” This is interesting—not broadly saying “AI technology,” but pointing out specific directions.
What does this mean? The government has realized that general-purpose large models are just infrastructure—what actually solves problems are vertical applications. Agents can automatically execute tasks, code large models can lower development barriers—these two areas are precisely where AI is landing fastest today.
Last week I used Claude Code to refactor an old project. What originally took 3 days, I finished in 2 hours. This efficiency boost isn’t a pie-in-the-sky PPT promise—it’s genuine productivity release. The government naming these two directions shows decision-makers are truly looking at technology implementation, not being led by big model vendors’ marketing.
Third, this isn’t “encouragement,” it’s “eliminating institutional barriers.”
The document explicitly states to “eliminate institutional and mechanism barriers.” That’s pretty strong wording. The difficulty of AI landing hasn’t been technology—often it’s institutional problems: data silos, entry barriers, qualification requirements, compliance costs. These “invisible thresholds” are the real obstacles.
Let me give you a real example. A friend of mine does medical AI—the technology is solid, but he can’t get into hospitals. Why? Because hospitals have strict procurement bidding processes, and risk assessment mechanisms for new technologies haven’t caught up. The result: mature technology, strong demand, but it can’t be used. This document says “eliminate barriers”—if this can actually land, the release space will be enormous.
So here’s the question: how do you capture this opportunity?
Don’t chase the abstract stuff. My advice:
Stop staring at large model foundations. That space is already monopolized by giants like OpenAI, Baidu, and Alibaba. Startups entering now are just walking into a meat grinder.
Look at vertical industry applications. Healthcare, education, finance, manufacturing—every industry has massive amounts of “manual work” that can be automated with AI. The “productive services” named in the document are actually To B tracks—this is where opportunities lie for small and medium enterprises.
Factor in compliance costs. The document mentions “eliminating barriers,” but specific details aren’t there yet. My judgment: heavily regulated industries like healthcare and finance will still have high barriers in the short term; but relatively loose sectors like education, retail, and services will see the first wave of release.
Honestly, the signaling significance of this policy outweighs its substantive content. Real details, funding, and pilot programs—we’ll need to wait a bit more. But it shows one thing: the government is no longer treating AI as “cutting-edge technology to develop,” but as “a production tool to implement.”
That shift in perception matters more than any amount of money thrown at it.