China is getting worried about AI & jobs

And the CCP is worried about people worrying about it

One of the more interesting parts of my job is that occasionally I get to run small surveys — really more like focus groups — where I ask influential members of China’s AI policy community what AI-related risks worry them the most. In the exercise, we present the participants with a list of different risks: employment impacts, bias and discrimination, catastrophic risk, etc. We then ask them to rank the risks according to their level of concern about each one over the coming decade.

I did a version of this in early 2024 and again in early 2026 and the results were striking. In 2024, the Chinese participants ranked AI’s impact on jobs second to last—sixth out of seven. In 2026, they ranked it second from the top. It was virtually tied with “misinformation” for the top spot and far ahead of all the other options.

These are very unscientific and small-sample surveys. The participants are ~12 Chinese AI policy people who inform government regulation and company policy. There is selection bias and no guarantee that the rankings reflect someone’s deeply held beliefs. But sometimes the results map closely onto trends that I’m seeing across the entire Chinese AI policy ecosystem. The spike in concern about labor is one of them.

Over the past two years, worries about AI displacing workers and leading to structural unemployment have shot up in China. Those fears extend from ordinary people to the wider AI policy community to (as best as we can tell) high-level CCP officials. The fears are reflected in policy documents, state media, and the way Chinese people relate to the technology itself. And on top of these existing layers of concern is a new one: the CCP is also increasingly worried about Chinese people worrying about losing their jobs to AI.

In this piece I’ll break down the drivers of this shift, how it’s reflected in official documents and discourse, and then preview the “Do’s and Don’ts” of China’s emerging policy response.

Why weren’t Chinese leaders worried earlier?

I think the best explanation for the earlier lack of concern about AI-driven job displacement lies in the the last 45 years of Chinese history. Since the 1980s, China’s economy and society have been in a near-constant state of disruption. A Chinese person who is 60 years old today (about the average for high-level CCP leaders) has witnessed some of the fastest and most dramatic socio-economic transformations in human history.

You can throw out a bunch of statistics to try to illustrate this¹ but those can’t fully capture the visceral feeling of social, economic and physical churn that characterized China since the 1980s. And through it all Chinese people displayed a genuinely awe-inspiring ability to adapt and make use of all the new opportunities.² Between 1980 and 2020, China’s GDP per capita multiplied 25x.³

The lesson for government officials was pretty clear: as long as the size of the pie keeps growing, the overwhelming majority of people will be dramatically better off. The social disruption and job displacement will mostly come out in the wash.⁴ My guess is that CCP officials looked at projections for AI-driven productivity gains and figured that the same would be true for this technology. And lots of normal people likely had a similar, though maybe unarticulated, gut feeling. People have seen their lives and workplaces go through rapid technological upgrading for 45 years—and rapid diffusion of information technology for 25 years—and during that time they’ve gotten much richer and started working much better jobs.

But over the past two years these perception have changed. Driving these changes are a specific incident and a change in the wider social zeitgeist.

The Spark: The Wuhan Robotaxi Saga

For policymakers, a key turning point was a series of events surrounding the rollout of robotaxis in the city of Wuhan.⁵ It’s a bit of an odd story for a couple reasons.

1. There is a good amount of public writing about this, but I was told a slightly different version of it that I can’t find anywhere online.

2. Whichever version is true, the purported impact on policymaker thinking seems quite disproportionate to what happened. Perhaps there’s a lesson in there.

I’ll start with what’s publicly reported. In 2024, Baidu began rolling out robotaxis in Wuhan under the Chinese brand name “Radish Run” (萝卜快跑; the English brand is Apollo Go). This angered traditional taxi drivers and companies who had already been under pressure from ride-hailing apps for years. In late June of 2024, a Wuhan taxi company released a public letter decrying the shrinking margins for companies and declining income for taxi drivers. The letter actually spends more time on the way ride-hailing apps have hurt taxi drivers, but it was the parts about robotaxis that stirred up online debate.

It accused tech companies of “monopolizing resources” and “snatching the rice bowls” of drivers. “The original intent of technology was to make life better. In reality, it makes it so people at the bottom can’t even get enough to eat.”⁶

The letter sparked lots of conversation on social media, and that conversation really picked up a couple weeks later when a Baidu robotaxi struck a pedestrian in Wuhan. Public opinion was split on the accident; the person was jaywalking and wasn’t seriously injured. But the online conversation about robotaxis “stealing people’s rice bowls” continued to gain steam, becoming a top trending topic on Chinese social media.

Here’s where I heard something a bit different. The first time someone relayed this incident to me, they said taxi drivers had gotten together and led a “strike” against the robotaxis by repeatedly hailing them and then cancelling at the last minute, effectively paralyzing the system. I’ve searched online for any documentation or discussion of this “strike” but haven’t been able to find anything. Maybe it was just a rumor or a miscommunication, or maybe any discussion was effectively scrubbed from the internet (though it’s a bit unusual to do such a good job with that kind of scrubbing).

Either way, the result was the same: the outcry over robotaxis in Wuhan led to a significant shift in how the government and policy community thought about AI’s impact on jobs. It led officials to take the threat of job displacement—and also the public’s fear of AI-driven job losses—seriously. I feel relatively confident about the Wuhan robotaxi incident contributing significantly to this shift, because multiple influential members of China’s AI policy community independently relayed that causal relationship to me at different times.

This might seem a bit weird: how could a short-lived social media kerfuffle about robotaxis actually alter official thinking about AI’s impact on jobs? And admittedly, it does seem quite disproportionate. This is partly what leads me to give credence to what I heard about drivers organizing a strike against robotaxis. Collective action, especially around economic harms, tends to really get the attention of the CCP. The Party also tends to pay close attention to what is sparking anger or anxiety online. The flipside of maintaining a very tight censorship regime is that you’re also very attuned to what issues people are angry or fearful about.⁷

But the robotaxi incident was also happening against a wider social backdrop of mounting anxiety about jobs and what the future holds.

The Socio-Econo-Emotional Backdrop: Deep Jobs Anxiety

This is a very important part of the story, but one that I’ll be relatively brief with here. It’s a largely vibes-based thing and I’m just not in those streets the way I once was. But over the past ~4 years, the vibes around employment in China have been very bad. Things are nowhere near crisis levels, with mass unemployment or anything like that. But after the Covid lockdowns ended in late 2022, the promised economic recovery just never fully materialized for normal wage earners. Young people couldn’t find jobs, and people who had jobs started getting their salaries cut and stopped getting promoted.

It’s a very different picture of the economy than what you’d infer from reading headlines about DeepSeek models and BYD exports. Dinny McMahon of Trivium has done a good job describing this two-track economy of booming high-tech exports contrasted with anemic wage and consumption growth.

Over the past year, AI has further inflamed this anxious social zeitgeist. Tony Peng wrote about this in a good piece on growing “AI anxiety.” And here’s how Poe Zhao summarized it in an insightful short note about what’s really driving the popular frenzy over OpenClaw.

China’s labor market has been under sustained pressure since COVID. Youth unemployment remains stubbornly high. Career mobility feels stuck for millions. AI has become a psychological escape valve. From DeepSeek in 2025 to OpenClaw now, Chinese media and short video platforms have been hammering one narrative nonstop: learn this AI tool, get a high-paying job. … What looks like grassroots tech adoption is closer to grassroots career panic…

Mixing these things together and you have what could become a pretty potent cocktail of social discontent. The Chinese leadership clearly felt they needed to start formulating some responses, and during the second half of 2025 those responses started to show up in policy documents.

Initial Policy Response: Assessments, Standards, Action?

The first major policy document that touched on this was the “AI+” plan, China’s landmark plan for diffusing AI throughout its economy and society. But the document also included some of the most direct language yet on AI’s potential impact on jobs:

Strengthen employment risk assessments related to AI applications (人工智能应用就业风险评估), guide innovation resources toward areas with greater job-creation potential, and mitigate impacts on employment.

When I mentioned this to some people, they pointed out that this seemed a bit contradictory. If the government is so worried about AI-driven job losses, why is it releasing this document that’s all about turbocharging AI applications across the economy? To which the Chinese state might respond:

Do I contradict myself?
Very well then I contradict myself,
(I am large, I contain multitudes.)

or

"The fundamental cause of the development of a thing is not external but internal; it lies in the contradictoriness within the thing.”

Walt and Mao. I wonder how they would have gotten along...

When I first read the AI+ mention of “employment risk assessments,” I assumed these were a hand-wavy thing that wouldn’t see real implementation. But a couple months later the standards group that covers the social impacts of AI finalized a national standard that included guidance on how to conduct these employment impact assessments for generative AI applications. Below is a machine translation of the relevant part of the standard:

This remains very high-level guidance and these aren’t mandatory yet. But this is often how regulation and standardization of technology works in China. Regulators or standard setters will first take a high-level pass at the problem, and then over the course of the following years take several more passes at it, each time getting more precise and practical. This is exactly what happened with China’s mandate for applying labels to AI-generated content, a requirement that first appeared in a fall of 2021 regulation on recommendation algorithms (but wasn’t really enforced) and was then iterated on in a series of regulations and technical standards over the following years, before being fully nailed down in the fall of 2025.⁸

In the months after the AI+ plan, we saw policy documents and official discourse around job impacts tick up. September 2025’s AI Safety and Governance Framework 2.0 (original doc, analysis) described the risk bluntly: “The roles of capital, technology, and data in economic activities are increasingly prominent, while the value of labor as a production factor is diminished, resulting in a significant decline in demand for traditional labor.” That marked a major change from the vague and benign language in the previous year’s Framework 1.0, which described AI as “accelerating the reconstruction of traditional industry modes, transforming traditional views on employment”.

The next significant step came in late January 2026, when the Ministry of Human Resources and Social Security announced:

Our country will implement actions to stabilize jobs, expand capacity, and improve quality; roll out employment support measures for key industries; and issue documents on promoting employment in response to the impact of artificial intelligence.

Pledging future policy action made a bit of a splash, though we’re now two months past that pledge and we have yet to see anything concrete emerge.

Most recently in March of 2026, the 15th Five Year Plan included a brief but direct passage on this: "Comprehensively address the impact on employment of changes in the external environment and the development of new technologies such as artificial intelligence. Establish an evaluation system for high-quality and full employment."⁹

Sidebar: The Twisty Bureaucratic Politics of AI

It’s always tough to decipher what’s going in the fierce and never-ending competition among China’s many ministries and other Party bodies. Here I’ll give a bit of what I’ve heard on this as it relates to AI and especially employment issues, but take the following with several grains of salt. And I welcome feedback from anyone who has heard or seen something different.

The National Development and Reform Commission (NDRC) is a policy powerhouse that acts as China’s main macroeconomic planner. At some point in 2023-2024 (I believe the second half of 2023) China’s top leaders designated the NDRC to act as a coordinator across AI policy issues.

Up to that point, the NDRC hadn’t been very active in AI policy. The Ministry of Science and Technology had provided an early push with the 2017 national AI development plan, and it later pushed for the establishment of AI ethics review committees. And from 2021-2023 the most impactful AI policy documents were the regulations issued by the Cyberspace Administration of China (CAC), governing recommendation algorithms and “deep synthesis”.¹⁰

But in the wake of ChatGPT, the leadership decided that you couldn’t leave AI policy primarily in the hands of a control-focused content regulator (the CAC). They needed to better balance the dual imperatives for control and development of AI,¹¹ and so the NDRC was tasked with managing this holistic balancing. Exactly how the NDRC has played that role is unclear. In one version, the NDRC itself directly took on this task. In another version, a small interagency coordinating committee was formed, with a member of NDRC leadership at the helm. I’m not sure which one it is, but my guess is the latter.

While the NDRC taking the lead would generally seem bullish for AI policy (and on balance it has been), the NDRC is also an organization that has to worry about broader economic and employment impacts. And according to at least one telling, He Lifeng — vice premier and Politburo member who led the NDRC until 2023 — became personally quite worried about AI’s impact on employment. He apparently commissioned a study on this, though I haven’t heard of any results. This last part about He Lifeng is firmly in the realm of rumor, so again take it with substantial salt.

If these rumors are accurate, it has made for a bit of an odd role-reversal. For most of the past decade, the CAC was perceived as the “bad cop” of tech policy, focused primarily on information risks and unafraid to harshly regulate and punish companies. But in the wake of ChatGPT, the AI priorities of top leaders began to shift from control to catch-up, and the CAC quickly got the memo. It greatly watered down its generative AI regulation, and has reportedly taken a far more accommodating approach to companies (“let us help you comply with these regulations”). At the same time, the more development-focused (“good cop”) NDRC is allegedly beginning to worry more and more about AI’s impacts on employment.

What’s Next for AI x Employment Policy?

The policy documents mentioned above give a picture of growing concern, but aside from “employment risk assessments” there hasn’t yet been much in the way of concrete responses.

We get a clearer picture of likely policy responses from how state media has been writing about AI and jobs. The past few months have seen a slew of editorials and commentaries that are both hashing out potential responses and “guiding public opinion.” We can lump these responses into some Do’s and Don’ts.

The Do’s are nice ideas, but probably not super high impact in the short term. The idea of steering AI development toward job-creating applications sounds good, but I start from a place of skepticism about this. Yes, the government can give nudges around the edges and it can really rev up activity in sectors that already have momentum. But short of very heavy-handed interventions, the profit motive will still be the giant magnet that pulls AI research and adoption forward. And that profit motive doesn’t care about workers—in most cases, the fewer of them the better.

On education, a March People’s Daily commentary on why AI is not stealing people’s rice bowls tried to calm nerves and put the focus on education. In one paragraph, it lists a number of education-related proposals from the 15th Five Year Plan (especially, “lifelong learning” 终身学习) and describes these initiatives as “providing a safety net for workers.” While education might be helpful in navigating a changing labor market, it’s telling that none of these components of the “safety net” actually involve redistributing financial resources.

These references to lifelong learning, job retraining and workers adapting show up in many other places of official discourse. As mentioned above, during my time living in China I was regularly in awe of how adaptable Chinese people were over the course of 45 years of economic transformation. But personally I think this is not going to cut it with AI.

The Don’ts are, in my opinion, more interesting:

Panic! The Science and Technology Daily made this clear in a recent piece: “AI Must be Controllable, and Technological Anxiety Must Also be Controllable.”¹² It covered the recent OpenClaw mania in China and then the social currents it reflects.

“Looking back at this wave of enthusiasm, OpenClaw's impact has long since transcended the tool itself. It functions more like an emotional amplifier, reflecting a pervasive and hard-to-place anxiety of the moment — the fear of being "left behind" by the tide of artificial intelligence.”

The article calls on technology companies not to overhype capabilities or ignore security risks. And it tells online platforms to stop playing up content telling people that if they don’t use AI they will get left behind. “The technology is accelerating, but societal sentiment need to be buffered.”

This next policy response is maybe the most interesting one: Don’t let companies fire people just because AI can replace them. This specifically came out of a high-profile labor arbitration case highlighted by Beijing’s local Bureau of Human Resources and Social Security. In the case, an arbitrator ruled that firing someone because AI can now do their job constitutes a violation of the Labor Contract Law. It turned on the question of whether AI’s rise constituted “a material change in the objective circumstances on which the labor contract was based,” thus allowing for the termination of a contract. Chinese labor law is definitely not my area, so I’ll just relay how Xinhua framed it in a recent commentary:

The law is clear: "objective circumstances" of this kind must be both unforeseeable and beyond the parties' control. A company's decision to adopt AI is a voluntary business judgment, with all associated risks squarely within its own control. When an employer terminates a labor contract on the grounds that a position has been "replaced by AI," it is in substance offloading the normal risks of technological iteration onto the worker. … Companies that enjoy the dividends of AI must also fulfill their responsibility to protect workers' employment.

This came out of the Beijing bureau of the Ministry of Human Resources and Social Security, the group that has promised forthcoming action on AI x Jobs. So maybe this is a preview of that.

But it raises as many questions as it answers.

1. Is the Ministry’s position here shared across the government? The Xinhua commentary suggests a certain level of approval across the system, but isn’t the final word.

2. Is labor law an effective tool for protecting workers rights at scale? Again, not my area and my initial hunch was “no.” In the Chinese companies I worked at employees often had (or felt they had) essentially zero leverage, and I never heard of anyone taking their bosses to court. But when I asked a friend about this they said these types of lawsuit and arbitration are common and utilized successfully by workers.

3. Can this approach really hold up if competitive pressures mount on these companies, or the economy as a whole? (More on this below.)

My guess is that the authorities see this not as a permanent solution but as a useful source of friction to slow down the transition and buy some time. And maybe that’s enough.

Alright, final one: Don’t become a welfare state. Xi Jinping himself has made it very clear that he sees an overgenerous welfare state (“welfarism”) as a huge problem. He said as much in a 2021 speech and a 2022 piece in the ideological journal Seeking Truth, saying China must “steer clear of the idleness-breeding trap of welfarism.” In the speech he specifically laid into Latin American countries whose overgenerous welfare states created “a group of lazy people with unearned incomes.” That might seem surprising coming from an avowed Marxist-Leninist like Xi, but he tends to adhere to those ideologies in their more philosophical (dialectical materialism) and organizational (vanguard party) aspects.

Of course it’s possible that Xi changes his tune on this. Some of his criticism of welfare states focuses on governments going beyond their financial means and raising peoples expectations in a way they can’t satisfy. But there does seem to be a harder and more ideologically-engrained edge to this that goes beyond the pragmatic considerations. It’s a somewhat hand-wavy take, but Xi came up through a ruthless and brutally competitive system, and he came to dominate it through shows of individual strength and cunning — not really the kind of life experience that makes you big on handouts.

Putting it all together

If we try to put these policy responses together into a coherent plan, we get something like the following:

1. Don’t scare people and don’t become a welfare state.

2. Make AI companies put some thought into labor impacts and provide the government with information on this front.

3. On the margins, try to nudge the technology toward labor-intensive applications.

4. Build in some friction to layoffs and force companies that are profiting from applying AI to continue supporting their workers.

5. Encourage and provide resources for people to continue educating and retraining themselves throughout their careers.

Laid out like that, it doesn’t look too bad. To be honest, when I started the research for this piece I came in with a much lower estimation of the Chinese state’s thinking on these issues. After combing through the material, I found some more sophisticated thinking than I’d expected (especially given how recently this came on the radar) and also some alternative tools that I hadn’t considered.

But, for me at least, #4 in that list is doing a lot of the work. Maybe there’s a meaningful play to make in this space, especially given the dominant and often unique role China plays in global manufacturing supply chains. Companies with hundreds or thousands of global competitors nipping at their heels might be legitimately unable to keep already-automated workers on their payrolls. And that might have been the case for China 25+ years ago, when it was just another replaceable module in the global market for cheap labor. But given the way China has come to dominate these supply chains, and its uniquely tight-knit and sophisticated manufacturing ecosystem, maybe those pressures won’t apply to China in the same way today.

Ok, that’s all for now. This piece began as a “I’ll just spend the next couple hours throwing together a quick take on…” and then snowballed into something much bigger. I hope it’s been helpful.

Thank you to Carnegie Junior Fellow Sophie Zhuang for her help with research on this piece.

1. China gained 600 million new urban residents from 1985-2020. State-owned enterprises laid off 25 million workers from 1998-2001. China used more cement from 2011-2013 than the U.S. did in the entire 20th century. The list goes on.

2. There are tons of great books on how people experienced and adapted to this multi-decade churn. Two of my favorites are Country Driving by Peter Hessler (especially the sections on “The Factory” and “The Village” — just exquisite stuff), and Factory Girls by Leslie Chang.

3. For comparison, during that same period US GDP per capita grew 2x. Americans obviously started from a much higher base, and remain at a much higher absolute level. That 2x growth also doesn’t account for distributional effects and growing inequality.

4. Tough beat if you were an older SOE worker in China’s northeast, an area that still hasn’t really recovered from reforms of the 1990s.

5. I know to the vast majority of people around the world Wuhan is, and probably always will be, the place where Covid came from. But it has a very special place in my heart. My first visit there for a frisbee tournament in 2010 was a big turning point for my experience in China. I wrote about that (in Chinese) in a Medium post back in 2020. Very different vibe, but if you want a visceral document of Wuhan during Covid, I’d highly recommend Hao Wu’s documentary 76 Days, which tracked workers and patients at one hospital through that period.

6. The reference to people not getting “enough to eat” is metaphorical. In Chinese, the “rice bowl” is the go-to metaphor for someone’s livelihood. The classic version is the “iron rice bowl” (铁饭碗) used to refer to lifelong (hence “iron”) employment and benefits for people working in the Chinese public sector.

7. People have told me this is especially true of the Cyberspace Administration of China (CAC), the group charged with online censorship and most AI regulation to-date. And that checks out. A few years ago I wrote about how a viral online magazine story shaped China’s first AI regulation. The same was true for the Party’s initial interest in deepfakes, which was sparked by online debates about face-swapping apps.

8. Stay tuned for a forthcoming piece on the evolution of this standard and what it reveals about the growing role of technical standards in Chinese AI policy.

9. A Claude translation that I slightly tweaked. The last sentence about the evaluation system comes out a bit odd and someone probably has a better translation and/or explanation.

10. The Ministry of Industry and Information Technology (MIIT), the Ministry of Public Security, and the State Administration of Market Reform were junior partners on some of these CAC regulations. MIIT and various technical organizations that report up to it (CAICT) provided a fair amount of technical and policy know-how for these.

11. The go-to phrase for this, which first showed up in the AI context in the 2023 gen AI regulation, is 发展和安全并重: “attach equal importance to development and security.” It’d be a nice little project for someone to trace the origins and evolution of this phrase. My cursory understanding is that it originated with/around Xi’s Comprehensive National Security Concept in 2014. At that time it was used to push for greater emphasis on security, relative to the development-first agenda of his predecessors. But when it showed up in the 2023 gen AI regulation, it was pushing the opposite way, indicating a move away from the security-dominant approach to tech and AI policy of the 2020-2023 era, and pushing for greater emphasis on development.

12. AI being “controllable” is one of the most longstanding positions of the Chinese state on the technology. What they mean by “controllable” is complicated and has evolved a lot over the past decade. Keep an eye out for an upcoming deep dive on this.