The AI Job Suck Is the China Shock of Today

Big economic changes tend to leave some Americans behind. The Trump administration needs to look forward rather than focus on the past.

May 29, 2025 at 10:00 AM UTC

Rearview-mirror policymaking seems as unavoidable as it is self-defeating. President Donald Trump is falling into this trap with his focus on reversing the past quarter-century of trade policy — trying to put the toothpaste back in its tube. In attempting to undo the so-called China shock, he is missing the opportunity to preempt collateral damage from the coming artificial intelligence shock, which will reshape labor markets over the coming decade.

Even relatively positive economic changes hurt some workers. As did the decline of America’s manufacturing hubs, AI is likely to prove a challenge for millions of workers. At the more apocalyptic extreme, Anthropic Chief Executive Officer Dario Amodei told Axios this week that AI could eliminate half of entry-level white-collar jobs and push unemployment as high as 20% over one to five years. While I’m not expecting anything that dire, there are subtle signs — as the Atlantic’s Derek Thompson pointed out last month — that the impacts may be already materializing in the unique and recent increase of the unemployment rate for recent college graduates to the highest since 2021.

Brookings Institution research projects that about 30% of the workforce could see at least half of their tasks disrupted by generative AI. That could include close to 19 million people in office and administrative support; 13 million in sales and related jobs; and 10 million in business and financial operations, according to Brookings’ analysis of OpenAI and Bureau of Labor Statistics data. Geographically speaking, economists Scott Abrahams and Frank Levy found that such work is most concentrated in expensive coastal areas, including the Bay Area and the nation’s capital. For the government, the key is to stand ready to provide help to those who need it.

The wildly uncertain fallout from AI requires modern tools for monitoring trends in employment and wages. In the China shock, the negative outcomes were concentrated in manufacturing communities. By failing to appreciate the scope of the problem early, policymakers allowed parts of the country to fall into a self-reinforcing cycle of decay, engendering a sense of unfairness and lighting a fire under a populist backlash in American politics. In contrast, the Abrahams and Levy study shows that a generative AI shock could nudge workers away from expensive coastal cities to places such as Savannah, Georgia, or Greenville, South Carolina, which offer affordable housing and economies that are less exposed to the job losses.

One source of forward-looking data is online job postings, which can be mined for keywords related to AI, Abrahams told me this week. Such data is far more comprehensive than it was in earlier shocks, and it reveals in real time the areas where companies are expanding and replacing workers — and, equally important, the areas where they aren’t. As Abrahams pointed out, AI’s impact may play out in large part through job-leavers that go unreplaced, rather than large and obvious layoffs.

In terms of the latter, improved disclosure would help. Kevin Frazier, the AI Innovation and Law Fellow at the UT Austin School of Law, has suggested updating the Worker Adjustment and Retraining Notification Act, which generally requires companies to provide 60 days advance notice of closures and layoffs of 50 workers or more. Frazier has suggested that medium and large-sized firms be required to disclose “widespread integration of new AI tools,” whether or not the new technology corresponds with immediate job cuts. Though compliance could be a challenge, this would add a layer of forward-looking visibility. Frazier also wants to change the WARN policy to capture more layoffs and give communities and policymakers more time to respond.

Second, the US should make preparations to provide a strong response to any visible labor market disruptions, including getting its fiscal house in order.1 While some people believe that AI will eventually be so disruptive that it demands a version of Universal Basic Income, the near-term solution is likely to look like an improved and probably costlier version of Trade Adjustment Assistance, the program rolled out during John F. Kennedy’s presidency to help workers sidelined by trade. That program was too bureaucratic and small to blunt a development as big as the China shock. Any new effort would have to cut down on red tape and be better funded. Frazier has suggested businesses themselves be required to pay into rainy-day-type funds for worker retraining.

Third, policymakers should make it easier for workers to move for new opportunities. Many lost jobs will be replaced by new and even better ones, but we can’t take for granted that the labor supply will automatically migrate to the sectors and regions with the greatest opportunity. In fact, one key takeaway from the China shock literature is that, while the labor market migrated, many individuals didn’t. David Autor, David Dorn and Gordon Hanson have found that incumbent workers were “largely frozen in the declining manufacturing sector in their original locations.”

It’s not clear why so many stayed. Personal ties may have motivated some, but others may have faced financial constraints — a problem exacerbated by today’s housing affordability crisis. As for Trade Adjustment Assistance, it provided only a laughable relocation allowance of no more than $1,250, a figure that should be much larger if it’s going to promote labor mobility.

Finally, the US must ensure that the next generation is equipped with the skills of the future, including general AI literacy as well as domain expertise around AI and robotics engineering. The future is also likely to place a premium on the general critical thinking skills and emotional intelligence that liberal arts degrees engender. While the payoffs of higher education may become less clear, it’s likely to remain essential to America’s success. Policymakers can support it through thoughtfully allocated student and research grants, and immigration policies that bring the best inventors and entrepreneurs to our country.

Unfortunately, Trump has paid short shrift to the AI challenge. He’s spending much of his time pursuing a policy of ex post protectionism, seemingly trying to reverse the outcomes of a shock that the US inadequately prepared for a quarter century ago. Instead, if he wants to leave an economic legacy, he should take steps to ensure that AI maximally benefits Americans by mitigating the inevitable dislocations along the way. Left unaddressed, America could face another populist backlash against uneven labor market outcomes, and the Republican Party may well find itself on the wrong side of this one.

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48970555)

The Trump admin is actually doing stuff to try to get in front of AI. In comparison, the Chinese government is doing nothing. They have no fucking idea what is going on. DeepSeek is doing it all themselves

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48970564)

The Trump admin has cultivated relationships with people in tech in order to understand and integrate themselves into developments in the AI industry. They understand what's coming and I would not be surprised if they are already coordinating with industry leaders behind the scenes. I predict that a focus on AI will be a major part of Vance's platform in 2028

They are trying to repatriate critical industries that will allow us to produce robots and the inputs for robots en masse. They are trying to vastly increase energy capability. Both of these things will be extremely important in the coming years

China on the other hand is doing absolutely nothing. Their leaders don't understand AI at all. They could be throwing their entire state apparatus at it and yet they are doing nothing. DeepSeek is operating almost entirely independently with little if any help from the state. They are doing a great job nonetheless but there is essentially no one else in China developing frontier AI models or integration outside of independent open source users

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48970697)

Worthless fucking boomer portfolio watchers. They rather get their lower half obliterated rather than deal with a fingr up their anuse for a bit.

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48972574)

Just remembered another one from my biglaw days when I got pulled into some deal, I think this was the first time I wrote python for work: The data room was some shit provider and it didn't provide metadata. The deal was ongoing and new documents were being uploaded routinely/old ones being updated. They were, no joke, relying on a representation from counsel that they would alert us if they added or modified anything. I said something like, "Why don't you just pull all the files, run an SHA-2 hash, then use that as a baseline? When you run it again, anything new or that hashes differently need to be looked at." This had never occurred to them and I ended up writing a python script for it. This is like stuff a first year computer science student could do even before AI, but a V20 didn't think of.

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48971076)

md5 is only shitty for cryptographically secure hashing

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48972109)

https://i.pinimg.com/originals/0b/aa/bd/0baabd60a870970ff7cdda4d489075a1.gif

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48972268)

https://x.com/stocktalkweekly/status/1927701640406081709

He said it in All Caps btw. Because he's Extra Serious about wanting you to understand that His Product Is A Gamechanger In The New Computer Ontology

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48971157)

### 1. **AI Transparency and Accountability Laws**

- **Algorithmic Impact Assessments (AIAs):** Require companies deploying AI in hiring, firing, or task automation to assess and disclose potential job impacts before implementation.

- **Right-to-Explanation Laws:** Mandate that workers and employers understand how AI-driven decisions (e.g., layoffs, promotions) are made.

### 2. **Job Displacement Mitigation Policies**

- **Automation Tax or Robot Tax:** Levy taxes on companies that replace human labor with AI/automation, with revenue funding retraining programs.

- **Severance and Transition Support:** Require companies displacing workers via AI to provide extended severance, healthcare, and retraining benefits.

### 3. **Workforce Reskilling & Education Reform**

- **Lifelong Learning Accounts (LLAs):** Government-matched savings accounts for workers to upskill (similar to 401(k)s for education).

- **Expanded Pell Grants for Tech Training:** Subsidize certifications in AI-resistant fields (healthcare, trades, creative industries).

- **National AI Apprenticeship Program:** Fund employer partnerships to train workers in AI-augmented roles.

### 4. **Labor Protections & New Worker Rights**

- **Shortened Workweek Incentives:** Tax breaks for companies adopting 4-day weeks (distributing work more evenly).

- **Collective Bargaining for Tech Transitions:** Strengthen unions’ role in negotiating AI integration terms.

- **Job Guarantee Programs:** Public-sector employment as a backstop for displaced workers (e.g., infrastructure, care work).

### 5. **Incentivizing Human-AI Collaboration**

- **Tax Credits for "Augmentation over Automation":** Reward companies using AI to assist (not replace) workers.

- **Small Business AI Grants:** Help SMEs adopt AI tools without reducing headcount.

### 6. **Sector-Specific Safeguards**

- **Healthcare/Education AI Guardrails:** Limit AI’s role in fields requiring human judgment (e.g., ban fully automated teaching/nursing).

- **Creative Industry Protections:** Copyright reforms ensuring artists/writers are compensated when AI trains on their work.

### 7. **Long-Term Structural Reforms**

- **Universal Basic Income (UBI) Pilots:** Test unconditional cash transfers to offset job market volatility.

- **Data Dividend Laws:** Require AI firms to share profits with workers whose data trained their models (e.g., California’s proposed data dividend).

### **Political Challenges & Trade-Offs**

- **Innovation vs. Protection:** Overregulation could stifle AI’s productivity gains, but inaction risks mass unemployment.

- **Global Competition:** U.S. policies must balance worker protections with maintaining competitiveness against less-regulated nations.

### **Key Precedents**

- The **WORKER Act** (proposed in 2021) aimed to tax automation and fund retraining.

- The **EU AI Act** includes risk assessments for high-impact AI, a model for U.S. rules.

Would you prioritize certain sectors (e.g., manufacturing, white-collar jobs) for protection, or focus on economy-wide solutions?

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48971217)

These laws are a great example of lib stupidity. It's supposed to protect workers, but the end result is that Google has to announced its laying off 10% of their workforce, so then Microsoft leadership and investors both go "shit we better lay off 10% of our workforce."

Amazing they do't have the foresight to realize forcing companies to announce they're using AI is goig to lead to more companies using AI...

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48972304)

https://www.newyorker.com/cartoon/a16995

People will fall all over themselves to be the ones who bring AI into existence as long as they get (brief) wealth, recognition, and power

(http://www.autoadmit.com/thread.php?thread_id=5731020&forum_id=2#48972858)