The AI Regulation Debate Intensifies: U.S. Policy Battles and China's Diverging Position

AI governance is entering an entirely new phase. On one side, the Trump administration is loudly announcing plans to create an "AI Force," hunting for an "AI Czar," and even trying to rebrand the entire industry. On the other, state regulators are rushing out "AI kill switch" bills and new data center rules. And hovering over all of it is one unavoidable question — China. If China refuses to join any kind of "pause" or "pace control" arrangement, do these policies mean anything at all? This week, Chinese President Xi Jinping's visit to the U.S. — with trade delegations already in talks on AI — brings the entire debate to a rare moment of real consequence.

Anthropic Delays IPO: When Safety Narratives Collide with Financial Reality

Anthropic's much-anticipated IPO, originally planned for this month or next, has been pushed back to November. According to the Wall Street Journal, insiders stress the delay "has nothing to do with AI safety" and is instead designed to let the company disclose an expected strong Q3 earnings report. Notably, this decision was made before CEO Dario Amodei began publicly calling for AI to slow down.

Leaked financial details suggest Anthropic's operating picture is improving rapidly: in Q2 of last year, the company spent $2.30 for every dollar of revenue it earned; by Q2 of this year, it had reached "marginal profitability." The company also claims positive operating income excluding stock-based compensation, and gross margins above 80% once revenue-sharing agreements and model training costs are stripped out.

Industry critics are deeply skeptical of any profitability claim that relies on stripping out major costs, arguing it fails to reflect genuine financial health. Kevin Shue of Interconnected Capital put it bluntly: public markets want to believe Anthropic can self-fund its capex, and debt should be a bridge to profitability — "not borrowing with no end in sight" — which is the core challenge facing the entire AI supply chain right now. Anthropic claims its annualized revenue could hit $10 billion by year-end, up sharply from $6.5 billion in late July, but some investors came away from meetings with the impression that "profitability may be transient."

Even more intriguing: Anthropic was quietly reported to have established its own wet lab in the Bay Area, conducting biological experiments. This sparked sharp questions. Flexport CEO Ryan Petersen asked why, if Anthropic is worried that an out-of-control AI will kill everyone, it would let one control a robotic biology lab. Investor Nick Carter floated a more "cynical" theory — that the recent wave of "fear marketing" may be designed to get investors to accept Anthropic's logic of internalizing all major breakthroughs rather than sharing the benefits with customers.

A wet lab is a traditional biology or chemistry laboratory where physical experiments are conducted using liquids, biological samples, and chemical reagents — as opposed to a "dry lab," which relies primarily on computer simulation and data analysis. The backdrop to AI companies moving into wet labs is the rapid growth of AI-assisted biological research in recent years. DeepMind's AlphaFold has revolutionized protein structure prediction, and AI-designed proteins and enzymes are now being used in drug development and even bioweapon defense research. Anthropic's wet lab ambitions are widely speculated to include using Claude to assist with experimental design and results analysis, validating AI-generated biological hypotheses, and exploring the intersection of AI and synthetic biology. Critics' concern is clear: a company that has built its brand around "AI safety" simultaneously controlling robotic systems capable of manipulating real biological materials creates an obvious tension with its publicly stated posture of caution.

Data Center Debt Markets: The Real Warning Signal for the AI Trade

More than stock prices, the financing market for data centers may be the most important corner to watch if you want to know whether the AI bubble is starting to deflate.

Several signals are worth watching. Bonds tied to a data center leased to trading firm Jane Street were issued in August at an 8.9% yield; they now trade at 11.3% — a move that cannot be explained by Federal Reserve rate hikes. Meta entered the junk bond market for the first time, raising $2.3 billion through a deal with CleanSpark at 8.25%, attracting over $10 billion in orders, yet investors clearly demanded a higher premium. More seriously, $1.8 billion in debt linked to Oracle's New Mexico data center has fallen to distressed levels around $0.89 on the dollar; a downgrade of Oracle's credit rating could trigger forced institutional selling.

A tweet from infrastructure investor Meltem Demirors — "hearing banks are stopping all compute lending, credit crunch beginning" — racked up 800,000 views. She later clarified: investment-grade borrowers can still access financing, but with tighter scrutiny, and "the long tail is drying up fast, with an extreme divide between the haves and have-nots." Steel investor Jigar Shah thinks panic is premature, pointing out that many smaller data centers under 25 MW can be built at 50% lower cost within 120 days. Regardless, this market is a core barometer for whether the AI trade has peaked.

Rising yields mean falling bond prices — a direct signal that markets are repricing the risk of a given asset. In the case of the Jane Street–linked data center bonds, a move from 8.9% to 11.3% means early buyers are sitting on paper losses, and more importantly, new debt issuance now requires paying higher interest, directly pushing up data center financing costs. "Junk bonds" (also called high-yield bonds) are debt instruments rated below investment grade (below BB+ on the S&P scale), typically issued by companies with weaker financials or newer business models; investors demand higher returns to compensate for default risk. Meta, a financially powerful tech giant, entering the junk bond market for the first time signals that it is outsourcing data center expansion to lower-rated partners to avoid balance sheet pressure. Oracle's New Mexico debt falling to "distressed levels" (typically below $0.80 on the dollar) suggests the market is questioning the project's ability to service its debt; if forced selling is triggered, it could create a vicious cycle affecting sentiment across the entire AI infrastructure debt market.

Trump's "AI Force" and the States' "Kill Switches"

Just before the U.S.-China talks, the Trump administration was busy over the weekend. Trump announced on Truth Social that he would create an "AI Force" — modeled on the Space Force he established in his first term — and would search for an "AI Czar," declaring that "only high-IQ individuals need apply." He also spent considerable time trying to rebrand AI itself, running a poll on X in which "Superior Intelligence" edged out the competition.

These moves drew mockery from left and right alike — right-wing commentator Matt Walsh demanded to know "why the president is focused on this." But some argue these proposals shouldn't be dismissed out of hand. The Space Force precedent is instructive: it was widely ridiculed at the time, but effectively consolidated satellite operations and defense assets that had been managed separately by the Army, Navy, and Air Force. Setting aside debates about existential risk and AI kill switches, advanced AI will undeniably introduce new cybersecurity challenges, and establishing a centralized institution to provide cybersecurity support across military branches is not an inherently absurd idea.

The "AI kill switch" is becoming a hot policy concept. California Governor Newsom signed an executive order accelerating related policy work, convening a task force to provide guidance within two months and requiring the introduction of third-party safety inspectors. As a frontrunner for the 2028 Democratic presidential nomination, Newsom's shift in tone is worth tracking as a bellwether for how Democrats are approaching AI politics. Andrew Yang and California Democratic Congressman Ted Lieu are also pushing kill-switch legislation. Virginia Governor Spanberger signed an executive order tightening data center construction rules, eliminating the "by-right approval" process that had created the state's famous "data center corridor" — leaving both developers and opponents unhappy. In a democracy, when everyone is moderately dissatisfied, it sometimes means a policy has found the right middle ground.

The Fundamental Divide Between the U.S. and China on AI

Behind all the talk of pauses and slowdowns lurks the China question. If China doesn't agree to such policies, they are effectively stillborn — they would only serve to exclude the U.S. from the AI race. California Congressman Ro Khanna has written to Chinese AI companies including Alibaba, DeepSeek^Ad, and Moonshot AI, urging them to join binding international agreements to control the pace of frontier development, and calling for an emergency congressional hearing.

Xi Jinping is scheduled to visit the U.S. from September 23–25, with his meeting with Trump reportedly set for September 24. While AI is dominating headlines, the most urgent economic priority is extending the trade truce set to expire in November. In Sunday's preparatory talks, Treasury Secretary Scott Bessent said he had raised a proposal for an "AI incident alert system" — akin to the Cold War–era hotline between the Kremlin and the White House — advocating for a notification mechanism between the world's top two AI powers to move "from opacity toward greater transparency."

There are several fundamental differences between the U.S. and China. First, there is almost no public discourse about "existential risk" in China's AI community; the Chinese government is far more concerned about AI's threat to social control and regime stability. Minister of State Security Chen Yixin has called for strengthening the Party's grip on AI and bolstering government regulation to maintain domestic political stability, defend against cyberattacks and disinformation, and compete militarily with the United States. Henry Gao, a law professor at Singapore Management University, notes that Beijing views AI safety through an "adversarial lens" rather than as a shared humanitarian mission: "data sovereignty and political security will never be traded away for international safety agreements."

Second, the two countries' economies are affected by AI in vastly different ways. In the U.S., AI capital expenditure is widely seen as the primary engine of economic growth over the past year — arguably the key factor keeping the economy from recession. China's economy has seen no comparable AI-driven boost: youth unemployment stands at 18.9%, car sales are down 20% year-on-year, and home prices have fallen another 14%. Former central bank official Li Daokui has said the economy is "running too cold, with a prosperous tech sector unable to lift a much larger foundation."

Anthropic's data security practices have also become a major concern for Chinese officials. In July, researchers discovered a mechanism in Claude Code that tracked Chinese IP addresses and reported activity — Anthropic described it as a necessary measure to block distillation attacks, but Chinese officials viewed it as "delivering malware" to some of China's largest tech companies, and Alibaba promptly banned Claude Code. Anthropic also disclosed in a risk report that Chinese government officials using Claude had uploaded sensitive documents including details of military operations. It is safe to predict that setting rules for AI-enabled espionage will receive far more attention from Chinese officials than existential risk ever will.

Microsoft AI CEO Mustafa Suleiman offered a different perspective: "I don't think we should use China as a scapegoat for holding back our own progress." He argues that each AI lab should be responsible for its own safety measures, and that regulation should be seen as "establishing shared norms and standards to improve safety" rather than a reason to slow development. Whether this view can become mainstream remains to be seen, but this week's U.S.-China talks will undoubtedly push the debate forward in one direction or another.

"Model distillation" refers to the technique of transferring the capabilities of a large model to a smaller one by having the smaller model mimic the larger model's outputs — in short, using a competitor's AI outputs as training data to replicate its performance at low cost. The mechanism discovered in Claude Code was, according to Anthropic, designed to detect and block large-scale automated distillation requests from Chinese IP addresses, preventing its technology from being cheaply copied. However, the way this protection was implemented — identifying user IPs and reporting activity — closely matches the definition of spyware under Chinese regulatory frameworks. This incident vividly illustrates the "dual-use" dilemma of AI software: the same piece of code can be framed as an intellectual property protection tool or as a data-collection backdoor. The technical facts are identical; geopolitical context determines radically different interpretations. This is precisely the kind of trust deficit that makes U.S.-China AI governance negotiations so difficult at the micro level.