Chinese parts already power American cars, and that’s exactly why Congress is panicking


TL;DR

Bipartisan US lawmakers introduced the Connected Vehicle Security Act to ban Chinese-linked vehicles, software, and hardware from the American market, as Trump meets Xi Jinping in Beijing. But with 60+ Chinese-owned suppliers already embedded in the US auto supply chain and BYD now the world’s top EV seller, the push exposes a tension between national security concerns and economic reality.

 

Somewhere in the wiring of the car you drove this morning, there is almost certainly a Chinese component. An airbag inflator. A windshield. A steering column bearing. According to global consulting firm AlixPartners, more than 60 US-based auto suppliers are now owned by Chinese companies, making everything from axles to electronic control units for vehicles that roll off assembly lines in Michigan, Ohio, and Tennessee.

It is against this backdrop, Chinese technology already threaded through the American automobile, that lawmakers in both parties are urging President Donald Trump not to trade away the US car market during his state visit to Beijing this week. The message from Capitol Hill has been blunt: do not use automobiles as a bargaining chip with President Xi Jinping.

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The concern is not hypothetical. In January, Trump told the Detroit Economic Club that he would welcome Chinese automakers building factories on American soil, provided they employed US workers. The remark sent a jolt through an industry that had spent years lobbying successive administrations to keep Chinese vehicles out. It was later walked back, but the damage to nerves, and to legislative calendars, was done.

On May 12, Representative John Moolenaar, the Republican chairman of the House Select Committee on China, and Democratic Representative Debbie Dingell introduced the Connected Vehicle Security Act of 2026. The bill would ban the importation, manufacture, and sale of connected vehicles, software, and hardware linked to China, Russia, North Korea, and Iran. Software prohibitions would take effect on January 1, 2027; hardware restrictions would follow by January 1, 2030. Each violation would carry civil penalties of at least $1.5 million, or five times the transaction’s value, whichever is greater.

A companion version was filed in the Senate by Elissa Slotkin, a Democrat from Michigan, and Bernie Moreno, a Republican from Ohio. Senator Slotkin described Chinese-made connected vehicles as “TikTok on wheels”, a reference to the data-harvesting fears that fuelled the push to divest TikTok from its Chinese parent company. The comparison is not entirely rhetorical: as TNW has reported, Chinese EV content is already flooding American social media through platforms owned by the same conglomerate, shaping consumer demand for vehicles that cannot legally be sold in the US.

The legislation codifies and expands restrictions first put in place under President Biden, whose Commerce Department in January 2025 finalised rules prohibiting connected vehicle technology linked to China and Russia. The legal foundation dates back further still: a 2019 executive order signed by Trump during his first term declared a national emergency over foreign threats to America’s information and communications technology supply chain.

The political logic is straightforward. Michigan and Ohio are battleground states heading into the 2026 midterms and the next presidential race. The auto industry directly employs roughly half a million people in Michigan alone, according to Governor Gretchen Whitmer, who released a statement applauding the legislation. For lawmakers in these districts, even the appearance of opening the door to Chinese carmakers is a political liability.

But the economic logic is more tangled. The average new car in the US now lists for more than $49,000, according to Kelley Blue Book, a figure that has become a quiet crisis for American consumers. In China, shoppers can choose from more than 200 battery-powered models priced below the equivalent of $25,000, according to DCar, a Chinese automotive content platform. BYD’s most popular model, the Seagull, starts at roughly $10,300. The cheapest new electric vehicle available in the US, the Chevrolet Bolt, is expected to retail for $28,995.

BYD overtook Tesla in 2025 to become the world’s largest seller of battery electric vehicles, moving 2.26 million units compared to Tesla’s 1.64 million, a 28% year-on-year increase against Tesla’s roughly 9% decline. The company that Elon Musk laughed at in a 2011 Bloomberg interview has become the industry’s most formidable competitor. Tesla briefly reclaimed the quarterly crown in Q1 2026, but the full-year gap of more than 600,000 units tells the structural story.

Outside the US, the playbook is already running. Chinese-made vehicles captured roughly 19% of sales in Mexico in 2025, according to data from the national statistics agency INEGI and industry bodies, up from less than 1% five years earlier. Mexico has since raised tariffs on Chinese vehicles to 50%. Across Europe, Chinese brands have made significant inroads, and BYD is reportedly in talks to take over certain Stellantis plants to expand production capacity on the continent. Europe’s cumulative EV investment has now passed €200 billion, but much of that capacity is being built by Chinese and Korean firms rather than European champions.

This is the pattern that alarms Washington. Industry groups, steelmakers, unions, and automakers have all pressed the same argument: China’s state-subsidised manufacturers will undercut domestic competitors on price, hollow out the supply chain, and then raise prices once the competition has been eliminated. Dingell invoked the solar panel industry as a cautionary example during a press conference on May 12.

China has a pattern of coming in, subsidising the cost to keep the price lower, destroy an industry and then jack up the price,” Dingell said. “This is about America’s future.”

The Information Technology and Innovation Foundation, a Washington-based think tank that has previously criticised some of Trump’s tariff policies, backed the legislation. Stephen Ezell, the foundation’s vice president for global innovation policy, described Chinese automakers as products of decades of state-backed mercantilism, not normal market competitors. The implication: conventional trade rules cannot apply. Even foreign automakers operating inside China are now partnering with Chinese tech firms because they cannot develop competitive software fast enough on their own, a dynamic that underscores just how far the technology gap has shifted.

The White House, for its part, pushed back on the premise. Spokesperson Kush Desai said in an email that the administration was always seeking investment in America’s industrial resurgence, and dismissed any suggestion that it would compromise national security as “baseless and false.

The question that the legislation cannot fully answer is the one embedded in the supply chain itself. More than 60 Chinese-owned suppliers are already manufacturing in the US. Chinese-made components sit inside vehicles built by American, Japanese, Korean, and European automakers on American soil. Banning finished vehicles and connected technology is one thing; disentangling a supply chain that has been quietly integrating for years is quite another.

Most industry experts agree it is only a matter of time before Chinese cars arrive in the US in some form. One scenario gaining traction involves requiring Chinese companies to partner with American automakers domestically, the same model China itself imposed on foreign manufacturers in the 1990s to build its own industry. The broader tariff landscape adds further complexity, with trade restrictions reshaping technology supply chains on both sides of the Atlantic.

For now, the legislative push represents a rare point of bipartisan consensus: more than 120 House lawmakers signed a letter last month urging Trump to keep Chinese automakers out. The bill’s sponsors are betting that the political cost of inaction outweighs the consumer cost of keeping affordable vehicles off the market.

Whether that calculus holds may depend on what Trump brings back from Beijing. The president said China has agreed to buy 200 Boeing jets and American oil, and not to supply military equipment to Iran, though no independent confirmation from Chinese officials has been found at the time of publication. He described Xi as “all business, no games.” On automobiles, there was silence.

The American auto industry will take that silence as a win. For how long remains an open question.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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