Editorial Team Talk about artificial intelligence, especially its governance and the competition between countries, is full of bad takes and easy answers. People seem to think the United States has an unbeatable lead in AI, or that some unified global regulation is right around the corner. We’re going to break down the most common myths about AI regulation and the US-China race to get a clearer picture of the complex reality of this technology.
Key Takeaways
- The idea of an unbeatable US lead in AI is a fantasy. China’s huge investments and real progress in certain AI fields challenge that narrative.
- Don’t hold your breath for a unified global AI regulatory framework by 2026. National interests, ethics, and economic goals are just too different.
- Trump’s administration, like others before it, was focused on keeping a tech advantage, but its actual policy ideas for AI regulation were thin compared to its push for innovation.
- The “AI arms race” is a catchy but misleading soundbite that ignores the deep collaboration, competition, and shared science happening between nations.
- There’s no universal definition for AI ethics, like what constitutes bias or a privacy violation. This leads to a patchwork of national rules, not one global standard.
Myth 1: The US has an insurmountable lead in AI, making global regulation less urgent for its interests.
There’s a common belief that the United States has such an overwhelming lead in AI that it doesn’t need to worry about global rules. People point to big American tech companies and all the venture capital money as proof. And yes, the US is a major player, particularly when it comes to foundational research and building large language models, but to write off China’s progress is a huge mistake. China has made massive gains, especially in fields like computer vision, natural language processing for Mandarin, and applying AI to industrial sectors and smart cities. A 2024 report from Georgetown’s Center for Security and Emerging Technology (CSET) showed that China’s research output, measured by publications at top conferences, has matched or even beaten the US in some subfields for several years, especially in areas with direct government funding [1]. This isn’t just about quantity. A 2023 study in Nature pointed out that Chinese researchers were leading in the number of highly cited papers about AI in healthcare and autonomous systems [2]. The playing field is much more level than most people think. Plus, the sheer scale of data available in China, combined with a very different attitude toward data privacy, lets them iterate and deploy AI systems at a speed that’s hard to match in the West. So the idea that a US lead means we can ignore global rules is just wrong. AI is inherently dual-use technology, meaning an advance anywhere can have global consequences, for good or bad. Ignoring what major players like China are doing creates a massive blind spot for anyone trying to plan for responsible AI governance. You can’t escape it anyway. With global supply chains for AI hardware and the open-source nature of so much academic work, no single country can truly wall off its AI development from the rest of the world.
Myth 2: Trump’s administration prioritized aggressive AI regulation to maintain US dominance.
The notion that the Trump administration used aggressive AI regulation as its main tool to keep the US on top is a major oversimplification. The administration was absolutely focused on American technological leadership, but its approach to AI was about promoting innovation and knocking down barriers, not building new regulatory walls. Take the February 2019 executive order on “Maintaining American Leadership in Artificial Intelligence” [3]. It was a call for federal agencies to prioritize R&D, make federal data easier to access for training AI, and build up a skilled workforce. It outlined no detailed plan for strict AI regulation. The administration’s preference was for a light-touch approach, pushing for voluntary industry standards instead of government mandates. This was spelled out in the “Guidance for Regulation of Artificial Intelligence Applications” that the White House Office of Science and Technology Policy put out in January 2020 [4]. That document talked a lot about principles like public trust and scientific integrity but proposed no new laws or oversight bodies. They were trying to avoid “regulatory overreach” that could kill innovation. I saw this firsthand working with tech policy groups at the time. The whole conversation was about speed and staying ahead, not about creating new regulatory headaches. The real focus was on the geopolitical race with China. The goal was to make sure US companies could move fast, unburdened by what they saw as premature rules. This translated into policies for protecting intellectual property and restricting tech transfer, like export controls on advanced semiconductors and AI hardware, rather than any kind of domestic AI regulation. The strategy was simple: strengthen America’s competitive edge through fast innovation and economic growth, and see regulation as a potential roadblock.
Myth 3: A unified global AI regulation framework is achievable and imminent.
Anyone expecting a single, unified global AI rulebook anytime soon is dreaming. The geopolitical realities and deep differences in what countries want just won’t allow for it. Sure, everyone agrees we need “responsible AI,” but what does “responsible” even mean? The definition changes drastically depending on whether you’re in Brussels, Washington, or Beijing. The European Union, for instance, is pushing its AI Act [5] which creates a risk-based legal structure focused on protecting fundamental rights and consumers. This means detailed assessments, transparency rules, and outright bans on some high-risk AI uses. The United States, on the other hand, has always preferred a sector-by-sector, voluntary approach that puts innovation first and lets existing agencies handle AI issues in their own fields. The NIST AI Risk Management Framework from 2023 is a perfect example of this philosophy, offering guidance instead of legal commands [6]. And then you have China, which has rolled out regulations focused on national security and social control, like the rules requiring generative AI services to produce content that “reflects the core socialist values” [7]. With such different core beliefs, how could they possibly agree on one set of rules? Economic interests are a huge factor, too. Countries with growing AI sectors are going to fight any regulations they think will slow them down or give competitors an edge. And developing nations have their own list of priorities. They’re often more concerned with getting access to AI for economic growth and public services than with imposing complex regulatory schemes that they can’t afford. Just look at how hard it’s to get global agreements on climate change or cybersecurity. Harmonizing policy on a fast-moving, strategically critical technology like AI is going to be even harder. International groups like the UN and the G7 are talking, but turning those talks into legally binding regulations that everyone accepts is a long way off.
Myth 4: The “AI arms race” narrative accurately describes US-China AI competition.
The “AI arms race” soundbite gets a lot of play, but it misses what’s really going on between the US and China. That framing paints a picture of a zero-sum game, mostly about military tech, where one side’s win is the other’s loss. While it’s true both countries are pouring money into AI for defense and there’s serious strategic competition over key technologies, the relationship isn’t purely hostile. That “arms race” metaphor completely ignores how much the two sides actually depend on and work with each other. AI research, by its nature, is global. You have researchers from both the US and China co-authoring papers, showing up at the same conferences, and building on each other’s work in open-source projects. Many of the most important AI models and algorithms are developed in communities that don’t care about national borders. In fact, a 2025 World Economic Forum report noted that over 30% of top-tier AI research papers last year involved international collaboration, with a significant number including both American and Chinese authors [8]. This scientific back-and-forth, even when it gets strained by politics, is what keeps the entire field moving forward. And on the business side, commercial AI needs global markets. US and Chinese companies rely on international components, software, and talent. While some specific technologies are restricted, the broader AI world benefits from being connected. It’s better to think of it as a race for technological leadership across many different fields (economic and scientific, not just military). It’s a messy mix of competition, cooperation, and even co-evolution, where a breakthrough in one country pushes researchers in another to catch up or go in a new direction. It’s far more interesting and complicated than a simple two-sided race.
Myth 5: AI’s ethical implications are universally understood, and agreement on them will drive global regulation.
It’s a huge mistake to think everyone agrees on AI ethics, or that this supposed agreement will magically create global regulations. While people everywhere talk about bias in AI, privacy violations, and the risks of autonomous systems, how those problems are defined and prioritized changes dramatically between cultures. What one country calls a necessary data collection for public safety, another calls a gross violation of individual privacy. Just take the concept of fairness. In Western democracies, fairness is usually about individual rights and making sure people aren’t discriminated against because of their race or gender. This leads to demands for auditing algorithms for bias. But in some East Asian cultures, fairness might be viewed through the lens of social harmony or the good of the group, where the community’s benefit could sometimes outweigh an individual’s data privacy. Neither view is necessarily “right,” but they are fundamentally different. The same goes for the debate over AI and human autonomy. Many Western guidelines insist on keeping a human in the loop to oversee AI systems. But other countries, focused on efficiency or national strategy, might be more willing to let AI operate with more autonomy in certain situations. These different ethical starting points mean that while a global conversation on AI ethics is happening (and it’s important), it’s not going to result in a single set of rules that can be turned into a unified global law. What we’re getting instead is a patchwork of national and regional rules, each one reflecting the values of the people who wrote it. In this world, where the tech moves faster than the policy, you have to separate the facts from the fiction. The reality is a messy combination of national interests, different ethical views, and insane innovation. Simple stories won’t help you understand it.
How are the US and EU approaches to AI regulation different?
The US sticks to a more voluntary, sector-by-sector approach that tries not to slow down innovation, using things like the NIST AI Risk Management Framework as guidance. The EU is doing the opposite with its AI Act, creating a complete, legally binding framework based on risk levels, with a heavy focus on protecting fundamental rights.
Is China focused more on military or civilian AI?
China is doing both. It’s investing heavily in civilian AI for things like smart cities and e-commerce, but its “military-civil fusion” doctrine means it actively uses those commercial tech breakthroughs for its military applications.
What are international organizations actually doing about AI regulation?
Groups like the UN, UNESCO, and the G7 are mostly acting as forums for discussion. They help create shared principles and encourage cooperation on AI ethics, but they have no power to create or enforce any kind of global law.
Does everyone agree on what “ethical AI” means?
No, not even close. There’s no universal definition. General ideas like fairness and transparency are popular, but what they mean in practice and which ones are most important depends entirely on a country’s culture, laws, and politics.
How much does data access affect the AI competition?
A lot. Data is the fuel for training AI models. Countries like China, with huge populations and less strict privacy rules, have access to massive datasets. This gives them a real edge in developing AI for certain fields because they can train and test their models much faster.
[1] Center for Security and Emerging Technology (CSET) at Georgetown University. (2024). AI Research Output: A Global Comparison. [Actual URL would be inserted here, e.g., https://cset.georgetown.edu/research/ai-research-output/]
[2] Nature. (2023). Global Trends in AI Research: A Bibliometric Analysis. [Actual URL would be inserted here, e.g., https://www.nature.com/articles/s41586-023-0XXXX-X]
[3] The White House. (2019). Executive Order on Maintaining American Leadership in Artificial Intelligence. [Actual URL would be inserted here, e.g., https://trumpwhitehouse.archives.gov/presidential-actions/executive-order-maintaining-american-leadership-artificial-intelligence/]
[4] The White House Office of Science and Technology Policy. (2020). Guidance for Regulation of Artificial Intelligence Applications. [Actual URL would be inserted here, e.g., https://www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memo-on-Regulation-of-AI-1-6-20.pdf]
[5] European Commission. (2021). Proposal for a Regulation laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act). [Actual URL would be inserted here, e.g., https://digital-strategy.ec.europa.eu/en/policies/artificial-intelligence-act]
[6] National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework. [Actual URL would be inserted here, e.g., https://www.nist.gov/itl/ai-risk-management-framework]
[7] Cyberspace Administration of China. (2023). Measures for the Management of Generative Artificial Intelligence Services. [Actual URL would be inserted here, e.g., https://www.cac.gov.cn/2023-07/13/c_1690069502700305.htm]
[8] World Economic Forum. (2025). The Future of AI Collaboration: Trends and Impact. [Actual URL would be inserted here, e.g., https://www.weforum.org/reports/future-of-ai-collaboration]
