The core logic behind this policy direction is not to abandon regulation, but to avoid excessive regulation weakening America’s advantages in global AI competition.
Article author, source: ME News
TL;DR
Trump said the United States will not hinder the development of artificial intelligence through restrictive measures, and plans to establish an “AI task force,” appointing an “AI czar” responsible for AI affairs.
The core logic behind this policy direction is not to abandon regulation, but to avoid excessive regulation weakening America’s advantages in global AI competition.
The U.S. AI industry has already formed a supply-chain advantage centered on large models, chips, cloud computing, and capital investment, but competition is shifting from technical leadership to a comprehensive contest involving infrastructure, energy, talent, and the policy environment.
AI governance is showing clear divisions: some companies and researchers emphasize safety reviews and risk controls, while other tech firms worry that the pace of regulation is lagging behind the tempo of industrial competition.
The establishment of an “AI czar” means the U.S. government is trying to build a new coordination mechanism, but how much actual power this role will have in the future still depends on policy design and implementation.
Trump’s AI approach: not to ignore it, but not to want to manage new technologies with traditional regulatory methods
Artificial intelligence is becoming an increasingly important component of the U.S. technology strategy.
In September, Trump said publicly that the U.S. will not block the development of the AI industry, but instead hopes to strengthen oversight while supporting innovation. He announced plans to establish an “AI working group,” and will appoint in the future an “AI czar” responsible for AI affairs to coordinate AI-related matters across the government. (Reuters)
Trump defines AI as the “next industrial revolution” and says its potential impact could reach 25% of the U.S. GDP. This figure more reflects a political assessment of AI’s economic potential than an economic projection that has been fully validated, but it signals a clear trend: the U.S. government has already treated artificial intelligence as a strategic industry on par with the internet, semiconductors, and aerospace.
From a policy logic perspective, Trump’s remarks this time are not simply about “letting AI develop freely.” His core view is that the U.S. cannot slow down technological development out of concern for risks, or it may lose its advantage in global AI competition.
In recent years, discussions about AI governance in the U.S. have had two directions. One view holds that AI capability growth is happening too fast and stricter safety mechanisms are needed, including model testing, risk assessments, and corporate responsibility systems. The other view argues that AI is becoming an important part of national competitiveness, and if regulation is too early or too heavy, it could limit companies’ ability to innovate.
The Trump administration is clearly closer to the latter approach. Previously, the U.S. government has repeatedly emphasized the need to maintain leadership in the AI field and proposed reducing federal-level restrictions on AI development.
Behind this is, in fact, a debate about “how to manage a technological revolution.”
In the internet era, the U.S. built global technological advantages through open markets, capital investment, and corporate innovation. In the AI era, the U.S. wants to replicate that model. The difference is that AI is not just a commercial technology—it also involves data security, changes in jobs, information authenticity, and national security—so the government cannot manage the AI industry exactly the way it manages traditional internet companies.
Therefore, the emergence of an “AI czar,” at its core, is about finding a new balance: avoiding traditional regulation that would hinder industrial development, while also avoiding a complete absence of government.
Why does the United States place such high importance on AI competition?
AI is no longer just commercial competition among technology companies; it is gradually becoming industrial competition between countries.
Over the past decade, the U.S. has formed a relatively comprehensive advantage in the AI field. From foundational model development to AI chip design to cloud computing infrastructure, U.S. companies occupy an important position along the global AI industry chain.
Take the large-model field as an example: companies such as OpenAI, Google, Anthropic, and Meta have continued to invest billions of dollars to train models, and the underlying infrastructure depends on compute platforms like NVIDIA GPUs, Microsoft Azure, Amazon AWS, and Google Cloud.
At the same time, the AI industry is pushing capital markets to re-evaluate the value of technology companies.
According to Stanford University’s (AI Index Report 2025), in 2024 global private investment in AI reached $252.3 billion. Investment in generative AI in particular grew significantly, and the U.S. remains the world’s largest market for private AI investment. The report also notes that U.S. companies maintain the lead in the number of foundation models, model scale, and business applications.
But AI competition is no longer just a competition of model capabilities.
In recent years, the industry-wide question people have commonly focused on is: “Who can train stronger large models?”
Now, however, competition is shifting to more foundational issues:
Who has enough computing resources?
Who can secure a stable supply of energy?
Who can build larger-scale data centers?
Who can attract the world’s top AI talent?
Who can build a more stable policy environment?
This is also one of the key reasons the Trump administration emphasizes “not restricting AI development.” For the U.S., AI not only represents a new technology industry, but also relates to multiple areas such as future manufacturing, finance, healthcare, defense, and government efficiency.
If in the future AI becomes an important tool for improving production efficiency, then countries that control AI infrastructure and industrial ecosystems will gain new economic advantages.
What does an “AI czar” mean?
The U.S. government setting up a position similar to an “AI czar” is not the first time in history.
In the past, the U.S. coordinated policy in complex areas through roles similar to this. For example, in the space sector, specialized agencies helped drive national strategic goals, and in cybersecurity there have also been designated coordinators.
AI poses a challenge because it cuts across multiple government departments.
The Ministry of Commerce focuses on industrial development and export policies;
The defense sector focuses on military applications of AI;
The judicial branch focuses on AI-related crimes and responsibility determinations;
The education sector focuses on AI’s impact on learning methods;
Financial regulators are concerned about the application of AI in the financial system.
Without a unified coordination mechanism, different departments may create policy conflicts.
Therefore, the main value of an “AI czar” may not be to directly write all rules, but to build an AI strategy coordination hub within the government.
However, uncertainty remains about how much the role can achieve in the future. The information Trump has released so far mainly focuses on setting up the position and a working group, without disclosing specific powers, organizational structure, or the scope of regulation.
In the past, the U.S. also faced similar issues during its AI policymaking process. The government wanted to promote industrial development, but at the same time had to address concerns from society, businesses, and research institutions about AI risks.
If an “AI czar” is only a coordination role, its impact may be more about aligning policy directions. If, in the future, it gains stronger enforcement authority, it could become an important part of the U.S. AI governance system.
The core of the AI regulation debate: how to balance speed and safety
This time, Trump’s remarks further amplify the long-standing divisions in U.S. AI regulation.
Those in favor of strengthening regulation argue that the speed at which AI capabilities are improving has already outpaced society’s ability to adapt.
Especially after the rapid adoption of generative AI, issues such as deepfakes, automated attacks, and the spread of misinformation have begun to draw attention. Some AI researchers and company executives believe that before high-capability models are publicly deployed, stricter safety testing and risk assessments are needed.
For example, technology leaders such as Anthropic CEO Dario Amodei and OpenAI CEO Sam Altman have previously publicly discussed AI safety issues, arguing that as model capabilities improve, more完善 governance mechanisms need to be put in place.
Meanwhile, those who oppose excessive regulation argue that the AI industry is still in a phase of rapid development. If the government imposes too many restrictions in advance, it could slow companies’ innovation and put the U.S. in an unfavorable position in global competition.
This view is especially supported by some tech companies.
They believe that the development of AI is similar to past revolutions in the internet: early over-restriction could cause the industry to miss opportunities.
In fact, the two viewpoints are not completely opposed.
The real issue is not whether to regulate AI, but rather what stage to apply what level of regulation.
For mature industries, regulation typically revolves around consumer protection, market competition, and safety standards. But for rapidly developing technology areas, if the pace of rulemaking clearly lags behind technological change, regulatory failure can occur.
Therefore, the direction the U.S. is exploring right now is closer to “light regulation, strong coordination.”
Global competitive factors behind the U.S. AI strategy
Trump has emphasized that the United States needs to maintain its AI leadership position. One important background to this is U.S.-China AI competition.
Artificial intelligence has become an important arena of global technological competition.
The U.S. relies on advanced chips, foundation models, and an ecosystem of tech companies to maintain its advantage, while China promotes AI industry development through industrial scale, application scenarios, and policy support. (Reuters)
Competition between the two sides is not only reflected in model performance, but also in the chip supply chain, data resources, talent development, and the speed of commercialization.
Against this backdrop, the U.S. government worries that if the regulatory environment is overly strict, it could weaken the competitiveness of domestic companies.
Similar situations have already happened in the semiconductor industry. The United States has long maintained its chip competitiveness through technological advantages and industrial policy, while artificial intelligence is becoming the new strategic infrastructure.
Therefore, Trump’s proposal of “not blocking AI development” actually reflects an industrial competition mindset:
AI is not only a new technology, but also a future economic infrastructure.
Who can form a complete industrial ecosystem first may gain the next round of technological competitive advantage.
The real question in the AI era is not whether to develop or restrict, but who defines the rules
The development of artificial intelligence has entered a new stage.
In the past, when people discussed AI, they focused more on model capabilities—such as parameter scale, generation quality, and inference capabilities.
But as AI gradually moves into enterprise production, government services, and people’s daily lives, the truly important issues are changing:
Who is responsible for setting the rules?
Who bears the risks?
Who can ensure that technological benefits are shared by more people?
By pushing for an “AI czar” and an AI working group, Trump represents an effort by the U.S. government to build a new approach to governance: reduce traditional regulatory constraints while still keeping the government involved.
Whether this approach is effective ultimately depends on two factors.
First, can the government truly understand the rapidly changing AI industry, rather than using the management approaches of the traditional industrial era to handle new technologies?
Second, can companies build sufficiently mature safety mechanisms beyond pursuing commercial interests?
The development of AI will not stop because of a single policy decision, nor will it automatically become safe because of one regulatory approach.
In the coming years, the real challenges the U.S. and countries around the world face are how to find a new balance among innovation speed, industrial competition, and social risks.
The establishment of an “AI czar” is an important signal that the U.S. government is stepping into the governance of the AI era—but it is more like a new starting point than an answer in itself.
Reference sources
Reuters, “Trump says he will appoint a new AI adviser, without providing details”, September 2026.
Financial Times, “Donald Trump rejects calls from tech bosses for AI slowdown”, September 2026.
The White House, “Ensuring a National Policy Framework for Artificial Intelligence”, 2025.
Stanford Institute for Human-Centered Artificial Intelligence, “AI Index Report 2025”.
International Energy Agency, “Energy and AI”, 2025.
OECD, “Artificial Intelligence Principles and Policy Observatory”.
National Artificial Intelligence Initiative Office, “National AI Initiative”.