US President Donald Trump is scheduled to speak on AI on July 23 at an event titled “Winning the AI Race.”

Organized by White House AI adviser David Sacks, the event will outline Trump’s vision for maintaining US leadership in AI technology.

The speech will coincide with the anticipated release of an AI action plan later this month.

This directive, initiated by Trump shortly after taking office, has been developed with input from industry leaders. It has been guided by Sacks and White House technology adviser Michael Kratsios.

Following the plan’s release, Trump is expected to sign an executive order to implement its policies.

The Trump administration has highlighted the importance of AI in addressing competition from China, which has made substantial investments in the sector.

Trump is also set to visit Pennsylvania on Tuesday to announce new investments in AI and energy infrastructure.

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🔗 Source: Bloomberg

🧠 Food for thought

1️⃣ AI policy reflects the ongoing cycle of hype, restriction, and pragmatism

Trump’s upcoming speech continues a pattern of presidential AI initiatives that historically swing between enthusiasm and caution.

The prior Trump administration emphasized AI as a transformative technology in 2018, with priorities focused on funding research, removing regulatory barriers, and workforce training 1.

This cyclical approach mirrors the broader history of AI development, which has experienced multiple “booms” and “busts” since the 1950s, with periods of over-promising followed by “AI winters” when funding dried up after expectations weren’t met 2.

The reversal on Nvidia’s H20 chip sales to China demonstrates how even tough national security stances eventually confront market realities, as these restrictions threatened to push Chinese firms toward non-U.S. alternatives 3.

The policy correction aligns with Nvidia CEO Jensen Huang’s position that export controls were putting “U.S. competitiveness in global markets at risk,” a concern that appears to have resonated with the administration 4.

2️⃣ The US-China AI competition features fundamentally different strategic approaches

While the US leads in private AI investment with $109.1 billion in 2024, China is pursuing a comprehensive industrial policy across the entire AI technology stack 5.

China’s approach emphasizes accessibility and cost-effectiveness, as demonstrated by DeepSeek’s R1 model, which showed AI could be developed at much lower costs 6.

The Chinese government is promoting self-reliance through substantial funding for startups and state-backed AI labs, with a long-term strategy aiming for global AI leadership by 2030 7.

In contrast, the US strategy relies more heavily on private-sector innovation and dynamism, with companies like Nvidia (whose revenue reached $130.5 billion in fiscal 2025, up 114% year-over-year) driving advancement 8.

Despite different approaches, the performance gap between U.S. and Chinese AI models is narrowing, creating a genuine competitive landscape rather than clear dominance by either country 5.

3️⃣ Technology trade decisions increasingly reflect a complex balancing act

The Biden administration’s decision to allow Nvidia’s H20 chip sales to China represents a significant recalibration in how the U.S. approaches technology transfer in strategic sectors 9.

This policy shift attempts to balance multiple competing interests: maintaining U.S. technological leadership, protecting national security, and ensuring American companies can compete globally 10.

The timing aligns with Nvidia’s extraordinary financial performance, which saw quarterly revenue of $39.3 billion (a 78% year-over-year increase), demonstrating the massive economic stakes in AI chip markets 8.

This pattern of restriction followed by selective opening mirrors historical approaches to managing technology transfer during periods of great power competition, where complete isolation proves impractical 4.

The decision acknowledges that overly rigid export controls risk backfiring by accelerating China’s development of domestic alternatives. Chinese AI models are already rapidly closing performance gaps with U.S. models despite previous restrictions 7.

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