Abstract

The development of artificial intelligence (AI) in the U.S. has reached a stage where fundamental limitations—shortages of computing power, a workforce deficit, and increasing global competition—threaten to slow technological progress.

This article examines the key challenges facing the industry, their causes, and potential strategies for overcoming them.

We analyze data on chip shortages and computational resource constraints, difficulties in training AI specialists, and the impact of the global technology race, particularly from China.

The article proposes solutions, including innovations in computing, regulatory measures, and international collaboration strategies.

1. Introduction

Artificial intelligence is no longer just a technology—it is a fundamental force shaping the economy, science, and society.

The U.S. has long been a leader in this field, but its status as an “AI superpower” can no longer be taken for granted.

Today, the industry faces three primary challenges:

• Shortage of computing power– a lack of GPUs, limited semiconductor supply, and the high energy consumption of supercomputers.

• Workforce shortage– an insufficient number of engineers and researchers, difficulties in talent development, and restrictions on attracting foreign specialists.

• Growing international competition– China, the EU, and other regions are heavily investing in AI, rapidly closing the technological gap.

These challenges require systemic solutions. Let’s explore them in detail.

2. Computing Power Deficit: A Threat to AI Advancement

AI models like GPT-4, Gemini, and Claude require enormous computing resources.

Training a single large model can take weeks or even months, consuming massive amounts of energy and requiring thousands of high-performance chips.

Key Issues:

1. Chip Supply Shortages

• The U.S. relies heavily on Taiwan (TSMC) and South Korea (Samsung), posing geopolitical risks.
• Domestic production (Intel, GlobalFoundries) is still unable to meet demand.

2. Energy Consumption

• Training AI models consumes megawatts of power. For example, training GPT-3 required as much energy as 1,200 homes use in a year.

• More energy-efficient solutions are needed: quantum computing, spiking neural networks, and neuromorphic processors.

Possible Solutions:

• Expanding U.S. semiconductor manufacturing(CHIPS Act, $52 billion in investments).

• Optimizing AI models– creating lighter architectures that run efficiently on weaker devices.

• Alternative computing approaches – quantum and optical processors.

If the computing crisis is not addressed, AI development in the U.S. will slow down, and leadership in the field may shift to other countries.

3. Workforce Shortage: A Crisis of Talent and Closed Doors

AI is not just about algorithms—it’s about the people who develop them.

However, the U.S. is currently facing a shortage of AI specialists in machine learning, mathematics, and computational sciences.

Facts and Figures:

• According to the Stanford AI Index 2023, there are over 300,000 AI-related job openingsin the U.S., but far fewer qualified professionals.

• 70% of students studying AI at top U.S. universities areinternational, yet visa restrictions force many to leave.

• China now produces more STEM graduates than the U.S. [(World Economic Forum, 2022)].

Main Barriers:

• Difficult training process – AI requires expertise in mathematics, programming, and neuroscience.

• Immigration restrictions – the H-1B visa system makes it difficult to attract top talent.

• Big Tech competition– companies like Google, OpenAI, and Meta recruit the best minds, leaving universities and government projects understaffed.

Solutions:

• Easing immigration policies for AI specialists.

• Expanding AI education and training programs in universities.

• Increasing government grants for researchers and startups.

If the workforce crisis is not resolved, the U.S.

risks losing ground to China, which is heavily investing in education and research.

4. Global Competition: China is Gaining Ground

China is making AI a national priority. The AI Development Plan 2030 aims to make the country the world leader in artificial intelligence.

China’s Advantages:

• Government-backed funding– billions of dollars invested in AI startups.

• Access to massive data sets– less strict privacy laws facilitate AI training.

• Supercomputer development– China has more supercomputers in the global top 500 than the U.S.

How Can the U.S. Respond?

• Increase investments in AI research.

• Strengthen strategic partnerships with the EU and India.

• Restrict the export of critical AI-related technologies.

Competition with China is not a future concern — it is happening right now.

5. Regulation, Ethics, and Emerging Risks

AI is evolving faster than regulations can keep up. Among the major concerns:

• Deepfake and disinformation– how can false AI-generated content be controlled?

• AI accountability– who is responsible when an autonomous system makes an error?

• Automation and job displacement– will AI replace millions of jobs?

The U.S. must find a balance between regulation and innovation to foster responsible AI development.

6. Conclusion

The U.S. AI sector is facing three fundamental challenges: a shortage of computing power, a workforce deficit, and increasing international competition.

These issues will not be solved overnight, but the future of the country’s technological leadership depends on addressing them.

To maintain its AI dominance, the U.S. must:

• Invest in semiconductor manufacturing and new computing technologies.

• Strengthen the AI education pipeline and simplify talent immigration.

• Build international alliances and regulate AI in a way that promotes innovation.

The world is entering a new technological race. The question is—who will emerge as the winner?

Dr. Vadym Chernets

AI Expert, New York