OpenAI Asked the Trump Administration to Expand a 35% Chips Act Tax Credit

In a significant policy move, OpenAI asked the Trump administration to expand the 35% Chips Act tax credit to include artificial intelligence (AI) infrastructure — particularly data centers, energy systems, and AI-focused semiconductor manufacturing. The proposal highlights the escalating costs of building and running advanced AI systems, which now rival those of national-scale industrial projects.

Why OpenAI Is Pushing for Expansion

Currently, the Chips and Science Act, signed into law in 2022, provides substantial incentives for domestic semiconductor production, including a 35% tax credit on capital investments in chip manufacturing. However, OpenAI argues that these benefits should also apply to AI compute infrastructure, which forms the backbone of innovation in areas such as machine learning, robotics, and generative AI.

In OpenAI’s submission to U.S. policymakers, the company emphasized that the cost of AI infrastructure — from high-performance GPU clusters to advanced cooling and energy systems — has become a major bottleneck in scaling up research. By expanding the tax credit, the administration could help ensure that the U.S. remains globally competitive in the AI race against regions like Europe and East Asia.

According to industry estimates, building a top-tier AI data center today can exceed $10 billion in total investment, largely due to hardware and energy expenses.

A Strategic Play for the U.S. AI Ecosystem

OpenAI’s request aligns with the broader goal of strengthening America’s AI leadership and ensuring that large-scale model development remains rooted in the U.S. economy.
The company’s position also reflects growing concern that without targeted incentives, AI research and production might migrate overseas, where costs are lower and regulatory barriers are fewer.

If approved, the expansion of the 35% tax credit could dramatically reduce the capital burden for U.S.-based AI firms, encouraging private investment and public–private partnerships.

For context, the Chips Act has already mobilized over $200 billion in private-sector investment since its inception (whitehouse.gov/chipsact). Extending similar support to AI infrastructure could potentially multiply the innovation output across industries — from healthcare and education to manufacturing and cybersecurity.

The Cost Challenge: AI’s Energy and Compute Demands

One of the primary drivers behind OpenAI’s appeal is the explosive energy demand of AI systems. Training state-of-the-art models like GPT-5 can consume millions of kilowatt-hours of electricity and require tens of thousands of GPUs, each costing thousands of dollars.
In addition, energy efficiency and sustainable compute have become national priorities, given AI’s projected carbon footprint growth of over 150% by 2030, according to clean tech analysts.

Expanding the Chips Act credit could help AI companies invest in greener energy solutions, such as advanced cooling systems, carbon-neutral data centers, and AI-optimized chip fabrication within U.S. borders.

Building a Foundation for the Next Decade of AI

Supporters of the proposal believe that this move could help cement U.S. dominance in AI infrastructure and attract global AI research investments.
It would also accelerate projects focused on AI safety, alignment, and multimodal reasoning, which depend heavily on large-scale compute resources.

Moreover, aligning AI infrastructure incentives with semiconductor policy could foster synergy between chipmakers and AI labs, creating an integrated ecosystem for innovation — something OpenAI considers crucial for the next wave of breakthroughs.

Conclusion: A Policy Test for AI Leadership

OpenAI’s request to extend the 35% Chips Act tax credit signals a pivotal moment in U.S. technology policy. As global competition intensifies, this proposal challenges policymakers to decide whether AI compute infrastructure deserves the same national support as semiconductor manufacturing.

If enacted, this expansion could mark a transformative step in lowering AI infrastructure costs, fostering innovation, and ensuring that the future of AI remains built — and trained — in America.