The buildout of AI infrastructure is quickly becoming a strategic priority, not just for companies, but for national competitiveness. With trillions of dollars expected to be invested globally in AI over the coming years, the U.S. government has incentives designed to accelerate domestic development and maintain leadership in the space.
These incentives are meant to encourage the technical work required to build, scale, and sustain AI systems at a national level, from advanced computing environments and energy-efficient facilities to the underlying technologies that make those systems usable.
While semiconductors and data centers are part of the equation, software is what orchestrates and optimizes them to deliver value at scale. It enables AI infrastructure to function efficiently, manage workloads, reduce costs, and translate raw computing power into real-world capability. In many ways, it sits at the center of the AI ecosystem, connecting hardware, data, and applications to make large-scale AI possible.
This has important implications for software companies. The same activities required to stay competitive are often exactly the types of work these government incentives are intended to support.
Why Incentives Matter for Those Building AI Capabilities
For software companies, the cost of staying competitive in AI continues to rise. Engineering talent is expensive, computing demands are growing, and development cycles are accelerating. Meanwhile, customers and investors expect faster delivery of new capabilities.
To keep pace, many companies are investing heavily in experimentation, product development, and performance optimization. They are building AI-powered products, supporting AI infrastructure, and creating software that helps advanced hardware perform more efficiently.
What many don’t realize is that this work may qualify for government incentives.
The AI Supply Chain Extends Beyond Hardware
When most people think of the AI supply chain, they think of semiconductors, GPUs, and data centers. These components are critical, but hardware alone doesn’t create value.
Software drives how that hardware operates. It manages workloads, optimizes performance, and enables AI systems to function at scale. The software layer supports nearly every part of the AI ecosystem, from training models to connecting infrastructure and end users.
Without it, even the most advanced hardware wouldn’t deliver meaningful results.
Many Software Companies Are Solving New Problems
AI development rarely follows a clear roadmap. Teams spend months reducing latency, improving model performance, or lowering infrastructure costs. Others focus on optimizing GPU workloads or building proprietary applications for industry-specific challenges.
The outcome is uncertain, and solutions are not always obvious. Teams test, iterate, and refine until something works. Some efforts succeed, others don’t. But that’s part of the process. In a competitive market, even modest gains can create advantages. More efficient systems reduce costs, and better models win customers. For software companies in AI, standing still isn’t an option.
Why Business Leaders Should Pay Attention
Software companies must balance innovation with cost control, talent acquisition, and growth expectations. As a result, executives are examining internal activities more closely.
What appears to be routine development may involve significant experimentation, technical uncertainty, and resource investment. For companies supporting AI infrastructure or building AI-enabled products, that distinction may be worth exploring.
If your company is developing AI software, supporting infrastructure, improving hardware performance, or using AI to accelerate development, you may already be playing a role in the broader AI supply chain. The key question is whether your organization is capturing the full incentive value of the work it already performs.
