Nvidia CEO Jensen Huang believes the artificial intelligence revolution is entering its next phase, one that could transform the semiconductor industry into a market approaching $7.9 trillion over the next decade.

Speaking about the rise of agentic artificial intelligence—AI systems capable of autonomously completing tasks and making decisions—Huang said the technology will require dramatically larger computing infrastructure than exists today. Considering the semiconductor industry was valued at roughly $792 billion last year, his projection implies nearly a tenfold expansion as AI adoption accelerates globally.

If that vision materializes, companies supplying the critical hardware behind AI computing could become some of the largest beneficiaries. Among them, Nvidia and Micron Technology stand out due to their leadership positions in AI processors and memory technologies.

Nvidia Expands Beyond Graphics Processors

Nvidia continues to dominate the market for graphics processing units (GPUs), the primary chips powering AI model training and inference across cloud data centers.

The company’s financial performance reflects that demand. During the first quarter of fiscal 2027, Nvidia generated revenue of $81.6 billion, an 85% increase from the previous year. Gross margins expanded to 74.9%, while adjusted earnings per share climbed 140% year over year to $1.87.

Management has projected approximately $1 trillion in cumulative purchase commitments for its Blackwell and Vera Rubin AI platforms through 2027, highlighting continued investment from hyperscale cloud providers and enterprise customers.

Nvidia is also broadening its AI ecosystem beyond GPUs. Its latest Vera architecture includes a standalone central processing unit (CPU), allowing the company to compete in an additional segment expected to become increasingly important as agentic AI workloads expand.

According to Nvidia, standalone CPUs represent a potential $200 billion addressable market, with the company forecasting roughly $20 billion in CPU revenue through the end of 2026. This reflects Nvidia’s strategy of offering complete AI infrastructure, including processors, networking equipment, software platforms, and integrated systems.

Despite its strong performance, Nvidia trades at approximately 22.9 times forward earnings, only modestly above the broader technology sector’s average valuation, suggesting investors continue to view its growth prospects favorably.

Micron Benefits From AI Memory Demand

Micron Technology occupies another critical position within the AI supply chain by manufacturing memory and storage solutions used in AI servers, cloud infrastructure, and advanced computing systems.

The company’s recent financial performance has been driven by extraordinary demand for high-bandwidth memory (HBM), a key component paired with AI accelerators to improve performance and energy efficiency.

During its fiscal third quarter of 2026, Micron reported revenue of $41.46 billion, representing approximately 346% year-over-year growth. Adjusted earnings per share increased more than twelvefold to $25.11 as constrained supply and strong pricing significantly expanded profit margins.

Industry conditions continue to support Micron’s outlook. Samsung Electronics recently indicated that the global memory shortage could persist until at least 2028, suggesting supply constraints may continue supporting elevated pricing for advanced memory products.

Micron has also secured multiple long-term supply agreements with customers, providing greater revenue visibility while helping reduce exposure to short-term fluctuations in memory pricing.

Despite its exceptional earnings growth, Micron currently trades at approximately 5.3 times forward earnings, reflecting investor caution over the historically cyclical nature of the memory industry.

Agentic AI Could Reshape Semiconductor Demand

Agentic AI represents a significant evolution from today’s generative AI applications.

Rather than simply responding to user prompts, AI agents are expected to perform complex workflows independently, requiring enormous increases in computing capacity, memory bandwidth, networking infrastructure, and storage.

If billions of AI agents become commonplace—as Huang predicts—the demand for advanced semiconductors would extend well beyond GPUs to include CPUs, memory chips, networking hardware, and complete AI systems.

This broader infrastructure buildout could benefit multiple segments of the semiconductor industry, creating opportunities for companies supplying complementary technologies rather than only AI accelerators.

Outlook

Jensen Huang’s projection of a $7.9 trillion semiconductor industry underscores the scale of investment that could accompany widespread adoption of agentic AI. Nvidia appears well positioned to capitalize through its expanding AI computing platform, while Micron stands to benefit from sustained demand for advanced memory solutions supporting next-generation AI infrastructure. Although both companies face differing valuation and cyclical risks, the continued expansion of enterprise AI deployments and data center investment suggests semiconductor demand could remain a powerful long-term growth driver for the industry.