Intel's (INTC +4.88%) stock has climbed an impressive 122% so far in 2026, a surge that I think is still a catch-up trade after years of underperformance. The company's first-quarter results offered compelling evidence that Intel's focus on advanced process technology, domestic manufacturing scale, and specialized silicon are starting to pay off at the exact moment when global artificial intelligence (AI) demand is shifting from experimentation to practical industrial realities.
As investors grapple with questions about which companies will actually supply the compute fabric for the next decade of intelligence, Intel's most recent numbers suggest this is not a one-off rebound but rather the opening chapter of a multiyear rally rooted in the transformation from a struggling chipmaker into a foundational layer of the AI infrastructure revolution.
Image source: Intel.
Analyzing Intel's monster Q1 performance
Intel's three most strategically important businesses witnessed robust growth during Q1. The company's combined Data Center and AI (DCAI) group grew 22% year over year, generating $5.1 billion in revenue.
Growth in the DCAI segment was driven by a wave of new Xeon 6 processor deployments with Alphabet and Nvidia, as well as the ramp of Gaudi 3 clusters utilized by major cloud computing platforms such as Amazon Web Services (AWS) and International Business Machines. In addition, Intel's application-specific integrated circuit (ASIC) division doubled year over year -- an encouraging sign that the company is positioned to benefit from rising demand for custom silicon across hyperscale workloads.
At the same time, Intel's Foundry segment grew 16% year over year to $5.4 billion as the company's 18A process node moved into high-volume production amid surging demand for AI-driven advanced packaging. This newfound scale combined with Intel's improving factory yields suggest a much-needed turning point is materializing for the company's manufacturing turnaround efforts.
NASDAQ: INTC
Key Data Points
What does the next decade look like for Intel?
Looking out to the next decade, Intel's positioning looks formidable thanks to deepening technical collaborations with big tech combined with support from the U.S. government.
Take Nvidia as an example. While Nvidia's dominance in training AI models is undisputed, the cost economics of large-scale inference seem to favor a heterogeneous approach. These dynamics have essentially forced Nvidia and Intel to move beyond a polite coexistence and actively co-engineer the next phase of advanced packaging and interconnect technologies.
By doing so, Intel is strategically embedding its core Xeon central processing unit (CPU) platform within Nvidia graphics processing unit (GPU) architecture to build a unified AI system. In turn, Intel is better able to secure a seat at the table in Nvidia-centric chip deployments rather than being shut out entirely.
Meanwhile, the U.S. government has been pushing for sovereign AI infrastructure for a few years now. This mindset has turned Intel's foundry into a national asset, helping the company secure billions in CHIPS Act funding. In theory, federal support could help ensure reliable demand and de-risk the capital-intensive nature of designing and building next-generation AI fabs.
For Intel, broadening its relationships with other leading AI developers combined with a favorable view from the government should serve as the foundation of a multi-year runway featuring subsidized capacity utilization, accelerating government and enterprise contracts, and a competitive moat that foreign rivals struggle to cross.
Intel and the AI inference supercycle
In my eyes, the hidden macro theme fueling Intel's growth for the foreseeable future is the impending explosion in AI inference. While training AI models is a one-time process, inference is more of a continuous, compounding workload that runs around the clock across millions of applications.
As the adoption of more sophisticated applications scale, customers will discover that relying solely on GPU clusters is prohibitive for most inference use cases. Instead, optimizing CPUs alongside GPUs can deliver the optimal balance of performance, power efficiency, and total cost of the chip stack.
The macro backdrop only amplifies Intel's opportunity: According to data compiled by McKinsey & Company, global AI infrastructure capital expenditure (capex) is expected to accelerate into the trillions of dollars annually by 2030, with inference set to claim the major share of this spending. This means that every incremental dollar spent on inference expands Intel's addressable market. In turn, the company's revenue visibility should improve as more long-term supply agreements are secured.
While it's still quite early in the AI infrastructure era, Intel is doing an impressive job of aligning its technology, manufacturing, and strategic partnerships with two key forces that will define the next decade of accelerated computing: sovereign AI infrastructure and the inference supercycle. As these forces come into focus, Intel no longer needs to chase relevance. Rather, the company is poised to become a key supplier of foundational computing horsepower to AI's most influential businesses for years to come.
