A Growing Challenge to Nvidia’s Dominance

Nvidia (NASDAQ: NVDA) has long reigned as the undisputed leader of the artificial intelligence chip market, powering everything from ChatGPT to cloud-based supercomputers. But a new trend among its own customers — the world’s largest technology firms — could threaten that dominance. Companies like Google, Amazon, Meta, and Microsoft are rapidly developing their own custom AI chips, signaling a shift that could reshape the semiconductor landscape and pressure Nvidia’s once-unrivaled profit margins.

Recent announcements highlight the accelerating pace of this shift. OpenAI revealed plans to co-design custom chips with Broadcom, while Meta confirmed the acquisition of chip startup Rivos to expand its internal silicon capabilities. Amazon’s massive Project Rainier, centered on its Trainium2 chips, is also well underway, with rising adoption from AI developer Anthropic. Each of these developments points to a strategic drive among hyperscalers to lessen their dependence on Nvidia’s expensive graphics processing units (GPUs).

Strategic Shifts Among Big Tech Players

The underlying rationale for in-house chip production is straightforward: control costs, enhance performance, and reduce reliance on a single supplier. According to JPMorgan, custom-designed chips will account for 45% of the AI chip market by 2028, up from 40% in 2025. While Nvidia and AMD currently dominate with their GPUs, Big Tech firms are steadily claiming larger slices of the market through proprietary chip architectures optimized for their software ecosystems.

Google remains the pioneer in this space with its tensor processing units (TPUs), which it has been developing for over a decade. Analysts estimate Google’s TPU business, when combined with DeepMind, could be worth as much as $900 billion. Amazon followed closely, acquiring Annapurna Labs in 2015 and launching its first Trainium chip in 2020. Microsoft, though a later entrant with its Maia AI chip introduced in 2023, has also begun scaling its AI infrastructure strategy.

This wave of in-house chip innovation represents not just a technical pivot but a strategic one. As Seaport analyst Jay Goldberg put it, “All the hyperscalers are looking at custom silicon because they don’t want to be stuck behind an Nvidia monopoly.”

“Death by a Thousand Cuts” for Nvidia?

Despite its leadership, Nvidia now faces what some analysts describe as “death by a thousand cuts.” While no single company poses an existential threat, the combined efforts of these hyperscalers could gradually erode Nvidia’s pricing power. Futurum Group’s David Nicholson argues that over time, Nvidia’s high margins may shrink as customers increasingly opt for lower-cost internal alternatives tailored to their specific AI workloads.

Yet Nvidia CEO Jensen Huang has publicly downplayed these concerns. Speaking in September, he noted that Nvidia is more than a chipmaker — it designs entire AI computing systems, from GPUs and CPUs to networking hardware. “We’re the only company in the world today that builds all of the chips inside an AI infrastructure,” Huang said, underscoring Nvidia’s integrated ecosystem as its competitive moat.

Even so, the dynamics of the AI chip market are changing rapidly. As cloud providers begin selling or renting out their own chips — as Google reportedly did in September — Nvidia’s traditional advantages could weaken, particularly in price-sensitive markets.

What Lies Ahead for Nvidia and the AI Chip Market

Analysts remain divided on whether custom chips will significantly threaten Nvidia’s future. Bank of America’s Vivek Arya and DA Davidson’s Gil Luria both contend that the overall AI market is expanding so fast that Nvidia’s growth trajectory will remain intact despite increased competition. However, others warn that as hyperscalers internalize more of their computing infrastructure, Nvidia may find its margins squeezed and its negotiating leverage diminished.

For investors and industry watchers, the next two years will be pivotal. By 2026, custom silicon production is expected to surge across the AI supply chain. Nvidia’s challenge will be to continue evolving beyond chips — into full-stack, integrated AI systems — to stay indispensable in a market it helped create.