Nvidia Faces Potential Margin Pressure as Big Tech Develops Internal AI Chips

By COINOTAG

Publication date: 2025-10-20 | Last updated: 2025-10-20

Nvidia faces mounting pressure as Big Tech builds internal AI chips to reduce reliance on its GPUs. Google, Amazon, Meta, OpenAI, and others are pursuing custom silicon, risking tighter margins and lower external demand. The shift could reallocate a meaningful share of AI compute away from Nvidia, particularly for training workloads and large-scale data centers that today depend on high-end GPUs. At the same time, Nvidia’s ecosystem—comprising software, tooling, and scalable systems—continues to offer a compelling value proposition for customers who want end-to-end solutions and strong performance across diverse AI workloads.

OpenAI, which rents Nvidia chips through Microsoft and CoreWeave, has begun designing its own custom chips in collaboration with Broadcom. Meta Platforms disclosed plans to acquire Rivos, a chip startup, signaling a broader strategy to expand in-house silicon capabilities. On the cloud side, Amazon’s expansive data-center program, Project Rainier, is already “well underway,” with hundreds of thousands of Trainium2 accelerators powering AI workloads from partners like Anthropic. Analysts note that demand for Amazon’s in-house hardware has risen quickly, hinting at a future where cloud providers rely less on Nvidia’s GPU supply chain.

Big Tech turns inward with custom chip designs

While Nvidia’s GPUs continue to dominate the AI market, cloud providers are accelerating internal chip designs with players such as Broadcom and Marvell Technology. These processors are generally cheaper, tightly tuned to a provider’s software stack, and designed to manage total cost of ownership across large AI deployments. Because they are used internally rather than sold broadly, these chips give cloud operators greater price leverage when offering services to customers.

In a June research note, JPMorgan projected that Google, Amazon, Meta, and OpenAI will account for roughly 45% of the AI-chip market by 2028, up from about 37% in 2024 and 40% in 2025. The remainder would stay with GPU incumbents like Nvidia and AMD. Jay Goldberg, an analyst at Seaport Research, said the big hyperscalers are building custom silicon to avoid being “locked behind an Nvidia monopoly,” emphasizing that Nvidia now faces competition from its own customers.

There are signs of that shift. Google reportedly began selling its tensor processing units, or TPUs, to a cloud provider in September, placing it in direct competition with Nvidia on certain workloads. Gil Luria, an analyst at DA Davidson, estimated that Google’s TPU and DeepMind units could be worth as much as $900 billion, describing them as arguably among Alphabet’s most valuable businesses. He noted that Google’s chips remain a strong alternative to Nvidia, with the gap closing rapidly over the past year to year and a half. Goldberg expects a flurry of activity around custom silicon through 2026 as the AI chip supply chain continues to evolve, though he cautions that not every project will succeed.

Google’s TPUs have a long-running lead in the space, having evolved over more than a decade. Amazon moved into the arena after Google, acquiring Annapurna Labs in 2015 and releasing Trainium in 2020. Microsoft, with its Maia AI chip introduced in 2023, remains a step behind the leaders in scale and deployment. The diversity of timelines underscores that progress in custom silicon is uneven across the industry, with each hyperscaler pursuing distinct architectures and software optimizations.

Analysts warn of slower growth for Nvidia

Despite Nvidia’s still-dominant position in AI infrastructure software and hardware ecosystems, analysts warn that the competitive pressures from custom silicon could erode margins over time. David Nicholson of Futurum Group said the risk to Nvidia’s margins is real and could become “death by a thousand cuts” as multiple custom accelerators enter the market and broaden alternatives for customers. He argued that the broader AI compute opportunity remains sizable, but the mix is shifting toward tailored silicon that competes on cost and efficiency, not just raw performance.

When asked about the threat in a September podcast, Nvidia founder and CEO Jensen Huang downplayed the risk, stating that Nvidia remains the only company in the world that builds all of the chips inside an AI infrastructure. He underscored the company’s integrated approach, highlighting Blackwell GPUs, Arm-based CPUs, and networking units designed to work together across racks, rather than relying on single-chip offerings. That perspective aligns with Nvidia’s historically strong position as an end-to-end platform vendor, even as customers pursue internal accelerators.

Not all investors see an imminent threat to Nvidia’s growth. Vivek Arya of Bank of America and Gil Luria of DA Davidson both argued that the rise of custom silicon may not derail Nvidia’s trajectory. Arya noted that Nvidia’s total addressable market may expand as AI compute demand grows, citing PitchBook data that show substantial investments in AI and neocloud initiatives from 2020 through September 2025. Luria added that the AI opportunity remains large and that demand for compute—both for training and inference—could outpace supply for many years, potentially allowing Nvidia to grow even as a portion of spend migrates to custom silicon. Still, Goldberg cautioned that designing and integrating new silicon alternatives is complex, and not all efforts will succeed, potentially creating a mixed outcome for the sector.

As the industry weighs these dynamics, Nvidia’s strategy continues to emphasize a complete stack approach—hardware, software, libraries, and orchestration tools that enable customers to scale AI workloads across data centers. The company has long argued that its platform advantage—encompassing performance, reliability, and a robust software ecosystem—will enable it to defend a meaningful portion of the compute market even as custom silicon proliferates. The evolving landscape will likely shape Nvidia’s product roadmap, partnerships, and pricing over the next several years.

Frequently Asked Questions

What is the potential impact on Nvidia’s earnings if more Big Tech builds internally?

Analysts expect some margin pressure as custom silicon reduces external GPU demand. The exact effect depends on how Nvidia expands its own platform offerings, integrates new technologies, and maintains customer lock-in through software and ecosystem advantages. While near-term margins may face headwinds, the long-term growth of AI compute could still support Nvidia’s leadership in end-to-end solutions.

Could Nvidia respond by accelerating its own silicon initiatives or partnerships?

Nvidia may continue to strengthen its software and platform stack while pursuing selective collaborations to ensure compatibility with customers’ custom silicon architectures. Huang has stressed the importance of integrated systems, which could inform a strategy that emphasizes cohesive hardware, software, and networked infrastructure rather than a pure single-chip race.

Key Takeaways

• Takeaway 1: Internal AI chips threaten external GPU demand and could compress Nvidia’s margins
• Takeaway 2: Custom silicon is cheaper and software-tuned, driving cost control for hyperscalers
• Takeaway 3: Analysts expect a meaningful market-share shift by 2028, with Nvidia defending a scaled but evolving role

Conclusion

Nvidia remains a central pillar of AI infrastructure, but the ascent of custom silicon from Google, Amazon, Meta, and OpenAI signals a structural shift in the AI compute landscape. The company’s future success will likely hinge on its ability to extend its platform advantages, maintain a broad software ecosystem, and adapt to evolving customer needs. As the market recalibrates, investors should watch how cloud-provider silicon initiatives affect pricing, supply dynamics, and Nvidia’s ongoing investments in AI systems. COINOTAG will continue to report on these developments as they unfold.

Author: COINOTAG