Cracks in NVIDIA's AI Chip Monopoly? Big Tech's De-NVIDIA Rush

On February 12th, OpenAI launched GPT-5.3-Codex-Spark, a new coding model generating over 1,000 tokens per second. But what truly shook the industry wasn't the speed -- it was the fact that this model runs on Cerebras wafer-scale chips, not NVIDIA GPUs.

Beyond OpenAI, Google, Microsoft, Amazon, and Meta are all investing hundreds of billions of dollars in custom AI silicon. Is NVIDIA's de facto monopoly as the AI era's dominant chip supplier truly beginning to crack? Or will these challengers ultimately hit a wall against NVIDIA's entrenched ecosystem?

1. The Cerebras-OpenAI Alliance: Can It Shake NVIDIA's Grip?

Cerebras makes the world's largest chip. The WSE-3 (Wafer-Scale Engine 3) spans 46,225mm² and packs 4 trillion transistors -- 57 times larger than a typical GPU. The advantage of this massive chip is clear: data doesn't need to travel between chips, dramatically reducing inference latency.

On January 14th, Cerebras and OpenAI announced a multi-year deal worth over $10 billion, centered on building a 750MW wafer-scale system by 2028. A month later, the first fruit of that partnership arrived as GPT-5.3-Codex-Spark.

Market research firm Futurum called Cerebras "the single biggest threat to NVIDIA." For OpenAI, the Cerebras card is a strategic move to reduce dependency on any single chip supplier while securing tangible cost advantages in inference workloads.

2. OpenAI's Chip Diversification: Can It Really Work Without NVIDIA?

OpenAI's de-NVIDIA strategy doesn't stop at Cerebras. The company is developing its own chip called 'Titan' in collaboration with Broadcom, targeting mass production on TSMC's 3nm process by late 2026. Once OpenAI secures its own silicon, inference costs could drop dramatically.

OpenAI has also signed a multi-year supply agreement with AMD for Instinct 6GW chips, diversifying its GPU sources. Add a $38 billion cloud deal with AWS, and the intent is unmistakable: no more single-source dependency.

The driving force behind this strategy is economics. As the AI inference market explodes, NVIDIA GPUs alone can't meet demand, and costs are soaring. Unlike training, inference requires processing massive volumes of requests in real time, making power efficiency and unit cost critical. In this domain, there are growing signs that custom chips may hold a cost advantage over NVIDIA.

3. Google's Ironwood TPU: Can It Reshape the Inference Market?

Google isn't sitting idle either. Its latest TPU v7, 'Ironwood,' is the first Google chip built specifically for inference. Each chip delivers 4,614 TFLOPS at FP8 and carries 192GB of HBM. A SuperPod linking 9,216 chips produces a staggering 42.5 ExaFLOPS of inference performance.

Google has pledged $185 billion in AI infrastructure investment. By powering all its AI services -- including Gemini -- on its own TPUs, Google is systematically reducing its NVIDIA dependency. Anthropic has also announced plans to access one million Google Cloud TPUs, signaling that the TPU ecosystem is expanding well beyond Google's internal use.

4. Microsoft's Maia 200 and AWS Trainium3: Big Tech's Custom Chip Arms Race

Microsoft is deploying its custom AI accelerator Maia 200 at scale this year. Built on a 3nm process, it boasts 10+ PFLOPS at FP4 and is already running GPT-5.2. As it rolls out across Azure data centers, Maia 200 is becoming a key weapon for reducing NVIDIA GPU dependency.

AWS has also launched Trainium3, built on a 3nm process with 3x the performance of its predecessor Trainium2. Amazon aims to cut AI service costs on AWS by over 40% compared to NVIDIA-based solutions through its custom silicon.

AMD is also aggressively targeting the inference market. Its next-generation MI350 promises a 35x leap in AI inference performance. Meta, too, is developing its own MTIA (Meta Training and Inference Accelerator) chip. It's hard to find a Big Tech company that isn't building custom AI silicon at this point.

5. NVIDIA's Dominance: Is It Crumbling?

Despite all these challengers, NVIDIA is far from endangered in the near term. The latest GB300 (Blackwell Ultra) platform is projected to capture 70-80% of the 2026 AI server market. Its lead in training remains overwhelming, and the CUDA software ecosystem continues to serve as a formidable moat.

That said, the inference market tells a different story -- or at least, it might. There are signs that custom chips may be closing the gap with -- or in some cases matching -- NVIDIA on cost-performance in inference workloads. As AI shifts from training-heavy to inference-heavy, this segment's importance keeps growing. The key question is whether this represents a brief disruption or the start of genuine structural change.

The momentum across Big Tech toward custom silicon is increasingly hard to ignore. But developing custom chips takes years, and building a software stack to replace CUDA is arguably harder than designing the hardware itself. Between a single customer's defection and an industry-wide structural shift, there remains a significant gap.

Conclusion: A Real Crisis for NVIDIA, or Just a Blip?

The OpenAI-Cerebras alliance, Google TPU's inference-first evolution, Microsoft and AWS mass-producing custom chips, and OpenAI developing its own 'Titan' silicon. The scale and pace of these challenges are unprecedented. But whether this leads to the end of NVIDIA's monopoly, or merely a reorganization into a 'new coexistence' where NVIDIA remains at the center while alternatives emerge around it, remains an open question.

NVIDIA still dominates training, and the switching cost of the CUDA ecosystem is enormous. Meanwhile, custom chips are proving increasingly competitive on cost in inference workloads. What the AI chip market will look like in two to three years -- whether 'NVIDIA-centered order' holds or an entirely different landscape emerges -- is shaping up to be one of the most fascinating storylines to watch in tech.