Nvidia's position as the undisputed king of AI hardware looks unassailable right now. The company commands more than 90% of the data-center GPU market and over 80% of AI processors, according to industry analysts. Its stock has soared more than 200% in the past year, making it the world's first $5 trillion company by market capitalization.
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But here's the thing: the very cloud giants that made Nvidia rich are now quietly building their own escape routes.
Microsoft's chief technology officer Kevin Scott said in an October 2025 fireside chat that up to this point, Nvidia has offered the best price-performance for AI workloads. But then he added the kicker: Microsoft is willing to entertain anything to meet demand. That "anything" includes shifting the majority of its AI workloads from Nvidia GPUs to its own Maia AI accelerators, which the company first revealed in late 2023.
Microsoft isn't alone in this quiet rebellion. Google has been building custom tensor processing units (TPUs) since 2018, and Amazon has its Trainium and Inferentia chips. These aren't just side projects anymore. In November 2025, Nvidia shares fell 4% after reports surfaced that Meta was considering using Google's TPUs in its data centers by 2027. The Information reported that Meta might even rent TPUs from Google's cloud unit as early as next year.
And Meta isn't the only one diversifying. Google has quietly signed a deal to supply up to one million TPUs to Anthropic, the AI safety company behind Claude. That agreement represents perhaps the strongest validation yet that TPUs have matured into a credible alternative to Nvidia's GPUs, not just for inference, but increasingly for training frontier models.
What's happening here is the beginning of a fundamental shift in how AI compute gets delivered. For years, Nvidia benefited from what economists call a "platform effect." Its CUDA software ecosystem became the de facto standard for AI development, making switching costs prohibitively high. Researchers loved it because their existing code just worked, only faster with each new GPU generation. But that advantage starts to erode when your biggest customers decide they'd rather own the whole stack.
The economics are becoming impossible to ignore. Nvidia's latest Blackwell GPUs reportedly cost above $30,000 per unit, with full rack systems such as the GB200 NVL36 costing around $1.8 million and the NVL72 around $3 million. When you're deploying tens of thousands of these chips, as Microsoft, Google, and Amazon are, those numbers add up fast. Custom silicon offers hyperscalers two critical advantages: cost control and performance optimization for their specific workloads.
Microsoft's Maia 100 accelerator, for instance, was able to free up GPU capacity by shifting OpenAI's GPT-3 workloads. The company is reportedly bringing a second-generation Maia to market next year that will offer more competitive compute, memory, and interconnect performance. Google Cloud, meanwhile, is experiencing accelerating demand for both its custom TPUs and Nvidia GPUs, but the company has made clear it's committed to supporting both paths.
Amazon is making similar moves. Anthropic's Claude Opus 4 now runs on AWS's Trainium2 chips, with the company's Project Rainier deployment using more than 500,000 of them. These are workloads that, just two years ago, would have gone exclusively to Nvidia. AWS is already preparing Trainium3 for later this year, promising double the performance and 50% better energy efficiency. The hyperscalers aren't just hedging anymore, they're actively shifting production workloads onto their own silicon.
This isn't just about saving money, though. It's about control. As AI moves from the training phase to widespread inference deployment - where models actually get used by millions of people, the hardware requirements change dramatically. Training costs scale with the number of researchers, but inference costs scale with the number of users. And there are a lot more users than researchers.
While Nvidia continues to post record growth through 2025, industry insiders suggest the company will eventually face new competitive pressures as custom chips mature, though the timeline for any huge market share shifts remains uncertain. Growth after 2026 will need another large inflection point, something which, of course, may never come. The question isn't if Nvidia will face a slowdown, but when.
Even Wall Street is starting to notice the cracks in the armor. Recently, DA Davidson upgraded Nvidia from "neutral" to "buy" earlier this year, but that move came after the firm had projected as much as a 48% downside for the stock. The analysts acknowledged that their outlook had "shifted quite a bit" due to the rapid pace of AI adoption across industries, but they also cautioned that AI applications needed to mature to justify the billions flowing into Nvidia's chips.
The competition isn't just coming from hyperscalers building their own chips. AMD and Intel are actively positioning themselves as viable alternatives to Nvidia's H100 and newer Blackwell platforms. AMD stock has rallied nearly 59% in the past year, reaching a market cap of $359 billion that dwarfs Intel's struggling $197 billion valuation. While neither company threatens Nvidia's dominance in training workloads, the inference market, where AI models get deployed at scale is becoming increasingly competitive.
Here's where things get really interesting: the AI chip market is starting to look like the smartphone market did a decade ago. Early smartphones used off-the-shelf processors, but as the market matured, companies like Apple started designing their own chips optimized for their specific needs and software. The result was better performance, better battery life, and better integration. We're seeing the same pattern emerge in AI infrastructure.
OpenAI's reported $10 billion partnership with Broadcom to develop custom AI chips signals that even AI companies themselves want to control their destiny. The partnership, with mass production targeted for 2026, would mark OpenAI's transition from a buyer of compute to a direct architect of silicon. Broadcom's custom silicon business has quietly become one of the most profitable in the sector, with Google TPU revenue alone growing into billions.
Yet OpenAI isn't putting all its eggs in one basket. In September 2025, Nvidia and OpenAI announced a $100 billion partnership that reveals the tangled economics of AI infrastructure. Under the deal, Nvidia invests heavily in OpenAI, which then uses those funds to buy Nvidia chips for new data centers. The circularity is striking: for every $10 billion Nvidia invests, OpenAI is expected to purchase roughly $35 billion in Nvidia hardware.
It's a clever arrangement that locks in demand while giving OpenAI the capital it needs to scale, but it also highlights just how dependent both companies have become on each other.
So what does this mean for the broader AI ecosystem? First, we're likely to see a bifurcation in the market. Nvidia will continue to dominate the high-performance training segment, where flexibility and raw compute power matter most. But for inference workloads, which will eventually dwarf training in total compute consumption, custom chips optimized for specific models and use cases will gain traction.
Second, the economics of AI deployment will change. As inference becomes the dominant cost center, hardware priorities will shift toward reducing inference costs rather than maximizing training speed. This could benefit traditional CPU platforms and specialized inference accelerators over general-purpose GPUs.
Finally, the supply chain dynamics will evolve. Companies that control their silicon have more flexibility in managing supply constraints and can optimize their hardware-software stack end-to-end. This vertical integration becomes increasingly valuable as AI models become more complex and compute-intensive.
Nvidia isn't going anywhere, of course. The company has responded to these trends by forming its own custom chip division targeting a $30 billion ASIC market. Its software ecosystem remains incredibly valuable, and its GPUs will likely remain the gold standard for AI research and development for years to come.
But the era of Nvidia's near-total dominance may be peaking. As one industry insider put it, "The question isn't if NVIDIA will face a slowdown, but when." With hyperscalers building their own chips, AI companies designing custom silicon, and competition heating up in the inference market, 2026 could mark the high-water mark for GPU-centric AI infrastructure.
The next phase of AI hardware won't be about who has the fastest GPUs, but who can deliver the most efficient, cost-effective compute for specific workloads. And in that race, having control over the entire stack : from silicon to software, might just be the ultimate advantage.
