Nvidia’s $1.4 Trillion Nightmare: Why Amazon’s $25 Billion Chip Dream Keeps Them Up at Night
The dominant player’s sky-high margins are attracting hungry rivals and ambitious internal teams.
Amazon.com Inc.’s chief executive officer Andy Jassy has begun to make an audacious boast during conference calls this year. Despite the company remaining a top customer of Nvidia Corp., it is also one of the world’s largest makers of data center chips in its own right. If Amazon’s semiconductor operation were a standalone business, it would generate an annual revenue of more than $25 billion, Jassy told analysts in July. And the sales are growing by a percentage in the triple digits. “We’re quite excited about what’s happening in our chips business,” Jassy said. This swagger underscores how much the semiconductor landscape is shifting and how quickly. While Nvidia remains the dominant maker of data center processors, particularly ones that help train artificial intelligence models, it faces increasing competition from all sides, including its biggest customers. Amazon and other cloud-computing providers, who remain the biggest buyers of Nvidia chips, have been developing in-house silicon designs and even selling them to third parties. Rival chipmakers such as Advanced Micro Devices Inc. and Broadcom Inc. are booking tens of billions of dollars in data center revenue. A raft of startups are raising billions more from investors keen to back the next breakthrough. Just in the past few days, Google forged a deal with Marvell Technology Inc. to further its chipmaking ambitions while Nvidia rival Cerebras Systems Inc. announced a new computer that it says can process AI prompts faster than any other. Anthropic PBC, meanwhile, has hired a Google silicon veteran at a time when it is plotting its own foray into chips. Nvidia, the world’s most valuable business, will be hard to catch. The company is still growing faster than many of its challengers. The company has also expanded into software, networking and other technologies to ensure it remains essential to the AI economy. But with the AI chip market poised to cross $1 trillion in revenue, the onslaught is only going to grow more intense. “Nvidia has a strong ecosystem,” said Hendi Susanto, a portfolio manager and analyst at investment firm Gabelli. Shifts in the market, like Amazon and Google suddenly selling their own chips to others, have been unpredictable, though. “That came on us relatively fast,” he said.
Nvidia’s earnings report on Wednesday will likely serve as another reminder of the company’s commanding lead. Analysts estimate that revenue nearly doubled last quarter from a year earlier to $92 billion. That is more money than any of its rivals get in a full year. However, the flood of cash with sky-high profit margins only makes its turf more attractive to potential predators. Meanwhile, some of Nvidia Corp.’s biggest customers have been told that the prices of servers containing its artificial intelligence chips are going up more than 15 percent in many cases, people familiar with the matter have said. The Santa Clara, California-based company did not respond to a request for comment. The threats to Nvidia can be divided into a few broad categories. On one side, existing chip competitors and a growing group of upstarts are both vying for a bigger share of the market. On the other, you have AI labs and so-called hyperscalers, the largest cloud-computing providers, designing their own chips.
Longtime rivals helped Nvidia ascend to the top of the semiconductor industry after developers realized that its graphics processing units, technology popularized by computer gamers, were well-suited for training AI models. Demand for these GPUs, also known as AI accelerators, exploded after OpenAI’s ChatGPT hit the scene in late 2022. That opened up an opportunity for Nvidia’s longstanding competitors. AMD, the second-largest maker of GPUs, saw its data center revenue more than double last quarter to $6.7 billion. And CEO Lisa Su expects the total market for AI accelerators to reach $1.4 trillion by 2030. Still, it is unclear how much of that category AMD will claim. Nvidia retains roughly 90 percent of the accelerator market. Intel Corp., which once eclipsed Nvidia in the chip industry, has attempted to sell its own accelerators with limited success so far. But it has benefited from the broader spending spree on AI data centers, fueling a resurgence in its sales and share price. Broadcom and Marvell, which make customized components for clients, have beaten their own path to the AI market. They are helping Google and other so-called hyperscalers, the biggest cloud-computing companies, develop chips to fill a fast-growing fleet of data centers. Then there are competitors in China, where Nvidia is grappling with trade restrictions. US export controls mean the company cannot sell its best products to Chinese buyers. And though Nvidia has lobbied hard to overturn them, the rules have lit a fire under Chinese efforts to develop domestic alternatives. Companies such as Huawei Technologies Co., blacklisted in the US, are making rapid strides toward developing products that are directly competitive, Nvidia has said.
A turning point in the AI industry is helping give Nvidia’s challengers a lift. Data centers are shifting from the training of AI models, a stage that involves bombarding them with data, to a phase known as inference. That is the point when the software begins responding to real-world inputs and spitting out responses. In other words, the AI models have been built and are now being put into use. Nvidia’s GPUs are an undeniably powerful tool for training models, but the inference stage is creating opportunities for a wider range of processors. That is set up a potential land grab by the chip upstarts. There are currently 150 companies working on more than 200 different AI semiconductor designs of one form or another, according to Jon Peddie Research. The idea is to tailor chips to handle inference faster or more efficiently. Products can also be fine-tuned to accommodate certain kinds of models. “Inference is not a one-size-fits-all, so brute-forcing inference with a single chip is not going to work,” said Sid Sheth, who is CEO of d-Matrix, a startup focusing on the space. Another motivation? A desire to keep Nvidia from being quite so dominant. The company charges about $30,000 per chip for its high-end accelerators. Its profit margins have expanded to a remarkable 75 percent, roughly 20 points wider than those of its nearest rivals. “The markets prefer a little bit of an oligopoly structure rather than a monopoly,” said Mitesh Agrawal, CEO of chip startup Positron. The public and private markets are awash with cash for anyone with a plan to make a dent in Nvidia’s lead. Cerebras held the semiconductor industry’s largest-ever initial public offering in May, raising a record amount in an oversubscribed debut. That company’s CEO, Andrew Feldman, has a simple pitch. His chips are much faster than Nvidia’s at providing an answer to an AI prompt. “This is a market that cares desperately about speed,” he said. OpenAI and Amazon have both forged deals with Cerebras, which relies on dinner-plate-sized chips to boost performance. At the same time, previously obscure startups like Positron, Fractile and Etched are attracting rapidly growing valuations in funding rounds aimed at helping them field novel chips.
None of this is easy. Designing semiconductors is not for the impatient, inexperienced or short of cash. Chips can take years to develop, at a cost of hundreds of millions of dollars or more, and companies rarely get them right on the first try. Sheth, of d-Matrix, has personally overseen about 20 chip tape-outs, the final stage in design before a company starts manufacturing a processor in mass quantities. “I don’t think it gets easier the 21st time,” he said. Hyperscaler companies, so named because they run computing operations with unmatched capacity, have a problem. They are adding data centers too quickly for the chip supply to keep up. That has made it a near-necessity to seek alternatives to Nvidia’s hardware. The sheer volume of money flowing into the sector has created an ecosystem where established giants and scrappy startups alike are attempting to disrupt the status quo. To understand the financial risk here, one must look at leverage. Nvidia’s customers are currently paying a premium for performance, but that premium funds the very research needed to build alternatives. As Amazon moves toward selling its own silicon to third parties, it captures a margin that would have otherwise gone to Nvidia. This shift from a vendor-client relationship to a competitive landscape fundamentally alters the economics of the cloud infrastructure market. The presence of 150 companies working on over 200 designs suggests a fragmentation of the market that favors those with the deepest pockets and the longest time horizons.
Google’s recent deal with Marvell Technology Inc. illustrates this trend. By partnering with established foundry partners, Google is not just buying hardware, it is investing in the supply chain that supports its own infrastructure. This insourcing strategy reduces dependency on a single supplier and secures capacity within a constrained global supply chain. Meanwhile, the focus on inference rather than training shifts the emphasis from sheer raw power to efficiency and cost-per-inference. This creates openings for companies that specialize in specific architectures or workloads. The emergence of a $1.4 trillion market by 2030 provides ample runway for these competitors, but it also means the winner-takes-most dynamic could persist longer than initially anticipated. The capital intensity of the semiconductor business means that the barrier to entry is formidable. While the hype cycle generates headlines, the engineering reality of creating a competitive AI chip requires sustained investment and expertise. The race is no longer just about who has the best graphics card for gamers, but who can solve the complex architectural challenges of artificial general intelligence. As Nvidia navigates this changing terrain, it must balance its current dominance with the need to maintain good relationships with its largest clients who also happen to be its potential replacements.
First reported by siliconvalley.com.
More in Business
- IMF Chief Warns of Economic Divergence and Debt Risks Amid Artificial Intelligence Boom
Kristalina Georgieva suggests that while AI could significantly boost global growth, it simultaneously threatens to widen inequality and exacerbate inflationary pressures and public debt burdens.
- California Limits Utility Profits and Expands Home Energy Role
Gov. Gavin Newsom signed laws capping utility returns on lower-risk projects and accelerating virtual power plant development.
- Lyft to Pay $272.5 Million in Record California Wage-Theft Settlement
Largest such settlement in state history resolves claims that Lyft misclassified drivers as independent contractors from 2016 to 2020, denying them core wage protections.