Chip race: Microsoft, Meta, Google, and Nvidia battle it out for AI chip supremacy

Nvidia’s predicting it will pull in $108 billion in revenue within just a few months. It wouldn’t be the first company to rake in over $100 billion in quarterly revenue — Amazon, Apple, and Alphabet have repeatedly reached the milestone.

Nvidia said in its latest earnings report that it brought in a record $96.2 billion in overall revenue in the past quarter, a jump of over $10 billion from the previous quarter. Its data center revenue alone more than doubled year-over-year to a record $89 billion, and the company’s profits more than doubled to $59.7 billion.

OpenAI says its new AI chip, Jalapeño, completes tasks more efficiently and returns responses faster than other AI systems, according to a blog post published on Tuesday. During a briefing with reporters, OpenAI hardware vice president Richard Ho said Jalapeño offers the “best of both worlds” with lower latency and higher throughput, as AI systems typically “have to make a trade-off between the two.”

First introduced in June, Jalapeño is an Application-Specific Integrated Circuit (ASIC) made in partnership with Broadcom. It’s designed for AI inference — the process of running a trained AI model to complete a task or deploy an agent.

OpenAI has just revealed a new “intelligence processor” chip for AI servers made in partnership with Broadcom. The chip, called Jalapeño, is designed to power current and future large language models, according to an announcement on Wednesday.

Jalapeño is an ASIC (Application-Specific Integrated Circuit), meaning it’s designed for a specific purpose: AI inference. With AI inference, models process a user’s request to run an agent like Codex or offer a response from ChatGPT, while AI training involves a model consuming vast amounts of data to inform its responses.

Public pushback against data centers has emphasized their water and energy consumption, and now Nvidia is highlighting its claim that the Rubin generation reference design for a fully liquid-cooled data center has “eliminated massive amounts of power usage and pretty much all water usage.” Still, it doesn’t address all of the concerns around AI data centers, including during their construction, and for the power generation requirements of the massive facilities. Also, as Gizmodo points out, Nvidia’s blog post doesn’t mention the cost of building this style of data center vs. one using less efficient air cooling, but claims that “every cloud provider and data center operator building for [Rubin] is making the transition.”

The efficiency gains are partly due to running AI servers hotter, as high as 113 degrees Fahrenheit (45 degrees Celsius). In a recent report, Amazon similarly touted higher heat tolerances as part of making its mostly air-cooled data centers more efficient.

After decades of only licensing its chip designs for others to use, UK-based Arm revealed the first chip it’s producing on its own, and the first customer. Dubbed the Arm AGI CPU, it’s another chip designed for inference, or running the cloud processing for AI tools like AI agents that can continue to spawn more and more tasks to run at once. The first company in line to use it is Meta, which has reportedly struggled to launch its own AI chips.

Meta says it’s both the lead partner and co-developer, and plans to work on “multiple generations” of the data center CPUs, for use along with hardware from other vendors like Nvidia and AMD. Arm customers like Amazon AWS, Microsoft, Google, Marvell, Nvidia, Samsung, and others included congratulatory notes with the announcement. However, Qualcomm, which said it had achieved “complete victory” over Arm with a court ruling last fall in their case over the terms of licensing agreements, was not one of them.

Nvidia announced on Monday that it’s investing $2 billion each into Lumentum and Coherent, which are both developing photonics technology for data centers, like optical transceivers, circuit switches, and lasers, which are used to move data at high speeds over long distances. Their tech could improve energy efficiency, data transfer speeds, and bandwidth in future AI data centers, after Nvidia already capitalized on its 2020 acquisition of the network hardware company Mellanox to beef up NVLink and increase the amount of data moving between its GPUs.

For Lumentum, the nonexclusive multiyear deal includes a “multibillion purchase commitment and future capacity access rights for advanced laser components,” as well as support for expanding R&D and manufacturing. Coherent’s deal is described similarly, with a “multibillion-dollar purchase commitment and future access and capacity rights for advanced laser and optical networking products.”

OpenAI has closed another round of funding, totalling $110 billion being newly committed to the maker of ChatGPT, which it says has more than 900 million weekly active users and over 50 million consumer subscribers. Amazon is investing $50 billion and striking a deal that includes plans for custom models and more. Nvidia and SoftBank are each contributing $30 billion, as well, even as the Wall Street Journal notes that Nvidia’s previous $100 billion investment plan is “on ice.” This marks another massive influx of cash for the company that’s now valued at $730 billion, and previously closed a $40 billion round in 2025. At the time, it was the largest private tech deal on record.

The investment from Amazon is more than just an injection of cash. The companies are entering a partnership that will potentially allow Amazon to play catch-up in the AI market. The two companies will be collaborating on custom models intended to power “customer-facing applications” like Alexa.

The AI data center build-out, as it currently stands, is dependent on two things: Nvidia chips and borrowed money. Perhaps it was inevitable that people would begin using Nvidia chips to borrow money. As the craze has gone on, I have begun to worry about the weaknesses of the AI data center boom; looking deeper into the financial part of this world, I have not been reassured.

Nvidia has plowed plenty of money into the AI space, with more than 70 investments in AI companies just this year, according to PitchBook data. Among the billions it’s splashed out, there’s one important category: neoclouds, as exemplified by CoreWeave, the publicly traded, debt-laden company premised on the bet that we will continue building data centers forever. CoreWeave and its ilk have turned around and taken out debt to buy Nvidia chips to put in their data centers, putting up the chips themselves as loan collateral — and in the process effectively turning $1 in Nvidia investment into $5 in Nvidia purchases. This is great for Nvidia. I’m not convinced it’s great for anyone else.

AMD has sealed a $1 billion deal with the US Department of Energy to develop two supercomputers, Lux and Discovery, in collaboration with Oracle and Hewlett Packard Enterprise (HPE). Both supercomputers will live at Oak Ridge National Laboratory (ORNL) in Oak Ridge, Tennessee. Lux is slated to come online fairly soon in early 2026, with Discovery following in 2029.

Both build on the work that went into the Frontier supercomputer, which is also housed at ORNL and was the fastest in the world until El Capitan came online last year at Lawrence Livermore National Laboratory. AMD also helped develop those supercomputers, so this isn’t its first time working with the US government on a project like this.

Qualcomm is launching a pair of new AI chips in an attempt to challenge Nvidia’s dominance in the market. On Monday, Qualcomm announced plans to release its new AI200 chip next year, followed by the AI250 in 2027 — both of which are built on the company’s mobile neural processing technology.

The new chips are built for deploying AI models, rather than training them. The launch marks a notable change for Qualcomm, which has primarily made processors for mobile phones, laptops, tablets, and telecommunications equipment.

With Windows 10 on its last legs, Intel is looking forward to the PC industry growing more than it has in years — the most since 2021, when the covid-19 pandemic revived industry growth by creating a huge surge in demand. But it seems the struggling Intel, which just received lifelines from Nvidia, Softbank, and the US government, isn’t fully ready to take advantage and is prioritizing AI instead.

Today on the company’s Q3 2025 earnings call, where Intel saw its first profit in nearly two years due primarily to those lifelines, CEO Lip-Bu Tan and CFO David Zinsner explained how the company doesn’t yet have enough chips. It’s currently seeing shortages that it expects to peak in the first quarter of next year — in the meantime, leaders say they’re going to prioritize AI server chips over some consumer processors as it deals with supply and demand.