Google quietly acquires Qualcomm: the mobile chip giant sets its sights on data centers, aiming for annual revenue exceeding 15 billion dollars

On November 6 local time, Alphabet, Google's parent company, disclosed that as of the end of the third quarter of 2026, the company held approximately 2.977 million shares of chip giant Qualcomm, with a market value of about $548 million. The documents from the end of June did not include Qualcomm.

After the news was released, Qualcomm's pre-market stock price rose by more than 3%. Given that Qualcomm is trying to reduce its reliance on the mobile chip business, this new position was quickly linked by the market to Qualcomm's data center business: Qualcomm is attempting to transition from mobile chips to AI infrastructure, while Google is also expanding its computing power to meet the growing demand for AI.

In the latest fourth-quarter earnings report for fiscal year 2026, Qualcomm's CEO Cristiano Amon reiterated that the company plans to achieve $5 billion in data center revenue by fiscal year 2027; according to previously announced targets, this figure will exceed $15 billion by fiscal year 2029.

However, as more documents were uncovered, Alphabet's holdings in Qualcomm stocks also led to another interpretation.

According to The Information, this shareholding may be related to Qualcomm's acquisition of the American AI software startup Modular. Alphabet's investment arm GV had previously invested in Modular, and when Qualcomm acquired the company this year, it primarily used its own stock as the transaction consideration. In the SEC prospectus subsequently submitted by Qualcomm, two funds under GV were listed as selling shareholders of Qualcomm stock, holding a total of approximately 3.196 million shares.

However, this number does not match the 2.977 million shares disclosed by Alphabet, and the shareholding quantities in the two documents have not been fully reconciled.

### Qualcomm is Seeking Its Next Growth Point

For a long time, Qualcomm's business model has been closely tied to the smartphone market. As one of the world's major mobile chip suppliers, its Snapdragon processors are widely used in Android phones, and its baseband chips and communication patent licensing business have provided the company with long-term revenue.

However, as the smartphone market matures, this business structure has begun to face new pressures. On one hand, global smartphone shipments are growing at a slower pace, and phone manufacturers are increasingly focusing on cost control; on the other hand, Apple's continuous push for self-developed baseband chips also poses a long-term risk of customer loss for Qualcomm. The latest earnings report for the fourth quarter of fiscal year 2026 disclosed by Qualcomm has already shown this pressure: affected by the weakening smartphone business and other factors, its mobile chip revenue for the quarter fell by about 26% year-on-year, down to $5.14 billion.


(Source: Qualcomm)

In recent years, Qualcomm has gradually increased its investment in automotive, Internet of Things, personal computing, and industrial sectors, hoping to transfer the low-power computing, communication, and chip design capabilities accumulated in the mobile chip field to more scenarios.

However, with the development of generative AI, Qualcomm sees a larger market: data centers.

In the past, AI computing in data centers was mainly dominated by NVIDIA GPUs. But as the scale of AI applications expands, computing demands are also changing. Especially as AI moves from simple question-and-answer to intelligent agents, models need to continuously call tools, execute tasks, and handle more requests, leading to an increase in inference computing volume. For cloud service providers, in addition to chip performance, they also need to consider computing costs, energy consumption, and the overall operational efficiency of the data center.

This provides Qualcomm with an entry opportunity. Its long-accumulated low-power computing technology, as well as the collaborative design capabilities of different computing units such as CPU, GPU, and NPU, can theoretically play an advantage in certain data center workloads. Particularly in the AI inference market, as the scale of model deployment continues to expand, customers are increasingly focused on balancing performance, energy consumption, and costs.

In June 2026, Qualcomm officially announced its data center business roadmap at its investor day, launching the Dragonwing product line for data centers, including the C1000 CPU, AI300 inference accelerator, and high-bandwidth computing technology (HBC). Unlike traditional GPU routes, Qualcomm aims to provide computing solutions covering different aspects of data centers through a combination of CPU, AI accelerators, memory, and interconnect technologies. The company also stated that by fiscal year 2029, data center business revenue will exceed $15 billion, while non-mobile business revenue will reach $40 billion.

This means that Qualcomm is not satisfied with merely being a chip supplier; it hopes to extend the software and hardware synergy capabilities formed in the mobile terminal field to cloud computing infrastructure.

The acquisition of Modular perfectly fills the software gap in this strategy. This startup focuses on AI software infrastructure, with core products including the programming language Mojo and the AI inference platform MAX, aiming to solve software adaptation issues between different chip architectures, allowing developers to more easily deploy and optimize AI models on different hardware.

This is particularly important for Qualcomm. In the past, its advantages were more reflected in mobile terminals, and the developer ecosystem and software toolchain were areas that needed to be supplemented to enter the data center. After acquiring Modular, Qualcomm can integrate its software platform with its own chips, lowering the threshold for developers to use Qualcomm hardware.

### The Chip Competition in Data Centers

Qualcomm's entry into data centers is not an isolated business move. Over the past two years, the demand for AI computing power has continued to grow, and competition in data centers has gradually extended from simply purchasing GPUs to multiple aspects such as chip design, memory, network interconnect, and software ecosystems. Large technology companies are increasingly participating in chip design, hoping to reduce costs, improve energy efficiency, and decrease reliance on a single supplier through customized solutions.

NVIDIA still occupies the most important position. Its advantages lie not only in the GPUs themselves but also in the CUDA software ecosystem, network interconnect technology, and complete solutions from chips to entire systems. However, high procurement costs and supply constraints have also prompted large cloud vendors to seek alternative solutions.

AMD is competing for the GPU market with its Instinct series accelerators; Broadcom is focusing on designing customized AI chips (ASICs) for large tech companies. Compared to general-purpose GPUs, ASICs can be optimized around specific workloads, creating differentiation in energy efficiency, cost, and performance. Chip companies like Marvell are also seeking opportunities in network interconnect and customized computing.

At the same time, cloud computing giants are also beginning to design their own chips. Google has TPUs, Amazon has Trainium and Inferentia, Microsoft has Maia, and Meta continues to advance MTIA. For these cloud vendors, self-developed chips are not contradictory to external procurement; the key is to find more suitable hardware solutions for different computing tasks.

Qualcomm's entry is happening amid this round of industrial division changes.

After announcing its data center roadmap in June this year, Qualcomm began to accelerate its efforts to collaborate with large tech companies. In September, the company announced a multi-generation customized chip collaboration with Amazon Web Services (AWS), focusing on AI inference computing and data center optical interconnect. The collaboration shows that Qualcomm has begun to engage with customers that have large-scale computing power needs. Additionally, Qualcomm is also advancing its data center chip business with companies like Microsoft and Meta.


(Source: Qualcomm)

It is worth noting that Qualcomm's cooperation with Amazon is no longer just a traditional chip procurement relationship. According to Reuters, the agreement allows Amazon to purchase up to $60 billion worth of Qualcomm AI data center chips and related products in the future, while Qualcomm has granted Amazon warrants to purchase its own stock, involving up to 25 million shares. This means that Amazon's equity arrangement is linked to actual procurement business.

However, obtaining customer orders is just the first step into the data center market; whether it can deliver on time is also a real test. Qualcomm CEO Amon mentioned in an interview that the current construction of AI infrastructure faces supply chain constraints, and the production capacity of key areas such as advanced process chips is still limited. For Qualcomm, which has just entered this market, in addition to securing customers, it also needs to ensure that chip manufacturing and product delivery can keep up with demand.

At the same time, Qualcomm also needs to face NVIDIA, AMD, and Broadcom and Marvell, which have already deeply participated in the supply chains of large cloud vendors. It needs to prove not only chip performance but also software compatibility, system integration capabilities, and long-term supply capabilities.

### Google's Computing Power Landscape

If Qualcomm is seeking opportunities to enter data centers, then Google represents another typical case: it is both a huge demand side for AI computing power and a chip designer and cloud computing service provider.

Google began developing its own AI chips early on. In 2015, Google launched the first generation of Tensor Processing Units (TPUs), initially used to accelerate internal machine learning tasks. As the demand for large model training and inference grew, TPUs gradually became an important part of Google's AI infrastructure. Today, Google not only uses TPUs to support the training and operation of models like Gemini but also provides TPU computing power to external customers through Google Cloud.

However, self-developed chips do not mean that Google can completely eliminate external suppliers. Chip design is only one part of data center construction; processors, memory, network switching chips, high-speed interconnects, and servers all require the participation of different companies. Especially for customized AI chips, it often requires collaboration between chip design companies, foundries, and cloud service providers.

Broadcom is one of the most important partners in Google's ecosystem. The two have collaborated in the TPU field for many years, with Broadcom participating in the design and supply of multiple generations of TPUs for Google. In April this year, Broadcom disclosed a long-term agreement with Google to continue providing design and supply support for Google's future generations of TPUs, as well as network and other components for the next generation of AI server racks, with the collaboration potentially extending until 2031.

Alphabet previously held shares in chip design company Arm and established a partnership with Marvell involving customized AI chips. In August 2026, Reuters reported that Marvell provided Google with a potential stock purchase arrangement of up to $12.2 billion, with the related rights linked to the goals of their customized chip collaboration.

This means that Google is developing its own TPUs on one hand while also establishing connections with external chip companies in various forms. Broadcom participates in TPU design and supply, Marvell is involved in customized chip development, and NVIDIA GPUs continue to meet some computing needs. For Google, self-developed chips are not intended to completely replace external suppliers but rather to reduce reliance on a single supplier through multiple technological routes and partnerships, gaining greater flexibility in computing power allocation.

So, does Qualcomm have a chance to enter Google's ecosystem?

From a technical perspective, there are indeed some potential intersections between the two companies. Qualcomm's long-accumulated low-power computing, CPU design, and heterogeneous computing capabilities have certain synergies with Google's continuously expanding AI infrastructure. Especially with the growing demand for AI inference, data centers are increasingly focused on computing efficiency and unit token costs, which provides more application scenarios for the computing architecture Qualcomm hopes to promote.

However, this does not mean that Google has become a potential customer for Qualcomm's data center chips. Currently, there is no evidence in the public domain indicating that the two parties have reached a cooperation agreement regarding data center chip procurement or custom computing. The globally renowned venture capital and technology ecosystem data platform Dealroom also reminded that 13F filings only reflect what stocks institutions hold at the end of the quarter and do not disclose the timing, cost, or investment purpose of purchases.

The existing cooperation foundation between Google and Qualcomm mainly comes from the mobile terminal and automotive sectors. Their collaboration has lasted for over a decade, from Snapdragon chips and the Android mobile ecosystem to recent developments in the automotive and personal computing fields. In January 2026, the two parties announced an expansion of their cooperation in the automotive sector, combining Qualcomm's Snapdragon digital chassis with Google's Android Automotive OS and cloud services; in September, Google launched Googlebook, with Qualcomm's Snapdragon X Elite becoming one of its first processor platforms. The scope of cooperation has also extended from mobile terminals to more computing scenarios.

It is still unclear how much of Alphabet's disclosed stake is related to the Modular acquisition. However, this does not prevent us from observing the industrial layout of the two companies. Qualcomm is attempting to transition from mobile chips to data centers, while Google is continuously expanding its computing supply chain beyond self-developed chips.

Whether there are deeper industrial connections behind this equity stake remains difficult to conclude. However, it is certain that as the competition for AI computing power deepens, the relationship between chip companies and tech giants is changing. Whether Qualcomm can become a new player in the data center market and whether it can generate new collaborations in the computing power field with tech giants like Google is worth continued attention.

Reference links:

1. https://www.mobileworldlive.com/google/alphabet-reveals-548m-qualcomm-stake/

2. https://s204.q4cdn.com/645488518/files/content_files/FY2026-4th-Quarter-Earnings-Release.pdf

3. https://www.qualcomm.com/content/dam/qcomm-martech/dm-assets/documents/Investor-Day-2026_TPialis_Data-Center.pdf

4. https://www.qualcomm.com/news/releases/2026/06/qualcomm-unveils-comprehensive-data-center-roadmap-for-the-agent

5. https://www.reuters.com/business/alphabet-discloses-548-million-worth-qualcomm-stake-2026-11-06/

Note: The cover/featured image is AI-assisted generated.