6G and AI: How Wireless Technology and Edge Devices Are Shaping the Global AI Race

Commentary by Matt Pearl

Published October 6, 2025

Technological revolutions result when practical insights combine with innovative engineering and risk-taking. Marty Cooper, who led the team that developed the first modern cell phone, was convinced-in the face of skepticism both inside and outside of his employer, Motorola-that millions of people would want to use the product he was developing. While many others believed that a cell phone would be viewed as a frivolous invention, Cooper pursued his quest based on two seemingly simple insights: "First, people are mobile," and "second, people connect with people-not places." Thus, Cooper understood that a modern commercial cell phone would not be seen as a novel gimmick but as a practical tool for people to connect as they moved through the world. His insights proved extraordinarily correct, with more than 700 million people owning cell phones by 2000. However, his efforts contributed to the mobile revolution and his company's bottom line only because they were combined with engineering insights and risky investments. Cooper led a team of brilliant engineers, and Motorola spent tens of millions of dollars developing the cell phone before it made a single dime on it.

Subsequently, the mobile phone industry underwent a second, equally significant revolution in the late 2000s. This transformation once again combined practical insight, engineering breakthroughs, and massive investments. The practical insight was that mobile phones need not function solely as phones but instead could be mobile devices capable of video streaming, video calls, and other applications. To make this happen, wireless networks needed to be upgraded to carry massive amounts of data rather than only voice traffic, with mobile carriers spending hundreds of billions of dollars acquiring spectrum and upgrading their networks. These advancements gave birth to the app economy and enabled numerous use cases that no one could have predicted, from ride sharing to on-demand therapy.

The wireless industry is now embarking on an evolution that may be of equal or greater magnitude. Appreciating the stakes requires understanding that the AI technological revolution will be inseparable from the evolution of wireless technology to 6G. As a result, it is one in which the United States, with its allies and partners, must take on a leadership role and will again require the combination of practical insight, technical skill, and massive investment.

6G springs from at least two insights, both of which have to do with the diffusion of AI. The first insight involves the types of devices that are served by mobile networks. Currently, wireless networks primarily serve two types of mobile devices: smartphones and tablets. Recent expansions have occurred in certain other types of devices, such as Internet of Things devices and fixed wireless terminals (which more than 7 million U.S. consumers use for home broadband), but this still constitutes a minority of wireless traffic.

As AI transforms virtually every type of operation in the physical world, other devices will play an equally significant role as the smartphone and the tablet. As a result, telecommunications networks will need to be smarter and more agile, capable of adjusting resources and meeting the unique needs of numerous other devices at the edge, including drones, autonomous vehicles, industrial robots, and various types of sensors. Additionally, consumers will increasingly use other types of mobile devices, including smart glasses and other wearables, such as pins, pendants, earbuds, and rings. While mobile networks will need to reach these new devices, smartphones and tablets will remain, with greater data needs than ever before due to AI, as people use these devices for new applications, including AI-powered photo and video editing and virtual agents.

While the demands of edge devices will transform mobile networks, there will be another, equally significant development in 6G: While wireless networks today are almost exclusively used for communications, in the future, they will increasingly perform other innovative functions. As wireless networks become fully software-defined, they will be able to run AI workloads on the same infrastructure that they use to carry data. This will be economically attractive because communications networks typically have spare capacity, since they must be designed to meet peak demand, which only occurs a couple of times per day. Thus, it will be possible to use them to handle computational workloads at the edge, near all those new devices. Therefore, with 6G, there may be a convergence of computing and communications, making mobile networks a distributed and global inference engine. Centralized data centers will continue to have a significant role in data processing, training, and inference, but wireless networks may have a larger place in the inferencing and rendering computation ecosystem.

Additionally, 6G will incorporate integrated sensing and communications (ISAC), meaning that the same networks that are used to transmit and receive data will also gather localized situational and environmental information about the physical world. These network-as-sensors services will enable new, innovative use cases such as the detection of adversaries' drones (an asymmetric threat that militaries are struggling to combat), smart traffic management, improvements in emergency response (e.g., locating victims trapped under rubble), and unobtrusive health monitoring. With the advent of AI-powered robotics, ISAC will enable users to precisely monitor and guide such machines.

What are the policy implications of all these developments? Although caution is warranted regarding how emerging technologies may affect policy at this stage, several policy ramifications are already clear:

• Global Stakes in AI and Telecom Leadership: The global races to control the future of AI and telecommunications are inextricably intertwined. Because wireless networks will perform many new functions, control over communications will affect which nations and companies control computing and ultimately the entire AI stack. If the United States and its allies and partners don't lead in the deployment of this infrastructure globally, then China surely will.
• Diffusing U.S. and Allied Technology: To effectively compete in the AI race, the U.S. government should incorporate telecom companies and networks into its effort to export the AI stack. It should focus on ensuring that U.S. and allied companies that have successfully competed in the market can diffuse their products and services globally, while avoiding the temptation to dictate specific outcomes. Thus, policies that encourage the global, market-based diffusion of such technology should be adopted, and policies that discourage it should be scrapped.
• Edge Devices as a Vital Frontier: This is not only a global competition over AI models, telecom networks, and data centers. While competing in those areas is critical, the United States and its allies and partners also should ensure that they are competitive in as many categories of edge devices as possible. All those AI applications will be run on such devices, and there is a risk that competitors and adversaries could leverage dominance over devices into control over the AI stack.
• The War for Talent: The AI race is also a factor because technical innovation is central to this competition; the United States is also in a competition for talent. The Trump administration should avoid adopting policies that make it harder to recruit highly skilled talent to the United States and should work with Congress to adopt policies that attract the best technical talent in the world.
• Success Requires Making Spectrum Available and Removing Barriers to Infrastructure Deployment: All the new innovative uses from AI will increase data traffic on mobile networks and, therefore, require access to new spectrum bands, many of which are currently used by federal agencies. The good news is that to address the increased demands of AI on mobile carriers in the One Big Beautiful Bill Act, Congress mandated that the federal government make a significant amount of licensed spectrum available. It is critical, however, for the United States to open the bulk of the spectrum required by the bill in large, contiguous blocks of mid-band spectrum by January 2030, and to ensure that mid-band spectrum in large blocks. By doing so, U.S. mobile operators can meaningfully deploy 6G when Europe, India, and China are expected to be deploying the technology. Removing barriers to deploying high-speed wireless infrastructure will help to ensure that the United States has the most extensive 6G deployments in the world.
• Strengthening Research-to-Commerce Production Linkages: Like AI, the path from research to production in 6G should be reduced significantly. Providing leading U.S. wireless researchers with sufficient resources, including tools that lower barriers to entry so that research can be demonstrated in the real world, is vital to these researchers getting adopted by global standards bodies.
• Policy Support for Risk-Taking: As was the case in Marty Cooper's time, for companies to succeed, they will need to make significant investments in capital. It will be critical for the Trump administration and Congress to create an environment that supports such risk-taking through consistent and predictable policy and, when appropriate, incentives such as tax breaks. To enable all facets of the AI and telecom ecosystem to thrive, these policies should be technology and end-result-neutral; for example, the government should provide support not only for massive new data centers but for smaller edge compute projects.

There is much to praise in the Trump administration's AI Action Plan. Delivering on its promises, however, will require effective implementation, and this in turn will necessitate recognizing critical policy dependencies, including recognizing the criticality of telecommunications networks, the competition for technical talent, the necessity of making spectrum available, the importance of research-industry linkages, and the paramount importance of government support for risk-taking.

Matt Pearl is the director of the Strategic Technologies Program at the Center for Strategic and International Studies in Washington, D.C.

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