Artificial intelligence dominated discussions throughout MWC 2026, but Keynote 6 moved beyond the usual conversations about deployment and market opportunity. Instead, the session examined a more fundamental question: how the systems being designed today—chips, networks, platforms, and identity frameworks—will shape the technological and societal landscape of the next several decades.
The keynote brought together leaders from across the technology ecosystem, spanning semiconductor design, telecommunications infrastructure, AI-powered service platforms, academic research, and public-interest technology policy. Each perspective illustrated a different layer of the emerging AI stack, revealing how computing, connectivity, and software are increasingly converging into a single integrated system.
What emerged from the discussion was a clear recognition that artificial intelligence is no longer simply a software capability layered on top of digital infrastructure. It is becoming a structural force that is reshaping the design of networks, devices, and digital platforms themselves.
Architecting 6G for the AI Era
Cristiano R. Amon – President & CEO, Qualcomm
AI as the Driver of the Next Wireless Evolution
Cristiano Amon opened the keynote by outlining how artificial intelligence is redefining the roadmap for wireless technology. While the global industry continues to expand 5G deployments, research and development for 6G is already underway. According to Amon, the next generation of wireless networks will be fundamentally different from previous generations because they will be designed from the beginning to support an AI-native digital environment.
Rather than focusing purely on higher data rates or lower latency, future wireless systems must support massive numbers of intelligent devices that continuously interact with both edge and cloud computing environments. In this context, connectivity becomes the enabling layer for distributed intelligence.
Amon explained that billions of connected devices—from smartphones and autonomous vehicles to industrial machines and consumer electronics—are increasingly capable of running sophisticated AI models locally. This shift toward on-device intelligence changes the demands placed on network infrastructure.
The Rise of Edge AI
One of the most significant changes highlighted by Amon is the rapid growth of edge-based artificial intelligence. Traditionally, most AI workloads have been processed in centralized cloud data centers. However, advances in chip design and machine learning optimization are enabling AI inference to occur directly on devices.
This evolution allows applications to operate with lower latency, improved privacy, and greater reliability. For example, autonomous systems, real-time translation tools, augmented reality applications, and advanced robotics require immediate decision-making capabilities that cannot rely solely on distant cloud servers.
As a result, networks must support a distributed computing model where intelligence operates simultaneously at multiple layers: device, edge, and cloud.
Amon described this as a transition from a “connected device ecosystem” to an “intelligent device ecosystem,” where AI-driven functionality becomes a core component of everyday digital experiences.
The Convergence of Connectivity and Computing
Amon also emphasized that the boundary between telecommunications networks and computing infrastructure is rapidly disappearing. Historically, wireless networks were designed primarily to transport data between devices and centralized servers. In the AI era, networks themselves are increasingly expected to participate in computational processes.
Future networks may support distributed AI workloads, dynamically allocate computing resources across edge nodes, and enable collaborative intelligence between devices. This integration will allow applications to process large volumes of data in real time while maintaining energy efficiency and performance.
The implication is that wireless infrastructure will evolve from a passive transport layer into an active participant in digital intelligence systems.
Toward AI-Native Networks
Looking ahead to 6G, Amon suggested that next-generation networks will likely incorporate advanced sensing capabilities alongside traditional communication functions. By integrating sensing, computing, and connectivity into a unified platform, future networks could enable entirely new classes of applications.
Examples include immersive digital environments, advanced industrial automation systems, precision location services, and intelligent infrastructure capable of interacting dynamically with the physical world.
These developments will require extensive collaboration across the global technology ecosystem. Semiconductor manufacturers, network operators, software developers, and device makers must work together to ensure interoperability and scalability.
Ultimately, Amon argued that wireless technology is entering a new phase of evolution. Instead of merely connecting people and devices, networks will become the foundation for distributed intelligence operating at planetary scale.
Global Infrastructure and the Intelligent Network
Chaobin Yang – Executive Vice President, Huawei
AI and the Transformation of Digital Infrastructure
Chaobin Yang followed by examining how telecommunications networks must evolve to support an increasingly intelligent digital economy. Yang described artificial intelligence as a powerful catalyst for digital transformation across industries, from manufacturing and healthcare to transportation and financial services.
As organizations adopt AI-driven systems to automate operations and analyze vast datasets, the underlying network infrastructure must evolve to support these new workloads. Reliable connectivity, low latency, and scalable performance are essential for enabling real-time AI applications.
Yang emphasized that networks are no longer simply enabling communication—they are becoming foundational platforms that support data-intensive computing and digital services.
Building Networks for the Intelligent Era
Yang argued that the rapid expansion of AI-driven applications requires telecommunications infrastructure that can support unprecedented levels of traffic and computational demand. This includes networks capable of delivering consistent performance across diverse environments, from dense urban areas to remote industrial sites.
Telecom operators must therefore continue investing in advanced network architectures, including high-capacity optical transport systems, cloud-native core networks, and intelligent network management platforms.
Artificial intelligence itself is also being integrated into network operations. AI-driven automation can help optimize traffic flows, predict equipment failures, improve energy efficiency, and enhance overall network resilience.
This integration allows operators to manage increasingly complex infrastructure environments while maintaining service quality and operational efficiency.
Enabling Industrial Digitalization
Another important theme in Yang’s remarks was the role of connectivity in supporting industrial digitalization. Industries such as manufacturing, logistics, mining, and energy are deploying connected sensors, automation systems, and real-time analytics platforms to improve productivity and safety.
These applications require reliable and high-performance networks capable of supporting large numbers of connected devices and mission-critical operations.
Yang suggested that next-generation connectivity solutions—including private networks and edge computing platforms—will play a key role in enabling these industrial transformations.
Global Collaboration and Ecosystem Development
Yang also stressed the importance of global collaboration in advancing the digital ecosystem. Building intelligent infrastructure requires cooperation across multiple sectors, including telecommunications providers, technology vendors, research institutions, and government regulators.
Standards development, technology interoperability, and international partnerships are critical for ensuring that digital infrastructure continues to evolve in a way that supports innovation while maintaining global connectivity.
According to Yang, the transition toward an intelligent digital world will depend not only on technological breakthroughs but also on the ability of the global technology community to collaborate effectively.
Networks as the Foundation of the AI Economy
Yang concluded by reinforcing the idea that connectivity infrastructure forms the backbone of the emerging AI economy. As artificial intelligence becomes embedded across industries and digital services, networks must evolve to support the growing scale and complexity of digital interactions.
The development of intelligent infrastructure—capable of supporting distributed computing, real-time analytics, and large-scale data exchange—will be essential for enabling the next phase of digital transformation.
In this vision, telecommunications networks are not simply part of the digital ecosystem; they are the platform upon which the AI-driven economy will operate.
Architects of the AI Age: Creating an Experience of One
Michael Weening – President & CEO, Calix
Kate Crawford – Research Professor, University of Southern California
Personalization as the Next Digital Frontier
Michael Weening framed the next phase of the AI era around a concept he described as “the experience of one.” As artificial intelligence becomes more deeply embedded into digital platforms and network infrastructure, technology will increasingly move away from standardized services toward highly personalized experiences tailored to individual users.
Historically, digital platforms have largely operated through mass-scale models. Services were designed to reach millions or billions of users simultaneously, often delivering identical interfaces and capabilities regardless of individual context. AI changes that paradigm. By continuously analyzing user behavior, preferences, and environmental data, intelligent systems can adapt services dynamically in real time.
Weening emphasized that connectivity providers and digital service platforms must rethink how they design networks and digital ecosystems if they want to enable this level of personalization. The future digital environment will involve billions of AI-driven interactions occurring simultaneously across homes, enterprises, and cities.
In this context, broadband and network providers are no longer simply delivering bandwidth. They are becoming enablers of digital experiences that integrate connectivity, cloud services, AI-powered applications, and edge computing into seamless platforms for everyday life.
AI and the Evolution of Service Platforms
Weening highlighted how service providers are increasingly using artificial intelligence to improve customer experiences and operational efficiency. AI-driven analytics can help providers better understand how users interact with networks and applications, allowing them to deliver services that respond more intelligently to individual needs.
For example, network operators may use AI to optimize bandwidth allocation, enhance home connectivity performance, or provide personalized digital services tailored to specific households or enterprises. As networks become more intelligent, they can anticipate user requirements rather than simply reacting to traffic demands.
This approach represents a shift from infrastructure-centric models to experience-centric models. Instead of focusing solely on connectivity metrics such as speed or capacity, service providers must consider how networks contribute to the overall quality of digital experiences.
In a world increasingly shaped by AI-driven personalization, the competitive advantage may lie in how effectively organizations can combine connectivity, computing, and data to deliver highly individualized digital environments.
The Social Implications of AI Systems
Kate Crawford expanded the conversation beyond technological capability to address the societal implications of artificial intelligence. As a researcher focused on the social and political dimensions of AI, Crawford emphasized that the systems being developed today will influence not only technological progress but also social structures and power dynamics.
Crawford noted that AI systems are often described in terms of innovation and efficiency, yet they are also embedded within broader economic and institutional frameworks. Decisions about how AI systems are designed, trained, and deployed can shape outcomes across labor markets, public services, governance structures, and cultural norms.
One key concern raised in the discussion was the concentration of power within large technology platforms that control significant computing resources, datasets, and AI development capabilities. As artificial intelligence becomes a core driver of economic value, access to these resources will play a major role in determining which organizations and regions benefit most from the AI economy.
Crawford argued that understanding the social impact of AI requires examining the full lifecycle of these systems—from the data used to train models to the infrastructure that supports their deployment.
Ethics, Governance, and Accountability
Another important dimension of Crawford’s remarks centered on the ethical governance of AI technologies. As AI systems increasingly influence decision-making processes in areas such as healthcare, finance, employment, and law enforcement, ensuring transparency and accountability becomes critical.
Crawford emphasized that responsible AI development requires interdisciplinary collaboration. Engineers, policymakers, social scientists, and industry leaders must work together to address questions around fairness, bias, transparency, and long-term societal impact.
The challenge is not simply to build more powerful AI systems, but to ensure that these systems operate in ways that align with societal values and public trust.
The conversation highlighted the tension between rapid technological innovation and the need for governance frameworks that can guide responsible development.
An Insider Perspective: Are We Ready for the AI Future?
Dex Hunter-Torricke – Founder & President, The Center for Tomorrow
The Acceleration of AI Innovation
Dex Hunter-Torricke closed the session with a broader reflection on the pace and implications of artificial intelligence development. Drawing on his experience working within major technology companies and public policy environments, he explored how quickly the AI landscape has evolved and what that means for the future.
Hunter-Torricke described the current moment as a period of extraordinary technological acceleration. Breakthroughs in large language models, generative AI systems, and advanced machine learning architectures have dramatically expanded the capabilities of digital systems.
These technologies are now influencing nearly every sector of the economy—from software development and scientific research to media production and enterprise operations.
However, Hunter-Torricke argued that society is still in the early stages of understanding how these capabilities will reshape institutions and economic systems.
Navigating the Opportunities and Risks of AI
While artificial intelligence promises significant benefits, it also raises complex questions about governance, accountability, and societal adaptation. Hunter-Torricke emphasized that policymakers, industry leaders, and civil society organizations must work together to address these challenges.
One of the central concerns surrounding AI development is the potential for unintended consequences. Powerful generative models can produce new forms of content and automation, but they can also introduce risks related to misinformation, security vulnerabilities, and misuse.
Hunter-Torricke suggested that the next phase of AI governance will require new frameworks capable of balancing innovation with risk management. Rather than slowing technological progress, these frameworks should aim to ensure that AI systems are deployed responsibly and transparently.
Preparing Institutions for an AI-Driven World
Another important theme of Hunter-Torricke’s remarks was the need for institutions—both public and private—to adapt to the realities of the AI era. Governments, businesses, educational systems, and regulatory bodies must develop the expertise required to understand and manage AI technologies effectively.
This includes investing in digital literacy, workforce training, and research initiatives that can help societies navigate the transition toward AI-driven economies.
Hunter-Torricke emphasized that preparing for the AI future is not solely a technological challenge. It is also a governance challenge that requires strategic planning and long-term thinking.
Shaping the Next Phase of the Digital Era
The keynote concluded with a reflection on the broader significance of the AI transformation now underway. Artificial intelligence has the potential to redefine productivity, creativity, and human collaboration with machines.
Yet the outcomes of this transformation are not predetermined. The decisions made today—about infrastructure, governance, ethics, and collaboration—will determine how AI influences society in the decades ahead.
Hunter-Torricke’s message was clear: the AI future is being built now, and the responsibility for shaping it rests with the institutions and leaders guiding technological development today.
TeckNexus Strategic View: The AI Stack Is Becoming the New Digital Infrastructure
Keynote 6 at MWC 2026 brought into focus one of the most consequential shifts underway in the technology industry: artificial intelligence is no longer an application layer innovation. It is becoming the organizing architecture of the entire digital ecosystem.
From semiconductor design and network evolution to personalized digital services and governance frameworks, the discussion revealed how AI is rapidly redefining the structure of modern computing. The keynote speakers approached this transformation from different vantage points—chips, networks, platforms, policy, and ethics—but a clear pattern emerged.
Artificial intelligence is reshaping the full technology stack simultaneously.
This transformation has profound implications for telecom operators, infrastructure providers, hyperscale cloud companies, governments, and enterprises alike.
The Emergence of the AI-Native Infrastructure Stack
One of the strongest themes throughout the keynote was the convergence between computing and connectivity. Historically, digital infrastructure evolved in layers: semiconductor innovation enabled computing devices, telecommunications networks enabled communication, and cloud platforms enabled scalable applications.
In the AI era, these layers are increasingly merging into a unified system.
Advanced chip architectures are enabling AI inference directly on devices. Edge computing platforms are distributing intelligence closer to where data is generated. Networks are becoming programmable environments capable of supporting real-time machine learning workloads. Meanwhile, cloud platforms continue to provide the massive training infrastructure required to build increasingly sophisticated models.
The result is the emergence of what can be described as an AI-native infrastructure stack.
In this model, intelligence operates simultaneously across devices, networks, and data centers. Connectivity is no longer simply a transport layer—it becomes an active participant in distributed computing workflows.
This shift represents a fundamental change in how digital infrastructure is designed.
Distributed Intelligence and the Rise of the Edge
Another critical theme from the keynote was the rise of distributed intelligence. For much of the past decade, artificial intelligence development has been centered around large centralized cloud environments where models are trained and deployed.
However, advances in semiconductor efficiency and machine learning optimization are enabling AI workloads to move closer to the edge of the network.
Edge-based AI allows devices to process data locally, enabling faster response times, reduced latency, improved privacy, and greater reliability. Applications such as autonomous vehicles, industrial robotics, augmented reality, and smart infrastructure all depend on real-time decision-making that cannot rely entirely on distant cloud servers.
As a result, future digital architectures will likely operate as hybrid systems where intelligence is distributed across device, edge, and cloud layers.
Telecommunications networks become critical in enabling this distributed intelligence model. High-performance connectivity is required to coordinate interactions between billions of intelligent systems operating simultaneously across the global digital environment.
The Shift from Connectivity to Experience
Another insight emerging from the keynote discussion was the growing importance of experience-centric digital services. AI enables a transition from standardized services to highly personalized digital environments tailored to individual users.
Instead of delivering identical digital experiences to millions of people, intelligent systems can dynamically adapt interfaces, services, and recommendations based on individual behavior and context.
This concept—described during the keynote as the “experience of one”—signals an important shift in how digital ecosystems may evolve. Networks, applications, and platforms are increasingly expected to collaborate in delivering seamless personalized experiences across devices and services.
For telecommunications providers, this shift raises an important strategic question: will operators remain providers of connectivity alone, or will they participate more directly in delivering AI-powered service platforms that shape digital experiences?
The Expanding Governance Challenge
While the keynote highlighted enormous technological opportunity, it also underscored the growing complexity of governing artificial intelligence.
AI systems increasingly influence decisions in areas such as employment, healthcare, finance, education, and public administration. As these technologies become embedded in critical systems, questions about transparency, fairness, and accountability become more urgent.
The challenge is not simply technical—it is institutional.
Governments must develop regulatory frameworks capable of managing the risks associated with powerful AI systems without stifling innovation. At the same time, technology companies must ensure that AI systems are designed responsibly and deployed in ways that maintain public trust.
The governance of artificial intelligence is likely to become one of the defining policy debates of the coming decade.
Concentration of Power in the AI Economy
Another issue raised implicitly throughout the keynote is the concentration of resources required to develop advanced AI systems.
Training state-of-the-art models requires enormous computing power, vast datasets, and highly specialized expertise. These requirements create significant barriers to entry, potentially concentrating AI development within a relatively small number of large technology companies and research institutions.
This concentration raises questions about economic competition, technological sovereignty, and equitable access to AI capabilities.
Countries and industries may increasingly view AI infrastructure—data centers, semiconductor manufacturing, and advanced research capabilities—as strategic assets.
This dynamic is already influencing national technology strategies and global competition.
Telecom’s Strategic Role in the AI Era
For the telecommunications industry, the AI transformation presents both opportunity and risk.
On one hand, AI-driven services will generate unprecedented demand for high-performance connectivity. Autonomous systems, immersive digital environments, industrial automation, and distributed computing architectures all depend on reliable, low-latency networks.
On the other hand, the economic value generated by AI may increasingly accrue to companies controlling software platforms, cloud infrastructure, and computing resources.
This dynamic raises an important strategic question for telecom operators: how deeply will they integrate into the AI stack?
Operators that remain purely connectivity providers may capture only a small portion of the value created by AI-driven digital services. Those that invest in edge computing platforms, AI-enabled network services, and enterprise digital solutions may position themselves more centrally within the emerging AI ecosystem.
The decisions made during the next several years will likely determine the telecom industry’s long-term role in the AI economy.
The Long-Term Transformation
Perhaps the most important takeaway from Keynote 6 is that artificial intelligence should not be viewed as a discrete technology wave. Instead, it represents a structural shift in how digital systems are designed and operated.
AI will influence semiconductor architectures, network design, software development, enterprise operations, and public policy simultaneously. The systems being built today will shape the technological landscape for decades.
This moment resembles earlier inflection points in computing history—the emergence of the internet, the rise of smartphones, and the expansion of cloud computing.
However, the AI transformation may prove even more far-reaching because it touches every layer of the digital stack.
Bottom Line
Keynote 6 highlighted a fundamental transition now underway across the technology ecosystem.
The digital world is evolving from a network of connected devices into a network of intelligent systems.
Connectivity, computing, and artificial intelligence are converging into a unified infrastructure that will power the next generation of digital services.
The architecture decisions being made today—about chips, networks, platforms, and governance—will determine how intelligence is distributed across the global digital ecosystem.
Artificial intelligence will not simply change what technology can do.
It will redefine how the digital world itself is built.
