Plenary Speakers

This is no longer a theoretical forecast; it is an operational reality defined by industrial-grade deployments. The convergence of supercomputing, advanced semiconductors, next-generation networks, quantum computing, generative materials science, autonomous AI/ML/AGI/ASI, humanoid robotics / automation / drones, biomedical innovation/programmable biology, BCI/neurotechnology, clean energy and storage, and advanced manufacturing and space, the new orbital economy – all compressing innovation cycles from decades into months.

Defining the Convergence

The S10 + 1 SHIFT framework is not merely a list of technologies; it is a systems-thinking model describing the recursive feedback loops between them. The framework posits that advancements in one domain instantaneously accelerate progress in others, creating a flywheel effect of innovation.

• Synergistic Drivers (S): The distinct boundaries between sectors are dissolving. Example: AI is now the primary architect of advanced chips (Driver 2 + Driver 6). Supercomputers are the laboratories for discovering new materials (Driver 1 + Driver 5).

• Hyper-acceleration (H): Innovation timelines are compressing by a factor of two. Example: What was once a ten-year R&D roadmap for drug discovery is now being executed in twenty-four months via AI-driven wet labs.

• Impact Magnification (I): The convergence generates non-linear outcomes and more than 5 times impact magnification. Example: An AI diagnostic agent does not just improve accuracy by 10%; it enhances it by 400% compared to human practitioners, fundamentally altering the economics of healthcare.

• Frontier Markets (F): Entirely new "white space" industries are emerging. Example: The "Materials-as-a-Service" (MaaS) market and the "Robotics-as-a-Service" (RaaS) labor market were theoretical concepts in 2020; in 2026, they are billion-dollar order books.

• Transformative Challenges (T): with these profound technology changes comes challenges: Example: geopolitical, economic, social, safety/ethical, privacy, risk, governance, ART—accountability, responsibility, transparency). And the Trilemma of Talent, Energy, and Governance represents the "great filter" for this era. Organizations that cannot secure power, talent, or regulatory license to operate will be left behind regardless of their technological prowess.

This synergistic interplay will generate over $20 trillion in new economic value by 2035. This valuation is underpinned by the explosive scaling of the artificial intelligence sector, which alone is forecast to contribute $16 trillion to the global economy by 2030 and Quantum, $2 trillion by 2035. Complementing this is the commercial space economy, transitioning from government-led exploration to a $1.8 trillion orbital market, and the multi-trillion-dollar impact of pervasive 6G connectivity and autonomous industrial systems.

However, this trajectory of hyper-acceleration faces an existential governor: then transformative challenges (geopolitical, economic, social, safety, ethical, privacy, risk, governance, transparency, responsibility, accountability, and the Talent-Energy-Governance Trilemma. The constraint on growth is no longer scientific feasibility but also physical and human capacity. The demand for AI compute is colliding with the "Power Wall," where gigawatt-scale data centers require the entire output of nuclear power stations. Simultaneously, a profound scarcity of specialized human capital—engineers capable of navigating the intersection of biology and silicon, or quantum mechanics and software—threatens to stall progress. Finally, the regulatory lag in governance creates a volatile operating environment where the rules for AI agents, genetic editing, and neural data privacy are being written in real-time, often fragmenting across geopolitical blocs.

The keynote and session serves as a definitive strategic guide on investments and innovation for all global leaders/investors/innovators. We examine the S10+1 technology drivers, identifying critical examples for each—that define the state of the art in 2026. It provides a roadmap to 2030 and beyond, moving beyond the hype to the hard engineering realities that will determine the winners of the next decade.

The role of technology leaders in this transformation is more critical than ever—requiring not just technical expertise, but also strategic vision, adaptability, and the ability to navigate complex challenges. As healthcare embraces AI, automation, precision medicine, and digital health, leaders must not only drive innovation but also ensure its successful implementation and adoption.

Yet, healthcare is not alone in facing these disruptions. Almost every industry is now a technology industry—from banking to agriculture—where success depends on integrating emerging technologies, fostering innovation, and leading teams through rapid change. This session will explore the universal principles of technology leadership, drawing on lessons from healthcare while providing insights applicable across sectors.

This talk will also highlight the evolution of Mayo Clinic’s strategic plan, demonstrating how the organization is positioning itself as a disruptor rather than the disrupted and the importance of proactively shaping industry transformation.

It requires trusted ecosystems that connect innovation, career / workforce development, and standards with meaningful collaboration across industry, academia, startups, and technical communities. The IEEE Industry Engagement Committee (IEC) is driving a portfolio of initiatives designed to strengthen how IEEE engages with industry and accelerates the practical adoption of emerging technologies. This talk will highlight IEEE Industry AI Labs and the broader IEEE for Industry effort as examples of how IEEE is helping bridge the gap between AI innovation and real-world industry impact. It will examine how Industry AI Labs can support hands-on experimentation, career development, and deeper industry engagement. Drawing on Matthew's perspective as Co-founder and CTO of Minset, the talk will connect IEEE's ecosystem-level role with lessons from building AI systems deployed at scale in industry.