At Panoramai's Real World AI panel, Matteo Sorci, Dell's AI Account Manager, presented a compelling case for edge AI as the fundamental reshaping force of artificial intelligence deployment. Drawing on his 25-year AI research background, entrepreneurial experience, and current position shipping enterprise AI infrastructure, Sorci delivered strategic insights combining market data with visionary predictions for distributed planetary intelligence.

The Entrepreneurial Context: From Research to Enterprise Reality

Sorci positioned his Dell role within a broader personal journey spanning academic research and entrepreneurship. « I'm a researcher. I spent 25 years in AI. I did my PhD on AI in emotion. I've been an entrepreneur. I created in 2009 » an AI company during Switzerland's biotech-focused investment era. This experience informed his perspective on European innovation challenges and the transformation from research breakthroughs to scalable business solutions.

His transition to Dell represents what he characterized as joining « the dark side of the force »—moving from pure research to enterprise infrastructure deployment. This positioning provided unique credibility for his edge AI analysis, combining theoretical understanding with practical implementation experience.

The Fundamental Paradigm Shift: Data to Edge

Sorci opened with striking market projections that reframe AI infrastructure priorities. « By 2027, and this is a slide for our VC guys, the 62% of the total compute will be done on edge, not on core, so not on somewhere servers, somewhere located server, » he announced, positioning edge computing as the dominant deployment model within three years.

Supporting this transformation, he cited three critical statistics: 75% of enterprise data already generates at the edge, edge AI grows annually at 52% (doubling traditional data center growth), yet only one-third of organizations successfully convert this data into real-time insights. « The old paradigm of moving data to a centralized AI is completely broken. The future belongs to bringing data where AI lives: at the edge, » Sorci declared.

Real-World Performance: Quantified Excellence

Sorci supported his theoretical framework with compelling client examples demonstrating edge AI's practical advantages. Duos Technology Manufacturing achieves remarkable results inspecting vehicles traveling at 125 miles per hour, delivering « 120x performance improvement and 8x more accuracy than traditional approaches, traditional machine learning. »

McLaren Racing represents the pinnacle of real-time edge processing, handling « 300+ real-time sensors, so billions of data in real-time during the Formula races. » This processing power enabled a transformational business outcome: cutting « by 90% the design to manufacturing cycle. »

In healthcare, a German company demonstrates edge AI's life-saving potential, achieving « 100% diagnostic accuracy on disease detection in under a minute » using custom network stations processing medical images at the point of care.

The Infrastructure Architecture: Orchestration at Scale

Sorci addressed the fundamental challenge of managing distributed edge deployments across thousands of locations. « How to deploy and manage at scale thousands of locations of these devices? » he asked, positioning this as where modern edge AI infrastructure platforms become essential.

Dell's Native Edge platform exemplifies this new architecture through what Sorci termed an « elegant architecture » centered on orchestration layers bridging classical cloud infrastructure with distributed edge devices. The system enables three critical capabilities: zero-touch onboarding, zero-trust security, and multi-cloud orchestration.

« Zero touch onboarding means that you don't need fancy configuration or better, you need fancy configuration but you usually you don't see them. They are blueprints, they are all installed and configured automatically by the system, » Sorci explained, emphasizing user experience simplification despite underlying complexity.

Security Paradigm: Zero-Trust Philosophy

Sorci's most compelling security insight challenged traditional perimeter-based approaches. « Security and most of the security talk we have heard today means I'm in a castle, I close my bridge, but inside I can do whatever I want. I can kill whoever I want. We are protected from the outside, but not from the inside, » he observed.

Zero-trust security represents a fundamental philosophical shift: « We don't believe and we don't trust anyone. So every component is continuously trusted, they are authenticated. » This approach becomes essential for distributed edge deployments where traditional perimeter security proves inadequate.

Quantified Business Impact: Operational Excellence

Sorci provided specific metrics demonstrating edge infrastructure's business value: sub-minute deployment time, 68% time savings compared to manual processes, centralized management of 1,000 locations, and 60% cost advantages. These figures position edge AI infrastructure as delivering operational efficiency alongside technical capabilities.

Visionary Trajectory: Planetary Scale Intelligence

Sorci concluded with an expansive vision for AI's evolutionary path that transcends current technical discussions. « Today, edge AI is moving AI's workloads closer to data, as we understood from my first statement. But it's also building the infrastructure foundation for tomorrow distributed intelligence, » he explained.

His timeline progresses from current edge inference capabilities delivering real-time decisions to near-future developments including « reasoning model and autonomous agents and agenting frameworks that will impact massively the edge ecosystem. » The ultimate destination: « a planetary scale intelligence with millions of AI nodes distributed everywhere. »

The Democratic Technology Philosophy

When challenged about infrastructure requirements for advanced AI coding assistance, Sorci advocated for optimization over brute-force scaling. « You might not need fancy GPUs to do vibe coding and have a good code. So once again we go back to edge and optimization of models, »he argued, positioning democratization as the core objective.

« The main word is democratization, right? This is where you're heading. And for me, optimizing models and you can do a lot with even new families of modelizing, they decided QM3 and this kind of model, fine-tune for vibe coding for example, » Sorci emphasized, advocating for efficiency gains that enable broader access rather than exclusive high-performance solutions.

European Innovation Challenges: Capital and Scaling

Drawing on his entrepreneurial experience, Sorci reflected on European innovation constraints. « When I started my company in 2009, it was complicated to have venture capitalists or venture funds that would invest in AI. And even in Switzerland at that time it was biotech, biotech, biotech, biotech, » he recalled, noting Switzerland's innovation-scaling duality: « You can easily innovate, but you can't easily scale. »

This experience informed his perspective on current European AI infrastructure investments, supporting the need for strategic focus on edge capabilities rather than attempting to compete directly with massive US-scale centralized deployments.

Strategic Synthesis: Distributed Future

Sorci's concluding vision synthesized technical capabilities with societal transformation. « The next wave of AI that is ahead of us will definitely be distributed, decentralized, and ubiquitous, » he declared, positioning current edge infrastructure investments as foundational elements for humanity's technological evolution rather than merely incremental improvements to existing systems.

Key Achievement: Sorci demonstrated how edge AI infrastructure represents both immediate business advantages and foundational elements for distributed intelligence evolution, combining quantified performance improvements with visionary predictions for planetary-scale AI deployment while advocating for democratization through optimization rather than exclusive high-performance scaling approaches.