AI in the Physical World: Robotics, Drones, and the Compute Behind It
Session Overview
AI in the Physical World: Robotics, Drones, and the Compute Behind It
AI is moving from screens to physical systems. Robots work in factories, drones navigate environments, autonomous systems see and adapt in real time. Embodied AI puts intelligence into robotics for manufacturing and logistics, drones for inspection and delivery, autonomous vehicles, and sensor systems in agriculture and infrastructure.
What's working in production versus stuck in labs? Where are technical bottlenecks—real-time processing, safety standards, integration? Which applications make economic sense now versus 3-5 years out?
Behind every AI application is compute infrastructure. Topics include AI data center challenges—energy consumption, cooling, chip supply, real estate—and trade-offs between centralized cloud and edge computing. Where are infrastructure bottlenecks, and how do companies think about compute strategy?
Companies building embodied AI, robotics firms, data center operators, chip manufacturers, and infrastructure investors discuss what's scaling and where capital goes.
AI Data Centers and Compute Infrastructure
Behind every AI application—whether embodied or not—is compute infrastructure. Training large models requires massive data centers. Deploying AI at scale requires distributed compute, whether in the cloud or at the edge.
Energy and CoolingAI workloads consume more power than traditional computing. Data centers are hitting energy limits. Where does the power come from—grid, on-site generation, nuclear? How do operators handle cooling at scale? What's the real constraint: power availability, cooling capacity, or both?
Chip Supply and ArchitectureAI depends on specialized chips—GPUs, TPUs, custom accelerators. Supply is concentrated. Lead times are long. How do hyperscalers, enterprises, and governments secure chip access? What's the impact of export restrictions? Where are alternatives emerging?
Data Center CapacityDemand for AI compute is growing faster than data center capacity. What's the timeline for new capacity—months, years? Where is capacity constrained geographically? How do enterprises that can't build their own data centers secure access?
Edge vs. CloudSome AI applications need low latency and can't wait for cloud responses. Edge computing moves compute closer to where data is generated. When does edge make sense versus centralized cloud? What's the cost trade-off?
Compute StrategyHow do hyperscalers (AWS, Azure, Google) think about AI infrastructure? How do enterprises plan compute needs when AI adoption is accelerating? What role do governments play in ensuring access to compute for national competitiveness?
Investment and Business ModelsData centers require massive capital. Who's investing—hyperscalers, infrastructure funds, governments? What business models work—cloud services, co-location, AI-as-a-service?
Data center operators, hyperscalers, chip manufacturers, infrastructure investors, energy providers, and enterprise AI leaders discuss what's needed, where bottlenecks exist, and how compute infrastructure is evolving.
Host / Speakers
Tammy Schuring — CEO & Cofounder Polymathic
Apoorva Raut — Chief Technology Officer RRP Electronics Ltd
Bettina Scheibe — Managing Partner United Founders
Embodied AI: Robotics, Drones, and Autonomous Systems
AI is moving from screens to physical systems. Robots need to work in factories. Drones need to navigate unpredictable environments. Autonomous systems need to see, move, and adapt in real time.
Embodied AI puts intelligence into physical systems that interact with the world—robotics for manufacturing and logistics, drones for inspection and delivery, autonomous vehicles, sensor-driven systems in agriculture and infrastructure.
What's Working vs. What's StuckWhat applications are in production environments versus still in labs? Manufacturing automation, warehouse robotics, inspection drones, agricultural systems—where is embodied AI delivering value, and where is it overpromised?
Technical ChallengesReal-time processing requirements. Physical constraints—weight, power, durability. Safety standards and certification. Integration with existing systems. Edge computing for low-latency decisions. Hardware reliability at industrial scale.
Economic RealityWhich use cases justify the cost? What's the ROI for deploying robotics in manufacturing versus hiring more people? Where does embodied AI make economic sense now, and where is it 3-5 years away from viability?
From Pilots to ScaleMany robotics companies have impressive demos. Fewer have scaled deployments. What does it take to move from pilot projects to production at scale? What breaks—technology, business model, customer readiness, or integration complexity?
Investment and CompetitionHow do investors assess embodied AI when development cycles are long, capital requirements are high, and margins can be thin? Where is competition heating up—humanoid robots, inspection drones, warehouse automation?
Robotics companies, operators deploying embodied AI, hardware manufacturers, AI researchers, and investors discuss what's scaling, what's stalled, and where the real opportunities are.
Host / Speakers
Jonathan Berte — Chief Visionary Officer Robovision.ai
Guillem Martinez Roura — AI and Robotics Programme Officer International Telecommunication Union
Dr.Milan Kumar — Chief Information Office & Member of the Board SCF
Nikolas Bullwinkel — CEO & Founder Circus SE
Pascal Kaufmann — Neuroscientist-turned-Entrepreneur Lab42 AI Lab Davos
Deep Tech
Space, defense, quantum, robotics, data & compute—founders, funders, builders.