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 Cooling AI 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 Architecture AI 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 Capacity Demand 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. Cloud Some 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 Strategy How 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 Models Data 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.