A new top-level effort unifies data center and network oversight as the company targets multi-gigawatt AI scale.
Meta has elevated AI infrastructure to a top-level strategic priority with the launch of Meta Compute, a new initiative that brings responsibility for building and operating data centers and networks under a single leadership structure.
“Meta is planning to build tens of gigawatts this decade, and hundreds of gigawatts or more over time,” CEO Mark Zuckerberg said in a social media post. “How we engineer, invest, and partner to build this infrastructure will become a strategic advantage.”
The initiative will be co-led by Santosh Janardhan and Daniel Gross, the company said. Janardhan will continue to oversee the company’s data center and network foundations, while Gross will lead long-term capacity planning, supplier strategy, and business modeling for AI infrastructure.
“They will work closely with Dina Powell McCormick, who just joined Meta as president and vice chairman, to work on partnering with governments and sovereigns to build, deploy, invest in, and finance Meta’s infrastructure,” Zuckerberg added.
Powell McCormick previously served as the US Deputy National Security Advisor for Strategy to President Donald Trump. Her husband, Dave McCormick, is a US Senator from Pennsylvania and a Senate energy subcommittee chair.
The move comes as hyperscalers race to deploy ever-larger AI clusters that place extreme demands on both network performance and power delivery, leading to tighter coordination across infrastructure planning.
At that scale, infrastructure constraints are becoming a binding limit on AI expansion, influencing decisions like where new data centers can be built and how they are interconnected.
The announcement follows Meta’s recent landmark agreements with Vistra, TerraPower, and Oklo aimed at supporting access to up to 6.6 gigawatts of nuclear energy to fuel its Ohio and Pennsylvania data center clusters.
Implications for hyperscale networking
Analysts say Meta’s approach indicates how hyperscalers are increasingly treating networking and interconnect strategy as first-order concerns in the AI race.
Tulika Sheel, senior vice president at Kadence International, said that Meta’s initiative signals that hyperscale networking will need to evolve rapidly to handle massive internal data flows with high bandwidth and ultra-low latency.
“As data centers grow in size and GPU density, pressure on networking and optical supply chains will intensify, driving demand for more advanced interconnects and faster fiber,” Sheel added.
Others pointed to the potential architectural shifts from this.
“Meta is using Disaggregated Scheduled Fabric and Non-Scheduled Fabric, along with new 51 Tbps switches and Ethernet for Scale-Up Networking, which is intensifying pressure on switch silicon, optical modules, and open rack standards,” said Biswajeet Mahapatra, principal analyst at Forrester. “This shift is forcing the ecosystem to deliver faster optical interconnects and greater fiber capacity, as Meta targets significant backbone growth and more specialized short-reach and coherent optical technologies to support cluster expansion.”
The network is no longer a secondary pipe but a primary constraint. Next-generation connectivity, Sheel said, is becoming as critical as access to compute itself, as hyperscalers look to avoid network bottlenecks in large-scale AI deployments.
Impact on network architects
Planning for tens of gigawatts of AI capacity will require data center designers and network architects to integrate power and networking considerations far more closely than in previous generations of facilities.
“Architects will need to balance energy consumption, heat dissipation, and workload placement while ensuring resilience through redundancy and intelligent routing,” Sheel said. “At this scale, AI infrastructure demands power-aware design and latency-optimized networks to maintain performance and reliability.”
Mahapatra added that large AI superclusters like Prometheus and Hyperion demand resilient regional interconnects, flexible layouts, and temporary deployment structures that support continuous scaling while distributing workloads across facilities designed for uncertain future AI requirements.
