Technology

Zuckerberg's Meta will beam sunlight from space to power AI data centers, solar-collecting satellites will orbit 22,000 miles above Earth — firm reserves 1 Gigawatt of orbital solar energy and 100 Gigawatt-hours of long-duration storage

Source

Tom's Hardware

Published

Apr 29, 2026

TL;DR

AI Generated

Meta, led by Zuckerberg, plans to power its AI data centers with sunlight beamed from space using solar-collecting satellites in geosynchronous orbit 22,000 miles above Earth. The company has reserved 1 Gigawatt of orbital solar energy and 100 Gigawatt-hours of long-duration storage to address the increasing energy demands of its AI infrastructure. This move is part of Meta's strategy to secure long-term energy supplies for its expanding AI operations, with a first orbital demonstration planned for 2028 and potential commercial delivery by 2030. The partnerships with Overview Energy and Noon Energy aim to tackle the challenges of intermittency and long-duration energy storage in renewable energy systems.

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Global memory shortage expected to get worse before it gets better

The global memory shortage is expected to worsen, with reports indicating that DRAM shortages may persist until the end of the decade. Major manufacturers like Samsung, SK Hynix, and Micron are investing in expanding production facilities, but the additional capacity won't be fully operational until 2027 or later, leading to a multi-year supply gap. The rise in AI infrastructure demand for high-bandwidth memory is prioritizing production over traditional DRAM used in consumer devices, causing further supply constraints. Analysts predict a shortfall in production growth compared to demand, potentially extending the memory shortage until 2030, resulting in continued high prices for consumers.

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Analytics group signals possible delays at 40% of AI data center construction sites — companies deny schedule holdups, but satellite imagery indicates otherwise

Several U.S. data center projects, including those involving Microsoft, OpenAI, and Oracle, are facing potential delays due to regulatory challenges, supply chain issues, and utility availability. Satellite imagery analysis by SynMax suggests that construction progress is slower than expected, with some projects possibly missing deadlines by over three months. Despite denials from companies involved, reports indicate a shortage of specialist workers and delays in construction. The increased demand for electricity to power AI data centers is also straining local utility providers, leading to further complications in project timelines.

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South Korea's three major carriers, SK Telecom, KT, and LG Uplus, will offer over seven million mobile subscribers unlimited 400 Kbps data after they exceed their monthly data limits. This initiative, declared a 'basic telecommunications right,' aims to provide essential internet access even after monthly plans are exhausted. Despite the modest speed, 400 Kbps can support basic online activities like messaging and VoIP calls. The carriers will also enhance services for seniors, improve public transport Wi-Fi, introduce affordable 5G plans, and invest in network infrastructure for AI applications. This move follows security breaches in the companies, prompting the government to emphasize the importance of ensuring basic telecommunications rights for all citizens.

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Architecting Intelligence: The Rise of RISC-V CPUs in Agentic AI Infrastructure

SiFive's recent $400 million Series G financing marks a significant milestone in the development of high-performance RISC-V CPUs tailored for agentic AI data center workloads. The funding aims to accelerate next-gen CPU IP, software ecosystem growth, and hyperscale deployment capabilities to address emerging compute challenges in AI infrastructure. CPUs are increasingly crucial in agentic AI systems due to their efficiency in handling complex control flow and orchestration tasks compared to GPUs and specialized accelerators. RISC-V's modular architecture allows for tailored extensions that enhance efficiency in handling diverse AI workloads. The focus is on integrating scalar pipelines with vector and matrix compute units to reduce memory bandwidth overhead and improve power efficiency, crucial as AI clusters scale. The investment also emphasizes expanding software compatibility and enabling customer-specific CPU customization to meet evolving AI workload demands.

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3 weeks ago