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Google will pay SpaceX $920 million per month from October 2026 through June 2029 for AI compute capacity using approximately 110,000 NVIDIA GPUs, as a bridge to meet demand for Gemini Enterprise while Google expands its own AI infrastructure.
Google will pay SpaceX $920 million per month from October 2026 through June 2029 for access to approximately 110,000 NVIDIA GPUs and other components, in a deal similar to SpaceX's recent agreement with Anthropic.
As part of the Stargate initiative, OpenAI has locked in nearly 7GW of computing capacity, planning over $400 billion in investment over three years, with a target of $500 billion and 10GW in commitments by year-end.
Microsoft announces the Surface Laptop Ultra with Nvidia's RTX Spark Arm-based chip, promising the most powerful Surface ever with up to 128GB unified memory and 1 petaflop of AI compute.
Microsoft unveils the Surface Laptop Ultra, a high-performance laptop co-engineered with NVIDIA, featuring up to 128GB unified memory, a Blackwell RTX GPU, and 1 petaflop of AI compute for creators and AI builders.
This paper proposes a Cognitive Kardashev Scale ranking civilizations by their sustained AI-grade computation capacity, using total power and efficiency. It places current humanity at K≈0.73 and explores future scaling trajectories.
Chamath explains the two key phases of AI compute: prefill, which is compute-bound and favors parallel GPUs like Nvidia's, and decode, which is memory-bandwidth bound and depends on scanning previously generated tokens.
An analysis of AI compute usage reveals that frontier labs like OpenAI, Anthropic, xAI, Google, and Meta currently use less than half of global AI compute, but their share is growing rapidly, which could impact scaling trends.
SpaceX's S-1 filing reveals a $1.25 billion per month cloud services agreement with Anthropic for compute capacity on COLOSSUS and COLOSSUS II through May 2029, supporting training of Grok 5.
Anthropic agreed to pay SpaceX $1.25 billion per month through May 2029 for cloud computing infrastructure, as revealed in SpaceX's IPO filing. The $15 billion annual deal highlights the critical importance of compute resources in AI development.
DARPA's MARRS program explores solid-state fusion near room temperature, with parallel international developments in LENR from India, Japan, and China, potentially transforming AI compute infrastructure if realized.
Stanford CS153Systems lecture featuring Jensen Huang of Nvidia discussing the compute behind intelligence.
A quick breakdown of ballpark numbers for a 100k H100 GPU datacenter, covering GPU costs (~$3B), full datacenter build (~$5B), power consumption (~0.2GW), and annual energy costs (~$50M).
OpenAI and NVIDIA announced a landmark strategic partnership to deploy at least 10 gigawatts of NVIDIA AI systems, with NVIDIA investing up to $100 billion as infrastructure is deployed starting in late 2026 using the Vera Rubin platform.
OpenAI and Oracle announced a partnership to develop 4.5 gigawatts of additional Stargate data center capacity in the U.S., bringing total Stargate infrastructure to over 5 gigawatts and supporting OpenAI's $500 billion investment commitment. The project is expected to create over 100,000 jobs and represents a major milestone in advancing U.S. AI infrastructure leadership.
OpenAI releases an analysis demonstrating that compute used in largest AI training runs has grown exponentially at a 3.4-month doubling time since 2012, representing a 300,000x increase and vastly outpacing Moore's Law. The analysis suggests this trend will likely continue and calls for increased academic AI research funding to address rising computational costs.