Scientific discovery has long been one of America's greatest strengths. That's why we're deepening our partnership with the U.S. Department of Energy's #GenesisMission# through new investments in AI infrastructure, scientific computing, engineering expertise, and a collaboration hub designed to help researchers across America's national laboratories, universities, and industry work together more effectively. Our goal is to accelerate AI-driven scientific discovery. From energy and medicine to advanced materials, faster scientific breakthroughs can strengthen America's competitiveness and create lasting value for generations to come. @ENERGY @Microsoft

Intel is proud to be a member of the Genesis Mission Consortium, a historic national effort that brings together @ENERGY, other U.S. federal agencies, industry, and academia to harness AI to accelerate innovation and scientific discovery. We look forward to collaborating with consortium partners to advance discovery, strengthen national security, and drive energy innovation. Watch the inaugural Genesis Mission Summit webcast to learn more:

Stanford and the U.S. Department of Energy’s @SLAClab will lead six Genesis Mission projects that will harness AI to accelerate advances in critical areas including battery waste, understanding the evolution of the universe, and plant genetics.

We’re expanding our work with the US Dept. of @ENERGY on the Genesis Mission – an initiative to double the pace of scientific discovery within a decade. 🧪 By committing $40M in AI tokens and @GoogleCloud credits, more lab researchers will gain access to Gemini and other AI models. →

Building, deploying, and governing AI "on your own terms" — what sovereign AI means for nations, and why speed is decisive 🌐 Title: What Is Sovereign AI—and How Cerebras Helps Nations URL: 🌐 Overview Sovereign AI is a nation's capacity to build, deploy, and govern AI on its own terms — spanning infrastructure, models, data practices, and institutions that reflect national priorities and security needs. The essence is control over the AI stack, not isolation. ❓ Challenges Solved Countries increasingly view AI infrastructure as a critical national asset. ・AI is becoming foundational to science, industry, security, and public services ・Secure data handling and domain-specific model development are needed ・There's competition for limited compute capacity 💡 Methodology & How Cerebras Helps It supports nations through three pillars. ・AI supercomputers for training and inference ・Model co-development partnerships ・Local education, workforce, and policy investment Technically, inference runs up to 15x faster than leading GPU-based solutions, and training delivers more than 10x faster time-to-solution vs GPUs. That speed enables reasoning and verification within real-world constraints. 🌍 Use Cases ・U.S.: scientific research via the Genesis Mission ・UAE: production of the Arabic LLM JAIS 2 ・India: national-scale 8-exaflop computing under Indian governance The piece argues speed is what separates theoretical capability from functional sovereign AI. #SovereignAI# #Cerebras#

CEREBRAS $CBRS IS ABOUT TO GO PUBLIC ... HERE IS A DEEP DIVE ON WHAT THEY ACTUALLY DO Most people have no idea what this company actually does. Here is the plain-English version: THE BIG IDEA Every AI model you have ever used (ChatGPT, Claude, Gemini, Llama) is trained on chips. The dominant company is NVIDIA $NVDA, which sells lots of small chips that get wired together into clusters by the thousands. Cerebras went the opposite direction. They build ONE giant chip the size of a dinner plate. Not exaggerating. Their flagship "Wafer-Scale Engine" (WSE-3) is 46,225 square millimeters of silicon. A normal AI chip is smaller than a postage stamp. THE NUMBERS On that one giant chip: - 4 trillion transistors (a top-end NVIDIA GPU has roughly 80 billion) - 900,000 AI cores - 44 GB of on-chip memory - 125 petaflops of compute power - Built on TSMC's 5nm process WHY THAT MATTERS When you wire thousands of small chips together, the wires become the bottleneck. Data has to travel between chips constantly. That eats time, power, and money. Cerebras keeps everything on one piece of silicon. No cables between chips. No slow networking. Just one giant brain. The pitch: faster training, faster inference, fewer engineers needed to manage cluster bottlenecks. WHAT THEY SELL Three ways to use Cerebras: - Buy the system outright (the "CS-3" is the box that holds the chip) - Rent compute via Cerebras Cloud - Dedicated capacity contracts for big customers They have 6 new AI inference data centers coming online across North America and Europe. WHO ACTUALLY USES IT The customer list is the validation: - OpenAI: $20B+ committed over three years - Meta $META: powers the Llama API for developers - Perplexity: runs its Sonar search model on Cerebras - Mistral: the French AI lab runs Le Chat on Cerebras - Mayo Clinic: trains genomic AI models on Cerebras infrastructure - GSK $GSK: trains biological language models - Argonne National Lab: has used Cerebras hardware since 2019 - AWS: hosts Cerebras chips inside Amazon data centers, accessed through Bedrock - US Department of Energy: signed an MOU for the Genesis Mission THE TRADE-OFF Cerebras is small compared to NVIDIA. 2025 revenue: $510M. 2025 operating loss: $146M. Concentration is the risk most coverage will not flag: - G42 (the UAE conglomerate) was 85% of 2024 revenue per Reuters - G42 plus MBZUAI (the Abu Dhabi AI university) were 86% of 2025 revenue per FT - The OpenAI deal is the big bet to diversify away from that concentration THE STORY IN ONE LINE NVIDIA bet that the future of AI is millions of small chips working together. Cerebras bet on one giant chip doing the work in one place. The market just decided their bet is worth nearly twice what they priced it at.