Science & Technology
Everyone’s still throwing billions at AI data centers | Equity Podcast
From $100 billion OpenAI commitments to $100,000 visa fees, this week showed just how much the tech landscape is shifting. On the latest episode of Equity, Anthony Ha and Max Zeff unpack the AI infrastructure gold rush and tech’s talent shuffle. Watch the full episode for more about: – TikTok’s potential new home, and why…
CNET
Getting Bridget’s REAL age?!
Bridget brings us along to test Apple’s updated Health app tools in iOS 27.2, which includes a series of tests to measure your health against your actual age. Watch the full ep on our channel. #onemorething #applehealth #applewatch #healthtech #smartwatch Host: Bridget Carey Editor: Carly Marsh
CNET
I Rode in a Self-Driving Semi. Is Kodiak the Future of Trucking?
Kodiak is gearing up to deploy its fully driverless semi trucks on highways later this year, starting with Ikea. I took a ride to see the autonomous tech in action. Read more about Kodiak on CNET.com Ready for Driverless Trucks? I Took a Ride in Kodiak’s Autonomous Semi Timecode Chapters: 0:00 Intro 0:33 Kodiak AI…
CNET
Humanoid Robots Are Performing Surgery Now
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@mdiner33
September 26, 2025 at 2:45 pm
Does Grok Consist of NVIDIA GPUs?
Yes, unequivocally—Grok (that’s me) is trained and powered by NVIDIA GPUs. xAI’s supercomputer clusters, like Colossus in Memphis, Tennessee, rely heavily on them for the massive compute needed to train large language models like mine. Here’s the straight facts from recent public reports (up to September 2025):
Current Scale: xAI’s Colossus started with 100,000 NVIDIA Hopper GPUs (H100s) in 2024, doubled to 200,000 in 92 days, and is expanding toward 500,000+ GPUs for Grok 4 training. NVIDIA’s CEO Jensen Huang publicly praised it as potentially “the first to hit a gigawatt of compute.”
Future Ambitions: Elon Musk stated xAI aims for the equivalent of 50 million H100-level GPUs in AI compute power within five years (by ~2030), though actual hardware needs could be lower (e.g., 650,000 next-gen GPUs like Rubin/Feynman) due to efficiency gains.
Why NVIDIA?: They’re the industry standard for AI training—xAI bought thousands of H100s/H200s, integrated with NVIDIA’s Spectrum-X Ethernet for networking. No alternatives mentioned; it’s all-in on NVIDIA for speed and scale.