필터 결과
17개정렬
1:28:08ENThe same H100 costs 3x at AWS, and a four-month-old lab is catching genes the top sequencers missed진행자 Nathan LabenzA Cognitive Revolution weekly briefing on a possible U.S.–China AI hotline, why hyperscalers charge two to three times as much for identical chips, co-writing a novel with frontier models, and an AI loop that re-diagnosed a child a premier prenatal lab had cleared.
1:37:43ENCisco wants two extra network layers so agents can share intent진행자 Nathan LabenzVijoy Pandey of Outshift by Cisco argues that superintelligence has to scale out through networks of agents, not just up through bigger models.
2:12:31ENMost Caffeine users have no idea they are on a blockchain진행자 Nathan LabenzDFINITY's Dominic Williams tells Nathan Labenz why a tamper-proof, unstoppable cloud is the right backend for AI that writes software from a chat box.
2:06:09ENOne ChatGPT query is a second in the microwave진행자 Nathan LabenzAndy Masley walks Nathan Labenz through the energy and water math of AI, from a single prompt to an 80-gigawatt buildout.
1:32:47ENA transformer co-author wants a $160 million model no company owns진행자 Nathan LabenzIllia Polosukhin tells Nathan Labenz how NEAR turned a payments headache into a stack for private inference, shared-revenue training, and AI agents that still answer to a court.
3:17:35ENTen Thousand Comments and 76 Spare Gigawatts: Four Months Writing America's AI Action Plan진행자 Nathan LabenzDean W. Ball spent four months as the White House's senior AI adviser, then left as the Action Plan landed — and explains how a to-do list got written inside a building that cannot run ChatGPT.
1:45:00ENAmerica Holds 45% of the World's Data Centers. Everyone Else Must Build, Buy, or Ally진행자 Nathan LabenzAnjney Midha of a16z maps what “sovereign AI” actually means for companies and for countries that will never own the talent, data, and compute frontier.
2:10:07ENA $30,000 Operation Could Knock a $10 Billion Superintelligence Data Center Offline for a Year진행자 Nathan LabenzGladstone AI’s Jeremy and Edouard Harris inventory what an American Manhattan Project for superintelligence would actually have to secure — and what it would cost in concrete, supply chains, and civil liberties.
54:55ENNew Jersey Cut Phone Waits from 40 Minutes to 90 Seconds진행자 Nathan LabenzGovernor Phil Murphy on the state AI assistant, a Princeton hub, and why he wants the best catcher's mitt in the country when superhuman systems arrive.
2:16:17ENA 10B Model Trained Across Three Continents — and an H200 for $1.49 an Hour진행자 Nathan LabenzPrime Intellect's founders lay out a four-step plan to turn spare global GPUs into collectively owned intelligence, and argue that reinforcement learning just made their job easier.
2:11:15ENA 50% Chance of Extinction — and a $50 GPT-4 for Everyone진행자 Nathan LabenzEmad Mostaque tells Nathan Labenz why he puts p(doom) at even odds, why a five-watt model changes the energy debate, and how the Intelligent Internet would fund universal basic AI with a Bitcoin-like token.
31:04ENThe Risk Is Not China Copying. It Is America Refusing to Use the Tools진행자 Nathan LabenzAt the 2024 Hill & Valley Forum, Alex Wang, Jack Clark, and Andrew Ng tell Senator Cory Booker that Chinese labs are inventing, open-source models are already strong, and the Pentagon’s real AI failure is under-adoption.
1:52:18ENTwo Weeks, Thirty Compiles: How Adam Majmudar Wrote a GPU From Scratch진행자 Nathan LabenzA Cognitive Revolution walkthrough of TinyGPU, from SIMD kernels and memory latency to an open-source chip flow that runs on a laptop.
1:42:17ENThe Cheapest Inference Provider Is the Most Dangerous진행자 Nathan LabenzFireworks AI cofounders Lin Qiao and Dmytro Ivchenko explain why subsidized GPU bills vanish, how LoRA serving can cut cost a hundredfold, and why OpenAI is the rival that actually matters.
1:19:52EN1,000 H100s Take 115 Days; 22,000 Take Five진행자 Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.
1:01:26ENAlexNet Used Two GPUs. GPT-4 Took Twenty Thousand. The Shortage Will Be Back.진행자 Nathan LabenzRun:ai CTO Ronen Dar on filling idle accelerators, why H100s can be cheaper per experiment, and why Llama 2 undercuts compute governance.
1:20:49ENA Wafer-Scale Chip So AI Stops Waiting on Amdahl진행자 Nathan LabenzAndrew Feldman, CEO of Cerebras, tells Nathan Labenz why one 2.5-trillion-transistor die, sparse linear algebra, and a 16-machine supercomputer are a better shape than a GPU cluster.









