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69개정렬
1:50:48ENWhy Inference Matters 12× More Than TrainingFrançois Chaubard, Dan Fu, Jeff Morgan, Joe Fioti, Liangsheng Yin · Y CombinatorYC Paper Club walks the inference stack from an API request down to GPU kernels. Four talks from Together AI, Ollama, Luminal, and SGLang argue that serving quality, not just training FLOPs, is where most of the compute and the money now sit.
1:13:26ENNaveen Rao on Energy, AI Hardware, Verification and the Future of Robotics게스트 Naveen Rao · 진행자 Harry StebbingsThe Unconventional AI founder argues that energy efficiency will shape AI hardware, while the growing volume of model output makes verification more valuable.
1:25:33ENWhy GPU Capacity Comes in Three-Year Rectangles — Erik Bernhardsson, Modal게스트 Erik Bernhardsson · 진행자 Vibhu SapraModal’s CEO tells Latent Space why AI demand almost never looks like a three-year GPU contract, how the company pools compute across thousands of customers, and why sandboxes grew from a product they almost killed into a third of revenue.
1:19:50ENSameer Dholakia on AI Investing, Bessemer's Growth Strategy and Company Culture게스트 Sameer Dholakia · 진행자 Harry StebbingsThe Bessemer partner explains his approach to pricing fast-growing AI companies, backing founders and building teams, while discussing the risks behind his optimistic forecasts.
1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go Past진행자 Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.
1:04:26ENAt 100,000 Accelerators, Something Fails Every HourGoogle AI infrastructure chief Amin Vahdat tells Sequoia's Sonya Huang why FLOPS is a vanity metric, why the TPU line split in two, and why he would rather wait on a utility than build his own gigawatt.
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Lab진행자 Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
1:05:01ENChase Lochmiller on AI Data Centers, Energy and GPU Economics게스트 Chase Lochmiller · 진행자 Harry StebbingsCrusoe’s CEO discusses the constraints on AI infrastructure, the economics of GPU capacity and his company’s approach to energy and managed inference.
35:39ENCompute Scaled a Millionfold. Bandwidth Only 40x. Fractile Wants to Fix That Ratio진행자 Sarah Guo, Elad GilIn the summer of 2022, Walter Goodwin founded Fractile in London to build very fast inference chips for the world's largest models.
1:20:28ENAI Valuations, Talent Wars and the Venture Capital Tradeoffs Behind Them게스트 Jack Altman, Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jack Altman, Jason Lemkin and Rory O’Driscoll debate large early-stage bets, open-weight models and the changing market for exits.
1:15:42ENAlex Mashrabov on Higgsfield's AI Video Business, Model Costs and Growth게스트 Alex Mashrabov · 진행자 Harry StebbingsHiggsfield's founder explains the search for product-market fit, how he calculates annualized revenue and why creative workflows matter more than chasing model benchmarks.
1:20:43ENAI Agents, Venture Pricing and the Tradeoffs Behind the Funding Boom게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate AI products, high-priced venture rounds, enterprise software and infrastructure risk.
1:19:54ENThomas Sohmers on AI Inference, Memory and Data-Center Growth게스트 Thomas Sohmers · 진행자 Harry StebbingsPositron’s co-founder discusses the engineering economics of inference, his views on AI infrastructure and regulation, and the risks he sees in financing and concentration.
38:14ENDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom Silicon진행자 Sarah Guo, Elad GilThe Stanford diffusion pioneer tells No Priors why a 50-person lab is training language models that generate many tokens at once — and why that maps to GPUs the way transformers once mapped to training.
2:19:30ENThe investigators were allowed six days. The word protein appeared once.진행자 Nathan LabenzA week of Cognitive Revolution morning shows on OpenAI’s agent-swarm incident, GPT-6 Astra’s loop transformer, and whether the pause debate is already over.
1:20:29ENAI Compute, Agent Security and the Race to Build More Software게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll discuss chip demand, personal agents, software competition and how AI may change enterprise buying.
1:08:25ENAlex Atallah on OpenRouter, Model Competition and AI Marketplaces게스트 Alex Atallah · 진행자 Harry StebbingsThe OpenRouter CEO discusses inference providers, open-weight models, enterprise safety and the economics of routing.
1:20:22EN20VC Roundtable on Open Weights, AI Spending and Software Investing게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate AI safety, chip investment, enterprise budgets and the future of software.
1:27:39EN20VC Roundtable on Open Models, AI Economics and Payments Deals게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll discuss model security, inference investment and a reported PayPal proposal.
1:28:42ENLin Qiao on Specialized AI, Open Models and the Economics of Inference게스트 Lin Qiao · 진행자 Harry StebbingsThe Fireworks AI CEO explains her case for customized models, discusses infrastructure trade-offs and forecasts lower AI costs and higher use.
2:06:41ENZero the J-Space and a Model's Hidden Goal Lights Up on Token One진행자 Nathan LabenzA weekly Cognitive Revolution cut of Anthropic's global-workspace paper, AI superforecasters, open video world models, and a chip that treats inference as data movement.
1:11:53ENSierra Co-founder Clay Bavor on Open Models, Enterprise AI and Building Teams게스트 Clay Bavor · 진행자 Harry StebbingsBavor discusses model selection and compute costs, deploying AI agents for enterprises, and how Sierra approaches hiring and company culture.
1:10:15ENThe 100x lives between the layers: Dylan Patel on co-design and a 60x yearSemiAnalysis’s founder argues that matching models, kernels, and silicon is what turns a 2x here and a 2x there into 100x, while equivalent-quality inference has gotten about 60 times cheaper in a year.
1:35:16ENPerplexity CEO Aravind Srinivas on AI Agents, Compute and Competition게스트 Aravind Srinivas · 진행자 Harry StebbingsSrinivas discusses the future of AI products, infrastructure constraints, export controls and the challenge of building a business around model advances.
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