필터 결과
75개정렬
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.
32:20ENWho Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.
2:00:51ENResample Every Token and You Can Watch the Model Decide진행자 Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:09:44EN10,000 GB300s for Four Weeks: Reflection’s Open-Model Ledger진행자 Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.
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.
1:20:28ENAI 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.
1:10:08ENJerry Murdock on AI Financing, Open Models and Technology Cycles게스트 Jerry Murdock · 진행자 Harry StebbingsInsight Partners' co-founder discusses credit risk, specialized models, security and how investors might navigate an uncertain AI cycle.
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models Banned진행자 Nathan LabenzFlo Crivello tells Nathan Labenz how Lindy Teammate lives in Slack, reaches two billion tokens of memory in two model calls, and why he would still outlaw the cheap Chinese models that currently power the product.
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.
1:09:26ENAnastasios Angelopoulos on Arena, Open Models and AI Evaluation게스트 Anastasios Angelopoulos · 진행자 Harry StebbingsArena’s CEO discusses real-world model comparisons, enterprise AI control, security and the economics of evaluation.
2:16:29ENFrom Two Papers a Month to Sixty: What Two Weeks in China Revealed About AI Safety진행자 Nathan LabenzNathan Labenz argues that the familiar 'China will never slow down' line does not survive contact with the actual Chinese AI safety ecosystem.
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.
51:31ENThey Refunded Nearly $2 Million. Then Factory’s CEO Explained the Dark FactoryMatan Grinberg on being two years early, why a model-agnostic harness beats lab co-design, and why 90 percent of coding tokens will soon run with the lights off.
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.
1:00:08ENArvind Jain on Enterprise AI, Open Models and the Future of Work게스트 Arvind Jain · 진행자 Harry StebbingsThe Glean founder debates model competition, enterprise AI returns and whether productivity gains will shrink teams or raise the bar for everyone.
1:11:01ENMike Mignano on AI Applications, Open Models and Venture Strategy게스트 Mike Mignano · 진행자 Harry StebbingsThe USV partner discusses AI agents, token spending and the opportunities and risks for startups building above the model layer.
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.
1:47:38EN19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chip진행자 Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.
1:28:02EN20VC Roundtable: Open Models, AI Spending and the Cost of Competition게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate AI talent, model economics, consulting and whether labs should build their own chips.
1:06:36ENBills Doubled Overnight. Next, the APIs Might Vanish.진행자 Jacob EffronSix months after their last roundup, Ari Morcos and Rob Toews tell Jacob Efron why coding agents finally work, why near-frontier open weights may have peaked, and why a compute famine could pull the plug on public APIs.
1:14:30ENRoman Chernin on AI Compute Demand, Inference and Infrastructure게스트 Roman Chernin · 진행자 Harry StebbingsNebius’s co-founder discusses customer demand, GPU economics, managed inference and the risks of a concentrated market.
42:57ENInference Is the Last Market: Baseten’s 30x Year진행자 Sarah Guo, Elad GilCEO Tuhin Srivastava on a billion-dollar inference cloud, why the app layer survives, and a GPU crunch worse than the narrative.
54:26ENTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-Ready진행자 Nathan LabenzMiniMax researcher Olive Song explains how M2 was trained with expert developers as reward models, interleaved thinking, and a perturbation pipeline that treats every scaffold change as the real generalization test.
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