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1:04:26EN
Sequoia Capital· 6. Okt. 2026PodcastAt 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.Infrastruktur & DeploymentAI cost & performanceInference+3
1:10:15EN
Sequoia Capital· 30. Juni 2026PodcastThe 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.Infrastruktur & DeploymentInferenceBenchmarks+3
44:19EN
Sequoia Capital· 17. März 2026PodcastThe $500-per-Kilogram Line: Why Starcloud Thinks Data Centers Belong in SpaceStarcloud founder Philip Johnston walks through the physics, launch economics, and business model of putting AI compute in orbit.Infrastruktur & DeploymentInferenceAI cost & performance+2
1:02:19EN
Sequoia Capital· 10. Dez. 2025PodcastA Top Video Model's Half-Life Is 30 Days. fal Runs 600 AnywayThe fal founders tell Sequoia why generative video is a compute-bound problem that language-model shops keep underestimating, and why they will not let any customer marry a single model.Infrastruktur & DeploymentGenerative mediaVision & Video+3
41:32EN
Sequoia Capital· 28. Okt. 2025PodcastMoore's Law Is Slowing. Nvidia's CTO Still Wants 10× a YearMichael Kagan on why Nvidia's $7 billion Mellanox deal, not just more transistors, is how you turn a data center into one computer.Infrastruktur & DeploymentDistributed training & inferenceInference+2
39:18EN
Sequoia Capital· 13. Aug. 2024PodcastOpen and Closed Will Tie at the Same Size: PyTorch's Former Lead on Fast InferenceA six-month library swap at Meta became a five-year rebuild. Lin Qiao wants the rest of the industry to get that stack in five weeks.Infrastruktur & DeploymentInferenceFine-Tuning+3
1:00:28EN
Sequoia Capital· 9. Juli 2024PodcastScaling Laws Aren't Done. Don't Lock Yourself Into Yesterday's Architecture.Microsoft CTO Kevin Scott on why the exponential is still compounding, why inference will dwarf training, and why a language model is the world's crappiest database.Unternehmen & StrategieScaling LawsInference+3