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1:04:26EN
Sequoia Capital· 2026年10月6日ポッドキャストAt 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.インフラとデプロイAI cost & performanceInference+3
1:10:15EN
Sequoia Capital· 2026年6月30日ポッドキャストThe 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.インフラとデプロイInferenceBenchmarks+3
44:19EN
Sequoia Capital· 2026年3月17日ポッドキャストThe $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.インフラとデプロイInferenceAI cost & performance+2
1:02:19EN
Sequoia Capital· 2025年12月10日ポッドキャストA 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.インフラとデプロイGenerative media画像と動画+3
41:32EN
Sequoia Capital· 2025年10月28日ポッドキャストMoore'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.インフラとデプロイDistributed training & inferenceInference+2
39:18EN
Sequoia Capital· 2024年8月13日ポッドキャストOpen 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.インフラとデプロイInferenceファインチューニング+3
1:00:28EN
Sequoia Capital· 2024年7月9日ポッドキャストScaling 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.企業と戦略Scaling LawsInference+3