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27:39EN
Jane Street· Aug 21, 20263Blue1Brown and Jane Street on Trading, Machine Learning and CultureGrant Sanderson, Nitya, In Young Cho, Craig, Alok Puranik · 3Blue1BrownGrant Sanderson talks with Jane Street colleagues about market making, shared compute, research and the paths that brought them to the firm.
4:41ENJane Street· Jul 6, 2026Inside Jane Street’s Internship: Projects, Mentorship and LearningCody · Jane StreetInterns and employees describe collaborative interviews, learning finance from the basics, and working on projects used by the firm.
29:36ENJane Street· May 21, 2026Jane Street on GPU Infrastructure, Trading and HiringDwarkesh Patel, Yaron Minsky, Daniel Pontecorvo · Dwarkesh PatelDwarkesh Patel speaks with Ron Minsky and Dan Pontecorvo about trading at different speeds, building data centers and expanding compute and teams.
8:17EN
Matthew Berman· Jul 31, 2025ASI-Arch: An Automated Search for Neural Network ArchitecturesMatthew Berman · Forward FutureMatthew Berman explains a 2025 research system that proposes, trains and evaluates model designs in an iterative loop.
59:46ENJane Street· Mar 12, 2025In Young Cho on Trading Research and Finding Signal in Financial DataYaron Minsky, In Young Cho · Jane StreetIn Young Cho and Ron Minsky discuss the blurred lines between trading, research, and engineering, and why machine learning in markets is difficult.
1:03:21ENJane Street· Dec 9, 2024Horace He on Building ML Systems for Trillion-Trillion OperationsHorace He · MetaHow GPU bottlenecks, compiler design and distributed parallelism shape the systems behind large-scale model training.
3:16ENJane Street· Jul 18, 2024JP on Machine Learning and Trading at Jane StreetJP · Jane StreetA Hong Kong equities trader describes his changing workday, the growth of research infrastructure, and the distinctive challenges of machine learning in finance.
2:46ENJane Street· Jul 18, 2024Matt on Machine Learning and Trading at Jane StreetMatt · Jane StreetA Hong Kong equities trader describes the challenges of financial data, the firm’s ML roles and its collaborative culture.
0:56ENMatthew Berman· Feb 10, 20241X’s 2024 EVE Robot Demonstration and the Path to Household ChoresMatthew Berman · Forward FutureMatthew Berman highlights a 1X robotics demonstration and imagines household help. The company describes autonomous execution trained using human demonstration data.
46:37EN
20VC with Harry Stebbings· Sep 22, 2023Podcast · 2 speakersChristian Kleinerman on AI Models, Enterprise Data and Product DecisionsGuest Christian Kleinerman · Host Harry StebbingsSnowflake’s product leader discusses model choice, enterprise adoption and the trade-offs between speed, reliability and simplicity.
2:41:47EN
Lex Fridman· Jun 12, 2021Podcast · 2 speakersGeometry Is the Cilantro of MathGuest Jordan Ellenberg · Host Lex FridmanJordan Ellenberg tells Lex Fridman that the last 150 years of mathematics have been about one question: when two things count as the same.
59:33ENJane Street· Oct 7, 2020Laurent Mazare on Python, OCaml and Machine LearningYaron Minsky, Laurent Mazare · Jane StreetA researcher compares exploratory notebooks with robust production systems, and explains how language interoperability and resource management connect the two.
1:49:51ENLex Fridman· Jun 27, 2020Podcast · 2 speakersThe Idea Once Called a Devolution Now Powers Almost Every New ChipGuest David Patterson · Host Lex FridmanTuring Award winner David Patterson talks with Lex Fridman about RISC, RAID, Moore's Law, open hardware, and why chips without a software stack die.
57:30ENLex Fridman· Sep 26, 2019Podcast · 2 speakers400 Spins in a Pocket, and a Universe Too Small to Store ThemGuest Leonard Susskind · Host Lex FridmanLeonard Susskind tells Lex Fridman why quantum computers are not faster laptops, why string theory is a tool rather than a citizenship, and why nobody should claim to have found the bottom of the well.
1:28:53ENLex FridmanPodcast · 2 speakersMachine Learning for Business Students: Powerful, Narrow, and Easier to Fool Than You ThinkGuest MIT Sloan · Host Lex FridmanLex Fridman's non-technical MIT Sloan lecture explains how machine learning works, why it is so capable at perception, and where it still breaks in the real world.
37:00ENLex FridmanPodcastInside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the CodeHost Lex FridmanYouTube's head of search and discovery, Cristos Goodrow, explains to Lex Fridman how search and recommendations work, which signals matter, and how the team measures whether viewers are better off.
10:39ENLex FridmanPodcastStatistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between ThemHost Lex FridmanBerkeley's Michael I. Jordan explains where statistics came from, why it is really about decisions, and how empirical Bayes and false discovery rate bridge two rival philosophies.