Latest Interviews
Showing 1–5 of 5 transcripts.
Clear all filters- Sequoia Capital49 min
The Breakthroughs Needed for AGI Have Already Been Made: OpenAI Former Research Head Bob McGrew
Bob McGrew, Stephanie Zhan, Sonya Huang
Bob McGrew defines 2025 as the "year of reasoning," a pivotal shift where immediate compute utilization and tool-augmented chain-of-thought capabilities drive rapid progress toward AGI while pre-training faces diminishing returns. This paradigm reframes post-training as a behavioral challenge and positions AI agents to democratize intelligence by pricing services at compute costs rather than professional market rates. Consequently, economic value accrues to application layers requiring proprietary enterprise context, while sectors ranging from robotics to software engineering transition to hybrid human-agent workflows that leverage these new efficiency gains.
- Sequoia Capital56 min
The Quest to ‘Solve All Diseases’ with AI: Isomorphic Labs’ Max Jaderberg
Founded to construct a general AI-driven drug design engine, Isomorphic Labs has leveraged Demis Hassabis's Nobel Prize-winning leadership and Max Yoderberg's AI expertise to release AlphaFold 3, a diffusion-based model that predicts the 3D structures of proteins, DNA, RNA, and small molecules. By replacing months of traditional crystallization with instant in-silico analysis, the company collaborates with industry partners like Eli Lilly and Novartis to navigate the 10^60-sized chemical space and advance programs in oncology and immunology without operating its own wet labs. This approach positions AI as a fundamental, indispensable tool for the entire pharmaceutical industry within five years, while the firm advocates for new regulatory frameworks to validate these predictive models for clinical trials.
- Sequoia Capital53 min
From AlphaGo to AGI ft ReflectionAI Founder Ioannis Antonoglou
Ioannis Antonoglou, Stephanie Zhan, Sonya Huang, Giannis Antinoglou
DeepMind founders Demis Hassabis and Shane Legg pioneered Artificial General Intelligence research by utilizing video games as controlled testbeds, evolving from AlphaGo's human-supervised neural networks to the self-learning AlphaZero and MuZero architectures. This strategic shift addressed critical limitations like hallucination and the "data wall" by prioritizing reinforcement learning and planning over static data pre-training, a methodology now considered essential for future AGI development. Looking ahead, experts predict that within five years, increased compute will directly yield higher intelligence in autonomous agents, marking a transition toward systems capable of independent reasoning and novel scientific discovery.
- Sequoia Capital1h 8m
GitHub CEO Thomas Dohmke on Building Copilot, and the the Future of Software Development
Thomas Dohmke, Stephanie Zhan, Sonya Huang
GitHub CEO Thomas Domke outlines a strategic vision to empower one billion developers by 2030 through AI integration, highlighting that Copilot has already secured over 1.8 million paid subscribers and drives significant productivity gains by automating up to 40% of current coding tasks. The platform is expanding beyond basic code generation with new Enterprise customization, Autofix security protocols, and a multi-agent Workspace designed to guide workflows from specification to implementation. Domke anticipates a hybrid model of open and closed-source architectures and a future beyond transformers, while maintaining a philosophy that AI should augment rather than replace human developers in an ecosystem spanning software to physical robotics.
- Sequoia Capital1h 7m
Reflection AI’s Misha Laskin on the AlphaGo Moment for LLMs | Training Data
Misha Laskin, Ioannis Antonoglou, Stephanie Zhan, Sonya Huang, Peter Abbeel, Rich Sutton, Joe Bardeen, Einstein, Michael Jordan
Founders Misha Laskin and Giannis, leveraging their DeepMind and Google experience, established Reflection AI to solve the reliability bottleneck in autonomous agents by replacing heuristic prompting with scalable search and reinforcement learning. The company addresses the "depth problem" in current LLMs by treating post-training as an AlphaGo-style pipeline that minimizes error accumulation to transition task completion rates from approximately 13% to near-perfect reliability. With a strategic vision targeting digital AGI within three years, Reflection aims to deploy universal agents capable of complex multi-step reasoning while prioritizing pragmatic safety through operational consistency.