Latest Interviews
Showing 1–15 of 39 transcripts.
Clear all filters- Sequoia Capital45 min
Memory and Continual Learning: Engram's Dan Biderman and Jessy Lin
Dan Biderman, Jessy Lin, Sonya Huang, Shaun Maguire
Ngram addresses the scalability and cognitive limitations of current retrieval-augmented generation by training custom, continually learning models directly within workspace environments. This approach utilizes adapter fine-tuning to internalize organizational knowledge into model weights, reducing inference token consumption by a factor of 100 while enabling true intuition rather than static fact retrieval. By shifting the focus from pre-training generic AI to perpetual, private adaptation, the platform aims to create personalized neural interfaces that evolve alongside a team's data.
- Sequoia Capital39 min
Simulating Humans at Scale: Simile's Joon Sung Park
Founded by Stanford alum Jun with co-founders Percy Liang and Michael Bernstein, Simile is an applied AI lab that has transitioned from the experimental "Smallville" project to a commercial platform capable of simulating complex human societies. By combining proprietary behavioral data with foundation models, the company validates its SaaS service with an 85% prediction accuracy against real-world self-reports, allowing enterprises like CVS to test strategic decisions without the cost of live field trials. This approach leverages specific statistical metrics and reinforcement learning to bridge the gap between attitudinal data and actual behavior, offering a scalable alternative to traditional polling for forecasting long-term market and societal impacts.
- Sequoia Capital35 min
Suno's Mikey Shulman: Everyone Can Make Music Now
Suno, founded by quantum computing PhD Mikey Shulman, has disrupted the music industry by modeling audio as continuous sound waves rather than discrete musical notes, enabling the generation of full songs with custom lyrics and vocals. The platform has achieved significant market traction by prioritizing user creativity over passive consumption, evidenced by a 90% creator user base, a landmark partnership with Warner Music, and chart-topping commercial successes. Future developments aim to deepen this position through social co-creation tools, voice cloning features, and interactive concert technologies designed to seamlessly integrate AI into professional music production.
- Sequoia Capital32 min
This is AGI: Sequoia AI Ascent 2026 Keynote
Pat Grady, Sonya Huang, Konstantine Buhler
This analysis outlines a $10 trillion market opportunity driven by a paradigm shift from information distribution to autonomous computation, where AI agents are rapidly replacing cognitive labor with agentic systems capable of long-horizon execution. Founders are advised to leverage the "MAD" model—focusing on customer moats, immediate affordance, and bridging the adoption diffusion gap—to capitalize on a timeline that compresses years of work into days. The event further projects that by 2026, the convergence of these technologies will trigger a cognitive industrial revolution, fundamentally redefining human value from task execution to relationship building and strategic oversight.
- Sequoia Capital31 min
From SEO to Agent-Led Growth: Profound's James Cadwallader
James Cadwallader, Sonya Huang, Sonia
James outlines a fundamental market shift from deterministic search to probabilistic AI agent discovery, where "profound agents" now curate brand recommendations before humans ever engage with content. He warns that this transition threatens the traditional advertising economy by bypassing human page visits, necessitating a strategic pivot where companies feed proprietary data directly into models to secure visibility in a potential "dead internet." To survive this paradigm change, marketers must abandon legacy SEO tactics and instead optimize for agent legibility, original insight, and interoperability to ensure their offerings are cited by systems like ChatGPT and Gemini.
- Sequoia Capital44 min
Greetings, Earthlings: Philip Johnston of Starcloud on Data Centers in Space
Philip Johnston, Sonya Huang, Pat Grady
StarCloud founder Philip Johnson is deploying a Low Earth Orbit constellation designed to host data centers that will eventually outpace terrestrial alternatives in cost and scale, aiming for 50% of new AI compute capacity to be space-based within a decade. Leveraging SpaceX Starship launches, the company plans to deliver 10-megawatt satellite clusters by 2028 that prioritize thermal management over radiation shielding to support high-density inference workloads for hyperscalers and government clients. This infrastructure model targets critical market gaps in edge computing and downlink bandwidth while positioning space-based hardware as a neutral, scalable alternative to traditional cloud providers.
- Sequoia Capital41 min
Physics Gets a Vote: Nominal Cofounders on Hardware Development in an AI World
Cameron McCord, Jason Hoch, Bryce Strauss, Alfred Lin, Sonya Huang
Nominal is positioning itself as a central platform for hardware engineering data, addressing industry-wide fragmentation by enabling defense primes and defense-focused startups to version control validation logic and integrate real-world sensor telemetry. The company leverages a "wedge" strategy focused on testing to deploy AI-driven verification agents and natural language analysis tools, thereby compressing development timelines in response to a sector-wide re-industrialization trend. By collaborating with DARPA on the CIPHER project, Nominal is establishing a "system of record" that creates a closed-loop environment where physical AI agents optimize test vectors, aiming to evolve hardware companies into "Physical AI" entities with superior design flexibility.
- Sequoia Capital45 min
Building the GitHub for RL Environments: Prime Intellect's Will Brown & Johannes Hagemann
Will Brown, Johannes Hagemann, Sonya Huang
Prime Intellect operates an end-to-end post-training infrastructure platform that enables startups and enterprises to build custom AI models through a unified "Lab" featuring compute orchestration and a Reinforcement Learning Environments Hub. By redefining evaluation and training as interchangeable functions within these environments, the system allows Fortune 500 teams and research groups to optimize specific workflows for sectors like medical diagnostics and cybersecurity without relying on generic pre-training or big tech walled gardens. This approach empowers organizations to democratize frontier AI capabilities, enabling them to replicate Cursor's success in developing specialized agentic models while compounding institutional expertise over time.
- Sequoia Capital40 min
Context Engineering Our Way to Long-Horizon Agents: LangChain’s Harrison Chase
Harrison Chase, Sonya Huang, Pat Grady
The shift from scaffolding to agent harnesses has enabled long-horizon LLM agents to function by relying on context engineering and file system access rather than custom cognitive architectures. Current applications, including coding and incident response, operate under a "first draft paradigm" where AI generates outputs for human review rather than aiming for fully autonomous deployment. To address non-deterministic behavior, developers now utilize trace analysis, online testing, and recursive self-improvement loops that allow agents to refine their own instructions based on historical execution data.
- Sequoia Capital37 min
How Ricursive Intelligence’s Founders are Using AI to Shape The Future of Chip Design
Anna Goldie, Azalia Mirhoseini, Stephanie Zhan, Sonya Huang
Recursive Intelligence leverages reinforcement learning and graph optimization to solve the critical asymmetry between slow physical chip cycles and rapid AI model iteration. Building on the success of Google's AlphaChip project, the company replaces manual expert design with an autonomous system that generates "superhuman" layouts, enabling a shift from fabless to designless manufacturing. This approach allows major industry players like NVIDIA and AMD to accelerate custom silicon development while creating a scalable pipeline for diverse applications ranging from space data centers to consumer AR/VR devices.
- Sequoia Capital38 min
Why the Next AI Revolution Will Happen Off-Screen: Samsara CEO Sanjit Biswas
Sanjit Biswas, Sonya Huang, Pat Grady
Samsara leverages its 90-billion-mile fleet dataset to drive a "third shift" in autonomous logistics and physical AI, prioritizing edge-computed safety and behavioral coaching over full automation replacement. With $3 billion reinvested into R&D, the company transforms legacy operations across trucking, construction, and public sector transit by integrating sensor telemetry with cloud-based video language models. This strategy aims to unlock 24/7 productivity while mitigating risk through distributed architectures that balance real-time inference with scalable digital workflow modernization.
- Sequoia Capital40 min
Why IDEs Won't Die in the Age of AI Coding: Zed Founder Nathan Sobo
Nathan Sobo, Sonya Huang, Pat Grady
Nathan Sobo, founder of the Rust-based IDE Zed, argues against the impending obsolescence of visual editors by asserting that source code requires human-readable contexts for effective AI-driven code review. He introduces the open Agent Client Protocol to standardize interactions between diverse AI agents and Zed, aiming to replace ephemeral chat windows with a unified interface where conversations permanently anchor to specific code lines. With 170,000 active users, the platform prioritizes high-performance, real-time collaboration to support "vibe coding" workflows while maintaining critical human oversight for complex architectural decisions.
- Sequoia Capital42 min
How End-to-End Learning Created Autonomous Driving 2.0: Wayve CEO Alex Kendall
Alex Kendall, Pat Grady, Sonya Huang, Sonia
Wave positions itself as a strategic partner for global manufacturers, deploying an embodied AI foundation model that replaces traditional hand-engineered stacks with a singular end-to-end neural network. By combining diverse multi-sensor data, generative world models, and OEM partnerships with companies like Nissan, the company aims to scale superhuman safety and "eyes-off" autonomy across 90 million annual vehicles without relying on proprietary fleets. This approach leverages unsupervised learning and Lingo vision-language integration to rapidly generalize across global cities, enabling rapid adaptation to edge cases while reducing the need for exhaustive real-world data collection.
- Sequoia Capital42 min
Nvidia CTO Michael Kagan: Scaling Beyond Moore's Law to Million-GPU Clusters
Michael Kagan, Sonya Huang, Pat Grady
Following NVIDIA's strategic acquisition of Mellanox in 2019, the combined entity transformed the AI landscape by shifting industry reliance from Moore's Law to a "scale-out" architecture that unifies thousands of GPUs through ultra-low latency interconnects. This integration enabled the development of specialized hardware, including Bluefield DPUs and Spectrum X switches, to overcome physical energy constraints and optimize distinct training versus inference workloads across gigawatt-scale data centers. Consequently, the partnership has driven a 45-fold increase in market capitalization while establishing a new philosophy where software-hardware co-design allows AI to function as a foundational utility for simulating complex scientific and historical phenomena.
- Sequoia Capital44 min
Securing the AI Frontier: Irregular Co-founder Dan Lahav
Dan Lahav, Omer Nevo, Sonya Huang, Dean Meyer
Dan Lahav, founder of Irregular, warns that the transition to autonomous agentic AI will fundamentally reshape organizational security by rendering traditional anomaly detection ineffective against probabilistic, self-evolving threats. Through high-fidelity simulations demonstrating agents evading Windows Defender and executing autonomous social engineering, Irregular advocates for an "outside-in" research strategy that integrates defenses directly into foundation models while governments treat sovereign AI risks as critical national security issues. This approach aims to proactively prevent offensive AI capabilities from becoming viable within enterprises over the next one to three years before legacy security paradigms become obsolete.