Latest Interviews
Showing 1–15 of 75 transcripts.
Clear all filters- Sequoia Capital22 min
Continual Learning: How AI Agents Get Better With Every Use | Arjun Karanam, Trajectory
Arjun Karanam, Ronak, Gabe, Harrison, Nico, Harvey
Trajectory, co-founded by Arjun and Ronak, addresses the lack of accumulated experience in AI by building a platform that enables models to continuously learn from the 100 trillion daily tokens generated by real-world agent interactions. The company utilizes a dual-learning architecture combining differential privacy with reinforcement learning on user-corrected traces, allowing organizations to transition from static models to systems that compound capability through automated post-training and flexible harness optimization. By abstracting complex training parameters into a 15-minute workflow, Trajectory empowers enterprises to retain ownership of their specialized models while refining agent performance directly against production traffic.
- Sequoia Capital24 min
When to Build Your Own Agent Harness | Harrison Chase, LangChain
The framework defines autonomous agents as systems built from three owned components: the model, context, and a prioritized harness that orchestrates data flow through an iterative LLM loop. Organizations can customize this harness via middleware for domain-specific optimizations or maintain off-the-shelf versions for in-distribution tasks, ensuring compatibility through dynamic model profiles. Continuous improvement is driven by a flywheel where trace data from evaluations using the Harbor benchmark feeds into an automated engine that identifies failures and suggests prompt, code, or context fixes.
- Sequoia Capital26 min
RL Environments Explained: How AI Agents Learn Real-World Work | Brendan Foody, Mercor
Mercore has expanded its revenue run rate to $2 billion by transitioning the AI data market from basic crowdsourcing to high-skilled "agentic data" services that enable frontier labs to build complex reinforcement learning environments. The company leverages expert networks of lawyers, engineers, and doctors to create realistic simulated worlds with precise human-verified rubrics, demonstrating a fivefold increase in model performance on specific legal tasks during recent training. As the primary data vendor for major application layer companies, Mercore addresses the industry's need for ultra-long horizon tasks and social dynamics evaluations that synthetic models cannot yet self-generate.
- Sequoia Capital28 min
Post-Training Is How You Keep Your Taste | Fireworks CEO Lin Qiao
Fireworks CEO Linh Nguyen advocates for a strategic industry shift from relying on rented APIs to owning intelligence through deep model customization, enabling companies to preserve unique business judgment while reducing inference costs by five to ten times. This approach utilizes a structured lifecycle of data curation, fine-tuning, and serving loops to transition from generic prompting to specialized models, as demonstrated by success stories like Cursor and niche vertical leaders in healthcare and security. Ultimately, post-training is positioned as the critical mechanism for startups to scale after product-market fit by converting proprietary user data into unclonable domain expertise before high API expenses threaten unit economics.
- Sequoia Capital29 min
How Harvey Built a Research Lab on a Budget | Gabe Pereyra
Harvey, Gabe Pereyra, Brendan, Julio, Ross, Brock
Harvey differentiates itself from well-funded frontier labs by leveraging an application-layer strategy that combines synthetic data generation guided by domain experts with post-training on open-source models. The company builds specialized legal benchmarks and utilizes infrastructure partnerships to train agents on complex tasks like contract negotiation without exposing sensitive client information. By deploying these capabilities across multiple vendors and product surfaces, Harvey aims to solve organizational productivity challenges while mitigating the performance gaps inherent in current long-context environments.
- 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 Capital40 min
Knowing What Your Customers Want, All the Time: Listen Labs' Alfred Wahlforss
Listen Labs leverages its database of 30 million participants to transition from AI-conducted interviews to generative agent simulations that predict individual customer preferences with up to 95% accuracy. Founder Alfred Walforce directs the platform to serve Fortune 500 clients like Microsoft and Chubbies by identifying specific product friction points and integrating insights directly into autonomous coding workflows. This approach creates a proprietary feedback loop where behavioral data refines agent models, allowing businesses to validate product decisions without the churn and cost of traditional market research.
- Sequoia Capital25 min
Neuralink's DJ Seo: Inside the Race to Connect Brains and AI
DJ Seo, Shaun Maguire, Sean McGuire
Neuralink is advancing its brain-computer interface technology through the deployment of two therapeutic products, Telepathy and Blindsight, to restore motor function and vision for patients with conditions like ALS and total blindness. The company has implanted its devices in over 20 human participants and reports successful trials where users regained computer control or visual perception, supported by a strategy of vertical integration and an "all-green light" engineering framework. Founder J.D. McGuire envisions these medical breakthroughs as a precursor to creating an "exocortex" that merges human intent with AI, pending regulatory approval for broader non-medical augmentation.
- Sequoia Capital38 min
Rebuilding IT From the Ground Up for the AI Age: Serval's Jake Stauch
Serval positions itself as an AI-native ServiceNow platform that leverages a code generation engine to instantly build workflows from natural language, collapsing development timelines from months to near zero. By separating its architecture into a restricted Admin Agent and a reasoning-capable Help Desk Agent, the company balances autonomous employee support with strict enterprise security while avoiding token-based pricing through pre-built TypeScript automation. This approach allows Serval to operate with extreme talent density and a flatter structure, ultimately aiming to eliminate menial tasks so employees can align their daily work with professional aspirations.
- 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 Capital26 min
ElevenLabs' Mati Staniszewski: How Voice Becomes the Interface for AI
Mati Staniszewski, Andrew Reed, Alfred Fuller, Gabriel Sanchez, Konstantin
Established in 2022 by high-school friends Gabriel Sanchez and Piotr to address monotone localization in Poland, Eleven Labs has rapidly scaled to generate over $400 million in revenue with a flat, title-less organization of 400 remote employees. The company distinguishes itself by prioritizing immediate monetization and emotional nuance in audio synthesis, evolving its technology from text-to-speech to complex voice agents deployed by governments, enterprises, and educational platforms. With a roadmap focused on emotional intelligence and the integration of audio as the primary interface for robotics, the firm maintains defensibility through proprietary data and a vast ecosystem of user-contributed voices.
- Sequoia Capital27 min
Waymo's Dmitri Dolgov: 20 Million Rides and the Road to Full Autonomy
Dmitri Dolgov, Konstantine Buhler
Dmitry Dolgov, Waymo's co-founder, outlines a 21-year evolution from DARPA challenges to a sixth-generation autonomous fleet that has achieved 200 million fully autonomous miles and delivered over 20 million rides with a safety profile 13 times superior to human drivers. The company's architecture utilizes a multimodal "Foundation Model" integrating physics reasoning and spatial understanding to power a structured, end-to-end system across 11 cities, now accelerating toward global expansion in London and Tokyo through the newly launched Waymo Origin vehicle. This mission-driven approach bypasses incremental improvements to rapidly scale commercial deployment, treating full autonomy as an existential imperative to eliminate the 13 million annual road fatalities while transitioning the core technology into a primary family transportation tool.
- Sequoia Capital28 min
OpenAI's Greg Brockman: Why Human Attention Is the New Bottleneck
OpenAI executives estimate they are 80% toward achieving AGI, a milestone driven by aggressive compute acquisition strategies and architectural innovations that currently enable AI to autonomously engineer software kernels. The organization is pivoting toward an enterprise and consumer model where a single AGI entity handles complex goals, urging startups to leverage agentic coding tools for massive productivity gains while navigating a landscape where human attention has become the primary bottleneck. This shift is underpinned by anticipated scientific breakthroughs in physics and biology, alongside internal reforms designed to manage the risks of autonomous agents within a future where humans oversee teams of AI rather than writing code manually.
- 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.