Latest Interviews
Showing 1–3 of 3 transcripts.
Clear all filters- Sequoia Capital52 min
Turning Graph AI into ROI ft Kumo’s Hema Raghavan
Hema Raghavan, Konstantine Buhler, Sonya Huang, Constantine, Sonia
Kumo AI co-founder Hema Raghavan presents a GPU-accelerated AutoML platform that automatically constructs graph structures from relational data to enable predictive SQL-like queries without manual engineering. The system delivers rapid four-week proofs of concept for sectors ranging from fintech fraud detection to healthcare demand forecasting by integrating directly with Snowflake and Databricks while maintaining strict data residency. By combining optimized cost architectures with explainability features and LLM synergies, Kumo lowers the barrier to graph learning for diverse enterprises requiring immediate, accurate model insights.
- Sequoia Capital52 min
Cracking the Code on Offensive Security With AI ft XBOW CEO and GitHub Copilot Creator Oege de Moor
Oege de Moor, Konstantine Buhler, Sonya Huang
Former Oxford professor and GitHub Copilot creator Uge Demore's company Expo deploys an autonomous AI system to automate offensive security testing, achieving an 85% success rate on proprietary benchmarks while matching top human penetration testers in 28 minutes instead of 40 hours. The platform continuously identifies critical vulnerabilities in major financial institutions and replaces traditional $18,000 manual tests with a scalable, subscription-based service designed to outpace AI-assisted cyber threats. Operating on a foundation of proprietary training data and strict cloud-based guardrails, Expo aims to transform web security standards by making continuous, automated offensive testing accessible to organizations of all sizes.
- Sequoia Capital1h 3m
Getting the Most From AI With Multiple Custom Agents ft Dust’s Gabriel Hubert and Stanislas Polu
Gabriel Hubert, Stanislas Polu, Konstantine Buhler, Pat Grady
Dust positions itself as a horizontal platform for AI adoption, predicting a bimodal future where enterprise users seamlessly switch between frontier APIs and local models to navigate varying technological plateaus. By prioritizing product-market fit over proprietary model training and leveraging Retrieval-Augmented Generation to unlock data silos, the company enables diverse teams to build specialized agents that augment human work rather than replace it. This strategy targets a demographic of young power users and aims to scale from isolated pilots to organization-wide adoption, facilitating everything from cross-functional translation to global expansion despite current limitations in reasoning breakthroughs.