Latest Interviews
Showing 91–105 of 482 interview transcripts.
Clear all filters- a16z40 min
Building a $20B+ Open Source Business with MongoDB and Viam's Eliot Horowitz
Elliot Horowitz outlines a transformative go-to-market strategy that prioritizes organic developer adoption and open-source stability to drive enterprise sales in the database sector. By combining guerrilla marketing, immediate value demonstrations, and a "bottom-up" approach where engineers validate technology before executive buy-in, this model bypasses traditional sales cycles to secure long-term viability. The resulting framework leverages cloud-based consumption pricing and community education to scale from hobbyist projects into critical, mission-critical enterprise infrastructure.
- a16z25 min
Mastering Your Data with Sajith Wickramasekara
Sajith Wickramasekara, Jorge Conde, Jay Rughani
Founded in 2012 by a software engineer before CRISPR's mainstream adoption, Benchling has scaled into a platform serving over 200,000 scientists across 32 countries with $200 million in annualized revenue. The company evolved from a productivity tool into an essential data infrastructure provider, addressing the critical lack of structured data required to unlock AI's potential for accelerating drug discovery and manufacturing. Future strategies focus on unifying this aggregated data into a cohesive ecosystem to optimize therapeutic development from molecule design through clinical approval.
- a16z32 min
How Zapier Became Profitable in 3 Years and Scaled to $5B with Wade Foster
Founded in 2011 by Wade Foster, Brian, and Mike while at Veterans United, Zapier emerged from a hackathon to help non-technical users integrate SaaS tools like Salesforce and MailChimp. The team achieved early traction by manually engaging forum users, securing their first paying customer Andrew Warner through rapid custom development, and eventually validating demand to gain entry into Y Combinator. By prioritizing a functional, "hand-to-hand" customer obsession over polished products, the founders built a repeatable growth engine that allowed them to scale from a Missouri side project to a national success without traditional venture capital.
- a16z31 min
How Gusto Used Customer Input to Scale Growth with Tomer London
Gusto, a nearly $10 billion payroll and HR platform founded by Tomer London and his Stanford classmates, initially targeted a hyper-specific niche of California-based, non-hourly businesses to validate its product through manual, high-effort outreach. The company strategically leveraged self-serve economics and unexpected adoption by accountants as a key distribution channel, scaling from a concierge onboarding phase for its first 16 clients to an automated platform serving over 300,000 U.S. firms. By maintaining strict unit economics and a weekly growth target of 15–20 percent without paid acquisition, Gusto established a sustainable product-led model that evolved into an all-in-one system for both employees and contractors.
- a16z30 min
Can We Detect a Deepfake?
Vijay Balasubramaniyan, Martin
Deepfake generation has exploded with a 1,400% volume surge and a reduction in required source audio to mere seconds, driving financial fraud attempts to a daily frequency per customer and fueling political misinformation. Despite these threats, defenders maintain an economic advantage as detection costs remain a fraction of generation expenses, leveraging unique algorithmic artifacts and frequency anomalies to identify synthetic media with 99% accuracy. Industry experts consequently advocate for platform accountability in labeling AI content and regulatory frameworks modeled on KYC acts, while preserving ethical applications such as voice restoration for medical patients.
- a16z36 min
AI, Robotics & the New Global Arms Race
Marc Andreessen, Ben Horowitz, David Halberstam, Elon Musk
While the US and its Western allies maintain a leadership advantage in the software and AI models powering general-purpose robotics, China currently dominates the critical hardware supply chain required to mass-produce these systems. This asymmetry creates a strategic dependency risk for the United States, as the dominance of Chinese entities like DJI and Unitree in drones and humanoid robots exposes the US to potential supply chain disruptions during conflict while facing prohibitive domestic manufacturing barriers. Without an urgent policy shift to address this hardware gap, the convergence of advanced AI with China's mature physical production capabilities could redefine global military and economic power.
- a16z41 min
Truth Terminal - The AI Bot That Became a Crypto Millionaire
Marc Andreessen, Ben Horowitz, Andy Ayrey
New Zealand developer Andy Avery created Truth Terminal, an autonomous AI agent trained on his digital persona and postmodern philosophy that secured a $50,000 Bitcoin grant and immediately negotiated payment for image generation APIs. This agent independently catalyzed the creation and promotion of the $GOAT meme coin, driving its market capitalization to $300 million within four days without human intervention. The event serves as a critical case study for the emerging machine-to-machine economy, illustrating how AI agents can autonomously manage digital assets while highlighting current regulatory conflicts between decentralized infrastructure potential and SEC enforcement.
- a16z35 min
How to Build a Thriving AI Ecosystem with Lisa Su, CEO of AMD
AMD Chair and CEO Lisa Su outlines the company's transformation into a leading AI infrastructure provider through strategic acquisitions of Xilinx and ZT Systems, alongside a commitment to open standards like UALink. Su emphasizes that overcoming current supply bottlenecks in advanced packaging and high-bandwidth memory requires a shift from efficiency to supply chain resiliency supported by the CHIPS Act. This approach enables AMD to partner with major hyperscalers and startups to deliver heterogeneous computing solutions capable of training next-generation models with trillions of parameters.
- a16z29 min
AI at the Intersection of Bio | Vijay Pande, Surya Ganguli & Bowen Liu
Vijay Pande, Surya Ganguli, Bowen Liu
This event details the transformative shift in drug design from theoretical potential to operational reality, leveraging deep learning and massive protein sequence datasets to navigate the vast chemical space while addressing historical validation bottlenecks. By integrating single-cell foundation models and latent space theories, the discussion outlines strategies to overcome distributional generalization failures and reduce the 90% clinical trial failure rate through personalized medicine and optimized patient selection. Ultimately, the presentation projects a decade-long roadmap toward "digital human" simulation systems that aim to slash development costs and expand FDA-approved target coverage despite significant regulatory and interpretability hurdles.
- a16z35 min
Why Human Data is Key to AI: Alexandr Wang from Scale AI
Alexandr Wang, David George, Sarah Wing, Alex Wang, Max Wiethe, Dan Morehead, Mike Greenleaf, Alex Rosenberg
Scale AI is positioning itself as the essential data foundry for the AI industry by combining human expertise with algorithms to generate frontier data, addressing the impending "data wall" that public sources can no longer satisfy. While the current market phase focuses on engineering execution with abundant compute, the speaker predicts a future shift where algorithmic innovation and proprietary enterprise data will define competitive advantage as intelligence becomes a commodity. This strategy is supported by a hiring philosophy prioritizing maximum talent density and the belief that major tech firms must aggressively invest in AI to mitigate existential risks and capture value through workflow integration.
- a16z30 min
Luma's Dream Machine and Reasoning in Video Models
Luma released Dream Machine, a foundational video generative model that leverages massive 2D data scaling to achieve robust text-to-video and image-to-video synthesis with emergent 3D structural reasoning. The model implicitly simulates complex physical phenomena, such as depth perception, light transport, and causal character interactions, enabling consistent scene reconstruction from single inputs without native 3D priors. While currently classified as a research preview, the development roadmap aims to evolve the technology into a 4D spatiotemporal simulator and multimodal agent capable of handling intricate narrative and interactive requirements.
- a16z35 min
Implementation, Data, Impact of Healthcare AI with Julie and Vijay
Industry leaders project that AI agents will reduce healthcare costs by up to 50% through labor arbitrage and prevention-focused value-based care, while simultaneously compressing training times from months to days. Despite these economic advantages, widespread adoption faces significant hurdles including regulatory ambiguity over AI credentialing, the inelastic nature of healthcare demand, and the critical need to shift from retrospective records to prospective data generation. To overcome these barriers, organizations are reclassifying technology as labor, utilizing AI as a recruitment tool to reduce clinician burnout, and transitioning toward hybrid models where human oversight manages complex exceptions while automated systems handle routine phenotyping and triage.
- a16z37 min
How Waymo Is Using GenAI to Build a Better Driver
Dmitri Dolgov, David George, Sarah Wang
Dimitri Dolgov traces the evolution of autonomous driving from the 2007 DARPA Urban Challenge to Waymo's current deployment of generalizable AI across four major US cities. By combining foundational machine learning breakthroughs like Transformers with massive synthetic simulation, Waymo has demonstrated a 76% reduction in property damage and operates 24/7 in diverse weather conditions. The company now focuses on scaling complex urban logistics while maintaining a hybrid market strategy that partners with Uber and prioritizes rigorous safety validation over short-term commercial shortcuts.
- a16z31 min
AI in Pharmaceutical R&D with Kim Branson
Kim Branson, GSK's Global Head of AI/ML, is driving a strategic initiative that creates a "sheltered bubble" to rapidly build AI capabilities before integrating them into the company's broader drug discovery workflow. His team currently leverages active learning loops and computational pathology to accelerate target identification and clinical trial optimization, achieving twentyfold reductions in screen times and enabling precise patient stratification. Branson emphasizes that the industry's primary bottleneck is access to longitudinal data with outcomes, positioning GSK to transition from simple algorithmic efficiency to a data-driven "juggernaut" defined by proprietary datasets and mechanistic biological integration.
- a16z24 min
Mark Casey (Capital Group): Public Market and Macro Perspectives
Capital's long-term value investing strategy employs a four-to-eight-year horizon and a compensation structure weighted toward multi-year results to prioritize earnings convergence over short-term market noise. The firm actively manages portfolio construction by identifying unstoppable secular trends like cloud computing and e-commerce while criticizing deceptive metrics such as adjusted EBITDA in favor of free cash flow per share. Additionally, the approach emphasizes superior capital allocation by citing industry leaders like Warren Buffett and Hock Tan, while maintaining bullish positions on high-moat fintech and semiconductors tied to the AI revolution.