Latest Interviews
Showing 1–3 of 3 interview transcripts.
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Ben Horowitz and David Solomon: The Sweetest Macro Spot in 40 Years
Ben Horowitz, David Solomon, David Haber
Goldman Sachs is expanding its balance sheet to between $2.5 and $3.5 trillion by shifting to a stable deposit model, while simultaneously deploying $6 billion in technology to automate operations and drive efficiency. Concurrently, Andreessen Horowitz has solidified its position as the largest U.S. venture firm by capturing 18.3% of capital in 2025, leveraging AI to scale investments from 15 to 150 major successes annually. These strategies converge within a stimulative macroeconomic environment characterized by fiscal support and deregulation, which is expected to trigger record M&A activity and accelerate the public listings of AI-driven companies despite lingering geopolitical risks.
- a16z30 min
Marco Argenti (Goldman Sachs): Turning Developers into Clients
Marco Argenti, a veteran engineer whose career spans four major technology shifts, now leads Goldman Sachs' engineering strategy by treating internal developers as primary customers to drive a culture of rapid, standardized innovation. Leveraging a "look outside first" approach and a selective AI pilot program, the firm has achieved productivity gains of up to 40% while preparing to expand generative tools to knowledge workers across its financial services division. Argenti predicts that within a decade, this hybrid AI architecture will transform the industry into a real-time business where speed of pricing and decision-making becomes the defining competitive advantage.
- a16z37 min
Marty Chavez (Sixth Street): Finding a Single Source of AI Truth
Marty Chavez, a former Goldman Sachs executive and current partner at Sixth Street Partners, traces the evolution of computational simulation from his foundational work on the SECDB system that navigated the 2008 financial crisis to his current advocacy for AI boundaries in finance and biotech. Drawing on decades of experience modeling complex systems for everything from currency trading to protein folding, he argues that future AI regulation should focus on physical interface constraints rather than internal algorithmic logic while emphasizing the critical need for clean data to avoid generative hallucinations. Chavez further notes that while current AI adoption in the Fortune 100 is driven by productivity demands, its transformative potential in life sciences depends on successfully bridging the gap between vast theoretical chemical spaces and the high costs of physical drug fabrication.