David Solomon & Ben Horowitz on Building Organizational Resilience & Navigating Macro Uncertainty

Episode Summary

Executive Summary: The conversation explores how AI is changing competitive advantage, capital formation, enterprise operations, and policy. Goldman’s David Solomon emphasizes scale, funding stability, and AI-driven productivity to keep the firm relevant, while Ben Horowitz argues AI and crypto are forcing a rethink of venture, enterprise adoption, and U.S. policy. Both see faster company growth, more M&A/IPO activity, and a more capital-intensive technology race.

Main Topics: Goldman Sachs: scale, funding, and strategic relevance (Priority: 5/5): David Solomon explains how Goldman evolved from a partnership to a public company while preserving partnership culture, and why scale plus stable funding are essential to long-term competitiveness. AI as a reset of software and startup moats (Priority: 5/5): The discussion argues that AI weakens traditional defensibility: leads can be closed faster, money and compute matter more, and companies can scale to major revenue faster than ever before. Venture capital strategy after 2009 (Priority: 4/5): Ben Horowitz describes a post-crisis strategy built around founder-first venture capital, scaling the firm for a larger software market, and becoming top tier through product differentiation. Macro environment and capital markets (Priority: 4/5): Solomon frames the U.S. environment as highly stimulative due to fiscal and monetary policy, AI investment, and deregulation, which supports asset prices and deal activity. M&A and IPO outlook (Priority: 4/5): Both speakers expect a strong year for transactions and public offerings, though regulatory uncertainty—especially from the FTC—may shape deal structure and timing. Policy battles over crypto and AI (Priority: 5/5): Horowitz outlines A16Z’s agenda in Washington: crypto market structure, stablecoins, AI regulation, copyright, and preventing fragmented state-by-state rules that could slow innovation. AI inside the enterprise (Priority: 4/5): Goldman is using AI to boost employee productivity and redesign core processes through OneGS 3.0, aiming to free capacity for growth without sacrificing returns.

Key Arguments: AI changes the economics of competition: unlike the classic software era, a lead is not necessarily durable if rivals can buy compute, data, and models to catch up. In mature financial institutions, scale matters more in turbulent markets because it creates resilience, leverage, and funding flexibility. Goldman’s long-term risk is not just execution but losing relevance unless it keeps growing, funding efficiently, and adapting technology across the enterprise. A public-company Goldman needed permanent capital to stay globally relevant; remaining a private partnership would have limited expansion. Venture capital must be top tier to attract the best founders, and a founder-friendly product was necessary for A16Z to break in without legacy prestige. Software-eating-the-world implied a much larger venture market, requiring venture firms to scale their org design beyond the traditional small-partner model. The U.S. macro setup is unusually supportive for asset owners and technology businesses because fiscal stimulus, rate cuts, capex, and deregulation are all simultaneously expansionary. M&A and IPOs depend heavily on confidence; as confidence improves, deal volume should rise, though regulators may still constrain big transactions. Crypto policy is framed as a civilizational and market-structure issue: rules are needed to clarify token types, protect innovation, and prevent government overreach. AI policy should regulate harmful uses, not the mathematical model itself, and should avoid fragmented state laws that would burden startups and weaken U.S. competitiveness. Enterprises are still at the start of AI adoption; the biggest gains will come from reengineering workflows and processes, not just adding copilots for individuals.

Data Points: Goldman Sachs total deposits: about $500 billion - Solomon says Goldman now has a meaningful deposit base after having zero 15 years ago. Goldman digital deposit platform deposits: over $200 billion - He cites this as part of the firm’s stable funding transformation. Goldman deposit funding share: about 40% - Goldman funds roughly 40% of the firm with deposits rather than wholesale funding. Goldman technology spend last year: $6 billion - Solomon says they spent $6B on technology and would have preferred $8B. Potential tech spend gap: $2 billion - He argues process reengineering could free up enough efficiency to spend more without hurting returns. Largest companies’ GDP contribution: 1% of GDP growth - Solomon says the four largest companies drove 1% of GDP growth through $400B of spending. Largest companies’ spending: $400 billion - Used to illustrate the scale of current AI and capex investment. VC fundraising share: 18.3% of all U.S. venture capital raised in 2025 - Horowitz says A16Z reached this share, showing its scale in venture fundraising. AI company growth: zero to over $100 million and even zero to over $1 billion in less than a year - Horowitz cites unprecedented speed of AI-native company scaling. Number of special processes in OneGS 3.0: 6 - Goldman selected six processes for deep AI-driven reimagining. Rating of public-market environment: "as sweet a spot" as seen in 40+ years - Solomon’s view of the current macro setup for financial assets and investable assets.

Pivotal Quotes: "The best time to raise money is when nobody has money." — Ben Horowitz: Explaining why A16Z’s post-crisis fundraising in 2009 turned out to be advantageous. "If you have prioritized data and you have enough GPUs, you can solve almost any problem. It is magic." — David Solomon: Describing why AI is especially powerful for firms tied to financial assets and data-heavy workflows. "You can't just put a thousand software engineers on a product and wipe out a startup... That's not true with AI." — Ben Horowitz: Contrasting classic software defensibility with AI-era competition and capital intensity.

Implications: AI is making growth faster, competition harder, and capital needs larger. Firms and founders must rethink moats, funding, regulation, and operating models; those who scale data, compute, and process redesign fastest may gain the edge.