CHAPTERS
Goldman’s funding pivot: why being the biggest wholesale funder is a bad trophy
David Solomon explains how Goldman shifted from heavy reliance on institutional/wholesale funding toward more stable deposit funding. He frames funding and liquidity as a core strategic risk for institutional financial firms and describes the progress Goldman has made building deposits from essentially zero.
- •Goldman previously became the world’s largest wholesale funder—an undesirable position in stressed markets
- •Deposits are more stable than wholesale funding and reduce liquidity risk
- •Goldman built a digital deposit platform and grew to roughly $500B in total deposits
- •Strategic focus: improving funding mix to support long-term resilience
From private partnership to public company—keeping the culture while adding strategy
Solomon reflects on Goldman’s evolution since its 1999 IPO and why going public was necessary to remain globally competitive. He describes the tension between maintaining a partnership ethos and operating with top-down strategic direction as a public company.
- •Private partnerships enable entrepreneurial autonomy and mutual accountability via periodic reevaluation
- •Goldman went public to access permanent capital and avoid stagnating relative to global peers
- •The firm has preserved an aspirational partner culture even as a public company
- •Leadership challenge: pairing partnership values with cohesive enterprise-wide strategy
CEO priorities: scale as a competitive moat for institutional financial firms
Solomon outlines how scale matters in mature, asset-linked businesses—especially during turbulence. He contrasts Goldman and Morgan Stanley’s “institutional” positioning with larger retail bank peers and argues that balance-sheet scale becomes increasingly important over time.
- •US financial system advantages: globally dominant US institutions
- •Goldman/Morgan Stanley are smaller than retail-bank peers; smaller size reduces leverage in volatility
- •Balance-sheet scale has expanded dramatically (Goldman ~ $1.9T) but peers are larger (JPM ~ $4.5T)
- •Hard truth: mature businesses struggle to build scale purely organically
a16z’s origin story: raising capital when nobody else can
Ben Horowitz recounts founding a16z in 2009 and taking criticism for fundraising during a downturn. He argues that contrarian timing—raising when others can’t—is often optimal for long-horizon investing.
- •a16z launched post–financial crisis during early mobile/cloud inflection
- •Fundraising in a downturn drew skepticism but created opportunity
- •Investor psychology is pro-cyclical: buy high, retreat low
- •Down-cycle capital formation can be a durable strategic advantage
Building a “top-tier” venture product: designing around the founder
Horowitz explains that VC survival depends on being “top tier” to attract the best entrepreneurs. Because reputation advantages were hard to replicate in 2009, a16z differentiated by creating a better product for founders—helping them stay CEOs and scale their companies.
- •Top-tier status determines access to elite founders and deals
- •Legacy firms built reputations from decades of iconic wins; new entrants need a different path
- •a16z focused on founder empowerment rather than founder replacement
- •Firm design aimed to provide brand, access, and operational leverage to entrepreneurs
Scaling venture for ‘Software Is Eating the World’—from boutique team to platform
Horowitz describes a16z’s second phase: scaling the firm to match an expanded universe of software winners. He challenges the traditional ‘basketball team’ VC model and explains how a16z structured itself to invest across far more opportunities without bloating decision-making.
- •‘Software is Eating the World’ implied many more breakout companies than historical norms
- •Traditional VC model doesn’t scale to a world with far more investable winners
- •Organizational design: scale coverage while keeping tight investment committees
- •Outcome: a16z grew from top tier to raising ~18.3% of all US VC (2025)
Industry leadership and national competitiveness: policy as a growth strategy
Horowitz connects a16z’s scale to a broader responsibility: growing the market and strengthening US technological leadership. He frames policy work as essential to US competitiveness—particularly versus China—and as a prerequisite for long-term innovation.
- •Andy Grove lesson: industry leaders must grow the market; no one else will
- •a16z’s policy work ties to ‘American dynamism’ and long-term national competitiveness
- •International posture shaped by strategic competition with China
- •Firm ambition extends beyond returns to shaping conditions for innovation
‘The sweetest macro spot in 40 years’: stimulus cocktail, AI productivity, and real risks
Solomon lays out why he views the current US macro environment as unusually favorable for asset-linked businesses. He cites overlapping fiscal stimulus, monetary easing, a capital investment supercycle, and deregulation, while warning that geopolitics and information volatility raise tail risks.
- •Drivers: fiscal stimulus, rate-cut cycle, major capital investment, and deregulation
- •AI-driven productivity expectations are being pulled forward by markets
- •Consumer stress persists because prices remain 25–30% higher despite lower inflation
- •Risks: multipolar geopolitics, social-media-driven volatility, and fragility of confidence
M&A and IPO outlook: ‘Whatever the question is, the answer is maybe’
Solomon argues that deal activity is fundamentally confidence-driven and predicts a strong year for M&A and IPOs as regulatory headwinds ease. Horowitz agrees on IPO momentum but flags uncertainty around FTC posture, especially in tech, potentially pushing activity toward alternative structures like IP transactions.
- •Past four years: strategic M&A confidence was low—answers were often ‘no’
- •Now: more openness and deal exploration—‘maybe’ replaces ‘no’
- •Solomon predicts a potentially record-setting M&A year and a stronger IPO pipeline
- •Horowitz: FTC uncertainty could reshape tech deals toward IP-focused transactions
AI changes competitive dynamics: why ‘leads aren’t what they once were’
Horowitz explains that AI reduces the durability of product leads that once protected startups from incumbents. With proprietary data and sufficient compute, well-capitalized players can close gaps quickly—pushing fast-growing AI companies toward IPOs to fund the compute/data arms race.
- •Classic software: engineering throughput limits create defensible time-based leads
- •AI era: proprietary data + GPUs can solve many problems quickly; money can buy speed
- •Competition becomes capital-intensive; startups can’t simply ‘sit on a lead’
- •Rapid growth rates (to $100M/$1B revenue quickly) increase pressure to access public capital
Crypto policy agenda: GENIUS Act wins and the push for market-structure clarity
Horowitz outlines a16z’s crypto policy priorities, arguing crypto is a foundational societal and economic technology. He criticizes prior regulatory approaches (including de-banking and broad enforcement) and highlights legislative efforts focused on stablecoins and clearer token classification rules.
- •Crypto framed as a breakthrough beyond fintech: property rights, creator economics, stakeholder coordination
- •Claims of informal ‘ban’ tactics: enforcement pressure, de-banking, Wells notices
- •GENIUS Act / stablecoin legislation passed into law
- •Clarity Act (market structure) aims to define when tokens are securities vs other instruments
AI policy: ‘Don’t regulate math’ and the fight against a patchwork of state laws
Horowitz argues AI should be regulated at the application level rather than restricting model development itself. He warns that overregulation could cede advantage to China and highlights key near-term battles: state-by-state compliance fragmentation and copyright/training rules.
- •Core principle: models are mathematical predictors, not inherently sentient entities
- •Regulate harmful uses (fraud, weapons, intrusion), not the underlying math
- •Avoid 50-state AI law patchwork that blocks startup innovation
- •Copyright/training debate: statistical learning over data vs reproduction; competitiveness vs China
Goldman’s ‘One GS 3.0’: reimagining enterprise processes with AI
Solomon describes two AI thrusts at Goldman: boosting individual productivity and redesigning core operating processes end-to-end. He emphasizes that process reengineering can free up capacity to invest in growth while preserving annual return discipline, and notes regulatory constraints slow experimentation.
- •Track 1: equip employees with AI tools/models to augment daily client work
- •Constraint: heavy regulatory clearance and governance before deploying tools
- •Track 2: redesign foundational processes to automate and boost efficiency
- •One GS 3.0 targets six processes first, aiming to reinvest savings into growth while maintaining returns
AI agents and investing: promise, limits, and the ‘unknown unknowns’ problem
Solomon and Horowitz explore how agentic AI might affect investing, noting that models excel on available facts but struggle with genuinely novel regime shifts. They question whether AI trained on widely available information can outperform when most humans underperform, while acknowledging rapid model adaptation once new information appears.
- •Investing often turns on unexpected novelty that cannot be modeled from historical data
- •Models can incorporate new facts quickly once events occur, but start from existing distributions
- •Skepticism: if most humans underperform, training on their information may not yield edge
- •Open question: whether agentic systems can develop differentiated insight vs faster consensus
Lightning round: favorite DJs and closing reflections
The conversation ends with a personal detour into music, revealing Solomon’s current pick and Horowitz’s classic hip-hop choice. The hosts wrap by thanking both guests for a wide-ranging discussion across finance, venture, AI, crypto, and policy.
- •Solomon’s pick: John Summit for energy and evolving club/house performance
- •Horowitz’s pick: DJ Jazzy Jeff as an underrated all-time great
- •Light closing after dense macro/policy discussion
- •End of session and acknowledgments
