“I don’t think venture is an ‘asset class’ in the sense many LPs think… You need dozens of Figma-sized outcomes every year to make that math work; I don’t see that many. So the only thing that breaks is the return assumption. Venture is return-free risk, not a risk-free return.” – Roelof Botha – Senior Steward, Sequoia
Botha’s mathematical argument is straightforward and devastating. With approximately $250 billion flowing annually into US venture capital and limited partners expecting net IRRs in the 12% range, the implied arithmetic requires roughly $1 trillion in annual exit value over typical fund horizons. Yet historical data reveals only about 20 companies per year achieve realised exits worth $1 billion or more. Even if we generously assume that frontier AI companies will produce larger outcomes than historical norms, the gulf between required and probable returns remains vast. The statement “you need dozens of Figma-sized outcomes every year to make that math work” underscores the sheer improbability of meeting aggregate return expectations.
This is not merely academic scepticism. Botha speaks from the vantage point of having navigated multiple market cycles whilst maintaining Sequoia’s position at the apex of venture performance. His perspective is informed by personal experience across technology’s most significant transitions: he took PayPal public as CFO at 28 in 2002—the first “dotcom” to go public after the crash—advocated for YouTube’s acquisition two years before Google bought it for $1.65 billion, and has since been instrumental in investments spanning Instagram, Square, MongoDB, Unity and DoorDash. When someone with this track record states that venture doesn’t function as an asset class in the conventional sense, it merits serious attention.
The Institutional Memory Problem
Botha’s critique exposes a fundamental tension in how institutional capital thinks about venture. The asset class framework assumes diversification, scalability and predictable return distributions—characteristics that venture capital demonstrably lacks. The data consistently show extreme power law dynamics: top-decile and top-quartile performance diverge dramatically from median outcomes, and the gap has widened as more capital has entered the market. Limited partners treating venture as they would bonds or equities—allocating based on target portfolio weights and rebalancing mechanically—are applying frameworks designed for normally distributed returns to a domain where outcomes follow profoundly skewed distributions.
The historical precedent supports Botha’s scepticism. When one examines the roster of leading Silicon Valley venture firms from 1990, most have ceased to exist or have faded into irrelevance. Even amongst firms that survived, maintaining top-tier performance across multiple decades and generational transitions remains vanishingly rare. Sequoia itself has institutionalised “healthy paranoia” through daily rituals—including wall-to-wall printing of “we are only as good as our next investment” in each partner’s handwriting—precisely because sustained excellence is so improbable.
Cost Structure and Margin Dynamics
Botha’s broader investment philosophy, evident throughout the conversation, provides essential context for understanding why he believes current capital deployment is fundamentally misaligned with probable outcomes. His emphasis on cost structure and unit economics—”cost is an advantage, not price”—reflects a disciplined focus on companies that can achieve sustainable margins rather than those burning capital to chase topline growth. This stands in sharp contrast to the behaviour incentivised when excessive capital seeks deployment: founders are encouraged to prioritise scale over efficiency, and investors compete on valuation rather than value-add partnership.
The contemporary challenge in AI applications illustrates this tension. Many AI-enabled software companies currently exhibit compressed gross margins—perhaps 40% rather than the 80% typical of pre-AI SaaS businesses—due to inference costs. Botha’s view is that these margins will improve materially over time as algorithms become more efficient, open-source models compete with proprietary offerings, and founders deploy model ensembles that match use cases to cost-value ratios. However, this requires patient capital willing to underwrite margin expansion paths rather than demanding immediate profitability or hyper-growth at any cost. The current abundance of venture capital undermines this discipline.
Decision-Making Architecture and Team Composition
Sequoia’s internal governance mechanisms reveal how a firm can maintain investment discipline amidst market exuberance. The partnership employs anonymous preliminary voting across approximately 12 participants per fund meeting, premortems that explicitly name cognitive biases in investment memoranda, and a culture of “front-stabbing” where dissent must be voiced directly and substantively. This architecture is designed to surface honest disagreement whilst preserving the conviction necessary for outlier bets. Critically, Sequoia has deliberately kept its investment team small—roughly 25 investors total—to maintain the trust required for candid debate. This stands in stark contrast to firms that have scaled headcount aggressively to deploy larger funds.
The personnel profile Botha describes—”pirates, not people who want to join the navy”—reflects a specific cultural DNA: competitive, irreverent, non-conformist individuals who nonetheless possess high integrity and play as a team. This is not window dressing; it’s a functional requirement for maintaining the dissonance between institutional humility (“we are only as good as our next investment”) and individual conviction (the willingness to champion contrarian positions). The challenge for most organisations is that these traits—competitive individualism and collaborative teamwork, paranoia and boldness—create inherent tensions that require active cultural management.
Implications for Founders and Emerging Managers
For founders, Botha’s analysis suggests that the current abundance of venture capital may be more liability than asset. Excess funding often undermines the discipline required to build durable businesses with strong unit economics and sustainable margins. The historical pattern he references—spreading talent thin, similar to 1999—implies that many startups are overstaffed, over-capitalised and under-focused on the cost structures that ultimately determine competitive advantage. Founders who resist the temptation to raise at inflated valuations and instead prioritise capital efficiency may find themselves better positioned when market conditions normalise.
For emerging fund managers, the message is equally stark: network development, relationship cultivation and demonstrable value-add matter far more than deploying large pools of capital. Botha’s advice to “build the network and the tributaries” reflects a business model predicated on access and partnership rather than balance sheet scale. Managers who attempt to compete by raising ever-larger funds are swimming against the arithmetic Botha outlines—there simply aren’t enough outsized outcomes to justify the capital deployed.
Theoretical Foundations: Power Laws and Portfolio Construction
Botha’s argument intersects with longstanding academic debates about venture capital portfolio construction and return dynamics. The seminal work by Korteweg and Sorensen (2010) on risk adjustment in venture returns demonstrated that much of venture’s apparent outperformance disappears when properly accounting for risk and selection bias. Subsequent research by Ewens, Jones and Rhodes-Kropf (2013) on the price of diversification showed that venture returns exhibit extreme skewness, with top-decile funds capturing disproportionate value. Harris, Jenkinson and Kaplan (2014) found that whilst top-quartile venture funds consistently outperform public markets, median and below-median funds underperform even after adjusting for leverage and illiquidity.
The theoretical challenge is that venture capital has always been characterised by power law dynamics—Chris Dixon and others have popularised Nassim Taleb’s observation that venture returns follow a power law distribution where a small number of investments generate the majority of returns. What Botha is arguing is that current capital inflows have pushed the industry beyond the point where even sophisticated portfolio construction can reliably generate attractive risk-adjusted returns for the typical investor. This is distinct from claiming that no attractive opportunities exist; rather, he’s asserting that the quantum of attractive opportunities relative to deployed capital has reached unsustainable levels.
Historical Analogues and Market Cycles
The 1999 parallel Botha invokes is instructive. During the dotcom bubble, venture capital fundraising surged from roughly $35 billion in 1998 to $106 billion in 2000. The subsequent crash saw fundraising collapse to $10 billion by 2003. What’s often forgotten is that the best-performing funds from that era—those that generated genuine alpha—tended to be smaller, more selective vehicles that maintained investment discipline even as capital availability surged. Sequoia itself raised a relatively modest $450 million fund in 1999, resisting the temptation to scale fund size aggressively.
The 2021 parallel is equally relevant. As growth-stage valuations reached unprecedented levels and tourists flooded into venture capital, established firms faced pressure to compete on valuation, deploy capital faster and compromise on diligence. Firms that maintained discipline—insisting on demonstrable unit economics, sustainable margins and realistic growth assumptions—found themselves losing competitive processes to investors willing to accept flimsier evidence of value creation. Botha’s framing suggests that this dynamic represents not temporary market froth but rather structural oversupply.
The Broader Context: Technology Adoption and Market Scale
Botha’s longer-term optimism about technology’s impact provides important nuance. He acknowledges that the scale of technology markets has expanded dramatically—from 300 million internet users during his PayPal tenure to four billion people with high-speed mobile devices today. He’s explicit that frontier technologies like AI, robotics, genomics and blockchain-based financial infrastructure will create substantial value. His scepticism is not about innovation potential but rather about the mismatch between capital deployed and capturable returns.
This distinction matters for interpreting the “return-free risk” characterisation. Botha is not arguing that venture capital cannot generate exceptional returns for skilled practitioners with disciplined processes and selective deployment. Sequoia’s portfolio—representing roughly 30% of NASDAQ market capitalisation from companies backed whilst private—demonstrates that outlier performance remains achievable. Rather, he’s asserting that treating venture as a passive, diversifiable asset class suitable for broad institutional allocation is fundamentally misconceived.
The Economics of Intelligence and Margin Evolution
The AI-specific dimension of Botha’s analysis deserves separate consideration. His framework for evaluating AI application companies combines near-term pragmatism with medium-term optimism about cost curves. In the near term, many AI-enabled products exhibit compressed margins due to inference costs, and investors must assess whether unit economics or pricing power justify those margins. Over the medium term, he expects substantial margin improvement driven by algorithmic efficiency gains, open-source model competition, economies of scale and intelligent model selection (deploying frontier models only where value justifies cost).
This view has profound implications for how investors should evaluate AI companies today. Those applying conventional SaaS valuation multiples without adjusting for current margin compression may be overvaluing companies whose competitive position depends on unsustainable subsidisation of compute costs. Conversely, those dismissing AI applications entirely based on current margin profiles may be underestimating the trajectory of cost improvement. Disciplined diligence requires explicit modeling of margin evolution paths, sensitivity to underlying cost curves and realistic assessment of pricing power as intelligence commoditises.
Governance, Conflict and Confidentiality
The operational challenges Botha describes—managing portfolio conflicts, preserving confidentiality and navigating situations where portfolio companies evolve into competitive adjacencies—illuminate the practical tensions that arise when firms operate as deep business partners rather than passive capital providers. His example of Stripe and Square converging into overlapping domains, requiring recusal from certain meetings and investment memos, illustrates that even well-intentioned conflict management involves trade-offs and constraints.
This dimension connects to the broader question of whether venture capital should be structured as a relationship business or as a capital-allocation optimisation problem. Firms pursuing the former model—exemplified by Sequoia’s emphasis on board service, operational partnership and long-term stewardship—necessarily accept constraints on portfolio breadth and sector coverage. Firms pursuing the latter can achieve greater diversification and sector coverage but sacrifice depth of partnership and founder alignment. Neither model is categorically superior, but they imply different return profiles and different sources of competitive advantage.
Implications for Limited Partner Strategy
For institutional investors, Botha’s analysis suggests a fundamental rethinking of venture allocation strategy. The orthodox approach—establishing a target allocation to venture capital as an asset class, selecting a diversified portfolio of fund managers across vintage years and strategies, and rebalancing mechanically—is predicated on assumptions that Botha’s data directly contradict. If venture exhibits power law returns at both the company level and the fund level, and if capital oversupply has pushed the industry beyond the point where diversification reliably captures attractive risk-adjusted returns, then LPs should concentrate capital with demonstrably superior managers rather than pursuing broad diversification.
This implies dramatically different behaviour: willingness to pay premium economics for access to top-decile managers, acceptance of capacity constraints and queue positions, focus on relationship development and value demonstration rather than purely financial negotiation. It also implies scepticism towards emerging managers unless they can articulate genuine edge—proprietary deal flow, differentiated value-add, or domain expertise that translates to selection advantage.
The alternative—acknowledging that venture capital allocation is effectively a form of economic development or innovation subsidy that happens to generate modest risk-adjusted returns—is intellectually honest but conflicts with fiduciary obligations. Endowments, pension funds and sovereign wealth vehicles investing primarily for financial return should perhaps treat venture capital as a satellite allocation justified by lottery-ticket optionality rather than as a core portfolio component meriting multi-billion-pound allocations.
Roelof Botha’s path to this perspective reflects an unusual combination of operating experience, investment track record and institutional leadership. Born in Pretoria in September 1973, he studied actuarial science, economics and statistics at the University of Cape Town before earning an MBA from Stanford’s Graduate School of Business, where he graduated as the Henry Ford Scholar—the top student in his class. His actuarial training instilled a probabilistic framework and long-term thinking that pervades his investment philosophy.
After working as a business analyst at McKinsey in Johannesburg from 1996 to 1998, Botha joined PayPal in 2000 as director of corporate development whilst still a Stanford student. He became PayPal’s chief financial officer in September 2001 at age 27, navigating the company through its February 2002 initial public offering and subsequent October 2002 acquisition by eBay. PayPal’s IPO occurred during a period of profound scepticism about internet businesses—one 2001 article titled “Earth to Palo Alto” essentially ridiculed the company—yet PayPal’s financial discipline and clear path to profitability vindicated the decision.
Botha joined Sequoia Capital in January 2003, working closely with Michael Moritz, who had been PayPal’s lead investor. He was promoted to partner in 2007 following Google’s acquisition of YouTube, an investment he had championed two years earlier. The YouTube founders were friends from his PayPal days, and Botha worked with them in Sequoia’s offices iterating on the product during its formative stages. His subsequent investments include Instagram (acquired by Facebook for $1 billion in 2012), Square (public market capitalisation exceeding $40 billion at peak), MongoDB (public since 2017), Unity Technologies (public 2020-2023), Natera and numerous others.
He became head of Sequoia’s US venture operations in 2010 alongside Jim Goetz, assumed sole leadership of the US business in 2017 whilst Doug Leone served as global senior steward, and was elevated to senior steward of Sequoia’s global operations in July 2022. His tenure has coincided with Sequoia’s organisational evolution—including the controversial 2021 introduction of the Sequoia Capital Fund, a permanent capital vehicle designed to hold positions indefinitely rather than liquidating according to traditional fund timelines—and with substantial turbulence in technology markets.
Botha’s intellectual formation reflects the intersection of actuarial risk assessment, McKinsey-style structured problem-solving and the crucible of operating in a high-growth technology company during both exuberance and crisis. His repeated emphasis on cost structure, margin dynamics and unit economics reflects operating experience rather than purely financial analysis. The actuarial lens—thinking in terms of probability distributions, long time horizons and avoiding ruin—distinguishes his analytical framework from investors whose backgrounds emphasise pattern recognition or momentum-driven investing.
