“‘What would have to be true?’ forces you to identify assumptions. It forces you to make the implicit explicit. And once you do that, you can test assumptions. You can see where you might be wrong. It’s a way to avoid getting swept up in narrative.” – Bill Gurley – GP at Benchmark

Bill Gurley, General Partner at Benchmark Capital, articulated this deceptively simple yet profoundly transformative question during a conversation with Tim Ferriss. The quote encapsulates a methodology that has become foundational to rigorous decision-making across venture capital, strategic planning, and organisational leadership. What makes Gurley’s formulation particularly powerful is its recognition that most flawed decisions stem not from lack of information, but from unexamined assumptions buried beneath compelling narratives.

Bill Gurley: The Architect of Disciplined Investing

Bill Gurley joined Benchmark Capital in 1999, during the height of the dot-com bubble, and has since become one of the most respected voices in venture capital. His career trajectory reveals a consistent commitment to analytical rigour over herd mentality. Benchmark, founded in 1995, distinguished itself by maintaining a partnership model that prioritised long-term value creation over rapid fund growth-a philosophy that directly shaped Gurley’s investment approach.

Gurley’s early investments included stakes in companies like eBay, Evan Williams (later acquired by Twitter), and OpenTable, demonstrating an ability to identify transformative business models before they achieved mainstream recognition. However, what distinguishes Gurley’s reputation is not merely his investment returns, but his intellectual framework for evaluating opportunities. He has become known for asking uncomfortable questions that force founders and fellow investors to confront the assumptions underlying their theses.

The “What would have to be true?” framework emerged from Gurley’s observation that venture capital and strategic decision-making are frequently hijacked by narrative momentum. A compelling founder story, an attractive market size projection, or a persuasive pitch can create what behavioural economists call the “narrative fallacy”-the human tendency to construct coherent stories from disparate facts, often at the expense of critical analysis. Gurley’s question serves as an antidote to this cognitive bias.

The Intellectual Foundations: Scenario Planning and Counterfactual Thinking

Gurley’s approach draws from several intellectual traditions that predate his articulation but which he synthesised into a practical methodology.

Scenario Planning and Strategic Foresight: The roots of “What would have to be true?” extend to post-World War II strategic planning methodologies. Royal Dutch Shell pioneered scenario planning in the 1970s under the leadership of Pierre Wack, developing frameworks to anticipate multiple futures rather than predict a single outcome. This approach proved invaluable when Shell anticipated the 1973 oil crisis whilst competitors were caught unprepared. The underlying principle-that explicit assumption testing prevents strategic blindness-directly parallels Gurley’s methodology.

Counterfactual Reasoning: Philosophers and historians have long employed counterfactual analysis-asking “what if?” questions to understand causation. Niall Ferguson’s work on counterfactual history and David Lewis’s philosophical framework on counterfactuals both emphasise that understanding what would have to be true for alternative outcomes illuminates the actual causal mechanisms at work. Gurley’s question inverts this: rather than asking what might have been, it asks what must be true for a proposed future to materialise.

Charlie Munger’s Mental Models: Gurley’s intellectual framework also reflects the influence of Charlie Munger, Vice Chairman of Berkshire Hathaway, who has long advocated for identifying and testing the assumptions embedded in investment theses. Munger’s emphasis on “inverting, always inverting”-asking what would have to be false for an investment to fail-complements Gurley’s approach. Both methodologies share a commitment to making implicit reasoning explicit and subjecting it to scrutiny.

Howard Marks and Second-Level Thinking: Howard Marks, co-founder of Oaktree Capital, has written extensively about “second-level thinking”-the practice of thinking beyond the obvious to identify what others might be missing. Marks emphasises that superior returns come from identifying market inefficiencies, which requires questioning consensus assumptions. Gurley’s framework operationalises this principle by providing a systematic method for uncovering hidden assumptions that the market may have overlooked or misweighted.

The Mechanism: From Implicit to Explicit

The power of Gurley’s question lies in its three-stage mechanism:

Stage One-Surfacing Assumptions: When someone proposes a business strategy, investment thesis, or strategic initiative, they typically present a narrative: “This market is growing at 40% annually. Our product is superior. We have first-mover advantage.” These statements rest on foundational assumptions that often remain unspoken. Gurley’s question forces these assumptions into the open. For a market-growth projection to be accurate, what would have to be true about customer adoption rates, competitive dynamics, regulatory environments, and macroeconomic conditions?

Stage Two-Testing Assumptions: Once assumptions are explicit, they become testable. Rather than accepting a narrative wholesale, one can interrogate each assumption: Is this assumption supported by evidence? What would falsify it? How sensitive is the overall thesis to this particular assumption? This stage transforms decision-making from an intuitive, story-driven process into a more empirical one.

Stage Three-Identifying Vulnerability: By mapping assumptions, one identifies which are most critical and most uncertain. This reveals where the thesis is most vulnerable to being wrong. A founder might discover that their entire business model depends on an assumption about customer acquisition costs that has never been validated. An investor might realise that a seemingly attractive opportunity depends on a regulatory change that is far from certain.

Application in Venture Capital and Beyond

Within venture capital, Gurley’s framework has become particularly influential. The industry is inherently forward-looking, requiring investors to make bets on futures that do not yet exist. This creates fertile ground for narrative-driven decision-making and herd behaviour. Gurley’s question provides a disciplined counterweight.

Consider a seed-stage investment in a marketplace company. The pitch might emphasise a large addressable market and network effects. Applying Gurley’s framework, an investor would ask: What would have to be true for network effects to materialise? What would have to be true for the company to achieve sufficient scale before competitors enter? What would have to be true about unit economics? What would have to be true about founder execution capability? Each answer reveals assumptions that can be tested through due diligence, founder conversations, and market research.

The framework has also proven valuable in strategic planning beyond venture capital. Corporate strategists use it to evaluate new market entries. Policymakers employ it to stress-test regulatory assumptions. Entrepreneurs use it to identify the riskiest elements of their business plans. In each context, the mechanism is identical: make assumptions explicit, test them rigorously, and identify where the thesis is most vulnerable.

The Narrative Problem: Why This Question Matters

Gurley’s emphasis on avoiding “getting swept up in narrative” addresses a well-documented cognitive vulnerability. Humans are narrative creatures. We construct stories to make sense of complexity, and these stories are often more persuasive than raw data. A compelling founder narrative-the scrappy entrepreneur overcoming obstacles-can be more influential than unit economics. A coherent market story-“mobile is the future”-can drive investment decisions regardless of whether specific applications are viable.

This narrative bias has contributed to numerous investment bubbles and strategic failures. The dot-com bubble was sustained partly by a compelling narrative about the transformative power of the internet, which was true in broad strokes but masked unsustainable unit economics in many specific cases. More recently, the 2021-2022 venture capital cycle saw inflated valuations sustained by narratives about growth at all costs, narratives that collapsed when assumptions about capital availability and customer acquisition costs were tested against reality.

Gurley’s question provides a systematic method for interrogating narratives without dismissing them entirely. The question acknowledges that narratives can contain truth-the internet was transformative, mobile is important-whilst demanding that the specific assumptions underlying a particular thesis be made explicit and tested.

Intellectual Lineage and Contemporary Influence

Whilst Gurley articulated the question in a form that has become widely adopted, the underlying intellectual tradition is deep. The question reflects principles articulated by:

Karl Popper on falsifiability: Popper argued that scientific progress depends on formulating hypotheses that can be proven false. Gurley’s framework operationalises this principle in a business context, treating investment theses as hypotheses to be tested rather than narratives to be believed.

Daniel Kahneman and Amos Tversky on cognitive biases: Their research on heuristics and biases demonstrated that humans systematically misweight information and fall prey to narrative fallacies. Gurley’s question provides a practical method for counteracting these biases.

Nassim Taleb on antifragility and tail risk: Taleb emphasises the importance of identifying hidden assumptions and tail risks that could invalidate a thesis. His work on “black swans”-high-impact, low-probability events-complements Gurley’s framework by highlighting that the most important assumptions are often those that seem least likely to be violated.

Contemporary venture capitalists and strategists have adopted and adapted Gurley’s framework. Mike Maples, founder of Floodgate, employs similar questioning methodologies when evaluating startups, asking what would have to be true for a company to achieve 100x returns. This approach has become increasingly common amongst disciplined investors seeking to distinguish signal from noise in an information-rich but wisdom-poor environment.

The Practical Power: Making the Implicit Explicit

The phrase “make the implicit explicit” is central to Gurley’s formulation. Most decision-making involves implicit assumptions-beliefs so foundational that they are rarely articulated. A founder might assume that their target customer segment will adopt their product because it is superior, without explicitly testing whether superiority translates to adoption. An investor might assume that a large market size guarantees opportunity, without explicitly examining whether the company can capture a meaningful share.

By forcing these implicit assumptions into explicit form, Gurley’s question enables several outcomes:

Improved Communication: When assumptions are explicit, teams can align around them or identify disagreements. A founder and investor might discover they have fundamentally different assumptions about customer acquisition costs, enabling them to either resolve the disagreement or recognise a misalignment that should affect their working relationship.

Better Risk Management: Explicit assumptions can be prioritised by criticality and uncertainty. Resources can be allocated to testing the most important and uncertain assumptions first, reducing the risk of discovering fatal flaws late in execution.

Enhanced Learning: When assumptions are explicit, they can be tested and updated as new information emerges. This enables iterative learning rather than narrative-driven persistence in the face of contradictory evidence.

Limitations and Complementary Approaches

Whilst powerful, Gurley’s framework is not a panacea. Some limitations warrant acknowledgement:

Assumption Blindness: The framework depends on identifying assumptions in the first place. Assumptions so fundamental that they are invisible to all parties involved-what Donald Rumsfeld called “unknown unknowns”-may escape scrutiny. Complementary approaches, such as red-teaming or seeking perspectives from outside one’s domain, can help surface these deeper assumptions.

Analysis Paralysis: Taken to an extreme, the framework can lead to endless assumption-testing without decision-making. Effective application requires judgment about which assumptions are most critical and when sufficient testing has occurred to warrant action.

Narrative’s Legitimate Role: Whilst Gurley warns against being “swept up in narrative,” narratives serve important functions in motivation, communication, and sense-making. The goal is not to eliminate narrative but to ensure that narratives are grounded in tested assumptions rather than wishful thinking.

Enduring Relevance

Gurley’s framework has proven remarkably durable because it addresses a persistent human vulnerability: the tendency to construct compelling stories and defend them against contradictory evidence. This vulnerability is not diminished by technological change, market evolution, or generational shifts. If anything, the acceleration of change and the proliferation of information have made disciplined assumption-testing more valuable, not less.

In an era of artificial intelligence, machine learning, and algorithmic decision-making, Gurley’s question remains profoundly relevant. Algorithms can process vast amounts of data, but they too can be trained on narratives rather than ground truth. The question “What would have to be true?” provides a method for interrogating algorithmic recommendations and ensuring that they rest on sound assumptions rather than patterns in biased training data.

For leaders, investors, entrepreneurs, and strategists, Gurley’s framework offers a practical tool for moving beyond narrative-driven decision-making towards more rigorous, assumption-based reasoning. By making the implicit explicit and subjecting assumptions to scrutiny, organisations can reduce the risk of being blindsided by reality and increase the likelihood of making decisions that withstand contact with the actual world.