Entrepreneurial Uncertainty: Building in the Fog

May 2025 

I. The Mirage of Predictability

Venture capitalists have long styled themselves as prophets of the future. More recently, some have claimed to become engineers of it. A growing number of firms now tout their use of advanced algorithms and AI to identify the next unicorn before others have even noticed the horn. As marketing gimmicks go, it is well timed: artificial intelligence is enjoying its cultural moment. But beneath the pitch lies an old and unresolved tension between the allure of science and the irreducibly human nature of entrepreneurial judgment.

Marc Andreessen, co-founder of Andreessen Horowitz, is blunt in his dismissal of the algorithmic turn. "Venture capital is 100% a game of outliers," he says. Of the 4,000 or so companies that seek institutional funding each year, only about 200 will receive backing from top-tier firms. Of those, perhaps a dozen will generate the lion’s share of returns. That’s not a diversified portfolio; it’s a power-law distribution. The implication is stark: venture investing is not about improving the average outcome. It is about identifying the rare exception.

And this, Andreessen argues, cannot be computed. No statistical model can anticipate the psychological makeup of a founder, the emergence of an unforeseen market, or the cultural inflection point that turns a curiosity into a category. Despite advances in machine learning and predictive analytics, the essential challenge remains unchanged: making decisions under radical uncertainty.

II. Understanding Radical Uncertainty

Entrepreneurship is often romanticised as a bold leap into the future. Yet what truly distinguishes it is not risk-taking in the conventional sense, but a willingness to act amid profound uncertainty. This is not the tidy kind of risk familiar to actuaries or hedge-fund quants—where outcomes are known and probabilities can be assigned—but something altogether more elusive. Entrepreneurs inhabit a “large world”, to borrow a term from decision theory, where neither the full set of outcomes nor their likelihoods can be known in advance.

In contrast, “small world” problems—like roulette wheels or insurance underwriting—are governed by probabilistic logic. Such risks, while potentially costly, are at least tractable. They can be priced, hedged, and, if need be, reinsured. Not so with entrepreneurial uncertainty. When a founder considers launching a product in an untested category or a venture capitalist weighs a bet on a nascent technology, the uncertainty is not merely quantitative but ontological: the future they seek to shape has no precedent, no stable distribution, and no reliable map.

This renders most financial models impotent. Discounted cash-flow forecasts become little more than speculative fiction; Monte Carlo simulations yield output divorced from empirical grounding. Instead, entrepreneurs and their backers must lean on judgment—an interpretive, context-dependent faculty shaped by experience, intuition, and narrative sense-making.

Matters become thornier still under conditions of deep uncertainty—when stakeholders cannot even agree on the appropriate conceptual model for understanding a system’s behaviour. Is the electric-vehicle transition best thought of as a linear substitution or a platform shift? Will generative AI augment human labour or automate it away? These are not questions that lend themselves to probabilistic finesse.

In 1921, Frank Knight drew a line that modern finance has since blurred. Risk, he argued, can be measured; uncertainty cannot. Tossing a coin, insuring a pensioner, or hedging a currency all operate within the realm of calculable probabilities—known distributions, historical frequency, statistical models. But entrepreneurship, as Knight saw it, belongs to a different realm: one-off decisions under conditions that defy replication, quantification, or even consensus about what the future might hold.

Knight’s distinction was later echoed by Austrian economist Ludwig von Mises, who placed entrepreneurial action squarely in the domain of radical uncertainty. Here, no model suffices, no spreadsheet reassures. The future is not hidden in the data, waiting to be coaxed out with a better regression; it must be imagined, constructed, tested—and often, reimagined. In such terrain, foresight trumps forecasting.

Modern finance, for all its sophistication, has largely abandoned this view. Volatility is treated as a stand-in for uncertainty, and tools like beta coefficients and value-at-risk (VaR) are applied with the confidence of physics. But the attempt to reduce all ambiguity to measurable inputs masks a deeper truth: the most important decisions in investing—those involving new markets, technologies, or strategies—are made under conditions of genuine uncertainty. There are no historical averages for AI regulation, no frequency tables for global supply-chain realignments, no model that predicts the emergence of entirely new consumer behaviours.

Startups inhabit this world by default. Venture capitalists stake millions on companies without revenue, markets without precedent, technologies without roadmaps. Private equity firms buy assets knowing full well that the world they are modelling may not exist in three years. Yet the industry remains wedded to probabilistic tools that work best in stable, repeatable environments—precisely the kind entrepreneurs aim to disrupt.

Innovation itself is rarely linear. It advances through recombination, serendipity and error. Entrepreneurs who succeed in such an environment often come from backgrounds that afford them the luxury to fail safely—family money, elite networks, or access to early liquidity. But their success also rests on a peculiar blend of disposition: long-term optimism paired with short-term paranoia. Vision fuels ambition; vigilance ensures survival.

The judgment they exercise is not analytical in the traditional sense. It is narrative, contextual and imaginative—a far cry from the formal rationality prized in standard economic models. Yet it is precisely this form of judgment that matters when the playbook does not exist. To act entrepreneurially is to accept that not all uncertainty is bad. It is to embrace the unknowable and wager that one’s judgment can produce value where models fail. In this view, profit is not a “normal” return; it is a reward for successfully engaging with the unknown.

…As Von Mises writes, like every acting man, the entrepreneur is always a speculator. He deals with the uncertain conditions of the future. His success or failure depends on the correctness of his anticipation of uncertain events. If he fails in his understanding of things to come, he is doomed. The only source from which an entrepreneur's profits stem is his ability to anticipate better than other people the future demand of the consumers. If everybody is correct in anticipating the future state of the market of a certain commodity, its price and the prices of the complementary factors of production concerned would already today be adjusted to this future state. Either profit or loss can emerge for those embarking upon this line of business…The ultimate source from which entrepreneurial profit and loss are derived is the uncertainty of the future constellation of demand and supply.

III. Judgment in Action

The Austrian School of Economics places the entrepreneur not at the periphery, but at the centre of all economic life. Ludwig von Mises argued that because all human action unfolds under uncertainty, every decision is, in essence, entrepreneurial. Were the future knowable, human beings would act as automatons—reacting to stimuli rather than exercising judgment. But action implies choice, and choice presupposes an uncertain outcome. Success results when chosen means achieve the desired end; failure when they do not.

Entrepreneurship, then, is not confined to hoodie-clad founders or Silicon Valley garages. It is a universal function, exercised by individuals in new and established firms alike—by managers, employees, investors, and anyone who bears the consequences of uncertain decisions. Even within large corporations, those tasked with navigating ambiguity—whether intrapreneurs, executives, or capital allocators—are acting entrepreneurially. Mises drew a useful distinction here: between entrepreneurs in general, who act under uncertainty, and "promoters"—those visionary few who reconfigure markets and drive radical innovation.

Understanding this realm requires another distinction—between what Richard von Mises called class probability and case probability. The former applies to repeatable events governed by known distributions: the odds of rolling a six, or the likelihood of mortality in a given age group. The latter concerns one-off, non-repeatable events—where no statistical regularity exists and judgment is paramount.

Nowhere is this more relevant than in venture capital. When a VC considers a seed-stage startup, they are not consulting actuarial tables. Each investment is a singular case, shaped by a unique combination of founder psychology, product-market timing, and technological viability. The question is not “What do the data say?” but “Does this feel right?” Pattern recognition, narrative plausibility, and intuition take the place of frequency-based models.

A new product may resemble a past success, but the context—consumer sentiment, market structure, regulatory backdrop—may have changed profoundly. Decisions to pivot, fundraise, or scale are taken amid radical uncertainty. Here, conviction and story matter more than spreadsheets.

The trouble arises when decision-makers mistake case probability for class probability. Statistical tools designed for stable domains—Gaussian curves, Monte Carlo simulations, or scenario trees—are often deployed in highly volatile and non-stationary environments. The result is not clarity but a veneer of precision. Assumptions multiply; insight does not.

This is why veteran investors so often defer to heuristics and hunches. In domains governed by radical uncertainty—where probabilities are unknowable, outcomes are non-repeatable, and the future resists modelling—classical decision theory falls apart. Innovation unfolds not on the basis of known risks, but in the shadow of the unknowable.

IV. Narrative as Strategy

Standard economic theory, whether classical or behavioral, rests on a reassuring premise: that the right data will lead to the right decision. Yet in the real-world terrain of entrepreneurship and innovation, such certainty rarely exists. Decision-makers operate in environments where the rules are unclear, the future is unformed, and outcomes cannot be reliably inferred from the past. In such conditions, statistical reasoning falls short—and narrative rises in importance.

Entrepreneurs do not simply calculate; they construct meaning. Rather than optimising known variables, they build stories that make sense of ambiguity and commit to visions that others may dismiss as premature or implausible. Narrative becomes not a substitute for rationality but a different species of it—an interpretive logic suited to situations where action must precede full understanding.

People do not act on facts alone; they act when those facts are woven into emotionally coherent stories about the future. These stories serve as scaffolding for belief and enable action amid doubt. Entrepreneurs and investors manage their uncertainty through mechanisms that sustain confidence under stress—such as due diligence, symbolic commitments, or rituals of reaffirmation. These tools don’t eliminate ambiguity, but they help make decisive action possible in the absence of certainty.

The behavioural economist Robert Shiller similarly argues that markets are shaped less by fundamentals than by narrative epidemics: viral stories that spread because they resonate emotionally, not because they are empirically grounded. During speculative booms, markets often enter what Tuckett calls “divided states,” in which inconvenient data is screened out and group consensus hardens into dogma. These illusions of clarity can be as dangerous as they are motivating.

This narrative mode of reasoning is not unique to finance. Founders navigating ambiguous environments rely on narrative frames to make decisions when information is partial, noisy, or conflicting. Stories provide coherence where analysis cannot. They offer emotional orientation, moral clarity, and a shared grammar for team coordination.

Indeed, in such settings, courage matters more than certainty. What separates the committed from the cautious is not superior foresight but the ability to act without guarantees. Clarity, in retrospect, is often a narrative imposed on what was once a chaotic process. Entrepreneurs endure “wall-kicking” moments, near-death pivots, and existential ambiguity not because they have solved uncertainty, but because they have constructed a story that makes the struggle worthwhile.

Importantly, some actors are better suited to this environment than others. Contrarian investors, visionary founders, and experimental scientists often thrive not because they have better models, but because they have different dispositions. They resist social conformity, tolerate ambiguity, and derive conviction from mental frames that others do not share.

In this light, markets are not just arenas for resource allocation. They are theaters of belief, where the most consequential acts are not those of calculation but of imagination. It is not the volume of data but the power of the narrative that often determines who acts, who hesitates, and who leads.

V. Building in the Fog

Uncertainty in entrepreneurship is not a lack of data but a lack of structure. Outcomes are ambiguous, probabilities unknowable, and precedent offers little guidance. In such conditions, the traditional tools of rational choice, statistical optimisation, or risk-adjusted returns fall short. What is required instead is a toolkit grounded in inference, intuition, and narrative sense-making.

In this world, success rarely stems from consensus. Skilled founders and venture investors often act in defiance of prevailing opinion, guided less by data than by conviction. In a power-law environment—where a small number of outliers generate most returns—the costliest error is not failure but omission: missing the one company that could redefine a category.

This asymmetric reality alters the calculus of decision-making. Optimising for averages or minimising loss rates leads to mediocrity. Instead, decision-makers calibrate their bets, impose thresholds, and experiment systematically. Methods like short pilots, MVPs, and simulated environments allow for learning and adaptation—forms of feedback that outperform stylised models. In contrast to static frameworks like the "secretary problem," real-world entrepreneurial contexts allow for iteration, course correction, and reversal.

Good entrepreneurs and investors employ ecological rationality—selecting tools that fit unstable terrain. In volatile, poorly structured environments, simple heuristics often outperform complex models. Decomposition—breaking grand strategic questions into manageable tactical decisions—is one such approach. Judgment here becomes modular and situational: a repertoire of strategies adapted to the moment, not a fixed rulebook.

Narrative plays a pivotal role. Where logic and induction falter, abductive reasoning—constructing plausible, coherent stories from fragmentary signals—takes over. These narratives help interpret weak data, simulate emerging futures, and guide strategic focus. Framing, not forecasting, becomes the mode of navigation. Steve Jobs’s “wait for the next big thing” was not an analytic prediction but a narrative posture—attuned to coherence, emergence, and timing.

Underlying this is the deeper faculty of judgment: the disciplined capacity to connect cause and effect, separate signal from noise, and act toward a long-term goal under constraint. This judgment extends not only to situations but to people—whom to trust, whom to hire, whom to back—especially when maps are missing and terrain is shifting.

Because early-stage ventures lack external validation, successful entrepreneurs often rely on internal leverage: conviction, identity, and purpose tightly aligned with their vision. They inhabit the future they are building before others can see it. Yet this inner clarity must coexist with outer adaptability. Learning often reveals the limits of what we know. Survival demands flexibility: shoring up weaknesses while scaling emergent strengths.

Entrepreneurial growth, then, is not merely technical—it is existential. It requires accountability, intentionality, flexibility, capability, and vulnerability. Each enables the others. Courage to act must be matched by humility to revise. Structured communities—those that share language, values, and feedback—can accelerate this transformation by offering mirrors, scaffolds, and sounding boards.

The central question for any entrepreneur—“What is going on here?”—has no definitive answer. But in its framing lies the beginning of good judgment. Radical uncertainty may be unquantifiable, but it is not paralyzing. Entrepreneurs do not wait for clarity. They create it.