I. The Unexpected Proposition

A tiny career miracle happened recently—not the sort that arrives with celestial trumpets and angelic choirs, but the kind that sneaks into your professional life with the casual nonchalance of finding twenty dollars in last winter’s coat pocket, simultaneously delivering financial relief and questions about your organizational competence. A few days after I’d abandoned my ritual head-banging against the corporate drywall (both metaphorical and, during one particularly grim quarterly review, literal enough to prompt concerned glances that suggested my colleagues had finally noticed my existence), I received an unexpected call from a friend who wanted me to be her tech company’s co-founder and CMO.

The venture possessed all the requisite talismans of promise: an accomplished friend as CEO (whose career trajectory had the elegant upward sweep that makes résumés read like mathematical proofs of inevitable success), a technically formidable CTO, actual capital (the kind that appears in bank accounts rather than PowerPoint projections), a promising industry position, and a technological foundation that resonated with my contrarian sensibilities—one which, under favorable conditions, might transform us from speculative optimists into visionary geniuses through that peculiar alchemy that only nine-figure exits can perform.

So I entered a state of professional quantum superposition—simultaneously inhabiting and not inhabiting the co-founder role, allowing myself to experience the company’s inner workings while preserving the potential to collapse this waveform of possibility into a definitive “no” should my observations warrant it. I became a walking thought experiment: Schrödinger’s Co-founder, neither committed nor uninvolved, occupying that liminal space where evaluation remains possible and decisions haven’t yet crystallized into contractual obligations. As the days unfolded through the liturgical rhythms of startup life—the morning stand-ups that function as secular prayer circles, the weekly product reviews with their ritualistic presentation of progress, the investor updates that bear an uncomfortable resemblance to confession—I found myself increasingly troubled by something deeper than the standard anxieties that hover around fledgling companies like remora fish around sharks.

I still didn’t know what this company was truly about—not in the superficial sense captured in elevator pitches, but in its fundamental orientation toward the world. I needed to understand the hidden armature upon which all visible manifestations of the company would inevitably be structured—the intellectual skeleton that would determine whether this particular configuration of humans, capital, and ideas might defy the statistical probability of magnificent failure that haunts the startup landscape like Banquo’s ghost at the entrepreneurial banquet.

So before I could agree to become a co-founder, I posed what seemed a deceptively simple question to everyone on the team:

“How do you think about founding a company? What do you value in a company?”

The responses proved illuminating not just in their content but in their form and orientation—like personality tests that reveal less in the specific answers than in the approach to answering. For me, this exercise affirmed two fundamental truths: first, that how we think determines what we can build, in the way that the conceptual vocabulary available to an architect constrains what structures they can envision; and second, that ideas transform from nebulous cognitive vapors into structures with real-world utility only through rigorous articulation and capture—a process of intellectual precipitation that converts the atmospheric into the tangible.

What follows is my response—a letter I wish I’d written years earlier, before previous ventures succumbed to the gravitational pull of unexamined assumptions with the same inevitability that planets fall into established orbits. I share it now in hopes of inspiring similar thought experiments in others facing crossroads, a philosophical stress test that might save you from the particular heartbreak of realizing, several million dollars and countless sleepless nights later, that you’ve been building on intellectual quicksand.

II. The Nature of Company Building: Vectors, Paradigms, and Cognitive Frameworks

To: [redacted tech company] Team

The invitation to join as co-founder carries both honor and responsibility—like being handed the architectural plans for a structure in which actual humans will someday live, work, and create meaning, all while being told “don’t worry that this is your first building; we’ll figure out the load-bearing walls as we go.” What follows is my attempt to articulate how I think about company building, recognizing that this response, like all human creation, suffers from both the limitations of language and the inevitable incompleteness of any framework that attempts to encapsulate something as complex and adaptive as an organization—a bit like trying to explain consciousness using only emoji or map the ocean floor with a pool noodle.

The Mind-World Problem in Startups: Beyond the Empirical Fetish

I’ve observed a peculiar tendency, especially in domains where rapid execution is valorized, to take for granted that one already understands the task before undertaking it and, if not, that building, launching, and iterating will naturally lead to needed insight. This is the “lean startup” model, which is common sense in the startup ecosystem, but which glosses over several questionable assumptions:

  • If sales are low but we know the product is good, we just need to optimize the conversion funnel (a syllogism that assumes the product’s “goodness” exists independently of market response, like believing a tree falling in an uninhabited forest definitely makes a sound).

  • We can optimize emails and ads by A/B testing every element over time (neglecting that the universe of possible permutations approaches infinity, making this approach akin to navigating the Pacific Ocean by examining one cubic meter of water at a time).

  • Customer feedback, usage, and purchases constitute infallible validation (overlooking that customers are remarkably skilled at accommodating suboptimal solutions until something fundamentally better appears, like how humanity spent millennia perfectly satisfied with horses as transportation until the internal combustion engine suggested we might prefer not to shovel excrement from our streets).

What’s happening here is a pernicious epistemological dualism: we study the concrete aspects of business (revenue, user metrics, market size) with empirical rigor but approach the conceptual foundations (what problems are worth solving, what human needs actually are, what valuable solutions look like) with a surprising casualness—a kind of mind-body problem transposed into the business domain, where we’ve somehow convinced ourselves that only the body requires careful examination while the mind will take care of itself.

This dualism feels like the startup world’s version of what Russell once described as “the doctrine of two kinds of truth”—the peculiar notion that some truths require rigorous verification while others can be accepted on convenience or convention alone. We’d never ship code without testing, yet we’ll often ship entire business models based on unexamined assumptions about what people need or how markets function.

But as any philosophically inclined engineer knows, there’s no patch for a fundamentally flawed algorithm. Success metrics tell us nothing about the quality of a company’s solutions—they merely show that something works well enough right now relative to a common definition of success, which is itself often arbitrary and historically contingent. Yahoo’s $125 billion market cap in 1998 didn’t validate their approach to search any more than Ptolemy’s ability to predict planetary positions validated geocentrism; both merely demonstrated effective exploitation of existing frameworks until someone proposed more elegant explanations that better accounted for observable phenomena.

This pattern—of success masking conceptual limitations until a fundamental shift renders them obvious—recurs with the reliability of seasonal migrations: Blockbuster dominated physical media distribution until Netflix reconceptualized first how media could be delivered and then how it could be consumed; IBM ruled computing until Apple reimagined the human-computer relationship; Kodak perfected chemical photography until digital imaging rendered their expertise irrelevant. In each case, the incumbent’s success metrics remained impressive right until the moment they became historical footnotes—like the patient who appears perfectly healthy immediately before the heart attack.

A More Stable Framework: The False Idol of Discontinuity

The popular conception of progress—in science, business, and indeed most domains of human endeavor—often draws heavily on the notion of paradigm shifts: the idea that advancement occurs through periodic revolutionary breaks with previous frameworks, separated by periods of “normal” activity where practitioners merely solve puzzles within the established paradigm. This view, while intellectually seductive (and conveniently flattering to those who fancy themselves paradigm-shifters), fundamentally misrepresents how genuine progress occurs.

I’d like to propose something more radical by being less radical—a theory of continuity rather than discontinuity. What if the truly revolutionary companies aren’t those that create dramatic breaks with the past but those that better align their products and services with stable, pre-existing structures of human cognition, social behavior, and technological possibility? What if Apple’s genius wasn’t in creating a new cognitive paradigm but in recognizing something fundamental about how humans prefer to interact with objects—something that existed long before the first GUI and will persist long after touchscreens become antiquated?

This notion of progressive refinement rather than revolutionary rupture reminds me of Popper’s insight that scientific progress advances through “conjectures and refutations” rather than periodic revolutions—each new theory attempting to solve problems left unsolved by its predecessors while preserving their explanatory successes. Apple didn’t scrap the entire concept of computing; they refined our understanding of how humans interact with information technology, creating something that felt new precisely because it better aligned with how we’ve always preferred to interact with our tools.

Consider Google’s PageRank algorithm. Its elegance came not from inventing a new way of thinking but from recognizing that human knowledge has always been organized through networks of reference and trust—that the value of information has always been partially determined by who else values it. This wasn’t a paradigm shift so much as an algorithmic implementation of how humans have assessed information credibility for millennia. The true innovation wasn’t discontinuity but unprecedented alignment between technological capability and stable cognitive reality.

This perspective suggests a more pragmatic approach to business innovation—one focused less on revolutionary disruption and more on deep understanding of enduring human needs, cognitive patterns, and social dynamics. It’s less about breaking with the past than about seeing the present more clearly and aligning your business more precisely with underserved aspects of human reality.

To grasp this distinction, consider a parable of two island tribes, each facing water scarcity but adopting radically different approaches to this existential challenge:

Tribe A perfects the art of surface-water finding through generations of incremental optimization. Their best water-seekers become tribal heroes, celebrated for their ability to extract maximum value from existing resources. They develop elaborate rituals and sophisticated methods, all oriented around the central challenge of finding more surface water—an approach that works until external conditions change and the rains simply stop falling, at which point their optimized methods reveal themselves as elaborate ways of solving the wrong problem.

Tribe B begins similarly but evolves differently when one member notices patterns in soil composition that correlate with water presence, leading to a 10% improvement in search efficiency. Rather than merely exploiting this discovery, they develop a culture of systematic experimentation that leads to the discovery of groundwater reserves, and eventually to desalination techniques that transform their relationship with water entirely. Their approach isn’t merely better at solving the same problem; it redefines what problem they’re solving and what solutions are possible.

The key insight here isn’t that Tribe B experienced a series of “paradigm shifts” while Tribe A remained stuck in one paradigm. Rather, Tribe B developed a stable meta-capability—the ability to identify and transcend limiting frameworks—that allowed them to progressively align their water-seeking activities with a deeper understanding of hydrological reality. Their success didn’t come from revolutionary discontinuity but from evolutionary continuity guided by increasingly accurate models of how water actually behaves in their environment.

This parable illustrates what Whitehead might have called “the fallacy of misplaced concreteness” in business—mistaking our models and methods (abstract) for the reality they’re supposed to represent (concrete). Tribe A confuses their water-finding rituals with water itself, while Tribe B maintains a clear distinction between their theories about water and water’s actual nature.

In business terms, this suggests that companies like Amazon didn’t succeed by creating revolutionary breaks with retail tradition but by progressively aligning their business model with more accurate understandings of what consumers actually want (greater selection, convenience, reliability) and what technology actually enables (efficient logistics, personalization, seamless transactions). The “revolution” wasn’t in the ends but in the means—not in what was desired but in how those desires could be satisfied.

Strategic Simplification: The Galilean Method Applied to Business

This brings us to what might be called the Galilean method—a powerful approach to understanding complex phenomena that transcends the false dichotomy between pure empiricism and pure theory. Standing beside the Arno River in Pisa, Galileo confronted a problem similar to what every business leader faces: how to make sense of overwhelming complexity. Predicting the river’s exact state even hours ahead would require tracking countless variables—water molecules, pressure differentials, wind patterns—an impossible task even with modern computational resources.

Galileo’s revolutionary insight wasn’t choosing sides in this false dichotomy but developing a method that integrated both through strategic simplification. By stripping away what he termed “accidental” properties—surface tension, air resistance, contextual noise—he revealed underlying patterns and principles that no amount of surface observation could discern. He constructed idealized models—perfect vacuums, frictionless planes, bodies falling without air resistance—not because these conditions existed in nature but because they allowed fundamental relationships to become visible beneath contextual complexity.

This approach brings to mind Wittgenstein’s notion of “showing the fly the way out of the fly-bottle”—creating clarity not by adding complexity but by removing the unnecessary, allowing the essential structure to reveal itself. Just as philosophical problems often arise from misunderstandings of language, business problems often arise from misunderstandings of markets and human needs.

This approach—strategic simplification to reveal essential dynamics—offers a powerful method for business innovation that transcends both blind empiricism and untethered theorizing. Rather than reactively addressing each sales fluctuation or competitor move, consider how a luxury skincare brand facing declining performance might apply the Galilean method:

Instead of frantically responding to each quarterly dip, marketing trend, or competitor launch, they would step back and ask: What fundamental relationships between expertise, trust, and status signaling must exist for any premium product to succeed? What underlying human needs does skincare fulfill beyond functional benefits? What principles of consumer psychology remain constant regardless of market trends?

Through this investigation, they might discover that premium purchasing decisions always involve specific psychological elements—not just correlation but necessary causation. For example, they might identify that perceived expertise, social signaling, and ritual self-care are not just features of skincare marketing but essential components of the value proposition itself. This understanding would then guide specific strategic decisions:

  • Instead of simply increasing ad spend, they might redesign their packaging to better signal expertise through specific design elements that research shows trigger trust responses
  • Rather than discounting to boost sales, they might invest in creating more sophisticated application rituals that enhance the self-care experience
  • Instead of chasing competitors’ ingredient trends, they might focus on creating distinctive sensory experiences that strengthen brand association

This transforms leadership from pattern-matching (reacting to what is) to principle-finding (understanding what must be)—from playing the game better to rewriting the rules that make the game possible. It’s not about revolutionary discontinuity but about progressive alignment with deeper realities that were always there but previously unrecognized or underserved.

The Structure of Inquiry: A Dynamic System of Understanding

To operationalize this approach, companies need a systematic framework for extracting understanding from information—a continuous feedback loop where observation, pattern recognition, explanation, and application mutually inform and refine each other. This process feels reminiscent of what Dewey described as “the pattern of inquiry”—a systematic approach to knowledge creation that integrates experience, hypothesis formation, and experimental verification.

  1. Observation: Collecting data not indiscriminately but selectively, designing observation processes that capture what matters rather than drowning in noise. For Netflix, this meant tracking not just what people watched but how they watched—patterns of pausing, rewinding, abandoning, and binging that revealed deeper engagement dynamics than simple viewing counts.

    Bacon might have recognized in this the interplay between “experiments of light” (observations designed to illuminate understanding) and “experiments of fruit” (observations designed to produce useful results)—both necessary, but neither sufficient alone.

  2. Description: Recognizing patterns in observed phenomena but understanding that pattern recognition without explanation is like noticing that tides correlate with lunar cycles without understanding gravitational influence. Amazon didn’t just observe that people abandoned carts when shipping costs appeared; they recognized that this reflected a deeper psychological principle of perceived transaction fairness.

    This distinction echoes Hume’s insight that we must differentiate between mere correlation and meaningful causation—not all patterns in data represent actual relationships in reality.

  3. Explanation: Moving beyond prediction to insight by uncovering causal mechanisms and fundamental principles. Apple didn’t just notice that people struggled with complicated interfaces; they developed a deeper understanding of how human cognition processes visual information and how physical gesture serves as a natural extension of intention.

    This focus on explanatory depth reflects what Peirce called “abduction”—forming hypotheses that explain observations in ways that go beyond mere pattern recognition.

  4. Application: Translating understanding into products, services, and strategies that test and refine theoretical insights. Tesla didn’t just apply battery technology to cars; they created products that simultaneously solved transportation problems and generated data to refine their understanding of how people actually use electric vehicles, creating a virtuous cycle of improvement.

    This integration of theory and practice recalls Kant’s insight that “thoughts without content are empty, intuitions without concepts are blind”—neither pure theory nor raw experience alone can generate meaningful innovation.

To navigate this process effectively, organizations must develop the flexibility to move between levels as circumstances demand, recognizing that different challenges require different balances of empirical testing and theoretical reconception. Some problems benefit from rapid iteration and market validation (finding the optimal button color for checkout completion); others demand deeper rethinking of fundamental assumptions (questioning whether buttons are the right interaction paradigm at all).

The Deferral Cascade: Why Smart Organizations Do Dumb Things

One of the great paradoxes in organizational life is that deep inquiry often appears luxuriously impractical while simultaneously serving as the primary determinant of long-term success—like dismissing regular health checkups as an unnecessary expense until you’re in the emergency room with something that could have been prevented with early detection. This creates what I call a “deferral cascade”: employees defer intellectual responsibility to managers, managers defer to executives, executives defer to market signals and conventional wisdom, creating an impression that the fundamental thinking has already been done somewhere by someone—an organizational manifestation of the bystander effect where everyone assumes someone else has checked whether the emperor is actually clothed.

The pattern feels oddly reminiscent of what Arendt observed about the “banality of evil”—not in its moral implications but in its structural mechanics of responsibility diffusion. Just as bureaucracy can distribute moral responsibility until it effectively disappears, business hierarchies can distribute intellectual responsibility until no one is actually thinking about the foundational questions that matter most.

The problem isn’t intellectual capacity or intention but structure. Most organizations lack explicit mechanisms for surfacing and examining assumptions, measuring the cost of unexamined beliefs, or allocating resources to fundamental inquiry. They can calculate customer acquisition costs to three decimal places but have no metric for whether they truly understand what problem they’re solving or how durably they’re solving it.

To combat this structural deficiency, successful companies institutionalize philosophical inquiry—not as an academic exercise but as a practical business function. Here’s how several innovative organizations have done this:

  • Amazon’s Six-Page Narratives: Before launching any significant initiative, teams must write a complete six-page narrative explaining their reasoning, assumptions, and expected outcomes. This forces rigorous thinking and surfaces hidden assumptions before resources are committed.

  • Pixar’s Braintrust: Regular sessions where projects are critically evaluated not by executives but by peers with no decision-making authority, creating a space for honest feedback without political considerations.

  • IDEO’s Design Thinking: A systematic process that begins with questioning the problem definition itself, often revealing that the initial framing was itself the primary obstacle to meaningful innovation.

These approaches share a common recognition: that execution without periodic reassessment of fundamental assumptions tends toward local optimization at the expense of transformative opportunities. Motion without direction isn’t strategy; it’s entropy disguised as progress.

Ryle might have recognized in this the classic “category mistake”—treating strategy as something separate from execution rather than a quality of how execution is conducted. Strategy isn’t something that exists apart from day-to-day operations; it’s a quality of how those operations are conceived and carried out.

From Conceptual Foundations to Practical Vision

If integrated inquiry is how we understand the world, vision is how we shape our company. Building an extraordinary organization isn’t just about financial outcomes but about creating something that expands possibilities, attracts exceptional minds, and develops products people don’t just use but find transformative. It should be something worth dedicating yourself to even if you never needed to work again, that opens doors you couldn’t open alone, and lets you build alongside remarkable people on ideas so ambitious and well-executed that competitors aren’t just left behind but left in a different paradigm entirely.

This conception echoes Berlin’s distinction between negative and positive liberty—between freedom from constraint and freedom to self-actualize. The truly great company doesn’t just liberate itself from competitive threats but liberates its employees and customers to achieve things previously impossible.

Here’s what this might look like in practice:

Principled Curiosity as Competitive Advantage

The most valuable questions don’t ask how to optimize what exists but what isn’t even being considered—like asking not how to make a better candle but whether light can be created without flame. Every domain contains both accumulated wisdom and unexamined assumptions; our job is to leverage the former while systematically questioning the latter, creating explicit mechanisms for fundamental inquiry within regular operations.

This approach brings to mind Socrates’ gadfly metaphor—the persistent questioner who stings the comfortable into action by exposing contradictions in received wisdom.

This might take the form of:

  • Regular “assumption audits” where teams explicitly identify and evaluate the core beliefs underlying their strategies
  • Cross-functional exploration teams tasked with investigating adjacent domains for transferable insights
  • Dedicated time for employees to pursue curiosity-driven projects without immediate justification

Hard Problems as Developmental Crucibles

The easiest way to fail is to avoid hard problems; the easiest way to win is to seek them out—not with masochistic enthusiasm but with strategic recognition that difficulty often correlates with opportunity. Complex, messy challenges develop the meta-capabilities that enable sustained innovation, like muscles that grow stronger through progressive resistance.

Nietzsche might have recognized in this his insight that “what does not kill me makes me stronger”—not as a celebration of suffering but as a recognition that obstacles, when engaged properly, become opportunities for growth.

Tesla didn’t choose electric vehicles because they were easy but because they were hard—and in solving that hard problem, they developed capabilities in battery technology, manufacturing, and digital integration that created enduring competitive advantages across multiple domains.

Meaning as Organizational Gravity

The companies that endure aren’t just technically impressive but deeply resonant—they matter to people in ways that transcend specific features or benefits. Meaning isn’t a marketing overlay; it’s the foundation that enables everything else, creating gravity that holds together customers, employees, and stakeholders even as surface features evolve.

This echoes Frankl’s insight that “those who have a ‘why’ to live can bear almost any ‘how'”—organizations with a compelling purpose can weather setbacks that would destroy companies held together only by financial incentives.

Patagonia doesn’t just sell outdoor clothing; they embody a relationship with nature that attracts customers who share those values. This alignment creates loyalty far more durable than any feature or price advantage could achieve—and it guides decision-making throughout the organization in ways that purely financial metrics never could.

Structure as Intellectual Infrastructure

Brilliant minds don’t want to be micromanaged. They want meaningful challenges and the autonomy to solve them. The best companies create environments where ideas win based on merit rather than hierarchy, work is guided by shared purpose rather than rigid oversight, and there’s a culture that values both intellectual exploration and practical execution.

Mill might have recognized in this his concept of “experiments in living”—the idea that progress depends on creating spaces where diverse approaches can flourish and be evaluated on their results.

This requires intentional design of organizational structures and processes that:

  • Minimize approval chains for experiments and innovations
  • Reward intellectual courage and constructive dissent
  • Create regular forums for cross-functional dialogue and debate
  • Allocate resources based on idea quality rather than political capital

III. The Decision: A Vector Toward Different Fields

In the end, I turned down the offer—not because the company lacked potential or the team lacked capability, but because our foundational understanding of what we were building and why diverged in ways that no amount of tactical alignment could overcome. The exercise and conversations that ensued made it undeniably clear what kind of company we had the potential to be and, just as clearly, that it wasn’t the right fit for me at this time.

Yet far from leaving me in doubt, this process has given me greater clarity and purpose—sharpening my understanding of what I value in companies and what I want to build. If nothing else, I hope that in reading this, others facing similar crossroads might find value in conducting their own philosophical stress tests before leaping into founding partnerships. The questions we ask at the beginning shape everything that follows—not just what we build, but who we become in building it.

Camus might have recognized in this a form of entrepreneurial existentialism—finding meaning not in external validation but in the integrity of one’s engagement with self-chosen challenges.

There are many paths to creating successful companies. Some thrive through rapid iteration and market responsiveness; others through deep reconceptualization of fundamental problems. Most require some integration of both approaches, adapted to specific domains and challenges. The key isn’t finding the one “right way” to build a startup, but developing the discernment to know which approach best fits the specific problems, markets, and teams we’re working with—and the flexibility to shift between them as circumstances change.

For me, that means continuing to search for opportunities where I can apply these principles—not as dogma, but as a flexible framework for building something meaningful, valuable, and enduring. The journey continues, guided not by the false certainty of paradigmatic thinking but by the humble recognition that reality is structured in ways we can progressively uncover, even if we never grasp it completely.