Damian Mingle

Author: Damian

  • Why Most AI Companies Fail

    Most AI companies fail because they are designed as products rather than as operating layers embedded in real decision systems.

    Much of the current enthusiasm around artificial intelligence has taken the form of company creation. New firms appear daily, each presenting a novel application, interface, or workflow powered by increasingly capable models. The energy is real, and so is the technical progress. Yet the volume of activity obscures a quieter question: whether most of these companies need to exist at all.

    This is not a claim about the usefulness of the technology. AI is already valuable and will remain so. The concern is about organizational form. Treating AI as the primary reason for a company’s existence conflates capability with durability. It assumes that the presence of a powerful tool is sufficient justification for a standalone business. In many cases, it is not.

    The misconception lies in confusing a technological moment with a structural opportunity.

    The prevailing framing suggests that AI creates new categories of companies simply by enabling new kinds of outputs. If a model can generate text, images, or decisions more efficiently than before, then the reasoning goes, a company can be built around delivering those outputs. This framing treats AI as a differentiator in itself.

    What this misses is that most of what AI enables is not unique to the firm deploying it. The underlying capabilities are widely accessible, improving rapidly, and increasingly standardized. When the primary value proposition of a company rests on access to a general-purpose capability, it becomes difficult to defend over time.

    Historically, enduring companies are not built around tools. They are built around positions within systems: ownership of workflows, control of interfaces, or responsibility for outcomes that others cannot easily assume. Tools come and go. Systems persist.

    AI, when treated as a product, invites commoditization. When treated as an operating layer, it invites integration. Many “AI companies” struggle because they sit awkwardly between these two modes, offering intelligence without owning the system in which that intelligence matters.

    At a system level, organizations exist to coordinate behavior under constraints. They manage incentives, allocate responsibility, and absorb risk. Technology can enhance these functions, but it rarely replaces them. A company that offers AI-generated insight without bearing responsibility for decisions remains external to the system it seeks to influence.

    This externality is costly. Decision-makers discount advice when they are accountable for outcomes but the advisor is not. This is not cynicism; it is rational behavior under asymmetric risk. An AI system that produces recommendations but does not share in the consequences will be treated as optional input rather than authoritative guidance.

    The most durable value emerges when intelligence is embedded where accountability already exists. This requires domain depth, operational ownership, and an understanding of incentives. It is easier to build a model than to integrate it into a system that must perform reliably under pressure.

    Variance provides a useful lens here. Many AI companies aim to improve peak performance: better predictions, faster responses, more creative outputs. But organizations are rarely constrained by their best moments. They are constrained by inconsistency. The systems that matter most are those that reduce variance in decision-making and execution.

    Reducing variance requires intimate knowledge of how decisions are actually made, where noise enters, and which constraints are binding. This work is specific, contextual, and resistant to abstraction. It does not scale easily across domains, which is why it is often neglected. Yet it is precisely this specificity that creates defensibility.

    Most AI companies avoid this terrain. They position themselves as horizontal solutions, applicable everywhere. In doing so, they sacrifice the very conditions that would allow them to matter deeply anywhere.

    Incentives reinforce this pattern. Capital often favors narratives of broad applicability and rapid growth. Builders respond by emphasizing generality over integration. The result is a proliferation of tools that demonstrate technical competence but lack systemic relevance. They can be impressive in isolation and inconsequential in practice.

    Behavioral science helps explain why this persists. Humans overvalue visible novelty and undervalue quiet reliability. Demos are persuasive; stable operations are not. It is easier to sell intelligence than discipline, even though discipline is what compounds.

    For builders, the implication is uncomfortable. Creating a company around AI requires more than technical skill. It requires choosing a system to belong to and accepting the constraints that come with that choice. This may involve narrower markets, slower growth, and deeper responsibility. It may also involve subordinating the technology to the problem rather than the other way around.

    Many would-be AI companies are better understood as features, integrations, or internal capabilities of existing organizations. This is not a failure. It is a recognition of where value actually accrues. When intelligence enhances an existing system, it strengthens that system’s owner. Spinning it out as a separate company often adds friction rather than leverage.

    For capital allocators, this perspective suggests a different set of questions. Instead of asking how advanced the technology is, ask where accountability lies. Who bears the risk if the system is wrong? Who owns the workflow that the AI touches? How easily can the capability be replicated by others with access to the same models?

    Answers to these questions reveal whether a company is building around a durable position or merely assembling a transient configuration of tools.

    Execution further clarifies the issue. Many AI companies struggle not because their technology fails, but because adoption stalls. Users test the system, appreciate its potential, and then revert to established processes. This is often interpreted as resistance to change. More often, it reflects misalignment with incentives and responsibilities.

    A system that improves outputs but complicates accountability will be sidelined. A system that reduces cognitive load and stabilizes decisions will be adopted quietly. The difference is not intelligence, but fit.

    None of this implies that AI will not reshape industries. It already is. But reshaping industries does not require a new company for every application. In many cases, it requires existing organizations to absorb new capabilities and reorganize around them. The winners are often those who integrate intelligence into their operations rather than those who attempt to sell intelligence as a standalone good.

    This reality can be difficult to accept in moments of rapid technological change. New tools create the illusion of new categories. Over time, most of these categories collapse back into the systems that matter: finance, healthcare, logistics, manufacturing, governance. The enduring companies are those that understand these systems well enough to embed intelligence where it changes behavior, not just outputs.

    In this light, the question is not whether AI companies can exist, but whether they should. A company justified primarily by the presence of AI may struggle to maintain relevance as the technology becomes more accessible and standardized. A company justified by its role within a complex system can use AI as a lever rather than a crutch.

    The distinction is subtle but decisive. Technology accelerates whatever structure it enters. When the structure is coherent, acceleration compounds value. When it is not, acceleration magnifies fragility. Most “AI companies” fail not because the technology disappoints, but because the structure they have chosen cannot sustain it.

    Seen clearly, this is not a pessimistic view. It is a clarifying one. AI is most powerful when it disappears into the systems that already govern decisions and outcomes. The fewer companies built solely around the technology itself, the more likely it is that intelligence will be applied where it actually matters.

  • Capital, Complexity, and Decision Quality

    In most discussions about capital, attention gravitates toward scale. How much is deployed, how quickly it can be deployed, and what returns it might produce. Complexity, when it appears, is treated as an external condition to be managed or avoided. Decision quality is often assumed to be a function of intelligence, experience, or access to information.

    This framing is comfortable, but it obscures the real source of both success and failure. In complex environments, outcomes are determined less by the amount of capital available than by the consistency and discipline with which decisions are made under uncertainty. Capital amplifies whatever decision process it encounters. When that process is coherent, capital compounds. When it is noisy, capital accelerates error.

    The misconception is subtle but persistent: that better outcomes primarily require more insight or more resources. In reality, they require better decision systems.

    The common framing treats complexity as an obstacle and decision-making as a discrete act. In practice, complexity is the environment in which decisions live, and decision-making is a continuous process shaped by constraints, incentives, and feedback loops. Capital does not operate in isolation; it moves through organizations composed of people, processes, and norms. Each of these elements introduces variance.

    When complexity increases, variance does not rise linearly. It compounds. Small inconsistencies in judgment, timing, or interpretation can produce large divergences in outcomes over time. The same strategy, applied by different teams or at different moments, yields materially different results. Capital is often blamed for this volatility, but the underlying issue is decision quality under load.

    Decision quality is not synonymous with correctness. In complex systems, correctness is often unknowable in advance. Decision quality is better understood as the ability to make choices that are coherent with objectives, repeatable across contexts, and robust to uncertainty. A high-quality decision process does not guarantee success, but it constrains failure.

    Seen this way, capital becomes less a driver of outcomes and more a stress test. As capital scales, weaknesses in decision processes are exposed. Informal rules become inconsistent. Tacit knowledge fails to transfer. Incentives drift. The system begins to behave differently than intended, not because anyone is acting irrationally, but because complexity has outpaced structure.

    At the system level, capital, complexity, and decision quality are tightly coupled. Capital increases the number of decisions that must be made and the speed at which they must be made. Complexity increases the number of interacting variables and the opacity of cause and effect. Decision quality determines whether the system remains stable under these pressures.

    Most organizations underestimate how much of their performance is driven by variance rather than averages. They celebrate peak outcomes and rationalize failures as anomalies. Over time, however, it is the distribution of decisions—not the best decisions—that determines results. A system that occasionally performs brilliantly but frequently deviates from its own standards is fragile. A system that performs consistently within known bounds is resilient.

    Behavioral science helps explain why this distinction is often missed. Humans are pattern-seeking and outcome-oriented. We overweight salient successes and underweight the quiet cost of inconsistency. We attribute outcomes to skill rather than structure and to individuals rather than systems. As complexity rises, these biases become more costly.

    Decision environments shape behavior. When criteria are ambiguous, people substitute intuition. When incentives are misaligned, people optimize locally. When feedback is delayed or noisy, learning stalls. None of this requires bad actors. It is the natural result of operating without sufficient structure.

    High-quality decision systems address these issues by reducing unnecessary discretion and clarifying trade-offs. They make implicit assumptions explicit. They define thresholds, escalation paths, and review mechanisms. They separate reversible from irreversible decisions and allocate attention accordingly. In doing so, they reduce variance without attempting to eliminate judgment.

    Capital responds to this stability. Investors and lenders do not require perfection; they require predictability. A system that behaves consistently under stress is easier to finance than one that relies on exceptional judgment at every turn. Reduced variance lowers perceived risk, which in turn lowers the cost of capital. This relationship is often indirect, but it is durable.

    For builders, the implication is that scaling is not primarily a function of ambition or resources. It is a function of whether the decision system can absorb increased complexity without degrading. This requires deliberate design. Processes must be revisited, not because they are inefficient, but because they no longer constrain behavior in the way they once did. As organizations grow, informal norms must be replaced with explicit structures, or variance will increase.

    This work is rarely glamorous. It involves documenting decisions, codifying criteria, and resisting the temptation to treat every case as exceptional. It requires accepting that some decisions should be automated or standardized, not because humans are incapable, but because consistency matters more than expressiveness in many contexts.

    For capital allocators, the lesson is to look beyond narratives of growth and innovation and examine how decisions are actually made. How does the organization handle uncertainty? How does it learn from error? How are incentives aligned across roles and time horizons? These questions reveal more about long-term performance than any single metric.

    Execution is where these dynamics become visible. Strategies fail not because they are unsound, but because they are executed unevenly. Decision quality degrades as complexity increases unless the system is designed to counteract that tendency. Capital accelerates whatever execution environment it encounters. It does not correct it.

    The most effective organizations treat decision quality as an asset. They invest in it deliberately and protect it as they scale. They recognize that complexity cannot be eliminated, but it can be managed through structure. They understand that capital is most powerful when it amplifies coherence rather than compensates for its absence.

    In this context, success looks less like brilliance and more like discipline. Fewer surprises. Narrower outcome distributions. A system that behaves the same way on difficult days as it does on easy ones. These qualities are easy to overlook and difficult to retrofit, but they are what allow capital to compound over time.

    Ultimately, capital does not solve complexity. It reveals how well decisions are made within it. When decision quality is high, complexity becomes navigable. When it is low, complexity becomes destabilizing. The difference is not intelligence or effort, but the quiet work of building systems that make good decisions repeatable.

  • Why Variance Reduction Is the Real Value of AI

    Most conversations about AI value begin with speed, scale, or intelligence. Faster analysis. More output. Smarter decisions. These claims are not wrong, but they are incomplete. They describe visible effects rather than the underlying mechanism that actually changes outcomes in real systems.

    In practice, organizations rarely fail because they lack ideas or insight. They fail because their decisions are inconsistent, noisy, and unevenly applied over time. The same team can make a strong choice on Monday and a weak one on Thursday, using the same information, under similar conditions. The variance between those decisions compounds far more than any single error.

    AI is often positioned as a tool for optimization or automation. Its more durable contribution is quieter: reducing variance in judgment where inconsistency is costly.

    The prevailing framing treats AI as a way to make decisions better. A more accurate model. A more complete dataset. A more rational process. This framing assumes that the primary problem is decision quality in isolation.

    In most operating environments, the problem is not that decisions are bad on average. It is that they are unstable. Outcomes vary widely based on who is involved, when the decision is made, what mood the system is in, and how much cognitive load is present at that moment.

    Two underwriters review the same deal and reach different conclusions. Two physicians interpret the same case differently. Two operators apply the same policy with different thresholds. Over time, this inconsistency erodes trust, capital efficiency, and system performance.

    AI does not need to outperform the best human judgment to create value. It only needs to narrow the spread between the best and the worst decisions that occur inside the system.

    Variance is not an abstract statistical concept. It is a lived property of complex systems.

    In organizations, variance emerges from human limitations: fatigue, bias, incomplete recall, shifting incentives, and context switching. These factors do not disappear with experience or intelligence. In fact, high-performing environments often amplify variance because decisions are made faster, under pressure, and with partial information.

    Systems absorb variance unevenly. Some domains tolerate it. Others do not. In capital allocation, healthcare, risk management, and operations-heavy businesses, variance is expensive. A single outlier decision can erase the gains of many good ones.

    AI functions as a stabilizing layer when it is embedded into the decision process itself. Not as a replacement for judgment, but as a constraint system that enforces consistency. It remembers what was decided before. It applies criteria the same way every time. It does not drift under cognitive load.

    This does not eliminate human judgment. It changes its role. Humans move from making every decision from scratch to supervising, exception-handling, and adjusting the rules that govern the system.

    The value emerges not from intelligence, but from reliability.

    Variance persists because most organizations lack strong feedback loops. Decisions are made, outcomes unfold slowly, and attribution is unclear. By the time results are visible, the context that produced the decision has changed.

    AI systems can encode constraints that humans struggle to maintain. Thresholds. Guardrails. Historical comparisons. Explicit trade-offs. These constraints do not make decisions optimal in a theoretical sense. They make them repeatable.

    Repeatability changes incentives. When outcomes are more predictable, capital can be deployed with greater confidence. When decisions are explainable, trust increases. When processes are consistent, systems become improvable.

    This is where many AI initiatives fail. They aim to optimize locally rather than stabilize globally. They chase marginal accuracy improvements instead of reducing tail risk. They build tools that assist individuals instead of shaping system behavior.

    Variance reduction is a systems problem, not a feature problem.

    For builders, this reframing changes what success looks like.

    The goal is not to create the smartest model. It is to design decision infrastructure that behaves the same way under pressure as it does under ideal conditions. This requires understanding where variance actually enters the system: handoffs, subjective thresholds, ambiguous criteria, and moments of human overload.

    Successful AI implementations tend to be boring on the surface. They formalize rules that already exist but are inconsistently applied. They surface historical context that humans forget. They narrow discretion where discretion adds noise rather than value.

    This kind of work is less visible than building a product demo. It requires deep integration into workflows and a willingness to prioritize system health over novelty.

    For capital allocators, variance reduction is often more valuable than upside.

    A system that produces steady, explainable outcomes is easier to finance than one that occasionally produces exceptional results but cannot explain its failures. Reduced variance lowers perceived risk, even if average performance remains unchanged.

    This is why mature industries adopt checklists, protocols, and standard operating procedures. AI extends this logic. It allows systems to encode judgment at scale without relying on perfect human execution.

    Capital responds to predictability. AI that reduces variance increases the reliability of returns, which in turn lowers the cost of capital and expands strategic options.

    Execution is where variance quietly destroys value.

    Most strategies fail not because they are wrong, but because they are unevenly implemented. AI can act as an operating layer that enforces execution discipline across time, people, and conditions.

    When decision criteria are explicit and consistently applied, learning becomes possible. When learning compounds, systems improve. When systems improve, performance follows.

    This is not a promise of transformation. It is a description of how stable systems evolve.

    AI’s most durable contribution is not intelligence, creativity, or speed. It is the reduction of variance in decisions that matter. When systems behave more consistently, outcomes improve quietly, capital flows more confidently, and complexity becomes manageable. In that sense, AI’s value is less about thinking better and more about thinking the same way, every time it counts.

  • AI Is an Operating Layer, Not a Product

    Much of the confusion surrounding artificial intelligence today comes from a category error. AI is routinely discussed, purchased, and evaluated as if it were a product: something discrete, self-contained, and valuable on its own. This framing is appealing because it fits existing commercial patterns. Products can be marketed, priced, compared, and sold. They can be deployed with a start date and evaluated against a feature list.

    But this framing quietly distorts expectations. It encourages organizations to ask whether an AI system is “good,” “powerful,” or “advanced” in isolation, rather than whether it meaningfully changes how decisions are made inside a system. As a result, many deployments feel impressive at a demo level yet inconsequential at an operational level. The technology appears present, but outcomes remain largely unchanged.

    The problem is not that the systems are incapable. It is that they are being treated as the wrong kind of thing.

    AI does not behave like a product because it does not create value independently. It behaves like an operating layer: a set of capabilities that alters how information flows, how decisions are formed, and how actions are sequenced across an organization. Its impact is inseparable from the processes, incentives, and constraints into which it is embedded.

    When framed as a product, AI is expected to “do work” on behalf of the organization. When framed as an operating layer, it is understood to reshape work by modifying the structure through which work happens. This distinction matters. Products can be evaluated at the point of delivery. Operating layers can only be evaluated through their downstream effects on behavior and outcomes.

    Historically, foundational technologies follow this pattern. Databases, operating systems, and networking protocols were not valuable because of their features alone. They became valuable because they changed what was possible to coordinate, measure, and execute at scale. AI occupies a similar role. Its value does not reside in outputs such as predictions, classifications, or generated text. It resides in how those outputs alter the decision environment.

    The mistake, then, is not overestimating AI’s capabilities, but underestimating the degree to which value depends on integration rather than acquisition.

    Viewed at the system level, organizations are collections of interacting decisions operating under uncertainty. Each decision is constrained by limited information, time pressure, incentives, and human cognitive limits. Errors compound not because individual actors are irrational, but because variance accumulates across many small judgments made under imperfect conditions.

    AI changes this landscape only when it is woven into the decision fabric. A model that produces accurate predictions but sits outside the workflow does little to reduce variance. A model that is tightly integrated—shaping when decisions are made, what information is surfaced, and how alternatives are evaluated—can materially change outcomes even if its raw accuracy is modest.

    This is where incentives and constraints matter. People do not simply adopt better information because it exists. They adopt it when it aligns with incentives, reduces friction, and fits within existing accountability structures. An AI system that introduces cognitive or operational friction will be bypassed, regardless of its technical sophistication. Conversely, a system that quietly reduces effort while improving consistency will be used even if its presence is barely noticed.

    From a behavioral perspective, this is unsurprising. Humans rely on heuristics to manage complexity. Systems that lower cognitive load and stabilize decision inputs are trusted over time, while systems that demand attention or justification are treated as external advice and discounted. AI functions best when it operates below the level of conscious deliberation, shaping the environment rather than competing for authority within it.

    Variance reduction is the key concept here. Most organizations do not fail because they lack peak performance. They fail because they cannot reliably reproduce acceptable performance across time, people, and conditions. An operating layer that narrows the distribution of outcomes—by standardizing information quality, timing, and framing—creates value even if it never produces a dramatic improvement in any single instance.

    For builders, this reframing demands a shift in focus. The central design question is not “What can the model do?” but “Which decisions does this system influence, and how?” Success depends less on model novelty than on architectural discipline: understanding workflows, identifying leverage points, and designing interfaces that align with human behavior. The most effective systems often feel unremarkable because they do not announce themselves. They quietly remove sources of noise and inconsistency.

    For capital, the implications are equally significant. Evaluating AI initiatives as products encourages shallow metrics: feature comparisons, model benchmarks, and adoption statistics. Evaluating them as operating layers requires patience and systems thinking. The relevant questions become: Does this change decision quality? Does it reduce downside risk? Does it improve repeatability? These effects are harder to measure quickly, but they are far more durable.

    This perspective also explains why many so-called AI companies struggle to justify their existence. If the technology is separable from the system it serves, it is likely to be commoditized. Sustainable value accrues to those who understand the domain deeply enough to embed intelligence where it alters behavior, not merely where it produces outputs. In this sense, the moat is rarely the model itself. It is the integration of intelligence into a complex, constraint-laden environment.

    For execution, treating AI as an operating layer changes how success is managed. Deployment is not an endpoint but a beginning. Continuous calibration, feedback loops, and organizational learning become central. The system evolves alongside the organization, and its effectiveness depends on governance as much as on code. This is less glamorous than shipping a product, but it is more aligned with how real-world systems improve.

    AI creates lasting value not by standing apart as a finished artifact, but by disappearing into the structures that shape decisions. When treated as an operating layer rather than a product, its role becomes clearer: not to replace judgment, but to quietly improve the conditions under which judgment is exercised.