Clayton Christensen’s disruptive innovation theory is one of the most influential frameworks in business. It is also one of the most misused and misunderstood. The theory explains historical cases well: looking backward, we can see how Netflix disrupted Blockbuster, how digital cameras disrupted film, how personal computers disrupted minicomputers. The problem is that the theory does not predict disruption forward. It does not tell you which emerging technology will actually disrupt your industry. And knowing the theory does not prevent incumbents from repeating the same failures.
The real story of disruption is not about missing obvious signals. It is about misaligned incentives that make the theoretically obvious response organizationally impossible.
What Disruptive Innovation Theory Actually Says
Christensen’s original insight was simple but powerful: incumbents often lose despite being aware of technological change, having resources to respond, and being well-managed. Why?
The theory proposes that disruption follows a pattern:
-
A new entrant targets the low end of a market or creates a new market entirely, offering a product that is inferior on traditional performance metrics but superior on dimensions incumbents ignore (price, convenience, accessibility).
-
Incumbents rationally dismiss the threat because their best customers do not want the product and the market is small and unprofitable.
-
The new entrant improves rapidly while incumbents focus on sustaining innovation for their most demanding customers.
-
Eventually the entrant’s product becomes good enough for mainstream customers while offering advantages incumbents cannot match.
-
By the time incumbents recognize the threat, their business model, organizational structure, and customer relationships trap them. They cannot respond.
This is a coherent narrative. It fits many historical cases. It is also largely unfalsifiable because when a company fails to disrupt itself, we can always retroactively explain why it fell into the trap.
The Unfalsifiability Problem
Disruptive innovation theory is powerful in part because it is unfalsifiable. After the fact, we can construct a disruption narrative for almost any industry shift.
Did Microsoft miss the smartphone revolution? Disruption theory explains it: Microsoft optimized for desktop computing, enterprise customers did not want phones, the phone market was small, the iPhone was initially inferior for business use, and organizational capabilities designed for PCs could not adapt.
This explanation is coherent. It is also not testable. We cannot use it to predict whether any given product will be disruptive.
Consider: if a startup builds an inferior product targeting a low-end market and fails, was it a failed disruption or just a failed startup? The theory does not tell us. If the startup succeeds, we declare disruption. If it fails, we say the team did not execute well enough or the market was not ready. The theory survives either outcome.
This is not to say the theory is useless. It is to say that using it requires careful attention to what it can and cannot do. It can explain past events. It cannot reliably predict future disruption.
When Incumbents Know and Fail Anyway
The theory predicts that incumbents fail because they do not recognize disruption. But many incumbents do recognize the threat and fail anyway. This is harder to explain with the framework.
Kodak’s executives understood digital photography. They did not lack technical capability or resources. They made a deliberate choice to prioritize film, which generated 70% of company revenue and was genuinely still profitable. Kodak did not go out of business because leaders were blind. It went out of business because the CEO is evaluated on quarterly earnings, and cannibalizing the film business would tank the stock price on his watch. By the time digital had clearly won, the company had no film business left to defend the digital business, and it was too late.
Blockbuster’s CEO did not lack awareness that Netflix existed. The company explicitly considered acquiring Netflix for $50 million and rejected it because late fees generated $800 million annually. The decision was rational from the perspective of current shareholders and the CEO’s compensation. It was catastrophic from the perspective of long-term shareholder value. But the CEO would not be held accountable for that long-term failure.
Microsoft’s mobile executives understood the iPhone’s threat. Ballmer was not ignorant of smartphones. The company simply concluded that smartphones would not displace PCs for productivity work because PCs were more capable. This was reasonable. The company also had incentive to defend Windows and Office, which generated enormous profits. The cost of betting on mobile was internal cannibalization and organizational friction. The reward was speculative. Rational actors make that trade-off.
These are not cases of disruptive innovation blindsiding incumbents. These are cases of incumbents making economically rational choices given their incentive structures, and those choices proving catastrophic in the long term.
The disruption theory obscures this. It suggests the problem is organizational blindness or structural inability to respond. The real problem is that decision-makers are optimized for short-term metrics and face consequences only within their tenure. Long-term consequences are someone else’s problem.
The Predictive Failure
The test of any framework is whether it predicts. Disruptive innovation theory fails this test.
Consider cloud computing. In 2006, AWS launched and offered computing services that were, by traditional metrics, worse than on-premise infrastructure: less control, less customization, reliability concerns, vendor lock-in. By disruption theory, this should have gradually displaced traditional data center infrastructure as it improved.
It did. But the theory did not predict this. Many industry observers and incumbent IT vendors claimed cloud was overhyped and would never handle serious workloads. The theory should have predicted otherwise, it should have highlighted the asymmetry (startups had no existing infrastructure to defend, enterprises did) and predicted gradual improvement and eventual dominance. But industry analysis did not use the framework this way.
Or consider smartphones. Before the iPhone, many analysts predicted that phones would become more computer-like (they did) and that converged devices would dominate (they did). But most did not frame this as disruption of PCs. The iPhone was disruptive to phones and cameras, not to PCs (which remain essential for knowledge work). Disruption theory does not clearly delineate the boundaries of which products disrupt which.
Or consider AI. Is AI disrupting software development? Will it disrupt knowledge work? Will it disrupt data analysis? The theory does not help answer these questions. We can construct disruption narratives retrospectively once we know what happened, but the theory does not predict which AI applications will succeed and which will fail.
The deeper problem: determining whether something is disruptive requires predicting whether the new product will improve fast enough to satisfy mainstream customers, whether incumbents’ business models will prevent response, whether the new value proposition will eventually matter to mainstream customers, and how quickly the new technology will progress relative to customer demands.
None of these are addressed by the framework. They are all empirical questions that require detailed understanding of specific markets, specific customers, and specific trajectories.
Why Companies Fail at Adaptation
The theory suggests companies fail because they are trapped by existing processes, customer relationships, and business models. This is partially true but misses the core mechanism.
Companies fail at adapting to disruption because the people making the decisions face personal consequences for short-term performance and none for long-term decline.
The VP of Product proposes entering the low-end market. This requires reduced pricing, which immediately lowers company margins; resource allocation away from the high-margin business; organizational friction with the legacy business unit; and risk of cannibalizing existing revenue.
The VP will be held accountable for this year’s and next year’s margin compression and revenue shifts. The VP will not be held accountable for the company’s market share in 2035. This dynamic is not limited to product teams. Leadership accountability operates within organizational tenure, not across it.
Given these incentives, the rational decision is to milk the high-margin business for as long as possible, then claim disruption was unavoidable when the business collapses.
This is not organizational blindness. This is accurate alignment of personal incentive with individual behavior. The problem is systemic: we compensate executives for quarterly performance, so quarterly performance is what they optimize for.
The Cases the Theory Struggles With
Some of the theory’s canonical examples do not fit perfectly.
Netflix: The theory predicts Netflix disrupted Blockbuster because it targeted convenience and price-sensitive customers with a model that improved over time. But Netflix’s path was more contingent than the theory suggests. The DVD format (not invented by Netflix) was critical. Postal logistics (a pre-existing service) enabled the model. Streaming required sufficient broadband penetration (driven by separate industry forces). Netflix succeeded because multiple factors aligned, not because the theory predicts.
The iPhone: Christensen’s own analysis of the iPhone is instructive. He initially predicted the iPhone would not disrupt traditional phones because it targeted the high end of the market and did not follow the low-end disruption pattern. He was wrong about this specific case, and his framework did not account for how the iPhone would shift what “the market” meant (from phones to pocket computers). Post-hoc explanations claimed disruption happened in a different way, but this moves the framework away from predictive value toward unfalsifiable post-hoc narration.
Discount retail (Walmart, Target): The theory explains these as low-end disruptors that gradually moved upmarket. But they mostly did not move upmarket, they created a parallel market for value-conscious consumers and coexisted with department stores for decades. The disruption narrative fits less neatly than the theory suggests.
When Disruption Theory is Actually Useful
The theory is most useful not for prediction but for orienting your thinking about market dynamics.
If you are an incumbent, the framework highlights risks: watch for competitors targeting segments you consider unprofitable; monitor whether emerging products are improving faster than your traditional performance metrics; recognize that your best customers might not want the product that will kill your company; understand that rationality within your current business model might be irrationality for long-term survival.
If you are a startup, the framework highlights strategy: target customers incumbents are happy to lose; compete on dimensions incumbents are not optimized for; understand that you do not need to beat incumbents on their best metric, you need to improve fast enough on your metric to eventually matter; recognize that business model alignment with your value proposition matters more than technology.
But recognizing these patterns does not guarantee adaptation. Knowing you are vulnerable to disruption does not change your incentive to defend current revenue. Knowing that the upstart might win does not guarantee that will change your competitive response.
The Misapplication Problem
Disruption theory has been so widely adopted that it is applied to everything, stripping it of meaning.
Any new technology that threatens an incumbent is labeled disruptive. Any startup that takes market share is called a disruptor. The theory becomes a synonym for “technology that changes things” rather than a precise description of a specific market dynamic.
This misuse obscures the real dynamics. A company might lose market share to a better competitor without disruption occurring. A new technology might be sustaining innovation (making existing products better) rather than disruptive. A startup might fail despite being disruptive because the improvement trajectory was not fast enough.
What Actually Determines Disruption Success
Beyond theory, the practical factors that determine whether disruption succeeds are market size and growth, improvement trajectory, business model alignment, competitive response, technology constraints, and regulatory and structural constraints.
Market size matters: low-end disruption works when the low-end is large enough to sustain a business while improving. If the low-end is too small, the disruptor cannot fund fast improvement.
Improvement trajectory is critical: the disruptor must improve faster than customers’ needs evolve. This is context-specific.
Business model alignment determines whether the disruptor can execute: the disruptor’s cost structure and capabilities must align with the new value proposition.
Competitive response sometimes happens: incumbents respond effectively when they recognize the threat early, have not yet maxed out market consolidation, can credibly offer the new value proposition without cannibalizing existing business, and have leadership willing to accept short-term margin compression.
Technology has limits: if the disruptor’s technology fundamentally cannot scale to handle mainstream use cases, disruption stops.
Structural protection matters: some industries have regulatory barriers, capital requirements, or network effects that protect incumbents. Disruption is easier in industries with low barriers to entry.
None of these are addressed by pure disruption theory. They are all industry-specific and require detailed knowledge of actual markets.
The Real Lesson
The deepest lesson of disruptive innovation is not about strategy. It is about incentives.
Companies are not disrupted because leaders are blind. They are disrupted because the incentive structures that determine individual success are misaligned with organizational long-term survival. Executives optimize for metrics they will be measured on within their tenure. Companies optimized for quarterly earnings are not set up to invest in markets that will mature after the current leadership departs.
This is not a flaw of specific leaders. It is a structural feature of how we compensate executives and measure corporate success. Until those structures change, companies will continue making the same trade-offs: defend current revenue, optimize current margins, let disruption happen slowly, and deal with the consequences when they become unavoidable.
Knowing the theory does not change this. Understanding that you are vulnerable to disruption does not change your incentive. The VP tasked with entering the disruptive market still faces the organizational friction of cannibalizing existing business. The CEO still has quarterly earnings targets. The structure remains even with perfect understanding of theory.
The companies that respond effectively to disruption do so not because they understand the theory better. They do so because their current business is already under threat, making new market exploration less cannibalistic; they have leadership with long enough tenure to make decisions that hurt short-term results; they create autonomous units with separate incentive structures aligned with disruptive opportunities; and they accept that disruption requires sacrificing some current business to avoid losing all of it.
This is not about theory. It is about organizational structure and the alignment of incentives with survival.