I deliberately try to leave my own field from time to time and listen to people who know far more than I do about something completely different.
I have written about this before in The Innovator’s Learning Pod. I strongly believe in cross-pollination because it exposes us to perspectives, questions, and mental models that are difficult to generate from inside our own field.
If new ideas emerge, at least partly, through new connections between things we already know, there is little reason to search for those connections only within the boundaries of our own expertise. Behavioral science has changed how I think about decisions. Poker has sharpened my view of uncertainty, outcomes, and luck. Urban planning has made me reconsider how environments shape behavior. More recently, while reading about Taoist thought, I came across an image that made me look at innovation differently.
That image was water.
I want to be careful with this because Taoism is not my field, and I am not trying to turn an ancient philosophical tradition into another innovation framework. I am simply borrowing one perspective and asking whether it helps me see something in my own domain more clearly.
Water appears prominently as a metaphor in the Dao De Jing. Scholarly treatments of Daoism describe water as an important model because of its capacity to yield, adapt to conditions, follow available paths, and avoid relying exclusively on direct force.[1]
What interests me is not the familiar interpretation of “go with the flow.” It is something more demanding.
Water does not respond to every obstacle in the same way. It can move around something, pass through an opening, accumulate behind a barrier, change direction, or gradually wear down what initially appears immovable. Its response depends on the terrain.
Innovation has a more complicated relationship with resistance. We often admire the person who pushes through it.
That admiration is understandable. If innovators changed direction every time an early customer disliked a prototype, an investor rejected a pitch, an incumbent dismissed a new approach, or adoption took longer than expected, many genuinely new things would never survive long enough to become useful. Innovation requires conviction, and conviction often means continuing in the presence of doubt.
I have explored that tension before in The Founder’s Forward Bias: the same ability to orient decisions toward a future that does not yet exist can create extraordinary conviction, but it can also make contradictory evidence easier to reinterpret than accept.
At the same time, resistance can contain information. The difficulty lies in knowing whether we are facing an obstacle that deserves persistence or a signal that should make us reconsider what we are doing.
That distinction has become increasingly important to me.
We just need to put another log on the fire
Years ago, I worked with a startup whose CEO had a phrase he repeatedly used when things were not developing as expected:
We just need to put another log on the fire.
I understood the reasoning behind it. New ventures need energy. Products rarely start out right. Markets take time to develop. Customers may need to experience something before they fully understand its value. Sales cycles can take longer than expected, and a viable business model rarely emerges fully formed from the first presentation.
There are moments when another log is exactly what the fire needs.
The problem was that, over time, almost every signal coming back from the market could be interpreted as another reason to add one. If customers did not respond, we needed more sales effort. If the proposition did not resonate, we needed better communication. If expected momentum failed to appear, we needed another iteration, another initiative, more time, or more money.
What gradually disappeared was the possibility that the resistance itself might be telling us something about one of our assumptions.
Eventually, this became the most expensive innovation failure I have personally witnessed in my career.
Looking back, I do not think the lesson is that somebody should simply have stopped earlier. That conclusion would be too easy, particularly with the benefit of hindsight.
Innovation happens under uncertainty. We make decisions with incomplete information, and the quality of a decision cannot be inferred cleanly from the outcome that follows. A good decision can produce a bad outcome, while a poor decision may occasionally benefit from timing, market conditions, or luck. I explored that distinction more deeply in Innovation Is Not Chess. It Is Poker, where the central argument is that innovation success depends on decision quality, learning, and adaptation without ever eliminating uncertainty.
Early evidence is often noisy, experiments are imperfect, and reality rarely provides an unambiguous signal telling us to continue or stop.
The more useful question is therefore not whether we should have shown more or less persistence in general.
It is this:
What evidence should tell us whether a particular form of resistance deserves more commitment?
Research on escalation of commitment makes that question more than a philosophical curiosity. Decades of work have examined why decision-makers sometimes continue investing in a course of action despite negative feedback, including the effects of previous investments, responsibility for earlier decisions, self-justification, and social or political pressure.[2]
That does not mean persistence after negative feedback is automatically irrational. Sometimes the underlying hypothesis remains sound and the obstacle lies elsewhere.
This is exactly why “persist or pivot?” is too crude a question.
Before deciding what to do, we first have to understand what exactly is resisting us.
Interested in working together?
Resistance is not one thing
When an innovation does not develop as expected, we often compress very different situations into the same statement: it isn’t working.
But that tells us very little.
A customer may value the solution but face a painful switching cost. A user may struggle with an unfamiliar interface while still experiencing significant value after the learning curve. A buyer may want the product but be unable to get it through procurement. A promising technology may depend on infrastructure that is not yet ready. The person experiencing the value may not control the budget. The problem may be real while the chosen customer segment is wrong. The solution may work while the economics around it do not.
And there is another possibility: people simply do not care enough.
Those situations should not lead to the same decision.
I find it useful to distinguish several broad forms of resistance.
There can be discovery resistance, where we simply do not understand the problem or context well enough yet.
There can be adoption resistance, where customers must overcome habits, switching costs, uncertainty, learning, or perceived risk.
There can be value resistance, where the progress we offer is not important enough to justify changing behavior.
There can be system resistance, where regulation, procurement, infrastructure, channels, incentives, partners, or economics stand between potential value and actual adoption.
And there can be what I would call evidence resistance: reality repeatedly behaves differently from our hypothesis, while we become increasingly sophisticated at explaining why the evidence should not count.
That last form is particularly dangerous because it can look remarkably similar to conviction.
Be like water does not mean follow the easiest path
This is where I find the water metaphor especially useful.
“Be like water” does not mean choosing the easiest path, abandoning direction, or yielding at the first obstacle. Water can persist. It can stay. It can accumulate. It can eventually alter the terrain itself.
What it does not do is insist that every obstacle must be treated in exactly the same way.
For innovation, the equivalent is not to pivot whenever things become difficult. It is to understand the nature of the resistance before deciding whether to push through it, work around it, change the path, wait, or stop.
This is also where research on entrepreneurial experimentation becomes relevant. Camuffo and colleagues examined what happens when entrepreneurs are taught to articulate theories, formulate hypotheses, and test those hypotheses systematically rather than relying mainly on intuition.[3] The value of such an approach is not that it gives founders certainty. It makes their beliefs more explicit and therefore easier to revise when evidence changes.
A later large-scale replication across 759 firms found that this scientific approach influenced idea termination and produced more nuanced patterns of radical pivoting, reinforcing the value of disciplined belief updating rather than either unconditional persistence or repeated arbitrary change.[4]
Research on A/B testing among technology startups points in a related direction. Koning, Hasan, and Chatterji found that experimentation was associated with organizational learning and improvements in several startup performance measures, including increased product launches.[5]
None of this research tells an innovator exactly what to do with a specific venture tomorrow morning. What it does suggest is that the quality of persistence depends partly on whether we can turn our explanation for resistance into something testable.
“The market isn’t ready” may be true, but it is still a hypothesis. So are “customers need education,” “we need more awareness,” or “the product needs another six months.”
This is also the reasoning behind GPT Makes Ideas Cheap. Wrong Innovation Bets Remain Expensive.: a plausible explanation, sophisticated business case, or polished solution does not reduce uncertainty by itself. Before the next commitment, the more useful question is what would have to be true for the opportunity to work and what evidence could tell us whether those conditions actually hold.
Before adding another log, we should therefore be able to explain what we believe the additional commitment will change, why we believe that, and what evidence would eventually make us abandon that belief.
That leads me to four questions that I would carry through the entire innovation journey:
What did we expect to happen?
What actually happened?
What are the plausible explanations for the gap?
What evidence would distinguish those explanations before we increase our commitment?
The following paid section applies those questions to the major commitments that accumulate as an innovation develops from an observed problem into a functioning business model and, eventually, something that may deserve to scale.
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Following resistance through the innovation journey
The nature of uncertainty changes as an innovation develops.
At the beginning, we may know very little about the problem or about who experiences it strongly enough to act. Later, uncertainty shifts toward the solution, behavior change, willingness to pay, channels, economics, dependencies, and eventually scalability.
The cost of being wrong changes as well. A mistaken assumption during an interview is relatively cheap. The same assumption embedded in a product, a sales force, infrastructure, or a multi-million-dollar investment is not.
The questions below are therefore not intended as another process. I see them as moments of interruption, especially when the instinct is to respond to weak evidence with more activity.
