Most founders think weak traction is a market problem.

That is true, but incomplete.

Weak traction is also a psychological event. More precisely, it is a social one. The numbers may be weak, but numbers rarely arrive inside a company as numbers alone. They arrive filtered through role, fear, incentive, ego, memory, and what each person can least afford to conclude.

That is why weak traction is so hard to read.

Not because the signal is absent. Because the signal is still permissive. It has not yet forced a single interpretation. The same chart can sit in front of three intelligent people and produce three coherent but conflicting conclusions. One sees ordinary early-stage noise. Another sees emerging proof that demand is weak. A third sees a problem of timing, channel, or positioning. All three can sound credible. All three may even contain some truth.

This is what makes weak traction expensive.

Strong growth simplifies interpretation (wether you are experiencing a true positive or a false one is another question). Obvious failure does too. In both cases, reality starts to narrow the range of permissible stories. Weak traction does the opposite. It keeps the range open. It leaves enough space for belief to survive, for doubt to remain private, and for costly decisions to be postponed under the cover of reasonable disagreement.

That middle state is where a great deal of drift is born.

Founders often describe it as uncertainty. But uncertainty is only part of it. The deeper problem is that weak traction becomes socially interpreted data. It is not merely measured. It is read. And each person reads it according to the psychological burden of their role.

So the real question is not just what the market is saying.

It is why different people are able to hear different things.

Founders Do Not Read Weak Traction as Outsiders

A founder is never looking at weak traction as a neutral observer.

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A founder is not defined by optimism.

They are defined by direction.

Most people orient themselves by reference points. Competitors. Benchmarks. Precedent. Quarterly results. Career risk. Social proof. The past is concrete. The present is measurable. The future is vague.

Founders reverse that weighting.

They look forward.

Not in a motivational sense. In a structural sense. They anchor decisions to a future that does not yet exist and behave as if it could.

That is what i call “defining bias”.

Most Decision-Making Is Backward-Looking

Corporate systems reward backward reasoning.

What worked last year?
What does the market currently demand?
What are competitors shipping?
What will the board accept?

These are stabilizing questions. They reduce variance. They protect capital.

But they also reinforce the current equilibrium.

Looking backward optimizes within an existing model. It rarely breaks it.

Looking sideways optimizes relative position. It rarely changes the game.

Looking downward, at constraints and risk, protects survival. It rarely creates step-change growth.

The founder’s posture is different.

They start from a point in time that has not yet happened.

Vision as a Decision Anchor

A real founder carries a mental model of a future state:

A product that shifts behavior.
A market that did not previously exist.
A cost curve that changes.
A new default.

This vision acts as a decision filter.

Resources are allocated based on whether they move toward that state.
Talent is hired based on whether they can operate in that state.
Partnerships are chosen based on whether they accelerate that state.

The present becomes secondary.

This is why founders often appear irrational in early stages. The data does not justify the bet. The market does not validate the direction. The incumbents look stronger.

From the outside, it looks like overconfidence.

From the inside, it feels like coherence.

The Power of Forward Bias

Forward bias creates several advantages.

First, it allows action under uncertainty.
If you require full evidence before moving, you never enter new territory.

Second, it creates narrative gravity.
Investors, employees, and customers align around a directional story. Momentum follows clarity.

Third, it breaks local optimization.
When you optimize for today’s metrics, you protect today’s model. When you optimize for a different future, you are forced to redesign the system.

History is full of examples of founders who acted against present evidence because they believed in a structural shift.

They were not responding to demand curves. They were anticipating them.

Without forward bias, nothing discontinuous emerges.

The Dark Side of Forward Bias

The same mechanism that creates breakthrough potential also creates blind spots.

Forward bias can distort evidence.

Signals that contradict the vision get reinterpreted as timing issues.
Weak demand becomes “education needed.”
Long sales cycles become “enterprise complexity.”
Flat engagement becomes “early market.”

This is not dishonesty. It is identity protection.

A founder’s vision is not just a strategy. It becomes personal. The future they see is tied to who they are.

Killing the vision feels like killing the self.

This is where many ventures fail.

Not because the future was impossible.

But because the founder refused to update the picture when reality provided counter-evidence.

Conviction vs. Adaptation

The tension is simple:

If you update too quickly, you abandon before compounding.
If you update too slowly, you burn capital defending a false premise.

The skill is not vision.

It is calibration.

Great founders hold two positions at once:

Externally, they project directional clarity.
Internally, they maintain probabilistic doubt.

They ask:

What would prove this future wrong?
What evidence would force a redesign?
Which assumptions are carrying the weight of this vision?

Forward bias without falsification becomes delusion.

Forward bias with structured learning becomes entrepreneurship.

Why Most People Don’t Think Forward

There is a reason forward bias is rare.

Looking forward is costly.

There is no social proof.
There is no salary guarantee.
There is no historical anchor.
There is no consensus.

You trade certainty for optionality.

In corporate environments, this trade is often punished. Deviating from the current model threatens performance metrics and political stability.

Employees optimize for career survival.
Founders optimize for future existence.

Different incentives produce different temporal orientations.

The Existential Layer

For founders, the venture is often existential.

It carries financial risk, reputation risk, identity risk.

This intensifies forward bias. When survival is tied to a projected future, protecting that projection becomes rational.

In established companies, the mechanism is similar but weaker.

Innovation leaders can become attached to their projects. Killing the initiative may threaten status or role security.

In both cases, when personal survival is tied to project survival, evidence becomes negotiable.

Forward bias hardens.

This is why the difference between healthy vision and destructive persistence often depends on structural incentives.

Can someone abandon the future they imagined without losing their livelihood?

If not, the bias will dominate.

Building With a Forward Anchor

Despite its risks, forward bias is essential.

Incremental improvement does not require founders.
Systemic change does.

The discipline lies in how the bias is operationalized.

A productive forward bias:

• Defines a specific future state.

• Makes explicit the assumptions required to reach it.

• Designs experiments that could disconfirm those assumptions.

• Allocates capital in stages, not all at once.

The founder still looks forward.
But they let reality negotiate the path.

They are not attached to the first version of the future.

They are attached to solving the underlying shift.

The Founder’s Dilemma

Every founder faces a recurring question:

Is the market early?
Or am I wrong?

There is no external authority that answers this.

Too much skepticism kills momentum.
Too much conviction kills capital.

The difference between legendary persistence and catastrophic stubbornness is often visible only in hindsight.

This is why forward bias cannot stand alone.

It must be paired with explicit kill criteria.

At what point do we admit this future is not emerging?

Without that boundary, the founder becomes the last believer in a market that never forms.

Looking Forward, Responsibly

A founder looks forward.

That is the edge.

But the strongest founders also look inward.

They separate identity from hypothesis.
They treat vision as a testable model, not a sacred truth.
They allow reality to reshape the path without abandoning ambition.

Forward bias builds companies.

Unexamined forward bias destroys them.

The defining trait of a founder is not that they see a different future.

It is that they are willing to move toward it before it is obvious.

The defining trait of a great founder is that they know when the future they imagined is not the one that is forming — and they adjust before capital, credibility, and time are locked beyond return.

Looking forward creates possibility.

Updating the picture preserves survival.

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Startups love the word democratization.

We democratize finance.
We democratize mobility.
We democratize education.
We democratize access to infrastructure.

The story is compelling. Technology lowers barriers. Prices fall. Scale increases. Participation expands.

From a classical disruption lens, the pattern is familiar. New entrants begin with inferior quality but greater accessibility. Over time, they improve performance while maintaining scale.

Streaming once looked inferior to cinema.
Ride-sharing once looked unreliable compared to licensed taxis.
Commission-free trading once looked simplistic compared to full-service brokerage.

Yet scale won.

But there is a second layer rarely discussed.

Every expansion of access also redistributes risk.

The question is not only: Who gains access?

It is: Who absorbs the downside when the model strains or fails?

The First Layer: Access Expands

Consider a few obvious cases.

Netflix turned on-demand video into a mass-market service. Lower initial quality, high convenience, rapid global reach.

Spotify shifted music from ownership to access. Compressed audio, subscription pricing, universal availability.

Uber made urban transport accessible with a tap. Early inconsistency, regulatory friction, enormous scale.

Airbnb unlocked underutilized housing supply. Variable quality, rapid network expansion.

Robinhood removed commission barriers in stock trading. Minimal interface, mass retail participation.

In each case, access widened dramatically.

Once access expands, investment follows. Infrastructure scales to support demand. Capital flows into supply. Market penetration accelerates.

This is the optimistic arc of disruption.

Lower barriers → more participation → infrastructure build-out → improvement in quality → normalization.

But there is a tension.

The Second Layer: Purchasing Power

Access does not automatically mean affordability.

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Founders say it all the time:

“We just need a bit more polish.”

It sounds responsible. No one wants to ship something half-baked, damage trust, or waste a first impression.

But this usually is not a quality decision.

It is a decision about exposure.

Once you see launch that way, “ready” stops looking like a standard and starts looking like a defense.

Private Conviction Feels Stronger Than It Is

Before launch, a product lives in a protected environment.

Inside that environment, the team understands the logic. They know what the product is supposed to do. They can explain what is missing. They can interpret weak signals generously. They can see the future version of the product, not just the current one.

That creates an asymmetry.

The builders are not judging only what exists. They are also judging what they believe will exist soon.

The market will not do that.

The market does not reward intention. It responds to what is there.

That is why teams stay “almost ready” longer than they should. In private, conviction compounds faster than evidence.

“More Polish” Hides Two Different Problems

The phrase works because it covers two separate issues under one label.

The first is surface roughness. A flow is awkward. The copy is weak. Parts of the experience feel unfinished. The product lacks grace.

The second is structural weakness. The product does not deliver the core value reliably. Users cannot complete the main job. The promise is ahead of the experience.

These are not the same.

One creates embarrassment.

The other creates misjudgment.

Yet founders describe both as if they were simple “readiness issues.”

That is where the mistake starts.

If the issue is structural, delaying launch may be correct. But then the work is not polish. The work is repair.

If the issue is surface roughness, delay usually serves the team’s emotions more than the business.

There Is a Point Where More Work Stops Teaching

Every product reaches a point where more internal work no longer reduces the main uncertainty.

Most teams miss that point.

They assume progress in the product means progress in the decision.

It does not.

A cleaner interface is not necessarily a better bet. A smoother onboarding flow is not necessarily stronger evidence. A more complete product is not necessarily a more viable one.

Sometimes the only thing that improves is the team’s ability to refine its own assumptions.

Launch should not be treated as a reward for craftsmanship.

It is a shift in where truth comes from.

At first, truth comes from building. Later, truth comes from contact.

The mistake is staying too long in the first mode because it feels more controlled than the second.

The Real Question Is Not “Is It Ready?”

Founders ask, “Is the product ready?”

Usually that is the wrong question.

The better question is this:

Is the next week of internal work more likely to reduce uncertainty than the next week in the market?

That is the real decision.

Not whether the product looks good enough. Not whether the team feels proud enough. Not whether every visible flaw has been removed.

What matters is whether waiting still teaches more than exposure would.

That is a different threshold.

And it is much harder to hide behind.

The Market Cares More About Relevance Than Refinement

Founders overestimate how much users care about refinement and underestimate how much they care about relevance.

Most users are not asking:

Is this polished enough?

They are asking:

Does this solve something I care about enough to continue?

That distinction matters.

Users will tolerate roughness if the value is clear.

They will not tolerate irrelevance.

That is why an incomplete-looking product can still work. It is also why a beautiful one can disappear without consequence.

The issue was never the finish.

It was whether the product mattered.

Strong Teams Are More Vulnerable to This Trap

The better the team, the easier it is to wait too long.

Weak teams often ship before they feel ready because they have no alternative. They do not have the capacity to perfect.

Strong teams have the opposite problem. They can always improve another screen, another flow, another message, another edge case.

There is always another valid refinement available.

That is the trap.

If launch depends on exhausting all possible improvements, launch never happens.

So the question cannot be whether more work is possible.

It has to be whether more work is still changing the decision.

That is a stricter standard.

It is also the healthier one.

Waiting Creates False Confidence

The obvious cost of delay is time.

The less obvious cost is false confidence.

The longer a product stays inside the company, the more the company mistakes internal coherence for external demand. The product feels stronger because it is more complete. The story feels more convincing because the team has repeated it often enough. The roadmap feels justified because so much effort is already behind it.

None of that means the market agrees.

In fact, waiting too long usually makes the truth harder to hear.

The business has invested more. The team has attached more identity. The story has hardened.

By the time the market rejects the premise, the product is not just more polished. It is more expensive to rethink.

A Better Threshold for Launch

There is a better way to judge readiness.

Not: are we proud of it?
Not: is it complete?
Not: will people notice flaws?

Ask this instead:

Can a real user complete the core job well enough that their reaction teaches us something we cannot learn internally?

That is the threshold.

Because it ties launch to learning, not appearance.

It separates cosmetic discomfort from strategic ignorance.

It also forces the honest question inside the team: are we still improving the product, or are we protecting ourselves from the verdict?

What Delay Often Really Means

That is what “just a bit more polish” often conceals.

Not a commitment to excellence.

A reluctance to test the bet.

That reluctance is understandable. Launch exposes more than the product. It exposes judgment. It exposes priorities. It exposes whether the thing the team has spent months defending deserves to continue.

That is why delay feels safer.

But safety is not neutral.

Sometimes it is just the decision to postpone contact with reality.

The Founder’s Real Job

The founder’s job is not to remove every imperfection before exposure.

It is to know when internal improvement is no longer the best source of truth.

That requires a harder discipline.

It means separating embarrassment from real business risk. It means admitting that some unfinished products are ready to teach, while some polished ones are still unfit to test. It means recognizing that launch is not the end of building. It is the start of evidence.

Evidence is what turns a product from belief into decision.

The Line That Actually Matters

The right time to launch is not when the product stops feeling uncomfortable.

It is when waiting starts protecting the team more than informing the business.

That is usually the moment founders call “not ready.”

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We are no longer operating in a dual-process world.

For decades, decision theory assumed two systems:

System 1 — fast, intuitive.
System 2 — slow, deliberative.

Both lived inside the brain.

That assumption no longer holds.

We now reason inside a triadic architecture.

System 3 — external, artificial cognition.

Large language models, copilots, embedded AI agents. They do not merely assist thinking. They participate in it.

And that participation is restructuring innovation.

The Collapse of Cognitive Distance

Strategic differentiation once reflected cognitive asymmetry.

Some firms had better analysts.
Some had deeper research capacity.
Some tolerated slower, more disciplined reasoning.

System 2 capability varied.

System 3 flattens that variance.

Today, any team can:

• Map competitors instantly

• Generate positioning options on demand

• Draft coherent business cases in minutes

• Model scenarios with minimal friction

The marginal cost of structured analysis approaches zero.

Cognitive distance collapses.

When cognitive distance collapses, variance collapses.

Not because intelligence disappears.
Because everyone searches the same terrain with the same amplifier.

The Behavioral Shift: Cognitive Surrender

Tri-System Theory formalizes what this means. It introduces System 3 as an external cognitive agent and identifies a pattern called cognitive surrender .

Cognitive surrender is not strategic delegation. It is the adoption of AI outputs with minimal scrutiny, overriding both intuition and deliberation.

In controlled experiments:

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In the late 1990s, the internet boom looked unstoppable.

Capital flooded into companies with no revenue. Valuations detached from cash flow. Fiber was laid faster than demand could absorb it. Startups hired aggressively, spent aggressively, and collapsed just as aggressively.

From a micro perspective, it was destruction.

Pets.com disappeared. Billions were written off. Investors were humbled. Boards were embarrassed.

The verdict seemed clear: irrational exuberance.

And yet.

Zoom out twenty-five years.

The same overbuilt fiber networks became the backbone of global broadband.
The data centers became the foundation of cloud computing.
The protocols and standards built during the frenzy became invisible infrastructure.

The companies died.
The infrastructure remained.

Micro failure.
Macro acceleration.

That tension matters today.

What If the Internet Boom Had Never Happened?

Imagine an alternative path.

No speculative capital rush.
No massive overcapacity.
No aggressive infrastructure build-out.

The internet still emerges, but slowly. Incrementally. Carefully funded.

Would we have:

• Global e-commerce at today’s scale?

• Real-time video streaming?

• Cloud-native startups?

• Platform ecosystems?

• Remote-first work structures?

• Digital payments embedded in daily life?

Probably. Eventually.

But not at this speed. Not with this global uniformity.

Bubbles compress time.

They overinvest in infrastructure ahead of proven demand.
They build capacity that only makes sense in hindsight.

Boards hate bubbles because they destroy capital.
History often depends on them because they build foundations.

The Structural Pattern of Transformative Waves

This pattern repeats.

Railways in the 19th century.
Electricity grids in the early 20th.
Telecom in the 1990s.
Now artificial intelligence.

In each case:

1. A breakthrough technology lowers the cost of a core capability.

2. Capital floods in.

3. Speculation outruns cash flow.

4. Many players collapse.

5. Infrastructure survives.

6. A second wave builds sustainable businesses on top of it.

The first wave finances exploration.
The second wave extracts durable value.

From a board perspective, this distinction is critical.

If you evaluate every investment solely on short-term firm survival, you miss the systemic dynamic.

If you ignore capital discipline entirely, you finance chaos.

The job is not to avoid waves.
It is to understand which layer you are financing.

Micro Rationality vs. Macro Progress

At the firm level, prudence is rational.

• Protect balance sheets.

• Avoid unsustainable burn.

• Demand unit economics.

• Reduce exposure to hype.

At the system level, overinvestment accelerates capability diffusion.

The uncomfortable truth:
Macro revolutions are often financed by micro miscalculations.

That does not mean waste is desirable.
It means timing and positioning matter.

What This Means for Boards

Boards face a structural dilemma during technological inflection points.

If you stay entirely conservative, you preserve capital but risk irrelevance.

If you chase every narrative, you risk destruction.

The better question is:

Are we funding speculative positioning,
or are we building capabilities that will remain valuable even if specific bets fail?

During the internet boom, companies that invested in internal digital literacy, infrastructure, and experimentation survived the crash better than those who merely chased valuation optics.

The infrastructure mindset is different from the hype mindset.

Infrastructure compounds.
Hype evaporates.

What This Means for C-Level Leaders

Executives often oscillate between two extremes during bubbles:

1. Defensive denial.

2. Aggressive imitation.

Both are reactive.

The more strategic stance is architectural.

Ask:

If this wave proves real at the macro level,
what structural capabilities must we possess to remain relevant?

Not:
Which startup should we copy?

But:
Which capabilities will become baseline?

During the internet era, that meant:

• Digital distribution.

• Data analytics.

• Online customer interaction.

• Software talent density.

Today, in AI, it likely means:

• Data governance maturity.

• Automation literacy.

• Decision augmentation systems.

• AI-native workflow redesign.

Even if individual vendors fail, those capabilities will not disappear.

What This Means for Innovators and Founders

Founders are often told: avoid bubbles.

That advice is incomplete.

Bubbles create:

• Abundant capital.

• Accelerated customer education.

• Infrastructure access.

• Talent mobility.

They also create noise, inflated expectations, and brutal correction cycles.

The opportunity is asymmetric.

If you build something that depends purely on valuation momentum, you collapse when sentiment shifts.

If you build something that sits on top of newly created infrastructure and solves a real constraint, you benefit from the excess capacity left behind.

After the dot-com crash, bandwidth was cheap. Servers were available. Talent was abundant.

That enabled the next generation.

The question for founders is not:

Is this a bubble?

It is:

If this collapses, what remains?

Build where the answer is “a lot.”

The AI Parallel

Today, venture capital flows heavily into AI infrastructure.

Compute capacity expands rapidly.
Model development accelerates.
Tooling proliferates.

Some companies will not survive.

That is almost certain.

But imagine the macro counterfactual.

If this wave did not happen, would we:

• Have broadly accessible cognitive systems?

• See automation in knowledge work at scale?

• Rewire enterprise workflows around intelligence augmentation?

Possibly. But slower.

Much slower.

The current wave may overshoot demand.
It may misprice risk.
It may destroy capital at the firm level.

But it may also build the baseline of the next economic layer.

The Real Strategic Question

The mistake is framing the debate as:

“Is it a bubble?”

That is a binary lens.

The better questions are:

• Which layer is speculative and which is foundational?

• Are we investing in narrative or capability?

• If valuations reset, what tangible advantage remains?

• Are we prepared for consolidation?

Macro acceleration does not excuse micro recklessness.

But micro discipline must not blind you to systemic shifts.

The Governance Imperative

For boards and executives, this requires two parallel logics:

1. Protect downside exposure.

2. Secure structural positioning.

You do not need to fund every startup.
You may need to fund learning, capability, and optionality.

The winners of the internet era were not necessarily the loudest during the boom.

They were often those who:

• Built quietly.

• Survived correction.

• Scaled on stabilized infrastructure.

Timing matters.

Survival matters.

But so does placement.

Final Reflection

The internet boom destroyed enormous investment.

From a micro perspective, it was painful and wasteful.

From a macro perspective, it compressed decades of digital transformation into years.

If that wave had not occurred, our world would be slower, less connected, less digitally integrated.

The next technological wave will follow a similar arc.

The question is not whether destruction will occur.

It will.

The question is:

When the dust settles, will you have merely avoided loss,

or will you have positioned yourself on the infrastructure that remains?

That is the difference between being right in the short term

and being relevant in the long term.

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Introduction

I kept running into the same boundary when working with innovation teams. Conversations moved quickly to solutions, validation, and execution, only to move back and forth again and again. The harder questions stayed unanswered: When is an idea worth real commitment? When does learning become expensive? And who is actually accountable for decisions under uncertainty?

To explore that gap, I spoke with an experienced independent filmmaker who has worked across writing, directing, editing, and production. We did not talk about film as an industry. We talked about decisions, learning, commitment, and responsibility. His latest film offers a useful comparison, not for romantic reasons, but because it openly deals with uncertainty, delayed feedback, and irreversible decisions.

This conversation does not aim to replace innovation methods. It confronts innovation thinking with a different logic from outside the industry.

The interview is published anonymously at the filmmaker’s request.

How do you recognize that an idea is more than just interesting?

I constantly have ideas. Every person is interesting. Every situation could theoretically become a film. Sometimes I carry protagonists or events with me for weeks. In the end, it is always a gut decision.

With my last film, it happened like this: I heard about what the two protagonists were planning. I found it interesting, nothing more. Then I stopped thinking about it. Months later, I heard they had actually started working together. I drove there, met them, and in that moment it became clear. I was sitting across from them and knew immediately: This is a film. Or more precisely: For me, this is a film.

That “for me” is decisive. At the beginning, you do not know where the journey will lead. You will get rejections. Funding bodies will say no. People will comment, “That’s supposed to be a film?” On top of that come your own doubts. But once that inner yes is there, you stay with it. Without that commitment, an idea becomes something you set aside at the first resistance.

How did you start working concretely without making large investments?

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Over the last few years, many companies quietly reversed course. Innovation budgets were cut. Digital initiatives were paused. Transformation programs were “temporarily” stopped if they were not contributing to cost savings programs, often indefinitely. Teams that once symbolized the future were reduced to a fraction of their former size.

This correction surprised many people. It should not have.

The collapse was predictable. What can be cut first is never essential.

What disappeared first under pressure was not what was strategically essential. It was what had always been optional. That distinction matters. When capital tightens and the core business comes under stress, organizations reveal what they truly depend on. What gets cut first was never tied to survival, advantage, or a clearly articulated future.

This was not a failure of innovation. It was a failure of innvovation strategy.

When Strategy Is Missing, Activity Fills the Void

In principle, innovation should serve strategy. In practice, it often replaces it.

When leadership cannot clearly diagnose what threatens the core business, or what obstacle stands between the organization and its aspiration, innovation becomes a proxy. New initiatives appear where direction is missing. Labs, pilots, partnerships, and technology programs multiply, carefully mapped onto H1, H2, and H3 horizons, not because they are needed, but because they are convenient.

This behavior is not irrational. It is organizationally convenient.

Trends provide legitimacy. Technology provides cover. Being seen as “working on the future” buys time and political safety. Saying “we are exploring” feels safer than saying “we have informedly chosen this path based on small bets, and we are not pursuing those others.”

But strategy is not the avoidance of choice. It is the discipline of choice under scarcity and uncertainty.

Innovation as a Substitute for Strategy

This is where most waste is introduced, and it is often misunderstood.

The waste does not come from experimentation.
It comes from committing to a direction without first understanding whether it matters.

Once a hype-driven direction is implicitly chosen, innovation spending follows. Experiments are framed as learning, but their real function shifts. Instead of testing whether the direction is worth pursuing, they are used to justify the choice already made. Evidence that supports the narrative is amplified. Evidence that challenges it is reframed, postponed, or ignored.

At that point, innovation no longer reduces uncertainty. It masks it.

This is why these initiatives collapse so quickly under pressure. They were never anchored in a constraint the organization could not ignore. They were anchored in a story about the assumed future the organization wanted to believe in.

Red Flags That Innovation Has Replaced Strategy

There are warning signs that innovation is no longer serving strategy but standing in for it.

One red flag is the absence of a named constraint. If leaders cannot clearly articulate what blocks progress, what threatens relevance, or what must change to reach a chosen future, innovation activity will expand to fill the gap.

Another red flag appears when direction is justified by trends rather than trade-offs. Statements like “we need to do something with AI” or “we cannot afford to miss this” are not diagnoses. They are social signals. Strategy requires exclusion. If nothing meaningful is being ruled out, no real choice has been made.

A third red flag is when learning cannot kill the initiative. If no one can name the evidence that would stop funding, reduce scope, or invalidate the direction, experimentation has lost its strategic role. Learning becomes protection, not discovery, and strategy degrades into momentum without correction.

The most revealing red flag shows up under pressure. When margins tighten, innovation is often cut first. Not because it is long-term by nature, but because it was never tied to a constraint the organization could not ignore.

Why Hype-Driven Strategies Feel Rational Inside Organizations

From the outside, hype-driven strategies look naive. From the inside, they are often rewarded.

Boards respond to familiar narratives. Executives are evaluated on visible action. Innovation programs signal ambition, attract talent, and demonstrate alignment with what the market currently celebrates. Few incentives reward saying no early, killing initiatives before they gain visibility, or explicitly deciding not to play in a fashionable space.

There is also a deeper driver: identity.

Many organizations implicitly assume they can become anything. A tech company. A platform. A data business. An ecosystem orchestrator. Heritage is treated as a limitation to be just overcome rather than a boundary condition to be respected.

But strategy begins with accepting limits.
Not every future is reachable from every past.

Ignoring this does not create freedom. It creates waste.

Strategy Is an Assumed Path Under Scarcity

A useful definition of strategy is demanding but clarifying:

Strategy is an assumed path toward a deliberately selected future, chosen under resource constraints, in the presence of uncertainty and real obstacles.

Every part of this matters.

• The path is assumed, not guaranteed

• The future is selected, not imagined

• Resources are scarce, not optional

• Obstacles are real, not abstract

Innovation, digitalization, or transformation only make sense inside this logic. Without it, they become labels for motion.

From Exploration to Hallway Sprinting

Many organizations today are very good at moving.

They explore widely. They pilot constantly. They partner, prototype, and iterate. From the outside, this looks like momentum. From the inside, it often feels like running.

The problem is not speed.
The problem is direction.

Without an informed decision about which futures are worth pursuing, organizations end up running corridors instead of committing to paths. They move quickly from door to door, opening one after another, only to discover that most rooms contain nothing that matters for them.

This is how companies become hallway sprinters.

Fast. Busy. Visibly active.
But optimized for motion, not impact.

Hallway sprinting happens when learning is decoupled from selection. When exploration is celebrated, but commitment is deferred. When speed is praised, but trade-offs remain implicit or politically avoided.

Strategy decides which doors are worth approaching.
Innovation, done well, tests whether opening one of those doors makes sense.

What Strategic Learning Actually Looks Like

Strategic learning is not about exploring everything.
It is about learning “the right arenas to play” early enough to change a decision.

It asks:

• What must be true for this future to matter for us?

• What obstacle, if unresolved, would make this path unviable?

• What evidence would justify further commitment?

• What evidence would tell us to stop?

This kind of learning reduces risk before capital is scaled. Everything else is activity.

Why This Work Needs a Different Name

This is why I believe we need to rename what is currently hidden behind words like innovation, digitalization, or transformation.

Those labels are too abstract. Too detached from identity. Too easy to inflate.

The real work is deciding which future is worth pursuing from where the organization stands today, and what learning is required to justify that pursuit before scaling commitment. That work must respect heritage, accept scarcity, and confront trade-offs.

It is not about becoming something else.
It is about choosing deliberately.

Closing

Innovation should shorten the distance to a decision, not lengthen the corridor.

Without choosing a future to commit to, every hallway looks attractive, until it proves empty.

Most strategic failures do not come from bad execution.
They come from good execution of the wrong idea.

This is uncomfortable for experienced leaders because it suggests that discipline, intelligence, and effort are not sufficient. Entire organisations can move with precision, speed, and commitment, and still walk confidently in the wrong direction.

The underlying reason is rarely a lack of historical expertise and data. It is almost always a framing error.

More specifically: bad analogy.

When you move outside your known business, ctrl C/V thinking becomes fear-driven.
It borrows certainty from the past instead of building insight from the new terrain.

Strategy Is Always Built on Analogy (Whether You Admit It or Not)

Strategy never starts from a blank sheet.

Every strategic discussion implicitly answers a quiet question first:

“What kind of thing is this?”

A market, a product, a technology, a competitor, a threat, a future.
Before numbers appear, before roadmaps are drawn, before investments are approved, leadership teams decide—often unconsciously—what this situation is similar to.

That similarity becomes the reference frame.

• Is this a better version of something we already know?

• Is this a cost play?

• Is this a channel shift?

• Is this just automation?

• Is this another IT project?

• Is this a niche that will mature later?

Once the analogy is chosen, most decisions become almost automatic.

What to optimize.
What to protect.
What to postpone.
What to ignore.

And that is where things quietly go wrong.

Why Bad Analogies Are So Dangerous

Bad analogies are dangerous for three reasons:

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I did not grow up surrounded by abundance, as the child of emigrants with two brothers. There were no ready-made toys, no money to buy whatever I wanted, and no catalogue of options to choose from. When you grow up like that, you do not learn how to shop for solutions. You learn how to arrive at them.

If you cannot afford ready-made toys and sport utilities, you start building them yourself, using your imagination. You do not get to skip understanding. You cannot outsource thinking to a product made by an expert. Before you can build anything, you need to figure out what it is supposed to do and why it might work at all. And when you get it wrong, there is no replacement waiting. You try again.

What looks like scarcity from the outside is, in practice, a long education in framing, reframing and causality. I learned how things worked because I had to make them work. I learned why they worked because copying without understanding only led to broken outcomes. And I learned that most solutions only show themselves after you exhaust the first dozen that seemed reasonable but were not.

You do not call this innovation as a child. You call it curiosity, or boredom. Only later do you realize that this was training in thinking beyond the obvious, because the obvious was usually unaffordable.

The Habit That Never Left

That pattern stayed with me. I still cannot encounter something interesting without wanting to understand how it is built. Building toys, sportswear, musical instruments, furniture, lamps, fermenting veggies and drinks, brewing beer, baking and cooking, drawing and sketching, writing code, mechanical systems, digital workflows, business models, it does not matter. If I cannot afford to treat it as a black box, I am forced to open it.

This is not about creativity. I never thought of myself as the creative type. It is something more basic and more demanding: the need to understand a system’s causality well enough that you could rebuild it under different constraints. Not perfectly. Not elegantly. But well enough to see what is essential and what is optional.

That process has a humbling side effect. The deeper you go, the clearer it becomes how much expertise is embedded in even simple things. Nothing that works reliably is trivial. You stop assuming ease. You stop mistaking polish for simplicity.

Most people stop at preference. I cannot. If something draws my attention, the next questions arrive automatically: How does this function? Why this and not something else? Where does it fail? Once you start thinking that way, it becomes difficult to ever accept a finished surface again.

Divergent Thinking Is Learned, Not Gifted

Creativity, divergent and convergent thinking is often romanticized as a personality trait, something you either have or do not. In reality, it is a byproduct of repetition under constraint. When you cannot buy your way to an answer, you must tune in, understand and generate alternatives. Many of them. And most of them will be wrong and you need to let them go.

You learn to distrust your first idea because it is almost always the most obvious one. You learn to keep going, not because quantity magically produces quality, but because nothing interesting appears until the predictable answers are exhausted and you are entering the learning mode. Iteration is not a method. It is a consequence of not having shortcuts.

This is the part people avoid. They want the breakthrough without the mess. The elegant solution without the pile of failed attempts behind it. But divergent thinking is built in discomfort. In reframing. In trial and error. In refinement. In reshaping half-working ideas until something finally holds.

That discipline stays with you. It teaches range. And range matters more than inspiration.

Why This Matters in the Age of AI

Large language models make this dynamic visible. Ask for a tool and you get the usual suspects. Ask for a flower and you get the same few names. Ask for a business idea and you get something that sounds suspiciously like a template.

The model is not failing. It is answering the most affordable question. To get something useful, you have to push it. Stress it. Reframe the prompt. Change the constraints. The moment you stop buying the first answer, better ones start to appear.

Human thinking works the same way. If you want an unusual but fitting solution, you must be willing to generate many paths, understand their mechanics, and discard most of them. Not creativity. Range. And range only emerges when you stay with the problem long enough to understand its inner logic.

The Humility of Understanding

There is another layer to this. Understanding how things work keeps you grounded. It makes it harder to overestimate yourself. You start noticing the invisible skill embedded everywhere. A good loaf of bread is not an accident. A smooth app experience is not simple. A strong service interaction is not luck.

When you understand mechanics, physical, digital, or human, respect follows naturally. You stop assuming progress is obvious. You stop confusing confidence with competence. And you get better at your own work because ego has less room to hide.

Humility is not softness. It is an operating advantage. It slows you down just enough to notice what others skip. And it keeps you from mistaking early certainty for truth.

Divergent Thinking as a Way of Living

Looking back, the constraints of my childhood taught me something I still rely on: nothing is fixed, everything can be rebuilt, and most problems have more viable solutions than people admit and are willing to explore.

If you can generate those paths, understand why they fail, and refine them without rushing to closure, you eventually arrive at an answer that looks unusual but feels inevitable once seen.

That is the real value of divergent thinking. Not genius. Not talent. Just the willingness to stay with curiosity and uncertainty longer than others, to raise the “dumb” questions followed by why or how, to understand before building, and to keep rebuilding (hardware, software, business models) until the logic holds.

This is not nostalgia. It is a reminder. Constraints did not limit me. They removed shortcuts. And in doing so, they trained a way of thinking that still shapes how I work and how I solve problems today.

If there is one thing I trust in innovation, it is this:
the person who cannot afford certainty learns faster than the one who buys it.

Most companies talk about the future as if it is a single road waiting ahead.
A plan, a vision deck, an investment cycle, a transformation program.
The future becomes a destination you intend to reach by extending the past.

But futures do not arrive this way.
They emerge, collapse, fork, and return in loops.
Some demand that you reinvent your core.
Some ask you to build something entirely new.
And some require you to let go of what once worked but no longer serves you.

Every company carries a legacy, but only few question it.

Innovation is framed as adding something new.
Rarely is it framed as stopping something old.

Yet the first step toward any plausible future is not invention.
It is liberation.

The Most Underrated Strategic Question

When companies explore their future, they usually begin with capability maps, megatrends, TAM analyses, or customer insights.
All useful tools, but none tackle the real foundation of strategy.

The question that cuts deeper than any trend report is this:

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I recently found myself revisiting Jobs to Be Done from an unusual angle, somewhere between the shower and a growing irritation. Not to refine a framework or compare interview techniques, but to understand why so much “innovation” still feels familiar.

Looking across the different JTBD streams—outcome-driven, switch-based, service-oriented, strategic—a pattern became hard to ignore. Many of these approaches produce better products, clearer priorities, and sharper decisions. Very few produce anything that could honestly be called new.

That realization kept pulling me back to the most famous JTBD example of all: the Milkshake case popularized by Clayton Christensen. It deserves its status. It clarified a job, reframed a situation, and corrected flawed assumptions.

And yet, stripped of mythology, it is anything but radical.

No new category.
No new technology.
No new business model.

Just a better alignment between an existing product and a specific moment in people’s lives.

That led to an uncomfortable conclusion. If even the canonical JTBD case is incremental, what does that say about innovation itself?

Viewed through this lens, the pattern becomes clear. Most innovations are not inventions. They are reconfigurations—substitutions and recombinations of existing products, services, and business models.

Often valuable. Often well executed.
But rarely new in the way innovation rhetoric suggests.

And perhaps the problem is not innovation.
Perhaps it is our misunderstanding of what innovation actually does.

The Category Illusion

We talk about innovation as if novelty were the point. New features. New markets. New categories. New technologies. Entire industries are built around the promise of “what’s next.”

People, however, do not experience life in categories.

They experience it in situations.

They sit in traffic. They postpone difficult decisions. They tolerate inefficiencies. They avoid embarrassment. They manage risk, visibility, and trade-offs.

Categories are retrospective abstractions. Jobs are the continuity beneath them.

Jobs change remarkably slowly. What changes is the surrounding context: cost, risk, legitimacy, timing, and exposure. In other words, what becomes acceptable.

This is where category thinking quietly distorts innovation efforts. When teams ask “What’s new?”, they are already looking in the wrong place.

A more revealing question is:
What was previously unacceptable, and why?

The secret of business model transformation? Renew around a stable job to be done, not inside a product category that is already collapsing.

The Strongest Competitor Is Doing Nothing

In almost every serious JTBD analysis, the same insight eventually emerges. The strongest competitor is rarely another product.

It is doing nothing.

Waiting. Tolerating. Improvising. Working around the problem. Accepting friction as normal. Accepting loss as the price of stability.

This is not inertia. It is rational behavior under uncertainty.

Many innovations fail not because they are inferior to existing solutions, but because they misunderstand what they are competing against. They design for feature superiority while ignoring the real alternative: avoidance.

Innovation rarely wins by being better.
It wins by making inaction less acceptable.

The real battleground is not against competitors, but against a status quo people have learned to live with.

What Feels Radical Is Often a Category Shift

When innovation feels disruptive, it is usually because a familiar job is suddenly handled by an unfamiliar category.

The job remains stable.
The means change.

Ownership becomes access.
Planning becomes immediacy.
Effort becomes automation.

The disruption comes not from novelty, but from reallocation. Value moves. Risk moves. Control moves.

This is why so many “breakthroughs” appear obvious in hindsight. The job wfas always there. What changed was the feasibility of serving it differently.

What feels radical is often not the job itself, but the collapse of an old category boundary.

Incremental Innovation Is Not a Failure

The Milkshake case illustrates this clearly. It was incremental. And it was appropriate.

No existing power structure was challenged.
No revenue logic was threatened.
No identity was destabilized.

The innovation worked precisely because it did not demand sacrifice.

Incremental innovation is often the only form that is politically, organizationally, and economically viable. It preserves incentives, protects roles, and avoids visible losers.

The problem is not incremental innovation.
The problem is mistaking it for change (transformation).

Organizations frequently believe they are innovating boldly when they are, in reality, optimizing safely.

The Threshold of Acceptability

If innovation is not primarily about creating new jobs, what is it really about?

It is about shifting the threshold of acceptability.

Revisiting the milkshake case with its original context in mind matters. This work was done in the early 2000s—before smartphones, mobile ordering, or digital subscriptions. The morning commuter was not lacking convenient alternatives. A banana or a prefabricated chocolate bar was already available at home, required no extra stop, and demanded less effort than pulling into a drive-through.

And yet, people still stopped at McDonald’s.

Convenience alone cannot explain that choice. Stopping the car, waiting in line, and ordering is objectively more effortful than grabbing something already at home. The decision only becomes coherent once acceptability is considered in situational terms rather than in abstract measures of speed or effort.

The banana and the chocolate bar failed not because they were inconvenient, but because they failed to fit the full duration and constraints of the commute. They ended too quickly, demanded attention at the wrong moment, or introduced small but real risks—mess, distraction, interruption. They solved hunger, but not the experience of a long, dull drive.

The milkshake lowered the threshold of acceptability within that situation. It lasted long enough. It could be consumed with one hand. It required no decisions mid-drive. It reduced cognitive and physical friction. The job itself was not new. Making the commute tolerable had always existed. What changed was what became acceptable while driving.

What is striking, however, is where this innovation stopped.

The response focused on the product—its thickness, packaging, and pacing—while leaving the surrounding circumstances untouched. The when remained the early morning rush. The where remained the car. The how remained a drive-through purchase followed by consumption in traffic. Even in the early 2000s, McDonald’s could have experimented with flow, ritual, or decision elimination around the commute. Instead, innovation stayed safely inside the product and its category.

That boundary explains why the outcome was incremental rather than transformative. The milkshake absorbed competition from adjacent categories like bananas and chocolate bars, but it did so by optimizing within a fixed situation. The job was served better, but the conditions under which it existed were left intact.

Innovation shifts what people are willing to tolerate.
It changes the balance between effort and outcome, risk and reward, exposure and protection.

It does not create new wants.
It lowers the threshold of what becomes acceptable.

An alternative past?: From a 2000 perspective, a truly category-challenging innovation by McDonald’s would have required imagining a future in which food no longer carried the burden of making the commute tolerable. If the technologies that later emerged had been available or even clearly foreseeable, McDonald’s could have redefined its role from selling breakfast to orchestrating the commute itself. Instead of designing a milkshake to last thirty minutes, the company could have designed the thirty minutes: eliminating the need to stop through predictive ordering, removing decision friction entirely, embedding itself into cars and routines, and shifting from calories to attention, pacing, and experience. In that imagined future, boredom would be managed through content, structure, and automation rather than consumption. The milkshake would not be improved, it would be made unnecessary. From the vantage point of 2000, that would have sounded like science fiction. Not because it violated the job logic, but because it violated the category boundary. It required accepting that the real innovation was not a better product for the car, but a future in which the car no longer needed a product at all.

Why Value Propositions Explain So Little

This is also why value propositions are weak predictors of adoption. They focus on benefits while ignoring fear.

People rarely switch because something is better.
They switch when staying becomes worse.

Loss aversion is not a psychological quirk. It is a structural force. It explains why technically superior solutions struggle while inferior but familiar ones persist.

When innovation succeeds, it is often because it neutralizes loss: loss of time, loss of face, loss of control, loss of legitimacy, loss of identity.

Many innovation efforts fail quietly because they argue upside in a context defined by downside.

When Innovation Stops Being Incremental

Innovation becomes non-incremental when it forces a confrontation with loss.

Not customer loss, but internal loss.

It threatens existing competencies.
It devalues experience.
It breaks financial logic.
It challenges identity.
It demands exclusion.

At that point, the question is no longer “Is this a good idea?”
It becomes “Who absorbs the cost of change?”

Most innovation programs are designed to avoid that question, not answer it.

That is why truly transformative innovation is rare. Not because it is technically difficult, but because it is politically expensive.

The Myth of the Unserved Job

This brings us back to the original intuition. Are there truly unserved jobs?

Very few.

Most so-called “unserved” jobs are simply jobs that long remained below the threshold of acceptability (underserved). Not because they lacked value, but because acting on them demanded too much. They were too risky, too costly, too slow, too visible, or too socially and institutionally illegitimate.

What changes over time is not the job itself, but the conditions around it. New technologies reduce effort and risk. New regulations legitimize previously unacceptable behavior. New social norms and habits lower psychological and reputational costs. Together, they shift how people perceive what is acceptable to do.

What appears as a new job is often an old one that has crossed a feasibility threshold. Something that was once unreasonable, awkward, or irresponsible suddenly becomes normal, even expected.

This does not diminish innovation.
It grounds it.

Why This Perspective Matters

Believing that innovation is about new jobs encourages exploration without accountability. It rewards novelty over relevance and ideation over decision.

Understanding innovation as a shift in acceptability changes the conversation. It brings trade-offs to the surface. It exposes constraints. It makes loss visible.

It also restores humility. We are not inventing human motivation. We are negotiating with it.

A Better Question

Instead of asking what is new as a innovation-creator or innovation-buyer, a more productive question emerges:

What are we still tolerating today, not because it works, but because every alternative has been too costly to accept?

That question redirects attention from imagination to reality, from brainstorming to diagnosis, from aspiration to courage.

Innovation does not begin with ideas.
It begins when inaction drops below the threshold of acceptability.

And that is usually enough to change everything.

The article was inspired by something ordinary: a couple arguing.

It happened on a train ride through the Swiss mountains, on the way to visit a close friend. The landscape outside moved slowly, deliberately. Snow-dusted slopes, quiet villages, long tunnels that forced reflection simply because there was nothing else to look at.

Across the aisle, a couple was in a low but intense discussion. The husband insisted he had a clear opinion. He always did. On politics, decisions, plans, almost anything. The wife pushed back. Not on the substance, but on the posture behind it. Her point was simple: he wasn’t open to alternative views. His certainty closed the conversation before it had a chance to begin.

What struck me wasn’t who was right. It was how familiar the dynamic felt.

I have seen the same pattern play out repeatedly in strategy discussions, innovation initiatives, and business model transformations. Leaders with strong opinions. Teams with well-rehearsed arguments. Decisions that feel crisp and decisive. And yet, beneath that clarity, something fragile: assumptions that are never named, alternative perspectives that are never explored, and paths that look deliberate but quietly converge toward the same outcome everyone else is choosing.

The problem is not conviction. The problem is unexamined conviction.

Strategy doesn’t fail because leaders lack opinions. It fails because those opinions are treated as facts rather than as hypotheses that deserve pressure.

The Comfort of Certainty

Certainty feels productive. It signals leadership. It creates momentum. It reassures boards, teams, and investors that someone is in control.

On that train, certainty sounded efficient too. The husband wasn’t shouting. He was composed, logical, articulate. From the outside, it looked like strength.

But certainty has a shadow.

When we are certain, we stop exploring. When we stop exploring, we stop learning. And when learning stops, strategy quietly turns into execution of yesterday’s logic.

Most organizations don’t suffer from a lack of ideas. They suffer from an excess of unquestioned assumptions. Assumptions about customers, markets, costs, scalability, technology, timing, regulation, or internal capabilities. These assumptions are rarely written down. They live in slide decks, habits, and phrases like “we know our customers” or “this is how the industry works.”

Certainty turns these assumptions invisible.

And invisible assumptions are dangerous, because they guide decisions without ever being challenged.

The Phantom You End Up Following

As the train entered another tunnel, the conversation paused. Silence filled the space, briefly. It felt symbolic.

When assumptions remain implicit, something similar happens in organizations. You think you are making strategic choices, but you are actually following a phantom.

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Every leadership team claims it values “diverse perspectives.”

Most of them mean stylistic diversity, not cognitive diversity.

There is a difference.

Groups can outperform individuals. Under the right conditions, they expand the search space of possible explanations, surface hidden assumptions, and stress-test fragile logic. But the same structure that enables better thinking can also institutionalize error.

A group does not automatically improve decision quality. It amplifies whatever dynamic dominates the room.

The question is not whether you have a group.

The question is what the group is optimizing for.

Why Groups Can Increase Decision Quality

A single mind is narrow by definition. It is constrained by its own priors, incentives, and blind spots.

A group, in theory, reduces those constraints in three ways:

1. Exploratory Expansion

Individuals tend to converge quickly. We fall in love with the first coherent explanation that fits the facts.

Groups can delay convergence.

When multiple hypotheses are voiced, the decision space widens. Alternative causal explanations emerge. Weak signals get challenged. Someone asks, “What would have to be true for this to fail?”

This exploratory function is the group’s greatest asset.

In uncertain environments, decision quality correlates with how well you map the possibility space before committing.

Groups can improve that mapping.

But only if exploration is allowed before alignment is demanded.

2. Bias Detection

No executive is immune to confirmation bias, sunk cost fallacy, or status protection.

In a functioning group, someone can interrupt the narrative and say:

• “We’re protecting our initial thesis.”

• “We’re ignoring disconfirming evidence.”

• “We’re optimizing for optics, not for truth.”

That interruption is priceless.

It converts implicit bias into explicit tension.

But this only works if disagreement is treated as diagnostic, not disloyal.

3. Distributed Pattern Recognition

Different backgrounds detect different anomalies.

A product leader sees usability friction.
A finance lead sees unit economics fragility.
A sales leader sees demand misfit.

When these lenses interact productively, the outcome can exceed what any individual would have produced alone.

This is the optimistic case for collective reasoning.

It is real.

It is also fragile.

Why Groups Often Make Decisions Worse

The same mechanisms that expand thinking can collapse it.

Three distortions are especially common.

1. Conformity Pressure

Humans are social creatures. We calibrate to status and tone in the room.

If the CEO speaks first, the probability distribution of acceptable opinions narrows instantly.

If dissenters are subtly penalized, future dissent disappears.

Agreement becomes safer than accuracy.

You still have a group.
You no longer have independent cognition.

The outcome looks aligned. It is simply synchronized.

2. Echo Chambers at Scale

Groups embedded in the same industry tend to share similar assumptions.

They read the same newsletters.
Attend the same conferences.
Benchmark the same competitors.
Hire from the same talent pools.

Over time, the industry develops a cognitive monoculture.

Certain beliefs become “obvious.”
Alternative models become invisible.

This is how entire sectors converge on the same playbook.

And this is why competitors frequently fail together.

Not because they lacked intelligence.
Because they shared the same blind spot.

3. Defensive Cohesion

When external pressure rises, groups often tighten.

The instinct is protective: defend the identity, defend the strategy, defend past commitments.

Exploration feels destabilizing.

So the group stops searching and starts reinforcing.

In that mode, meetings become rehearsals of what is already believed.

Decision quality declines precisely when uncertainty increases.

The Hidden Variable: What Is the Group’s Real Job?

Every group claims its job is “better decisions.”

In practice, groups often optimize for something else:

• Maintaining harmony

• Protecting reputations

• Preserving past investments

• Avoiding visible conflict

• Signaling decisiveness

These goals are human. They are understandable.

They are also incompatible with exploratory reasoning.

If the implicit job of the group is cohesion, truth becomes secondary.

If the implicit job is speed, exploration becomes waste.

If the implicit job is optics, dissent becomes risk.

The structure of incentives determines the cognitive output.

Industry Convergence: When Everyone Thinks Alike

Look at most sectors during a hype cycle.

AI.
Blockchain.
SaaS land-and-expand.
Marketplace aggregation.
Roll-ups.
Direct-to-consumer.
You can replace the label.

Inside each wave, narratives synchronize.

“We must add this.”
“This is inevitable.”
“Everyone is doing it.”

This is not always wrong.

But it is rarely independently verified.

Groups within the same ecosystem reinforce each other’s logic until deviation feels irrational.

That is not collective intelligence.
It is distributed conformity.

The danger is not that your team lacks intelligence.

The danger is that your team’s intelligence is highly correlated with your competitors’.

Correlated intelligence produces correlated mistakes.

When a Group Actually Outperforms

A group becomes greater than the sum of its members under three structural conditions:

1. Independence of Thought

Members must form preliminary judgments before discussion.

If opinions are shaped in real time by status signals, diversity collapses.

Pre-commitment of hypotheses forces independence.

2. Structured Dissent

Disagreement must be expected, not tolerated.

Assign roles:

• Red team

• Assumption challenger

• Kill-criteria guardian

Not to create theater, but to institutionalize friction.

Without friction, exploration collapses into confirmation.

3. External Cognitive Input

The group must regularly expose itself to perspectives outside its domain.

Not adjacent competitors.

Not industry analysts.

Outs(AI)ders.

Biologists for strategy.
Filmmakers for product design.
Anthropologists for customer behavior.
System engineers for organizational design.
Architects for innovation.

Cross-pollination reduces correlation risk.

It reintroduces variance into thinking.

Without variance, there is no learning.

Why Cross-Pollination Matters More Than Diversity Theater

Corporate language loves “diversity.”

Most implementations focus on demographic variables or surface-level experience.

Those matter socially.

They do not automatically generate cognitive divergence.

Cognitive divergence comes from exposure to different problem frames.

A biologist thinks in evolutionary trade-offs.
A military strategist thinks in constraints and leverage.
A behavioral economist thinks in incentives and biases.
A filmmaker thinks in narrative tension and audience experience.

These frames are portable.

When imported into a new domain, they destabilize assumptions.

That destabilization is uncomfortable.

It is also the source of genuine learning.

If everyone in the room learned strategy from the same business school cases, you have alignment.

You do not necessarily have insight.

The Cost of Staying Inside the Bubble

Remaining inside an industry echo chamber feels efficient.

Language is shared.
Benchmarks are known.
Metrics are standardized.

But efficiency in thinking can mask fragility.

When the environment shifts, monocultures struggle.

They optimized around yesterday’s constraints.

They miss early signals because those signals do not fit the dominant frame.

True exploration requires temporary inefficiency.

It requires translating between disciplines.

It requires tolerating conceptual friction.

Most organizations prefer fluency over friction.

That preference is expensive over time.

New Series: The Learning Pod

Most strategy conversations happen inside the same bubble.

Same industries.
Same references.
Same mental models.

The Learning Pod is a new article series summarizing cross-disciplinary interviews with thinkers outside the usual innovation circuit.

Not networking.
Not trend commentary.

Each piece distills how different domains approach uncertainty, trade-offs, incentives, and judgment, and applies those lenses to real strategic questions.

If your competitors consume the same inputs, your decisions will correlate with theirs.

This series is about importing variance before you commit capital.

First article coming soon. Subscribe to receive it.

Subscribe now

The Discipline Behind Collective Intelligence

Collective intelligence is not automatic.

It is engineered.

Engineered independence.
Engineered dissent.
Engineered exposure to alternative frames.

Without structure, groups drift toward comfort.

Comfort reduces variance.

Reduced variance increases correlation.

Correlation increases systemic risk.

You do not notice it when times are stable.

You feel it when the environment changes.

A Hard Question for Leadership Teams

Ask your team:

• When was the last time someone changed their mind publicly?

• When was the last time an external discipline reframed your strategy?

• When was the last time you killed a project because your assumptions failed, not because funding ran out?

If the answers are vague, your group may be aligned.

It may not be learning.

Alignment without exploration produces coherence.

Learning requires dissonance.

Final Thought

A group can be a laboratory for better thinking.

Or it can be a resonance chamber for shared illusion.

The difference is structural, not emotional.

If you want decisions that outperform your competitors, reduce correlation in your reasoning.

Import variance.

Institutionalize dissent.

Design the group for exploration before commitment.

Otherwise, you may not just be wrong.

You may be wrong together.

Companies say they struggle with execution.

They respond by adding goals, metrics, dashboards, and OKRs. They increase visibility and pressure, convinced that clearer targets and tighter cadence will close the gap between strategy and results.

And yet, execution keeps degrading.

Teams deliver what they are asked to deliver. But relevance erodes. Decision quality declines. Learning slows. The organization looks busy and disciplined, yet strangely stuck.

At some point, blaming execution stops being plausible.

The more uncomfortable possibility is this:
many companies misunderstand how strategy becomes performance in the first place.

A useful comparison comes from professional football. Not because football is elegant or clean. It isn’t. Clubs are political, messy, and emotional. Coaches get it wrong. Owners panic. Fans overreact.

But football has something most companies lack:
fast, public, and unforgiving feedback loops.

You cannot narrate your way out of a 0:3.

Vision in football is identity, not aspiration

In football, vision is rarely poetic. It is grounded, sometimes unflattering, and tightly coupled to reality.

“We are a development club.”
“We survive by intensity, not talent.”
“We cannot outspend our rivals.”
“We prioritize staying up over style.”

This is not marketing language. It is identity.

Vision in football answers a simple question:
What kind of club are we, even when results fluctuate?

It is shaped by history, budget, league position, supporter expectations, and ownership. It changes slowly because identity constrains choice.

Most companies treat vision differently.

Corporate vision statements describe who the organization wants to become, not who it is. They are aspirational by design, written to inspire rather than to constrain. “Customer-centric.” “Digital leader.” “Best-in-class.”

The first break in the chain happens here.

When vision becomes aspiration instead of identity, it stops excluding options. Everything can be justified. Strategy begins without boundaries.

Football does not avoid this because it is wiser.
It avoids it because it cannot afford ambiguity about who it is.

Mission is boring on purpose

A football club’s mission is operational and stable: compete, develop players, entertain supporters, remain solvent.

It does not change with every season or coach. It is not asked to carry moral weight. It exists to define the organization’s role, not its ambition.

In companies, mission often absorbs purpose, values, and employer branding. It becomes expansive, emotional, and vague.

The cost is subtle. Mission stops constraining strategy. It cannot help leaders distinguish between contribution and activity.

Effectiveness begins with contribution.
Contribution to what result, for whom?

Football answers that question relentlessly. Most companies soften it.

Diagnosis is unavoidable in football

Every football season begins/ends with a diagnosis, explicit or implicit.

How strong is our squad relative to the league?
Where are we structurally weak?
Which matches matter most?
What risks could sink the season?
What can we not afford to get wrong?

This is constraint analysis, not ambition.

If a club misdiagnoses its situation, it does not get another narrative cycle to correct it. The league table exposes weak assumptions quickly. Injuries, fatigue, fixture congestion, and opponent strength remove illusion.

Companies often treat diagnosis as optional.

Constraints are reframed as “complexity.” Problems become “headwinds.” Leaders jump from vision straight to initiatives, skipping the hard work of naming what actually blocks progress.

This is where strategy quietly collapses.

Strategy without diagnosis is not strategy.
It is planning without understanding.

Strategy in football is enforced choice

Football strategy is not a list of priorities or tasks. It is a set of enforced trade-offs.

You cannot press high and conserve energy.
You cannot dominate possession without defenders who can build play.
You cannot prioritize youth development and demand immediate results without tension.

Strategic choice shows up everywhere: recruitment, training design, match tactics, rotation policies.

In companies, strategy often becomes additive. Growth, efficiency, innovation, transformation. All at once.

This is not a failure of intelligence. It is a failure to accept constraint.

Richard Rumelt put it plainly:
if your strategy does not force you to give something up, it is not a strategy.

Football makes incoherence visible on the pitch.
Companies often hide it in governance structures.

Objectives are few because attention is scarce

A football club typically enters a season with one to three real objectives.

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Leaders know how established companies work. Specialists own their domains, management orchestrates the whole, and the system is stable enough that responsibilities can be cleanly separated. That logic collapses the moment you enter a new venture.

A venture is not a company.
It is a hypothesis of a business idea.
A bundle of assumptions pretending to be a business.

It has no guaranteed customers, no proven economics, no validated system. Treat it like a mini-corporation and you create a dangerous illusion: that progress in isolated parts equals progress in the whole.

“Management Owns the Business Model” Sounds Right — Until You Think

Someone told me recently that specialists should own their areas and management should own “the business model and the validation.” It sounded neat but fell apart immediately. In a corporate setting, maybe. In a venture, impossible.

Validation is not a management function.
Validation is the work.

No part of the business model is done, until the whole model is done.

Every piece of research, design, engineering, marketing, and finance ties directly into the question:
Does the system work when all parts interact?

A venture does not care how polished a component is. It only cares whether the entire logic holds under reality.

Why Ownership Becomes a Trap

Ownership works in stable companies because the system is known and the job is optimization. In ventures, ownership isolates people inside their craft. It blinds them to the interdependencies that decide viability.

A user researcher can say “my insights are done.”
A designer can say “my flow is done.”
An engineer can say “my API is done.”
A marketer can say “my campaign plan is done.”
A finance expert can say “my model is done.”

In a venture, none of this is true.

Nothing is done until everything works together.
And everything is provisional until the system is validated as a whole.

The Domino Effect That Teams Underestimate

Here’s the classic trap.

A SaaS B2B venture believes its model is validated.
Digital acquisition. Paid search. Content. Clean funnel. Predictable CAC.

Then reality strikes. The ads don’t convert. Inbound traffic is weak. Quality leads disappear. The founders switch to field sales.

Suddenly the economics flip:

• higher CAC

• slower deal cycles

• shrinking margins

• rising price pressure

• new customer expectations

• geographic constraints

• exploding cash needs

One tactical change destabilizes the entire system.
The funnel breaks the margin, the margin breaks the price, the price breaks the segment, the segment breaks the product, the product breaks the roadmap, the roadmap breaks the funding logic.

What looked like a small correction becomes a systemic collapse of the “validated” business model.

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A Business Model Is a System, Not a Collection of Artifacts

A business model is not a pitch deck container filled with “product,” “pricing,” “marketing,” “ops,” and “finance.” It is a set of interdependent relationships. Move one element and every other element recalibrates.

This is why the real unit of work in a venture is the system, not the tasks.

• Does the segment match the price?

• Does the price match the margin?

• Does the margin match the acquisition model?

• Does the acquisition model match the job-to-be-done?

• Do the switching forces support retention?

• Does the capital strategy support the growth plan?

You cannot answer these questions in isolation.
You can only answer them by validating how parts behave together.

JTBD Makes the System Visible

Jobs to Be Done forces you to see the deeper structure of demand: goals, constraints, anxieties, switching logic, and trade-offs. It shapes product choices, pricing boundaries, acquisition strategy, retention forces, and economic viability.

When you understand the job:

• you see the limits of the model

• you see where the system can flex

• you see the assumptions that must be tested

• you see whether the economics can work at all

JTBD exposes systemic risk.
Systemic risk drives validation.
Validation shapes the model.

Ignore the job and you misalign the system.
Ignore the system and you validate too late.
Validate too late and you don’t get a second chance.

The Human Side: Craft vs. Truth

Specialists naturally defend their domain. Their craft is their identity. It feels safer to perfect your area than to confront the fact that the business job “validate its right to exist” (you are a part of) may not work.

It feels safer to say “my part is done.”
It is much harder to say “we still don’t know if the system works.”

Venture building demands people who can detach from their craft when the system requires it. Sometimes a beautiful design must be thrown away. Sometimes architecture must be rewritten. Sometimes the pricing strategy must shift. Sometimes the segment must change entirely.

None of this is a failure.
It is the work.

Are You Optimizing for Craft or for the System?

If you lead a venture team, ask yourself:

• Do people optimize their area or strengthen the model?

• Do they understand the interdependencies?

• Do they know which assumptions keep the system alive?

• Do they see how one change affects the economics?

• Do they understand that validation is their responsibility?

If not, you are running a team that builds in slices instead of systems. And slices do not survive.

Real Ventures Behave Like Organisms

Successful ventures adapt, integrate learning, and update the entire model when a single piece shifts. They treat the business model as a living system, not a checklist.

Wannabe ventures behave like miniature corporations.
They polish parts.
They protect domains.
They treat validation as someone else’s job.

The difference decides survival.

Conclusion

In a venture, nothing is validated until everything works together.
And when one piece moves, the whole system shifts with it.

Once you understand this, you see venture work differently.
You stop asking who owns what.
You start asking what the system needs next to get done.

And that is the moment a team becomes aligned and capable of building something real.

Across the companies I know, a strange pattern is emerging.
The last standing corporate innovation teams—the ones that survived budget cuts, reorganizations, and shifting leadership priorities—are no longer focused on innovating for their own organizations. They are selling their skills to the outside world.

Banks offer their design and prototyping teams to others.
Insurers run paid training for industries far from their own.
Industrial players pitch their innovation staff as consultants for whoever wants to pay for a workshop.

It is happening quietly, but it is happening everywhere.

And this shift says more about the state of corporate innovation than any strategy document.

The Bitter Truth: Innovation Got Cut to the Bone

Let’s be honest.
Most corporates shrank or eliminated their innovation teams in the last years. The combination of economic uncertainty, the pressure to optimize costs, and the temptation to “focus on the core business” pushed long-term bets to the edge of the agenda.

Innovation was framed as too slow, too risky, or too disconnected from the immediate financial targets.

The irony is that uncertainty is exactly when companies need clarity beyond the operational noise. When the future of your industry is up for grabs, cutting the capability that helps you see what is changing is a strange decision. But it is a widespread one.

And here we are now.
The corporate innovation teams that survived the cuts are left with too little internal demand to stay relevant. Their pipeline of innovation candidates dried up. The strategic conversations moved elsewhere. The mandate became unclear.

So they did what any team does when its relevance is questioned:
They tried to prove their value by finding work somewhere else.

The Rise of the Corporate Innovation Agency

Instead of driving exploration internally, these teams now act like agencies. They sell design sprints, foresight programs, trend reports, ideation workshops, and capability training to anyone who shows interest.

On the surface, this looks like a clever survival tactic.
Better to use the skills than let them rot.
Better to bring revenue than look like a cost center.
Better to demonstrate demand than fight political battles internally.

But scratch the surface and you see something deeper.

This shift mirrors what happened to startups during the early COVID period. Many young companies suddenly faced existential risk because their business models froze overnight. As a temporary tactic, they lent their workforce to other companies, built unrelated projects, or pivoted to short-term service work. It was a bridge to survive.

It worked for some.
Others never returned to their original mission (or died).

Corporate innovation teams are now doing something similar.
And the risk is the same.

The Consequence: Innovation Loses Its Mandate

When a corporate innovation team starts selling expertise externally, the company admits something without saying it aloud:

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We live in an age where every platform is a stage for behavioral engineering. Attention has become the raw material, and time—the currency. LinkedIn is no exception. Unlike TikTok or Instagram, LinkedIn carries the veneer of “professionalism”. Yet scratch the surface, and you’ll see the same underlying principle: the platform is a data-generating machine, designed to monetize human activity.

The question we rarely ask: what is the job LinkedIn was hired to do? And just as important, who are the job performers, and what jobs are they really performing?

This isn’t just an academic thought experiment. Understanding it changes how we use LinkedIn, how founders, recruiters, and marketers interact with it, and even how we think about attention in professional networks.

Jobs-to-Be-Done: A Quick Refresher

The Jobs-to-Be-Done (JTBD) framework is simple: people don’t buy products—they hire them to get things done. The focus is on outcomes, not features.

For example: a founder doesn’t “use LinkedIn”; they hire it to amplify their voice, connect with potential partners, or attract talent. A recruiter doesn’t “have LinkedIn”; they hire it to find the right candidate faster than competitors.

But in every JTBD scenario, context matters. The same platform can be “hired” for entirely different jobs. And in LinkedIn’s case, there’s a twist: some of the most critical jobs aren’t even consciously hired by users—they’re extracted by the platform.

LinkedIn as a Job Performer Network

To understand LinkedIn’s JTBD, you can’t look at isolated users. Think of it as a network of job performers, where each participant’s actions create value for others—intentionally or not.

At the centre of this network are two broad categories: “payers” and “non-payers”. Each has distinct jobs, yet their actions are co-dependent.

“Paying” Job Performers: Amplifiers of Influence

“Paying” users hire LinkedIn for enhanced reach, visibility, and efficiency. This includes:

• Advertisers and marketers: “I need to push messages into the feeds of the right professional audience, in the right context, at the right moment.”

• Recruiters and HR professionals: “I need to identify, reach, and evaluate candidates faster than anyone else.”

• Founders and entrepreneurs: “I want my voice amplified, my network expanded, and opportunities unlocked.”

How they work the network:

“Paying” users do not generate value in isolation. Their success depends entirely on the attention, engagement, and behavior of non-paying users. Every like, comment, connection, or view is a piece of data that enhances the reach, relevance, and precision of payer activity. Without a receptive network of “non-payers”, a promoted post is just a message lost in the void.

“Non-Paying” Job Performers: The Invisible Data Workers

Here’s the uncomfortable truth: most LinkedIn users are not the primary beneficiaries of the platform’s monetization—they are the raw material.

“Non-payers” hire LinkedIn for outcomes like:

• Consuming content: industry updates, thought leadership, trends.

• Exploring opportunities: job postings, networking for career moves.

• Maintaining professional visibility: liking, commenting, occasionally posting.

But the real job they perform, often unconsciously, is generating data that can be monetized by “payers”. Every interaction feeds algorithms, informs ad targeting, and validates sponsored content. Their attention is converted into actionable insights for paying users—and revenue for LinkedIn.

Peripheral / Hybrid Job Performers

Then there are hybrids: freelancers, small founders, side hustlers. Their jobs oscillate between “free” and “paid features”: promoting content, networking, or exploring career opportunities. They serve as bridges in the network, amplifying “payer” content while benefiting from select free features.

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LinkedIn’s Own Job-to-Be-Done

LinkedIn itself is more than a connector of professionals. Its JTBD, from an organizational perspective, is clear:

“Turn human attention and engagement into monetizable signals for paying customers while keeping free users hooked enough to generate that attention.”

Behavioral science and AI algorithms are deployed to maintain endless engagement loops, subtly nudging users to spend more time scrolling, commenting, and posting.

This creates a tension between the platform’s JTBD and the user’s JTBD:

• Users want career growth, networking, or content.

• The platform wants engagement first, monetization second, and human outcomes a distant third.

The Monetization Loop

Here’s the feedback loop in practice:

1. “Non-payers” scroll, like, and post, creating engagement signals.

2. Algorithms surface “payer” content to maximize reach and relevance.

3. “Payers” refine targeting, content, and distribution using the data generated by “non-payers”.

4. LinkedIn collects fees and ad revenue, completing the monetization cycle.

In essence, the “non-paying” job performers are producing the very outcome that “payers” are hiring the platform for, without realizing it.

Implications for Understanding JTBD

Several lessons emerge when you see LinkedIn as a job performer network:

1. JTBD are co-dependent: “Payers” outcomes are only possible because “non-payers” provide attention and data.

2. Most users are unknowingly performing critical jobs: Passive scrolling and minor engagement are extremely valuable to payers.

3. Network orchestration matters: LinkedIn’s algorithms are not neutral—they are designing behavior to maximize monetization.

This reframing changes how we see success on LinkedIn. For example, a founder endlessly scrolling for inspiration is performing a job for others (“payers” and the platform), not necessarily for themselves.

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Reclaiming Your Attention: Intentional JTBD

Understanding the network allows users to reclaim agency. Ask yourself:

• Which job am I really hiring LinkedIn for?

• Am I generating data for someone else, or am I achieving my intended outcome efficiently?

• How can I use the platform without being hijacked by the engagement loops?

Practical strategies:

• Define outcomes before opening LinkedIn.

• Limit time spent scrolling; focus on posting, connecting, and engaging intentionally.

• Evaluate features: which ones serve my JTBD, and which serve someone else’s?

The Bigger Picture: LinkedIn as a Mirror

LinkedIn reflects more than professional networks—it reflects attention economies and the subtle monetization of human behavior. Every post, comment, or profile update is a signal. “Payers“ capture it. Algorithms amplify it. “Non-payers” feed it.

The question is not “what can I get from LinkedIn?” but:

“Whose job is LinkedIn really doing—and am I the beneficiary or just the raw material?”

Recognizing this networked JTBD perspective is the first step to using LinkedIn intentionally rather than being used by it.

Closing Thought

LinkedIn is not neutral. It is a complex network of job performers, where value is co-created, attention is harvested, and monetization is inevitable. If you are conscious of your role—defining your JTBD, using the platform intentionally—you can make it work for you. If not, you are simply producing data that someone else pays for.

In the end, understanding the real job LinkedIn performs is less about features or content strategy, and more about awareness, agency, and purpose in a system designed to profit from every click, like, and scroll.

There was a time when electricity was the headline.

Newspapers wrote about it as a miracle. Investors chased it. Entrepreneurs built companies around it. Engineers debated AC versus DC as if the future of civilization depended on it. In some sense, it did.

Factories reorganized around electric motors. Cities extended working hours beyond daylight. Entire industries restructured. Electricity was not incremental improvement. It was a new layer of capability.

And then something strange happened.

Electricity disappeared.

Not physically. Economically. Culturally. Strategically.

Today, no serious company claims competitive advantage because it “uses electricity.” No board approves a budget because the firm has successfully “adopted power.” Electricity is assumed. It is infrastructure. It is a platform upon which value is created, not the value itself.

The transformation was real. The conversation faded.

We are watching the same pattern unfold with AI.

The Illusion of the Tool

In the early phases of any general-purpose technology, people mistake it for a tool.

Steam power was a tool. Water wheels were tools. Horses were tools. Electricity looked like a tool. Early adopters plugged it into existing processes. They replaced steam engines with electric motors without redesigning factories. Productivity gains were marginal.

The real shift came later.

Electricity allowed decentralization of power inside factories. Machines no longer needed to be physically arranged around a central shaft. Layouts changed. Workflows changed. Management changed. Entire industries reorganized around the flexibility electric power enabled.

The technology did not simply improve the old model. It made new models possible.

AI is at the same stage many factories were in 1905: swapping steam for electricity without rethinking the factory.

“We use AI for customer support.”
“We integrated AI into our workflow.”
“We automated parts of marketing.”

This is the electric motor bolted onto a steam factory.

The deeper shift is not automation. It is cognition as infrastructure.

From Power to Cognitive Power

Electricity provided physical power everywhere. Cheap, reliable, distributed energy.

AI provides cognitive power everywhere. Cheap, scalable, distributed intelligence.

Not intelligence in the human sense of wisdom or judgment. But pattern recognition. Prediction. Language generation. Optimization. Classification. Simulation.

For most of history, cognitive labor was scarce. Skilled analysis required trained humans. Scaling thinking required hiring.

Now cognition is becoming ambient.

When a capability becomes ambient, its economics change.

Electricity reduced the marginal cost of energy close to zero in relative terms. That enabled refrigeration, computing, telecommunications, and global manufacturing networks.

AI reduces the marginal cost of certain forms of cognition close to zero. Drafting. Summarizing. Translating. Coding. Designing variations. Running scenarios. Generating alternatives.

The question is no longer: “Do we use AI?”

That question will sound as outdated as “Do you use electricity?”

The real question becomes:
What value can you create because cognitive power is ubiquitously available?

The Strategic Misunderstanding

Calling it an “AI strategy” already reveals confusion.

No company ever had a serious “electricity strategy.”

They had manufacturing strategies. Distribution strategies. Cost strategies. Scale strategies. Electricity was embedded inside them.

If your competitive advantage depends on “using AI,” you do not have an advantage. You have access to a commodity.

The strategic work lies elsewhere:

• Which constraints disappear because cognition is abundant?

• Which bottlenecks remain scarce?

• Which business models become viable only when thinking is cheap?

When electricity spread, illumination was not the final value. It enabled nightlife economies, refrigerated supply chains, elevators that allowed skyscrapers, data centers that enabled the internet.

Electricity was not the end product. It was the substrate.

AI is becoming substrate.

The Plug-in Fallacy

Many companies treat AI as a plug.

“We have integrated AI into our CRM.”
“We use AI for forecasting.”
“We deployed AI chatbots.”

This framing assumes AI is a feature layer.

But general-purpose technologies do not stay features. They reshape system architecture.

Electric motors did not just replace steam engines. They dissolved the architectural constraints that steam imposed. Central shafts disappeared. Flexible layouts emerged.

AI dissolves cognitive bottlenecks.

Previously:

• Analysis required analysts.

• Prototyping required designers.

• Coding required developers.

• Research required time-consuming synthesis.

Now these tasks can be generated, iterated, and stress-tested instantly.

That changes cycle times. It changes experimentation economics. It changes decision quality. It changes the boundary between exploration and execution.

If cognitive iteration is cheap, then the bottleneck shifts.

The constraint moves from “can we generate options?” to “can we judge them well?”

Judgment becomes the scarce resource.

The Real Scarcity

Electricity did not eliminate scarcity. It shifted it.

Once power was abundant, coordination became critical. Supply chains expanded. Logistics complexity grew. Organizational design mattered more.

Similarly, AI does not eliminate uncertainty. It shifts it.

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Innovation loves numbers.
Everyone talks about (financial) outputs and outcomes. More features shipped. More revenue. More margin. Faster cycles. Lower costs. Higher productivity. These terms fill decks, OKRs, and quarterly reviews. They feel objective. They feel solid. They feel like the truth.

But numbers are not the truth.
Impact is the truth.
And impact is never functional.
It is socio-emotional.

I did not see this clearly at the beginning of my career. I saw it the moment I created the JTBD Insight Canvas. When you follow the chain of causality behind any functional result, you end up somewhere entirely different from where you thought you would. Behind every metric sits a person. Behind every KPI sits a fear, a hope, a status game, a sense of identity. Behind every “business case” sits someone who wants to feel safe, respected, valued, or proud.

This article is about that quiet shift.
The one most teams ignore.
The one that decides whether your innovation creates relevance or becomes another forgotten feature in a crowded market.

This is not a tactical article.
It is reflective.
Because innovation only becomes meaningful when you are ready to see the human story hiding behind the data.

If you want your innovation to matter design for the socio-emotional shift, not the KPI.

The Illusion of Functional Impact

Let’s start with a basic assumption: functional impact matters.
Better margin, shorter cycles, fewer errors, higher throughput, smoother processes — these things are not irrelevant. The problem is not their existence. The problem is our obsession with them. They give us a false sense of clarity, a sense that we understand why people choose or reject a solution.

But functional impact without emotional relevance is decoration.
It is a door without a room behind it.

Teams celebrate functional wins as if they speak for themselves. “We reduced the time from 14 days to 4.” “We increased productivity by 27 percent.” “We cut costs by 18 percent.”

These numbers sound like progress.
And in a narrow, mechanical sense, they are.

But no user — and no decision-maker — hires a solution for a number.
They hire it for a shift.

A shift in how they feel.
A shift in how they are perceived.
A shift in what they can do next.
A shift in how much fear they carry.
A shift in their sense of identity, agency, or control.

Functional impact is the shadow.
Socio-emotional impact is the object casting it.

Until you understand the object, you are innovating in the dark.

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The Higher Margin Fallacy

Take one of the most common claims in innovation: “Our solution increases margins.”

Margins feel like the ultimate functional goal. They are clean, financial, quantifiable. They suggest efficiency, discipline, and superior value creation. Teams love talking about margins. Boards love listening. Investors smile. It signals competence and control.

But if you spend time listening to decision-makers, something else becomes clear.
Margins matter less than what margins represent.

Higher margins are not about the number.
They are about human consequences.

Let me break down what “higher margins” really mean inside an organisation:

1. Higher margins mean protection from judgement.

If you deliver the numbers, you walk into meetings without fear. You avoid blame. You stay off the radar of those who might question your competence. The number is a shield.

2. Higher margins mean status.

Within organizations, numbers are a currency of respect. If your business unit “turned the margin trend around,” people see you differently. You are trusted, listened to, and included in conversations where influence is decided.

3. Higher margins give you credibility for the next initiative.

People who deliver numbers get permission to try new things. They get budget. They get support. They are not challenged with endless scrutiny. Margin is social capital.

4. Higher margins justify your past decisions.

Most leaders carry silent fears about choices they made. Results give them the ability to tell a coherent narrative. Numbers become a story that saves them from doubt or embarrassment.

5. Higher margins enable generosity.

With better margins, leaders can reward their teams. They can approve bonuses, trainings, promotions. Generosity strengthens loyalty and identity. There is deep emotional satisfaction in being able to say yes.

6. Higher margins reduce existential fear.

When the numbers fall, people worry about layoffs, political battles, reorganisation, being perceived as “dragging the company down.” When the numbers rise, they feel anchored, stable, grounded.

7. Higher margins unlock agency.

With good numbers, leaders can shape the future instead of reacting to crises. They move from survival to creation. Agency is emotional oxygen.

This is the real impact.
Economic metrics are always socio-emotional stories wearing a suit.

Once you see this, you cannot unsee it.

The Hidden Emotional Life of Productivity

Another classic example: productivity.

No one actually wants productivity.
They want what productivity allows them to feel.

People do not hire a tool to be “more productive.” They hire it to gain emotional safety.

• Productivity makes you appear competent, even if you feel insecure.

• Productivity helps you stay ahead of demands, avoiding that creeping sense of being overwhelmed.

• Productivity protects you from the fear of being replaced.

• Productivity gives you pride — “I can keep up; I still matter.”

• Productivity reinforces your identity — “I am not the bottleneck; I am the contributor.”

These are not functional benefits.
They are emotional consequences.

And yet most innovation teams talk about productivity as if humans were machines.

You see the problem.

Why Innovation Conversations Stall

Most innovation efforts stall for the same reason: teams fall in love with their own numbers. They design solutions that hit functional KPIs but ignore emotional causality. They start conversations by quoting metrics. They present dashboards. They talk about cost savings, cycle times, margins.

But users decide based on emotions, not spreadsheets.
Decision-makers switch based on personal risk and identity, not efficiency arguments.

If your solution does not create a shift in the person using it, no amount of functional improvement will earn adoption.

This is why so many technically superior solutions lose to inferior ones: they miss the socio-emotional layer.

When teams start and end with KPIs, they guarantee mediocrity.
When they start with socio-emotional shifts, they have a chance at relevance.

The Shift You Need to Trigger

Let’s be precise.

Impact happens when people change.

Not systems.
Not processes.
Not dashboards.
People.

If you want your innovation to matter, ask different questions:

• What does this let the user feel?

• What fear does it remove?

• What story does it allow them to tell?

• What identity does it reinforce?

• What social status does it unlock?

• What silent burden does it lift?

• What pride does it generate?

• What shame does it prevent?

• What permission does it create?

These questions are uncomfortable for many teams. They require honesty. They force you to step out of the logic of the business case and into the logic of human psychology.

But this is where relevance lives.

A solution that does not move people emotionally does not move them at all.

The Innovation Mistake Nobody Talks About

There is a mistake I see across companies, founders, and teams: they measure their progress by functional outcomes, but they pitch their innovation without any emotional grounding.

They talk about their product as if the world were rational.
The world is not rational.
It is emotional with data on top.

You can reduce cost by 20 percent and still lose the deal.
You can speed up processing time and still lose adoption.
You can automate tasks and still trigger resistance.
You can improve margin and still not be chosen.

Because you did not design for the socio-emotional shift.
Because you talked about efficiency when the user cared about identity.
Because you talked about features when the stakeholder cared about reputation.
Because you talked about KPIs when the person cared about safety.

When there is no emotional shift, the decision becomes easy:
stick with the status quo.

Impact You Will Never See

The most important impact your innovation creates is the impact you will never see.

You will not see the pride a leader feels when they can finally show progress.
You will not see the relief someone feels when they look at their workload and think, “This is manageable now.”
You will not see the confidence someone gains when they can explain their choices to their boss without fear.
You will not see the way a team starts to trust each other again because the pressure eased.
You will not see the internal respect someone earns because they championed the right project.
You will not see the sense of belonging someone experiences when their department is suddenly seen as “the one that turned it around.”

These moments do not show up in dashboards.
But they define whether your innovation will survive.

Impact is human.
Always.

A Quiet Reflection on My Own Work

Let me speak honestly for a moment.

Like many people building something on their own, I have had long periods without income. Periods filled with self-doubt, frustration, and the sense that I should be “further” by now. When you do not have the numbers to justify your path, you start to question your value.

But the more I speak with innovators, teams, and leaders, the more I see the same pattern: we measure ourselves with outputs and outcomes, yet we judge our lives through impact.

Not financial impact.
Human impact.

You can be the person with the biggest revenue and the smallest influence.
Or you can be the person with modest numbers and massive human impact.

People do not remember features.
They remember how you made them see differently.
They remember the clarity you helped them find.
They remember the shift you triggered in their thinking.
They remember the struggle you helped them name.
They remember the courage they gained from your work.
They remember the story you helped them rewrite about themselves.

That has nothing to do with KPIs.
That is pure human causality.

And that is the kind of impact I want to create in my own work.

If You Want Your Innovation to Matter

Here is the truth I hope this article leaves behind:

Design for the shift, not the KPI.
Metrics follow meaning.
Impact follows emotion.
Relevance follows identity.
Adoption follows safety.
Loyalty follows pride.
Change follows the feeling of “I want this, and I want to be the person who uses this.”

If you want your innovation to cut through the noise, stop chasing functional superiority.
Everyone can copy features.
No one can copy the emotional shift you create.

And if you want support uncovering the socio-emotional causality behind your solution — the human logic that makes innovation relevant — this is the work I do with teams, founders, and organisations.

Because once you understand the emotional drivers of impact, innovation stops being guesswork.
It becomes a way of shaping how people live, decide, and move.

And that is the work worth doing.

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