Stage 1: What is “the Honest Unknown Principle”?

I would define the concept provisionally as:

We can fail epistemically in two directions:

False certainty:

“I know X.”…when our evidence only supports: “X seems plausible.”

But we can also fail in the opposite direction:

False uncertainty:

“We just can’t know.”…when sufficient evidence actually exists to make a useful judgment.

So the principle isn’t really:

Admit what you don’t know.

That’s too easy. It is closer to:

That distinction matters enormously.

Sometimes the honest answer is: Known.

Sometimes: Probably.

Sometimes: Plausible, but weak evidence.

Sometimes: We don’t currently know.

And occasionally: We cannot know from the information available.

Those are very different epistemic states.

Where the idea comes from

There isn’t one bloodline. There are several tributaries feeding the same river.

One of the oldest is Socratic ignorance. Socrates’ intellectual advantage is traditionally framed not as knowing everything, but recognizing where his knowledge ends.

Socrates recognizing his own ignorance while others mistakenly believed themselves knowledgeable.

That’s epistemic humility.

But humility alone isn’t enough for building our model of Honest Unknown.

Much later, statistics and decision theory introduced another distinction: “…uncertainty should itself be represented rather than erased.”

Modern judgment-and-decision research calls one important version of this calibration: whether your confidence actually corresponds to how often you’re right. Overconfidence becomes dangerous, precisely because it causes us to behave as though uncertain information were settled.

Then we get another useful branch from strategic planning: known unknowns versus unknown unknowns.

The terminology became famous through Donald Rumsfeld, although the underlying distinction predates him. RAND has used the framework explicitly in reasoning and planning under uncertainty.

Put those together and we get something stronger than “know that you don’t know”:

Instead:

That’s not a historical equation, just my compact representation of the idea. And this gives us our first important distinction.

We could have an unknown and pretend it’s known.

Or have an unknown and correctly preserve it as unknown.

We follow the latter.

The first trap

I don’t want you walking away from Stage 1 believing that intelligent people say “I don’t know” more often.

That’s not necessarily true.

A person can hide behind uncertainty just as easily as they can hide behind certainty.

The harder discipline is:

Don’t know less than you could know.

Don’t claim to know more than you do.

And that’s exactly where we’ll start Stage 2: the taxonomy of unknowns, because “I don’t know” turns out to contain several completely different conditions that require different actions.

Stage 2: Not all unknowns are the same

This is where the idea becomes useful.

When someone says “I don’t know,” I think the first question should be: What kind of not-knowing is this?

Because different unknowns demand completely different behavior.

There is a formal distinction from statistics, engineering, and risk analysis that gives us a strong starting point.

NASA, for example, separates uncertainty into:

• epistemic uncertainty, caused by lack of knowledge, and

• aleatory uncertainty, caused by inherent variability or randomness.

But for thinking, I want to expand that into a more useful taxonomy.

1. The knowable unknown

“I don’t know, but I could.”

You don’t know how much revenue Customer X generated last year? The information exists. You haven’t retrieved it.

You don’t know whether a competitor offers Feature Y. You could investigate.

You don’t know whether customers actually care about something. You could interview them, observe behavior, or run an experiment.

This is epistemic uncertainty. More information can reduce it.

NASA explicitly treats epistemic uncertainty this way: it represents the state of our knowledge and can shrink as information is acquired.

This is perhaps the most abused category in business.

People say: “We don’t know whether customers will pay for this.”

True.

But that doesn’t mean: “Therefore, let’s have a meeting and debate it.”

😂

It means go get or buy information.

That leads to our first operating rule:

2. The probabilistic unknown

“I can know the odds without knowing the outcome.”

Will a fair coin land heads? Unknown.

Can we improve our knowledge until we know what the next flip will be? No.

But we know something extremely useful:

P(H)=0.5

i.e. probability of heads is 50%

So unknown does not mean absence of knowledge. We can have considerable knowledge about something whose outcome remains unknown.

Business is full of these:

“Will this salesperson close this particular deal?”

“Will this investment outperform?”

“Will this customer churn?”

“Will this drug work for this particular patient?”

We may never be able to answer the binary question beforehand. But perhaps you can move from:

¯\(ツ)/¯

to:

“Given what we know, I’d put it at roughly 65%.”

Now we’ve transformed ignorance into calibrated uncertainty. And calibration has a precise meaning.

If you repeatedly assign 70% probability to events, roughly 70% of those events should occur. Tetlock describes exactly this property in forecasting research.

So:

Notice what we’re not doing. We’re not forcing an answer. We’re improving the shape of the unknown.

3. The unknown unknown

“I don’t know that I don’t know.”

This is the dangerous one. Imagine you’re evaluating whether to launch a product. You have modeled:

market size

pricing

CAC

conversion

retention

engineering cost.

Beautiful spreadsheet. But there’s some variable X you haven’t even conceived of.

Maybe a regulatory constraint. Maybe distribution behaves completely differently. Maybe customers use the product for something you never anticipated.

Your model contains no cell labeled X. That means adding more precision to the existing spreadsheet doesn’t solve the problem.

You can make: Customer Acquisition Cost (CAC)

CAC = $47.31

instead of:

CAC ≈ $50 (approx)

and feel wonderfully sophisticated while completely missing the variable that kills the company.

This is where intellectual humility becomes structural rather than emotional.

You need mechanisms designed to discover variables you haven’t modeled. We’ll eventually get into those.

4. The currently unknowable

This is another category people often confuse with #3 (The Unknown Unknown).

“The answer exists eventually, but cannot presently be observed.”

Who will win the 2032 presidential election?

What will Apple’s largest product category be in 2040?

What will Bitcoin trade at on August 17, 2036?

There will eventually be an answer.

But there is no amount of Googling/GPTing today that retrieves it. You can gather evidence and create a probability distribution, but you cannot collapse it into fact.

This distinction matters because humans are extraordinarily tempted to convert:

prediction

into

knowledge.

The honest unknown says: “I have a thesis. I do not have a fact.”

5. The malformed unknown

This is my favorite category, and it’s most overlooked.

Sometimes you don’t know because the question is garbage.

🤯

“What will AI do to our company?” Impossible to answer usefully.

“What is our AI strategy?” Possibly meaningless.

“Is this a good investment?” Compared with what? At what price? Over what time horizon? Under what assumptions?

Douglas Hubbard’s work on measurement makes essentially this point:

Often that reframing changes what should be measured in the first place.

So sometimes:

And the correct response isn’t research. It’s reformulation.

Now we have our first working map

When we encounter: “I don’t know.”

don’t stop there. Ask:

Could the answer be obtained?

→ investigate.

Is the outcome inherently uncertain?

→ estimate probabilities.

Might important variables be outside my model?

→ search for missing assumptions.

Does the answer only become knowable in the future?

→ maintain a forecast, not a belief masquerading as fact.

Is the question itself poorly formed?

→ reformulate it.

And here’s where the research gets particularly interesting.

A 2017 Management Science paper by Walters, Fernbach, Fox, and Sloman tested something almost eerily close to what we’re calling the Honest Unknown. They found that overconfidence was substantially reduced when people were explicitly asked to consider what information was unknown or missing before stating their confidence.

It worked better than simply asking people to “consider the alternative.”

That’s a big clue.

The useful question may not be: “Am I wrong?”

It may be: “What don’t I know that would matter if I knew it?”

Those questions sound similar. They’re not.

We’ll stop here, because Stage 3 is the research behind why us humans are so bad at preserving unknowns: overconfidence, ambiguity, anchoring, narrative completion, calibration, and why intelligent/expert people can actually be especially vulnerable in certain circumstances. That’s where the psychology gets juicy.

Stage 3: Why our brains hate an honest unknown

Now we get to the machinery underneath it. The Honest Unknown Principle is necessary because our human mind doesn’t naturally preserve uncertainty very well.

It tends to compress uncertainty into a story.

And once the story feels coherent, something dangerous happens:

This is one of the central themes in Daniel Kahneman’s work on judgment. He used the wonderfully ugly acronym WYSIATI: “What You See Is All There Is.”

The basic idea is that when forming judgments, we construct the best explanation from the information immediately available while often failing to adequately account for information that is absent. Kahneman connects this directly to overconfidence.

That gives us our first enemy.

1. The brain doesn’t leave empty fields empty

Suppose I’m considering acquiring a small software company.

I know:

Revenue: $8M

Growth: 35%

Gross margin: 78%

Founder wants out

Customers seem happy

Purchase price: $30M

I start constructing a story:

“Founder is tired… Business is healthy… Strategic buyer hasn’t noticed it… We can improve distribution… $30M might be cheap.”

Notice what happened.

I didn’t necessarily fabricate facts.

That’s much more subtle.

I connected facts.

And the connections themselves may be assumptions:

Founder wants out → probably not because something is wrong.

Customers seem happy → probably low churn.

35% growth → probably sustainable.

No strategic buyer → probably overlooked.

Every individual inference sounds reasonable.

Collectively, they create a world.

Once I can see that world clearly, my confidence rises.

But the evidence hasn’t changed.

This gives us an important distinction:

A story can be complete while the evidence underneath it is Swiss cheese.

2. We don’t just suffer from ignorance. We suffer from unrecognized ignorance.

This connects beautifully to work by psychologist Baruch Fischhoff on hindsight bias.

After an event occurs, people tend to perceive that outcome as having been more predictable beforehand than it actually was.

In Fischhoff’s classic experiments, telling people which historical outcome occurred increased the probability they retrospectively assigned to that outcome.

Think about the consequence.

Before: “Could go either way.”

After: “Of course that happened. The signs were there.”

• The information that didn’t predict the outcome gets mentally demoted.

• The information that did gets promoted.

That’s devastating if you’re trying to become a better decision-maker because:

A good outcome can teach you the wrong lesson about a bad decision.

And:

A bad outcome can teach you the wrong lesson about a good decision.

If I make a 90% probability bet and lose, that doesn’t prove my reasoning was bad.

If I make a 10% probability bet and win, that doesn’t prove I’m a genius.

So we need to preserve what we knew at the time.

Remember this. It becomes important when we build our checking system.

3. Intelligence doesn’t automatically save you

This is one of the most interesting parts.

You might assume:

More intelligence leads to better reasoning equals fewer false beliefs. This is wrong!

Because, reality is a lot messier.

Keith Stanovich and colleagues have spent decades distinguishing intelligence from rational thinking.

Cognitive ability helps enormously, but many rationality errors aren’t eliminated simply by being intelligent.

There’s a nasty implication here.

A highly capable thinker may sometimes be better equipped to defend a conclusion they’ve already reached.

More intelligence gives us a larger rhetorical and analytical arsenal.

So intelligence can be used for:

Seeing Evidence, jump to Conclusion ❌

but also:

Starting with Conclusion, working backwards to Sophisticated justification ❌

Those are completely different processes.

That’s why the Honest Unknown isn’t fundamentally an IQ problem.

It’s a discipline problem.

4. Experts have another problem: the domain boundary

Expertise is enormously valuable inside environments where expertise is actually predictive.

Kahneman and Gary Klein, who came from rather different traditions of decision research, published a fascinating joint paper on when expert intuition can be trusted.

Their conclusion wasn’t “intuition is good” or “intuition is bad.”

Expert intuition becomes reliable when two conditions exist:

The environment contains sufficiently regular patterns, and the person has had enough opportunity to learn those patterns through feedback.

Chess?

High validity.

Experienced chess players repeatedly encounter patterns and receive rapid feedback.

Long-term stock-market prediction?

Far uglier.

Feedback is noisy. Causality is ambiguous. Regimes change.

Now imagine someone who has spent twenty years successfully building software companies.

They probably have genuine expertise.

But then:

“I’ve built companies for twenty years. I know this market will develop this way.”

Hold on.

You’ve crossed a boundary.

Your expertise may justify confidence about:

engineering organizations,

product development,

hiring,

sales processes.

It may not justify the same confidence about:

interest rates,

geopolitics,

regulation,

consumer psychology five years from now.

The dangerous sentence is:

“I’ve seen this before.”

Sometimes you have.

Sometimes you’ve seen something that rhymes just enough to seduce you.

5. Then there’s motivated reasoning

Now we add gasoline.

Sometimes we aren’t neutral about the answer.

You want the acquisition to work.

You own the stock.

You hired the executive.

You built the product.

You publicly predicted something.

Now evidence doesn’t enter an empty courtroom.

The defendant already has a lawyer.

Research on motivated reasoning, particularly associated with Ziva Kunda, shows how people’s goals can influence the cognitive processes they use to evaluate evidence and reach conclusions.

People don’t necessarily consciously decide to deceive themselves. Rather, reasoning can become selectively recruited toward conclusions they prefer, provided those conclusions can still be justified. Cherry-picking.

That’s much scarier than lying.

A liar knows the truth and says something else.

A motivated reasoner can genuinely believe they’ve evaluated the evidence objectively.

So here’s our emerging model

When reality gives us:

Evidence + Missing Information

our brains don’t reliably preserve that state.

They tend to do something closer to:

Evidence + Assumptions + Narrative + Motivation → Confidence

And the output doesn’t come with labeled warning like “37% OF THIS CONCLUSION WAS FILLED IN BY YOUR BRAIN.”

😂

It just feels like your judgment.

That is why I don’t think the Honest Unknown Principle should ultimately be about humility.

But…“Be humble” is weak operational advice.

We can’t inspect our brains and turn the humility knob from 6 to 8.

Instead, we need to externalize uncertainty.

We need a method that forces:

facts to remain facts,

inferences to identify themselves as inferences,

assumptions to expose themselves as assumptions,

unknowns to remain visibly unknown,

and predictions to carry probabilities rather than disguising themselves as facts.

And I think this is where we’re going to turn the concept into something genuinely useful: a practical protocol for making decisions under uncertainty.

Not a 27-item checklist. Something compact enough that we could actually run it mentally during an investment decision, strategy meeting, product bet, acquisition, hiring decision, or argument.

Stage 4: Turning the Honest Unknown into an operating method

Now we stop admiring the philosophy and make it earn rent.

I think the mistake would be creating an “Honest Unknown Framework™” with twelve boxes. You’d use it twice and then it would fossilize in Notion. We’ll still do that, but later…

Instead, I want to derive something small enough to become instinct.

Start with any consequential claim: “I think X.”

Before acting on X, interrogate it through four moves.

1. Separate what we know from what you infer

Imagine:

“This competitor is struggling.”

What do I actually know?

Revenue growth fell from 40% to 12%.

They laid off 18% of staff.

The founder stepped down.

Three senior salespeople left.

Those are observations.

Then comes:

“They’re struggling.”

That’s an inference.

Maybe it’s an excellent inference.

But it still belongs in a different bucket.

This distinction sounds almost insultingly simple. In practice, it’s incredibly powerful because normal speech quietly erases it.

We say:

“Customers don’t want this.”

when what we actually observed was:

“Nine of twelve customers didn’t use it within 30 days.”

The first is an explanation.

The second is evidence.

So our first discipline becomes:

Don’t eliminate interpretation. Label it.

2. Ask the Honest Unknown question

Once you’ve formed your thesis, don’t ask:

“Why might I be wrong?”

That’s useful, but I think there’s a better question:

What would I want to know before being allowed to become more confident?

Notice the framing.

You’re not trying to destroy your thesis.

You’re identifying missing information with decision value.

Suppose you’re considering investing in Company X.

You know:

Revenue growing 60%.

Margins improving.

Large TAM.

Management sounds excellent.

Valuation looks reasonable.

You’re bullish.

Now:

What would I want to know before being allowed to become more confident?

Maybe:

How much growth comes from one customer?

What does retention look like by cohort?

How much revenue is usage-based?

What happens to margins if inference costs don’t decline?

Are competitors seeing similar growth?

Why are insiders selling?

Now we’ve found the interesting territory.

Not:

“What don’t I know?”

That’s infinite.

You don’t know the CEO’s favorite sandwich either. 🥪

We care about:

Some unknowns are irrelevant.

Some can flip the decision.

Those are critical unknowns.

3. Ask the most important question: Can I collapse this unknown?

Take each critical unknown and classify it using Stage 2.

Suppose your question is:

“I don’t know whether customers are retaining.”

Can you know?

Yes.

Find cohort data.

So:

But if the question is:

“I don’t know whether this market will be worth $50B in 2032.”

You cannot retrieve the 2032 answer.

So stop treating additional research as though eventually Google will reveal the future.

Instead:

And if it is:

“I don’t know how customers will react to $99 pricing.”

Maybe research helps.

But perhaps the cheapest information source is reality:

Ask them or simply charge a few $99. See what happens.

This connects to a real concept from decision theory called Value of Information, which very roughly means:

Formal decision analysis asks how much better your expected outcome would be if particular uncertainty were resolved before choosing.

That gives us a gorgeous little rule:

You could spend three weeks improving your confidence from:

73% → 76%

when either probability produces exactly the same action.

That’s intellectual procrastination dressed in a lab coat.

4. Finally: state the decision without killing the unknown

This is the part I care about most.

Eventually, we have to act.

We cannot preserve uncertainty forever.

Suppose you’ve done everything above and arrive at:

“I think we should launch.”

The temptation is to mentally transform that into:

“Launching is the right decision.”

Don’t.

Instead:

“Given what we know, launching is the best decision. I still don’t know X and Y.”

That’s a fundamentally different intellectual posture.

You have separated:

We can be highly confident in the decision while remaining uncertain about the outcome.

This distinction is huge.

Consider poker

You hold pocket aces pre-flop.

Your opponent goes all-in with 7♣ 2♦.

You call.

Excellent decision.

Then the board comes:

7♥ 2♠ 9♣ 4♦ K♠

You lose.

Was calling wrong?

Of course not.

The outcome was uncertain.

The decision wasn’t arbitrary.

This is why Annie Duke’s work on decision-making uses poker so effectively: decision quality and outcome quality must be separated when luck is involved.

And this brings us somewhere surprisingly deep.

The goal of the Honest Unknown Principle is not to reduce uncertainty to zero.

That’s impossible.

The goal is:

Then act. That’s very different from:

“Wait until we know.”

And equally different from:

“We know enough.”

The first can create paralysis.

The second can conceal recklessness.

So our mental protocol is becoming very small

You believe X.

Ask: What do I actually know?

Then: What would I want to know before allowing myself greater confidence?

Then: Can I cheaply reduce any of them?

Then: Would resolving this unknown change my decision?

And eventually:

That last part matters.

Because the unknown doesn’t disappear when the meeting ends. It becomes something we can watch.

And that takes us directly into Stage 5: the audit.

This is where we’ll build ways to catch yourself after you’ve formed a strong conviction, especially when you really like your thesis.

We’ll look at falsifiability, disconfirming evidence, premortems, prediction logs, confidence calibration, and one particularly powerful question:

“What would have to be true for me to change my mind?”

That is where Honest Unknown starts becoming a defense against your own intelligence.

Stage 5: How do you check yourself?

Now assume the dangerous case. You’ve done the research. You’ve separated facts from inference. You’ve identified unknowns.

And you still have a strong thesis.

The problem now isn’t ignorance. It’s attachment.

Once I believe X, my brain becomes very good at making new information compatible with X.

So we need something stronger than: “I’ll stay open-minded.”

Open-mindedness is an intention. We need tripwires.

1. Define what would change your mind before reality happens

Suppose your thesis is: “This company is undervalued because the market is underestimating its AI business.”

Before investing, write:

I would materially weaken this thesis if...

Maybe:

AI revenue growth falls below 20% for two consecutive quarters.

Gross margins deteriorate despite revenue growth.

The supposed AI growth turns out to be mostly cannibalization.

A competitor demonstrates materially better economics.

Management changes how it defines or reports the segment.

Now something important has happened.

You’ve made your belief falsifiable.

Karl Popper made falsifiability famous in philosophy of science. His deeper point was about distinguishing scientific theories from claims that could accommodate essentially any observation.

We can steal the useful part for ordinary reasoning:

That’s our first tripwire.

2. Separate the thesis from the position

This matters enormously in investing, strategy and leadership.

Before buying:

“At $40 this stock is undervalued.”

After buying:

“My stock is undervalued.”

One word changed.

My.

Now selling doesn’t merely mean updating a forecast.

It means admitting I was wrong.

That introduces ego, sunk cost, public consistency and loss aversion.

The position begins defending the thesis.

So:

If anything, once you have skin in the game, your standard for checking yourself should rise.

This generalizes far beyond stocks:

my hire

my company

my strategy

my product

my prediction

The possessive pronoun is epistemically expensive. 😄

3. Run a premortem

Gary Klein popularized a wonderfully simple technique called the premortem.

Instead of asking:

“What could go wrong?”

Imagine something stronger:

“It’s two years from now. This decision was a disaster. What happened?”

Research by Klein and colleagues connects prospective hindsight of this sort with identifying explanations that ordinary forecasting may overlook.

The psychological trick is clever.

“What could go wrong?” makes failure hypothetical.

“It failed. Why?” makes your brain explain an observed world.

Suddenly you get:

“Our largest customer built it internally.”

“Inference costs never came down.”

“The regulatory exemption disappeared.”

“Our sales cycle was 14 months instead of six.”

“The technical advantage became commoditized.”

Some of those possibilities were previously unknown unknowns.

The premortem changes it:

It turns Unknown Unknown to a Known Unknown

That’s epistemic progress even though you haven’t learned a new fact.

You’ve discovered a new question.

4. Keep a decision record, not just an outcome record

Remember hindsight bias from Stage 3.2?

This is the antidote.

Before an important decision, preserve:

What I believe.

Why I believe it.

What I know.

What I’m assuming.

What I don’t know.

What probability I’d assign.

What would change my mind.

Then leave it alone.

Six months later, don’t ask:

“Did I win?”

Ask:

“Was my model of the world well calibrated?”

Suppose you make ten decisions that you believe each have roughly a 70% probability of succeeding.

Seven work. Three don’t.

Those three failures aren’t necessarily evidence of poor judgment.

In fact:

You hit a 10 on 10 (100%)

success on repeated genuinely 70% propositions would eventually make me suspicious that you’re understating your confidence.

Good uncertainty should sometimes produce surprises.

That’s what probability means.

This is precisely why Philip Tetlock’s forecasting research is so interesting. In the Good Judgment Project, forecasters made explicit probabilistic predictions that could later be scored and calibrated. Research emerging from the project found that some people consistently became remarkably good forecasters, and that forecasting skill could be improved through techniques including training, aggregation and active updating.

We don’t improve judgment merely by thinking harder.

You need feedback against predictions you actually made.

Otherwise hindsight quietly edits the history.

5. Now we get to my favorite audit question

Most people ask:

“What evidence supports my thesis?”

Better thinkers ask:

“What evidence contradicts my thesis?”

But I want you to go one step further:

What evidence should exist if my thesis were true, but doesn’t?

That’s a different beast.

Suppose I say:

“Customers absolutely love our product.”

Okay.

If that’s true, what else should I expect to observe?

High retention.

Organic referrals.

Usage expansion.

Customers complaining when service goes down.

Some willingness to pay.

Maybe competitors copying features.

If none of those things exist, that’s interesting.

You’re no longer merely looking for contradictory evidence.

You’re looking for missing expected evidence.

Formally, this touches Bayesian reasoning.

If hypothesis (H) predicts evidence (E), then observing (E), or conspicuously not observing it, should affect how much credence we give (H).

Bayes’ theorem gives us:

• H (Hypothesis) - Q: “Is my idea true?”

• E (Evidence) - Q: “What did I observe?”

• P(H) (Prior probability) - Q: “Before seeing this evidence, how likely did I think my idea was?”

• P(E \mid H) (Likelihood) - Q: “If my idea were true, how likely would I be to see this evidence?”

• P(E) (Overall probability of the evidence) - Q: “How often would I see this evidence for any reason?”

• P(H \mid E) (Posterior probability) - Q: “After seeing the evidence, how likely is my idea now?”

You don’t need to calculate this numerically every time.

The conceptual habit is enough:

Then look at the world.

6. The stopping rule

There’s one final trap.

Once you learn all this, you can become the guy who perpetually finds another uncertainty.

Every decision becomes:

“But have we considered...”

Eventually your epistemic rigor turns into sophisticated cowardice.

So you need a stopping condition.

I proposed this in Stage 4:

Once further information is unlikely to change your action enough to justify its cost:

Stop.

Make the decision.

Carry the remaining uncertainty.

Watch the tripwires.

Update when reality changes.

That last one is crucial:

Update when reality changes.

So now we have a loop

We started Stage 4 with a belief:

We separated:

We reduced valuable unknowns.

We acted.

Now Stage 5 adds:

And then the whole thing starts again.

The Honest Unknown isn’t therefore a state of saying:

“I don’t know.”

It’s a continuous relationship between belief and evidence.

Now, we’ve accumulated enough pieces. Let’s derive our framework from first principles.

We’ll temporarily throw away Socrates, Kahneman, Tetlock, Popper, Klein, Bayes, and even the phrase Honest Unknown.

Let’s start with reality itself and ask:

If I knew absolutely nothing about epistemology, what must logically be true about making decisions in a world where my information is incomplete?

Then we’ll derive a small set of axioms from scratch.

And from those, I think we can arrive at our version of the principle, including what it means, when you invoke it, and potentially a compact formulation you could carry around as part of your broader first-principles thinking.

Stage 6: Build it from first principles

Start with the world.

Axiom 1: Reality exists independently of what I believe about it

Suppose a company has $4 million of cash.

I believe it has $10 million.

My confidence doesn’t create another $6 million.

Obvious, but foundational:

“Reality” is NOT “My model of reality”

Everything we think you “know” is actually mediated through a model.

We don’t possess reality.

We possess information about reality.

That gives us:

• R = reality

• M = my model of reality

The goal of reasoning isn’t:

• M = a convincing story (this is wrong)

It’s:

• M ≈ R (approximately)

Being wrong isn’t the fundamental failure.

Our models will inevitably be wrong in places.

The failure is refusing to modify (M) when (R) gives you contrary information.

Axiom 2: My information about reality is always incomplete

For almost every meaningful decision, we possess only some subset of relevant information.

I_R = everything potentially knowable about reality

I_M = what we currently know

Then necessarily:

I_M ⊂ I_R i.e. what we know is a subset of everything knowable about reality

Then, the gap is:

U = I_R - I_M

Our unknown (U).

But here’s the problem.

We don’t necessarily know the contents of (U).

That’s why:

“I’ve listed everything I don’t know.”

is logically suspicious.

If we could enumerate everything inside the unknown, it wouldn’t really be entirely unknown anymore.

So uncertainty isn’t a bug we eventually eliminate.

It’s structural.

That immediately kills one fantasy.

Otherwise we’d barely act at all.

Axiom 3: Decisions must nevertheless be made

Reality doesn’t wait for epistemology.

Hire her or don’t.

Invest or don’t.

Build it or don’t.

Enter the market or don’t.

And importantly:

If you have $1M sitting in cash while deciding whether to invest it, you’ve chosen cash for another day.

So we have:

Incomplete information → Required action

That’s the fundamental problem.

Axiom 4: Therefore, a good decision cannot require certainty

If information is inherently incomplete and decisions nevertheless must occur:

Decision quality cannot depend on outcome certainty

Instead, a rational objective has to look something more like:

This is why the poker example mattered.

We can make:

the right decision and get the wrong outcome.

And:

the wrong decision and get the right outcome.

Outcome alone cannot tell us whether reasoning was sound.

Axiom 5: More information is not automatically better

This one’s important because otherwise our principle becomes:

“Research everything.”

Suppose you’re buying a $100,000 piece of equipment.

There’s an unknown that has a 40% chance of changing your decision.

Worth investigating.

There’s another unknown with effectively no chance of changing your decision.

Researching it costs three weeks.

That information might be true and completely useless.

Therefore:

Its value comes from its ability to alter the action or materially improve its expected consequences.

That’s a surprisingly important distinction.

Axiom 6: Confidence should therefore be earned, not chosen

Now consider two people.

Person A:

“I’m 95% sure.”

Person B:

“I’m 65% sure.”

Which is the better thinker?

Impossible to tell.

Confidence itself isn’t a virtue.

If reality warrants 65%, then:

65% = good reasoning

and

95% = bad reasoning

Likewise, if evidence warrants 95%, saying 65% isn’t humility.

It’s miscalibration.

This is where I think our principle should explicitly reject the cult of uncertainty.

The objective isn’t lower confidence. It’s justified confidence.

That gives us:

Strength of belief should be proportional to strength of evidence.

Again, that’s our notation, not a scientific law.

And now something falls out of these axioms

Let’s combine our Axioms to a belief system:

1. Reality exists independently of me.

2. My model of reality is incomplete.

3. I cannot know everything.

4. I must act anyway.

5. Additional information has cost.

6. My confidence should reflect my evidence.

Therefore the rational objective cannot be:

Know everything before acting.

Nor:

Trust yourself and act confidently.

It has to be something between them:

That, to me, is the Honest Unknown Principle we’ve been circling. But I think we can sharpen it further.

I think there are actually two forms of intellectual dishonesty

We’ve talked mostly about:

False certainty

which says, Evidence says uncertain and I say known

But there’s an inverse:

False uncertainty

where, Evidence supports conclusion and I say unknowable

This happens constantly.

Someone presents overwhelming evidence and another person retreats into:

“Well, nobody really knows.”

That’s not epistemic humility.

That’s an escape hatch.

So our principle should punish both errors.

Think of a scale:

Underclaim (on the left) ← Evidence (middle) → Overclaim (on the right)

The objective isn’t to move left.

It’s to stay in the middle, as close to the evidence.

That suggests a stronger formulation:

I like that considerably more than:

“Be comfortable saying I don’t know.”

Meh! That’s obvious.

Because now the Honest Unknown isn’t a philosophy of doubt.

It’s a philosophy of fidelity.

Our belief should faithfully represent the information available.

And now I want to add one final axiom

This one isn’t necessary for the logic above.

But I think it’s necessary for our operating principle.

Axiom 7: Beliefs are temporary; the method is permanent

Today:

P(X) = 0.70 i.e. probability of X happening is 70%

Tomorrow you learn something important.

Now:

P(X) = 0.42, probably dropped down to 42% with new findings

Nothing has gone wrong.

In fact, something has gone right.

Your model moved toward reality.

So:

That produces a very different relationship with conviction.

You can hold a thesis strongly without needing to defend it permanently.

I’d put it this way:

That might be the most important sentence in this entire exercise.

Where we’ve arrived

We started six stages ago with a vague phrase:

The Honest Unknown.

Now we’ve derived something much richer:

1. Observe honestly

↓ ↓ ↓ ↓

2. Separate evidence from inference

↓ ↓ ↓ ↓

3. Expose decision-relevant unknowns

↓ ↓ ↓ ↓

4. Reduce the ones worth reducing

↓ ↓ ↓ ↓

5. Calibrate belief to evidence

↓ ↓ ↓ ↓

6. Act despite residual uncertainty

↓ ↓ ↓ ↓

7. Update when reality answers back

But I wouldn’t yet call that our principle.

It’s still too elaborate.

A first principle should compress.

Something you can invoke in ten seconds while someone is pitching you an investment, presenting a strategy, explaining why a product failed, or telling you something with suspicious certainty.

So I think there’s one final step worth doing.

Stage 7 should be synthesis.

We’ll take everything we’ve developed and compress it into perhaps 3 first principles, a handful of diagnostic questions, and one concise formulation of our Honest Unknown Principle.

Not borrowed philosophy. Not consultant-framework soup.

Something you can actually think with.

Stage 7: Our first-principles version

Now let’s compress.

If a principle needs a slide deck to invoke it, it has already lost.

I think everything we’ve covered reduces to three first principles.

I. Reality over narrative

Start with:

What do I actually know

Not: What do I think happened?

Not: What’s the most convincing explanation?

Not: What does my experience tell me?

Strip the claim down to observations first.

Fact: Revenue declined 18%.

Inference: Customers are leaving.

Explanation: Customers are leaving because the product is getting worse.

Prediction: Revenue will continue declining.

Four completely different epistemic objects.

Normal conversation casually welds them together:

“The product is getting worse, customers are leaving, and this business is declining.”

Sounds like one fact.

It might contain one fact and three layers of reasoning.

So:

I think that’s worth remembering.

II. Unknowns are obligations, not weaknesses

Now we encounter something we don’t know.

Our instinct shouldn’t be embarrassment.

Nor should it be:

“Who knows?”

Instead:

Unknown? → What kind?

Can I look it up?

Can I measure it?

Can I test it?

Can I only estimate it?

Is it currently unknowable?

Or have I asked a bad question?

Then the crucial filter:

Would knowing it change what I do?

If yes, the unknown creates an obligation to investigate.

If no, perhaps it deserves to remain unknown.

This produces Principle II:

That’s very different from “do more research.”

Sometimes the intellectually correct move is to stop researching.

III. Decisions are commitments to action, not claims of certainty

Eventually you arrive here:

“I still don’t know.”

Good.

Now what?

You act. Uncertainty does not mean inaction.

You might say:

“Given what I know, I think acquiring this company is the best available decision. I’m about 70% confident. The biggest unresolved variable is customer concentration. If retention falls below X, my thesis changes.”

That’s enormously stronger than:

“This is a good acquisition.”

And stronger than:

“There’s too much uncertainty to know.”

You haven’t eliminated the unknown.

You’ve bounded it.

Then you’ve acted around it.

That leads to our third Principle:

Now compress those three

We have:

1. Reality over narrative

What do I actually know?

2. Unknowns as obligations

What matters that I don’t know?

3. Action without certainty

Given both, what should I do?

Which gives us a remarkably compact sequence:

Known (on the left) → Unknown, in middle → Action (on the right)

But there’s one missing piece.

After action:

Reality (when observed) → Update (your model)

So our actual loop becomes:

Know → then, Expose → then, Act → and, finally Update

That’s enough of a framework.

I wouldn’t make it any bigger.

Our Honest Unknown Principle

Now I’d formulate the principle itself this way:

I like every word there.

Know only what the evidence earns.

Prevents false certainty and false humility.

Preserve what remains unknown.

Don’t allow assumptions, narratives or confidence to silently fill the blanks.

Reduce the unknowns that matter.

Unknowns aren’t sacred. Attack the valuable ones.

Then act.

Uncertainty isn’t permission for paralysis.

That’s our principle.

But we need a check against it

Frameworks tend to become ceremonial. We don’t want to sit in a meeting thinking:

“Time to execute Stage IV of my Honest Unknown Methodology.” 🤓

So I’d reduce the audit to four questions.

When you have a strong opinion, especially one you’re excited about:

1. What do I actually know?

2. What am I assuming?

3. What don’t I know that could change my decision?

4. What would make me change my mind?

That’s it.

The first separates reality from narrative.

The second exposes inference.

The third finds the Honest Unknown.

The fourth prevents the thesis from becoming religion.

And if #3 reveals something knowable and important:

go find out.

If it reveals something unknowable:

carry the uncertainty.

If #4 has no answer:

you’re probably defending something rather than reasoning about it.

There’s an even faster version

In ordinary conversation you won’t run four questions.

So I’d train one reflex:

“What would have to be true?”

Someone says:

“AI agents will replace SaaS.” Ask: What would have to be true?

“This stock is undervalued.” Ask: What would have to be true?

“Customers don’t care about this feature.” Ask: What would have to be true?

“We should hire this person.” Ask: What would have to be true?

That question immediately begins exposing the hidden structure underneath a conclusion.

Then ask:

Which of those things do we actually know?

Now you’ve cracked the claim open.

And there’s one behavior I’d deliberately train

When you catch yourself saying: “I know...”

mentally substitute:

“What evidence would I need to deserve the word know?”

And when you catch yourself saying:

“We can’t know...”

ask:

“Can’t know, or haven’t found out?”

Those two questions protect opposite sides of the principle.

One attacks unjustified certainty.

The other attacks intellectual laziness disguised as humility.

The deepest version

After going through all seven stages, I actually think “Honest Unknown” undersells the philosophy we’ve built.

Because it sounds like the virtue is admitting ignorance.

That’s only one component.

The deeper principle is about maintaining an honest boundary between:

Reality

and

My representation of reality

Everything else follows from that.

Facts are pieces of the representation.

Inference extends it.

Unknowns mark its boundaries.

Probability describes uncertainty inside it.

Research expands it.

Decisions operate from it.

Feedback corrects it.

And ego corrupts it by confusing:

“My model says X”

with:

“X is true.”

So if I had to compress everything we’ve built into one sentence underneath the entire thing, it wouldn’t actually be about uncertainty.

It would be:

Our model is disposable.

Reality gets the final vote.

That’s the endpoint of the formal lesson. From here, the interesting part would be pressure-testing this philosophy against real situations: an investment thesis, a product decision, a business strategy, a hiring decision, even one of your strongly held beliefs. That’s where we’ll find out whether the principle actually survives outside the laboratory.

Subscribe now

Share

Leave a comment

Skill

You can install The Honest Unknown Principle as a Skill.md for your choice of LLM.

Download the Skill.md

Cheatsheets

Download The Honest Unknown Principle Worksheet as PDF

References

Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models

Ancient Skepticism — Stanford Encyclopedia of Philosophy

Superforecasting: The Art and Science of Prediction — Philip E. Tetlock and Dan Gardner

Donald Rumsfeld’s “known knowns” remarks — CNN

RAND: Defense Supply Chain Resilience Research Agenda

NASA: Epistemic and Aleatory Uncertainties

How to Measure Anything — Douglas W. Hubbard

Known Unknowns: A Critical Determinant of Confidence and Calibration — Walters, Fernbach, Fox, and Sloman

Thinking, Fast and Slow — Daniel Kahneman

Hindsight Is Not Equal to Foresight — Baruch Fischhoff

Rationality and Intelligence — Keith E. Stanovich

Conditions for Intuitive Expertise: A Failure to Disagree — Daniel Kahneman and Gary Klein

The Case for Motivated Reasoning — Ziva Kunda

A Simplified Method for Value of Information Using Constructed Scales

Thinking in Bets — Annie Duke

Karl Popper — Stanford Encyclopedia of Philosophy

Performing a Project Premortem — Gary Klein

Forecasting Tournaments: Tools for Increasing Transparency and Improving the Quality of Debate — Tetlock, Mellers, Rohrbaugh, and Chen

Psychological Strategies for Winning a Geopolitical Forecasting Tournament — Mellers et al.

Here’s the thing. Knocking off incumbents has always been tough. Switching costs have always been a tradeoff most organizations just can’t justify.

Their operating leverage of growing revenue faster than OpEx goes out the proverbial window.

Economies of scale aren’t the same either, it doesn’t plateau with growth. It keeps going up. Jobs to be done, as they get done, need LLM tokens unlike the marginalized CPU compute.

Incumbents sitting on healthy profits have to embrace going in red (at least pink) and justify reinvesting in their tech, archaic schemas unsuited for LLMs. Whereas, new Challengers, can come in with VC subsidized plans, newer, much cleaner infra and hit them where it hurts – right in their tokens.

Goliaths, the incumbents, are merely enjoying the unearned benefits of switching costs: be it data migrations, integration rebuilds, workflow and training costs, implementation risks, relationship or ecosystem loss. Either or all of these. Healthy enterprises seldom switch a critical piece of their ops workflow because of features or cost.

Even hyperscalers haven’t figured this out, they just have deeper pockets to spend a billion (or ten) today and hope to make it back in thousands later, indefinitely. Only model providers have done this successfully i.e. knocked off incumbents. They’re also highly subsidized, that’s more like Goliath v New Goliath, but worth a look on how they’ve taken down industries and not only managed to kill startups but established enterprises that never saw it coming. Look at Chegg, StackOverflow, Quota, WebMD…all were thriving, untouchable at one point. There’s plenty more to come as the model providers become application providers themselves.

Anyway, I digress. Back to the fumes of switching costs that incumbents are still running on. Besides being (arguably) better or not, how have model providers done this [knocked off incumbents]?

- Low Cost, which is highly subsidized currently

- More Appeal, of a magical, addictive product

But…they also went after people first, not their jobs. May not have been an intentional move, but it was brilliant.

Build tools for the day-to-day use, and people will innately trust you with work too.

This personal and work dependency osmosis, while common in physical world and hardware, rarely happens with software. Gmail and Google Workspace is a good example of being done before, but that’s not David, that’s just Goliath playing David.

For the Davids, now, the key is figuring out how not to face Goliath head-to-head. Not to compete on features, market share, SLA response times, or even cost. It’s guerrilla warfare. Jump out from behind the trees, through the trenches; get to the decision makers beyond their work stations. From outcomes perspective, build and launch addictive daily use tools, tangential to the core business need. Things the incumbents could never justify or prioritize in their quarterly plannings.

Hubspot, I’m not a paid user, does this really well. From their sponsored podcasts, signature generator, Dharmesh’s Agent.ai play…maybe unintentional but brave and brilliant regardless. Way to be in the arena. They’ve managed to go from David to Goliath while keeping all of David’s qualities of being nimble, fast, unafraid to experiment and deliver.

Now, how’s any of this practical, tactical? Great question, Daniel-san (or more like David-san in this metaphor).

Building the next payroll/HR platform? Build the free “what’s my real take-home” and RSU/equity tracker that employees check obsessively. Get the *worker* hooked on understanding their own money, then sell their employer the backend that feeds it.

Starting the next dev/observability platform? Give individual devs a gorgeous free status page and side-project monitoring they’ll use on weekend projects. They drag it into work on Monday.

Creating the next AP / Vendor Management tool for Industry X? Launch a killer app for Lemonade Stand finances, a financial learning aid for kids teaching them how to manage money-in/money-out.

Pick the tangential daily-use tool that reaches your decision-maker *as a person*, at 11pm, on a night out or at home — not as a work-time buyer. That’s the trench Goliath won’t climb into.

Read the official AKOS Blog here: https://akos.ai/blog

In a world where founders, investors, marketers, statisticians, executives and HiPPOs (Highest Paid Persons Opinion); all live and die by data metrics, it's imperative that we're looking at the right data. Data is often considered to be impartial and irrefutable; few have the confidence to doubt it, even fewer can constructively argue its source, sample and presentation methodologies. But, data analytics is a complex amalgamation of puzzles and mysteries.

Puzzles vs. Mysteries

A New Yorker article Open Secrets by Malcolm Gladwell explains the difference between Puzzles and Mysteries. I'm paraphrasing for relevance to our current context:

Puzzles signify a lack of clarity or information available or visible to us. A puzzle may require an information gathering session or a simple restructuring of information to devise an actionable solution.

Mysteries require judgments and the assessment of uncertainty, and the hard part is not that we have too little information but that we have too much.

The KPI Paradox

Doesn't matter your role or rank in the organization, you're accountable for a KPI, or several. Whether you know the exact KPIs or not is a whole other story. We've bastardized KPIs by building dashboards and scorecards, churning and burning through them every quarter. We’ve been writing stories and cherry-picking data to justify our stories, a post hoc ergo propter hoc fallacy.

Trouble stems when we try to define KPIs and “find data” to solve Mysteries, albeit they are really meant to solve Puzzles. KPIs are leading indicators, and each needs to be tied to a definite action, sort of like the gas light going off on your car.

KPI = gas light on OR off → Action = Stop for gas OR keep going

Business decisions aren't as binary as the level of gas in your car, but they require solving both Puzzles AND Mysteries, and countless KPIs or more data isn't going to get us there. We really need to stop using "more data" as an answer to our decision-making problems. This isn't Hollywood, you don't get to have a script and then find locations, actors, costume et al. to match your narrative.

What we need to do is to differentiate Puzzles from Mysteries; and doing so will lead to effective distinctions between actionable KPIs and colloquial KPIs. The distinction sometimes gets murky, and requires practice and restraint. And oftentimes, a Mystery is only uncovered after solving the precursory Puzzle(s).

Solving Puzzles vs. Deciphering Mysteries

With the ability of tracking every visitor/user action and behaviors, we’re in an entirely new era of passive data collection, an enormous amount of data. Some of this data is invaluable in solving linear Puzzles by answering the “who”, “what”, “where”, “when” and the “hows”; and solving them is key to the survival of our marketing campaigns, product roadmap, and our businesses' survival. But, should we stop there? No! Each time we solve a Puzzle - there’s likely a larger Mystery lurking in the end. These Mysteries are often discovered by asking "What if?", "What then?", "What else?", "What now?."

“Solve Puzzles to Survive, Decipher Mysteries to Scale.”

The expected outcomes of asking those questions is also significantly different between the two.

• Solving a puzzle will lead to actionable insights for effective progress towards your objectives.

• Deciphering a mystery will merely lead to identifying uncontrollable patterns in your users and market behaviors. It requires less data, more foresight and risk tolerance.

Question to ask?

Will solving this (Puzzle or Mystery) lead to actionable insights for me to make changes or will it simply lead me to identifying more of the unknown?

• If the answer is Actionable Insights. It's a Puzzle. Dig into the data and find a way to solve it.

• If it's More of the Unknown, you have a Mystery on your hands. You'll need to unravel enough of the nesting dolls to reveal the true mystery. And, at some point, maybe at the last atomic level, make decisions and take actions based on gut feeling, experience, and/or optimism.

For it is this unhinged resolve that could lead to groundbreaking insights towards market-gap and revenue stream opportunities. That said, it definitely does not involve more chaotic data collection. We got enough of it already, and more data isn't going to give us an answer.

“Puzzles come to satisfying conclusions. Mysteries often don’t, rather they are the doorway to a journey towards exponential growth or complete demise.”

In Conclusion

Puzzles are straightforward – questions that have answers, crosswords that can be filled in. Day-to-day business decisions require solving these puzzles, defining your KPIs based on levers you can pull or push to incrementally improve and survive the ever evolving competitive matrix.

On the contrary, Mysteries don’t need revelation; they require prevision, a prophetic foresight based on trends analysis, market forecasting, loony optimism, and plenty of moxie.

Patiently waiting and doing nothing is the hardest thing I do. Most often I fail at it. While my impatient actions may not have a negative impact on the desired outcome, it is the frustration associated with delayed reactions from continuous inconsequential actions that gets to me. I anger, I victimize myself, I deflect blame and act irrationally.

When enough time has passed, slowly, too slowly, I am able to look back at the situation. 20/20 hindsight and my toddler-like ability to connect the dots allow me to deduce which actions led to the desired outcome. That epiphany, in turn, leads to disappointment for all the time vested and wasted towards unfruitful attempts.

What I often forget is that business, family, and friends all require The Compound Effect. That is to say, it is not one grand gesture that wins the heart, it is through the compounding of smaller increments. The author, Darren Hardy, provides a clever metaphor for our impatience towards the compound effect – he explains it as our desire to keep turning that hot water knob while impatiently waiting for the right water temperature. For me, that outcome is almost always a good reminder to be patient.

In other scenarios, it is not as apparent as burning skin. So, I have built a framework for myself. Not necessarily a solution, just an over analytically derived step-by-step guide to stay patient.

Realization and Acceptance

The framework starts with a reminder to myself and acceptance of who I am. I, like most in my circle, am a builder and a technician – a DIM (do-it-myself) entrepreneur. Doing something, even if it’s chaotically and frantically pedaling, is an innate trait.

Expect latency and account for it

Next, acknowledge that The wait is where it’s at – the delay between action and response. Making quick decisions solely for the sake of moving fast is only fruitful if I am patient enough to wait for enough data (or the right data) to reveal itself, allowing me to base the next decision off of it. There are essentially three types of latencies I account for:

• Time bound latency: Setting a specific time-based event to delay making the next decision.

• Data bound latency: Setting a specific data threshold to delay further analysis.

• Accountability bound latency: Entrusting someone else to be accountable for further changes, follow-up decisions, or even making the decision to pull me back in.

Puzzle vs Mystery

Call it manifesting or wishful thinking. A question I started asking myself is “What does ‘done’ look like?” Answering this requires understanding if we’re solving a puzzle or deciphering a mystery.

Solve a Puzzle -> Actionable Insights -> Next Steps

Decipher a Mystery -> Identified Patterns -> Further Analysis

As I’ve grown more and more accustomed to thinking in puzzles and mysteries. I generally jump to asking “Will decoding this (Puzzle or Mystery) lead to actionable insights for me to make changes or will it simply lead me to identifying more of the unknown?” Setting clear expectations with myself helps avoid future heartache and unnecessary dwelling over what I cannot control. And, because of that cognition, patience becomes a mere necessity and not an unachievable virtue.

Mind mapping Outcomes

If this, then that. This is a cheat of sorts. I only do this if it is a Mystery with multiple predictable outcomes. A tool like Coggle or FigJam to mind-map predictable outcomes. In most cases, the real outcome does land tangentially close to one of the predictions to get a head start on the next set of decisions and actions.

Conclusion

There’s only so much planning you can do before you eventually take the leap and test against your assumptions. As an entrepreneur and business owner, it is a harsh realization that my engineering mindset is actually a curse. A challenging innate persona of who I need to be most of the time – a patient observer, and the framework above helps me to be one.

Let’s start with Passion and a brief exercise to show why passion alone is not enough.

Grab a piece of paper (No, a Notion doc is not okay for this, get a real piece of paper). Create a list: 1, 2, and 3. Now follow the instructions below:

1. Write what you’re passionate about? I understand that for some, the words ‘get rich’ or ‘die trying’ might come up, but try to avoid it. Seriously, what are you passionate about? Write down a phrase. This is your What.

2. Now, write Why you are passionate about it.

3. Write out the plan of your pursuit in achieving that What.

Finally, write the plan for pursuing your passion — the how.

As an entrepreneur and a math nerd, I believe our successes, failures and plateaus can be plotted through simple exponential and logarithmic functions. In successes, as the value of x gets larger, the function value of f(x) increases more and more dramatically. In failures, as the value of x gets larger, the function value of f(x) drops more and more drastically. A little impact to x, can increase or decrease the outcome radically. And, then there are logarithmic functions that might come into play from time to time. Where beyond a certain point, the value of x increasing doesn’t lead to a radical shift in the outcome. It’s when you’re not winning, or losing — you’re just surviving.

Passion is necessary for exponential growth, but the same passion can also lead to satisfaction in survival for just long enough before the exponential demise. Passion alone isn’t enough. The answer then lies in the plan. And, realistically, even then it might not be enough, you might scale or you might burn out, but the plan gets you to the moment of realization sooner rather than later.

In my line of work, I often meet people with passion who need help devising a plan and executing it. But, I also meet individuals with a solid plan who lack the passion for their project in general. In the latter situation, I can’t help, but the former is why I rise and stay awake. The words following are for the insanely passionate.

I was wrong

It used to be tough saying these words. Initially in my career, my ego wouldn’t let me. I am finding that the older, wiser me is saying it more often now, and it’s not because of some newly discovered humility; it comes from acceptance. I accept that I will not get it right the first time, maybe even the second or the third, but eventually I will. Similarly, a successful blueprint isn’t based on immediate perfection but on the elimination of iterations not leading to success — accepting when you’re wrong, and moving on, over and over again until you’re right.

Perfection is unnecessary

Looking back, I regret almost every time I tried to achieve the perfect solution, including the times when I did achieve it. The trade-off of additional time, money and resources was almost never worth the outcome. The satisfaction factor and outcome margin between excellence and perfection is narrow, too narrow to justify insane passion associated with perfection.

Quick and cheap can be good enough

It’s ironic that I believe this because my job relies on convincing people that it isn’t true. This opinion, however, has an expiration date and there comes a time where quick and cheap will not make the needle move, at least not forward. But, I do believe that we now live in a Deploy or Die world for passionate ideas. Initially, your plan needs to quickly and cheaply validate your idea in market adaptation and financial feasibility before it’s too late and your passion becomes unrealistic infatuation. A key part of your plan should be to verify if it’s worth the sacrifices you’re going to make going forward. If the answer is yes, then and only then, you stride forward.

Being unique is overrated

Uber and Lyft are similarly competing with virtually the same features for riders and drivers, down to vehicle options, rates and tips. They shamelessly steal from each other. Their drivers are often the same…. even their goals are the same.

Being the first of your kind may have it’s benefits, but it has little to do with success. Legality isn’t a concern unless you’re blatantly stealing, and tenacious passion and will can justify the ethics of it. Where it matters most is defining and delivering on your own key differentiators (what we call the X-factor). Your launch plan needs to shout that differentiator from the rooftops.

I can’t do this alone

An idea’s conception may be a solo act but it is impractical for one person to continuously scale. Even the most introverted among us will need others to grow. Sharing your passion will attract the support you need to stay on the path of exponential growth, and expanding your support group at key intervals will help you skip pitfalls you might otherwise be too close to see. Plan to bring in trusted advisers during each stage so that you’re not going too far into alone. They will not only bring new perspectives but also dedicated expertise in areas you may lack.

Now that you have your passion written down, write down your assumed constraints, write your needs to overcome those constraints, then define what success looks like and finally, get started on the first draft of your plan.

Bonus: Look back

The future may always be a mystery, but the past is written. Go back to the beginning from time to time to look at the paper you inked your passion on, it’s going to ground you back to the wistful love affair that started you on your journey. That optimistic innocence can sometimes be lost in the practical pessimism that comes along with executing plans in the real world.

One of my next posts will go deeper into coming up with an iterative blueprint plan for your passionate idea. Stay tuned...