You cannot know both the position and the momentum of a particle with arbitrary precision. The more precisely you fix one, the more uncertain the other becomes. Heisenberg's uncertainty principle is a limit of nature, not of instruments. It states that Δx·Δp ≥ ℏ/2: the product of the uncertainties in position and momentum has a minimum. Improve measurement of one quantity and you degrade knowledge of the other. The principle applies to conjugate variables — pairs linked by the mathematics of quantum mechanics. Energy and time obey a similar relation: sharp energy implies fuzzy time, and vice versa.
Heisenberg derived this in 1927 from the mathematics of quantum mechanics and the behaviour of measurement. Measuring position (e.g. by bouncing a photon off a particle) necessarily disturbs momentum; measuring momentum leaves position uncertain. The uncertainty is not ignorance that better technology will fix. It is built into the description of the system. The principle undercuts the classical ideal of exact prediction. You can have precision on one axis or the other, not both. Strategy and decision-making face an analogue: optimising for one variable often increases variance or opacity on another. Measuring and rewarding one outcome can distort behaviour and obscure the rest. The frame is not that "everything is uncertain" but that some pairs of things cannot be jointly optimised or known to arbitrary precision. Choose what you want to pin down; accept trade-offs on the conjugate.
Section 2
How to See It
Uncertainty-principle dynamics appear when improving knowledge or control on one dimension degrades it on another, or when measurement itself changes the system. Look for conjugate pairs: two quantities that cannot both be maximised at once.
Business
You're seeing the Heisenberg principle when a company ties bonuses to quarterly revenue. Revenue becomes sharply measured and optimised; long-term product quality and culture become noisier and harder to track. The act of measuring and incentivising one variable (revenue) distorts behaviour and reduces effective information about the other (sustainable value). You get precision where you point the measurement; you get uncertainty or gaming elsewhere.
Technology
You're seeing the Heisenberg principle when A/B testing optimises click-through rate. CTR is measured with high precision; brand perception, trust, and long-term engagement are harder to measure and often degrade when CTR is pushed. The optimisation narrows uncertainty on one metric and increases it on others. The "measurement" (the test) changes what gets built — the system is disturbed by the act of observation.
Investing
You're seeing the Heisenberg principle when a fund reports daily NAV. Precise, frequent marking gives limited partners certainty about current value. It also encourages short-term positioning, reduces tolerance for illiquidity, and can distort management toward smoothing NAV instead of long-term compounding. Precision on "current value" trades off against clarity on "durable strategy" and the behaviour of the manager.
Markets
You're seeing the Heisenberg principle when central banks target inflation with high-frequency policy. Fine control over near-term inflation can require interest-rate volatility and large balance-sheet moves, which increase uncertainty in financial conditions and market functioning. Sharpening one variable (inflation path) can blur another (stability of the monetary regime).
Section 3
How to Use It
Decision filter
"When designing metrics, incentives, or decisions, ask: what is the conjugate variable? If we maximise precision or control on X, what becomes harder to know or control? Accept the trade-off explicitly. Do not assume we can have full precision on both. Choose which variable we need to pin down and tolerate uncertainty on the other."
As a founder
You cannot optimise every dimension at once. Optimising for growth can obscure unit economics; optimising for margin can obscure growth and retention. Pick the variable that matters most for the current phase and accept that others will be noisier. Be explicit about the trade-off. Avoid building incentives that demand precision on conjugate pairs — e.g. "maximise revenue and maximise quality" with a single metric. One will be gamed; the other will be neglected. Design metrics and reviews so that the conjugate is monitored, not optimised into distortion.
As an investor
Due diligence that demands full precision on both "current traction" and "long-term optionality" is asking for the impossible in many cases. Early-stage companies often have clarity on one and noise on the other. Use the principle to set expectations: we may have a sharp read on growth and a fuzzy read on durability, or the reverse. Allocate and size positions accordingly. When a manager or company claims perfect precision on multiple conjugate dimensions, treat it as a red flag — they may be overconfident or gaming one variable.
As a decision-maker
Recognise that gathering more information on one dimension can reduce the reliability of another. Intense monitoring of output can change how people work and what they report. The act of measurement can alter the system. Decide which variable must be known or controlled, accept uncertainty on its conjugate, and avoid incentive structures that pretend both can be maximised simultaneously.
Common misapplication: Treating the principle as "we can never know anything." The principle says that certain pairs of quantities cannot both be known with arbitrary precision. It does not say that all knowledge is impossible. Use it to identify conjugate pairs and make conscious trade-offs, not to justify inaction or vagueness.
Second misapplication: Ignoring that measurement disturbs the system. In business, measuring and rewarding a metric often changes behaviour in ways that degrade other outcomes. The uncertainty is not only statistical; it is causal. Design measurement and incentives with the disturbance in mind — or you will get exactly what you measure and lose sight of the rest.
Buffett avoids demanding precision on conjugate pairs. He does not ask managers for detailed short-term earnings guidance that would incentivise smoothing or myopia. He prefers "fuzzy" reporting on quarterly results and sharp focus on long-term capital allocation and business quality. The trade-off is explicit: less precision on near-term numbers, more alignment on durable behaviour and less gaming. The uncertainty principle in practice: do not try to know both "this quarter's number" and "unchanged long-term strategy" with high precision at once.
Feynman popularised the physics and its implications: "Nature has a kind of uncertainty built in." He stressed that the principle is not about experimental error but about the structure of physical law. The strategic takeaway he embodied: accept fundamental limits on what can be known or controlled. Do not build systems that assume infinite precision on multiple conjugate dimensions. The principle is a guard against overconfidence in measurement and control.
Section 6
Visual Explanation
Heisenberg uncertainty — Conjugate variables: precision on one axis increases uncertainty on the other. Choose which to pin down; accept the trade-off on its conjugate.
Section 7
Connected Models
The Heisenberg principle connects to observer effects, measurement limits, and incentive design. The models below either explain why measurement disturbs (Observer Effect), how to handle uncertainty (Unknown Unknowns, Signal vs Noise), or how metrics can backfire (Goodhart's Law).
Reinforces
Observer Effect
The observer effect: measuring a system can change it. Heisenberg's principle is the quantum expression of this — measurement disturbs conjugate variables. In organisations, measuring and rewarding an outcome changes behaviour; the "observation" is not passive. The reinforcement: assume that measurement has a cost or side effect. Design for it.
Reinforces
Goodhart's Law
When a measure becomes a target, it ceases to be a good measure. Goodhart's law is the behavioural counterpart of the uncertainty principle: optimising for one variable degrades its informational value and often degrades other outcomes. The principle says you cannot have arbitrary precision on both of a conjugate pair; Goodhart says that trying to optimise one metric distorts the system. Use both when designing KPIs and incentives.
Reinforces
Signal vs Noise
Precision on one dimension often means more noise on another. Signal vs noise is about extracting information in the presence of uncertainty. The uncertainty principle says that for conjugate variables, reducing noise on one increases it on the other. The link: not all uncertainty can be eliminated; some is structural. Allocate measurement and attention accordingly.
Leads-to
Unknown Unknowns
Some uncertainty is irreducible — we do not know what we do not know. The Heisenberg principle adds: even for known variables, conjugate pairs impose a floor on joint precision. Unknown unknowns and uncertainty-principle trade-offs together argue for humility in forecasting and for designing systems that do not assume full precision.
Section 8
One Key Quote
"What we observe is not nature itself, but nature exposed to our method of questioning."
— Werner Heisenberg, Physics and Philosophy (1958)
The method of questioning — what we measure, how we incentivise — shapes what we see and how the system responds. We do not get a view from nowhere. We get a view that is conditioned on our choice of variables and our act of measurement. The practitioner's job is to choose the right variable to pin down and to accept that the conjugate will be less precise or more disturbed. Do not pretend the method of questioning is neutral.
Section 9
Analyst's Take
Faster Than Normal — Editorial View
You cannot maximise precision on conjugate variables at once. Identify the pairs that matter: short-term results and long-term behaviour, growth and margin, one metric and the unmeasured dimensions it crowds out. Choose which to optimise; monitor the other; do not design incentives that demand both to be perfect.
Measurement disturbs the system. When you attach consequences to a metric, people adapt. The metric can become a target and lose information (Goodhart). The act of measurement can also increase effective uncertainty on other dimensions because effort and attention shift. Design metrics and reviews with the disturbance in mind.
Be explicit about the trade-off. Stakeholders often want precision on multiple dimensions. The uncertainty principle says that for conjugate pairs, that is not possible. Make the trade-off visible: "We are optimising for X this phase; Y will be noisier." Reduces surprise and blame when Y moves.
Use the principle to resist overmeasurement. More metrics do not always mean more knowledge. For conjugate variables, sharpening one can blur the other. Add metrics only when the gain on the new variable outweighs the cost on its conjugate or the cost of added noise.
Apply to incentives. Bonus schemes that reward one number and assume "everything else stays good" ignore the principle. People will optimise what is measured; the conjugate (culture, quality, sustainability) will often degrade. Either measure and reward the conjugate in a way that does not game it, or accept that it is not being optimised.
Section 10
Summary
The Heisenberg uncertainty principle states that conjugate variables (e.g. position and momentum) cannot both be known with arbitrary precision; improving one degrades the other. Strategically: optimising or tightly measuring one variable often increases uncertainty or distortion on another. Choose which variable to pin down, accept the trade-off on its conjugate, and design metrics and incentives so that measurement does not distort the system more than intended.
How metric fixation distorts behaviour and degrades unmeasured dimensions. Practical counterpart to the uncertainty principle in organisations.
Leads-to
Measurement
Measurement is the act of reducing uncertainty on a chosen variable. The principle says that for conjugate variables, that reduction comes at the cost of uncertainty elsewhere. When choosing what to measure, consider the conjugate and the disturbance. Measure what matters; accept that you are not measuring something else as well.
Tension
Second-Order Effects
Optimising one variable can trigger second-order effects on others. The uncertainty principle makes this structural for conjugate pairs: you cannot avoid the trade-off. Second-order thinking says to anticipate knock-on effects; the principle says that for some pairs, the trade-off is fundamental. Combine both when evaluating metrics and incentives.
