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First principles thinking

How to take a problem apart until you reach things that are actually true (physics, arithmetic, hard constraints), throw away what is merely customary, and rebuild a solution from those pieces. Covers where the idea comes from, when it beats reasoning by analogy and when it does not, a step-by-step method, the supporting tools (Socratic questions, Five Whys, Fermi estimates, unit economics, the idiot index, inversion, constraints), worked examples and the ways it goes wrong. It pairs with systems thinking, which puts the parts back together, and with the wider toolkit in general thinking models.

Definition and origin

A first principle is a proposition you cannot usefully derive from anything more basic within the problem at hand: a law of nature, a definition, an arithmetic identity, a measured fact. First principles thinking is decomposing a problem down to those propositions and reasoning upward from them, instead of starting from how things are usually done.

SourceIdeaWhat it contributes
Aristotle, Metaphysics Book V (Δ), 1013aa "beginning" (archē) is the first point from which a thing is, comes to be, or is knownthe name: a starting point of knowledge that is not itself derived
Aristotle, Posterior Analytics I.2demonstrated knowledge rests on premises that are true, primary and immediate, better known than and prior to the conclusion (paraphrased)reasoning is only as good as the premises it rests on
Descartes, Discourse on the Method (1637)four rules: accept only what is clearly known, divide difficulties into parts, go from simple to complex, enumerate completelythe procedure: doubt, decompose, rebuild, check
Descartes, Meditations (1641)method of doubt: suspend every belief that can be doubted to find what survivesthe attitude: treat inherited beliefs as unproven
Physicsderive behavior from conservation laws, measured constants and unitsquantification: fundamentals come with numbers
Musk (Wired, 2012; Kevin Rose interview, 2012)"reason from first principles rather than by analogy"the popular business framing, applied to cost

Aristotle's wording in W. D. Ross's translation: "It is common, then, to all beginnings to be the first point from which a thing either is or comes to be or is known." The line often quoted online, "the first basis from which a thing is known", is a compressed rendering of the same passage (1013a), not Ross's text.

Descartes' first two rules, in John Veitch's translation of the Discourse: "never to accept anything for true which I did not clearly know to be such" and "to divide each of the difficulties under examination into as many parts as possible, and as might be necessary for its adequate solution." The third rule (ascend from the simplest objects to the complex) is the rebuild step; the fourth (enumerations "so complete" that nothing is omitted) is the check.

Musk's version, from his October 2012 interview with Chris Anderson in Wired: "I tend to approach things from a physics framework. And physics teaches you to reason from first principles rather than by analogy." In the Kevin Rose interview the same year he described it as a way to "boil things down to the most fundamental truths and then reason up from there".

First principles vs reasoning by analogy

Reasoning by analogy means copying what worked in a similar case, with small changes: "competitors charge $29, so we will", "every startup raises a seed round", "rockets cost what rockets have always cost". It is how most decisions are made, and usually rightly so.

Reasoning by analogyReasoning from first principles
Starts fromwhat others did in similar caseswhat must be true (laws, constraints, measured facts)
Costcheap: minutes, borrows others' learningexpensive: hours to months, needs domain knowledge and data
Typical outputincremental improvement on the status quoa different design, or confirmation that the status quo is near optimal
Error modeinherits the reference class's mistakes and outdated assumptionsmisses tacit knowledge, reinvents wheels, overconfident models
Best whenthe domain is stable, the stakes are low, many others have iterated on itconventions are wrong, the domain or its inputs are changing, stakes are high
Examples of good usechoosing a CRM, standard legal docs, office layoutcost-down of a core component, new product category, pricing a new kind of service
Signal that first principles will pay offWhy
the price or cost is far above the cost of inputs (high idiot index)the gap is made of conventions and process, which can change
an input changed recently (cost of compute, a new material, a regulation)industry habits were optimized for the old input
"that's how it's done" is the only justification anyone can giveno one has checked the reasoning recently
experts disagree on basic numbersthe reference class is not trustworthy
the decision is large and irreversiblethe cost of the analysis is small relative to the stakes
incumbents are all doing the same thing for the same reasonshared assumptions are shared blind spots

The practical rule: default to analogy, escalate to first principles for the few decisions where the gap between convention and physics looks large and the payoff from closing it matters.

The method

  1. State the goal as an outcome, not a solution

    "Get 5 people from A to B daily for under $X" rather than "buy a van". "Store energy at under $Y/kWh" rather than "buy cheaper battery packs". A solution-shaped goal smuggles in assumptions before you start.

  2. List every assumption

    Write down what you, the industry and your team believe about the problem: costs, constraints, customer behavior, required features, regulations, timelines. Include the ones that feel too obvious to write.

  3. Classify each assumption

    Sort into law of nature, hard constraint, convention, or opinion (table below). Most of the value is found here: conventions masquerading as laws.

  4. Decompose to fundamentals

    Break the cost, time or performance into its components: materials, energy, labor hours, machine time, information flows, approvals. Keep going until each piece is something you can measure or look up.

  5. Quantify

    Put a number and a unit on each component, with a range. Fermi estimates are fine at this stage; you need orders of magnitude, not precision. Check units add up.

  6. Rebuild from the fundamentals

    Ask: given only the laws and hard constraints, what is the cheapest, fastest or simplest design that meets the goal? Then add back only the conventions that earn their place.

  7. Test against reality

    Build the smallest experiment that could prove the rebuilt design wrong: a prototype, a quote from a supplier, a price test, a pilot. Update the model with what breaks. Theory that has not met a supplier or a customer is not done.

Classifying assumptions

ClassTestCan you change it?Examples
Law of naturewould violating it break physics, math or logic?noenergy conservation, speed of light, a lithium-ion cell's theoretical energy density limit, compounding arithmetic
Hard constraintis it enforced by something outside your control, at real cost if breached?rarely, slowly, at high costlaw and regulation, contracts in force, available capital, human biology, current supplier capacity
Conventiondid people choose it, and could a competitor do it differently tomorrow?yespricing norms, org structure, batch sizes, approval steps, standard part choices, "enterprise needs a sales team"
Opinionis it a prediction or preference without data behind it?yes, by testing"customers won't pay for that", "this market is too small", "users hate onboarding flows"

Two useful probes: "Who decided this, and when?" (conventions have authors and dates; laws do not) and "What would have to be true for this to be false?" (if the answer is "physics would have to change", it is a law).

Tools

Socratic questioning

Disciplined questioning to expose assumptions and test reasoning, named after the method Plato shows Socrates using. The six categories below are the standard classification taught in critical-thinking courses (see the Wikipedia entry in References).

CategoryPurposeQuestions to ask
Clarificationmake the claim preciseWhat exactly do you mean by "expensive"? Compared with what? Can you give an example?
Probing assumptionssurface what is taken for grantedWhat are we assuming here? Is that always true? Why do we think it holds in this case?
Probing reasons and evidencetest the supportHow do we know? What is the source? What would change our mind? Is there reason to doubt this evidence?
Viewpoints and perspectivesfind alternativesHow would a competitor, a customer, a regulator see this? What is the strongest counter-argument?
Implications and consequencesfollow it throughIf this is true, what else must be true? What happens next? Who is affected?
Questions about the questioncheck you are solving the right problemWhy does this question matter? Is there a better question? What would answering it let us do?

Five Whys

Ask "why?" repeatedly until you reach a cause you can act on structurally. Taiichi Ohno, architect of the Toyota Production System, described it as "the basis of Toyota's scientific approach by repeating why five times the nature of the problem as well as its solution becomes clear" (Toyota Production System, English edition 1988).

Ohno's own example, condensed:

Why?Answer
1. Why did the machine stop?there was an overload and the fuse blew
2. Why was there an overload?the bearing was not sufficiently lubricated
3. Why was it not lubricated?the lubrication pump was not pumping sufficiently
4. Why was it not pumping sufficiently?the pump shaft was worn and rattling
5. Why was the shaft worn?there was no strainer, so metal scrap got in

Replacing the fuse fixes the symptom; fitting a strainer stops the recurrence.

LimitWhat to do instead or as well
"Five" is arbitrary; the root may be at 3 or 9stop when you reach a cause you can change structurally, not at a count
produces one causal chain; real failures usually have several contributing causesbranch the tree at each level; use a fishbone (Ishikawa) diagram or a causal loop diagram
investigators stop at symptoms or at "human error"ban "someone made a mistake" as an answer; ask why the system allowed the mistake
results depend heavily on who is asking and what they already believedo it as a group with people from each part of the process; check each link with data
critics: Teruyuki Minoura (ex-Toyota) called it too shallow in practice; Alan Card (BMJ Quality & Safety, 2017) argued it should be abandoned for serious incident analysistreat it as a quick triage tool, not a method for complex incidents

Fermi estimation

Estimate an unknown quantity by multiplying rough, independently estimated factors. Named after Enrico Fermi, who was known for good order-of-magnitude answers from little data. Errors in the factors tend partly to cancel, so the product is often within a factor of a few of the truth, provided no single factor is badly biased.

Worked example 1: piano tuners in Chicago (the classic)

FactorEstimateReasoning
population2.7 million2020 US census, rounded
people per household~2.5typical US figure
households~1.1 million2.7 M ÷ 2.5
share of households with a piano~1 in 20guess; the weakest factor
household pianos~55,000plus schools, churches, venues: call it ~60,000
tunings per piano per year~1recommended once a year; many are tuned less often
tunings a tuner can do per year~1,000~2 hours each with travel → 4 a day × 250 working days
tuners~6060,000 ÷ 1,000

The answer is "tens, not thousands". If a directory showed thousands, the estimate tells you which factor to question.

Worked example 2: how many servers for 1 million daily active users?

FactorEstimate
requests per user per day50 (assumption; measure it)
requests per day50 million
average requests per second50 M ÷ 86,400 s ≈ 580
peak-to-average ratio3× (assumption) → ~1,750 req/s
throughput per instance500 req/s (from a load test)
instances at peak1,750 ÷ 500 = 3.5 → 4, plus 50% headroom → 6

Dimensional analysis and unit economics

Dimensional analysis: every term in an equation must have the same units, and units multiply and cancel like algebra. It catches most spreadsheet errors ("users × $/month = $/month", not "$").

requestsuser⋅day×users×day86,400 s=requestss\frac{\text{requests}}{\text{user}\cdot\text{day}} \times \text{users} \times \frac{\text{day}}{86{,}400\ \text{s}} = \frac{\text{requests}}{\text{s}}

Unit economics is first principles applied to a business: decompose the P&L into what one unit (a customer, an order, a ride) earns and costs.

QuantityFormulaExample
gross margin per customer per monthARPU×GM%\text{ARPU} \times \text{GM\%}$50 × 80% = $40
expected customer lifetime (constant monthly churn c)1 ÷ c months1 ÷ 0.03 ≈ 33 months
lifetime value (LTV)ARPU×GM%/c\text{ARPU} \times \text{GM\%} / c$40 ÷ 0.03 ≈ $1,333
CAC paybackCAC/(ARPU×GM%)\text{CAC} / (\text{ARPU} \times \text{GM\%})$400 ÷ $40 = 10 months
LTV : CACLTV ÷ CAC$1,333 ÷ $400 ≈ 3.3

The same decomposition works for anything: cost per delivered parcel, cost per inference, cost per hire, cost per kilogram to orbit.

The idiot index

The ratio of what a finished part costs to what its raw materials cost. Walter Isaacson's 2023 biography Elon Musk describes Musk using it at SpaceX and Tesla: a component with a high index is a sign that the design is too complex or the manufacturing process too inefficient.

idiot index=cost of finished partcost of its raw materials\text{idiot index} = \frac{\text{cost of finished part}}{\text{cost of its raw materials}}
Index (rough heuristic)Reading
~1–3commodity-like; little room except in materials
~10typical of machined or assembled parts; look at process, tolerances, supplier margin
~50+the cost is almost all process, overhead, low volume or margin: first principles territory

Caveats: it ignores the value of design, qualification, testing, certification and low volume, which are real costs. A high index is a question ("where does the money go?"), not a verdict.

Inversion

Solve the problem backwards: instead of "how do we succeed?", ask "what would guarantee failure?" and avoid those things; instead of "how do we make this faster?", ask "what makes it slow?". The mathematician Carl Jacobi is said to have told students to "invert, always invert" (man muss immer umkehren); Charlie Munger popularised it as a thinking tool. In first principles work it finds the constraints you forgot to list.

Forward questionInverted question
How do we reduce churn?What would make a happy customer leave next month?
How do we ship faster?What stops a finished change reaching users today?
What should this product do?What must it never do?
How can this plan succeed?It is a year from now and the plan failed: why? (a pre-mortem)

Constraints analysis: physics vs policy

For every limit on performance, ask whether it is physics (a law or hard constraint) or policy (a rule, habit, or decision someone made). Most bottlenecks in organizations are policy.

Eliyahu Goldratt's theory of constraints (The Goal, 1984) adds the discipline of working on one constraint at a time: the throughput of a system is limited by its tightest constraint, so improving anything else does not help.

Step (Goldratt's five focusing steps)In practice
1. Identify the constraintwhere does work queue up? what is at 100% utilization?
2. Exploit itget the most out of the constraint as it is: no idle time, no low-value work on it
3. Subordinate everything elsepace the rest of the system to the constraint
4. Elevate itinvest to increase its capacity (hire, buy, redesign)
5. Repeatthe constraint has moved; do not let inertia become the new constraint
ConstraintPhysics or policy?First-principles response
"Releases happen fortnightly"policyask what risk the cadence controls; control it directly
"A London server cannot answer a Sydney user in under ~170 ms"physics: ~17,000 km each way at ~200,000 km/s in fibermove the data or the compute closer, not the code
"Enterprise deals need 6 months"mostly policy (buyer procurement), partly hard constraintfind which steps are legal requirements; remove or parallelize the rest
"We can't hire senior engineers"usually policy (pay bands, location, process)test each: which would a competitor change?

Musk's "algorithm" (as reported)

Isaacson's biography reports a five-step process Musk repeated to his teams, in this order (paraphrased):

StepIdea
1. Question every requirementeach requirement should come with the name of the person who made it, not a department
2. Delete any part or process step you canif you are not adding some back later, you did not delete enough
3. Simplify and optimizeonly after deleting; the common mistake is optimizing something that should not exist
4. Accelerate cycle timespeed up only what survived steps 1–3
5. Automatelast, not first

It is first principles thinking turned into an operating routine: steps 1–2 are "classify assumptions", step 3 is "rebuild".

Worked examples

Battery packs (Musk, 2012)

In a September 2012 episode of Kevin Rose's Foundation interview series, Musk used battery packs to illustrate the method. The common assumption he described was that packs had historically cost "$600 per kilowatt hour" and always would. His first-principles version: list the material constituents (cobalt, nickel, aluminum, carbon, polymers for separation, a steel can), price each at London Metal Exchange rates, and add them up: "It's like $80 per kilowatt hour."

Elon Musk and Kevin Rose (opens in a new tab) (Kevin Rose, YouTube)
StepContent
goalcheap stored energy for cars, in $/kWh
analogy answer~$600/kWh, "historically", so electric cars stay expensive
decompositioncell chemistry → materials by mass per kWh → commodity prices
fundamental floor~$80/kWh in materials (Musk's 2012 figure)
conclusionthe ~$520 gap is manufacturing, design and scale, which can be engineered down
what happenedBloombergNEF's December 2025 survey put the average lithium-ion pack price at $108/kWh, and battery-electric-vehicle packs at $99/kWh

Caveats: the materials floor is not an achievable price (processing, cell manufacturing, packs, margins are real costs), commodity prices move (lithium prices spiked in 2022, for example), and the fall in pack prices came from the whole industry's scale and chemistry changes, not one company's reasoning. The method's value was showing that the high price was not a law.

Rocket materials (Wired, 2012)

Musk told Chris Anderson (Wired, October 2012) that he asked what a rocket is made of ("aerospace-grade aluminum alloys, plus some titanium, copper, and carbon fiber") and what those materials cost on the commodity market. His answer: "the materials cost of a rocket was around 2 percent of the typical price", against "probably 20 to 25 percent" for a Tesla car. An idiot index near 50 said the cost was almost all process, overhead and low volume. SpaceX's response over the following decade (vertical integration, in-house manufacturing, then reusable boosters) attacked that gap.

The Wright brothers' lift data

After disappointing glider flights in 1900–1901, the Wrights stopped trusting the published aerodynamic data they had inherited (including the long-accepted Smeaton coefficient, which turned out to be too high) and built their own wind tunnel in late 1901 to measure lift and drag on many small wing shapes. The accepted numbers were a convention that everyone had copied; the measurements were the fundamentals. Their 1902 glider, designed from their own data, worked.

Pricing a product from its cost structure (illustrative numbers)

A B2B scheduling tool. Analogy says: "competitors charge $29 per seat, so charge $25."

ComponentMonthly cost per customerNote
compute and storage$4from the cloud bill ÷ active customers
support$6~20 minutes per customer per month at a loaded $18/hour
payment fees~3% of pricecard processing
onboarding (amortized)$2$48 of setup time over 24 months
unit cost~$12 + 3%
AnchorCalculationResult
price floor at 80% gross margin$12 ÷ (1 − 0.80 − 0.03)~$71/month
value ceilingsaves 5 staff hours/month × $40/hour$200/month
sensible rangebetween floor and roughly a third to a half of value~$70–100/month per location

The first-principles answer prices per location, not per seat (the value scales with appointments, not logins), at roughly 3× the analogy price. Then test it (step 7): a price test is cheaper than a debate.

Redesigning a process: the weekly change board

"Every production change needs sign-off from the Thursday change advisory board." Classified: convention.

QuestionAnswer
What is the goal?few customer-visible failures, fast recovery when they happen
What are the fundamentals?failure probability per change, blast radius, time to detect, time to roll back
What does the board do to each?little to failure probability; batches changes weekly, which increases blast radius and makes causes harder to find
Rebuilt designsmall changes, automated tests, canary releases, feature flags, one-click rollback, peer review in the pull request
EvidenceDORA's 2019 State of DevOps report found no evidence that formal external review was associated with lower change fail rates

Personal: do I need a car? (illustrative numbers)

Analogy says: "adults own cars." First principles says: the goal is trips, not a car.

FundamentalHow to get it
trips per month by type (commute, shopping, weekend, emergency)a week of logging, scaled up
full annual cost of ownershipdepreciation + insurance + fuel or charging + maintenance + parking + tax, from your own quotes
cost of the alternative mixpublic transport pass + taxis for the awkward trips + rentals for weekends away
time costdoor-to-door minutes each way × your value of an hour
hard constraintsdisability, children, rural location, shift work at night

If ownership costs, say, $8,000 a year and the alternative mix for your actual trips is $3,500 plus 80 hours of extra travel time, the question becomes "is an hour of my time worth more than $56?", which is answerable. Put in your own numbers; the structure is the point.

Worked template

FIRST-PRINCIPLES BREAKDOWN: <problem>
 
1. GOAL (outcome, with a number and a unit)
   e.g. "cut cost per delivered order from 9.40 to under 5 USD"
 
2. CURRENT ANSWER BY ANALOGY
   what the industry / we currently do, and its cost/performance
 
3. ASSUMPTIONS                         CLASS        EVIDENCE
   a. ...............................  law/hard/conv/opinion  ...
   b. ...............................  ...          ...
   c. ...............................  ...          ...
 
4. DECOMPOSITION (tree down to measurable pieces)
   total = component A + component B + ...
     A = (units) x (price per unit)       low / likely / high
     B = ...
 
5. FUNDAMENTAL FLOOR
   cost/time/performance if only laws + hard constraints applied
   gap between current and floor = ........ (the opportunity)
 
6. WHERE THE GAP LIVES
   component   current   floor   gap   convention causing it
 
7. REBUILT DESIGN
   the simplest design that meets the goal using the floor;
   conventions added back ONLY with a stated reason
 
8. RISKS AND CHESTERTON'S FENCES
   conventions we are removing - do we know why they exist?
 
9. CHEAPEST TEST THAT COULD PROVE THIS WRONG
   experiment, owner, date, pass/fail threshold
 
10. DECISION AND REVIEW DATE

Checklist

  • The goal is an outcome with a number and a unit, not a solution.
  • Every assumption is written down and classified; "law" is used only for physics, math or logic.
  • Each convention has an owner or origin you can name, or is marked "origin unknown: investigate".
  • The decomposition bottoms out in things you can measure or look up, with units that check.
  • Each number has a range and a source; the weakest estimate is flagged.
  • The floor (physics-only answer) is computed and compared with the current answer.
  • You know why each removed convention existed (Chesterton's fence) and have spoken to someone who operates it.
  • An expert in the domain has tried to break the model.
  • There is a cheap test with a pass/fail threshold before any large commitment.
  • You have checked whether the analogy answer was, in fact, close to optimal.

Failure modes

Failure modeWhat it looks likeGuard
Convention mistaken for law"enterprise software needs a 6-month sales cycle", treated as fixedask "who decided this and when?"; look for anyone who does it differently
Law mistaken for convention"latency is just an engineering problem" when it is the speed of light; "we can grow 20% a month forever"check against physics, math and base rates before rebuilding
Reinventing the wheela startup designs its own database or payments stack because "we reasoned from scratch"first principles is for the core problem; buy or copy everything else
Infinite regressdecomposing to quarks when the question is office rentstop at the level where the numbers become stable and measurable
Ignoring tacit knowledgeremoving a rule, step or margin whose purpose is invisible on paperChesterton's fence: find out why it is there before you remove it
Materials floor treated as a target"the parts cost $80, so we can sell at $100"the floor excludes processing, labor, testing, warranty, overhead, margin
Overconfidence in the modela neat spreadsheet replaces contact with suppliers and customersstep 7: every model gets a real-world test
Selective scepticismdoubting others' assumptions but not your own favoriteclassify your own assumptions with the same table
Cost of analysis ignoreda week of first-principles work on a $200 decisionuse analogy for small, reversible decisions
Using it as rhetoric"I'm thinking from first principles" as a way to dismiss experienceshow the decomposition and numbers, or it is just an opinion

Chesterton's fence

From G. K. Chesterton's The Thing (1929): a reformer comes across a fence across a road and says, "I don't see the use of this; let us clear it away." The wiser reformer answers: "If you don't see the use of it, I certainly won't let you clear it away. Go away and think. Then, when you can come back and tell me that you do see the use of it, I may allow you to destroy it." The point is not "never remove fences"; it is "understand the reason before deciding the reason no longer applies". In first principles work, that means every convention you delete gets a line in step 8 of the template.

Critiques and when not to use it

CritiqueSubstance
It is not a distinct methodit is ordinary scientific and engineering reasoning; the name adds branding, not technique
Survivorship biasfamous examples (batteries, rockets) are remembered because they worked; many "first principles" ventures failed on things the model left out
Fundamentals are not always knowablein markets, organizations and people, the "laws" are statistical and change; decomposition can give false precision
Analogy encodes accumulated learningconventions often exist because many people tried alternatives and they failed; discarding them wastes that knowledge
Decomposition misses interactionsreducing a system to parts can miss feedback, delays and emergent behavior (this is what systems thinking is for)
It can justify arrogance"reasoning from scratch" is sometimes a license to ignore experts, regulators or safety margins

Do not use it when:

  • the decision is small, cheap and reversible: copy a good default and move on;
  • the domain is well understood and stable, and others have iterated on it for decades (tax filing, accounting standards, most HR processes);
  • the "conventions" are safety-critical rules written after accidents (aviation, medicine, electrical codes) unless you have the expertise and the regulator's agreement;
  • you do not have the domain knowledge to tell a law from a convention, and cannot get someone who does;
  • speed matters more than optimality, and the analogy answer is good enough.
ToolRelationship
Systems thinkingfirst principles takes a problem apart; systems thinking studies how the parts interact over time (feedback, delays, stocks). Use both: decompose to find the fundamentals, then map the loops before changing anything.
General thinking modelsinversion, second-order thinking, map vs territory and circle of competence are the companion checks on a first-principles model
Decision-making modelsreversibility tells you how much first-principles effort a decision deserves
Probability and riskranges, base rates and expected value turn estimates into decisions
Cognitive biasesanchoring on the current price and status quo bias are what analogy reasoning is most exposed to
Microeconomicscost curves, marginal cost and opportunity cost are first principles for business questions

Practice exercises

  1. Your biggest line item. Take your company's (or household's) largest monthly cost. Decompose it to units × price per unit, compute a floor, and list which conventions sit in the gap.
  2. Fermi warm-ups. Estimate, with ranges: liters of coffee drunk in your city per day; the number of software engineers in your country; the energy in a fully charged phone battery, in joules.
  3. Classify ten assumptions. Write ten beliefs your team holds about your market. Classify each; for every "convention", name who decided it.
  4. Idiot index hunt. Pick three products you buy (a charger cable, a sofa, a SaaS subscription). Estimate raw input cost and compute the index. Where does the gap go?
  5. Invert your roadmap. List five ways next quarter could fail. Which one is not addressed by any current plan?
  6. Five Whys, with branches. Take a recent incident. Do five whys, but allow two answers at each level. Compare the tree with the single chain.
Worked answer: energy in a phone battery

A typical phone battery is around 4,000–5,000 mAh at a nominal ~3.8 V (read the numbers printed on yours).

4.5 Ah×3.8 V≈17 Wh,17 Wh×3,600 JWh≈62,000 J4.5\ \text{Ah} \times 3.8\ \text{V} \approx 17\ \text{Wh}, \qquad 17\ \text{Wh} \times 3{,}600\ \tfrac{\text{J}}{\text{Wh}} \approx 62{,}000\ \text{J}

About 60 kJ: roughly the energy needed to lift a 60 kg person 100 m. Dimensional analysis (Ah × V = Wh) is what makes the conversion safe.

Worked answer: classifying "we need an office to build culture"

Opinion, dressed as a hard constraint. The fundamentals are: how often people need synchronous, high-bandwidth contact (design reviews, onboarding, conflict), how much that costs remotely vs in person, and what rent costs per employee per year. A first-principles design might be a remote team with paid quarterly offsites, or an office used two fixed days a week; either can be tested for a quarter against retention and delivery metrics.

References