Industry Learning Sprint

intent

Build a falsifiable, non-consensus view of an unfamiliar industry in approximately one week using a strict three-step sequence: financial structure mapping, hypothesis stress-testing via expert dialogue, then unique view synthesis. use this when you're entering a sector for the first time and have a real decision to make (invest, acquire, advise, pitch) under time pressure. the output is not comprehensive understanding but one contested hypothesis backed by primary documents and expert correction. skip this if you already know the domain deeply or if your timeline is under 48 hours.

---

inputs

Required context:

• target industry name and sector classification
• your specific decision or recommendation that depends on understanding this industry
• available time (ideally 5-7 days; minimum 3 days for abbreviated sprint)
• access to public company annual reports and SEC/equivalent filings

External connections:

• sec.gov or equivalent national securities filings database (no auth required)
• company investor relations pages (no auth required)
• your network for expert outreach (operators, investors, ex-employees, regulators in or adjacent to the target industry)

Knowledge prerequisites:

• ability to parse a financial statement (revenue, gross margin, capex, customer concentration). no advanced accounting required. four numbers suffice.
• willingness to treat domain expert opinions as stress-tests, not gospel
• commitment to form a view by day 7, even if incomplete

---

procedure

Step 1: Financial Structure Mapping (Day 1-2)

1. Identify 2-3 leading public companies in the target industry. check: at least 3 years of annual reports available. if <3 years public, add a pre-ipo competitor or adjacent public proxy.

2. Extract financials without analyst commentary. download annual 10-Ks or equivalent. read the company filings directly, not analyst summaries. for each company, document:

   • revenue model: what are they selling? who pays? monthly/one-time/recurring?
   • gross margin: (revenue minus direct cost of goods sold) / revenue. document both overall and by segment if available.
   • capex vs. opex split: annual capital expenditure as % of revenue; operational expense as % of revenue. this split determines barriers to entry and profit sustainability.
   • customer concentration: is >30% of revenue from a single customer or customer class? flag as systemic risk.
   • disclosed risk factors: read the 10-K section titled "Risk Factors" as the most honest document a public company produces. each risk factor encodes a potential failure mode. extract top 3.

3. Source each data point. create a one-page financial structure map. format: table with columns [metric | company A | company B | company C | source document]. include row for gross margin by product line if available.

4. Output: one-page financial structure map in template format below.

Input to Step 2: the financial structure map becomes your hypothesis base. do not proceed to expert conversations without it.

---

Step 2: Expert Dialogue (Day 3-5)

1. Design 3-5 expert conversations as hypothesis stress-tests. for each expert, prepare 2-3 specific financial questions: "I saw gross margin drop 8% year-over-year in Q3 2023. is that because [Y: customer mix shift] or [Z: pricing pressure]?" questions should cite specific numbers from Step 1.

2. Target four expert categories. each has structurally different information:

   • operators: people running companies in the sector (ceos, heads of sales, supply chain leads). they know bottlenecks and unit economics.
   • investors: ve, pe, or public market investors focused on the sector. they know competitive positioning and market share dynamics.
   • ex-employees: people who left companies in the sector. they know what insiders don't say publicly.
   • regulators / industry bodies: people in compliance, standards organizations, or government oversight. they know structural constraints.

3. Conduct each conversation. start with your financial hypothesis. format: "In the financials I see [specific finding]. I hypothesize [Y or Z]. Is that right? What does the financial structure miss?"

4. Document corrections and new structural insights. for each conversation record: hypothesis confirmed or corrected (with specific correction); name and role of expert; date. also capture 1-2 structural insights not visible in financials (e.g., informal customer agreements, regulatory change in progress, technology adoption rates not in published data).

5. Output: spreadsheet or table with columns [hypothesis tested | finding | expert name/role/date | correction or confirmation | structural insight not in financials]. minimum 3-5 conversations, at least 2-3 of the four expert categories covered.

Input to Step 3: the corrections and structural insights. if >50% of your hypotheses were wrong, loop back and revise the financial structure map interpretation.

---

Step 3: Unique View Formation (Day 6-7)

1. Synthesize into one non-consensus hypothesis. it must be specific, falsifiable, and contested. format:

   • Mechanism: name what actually drives the industry that most people are wrong about. not "it's a great market" but "gross margin compression is structural because [reason], not temporary."
   • Evidence base: cite one financial finding + one expert correction + the tension between them. example: "10-K shows customer concentration >40% (risk), but three operators say customer switching costs are low (contradicts risk factor). this suggests the risk disclosure is outdated."
   • Falsifier: state what would prove this hypothesis wrong. example: "if customer churn in the top account drops below 5% over 18 months, the switching cost assumption is wrong."
   • Confidence: 0-100% based on agreement across expert categories and financial consistency. if you have 80% confidence, state what the missing 20% is.

2. Apply the stop-rule. ask: "Would a domain expert be surprised by this view?" if the answer is no, you have summarized consensus, not generated a view. revise. keep iterating until at least one expert category would push back.

3. Document known gaps. explicitly list what was not covered: regulations not researched, geographies not explored, customer segments not verified, technology assumptions not validated. state what would change the view if true.

4. Output: one-page unique view synthesis in template format below. include falsifier, confidence, and known gaps.

Input from Step 3: final recommendation or thesis statement grounded in the three-step sequence.

---

decision points

If you have <3 years of public financial data:

• use the oldest available data plus any pre-ipo filings or proxy statements. note the data limitation in the known gaps section. lower confidence accordingly.

If >50% of your expert conversations contradict the financial structure map:

• do not average them into a muddled view. instead, loop back to Step 1 and re-read the financials for a different interpretation. the financial data is fixed; expert opinion varies. find which expert category (operators vs. investors vs. regulators) disagrees and why.

If you cannot identify any expert who would contest your view:

• you are restating consensus. revise the hypothesis. if the view is "this is a healthy industry with good margins," that is not falsifiable and every expert will agree with the obvious. pick a structural claim about why most capital allocators are misalloc ating or why a competitor will win.

If your timeline is <3 days:

• mark all output as preliminary. run abbreviated Step 1 (one company, two-year data only) and skip Step 2 or conduct only 1-2 expert conversations. do not form a strong unique view; instead, output the financial structure map and flag what you need to validate.

If the industry is primarily informal or unregistered (gray market, unlicensed service, private networks):

• financial reports will be unrepresentative. in this case, Step 1 data quality is degraded. emphasize expert dialogue (Step 2) and regulatory filing analysis in adjacent industries. note the limitation up front.

If expert conversations reveal a material regulatory change in progress:

• pause the sprint. obtain primary regulatory documents (draft rules, public comments, regulatory timeline). reintegrate into the financial structure interpretation. do not ignore this signal.

---

output contract

Deliverable 1: Financial Structure Map

• format: one-page table or markdown
• contents: [metric | company A | company B | company C | source]
• required rows: revenue model (description + unit economics if available), gross margin (%), capex as % of revenue, opex as % of revenue, customer concentration (% from top customer), top 3 risk factors (quoted from 10-K)
• file location: /outputs/financial-structure-map.md or equivalent
• validation: all numbers traced to annual report page number and year

Deliverable 2: Expert Dialogue Summary

• format: spreadsheet or table
• contents: [hypothesis tested | financial finding | expert name | expert role | company/organization | date of conversation | confirmation or correction | structural insight not in financials]
• minimum: 3-5 expert conversations across at least 2 of 4 categories (operators, investors, ex-employees, regulators)
• file location: /outputs/expert-dialogue-log.csv or markdown table
• validation: each row is traceable to a real conversation with named participant and date

Deliverable 3: Unique View Synthesis

• format: one-page document
• contents: specific hypothesis statement, evidence base (1 financial + 1 expert + tension), falsifier, confidence %, known gaps
• example falsifier: "if [metric] reaches [threshold] by [date], this hypothesis is false"
• file location: /outputs/unique-view.md
• validation: view would generate disagreement or questions from at least one expert category; contains a falsifiable claim; known gaps section is specific, not vague

Overall validation:

• sprint completed in 5-7 days or explicitly marked as preliminary if <3 days
• all three deliverables completed and dated
• no deliverable relies entirely on secondary sources (analyst reports, news); each cites primary documents (10-K, expert conversation notes, regulatory filings)

---

outcome signal

You know the skill worked when:

1. You can state a specific hypothesis that contradicts at least one thing you believed before the sprint. not "the industry is competitive" (already known) but "customer switching costs are 40% lower than the risk disclosure suggests because [financial evidence] + [operator told me] = [implication]."

2. You can articulate what would prove your view wrong. if you cannot name a falsifier, you have an opinion, not a hypothesis. the skill requires a hard no condition.

3. At least one domain expert challenges your view in conversation. if every expert agrees with you, the view is not non-consensus; it is consensus disguised.

4. Your confidence level is ≤85%. if you claim 99% confidence, you are overfit to the data you collected. 70-80% confidence after seven days is realistic for a genuine view.

5. You can explain the industry's actual unit economics to someone unfamiliar with it using only numbers from the financial structure map. if you cannot walk through revenue model → gross margin → capex → customer concentration in two minutes, you are missing the structure.

6. Your known gaps section is specific and non-empty. if you claim to have no gaps after one week, you are either done with a shallow analysis or you are in denial. the sprint is designed to expose gaps, not hide them.

7. You can make a recommendation or investment decision grounded in the three-step sequence. the view should directly support why you will or will not invest, acquire, partner, or advise in this industry. if the view is disconnected from your decision, the sprint did not close.

---

coaching novices (adaptive front door)

Two activation modes:

• Engine mode: you have a concrete industry target and basic financial literacy. run the three-step process directly.
• Coach mode: you are new to financial analysis or uncertain about scope. use guided prompts one step at a time.

Coach mode flow:

1. Reframe the goal: "You don't need to become an industry expert. you need one falsifiable hypothesis that would be contested by an insider. not comprehensive. specific."

2. Check fit: "Is this a brand-new industry for you, or do you already have domain experience?" if already expert, exit skill. recommend first-principles instead to stress-test existing knowledge.

3. Elicit the real case: "Which industry, and what decision are you making at the end of this?" wait for their answer. do not guess.

4. Run Step 1 with their input: guide them to one company's 10-K. walk through: "Find the revenue section. what are they selling?" extract the four numbers together.

5. Run Step 2: help them identify one operator and one investor to reach out to. provide a template question based on their Step 1 findings.

6. Run Step 3: "Name the thing most people are wrong about in this industry. what would an insider disagree with you on?" push for specificity.

7. Close with the view: "Your non-consensus hypothesis is [X]. here is why an insider would contest it. here is what would falsify it."

In coach mode, place [WAIT] stops between each major step. do not advance until the user responds.

---

applying it well

• sequence strictly. financials before experts. experts before view. violating this order produces low-signal output.
• read primary documents. 10-K, annual report earnings transcript. analyst summaries are consensus laundering. skip them.
• design expert conversations as hypothesis tests. "what should i know about this industry?" produces orientation. "i saw X in the financials, is that because Y or Z?" produces signal.
• target four expert categories. operators know execution. investors know capital flows. ex-employees know gaps between public narrative and reality. regulators know structural constraints. each sees a different part of the elephant.
• apply the stop-rule relentlessly. "would a domain expert be surprised by this?" if no, you are summarizing. revise until yes.
• document known gaps. prevents overconfidence. increases credibility. makes clear what additional research would change the view.
• specify falsifiers. vague views feel safe. falsifiable views enable decision-making. commit to conditions that would prove you wrong.

---

common rationalizations

fake move	reality
[D] "I've read five analyst reports."	Five consensus documents give higher-confidence consensus, not independent analysis. analyst reports hide the financial structure under narrative. read 10-Ks.
[D] "I talked to insiders for hours."	Without a financial hypothesis base, expert talk produces orientation, not stress-testing. you collect anecdotes instead of testing specific claims.
[D] "My view is that this is a great industry."	That is the marketing narrative. a view names the structural mechanism most people are wrong about. "great" is not a hypothesis.
[D] "I don't know how to read financials."	The sprint requires only four numbers: revenue model, gross margin, capex/opex, customer concentration. you do not need to be an accountant.
[D] "All the experts agree, so the view is right."	Expert consensus is what the sprint is designed to think against. if all experts agree, you have found consensus, not a unique view.
[D] "I need much more research before forming a view."	More research without a view target produces information, not insight. seven days is a feature, not a limitation. commit at day 7.
[D] "My unique view might be wrong."	Specify what would falsify it. being wrong about a falsifiable view beats vaguely right about consensus.
[D] "I ran out of time and formed a view anyway."	Mark it preliminary. state what you would validate next. do not claim confidence you have not earned.
[D] "The industry is too complex to understand in a week."	you are attempting comprehensive understanding instead of a falsifiable view. a view requires less data than full understanding. target a specific mechanism, not the whole system.

→ add observed rationalizations [O] here after real use. describe what failed and why.

---

red flags

• No view that would be contested by a domain expert. you produced a summary, not an analysis.
• Expert conversations conducted before reading financial reports. sequence reversed. you are not testing, you are collecting anecdotes.
• Financial structure map missing gross margin. this is the most diagnostic number. if it is absent, the foundation is weak.
• Sprint took >14 days. time pressure is a design feature. if you spent two weeks, you are optimizing for comprehensiveness, not decision-making.
• Unique view is not falsifiable. it is an opinion, not a hypothesis. opinions do not drive decisions.
• Only one expert category consulted. one-dimensional model. missing structural perspectives from other actors in the industry.
• Sprint conducted entirely from secondary sources. primary documents (10-K, expert dialogue) skipped. output is secondhand consensus.
• Confidence >90% after seven days. overfit. you are too certain. real views have gaps.
• Known gaps section is empty or vague. "there may be other factors" is not a gap. specific gaps: "i did not validate customer switching cost assumptions in verticals outside north america" or "i did not research pending regulatory changes in eu."

---

verification checklist

• annual reports for 2-3 leading companies (3+ years of data) read as primary sources
• financial structure map contains all four dimensions: revenue model, gross margin (%), capex/opex (%), customer concentration (%)
• top 3 risk factors extracted directly from 10-K risk factor section
• expert conversations designed as hypothesis stress-tests with specific financial findings as the agenda
• at least 3 distinct expert conversations conducted across at least 2 of 4 categories (operators, investors, ex-employees, regulators)
• unique view is specific, falsifiable, and would be contested by at least one domain expert
• falsifier is explicit and measurable (not vague)
• stop-rule applied: "would an insider be surprised by this?" if no, view was revised
• known gaps explicitly documented and specific (not generic)
• sprint completed within approximately one week or explicitly marked preliminary if <3 days
• confidence level ≤85%

---

related skills

• probabilistic-thinking: assign confidence intervals and bet sizing before expert conversations. use before step 2.
• first-principles: stress-test the unique view by decomposing it to first principles