Technology Evolution & Disruption
Crossing the Chasm
Moore's argument: new technology does not spread smoothly from early buyers to the mass market. Between the enthusiasts and the mainstream sits a gap where growth stalls, and the companies that get across do it by winning one narrow niche completely before going wide. For investors, the stage a company is in shows up in its retention, concentration and sales-efficiency numbers.
The big picture
Moore, a Silicon Valley marketing consultant, took the adoption curve from diffusion research — innovators, early adopters, early majority, late majority, laggards, roughly 2.5 / 13.5 / 34 / 34 / 16 percent of a market — and pointed out that the groups are not neighbours who pass a product along. Early adopters (he calls them visionaries) buy a break with the past and accept rough edges to get a head start. The early majority (pragmatists) buy an improvement to what already works and want proof from companies like their own. Because each group distrusts the other's reasons, references from visionaries do not persuade pragmatists, and a product can sell well for a few years and then stall. That stall is the chasm. The first edition appeared in 1991; the third edition (Harper Business, 2014) updates the cases.
Why it matters now: in 2026 dozens of AI-agent and AI-infrastructure vendors are posting early-market growth rates. The book gives a way to ask which of them have pragmatist customers yet — and the answer is visible in reported metrics long before it is visible in the narrative.
The 3 strategic pillars
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Visionaries and pragmatists want different things
The early market is bought by people who want a leap and will tolerate risk; the mainstream is bought by people who want a safe, complete, supported improvement and wait until others like them have bought.
Revenue from visionaries tends to be lumpy, custom and spread across unrelated industries — large deals, heavy services, a few customers carrying the total. When that pool runs out, growth decelerates sharply even though the product works. In the numbers: high growth with high customer concentration and no dominant vertical, followed by a deceleration without a matching rise in retention.
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Beachhead and whole product
The way across is to pick one narrow segment with an urgent problem and serve it completely — the core product plus every integration, service, partner and standard the buyer needs to get the result (the whole product).
Concentration is deliberate: a large share of revenue from one vertical is a feature at this stage, because pragmatists in that vertical reference each other. Success shows as rising net revenue retention (existing customers spending more each year), falling churn and a shorter CAC payback (months of gross profit needed to repay the cost of winning a customer).
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Bowling pins, then the tornado
Each won niche becomes the reference for an adjacent one, like one bowling pin knocking over the next. Once enough niches have tipped, the mass market adopts at once and demand outruns supply (the tornado); after that, growth settles into extension and upgrades on main street.
In the tornado, sales efficiency jumps — the magic number (new annualised revenue per dollar of prior-quarter sales and marketing spend) moves above 1 — and customer concentration falls fast. On main street, growth slows, churn is low and value comes from margin and add-ons, which is where the Rule of 40 (growth % plus free-cash-flow margin %) becomes the main test.
What a Closelooknet reader does with it
The working use is a stage diagnosis from reported numbers. Growth rate alone cannot tell an early-market company from one in the tornado, and a deceleration alone cannot tell a chasm stall from a maturing main-street business. Read together — growth, net revenue retention, gross churn, top-customer concentration, share of revenue from the largest vertical, magic number and CAC payback — the metrics separate the stages reasonably well. The mistake it prevents is extrapolating early-market growth as if the mainstream were already buying, or treating a deliberately concentrated beachhead as a risk when it is the strategy. The stage is a description of evidence, not a forecast: companies can stay in the chasm for years or skip steps.
The bridge to the Closelooknet approach
The same metrics run through Closelooknet's software coverage. SaaSpocalypse asks whether AI agents break the seat-based pricing model, and Work-as-a-Service describes the outcome-priced newcomers — most of which are still selling to visionaries. The glossary entries for net revenue retention, magic number, CAC payback, churn and the Rule of 40 define the pack's inputs. AW40 tracks companies positioned to capture value from agents, which is where chasm stalls would show up first. The companion reads: Christensen explains why incumbents ignore the entrant while it is small, and Perez describes the same diffusion at the scale of a whole technological revolution. The pack scores the reader's own company data; the stage it returns is a research note, not a signal.
Action-Kit — from theory to practice
Tooling & data
| What you need | Where to get it | Cost |
|---|---|---|
| Company filings and shareholder letters Revenue, sales and marketing expense, free cash flow, net revenue retention and customer-concentration disclosures | SEC EDGAR (US) and company investor-relations pages Net revenue retention and top-customer share are often disclosed only in the 10-K or the earnings deck — record the definition each company uses, they differ. | Free |
| Public software benchmarks Peer medians for growth, net retention, magic number, CAC payback and Rule of 40 to calibrate the stage profiles | Meritech Capital public comps | Free |
| Fundamentals screener Quarterly revenue and free-cash-flow history to fill the pack's input sheet | stockanalysis.com (free tier) or Koyfin | Freemium |
The formulas
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Stage fit score
Fit(stage) = 100 × (1 − Σ wᵢ × min(1, |xᵢ − pᵢ,stage| / sᵢ) / Σ wᵢ); placed stage = the stage with the highest fit- xᵢ: the company's metric (growth, NRR, gross churn, top-10 share, largest-vertical share, magic number, CAC payback)
- pᵢ,stage: the reader-editable prototype value for that stage
- sᵢ: scale (distance at which a metric counts as a full miss)
- wᵢ: weight
The prototypes are a Closelooknet heuristic, not figures from the book. A margin under 10 fit points between the top two stages means the evidence is mixed.
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Magic number (revenue-based)
Magic = (Revenue_q − Revenue_q−1) × 4 / S&M expense_q−1- Quarterly revenue, this quarter and last
- Sales and marketing expense, last quarter
Above 1 means each dollar of go-to-market spend added more than a dollar of annualised revenue; below 0.5 usually means the sales motion does not yet repeat.
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CAC payback
Payback (months) = CAC / (new ARR per customer × gross margin / 12)- CAC: sales and marketing cost per new customer
- Annual recurring revenue per new customer
- Gross margin %
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Rule of 40
R40 = revenue growth YoY % + free-cash-flow margin %- Revenue growth, year over year
- Free cash flow / revenue
A sanity check next to the stage: early-market companies often clear 40 on growth alone; a main-street company has to clear it on margin.
Applied Pack · free members
Moore Applied Pack
The chasm-stage metrics sheet: enter your own quarterly numbers and a weighted scorecard places the business on the adoption curve — early market, chasm, bowling alley, tornado or main street — with the Rule of 40 alongside.
- Moore_Chasm_Scorecard.xlsx — READ ME, a quarterly input sheet (revenue, sales and marketing, FCF margin, NRR, gross churn, top-10 share, largest-vertical share, CAC payback) for an EXAMPLE_ company you overwrite, live derived metrics (YoY growth, magic number, Rule of 40), editable stage profiles and weights, and a scorecard with fit per stage and the placed stage per quarter
- moore_chasm.py — stdlib-only: reads a CSV of your own quarterly metrics and prints growth, magic number, Rule of 40, fit per stage and the placed stage
- quarters_sample.csv — EXAMPLE_ rows showing the input format
- README.txt — metric definitions, inputs, how to run, and the educational-use disclaimer
Pack security
Macro-free Excel · plain-text Python you can read before you run it · no installers, no network access — the code works only on files you provide. Served only from closelook.net; we never distribute through download portals or email attachments. How to verify in 30 seconds →
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Educational templates — a research diary companion, not investment advice.
Closelooknet publishes a market diary, not investment advice. This condensed read restates the book's ideas in our own words for education — for the author's full argument, go to the source.