Tracks/Capstone & Applied Practice/Module 4.1
Track 4 — Capstone & Applied Practice

Module 4.1: Startup Engineering Economics

Runway-aware engineering, MVP economics, cost-per-experiment, and positioning your technology for Series A due diligence.

3 Lessons~50 minIntermediate
1

Lesson 1: Runway-Aware Engineering

Startups operate under a countdown timer: runway. Every engineering decision must be evaluated against "will this extend our runway or shorten it?" Building the wrong thing at the right quality, or the right thing at the wrong quality, both burn cash.

Burn Rate per Engineer

Fully-loaded cost including salary, benefits, tools, and infrastructure. For a seed-stage startup: $15K-25K/month per engineer.

A 5-person eng team burns $75K-125K/month. 12 months runway = $900K-1.5M minimum.
Velocity-to-Revenue Ratio

How much engineering velocity translates to actual revenue growth? If 100% of velocity goes to features but revenue is flat: wrong features, wrong market, or wrong pricing.

Healthy: each sprint delivers measurable movement toward revenue milestones.
Technical Debt at Seed Stage

At seed stage, some technical debt is GOOD. Shipping fast and learning beats building perfectly. The question: "Will this debt block us before Series A?"

Acceptable: debt that can be remediated in 2-3 sprints. Dangerous: architectural debt requiring rewrites.
📝 Exercise

Calculate your engineering burn rate per month. Map each current initiative to a revenue milestone. For each: does the expected revenue impact justify the engineering cost?

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Lesson 2: MVP Economics

The MVP isn't about building the minimum product — it's about finding the minimum engineering investment that validates a revenue hypothesis. Every feature in V1 not tied to a testable hypothesis is waste.

Feature-Hypothesis Mapping

For each planned feature: "We believe that [feature] will cause [user behavior] which will result in [metric change]." No hypothesis = no justification for building.

V1 should have 3-5 hypothesis-backed features, not 20 nice-to-haves.
Time-to-Validation

How quickly can you put this feature in front of users and measure the hypothesis? 2-week cycles are ideal. 3-month cycles burn too much cash before learning.

Target: every feature validated within 2-4 weeks of engineering start.
Cost per Experiment

Engineering cost of building + deploying + measuring one feature hypothesis. Lower cost = more experiments per runway dollar = faster learning.

Elite startups: $5K-15K per experiment. Over-built: $50K+ per experiment.
📝 Exercise

For your current MVP plan: list every feature. For each, write the hypothesis it tests. Remove features without clear hypotheses. Calculate cost savings.

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Lesson 3: Pre-Series A Positioning

Series A investors evaluate technology as an asset. Your engineering metrics, architecture decisions, and technical debt profile directly impact your valuation multiple.

Investor Tech Due Diligence

Series A due diligence includes: architecture review, code quality assessment, team capability evaluation, and scalability analysis. Poor results = lower valuation or killed deals.

Prepare: clean architecture docs, test coverage > 70%, CI/CD pipeline, no critical security issues.
Scalability Story

VCs need to believe your architecture can handle 10-100x growth. If your MVP architecture cannot scale without a rewrite: red flag.

Show: current load, architectural ceiling, and the plan to scale beyond it.
Team Leverage

Revenue per engineer ($500K+ is strong at Series A), deployment frequency, and DORA metrics signal to investors that the team is high-performing.

Series A benchmark: 4-6 engineers generating $2M-3M ARR = strong signal.
📝 Exercise

Create a "Tech Due Diligence Prep Pack" for your startup: architecture overview, key metrics dashboard, scalability plan, and team capability matrix.

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