Syllabus

This course teaches the business judgment behind a traditional MBA to builders who ship apps with AI coding tools, no-code/low-code platforms, and small technical teams.

It is for people who can already build or modify software but want the missing business spine: how to choose a market, price a product, read financial statements, model unit economics, sell, operate, hire, fundraise, and manage risk. Traditional MBA programs organize their core around topics such as accounting, finance, marketing, operations, strategy, leadership, and managerial decision-making; this course keeps that backbone but teaches it through the operating reality of app builders and AI-native software companies (HBS, Wharton, Stanford GSB, MIT Sloan).

You will see current case studies from AI app builders, coding agents, developer infrastructure, product-led SaaS, public filings, and official company materials. The point is not to memorize one hot company. The point is to learn how to analyze any app business: where value is created, who pays, what the costs are, why customers stay, what can break, and what the founder should do next.

What you'll be able to do

• Translate a prototype into a business model, value proposition, buyer, and market thesis.
• Read financial statements and build a simple SaaS/app-builder P&L, cash-flow forecast, and runway model.
• Calculate gross margin, CAC, payback, LTV, retention, net dollar retention, burn multiple, and token-cost sensitivity.
• Choose a pricing model for AI-assisted software: seat, usage, credits, outcome, marketplace take-rate, or hybrid.
• Analyze competition, substitutes, supplier power, platform risk, switching costs, network effects, and durable moats.
• Run customer discovery, segmentation, positioning, launch, product-led growth, founder-led sales, and retention loops.
• Design lightweight operations for a small AI-native team: roadmap, support, quality, security, incident response, vendor risk, and governance.
• Hire, contract, incentivize, communicate, and lead without prematurely importing big-company process.
• Understand incorporation, SAFEs, venture rounds, IP, privacy, AI risk, and basic governance well enough to know when to call counsel.
• Produce a capstone venture memo, financial model, operating dashboard, GTM plan, risk register, and board-style update for a real or plausible app business.

Prerequisites

You should be able to ship, meaningfully modify, or deeply inspect a small web app. You do not need an undergraduate business degree, accounting background, or prior MBA coursework.

You should have:

• Builder fluency. You can use tools like Cursor, Claude Code, Codex, Replit, Lovable, v0, Supabase, Stripe, or similar systems without needing every button explained.
• Spreadsheet comfort. You can read rows, columns, formulas, percentages, and simple charts.
• Basic SaaS literacy. You know what subscriptions, APIs, hosting, users, churn, and usage limits are, even if you have never modeled them.
• Willingness to talk to users. Many exercises require writing down real customer hypotheses, running interviews, or testing pricing assumptions.

The existing LearnOS AI Engineering course is an optional technical complement. It is especially useful if you want deeper context on inference economics, model choice, agent infrastructure, evaluation, and observability before this course turns those technical facts into business decisions.

The roadmap

Read the course in order. The first module turns a prototype into a business thesis. The next two modules make that thesis financially legible. Marketing and product modules test whether customers actually want it. Operations, people, and law make the business durable enough to survive contact with users, employees, vendors, investors, and regulators. The capstone pulls everything into one operating system.

Time commitment

Expect 39 lessons at roughly 35-45 minutes each, or 26-32 hours of focused study. The capstone adds another 12-20 hours depending on whether you use an existing app or invent a new venture for the course.

A practical cadence is one module per week:

• Read 4-5 lessons.
• Complete the module's practice work.
• Update your venture memo and dashboard.
• Review flashcards and notes before moving on.

That pace finishes the course in 8-12 weeks at about 4-5 hours per week.

How to study in LearnOS

• Use active recall. After each lesson, close the page and explain the idea without looking. Retrieval practice improves long-term retention more reliably than passive rereading (Roediger & Karpicke, 2006).
• Chat with the lesson. Ask targeted questions like "What metric would catch this failure?" or "How would this apply to a usage-priced AI app?" Keep the question tied to the lesson you just read.
• Make the flashcards earn their place. Keep cards for concepts you need to recall under pressure: contribution margin, payback period, NDR, SAFE dilution, Five Forces, risk registers, and cap-table mechanics.
• Keep one venture notebook. Use the same app idea throughout the course. Each module adds one layer: strategy, model, pricing, GTM, operations, team, legal risk, and final memo.
• Build the spreadsheet early. Do not wait for the capstone. Start with rough assumptions, then revise as the course gives you better tools.
• Treat cases as decision labs. For each company case, ask what decision the leader faced, what constraints mattered, what data would change your mind, and what you would do next.

The capstone

You will ship a Venture Operating System for a real or plausible app business. It is not a slide deck full of vibes. It is a working founder artifact.

Your final capstone includes:

• A one-page venture thesis: customer, problem, wedge, market, business model, and strategy.
• A simple financial model: P&L, cash-flow forecast, runway, pricing assumptions, AI cost sensitivity, and SaaS metrics.
• A GTM plan: segmentation, positioning, pricing/package, first channel, launch plan, and sales or PLG motion.
• An operating dashboard: acquisition, activation, revenue, retention, quality, support, cost, runway, and risk.
• A risk register: legal, privacy, AI, security, vendor, platform, hiring, and financial risks.
• A board-style update: what changed, what you learned, what metrics matter, what decision is required next.

The goal is that by the end of the course, you can look at your own app like a founder, operator, investor, and customer at the same time.

What this course omits

• MBA admissions signaling and networking. This is a skills course, not a substitute for the credential, alumni network, recruiting pipeline, or social capital of an MBA program.
• Deep accounting certification. You will learn founder-level accounting literacy, not CPA-level accounting.
• Wall Street finance depth. You will learn enough corporate finance and valuation to make founder decisions, not enough to become an investment banker.
• Jurisdiction-specific legal advice. The law module is a map of issues and documents. It is not legal, tax, or accounting advice.
• A generic startup hype tour. Current AI-builder cases appear because they teach business mechanics: pricing, costs, distribution, strategy, governance, and operating leverage.
• A guarantee that any case fact stays current. Private-company revenue, pricing, funding, product packaging, AI regulation, and platform policies change quickly. Lessons date volatile claims and point to sources you should re-check.

Sources

• Harvard Business School - MBA Curriculum - MBA required/elective curriculum context.
• Wharton - MBA Curriculum - MBA core structure and business-discipline coverage.
• Stanford Graduate School of Business - MBA Curriculum - MBA curriculum and first-year foundations.
• MIT Sloan - MBA Curriculum - MBA core and action-learning orientation.
• Roediger & Karpicke, 2006 - retrieval practice and long-term retention.

Practice

1. Pick one app idea you might use for the capstone. Write one sentence each for the customer, painful problem, current alternative, proposed product, and why now.
2. List the three business questions you least know how to answer today. Tag each as strategy, finance, marketing, product, operations, people, or risk.
3. Choose one current AI-builder company you want to track through the course. Write down the business model you think it uses and what would make that model fragile.