For a decade, my instinct for solving any problem has been identical: open an IDE, write clean code, and push to production. But making the leap from software engineer to business creator, specifically while exploring how to commercialize OpenML Aperture, requires completely rewiring that reflex. When I first looked at turning this project into a viable product, my immediate urge was to do what engineers do best: build more features, analyze competitor matrices, and calculate theoretical market sizes. It turns out, that is the exact wrong first step. Building a sustainable business doesn’t start with beautifully abstracted code or secondary market research; it starts with stepping away from the keyboard to find out if an economic buyer is actually bleeding from the problem we want to solve.
First-Principle-Based First Step
When engineers transition to business creators, the default instinct is either to write more code or perform academic “market research” (estimating TAM/SAM, reading Gartner reports, analyzing competitor feature matrices). From first principles, a commercial business is fundamentally a repeated economic transaction: an exchange of money for the relief of an acute, expensive problem.
What Do TAM, SAM, and SOM Mean?
- TAM (Total Addressable Market): The total maximum market demand or 100% revenue opportunity for a product or service if every potential customer bought it, ignoring competition or limits.
- SAM (Serviceable Addressable Market): The specific slice of the TAM that your business model, geography, and constraints can realistically target
- SOM (Serviceable Obtainable Market): The actual share of the SAM that your business can realistically capture in the near term while facing active competitors
Macro-market research cannot tell us if an individual has an acute enough problem to pull out a credit card today. A “market” is merely an aggregate group of people who share the exact same unsolved problem and have the budget to eliminate it. If we haven’t validated that the problem is urgent and expensive, sizing the market is calculating zero multiplied by a very large number.
Deconstructing Step Zero: The First-Principles Sequence
1. Formulating the “Urgent & Expensive” Problem Hypothesis
Before writing a line of business plan, isolate what pain our concept actually addresses and why an organization cannot simply ignore it:
- Is it a vitamin, a painkiller, or a tourniquet? Code documentation and clean architectural diagrams are historically “vitamins” - everyone agrees they are nice, but nobody buys them with urgency. In contrast, “AI agents hallucinating and pushing breaking schema changes to production” is a tourniquet.
- What is the cost of non-action? Calculate the friction in terms of wasted developer hours, blown LLM inference token budgets, failed deployments, or compliance liabilities. If the cost of the problem is negligible, the price we can command is negligible.
2. Separating the End User from the Economic Buyer
Engineers build for users; businesses sell to buyers. In technical tooling, these 2 entities rarely share the same incentives:
- The User: The individual engineer or autonomous coding agent utilizing the context files, skills, and graph interface. Their metric is convenience and workflow speed.
- The Buyer: The Engineering Director, VP of Infrastructure, or Head of Platform Engineering. Their metrics are developer throughput, compute/token expenditure, system reliability, and headcount leverage.
- First-Principle Question: Who has discretionary budget right now to solve this problem, and what exact OKR or KPI does it hit for them?
3. Conducting Behavioral Discovery (Not Feature Pitching)
Instead of asking people, “Would you buy an open-source structural context hub for AI agents?” (to which everyone politely says yes, but never buys), you must interrogate their historical behavior:
- How does your team currently prevent coding agents (Cursor, Claude Code, etc.) from hallucinating deprecated libraries or violating architectural rules?
- What tools or workflows have you built internally to solve this, and how many engineering days did it consume?
- Have you allocated budget to solve this problem in the current fiscal cycle? If so, what products did you evaluate?
If prospective buyers have not already attempted to solve the problem, either through internal scripts, custom prompt routing, or messy wikis, the pain is not acute enough to sustain a commercial product.
Shifting Mental Models
| Dimension | Engineering Mindset | Secondary Market Research | First-Principles Business Creator |
|---|---|---|---|
| Starting Point | Features, tech stack, codebase capabilities | Industry analyst reports, top-down TAM estimations | Identifying an acute, recurring business expense or operational bottleneck |
| Validation Metric | System architecture, test suites, clean abstractions | Spreadsheet projections, market growth percentages | Documented customer desperation and budget allocation |
| Feedback Loop | Code compiles and works reliably | Theoretical survey responses | Direct conversations evaluating how customers currently spend time and money |
| Goal of Step 1 | Build the MVP | Estimate total addressable dollars | Find 10 people experiencing the exact same acute pain |
This brings up a fundamental mindset shift for transitioning from engineer to business creator: in mathematics or computer science, axioms are static, logical, and universally true. In business, our foundational axioms are human psychology, pain tolerance, and budget cycles. Humans are messy, irrational, and frequently don’t even understand their own problems, i.e. In business, your axioms are humans, not numbers.
If we try to sit in a room and deduce a perfect business model entirely on a whiteboard, the same way a software engineer would architect a clean React codebase, we risk building a logically flawless product that no one actually buys. In business, we cannot derive our axioms in isolation; wee have to discover them through collision. We have to go out into those Discords and GitHub issue trackers, throw our hypothesis at real engineering managers, and see if it shatters.