Why Profitable Systems Can Still Have No Real Value

*Applying insights from **Good Strategy / Bad Strategy** to real-world software businesses*

Introduction: Profit Is Not the Same as Value

Many software founders proudly say:

“Our product makes money.”
Related Posts
Classic Programming Thinking: What We Still Learn from Kernighan & Pike
Before Writing Code: The 5 Questions We Always Ask Our Clients
Fix Discovery & Access First: The Fastest Way to Restore the University Library’s Strategic Value
How to Develop a Manufacturing Execution System (MES) with Python
Why Learning Software Development Feels So Painful — and How to Fix It
ChatGPT 5.2 vs 5.1 — Explained with Simple Analogies

But Richard Rumelt, in Good Strategy / Bad Strategy, challenges this assumption with a powerful thought experiment — the famous “UFO machine” that produces $10 million per year.

The twist?

The machine may generate cash, but it has little strategic value once sold to another investor.

This idea is uncomfortable — and extremely relevant — for software businesses today.

The UFO Machine, Reframed for Software

Imagine this scenario:

A SaaS product earns $1M per year
It has customers and steady usage
Anyone with a similar team can rebuild it in 6–12 months

On paper, it looks valuable.

Strategically, it may not be.

Like the UFO machine, its profits exist without a defensible reason.

Why Profit Alone Is a Weak Signal

Traditional business thinking equates value with:

Revenue
Growth
EBITDA
Multiples

Rumelt argues that strategy asks a deeper question:

Why does this profit exist — and why hasn’t competition erased it?

If the answer is vague, the value is fragile.

Transferability Test: A Simple Value Filter

A powerful way to test real value is this:

Does the advantage survive a change of ownership?

If selling the software means:

Customers leave
Margins collapse
Differentiation disappears

Then the software was never the source of value — context was.

Boring Value vs. Interesting Value in Software

Boring Value

Generic CRUD SaaS
Feature-driven differentiation
Easily copied UX
Competes mainly on price

This is UFO-machine value: profitable but strategically thin.

Interesting Value (Rumelt’s Idea)

Interesting value comes from asymmetry:

Knowledge others lack
Position others cannot take
Frictions others cannot cross

In software, this often looks like:

Deep integration into operations
Accumulated domain-specific data
Workflow ownership
Regulatory or cultural specificity

Real Software Examples of Interesting Value

1. Embedded Workflow Software

Software that becomes part of how work is done:

MES inside factories
Accounting systems tied to local regulations
Industry-specific operational tools

Replacing it is painful — that pain is value.

2. Data That Improves the System

When usage creates data that:

Improves predictions
Refines workflows
Increases switching costs

The value compounds over time and cannot be sold as code alone.

3. Relationship-Centered Platforms

Some systems work because of:

Trust
Long-term support
Operational knowledge

The software is only one layer — the value lives in the relationship.

Why Many Profitable SaaS Still Struggle

Founders often optimize for:

Feature velocity
Short-term MRR
Investor narratives

Instead of:

Durable advantage
Strategic position
Long-term defensibility

The result?

A profitable product with no moat — easy to replace, easy to ignore.

graph LR
    A["Profitable System ≠ Strategic Value"]

    A --> B["Cash Flow"]
    B --> B1["Revenue"]
    B --> B2["EBITDA"]
    B --> B3["Short-term Profit"]

    A --> C["Boring Value"]
    C --> C1["Generic Software"]
    C --> C2["Copyable Features"]
    C --> C3["Price Competition"]
    C --> C4["UFO Machine"]

    A --> D["Interesting Value"]
    D --> D1["Asymmetry"]
    D1 --> D1a["Unique Knowledge"]
    D1 --> D1b["Unique Position"]
    D1 --> D1c["Unique Context"]

    D --> D2["Embedded Systems"]
    D2 --> D2a["Hardware + Software"]
    D2 --> D2b["Operational Workflow"]
    D2 --> D2c["Physical Constraints"]

    D --> D3["Data & Learning"]
    D3 --> D3a["Accumulated Data"]
    D3 --> D3b["System Improvement"]
    D3 --> D3c["Switching Cost"]

    D --> D4["Relationships"]
    D4 --> D4a["Trust"]
    D4 --> D4b["Long-term Support"]
    D4 --> D4c["Consulting Know-how"]

    A --> E["Strategy Test"]
    E --> E1["Transferability"]
    E1 --> E1a["Ownership Change"]
    E1 --> E1b["Does Advantage Survive?"]

    E --> E2["Defensibility"]
    E2 --> E2a["Moat"]
    E2 --> E2b["Friction"]
    E2 --> E2c["Replacement Pain"]