In software architecture there is a “delicate dance of compromises”, a concept emphasized in the “Fundamentals of Software Architecture” by Mark Richards and Neal Ford. Two particular “laws” in that book have always resonated with me, influencing not just how I design systems, but also how I approach new technologies.
Let’s dive in:
The First Law of Architecture
Everything in software architecture is a trade-off.
Software architecture is about balancing competing priorities: performance vs. simplicity, security vs. ease of use, scalability vs. cost, and so on. No solution optimizes for all aspects at once. There is always a push and pull between constraints.
Corollary to the First Law
If an architect thinks they have discovered something that isn’t a trade-off, more likely they just haven’t yet identified the trade-off.
I interpret this quite strictly: if you have not spotted any trade-offs in your proposed solution, you probably have not delved deep enough into how the solution will behave under various circumstances. Real-world usage often exposes hidden costs—be it higher operational overhead, licensing fees, or dependencies that complicate future updates.
The Second Law of Architecture
Why is more important than how.
A frequent pitfall in software development is jumping straight into the “how” (tools, frameworks, or libraries), without first asking “why”. Pinpointing the “why” ensures that your technology choices serve real business or user needs, rather than following hype or convenience.
First Law in the AI Context: Everything Is a Trade-off
When choosing or designing an AI solution, you are always balancing competing goals. Below are two common examples:
- Local vs. Cloud
- Local Models: Full control over data and potentially lower latency, but you’ll incur high hardware and maintenance costs.
- Cloud Models: Easier scalability and lower initial costs, but you face recurring fees and potential compliance concerns.
- Complexity vs. Speed
- Complex Models: Often more accurate but can be expensive to train and slow to run.
- Simplicity: Faster, cheaper, and easier to maintain, though potentially less precise.
Trade-offs are unavoidable. If you think you have found a free lunch, you likely have not spotted the hidden cost yet.
Second Law in the AI Context: Why Matters More Than How
Defining the rationale (“why”) comes before deciding on the technical approach (“how”). Examples include:
- Business Alignment
Ask why you need the AI solution at all. If it does not solve a genuine business or user problem, no amount of technical brilliance will create real value. - Measuring Success
Pinpoint the problem you aim to fix (reduced costs, higher customer satisfaction, deeper analytics) and let that guide your choice of tools and methods.
Knowing why you are building an AI system ensures you pick the right trade-offs when weighing performance, cost, complexity, and transparency.
