Still Holds: Gall’s Law

AI took away the constraints that brought discipline to MVPs. You must impose them yourself.

Complex systems evolve from simpler systems. The ones that thrive do so because they’ve adapted to real-world conditions — and not because they were designed to address all possibilities.

In systems thinking, this principle was best articulated by John Gall:

A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over with a working simple system.

I’ve long promoted Gall’s law to students and clients. It’s been hard going. We want to see products and services in their full glory ASAP. But you can only throw so much cash and person-hours at a problem. Ergo, we got the time-tested idea of a minimum viable product. (It’s no coincidence that orgs with more resources violate Gall’s law more often than scrappy startups.)

But the value of an MVP isn’t just that it allows you to get something that works quickly and cheaply. Instead, the value is that that first try isn’t overspecified to theoretical conditions. It’s only a draft meant to kick off an evolutionary process that leads to a system that meets real-world customer needs.

AI removes these constraints. An afternoon with Claude can yield a comprehensive spec for a very complex system. Further sessions can architect the system and build an initial release that includes bells, whistles, timpani, harps, violas, and all the other instruments in the orchestra. All this, at a fraction of the cost and time it would’ve taken in the past.

That’s amazing. It means we can now design and build much larger systems, faster. This opens new possibilities. Not just one new feature, a new product. Not just a new product, a suite. Not just a suite, a platform. The possibilities seem endless.

But in removing architecture and development constraints, we’re also removing the need to focus on what matters most and the discipline to run it by the market. All that upfront complexity doesn’t necessarily address real-world needs. Instead, it reflects a singular top-down vision that may or may not provide value to others.

Whereas MVPs in the “before times” called for minimal investment before validation, LLMs promise a much more realized vision from the first go. But there’s a significant difference between a sexy concept car and a vehicle customers will take grocery shopping. AI-augmented workflows will give you the former, but not the latter.

Here’s an example. Two years ago, I started building a product called SiteRanger, an AI-powered agent to help small teams manage large websites. This was before Claude Code or any of the current coding agents. Still, I got surprisingly far by using Claude to augment my basic PHP skills.

Together, we built a functional MVP that implemented what I considered to be the core functionality. The problem: my “core” was in fact an open-ended platform. Rather than solve a particular customer problem, it was designed to solve classes of problems.

By the time I got alpha users on board, the system was wildly over-architected. Worse, I learned new agentic systems could provide ~80% of its value. When I looked to pivot, I realized I’d have to move in a completely different direction, one with entrenched incumbents. It wasn’t worth it.

It wasn’t all a loss. This experiment taught me a lot about developing AI-powered software products using AI. But the most important lesson I learned is that AI makes it VERY easy for individuals and small teams to land in the same trap as resource-rich orgs: no constraints.

Which isn’t to say you shouldn’t use AI. To the contrary, I’m all for accelerating MVP design and development. But the word “viable” is fungible, especially when you have robot engineers. You want to expose the product to the discipline of the market. That means releasing something embarrassingly simple at first. And that requires discipline and constraint — the two things most scarce when working with LLMs.

My friend Karl Fast pointed me to a wonderful scene in the movie A RIVER RUNS THROUGH IT. The main character, a child, brings an essay to his dad, a strict preacher, for evaluation. The dad’s only reply: “Half as long.” The child does, and returns with the edit. After scribbling with a red pencil, the preacher looks at him and says: “Again, half as long.”

Step away from the console and ask yourself: What would I cut if there were no AI building it? Cut, cut, cut. Then imagine Tom Skerritt staring at you over his schoolmaster glasses and saying drily, “Again, half as long.”

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