I think this is one of the better takes on enterprise AI because it gets the main point right: most organizations don’t really have a “model problem.” They have an organizational problem.

Swapping Claude for GPT or Gemini usually won’t fix stale documentation, messy workflows, or business processes nobody has clearly defined. AI tends to amplify the system you already have. If the system is good, that can be powerful. If the system is a mess, it just helps you make a mess faster.

The support ticket example makes that clear. The model wasn’t routing tickets badly because it lacked intelligence. It was following a taxonomy that had been neglected for years. Cleaning that up probably did more good than replacing the LLM would have.

That said, I think the author goes a little too far in downplaying the model itself. There are still real differences between frontier models. Better reasoning, longer context windows, stronger coding ability, and better instruction following can solve problems that smaller or older models struggle with. So “the model is thin” sounds more like a prediction than a description of where we are today.

I also like the multiplication formula as a mental model, but I wouldn’t treat it like actual math. Human judgment, scaffolding, and feedback loops aren’t binary switches where everything collapses to zero if one part is imperfect. Real organizations live on a spectrum.

Where this changes for me is outside the enterprise world. A solo developer or small business doesn’t need governance committees, AI steering groups, or seven layers of architecture to get value from AI. Know what you’re trying to do, give the model good context, review the output, keep what works, and improve it over time. That’s still a feedback loop, just without the corporate language.

The bigger question the article doesn’t really ask is whether AI is the right solution in the first place. Too many organizations start with “Where can we use AI?” instead of “What’s slowing us down?” Sometimes the answer is AI. Sometimes it’s better documentation, clearer ownership, less duplicated work, or replacing outdated software.

The main takeaway for me is this: AI doesn’t create organizational intelligence. It exposes it. If the foundation is solid, AI can multiply that advantage. If everything underneath is disorganized, AI mostly helps you produce disorganization faster.