Why the Best AI Teams Will Think Like Architects

Everyone has access to the same models. The same APIs. The same frameworks. The technology is commoditizing faster than anyone expected. If your competitive advantage is which model you’re using, that advantage has a shelf life measured in months.

So what separates the teams that ship production AI systems from the teams that produce impressive demos?

Architectural discipline.

The teams I see succeeding — and the approach that’s worked in my own production systems — share the same patterns. They decompose complex problems into modular components. They define clear interfaces between them. They document not because someone told them to, but because the system demands it. They build for change, knowing that the model they’re using today will be replaced by something better tomorrow.

These aren’t AI-specific skills. They’re architecture skills. The same principles that make any complex system maintainable, adaptable, and composable.

The difference is that agentic systems punish the absence of these skills faster and harder than traditional software ever did. When agents are operating autonomously, making decisions, chaining actions together — every undocumented interface, every ambiguous boundary, every architectural shortcut compounds. At human speed, you could compensate with tribal knowledge and ad hoc fixes. At agentic speed, you can’t.

The best AI teams in the next few years won’t be defined by their model expertise. They’ll be defined by their ability to architect systems that can evolve as fast as the technology underneath them. That’s an EA skill. And it’s in desperately short supply.