When AI is discussed in enterprise strategy, the conversation often centers on more; more projects completed, more tickets closed, more output in less time. It’s productivity defined by volume. But perhaps the deeper opportunity lies not in scaling up output, but in scaling down complexity.

What if the real promise of AI isn’t just doing more things faster, but needing fewer things done at all?

Software engineering, at its core, is the art of taming complexity. Ever since entropy in thermodynamics was connected to entropy in information theory, we’ve understood that systems trend toward disorder unless shaped by thoughtful intervention. Engineers have always been that shaping force.

AI offers a new lever. Not to push more code through the pipeline, but to question whether the pipeline needs to exist in the first place. It’s not just about completing projects—it’s about discovering which projects no longer need to happen.

AI as an Entropy-Reducing Force

AI enables a new class of simplification:

Code synthesis: Generating abstractions instead of building them by hand.

  • System consolidation: Identifying duplicate or low-value functionality.

  • Insight-driven design: Letting data inform what should not be built.

  • Language interfaces: Replacing layers of UI and logic with natural conversation.

Each of these reduces surface area, not expands it.

Productivity Isn’t Just Throughput

A troubling pattern in AI strategy is the uncritical celebration of throughput. But throughput without insight is just acceleration on a treadmill. Enterprises should ask not just, “How fast can we move?” but “What can we stop doing altogether?”

The greatest impact AI can have on engineering organizations might be measured not in delivery velocity, but in entropy reduction: fewer systems, fewer lines of code, fewer decisions needing human intervention.

Imagine an engineering organization where AI helps:

  • Sunset redundant services.

  • Refactor sprawling codebases into lean, understandable modules.

  • Automate away not just tasks, but whole classes of problems.

That’s not just productivity—it’s evolution. It’s a shift from building to distilling. From motion to meaning.

In this light, AI strategy isn’t about more. It’s about better. And often, better begins with less.

(Brought to you by an AI assistant :D)