AI Can Improve Itself. But Who Decides What 'Better' Means?

Vahe Andonians is the founder and CEO at Cognaize.

AI is becoming part of its own development process. It can improve software, find more efficient ways to use computing resources and help improve how new models are built. Those gains can make the next round of development faster and more effective.

That is a real improvement loop but not self-sufficient alone. It still depends on goals, tests and feedback that come from outside the system.

An AI can get better at reaching a target without knowing whether that target is worth pursuing. It can improve its test results without delivering a better real-world outcome.

For business leaders, the opportunity is to automate more of the work of improvement. The responsibility is to decide what counts as progress, and ensure the system is being measured against it.

The score is not the goal.

Every improvement loop needs a way to distinguish progress from activity. In coding, that might mean passing a test. In operations, it might mean lower costs.

Suppose a customer-service system is rewarded for closing cases quickly. It could improve that metric while making customers feel dismissed. A coding agent could pass the available tests while introducing a vulnerability those tests do not cover.

The management problem is familiar: A target can become a substitute for the purpose it was meant to serve.

A system that rapidly generates and tests alternatives may discover weaknesses in a poorly designed measure faster than a human team. It does not need to share the organization’s broader purpose to optimize the number it has been given.

The right response is not to abandon measurement. It is to treat the design of the measurement as seriously as the design of the system.

Before asking an AI to improve something, leaders should be able to answer two questions: What outcome do we actually want? And what could get worse while our chosen score gets better?