We measure delivery.
We measure usage.
We measure outcomes.

But what do we measure for good discovery?

We re starting to rely on AI more and more in product decisions.
But here s something we ve been thinking about:

When AI is wrong about your product - who notices first?
The PM? The engineer? The user?

Or worse - no one?

As we build Athena, we keep asking ourselves how a system can stay grounded in reality, not just generate convincing answers.

We re building Athena as a system that tries to connect product intent with technical reality using AI subagents.

Instead of just managing tasks, it tries to understand how the product actually behaves - architecture, constraints, and decision history.

Before we go deeper:
If you could give an AI workspace one ability related to product understanding, what would it be?

Would love to hear different perspectives from PMs, engineers, and founders.

We ve been looking a lot at how product discovery changes as teams grow.

At small scale it s fast and intuitive, but at larger scale it often becomes fragmented, slow, or disconnected from the actual system.

From your experience-what s the first thing that breaks in discovery workflows when teams scale?