There s a point where products stop being fully understandable.

Too many features
Too many dependencies
Too much history

And decisions start getting made with partial context.

Most teams can build.
Ship.
Iterate.

Execution isn t the problem anymore.

But clarity is.

Most teams don t struggle to generate ideas.
They struggle to choose the right ones.

Discovery produces insights.
But decisions still rely on partial context, bias, and pressure.

The bottleneck isn t finding opportunities - it s knowing which ones are actually worth building.
Athena is built around this gap - not just surfacing ideas, but helping teams evaluate and decide with full context.

AI surfaces more:
More insights
More ideas
More opportunities

But product is still about focus.

So the real question becomes:  What do you choose to ignore?

Traditionally, PMs are the ones connecting everything: users, business, engineering.

But now with AI systems that can map, analyze, and connect - what happens to that role?

Does AI amplify the PM?
Or start replacing parts of the job?

A slightly uncomfortable question:

Are we still doing product discovery - or mostly validating decisions we already made?
As teams grow, processes get heavier, but it sometimes feels like real exploration gets lost.

We ve been thinking about how Athena could push teams
back toward actual discovery - not just confirmation.

How honest do you think discovery really is today?

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 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?

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

But what do we measure for good discovery?

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.