We ve all been there: the engineering team ships an incredible feature, the marketing team blasts the launch, the metrics show a temporary spike in usage-and then... nothing. Silence. The feature slowly turns into product debt, and the actual value delivered to the user drops to zero.

As builders, we are constantly obsessed with shipping. We measure velocity, sprint completions, and launch dates. But somewhere along the way, we forgot to measure whether the things we build actually move the needle for our users bottom line.

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.

We try to estimate impact and we try to assess effort.

But most evaluations happen with:
Limited data
No real validation
Weak assumptions

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?

Product complexity It s about:

1-how many assumptions are embedded in decisions

2-how hard it is to trace why something exists

3-how many systems each change quietly affects

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?

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?

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?

For a long time, we optimized for speed:
Faster iterations.
Faster validation cycles.
Faster decisions.

It felt like progress.

But then we started noticing a pattern:
We were getting to answers faster - but not necessarily to better answers.

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.