But it will raise the bar on what good decision-making even means.

Faster insights will stop being impressive - they ll become expected.
Broader analysis won t be a differentiator - it ll be table stakes.
Deeper visibility won t be optional - it ll be assumed.

And once that happens, a lot of traditional discovery work will start to feel outdated. Not because it was wrong, but because it was designed for a world where you could afford to be slow, partial, and reactive. That world is gone.

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.

There s a growing pattern of using tokens to generate AI code and documentation slop. Then use even more tokens to understand and review that slop.

Then judge engineers by token usage instead of how empathetic and clear their docs and code actually are

At some point, the system starts optimizing for the wrong thing. Instead of asking Can a human actually work with this? , we continue asking How much did we generate? or How many agents did we spin up today? - are those the success metrics we want?

A clear example of this is what we re seeing in AI-generated UIs for landing pages. Tools like Claude (and others) can produce interfaces quickly, but they often converge into a very recognizable template. Same layout patterns, same spacing, same visual language. It becomes less design and more average of all designs the model has seen.  

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

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

Every single week, my X feed and GitHub trending list tell me the same thing: the tech stack we chose three months ago is already obsolete.

There s a new AI wrapper that promises 10x speed, a new framework that claims to solve all state-management nightmares, and a database that apparently runs on pure magic. It s exhausting.

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?

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

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?