Tal Elor

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And once that happens, a lot of traditional discovery work will start to feel outdated.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.

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.  

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.