I keep noticing the same pattern. A team has a real model capability, decent engineering, and a working UI, but the product still feels flat. You try it once, nod politely, and never think about it again.

That does not usually happen because the team forgot to add enough AI. It happens because the product has no taste in the interaction. The first move is cold, the reveal is weak, the controls feel generic, or the system asks for trust before it has earned any curiosity.

When I say I care about AI product work, this is part of what I mean. I do not only care whether the system technically works. I care whether the experience has enough life in it to create pull without drifting into theater.

A few things seem to matter over and over:

1. The first interaction needs a point of view. The product should not feel like a blank chat box waiting for the user to do all the creative work. Good AI products usually make one strong suggestion about how the interaction should begin. The user should feel invited into a shaped experience, not dropped into a void.

2. The output needs presence, not just correctness. A result can be useful and still feel dead. The products people remember usually give the output enough contrast, motion, framing, or specificity that it lands as a moment instead of a line item.

3. Surprise only works when the user still feels agency. I like products that can surprise me, but only if I still feel like I can steer the taste. The best AI interactions are often the ones where the system makes an unexpectedly good move and then immediately gives me a clean way to shape what happens next.

4. The second session matters more than the first wow. A lot of AI products are optimized for screenshot energy. That is not nothing, but it is not enough. If the product has no reason for me to come back, remember context, refine a previous result, or build a small ritual around it, the coolness collapses into demo energy.

5. Trust still has to be designed into the experience. The goal is not to make the interface magical by hiding everything important. The products I like most are the ones that feel alive while still making provenance, control, and recovery feel natural.

This is part of why I built AI Product Reactor. I wanted a small interactive tool for thinking through product taste in a more inspectable way: first-use energy, reveal moments, return loops, and what kind of interaction is actually worth building.

It also connects pretty directly to AI Interface Lab. That lab is more about surface shape, trust burden, and where autonomy belongs. Reactor is more about magnetism: what makes a product feel vivid, shared, replayable, or worth returning to after the first curiosity spike.

None of this is meant as an argument for shallow polish. The best AI product taste is still grounded in real user behavior. If the workflow is fake, the visual energy will not save it. If the workflow is real, taste is what helps the product feel like an experience instead of a feature demo.

That is also why I think terms like AI product builder, AI product operator, or even product people using Claude Code and Codex are only useful if they point back to actual artifacts. The claim should cash out in product surfaces, not just in language.

If you want the most direct proof around this part of the work, start with AI Product Reactor, AI Interface Lab, what I've actually built with Claude Code and Codex, and Projects.