The point of a prototype is collaboration. It’s to get feedback from colleagues and end users.
Previously we’d whiteboard that out, spend a few days writing some code or stitching together a figma prototype to achieve a similar results.
I feel ya on the energy use, but don’t see how this is going to get me sued or isn’t allowing me to collaborate. The prototype code is going to get burned anyway, and now I my coworkers and I can pressure test ideas instantly with higher fidelity than before.
Once again, it all depends on the use case. The other day I used an LLM quickly mockup a carousel UI so I could see if it was worth writing real code for. It helped me explore a couple bad ideas before I committed to something worth coding.
I’m not actually checking that code in. I’m using the LLM like a whiteboard on steroids.
All I’m saying is that is you ask people about AI with no use case, you’re going to get different answers than if you ask people about AI when it’s contextualized to a specific problem space.
If I ask a bunch of people about “what do you think about automobiles,” I’m going to get a very different answer than if I ask “what do you think about automobiles that are used as ambulances” or “what do you think about automobiles instead of mass transit.”
Context will give you a very different response.
people don’t really like ai
Once you start asking about AI in regard to specific use cases, I think you’ll find that quickly changes.
My company and I have been running a lot of studies around how and where people find value in these tools, and a LOT of people find LLMs useful for copy writing, doing quick research, data visualization, synthesis, fast prototyping, etc.
There’s a lot of crap that AI is bad at in 2025. Especially the poor in-app integrations that everyone is trying to standup. But there are a lot of use cases where it does provide a lot of value for people.
Did Great Britain move in with Mexico after it broke up with Europe?