Building AI products, building products with AI — interview debrief 04

In one interview, I was asked: what’s the difference between “building AI products” and “building products with AI”?

I didn’t give a particularly good answer at the time. Thinking about it afterward, I’d break it down along a few dimensions:

01 | Building products with AI: AI changes how things get made

PMs use AI for research and organizing requirements, designers use AI to produce prototypes, engineers use coding agents to write code.

AI makes the team more efficient at building the product, but the product it ships may contain no AI at all.

So a team that relies heavily on ChatGPT, Claude or coding agents isn’t necessarily building an AI Product.

02 | AI products: AI enters the process through which users get value

For example, a model handles generation, classification, recommendation, search or conversation, or makes some of the judgment calls within a workflow.

At this point, AI is no longer an internal tool for the team—it’s part of what the product can do.

The PM also has more decisions to make: how to define quality, what happens when the model gets it wrong, where deterministic rules should be used instead, whether human review is needed, whether the latency and cost are acceptable.

But “the product uses AI” doesn’t necessarily mean it’s AI-native.

03 | AI-native: the product itself is redesigned around AI

This term doesn’t have a fully agreed-upon boundary. I use one simple question to decide:

If you take the AI out, do the core workflow and value proposition still hold up?

If all you lose is an AI assistant and the original product basically still works, I’d lean toward calling it AI-enabled.

If taking the AI out means the core workflow has to be redesigned, then it’s closer to AI-native.

So this is how I distinguish them now:

- AI changes how the team builds products → building products with AI

- AI becomes part of what the product can do → AI products

- AI changes how the product itself should be designed → AI-native

For a PM, this distinction isn’t just terminology.

The first mainly changes how efficiently you work; with AI products, you start having to manage model behavior and risk; with AI-native, the question goes one step further:

Not “where can we add AI to the existing product?” but “now that we have AI, should this workflow still exist the way it does?”