AI gets talked about like a single thing you either adopt or don't. In practice, what it changes depends entirely on what you're building.
The four types of product work — Workflow, V2, Innovation, and Social — each feel AI differently. Here's where it moves the needle, and where it doesn't.
Workflow: AI Compresses the Known
When the best practices are already understood, AI's value is speed. It drafts the predictable parts, fills the obvious gaps, and gets a working version in front of users faster. The risk is mistaking speed for fit. AI can produce the standard answer. It still takes judgment to adapt that answer to a specific user and goal.
V2: AI Reads What Users Already Do
A V2's hardest question is what's actually working. AI is good at that question now, surfacing patterns in usage, support tickets, and feedback that a team would take weeks to find by hand. Used well, it sharpens the read on the existing product. Used lazily, it generates more features no one asked for. The discipline is the same as before: refine in service of real metrics, not novelty.
Innovation: AI Lowers the Cost of Being Wrong
First movers fail mostly because exploration is expensive. AI changes that math. You can prototype, simulate, and throw away ten directions in the time it used to take to build one. That makes the leap less risky. It doesn't make the leap for you. The vision still has to come from somewhere a model can't reach: a point of view about what should exist.
Social: AI Is the Part to Handle With Care
This is where AI needs the firmest hand. Social products depend on cultural nuance and human sensitivity, exactly the things models tend to flatten. AI can help you move faster, but it doesn't understand context, and it will confidently get tone wrong. Here the human read isn't a nice-to-have. It's the product.
What It Means Moving Forward
The throughline is simple. AI changes the cost of building, not the need to know what you're building and for whom. It makes the known faster, the existing clearer, and the unknown cheaper to explore. What it doesn't do is decide which kind of product you're making, or what it should mean to the people who use it. That's still the work. It's still ours.
If you're not sure which type you're in, or how much of it AI should touch, that's a good conversation to have before you build, not after. At Loopdash, that's where we like to start.