Marily Nika

There is something called the shiny object trap, and I'm always telling people, 'Hey, don't do AI for the sake of doing AI.' Make sure there is a problem there. Make sure there is a pain point that needs to be solved in a smart way.

From a product perspective I can imagine like three bubbles in my head. So you want to find the intersection both something that's desirable by users, something that is going to be a viable business and something that is going to be feasible from a research scientist and technical perspective.

It's all about changing the mindset of PMs. Taking a step back and just thinking about, okay, I have all this data that's just lying and sitting around what is it that I can do with it?

Don't do it for your MVP. It makes zero sense. Do not waste time of data scientists that can train models with using powerful machines that are going take weeks to train.

I believe that all product managers will be AI product managers in the future. And this is because we see all products needing to have a personalized experience, a recommender system that is actually good.

The generalist PM helps their team and their company build and ship the right product. But the AI PM helps their team or company solve the right problem.

If you have an MVP and you just want to get buy-in for an idea or feature that may use AI in the future, fake it, create a little figma prototype and just show it some users, just fake what the AI is going to be doing.

Usually product managers get ahead the more they launch. But if you're in a research or if you're not going to launch as often, so you need to make sure to clarify with the hiring managers early on, 'Hey, what does progress mean?'