Microsoft CPO: If you aren't prototyping with AI you're doing it wrong

"Priors" refers to preconceived beliefs or assumptions about AI capabilities based on past experience with the technology.

The updating of the priors is really hard. The models couldn't do some things one year ago. I mean, image generation was full of spellings or reasoning. You just couldn't have deeper and smarter answers. You couldn't do data analysis. So my impression of it from change, trying it a few months ago, that prior needs to be updated.

What I'm seeing is that the time to first demo is much shorter, but the time to a full deployment is going to take longer. So I think that there's going to be an uneven cadence.

NLX stands for "natural language experience" or "natural language interface" - AI-powered conversational interfaces that replace traditional user interfaces.

NLX is the new UX. Conversations also have grammars. They have structures. They have UI elements. They're invisible. What are the new principles, new constructs in natural language as an interface?

In consumer, you're kind of like, 'Oh, we have a playbook for make the product work or make the feature work and make it delightful,' but I think in the enterprise, you almost have... Every time you think you have one use case, you have really two, which is how do you make sure that the feature works well and there's governance of the feature.

TPS reports reference mundane administrative paperwork from the movie "Office Space," used here to describe process-heavy, low-value product management work.

If you are a TPS report, mostly process person, and a lot of companies do get confused about product management and process and project management, I think then you do have a question of, 'Hey, what is the value add here,' especially if AI can read and write 50,000 meeting notes and track things and send emails and so on, but what I do think on the flip side is the taste making and the editing function becomes really, really important.

The danger of metrics. When you're looking at something zero-to-one. If you decide on a metric too prematurely, that's false precision first of all, right? I mean, CTR. When you have a thousand people, it doesn't mean anything.

When I think about agents, I think about these three things. So one, it's autonomy like being... And it's a spectrum, it's not a zero-one, it's how do I actually delegate things that it can do. Second, I think of as complexity. It's not a one-shot, 'Hey, summarize this document, generate this image, but it's build me this prototype or help me knock this meeting out of the park.' And then the third one I think of is it's a much more natural interaction.

I have a cheesy Chrome extension. Literally whenever I open a new tab, it just says, how can you use AI to do what you're going to do right now?

I've repeatedly learned that when you're doing something new, zero-to-one, the temptation is to kind of think about... rush and say to go to scale before solve. So I've always said to my teams solve before scale.