Adam Mosseri

speaker

647 appearances 6 recordings 4 series first heard Nov 2024 last heard 9 Jul

Adam Mosseri’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 2 in all, peaking in Jul 2026 with 1.

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Lenny's Podcast: Product | Career | Growth · Adam Mosseri: AI is a tailwind for authenticity · 9 Jul 2026 podcast

If you're doing something that is really novel from an experience standpoint, you need a very senior product designer.

So we try to build a team based on the needs of the work a bit, but then end up with a much smaller core, which is more on the order of six or seven usually.

And that is a very big shift that's just happening to us this year.

But they...

Just by virtue of having less people to coordinate, they can often move faster and make better decisions, a little bit less designed by committee.

So we talk a lot about AI adjusting and improving productivity, and that's part of it.

But I think another part of it is just the small teams, I think, often are just more effective.

You just have less specialists, right?

So you might not have any, you might be four engineers and a product staff, and there's no data scientist, there's no designer, there's no researcher, there's no content designer.

The product staff is the generalist that sort of supports all of those things.

I mean, what's clearly happening is all the functions are starting to bleed into each other and the whole industry is wrestling with what that means.

You know, a lot of what a data scientist does at a big company, for instance, is relatively mechanical.

And so, you know, there's stuff that they do that is really more like, you know, art and science and the stuff that's really more like just pulling data, data management.

You know, so some of the tools that we're building internally to understand, for instance, a traditional data science question would be a waterfall.

So if you wanted to look at people creating reels, you would look at all the steps and then how people fall off on each step and try to figure out where there might be opportunities.

to improve things.

That kind of basic waterfall analysis is like much easier now to use some of our internal tools to just pull automatically as opposed to having to have a data scientist do a bunch of bespoke work for that.

So a product staff might be able to do that now, and they couldn't do that a year ago.

So you just end up with these people who have more generalist shapes.

And then when you need it, when you really need it, you have a more senior, ideally, or just more creative specialist.

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