The Collaboration Muscle: LinkedIn’s Ya Xu
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Today, we're airing an episode produced by our friends at the Modern CTO Podcast, who were kind enough to have me on recently as a guest. We talked about the rise of generative AI, what it means to be successful with technology, and some considerations for leaders to think about as they shepherd technology implementation efforts. Find the Modern CTO Podcast on Apple Podcast, Spotify, or wherever you get your podcast.

Most of us have used LinkedIn to search for a job or to make new professional connections. But how can AI help facilitate all the many ways users engage with LinkedIn? Find out today when we talk with Yashu, LinkedIn's head of data. Welcome to Me, Myself and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, professor of information systems at Boston College.

I'm also the guest editor for the AI and Business Strategy Big Idea program at MIT Sloan Management Review.

And I'm Sherwin Kodobande, senior partner with BCG, and I also co-lead BCG's AI practice in North America. Together, MIT SMR and BCG have been researching AI for six years now, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and deploy and scale AI capabilities across the organization and really transform the way organizations operate.

Sherva and I are excited today to be talking with Yashu, Head of Data at LinkedIn. Yashu, thanks for taking the time to join us. Welcome. Thank you for having me. Let's start with your current role. What do you do for LinkedIn?

I am part of the engineering organization. I lead the team that's called data. I know it's very confusing to people outside of LinkedIn to realize what data really means. But essentially, if you think about all the data science, AI, and privacy engineering is all happening in my organization. So we do all the way from research to production. So it's pretty

Pretty big organization that is really helping the company realizing our ambition in data in AI. And what is that ambition? It's really using AI, using data to help create economic opportunity for the global workforce.

Comment a little bit more about that. I like, by the way, how you said, look, it's all about creating opportunity and economic value. And you didn't say it's about bringing the most advanced technologies and building sophisticated algorithms, which is all, I'm sure, part and parcel for what you do. But you started with a very higher meta framing and positioning approach.

Really encouraging to hear that. But just generally comment on what that is for LinkedIn and what role does AI play in that? First of all, I think that creating advanced technology, to me, that is how. The end goal is not just creating technology, but really, like I mentioned, creating opportunities. Maybe I would break it down a little bit. I don't know how...

well, the audience understand LinkedIn in general, like we like to think of ourselves as we have three key marketplaces, right? We have our knowledge marketplace, where if you think about as people creating content on LinkedIn and people consuming content and getting informed so that they can advance in their career and making the right connections to the right people. So that's what we call knowledge marketplace. We also have

The talent marketplace, this is when job seekers come to LinkedIn and also employers and companies posting jobs and recruiters, you know, finding the right talent for the companies, right? So that's our talent marketplace, which also include our learning, thinking about individuals or professionals who are reskilling themselves so that they can continue to advance in their career or finding new opportunities.

The third marketplace is really what the product services marketplace. This is where marketers come to LinkedIn platform and then really try to identify the buyers for their product and services, sales individuals coming to LinkedIn and really try to identify the buyers as well for them. So if you're thinking about the role that AI plays, hopefully with that context, it's very clear that obviously in the knowledge marketplace, we are really trying to match customers

the content creators with the right content consumers. In the talent marketplace, we really just try to match the job seekers with the right companies and with the right opportunities.

And then for the product and services marketplace, we're just trying to match buyers and sellers, et cetera, right? So when you're putting in that context, I hope it's very straightforward to see that obviously data and AI plays such an essential role because how do you match them? It's really through advanced technologies that we have in data and AI. So how do you match them then takes us to...

variety of use cases and experiences. And just curious, how do you come up with those? What is the process where you've got the business side and you've got the technology and engineering and data side working together? Take us through the recipe. Yeah.

I would actually maybe start getting to the tactical aspects of things. I think LinkedIn is a very unique place because the way that I describe LinkedIn as a three different marketplaces, and then the way that data and AI play a role, that's how our CEO described LinkedIn. What I've described is not so much like, oh, this is our data or AI view of LinkedIn. This is

LinkedIn as a company's overarching view of LinkedIn. So hopefully that set the stage for how integrated

data and AI is for the mission and the vision of the company, for how the company operates, for the collaboration that we can talk more about, for how different product development processes work and collaboration happens across different team boundaries. That's where I wanted to start with because it's just one word. It's very integrated. Yep.

Tactically, and maybe just talk about a little bit of how we are organized. LinkedIn is very functionally organized. So if you're looking at who sits at our CEO's table, you have essentially all the functional heads, right? Head of engineering, head of product, head of sales, head of marketing, head of legal, HR, head of obviously finance. So we are having a very strong muscle because of that to work cross-functionally.

And so even though that all the data science and AI functions are in my team, we have the muscle and the structure to enable really strong collaboration between all the other functions that we work with in order to bring the AI solutions to production, to life, and to deliver those values to our members and customers. So concretely, my team, I have leaders who are focused on different areas.

And as an example, I have a leader who is focusing on our consumer experience in particular, right? So the way that he would operate is that he would work very closely in a very embedded fashion with other cross-functional leaders who focuses on consumer experience. So what does that mean is that they have...

many many touch points all the way from quarterly planning how do you set the okr how do you do reviews

When it comes to a particular initiative, then you got functional heads all come together and then strategize what needs to happen, who to work on them, like planning our roadmaps. All these are happening just very seamlessly as a cross-functional team. And like I said, right, because we as a company has always, ever since the existence of LinkedIn, it's always operated in this way that everyone has their muscle.

It's brilliant because Sam and I have done a lot of research here and talked with companies across sectors. And it's really interesting. You said the three key words which have been really challenges for most who aren't getting any value from AI, right? You said strategy and mission. That's where you started with. You said integrated cross-functional teams. And then you said collaboration.

And in fact, when you look at the data of 90% of companies who aren't really getting as much value for their investments in AI than those 10% who are getting, it comes down to these very three points. And it's interesting that LinkedIn's inception has been

that kind of a place where it's been data first and in an integrated way completely resonates. And also very refreshing that in your role as the head of engineering and data, that that's also where you first go before you go and you talk about everything else, which is all important, but it's the how, it's not the what.

Yeah, absolutely. And I always look to the leaders in my team as well. They are not like, I'm just a leader of AI. I'm just a leader of data. Like they really need to understand where the end goal is, right? The end goal is never just creating maybe the largest model or the best state of art. It's really about delivering the value. When you understand that, and then when you got the cross-functional team, all having the same shared goal and purpose, that's

then that also brings that collaboration to life really easily. How hard was that to put that in place and align the incentive and the organization and keep, you said different muscles, but how hard was that to get to that place? Was it always there? Were there some very hard decisions that had to be made? I have been at LinkedIn for close to nine years now.

I want to say it's pretty much always there because this is how we've always been organized. The culture is so ingrained that I think the new employees will just very quickly assimilate it into this is sort of how we operate.

So you mentioned getting better in collaboration, and I'm kind of thinking back on what you were saying about matching. You used the word matching in all three of your scenarios. What risk is there that you get down this path of a bunch of engineers ever improving the existing matching algorithms and perhaps missing out on a fourth algorithm?

area that you need to be focusing on. Is there a tension that you're feeling between ever improving that matching process through better and better algorithms and data and figuring out where to apply it in some of your newer products like your newsletter or LinkedIn lives or some of those kinds of new things? Where's that tension?

There are nonstop new areas that pops up, right? With regarding how we can innovate and should be innovating. Like, and even let me just like starting with even matching. It's very simplistic to say, hey, we just kind of try to do better matching. But what does better matching even mean, right? Let's take our talent marketplace as example. Like, obviously we wanted to match the best qualified candidate with the best company.

But how do you even define that? What is best? What is best? What is the best not only for the job seeker,

But also, what's the best for the other side of the marketplace, which is the companies, right? Like, if I were to try to hire somebody, my ultimate ideal state is I really only talk to one candidate, and that is a dream candidate I wanted to hire. And then so same thing with, let's say, in the knowledge marketplace, as we try to connect the content creators to people who are interested in reading their articles, like,

What does that even mean by the right matching as well? Is it that we wanted to maximize the engagement that people have on a particular post? How do we think about distribution of those engagements across? How do we think about, maybe I'm a new creator, first time writing a post on LinkedIn, and I didn't get any response. I would be so discouraged and never post again. How do we think about that shorter-term trade-off and the long-term trade-off?

There's so much more, even just in this very simplistic framework of we're just doing matching. So now coming out of the matching aspect of it, there's again, way more, right? Like thinking about how do we help people do content discovery? And how do we think about when advertisers come to us, how do we actually help them pace their budget? How do we help them utilize their budget better?

Given the progressive culture and the highly collaborative, integrated, functional culture of LinkedIn, what makes a good candidate for your team? What are you looking for in addition to the hard skills of technology and data and AI and data science? What do you think is the secret sauce there?

I would say number one, someone who I think Satya was the one who said that, well, would you want to hire a learn-it-all or know-it-all? I am a...

strong believer of learning it all. And what makes a candidate successful in the past doesn't necessarily mean that they will be successful in the future, right? But that attitude of, I'm going to learn, I'm going to adapt. I think that's just, that's so important. So I would say learning somebody who is really a big learner. And the second one is someone who is

Just curious. Because when you are curious, you have that drive, you have that, I'm going to get to the bottom of it, right? Like so much of the amazing progress we've made is because someone is like, you know what? I'm not here for all the fluffy things. I'm just going to be here to really focus on this problem that I saw that I'm just trying to figure out how to solve it. You know, it reminds me, I had a mentor that used to tell me,

There is no boring projects, only boring people. And so every time I was like, I'm not sure I'm crazy about this project. He says, you could make it interesting. You have ability to learn, you have ability to complain constructively. So now you're saying that it brought back to my memory. There's only boring people, no boring project. Absolutely. I love that. I may start quoting that as well.

What's interesting, and I think that ties into you're a bit of a hero in the academic community. I think maybe you're attracted to those people. Academics complain a lot. Maybe you're attracted to those academics that complain a lot. But I wanted to tie that to another way that you're creating economic value through LinkedIn. And I think one thing that's really fascinating here is that you've got a platform that has insight into what's going on into the economics of LinkedIn.

the invisible hand, making the invisible hand visible, you've got unparalleled insight. I was hoping you could talk a little bit about the things that you, I mean, I'm aware of some of the things you've done with code and with your LinkedIn Graph projects, with the academic world. What are you doing in that aspect to get the insights into those things that we've just never had insight into before? Really, really good point.

Because of the volume that we have on the platform and how much economic opportunity activity that happens on the platform, that we particularly have that insights into like future work, what skills are in demand, like how different companies are hiring, which industry is hiring more and hiring less. And even just like thinking about the equity aspects of it as well, right? I do women

when having different rate of job changing, et cetera, advancements in their careers, all that. So tons of insights that we have on our platform. So what we have done, and Sam, you kind of alluded into is, by the way, we call all these activities, this vibrant activity on our graph, what we call economic graph. And we have started a particular effort on economic graph

probably like seven, seven, at least six years ago, where we essentially stand up a team that includes a bunch of folks on my team with our policy teams, with our comms teams, with our editorial teams that really try to share and bring some of those data and insights to the external communities. And we have been very successful. As a matter of fact, for example, last year, we have sent a report to

most of the congressmen, women, on what is the labor marketplace look like for their region. We have collaboration partnership, for example, with Singapore Education Department to help them figure out what are the skills that is in demand and it's lacking so that they can change their educational curriculum to help. That's just huge.

And then we have worked with obviously like a lot of other institutions, either much more directly with a particular local government or with some like World Economic Forum, you know, G20. We share a lot of our reports with them to help influence some of the policies they have. Another simple example is we are really...

helping the border community know what the green skills, people who are either hiring for green skills, people who are like, where is that talent going? So that as we invest more in green energy, that both the governments and those industries can be more guided in that from talent perspective. That's huge.

You've been featured in Fortune's 40 Under 40. You've written a book. You've given numerous speeches. You're a very successful practitioner in a very successful company. I'm curious, what would your advice be to your peers in other organizations that are

leveraging AI as the how to achieve whatever the what of their company is. What would be like the two things that you think might not be obvious to others? Maybe my first thing that came to my mind is maybe not so provocative. It's really just have the best talent. And it's so important. I am such a strong believer that when you bring the best people around,

All you need to do is to get out of the way and then helping them to be successful. And then wonder just happens. Many of folks on my team can do their job way better than I can do their job. Especially in a field that is constantly innovating. I always joke about it's like how the pace that this field, this domain is changing is

It's just like 300, 400, 500 miles per hour. It's crazy. I mean, I got my PhD in this domain and I was in this workshop that was talking about graph neural network and just graph learning in general. And what the practitioners are doing today, like versus, you know, when I was doing my thesis 10 years ago, it's entirely different. And what was state of art 10 years ago is nowhere to be found today.

in today's practice. So I think that's, again, just re-emphasize on how important it is to bring the best people and the talent, especially in a very innovative space. And the second thing I want to say is, maybe this is a little less obvious, is to make data and AI work in a company, the way to do it is not to build a wall, not to build a wall between folks who knows data and AI and people who don't know data and AI.

And by the way, this is a general pitfall, either in the mindset of people or how companies are organized. If you have this, let's say you got an expert team who is like the world class in data and AI, and then you just expect, hey, you know what, like the rest of the company knows nothing about data and AI.

I think what you said about talent is probably not obvious to many. And I also think it really corroborates your earlier point about curiosity and learning. I mean, if those are the ingredients, then of course, talent matters a lot. So thank you for that. We're just seeing that over and over again. Maybe it's just the kinds of people that we're perhaps attracted to on the show, but it does seem to be showing up a lot. Absolutely.

Okay, so is this time for the five questions? Should we do that? Yeah, do that. Do you know about this? I do not know about this, but fire away. Oh, we didn't tell you? All right. Surprise. I like surprises. We have this thing where we have like five questions. You could just riff, give an answer. So what's your proudest AI moment? I would probably go back all the way to when I was in grad school.

And I was taking this class and it was probably the very first time I really saw AI in application in the way that you can feel, you can touch. I was taking this class with Andrew Ng where we are just supposedly building an algorithm that is able to give in a stream of video, identify objects in the video.

And I worked really hard with a classmate of mine and a close friend of mine. And then at the end of the day, they had this competition of the accuracy and precision recoil, et cetera, et cetera. In a pretty large class, we won our second place. So not the first place, still room for improvement, but very proud that, especially like I was, you know, second year in my PhD. And before that, a lot of my experience was a little more contrived examples, right?

Very good. What worries you about AI? On one hand, obviously, I'm super excited for the potential. But what worries me about AI would be in the responsible AI space in particular. Obviously, I'm really glad the attention that responsible AI is able to get in the public and in the research community, in industry as well. But at the same time, it's not just the buzzword. We got to really

put it in practice and making sure that we are continue to research on how we are able to identify biases that AI system can bring in. And it's a super challenging space. You know, I've been working in this space for, I want to say like, you know, extensively a couple years now, and just knowing how challenging this is. So my call to action to your audience is definitely leaning in the space and research, continue to push the boundaries on what's possible.

Very well said. What is your favorite activity that does not require technology? That's easy. It's skiing. That requires technology. It does not require technology. The ski.

Oh, come on. Bite the head, argument. Yeah, you could explain that to anything. Come on. Like about four years ago, I really got into skiing. And the reason I love about skiing, it's a forced meditation.

It's like you go down the hill and I'm not nearly a good skier, but like all I think about is how can I get down safely? Yes. Nothing else came to mind. So that. I was sure you were going to say gradient descent. That's funny. But I also like the every time when you go out.

It's different because the snow condition, the weather, the slope, all adds variability to how you actually do when you ski in that day. So I love that just because, you know, it's never status quo. What was the first career you wanted, like in your childhood? What did you want to be when you grew up? Well, if you say way back, I joke about it still today. Like I told my mom I wanted to be president. I'll vote for you.

What's your greatest wish for AI in the future? I just really hope AI should serve the people and help everything that we're doing to be more efficient and better. And I mean, I would say that in general about technology, right? Like so many things that we were not able to do, now we can do because of AI and technology. So I continue to be very bullish about that. And I'm very excited that I can be part of it. Thank you.

Yeah, it is absolutely wonderful talking to you. You know, I was thinking back at what you're talking about muscles. And when you first said muscles, I have to say I was thinking biceps. You know, I'm thinking about big muscles.

But as we've talked, I'm now thinking more like eye muscles, like focus, like getting the granularity. Because what you're talking about is the data that's going to let us see things that are happening in the world that we just have not been able to see before. And I just think that's fascinating. And we really enjoyed talking to you today. Thank you so much. Thank you. Thank you for having me. Yeah, it's been truly, truly insightful. And thank you for making time. Of course.

Thanks for joining us today. On our next episode, Shervin and I talk with Nitsan Mekel-Brobov, eBay's chief AI officer. Hope you can join us then. Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn't start and stop with this podcast. That's why we've created a group on LinkedIn specifically for listeners like you. It's called AI for Leaders, and if you join us, you can chat with show creators and hosts, ask your own questions, share your insights,

and gain access to valuable resources about AI implementation from MIT SMR and BCG, you can access it by visiting mitsmr.com forward slash AI for Leaders. We'll put that link in the show notes and we hope to see you there.