Ryan Roslansky: Turning AI anxiety into skills for the future of work

0:00 It's an uncertain time right now in the world of work.

0:02 There's a lot of anxiety.

0:04 It feels like the old playbooks, you know, aren't relevant anymore.

0:08 Maybe the new playbooks haven't even been written yet.

0:10 And sometimes when you're mired in the technology, and especially with AI,

0:14 and you kind of draw out where you know,

0:16 where this could potentially going to go,

0:18 it leads you to, you know, some dark places and some uncertain places.

0:21 And Aneesh and I wanted to take this moment to write the book

0:25 not as a crystal ball of what's going to happen in the world,

0:28 but more, I think, as a framework to help people

0:31 start to think through how you can turn uncertainty into opportunity.

0:34 how you can turn uncertainty into opportunity.

0:35 The future isn't written on this.

0:37 It's in our hands.

0:39 That's Ryan Roslanksy, CEO of LinkedIn and a Microsoft executive vice president.

0:44 Ryan leads the LinkedIn platform that connects

0:46 more than a billion professionals worldwide.

0:49 Ryan and his colleague, Aneesh Raman, have written a new book.

0:52 It's called “Open to Work.” A practical guide

0:55 to navigating your career in the age of AI.

0:58 We talk about AI’s potential impact on all our jobs and careers.

1:02 It's both a challenge and an opportunity.

1:05 After all, the human brain predates the Industrial Age by millennia,

1:09 and human beings have shown an extraordinary

1:11 ability to adapt to each technological advance.

1:15 But to make the most of this opportunity, we each need to combine a sense

1:18 of where technology is going with practical advice.

1:21 And Ryan has a lot of good practical advice to share.

1:25 Including how to think about your current job not as a title,

1:29 but a collection of tasks, and then use this understanding to build

1:34 on your strengths and add to your skills.

1:36 The future may be a lot more promising than you think, if we get this right.

1:41 My conversation with Ryan Roslansky, up next on Tools and Weapons.

1:48 Ryan, it's great to sit down.

1:50 I have been looking forward to this conversation for a long time.

1:53 Because you and your colleague, Aneesh Raman at LinkedIn,

1:57 have been working on a book that has now reached the shelf.

2:02 “Open to Work– How to Get Ahead in the Age of AI.” I love the title,

2:07 because a lot of times people are asking hard questions like,

2:12 “Is there a way for me to get ahead?” “Will I be left behind?” You and Aneesh

2:17 offer so many insights about where the world

2:19 and where the world of AI are going.

2:21 Thanks for having me, Brad.

2:22 Thank you for, you know, the inspiration that you,

2:25 kind of gave to write- to write the book as well.

2:27 And so my colleague, Aneesh Raman and I, from LinkedIn,

2:32 had the idea basically to evolve what we've

2:35 been doing for decades on the LinkedIn platform.

2:38 A platform that exists to create economic

2:40 opportunity for every member of the global workforce.

2:42 And right now, there's a lot of uncertainty,

2:44 a lot of anxiety with the world of work through AI.

2:47 And while this is by no means a crystal ball to what the future of work holds,

2:53 it is a starting point to start a discussion,

2:56 and to start giving people a framework for how

2:58 they can think through what's happening around them right now.

3:01 How they can, you know, connect some of the dots that, you know,

3:04 maybe creating some of the anxiety or the uncertainty,

3:07 and hopefully then turn that uncertainty into opportunity.

3:11 And, you know, I think one of the most important things that I've

3:14 thought about in writing this book and talking to people about it,

3:17 is the future isn't written on this.

3:20 It's in our hands as workers,

3:22 as employees, as companies, as economies, as societies,

3:26 to, to figure this out together, to find the opportunity,

3:31 and to create a better, world of work moving forward.

3:33 So, it's exciting to get this book out there

3:37 and I look forward to talk to you about it.

3:38 Well, I mean, one of the things I think your comments reflect is you,

3:43 or you and Aneesh, really have in many ways,

3:46 some unique perspectives, I will say uniquely broad perspectives,

3:50 because of all of the data that you get from LinkedIn,

3:55 from the LinkedIn Economic Graph, and not just the data about where the world

3:59 is and where the world of work is today,

4:02 but I think the perspective gained over time.

4:04 Because, as you say, for two decades,

4:06 the world has been changing, jobs have been changing.

4:09 You all have been seeing those changes.

4:12 So now, as you put it, we look ahead.

4:16 Let me start with where you started in the book, the introduction.

4:22 And I think you've already alluded to a little bit of this, but I even love

4:25 the title “Failure is Not an Option.” And what

4:30 you start with is really the Apollo 13 story.

4:34 Many people know it,

4:35 either having read about it or watched the movie and other things.

4:40 This extraordinary story of human ingenuity,

4:43 people doing things that I don't think they thought were necessarily possible,

4:47 having to do them in a few days to keep three astronauts alive.

4:52 And it really was about human ingenuity.

4:55 Why did you choose that as the story with which to begin?

4:59 Yeah, it's a great question.

5:00 As you said, I mean, it's this beautiful day in 1970.

5:04 You have a set of astronauts that are taking the fifth trip to the moon.

5:07 Things are going well, and out of nowhere,

5:11 you know, in a quick stir of the oxygen tank,

5:14 “Boom!” And the astronauts start to realize that they are spewing oxygen

5:18 out into space and send that now famous message back to Mission Control,

5:23 “Houston, we have a problem.” And, you know,

5:25 it was the the flight director, Gene Kranz, who sat there in Mission Control,

5:30 started to understand what was going on, and came

5:33 to the conclusion that “Everyone, stay calm.

5:36 And let's remember failure is not an option.” And I

5:40 think it just resonates so well at this moment in time,

5:43 which is that when you're hit with this uncertainty,

5:45 we have to come together to figure this out,

5:47 to use human ingenuity to think creatively

5:51 is the right mental framework to be in.

5:54 This is a great example of, technology wasn't going to solve this problem.

5:59 This is a set of astronauts that had

6:01 to make do with what was literally, you know,

6:05 what would right now would look at like super

6:07 antiquated technology that exists on this, on this spacecraft.

6:11 You know, literally in certain cases, you know,

6:14 fit a square peg into a round hole and figure out a way to save themselves.

6:19 And, you know, it's a bunch of people working together, across different,

6:23 you know, departments and agencies, and in space and on the ground.

6:26 And they succeeded and they, you know, they, they brought it together.

6:30 They brought themselves home.

6:31 And it's just, it's just a great, in my view,

6:36 a great story for what happens when, you know,

6:39 you realize failure's not an option, you bring human ingenuity to the forefront,

6:43 and you're able to do great things.

6:54 One of the interesting threads that you pull on is

6:58 this notion that we don't know entirely where things are going,

7:02 but that doesn't mean it’s predetermined and that we don't have agency.

7:06 I have to admit, I love this because, to be honest,

7:10 I feel like it's sometimes missing in our industry.

7:14 In the world of technology people can be a futurist and make a lot

7:18 of predictions about what is going to happen in five or ten years.

7:23 I don't know that there's a lot of accountability or even review.

7:27 I will admit, a few weekends ago I was thinking

7:30 about this and I used the Researcher agent in Copilot,

7:34 and I put in a lot of the names that everybody would recognize.

7:37 And I asked for an assessment and a grade

7:41 of all of the, I'll just say, luminaries,

7:46 and how well they did with their predictions

7:48 about what would happen in defined periods of time.

7:52 And almost everybody got between 20 and 30%.

7:56 So, you know, nobody got a passing grade.

7:59 But rather than have people make fewer predictions,

8:03 people just keep making them.

8:05 But this is not what you guys do.

8:08 You and Aneesh.

8:09 In fact, what you say is,

8:11 “...we won't get a lot of these answers for some time, in some cases decades.

8:16 We don't need these answers, however, to know what to do right now.

8:21 The most important part of all

8:23 of this is that these answers are not predetermined.

8:27 Nothing about this moment is.

8:30 Where we go next comes down to one thing and one thing only:

8:34 the choices we make right now as individuals, organizations, economies,

8:40 societies...” You use that to frame the mission and what you

8:45 think the mission is for the two of you as authors,

8:47 really, in my view, the view of LinkedIn and Microsoft, about human innovation.

8:55 Can you say a little bit about how you think about human innovation?

8:59 What is it, and what is it that makes it most special?

9:02 What makes so many of the, you know, the greatest technology thinkers great is

9:07 this idea of what the future can become.

9:10 And sometimes when you're mired in the technology,

9:13 and especially with AI, and you kind of draw out where,

9:16 you know, where this could potentially go, it leads you to, you know,

9:20 some dark places and some uncertain places sometimes.

9:23 But oftentimes that that assumes that nothing else changes around it.

9:28 That if we were to all to stay

9:29 in one place and not change and let the technology,

9:32 you know, run wild, we could end up

9:34 in a place that seems uncertain or scary to us.

9:37 I don't believe in that.

9:38 I believe that, you know, that humans play such an integral role

9:43 in shaping where that technology should go, and understanding not only,

9:48 you know, what technology can bring to bear on the upside,

9:52 but how people can adapt and evolve with that along the way as well.

9:57 So much of what's exciting about AI, the AI that we use every day right now,

10:01 is its ability to take certain tasks that, you know,

10:03 have somehow felt mundane to us in the past, maybe we didn't want to do them,

10:06 and help us do them better, give us actually more agency.

10:10 But more importantly, I think we're all starting to realize that the set

10:13 of what we historically maybe called “soft skills”,

10:15 which didn't seem as important, these are really important, by the way.

10:18 You know, curiosity, courage, communication, compassion.

10:22 Wow.

10:22 These turn out to be some really,

10:24 really important skills to, you know, do your job well.

10:27 And the focus and emphasis on those, along with AI,

10:30 is what I think gives us the opportunity to dream big and paint

10:32 a much more positive picture that exists

10:35 with humans and technology together moving forward.

10:38 And as you say, you don't have to know every step.

10:43 No one knows every step they're going to need to take.

10:47 The most important thing is to take the first step.

10:49 That’s right.

10:50 You know, if you're early in your career, if you're later in your career,

10:54 if you're a parent wanting to think about what a career

10:58 for your son or daughter might mean in the future,

11:00 I think this book has very practical, helpful advice.

11:05 You have a title for a chapter, “Jobs Are Tasks,

11:09 Not Titles.” But you do put tasks into different buckets.

11:13 Can you say a little bit about the three buckets that you define?

11:16 It was maybe two and a half or three years ago when, you know,

11:20 you and I were first exposed as part of, you know,

11:23 this great company to some of the, groundbreaking AI moves.

11:28 And someone in a meeting said something like, “Well,

11:30 where does this all go?” And off the cuff, you turned and said, “You know what?

11:34 Everybody's job is a set of tasks.

11:36 And we need to look at it like that.

11:38 And if your job is just a set of tasks that can be automated,

11:41 you may need to start looking for a new job.” And I mean,

11:44 I remember writing that down really quickly when you said it,

11:47 Brad, because- I have no recollection.

11:49 That's what I assume.

11:51 I'm glad you took notes.

11:52 But it's a really important framing.

11:54 And it does start to give people something practical to think about,

11:56 which is that, historically, we've talked about what we do as a title.

12:00 “I'm a product manager.” “I'm a marketer.” “I'm a salesperson.”

12:04 And it's a great shorthand to basically bundle up what, at the end of the day,

12:08 is a set of tasks to be done and how you communicate what you do.

12:11 It's important for us to actually think about our jobs not as a title,

12:15 but as that set of tasks, because the more that you think about it like

12:19 that, the more you can start to realize that, “Wow,

12:21 there are certain tasks that AI can do really well, and that's a great thing.

12:27 But if my job is only a set of those tasks,

12:30 I need to start thinking about what that means

12:31 for me.” We bucket those tasks into three buckets.

12:34 One, tasks that we, you know, feel pretty,

12:37 pretty good that AI is going to automate those and can do a great job with them.

12:42 You know, “Summarize this document.” “Translate this piece of text.” You know,

12:47 AI is going to be great at that.

12:49 The second bucket are those tasks that we feel like

12:52 can really help augment what you do as a human being.

12:57 So, you know, AI can't go all the way there,

12:59 but they can really give you a superpower as you realize how to use them.

13:04 The last set of tasks, the third bucket,

13:07 are those things that are, so innately human or messy sometimes,

13:12 that we don't believe that AI is going to be able to do those.

13:16 When you're in a meeting and people aren't agreeing

13:18 on something and you have to get them to agree.

13:21 Or people who are on different pages about the direction to go

13:23 when you got to bring people together talk about a strategy.

13:26 The ability to really communicate and galvanize

13:29 a group of people around going a certain direction.

13:31 We bucket those into those tasks and then it's a really easy exercise,

13:35 you can start to think about in your job.

13:36 “My goodness, like what do I do on a daily basis?

13:39 And you know, if I really boil that down,

13:41 what percentage of my job fixing fits into each of these buckets?” Again,

13:45 it's not a crystal ball.

13:47 It's not the answer,

13:48 but it's a way to start thinking about “Am I in a role right now that I

13:52 need to be thinking about making a quick change?”

13:54 Or, “Am I in a role right now where,

13:56 wow, I feel really, insulated into the future?” Or, “Am I in a role where,

14:02 you know what, like most people are,

14:04 some of my tasks are in this bucket one, some of them are bucket two,

14:07 some of them are bucket three,

14:07 and how can I be great at all of these?” But I mean,

14:21 when you think about, you know, say,

14:23 just that dividing line between what you rely on AI

14:27 to do versus what you're now using AI to do, but you're doing it with AI.

14:32 How do you find that changing your own work?

14:35 One of the greatest things that I found so far with AI is,

14:40 having Copilot built directly into Outlook,

14:43 and my ability to get this long email where people are debating something,

14:47 and just say, “Hey, can you explain this to me really quick?

14:49 Like, what's going on or what do I need to do?”

14:51 And there's so much nuance in a lot of these discussions,

14:54 and there's so much decisions that I have to make

14:59 before I can make a decision or reply to email.

15:01 But I can save a good ten to 15 minutes

15:04 on a topic when I can just come up to speed really quickly.

15:07 So, I love that.

15:08 And then the second thing that I've started to do quite frequently,

15:11 I'll be in a situation where I have

15:14 to send an important email to you or to Satya, and, and I'll ask Copilot, “Hey,

15:20 how can I make this better?” Or, “What am

15:22 I missing here?” I'm not asking to make a decision.

15:25 I'm asking it to challenge me or help me as a thought partner

15:28 to make a better end product that I'm trying to make a point on.

15:32 You know, a lot of these that I'm talking about, these are bucket two tasks.

15:35 You feel like you have this superpower all

15:37 of a sudden to kind of do more, with AI.

15:40 But it doesn't just happen and it does- You can't just, you know,

15:44 assume that you're going to type something into some random AI

15:46 and it's going to be there for you and help you.

15:48 There's a lot of work and effort that goes

15:51 into this in helping it become that thought partner for you.

15:54 And, you know, your reference to curiosity.

15:56 I mean, I felt for so long that that actually

16:00 is the fundamental fuel for growth, for learning.

16:05 But it is a bit of a craft and a bit of an art, even to learn how to use AI.

16:13 And in your work, how are you

16:16 seeing the development of that capability, that skill?

16:20 I think everyone has their own, you know, approach to working with these tools.

16:25 And I actually think that's a great thing,

16:27 because to be building this thought partner with you,

16:29 it has to become so personalized to you

16:31 and who you are and how you work and think.

16:33 The LinkedIn feed these days is flooded with people who are so excited to share

16:40 what they are doing or learning with the various AI products that they're using.

16:45 It’s symbolic of a couple of things.

16:47 Number one, people are trying a lot of different things in their flow of work.

16:53 Right.

16:54 And not all of them hit.

16:56 But when they do, when you're able to really

16:59 find like a bucket two task that makes sense,

17:03 you're so excited about it that you want to share it with the world.

17:07 You know, number two, the feedback that you see on these LinkedIn posts,

17:10 like, “Did you try this”, or, “Try it this way”, or, “Oh my goodness,

17:13 I'm going to go and try that.” It kind of goes back to this human idea of like,

17:17 we're all trying to figure this out together,

17:19 and this building of a community around these topics and how you're using it.

17:22 It's just really inspiring to watch people kind of motivating each

17:26 other on and kind of talking about it and doing it.

17:30 Anyboy who's been around computers for the last ten or 20 or longer years,

17:36 knows, hey, some people, they were like PowerPoint or Excel wizards.

17:40 They could do things, that most of us would go, “Wow, I wish I could do that.

17:44 I cannot.” And, it in part can be people get some formal courses,

17:51 it can be online, it can be in some other format.

17:54 It might be watching a video,

17:56 might be LinkedIn learning, or it might just be practice.

18:00 Yeah.

18:01 The same thing is happening here.

18:03 Even just being inside, you know, a team.

18:07 The comparisons, people say, “Wait,

18:09 how did you get it to do that?” It often- “How did you prompt it?” “How did

18:15 you use it?” You think of writing as something

18:19 that you would either do yourself or rely on.

18:22 Maybe you take a shortcut and, you know, you let your Copilot or some other,

18:28 you know, chatbot write for you, and it does a good job.

18:31 But wow, I have been so struck when people really just work with it— Yeah.

18:37 —and they keep using it.

18:39 And, one of my colleagues has a habit when she gets something,

18:43 she doesn't like, she says, “That's terrible.

18:48 You got to do that again.” And it'll come back and say, “Yeah, you're right.

18:53 It's terrible.” Takes feedback better than maybe

18:56 the some of the humans we work with.

18:59 But it it is a skill.

19:01 Yeah.

19:01 And it is a skill, in part, honed through practice.

19:04 We talk a lot in the book about this importance of a mindset

19:08 shift of really adapting AI in your workflow or pushing yourself to adapt it.

19:15 For the future of your career it's really important to be trying these tools,

19:23 and learning these tools as you would with any other skill.

19:26 We live in this technology world, and it seems like, you know,

19:29 according to you and I, everyone in the world is using AI all day long.

19:32 That's not true.

19:32 To really kind of push yourself to learn about it, read about it, try it out.

19:36 I think that's an important mindset shift that folks need to be making.

19:40 You have this notion, bucket three.

19:42 You know, the tasks that are uniquely human.

19:46 And that hence AI won't do or can't do.

19:50 For a huge number of jobs,

19:52 there are significant parts of it that are uniquely human.

19:56 If you just, you know, think about what it means to work in sales.

20:01 You know, every company that has a product has people who are in sales.

20:08 You know, to be a lawyer.

20:10 And so often success turns on relationships that build trust.

20:17 So much of the work that matters actually involves investments of time,

20:23 of building relationships, of understanding other people.

20:28 And the more people can move tasks from bucket one to bucket two,

20:33 and then free up some more time for bucket three,

20:36 which I think is frankly where we're often the most time constrained,

20:39 people are so busy, they just think, “Wow,

20:42 I think I could be a better manager of my team if I just had

20:46 some more time to ask the people I work with, you know, how are they doing?

20:52 What's up with their kids and their family?”

20:54 And that is actually an important part of what it means,

20:58 I think, to be successful.

20:59 So, I think it's just a different way

21:01 that you offer of reimagining a world of work

21:06 that can be more meaningful if people figure out how to use AI the right way.

21:12 These skills, they're important,

21:13 but they've historically been talked about as soft

21:16 skills and almost like put to the side.

21:17 Like, “Those don't matter as much.” We've always

21:21 kind of known they really matter, quite frankly.

21:23 Like, having strong EQ in your job is

21:25 is a really important thing for for many people.

21:28 The fact that, you know,

21:29 so many of these hard skills feel like they have the ability to be automated,

21:32 that now all of a sudden it's kind of, shining

21:34 the light on the true importance of some of these soft skills.

21:37 If you think about, I mean, you know,

21:40 If you think about your your day today or my day today.

21:43 My goodness.

21:43 Like the number of the number of times I've had to use,

21:46 you know, some uniquely human skill around,

21:48 you know, compassion or, you know, EQ,

21:52 or conflict mediation I mean, these are- That's what these jobs are.

21:55 And to think that those aren't important, I think makes no sense.

22:00 But to think that, wow,

22:02 what will happen in a in a professional world where people are actually

22:05 much better at these skills and have really honed in their craft on it?

22:09 I think that it makes things a lot better.

22:11 Most people in some way have jobs where at some point

22:16 in time you need people to bet on you, to trust you.

22:20 And that usually comes with time well spent

22:23 in investing in those relationships that make the difference.

22:27 I also think this connects- You go from jobs to careers.

22:30 And you have this phrase that really “a career is

22:34 not a ladder,” maybe the way we’ve often thought about it,

22:36 “but a climbing wall.” Can you say a little bit more about that?

22:39 The most requested feature by far that I've

22:42 seen in my time at LinkedIn is you know,

22:45 people say, “Hey, Ryan, LinkedIn has all this data.

22:47 Can you can you just use that data to show us exactly

22:50 what career paths are supposed to be or what they look like?

22:53 If I want to become a CFO, what am I supposed to do?

22:56 Or I want to, you know, be a marketer,

22:58 where should I go to school now?” And the truth is,

23:00 you look at the data and there is no such thing as a linear career path.

23:04 You have to realize that you need to take your career into your own hands.

23:07 We're moving from, you know, just this idea of roles into specific tasks,

23:13 and the importance of those tasks.

23:14 All of a sudden, you know,

23:15 this kind of linear thinking of a, of a career or hierarchy

23:19 inside of a company starts to look a little bit different.

23:22 And you need to be thinking about your career less about,

23:24 “I need to be climbing this ladder,

23:26 or getting this new title or promotion”, and more about,

23:30 you know, “What adjacent skills or tasks can I be picking up?

23:33 What did I think I couldn't do that now, if I really embrace AI,

23:37 I can actually do more of that?” It's a reframing on what,

23:41 you know, we've historically thought it meant to be in a career,

23:45 or what a career was made of.

23:46 We’re seeing phenomenal, you know, employees at LinkedIn, or Microsoft,

23:52 or many companies who historically may have thought that their, you know,

23:57 path to success was, “I'm a great IC and now I'm supposed to become a manager.

24:01 Like, that's what I'm supposed to do.” Yeah.

24:03 But now it's like, “Wow.

24:05 As an IC, I can, you know, leverage some of these AI tools, get a lot more done,

24:10 be more fulfilled in my life, and that's what's rewarded now.

24:14 That's what seems exciting, my ability to help my company in greater ways.”

24:18 These hierarchies are so embedded in the human psyche.

24:20 And I don't think these get changed overnight.

24:22 But, I am starting to see that trend.

24:25 I think it's important to think about, you know,

24:26 “I'm not just trying to be the next, you know,

24:29 seniority or level, but I'm trying to expand my skill

24:31 set.” That's going to be the path to success moving forward.

24:41 As you get to this part of the book,

24:43 you're basically encouraging people to think about three questions as they

24:48 think about their their own careers and where they want to go.

24:52 The first one is, Why do you work?

24:56 The why.

24:56 Say a little bit about that question and why you make that the first question?

25:00 I don't want to discount that the answer to many

25:02 people to that question is “I need I need to live,

25:05 I need money to live.” And I think that that's fair, but, and really important.

25:09 But I think it's a great place to start, which is inside of myself.

25:12 Like, “Why am I doing this?

25:14 What do I care about?

25:15 What is going to make me get out of bed every day?

25:18 What is motivating me?” And I think that then

25:21 helps you make the next decisions and what,

25:23 you know, you should be trying to do or learn in your career.

25:25 But it also connects with then your second question, which I think is even more,

25:30 perhaps beneficial for people as they're

25:32 really trying to to think about themselves, which is what do you uniquely do?

25:37 How do you think about that question?

25:39 If I want to have a fulfilling career, if I want to be able to, you know,

25:43 leverage those skills and tasks that I know how to do,

25:46 in a world where, you know,

25:48 some of these bucket one tasks may become more commoditized, like,

25:51 “What is it unique about me that helps me

25:53 stand out in this field?” What are those, you know,

25:56 human skills that you uniquely bring?

25:58 What are those other skills and tasks that you uniquely know how to do,

26:01 you know, with or without AI tools?

26:03 To paint a picture that's more at a task-based level than on a role-based level.

26:07 And again, I think it's just more of this mentality shift from roles to tasks.

26:12 And then you go to the third question,

26:14 “Where do you want to go?” How do you and Aneesh think

26:18 about that question and how people best think about it for themselves?

26:22 I think the good thing is that if you're able to answer one and two, you know,

26:25 it starts to give you a pretty good ability to feel

26:28 like you have agency over what number three looks like.

26:32 Again, to so many people, you know, careers and jobs, it feels like a black box,

26:39 you know, that “Someone else is taking, you know, care of this for me.

26:42 I get on some path and it.

26:44 And again, I might.

26:45 That's how it's supposed to work.” In reality, no,

26:48 no, you have to take it into your own hands.

26:50 You have to take that agency of what you

26:52 want to become into your own hands by again,

26:54 going back to “Why am I doing this?” You know,

26:56 “What is unique to me?” And then with those two

26:58 questions I think you're really able to answer,

27:00 you know, a variety of ways that you can go with your career that match

27:03 those two things that will then end up

27:04 with something that's fulfilling and unique to you.

27:07 So many people end up in jobs that, you know, are not what they meant to be.

27:10 They're not what they meant to be doing.

27:11 They don't enjoy doing that.

27:13 And I think if you can really foundationally,

27:16 end up principally with those first two questions,

27:18 it will lead to a much more fulfilling career,

27:20 especially in a time where it feels like, you know,

27:22 there's so much uncertainty and you don't have control of it.

27:24 You do have control of it, but you have to put in the time and the effort,

27:27 and the work, to really ground yourself in those principles

27:29 and those questions to know which way to go.

27:31 There's also this really terrific, logical flow in your book.

27:37 “Understand the impact of the technology.” The part we've just talked

27:41 about sort of that comes after that is actually the “Know yourself.

27:44 What's uniquely your strength?” Then it goes to a third part,

27:50 which is putting it in the context of the world as a whole,

27:53 where economies are going.

27:55 Which I think is helpful as well.

27:58 And you entitle the chapter, “Economies Need Innovation From All for All.”

28:04 What do you and Aneesh mean by that?

28:07 I think if you start with the mindset that, you know,

28:10 there is a very positive future of the world of work,

28:15 and you think about what some of these AI

28:18 tools can go and do and unleash for the world.

28:22 There’s some really positive outcomes to all of this.

28:27 I have a 20-year-old daughter who, you know,

28:29 for much of her life has been really focused on, you know,

28:33 “Dad, how can I make an impact on climate change?

28:36 It feels like there's nothing that can be done here.” Now,

28:40 the conversations we have are more like, “Oh,

28:42 you know, these tools are going to be so powerful

28:45 that some of these really important questions in the world,

28:48 like climate change, and health care, and poverty,

28:51 we may be able to find step change breakthroughs in solving

28:55 some of these problems.” When you look at it through that lens,

28:58 all of a sudden it becomes so inspiring what can happen.

29:02 And then you take the logical steps back to say, “Okay, well,

29:06 what needs to happen to get there?” It requires, you know,

29:11 these strong partnerships between the public sector, the private sector,

29:16 the infrastructure that needs to exist in the world

29:19 to allow some of this prosperity to flourish, the skilling that needs to exist,

29:24 to help people learn what they can do with these tools.

29:27 There's so much at the foundational level of work that needs

29:31 to be done that can't just be done by, you know,

29:34 individuals or companies, it needs to be done by, you know,

29:36 kind of, schools and governments and everyone involved.

29:39 But when you when you put yourself in that mindset,

29:41 wow, how exciting can this be?

29:43 That almost brings us back to where we began,

29:47 talking about an outlook on the future.

29:50 And this– Should we be optimistic?

29:54 Should we be pessimistic?

29:55 Should we be excited?

29:57 Should we be anxious?

29:59 The first thing I would note is, even if AI may automate or make it possible

30:06 to automate many of the tasks in your work,

30:10 what you do is more than just the sum of the tasks.

30:13 We at Microsoft see so much about how great AI tools are automating coding.

30:21 The job of a software engineer is not going away, but it is changing.

30:27 The data that we have right now on LinkedIn.

30:30 We're seeing an increase in the number

30:32 of software engineer postings that are happening right now.

30:36 And it feels like that should be ironic, but it's not necessarily, at all.

30:39 Exactly.

30:40 Exactly when you think about the power that comes through the creation,

30:44 you know, through software engineering.

30:46 Now, that ability that can be in more

30:47 people's hands is actually really, really exciting.

30:49 But to your point, the job is changing.

30:51 Take the job of a product manager, where you know,

30:54 historically what you used to spend a lot of your time on was,

30:57 you know, number one, talking to customers to understand the problem.

31:00 Most important.

31:01 Number two, writing a bunch of, you know,

31:04 specs to hand over to engineers about how the product should look,

31:08 how the features should come together, how we’re going to measure success,

31:12 etc., and then help manage the kind of project to be done.

31:16 Directionally, that job is still the same today,

31:18 except that second step is different.

31:20 You're still out talking to a lot of customers,

31:23 but the next thing you're doing is actually

31:25 you're writing a bunch of evals for the model.

31:28 It's not an antiquated product- Explain what

31:30 an eval is for people who are not familiar.

31:31 For most people it's, you know, the idea that a customer has this need.

31:37 We have to help our product,

31:39 which is an AI-enabled model-driven product to fulfill that customer need.

31:45 So, I need to help teach the AI through what we call evals.

31:49 So, a scenario, the product needs to do

31:51 this, and the AI needs to respond like this.

31:54 I need to teach it how to do that in order to fulfill that customer need.

31:58 A product manager's job is still talking to a customer.

32:01 It's still writing something that looks like a spec.

32:03 The spec now is just a set of like,

32:06 “If this scenario, here's a good or bad response.” “If this scenario,

32:10 here's a good or bad response,” and helping to train

32:12 the model to help the customer do their job, still working in the process.

32:16 So, it's not like the job is going away, but it is changing.

32:19 And the product managers who are, you know,

32:21 really succeeding in their careers right now are the ones that are embracing,

32:25 you know, that AI eval loop and really understanding it,

32:28 how it works, and in the flow.

32:29 So, I think everone’s job is changing.

32:32 In fact, it's not a new thing.

32:34 If you look over the past eight years alone on LinkedIn,

32:37 you take the average role.

32:38 This is not even an AI-related comment.

32:41 The skills required to do that job over

32:43 the past eight years have changed by 25%.

32:47 And we expect that by 2030, they will change by 70%.

32:51 So, even if you're not changing your job, your job is changing on you.

32:55 And quite frankly, that's always been the case through history.

32:58 It may just be accelerated now in the AI age.

33:03 The other point that you make in the book is that there's

33:07 probably more premium and opportunity in the future for cross-disciplinary work.

33:13 The ability to combine that, you know, what might be a specialist,

33:19 but with also, the ability to think across specialties.

33:22 Do you see that actually becoming one of the soft skills that's more necessary,

33:28 or in-demand, or valuable because of AI?

33:31 I think a couple things there.

33:33 You know, first, and we've we've tried this inside

33:35 of the LinkedIn company over the past year,

33:37 and it's been really exciting to see.

33:40 Historically, we have siloed roles in the software production process.

33:45 You know, I talked about one just a second ago of a product manager.

33:48 But in that process that you also have a product marketer, a designer,

33:52 a front-end engineer, to create, you know, part of the LinkedIn product.

33:59 What we've learned is that when you enable

34:03 people to use some of these great AI tools,

34:09 oftentimes people are able to do many more

34:11 of those tasks that are required in those siloed roles.

34:14 So we tried something recently.

34:17 We created a role at LinkedIn, which is called a builder.

34:21 And historically, you'd think of it as those four roles.

34:25 And now it's just this one role with a great set of AI

34:29 tools that enable you to build a product from start to finish,

34:33 leveraging a lot of the insight typically held siloed in those roles.

34:37 When roles are siloed, it requires a communication path,

34:41 and that takes time to do and build.

34:44 When you can enable a single builder with the ability

34:46 to do this, they can do things much more frequently.

34:49 For a company like LinkedIn, like the speed and the quality of the decisions

34:54 to make and get a product out there, are really all that matters.

34:57 We try a lot of things and we test it and we pull a lot of things back.

35:00 So, enabling this new class of builders to go and build

35:03 more things and test more things helps our company to thrive.

35:08 Quite frankly, doesn't mean anyone's role is going away.

35:10 We're making more builders.

35:12 And we've created a new program at LinkedIn for associate builders.

35:15 People who are just, you know, maybe coming out of college,

35:18 maybe not even going to college at all,

35:20 but are proficient in building things and understanding these tools

35:22 as a new way of entry-level work to come into our company.

35:25 Because it's so valuable for what we need right now at LinkedIn.

35:29 I think we'll see a lot of roles start to change like that as well.

35:32 Again, not something going away,

35:34 but adapting the way that work is done to help a company succeed.

35:38 Well, one of the things I love about that, and even

35:41 though you say a job is not a title,

35:43 and as somebody who didn't major in computer science,

35:47 if I could choose between being a coder and a builder,

35:51 I think it's pretty cool to be a builder.

35:53 Builder sounds great.

35:54 Yeah.

35:54 So, let’s conclude with what I think is maybe the, I'll

36:01 call it the emerging great debate in the tech sector.

36:05 I might even argue the emerging great debate for this century as a whole.

36:11 What are we trying to do?

36:13 Are we trying to use AI to outperform people?

36:18 Or are we trying to use AI to help

36:22 people develop the capacity to do more in work,

36:26 with their lives, in short, help people perform at a higher level?

36:31 I actually think there's a bit of a schism in the tech sector.

36:37 If you just look at what people have as their mission,

36:40 or just their great quest.

36:43 There are some who say, “No, we are in search of AGI.” What is AGI?

36:47 Well, there are differing definitions, but they say, “It is the ability,

36:51 to have some autonomous system that can outperform humans at, say,

36:57 most economically valuable work.” Or you have our mission,

37:02 which is basically to create technology that empowers people to do more,

37:07 to achieve more, or to be more that they want to be.

37:11 Those are two very different visions

37:15 of what different companies are trying to accomplish.

37:20 I still remember when LinkedIn and Microsoft came together,

37:23 and part of the conversation was Satya Nadella and Jeff Weiner,

37:28 realizing that the two companies had very similar mission statements.

37:33 So, as we look at the future.

37:34 You know, you uniquely, you've you've had this experience at LinkedIn,

37:38 you've been part of Microsoft, you look to the future.

37:41 What do you hope we will do with AI for people in the world?

37:47 One of the most important reasons that Aneesh and I felt like

37:50 we needed to write this book right now is to, more than anything,

37:54 start the discussion and help people to start understanding what's going on.

37:59 The reason that I work at LinkedIn, and the reason that I work at Microsoft

38:03 is because of the missions of these companies.

38:06 Like, LinkedIn is a platform for 1.3 billion

38:10 professionals around the world to find economic opportunity.

38:14 It has been that way for 20 years.

38:16 And, you know, through different economic changes through Covid,

38:20 we've been there as a platform to help

38:22 people navigate their careers through their community,

38:27 through how they learn new skills,

38:29 through how they, you know, adapt as human beings.

38:34 And I think now more than ever,

38:36 people need to be understanding and having these discussions again.

38:40 And it's not just unfortunately, up to, you know,

38:42 some tech companies or you and I, but it's governments,

38:45 it's economies, it's educational institutions to really paint

38:51 a positive future for what this can all mean.

38:55 AI is an enabler for us to do bigger and better things as a society.

39:04 We have to be thoughtful about the approach and how we get there.

39:08 Everyone has to have a framework so they, you know,

39:11 can deal with the uncertainty in the messy middle we're going through,

39:14 to how to navigate it right now.

39:16 But my goodness, if we are able and when we're able,

39:19 to navigate through it, to give humans these superpowers,

39:23 to enable and to do more than they ever thought possible,

39:26 I think we're going to end up in a pretty special place.

39:28 I know you share the same thought as well, and that's why you're here.

39:31 Every book is an argument.

39:34 Every book starts a conversation.

39:36 I think the book that you and Aneesh

39:38 have written really is about two conversations.

39:41 One is how can an individual or a parent coaching a kid,

39:48 or somebody who's 20 years into their career and looks ahead to another 20,

39:53 get some practical advice and think in some new and helpful ways?

39:58 In this changing time,

40:01 how can individuals use AI to be more successful in so many ways?

40:06 But there's a second argument you're making as well, that there is a path.

40:11 If we put our minds to it, we will decide the future as people.

40:16 Machines won't decide it, people will.

40:18 There is a path, where as a society,

40:21 we can use AI to fashion a broader and better future for lots of people.

40:27 And it's not about being replaced by machines,

40:29 it's by using this to achieve things that we couldn't otherwise contemplate.

40:35 I think you and Aneesh together have written

40:38 a book that creates the basis for these conversations.

40:43 I think it's the conversations that people want to have.

40:46 But more than that, I think it's the conversations that we need to have.

40:49 And I'll say it helps reinforce what all

40:53 of us who work here at Microsoft and LinkedIn.

40:56 This is why we joined these companies in the first place.

41:00 It’s why we’re still here today.

41:02 because we actually want to see technology do good for people.

41:07 And I think you show us how.

41:09 So thank you very much.

41:10 Thanks, Brad.

41:11 Appreciate it.