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Spatial co-ordinations are initially random, and will automatically re-arrange to minimize complexity based on distance between relationships. Mouse down and drag to pan. Right click on the strategic diagram toggles between motion and stationary. Hover over abstract node (orange) to view abstractions. Hover over leaf node to view corresponding narrative. Left click on the leaf node expands the narrative to view full text.

Narrative Analysis - Report

Key Focus

My best advice is ironically, something that we've been talking about for a while in terms of how we create a much more equitable and efficient labor market across LinkedIn which is, it's really important for people to change the way you think about jobs ...

And so in order for us, you know, fundamentally, we built the largest global labor market on LinkedIn to help people find jobs. And in order to help people find jobs, ensure that we can match talent with the opportunity or the people with the jobs ...

Momentum supporting factors

(linkedin,answer,question)

(linkedin,answer,qualifying)

(linkedin,answer,augment)

No challenge supporting factor found

Work-in-progress supporting factors

(linkedin,jobs,market,labor)

(linkedin,company,microsoft,gen)

(linkedin,microsoft,gen)

(linkedin,company,internet)

(linkedin,consumer,internet)

(linkedin,company,consumer)

(linkedin,company,workforce)

(linkedin,company,start)

(linkedin,company,recognizing)

(linkedin,consumer,recognizing)

(linkedin,company,questions)

(jobs,labor,talent)

(jobs,market,labor,talent)

(jobs,market,talent)

(jobs,talent)

Time Period	Challenge	Momentum	WIP
Report	0.00	2.82	97.19

High Level Abstraction (HLA) combined

High Level Abstraction (HLA)	Report
(1) (linkedin,jobs,market,labor)	100.00
(2) (linkedin,company,microsoft,gen)	57.19
(3) (linkedin,microsoft,gen)	56.39
(4) (linkedin,company,internet)	55.59
(5) (linkedin,consumer,internet)	55.43
(6) (linkedin,company,consumer)	55.27
(7) (linkedin,company,workforce)	53.83
(8) (linkedin,company,start)	53.04
(9) (linkedin,company,recognizing)	52.40
(10) (linkedin,consumer,recognizing)	51.44
(11) (linkedin,company,questions)	51.12
(12) (jobs,labor,talent)	48.56
(13) (jobs,market,labor,talent)	48.08
(14) (jobs,market,talent)	47.76
(15) (jobs,talent)	47.60
(16) (linkedin,jobs,labor,talent)	47.28
(17) (linkedin,jobs,market,talent)	46.81
(18) (linkedin,jobs,talent)	45.21
(19) (jobs,labor,global)	44.89
(20) (jobs,market,global)	42.81
(21) (jobs,market,labor,global)	42.17
(22) (linkedin,jobs,labor,global)	40.89
(23) (linkedin,jobs,market,global)	39.14
(24) (jobs,labor,equitable)	37.86
(25) (jobs,market,equitable)	35.78
(26) (jobs,market,labor,equitable)	35.14
(27) (linkedin,jobs,labor,equitable)	33.87
(28) (linkedin,jobs,market,equitable)	32.11
(29) (jobs,labor,advice)	30.83
(30) (jobs,market,advice)	28.75
(31) (jobs,market,labor,advice)	28.12
(32) (linkedin,jobs,labor,advice)	26.84
(33) (linkedin,jobs,market,advice)	25.08
(34) (linkedin,jobs,wow)	23.00
(35) (company,jobs,recruiters)	22.20
(36) (linkedin,jobs,recruiters)	21.25
(37) (linkedin,jobs,professionals)	20.45
(38) (company,jobs,momentum)	19.49
(39) (company,recruiters,momentum)	18.69
(40) (linkedin,jobs,momentum)	18.37
(41) (linkedin,microsoft,technology)	17.25
(42) (linkedin,microsoft,revenues)	16.93
(43) (linkedin,microsoft,paradigm)	16.61
(44) (linkedin,consumer,overtly)	15.97
(45) (linkedin,consumer,microsoft)	15.65
(46) (linkedin,consumer,hey)	14.70
(47) (linkedin,consumer,gen)	14.54
(48) (linkedin,answer,questions)	13.74
(49) (linkedin,answer,question)	13.26
(50) (linkedin,answer,qualifying)	12.78
(51) (linkedin,answer,product)	12.30
(52) (linkedin,questions,product)	11.50
(53) (linkedin,answer,augment)	11.34
(54) (linkedin,product,company)	11.18
(55) (jobs,thinking,start)	10.06
(56) (jobs,wow)	9.90
(57) (company,jobs,technology-driven)	8.79
(58) (jobs,technology-driven)	8.47
(59) (company,jobs,technology)	7.51
(60) (company,questions,technology)	7.03
(61) (jobs,technology)	6.87
(62) (company,jobs,questions)	6.23
(63) (company,jobs,misfits)	5.43
(64) (company,jobs,linkedin)	4.47
(65) (company,recruiters,linkedin)	1.92
(66) (company,hiring,entry-level)	1.44
(67) (company,recruiters,candidates)	1.28
(68) (company,questions,chros)	1.12
(69) (company,microsoft)	0.80
(70) (company,internet)	0.64
(71) (company,gen)	0.32

Supporting narratives:

Please refer to knowledge diagram for a complete set of supporting narratives.

momentum - Back to HLA

Have AI create a draft of what the answer to that might look like and then feed that out to the people on LinkedIn who have skills qualifying them to answer that question, have them come on top of it and to augment it and to make it better ...
High Level Abstractions:

(linkedin,answer,qualifying)
(linkedin,answer,question)
Inferred entity relationships (4)
(answer,linkedin,question) [inferred]
(answer,linkedin,qualifying) [inferred]
(answer,linkedin,questions) [inferred]
(answer,linkedin,product) [inferred]

momentum - Back to HLA

Have AI create a draft of what the answer to that might look like and then feed that out to the people on LinkedIn who have skills qualifying them to answer that question, have them come on top of it and to augment it and to make it better. ...
High Level Abstractions:

(linkedin,answer,augment)

WIP - Back to HLA

Lev-Ram: So, I want to hear more about Microsoft and some of the specific ways that you feel being a part of this larger company who has so much of the IP and the ties to what's going on with AI and gen AI in particular, how that's benefited you. ...
High Level Abstractions:

(linkedin,microsoft,gen)
(linkedin,company,microsoft,gen)
Inferred entity relationships (5)
(gen,linkedin,microsoft) [inferred]
(linkedin,microsoft,technology) [inferred]
(company,gen) [inferred]
(linkedin,microsoft,revenues) [inferred]
(linkedin,microsoft,paradigm) [inferred]

WIP - Back to HLA

Then globally what's happening is I think that a lot of the world is recognizing that they need LinkedIn more than ever. So we're a 20-year-old consumer Internet company, and that's that's a long time for consumer Internet companies ...
High Level Abstractions:

(linkedin,company,recognizing)
(linkedin,consumer,recognizing)
(linkedin,company,consumer)
(linkedin,consumer,internet)
(linkedin,company,internet)
Inferred entity relationships (9)
(consumer,linkedin,overtly) [inferred]
(company,internet) [inferred]
(company,linkedin,product) [inferred]
(consumer,linkedin,microsoft) [inferred]
(company,linkedin,questions) [inferred]
(consumer,linkedin,recognizing) [inferred]
(company,linkedin,start) [inferred]
(company,linkedin,recruiters) [inferred]
(company,linkedin,workforce) [inferred]

WIP - Back to HLA

You know, remote or hybrid. Oh, and not only for LinkedIn, our company, but the global workforce is like staring at this platform trying to figure out what to do ...
High Level Abstractions:

(linkedin,company,workforce)
Inferred entity relationships (5)
(company,linkedin,recognizing) [inferred]
(company,linkedin,start) [inferred]
(company,linkedin,recruiters) [inferred]
(company,linkedin,product) [inferred]
(company,linkedin,questions) [inferred]

WIP - Back to HLA

I want to start out by asking you a little bit about this transition to AI, which a lot of people are talking about now, but you've been talking about for a little bit longer than just the immediate present ...
High Level Abstractions:

(linkedin,company,start)
Inferred entity relationships (5)
(company,linkedin,recognizing) [inferred]
(company,linkedin,recruiters) [inferred]
(company,linkedin,product) [inferred]
(company,linkedin,workforce) [inferred]
(company,linkedin,questions) [inferred]

WIP - Back to HLA

And then all of a sudden my questions that I have to deal with is like, oh, like, what do you do with 38 offices around the world ...
High Level Abstractions:

(linkedin,company,questions)
Inferred entity relationships (5)
(company,linkedin,recognizing) [inferred]
(company,linkedin,start) [inferred]
(company,linkedin,recruiters) [inferred]
(company,linkedin,product) [inferred]
(company,linkedin,workforce) [inferred]

WIP - Back to HLA

And so in order for us, you know, fundamentally, we built the largest global labor market on LinkedIn to help people find jobs. And in order to help people find jobs, ensure that we can match talent with the opportunity or the people with the jobs ...
High Level Abstractions:

(jobs,market,global)
(linkedin,jobs,market,global)
(jobs,market,labor,global)
(jobs,labor,global)
(linkedin,jobs,labor,global)
(linkedin,jobs,market,labor)
Inferred entity relationships (16)
(global,jobs,labor) [inferred]
(global,jobs,market) [inferred]
(jobs,labor,linkedin,market) [inferred]
(jobs,market,talent) [inferred]
(jobs,labor,talent) [inferred]
(jobs,linkedin,professionals) [inferred]
(global,jobs,labor,market) [inferred]
(jobs,linkedin,talent) [inferred]
(jobs,labor,linkedin,talent) [inferred]
(jobs,linkedin,recruiters) [inferred]
(jobs,labor,market,talent) [inferred]
(jobs,linkedin,wow) [inferred]
(global,jobs,linkedin,market) [inferred]
(jobs,linkedin,market,talent) [inferred]
(global,jobs,labor,linkedin) [inferred]
(jobs,linkedin,momentum) [inferred]

WIP - Back to HLA

My best advice is ironically, something that we've been talking about for a while in terms of how we create a much more equitable and efficient labor market across LinkedIn which is, it's really important for people to change the way you think about jobs ...
High Level Abstractions:

(linkedin,jobs,market,equitable)
(jobs,market,equitable)
(linkedin,jobs,market,advice)
(linkedin,jobs,labor,advice)
(linkedin,jobs,market,labor)
(jobs,labor,equitable)
(linkedin,jobs,labor,equitable)
(jobs,labor,advice)
(jobs,market,labor,advice)
(jobs,market,advice)
(jobs,market,labor,equitable)
Inferred entity relationships (21)
(advice,jobs,labor,market) [inferred]
(jobs,market,talent) [inferred]
(jobs,linkedin,talent) [inferred]
(jobs,linkedin,wow) [inferred]
(equitable,jobs,labor) [inferred]
(equitable,jobs,linkedin,market) [inferred]
(advice,jobs,labor) [inferred]
(equitable,jobs,labor,market) [inferred]
(equitable,jobs,market) [inferred]
(jobs,labor,linkedin,market) [inferred]
(jobs,labor,talent) [inferred]
(jobs,linkedin,professionals) [inferred]
(equitable,jobs,labor,linkedin) [inferred]
(advice,jobs,labor,linkedin) [inferred]
(advice,jobs,linkedin,market) [inferred]
(jobs,labor,linkedin,talent) [inferred]
(jobs,linkedin,recruiters) [inferred]
(jobs,labor,market,talent) [inferred]
(jobs,linkedin,market,talent) [inferred]
(advice,jobs,market) [inferred]
(jobs,linkedin,momentum) [inferred]

WIP - Back to HLA

And so in order for us, you know, fundamentally, we built the largest global labor market on LinkedIn to help people find jobs. And in order to help people find jobs, ensure that we can match talent with the opportunity or the people with the jobs. ...
High Level Abstractions:

(jobs,talent)
(jobs,labor,talent)
(linkedin,jobs,market,talent)
(jobs,market,talent)
(linkedin,jobs,talent)
(linkedin,jobs,labor,talent)
(jobs,market,labor,talent)
Inferred entity relationships (10)
(jobs,labor,linkedin,market) [inferred]
(jobs,labor,talent) [inferred]
(jobs,linkedin,professionals) [inferred]
(jobs,linkedin,talent) [inferred]
(jobs,labor,linkedin,talent) [inferred]
(jobs,linkedin,recruiters) [inferred]
(jobs,labor,market,talent) [inferred]
(jobs,linkedin,wow) [inferred]
(jobs,linkedin,market,talent) [inferred]
(jobs,linkedin,momentum) [inferred]

WIP - Back to HLA

And that's where we started to identify and notice, wow, there's a real uptick in people requiring or posting the need for AI-type skills in jobs and subsequently, professionals on LinkedIn learning those skills, adding them to their profile, like, probably two years ago was like, oh, this AI thing is going to be real ...
High Level Abstractions:

(linkedin,jobs,wow)
(linkedin,jobs,professionals)
Inferred entity relationships (6)
(jobs,linkedin,recruiters) [inferred]
(jobs,linkedin,wow) [inferred]
(jobs,linkedin,market,talent) [inferred]
(jobs,linkedin,momentum) [inferred]
(jobs,linkedin,professionals) [inferred]
(jobs,linkedin,talent) [inferred]

WIP - Back to HLA

And it's across the board for everything that we do from how you share content on LinkedIn to how you find jobs, to how recruiters do their jobs, and we're starting to see real momentum and real wins across the board ...
High Level Abstractions:

(company,jobs,recruiters)
(company,jobs,linkedin)
(linkedin,jobs,recruiters)
(company,recruiters,linkedin)
Inferred entity relationships (18)
(company,jobs,linkedin) [inferred]
(company,jobs,misfits) [inferred]
(company,jobs,momentum) [inferred]
(jobs,linkedin,professionals) [inferred]
(company,linkedin,product) [inferred]
(jobs,linkedin,talent) [inferred]
(company,linkedin,questions) [inferred]
(jobs,linkedin,recruiters) [inferred]
(company,jobs,technology) [inferred]
(jobs,linkedin,wow) [inferred]
(jobs,linkedin,market,talent) [inferred]
(company,jobs,questions) [inferred]
(company,linkedin,recognizing) [inferred]
(company,linkedin,start) [inferred]
(jobs,linkedin,momentum) [inferred]
(company,jobs,recruiters) [inferred]
(company,linkedin,workforce) [inferred]
(company,jobs,technology-driven) [inferred]

WIP - Back to HLA

And it's across the board for everything that we do from how you share content on LinkedIn to how you find jobs, to how recruiters do their jobs, and we're starting to see real momentum and real wins across the board. ...
High Level Abstractions:

(company,recruiters,momentum)
(company,jobs,momentum)
(linkedin,jobs,momentum)
Inferred entity relationships (11)
(company,jobs,linkedin) [inferred]
(company,jobs,misfits) [inferred]
(jobs,linkedin,professionals) [inferred]
(jobs,linkedin,talent) [inferred]
(jobs,linkedin,recruiters) [inferred]
(company,jobs,technology) [inferred]
(jobs,linkedin,wow) [inferred]
(jobs,linkedin,market,talent) [inferred]
(company,jobs,questions) [inferred]
(company,jobs,recruiters) [inferred]
(company,jobs,technology-driven) [inferred]

WIP - Back to HLA

We started to really see some new breakthrough in technology. And we need to embrace this as Microsoft.. . And for LinkedIn, that really meant navigating the paradigm shift on two fronts ...
High Level Abstractions:

(linkedin,microsoft,technology)
Inferred entity relationships (2)
(linkedin,microsoft,revenues) [inferred]
(linkedin,microsoft,paradigm) [inferred]

WIP - Back to HLA

And look, Microsoft, you know, is a massive business and LinkedIn has a great huge business, $16 billion revenues. ...
High Level Abstractions:

(linkedin,microsoft,revenues)
Inferred entity relationships (2)
(linkedin,microsoft,technology) [inferred]
(linkedin,microsoft,paradigm) [inferred]

WIP - Back to HLA

And we need to embrace this as Microsoft.. . And for LinkedIn, that really meant navigating the paradigm shift on two fronts. ...
High Level Abstractions:

(linkedin,microsoft,paradigm)
Inferred entity relationships (2)
(linkedin,microsoft,technology) [inferred]
(linkedin,microsoft,revenues) [inferred]

WIP - Back to HLA

But first, you know, as a consumer of LinkedIn, I don't know that I have noticed any shift and like, sure, I see new features, but it's not like you're overtly saying like, Hey, look what we can do with AI. ...
High Level Abstractions:

(linkedin,consumer,overtly)
(linkedin,consumer,hey)
Inferred entity relationships (2)
(consumer,linkedin,recognizing) [inferred]
(consumer,linkedin,microsoft) [inferred]

WIP - Back to HLA

Lev-Ram: So, I want to hear more about Microsoft and some of the specific ways that you feel being a part of this larger company who has so much of the IP and the ties to what's going on with AI and gen AI in particular, how that's benefited you ...
High Level Abstractions:

(linkedin,consumer,microsoft)
Inferred entity relationships (5)
(linkedin,microsoft,technology) [inferred]
(consumer,linkedin,recognizing) [inferred]
(consumer,linkedin,overtly) [inferred]
(linkedin,microsoft,revenues) [inferred]
(linkedin,microsoft,paradigm) [inferred]

WIP - Back to HLA

Lev-Ram: So, I want to hear more about Microsoft and some of the specific ways that you feel being a part of this larger company who has so much of the IP and the ties to what's going on with AI and gen AI in particular, how that's benefited you. But first, you know, as a consumer of LinkedIn, I don't know that I have noticed any shift and like, sure, I see new features, but it's not like you're overtly saying like, Hey, look what we can do with AI ...
High Level Abstractions:

(linkedin,consumer,gen)
Inferred entity relationships (1)
(gen,linkedin,microsoft) [inferred]

WIP - Back to HLA

And the first product that we launched was something called collaborative articles, which is where we want to help any professional on LinkedIn to answer professional questions that they have. How do I ask for a raise. ...
High Level Abstractions:

(linkedin,answer,questions)
Inferred entity relationships (3)
(answer,linkedin,question) [inferred]
(answer,linkedin,qualifying) [inferred]
(answer,linkedin,product) [inferred]

WIP - Back to HLA

And the first product that we launched was something called collaborative articles, which is where we want to help any professional on LinkedIn to answer professional questions that they have ...
High Level Abstractions:

(linkedin,answer,product)
(linkedin,questions,product)
Inferred entity relationships (4)
(answer,linkedin,question) [inferred]
(answer,linkedin,qualifying) [inferred]
(linkedin,product,questions) [inferred]
(answer,linkedin,questions) [inferred]

WIP - Back to HLA

And then secondly, how do we ensure that LinkedIn, our products and our company, we can embrace AI to make a more valuable product and platform for the world ...
High Level Abstractions:

(linkedin,product,company)
Inferred entity relationships (6)
(company,linkedin,recognizing) [inferred]
(linkedin,product,questions) [inferred]
(company,linkedin,start) [inferred]
(company,linkedin,recruiters) [inferred]
(company,linkedin,workforce) [inferred]
(company,linkedin,questions) [inferred]

WIP - Back to HLA

So, you know, it's important to start thinking about a new job. The majority of jobs though in the world are the types of jobs where a specific portion of those tasks will be automated, and you need to learn how to leverage those tools to help in that part of your job ...
High Level Abstractions:

(jobs,thinking,start)

WIP - Back to HLA

And then to your point, one of the things we really identified early on is changes in skills that are required to do specific jobs. And that's where we started to identify and notice, wow, there's a real uptick in people requiring or posting the need for AI-type skills in jobs and subsequently, professionals on LinkedIn learning those skills, adding them to their profile, like, probably two years ago was like, oh, this AI thing is going to be real ...
High Level Abstractions:

(jobs,wow)

WIP - Back to HLA

And what he talked about was how having all the data they have about who's looking for jobs, who's looking to hire, understanding where the matches are and the misfits are, gives them a really interesting window on this very rapid transformation that's taking place in the technology-driven business world. ...
High Level Abstractions:

(company,jobs,technology-driven)
(jobs,technology-driven)
Inferred entity relationships (7)
(company,jobs,technology) [inferred]
(company,jobs,questions) [inferred]
(company,jobs,linkedin) [inferred]
(company,jobs,misfits) [inferred]
(company,jobs,momentum) [inferred]
(jobs,technology-driven) [inferred]
(company,jobs,recruiters) [inferred]

WIP - Back to HLA

So that's why, it's funny, you know, a lot of the questions that I'm getting these days are coming from both CIOs and CHROs, because for the first time, I think what's going to happen is when you do look at jobs as a set of skills and tasks and you look at the future of what your company needs to look like, it becomes more overlapping with technology and people. ...
High Level Abstractions:

(jobs,technology)
(company,jobs,technology)
(company,questions,technology)
Inferred entity relationships (7)
(jobs,technology) [inferred]
(company,jobs,questions) [inferred]
(company,jobs,linkedin) [inferred]
(company,jobs,misfits) [inferred]
(company,jobs,momentum) [inferred]
(company,jobs,recruiters) [inferred]
(company,jobs,technology-driven) [inferred]

WIP - Back to HLA

So that's why, it's funny, you know, a lot of the questions that I'm getting these days are coming from both CIOs and CHROs, because for the first time, I think what's going to happen is when you do look at jobs as a set of skills and tasks and you look at the future of what your company needs to look like, it becomes more overlapping with technology and people ...
High Level Abstractions:

(company,jobs,questions)
(company,questions,chros)
Inferred entity relationships (7)
(company,jobs,technology) [inferred]
(company,questions,technology) [inferred]
(company,jobs,linkedin) [inferred]
(company,jobs,misfits) [inferred]
(company,jobs,momentum) [inferred]
(company,jobs,recruiters) [inferred]
(company,jobs,technology-driven) [inferred]

WIP - Back to HLA

And what he talked about was how having all the data they have about who's looking for jobs, who's looking to hire, understanding where the matches are and the misfits are, gives them a really interesting window on this very rapid transformation that's taking place in the technology-driven business world ...
High Level Abstractions:

(company,jobs,misfits)
Inferred entity relationships (6)
(company,jobs,technology) [inferred]
(company,jobs,questions) [inferred]
(company,jobs,linkedin) [inferred]
(company,jobs,momentum) [inferred]
(company,jobs,recruiters) [inferred]
(company,jobs,technology-driven) [inferred]

WIP - Back to HLA

Then you have all these companies that are hiring entry-level marketers, but these two things are not connecting. And again, back to my previous point, like that's our job as a company ...
High Level Abstractions:

(company,hiring,entry-level)

WIP - Back to HLA

So, for example, you see a lot of recruiters do things like, Well, I just want to see candidates that went to Princeton, someone went to Princeton, they must be smart ...
High Level Abstractions:

(company,recruiters,candidates)

WIP - Back to HLA

LinkedIn is a subsidiary of Microsoft now, it's owned by Microsoft, but it's a huge company. It has something like $16 billion in revenue ...
High Level Abstractions:

(company,microsoft)

WIP - Back to HLA

So we're a 20-year-old consumer Internet company, and that's that's a long time for consumer Internet companies ...
High Level Abstractions:

(company,internet)
Inferred entity relationships (1)
(company,internet,linkedin) [inferred]

WIP - Back to HLA

You've got a plan already for the year ahead and you've got to go into the company and kind of say, hey, you know, time out, we're going to have a pivot and we're really going to focus on AI and gen AI and ensuring gen AI works throughout the company. ...
High Level Abstractions:

(company,gen)
Inferred entity relationships (1)
(company,gen,linkedin,microsoft) [inferred]

Semantics Relevance

LinkedIn CEO says galvanizing 18,000 employees to shift objectives to AI ...