Explore how global leaders at the WEF Annual Meeting 2026 in Davos are debating strategies to harness AI‑led productivity while ensuring job growth and shared prosperity.
At Davos 2026, leaders framed “preventing jobless growth” as a productivity design problem: raising output without shrinking opportunity. Erik Brynjolfsson warned early signals already show stress for younger workers in highly AI-exposed roles, citing a 13% employment decline for ages 22–26 (now 16% in newer data). Yet the same tools can expand jobs when used to augment rather than automate: “the augmenters…had growing employment.”
Cognizant CEO Ravi Kumar argued the productivity boom has lagged because AI is “probabilistic,” requiring “context engineering” and workflow reinvention, not bolt-on automation. He described a shift to agentic work where people “macro delegate…[and] micro steer,” and claimed AI can broaden opportunity: the bottom 50th percentile saw a 36% productivity bump versus 17% for the top half.
UC Berkeley’s Laura Tyson emphasized history suggests no permanent technological unemployment, but transitions are hard and benefits often accrue to capital: the key question is “how are the productivity benefits shared?” EU Commissioner Valdis Dombrovskis highlighted Europe’s productivity gap and aging workforce, arguing AI adoption plus large-scale reskilling is essential.
AFL-CIO President Elizabeth Shuler pressed for worker-centered governance: “include workers…upstream,” not after deployment, and build “guardrails” so AI makes jobs safer and better rather than “de-skilled…replace[d].”
Hello and welcome to this panel on preventing jobless Growth. This has been the big topic here in Davos. And we have a lot of drivers of both the growth side and the job side. Technology, geopolitics, demographics and other factors. There's really two narratives going on here that are surprisingly disjoint. There's the positive narrative talking about growth and how we all have more wealth creation. There's the negative narrative about how where the jobs are going to come from. And I would like to highlight that these are actually two sides of the same coin. Productivity. What is productivity? Productivity is output divided by input. And for some reason a lot of people focus on the numerator. And other people focus on the denominator on the numerator side. We have some amazing progress on a lot of dimensions where as productivity grows, we have the potential for more not just more wealth creation, but cleaner environment addressing poverty, better, longer lifespans in healthcare and technology has been helping with that. For instance, we did a study, looking at how llms were affecting call centers, and we found that there was about a 14% average increase in productivity when people are using it up to 35%. There have been studies in, software and coding where you sometimes get even higher double digit or even triple digit productivity levels and recent data. It's early in the United States and other countries suggest that we may be see the early inklings of a productivity revival. The other side of productivity, though, is the denominator. And there the data is more worrisome because as you increase productivity, that ratio of output to input, you can either have the numerator grow or you can have the denominator shrink or just lay flat. And that's a bit of what we've been seeing lately. Job growth has been somewhat discouraging. We did a paper we call Canaries in the coal mine, looking at some early indicators of what was happening, in jobs that were highly exposed to artificial intelligence using data from ADP. We found that those in the youngest group, age 22 to 26, in the most exposed occupations, like I mentioned, call centers, software. But we ranked all 900 occupations based on their tasks. That age group actually had about a 13% decline in employment relative to before llms, especially in 2024 and 2025. We started seeing the that effect grow bigger. The older age groups did not have that effect, so the overall effect was fairly muted and some encouraging information. If you sliced it based on people who are augmenting using the llms and generative AI to augment their work versus people who are using it to automate work, what we found was that the augmenters, those who are learning new things, had growing employment, so they both had more output and more employment, so more shared prosperity. Sadly, that was a minority of the folks. But if we can get more people doing that, maybe that's a potential path. It's still early. That was only about 13% I mentioned, for that one age group. We just got some new data in which not in the new paper yet, but it's now about 16%. I'm not sure whether it's going to continue to grow over the coming months and years, but it's certainly, as I say, a canary in the coal mine to look a little bit more closely at it. Our goal, of course, is to have, to prevent jobless growth, to get the benefits of productivity that not only increase the top line, but also don't hurt people by eliminating jobs. And we've assembled a set of the world's leading experts here, and I'd like to take a minute to introduce them. And then we'll each have them weigh in on these issues. So let me start over here with Valdez Dombrovskis. He's a commissioner at the European Commission. He's focused on the economy and productivity. Ravi Kumar is the CEO of cognizant. Jonas Pricing is the CEO of the manpower Group. Liz Shuler is the president of the AFL-CIO, the largest labor union. And Laura Tyson is a professor at the Graduate School of Business at UC Berkeley. My neighbor at Stanford. So welcome, all of you. And let me just start with a question. I think we'll start with you, Ravi, if that's okay. I'd like to ask you, you know, we've heard a lot about I just mentioned about the potential for productivity boost from artificial intelligence, but we don't really see it in the aggregate statistics. A whole lot that there's been these amazing capabilities, but the aggregate numbers are still somewhat muted. Can you make it a little bit more tangible for us and tell us a little bit about how AI is augmenting work, replacing work, affecting productivity? You're very much on the front lines in your organization. And what can organizations do to harness the potential better?
Thank you. Thank you, Eric, for this question. You know, we did this study with 18,000 tasks and a 1100 occupations from the owner database. Yes. We are pretty sure now that the technology is pretty advanced. I mean, the amount of innovation which has happened around it, the drift of value to businesses is not happened yet. Yeah. And that's, you know, that's true for most technologies. It takes like eight to 8 to 10 years. In this case, the drift has been slower than what we think it should have been. And the reasons why the drift has been slower, and therefore the productivity has been a little bit of a flattish curve. You know, first and foremost, this is a technology which is very probabilistic in nature. It is unlike traditional software, classical software, which is very deterministic. The implementation of it and the reinvention of businesses, which was relatively easier when we applied enterprise software 30 or 40 years ago. When you have probabilistic software and you're trying to do things which are very different to what classical software did, which has human judgment integrated into the decision making, into the work patterns, into the problem solving, you would have to do contextual engineering of the technology which is available. So one of the things we've been working on is to make sure that how do we teach AI to be a part of your team? Until you do that, productivity is not going to be.
Teach the AI.
To be a part of your team. And I've written an HBR article on it. Yeah, okay. Have a look at it. I mean, it's in the public domain. How to teach AI to be a part of it, how to teach AI to understand the hustle of the company. Yeah, the work patterns of the company, the tribal knowledge of the company. And that is integral to what we are trying to codify and create digital labor using AI. Remember, productivity has been very high for software development. If you just take that specific function.
For for decades.
Yeah. Companies like ours, we have seen a significant bump in productivity just on software development cycles. But that's very deterministic. That's deterministic work. It's riding logic, writing code. In fact, I would say one of the points you made early careers have got impacted. In our case, we actually hired more school graduates last year than ever before. Really. Yeah. And we think we can amplify the potential of people at the bottom of the pyramid create. And, you know, you know, eliminate entry barriers, create more productivity, more throughput, provided you can use it to amplify the potential of the people at the bottom.
I'd love to hear a little bit more about that.
And we did the stat for the bottom 50 percentile. We had a bump of 36% and the top 50 percentile. We only had a bump of 17%, right. And that gives us hope that you could broaden the pyramid. You could take paths to expertise much quicker. Learnability is much faster.
Can I just ask a on that point in particular? I mean, by the way, we found a very similar pattern in our call center study that the less skilled workers actually had the bigger boost from, from using the technology. But but what you're doing and continuing to hire a lot for these entry level people could be a roadmap for a lot of people. So I want to hear more about how you're doing, because it is true that a lot of the skills that they were doing are increasingly done by machines. So are you upskilling them in a way? Are you are you teaching them new things that they can do so that they can continue to be?
I think the good news is early careers, they do not they do not even know how the old stuff was done. This is the first time they're starting to write software code and this is the only way they knew it. So it's a relatively easy transition to this, to this process. The second bit I would say is, you know, code assist platforms, which helped people to work with machines together. We're very synchronous. We have now started to get to asynchronous work, which is agents do the work. You might you macro delegate it and you micro steer it.
Yes.
And if you macro delegate it in micro steer it. You're like you know you're sending agents for work delegating them and you're taking it and you're sharing it. So I'm going to touch a few other points so that I don't hijack the time for the rest of the panelists. So context engineering is a space we have deeply researched on. We think that's very important in the contextual computing era. You know, the past was, you know, we wrote technology around the microprocessor. We're going to write technology around the LM, which is very probabilistic in nature. Context engineering is a very important point. You know, integrating work between machines and people is very important. The infrastructure of enterprises has predominantly been built for humans. It has not been built for machines. And if you look at what's happening with with autonomous cars, you know, the to to implement autonomous cars has been harder because the infrastructure has been built for humans. Right. And now you're trying to play you're trying to build an interplay between humans and machines. If it was just machines or just humans, it would have been relatively easier. If it's machines and humans coming together and amplifying each other, it's going to be harder. So that infrastructure integrating work patterns. In fact, AI is going to be in the middle of a workflow. You're going to have people on the front and people at the back, people on the front doing, you know, authentication problem finding, you know, and, you know, ideation and stuff like that. People at the back doing validation and verification. So to integrate work productivity is only going to come if you start to integrate the work from human and and machine labor. The last point I want to make and we can open it up subsequently after all of us talk. This is not about applying technology to old stuff we already had. So what will really happen is the old stuff will remain as is, and we're going to do it in a cheaper way. We have to reinvent the business, reinvent a process, reinvent a flow, and that reinvention and reimagination will will drive productivity. So integrating workflows between machines and humans, context engineering, you know, amplifying the potential of humans and reinvention of businesses rather than just eliminating work or just trying to apply this on top of something which is already bad.
Historically, that's been where most of the biggest gains come from. And I want to come. I'm sure we'll come back to that point a little bit, but I want to get some of the other folks involved. Let's go. Let's go to you, Laura, because there's you know, I talked about this tension between productivity and jobs. But, you know, economists know that historically, whether it's in the Industrial revolution or all the changes since then, every time there's been an amazing technology, it hasn't eliminated jobs. People were worried about it. And you can quote all the times, but, you know, there have been more jobs historically. Is this time different? Are you worried? What are your expectations?
So. So first of all, I want to say I certainly espouse those views. I certainly feel that past technological revolutions have actually led. There's no such thing in the evidence so far of long term technological unemployment. I mean, what happens is the technology over time, along with changes in demand, changes the composition of employment, changes what people do, changes what sectors are demanded to grow, and labor is hired to do that. So so that's the first thing. The second thing though is and I think it's fair, and it explains some of the concern that people have is the disruption effect can be pretty big. The disruption effect.
Even if you're moving from one set of jobs to another job. that's not the easiest thing to do.
You know, it's like you historically speaking, again, the technology oftentimes will eliminate or reduce the number of jobs. It might increase productivity in that place, but the number of jobs actually declines, whereas the new jobs are someplace else. Or the new jobs require some set of skills that people who are displaced.
An example of that in the past that we've seen.
Have, I mean, I would say the best work here is really by David OTR and you can kind of see all the new jobs. But again, it's a 40 year process. It's not it's not a 40 day process or four years. So I would say that the concern people have is about the transition and then the policymakers, I mean, I tend to look at this all in terms of policy. So what should policymakers do? Well, number one, I just want to start with the notion that we really need to have very strong aggregate demand. I mean, if we have a weak labor market, then the growth of new jobs and, is going to be slow. So first of all, let's just say you got to get macro policy, right. Okay. That's but then you have to do things like, like, help train. And I heard this morning and I think this has been really important. California does a lot of its training through community colleges. And so basically, the kids going into community colleges are very concerned about your kind of data. Oh my God, I'm not going to get into an entry level job in this kind of office situation. So what the I would say, what community colleges and educators need to do is have very tight links with the business community. What do you what what's happening with you? What sorts of jobs do you feel you are going to create?
Like the sort of like the Ravi described that. exactly 900 different occupations.
My view would be work with Ravi. The policymakers should. Absolutely. Because in our market system, these kinds of decisions are going to be made ultimately by the companies. The policymakers actually have to respond. So I would go that far. I do want to say, though, that I have a dystopian view in the following sense. I certainly believe that the productivity benefits are there and can be substantial. The question is, how are the productivity benefits shared? This is a huge issue going forward because if you look at the technological revolution, say, the the, the digital revolution that came in between the industrial revolution and where we are today, you see very significant polarization of the labor market. You see a loss of middle skill, middle income jobs. Many people benefit with education at that point by moving top. But a lot of people fall bottom, fall bottom, and the productivity growth of the economy measured in a variety of ways. The real wage growth does not keep up, does not keep up what has happened. And I'm really just talking about the digital revolution so that we're you can see that not only is there a polarization, but the labor share of income declines, the capital share of income rises, the productivity surplus is going to capital, it's going to capital. And so I think those are things we really have to worry about. It's not just the number of jobs. It's not just the composition of jobs is.
Going to we are beginning to see in the data.
It really is how you share the benefits. And therefore, from a policymakers point of view, it's a challenge to think about how to do that. But I think we should get that on the table as one of the things to discuss. I believe in the productivity benefits. I believe that employment will change over time. I believe that there's no long term unemployment, but my concerns are about real wage growth, about.
Division of the benefits.
About.
The division. Well, you mentioned.
The transition.
And the of.
Course, and the transition, the transition.
And let me just hold it there and let us get some other voices in. And so you mentioned policymakers. And so why don't we have one here. Valdes, if you want to weigh in, like, you know, how are you thinking about, you know, this risk of jobless growth and what are the policy targets we should be thinking about? Is that on your agenda?
Well, good morning, everyone. Obviously, it's, on our agenda. Well, all in all, what we are looking, if we first look at the productivity that in terms of productivity, EU is lagging behind other major economies already for a few decades. So there is some catching up to do. And if we look why we are lagging behind in productivity growth, it's largely explained by tech sector. So from that point of view, we are viewing AI as an opportunity. And we are now investing a lot of actually, developing and applying AI, setting up what we call our AI continent action plan, how we promote and develop the AI, including AI, Gigafactories and so on. And second, apply, AI strategy where we, facilitate the use of AI across different sectors of economy and also the public sector. So in terms of jobs, jobs, right now, labor market in the EU is robust. And what we had seen in post-Covid recovery, actually, our economy has created more jobs than you would normally expect with this kind of economic growth we are having right now. So, our, it's.
Almost the opposite of.
Our employment growth continues, to, our employment continues to grow and unemployment is at historically low levels. And also, if we look at the OECD data, what the OECD data says, that, approximately one third of job vacancies are highly exposed to AI in terms of requirement of AI skills, and it probably will increase to two thirds in the coming years. So, obviously, as some colleagues mentioned, the important question here is about managing transition, how we equip, people with a right AI skills, actually, to be able to use it as a factor of production, and, in Europe, in any case, we need to offset it against another tendency. We are aging, continents. So labor supply is shrinking. Also, to sustain the level of prosperity, we need to increase productivity. So we mainly see it as a challenge of properly managing transition, making sure that the labor has the right skills, for, also deployment of the AI.
Great. That's terrific list there. So that's the policy makers. Let's talk about employers. I think they also have a responsibility. And and we have maybe the world's biggest one of the world's biggest employers. You can tell me, at manpower. Jonas, can you tell us a little bit about how you think about it? Like, what is the responsibility? What's the potential for employers to address this?
Yeah, no, I think so. As a starting point, I think it's great to hear that. You know, we all seem to believe that long term jobless growth is not going to be the main issue to contend with the transition. Certainly. And how can we ensure that this is evenly distributed? To your early comments, though, Eric, when we look at youth unemployment in the US, for instance, we see this disparity now. But we always see this disparity when the labor markets are going through a tougher time, as they have in 25. I know the the economic growth is strong in the US, but the labor markets have been weakening all the way through 25. So not only college graduates are having a tougher time finding their first job and high school graduates.
They're kind of on the front lines of this.
And this is a tradition. This is what happens as companies go through an economic cycle. They hire experienced workforce first or specialized skills. So I don't know if your research sort of eliminated that because I.
Well, let me just very briefly. Yeah. So if we looked at that age group, but we also looked at the exposure to AI and the folks that are most exposed had a big fall, the less exposed had a lesser fall. And then interestingly, the least exposed like home health aides, they actually had growing employment. So it wasn't just a level shift. It was kind of a twist depending on on how much they were exposed. So that's a little bit more concerning. I have to be clear, this was not a a causal test. We didn't like, you know, have a, a group that was exposed to AI and a B group that didn't get exposed. So it's all correlational but the patterns are a little concerning.
Yeah. Well we're not really we're seeing that in some job categories. And you mentioned the two really the only ones where we can see some effect. But broadly speaking value realization is lagging the strength and the advancement of the technological.
As Ravi said.
As we said, just as you said, Ravi. Yeah.
And in fact, we did a quantification of that by looking at those 18,000 tasks in the United States, $15 trillion. Is the labor economy or the labor value, $4.5 trillion is already theoretically exposed to AI, but that $4.5 trillion is not realized yet. No.
No, no.
And I think a lot of those reasons is that to get the full benefit of AI, it's not about an AI applied to task, but it is about an AI applied.
To the workflow.
To a workflow. And that application of the design, the process redesign on how work gets done will create new jobs, will require new skills, and it will take time.
The reinvention.
The reinvention. It's easy to reinvent with five people in a startup, you know, large organization, public sector. It takes time. The culture has been established and, you know, the ability to attract and grow the skills. Which comes back to the point you asked me about around employers. And of course, employers have a big responsibility to avoid this notion of a divide. Now, when it comes to the digital divide, I have a bit of a contrarian view because AI commoditizes expertise. So instead of creating a divide between the haves and the have nots, everybody has access essentially to an infinite extension of your mind.
Of their capabilities.
Of capabilities.
So on observation, I think the digital divide.
If you let me finish, Ravi. So I think that is a is a positive. But what we're observing today that companies are already doing those at the front edge, they are really training their people in AI skills. And you say, what is an AI skill? If what is true is a process redesign is needed, what kind of skills are you training for? Well, they're being very pragmatic. You can think about this almost as the evolution of, you know, word, Excel and PowerPoint.
Exactly.
First of all, know how to write a really good prompt.
Second.
Know how to integrate a document and analyze this document. Draw the insights, ask conclusions. You know, do that. And then last but not least, moving to the last phase is how can you augment your skills when you're accompanied by a large language model and you can now work get work done in a different way?
I like that.
So that's sort of the the thinking that I, we are starting to see coming through. So you can think about, you know, the skills, or rather you can think about the skills being the technology infrastructure equivalent. So they are needed. And the training is really all about the R&D and how people can use it. Use training. We see the most successful candidates for jobs today. List. I know how to write a prompt. I know how to do ChatGPT. I work with Claude, much as you remember seeing. Yes, I know I can do the Microsoft suite of office. Very well. You know, this is now just ubiquitous in applications, and those that have those employers will say, oh, well, this is a person who's learning new skills much more employable. I think we are interested in that.
This is so important because as you're highlighting, you know, you can use AI to augment yourself and humans and machines working together. It's hard. It's harder than just having one separate. But ultimately, if you do that now, now the person becomes an extension. They're creating more value and ultimately that makes them more employable. So that's a very promising path.
So just to add to it, I mean, you raised this point, the divide which was created for digital technologies, was also because there were skills needed to get those jobs, and it wasn't as commoditized. I mean, I actually think AI is an equalizer in many ways because it takes the entry barriers out and you can it diffuses fast and you can actually access jobs, which you couldn't access. If we pivot the skilling in the right way.
I just point out we should go on. But a question I have in my mind, if we live, it's now let's go to a very specific structure. The structure we live in is a structure in which companies make these decisions, okay. They make these decisions for why does a company decide to use the technology to augment skills versus to automate skills? If you have a high percentage of labor costs in your overall cost structure, and it tends to be in fairly low skill categories that can be easily automated away, your incentive is to automate it away. I believe, I.
Believe, I think, I think.
You're both talking about companies that are doing something different. And I think what I want to understand is what can we do in the structure, to encourage companies to behave that way because there's a natural incentive.
Yeah. I'll give you a chance to think about an answer to that question. I have some thoughts as well, but I really want to. We've all been talking about workers. I agree. And here we have. I agree, Liz Shuler representing the largest labor union. So I think we should hear from this. You're the anchor person here. Tell us a little bit about, you know, given the scale and some of the things you've just heard here, you know, tell us about how you think we can ensure that workers get a fair share. Have a voice in all of this. What are you seeing?
Yeah. It's been I've been sitting here waiting to speak because we represent all of the people that you're talking about. Right. Exactly. In the US, the AFL-CIO is an umbrella organization of 64 unions, 15 million workers, ranging from every industry from professional athletes to actors and bus drivers, nurses, health care workers, the people, you know, making this meeting happen behind the camera. So we absolutely, are paying attention to what is happening with this transition and this discussion. We are seeing just as a backdrop the economy, at least in the US and around the world, isn't working for working people now, right? We have inequality at its highest levels. You know, people are working harder and harder for less. They're working two and three jobs just to keep up. In the US, workers, 40% of workers do not have $400 for an emergency. Now put AI on top of that. The insecurity that we're all experiencing, the fact that people are waking up and some new technology is landing on them in their jobs without training, without them having a say, of course they're going to be anxious. Of course they're going to be feeling insecure about what the future holds. And so I think we really need to stop and say, who are we doing this for? What are the results we want and how do we get there? Well, we get there by including workers in the process. They shouldn't have to be at the end of the cycle. They should be actually upstream in the cycle. And in fact, we have a partnership with Microsoft for that very reason to get workers in the labs to say, if we're going to start using AI in transportation, we should have bus drivers in that lab working with developers to make this as successful as it can be, because we are not anti-technology. If you think about every industrial revolution that we've been through, working people have helped us make that transition, and it's really because we've helped tame the technology. We've helped, you know, figure out how to use it in the most effective way. So I think your question about augmentation versus replacing that is the big question we have, because if we can all agree that this is to make our jobs better and safer and easier, more productive than we're all in. But if you're looking to just de-skilled, dehumanize, replace workers, you know, put people out on the street with no path forward, then absolutely, you're going to have a revolution. So I think that's something we all need to be very real about and thinking about if we are going to have productivity gains, working people, the ones who make these industries happen, need to share in that. So there's not been a lot of discussion about that here. Of course, the, you know, in terms of, you know, how we create policies, how we create tax, infrastructure, you know, whether or not we are redistributing that word, you know, as a dirty word around here, but we need to talk about it and confront, how we're going to make sure that working people share in the gains of these, these technologies. And if you look at the numbers of jobs, let's talk about job quality, because, yes, maybe there are a lot of jobs created, but what kind of jobs are we talking about? Are they jobs that can sustain a family? Is it a job that, you know, people can actually work? One job, one job should be enough. So I think these are the things that are on people's minds. And also I have to say a word about privacy, data security, and how the data is used. I'm going to use the WNBA players as an example. Right now, professional athletes who are fighting for a fair contract, data and AI in their workplaces is part of it because AI is actually judging how players are making decisions and how they play a game. Right? Are we are we now saying that that's what we're getting to, that an athlete can't use professional judgment. An artist can't use professional judgment without an AI system evaluating them at every turn, saying whether they made the right decision to make a pass to this player or that player. So collective bargaining actually is a tool that really undergirds this and gives workers a seat at the table to determine how that data is used.
Well, I think we all agree we need to work towards shared prosperity. I want to pick up on that theme of augmentation. But first I want to just give a heads up. I'm going to open the discussion up to questions from the audience. So if some of you have a question you want to raise, you formulate that. But let me just pick up on what you're saying. And I think almost everyone here was talking about this distinction between automating and replacing workers versus augmenting them and extending what they can do. And, you know, I've done some work on this. And one of the challenges is that, you know, we can have much more shared prosperity if we use AI to augment people. But there's a set of institutional incentive policy levers that are steering us excessively towards automation.
The other.
Direction and the wrong direction. And I wrote a paper called The Turing Trap that that, many of you may be familiar with the Turing test, this idea that we try to make AI that perfectly imitates humans and can replace them, it's been an inspiration for a lot of technologists. I think it's a terrible direction for the technology we should be doing. As Ravi and others said, finding ways for humans and machines to work together. Most of the benchmarks out there that the AI researchers are working towards, they are a black box of just how well can this AI do some particular task? And then they take it and they put it in a hospital in an other organization. It turns out it doesn't work nearly as well as it did in the laboratory. Surprise, surprise. You know, if a if a medical imaging system says, oh, you know, cut out the patient's left lung and the doctors or nurse says, well, why are you saying that? Well, probability 0.87. Like what do you do with that? You know, do you, do you operate or not? It has to be designed from the beginning to work with the workers and have them coordinate. So we're creating a new set of what we call center benchmarks, where it's not cheating to have the human help the machine or the machine help the human. It's the whole point is the two of them need to work together, a whole group of them work together, and we have some tax incentives that spares too much towards automation. A lot of CEOs, I think they overly focus on some of the bottom line metrics of cutting costs, when they should be thinking about how they can create more value. So there's a whole set of cultural and financial incentives that we can change to lead us more towards this augmentation. I think that would be a great takeaway from this panel, is we can have productivity growth increasing that numerator I talked about before and not simply cutting the denominator. Sure. If you want to weigh in and then let's let's get a question from.
The audience very quickly. Just one point I wanted to make to put more emphasis is also on labor skills, because we have been dealing with also in a previous years with a digital skills more broadly, where the huge shortage of digital skills and we are seeing right now the same with AI. Indeed companies are we see it also in microdata, massively retraining their workforce because there is not enough graduates with the necessary AI skills. So if we want to have this transition successful, we also need to focus very much on having the right skills of people entering the labor force and also people already in the labor force.
Absolutely. So is there someone in the back here? If we get a microphone over here? Here comes the mic. And just say your name. Thank you. Brief.
Brief as I can. Simon O'Connell, SMB global development partner. Fascinating discussion. I love the kind of framing moving towards the the the shared productivity gains. Official development assistance last year was about 212 billion globally. It's being dismantled, unraveled, depleted, etc., etc.. I wonder if there's a whole new framework here around shared productivity gains where those doctors, nurses, teachers, even farmers in more affluent societies who gain time and the companies gain in growth through that increased productivity that can be shared. It's really metrocable it's really Evidence-Based and it can be shared. For sure it can be shared, you know, in more affluent societies as well, but a whole different model moving away from the more traditional official development assistance structures to shared productivity gains. And I'd love to hear thoughts from the from the panelists on.
I think I just heard a really compelling presentation from the president of the world Bank. And what what they're doing is, is they believe that that you can use edge AI models to help increase productivity in the key sectors that are for, to bring a, a country or, bring a nation continent out of poverty. So he sort of talked about the edge AI in agriculture. So helping small scale, low income farmers in Africa who had a tendency to just leave the farm, sell the farm, go to urban, not be employed. This now makes them much more productive. Okay. He talked about that in terms of health care. He talked about that in terms of tourism. So I think it's possible if we if we here we tend to focus all on the big models and the big corporations. But actually there's an amazing set of things being done at the sectoral level. And that probably also would be involved with you. I mean, you talk about voice and labor representation. If we're going if the technology is developing, getting the workers to work in a sector with the technology, I love the I love the bus transport example, I love it, my husband is a writer. What about the the Writer's Guild Association and how you protect your intellectual property? So it's the basketball players. It just strikes me that we can actually get very a huge increase in productivity, performance, wages, everything else. If we actually design the edge applications to really work in, in them.
So we're running low on time here.
I want to just.
One one. Yeah. I'll just call it I just want to give everybody a chance to weigh in, maybe for like a minute or so. And let me start with you, Ravi.
Yes. So I think you raised this very important point on higher demand.
Higher demand.
And that, I think, is a trigger to I mean, why are corporations focused on bottom line? Because they're not focused. They're not able to do growth. Okay. So the ability to take the pivot back to growth and create a flywheel on that. And productivity is a flywheel. I mean, it creates deflationary growth, of course. So, so the point you made on sectors to look at, I think is very important. I mean, a lot of productivity is looked at in context to the knowledge industry and a lot of jobs which got created in the last two decades were in the knowledge industry. The reality is you need more productivity in agriculture, manufacturing, care sectors, sectors, health care, and the throughput you can get there is significantly higher. And we did exposure scores and velocity of change in the last three years. The exposure scores are very low in construction and transportation, but the velocity at which that exposure is going to go up is very high there. And you're going to create more jobs because the productivity is going to go up. I mean, let's take software development. Productivity is very high.
Yes.
So the the exposure scores are very high. The velocity of change is going to be lower because it is so high. I mean, in software at least our study says the exposure is 70%, which means the change, the rate of change every year is going to be much lower. It's already rebaseline the jobs of the future are already here. While manufacturing, health care, construction, transportation, construction these are sectors where you could do digital enhancement of physical jobs using AI technologies.
And some of those physical changes are going to happen a little bit later. We're seeing it first in the cognitive side. Jones do you want to weigh in with some closing thoughts?
Yeah. You know what strikes me? Over time, we always get very mesmerized by the technology itself. That's ten years ago. We're all worried. Blockchain. We were curious. The whole Davos, that was all. It was all about blockchain that lasted one year and then that was it. Then we moved to driverless cars. What are all the bus drivers going to do? What's going to happen with them. And you know we're nowhere near there. And when we are the buses or the trucks have a driver in the cabin, because as humans, we prefer that certainty that just in case, just like airplanes, just in case, please, let's have three pilots. Even though evidence and data would say pilots are the ones that cause issues more than the technology in most cases. So in the end, the technological progress and the speed of which I think is all about humanity.
Yeah.
The work of the future, enabled by technology, is decided by the workers of the future and the skills that they acquire. Yet we always tend to move into the notion of what can technology do and you know, what is going to happen. So, so this notion of we need to take care of the people, provide humans the edge, which we believe is going to be the case augmenting with with AI. So the human age edge and the human age is really what we need to focus on, because the notion of reskilling at scale, we don't have to worry about large enterprises. Large enterprises will retrain to reskill, upskill their people because they're competing and they have the resources we need to worry about all of the major, all of the other employers, which are the majority of employment providers. What are they going to do? And that's where policymakers, distribution policies and things like.
That.
And highlighting.
That it's so important.
That this amazing technology gets a lot of attention. But making all those other changes, really, that's where the rubber.
That's where.
It comes to life.
And that's been lagging much more. We can close that gap and boost the productivity. That will make a big difference. Let's go to Valdez and then Liz, you get to wrap up and I'll say a final word. Yeah, yeah.
Maybe to come back to the original topic of our discussion on, jobless growth. I think we all concur that there's not going to be a jobless growth, but we have to admit that there is also going to be some replacement of, work. Professions will change. It will not be able to preserve all the works. But the nature of jobs will be changing. And it's also about managing transition, about reskilling the people, because there will be jobs which will be phased out. It has been the case since the first industrial revolution. But the question is what happens on jobs with aggregates? And there are reasons to believe that there will be jobs. And the question is how we properly equip people for those new jobs will not be able to hold to every old job we had.
Thank you. Liz.
Can anyone give me an example of a transition that really went well? I think we should learn from our past. And I think about the US when we lost manufacturing, hollowed out the industrial Midwest and left workers behind. That did not go well. Why are we not learning the lessons from these previous transitions and choosing a different way? So we have the opportunity to do that this time. And so I would argue that this transition needs to be worker centered, that we put workers at the front instead of them, always the losers, and that training partnerships not just top down, but workers again at the table. And then thinking about guardrails, guardrails to make sure that the technology is not running over us. And that working people, again, are the ones driving it.
Absolutely. And that's the interest not just of workers, but all of us in society and enlightened self-interest. So we need to do that. And I think one of the things that we can do is get better visibility into how the world is changing better data and statistics, and try and align the incentives a little bit better. So thank you very much. This has been an amazing discussion, and we're very privileged to have all of you be able to weigh in on this, I appreciate it. Let's give them all a round of applause.
Thank you.