From self driving cars to robot powered factories. Artificial intelligence is taking over significant pieces of the global economy. This is great for the businesses embracing AI, but there is a downside. More robots in the workforce also means more people losing their jobs to computers. So how bad will the robot revolution be and how will it reshape the global economy? Welcome to Benchmark. I'm Scott Landman and economics editor with Bloomberg News in Washington. Returning as a guest co host is my colleague Chris Content. He's a reporter covering the Federal Reserve and u S economy also here in d C. Chris, glad to have you back, Happy to be here. So this week we're talking about the rise of the robots and how they will impact the economy. As someone who writes about the Federal Reserve, Chris, what interest you and AI? Well, I can tell you, Scott, that there is a great capacity actually of artificial intelligence to transform one of the biggest jobs of central banks. As you know, central banks are trying to regulate economies, keep them in that Goldilock zone, and they do this through short term interest rates. But that tool has a lag to it. It takes a good six st eighteen months before a move and interest rates has an impact in the real economy. So these central bankers they've got to forecast what what's the economy gonna be like a year from now, eighteen months from now. But they're not very good at forecasting the future when it comes to things like inflation, unemployment, GDP growth. So some central banks are starting to turn for a little help to artificial intelligence because artificial intelligence can be used quite effectively at spotting patterns in past events and then using that to predict the future. So it's an area it's going to take a while, but it's an area of great promise for economics. It's not just economics, but it's business and the broader economy as a whole. Forecasting prediction, it's it's a really big part of what AI is meant to do. And our guest is here to talk about that today. His name is Joshua Gains and he's a professor at the University of Toronto's Rotman School of Management. He's one of the authors of a brand new book called Prediction Machines, The Simple Economics of Artificial Intelligence, just published by Harvard Business Review Press. Joshua, thanks for joining us. Good to be here. Thanks. So we've already sort of alluded to this, but why is the book called Prediction Machines. Well, it's uh called that and not some more interesting title such as, um, you know you're wonderful, great new robot and how your life is going to get better, simply because the recent developments in artificial intelligence are not about completely replacing human intelligence per se, but actually all of being about one thing, and that is prediction. That is taking a whole lot of information that you do have and converting it into information you do not have. That's very, very different from something that does all of your choices for you and things like that. Uh, it really really just does prediction. And when we talk about this prediction function and getting all this information, can you bring us into the real world here and what kinds of industries or jobs would be most likely to or have the most room to benefit from this kind of additional knowledge. Well, prediction is something and from your business that you're in, you think of it mainly about forecasting, for instance, economic variables and things like that, and something of course I've worried about as well. But what's really interesting about these new developments is that they highlight problems that turn out to be prediction problems. For instance, the whole issue of having a digital image served up to you and knowing what's in it is a prediction problem. Invariably, when you get an image from the Internet, the label that's being attached to it is the label that human would attach to it. And so basically what Google are doing when you're searching for a picture is predicting which pictures correspond to that label. So that's a form of prediction. And it turns out that that and language translation and understanding machines, understanding human speech, and even self driving cars are all mainly a prediction problem, and so that is where this new artificial intelligence is being implemented. You wouldn't normally call that prediction problems. We normally think about forecasting the weather or forecasting an earthquake or something, but basically prediction is all around us. Joshua, I think it's clear that this can have all sorts of benefits in our lives and in the economy, but I can also have some downsides, and particularly how how these things are distributed throughout our society. Maybe a big question mark first about the benefits. Where where do you see that societally and also economically. So I think the benefits come from wherever you think about where would it be good to know things with greater certainty? And so any kind of decisions that you're doing under uncertainty are going to benefit from having better prediction so that you can imagine being applied. For instance, you're trying to manage inventory. If you've got better predictions regarding demand you're going to face, you're going to be able to manage that inventory better. You're going to be able to correctly adjust your behavior to prevent shortfalls or worse, to prevent surpluses that end up to over stocking your inventory. Those are the sorts of places where prediction machines are going to work quite well. Uh. And so basically anywhere where there's decision making being done and there's uncertainty, there is room for better prediction, and the machines might serve that up right, And that sounds, of course like it will make our companies more efficient, more productive, but also I think will disrupt how companies operate and may disrupt people's lives. Do you think this time is going to be any different. We've had lots of technology disruption in our economy over the decades, in fact, in centuries, um is the pace of change these days going to be more disruptive and more problematic to adjust to. I think there's a chance, as usual, whenever you've got a very large and radical innovation occurring and being adopted, there is the potential for disruptive change. But the hard part is trying to predict exactly where that will be. When we look back and we think about the rise of the automobile, it's no surprise that we also point to horses as being the disruptive workers in that disrupted workers in that equation. When it comes to things like better prediction, however, it's far subtler um. Some of the discussion that's going around is saying, well, this is the first Why is this time different? It's because it's the first time you really have machines taking over cognitive tasks. Well that's not true. We've had machines take over cognitive task with computers, and you know, we seem to most of us seem to be still be gainfully employed, and the adjustments that took place have been worked out uh, this time, you know, Okay, the computers are doing much much more. They're doing much more in terms of thought. Yes, But as we identify in the book, the one thing that computers can't do is set goals. You still have to have a human what we call human judgment, to set the goals the trade offs. No prediction is going to be perfect, so you have to work out what how you're going to stomach errors and things like that. Those are still roles for people to get into. It's only where better prediction was like the final thing towards getting full automation that you might see jobs actually replaced. Speaking of not getting to full automation, one interesting example you discussed a little bit in the book is that of doctors and how their jobs might change under advances in AI, in terms of they just won't have to do the diagnoses as much anymore. The computer is going to do it for them, and their jobs are going to substantially change. Can you talk about that a bit well, basically in a conference a couple of years ago. Jeff Hinton, who is one of the pioneers of the new development it's in AI. He now it works part time at Google. He basically said to the conference, well, I think we should stop training radiologists. Now, what he meant by that was his view of what a radiologist does is look at images and then decide, you know, is there a problem or not that requires further treatment. Well, obviously, prediction machines have the capability and have been demonstrated in some settings to be far superior to people looking at those pictures and identifying exactly what's going on with a far greater degree of accuracy. So if that's your view of what a radiologist does, well it sounds like curtains for them. But actually radiologists have been dealing with these sorts of issues for fifty years and they're quite aware of them. That has technology improves their jobs change. What happens in terms of radiologists is it's not some a simple choice. You get a prediction of something and you know exactly what to do. You're right. If that was the case, then anyone could just look it up in a manual and you wouldn't need a radiologist. But invariably there are other factors, other criteria, and in particular the personal dimension of the patient situations that will also impact on the what the treatment decision actually is. So prediction is an important input into that, but there are other factors going on, and we're a long way off being able to automate all of that. And thus far, the evidence is that you make the radiologists actually better at their jobs by having these prediction machines. They are able to act with more certainty and therefore able to come up with more confident recommendations. This allows them to save some time and save some other things, and develop other skills, and may change the allocation of tasks between radiologists and other medical practitioners quite a bit. So I don't necessarily see it is obvious that those jobs are going to be disrupted as quickly as some of the engineers do. What kind of jobs, Josh, would you say are the least vulnerable to being replaced or even disrupted by this kind of technology. Well that's a really interesting question. You know. We tend to, when confronted with this thing, point to all the important things that we do and how it can't be replaced by a machine. For instance, some of the jobs that people talk about as being hard to be replaced by machines are ones that require emotional input. Interestingly, Danny Khneman at a conference here just last year. He's the Nobel Prize winning economist who has been responsible for behavior or economics and thinking about decision making and judgment in the context of psychology, and his answer was equivocal. He sees humans as ultimately very flawed and doesn't see any reason why machines wouldn't take over. His view was, do you really want your care to be managed by disinterested children when you've become elderly, or would you rather have a robot who's been trained to work out exactly what your needs are. So it's very hard to work out what is safe and what isn't. All we can say right now is that the tools in their current instantiation and what we're going to see probably over the next five years and maybe ten years, is all about that prediction function, which leaves a lot of room open for people. Speaking of jobs that are likely safe from AI disruption, I think Chris will appreciate being in Washington, d C. Federal government, Congress, etcetera. Those will probably be pretty safe from disruption for a long time. So if we ever get some disruption that sector, that would be something definitely to watch. But josh I wanted to turn to another issue that you discuss when when you're talking about economic impact in the book. Uh, it's really intriguing that you talk about inequality and how that would evolve if AI were to take hold in the economy. Why could that get worse with the rise of AI. Well, it's you know, as usual, it's difficult to forecast these things. You never should trust economis on on those sorts of big trend forecasts. But the concern that we have is that certain skills become more valuable valuable and other skills become less so. And one of the problems is if you wanted to be the sort of person who could take advantage of AI, it's invariably not going to be a skill that is like routine. Necessarily, once you know, once you have the sort of job where you can look at a prediction and you know exactly what to do, your place in that is devalued. On the other hand, there are some situations where it's going to take a bit of art to understand what the prediction really is. We don't tend to think of machine learn in computer sciences art, but there are so many variables that the engineers have to control and think about that. It does tend to have that quality to it, and similarly, how we use those tools also has a bit of an artistic flare to it. And by that I mean that it's not a dents sure why someone who's more productive is more productive, but somehow they get it more and so the people who are able to do that are probably going to fare better. The one concern we have is when I'm a person who's using an AI tool, I can use it at a much larger scale and make decisions for many more across a greater domain. And in that regard, I'm sort of made a superhuman. But you can only have so many superhumans, and that's where we might worry about inequality. Josh, talk to us a little bit also about this idea explored in your book on how artificial intelligence might actually contribute to the concentration of certain industries in our economy, even aid the creation of monopolies. Well, I think this is something that has occurred with any digital platforms, especially ones that work better with larger scale. So the current AI runs on machine learning, and I have to emphasize the learning part. You get better by putting your machines out in the field and continuously adjusting them with new data and new learning. In so, what that means is that a company such as Google that has a lot more people using its search engine is able to use AI to improve that those search engines at a greater rate than competitors such as Being and Duct Duct Go, and so in that regard, you could have the preservation of a dominance or the emergence of dominance. And similarly this might hold for companies like Facebook, and it might hold to a degree for companies like Apple and Amazon as well. They just have a lot more activity and so their potential for to to use AI to learn at a faster rate might be there. But as with all of these things, is sometimes these firms do get into a rut, and sometimes people find better ways of learning and doing things that might initially perform worse but have a better trajectory. So it's not a given that we're going to have the current monopolies, be the future monopolies or anything like that, or the current large companies, but we might see new ones develop on the basis of new tools. Every other time, we've had a large technological revolution that has occurred, I'd expect it to occur this time too. Just taking a broader view of competition, you also get into which country might be the dominant force in AI. If there's a dominant force, the US has already has a clear lead, and yet there's a lot of activity going on in China. How do you see China ascending and competing against the United States in AI. Well, this is where being able to have access to the right sort of data comes to play, not just data but also talent. So let's talk about data first. The United States is sort of a middle ground in terms of privacy regulation. Europe is far more stringent, but China there's none at all, and so Chinese firms have the ability to appropriate and use consumer data to develop AI at a much greater rate than you would be able to do inside the United States. So there's a benefit. Secondly, there's the issue of capabilities. At the moment, AI resources are thin on the ground. It's hard to hire engineers they command six or seven figure sums. But the United States at the moment is cutting itself off from the global pool of talent in machine learning, and this is something other countries aren't doing. Not only they not cutting them selves off, They're also providing resources. China is spending several billion dollars on a technology cluster in this area. Russia are doing the same, and even countries like Canada, the government's actively supporting the development of news superclusters in the space. That's going to help attract talent around the world, because talent wants to be able to work, and I think that's another area where the United States faces some risks. Let's end this interview with the existential question that you address in your book or try to address. We've all seen our share of science fiction movies. The Rise of the Robots Terminator to looms very large in my mind for me, josh is this the end of the world as we know it? Well, as we say in the book, not enough time yet to tell. But you've got enough time to read our book and be on the right side of it. All right, Well, let's send it there. Joshua Against from the University of Toronto, author of Prediction Machines, thank you very much for running us on Benchmark. Thank you. Benchmark will be back next week. Until then, you can find us on the Bloomberg terminal Bloomberg dot com. Our Bloomberg app and podcast destinations such as Apple Podcast, Spotify, or wherever you listen. We'd love it if you took the time to post a review of the show so more listeners can find us. You can also check us out on Twitter, follow me at at scott Landman, Chris You're at Chris Ja Conden, and our guest Josh Gannes is at Josh gannes g. A n S Benchmark is produced by Toper Foreheaz. The head of Bloomberg Podcasts is Francesca Levy. Thanks for listening. To see you next time.

From self driving cars to robot powered factories. Artificial intelligence is taking over significant pieces of the global economy. This is great for the businesses embracing AI, but there is a downside. More robots in the workforce also means more people losing their jobs to computers. So how bad will the robot revolution be and how will it reshape the global economy? Welcome to Benchmark. I'm Scott Landman and economics editor with Bloomberg News in Washington. Returning as a guest co host is my colleague Chris Content. He's a reporter covering the Federal Reserve and u S economy also here in d C. Chris, glad to have you back, Happy to be here. So this week we're talking about the rise of the robots and how they will impact the economy. As someone who writes about the Federal Reserve, Chris, what interest you and AI? Well, I can tell you, Scott, that there is a great capacity actually of artificial intelligence to transform one of the biggest jobs of central banks. As you know, central banks are trying to regulate economies, keep them in that Goldilock zone, and they do this through short term interest rates. But that tool has a lag to it. It takes a good six st eighteen months before a move and interest rates has an impact in the real economy. So these central bankers they've got to forecast what what's the economy gonna be like a year from now, eighteen months from now. But they're not very good at forecasting the future when it comes to things like inflation, unemployment, GDP growth. So some central banks are starting to turn for a little help to artificial intelligence because artificial intelligence can be used quite effectively at spotting patterns in past events and then using that to predict the future. So it's an area it's going to take a while, but it's an area of great promise for economics. It's not just economics, but it's business and the broader economy as a whole. Forecasting prediction, it's it's a really big part of what AI is meant to do. And our guest is here to talk about that today. His name is Joshua Gains and he's a professor at the University of Toronto's Rotman School of Management. He's one of the authors of a brand new book called Prediction Machines, The Simple Economics of Artificial Intelligence, just published by Harvard Business Review Press. Joshua, thanks for joining us. Good to be here. Thanks. So we've already sort of alluded to this, but why is the book called Prediction Machines. Well, it's uh called that and not some more interesting title such as, um, you know you're wonderful, great new robot and how your life is going to get better, simply because the recent developments in artificial intelligence are not about completely replacing human intelligence per se, but actually all of being about one thing, and that is prediction. That is taking a whole lot of information that you do have and converting it into information you do not have. That's very, very different from something that does all of your choices for you and things like that. Uh, it really really just does prediction. And when we talk about this prediction function and getting all this information, can you bring us into the real world here and what kinds of industries or jobs would be most likely to or have the most room to benefit from this kind of additional knowledge. Well, prediction is something and from your business that you're in, you think of it mainly about forecasting, for instance, economic variables and things like that, and something of course I've worried about as well. But what's really interesting about these new developments is that they highlight problems that turn out to be prediction problems. For instance, the whole issue of having a digital image served up to you and knowing what's in it is a prediction problem. Invariably, when you get an image from the Internet, the label that's being attached to it is the label that human would attach to it. And so basically what Google are doing when you're searching for a picture is predicting which pictures correspond to that label. So that's a form of prediction. And it turns out that that and language translation and understanding machines, understanding human speech, and even self driving cars are all mainly a prediction problem, and so that is where this new artificial intelligence is being implemented. You wouldn't normally call that prediction problems. We normally think about forecasting the weather or forecasting an earthquake or something, but basically prediction is all around us. Joshua, I think it's clear that this can have all sorts of benefits in our lives and in the economy, but I can also have some downsides, and particularly how how these things are distributed throughout our society. Maybe a big question mark first about the benefits. Where where do you see that societally and also economically. So I think the benefits come from wherever you think about where would it be good to know things with greater certainty? And so any kind of decisions that you're doing under uncertainty are going to benefit from having better prediction so that you can imagine being applied. For instance, you're trying to manage inventory. If you've got better predictions regarding demand you're going to face, you're going to be able to manage that inventory better. You're going to be able to correctly adjust your behavior to prevent shortfalls or worse, to prevent surpluses that end up to over stocking your inventory. Those are the sorts of places where prediction machines are going to work quite well. Uh. And so basically anywhere where there's decision making being done and there's uncertainty, there is room for better prediction, and the machines might serve that up right, And that sounds, of course like it will make our companies more efficient, more productive, but also I think will disrupt how companies operate and may disrupt people's lives. Do you think this time is going to be any different. We've had lots of technology disruption in our economy over the decades, in fact, in centuries, um is the pace of change these days going to be more disruptive and more problematic to adjust to. I think there's a chance, as usual, whenever you've got a very large and radical innovation occurring and being adopted, there is the potential for disruptive change. But the hard part is trying to predict exactly where that will be. When we look back and we think about the rise of the automobile, it's no surprise that we also point to horses as being the disruptive workers in that disrupted workers in that equation. When it comes to things like better prediction, however, it's far subtler um. Some of the discussion that's going around is saying, well, this is the first Why is this time different? It's because it's the first time you really have machines taking over cognitive tasks. Well that's not true. We've had machines take over cognitive task with computers, and you know, we seem to most of us seem to be still be gainfully employed, and the adjustments that took place have been worked out uh, this time, you know, Okay, the computers are doing much much more. They're doing much more in terms of thought. Yes, But as we identify in the book, the one thing that computers can't do is set goals. You still have to have a human what we call human judgment, to set the goals the trade offs. No prediction is going to be perfect, so you have to work out what how you're going to stomach errors and things like that. Those are still roles for people to get into. It's only where better prediction was like the final thing towards getting full automation that you might see jobs actually replaced. Speaking of not getting to full automation, one interesting example you discussed a little bit in the book is that of doctors and how their jobs might change under advances in AI, in terms of they just won't have to do the diagnoses as much anymore. The computer is going to do it for them, and their jobs are going to substantially change. Can you talk about that a bit well, basically in a conference a couple of years ago. Jeff Hinton, who is one of the pioneers of the new development it's in AI. He now it works part time at Google. He basically said to the conference, well, I think we should stop training radiologists. Now, what he meant by that was his view of what a radiologist does is look at images and then decide, you know, is there a problem or not that requires further treatment. Well, obviously, prediction machines have the capability and have been demonstrated in some settings to be far superior to people looking at those pictures and identifying exactly what's going on with a far greater degree of accuracy. So if that's your view of what a radiologist does, well it sounds like curtains for them. But actually radiologists have been dealing with these sorts of issues for fifty years and they're quite aware of them. That has technology improves their jobs change. What happens in terms of radiologists is it's not some a simple choice. You get a prediction of something and you know exactly what to do. You're right. If that was the case, then anyone could just look it up in a manual and you wouldn't need a radiologist. But invariably there are other factors, other criteria, and in particular the personal dimension of the patient situations that will also impact on the what the treatment decision actually is. So prediction is an important input into that, but there are other factors going on, and we're a long way off being able to automate all of that. And thus far, the evidence is that you make the radiologists actually better at their jobs by having these prediction machines. They are able to act with more certainty and therefore able to come up with more confident recommendations. This allows them to save some time and save some other things, and develop other skills, and may change the allocation of tasks between radiologists and other medical practitioners quite a bit. So I don't necessarily see it is obvious that those jobs are going to be disrupted as quickly as some of the engineers do. What kind of jobs, Josh, would you say are the least vulnerable to being replaced or even disrupted by this kind of technology. Well that's a really interesting question. You know. We tend to, when confronted with this thing, point to all the important things that we do and how it can't be replaced by a machine. For instance, some of the jobs that people talk about as being hard to be replaced by machines are ones that require emotional input. Interestingly, Danny Khneman at a conference here just last year. He's the Nobel Prize winning economist who has been responsible for behavior or economics and thinking about decision making and judgment in the context of psychology, and his answer was equivocal. He sees humans as ultimately very flawed and doesn't see any reason why machines wouldn't take over. His view was, do you really want your care to be managed by disinterested children when you've become elderly, or would you rather have a robot who's been trained to work out exactly what your needs are. So it's very hard to work out what is safe and what isn't. All we can say right now is that the tools in their current instantiation and what we're going to see probably over the next five years and maybe ten years, is all about that prediction function, which leaves a lot of room open for people. Speaking of jobs that are likely safe from AI disruption, I think Chris will appreciate being in Washington, d C. Federal government, Congress, etcetera. Those will probably be pretty safe from disruption for a long time. So if we ever get some disruption that sector, that would be something definitely to watch. But josh I wanted to turn to another issue that you discuss when when you're talking about economic impact in the book. Uh, it's really intriguing that you talk about inequality and how that would evolve if AI were to take hold in the economy. Why could that get worse with the rise of AI. Well, it's you know, as usual, it's difficult to forecast these things. You never should trust economis on on those sorts of big trend forecasts. But the concern that we have is that certain skills become more valuable valuable and other skills become less so. And one of the problems is if you wanted to be the sort of person who could take advantage of AI, it's invariably not going to be a skill that is like routine. Necessarily, once you know, once you have the sort of job where you can look at a prediction and you know exactly what to do, your place in that is devalued. On the other hand, there are some situations where it's going to take a bit of art to understand what the prediction really is. We don't tend to think of machine learn in computer sciences art, but there are so many variables that the engineers have to control and think about that. It does tend to have that quality to it, and similarly, how we use those tools also has a bit of an artistic flare to it. And by that I mean that it's not a dents sure why someone who's more productive is more productive, but somehow they get it more and so the people who are able to do that are probably going to fare better. The one concern we have is when I'm a person who's using an AI tool, I can use it at a much larger scale and make decisions for many more across a greater domain. And in that regard, I'm sort of made a superhuman. But you can only have so many superhumans, and that's where we might worry about inequality. Josh, talk to us a little bit also about this idea explored in your book on how artificial intelligence might actually contribute to the concentration of certain industries in our economy, even aid the creation of monopolies. Well, I think this is something that has occurred with any digital platforms, especially ones that work better with larger scale. So the current AI runs on machine learning, and I have to emphasize the learning part. You get better by putting your machines out in the field and continuously adjusting them with new data and new learning. In so, what that means is that a company such as Google that has a lot more people using its search engine is able to use AI to improve that those search engines at a greater rate than competitors such as Being and Duct Duct Go, and so in that regard, you could have the preservation of a dominance or the emergence of dominance. And similarly this might hold for companies like Facebook, and it might hold to a degree for companies like Apple and Amazon as well. They just have a lot more activity and so their potential for to to use AI to learn at a faster rate might be there. But as with all of these things, is sometimes these firms do get into a rut, and sometimes people find better ways of learning and doing things that might initially perform worse but have a better trajectory. So it's not a given that we're going to have the current monopolies, be the future monopolies or anything like that, or the current large companies, but we might see new ones develop on the basis of new tools. Every other time, we've had a large technological revolution that has occurred, I'd expect it to occur this time too. Just taking a broader view of competition, you also get into which country might be the dominant force in AI. If there's a dominant force, the US has already has a clear lead, and yet there's a lot of activity going on in China. How do you see China ascending and competing against the United States in AI. Well, this is where being able to have access to the right sort of data comes to play, not just data but also talent. So let's talk about data first. The United States is sort of a middle ground in terms of privacy regulation. Europe is far more stringent, but China there's none at all, and so Chinese firms have the ability to appropriate and use consumer data to develop AI at a much greater rate than you would be able to do inside the United States. So there's a benefit. Secondly, there's the issue of capabilities. At the moment, AI resources are thin on the ground. It's hard to hire engineers they command six or seven figure sums. But the United States at the moment is cutting itself off from the global pool of talent in machine learning, and this is something other countries aren't doing. Not only they not cutting them selves off, They're also providing resources. China is spending several billion dollars on a technology cluster in this area. Russia are doing the same, and even countries like Canada, the government's actively supporting the development of news superclusters in the space. That's going to help attract talent around the world, because talent wants to be able to work, and I think that's another area where the United States faces some risks. Let's end this interview with the existential question that you address in your book or try to address. We've all seen our share of science fiction movies. The Rise of the Robots Terminator to looms very large in my mind for me, josh is this the end of the world as we know it? Well, as we say in the book, not enough time yet to tell. But you've got enough time to read our book and be on the right side of it. All right, Well, let's send it there. Joshua Against from the University of Toronto, author of Prediction Machines, thank you very much for running us on Benchmark. Thank you. Benchmark will be back next week. Until then, you can find us on the Bloomberg terminal Bloomberg dot com. Our Bloomberg app and podcast destinations such as Apple Podcast, Spotify, or wherever you listen. We'd love it if you took the time to post a review of the show so more listeners can find us. You can also check us out on Twitter, follow me at at scott Landman, Chris You're at Chris Ja Conden, and our guest Josh Gannes is at Josh gannes g. A n S Benchmark is produced by Toper Foreheaz. The head of Bloomberg Podcasts is Francesca Levy. Thanks for listening. To see you next time.