Podcast appearances and mentions of michael chui

My fellow pro-growth/progress/abundance Up Wingers,The innovation landscape is facing a difficult paradox: Even as R&D investment has increased, productivity per dollar invested is in decline. In his recent co-authored paper, The next innovation revolution—powered by AI, Michael Chui explores AI as a possible solution to this dilemma.Today on Faster, Please! — The Podcast, Chui and I explore the vast potential for AI-augmented research and the challenges and opportunities that come with applying it to the real-world.Chui is a senior fellow at QuantumBlack, McKinsey's AI unit, where he leads McKinsey research in AI, automation, and the future of work.In This Episode* The R&D productivity problem (01:21)* The AI solution (6:13)* The business-adoption bottleneck (11:55)* The man-machine team (18:06)* Are we ready? (19:33)Below is a lightly edited transcript of our conversation. The R&D productivity problem (01:21)All the easy stuff, we already figured out. So the low-hanging fruit has been picked, things are getting harder and harder.Pethokoukis: Do we understand what explains this phenomenon where we seem to be doing lots of science, and we're spending lots of money on R&D, but the actual productivity of that R&D is declining? Do we have a good explanation for that?I don't know if we have just one good explanation. The folks that we both know have been both working on what are the causes of this, as well as what are some of the potential solutions, but I think it's a bit of a hidden problem. I don't think everyone understands that there are a set of people who have looked at this — quite notably Nick Bloom at Stanford who published this somewhat famous paper that some people are familiar with. But it is surprising in some sense.At one level, it's amazing what science and engineering has been able to do. We continue to see these incredible advances, whether it's in AI, or biotechnology, or whatever; but also, what Nick and other researchers have discovered is that we are producing less for every dollar we spend in R&D. That's this little bit of a paradox, or this challenge, that we see. What some of the research we've been trying to do is understand, can AI try to contribute to bending those curves?. . . I'm a computer scientist by training. I love this idea of Moore's Law: Every couple of years you can double the number of transistors you can put on a chip, or whatever, for the same amount of money. There's something called “Eroom's Law,” which is Moore spelled backwards, and basically it said: For decades in the pharmaceutical industry, the number of compounds or drugs you would produce for every billion dollars of R&D would get cut in half every nine years. That's obviously moving in the wrong direction. That challenge, I don't think everyone is aware of, but one that we need to address.I suppose, in a way, it does make sense that as we tackle harder problems, and we climb the tree of knowledge, that it's going to take more time, maybe more researchers, the researchers themselves may have to spend more time in school, so it may be a bit of a hidden problem, but it makes some intuitive sense to me.I think there's a way to think about it that way, which is: All the easy stuff, we already figured out. So the low-hanging fruit has been picked, things are getting harder and harder. It's amazing. You could look at some of the early papers in any field and it have a handful of authors, right? The DNA paper, three authors — although it probably should have included Rosalyn Franklin . . . Now you look at a physics paper or a computer science paper — the author list just goes on sometimes for pages. These problems are harder. They require more and more effort, whether it's people's talents, or whether it's computing power, or large-scale experiments, things are getting harder to do. I think there's ways in which that makes sense. Are there other ways in which we could improve processes? Probably, too.We could invest more in research, make it more efficient, and encourage more people to become researchers. To me, what's more exciting than automating different customer service processes is accelerating scientific discovery. I think that's what makes AI so compelling.That is exactly right. Now, by the way, I think we need to continue to invest in basic research and in science and engineering, I think that's absolutely important, but —That's worth noting, because I'm not sure everybody thinks that, so I'm glad you highlighted that.I don't think AI means that everything becomes cheaper and we don't need to invest in both human talent as well as in research. That's number one.Number two, as you said, we spend a lot of time, and appropriately so, talking about how AI can improve productivity, make things more efficient, do the things that we do already cheaper and faster. I think that's absolutely true. But we had the opportunity to look over history, and what has actually improved the human condition, what has been one of the things that has been necessary to improve the human condition over decades, and centuries, and millennia, is, in fact, discovering new ideas, having scientific breakthroughs, turning those scientific breakthroughs into engineering that turn into products and services, that do everything from expand our lifespans to be able to provide us with food, more energy. All those sorts of things require innovation, require R&D, and what we've discovered is the potential for AI, not only to make things more efficient, but to produce more innovation, more ideas that hopefully will lead to breakthroughs that help us all.The AI solution (6:13)I think that's one of the other potentials of using AI, that it could both absorb some of the experience that people have, as well as stretch the bounds of what might be possible.I've heard described as an “IMI,” it's an invention that makes more invention. It's an invention of a method of invention. That sounds great — how's it going to do that?There are a couple of ways. We looked at three different channels through which AI could improve this process of innovation and R&D. The first one is just increasing the volume, velocity, and variety of different candidates. One way you could think about innovation is you create a whole bunch of candidates and then you filter them down to the ones that might be most effective. Number one, you can just fill that funnel faster, better, and with greater variety. That's number one.The candidates could be a molecule, it could be a drug, it could be a new alloy, it could be lots of things.Absolutely, or a design for a physical product. One of the interesting things is, this quote-unquote “modern AI” — AI's been around for 70 years — is based on foundation models, these large artificial neural networks trained on huge amounts of data, and they produce unstructured outputs. In many cases, language, we talk about LLMs.The interesting thing is, you can train these foundation models not just to generate language, but you can generate a protein, or a drug candidate, as you were saying. You can imagine the prompt being, “Please produce 10 drug candidates that address this condition, but without the following side effects.” That's not exactly how it works, but roughly speaking, that's the potential to generate these things, or generate an electrical circuit, or a design for an air foil or an airframe that has these characteristics. Being able to just generate those.The interesting thing is, not only can you generate them faster, but there's this idea that you can create more variety. We're usefully proud as humans about our creativity, but also, that judgment or that training that we have, that experience sometimes constrains it. The famous example was some folks created this machine called AlphaGo which was meant to compete against the world champion in this game called Go, a very complex strategic game. Famously, it beat the world champion, but one of the things it did is this famous Move 37, this move that everyone who was an expert at Go said, “That is nuts. Why would you possibly do that?” Because the machine was a little bit more unconstrained, actually came up with what you might describe as a creative idea. I think that's one of the other potentials of using AI, that it could both absorb some of the experience that people have, as well as stretch the bounds of what might be possible.So you come up with the design, and then a variety of options, and then AI can help model and test them.Exactly. So you generate a broader and more voluminous set of potential designs, candidates, whether it's molecules, or chemicals, or what have you. Now you need to narrow that down. Traditionally you would narrow it down either one, through physical testing — so put something into a wind tunnel or run it through the water if you're looking at a boat design, or something like that, or put it in an electromagnetic chamber and see how the antenna operates. You'd either test it physically, and then, of course, lots of people figured out how to use physics, mathematical equations, in order to create “digital twins.” So you have these long acronyms like CFD for computational fluid dynamics, basically a virtual wind tunnel, or what have you. Or you have finite element analysis, another way to model how a structure might perform, or computational electromagnetic modeling. All these ways that you can use physics to simulate things, and that's been terrific.But some of those models actually take hours, sometimes days, to run these models. It might be faster than building the physical prototype and then modeling it — again, sometimes you just wait until something breaks, you're doing failure testing. Then you could do that in a computer using these models. But sometimes they take a really long time, and one of the really interesting discoveries in “AI” is you can use that same neural network that we've used to simulate cognition or intelligence, but now you use it to simulate physical systems. So in some ways it's not AI, because you're not creating an artificial intelligence, you're creating an artificial wind tunnel. It's just a different way to model physics. Sometimes these problems get even more complicated . . . If you're trying to put an antenna on an airplane, you need to know how the airflow is going to go over it, but you need to know whether or not the radio frequency stuff works out too, all that RF stuff.So these multiphysics models, the complexity is even higher, and you can train these neural nets . . . even faster than these physics-based models. So we have these things called AI surrogate models. They're sort of surrogates. It's two steps removed, in some ways, from actual physical testing . . . Literally we've seen models that can run in minutes rather than hours, or an hour rather than a few days. That can accelerate things. We see this in weather forecasting in a number of different ways in which this can happen. If you can generate more candidates and then test them faster, you can imagine the whole R&D process really accelerating.The business-adoption bottleneck (11:55)We know that companies are using AI surrogates, deep learning surrogates, already, but is it being applied as many places as possible? No, it isn't.Does achieving your estimated productivity increases depend more on further technological advances or does it depend more on how companies adopt and implement the technology? Is the bottleneck still in the tech itself, or is it more about business adaptation?Mostly number two. The technology is going to continue to advance. As a technologist, I love all that stuff, but as usual, a lot of the challenges here are organizational challenges. We know that companies are using AI surrogates, deep learning surrogates, already, but is it being applied as many places as possible? No, it isn't. A lot of these things are organizational. Does it match your strategy, for instance? Do you have the right talent and organization in place?Let me just give one very specific example. In a lot of R&D organizations we know, there's a separate organization for physical testing and a separate organization for simulations. Simulation, in many cases, us physics-based, but you add these deep-learning surrogates as well. That doesn't make sense at some level. I'm not saying physical testing goes away, but you need to figure out when you should physically test, when you should use which simulation methods, when you should use deep-learning surrogates or AI techniques, et cetera, and that's just one organizational difference that you could make if you were in an organization that was actually taking this whole testing regime seriously, where you're actually parsing out when the optimal amount of physical testing is versus simulation, et cetera. There's a number of things where that's true.Even before AI, historically, there was a gap between novel, new technologies, what they can do in lab settings, and then how they're applied in real-world research or in business environments. That gap, I would guess, probably requires companies to rewire how they operate, which takes time.It is indeed, and it's funny that you use the word “rewiring.” My colleagues wrote a book entitled Rewired, which literally is about the different ways, together, that you need to, as you say, rewire or change the way an organization operates. Only one of those six chapters is around the tech stack. It's still absolutely important. You've got to get all that stuff right. But it is mostly all of the other things surrounding how you change and what organization operates in order to bring the full value of this together to reach scale.We also talk about pilot purgatory: “We did this cool experiment . . .” but when is it good enough that the CFOs talks about it at the quarterly earnings report? That requires the organization to change the way it operates. That's the learning we've seen all the time.We've been serving thousands of executives on their use of AI for seven years now. Nearly 80 percent of organizations say they're regularly using AI someplace in the business, but in a separate survey, only one percent say they're mature in that usage. There's this giant gap between just using AI and then actually having the value be created. And by the way, organizations that are creating that value are accelerating their performance difference. If you have a much more productive R&D organization that churns out products that are successful in the market, you're going to be ahead of your competitors, and that's what we're seeing too.Is there a specific problem that comes up over and over again with companies, either in their implementation of AI, maybe they don't trust it, they may not know how to use it? What do you think is the problem?Unfortunately, I don't think there's just one thing. My colleagues who do this work on Rewired, for instance — you kind of have to do all those things. You do have to have the right talent and organization in place. You have to figure out scaling, for instance. You have to figure out change management. All of those things together are what underpins outsized performance, so all those things have to be done.So if companies are successful, what is the productivity impact you see? We're talking about basically the current technology level, give or take. We're not talking about human-level AI, superintelligence, we're talking about AI more or less as it exists today. Everybody wants to accelerate productivity: governments around the world, companies. So give me a feel for that.There are different measures of productivity, but here what we're talking about is basically: How many new products, successful products, can you put out in the market? Our modeling says, depending on your industry, you could double your productivity, in other words, of R&D. In other words, you could put out double the amount of products and services — new products and services — that you have been previously.Now, that's not true for every industry. By the way, the impact of that is different for different industries because for some industries you are dependent — In pharmaceuticals, the majority of your value comes from producing new products and services over time because eventually the patent runs out or whatever. There are other industries, we talk about science-based industries like chemicals, for instance. The new-product development process in chemicals is very, very close to the science of chemistry. So these levers that I just talked about — producing more candidates, being able to evaluate them more quickly, and all the other things that LLMs can do, in general, we could see potential doubling in the pace of which innovation happens.On the other hand, the chemicals industry — let's leave out specialty chemicals, but the commodity chemicals — they'll still produce ethylene, right? So to a certain extent, while the R&D process can be accelerated a great deal, the EBIT [Earnings Before Interest and Taxes] impact on the industry might be lower than it is for pharmaceuticals, for instance. But still, it's valuable. And then, again, if you're in specialty chem, it means a lot to you. So depending on where you sit in your position in the market, it can vary, but the potential is really high.The man-machine team (18:06)At least for the medium term, we're not going to be able to get rid of all the people. The people are going to be absolutely important to the process.Will future R&D look more like researchers augmented by AI or AI systems assisted by researchers? Who's the assistant in this equation? Who's working for who?It's “all of the above” and it depends on how you decide to use these technologies, but we even write in our paper that we need to be thoughtful about where you put the human in the loop. Every study, the conditions matter, but there are lots of studies where you say, look, the combination of machines and humans — so AI and researchers — is the most powerful combination. Each brings their respective strengths to it, but the funny thing is that sometimes the human biases actually decrease the performance of the overall system, and so, oh, maybe we should just go with machines. At least for the medium term, we're not going to be able to get rid of all the people. The people are going to be absolutely important to the process.When is it that people either are necessary to the process or can be helpful? In many cases, it is around things like, when is it that you need to make a decision that's a safety-critical decision, a regulatory decision where you just have to have a person look at it? That's the sort of necessity argument for people in the loop. But also, there are things that machines just don't do well enough yet, and there's a little bit of that.Are we ready? (19:33). . . AI is one of those things that can produce potentially more of those ideas that can underpin, hopefully, an improved quality of life for us and our children.If we can get more productive R&D, and then businesses get better at incorporating this into their processes and they could potentially generate more products and services, do we have a government ready for that world of accelerated R&D? Can we handle that flow? My bias says probably not, but please correct me if I'm wrong.I think one of the interesting things is people talk about AI regulation. In many of these industries, the regulations already exist. We have regulations for what goes out in pharmaceuticals, for instance. We have regulations in the aviation industry, we have regulations in the automobile industry, and in many ways, AI in the R&D process doesn't change that — maybe it should, people talk about, can you actually accelerate the process of approving a drug, for instance, but that wasn't the thing that we studied. In some ways, those processes are applied now, already, so that's something that doesn't necessarily have to changeThat said, are some of these potential innovations gated by approval processes or clinical trials processes? Absolutely. In some of those cases, the clinical trials process gait is not necessarily a regulation, but we know there's a big problem just finding enough potential subjects in order to do clinical trials. That's not a regulatory problem, that's a problem of finding people who are good candidates for actually testing these drugs.So yes, in some cases, even if we were able to double the amount of candidates that can go through the funnel on a number of these things, there will be these exogenous issues that would constrain society's ability to bring these to market. So that just says, you squeeze the balloon here and it opens up there, but let's go solve each of these problems, and one of the problems that we said that AI can help solve is increasing the number of things that you could potentially put into market if it can get past the other necessities.For a general public where so much of what they're hearing about AI tends to be about job loss, or are they stealing copyrighted material, or, yeah, people talk about these huge advances, but they're not seeing them yet. What is your elevator optimistic pitch why you may be worried about the impact of AI, but here's why I'm excited about it? Why are you excited by it?By the way, I think all those things are really important. All of those concerns, and how do we reskill the workforce, all those things, and we've done work on that as well. But the thing that I'm excited about is we need innovation, we need new ideas, we need scientific advancements, and engineering that turns them into products in order for us to improve their human condition, whether it's living longer lives, or living higher quality life, whether it's having the energy, whether it's to be able to support that in a way that doesn't cause other problems. All of those things, we need to have them, and what we've discovered is AI is one of those things that can produce potentially more of those ideas that can underpin, hopefully, an improved quality of life for us and our children.On sale everywhere The Conservative Futurist: How To Create the Sci-Fi World We Were PromisedMicro Reads▶ Economics* The Tariffs Kicked In. The Sky Didn't Fall. Were the Economists Wrong? - NYT Opinion* AI Disruption Is Coming for These 7 Jobs, Microsoft Says - Barron's* One Way to Ease the US Debt Crisis? Productivity - Bberg Opinion* So far, only one-third of Americans have ever used AI for work - Ars▶ Business* Meta and Microsoft Keep Their License to Spend - WSJ* Meta Pivots on AI Under the Cover of a Superb Quarter - Bberg Opinion* Will Mark Zuckerberg's secret, multibillion-dollar AI plan win over Wall Street? - FT* The AI Company Capitalizing on Our Obsession With Excel - WSJ* $15 billion in NIH funding frozen, then thawed Tuesday in ongoing power war - Ars* Mark Zuckerberg promises you can trust him with superintelligent AI - The Verge* AI Finance App Ramp Is Valued at $22.5 Billion in Funding Round - WSJ▶ Policy/Politics* Trump's Tariff Authority Is Tested in Court as Deadline on Trade Deals Looms - WSJ* China is betting on a real-world use of AI to challenge U.S. control - Wapo▶ AI/Digital* ‘Superintelligence' Will Create a New Era of Empowerment, Mark Zuckerberg Says - NYT* How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index - Arxiv* Mark Zuckerberg Details Meta's Plan for Self-Improving, Superintelligent AI - Wired* A Catholic AI app promises answers for the faithful. Can it succeed? - Wapo* Power Hungry: How Ai Will Drive Energy Demand - SSRN* The two people shaping the future of OpenAI's research - MIT* Task-based returns to generative AI: Evidence from a central bank - CEPR▶ Biotech/Health* How to detect consciousness in people, animals and maybe even AI - Nature* Why living in a volatile age may make our brains truly innovative - NS▶ Clean Energy/Climate* The US must return to its roots as a nation of doers - FT* How Trump Rocked EV Charging Startups - Heatmap* Countries Promise Trump to Buy U.S. Gas, and Leave the Details for Later - NYT* Startup begins work on novel US fusion power plant. Yes, fusion. - E&E* Scientists Say New Government Climate Report Twists Their Work - Wired▶ Robotics/Drones/AVs* The grand challenges of learning medical robot autonomy - Science* Coal-Powered AI Robots Are a Dirty Fantasy - Bberg Opinion▶ Up Wing/Down Wing* A Revolutionary Reflection - WSJ Opinion* Why Did the Two Koreas Diverge? - SSRN* The best new science fiction books of August 2025 - NS* As measles spreads, old vaccination canards do too - FT Faster, Please! is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

The future of work is changing, and we can't afford to have our leaders evolve backward by sticking to outdated leadership styles. It's high time for a major upgrade because this new era of work won't wait for you to catch up. In this three-part episode, we explore what it takes to thrive at the highest levels of leadership across three urgent dimensions: executive readiness, AI fluency, and hybrid work strategy. We begin with Mark Thompson, world's #1 CEO coach and author of CEO Ready, who shares what it really takes to step into the top seat: mastering leadership languages, building trust with your boss, and aligning ambition with the real demands of executive life. Then we dive into the age of AI with Dr. Michael Chui, Senior Fellow at McKinsey and QuantumBlack AI, who explains how agentic AI is reshaping leadership itself—from decision-making and team structure to the rising need for leaders who can manage machines as skillfully as people. Finally, we turn to the workplace of the future with Stanford Professor Dr. Nicholas Bloom, the world's top expert on remote work, who debunks common myths, lays out why hybrid work is the sweet spot, and reveals how AI might accelerate the decline of fully remote roles.   ________________ Start your day with the world's top leaders by joining thousands of others at Great Leadership on Substack. Just enter your email: ⁠⁠https://greatleadership.substack.com/

AI is becoming more powerful and autonomous. What we once thought was a future trend is now reshaping how organizations are operating today. But the biggest risk of AI isn't replacement. More likely, it's reliance. Are you prepared for what's coming? In today's episode, Dr. Michael Chui, Senior Fellow at McKinsey and QuantumBlack AI, reveals how AI is rewriting the rules of competition, leadership, and even hiring structure. You'll learn why over-relying on AI can erode human judgment and creativity, how to build organizations that balance human decision-making with machine efficiency, and why managing AI agents will soon be as important as managing people. Dr. Chui shares how leaders can prevent “silent automation” from taking over their teams, what skills future managers must develop (including prompt engineering and oversight), and how companies can redesign roles, workflows, and organizational structures to thrive in the era of agentic AI.   ________________ This episode is sponsored by Workhuman. Here's a good question for you: Who in your marketing department is a flight risk? How about: Where are your talent or skills gaps? Or: Which employees make up your next generation of leaders? If you couldn't answer any of these, then you need to check out Human Intelligence™, from Workhuman. By combining AI with the rich data of their #1 rated employee recognition platform, Human Intelligence unlocks insights and capabilities that redefine talent management, cultural transformation, and employee engagement. Want to learn more? Go to workhuman.com and learn how today's leading companies are turning AI into a force for good with Human Intelligence. ________________ Start your day with the world's top leaders by joining thousands of others at Great Leadership on Substack. Just enter your email: ⁠⁠https://greatleadership.substack.com/

Kevin Werbach speaks with Medha Bankhwal and Michael Chui from QuantumBlack, the AI division of the global consulting firm McKinsey. They discuss how McKinsey's AI work has evolved from strategy consulting to hands-on implementation, with AI trust now embedded throughout their client engagements. Chui highlights what makes the current AI moment transformative, while Bankwhal shares insights from McKinsey's recent AI survey of over 760 organizations across 38 countries. As they explain, trust remains a major barrier to AI adoption, although there are geographic differences in AI governance maturity.  Medha Bankhwal, a graduate of Wharton's MBA program, is an Associate Partner, as well as Co-founder of McKinsey's AI Trust / Responsible AI practice. Prior to McKinsey, Medha was at Google and subsequently co-founded a digital learning not-for-profit startup. She co-leads forums for AI safety discussions for policy + tech practitioners, titled “Trustworthy AI Futures” as well as a community of ex-Googlers dedicated to the topic of AI Safety.  Michael Chui is a senior fellow at QuantumBlack, AI by McKinsey. He leads research on the impact of disruptive technologies and innovation on business, the economy, and society. Michael has led McKinsey research in such areas as artificial intelligence, robotics and automation, the future of work, data & analytics, collaboration technologies, the Internet of Things, and biological technologies. Episode Transcript The State of AI: How Organizations are Rewiring to Capture Value (March 12, 2025)  Superagency in the workplace: Empowering people to unlock AI's full potential (January 28, 2025) Building AI Trust: The Key Role of Explainability (November 26, 2024) McKinsey Responsible AI Principles

José Antonio Bowen: Teaching With AI José Antonio Bowen has won teaching awards at Stanford and Georgetown and is past president of Goucher College. He has written over 100 scholarly articles and has appeared as a musician with Stan Getz, Bobby McFerrin, and others. He is the author of multiple books in higher education and is a senior fellow for the American Association of Colleges and Universities. He is the author with C. Edward Watson of Teaching With AI: A Practical Guide to a New Era of Human Learning*. AI will change how we work, but it's also going to change how we think. In this conversation, José and I explore where to begin working with AI and why those who can use it will serve a critical role in shaping what's next. Key Points Physical maps make you smarter than GPS, but GPS is more practical for daily use. AI isn't inherently good or bad, but like the internet, it will change how we work. AI will eliminate some jobs, but it will change every job. Those who can work with AI will replace those who can't. Rather than thinking about creativity through the lens of responses from AI, focus on bringing creativity into your prompts. Most of the AI progress for companies is coming from non-tech folks that are figuring our how specific tasks get more efficient. AI is very good at some things and not good at others. You'll discover how this relates to your work by experimenting with different prompts. Resources Mentioned Teaching With AI: A Practical Guide to a New Era of Human Learning* by José Antonio Bowen and C. Edward Watson Example AI Prompts by José Antonio Bowen The Human Side of Generative AI: Creating a Path to Productivity by Aaron De Smet, Sandra Durth, Bryan Hancock, Marino Mugayar-Baldocchi, and Angelika Reich Moderna and OpenAI partner to Accelerate the Development of Life-Saving Treatments The State of AI in Early 2024: Gen AI Adoption Spikes and Starts to Generate Value by Alex Singla, Alexander Sukharevsky, Lareina Yee, Michael Chui, and Bryce Hall Interview Notes Download my interview notes in PDF format (free membership required). Related Episodes Make Your Reading More Meaningful, with Sönke Ahrens (episode 564) Principles for Using AI at Work, with Ethan Mollick (episode 674) How to Enhance Your Credibility (Audio course) Discover More Activate your free membership for full access to the entire library of interviews since 2011, searchable by topic. To accelerate your learning, uncover more inside Coaching for Leaders Plus.

What is a geek? What geek norms are associated with success?  Andrew McAfee will answer these questions and more. He's a principal research scientist at the MIT Sloan School of Management and author of the new book, The Geek Way: The Radical Mindset that Drives Extraordinary Results. This is a guest episode from McKinsey's Forward Thinking podcast, with co-host and McKinsey partner Michael Chui.See www.mckinsey.com/privacy-policy for privacy information

Co-host Michael Chui talks with Nan Ransohoff. Ransohoff is the head of climate at Stripe and leads Frontier, an advanced market commitment for carbon removal. She answers questions including: Is carbon removal a get out of jail free card for emitters? What are the most promising carbon removal technologies? Is it possible to scale up effective technologies quick enough? How much do costs need to come down before scaling is possible? What is an advanced market commitment? See www.mckinsey.com/privacy-policy for privacy information

Artificial intelligence (AI) is advancing at breakneck speed. Michael Chui, a partner at the McKinsey Global Institute (MGI), leads research on how this technology is reshaping business, the economy, and society more broadly. According to MGI, the next wave of AI-generated productivity improvements will send shockwaves through the labor market as it reaches–and surpasses–human-level ability across a wide range of skills but ultimately make the entire world wealthier and healthier. We hope you enjoy this conversation.Mentioned in this EpisodeMcKinsey Global Institute (MGI)MGI report: The Economic Potential of Generative AIAccenture report: Strategy at the Pace of TechnologyStudy on AI's capacity for theory of mindJohn Maynard Keynes

Co-host Michael Chui talks with Andy McAfee. McAfee is a principal research scientist at the MIT Sloan School of Management, co-founder and co-director of MIT's initiative on the digital economy, and the inaugural visiting fellow at the Technology in Society organization at Google. See www.mckinsey.com/privacy-policy for privacy information

Good things will come to those companies that don't wait. McKinsey AI experts Michael Chui and Alex Singla discuss McKinsey's new reportabout the generative AI opportunity with global editorial director Lucia Rahilly. Hear how companies should immediately seize the gen AI opportunity to gain competitive advantage.But first, McKinsey senior partner Kelsey Robinson points out a consumer spending dichotomy in her first of a series of a quick US consumer behavior check-ins.See www.mckinsey.com/privacy-policy for privacy information

Co-host Michael Chui talks with business professor Ethan Mollick. He is an associate professor at the Wharton School at the University of Pennsylvania. Mollick covers topics including the following: What is generative AI? How substantial are the performance improvements workers can gain from using generative AI How to use human management skills to get better results from generative AI What generative AI means for the future of work and trust What he learned when he made the use of generative AI mandatory in his classes See www.mckinsey.com/privacy-policy for privacy information

Co-host Michael Chui talks with economist Hans-Helmut Kotz who is a visiting professor of economics at Harvard University, a senior policy fellow at the Leibniz Institute for financial research at Goethe University, Frankfurt, and on the economics faculty of Freiburg University. Kotz covers topics including the following: Parallels between the 2007–09 global financial crisis and today's financial turbulence. The balance that banking regulators need to strike to protect the economy but encourage innovation. Being prepared by taking eclectic perspectives. See www.mckinsey.com/privacy-policy for privacy information

Co-host Michael Chui talks with Nouriel Roubini. Roubini is professor emeritus of economics at the Stern School of Business at New York University, and CEO of Roubini Macro Associates, a global macroeconomics consultancy. He covers topics including the following: The “mother of all” debt crises and what to do about it Likely future trends in the global balance sheet—the world's economic health and wealth The trajectory of globalization Which “megathreat” worries him most See www.mckinsey.com/privacy-policy for privacy information

Marketing, Vendas, Operações, Tecnologia da Informação... São inúmeros os setores com potencial para o uso de ChatGPT, a tecnologia que já registra o crescimento mais vertiginoso da história da internet para um aplicativo de grande público. Esse modelo de Inteligência Artificial, capaz de responder perguntas complexas de forma criativa, foi lançado no final de novembro de 2022 e, em apenas dois meses, superou 100 milhões de usuários e 13 milhões de visitantes diários. Mas como aproveitar as oportunidades e contornar os riscos éticos, legais e práticos que essa revolução apresenta? No próximo episódio do McKinsey Talks, você vai saber como as empresas estão usando a Inteligência Artificial Generativa, conhecer casos concretos com potencial para uso dessa tecnologia e até onde essa novidade pode nos levar. O bate-papo será com Pepe Cafferata, Sócio da McKinsey em São Paulo e líder da QuantumBlack na América Latina, e Michael Chui, Sócio em São Francisco e Líder do McKinsey Global Institute.

Co-host Michael Chui talks with Justin Adams. Adams is the head of partnerships at Just Climate, a climate-led investment business. he answers questions, including: How does nature or the ecosystem provide services to the economy? How much needs to be invested in nature? What role can capitalism play in addressing issues around sustainability? What kind of innovations have real potential to mitigate carbon emissions? See www.mckinsey.com/privacy-policy for privacy information

In this episode, co-host Michael Chui talks with Nicholas Bloom. Bloom is the William Eberle Professor of Economics at Stanford University and a senior fellow at the Stanford Institute for Economic Policy Research. He covers topics including the following: The history of remote work How remote work can enhance productivity What practices make hybrid work most effective How the views of employers and employees compare on working remotely See www.mckinsey.com/privacy-policy for privacy information

McKinsey analyzed 14 new trends in its latest report on business technology. Here are the three that leaders need to know about most. In this episode of The McKinsey Podcast, partners Michael Chui and Roger Roberts speak with McKinsey editorial director Roberta Fusaro about McKinsey's latest report on technology trends, with a particular focus on the benefits and challenges of applied AI, cloud and edge computing, and bioengineering. After, we'll hear from McKinsey partner Brian Rolfes, who came to McKinsey firmly “in thecloset.” But a chance meeting with a client showed him the value of being authentic—with himself and others. Theme music composed, performed, and produced by Joy Ngiaw.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, co-host Michael Chui talks with Jayshree Seth, a corporate scientist and the chief science advocate at the 3M company. She covers topics including: How the human context can be brought into the practice of science and engineering Expanding talent pipelines into STEM fields The role of leadership in technical fieldsSee www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, Michael Chui talks to Tyler Cowen, Holbert L. Harris Chair of Economics at George Mason University, who serves as chairman and general director of the Mercatus Center at George Mason University. With colleague Alex Tabarrok, Cowen is coauthor of the popular economics blog Marginal Revolution and cofounder of the online educational platform Marginal Revolution University. This conversation was recorded in September 2022. To read a transcript of this episode, visit: https://mck.co/TylerCowen Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, Michael Chui talks to Tyler Cowen, Holbert L. Harris Chair of Economics at George Mason University, who serves as chairman and general director of the Mercatus Center at George Mason University. With colleague Alex Tabarrok, Cowen is coauthor of the popular economics blog Marginal Revolution and cofounder of the online educational platform Marginal Revolution University. This conversation was recorded in September 2022. To read a transcript of this episode, visit: https://mck.co/TylerCowen Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 47:57) >

Michael Chui, Kweilin Ellingrud, and Jackie Wong of McKinsey & Co, join Jerry to talk about the recently released report Asian American workers: Diverse outcomes and hidden challenges. Michael, Kweilin, and Jackie each share why this particular report means so much to them individually. Listen in as they discuss a wide range of topics with Jerry, including their routes to the corporate world and McKinsey, the importance of disaggregated data, the challenges that lie ahead, and what actions we can take to continue raising awareness and fighting for change for the Asian American diaspora in the workplace and beyond.Meet MichaelPartner at the McKinsey Global Institute (MGI), McKinsey's business and economics research arm. Lead research on the impact of disruptive technologies and innovation on business, the economy, and society. Also led McKinsey research in such areas as Big Data, social and collaboration technologies, and the Internet of Things.Frequent speaker at major global conferences, and research has been cited in leading publications around the world. PhD dissertation, entitled "I Still Haven't Found What I'm Looking For: Web Searching as Query Refinement," examined Web user search behaviors and the usability of Web search engines.Connect with Michael on LinkedInMeet KweilinAs a Senior Partner at McKinsey, I am a Director of McKinsey Global Institute where I lead our research on global productivity, the Future of Work and impact of automation, impact from COVID-19, and gender equality and racial equity. I am also a leader in our Operations and Insurance practices, implementing global performance transformations. I am the instigator and co-author of The Power of Parity, McKinsey Global Institute research on unlocking the potential of women to drive greater economic growth. I write and speak on the untapped potential of women globally, and strategies to achieve gender parity. My writing has appeared in TIME and Harvard Business Review and I have spoken at the UN Women's Empowerment Principles Event, the Council on Foreign Relations, the Wilson Center, the U.S. Treasury, and the U.S. EEOC. Connect with Kweilin on LinkedInMeet JackieJackie Wong is an engagement manager in McKinsey's Philadelphia office and leads the firm's Race in the Workplace research study. In addition to the Asian American workers research, he has also co-authored other workplace equity research for Black workers, frontline workers, and LGBTQ+ workers. With his research expertise, he advises clients on talent strategy, with a focus on diversity, equity, and inclusion. He also leads McKinsey's LGBTQ+ talent recruiting in North America.Connect with Jackie on LinkedInRead the report: Asian American workers: Diverse outcomes and hidden challenges// Support Dear Asian Americans:Merch: https://www.bonfire.com/store/dearasianamericans/Buy Me a Coffee: https://www.buymeacoffee.com/jerrywonSubscribe to the Newsletter: https://subscribepage.io/daanewsletterLearn more about DAA Creator and Host Jerry Won:LinkedIn: https://www.linkedin.com/in/jerrywon/Instagram: https://www.instagram.com/jerryjwon/// Listen to Dear Asian Americans on all major platforms:Transistor.fm: http://www.dearasianamericans.comApple: https://apple.dearasianamericans.comSpotify: https://spotify.dearasianamericans.comStitcher: https://stitcher.dearasianamericans.comGoogle: https://google.dearasianamericans.com  Follow us on Instagram:  http://www.instagram.com/dearasianamericans Like us on Facebook: http://www.facebook.com/dearasianamericans Subscribe to our YouTube: http://www.youtube.com/dearasianamericans // Join the Asian Podcast Network:Web: https://asianpodcastnetwork.com/Facebook Group: https://www.facebook.com/groups/asianpodcastnetwork/Instagram: https://www.instagram.com/asianpodcastnetwork/Dear Asian Americans is produced by Just Like Media:Web: http://www.justlikemedia.comInstagram: http://www.instagram.com/justlikemedia

In this episode of the McKinsey Global Institute's Forward Thinking podcast, co-host Michael Chui talks with Alec Stapp, co-CEO of the Institute for Progress, a Washington, DC, think tank he co-founded in January 2022. Progress is a policy choice, its founders say, and they have chosen to focus initially on three topics—meta-science, high-skill immigration, and biosecurity. Why those three? Their view is that each one is important, neglected by other researchers, and potentially tractable politically. This conversation was recorded in July 2022. To read a transcript of this episode, visit: https://mck.co/AlecStapp Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, co-host Michael Chui talks with Alec Stapp, co-CEO of the Institute for Progress, a Washington, DC, think tank he co-founded in January 2022. Progress is a policy choice, its founders say, and they have chosen to focus initially on three topics—meta-science, high-skill immigration, and biosecurity. Why those three? Their view is that each one is important, neglected by other researchers, and potentially tractable politically. This conversation was recorded in July 2022. To read a transcript of this episode, visit: https://mck.co/AlecStapp Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 42:31) >

In this episode of the McKinsey Global Institute's Forward Thinking podcast, co-host Michael Chui talks with Avinash Collis, professor of digital economy at The University of Texas at Austin, and Digital Fellow at Stanford and MIT. He covers topics including: — One of the greatest inventions of the 20th century — How to run economics experiments to figure out how much digital services are worth — How much value digital services provide that GDP doesn't capture This conversation was recorded in April 2022. To read a transcript of this episode, visit: https://mck.co/AviCollis Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, co-host Michael Chui talks with Avinash Collis, professor of digital economy at The University of Texas at Austin, and Digital Fellow at Stanford and MIT. He covers topics including: — One of the greatest inventions of the 20th century — How to run economics experiments to figure out how much digital services are worth — How much value digital services provide that GDP doesn't capture This conversation was recorded in April 2022. To read a transcript of this episode, visit: https://mck.co/AviCollis Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 41:08) >

In this episode of the McKinsey Global Institute's Forward Thinking podcast, Michael Chui talks with Benedict Evans, an independent technology analyst. Evans covers topics including the following: ● The generational shifts in technology that occur every 15 years or so ● What characterizes a “universal product” ● Why Web3 generates the most polarizing views among senior tech people ● Whether tech is becoming a regulated industry This conversation was recorded in February 2022. To read a transcript of this episode, visit: https://mck.co/BenedictEvans Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, Michael Chui talks with Benedict Evans, an independent technology analyst. Evans covers topics including the following: ● The generational shifts in technology that occur every 15 years or so ● What characterizes a “universal product” ● Why Web3 generates the most polarizing views among senior tech people ● Whether tech is becoming a regulated industry This conversation was recorded in February 2022. To read a transcript of this episode, visit: https://mck.co/BenedictEvans Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 39:33) >

McKinsey's most recent research found that too often organizations are not using IoT to its best advantage, in large part driven by the perception that the Internet of Things is a siloed, technical process, opposed to one that's holistic. Hear from partners Mark Collins and Michael Chui in conversation with executive editor Roberta Fusaro to discuss how companies can get it right. After, a piece of space junk is supposed to hit our moon soon. That piece of debris is not the only one hurtling through orbit. Hear from associate partner, Chris Daehnick, about the implications of space trash.   Theme music composed and performed by Joy NgiawSee www.mckinsey.com/privacy-policy for privacy information

McKinsey's most recent research found that too often organizations are not using IoT to its best advantage, in large part driven by the perception that the Internet of Things is a siloed, technical process, opposed to one that's holistic. Hear from partners Mark Collins and Michael Chui in conversation with executive editor Roberta Fusaro to discuss how companies can get it right. After, a piece of space junk is supposed to hit our moon soon. That piece of debris is not the only one hurtling through orbit. Hear from associate partner, Chris Daehnick, about the implications of space trash.   Theme music composed and performed by Joy Ngiaw Read more > Listen to the podcast (duration: 27:47) >

In this episode of the McKinsey Global Institute's Forward Thinking podcast, Michael Chui talks with David Autor, Ford Professor of Economics at the Massachusetts Institute of Technology. Autor talks about the paradoxes created for labor markets by the pandemic, the effect of trade globalization, the rise of China as a world economic power, and the economic impact of automation. He answers questions including: ● How have labor markets changed as the result of the pandemic, and how might they evolve? ● How has the globalization of trade affected the US labor market? ● What could have been done, and should be done now, to mitigate localized negative effects of trade shocks? ● Does ‘cowboy capitalism' give us higher growth? ● What's the most surprising thing you've learned during the pandemic? This conversation was recorded in February 2022. To read a transcript of this episode, visit: https://mck.co/DavidAutor Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, Michael Chui talks with David Autor, Ford Professor of Economics at the Massachusetts Institute of Technology. Autor talks about the paradoxes created for labor markets by the pandemic, the effect of trade globalization, the rise of China as a world economic power, and the economic impact of automation. He answers questions including: ● How have labor markets changed as the result of the pandemic, and how might they evolve? ● How has the globalization of trade affected the US labor market? ● What could have been done, and should be done now, to mitigate localized negative effects of trade shocks? ● Does ‘cowboy capitalism' give us higher growth? ● What's the most surprising thing you've learned during the pandemic? This conversation was recorded in February 2022. To read a transcript of this episode, visit: https://mck.co/DavidAutor Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 38:06) >

Inaugurando a série sobre Inteligência Artificial nos Negócios, Pepe Cafferata, sócio da McKinsey em São Paulo, e Michael Chui, sócio da McKinsey em São Francisco, discutem (em inglês) o panorama da adoção dessa nova tecnologia nos mais diversos setores a partir de um estudo internacional realizado.

In this episode of the McKinsey Global Institute's Forward Thinking podcast, co-host Michael Chui talks with Matthew J. Slaughter, Paul Danos Dean and Earl C. Daum 1924 Professor of International Business at the Tuck School of Business at Dartmouth College. Slaughter takes the temperature of globalization, discussing ways of tackling distributional challenges and ensuring it works for ordinary families. He also reflects on the evolving demands on leaders of corporations in the 21st century, positing that those who recognize their wider purpose in society tend to outperform on productivity growth and profits. He answers questions including the following: ● At a time of great challenge in terms of globalization, what has worked, what hasn't worked, and what have we learned? ● What can be done to achieve the benefits of globalization while making sure distributional challenges are addressed? ● How have the demands placed on leaders in the 21st century changed? ● In late 2021, there is a lot of discussion about supply chain issues. What is going on here and what is going to happen? This conversation was recorded in November 2021. To read a transcript of this episode, visit: https://mck.co/MattSlaughter Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, co-host Michael Chui talks with Matthew J. Slaughter, Paul Danos Dean and Earl C. Daum 1924 Professor of International Business at the Tuck School of Business at Dartmouth College. Slaughter takes the temperature of globalization, discussing ways of tackling distributional challenges and ensuring it works for ordinary families. He also reflects on the evolving demands on leaders of corporations in the 21st century, positing that those who recognize their wider purpose in society tend to outperform on productivity growth and profits. He answers questions including the following: ● At a time of great challenge in terms of globalization, what has worked, what hasn't worked, and what have we learned? ● What can be done to achieve the benefits of globalization while making sure distributional challenges are addressed? ● How have the demands placed on leaders in the 21st century changed? ● In late 2021, there is a lot of discussion about supply chain issues. What is going on here and what is going to happen? This conversation was recorded in November 2021. To read a transcript of this episode, visit: https://mck.co/MattSlaughter Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 34:59) >

In this episode of the McKinsey Global Institute's Forward Thinking podcast, Michael Chui talks to Byron Auguste and Beth Cobert whose professional life is dedicated to fostering a more skills-based labor market. Their focus is on the United States, but their diagnostic can just as easily apply to other countries and regions. Both worked at McKinsey & Company for many years on labor-market issues before going on to work in the Obama administration. Today, Beth is the chief operating officer of the Markle Foundation and CEO of Skillful, a Markle Foundation initiative. Byron Auguste is the co-founder and CEO of Opportunity@Work. They answer questions including the following: — Who is actually working in the US labor market? — How does the language we use—“low skill”, “high skill”—cause problems? — What could a potential future of more of a skills-based workforce look like? — If employers and people want a skills-based workforce, why aren't we there already? — Do we all have to wait until there's some global skills taxonomy we all use? How does this thing evolve so that it actually becomes practical? This conversation was recorded in October 2021. To read a transcript of this episode, visit: https://mck.co/BethCobertByronAuguste Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, Michael Chui talks to Byron Auguste and Beth Cobert whose professional life is dedicated to fostering a more skills-based labor market. Their focus is on the United States, but their diagnostic can just as easily apply to other countries and regions. Both worked at McKinsey & Company for many years on labor-market issues before going on to work in the Obama administration. Today, Beth is the chief operating officer of the Markle Foundation and CEO of Skillful, a Markle Foundation initiative. Byron Auguste is the co-founder and CEO of Opportunity@Work. They answer questions including the following: — Who is actually working in the US labor market? — How does the language we use—“low skill”, “high skill”—cause problems? — What could a potential future of more of a skills-based workforce look like? — If employers and people want a skills-based workforce, why aren't we there already? — Do we all have to wait until there's some global skills taxonomy we all use? How does this thing evolve so that it actually becomes practical? This conversation was recorded in October 2021. To read a transcript of this episode, visit: https://mck.co/BethCobertByronAuguste Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 44:27) >

In this episode of the McKinsey Global Institute's Forward Thinking podcast, co-host Michael Chui speaks with Mary “Missy” Cummings, one of the first female fighter pilots in the US Navy and now a professor in the Duke University Pratt School of Engineering and the Duke Institute for Brain Sciences, as well as the director of Duke's Humans and Autonomy Laboratory. Cummings talks about her life as a fighter pilot and her journey into automation and robotics. She also answers questions like: • What are your reflections on diversity across different fields? • What are some interesting developments you're seeing in the automation of vehicles? • Are there things that car designers should be learning from the aerospace industry, or vice versa, as they're starting to implement more levels of automated technology and driver assistance? • What is the perfect use case for automation? • What excites you most about advances in technology? This conversation was recorded in August 2021. To read a transcript of this episode, visit: https://mck.co/missycummings Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, co-host Michael Chui speaks with Mary “Missy” Cummings, one of the first female fighter pilots in the US Navy and now a professor in the Duke University Pratt School of Engineering and the Duke Institute for Brain Sciences, as well as the director of Duke's Humans and Autonomy Laboratory. Cummings talks about her life as a fighter pilot and her journey into automation and robotics. She also answers questions like: • What are your reflections on diversity across different fields? • What are some interesting developments you're seeing in the automation of vehicles? • Are there things that car designers should be learning from the aerospace industry, or vice versa, as they're starting to implement more levels of automated technology and driver assistance? • What is the perfect use case for automation? • What excites you most about advances in technology? This conversation was recorded in August 2021. To read a transcript of this episode, visit: https://mck.co/missycummings Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 30:17) >

In this episode of the McKinsey Global Institute's Forward Thinking podcast, host Michael Chui speaks with Laura Tyson, Distinguished Professor of the Graduate School at the Haas School of Business, University of California, Berkeley. Tyson shares her journey in economics, from discovering Econ 1 in college to chairing the President's Council of Economic Advisers. She also answers questions like: • Are we in a “golden age” for women in economics? • What impact has the pandemic had on gender equality? • What can other countries learn from Germany's apprenticeship and skills programs in preparing for the future of work? • Why doesn't she expect a slow recovery after the pandemic, as we experienced after the global financial crisis? • Why aren't predictions about the economy more accurate, like predicting the weather? This conversation was recorded in June 2021. To read a transcript of this episode, visit: https://mck.co/lauratyson Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, host Michael Chui speaks with Laura Tyson, Distinguished Professor of the Graduate School at the Haas School of Business, University of California, Berkeley. Tyson shares her journey in economics, from discovering Econ 1 in college to chairing the President's Council of Economic Advisers. She also answers questions like: • Are we in a “golden age” for women in economics? • What impact has the pandemic had on gender equality? • What can other countries learn from Germany's apprenticeship and skills programs in preparing for the future of work? • Why doesn't she expect a slow recovery after the pandemic, as we experienced after the global financial crisis? • Why aren't predictions about the economy more accurate, like predicting the weather? This conversation was recorded in June 2021. To read a transcript of this episode, visit: https://mck.co/lauratyson Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 33:27) >

In this episode of the McKinsey Global Institute's Forward Thinking podcast, hosts Michael Chui and Anna Bernasek speak with Daron Acemoglu. Daron is a professor of economics at the Massachusetts Institute of Technology (MIT), a coauthor (with James A. Robinson) of Why Nations Fail: The Origins of Power, Prosperity and Poverty, and the author of many influential academic papers. His research covers a range of topics, including political economy, economic development, economic growth, technological change, inequality, labor economics, and economics of networks. The conversation focuses in on how we make growth happen in a world of technological change that is largely disrupting labor markets. As Daron puts it, “I believe that the political economy of growth ... and technology, automation, AI—those [things] are intimately connected.” The influential economist connects the dots between artificial intelligence, productivity, wages, and inequality, and how to counterbalance the impacts of automation. This conversation was recorded in April 2021. To read a transcript of this episode, visit: https://mck.co/daron Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of the McKinsey Global Institute's Forward Thinking podcast, hosts Michael Chui and Anna Bernasek speak with Daron Acemoglu. Daron is a professor of economics at the Massachusetts Institute of Technology (MIT), a coauthor (with James A. Robinson) of Why Nations Fail: The Origins of Power, Prosperity and Poverty, and the author of many influential academic papers. His research covers a range of topics, including political economy, economic development, economic growth, technological change, inequality, labor economics, and economics of networks. The conversation focuses in on how we make growth happen in a world of technological change that is largely disrupting labor markets. As Daron puts it, “I believe that the political economy of growth ... and technology, automation, AI—those [things] are intimately connected.” The influential economist connects the dots between artificial intelligence, productivity, wages, and inequality, and how to counterbalance the impacts of automation. This conversation was recorded in April 2021. To read a transcript of this episode, visit: https://mck.co/daron Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 34:15) >

In this episode of Forward Thinking, host Michael Chui speaks with Jeffrey Ding, researcher and founder of the ChinAI newsletter, about information asymmetry in artificial intelligence between China and the West. They cover why data may not be like oil, the Chinese industry adage on products, platforms, and standards, “unsexy AI” and more. There's a lot of talk right now about artificial intelligence, or AI, and what it means for global competition. Today's conversation features somebody you probably don't know yet but probably should. He's famous in certain corners of the internet but his work, it turns out, is relevant everywhere. MGI research suggests that while there's AI happening all around the world, there are two places where the most AI development is taking place, and it's the US and China. What's interesting about that is that while a lot of the Chinese AI developers are reading and even coauthoring English-language papers, very few Western AI practitioners are able to keep up with the flow of information in the Chinese language, even when a lot of it is published openly. It's almost like a one-way mirror—and this asymmetry might seem strange in a field where a lot of the work is openly available on the internet. But our guest, Jeffrey Ding, has been helping to make sure more AI information flows back from China to the West. This episode's guest, Jeffrey Ding, is a PhD Candidate in international relations at the University of Oxford and a pre-doctoral fellow at Stanford's Center for International Security and Cooperation, sponsored by Stanford's Institute for Human-Centered Artificial Intelligence. He is also a research affiliate with the Centre for the Governance of AI at the University of Oxford. This conversation was recorded in March 2021. To read a transcript of this episode, visit: https://mck.co/forwardthinking Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more.See www.mckinsey.com/privacy-policy for privacy information

In this episode of Forward Thinking, host Michael Chui speaks with Jeffrey Ding, researcher and founder of the ChinAI newsletter, about information asymmetry in artificial intelligence between China and the West. They cover why data may not be like oil, the Chinese industry adage on products, platforms, and standards, “unsexy AI” and more. There's a lot of talk right now about artificial intelligence, or AI, and what it means for global competition. Today's conversation features somebody you probably don't know yet but probably should. He's famous in certain corners of the internet but his work, it turns out, is relevant everywhere. MGI research suggests that while there's AI happening all around the world, there are two places where the most AI development is taking place, and it's the US and China. What's interesting about that is that while a lot of the Chinese AI developers are reading and even coauthoring English-language papers, very few Western AI practitioners are able to keep up with the flow of information in the Chinese language, even when a lot of it is published openly. It's almost like a one-way mirror—and this asymmetry might seem strange in a field where a lot of the work is openly available on the internet. But our guest, Jeffrey Ding, has been helping to make sure more AI information flows back from China to the West. This episode's guest, Jeffrey Ding, is a PhD Candidate in international relations at the University of Oxford and a pre-doctoral fellow at Stanford's Center for International Security and Cooperation, sponsored by Stanford's Institute for Human-Centered Artificial Intelligence. He is also a research affiliate with the Centre for the Governance of AI at the University of Oxford. This conversation was recorded in March 2021. To read a transcript of this episode, visit: https://mck.co/forwardthinking Follow @McKinsey_MGI on Twitter and the McKinsey Global Institute on LinkedIn for more. Read more > Listen to the podcast (duration: 32:17) >

Ahead of Juneteenth, now a federal holiday, we examine how America's economy remains marred by the legacies of slavery and racial discrimination. Dan is joined by McKinsey & Co.'s Shelley Stewart III and Michael Chui to discuss a new report from the McKinsey Global Institute and McKinsey's Institute for Black Economy Mobility, digging into the economic inequities between Black and white Americans, including massive wage and wealth gaps, and what can be done to address them. Learn more about your ad choices. Visit megaphone.fm/adchoices

The Bio Revolution has the potential to transform our lives, and genome editing—the ability to change the DNA sequence in a targeted way using CRISPR-Cas9, is one of the key innovations that has sparked imaginations while also raising its fair share of controversy. What is the origin of this technique? How do we weigh the enormous benefits against the potential risks? And what is its role in solving the global coronavirus pandemic? As part of the McKinsey Global Institute's research on the Bio Revolution, partner Michael Chui spoke with Jennifer Doudna, PhD, one of the scientists who discovered the genome-editing technique CRISPR-Cas9 and leading proponent of its responsible use. Jennifer is a professor of molecular and cell biology and chemistry at the University of California, Berkeley. The Doudna lab pursues a mechanistic understanding of fundamental biological processes involving RNA molecules. To read a transcript of this episode, visit: mck.co/3dEJWhJ To read more about the Bio Revolution, visit: mck.co/biorevSee www.mckinsey.com/privacy-policy for privacy information

The Bio Revolution has the potential to transform our lives, and genome editing—the ability to change the DNA sequence in a targeted way using CRISPR-Cas9, is one of the key innovations that has sparked imaginations while also raising its fair share of controversy. What is the origin of this technique? How do we weigh the enormous benefits against the potential risks? And what is its role in solving the global coronavirus pandemic? As part of the McKinsey Global Institute's research on the Bio Revolution, partner Michael Chui spoke with Jennifer Doudna, PhD, one of the scientists who discovered the genome-editing technique CRISPR-Cas9 and leading proponent of its responsible use. Jennifer is a professor of molecular and cell biology and chemistry at the University of California, Berkeley. The Doudna lab pursues a mechanistic understanding of fundamental biological processes involving RNA molecules. To read a transcript of this episode, visit: mck.co/3dEJWhJ To read more about the Bio Revolution, visit: mck.co/biorev Read more > Listen to the podcast (duration: 35:19) >

Read more > Listen to the podcast (duration: 00:90) > Forward Thinking is a new podcast series hosted by Michael Chui and Anna Bernasek. We want to inform and provoke your thinking by interviewing names you may know—like Nobel prizewinners Jennifer Doudna and Sir Christopher Pissarides—and by introducing up-and-comers you should know. We ask experts to tell us about the key changes they see that will shape our lives, our businesses, and our societies…Including what comes next after COVID-19, global health, climate change, the future of work, diversity, inclusion, inequality, and cutting-edge technology like the bio revolution and artificial intelligence. Interviews are underpinned by deep McKinsey Global Institute research that provides fundamental insights needed by leaders like you.See www.mckinsey.com/privacy-policy for privacy information

Forward Thinking is a new podcast series hosted by Michael Chui and Anna Bernasek. We want to inform and provoke your thinking by interviewing names you may know—like Nobel prizewinners Jennifer Doudna and Sir Christopher Pissarides—and by introducing up-and-comers you should know. We ask experts to tell us about the key changes they see that will shape our lives, our businesses, and our societies…Including what comes next after COVID-19, global health, climate change, the future of work, diversity, inclusion, inequality, and cutting-edge technology like the bio revolution and artificial intelligence. Interviews are underpinned by deep McKinsey Global Institute research that provides fundamental insights needed by leaders like you. Read more > Listen to the podcast (duration: 00:90) >

Thank you for listening to the Leaders, Innovators and Big Ideas podcast, supported by Rainforest Alberta.The podcast that highlights those people who are contributing to and/or supporting the innovation ecosystem in Alberta.Michael Chui Michael Chui enjoys learning about different perspectives and challenges assumptions through questions. He had a diverse career in finance and operations, technology (software), product management (product owner) and regulatory economics including times training as a locomotive engineer. His diverse experience allows him to look at complex ideas through multiple lenses and pinpoint its simplest core values. He is currently unlearning && re-learning how to build intuitive (web) applications and effective architecture solutions as a full stack developer. He is also volunteering with Chartered Professional Accountants (CPA) Alberta to help accountants better adapt to the latest in software and technology.Dr. Byron Chu Byron has a Ph.D. in Biochemistry from the University of Calgary. His studies focused on intrinsic properties of proteins, and today he applies the same scientific principles to data science projects. As a data scientist, Byron is interested in the intersection of data analysis and storytelling, and how numbers can inform and impact on every level of society: from education and the environment to the health issues we may potentially face.Please be sure to share this episode with everyone you know. If you are interested in being either a host, a guest, or a sponsor of the show, please reach out.We are published in Google Podcasts and the iTunes store for Apple Podcasts We would be grateful if you could give us a rating as it helps spread the word about the show.Show SummaryIn an online article, the business publication Forbes recently discussed the importance of teaching K-12 students “computational thinking”. Computational thinking is a way to solve complex problems by breaking them down into steps and finding trends and patterns. In this episode, Rainforest Alberta host Michael Chui chats with Dr. Byron Chu, project manager and data scientist for the Callysto program. Byron talks about why it’s important to teach students computational thinking and what Callysto is doing to bring computational thinking into Grades 5-12 classrooms across Canada. About Callysto, Callysto is a free, online learning tool that helps Grades 5-12 students and teachers across Canada learn and apply in-demand data science skills, including data analysis, visualization, coding, and computational thinking. The online tool’s interactive learning modules are available in a variety of subjects – from math to history – and are aligned with existing curriculums. The Callysto is federally-funded and is led by the non-profit organizations Cybera and the Pacific Institute for the Mathematical Sciences. Callysto: https://callysto.ca/ Callysto Youtube channel: https://www.youtube.com/channel/UCPdq1SYKA42EZBvUlNQUAng Cybera: https://www.cybera.ca/ Jupyter notebook: https://jupyter.org/ Forbes article: "Stop Calculating And Start Teaching Computational Thinking" by Tom Vander Ark. https://www.forbes.com/sites/tomvanderark/2020/06/29/stop-calculating-and-start-teaching-computational-thinking/#3f1856d83786Show Quote:"Computational thinking in combination with data science and open data is a very powerful phenomenon...where industries can share a lot of the knowledge to come up with solutions to apply to teach and educate. ""In the student's first hackathons, we had challenges on Pokemon, Spotify, and Alice in Wonderland. Books can be turned into data. Words can be turned into data. We had a whole exploration of what you can do with natural language processing, even though (the students) don't recognize they are doing natural language processing."Credits...This Episode Sponsored By: Community Now! MagazineEpisode Music: Tony Del DeganCreator & Producer: Al Del DeganSystem Engineering Sponsor: Kris Chase - PODMaster 2020  

Guests: Erik Wasson, Bloomberg Congressional reporter, Max Abelson, Bloomberg Businessweek reporter, David Tafuri, Former Obama Campaign Foreign Policy Adviser, and Dr. Michael Chui, partner at the McKinsey Global Institute.

Guests: Erik Wasson, Bloomberg Congressional reporter, Max Abelson, Bloomberg Businessweek reporter, David Tafuri, Former Obama Campaign Foreign Policy Adviser, and Dr. Michael Chui, partner at the McKinsey Global Institute.

How will Artificial Intelligence shape the next decade? Will thisrevolution be a positive force, spurring global growth and improving lives around the world? Or will the benefits flow heavily to those who already have the knowledge and wealth to use these revolutionary technologies?  Edie Lush and Claudia Romo Edelman pursue those questionsaround the world. They speak to an author of a major United Nation’s report who says that AI will spur global growth more than earlier innovations like steam power. But who benefits from that growth will be shaped by how well Africa, Latin America and the rest of the Global South absorb and adapt these powerful tools and manage the inevitable disruptions to work. “In some ways, the Luddites weren’t wrong,” says the co author of the report, Michael Chui of McKinsey.  In other words, AI can either help achieve the Sustainable Development Goals or move them out of reach.  To understand what is already being done in Africa, Edie and Claudia speak with two African experts, Nathalie Munyampendaof the Next Einstein Foundation and Abdigani Diriye from IBM Research in Nairobi. They stress the importance of Africans developing African solutions to solve Africa’s challenges. “The conversation really needs to be around how we can effectively use artificial intelligence to improve the human condition and how we can prepare ourselves and the next generation,” says Diriye. Two special guests cite one basic challenge: inclusion. Christopher Fabian, innovation expert from UNICEF, and Rosemary Leith of the World Wide Web Foundation, note that half the world is not yet on the internet. Those who are not connected do not and will not have access to the powers of AI. This episode also features a conversation about the gig economy with Jennifer Rademaker, Executive Vice President of Global Customer Delivery at Mastercard, the sponsor of Season Two of the Global GoalsCast.

Dr. Michael Chui, partner at the McKinsey Global Institute, McKinsey's business and economics research arm shares what businesses should focus on in terms of AI to move their business forward. 

Michael Chui, Senior Partner at McKinsey Global Institute (MGI), and an expert in artificial intelligence, believes that many jobs are going to disappear – including those done by MBAs and doctors – just not as quickly as people think. As he says to Bill Kerr, there is a lag between the rate at which technology advances and the rate at which it is adopted. Just think Star Trek.

Bill Gates portends doomsday is coming. The late Stephen Hawking said we should prepare for our robot overlords to take their thrones. But is the future as glaring as HAL’s red eye? Or is it more complicated than that? What does a future powered by algorithms and big intelligence mean for our lives? What are the game-changing developments made possible by AI? How will AI transform industry and disrupt business? A panel of tech and business experts, including Tim O’Reilly, Gary Marcus, and Michael Chui, discuss how AI will impact our lives, and what business sectors might be most affected. Their conversation is led by Bloomberg Television’s Erik Schatzker. Show Notes Listen to the Aspen Ideas to Go episode, WTF (What's the Future), featuring Tim O'Reilly. Follow our show on Twitter @aspenideas and Facebook at facebook.com/aspenideas. Email your comments to aspenideastogo@gmail.com.

Artificial intelligence research: Dr. Michael Chui, partner at the McKinsey Global Institute, speaks with CXOTalk about his latest report on AI, automation and the impact of technology in the workplace.Chui leads McKinsey’s business and economics research arm in analysis of Big Data, Web 2.0 and collaboration technology, and the Internet of Things.

Artificial intelligence research: Dr. Michael Chui, partner at the McKinsey Global Institute, speaks with CXOTalk about his latest report on AI, automation and the impact of technology in the workplace.Chui leads McKinsey’s business and economics research arm in analysis of Big Data, Web 2.0 and collaboration technology, and the Internet of Things.

Martin Feldstein, Harvard University George F. Baker Professor of Economics, says now is the time for tax reform because the politics are right. Henry Olsen, EPPC Senior Fellow, says President Trump needs to recover his populist mojo. Matt Hornbach, Morgan Stanley Global Head of Interest Rates Strategy, says it's time for the next bond market phase. Michael Chui, McKinsey & Co. Senior Fellow, says there is enough work for people to do, even with the increased use of robots.  Learn more about your ad-choices at https://www.iheartpodcastnetwork.com

Martin Feldstein, Harvard University George F. Baker Professor of Economics, says now is the time for tax reform because the politics are right. Henry Olsen, EPPC Senior Fellow, says President Trump needs to recover his populist mojo. Matt Hornbach, Morgan Stanley Global Head of Interest Rates Strategy, says it's time for the next bond market phase. Michael Chui, McKinsey & Co. Senior Fellow, says there is enough work for people to do, even with the increased use of robots. 

In a speech to the International Center for Journalists, Fox News Sunday host Chris Wallace put forth the idea that the news media is hastening its own fall from grace in the Trump era. Wallace implored journalists to abandon partisan rhetoric and commentary on President Trump and return to giving context and reporting the facts. Wallace joined Steele & Ungar to elaborate on his argument and weigh in on the firing of Today Show co-host Matt Lauer due to allegations of sexual misconduct. Michael Chui of the McKinsey Global Institute talked about the group's shocking report that automation could wipe out 30 percent of America's workforce over the next two decades.

Dr. Michael Chui of the McKinsey Global Institute. Dr. Michael Chui is a technologist by training, a computer and cognitive scientist, author, and frequent speaker at major global conferences. Stephen W. Maye is your host for Projectified with PMI. In this episode, Stephen talks with Michael Chui who has conducted lots of fascinating research around automation, artificial intelligence, machine learning, robotics, and digitization.   Michael shares research on the impact that artificial intelligence will have on the workforce, why A.I. presents new opportunities in the project world, how it affects work and the future potential of work, and how project professionals can prepare themselves to thrive in an emergent, exciting and uncertain landscape.   For an easy way to stay up-to-date on Projectified with PMI, follow the podcast at: Itunes, Stitcher, Google Play or pmi.org/podcast.   Key Takeaways: [:28] Steven introduces Dr. Michael Chui and he gives us some background into his work [1:40] What Dr. Michael Chui’s research has been about, the motivation behind it and the biggest surprises to come out of it [8:40] Where Michael sees the significant differences in the project world with the adoption of technological automation [12:17] The good news: Emphasis on leadership roles and developing connections with people [14:20] Where Michael anticipates the most significant impacts from automation and digitization [16:25] Where we are from a social acceptance perspective about machines and robotics [19:20] Awareness of robotics and automation adoption across the country and on socioeconomic levels [22:17] How people in professional roles will prepare for a world of increasingly smart technology [24:48] The good news for project management and project leadership: Immense value placed on leadership and motivation skills   Links: Michael Chui’s McKinsey & Company Profile

Data and automation have the power to transform business and society. The impact of data on our lives will be profound as industry and the government make greater use of techniques such as artificial intelligence and machine learning. Explore this important topic with two world experts.

Data and automation have the power to transform business and society. The impact of data on our lives will be profound as industry and the government make greater use of techniques such as artificial intelligence and machine learning. Explore this important topic with two world experts.

CSIS Senior Fellow and Deputy Director of the Strategic Technologies Program Denise Zheng, speaks with Michael Chui and Sokwoo Rhee about opportunities for economic growth and the role of government in enabling the Internet of Things. Michael Chui is a partner at the McKinsey Global Institute and Sokwoo Rhee is Associate Director of Cyber-Physical Systems the National Institute of Standards and Technology (NIST).

Next-generation genomics can be described as the combination of sequencing technologies and big data analytics. The potential impact of this disruptive technology in health care will be primarily realized through extending and enhancing lives through faster disease detection, more precise diagnoses, new drugs, and more tailored disease treatments. The technical challenges inherent in genetic engineering technology are great but may be less formidable than the social, ethical, and regulatory concerns it may generate. Please join us for a discussion of the possibilities and the challenges of next-generation genomics and implications for health care worldwide.The panel of speakers include Timothy Behrens, Senior Director, Human Genetics, Genentech, Francis deSouza, President, Illumina Corporation, Robert L. Nussbaum, Chief, Department of Medicine & UCSF Institute for Human Genetics, UCSF.The discussion is moderated by Michael Chui, Partner, McKinsey Global Institute.For more information about this event please visit: http://www.worldaffairs.org/event-calendar/event/1298

Michael Chui est “Senior Fellow” pour le McKinsey Global Institue. Il vit à San Francisco, Californie, où il dirige les recherches sur les impacts que peuvent avoir les technologies de l’information sur l’économie et le business ; technologies telles que les Big Datas, le Web 2.0 et l’internet des objets. Il est le co-auteur de “Big data: The next frontier for innovation, competition and productivity.” Il a travaillé pour des clients dans le secteur High Tech, des medias et de l’industrie des télécoms sur la stratégie; l’innovation et le développement produit, dans l’IT, pour les ventes et le marketing. Ses recherches ont été citées à plusieurs reprises dans des publications du Wall Street Journal, du New-York Times, du Financial Times, de la Fast Company, de Forbes, The Economist, The times of London et des Echos.

Michael Chui est “Senior Fellow” pour le McKinsey Global Institue. Il vit à San Francisco, Californie, où il dirige les recherches sur les impacts que peuvent avoir les technologies de l’information sur l’économie et le business ; technologies telles que les Big Datas, le Web 2.0 et l’internet des objets. Il est le co-auteur de “Big data: The next frontier for innovation, competition and productivity.” Il a travaillé pour des clients dans le secteur High Tech, des medias et de l’industrie des télécoms sur la stratégie; l’innovation et le développement produit, dans l’IT, pour les ventes et le marketing. Ses recherches ont été citées à plusieurs reprises dans des publications du Wall Street Journal, du New-York Times, du Financial Times, de la Fast Company, de Forbes, The Economist, The times of London et des Echos.