Podcast appearances and mentions of Erik Brynjolfsson

Will artificial intelligence help you do your job, or will it just straight-up do your job and leave you unemployable? Or will the future bring something else entirely — either between those two extremes or a world that we simply cannot imagine yet? And are we already starting to see signs of that future emerging? On this episode of The New Bazaar, Cardiff is joined by economist Nathan Goldschlag, Research Director at the Economic Innovation Group. Until recently, Nathan was Principal Economist at the U.S. Census Bureau's Center for Economic Studies, where among other things he led research on the impact of technology, including AI, on the economy. Any worthwhile list of the world's best economists on the subject of AI and work would have to include him. Cardiff and Nathan go through Nathan's own research* and also filter out the megaton of nonsense on the topic and discuss some of the work done by others — research, essays, meanderings — that they think is actually worth sharing with listeners. They discuss, among other things: How many businesses are now using AI to produce goods and servicesHow have things changed since the launch and popularization of large language modelsEconomic growth consequences of AIWhether “learn to code” is still good advice The skills that still matter To steer or not to steer the AI future* Nathan's research on AI was done in collaboration with a large team of researchers at the Center for Economic Studies at the U.S. Census Bureau including Emin Dinlersoz, Lucia Foster, David Beede, John Haltiwanger, Zach Kroff, Nikolas Zolas, Gary Anderson, and Eric Childress, along with program area partners including Kathryn Bonney, Cory Breaux, Cathy Buffington, and Keith Savage, as well as academic partners including Daron Acemoglu, Erik Brynjolfsson, Kristina McElheran, and Pascual Restrepo. Related links:The impact of AI on the workforce: Tasks versus jobs?Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey.The Rapid Adoption of Generative AI | NBERAnswering the Call of AutomationAI-2027.comTyler Cowen - the #1 bottleneck to AI progress is humansDriverless trucks are coming and unions aren't happy about itGenerative AI at Work Hosted on Acast. See acast.com/privacy for more information.

The Stanford economist unpacks AI's impact on work and productivity, its limitations, and wider implications. He also lays out what organizations can do to get more out of the technology as they invest in the transformation. And he updates his longstanding research into augmenting traditional GDP metrics to capture the value of digital goods and services.

When are you most productive? How do you increase your productivity? And is technology a help or a hindrance?! The amazing Erik Brynjolfsson learns the Chinese word for "productivity" and discusses with ShaoLan the technologies that can cause ripples of productivity growth around the world. ✨ BIG NEWS ✨ Our brand new Talk Chineasy App, is now live on the App Store! Free to download and perfect for building your speaking confidence from Day 1. portaly.cc/chineasy Visit our website for more info about the app.

Impress your Chinese friends with the word for Artificial Intelligence! World-leading economist Erik Brynjolfsson chats with ShaoLan about how smart robots could really get and how that might change the world as we know it. ✨ BIG NEWS ✨ Our brand new Talk Chineasy App, is now live on the App Store! Free to download and perfect for building your speaking confidence from Day 1. portaly.cc/chineasy Visit our website for more info about the app.

This is the last and amongst the liveliest of my interviews at Munich's DLD Conference this year. An old friend who has appeared on KEEN ON several times before, Andrew McAfee is a MIT professor who co-wrote the 2014 classic The Second Machine Age. In our conversation, celebrating the 20th anniversary of the DLD Conference, McAfee reflects on the technological changes of the past 20 years,. He acknowledges that while he accurately predicted the broad trajectory of technological advancement, he underestimated AI's capabilities in areas like language processing and creative tasks. McAfee discusses the emergence of deep learning around 2012 and its evolution into today's generative AI. While maintaining overall optimism about technology's impact, he expresses concern about increasing social polarization and anxiety, particularly related to social media use, though he notes these trends actually preceded current technology. On economic matters, McAfee challenges the notion that tech innovation is stagnating, pointing to newcomers like Nvidia and OpenAI as evidence of continued inventive dynamism. He discusses Europe's technological lag behind the United States, citing regulatory challenges like GDPR as potential factors. Regarding climate change, McAfee believes technological solutions, particularly nuclear fusion, could address environmental challenges, though he acknowledges the severity of the crisis. He concludes by warning how traditional companies must adapt to survive in an era of rapid technological change, particularly facing competition from more agile, tech-savvy competitors.Andrew McAfee (@amcafee) 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 and Society organization at Google. He studies how technological progress changes the world. His next book, The Geek Way, will be published by Little, Brown in 2023. His previous books include More from Less and, with Erik Brynjolfsson, The Second Machine Age. McAfee has written for publications including Foreign Affairs, Harvard Business Review, The Economist, The Wall Street Journal, and The New York Times. He's talked about his work on CNN and 60 Minutes, at the World Economic Forum, TED, and the Aspen Ideas Festival, with Tom Friedman and Fareed Zakaria, and in front of many international and domestic audiences. He's also advised many of the world's largest corporations and organizations ranging from the IMF to the Boston Red Sox to the US Intelligence Community. McAfee and his frequent coauthor Erik Brynjolfsson are othe nly people named to both the Thinkers50 list of the world's top management thinkers and the Politico 50 group of people transforming American politics.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.Keen On 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 keenon.substack.com/subscribe

Stephen Dubner, live on stage, mixes it up with outbound mayor London Breed, and asks economists whether A.I. can be “human-centered” and if Tang is a gateway drug. SOURCES:London Breed, former mayor of San Francisco.Erik Brynjolfsson, professor of economics at Stanford UniversityKoleman Strumpf, professor of economics at Wake Forest University RESOURCES:"SF crime rate at lowest point in more than 20 years, mayor says," by George Kelly (The San Francisco Standard, 2025)"How the Trump Whale and Prediction Markets Beat the Pollsters in 2024," by Niall Ferguson and Manny Rincon-Cruz (Wall Street Journal, 2024)"Artificial Intelligence, Scientific Discovery, and Product Innovation," by Aidan Toner-Rodgers (MIT Department of Economics, 2024) EXTRAS:"Why Are Cities (Still) So Expensive?" by Freakonomics Radio (2020)

Stanford University's Erik Brynjolfsson joins us to share insights on how business can leverage AI to boost productivity, innovation and growth. In this episode, Erik explores the key role of a CFO in the AI era, strategies for staying competitive in today's digital world, and the AI skills professionals need to stay ahead. He also dives into the future of work, the growing importance of intangible assets, and the ethical implications of AI development. Join us to gain valuable insights from a leading AI thought leader and academic. Host: Aidan Ormond, digital content editor, CPA Australia Guest: Erik Brynjolfsson, Senior Fellow at Stanford's Institute for Human-Centered AI and Director of the Stanford Digital Economy Lab. He is also the Ralph Landau Senior Fellow at Stanford's Economic Policy Research Institute, a Professor by Courtesy at the Graduate School of Business and Department of Economics, and a Research Associate at the National Bureau of Economic Research. Known for his groundbreaking research on the economics of information, Erik has written bestsellers and holds a PhD from MIT and degrees from Harvard. For more information on Erik's research work at Stanford University, head to the Stanford Graduate School of Business faculty research page. Erik is also appearing at CPA Australia's Congress 24 in Canberra this October. It's an in-person and virtual event, featuring insightful and inspiring thought leaders who'll share their ideas on a variety of topics to help level up your professional knowledge. Would you like to listen to more INTHEBLACK episodes? Head to CPA Australia's YouTube channel. CPA Australia publishes four podcasts, providing commentary and thought leadership across business, finance, and accounting: With Interest INTHEBLACK INTHEBLACK Out Loud Excel Tips Search for them in your podcast platform. Email the podcast team at podcasts@cpaaustralia.com.au

Erik Brynjolfsson returns for another fascinating episode in which you can learn how to say the word for robot in Chinese. He and ShaoLan also discuss the future of robotic technologies and how they will continue to shape our lives in the coming years. ✨ BIG NEWS ✨ Our brand new Talk Chineasy App, is now live on the App Store! Free to download and perfect for building your speaking confidence from Day 1. portaly.cc/chineasy Visit our website for more info about the app.

Which characters does the Chinese language put together to make the word computer? It will definitely surprise you with its sci-fi nature. In this episode, ShaoLan and leading expert on digital technologies Erik Brynjolfsson share how to construct Chinese words such as computer, TV, movie and telephone. ✨ BIG NEWS ✨ Our brand new Talk Chineasy App, is now live on the App Store! Free to download and perfect for building your speaking confidence from Day 1. portaly.cc/chineasy Visit our website for more info about the app.

Our guest in this episode is David Wakeling, a partner at A&O Shearman, which became the world's third largest law firm in May, thanks to the merger of Allen and Overy, a UK “magic circle” firm, with Shearman & Sterling of New York.David heads up a team within the firm called the Markets Innovation Group (MIG), which consists of lawyers, developers and technologists, and is seeking to disrupt the legal industry. He also leads the firm's AI Advisory practice, through which the firm is currently advising 80 of the largest global businesses on the safe deployment of AI.One of the initiatives David has led is the development and launch of ContractMatrix, in partnership with Microsoft and Harvey, an OpenAI-backed, GPT-4-based large language model that has been fine-tuned for the legal industry. ContractMatrix is a contract drafting and negotiation tool powered by generative AI. It was tested and honed by 1,000 of the firm's lawyers prior to launch, to mitigate against risks like hallucinations. The firm estimates that the tool is saving up to seven hours from the average contract review, which is around a 30% efficiency gain. As well as internal use by 2,000 of its lawyers, it is also licensed to clients.This is the third time we have looked at the legal industry on the podcast. While lawyers no longer use quill pens, they are not exactly famous for their information technology skills, either. But  the legal profession has a couple of characteristics which make it eminently suited to the deployment of advanced AI systems: it generates vast amounts of data and money, and lawyers frequently engage in text-based routine tasks which can be automated by generative AI systems.Previous London Futurists Podcast episodes on the legal industry:Ep 53: The Legal Singularity, with Benjamin AlarieEp 47: AI transforming professional services, with Shamus RaeOther selected follow-ups:David WakelingA&O ShearmanContractMatrixHarvey AIRAG - Retrieval-Augmented GenerationDigital Operational Resilience Act (impacts banking)The Productivity J-Curve (PDF), by Erik Brynjolfsson, Daniel Rock, Chad SyversonAgentic AI: The Next Big Breakthrough That's Transforming Business And Technology, by Bernard MarrMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

In this episode of Cisco Champion Radio, we dive into all the exciting details of the upcoming Webex One 2024 conference, Cisco's flagship event dedicated to hybrid work collaboration and AI innovation. Join us as we explore the key highlights, from inspirational keynote speeches by Fareed Zakaria and Erik Brynjolfsson to hands-on product demos and a bustling expo floor featuring Webex integrations and an AI hub. Whether you're attending in person or virtually, we've got you covered. Learn about the customizable swag options, discover the must-see sessions, and get tips on how to navigate the agenda to maximize your experience. Plus, don't miss the chance to hear about the epic concert party featuring Alter Ego, the Canadian party band known for their electrifying two-hour sets with costume changes! WebexOne 2024 promises an exciting, packed schedule from October 21 to 24, in Fort Lauderdale, FL, offering a unique mix of training programs, keynotes and breakouts, expo and product demos, networking opportunities, and an awards ceremony. Tune in to get all the insights you need to make the most of this transformative event, whether you're attending live or catching up on- demand. As a podcast listener, use this special discount code WX12024 at WebexOne.com to save hundreds on your ticket! Resources https://www.webexone.com/ Cisco guest: Andrew Pearson, Director, Event Marketing, Cisco Cisco Champion hosts: David Macias, Independent Consultant Ruth Duncan, Customer Success Manager, ScanSource Rickey Keith, Consulting Systems Engineer, World Wide Technology Moderator Danielle Carter, Customer Voices and Cisco Champion Program, Cisco

Stanford University's Erik Brynjolfsson joins us to share insights on how business can leverage AI to boost productivity, innovation and growth. In this episode, Erik explores the key role of a CFO in the AI era, strategies for staying competitive in today's digital world, and the AI skills professionals need to stay ahead. He also dives into the future of work, the growing importance of intangible assets, and the ethical implications of AI development. Join us to gain valuable insights from a leading AI thought leader and academic. Host: Aidan Ormond, digital content editor, CPA Australia Guest: Erik Brynjolfsson, Senior Fellow at Stanford's Institute for Human-Centered AI and Director of the Stanford Digital Economy Lab. He is also the Ralph Landau Senior Fellow at Stanford's Economic Policy Research Institute, a Professor by Courtesy at the Graduate School of Business and Department of Economics, and a Research Associate at the National Bureau of Economic Research. Known for his groundbreaking research on the economics of information, Erik has written bestsellers and holds a PhD from MIT and degrees from Harvard. For more information on Erik's research work at Stanford University, head to the Stanford Graduate School of Business faculty research page. Erik is also appearing at CPA Australia's Congress 24 in Canberra this October. It's an in-person and virtual event, featuring insightful and inspiring thought leaders who'll share their ideas on a variety of topics to help level up your professional knowledge. Would you like to listen to more INTHEBLACK episodes? Head to CPA Australia's YouTube channel. CPA Australia publishes four podcasts, providing commentary and thought leadership across business, finance, and accounting: With Interest INTHEBLACK INTHEBLACK Out Loud Excel Tips Search for them in your podcast platform. Email the podcast team at podcasts@cpaaustralia.com.au

Hey Strangers, #google #ai #art Former Google CEO and chairman Eric Schmidt has made headlines for saying that Google was blindsided by the early the rise of ChatGPT because its employees decided that “working from home was more important than winning.” The comment was made in front of Stanford students during a recent interview, video of which was removed from the university's YouTube channel after Schmidt's gaffe was widely picked up by the press. I managed to watch most of Schmidt's chat with Stanford's Erik Brynjolfsson before it was taken down, however, and something else he said stands out. (You can still read the full transcript here.) While talking about a future world in which AI agents can do complex tasks on behalf of humans, Schmidt says: If TikTok is banned, here's what I propose each and every one of you do: Say to your LLM the following: “Make me a copy of TikTok, steal all the users, steal all the music, put my preferences in it, produce this program in the next 30 seconds, release it, and in one hour, if it's not viral, do something different along the same lines.” That's the command. Boom, boom, boom, boom. A bit later, Schmidt returns to his TikTok example and says: So, in the example that I gave of the TikTok competitor — and by the way, I was not arguing that you should illegally steal everybody's music — what you would do if you're a Silicon Valley entrepreneur, which hopefully all of you will be, is if it took off, then you'd hire a whole bunch of lawyers to go clean the mess up, right? But if nobody uses your product, it doesn't matter that you stole all the content. And do not quote me. ======================================= My other podcast https://www.youtube.com/channel/UCKpvBEElSl1dD72Y5gtepkw ************************************************** Something Strange https://www.youtube.com/watch?v=GRjVc2TZqN4&t=4s ************************************************** article links: https://www.theverge.com/2024/8/14/24220658/google-eric-schmidt-stanford-talk-ai-startups-openai ====================================== Today is for push-ups and Programming and I am all done doing push-ups Discord https://discord.gg/MYvNgYYFxq TikTok https://www.tiktok.com/@strangestcoder Youtube https://www.youtube.com/channel/UCe9xwdRW2D7RYwlp6pRGOvQ?sub_confirmation=1 Twitch https://www.twitch.tv/CodingWithStrangers Twitter https://twitter.com/strangestcoder merch Support CodingWithStrangers IRL by purchasing some merch. All merch purchases include an alert: https://streamlabs.com/codingwithstrangers/merch Github Follow my works of chaos https://github.com/codingwithstrangers Tips https://streamlabs.com/codingwithstrangers/tip Patreon https://www.patreon.com/TheStrangers Webull https://act.webull.com/vi/c8V9LvpDDs6J/uyq/inviteUs/ Join this channel https://www.youtube.com/channel/UCe9xwdRW2D7RYwlp6pRGOvQ/join Timeline 00:00 intro 00:20 What Talking We Talking About 03:16 Article 08:37 Steal it 13:03 My Thoughts 14:09 outro anything else? Take Care --- Send in a voice message: https://podcasters.spotify.com/pod/show/coding-with-strangers/message

While the rapid development of AI technology promises unprecedented productivity gains and innovations, concerns of job displacement and increasing inequality persist. How can we ensure AI complements human labor rather than replacing it? What measures can we take to prevent a dystopian AI future?Erik Brynjolfsson is a professor at Stanford University and has pioneered research on the economics of information technology and AI. In this episode, Erik discusses the potential for AI to enhance job productivity, complement our workforce, and boost economic growth at scales comparable to the Industrial Revolution while also exploring potential negative futures and strategies to avoid them.(03:16) Imagining the future(09:41) Impacts on Productivity and Labor(21:14) GPTs are GPTs!(31:02) Workhelix(32:07) Self Driving Cars(36:25) AI Helping Innovation(38:02) Negative Impacts(38:39) Historical Context and Economic Concerns(42:56) AI's Impact on Job Markets and Productivity(48:22) Shared Benefits(49:46) Pessimism and Belief about AI(01:03:59) Strategies for a Positive AI Future(01:12:17) Last Question

(0:00) Intro.(1:24) About the podcast sponsor: The American College of Governance Counsel.(2:12) Start of interview.(4:04) Sonita's "origin story." (5:45) Her professional career, starting with a startup in the gaming industry.(8:15) Her guiding principles for her career at the intersection of innovation, sustainability and digital transformation.(9:30) Her roles at HP, Siemens and PG&E.(11:00) Her board "portfolio" life starting in 2022: SunRun and TrueBlue. Advisor to Sway Ventures.(14:02) About the NACD Blue Ribbon Commission on Board Culture (where she served as a Commissioner).(17:00) Surprises and takeaways from the report.(22:30) Recommendations for handling the increasing politicization in the boardroom. (26:42) On geopolitics in the boardroom. Supply-chain vs consumer market.(31:30) On the solar and battery industry geopolitical landscape. (38:23) How should directors think about AI in the boardroom. "Everyday AI" vs "Game-changing AI". Use cases: 1) Back-office capabilities, 2) core capabilities, 3) front office, 4) New products and services. AI code of conduct. Use of data. Cybersecurity.(43:51) On the impact of AI in the workplace. *reference to study by Erik Brynjolfsson(47:09) Books that have greatly influenced her life: The Five Levels of Leadership,  by John Maxwell (2011)Venture Mindset, by Ilya Strebulaev and Alex Dang (2024)Last Lecture Series at the Stanford Graduate School of Business (July 2023), by Graham Weaver.(48:06)  Her mentors. (49:22)  Quotes that she thinks of often or lives her life by.(50:44) An unusual habit or absurd thing that she loves.(51:30)  The living person she most admires. Sonita Lontoh is a public company board director, strategic advisor, and former Fortune 100 senior executive who focuses on digital innovation, artificial intelligence (AI), and sustainability — contributing positive impact to businesses, consumers, and society. You can follow Evan on social media at:Twitter: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/__You can join as a Patron of the Boardroom Governance Podcast at:Patreon: patreon.com/BoardroomGovernancePod__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License

In this episode 25, Teodora Groza & Thibault Schrepel talk with Erik Brynjolfsson (Stanford University) about how antitrust agencies can document market dynamism, gain a better understanding of the digital economy using the GDP-B measure, track AI dynamics, and more. Follow Stanford Computational Antitrust at https://law.stanford.edu/computationalantitrust

World leading economist Erik Brynjolfsson visits the Talk Chineasy studio in London to learn a hugely important word for our lives in Chinese, “work”! Also, find out how to say “I want to work!” and “I don't want to work”!

The economy affects almost every area of our lives and who better to learn the Chinese word for the economy than world leading economist and author of “The second machine age” Erik Brynjolfsson.

When are you most productive? How do you increase your productivity? And is technology a help or a hindrance?! The amazing Erik Brynjolfsson learns the Chinese word for "productivity" and discusses with ShaoLan the technologies that can cause ripples of productivity growth around the world.

Welcome to episode #923 of Six Pixels of Separation - The ThinkersOne Podcast. Here it is: Six Pixels of Separation - The ThinkersOne Podcast - Episode #923. He's a hugely respected thought leader and practitioner at the intersection of technology and business. Andrew McAfee offers a compelling exploration of The Geek Way in his latest book, which redefines our approach to innovation and leadership. As a Principal Research Scientist at the MIT Sloan School of Management and the co-founder of MIT's Initiative on the Digital Economy, Andy has been at the forefront of how technological progress reshapes our world. Andy unpacks the essence of The Geek Way, revealing it as more than just a cultural shift. It's a transformative approach to achieving extraordinary results across industries. The book, characterized by an unwavering commitment to science, speed, ownership, and openness, emerges not only as a pathway to success but as a better model for realizing company goals and fostering innovation. As geek culture transitions from the fringes to the mainstream (look no further than Marvel movies), admired for its dedication to evidence-based decision-making and problem-solving, Andy highlights the profound impact of this mindset on business practices and societal progress. One of the most compelling aspects of Andy's work is the application of The Geek Way to the realm of artificial intelligence. In an era where AI's potential to revolutionize industries is often met with equal parts enthusiasm and apprehension, Andy provides a balanced perspective. He acknowledges the transformative power of AI as a tool for economic progress while addressing the societal implications of job displacement, advocating for iterative learning and adaptation as keys to harnessing AI's benefits. His previous books include More From Less, Machine. Platform. Crowd, The Second Machine Age (with Erik Brynjolfsson - which I adored), Race Against The Machine and Enterprise 2.0. For leaders, innovators, and anyone curious about the intersection of technology and business, this podcast and Andy's insights are indispensable. Enjoy the conversation... Running time: 1:04:06. Hello from beautiful Montreal. Subscribe over at Apple Podcasts. Please visit and leave comments on the blog - Six Pixels of Separation. Feel free to connect to me directly on Facebook here: Mitch Joel on Facebook. Check out ThinkersOne. or you can connect on LinkedIn. ...or on Twitter. Here is my conversation with Andrew McAfee. The Geek Way. Second Machine Age. Race Against The Machine. More From Less. Machine. Platform. Crowd. Enterprise 2.0. MIT's Initiative on the Digital Economy. MIT Sloan School of Management. Follow Andrew on X. Follow Andrew on LinkedIn. This week's music: David Usher 'St. Lawrence River'. Takeaways: Geek culture has evolved from being stigmatized to being admired and accepted. The Geek Way is characterized by norms such as science, speed, ownership, and openness. The Geek Way can lead to better outcomes for companies and is more effective in achieving goals. Leadership plays a crucial role in driving the adoption of the Geek Way and overcoming challenges.  Visionary leaders are not essential for the 'geek way' to thrive in various industries. Artificial intelligence is a powerful tool that can accelerate economic progress. Concerns about job displacement and societal implications of AI. Letting go of personal hangups is crucial for embracing new opportunities and growth. Chapters: 00:00 - Introduction and Geek Culture 03:34 - The Evolution of Geek and Geek Culture 09:44 - The Geek Way and Business Geeks 14:11 - The Geek Way and Big Tech 19:11 - The Geek Way and the Post-Pandemic Workforce 25:34 - The Geek Way and Technology Impact 30:53 - Geek Leaders and Their Characteristics 36:55 - The Geek Way in Other Industries 45:21 - The Heart of Science 46:09 - Geek Way in Solving Wicked Problems 47:38 - Geek Way in Prosaic Industries 47:46 - Artificial Intelligence and its Impact 53:27 - Concerns and Optimism about Artificial Intelligence 56:23 - The Role of Critical and Emergent Thinking 59:48 - Letting Go of Hangups

Impress your Chinese friends with the word for Artificial Intelligence! World-leading economist Erik Brynjolfsson chats with ShaoLan about how smart robots could really get and how that might change the world as we know it.

Erik Brynjolfsson returns for another fascinating episode in which you can learn how to say the word for robot in Chinese. He and ShaoLan also discuss the future of robotic technologies and how they will continue to shape our lives in the coming years.

Which characters does the Chinese language put together to make the word computer? It will definitely surprise you with its sci-fi nature. In this episode, ShaoLan and leading expert on digital technologies Erik Brynjolfsson share how to construct Chinese words such as computer, TV, movie and telephone.

In policing, as in most vocations, the best employees are often promoted into leadership without much training. One economist thinks he can address this problem — and, with it, America's gun violence. SOURCESKenneth Corey, director of outreach and engagement for the Policing Leadership Academy at the University of Chicago and retired chief of department for the New York Police Department.Stephanie Drescher, operations captain in the City of Madison Police Department.Max Kapustin, assistant professor of economics and public policy at Cornell University.Jens Ludwig, economist and director of the Crime Lab at the University of Chicago.Sandy Jo MacArthur, curriculum design director for the Policing Leadership Academy at the University of Chicago.Sean Malinowski, D.O.J. strategic site liaison for the Philadelphia Police Department and retired chief of detectives from the Los Angeles Police Department.Sindyanna Paul-Noel, lieutenant with the City of Miami Police Department.Michael Wolley, deputy chief of operations with the Indianapolis Metropolitan Police Department. RESOURCES:"Policing Leadership Academy (PLA) Graduation of Inaugural Cohort," by the University of Chicago Crime Lab (2023)."Policing and Management," by Max Kapustin, Terrence Neumann, and Jens Ludwig (NBER Working Paper, 2022)."Getting More Out of Policing in the U.S.," by Jens Ludwig, Terrence Neumann, and Max Kapustin (VoxEU, 2022)."What Drives Differences in Management?" by Nicholas Bloom, Erik Brynjolfsson, Lucia Foster, Ron S. Jarmin, Megha Patnaik, Itay Saporta-Eksten, and John Van Reenen (NBER Working Paper, 2017)."Management as a Technology?" by Nicholas Bloom, Raffaella Sadun, and John Van Reenen (NBER Working Paper, 2017)."Measuring and Explaining Management Practices Across Firms and Countries," by Nick Bloom and John Van Reenen (NBER Working Paper, 2006)."Crime, Urban Flight, and the Consequences for Cities," by Julie Berry Cullen and Steven D. Levitt (SSRN, 1997). EXTRAS:"Why Are There So Many Bad Bosses?" by Freakonomics Radio (2022)."What Are the Police for, Anyway?" by Freakonomics Radio (2021).

Science. Ownership. Speed. Openness.These are the four pillars of Andrew McAfee's observed structure for successful companies. It is the “geeks,” the leaders at the forefront of cross-industry innovation, who embrace these norms and have the potential to redefine business as we know it. In order to break ground and create the kind of future we dream of, organizational leaders need to banish the fear of failure, embrace mistakes, and accept hard feedback with open arms.Andrew is a best-selling author, Principal Research Scientist at the MIT Sloan School of Management, and co-founder of MIT's Initiative on the Digital Economy. His books include More from Less and The Second Machine Age, co-authored with Erik Brynjolfsson. Today on the podcast, we discuss the ideas captured in his most recent book, The Geek Way: The Radical Mindset that Drives Extraordinary Results. In This Episode* The universal geek (1:35)* The four geek norms (8:29)* Tales of geeks and non-geeks (15:19)* Can big companies go geek? (18:33)* The geek way beyond tech (26:32)Below is a lightly edited transcript of our conversation.The universal geek (1:35)Pethokoukis: Is The Geek Way really the Silicon Valley Way? Is this book saying, “Here's how to turn your company into a tech startup”?McAfee: You mentioned both Silicon Valley and tech, and this book is not about either of those—it's not about a region and it's not about an industry, it's about a set of practices. And I think a lot of the confusion comes because those practices were incubated and largely formulated in this region called “Silicon Valley” in this industry that we call “tech”. So I understand the confusion, but I'm not writing about the Valley. Plenty of people do that. I'm not writing about the tech industry. Plenty of people do that. The phenomenon that I don't think we are paying enough attention to is this set of practices and philosophies that, I believe, when bundled correctly, amounts to a flat old upgrade to the company, just a better way to do the thing a company is supposed to do. That needed a label, because it's new. “Geek” is the label that I latched onto.But there's a universal aspect to this, then.Yeah, I believe there is. I understand this sounds arrogant—I believe it's a flat better way to run a company. I don't care where in the world you are, I don't care what industry you are in, if you're making decisions based on evidence, if you're iterating more and planning less, if you're building a modular organization that really does give people authority and responsibility, and if you build an organization where people are actually comfortable speaking truth to power, I think you're going to do better.One reason I'm excited about this book is because, you as well, we think about technological progress, we think about economic growth and productivity and part of that is science and coming up with new ideas and a new technology, but all that stuff has to actually be turned into a commercial enterprise and there has to be well-run companies that take that idea and sell it. Maybe the economist's word might be “diffusion” or something like that, but that's a pretty big part of the story, which I think maybe economists tend not to focus as much on, or policy people, but it's pretty darn important and that's what I think is so exciting about your book is that it addresses that: How to create companies that can do that process—invention-to-product—better. So how can they do it better?Let me quibble with you just a little bit. There are alternatives to this method of getting goods and services to people, called “the company.” That's what we do in capitalist societies. Jim, like you know all too well, over the course of the 20th century, we ran a couple of experiments trying it a different way: These collectivist, command-and-control, centrally planned economies, those were horrible failures! Let's just establish that right off the bat.So in most of the parts of the world—I think in all the parts of the world where you and I would actually want to live—I agree with you, we've settled on this method of getting most goods and services to people, most of what they consume, via these entities called companies, and I don't care if you're in a Nordic social democracy, or in the US of A, or in Southeast Asia, companies are the things getting you most of what you consume. I think in the United States, about 85 percent of what you and I consume, by some estimates, comes from companies. So, like them or hate them, they're incredibly important, and if a doohickey comes along that lets them their work X percent better, we should applaud that like crazy because that's an X percent increase in our affluence, our standard of living, the things that we care about, and the reason I got excited and decided to write this book is I think there's an upgrade to the company going on that's at the same level as the stuff that [Alfred] Chandler wrote about a century ago when we invented the large, professionally managed, pretty big company. Those dominated the corporate landscape throughout the 20th century. I think that model is being upgraded by the geeks.It's funny because, I suppose maybe the geeks 50 years ago, maybe a lot of them worked at IBM. And your sort-of geek norms are not what I think of the old Big Blue from IBM in the 1960s. That has changed. Before we get into the norms, how did they develop? Why do we even have examples of this working in the real corporate world?The short answer is, I don't know exactly. That's a pretty detailed piece of corporate history and economic history to work on. The longer answer is, what I think happened is, a lot of computer nerds, who had spent a lot of time at universities and were pretty steeped in that style of learning things and building things, went off and started companies and, in lots of cases, they ran into the classic difficulties that occur to companies and the dysfunctions that creep in as companies grow and age and scale. And instead of accepting them, my definition of a geek is somebody who's tenacious about a problem and is willing to embrace unconventional solutions. I think a lot of these geeks—and I'm talking about people like Reed Hastings, who's really articulate about what he did at Netflix and at his previous company, which he says he ran into mediocrity—a lot of these geeks like Hastings sat around and said, “Wait a minute, if I wanted to not repeat these mistakes, what would I do differently?” They noodled that hard problem for a long time, and I think via some conversation among the geeks, but via these fairly independent vectors in a lot of cases, they have settled on these practices, these norms that they believe—and I believe—help them get past the classic dysfunctions of the Industrial Era that you and I know all too well: their bureaucratization, their sclerosis, their cultures of silence. They are just endless stifling meetings and turf wars and factions and things like that. We know those things exist. What I think is interesting is that the geeks are aware of them and I think they've come up with ways to do better.The four geek norms (8:29)It's funny that once you've looked at your book, it is impossible to read any other sort of business biography of a company or a CEO and not keep these ideas in your head because I just finished up the Elon Musk biography by Walter Isaacson, and boy, I just kept on thinking of speed and science and the questioning of everything: Why are we doing this? Why are we building this rocket engine like this? Who told us to do that? Somebody in legal told us to do that?Exactly.So certainly those two pop to mind: the speed and the constant iteration. But rather than have me describe them, why don't you describe those norms in probably a much better way than I can.There's a deep part of the Isaacson Musk biography that made my geek eyes light up, and it's when Isaacson describes Musk's Algorithm—I think it's capitalized, too, it's capital “The,” capital “Algorithm,”—which is all about taking stuff out. I think that is profound because we humans have a very strong status quo bias. We're reluctant to take things out. It's one of the best-documented human biases. So we just add stuff, we just layer stuff on, and before you know it, for a couple different flavors of reason, you wind up with this kind of overbuilt, encrusted, process-heavy, bureaucracy-heavy, can't get anything done [corporation]. You feel like you're pushing on a giant piece of Jell-O or something to try to get any work done. And I think part of Musk's brilliance as a builder and an organization designer is to come up with The Algorithm that says, “No, no, a big part of your job is to figure out what doesn't need to be there and make it go away.” I adore that. It's closest to my great geek norm of ownership, which is really the opposite of this processification of the enterprise of the company that we were super fond of starting in the '90s and going forward.So now to answer your question, my four great geek norms, which are epitomized by Musk in a lot of ways, but not always, are:Science. Just make decisions based on evidence and argue a lot about that evidence. Science is an argument with a ground rule. Evidence rules.Ownership. We were just talking about this. Devolve authority downward, stop all the cross-communication, coordination, collaboration, process, all that. Build a modular organization.Speed. Do the minimum amount of planning and then start iterating. You learn, you get feedback, you see where you're keeping up to schedule and where you're not by doing stuff and getting feedback, not by sitting around asking everybody if they're on schedule and doing a lot of upfront planning.Finally, openness, this willingness to speak truth to power. In some ways, a good synonym for it is “psychological safety” and a good antonym for it is “defensiveness.”If anything, from what I understand about Musk, the last one is where he might run into challenges.That's what I was going to say. The ownership and the speed and the science struck me and then I'm like… the openness? Well, you have to be willing to take some abuse to be open in that environment.There are these stories about him firing people on the spot and making these kind of peremptory decisions—all of that is a violation, in my eyes, of the great geek norm of openness. It might be the most common violation that I see classic Silicon Valley techies engage in. They fall victim to overconfidence like the rest of us do, and they're not careful enough about designing their companies to be a check on their own overconfidence. This is something Hastings is very humble and very articulate about in No Rules Rules, the book that he co-wrote with Erin Meyer about Netflix and he highlights all these big calls that he was dead-flat wrong about, and he eventually realized that he had to build Netflix into a place that would tell him he was wrong when he was wrong, and he does all these really nice jobs of highlighting areas where he was wrong and then some relatively low-level person in the organization says, “No, that doesn't make sense. I'm going to go gather evidence and I'm going to challenge the CEO of the company with it.” And to his eternal credit, Hastings goes, “It's pretty compelling evidence. I guess I was wrong about that.” So that, to me, is actually practicing the great geek norm of openness.So someone reading this book is thinking that this book is wrong. Where would that come from? Would that come from overconfidence? Would it come from arrogance? Would it come from the idea that if I am in the C-suite, that obviously I have it figured out and I can probably do all your jobs better than you can, so why are you challenging me? Why are you challenging the status quo? “Hey, that's how we got here was through a process, so trust the process!”It's one of the main flavors of pushback that I hear, and it's very often not as naked as you just made it, but it is, “Hey, the reason I'm sitting in this executive education classroom with you is because I'm fairly good at my job. I made some big calls right, and my job is to provide vision to my team and to direct them not to be this kind of lead-from-behind more coach-y kind of leader.” That's one flavor of pushback I get. Another one is a very pervasive tendency, when we come across some challenging information, to come up with reasons why this doesn't apply to us and why we're going to be just fine. It's some combination of the status quo bias and the overconfidence bias which, again, two of the most common human biases. So very often when I'm talking about this, I get the idea that people in the room are going, “Yeah, okay, wow, I really wouldn't want to complete with SpaceX, but this doesn't apply to me or to my industry.” And then finally, look, I'm clearly wrong about some things. I don't know exactly what they are. Maybe the incumbents of the Enterprise Era are going to mount a surprising comeback by falling back on their 20th-century playbook as opposed to adopting the geek way. I will be very surprised if that happens and I'm taking bets like, “Let's go, let's figure out a bet based on that,” but maybe it'll happen. I'm definitely wrong about some things.Tales of geeks and non-geeks (15:19)Given what you've said, I would certainly think that it would be easier to apply these norms at a newer company, a younger company, a smaller company, rather than a company with a hundred thousand employees that's been around for 30 years. But it's possible to do the second one, right?It is possible. Let me violently agree with you, Jim. You and I are of a vintage and we're both Midwesterners. We both remember Arthur Andersen, right? And what an iconic American Midwestern symbol of rectitude and reliability and a healthy culture that kept the business world honest by auditing their books. Remember all that? Remember how it fell apart?I knew people, and if you got an interview with Arthur Andersen, they're like, “Wow, you are with the Cadillac of accounting consulting firms.”But beyond that, you were doing a valuable thing for society, right? These people had status in the community because they kind of kept companies honest for a living.That's right. That's right. You were true of the truth tellers.Yeah. It was a big deal and a lot of your listeners, I think, are going to be too young to remember it firsthand, but that company became a dysfunctional, unethical, ongoing, miserable train wreck of an organization in its final years before it finally fell apart. It could not have been more surprising to people of our vintage and where we came from. I tell the story of how that happened a little bit in the book to drive home that cultures can go off track in profound ways and in AA's late years, if someone had teleported The Geek Way and waved it around, would it have made any difference? I'd like to hope so, but I kind of don't think so.However, to tell a more optimistic story, I had the chance to interview Satya Nadella about his turnaround at Microsoft, which I think is at a level maybe even above the turnaround that [Steve] Jobs executed when he came back to Apple. The amount of value that Nadella has created at Microsoft in nine years now is staggering, and Microsoft is back. Microsoft has mojo again in the tech industry. But when he took over, Microsoft was still a large profitable company, but it was dead in the water. It wasn't innovating. The geek elite didn't want to go work there. The stock price was flat as a highway for a decade. It was absolutely an afterthought in anything that we care about. And so I use Nadella and I learned from him, and I try to tell the story about how he executed this comeback, and, to my eyes, he did it in a very, very geek way kind of a way.Can you give me an example?My point in telling that story is: I do think it's possible for organizations that find themselves in a bad spot—Established organizations.Established. Large, established organizations find themselves in a bad spot. Those kinds of leopards can change their spots. I firmly believe that.Can big companies go geek? (18:33)What are the first steps to change the corporate culture of a big company?That's why I'm so blown away by what Nadella and his team were able to do. Let me pick out a couple things that seem particularly geeky to me that he did. One was to say that—it doesn't matter if you develop them or not—you do not own code or data at Microsoft. What he meant by that was, subject to legal requirements and safety and some guardrails, if you want to grab some of the code repository at Microsoft to go try something or some data and go try something, you have the right to do that. That just eliminates huge amounts of gatekeeping and hard and soft bureaucracy and all of that inside the company. And that led to things like Copilot. It's a very, very smart way to start dealing with bureaucracy: just saying, “No, you don't get to gatekeep anymore.”He also did fairly obvious things like make sure that their really dysfunctional evaluation system was over. He also emphasized this thing that he called “One Microsoft,” which at first sounded like just CEO rah-rah talk. And it is to some extent, but it's also incredibly clever because we humans are so tribal. In addition to the status quo bias and the overconfidence bias, the third easy, easy bias to elicit is “myside” bias. We are tribal. We want our tribe to win. I think part of Nadella's brilliance was to say, “The tribe that you belong to is not Office versus Windows versus Bing versus… the tribe you belong to is Microsoft.”And he changed compensation, so that it also worked that way. He worked with incentives—he took an Econ 101 class—but he also kept emphasizing that “we are one tribe,” and that makes a difference if the leader at the top keeps saying it and if they behave that way. I think one of the deepest things that he did was act in an open way and demonstrate the norm of openness that he wanted to see all over the place. He got a ton of help with it, but if you talk to him, you immediately realize that he's not this table-pounding, my-way-or-the-highway kind of a guy. He's somebody that wants to get it right, and if you have an idea, you might get a fair erring for that idea. He also embraced agile methods and started to move away from the old ways that Microsoft had to write software, which were out of date, and they were yielding some really unimpressive projects.So as he and I were talking, I was doing my internal checklist and I kept on saying, “Yep, that's speed. That is science. That is ownership. That is openness,” and just emphasizing, as I listened to him, I just kept hearing these norms come up over and over. But one thing that he clearly knows is that this ain't easy and it ain't fast, and cultural change is a long, slow, grinding process, and you've got to keep saying the same thing over and over. And then I think, especially as a leader, you've got to keep living it because people will immediately sense if what you're doing is not lining up with what you're saying.One bit that popped out, because obviously I'm in Washington and I see a government that doesn't work very efficiently, and you wrote, “To accelerate learning and progress, plan less and iterate more,” and to iterate means to experiment, it means you're going to fail. And boy, oh boy, failure-averse organizations, you can find that in government, you can find it in corporate America, that acceptance of: try something and if it fails, it's a learning experience. It's not a black mark on your career forever. Now let's go try the next thing.Exactly. To me, it's the most obvious thing that the geeks do that's starkly different from Industrial Era organizations, “plan less, iterate more.” The great geek norm of speed, and there are a bunch of exemplars of that. The clearest one to me is SpaceX, where they blow up a rocket and that is a win for them, not a loss. And even if it gets written up in the press as, “Oh, Starship blew up, or whatever”—they don't care, right? They'd rather that it didn't blow up or that it stayed together longer, but if they got the learning that they were looking for, then they're like, “Great, we're going to incorporate that, we're going to build another rocket, we're not going to put any people on until we're very, very, very sure, but we're going to blow up a bunch of rockets.” From the start of the company, that has been an okay thing to do.They also are willing to embrace pretty big pivots. The first plan for Starship was that it was going to be a carbon fiber rocket because carbon fiber is so strong and lightweight, but their method for making it was too slow, too expensive, and had a reject rate that was too high. The thing's now made out of stainless steel! It's the opposite kind of material! But they said, “Look, the goal is the goal, and the goal is not to stick to the original plan, the goal is to build a great big rocket that can do all kinds of things. The way we get there is by trying—legitimately trying—a bunch of stuff and failing at it with the eyes of the world upon us.”I want to draw a really sharp distinction between the process and the product, and what I mean by that is a failure-tolerant process can yield an incredibly robust, safe product. We don't need to look any farther for that than the Dragon Capsule that SpaceX makes, which is the only capsule currently made in America that is certified by NASA to take human beings into space. It's how all Americans these days get back and forth to the ISS. NASA doesn't have one. NASA gave a contract to Boeing at the same time it gave one to SpaceX. Boeing still has not had the first crude test of its capsule. This geek way of speed, it's uncomfortable, and you got to be willing to fail publicly and own it, but it works better.Is the geek way, to some degree, an American phenomenon?So far.I was going to say, can the geek way be implemented in other countries? Is there something special about American culture that allows the geek way to work and to be adopted—I said universal earlier, maybe I meant, is it truly universal? Can it be implemented in other places?Jim, you and I, as proud Americans, like to believe that we're an exceptional country, and I do believe that. I don't believe the geek way only works with a bunch of Americans trying it. I travel lots of different places, and especially the energy that I see among younger people to be part of this transformation of the world that's happening (that you and I are lucky enough to get to observe and try to think about), this transformation of the world in the 21st century because of the technological toolkit that we have, because of the amount of innovation out there, the thirst to be part of that is very, very, very widespread. And I don't think there's anything in the drinking water in Munich or Kyoto or Lima that makes this stuff impossible at all. It is true, we're an individualistic culture, we're kind of mouthy, we celebrate these iconoclastic people, but I don't think any of those are absolutely necessary in order to start following norms of science, ownership, speed and openness. I hope those are universal.The geek way beyond tech (26:32)We've been talking a lot about tech companies. Are there companies which really don't seem particularly techie (even though obviously all companies use technology) that you could see the geek way working currently?I haven't gone off and looked outside the tech industry for great exemplars of the geek way, so I have trouble answering this question. But think about Bridgewater, which is really one of the weirdest corporate cultures ever invented, and I haven't read the new biography of Ray Dalio yet, but it appears that all might not be exactly as it appears. But one thing that Bridgewater has been adamant about from the get-go, and Dalio has been passionate about, is this idea of radical transparency, is the idea of openness. Your reputation is not private from anybody else in the company at any point in time. So they've taken this norm of openness and they've really ran with it in some fascinating directions. In most organizations, there's a lot of information that's private, and your reputation is spread by gossip. Literally, that's how it works. Bridgewater said, “Nope. We really believe in openness and everything that's important about your performance as a professional in this company, you're going to get rated on it by your colleagues, and you're going to have these visible to everybody all the time inside the company so that if you start espousing how important it is to be ethical, but your score as an ethical leader is really low, nobody's going to listen to you.” I think that's fascinating, and I think as time goes by, we're going to come across these very, very geeky norms and practices being implemented in all kinds of weird corners of the global economy. I can't wait to learn about it.I would think that, given how every country would like to be more productive, every country's having a white paper on how to improve their productivity, and this, to me, is maybe something that policymakers don't think about, and I'm not sure if there's a policy aspect to this, but I hope a lot of corporate leaders and aspiring corporate leaders at least read your book.Well, the one policy implication that might come up is, what happens when the geeks start unignorably beating up the incumbents in your favorite industry. When I look at what's happening in the global auto industry right now, I see some of that going on, and my prediction is that it's going to get worse instead of better. Okay, then what happens?Save us! Save us from this upstart!Exactly, but then there could be some really interesting policy choices being made about protecting dinosaur incumbents in the face of geek competitors. I hope we don't retreat into nationalism and protectionism and that kind of stuff. What I hope happens instead is that the world learns how to get geeky relatively quickly and that this upgrade to the company spreads.The only thing I would add here is I would also urge business journalists to read the book so you understand how companies work and how these new companies that work, companies that look like they are—and not to keep harping on SpaceX, but so many people who I think should know better, will look at SpaceX and think, “Oh, they're failing. Oh, that rocket, as you said earlier, the rocket blew up! Apollo had a couple of problems, they're blowing up a rocket every six weeks!” And they just simply do not understand how this kind of company works. So I don't know. So I guess I would recommend my business journalists to read it, and I imagine you would think the same.That recommendation makes a ton of sense to me. Jim. I'm all on board with that.Andrew. This is an outstanding book and a wonderful companion piece to your other work which is very pro-progress, and pro-growth. I absolutely loved it, and thanks so much for coming on the podcast today,Jim, thanks for being part of the Up Wing Party with me. Let's make it happen.Absolutely. Thank you.Thank you, sir. 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

In CXOTalk episode number 812, Michael Krigsman speaks with Andrew McAfee, a principal research scientist at MIT, for a detailed discussion on creating a business culture that supports AI. As the author of 'The Geek Way, McAfee shares lessons drawn from his extensive research on how technological advancements impact business operations and organizational culture.This conversation is particularly valuable for business leaders interested in learning how to create culture that supports the strategic role of AI in their organization.Key highlights from this episode include:►*The Intersection of AI and Business Culture:* Insight into how AI is reshaping business strategies and influencing organizational dynamics.►*'Geek Culture' in Organizations:* Exploration of the concept of 'geek culture' within enterprises and its significance in fostering innovation.►*Ethical and Strategic Implications:* Discussion on the ethical aspects of AI integration and strategies for effective implementation in corporate settings.►*Adapting to Technological Change:* Guidance on how businesses can evolve to embrace technological advancements and the future of work.*Andrew 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 and Society organization at Google. He studies how technological progress changes the world. His next book The Geek Way will be published by Little, Brown in 2023. His previous books include More from Less and, with Erik Brynjolfsson, The Second Machine Age.McAfee has written for publications including Foreign Affairs, Harvard Business Review, The Economist, The Wall St. Journal, and The New York Times. He's talked about his work on CNN and 60 Minutes, at the World Economic Forum, TED, and the Aspen Ideas Festival, with Tom Friedman and Fareed Zakaria, and in front of many international and domestic audiences. He's also advised many of the world's largest corporations and organizations ranging from the IMF to the Boston Red Sox to the US Intelligence Community.*Michael Krigsman* is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world's top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.#cxotalk #enterpriseai #culture #culturetransformation

Andrew McAfee (@amcafee) stops by The Business Brew to discuss his new book The Geek Wayhttps://www.amazon.com/Geek-Way-Radical-Mindset-Extraordinary/dp/B0C1DQW5FC/ref=sr_1_1?keywords=Andrew+McAfee&qid=1700075448&s=audible&sr=1-1Andrew 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 and Society organization at Google. He studies how technological progress changes the world. His previous books includeMore from Less and, with Erik Brynjolfsson, The Second Machine Age.McAfee has written for publications including Foreign Affairs, Harvard Business Review, The Economist, The Wall Street Journal, and The New York Times. He's talked about his work on CNN and 60 Minutes, at the World Economic Forum, TED, and the Aspen Ideas Festival, with Tom Friedman and Fareed Zakaria, and in front of many international and domestic audiences. He's also advised many of the world's largest corporations and organizations ranging from the IMF to the Boston Red Sox to the US Intelligence Community.McAfee and his frequent coauthor Erik Brynjolfsson are only people named to both the Thinkers50 list of the world's top management thinkers and the Politico 50 group of people transforming American politics.

Co decyduje o tym, jak się nam żyje? Czym jest dobrobyt i jak go mierzymy? Czy jakość powietrza i ochrony zdrowia mają znaczenie? Co dobrobyt ma wspólnego z produktywnością? Dlaczego Niemcy są bardziej produktywne od Polski? Czy rządy prawa robią różnicę? Czy podwyżka płacy minimalnej może coś zmienić? Dlaczego bankructw firm powinno być jak najwięcej? Oraz jakie jest najbardziej produktywne miejsce na świecie? O to wszystko pytamy ekonomistę Marka Ignaszaka. Zapraszają: Piotr Żoch & Jakub Bodziony (Kultura Liberalna).  

MIT professor and economist Erik Brynjolfsson said recently that he'd be disappointed if AI didn't lift the  current anemic 1.2% productivity growth rate to 3% or even 4%. This would be a good thing for business and government as it could potentially help with the labor shortage, drive earnings growth and increase tax revenues, which would ostensibly help address current debt levels. This is one of the promised impacts of AI. While the hype surrounding Gen AI has narrowly propped up certain sectors of the market, like AI startups and the magnificent seven, the macro effects have not been felt thus far as adoption remains largely experimental. In this Breaking Analysis and ahead of Supercloud 4, ETR's Erik Bradley and Daren Brabham join the program to share the latest trends on AI adoption, how Gen AI is being used, some of the deployment models and the AI leaderboard based on spending momentum and presence in the market. Historical trends in the music industry:https://open.lib.umn.edu/mediaandculture/chapter/6-4-current-popular-trends-in-the-music-industry/#:~:text=The%20Big%20Four%20control%20over,current%20distribution%20of%20market%20share.&text=Four%20major%20music%20labels%20control,the%20U.S.%20recording%20music%20industry.Current trends in the music industry:https://musicandcopyright.wordpress.com/2023/04/25/recorded-music-market-share-gains-for-sme-and-the-indies-publishing-share-growth-for-umpg-and-wcm/IDC sponsored content on AI w/ some market data:https://content.dataiku.com/idc-infobrief-2023

This podcast is a commentary and does not contain any copyrighted material of the reference source. We strongly recommend accessing/buying the reference source at the same time. ■Reference Source https://www.ted.com/talks/erik_brynjolfsson_the_key_to_growth_race_with_the_machines ■Post on this topic (You can get FREE learning materials!) https://englist.me/121-academic-words-reference-from-erik-brynjolfsson-the-key-to-growth-race-with-the-machines-ted-talk/ ■Youtube Video https://youtu.be/CSWYFBF48jY (All Words) https://youtu.be/QAK5o9YLQus (Advanced Words) https://youtu.be/9b7zW4ztpEI (Quick Look) ■Top Page for Further Materials https://englist.me/ ■SNS (Please follow!)

We often speak about the AI revolution as though it were something that might happen in the far future. But, what about today, when AI is modifying our culture, society, and the economy as we speak? In this episode, we consider how AI is currently creating massive shifts, and the potential benefits and challenges we face right now. Is AI revitalizing the economy, or is it on track to displace countless workers? Will AI render filmmakers and writers obsolete, or are we at the beginning of a modern-day cultural renaissance? And is AI being used to help preserve Indigenous cultures, or is it a tool to harvest their data for corporate gain? To address these questions, host Raffi Krikorian speaks with Erik Brynjolfsson, Director of the Digital Economy Lab; Justine Bateman, director, writer, producer and author; Ari Melenciano, artist and creative technologist; and Keolu Fox, assistant professor at the University of California, San Diego and co-founder and co-director of the Indigenous Futures Institute. Together, they chart a path to help us navigate AI in the current moment. To learn more about Technically Optimistic and to read the transcript for this episode: emersoncollective.com/technically-optimistic-podcast For more on Emerson Collective: emersoncollective.com Learn more about our host, Raffi Krikorian: emersoncollective.com/persons/raffi-krikorian Technically Optimistic is produced by Emerson Collective with music by Mattie Safer. Email us with questions and feedback at technicallyoptimistic@emersoncollective.com. Subscribe to Emerson Collective's newsletter: emersoncollective.com To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

In a rich career as a photographer, he's brought his own distinct gaze to every place he's been to. Rick Smolan joins Vasant Dhar in episode 65 of Brave New World to talk about his rich experiences -- and what he's learned from them. Useful resources: 1. Rick Smolan on Wikipedia, Instagram, Amazon, Ted Talks and Against All Odds. 2. The Good Fight: America's Ongoing Struggle for Justice -- Rick Smolan and Jennifer Erwitt. 3. The Human Face of Big Data -- Rick Smolan and Jennifer Erwitt. 4. Alone Across the Outback -- Rick Smolan's talk for National Geographic. 5. Natasha Pruss & Rick Smolan -- Their Profound Story. 6. Tracks -- John Curran. 7. The Coddling of the American Mind — Greg Lukianoff and Jonathan Haidt. 8. How Social Media Threatens Society — Episode 8 of Brave New World (w Jonathan Haidt). 9. A Day in the Life of Australia -- Rick Smolan. 10. The Player -- Robert Altman. 11. The Birth of a Word -- Deb Roy. 12. George & Jerry Invent A Show About Nothing -- Seinfeld. 13. Generative AI at Work -- Erik Brynjolfsson, Danielle Li and Lindsey Raymond. 14. Erik Brynjolfsson on the Second Machine Age — Episode 18 of Brave New World. 15. Andrew Yang on the New Politics America Needs -- Episode 27 of Brave New World. 16. Forward: Notes on the Future of Our Democracy — Andrew Yang. 17. Bias, Lies, and Democracy — Episode 14 of Brave New World (w Ali Velshi). Check out Vasant Dhar's newsletter on Substack. Subscription is free!

Erik Brynjolfson joins host Jeanne Meserve to discuss the timelines of the AI industrial revolution, the future of generative AI, and democratic values. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit scsp222.substack.com

World leading economist Erik Brynjolfsson visits the Talk Chineasy studio in London to learn a hugely important word for our lives in Chinese, “work”! Also, find out how to say “I want to work!” and “I don't want to work”!

The economy affects almost every area of our lives and who better to learn the Chinese word for the economy than world leading economist and author of “The second machine age” Erik Brynjolfsson.

When are you most productive? How do you increase your productivity? And is technology a help or a hindrance?! The amazing Erik Brynjolfsson learns the Chinese word for "productivity" and discusses with ShaoLan the technologies that can cause ripples of productivity growth around the world.

Erik Brynjolfsson is a researcher, author, senior fellow at the Stanford Institute for Human-Centered AI, and director of the Stanford Digital Economy Lab. He joined WorkLab to offer business leaders an overview of how AI will transform productivity. Brynjolfsson is the first guest of season 4 of the WorkLab podcast, in which hosts Elise Hu and Tonya Mosley have conversations with economists, designers, psychologists, and technologists who explore the data and insights into why and how work is changing.   WorkLab 

Impress your Chinese friends with the word for Artificial Intelligence! World-leading economist Erik Brynjolfsson chats with ShaoLan about how smart robots could really get and how that might change the world as we know it.

"You can see the computer age everywhere but in the productivity statistics," said Nobel laureate economic Robert Solow in 1987. A decade later, the '90s productivity boom was in full swing. Likewise, it took decades for electrification to have an impact on productivity growth in the early 20th century. Today, artificial intelligence can write a coherent paragraph or generate an image from a simple prompt. But when will AI show up in the statistics, boosting productivity and then economic growth? Avi Goldfarb joins Faster, Please! — The Podcast to discuss that question and more.Avi holds the Rotman Chair in Artificial Intelligence and Healthcare at the University of Toronto's Rotman School Of Management. He's also co-author, along with Ajay Agrawal and Joshua Gans, of 2022's Power and Prediction: The Disruptive Economics of Artificial Intelligence.In This Episode* Prediction at scale (1:34)* How AI has transformed ride hailing and marketing (5:37)* The potential for “system-level” changes (11:26)* When will AI show up in the statistics? (16:12)* The impact of ChatGPT and DALL-E (19:46)Below is an edited transcript of our conversation.Prediction at scaleJames Pethokoukis: What this book is about—and then you can tell me if I've gotten it horribly wrong—this is a book about machines making predictions using advanced statistical techniques. 1) Is that more or less right? And 2) why is that an important capability?Avi Goldfarb: That's more or less right. The only place where I [would offer] a little correction there is, the reason we're talking about artificial intelligence today is almost entirely due to advances in computational statistics. Yes, it is just stats and that sounds kind of unexciting. But once we have prediction at scale, it can be really transformative to all aspects of business in the economy. There's a reason why we're calling computational stats “artificial intelligence” and we didn't use to.Prediction at scale. That's a great three-word description. Probably why you used it. To what extent is that now happening? The name of the book is Power and Prediction: The Disruptive Economics of Artificial Intelligence. Is this prediction at scale already disruptive to some degree or is it, will be disruptive?The technology, for the most part, is pretty close to there, in the sense that we can do prediction at scale because we have the data and we have computational power to do all sorts of amazing things. For the most part, it hasn't been disruptive yet. And it hasn't been disruptive yet, just because we have the technology doesn't mean we know how to use it well and we know how to use it productively in our processes and systems in order to get the most out of it.Are there sectors currently doing this, but they're not doing it well yet? It's in a variety of sectors, but not enough companies doing it? Lots of companies are already using these machine learning tools, but they tend to be using them for things they were already doing before. If you had some prediction process to predict, if you're a bank, whether somebody's going to pay back a loan. In the very old days you'd have some human, the loan officer, look the customer up and down and go with their gut. And then, starting in the 1960s and especially in the ‘90s and beyond, we started to use scoring rules, partly your credit score and partly other things, to get a sense of whether people are going to pay them back. And so we were already doing a prediction task done by a machine. And now increasingly we're using these machine learning tools. We're using what we're calling AI, over the past five to 10 years, to predict whether people are going to pay back a loan. We're seeing those kinds of things all over the place, which is: You had some prediction, maybe you've used even a machine prediction before, and now we're using machine learning. We're using AI to make those predictions a little bit better. Lots of companies are using that.That sounds incremental. That sounds like an incremental advance.It's absolutely an incremental advance. We call these point solutions, which is, you look at your workflow, you identify something that a human is doing. You take out that human; you drop in a machine. You don't mess with a workflow because it's always easier to do things when you don't mess with a workflow. The problem is, when you don't mess with a workflow, there's only so much gain you can get. We've seen AI-based point solutions, prediction point solutions, all over the place. We haven't seen real transformation in very many industries. We've seen it in a couple. We haven't seen it in very many industries because real transformation requires doing things differently.How AI has transformed ride hailing and marketingDo you think that it has happened in one or two industries that you think would actually meet that bar of transformational? Can you give me an example?Absolutely. If you wanted to be a cab driver in the city of London 20 years ago, or even today, it takes three years of schooling. Learning to navigate those streets is really, really hard. And especially learning to navigate and predict where the traffic is going to be is really, really hard. And so there is a really rigorous process to screen people to be taxi drivers. In the US 30 years ago, there was something like 200 or 300 taxi drivers in the whole country. About 15 years ago, two technologies came about. The first one being digital dispatch, which is essentially tools for drivers to find riders, sometimes through prediction and sometimes through other tools. And then the second part was what's been disruptive with respect to that three years of schooling in the city of London, which is prediction tools for navigating a city. This is your GPS system.In the early days, many people selling digital dispatch and navigational predictions were selling them into professional driving companies, into taxi companies. “Hey, your taxi drivers can be 15 percent more efficient if they know the best route at this time.” That's what we call a point solution. You're already doing this, you take out some part of the human process, you drop in a machine, and you do it a little bit better. A couple of companies realized that digital dispatch combined with navigational prediction could create an entirely new type of industry. And this is the ride-hailing industry led by Uber and Lyft and others. That's a totally new kind of way to do personal transportation that made millions of amateur drivers as good as professional because they could navigate the city and find riders.Example number one is the taxi industry. Personal ride-hailing, for lack of a better word, has been transformed partly through digital and really those maps are important—and a big part of those maps is machine learning tools and figuring out where the traffic is, etc. So industry number one.Industry number two is advertising. I don't know if you've seen the TV show Mad Men. That was really how the advertising industry operated well into the ‘90s. Maybe not the soap opera aspect of it. Maybe, maybe not. I don't know. But the idea that there's a lot of wining and dining and charming people to convince them to spend millions of dollars on an ad campaign. And whether a campaign worked or not was largely based on gut feel. And which kinds of customers you targeted and which TV show and which magazine, all of that was priced based on intuition and not much else.Digital advertising came along in the late 1990s, and the first ways we thought about digital advertising was that it was like the magazine industry. So instead of advertising in People magazine, you're going to advertise on Yahoo using the exact same processes you did in People magazine. There was a rate card and it was going to be so many dollars per thousand users. And if you were doing general search, it might be $10, and if you're looking for real estate, it might be $50. And that's exactly how the magazine industry was priced. Some magazines were more than others based on readership and topic. And it was all based on personal selling, intuition, deals, etc.Then people realize that digital advertising created an opportunity to predict who the user was, who might see your ad. A user arrives at a publisher and an ad needs to be served, and you can predict who that user is and what they might want and when they might want it. Based on those predictions, rather than just do the magazine industry old way of doing things, you can now serve the right ad to the right person at the right time. Starting around 2000, there were all these innovations in online advertising that led to an industry that today looks almost nothing like the industry that you saw in Mad Men. Every time a user goes to a website, there is a real-time auction, in fractions of a second, between, in effect, thousands of advertisers for that user's attention. And there are all these intermediary steps, lots and lots of intermediaries—largely led by Google, but some other players that complement Google in that process—to create an entirely new kind of ad industry. The ad industry has had a system-level change because we can now predict, for a given impression or given user who's looking at a page, what they might want and when they might want it. Predictions changed the industry.The potential for “system-level” changesHow confident are you that this technology is powerful enough that we'll see system-level changes across the economy? That this is a general-purpose technology that will be significant? And do we have any idea what those changes will be, or is it, “They'll be big, but we don't know exactly what they are.”The technology itself is pretty extraordinary. And so in lots and lots of contexts, I'm pretty confident the technology's going to get there. There are some constraints on it, which is that you need data on the thing you're trying to predict in order for the predictions to work. But there are lots and lots of industries where we have great data. The technology barriers, I think, are being overcome. In some industries faster than others, but they're being overcome in lots and lots of places.That's not the only barrier. The technology is barrier number one. Think of an industry that I'm particularly excited about the potential of the technology, which is healthcare. Why is it so exciting for healthcare? Because diagnosis is at the center of how healthcare operates. If you know what's wrong with somebody, it's much easier to treat them, it's much less costly to treat them, and you can deliver the right treatment to the right person at the right time. Diagnosis, by the way, is prediction. It wasn't obvious, the way we thought about that in the past. But really, what it is, it can be solved [with] statistical prediction by using the information you have, the data on your symptoms, to fill in the information you don't have, which is what's actually causing your symptoms. If you do a Google Scholar search for something like “artificial intelligence healthcare,” you'll get a few million hits. There are lots of people who've done research producing AI for diagnosis. The technology, in many cases, is there. And in lots of other cases, it's pretty close.That doesn't mean it's going to transform healthcare. Why not? What's an AI doing diagnosis? They're doing a thing that makes doctors special. Yes, a good doctor in their workflow does all sorts of other things — they help patients navigate the stress of the healthcare system, they provide some treatments, etc. — but the thing that they went to school for all those years for, and for many of them the thing that they have that nurses and pharmacists and other medical professionals don't, is the ability to diagnose. When you bring in machine diagnosis into the healthcare system, that's going to be very disruptive to doctors. There are lots of reasons why, then, doctors might resist. First, they might be worried about their own jobs. Second, they might just not trust the machines and believe they're good enough. Because [in] the medical system doctors are a core source of power—they help determine how things work—they're going to resist many of the biggest system-level changes from AI-based diagnosis.And so you may have regulatory barriers, you may have organizational incentive barriers, and you may have barriers from the individual people on the ground who sabotage the machines that are trying to replace them. All of these are reasons — even if the technology is good enough — that AI in healthcare may be a long way away, even though we can see what that vision looks like. In other industries, it might be closer. In lots of retail contexts, you're trying to figure out who wants what and when — Amazon's pretty good at that in lots of ways — and in-store retailers can do that too. And so there are reasons to think that disruption in many retail industries will come faster.I just want to be a little careful here. I see the technology is there. There are some barriers on the technology side. If the payoff is big enough, I think most of the technology-related barriers can be overcome. To give you a sense of this: We hear a lot something like, “We don't want to do AI in our company because it's just so difficult to get the data organized and get the right data to build those predictions.” Well, yeah, it's difficult. But if the payoff is going to be transformative to the company and make the company millions or billions of dollars, then they'll spend thousands or millions in order to make it happen. And so a lot of the challenges aren't tech specific. They're incentives and organization based.When will AI show up in the statistics?I think of the classic Paul David paper about the dynamo. It took a while before factories used electricity, and they actually had to redo how the factory was designed to get full productivity value. And you say that we are sort of in the “between times.” And that makes me think of a classic Solow paradox: We see computers everywhere but in the statistics. He said that in '87. Are we, like, in the 1987 period with this technology? Or are we now in the late ‘90s where it's starting to happen and the boom is about to begin?I think we're in the early ‘80s.Not even the late ‘80s?He said that in 1987. By 1990 it was showing up in the data. So he just missed it.[We're in the early 1980s] in the sense that we don't quite know what the organization of the future looks like. There are reasons to think for many industries it might take a long time, like many years or decades, for it to show up in the productivity stats. While I do say we're in the early ‘80s because we haven't figured it out yet, I'm a little more optimistic that maybe it won't be 30 years to really have the impact. Mostly because we just have the lessons of history. We know from past technologies, and business leaders know from past technologies, electricity and the internet and the steam engine and others, that it requires some system-level change. And we now have the toolkits to think through, how do you build system-level change without destroying your company?When electricity was diffusing in the 1890s, there wasn't really any idea that this might take 40 years to figure out what the factory of the future looks like. It just wasn't on anybody's mind. The management challenges of redesign were unstudied, and there was no easily accessible knowledge to figure that out. Jump forward to the ‘80s and computing: Again, we hadn't even learned the lessons of electricity back then. Paul David's paper came out in 1990. It was a solution to the Solow paradox.But since then, we have a much better understanding of what's required for technological change. There has been decades of economics literature Erik Brynjolfsson, Tim Bresnahan, Paul David, and others. And there's been decades of management literature taking a lot of those ideas from econ and trying to communicate them to a broader audience to say, “Yes, it's hard. But doing nothing can also be a disaster. So being proactive is useful.” Then there's another piece about optimism here, which is that the entrepreneurial ecosystem is different than it used to be. And we have lots and lots of very smart people building tech companies, trying to make the system-level change happen. And that gives us more effectively more kicks at the can to actually figure out what the right system looks like.The impact of ChatGPT and DALL-EChatGPT and these text-to-image generators like DALL-E, are these significant innovations that can cause system change? Or are they toys that can't figure out how many arms people have and are able to produce B-level middle school essays?They're both. What do I mean by that? The technology is incredible. What ChatGPT can do and DALL-E can do is really, at least to me, it's amazing. Especially what ChatGPT can do. It's much better than I… That came much faster than at least I thought it was going to come. When I first saw it, I was blown away. So far it's a toy. So far, most applications have been “Hey, isn't this cool? I can do this kind of thing.” In a handful of places, it's moved beyond a toy to a point solution. Joshua [Gans], Ajay [Agrawal], and I wrote a piece in HBR. We drafted it out, and rather than reread it and edit it 60 times like we normally do, we sent it into ChatGPT and said, “Write this in a way that's easy to read.” And it did. We had to do some final edits afterwards. But like, we are already doing the same thing. It made a piece of our workflow a little bit more efficient. Point solution.A lot of the talk here in universities, “Uh-oh, we have to change the way we do final exams because ChatGPT can write those exams for our students.” Sure. But that's really not thinking through the potential of what the technology can do. What we've seen so far are toys and point solutions, but I do see extraordinary potential for system solutions in both. Both DALL-E and ChatGPT, and all these generative models. ChatGPT, if you think about it, what does it do? One thing it does is it allows anybody to write well. Like I told my students, you no longer have an excuse to write a bad essay with terrible grammar and punctuation that's not structured like a five-paragraph essay. No excuse anymore. It used to be, okay, maybe there's an excuse because there was some time crunch and you had other things due. Or your language skills — you're a math person, not an English person. No excuse anymore. ChatGPT upskills all those people who are good at other things but whose opportunities were constrained by their ability to write. So what's that new system? I don't know. But there are a lot more people around the world who are bad at writing English than are good at writing English. And if now everybody is a B high school-level student, able to write an essay or able to write well in English, an email or whatever it might be, that's going to be amazing. We just have to figure out how to harness that. We haven't yet.You've sort of given us a potential timeframe, broadly, for when we might see this in the data. When we see it in the data, how significant do you think this technology can be? What is, do you think, the potential impact once you can find it in the data, the productivity growth, which is kind of the end goal is here?That's a great question. Let me reframe it and say, the thing I'm worried about is that it won't reach its potential. A lot of people are worried about the impact of AI on jobs and what are people going to do if machines are intelligent? Jason Furman attended our first Economics of AI conference. This was in 2017. He was formerly chair of Obama's Council of Economic Advisors. And the thing I'm worried about is that there's not going to be enough AI. The productivity booms that we've had in history from way back to the steam engine and then electricity and then the computer age and the internet have been driven by system-level change, where we've figured out how to reinvent the economy. And that's led to sustained productivity growth: first the steam engine at 0.5 percent and then maybe 1 percent with electricity and then 2 percent after the war or more. I don't know what the number is going to be. I know you wanted me to give you a number. I don't know what the number's going to be. But this technology has potential to be like all those others, assuming we figure out what that system-level change looks like and we overcome the various sources of resistance.To sum it up, your concern is less about, can we solve the technical problems, versus, will society accept the results?Exactly. 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

Emily Nix talks about how violence against women at work affects the victims, perpetrators, and firms. “Violence Against Women at Work” by Abi Adams-Prassl, Kristiina Huttunen, Emily Nix, and Ning Zhang. *** Probable Causation is part of Doleac Initiatives, a 501(c)(3) nonprofit. If you enjoy the show, please consider making a tax-deductible contribution. Thank you for supporting our work! *** OTHER RESEARCH WE DISCUSS IN THIS EPISODE: “Sexual Harassment and Gender Inequality in the Labor Market” by Olle Folke and Johanna Rickne. “Incentives for Managers and Inequality Among Workers: Evidence from a Firm-Level Experiment” by Oriana Bandiera, Iwan Barankay, and Imran Rasul. “What Drives Differences in Management Practices?” by Nicholas Bloom, Erik Brynjolfsson, Lucia Foster, Ron Jarmin, Megha Patnaik, Itay Saporta-Eksten, and John Van Reenen. “When Harry Fired Sally: The Double Standard in Punishing Misconduct” by Mark Egan, Gregor Matvos, and Amit Seru. “Monitoring Harassment in Organizations” by Laura Boudreau, Sylvain Chassang, and Ada Gonzalez-Torres. [Working paper.]

There's been a lot of anxiety lately about AI replacing workers. But what many economists are really worried about is not mass unemployment, but polarization. Emerging technology, they say, isn't coming for all our jobs—it's shrinking the middle class, specifically. Experts warn that we've seen this movie before with globalization a generation ago. Without a smart policy response, the coming shifts in the labor market could not only heighten economic hardship, but also sow even more division in our increasingly polarized society. In this episode, we ask: Could the robots come between us? And what can we do about it? MIT's Frank Levy and David Autor, Stanford's Erik Brynjolfsson, and CMU's Lee Branstetter suggest ways we can work together to ensure the Fourth Industrial Revolution is an economic reboot for the better.

Erik Brynjolfsson returns for another fascinating episode in which you can learn how to say the word for robot in Chinese. He and ShaoLan also discuss the future of robotic technologies and how they will continue to shape our lives in the coming years.

Which characters does the Chinese language put together to make the word computer? It will definitely surprise you with its sci-fi nature. In this episode, ShaoLan and leading expert on digital technologies Erik Brynjolfsson share how to construct Chinese words such as computer, TV, movie and telephone.

With everyone talking about ChatGPT and the acceleration of AI today's conversation is with economist and author Erik Brynjolfsson on why we should race with the machine not against the machine. He shares why we have to return to a conversation about our values and vision to guide the decisions we make as we reinvent education for what he calls The Second Machine Age.  When we step back and assess the risks and rewards of existing and emerging technologies in the context of the needs of our learners we create a culture of innovation built on empathy.  Template: Dowload the free template to have a conversation about "Obstacles and Opportunities" with your team. If you are talking about AI you can use this episode for inspiration Learn more about Erik's work here.  Learn more about Designing Schools here. 

Erik Brynjolfsson's paper “The Turing Trap: The Promise and Peril of Human-Like Artificial Intelligence” argues that the “imitation game” of creating tech that mimics humans has increased productivity and living standards, but does not exist without costs. Those costs make up “The Turing Trap” which happens when humans not involved in creating AI cannot compete with the productivity and efficiency of the robots designed to do their jobs, and lose control of their economic and political futures. The Turing Trap sits at the center of contemporary labor force struggles, including the Great Resignation, the fight for “good jobs” and cratering male labor force participation. Michael Strain, who directs AEI's Economic Policy Studies, joins Dr. Brynjolfsson and I to discuss what economic policy can do to encourage more innovators aim higher and create machines that augment rather than replace human labor, and how that effort is crucial to the American Dream. Mentioned in the episode https://www.brynjolfsson.com/ (Erik Brynjolfsson) https://www.amazon.com/Utopia-Thomas-More/dp/1512093386 (Utopia Paperback by Thomas More) https://www.amazon.com/Foundation-Isaac-Asimov/dp/0553293354 (Foundation Mass Market Paperback by Isaac Asimov) https://www.amazon.com/Worldly-Philosophers-Economic-Thinkers-Library/dp/1441743669 (Heilbronner's Worldly Philosophers) https://newsinfo.iu.edu/news/page/normal/5075.html (Doug Hofstadter) https://digitaleconomy.stanford.edu/news/the-turing-trap-the-promise-peril-of-human-like-artificial-intelligence/ (The Turing Trap by Erik Brynjolfsson) https://www.aei.org/profile/michael-r-strain/ (Michael R. Strain) https://www.amazon.com/American-Dream-Not-Dead-Populism/dp/159947557X (The American Dream is Not Dead) https://www.city-journal.org/html/when-high-schools-shaped-americas-destiny-15254.html (The High School Movement) https://taxfoundation.org/tax-basics/pigouvian-tax/#:~:text=A%20Pigouvian%20tax%2C%20named%20after,sugar%20taxes%2C%20and%20carbon%20taxes. (Pigouvian Tax) https://taxfoundation.org/tax-basics/consumption-tax/ (Consumption Tax) https://www.investopedia.com/terms/t/taxreformact1986.asp (Tax Reform Act of 1986) https://scholar.harvard.edu/files/mankiw/files/smart_taxes.pdf (Greg Mankiw Pigou Club)

On September 21, Justin Hendrix moderated a panel discussion for the McCourt Institute at a pre-conference spotlight session on digital governance ahead of Unfinished Live, a conference on tech and society issues hosted at The Shed in New York City. The topic given by the organizers was Digital Governance and the State of Democracy: Why Does it Matter?  Panelist included:   Erik Brynjolfsson, the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow, Stanford Institute for Human-Centered AI (HAI) and Director of the Stanford Digital Economy Lab Maggie Little, Director of the Ethics Lab at Georgetown University Eli Pariser, Co-Director of New_Public, an initiative focused on developing better digital public spaces; and Eric Salobir, the Chair of the Executive Committee, Human Technology Foundation, a research and action network placing the human being at the heart of technology development

Erik Brynjolfsson is the director of the Stanford Digital Economy Lab and professor at the Stanford Institute for Human-Centered AI. He joins Big Technology Podcast for a discussion of why our fears that artificial intelligence would take human jobs haven't yet come to fruition. We also cover how humans and AI can work together and how AI is changing work already. Stay tuned for the second half where we discuss the latest on robotic process automation and address why we're working at all in the age of machines. Check out Prof. Brynjolfsson's paper: The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence

World leading economist Erik Brynjolfsson visits the Talk Chineasy studio in London to learn a hugely important word for our lives in Chinese, “work”! Also, find out how to say “I want to work!” and “I don't want to work”!

The economy affects almost every area of our lives and who better to learn the Chinese word for the economy than world leading economist and author of “The second machine age” Erik Brynjolfsson.

When are you most productive? How do you increase your productivity? And is technology a help or a hindrance?! The amazing Erik Brynjolfsson learns the Chinese word for "productivity" and discusses with ShaoLan the technologies that can cause ripples of productivity growth around the world.