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Coming up next on Passion Struck.
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I think in the long term, I am worried the subtle stuff starts to matter.
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If we give up more authority and control, if we re not in the loop, then I worry a lot about the kind of biases that AI has in making decisions.
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If you re still in the loop and it s one of the voices you re listening to, like another human mentor, then there s a lot more value in it.
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Welcome to Passion Struck.
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Hi, I m your host, Jon R. Miles, and on the show, we decipher the secrets, tips, and guidance of the world s most inspiring people and turn their wisdom into practical advice for you and those around you.
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Our mission is to help you unlock the power of intentionality so that you can become the best version of yourself.
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If you re new to the show, I offer advice and answer listener questions on Fridays.
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We have long form interviews the rest of the week with guests ranging from astronauts to authors, CEOs, creators, innovators, scientists, military leaders, visionaries, and athletes.
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Now let s go out there and become Passion Struck.
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Hello everyone, and welcome back to episode 437 of Passion Struck, consistently ranked the number one alternative health podcast.
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A heartfelt thank you to each and every one of you who return to the show every week, eager to listen, learn, and discover new ways to live better, to be better, and to make a meaningful impact in the world.
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If you re new to the show, thank you so much for being here.
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Or you simply want to introduce this to a friend or a family member, and we so appreciate it when you do that.
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We have episode starter packs, which are collections of our fans  favorite episodes that we organize into convenient topics that give any new listener a great way to get acclimated to everything we do here on the show.
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Either go to passionstruck.com slash starter packs or Spotify to get started.
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Are you curious to find out where you stand on the path to becoming passionstruck?
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Dive into our engaging passionstruck quiz crafted to reflect the core principles shared in my latest book,
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This quiz offers you a dynamic way to gauge your progress on the PassionStruck continuum.
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Just head over to PassionStruck.com to embark on this insightful journey.
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With just 20 questions and roughly 10 minutes of your time, don t miss this chance to gain valuable insights into your PassionStruck journey.
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Take the quiz today.
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In case you missed my interview from earlier in the week, we dove into the world of high-performance coaching with Sean Foley, the renowned golf coach behind some of the biggest names in the sport.
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From his unique coaching philosophy to the mental health strategies that can turn a struggling golfer into a champion, Sean shares insights you won t want to miss.
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And if you liked that previous episode or today s, we would so appreciate you giving it a five-star rating and review.
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They go such a long way in strengthening the Passionist Rock community, where we can help people to create an intentional life.
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And I know we and our guests love to hear your feedback.
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Today, we re venturing into the rapidly evolving world of artificial intelligence with none other than Ethan Mollick, a Wharton professor, author of the groundbreaking book Cointelligence, Living and Working with AI, and the mind behind the popular One Useful Thing substack.
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Ethan is recognized as one of the leading experts on AI, with his insights featured in prestigious outlets like The Atlantic, NPR, and The New York Times.
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In Cointelligence, Mollick addresses the mixed feelings surrounding AI,
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from the excitement about its potential to enhance productivity and creativity to the fears about job security and the broader implications for humanity.
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Through his work, Malek aims to demystify AI, presenting an honest, research-backed view on how these tools can transform our world for the better if used wisely.
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Today, we ll discuss reimagining work in an AI-enhanced world, the importance of focusing on the positive impacts of AI, how AI can amplify human creativity,
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its revolutionary role in education and the democratization of talent and ability through technology.
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Join us as Ethan challenges us to see AI not as a threat, but as a transformative tool that when harnessed correctly, can lead to unprecedented growth and innovation.
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This conversation is not just about understanding AI, but about envisioning a future where technology and humanity coalesce to create a more empowered and equitable world.
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Thank you for choosing PassionStruck and choosing me to be your host and guide on your journey to creating an intentional life.
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Now, let that journey begin.
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I am absolutely thrilled and honored to have Ethan Mollick on PassionStruck.
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Welcome, Ethan.
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Thank you for having me.
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But before I get into that, I understand through my research that you have a love for cheese curds.
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I thought it was a really fun detail about your background.
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Can you share a memory or a story that embodies your connection to Wisconsin where cheese curds are well-known and how it stayed with you over the years?
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Oh, definitely.
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I m born and raised Wisconsin.
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I may sound like a New Yorker because I talk fast, but born and raised in Wisconsin.
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And the great thing about Wisconsin is it is a very grounded Midwestern state.
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So one of the great joys there is going to the state fair every year.
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And so I think they sell something, the state has only populated a few million.
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They sell several times that number of cream puffs every year.
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There s of course, butter carvings of cows, but they also sell cheese curd.
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And one of the things that like you can t get outside of Wisconsin is a real cheese curd.
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Because when you bite a real cheese curd, it squeaks like you re biting into a balloon.
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I mean, that s how you know it s good.
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So there s an authenticity piece there of you can tell a real Midwesterner because they know what cheese curd sounds like.
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And so it s nice to have a grounding back there, even as I ve moved to the East Coast and my career has grown in other kinds of ways.
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I live here in St.
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Petersburg, Florida, and we have a weekend farmer s market every Saturday.
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One of the vendors sells cheese curds.
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So now I m going to have to go and put it through the litmus test of is it an authentic cheese curd or not?
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You ll have to report back on how that works.
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Well, it d be interesting if I could have AI do it for me, but we re not there yet.
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Not quite.
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Although GPD 4 beats humans in the sommelier test.
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So it s actually the written wine tasting it does pretty well on, but it can t taste anything yet.
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I could understand, given how much effort you have to put into being a sommelier, why it might be better at doing that than a human who might pick up other things.
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Well, I wanted to get into your academic pursuit because you decided to pursue an MBA at MIT, and that was a really pivotal moment for you.
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I wanted to ask what were some of the most valuable lessons that you learned during your MBA?
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And more importantly, what led you to then want to go on and pursue getting a PhD?
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It s a great question.
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So I was a co-founder of a software startup company with my college roommate, who is the sort of technical genius behind it.
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I was the business guy and we did it right after school.
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And I made literally every mistake possible.
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Every error, we were successful.
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We invented the paywall.
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I still feel a little bad about that, but in the late 1990s, but you know, 20 something guys selling this thing and he, again, technical genius.
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I was the sort of sales guy.
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And anything we could do wrong, we did wrong from hiring to how we set up equity to how we did everything was a mistake.
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We succeeded, but it was a tough run.
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And I thought I should learn how to do this for real.
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And I decided to get the MBA.
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And one of the things I learned about entrepreneurship that led me to the PhD is nobody knew anything.
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Right.
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So there s some stuff we know about how to be an entrepreneur, but we don t know that much.
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And I thought, I want to study this.
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I want to figure out how this works.
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So that was a transition.
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As to the big thing I learned, I think the thing I learned, the lesson I learned most of the PhD program, which is a basic economics lesson, which is people do what they re incentivized to do.
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And I think once you realize it s actually true, it s fairly profound because people often do the wrong things because they re incentivized in the wrong way.
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Organizations are often broken so that you re incentivized to do something that s not actually helpful.
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And that lesson has stuck with me ever since.
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Yeah, well, that is definitely true.
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And it s something that I ve seen throughout my business career as well, before I started this is those incentives really drive people s actions sometimes in good ways, sometimes in the exact opposite way that you would like them to be.
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I think it s important for people to understand a foundation of your entrepreneurial journey because you were part of several startups, one that s infamous at creating a paywall, if I understand it correctly.
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Can you share a little bit of that background?
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Sure.
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I spread it to my co-founder who was one of my college roommates, and he really knew about this market, had resources to bring to bear, and he brought me into the company when it was just the two of us working together.
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And it was a very typical entrepreneurship story, learn by doing, right?
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Among the other things I learned was we were trying to sell a product to companies that had no idea
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what the internet was for publishing.
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So I literally at one point had a sales call with a company that had an actual castle because they had started their printing press in 1500.
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And then I m going to them and saying, Hey, the internet s going to be a big deal.
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You should sell things on the internet.
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And I m glad there were no archers left at the castle from my reception.
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So it was a lot of this sort of like realizing, and it s something that s been common in my career.
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that I m usually a little bit ahead on technologies.
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And as a result, it s been nice proof after the fact that yes, we were correct on this.
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Like the New York Times and Wall Street Journal experimented with our paywall and actually started using it.
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But we were very early on in software as a service and all of these kinds of things.
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And I think that that lesson has stuck with me.
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And I ve been in that kind of place multiple times.
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The funny thing about the AI piece in some ways is I ve been ahead of the curve, but the curve s finally catching up.
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So suddenly people are listening to me where they probably didn t before.
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But the struggle has been, how do you communicate these ideas?
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And that s been part of what being a professor is.
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How do I talk about this stuff?
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How do I make people feel the sort of passion and importance of these topics?
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Are you familiar at all with Jim McKelvey?
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The name is familiar.
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name.
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So Jim is a friend of mine.
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He co-founded Square with Jack Dorsey.
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Of course.
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Yes.
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Yes.
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But he s got this new company called Invisibly, and I m just ad-libbing here because I m interested in this.
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I have never liked the paywalls that publications like the New York Times, Wall Street Journal, Time, Forbes, they re all using it now.
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So Invisibly
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was really a breath of fresh air because what Jim is trying to do is disrupt the whole publishing industry by actually paying the consumer to consume content.
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And it sounds counterintuitive, but
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For every piece of content that you access, they re going to pay both the publisher and depending on how valuable your reading is, your consumption, you also get paid.
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So it s a really interesting model if you haven t examined it.
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I think the paywall is, I think publishers needed it, but you would have hoped we would have transitioned to a better model by now.
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And I think that in general media is stuck between models.
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It s fascinating.
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In the 1940s, people subscribed to an average of three newspapers and eight publications for each family, like crazy numbers.
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And now nobody subscribes to things and the market s just looking for desperate solutions.
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I think the paywall was a bridge, but that sounds like a better evolution forward.
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So I ll check that out.
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So today we are going to be doing a deep dive on AI, and AI has been around for a while.
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I ve actually been studying it for a while.
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However, November, 2022 marked the beginning of a new era, the great awakening of generative AI through the emergence of creative machines.
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Can you share a little bit about what went through your mind when you first realized the implications of this development?
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Because it was far different than the previous versions of AI.
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So just for background, there s two paths of AI.
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AI has been a big deal for, as you said, a while.
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At the MIT Media Lab, I worked with one of the sort of founding luminaries of AI, Marvin Minsky at the time.
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So like AI had always been like this boom and bust kind of cycle.
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And the latest boom actually started in the 2010s, which was around analytics, the idea that we can do machine learning and build predictive algorithms.
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So Amazon uses that to figure out where to put their warehouse, because they can take all their data or what products to recommend.
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When you re going to a podcast site, that s what s crunching all the data and suggesting what other podcast you might want to listen to.
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And that was very valuable.
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But those systems were really bad at one kind of prediction, which was predicting the next word in the sentence.
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Because if your sentence ended with the word filed, it didn t know whether you were filing your taxes or filing your nails.
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And then in 2017, this paper came out called Attention is All You Need that set up a new way of looking at AI called the Transform with Attention Mechanism.
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They let the AI pay attention to the entire sentence the word was in, the page, the paragraph, and then that let us create realistic language as a result of this.
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And for a while there were bugs.
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So I ve been following that large language model thing for a while, and there were earlier versions of it that were really interesting because they wrote like fifth grade writers.
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And it was like, wow, that s amazing that you can write like a fifth grader.
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But it wasn t particularly insightful.
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And then when ChatGPT came out, for reasons that are still unclear to even the insiders, I talked to OpenAI all the time, I talked to Google, I talked to Microsoft, all the players, nobody quite knows why it s as good as it is.
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But suddenly we had a model that just by virtue of being larger, even though it wasn t a technological leap forward over previous models necessarily, it could score high on the SAT.
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And it could, it seemed to be, there seemed to be someone that we were talking to and it could write at a high school level.
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And I remember as soon as it came out, I tried it and there s a whole Twitter thread of me doing experiments.
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Oh my gosh.
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I like, wow.
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It writes a poem.
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Wow.
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It could do, it writes a memo.
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It can do a strategy document.
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And it was like a dawning revelation.
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I started teaching it to my classes and it really became a big deal overnight.
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One of the things I wanted to go back to, and thanks for sharing that story, is my positions were in the technology area of major corporations.
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And during the mid to late 2000s, I was the chief data officer at Lowe s.
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And Lowe s absolutely competed by using data.
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So we were probably one of the two or three largest customers of MicroStrategy.
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We had a huge SAS implementation and a lot of behavior scientists and other PhDs who were writing these models for those tools.
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And just to give an example for the audience of how powerful this was,
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We created a model around the single view of the customer, which without having a loyalty program, we were able to get a 92% match, meaning we knew who was coming in the store.
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And we also knew if you were in your second home, when you were visiting it, because we had trigger events that would tell us, and then we would market to you based on knowing who you were and your shopping habits and
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indicators that would tell us whether you re starting a project or not.
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And that was almost 20 years ago.
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How could, just to give a sense for a listener, if we could do that then with some of these new models that we have now, what would it add to that sophistication?
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So what s interesting about that is that form of AI, doing the predictive analytics based on customer data, has advanced.
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The models are much better than they were.
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They can do better predictions, maybe be to 99%.
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But that actually hasn t been what s behind the latest boom of AI.
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In fact, you are early to this market and other people have been struggling to catch up with what Lowe s could do and what you could do.
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That s been like the last 10 years, right?
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It s been a steady stream of it s getting easier to do.
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It s easier to build your data stores.
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It s easier to do the analysis.
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There are better visualization tools.
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So the stuff that you got a competitive advantage of is now much more democratized.
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The thing that s causing all the insanity around AI right now is this completely separate thread that
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would not be good at identifying necessarily customer patterns, because that s not what large language models do that well.
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But what would be is would make it so that every customer service representative who talks to them gets a perfect script, or even the AI can speak to the customer and say, hey, it looks like you re trying to build a house.
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Let me give you some advice.
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It looks like you actually bought a little bit too few of this.
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And the latest design is not to have exposed brick.
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So it looks like that s what you re trying to do.
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Can I help you out with that a bit?
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It s that idea of having almost a person there that you can deploy on demand, and that s what large language models do.
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OK, and Ethan, I wanted to give the audience a background as well, in case they re novice in this area.
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My understanding is that there are three different AI models, depending on their scope of capabilities and methodologies.
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If you re able to, can you discuss the difference between foundation, frontier, and specialized models?
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Yeah, so that s a great question.
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Okay.
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So there are many large language models out there.
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Probably most of the people watching or listening have played with ChatGPT.
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All right.
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ChatGPT, if you re using the free version, it uses a model called GPT 3.5, which is completely fine, but not great.
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There are now, that s an example of foundation model.
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There are now many models that are as good as the free version of ChatGPT.
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I actually have one running on my home computer that s open source.
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That s almost as good as ChatGPT.
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Those are called foundation models.
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There s many of them out there.
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Right.
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Different sizes.
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Elon Musk has his rock and there is Lama from Meta.
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All of these are foundation models and they all have a little bit different personalities.
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They re all and there s lots of different ability levels.
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Some of them are very small and cheap to operate.
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Some are very expensive.
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There is a subset of those models that are called frontier models.
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Because in AI right now, there is a scaling law, which means that the larger my model is, which means the more information I fed it, the more computing power, and therefore the more expensive it was to build, the more time it took to build, the smarter it is.
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So only a few companies in the world can afford to build these.
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Right now, there s three frontier models, which are GPT-4, which is the paid version of Chatsy, Google s Gemini Advanced, which is the paid version of Google s AI, and Cloud 3, which is also paid, which just got announced.
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Now, those models are smart.
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The reason why it matters that your model is smarter is because lots of people are building smaller foundation models that are specialized.
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So famously, Bloomberg spent $100 million building a financial GPT, and they trained it on all Bloomberg s secret proprietary data, and it was supposed to help them with stock picks.
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Well, a new paper just came out that showed that actually GPT-4, the same thing that you can get access to anywhere in the world for 20 bucks a month, actually outperforms Bloomberg GPT, and they spent $100 million on stock picks, even though it s not built for stock picks.
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Google will build a specialized model called Palm 2 for medicine.
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It s Palm 2 Med, which is supposed to be really good at medicine.
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GPD 4 beats Palm 2 in medical advice.
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It actually beats most doctors in medical advice as well.
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larger models, the frontier models are smarter and they tend to be very good at lots of different things rather than going with a specialized or smaller model.
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There s still reasons to do that if you re a company.
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Thank you for that explanation.
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And I understand that Microsoft now has something called Co-Pilot that I was just talking to a friend of mine from Microsoft, that they re basically integrating into their suite.
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So you could have it build PowerPoints for you.
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You could have it go in there and you could ask it to lay out a keynote speech and word, et cetera.
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What type of model is Co-Pilot using?
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So co-pilot, Microsoft has partnered with OpenAI.
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So co-pilot is actually GPT-4, the frontier model that OpenAI does.
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So you can get access to it two ways.
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You can get access to it directly by paying ChatGPT+, or you can get access to it through Microsoft s co-pilot tools.
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You don t have to charge $20 a month for something like that.
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Co-pilot is
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both much easier to use.
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There s a little button right in Word that you can click and tell it what you want.
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It literally will write a Word document for you.
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It ll write a 30-page PowerPoint.
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It s pretty miraculous.
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So it makes it easier to use, but also doesn t necessarily give you all the power of using the model directly because you re not talking to the model.
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It s helping you make it easier.
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It s a copilot.
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So part of the choice, I like Copilot as a starting place for people because it s a very powerful way to use these tools.
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But if you really want to get good at AI, you have to start using the models directly, which means getting access to one of the frontier models as a chatbot and working with it.
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Okay.
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And I know a lot of people are getting very worried about this disrupting their livelihoods, but there are certain areas of the market that are harder to penetrate because of regulation.
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I think about this as being the government, healthcare, finance.
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So as we think about these frontier models and these specialized models for niche applications, such as healthcare, government, or finance,
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What are the challenges, so people can be aware of them, of creating models that perform at higher levels of accuracies that are needed in these domains?
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How long do you think it s going to be until they can actually enter these domains and do it in a proficient enough manner that it can start disrupting them?
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SPEAKER_01
So it s an interesting question because the important thing to realize about AI is nobody knows the answer to these questions.
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SPEAKER_01
And I talk to all the people who build these models because we don t know how good they re going to get.
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And we don t even know everything that we can do.
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I signed my MBA students an assignment this last month where I taught them how to build AI tools using chat GPT.
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And I gave them the assignment.
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I want you to replace the job you are applying for so that when you go for a job interview, you can give them the GPT you created, the AI tool you created.
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and say, I would like a raise now, please.
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And what was amazing, the MBAs represent all these different jobs.
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I had Navy pilots.
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I had hip-hop promoters.
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I had lots of private equity people, consultants.
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So the private equity people made something that read 10K financial statements and wrote Abdul memos.
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The Navy pilot had a way that it filed Navy flight reports for him in a way that saves him two hours a day.
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And all of these people were creating these specialized tools for their jobs
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SPEAKER_01
Within just a few hours, and actually, three of them told me they got jobs as a result of this so that was pretty neat.
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SPEAKER_01
But the idea is I don t know the full range because the expert in your field and open AI isn t the expert field.
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SPEAKER_01
Models are already more capable than people think.
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SPEAKER_01
Now, in the bigger question about regulation, that s very uncertain.
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SPEAKER_01
When I talk to regulated industries like banking though, part of the deal is it shouldn t be working with the regulated part with AI because it s not legal to do that yet.
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SPEAKER_01
But there s lots of other parts of what their business does that can be enhanced by AI.
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SPEAKER_01
Their marketers could use this to do better marketing work.
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SPEAKER_01
The customer service team can do this.
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SPEAKER_01
You could do this in ways that are not necessarily exposed to regulation, if that makes sense.
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SPEAKER_00
Yeah, no, I think it s a helpful explanation.
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SPEAKER_00
And I just wanted to put it out there so people understand the full spectrum that s going on here and what s going to need to be done to take these things to the next level.
2
SPEAKER_00
So I want to talk about motivations for a second, because you brought them up earlier.
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SPEAKER_00
And in the book, you discuss the motivations behind developing AI.
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SPEAKER_00
And you highlight not only the potential profitability, but the belief among some researchers and figures like Sam Altman, that superintelligence could offer boundless upside for humanity.
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SPEAKER_00
Now I hear that, and I think there s two sides of the coin.
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SPEAKER_00
There s this optimistic view, but how do you reconcile that enthusiasm for superintelligence potential benefits, such as curing diseases or solving global warming, with the significant downsides and risks that come about with AI alignment and the fear of runaway AIs?
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SPEAKER_01
That s a really great question.
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SPEAKER_01
The crazy thing about this is that the belief, it s ideology right now that s guiding things.
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SPEAKER_01
So there s groups of people who really believe that they could solve this, that they, first question, can you build a super intelligence?
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SPEAKER_01
That s absolutely unknown at this point.
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SPEAKER_01
I think people are assuming it s doable.
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SPEAKER_01
I don t know.
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SPEAKER_01
If you build a super intelligence, would it have its own goals or would it just be like a very smart machine that will do like,
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write a really great script for listening to the conversation I m having with you right now, and feed into my ear the optimum thing I can say right now to be very impressive to you.
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That s a version of AI where it s not taking charge, but it s helping us in different ways.
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SPEAKER_01
But assuming you can build something that is super intelligent and that has some
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self-driven approach is autonomous to some degree.
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SPEAKER_01
Then there s a complete gap between people who either call them the doomers and the accelerationists.
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SPEAKER_01
And it s a real fight here.
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SPEAKER_01
It s amazing.
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SPEAKER_01
I m calling from San Francisco right now.
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SPEAKER_01
And it s a genuine debate.
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SPEAKER_01
Their organization, their open AI is all in on believing that they can build a super intelligent AI that will help humanity.
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SPEAKER_01
There are other groups that are incredibly worried about this and don t believe that it s possible.
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SPEAKER_01
Some people think the whole thing is fascinating, but the only way to really understand why these labs are doing what they re doing is to understand their beliefs, because OpenAI could make a lot more money if they were trying to do other things other than build a superintelligence, but that s what they re focused on right now.
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SPEAKER_01
Whether they succeed or not, nobody knows, including them.
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SPEAKER_01
They believe they can do it, though.
2
SPEAKER_00
So I had the honor of interviewing Yael Eisenstadt.
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SPEAKER_00
I m not sure if you know who she is, but for a period of time, she was at Facebook and she was looking at how they were humanely using their technologies or not.
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She ended up leaving.
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SPEAKER_00
But I had a really fascinating conversation with her about how oftentimes, whether it s Sam Altman or another company who s maybe built the Fitbit or some other application, doesn t have to be AI.
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They come up with these great ideas.
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They get funding from VCs.
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They put this out in the world with all the good intentions that there are.
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SPEAKER_00
but they don t anticipate the nefarious actions that bad actors can do with that technology, such as groups of special forces wearing the Fitbits and another country being able to track their whereabouts.
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SPEAKER_00
How do you think we go about
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putting advisors in important companies so that we have people who are looking at this from a completely different view than the people who are creating them.
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Someone who could see the possibilities of how these things could be used if they got in the hands of the wrong people.
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SPEAKER_01
So there is effort to do that, right?
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And I ve spoken to a lot of people in lots of industries.
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There are people who are very worried about this.
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For example, one of the initial conversations I had with companies like OpenAI and Microsoft were things like, hey, you didn t realize this, but when you released Chakchivuti, you created a universal cheating bot.
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SPEAKER_01
And I m a professor at a university and I know what s coming and other people don t because they don t think they haven t tried it yet.
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SPEAKER_01
But this will destroy every essay, and essays are a useful way of teaching people things.
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They had no idea it would do that.
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SPEAKER_01
So when you re creating something of general purpose use, you can t anticipate all the use cases.
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The biggest concern I ve seen from a lot of people that s become the focus of bad actor concerns is that mostly terrorists and criminals are dumb, or at least the ones we catch are dumb.
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But if this
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does, as we re seeing in other spaces, moves everyone s performance up the eighth percentile of performance, then if you ve got a good plan for how to rob the bank, you learn how to write the perfect ransom note, you don t mess up, that is one of the concerns.
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SPEAKER_01
I don t know what you do about that.
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SPEAKER_01
Right now, the systems have guardrails that try and stop you from saying how you want to rob a bank, but I can get these systems to tell me that by convincing them that we re in a one-act play, or it s helping me write a crime novel, and then it s okay, well, this is fictional, I m happy to tell you how to rob a bank.
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SPEAKER_01
So I think that s a set of concerns, but I don t think we can even anticipate the full range of negative or positive implications, because we don t know yet.
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SPEAKER_01
We don t also know how good these models are going to get.
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SPEAKER_00
Thank you for sharing that.
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SPEAKER_00
And you ve been a prominent voice, as you mentioned earlier, in explaining the practical aspects of AI.
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SPEAKER_00
And I just want to talk about education for a little bit, because that s one of your main focus areas.
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SPEAKER_00
I remember when I was going through my bachelor and graduate studies, I couldn t stand the lectures.
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SPEAKER_00
It was just for some of the auditory processing issues that I have personally, the lectures would just bore me out of my mind.
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I wanted something that was more interactive.
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Potter is now a sophomore at the University of Florida, and she constantly complains about the lectures that are still going on.
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SPEAKER_01
It seems like we ve been teaching things the same way now for a very long time.
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How do you think AI might be able to disrupt the way that we re educating people, maybe allowing us to do it in a better way?
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That has been my real passion over the last 10 years before AI, and the reason I got interested in AI was about transforming teaching.
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It s very frustrating because we know how to teach better.
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There s a ton of research, but we don t do it.
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Lectures are 2,000 years old, 2,500 years old.
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It is a way of teaching.
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SPEAKER_00
There s value in lectures, but not as much as people say.
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Passively sending people information isn t a very useful way to teach.
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SPEAKER_00
Increasingly, research has been showing that active learning, where you actually do things in the classroom, make a big difference.
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SPEAKER_00
Now, there s two problems with this.
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One of them is there s a great study at Harvard with their introductory physics classes, where they divided up the classes into lectures and active learning, where you had to do problem sets and do work inside of class.
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The people in the lectures reported that they learned more and that they felt they enjoyed it more, but they did much worse on tests.
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The people who had to do active learning learned it much better, but reported not liking it as much.
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And part of the reason they didn t like it is part of what being an active learner does is exposes you.
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While you re in a lecture, you can sit back and be like, I m being entertained, maybe if it s a good lecture, or I know this stuff.
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SPEAKER_00
There s a couple challenges.
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One is that students actually, even though you and your daughter are pushing ahead on this,
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The lectures feel good because you just sit there and listen.
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But the other issue has been it s very hard to do active learning because active learning requires us to teach outside the classroom.
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We call this a flipped classroom.
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So the way we ve had to do that is we assign you textbook reading.
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That s not very good.
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Then we started to assign you videos.
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Still not great.
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So the really amazing thing about A.I.
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is we could do that outside the classroom tutoring and teaching with A.I.
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and then the classroom time we spent in applications active learning doing things doing activities simulations experiences.
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And I think that is a really positive transformation that we re going to see happen soon.
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Yeah, to me that when you re acting with something, I don t care whether it s trying to improve your personal life and you re trying to maybe work on your physical health, or you re trying to learn chemical engineering, either one, the more you can play with it, the more you re going to learn it.
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Absolutely agree.
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And something I ve been wondering, AI is going to be a cheat sheet for students, given what I know it could do.
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If I were studying, I d ask it a lot of questions.
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My worry about this is that when you start using another source for information, A, it can produce inaccuracies, but more importantly,
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SPEAKER_01
I worry about, do we start retaining the information less because we re relying on something?
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SPEAKER_01
It s like using a calculator and it s not allowing you to fully immerse yourself with the learning that s going on.
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SPEAKER_01
Do you have any perspectives on that?
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SPEAKER_00
You re nailing a major problem.
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SPEAKER_00
Okay.
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SPEAKER_00
So we already know when someone Google something, they don t retain it as much because they ve outsourced that part of their learning process to getting the answer immediately.
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SPEAKER_00
My students have stopped raising their hands as much in class.
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SPEAKER_00
I asked why, and they re like, I d rather ask the AI to explain something like M10.
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But that again, you just get the explanation.
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SPEAKER_00
Raising hand in class is a big part of how we have interaction.
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SPEAKER_00
So we re gonna have to rethink some of this.
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SPEAKER_00
Now we ve been designing, so explaining M10 is a very bad way to learn because you just get an answer given to you and it s often a dumbed down answer.
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SPEAKER_00
But the really cool thing about the AI is it can actually act as a good teacher or tutor.
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SPEAKER_00
So a good tutor asks you questions.
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SPEAKER_00
They re like, can you explain to me what you know about the sales process?
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And then that helps you think about what you know and don t know.
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SPEAKER_00
And then maybe I would say to you, actually, could you expand on the first point?
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SPEAKER_00
You re almost right on that.
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SPEAKER_00
But think about why that might not work for enterprise sales.
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SPEAKER_00
And now that is a way of making you actively learn.
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SPEAKER_00
And a good tutor, that s why tutoring is magic.
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A good tutor is drawing things out from you.
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SPEAKER_00
They re not teaching you by telling you stuff, like a lecture.
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SPEAKER_00
We can get AI to do that.
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SPEAKER_00
We already have working AI tutors.
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SPEAKER_00
The issue is not we can get to that better point in education.
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SPEAKER_00
Actually, a study just came out a couple of days ago in Ghana, of all places, where they use the AI math tutor for students in grade school math and found that the tutor significantly increased how much math they were able to do in their math tests because they got that extra tutoring to help us out of class.
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SPEAKER_00
Tutoring is very expensive.
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they were able to do this for less than $10 a student, which is like an insane intervention.
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SPEAKER_00
Similarly, there s another case in entrepreneurship in Africa, where being given an AI advisor helps small business people make 20% more profits.
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SPEAKER_00
So if we build this stuff, we can build it in a way that gets around that problem.
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SPEAKER_00
But the default version, and this is why I m so passionate about it, experts need to be using AI to figure out how to use it for their fields, because
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SPEAKER_00
The default is it explains stuff to you in a kind of dumbed down voice and it s annoying, but give it the right paragraph of a prompt and I can make it sing.
3
SPEAKER_01
Well, this leads me to an interesting area that I wanted to discuss with you.
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SPEAKER_01
I ve been reading reports that not only are social media companies leaning towards the left, so are some of the models that are being built by AI systems.
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SPEAKER_01
Whether we want them or not, there s this issue of inherent biases within AI systems.
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SPEAKER_01
How do we start ensuring the accountability when it comes to these biases, which are emanating from what seemingly appear to be objective machines that are influencing our perceptions and actions, however, they re not objective?
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SPEAKER_00
Yes.
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SPEAKER_00
The funny thing is that concern actually was originally a very strong concern from progressives that these systems would be seeming stating things that seem to be true without there s people unhappy on both sides about how these machines work.
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SPEAKER_00
So there s a couple of things to mention here.
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One is I think that this is a genuine concern a real issue.
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The second is it s very hard to measure because the AI changes personality based on you.
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SPEAKER_00
So you give it a political test and you seem to be left-leaning and it ll want to make you happy and generally become more left-leaning as a result.
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SPEAKER_00
And for example, if you give a personality test in Korean to the AI, it gives you answers that are more like a Korean would give answers to the question than if you did it in English.
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SPEAKER_00
So it s very hard because it changes.
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SPEAKER_00
Now, one of the big sets of controversies has been about image generation.
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SPEAKER_00
Image generation is not a very good way to know what AIs do because the image generation systems actually use a very different technology and it s a gimmick.
3
SPEAKER_00
They re not actually how these large language models work.
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SPEAKER_00
And the problem with the image generation AI is there is a bias in the systems, which is that because they re trained on images, whatever they see the most of, they tend to produce.
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SPEAKER_00
So if you ask for a picture of an entrepreneur, you would get a white male entrepreneur all the time, like 100% of the time.
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SPEAKER_00
So the AI system is trying to make that a little bit less biased, then nudge it a little bit so that every so often it produces a more diverse entrepreneur.
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SPEAKER_00
Then what happened with Google is they turned that up to a very high level, not necessarily even on purpose.
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SPEAKER_00
And it started to produce only the extreme diversity.
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SPEAKER_00
And if you ask for a 1940s German soldier, you would get like a Hispanic soldier and a soldier and African soldier.
3
SPEAKER_00
And it was very strange.
3
SPEAKER_00
So part of this is these systems are trying to tune them to try and make them not embarrassing and not overly biased.
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SPEAKER_00
And they swing the needle too far one way or another.
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SPEAKER_00
I don t, but I think the overall picture that we re talking about here, which is about the issue of bias is a real one, but it s hard to know what you do about it, because these systems are really subtle.
3
SPEAKER_00
If you give them political tests, they actually tend to turn out slightly right of center in most tests, but it s very easy to get them to act.
3
SPEAKER_00
Like the guardrails are very, like another thing with Google that caused the problem was you asked it to compare Hitler and Mother Teresa, and it wouldn t take a stand on which was better.
3
SPEAKER_00
But it was very clearly they didn t think about this.
3
SPEAKER_00
They said, don t take sides on controversial issues.
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SPEAKER_00
And instead, it didn t take a side on even issues that should have been obvious.
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SPEAKER_00
So it s very hard to know about the bias, but I am worried about that.
3
SPEAKER_00
I think everyone should be.
3
SPEAKER_00
But I think it s also the systems, the people who are building these systems don t have as much control as we think about them.
3
SPEAKER_00
And I think that s part of the worries we have here.
3
SPEAKER_00
I don t know how you create it.
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SPEAKER_00
Because they re trained on all of human writing, that creates a bias in the system that s really hard to know what to do with, because it turns out that s
3
SPEAKER_00
what our writing is biased towards, at least in writing on the internet.
3
SPEAKER_00
So it s a very complicated question.
3
SPEAKER_01
Ethan, I want to switch to another area that you cover in the book.
3
SPEAKER_01
And these are your four principles of working with AI.
3
SPEAKER_01
And I m going to give them all and then ask you a couple of questions about them.
3
SPEAKER_01
The first one is always invite AI to the table.
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SPEAKER_01
The second is be the human in the loop.
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SPEAKER_01
Third, treat AI like a person.
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SPEAKER_01
And fourth, assume the worst.
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SPEAKER_01
Assume that the AI that you re using today is the worst AI that you will ever use.
3
SPEAKER_01
In light of your second principle, being the human in the loop, how do you see the role of human judgment evolving as AI becomes more integrated into the decision-making processes?
3
SPEAKER_01
This is really an intriguing one for me.
3
SPEAKER_00
Yeah, and I think it rides right into your bias issue before, which is if the AI seems like an omniscient advisor, but it actually has right-wing or left-wing or other kind of bias, would we even know?
3
SPEAKER_00
And I think that s a worry.
3
SPEAKER_00
All the early evidence is that the AI is a very good judge, but it has biases, that those biases are subtle in the way human biases are.
3
SPEAKER_00
I ll just pick one example of a study that showed
4
SPEAKER_01
that when you ask the AI to write letters of recommendation, it tends to, when it writes about women, it tends to talk about how warm they are, and for men it tends to talk about how confident they are.
4
SPEAKER_01
It s not an easy thing to spot, but once you do the other, you re like, oh, well, there s a bias.
4
SPEAKER_01
Another bias, we have a study coming out where we have the AI generate, pick what ideas are best out of an idea, and it s better than humans at picking the idea that ends up being best, any individual human.
4
SPEAKER_01
But it has the same bias humans do, which is it likes the first idea and the last idea better than ideas in the middle, which is a human bias.
4
SPEAKER_01
And it s weird the AI has the same thing.
4
SPEAKER_01
So I think these systems are very biased in ways that are both subtle and obvious, but they re also really good advisors because humans are also biased.
4
SPEAKER_01
So part of the question is, I would be taking AI advice with a grain of salt.
4
SPEAKER_01
Right now, the AI is not going to be as good as you, whatever your job is.
4
SPEAKER_01
But I have talked to physicists at Harvard who are like, all my best ideas come from talking to AI.
4
SPEAKER_01
There s a nice paper on business strategists that say the AI does as good a job as a business school professor in giving you strategic advice.
4
SPEAKER_01
And whether you think of business school professors, I am one, but it s a separate issue.
4
SPEAKER_01
But there is a lot of, it is a pretty good advisor.
4
SPEAKER_01
But I think in the longterm, I am worried about the exact same thing you are, John, which is
4
SPEAKER_01
then the subtle stuff starts to matter, right?
4
SPEAKER_01
If we give up more authority and control, if we re not in the loop, then I worry a lot about the kind of biases that AI has in making decisions.
4
SPEAKER_01
If you re still in the loop and it s one of the voices you re listening to, like another human mentor, then there s a lot more value in it.
4
SPEAKER_00
OK, I appreciate that perspective so much.
4
SPEAKER_00
And the other one that really intrigued me is your suggestion of treating AI like a person, but it s also defining its role clearly.
4
SPEAKER_00
And in my latest book, I had a whole chapter on the importance of setting boundaries.
4
SPEAKER_00
And to me, there needs to be boundaries that are set with how you re using AI to ensure it s acting within a desired scope that you set out.
4
SPEAKER_00
I wanted to ask you if you agree with that sentiment.
4
SPEAKER_01
Yeah.
4
SPEAKER_01
In fact, it brings me to a wider point that I think is good too, which is if you are a good manager, if you re a good manager of yourself or people, if you do a good job setting boundaries, explaining rules, telling people what you want, you re going to be really good at working with AI.
4
SPEAKER_01
People think of AI as a coding thing, but even though it s not a person, it s not thinking, it s not alive, it works like a person because it s trained on human knowledge.
4
SPEAKER_01
So the best users of AI are actually people who are good managers, who are good organizers and thinkers.
4
SPEAKER_01
Teachers tend to be really good with AI.
4
SPEAKER_01
So it s a very interesting, like it follows rules really well.
4
SPEAKER_01
So I love the idea of setting boundaries.
4
SPEAKER_01
I wouldn t be surprised if taking the principles from your book and applying them to AI wouldn t get the AI to do a good job for you.
4
SPEAKER_01
Because it tends to work on those kinds of psychological principles.
4
SPEAKER_01
There s a set of papers that show that if you offer the AI a tip, even though you can t tip it, it does better.
4
SPEAKER_01
There s a paper showing that, there s a study showing that it does worse in December than in May and produces lazier answers because it seems to know about winter break.
4
SPEAKER_01
Telling it to take a deep breath and think things through resulted in more accurate math answers, even though it can t take a deep breath or think things through that way.
4
SPEAKER_01
So treating it like a human gets you a large part of the way there.
4
SPEAKER_01
And then the question is role.
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SPEAKER_01
Telling it what kind of human to be helps a lot.
4
SPEAKER_01
You are a marketer and that gives the AI context in which to operate.
4
SPEAKER_01
So the AI sort of gives you a default sort of answer.
4
SPEAKER_01
And your job as somebody prompting the AI is to get to do something other than the default answer.
4
SPEAKER_01
And the way you do that is you tell it who it is.
4
SPEAKER_01
You re a marketer who focuses on providing SaaS sales to large corporations.
4
SPEAKER_01
it knows a lot about the world already.
4
SPEAKER_01
So you re going to get very different answers if you tell it it s a marketer focused on sass tales, than if you say it s your marketer focused on the youth market, than if you say you re a PR writer, than if you say you are a hip hop promoter.
4
SPEAKER_01
And that s going to vet ref to change the results.
4
SPEAKER_00
This leads me to what is really eye-opening to me, your principle that whatever AI you re using right now is going to be the worst AI you will ever use, given how powerful these things are already.
4
SPEAKER_00
One of the questions that I get all the time, especially from my younger audience who are concerned about where they should focus their education or their talents is,
4
SPEAKER_00
How do they possibly prepare for a future of work and their life where AI s capabilities are constantly evolving?
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And the second point to that would be, where would you recommend that if they were going to be focusing on areas to master, what areas would you recommend people focus on?
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So those are the questions.
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And they actually break down to another question, which we don t know the answer to, which is ultimately why I m not going to be at that.
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I ll give you my advice, but it may not be that satisfying, which is we don t know how good these systems are going to get, and we don t know how fast.
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When I even talk to people building these systems, there s division between will we be able to achieve superintelligence?
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Are we near the end of how good this run is?
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Will it be too expensive to build better models?
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We don t know the answer.
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So the most important question is how good, how fast, right?
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Is AGI, this artificial general intelligence achievable or not?
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When would that happen?
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If not, are we getting close to how good this technology can get?
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And we re about to plateau and nobody knows.
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So you have to kind of plan for a world where you don t know what s going to happen because I don t have answers to those and the people training the models don t have answers to those.
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So there isn t an instruction manual forthcoming.
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And we don t even know what the AIs are good at or bad at.
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I would say systems change much slower than jobs.
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Tasks and jobs might change more quickly.
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When we survey people after using AI, they always react in the same two ways.
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They re nervous about the future of their job, but they re happy.
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They re happy because they outsource the stuff they re least good at, they re most bored by, to the AI, which lets them focus on what they re good at, which is usually what they re passionate about, which is usually what they re better at the AI at.
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People often feel really good after using AI.
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I hate doing this kind of form or I hate writing this kind of email.
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The AI does it for me.
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It does my expense reports.
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That s an exciting world.
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So in some ways it s about leaning into what you are most passionate about and what you re best at and sharpening that skill on the idea that even if AIs get really good, you ll be in the top 1% of that skill.
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You re still going to beat them under lots of different scenarios.
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And even if AI gets as good as a human, you ll still be the person who can better tell what the AI s job is doing bad or good or at.
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So I think it s less about changing your entire career and more about leaning into this.
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Now, I do think that means if you want to be a marketing writer, you have to be a pretty good marketing writer to survive in this next open world.
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So I think if you like this and love it, you need to double down and become an expert on this.
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You need to double down and be sharpening your skills in that direction.
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I wouldn t change careers at this point.
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I think there are some things that are more risk than others.
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But I think if you re a really good photographer, there ll be room for you.
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If you re a mediocre photographer, there s more danger to your job.
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If you re a very good salesperson, you re going to beat the AI.
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If you re a mediocre salesperson, you may not be.
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But if you re a mediocre salesperson, you probably don t like sales that much.
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And then maybe you re okay to outsource that part of your job so you can focus on the part of it that s really passionate.
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So when people ask me what they should do, I think you should do what you re passionate about and care about, but you should double down on becoming an expert on that field.
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You don t need to be an expert in everything.
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Being an expert is something important and narrow.
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Yeah, thank you for that advice.
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And one of the companies that I like to highlight because I m an alumnus is Accenture.
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And I like the fact that they are looking at this to replace skill sets like data entry, or maybe going through some of the tax models.
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But instead of just getting rid of the employees, what they re trying to do is set a new baseline and to try to retrain them so that they re now using their human intellect to work on top of this foundational layer that AI can provide, which I think is a great approach for companies to follow.
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I love it.
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Lean into using this to remove drudgery and help us thrive as people.
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And companies that do that will succeed.
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And that is the model we have to follow.
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You think about it, it s the same thing as the industrial revolution with like hand stuff, like manual labor.
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Like you, what nobody really wants to be digging a ditch, right?
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Like it s more interesting to be somebody who could run a set of machines to do this stuff.
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And I think we have to think about the same thing.
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What mental, people are bored at work.
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Like, most people in surveys report being bored at least five or ten hours a week at work.
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Why do we have to accept that that s the way it s going to be?
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Similarly in education, you were talking about being bored from lectures.
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This gives us a chance to think about how we help humans thrive more, and I think that s going to be the key to success in the future.
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And Ethan, I d like to not give out your whole book.
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And one of my favorite portions of your book is one of the ones I think that readers need to read the most.
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And that s the sections where you go through AI as a person, a creative, a coworker, a tutor, a coach, et cetera.
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Because it s really fascinating to understand if you think about it, how these different roles are going to be fulfilled.
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You brought up Harvard earlier.
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I think one of my favorite conversations last year on the podcast was with Professor Bob Waldinger, who currently runs the Harvard Adult Study of Advanced Aging, which shows that human relationships are the key to happiness above everything else.
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When I think about AI as a person, creative co-worker, tutor, etc.,
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It s leading people to have a profound attachment and possibly even feeling married to their AI.
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What implications do you see for human relationships, which are so important for
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are feeling fulfillment?
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Yeah, I think that is a giant question.
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There s a bunch of studies coming out that s showing that talking to AI relieves loneliness for people, but they do form attachments, right?
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Right now, the people who are using AI in the early surveys report that the AI both helps them feel more socially connected, but also doesn t reduce their, sometimes increases their willingness to talk to other people.
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Because they get over some of their anxiety and other issues with the AI and then talk broadly.
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But we don t know the answer yet.
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And I do worry about AI becoming addictively interesting to talk to.
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For people who want to get their hands on AI, I think it s great to have a conversation, but I think it s important to do things.
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And the two things I think everybody should do is, like we talked about earlier,
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spending your 10 hours using a frontier model like GPT-4 and trying to do all your work with it and seeing what it s good or bad at.
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At first, it won t do very well, and then you ll learn how it works better.
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The other thing I think people should do is download a chatbot.
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There s one called Pi from Inflection, and it s a very advanced AI model.
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It s optimized for chitchat.
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It s free for right now.
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and talk to it, because I think you will be both impressed and unnerved by how good it is, right?
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It s genuinely interesting to talk to.
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It s interesting to you.
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It s smart.
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And that is the future we re heading towards.
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And I don t know what that means for human relations.
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I hope that there ll be some pushback or connections to other people still matter.
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Maybe this will free us up from our anxieties, our deeper, our darker parts.
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We have this conversation with the AI, and then we have better, deeper, meaningful conversations with other people.
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But I don t know.
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And I think everyone should experience it because it s worth seeing where things might be heading.
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Okay.
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And I have just a couple of questions left for you.
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So the Holy Grail is artificial superintelligence.
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Do you think that s possible?
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And if it is, what happens if it s created?
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Okay.
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So I am a long-term skeptic on this because I ve lived through many AI booms and busts, but I have gotten less skeptical over the last year.
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Right.
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And I think that s the general reason.
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Like if you look at surveys of computer scientists, there are many who think this is completely impossible and it s irresponsible to even talk about because it can never happen.
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But the computer scientist surveys have generally moved in the direction of people feeling it s more possible.
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The timeline to AGI, artificial general intelligence, the first stepstone to superintelligence, shrank by 14 years in a survey of computer scientists that s done every year, last year.
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So I don t know whether it s possible.
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I really don t.
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I think it s also important not to focus on it too much because it becomes something that either happens or doesn t, and we don t have control over that.
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And that makes us feel we don t have control over AI, but we do have control over how we use it today and in the near future.
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But if artificial superintelligence happens, then we have to hope we got everything right.
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We build a machine God that does things that are incomprehensible to us.
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And then it s a question of like, does it like us or not?
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And there s very scary scenarios and very positive scenarios, but we ve had 2 million years where Homo species have been the dominant intelligent creature on the planet.
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And that ends as soon as this happens.
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And we don t know what happens afterwards.
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It is worth noting that this is the explicit goal of many of the AI companies is to build this thing.
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So I don t know if it s possible or not, but I do know they re trying, and I don t know what the timeline would look like.
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But I also don t spend a lot of time worrying about that relative to the AIs that are here now, and they ll be here in the next year or two.
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How do we use those to make life better?
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OK, and this is just a fun question I just thought of.
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Would you be comfortable today flying on a commercial airline that had no human pilot and was completely flown using AI?
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I am completely comfortable in a AI-driven Tesla, but I also know the limitations of where you need a human to help out.
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If a road has a lot of construction and there s crazy drivers, I d take over by hand.
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Most of your planes are driven by autopilot right now, right?
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By the way, I just saw the most amazing passenger statistic, which is we now have the fatality rate on American airplanes has dropped to one person per light year.
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We ve had no fatal plane crash in 13 years without a fatal crash in America or something like that.
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I don t remember the exact number.
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And that s because AI has taken over, but is also working with humans to some extent.
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So I would not, like, I m in San Francisco, there are driverless cars driving around all over the place, but there s also human operators who the car can notify if there s a real problem, they can take over in a moment s notice.
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So with the state of AI right now, I actually wouldn t feel that unnerved flying on a plane that I know is being revived by AI, but I want a human pilot as a backup for all those issues that might come up that AI can t handle yet.
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Will that still be the same thing in two years?
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I don t know, but I would feel more comfortable with an AI mostly flying the plane for all the regular stuff and then the human there, well great for the Sully Sullenberg moment where something bad happens and where I don t trust the AI.
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Okay, and then lastly, your vision as you ve talked about today for AI is both optimistic and a little bit cautionary.
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As we navigate this new era, are there any principles or deadlines that you believe the greater society as a whole should adopt to ensure AI s gifts are harnessed for creating a better human future?
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OK, so I think that s a really great question.
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I think there s a few things.
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One is, I think we need to recognize it s our responsibility.
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I think we re just used to thinking about technology.
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It happened with social media.
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Social media, we never took responsibility as people for what social media was doing.
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People post nasty stuff, and we don t condemn them for doing it.
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We never made social media a nice place, and we let the companies be in charge of it.
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And I think that s a mistake.
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I think we have a destiny over what AI is.
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We don t want to do something.
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We should be agitating for a law that should never replace doctors or whatever else you want.
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And you get to use it in a way that s ethical or not.
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I ve spoken to companies that as soon as they get AI in and improves performance for their employees, they fire employees until the performance level drops right back to where it was before.
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I ve talked to other companies that are like, this is great.
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Now our employees can do 10 times as much work and more interesting work because they have this assistant that does the boring stuff.
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We get to make these choices at an individual level.
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We have agency.
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That s the main message I want to communicate is that we have control over this and we shouldn t pretend we don t and we shouldn t give up the way we did in social media and then just say the companies will handle this all.
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You don t want that happening for all the reasons we talked about bias and about decision making.
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So I think that is the main message I want to have is that you need to start taking charge of your own destiny right now and you can use AI personally right now to make a difference in your life if it s positive or negative.
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And ignoring it is the worst thing you could do, because it s not going away.
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There s a lot of people who I talk to who are like, oh, the lawsuit by the New York Times will shut down, it s not going to make a difference.
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That regulation will step in and shut down, it s not going to happen.
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That this particular paper I read says that AI development is going to stop this year, that s probably not going to happen.
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We have to treat this as a real thing that s occurring and take control over it.
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So what I m hearing from that is something that I talk about a lot on the show, which is the power of choice.
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And we do have a choice and the choice right now should be to start experimenting with it.
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So you understand what its capabilities are and get used to it being a part of your life going forward.
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So I think that s a wonderful advice.
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So Ethan, if someone is interested in learning more about you, what you re teaching, et cetera, your book, where s the best place for them to go?
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So I publish almost weekly kind of AI, try and explain what s happening in AI at a website called One Useful Thing.
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And or you can go to more useful things where I put all the AI prompts we have linked to the book.
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I have videos on YouTube explaining how prompting works and how to use AI.
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Order the book.
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It s Cointelligence.
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I m very excited about it.
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It s coming out soon.
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But also the One Useful Thing is free and you can read a lot of the articles and updates and information.
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I m an academic, so it s nice.
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I don t have to.
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I put out a lot of free information to the world, lots of papers.
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So that would be a starting point you could look at.
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Make sure audience you check out Ethan s sub-sack and thank you so much for being here.
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It was such a fascinating conversation and congratulations on the launch of this incredible book.
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Thank you for having me.
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These were great questions and I think important ones everyone should be asking.
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What an incredible honor that was to interview Ethan Mollick.
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And I wanted to thank Ethan and Penguin Random House for the honor and privilege of joining us on today s episode links to all things.
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Ethan will be in the show notes at passionstruck.com.
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Please use our website links.
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If you purchase any of the books from the guests that we feature here on the show videos are on YouTube at both our main channel, John R miles, and also our clips channel at passion struck clips.
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Also, if you want to tune in to our newsletter, you can go to passionstruck.com and sign up for Live Intentionally.
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On the next episode of Passion Struck, I interview Dr. Jeff Karp, a luminary in the realm of bio-inspired engineering, who s a distinguished professor at Harvard Medical School, as well as MIT.
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His journey as a curious child grappling with learning deficiencies and ADHD
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to a titan in biotech innovation is a testament to the transformative power of being lit, a state of heightened awareness and engagement.
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In our interview, Dr. Karp discusses how to tap into the secrets of this dynamic state.
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Activation energy is something that I learned about a long time ago in a chemistry class, and it just really jumped out to me.
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You put, let s say, two molecules in a beaker of water and they don t react, nothing s really happening.
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And then you add some heat
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to it and the molecules start moving around a little bit more, not really bombarding and reacting.
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Then you add more heat.
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Now they re really moving at a little bit more heat.
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And now they interact, they bombard, they hit each other, and then a reaction takes place.
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So the amount of heat that you add to the system is the activation energy, the amount of energy that you ve put into the system.
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And that jumped out to me because I was like, wow, this really applies to everything in my life, all the things I want to do.
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Remember that we rise by lifting others, so share the show with those that you love and care about.
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In the meantime, do your best to apply what you hear on the show so that you can live what you listen.
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And until next time, go out there and become PassionStruck.