476 What Happens When AI Starts Doing the Thinking? With Eric So

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Welcome to Partnering Leadership, a top global leadership podcast for purpose-driven leaders with a growth mindset.
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Seeking to learn from the leadership journey of change makers and business insights from leading global thinkers.
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For additional leadership insights and bonus content, visit us at partneringleadership.com.
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Now here's your host, Mahan Tavakouli.
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Eric, so welcome to Partnering Leadership.
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I am thrilled to have you in this conversation with me.
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Thanks so much, Ron.
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I really appreciate you having me.
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Can't wait to talk about the collision, what AI does to us, Eric.
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You were recommended by Senge Paul Chowdhury, episode 422, one of the books and episodes I most find myself recommending.
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And I've no doubt the conversation with you will become one of those as well.
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Before we get to that though, would love to know a little bit more about you, Eric.
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Whereabouts did you grow up and how did your upbringing help contribute to who you've become?
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I grew up in Maryland, not too far from where you are now.
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And I grew up the child of two Chinese immigrants.
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It shapes a lot of my outlook on life because so much of what they emphasized to me growing up was the importance of hard work, of grit, of persevering through problems.
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And that has shaped a lot of where come as a person.
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I certainly worked very hard to put myself through grad school.
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I received my PhD from Stanford and have now spent, this is actually my 15th year as a professor at MIT.
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And the reason why I mentioned that in conjunction with my parents is as I've sat at the forefront of AI, at least from an educational standpoint, I've really seen how much it has changed the way that we work and how many young people are learning these days, or perhaps substituting learning.
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And it's really this threat to a lot of the incentives for hard work and how people are operating that gave me some inspiration for writing this book, wanting to really understand how AI is changing the way that we think about the way that we process information, what we remember, and how we see the world.
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That kind of led me down this journey of where I am today.
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I wonder whether there was a trigger point or what you saw that made you say, hold on a minute, this is impacting us more than many of us expect or think.
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Probably wasn't a single moment, but it was a collection of moments clustered together.
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One is that early on, after the arrival of ChatGPT, took an assignment that I would typically give to my grad students at MIT to work on in groups.
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And they would typically spend six to eight hours working through this problem set.
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And what I realized late in 2022 or early 23 was that I could instantly upload the homework and I could get not just a good answer, but really a perfect answer, one that our students would be very proud of in the span of a couple minutes.
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And this was a big aha moment to me of how revolutionary the technology was even at that time and how much it was going to change education.
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But I also noticed in my own habits, I started out the book talking about my own experience where I found myself instinctively reaching for AI in times perhaps when I shouldn't have.
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And I really sat back and thought about what I had just done and how I had removed myself from the process and began to really wonder what that meant for my own thinking and my own skill development and authenticity.
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And I had a series of these moments where I caught myself being too tempted to use the technology, even as someone who's cognizant of what it may be doing to us.
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And then I really started to notice it everywhere.
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I would see it in my students.
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Many of my students would skip the prep work that's involved with digging really into a case or a problem set instead of outsourcing it to AI.
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One student in particular, I remember it's really burned in my mind.
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I asked my class, tell me your thoughts on how technology is changing our thinking.
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And before I could even get the question fully out to the class, a student raised her hand and said, I asked Chat GPT, and here's the answer.
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So here was a question about technology and our thinking being answered by technology doing our thinking.
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And it was really the collection of efforts that began to really strike me as one of the most important issues of our time in this AI landscape.
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And as you alluded to, one that's getting overlooked.
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It's not just what AI can do for us, it's really we should be asking what AI does to us.
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And that's the focus of the book.
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So here's what I wonder, Eric, you are not that old.
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However, is this an old guy saying, oh, we used to do it a certain way?
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Now people are losing their capabilities.
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So, for example, my dad was wonderful with math.
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He could sort out all kinds of things in his mind.
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He was a little frustrated that his sons were good at math, but not that good and sometimes needed to use tools or calculators.
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And then when I see my girls, they have zero capability of working out math in their heads, but even the SATs that they have to take, they can use their calculators.
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So is this sort of the next version of having a calculator?
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What is wrong with us not knowing how to do math in our heads?
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So, what do you see as being wrong with this?
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I would take it in two parts.
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The first thing I would say is that there's a lot of past technologies like calculators that have undermated certain parts of our thinking.
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I think the critical difference of AI in this era is that AI offers to do the complete chain of thinking.
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So there's a famous quote that calculators can only calculate, they cannot do math, in the sense that calculators can do simple arithmetic, but they don't really choose the problems to be solved.
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They don't really understand the broader context, they don't think about how to apply it.
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With the rise of AI, in particular agentic AI, these systems are increasingly offering to remove us entirely from the thinking process.
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And I think that really poses a threat to young developing minds in the sense that I think people of a certain vintage, so I'm 46.
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I certainly remember struggling through problems growing up, and that's really a big part of how I develop my mastery.
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I suspect the same as you and many of your listeners.
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The thing I really worry about is the people who are developing some foundational mastery or attempting to are outsourcing that struggle component that was so critical for our development to an AI system under the belief that the output is sufficient evidence of mastery.
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And that's really not the case.
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I couldn't agree with you more, Eric.
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A lot of times I find myself writing and in struggling with trying to convey a concept and write about it.
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I end up understanding the ideas much better myself.
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So that struggle is really important.
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But I still want to understand what the issue is with this.
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In that couple of months ago, Eric, my family and I were visiting Orlando, and now they have Waymo's running all around.
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We took a Waymo, it was fantastic.
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So I could visualize a day when people don't need to drive.
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Maybe some people will drive.
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And that automated system that doesn't allow humans to think, at least based on current safety tests, is a lot better than the systems where humans are thinking and acting.
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So again, why should we protect that thinking, that struggle, that acting on the human side when the machine, in this case, can do it so much better?
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I would say a couple things.
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One is that there are many parts of work that are not directly separable from the work that you can produce with AI.
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So, for example, I have many students who will generate a really polished presentation with AI.
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But then when it comes to the final portion of that deliverable, that is standing up and defending it and having discussions with people, they really struggle through it because they've skipped the sort of the foundational component.
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And I think that reflects many parts of work where you need to make sure you understand what you're talking about or what you've developed in order to really complete your side of the business transaction.
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And in the absence of that initial struggle of really thinking through what goes onto the paper and how it's formulated, I think you lack that ability.
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These are what economists often refer to as sort of strong bundles.
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You can't really separate the output from the knowledge necessary to defend it if you want to have value.
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I think that it speaks to this really important point that in the absence of being able to think independently, to really stand outside the AI, it really begs the question on a personal level: why would I pay you the salary that you hope to command?
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In the sense that I can hand off all of these tasks to an AI system.
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So from a personal level, I think it's critically important to develop that mastery as a means to have something that you can contribute.
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It's a big reason why you're in the room and why people pay you the salaries that they do.
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I wonder, Eric, are universities well suited to develop that mastery?
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I agree with you in that when I use AI and I use it sometimes too much to your point.
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Sometimes I catch myself outsourcing thoughts before I should.
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That said, I can gauge the output, the response based on my experience, based on my judgment, which has been developed over a series of years, experiences, and time.
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Part of what I hear from you is that younger people also need to struggle with ideas to develop that judgment in order to be able to add value to the output that AI gives.
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But I don't know if our education system is well suited to help them become capable in doing that.
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I think that AI poses a huge threat to the existing models of education on many fronts.
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And I think it really poses an important challenge that education adopts around AI.
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There's no doubt that future leaders will require people to use AI, push people, and it'll be part of our work.
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And so it's not a matter of yes, no, but I think it's really a matter of training people to use AI well.
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As I talk about in the book, AI isn't inherently good or bad.
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It really depends on how you use it.
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And I would go a step further to say it really is, it matters a lot about how we interface with the technology.
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Meaning a lot of choices that we make, both in terms of querying these systems, but also how we see the information that is presented, really can change whether we grow through using AI versus weaken our abilities.
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The additional thing I would say is that there's a lot of argument in favor of giving people real-world problems to solve with AI and then using the educational sphere to help inform the right approaches, but also to bring people together to think about those problems like jointly.
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And I think that's really where education can help in assembling people with a common problem, an idea, give them a training ground for developing this expertise.
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So I think education can help quite a bit with that, but I think you're totally right.
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Then it's current formulation, the way that you and I perhaps were grew up in the standard education system probably is not very well suited because it's so based on the deliverable that now you can hand off to AI.
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And it is a significant issue, Eric.
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Part of what I see is when you add AI with the fact that now through pendants, glasses, earbuds, it can take in all the information in our world and therefore give us recommendations and then we can act.
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We can to a certain extent become just the transmitters of the AI information that is coming into us if we don't take ownership over our own thinking.
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And one of the points that you make is about AI gravity.
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So the concept of AI gravity is really this idea that AI really pulls at people and many aspects of the way that we are as humans and our economic systems that kind of nudge us towards more and more AI use.
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So, what are some of these components?
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One is simple a biological reality that thinking is like it turns out to be like an incredibly expensive biological habit in the sense that our brains are this paradox of biology.
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They represent only 2% of our body weight, but consume over 20% of our calories, even when we're resting.
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And when you put people into these high stress situations where it's really intellectually demanding, consumes a lot of those calories that our body and our evolution has trained us to try to conserve.
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So it's really no surprise that people feel this urge to have AI do their thinking for them.
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It's hardwired as a temptation into our thinking.
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The same thing is true of many economic systems.
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If you look at the way society operates, much of how we're compensated is not on the basis of necessarily the level of where we sit, but rather a comparison.
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How do we stack up against other people?
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And so when you put people into a situation where I don't really know if you're using AI or not, but I worry that you're using it to produce more, to get ahead, to generate more reports, it really creates this arms race, this concern that if I don't use it, I'll fall behind.
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And it puts further pressure on me to want to use these tools and outsource even more of my thinking.
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And then you combine that with the fact that detection is really difficult with AI systems in the sense it's very hard for me to figure out if you're using those glasses or those pendants, unless I really kind of press further.
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And I think when you think about the situation that people are put in, where it's a strong biological temptation, there's huge economic rewards in terms of productivity that can be accomplished if you use it well.
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You are not really penalized or detected if you use it.
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And it just creates this ecosystem in which it's very hard to resist.
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And so one of my key messages in the pull-up is that not enough to say to people, just go think for yourself.
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I think it really doesn't acknowledge or give credit to the fact that there are many societal systems that are putting emphasis on people to use it.
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And that's really where the book tries to urge people to think about system design as a means to address some of these issues.
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It's not about wagging your finger or having a stern lecture with young people in your life.
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It's about recognizing the situation they're in and helping them to develop strategies where they can use AI, as is going to be increasingly expected, but to use so in a way that preserves what makes us valuable.
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So I would love to touch on that systems design a little bit more, Eric.
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Because on one end, I have been encouraging, and a lot of my guests talk about how to get people in the organization to actually embrace AI and use AI more.
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You talk about competitive ratchet effect.
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So there are organizational pressures, there are pressures from everyone around us.
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But you are saying that we need to think about systems design to make sure it's done well.
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I think I would say a couple pieces to it.
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One is that I think any reward system that incentivizes that sort of measures people based on how much they use AI is destined to create this problem.
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That basically you just outsource more of your thinking to AI.
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It's a simple tenet of life.
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The whole concept of economics in a nutshell is people respond to incentives.
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And so when you give people incentives to use AI more, naturally that emerges.
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What's the counter proposal?
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The counter proposal is to measure and reward people for the things that you really care about that they can accomplish with AI, but that place priority on what makes you valuable.
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So for example, if I create an incentive system where it's just the number of presentations you can generate, you just upload these to AI, it could generate a hundred different presentations in any given day.
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But if you create incentive systems where it's not just about the presentation, but it's effectiveness of the delivery, the ability to generate sales or convert those to actual revenues, then I think it forces people to deal with this reckoning where just having AI generate these things for you is not what's going to get you ahead because you're going to miss out on the other pieces outside of the presentations, which are really valuable.
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And I would also add to this that I think what I've noticed is that the people use AI often to outsource the things that really make them valuable.
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So in the book, I talk about the fact that it's useful to really think carefully about what are the things that define your value as a thinker.
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I call them signature thinking skills.
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So maybe it's a lawyer's like rhetorical ability or sort of a physician's diagnostic eye.
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And to really be thinking about designing your work with AI in a way that preserves those signature thinking skills.
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I'm all in favor of using AI for the parts of my work, which may be rote or like unimportant for the purposes of doing my signature work, but not to outsource everything.
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And it really makes a difference in terms of which things you decide to outsource to AI.
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So part of what I encourage people to do is to engage in an exercise where they think about what are the things that they want to have as signature thinking skills.
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And to me, if you work backwards, it writes the rules for how you should be using AI.
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Because if you're using AI to do those tasks that are like really that cultivate signature skills, then you're really just risking and losing those things.
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And I think it clearly poses a threat to our value.
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You draw an important distinction between what we can accomplish with AI and what we remain capable of doing without it.
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So what are the types of signature thinking skills that we should focus in on?
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I think it varies a lot by profession.
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So, in the sense that myself as a professor, one of the things that I really value in myself is in my ability to understand the connections between different streams of research and how they fit together, the sort of the pieces they tell us, and you know, but also the gaps between them.
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But if I were to have AI synthesize all those papers.
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So like I go to like notebook LM and have it generate a podcast, it's really removing myself from that process of kind of investigating or doing the deep synthesis.
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And like many of my colleagues have reported, they found that has weakened their ability to do that critical work, the thing, identifying the right questions, pulling the pieces together to be able to articulate that to other people.
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And so that's an example from my own work.
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But you can imagine that for many professions, there are critical things that are important and things that are not.
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For example, in my work, I think it's really important to be able to figure out what are the right questions to ask, and how do you design, for example, an experiment to test that.
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But what's less important to me are things that, like maybe searching and retrieval, like kind of finding papers that might be relevant.
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I think I have no problem outsourcing those things to AI.
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The same thing is true for like certain coding tasks.
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It's really important that I know what I want to code, how to evaluate the output and how to structure tests.
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But that's different than the simple mechanics of sitting down to actually write out that computer code.
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I don't think of that as being very critical to who I am.
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Now, a software developer may feel differently, but that's what I mean by it's very situational or occupation specific.
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It takes a lot of intentionality to do what you're talking about, Eric.
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And I use the simple example of you said you grew up in Silver Spring, pretty close to where I live.
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I grew up in the DC area in Maryland, Montgomery County, and how I use Waze quite often.
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A few times I didn't use Waze, I got stuck in traffic jams.
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So, in essence, I learned from the AI that I better follow the AI.
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Now I follow the AI a lot, to the point that if I don't pay attention, sometimes I drive my daughter for volleyball to new areas.
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If you ask me to drive there without ways, I wouldn't be able to.
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Without being focused on what you're talking about, it's pretty easy for us to defer all of the decision making to the AI rather than the struggle of the thinking.
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Yeah, I think that's right.
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I think that AI presents us with a trade, and like any other trade.
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The sort of the appropriateness of that trade depends entirely on what you're giving up and what you're receiving.
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I think with GPS, I think it offers a lot of convenience.
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There's an argument to be made, it offers a great deal in terms of safety.
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And so I understand why people make this trade-off and why there may be less of a concern.
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I think, though, in the context of our intellectual work, I think it's just too critical to offload and to risk-threatening.
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It's too important to our functioning and society and who we are and our value that it doesn't present a palatable trait to me.
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And you're absolutely right that you have to be intentional about it simply because otherwise it invites a lot of short-term thinking.
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I just offload something to AI, and in the short term, it does work.
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But cumulatively, those hacks really add up to threaten to who we are.
00:22:51.920 --> 00:22:57.759
And oftentimes people don't realize it until they're in a moment where suddenly they can't access the technology.
00:22:57.839 --> 00:23:07.119
They don't have access to the AI chat bug, their AI pendants or glasses are not working, and where they really feel the pinch of having not gone through the struggle.
00:23:07.519 --> 00:23:11.359
As you also say, it's hard.
00:23:11.519 --> 00:23:16.480
It goes against some of our human instincts and human desires.
00:23:16.799 --> 00:23:21.599
Yeah, in the book, I talk about different rules or principles for thriving in this era.
00:23:22.079 --> 00:23:32.480
One of them, the first, is to value the struggle in the sense that, as we alluded to earlier, struggle in our society is often treated as a problem to be solved.
00:23:32.559 --> 00:23:35.200
It's often the basis of products that we are sold.
00:23:35.359 --> 00:23:37.920
But the reality is the struggle is formative.
00:23:38.079 --> 00:23:41.359
You may have heard the saying, there is no learning without struggle.
00:23:41.519 --> 00:23:46.079
And I think it's in part requires a reframe of how we think about struggle.
00:23:46.240 --> 00:23:49.759
And if you think, if you actually look back in history, there's precedent for this.
00:23:49.920 --> 00:23:59.599
So post-industrial revolution, our society had to be reformulated in the sense that you no longer needed to be physically active to be economically productive.
00:23:59.759 --> 00:24:04.559
And the implication of that is that many people saw their physical bodies atrophy.
00:24:04.880 --> 00:24:11.599
And what this required was an intentionality to develop physical exercise as something you do deliberately.
00:24:11.839 --> 00:24:17.599
It's crazy to think about in modern society, but there used to be a period where people did not exercise on purpose.
00:24:17.759 --> 00:24:19.200
It was something they were paid to do.
00:24:19.359 --> 00:24:20.799
But we understand this now.
00:24:20.960 --> 00:24:26.319
We go to the gym, we take the stairs, we feel the burn, we realize we're doing something good for ourselves.
00:24:26.480 --> 00:24:31.039
And I think that's part of the reframe that this technological shift is asking of us.
00:24:31.440 --> 00:24:46.640
Now, one of the things that you mention that counters the way a lot of people talk about AI, Eric, is that typically we've been talking about great human plus great AI.
00:24:47.039 --> 00:24:51.680
So that augmentation means much better performance.
00:24:52.000 --> 00:24:54.640
But you call that dream team fallacy.
00:24:55.119 --> 00:25:04.160
Yeah, the dream team fallacy conjures images that if I bring elite people and elite technology and I put them together and I get incredible results.
00:25:04.400 --> 00:25:06.960
But the data often tells us something different.
00:25:07.200 --> 00:25:15.359
And the reason is that technology and our human brains, they're not additive in the way that you might hope, or even multiplicative.
00:25:15.599 --> 00:25:17.039
They're really kind of reactions.
00:25:17.200 --> 00:25:25.759
And there's so many evolutionarily wired aspects of our thinking that makes us often worse off when using the technology.
00:25:25.920 --> 00:25:27.039
So what's an example?
00:25:27.279 --> 00:25:33.759
A simple example is that when you work with an AI system, they often provide you with a recommendation.
00:25:34.000 --> 00:25:45.599
But because our brains are wired to engage in shortcuts, we often anchor on the first thing that the AI presents us without critically evaluating whether it's true.
00:25:45.920 --> 00:25:55.119
And what that often does is it removes the value of me being involved in the first place, if I simply have, if I just latch onto the first thing the AI says.
00:25:55.359 --> 00:26:00.079
Additionally, there is a tendency of us in becoming complacent.
00:26:00.160 --> 00:26:08.319
Sometimes they call this complacency bias, is that if I work with an AI system that gets good enough, I stop bothering to check if it's right at all.
00:26:08.480 --> 00:26:11.200
And so I remove my thinking from the process.
00:26:11.359 --> 00:26:14.799
And so you're not really getting this like elite human anymore.
00:26:14.880 --> 00:26:18.960
You're getting the distracted or thoughtless human combined with the AI.
00:26:19.119 --> 00:26:23.039
And that removes the value of having a person there altogether.
00:26:23.279 --> 00:26:36.240
And so one of the striking conclusions of my research and sort of this broader field is that contrary to a lot of people's intuition, actually embedding simple things like friction in your interface with the AI can help a lot.
00:26:36.400 --> 00:26:37.599
And so, what do I mean by that?
00:26:37.759 --> 00:26:42.400
Is that oftentimes the models, if you ask them a question, will immediately give you an answer.
00:26:42.480 --> 00:26:44.799
And the human brain naturally anchors on that.
00:26:44.880 --> 00:27:04.400
But instead, if the model either pauses for you to think, it forces you to think first, or if it prompts you with additional questions that can help you really get to the substance of the issue, not only do you get better outcomes, but you get retention in the sense that because I struggled through it, I'm much more likely to remember it afterwards.
00:27:04.640 --> 00:27:09.759
And so that's not what we think of typically in terms of a dream team, slapping the pieces together.
00:27:09.920 --> 00:27:21.039
But it turns out the evidence shows us we have to be very thoughtful as educators, as parents, as leaders in designing interfaces that are going to bring out the best of both the technology and the human.
00:27:21.359 --> 00:27:25.200
Eric, I've now become more hopeful as I have listened to you.
00:27:25.359 --> 00:27:34.960
I had read the book, and I'm now becoming more hopeful, both thinking about the analogy for the exercise that you mentioned.
00:27:35.119 --> 00:27:40.160
And my undergraduate degree, actually, before going to business school, was in human nutrition.
00:27:40.240 --> 00:27:43.759
And then I had done some graduate research in human nutrition as well.
00:27:44.079 --> 00:27:54.160
Our human biology is suited to want fat, salt, and sugar, which is why we crave it in the environment.
00:27:54.480 --> 00:28:10.079
However, as we have become more aware of the impact of it, to a certain extent, we exercise control over how much fat, sugar, and salt we consume trying to be healthy.
00:28:10.319 --> 00:28:36.319
So it's by being aware of what our tendencies are, what to a great extent some of these providers benefit from, which is engaging us on an ongoing basis, then we can add friction and we can do things in a way that benefits us rather than benefits them or detracts from our capabilities and abilities.
00:28:36.720 --> 00:28:38.000
Yeah, I completely agree.
00:28:38.240 --> 00:28:58.319
On the point about usage, one of the things I really worry about and talk about in the book is this sort of downward spiral that I sometimes see where someone begins to use AI, and slowly, because they're using AI, they're not practicing that particular task, and they lose fluency or they lose proficiency with that task.
00:28:58.559 --> 00:29:07.680
And as a result, we become less comfortable doing the task without AI, which in turn makes us reach for AI even more, and then just kind of spirals.
00:29:08.000 --> 00:29:21.920
And I've seen in presenting this book at various audiences, I've had so many people, both business leaders, but also parents, come to me and just say, I've seen AI take a toll and where people can no longer think without it.
00:29:22.000 --> 00:29:24.079
They just become dependent on it.
00:29:24.319 --> 00:29:32.000
And I think it's in part the book is trying to raise awareness of this sort of potential downward spot if we don't step in.
00:29:32.319 --> 00:29:42.799
It also raises a point that you alluded to, which is that oftentimes in the moment, we're poor judges for what we really understand and what we own.
00:29:42.960 --> 00:29:46.400
There's this notion of processing fluency that I talk about in the book.
00:29:46.640 --> 00:29:52.960
You see someone deliver this polished lecture, or you watch a YouTube video and suddenly you feel like you understand it.
00:29:53.200 --> 00:30:01.519
But when you actually task those same people with trying to explain it or answer questions about this struggle, AI presents us with something very similar.
00:30:01.680 --> 00:30:07.039
We feel as though we understand it because AI explained it very polished in a polished way to us.
00:30:07.200 --> 00:30:14.799
But when you look at the data, it shows us that oftentimes we mistake the ease of us ingesting it with our real command in it.
00:30:14.960 --> 00:30:18.160
And that's one of the things I'm trying to raise awareness of as well.
00:30:18.480 --> 00:30:21.359
You do an outstanding job of that, Eric.
00:30:21.519 --> 00:30:27.680
As a fun aside, uh a couple of weeks ago, I got an email as a podcaster.
00:30:27.759 --> 00:30:43.519
You get pitched all kinds of things, but I got pitched on an AI tool which listens in real time to the podcast conversation and gives you questions based on what the guest said.
00:30:43.759 --> 00:30:47.200
I can just step aside and the AI interview you.
00:30:47.440 --> 00:30:51.119
Yeah, I think that's really what I'm worried about broadly.
00:30:51.359 --> 00:30:54.640
And it's not, I want to put it just about capabilities.
00:30:54.880 --> 00:31:05.039
Part of what I talk about in the book is how AI is changing our sort of view of the world, our understanding of what is truth, and our ability to relate to others.
00:31:05.200 --> 00:31:12.000
So there's a lot of research, just fascinating research about how AI changes our interactions with other humans.
00:31:12.160 --> 00:31:22.880
One of the central tendencies of AI, because it is trained to get your approval, those little thumbs up buttons, like when you work with ChatGPT, it really wants to make you happy.
00:31:23.039 --> 00:31:26.319
And so that means that the models are programmed to be sycophantic.
00:31:26.559 --> 00:31:30.480
More likely than not, if you suggest an idea, it's going to tell you it's a great idea.
00:31:30.799 --> 00:31:38.400
But this kind of creates this new type of echo chamber, a personalized one that I think takes what we've seen with social media to another level.
00:31:38.559 --> 00:31:44.240
And it surrounds ourselves with an AI system that just naturally says that everything we do is great.
00:31:44.480 --> 00:31:55.200
And where I've seen this really come into play is imagine you have two business partners or spouses, and they are in some sort of disagreement, and they both go to their AIs and say, Here's my position.
00:31:55.279 --> 00:31:58.000
And the models say, Oh, I can't believe that other person.
00:31:58.079 --> 00:31:59.279
They're completely wrong.
00:31:59.519 --> 00:32:04.720
And what happens is they come back to the table, even more entrenched in their original thinking.
00:32:04.880 --> 00:32:15.759
And so it's not only that it can send us into sort of this like more of an echo chamber, it creates divides between other people in the sense that I think I'm right, I think you're wrong.
00:32:15.920 --> 00:32:19.920
But I also just mentally really like that AI is always kind to me.
00:32:20.000 --> 00:32:21.200
It says nice things to me.
00:32:21.359 --> 00:32:25.920
So I don't like talking to the human who disagrees or tells me my ideas are bad.
00:32:26.240 --> 00:32:27.200
That's true.
00:32:27.359 --> 00:32:49.359
And if you look at sites like Character AI, Replica, which is much more romantic relationships, and just see some of the testimonials people have of the relationships they've established with AI, you can see why, to a certain extent, we then lose the capability to deal with another human where there is friction.
00:32:49.599 --> 00:32:59.119
It doesn't matter how happily married you are, there's always friction in that no matter how effectively the team works with each other.
00:32:59.359 --> 00:33:05.200
So there are capabilities we need to maintain to be able to interact with other humans.
00:33:05.519 --> 00:33:06.640
Yeah, absolutely.
00:33:06.960 --> 00:33:19.440
You may have seen in headlines recently that China is putting in policies in place to crack down on AI romantic partners in part because they're so worried about this problem that they're concerned about population growth.
00:33:19.759 --> 00:33:30.640
And I think it's a simple reflection of the fact that AI is so tempting to us, not only to outsource our thinking, but to use it as a means to fill our need for social engagement.
00:33:30.880 --> 00:33:37.200
I talk about this in the book, but there's all this fascinating research about what goes on in our brain when we interact with these systems.
00:33:37.440 --> 00:33:57.839
And it turns out that flattery that we get from AI, even if we are very rational about it and we know that it's a machine and it's flattering us, it just tends to trigger this chemical reaction in our brain in a way that is almost unmistakable or undistinguishable from real human interaction with the where we get genuine flattery.
00:33:58.000 --> 00:34:01.119
And I think this kind of invites us to keep coming back for more.
00:34:01.200 --> 00:34:07.200
It only makes us more addicted, and then we turn to it not just for personal things, but intellectual things.
00:34:07.440 --> 00:34:12.639
And this is really, I think, where people can become just taken over by AI if they're not careful.
00:34:12.960 --> 00:34:20.719
It's hilarious because ChatGPT compliments me when I ask it to disagree with me.
00:34:20.960 --> 00:34:22.800
So you're absolutely right.
00:34:23.039 --> 00:34:28.159
Where the way the interactions work, on one end, you laugh at it.
00:34:28.320 --> 00:34:31.760
On the other end, as you just made the point, we like that.
00:34:31.920 --> 00:34:33.599
As humans, we like that.
00:34:33.920 --> 00:34:36.239
So there is something for us to be aware of.
00:34:36.400 --> 00:34:42.400
But what I wonder, Eric, is you also used AI to write this book, and you mentioned that.
00:34:42.639 --> 00:34:52.320
So how do you recommend for people to approach both what they use AI for, also how they use it?
00:34:52.639 --> 00:34:57.360
Yeah, in the book, I talk about four sort of general rules or principles.
00:34:57.599 --> 00:35:01.280
One is to value the struggle, which is a mentality shift that we talked about.
00:35:01.599 --> 00:35:05.519
The second is to value who you are without AI.
00:35:05.599 --> 00:35:18.079
And what that means is recognizing that many important life moments happen outside of the digital sphere, and that we need to preserve those capabilities in order to function when the technology fails.
00:35:18.320 --> 00:35:23.039
A third, which speaks more to your point, is to reinvest your cognitive surplus.
00:35:23.280 --> 00:35:31.599
So what oftentimes I see people do is they'll use AI as simply a means to do the same things, but just a little bit faster or without thinking.
00:35:31.840 --> 00:35:40.000
But if you look back in history, the most transformative technologies were not about automating processes or doing things faster.
00:35:40.239 --> 00:35:42.880
They were for unlocking new human capabilities.
00:35:42.960 --> 00:35:49.199
Like you think about the airplane or the microscope, these things allowed us to chart new territory and thinking.
00:35:49.360 --> 00:35:51.840
And that's part of what I advocate with AI.
00:35:52.000 --> 00:36:01.920
The time that AI saves you for rote tasks is to use it to continue to grow intellectually, to challenge your thinking and to push yourself further to take on new tasks.
00:36:02.239 --> 00:36:06.079
The fourth rule is to make AI your cognitive trainer.
00:36:06.480 --> 00:36:07.679
What does that really mean?
00:36:07.920 --> 00:36:13.599
It means that AI is this incredible tool for learning if you use it properly.
00:36:14.079 --> 00:36:23.280
In the sense that AI can be personalized, it can be a useful tutor, but it requires the discipline to not use it as a simple answering machine.
00:36:23.679 --> 00:36:29.039
There's this long-standing notion in education research called the two sigma problem.
00:36:29.280 --> 00:36:43.199
And what that refers to is this very persistent gap where personalized tutoring often gives people educational outcomes that are two standard deviations better than the bulk class where everyone gets the same material.
00:36:43.360 --> 00:36:48.559
So if you have a really dedicated tutor, they can meet you where you are, you're just far ahead of others.
00:36:48.719 --> 00:37:07.039
And what research shows is that when you use AI with the right scaffolding, where it's a tutor, not an answering machine, it can meet you where you are, it allows people to achieve these remarkable gains in learning, in educational productivity, but it requires the discipline to not just take the easy way out.
00:37:07.199 --> 00:37:14.400
And so I talk about these four rules throughout the book as a means to frame how we go about approaching this era with AI.
00:37:14.559 --> 00:37:23.679
Some of that is about individual agency, some of that is about designing policies that don't encourage people to just use AI for the sake of using AI.
00:37:23.920 --> 00:37:34.320
And some of that is about designing interfaces between humans and AI that they sort of pay tribute to this dream team myth that we talked about earlier.
00:37:34.480 --> 00:37:47.599
That's a fallacy, that oftentimes we need to embed friction into these systems just to keep ourselves in the mix, which feels very silly, like of adding friction into something intentionally, but that turns out to make all the difference.
00:37:47.920 --> 00:38:19.519
So I wonder, Eric, I was in a meeting a couple of months ago where there was a high-level person involved in AI in what is now Department of War, was formerly Department of Defense, and the person was making the case for why there shouldn't be human involvement or human in the middle, in part because of speed of decision making and speed of action.
00:38:19.760 --> 00:38:35.920
And as I'm sure you're also aware, that's where Anthropic got in trouble with Department of War, Department of Defense, where they wanted a human in the middle for certain decisions, and Department of War didn't want that.
00:38:36.400 --> 00:39:00.159
So would love to get your thoughts on that because the argument the person was making, and the same argument could be made in a lot of organizational decision making, is that the speed and accuracy of decision making of your adversaries can be drastically higher without the human in the loop.
00:39:00.320 --> 00:39:02.159
So what do you think about that?
00:39:02.639 --> 00:39:09.679
I think it really speaks to the broader AI gravity and the competitive landscape that we face.
00:39:09.920 --> 00:39:14.639
If you listen to AI leaders to government leaders, they'll talk about this arms race.
00:39:14.800 --> 00:39:31.280
You may have seen at the 2026 World Economic Forum, Dennis Sasabas was on stage with Dario Amadei, and they said, gosh, I wish we could slow down model development because it's happening so fast, but we can't because China is developing the next model where adversaries are developing.
00:39:31.519 --> 00:39:38.400
And it really speaks to this idea that there's pressure to develop and use these systems just as a means to keep afloat.
00:39:38.639 --> 00:39:50.639
I think in the context of decision making, in the context of war, gosh, that's such a difficult ethical issue to tackle and one that I don't claim to have easy answers.
00:39:50.800 --> 00:40:07.679
But I think there are so many instances in which sort of human ethics and judgment are necessary to make the right decision that I would be very leery of removing AI unless it was like removing humans from that process, unless that was really the only possibility.
00:40:08.079 --> 00:40:09.440
It is very hard.
00:40:09.679 --> 00:40:21.440
You also give example, this is pre-AI, but still it's automated systems, USS Vincent's and issues there where they shot down a passenger airliner.
00:40:21.679 --> 00:40:38.000
There are cases where F-16s with AI pilots outperform the best fighter pilots because the AI can more quickly adjust, adapt, and come up with decisions in a dog fight.
00:40:38.159 --> 00:40:53.039
So there are a lot of challenges around this, but it is an issue that is a struggle where on one end you have people that say others are going full force ahead, whether it's with military or it's organizationally.
00:40:53.519 --> 00:40:55.440
Therefore, we have no choice.
00:40:55.599 --> 00:41:00.559
And some say that we do need to have that human judgment involved.
00:41:00.880 --> 00:41:09.280
Now, you spend much of your book, Eric, focused on how AI impacts an individual's mind.
00:41:09.679 --> 00:41:18.239
Most of my audience, they are executives, they are individuals, as well as leaders of organizations.
00:41:18.800 --> 00:41:27.440
What happens in your view when this kind of cognitive outsourcing occurs across an entire organization?
00:41:27.840 --> 00:41:28.000
Yeah.
00:41:28.400 --> 00:41:29.360
Thank you for raising this.
00:41:29.519 --> 00:41:33.039
I think that I would say a number of things to organizations.
00:41:33.360 --> 00:41:40.880
One is that it really threats to hollow out many aspects of continuity within organizations.
00:41:41.039 --> 00:41:46.800
So, for example, some people are turning to AI instead of hiring junior employees.
00:41:47.039 --> 00:41:55.599
And the problem with that, of course, is that if you don't bring in junior employees to do the quote unquote club work, struggle through problems, that's how they develop expertise.
00:41:55.679 --> 00:42:03.280
But that's also how they become the next person, the manager or executives who has the experience to understand the particular context.
00:42:03.440 --> 00:42:07.440
And so I really worry about this being a short-term solution.
00:42:07.679 --> 00:42:18.480
It's true, you can save money potentially by not hiring as many junior employees, but at the threat of preserving a lot of information which is not actually codified or written down.
00:42:18.719 --> 00:42:25.840
A lot of an organization is like this living memory, and it's not necessarily in manuals or PDFs or spreadsheets.
00:42:26.000 --> 00:42:30.400
It's in the dialogue, it's in the conversation, it's in the minds of employees.
00:42:30.639 --> 00:42:38.400
And the problem is if people are not developing that expertise, they don't have the ability to carry it forward or to pass it down to the next generation.
00:42:38.719 --> 00:42:55.920
It's also the case that when you introduce AI systems, there's this really subtle thing that happens, which is previously, if I were your coworker, I would bring to the table my set of expertise and you would bring your set of expertise, and we would essentially engage in a trade.
00:42:56.079 --> 00:42:58.719
I would help you with some aspects and you would help me with others.
00:42:58.880 --> 00:43:04.239
And we develop a sort of a shared bond, a trust within an organization as a team.
00:43:04.480 --> 00:43:08.079
But increasingly, I don't turn to you to fill my sort of gap.
00:43:08.239 --> 00:43:09.360
I turn to the AI.
00:43:09.519 --> 00:43:19.840
And what that means is we lose a lot of that connection that forms trust as the basis for partnership and loses a lot of that communication that happens along with it.
00:43:20.000 --> 00:43:24.719
And so it reminds me a little bit of the evidence of what happens from work from home.
00:43:24.880 --> 00:43:30.880
A lot of times people they can get a lot of work done because they don't have to commute, but they miss that interaction with other people.
00:43:31.039 --> 00:43:36.880
They miss the information sharing, the connection, the trust that I think is can be very important for organizations.
00:43:37.360 --> 00:43:43.519
So I think what I would say is that you're right, we shouldn't just be thinking about AI on an individual level.
00:43:43.679 --> 00:43:54.800
We should be thinking about what it does to us in terms of our teamwork, our trust, our culture, but also in terms of not just where we are today, but where the organization is going in the future.
00:43:54.960 --> 00:43:59.760
And I think smart leaders will recognize that and take actions now rather than just take.
00:44:00.159 --> 00:44:03.840
Taking the short-term benefit of saving costs of AI.
00:44:04.480 --> 00:44:18.079
I repeatedly say, Eric, and mention it to my clients as well that without that human judgment, if you purely rely on the AI, that can be duplicated instantaneously.
00:44:18.320 --> 00:44:21.360
There is no competitive advantage to that.
00:44:21.599 --> 00:44:27.440
For a month or two, you could be ahead, but that's it at best, at most.
00:44:28.320 --> 00:44:43.679
Therefore, you do need to focus a lot of effort on the parts where human judgment adds value and you need to maintain, and sure, on the parts that AI can effectively do separately.
00:44:43.840 --> 00:44:54.800
So that is part of the unique challenge that we have as we go through this transformation, which I believe is very different than what we have experienced before.
00:44:55.119 --> 00:45:02.480
Now, I want to find out a little bit more about your practices and the way you approach AI, Eric.
00:45:02.719 --> 00:45:11.119
First, out of your four rules that you went through yourself, which ones do you find hardest to follow?
00:45:11.840 --> 00:45:22.239
I think it's probably the rule four to use AI as your cognitive trainer in the sense that there are a lot of topics that I would like to engage and learn more about.
00:45:22.639 --> 00:45:27.519
I think, though, AI is so tempting as an answer machine, as a quick shortcut.
00:45:27.920 --> 00:45:40.880
And so rather than me asking the AI to be in tutor mode to help me understand something like quantum physics, the temptation is so strong to just say, explain to me quantum physics in two sentences or something along those lines.
00:45:41.199 --> 00:45:51.440
In doing so, I'm being unfaithful to this value, the struggle point, that you really have to engage in friction of the process of learning to encode it, to develop that type of mastery.
00:45:51.679 --> 00:45:55.119
So that's definitely a part that I catch myself doing a lot.
00:45:55.280 --> 00:46:02.559
And what I've noticed is that most of the leading frontier labs, like Anthropic and ChatGPT, they offer a tutor mode.
00:46:02.639 --> 00:46:06.159
You can enable them where they don't and give you instant answers.
00:46:06.320 --> 00:46:10.639
But it requires you to actually use it and to turn it on.
00:46:10.880 --> 00:46:27.199
And more for parents and for educators, what I've noticed is that if you give people a system where they have tutor mode built in, the temptation then is to reach for their phone and then open up the web browser to go to ChatGPT, wherever that doesn't have the tutor mode in place.
00:46:27.440 --> 00:46:31.760
And so I think this is in part why I think we need a societal reframe.
00:46:31.840 --> 00:46:35.440
We need to understand sort of ethics and value in using these systems.
00:46:35.679 --> 00:46:42.159
And we need to have and teach people about this in the same way that we've taught people about responding to past revolutions.
00:46:42.480 --> 00:46:44.159
It does take effort, Eric.
00:46:44.239 --> 00:46:57.679
I've used Khan Academy with my daughters, and the tutor mode, as you're saying, becomes very frustrating for someone who is used to typical AI answers.
00:46:57.920 --> 00:47:03.119
Because the tutor mode is brilliant in that it wants to get you to think.
00:47:03.519 --> 00:47:06.000
But as you've mentioned, we want to skip that.
00:47:06.159 --> 00:47:07.039
Give me the answer.
00:47:07.199 --> 00:47:08.480
Give me the answer.
00:47:08.800 --> 00:47:13.119
Assuming if we get the answer, we will learn, but that's not how we learn.
00:47:13.360 --> 00:47:22.320
So that's a great practice for us all to use, including as you mentioned, whether with GPT or Claude.
00:47:22.719 --> 00:47:38.960
Now I wonder if we're five, 10 years from now, Eric, what do you hope that we as humans are better at because of AI rather than merely better at doing with AI?
00:47:40.159 --> 00:47:57.519
What I really hope is that AI is a big unlock for us, that it removes frictions and impediments that stopped us from pursuing our intellectual interest, our artistic pursuits, that it reduces barriers to allow us to reach for more.
00:47:57.679 --> 00:48:01.679
I think that's where we've seen a lot of the transformative change with past technologies.
00:48:01.840 --> 00:48:07.280
And so it's not simply about producing more of the same, it's about reaching for higher aspiration.
00:48:07.519 --> 00:48:16.239
And clearly, the examples that the IA companies often talk about is curing disease or preventing mass outbreaks or something along those lines.
00:48:16.400 --> 00:48:19.840
And I think that's really where I see a lot of the promise.
00:48:20.000 --> 00:48:22.880
That's not necessarily how I see people using it now.
00:48:23.039 --> 00:48:35.039
But my hope is that by reducing some of those barriers, it can just unlock what is so special about humanity, our creativity, our sort of understanding of parts, our connections to others.
00:48:35.199 --> 00:48:39.360
And that, of course, requires more than one thing to get us there.
00:48:39.679 --> 00:48:41.280
It's going to take a lot of effort.
00:48:41.360 --> 00:48:59.199
And again, connecting it to one of my passions, which is, I mentioned nutrition, is that processed foods go through the system a lot faster and cause a lot of issues that now we have become a lot more aware of.
00:48:59.440 --> 00:49:06.719
The processing of food doesn't help the human digestive system at all and causes issues.
00:49:06.960 --> 00:49:13.760
This is the same way in that we can have AI process the thoughts for us and give us the answer.
00:49:14.000 --> 00:49:24.239
That does not produce the kind of results that we want, whether in terms of our cognitive capabilities or the outcomes.
00:49:24.480 --> 00:49:38.800
So that's why I appreciate you raising the issue, Eric, because I do see all around me, including in organizations, just a couple of months ago, I had a CEO of a major telecom saying that you know what?
00:49:39.119 --> 00:49:47.519
I now have executive VPs present beautiful PowerPoint decks on strategy to me.
00:49:47.760 --> 00:49:54.960
When I ask them a few questions, they have very little idea on the point that they're making on the PowerPoint.
00:49:55.199 --> 00:49:58.800
So this is not just an issue that some students are having.
00:49:58.960 --> 00:50:06.480
This is an issue all across society, including some of the most senior executives in organizations.
00:50:06.880 --> 00:50:13.599
So for my audience to find out more about your book and follow your work, where would you send them to, Eric?
00:50:14.079 --> 00:50:17.599
My book is available on all the major retailers like Amazon.
00:50:17.679 --> 00:50:20.159
It's the collision, what AI Does To Us.
00:50:20.400 --> 00:50:27.280
I would also recommend that people check out my website, which is ericsow.pro, where pro is short for professor.
00:50:27.519 --> 00:50:35.599
And on my website, you'll find a workbook where I provide people with exercises to try to make the rules that I talk about in the book tangible.
00:50:35.840 --> 00:50:46.880
I've led a number of organizations through this exercise, and it's meant to really force people to design how they work with AI in a way that will make them better off, not just faster.
00:50:47.679 --> 00:50:49.199
I agree with that, Eric.
00:50:49.280 --> 00:50:51.840
I am a big advocate for AI use.
00:50:52.639 --> 00:50:55.840
But we need to use it properly.
00:50:56.239 --> 00:51:01.280
So that's why your insights are of tremendous value.
00:51:01.440 --> 00:51:04.559
And I so appreciate your book as well.
00:51:04.719 --> 00:51:10.320
Thank you so much for the conversation on the collision, what AI does to us.
00:51:10.559 --> 00:51:12.239
Thank you for joining me, Eric So.
00:51:13.679 --> 00:51:14.880
Thank you so much for having me.
00:51:14.960 --> 00:51:15.840
It's been a real pleasure.
00:51:15.920 --> 00:51:18.239
I really appreciate you engaging with the topic.
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And you've been a wonderful host.
00:51:19.920 --> 00:51:20.719
Thank you.
00:51:21.840 --> 00:51:26.159
You have been listening to Partnering Leadership with your host, Mahan Tavakoli.
00:51:26.719 --> 00:51:42.239
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00:51:42.480 --> 00:51:48.639
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