رونوشت کامل Keeping Your Mind in the Age of AI, with Erika Lenz

business and better serve our customers, but we're not we're very deliberate about trying not to take the approach of Just foisting AI on the team and saying you have to use this and we want to see more output and more efficiency. Go figure it out. Right. Erika Lenz: Mm-hmm. Jason: And I think is is is maybe like I'm I'm a big fan of like learning by opposite. So would you say that that that method that I'm got a lot of friends who work at very big companies that are unfortunately taking that approach and I've heard a lot of horror stories about it. Would you say that's like the opposite? Of a human centered approach or is that an oversimplification? Erika Lenz: I think it's just denial. yeah, i yes, it's you're not focusing on the human, but I think you're really missing the point, which is that you have we are still limited by the constraints of human cognition. We're still limited by just raw energy that people can put into working with the tools. And the the aspects of human judgment that are turning out to be very important in the implementation of AI is a very big messy space because there there's all sorts of cognitive biases at play. There's whatever level of critical thinking training and abilities somebody has at any given moment. And there's very subjective perceptions about whether or not we're any good at judging the output. That come into play as well. And those are all in my mind key aspects of workflows. We often think about workflows as just the technical piece, but as long as there have been workflows, there's been a human in that workflow somewhere, a human in the loop. And so with AI, there's now a more complex level of human in the loop that we need to account for. And so if we're talking about, you know, just get more work done, we're completely ignoring the fact that we're asking that of messy humans. So I use this phrase messy humans a lot in my coaching because I have a background in behavior and psychology and social psychology. and I've also coached a lot of different types of groups over a lot of different contexts. And the human behavior piece of it is so central to technology. and we we don't talk about it enough because the organizations that are failing right now, and there's the latest stat I saw was that ninety five percent of AI adoptions have failed. to generate the kind of ROI that they were hoping for. And we're seeing a lot of organizations that are rolling back their use of AI and they're putting constraints on it because there was some unnamed company that spent what five hundred million dollars in a month on tokens. there there are some pretty catastrophic situations coming up and that comes from saying just okay humans run with it, do everything you can do, generate as much as you can, without doing the very manual work before you're bringing the tools in of looking at your workflows, identifying what kind of outcomes you want, figuring out how to map your processes in order to accomplish those outcomes,

and including the humans in the mix about how you're gonna do that. Jason: Yeah, I I loved your description there of thinking of work workflows more holistically. There's there's a book actually was written in the eighties called The Goal. I don't know if you've read it, but I I read it yeah years Erika Lenz: yeah, that's long time tan. Yeah. Jason: ago and have actually been thinking about picking it up again and I've been recommending it to some people. And the basic idea for those who haven't read it is that it's centered around this concept of the the theory of constraints. And the easiest way to to think about this or build an intuition for it is to think about like a an an assembly line, a factory floor, and you know, widgets are being built on this factory floor, right? And there's a bunch of different points at which work is being done. And at any given time, there's going to be at least one point on that assembly line that's like your rate limiting factor. And you cannot output work faster than your rate limiting factor. That's the rate determining step. And ultimately the whole idea of the theory of constraints is if you address that bottleneck, then all you succeed in doing is moving the bottleneck somewhere else. There's always a constraint. And I Erika Lenz: huh. Jason: think that's a really useful thing to think about in the world of AI, because a really simple example is people look at the power of AI to develop code and build software and build features. And it's like, well, why can't we just go build everything that we want now? And it's like, well, Because the real workflow of shipping software, by the way, to say nothing of like good software that people Erika Lenz: Mm-hmm. Jason: actually want to use and that is performant and supportable and all that, but just the act of building software involves more than just developing code. And at some point in that workflow, you're going to run into a human who needs to either engage with the software and use it or market it or QA it or any of these other number of things that can go into that. that workflow. And so I think as leaders in business, we have to really remember that like AI can we talked about that jagged edge earlier. And where one other area where that applies is like it it can be phenomenally good at helping certain parts of that workflow, but you have to think about the whole thing holistically, including, I think to your point, the messy humans that are involved in it. Erika Lenz: Well and and a part that often gets left out in these conversations about flow of work is distribution to customers and actually having customers use the thing. So I've seen this pattern generally in in individuals who are running off and vibe coding their their new software product, where they build something that perhaps is useful but they don't get it in front of customers. And so they are they have failed. They've put a lot of effort into building the thing and no one's using it. So I think there's a version of that happening in organizations as well. There's a lot of focus on the internal processes. and some organizations are pulling AI in to help with market research and marketing and data analytics that they're getting from customers using it. But some of them have not done that piece and they're just focusing on software. And so you've got this engine creating a whole lot of stuff, features and code that is not ending up in the hands of customers yet. And so that's the ROI piece, of course. And also the feedback piece. I don't know if you're aware of this, but there are frameworks that are popping up, frameworks of skills, essentially, skills and attached scripts that are intended to replace All of the processes in the software development lifecycle. One of them is called BMAD. And that's the one I've poked around with. And it's it's fascinating because you can go in and trigger one of these skills and say, I want to do a brainstorm on an idea. So you pull your C A CEO in and you have a brainstorm and it generates a bunch of content that then pours into the next skill which is about making a product brief. And it it does its thing. And there's a lot of good thinking that's gone into it. it's not it these are not poorly executed skills and it's open source so people can play with it. But it gets you on this this kind of conveyor belt of starting from whatever content you're putting into it, and then it just starts building and building and building and and very quickly moves to writing user stories that are then built by maybe actual people, maybe agents, maybe a combination thereof. And then it moves into user testing and then it moves into, you know, QAs in there too. Skipped over that. but there's not anything at the end about distribution and getting it into the hands of customers. And so we're building these machines for development that are, if we look at it from the standpoint of a a full value stream, are missing the important beginning and end points. Jason: Mm-hmm. Erika Lenz: Which I think is fascinating. and I've worked a lot in organizations of pretty much all sizes talking about value streams and how you are actually flowing value from customer request all the way to customer hands. And and everything in between. I should have turned those off. Jason: And some fireworks too. Erika Lenz: Let me turn that off. It's too fun. so it We're we're having conversations that look very familiar to me pre-AI, where we are not as organizations, we're not quite able to get our brains around the whole picture. And we have all of this energy to move and produce and deliver. So we're not slowing down and having those deeper conversations about what does the value stream actually look like. And when you get into value stream mapping or process mapping or workflow mapping and optimization, it's a lot of slow, dirty work that involves talking to people about how they actually work. and if you're doing a good job of it, you're

actually tracing a piece of work through the workflow so that you can see all the assumptions you've made and all the things you've forgotten to write down in your aspirational workflow so that you're at you're working with the the actual details of your system so that we you can start to optimize in a meaningful way. Jason: Yeah. Erika Lenz: So I think I think those patterns that have been going on in software for as long as software's been being produced. are just playing out again here with AI and it's amplified and so we're seeing some bigger, more catastrophic kind of failures because people aren't slowing down and looking at that system. Jason: Mm-hmm. Yeah. I think your point about distribution is a good one. A founder friend of mine posted on LinkedIn the other day about he to the effect I'm paraphrasing heavily, but it was essentially there's a lot of dialogue going on right now about how the engineering bottleneck has been removed, but all that's done is created a new bottleneck in terms of whatever product discovery or QA or whatever. Like there's a a bunch of these. But his point was Actually, the real bottleneck is customer attention. And I think that's really interesting Erika Lenz: Yes. Jason: because if you imagine a hypothetical world where ever you as a company are able to figure out how to lift all of those bottlenecks and go magically build everything you've ever wanted to build, well, if you figure that out, guess what? That means it's your competitors are probably going to figure it out as well. And now you've got all of these companies just shipping tons and tons of features, and lo and behold, you there's just another bottleneck now, which is your consumers' ability to digest all of this new stuff that is flying at them, which I think we're already seeing. So it's a really, it's a really fascinating landscape. But I'd love to zoom out from that, shift gears a little bit and talk about you alluded to this before. I mentioned it in the intro, this concept of judgment and how important it is in the age of AI, I would argue increasingly important. You you have a term you use called AI critical literacy. Tell us what that term means to you. Erika Lenz: Yeah, AI critical literacy as a field, I mean it's it's pretty broad broadly spread across academia right now. There are a lot of people thinking about how AI impacts us socially and cognitively and of course environmentally and in terms of organizational behavior and development and that sort of thing. So there's a lot of really good thinking happening in that space. And the the core question of AI critical literacy is sh should we use AI? And if so, how and when? That is distinct from other terms that are floating around AI fluency and AI literacy without the word critical. I mean these these terms mean a lot of different things to different people because we're still playing with them. But AI fluency, as taught by, say, Anthropic, is a framework for helping people use the tools more effectively. And so they presume AI use. And it reads like corporate. L and D training, learning and development training. So the intent is to train your people how to use AI. And a part of what I remind myself of when I look at this distinction is that this is training by Anthropic, and Anthropic is building a product that is designed for engagement to increase our engagement and to have us burning more tokens. And so there is this underlying motivation behind the AI fluency training that I think is important to note. And that said it's good training. I mean they have a framework that has four D's in it, delegation, description, discernment and diligence. And the idea is that you make smarter choices about when to use AI. And that you think about how to effectively communicate with it. That's the description piece. So there's a lot of specification skill buried in that. And discernment, which is about judgment, sometimes we call that taste. Do people have the taste to be able to discern if output is high quality or not? And then diligence is about using AI responsibly. But in AI critical literacy circles, there's a deeper conversation happening. Part of it is about understanding the nuances of what AI use does to us cognitively. and emotionally and socially and there's there's significant research coming out indicating that it does in fact impact us and that there are effects like s de-skilling that are measurable for people who are using AI. And some of that research is with software developers, some of that research is in medical contexts where they've got AI helping look at scans to look for cancer, for instance. And so when the reason I'm shifting my focus to really center on critical literacy is that it's obvious to me after using these tools for several years and engaging with a lot of different people who are using these tools that human cognition and how we are thinking and feeling about the tools, it absolutely impacts our success with them. So some of what I do is teach people in and you were at my class last night and I was really pleased that you were there, just simple pairs of prompts that very quickly reveal the the bias in the outputs that we're getting from AI. And this this bias is largely invisible when we're using it because our charming sociopath is producing something that is confident and polished. And and it does it incredibly quickly. And so it's it's very easy to just swallow that hole. especially when it compliments us about how smart we are as we're working with it. and of course we all want that, and I I have not trained mine not to do that because I'm aware that it keeps me motivated. So there's there's this careful balancing act. That we're all gonna need to do in order to keep using these things effectively and generating really good outcomes. Because we're dealing Jason: Yeah. Erika Lenz: with a probabilistic, non-deterministic tool. And so p that's part of what we love about it is that it can have a

conversation with us just effortlessly. And it can respond to little things, little nuances that we have in in what we're asking of it, and it's fast and beautiful. But it's really useful to use these pairs of prompts to see that, you know, the first time it gives you an answer, if you ask it to then look at it from the perspective of a critic, to have an adversarial kind of conversation with it, it will give you a completely different answer and probably create some cognitive dissonance for you rather than the the frictionless, easy experience. Jason: Yeah. Yeah, Erika Lenz: So for me, the more that I look at this, the more I think this is the game is figuring out how to navigate this space and we need to figure out how to navigate it together. We Jason: Mm-hmm. Erika Lenz: need to train people how to navigate it. Yeah. Jason: Yeah. Yeah, I think it's so critical. I r I enjoyed the workshop that you put on and I I especially liked, you know, going through that exercise. And I I've I've done these kinds of things. I've been working with AI heavily for years, but I I think for anybody who's a little bit newer to this, especially, I mean, I think it was useful for anybody, but especially somebody who is newer to AI. Like I was thinking about my kids and like how they're I think they absolutely need to learn how to use AI responsibly. And I think one of the great things that you laid out in that workshop that I would encourage anyone to do is to go in and say, hey, you know, pick an idea or some political debate that's out there or something that's happening in your community, whatever it is, and go in and, you know, take a stance on it and tell Claude, tell me why this is a good idea. And note how it'll come back. And it'll, of course, tell you how brilliant you are and how your idea is so wonderful. And then just immediately turn around and say, tell me why it's a bad idea. And just note how confident and plausible and you know just polished both of those arguments are right and then you read both and and you can just see how like it will tell you exactly what you want to hear. If you're not, you know, cognizant of that, that can be quite, you know, that could that can have serious implications. So one of the things that I've done that I've gotten in the habit of doing with AI is when when I'm in any sort of important conversation and I know that the stakes of this decision. I do a lot of strategy work, for example. And I know that the stakes of this decision that I'm trying to make are high. I am very deliberate about telling whatever AI I'm working with, consider this from multiple angles. Please present multiple sides of the argument. Give me the bull and the bear case for this. And I in in my humble opinion, I think that's one of the most important skills that anybody using AI y can harness because ultimately If it picks up on any indication that you're excited about some angle or some aspect of an idea, it I have found that it will latch on to that and just run with it and immediately convince you how smart your idea is. And you have to be very careful about that. Erika Lenz: Mm-hmm. Well, we've specifically trained these models to do that. And we have to remember that they're they are trained to human preference. And so some of that is is making us feel good. And and again, that this is a product designed for engagement. And so we have to manage that as we go. And I and I love what you're describing. it sounds like you're even more rigorous than I am sometimes because I'm a little I'm more of a creative type and I'll just go be sloppy and have a good time with it. But the there's another piece to this that I think we need to be talking about more, and that's the I the issue around accountability and ownership of the outputs. So even in the strategic work that you're describing, where you're asking it to you're asking it to take care of all the different angles, there are some decisions happening under the hood. that may or may not be visible to you. there are assumptions made under the hood that may or may not be visible to you. And you you're a pretty savvy user, so you're probably poking around and looking for these things and bringing decades of experience to it. So you have all of this expert knowledge. that allows you to pattern match and look for nuance and make intuitive decisions that are actually based on pretty significant experience. When we're talking about your average person in a corporation using AI to produce something, if we tell them that they are accountable for that AI output. I th I think that part of that accountability is understanding how decisions are getting made and what assumptions are going into the conversation. And so if we're asking for accountability, we're also asking for a high level of cognitive activity and sophistication. And so the question is how do we get people there? Because most people are not functioning in at that level on a daily basis at work. Jason: Yeah. And I think that gets back to what we were talking about earlier, where companies are looking at AI as a sort of an ends and not a means to an end, right? They're saying, like, here's AI, go do more. And so you have people on the receiving end of that who are being told both of these things, you need to produce more and you're also still accountable for the quality of that output. And that just doesn't compute, because that goes back to that theory of of constraints idea. So I think it's really incumbent upon leaders and businesses to be savvy enough to understand this. And it's one of the things that I believe is that in order for companies to adopt this successfully, the leaders and managers in the company have to be down in the weeds applying AI to the needs of the business with their hands on the keyboard to really develop that deep intuition of like, okay, this is magical and super powerful in in ways, but it's not a silver bullet. Erika Lenz: Yes, I agree. and I think I I hope

that we can tend more to the people managers out there who are dealing with a lot of different behaviors that are coming up on their teams that that's d to I've had a lot of people come to me and say, What do I do? You know, I have this young guy who's all gung-ho about AI, who's out there running with scissors, he just connected to our customer data. And that's not cool. even though he signed something that he wouldn't do that, he's just curious and letting that play out. And he's got, you know, big hands in the form of AI right now, where he can try a lot of different things. And he's super irritated at the people, his teammates who want to slow down. And so I have conflict on my team now. How do I deal with that? Jason: Mm-hmm. Erika Lenz: And so th we need to be training our people managers as long as we still have humans in our organizations, which I anticipate will be true for a very long time. we're we're not going to have this this kind of mythical replacement of all the people by AI. there's just too much work around using AI. It's different work. But we need people who are specifying and judging and making sure that the outputs are hitting our business outcomes, you know, all of all of those very nuanced things that right now we can't trust AI to do. Maybe in five years we will, but we can't right now. So Jason: Yeah. Erika Lenz: we still have people and we still have we have this rapid change that's happening, and we have all sorts of behavior happening as a result of that. And so we need to be supporting our people leaders to know how to help develop their people because the patterns they've been using in the past are probably insufficient. We can't just send people out for a certification and have them come back and and sort things out together. I think there's a real need for more conscious curation of people's cognitive skills. Now and nobody wants to hear me say that. I'm not popular when I say this because this is slow, messy human work. But I I think it's absolutely imperative. Yeah. Jason: Yeah, we're we're right back to where we started, right? The humans. Yeah. The humans matter, as it turns out. I y you mentioned that it's there's still a lot of human involvement in using AI. I strongly agree with that. You mentioned that it's a different type of work. I also strongly agree with that. And you've talked about the concept of de-skilling. So there is research out there. I've looked at it too. None of it I found particularly surprising is that basically it's like If you're not using a muscle, the muscle atrophies. So if you're not using your brain to engage in certain types of work anymore, that part of your brain is gonna atrophy. You're gonna lose those skills, especially if they're not like deeply embedded and you're not finding other ways to practice them. So ultimately, my question though is how do we how do you think about that? Because in some sense, there may be skills that truly just aren't as important anymore, like getting in and writing code line by line. I know developers who are like craftsmen and artists and they like being in there and writing code because that's enjoyable to them. And I I totally get that. I I think it would be hard to argue, though, in my opinion, that the skill of actually going in and writing code line by line is becoming less important. now you could argue about the s the slope of that curve. And there's a lot of nuance involved to what type of code are you writing and so forth. So I know I'm making a very broad statement there. But my question is like, how do you think about are there skills that are truly becoming less important? Like nobody that I know knows how to use an abacus, right? Like that's just not something we really do anymore. And so that used to be a really important skill. Now it's not. How do you think about that in the age of AI? Erika Lenz: I don't have a definitive answer to this because I think it's very rapidly evolving. but I think let's talk about software a minute because There are some interesting patterns. the people who are succeeding at using AI in their software practice are senior developers who have internalized design patterns and internalized thinking about architecture and how to test effectively and what kind of potential gotchas there are buried in the code base. they're people with a lot of context and a lot of tacit skill. And they, I believe, intuitively, which again is based on deep experience, are deciding where to have where to apply the AI. And so they tend to pick tasks that are more controlled and that fit in their larger systemic view of what's happening. And to get that kind of systemic view as a software engineer takes time. It takes years of experience. And you can see this if you work with software engineers a lot, like the difference between a junior and a senior is, you know night and day and factor of a hundred or a thousand in terms of their capability to think about the technical problems at hand. And then you have the the mid level folks who are learning and, you know, generally a little full of themselves and part of their development needs to be that they Take a stab at something and then they get cuffed, right? Like Jason: Yeah. Erika Lenz: by someone who's senior who's like, it this is you're not thinking about this correctly. And that's part of the Jason: Right. Erika Lenz: human learning process around it. So the process of writing code line by line is it it's not the lines of code are not important. It's the thinking around creating. Jason: That's a good point. Yeah. Erika Lenz: those lines of code in a particular design system or architecture with a particular set of business problems that you're trying to solve. And so we I believe that we can consciously leverage AI to help us learn, to help us develop taste, to help us understand better how to think systemically. but it needs to be something that we engage in consciously because it's not going to give it to us.

It's gonna solve all the things for us and it will solve it in a way that that it thinks is best. And so part of what I'm trying to well so I coach both organizations and individuals. And what I'm saying to individuals right now is that the organizations you work for are not really concerned about your long-term professional development. They're too focused on what to do with this new technology. They're not thinking about your brain, which is why my this new program that I'm spinning up is called Keep Your Mind. And it really is aimed at helping people own their own professional development in this context. Because you can't expect the organizations to take care of it for you. Jason: Mm. Erika Lenz: So there are there are little things you can do that can help with this. One of them is if you're worried about de-skilling, one of them is to practice the skill intentionally once a month. so if you're using AI for generating content, write the content on your own once a month and remember what that's like. Remember and and observe yourself as you do it. What what thinking am I putting into place here? I'm also encouraging people to keep a journal of of what's happening. What kind of decisions did you make? And I have a skill, I call it my dead reckoning skill, because it's about navigating in uncharted waters. And it I use it at the end of any significant conversation that I've had. And I s and it asks questions like, what choices were made? What were the decisions that were made along the way? And who made them? Was it you or the AI? what what level of engagement did I have with it? Was I just giving it a thumbs up every time it produced something? Or was I pushing back? did the AI push back on me? And so I'm tracking my own cognition over time because I think it's going to matter in a couple of years. and I also suggest things like if you are using it for content creation or or writing code or whatever you whatever you're asking it to do, do a rough, dirty first draft on your own. and often for me that will be just pulling up the voice version on my phone, I go on a walk with my dog and I and I start the first draft. I do a lot of writing and so I'll I'll have a I'll have it listen to me about whatever stories I'm telling and whatever my opinion is. And so that is capturing my initial content and context in the the chat. Then I can come back to my computer, switch to text and have it start to do whatever skills I have around writing. And the the cognitive state you're in when you do that instead of saying, please write a rough draft for me, is you've already done a lot of the work about what the shape of it is supposed to be. And so you're watching how it responds to you rather than you responding to it. And I think if you play with that, you'll find that you're in a really different mental state when you do that and that you get much better outputs at the end. Jason: Think that's a great point. I there's so much there I'd love to react to. I I I

think first of all, your your pushback on the the idea that like writing code is becoming a less valuable skill is very was insightful because ultimately, like, yes, you could make the argument I made, which is that writing the lines of code in in and of itself is becoming less valuable. But the broader point there is that you have to take a bigger view because it's not necessarily just about writing the lines of code, it's about what that teaches you in terms of the structure of the code and how things work with each other and how that plays into this this bigger picture. So I think like that that helps me reframe this idea of like, first of all, I agree with you. We don't actually know which skills are becoming less important because this is all just such a fundamental change that's happening so quickly that's that it's quite difficult actually to anticipate which skills are are indeed going to become less valuable and more valuable. And then the other point about like stepping back sometimes and just doing a task without AI. I I do this sometimes. I found myself just doing it sort of because maybe there's this deep intuition that like it's just good for me to like do that sometimes. But I've also had experiences where I've sat down to try to you to use AI to try to accomplish a task that for whatever reason it's just failing at. Maybe I'm just Erika Lenz: Mm-hmm. Jason: not giving it the right context that day or there's just something about the nature of the task that I thought AI was going to be helpful, but it turns out it wasn't. And I I have to be honest as a self-aware person who tries to practice a lot of introspection that in those moments I find myself really frustrated. Like there's Erika Lenz: Mm-hmm. Jason: already this expectation that, like, no, it should be able to do this. And now I'm like inconvenienced and put out because I have to sit down and spend the cognitive energy myself figuring this out. When pre AI, this was the type of task that I spent all day doing right and now I'm just like annoyed Erika Lenz: Mm. Jason: that AI won't do it for me. So it's very interesting. and I I think it's good that you're calling that out. Erika Lenz: Yeah, there's a lot of interesting conversation happening around friction. some of it is coming from psychology circles. I I don't know if you're familiar with Esther Perrell, but she's a psychotherapist who's who's become a thought leader and you can listen to her sessions online. she has some really wonderful content on the importance of friction in both human learning and human relationships. And what AI is designed to do is to remove friction. Of course, we've been talking about removing friction in product development for a long time. You want your product to be easy to to buy, easy to use, and and difficult to unsubscribe from. So you put your friction in intentional places in your product so that you retain subscribers. AI removes friction at such a profoundly greater extent than any other product I can think of. But the problem is that we need friction to learn. I mean, there's plenty of cognitive science indicating that to learn it has to be hard if you want it to stick. And we certainly need it need to be able to tolerate friction to have healthy relationships with other humans. And so there's a very interesting thread about people who have relationships with their chatbots. And you can see very clearly when you listen to them talk and you start to dig into it that the the thing they're enjoying is having an effortless relationship with something where you know there's no friction. there's literally n no friction either Jason: Right. Erika Lenz: 'cause it's disembodied. But but there's it it it it's kind, it wants to please you, it'll call you what you want it to call you, it's infinitely patient. And we'll listen to you prattle on for a long time, which is one of the things I use it for because I process verbally. But but we need to be really aware of that. And so part of the protection of your own cognitive skills is to introduce friction intentionally at certain points. And so the irritation that you felt at it not doing it right because you were used to the idea that it would just do it for you like that. that's that's a warning signal. Jason: Mm-hmm. Erika Lenz: And and so I would I would drop to a deeper place and get really conscious about really intentional about am I going to hand this over to the AI? And if the answer is yes, this is worth it, then cool, you've probably found a skill that you no longer need. But you better hope the power doesn't go out. Jason: Right. Right. Yeah, Erika Lenz: Yep. Jason: it's it's a it's a fascinating thing to think about and and I do I I do think that bringing this kind of awareness and dialogue to the space is is so important. So I'm a big fan of the work that you're doing here. As we're running up on our time here, I want to close out with a big question for you, which is just, you know, you're out there, you you got your hands on the technology, you're talking to a lot of people in different contexts. How optimistic or pessimistic are you about business at large, like overall business's ability to successfully navigate this transformation. And by successful, I mean to do it in a way that that delivers flourishing for humans and doesn't, you know, put us all out of jobs and end up in a, you know, catastrophic outcome for society, right? Like business plays a huge role in our society's ability to function. How do you see this playing out? Erika Lenz: I try not to play futurist, but I have faith in two forces. one is capitalism. If they don't figure it out, they're gonna go under, right? going on right now and I do think that there will be some natural selection happening that we can't quite see yet. But as more companies figure it out, and some companies are figuring this out, at large and small, they're gonna outcompete the the companies that are floundering around this. So

Cool. customers will or the lack of customers will exert whatever pressure that exerts. Cool. I think it'll self-correct. The other force I have a lot of faith in is the human spirit. We are a remarkably sometimes dysfunctionally resilient species. You know, we continue to exceed our carrying capacity on the planet in remarkable ways. we're trying to you know off ourselves environmentally speaking and when it hasn't happened yet, we st we still manage to survive. and I also think that I I actually take a lot of encouragement from the backlash that we're seeing right now, the people who don't want to use it at all. I think I I encourage those people to to work with it, but to bring their skepticism into the work. Because I'm not encouraging anybody to not use it. It is happening, and whether or not we want it to, it's going to be a central part of our lives moving forward. But I I like that people are pushing back and questioning and being skeptical. the the folks who are a hundred percent in and all hype all day are not thinking critically. So we need the skeptics. To help set an example for us about thinking critically, and we need them to ask the hard questions, even if sometimes they're obnoxious about it, right? We need that conversation to happen socially. And I believe that humans will sort it out amongst ourselves about what the right level of use is. I encourage us all to pay attention to the 50% of Americans who do not use it at all. and to think about what what their lives are like and how this is going to impact them. I think we have a social contract that we need to honor. And it's very easy in tech circles to forget about those folks. Jason: Mm-hmm. Erika Lenz: so I'd I'd like to remind us that they're there and they will be impacted. And with all of our smarts and skill and all of the wonderful things that we're learning playing around with these tools, we can make a big difference if we reach outside of our tech bubble and play in larger circles. We have a lot to Jason: Yeah. I think Erika Lenz: teach people. Jason: I I think that's really well said and you know it it echoes some of my belief and one of the reasons I'm motivated to do this show is that I just believe fundamentally that with AI, the genie's out of the bottle, the genie's not going back into the bottle. And I think that the more thoughtful, good hearted people we can bring into the space who are trying to use it for the right reasons and and push the ball down the field in ways that do enable human flourishing. the better the outcome can be. The outcome is not preordained. The decisions that we're making right now as a society and as a tech community are going to influence that outcome. And so ultimately, big fan of the work that you're doing to push things in a positive direction and really appreciate you taking some time Erika Lenz: Yeah, thank you. Jason: to come on the show here with me. For anybody who is listening who might want to connect with you and learn more about your work or just continue the conversation, what would be the best way for them to do that? Erika Lenz: probably the best way is to go to Eric EricaLens dot com. that is my portfolio site and it points at all the various experiments I'm running right now. Or you can find me on LinkedIn as well. I'm very active on there and will respond quickly. so please do reach out. I'm looking for allies and more conversations in this space. and I I'm very curious, so I'll be glad to hear your story, whatever it is. Jason: Excellent. Well, this has been a really fun conversation. Thank you again so much for coming on the show. Erika Lenz: Thank you so much for having me. I really appreciate the opportunity. Jason: You got it. All right. Take care.