Transcript
Welcome to a new edition of Identity Matters. I'm Mark McClain, CEO and founder at SailPoint, and I'm very pleased to be joined today by Ethan Mollick. Ethan is a professor at the Wharton School of Business, very impressive, and an innovation and AI expert. And I checked, and I think he actually, actually invented the internet, not Al Gore. But anyways, we'll talk about that later. Ethan, welcome to Identity Matters. So glad to have you. Thrilled to be here. Thank you. So we're going to talk about something that everybody's talking about, which is AI and the future of work. And I think a couple of directions we might take here. One is, as folks know, we're in the identity security business, and there's a lot of discussion about AI and how secure it is, agentic AI. But we are a company full of intelligence workers, software developers, and highly trained customer service people, and sales people, and everybody's wondering, what does this mean for me? So I'd love to kind of poke at both of these as we kind of get into it. Let's start about just the general impact of generative AI, particularly, obviously, AI has been around a while, but generative AI on the workforce. What kinds of things are people asking you about? What are you seeing that companies are looking at? What are the concerns? Let's just talk about what this is going to do to the workforce. I mean, so obviously a big question. The thing to know about generative AI is it's a general purpose technology, right? So it's going to affect everything in different kinds of ways, in ways that are really hard to predict, right? I'm deep into this as an expert. I know, I talk to the AI labs all the time, I talk to policymakers, and the answer is nobody knows anything. So we're kind of figuring this out as we go along, right, is the first thing. So if you're confused and a little, you know, that's the right place to be. I think we do know that the transformation involved in AI is going to hit more educated workers, more highly paid workers, more creative workers first, right, the kind of white collar professional work. It's important to recognize the difference between, like, transformation and destruction. So there are two different things, right? Your job is going to change. That doesn't necessarily mean your job is going to end. Yeah, some jobs may. I think we're all recognizing, look back to industrial automation and the industrial revolution. Some jobs absolutely went away. A lot of jobs transformed, and I think this is the interesting one. I'd love to get your thoughts on this. A lot of new jobs came into being that we couldn't imagine before some of those technological shifts, and I think we're going to see the same thing. There's going to be job descriptions three to four years from now, maybe less than that, that we don't even think are possible today. What do you think are some of the new things that are going to unfold for people? So I mean, part of what I think, the way we think about jobs when we analyze it is jobs are actually bundles of tasks, right? You don't do one thing, you do many things. So as CEO, you're doing podcasts, and this is one part of what you do, right? You're doing leadership roles, right, everything. You have meetings. You keep up with data on the technology, right? You probably go ahead and sell calls yourself. You talk to policymakers. Those are all tasks that you do. If AI takes or transforms some of those tasks, that will change the bundle of what you do and work. So I have a feeling that maybe less job category is being created as much as people's jobs transforming about what parts of the job they specialize in. No, that makes a lot of sense because I think we're already seeing, like I think of it already in a lot of these kind of intelligence white-collar work where I would say it's the first draft thing, right? Like why would you create something from a clean sheet of paper today where you can give a few thoughts, get a, quote, first draft of a new blog post, a marketing paper, and let it do that initial first creation and then bring your knowledge, intuition, whatever on top of that? That saves a lot of that upfront part of many, many tasks. That's certainly an obvious one to me. So I actually want to push back on two different ways on that because I think that there are two other ways. There's a risk of doing that, right? Okay. So if the first draft goes to the AI, then you're not doing a lot of thinking, and you will end up following the AI's instructions. So I actually recommend writing a very rough first draft yourself before handing anything to the AI. Or you're just going to be saying the same things everyone else does because the AI is going to give you fairly similar answers. Because it's going to grab it from... And the second thing I would say is with the rise of agent AI for real, and look, people have been talking about agents for the last few years, agents became like a real thing for most people in the last three to six weeks. And as a result of that, increasingly the way you want to work with AI is you just want to delegate work to it. So it's not even saying, oh, give me a first draft. My book, Co-Intelligence, was all about how you work back and forth with AI. We're entering a world of AI management though, where you tell an AI to do something, it does something and it might save you seven hours or whatever work. Yeah. Instead of like sharing a task with it and having it enhance your production of the task, just give it the task, let it bring the work back to you, I guess would be one thought. But for creative work, you're saying, that's kind of counterintuitive to what I've seen and read before. It's better to use your initial thinking as an expert in some field to make sure your imprint starts it down a path, then let it enhance that path. That's right. Otherwise, you get fixed out of position. It's the same way with ideation, actually it turns out brainstorming is the worst technique for generating ideas as a group, because as soon as someone says something out loud, everyone fixates on that idea. Right. Right? And it looks at the most senior person in the room. So you actually always want to start a brainstorming session before AI with writing down your ideas privately first, so you didn't reveal all your thoughts and advancing. And that's always true in a room when you have various hierarchy of decision makers, everyone can say, oh, we're all equal in this room, but you're not. People can't separate themselves from the structure of the organization. And to your point, you always say, there's no bad ideas, let's get everything out there. But then almost people can't help themselves, like, oh, I don't like that thing. And they start attacking it before you've even had a chance to- Or they self-censor right at the beginning, right? Yes. Right. So the AI has that same kind of aspect, which is like, hey, it wrote me a blog post. These are good points. I'm going to focus on these points rather than the points in this way. So you've said in the past when you want to, you're, okay, boy, that's really interesting. That's kind of counterintuitive for me. You've said, I think the quote was, it's going to make everybody good at work. Does that mean kind of take whatever we're doing, and it's just going to enhance either our productivity or the quality of our work? What do you mean by that? So there's actually a lot to unpack, right? So some of our initial studies with my colleagues at Harvard, MIT, and University of Warwick, we went to Boston Consulting Group, and we gave everyone access to GPT-4, half of, 8% of the workforce, half of them got access, half did not. We saw a 40% improvement in quality right away, right? And the lower performers had the biggest quality boost. And that one, so one way that AI makes people better is whenever you're worse at, the AI is probably better than you. So it moves everybody up to the 80th or 90th percentile. So that's one way it makes everybody good. It closes skill gaps. Closes gaps. But in some ways, that's the AI doing the work, not the humans. A second way to make you good is whatever your best at, it probably works as an enhancer, right? So if you're a good coder, you're probably a really good coder right now. So it's doing both things. It's doing a little compression of making everyone the same and also, you know, dispersing skills more. So I had read something that was a little potentially counter to that, and maybe it's an older piece of data now. I think I read something that early on, I think we thought, and this was particularly about software development and coder, that it would bring everybody up. Then I read something that said, no, actually, it's going to take that really good coder and make them like 10 times better than the average coder who might come up to be a good coder. In other words, it was almost going to have more outsized positive impact on taking your great people to super great or whatever. Have you seen any of that kind of thinking or work? So there's really four things that are happening all at once. One is this idea of skill compression. The floor gets raised for everybody, right? The second is that you see everybody gets elevated to some degree, right, is the other option. That's right. And then there's the idea that maybe, you know, that some of your best performers get very large boosts, and there's some evidence for that as well. And then there's a fourth thing, which we don't talk about as much, which is some people are just good at AI, and they get a very large boost. So people who were not necessarily the top performers before, maybe they're really good at agentic coding, and suddenly they're producing 100 times more code than anyone else. So all of those things are happening at the same time inside organizations. That last one, I had not heard much about. And to your point, remember, not so long ago, we were talking about digital natives. I'm sure pretty soon we're going to talk about AI natives, people who grew up using AI just in their normal course. I actually worry about the digital natives analogy, because when you talk about digital native, it was like, there's a lot of stuff you have to know about making the internet work, right? So how the culture works, how the language works. I think people are over-applying the idea of digital native to AI. One of the more depressing things I heard from a CHRO recently was that they said, oh, the kids we just hired, they're great at AI. And I'm like, they're not great at AI. They're just typing everything you say into Cloud and handing you the results back. That's not great at AI. Great at AI is not a using it. What is the great at AI skill you think people are getting? If you don't have any experience, you don't have any knowledge, you're not great at it. You're just handing in AI work. This is actually something where experienced workers have the biggest benefit. Well, that's a segue to one of the other places I wanted to go, which is, OK, one of the things I think a lot of people are wrestling with is, so if AI is going to take over a lot of parts of our work, entire tasks on our behalf, where are we going to get the value from the humans? Like, this is sort of your point. Like, if a young person right out of school has zero experience, but they throw things into AI, they don't have all this, quote, domain knowledge that would then theoretically allow them to use that AI more effectively. Someone who's been in the industry 10 years doing whatever, software coding, investment banking, talk about what are we going to value in the humans as we get more and more dependent on AI? All right. So this is sort of a bad news, uncertain news, good news situation. OK. Let's go in that order. OK. So the bad news side is I think a lot of people want to have bright, shining lines that AI can't do this, right? So I was talking to a really well-known social psychologist. He said when the AI first came out, I thought it would be empathy and creativity would be human traits. And in almost every survey we have, like, for example, people would rather talk to GPT-4 than talk to a doctor because of its higher empathy levels, right? And so we're not finding those bright lines. So I think bright lines are a really dangerous place to be, right? I think that on the kind of good news side, AI is jagged. It's good at some stuff you wouldn't expect, bad at some stuff you wouldn't expect. And there is a need for humans to kind of fill lots of gaps about what AI can do. I don't know how much people will like what gaps there are. The gaps are going to be different in every field. But there's a reason you can't just say, AI, do my job, right? And there's a lot of pieces that are missing there. And then I think on certain news is in the long term, what does this look like, right? When you have agents that you assign work to, what does that mean for work? We just don't know. And anyone tells you with certainty, like, there's always some viral post one way or another. We just don't have answers. Yes. Especially, and I think in the world we're going to maybe move to next on some of the security aspects, agents interoperating with other agents without the new phrase humans in the loop. We talk a lot about AI with humans in the loop. I think that's sort of everybody's initial comfort zone, like, it's still a person to check on it, make sure it's working. But pretty soon, I think we all think there's going to be a level of agents interoperating with other agents. And now that's completely unknown, kind of a landscape for all of us, I would assume. I mean, unknown for literally everybody on the planet, right? So the frontier of knowledge is not that far away from where you are right now, right? So anyone, by just saying agents interoperate with agents, you're already in the 0.01% of people who kind of understand what's happening in that space. That's fair. Well, and let's go to some of this, because again, our company and a lot of the folks that listen in on this are folks that are dealing with enterprise companies, right? There's a ton of interesting things happening in the world of consumers and in the world of small business. But for the most part, SailPoint, a lot of the companies that we interact with, we're in that world of enterprises, right, from, say, mid-sized, few thousand employees up to the giant corporations. And I know you've done a lot of work with those same kind of companies, the big brands we all know. In that realm, we're certainly hearing a lot about a lot of interest. You said you've done some work with boards. I'd love to hear kind of what you're hearing in boardrooms about, well, we've got to step in and take advantage of this tech. And on the flip side, a lot of concerns about the safety, the security, the data protection. So just kind of, what are you hearing in some of those business-level conversations about concerns with security of data and access to data, those kind of things? I mean, so, you know, I talk to the heads of all the AI labs on a regular basis, not as much on the CEO side, but the people building the products, and, you know, like, they don't actually have that much of a security background. It was absolutely, like, if you've used any of these enterprise tools, they don't have anything, right? They're just starting to add auditing and things that, like, are basic stuff, right? And so, you know, they're trying to build a product that does everything. It's hard to have that happen. I would say on the security side, it's also kind of a mess right now, right? Like, part of the issue is that these tools are really effective, right, until very recently it was all about co-intelligence, right? Not to steal my book title, but, like, it was working back and forth with the AI, which made it very easy to attribute problems or issues. So you give someone the tool, they're responsible for the output of the tool. And the truth is, the initial set of worries about what AI could do focused on stuff that wasn't really that much of a threat, right? Will it exfiltrate our data? You know, will they train our data? That stuff has been solved problems for a while. The more complicated issue of what does an agent have access to under what circumstances, that's a harder problem, especially because the agents are pretty good at finding information and figuring out ways around that. And I think that people haven't grappled with that really yet. At the same time, when you can get large-scale productivity gains, and when the survey work that my colleagues at Ward have been doing have been showing that now 75% of companies report positive ROI from their gen AI experiences. So, like, they're seeing gains from this. It makes it hard to say, okay, let's slow down and wait for a full solution. So, you know, this is one of those building the planes in flight situation. But as the decision-making shifts from the CIO office, the tech office, which is the CISO office, into the CTO, the CMO, the CEO's office, some of this is we just need to get this going. So, I think that part of this is when I see security people try and slow this down, it either results in a complete stop to everything, in which case the company starts falling behind on AI work, right? Or they end up getting overruled. And I think that what we need to start seeing is a new kind of partnership where the people who are worried about security identity are doing this in a, like, in a how do we enable as much as possible, as quickly as possible, rather than how do we slow this down to be sane. Totally get the impulse, right? There's all kinds of problems underway, but there's also a big risk in companies' views of not adopting. You hit the word I wanted to hit next, so good segue on where our brains are tracking here, because I was going to bring up the word risk and say, obviously, what I think a lot of us are seeing is there's two kinds of risk. There's the risk of being too aggressive, perhaps, and not understanding some of the risks, and then waltzing into something, and all of a sudden, all of your IP is all over the model, and you've lost your IP. The other risk that we're not talking about as much is what about going too slow and getting lapped by your competitors who are more aggressively adopting it? How do you hear, particularly senior exec teams or even boards, discussing kind of those two risks? They are less worried about the security risk than they were. I mean, part of that is, like, look, you can go through the list of, like, there's, they have enterprise chat GPT at, you know, JP Morgan and Novo Nordisk. Like, it's not like it's, like, that there is, you know, there's enough big players that have jumped that it's no longer quite the same kind of thing that it was a few months ago. And by the way, when you see big players jump, it's often because the CEO gets the religion, right? Like, it doesn't take long of playing with money systems for people to go, oh, my God, like, this is, we have to figure something out here. Like, the idea that you're not using, you know, agentic coding tools is already ridiculous, right? Like, it, like, of course, you're going to, and every, you know, I know, you know, we talked about cursor and some of the ID networks, but like, the next step is cloud code or codex where, you know, and so how can you not, if you get 10 times or a hundred times productivity gain, it's, you know, there's risks everywhere. I mean, you built this company, right? I'm an entrepreneurship professor. You're an entrepreneur. Like, part of what an entrepreneur does and why, why your entrepreneurial CEOs are so successful is they know when to take a risk and not when to not to balance it. You're not just a custodian, you're, you're transforming. And I think the same thing happens here. Visionary CEOs have to take a risk. There's danger here, but there's danger everywhere right now. Yeah. There's an old, you, I'm sure probably heard it somewhere along the way. We've repeated it on this podcast a few times just because it's such a simple, but great metaphor, which is the, why do F1 race cars have great brakes answer? So they can go even faster, right? If you trust your brakes, you can come into a turn at 150 miles an hour, brake to 80, navigate that turn and get right back up to 150. You'd need a great engine. That's the, the ability to take advantage of this technology, but, but there is this sense of, but I want to trust that I have a break when I need it. Right. And that is that risk management. Like you can get the, the, the most successful entrepreneurs weren't necessarily risk blind. They, they took reasonable risks, sometimes pretty edgy risk. Right. But then they also understood the risks that were not okay to take to put the business and I think that we're, we're in that unknown area right now. And leadership matters more than ever right now. Right. Like these are decisions to be made. What do you want to, I, and I, you know, I talked to a lot of CEO type people, right? And one of the things I've come to believe is like heavy as the head that wears the crown right now because you have to transfer, like it's not, it's not enough to say everyone use AI, right? 90% of people must use AI because that's not a KPI that makes any sense. Right. And when it's happening is everyone just records Microsoft teams calls and they're like, I've checkmarked. Yeah, exactly. Right. And Microsoft teams becomes even more Microsoft teamy. Don't worry. We love you. Microsoft teams. I'm shaking my head. Yeah. Everyone's, everyone, everyone in the room is shaking their heads. But the, but I think that the issue is that you have to actually have a vision, like what does coding look like? You know, we're talking about like everyone is, so your coders are now coding 10 times as much as they did before. So if you haven't changed how a sprint works, what difference does that make? They're standing up at a standup meeting and saying, you know, Claude did all my code yesterday. It's doing all my code today. No blockers and sits back down. Like what does that mean? What does it mean that a marketer can do more? Are you changing how marketing operates? So you have to re-envision the entire business on the go. So that's where the security piece comes in. It needs to be a compliment, right? Providing the brakes as a backup, working with the system. But if you stand in the way and you say, no, no, this isn't secure, you're a hundred percent right. It does not matter in some ways, right? Your job is to, is, you know, the space shuttle still has to fly. Your job is to make sure it's as safe as it can be to do that. That's it. I mean, again, the nature of all business is managing the risk. You can insure it away, you can try to mitigate it, or you can even try to eliminate it. But in this case, like nobody's going to eliminate this risk because that would be not using AI. And that's going to be an unacceptable answer. You can't insure it away. And we don't even know all the ways to mitigate it. So this is where, if you're a security professional listening to this, part of your job shifts from your threat profile is completely unknown. We have no idea what the threat surfaces are. We have no idea, like literally none. If you haven't seen it, right, like, you know, open AI and Anthropic both have entered the next level of autonomous coding and threats. We don't know what the security threats look like in this space. I think there's a tendency, and you know, sometimes this falsely moves markets, to believe that those companies know what's going on, right, and that they have like some insight. Some grand plan. There is no, I mean, they are really good at producing large language models. Nobody knows what those large language models do, including them, right. Their product people are just producing everything. But there's like, but part of what you don't want to do is be left behind where they're the only ones providing solutions that think about this space and your answer is don't use it, right, which is not what you say. But I think that I see that a lot from security professionals inside organizations. It's not realistic, I think, and we joked about you inventing the internet. I'm sure you contributed. You probably weren't the sole inventor of the internet back in the day, but when we think about that metaphor, yeah, a little bit, me too, you know, and when you think about that metaphor, that's similar in some ways, isn't it, where that team, the set of people that first thought about this interchangeable internet thing, they were not security professionals. It was designed with an openness. They wanted to share everything. And even after that, we had a bunch of folks come along and go, wait, what about security where it's needed? And it was, quote unquote, an afterthought. I kind of feel like we're watching the same movie where these folks are amazing technologists that are developing these capabilities at Tropic and OpenAI and now Google and Microsoft and everywhere else, but their first instinct isn't always security. And so there's going to be, I think, this interesting challenge again for the, quote, security industry to say, how do you come alongside this and say, we're not trying to shut it down. As you said, we're not trying to stop it, but you probably need some controls at least to be able to dial it up and down as appropriate. Do you think that's the same kind of thing we're going to see evolve? Yeah. I mean, and, you know, I think that there's going to be, like, this is a chance for organization. If you're interested in the security side, it's time to rebroaden your horizons, right? You can think about fields as things that kind of narrow down, there's business as usual, and there's these periods of ferment where lots of disruptions happening. That's what's happening right now. So what security means is going to change. What a security professional is supposed to be involved in is supposed to change. Like, do you care about, like, you could expand your field to do things like agent psychology, to be honest. Like, no, I mean, it really matters. Like, what is the agent, like, what should they be talking to each other about? Are we monitoring how they're thinking about stuff? Are we doing, you know, like, is that an issue that we care about? Does security mean, you know, helping everybody understand what the risks associated with things are? Does it mean, like, there's a chance to do much more? And I think this goes back to the job conversation. My anxiety is that people view this as, how do I defend the borders of my job, right? And then you're in a world where you're probably going to shrink what you do, because your turf is probably, like, other people are going to intrude on your turf and your turf is changing. If you give this an opportunity to expand what you do, that security, like, and I think, you know, part of this is just rethinking what security means, right? So part of this is, what other things are part of security? What's it mean to secure an agent? Maybe we have more interaction with the agents themselves and don't just think about, you know, what content you can access or not. Maybe we have to be explaining to agents what's secure and what isn't. Those kind of things are potentially really powerful ways to change what we do. Train the agents how to think that way, quote, unquote, think that way. I mean, that's all Claude's skill is. Yeah. There's a new term you probably already have heard. I haven't heard it very long ago again, how fast this world is evolving. We're going to be managing intent, agentic intent. Like, what was this agent supposed to be trying to do? What problem is it set out to solve? And is the thing it's now doing in line with that intent, or has it veered off from that intent somehow? And that's going to be apparently a new discipline for how do you make sure that the agent, as you said, stays within its designed intent at some level so it doesn't run off and try to grab a bunch of data it really wasn't supposed to grab to accomplish its objective. But, well, you have to be one step further because the agents are very good. So if they're trying to grab data they didn't have access to, part of your question is not how do I punish this and prevent a line? It might be like, okay, so why, what do we need to do? And that's kind of why, but that's why I think the security space expands, right? It can't just be like, this is yes or this is no. And security can't just be controlled by an information security officer because you now have, I mean, you can think about agents as having another million employees. They have their own sets of needs. How do we dynamically create them? And we haven't even started talking about them. One of the most effective ways to do this is hierarchies of agents, right? So I would imagine that you're going to be creating security agents that are helping do audits and conversations, but you don't want those to be too in the way. But we have to invent all this. None of this exists, right? I mean, you just said intent. I am in this field every day, right? I talk to everyone. I mean, I've only heard intent used in a completely different way. So we're in a world of completely conflicting ideas. And so if you think everyone's settling on something, they're not. And you've heard, we've all heard now the term, the digital workforce. And as you said, well, like every other workforce, theoretically, there's going to be hierarchies, there's going to be domains of expertise, there's going to be a need to interoperate and communicate. I guess a digital workforce will have a lot of characteristics of a human workforce over time. As far as we can tell, a lot of it. When I'm doing fairly complex agenda projects, I actually have a small organizational hierarchy. So I have a research team of sub-agents and I have an orchestrator that kicks off the research and then I've got an art team. And some of the early evidence is the more you organize agents like an organization, the better off you are. Yeah. I've heard some of that same thing. All right. Well, Ethan, we've talked a lot about the technology, but let's come back to the humans in the equation here. How should leaders at various levels in organizations be thinking about this whole evolving world? What is your kind of counsel to leaders today? There's a few basic things, a few complicated things. The basic things, I talk about this a lot, are you actually need three things in your organization to succeed. Leadership, lab, and crowd. Leadership is the leaders we're talking to right now who make decisions. They're going to have to think about incentives in a hard way. They're going to have to think about organizational structure. Crowd is giving people in your organization access to these tools. Security concerns are real, but at least giving them access to tools is really important. And then they're the ones who are going to come up with the good ideas. Nobody has a good idea for you. It's people who invent them inside the organization. And then finally, lab, you need people doing 24-7 gen AI work. And they need to be taking ideas from the crowd and turning those into products. They need to be helping and giving you a sense of where things are heading in the space. So you need those three elements, leadership, lab, and crowd. And then the other thing leadership should be doing is you've got to be using these systems. So two-thirds of CEOs have said they've used AI, but very few of them have used it as intensely as they should. Don't make time to do this. Just use the system for a week and just put every decision you put through it and you'll see what happens. Yeah. In full confession, I'm right there. I've been kind of dabbling and I'm going to like kind of shift gears. Literally, I've had some conversations with other, what did you call us, CEO-type people? I'm a CEO-type person. Yes, you are. I like that. And this idea that there's this like, oh gosh, I've been thinking about all the ways my team ought to be leveraging this and not enough about, well, how does this change my work? And it's really important for the leadership side because ultimately that's where change is going to come from. And if you either are going to, you will get this at some point. You're going to have a small existential crisis, but then you'll get it. It's not a joke. You will have a small existential crisis, but then you'll get it. I have those weekly. Okay, good. Then we're in good shape. I'm good on existential crises. Okay, look. One more thing, and this is really fun given that we've talked about how things have been changing literally weekly if not monthly. If you try to look out five years, that's a pretty nice long horizon. What do you think we're going to be talking about that we got right right now and how we were doing things? And what do you think we are misunderstanding that we'll maybe have different view or different clarity on in five years? I think the broad picture is I think people are underestimating the technology and overestimating how quick things change in organizations. So I promise you that almost anyone who's listening to this doesn't know what these false systems can do at their rate. And I've actually heard enough early stuff, the curve is still going. So we're going to end up with incredibly capable AI systems that we already do, but they're continuing. And we have trouble modeling that in our head. It's just hard to know what really better looks like. At the same time, all the AI labs and all the viral panic that keeps taking over Twitter, right, or X or whatever is all about the idea that they think things change faster than they do, right? I talk to organizations all the time. Almost nothing has happened. Organizational change has not happened, didn't happen in 2025 with AI, right? It's going to start happening in 2026, but it'll take a while. Change is going to occur, but organizations are complicated. Human society is complicated. People who think companies are just built for efficiency haven't ever dealt with company politics or direction or the fact that conflict is part of what we do. And so I think underestimating where the technology is going to go versus and overestimating the speed at which change gets adopted. Yeah, for a company like ours that mostly deals in the Fortune 5000, like the larger organization in the world, absolutely. The old Mark Twain quote about the death of the mainframe turned out to be greatly exaggerated. We have tons of mainframes running tons of our businesses today. And that's because if it's working, I'm not going to change it. I've got a lot of other things I'm going to change. So the pace of change, not only just, there's not some single variable for an organization. It's within the organization, all kinds of different variables about how they change certain things. And you can watch how that frontier changes because like basically I've been taking to a lot of large financial companies who have, you know, COBOL based mainframe systems and their modernization efforts just leapt up by many years because it turns out you can do good COBOL work with these systems and you don't need to wait on the one person from, you know, the vendor to come out to you. So like that's a nice example of change in both ways, right? On one hand, it's going to take a while for them to move the legacy systems to the other. That was not in their 10-year plan or five-year plan and now they're going to do this in the next six months. So huge changes at the same time as slow changes. Oh, that's really good. Well, this has been great. I guess we'll just kind of say that was a lot of fun. I feel like we could talk for another hour and probably not even still scratch the surface of a lot of what people are, again, thinking about in their organizations and in their personal lives and how they use this. But thanks for all the thought provoking thoughts. I love the counteractions to some things I thought I had heard and you're challenging those in a great way. I'm like, oh, well, I'll think differently about that. That's always what I think is most useful about these podcasts, get people to think multiple sides of an issue, multiple views on an issue. Super, super helpful. So Ethan, last thing as we begin to wrap up, I like to always ask my guests what books are, and a lot of times I hear people aren't reading very many books, mostly reading blogs or listening to podcasts, but if you think some of the influential voices that you're absorbing, what are some of the things you think are really helpful right now? So on the book side- You said your own book, of course. Yes. My own book, Cointelligence. Cointelligence. Yeah. There. I just finished rereading a book called The Knowledge, which is basically how would you rebuild civilization from scratch technically. And I found that was really interesting because you kind of see the complexity of how many systems are in place that in order to be able to build everything we build around us. So that was a lot of fun. I also just finished just, you're starting to see all of these really interesting new connections forms. So I just finished a book called Proto on Proto-Indo-European, which was the first sort of language that birthed almost all of Western European languages, Sanskrit, and just another nice example of technological breakthroughs are changing how we start to think about our own past. So those have been fun. I have a blog, One Useful Thing, that's pretty widely read that I do. The truth is almost all of the interesting discussion on AI is happening online, right? So like there's- Your books are too slow. But that's where a lot of really interesting stuff is going on. So for better or for worse. Great. Well, I'm sure you get a lot of interesting folks on that podcast because you're attracting people that are thinking deeply about this. So that's great. Well, this has been fun. I really enjoyed it. Thanks again for coming to our sales kickoff. That's why you and I happen to be sitting here. You're speaking to our team to get them thinking about how our customers and they themselves are processing all these rapid changes. So I really appreciate that. Thanks man.
