Is People Analytics Having a Mid-Life Crisis? & Employee Listening at Netflix - Colby Nesbitt - #181

[00:00:00] Hello friends of the podcast and welcome to Directionally Correct, a People Analytics Podcast with your host Cole Knapper and today's guest Colby Nesbitt, Senior Manager of Employee Listening at Netflix. In this episode we will cover whether People Analytics is having a full-blown midlife crisis or not. Is People Analytics having a midlife crisis? The hot one, PSYOP session that everyone is still talking about. I did hear a story that someone changed their flight to come to our session.

[00:00:30] What it's actually like being the lone IO psychologist in a performance management company? What is it like doing performance or being an IO psychologist at a performance management company that has no IO psychologist other than you? What's genuinely new in employee listening at Netflix? The Culture Memo has been called perhaps the most important document to come out of Silicon Valley. Whether performance actually follows a power law? Is performance a power law and why does that matter?

[00:01:00] And why sports might be a terrible metaphor for work? How I handle the ways that sports break from more traditional organizations. Now, let's get down to business. Hey, Directionally Correct fans. This podcast is dedicated to you to help democratize people intelligence for the world of work. If you're looking to support the podcast, please make sure to listen weekly. Subscribe to the Directionally Correct Substack newsletter.

[00:01:27] Sign up for the Data Driven HR Academy at datadrivenhracademy.com. Purchase Cole's book, People Analytics. Or check out everything else at colnapper.com. Before we get into it, a quick word about HR Bench, the company powering this podcast. You know, when we all started in people analytics, we wanted to do strategic work.

[00:01:50] Building predictive models, workforce planning, advising the C-suite, and most of all, quantifying the impact for the business. Instead, we spend months building dashboards and reports that should already exist. HR Bench eliminates that entire phase. Your HR is connects, metrics calculate, your benchmarks populate. This is not novel. This is day one, not quarter two.

[00:02:15] That means skipping straight to prescriptive analysis, storytelling, and taking action for the business. Want to learn more? Book a demo at hrbench.com slash directionally correct. Find out more about the company powering this podcast and building the future of people intelligence. As always, all opinions are our own, and thanks for being a listener. Well, Colby, thanks so much for joining me on the podcast today.

[00:02:40] Before we get too down to business, I wanted to say a quick shout out to a friend of the podcast, previous guest, Yuyen Sun, who couldn't make it today for personal reasons, but we were going to cover some of the topics that you guys have been talking about together. So we'll bring Yuyen on later and get her thoughts on it. But is people analytics having a midlife crisis? What's going on, Colby? I think it is. And yes, Yuyen is phenomenal.

[00:03:08] You should follow her on LinkedIn and Substack if you don't already. She's one of my favorite voices in the space on AI and the Future of Work. And this question came up for us. This is people analytics in a midlife crisis. Really the first time we ever spoke, we had crossed paths in the comments on LinkedIn, and I asked if she wanted to chat.

[00:03:29] And we had this great conversation about whether, about how it felt as though people analytics was kind of like at an existential plateau. And at the end, I was like, do you want to write about this? And she graciously did. And so we, our thesis essentially is that people analytics was predicated on this idea that employee headcount had inherent meaning.

[00:03:54] And that meant that everything that was built from that also had inherent meaning, like more hires were better. More attrition was worse. We study the structure of organizations and where people sit. And it just, it was all on this foundation of this idea that more people meant more productivity. And that was, that was true for a while for honestly much of history and into the modern era.

[00:04:23] But it's starting to break. And we looked at this from a few different angles. We looked at, at the national level at like GDP data from the Bureau of Labor Statistics. We looked at revenue per employee benchmarks are climbing. And even at individual companies, the employee headcount is kind of decoupling from, from operational income. And that happened, I mean, it's for a few reasons, I think.

[00:04:50] We're seeing it mostly at a tipping point around like 2022, 2023, where you have a huge change in the macroeconomic environment. You have the introduction of AI. And there's just this shift between assuming that more headcount means more productivity. And for a long time, like Wall Street would reward companies growing in headcount by assuming that meant they were capturing greater market share.

[00:05:17] And a company with a larger employee base was more productive than a company that wasn't. And that's kind of being turned on its head. And so like now companies get rewarded for doing layoffs in the market, which is, I'm not saying it's good, but it's what's happening. And, and so that all of the assumptions then that rest on that headcount metric don't hold anymore.

[00:05:38] And it was always kind of a lazy metric, if I'm being honest, because it assumes that people are kind of interchangeable and that people are adding value. And like, we don't know that that's true. That's an assumption that's mostly untested. And so then we have this system where, especially when you introduce AI, the, the productivity of a system no longer depends just on the person in the seat. And it's more a product of the, the work system.

[00:06:07] And so what we propose instead is that we should measure productive capacity, which, which is a term we borrowed from like the industrial revolution. And, and, and, and as far as I know, it hasn't been used in this context before, but really the idea of like the, the unit of work has changed where we shouldn't, we never should have relied on headcount, but especially now. We can't assume that that translates in the same way.

[00:06:33] So it's instead of an equation that includes headcount, essentially the tasks to be done, whether those are executed by humans, by AI and what the value that those tasks bring to the business, what they cost and what the other risks are. And we see this on the flip side of the equation, like on the AI side, we have the same headcount issue. It's more just like adoption and like token maxing.

[00:06:57] And we see how that's playing out just a lot faster where it's unclear whether usage actually corresponds to value. And so it's a lot harder to measure obviously. And so it's not, it's not the sense that we have all the answers, but more like we're trying to figure this out in real time and saying we need to be asking different questions.

[00:07:15] And it's not that we stop measuring headcount, but we need to push it further and actually understand the question that we should have been asking all along, which is how do our people and work systems create value for the business? Mm-hmm. Well, and this showed up in your sub stack, Variance Explained, as well as Yuyan's Amazing Lives. So two really great sub stacks that if you're not out there following, you should be following.

[00:07:41] One of the things that I thought was really interesting about it is, and you talked about in terms of productive capacity. I've been speaking about in terms of unit economics. And so what firms have been trying to optimize is for every unit of a human increase, does that either A, increase our revenue as a company, make it stay the same or decrease our revenue as a company? Mm-hmm.

[00:08:06] And the question you have to ask yourself is, okay, what level of revenue per employee, i.e. the unit economics, do we need to get to? And the answer is whatever our investors say or whatever our competitors is, right? Mm-hmm. And if we're not at that level, then that makes justification many times for cutting headcount.

[00:08:40] Mm-hmm. So what is going on in people analytics, though, in particular that, you know, diagnosis it as a midlife crisis? And then where do we go from here? Yeah. I think one thing that's interesting about people analytics is it's relatively young as a field within HR compared to, like, recruiting, which has existed since the dawn of organizations. Somebody has to bring in people. You know, somebody has to make sure people get paid. Like, those have existed in different forms, sure.

[00:09:09] But people analytics came up at a very particular time and was shaped by those conditions. And we're reaching a point where we're realizing that the rules are not the same in the world that we're in. And so we need to continue reinventing ourselves and not act as though the same assumptions that we made under certain, like, business and economic conditions will continue to hold.

[00:09:31] And so something I noticed that was really interesting was we shared this article right around the same time that you shared a similar one or a similar in, like, the topic on the People Intelligence Manifesto. And I think it was, like, a day or two apart, which wasn't on purpose. But I think we were observing some of the similar things.

[00:09:55] And so I want to I think we saw some similar themes, but arrived at, like, different structural proposals, which I want to get into in a second. But before I do something that immediately stood out to me was the language we used. Like ours, we called it a crisis, you know, a midlife crisis. We use, like, militaristic language like Genghis Khan and this, like, war story from the Old Testament. And, like, also kind of, like, existential, like asking if, like, what if headcount is, like, counting the number of souls on board a ship?

[00:10:24] Like, these are, like, meaty questions and big language. And, like, the manifesto is, like, a loaded word that's political. It's controversial. It's a deeply held belief. Like, why do you think we were both drawn to that type of language? And, like, why is that necessary for, like, a Substack article about people analytics operating models? Yeah, I think it's for a few reasons. One is loaded words get more clicks. You know? It's so practical. Let's fun the game a little bit.

[00:10:53] Let's call it out for what it is. But I would also say that it's been a fraught time. Yeah. End of the last few years. Like, I've been on kind of this journey where in 2023 I was giving this presentation called Is People Analytics a Luxury? In 2024 I was giving the presentation about, you know, people analytics had peaked in terms of its head count and then started to decrease. And I had written a series about that.

[00:11:22] And then, like, what I thought about in 2025 I was, I published the book on people analytics and how generative AI was going to kind of, you know, disrupt the whole field and how that's actually not an opportunity. It's a threat, but it's also an opportunity. And then the manifesto is like the 2026 version, which is like, I'm past this. Like, you guys got it like the midlife practice you're talking about. Like, I feel like I went through it and like, I'm ready to get on the other side. I bought the sports car. I want to go move forward and say, what does the future look like?

[00:11:51] And is that future exciting? Because for a time there, I didn't feel like it was. Right. I was like, I was kind of worried. And I've been having these conversations with folks like folks lately. And I know it's not just me is like, I actually think people are starting to see kind of a playbook of where to go. And it's sort of the playbook of when people analytics started. And and that's why I keep saying that we're so well positioned for this future, which is the people who got into this field early.

[00:12:19] There were no laurels to rest upon. Like you had to be a builder. You had to be a first principles thinker. You had to be a person who is willing to be entrepreneurial. You had to be a person who was willing to go out and take big swings. And therefore, the field culturally kind of manifested that. Right. And then it became a hot field and everybody and their brother wanted to join. But if you were early, that's what you had to do. And so what I've started to see now kind of my friend group is those.

[00:12:48] And those are the people that came in early is they're like looking for what's the next big swing to take. Mm hmm. And what you know, some people are calling AI workforce transformation or all of the above or none of the above is kind of coalescing. And you even talked to think about called at work analytics.

[00:13:12] And in your article, it's kind of coalescing to be this isn't a new, interesting time to take big swings. Most people in HR and most people, frankly, and people analytics that have come like post 2017 aren't comfortable with that. But, you know, the people who are the people who are early adopters and kind of the first instantiation. So this is actually an exciting, transformative time. And to quote the great little finger from Game of Thrones, chaos is a ladder. Right.

[00:13:40] This is a chance as a chaotic moment to move up in the world and kind of improve your station in life. The chaos is coming. Let's use it to our advantage. Yeah, I agree with that in terms of the reorganization and the energy around it for sure. And what I thought was interesting about the way that we approached kind of like the what to do next was the way that you and I talked about it was kind of a collapsing of analytics functions across functions.

[00:14:06] So like the HR having, you know, people analytics or whatever we were calling ourselves now. And along with like IT and business analytics and having more touch points cross functionally. And the way that I read your article is kind of like collapsing people in people analytics and like the analytically driven HR functions that way. And so we were kind of like on different collapsing across different axes, if you will.

[00:14:32] And I think I've even heard you say that in the future, people, HR in general may look more like an expanded people analytics function kind of built on that analytical capacity. So like what would that look like to have like a people intelligent L&D function or recruiting function? I think that might be an oxymoron. But that's just sorry shot across the ballot. It's getting spicy. Yeah, but I believe it.

[00:15:01] And I actually cover this. You haven't heard yet because it hasn't come out yet. But the episode with Andrew Bartlow, we talked about this a lot in terms of kind of the creative destruction that's happening in the start of the world. And how you're really creating HR functions for the first time. And in the past, you know, there was a playbook for that. But what does that mean with AI and like how would you build an HR function from scratch today if you were starting one? And that was a fascinating conversation.

[00:15:26] But I think the way I look at it is through the lens of like, let's say you're the CHRO for a second. You never wanted to get intelligence in data from like 17 different places in HR. That's just annoying. That's a toll you don't want to have to pay. So CHRO has always wanted intelligence just to come from one place. And so for the first time, I actually feel like we have the capability to do that. And so I think in net net, that's a good thing.

[00:15:53] Right now, should you have L&D intelligence and talent acquisition intelligence and all of these different fragmentations? I think really what you want to have is the ability to ask questions, to get answers and to make decisions that are boundary spanning. And that's why I think you need that intelligence layer that collapses down to again, just data and just intelligence.

[00:16:16] Like that's kind of my frame of reference is like, if you take a user centered approach to this, the people who actually want to be using this data, what do they do? Now, if you think about like the entirety of an HR operating model, I think if like, let's take Dave Ulrich, you know, you've got shared services, you've got centers of excellence, and you've got HR business partners. We never really lived up to it, frankly, but let's say for the sake of argument that we did, shared services is going to get completely eaten by AI. There's no reason why it should, right? It's just transactional data. That's all it takes.

[00:16:45] A lot of the expertise and centers of excellence are going to get hollowed out, right? Stacia and Danny have been talking about this concept of hollowed out expert because, you know, they believe that you can demonstrate expertise without actually having it because of AI. And so I think that that's going to get largely hollowed out. And then the HR business partners, I think they're going to be fewer but more powerful. Right.

[00:17:08] And so in the future, I actually see it where the HR business partners will probably continue to rule the roost, but they're going to be incredibly overpowered with intelligence that they've never had. And the skills that they need to do that job is probably going to change dramatically and actually think that the legacy people analytics folks of let's say yesteryear of 2022 might actually be a great HR business partner in 2032. And that's kind of the mental model that I have.

[00:17:37] How does that jive with how you were thinking about things? I agree with that. I think what will eventually exist is HR business partners and people analytics. And I think the this complimentary skills will become really more important. I see those really as two sides of the same coin or like, you know, I like a metaphor.

[00:17:58] So like people analytics is like a plane over, you know, it's like they're seeing the ground from 30,000 feet and HR business partners are like in the forest on the ground. And so if you can have those two talking to each other, then it's incredibly more powerful than just one or the other. And I do think having the business acumen that HR business partners have and the political savvy alongside the analytical skill set is essentially what is needed.

[00:18:25] But I also think the relationship with other functions like finance and IT will become increasingly important as we're dealing with different types of data and tying that more and more closely with financial outcomes. Yeah, I think. I mean, there's a lot of really interesting discussions that are going on, not just about like how HR is constructed, but like how businesses are constructed.

[00:18:46] I think again, we I was giving the vantage point of the CHRO. But if we were talking about the vantage point of the CEO, I really think they're going to start to ask questions like why do we need IT and finance and HR and any kind of corporate functions to be separate? Right. Yeah, that's a comfortable question. And I don't think that we necessarily have the answers to that question right now. But when you when I see like there's going to be a forcing function, that means HR and finance in particular and IT are going to have to collaborate a lot more than they have historically.

[00:19:16] Yeah, I agree with that. Yeah. But what do you what role do you think that all this discussion has to play on employee listening? Hmm. That's a good question. I think. In my view, employee listening has moved deeper and deeper into the like backbone of people analytics, or at least it should be. And so it used to be sort of this like layer on top. And for a long time, it was about employee satisfaction.

[00:19:41] It was about retention, again, because that was sort of the tenor of the time and we're all products of our time. But listening needs to evolve with the needs of the business. And so something that I find myself saying over and over is like we assume that listening means listening for sentiment. And that is true. But I also really love the idea of listening for capability.

[00:20:02] And I think maybe this is just a broad and self serving view, but but I think listening, I take a really broad approach to what is listening and really like any sort of employee data can be looked at in terms of listening because it's either behavioral. It's stated, you know, like in a survey, it is qualitative. It's, you know, measured through any sort of means. And so I kind of take license to make my sandbox bigger than just surveys in particular.

[00:20:30] And I think the field is becoming much, much more important than that. And so I think the the way that I think that links to what we were just talking about is really how do we better articulate? How do we gather better data about the work that is happening? And some of that is happening through new channels. And then how do we report on that in a more meaningful way?

[00:20:52] And like something that drives me crazy, that's just it's not really about like a new method entirely, but it's just a kind of a reporting thing is like the way that we report on people data, whether it's listening data, attrition, what have you, is hierarchical according to the org chart. And that's not not how work happens. And it's increasingly not how work is going to happen. And I think, you know, even as teams get larger span of control is increasing, cross-functional relationships and work will become more important.

[00:21:19] And that will be much more of how work actually gets done. And so if we don't understand the linkages there and are able to reflect data across the actual structures that are underlying the work, then the if we're not able to do that, then the data will be less and less useful. What is the most useful data in this space at the moment? And where do you see it going? Oh, that's a good question.

[00:21:45] I think I'm going to take this in like a little bit of like a meta direction, something that I think about in terms of like evaluating the quality of listening data is like what are people not telling you or what processes are they not engaging and what does that say? And so I think we're quick to analyze data from any survey we get without really scrutinizing like, what is our response rate?

[00:22:11] It seems so basic, but what is our response rate? Who are we hearing from and who are we not hearing from? And that like non response can be very telling. And so I think that's a step that we skip sometimes. And so interpreting like the difference between stated and behavioral intentions and reasons for things can be really powerful. Another example is like exit reasons. It's like, sure, people say certain things in an exit survey or interview.

[00:22:37] But it's like what actually happened before that that predicted their exit and using those things in conjunction can be really telling. Yeah. State and reveal preferences. Silence. I love it. This is a lot of the intelligence framework. I was talking about people intelligence manifestos. This is good stuff. But you had a really, I mean, I might even call it like a generationally popular session at PSYOP this year.

[00:23:07] What was the back story on that? So what was the session? What did you guys cover? But it was, I mean, I heard more about that session than anything I've heard about in years. That's very generous. I don't know. I don't know what metric you're using for that generational claim, but I will take it to the bank. I actually, this is probably apocryphal because it was like secondhand, but I did hear a story that someone changed their flight to come to our session.

[00:23:32] And that I think is pretty funny. So if you're listening and you change your flight to come to Hot Ones, like shoot me your flight confirmation and I will buy you a drink at next time or some wings. But yeah, there is a funny backstory to this where I got together with a couple of my good friends from graduate school, Anna Hewlett and Taylor Sullivan. And last summer we were planning a PSYOP submission mostly because we wanted an excuse to get together a few times and chat about it because now we live all over the country.

[00:23:59] We don't work together anymore. And we were asking ourselves the question, what session would we be really excited to get out of bed on Saturday morning and go to? And we were having trouble coming up with one. And so I went to bed that night and I had a dream that we did Hot Ones and I texted them the next morning. I was like, I either had the best idea. What's Hot Ones for the night? Oh yeah. Yeah. Thank you. Thank you.

[00:24:22] So Hot Ones is a YouTube interview show hosted by Sean Evans, who is a standup comedian, but he's an incredible interviewer, like so sharp, so well researched, like very, very good. And so he brings on these guests and they, he gets phenomenal guests and they eat progressively hotter and hotter wings, 10 of them. And hilarity ensues. And the questions range from like, you know, very, it's not like a gotcha show. It's, it's very genuine.

[00:24:50] He asks deep questions. He asks funny questions and it is genuinely hilarious. Like some of my favorites are, uh, uh, Shaq is great. Uh, uh, Paul Rudd is a gem. Uh, Lewis Hamilton, um, Conan is like unhinged, but anyway, really funny stuff. Um, and so I had this dream that we did this at PSYOP with, you know, some great folks from the field and that they, you know, shared openly and that it was just this really lovely moment.

[00:25:18] And I texted them and I was like, I, I just had either the best idea or worst idea I've ever had, but I think we have to do it either way. Uh, and so we did, we got some great, um, great panelists who were so thoughtful and generous and trusting to just like do this crazy thing. And I'm actually really, I'm so grateful for them. I'm really grateful for, uh, the reviewers and the committee to like take a chance on something pretty silly, um, and seeing the value in it.

[00:25:43] And there was like really genuine value. Like we had some great takes, like what we were just talking about, Alexis Fink was talking about how people analytics needs to be more embedded with IT and data science and leaning that direction. We had Nathan Carter, who's one of my, my like favorite methodologist, uh, saying, yes, he's been on Alexis too. Um, he was saying, you know, guys like, this is how you do a meta analysis. Here's how, you know, it's legit.

[00:26:11] And like, there's no more authority I need than that. We had, um, Tori Howes who like literally wrote the book on applied psychology. And she was talking about how, like some of the most fundamental theories we have about like psychological cons contracts and how employees relate to our work are fundamentally changing. And how we need to revisit some of those as we better understand like the future of work.

[00:26:33] We had, uh, JP Elliott, who's a phenomenal. He was talking about, um, really like embeddedness with the business and encouraging IOs to get out of people analytics and take a role in talent management, take a role as an HR BP. Like it will humble you. I did that. It changed my life. Like I'm not exaggerating. And, and so really like genuine insights.

[00:26:55] And at the same time, it was actually hilarious. I thought it was hilarious anyway. Like I'm looking over, it's humanizing. Like I'm looking over and Alexis is like gargling milk. Like Nathan and Tori are like passing Tums around and JP, one of my, JP was a sneaky one actually. Like he was the only one I think by the end of us, by the end of the five of us who could actually like string together a coherent thought.

[00:27:18] Um, but I asked him a question about his time at Taco Bell. And I was expecting like, you know, a leadership lesson, something profound. Like here's, you know, what I learned about business acumen. And he was like, listen, here's how you make a bunch of tacos really fast. You hold your hand like this, you put the tacos in between and it was just like so perfect.

[00:27:39] And so there was, I think it's humanizing in a way, um, alongside like the actual wisdom. And so, uh, I would love frankly to see more of that at conferences. And I think, you know, like I love a good debate, like, and not a performative one, like in good faith debate, um, unscripted. I love, I would love to see a session like, these are all the experiments that I did that failed. Like, this is what I learned. Like that would actually be really valuable. And I would love to see more of that. So like, I don't know.

[00:28:08] Do you agree? Like, do you think there's room for that? And like, if so, how do we as leaders in the field, like promote that sort of, um, like discourse that helps folks learn and kind of humanize in a real way? Yeah. I mean, that's kind of the journey I'm on with this, frankly, because I've been trying to figure out a way. And frankly, the number one thing holding us back is it's hard to get people to open up. It just is.

[00:28:32] Yeah. And I think the great thing about hot wings in particular is it almost lowers people's like cognitive load or something because they're having to process like all the hotness. Yeah. That's exactly it. Exactly. Yeah. Yeah. And so you actually get the self monitoring to go down a little bit for a second and you get people like JP, you know, again, friend of the podcast, previous guests talking about how to make tacos between his fingers.

[00:28:58] Yeah. And that's fun stuff. Yeah. And what they really think about the issues and like some hot takes. Yeah. Literal hot takes. Literally. Yep. Yep. Well, one of the things you mentioned there is your journey kind of not just doing people analytics, but getting into a kind of a broader talent space. What is it like doing performance or being an IO psychologist at a performance management company that has no IO psychologist other than you?

[00:29:27] Yeah, that was a really interesting experience. So I was at Lattice for about three years and I was the first people analytics hire and went on to lead talent management and compensation as well. And so it was, it was actually, it was incredibly fun. It was also incredibly humbling. And I think I came in a little, I don't know why I thought this before. I think when I came out of my PhD program, I thought that IOs like ran HR. Like we absolutely do not.

[00:29:57] What are the common misconceptions of faculty that are out there, which I've tried to rebut many, many times. Yeah. Like I thought, okay, like, you know, the average, like L and D practitioner or like TA leader was an IO. And that is just, I don't know why I thought that my dad's in HR. He's not an IO. Like, I don't know what possessed me, but.

[00:30:17] And so I, I, I came in a little hot and was like, you know, I think I can like teach people some things. And like, I'm sure I did, but I learned a lot more from them than I taught them. I'm sure.

[00:30:30] And because of the, the, the gap between, and we talk about like the science practitioner gap, like literally there's a gap. Like I studied selection, for example, in graduate school, I did selection and consulting for a number of years, like high volume, high stakes selection for the military, for medicine, like really interesting high stakes industries.

[00:30:49] Then I went to a startup and it's not selection anymore. It's recruiting. And there's a big difference and none of the rules applied. And so I was trying to figure out like, how do I, how do I do this? How do I make myself useful when the laws of the universe have changed? And really like, if I had tried to come in and be like, Oh, let's do a predictive validation study. Like it would have taken two years. It would not have been useful by the end of it because the business would have completely changed directions by that point. And it just was not at all the same.

[00:31:20] The goal wasn't the same. And so it was helpful for me to see like that mechanism and how the, I still was able to provide some value by being like, okay, fundamentals, like data cleanliness. Like how are we thinking about measurement? Like how are we thinking about consistency in the process and like bringing in some of those principles? It's like, we don't have to do the research here. Like we, we can rely on external research to bring that in, but we, so like, we're not reinventing the wheel,

[00:31:48] but we can bring that to, to give it like what the business needs at this size and stage. And something else that I, something else I found was really interesting over time was, I think a lot of the goal of people analytics is to, or maybe not the goal, but the, the mechanism for being effective is like making something complicated seems simple.

[00:32:11] And I think there's like an intimidation factor for folks who aren't like data native that can be really dangerous. You know, it can be a real barrier to folks engaging with the work. Like I had one example where a stakeholder of mine reached out to pull some data on like benefits usage. And I was like, sure, happy to pull this for you. You also have access to this. You know, here's how you pull the report and you would just like count up the names in the file.

[00:32:36] And, and she said, okay, but like data analysis isn't really my forte. And I was like, this is counting names in a CSV file. Like that is not data analysis, but just, just like interacting with the data in that way can be intimidating. And so a lot of that is like, how do I make this simple? How do I invite people in and like help bring them along in the journey of how to use data so that I'm not the bottleneck for pulling reports and also interpreting data out in the wild.

[00:33:02] And in talent management and compensation, you kind of have the opposite problem where like everyone is telling you how to do your job and thinks they could do it better than you. And I think, you know, it's not because those things are simple, but I think everyone has like their kind of experiential theories of like what makes good performance management and compensation.

[00:33:21] And it actually is like, there is a discipline behind it. And you kind of have to like bring people along that of like something that seems simple, but actually maybe more, more complicated and require some more, some more thought on the back end. So that was just like an interesting way of seeing like how different strategies play out in, in HR functions. But I'm curious for you too, like you were in a different but similar capacity working at a skills company, being an IO and one of the only or few, right?

[00:33:50] Yeah, that was interesting. So when I was at Lightcast and again, they do more than just sell skills data, but this is a big part of what they're selling nowadays. And I was the first IO psychologist they had ever hired. And that was very interesting because it was a company founded and primarily run by labor economists. And they have a very different view on the world. It's a very interesting view, I had to admit. And they have a lot of really good data and a lot of really good theory and a lot of things that frankly, I think IO psychologists can learn from.

[00:34:18] But I also think that there's a lot of things that blind spots that they have that I would come in and I would mention things and be like, we've never heard of this. Like we've existed for over 20 years. And so a lot of my job, I feel like was just like, all right, I'm just going to say some really white bread, like IO psychology stuff and blow people's minds. Like what? Like what? I mean, it would just be like, they're in addition to skills, they're also tasks.

[00:34:46] And if you combine the two, those two things make up what's called a job analysis. And I mean, I give a lot of conference talks on job analysis and how to do it at scale. When I was at Lightcast, that was like a big considerable part of my job. And again, that's very, you know, very basic stuff that again, I think IO psychologists figured out in like the 1970s and 80s.

[00:35:09] So it was an interesting experience. I was probably more externally facing in that role than you were at Lattice, but it's fascinating nonetheless. But you are now at Netflix. We touched a little bit on the employee listening earlier, but Netflix is, I think, probably core contribution in the HR space, even though it's not really about HR at Netflix is the culture memo that came out.

[00:35:33] It was very, very hot. I assume it's still relevant at the organization. Can you talk about how that plays a role and how it affects kind of your job at Netflix as well? Yeah, absolutely. I think, you know, the culture memo has been called perhaps the most important document to come out of Silicon Valley. And I don't think that's an understatement. And it really is important at the company currently.

[00:35:55] And what strikes me when I joined is that it's it doesn't feel like it's owned by HR and everywhere else that I've been pretty much, it feels like the values are owned by HR. And that is just a really uphill battle to to fight. And so what I think is most portable. Here's what I'll say.

[00:36:14] I think big tech and tech in general has a really dominant voice in the space of people analytics because people analytics was kind of like born out of Google in some ways. And, you know, there's like a disproportionate focus in the media on certain industries. And it just it feels like it's a leader in the space, not good or bad. It just is what it is.

[00:36:36] And I think sometimes people mistake that leadership as like portability of culture and try to copy and paste certain elements of what people are doing to their organizations. When really, like I think the the primary lesson of the culture memo is like your culture is a product. It's it is a thing to cultivate is isn't something that should happen by accident.

[00:37:00] It is something that exists with or without you. And so you might as well build it as opposed to just seeing what happens because you might not not like what happens. And so what's really important, I think, about that is the exercise that you go through to get to that. And like, are you willing to do the work? Are the leaders invested in articulating what the what that culture is, what the values are and and how you get there is really the work of it.

[00:37:24] And so it's not just as simple as saying, like, OK, we're going to like copy and paste, you know, radical candor to our organization and we're going to have, you know, Apple's success. It's like, no, that's not how it works. And so it's much more about the internal consistency of what you're building and what that looks like there. I think there's no like good I mean, even similar to like, you know, like global and national cultures, like there is no good or bad.

[00:37:47] It's like, are you consistent? Are you do you do what you say you value? And are you clear about that? And that's like the real value of a culture. Yeah, I will say this, this episode and back when Uyen was still going to come on, I had to do more prep for this episode than like five episodes because I had to read so many freaking articles. So I kind of resent you for that. I went back and I re-listened to Uyen's podcast I've done with her in the past and and a bunch of different things.

[00:38:16] One of the things that came up in that process is finding out how much of a precocious child you were and that the New York Times actually profiled you in high school. Do you care to talk about that at all? Yeah, yeah, that's funny. So when I was a senior in high school, the New York Times wrote a feature on a handful of girls at my at my high school, including me.

[00:38:45] And it was really about the experience of growing up. So like, you know, these we grew up in the 90s and aughts. And it was the zeitgeist of the time was like, OK, girls can do anything, so they must do everything. And really about that, like climate. And I grew up in a suburb of Boston. It was very highly educated, highly competitive, affluent. And there was sort of this like pressure cooker, especially as it related to applying to college.

[00:39:13] And so the title of the article is for girls. It's be yourself and be perfect, too. And it's really interesting because. I didn't realize at the time what it would mean to like have my picture on the front page of the New York Times. I was 17 and I was famous for about 15 minutes and that was plenty. But I go back and read it like every few years and it always hits a little bit different, like it hit different after I had kids.

[00:39:40] And, you know, the way that I think about the questions that I was asking and a lot of that. I don't think you also realize like how you grow up until you leave. And so there were a lot of things about like the privilege that I had, the pressures that I was dealing with, the things that I was concerned with that I didn't realize until I went elsewhere. And but one of the consistent questions that it raises and even a quote that I had is like, we do all this striving and like, what is it for?

[00:40:09] And that's a question I keep coming back to, whether it's like, you know, that from high school, the like competitive spirit from being an athlete, like going to a Ph.D. program, like being in the work that we do, literally like studying why people do what they do at work and how, you know, how we approach our work. And something I said was, you know, I want to live up to my own expectations.

[00:40:33] And I truly don't know if I genuinely meant that or if I was kind of like aspirationally speaking it into existence. But it's something that I come back to. And like the thing I take from that is like the the doing things for the joy of them, like something that one part I really like is talking about my cross country coach. And he was an older gentleman who retired not too long after I graduated.

[00:40:57] But he he said on our first day of practice, running is a great metaphor for life. And I was like 14 years old. I had run like a mile in gym class. I must have been like, what the heck are you talking about? But he seemed to know that girls were under a tremendous amount of pressure. And so he never used fear or shame as motivators, which I think unfortunately can happen in sports and can happen for for girls.

[00:41:27] And so his philosophy was like, turn loose and just like let him run and run for the joy of it. And that I keep coming back to it. And it wasn't like we were bad. We won states three out of four years I was there. And so it's it's about like what what motivates us and what do we how do we continue to ask ourselves that in a world where it feels like there's all these external things of saying what we should do. We really need to keep asking ourselves, why are we doing what we're doing?

[00:41:55] So what motivates you and how would you live up to your expectations right now? Hmm. What motivates me is really asking big questions, thinking about big ideas that are surprising and scary. And some of this comes out in writing, I think.

[00:42:20] And, you know, that's something that I've started doing more over the last couple of years and especially more recently. And it reminds me, I I reread letters to a young poet recently by Rainer Maria Relka. He's an Austrian poet and posthumously they published some letters that he had written to a younger poet that he was mentoring who had sent him some pieces of work and asked, like, you know, for feedback.

[00:42:46] Do you think it's good? And he was gracious enough to reply to this aspiring poet. But he was like, they're like, fine, not great. But like, you shouldn't be asking me for this. This is not why you should do any of this. You should ask yourself, like, why do you write? And he even went so far to say, if it were denied you, like, if would you die if it were denied you to write? And I don't I don't know if he was being literal.

[00:43:14] But what I take that to mean is, like, if you're not doing this for you, then you should not be doing it and you're just going to hate it. And so it's really about, like, understanding our own motivations. And when it comes to writing in particular, like, I value that because I find out what I think through writing. And it's something that brings my attention into the present.

[00:43:41] It helps me work through an idea and figure out, you know, it just it feels very satisfying to do and to come to a conclusion. And so that's something that I keep coming back to of, like, what is it about that intrinsic motivation? And that's different for me than it might be for somebody else. But it's figuring out what that is for you. And I feel like that the more time that we can spend in our work in those states, the better our lives will be. It sounds cliche, but I think it's true.

[00:44:06] You know, when you started a sub stack, right, you have the intrinsic motivation. Should other people be doing that? I think they, you know, could take Rilke's advice of, like, ask yourself why you want to. And so, you know, I think if the motivation is there, if it feeds something in your soul, yeah. I think there are also fringe benefits. If that's the primary motivation, I don't know that it's worth it.

[00:44:36] But there are benefits of, like, you know, when you start to share those things, you start to find people who think and are interested in similar ideas and, like, want to be in conversation about it. And I think even, you know, professionally, I think it is increasingly an advantage to have a brand and a voice that's different than just a resume and that distinguishes how you think.

[00:45:00] And something I value a lot about writing is, like, the storytelling where, like, I mean, I think you can write about a lot of things, especially these days. You can tell AI to write about anything for you. But I try to think about, like, what are the things that only I can write? And, like, let me own that. And so when you think about writing, if, you know, if someone's interested, like, what are the things that only you can write? Write about that. Are you the only person that can write about baseball? Well, definitely not.

[00:45:30] But I do write about sports quite a bit. It's funny. I think I'm just pretty steeped in it. Like, my dad is a big sports fan, always was. I think one of my first outings as an infant was to Fenway Park, like, in a little carrier. And I, like, I grew up in Boston. Like, just off the marathon route, I was always involved in sports.

[00:45:53] I went to, you know, really competitive, big athletic schools, Southern Cal in Georgia. Go Dawgs. Bye Dawg. And my husband is a baseball journalist. And so, like, it's very much a part of every stage of my life. But, and I think there are some interesting parallels for organizational research and people analytics.

[00:46:21] But before I get into that, like, why do you think sports are, why do you think sports exist? And not like recreational sports, like professional sports. Like, why is this like a multi-billion dollar industry? You want my real answer? Yeah. It's a proxy for war during peacetime. Mm-hmm. Right. So people are, right, it's like playing war games, essentially, in advance.

[00:46:46] And it's a way of, for people to demonstrate competence and potential for if a war would ever break out, that they could be athletic enough to actually, you know, conquer their, you know, people that are on the other side of the fence that they're fighting. Yeah. Yeah. Yeah. Reminds me of this social psych, yeah, social psych study I read in undergrad about.

[00:47:12] They, like, measured the cortisol and testosterone levels of soccer fans, like before, during, and after a game, and, like, whether their team won or lost. And it was fascinating. It's like, it makes us feel things. And it's very primal. And I think there's so many, like, it has everything. It's got, like, triumph and redemption and pathos. And it's got heroes. And it's real, right? Like, in a way that a lot of entertainment is scripted. Sports is real.

[00:47:36] And selfishly, it's got a lot of data for someone who has that proclivity. But I think for, in an organizational sense, it's a really nice, like, playground to experiment with some of the ideas that we think about every day, like leadership and teamwork and performance and selection. Like, all of those things exist in a literal sense, you know, like sports are organizations that people work for. And also, you know, they're on display in a different way.

[00:48:05] And so I think it's a valuable, like, foil for a lot of the thinking that we do. So that's, it's, it is definitely part of the, like, why I write because I like it. And that's a good enough answer for me sometimes. I think about, it's one of my, one of my favorite theories from the IOPS literature is idiosyncrasy credit theory. It was just Hollander, 1958. It's an oldie but goodie.

[00:48:32] And it says that our interpersonal interactions are governed by this social currency called idiosyncrasy credits. And you gain those by demonstrating competence and you spend them by being idiosyncratic, by deviating from the norm. And I think it's interesting because I say this because I don't know that I would have written a lot of what I wrote if I was, like, my first year out.

[00:49:01] A, like, I don't know that I would have, like, earned the wisdom to actually, like, write what I'm writing. But also, you know, you might be, especially if you're, like, challenging the field, it can be difficult. And you're deviating from a norm. And so it helps if you have, like, demonstrated a certain degree of competence, which I hope that I have.

[00:49:20] And then you have that space to, like, do something a little different and, like, talk about something that no one else is talking about or bring a challenging idea and saying, like, hey, I am of us and I'm challenging us to that. So I think some of that is, like, how I've been experimenting with, like, what do I write about? And I always try to, like, have an interesting angle on something, whether it's sports or poetry or philosophy or something. Colby, you ready to join me in Cole's Corner? Let's do it.

[00:49:50] Welcome to Cole's Corner. All right, let's start out with some rapid fire. If you weren't doing people analytics and employee listing, what would you have done with your career? I'm not really good at rapid fire. You can probably tell that by now. I truly think I'm, like, made for this. So it's hard for me to choose. But for a long time, when I was growing up, I wanted to be a novelist.

[00:50:19] I liked writing. And so I liked the idea of building a character. Now, I'm not so sure. I don't think I would have liked to spend that much time, like, alone in my brain. I really need that balance of working with people. But, yeah, I wanted to be a writer. In fiction, I'm assuming, based on what you just said, like drama, something like that? Yeah, very character driven. Okay. What's the place you've never been to that you most like to go and why? Hmm.

[00:50:47] I have never been to Denmark. My mom's mom is Danish, was Danish. And I feel, like, Danish in my soul. Like, yeah, there's this concept of, I think it's called hygge. Hygge, I pronounce it's H-Y-G-G-E. It doesn't have a great English translation, but the closest I've found is, like, coziness.

[00:51:14] And there's this idea of, like, slowing down, enjoying simple things, being cozy, like candles, and, like, reading books, and just, like, enjoying. And they have, the Danes have, like, some of the worst weather in the world, and yet some of the highest, like, happiness indices. And I think that is why, is, like, they're kind of embracing some of that. So, that's, I'd like to go to Copenhagen. Feel the hygge.

[00:51:40] If you were a character in any book, TV show, or movie, who would you be and why? Hmm. Well, I already said I wanted to be a novelist, so I feel like I need to pick a book. Honestly, I find this question really difficult for a few reasons. There's so many universes to choose from. A lot of the books that I like are, like, devastatingly sad. No one's reading, like, Sophie's Choice and, like, oh, I'd like to play Sophie.

[00:52:07] Or, you know, most of the characters that I find interesting are very flawed, and so it's hard to, like, see ourselves that way, but it's probably true. Listen up, this is how you buy yourself time. Oh, man. Yeah, you know, this is not my first rodeo. I really like Elizabeth Bennett from Pride and Prejudice. I think Jane Austen is a genius, was a genius.

[00:52:35] But the reason I like her is because she's very intuitive and, like, a systems thinker, and yet, like, able to incorporate new information and new perspectives and change her mind. And so I aspire to that anyway as someone who, like, knows my own mind but is also open to new information and curious. So I got one big question for you. We touched on it a little bit a minute ago.

[00:53:03] I find this that I run and I walk sometimes in the morning, and you kind of always end up right back where you started. Otherwise, you're somewhere else. And at a certain point, I'm always like, why the heck am I doing this? And so the question for you is, you're a big runner. And why, like, what's the point? And you mentioned your coach earlier who was saying you just let him run or whatever it was that you said. Like, what's the point and what do you get out of it?

[00:53:34] Very existential. I think running, I don't know, running and, you know, probably, like, fighting of some sort are, like, the oldest sports of humanity. And I think there's something deeply primal about it where it's like this is part of how we were made. And I think something that I come back to often is that running teaches you a lot.

[00:54:04] I mean, I mentioned my coach saying it's a metaphor for life. Like, it truly is that. And it teaches you a lot of things through pain. Because running is just, like, how much pain are you willing to endure and for how long? And that's, like, an uncomfortable question to ask because we want to resist it. And so we think about, like, I think it teaches you the difference between good pain and bad pain.

[00:54:31] Because you, there's a difference between, like, running in a way that will, like, maybe you have, like, a slight injury. And if you push through it, that pain, you'll just make yourself worse. But then there's another type of pain that makes you better. And it's learning to tell the difference between those. But when you, like, the first time you run a marathon, like, you think it's going to kill you. That's what your brain is telling you. And so you learn through a few reps that you're not actually going to die. That it actually makes you stronger.

[00:55:01] And so there were parts of that that, like, truly made me, like, not afraid to go into labor with my children. Because, like, I knew even no matter how much it hurt, like, it wouldn't kill me. And I think to bring it back to work a little bit, you know, I think we're leaning a little metaphysical here. So I'll rein it back in. But I think something that we don't talk about enough is that development and growth is painful. And being in over your head is difficult.

[00:55:30] And it's also what makes you better. And so I think there's an element, I find, of, like, work where we don't listen to our bodies. And it's like we call it a gut feeling for a reason. Like, you need to learn the difference between, like, what is a growth opportunity that will make you better? And what is being in a position where you're just going to get burned out? Or what is being in a position where your job is going to ask you to sacrifice your principles? Like, your body will tell you.

[00:55:58] And so that's something that I take from running in, like, a kind of more mental sense. But running is one of those things that is full of contrast. Like, it feels so simple. And yet it's so complex. And it feels painful. And yet it's so joyful. Yeah. Sounds like I need to figure out what some of that good pain is. I'm like another kind. There's only one way to get to it. And you're not going to like how. All right. Let's do some what am I reading. Yeah.

[00:56:26] So the first one, which is like a whole other topic that I feel like we could talk a whole podcast about, but we're not, is from your newsletter called Is Performance a Power Law? Meaning Metrics and Misattribution. I have talked extensively about the power law over the last few years, but especially in the last year or so. And I see you've written about it. So maybe I'll just let you share.

[00:56:56] What is performance a power law? And why does that matter? Yeah, it's a good question. I was actually quite nervous to publish this. And it's funny because, you know, it seems so innocuous where you're like, okay, we're having a debate about distribution. Because it's very controversial. Why is it controversial? Well, that's a really good question. And I actually, that was a question I was going to ask you, but you beat me to it.

[00:57:23] I think it's controversial because it goes to our like existential curiosity of like, where does our value come from? And if I'm being honest, I am going to say something that like would have shocked my graduate student self, which is that like, I think performance ratings are kind of like neither here nor there. And like, they're not an exercise in measurement accuracy at all. That's not to say that they don't matter or like that performance management doesn't matter.

[00:57:53] But its primary purpose is to signal what is valued behavior, give feedback, inform decisions. It's not to like get to the most accurate measure. And so a lot of times like I, where it gets really interesting for me is like, how does this relate to compensation?

[00:58:17] And so when you get into like power law, I think it gets very lightning roddish when you get to like a, you know, how are people paid? Should some people be paid like 6x someone in the same role? Like I think we have like an evaluative or like an equity reaction to that. Like what does that say about my value as a person? And so I think, I think that's like why it's so controversial.

[00:58:39] The kind of like nitty gritty, unsatisfying answer of it is like, I don't, I don't disagree with most, with folks on either side of this debate really, because I think we're talking past each other. And I think we gloss over the kind of like boring definitional stuff and jump to like pretty charts. But really it's about like, are we measuring behavior? Are we measuring performance outputs? Because I think behavior tends to follow a more normal distribution.

[00:59:07] Outputs tend to follow a power law distribution. And that's all fine and good. I think, like I don't disagree with either of those points. I think the real question is like, do we reward behavior or do we reward outputs? And I also think that reasonable people can disagree about that.

[00:59:27] I think there's, so here's what, here's what I'd say is like, once we get to that question, the natural, the natural next question is like, okay, so do we pay people according to a power law or do we pay them according to a normal distribution? And if we're saying that we should pay for output, then most compensation systems currently don't follow a performance for a power law distribution. And they do, they kind of like make news when they do.

[00:59:58] It's rare. And so if we take that to be true, then we could say that our highest output employees who are making the most value of the company are vastly underpaid according to their output versus the distribution of pay. And I think, I think in IO psychology, we like over rotate on performance and not enough about pay.

[01:00:24] I remember like reading dozens of articles on performance and like, you know, a few on compensation. And it's, it's pretty like backward to me. I think it's neglected. You have a theory about pay and that everyone has paid the same, not literally in dollars, but basically that like the expertise and energy and effort and hardship required of a job. Miscellaneous part, sure.

[01:00:54] Is, is a commensurate with how someone is paid. And if those two like sides of the equation are out of balance, there's a market correction that brings them back to equilibrium. So if that's true, then do you think that high performers are paid the same? And like, are we going to see a market correction? Are we, are we already seeing one? So when I talked about everyone gets paid the same, I said that there's really three things that are intention.

[01:01:23] And compensation professionals usually only care about one of them. And it's equity, proportionality and equality. Right. So proportionality is, are your efforts rewarded in proportion to your outputs? Right. We don't pay that way according to what you just said. So proportionality does not seem to be considered. The quality would just be literally, you know, everybody's paid the exact same amount of money.

[01:01:49] And then equity is more about like, are you paid kind of like what you're worth? Right. Right. And we have a whole discipline now in HR that used to not exist called pay equity. And so you can see which one of the three is optimized for. Right. In the pay equity space. And the reality is, that's where the divergence occurs between, it's between the equity and the proportionality.

[01:02:15] Mostly, unless you're truly going to go full communism, you know, you're not going to go just everybody literally gets paid the same. So it's really just between equity and proportionality. And that's where the tension exists. And then I would also say what's upstream of that is you talked about people get paid via, or not paid, but they, that performance is either normally distributed in terms of what you used, like the processes that go into it.

[01:02:42] So procedural justice, and then it is, but it is, it's power law distributed when it talks about outcomes, which is just, you know, distributive justice, which are two different pillars. And so the question you're really optimizing for is, are you optimizing for procedural justice or distributive justice? And it seems like we optimize more for procedural justice than distributive, right?

[01:03:06] So they're like, what I think people are unwilling to confront most of the times is that they only see that we're optimizing for one thing. And really, in reality, we're completely neglecting whole other areas that do matter. And I think that's why you see, again, in terms of like principal agent theory, that executives aren't paid the same, right? They're not. They're paid exclusively almost sometimes.

[01:03:34] I mean, you even see CEOs that make a single dollar, and then they're paid all in equity, right? Not equity in terms of like pay equity, but we're talking about equities, securities, like security exchange commission. So they're paying in stock for the firm. Stock for the firm is nonlinear, right? You can make zero dollars and you can make infinity dollars in that type of world. And that means that we have maximized their pay for outcomes, right?

[01:04:01] So if you want an outcome-based model, if you want a distributive justice-based model, become an equity holder. And if you want a, I know this is where words and terms get confusing, but if you want an equity-based model, go to the procedural justice side and be a worker, be a W-2 employee. And we've kind of chosen two different universes in this space.

[01:04:24] And one is governed by kind of one theory of fairness, and another one is governed by another theory of fairness, and never the two shall meet. Another way of looking at this is something I believe I've heard you say is that talent is output minus effort.

[01:04:46] And I think there's a fundamental question in the area of compensation is like, should we compensate people based on their talent, on their effort, or their output? Well, I think one of the things that's upstream of that is not even before you get to compensation is what should you select for? Sure. If you have two candidate, candidate A, candidate B, they both have equivalent output, but candidate A has half of the effort,

[01:05:13] and candidate B has twice the amount of effort, and thus effort presuming that that will take twice the amount of time, the same output, but twice the amount of time, are these two people actually equivalent, even though they had the same output? And how can you be sure that you're actually measuring their output too? Let's say for the sake of argument that we're doing this perfectly, and we know that will never exist, but let's say for the sake of argument we are.

[01:05:38] Then it becomes a selection issue, generally speaking, because we try to optimize for selecting folks with talent. Right? And that looks like, and that's obviously a multifaceted, you know, many different constructs of what talent means for different jobs, which brings us back, I think, to the performance law, which I don't think you ever actually answered the question, is performance a power law? Because I think your argument is, like a lot of the thing that you brought up in your answer about,

[01:06:06] you know, is it fair or whatever we were talking about a second ago, was based on the premise that performance is a power law, and therefore should we pay people based on their outputs or their inputs, essentially. But in the article, you actually take issue with the whole concept that is performance power law based? And so I want you to answer that question. Is it power law based? Performance is not a power law. You heard it here first, not even close to first.

[01:06:36] Performance is behavior, and that is normally distributed in nature. Performance outputs are power law distributed oftentimes. Something I think is interesting too, is like we talk a lot, a lot of these are by nature of being counts. They're bounded at zero. You know, you can't make like negative widgets. You can make a million or five.

[01:07:00] Something that is interesting to me that we rarely consider is like people can generate value for the business. Yes, but they can also cost value to the business. Like there's a negative part of the axis that we're not looking at. They're considered a, not an asset, they're considered a liability. Yeah, right. But like we don't think about, you know, we always think about the positive value that people add. Yeah. So, or the negative in terms of like their literal salary, but not necessarily like what they might cost the business if you have a poor performer.

[01:07:29] So anyway, I think that's kind of an interesting wrinkle. But yeah, that's, that's my take. And, and I, I stand by it. I do think it's like rather like epistemological. I don't know if I said that right. But again, like I, I come back to what are we doing with this? And that's why it matters. Not necessarily like the essence of it, but I think it's the definition that we're talking past each other.

[01:07:55] Well, I said this a while back on an episode where I said, I think it just gets to the people's fundamental belief. Are we all equal or are we not? And there's some people that are like, oh, we're not. And that's good. And there's some people that are like, we're all equal and that's good. And those two people are going to go to war. And I think that's really what it comes down to. Well, let's, let's move on to our next, what am I reading article? It's somewhat related. I think you'll see it.

[01:08:21] I think you're, you're probably already on to me and it's called, it's from the, the substack called the right path. And right is spelled like writing, not like right, like as in, you know, the right answer to a question. Oh, I love a good pun. And it's called the mathematical reason most people never quote unquote make it. The uncomfortable truth about effort, outcome, and the math that connects them all.

[01:08:45] And I found this to be more of a fun article about like how to like optimize, like getting clicks or something like this than anything. But it covers some, some deeper subjects, which is in 1963, the physicist named Derek DeSolo Price created what started studying scientific publications to understand why some researchers dominated their fields. While others got zero attention and it followed what is called Price's law.

[01:09:13] Price's law states that the square root of the number of people in a domain does 50% of the work, which again, this leads to a power law based distribution based on what we were just talking about. And he gives some examples about articles that he himself has written. And he said something, I'm trying to find it here, which is he published, he said, let's use my own substack as an example. In the last eight months, I've posted about 30 newsletters, over 700 notes and thousands of comments.

[01:09:42] In that time, only about 15 notes, two newsletters went quote unquote viral. And that brought me an overwhelmingly large amount of my subscribers. That 17 pieces out of 730 total outputs, roughly 2.3% driving the majority of my growth. The real question is, I never would have found those 17 winners without doing the other 713 other things.

[01:10:07] And so that's kind of the crux of what I wanted to dig into with you is he talks about this kind of this paradox where you have to do all of the outputs. You can even figure out what are the small amount of outputs that give you a disproportionate amount of the rewards. And that does follow a power law distribution in terms of outputs. And so what did you think about this one in relation to what you wrote as well? Yeah, I thought it was really clever.

[01:10:37] And I agree with your read. I mean, I thought it was funny that the concluding section is called like why you have to write the other 90%, even if 10%. And so the practical side of me is like, okay, so what's the point? I think in particular with this one, I think the corollary to what we were talking about before is kind of the behavior versus output is like quantity and quality are not necessarily the same.

[01:11:05] There's a couple examples, I think, the way that you can draw this out is one is like, I mean, what you just said about why you have to do the other, why you have to write all the articles reminds me of this, the kind of other implication of what the power law distribution, like what that worldview means and maybe why it's threatening, which is the like kind of unsaid implication beyond that is like,

[01:11:34] well, maybe we should just have the tail, right? Like and get rid of everybody else. And that, you know, is an implication that is dangerous or at least threatening to folks throughout the distribution, right? But it's like, what happens if you say, okay, you know, the top 10% of our company brings in half the value. Maybe let's say we're generous and we don't just keep the top 10%, we keep the top 50%. Like, will that hold?

[01:12:02] Will you still have the like that 50% value capture without the rest of the distribution? Or is it the case that having that bottom part of the distribution actually enables the high performers to do what they do best? Or are they going to be stuck doing all the stuff that the lower performers were doing? Is it structural? And even then, if you, you know, if you cut the distribution at 50%, then, you know, do you have it? Does it create another power law distribution?

[01:12:28] You do that, like, you know, ad venetian, like, how do you? According to Price's law, it would. Right. And so what company exists at the end of that? Like what and what is the value of that business? Is it? I think it's a really great exercise that is to think about things and the limit, right? And I've talked about it, I think, before on the podcast. I can't remember when.

[01:12:50] But there is this fascination right now in Silicon Valley until how quickly, like there's like literal wagers being put on this. How long is it going to take until we get a one person unicorn? Yep. So a company that's valued at a billion dollars just by one person. And that's the logical conclusion to the argument that you're making. And so the answer to that, I think it was more of a rhetorical question, but the answer to it is that's where people want to go, at least in the investment communities.

[01:13:20] They want to go to where you literally need one, perhaps even zero people to get literally infinite gains. And then there is, again, another wing. So let's call that the distributive justice wing. Let's go to the procedural justice side of things. And there are folks that are saying, well, what about the rest of us? Right. And what are we going to do? How are we going to eat bread? You know? And I think it lends itself for a really interesting discussion.

[01:13:50] The thing that I kind of latched on to in the end of this article about the paradox and do you still need to write the other 90% of articles? Is I think I've kind of come to this conclusion that I just like the other 90% of stuff better. I was going to ask you about that. I have all the data. I've got all the metrics. I can optimize for all of these things.

[01:14:13] And a lot of times I consciously go for the things that I know will be in the 90% just because that keeps me sane and I like it more. Yeah. I thought about that because of the intro to the article, which is an interesting take. It's about looking at Spotify and saying that Spotify has 11 million artists. Only 50% of the streams are generated by only 3,000 of those. I can't do math and fast enough to know what the percentage is.

[01:14:41] But I think it's interesting for a couple of reasons. One is that if we're talking, again, I keep coming back to this, if we're talking about output and like viewership, listenership, whatever you call it, then certainly that's power allowed distributed. If we're talking about like musical capability, we're really like truncating the distribution of like anyone who has music on Spotify is probably like talented somewhat just, you know, statistically. And so we're, yeah, so we're cutting off the whole distribution. So, of course, it looks like a tail. But I digress.

[01:15:10] But also, I mean, I think the conversation is qualitatively different when we're talking about value to the business where it's like, okay, that like it kind of is what it is in a sense. But when we talk about art, it's like there's this friend's quote where Phoebe's like, I would give anything not to be appreciated in my own time. There's like something to like, you know, I was going to say, you know, you've written, you know, dozens or maybe hundreds of articles.

[01:15:36] I'm like, do you think there's even a correlation between the number of likes that they got and how valuable you think it is? What I would say, I would kind of take a different tact on it, which is I have found the archetypes that work. And I've found the archetypes that don't. And what I would I have found, like, I'll give an example, just just and I see why so many people do it now is I wrote an article.

[01:16:05] It's my most popular article in the past year called Claude in Excel is insane. Right. So I was talking about Claude got released to have a functionality in Excel and I wrote it up pretty quickly, published it out there and it just blew up. Went very boring. And the thing that I noticed is that there are people out there that they only publish posts like that. That they're like and they'll offer like training of how to do Claude in Excel or training of how to use the new fanciest thing.

[01:16:34] And I realized, like, that's an archetype. Right. And I've almost come to this conclusion is every time I stumble over an archetype that works is I just don't like it anymore. And it's because I want to be able to not be governed insofar as like other people are governing me by clicks and likes and all that kind of stuff. I don't want to be governed by what the masses say. I want to be governed by where I get inspiration.

[01:17:02] So you're like climbing down the spectrum on the power law distribution on purpose. Or it's just like going into it eyes wide open. Knowing that like it's like what actors say, like famous actors sometimes will make like one popular film and then one like indie film. And they said one for me. You know, I will do it's like, OK, I'm going to do one for the people and then one for myself. Right. Yeah. Not everybody's going to like the camera or the system, but I'm going to write it for me.

[01:17:30] Not everybody's going to like everyone gets paid the same, but I write it for me because it's good. You know, and that that's what I do. And I don't know if you have kind of a similar viewpoint on it because you do write quite a bit about sports. I don't know if that's an archetype out there. I've never written about it, so I couldn't tell you. But maybe maybe that's a good segue actually into our next one. You were an article called Sports are Not the Perfect Metaphor for Work. That's why they're useful.

[01:17:57] And so maybe maybe you could actually walk us through this and kind of answer the question I was giving a second ago. Yeah. Yeah. This was a fun one. I was actually in response to a conversation with Tyler Weeks, who I know has been on the podcast before. Tyler's great. He's also got a great sub stack and writes about really interesting and different stuff.

[01:18:17] And he asked me something about like why I write about sports or how I handle the ways that sports break from more traditional organizations and like how I handle that. Because there are a lot of things about sports that are different, like radical pay transparency where we know literally what everyone is paid. We don't have that in businesses.

[01:18:39] We have radical performance metrics that are radically transparent performance metrics, stuff like that. And so this was really kind of like an explanation of that. And it's interesting because it actually goes further back.

[01:18:58] I started writing about sports and its overlap with organizational science back in graduate school with my advisor, Brian Hoffman, who's also a big sports fan and just, I think, wanted a reason to make sports part of his job. And so he would do research. And it's a really and also you don't have to like collect data from an organization to do it.

[01:19:17] And so it's a really like interesting place to extend those questions about asking, like, how can we better understand performance and its dynamics? That was the one that I kept coming back to. But it's funny because we always had to like pressure test it with reviewers to say, like, this is actually relevant for understanding teams, understanding leadership. But I think we see a lot of those same themes play out just in different ways.

[01:19:45] And so where I went with this article is something I think is interesting and understudied about the way that we look at performance in organizations is that we treat performance, regardless of its distribution. We treat someone's placement on that distribution as like a pretty stable trait, like more trait than behavior state. And I think there's a lot more within-person variability than we like to think.

[01:20:13] I love the theory of typical and maximum performance. That kind of gets back to what we were talking about in the like selection and compensation conversation about like, do we, it's much harder to select for typical performance because you can't observe how someone works in there every day. They show up to an interview and that's more like max performance.

[01:20:35] But there's, you know, there's, there's dynamics that we often ignore in our systems of just putting someone in like a nine box and like that's the type of performer they are. And I think that ignores a lot of meaningful variance in terms of what are they capable of? What do they do on, you know, an average day? How do we actually develop that is probably the most important question of like, what do we do with that information rather than just treating it as an object?

[01:21:04] Hey, this is Brent Skinner, host of Small Talk Window here at the Work Defined Podcasting Network. Have you ever wanted a short podcast to listen to during a coffee break of your work day? You've come to the right place. My guests dispense with a small talk as quickly as possible and settle into a meaningful conversation around the world of work. Well, I had, I had one more article about the power law in there, but I think we've beat that dead horse enough.

[01:21:32] But, uh, do you have any questions for me before we wrap up? Yeah, I've got a lot of questions. Um, I, it's funny, I actually, um, at one point I put, um, professional question asker in like my tagline on LinkedIn. It was kind of a cheeky joke with myself that like, I, I like write surveys for a living. You also ask questions, you know, for a living in a way. But I do think like curiosity is probably like my most valuable, like skill in my, my job.

[01:21:58] So, um, I, let me think about where to even start here. Um, something that I think about a lot is the way that we talk about intelligence, especially in the like vein of artificial intelligence. Um, you've written about like intelligence of different kinds. Something I was reflecting on recently, it was like, is the nature of intelligence changing or are the indicators of intelligence changing?

[01:22:24] And like, one reason I thought about this is like one of the reasons, one of the times I first believed that I was smart was because I was really good at spelling. It's like, who needs to be good at spelling anymore? Uh, and so like, when you think about the, the indicators or the actual underlying constructs, like, how do you think that's changing? Um, when, and you were talking about the three meanings of intelligence article that I wrote with Kristen Sabo, just want to give her a shout out previous guest from the podcast, all that stuff.

[01:22:52] Um, yeah, it's, it's interesting concepts in the sense that I think we're playing this game right now as a society, which is, we think that there, that if you just ramp up intelligence enough, like true, like whatever your definition of it is, but artificial or human level, whatever. If we ramp it up enough, do you kind of become God and, or do you kind of become conscious?

[01:23:24] And, and I think that we're going to find that those are like three different spectrums, right? So that there may be that maybe there's a limit to intelligence. Maybe there's not, I really have no clue, frankly. I don't think I'm smart enough to know the answer to that question, but the thing I am pretty sure of is it doesn't matter how intelligent you get. That doesn't make you conscious and it doesn't matter how intelligent you get. That doesn't make you God. Right.

[01:23:49] And, and I think that people are conflating those three things into one that they're creating a God and they're creating a super intelligence and it will become conscious and then it'll kill us all or whatever. And I just think that those are three separate things. And I'm just curious how far the one spectrum of intelligence can go. Cause I think it's fascinating. Yeah. Yeah. What that reminds me of something that I've been thinking about lately and it's kind of half baked, but I'm curious what you think.

[01:24:16] And it's something that I call like the disembodiment of work. And I mean that like, if we look at the course of human history, like work for a lot of that time had both a physical and intellectual component in a lot of cases, some more physical, some more intellectual. And different technological advances have kind of changed the nature of that type of work.

[01:24:42] And we've seen things like, you know, the factory line and manufacturing and, and how that changed physical work in a lot of ways, like craftsmanship. And I think sometimes those technological revolutions affected certain professions that were like more or less high status. And like that influences the discourse around it.

[01:25:04] And it kind of pushed like the higher status work out of like crafts, things that have, you know, a physical component. I think maybe one exception now is like physicians. And even then like we're starting to see like more robots. They're coming for them. Yeah. And so like we have this kind of move toward, okay, work for those who have an option becomes knowledge work.

[01:25:28] And we become kind of like these like brains in a jar is like this image that I have of like, that's, that's what work is. And I, and now we have AI coming for some of that knowledge work. And so there's like a different kind of tenor around that conversation. And there's a part of me that like, I don't just, I don't subscribe to like dualism. I think the work that we do, like a purely knowledge worker job is deeply physical.

[01:25:56] And by that, I mean like the way that we make decisions is emotional, not rational. And those emotions have a mental component, sure. But like they originate in our body. And like, if you don't believe me, like try to do a presentation when you're eating hot sauce because like you can't think. And so when you think about like, what does that mean to be disembodied?

[01:26:20] Like literally like separating ourselves from our work and like talking to, talking about like a type of intelligence that just literally does not have a body. Like, does that, like, are you seeing a trend like that? Does that mean anything to you? No, I think, I think you're actually kind of saying what I was saying in a different way, frankly. Um, I think that the root of consciousness is biological. And if you create something that does not have biology, therefore it cannot be conscious. Right.

[01:26:50] And again, I might be proven wrong at some point and willing to be proven wrong. But that embodiment is why robots have become such an interesting question because the, they are embodied. Right. They do interact with the world and they do have the capability for putting artificial intelligence in them. And the, really the big question is, is once it gets sufficiently good, right. Cause I just don't think it's there yet. People say it's there. It's not.

[01:27:15] Um, once the robots and the AI get sufficiently good, do, does the kind of the emergent gestalt of those two things combined create something that rivals biology. Right. And that's, that's kind of where my head is at right now is once you solve the embodiment problem, does that tell you something qualitatively new? I don't know the answer to it, but I think it, it might be the most important question of

[01:27:43] the 2030s because that is when I think you're really going to see the robot boom happen. Yeah. I think there's like debate on this. It'll be interesting. I've got another one for you if you're willing. Okay. Um, something I always like to ask people about is their dissertations because it's something that we spend a lot of time thinking about for a long time. I don't know why, but I just had a feeling. That's funny. I love to ask people. I think it's because you studied under Brian Hoffman, who I cited extensively in my dissertation and I wrote about 360 degree feedback.

[01:28:11] And so I was like, there is this weird connection here that I don't think we've ever talked about, but I was just, I was, I was curious. Yeah. Yeah. So I'm curious about your, your dissertation and what, what you learned from it and whether you think what you found, like, what was the finding and do you think it still holds? Uh, I know it doesn't hold because I tried it out. Uh, and this is, uh, this is definitely something I have not talked about in the podcast for.

[01:28:38] So one of the roles that I had at one of the startups I worked for was to, uh, implement a 360 degree based performance management process for the firm and to try it out. And it was a huge failure. And, uh, that was a really cool thing. And, and, uh, a humbling thing to learn is that the thing that you had studied quite profusely for a long amount of time, uh, might, when it comes into contact with reality and from

[01:29:07] a first principle standpoint has, uh, if you have a flaw in a closed loop system, uh, and you try to do it at scale, the flaw is the thing that scales, not the system. Right. And that's an interesting thing to learn. And, uh, yeah, I think that's all I want to say about that. I try not to get in trouble with a prior employer or anything like that. Fair enough. It reminds me of, this wasn't my dissertation, but it was my, my master's thesis.

[01:29:34] And speaking to Brian Hoffman, I don't know if you had this, um, when I was in graduate school, I remember when we started writing papers and our faculty told us like, don't invent new things. Like don't make a new theory. Don't make a new construct. We have enough proliferation without like grad students running around making, making new things, but I couldn't help myself. Um, I had some help from Hoffman. Um, from Brian Frost and Bill Balmer and a few others.

[01:30:03] But I, uh, invented a construct or introduced one called off-duty deviance, uh, which was like employee behavior away from work that, um, might be in conflict with the organization's policies or values. And the closest kind of corollary, uh, that existed in the literature was CW counterproductive work behavior. Um, which is of course something like deviant that's directed at the company itself or, or other, um, coworkers.

[01:30:33] But that's much more determined by like organizational attitudes, different mechanisms there. Um, and it was interesting. We had some pushback from like trying to publish this work where folks were like, this is the psychology of work. Like, why are you trying to talk about outside of work things? But I think it was kind of like prescient in a few ways. Um, I mean, it mostly came out of like news articles where we had, uh, people getting fired for getting arrested or posting something on social media that got them in trouble with their employer.

[01:31:03] And we were like, people are losing their jobs over this. We need to understand like what, how organizations are thinking about this, what the practices are. And there weren't a lot of materials. And so like, I, I combed through a bunch of like news articles. I read case law, which gosh, if, uh, if journal articles are tough, like case law is like watching paint dry. But, uh, I took a sociology class on deviance, which I think everybody should take a sociology class. You mentioned one about, you mentioned economics and they're great, like systems thinkers theory.

[01:31:32] Um, I loved that. And, uh, the reason that I think it was prescient was like, I might even like change the title, the name of the concert. Like what is off duty when you work from home? Uh, like in a, in a time when like everything is always on and, um, is, does that really exist? Uh, and then of course, when we think about like what it means to, it makes me think about like, what does it mean to employ a human who has an out of work life that might be in

[01:32:02] conflict with, uh, an organization versus like an agent, which like does not have to my knowledge, like desires or motivations outside of its work. It doesn't care if you turn it off, uh, you know, or get rid of it. And, uh, I don't know. I think it brings up some of those more like questions about consciousness and the, the fundamental relationship between people in our work and what that means. So I don't know. Is that related to anything you've been thinking about? Yeah. I would say it's related to every way it gets paid the same.

[01:32:31] Some jobs, if your life outside of work fundamentally does not matter, you're probably paid a little bit less. And if your life outside of work matters a great deal in terms of your reputation and the things that matter to you, you probably get paid more. And it is that sacrifice or what I would call the miscellaneous heartache associated with that, that makes it more or less relevant depending on your level of how that those things matter.

[01:32:56] And as far as I can tell, nobody has ever put that into an employee value proposition model, uh, that I've seen out there. And so I think it's, uh, I don't know, very ripe for the plucking if somebody wants to go work on that. Yeah, I think so. It didn't really catch on. So, you know, I don't know how successful a new construct was, but, uh, okay. I got one last one for you and I'll, I'll, I'll, uh, leave you alone. Um, we're already at the longest episode ever. So what, what's one more? Okay. What's one? Um, I, I know you have a policy.

[01:33:30] It's not really a debate, but it was still fun to try it out. Okay. Well, that'll be a good one. Um, but what debate do you think we are not having in our field that we need to have? Uh, I mean, I, I, gosh, um, I'm going to say this. People are going to hate me when I say this, but I kind of don't care. Uh, the debate on whether or not we want to be relevant. Hmm.

[01:33:58] I think we consciously choose to choose the kind of the kid glove topics that we know we're comfortable with, like selection or like, you know, all the things. Um, like think about all the big things that are happening in the world of HR, right? I was psychologist. If they have a choice to study big thing or study thing that they're more comfortable with, that's more in their wheelhouse. A hundred times out of a hundred choose to study the thing that's more comfortable in their wheelhouse.

[01:34:26] And, and I see that as a conscious neglecting of being relevant. And so we, we talked about economists earlier. One of the reasons why economists are more powerful than our psychologists in the world. And things like the reason why the federal reserve is like a high status thing. And I don't know, the president of the PSYOP is, is not as high status as that is because they always go and study the things that matter. And it is a conscious. And again, I see this as eyes wide open. It's a very conscious choice.

[01:34:55] Again, people are going to hate me for saying this, but I don't care because I want to be relevant and I want our field to be relevant. And I want us to be the people that are out there. I mean, I had this episode recently with J Ben Babel and he talks about dissenting viewpoints and how the people that are in a community that have the dissenting viewpoints typically are the people that care about the community the most. I care about this community a lot. And that's why this dissenting viewpoint, I think actually matters a lot.

[01:35:22] It's like we want, if we want to keep, to keep having a voice, we have to choose the things that matter first and not our personal kind of idiosyncratic things that we prefer. However, those need to be second. So maybe we do need to generate new theories, you know, because maybe those theories would be relevant to the things that are actually in the zeitgeist. Yeah.

[01:35:44] Well, I think that brings us pretty full circle to the beginning of the conversation about why we're talking and thinking about what the future of this field is and why that is challenging. Like, here's like what one I've probably been on this one before, not as much on the podcast, but outside of the podcast, especially at SIOP is like, why are we comfortable with like a 0.3 correlation being the ceiling of how good we can do it predicting job performance? You know, like with a pre-art assessment, why is that like agreed upon everybody in the field?

[01:36:13] Nobody's trying to kind of go past that. 0.3 is a bit of a ferocious. Yeah. It's ferocious. I think we were so obsessed with individual differences and like, yeah, psychology, of course. But like we study the heck out of people. And then we say, you know, we ignore the situation, the team, the within-person variance. And we're like, oh, everything else is error. So convenient. And it's not. It's not. And I think Tyler and I even wrote an art, Tyler Weeks. I know.

[01:36:42] He always calls us artists. Because I brought this up to him at a dinner. And I was just like, yeah, I don't understand. This 0.3 thing drives me crazy. He's like, let's write about it. And again, I think he's a physicist by training or something like that. And so he's like, okay, let's talk about it. I'm like, great. And then we went out there and I feel like destroyed the concept, which I am. Never mind. But I'm bringing on a very prominent person to talk about it. And I think it will be eye-opening, let's say. Yeah. Yeah. I like it.

[01:37:13] Yeah. But Colby, you have been a fantastic guest. This is the longest episode we've ever recorded. I've kind of been on a trend lately of doing longer episodes. Just a personal thing I want to do is like, I just want to have the conversation and see where it goes. Why limit it to a certain amount of time? If people quit listening, that's their prerogative. I don't care. I want to do the 90% things, not the 10%. Right. I was going to say, we're not in the tail anymore. Yeah. Yeah. We're not optimizing for the archetypes that are out there, but I really appreciate you being a good sport with this.

[01:37:41] If people want to learn more about you or reach out or what have you, where can they find you? Yeah. You can find me on LinkedIn. And I mentioned my sub stack is called Variance Explained. And you can find me there. But thanks so much for having me. This has been fun. Awesome. Well, you've been listening to Directionally Correct, a People on LX podcast with your host, Cole Napper. And today's guest, Colby Nesmith. Thanks for joining me, Colby. Thanks for joining me. Thanks for joining me. Thanks for joining me. Thanks for joining me. Thanks for joining me. Thank you.