Intelligence at Databricks Where the Future is Now - Marina Pearce - #197

[00:00:00] Hello friends of the podcast and welcome to Directionally Correct, a People Intelligence Podcast with your host, Cole Knapper, and today's guest, Marina Pearce, Senior Director of Workforce Intelligence at Databricks. In this episode, we will cover what workforce and people intelligence really means. What does it mean to create an intelligence layer? What goes into that? And how is it different than maybe, you know, people analytics from three to five years ago?

[00:00:27] A shift from analysts as reporters to analysts as architects. I don't even really like to call people analysts, if I'm being totally honest, because I think it puts them in a box. And Marina's philosophy of being loud and opinionated about your work internally so everyone can poke holes in it. You don't really like talking publicly about a lot of this stuff. You're very opinionated, but you don't necessarily share them. That's why I've been, like, encouraging you. Now, let's get down to business.

[00:00:55] Before we get into it, a quick word about HRBench, the company powering this podcast. Most HR tech was built for the problems of a decade ago. HR Bench is built for what's next. Analytics, workforce planning, surveys, data health, job architecture, and AI workforce transformation unified in one AI native platform. HRBench innovates faster than anyone in the market and scales to solve real organizational problems.

[00:01:23] The future of people intelligence isn't more dashboards. It's providing solutions for the business in real time. 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. So, Marina, what's it like being super, super spoiled and having everything at your disposal at Databricks?

[00:01:53] It's pretty fun. It's just like, Cole's coming in hot. It's a question. I mean, it is. We like to try out all the new tools, all the new sort of toys. It's fun to explore. It can be a little overwhelming too, because you're in a position where you have access to everything as it releases. So you need to make decisions about where to optimize and what to teach others, but fun. What is everything? What are these tools that maybe other people don't have that you have? Hmm.

[00:02:23] So at Databricks, we produce a data and AI platform. And so internally, we use that platform. We have access to foundation models as a result of using that platform, but we're often playing with features before they sort of release to everybody or before everyone knows about them. So perhaps that's mostly what I mean. But we also use AI tools that are, you know, owned and sold by other companies. Isn't that wild?

[00:02:54] I'm sorry. I'm just coming here. I'm like super jealous of you. You get to do such cool stuff. So I've been looking forward to this one for quite some time. Finally getting you out and about talking to people. This is good stuff. That's great. Yeah, it's going to be great. I feel like it's been the right time to be at Databricks and play with all of these tools because the past year, year and a half is when we've released so many that are sort of optimized for people like us, people in people intelligence, people working in this space.

[00:03:23] And so something will release that makes something that used to be very annoying, very easy and also sort of delightful. And then you have it first and you get to play with it first and get feedback on it first and think about how would I use this? Yeah. So, yeah, I mean, it is kind of a joy. I it is hard to imagine what it was like before or sometimes what it could be like elsewhere. Well, we forget so quickly.

[00:03:50] I mean, it's you just get so accustomed to, you know, being able to make everything so easy. You mentioned people intelligence. What does it mean to create an intelligence layer? What goes into that and how is it different than maybe, you know, people analytics from three to five years ago? So to me, so I am always curious about how we sort of as a discipline define ourselves, because I think we have phrases that mean a bajillion different things and whatever.

[00:04:18] But I think years ago when I was working in, quote unquote, people analytics or whatever it was that I was calling my teams at the time, what it meant was some self-serve tooling, some advanced analytics and research. So modeling things that perhaps folks didn't have had the interest, but perhaps not the skills or time to really deeply dive into to answer a research question. And then sort of organizing rhythms around understanding something like we care about X factor.

[00:04:47] Therefore, we're going to have a monthly or quarterly indicator of how that's going and predict it and so on. And we were sort of in this position as people analytics folks where we were the reporters, the analyzers. We had the X. We understood the data. We knew the exact definition of the denominator of the attrition metric. Nobody else did. We had sort of all the keys to the castle.

[00:05:07] And I think what's quite different today from my perspective is with technology, we have the opportunity to disperse that capability outside of our human brains and sort of give our keys to everybody. Democratized capability, not democratized data sort of. And so today where I focus is not on like helping a particular person answer a question or allocating someone on my team on like a three week project to research something or to come up with a new metric.

[00:05:37] I think a lot more about how do I create a foundational ecosystem where everybody can ask and answer the types of questions that we individually used to have to ask and answer as people analytics professionals. So how do you like unify all your information together? How do you secure it? How do you govern it? How do you keep it up to date? Then how do you create really, really easy interaction layers on top of it so that you can talk to your editor directly if you want a conversational experience. You can interact with agents or apps or dashboards, whatever it is that you need.

[00:06:06] And where my team spends a lot of time right now is optimizing the foundations of that ecosystem. So like make sure our goal tables are just right. Make sure all the dashboards and agents and apps we certify are super reliable so that if anybody is using them, they don't have to question it or come to a human to check. They know that what they're getting is correct.

[00:06:24] And so if I sort of zoom out, I feel like the shift is maybe from being like the only people who can ask and answer a question to being the people who enable everyone to ask and answer a question. And increasingly, you didn't sort of ask for this, but I feel like there's a third step beyond just put all your data together and then interact with it.

[00:06:43] I think there's a help people automate their work step that we're just sort of starting to dip our toe into, which is not just analytics oriented, but like change the way you operate at work as a result of you being able to interact with the kind of people intelligence platform. I love all of that. What do you think? Do you feel differently?

[00:07:07] No, I, well, the thing I like the most about what you said, because most of these conversations I have, they say a lot of the same things that you say, but they're talking about when we get there in the future. And you said today, today we do this. And that's why I like talking to you because I think a lot of folks are kind of playing an idea land of like, oh, it could be this way.

[00:07:33] And you're saying, no, it is this way because we have, we have made the changes. We've done what's necessary. We've made the leap and guess what? Get in the water. The water feels great. Right. And one of my personal frustrations has been folks who, who see the idea for the future and they're scared or they think it's going to be bad or they think AI is going to take all of our jobs or, and all that, you know, you talked about automation and automation is going to be a bad thing, not a good thing.

[00:08:01] And the reality is, again, it's great. Like, just, just be early, you know, just be the one who's doing it today, not tomorrow. And guess what you might find that you're in the center of all of the good decision making that's happening at your organization and helping democratize that. Like you said, that's, that's what I'm seeing. And it's like my definition of leadership is somebody who can define a compelling and positive vision for the future. And you're living it.

[00:08:30] And so I love it. I love it, Marina. I'm so glad you're here. I do think it's, it's fun. I do. I have been on this sort of journey for a while. I feel like this idea of democratizing capability has been an interest of mine. And I feel like for years, I have gotten the reaction of sort of, but what will we do? And I have, I have never observed a lesser need for people on the team.

[00:08:55] Once you start setting these ecosystems up, what you observe is the people on the team stretch into new skills. They start becoming different. And you need more of them, not fewer of them. And so it's, I don't know, I guess that's maybe one thing I would say just in response to what you shared too, is I think sometimes the fear reaction, I'm not observing that to be the case.

[00:09:22] Well, and one of the things that maybe I see that you and many others don't is the full spectrum of analytics teams. Like I've always talked about this kind of looking up philosophy. And I think we may have even chatted about this before. It's like, when you're really good, you're always looking up to who's doing it better. All right. So you're always looking up and you don't necessarily look behind you to say, oh, what are the people that are looking up to me? Right. What are the people that are trying to aspire to be better in their own respect?

[00:09:53] I talked to a very, very wide spectrum of people in this field. And so I see a lot of folks that maybe, you know, you don't see if you're only looking up. And I think it's really, really important that we paint a compelling vision for the future for those folks because they're not there and we need to show them how to get there. And I think a part of that, which you were touching on just a second ago when talking about the skills. And I really love for you to talk.

[00:10:21] Feel free to talk for a while on this one because I love to see where your brain goes. But if you're building a team from scratch today. What what does it look like? How is it composed? What technology do you need? What skills do you need? But also I would love to see from your perspective, because you've been at some pretty impressive companies, some pretty impressive stops. I'm going to make you feel very uncomfortable today, by the way, because I know you're extremely humble person. I'm just gonna say how awesome I think you are probably 20 times. And again, that's going to make it weird and that's going to be OK.

[00:10:52] But like you've been in some really impressive places, had some really impressive teams. How is what you would do today different from those prior stops? Hmm. Yeah, that's a good question. Great question. I think one thing I think about a lot is context matching. I think sometimes we're tempted when you're really heavy into a discipline like the history of people analytics to maybe apply a playbook and say like,

[00:11:22] Hmm, you need one of these, you need one of those, you need to report in this way in this layer to this structure. You need these three priorities because they're hot. I haven't I I don't really believe in that approach. I believe in really, really, really, really, really deeply understanding what your company is doing, what problems they're solving their operating rhythms and then figuring out how a people function can help. And that's sort of where I live. And I'm always optimizing against that.

[00:11:49] And so every time I build a team at these companies, I think about what is the thing that is stopping our company from being maximally successful? How can the people team help? And then what am I going to do? And so can I pause you there for a second? I just want to say one thing. Sure. One thing. Wait, you're not saying that there's just some cookie cutter approach to people analytics that you lift and shift to every organization. All right. You know, tangent over. Yeah. That's a good thing. I don't know.

[00:12:17] I would be curious if you find that ever to be successful. I don't know. I feel like I haven't. So like there's full furbearers of that methodology that are quite popular. Yeah, I don't know. That hasn't that hasn't been my, my go to, I guess. But I think that's why I had to tell you that because in order to answer your question about how I would do it today, it's very dependent on the context you're operating in.

[00:12:39] So the context I operate in is a data and AI company that produces a platform that has a people team that's quite lean by design. We prefer a lean operation. So imagine a world where we just don't have people who sort of have spare time to do things that might be slow or friction full or redundant. We have people who are hungry for any opportunity to get their time back and to optimize. There's very low sort of ego territorialism.

[00:13:08] There's more like, let's just get things done. And so when I'm in that situation, and I know I need to build a platform, everybody's very busy. I have to think about like, how am I going to get this done? What other teams can I rely on around me for help? What kind of help can I rely on? In my context, a lot is sort of self sustained. So our team manages a lot about the platform itself in a way that might not be true at other companies, depending on what how their partner model is set up and how their company operates. But for us, it means we go very full stack.

[00:13:37] So each member of my team can set up an ingestion can configure an MCP can transition their bronze to silver to gold themselves can play in a get repo can run a machine learning model or build one can sit down with executive and talk about key takeaways from an analysis that someone has run can optimize the security infrastructure of a platform. Can run a survey and build one.

[00:14:06] It's sort of like what I need is a, is a set of people who can do a lot of different things. The model that maybe was very popular. More when I started in people analytics many years ago was you have a person who's a data engineer, you have a person who's a data scientist, you have a person who's an analytics partner, and they talk to people. I just, I don't really think that model could work here.

[00:14:30] It's just too much of a low continuity high expense type model we're looking for high continuity flexible model. And so if I were building a team from scratch anywhere, I would be trying to think about like what is the context I operate in with the problem I need to solve and then how do I match to it. And for me, it's very full stack. So it means the people on my team today, which is a team that has been pulled together over the last year since I've been at Databricks. Um, they don't necessarily come from people analytics backgrounds. I think maybe one or two have worked in people analytics before.

[00:14:59] I have someone who's worked in product. I have someone who started their career as a recruiting coordinator, but they are all very high G. I know we have a lot of IOS who listen, but they're very high G. They're very high curiosity. They're very high, like figure it out of nests. I don't know what the actual like trait of that label would be, but they're very willing to learn things.

[00:15:21] And they maybe don't even have like the foundational background, HR expertise that I may have really, really, really valued in the past or in a different context. So, yeah, classic IO answers. It depends on your context, I guess. But my current situation is very full stack, very figure it out of nests plus curiosity plus G.

[00:15:45] I would just, I could just sit here all day and just let you talk about this stuff because I just so violently agree. You do? Well, and again, because I think I probably get out and talk to a wider swath of people. A lot of what you said is very heretical. And that's why I like it. And I think you're right. And I think everyone else is wrong. So I'm just going to put a stake in the ground there because I am fully invested in what you just said.

[00:16:15] For what it's worth, I think it is scary. Like, I was just presenting about what we do at a conference this week. And when I'm talking with folks about it, like I can see some anxiety around like, well, I don't, I don't know how to set up a pipeline. Like, I'm not, you know, I don't. And the reality is none of us spike on literally every skill by default. Nobody on my team came in good at everything. Everyone was good at like 20% of everything they needed.

[00:16:42] And now they're probably good at 80% and they can figure out the other 20% as they go. But I think being able to figure it out as you go is the key. I know when we started using our platform more and more, we started with this sort of consumer vision of we're going to make it so easy for everybody to just consume what we produce as a central platform team. And then we released as a company, a tool that allows you to build really easily as a user.

[00:17:08] And we have this choice to make of like, do we want HR people to just be consumers? Or do we want to see what would happen if we let them build like with, you know, with certified assets, with things that we produce foundationally. We're not looking to create chaos here. And we really kind of pushed for that. And what we found was they wanted to build and not only did they want to build, they wanted to go fast and far.

[00:17:30] So all of a sudden, really, really, really amazing tech recruiters were building machine learning models to optimize their pipelines and processes. And people partners were building apps to help them scenario plan with their business leaders. And so what does that mean for my team? We have to know how to build machine learning models and apps so that we can help them do that. And we also have to know how to do everything else that they might potentially do.

[00:17:54] So as the tech evolves, we've sort of evolved the amount of skills we have to play with or how much we have to figure it out, I guess, along the way. And it's not scary if the tech helps you and if the team is styled in that curiosity forward way. Was any of this related to the concept I've heard you say about analysts, not as reporters, but analysts as architects? Yeah.

[00:18:23] I don't even really like to call people analysts, if I'm being totally honest, because I think it puts them in a box. I think it's sort of that idea of I don't need someone whose job is to answer a question anymore. I need someone whose job is to build and optimize a system where questions can get answered. So I need architect, not Q&A.

[00:18:48] And like you're for people who don't know you because like you're incredibly credentialed, credible person. Where are some of the places that you worked before before Databricks? Let's see, when I was like a baby IO, I did my PhD internship at Amazon when they were like 90,000 employees. And I worked with kind of the early version of IO there, which was awesome. I went on to build up the assessments practice at Ford Motor Company.

[00:19:17] So historic, traditional, Fortune 10, 200,000 plus company. Really interesting. Amazing products, obviously. But really interesting place to sort of grow up at first because you see everything optimized. Like all processes exist and are well-oiled machine. It's just a very fascinating place to start where you see everything work really well and then you kind of move into tech.

[00:19:42] But anyway, after I built up assessments at Ford, ended up doing another stint at Ford where I built up their global people analytics function. They had been really interested in that for a while. They were more sort of workforce planning, operational workforce planning leading and maybe tech leaning. And they really wanted this more like research and process optimizing version of people analytics where they were like, is everything working as well as it could help us all the time?

[00:20:08] And then I went on to Stripe right before as they were preparing for hyper growth. And Stripe sort of famously doesn't like titles and putting people in boxes. So you accumulate problems, not jobs at Stripe is how I kind of describe them. It's really fun place. But the sort of problems I was solving there and building teams around were. How do we make sure managers stay effective as their teams might grow as much as, you know, 100% year over year for a while?

[00:20:38] How do we make sure that organizations remain healthy? So do we have ideal designs in place? Are we trending towards those designs or not? Why? How do we tolerate culture breaks or like subcultures or countercultures? How do we kind of prepare? How do we optimize resource allocation? How do we sense across the workforce whether we are in fact growing too fast and therefore kind of need to step back a bit?

[00:21:01] I looked after their remote hub, which was sort of a side gig that I did, but I loved where I had a couple thousand remote employees that I was responsible for leading a team to support and organize benefits for. And then I ended up taking on all their hubs at one point and looking after all their offices globally, sort of as another side gig. But those are the sort of problems I accumulated there and listening. I took on at one point, redoing their listening strategy to be a little more continuous, a little more strategy aligned.

[00:21:32] I did a lot there around like radiating what we were finding in people analytics and sort of sharing with the business. And that's maybe stuff that wouldn't be surprising to you, Cole, or maybe to your listeners, but is really sometimes surprising and interesting for other people. We would publish these papers every couple of weeks. Like we published one called the fund to frustration ratio, which is basically like why people are or are not motivated at work and how you drive engagement and retention.

[00:22:00] Anyway, we got into sort of internal marketing of sort of human behavior optimization at one point. And then I went over to Salesforce and was there for a couple of years, ended up leading their what we call people strategy analytics area, and then transition that into what I called a workforce intelligence org to get more proactive and actionable and prescriptive about how the systems were running. They were really tech heavy there. They were really tech heavy there.

[00:22:28] So figuring out how to place agents beside humans and help them optimize their own workflows was starting to be more and more of a focus. And then from there, I came to Databricks and specifically came to help our people team with what we call unlock data and AI so that we can run the way we need to, not just in the people team, but as a company. So that's sort of where I've moved.

[00:22:53] I don't know, I feel like I've done a variety of different elements of like workforce planning or design, core people analytics, RNA. I did like user centered design for a minute. I owned culture and D&I at one point. I feel like I've sort of played all over the place, but I tend to like solving people problems for companies and whatever the biggest problems are. Those are the ones I try to solve. And I actually think folks in our area can, can solve so much.

[00:23:21] And so I try not to get in a box. Well, with that, you introduced this concept to me of radiated intent as a way of solving problems. Can you talk about that? Yeah. I don't remember where I picked this up. Maybe it was a stripe someone I was watching someone do this and I, and I asked them what they were doing and they explained this to me and it sort of caught fire in me.

[00:23:43] But it's this idea of when you are working on something, people don't know that you're working on it unless you invite them into it. And so I do this thing called radiating intent, which is basically just talking a lot and sharing a lot about what I am doing and trying to do and inviting other people into that. So I, I love to publish a roadmap. If you know me, if you ever worked with me, Marina's got a roadmap, there's always a go link and it's publicly available.

[00:24:09] And it shows like what's happening when what's the plan, what are the dependencies, what's the vision. And I invite people into that. I'll sit down with our leaders and say, is this right? Does this feel strategically aligned? What would we edit? And I make sure it's open so that, you know, I think it can be sort of, it can be hard in any team to know what's going on across the board. Everyone's quite busy.

[00:24:36] What I would hate is if someone is thinking of my team and wondering, what is it that they're doing? Like, why aren't they helping me? What is it? What are they so busy with? And I feel that if you are able to sort of publicly openly state, here is what I am up to. This is why it is the most important thing. I think that's what I'm doing for sort of more success than if I were just in my little bubble and setting up my little pipelines quietly.

[00:25:03] So that's what radiating intent is basically amplifying and inviting people in. So if you're radiating intent, you're defining your job as the problems that you solve, not just as like a job description. You're no longer kind of being the gatekeeper for insights. You're democratizing insights and letting people kind of build on their own. How do you know you're creating value? And how do you quantify that? Okay. We think it depends, right? Like what problem are you solving?

[00:25:33] Did you solve it? How micro can you get with it? I like to like solve problem by problem. That we're asking. Can I ask like one kind of like clarifying thing here? Yeah. The reason why I asked it that way is because I think a lot of folks think that their value comes from all those things that you are letting go of. Oh. Right? And so they're afraid of letting go of those. Like, why would you let, like, why would you democratize and let people build the bill?

[00:25:58] The building that I do is the value I create, but democratize that I no longer create, I'm no longer creating value. So that's why I asked the question the way that I do. It's interesting. I guess I think of it like you're becoming the infrastructure of the company. Your, your value is inherent. It runs because you're there. Um, I think there are sort of macro indicators I look for.

[00:26:27] Like I look to see. I like like an op X per employee, F per employee type indicator for a people team. And I like to see how many people are required to do the thing we need to do over time. Are we getting more efficient or less? What indicators do we have that we're operating better and better? And how can I show that? How can I make sure I am playing a role in making that true? Um, and if I can't, maybe that's okay. But if I can, is that perhaps a problem that my team should run at?

[00:26:56] So I, I, I, I like to play up there and like the, are we actually doing this well as a people team rather than in the past? Sometimes I've been sort of deep in these, like, did I do a study on a learning and development program and show that leaders who participate in one manager training cycle have two percentage points, better results on their culture survey. Those are more distal to me. Like, not that you shouldn't be checking the ROI of your own work and making sure that what

[00:27:25] you are responsible for in a company, you know, what is being invested, you're getting something back for it. But I guess I, I currently prefer to look more at the, how is the whole system running and how are we helping it run well? Or where is some indicator of operational effectiveness? Not so good. And our team can actually help make it better. Yeah. And you can even look into the counterfactuals of places who haven't adopted your system and how inefficiently they run.

[00:27:51] If you were so inclined to want to quantify your work, but who knows if that's what you would want to do. But thinking of kind of that company-wide lens, you rolled out a company-wide intelligence layer. Was that easy? Was it seamless? Were there no bumps? Was there any change management? What was that like? I mean, it's not easy. I think there's like technical difficulties, right?

[00:28:17] Like figuring out how to actually use the tools and secure them when you're dealing with people data. I think everyone sort of has a, who's going to see it? What's where, what's going to happen? What, where's the breach? And so really, you know, being paranoid about your ability to understand the technicals of the thing you're setting up is something that becomes very key. I almost don't think that is the hard part though. Like I think technicals are becoming easier and easier more and more as point and click more and more as conversational.

[00:28:46] If you have foundational understanding of what's going behind the scenes, when you were conversing with something that is helping you set up whatever it is you're set up, you're going to be, you're going to be more and more solid. I think it's, it's harder to change, manage and bring people along the journey in the way that you might need them to come. And so I don't know if everybody would agree with that, but for me, I think the tech part is getting easier and easier, but humans continue to prefer their own habits and norms, regardless of what technologies exist around them.

[00:29:14] And so you have to figure out like, for me, I think about expectations, tools, reinforcers, like what's this, what's the loop I'm going to set up so that people sort of edit whatever is maybe their default setting. Have we set really clear expectations of what good looks like? Have we communicated that openly? Is it, is it top down? So they feel supported on those expectations. Tools is where the tech part comes in. Like, do they actually have good tools to meet the expectations or are they kind of clunky? Are they annoying? Are they frictiony?

[00:29:41] Like, are they actually fun to interact with and therefore they want to meet the expectations or are you kind of adding something to their plate? And then the loop, the reinforcers is when they do it really well, are you shouting them out? Are you incentivizing them? How are you helping them kind of be successful and close that? I did the thing I was supposed to. Um, at data bricks we have actually, I, um, I like this little frame. I don't know if it's the perfect frame for everybody. We have this little frame we talk about with our people team. We call it mindset skills usage impact.

[00:30:11] And so we define these are sort of the four pillars that we think are necessary for proficiency around data and AI. So your mindset is like, are you open? Like open-endedness to experience, right? It's are you open-minded? Are you willing to try new technologies and new tools, new ways of working skills is how effectively can you do so? How well can you do it? Are you good at, are you good at it when you try? Um, usage is, are you in fact objectively doing it? Are you trying? What are you doing? What are you building? What are you consuming? What are you touching? What are you changing?

[00:30:41] And then impact is as a result of you doing these things, what changed about your job and your life at work. And so we observe people's behaviors in our platform. Everything's observable. We can see everything that everybody's doing. And we have indicators of these pillars. And then we survey people quarterly to get a sense as well. But that's not all we do. It's not just measurement. We're like, we have like a, it's very large. The investment we've made in making this successful, just to be honest, it's a huge priority for our people team.

[00:31:08] Um, we call it a P zero, but we have a whole AI champions network that we stood up where we have about 25 or so folks in the people team who have applied and been selected to be AI champions where 5% of their job is helping this be successful. We flew them out to SF and train them and supported them and enable them so that they could go back and help their peers more than perhaps my team can individually provide the type of hyper care that everybody would need. We do a lot of like checking in.

[00:31:36] Our leaders are very, very avid users of the platform. And so they're always trying to help their teams. My team went into hyper care mode for a couple of quarters where if you ever let out anything tech, there's like this hyper care idea. But basically what it means for us is anytime anyone needs something, we are there. So like you see something in a Slack channel and it's like, I'm, I'm struggling with X. I'm trying to build Y. I don't understand Z. Someone on my team is raising their hand and saying, do you want to give me a call? Can I help you?

[00:32:05] Um, so yeah, there's a lot of sort of measurement and change management and expectations and tools being delightful and reinforcers. But when you consume, when you set all this up together, what you get is change and adoption. And, um, something we find, which is quite nice is because the platform is smooth, like because it is set up well and somewhat fun to interact with in addition to being helpful. Once you try it, you get a little addicted.

[00:32:35] So we just need spark. Like that's really all we need is for people to just like dedicate a few minutes and get a spark. And then they're hooked. And if we made sure they're supported when they have questions, we made sure that they have the access they need to do, to do what they need. If we taught them how to automate things that again, in a lean environment, like they are hungry for such automation, then it kind of takes. Did that answer your question or what, what else were you curious about?

[00:33:03] Marina, I could, you know, just let you read the phone book conversation. Cause you're, you're just crushing it. So thank you for what you're doing. Um, it's a big deal. And I think if you're going to do it, you have to do it that way. Like it has to be the top priority for your team. We have to talk about it every day. We talk about it in every people team meeting.

[00:33:23] So a lot of what you describe is from the vantage point of looking at things from a high level systems perspective, trying to address problems across the entirety of the organization, not optimizing to, you know, a local maximum or something like that.

[00:33:42] And one of the things I've been talking about is at least historically is people analytics historically had, is the only other role in HR that had the same vantage point as the CHRO or the chief people officer, depending on how your company titles it. And you've been very much talking about things. You didn't actually mention it, um, in that way.

[00:34:05] Um, but you know, earlier this year I had, uh, Jennifer Wilson from Hydrogen Struggles who runs a CHRO practice there. And she came in and gave essentially the playbook of what it would take for people with our backgrounds to become the CHROs of the future and what we would need to kind of address the gaps that are there as well. I thought that was a, it was one of the best episodes, um, that I've had. And, um, I don't know what, what are your thoughts on it?

[00:34:32] And is that a career path for people that are in our position, especially with that transition to looking at things from an intelligence standpoint, not just a historical analytics lens? I mean, I am. Come on, do it. Yeah. I am extremely biased toward folks with the capabilities in this space. So I think sort of anything is possible.

[00:34:56] If you start playing with process optimization, operational effectiveness, redesigning work, technology systems, you're talking about sort of becoming like the machine that a people team runs on. If you wanted to take that to another level and lead or build a people team, you probably could. I do have these conversations with folks who are, who have these interests and there is some sentiment, whether objectively true or not.

[00:35:25] I cannot say, I think everything again depends on like what kind of company are you trying to join at what stage of their life cycle? Like what are their problems are trying to solve, but I think getting outside of our space and especially interacting directly with business leaders as much as you can in a, in a dependency way, like being an HR VP, being a coach in some manner is probably.

[00:35:50] The biggest, most frequent gap I see in folks that work in people intelligence or people analytics is perhaps they haven't done that yet. And when I, when I observe folks starting down that trajectory, that seems to be where they tend to start to hit. Do I think that, you know, when I, when I was working early in my career, there was a concept, I don't really know, I hope we still do this. I don't know that we talk about it much in tech right now, but this concept of career rotation.

[00:36:16] So like build your 25 year plan for the company. What are you going to, how are you going to move around? What roles are you going to take? Are you going to go off? That's a dinosaur. Nobody talks about that. Listen, that's, that was the vibe. Now you don't have that. So you have to sort of sense like, what is it that I want to do? What is the type of maybe CHR role I would have the most fun playing in and in order to have that role, what kind of experience was what I need?

[00:36:43] I don't know. I'm not closed off to it. If that's sort of a question behind your question. I'm curious about all the different roles that people analytics folks can move into and move out of over time. Something I've been interested in across companies is rotating folks from my team elsewhere. It's like switch trading someone. Cause you can, if you grab a people partner or a learning professional or a recruiter who is really curious and hungry and really wants to try something new.

[00:37:13] And you send one of your folks out to learn how things actually work today in a function. Then when they rotate back, you all of a sudden have like an empathy filled professional who knows your space and can help their function. And you know a lot about a domain that perhaps you wouldn't have seen up close before. So I really like that kind of stuff. And I think I personally think the more you can do that, the better for your career, no matter kind of what area you're in.

[00:37:37] Yeah. You're essentially, and I talked about this at Talreos earlier this year. I thought I was joking. I wasn't joking. I was like, just kind of saying like, what are the forward deployed engineer version of what we do? And it's, you're not just effective because you're better at solving problems. What you touched on is, is really one of the keys, which is it produces empathy, makes you really understand the problem from the perspective of the person who's trying to solve it.

[00:38:05] And therefore makes you a better problem solver. So I, I love that. Um, and a mutual friend of ours in an episode that has not come out yet. Uh, Ann Marie Johnson, um, it has moved out of, you know, this intelligence space into the HR BP arena. And I called my shot on the podcast that she will be a future CHO. Right. I'm sure she can be. I'm absolutely confident in it.

[00:38:30] And I'm curious for people that are in your shoes, because the way you described it, and I'm simplifying it, but you're creating the, you're the, you're the foundation. You said you're creating the infrastructure. And then that creates the ability to go in and do this somewhere else. If you wanted to build a people team around the infrastructure that's there. And one of the, the things that I put forward, I think in my book, I think it was in my book. I can't remember.

[00:38:58] It was either that or some of the articles I've written was around that in the future, if, uh, if enough of HR gets eaten up by this kind of AI automation, the only thing that might be left at the time I said was people analytics. But I would call it like this, this intelligence skillset is like this infrastructure, this foundational skillset might be the only thing that's left.

[00:39:25] And therefore you de facto become the chief people officers of the future. If that's all that's left. I know that was more heretical than even some of the stuff that you've said so far. What, how do you react to that? I'm just deciding what I want to say. Just say it, just do it. Nobody's listening. It's just me. It's just me, Marina.

[00:39:45] I do think, um, if we sort of take a step back about why a people team even exists, why a CEO would even hire a CHRO, right? That that's what I sort of think about sometimes. I think you always need infrastructure to run a company. That's how you get your operational layer done. Like you need some systems, automations, algorithms that run your company, whether it's humans or finances or whatever, whatever. It is you're trying to run.

[00:40:14] I think a lot about what are the first roles that people tend to invest in when they're starting to build a company from an HR perspective. And what, what are sort of people, what are kind of CEOs willing to pay for in people teams? Just to be like very direct about it. I wonder if you asked, you know, a new CEO, if they would be willing to pay for HRBPs. I would estimate they probably would. They probably want some coaching and support and side by side. Would they? I don't know.

[00:40:44] I'm curious. I'm actually saying study. But if you really metered out the list of dozens of job families that exist when we specialize in scale and domain optimize, like we perhaps have over time. If they really looked through all that and thought, do I need all of everything here? Do you know what I mean? I think there would be some perhaps ranking. And I do wonder sometimes about what are sort of the most critical enduring efforts for what it's worth.

[00:41:14] Most roles in HR are so related. And so it's quite, if you're interested, it would be quite simple to take something that you've already done in one COE and move into another or go from a COE role to a business facing role and back again. I personally love when people accumulate skills in that way. And I think it makes you able to sort of do anything in the future if you were, if you were tagged to do something with a new CEO who perhaps is trying to look for a valuable HR person.

[00:41:45] I think about that a lot. Like, would they? What is it? Yeah. Like, perhaps if you had to, you know, from a first principle standpoint, building an HR function from scratch, you know, this year or next year, what would that look like? Mm-hmm. I don't know. I don't know if you have any thoughts on that, Marina.

[00:42:03] I do think it's, someone said something recently to me, which was, if your company existed before 2022, 2021, you probably have a bunch of debt in how you think about how operations work and how organizations are structured and how work itself is. And you are trying to figure that out right now. You're trying to make that effective in the world that exists now with tech. Yeah.

[00:42:31] And if you are in a newer company than that, you get to play sort of with a blank slate and you get to decide what do you want to be true? What could your model be? Yeah. I've done sort of, I think, quite engaging, interesting scenario planning exercises with HR teams before where we look ahead, future of work style and say like three scenarios. One, stuff a little bit changes. Two, roles in these ways shift.

[00:42:58] Three, HR doesn't exist as a concept and nothing is real and everything is chaos and we're starting from scratch. And those are always the most interesting and also scary ones to think through, but. Yeah. I don't know. I don't know if I have specific stakes I want to make in the sort of set in, but. Well, how about you let me, let me lean into it for a little bit. One of the things you said is, you know, if you started post 21, 22, you kind of blank slate. HR bench started in 2021.

[00:43:27] I'm loving the blank slate. It's great. Right. Because what people don't realize or what they won't acknowledge publicly usually is what you mean when you say that is you have to tear down and then rebuild. Right. Thinking about it from like a, instead of just building for the first time. And, you know, the tear down actually costs a lot of money and time and heartache. Like, and that's even before you get to the rebuild. And that, so it's like, it's like two pain points. Right.

[00:43:54] But to your point from this is like, if you ask the CEO, you know, what, what are they willing to pay for? And, and I know from your experience, you know, in the automotive industry, kind of the lean six sigma standpoint, the value added task is something a customer is willing to pay for a non value added task. So they're not willing to pay for it or the value added tasks HR brings. Well, one value added task, and this is frankly what the HR profession, not, not just people analytics or I was psychology as trying to be getting away from is the risk mitigation. Yeah. Right. It does play a role.

[00:44:24] People are willing to pay for that. They don't want to get sued. They don't want to, you know, all the stuff that, that, that kind of mitigates risk that HR does. And the only other thing that I can see that they pay for, and it's kind of, there's two prongs to it is the value add is the ability to augment and help decision making around human capital. Right. Which is all the foundational stuff you were talking about a second ago.

[00:44:48] And the only leg to stand on for a traditional HR person is kind of the coaching facilitation, you know, being able to be a good communicator, the softer skills type of influencing that, that angle. And, you know, if you're not willing to pay for it, sometimes they'd be willing to pay for it. Maybe the, you know, just making sure that the, all of the C-suite doesn't kill each other, you know, that, that.

[00:45:18] Yeah. It becomes useful. Yeah. It becomes useful sometimes. Um, not that you can't pay an external person to do that instead of just doing it internally, which is also an option. Um, but the only thing that you, for sure that they'd be willing to pay for, that's a value added task that is not risk mitigation is really this foundation. It's not a foundational stuff that enables decision making, i.e. intelligence. Yeah. Right. That's it. It reminds me of, have you ever worked with an HR team that splits their operations into front office and back office?

[00:45:48] Do you know that? Uh, I mean, I've, I've had them do it in name, but not in practice. Yeah. Does that make sense? Yeah. Okay. Yeah. It seems similar to that idea to me where you're like, things need to run in some kind of scalable manner for my company. And then I need partners. I need support for specific things that a human sort of must do. It's interesting. I think the world that we're living in now is one where we're seeing more and more what's possible.

[00:46:14] And the more you in your contacts can sense what is value added and what is useful to you, I think the better. I don't know that. I guess I'm on the, I'm also on the side though, that like everything's not changing today. It's going to take a while for any kind of huge substantive changes. And so you already made a lot of the substantive changes. What was that? You've already made a lot of the substantive changes that people are talking about making. For our, for yeah. For our foundation.

[00:46:41] But I mean to our, to the operating model that everybody knows and loves around HR, to the roles that traditionally exist. I think. Oh, come on. Let's just say it. The Ulrich model's dead. Like it just is. And it's been dead. Dave Ulrich says the Dave Ulrich model is dead. Yeah. But it still exists though. Like. Yeah. It still exists, but it's like in stages of dying. Like, I don't know. Yeah. Like what you would call it. Like it's bleeding out. Yeah.

[00:47:08] I do think, do you think that as a profession, people teams are accepting that? Well, this is where I, I, this actually you're, I think you're finally understanding what I was talking about with the looking up aspect from earlier. I talked to a lot of folks that are in levels of resistance to it and, or levels of, you know, stages of grief, whatever you want to call it. Yeah.

[00:47:44] Yeah. Like, what are you doing? Can I go and live where you live? Because like, that's all like part of me. I love staying on the bleeding edge. And then part of me wants to go like live on a farm and never think about this stuff again. I'm the same. So in like nothing in between those two things. Yeah. I'm only thinking about this or I'm on the beach. Those are my two. Yeah. Yeah. And so like, I don't know, there is something that's like, kind of like, I am jealous of that too. Yeah. A little bit, but the people that are in between, I don't really understand it.

[00:48:14] And maybe I'm just too much of a black and white thinker in that respect. But like, if I see that the future is happening and it's unfolding and you can deduce exactly what's going to happen because you can see a series of dominoes that are, have started to fall and where they will continue to fall. It makes no sense to just like, well, I guess we're just not going to do anything different. Yeah. Make any sense. I do try to live in this, like things appear to be changing quickly to me today based on where I work.

[00:48:42] I think in a long term future, things will be quite different. The in between is probably sort of where the most usefulness of my energy is. And so I spend a lot of time like watching the ball, trying to figure out what is the best value play for a company and for a people team right now, based on all the guardrails that exist, based on all the variables underway. I find myself if I think too much about the forward future, I can't really act.

[00:49:11] And if I think too much about today, I kind of forget. You know what I mean? So I try to, I don't know, try to exist in between. Yeah. One of the things, words that I've heard you say multiple times on the podcast today is curiosity. Right. You talked about being open to new kind of experiences or new, just the new that's happening. Right. And one of the things I've really appreciated about getting to know you is how you're always looking for where you learn what's next. Where, what are the new things? What's, what's happening?

[00:49:40] Where should I be going? And you mentioned earlier in the podcast that you recently came back from a conference. We've been whinging about conferences sometimes. So what, what are your thoughts on kind of the conferences in the space? And is that where you go to quench your curiosity? Do you go and you learn new things there? Oh, it's a tough one. So I think conferences are useful, but not always for learning new things. Mm-hmm.

[00:50:08] I think they can be really useful for conversations with folks at companies who are maybe experiencing things similar to me, where I can really sit down with them and understand what is it they're trying? What did they learn? What did I learn? Where we can really share. And so if a conference creates an environment where that's possible, that, that is, that's useful to me. But if I'm thinking about sort of things people are willing to say on stage in front of everybody when being recorded, I don't know that those are, those tend to be quite useful to me.

[00:50:34] And I do actively seek out forums where I can have real conversations with folks who are in positions like mine. And we can really share what we are doing and what's happening and what we're observing in a way that doesn't have to feel maybe sometimes distant. But I don't, I don't do that always. I don't always get that in conferences, perhaps in a room at a conference or in a corner at a conference, but often in text threads and chats, you know, in live discussions. And I'm trying to be kind of the bridge between those two things.

[00:51:04] It's hard to do stuff publicly. It obviously you'll always have a higher level of trust in an intimate setting where, you know, you know, things won't be shared. But you can still, people can share more than they think they can. Yeah. I think at conferences, and this is why I've just really tried to start putting my thumb on the scales of what content is shared at conferences for at least the ones that I'm involved in. Yeah. Because I'm tired of all the cope. Yeah.

[00:51:29] And I'd like it, like conferences have turned into like where we agree on like, what are our talking points about how we're coping with what's happening, rather than saying how we're going to be proactive about how we're going to create the future. I want to create the future. I want to be the change. I want to be, you know, that kind of that leadership point that I was talking about from earlier about, you know, painting a compelling vision for the future. Yeah. Making it positive and people seeing themselves in it. Right.

[00:51:55] And so let's get over the cope, you know, and let's keep having, you know, good private conversations, too, because I get my fair share of those. Those are awesome. I think it's a good direction. You also know I like forums outside of HR. I often prefer to listen to product folks and folks, marketing folks, talk about people problems instead of people problems, talk about people problems. I find it very, very, very useful to hear from their vantage point.

[00:52:22] So that's the other thing I would add is I seek those out as well. So what's your I'm really glad you went this direction. What's your how do you curate your information diet? You've asked me this before. I don't I don't know that I have a very purposeful method. I think I only have so much energy in a day. I am not someone who learns on LinkedIn. I that is not the place that I go for organized information downloads.

[00:52:53] I have a couple of groups that I'm really active with a couple of friends who do what I do across companies that I'm sort of chattering with all the time. I find that very, very, very useful. Like we will just talk about problems we're facing and how we're getting through them. So part of my information diet is I put a lot of energy into specific connections and specific groups, I think, versus sort of like at scale information chaos that you might find on LinkedIn.

[00:53:18] I like podcasts that sometimes HR like I've been listening to yours, especially made a joke with me a while back about how I wasn't listening. And, you know, I've been I've been I've been on it since then. I've been here. I took the feedback. But I also really like like I know you picked an article from Lenny today for us. I love Lenny's podcast. I love like random like diary of a CEO.

[00:53:46] I just love people talking about things that I'm interested in who don't live in my discipline and maybe don't have the same underlying understanding of human behavior at work that we do, because often they're they're offering fresh perspectives that I might not think about. And I think it's honestly something we can get in our own way in HR and thinking playbooks and thinking like here's the way things should work and why and how it has been done. And that's just often not true.

[00:54:12] And so those those sort of outlets allow me to take a more first principles the how do people really think kind of perspective. Yeah, I've gotten to this point where I really find it hard to just talk to normal people. It's not good for you. It's tough. And I just. I don't know.

[00:54:37] That's why I asked the question about the information diet, because I have almost gotten to this point where I can't listen or read anything from like, like, just normal HR people anymore. Yeah. And my information diet is almost exclusively from either the smartest people in the field, but even less and less of that over time and more and more about things outside of our field. You're one of the few people I've met who has kind of a similarly cultivated sense of information.

[00:55:06] And it's like, and I told this to Roxanne Bisbee Davis when she was on the podcast, she's leading an intelligence function to like a real intelligence function, not just a rebranded one. And you are, too. And it's like, I just like the number of people I can actually talk to about what I think about the world is like gotten so small. And I'm just super appreciative of you because that because it's like, finally, it's like I can breathe again. Like there's another person who actually understands. I'm not trying to always just convince them.

[00:55:34] I feel like my whole day is like trying to convince people like what, like what, not the future. It's today. Like it's today. It's not tomorrow. And I was just like, oh, finally, it doesn't have to be exhausting for a little bit because one of the things that talked about with Roxanne is like she asked me like, you know, when people ask you what you do, what do you tell them? I said, I should avoid the question. Hmm. You do. I've done this. I've asked you this before. You're very good at avoiding it repeatedly, despite follow ups.

[00:56:03] It's because like there's only in there's only people like you who I can actually talk to about what I really think about things, you know? And so this to me is so refreshing again. One of the things you told me in preparation for the podcast that was kind of idiosyncratic about you is that and I can tell you're leaning into it like against this notion right now, which is you don't really like talking publicly about a lot of this stuff. And you're very opinionated, but you don't necessarily share them. So I've been like encouraging you.

[00:56:31] So I know what is that like and why? Why the change recently? You know, I don't like to make people feel isolated. Like I do feel like we I'm sometimes trying to do something different than perhaps what is the norm around me. And I don't want to make people feel anxiety because of that. But also, I think a lot again about like energy and what energy I have and what I can share with others.

[00:56:57] And sometimes it is more or less useful for me to share energy depending on. The forum, you know, and so sometimes people will talk. They'll like radiate their intent very openly on a sub stack or LinkedIn or something. And I don't know, maybe there is some ROI there for them personally. But for me, I'm not sure that I ever get enough sort of back to help me and sort of restore the energy that I'm given through that. I don't think I've changed recently.

[00:57:26] I think I just have been kind of heads down in tech at a couple companies now doing a lot and. I'm trying to pick my head up a little bit here and there and see like what else is going on. I do feel like I have a sense of what's happening in my space and in companies similar to mine with folks operating similarly to me. But I don't think I always have a great understanding of the broader market and the problems that everybody is facing.

[00:57:56] And you sort of have to force yourself to empathize with other contacts. I think a lot right now for what it's worth. I don't this is a longer answer than perhaps you wanted. I think a lot right now about what I what we have been able to do at Databricks as a result of having our technology and having our people team and having our clear top down expectations. I thought about like, could I pick that up? Could I just hand that to a friend at a large company with a bunch of debt and process debt and tech debt and preference debt?

[00:58:25] And could they just do it? They couldn't. Like, I know that they couldn't. And so I do. I am curious to understand what that is like right now and what would need to be true for especially really big companies with a lot they're already doing. To be able to kind of rebuild, like you said, like. From scratch, sort of. And so I don't know that is interesting to me right now when I am picking my head up.

[00:58:47] I'm looking for those other companies that have like the hunger to do it differently, but are perhaps blocked by silly things, bureaucratic things or because maybe they don't realize it's possible yet or something. And I am curious about breaking that stuff apart. I am personally glad that you are sharing this information. That is what I want to hear. So thank you. Okay. You wanna, you wanna join me in Cole's Corner? Sure. Welcome to Cole's Corner.

[00:59:16] Alright, let's start out with some rapid fire. Okay. If you weren't doing what you're doing now, what would you have done with your career? Hmm. Hmm. Is this like my ideal or is this like realistic? Do you know what I mean? What, what's the goal? Well, give me both. I mean, you know that I really just wanna live on a beach. Is there a career? Ideally, I don't have a career. I'm just on a beach thinking to myself about things.

[00:59:47] But I think realistically, my whole family is engineers, different flavors of engineers. I went to an engineering charter school. I thought I was going to be an engineer. And then I took sort of a hard turn and discovered this thing about human behavior at work and went like really hard at that rabbit hole. But I have always liked problem solving and tech. And so I realistically, I would probably be an engineer somewhere. Do you know Kelly Monahan? Remind me where she is.

[01:00:15] Her episode, you haven't heard it yet because it's coming out on Monday. But she said something very similar. Really? Yeah. And so if you guys haven't met, we can make that happen. That's great. Yeah. I look forward to hearing it too. Yeah. What's the place you've never been to that you most like to go and why? So I don't have like a location, but, and perhaps this is like a deeper answer than, then you were hoping for, but I lost my dad a long time ago.

[01:00:44] And so I would love to sort of go to a different time point and spend more time with him. But I don't know that I have a location. I would love to go. I feel like I go to the places I want to go, you know, but there are perhaps some places in time that I would like to return to. Any particular place? You mean like a location place? Yeah. Like with your dad. Oh, I think he would really like Alaska. Like one of these beautiful cruises people do to Alaska. I think he would really like that.

[01:01:15] Awesome. Yeah. My parents actually just got back from one of those. Did they? Yeah, they did. They loved it. Yeah. They said that there are bald eagles everywhere. Like just like, like crows. It just looks gorgeous and peaceful. Yeah. That's wild. If you were a character in any book, TV show or movie, who would you be and why? Okay. So I heard one of your recent guests, they said the doctor from Doctor Who, and that is the answer. That is such a good answer.

[01:01:42] Um, I recently started going through Indiana Jones movies with my nine year old. And so I'm having to go with Indiana Jones. He's got this like scholarly thing and swagger and curiosity and adventure and exploration mixture that I think could be really fun to live through. A lot of old movies don't hold up. Hold up. The Indiana Jones movies are great. They're really good. Yeah.

[01:02:09] They are surprisingly like scarier than I remember them being or maybe I just didn't like realize that things were scary when I originally saw them in my youth. But now that I'm watching the back with him, I'm like, oh, here we are. I think we've all just gotten weaker over time. Probably. Probably what it is. Probably. Well, you did steal my one big question from earlier. Um, because I kind of planned on asking you about a first principles approach to building an HR function as my one big question. So I'm going to ask you a different one on the fly. Mm hmm.

[01:02:38] Because a lot of what we've been talking about is like, or I keep saying I have to be in this kind of persuasion game about talking about what the future will be when the future is actually today. And this all of these things exist. And this is why you should be doing it today. Mm hmm. I think that's interesting.

[01:03:06] I think, um, I did sort of hint at this earlier, but I do think we were at a problem stage where it was really, really hard to put a bunch of stuff together to connect a bunch of systems and time consuming and annoying. And then I think it was really hard to optimize interactions. And so we lived in these worlds where everybody had hundreds of dashboards and reports and queries and things that were going on. And now I don't think we need to live in that world either.

[01:03:31] I think we can operate conversationally well, as long as our architecture is solid behind it and all of these structured formats are solid as well. I think what, what I am going to run at next is helping people change their own workflows, helping teams change their workflows, and then helping cross functional kind of programs change their workflows. And so I think about like, once you have a platform that has all your information. Great.

[01:04:02] Once you can connect it live to MCPs and you can see real time reads of everything in the ecosystem around you. Great. Once you can write back to that ecosystem. Why not automate most of the admin activity that is in your job? Why not automate most, most of the admin workflows that exist at your company? And so that's what I'm interested in is we sort of figured out data organization.

[01:04:28] We figured out interactions and insights propagation at scale. I want to figure out automation at scale, and I want it to feel ownable for individual people at a company. I don't want it to feel like it's sort of done to them. That's what I'm going to do next. Say more about that last part. You want them to feel like it's not done to them. What does that mean? I think a lot of times when we talk about augmentation or automation, maybe we talk about doing it to people.

[01:04:58] Like we're going to change their work. We're going to redesign their role. I'm a really big fan of job crafting. And I think if you give people the tools that they need to redesign their own work or make people responsible for redesigning a cross-functional workflow, I think they will be able to own and sort it out in perhaps a better way than some kind of central hub could. So I like to think that people are maximally engaged and productive when they have ownership over how work happens.

[01:05:27] Usually individual people understand their context best. And so I would love to create a system where people can sort of, I think like individual team org level, but I think there's a lot of money. There's a lot of meat at that individual level of like, I know what it takes to do this type of work at this company, and I can figure it out sort of on behalf of me and other people like me to make it easier. I love that. I know we're kind of early on that, so we'll see. But I really think that's the right next thing.

[01:05:57] I think we're going to find out. Well, let's do some what am I reading. Okay, sure. The first one I've got for you, which we've, I'm getting good at this podcasting thing because it's about, it's about curiosity. Since that's been a theme of today's episode. And the title of the article is, Is Your Workplace Draining Curiosity? And it's from the Human Resources Magazine, Human Capital Magazine. I don't know what it is. Some HR magazine.

[01:06:27] Excellent. And so they're summarizing some research that were done by Survey Monkey asking people, and it said 95% of people describe themselves as somewhat curious. 60% say they're very curious. And only 30% say their work for their workplace strongly rewards curiosity. And so digging into this, they look into some of the things that are draining curiosity at work.

[01:06:54] One is they call the AI middleman, where directors and VPs are nearly three times as likely to, as individual contributors, to use AI instead of asking a colleague a question. And then they show some of the impacts of losing curiosity.

[01:07:12] And then some of the ways of rebuilding curiosity at work, and some of the ways of rebuilding it are more opportunities to brainstorm with colleagues, more psychological safety, stronger connections across teams, reduced workload, and more unstructured time. Did you get a chance to look at this one? And what did you think about it? Yeah, I did. It threw me into a bit of a moment where I was trying to decide if you can run out of curiosity.

[01:07:43] And then I went back to my, I was like, would my grad professors have said this is a state or a trait? I went for a minute on sort of like a tangent of my own mind's creation. What'd you find in your tangent? I don't think you can run out of curiosity. I think we're mixing concepts, constructs there. But for the purpose of this article, I don't need to make that argument. For the purpose of this article, I think what they're trying to argue is work is changing a bit.

[01:08:07] And the way people interact, I think what they're talking about is maybe interpersonal curiosity, like talking to other people about things. And that's changing with different information being available differently, which I can get behind. What I liked about it was kind of what we often find. The recommendations are sort of tried and true. If you do these things, you will have a productive team environment sort of recommendations.

[01:08:31] Like we know for decades that creating psychological safety creates more potential for ideas and more potential for people to feel like they can contribute the way they need to. We know that if you create focused time in a day, people will ultimately produce better quality work for you. So I like that it was consistent, but I don't think I don't do you think that you can run out of curiosity if you are a curious person? I think you can get fatigued. Yeah. Energy. Yeah, I think that but that's not necessarily curiosity.

[01:09:01] That's kind of like the I think it's like a parallel question is like, can you run out of questions to ask? And I think the answer to that is no. So therefore, as long as the human has the energy and is still alive and their heart is beating, then yeah, you probably can't run out of curiosity. Yeah. And I think there's something there, something that they're trying to solve for, which is people are talking to tools instead of people.

[01:09:30] I don't know that talking to tools makes you less curious. So there are sort of a few like premise issues I had as I was reading it was like, I don't know. I think that I mean, I don't think they explicitly stated in the article, but it was sort of like an anti AI article where implicitly, you know, using AI is making us more isolated and isolation makes curiosity more, more sparse.

[01:09:56] And so they're saying, like, the good thing is human connection and bridging teams, psychological safety and all that when it's probably an and not an or. Yeah. But even within that, I do think that isolation is a real problem, even if you are using AI in a productive, curious way. And so I think you can have it both ways. Yeah. I did see a talk recently that was quite good from Michael Arena talking about. Hmm. Prior guest of the podcast, friend of the podcast. Okay. Lovely.

[01:10:25] I remember when he was doing ONA stuff at GM that was quite inspirational when we were at Ford, we were watching what he was doing and sort of the beginning of connected comments. But anyway, he was talking about what it looks like now when new hires join an organization versus how it sort of used to look and how so much is systematized that they can interact with in isolation versus previously having to rely on networks. I hope I'm explaining. I hope I'm representing him well, having to rely on networks. And so you observe them interacting differently.

[01:10:54] And therefore, like if you think about all the network effects, right, like their influence is probably slower to build. Their awareness is probably slower to build. Perhaps their expertise and context is slower to build. And so I do think there is something there about getting more isolated. I don't think that it affects curiosity, perhaps. I think it maybe affects other things. Is my net net. Okay. I feel like curiosity is more innate. Yeah.

[01:11:21] Well, let's, let's dig into the next one in terms of what am I reading? A frequent citation on the podcast. Paul Spector in his own website wrote an article called writing formulaic research papers. I know you have your PhD. We haven't really talked about it, but one of the things I like to do is when people have their PhDs, they know a lot of the, I don't know, let's call it baloney that goes into having to do research and then try to get it published in academic journals.

[01:11:49] I do cover a lot of academic journals in the, what am I reading section. And he talks about writing formulaic papers and some of the strategies that, you know, let's call them early career academics use to try to game the system to get their articles published. I'm leaving. I'm leaving. So he says leading with exaggerated and unsupported claims use citations to support those claims. Draw on theory, even if the theory is not really related to what you're talking about.

[01:12:18] Um, include lots of methodological details with the citations that people probably aren't going to look up. Use overly complex statistics. And he said, there is a better pathway forward. You can focus on what is the problem? What is known about the problem? Is there a theory that explains the phenomena? And what are, what is the most rigorous study method that answers my question rather than just the most complex statistics? What are the simplest statistics to shed light on the question?

[01:12:48] And what conclusions can I reasonably reach? And what new questions does this research raise as a consequence? What do you think about this one, Marina? I did. I thought I was going to be an academic. And so I did actually go through a phase of my life where I published and went through the cycles that you are, you know, she's like, I resemble this. Huh? I remember this.

[01:13:10] Um, I'm curious for your take two, because I know you also did a program, but I think published research papers are by nature formulaic. You are taught to write them as formulas and readers expect a certain formula. And I like, I liked his post and I think it was probably relevant. He could have posted it five years ago or 10 years ago or 15 years ago or 20 years ago or beyond. And it's still what it is probably five years from now, 10 years from now, 15 years from now, 20 years from now too.

[01:13:39] Yeah. Which leads me to really think about what is it that's perpetuating this? Like perhaps they are doing this because that is in fact what gets published and reinforced. And so if we really do want to edit something like for me as a practitioner, would I prefer simple methods that I can explain easily in a one page format, the clear problem statement and a clear outcome recommendation? Absolutely. I would love that.

[01:14:03] Um, but I think we would have to change something systematically about how research is produced to be able to make that change happen because it is pretty, in my opinion, pretty prolific across outlets, how we, how we do that today. But what do you think? So, uh, I'll make two points. Cause one of the things I, I, I've whinged on about a lot of this stuff in the past, I'm going to try to give two new angles of things I haven't talked about. Okay. One is, I love that you pointed out that, you know, there is a formula, you were taught the formula. This is why the formula is kind of self reproducing.

[01:14:34] Um, he produced, he didn't, it's not that the formula is the problem because he gave a separate formula. Yeah. He's saying it's not that the four, like using a formula is bad is that we have the wrong formula. That's true. And what if we had a right formula? And so take that one for what you will. Yeah. Cause I think his formula is better. I would read that. Yeah. So much more readable, right? The second part, um, and this is digging into kind of the complex statistics parties.

[01:15:03] You remember the journal, I've covered a couple or a few articles from there, but they're usually very dense ORM organizational research. Of course. When you're in an Iowa program, it's one of the best journals, right? It's all the new ground is broken there. It's really great. JP, PSYC, ORM. Yeah. But, but here's the problem. All those other journals you just started to cite over time started to sound like every article was an ORM article.

[01:15:28] Because every article had to introduce some new statistical flavor, some new scale that got validated, some new thing. And ORM has its place, right? But every article just to get published doesn't need to be introducing some new flavor of the week. Just to get published, a simple statistic would probably have been more reasonable if you really wanted to have a real finding, not a statistical artifact. Yeah. Right.

[01:15:53] And so I guess the one thing I would say is not everything needs to be ORM in terms of academic publishing. And if it's every journal turns into ORM, it's just because people are gaming the formula of the system, not because they're contributing to the knowledge of the field. Do you think there's any reason, so I am by default, like I'm likely to agree with you. I'm just trying to think like as a full argument.

[01:16:17] Do you think that there's any reason to suspect that by pushing people to try out new different methodologies, we will as a discipline find new simpler approaches over time that work? Do you know what I mean? Like, are we sort of stagnating now? Well, I mean, say it again to make sure I understand that I'm inclined to disagree. Sort of, when you are encouraging people to publish with new methodologies all the time, often more advanced, complex, fancy policies.

[01:16:46] When you're encouraging this, what you're doing is sort of testing new possibilities for the field and perhaps finding new methods that are better than previously understood methods. Okay. I thought you said it would lead to more simple methods in the future. Yeah. And I do not think that that will lead to that. Yeah. I think what you create is a system of incentives.

[01:17:09] This is one of my usual talking points I've talked about a lot, so I'll go ahead and kind of rehash it, which is you've created a system of incentives that people aren't trying to actually find findings or they know that it becomes more publishable, the more complex your analysis is. So it just creates this kind of arm trace of creating more and more complexity rather than actually more and more findings. And yeah, that's a problem. The whole systems issue though, Cole, like that's how people, you know, get.

[01:17:39] That's how they get. Yep. Your job. Show me the incentives. I'll show you the outcome. Yeah. It's, it's, it's definitely, but what's one of the reasons why science becomes less and less relevant over time. Yeah. Unless you can show that there's a relationship between the complexity of the methods and the impact that the finding will have, which as far as I can tell, there's no correlation between those two things. Yeah.

[01:18:01] I'll tell you when we were talking earlier about the papers we used to produce internally at Stripe, those papers were always two pages or less written for everybody to understand and simple truth-seeking, clear tests. Yes. And I think that's sort of why they worked is they were so direct and simple to apply. And they probably follow Paul Specker's formula. Probably, probably, probably less. Yeah. Probably less jargon.

[01:18:30] We were really low on jargon, you know, of course. Yeah. But that, that's a lot more useful. I don't know a lot, a lot of what I reflected on when reading this article was I do not read academic papers anymore. I have. Yeah. It has been years, perhaps. Do you read them outside of for this podcast? Oh, absolutely not. Never. But this you would read, right? If there were like simple, clear problem solving. Yeah. I mean, it would still have to be good.

[01:18:57] But, you know, even if, I mean, there's thousands of research articles published every year, even if only 10% of them are good, that would give me hundreds to read. Yeah. Right. And the difference between hundreds and zero is quite substantial. Yeah. So, you know, it's, it's not, it's not a quality research quality issue. It is a fundamental, the formula is broken issue. Yeah. I think the peer reviewed element is key. A lot of what hits me from all sources is non peer reviewed, just sort of.

[01:19:28] Slop. I don't know what to call it. It's just like information being thrown at me. But if there are some journal that had some peer reviewed element that was also following his formula, I think that'd be great. Yeah. Well, let's do the last one. I know you're excited about this one. Oh, you mentioned his name earlier. Um, so you, uh, introduced me to this whole Lenny's podcast, but I don't know how to pronounce the last name, but Lenny and, uh, Noam Siegel wrote a, uh, a, uh, sub stack article about a recent podcast.

[01:19:54] They had about how tech workers are feeling in 2026, the workforce is splitting into two. And so they, they did this huge, big research study on, on tech workers across the tech industry. And they had a bunch of big takeaways. I'm going to go through them. We can dig into whichever ones you want to Marina. One, the workforce is splitting into two tech workers either feel amplified by AI or shaken by it. Burnout is surging and optimism is fading. They're seeing a lot of burnout on the rise.

[01:20:22] Uh, tech workers wouldn't recommend their own field over half of people. So they wouldn't recommend their own field to a newcomer. Productivity is up, but quality is questionable. 82% say AI is making them measurably more productive, but they're not sure this is actually making the work better. Underlying fear is the underlying fear is being overworked. Only 22% worry about losing their job to AI, but everyone worries about essentially being overworked.

[01:20:50] Almost everyone is ambivalent designers and researchers are the most worried. Founders are still the happiest people in tech. I guess that's been a trend that they've seen over time. And small companies are still the best places to work. Hey, hey HR bench. Managers are still the biggest lever for happiness, which I'm going to let you dig into that one here in a second. And the industry and tech workers own words is chaotic. What did you, what did you think about this one, Marina?

[01:21:19] Well, you know, I like, I like that source of information. I thought it was that I think is a really good example of observing how people outside of a people function are understanding a people problem, by the way. Some of the insights in there are would not be surprising to us. We have been finding for many decades now that it's the manager, that the manager is the key. You want change management is the manager, productivity manager, curiosity and innovation manager. It's the environment they're creating retention and so on.

[01:21:48] And so it is always sort of fun when other people discover these like enduring findings that we have that, you know, and you're like, oh yeah, that makes sense. I was really interested in the finding in there that was around no one recommending their own field to others. On the surface, I think that's really concerning for future generations. I think I did sort of double click into it and the item they're using is an NPS item.

[01:22:18] And I think what they're doing is looking for that 910 promoter and that's the wood, you know what I mean? So maybe if I could see the full distribution, I wouldn't feel so concerned. I'm not sure. But I think the way they're positioning it in terms of designers and researchers are most concerned. I think data analysts were quite concerned. And then everyone else was also like would not recommend don't get into what I do. I just hope that is a method issue. Like, I hope that is not actually true, I guess.

[01:22:43] And I wonder if that may change as we hopefully move out of a more anxiety laced burnout driven place and into a more engaged and flow rhythm style of work. I hope in the next few years. What did you what did you think? Well, that one in particular. And this is.

[01:23:05] This is why I've been saying the whole leadership thing lately about my definition of leadership is somebody who paints a compelling and positive vision for the future that you can see yourself as a part of. Yeah, it was actually in reaction to using the system. It's just to me a few months ago. And it's because. If you look at the leaders of the field, the quote unquote leaders of the field of tech, they paint an apocalyptic future.

[01:23:29] So why reasonably would anybody recommend an apocalyptic future for a field to anyone else makes no sense. And it's because it is devoid of that definition of leadership, of painting a compelling vision of the future that is positive people see themselves as a part of. And that's why I've been trying to do this for our field.

[01:23:49] But I think the tech leaders could stand to take a taste of their own medicine, because I think one of the if you remember the podcast, I actually listened to that in the car after you had sent it to me that day. I was on a long drive that day and one of the things that talked about is the folks, they feel like they wouldn't recommend it to others. But if they actually look at like the content of the work that they're doing, they're like, oh, yeah, this is fine. This is great. Like this is still going to be around.

[01:24:18] You know, A is not getting rid of it. So there's apocalyptic future that they see for everybody else. They actually don't see for themselves. And so it's like almost like a collective action problem where everybody thinks that everybody else is going to be automated, but they're going to be fine. But they think that everybody is getting destroyed and therefore they're like, oh, this is terrible. Don't go into this. Don't do this field. And so it's like this collective psychosis almost. Yeah, what's going on? And I don't know if you saw that or recognize that from your own perspective.

[01:24:44] I do think that just sort of humans like we do sort of do that in unknown circumstances. But they don't have to. I think it's fair. I think maybe the other piece of that podcast that that stuck with me, I've been thinking about since I am obviously what they would call like an enthusiast. I get a lot of engagement out of interacting with these tools. I do feel like my work has substantively shifted. I'm happy as a result of that. I understand that not everybody feels the same.

[01:25:11] And one of the things I try to think about is this finding they had around the driver of anxiety being you are expected to produce more faster, less breathing room, less time on your own. And there was another article. I can't remember the source of a couple years ago. I feel like when the foundational models were really sort of becoming prolific, where they were talking about how.

[01:25:37] AI use is used most by your highest performers, and they don't take the time back. And so all of a sudden your highest performers are doing more. And is that okay or not? Are they still able to accumulate the same kind of quality? Or are you overburdening folks who used to be contributing in an outsized way? I think about that a lot with this finding. And when I talk to other folks around me who are not in that like, you know, enthusiastic, energized, whatever they call it, bucket. Yeah.

[01:26:04] I can't remember what episode it was on, but I covered an article from Ben Stancil while back on Substack where he talked about like high performers are getting addicted to work. Like kind of like you did to video games in the past. It's like it's become so fun. And so like you feel like you can't step away. Yeah. Right. And that I think is a real phenomenon. And it's all, it's leading to a lot of the burnout and things that this, this, this one discussed. I don't know where that goes.

[01:26:33] I like, I can see a future where that people start to chill out on a little bit. And I can also see a future where it's not just the high performers that are addicted, but everybody else becomes addicted to. Yeah. And then we just get stuck in this loop. And I'm not really sure what would be the breaking point to which of those two scenarios happens. Yeah. I do also wonder. We're so focused on AI right now as a topic of conversation. I hope soon. Yeah. I think that's the way that we're going to talk about.

[01:27:03] And then I'm just going to talk about AI as a topic conversation. And we will just talk about how work happens. We won't say like, it's an AI tool. We will just say it's a tool, you know? And I wonder if, I don't know, through like the methodology. What was that? I said, think of the foundation model companies. I was listening to another one that you liked to listen to, Dwarkeash, the other day. And I think it was Noam Brown, who's one of the researchers at OpenAI, was talking about. They have actually started reducing the headcount of the LLM team.

[01:27:31] And people are being applied to, you know, agents and building products and other places of like applications of the LLMs rather than just brute force improving the LLMs anymore. And that's where they're squeezing the value out of it. So like you can see we've already hit the point of diminishing returns in terms of what people think like, quote unquote, LLM driven AI is going to do. So now people are focusing on what's next. Yeah. And maybe, maybe what's next might be a little bit, you know, less addictive and more human and all that stuff.

[01:28:00] Or maybe it'll be way more addictive and maybe we're all going to die. I don't know. Maybe. I just wonder about when we ask people about AI, are we putting them into a situation where they are likely to tell us other things? Like, do you, I don't know if you remember like a decade ago when it was very trendy to ask about bureaucracy. And then as soon as you say the word bureaucracy, you cue the negative sentiment that perhaps might not have existed before. Yeah. When you hadn't asked about bureaucracy. And I wonder if you're in a survey asking about the effect of AI on your energy at work on your, you know, I don't know, I guess I'm.

[01:28:31] You're morally shading. Yeah. What you're mentioning when you talk about it. Yeah. Right. Because you could describe, I think there's something called like a Russell conjugation where it's like you use something to describe something in one way. So like, I sweat, keep her spires, she glows. Right. It's the same thing, but you're coding it in different ways morally. Um, and so like bureaucracy has a negative code, but you could just say structure. Yep. The structure of work.

[01:29:00] And that wouldn't have a negative code. Right. So you're actually shading and telling people what to feel just by how you lay in the question. Yeah. I always end up for what it's worth. Whenever we look at a research study, I always end up in methods for some reason. And I, I just think sometimes these papers are written or these podcasts are hosted and you're seeing the outcome and it's been shaped. The narrative has been shaped for you. Mm hmm. If you really kind of poke at it, you can understand more of what was really sort of collected and understood.

[01:29:30] Yeah. That's why a lot of times if I ever get a disagreement with somebody, it's very rarely about their opinion or something like that. I usually just disagree with the premise of the question. Yeah. Because I can tell it's being shaded one way or another. And it's like, well, I just disagree with your premise. So like, let's just like, let's unpack that. Yeah. These are good article picks. I enjoyed looking at them. Hey, I'm getting good. Yeah. It was very, it felt very personal to me too. I appreciated that. I see you. Uh huh. Yeah.

[01:29:58] Um, did you have any questions for me before we wrap up? I'm curious about your perspective. I think you asked me a lot of questions about how, how I would build what I would do. You talk to a lot more people in a lot of different contexts than I operate in. Yep. What would you do? Like what skills would you optimize for? What remits would you go after? Um, I think it's one of the things that you're going to have to give me, like, is it, am I a huge company or a small company? Yeah.

[01:30:28] But give me one more. Huge company. Huge company. So what's in the mode of what we were saying about the tear down and the rebuild. If you're at a huge company, that probably means you're already blessed with a lot of resources. All right. So if I'm blessed with a lot of resources, that means that if I want to stay cost neutral, I probably have a lot more to work with. I have a lot more wiggle room than I do at a smaller company. At a small company, anything I do new is probably a net new spend. And so I have to justify it in a new way.

[01:30:58] And I've been in both situations. And so it's a very different kind of formula depending on those. Um, the reality is like, let's say my net spend on my 50 person team is $20 million or something like that. I'm just throwing, throwing out numbers. Mm-hmm . I'm going to come back to them and I'm going to say, all right, we're going to tear down how we do this. And it's only going to cost us $5 million instead of 20. And we're going to produce 10 times as much work. And that work is going to be of a higher quality.

[01:31:25] And like you said from earlier, it's no longer going to be focused on us being the build, you become the builders. Mm-hmm . And then we're going to find ways of scaling that for the entire organization. Um, so that we can be the early adopters of the HR function to prove out what the future of HR is going to look like at our organization. In the hopes that when we get into that kind of that, not just focusing on intelligence, but focusing on automation and automation, that's not just happening to you.

[01:31:53] It's happening through you is upscaling the entire function to do the same thing for themselves. And that de facto positions my team as the leader of the future of HR, because we not only created it for ourselves. We not only disrupted ourselves first, but we created the playbook for enabling other people to be the builders themselves. And then ultimately somebody is going to have to completely redesign HR and who knows, maybe that'll be me. That's what I would do.

[01:32:20] I appreciate that you went context deep and you also did a tear down. I love a tear down. Mm-hmm . But yeah, I think you're right. Like reset the foundation and then your position for greater success as a people team. I always say this is like, when I say something that's true that I think everybody agrees with, but won't say publicly is like, that's so weird. Right? So weird. Um, because I think everybody knows what I just said is like the, is the way it's the way.

[01:32:48] And I don't think that they can say it publicly because you know, they're beholden to one thing or another. Yeah. And real constraints exist. I do wonder though, what would need to be true for those constraints not to exist? Like what, what is it that's holding you back? Is it just priority? Like, what is it really? Uh, I mean, I'll say from a system, I mean, there's a lot of different things holding it back, but I'll give you, I'll give you two. One is systems. One is politics, right?

[01:33:11] From a systems perspective, the incentive to get ahead that the, what the system is incentivizing is to grow your team, grow your budget, grow your head grout, grow your budget. Yeah. That's how you, you know, project power on your organization. It's how you get promotions. It's how the system reinforces itself, right? This goes in the face of that. And when something goes in the face of how a system is rewarded, you either have one option, you need to regress to the mean of the system or two. You have to change the system.

[01:33:39] So that's why I described a way of not only deconstructing what my team would be doing, but also changing the entire system of the HR function itself. Right. That's one other two is politics. Right. People have vested interests. They don't want to see, you know, they don't want you to call their baby ugly. They don't want to change. They, they have institutional power. You know, one of the things that I put in the preface of my book from last year is. That CHROs, I always felt like were the number one thing holding back the future.

[01:34:09] Right. It's not a common viewpoint. I was very provocative. Surprisingly, nobody really commented on it. They commented about a lot of other stuff in the book. I always said that we have a generational shift that's been going on in organizations that people that came into that CHRO position before data was a thing in HR and people who came in that position after data was a thing. And every year person retires from the before land. And every year somebody gets promoted from the after land.

[01:34:37] At a certain point you hit a critical mass where the ethos of the entire field changes because you no longer have the people from the before land anymore. Very provocative. It requires a lot of patience. Well, it's, it actually shows where I was wrong. Earlier in my career I actually thought that this is, we can win over hearts and minds. And I realized that no, this is just a generational problem. And that's a really depressing problem to have. Because that means that we can't change. And I always believe that people can change.

[01:35:06] So, I don't know, take that for what it's worth. Yeah. You'll have to tell me another time, not today, what you would do in a small company. Small company is a lot easier usually. The constraints are real, right? They're real. But you also can be, to use your word from earlier, much more scrappy. Scrappy is fun. You can get a lot done with scrappy. You can move fast. You can, I mean, being in a small company, there's a lot of, you know, hard things about it.

[01:35:34] And frankly, you might get snuffed out of existence pretty quickly. It's always a risk, but you can have a lot of fun at a small company. I agree. Ready to wrap up? I am, yeah. Okay. Well, Marina, it's been so fantastic having you. I want to, has this not been like a lot of fun for you? It has. Like, although it feels a lot like our conversations usually fail, but yeah, they're always fun. Well, I'm glad I got you there because it didn't start out that way, but we were ending that way.

[01:36:02] And I'm glad we got there because that was my hope for that. I had a lot riding on the expectations for this episode. Did you? Yes. I've been looking forward to it for a long time. And this is good stuff. Hopefully I met your expectations. You exceeded my expectations. I am so pleased. Okay. But if people want to reach out to you, where can they find you, Marina? They can, I guess, despite me earlier saying I'm not really on LinkedIn, the easiest way is in fact to find me on LinkedIn. I don't post much, but you can send me a message.

[01:36:31] Well, thank you so much, Marina. Marina, you've been listening to Directionally Correct, the People Intelligence Podcast with your host, Cole Knapper. Today's guest, Marina Pearce. Thanks for being here. Bye now.