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Kasia Chmielinski is the Co-Founder of The Data Nutrition Project, an initiative that builds tools to improve the health of artificial intelligence through better data. They are also a technologist at McKinsey & Company in Healthcare Analytics and previously worked at The U.S. Digital Service (Executive Office of the President) and Scratch, a project of the MIT Media Lab.
They studied physics at Harvard University. When not in front of a whiteboard or a keyboard, Kasia can be found birdwatching or cycling uncomfortably long distances on a bicycle.
Underland: A Deep Time Journey, by Robert Macfarlane.
What is an ethical product? In an industry whose mission is to build technology that does good in the world, you’d think that by now we’d have figured this one out. You know, develop a checklist of criteria that helps chip away at our assumptions and biases and answer questions like, “is what I am doing meaningful?” and “is what I am doing ethical?”
In this episode of the Product Momentum Podcast, Sean and Paul welcome Kasia Chmielinski, co-founder of the Data Nutrition Project and technologist at McKinsey & Company in Healthcare Analytics. Unfortunately, or perhaps fortunately, Kasia says, ethics are not black and white. They cannot be captured in a series of boxes that will be applicable in every situation. There are, however, processes and strategies to intentionally build an ethical product, they say.
“We already have these processes,” they add, “but the intent behind them is usually monetary or financial – something about growth or ROI. If we modify our processes and strategies to instead think about the end-user, think about the potential harms, think about how people are going to use it, we’d probably have better products for people.” It’s all about trade-offs and balance, they add.
It’s a significant challenge (pardon the understatement). We’re solving big, hairy, complex problems for an audience of users whose experiences and ethics are as varied as snowflakes. With so many combinations and permutations – and so many dependencies – it’s no wonder the question about meaning and ethics remains unanswered.
Or has it? Have a listen to the pod as Kasia methodically tackles the question – precisely as you would expect a trained scientist would – but with an added sprinkling of optimistic philosophy that suggests their answer will help us all create better products and do more good in the world.
[02:00] Use your powers for good. There are a lot of tools you can create that can be used for good or evil.
[03:02] The stories we tell should be true. But they can’t just be true. They have to be engaging, and appropriate for our audience.
[04:06] The user story is less about storytelling. It’s more about having the right components of the story…and phrasing it in a way that’s going to get you budget and people and resources.
[05:38] You can’t use a story to fix a bad product.
[07:44] In the realm of machine learning and AI, we’re so focused on the outcome of these models that we’re not really thinking about all the inputs that shape the outcome.
[11:05] Ethics are not black & white. And they can’t be captured in a series of checkboxes that answer the question: “Is what I am building ethical?”
[11:56] Tools are agnostic. It’s the use case that makes the difference. So we need to have the conversations and make the observations that help understand the necessary tradeoffs and balance.
[13:59] How are people using my product? And how did their use align with the moral compass we established to begin with?
[15:56] Iterate toward better products over time. That should be a big part of what we do as product managers.
[16:43] Keep your tech people really close. There are so many points at which you have to make decisions technically that also could seriously impact the product.
[18:45] It’s important to think about where we get our energy.
[20:31] When considering your next position…. Is it challenging technically? Is it interesting from a product management perspective? What are we trying to accomplish? How will it affect people?
[22:24] The Data Nutrition Project. Just this little team of people who are mostly volunteering our time on nights and weekends because we want to make the world a better place.
[23:10] The hardest thing about product management. You don’t have direct power over anything.
[23:56] ‘CEO of the Product’. I think they tell us that as a joke. It’s like, “don’t you wish?”
[24:23] Innovation. There are categories of innovation. And they’re all related by movement. Movement of an idea or a concept or a product in a direction that hasn’t been explored. Or movement further in a direction that has.
[25:44] Source of inspiration. The most inspiring things come from hanging out with like a 13-year-old. Nothing will change your mind like hanging out with a kid.
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Sean [00:00:18] Hi, welcome to the Product Momentum Podcast, a podcast about how to use technology to solve challenging technology problems for your organization.
Sean [00:00:28] Hey, Paul. How are you doing today?
Paul [00:00:30] I’m great, Sean. How are you?
Sean [00:00:31] I’m super excited. Kasha has such a wealth of experience, such an interesting background. They have done so many different things in our industry and other industries that we have to learn from. I can’t wait to get into this.
Paul [00:00:46] Yeah, from storytelling to non-linear career paths to my favorite, influence without authority, I think we’ve got a lot of ground to cover here. Let’s get into it.
Paul [00:00:58] Well hello and welcome to the podcast. Today, we are excited to be joined by Kasia Chmielinski. They studied physics at Harvard University. They’re the co-founder of the Data Nutrition Project, an initiative that builds tools to improve the health and artificial intelligence through better data. They’re also a technologist at McKinsey & Company in health care analytics and previously worked at the U.S. Digital Service Executive Office of the President and Scratch, a project of the M.I.T. Media Lab. When not in front of a whiteboard or a keyboard, Kasia can be found birdwatching or cycling uncomfortably long distances. And in their relation to McKinsey, I am asked to point out that their opinions are their own and not reflective of McKinsey & Company Kasia, welcome to the show. We’re so happy to have you.
Kasia [00:01:39] Thanks. Nice to be here.
Paul [00:01:41] Excellent. You’ve had a non-linear career path, as you put it, and storytelling plays a big part of your background. Your role in communications informs how you see product and how you see the world. Can you tell us a little bit about how storytelling fits into what product means to you today?
Kasia [00:02:00] Yeah, absolutely. I think it’s helpful to understand that I aim to understand storytelling through the lens of someone who has a more technical background where storytelling isn’t always front and center. So, you know, if we go back many years, I studied physics a while back and ended up getting my degree. Loved the work, loved the topic, but really couldn’t tell a story. And when I was determining what to do next, you know, I think we’ll get into this later, but there are a lot of tools you can create that can be used for good or evil and I was seeing a lot of things in physics that I could do with my career that didn’t really fit what I wanted to do mission-wise. And so I ended up looking around and saying, “well, what might be an interesting path to choose?” Technology was really interesting to me. And although I wasn’t a coder, I actually thought, “well hey, why don’t I jump ship a little and try out the communications path because I don’t really know how to tell a story. I can make things, I can technically do equations and all that. But, you know, being able to actually tell a story and paint a vision with technology is something I can’t do.”.
Kasia [00:03:02] And so that’s actually what I ended up doing when I graduated. I went and I worked at Google in the communications field, which to many people just seems bizarre, you know, my mother included. She’s like, “what on earth are you doing?” But it was actually, I think, one of the best moves that I could have made because what it did was it showed me the power of a story. And, you know, that’s what we do all day long. We tell stories, right. That’s how we communicate as human beings. Now, stories should be true, right. But they can’t just be true. They have to be engaging. They have to be appropriate for the audience. And, you know, the power of a story is basically the first step in making a product. No one buys an MVP, right? They’re all buying a vision. Even if you only ever get to MVP, the thing that they’re actually signing on to is a story that you tell about where it’s going. And to me, that’s the importance of communication when it comes to product.
Paul [00:03:48] We use these things called user stories all day long in our backlogs and we have a rough framework of a beginning, a middle, and an end of what we want the user journey to look like. Is that what you’re talking about when you talk about a story, or is it more contextual, more nuanced than that?
Kasia [00:04:06] That’s a really good question. I mean, again, I think it’s about communication and about making sure that you’re empathetic to everyone that you’re talking to your product about. And in some cases, you know, that is about interviewing people and being a really good listener and being able to understand what someone is saying to you and tell back to them what it is that you’re hearing in terms of what it is that they’re trying to do or they want to do. And obviously, all of those things turn into user stories. I think the kind of term of art ‘user story’ is less about storytelling, honestly, and it’s more about, do you have the right components of the story? So do you have the acceptance criteria? You know, all those things that you would need for a good story. So I mean less that, I mean more of, can you take a step back and look at what someone’s trying to say, how they’re communicating it? Can you speak that language when you’re explaining to them, you know, what you’re trying to build to see whether it matches what they want? Also, can you phrase what you’re trying to do in a way that’s going to get you budget and people and resources, right? These are also really important things to consider if you’re a product manager.
Sean [00:05:06] Yeah, so basically positioning of the product. And you tell different stories to different communities. You have to tell a story, or the people building the product need to understand the story as well as the people that are going to consume the product, and the people that are investing in the product, right, you have to weave it all together.
Kasia [00:05:21] Exactly.
Sean [00:05:22] April Dunford, she has a book out called Obviously Awesome. We had the chance to interview her on positioning, and it was a fascinating discussion about this stuff and how you position your product and the importance of story and storytelling.
Kasia [00:05:38] I think one thing that is worth pointing out is that the story by itself isn’t enough and you can’t use a story to fix a bad product. Right. So that’s kind of what I ended up learning when I was at Google and what then caused me to think, “maybe I actually want to become a product manager, not remain a communications manager,” was that I was kind of pulled in at the last minute to message a product that maybe wasn’t a great fit for where they were trying to launch it or how. And so when you’re pulled in at the very end, to tell a story, you know, that can’t fix something that doesn’t have a good fit. And in seeing that happen a few times, not a ton of times, Google’s a great place, but seeing that a few times, thinking, “well, hey, if I had been a part of the conversation earlier when you’re shaping what you’re actually building so that the story you tell aligns with the product that you actually want to launch, that would be way more fun.” And so that’s actually when I started to think, “Hey, I should probably try to find a way to move into product management,” and I did that by actually leaving Google and going and starting a company with some folks on the West Coast and trial by fire and learning by fire. I just was the first product person, but also everything else, and then eventually, you know, grew that function and that’s what I’ve been doing since.
Sean [00:06:52] Well, I’d like to hear the story of the Data Nutrition project, and I think the audience would as well because I think it’s fascinating.
Kasia [00:06:59] Yeah. This is a project I’m super excited about. It’s one of these things that, you know, is a labor of love for myself and everyone involved. We’re all kind of doing this on our nights and weekends. About two years ago, I received a fellowship along with a bunch of other people to join a cohort of individuals at the Media Lab and also the Berkman Klein Center at Harvard to think about the ethics of artificial intelligence and kind of the governance models. And it was very DIY. So we came in as a cohort and were told, “OK, self-organize and launch something by the end of the year, by the end of the semester. We don’t really care what it is. Figure it out yourselves. You’re all smart people. And we immediately started to break into groups based on our interests.”.
Kasia [00:07:44] My interest has kind of always been in data. I’ve always been adjacent to big data, big inference, whatever you want to call it, large datasets. And I know that especially in the realm of machine learning and A.I., that we’re so focused on the outcome of these models that we’re not really thinking about all the inputs that in a lot of ways shape the outcome. So you have a machine learning algorithm that’s, you know, quote-unquote racist, and the outputs that are coming at the end of it are not things that you want to be affecting real decisions in the world. And everyone’s really focused on, “how do we make that model better?” Well, what was the data that you fed that model? And if that data is historically racist or making decisions based on situations in the world and the world ends up being a fairly biased place, then obviously your model is going to be producing the same outputs, right.
Kasia [00:08:32] So a group of us were interested in that particular slice of AI, ethics, and governance. And so we decided, “well, let’s try to come up with something that is a tool, but also a methodology around better data.” And the analogy that we used was a nutritional label for food. So in the same way that, you know, you can pick up a candy bar, you can look on the back of it and say, “Is this healthy for me?” You should be able to pick up a dataset and very quickly scan it and say, “it’s healthy for my use case; is this is healthy for my model?” That’s a very hard problem. A candy bar is not really like a data set in a lot of ways. It’s hard to take the same thing that you put on a candy bar and also, you know, a muffin and also a bag of chips. It’s hard to do that with data sets, to make the same label, and put it on every single dataset. But that was the prompt that we had and so we actually came up with kind of a framework, a methodology around this, and a prototype that was based on a dataset that we worked with ProPublica to procure. So that was around marketing payments for doctors who were selling pharmaceuticals.
Kasia [00:09:30] So that was the first version. And since then, the fellowship ended, you know, everyone back to their lives, except the Data Nutrition Project, which became a real project. We incorporated and became a nonprofit and we continue to work on the question of how do you build something that’s going to be standardized and makes it really easy for people to understand what’s inside of the dataset before they build the model so that we can try to address some of the harms earlier in the pipeline. Because the farther you get along the pipeline, the more expensive it is to correct. So that’s what the project does. We’re working on the second version of the label right now, which is a little bit more context-based, so it’s a little bit more about use case and intent rather than something that’s standardized and generalizable for all datasets. And we’re having a really fun time doing it because it’s a problem that we see everywhere and we think a solution is on the right track.
Paul [00:10:20] Sounds fun. It sounds like a big problem that is challenging, but rewarding because you know it’s going to make a difference in the world. I want to ask maybe a question that’s probably too big. I don’t know a better way to ask it, though, but what is an ethical product? When we’re looking at something as shorthand is a Data Nutrition Project label and we can see these are the inputs, this is the validity of the model, we also have to look at the human being on the other side and understand what is it that they’re trying to do. And you said something in our chat before the show about, there is no checklist to go down and say, “this is an ethical product, this is not,” but we have to try. Right. So how do we start to chip away at some of the assumptions and biases and start to get at, “is what I’m doing meaningful, is what I’m doing ethical?”
Kasia [00:11:05] Yeah, I feel like if I had an answer to the question of ‘what is an ethical product,’ you know, I’d be really special and I would share that specialness of the world. I mean, unfortunately, and maybe, fortunately, ethics aren’t black and white, right. And they can’t be captured in a generalized series of boxes that will be applicable to every situation. So there is no checklist that I know of. But I do think that there are processes and strategies to intentionally build a product. And we already use these, right. But our intentions are usually monetary, you know, financial or something about growth or ROI. And I think that if we modified our processes and our strategies to instead think about the end-user, think about the harms, think about how do we think people are going to use it, we’d probably have better products for people.
Kasia [00:11:56] The reason that is so hard, right, why you can’t have a checklist is because there’s so many dependencies. Right. And really is based on how you plan to use something. In the same way I mentioned I studied physics, you know, tools are agnostic. It’s the use case that makes the difference. So the same basic physical principles drive, you know, nuclear energy as well as nuclear weapons. A pretty common example, I think, but it’s a fairly powerful one. And when you’re building an ethical product, the same thing can be true. You know, you’re building a tool or set of tools or something, a framework, and there are many different ways it can be used. And some are probably more, quote-unquote, moral than others. So to me, it’s always gonna be a conversation and the focus is going to be on trade-off and balance. So some examples that might be salient: contact tracing, you know, it’s about the public good, but then it’s also about personal privacy. Where’s that balance? What’s moral? What’s morally correct to do in that case? Right. You look at the Census. A giant database, essentially, with a lot of transforms and computed data to fill missing values. What about specificity, since there’s so many regulations, policies that depend on Census data, right. So you want to make sure that you’re pretty specific. But again, you don’t want to target any particular populations or make things obvious that could be sensitive. And I think there are a lot of examples like this where an ethical product is, I think how you approach it and the conversations you have and not so much, “am I doing the right thing?” Because that’s always gonna be a very difficult call to make. So you need to think about it in terms of tradeoffs and balance.
Paul [00:13:28] Yeah, it’s that trade-offs piece that I think is the key that I keep coming back to my mind. The thought process and the intentionality has to be upfront. It can’t be postmortem, so to speak. You can’t say that, “I had the best intentions, hurt a bunch of people,” intentionally or not, and then say you’re sorry. As product managers, part of our job is to think through those second- and third-order effects upfront and make those choices deliberately. I think it’s really becoming more part of the role more explicitly, now, more than ever.
Sean [00:15:11] Yeah. I think if you’re solving a big enough problem or a complicated enough problem, or you’re dealing with lots of data, there’s really no way to think through all of the possible permutations and problems that might arise. And like you said, somebody’s going to figure out a way to misuse it or abuse it or find a hole in it. It’s just going to happen or you’re not going get anything done because you’re gonna be spending way too much energy managing every single risk. And I think it’s about intent. To go back to your question, Paul, I think you have to go into it with the best of intentions and manage to the greatest degree possible. But you’re going to have problems. So to Kasia’s point, you have to have some sort of regular checks and balances in place and you’ve got [to have] some sort of regular cadence for looking for these permutations and these distortions, you know.
Kasia [00:15:56] Definitely agree. You know, how do we as people come up with our own morals and values? You know, we don’t like pop with them all. This is probably getting into psychology and developmental psychology. I don’t really want to have that conversation, but certainly, I think we can agree that it’s an iterative process. Over time, we learn things, right. And so if we imagine our products are living ecosystems, right, filled with people and movement and decisions and all of this, then we can also iterate towards better products over time. And I think that that should be part of what we do as product managers.
Sean [00:16:27] Cool. So you love to work with data. You’re working with things, like I saw in one of your posts, Jupyter Notebooks and Node.js, and doing some big projects. Do you have any advice for our community on product management in the big data space in particular?
Kasia [00:16:43] I mean, I think I have an affinity for these areas, but I wouldn’t say that I’m an expert in big data. I mean, part of the challenge always as project managers is that you know a little bit of a lot of things. I’d say find your tech leads and your architects and your ML people and make sure you keep them close and you ask them lots of questions because really, it’s like, devil’s in the details around a lot of this area of product, I think, and understanding kind of where the decision-making points are, right. So I mentioned computing data. Is it in the way that you are doing the ETL to get the data in? Is it in the transformations? Is it in how you’re managing missing data? You know, there’s so many points at which you have to make decisions technically that also could seriously impact the product. So I’d say, like, keep your tech people really close and don’t try to learn every little piece of the technical capacity. I mean, at least for me, like it’s not possible. So that’s like one big thing that comes to mind.
Sean [00:17:39] That’s a great piece of advice.
Paul [00:17:41] So I actually want to go back in time a bit, jumping off that, and talk about your experience at Scratch, because I think the impact that that’s having is generational in scope. When you think about the way that the team was put together, it was an odd situation for you to be in. The way that you’ve described, your product manager is nocturnal, so I can’t imagine what kind of hours you were working. But I think in terms of the outcome and having an open-source, free product for 30 million, now, kids to learn how to code is going to make an impact that we can’t even begin to fathom. A generation later, there’ll be an awareness about what goes into the things that we feel like we’re perhaps past the pioneering era, but still in a product management phase that’s in its infancy. But you’re going to have an impact in the work that you do that I don’t think many of us can really look down the road and see quite how far-reaching it’s going to be. Does part of that experience stay with you in what you’re doing now and do you look for opportunities to roll those kind of experiences into what you’re doing in the Data Nutrition Project?
Kasia [00:18:45] Yeah. That’s a series of fun questions. I mean, I think that it’s important for each of us to think about where we get our energy. And for me, it comes in a number of different vectors. So one is just the challenge of the products and is Scratch is very challenging in that it’s a research project out of a lab. When I started, you know, we were constrained in every direction. Everything was physically hosted on machines. I had never experienced that before. No automated scaling. Really no budget, you know, it’s a free product, again, a research project. And yet it was taking off all over the world. And when I joined I think we had four million users and the tech lead, who was nocturnal, so that was that was really fun. The tech lead said to me, “we will not hit five million users, there’s just, the system is not built that way.”.
Kasia [00:19:36] And so that was really fun. I mean, as a product manager getting pulled into a product that’s growing so fast just kind of organically. You know, we had no marketing team. None of that kind of stuff. It’s just word of mouth. The fact that if a free coding platform for kids of all ages, a lot of adults on there, too, that was really fun. In terms of the impact, that’s another thing that definitely drives me, and absolutely, I mean, Scratch is such a pivotal, you know, product and continues to be in the lives of so many kids and so many people. The mission of the team is extremely strong. It’s based in educational philosophies that come from Seymour Papert, and so there’s a legacy there that’s educational and academic, which you don’t often find in a lot of small teams that are building products. They don’t really have, you know, deep philosophical underpinnings. And in a way, the product was just the next incarnation of the philosophy and not really, you know, the other way around. It wasn’t really product-driven. It more philosophy driven, which is really fun to work.
Paul [00:20:30] Love that.
Kasia [00:20:31] So, yeah, I think a lot about both of those vectors in terms of any other position that I take. You know, is it challenging technically, is it interesting from a product management perspective? But also, you know, what’s the mission, and what are we trying to accomplish? How is this going to affect people? Both of those I really do factor into my decisions in terms of career.
Paul [00:20:51] That’s awesome. I think the next phase of your journey in the digital service is also really interesting. And I’m curious, how do you compare the different points in your career? Do you look at the Scratch versus Digital Service versus Nutrition Project as distinct, or do you see connections in this non-linear journey that you’ve been on?
Kasia [00:21:08] I think non-linear paths are becoming more and more common, which is great because it means that don’t have to explain it as much. You know, I think the overlapping variable in all of these decisions was me. It was really the only overlap. And so, as such, it feels very linear to me because I’ve just moved through them all. I can definitely see reasons that I enjoy, you know, each one of the places I’ve been. I actually think it comes back to the two points I just made before. Thanks for your good question there. I think it framed it up nicely in that it’s a difficult product management problem and it also has a lot of impact. So, you know, when you go to work at the U.S. Digital Service, which is part of the White House and government, you’re there for a short-ish amount of time. It’s two to four years, one to four years, however long you manage to stay. You get kind of deployed to projects that maybe are in need of product management or engineering or design talent. You work with people who are there, sometimes reluctantly, sometimes they welcome you with open arms, and you try to make government technology better. And you’re never gonna fix it entirely. You’re working with a lot of huge, legacy problems, like giant, giant legacy projects that are very, very humbling. And your total addressable audience is the whole population of the United States.
Sean [00:22:24] That’s huge.
Kasia [00:22:24] And sometimes people outside the United States as well. And so the impact that you can have is massive, right. But the problems that you face, my God, like, you know, “that program’s written in what? I don’t even know what that is. Who speaks that?” “One guy.” “Cool. Where is he?” Right. It’s like these kinds of things are really, really challenging and humbling and super fun. So I think that’s kind of the throughline. You know, on the Data Nutrition Project, the same kind of thing where we’re just this little team of people who are mostly volunteering our time on nights and weekends and we want to make the world a better place. Our mission, I think, is front and center. And it’s really technically hard. And not only that, it’s conceptually hard. And so, again, it’s like, the impact we can have and also the challenge technically and from a PM perspective.
Sean [00:23:10] What do you think is the hardest thing about product leadership, product management?
Kasia [00:23:15] You don’t have direct power over anything.
Sean [00:23:18] Yeah.
Kasia [00:23:18] So like any, you know, pop culture reference of managerial techniques that involve brute force are not applicable in this situation, I find. Right. It doesn’t work for me at least. I think this is also very, you know, based on your identity and the way you come across and all these things. But you’re mostly, again, telling stories to lots of different people about the same thing. You’re trying to get them all on board so that you can move forward. You often don’t really have control really over anyone’s actual backlogs or actual anything. And yet you somehow have to get everyone to kind of move in the same direction. That, to me, is the most challenging thing and also where I have the most fun.
Sean [00:23:56] That’s a great answer and it’s so true. It’s a recurring theme for us. They call you the ‘CEO of the product,’ but you have none of the power of a CEO. You can’t really make decisions.
Kasia [00:24:05] I think they tell us that as a joke. It’s like, “don’t you wish? Oh, but you aren’t.”
Sean [00:24:11] You’re not really, no. That’s great. All right. I have a quick question for you. How do you define innovation? You’re doing a lot of innovative things so I’d love to hear your opinion on that word.
Kasia [00:24:23] I never thought about that. I guess, you know, I think there are categories of innovation and in my mind, they’re all related by the fact that it is movement, movement of an idea or a concept or a product in a direction that hasn’t been explored or further in a direction that has. And some innovations take you on a totally different road and others just advance you a block on the same road. But I do think it’s about materially moving something forward. I guess I don’t use the word a lot, but that’s I imagine, how I would define it. Now I’m curious though, how would you define it?
Sean [00:25:01] I’d define it as anything that causes a consumer to be moved, touched, or inspired. So it produces momentum for the relationship.
Kasia [00:25:11] Well, that’s way better than mine. Yeah, that’s great.
Sean [00:25:12] I have my own language but thank you for turning that around on me. But yeah, I define momentum as the ability to cause a change in a relationship between a consumer and a product or a consumer and a business. And innovation is any tactic, that when you deploy it, it causes that to occur. That’s my definition.
Kasia [00:25:28] I might steal that. That’s great. I really like that. Thanks.
Paul [00:25:31] Well, we’re coming up on the end of our time together. I’ve been inspired listening to you and the experiences that you’ve had have had a big impact on the world, on a lot of different people. I’m curious, what is inspiring you?
Paul [00:25:44] Oh, that’s a fun one. Well, let’s see. So what am I inspired by? I say that, even though I’ve only worked in one place where I’m directly building things for younger folks, so for kids, also kids of all ages, I’ve always been pretty close to education. And so I’ve always tutored kids, taught on the weekends, mentored kids. I think that the most inspiring thing is to go hang out with like a 13-year-old, maybe even an eight-year-old. I mean, it’s just like nothing will change your mind like hanging out with kids. So I’m constantly, you know, finding innovation, momentum to be inspired, through kids.
Sean [00:26:25] Paul has a whole pile of them at home and I do think they make you a better product leader. I really do, because they’re constantly reminding you, to have fun and to produce anything better, you have to stay curious and you have to have that child’s mindset, like the world is fresh and new and there’s still so much that we can do.
Paul [00:26:40] Definitely.
Sean [00:26:41] I agree with that. There’s some great advice.
Kasia [00:26:43] Yeah. Just out of curiosity, how many kids do you have running around at home?
Paul [00:26:47] I have five. Oh man, you have a whole like, little troupe.
Sean [00:26:51] I wasn’t kidding.
Paul [00:26:52] Yeah.
Sean [00:26:54] I had four but mine are largely grown. My youngest is 15 now, so…
Kasia [00:26:59] Wow. Does that mean that you’ve all been like, quarantined with five kids? Oh wow, so much inspiration, so much innovation.
Paul [00:27:09] So much more innovation than you can imagine. Is there a book that you want to throw out as a recommendation?
Kasia [00:27:17] I read really strange books.
Paul [00:27:20] The stranger the better.
Sean [00:27:21] Yeah, really.
Kasia [00:27:22] I mean, I’ve been thinking a lot about sustainability and about how we can fit in better with cycles, like natural cycles that happen in the world. So I’ve been reading Robert McFarland’s newest book, but it has nothing to do with product. I think it’s called Underland. But yeah, there’s something about, especially right now, things that are bigger than us, things that will exist beyond the building for scale and building for sustainability. And he kind of goes and caves in a bunch of places and, you know, visits underwater rivers and talks about toxic waste and how long it’s going to take for the half-life to come, and yeah, I’ve been finding that really inspiring in like a strange way.
Sean [00:28:05] The long term impact of the decisions that we make today, right?
Kasia [00:28:09] Yeah, and especially right now, how a lot of things that we do are for short-term gain and we’re not thinking about longevity or what’s going to happen once we’re gone.
Sean [00:28:18] Roger that.
Paul [00:28:18] Yeah. Big thoughts.
Kasia [00:28:20] Sorry, I wish I had something quip-ier, but I don’t.
Paul [00:28:24] I think it’s making me really curious to uncover some of those blind spots in my own thinking and try to see, you know, “what tradeoffs am I making without realizing it?” And I can’t say enough how grateful I am for the time that we’ve had to chat. I’ve learned a ton and I’m super grateful for the time that you’ve taken to spend with us today. Thanks so much, Kasia.
Sean [00:28:43] Thanks, Kasia.
Kasia [00:28:45] Yeah, thank you so much for having me. Have a great rest of your day.
Paul [00:28:47] You too.
Paul [00:28:51] Well, that’s it for today. In line with our goals of transparency in listening, we really want to hear from you. Sean and I are committed to reading every piece of feedback that we get. So please leave a comment or a rating wherever you’re listening to this podcast. Not only does it help us continue to improve, but it also helps the show climb up the rankings so that we can help other listeners move, touch, and inspire the world, just like you’re doing. Thanks, everyone. We’ll see you next episode.