How to protect your creativity when everything is speeding up | Federico Casalegno, VP of Design, Samsung

Episode transcript

Federico Casalegno 0:00

Our goal is to support people's life, empower people to realize their dreams, to be healthier, to be happier, more creative through the products and the experiences that we have and we bring to market.

Randy Silver 0:21

Hi, welcome back to the product experience. I'm Randy Silver. One of the things that's going on that I hear from people day after day is how much of a challenge it is for them to actually implement real change with the new tools that we have available to us. And that's something that I see especially in medium to larger companies. I see it with people doing it with software, and it's even harder with hardware. So we've got someone on today who's going to tell us about how he's made real change at one of the largest companies in the world, uh, how things are different, and we're going to welcome him right now. So, Federico, do you mind giving us a quick introduction?

Federico Casalegno 1:01

Hi, thank you, Randy, for having me. My name is Federico Casalegno. I'm executive vice president at Samsung Electronics, and I'm the head of the Samsung Design Innovation Centers and Global Studies within the corporate design center in Samsung.

Randy Silver 1:18

And Federico, tell us a little bit about your past. You were at MIT for a long time. How did you get into design and into this world in the first place? That is correct.

Federico Casalegno 1:28

Um, I actually started doing my PhD here in Paris in social sciences. And while I was doing my PhD, I started to work with Philips Design. We're back in the 90s, and Philips Design was one of the great organizations that was applying, let's say, human-centered design for product development. When I finished my PhD, I moved to MIT in the year 2000 and I started to work with Bill Mitchell's. I started to work with him and a lot of many other colleagues at the Media Lab. So between the year 2000 and 2020, about 20 years, I've been uh doing research at MIT, I've been teaching at MIT, teaching at the Media Lab. I have created the MIT Mobile Experience Lab and the MIT Design Lab. And then I moved into Samsung in San Francisco at the beginning. So that's where I transitioned from MIT to Samsung.

Randy Silver 2:26

So you were working in corporate, you went to academia and consulting, and then back to corporate. And I'm curious, after all those years away, what was it like going from an advisory role uh and doing theory advisory and working in the academic environment back into a management and actual delivery role? What was that transition like?

Federico Casalegno 2:47

Well, actually, uh let me the way my research lab was working in MIT was basically uh working with companies. So I was definitely 100% in the academia, even though for uh four years I've been working with Motorola. In the year 2004-2008, I was working with Motorola in the advanced concept group. So even when I had MIT, I was full-time in Motorola, and then I started to uh I create my own research lab, and the research lab was funded by private corporations. So, in a sense, I was 100% in academia, but our research was sponsored by private companies or governments or or public institutions. So somehow our role at MIT was to push unbounded research in a way to uncover possibilities or where we could push human-centered design through technological innovation. But at the same time, we had a lot of companies coming to us. And at one point, I wanted to see how you can land your amazing ideas or your beautiful work, how we can bring it to business. So, in a way, I was used to have these um, in a way, double hat, on one hand, pushing the research and innovation, on the other hand, talking to companies that wanted to see how we could make profit out of the work that you do. And we had a lot of, in a way, experiences where we push digital experiences, we push products to market. Um, and while at MIT, I created one of the most funded research labs within uh the School of Humanities, for example, where we have a lot of sponsors, but also I had a lot of researchers and employees. So at one point, I had the management roles even within the academia.

Randy Silver 4:42

That makes sense. So when you went back to Samsung, or when you joined Samsung, you didn't just start with the design function, you started a computational design function. What does that actually mean? It sounds fancy.

Federico Casalegno 4:56

That's good. So when I joined Samsung, we are in 2018, and uh the name of Samsung was Samsung Design Bridges America, and I was in charge of uh Samsung Design in all the United States. And basically, I operated two transitions. The first one is how we can shift for purely product focus to experience driven innovation. We're product, we're part of the equation, but we still want to really keep and push forward the experience that we deliver to our customer through the lens of experience innovation, digital experience innovation, and also product innovation. So that was one of the areas. And in fact, um we had done a lot of work in bringing experience-driven design into our planning processes. So that was one area. The second area, when I really started to um to bring uh advanced AI and computing at MIT, I started the computational design function, which basically is the idea to apply advanced computing, AI, and machine learning for product development. So that's something new. We started new. And for me, um the two functions in a way are united by the fact that what we do is human-centered design. So at the core, we really put people and I would say the environment to the planet as main mission. How do you improve products and experiences for the people they use? How do you put people's dream first, people's aspirations, and then start from that point? How the product and the experiences that you design can help people to realize the dreams, aspirations to have a better lifestyle, a healthier lifestyle, to be more creative, more expressive, and so on. So that's one. Computational design, apply machine learning, artificial intelligence, and all set of tools and practices to improve products. So we apply computational design, strictly speaking, especially for wearable and product development.

Randy Silver 7:19

You said you this is going back to 2018. So this is long before the days of an LLM interface of things. What is the and this is not just about uh improving the design of a specific product, it's about improving the process of designing products. Correct. Correct. What is it? What does that mean? How did you approach that?

Federico Casalegno 7:40

So you it's right. Yeah, it's not it has not started in 2018. So I would say I was lucky enough in my life to have one amazing mentor in Paris, which was Michel Maffezoli, and his mantra was really the experience, so understanding humans from experience perspective. Another mentor was Stefano Marzano, was the CDO of Philips Design at the time. And I've been lucky to work with him during my time at Philips. And then finally, I've been working with Bill Mitchell. Bill Mitchell was the dean of the School of Architecture at MIT. He was really the person who operated the transition into the advanced computing world and apply AI and computational tools for product development and especially in architecture. Now, how the process looks like now uh in the way we do it in Samsung, basically, I would say there are four phases. The first phase is get the data. Data uh are the most important part. So what we do, we um we apply, we gather data with 3D scanning and 4D scanning, and we make sure that we cover a variety of demographics that really respect the demographics of our population. So we have all the gender, um, sex, ethnicities, and and and so on. So data are more important. Point number two, once that you get data, how do you establish the right KPIs in the way you need to use the data? What this means is, for example, if you design the next version of the ring, if you design the next watch or buds, how do you design for fit? How do you optimize for comfort? How do you optimize for the way the sensors get data from your body? So it's important once that you acquire the data, how do you optimize and define the KPIs? Point number three, how do you train the models and apply machine learning in order that uh you have reliable data, reliable models and information that AI can use to optimize the computing phase? And finally, how do you test and iterate in order to bring into product development the right ideas, the right uh refinements in order to improve the products and the way the products are? So, for example, we can uh have a 3D model of human ears, and you can simulate digitally in the digital twin how bad fits into your ears. And then you can operate a small modification to the physical form factor, to the product itself, in order to improve the form and the way the product fits into your ears, the way you feel more comfortable with it, the way you improve the acoustic elements and so on. So basically, digitally you can simulate variation of the same products very rapidly, and we use millions of data points to apply those variations into the product event. So that's you simulate digitally, but what we also do, we transfer this information into robots, for example, and uh the robot operates movement to mimic the human body. So you can film someone running with a butt and see how the device moves into your ears. You copy the movement, you give it to robots, and the robots simulate run and see how the product changes. And finally, we simulate how and we test with real users how the product feels, and we ask people you know how to use it, and etc. So basically, in our product development cycle, we do very quickly and fast simulation into the digital twin, into the uh robotic environment, and into the real environment with people. And it's really an iterative process that very quickly allows designers to iterate, bring innovation into the product development, and bring this information back into the product uh uh ideation cycle.

Randy Silver 12:23

So, what kind of difference has this actually made? I mean, it sounds great in theory, but how much faster? What kind of uh things has it allowed you to do? How do you measure the success of an improved cycle like this?

Federico Casalegno 12:37

Well, let's say in the past, you think how the design process went. Basically, designer needed to use like a 3D printing to print a new device, test it with the real users, ask the user to test it and provide feedback, go back to the digital file, redesign, and so on. And this was a very cumbersome in a way process, which of course is effective. But in this process, because we still believe that it's very important to 3D print, to fabricate the products and test it with people, but at the same time, you can do it digitally and improve how the digital environment can help to simulate product development. So, in certain phases, we really go from weeks of work into minutes of testing. And basically, in that way, you empower designers with amazing tools that can enable designers to make quick decisions, and that's really new at the speed and and uh and the precision that we can make.

Randy Silver 13:47

What's changed for the design team along the way? I mean, just the speed is one thing, but what kind of things have they been able to do? What have they learned and changed about their practice besides speed?

Federico Casalegno 13:59

So basically, it's not only speed as as you say, but it's really having access to data to make informed decisions in real time. So we still have amazing designers. My team is really supporting the amazing designers that we have in our organizations, which ultimately they are the ones who make decisions. We don't make decisions for them, we present data and we empower them to make decisions based on data. So we have fact-based decisions in real time. And that's actually absolutely new as a process where you still keep the creativity, the passion, the innovation, the desire that the designers put into the way they develop the products, but at the same time they have their fingertips, facts, and data that they can use to make their own decision, the same way RD can make decisions. Because in a product development, there is such a complex environment where you can have, it's not only about making decisions about the design, but every time you move an element where you put the battery, the audio, how do you design uh the wristband, how design this or that. Like they have a lot of implication to the whole system. It's always a systemic design. It's not only the form factor, the color, the shape, the battery, the dimension of the battery, and so on. But every decision implies a systemic modification of the whole system. And so design teams have definitely a better way and faster based uh faster way to make decisions informed by the data that we provide.

Randy Silver 15:42

What is a hardware cycle for you now? I mean, it used to be a generation, would be a year or two to three years on some things. Uh, how quickly are you moving on on iterating on new designs in the market?

Federico Casalegno 15:55

I think it's still very long. So I'm not the one that ultimately bring bring like the hardware to to market. We have amazing and and very fast engineering and manufacturing line. I think in the way we do it, definitely we optimize the design phases, we optimize the creativity and how to um design the product, and definitely we could optimize also the design, the manufacturing cycle. Right now, my team in particular is really based on really trying to understand humans and demographics and design to expand the comfort, the fit, and the way people use our product to make it better, more comfortable, to feel better, and to have a larger demographic into the the people we reach.

Randy Silver 16:49

So, what kind of things are you are you doing? One of the things that it sounds like it would come out of this is the as you said, you're measuring demographics across genders, across uh ethnicity, across any number of factors. Does that lead to a single model of something that tends that works across for much wider groups, or do you just start doing adaptable models, iterative designs uh that are in the market to ensure that you know you've got the right fit for everybody?

Federico Casalegno 17:21

I think it's both ways. So, for example, you think earbuds, you can have one model that fits a larger number of demographics on one end. On the other end, you have like uh ear tips, and you can optimize the dimension of ear tips based on the actual years model to cover a larger demographic and to fit better to a larger demographics. So it's always about at the end of the day, we ship millions of products. So, how do you optimize um the fit with one person versus to to reach a larger scale of consumers? But I guess for now, using these tools is really improved, we are really improving the reach. And and I have to say, the the reviews and and the feedback we have from from consumers, we really improve the comfort of our products to a level that we had uh before.

Randy Silver 18:13

So you're collecting data, Federico, uh during the testing environment, but I'm guessing you also now have access to data that you never had before from the actual production uh models that are out there, things that are in use in the market. How do you use that data to refine the next generation?

Federico Casalegno 18:31

We use all sorts of data, including actually data that we generate through generative AI. The point here is that uh the AI generation models that we use have been trained or our data that have been qualitatively checked almost one by one. So it's not a random application of generative AI and create more ears, more wrists, or more heads, but it's really how do you start from a qualitative control set of data and create new and generate new data that we can use. And of course, you can compare and see any other product or apply this the system to any other uh products in the market, including products that potentially is a process that will help us to think products that are not even existing today.

Randy Silver 19:28

Okay, I'm guessing you can't go too deep into that as much as that's a big tease. Yes. Um one of the things that comes up a lot, and this is probably more for for digital products than for physical products, but the use of synthetic personas and synthetic people versus actual data from from real lived experience, what's been the biggest variance? What's the biggest challenge in using uh synthetic and generated information, you know, prototypes versus what you find when you go to actual physical testing?

Federico Casalegno 20:01

So, what is the the biggest problem of synthetic generated data, in my view, is, and this is not only for products, like physical products, but also for interfaces, also for for everything for strategy for is how much you rely on data and how much the designers remain the owner of the way they use the data. So I think the whole process can be applied to generating new interfaces, new products, and so on. But at the end of the day, you really need to have teams that they know exactly what they're designing for, they understand their mission, they understand their goals, and utilize uh data and AI tools in order to achieve their missions. And they need to be expert, they need to be domain experts. Because at the end of the day, they really need to be the ones that make uh decisions. They have the they need to be the one to make the call. And that's why uh I was really saying like our role is really to bring fact and data-driven decisions to teams of designers and RD, because they really need to be the one that made the the call and drive the process.

Randy Silver 21:20

How do you marry those two things together? A lot of the what I see with the people that I work with is the first 80% of a project, the discovery, the possibilities, that goes so much faster and the the possibilities are incredible. But the second 80% of a project of actually turning it into something that is uh practical, that's usable, that's scalable, that's reliable, that's still incredibly hard in many, many cases. Um are you seeing the same phases? How do you manage the the challenge of both being bold but also being practical?

Federico Casalegno 21:59

Well, I think. You if you look, some of the tools that we have can definitely uh uh apply, for example, for automation. In the discovery phase, how can you um automate tasks that before could take like forever to be done? So in that case, you delegate the system to automate tasks and can bring um can improve or prefer products or or features. Another area is computation, which is exactly how much you can compute data and bring this data back to the design and the decision-making process. A third area could be how you use AI and tools for optimization. You know, you give the right KPIs to the system. Um, do you define what fit, what comfort, what uh you want to take out of the sensor, or even like how much material you want to be um uh taken from recyclable plastic versus non-recyclable material and so on, and can optimize the shapes and forms in order to match the initial goal. And finally, there is this area which is not, let's say, productivity, because you can produce images, you can produce forms, you can produce uh ideas. Uh, but um, I would say really uh productivity can be supported by those tools, but the creativity part is really what remain, and by creativity, it's like you know, how much you need to um wage and and understand how do you bring a product to market, what are the consequences, and there are many other complex elements from financial to demographic to uh uh portfolio planning and so on. And also there is, you know, all designers they do um they have a part of this rational, but they also are driven by instinct, right? They you know they they at one point they fell in love with the products that they do, with the what they can do and bring to the table, that there is a part that at the end of the day, when you need really to bring innovation to the table, there is a a certain part which is definitely unknown and cannot be otherwise.

Randy Silver 24:11

So we start you started off telling us that you've been doing this uh at at Samsung for uh about eight years now. So again, before the age of uh LOM interfaces, there's a lot of tools. I think when you say AI right now, you you're covering a lot more than just generative. You're talking about ML, you're talking about a number of other things. Can you give an overview of just the different types of technologies that you are using that are advancing things?

Federico Casalegno 24:39

Well, uh let me start actually by the fact that um I am lucky enough to have or to have built a team of creative technologists, designers, strategists that have a deep understanding of AI and those tools, and they apply those tools in their domain. So we started years ago even to apply how do you use LLM to understand consumer? How do you understand how do you apply LLM for strategy? How do you understand LLM to um to work to understand people? How do you is the so it's not only for product development, I would say. So I think we apply all sorts of tools and we create our own tools. I think what is very important today is that uh you have access to a certain number of tools, but you can use those tools to create the tools that you need. But in order to create tools that you need, you really need to know what you need, and that's a very critical and important part. It's in a way, before jumping into the uh design for engineering, we are in the phase of we we need to design the problem. We really need to capture and understand the product we need to design for. So that's the core. And once that you have understood this space, then you can design and has the right tools to engineer the product you want to uh design.

Randy Silver 26:19

You can get very lost in all this. The the promise of doing potentially anything is incredible. How do you get that team? And we'll talk about the team in in just a minute or two, but how do you get the team to stay focused on, as you said, as the product that you definitely want to ship, how do you know which is the right thing to focus on and prioritize?

Federico Casalegno 26:40

Uh twofold. I mean, on one end, we have let me say top-down requests. So because we support a team of designers, RD teams, so we receive from them very specific requests. They need to create the new watch, so they need to optimize the wristband, so they ask how we can do it. How can we study the movement of the brace and apply it? We need to create a new ring, so how we can study them the movement of the hands in order to optimize the ring size and the sensors' capabilities. So we receive very specific requests, but during the process, what is interesting, we may come up with interesting challenges and ideas. So, from one end, is top-down. We have requests and we need to uh empower the teams with the data to uh um uh and provide the right data to the teams. On the other hand, during the process and the ongoing conversation, we can come up with problems that we uh we didn't knew we had before. So, how do you apply those tools to the problem that uh uh raise in the design and uh in the design phase? So always these uh two phases, one is uh uh prototype problems, and the other one is prototype solutions in a way. You're often in the prototype problems phase, the more like design and the creative space, and you really need to understand what the problems are before you start to engineer the solutions. Both are very relevant, both are very important, but they're very different uh uh phases and require very different mindset in a way.

Randy Silver 28:18

And you've got a wide range of things there. You've got an object, you've got an interface, and you have an experience as well of how of how those things all marry up together. What is the team look like that you've put together for this? It's not going to be uh just people with a graphic design or a physical design experience. There it as you said, you've got a bunch of creative technologists. What does your your actual team look like?

Federico Casalegno 28:42

I don't know if I should define as AI designers or AI, but basically, we have a team that works across disciplines, and we started to I started to bring together people with very different set of skills. This was like from during all my career, actually. I start when I started to do my first project at Philips Design or in the Sorbonne University, I was working with a team of social scientists, industrial designers, engineers, strategists, ethnographers, etc. And actually in the middle of the 90s, when I first went to Philips Design and really see like that, you have like a psychologist, an ethnographer, and then a designer and a technologist in the same room, it was kind of uh both inspiring and surprising. And when I went to MIT, the work across disciplines was really the starting point. My classes uh really were bringing students for all the MIT departments, from School of Architecture, from computer science, from mechanical engineering, and so on. So working across disciplines, and somehow I have reproduced in different ways. This is in the way I create teams within Samsung. So our teams, in a way, have people with different sets of skills, but somehow they're all united by the fact that they are extremely open and curious talent. Because basically, when I created the computational design team, basically I couldn't hire people that could do the job as I wanted the job to be done. I really had to have smart people that could apply their own skills to the new jobs that they were going to do. So basically, the talents are really to learn how do you make something that you haven't done in your life and how do you apply your set of tools and capability and curiosity to design the tools and do the job that you need. So, right now the teams have a variety of skills, but uh united by talent and curiosity.

Randy Silver 30:57

What about the size and the disposition of the teams? You know, the in the past you would have needed uh a whole host of people to do these things, but now to given access to various tools, people are a lot more capable up to a point. They're not necessarily going to be expert in everything, but they can do 80% of something pretty well. Yeah. So, you know, we've gone from uh I I think I use the same example every time I in every conversation, but you know, a couple of years ago was Amazon's two pizza team, and now some startups and scale-ups are trying to get away with a product development team of one person, and so a happy meal team. What is what is the but I I maintain that it's important the discussion that you have with other people, the the um the friction that you have from discussing ideas and refining them is really important. But I don't know what the optimal team size is. So what does it look like for you? What what are you uh implementing these days?

Federico Casalegno 31:52

I I I agree 100% with you. I mean, uh the you know, you you can have one-man-band and having teams or having individuals using tools and and uh and agents that can really work for you and with you. But the friction that happens within the everyday discussions are really, really important. Right now, I have a team in Paris, I have a team in San Francisco, I have a team in Korea, but they're very small teams, so you don't need a hundred of people. Actually, it's really like a small number of people, you have 10, 15 people maximum, actually five, 10 people. But how efficiently they work together and they collaborate with other teams is key. And as again, I I wouldn't stop to repeat, but the fact that really there are discussions happening within the teams that really help to steer the teams and bring innovation where the team wants. So, one of the very important, I would say, roles that uh uh leaders they have to do while they are hiring or creating their own teams is hiring good people at the end of the day. What I mean by good people, otherwise, I mean in the Bay Area, we all have researchers and talents that went to similar schools, they work in similar companies and so on. So the difference is really not about the type of tools that you use, but how you work with other people, how you collaborate, how open, passionate, and and uh where is your ethical compass uh driving you. So I think the the teamwork is absolutely important and the team uh very agile small teams is is probably optimal for the solution rather than having large teams.

Randy Silver 33:41

One of the challenges we have right now is that um you've got these terrible examples of of the LLM saying finish a math problem and make a breakthrough, something like that. You know, it's come out and do something brand new, make a great leap forward, but you've also got the challenge of getting it right, getting something that is sellable, that is useful, that is reproducible and scalable, but also manufacturing deadlines and seasonal things are real things. How do you balance the challenge of doing things to a schedule and also trying to really push things forward?

Federico Casalegno 34:17

I think that's uh I would say two things. One, I see that there is a incremental growth in the speed in which uh products, ideas are produced. So productivity is not the issue, meaning is the issue right now. You can uh the speed of light you can produce something. You can ask an LLM to write a love letter, a love song in two seconds, but meaning and feeling loves is a very different story. So, how do you balance that? So that's why I keep insisting on yes, the value of having tips that master tools is a given. I mean, these are the tools you already know if you work in this industry, you need to have a set of tools in your toolbox and you need to be very good at that. But that's a given. But what makes really the difference is how are you capable to bring meaning into the picture, not letting LLMs or AI drive in your agenda, which is very easy, but how you keep it in a short leash and bring meaning into what you're doing. If you're doing a strategic work, a portfolio planning work, or a hardware product development, or uh a new product development or a new interface, you really need to be sure that you use the tools rather than be used by the tools itself.

Randy Silver 35:44

You just talked about mastering tools. Um, and the tools are changing so fast. The technology is moving at such an incredible speed. How do you stay sharp? How do you stay up to date? How do you keep making sure that you're taking advantage of everything that you can?

Federico Casalegno 36:01

Oh well, the teams are you know, young talents, passionate. So there is a passion, there's an attraction to see those tools and the possibility of those tools. There is a fascination. But at the same time, we have a very critical approach and we use the tools in the way we need. Because at the end of the day, and I said this is a beginning, for us, our main mission is human-centered product development, human-centered design. So, in a way, our role is still the same, which is how do you really understand people? You keep people as your North Star. How do you understand people's dreams? How do you understand how we can help people through the products and experiences that we deliver? And then, of course, we can apply all the tools that we have in our team, fingertips, to deliver that. But the mission is not to apply tools, the mission is to deliver meaningful products to people first. And we really keep this uh ethical compass within the teams and within our organizations.

Randy Silver 37:03

A lot of people are uh getting pressure from their CEOs and from their boards to just move faster, to just use AI and make it happen. Why can't we just ship something? What's the first question that they should be asking their design, their engineering teams that they seem to skip? What's where should they actually be starting with this?

Federico Casalegno 37:25

Let me bring again this back is our goal is not to deliver products. And I'm saying this from a design organization, of course, but my goal is not to deliver just because that's easy. Our goal is to support people's life, empower people to realize their dreams, to be healthier, to be happier, more creative through the products and the experiences that we have and we bring to market. So if this is the goal, then how we can make sure that we can deliver. In our organization, uh Mauro Porcini, our CDO, he we've been working really in how we can deliver, how we can define AI. And for us, AI is EI plus HI. AI is equal or is empowered by emotional intelligence plus human imagination. So we really need to bring together this emotional intelligence, empathetic part of the work that we do with not only the tools for uh that we have thanks to artificial intelligence, but also how we can unleash uh the human imagination abroad. So, really, AI is emotional intelligence plus human imagination applied to products and experience development.

Randy Silver 38:43

Federico, I think we've got time for just one last question. This has been a lovely chat, uh, but something you said recently caught my eye. You said that we're going to see incredibly new products that are inconceivable today because of computational design. I want you to to get out your crystal ball and make a prediction. What one five years from now, what's a product category or an experience that you think will that you're looking forward to seeing that does not exist today?

Federico Casalegno 39:11

So in terms of um I've I don't have a crystal ball, unfortunately. Uh, I still believe, for example, that the you know mobile devices as as we know and are still a very good platform of a hardware that provide the type of information that you need when you did it and empower you to do things. But the way the reason why I was saying that is that uh designers and and creative talents, they have teams of creative people in their at their fingertips that really empower them to think differently, to have faster iteration, be more creative, and so on. So the reason why we're saying that is that we see at the same time an evolution of the speed of manufacturing, of product development, and the creativity and materials as well, for example. So all these elements together would still the same ethical compass to impact in positively the people that we have. I'm sure that we are going to design new products that we haven't seen before. And that's exactly why we're we're forming people and talents that are incredibly creative, and they have way different tools that we had in the past and way different access to data that can really empower creativity.

Randy Silver 40:36

Well, you just brought up the ethical side of things, and I'm sorry we're not going to get a chance to talk about that today, but uh yeah, I'm really looking forward to both the new products and how we deal with the new problems that they they uh generate. But Federico, thank you so much. This has been a lovely conversation.

Federico Casalegno 40:53

Thank you, Randy. It was great talking with you.

Lily Smith 40:56

The product experience hosts are me, Lily Smith, host by night, and chief product officer by day. And me, Randy Silver, also host by night.

Randy Silver 41:06

And I spend my days working with product and leadership teams, helping their teams to do amazing work.

Lily Smith 41:12

Lou Ron Pratt is our producer, and Luke Smith is our editor.