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

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0:34Federico Casalegno: notes 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

1:01Randy Silver: 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, how things are different. And we're going to welcome him right now.

1:37Randy Silver: So Federico, do you mind giving us a quick introduction?

1:41Federico Casalegno: Hi, thank you, Randy, for having me. My name is Federico Casaleño. 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.

1:58Randy Silver: 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.

2:08Federico Casalegno: 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 Mitchells. 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 doing research at MIT. I've been teaching at MIT, teaching at the Media Lab.

2:51Federico Casalegno: 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.

3:06Randy Silver: 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, doing theory advisory and working in the academic environment, back into a management and actual delivery role? What was that transition like?

3:27Federico Casalegno: Well, actually, the way my research lab was working at MIT was basically working with companies. companies. So I was definitely 100 % in the academia, even though for 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, I created 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 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.

4:28Federico Casalegno: 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 this, in a way, double hat on one end, pushing the research and innovation, on the other end, talking to companies that wanted to see how we could make profit out of the world that you do. And we had a lot of, in a way, experiences where we push digital experiences, we push products to market. And while at MIT, I created one of the most funded research labs within the School of Humanities, for example, where we have a lot of sponsors, but also had a lot of researchers and employees.

5:16Federico Casalegno: So at one point, I had the management roles even within the academia.

5:22Randy Silver: 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.

5:36Federico Casalegno: That's good. So when I joined Samsung, we are in 2018. And the name of Samsung was Samsung Design Research America. And I was in charge of 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 to 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, we had done a lot of work in bringing experience-driven design into our planning processes.

6:30Federico Casalegno: So that was one area. The second area, when I really started to bring advanced AI and computing at MIT, I started the computational design function, which basically is the idea to apply advanced computing AI machine learning for product development. So that's something new. We started new. And for me, 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?

7:21Federico Casalegno: people's aspirations. And then starting from that point, how the product and the experiences that you design can help people to realize the dreams, aspiration to have a better lifestyle, a healthier lifestyle, to be more creative, more expressive, and so on. So that's one. Computational design applies machine learning, artificial intelligence, and all set of tools and practices to improve products. So we apply computational design, And strictly speaking, especially for wearable and product development.

7:58Randy Silver: You said this is going back to 2018. So this is long before the days of an LLM interface of things. And this is not just about improving the design of a specific product. It's about improving the process of designing products. What does that mean? How did you approach that?

8:20Federico Casalegno: So it's right. 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 Maffesoli. And his mantra was really the experience, so understanding humans from an experience perspective. Another mentor was Stefano Marzano, was the CDO of Philips Design at that 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.

9:13Federico Casalegno: Now, how the process looks like now in the way we do it in Samsung. Basically, I would say there are four phases. The first phase is get the data. Data are the most important part. So what we do, 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, sex, ethnicities, 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?

10:19Federico Casalegno: 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 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 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 refinements in order to improve the products and the way the projects are? So, for example, we can have a 3D model of human ears and you can simulate digitally in the digital twin how BUDS fits into your ears.

11:15Federico Casalegno: 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 the robot operates movement to mimic the human body.

12:06Federico Casalegno: So you can film someone running with the buds and see how the device moving to your ears. You copy the movement, you give it to robots, and the robots simulate run and see how the product change. And finally, we simulate how, and we test with real users, how the product feels, and we ask people how to use it, and et cetera. So basically, in our product development cycle, we do very quickly and fast simulation into the digital twin, into the 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 ideation cycle.

13:03Randy Silver: So what kind of difference has this actually made? I mean, it sounds great in theory, but how much faster? What kind of things has it allowed you to do? How do you measure the success of an improved cycle like this? Well, let's say in the past, you think how the design process went.

13:24Federico Casalegno: Basically, designers 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.

14:10Federico Casalegno: 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 the precision that we can make.

14:27Randy Silver: 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? So basically, it's not only speed, as you say, but it's really having access to data to make informed decisions in real time.

14:52Federico Casalegno: 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 fact and data that they can use to make their own decision the same way R &D 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 design but every time you move an element where you put the battery, the audio how do you design the wristband how designed these or that.

15:55Federico Casalegno: 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 of 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 way to make decisions informed by the data that we provide.

16:22Randy Silver: 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. How quickly are you moving on iterating on new designs in the market?

16:34Federico Casalegno: I think it's still very long, so I'm not the one that ultimately brings the hardware to market. We have an amazing and very fast engineering and manufacturing line. I think the way we do it, definitely we optimize the design phases. We optimize the creativity in how to 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 designed 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 people we reach.

17:28Randy Silver: So what kind of things are you doing? One of the things that sounds like it would come out of this is, as you said, you're measuring demographics across genders, across ethnicity, across any number of factors. Does that lead to a single model of something that works for much wider groups? Or do you start doing adaptable models, iterative designs that are in the market to ensure that you've got the right fit for everybody?

18:00Federico Casalegno: I think it's both ways. So, for example, you think earbuds, you can have one model that fit a larger number of demographics on one end. On the other end, you have like ear tips and you can optimize the dimension of ear tips based on the actual ears model to cover a larger demographic and to fit better to a larger demographic. So it's always about, at the end of the day, we ship millions of products. So how do you optimize the fit with one person versus to reach a larger scale of consumers. But I guess for now, using these tools is really improving. We are really improving the reach. And I have to say the reviews and the feedback we have from consumers.

18:45Federico Casalegno: We really improve the comfort of our products to a level that we had before.

18:52Randy Silver: So you're collecting data, Federico, during the testing environment, But I'm guessing you also now have access to data that you never had before from the actual production models that are out there, things that are in use in the market. How do you use that data to refine the next generation?

19:11Federico Casalegno: We use all sorts of data, including actually data that we generate to generative AI. The point here is that 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 wrist 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 system to any other products in the market, including products that potentially is a process that will help us to think products that are not even existing today.

20:07Randy Silver: Okay, I'm guessing you can't go too deep into that as much as that's a big tease. Yes. 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 synthetic and generated information? You know, prototypes versus what you find when you go to actual physical testing.

20:40Federico Casalegno: So what is 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 every, for strategy, 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 data and AI tools in order to achieve their missions.

21:30Federico Casalegno: And they need to be expert. They need to be domain expert. because at the end of the day, they really need to be the ones that make decisions. They need to be the one to make the call. And that's why I was really saying our role is really to bring fact and data-driven decisions to teams of designers and R &D because they really need to be the one that make the call and drive the process.

22:00Randy Silver: How do you marry those two things together? A lot of 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 possibilities are incredible. But the second 80 % of a project of actually turning it into something that is practical, that's usable, that's scalable, that's reliable, that's still incredibly hard in many, many cases. Are you seeing the same phases? How do you manage the challenge of both being bold but also being practical?

22:38Federico Casalegno: Well, I think if you look, some of the tools that we have can definitely apply, for example, for automation. In the discovery phase, how can you automate tasks that before could take forever to be done? So in that case, you delegate the system to automate tasks and can improve or perfect products 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 give the right KPIs to the system. Do you define what fit, what comfort, what you want to take out of the sensor, or even how much material you want to be 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.

23:46Federico Casalegno: 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 ideas. But I would say really productivity can be supported by those tools. But the creativity part is really what we mean. And by creativity, it's like how much you need to weigh and understand how do you bring a product to market? at what are the consequences. And there are many other complex elements from financial to demographic to portfolio planning and so on. And also there is, you know, all designers, they have a part of this rational, but they also are driven by instinct, right?

24:33Federico Casalegno: You know, at one point, they're feeling in love with the products that they do, 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 certain part which is definitely unknown and cannot be otherwise.

24:51Randy Silver: So you started off telling us that you've been doing this at Samsung for about eight years now. So again, before the age of LOM interfaces, there's a lot of tools. I think when you say AI right now, 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?

25:18Federico Casalegno: Well, let me start actually by the fact that I am lucky enough to have or to have built a team of creative technologists, designer, 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 work, to understand people? So it's not only for product development. So I think we apply all sorts of tools and we create our own tools. I think what is very important today is that you have access to a certain number of tools, but you can use those tools to create the tools that you need.

26:23Federico Casalegno: But in order to create tools that you need, you really need to know what do you need. And that's a very critically important part. It's in a way, before jumping into the design for engineering, we are in the phase of 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 the right tools to engineer the product you want to design.

26:59Randy Silver: You can get very lost in all this. So the promise of doing potentially anything is incredible. How do you get that team? And we'll talk about the team 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?

27:21Federico Casalegno: Twofold. I mean, on one end, we have, let me say, top-down requests. So because we support a team of designers, the R &D team, 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 wrist and apply it? We need to create a new ring. So how we can study the movement of the hands in order to optimize the ring size and the sensors capabilities. So we receive a very specific request. But during the process, what is interesting, we may come up with interesting challenges and ideas.

28:03Federico Casalegno: So from one end is top-down. We have requests and we need to empower the teams with the data and provide the right data to the teams. On the other end, during the process and the ongoing conversation, we can come up with problems that we didn't know we had before. So how do you apply those tools to the problem that raise in the design and in the design phase. So always these are two phases. One is prototype problems and the other one is prototype solutions in a way. You're often in a 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.

28:49Federico Casalegno: Both are very relevant. Both are very important, but they're very different phases and require very different mindset in a way.

28:57Randy Silver: 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 those things all marry up together. What does the team look like that you've put together for this? It's not going to be just people with a graphic design or a physical design experience. As you said, you've got a bunch of creative technologists. What does your actual team look like?

29:21Federico Casalegno: I don't know if I should define as AI designers or AI, but basically we have a team that work 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 when i started to do my first project at philips design or in the sorbonne universe i was working with a team of social scientists industrial designer engineers strategists ethnographer 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 and ethnographer and and the designer and the technologist in the same room, it was kind of both inspiring and surprising.

30:13Federico Casalegno: And when I went to MIT, the work across disciplines was really the starting point. My classes 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 the way I create teams within Samsung. So our teams, in a way, have people with different set 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.

31:07Federico Casalegno: 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 capabilities and curiosity to design the tools and do the job that you need. So right now, the teams have a variety of skills, but united by talent and curiosity.

31:37Randy Silver: What about the size and the disposition of the teams? In the past, you would have needed a whole host of people to do these things. But now, 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 I think I use the same example every time I in every conversation. But, you know, a couple of years ago is 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.

32:14Randy Silver: What is what is the but I maintain that it's important to discussion that you have with other people, the the 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 are you implementing these days?

32:32Federico Casalegno: I agree 100 % with you. I mean, you can have one-man band and having teams or having individuals using tools 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 100 of people. Actually, it's really like a small number of people. You have 10, 15 people, maximum. Actually, 5, 10 people. But how efficiently they work together and they collaborate with other teams is key.

33:17Federico Casalegno: And again, I will not stop to repeat, but the fact that really there are discussions happening within the teams. that really helped to steer the teams and bring innovation where the team wants. So one of the very important, I would say, role that leaders have to do while they're hiring or creating their own teams is hiring good people at the end of the day. What I mean by good people, 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 tool set you use, but how you work with other people, how you collaborate, how open, passionate, and where is your ethical compass driving you.

34:08So I think that the teamwork is absolutely important.

34:13Federico Casalegno: And team, very agile, small teams is probably optimal solution rather than having large teams.

34:21Randy Silver: One of the challenges we have right now is that you've got these terrible examples of the LLM saying, finish a math problem and make a breakthrough, something like that. You know, it's come on 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?

34:56Federico Casalegno: I think that's, I would say two things. One, I see that there is a incremental growth in the speed in which products, ideas are produced. So productivity is not the issue. Meaning is the issue right now. You can, at 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 love is a very different story. So how do you balance that? So that's why I keep insisting on, yes, the value of having teams that master tools is a given. I mean, these are the tools. 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.

35:50Federico Casalegno: 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 own 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 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.

36:24Randy Silver: You just talked about mastering tools. 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?

36:42Federico Casalegno: Well, the teams are 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 them. 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 are you going to understand how we can help people through the products and experiences that we deliver?

37:24Federico Casalegno: And then, of course, we can apply all the tools that we have in our 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 ethical compass within the teams and within our organizations.

37:43Randy Silver: A lot of people are 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? Where should they actually be starting with this?

38:05Federico Casalegno: Let me bring again this back as 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, Mauro Porcini, our CDO, we've been working really in how we can deliver, how we can define AI. And for us, AI is EI plus HI.

38:53Federico Casalegno: AI is equal or is empowered by emotional intelligence plus human imagination. So really need to bring together this emotional intelligence, empathetic part of the world that we do with not only the tools that we have, thanks to artificial intelligence, but also how we can unleash the human imagination abroad. So really AI is emotional intelligence plus human imagination applied to products and experience development.

39:23Randy Silver: Federico, I think we've got time for just one last question. This has been a lovely chat. 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 get out your crystal ball and make a prediction. 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? So in terms of,

39:55Federico Casalegno: I don't have a crystal ball, unfortunately. I still believe, for example, that mobile devices, as we know, are still a very good platform of hardware that provides the type of information that you need when you need it and empower you to do things. But the reason I was saying that is that designers and creative talents, they have teams of creative people in their fingertips that really empower them to think differently, to have faster iteration, be more creative, and so on. So the reason why I'm 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.

40:47Federico Casalegno: So all these elements together with still the same ethical compass to impacting positively the people that we have, ensure that we are going to design new products that we haven't seen before. And that's exactly why 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.

41:16Randy Silver: 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 yeah, I'm really looking forward to both the new products and how we deal with the new problems that they generate. Federico, thank you so much. This has been a lovely conversation.

41:33Federico Casalegno: Thank you, Randy. It was great talking with you.

41:36Randy Silver: 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. And I spend my days working with product and leadership teams, helping their teams to do amazing work.

41:52Federico Casalegno: Luron Pratt is our producer and Luke Smith is our editor.