Design can play a critical role in transforming R&D technology into products. R&D often is driven by the curiosity and it's a beautiful thing, some time we forget this, but these scientists, these engineers are driven by their own passion, their own curiosity. As a designer, our role is to understand it and try to materialize it into something tangible and concrete. Leonardo Giusti has spent his career in the spaces between disciplines, between art and science, between research and product, between the physical world and the digital one. It's not a conventional design path, but it's one that's led him to work that most designers never get near. He's the co-founder and chief design officer of Archetype AI, a company building foundation models trained not on text or images, but on the continuous stream of sensor data flowing from the real world like factories, power grids, and city intersections. Before that, he spent nearly seven years at Google's Advanced Technology and Projects Group, where he led design on Project Sully, a miniature radar chip that taught devices to understand human gesture and presence, and Project Jakarta, which wove interactivity into everyday objects, like Levi's jackets and Eve St. Laurent bags. He holds a PhD in human computer interaction from the University of Florence and spent years as a postdoctoral researcher at MIT's design lab. He's also filed more than 30 patents. What makes Leonardo's thinking distinctive is his insistence that the metaphors we use to describe AI shape everything. How we build it, how we regulate it, and who it ends up serving. He's skeptical of the dominant vision of AI as an autonomous agent that does things for us and is pushing towards something different, AI as a tool we think with. In this conversation, we get into his unusual path to design through cognitive science and robotics, but it actually means to treat emerging technology as a design material. By the chat, it's a primitive interface for the physical world, and why he believes augmenting human intelligence might be the most important design challenge of our time. This is Design Better, where we explore creativity at the intersection of design and technology. I'm Eli Wooler. And I'm Aaron Walter. If you're hearing this, you're not currently on our premium subscriber feed. Design Better Premium subscribers enjoy weekly episodes. That's four episodes per month rather than just two, and all of them are ad-free. Plus, you'll get an invitation to our monthly AMAs with the smartest folks in design and tech. And if you subscribe at the annual level, you'll also get our toolkit, a collection of our favorite design and productivity tools like Proplexity, Mirror, Read AI, and War. You'll hear a preview of this episode, but if you'd like to hear the full conversation, please consider becoming a premium subscriber at designbetterpodcast.com/subscribe. The podcast is available to everyone through our scholarship program, so if you can't afford a subscription, just shoot us an email at
[email protected]. We'll help you out. We'll return to the conversation after this quick break. Design Better is brought to you by WICS Studio, the platform built for all web creators to design, develop, and manage exceptional web projects at scale. Learn more at WICS.com/Studio. And now, back to the show. Leonardo Juiste, welcome to the Design Better Podcast. Thank you for having me. We're excited to have you. You have a very interesting background. I've worked at Google for a number of years as a head of design, and now you're working at a, what looks like a pretty unique company working at this intersection of physical and AI products. So we've got a lot of stuff to talk to you about, but maybe first tell us about that transition you made from working on Google on some really interesting cool products to starting your own thing here with Archetype. As you said, I'm now one of the co-founder of Archetype AI, which is a physically AI startup, where we are building this AI Foundation model that is designed from the ground up to live in the physical world, helping people run machines, factories, infrastructures, cities, et cetera. So it's really like a deep tech startup, but it has this very strong R&D component. And so as a designer, I always found myself working very close with R&D. I'm always being more comfortable, you know, to work with engineer and scientist. A bit outside to what you may consider to be the mainstream of the design community. I believe this is a little bit the result of my upbringing as a designer. I didn't really take a straight path, but many different detours that ultimately brought me where I am today. So I have a very mixed background as a designer. As you can probably say from my accent, I'm Italian, I study there. And when I was in college, I was starting to be very interested in cognitive science and artificial intelligence and human machine interaction. And I stumbled upon design later in my career. I was doing my PhD at the time. I was working on robotics, trying to design them in a way that they can interact socially with people. And we did this project with Domus Academy. It's a design institution in Italy that captures the outcomes of the radical design movements in Italy in the 60 and the 70s. The design school is a design institution. I also do research. We started working together. And I was exposed to this idea of design. Design, though, as a critical practice, it was this idea that design can be seen as an inquiry method, a way to question the status quo. And so this really clicked with me. This idea that design is not just about solving problems, but really it's about discover the problem that are worth solving, you know, to outline unilaterally and vision for the future. In this context, I learned to see prototypes. For example, not just a way to approximate the final product, more like provocations, to think critically about what you're building, about the implication in society and so on. All these things clicked for me and I found my passion and I took all these bag of experience and I moved to US in 2011 when I was a researcher at the MIT design lab in Boston. I spent a couple of years and then worked a little bit as Samsung. And finally, I landed a Google 8 up. Google 8 up is advanced technology and projects. So I was actually working in a R&D lab that really helped me refine my design practice as a designer in this highly technical environment. When I was there, we were working on some very interesting projects. One of them was Project Soli, which was this tiny radar that we were able to embed inside consumer electronics and it was able to understand what was happening around them. When we started, really, we didn't really know about what do we do with this, you know, it really started from the curiosity of some scientists and some researchers. And a designer, when you are there, it's kind of interesting because you can really think already about the products, the product features, how wide it's useful. But really, the first thing that you need to do, if you want to sit at the table with these people, is to work with them and try to understand why they are curious about these things and what are the properties of these new materials, basically, that you're working on. The core idea of Soli was that this tiny radar was embedded in consumer electronics that now become aware of their surroundings and aware of people's behavior. And this thing that was able to understand different aspects of the global communication, for example, was able to understand when you were moving toward a device or away from a device, they were able to understand different types of gestures so that you can start to establish a more famous interaction with these devices. What brought me to archetype, the team that was building Soli, realized very quickly that in order to make it work, to understand all these different behaviors, you have to use very advanced machine learning. And machine learning was very expensive at the time. In order to recognize, for example, even one single gesture that you do on top of a nice device or a pixel phone devices, it required a huge amount of data collection. For example, we spend like years to collect data of a simple, wipe gesture and we collect 5 million samples of this gesture all around the world to think into consideration different cultural differences, demographic differences and so on. And it was very expensive. Every time you want to do a new gesture or a new behavior, you have to redo this training once again. And so very quickly we realized that this really doesn't scale much. And it's probably one of the key problems, the IoT head in the past. In parallel, there were all these interesting new things happening in AI, these foundation models. And so we thought, what if we actually build a foundation model for sensor? So every time we want to create a new use cases, we don't have to retrain it from scratch, but we can actually just fine-tuning with a smaller set of data. And this is a little bit how archetypes started with this idea to build a foundation model for sensor data so that we can develop very quickly new use cases, a sort of like horizontal platform for the physical world. Based on the continuous understanding of sensor data. So we are able to translate this sensor data into meaningful information. And the projects were really. I'm curious, did you bring any of your cultural background to this project? And I'm just thinking that Italians like to speak a lot with their hands, which I love. So there's naturally, like, I'm sure maybe an interest. Like, what can we do with sort of a gestural communication? But perhaps it doesn't translate across different cultures. Yeah, just curious, what part of your perspective is you brought to that? Definitely. When you look at the way we interact with each other as humans, it's very intuitive, right? If I see you walk behind me, I can probably keep a door open for you, even without saying a single word. I immediately understand that you want to come through the door as soon as I keep open to you. If I see that you are touching your glass and maybe pour water for you, that there is the intuitive understanding that we have between people, that is based on this understanding of subtle cues, these little nonverbal communication cues. But this sort of understanding doesn't really exist with technology. Technology is, if you want, very rude. It interrupts at the wrong time. It doesn't have the ability to understand any of these social cues that make our interaction with other people very smooth, easy, and go. And so really the idea there was, can we bring some of these social intelligence in two consumer electronics? Can we design these device in a way that can participate in our everyday life in a more polite way, if you want? It's not that we need to make them smarter, but maybe there can be a little bit more polite and participate in a more harmonious way in our day-to-day life. And so we took inspiration from the way people interact socially to build a design language and interaction design language for consumer electronics. And this was in an nutshell what project solubos in the end. So project solubos it shipped in Pixel 4 and also in the Nest Hub. So it went from being this abstract idea to something that was like a commercial feature in existing Google products. You also worked on project Jakarta as well, which was building interactive textiles that were deployed commercially with Levi's jackets and Eiff's Sontler Out bags as well. What did you learn from that project project Jakarta? I mean, it feels like it is related to Soli, but different in many ways. Project Jakarta, as you said, was this conductive fabric that we could embed into different products. The core idea was to bring interaction capabilities to everyday objects. There was this idea that we can bring computational capability into everyday object, but often it results in the design of new things that after six months end up into a drawer and no one use them anymore. We have seen in the past this explosion of internet of things, that they've tried to do all sorts of things that are cool for a little bit, but then this appeared. What we really tried to do with Jakarta was to really try to understand how to augment the things we already use, like a jacket, a backpack, a pair of shoes, for example, a project with it with Adidas. It wasn't to create something new, but to really try to leverage what we already used and exist and make them better. And in order to do that, we have to really study how things were made. So we work with this company to understand their manufacturing process, for example. To be sure that the technology we were building could be part of the way they do things. So they didn't have to change, for example, how you make shoes or how you build a jacket. So this was to me a very important understanding coming from Jakarta when we want to bring technology and intelligence and connectivity to the physical world. We should start from what we already have and try to make it better. In order to do that, we need to deeply understand as designers how things are made. Because if we create friction to the manufacturing process, it will never happen, right? I mean, the costs start to raise and so on. So a few of your projects have made it from this R&D phase into actual commercial applications. And that's not of the truth for R&D projects. And we've talked to the Google X folks a few months ago. And they're sort of in this similar arena of developing very speculative in some ways, products and experiences, and some number of them make it on. What are your insights about bringing something from a more experimental phase into something that's more commercial? I believe that design can play a critical role in transforming R&D technology into products. I do believe that design is sort of like this cog that sits in between R&D and productization. And very often, one of the mistakes that I have done, I've seen other people doing, is going to work with the people that do R&D, it's a product design mindset. And I'm trying to explain what. If you go there and you start immediately talking about product features and users, you create constraints to the R&D process that at that time are not really necessary. R&D often is driven by the curiosity. And it's a beautiful thing. Some time we forget this, but these scientists, these engineers are driven by their own passion, their own curiosity. And of course, this is to me is always the first step. When I start a new R&D process, I try to work with these people, try to run what I call material study. I try to understand any type of technology as a new material. And I try to start it first, try to understand what are the characteristics of this new material. For example, with Soli, it was radar. It worked on the electromagnetic spectrum. But you really didn't understand from an interaction point of view what can I actually do. So the first study we did was to understand, OK, how does it react to the human body? Can you understand large movements? Like, I'm here, I'm not here. But surprisingly, as we were running this study, we discovered that it can also detect tiny, tiny movement of your fingers. And so in this first phase, really as a design, we need to be very humble. And we need to work with these people, try to work with them to take what they're building and try to materialize into prototypes, experiments that allow you to understand the affordances and the properties of this new technology. Because then these can give you the ideas. When we realize that Soli can understand large body movement and tiny, more movement, then we're all interesting. We can now understand different aspects of nonverbal communication, like we do as human, like presence, large body movement, gestures. And then from there, we build a coherent interaction design language. And of course, this is the first phase. And this allows us to have a very material understanding on this technology of the properties that they have, which is quite common in other disciplines, outside interaction design, like in architecture, the importance of material, for example. It's critical for the development, then, of the final out. This is the first step. And then you need to understand how to translate that. What you do by doing this, basically, you build almost a map, a legible map on the technology. So that someone with a more product mindset can look at this map and understand what journey they want to take into the product development. And so I think at the very early stage, the role of design is to build these legible maps of the technology to inform further on the development of new products. The only example that I've seen, like-- I mean, there are a lot of people. The Bauhaus was big in these. They had these basic courses, like when they were teaching the student how to explore the properties of materials, for example. I think another interesting reference is what they were doing, the Pixar. And there is this famous quote, like, technology inspires art, art's challenge technology. And I see at the very early stages, a very similar approach where technology really inspired design, but design has the opportunity to push the technology from the very beginning if you're willing to sit at the same table with these people so that you can accelerate and try to find a path over time that makes sense from a product point of view. History is filled with examples of consumer products that came from some sort of R&D scenario, but they just showed up at the wrong time. There's the Apple Newton, which was a brilliant device. Google Glass, the Segway, all of these things now are in our lives and normal and not that magical, though they were at the time when they were introduced. But they just weren't timed very well. In that R&D to productization cycle, how do you know when something is ready for consumers? Or vice versa, when consumers are ready for that device? So the way I see the innovation cycle, right? Very often we talk from zero to one, which is like from an innovation to a product. And as a designer, we have a lot of techniques, design thinking and so on, to really work this through. We take some existing technology, we shuffle them, we play with them, we understand how they fit into society, use cases and we build beautiful products. But there is another phase that we, it's often not considered, especially as a designer. And it is the phase that goes from the invention to the innovation and the invention of a new technology like radar or AI, for example, as well. This phase from minus one to one is where we translate some early stage ideas into a technology, it is an innovation itself and then later on we translate into a product. I think there are different ways and I've talked a little bit about how you can translate early stage invention into possible innovation, like a new technology with coherent, for example, interaction model for solians, so on.
And that innovation is going to become a product that's, I think, when it gets quite hard and it really depends on timing, it's society ready for that, is the economic context and cultural context able to receive that technology. And the reality is a lot of people run studies and you can try to predict and figure it out, but it's typically really hard, especially with Project Jakarta. You weren't really sure about the viability of Jakarta as a product that actually fit into people everyday life. We have a vision, we have an idea, what we were able to do was to start really, really small. And so not every company can afford this, of course, but the idea that you have a branch of your company that is dedicated to create these products which you build in small batches and you actually tested before you go a full scale. The idea that you can try different versions of a product, it's typical in software, it doesn't really happen in hardware very often because of obvious reasons. But I think it's what we're trying to do with Jakarta. We're seeding products into the market to understand and study how people react if it works and if it doesn't. So understanding the right timing for a technology and if there is the right social cultural content, I believe it's really hard to do on paper and I think there are ways in which we can, these days especially try to test it even if it is an hardware product, the same way we do with software. Let's shift and talk a bit about what you're doing now with archetype and a lot of our audience is familiar with large language models and what they're trained on, let's say, all the text that exists on the internet or all the imagery. And what you're doing is slightly different. You're building models that are based from data from the physical world via sensors and other technology. Maybe give us a specific example of the way that your technology works and a project that you're working on. Our foundation model is designed from the ground up to continuously analyze sensor data and translate them into an interpretation that is useful for certain tasks. The model is able to receive as input data such as accelerometer, cameras, any type of time series data and is able to translate them into a description and is able to understand hidden patterns in the data like anomalies and so on. The core idea of the model is that we are able to map the sensor data into the same mathematical space that language. And so by aligning them together, we can both control the model using natural language and actually have a description of what's happening in the world in a language that we humans understand. Sometimes we refer it to like a Rosetta stone that is able to map different types of input to language and make them intelligible. We are working with many different customers mainly in the area of industrial technology. We have several partnerships with manufacturing companies, for example, where they have a lot of data. There was a time 15 years ago where factories have embedded a lot of sensors into machines and they are sitting now there with a huge amount of data and they don't know what to do with that. And now finally we have AI that can actually able to understand this data. And so you have entirely factored that have been already sensorized that we can actually provide additional value there. And in use case like for example, one of the most typical one is predictive maintenance. We know for example when a machine is about to break. We can spot for example signal in the machines before it happens that is going to break. And that's very important because these machines are very expensive and even a few hours of downtime is worth a lot of money. We are able to identify anomaly for example, in certain data in a certain machine that may prompt an operator in the field to go there and check what's going on if there is something is broken or not. Other use cases that we have outside the manufacturing and industrial setting is for example, we are working with different cities as well. In this case we are using camera as input. And one of the projects that we have is with the city of Belpuy and Seattle where they have a lot of cameras at the intersections and they are interesting in optimize and increase the safety of this intersection. And so our model is able to identify what they call near misses when an accident is about to happen but didn't really happen. And by analyzing this data across all the different intersection they can build a sort of like a hit map of the city understanding what intersection for example are more problemathing than other and then potentially intervene. A follow-up project they were also doing with them is our model can also recognize if someone with disability for example or a mother with kids is crossing the street. And if you take it longer then the lights allow they can automatically extend the lights so that they can finish and cross the street. So these are completely different just case one in factories, one in cities but the idea is like wherever there are sensors our model is this general purpose intelligence that can have to extract insight from this data from the real world and provide the people working there with additional intelligence if you want we are trying to augment them to make the right decisions. How do you think about privacy and ethics in this space? There's tons of very valuable scenarios where this could be helpful. You describe some that are very business to business kind of manufacturing efficiencies and operations. In the hands of a government it could be helpful at an airport to detect potential threats. You know is this person walking differently is there something different about this person's luggage so could TSA for example screen more effectively or get some clues about where to look for threats. I can also think of government scenarios where if you've got an authoritarian government and they've got the sensor data how might they use this to change behavior or control masses. There's a lot of different ways that this could be used potentially to dangerous outcomes. How do you think about those sorts of ethical implications? I think we have a problem as a society that is beyond what archetype is doing what you can do with sensor because the sensors are ready there some of these capabilities are ready there and I do believe. If you'd like to continue listening to this conversation you'll need to subscribe at designbetterpodcast.com/subscribe. 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