Jay Park (2:55)

Thank you. Thank you for having me, Jay.

Aaron Walter (2:57)

You're doing really interesting work, and you have been doing some fascinating work throughout your career. But where you are right now, you're at Ford. You're working specifically on electric vehicles. So F150, Mach E, all of those different vehicles as well, including other propulsions.

Jay Park (3:14)

Too, by the way, which makes it really fascinating and complicated.

Aaron Walter (3:18)

Yeah, we want to talk about all of that stuff, but maybe we could just talk a little bit about your experience in general because you have a unique perspective that you spent such a long period of time working at Microsoft and Amazon, working on the Echo show, which is a ubiquitous product. Spent a couple years working at Google in a very important leadership role and, and now you're at Ford and they're some of the most influential companies in the world. How are problems solved differently across these different companies and cultures? Because culturally, like people are going to look at things differently between Microsoft, Amazon, Google and now Ford. What's the difference between the problem solving approach?

Jay Park (4:02)

With the exception of Amazon, Microsoft was software. Although the fun part was we did work with the partners on what the hardware should be. That's the whole PC industry. And then you see that played out on the Windows Phone, for example. I think Amazon, we had first row seat at defining the hardware in house while trying to connect the software to enable the service. I think the first kind of company that was trying to bridge all that together. You hear about Apple doing really well on services now through their software and hardware. So having a first row seat to that was very enlightening. Google was definitely more software. We had some hardware components for like meeting speakers and stuff like that. But we primarily stayed at billions of users. Even like a 0.5% negative impact. There's millions of people. It's a different experience working at Ford. It's primarily a body company. Right. You're building physical bodies. And then the big shift is adding the mind, the idea of having service. So having these three things connect is sort of where most products are going.

Eli Woolery (5:10)

Talk maybe a little bit about the Ford's design legacy. I mean, certainly the cars themselves have an aesthetic design legacy, but in more modern years, Ideos worked with ford in the 1990s. We talked to Sandy Frci who ran their innovation lab for a while. So this idea of design and innovation is not new to Ford and it's something they seem to invest in quite a bit. Could you talk to us about that?

Jay Park (5:31)

I think there's a lot of great movement in design, of course, in Ford. I think Ford is trying to find its footing on what it is to be a product company from a manufacturing company. And you start with the body Again, aesthetics. And then at some point you have to start doing product design and you have to know what are the big bets and what do the customers want. So I think all of the human centered investment that went in was very helpful. At some point though, it also made sense that, hey, you need to have experts who can work side by side with engineering to actually build something. So that takes a different kind of expertise. So I think the last two and a half years, almost three years I've been here, what I've been tasked to do is make digital product design appear to the physical design. And typically in OEMs, digital team is under a studio. In our company, my team used to be called digital asset delivery team, very production heavy, service oriented. And so part of my charter was to come in and build out that team of experts and we like to call it as expert, leading experts, model. And then so you work inside out rather than outside in. So that's been the biggest shift in how we work.

Aaron Walter (6:46)

Tell us more about that. Just the integration of software into cars. I mean there are some car companies where that is so inherent in their philosophy and their, their strategy really, and that Ford is an expert in making cars. They are the ones who innovated the production process. But integrating in a legacy company like this that's been around for so long, integrating new technology, in some sense it's a challenge, you know, like it's a challenge to try to bring that into the entire experience. How do you think about that and how does the leadership at Ford think.

Jay Park (7:25)

About that integration in auto industry? When I arrived, there was three big disruptions. People like to think about electrification, which is a big one. The adoption rate has slowed down, but it's going to be the end state for most of the customers, we believe still. Then there is autonomous driving, different levels of it, and then there's digitization, which is a broad term, but that's really important because that's the bridge to the interface. And then we talk about binding that to service. But now thinking about AI more holistically is becoming that next big thing that has to bind all of these together. So for Ford, even before I got here, they went through a huge disruption to adopt touch interface a while back. And those are some of our existing vehicles. It runs on a software called Sync for and you know, it's kind of, to be honest, dated. But that was a huge shift for Ford, right, to take that risk, you know, many years ago. And now we are starting to invest a lot more in embedded experiences. So last year we shipped Five programs that uses Google's aaos, including their services. And we have a good tailwind on those products this year. And we're working through the next two generations of products where we invest even more on our embedded experiences. So it's early days, but we're definitely investing there quite a bit.

Aaron Walter (8:55)

So should we read this as the strategy of let's shift the responsibility of software over to our phones instead of the car? Is Ford's strategy that we need to bring more of that in house?

Jay Park (9:08)

So, for example, if you go to China, they have fundamentally changed how a car is made. They sort of jumped over a couple of generations and went straight to using EV vehicle as a clean canvas. They're building in more like a product. And if you're a phone manufacturer, the phone and the car is the same thing. It just simply blends. So they're ahead in some ways on the customer experience. And we have different opportunities and some headwinds. We don't own the phone oss, but we do think there's definitely a big theme around how does phone and the vehicle interact together. And it doesn't always have to be that it's the same os, but like imagine your phone when you're away from the car, you can still have your car in your pocket and there's quite a bit of work you can do to make that experience really great. And you've seen some of other manufacturers do this, especially EV companies, but that's just the beginning of surfaces that we can bring. Rather cool experiences.

Eli Woolery (10:13)

Tell us a little bit more about Aaron and I. I think we're in the same boat, not having experienced EVs in China. So when you say the car and the phone are the same thing, they essentially have control over the design of both these systems so they can integrate them.

Jay Park (10:25)

Yes. So like Xiaomi and Huawei, they are phone manufacturer. There's a book that you all should read if you haven't. It's called Apple in China and it talks about tech transfer. They also mentioned Tesla there as well. So what we've done is created a knowledge base for the Chinese to pick up how to build complicated, sophisticated products. And they've used that as a flywheel to create their own. So they are number of steps ahead of what most people don't even know is happening. Like Huawei has a foldable. That thing's pretty amazing. I think there's a video of Jensen Huang out there playing with it and going, oh my gosh, this thing is crazy. Like, I haven't even seen anything like this. So not only are they good at manufacturing at cost, but they're also being Creative. They've had 10 year Runway on that on phones. They leverage it to the EV when Tesla came in, so they have a strong Runway of creating software to defined vehicle. They took more of a first principles approach. They built the EV more like a product. Right. Versus having to deal with, oh my gosh, I have to manage all these modules. We ventured in them out, we have to spec them. It takes a year and a half to get all those parts back. So we integrate and chip. They don't think that way. They can have a very clean architecture, build it almost like a computer on wheels. There's a couple of layers that is a little bit more complicated than a phone. Like you know, you need to have a driving powertrain, manage the power, all that stuff. But fundamentally it works like a phone on wheels. And they approach the product experience from that perspective and then they add things like how would you control actuators and motors, sensors and actuators to have what we call third space? I mean you go there in a standard ev, SUV will fall out of your way, give you a zero gravity experience, screens everywhere. I mean it's pretty magical. It really will blow you away in their approach to what a car is. And I think there's quite a bit of opportunity, of course, for Ford and how we reimagine that as well, that's fascinating.

Aaron Walter (12:40)

I mean this is ultimately what Apple, they dealt with before the iPhone. They experimented with creating software for someone else's device, bumped into a lot of limitations and decided it's inevitable. We have to have full vertical integration where we're controlling all of things.

Jay Park (12:58)

For us and the west, having displaced how you manufacture is a disadvantage.

Aaron Walter (13:04)

Talk more about that, like say more about what the disadvantage is. I mean, Ford has all of the manufacturing and is making some pretty incredible changes in expanding and modernizing the manufacturing process in the U.S. absolutely.

Jay Park (13:20)

So you know, as you can tell, like we have majority of our vehicles that are manufactured here. So that's a great thing. I would say the Achilles heel is supply chain, the ingredients. Name a big tech company that's creating modern devices, glasses, speakers, name them. And then if you listen to Mary Meeker, the person, she forecasts the future a lot. In the last AI report she mentioned we're going to have trajectories like billions of smart intelligent devices. Most of them are going to be manufactured in China. Especially the supply chain. Right. Of ingredients. I think that's a disadvantage as we think about how you compete when there's a shift of going from sort of a globalistic distribution of supply and building things to more regional. And I think that's going to create tensions and I think that's happening naturally because as products get smarter, the moment you have cameras and you have sovereign cloud processing it, you're going to have issues whether it's a car or a small device, especially if another country is trying to sell that into the US and you're seeing some of that play out already. So I think those are the challenges you have. The real topic really I think to me is it is relevant to design field and design leaders, is I believe in this new era of AI, the way we have to think about the new opportunities is how the digital experience live on a specific vessel, whether it's digital or physical. Because the input method drives the new paradigms. And right now there's a bit of a displacement of the input method that we're applying. We're using the old ones, but the capability of the thing we're trying to leverage, the AI is completely different. So then you have to think about the vessel and the input. And so the vessel you have to talk about where is it going to be made. The broad picture is like you want to have total control over everything you build and you can't just be responsible for the digital portion and say you're okay, you're not okay. Because if you think about what a modern industrial core is, is there's two factories that's happening that's being built. One is the AI factory. We're aware of that one because there's a lot of press on that. I think we're over indexed on that. The other one is intelligent product factories, things that can build intelligent products, including cars. That one's really important because that's where you're going to get a lot of parts, compute, sensors, other supplies that comes together. Whether it's an EV or a robot. It's the same kind of architecture in a way, you want to get really fit at that so that you can bring the cost down and have ownership of the supply. Right. Especially when we go regional from global. And then it's power, what's going to power those factories. That's how I see the two big factories, energy, power and supply chain. And if you look at us right now, you're starting to hear news about, oh, this company is now investing in bringing the chips back here and billions of dollars, but it's sort of scattered. The bigger news is we're all Building these AI factories, which is great. But if you look at those big seven companies, they're spending 50, 60 billion dollars annual to invest. If you think from a sovereign point of view, you might be overspending. Do you need six times that much spend when China probably is going to spend one or two times that? That's like 300, $400 billion difference. And where are they putting that money? The intelligent product factories, they're going to go from 2 terawatt to 3 terawatt to 8 terawatt by 2040 terawatts. About 750 million homes that you can power us is going from like 2 to 3 terawatt. So we don't compete it even at the power level. Those super clusters that you see being built, they run on about 1 to 2 gigawatt power, which is like one or two nuclear reactors. So there's a lot of stuff going on that people are not aware of. But I think if you have so much redundancy building that power network, I don't think it's very efficient versus putting some of that investment and policy into building intelligent product factors including EVs.

Eli Woolery (17:49)

China's also demonstrated that they can develop very efficient models like Deep Seq without the huge investment and the types of infrastructure that we have here too.

Jay Park (17:58)

Yeah, you have to read between the lines there. But clearly talent wise capability, they are as smart or smarter. The best thing about design sometimes is right, like constraints. You find innovations when you have constraints. So one thing Deepsig did was they notched their compute from an 8 bit to 4 bit and just got an exponential efficiency. It's just like if you didn't have to do that, why would you, Right? I mean humans just show ingenuity all the time.

Aaron Walter (18:26)

It's crazy. I heard a stat recently that AI spending investment in AI has surpassed consumer spending in the US contributing to GDP growth. That's just like an insane amount of investment in AI.

Jay Park (18:40)

And that's my point. It's a little bit overindexed on building these hyperscalers.

Aaron Walter (18:45)

So let's shift gears a little bit and maybe talk about electric cars. Ford has taken a really interesting approach to EVs. The F150 Lightning in particular. I think there was risk and a lot of investment and I see a lot of them on the road. I have friends who own them and they love them. But we're also seeing that the adoption rate has slowed and not been quite as intense as we expected. And there's been a lot of changes politically in the United States just kind of general cultural shift as well. What's the state of EVs in the US and maybe globally if you have that perspective?

Jay Park (19:25)

It's what we call the early majority blues. There's an early adopter leading indicator in excitement, and then there's what I call the early majority blues. And it comes in few factors. One is average electric vehicles still tend to be expensive. Getting that battery cost is a difference. Getting that range is important. And some of this is due to again, how we get our parts right. So that's why we did a hard pivot about a year ago. You know, we loaded a whole bunch of EV that were highly priced and potentially harder on how we earn revenue off of that net revenue. And seeing how the environment's changing, we pivoted away, we unloaded them and instead changed that strategy to load more affordable EV platform from ground up. So that's where we focused our attention to seeing where China was going. So we wanted to make sure we were fit and competing against an affordable ev. And we tweaked our battery technology at the same time to something that is less dense, less flammable. It means it might not have as much range, but you can design that into the aerodynamics of the car to make sure you get the right range. So we've been very smart in my mind about pivoting. And then because we have multi propulsion portfolio, we also are turning a lot more of PHEB hybrids around because even those big trucks, they love having power on board. We serve a lot of essential workers who do a lot of building and fixing and moving. So they need the range, but they also don't mind getting that extra efficiency in cities and urban areas, but also to power their work, which is something that they really love about lightning, of course, Right?

Aaron Walter (21:14)

Yeah. You get like a 220, you get bidirectional charging for your house. It's an amazing vehicle, like what it can do.

Jay Park (21:21)

Yeah. And so I think that shift is good for us. There's an analogy I like to give. So, you know, in strategy, people have to draw S curves, an S curve, you know, you look at the climb, you're kind of blind to how short it is or how long it'll last. And then, you know, you can draw a double S curve to show disruption, but then it's hard to understand the disruption. Like I've done this over the time where I draw these things, and then I realize I'm not really helping understand that disruption, the intersection of those two S curves. What most humans do is they look at. I've heard this term before, it's not mine. Local to global maximum. Think of it as two hills. You're on a small hill, you see a bigger hill. Of course that's where you want to go and it's a very first person perspective. But in reality when you turn it around into a third person view, you see the small hill and then you see a big valley and then you get to the top of the hill. And so EV journey is just that. We're going through that valley. But in our mind we have made the right strategic call to unload some of the EVs that we didn't think were going to be profitable and really double down on affordability that we think would be a better fit for our customer and making sure technology, the way we manufacture things, the battery we use is super efficient to give us that right viability, if you will. And then, you know, we're not talking about other OEMs, but you'll notice that some of the OEMs, they've already loaded their plant with some of these EVs that they can't unwind. That's not a good place to be.

Aaron Walter (22:59)

We'll return to the conversation after this quick break.

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Aaron Walter (26:06)

And now back to the show. One thing that's interesting to to me about the design problems that you all face is that most industrial design products, you design a product you've learned from a customer, you sort of dial that in, you make that thing and you distribute it. But trucks in particular, and the Ford F150 has been the most popular vehicle in the United States. Not just the most popular truck, but the most popular vehicle since the 80s. I live in Georgia. They are ubiquitous. They're everywhere. And this product segment has a different type of design problem in that a lot of people want to customize their truck. So they get one from the dealership. And now dealerships are building in these like elaborate customization arms. So you just bought this brand new truck, you send it over to this other arm, they change the wheels, the bed, like lift kits, all kinds of different things that you're getting. And electric vehicles, which seem to be inevitable and superior in many respects, they are not yet in that space where they can be Quite so customized because when you make all these customizations, it affects the software, the way that the whole thing fits together. Is this part of the design challenge that you and your team are working on?

Jay Park (27:29)

Yeah, I would say if I could abstract it a little bit, we sort of classify our company as a middleweight. We're not as big as, let's say, Toyota. So we have a very structured portfolio. I think the company for a while worked on trimming down unnecessary products. Like we're basically, we had performance product, we had working products, we have adventure products, maybe family. So it's pretty tight. And even there though, what's music to my ear is the way we're approaching software is from platform first point of view, so we can scale. And so we have this 80, 20 rule about like, how do we build this new OS that can serve all the portfolio, millions of vehicles, and then still find that uniqueness that makes each vehicle or each domain special? It's fun and at the same time very challenging, to be honest. Because for example, another thing we work together with our studio team is what we call cabin control strategy. And of course, torque customers probably want different kinds of interface than a family suv. But we want to make sure we don't create the complexity that hurts us later with quality because at some point the sheer scale of complexity will make us be less efficient on how we build and update our products. And because we're also probably spending more time servicing the variations. Right. So it's not any different than even software companies have to manage this stuff. So we try to bridge that fine line of being platform centric but not be tone deaf to the program. So we have this healthy tension. And I have to say, in my three years, I love working on a ground up EV, but I almost love better working with the 150 and Super Duty programs because they're so passionate and we're both learning from each other to build the best product for their customers, our customers. But we're both doing a lot of learning, you know, and how do you have a critical thinking mindset to solve that problem?

Aaron Walter (29:39)

I mean, for a lot of people, their truck is a big part of their identity. I see that here in Georgia.

Jay Park (29:45)

Yeah, I'm pretty excited about what's going to come out next.

Aaron Walter (29:49)

Okay, what do you think design is going to be in the next five years? I mean, there's so much right now we're recording this a day after GPT5 came out already. I can see like some of the early demos that's just going to change design Dramatically and engineering. And when we project this out, I do a lot of reading and watching of YouTube with folks like Geoffrey Hinton, Mo Gaudet, all these folks who have been pioneers in AI and products in that space, and they're all projecting very short periods of time, dramatic change. And I can't help but think it's all going to be different for us. How do you see it?

Jay Park (30:34)

Well, yes, I think the sheer rate of growth is overwhelming. And I don't listen to people go, everything's going to be okay. They're the people who are looking at the beach, people on the beach and not looking at the big wave coming behind them. And I think we should embrace the uncomfortableness back to that valley. And I think our job as design is to figure out what part of that valley do we feel uncomfortable about and what's actionable. And so from interface point of view, there are a couple of things that I think is really interesting. I did mention a concept at Config Panel, but it's this idea of like before, sort of the generative AI, our input method was all about us pushing it in. It's very instructional, right? So if you want to design a chair, you use a mouse and you're inputting and you're instructing, basically, that's the interface with AI, especially when it's generative, you're pulling. You can say, let there be a chair. You're asking a question first. It's very Socratic. Then you're refining that idea. So from a design process, that's why I keep saying I don't think we have the right interface for it. Because the folks who are creating these large capabilities, it doesn't come with new interface. So everyone's struggling, and that's why everyone's trying to design a new browser. I literally had this talk a couple of weeks before Johnny and Sam got together. I'm not saying I predicted it, but I think it's in the air. And in the talk. I was saying, don't make fun of rabbits and human AI pins. There's going to be more of them. We're just trying to figure out what that interface is, right? So I think that's at one level, at a working level. You know how we use diamonds a lot to talk about diverge and converge. And I've been around the block. Most companies, they don't have the left side of the diamond. They're just converging. How many places have you been where you're just converging? Here are the KPIs converge. And so the design job is to create the Runway to go. No, no, no. You want to have insights, you want to be stimulated by our prototypes. There's that gray area that can really drive the direction and points of view. Especially on V1 products like @ Amazon, it was perfect environment where a design could go show that hand more. But when you're in a very operational place, it's a little bit harder. Right. But most places, they tend to start at the top of that peak of the diamond and they never see the left side. I think with AI, if you kind of entertain this idea of that, hey, we can bring this self credit model back. What that means is you don't start with the what, you start with the question. It's very design friendly, right? And if you look at your own workflows on how you use these LLMs, at least this is why I do it. It's very Socratic. I ask questions, I ask questions, questions, questions. I generate different options and we debate and then I have it summarize it for me. And you don't want to get into paralysis of this, but it's a very interesting partnership that I'm finding that you can apply that. I've been doing it without AI to do. As a design leader in companies, when someone gives you a prd, I inspect was the paper written about the why and what or the how? And it's going to be a distribution of sum, right? But usually if it's very weighted on the how, which is typical, I go, no, no, no, that's our job. Let's define the why and what. And we can help you with that. We can give you research, we can prototype, we can ideate, it'll help your output be better. And then let us design. That's the help part. It doesn't come for free. I haven't been to any place that that is part of the culture. You kind of have to establish it. So I think AI in five years. Well, five years is a long time, but I think those are some of the things that we can do. The challenge with exponential growth is also the rate of how different class of workers would get displaced. So if you think about kind of peak factory era to peak first office and then peak office with PC, you can see a displacement and distribution of the workforce from like. I think farming now is like less than 2% but it used to be 60, 70% pre industrial, then was majority was factory. And then the office worker, more clerks. And then when Pete knowledge worker kicked in, Peter Drucker is knowledge worker. That starts to become a big body of workers. As we start to de. Industrialize, that becomes our main way. And then now we're sort of holding onto that peak office and trying to embrace the peak AI hasn't reached yet. But in five years it probably will. Right. If you talk to folks and when that happens, the displacement might be like 30 million plus on Knowledge Worker and another 20 million on Essential Worker. So I've been trying to figure out like, okay, where do these people go? Because like at first you go, oh, there may be no jobs, but like every time there's a disruption, we did create new places where people did work. And I'm just trying to figure out what that looks like. And it's possible that most people are going to a larger bucket of essential work, whether service, hospitality. But this is sort of like if you assume, I'd say the projection of AI and robotics, I think in five years, I think that's chapter one, chapter one of displacement and probably another 10, 15 years for chapter two and three. So that's again back to that valley. You can't go from one small peak to the next one. Like it's happened every time in the history. We went from farming to factory factory knowledge worker, knowledge worker to even more amped up knowledge worker. But no one talks about the valley. And I think it's just really interesting to examine what happened there for the people. It did not come for free. And we're about to go through a big one in my mind.

Eli Woolery (36:49)

I think we want to dive more into that subject because it's super interesting. But before we depart, you talked about the diamond and the divergent phase of design. And it seems like we're in a space where these AI tools make that so much more accessible and fast to iterate and create prototypes. Maybe talk a little bit about that, how that's, you know, you've seen that applying even now in your own team.

Jay Park (37:11)

I have like mixed feeling about it, to be honest. There are tools now where you don't have to draw, right? So your question could be a sketch. I call the input a question now and then, boom, it will render things for you. And I asked myself, as a designer or as a human, what happened to the art of approaching a blank canvas? How fast do you want things to come to you is something that I just question. Like there's something good about the slowness of things, you know. I don't know if you've ever read Milan Kundera. He's known for writing a book called Unbearable lightness of being. He's a philosopher. But then he wrote a book called Slowness. And it's a really cool book because the first chapter is all about these two people, men and women. They're on a carriage going down a countryside, it's a long ride. And he talks about how just them inadvertently bumping into each other creates emotions. We've lost that part of creation if we just go too fast. And I'm trying to figure out what is that version because right now I feel like everything we talk about is fast speed. Velocity is almost like microwaving your way into production. And I'm like, okay, maybe you can do it with ads, but it's not how you really want to live. Like, I don't know, I don't know. But I think when you're moving things at scale, of course, I mean people were talking about Microsoft Office as digital human lifts. Right. This is that times a lot more. And so I think you can leverage it. I think the collateral damage is we're going to need less humans. It's just like, I mean that's just the reality.

Aaron Walter (38:51)

Yeah. And the rise of robotics is part of this as well. It's reported, but maybe it's not quite as in focus. There's amazing stuff happening with robotics right now that will converge with AI that's happening. I mean it's sort of this perfect storm of things that will change our culture, potentially impacts capitalism in major ways. I mean what you just described was about 10 to 15% of the American population being displaced by AI one way or another. And when the robotic revolution hits as well, when those things combine, that could be considerably higher.

Jay Park (39:32)

Very much. And in fact that's what I was trying to express at the config. It's just underreported. You might hear about figure and stuff like that. Right. But what's under reported is if you look at again back to China, they have an existential crisis in general, world is aging and we're not replicating as fast. But China, remember they had a one child policy. So that is a huge one way door that's costing them probably 80 million labor force in the next 20 years. They're moving straight into humanoid robotics using their EV platform. And that's what I mean about having been fit on core, in industrial core. They have it, they're shifting it over and you're going to like Xiaomi. Phones are run on a dark factory, they make one every five seconds.

Aaron Walter (40:24)

So no lights on, no humans. That's what you mean by a Dark factory. Like it's just like production happening and there's nobody there.

Jay Park (40:33)

Yeah. And then so what you're going to move is from human supervised humanoids to system supervised human nodes very quickly. And again they're going to have access power supply and they have integrated all their supply chains. So that's a pretty mighty leverage to have in the world geopolitics or geoeconomics. And in the US I feel like individual companies isn't going to scale. You need like a national plan. You're not going to secure enough supply to get the cost down. If you study the drone industry, this is what happened. I forget the manufacturer here but like they couldn't compete against China. They were distributed, location didn't have the supply under control and China just outmuscled them and out innovated them.

Aaron Walter (41:23)

Are you describing basically like a government based operations to supply companies?

Jay Park (41:30)

It's an open question like again back to the valley between the local and global Maximus. I'm not a politician so don't get me wrong but like why are there tariffs? Well, we are $2 trillion. There's a 2 trillion gap between us and China. We're just outputting less plain and simple. But it's kind of our system globalism. Right. You need policies, we got AI act, we got BUN Chip Act. You need policies to try to drive some of that. But then we need to do more. I think we have to figure out. I haven't architected this the wrong way, but I think we have to start thinking about this in terms of well, what is the pan even pan company level? How do you do this? How do the OEMs all get together to secure supply together you need scale. You can't do this as a startup alone. I don't think. I think that's what I'm learning. Being in a big manufacturing company, it's like the beauty of us is the individualism, individual company making it standing ground. But when another player is playing at a national level, I just ask about the scale. How do you compete at the scale?

Aaron Walter (42:38)

You alluded to something that I think is very important for designers to start thinking about, which is that you know, we're moving into AI. AI is kind of the rethinking of everything that we do as designers in many ways and yet we're still operating with kind of antiquated UI models. There's a long history of this as media evolves that it sort of imitates what was before until it can figure out its own identity. One small example is I Remember in the late 90s, CD ROMs and even websites would design books. Like this was a metaphor that people often used like you're clicking through from book to book. HyperCard was sort of like that as well. Like you're jumping from place to place and we're still operating with like click and tap. Natural user interfaces seem inevitable. With AI, what do you and your team, what are you experimenting with? Or how are you thinking about a different opportunity for interacting with computers?

Jay Park (43:38)

Yeah, and I won't give away what exactly we're doing, but I can tell you we've been starting to look around the corner. So then I had to pick sort of like, I'm not saying I'm not a futurist, but like I'm practical. But I wanted to figure out, okay, like especially in Ford, we have core demographics that work in certain way, our customer base. So it's very hard to defy that gravity, if you will. It takes a lot of energy to pull off of that gravity, to think a little bit differently. But one provocation I provided to the team was I was just charting different evolution of things, but including generational. So if you map out post war folks to boomers all the way to Gen Alpha and you look at the years, so we're 20, 25 in 10 years. In 2035 a lot of the boomers will be much older and then you're going to have half of Gen Alpha in a driving age. That's a huge change. And if you think about Gen Alpha, they are AI native. Do you know what an agent is in elementary school? That is a scary thought. I had Gen Zs, they knew that there was a service for that like Instagram and stuff like that. Maybe their middle school and high school, depending on how old your Gen Zs are. And millennials, of course, you know, there's an app for that and maybe high school or college. You know, I'm a Gen X and I knew there was a computer for riding a dog in college. To have a whole generation that is accustomed to knowing there's a AI for that, you really have to take first principles approach to what an in cabin experience is, what a vehicle is even. Because by then you're going to have quite a bit of interesting products out there. Not all of them will make it, but some of them might become the next ubiquitous thing. We just don't know.

Eli Woolery (45:37)

Jay, this has been a fascinating conversation. What's inspiring you right now? And it doesn't have to be work related. Before we started the interview, you and Aaron were riffing on your guitars in the background there. Is there anything in your music world, in the movies you've been watching or TV shows or anything you've been reading that you've been found particularly inspiring?

Jay Park (45:55)

I'm finding actually the basics is what's most inspiring. I tend to go down rabbit holes. Curiosity takes me to a lot of places, but at the end of the day, I'm realizing spending more time with your loved ones is what's inspiring and wanting to do more of that. It's really that simple? I think so. I love my walks. I'm in the Bay Area, so there's a place called Fort Fungston. It's a beach, it's got trails. I just love going there right now. My kids are also at home and so there's that intersection of like they sort of came back for a little period of time. So I'm trying to enjoy that time before they move out again.

Aaron Walter (46:34)

Where can people learn more about you and the work that you and your team are doing At Ford?

Jay Park (46:39)

I don't keep a big profile, so like at LinkedIn, I have some links to some talks. Hopefully maybe this interview also help. And we are working on like a Ford design website. We don't have one, to be honest. Last three years we've been busy building the next gen work, but it's not coming out yet. Right. So that's one of the thing that's engaging but also a little bit trying is like it takes a while to build a car. So we're excited about the announcement next week, but then we can't really talk about it. When some of those vehicles are more mature, then we can start talking about it. That's I think we will roll out more about our team, how we work, our story, the design language, all that fun stuff that we definitely want to share.

Aaron Walter (47:22)

Jay, thank you so much for joining us on Design Better. This has been super interesting.

Jay Park (47:26)

Thank you for having me.

Aaron Walter (47:32)

This episode was produced by Eli Woolery and me, Aaron Walter, with engineering and production support from Brian Paik of Pacific Audio. If you found this episode useful, we hope that you'll leave us a review on Apple Podcasts, Spotify or wherever you listen to finer shows or simply drop a link to the show in your team's Slack channel designbetterpodcast.com It'll really help others discover the show. Until next time.