The AR Show: Alex Hertel (Intuit) on Mixing Bits and Atoms within the World Computer

11:08PM Dec 16, 2020

Speakers:

Jason McDowall

Alex Hertel

Keywords:

computer

people

world

company

connect

augmented reality

spatial computing

intuit

ar

problem

startup

shockley

build

create

google

platform

hardware

shift

idea

data

Welcome to the AR show right dive deep into augmented reality. with a focus on the technology and uses of smart glasses and the people behind them. I'm your host Jason McDowall. Today's conversation is with Alex Hertel. Alex is the former co founder and CEO of Xperiel, and currently an inventor at Intuit, where he is a director in their futures group. Alex completed his PhD in computer science at the University of Toronto, and is an expert in no-code languages, as well as the use of immersive technologies to make the physical world digitally interactive. After his PhD program, he co founded Walleto, which was acquired by Google and became Google Wallet. After several years at Google, he left to become the co founder and CEO of Xperiel, where he helped to create a user friendly programming language called Pebbles, with a goal of democratizing AR and IoT programming in order to connect the internet to the physical world. In this conversation, Alex explains how he applies his research to a concept he calls the world computer, and how it relates to competing paradigms of the past.

If you think about all the emerging technologies, right now, we tend to focus on them in isolation. So a lot of people talk about augmented reality, a lot of people focus on the Internet of Things, a lot of people focus on 5g and edge computing and wearables. And I have a different take on it. I think that it's not very helpful to look at these different pieces in isolation from each other. I like to look at it as being components of what we call the world computer. And so what I mean by that is like the whole world is kind of literally going to become a computer. And all of these different technologies, these emerging technologies that I just mentioned, they are just replacing the old pieces in the Von Neumann sort of the narrative that we've been talking about.

Alice goes on to describe how a new no code language can make the process of creating AR experiences more accessible. He also describes lessons learned through his two entrepreneurial experiences, and the benefits of now being an inventor into its futures group. As reminder, you can find the show notes for this and other episodes at our website, the AR show.com. Let's dive in. Alex, where did you discover your passion for salsa dancing?

Well, my father used to work in South America. And so the family would fly down there during holidays and vacations and Christmas time and that kind of thing. And down in South America, it's part of the culture. And so I saw a lot of that. And then one time when I was growing up in Canada, there was some salsa dancing, and I got into it, and then I got really into it. And then eventually I even started teaching it. But yeah, that's the origin story there. And as a teacher, Was this something you just doing on the sidewall while going to school? Exactly, yeah, I taught while I was studying my undergrad for my undergrad degree there at the University of Victoria.

Got it. And so at University of Victoria, what is it you were studying at the time?

Computer science, so I completed my bachelor's degree in Computer Science there. And then after that, I went to grad school at the University of Toronto.

And there, your study of computer science really focused on what angle

it was theoretical computer science. So that was always what kind of drew my brother Philip and I went through the program together. And obviously, computer science has many facets, and what got us excited was sort of the foundational theoretical underpinnings of all of it. And so these are kind of the classes that most people hate, but we love them. And they're sort of in many ways, the most difficult classes, there is very little about computers in them. It's a very math and logic heavy. And that was really our focus. And we got into that right away in undergrad. And that kind of paved the way for grad school very nicely.

And so as you were exploring those theoretical computer science classes, and as you kind of apply that sort of thinking, that sort of model to where we are today, as we talk about this world of augmented reality, this world in which we have all of these devices around us with compute capabilities, what does that look like? What is the sort of next evolution of computing look like to you?

Yeah, so I guess that's one advantage that the theoretical background gave us is that in some sense, it's, it's all the same. And history repeats itself technologically, in a very strong sense. So for instance, you can chart the history of computing. And there's the Von Neumann architecture. So every every modern computer kind of follows this von Neumann architecture, and they kind of all do, right. So But nevertheless, we've gone through these platform shifts.

Can you explain the basic von Neumann?

Yeah, so the Von Neumann architecture is basically the idea that you are separating your computer into different parts and that there's the brains of the computer, and that's like the CPU and you have its memory. And you have all of the different devices and peripherals connected along with what's called a bus. And the bus can be kind of considered to be a kind of a highway, if Wait, and the data is like cars running along the highway, and you have a central bus, and then everything is connected to the central bus. And the advantage of that is that you don't need to connect everything to everything else. Right. So you can think of it almost like a imagine the telephone has just been invented, and you live in a village, and you want to set up a telephone system in your village? Well, the naive way to do it would be just to lay a wire between every two houses. So there'll be a there'll be a telephone line from you to Bob, and you to jack and you to Susie and you know, there'll be there's 100 people in the village, you need something like 100 squared, divided by two number of, of lines. So it's growing quadratically, which is a lot. But if you're smart, what you do is you set up a central telephone exchange, which is of course exactly what they did. And then you only have to connect every house to the central telephone exchange. So then you then you call the central telephone exchange, and it routes you to the right place. And now suddenly, you'll need 100 connections, which is far, far less it grows linearly. And that's exactly the same idea with that with computers. It's just is that the the obvious sort of intelligent way to do it? And so

this sort of basic architecture, you see repeating itself every time we that's been the basis, I guess, for all the computing that we've done since the 40s 50s. How does that apply? As you kind of look out today?

Yeah, well, obviously, computers have changed in many ways. But in this one fundamental way, that change stayed the same. And so we've gone through these platform shifts, where, once upon a time, the computers were huge, and they filled whole rooms and whole buildings, and only corporations and governments and universities could afford them military could afford them. And then there was this huge revolution with a PC. And I would call that a platform shift. Or suddenly, civilians, every home, in principle could have a computer in it. And but it was the same creature was just much smaller. And that the pieces were still the same, like if you remember, the days when you used to have a box that sat on your desktop, and that was the brains of the computer. And that's where you had the memory and the hard drive and the CPU and the brains of it. And then you would connect all these peripherals like a keyboard and a mouse and a scanner and a printer and a monitor to it. And then there was another platform shift after that to the World Wide Web. And now the brains work just sitting on your desktop. Now the brains were kind of far away in some data center somewhere. But again, it's exact same idea is that those computers there were playing the same role that they always have. And then the next platform shift was the mobile revolution. And so the mobile revolution is again, your habit, you have a supercomputer in your pocket now. And we kind of forget that it's it's much less a phone that it is a computer. And it's housed in this beautiful form factor. So again, it's very easy to forget that this is just a von Neumann computer, and all of its peripherals are still there. Instead of a mouse and a scanner and a printer, you have a camera and you have a touchscreen, and you have a microphone and a fingerprint scanner, but exact same idea, just in a different form factor. And then the big million dollar question is, well, what's the next big platform shift that's coming? And our hypothesis that my previous company, serial was what we call the world computer? And we can we can talk more about that. But that's how I like to think about it.

And what is this world computer? And how does it? How does it evolve, I guess from or is it a complete break from what we have seen before?

Yeah, well, it's in some ways, it's revolutionary in other ways. It's just the same old bondwoman computer just on a different scale and in a different form factor. And I think the way this maybe connects to the topic of the show of augmented reality is that if you think about all the emerging technologies, right now, we tend to focus on them in isolation. So a lot of people talk about augmented reality, a lot of people focus on the Internet of Things, a lot of people focus on 5g and edge computing and wearables. And I have a different take on it. I think that it's not very helpful to look at these different pieces in isolation from each other. I like to look at it as being components of what we call the world computer. And so what I mean by that is like the whole world is kind of literally going to become a computer. And all of these different technologies, these emerging technologies that I just mentioned, they are just replacing the old pieces in the Von Neumann sort of the narrative that we've been talking about. So augmented reality, for instance, it's going to be the pixels, right? So augmented reality at its core is basically an output mechanism. So that the monitors and the screens of the world will be will be replaced by augmented reality. And so my friend Charlie think has this great way of describing it. He says that the whole world will be painted with data. So we'll have this AR cloud And you'll be wearing your ar, ar wearables your glasses, you'll be walking down the sidewalk, and you'll be this, this overlay on the entire physical world. And those will be the pixels. And that's going to replace the monitor that used to sit on your desktop or that with the touchscreen in your hand. And then the Internet of Things, is just the 1000s and 1000s, or millions of different devices being connected to the internet. And those are just replacing the old, all the peripherals that used to sit on your desktop, in the scanner, and the printer and the microphone, and so on. It are now being replaced by things like thermostats and baby monitors and sensors and self driving cars, you know, all of that's connected, but it's exactly the same idea. So that theme hasn't changed, right, and all of its going to be housed in data centers, that's what the brains of the computer are going to be, instead of on a sort of a local CPU, those aren't going away, either. You're also going to have local CPUs. So that the paradigm shift here is that there'll be edge servers, it's all gonna be connected via 5g. And so ultimately, the internet is going to be basically an infinitely long extension cord that connects all of these different pieces together. But it's still just a von Neumann computer, just like they taught me in undergrad. And it's just that the form factor and the shape and the scale of it and its capabilities are changing. And each of these different revolutions, they have their strike zone in terms of what type of software is native to them. So for instance, a desktop computer, you would install applications on your on your hard drive, and then you'd be able to run them. And then same with mobile phones, you kind of go to the app store, and you install these applications locally. And then if you go and you look at the worldwide web, that was a sort of a different idea, their their webpages and the World Wide Web, that's sort of the native type of application that you can run on that platform shift. And so when we look at the world computer, it's what runs what what we call spatial computing, natively, spatial computing will be native to the world computer.

So have the internet is the bus, it's the connector, we have all these different peripherals which are represented by IoT devices by automobiles and the robots that will be amongst us in the world. The glasses are really wearable displays. They allow us to interact and view I guess, the informations coming back from these sorts of things. While the internet is a physical connector, in a sense, like the data is allowed to commingle and communicate all of the other elements on this network, how do they actually speak the same language? What is the equivalent of HTML in this sort of spatial computing era?

Yeah, well, that's, that's one of the challenges. And that's one of the that's where we have partial answers, but not complete answers. So the Internet has already grown up with all sorts of different standards. So So TCP, IP, HTTP posts, that kind of thing. Those are, are those aren't going away. And in many, in many ways, we're going to be able to leverage that the fact that the internet already has many different standards for communicating along like the bus as you've described it. Now, that being said, there are pieces that are missing. And so there right now, there is no operating system. And there is no there are no nice tools, or programming languages that are that are designed for this world computer, right. So every computer needs an operating system. And on the desktop world, we had windows and DOS and Mac OS. on smartphones, we have Android, we have iOS. So computers need operating systems in order to manage all of that. And right now, the world computer doesn't have a nice operating system like that to facilitate that piece of it. And it also doesn't have the development tools, right. So development tools, and programming languages very much mirror the platforms that they're that they're working on, and that they're designed for. Right. So HTML is very much a worldwide web, internet type programming language and those types of tools. And right now, there aren't any really nice tools for that. And actually, that's what that's what my previous company experience was working on exactly that which is the operating system for the world computer, along with programming languages designed from the ground up for this new paradigm and platform shift.

That sounds like an incredibly ambitious undertaking for a young, early stage company. Why does that make sense in a startup, as opposed to one of these major tech companies going to beginning to chip away at this problem?

Yeah. So I mean, in one sense, it's audacious and a little bit crazy for a startup to try for a moonshot like that. in another sense, that's kind of what startups are for. Right? That's, that's the strike zone. Whereas larger companies, that's just not really what they're geared for. so large, larger companies have found their success. By definition, they're already making millions or billions of dollars, and they so they tend to be much more conservative in terms of the risks that they're willing to take. I think that's why you don't see these types of sort of moonshot ideas coming out of large companies. And that's why startups which have nothing to lose, because they're so small, they're backed by venture capital. So obviously, that that part, they don't want to lose, but they don't have a billion dollar business to lose yet. And I think that's just kind of the way things are. And large companies just aren't geared to chase those types of stars.

How is tackling this problem similar to or different from, you know, the last time we did this for the web with HTML and JavaScript, or the other sorts of attempts to define the language and define the operating system? Yeah,

well, you can think of the web as being almost an early version of the world computer, it's kind of a primitive piece of it. And it's got some of the pieces already in place. That where this becomes much more difficult is that the World Wide Web was designed for a homogeneous world, where all of the different computers were basically the same, they were all desktop computers. And so the World Wide Web, at least initially was was designed for a homogeneous hardware world. But now we're in a world where basically everything is connected, right, including your car, including your phone, your watch your tablet, your thermostat, your baby monitors, you know, you name it, all of that's connected now. And that's a much much hairier problem, because all of these devices have different form factors they all have. They're all different sizes, they're running different operating systems, some of them have screens, some of them don't. So it's just a much heavier lift to have that heterogeneity there.

And is you kind of think through how to tackle the problem, I guess, it's really important to ultimately understand how these sorts of devices are going to interact with each other. What are the sorts of experiences that you imagine will be possible by better connecting these sorts of devices together?

Yeah, and it turns out that that's actually where a lot of the treasure is, is that people often like to think in buckets. And when you're starting to when you start to connect different devices, you can find out that there's treasure in the gaps. And so it's kind of like when you connect to devices in the right way, the sum is greater than the parts. And, and we found that she had experience with the previous company, we worked with a lot of pro sports teams. And the the pro sports teams, they already had a lot of hardware installed in their stadiums, especially the new ones, right, they have all their ticket scans, their turnstiles, their cash registers, their beacons, or they have a jumbotron, like all of it is was connected already. But they were independent of each other, they weren't working together in any kind of concerted way. So they could do one thing over here and one thing over there, but there was no nice way for them to kind of combine the different pieces. And that's what we help them do with our operating system in our programming language, we help them be able to combine these pieces to create greater experiences that span the hardware. So a good example of that is our our augmented reality t shirt cannon. So if you've ever been to a pro sporting event, you've seen a halftime the mascot will go up there with a T shirt cannon launched t shirts into the audience, and everyone loves this. And what we did is we built the next generation of this for the next generation of fans. And so the idea was to use augmented reality and take the jumbotron. And the jumbotron, the big screen becomes your T shirt cannon, only in a virtual sense. And it's launching these virtual t shirts out at you. And then the fans all have to take their phones and they have to use their phones to catch these virtual t shirts. And so basically, what we were able to do there is leverage the fact that the fans already have the team's apps, they all have phones, they're all you know, they're all there. It's a captive audience. They're ready to be engaged. And we took the the jumbotron, which was used to be just sort of a dumb screen, and we made it much more interactive. And so by connecting the fans phones to the screens, we wanted to create something new and really cool and interesting.

And so let's break it down from the theoretical computer scientists in you. What are kind of the key elements that are happening in this, you have certainly have disparate hardware elements, but from from a programming language from the sort of elements you're trying to make available through this sort of language. what's what's happening?

Well, so we were taking pieces, like a jumbotron used to be a basically a glorified huge television. And so it was an output mechanism. But it was only an output mechanism. And there was no way to interact with it. But computers are interactive. And so what we are able to do there is to let you through the phone, which is now an input mechanism. And the phone basically became a mouse cursor for the physical world and you can now use it essentially to interact with these virtual objects that are being watched out of the jumbotron at you. So we turned it into a computer, whereas it used to be a television. that's fundamentally what happened there.

And so here there's some notion of direction spatial awareness in whatever is being launched, the same t shirt being launched, the left side of the stadium is not going to be also catchable by the people on the right side of the stadium is that that element in this type of interaction?

Well, it depended on what the team wanted to do. So basically, they have little dials that they could turn, and they get like maybe maybe they only had 400 t shirts to give away. And so and they have a stadium full of 80,000 people or something like that. And so we they have the ability to kind of turn the dial and make it harder or easier. So we gave them that flexibility. And if if too many people are catching it, they can also kind of end it early, they hit that kind of a stop button. So we gave them those that level of control so they could do that.

Got it? And can you show another example of where this sort of world computer meets reality here?

Yeah, so another good example is another game that we created for them, which allowed fans to play along live with the action on the field. And so the idea is that you're, you're, again, you're your fan, you're sitting in the stands, or you're or you're sitting at home watching on TV. And normally, it's a sort of one way street, you are watching the players on the on the field, they're playing their game, and there's nothing really that you can do to interact with that. And so the idea here was that we let the fans make predictions, we let them like in football, for instance, what's the team going to do next? Are they going to punt it? Are they going to kick a field goal? Is there going to be a turnover? That kind of thing. And now again, there was a return that into a two way street? And you could we took basically what used to be an analog thing, like the players on the field where there were kind of the analog, and we digitized it. And we made it interactive. And so that gave an extra level of interactivity. And I think this is the way that things. It's always an arms race, in some sense, right, is that this is the next generation of fans, they grew up with video game controllers in their hands, and they become a little bit antsy if they just have to sit there and watch a television broadcast in this one one button uni directional way, the way the grandparents did. And we we brought that video game, two way interactivity to that type of experience as well.

And as you are thinking through the right way to abstract this, so that somebody who comes along wants to create their own version of this type of experience, whether it's within a sporting arena or anywhere else, what is the modality what is the methodology you've come up with to help people describe what they want to happen in this sort of environment?

Yeah, so So I mentioned that we were working on the operating system, as we talked about that a little bit. But the the part that I haven't really talked about is the development tools. And so we also created a programming language called pebbles. And so pebbles is a no code language that was designed exactly for this world for this world computer where everything is connected. And and pebbles is a unique invention, and a very different type of programming language in that it didn't have a grammar or syntax. You don't write your code, you draw your code. So this is a programming language that's designed for everybody. But people who are more visually oriented, like designers, marketers, or product managers who of course, smart, that don't necessarily come from a technical background, they're able to use this programming language. And normally, they wouldn't be able to build anything and with pebbles they are. So we found a way to take kind of regular civilians and turn them into software engineers. And that's I think, important because there just aren't enough software engineers on the planet, hey, there's something like 25 million, which sounds like a lot. But actually, that's far less than 1% of the population. And even even in the United States, it's only something like 1.3% of the population. So it's a very rare skill. And it's an ever increasingly important skill, right is that once upon a time, our grandparents, they only needed reading and writing and arithmetic in school to then they could graduate and they could they could get by. But we now live in an effort ever increasingly, technical world. And now if you're lacking computer skills, and software development skills are still the most fundamental part of that you're kind of at a disadvantage in this new economy. And so that was our biggest goal there is to democratize software and software creation, so that so that everybody could have a shot at these high paying Silicon Valley jobs and so that everyone could write their own software as well.

Yeah, definitely a mindset that resonates broadly, this opportunity. And I think the no code movement in general has seen a lot of traction because of this, this idea that if we can just make add an extra percent with 3% of the population that can access this dramatically expands the opportunity dramatically expands the sort of things we can create the value we can create, using these sort of basic building blocks. But as you kind of envision this sort of No code environment, you notice very graphical, right? You're basically drawing pictures connecting boxes between each other. What in this sort of environment? I'm still trying to wrap my brain around? How, what is it that we're defining what the box represents what the connection between the boxes represents? What in the sort of Yeah,

well, in some sense, again, nothing has changed. And in some sense, a lot has changed. So the part that hasn't changed is that you're still writing software, but you're doing it in a very different way. So I think what we did, which was special and different, was that we kind of figured out how to take software programming, which is normally a very left brained, very analytical and logical exercise. And we kind of pushed more into the right brain. So the monkey almost never used the keyboard. And there's no grammar, and there's no syntax in the public language. And so you're drawing all of your code, you're drawing a diagram. And then you have these pebbles on there, and the pebbles are moving around. And as the program is executing, the pebbles move around, that changes the state of your program, and it causes actions to happen. So a good example of this is something like a loop. So in a normal programming language, in a grammar based programming language, a loop is a very abstract thing, Susan, computers is going to step through this loop until it actually breaks out of the loop. And you, you have to kind of keep that in your head in this in this very abstract way. But in pebbles, the loop is very literal. Like it's actually a circle, it's kind of loop on the screen, and you'll see the pebble moving around in the circle, as many times as you want it to doing a bunch of work. And eventually it'll it'll leave the loop. And so we found that, that people who are coming from a non technical background they were they're able to wrap their heads around that core easily, then it was done in a very abstract way. And so kind of getting back to your question, that part hasn't really changed the way we've done it is different, but they're still doing that they're still making a loop. Right? So there's still branching and there's still logic in there. Like all none of that has changed. But we've just changed the form of it or the modality to to make it more accessible.

Interesting. So as I am a user in this world, in the spatial computer, and I have a device that allows me to interact with this spatial computer, whether it's a device in my pocket, or I happen to be wearing some sort of wearable display, how do I know how do I know that the thing that I'm looking at, is interactable. It's available for me to interact with?

Yeah, so there, there are many different methods. And there is sort of the the early stages of it are with Gio, for instance, you can have push notifications, right. So if you walk into a geo fence, or if you walk near a beacon, your phone might buzz in your pocket and notify you. haptic feedback from your phone is another new type of modality of interactivity from from this world computer. And you could also have visual code. So for instance, if you put a QR code, people know that they could scan a QR code is just another what we call triggers. So these triggers are interaction points in the physical world. So geolocation is a type of trigger a beacon is a type of trigger vision recognition or a QR code, those are types of triggers. Audio recognition, like Shazam is another one. I think, ultimately, where this is going, though, many of these triggers are going to be replaced in with a sort of very native AR type of trigger in terms of vision recognition. So if you imagine walking down the street, wearing your, your AR glasses of the future, there will probably be a camera in there that will constantly be looking at everything and scanning everything to be able to tell you exactly where you are, because there will be this digital twin of an AR cloud that's being mapped to the exact experience that you're having. And so you won't need geo locations, you won't need beacons, you won't need QR codes anymore. All of it will basically be vision triggers, associated and stored in this AR cloud. So when you walk into Starbucks, you know, Starbucks location number 1234, the camera in your glasses will know that because it'll look at the unique configuration of that Starbucks location and know exactly where you are, you know, perhaps the system a little bit with with GPS or, or even your your IP address. And then when you look at the counter, or if you look at the shelf of products or something like that, it'll be able to take an action based on that. And so that this this new type of AR cloud trigger, I think will become the main one, it'll sort of supersede all the others which are available right now.

I could imagine being inside the Starbucks that there might be a lot of potential triggers. How How would you keep it from becoming visually overwhelming? How do you filter things down so that it's not too much data that the world is being painted with in that moment? That's an important question.

And so there are easy cases and there are hard cases. So easy cases are things like walking into a Starbucks walking into a stadium because or like A restaurant or anything like that, because there we can be reasonably certain that the person is entering the stadium because they're a fan, they're entering the Starbucks because they're interested in buying something. Much harder cases are ones where, for instance, you walk into Times Square in New York City, right? And there's 1000 different brands that are vying for your attention. And we just, we can't just have 1000 different pop ups come and overwhelm you, like you said, and so ultimately, this is this is a problem that solve solvable by machine learning. And it'll be kind of like Google, right is that when you type a search into Google, and I type a search into Google, we're not necessarily going to get the same results, because it knows something about you, and it knows something about me. And there's machine learning in the background that will curate it and personalize it. And it'll be exactly the same with the AR cloud. So when you do walk into Times Square, you might get completely set a different set of pop ups than I do. And and of course, sometimes the right answer is to do nothing. Because we also don't want this to be spammy, right? The worst thing we can do for everyone is that the system is bothering you, every five steps down the sidewalk kind of thing. Right? So oftentimes, the answer should be do nothing. And then even when it does something, it should be very accurate. And it should give you very few number of choices, like, you know, maybe one or two or three choices above the fold. And if you want to scroll down and look at more choices, that's fine. But less is more, and accuracy is going to be critical in this world.

Yeah. Yeah. This also kind of brings up this other challenge that I think people are talking about, which is the one around privacy, like how do we maintain our security, manage our identity, make sure that we have that the computer itself doesn't know too much about us, while still giving us the sort of relevant information that's relevant just to us,

right? Yeah, so so privacy and security are going to be major considerations here. So in the spatial computing world, it's more complicated than the previous platforms. And there's a whole new dimension of safety that we need to consider. Like when you're immersed in your air glasses walking down the sidewalk, and you're not careful, you could step out into traffic, and you could you could be hit by a car. So this is suddenly you, it's very hard to kill yourself when you're sitting in front of your desktop computer. And so that's, that's a major new consideration that that everyone's going to have to take into account, suddenly, human safety. And then there are obviously many digital threats that come along with that. But I think this is also going to be a big opportunity. For us as we're, as this new platform shift is happening. There's an opportunity here to do it, right. Like we've we've actually kind of learned from the previous platform, from web and from mobile, that there have been a lot of companies in Silicon Valley getting into all sorts of trouble and being hauled in front of Congress, for playing fast and loose with user data. So I think this is going to be an opportunity to do that correctly. And to do it in a way where users in a very transparent way, are owners of their own permissions and their own data in a very, very clear and understandable way, as well. And we did a lot of work on this at Experian as well, in terms of what should the security and the privacy model look like. And what we came up with, looks a lot like an onion skin. And the idea is that an onion has many different layers. And your personal data also kind of has many different layers in the sense that it's not all equal in terms of how sensitive it is. So things like your social security number and your credit card number. Those should be buried deep, deep, deep inside the onion. And those should require multiple factors of authentication in order to get up. And then near the surface of the Union, there are things like your food preferences. So in this in the world computer, one of the really cool use cases will be that you'll be able to take your preferences with you. It'll be like there's this invisible bubble of data that just follows you around wherever you go. So even when you when you go into a coffee shop, even if you've never been to that coffee shop for it'll kind of be like Netflix, it'll be able to make recommendations to you because people who are like you have shopped there before and and maybe it knows from other coffee shops that you've been to that your favorite drink is a vanilla latte. So when you walk into this new coffee shop, it might just offset microphone might buzz and they'll say Hey, would you like me to order your favorite drink of vanilla latte? And then you can one click order their unit even though you've never been there before. Maybe you walk into a restaurant, right? And again, if you've never even if you've never been to this restaurant before the maitre D might give you a menu and the menu what might be on an iPad. And now there's an opportunity again to bring your preferences with you. So it could resort itself can just like me Netflix and take the items that it predicts through machine learning that you're going to like the most, and put those at the top of the menu. And it can take, maybe you're allergic to gluten or peanuts or something like that. And you can just flag those or take those off the menu entirely. And so this new world, I think, is going to offer a lot of opportunities for like, really valuable personalization. And then the challenge, as you're pointing out, is how to do that right, and to do it in a way that preserves kind of security and privacy and so that the onion skin handles this. So for instance, the your your food preferences, your restaurant preferences, your coffee preferences, those might be those aren't nearly as sensitive as your social security numbers. So those might be way outside in the first layer of the onion. And then what you can do is you can say, Okay, I own my data. But I am willing to give all my food and reference restaurant preferences, to cafes and restaurants that are authenticated on the system. And I'm only willing to give them that little sliver of my data, and then out and then from then on, every time you walk into one of these restaurants, you'll share your data. And they'll give you something valuable in return in the form of a curated menu. And the key idea here is that the user with eyes wide open is able to make that decision, they're able to see, here's what I'm getting, here's what I'm getting. And that has been obscured sort of in for the past number of years. And the idea of the users and very transparently owning their own data and getting clear permission and understanding what they're getting as a big opportunity for us to get that right in the real world web on this world computer.

I really like this idea of the onion and the multiple layers of sensitivity. And in having different thresholds for when it's acceptable, and how much effort is needed in order to unlock each of those layers, depending on who whom it is or what it is that we're interacting with. And I you know, I think there's part of the security model that comes to mind is also how transactional and to where the data is being transmitted. If that data is used in this moment, by a local compute, who is understanding, okay, I'm going to deliver this vanilla latte to you because that's what you love. And that stays local, whatever this exact scenario is, but if that stays local, I feel a lot more comfortable than I know, they've just now absorbed that into the cyborg right into the central compute. And now there's the building a profile of me some central competing systems building this profile, oh, he's allergic to peanuts, he loves vanilla lattes. And he travels he only eats at Starbucks on Tuesdays, you know, whatever, whatever this sort of profile is. There's this other layer of how is the data going to be used not just what the data is, but I think is ultimately necessary as we think about this next iteration of privacy.

Right? No. And again, all of that has kind of been obscured in the previous iterations. So I think transparency is just critical here. And the only time because it's hard to reform that, right, because there's so many billions of dollars at play, and then entrenched business interest, so but if there's ever a time to reform that it's when there's a platform shift going on. And that's an opportunity for us to to get it right.

Yeah. As you were working at Experian, and building out the operating system, the underlying programming language, you had these concepts around how security would evolve and be rethought and the sort of thing, where were you in the process? What's kind of the next step in bringing this thing fully to life and making it generally available?

Yeah, the next step was to develop the language to a point where we could release it into the wild, and let let everyone stretch out to work with it. And so that's the part we just did. We never quite got to, but yes, thought that was gonna be the next step.

Now, you and the team aren't into it. What's special about into it? Why did you get excited about joining the Intuit team?

Yeah, so I mean, into it, in many ways, is aligned with the many things that we value. So it's a particularly innovative, large company. And you can't say that about all the companies out there. So not not every software company is very innovative. But But Intuit is a good example of that. So many of your listeners might not know too much about into it, but it's the it's the company that creates QuickBooks and TurboTax. And permit, maybe the products are a little more familiar than a company is. And it's actually quite an old software company, as far as software companies go. So it's about 37 years old. And it turns out that there are only there are less than 10. Companies like that, that have survived this long. It's really hard to survive for many decades, in an industry in a high tech industry that is constantly shifting. So that tells you something about into it, it tells you that it's a very innovative and well run company that managed to navigate these very disruptive platform shifts, and whereas others were capsized. So in the beginning into it shipped all of its software on floppy disks, and it was it was a company that was squarely aimed at the PC. And then the internet was invented and into wisely figured out how to take advantage of that and ship their services online. And then the mobile revolution happened. And now now, Intuit has also again, shifted in the right way to mobile and watham products or mobile. And then whatever happens next, whether it's the world computer or something else, you can be pretty confident, but at Intuit will remain very innovative and will make the right decisions there. So So all of that makes a lot of sense. And we've joined the futures team, and the futures team, even within Intuit is particularly tasked with looking over the horizon and looking around corners, and, and keeping our eyes open and inventing and watching the really innovative technologies and the shifts that are coming out there. The other part that I really like about into it is that it's very customer focused. So Intuit, describes itself as being customer obsessed. And I didn't really know what that meant before I joined. But it really is. So Intuit was the company that kind of invented or at least brought, what they call follow me homes, to the tech industry. And so the idea is, you don't just want to design products from an armchair, you actually want to go out. And you want to make sure that your hypothesis is right. So it's actually a very scientific way of approaching the problem, you have a hypothesis, but then you want to test it. So they will actually go and they'll put their products in the hands of their users. And they will just ask the users what they think about the product, they'll watch them, and they'll watch them use it. Because you learn a lot more by watching them by then just by asking. And then they'll take that feedback, and they'll take what they learned. And they'll go and refine their hypothesis. Again, it's very scientific way. And so this is very customer centric approach to product development appeals to me a lot. I think that's a very smart way to do it. And then the final part that I really like about Intuit is is how how trusted it is. So you know, there are a lot of companies in Silicon Valley's in valley that aren't trusted, and that have kind of burned a lot of the trust. But Intuit has maintained its integrity. And they have another slogan, there is integrity, without compromise. And Intuit very much lives this as you would expect it to because it's so close to taxes, like it really does need users trust, but it's been able to deliver that and maintain that trust. And that's an important value. There's

all of that resonated very much with with us. He talked about this, this follow me home, it's kind of obsession with a customer. I remember when I first learned about the Lean Startup movement, this is before Eric Reese, even with with his book, you know, he was a student of Steve Blank, then even before then there was already this sort of this notion that product development was being done very haphazardly. It's we're talking about the 90s here, even before at least in the commercial world. And in there were companies at that time, there were really Vanguard's in this perspective that were building products for customers. So if that's the objective, then we should really understand what the customer is what they do, what's really important to them, how the thing that we're building actually fits into their lives. And the only way you can really do that is to is to follow them home and to really study them. And I remember into it and the management principles that that company had done at the time for me early in my product management career, was really the shining example of how to do it, right. It's not easy, it's really uncomfortable, it's actually difficult to build that sort of trust with customers and really get that sort of deep insight and to be able to understand what that insight means and apply it effectively to a product. But that was always stood out for me from into it.

Yeah. And I think I think it's very easy for tech companies to fall into the trap, because tech companies, by definition, have people working for them who are very technical, you know, software engineers at the extreme end of the bell curve in terms of how technologically knowledgeable they are. And so if you put them in charge of product development, and let them kind of just run wild according to their instincts, they're going to end up building products, by engineers, for engineers that are great for the tail end of the distribution. But people who are kind of in the middle of the bell curve aren't going to like those products. And this is this is something that Apple did very well, right, Apple always designed products, sort of for the fat part of the bell curve. And they did that very well. And I think that into it also has figured out through this customer obsession and the follow me homes, how to how to figure out how to appeal to the normal person and build products that solve problems and help normal people prosper. Rather than aiming at the tail end of the distribution. I'm surprised that Intuit doesn't get more credit for it, quite frankly, is that you know, Apple is kind of Steve Jobs in particular, they're kind of remembered as being the company that brought industrial design to high tech, and rightfully they get a lot of credit for that because Steve Jobs fully valued that and now everyone does it. But back Apple is kind of the Pioneer there. And I'm surprised that Intuit doesn't get more credit for having been the Pioneer To bring sort of this customer obsession and the follow me home that that that methodology to high tech.

Yeah, the trend is definitely deserved. But I think Steve Jobs had a platform maybe that Intuit didn't create for themselves to kind of prophesize there and talk about what it is that they're doing that so great,

right? Yeah, no, fair enough. He was, he's a one man PR army.

Going back a little bit of experience, you had an experience wasn't the first sort of entrepreneurial experience that you've had, you talked about your brother and coming out of coming school coming your Ph. D. program, and at that time, you created a very different sort of company. Can you describe that experience? what it is you created? Yeah, yeah. My

first company, as you mentioned, is coming out of grad school, fill up, my brother and I were at the University of Toronto, we were completing our PhDs in computer science. And we were trying to figure out, well, what what should we do next. And I guess we had a bunch of options open to us, we could have gone into academia and become professors, we could have gone and worked at a big company. And instead, we decided to become entrepreneurs, and to start our first company, which was called volevo. And that was a digital wallet. And the initial problem that we were solving, we just saw how, how inefficient and how insecure payments were online. And that was the problem that we started solving. But it kind of partway into into that the the mobile revolution suddenly started. And suddenly the iPhone came out. And then we saw the writing on the wall immediately, we thought, Okay, well, the problem that we're working on is, is important. But we're on the wrong platform, like with the web is kind of yesterday, what we need to do is take everything that we're inventing here and put it on smartphones. And so that's exactly what we did is we built a mobile wallet. And then we made a go of that we tried to get funding, but the timing was terrible. So this was sort of during the financial crisis, and everything was melting down. And we were first time entrepreneurs. So it's difficult to get funded, if you're a first time entrepreneur because you're an unknown quantity. And we couldn't raise 5 million, let alone the 50 million, we would have needed to kind of revolutionize the payment space. And so we've kind of shifted strategies there. And we ended up approaching Google and and being acquired by Google, and then joining the team there. And then what we built kind of fed into and help help create Google Wallet.

Yeah, that's great. That's quite a great spot to end up as you kind of build out that first entrepreneur experience that first bit of technology. Yeah, as you kind of think back on that first entrepreneurial experience. You were talking about the financial crisis, which is 2007 2008, which is a really difficult time for a startup. I was in a startup at the time. And I remember that our funding went backwards. It wasn't just that it dried up that we couldn't get any more. It's the the funding that we had been committed that the time disappeared. It was a really difficult time for a lot of startups. What were some of the things that you that you felt you got right, in that period?

Yeah, I mean, selling the company was certainly right, that wasn't written in stone, we easily could have failed. So we definitely got that right. I learned a ton at Google. So I definitely value my time there just that was our first time working at a big company. And so kind of making a lot of connections, learning how that works. All of that was really valuable. So I would consider the learnings to be, you know, much of what I what I value there got right?

And it was a difficult situation. But is there anything that you, as you reflect back would have done differently, that you would advise another startup CEO to do differently if I were to encounter a similar situation?

Well, I mean, almost everybody in the payment space that I've ever talked to, recognizes that it is a very difficult space. And so that I'm not sure I would ever build another startup in the payment space again, just because it is it is so competitive, and it is so difficult when there's so many entrenched players and and so on. So I my advice would be to, to choose an industry where that that's easier. And there are some industries that are very difficult, where there are entrenched players, there are others that are there, greenfields and if you can find a Greenfield and there are no entrenched players and nothing getting in your way. That's it, that's a path of less resistance.

Is that what appealed to you as you're at Google and thinking about experience was that it was more of a Greenfield opportunity?

Ah, I'm not sure I calculated it that thoroughly. It was more that we had our PhD research all of our all the technology at experior was actually an offshoot of our PhD research. And it had been sitting on the shelf for four years, as is often the case, we never actually thought it was going to be used for anything, but we found a use for it. And that was sort of the the appeal was to finally take our PhD research and put that to use what was the trigger? What were you seeing in the market or

in the technology at the time that told you that was time to take that off the

shelf? Well We constantly saw this pain point that there just weren't enough engineers, no matter which team you're on, I think this is probably a universal statement. Like in every, every software company on Earth, every single team, you could give them all sorts of more headcount, you can give them many more engineers, and all of them will be able to find work for those people and probably need even more. So engineers are always a bottleneck. And the and we saw this At Google, we saw this everywhere, we were actually in part of Google, that was kind of the creative part of Google. And that creativity was never the bottom of it. Lots of creative people come in with really, really cool ideas, but don't like something like 3% of it ever got built. And that was purely because of a lack of engineering. And so the pain point that we saw there was, well, what if we could empower the creative people to build the software themselves, so they didn't have to beg for help engineering headcount that would solve this problem. And so that's what kind of motivated us

that initial impetus was, could have been taken in different directions right there, it was more about no code, how do we reduce the barrier to effectively building with this sort of foundational computer chunks? You took a kind of this other thing? Like, what is the next sort of computer? Was there something else that kind of triggered that next leap?

Yeah. So while you're at it, if you are going to go and build a new computer programming language, where should you aim it? Should you aim it at the past? Or should you aim it at the future? And so we saw all of these emerging trends in terms of internet of things in terms of augmented reality? And we thought, well, while we're at it, why, why? Why should we aim at the past? Let's use it to build augmented reality experiences, let's use it to connect IoT devices, because that's, that's, that's what's gonna be important in the future.

Yeah, that was a very ambitious goal. At that time, especially as you can reflect on the experience experience, was at the right time, one of the things that I think startups really struggle with, and especially for somebody who is really forward thinking, right, the goal of an entrepreneur is often to create the future that you want to live in. And sometimes you're a little bit too far ahead of the curve, or, you know, yeah, how did you feel about what the timing of what you're trying to create relative to where the rest of the market was?

Yeah, well, that's, that's always the danger, right? As an entrepreneur, there are some factors that are inside your control. And there's some factors that are outside your control. And I think the whole augmented reality industry or even the Internet of Things industry, we kind of we were all expecting the world to change faster than it actually did. You know, we were all kind of expecting that sometime in the year 2017, maybe we would all have augmented reality glasses. And same on the IoT side, we expected home automation to take off much, much faster than it actually did. And so in that sense, you that's one of the forces of nature, that's kind of outside your control. And to your point, are you going to be on the leading edge or on the bleeding edge of it? And so I think probably most companies, especially in the consumer facing AR and IoT space, would say that they were more on the bleeding edge of things, and that we all kind of peaked a little bit too early. And then, you know, hopefully, in the next few years, someone's going to build an awesome set of miniaturized AR glasses. But I think everyone was expecting that to happen sooner than it did. And we saw what would happen with magically is that they made a good run at it. But again, everyone was maybe just a little bit too early. Yeah,

we all know there's an S curve coming, or at least we all anticipate, right, that we're gonna we're gonna hit this truly exponential part that's going to look very vertical or very linear up. Yeah, we're just not quite sure where that's going to happen. It's always about three years out, it seems.

Yeah. And that's, that's the part as an entrepreneur that it's very difficult to get, right, because it's not within our control. And you can, you can kind of make an educated guess. But if it doesn't happen, it doesn't happen. And with almost every company, probably the most important part is the timing. And that's, that's just luck, and you can't control for it.

You know, as you think about your own evolution as an entrepreneur, how have you changed the most?

Good question, I think, I think I probably listened a lot more than I used to. I think, you know, everyone always is in love with their own ideas. And I think it's very easy to do that. And to, to kind of barrel ahead with blinders on. And I think I or at least I hope that I've learned to maybe listen a little more and, and to, especially when surrounded by smart people giving advice to really absorb it and reflect on it, rather than, you know, be convinced that you're right. And so I think that's the sort of biggest lesson for me is to look at, learn to listen,

that's a really tough one. It's really tough one, it actually even ties back to what Intuit had ultimately learned. Because I think that in general, it is our nature to build the right idea that we have inside of our own heads. And sometimes the reality is that The idea can be made better, the more we're able to share it and hear the perspectives of others.

Yeah, no. And I think I think that's the same problem that maybe scientists have had forever, right is that they have their own hypothesis. They're in love with their own hypothesis, they go out and test it. At the end of the day, though, reality is a cruel mistress. And it doesn't matter how much you love your own idea. If reality or if the market doesn't like your idea, well, then too bad, you're wrong. And so that's a it's better to learn that lesson early and to get ahead of it early, and maybe not to be so attached to to love your own ideas so much that it blinds you. Yeah,

yeah. Let's wrap up with a few in lightning round questions here. Sure. What commonly held belief about spatial computing Do you disagree with?

I think one thing that I the way that I view things differently, and we've talked about this a little bit is just that everyone tends to look at these different emerging technologies in their own buckets, and not as part of a larger hole. And I like I like to compare it to the old parable of an elephant in the dark, like, imagine an elephant in a dark room, and you have three people touching it, and one person touches the trunk and says, Oh, I'm touching a snake. Right? The next person touches the leg. That's all I'm touching a tree. And then the final person touches the tail, there's, oh, no, this is a rope. Right. And so I think we've kind the kind of fall into that trap, too, is that people look at IoT in isolation, or people look at AR in isolation, I, to me, that feels a lot like the people touching the elephant. And actually, if you just take a step back, and if you look at the bigger picture, you see that they're part of this larger hole than what I've been talking about is the world computer. And I think that's maybe where I differ from, from a lot of people, I don't like to look at these technologies in isolation from each other.

Yeah, I think that as as builders, so many of the people listening are builders in this space, it sometimes feels like such an ambitious problem, just to solve the one little piece we're focused on right here in front of us. And it's hard to kind of have this larger, holistic perspective, especially when you feel like you have so much less control over the other pieces. Right, but it's necessary, because for us to plot the right course, we have to appreciate how these things are going to come together.

Well. And I think I think it might not be just sort of a nerdy observation, either. I think there might be something fundamental going on here. So like, if we look back at augmented reality, at least on the consumer facing side, as well as IoT, you're going to at least on the on the consumer facing side, why is it that they haven't taken off the way that we had hoped. And maybe a good analogy here is again, imagine we went back to the PC days. And if you have a scanner and a printer and monitor, and let's say you've tried to connect the scanner, and the printer and the monitor together, but you didn't have that box on your computer to connect them all together with. And I think that's kind of the trap that people are falling into, is that we're trying to build AR, which is essentially a monitor. And we're trying to build IoT, which is essentially the peripherals, but we don't have the computer or the operating system figured out. And so I would say that that's a prerequisite. And to me, it seems very clear, because all these things are the same. They're all just about knowing the computers. And if it's clear to you that you can't just connect the scanner and monitor together in the sort of the PC era. It should also be clear that you can't do this with AR and IoT, you need sort of the intermediating, bus and CPU and all that the operating system and the dev tools to bring it all together. So if you had to kind of point and analyze, like why things haven't taken off, I'd say that, largely it's because those important ingredients are missing.

Who's poised to solve that part of the problem today?

That's another good question. I think in principle, all of the big companies out there like Microsoft, and Google and Amazon, apple, you know, they all have tremendous resources. And if they wanted to, they could just sort of snap their fingers and bring all those resources. And if they could get them to kind of pull them the right direction. They could easily solve this problem. But that's, you could say the same thing, though, about Microsoft during the 1990s. Right? Like Microsoft used to be the world's only tech giant, and it dominated the PC era. And if they played their cards, right, they could own the internet today. But that's just not. That's not how big companies work. Right? Is that Along came a little startup called Google and ate their lunch. And Microsoft easily could we had way, way, way more resources and easily had they played their cards, right. And then it happened again, right? If you look at the mobile revolution, you know, Microsoft was an operating system company, they should have and could have built Android themselves.

They did. They had Windows CE and they had Windows Mobile, they dominated mobile computing up until they did

Yes. Well, that's true. But they also had a start button. Yes, they did, right. And so they fell into the trap of taking their desktop operating system and just trying to squeeze it into a smaller form factor, which, which Steve Jobs immediately understood was the wrong move, right. And they could have been they should have, but they didn't. And they had the resources, they could have spun up 10 different projects, or 20 different projects, each trying something different if they wanted to, right. But for some reason, that never happens, it's always some little startup like Android, you know, or, you know, I wouldn't call Apple startup at the time, but for different reasons, they were thinking in very innovative way. Sometimes, for some reason, it's always the little startups that come in and figure it out. And not not the big companies. So I in some, I think a lot of it has to do, ironically, the fact that they do have so many resources, and they do have a golden goose to protect. And so they're not willing to take the big risks and make the big bets and think very differently. And so I guess it's going to be probably a little startup and or it'll be income, it'll, it'll just be incremental improvements by big companies until they finally figure it out.

So it might be a long road still in front of us.

It might be unless some well funded, very intelligent startup, you know, with the right timing goes and figures it out.

Yeah. Besides the one that you've been working on the last few years, what tool or service Do you wish existed in this spatial computing market?

Well, I think everybody's hungry for a really good pair of AR glasses, that and like that, that just doesn't exist yet. And when it does, that will be the game changer. And hopefully, the catalyst that makes everything take off. So, you know, there's obviously rumors that Apple is working on stuff, and they're obviously a great hardware company, and you'd expect that what they believe is going to be very good. But that's sort of the single linchpin here is the actual hardware in the form factor that you can wear, that also doesn't sort of have a social stigma and makes you look weird. It has to be fashionable, and, you know, reasonable and, and lightweight and have the right battery life and and shooting power. Once we get back right, then maybe we're off to the races.

Yeah, that's a hard combination. But maybe it will be the one that that figures it out.

Well, and edge computing may be part of the solution here is to offload some of the heavy duty processing to a nearby edge server. And that might be a sort of a stopgap.

Yeah. Yeah. What book Have you read recently that you found to be deeply insightful or profound?

I read a book called broken genius, which is the story of William Shockley. And he's, he's not very well known. But he was the inventor of the transistor. And in many ways, in a Silicon Valley wouldn't exist without him is a the invention of the transistor. But B he's actually the person who literally came to Silicon Valley and started his company, Shockley semiconductor there. And he was he was a very successful talent scout. So he's the one who crisscross the country found all the smartest people are nice and and more like he hired all the Intel guys. But he was just a, he had many flaws, and just personality wise, he won the Nobel Prize. And he was very arrogant man, and bitter and hard to work with. And all of the Intel guys left, and they made Fairchild and they make Intel and everyone knows that story. But then Shockley kind of ended up ruining his own reputation. And he, he was a eugenicist. And so I found that to be an interesting story, because he was clearly a genius. And that's the title of the story of a broken genius. But it's always interesting to see someone who's so smart, but also so dumb, or so flawed at the same time. So and also ties into the history of Silicon Valley. So well, so in some sense, almost all of this companies in Silicon Valley and the the venture capital, they grew up there. So it all kind of came from that initial seed that he brought. And so it was an important part of the history. And it's just sort of sad to see that he ended up becoming a eugenicist and sort of ruining his reputation. Now, if he if he hadn't done that, they'd probably be a statue to him in every town and city in Silicon Valley. But he he ruined that for himself, which is just kind of sad, too bad.

Yeah, Shockley when he was at Bell Labs, that that was the breakthrough. That was the tipping point in our evolution in digital computing.

Yeah, yeah. It's arguably the transistor is arguably the most important invention since fire, right? It's just that if ever there was a civilization changing invention, that was it.

Yeah. Even in this week's Apple announcement, they're talking about their new Apple, silicon, the one chip, there is still a measure, like they're still measuring this and how many transistors have we crammed into this microprocessor? Because that's still such a critical element ultimately, and creating all the competing technologies.

Yeah, I'm always I'm really impressed with the hardware people like we in some sense, humans are much much better at hardware than we are at software and There's the miracles that those people work on tiny, tiny scales is amazing to me.

Yeah. If you could sit down and have coffee with your 25 year old self, what advice would you share with 25 year old Alex?

Well, I mean, there's the obvious thing I would do is tell myself which stocks to invest in now. And if a client side I know which, which ones are going to go up and which ones are going to go down, right. So there will be that obvious advice. But joking aside, I think I already touched on this a little bit, I think I would I would tell myself to be a better listener, and maybe explain to myself how to do that. And you know how to carefully reflect on what smart people tell you and you avoid making a lot of mistakes that way.

That's a tough one. Maybe something that Shockley also could have learned a little bit from. Right.

Yeah, for sure. Yeah.

Alex, any closing thoughts you'd like to share?

Yeah, I mean, I think that that, like I said, ar was supposed to have peaked by now. And it hasn't. But I'm still very optimistic that we're not far off. I think, in the next few years, we're probably going to have more breakthroughs. And I think that as, as the hardware comes, becomes mature, and as more connections are made, and more of these pieces are filled in, whether it's an operating system or the Event Bus, I think that this world computer is going to happen. It's just really a matter of when not not half, but I remain an optimist and that it's not gonna be too far away before some of the important pieces of the puzzle start walking into place. You know, where can

people go to learn more about you and your efforts now here into it?

Yeah, I'm not particularly active on social media. The best way to connect with me is on LinkedIn. So just look up Alex, your towel on LinkedIn, you'll you'll find me so feel free to send me a message there.

Awesome. Alex, thanks so much for the conversation.

Yeah, thanks for having me on. It's been great.

This was the last episode of 2020. And the first week of the new year, I'll post an episode sharing my perspective on where we are as an industry and some thoughts on what we may see in 2021. Please subscribe to the podcast so you don't miss this or other great episodes and happy holidays.