Join Zach Gardner, Chief Architect @ Keyhole Software, as he sits down with Jesse Tayler, CTO @ TruAnon and inventor of the App Store, to discuss his career journey in the Next Level podcast.

Jesse recalls how he got into programming with “borrowed” chips from MIT, through his foray into Internet-based food delivery, into novel use cases for GenAI in 2024. He also tells the story of when he presented the App Store to Steve Jobs. Jesse concludes with his advice to other introverts on how to become a CTO.

~~~Chapters~~~

00:17 Intro to the “Next Level” videocast series
02:26 Jesse’s career path 11:33
Interesting projects Jesse has worked on
25:16 Novel use cases for GenAI
34:33 Jesse’s path to becoming a CTO

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• YouTube: https://youtu.be/O91KyJPzldA
• Apple Podcasts: https://podcasts.apple.com/us/podcast/jesse-tayler-app-store-inventor/id1742833688?i=1000654312640
• Spotify: https://open.spotify.com/episode/1ciKB1I4A6wlMWuAYGF0iz?si=e45018f9a21a4cd1
• … or wherever you get your podcasts!

About Jesse Taylor:

Jesse Tayler has a storied career in Silicon Valley as the startup, crypto inventor of the first App Store which he personally demonstrated to Steve Jobs in 1993. His work spans digital solutions from pioneering early-web solutions to modern online identity verification, marking him as one of the most generally prolific software inventors of his time. His work includes the first ever private equity trading platform on the web and pioneering the first geo-coded food delivery, also a Steve Jobs related startup and curated in the Smithsonian Institute under inventions of the 20th century. Jesse’s lifelong contributions are widely recognized for changing the world of computing, with popular inventions showcasing the impact of his larger-than-life role. Jesse is a book author relating to expert software process and is retained for talks on technology, training and coaching for startups in their early and growth phase. Jesse is also a musician with a composition currently streamed on Spotify and iTunes.

About The Next Level Series:

Next Level is a Videocast for Aspiring Engineers with Keyhole Software’s Chief Architect, Zach Gardner. This series dives into the pivotal question every software engineer faces: what direction should my career take?

Like many of us, Zach grappled with this dilemma until he found guidance from incredible mentors. Now, Next Level brings these insights to you. Zach interviews tech leaders, delving into their diverse career paths and success stories. Spoiler: careers in tech rarely follow a straight line! Discover the stories, challenges, and strategies behind these industry giants, all aimed at helping you map out your own journey.

See All Episodes

Partial Episode Transcript

Note: this transcript section was created using generative AI tools like YouTube automated transcripts and ChatGPT. There may be typos, slight content changes, or character limits for brevity!

[Music]

Zach Gardner: Ladies and gentlemen, welcome to “The Future.” My name is Zach Gardner, the Chief Architect at Keyhole Software, and I have a very, very interesting guest with me today on my program called “Next Level.” It’s a videocast really geared towards the next generation of technical leaders—people that have fewer gray hairs than I do, people that are trying to find their way through this amazing, sometimes uncertain, landscape of technology and software development. Do they want to be architects? Do they want to go the staff engineering route? Or do they want to put on the suit and be part of the business, the bane of every developer’s existence? I don’t know; I don’t have the answer. But I do have a group of people who are willing, able, and very insightful about what their career journeys were like, what they’ve gone through in their time behind the keyboard, and maybe what recommendations they have for the next generation of technical leaders.

The person I’m talking with is the one and only Jesse Tayler, Chief Technology Officer at TranOn. Did I remember that right?

Jesse Tayler: That’s right, like QAnon but it’s the opposite.

Zach Gardner: Very, very good. Yeah, that’s QAnon; that’s a very different podcast from this one. So before we begin, just a reminder to our more litigious audience members: the views and opinions expressed in this program are those of the participants and do not reflect their employers, their trade organizations, or any yacht clubs they are affiliated with. Which, I don’t know if you are—I’m not—but it’s okay. This is just two dudes talking, just having a good time, that’s all.

So, to get us started off, Jesse, why don’t you give the audience kind of your background? Where did you start programming? Maybe what was your first program? People often like to reminisce about the glory days. What are some of the interesting projects you’ve worked on? That’ll get us teed up for the second question I have for you.

Jesse Tayler: Excellent. Yes, well, I got a very early start in computers basically by being one of those electronics kids in my generation. It was right at the crux where integrated circuits were beginning to be cheaply available, especially if your mother happens to work at MIT. There were chips that you could liberate from certain university projects. In fact, there were people at the university who I now realize are famous AI people like Marvin Minsky. As a kid, I had no idea, and that probably served me well.

I remember understanding engineering and electronics things. At MIT, I got my hands on the first sort of terminal, one of those big printers with the green-lined paper that is very wide, with the little holes on the sides of it. These things would just roll onto the floor endlessly. I looked at this device, they let me open it, and I looked inside. I pressed the letter G, and the little dot matrix print head would roll out of the way so that you could see what you were writing, then it would come over and print the G. I thought, “There’s not enough wires. How could this thing possibly work? This is total magic.”

I was stunned, and I still remember that very moment. I thought to myself, if you could do that… I saw that the keyboard had all the wires going into a little integrated circuit. This had enough memory to keep track of eight or ten keystrokes, and it would kind of pipe them into the serial clock when the opportunity of the computer arose to get that data. It would just boil down to a handful of wires; you could have this plug in between the keyboard. I thought, these things are the most profoundly interesting thing. Electronics is fine, but these machines—I want to know.

I think for a lot of computer people, there are so many smart engineers, and they love writing code and all that goes with it. I don’t really know if I’m one of those. Some of these folks are a lot more talented in engineering; my skills are very good, but some people are profoundly good. I realized that I have a need for the end result. I don’t like debugging; thinking that hard is not all that much fun. Frankly, I’d rather play golf. But there is this drive that comes in some personalities, especially those with the entrepreneur’s disease. Even as a young kid, I looked at that situation and thought, I want to make the computer do things, things that I want. I don’t want to have to explain to somebody else or listen to their explanation as to why it can’t be done. Therefore, I will learn it down to the bits and bytes, the chips and the assembly language, and figure out how compilers work.

I had no ambition in startups or building commercial software. First of all, I was too young to understand any of those kinds of ideas, but also it just wasn’t my motivation. So, my personal story, both in terms of being the kind of engineer that I am, and now in my 50s, I can say that I’ve spent my entire career doing early-stage software invention, startup stuff, trying to make new things. It’s an unusual origin story but one that’s really kind of a lot of fun. At this juncture, I realized that generation kind of came and went, and my children grow up with iPads and have a totally different world view.

As an inventor, I was blessed to be in a generation where I thought all the good stuff had been invented. But it turns out there was at least one career’s worth of more stuff to make. So, that’s how I got my start—fascination.

Zach Gardner: Very cool. It echoes pieces of what I recall from early in my career. The first computer we had in my house didn’t come from MIT; it came from a Gateway store, if I remember right. I remember my pops showing it to me, walking me through it because he had used them at work, but this was our first home computer. I don’t know why I vividly remember this, but it has stuck with me ever since—the screen saver. Realizing that I could right-click, go to display settings, change the screen saver—to me, that was the light bulb moment. I had the agency; if I could just figure out how to communicate with this machine correctly, I had the ability to actualize changes that I had in my brain and see them represented on the screen. For you, it was the letter G; for me, it was the screen saver.

There always seems to be that origin story behind all of us. One thing you said that stuck with me, too, as you were talking, was the idea of whether every problem has been solved. When you got into programming, there was that mythology of, “Oh, 10 or 16 megs—why do we need that much RAM? We could do everything we need to in eight.” I was actually talking with one of our co-founders about when they used to have to install software on floppy disks. You couldn’t deploy it through the web or have over-the-air deployments; everything had to be done over a floppy disk. We were like, “How big were those?” I think they were the 512-kilobyte ones, which today, I don’t know if I could even have an image that could be stored on there.

The folly we often get ourselves into is thinking that all Ls have already been uncovered, all problems already solved. Two years ago, something like ChatGPT and generative AI—no one had any idea of the amount of disruption that would cause or even what novel problems it could solve. I always appreciate talking with other people who get that the best problems are the ones we have yet to solve and maybe even yet to conceptualize. I’m curious if you can give the audience a little bit of background on some of the projects you’ve worked on. Do you have any patents to your name? Because I don’t, and I always like talking to people who do. What are some really cool things you’ve worked on over your career?

Jesse Tayler: You make a good point, and I won’t dwell on it too much before answering your actual question. I can’t remember the guy’s name, but email was invented, at least the one that transfers between computers. The at sign is really what makes email work. It is a username at a computer name. It is the simplest invention. As a kid, I viewed that as the last great frontier of software invention, having no inkling about the web or the app store or any of these other important things that would come very shortly. I felt like I was about 10 years younger than the Bill Gates-Steve Jobs generation and felt like I was just born here in the Upper West Side of New York City. It’s not Silicon Valley. I was 10 years late; email has already been invented. It’s all over.

But, of course, that turned out not to be true. Today, my calling card moniker is “App Store inventor.” It’s a funny, ironic, interesting story. We invented that product on the same computer that the web was being invented on at the very same time. These two inventions have a lot in common. The web is there to freely transfer information between computers, and the App Store is there to protect the rights of artists online. These two things now marry so beautifully and are kind of the legs on which the mobile computing world of the 21st century stands—20th-century inventions. The guy who invented the web has been knighted by Queen personally, I’m still waiting. I, I, you know, I, I think Kamala Harris is gonna call. But, in all seriousness, it was not the kind of thing where… I should say we understood what we were doing and we truly believed that the future would be electronic distribution. This was not in question for us. But you have to understand the way people reacted to it. Microsoft was just becoming very big when Windows 95 was on the horizon. Already, Windows 3.1 had just crushed WordPerfect and Lotus 123. The message was to sell software, which was previously worthless. IBM didn’t sell software; they sold you hardware and gave you the software because people didn’t want to buy nothing. They wanted a box, a disc, a book.

So, you walk around and tell people there’s a lot of efficiencies and advantages in a purely digital solution. You’re taking something digital, putting it on a floppy disc so you can put it on a shelf, and people can take it home and install it themselves. That seems normal—that’s what everybody did. We drove to a store called Egghead and bought software. However humiliating that may seem, that’s the way we did it, and we liked it.

A lot of times, you innovate and build things, and most of your time is spent communicating. How do you get people to understand a future vision that doesn’t exist? These things are very easy to see in retrospect, but sometimes very difficult to convince your peers.

I’ll give you an even more humorous and awful example. This wasn’t my invention, but I was one of the early geo-expert engineers at a company called CyberSlice. That gives you an idea of exactly what era this company was formed—like Terminator one. CyberSlice’s business was to deliver pizza online—geo-coordinated pizza. In other words, you would put in your address, we would understand where you were physically on the planet, find restaurants nearby, understand their menu, and you could actually pick a pizza with toppings and say go. This had been shown in a movie. Rather embarrassingly, people laughed at the scene where the character ordered a pizza online. The founders of the company literally saw the movie and decided this might be possible.

But the amount of money that it took—there was no Yahoo Maps, there was MapQuest, so there were some online maps, but we couldn’t use any of that. We had to buy the geo data from the source, which was like a million dollars a year to rent. It was a preposterously expensive organization with large numbers of extremely skilled engineers building out fantasy-level stuff.

Now you might think, well, you guys had Grubhub, and boy, the whole world is delivering right now—BCS must have been all over you. Well, now, this isn’t my thing to pitch, but I’m often in the room. Just imagine trying to sell this thing. We’re in the room with a real VC, going through this thing and showing how all this technology works. This guy’s getting irritated. This is really a lot of technology and expense, and the numbers are incredible. We make like 50 cents on every delivery, and he’s just like, ‘You people are so stupid, I don’t even know how to explain to you.’ So he starts giving that arm on the back of the chair, holding his face, and at one point, he just interrupts and says, ‘Wait, let me show you an invention. It’s called the telephone. Here’s one right here. Oh look, I pick it up. Hello, Bill’s Pizza? Yeah, I want a pizza. And when the guy comes to me, I give him 10 bucks at the door.’ That’s the problem you’re trying to solve? I don’t think there’s a problem, and I think this invention’s already been here before.

Needless to say, we didn’t do real well on that particular venture pitch. But, you know, that’s almost all of what you hear from people when you’re shepherding something to market that’s worthy. It’s almost by definition. And frankly, I’m glad I wasn’t really the one doing that demo. I got to demo the App Store to Steve Jobs. He was like, ‘I like it,’ and walked away. That was, you know, so we didn’t have a lot of interaction. He got the idea, and I didn’t have to have a really nicely honed demo with fancy words. I took him through the engineer—the 20-year-old engineer’s idea of why he should want to do electronic distribution. This guy selling the geo CyberSlice stuff was a pro—a pitch master. We had backing, we had seriousness, we had millions of dollars already, and yet we were still laughed out of the room.

So, yeah, that’s a lot of my success stories are muddled with failure, I guess. I think that is one of the true marks too of a senior software engineer. I’ve seen some people that have been somehow promoted from a junior to senior with one and a half years of experience, and I always kind of wonder—the true measure of a senior should be how many times you’ve brought down production. It should be measured in how many mistakes you’ve made because you get to understand and have a much better appreciation for the complexities that are just inherent in our field. There’s no way to predict all possible variables, so don’t even go through that futile exercise of trying. There’s no way to appreciate how something that you spend days, weeks, hours slaving over will be received by someone, and they’re just like, ‘Oh, okay, yeah, that’s cool, that’s fine.’ But something that you spend maybe a few minutes on, they’re like, ‘Oh wow, this is totally revolutionary.’ There’s just no way to appropriately explain that to people outside of the field.

The CyberSlice thing—I love the name, by the way, like A+ on the marketing for that—it got me thinking about one of the use cases I’ve heard about for generative AI, which wouldn’t be a video cast in 2024 if generative AI didn’t at least make a cameo appearance. In the future, which might even be quarter three 2024, there’s the potential of having consumer-grade conversational AI that allows us to, as consumers, interact with other humans but not have to do it directly. So, you would have an app on your phone—maybe it’s called CyberSlice, don’t know if that name is still trademarked—but you will just tell it, ‘Order me a pizza, and it needs to be pepperoni and it needs to be gluten-free.’ I don’t know why you’d subject yourself to that, and just, ‘Get it here in 30 minutes.’ The conversational AI will call using that very futuristic technology that the VP or whatever his title was, was talking about, and it will do audio-to-audio translation and actually have a chat with a person on the other end, giving them your address, what you want for your order. Then, if there are no anomalies or exceptions, it can just hang up the phone.

The crazier part is maybe that pizza place will then have a conversational AI that interacts with the people calling, so we might have a human talking to an AI, that AI talks to another AI, and that other AI puts into the point-of-sale system what the actual order is. So, the only pieces where you actually need a human are to describe the need, and then on the other side, once the need has been translated into the system, be able to actually fulfill the need—either through baking the pizza, delivering the pizza, yada yada yada.

I’m curious if there are other generative use cases that you’ve heard about or thought about, things that maybe aren’t getting enough air time. Other than CyberSlice—trademark.

Zach Gardner: Yeah, I think there’s a clear capacity of AI to do certain things that structured programming doesn’t do. One example that jumps out in my mind—or two, really. One is when we used to shop at Blockbuster Video and we’re able to sort of glance around and take in enormous amounts of data. We’ve fallen to a keyword search, and however good that is on Netflix, you know it’s crap. Everybody knows it’s crap. When you look for a piece of furniture, most of the time you can get the dimensions, but jeez, what does it really mean? It’s hard to actually see how those measures conform to the space that you need to fit that couch in. It’s really hard for you to look at the couch and see if it fits. In fact, half the time when you order from Amazon, it comes in and it’s about a third the size you thought it was. You’re like, ‘I didn’t know they made these this small.’

So, I think there are so many advantages in AI providing that cooperative assistance. Keyword search will either be dramatically changed or perhaps just unimportant in most cases. It’s much better to say, ‘I don’t like green and deco is out,’ and have it understand. The interesting aspect of that scenario is, of course, now Amazon is going to provide you with a very smart AI to help you find that couch, but they’re also going to guide you to the couch they prefer you to buy, as they do with keywords. So, that’s fine, but then AI could be—let’s say—rather persuasive. Yes, AI might ask me things, I might give it information that maybe I didn’t realize what it’s doing with. So, when I interact with Amazon’s AI, I better have my AI doing the work, and my AI better be trained to defend my privacy and my interests.

Now, I have a funny story and then I’ll follow that up with something that I’m sure will give you some nightmares. A funny toon I recently saw is with email communication, where the gal says, ‘Look, AI can take my bullet point and it expands it to a whole email as though I had written it.’ And then the receiver says, ‘Look, AI takes this giant email and reduces it to the one bullet point.’ This is the funnier version, but I imagine having a personal assistant who’s trained to be extremely persuasive, to know my preferences, to go out and order my pizza and order my furniture is really cool, but what if they’re still an idiot, and they have no capacity for judgment or thought? Because it doesn’t—nobody said they were intelligent, they were just artificially intelligent, or they were imitating intelligence. And so, the best thing I can imagine doing is also then providing my assistant with a very careful review process, a sort of senior judgment, to make sure I don’t end up doing something really dumb. And I think those kinds of scenarios get very interesting.

And I think, particularly, we’re at a phase where the focus will be on things like fraud, scams, and AI-based systems can be trained to identify fraud and other bad actors, if you will. But it’s also possible that AI systems will facilitate fraud, provide very difficult ways for those AI-based systems to protect themselves. A lot of these things will be a balance, which will be a bit scary, but it’ll be extremely important to make sure AI is working for you and not working for you against you.

Amazon AI is actually interesting that he brought that up. I saw something last week where someone was able to, and I actually looked it up—it’s still in beta as of the recording of this, March 19th, right? It was still in beta, and for whatever reason, my account has not yet been selected to have that feature flag turned on. But someone was able to convince the AI to give them a discount; it was like a 7% discount on a Mac, which, I don’t know, I was going to ask it, “Could you give me a 100% discount?” and see if they would honor it.

I mean, one of the airlines, maybe it was Canadian Air or Air Canada, someone was able to convince their generative AI chatbot to give them a 100% discount on their airfare. And the courts ruled that they had to honor that because it is a representative of your company, and it is just as legally binding as if a customer service rep, a human being like you or I, gave them a 100% discount. There is a piece of this that gives me hope: Amazon’s AI for recommendations. After I buy a toilet seat, it invariably asks me, “Hey, we’ve just seen that you’ve bought a toilet seat. Do you want to see these five other toilet seats?” And it’s like, “No, I just got a toilet seat. Maybe you want to show me toilet brushes? Maybe you want to show me toilets? Maybe you want to show me toilet paper?” But if I’ve already bought a MacBook Pro, I don’t need you to recommend me other MacBook Pros. So there is hope; we’re not extinct, we have not been made superfluous at least as of this recording.

To kind of wrap this up, I’m curious if you can provide recommendations for what are some of the things that you thought about as you were going through your career journey, as you were going from the zygote of a developer that looked at the letter “G” on a keyboard through the different evolutionary stages. What made you want to become a CTO instead of a manager, instead of staying on the individual contributor route? Where was your head at as you went through this journey?

Jesse Tayler: You know, I would like to take a lot more credit for directing my career than, um, you know, reality. You can almost tell by my origin story that I somewhat stumbled into a career of even being a software computer scientist. I was going to be in the band, you see. Turns out that you need all kinds of talent for that, which I just didn’t have. But it turns out people would pay me for software. This was something that my girlfriend at the time alerted me to—that apparently beer is not pay, and that in fact, I was not a professional musician, I was a computer scientist. This was literally a true story and shocked me.

So I think I ended up sort of taking on that CTO role partially because of the maturity of the industry and my age. Even at a young age, I knew more about computers than most people. Having access through my mom, who was an English professor at MIT—not cool, but you know, it gave me access that young people, you know, in grade school, don’t get access to computers like that at my age.

And I think because my personality is a little different than a lot of deep-thinking engineers, I was able to talk with the engineers, work with the engineers, build and invent with the engineers. I liken things to a band. You go through a period where you practice your instrument until your fingers bleed, but then you come to a point where you can just stand up and play, and suddenly it becomes much more productive and creative. Suddenly, there’s a sound. The same thing happened in software. I got to a point where it was like orchestrating, and typically the team was like, “Hey, send Jesse to the board meeting.” And I began to learn how to communicate to non-technical stakeholders and found that because I’m a regular kind of person in my everyday life but also have this experience and depth of engineering, I was able to train sort of the two halves of my brain to cooperate a little better.

And that’s a lot of what good CTOs do. They can inspire the engineers; they can attract great engineers; they can protect the principles of engineering in ways that non-technical people aren’t going to be able to really understand. And you need to be able to present confidence to non-technical stakeholders—transparency, visibility—so that they have comfort looking into a black box. Let’s not forget, software is invisible, right? I can’t see it either. We design a construction that we all agree looks like what’s in there. And this is the part that CTOs really get their heads around because that’s the part that communicates to the customer, the designers, the engineers, the marketing people—right? Everyone who has a stake and interest in the outcomes of software production who is not in that engineering team. And that’s most of the world.

So it’s a very tricky job. I think there’s a lot of different ways to do it. I clearly come at it from the hands-on experience. I’m with you in the trenches sort of thing. I’ve seen people who go to school, come out, and learn how to manage technology and engineers, and it’s a very different way to do it. But some of those people are also successful at being CTOs, so I don’t know what the recipe is on the other side. I can only describe how I ended up getting there.

Zach Gardner: No, no, that’s good to hear. I mean, I too love the marriage of technology solving business problems, being able to understand technically how to set up large architectures, understanding on the other side what it is that we’re actually trying to solve, doing things like ride-alongs and shadowing. I mean, I too, like you, love living in the trenches. There’s no place I would rather be. So, Jesse, thank you so much for your time, dude. I really appreciate it. It was really fun; it was a good time.

Jesse Tayler: Absolutely, Zach. I would love to talk geek with you anytime. And hey, this is the year to chat about AI, so whether or not we know what we’re talking about, we’re gonna talk anyway, right?

Zach Gardner: Oh yeah. Oh yeah. No, I definitely, next time I make it out to the city, I definitely owe you a chicken over rice. Or, I hope you’re a white sauce and a red sauce kind of guy because that’s the kind of guy I am.

Jesse Tayler: Come to New York not for the weather, but you know, yes, we’ll share a drink. And honestly, all the good stories are a two-drink minimum, so your audience is just not gonna get to the good stuff unless they come to New York.

Zach Gardner: Come for the Halal, stay for the drinks. It’s a good way to end the show. So no, it’s been a blast, Jesse. Really appreciate it. Ladies and gentlemen, we’ll catch you in the future.

[Music]