Afraid AI Will Replace You? Here's the One Skill It Can't

James Villarrubia went from building AI for NASA's drone and aerospace programs to becoming CTO of a travel tech company. In this episode, he and Mo get into why curiosity might be the most important skill in the AI era, what happens to our brains when we stop pushing back on the answers we get, and why the people most resistant to AI might actually be seeing something the rest of us are missing.

Guest: James Villurbia, Group CTO, AnyRes.com / Former Head of Digital Innovation & AI @ NASA CAS

🔗 Podcast: https://alice.io/podcast

Follow the show so you don’t miss the next episode.
New episodes every two weeks. Stay curious.

Transcript

SPEAKER_00 0:00

I think there is something unique to AI. I think there's something unique to the ecosystem of AI in terms of like development and its like rapid increase in only a couple years. I think we as a community of professionals just working in the world, like haven't maybe had time to adjust to the rapid uh growth and investment uh in AI. It's everywhere now. But the fact that it's everywhere, I think speaks to sort of this universality. It's sort of a major technological innovation that people find ourselves.

SPEAKER_01 0:26

If AI has ever made you stop and think, wait, what is happening? You're not alone. I'm Mo, and I'm a security researcher asking the same questions. On Curiouser and Curiouser, we're having open conversations with experts, researchers, and leaders working at the edge of this space, talking through how AI is taking shape, what's shifting, and how people inside the work are thinking about it as it happens. So join us and listen in as the conversation takes shape.

SPEAKER_00 1:06

Hello, everyone. Uh I'm James Villubia. Uh I'm a former uh presidential innovation fellow for AI at NASA, uh, hence the rockets. Uh sadly, uh Moe is wrong. I did not build rockets. Um I built AI for the team that did planes and drones and all the things in the atmosphere that people more interact with, um, which I think is actually cooler because uh there's you know more things you touch uh that we worked on uh that humans actually experience because only a few people ever get to go to the moon, uh to the moon or fly in spaceships. Uh so I, you know, I I like the non-s uh the non-spaceship side of NASA. But I'm excited to be here, uh excited to talk about all sorts of things.

SPEAKER_01 1:45

That's cool. I didn't even know there was a non-spaceship side, to be honest. Like I just know that there's like freeze-dried ice cream, and that's pretty cool.

SPEAKER_00 1:52

So the group I worked with was sort of um like a like a weird research group within the aerospace side of things. So, like, you think about like what's the future of drones? What's the future of like un you know, unmanned aerial vehicles, and like can, you know, will New York ever have like air taxis uh like that fly me from building to building? Um, those sorts of like weird, crazy ideas. Well, someone's gotta be thinking about them and someone's gotta be thinking about like, what's the policy regulation there? What's the safety concerns? Uh, how do we deal with the wind tunnels of New York, you know, uh streets, et cetera? So lots of cool stuff.

SPEAKER_01 2:25

And then now you're actually like the CTO of Is It Your Own Thing that you're that you're doing now?

SPEAKER_00 2:31

It's a collection of uh private equity uh rolled up uh travel companies. So we do a lot of data uh analysis trying to basically uh provide the sales and data services for all those small businesses around the world that, you know, like, hey, I'm I'm renting a kayak or I'm renting uh, you know, I'm doing a sunset cruise on my honeymoon, uh, all those things that, you know, Airbnb or uh get your guide or uh you know, all those sorts of companies they sell, they're not the ones actually providing the uh that data service, uh collecting credit cards, et cetera, on behalf of those customers. Uh they're usually buying that data from someone else. Uh so we are uh we are sort of one of those big companies uh that helps the small businesses actually, you know, play in the big ecosystem of the internet and also help you know people on their honeymoons or their travel, et cetera, have you know cool local experiences.

SPEAKER_01 3:16

So basically, yeah, I was gonna say something about Airbnb, but no, I think it's a lot better than Airbnb. Or it sounds cooler.

SPEAKER_00 3:22

It's a partner to Airbnb, right? Because they great, you're renting your house, you know, they're there, but like, but like, oh, I want to go, you know, do uh like a, you know, it could be some, you know, some guy who sells uh who's doing local tours, right? He has a tour business, like, hey, you know, I was in Lisbon, for example, uh, with my wife a while back, and we found a small company, uh, didn't know we were actually, we it was right before it took this job. We were using uh the uh one of the companies that was our customer of ours, and we were trying to book like a small local tour. It's like it's a guy, it's his like electric vehicle, he picks us up, he knows our names, he gives us like a personal tour of like two or three of the castles around Lisbon, uh, like in the various periods in history. I was like, this is this is awesome, this is perfect. I would have never found this. Uh, I would never booked it if it hadn't been so easy. Like, great, put in my credit card, book the time. You know, here's where he picks me up at my hotel. It was just all seamless uh to have like a really good experience. Um, and I didn't have to know anything about Lisbon beforehand. It was just great, we'll we'll take care of that for you. Um, and that's the stuff, that's the experience that we want to provide uh to the end user, the you know, the the tourist, et cetera. But also we want to make it easy for the businesses to like manage all that. So uh yeah, it's not, it's not uh we are trying to get more AI uh involved, and I think that's why they brought me on, because obviously the travel world, everything's getting you know consumed by AI. Uh but you think about sort of like AI helping you plan your trips, try to find those really weird niche experiences that are like that are really good fit for you or for your kids or your wife or your you know partner, whoever it may be. Like that's like navigating and finding those things and setting them all up is is a complex problem. And I think you know, AI is the feature of that. And hopefully we'll we'll bring this this company there.

SPEAKER_01 4:55

It's been fun to see how AI has kind of made its way into the consumer space in this way, right? So like it started off as like, oh, well, where's the best place I can go? And it was just doing like basic searching, and then we got agents, and it's like, okay, well, book this very specific trip for me, right? Where you go and now it like books the trip, but it might be one part of the trip, right? Now the next step is like more complex. You get a couple more steps in. How do you actually go and build like a travel experience? Oh, well, let's do all these couple of things. What do you like? Do you want to center it more around food? Or do you want to go visit more things, or you know, depending on what you want, and then go find flights for it. And then, you know, it's so cool how like everything's evolved and how we've seen kind of AI go from chatbot to um this planner, then go to actually executing on these things, and eventually travel almost a travel agent, um, for like a full stack travel agent from like, oh, you're going to all these places, let me find all the hotels for you. This is actually in a chain that is connected to your like Amex card. So why don't we use, you know, like it's crazy. Like the amount of complexity.

SPEAKER_00 6:05

I see you've got the Chase Blue Platinum card, and you get points if you stay here, and like we've negotiated and the point value is this. So, like, this is actually a savings, even though the dollar value is like it can do all that for you way faster than you could have otherwise. Um, yeah, no, it's it's the world is changing very fast in this regard. And I think part of the thing that got me excited because my background before, even before NASA was social impact startups in education, uh, healthcare, et cetera, and a prior stint uh at uh in the Obama White House uh and you know around around the you know Obama administration. So there's been this like you know public policy ben. So it's like, okay, this travel company is this really you know exciting for me. But I also saw that like AI is coming for all these businesses because you know, you used to be, oh, hey, I can Google, I can go, I can search all these things and find a tour or something that might be of interest to me. We had to put in the work, but it was still there. AI has made that work a lot easier. It's like, oh, just ask and it gives me a recommendation, but just one, maybe two. So like AI is sort of making a lot of choices on behalf of us. And I don't necessarily know if all those models are treating all of the sort of vendors, all the tour guys out there with the same, you know, degree of uh, you know, balance. Like, hey, like, are they pushing everyone to the same vendors just because that's the where the LLM sort of like that was what is what got remembered by the LLM when you know in the training process? So I think there's a there's a concern that a lot of small businesses will just get sort of left behind uh in the sort of coming AI replacing search. Uh and I think it's like, well, if we can help them tackle that and stay out of that and give people better experiences, give them more optionality, um, it's a it's a win-win for a lot of small communities that depend on tourism around the world.

SPEAKER_01 7:42

You built also um an AI, an educational AI for X Prize. I'm wondering how you feel about like AI in terms of reducing the or how people are using AI. So you you equivoc uh equivocated it to it like search, right? But I think we're seeing a lot of use cases where people are relying on AI for answers and like not just like going to and finding the answer from search,

AI Is Rewiring How Our Brains Form

SPEAKER_01 8:10

but then going, copying, pasting that same answer and just uh kind of downloading it as knowledge into their own like you know, memory.

SPEAKER_00 8:19

It is changing. I mean, I think there's some great psych papers out there right now. Like the use of AI is actually changing how brains are forming, how like neuropathways are forming, how we retain information. And I don't know if we've quite figured out, like we can see early on, we can see what's being diminished. I don't know if we quite know what is being grown. Like, what are the new neural pathways, uh, the types of analytical thinking that will like will have to be respond, will have to grow and respond uh in this sort of world of AI. But I think it's something we should we should be conscious of for sure.

SPEAKER_01 8:52

We are relying on AI so much. And like you said, um there was actually this really good podcast I was listening to a while ago, um, like the the cognitive something. It was cool. Um, but uh in one of the episodes, it was this educator who's or this researcher who was talking about um how there was a study and they gave people AI and they limited other another group to like not using AI, and they wanted to find out like who was learning better or whatnot. And it just showed that like people who were using AI in a way to like just get answers were actually performing worse than people who were like using AI in a sense to uh more give them um leading principles to find their own answers, right? So it's more like using AI as a guide versus using AI as the the you know the source of truth.

SPEAKER_00 9:47

There's a sort of a skill that develops in like I think all career paths, um, where eventually sort of you can sort of smell smell bull. Uh or you know, like you've been around like I don't know the answer, but that feels wrong. Um, and you put in the sort of the 10,000 hours mindset, you know, uh was it Malcolm Gladwell? Like you, whatever the framing is, the idea that like eventually you are exposed to enough things that like your brain actually does start to pick up on a lot of like subtle notions, subtleties that you maybe can't put to words, but you're like, this something's about this is off, and I want to like, you know, click in there a little bit more, see, answer, ask a few more questions. That is something that really comes with time, but that really is the skill that is best applied to AI, because AI is wrong a lot of times, mostly because it just doesn't know what you really want. It doesn't have the full context of the situation, and trying to give it all of that information is tedious and time consuming. So you sort of like summarize what you need, ask it, and it gets like, well, I think based off of this, here's you know, here's regress of the mean, here's sort of like the the optimal point. But it can't know everything. And if you can't push back, and that's where I think that sort of the 21st century skills in a lot of sort of the educational circles where they're focused, like we need to build AI to help kids have 21st century skills, which is mostly critical thinking about how to use AI, uh, how to push back on AI, how to have in you know, interrogatory, inquisitive question and answer sessions with AI saying, oh, it gave me this answer. Where do you think it might be wrong? Okay, well, ask it. Okay, well, like how do you know that the second answer was correct? Um, there was a great study that came out, I don't know if it was a great study, but it was a study that came out um a few weeks ago where it said, I think uh what is it, 50 to 60 percent of AIs will change their answer with just one pushback. So, like, hey, you know, plea, you know, like we recommend A. Or like, I don't know about A. It's like, oh, okay, you're right, we recommend B. And it's just like without hesitation, because they overalign on like trying to make people feel good, uh, on like their answers, like, oh yeah, you like my answer as opposed to the answer was correct. Um, and until we, I think, as a culture learn to think like how we converse with AI in a way that is sensitive to that like high variance, uh, high willingness to change answers, like we will struggle. People will struggle. But the people who are naturally maybe a little bit distrusting, I think will succeed. They are, they are sort of coming to the game with that sort of critical lens. And also, I think people later in their careers who've developed that critical lens can also do it. And I think that's why you're seeing a lot of more sort of the entry-level light-collar jobs are struggling, because it's it's hard to it's hard to hire people without that sort of growth yet. But you know, I think we as a culture will have to find a way to upskill those people into that critical thinking without having them, you know, waste 20 years doing the grunt work.

SPEAKER_01 12:36

I think one of the big parts about that too is ensuring that there's material that people can actually use and access. And um, again, like there, there's still like these tech deserts, right, in in the United States, even right, where people just don't have access to the technology, or they don't even have access to learn about the technology. And they kind of get it through this trickle-down effect where they hear rumors about it, or they see like the second or third lay um costs of the of the technology. So I am bringing this back to actually your project, which I want to I want you to also describe a little bit more. But it's been described as like the Wikipedia um like a new a new version of Wikipedia. At least that's how I kind of looked at it. Um again, it and I could be wrong about this, but it seems like it was just a self-improving textbook.

Building AI for Education

SPEAKER_00 13:27

Yeah, so this was a project uh called Mount Cleverist, uh, because puns were really, really cool at the time, and we we just loved that. But it was, I think, decidedly ahead of its time. Um, because we were hacking together without the billion dollar budget, we were hacking together NLP and AI models, trying to build um what could, you know, like you could knock out overnight right now with the current models. But the idea was um that schools, like there's so many there's technical tech, as you said, tech tech deserts, right? There's a lot of schools um whose you know, procurement process to get tech technology in the classroom is very slow, the tech that they're buying is bad, uh, or um it's they're disproven. It's like not, you know, it doesn't have like a lot of statistical validity. Most software used in schools does not have any strong efficacy analysis. Basically, like you can't prove that the students were smarter and better and retained more at the end of using your tool. Like almost none of them have that. It is very hard to do from a like science statistics perspective. The data is really hard to collect because you got student privacy rights and a lot of all these things. And the companies that can sort of both afford to operate at like educational tech margins and hire the data sci data, you know, data team to do that, there's a few and far between. And like uh, you know, the sort, you know, like Duolingo was able is able to get you up to a certain level. And like they, you know, have have, I think, you know, uh struggled to get to that next level of fluency. And a lot of companies say, hey, look, they, you know, even they, the top, this huge company, still is having trouble. So I think our goal with Mount Cleverist was to say, okay, well, how do I get something in the hands? There are there is all this information out there. It's Wikipedia, there's all these textbooks, like something more engaging than like the crappy textbook from the 1980s that, you know, like says our current president, Reagan, uh, that's being you know, hand-me-down uh in these sort of you know, rural school districts. How do we get something in them that is better, but not assume that our choice of what better is is always gonna be right. So the idea was well, we're gonna scrape internet content that the teacher selects, we'll build questions, and we're basically gonna assume that any content we create or pull and any questions we we you know pose or generate are just bad. That helped because our AI wasn't very good. So the questions were actually sometimes really bad. But the idea was that it would, as people, as students took it, and that maybe not every question they got exposed to was like a really good question. Sometimes it was just like a bad question. It was poorly formed, uh, hard to understand. But over time, we would be able to pull out like, oh, which questions actually led to long-term retention? And over time, you would start to see this like this slow growth of like, okay, well, what do those questions look like? How do we repeat those questions? Okay, so every time a student took a test, it improved the quiz for the next student and then the next student, the next student. And that it wasn't limited to one classroom. It's like anyone who ever, you know, our classic example is like War of 1812. Anyone who ever took a quiz on the War of 1812 would benefit from every teacher and every student who'd ever had been asked any question at that like grade level about the War of 1812. And like being able to sort of have this like this slow building roll-up of true efficacy-backed, uh statistically backed uh education and games was like was the dream. The problem was that the AI, like our AI, which is like we couldn't afford to get like to the top top-tier models. Um, and then COVID hit, uh, and like we had shut it down. I thought God, God bless. Um, I was working for two startups at the time, one in remote work uh analytics of how to make remote work profitable. Um, and then the other was in online education uh for like asynchronous online education at home. Both of them shut down in November 2019 because the investors are like, we don't think these things will all take off. Um that one, those, those sting. So yeah, so the the remote work one obviously, I think uh the the stuff that we wrote, the the stuff we published became like the default policies for I think like Deloitte and Facebook and all these big companies like just took it wholesale. It's like this is all policy now. Um, and they had some success. And the education, you know, investors call me up six months later, like, hey, is that still thing still thing still alive? We we use it. It's it's it's perfect. This is your this is your moment. It's like, well, so we shut it down. Like it's it's it's gone. Well, do you think that is the future of education and AI? Is that thinking that like this comes back to this conversation where you were talking about sort of AI and its growth, like there's a if you start with the assumption that what the AI is producing is distrusted, um, and it's like it's it's not great, but if we can sort of collectively start to, as a as a culture, as an organization, as a community, start to say, hey, but like this was good. This is what good looks like. Hey, everyone, let's learn what good looks like. Let's all share this learning. Um, suddenly we can sort of move forward. And that that applies to like all uses of AI, including this, you know, one uh in education that was just very sort of specific example. Uh, but is I think tied to this, like the next step that we need for all of these, you know, all of these industries that are trying to tap into AI is is saying, great, yeah, it's not great now. It will eventually get there. And the the fastest way we can sort of get over that hump is to share our learnings and to sort of cooperate. It doesn't need to be sort of like, you know, eat or be, uh, you know, kill or be killed out there.

SPEAKER_01 18:32

I think having something that's more focused around continuous learning, but is also able to iterate, super important to just keep people like the neuroplasticity, kind of keep the connections alive, right? Because we don't want to lose that ability to like have to actually critically think and then actually go and read the material. Um, I think there's a lot of interesting stuff with generating new questions.

SPEAKER_00 18:55

I think you hit the nail on the head. So I've been working with a uh a great uh nonprofit as part of the American Education Research and Development Fund. Uh it's a big, big, big fun sort of DARPA for education. It's a group called Augment Ed, and they are like focused on this research problem and they're they're you know kicking off some great work. But I think one of the conversations that we had uh sort of the early days of design was sort of thinking, well, like what is the modern experience with AI look like for homework for students? And it's like, well, if we've always said, hey, we can ask a question and the student will give us the answer, and we're basically measuring, did they memorize the answer? It's regurgitation to some degree. And having those sort of like deeper essay-based questions is harder. It's way harder in earlier grades where like writing isn't as strong. They don't need to have the skills yet to communicate those sorts of uh ideas. But it's always been a gap that we have this like just memorize all the facts, move forward. Um, that has been a problem in our education system. I think AI has like said, well, if that is, if cheating is effectively with AI so easy that everyone has sort of a copy of the test from the prior year, then the test is useless. Just stop pretending, stop trying to make the test more useful. It will never be useful again. But what then becomes useful? It's like, well, great. Have it, you have it, you know, and ask the question and say, okay, well, what questions do you have of the story for your to kill a mockingbird? It's like, great. Well, here's here's something like here's a statement that's like someone made about to kill a mocking bird. Do you agree? Why or why not? And if you can have an AI actually not give the answers, but just like probe. Uh it's like, well, I don't like what in the book makes you think that? Like, hey, like what was a part of the book? Like, you know, cite your source effectively, but like they don't have to like memorize the exact lines, but having that sort of interrogative conversation in a way that feels fluid and easy for you know a fifth grader or sixth grader or eighth grader, whatever it may be, to have, uh, not overly burdensome, not super technical. And the AI is not like, you know, it's not some strict rubric because you're like, I just want to make sure that like, did the student engage enough to ask some critical questions? They had a novel idea and maybe they were rewarded that. Oh, no one's asked that before. That's really cool. No one's posed that idea. Like that sort of reward structure is again only possible with AI. So I think, you know, the way out of the, oh, students are all cheating on tests with AI these days, is like, yeah, well, the only way out at scale is really to get AI more into the classroom. Um, and I think my concern for the next you know, 20 some odd years is that education will take, takes that long for like those sorts of innovations to really take seat in the in the ecosystem. Uh so the kids going, my kids, uh, you know, Gen Alpha, who will be going through school without, well, not, you know, with a lot, like call, as you point out, a lot of diversity in the accessibility of various tools and technologies. Like, there's we're gonna see a real chasm between sort of the haves and has nots in education. COVID pointed this out. I mean, my dad, like, even as of top top tier schools, I was, you know, talking to schools, like, well, how do we, how can we help? How can I help? Even if we deployed some of my my AI tooling, it's like, yeah, well, not every household has a laptop. Certainly. Not three or four laptops, one for each kid. No. So okay, now we have to take turns. Okay, well, you know, 9 a.m. to 11 a.m. is the sophomore's time to get on class. And okay, then you know, then near my fifth grader hops on because there's only one shared laptop. Well, they all have phones, but oh, the phones can't do this because none of the software was designed for phones. Because the for years, like, oh no, no, we don't want foo phones in schools. And then COVID comes along, and phones are the only at scale accessible device in the hands of every child to actually do that. But they weren't, none of it was designed for that. So these are sort of like all the weird things that like the gotches that are gonna come for this ecosystem that AI is, you know, gonna introduce more of. Um, and this is strangely, this is the stuff, these are the questions that that group at NASA, like this is what we asked, like this trying to play out these games and come up with solutions, usually to like drone and and and aerospace problems. But we were also looking at like, you know, at education, like what you know, what does AI's impact on education do to the crop of aerospace engineers that we coming up in 20 years? And will we have enough to support you know the space economy for the US? They're all all this is you know interrelated.

SPEAKER_01 23:09

At the beginning of our conversation, you also mentioned there's another neuropathway that we really need to get ready for. And we don't really know what it is. Um just this morning I was having a conversation with our CTO, and we're working on a couple of really cool projects. And one of them was like trying to figure out well, how do we like um we're trying to build like this agentic solution? How do you think about agents in how do you solve an agentic problem, right? Um, you have to solve it agentically, but the only way you do it is by understanding agents enough to be able to implement them. So, how do you learn about agents if you're not iterating and playing with agents? And I think this is like where we get to this new skill set that people need to have. And we've seen it pop up on job boards where it's now like AI coordinators and just AI engineers, which are basically prompt engineers, right, with extra steps. And we're seeing AI implementation engineers, which are engineers that know how to plug AI into workplace processes. So, you know, you've advised people to play with AI enough until you see it fail, right? And understand why it's making those mistakes, which is awesome. I think this is actually like a perfect example of like a new pathway that we need to start building, which I don't really know what it's called, right? Because this iterative motion isn't something that I think we've had to deal with, it's usually question-answer. But now it's like like, what if something is doing all the work for you? What is what does this new observability uh kind of pathway look like now that you need to become more observant? As humans, we've always been observant, right? It's uh it's biological, it's what we needed for survival at some point. But at some point it feels like um the technology was very much like okay, you do one thing, you give me a response, all right, we'll just keep going back and forth with this until something works. But now it's like sit back and watch the screen is what a lot of people are doing. So it doesn't mean that this is the worst thing that can happen. Um, it might mean that this is the next step in how we think about where engineering is today, where it needs to go. Like, how do we evolve this? Um, but I guess, in a sense, how do you kind of get people comfortable with playing enough with these tools and failing in an environment where they feel like they can experiment, right? And I can imagine that this is something that you had a challenge with at NASA for sure.

Institutional vs Human Language

SPEAKER_00 25:38

I mean, I think one of the things that excited me about uh when when you all reached out about sort of coming on the show was like the title itself, like Curious and Curiouser. And I was like, great, like I I talk at length, uh like when I give keynotes, like it's almost always like the title is Curiosity in the age of AI. It is, it's the focus. And I think that has been sort of the linchpin deciding factor of like I think people's ability to adapt in the current ecosystem. People who are like, I saw AI, it's like, nope, I I it's it's too complicated or it's too scary or I don't trust it, and I'm just gonna put it to the side. And then people are like, oh, I don't trust it, and I don't know why. Let me figure out why I don't trust it. Or uh, oh, that worked really cool. How did it work? That's like that's that saved me 10 minutes. But I wanna, I before I make built my whole life or process around this, like I want to understand the limits of it. And that that sort of willingness to ask that sort of second order question, not the oh, did it do what I asked, but like why or how? How far can I push it? Um, the second order level, which to me is just like just sort of core level curiosity, not the thing in front of you, but what comes next? That is gonna be the I think the deciding skill. Um and it really is, I think the the way that we sort of get to a better feature is trying to figure out uh from like a leadership perspective and orgs out there trying to do this, like, well, how do I foster curiosity in my team? Um, and that's a that's a hard question. Because like, hey, we've we've you know, we've been we've for so long focused on I want you know, work ethic, work ethic, work ethic. And now it's like, well, I, you know, if I have a AI coding all day, every day, and it's just stopping to ask me questions, the volume of code that it's producing might be the ex almost the exact same as everybody else. Fine. What distinguishes me from everyone else, a good developer from a bad developer? And like all the tools that for the last 20 years the ecosystem has been building, like to track software development as an ecosystem, or you know, like, hey, like how many, you know, uh how many changes did you make to the code? And like, you know, how many features did you deliver? You know, very straightforward metrics uh when like that was all that we could think about. Now that doesn't that doesn't really matter. And I think there's so many other places in ecosystems and industries where like it's gonna be hit. I think software is getting hit real hard right now with this problem, but it does go back to sort of your point of like the the skill that I see where people are thriving. And this was at NASA, this is at my current company. Um, it's sort of that growth mindset. Uh it's the thing that it's that 21st century critical thinking, um, but with sort of an eye towards what's next. Don't just ask, you know, don't be, don't criticize what's in front of you. Imagine what could could come and be critical of that before it gets here. Help your, you know, think of that planning loop in your mind. And if we could start to sort of frame a lot of both our sort of internal education, our strategies, our human capital investments around like creating space for people to feel like, you know, psychological safety, feel safe enough to be wrong and to try new things, then the ultimate result of that will be way better. Uh, because if people fail, then they will spend so much time haranguing and try to try to iterate to just be perfect before they show you anything. I would much rather have people fail and then show everyone, oh, here's what failed. Now you could all avoid that mistake and we can all move forward together. And it's again, you know, sharing those ideas across across the ecosystem, across teams. Um, and I think that was in NASA, the success that we hit when we started really cooking again for like the really heavy innovation of sort of using AI to really uh to push boundaries. Um, it wasn't that we were just using AI. Yes, we got people over the hump of yes, AI is cool, let's try it. But it's when they started getting AI to encourage them to bring in other people. So, hey, like you are. So we hired and we said, we're not gonna hire a wing guy, we're not gonna hire a fuel guy. Um, we're gonna hire someone who's curious. And yeah, they have a PhD in fuel, or you know, like these are locket scientists. We bring them in to say, you're not allowed to talk about what your PhD is in. You have to talk about literally anything else. And that forced them to suddenly, oh, I gotta, I gotta, I gotta go elsewhere. I gotta be comfortable being being ignorant again, because I'm I have a PhD and I work at NASA in fuel, and I can't talk about that. So I think they have to be like a pseudo-expert in something else, but they have to ask good questions of the experts. They're you know, interviewers, they're now uh they're you know, question askers, they're seekers, they're not havers, right? Um, they're not bringing just what they know. Um, and that that plus AI, like, oh man, that was a powerful combo. And I think like those are gonna be the jobs and the roles, uh, the new jobs uh that emerge are the people who like can do that really well, who just like come, you know, come into a company and ask the really hard questions uh and question everything and are good at iterating quickly and finding root cause to, you know, like whatever it may be to get you to that to

Why AI Skeptics Might Be Right

SPEAKER_00 30:32

your product or whatever it is to that next step.

SPEAKER_01 30:34

To flip that on its head, right? You at NASA for sure, and you know, me at other orgs too. We work with some brilliant engineers. Obviously, I haven't worked on with rocket scientists, but some really smart people. Um, I think that there's this like um, I don't know if you've ever faced this. I'm sure you have, but there are some people who kind of look at AI and they're still very resistant. I'm not saying this as a way to like paint these people in a bad light. Rather, it's like these minimal use people that are super brilliant, they see everything that's happening and they look at Gen AI and they say, no, thanks, I'm good. I can do this myself. Um rather than immediately demonizing people saying, oh my gosh, they're gonna be left behind, I'm wondering if there was something inherent about AI that makes folks maybe hesitant to adopt. And maybe they're right, are they right about something, or do you think that maybe they were right about something that we weren't seeing at the time? And maybe a couple months later we kind of think, you know, maybe they were right about the speed of adoption.

SPEAKER_00 31:38

I think there is something unique to AI. I think there's something unique to the sort of ecosystem of AI in terms of like development and it's like rapid up uh, you know, increase uh in only a couple years. Um so I think, you know, maybe like we as a community of professionals just you know working in the world, like haven't maybe had time to adjust to the rapid uh growth and investment uh in AI. It's everywhere now. But the fact that it's everywhere, I think speaks to sort of this universality. Um it's sort of a trying to think of like major technological innovations that people sort of undersell. Like, oh great, we the printing press, you know, big, big thing. Yes, it increased the accessibility of books, but for a while that didn't do much because no one could read. Uh but it increased literacy. And literacy then became this like powerful force across the world, um, exchanging ideas, the enlightenment, all like that spun out of literacy. If books had not been accessible, if reading had not become sort of a thing that you could teach, then you know the the press wouldn't wouldn't have had a you know a big issue. Um think of like, you know, metallurgy, nails, screws, the invention of a screw. Uh it sounds so simple, but like how much modern construction and buildings like depend on like that ability to just like put two pieces of wood together in a way that like like the sheer strength is different. Uh, like the the way it connects is different than a nail. It's not gonna come out like a nail. Nails were were huge. You know, like the I love the phrase like dead as a doornail. That comes from this idea that sorry, we're gonna random curios curiosity story. Hey, I hope you're ready for this. So you the yeah, the nails that we'd use in a door in the medieval times were nails that you would bang in and then bang, like the the, you know, the other side would have the tip pop out, and then you'd bend the tip down so that someone couldn't like pry open the door because it was usually the weakest part of like a castle or you know, some like you know, stone-built you know, thing. So doornails were like bent at the end. So they were usually not reusable because they had been bent so much that they're like, okay, it's you know, dead as a doornail is in fact, like it's the last time you can use a nail. Well, that's like, well, why would you reuse a nail? Because they were so expensive. So like nails were this thing of you'd born down the house uh and then go get the nails before you went to like move to another town. Um, because oh well, I gotta get the nails, um, sifting through through the ashes of your former house to get the nails because that was the thing that was worth keeping. So, like these technological things that were like subtle and small, and we don't think about them much today, but like they were the undercurrents of massive change. AI is one of those technologies, right? The internet, you know, was one of these technologies. And I don't, you know, I don't think that people like the people that are are resistant to it are are wrong. I think they might have like, in terms of like the fear about what's going to happen to jobs, to the economy, et cetera, like they're those are well-founded fears. Um, but I think that

AI as a Subtle but Massive Shift

SPEAKER_00 34:31

I would much rather have these, you know, brilliant people, much rather have them at the table, helping us come up with ideas of like, well, how it's coming. Like there's no stopping it. We might be able to slow it a little bit, but not by much. How do we make this work with us? How do we make it not an antagonistic thing, but like a partnership? Like, you know, and again, this was sort of the the approach that we took in NAS. Like, well, like I don't need it to be a fuel expert. I got a fuel expert, but how like how do we help it? How do we use it to help us come up with cool new things that that humans would never have come up with? Uh, new ideas, crazy ideas. How do we test it? How do we move faster at the stuff that we already wanted to do? And I think like that that shift, um, again, not demonizing, uh, like that shift has has to start with the curiosity they have to want to. On sort of like the AI ecosystem, part of the problem is that the, as said, you know, we had this AI winter for a long time. And then suddenly, you know, uh OpenAI releases Chat GPT and suddenly it's like, oh man, we crossed sort of a user experience threshold and suddenly the money the oh, the money's there. Now, now everyone wants it. Um, but there was sort of an AI winter, you have a lot of people interested. AI is also a piece of convergence of issues. AI is a technology that lends itself with the hallucinations, particularly the early models, to a lot of distrust. Distrust because it'll take my job, distrust because it's wrong, and I don't I have to double check all its work. So it comes to the market, there's a lot of distrust, there's a lot of fear. So you take that fear and you take this AI winter and you say, oh, well, uh let's say I'm a a I want to use AI. I am curious, I want to engage. Who do I ask to talk about it? Who can come explain it? Well, the only people in the ecosystem that had high levels of trust that weren't like, you know, sell, you know, uh, you know, selling selling you a bridge, uh, like I'm an AI influencer or whatever it is, were the PhDs, right? The deep experts who've just been living in it in, you know, in their basement uh at some company for a long time, just like pushing and pushing on the research. Those tend not to be the people who are the best at explaining, you know, what will impact the world, right? Because they have the blinders on, right? It's like asking the guy who designs the hammers uh for the, you know, like at Husky or some tool company to come in and talk to you at like an architecture conference. That dude probably knows nothing about architecture. But yes, the builders who build that home or what they'll probably use a hammer, sure. He could talk to you about hammer design, but there's such a chasm between like the expertise of designing a like a well-balanced hammer and the metallurgy and then architecture. But they are, you can sort of see that like chain of like AI, the guy who knows how to build a really robust LLM, is not the guy who can tell you what is gonna be the impact to like education or the economy once that LLM is deployed at scale in a business environment. And that gap of like, well, who are the few people in the middle who have done that work? It was like, I count myself like you know, one hand of people I knew before this current boom of people who's sort of trying to do that alongside me, who are asking those like bridging the technology to a real product, but oh yeah, let's try the AI. It's not perfect yet, but it's worth trying. There just wasn't a lot of it. But those, I think, that skill of bridging that gap is the is the piece that is going to make the difference. But AI has been lacking in that group for a while. And it'll take us four or five more years before we have, I think, a robust enough set of those experiences across all these uh these industries where like, oh, yeah, we we know someone who's done this, broken it, fixed it. They know how to deploy and roll out AI um safely, effectively, et cetera. Um and it can sort of bridge that gap. Um, which is why, you know, again, like if you go to an AI conference uh like two years ago, it would have, you know, you know, gouged your eyes out, you know, ripped your ears off, it would have been so boring. Uh because they just didn't know what they're uh, you know, here's a really, really super technical paper. And like no one's like, I'd what? Like I'm I'm a CEO. Like, well, what's this mean to me? Um, we are getting it is getting a little bit better, but it's still not fully there. We're not fully baked.

SPEAKER_01 38:23

Yeah, I was gonna say it's it's we're still cooking. Um still cooking, yes, yeah. Um it's definitely getting, I think, more accessible. Like research. Um, even like I've been in research for a while, and I say that loosely because I'm not I do not have a PhD, but um, my first job in the Bay Area was at a research company, or at least the research arm of a company, and I was surrounded by PhDs. Just I was the only one with BS, my title. Even my last job, it was uh it was being part of a research group, right? Um, so it was just like, okay, well, like you know, research is accessible nowadays, and everybody can have an opinion, and ever and I think even more so, everybody now has the tools to have an opinion that is validated back and it really reads, um, it's more approachable. I so I think this is a really fun time to be in research. Um, the really good research will always come up above like the AI-generated research, which is great. Um, and I'm very happy about that. But like now we're just getting more people who feel confident in being able to like talk about the things that they're working on or share about the things that they're doing. And you mentioned this really interesting part where you're like, oh man, there's like these genius people that just do not know how to communicate ideas. And our last podcast guest, we actually spoke about this as well. So there's something that you mentioned, and I'd love for you to explain it really quick. So you mentioned something about institutional language versus human language, right? And the different modes that like we kind of need to translate between. And I'm wondering like how these kind of these two different types of languages help in in um just like your the communication philosophy overall, and like what what this kind of means.

SPEAKER_00 40:11

Well, I would say institutional language is just like a subset of human language. Um it's like it's learned, uh, you can think of like onboarding at a you know at a company, like you you get the lingo. You know, like if if anyone has ever interacted with anything in the sort of the defense or security space, the amount of acronyms that you have to learn is insane. You know, it's just it's just it's absurd. And people like they just assume that you know. Um, so there is that sort of lingo in every large org about how they talk about their teams, their projects, the code names of that, you know, research thing, whatever it is. There's just a lot of that. And I think people get caught up in that and they default to that shorthand. And I think there's also then sort of an expectation that within large orgs, you say, like, oh, like I trust that person to be the expert. So they, you know, they, you know, if they say that, great, I'll just yes and. Um so there's not necessarily a lot of healthy pushback. And pushback in orgs also tend to be viewed as like hostiles, like, oh, how dare you question me? I'm, I, this is my job. It's like, okay, great. But and I think that's where, like, that's where finding common ground that isn't that institutional language, that isn't that transactional. Here's what I gotta do, here's what you do, here's what we do, here's our policy. If you want people to start sort of being curious, you know, stepping back, taking a step back and saying, hey, like our AI policies, our whatever approach to this is like, could we, could we redo this, we rethink this? Um, it's best to start with neutral territory. Uh, and this is why I have found myself like collecting stories, like weird stories from history and things, mostly because they are neutral territory. Uh, if I'm in a meeting and I start telling a story or like telling some, you know, like, you know, like uh telling some story about you know hammers and nails in indoor, you know, indoors, uh, or or something, you know, absurd and uh strangely disconnected. At the end, that will try to connect it. But it's not like either side of that conversation knows more about that random weird story. It's a it's about a like some metaphor to collect and say, hey, we're now sort of all on the same page to the point that is trying to be made. Now let's have a conversation. Um, and that sort of bridging with sort of the neutral territory is, I think, the most effective. And this, you can see this come out, oh man, um, in PhD's research communities we talk about, but any technical field, deep experts tend to use metaphors in trying to convert something that is also from their expertise. So, like, you know, someone trying to explain like how the internet works to you know, to a gardener, be like, oh, well, like the HTTP stack, all right, it's a lot like uh, and then they'll reference something in like hardware. It's like, oh, it's like the computer bus. Someone's like, what's that? Like they, their reference for sort of what is common in their industry is just so far removed from what that gardener or whatever might be. So the best communicators are the ones who have not only lots of stories, but stories that are, again, neutral to the parties involved. And I think that has been the most interesting way to bring like CEOs or CTOs that are like AI resistant and AI experts to the same conversation. Um, because again, I'm typically when I'm giving these talks or sharing these stories, I'm not there to like tell the AI person, here's here's what you invest in, here's what you build. No, no. They will always know that. And I'm not there to tell the CEO, here's, you know, like here's how to spend your money. What I am usually there to is like, here's a risk that neither side is fully appreciating. And you guys need to start talking about. Because you have like the finance side has a has a part to play in mitigating this risk, the technical side has a part to play, but you both got to be talking about the same risk. So I'm sharing stories from history of when people you know blew up the wrong thing or bought the wrong technology and it, you know, like it, you know, killed hundreds of people. Like terrible, you know, terrible stories. Like now, hey, you see how it's important. To do X, let's take that lens and apply it to AI. Let's, you know, like that metaphor, that allegory. What are the similar risks in the AI ecosystem? Okay, now, now we get it. Okay, now talk. And I think that has been, to me, the most successful way is just is you know, is to bring those sort of really sort of out of left field stories. Um, because, you know, then no one feels particularly empowered to say, like, well, you're wrong. That story is, you know, historically, you know, inaccurate. I was like, it doesn't matter. That's not really the point. And that that has worked really well. So, you know, for all the listeners out there, curiosity, you know, these stories really will help you in a lot of ways.

SPEAKER_01 44:36

You made this incredible crazy switch from NASA to travel. You know, and we brought up this part about institutional language, which no longer exists in this at least during this change. And you're stepping into an industry, like you said, where you are now the subject matter expert on AI, trying to bring it up, trying to get this adopted. Right. I think you now, as the builder, right, you're still building all the time, and you now also are kind of tasked with shepherding, I think, this organization into the modern era. And you don't necessarily have all of the same exact institutional language that they have, and you're challenged with kind of bringing them up to a matter of fact, your institutional language. So how is that how has that kind of been? You know, I can imagine that you know, there's obviously a reason why they hired you and why they brought you on right as CTO, obviously. There's also this other part as CTO where you're now an executive and you now need budget for things, and you need to go and get people to say, hey, like we want, we, or you need to say, hey, I need money for this, and they are gonna say why. And now you need to explain it in their language, right? So, what does bridging that gap look like? And I guess what are the different types of things uh or areas that you like to focus on when making these arguments for investments, either in different parts of AI, or maybe to um empower different teams that are using AI, um, or to, you know, even train ICs so that everyone has like a better skill set in AI.

SPEAKER_00 46:18

First thing I would say is that I I tend to start in any this this is not my first sort of change management with AI. I think the AI has gotten really better. The job is still, I think, largely the same. And I I tend to approach sort of like a lot of this stuff as like 90% carrot, you know, uh all carrot, no stick, 90% carrot, uh very little stick. Um, because people, if you want people to grow and be innovative and try new things, uh, you cannot fear tends not to be the best way, threats tend not to be the best way to get that uh that code of action. Um you won't get more deliverables, you will not get new ideas. Um you're trying to be innovative, you have to build coalitions of the willing. Um and that is a hard culture to build uh of like getting people excited. It's like, hey, like come come, you know, like uh you know, come on board. Uh, you know, I'm the I'm the I'm the strange new manager with candy. Like come to my my lunch meeting, um, uh my brown bag, and I'll I'll teach you stuff. And so a lot of it was sort of like showing them what could be done. Um, and you know, even with the ICs, like I've been having a lot of like one-on-ones and meetings and demos of like, hey, like let me show you how I code. Just like give me, you know, give me a ticket, give me a task, and like we'll do it, we'll do it together, all like 20 people in a room, and like we'll like we'll play this game. And they can see, oh wow, AI did a lot, but they also see like where I pushed back. And they can ask questions like, why did you ask that? Uh so they get to see again, sort of that what are the limits and and sort of learn and can mirror that, and then it hopefully brings up you know good patterns. Um so I think once you start getting sort of the people doing the hard work, the sort of in the weeds, and you understand like this, if they make these changes, this is what will happen in terms of speed, efficacy, et cetera. You now have at least a small data set that you can now take to management and say, hey, look, 70% of the team is not using this, 30% is this is how much more productive that 30% is. If it's not productive, okay, what are we doing wrong? Like you fix it, find, find, find something else. But usually that collision of the willing means you get more excitement, you means you can trust the data that's coming out of it because people are not lying to you about usage. Um, and then you can get real data, you can bring that, and you can make your business case of like, now let me get the laggards uh you know to come on board um in that long tail of sort of buy-in. Um and that's sort of like the how you sort of shift that culture towards towards sort of new things. It is slow. Um it is not something that can be done overnight, it's not something that can be done like you know, top down. Um you can't be like, you know, go be smart and use AI. Um you can just provide opportunities and space uh and uh maybe positively incentivize. Um but that's the really only the way the best way. Um and I think one of the things that is discounted a lot um as an expert is as an outsider, when I come in, I have no political cachet. They don't know who I am. And like, yeah, oh, you're a guy from NASA. Okay, whatever. I don't know, for example, the travel industry. I've had to learn a lot. And there are a lot of people in my company who've been in it for 15, 20 years. They know way more, but all the weird little gotches of like how, oh, well, how how they do this, you know, weird accounting to like, you know, to do bookings or you know, taxing this, but it's a reseller rate over here, like it's absurd. And I was like, I, you know, AY would I would never guess this, AI could never guess this. That expertise is still very valuable. And it's got to re respect that. It's like, I'm not here to blow up your life, I'm trying to make it better. But coming and say, hey, you can help teach me, I'll help teach you, and we can go together. Again, a coalition of the willing. And if you don't respect that, if you don't sort of acknowledge that you you don't know everything, then you won't, you won't get the buy-in. It's even better if you can train that person who's excited, that first, you know, that star student who's like, oh man, I really want to try. Thanks for helping. And then say, great, you teach the next class. Like you host the brown bag. I'll schedule it, I'll make everyone show up. Uh, I'm still a CTL, but you host it because they would much rather hear it from you. And if there's anything really hard, I'll step in and like, oh, actually, here, you know, here's that's a hard question. Let me take that one. But having it come from within, I think buys, gets gets better buy-in. People like, you know, to see their peers learn and grow. And it feels, again, it makes them feel like, oh, I can do this. Um, and I hope, you know, any of my teammates listening to this, they they get that. And if they are excited about new stuff, please volunteer for the for the next brown bag. Um, because you know, radical candor, like I want, you know, I want people to feel like they can grow and they are going. That is the only way I think we all will survive this coming storm because we're moving too fast. The system is moving, the ecosystem, the tech is moving too fast to not be constantly growing and learning. It's the only skill that will matter at this pace.

SPEAKER_01 50:52

You're now building AI systems at a massive scale, right? So here's the question. Every parent with a curious kid is actually wrestling with. Everyone. So lots of this is a lot of pressure on you. So your tenure adult years old, your 10-year-old comes home from school, plops down at the kitchen table, and says, Dad, my teacher says AI is going to do all the thinking for us. So why should I bother learning the hard stuff? What do you tell them? And more importantly, what do you show them?

SPEAKER_00 51:20

Yeah. This used to be easier because the models were worse. Uh, it's harder now. Uh, but I would say, I I am worried about this with my kids. I have a you know, three and a half year old and a one and a half year old. Like, I don't know what school will be like when they actually are like doing real learning. I would say it's the same thing I tell a lot of people who are professionals, is that I would say, okay, let's sit down and I'm gonna show you AI. And we're gonna, oh, look how cool it is. Look at all these fun things we can do. And then I'm gonna start showing these, like, well, where is it wrong? And I think the challenge you'd have like with you know, five-year-olds is that they don't know what's wrong yet. They don't know enough to like, oh, that's the wrong answer. A 10-year-old should, right? They should have some things that maybe like they just know about the world. Um, so showing it that AI can make mistakes is a good first step. And I think if you can sort of, if you want to gamify it, it's like, okay, well, now the game is like, how can we make the AI wrong? Like, what can what what questions can we ask of the AI to trip it up, to make it wrong, uh, to make it come back wrong. And that sounds insidious, um, but it's not. Because what you are really trying to do is to get, again, the framing of the AI isn't always right. And your job is to come with curiosity, to come frame the questions, come with like you have to know, go find enough information to ask a hard question. Um, and that means reading to kill a mockingbird or whatever it is, because you got to come in with a question that maybe be, oh, well, what about this? What about this weird reference that uh, you know, Jim says this and Scout says that. That is, I think, the way that I would approach it with it with a 10-year-old and with a kid. Um, I would probably have may send a note to the teacher like, hey, could I give you some better resources about how to think about AI in your classroom? Um, because if they're saying, you know, it's like it'd that'd be like a math teacher saying we don't need to learn math because we'll all have calculators in our pockets one day, which we now we do. But math is still valuable. And when you get to higher level math, no one is using a calculator because higher level math is is constructs. It's it's you know, it's all letters and Greek alphabet, and it's it's crazy, but you still need to know the the the core level stuff. Those, that level of pursuit will still exist in the future. And you know, like the calculator is a tool, but it has limits. AI is a tool, it has limits. Um, so let's figure out a way, teacher, parent, student, of finding those limits and then being creative about like how we sort of learn from both, right? It's you know, having both, you know, one foot into the AI world and one foot out, uh, and being critical about you know which way we lean. Cool.

SPEAKER_01 53:46

Well said. And honestly, I think one of the one of the things I'd be really I would love to do this. Maybe I should, maybe I should go volunteer somewhere. Um, I would love to like go to a classroom and just like pen test AI right there and just make it say a bunch of funny things. And I bet like not only would I create the the coolest generation of like pen testers, um, but I think showing showing them that like AI lies and AI isn't uh the smartest person in the room would really level set some of their expectations. And I think that's the problem. Right now, there's so many inflated expectations and around AI where it's like we've made it so perfect and it can't be wrong. Like it's just not beneficial for us if it's wrong. And I think uh with this next these next coming generations, I think showing the mistakes that it makes and showing how bad it can be is gonna prepare them for when it's really good. Um to be really like still skeptical, still curious, still, you know, knowing that yes, it may be right and it may be even more right than them every once in a while, but it's not gonna be right every time.

The Jobs AI Can't Do

SPEAKER_00 54:59

Let's say AI gets really good at coding. Okay, so we need fewer software developers, but we might need more product managers because AI is still pretty bad at like sitting down with a customer and like really like chatting with them, sitting over their shoulder, asking, like, oh, trying to understand what their needs are, like putting that in context of the the product market fit and the business, you know, and scalability and cost and margin. Yeah, it can help you with that. But it's actually still pretty bad because it can't leave the computer and go be curious. It can't ask those questions. Um, you can't force a customer to go sit in front of your AI and while it while it peppers it with questions. So, like even in software where we're seeing this massive uptick, yes, it's maybe sort of like collapsing AI sort of software jobs, but I would say it's actually creating more software jobs that like dabble in product. And it's enabling more product managers to dabble and prototype things with AI before they like try to build it at scale. And it's making more engineers aware of like, well, oh man, I have to do this a million times a day. Oh, like I how do I think about scale? You know, because my starp is growing way fast. Like there are new jobs that are emerging that are adjacent or wholly new in these fields. So, yes, AI is, I think, you know, like changing the way we we think about it. But if you're not being curious enough to see how it, how it could be transformed, how there's new opportunities opening up, then again, you're not you're not being curious enough. You're not you're not asking enough questions. Um and I encourage anyone who's is sort of super dire about AI to think about all, you know, try to think of like all the new jobs uh that could be created when we sort of level up one step. Yeah, we'll need more fewer software developers, but that means we have maybe more companies. You know, the same amount of software developers trying new things, building new things. The ecosystem becomes more frothy and more interesting.

SPEAKER_01 56:38

So you said your favorite example of interdisciplinary innovation is bread and cancer. Um, a Japanese engineer who built bakery scan to identify pastries, and somehow that led to better cancer detection across Japan. So I feel like you know more about this than me, but like the burning question on my mind is like, who was in the room when someone was like, you know what would really help oncology? Croissant recognition software.

SPEAKER_00 57:03

I'll be quick. Um, engineer pre is a sort of deep learning era in the US, like he's over in Japan where pastries are like really big thing. Like a grocery store chain is like, hey, we have real trouble like scanning like our 300 different types of pastries. Um, can you build like an image scanner at checkout that just like tells us how many, like how many bread rolls, which type of bread roll? Honey glazed, you know, pecan, whatever, like hundreds of options. And this is again like pre-our current AI capabilities. He's like, sure. He spends like six, seven years building it, almost bankrupts his company because he can't get he can't get the contract delivered. He finally cracks it. But like in Japan, it's such a big deal how fast this was and how how like quickly they were able to like scan all these. Like it, it blows up. Like local news is covering it. They're like, oh man, bakery scan, this is the coolest, hottest new thing at this grocery chain. News is covering it. It's like getting talked about. And it was covered so much that a doctor saw it on the local news. And the doctor was like, so you had one person being curious, like, hey, yeah, I think I can tackle this with AI. Let me invent a whole new field of image processing. And the doctor was like, hey, bread rolls sort of look like cancer cells under a microscope. Maybe we could work together. So he just calls him up out of book. He's just curious. He he reaches out to the engineer. He knows his name, it's on the local news. He's like, hey, do you want to get together and and figure out how to maybe this bread roll thing can be used to detect cancer? Lo and behold, he was right. It does. And it like, it works wonders uh in the era of oncology for several years before you know advanced AI in like deep learning caught up. Um, but like they were they were doing it the hard way, right? It was custom tuned uh for this particular problem, but it saved countless lives because one guy on a couch was curious about a local news segment about bread rolls. Um, so if you aren't thinking like that, uh then you are missing all of the opportunities that are gonna come up in this sort of AI era because there's so many more now. Um it's at everyone's fingertips, not just one engineer and one doctor. Everyone can be sort of tapping into new places, trying new things with it. So yeah, it's it's my favorite story because it's it's so absurd. But again, save lives.

SPEAKER_01 59:10

James, thank you so much. This has been a pleasure. Um honestly would listen to you tell stories all day. Uh, you're a great storyteller. Um, and it likely makes being at your org a fantastic time.

SPEAKER_00 59:21

Thank you so much for having me. It's been really great conversation. Um, love talking about this stuff.

SPEAKER_01 59:25

Always. And then um to wrap up, uh, where can people find you? Uh, where are you gonna be at next? Are you working on anything that you want to talk about?

SPEAKER_00 59:33

If you are interested in our travel software, uh it's a collection of companies, uh, Regiono, ResD, and Checkfront, uh, and a new company called Manifest, uh, are all in our sort of network and we're all sort of pushing the boundaries and growing. Um, so those are great to check them out if you are traveling. Um, and then the best way to follow me is on Twitter, uh James underscore MTC. That's where I you know wax philosophical about AI and politics and and how hopefully it'll change the world for the better. Um, yeah, catch me there.

SPEAKER_01 59:59

Cool. Well, James, thank you again. If this episode helped cut through the noise, like or subscribe so you don't miss what's next. Thanks for spending time with us. Until next time, stay curious.