251: Lessons from the Dot-Com era for today's AI Hype

Is today's AI hype cycle just dot-com boom 2.0?

In this episode, host Sophia Matveeva talks with David G. Ewing, who lived through the original tech bubble (literally in a closet in Palo Alto!) about the striking parallels between then and now.

Listen to learn lessons from dot-com history to navigate today's AI boom.

Listen to learn:

• Why the "more things change, the more they stay the same" when it comes to tech hype cycles
• The surprising success factor that determined which dot-com companies survived (and what it means for AI)
• A powerful career strategy that helps you thrive amid tech disruption
• Why measuring AI's impact requires looking beyond traditional ROI metrics

Timestamps

00:00 Introduction

01:00 The Wild West of Tech: Lessons from the Dot-Com Era

10:02 Navigating the AI Hype

19:53 Building Infrastructure for Success in AI

29:45 The Hydra vs. Phoenix Analogy

About Our Guest

David G. Ewing is the founder of Content Lion, a next-generation AI centralisation and enterprise content management system. He brings decades of technology experience and a pragmatic approach to digital transformation.

Resources Mentioned

• David's blog: davidgewing.com
• David's podcast: Content Kingdom
• David's company: contentlion.com
• Book recommendation: "What I Learned Losing a Million Dollars"
• For thinking about risk and Black Swan events: Nassim Taleb's "The Black Swan"

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Transcript

Sophia Matveeva (00:00.056)
Now look, every one of these situations has been, you know, the wild West over and over again, right? And the wild West comes with the period of time where there are no rules and, you can get away with some things that are not normally allowed. remember, for example, a friend of mine joining an e-commerce company back in the nineties and he had been a, I don't know, like a publicist or something. And I was like, Hey man, you're like the.com head for this. And he's like, yeah. And I said,

What makes you think you could do that? And he said, what makes you think I can't? Like nobody has any of these skills yet. Someday the people will expect you to be very good at e-commerce, but today's not that day. So I'm jumping in and I thought that was a really great attitude.

Sophia Matveeva (00:45.615)
Welcome to the Tech for our Techies podcast. I'm your host, tech entrepreneur, executive coach at Chicago Booth MBA, Sophia Matheva. My aim here is to help you have a great career in the digital age. In a time when even your coffee shop has an app, you simply have to speak tech. On this podcast, I share core technology concepts, help you relate them to business outcomes.

And most importantly, share practical advice on what you can do to become a digital leader today. If you want to have a great career in the digital age, this podcast is for you. Hello, smart people. How are you today? I am recording this to you from my hotel room in Abu Dhabi. And this afternoon, I'm off to Riyadh in Saudi Arabia. Isn't that cool?

I'm meeting with business leaders and government decision makers in the Gulf region because there is so much investment going into digital transformation and innovation in the region. And honestly, it's really exciting times here. Plus people, the beaches, white sand, turquoise sea, gorgeous sunsets, really, really recommend a visit. Anyway, let's talk about AI.

Today's guest is an entrepreneur who built his first company in the dot-com era. Isn't that interesting? So his name is David Ewing and David says that there are a lot of parallels between the AI hype that we're seeing today and the dot-com hype of the late nineties, early noughties. And remember there was this boom and then there was a massive bust. So fortunes were made and fortunes were lost.

Which is why I want you to learn from that time and see how you can apply those lessons to today. Hype times are exciting, but hype times are also financially dangerous. So if you're wondering, how should you be thinking about AI investment in your organization? Or if you are a founder, if you're wondering how should you approach fundraising in this hype time for your startup? Or if you're wondering,

Sophia Matveeva (03:02.072)
What job should you really take to benefit from this AI boom today? Then this episode is definitely for you. And my dear smart person, if you are enjoying all of this free education that I'm giving you, then spread the love. Send this podcast to one person who wants to also have a great career in the digital age. Because when you do that, you might give them exactly the insight in education that they're looking for.

And now let's learn from David. David, you started your career during the dot com boom. And so what are the similarity that you're seeing between then and now this AI hype era that we're in? Well, the Silicon Valley dot com boom was a hilarious time. I mean, I remember that it was so crowded in California that I had to live in a closet in Palo Alto with a bunch of

guys from Stanford Business School. You were all in the closet together or you had no, no, I was really fancy. had my own closet. So yeah, it was the Cadillac of Palo Alto. Stanford, you get your own closet. No, it's all right. But you know, the, the, the mania, the excitement, uh, the buzz, was, it was all there before. And, and gosh, you know, you watch what's happening now with all of this and it's, it's, you know, the more things change, the more they stay the same. We're

We're in the same boat right now where we have the same kind of hype. have the same people saying instead of.com, saying, Hey, I've got this dot AI type thing and look how great it is. And, and yet we're, we're skipping on fundamentals and, people are starting to lose their heads. And so, yeah, there's a, there's a tremendous amount of fun and excitement, from this new period of, of AI, but it's also following so many of the same patterns that I just think that it's a.

A great thing for everybody to take stock and say, remember the lessons we learned the last time we went through this. Let's remember those lessons again and do the same things and, do them better this time. So. So, I would like to begin by discussing some of those lessons because it was around that time that I believe there was this famous article about amazon.bomb. I think that's what the article called them.

Sophia Matveeva (05:24.888)
And Jeff Bezos was really annoyed about that. And it kind of made sense because there were so many companies that were going bust, but Jeff Bezos, well, I mean, he's the last one laughing now, right? So yes, there was this dot com boom and bust, but also that's when a lot of today's billionaires were minted. So what can we learn from this?

Well, a couple of things are really important to learn from this. One is, is that, yes, while there are examples of the heroes of the time, the survivors who came out of it, the Jeff Bezos, you know, how many zillions of people do you not remember? And, you know, if you were living in Silicon Valley at the time, you could remember a lot of these companies like the big heroes at respond.com that nobody knows anymore and all sorts of other.

You know, litany of scrap heap of companies that were there. We're going to have the same thing here. And we're also going to have massive winners. And so the key for me was how do I look at what was the underlying success factors that made the businesses that succeed succeed and drop the things that weren't and, a couple of things emerge. for example, one of the things that I think that is a sure fire bet in any era in any age is always been.

infrastructure and, you can look back even, you know, go back to 1849 when they found gold and in California, right? Which is the great metaphor for all of this. The, the, the 49ers, you know, there were people who won who, who found gold, but, but by and large, most of those people lost, the people who won consistently all the time were the people selling picks and shovels and carts and camping tents and tan.

And canteens and hotels and saloons, they won big time. the miners, not so much. And then if you look at Silicon Valley, you know, dot com dot bomb era, yes, we had this.com and that.com, but you know, there was pets.com and there was all sorts of failures, but what, what were the big winners underlying infrastructure? And really that's where Amazon ultimately won. Yes, they still happen to sell books on the internet, but that's not the Amazon that we have today. We have Amazon.

Sophia Matveeva (07:40.644)
you know, cloud and we have Amazon infrastructure and fulfillment and every retailer in the world sells through Amazon. They're, they're an infrastructure play. They have essentially became picks and shovels and saloons and, bars, not, you know, out there hunting for gold. And, and the same thing is true now, you know, right now we have, a period of time where people are putting together all kinds of top level applications for.

you know, AI trying to do this with that AI and some of those will succeed. That's for sure. But, but what's a sure fire bet infrastructure, which is where I wound up moving myself and my company. So content line, for example, the company that, that I created last year, we're all about consolidating an infrastructure for content. So every video, every image, every blog article, everything that's really part of your company, whether you want to use it to train on an AI model or whether you'd want to just share it with the world or, or to customers or portals.

that's the infrastructure play. so for people who are in startups and are thinking about it, trying to align with infrastructure is probably a much higher probability bet than trying to push all the way to the end application. And also the world did actually change maybe as part of the dot-com boom, but definitely kind of as part of that journey, you the way we watch movies, the way we date, the way we get, you know, our presents delivered.

the way we shop, that has definitely changed. So the general trend was correct, but not all of the companies, you you couldn't specifically say which companies was going to win or not, which is I think kind of where we are right now. And so kind of when somebody is thinking about how to make the most of this from a career point of view, you know, so let's say, you know, I know we've got quite a few people who are working

on corporate innovation and they're thinking, well, I want to be successful in the age of AI. I also don't know which horse to back, but I know I want to be in the race. And you know, there's that you want to take a risk, but also you don't want to take the kind of risk that is essentially going to be too much and career ending. So what would you say to that person? You're right, Sophia. This is a, this is a tricky time and

Sophia Matveeva (10:02.34)
You know, there's a couple of things that come to mind for me. One is I remember, back in the early days of home computing, which is actually my father's era and he started a company. And one of the things that people were asking themselves is what are we going to do with these computers? Right. If there's a computer in the home, what, what possible use could there be? And so, you know, in the late seventies or eighties, people looked around and said, well, I suppose you could, you could put your recipes in here. Right. And that was.

That was like kind of the best app application that people could think of for a home computer. then, you know, go into the nineties with dot coms and you know, everyone's like, what are we going to do with this internet thing? And people were like, well, we, could do e-commerce. And that became kind of the, the big thing, the, stuff you mentioned to like streaming video and you know, social media, all that came in the next decade.

Afterwards, after we had a time to accommodate and look at that. So, so where are we at with this AI period? I look at this and say, we're at the recipe stage, right? We're at the, the commerce stage where we're all sitting there looking at this going, you know, what, what, let's look at what we are familiar with and then let's just do that a little bit better with AI. that unfortunately is not the revolutionary thinking that we need in order to really harness what, what AI is capable of. So.

so we have to look past that and I'll give you an example of something that happened to me about 10 years ago. I had a client who went through a digital transformation. They went from old school systems, they upgraded, they were in the cloud. Everything was going great except for one problem. And that was their call center. And so this was a, a fancy, designer, material company. so fancy designers and architects would call them.

And they needed to, in a very quick way to fill a very large order. And what was happening was, you know, the systems were slow and clunky. were cloud-based and there was a lot of clicks. So the calls were over five and a half minutes. So I got hired and the customer said, bring the call time down. So we did all the things that you would expect, right? We, we, listened to the business. We, you know, came up with all the change management pieces. We streamlined all the technology and we got it so that the next release of the technology was super slick and you could do the call.

Sophia Matveeva (12:20.304)
in 90 seconds or less, but when we released it, you know, we went, we had the big fanfare, we did the whole thing. And, the first day in the call center, there was absolutely no change to the call times. was five and a half minutes. And we were like, what is going on? So, you know, but, but the head of the projects that don't worry, David, just, you know, we'll, we'll keep an eye on it. I called a week later, no change, five and a half minutes. And I'm starting to panic, right?

I call it the month mark and say, you know, we're watching the technology. There's absolutely nothing wrong. have zero defects. How is the call time? He said, so it's still five and a half minutes. And I'm like, how are you not furious with me? Like I, this is a giant failure, right? And said, Oh no, no, no, no, See what's happened is, is that our, call center agents have learned how to fill the time when they, with the old system. And so now what's happening is they're talking to them, but instead of just sitting there, filling the time with.

idle chit chat, they're having these very substantial conversations with the designers. Our order volume has tripled and this is the greatest ROI project in the history of the company. But it's not because the call time has gone from five and a half minutes to 90 seconds. It's because we just have a much higher quality experience with the customer. So the lesson from that, that I gathered was don't be so sure of what you want that you wouldn't take something better, right?

And I think when we look at the AI age now, that's where we are is that we're grabbing technologies and we think we know what we want, right? We're like, Hey, we, write press releases today. We write blog articles today. Wouldn't it be great if we just use generative AI to like write one for us? Well, that's okay. And sure. But, that's not really producing anything. That's amazing. It's not, it's not leading to people engaging with us.

at high, high levels. And so we have to look a little bit deeper and be not so sure of what we want that we wouldn't take something better. And that's really what the race is right now, what the exploration is. so anybody who's funding a project internally, you know, the takeaway that I have is don't be so wed to what you thought you'd get an ROI from that you wouldn't take something better. For example, you know, we had a company who recently thought that the greatest thing they could do

Sophia Matveeva (14:39.054)
with their AI generated chat bot was just respond to questions, but kind of like an FAQ. And that was okay. But what was far better was when we started to hook up the IoT system to their service system so that we could recognize that when a customer was having an outage or downtime, and then we could use the same chat bot to talk to the customer about their outage, or we could just.

proactively contact the customer through other channels like text, WhatsApp or email and let them know, hey, you're down. We already know you're down and we're working on getting you back up. And if you want more information, you know, authenticate, authenticate and talk to our chat bot. And, um, and that is a far, far better experience than what we had originally thought of. So, um, there's a little bit of groping right now. You know, this is a, this is a, this is a phase where everybody has to be a little bit more flexible and a little bit more curious.

and be willing to accept something that's kind of a surprise. And that's not easy for us to do. know, this actually reminds me of something that Brian Chesky, the co-founder and CEO of Airbnb said when they were thinking about how to create the Airbnb digital experience, because they thought, okay, let's think of what would be an 11 star customer experience. And, you know, you can search for this interview, essentially says, you know,

If you book something super, super expensive, like a super expensive vacation, okay, what would happen? Okay. You'll be picked up in a really fancy car and then you'll be flown in a helicopter to the location and then champagne. Okay. I think I added the champagne. But you know, basically like wonderful things would happen to you. And as I was listening to this perfect vacation, I was thinking, yeah, that's a reason to work hard. And you said, okay, well, so this is.

Like if money was absolutely no object, this would be the most super amazing experience. Okay. How can we take that and what can we do with design, with digital technology to recreate as much of that experience as possible? Okay. You know, for all of those of us who have used Airbnb, I would argue it is not necessarily an 11 star experience, but to be honest, it is much better than what it could be.

Sophia Matveeva (16:57.582)
Right? Like I do actually think that they are doing quite well in given what they've got. But it's essentially that idea that let's think of the best possible thing and then see if we can get there as opposed to, know, what they were looking at was, okay, we're basically connecting random strangers. And so maybe we just need to convince them that they're not going to kill each other. So they're not, you know, going for the lowest possible thing, which is

Okay, we will send you criminal record checks on each other and then hand over the keys, which would be terrifying. And instead they went for super luxury. Okay, let's try to create that. And so when a founder is hearing about this and you know, we do have quite an audience of non-technical founders and who are thinking, okay, this is the age of AI. I need to do something with it.

I'm listening to this podcast. What am I going to do now that I have this knowledge? Now that I also know that we are in this hype time, but maybe hype time means that I can raise a lot of money without having really thought things through. There are some benefits, right? Sure. So what would you say to that, Hamda? Well, first off, yeah, with your Airbnb thing, Sophia, I will concur that my last Airbnb experience was more like a more like

visiting criminals that it was a 11 star experience. but, but yeah, I, you know, right now, look, you know, every one of these situations has been, you know, the wild west over and over again, right. And the wild west comes with the period of time where there are no rules and, and you can get away with, with some things that are not normally allowed. I remember, for example, a friend of mine joining an e-commerce company.

back in the nineties and he had been a, I don't know, like a publicist or something. And I was like, Hey man, you're like the.com head for this. And he's like, yeah. And I said, what makes you think you could do that? And he said, what makes you think I can't like, nobody has any of these skills yet. Like these are someday that people will expect you to be very good at e-commerce, but today's not that day. So I'm jumping in and I thought that was a really great attitude. you know, one day we will expect people to be true experts at AI.

Sophia Matveeva (19:24.192)
but today's not that day. I mean, today you can learn a little bit about AI just by talking to AI and you know, that doesn't quite make you an expert, but you're on your way. so, you the water is, the water's warm and the, and the West is wild. so if we're, if we're thinking about what to do here, then yes, you can raise a lot of money. can, you can go out and explore. and you can, and you can be curious, but you know, going back to some lessons that, that we learned from the last time.

there's this great book called what I learned losing a million dollars. And it's about an investor who's an amateur who skyrockets and then falls. And you know, the, the really interesting thing is, is that what really separates the amateurs from the professionals is not the ability to make money because in stock investing, you know, an amateur can pick up or down and invest for long or short, just as well as anybody else in the short run. Right. But what makes the professional the professional is there.

ability to withstand and accept losses. And, and that's what we're going to find here with AI is that yes, you can go out and you can, you can do something, but if you're not ready for loss, if you're not ready to say, you know, everything that we're going to do is going to fail. We're going to learn and then we're going to adapt. we expect that we don't just, we're not just contingency planning in case that happens, but that's actually what's going to happen. Then then you're.

you're the amateur investor, you you're going to be shocked when you lose. And, and really, you know, there's no need for that because, through every one of these failures, through every failure in the.com era, we were able to learn. So the key is to position yourself in a place where failure is not catastrophic. And I think that's also, you know, if we think in the broader terms of people's careers, whether you're a founder or you're working in corporate innovation, essentially what I find is that if people

try new things, if they create new products, or they need some sort of digital change, even if it doesn't end up turning into Amazon, or even if that initiative doesn't end up being long lived in that corporate, often that person's career can actually really benefit from it. Because unless you do something

Sophia Matveeva (21:44.99)
massive without any experience, which I don't recommend that you do. know, don't just be like, I need $3 million to do this thing that I don't know anything about. Like that's not the right way to do it. But essentially, if you are smart about how you experiment and that thing doesn't work out, I find that generally people can use that story and use that experience to do the next thing, whether it's a startup or the next project.

that tends to be really lucrative. And I find that in corporates, boards that understand how this works, boards that understand digital transformation, they are accepting of that. There are also boards that are not. They're like, okay, everything that we do has to be perfect. But in the digital age, that's a very dangerous attitude to have that you can't really stick with. And so, David, you mentioned fundamentals.

What are the fundamentals that people were ignoring during the dot-com boom? And are they the same fundamentals that people are ignoring today? Well, I'm going to, I'll answer that question from a technical standpoint, but first I think there's an analogy that, really helps. you know, if you think about, there's a mythical beast in mythology called the, the Hydra, right? And it's like a

several headed monster and if you cut off one of the heads to grow back and and so the thing is the Hydra actually doesn't just withstand damage. The Hydra actually gets better every time it gets damaged. Every time you hurt it, it comes back stronger, right? And a lot of people's careers can be like a Hydra. And that's really what I think people need to be thinking about is when you go out and take a chance or a risk, take one that's going to help you learn.

with a chance for reward, course, but make sure that you come back with every head that you lose, come back with two, right? And I think that's, that's a better answer than the other analogy from history, which are, or from mythology, which is, you know, the Phoenix, which, you know, you get completely torched and then you rise from the ashes. Not so good. You're just, you're just back where you started. Right. So I, I encourage people. and when I look at

Sophia Matveeva (24:05.79)
risks and ventures that I'm going to take is how do I make sure that I'm in a Hydra situation, not a Phoenix situation. And, so, you know, going back to the, to the, to the.com era and looking at the fundamentals, what went wrong? Well, you know, people were talking about businesses and valuing them on eyeballs and, know, not having a clear path to revenue and doing all those things. And what are we doing here in the AI age? Well, you know, we're, doing the same thing where we're talking about how big

is degenerative AI and how big is the data that it's learned on and how, know, but really what's, what's the outcome and what's the value here. So, you know, the things that will never change is revenue growth, customer experience, you know, the fact that at the end of the day, it's people that are making decisions about all of these things and people make decisions based on emotion, not on logic. That's well known. So customer experience matters, customer lifetime value matters. And so having a clear path to.

understanding how these metrics would be affected by any AI solution is the fundamentals that we need. But if you're going out there and you're trying a venture after you've done all that, there's still no guarantee you're going to get it right. So look to be the hydro, not the Phoenix. Also, I was at the Economist Innovation Summit a couple of weeks ago and people were talking about AI ROI and I was actually quite surprised by how

little you had to know to be on the stage. was like, wow, this is, well, it kind of, I mean, it was, it was comforting because it's comforting to know that even people in positions of power are really trying to figure things out and don't necessarily have all of the answers. But what I was seeing there, kind of, you know, given

ROI in this Hydra analogy that you were talking about is that people were talking about measuring ROI not only in terms of money, but also in terms of other metrics. So, okay, are we increasing our time to learning? Are we increasing basically can our people, are our people happier now? So if our people are now doing less drudge work, basically is our staff retention going to increase?

Sophia Matveeva (26:27.96)
which is also an ROI metric. and I've given, given where we are in the AI journey, it is probably too early to ask for very concrete metrics, yet we also need to have an eye on them. You know, we can't completely let go of ROI and revenue, but also if we focus on it, like that's the only thing. And if we don't see ROI in the next three months, the project is out.

That's also not the right approach, which is why I think this time is full of opportunity, but it's also really difficult because it's so much easier to say, look, I've got a building, it's got a bunch of apartments, this is what they lease for, we have a spreadsheet, it all works. Whereas now, you know, even the eggheads at The Economist are like, well, we don't know, but we're giving it a go. Yeah.

Well, you know, for years I've, I've helped technology companies come up with ROI models for their products. And, know, I've really boiled it down to there's four possible levers that you can pull here. If you're thinking about how technology can apply to business, the easiest one, the one that's easy to sell with or to convince people of is revenue. Right? So great. can, I can increase your revenue. Here's how, you know, the sales time goes from 25 days to 22 days. And here's what that's worth. Great. When you can do that, that's.

wonderful, but we are not there yet, right? The second lever, of course, is where are the cost reductions I can make? So if I can take a business process and I can standardize it and I can cut X number of hours off of it or reduce headcount or do something, great, I can take the cost out of this process. There's an ROI. The third one that I think is where you're now starting to fly off into a little bit of a more nebulous area, but it's still very valid, is if I can make a balance sheet improvement.

Right. So if I can turn receivables into cash, if I can turn inventory into cash, if I can turn liabilities and I can reduce them, then great. You know, like that's a, that's also a little harder to make the average person understand, but you know, with a little bit of time and education, you can do it. So, so those are great, but we're, but none of those are so easy to find in this early era for, for AI. But, so we're left with the fourth category and the fourth category is this great big nebulous one that I call risk.

Sophia Matveeva (28:50.17)
And so being able to reduce risk is one of the things that I think is where we're really at. So in your example, for example, people are happier. So maybe retention goes down. Well, that's really reducing risk. The risk that we were going to lose people, we don't know if we were going to lose them or not, but now maybe we can look at retention numbers and see that they're down. Maybe we can make a causality between those two things. That's great.

So risk is really the last one, but it turns out that risk is actually the most interesting of all of the different variables because it's the wild card, right? The human mind is so poor at analyzing risks because risks can come out of nowhere. You know, there's this great story about a gambling casino in Las Vegas, and you would think, well, the big risks are, you know, somebody coming in and

and winning at the table. That's not a risk. It turns out that, you know, they've got that engineered. They win so many dollars per table every hour forever and ever. But the big risks are things like someone just decides to stop filing, you know, forms with the IRS for the, for the big winners and they could lose their license or, you know, terrorists come in and capture the daughter of the CEO and want them to go into the vault and hand them a hundred million dollars. Like those are risks that are really hard to see. And so when you're looking at AI and you're saying,

Okay, we're going to reduce risk with AI. you know, we, there's a lot of room to explore it. There's a lot of creativity in there and there's a lot of opportunity to consider things that are normally things that we wouldn't consider like, maybe we can lead to lower turnover because of, you know, a happier staff or other risks that, that we can see. so, you know, the, the, the double-edged sword about risk is that you can pretty much come up with anything. and it's kind of hard to prove.

that that risk might have happened, but at the same time, you you can start to work with it. And I think that that is a, it's an area right now that's, that's worth exploring. So for people who want to explore risk, loved Nasim Taleb's Black Swan. I really loved reading that book. So this, so I expect lots of interesting Black Swan events to come out of AI use cases. And David, I have my last question for you and it's a personal one. So you live

Sophia Matveeva (31:14.764)
as we have heard in a closet during the dot com era. And you know, you're at the start of your career, so I'm assuming you were, you know, young and full of hope and enthusiasm. And so when you look back at that, are there any regrets? You know, you were in this really crazy time. Are there things that you wish you wouldn't have done or things that you had the opportunity to do, but you didn't?

That's a tough question, Sophia, because anybody who says I live with no regrets, my personal opinion, they're lying. You know, I think that I have, I have five regrets a day and, and they usually come in the categories of, things I wish I had done that I did not do or things that I did do that I wish I had not done. one regret from that time period is, is the, the CEO of, of Sun Microsystem, Scott McNeely was, across the bar and, and he was a friend of a friend.

And so right out of college, my friend and I, who also had the same friend of a friend, we went over to go meet him and my friend tripped and spilled his beer all over Scott McNeely's, I think his wife and, and we wound up not introducing ourselves to them. So, yeah, so I had regret. Like I wish we hadn't tripped as we walked across the floor to go visit Scott, because who knows where that would have taken us. But, you know, getting to more serious answers on that, you know,

look at it this way. People could say, well, if only I had picked the right startup, right? If only I'd gone to Google, I'd have been set, you know, but it wasn't completely obvious that Google was going to be Google. In fact, my sister who joined Google really early in, uh, in the year 2000, I remember trying to talk her out of it because they had no revenue model at the time. And I thought it was so risky. And I was like, don't do this. And she said, no. And her reasons for doing it had nothing to do with the vision of what Google would eventually become. It was all about.

Google's culture and who they were as people. so, you know, the thing is, is that when we look back, so many things are obvious in hindsight, but they sure as heck weren't when we were there, right? So, where's the next Google and how do you step on the rocket ship so that you can be a part of it? Yeah, I think I have the same risk or same regrets everyone else said that I didn't make the perfect move at the perfect time. But that's really...

Sophia Matveeva (33:38.264)
Getting back to Nassim Taleb, that's not the point of the book. The point of the book is, you know, keep yourself, keep your options open so that you can benefit when, those unexpected black swans occur. Right. And so I think that's really what we're doing. So, you know, content line, my, business right now is all about options open. So we're centralizing the world's content for every single content creator and company so that they have all of their ability to have the option, but not the obligation.

to take their content and push it to a generative AI, take their content, push it to the website, take their content, push it to their mobile apps, take it to their portals, wherever they want to take their content, when they want to take it there, whether it's product or marketing or sales or IP, they have it protected and then they can use it where it's most effective for them. But that's really how we're kind of getting ourselves ready for the Black Swan events because that type of flexibility, that type of infrastructure, that's really where

we want to be. And I think as people look at this situation, try to make sure that you're in those situations where you can benefit like that. Awesome. Thank you so much. And so where could people learn about you and your company? I keep a running blog at davidgewing.com so you can go there. I also have a podcast myself called Content Kingdom. So if you're into content, Content Kingdom is definitely something to check out. And then Content Lion, which is our

Next Generation AI Centralization and Enterprise Content Management System can be found at contentlion.com. Awesome. Well, thank you so much, David. It has been a pleasure speaking to you. Thank you, Sophia. It's been real fun drive.