THE BIG WORD: SURVIVING THE AI DISRUPTION CYCLE WITH JOSHUA GOULD

E109 The Big Word: Surviving the AI Disruption Cycle with Joshua Gould

Transcript

THE BIG WORD SURVIVING THE AI DISRUPTION CYCLE WITH JOSHUA GOULD

Dalton Anderson: [00:00:00] Welcome to podcast, discuss entrepreneurship, industry trends, and the occasional book review. How do you take a successful service business and turn it into a a hundred million dollar tech titan? Our guest today did exactly that. After starting in the trenches of the company's telemarketer department, he rose to the top to lead a digital revolution in the language industry.

Joshua Gold, appreciate you coming on the show. Thank you for, for being here.

Joshua Gould: Thank you for having me, Dalton.

Dalton Anderson: So today we're just gonna be discussing a couple topics. The agenda, the planned agenda is just talk about how the CEO or professional managers are, are shifting away from. Like, I would say people, managers to like a technical product builder. And then talking about how the AI disruption cycle is similar to what we've seen in the past with the internet boom and bust.

And then how capital is the way to, to [00:01:00] create your moat and. Why internal profits aren't enough. And then if we have time, we'll touch into the apprenticeship programs versus like a traditional college approach. And I think there's a trend that we're seeing, and Joshua's knows more and Josh knows more about that than I do, and we can touch on his approach with the apprenticeship program and then.

The last thing is maybe some, some anecdotal information on entrepreneurship, and I think the premise of it is just getting stuff done. So before we dive into all of that stuff, would you wanna provide a background, more detailed background like of yourself, Josh,

Joshua Gould: Yeah, sure. I won't go back to, uh, kindergarten. I'll save the listeners, uh, from hearing that part of the story. But I moved to the US at 21. I never went to college. I started out in, uh, cos uh, brewers selling beer to, uh, pops in England, learned to sell and then came to [00:02:00] sell language services, uh, in the big word.

And I started out telemarketing. I did the night shift selling to the us and then we decided to come and open up a US office, which is how I ended up here. Um, 21 years ago, so half my lifetime. And I, I mean, that's the story I like to tell people, but the, the parties were a big draw as well in New York City, especially as I kind of like exhausted London and it was time to move on.

So I came to, uh, the, uh, the US and we were actually a language agency, a translation agency. We had built a really good web service, API, into the back end, the back end of a lot of banks, and we would do their equity research documents. So I started a small office on Wall Street and that was just before the collapse and, and the great recession. And the crazy thing then [00:03:00] is like I just got here, I just signed up some clients we were got going and all of a sudden, and I lived on Pine Street, behind Wall Street and I would be going home and I would see my clients walking with like boxes down the street and everyone was fairly optimistic even then there, you know, they would be like, okay, don't worry, we got you in the next bank we go to.

But it didn't work out that way. You know, it was a, a blood bath and it was much worse than people thought. And, uh, I realized I had to pivot to government contracting because George Bush was president at the time and he had this crazy bill to spend, you know, hundreds of billions if not trillions of dollars, uh, through government contractors.

So we pivoted at that point to selling to government rather than banks. And it was really the time when we decided as well that we had to automate, uh, workflows [00:04:00] because we just couldn't be competitive in a, in a government, uh, acquisition environment, which is a lot more stringent than relationship-based banking.

Dalton Anderson: Mm-hmm.

Joshua Gould: So we did that and, you know, we opened a defense business, the support in Afghanistan and Iraq and Syria with human intelligence. And, uh, the business has just become more and more technical and, and gone further and further away from the translation agency. It started with,

Dalton Anderson: Just a natural progression. And the, the translation is, is the big word, right? And then there's a portfolio of, of companies with an under that umbrella, correct.

Joshua Gould: Yeah, so the big word is the name of the company and our, uh, apps all are under something called word sync. So it works a bit like Uber. Uh, so you have your taxi, uh, app, and that's the one that you can click on and, uh, inter uh, uh, and in my case, an interpreter will show up, not a taxi [00:05:00] driver. Uh, you can also get one on video, like we could have an interpreter on the line.

Now if we didn't speak the same language.

Dalton Anderson: Maybe if you were from Liverpool, it'd be, we might need to

Joshua Gould: Yeah, I, we, yeah, we have some regional accents for sure. Uh, I need them myself. When I go to England, I've lived here too long. And then we, uh, and then we have AI in there as well. You can have an ai, uh, linguist. We have subtitling, dubbing, editing all the translation, which is the written work all through an app.

And you can get that on your equivalent of your Uber taxi app. But

Dalton Anderson: Hmm.

Joshua Gould: Uh, 15,000 linguists have the actual taxi app, which is, you know, their app. And we say that's a business in a box so they can accept and decline work. Uh, they're mainly 10 99 workers and they can invoices through the application. And then our own staff, uh, across the globe have their app, [00:06:00] which is really the, the administrative or or management side of it.

And about 80 plus percent of all of our requests are not touched by any one of our employees. It's entirely done through the applications, uh, whether it be the spoken word, whether someone shows up in person, in a police station in Amsterdam, uh,

Dalton Anderson: Mm-hmm.

Joshua Gould: we do about 50% of the Dutch police, uh, calls, uh, or whether it's someone in, um.

Medicare or Medicaid appointment in the us. It doesn't matter whether it's over the phone interpreting and that'll be done by human, whether it's done by ai. It's all really done through the app and that was the huge evolution and I, I can get into it if you want, but we went through many AI disruptions all the way back to 1998.

Dalton Anderson: Yeah, let's, let's dive into it. I think everyone's got the background of. Of the big word and the kind of the gist of it. [00:07:00] And I think now is a good pivot to discussing what changes you're seeing or that have seen over the years, and then bring that back to what people know historically is like the.com bust or these other type of big disruptions.

Joshua Gould: Sure. So a lot of people are saying We've never seen anything like this AI revolution, and I beg to differ. I don't think you have to go back to the industrial revolution. I think you can just go back to the nineties in the.com, uh, boom or boss and you know, I don't know how old your average audience member is, but uh, I'm

Dalton Anderson: I think the analytics is like 27 to 35 is the majority of the

Joshua Gould: Yeah, so I can explain very simply. There was this thing, it was invented by the US Army called the Worldwide Web. We now know that as the internet and, um, the, you know, everyone would say that [00:08:00] was gonna change the world, and it absolutely did. And all these companies, just like we see now with AI, started popping up.

And different to what we see now was, was a, a very immature investment market. So people were taking life savings. Uh, companies were, uh, balance sheet investing in these startups that didn't really have a product, but more of a promise. Uh, they were solutions looking for problems. And some of those were legit.

So your eBays, your PayPals, your Googles, they all started in those days. And um, all of a sudden there was this great big boom and we had the internet. And before that you had to fax, uh, your translation work to our office. And we would sit there with those clickers, you know, like when you're going in and out of the club.

And that's how we counted words.

Dalton Anderson: How do you manage that?

Joshua Gould: people. Yeah, [00:09:00] so there was no Microsoft Word, you know, uh, there was Lotus Notes. That was exciting. Oh, your viewers don't know Lotus. So, uh, but it was an IBM version of Microsoft Office and it was all, uh, insane. And then around, I think it was 98, 99, the bust happened.

And that was where a lot of these businesses, they didn't really any. Add any value on top of the internet. They weren't really doing anything and you know, they just went from billions of dollars of valuation overnight to zero, and people lost a lot of money. Uh, but there were some good companies that solve real problems, like Google, you know, sorted through all of the content and indexed it, and it was a good solution.

Like you could find the websites that you needed to find eBay allowed, like anyone to open up a virtual door. Uh, PayPal allowed us to pay, but [00:10:00] the, the, the big issue with it was logistics because. Everything had to catch up. You know, you could sell your antiques to a Chinese dealer in China through eBay, but how do you get your antique to China?

Well, there wasn't an easy way to ship it back in those days, and it was very expensive. So all the same stuff that you're seeing now, it, it, you know, talked with AI where we've got solutions to problems, but there's too many missing components, uh, to really make it, you know, electrifying yet. That's gonna come.

And I argue that we've had AI for a very long time. I, I, you know, I give the example of my thermostat since I was a kid. Um, and I'm in my forties. You could, you know, twiddle the dial and it would send an electronic signal to your boiler and change the, uh, temperature in your home. And that's artificially intelligent.

So the, you know, what we see now when people [00:11:00] talk about ai, they're talking about large language model, which is like generative ai. And, um, but we've had AI disruptions, uh, throughout history. And in the nineties in my business, there was, uh, something very similar to what we see today where you could reuse pret translated content.

And 70% of all the words that we were charging for. And we were charging like 15 cent a word, and 70% of them just disappeared, uh, overnight because there was something called translation memory. And what we found was, and there was a big argument in the boardroom, should we go and tell our clients our biggest client was Honda?

Should we tell them that 70% of the manuals don't need to be translated anymore? And we just acquired a company called Meka, uh, out of Japan for this client. And all of a sudden, [00:12:00] you know, they didn't look like such a good deal. So we decided to go ahead and tell them, and they were so happy. And they said to us, guyses, we weren't even spending 1% of what we needed to spend.

And they. Basically their revenue continued to grow, even though 70% of you know, there was a 70% discount essentially.

Dalton Anderson: Mm-hmm.

Joshua Gould: And then you get to say like 2013, and there was something called neural machine translation, which is a neural ai. And you know, we, we called it machine learning. I, I think all AI is machine learning.

If you ask me, it's just being rebranded. But neural is large language model. It is what we call AI today, except instead of it being large language model and therefore using the whole internet and YouTube and everything else to tune it, you are now really, you know, very focused on, uh, [00:13:00] small dataset and enough corporate.

So Google came out with this and their first big use case was. Uh, to essentially try and eradicate translation businesses, and we were pretty scared of it. But, you know, again, you know, necessity is a mother of all invention, and we built orchestration tools around it, and we built the wraparound. So the subtitling, the doubling, doubling and editing and e-learning took off because of this.

You know, it became really cheap to create content. Uh, and then sell. And the best value content is what is content that people will pay for. 'cause they're actually gonna learn something. So, you know, uh, e-Learning became big scorn packages, Moodle, you know, these are open source programs for e-learning content.

And we went into that business, uh, as well. And we were very much a government contractor, uh, at the time. So we were doing a lot of interpretation, and that's the spoken word. [00:14:00] But it was very expensive and it took around, uh, 18 different processes, um, to get an interpreter on site. Think of it as like a temporary employer, uh, uh, employment agency, like

Dalton Anderson: sure there's a lot of security in, in those

Joshua Gould: Yeah. So we automated that whole bit and we went from an operations cost in the teens of 20% of our revenue. Uh, down to 4% of our revenue, and that was through, uh. artificially intelligent workflows. I, I described them back in the day before. Everyone used AI and machine learning as self-driving workflows.

And that really, again, it saved the business and it made us very, very competitive price wise as well. And you know, so we continued to grow and you know, as of to today, we have 15,000 human interpreters. We have billions of combinations of, [00:15:00] uh, machine learning engines that can do, you know, to a far lesser extent what the humans can do.

And, um, every single day we receive tens of thousands of requests, mainly from police stations, from hospitals, uh, from military sites. Uh, a lot of our linguists are terrace cleared, uh, various versions of secret cleared. And it's, uh, an exciting business. And, you know, I wake up every single morning, Dalton, to try and eradicate the, the, the final barrier of communication, which is of course, language.

Uh, and really help governments communicate with their population so we can get them in work and we can get them paying taxes and we can get them operational because we are in a major, uh, crunch of people, you know. You know, you look at the jobs report and it's very, very short term, right? It's a month, it's looking at a month.[00:16:00]

You can go back a year. But over the next two, three decades, if we want to compete with China and India as a, as a nation in the US or or, or my other nation, which is the uk, we've got to deploy, uh, people. And, uh, we've got to deploy AI to make those people more efficient. And I think that, uh, our governments really understand that right now, which is why they're really working hard to get as many people in work as possible.

Dalton Anderson: Y Josh, you, you touched on a lot of points there. I mean, a lot of great information. Some things that I thought was interesting was your point about AI being a thing for a while, and that's completely true. You learn in school when you're doing these tech classes, like AI is like a sphere of many different operations, like AI is like.

One. It's not necessarily one thing. It's many different things. It could be automation, it could be machine learning, it could be all these things and [00:17:00] LLMs and its use cases, gin, ai. But it has been the, I guess, main weight or pull towards the word and term AI, where it's not necessarily ai. There's many things that are ai and AI has been around for a while, just not.

As prevalent as it is now with this Geni Gen AI revolution. And then another thing that I thought was interesting was just like the natural evolution of the company. After the pivot where it was, you saw that corporations were gonna be having a hard time with. Af after the events of the.com bust. So you pivoted to government contracts and I, I assume that's somewhat of a, a workflow, restructuring a contract approach.

And then after that, there was like a natural progression. So like from from governments to municipalities. And then now there's the, I, I guess the way you explained it [00:18:00] the best is like the Uber language, Uber translation app, which is really cool. And then. You just keep progressing and, and I would say improving the orchestration infrastructure for language translation.

Joshua Gould: Yeah, that, that's right. I mean, a lot of people, let's face it, no one's really building ai, you know, unless you are called Anthropic or Google.

Dalton Anderson: Yeah.

Joshua Gould: You're not really building ai. What you're doing is you're building orchestration tools. You are building improvement tools. You're building wraparound tools. But here's the good news, and a lot of people I know are very scared of ai and I tell them, you don't need to be scared of, it's not like Anthropic wants to make, um, a product exactly for your client's use case.

And even though my client's, a government and anthropic does sell to my clients, or at least it did until five minutes ago, they, they're not doing it. [00:19:00] They're not doing the Q Bs, you know, the quarterly business reviews. You know, you don't get a CEO to A QBR. Uh, if it's philanthropic and they're not doing the Cyber Essentials Plus, and they're not doing the ISO 27,001, uh, uh, and they're not guaranteeing it's staying.

Uh, you know, the data's gonna stay where they said it's gonna stay for that particular client. And they're not, uh, they're not giving you the ability to put human in the loop, and they're not giving you the ability to switch entirely. I, I have a lot of, uh, clients who start out with, uh, simple ai, uh, call into our software and then switch over midway into human video because it's become too technical, like a medical, this happens all the time in a medical setting. So Atropic is not gonna do that. And you know, unless you are solving a problem for 8 billion people, that's going to make potentially a hundred [00:20:00] plus billion in revenue. You are probably not on Anthropic or Geminis or any other of these few manufacturers of it, you, you're not at risk. And the other thing, this is an interesting statistic that the guy that built the AI for Microsoft told me, he said that 99% of every translated word on the internet is through ai. And I don't doubt that, but my industry grew last year 6%, and that we're doing a lot of translation. So that means that that 1% has got so huge. Uh, because we are so used to reading everything now in our own language. It used to, the whole internet was in English, uh, you know, not too long ago, but we're so used to buying now in English and calling up a call center and speaking English.

But guess what? You know? It wasn't the case for a Chinese [00:21:00] person or a TI speaker or a Haitian, I mean Miami, a Haitian Creole speaker. Now they can do that for the first time ever, so businesses are capitalizing on that. So the macroeconomics of AI are explosive. You know, you look, you know, people are very focused on, on the micro, you know, what they do their, their market, their addressable market, and I say the old market size will be thousands of times, not tens or hundreds, thousands of times bigger because of ai.

And yes, AI might take a good share of your market depending on what your market is, but the small percentage will be much bigger in real dollar terms. Than it is today. And that's what I've seen in our business. You know, our business over the last 30 years has grown, uh, by about an annual 6% by constant, uh, ai, uh, disruptions.

Dalton Anderson: I think the premise of that is just don't be scared. Just dive in. Right.

Joshua Gould: What's the worst that can happen?[00:22:00]

Dalton Anderson: So I think the next topic about that is you went through and led a big revolution and it's a, I think it's a difference of mindset and skillset with. Uh, this big transition of a technology stack for a lot of these companies, like the expectation isn't that you'll think about using LLMs or this Gen ai.

It's like how are you doing that to make your organization more efficient? And how are you making sure that the organization is galvanize and executing consistently on a pace that outpaces their competitors?

Joshua Gould: That's a huge question. Dalton. I think that this is, you know, I, I'm hearing by. Major investors and, you know, on a regular basis, uh, people aren't adopting the AI quickly. Uh, we're spending a lot of money on building out AI products that no one's buying. Uh, and it makes [00:23:00] total sense, right? Because if you are the call center manager and I have a call center of a couple of hundred people, and you know that bringing in an AI agent is gonna make your call center infinitely more profitable.

And it's more scalable. Do you want to bring in the AI agent now? In theory, the answer is yes. But we're dealing with humans and the humans are like, well now there's only one degree of separation between me and the robots. And they'll, uh, try and legitimize their decisions in their head of it. It can't do it as well as I can.

It's gonna upset people, it's gonna damage the brand. And their managers are now only two degrees from the robots. And I think it's in innate in humans to want empire build. So again, AI becomes a threat. So there's a natural bias against ai. Even if you understand its power, [00:24:00] even if you think it's great, even if you really know what you're talking about, you're still thinking there's a bias behind it.

On the other hand, most people don't have the ability to integrate AI because what does that actually mean? You know, like. What do I do? Like, do I go to, uh, philanthropics website, download a, a cloud engine or a cloud engine, whatever you wanna call it. Uh, and, and you, you know, then what do I do? Where do I plug it in?

And, and that's a big issue. You know, these manufacturers are making airplanes and then they're selling it to people who aren't pilots. And right now there's no air traffic control system and there's not enough, uh, runways to take off from. So, you know, it's another roadblock. Right. And all of this will be resolved in time,

Dalton Anderson: With the infrastructure. You made a good point earlier about the infrastructure. The technology's there, the infrastructure isn't there. The, the, even [00:25:00] though the worker skillset, like leader skillset is not necessarily mature to handle the new transition.

Joshua Gould: yeah. I read something really interesting. The other day, and that is that CEOs have been fired at alarming rates and 80% of all new CEOs are first time CEOs. And I can see exactly why because CEOs who are battle Harding, 'cause of COVID and all the disruption, they're saying, hang on a second. You know, they're like pilots.

They're saying, first we just gotta ate, you know? And then we've gotta navigate out the situation, and then we'll communicate exactly how we do it to you. And boards are saying, well, I can't wait that long. You know, I've just invested half a billion dollars into your business and, you know, I'm, I'm not gonna wait until you figure this out.

You know, if you, you know, and some junior, uh, senior leader sticks their hand up and says, I'll figure it [00:26:00] out. So that's why you're seeing 80% of CEOs being replaced by, uh, middle, middle and kind of senior middle management who are saying that they'll figure it out. And maybe that is the right thing to do.

Maybe you need that blind confidence, but even when you do that, you've gotta have great data. The big word has these applications and we learn the hard way that when the data isn't available and it's not good technology scales negatively. You know, it creates this, it creates a problem that exists in the manual human world, and it makes it very big very quickly, and it can get out of control.

So you've got to also have databases and data. And if you are not a tech company already or like a SaaS business, well it's really hard to do that. And that can take a, I think that will take five years for most companies to do that. But if you are enterprising and you can figure it out, I mean, it will give [00:27:00] you a short term advantage.

And, and that's all it's gonna give you. It's gonna give you a short term advantage. There will be a time when all your competitors have the exact same AI as you, and then it's a tax, you know, it's, it's just something you've gotta do and gotta pay for, but you've got no advantage over it. So I would encourage all your, your listeners, Dalton. To make sure that you get ahead of this. 'cause if you can get a two, three year advantage, uh, to your competitors, you can then build on that in other ways outside of AI and stay ahead. Uh, and that's what we're trying to do at the big word with ai.

Dalton Anderson: You, you had quite a bit of advantage though with all the data that you have, like 30 years of data that that's huge

Joshua Gould: I hope

Dalton Anderson: gold. Like a gold mine. The, I think. There was a point about the bias against ai and I, I thought about a similarity when somebody gives you pretty harsh feedback. People have the, like, the first tendency is to be defensive, but [00:28:00] the hard the, the feedback could be true or not be true, but the, the first action people typically have is to be defensive.

Like, oh, Josh doesn't know what he is talking about, or whatever it may be. But if you just take a breath and really think about the situation, it's clear. What you need to do, right? Like I don't, I think it, if you look at it with a wider lens and take that kind of perspective versus just about me or the micro instead of the macro and, and say, okay, well if I'm gonna automate this call center and, and maybe the.

Employees working on the call center are elevated to handle higher escalated calls, like maybe ones that are very serious or security related. Then the AI is not used. It's it's human, and then we're verifying that the calls were interpreted correctly or whatever it may be. [00:29:00] That may be uncomfortable, but if you don't do that, then the.

Issue that that gets created is if your competitors do the same thing that you did not want to do, then your opportunity is gone because you're not competitive and they took your contracts and then that hurts. That doesn't hurt just the person who who didn't make the decision. It hurts all the employees that were there and they were great people.

They could've, they don't necessarily have to do that job. They could do something else. If you've got great people, you can move them into another position. You can upskill them.

Joshua Gould: Yeah, there's nothing worse than working for a manager who loves their team so much that they love them death. What I mean by parents know what I'm talking about. Um, you know, they're trying to protect them by resisting ai, by resisting change, but actually all you're doing is harming their career. You're keeping them in a position in a job that's [00:30:00] probably not gonna exist in a few years, rather than allowing them to move on with their lives.

Uh uh, and I think I see this over and over again. In fact, some of the managers that I've hired who will love the most. We're very guilty of this. And you need that tough love. And I'm a father, and if I went to my two daughters and said, guys, don't worry. Do whatever you want to do. Be a musician, be this, be that.

I've got your back. Like, I'm gonna buy your house. I'm gonna buy your car. I'm gonna make sure food is always on the table by taking away the hunger and the uh, uh, and the necessity. And as humans, we won't do things without hunger and a necessity most of the time. And you know, so what you have to realize is your competition are going to adopt ai.

They may not all adopt AI and they may not all adopt it quickly, but a few will. And if you are seen as a lagger, [00:31:00] your, it doesn't matter what, how good your relationships are, how much they like you. They can't stay with you because AI speeds up delivery. It makes it cheaper. It can do quality control and it's can do so many things that you just can't compete with.

So you, what's gonna happen is you're gonna lose the contracts into your point, Dalton, then you've got no point, you know, sorry. Then you've got no choice but to lose the people and, and all of a sudden these people are gonna be lost on the job market in the worst time possible when every other person who close their eyes to the AI threats and opportunities, you know, are pushing their own workforce, you know, onto the street.

So it, it's going to be a, a, a massive challenge. And, uh, if you are working for a company who's not quick enough on this AI train, I would seriously, if I were you, I would be looking for another job. But no matter how much I like that company, [00:32:00] because I'd be very worried about what's gonna happen in the near future.

Dalton Anderson: And then another point about this analogy that we're working through is if those contracts are gone and the employees have to be let go 'cause there's not enough revenue, they never got upskilled. So they are now. Have to re-skill without a job versus if you made a decision first off, then you could up-skill your employees.

And if they wanted to leave and go somewhere else or move on or do their own thing, that's fine, but like, it just, it's almost like a critical failure point if that's,

Joshua Gould: It's absolutely right. Yeah. I mean, you're absolutely right. Like you can't learn these things, you know, at a 30-year-old or as a 30 5-year-old in the classroom. You are not realistically going to go back to school to do AI courses in the middle of your career. Just like my parents never went back to, you know, learn the internet.

Uh, you know, they did [00:33:00] some night school on Dreamweaver to try and understand what a website. Was, um, you know, that's when you would code a website.

Dalton Anderson: Yeah, dream was pretty cool.

Joshua Gould: Yeah. It'd be basic like HDML coding, uh uh, you know, but you might go and do that, and I would encourage people to do that. Or you'll just listen to podcasts like this and you get the same kind of education, but you are gonna have to learn on the job.

And that's how it, you know. My father used to, you know, always complain to me to say that our technology came with no instruction manual. And I said, but no one reads the instruction manual anymore. And if you do need to read it, you probably shouldn't be working for a tech enabled company like us. Uh, you know, and that's reality.

So you're right, you, you've gotta put your employees in positions that they have to figure it out, that they have to utilize it. It's an education in of itself, and you have to do [00:34:00] this as a thing because as a CEO of the big word, I don't have all the answers. I'm not a techie guy. I might understand a lot about technology, but I can't go and code this stuff myself.

And so I have to build a team around that. And we have to, you know, my receptionist now. Has to be able to use, uh, AI to figure out who someone is as they're walking in, and get as much information out the database as possible about them so we can greet them in a way that they wouldn't be able to be greeted normally.

Uh uh, and, and Ross, who sits on our reception, has to do that, but he also has to, like, when the toilet's backed up and broke and call the plot,

Dalton Anderson: Mm-hmm.

Joshua Gould: you know, or, or the facility manager. So it, it is hard. This is really hard, but it's exciting. So I would, you know, encourage everyone, enjoy this ride. It's not that serious, this thing called life.

You know, you're going to be dead at [00:35:00] some point and you're not gonna think to yourself. Uh, I wish I just hung on to, uh, to that job for a little bit longer or a few more days, or, I really liked the office space and the copy was good. You're not gonna think about it, you're gonna say, I wish I took life by the hands and you know, or the horn, sorry.

And just grabbed it and just went with it and just threw caution. So I promise you, you know, I've imagined myself, I know this makes me sound crazy on my death bed thinking what would I wish? And I do this as an. What would I wish I'd done differently? And it's nearly always, I wish I'd taken more risks. I wish I'd made that investment.

I wish I'd done that. Proof with my family. I wish I'd bought a boat. So guess what I do Dalton, I do it all the next year and uh, which makes me an exhausting husband.

Dalton Anderson: Yeah. No, I think that's a great point though, just to, don't get wary about it. Don't lean, like, just don't get wary. Don't get hesitant. [00:36:00] Lean in, you know, what's the worst that could happen? Like you, you learn a new skill or you upskill yourself and that's not utilized at your job, but that might make you a wiser person or make you more efficient or make you more interesting.

It's not a big deal. And if, if it's a complete waste of time, which it most likely isn't. So what? Like it is what it is. It's not that. It's not that serious.

Joshua Gould: You

Dalton Anderson: Yeah. Yeah.

Joshua Gould: You know, my, my favorite days, uh, on this planet are a day I'm a boater. So, you know, I, I, I have no idea where I'm going. You know, I come out my arena, my marina, and I have to decide, do I turn left? Do I turn right? And then I just go and I see and, and, and, and sometimes we barbecue and sometimes we anchor and sometimes we dock and dine.

But you know, it's a great metaphor to life. You don't need a boat, by the way. You can have a secondhand used kayak off Craigslist and you can have, you know, I think just as much fun. You [00:37:00] know, probably with less people, but you can have just as much fun. You know, I, I, it's a great metaphor, but like, just do it.

Go for it. As long as you know, I think as for a family household, you need to earn at least a hundred thousand bucks that's going to take care of your food, your healthcare. You're not gonna be looking over your shoulder. You're gonna have a little bit of money for a rainy day fund. And if you don't, it's just 'cause you're overspending and that's a choice.

But after that, go and have fun. This AI stuff is going to be fun. It is fun. I, I mean, I, I call up our, uh, AI call center all the time. She's called it Ava. And I ask her, can you sing me a song about the big words? Can you tell me a joke? You know, and it's not a real relationship. It's, you know, there's no, it doesn't mature.

It doesn't, there's no trust there. But it is fun and I love demoing it to people.

Dalton Anderson: Yeah, life can be a treat if you want it to. For sure. Speaking now about treats, what about capital treats? So know in our [00:38:00] pre-interview you discussed the like kind of the change of the what is needed to survive as a a company. And

Joshua Gould: Okay.

Dalton Anderson: those things is, is raising capital versus trying to rely on just internal revenues to scale.

Joshua Gould: yeah, I mean, since we spoke Dalton, I mean the private capital markets, it is been a blood, I would say this is. Probably the opening salvo to a major crisis. And, but the, for your listeners who, who, dunno what I'm talking about. Uh, there is a company, I believe it's called Blue Owl. There's Apollo, there's Blackstone.

These are companies with hundreds of billions of dollars of assets under management. And there's essentially been a run on the banks businesses have not performed as well. They, they, they. Uh, taken out a lot of money and they've deployed that money on Rolls Royce's when [00:39:00] sometimes, you know, a Ford will do. Uh, and we've all seen that if you are in the tech space, you, you know how much people are spending on building technology compared to probably what they need to, to build these minimal lovable projects. So companies that can generate cash now are becoming very favorable. The big word is a cash generative company.

And yes, we spend many millions of dollars in building AI tools, uh, a year, but we have to generate cash and you need cash to build these tools. You know, this isn't free. This is very, very expensive. They're capital intensive. And that's why you saw the Oracles of the world and all these huge companies going out, borrowing tons of money over the last six months, even though they generate a lot of money themselves because they're creating a war chest.

And they know that this is, AI is an infrastructure play for them as much as it is a software play. So [00:40:00] they've gotta build, I, I read that Elon Musk is actually building his own power plants now, and they, they even have changed the law to help. Silicon Valley companies being able to own and operate utility companies so that they're not diverting too much electricity away from me and you who just wanna watch YouTube,

Dalton Anderson: Yeah, we're a couple that, that's funny though 'cause we're like almost a couple steps away from big government. Like maybe, maybe that that is the next government client for you.

Joshua Gould: Yeah. Well, well, they are. I mean, some of these, some of them are our client. We, we have contracts with private sector. Uh, players that act by government and, and some of their revenue is, you know, if you look at a Google revenue or a Tesla revenue is bigger than the GDP of many countries. So, yeah, these are huge organizations and they need a lot of money.

But so do you, if you've got a, uh, company with 10, 20, 30 people, and you've gotta understand that you [00:41:00] don't just make AI happen, you don't just integrate it. When you integrate ai, you've got to often rebuild your own infrastructure, your own text stack. You've got to review all your databases. You've got to bring in experts who understand how to, uh, look at your human workflows and then, uh, incorporate them into, uh, artificial or digital workflows.

And then you bring process, uh, improvement people in because it's an opportunity to cut out waste. And if you don't do that, and a lot of people aren't doing that, Dalton, they're trying to cut corners. They're finding the, the AI is telling them what to do incorrectly, and they're finding that it's just not, it.

It's adding complexity rather than taking it away. So they're saying the AI doesn't work. It's not there yet. You've heard it, you've seen it online. I guarantee you it works. It's there, but [00:42:00] it's, it's that I don't wanna be the one to be blamed for it. And, you know, there's so many companies now that have spent tens of millions of dollars on building technology, but the technology was built on a tech stack that's now 15 to 20 years old, and AI doesn't plug, plug, and play nicely with that. You know, so if, if you are a CTO or a CIO listening to this and you haven't moved over to like this micro service architecture, and if you're not technical, that's basically like Lego, you know, where, where every little service within your technology has its own API, its own ability to integrate. It's going to be very hard.

So first you have to do that, that's your foundation and, and this requires massive capital. It's CapEx, you know, CapEx, it is, uh, it is capital expenditure and banks will lend for this because they see [00:43:00] your, um, your products that you're building have assets. They're intangible, so they're not like buildings, but they see it in a very much the same way.

And that's how companies, I believe Dalton will be valued in the near future. People will say. Okay, with a little bit more ai, can we proforma out what, how profitable this company could be? We've gotta get through this situation now where the capital markets have screeched to a halt and that's making it difficult for private equity and growth equity and VCs to invest.

So that's the other thing you've gotta think about. What's your source of capital for this? You know, maybe that's your house. Maybe you're gonna go and take a mortgage against it. Uh, maybe it's just. You know you really like Wilmer and you really like Mildred and you've kept them around a long time, but they're both earning $60,000 and all in costing you with taxes and OED $150,000.

And maybe it's time for Wilmer and [00:44:00] Mildred to retire because for the greater good of the other 20 people you've got working for you, we need to take that money. 'cause we now need to bring in. You know, a business process expert to map out our AI strategy.

Dalton Anderson: No, I, I agree. One of the points that you had was people integrating AI and then. Being disappointed with the results. I think one of the key points is when you're training your system or trying to do an orchestration or automation of whatever process it may be. It could be digital, it could be human. If you're trying to do that, you've gotta almost spend the same amount of time on that process with AI or these lms.

Than you would if you were an onboarding employee from scratch. Like whatever that process is, you've gotta take the time and train and curate and get the [00:45:00] data like perfect. Get the process, perfect, get the training recipe for the ai perfect. And, and take the time. But I think people think like, okay, well AI's supposed to know everything, so then if I just give it some stuff, then it's gonna know what to do.

That's not how it works. You've gotta be very strategic on the instructions and, and make sure that you don't input any bias. Like, oh, I think, or you should do something like that. 'cause if you tell it like, I think you should do something that, or like, I think you should suggest it's just gonna do what it asks what, what you asked it to do, like that's a request.

There's a lot of things that you have to get to think about when you're setting those up. And I think a lot of times they're rushed. And once again, we talked about it a little bit. Like there isn't a core skill set at every company. And so I think. That there's not enough time and or expertise in, in those areas where it's just, sometimes it falls flat and then, then you're on, on the hook.

Somebody's somebody's upset, and a lot of people are like, well, it's just ai.

Joshua Gould: Yeah. And then. Looking for someone to throw [00:46:00] under the bus, you know? And usually the A CEO or the CTOI, I like to think of it as a car. It's a car engine, right? The AI is an engine. You know, you go out and you buy AI from Gemini. You getting an engine, a great engine, and. You know, you've gotta plug it in so that it can get fuel, that it can get cool air, that it can, you, you've gotta get the timing of the spark exactly right.

It, and if you flood it with fuel, the whole thing blows up. Uh, so you need the, just the exact right amount of fuel and you've gotta put oil in because it can work really well for like five minutes with zero oil. And then the whole thing will just, you know, rip itself to shreds. So. When you go and buy these AI engines, just to your point, there are so many like parts to this and, and that's why people don't want to bring in AI even if they know how good it is.

Because it is so capital intensive and [00:47:00] it's so hard. And you could bring in a new brand, new create engines and the key and it can blow up if you get it wrong. And then you're back to the drawing board and you've upset a lot of clients. So you know, but again. You. This isn't quite an engine. I have faith in America and Europe's leaders and CEOs and CTOs to figure this out.

You know, sometimes you will not know exactly what you are doing, but you will figure it out. And that's the entrepreneurial spirit that the Western world was built on. And it's the entrepreneurial spirit that the eastern world is, is now being copying and in many ways doing it just as well, if not better than us.

So we've just gotta get back to what made us great, which is intrapreneurs, which are entrepreneurs who work for companies and entrepreneurial spirit. And those folks are the ones that don't have to worry about AI taking their job because the AI needs them. More than the other [00:48:00] way around.

Dalton Anderson: Correct. I think that's a great segue while we're closing out to the show, is just talking about like the truth about entrepreneurship or entrepreneurship and really emphasizing the getting stuff done or otherwise the adult version, getting shit done.

Joshua Gould: Yeah, no. It's funny because one of my friends watching podcasts, they're like, oh, you actually have a job. I didn't really realize it was a serious job. Yeah, and I have my own, uh, like first person podcast where I do like Ridealongs. I'm like, oh my God, you actually have an office And real people. I, you know, I thought you were just BSing me.

Uh, you know, because they don't see me as some kind of ivory tower suit. I know I'm in a suit today, but that's actually because I've gotta go to ceremony this afternoon. But that's what the new entrepreneur has to be, or, or CEO. You have to go and understand and you have to get things done. And business is, you know, is, [00:49:00] you know, more fragmented.

There is more, uh, questioning, there is more debate. And if you are not around it, if you're not in the weeds with your tea. You are not gonna be able to do that. In fact, just before I came, uh, on air with you, uh, my, uh, head of security called me up and he said, they don't want you to join the tabletop exercise for our, uh, cyber defense.

So every few months we do, uh, simulated ransom attack. And I said to him, what do you mean I'm a key part of our operating procedures? And, and they say, well, it's a new company and they've never seen a CEO who's been part of the, uh, cyber defense. I said, well, who's going to go on television? I mean, I deal with courts and police stations for some of the biggest cities in Europe and medical for the biggest states in America.

Like, someone's gotta go and deal with it. I've gotta understand, I've gotta be part of it. And who's gonna make that decision to kill [00:50:00] the server? You know, uh, is, is it gonna be you? And he is like, no, I, you know, I, I'll do, I'll make technical decisions, but you know, I can't make some of those decisions. And it just goes to show how, how radically quick the job of being an entrepreneur and a CEO is changing and you can't afford to be like.

My father, who was a very successful businessman and old companies for a lot of money, used to say, don't show me spreadsheets. I don't understand them. Well, you can't get away with that these days. You've gotta understand the technical data. You know, he's a very technical game, and I liken it to, if you compare the very best quarterback in the NFLA hundred years ago to the very worst quarterback in the NFL today, and this could be for any sport, by the way. The, the very worst is going to be significantly faster, [00:51:00] bitter, uh, more operational. And it's the same with entrepreneurs and CEOs and entrepreneurs. You know, we have to be so much better than we would've had to been 30 years ago. But the good news is your kids are gonna be able to see the same.

Dalton Anderson: Josh, what a great episode. You absolutely crushed it with the analogies today, I somebody hit the nail on the head, the, the car engine, the football players. You could do American football, you could do European football, however it may be. But that, that, that is a relatable topic for anybody and relatable to the, the next level of what it means to be a leader, of course, where we are in this world.

Good evening, good afternoon. Good morning. I think the last thing I forgot, Josh, if you want people to get in contact with you, how would you like that to be routed? Like you? Would you like people to reach out to you on LinkedIn or do you have a contact

Joshua Gould: Yeah, [00:52:00] you can find me on LinkedIn. I'm a LinkedIn, uh, whore as they say. You can, you know, I love contact, I love getting mail. Uh, you can sort of. Josh Gould, the big word. Uh, I have my own, like I said, talking Head podcast, uh, called Exec Craft. Uh, so please make my day and and subscribe to that and make sure you're doing the same with the Daltons.

You have a great show here.

Dalton Anderson: Yeah, I'll plug that in as well in the show. Okay. So if you're interested in, in, in Josh's show, I'll plug that in and then I'll also link his LinkedIn. Thank you everybody.