WEBVTT
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Hello and welcome to All the Ambition.
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I'm your still very bald host, OK Spitz.
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The one with all the ambition today is Mr.
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Brian Sampson.
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He is the founder of Plug E-L-U-G-G.
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And I'm excited to talk to you about quality versus cost, recruitment, and hiring, especially in this crazy epoch of AI.
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How do we sort this out, Brian?
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How do I get talent?
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How do I get good people who do good work?
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Rookie, I'm uh I'm honored to be here and uh excited to dive into it.
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Alright, so tell us a little bit about Plug.
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It sounds like you were having a stake in Buenos Aires, and you fell in love with Latin American culture.
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Just as backdrop, my sister is Venezuelan.
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Uh, that whole branch of my family, my nephews, nieces, cousins.
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What do you call the the son or daughter of a nephew or niece?
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Is that what is that?
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Is that a cousin?
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They're all Venezuelan.
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So uh I can relate.
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I was down in Caracas, I worked for a near shore Colombian agency for a while.
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And and I understand uh one of your core competencies is uh is Latam talent.
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Tell tell us all about it, Brian.
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Yeah, absolutely.
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Um so I was um so I I'll I'll first say I regret never having a chance to visit Caracas yet, so I'll have to have to learn learn more from you about your experience there.
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Um I went there before it got totally crazy.
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Yeah, yeah.
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And now now it's in a weird place.
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But you know, if Chevron can hang out, I guess, you know, maybe American tourists can start visiting again, too.
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Yeah, I feel like there's more optimism than pessimism today, you know, at least at least more than uh than that uh you know middle of the night helicopter capture.
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Yes, change the game, hopefully for the better.
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Let's see, let's see how it shakes out.
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Yeah, everyone I've talked to seems like they're more optimistic than they were six months ago.
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So I appreciate that.
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Um but on my side, I was lucky enough to um uh find an investor to fund an idea that I had about 11 years ago.
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Um, and that was really predicated on this world of fintech, uh financial technology.
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Man, this is like all the rage, just like AI is today, that's what fintech was a dozen years ago.
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I was in San Francisco.
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Um, all these new apps for wealth management and lending and all this was hot.
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And um I realized that a lot of these fintech companies had lots of capital, they had a great product vision and thesis, and they were totally agnostic into how their their tech startup was built, the actual, you know, uh uh layer in the tech stack.
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So my thesis was what if we could build a services business that uh built the tech stacks for all these fintechs.
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And I raised some money, um, but it wasn't uh it wasn't 20 million, it was two million.
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Not bad.
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Not bad.
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Um, but if you spent any time in San Francisco, uh it buys you, you know, a little bit of avocado toast and and not much more, uh just a very expensive place to do business.
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Um, we would have blown out our budget on engineers quickly.
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So I decided to look at Latin America, uh, and the place that I zeroed in on was Argentina.
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I had done a backpacking trip a few years prior, uh, really enjoyed my time there, thought the English fluency was out of this world.
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Um, it was just a fun place to visit.
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You know, I had uh some of the best steak of my life there, the best wine, empanadas, tree-lined streets.
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And the lasting uh thing for me was after spending five years in San Francisco, where everybody you know works in tech, they all want to talk about tech all the time, they're working like crazy.
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Um, and I appreciate you know the ambition and the mission-drivenness of it, but the quality of life like wasn't really there.
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So being in in Buenos Aires specifically, which is a mega city, it's just like New York City, and uh people people go to the clubs at 1 a.m.
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They're not coming home, they're going to the clubs at 1 a.m.
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So it's a city that doesn't sleep, um great culture.
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But what I what I just loved about it was they'd spend their Sundays just uh with their friends sitting down in the park.
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They'd have four-hour dinners on a Tuesday, but the only only the first hour was spent eating.
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The next three hours were spent just talking, you know, the phones are off the table, they're just talking, and I really needed that.
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So, you know, there are there are places in the world that I love to visit, there are places that I probably won't go back.
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Um, but Argentina was a place that, wow, this quality of life is through the roof, loved it and decided to build um a company there.
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And this was the company that was building the minimum viable products for fintech companies.
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Uh, grew that to about 85 people, uh, live there, you know, laundry and go to the, you know, it's like Europe where there's a fruit guy and a and a butcher shop, and uh, you know, uh, instead of the giant supermarkets, uh, it's it's really compartmentalized, and you know everybody on the street and you see the same people, amazing community.
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So anyway, uh had a blast there, you know, uh built this company, office space, figured out the tax situation and labor laws and everything.
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Um, and uh I've just been all in ever since.
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So 11 years.
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I'm on my third uh company that focuses on Nearshore.
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All three are still very much in business.
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Um, and Plug is more um uh diverse, I should say.
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So not just Argentina, but all of Latin America.
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Mexico is actually our biggest country.
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We've got about 120 people that are engineers that we uh basically build out to tech companies that need talent.
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And a shift has really happened, Mookie, in that a decade ago it was well, like what does it cost?
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Like, how much can I save by putting developers in Latin America?
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It's a different conversation today.
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Now it's a lot more about AI is happening.
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The only people that really understand how to put this together are senior engineers because you've got these massive code bases, you're trying to integrate different pieces of AI, AI, uh written code, you're trying to QA it, you're trying to do the architecture, the design, the UI, UX.
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There's a lot.
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Um, you need fewer engineers, but more senior, capable engineers.
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And if you're only looking in San Francisco and Austin, you run out of them pretty quickly.
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But there's millions of really capable, senior level engineers that are sure they're cheaper, but they work on the same time zone and they could do all this 10x level engineering work.
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And that's what we're doing.
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Um, we're making the introductions and handling all the payroll, the hardware, the customs, the logistics, kind of an end-to-end solution for people that are looking at Latin America as a place to grow their companies.
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I find it interesting that you've inverted the quality consideration for near shore.
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It used to be commoditized, as we all know, really just cost-driven.
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You got the three legs of the stool, right?
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Which is quality, time, and cost.
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And when it came to cost and more or less time, the near shore idea was appealing, but the quality concern was there off the top.
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Maybe even language issues, some cultural issues, that kind of thing.
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And what you're describing is interesting, especially through the lens of AI, where some of the remedial programming, the agents are doing that with tremendous facility.
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And what most people are coming to understand from a system management point of view, you need senior level programmers who look at groups of code, who understand the business use case, who can potentially even liaise with clients.
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That's the kind of staff that you need on your bench when the agents are there crunching through the JavaScript and putting the protocols together and compiling.
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But you need a brain behind the box.
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Yeah, you're absolutely right.
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You know, this used to be a body problem.
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Like, where can I find the most bodies to do it?
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It's not a body problem anymore.
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It's uh it's a quality problem, like you talked about.
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And um, by the way, as much as we we want it to be, AI isn't always correct.
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So we need uh the senior person who's seen it, uh, seen the reps, seen stuff work, seen stuff that doesn't work, and understands the context and the prompts, um, to ascertain is this is this AI uh result correct or not, and and reject it and push back if they need to.
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And how is the AI adoption in Latam?
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I'm tacitly assuming because you've integrated this talent with such fluidity that it's on a high level, but how is the absorption?
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We know the AI industry is just going a million miles an hour, the frontier models are all competing with each other, and now even the Asian models are coming in on a cost basis.
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Yeah.
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They're a little bit behind, but they're way cheaper.
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So, how does it shake out with your talent pool in terms of their understanding of the frontier models, their utilization of the agentic technology, and their ability to plug this all in?
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Yeah, great, great question.
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And uh, and uh um a really great insight about these Chinese models that are coming in.
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And uh wow, I mean it's it's really impressive what they can do on a pound-for-pound basis.
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Um, but in terms of Latin America, I think there's there's two threads here.
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The first is um I think Americans, we sometimes feel like the world is, you know, like we're we're right in the center of the world.
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We're the only ones that have access to this stuff.
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We don't.
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Uh it's all over, it's really penetrated Latin America.
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Um, and just you know, day-to-day stuff too, you know, Chat GPT and Claude and um basic, like I need a notary for this, or I need this for that, you know, not even like the code stuff, just daily life uh penetration, it's there.
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But in that that next level of the code and understanding how to use it to accelerate your work and be uh a 10x engineer, which is what we talked about for years, it is really well adopted.
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Um in my personal experience, I think uh um Brazil, Argentina, Mexico, and Colombia are probably in that like that first gear.
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Um, this isn't to say there's not individuals everywhere, but I think if you're looking for talent concentration with the AI tools, those are probably the countries I'd look at.
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And maybe the other thread that I'll say is we're in a unique spot where we're connecting, we're sourcing, introducing, and hiring talent for a wide variety of customers.
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And there is this bell curve, just like you see in everything in life, where um, you know, I remember uh, you know, probably 25 years ago, you know, maybe it was the year 2000, and um my grandma um was like, I'll never get a cell phone, this is just a fad, right?
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Um and then even, you know, uh, and you know, she lived another 15 years um and was adamant, you know, no cell phone for me.
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This is this is just a fad, right?
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And and I think uh all all technologies kind of go through that, where there are people that are first movers, um, there are people that are kind of in the middle after a lot of people have used it, and then there are people that you know will never use it.
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And uh AI is is the same thing.
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Um what sometimes we forget about because we're we're probably on that um in that grouping that's that's um more active, is that uh there's still a very small subset of the whole population that's using it.
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Um when you look at you know the 7 billion people on Earth, there's still a small subset that's using it.
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Um more people are not using AI than than are.
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Um and then you look at tech companies, there's still this same bell curve where there are some where it is their absolute DNA, and there are some that are still just kind of tinkering, and a lot of people in the middle that like maybe have a little success and a little bit of well, do we really need AI to do this?
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We're not really sure how to how to put this together.
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So our 120 engineers that we have on projects, they're kind of sliced all across that that bell curve continuum.
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A few, it's all they think about, and there's um the same number of people that haven't even even used it based on um their client environment.
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You bring up a great point about the early adopters versus even the Luddites on the other side.
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They're just like, no way, Jose, this is weird.
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And they're just uncomfortable with it.
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They just they just don't want to change.
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And you make an even better point that clients are like this too.
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So if we're early adopters and we're trending and we're talking about AI all the time, that does not mean that our clients all are.
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So we're shooting ourselves in the foot if we're ahead of the game, too far to the point where we even alienate them or provide them talent that they don't want or don't think they need.
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So that that's a terrific point.
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And part of that is the adoption curve itself, which is onboarding the technology.
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So introducing clients to it, being that good partner who can turn them on to it, and crawl, walk, run.
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You don't want to sort of throw an LLM at them and have them freak out.
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You wanna you wanna tickle them a little bit with the possibilities and then find the right talent to do it.
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That's right, that's right.
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Well said.
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And what what's wild is I I do consulting myself, and to your point, a lot of companies get it.
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Like we need to integrate AI, but there's a people component of that.
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That even if the technology is there, you need humans to use it, and you need the AI to be seamlessly integrated into business processes that have been going on fine without it forever.
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And people are threatened and people are freaked out, and there needs to be that onboarding that needs to take place, not only for talent that you bring in, but for your clients themselves.
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Is that part of your business as well?
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Where you're like kind of AI, AI 101, if it's FinTech or another vertical, hey Brian, can you show us what you got and how we could potentially use it?
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I love I love the way you phrase that because constraints are, you know, every every company has constraints whether they're they're man-made or or machine-made, right?
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Um, but from that, uh I think what's really surprised me is um it's not even enormous giant companies with thousands of employees.
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Companies that maybe have 50 people, um, maybe 30 of them are using AI.
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Um and there might be 12 different ways that they're using it, and they're not even telling each other how they're using it, and there's concurrent projects in parallel that are solving kind of the same thing.
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Um, and that that is so fascinating, is this call it like knowledge management integration plan um that is like the wild west today?
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Because I think companies are facing this problem of do we need like an AI officer that's kind of thinking about this, or um, is this a federal thing, like an AI officer, or is this a state thing, you know, and and each individual or department um decides how and to what extent they want to integrate it.
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Um, that probably needs to be solved first at the company level.
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Well, you know how that can go.
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Uh the the Council of Digital Excellence, just about every major corporation has had that, right?
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Going all the way back to the internets when people didn't know what that is, and then using search for research, and then bringing in the suite of Microsoft products.
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I mean, you you could tell I'm no spring chicken, so I've I've lived through a lot of this from a business case point of view, and every quantum leap that you need to make is incremental, and it and it takes time.
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And and I just have a high degree of skepticism that you're gonna have an executive level position that could bring the hammer on down from on high.
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That's right.
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It usually works from the bottom up in the sense where you make it happen, you offer solutions which are pragmatic and immediate, and then the culture starts to change and become more receptive to it, right?
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Yeah, yeah.
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I just wanted to say I I uh I I finally connected the acronym of the Center of Digital Excellence, which is code, which is which is great.
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Right, right.
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I was a little slow on that one, but um, you know, uh I think FOMO, another acronym, Fear of Missing Out, is really driving a lot of this, and that is um white collar people tend to be on LinkedIn and they read their thread, and it's 90% about my company's using AI for this, and my company's using AI for that, and and you just don't want to be left behind.
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Um, so all of a sudden you're the CEO and you tell your CFO, we need some budget for this.
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I know it wasn't in our plan, but we need some budget for this.
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And and then they hear about guys like uh Jensen, you know, at NVIDIA, who's who says, if my if my engineer making$500,000 a year in base salary is only spending$5,000 a year in tokens, I'm firing them.
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They need to pump it up by a couple zeros.
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That's right.
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And then you know, you're the CEO and the CFO and the CTO of these companies hearing about this, and all of a sudden, you know, there's a seven-figure budget, if not more, for your team to start token maxing, but nobody knows what they're supposed to token max for, right?
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It just this, yeah, yeah.
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That's right.
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It it's a KPI that makes no sense because it's utterly, utterly devoid of strategy.
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Yeah, it's it's it's a tactical approach based on the hype engine.
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That's right.
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And there's no better example than Wall Street itself with CapEx spending in the hundreds of billions.
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Anthropic might be profitable next year, maybe.
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And they're they're the cream of the crop, right?
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So even the frontier models in AI are burning through cash, and it's really expectation more than it is reality at this point.
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But that does beg the question that it is effective.
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Everyone knows that it's gonna be transformative if it isn't already, and it's a little bit like laying cable.
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Remember, laying the fiber.
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Optic cable in the early days of the interwebs.
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And people lost their shirts, and companies went bankrupt digging holes in the Atlantic and laying all that stuff down.
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And it wasn't used for like a decade.
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But now it's the backbone of the internet.
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And I think the data centers and all this stuff will be used, but the question is when and not if.
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And we're in that weird interim period where a scalable near shore solution like what you're offering makes a hell of a lot of sense.
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Because if I'm a CEO or a hiring manager and I need a bench, I need it smart, primed, senior level, scalable, and affordable.
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And it sounds like you've got the secret sauce that could be a viable solution to a company.
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What what kind of clients love you, Brian?
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What is an ideal customer for you?
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Well, that that might be the best uh setup question I've ever heard.
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So so sorry.
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I appreciate that.
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It's the slow the slow thrown fastball for you.
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Tell me why why people love me.
00:23:27.920 --> 00:23:36.240
And uh I think this is like the biggest opportunities, the ancillary uh power usage, the power grid to handle all this stuff.
00:23:36.319 --> 00:23:38.480
I mean, this is just enormous.
00:23:38.799 --> 00:23:51.759
And you know, I read something the other day that every year China's um adding essentially the um the capability of Germany's power grid every year.
00:23:51.920 --> 00:23:57.119
I don't know if you you're nodding, if you heard the same thing, they're adding that to their their their capacity.
00:23:57.680 --> 00:24:00.640
So uh that that I think is is the big driver.
00:24:00.799 --> 00:24:07.680
But in the meantime, you know, we're we're in 2026 and we're talking about how companies can be more effective.
00:24:08.000 --> 00:24:19.200
And it used to be all about price and what um, and that's where you know India, all the good things to say about it.
00:24:19.359 --> 00:24:26.079
Um, I mean, India was like the place to find your your cheaper alternative engineers.
00:24:26.960 --> 00:24:41.920
Um but there's a few things that I think maybe have frustrated and I had I've had uh more conversations than I can count, hundreds of conversations with uh VPs of engineering, directors of engineering, CTOs over the years.
00:24:43.119 --> 00:24:57.519
And um the the common frustration they had was um partially cultural, so uh it's so hierarchical over there, right?
00:24:58.079 --> 00:25:07.680
That um you might have to go through five uh chains in the ladder for your message to get to the engineer actually doing the code.
00:25:08.319 --> 00:25:19.279
Um and uh you'll get a lot of head nodding, which is to say they might acknowledge that they received it, not that they agree with what you're doing, right?
00:25:19.440 --> 00:25:21.599
So you might not get any pushback there.
00:25:21.920 --> 00:25:27.759
So you've got this hierarchy, which also means a lot more layers, which adds to the cost, right?
00:25:28.240 --> 00:25:32.400
Um always go down, feedback never comes back up.
00:25:32.559 --> 00:25:32.880
Okay.
00:25:33.599 --> 00:25:38.960
Um the second is uh wage convergence.
00:25:39.200 --> 00:25:48.000
You know, maybe the story 20 years ago was that you could get somebody for$5 an hour to, you know, rebuild your code base, but wages keep rising.
00:25:48.079 --> 00:25:50.079
And by the way, like that's a good thing.
00:25:50.240 --> 00:25:57.759
You know, the Philippines um, I think were just upgraded recently to what's considered an upper middle income country.
00:25:58.000 --> 00:26:08.880
I I believe the the per capita wage is still you know south of ten thousand dollars a year, but that's a big leak from people that were making like you know eighty dollars a month or something.
00:26:09.200 --> 00:26:19.519
So as wages rise in Asia, um there's less of a gap, you know, between that and Latin America, right?
00:26:20.160 --> 00:26:26.240
Uh so the conversation starts to look at um all the other things that come with it.
00:26:26.559 --> 00:26:29.039
Do I want to do a midnight call?
00:26:29.519 --> 00:26:37.039
Um, do I want to have to go through five layers um to get what I want to get done?
00:26:37.279 --> 00:26:40.720
And then you start to look more at value, right?
00:26:41.039 --> 00:26:59.359
So if I can collaborate with somebody, which is really the way that the tech industry is gone, instead of throwing stuff over the wall and you hope that it was understood and you've got it waiting for you in your inbox or your JIRA, you know, the next day, uh, you you want to be able to collaborate.
00:26:59.519 --> 00:27:09.680
And like, I've got this idea, or I want to debate something, or I want to talk through something, or there's an implementation, or a QA, or a stand up.
00:27:10.160 --> 00:27:14.000
You just you don't you you don't want to keep doing calls at midnight.
00:27:14.079 --> 00:27:16.880
You just want to work your regular hours in the states.
00:27:17.519 --> 00:27:24.720
Um you want to work with the engineer who's actually doing it, not five layers.
00:27:25.200 --> 00:27:33.119
So culturally, Latin America is a lot closer to how we talk and think in the US.
00:27:33.599 --> 00:27:42.480
I personally love that because as a founder, um I chase bright, shiny things and I can do a lot of dumb things.
00:27:42.960 --> 00:27:51.920
And these guys will say, Hey, um, I've got a different way for you to think about this, or stop doing that.
00:27:52.079 --> 00:27:57.839
You know, like they they they really have an opinion and they're passionate, and I want to hear that.
00:27:58.960 --> 00:28:08.799
I would also say that more people than not don't have like an 18-step process for everything.
00:28:09.599 --> 00:28:12.960
Um they're a little bit more spontaneous, you know.
00:28:13.039 --> 00:28:18.640
They like the world is moving fast, you know, we don't have time to document everything in an exact way.
00:28:19.039 --> 00:28:21.680
So we need some flexibility in the way that we work.
00:28:22.160 --> 00:28:35.279
So I can throw an an idea of an outcome or a vision to my team in Latin America, and they can like figure out the in-between, the how, how do we get this done?
00:28:35.440 --> 00:28:37.039
And I think they like that.
00:28:37.599 --> 00:28:47.119
Um, I might be you know in a unique situation, but I but I feel like um with Asia, I kind of have to process it out.
00:28:47.440 --> 00:28:51.200
Um, I think they're less comfortable going off script.
00:28:51.440 --> 00:28:57.440
I think they and and maybe a good example of that is uh it's not software but customer service.
00:28:57.599 --> 00:29:13.599
If you're connected to somebody, you know, call an airline and you've got an issue with your seat or you want to rebook a ticket, and it's not something that is like totally within their script, um, it can get ugly really quickly.
00:29:13.839 --> 00:29:19.039
Or Latin America, I think people kind of go with the rhythm of the flow, it's conversational.
00:29:19.440 --> 00:29:24.319
They they can they can you know move and adapt uh depending on where that's going.
00:29:24.559 --> 00:29:28.400
So now we've got this a lot of the cultural stuff is aligned.
00:29:28.799 --> 00:29:32.720
Um the cost differential is way smaller than it used to be.
00:29:32.880 --> 00:29:38.079
Latin America is still more expensive, but it's it's a lot it's a lot less of a gap.
00:29:38.319 --> 00:29:49.759
Um, you've got this exact same time zone, and in this day and age where you need senior engineers that can kind of see the whole picture, the whole roadmap.
00:29:49.920 --> 00:29:58.480
Um, they understand the tech stack, the priorities, how it all integrates, um, the design, the architecture, the QA.
00:29:59.359 --> 00:30:01.279
You got the whole package there.
00:30:01.519 --> 00:30:08.720
This is this is a continent that's just loaded with talent, a region that's loaded with smart, capable people.
00:30:09.039 --> 00:30:14.559
So it's shifted from cost to quality and talent leverage.
00:30:14.720 --> 00:30:17.039
And that's why I love Latin America.
00:30:17.519 --> 00:30:20.079
And we've been in this world for 11 years.
00:30:20.240 --> 00:30:21.599
We know it intimately.
00:30:21.839 --> 00:30:27.440
We have recruiters in a lot of these countries, operations people in a lot of these countries.
00:30:27.680 --> 00:30:33.279
We understand how to get hardware in and out of there, which is not easy, by the way, uh, with customs.
00:30:33.359 --> 00:30:35.440
We know how to payroll people.
00:30:35.759 --> 00:30:38.720
Not easy to do, you know, and some of these constraints.
00:30:38.960 --> 00:30:48.559
I mean, it's uh if you've ever had to hire somebody in California, you know, and you're and you're you're outside of California, it's it's the same thing, you know.
00:30:48.799 --> 00:30:52.480
There's a lot of complexities uh with the labor law.
00:30:52.960 --> 00:31:14.000
Um so we've done over 500 placements at Plug for clients of all shapes and sizes, and we love this space, uh, helping people understand the nuances of different countries, talent concentrations, hardware, scope, what if you want to scale, what if you want to travel.
00:31:14.240 --> 00:31:16.480
Um, we do these conversations all the time.
00:31:16.559 --> 00:31:22.720
So I really appreciate that question, Mookie, and uh, we'd be happy to talk to any of your audience anytime about that.
00:31:23.039 --> 00:31:23.839
That'd be great.
00:31:24.079 --> 00:31:31.519
Links in the description below to reach out to plug and uh do a discovery session, right?
00:31:31.759 --> 00:31:37.279
Find out what folks need, align it with the talent that you've got.
00:31:37.359 --> 00:31:43.440
And it also sounds like you're bringing a combination of agility and expertise.
00:31:43.920 --> 00:31:51.839
They were wondering years ago why airplanes were falling out of the sky with greater frequency in Asia than here in the States.
00:31:52.079 --> 00:31:56.640
And it literally boiled down to the relationship between the pilot and the co-pilot.
00:31:56.960 --> 00:32:12.400
Yeah, there were instances where literally like the the the 747 was running out of fuel, and the and the co-pilot didn't want to tell the pilot because it would call him out on being negligent, and the plane, and the plane flew into a mountain.
00:32:12.559 --> 00:32:14.000
This is documented, true.
00:32:14.400 --> 00:32:16.160
I I read that exact story, yeah.
00:32:16.240 --> 00:32:28.160
That it I think it was uh a South Korean airline and uh the cultural gap or like the hierarchical gap between the captain, between the captain and even the first officer.
00:32:28.400 --> 00:32:41.039
They had they had the uh what do they call that the black box where it like the conversation, it's like um, excuse me, sir, you know, like they're like a hundred yards away from hitting the mountain, you know.
00:32:41.119 --> 00:32:48.559
Um excuse me, sir, you know, not not not to um interrupt you, but we're gonna crash.
00:32:48.880 --> 00:32:51.440
But and then not to interrupt you, but boom.
00:32:51.759 --> 00:32:56.240
And and and the reason was because of this hierarchical culture.
00:32:56.400 --> 00:33:01.440
And it's not right or wrong, it's just how people live and what they're used to.
00:33:01.599 --> 00:33:04.400
And in Asia, these hierarchies are significant.
00:33:04.559 --> 00:33:11.279
Speaking of Korea, uh, you do not address a person by their name, you address them by who they are.
00:33:11.440 --> 00:33:17.920
So sister, brother, older brother, younger sister, and you say captain.
00:33:18.000 --> 00:33:20.720
You don't even say their name, you say captain.
00:33:20.960 --> 00:33:23.599
We are uh, by the way, out of gas boom.
00:33:24.160 --> 00:33:27.680
So uh that's the same case for software benches.
00:33:27.839 --> 00:33:38.319
So you've got the project lead, and then you might have the developmental guy, and then you have the coder, and it's these ladders of communication.
00:33:38.559 --> 00:33:49.279
And what you're describing is in Latin America, it's kind of like, hey, you know, let's we gotta we got some code to to build here, and let's go for it.
00:33:49.519 --> 00:33:53.200
And the smartest idea in the room wins.
00:33:53.599 --> 00:34:03.680
Uh, we all toss in our ideas, and we're happy to liaise with the client as well, because that hierarchical system creates opacity often.
00:34:03.920 --> 00:34:25.360
I've worked with Asian clients too, where there's this respect factor, and it backfires because it it eliminates the transparency and fluidity between these vital creative brainstorms, between the client who knows the business expectations and business rules, and then the bench who needs to implement, right?
00:34:25.760 --> 00:34:37.119
Yeah, you know, uh on a personal story, um, I was uh lucky enough to be part of the um I did my MBA uh where I got two degrees uh from the same program.
00:34:37.599 --> 00:34:49.119
One from UCLA, where most people uh know, and then the other is National University of Singapore, which if you don't know, it's actually like a top school period in all of Asia.
00:34:49.440 --> 00:34:57.599
Um and we would travel to Singapore at the NUS campus, and we would go to the UCLA campus.
00:34:57.840 --> 00:35:14.719
And whenever we had a UCLA professor, man, they loved to debate, they love to be challenged, like they were almost kind of thriving on uh debate between students, debate with them, and um, all these ideas were up for grabs, right?
00:35:15.519 --> 00:35:18.400
Who could articulate the best idea?
00:35:20.159 --> 00:35:34.880
It couldn't have been further from that in Singapore, where um, you know, we had uh probably 50% of the cohort was uh Americans, and you know, we're kind of used to that.
00:35:35.039 --> 00:35:41.440
And that first time they did that with a Singaporean professor, wow, it it did not go.
00:35:41.599 --> 00:35:42.880
It did not go over well.
00:35:43.039 --> 00:35:49.679
Um, you could feel this almost like flush of embarrassment that they were being challenged, right?
00:35:50.079 --> 00:35:53.920
Um, I'm professor, I'm I'm highest on the hierarchy.
00:35:54.559 --> 00:35:56.639
We don't we don't debate ideas.
00:35:56.960 --> 00:36:00.159
I set up my podium and I lecture and that's it.
00:36:00.400 --> 00:36:01.039
That's right.
00:36:01.199 --> 00:36:08.559
And Latin America is so much more closely aligned with how we communicate in in the US.
00:36:09.199 --> 00:36:15.360
And uh uh there's this this like natural kind of horizontal structure.
00:36:15.519 --> 00:36:32.079
You don't have these giant pyramids, and and the way we we do business today, it's a lot closer to um small teams of um mostly uh uh equal, at least on a meritocracy basis.
00:36:34.079 --> 00:36:38.079
Once again, it's agility, it's able to do it quick, be responsive.
00:36:38.239 --> 00:36:51.599
And to your point that you were making earlier, it's not just a bunch of guys who love yelling at each other and getting getting in deep, but they're bringing chops, they're bringing expertise, they're they're bringing uh know-how.
00:36:51.840 --> 00:36:55.840
And how do you substantiate that among your among your recruits?
00:36:56.000 --> 00:36:59.760
I think you know, listeners and viewers will be like, okay, Brian, this sounds terrific.
00:36:59.920 --> 00:37:10.000
I get uh the suavissimo Latin kind of stuff where they roll their sleeves up and their the soccer game is on is in the background and everyone is brainstorming.
00:37:10.159 --> 00:37:10.320
Yeah.
00:37:10.480 --> 00:37:13.199
But how do we know you're bringing us top talent?
00:37:13.519 --> 00:37:15.679
Yeah, yeah, great question.
00:37:16.079 --> 00:37:22.079
This is something that I've thought a lot about, and it's very important to the ethos of plug.
00:37:23.599 --> 00:37:36.239
My very first business in Latin America when I was living in Argentina, we set it up with the typical structure of hey, like we're a technical team, technical company.
00:37:36.800 --> 00:37:46.320
We have our very own CTO, our very own architect, our very own tech lead, project manager, so forth.
00:37:46.559 --> 00:38:05.760
So whenever we talk to the client, um, it was you know our tech guys and their tech guys, and and that that worked really good until you realize that everybody has their own egos and their own biases that they bring to hiring.
00:38:06.000 --> 00:38:08.480
And hiring is a lot like driving.
00:38:08.719 --> 00:38:12.719
Uh 90% of us think we're great drivers.
00:38:12.960 --> 00:38:14.880
Uh, we're probably not, you know.
00:38:15.039 --> 00:38:17.840
Um uh and it's just a bell curve, just like anything else.
00:38:18.000 --> 00:38:19.039
Same with hiring.
00:38:19.280 --> 00:38:25.679
A few people are great at great at hiring, but if you ask the average person, we all think we're we're pretty good at it, right?
00:38:26.079 --> 00:38:32.639
Um and you hear this adage of, man, all these companies, we only hire the top 1%.
00:38:33.360 --> 00:38:41.599
Well, if the unemployment rate is only 2% in tech, somebody's you know not uh vetting properly.
00:38:41.920 --> 00:38:49.679
So we I think after a little while I started to see this pattern of, well, we think this engineer is great.
00:38:49.840 --> 00:38:54.320
That client has no idea what they're doing if they don't think if they don't think the same way.
00:38:54.639 --> 00:39:03.360
Um, and everyone's got their own hiring bars, and they feel like everybody else's engineers suck, and they're the only ones that have great engineers.
00:39:04.079 --> 00:39:07.760
So um, so how do we how do we flip this?
00:39:08.320 --> 00:39:17.519
And plug from day one, um we got rid of the engineer technical vetting, which sounds crazy.
00:39:17.679 --> 00:39:18.559
Like, why would you do that?
00:39:18.719 --> 00:39:20.400
Aren't you wasting people's time?
00:39:20.960 --> 00:39:33.280
No, what we really try to do is align that particular hiring manager what their biases are, and meaning bias is not a dirty word.
00:39:33.519 --> 00:39:36.559
It doesn't have to be, it doesn't mean discrimination.
00:39:36.960 --> 00:39:41.199
It means that we all kind of bring our own baggage and stuff into it, you know.
00:39:41.360 --> 00:39:43.760
Um, for example, I grew up in the Midwest.
00:39:43.920 --> 00:39:54.880
I went to a school before grad school that nobody ever heard of, you know, and I think that um probably drove a lot of my own hiring decisions for the first part of my career.
00:39:54.960 --> 00:40:00.480
You know, I wanted the the kids with a chip on their shoulder, you know, I didn't want the elite institutions.
00:40:01.039 --> 00:40:11.440
Um, and you think about uh Google and Facebook, you know, a decade ago, where Google hired the PhDs and it was like academia style.
00:40:11.599 --> 00:40:23.840
Uh but Facebook was move fast and break things, and let's hire the hacker that um you know uh dropped out of high school, you know, to build dropped out of Harvard like himself.
00:40:24.159 --> 00:40:24.480
That's right.
00:40:24.639 --> 00:40:25.199
That's right.
00:40:25.360 --> 00:40:42.320
So, you know, we all have our own biases, and it's our job to just hey, like it is what it is, let's find out what really matters to you and make sure that we map up, uh map the experience, the types of projects, the types of companies.
00:40:42.480 --> 00:40:49.360
We vet for English level, uh, we vet for uh the environments, the technical skills.
00:40:49.599 --> 00:40:55.360
Um so by the time that they've met these candidates, it's a small short list.
00:40:55.679 --> 00:40:58.800
We're not sending big batches and praying.
00:40:58.960 --> 00:41:02.480
It's a small vetted short list that everything is aligned.
00:41:02.639 --> 00:41:09.920
And then usually um it's actually less than three to one uh um candidates we send that get an offer.
00:41:10.000 --> 00:41:11.199
So it's 2.8.
00:41:11.679 --> 00:41:20.880
So every every 2.8 candidates that we send over, uh one of them gets hired, and our retention rate is over 90%.
00:41:21.519 --> 00:41:35.280
So our clients are are very happy because they're treated as unique, and we understand that they they have their own bias, their own baggage, their own way of looking at the world, and that's perfectly fine.
00:41:35.360 --> 00:41:38.400
It's nothing to um to be worried about.
00:41:39.199 --> 00:41:56.079
So I'm here at bald ambition, and I might need a bench of agile experts who can come in with guns blazing, who are willing to brainstorm with me, who have a flat organizational structure so there's no red tape, and we can get the job done.
00:41:56.320 --> 00:41:57.519
How does it work?
00:41:57.840 --> 00:42:00.159
I uh arrange a meeting with you.
00:42:00.320 --> 00:42:09.760
Is there a discovery session that you conduct in terms of analyzing my baldness to see what kind of software needs I might have?
00:42:09.920 --> 00:42:12.880
Is is that the usual process for people who reach out?
00:42:13.119 --> 00:42:14.320
Yeah, I appreciate that.
00:42:14.480 --> 00:42:20.000
Um, so generally, um, in that very first call, um, we accomplish a lot.
00:42:20.159 --> 00:42:23.119
Uh, our clients are really impressed with what we can get done.
00:42:23.360 --> 00:42:28.320
So, uh, first and foremost, we want to understand the culture, the biases, the job description.
00:42:28.400 --> 00:42:31.280
You know, we usually bring a recruiter in on that as well.
00:42:31.360 --> 00:42:33.280
So they're asking their questions.
00:42:33.440 --> 00:42:36.400
We don't have stuff lost in the game of telephone.
00:42:36.559 --> 00:42:50.639
Um, and then the second part of that is based on your needs and constraints, whether it's budget or scale, or they need to use the souped up MacBook, or they're kind of open on that.
00:42:50.880 --> 00:43:03.119
Um, and there's like a wide variety of things where they want their team co-located, even if down in the same office, the same city, um, or they want to be able to fly within four hours to reach their team.
00:43:03.280 --> 00:43:08.719
You know, there's a lot of different things that that um um we've seen firsthand.
00:43:09.119 --> 00:43:29.679
So we want to be able to not dictate, but advise and say, hey, you know, Mookie, based on uh um the fact that you want four people um and you've got this pretty sophisticated skill set that you're looking for, you know, we think Brazil is gonna be the best option for you.
00:43:30.000 --> 00:43:40.480
Um or you know, uh based on this need, you know, if we could um recommend a country would be uh Nicaragua, you know, based on this.
00:43:40.719 --> 00:43:44.559
So we want to be able to offer suggestions, advice, counsel.
00:43:44.800 --> 00:43:55.760
And then uh usually within um uh 24 to 72 hours, somewhere in that window, you'll get uh some initial candidate flow because we we know all these people.
00:43:56.079 --> 00:43:58.400
11 years uh in this region.
00:43:58.960 --> 00:44:03.840
Uh we've got a really big network of people that trust us and they know us.
00:44:04.000 --> 00:44:07.119
And and I'd say that's something we didn't really cover.
00:44:07.360 --> 00:44:12.800
Um, but uh everyone's wondering, okay, like I'm ready to hire in Latin America, what do I need plug for?
00:44:13.599 --> 00:44:29.119
There is a kind of an inherent distrust, distrust of people in Latin America over people that aren't from there, and it takes a while to build credibility and do what you say you're gonna do.
00:44:29.519 --> 00:44:32.639
And we've been very steadfast on that.
00:44:32.800 --> 00:44:46.000
So if you're a brand new company trying to hire there, um, I mean, even you know, I I heard a story recently of DoorDash was trying to do this in Mexico, and people thought it was like a scam, you know, they didn't really know them, yeah.
00:44:46.239 --> 00:44:47.119
I read that too.
00:44:47.199 --> 00:44:51.519
It was a big fail because people did not trust what was going on.
00:44:51.679 --> 00:44:54.960
And yeah, and you've got you've been there a decade now, right?
00:44:55.199 --> 00:44:55.840
Yeah, that's right.
00:44:56.000 --> 00:44:56.320
That's right.
00:44:56.719 --> 00:44:57.440
That's important.
00:44:57.519 --> 00:45:05.199
So, from a trust point of view and an agile expert point of view, we'll put again the link in the description.
00:45:05.360 --> 00:45:07.119
Thank you so much, Brian.
00:45:07.440 --> 00:45:08.000
My pleasure.
00:45:08.079 --> 00:45:08.320
Thank you.
00:45:08.800 --> 00:45:10.800
What plug has to offer.
00:45:11.039 --> 00:45:20.159
Uh, and if you're out there as a as a client or potential client, don't just run the claw and swarm a bunch of agents.
00:45:20.639 --> 00:45:30.000
You need human talent on your bench, and Brian and Plug offer a scalable solution that can do it the right way.
00:45:30.320 --> 00:45:31.440
Appreciate that, Mookie.
00:45:31.599 --> 00:45:31.920
Thank you.
00:45:32.480 --> 00:45:34.719
Thank you so much, Brian, for your time.
00:45:34.880 --> 00:45:37.280
Like, comment, share, everybody.
00:45:37.519 --> 00:45:40.079
And uh give Plug a plug.
00:45:41.039 --> 00:45:43.280
They'll boost, they'll boost your juice.
00:45:43.519 --> 00:45:44.559
Thank you so much.
00:45:44.800 --> 00:45:45.679
Thank you.
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Hello and welcome to All the Ambition.
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I'm your still very bald host, OK Spitz.
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The one with all the ambition today is Mr.
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Brian Sampson.
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He is the founder of Plug E-L-U-G-G.
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And I'm excited to talk to you about quality versus cost, recruitment, and hiring, especially in this crazy epoch of AI.
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How do we sort this out, Brian?
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How do I get talent?
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How do I get good people who do good work?
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Rookie, I'm uh I'm honored to be here and uh excited to dive into it.
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Alright, so tell us a little bit about Plug.
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It sounds like you were having a stake in Buenos Aires, and you fell in love with Latin American culture.
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Just as backdrop, my sister is Venezuelan.
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Uh, that whole branch of my family, my nephews, nieces, cousins.
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What do you call the the son or daughter of a nephew or niece?
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Is that what is that?
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Is that a cousin?
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They're all Venezuelan.
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So uh I can relate.
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I was down in Caracas, I worked for a near shore Colombian agency for a while.
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And and I understand uh one of your core competencies is uh is Latam talent.
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Tell tell us all about it, Brian.
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Yeah, absolutely.
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Um so I was um so I I'll I'll first say I regret never having a chance to visit Caracas yet, so I'll have to have to learn learn more from you about your experience there.
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Um I went there before it got totally crazy.
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Yeah, yeah.
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And now now it's in a weird place.
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But you know, if Chevron can hang out, I guess, you know, maybe American tourists can start visiting again, too.
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Yeah, I feel like there's more optimism than pessimism today, you know, at least at least more than uh than that uh you know middle of the night helicopter capture.
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Yes, change the game, hopefully for the better.
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Let's see, let's see how it shakes out.
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Yeah, everyone I've talked to seems like they're more optimistic than they were six months ago.
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So I appreciate that.
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Um but on my side, I was lucky enough to um uh find an investor to fund an idea that I had about 11 years ago.
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Um, and that was really predicated on this world of fintech, uh financial technology.
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Man, this is like all the rage, just like AI is today, that's what fintech was a dozen years ago.
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I was in San Francisco.
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Um, all these new apps for wealth management and lending and all this was hot.
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And um I realized that a lot of these fintech companies had lots of capital, they had a great product vision and thesis, and they were totally agnostic into how their their tech startup was built, the actual, you know, uh uh layer in the tech stack.
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So my thesis was what if we could build a services business that uh built the tech stacks for all these fintechs.
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And I raised some money, um, but it wasn't uh it wasn't 20 million, it was two million.
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Not bad.
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Not bad.
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Um, but if you spent any time in San Francisco, uh it buys you, you know, a little bit of avocado toast and and not much more, uh just a very expensive place to do business.
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Um, we would have blown out our budget on engineers quickly.
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So I decided to look at Latin America, uh, and the place that I zeroed in on was Argentina.
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I had done a backpacking trip a few years prior, uh, really enjoyed my time there, thought the English fluency was out of this world.
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Um, it was just a fun place to visit.
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You know, I had uh some of the best steak of my life there, the best wine, empanadas, tree-lined streets.
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And the lasting uh thing for me was after spending five years in San Francisco, where everybody you know works in tech, they all want to talk about tech all the time, they're working like crazy.
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Um, and I appreciate you know the ambition and the mission-drivenness of it, but the quality of life like wasn't really there.
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So being in in Buenos Aires specifically, which is a mega city, it's just like New York City, and uh people people go to the clubs at 1 a.m.
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They're not coming home, they're going to the clubs at 1 a.m.
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So it's a city that doesn't sleep, um great culture.
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But what I what I just loved about it was they'd spend their Sundays just uh with their friends sitting down in the park.
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They'd have four-hour dinners on a Tuesday, but the only only the first hour was spent eating.
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The next three hours were spent just talking, you know, the phones are off the table, they're just talking, and I really needed that.
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So, you know, there are there are places in the world that I love to visit, there are places that I probably won't go back.
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Um, but Argentina was a place that, wow, this quality of life is through the roof, loved it and decided to build um a company there.
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And this was the company that was building the minimum viable products for fintech companies.
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Uh, grew that to about 85 people, uh, live there, you know, laundry and go to the, you know, it's like Europe where there's a fruit guy and a and a butcher shop, and uh, you know, uh, instead of the giant supermarkets, uh, it's it's really compartmentalized, and you know everybody on the street and you see the same people, amazing community.
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So anyway, uh had a blast there, you know, uh built this company, office space, figured out the tax situation and labor laws and everything.
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Um, and uh I've just been all in ever since.
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So 11 years.
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I'm on my third uh company that focuses on Nearshore.
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All three are still very much in business.
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Um, and Plug is more um uh diverse, I should say.
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So not just Argentina, but all of Latin America.
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Mexico is actually our biggest country.
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We've got about 120 people that are engineers that we uh basically build out to tech companies that need talent.
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And a shift has really happened, Mookie, in that a decade ago it was well, like what does it cost?
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Like, how much can I save by putting developers in Latin America?
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It's a different conversation today.
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Now it's a lot more about AI is happening.
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The only people that really understand how to put this together are senior engineers because you've got these massive code bases, you're trying to integrate different pieces of AI, AI, uh written code, you're trying to QA it, you're trying to do the architecture, the design, the UI, UX.
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There's a lot.
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Um, you need fewer engineers, but more senior, capable engineers.
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And if you're only looking in San Francisco and Austin, you run out of them pretty quickly.
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But there's millions of really capable, senior level engineers that are sure they're cheaper, but they work on the same time zone and they could do all this 10x level engineering work.
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And that's what we're doing.
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Um, we're making the introductions and handling all the payroll, the hardware, the customs, the logistics, kind of an end-to-end solution for people that are looking at Latin America as a place to grow their companies.
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I find it interesting that you've inverted the quality consideration for near shore.
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It used to be commoditized, as we all know, really just cost-driven.
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You got the three legs of the stool, right?
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Which is quality, time, and cost.
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And when it came to cost and more or less time, the near shore idea was appealing, but the quality concern was there off the top.
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Maybe even language issues, some cultural issues, that kind of thing.
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And what you're describing is interesting, especially through the lens of AI, where some of the remedial programming, the agents are doing that with tremendous facility.
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And what most people are coming to understand from a system management point of view, you need senior level programmers who look at groups of code, who understand the business use case, who can potentially even liaise with clients.
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That's the kind of staff that you need on your bench when the agents are there crunching through the JavaScript and putting the protocols together and compiling.
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But you need a brain behind the box.
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Yeah, you're absolutely right.
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You know, this used to be a body problem.
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Like, where can I find the most bodies to do it?
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It's not a body problem anymore.
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It's uh it's a quality problem, like you talked about.
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And um, by the way, as much as we we want it to be, AI isn't always correct.
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So we need uh the senior person who's seen it, uh, seen the reps, seen stuff work, seen stuff that doesn't work, and understands the context and the prompts, um, to ascertain is this is this AI uh result correct or not, and and reject it and push back if they need to.
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And how is the AI adoption in Latam?
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I'm tacitly assuming because you've integrated this talent with such fluidity that it's on a high level, but how is the absorption?
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We know the AI industry is just going a million miles an hour, the frontier models are all competing with each other, and now even the Asian models are coming in on a cost basis.
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Yeah.
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They're a little bit behind, but they're way cheaper.
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So, how does it shake out with your talent pool in terms of their understanding of the frontier models, their utilization of the agentic technology, and their ability to plug this all in?
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Yeah, great, great question.
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And uh, and uh um a really great insight about these Chinese models that are coming in.
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And uh wow, I mean it's it's really impressive what they can do on a pound-for-pound basis.
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Um, but in terms of Latin America, I think there's there's two threads here.
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The first is um I think Americans, we sometimes feel like the world is, you know, like we're we're right in the center of the world.
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We're the only ones that have access to this stuff.
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We don't.
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Uh it's all over, it's really penetrated Latin America.
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Um, and just you know, day-to-day stuff too, you know, Chat GPT and Claude and um basic, like I need a notary for this, or I need this for that, you know, not even like the code stuff, just daily life uh penetration, it's there.
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But in that that next level of the code and understanding how to use it to accelerate your work and be uh a 10x engineer, which is what we talked about for years, it is really well adopted.
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Um in my personal experience, I think uh um Brazil, Argentina, Mexico, and Colombia are probably in that like that first gear.
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Um, this isn't to say there's not individuals everywhere, but I think if you're looking for talent concentration with the AI tools, those are probably the countries I'd look at.
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And maybe the other thread that I'll say is we're in a unique spot where we're connecting, we're sourcing, introducing, and hiring talent for a wide variety of customers.
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And there is this bell curve, just like you see in everything in life, where um, you know, I remember uh, you know, probably 25 years ago, you know, maybe it was the year 2000, and um my grandma um was like, I'll never get a cell phone, this is just a fad, right?
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Um and then even, you know, uh, and you know, she lived another 15 years um and was adamant, you know, no cell phone for me.
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This is this is just a fad, right?
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And and I think uh all all technologies kind of go through that, where there are people that are first movers, um, there are people that are kind of in the middle after a lot of people have used it, and then there are people that you know will never use it.
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And uh AI is is the same thing.
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Um what sometimes we forget about because we're we're probably on that um in that grouping that's that's um more active, is that uh there's still a very small subset of the whole population that's using it.
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Um when you look at you know the 7 billion people on Earth, there's still a small subset that's using it.
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Um more people are not using AI than than are.
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Um and then you look at tech companies, there's still this same bell curve where there are some where it is their absolute DNA, and there are some that are still just kind of tinkering, and a lot of people in the middle that like maybe have a little success and a little bit of well, do we really need AI to do this?
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We're not really sure how to how to put this together.
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So our 120 engineers that we have on projects, they're kind of sliced all across that that bell curve continuum.
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A few, it's all they think about, and there's um the same number of people that haven't even even used it based on um their client environment.
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You bring up a great point about the early adopters versus even the Luddites on the other side.
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They're just like, no way, Jose, this is weird.
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And they're just uncomfortable with it.
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They just they just don't want to change.
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And you make an even better point that clients are like this too.
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So if we're early adopters and we're trending and we're talking about AI all the time, that does not mean that our clients all are.
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So we're shooting ourselves in the foot if we're ahead of the game, too far to the point where we even alienate them or provide them talent that they don't want or don't think they need.
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So that that's a terrific point.
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And part of that is the adoption curve itself, which is onboarding the technology.
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So introducing clients to it, being that good partner who can turn them on to it, and crawl, walk, run.
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You don't want to sort of throw an LLM at them and have them freak out.
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You wanna you wanna tickle them a little bit with the possibilities and then find the right talent to do it.
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That's right, that's right.
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Well said.
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And what what's wild is I I do consulting myself, and to your point, a lot of companies get it.
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Like we need to integrate AI, but there's a people component of that.
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That even if the technology is there, you need humans to use it, and you need the AI to be seamlessly integrated into business processes that have been going on fine without it forever.
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And people are threatened and people are freaked out, and there needs to be that onboarding that needs to take place, not only for talent that you bring in, but for your clients themselves.
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Is that part of your business as well?
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Where you're like kind of AI, AI 101, if it's FinTech or another vertical, hey Brian, can you show us what you got and how we could potentially use it?
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I love I love the way you phrase that because constraints are, you know, every every company has constraints whether they're they're man-made or or machine-made, right?
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Um, but from that, uh I think what's really surprised me is um it's not even enormous giant companies with thousands of employees.
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Companies that maybe have 50 people, um, maybe 30 of them are using AI.
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Um and there might be 12 different ways that they're using it, and they're not even telling each other how they're using it, and there's concurrent projects in parallel that are solving kind of the same thing.
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Um, and that that is so fascinating, is this call it like knowledge management integration plan um that is like the wild west today?
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Because I think companies are facing this problem of do we need like an AI officer that's kind of thinking about this, or um, is this a federal thing, like an AI officer, or is this a state thing, you know, and and each individual or department um decides how and to what extent they want to integrate it.
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Um, that probably needs to be solved first at the company level.
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Well, you know how that can go.
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Uh the the Council of Digital Excellence, just about every major corporation has had that, right?
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Going all the way back to the internets when people didn't know what that is, and then using search for research, and then bringing in the suite of Microsoft products.
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I mean, you you could tell I'm no spring chicken, so I've I've lived through a lot of this from a business case point of view, and every quantum leap that you need to make is incremental, and it and it takes time.
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And and I just have a high degree of skepticism that you're gonna have an executive level position that could bring the hammer on down from on high.
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That's right.
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It usually works from the bottom up in the sense where you make it happen, you offer solutions which are pragmatic and immediate, and then the culture starts to change and become more receptive to it, right?
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Yeah, yeah.
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I just wanted to say I I uh I I finally connected the acronym of the Center of Digital Excellence, which is code, which is which is great.
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Right, right.
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I was a little slow on that one, but um, you know, uh I think FOMO, another acronym, Fear of Missing Out, is really driving a lot of this, and that is um white collar people tend to be on LinkedIn and they read their thread, and it's 90% about my company's using AI for this, and my company's using AI for that, and and you just don't want to be left behind.
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Um, so all of a sudden you're the CEO and you tell your CFO, we need some budget for this.
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I know it wasn't in our plan, but we need some budget for this.
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And and then they hear about guys like uh Jensen, you know, at NVIDIA, who's who says, if my if my engineer making$500,000 a year in base salary is only spending$5,000 a year in tokens, I'm firing them.
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They need to pump it up by a couple zeros.
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That's right.
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And then you know, you're the CEO and the CFO and the CTO of these companies hearing about this, and all of a sudden, you know, there's a seven-figure budget, if not more, for your team to start token maxing, but nobody knows what they're supposed to token max for, right?
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It just this, yeah, yeah.
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That's right.
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It it's a KPI that makes no sense because it's utterly, utterly devoid of strategy.
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Yeah, it's it's it's a tactical approach based on the hype engine.
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That's right.
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And there's no better example than Wall Street itself with CapEx spending in the hundreds of billions.
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Anthropic might be profitable next year, maybe.
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And they're they're the cream of the crop, right?
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So even the frontier models in AI are burning through cash, and it's really expectation more than it is reality at this point.
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But that does beg the question that it is effective.
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Everyone knows that it's gonna be transformative if it isn't already, and it's a little bit like laying cable.
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Remember, laying the fiber.
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Optic cable in the early days of the interwebs.
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And people lost their shirts, and companies went bankrupt digging holes in the Atlantic and laying all that stuff down.
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And it wasn't used for like a decade.
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But now it's the backbone of the internet.
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And I think the data centers and all this stuff will be used, but the question is when and not if.
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And we're in that weird interim period where a scalable near shore solution like what you're offering makes a hell of a lot of sense.
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Because if I'm a CEO or a hiring manager and I need a bench, I need it smart, primed, senior level, scalable, and affordable.
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And it sounds like you've got the secret sauce that could be a viable solution to a company.
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What what kind of clients love you, Brian?
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What is an ideal customer for you?
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Well, that that might be the best uh setup question I've ever heard.
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So so sorry.
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I appreciate that.
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It's the slow the slow thrown fastball for you.
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Tell me why why people love me.
00:23:27.920 --> 00:23:36.240
And uh I think this is like the biggest opportunities, the ancillary uh power usage, the power grid to handle all this stuff.
00:23:36.319 --> 00:23:38.480
I mean, this is just enormous.
00:23:38.799 --> 00:23:51.759
And you know, I read something the other day that every year China's um adding essentially the um the capability of Germany's power grid every year.
00:23:51.920 --> 00:23:57.119
I don't know if you you're nodding, if you heard the same thing, they're adding that to their their their capacity.
00:23:57.680 --> 00:24:00.640
So uh that that I think is is the big driver.
00:24:00.799 --> 00:24:07.680
But in the meantime, you know, we're we're in 2026 and we're talking about how companies can be more effective.
00:24:08.000 --> 00:24:19.200
And it used to be all about price and what um, and that's where you know India, all the good things to say about it.
00:24:19.359 --> 00:24:26.079
Um, I mean, India was like the place to find your your cheaper alternative engineers.
00:24:26.960 --> 00:24:41.920
Um but there's a few things that I think maybe have frustrated and I had I've had uh more conversations than I can count, hundreds of conversations with uh VPs of engineering, directors of engineering, CTOs over the years.
00:24:43.119 --> 00:24:57.519
And um the the common frustration they had was um partially cultural, so uh it's so hierarchical over there, right?
00:24:58.079 --> 00:25:07.680
That um you might have to go through five uh chains in the ladder for your message to get to the engineer actually doing the code.
00:25:08.319 --> 00:25:19.279
Um and uh you'll get a lot of head nodding, which is to say they might acknowledge that they received it, not that they agree with what you're doing, right?
00:25:19.440 --> 00:25:21.599
So you might not get any pushback there.
00:25:21.920 --> 00:25:27.759
So you've got this hierarchy, which also means a lot more layers, which adds to the cost, right?
00:25:28.240 --> 00:25:32.400
Um always go down, feedback never comes back up.
00:25:32.559 --> 00:25:32.880
Okay.
00:25:33.599 --> 00:25:38.960
Um the second is uh wage convergence.
00:25:39.200 --> 00:25:48.000
You know, maybe the story 20 years ago was that you could get somebody for$5 an hour to, you know, rebuild your code base, but wages keep rising.
00:25:48.079 --> 00:25:50.079
And by the way, like that's a good thing.
00:25:50.240 --> 00:25:57.759
You know, the Philippines um, I think were just upgraded recently to what's considered an upper middle income country.
00:25:58.000 --> 00:26:08.880
I I believe the the per capita wage is still you know south of ten thousand dollars a year, but that's a big leak from people that were making like you know eighty dollars a month or something.
00:26:09.200 --> 00:26:19.519
So as wages rise in Asia, um there's less of a gap, you know, between that and Latin America, right?
00:26:20.160 --> 00:26:26.240
Uh so the conversation starts to look at um all the other things that come with it.
00:26:26.559 --> 00:26:29.039
Do I want to do a midnight call?
00:26:29.519 --> 00:26:37.039
Um, do I want to have to go through five layers um to get what I want to get done?
00:26:37.279 --> 00:26:40.720
And then you start to look more at value, right?
00:26:41.039 --> 00:26:59.359
So if I can collaborate with somebody, which is really the way that the tech industry is gone, instead of throwing stuff over the wall and you hope that it was understood and you've got it waiting for you in your inbox or your JIRA, you know, the next day, uh, you you want to be able to collaborate.
00:26:59.519 --> 00:27:09.680
And like, I've got this idea, or I want to debate something, or I want to talk through something, or there's an implementation, or a QA, or a stand up.
00:27:10.160 --> 00:27:14.000
You just you don't you you don't want to keep doing calls at midnight.
00:27:14.079 --> 00:27:16.880
You just want to work your regular hours in the states.
00:27:17.519 --> 00:27:24.720
Um you want to work with the engineer who's actually doing it, not five layers.
00:27:25.200 --> 00:27:33.119
So culturally, Latin America is a lot closer to how we talk and think in the US.
00:27:33.599 --> 00:27:42.480
I personally love that because as a founder, um I chase bright, shiny things and I can do a lot of dumb things.
00:27:42.960 --> 00:27:51.920
And these guys will say, Hey, um, I've got a different way for you to think about this, or stop doing that.
00:27:52.079 --> 00:27:57.839
You know, like they they they really have an opinion and they're passionate, and I want to hear that.
00:27:58.960 --> 00:28:08.799
I would also say that more people than not don't have like an 18-step process for everything.
00:28:09.599 --> 00:28:12.960
Um they're a little bit more spontaneous, you know.
00:28:13.039 --> 00:28:18.640
They like the world is moving fast, you know, we don't have time to document everything in an exact way.
00:28:19.039 --> 00:28:21.680
So we need some flexibility in the way that we work.
00:28:22.160 --> 00:28:35.279
So I can throw an an idea of an outcome or a vision to my team in Latin America, and they can like figure out the in-between, the how, how do we get this done?
00:28:35.440 --> 00:28:37.039
And I think they like that.
00:28:37.599 --> 00:28:47.119
Um, I might be you know in a unique situation, but I but I feel like um with Asia, I kind of have to process it out.
00:28:47.440 --> 00:28:51.200
Um, I think they're less comfortable going off script.
00:28:51.440 --> 00:28:57.440
I think they and and maybe a good example of that is uh it's not software but customer service.
00:28:57.599 --> 00:29:13.599
If you're connected to somebody, you know, call an airline and you've got an issue with your seat or you want to rebook a ticket, and it's not something that is like totally within their script, um, it can get ugly really quickly.
00:29:13.839 --> 00:29:19.039
Or Latin America, I think people kind of go with the rhythm of the flow, it's conversational.
00:29:19.440 --> 00:29:24.319
They they can they can you know move and adapt uh depending on where that's going.
00:29:24.559 --> 00:29:28.400
So now we've got this a lot of the cultural stuff is aligned.
00:29:28.799 --> 00:29:32.720
Um the cost differential is way smaller than it used to be.
00:29:32.880 --> 00:29:38.079
Latin America is still more expensive, but it's it's a lot it's a lot less of a gap.
00:29:38.319 --> 00:29:49.759
Um, you've got this exact same time zone, and in this day and age where you need senior engineers that can kind of see the whole picture, the whole roadmap.
00:29:49.920 --> 00:29:58.480
Um, they understand the tech stack, the priorities, how it all integrates, um, the design, the architecture, the QA.
00:29:59.359 --> 00:30:01.279
You got the whole package there.
00:30:01.519 --> 00:30:08.720
This is this is a continent that's just loaded with talent, a region that's loaded with smart, capable people.
00:30:09.039 --> 00:30:14.559
So it's shifted from cost to quality and talent leverage.
00:30:14.720 --> 00:30:17.039
And that's why I love Latin America.
00:30:17.519 --> 00:30:20.079
And we've been in this world for 11 years.
00:30:20.240 --> 00:30:21.599
We know it intimately.
00:30:21.839 --> 00:30:27.440
We have recruiters in a lot of these countries, operations people in a lot of these countries.
00:30:27.680 --> 00:30:33.279
We understand how to get hardware in and out of there, which is not easy, by the way, uh, with customs.
00:30:33.359 --> 00:30:35.440
We know how to payroll people.
00:30:35.759 --> 00:30:38.720
Not easy to do, you know, and some of these constraints.
00:30:38.960 --> 00:30:48.559
I mean, it's uh if you've ever had to hire somebody in California, you know, and you're and you're you're outside of California, it's it's the same thing, you know.
00:30:48.799 --> 00:30:52.480
There's a lot of complexities uh with the labor law.
00:30:52.960 --> 00:31:14.000
Um so we've done over 500 placements at Plug for clients of all shapes and sizes, and we love this space, uh, helping people understand the nuances of different countries, talent concentrations, hardware, scope, what if you want to scale, what if you want to travel.
00:31:14.240 --> 00:31:16.480
Um, we do these conversations all the time.
00:31:16.559 --> 00:31:22.720
So I really appreciate that question, Mookie, and uh, we'd be happy to talk to any of your audience anytime about that.
00:31:23.039 --> 00:31:23.839
That'd be great.
00:31:24.079 --> 00:31:31.519
Links in the description below to reach out to plug and uh do a discovery session, right?
00:31:31.759 --> 00:31:37.279
Find out what folks need, align it with the talent that you've got.
00:31:37.359 --> 00:31:43.440
And it also sounds like you're bringing a combination of agility and expertise.
00:31:43.920 --> 00:31:51.839
They were wondering years ago why airplanes were falling out of the sky with greater frequency in Asia than here in the States.
00:31:52.079 --> 00:31:56.640
And it literally boiled down to the relationship between the pilot and the co-pilot.
00:31:56.960 --> 00:32:12.400
Yeah, there were instances where literally like the the the 747 was running out of fuel, and the and the co-pilot didn't want to tell the pilot because it would call him out on being negligent, and the plane, and the plane flew into a mountain.
00:32:12.559 --> 00:32:14.000
This is documented, true.
00:32:14.400 --> 00:32:16.160
I I read that exact story, yeah.
00:32:16.240 --> 00:32:28.160
That it I think it was uh a South Korean airline and uh the cultural gap or like the hierarchical gap between the captain, between the captain and even the first officer.
00:32:28.400 --> 00:32:41.039
They had they had the uh what do they call that the black box where it like the conversation, it's like um, excuse me, sir, you know, like they're like a hundred yards away from hitting the mountain, you know.
00:32:41.119 --> 00:32:48.559
Um excuse me, sir, you know, not not not to um interrupt you, but we're gonna crash.
00:32:48.880 --> 00:32:51.440
But and then not to interrupt you, but boom.
00:32:51.759 --> 00:32:56.240
And and and the reason was because of this hierarchical culture.
00:32:56.400 --> 00:33:01.440
And it's not right or wrong, it's just how people live and what they're used to.
00:33:01.599 --> 00:33:04.400
And in Asia, these hierarchies are significant.
00:33:04.559 --> 00:33:11.279
Speaking of Korea, uh, you do not address a person by their name, you address them by who they are.
00:33:11.440 --> 00:33:17.920
So sister, brother, older brother, younger sister, and you say captain.
00:33:18.000 --> 00:33:20.720
You don't even say their name, you say captain.
00:33:20.960 --> 00:33:23.599
We are uh, by the way, out of gas boom.
00:33:24.160 --> 00:33:27.680
So uh that's the same case for software benches.
00:33:27.839 --> 00:33:38.319
So you've got the project lead, and then you might have the developmental guy, and then you have the coder, and it's these ladders of communication.
00:33:38.559 --> 00:33:49.279
And what you're describing is in Latin America, it's kind of like, hey, you know, let's we gotta we got some code to to build here, and let's go for it.
00:33:49.519 --> 00:33:53.200
And the smartest idea in the room wins.
00:33:53.599 --> 00:34:03.680
Uh, we all toss in our ideas, and we're happy to liaise with the client as well, because that hierarchical system creates opacity often.
00:34:03.920 --> 00:34:25.360
I've worked with Asian clients too, where there's this respect factor, and it backfires because it it eliminates the transparency and fluidity between these vital creative brainstorms, between the client who knows the business expectations and business rules, and then the bench who needs to implement, right?
00:34:25.760 --> 00:34:37.119
Yeah, you know, uh on a personal story, um, I was uh lucky enough to be part of the um I did my MBA uh where I got two degrees uh from the same program.
00:34:37.599 --> 00:34:49.119
One from UCLA, where most people uh know, and then the other is National University of Singapore, which if you don't know, it's actually like a top school period in all of Asia.
00:34:49.440 --> 00:34:57.599
Um and we would travel to Singapore at the NUS campus, and we would go to the UCLA campus.
00:34:57.840 --> 00:35:14.719
And whenever we had a UCLA professor, man, they loved to debate, they love to be challenged, like they were almost kind of thriving on uh debate between students, debate with them, and um, all these ideas were up for grabs, right?
00:35:15.519 --> 00:35:18.400
Who could articulate the best idea?
00:35:20.159 --> 00:35:34.880
It couldn't have been further from that in Singapore, where um, you know, we had uh probably 50% of the cohort was uh Americans, and you know, we're kind of used to that.
00:35:35.039 --> 00:35:41.440
And that first time they did that with a Singaporean professor, wow, it it did not go.
00:35:41.599 --> 00:35:42.880
It did not go over well.
00:35:43.039 --> 00:35:49.679
Um, you could feel this almost like flush of embarrassment that they were being challenged, right?
00:35:50.079 --> 00:35:53.920
Um, I'm professor, I'm I'm highest on the hierarchy.
00:35:54.559 --> 00:35:56.639
We don't we don't debate ideas.
00:35:56.960 --> 00:36:00.159
I set up my podium and I lecture and that's it.
00:36:00.400 --> 00:36:01.039
That's right.
00:36:01.199 --> 00:36:08.559
And Latin America is so much more closely aligned with how we communicate in in the US.
00:36:09.199 --> 00:36:15.360
And uh uh there's this this like natural kind of horizontal structure.
00:36:15.519 --> 00:36:32.079
You don't have these giant pyramids, and and the way we we do business today, it's a lot closer to um small teams of um mostly uh uh equal, at least on a meritocracy basis.
00:36:34.079 --> 00:36:38.079
Once again, it's agility, it's able to do it quick, be responsive.
00:36:38.239 --> 00:36:51.599
And to your point that you were making earlier, it's not just a bunch of guys who love yelling at each other and getting getting in deep, but they're bringing chops, they're bringing expertise, they're they're bringing uh know-how.
00:36:51.840 --> 00:36:55.840
And how do you substantiate that among your among your recruits?
00:36:56.000 --> 00:36:59.760
I think you know, listeners and viewers will be like, okay, Brian, this sounds terrific.
00:36:59.920 --> 00:37:10.000
I get uh the suavissimo Latin kind of stuff where they roll their sleeves up and their the soccer game is on is in the background and everyone is brainstorming.
00:37:10.159 --> 00:37:10.320
Yeah.
00:37:10.480 --> 00:37:13.199
But how do we know you're bringing us top talent?
00:37:13.519 --> 00:37:15.679
Yeah, yeah, great question.
00:37:16.079 --> 00:37:22.079
This is something that I've thought a lot about, and it's very important to the ethos of plug.
00:37:23.599 --> 00:37:36.239
My very first business in Latin America when I was living in Argentina, we set it up with the typical structure of hey, like we're a technical team, technical company.
00:37:36.800 --> 00:37:46.320
We have our very own CTO, our very own architect, our very own tech lead, project manager, so forth.
00:37:46.559 --> 00:38:05.760
So whenever we talk to the client, um, it was you know our tech guys and their tech guys, and and that that worked really good until you realize that everybody has their own egos and their own biases that they bring to hiring.
00:38:06.000 --> 00:38:08.480
And hiring is a lot like driving.
00:38:08.719 --> 00:38:12.719
Uh 90% of us think we're great drivers.
00:38:12.960 --> 00:38:14.880
Uh, we're probably not, you know.
00:38:15.039 --> 00:38:17.840
Um uh and it's just a bell curve, just like anything else.
00:38:18.000 --> 00:38:19.039
Same with hiring.
00:38:19.280 --> 00:38:25.679
A few people are great at great at hiring, but if you ask the average person, we all think we're we're pretty good at it, right?
00:38:26.079 --> 00:38:32.639
Um and you hear this adage of, man, all these companies, we only hire the top 1%.
00:38:33.360 --> 00:38:41.599
Well, if the unemployment rate is only 2% in tech, somebody's you know not uh vetting properly.
00:38:41.920 --> 00:38:49.679
So we I think after a little while I started to see this pattern of, well, we think this engineer is great.
00:38:49.840 --> 00:38:54.320
That client has no idea what they're doing if they don't think if they don't think the same way.
00:38:54.639 --> 00:39:03.360
Um, and everyone's got their own hiring bars, and they feel like everybody else's engineers suck, and they're the only ones that have great engineers.
00:39:04.079 --> 00:39:07.760
So um, so how do we how do we flip this?
00:39:08.320 --> 00:39:17.519
And plug from day one, um we got rid of the engineer technical vetting, which sounds crazy.
00:39:17.679 --> 00:39:18.559
Like, why would you do that?
00:39:18.719 --> 00:39:20.400
Aren't you wasting people's time?
00:39:20.960 --> 00:39:33.280
No, what we really try to do is align that particular hiring manager what their biases are, and meaning bias is not a dirty word.
00:39:33.519 --> 00:39:36.559
It doesn't have to be, it doesn't mean discrimination.
00:39:36.960 --> 00:39:41.199
It means that we all kind of bring our own baggage and stuff into it, you know.
00:39:41.360 --> 00:39:43.760
Um, for example, I grew up in the Midwest.
00:39:43.920 --> 00:39:54.880
I went to a school before grad school that nobody ever heard of, you know, and I think that um probably drove a lot of my own hiring decisions for the first part of my career.
00:39:54.960 --> 00:40:00.480
You know, I wanted the the kids with a chip on their shoulder, you know, I didn't want the elite institutions.
00:40:01.039 --> 00:40:11.440
Um, and you think about uh Google and Facebook, you know, a decade ago, where Google hired the PhDs and it was like academia style.
00:40:11.599 --> 00:40:23.840
Uh but Facebook was move fast and break things, and let's hire the hacker that um you know uh dropped out of high school, you know, to build dropped out of Harvard like himself.
00:40:24.159 --> 00:40:24.480
That's right.
00:40:24.639 --> 00:40:25.199
That's right.
00:40:25.360 --> 00:40:42.320
So, you know, we all have our own biases, and it's our job to just hey, like it is what it is, let's find out what really matters to you and make sure that we map up, uh map the experience, the types of projects, the types of companies.
00:40:42.480 --> 00:40:49.360
We vet for English level, uh, we vet for uh the environments, the technical skills.
00:40:49.599 --> 00:40:55.360
Um so by the time that they've met these candidates, it's a small short list.
00:40:55.679 --> 00:40:58.800
We're not sending big batches and praying.
00:40:58.960 --> 00:41:02.480
It's a small vetted short list that everything is aligned.
00:41:02.639 --> 00:41:09.920
And then usually um it's actually less than three to one uh um candidates we send that get an offer.
00:41:10.000 --> 00:41:11.199
So it's 2.8.
00:41:11.679 --> 00:41:20.880
So every every 2.8 candidates that we send over, uh one of them gets hired, and our retention rate is over 90%.
00:41:21.519 --> 00:41:35.280
So our clients are are very happy because they're treated as unique, and we understand that they they have their own bias, their own baggage, their own way of looking at the world, and that's perfectly fine.
00:41:35.360 --> 00:41:38.400
It's nothing to um to be worried about.
00:41:39.199 --> 00:41:56.079
So I'm here at bald ambition, and I might need a bench of agile experts who can come in with guns blazing, who are willing to brainstorm with me, who have a flat organizational structure so there's no red tape, and we can get the job done.
00:41:56.320 --> 00:41:57.519
How does it work?
00:41:57.840 --> 00:42:00.159
I uh arrange a meeting with you.
00:42:00.320 --> 00:42:09.760
Is there a discovery session that you conduct in terms of analyzing my baldness to see what kind of software needs I might have?
00:42:09.920 --> 00:42:12.880
Is is that the usual process for people who reach out?
00:42:13.119 --> 00:42:14.320
Yeah, I appreciate that.
00:42:14.480 --> 00:42:20.000
Um, so generally, um, in that very first call, um, we accomplish a lot.
00:42:20.159 --> 00:42:23.119
Uh, our clients are really impressed with what we can get done.
00:42:23.360 --> 00:42:28.320
So, uh, first and foremost, we want to understand the culture, the biases, the job description.
00:42:28.400 --> 00:42:31.280
You know, we usually bring a recruiter in on that as well.
00:42:31.360 --> 00:42:33.280
So they're asking their questions.
00:42:33.440 --> 00:42:36.400
We don't have stuff lost in the game of telephone.
00:42:36.559 --> 00:42:50.639
Um, and then the second part of that is based on your needs and constraints, whether it's budget or scale, or they need to use the souped up MacBook, or they're kind of open on that.
00:42:50.880 --> 00:43:03.119
Um, and there's like a wide variety of things where they want their team co-located, even if down in the same office, the same city, um, or they want to be able to fly within four hours to reach their team.
00:43:03.280 --> 00:43:08.719
You know, there's a lot of different things that that um um we've seen firsthand.
00:43:09.119 --> 00:43:29.679
So we want to be able to not dictate, but advise and say, hey, you know, Mookie, based on uh um the fact that you want four people um and you've got this pretty sophisticated skill set that you're looking for, you know, we think Brazil is gonna be the best option for you.
00:43:30.000 --> 00:43:40.480
Um or you know, uh based on this need, you know, if we could um recommend a country would be uh Nicaragua, you know, based on this.
00:43:40.719 --> 00:43:44.559
So we want to be able to offer suggestions, advice, counsel.
00:43:44.800 --> 00:43:55.760
And then uh usually within um uh 24 to 72 hours, somewhere in that window, you'll get uh some initial candidate flow because we we know all these people.
00:43:56.079 --> 00:43:58.400
11 years uh in this region.
00:43:58.960 --> 00:44:03.840
Uh we've got a really big network of people that trust us and they know us.
00:44:04.000 --> 00:44:07.119
And and I'd say that's something we didn't really cover.
00:44:07.360 --> 00:44:12.800
Um, but uh everyone's wondering, okay, like I'm ready to hire in Latin America, what do I need plug for?
00:44:13.599 --> 00:44:29.119
There is a kind of an inherent distrust, distrust of people in Latin America over people that aren't from there, and it takes a while to build credibility and do what you say you're gonna do.
00:44:29.519 --> 00:44:32.639
And we've been very steadfast on that.
00:44:32.800 --> 00:44:46.000
So if you're a brand new company trying to hire there, um, I mean, even you know, I I heard a story recently of DoorDash was trying to do this in Mexico, and people thought it was like a scam, you know, they didn't really know them, yeah.
00:44:46.239 --> 00:44:47.119
I read that too.
00:44:47.199 --> 00:44:51.519
It was a big fail because people did not trust what was going on.
00:44:51.679 --> 00:44:54.960
And yeah, and you've got you've been there a decade now, right?
00:44:55.199 --> 00:44:55.840
Yeah, that's right.
00:44:56.000 --> 00:44:56.320
That's right.
00:44:56.719 --> 00:44:57.440
That's important.
00:44:57.519 --> 00:45:05.199
So, from a trust point of view and an agile expert point of view, we'll put again the link in the description.
00:45:05.360 --> 00:45:07.119
Thank you so much, Brian.
00:45:07.440 --> 00:45:08.000
My pleasure.
00:45:08.079 --> 00:45:08.320
Thank you.
00:45:08.800 --> 00:45:10.800
What plug has to offer.
00:45:11.039 --> 00:45:20.159
Uh, and if you're out there as a as a client or potential client, don't just run the claw and swarm a bunch of agents.
00:45:20.639 --> 00:45:30.000
You need human talent on your bench, and Brian and Plug offer a scalable solution that can do it the right way.
00:45:30.320 --> 00:45:31.440
Appreciate that, Mookie.
00:45:31.599 --> 00:45:31.920
Thank you.
00:45:32.480 --> 00:45:34.719
Thank you so much, Brian, for your time.
00:45:34.880 --> 00:45:37.280
Like, comment, share, everybody.
00:45:37.519 --> 00:45:40.079
And uh give Plug a plug.
00:45:41.039 --> 00:45:43.280
They'll boost, they'll boost your juice.
00:45:43.519 --> 00:45:44.559
Thank you so much.
00:45:44.800 --> 00:45:45.679
Thank you.
