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This is the show where we go deeper than the hype. Where we go beyond just the prompt. On the podcast, we talk with product, engineering, and GTM leaders who are building AI-native products and using AI to supercharge how their teams operate.
If you’re looking to scale your business with AI or want to learn from those doing it at the frontier, then you’re in the right place.
Actually been really incredible how how many people were worried about the cost of LLL models. And then as we started building, every few months, it'd be like, it's cheaper and better. And then it's like, it's cheaper and better I get. And I think somebody mentioned that it's like the fastest depreciating technology that we've ever seen. It's been at an incredible pace.
Speaker 1:You were around during the adoption of personal computers and also the internet. Do you believe that this current wave in technology is quicker than either of those?
Speaker 2:Yes. I think there's no doubt. The pace of change and the speed of reduction in cost, they're I don't know whose law it's gonna get named It's
Speaker 1:more law anymore. Oh, no. Altman's law is
Speaker 2:what it is. It's orders of magnitude. I mean, it's crazy, but it's crazy good. I think the great news if you're a user is the pace of innovation is in sync.
Speaker 1:Oh, yes.
Speaker 2:Yeah. I'm old enough to have, you know, been through a few of these, starting back with, you know, PCs and Internet and then mobile and then cloud computing.
Speaker 3:Hey, everyone. Welcome to Beyond the Prompt. I'm your host, Sunny. This is the show where we go deeper than the hype, where we go beyond just the prompts, and that's where the name of the podcast comes from. I'm talking with product, engineering, and go to market leaders who are building AI native products and using AI to supercharge how their teams operate.
Speaker 3:If you're looking to scale your business with AI or want to learn from those doing it at the frontier, you're in the right place. And if you're interested in coming onto the podcast or just want to chat with me on the cool things you're doing with AI, then click on the link in the description to get in touch. Now this is the first episode of the podcast, and it was before I had set up this mic. So bear with me on
Speaker 1:the audio. It's not the best, and
Speaker 3:I promise the audio in the next episodes will be better.
Speaker 1:The audio on Anthony's side also changed, so just bear in mind, about ten, fifteen minutes into the podcast, the audio for Anthony actually gets a lot better. But the first ten, fifteen minutes, it's not as good.
Speaker 3:But the podcast still has incredible content, so
Speaker 1:I'm looking forward for you to enjoy it for sure.
Speaker 3:Regardless, I was super, super excited about my first ever guest on the podcast. My first ever guest on the podcast is Anthony Bay, a veteran product and marketing executive with decades of experience shaping some of the world's most impactful tech platforms. After starting his career in early startups, he spent eight years at Apple across The US and Europe, leading product marketing efforts and networking, communications, and media. He then moved to Microsoft, where he launched the original MSN. Yes, the original MSN.
Speaker 3:And led major product groups focused on e commerce and digital media through the 1990s. Later, he took on global leadership role at Amazon Prime Video in its earliest phase and went on to become CEO of Ardeo, a digital music streaming company acquired by Pandora. Today, he's CEO and founding partner at Techquity, an advisory firm of senior product and engineering leaders from companies like Amazon, Google, and Microsoft. Techquity today helps CEOs and investors navigate complex tech and AI decisions by embedding experienced operators directly into the process, from hiring and team building to product strategy and even infrastructure modernization. In this episode, Anthony and I talk about what AI means for modern execs, how non technical leaders can make smart bets, and how seasoned operators are guiding the next wave of transformation.
Speaker 3:Let's get into it.
Speaker 1:Awesome. Welcome to today's episode. We have a very exciting guest today. So, Anthony, thank you for for joining us. I'd love for you to tell me a little bit about your career background and tell the listeners about your career background.
Speaker 2:Sure, well first of all, thank you. Thanks for having me. I look forward to the discussion. So my background is, I spent many years in various roles in, if you will, the big tech companies you might recognize. I did a couple of startups first and then had two small children and decided I need a little more stability, so I had just to date myself.
Speaker 2:My first computer was an Apple II. Bought a Macs the day they came out and went to work for Apple. So I was with Apple for eight years in The U. S. And in Europe and ran various product marketing oriented initiatives on networking, communications, and media.
Speaker 2:Went to Microsoft, and spent most of the nineties, at Microsoft running various product groups. I ran I shipped, for those who may remember, the original MSN. I shipped the original MSN, and then I was responsible for all the e commerce platforms and then media, digital media at Microsoft. Left, used some angel investing and advising, went on some went on boards, public boards, private boards, So I've been on a of both. And realized that I actually kind of missed an operating role, so I went back, actually joined Amazon in the very early days of Prime Video.
Speaker 2:I ran Amazon Prime Video globally for a while and then went and, ran a company called Ardio, digital music company, which we sold to Pandora and then kind of shifted mode to, a bit of what I'm doing now, which is advisory, more of an advisory, work. And a few years ago with some other, other former senior not former, senior tech execs from product and engineering founded this company called Techquity. And so what Techquity does, and, you know, I won't spend too long because I'm sure we'll get into it, but the basic premise of Techquity is that the vast majority of companies, vast majority of CEOs and investors, frankly both private equity and venture, are not tech themselves, are not super strong technically. They're good, you know, great CEOs, good business people, it comes to investors, very good investors. But when it comes to navigating, you know, critical and complex, you know, tech decisions, teams, tech prioritization, you know, they, you know, they, they're trying to do that without the expertise.
Speaker 2:And so Techquity is a collection of senior tech executives, builders and operators ourselves, and we work with, you know, our clients, our CEOs and investors, we get involved as our copilot, helping them navigate complex, you know, tech decisions, hiring key people, you know, understanding where they are, which will lead to, you know, AI at the moment.
Speaker 1:Yeah. Can you talk a little bit about some of the folks that you partner with? I think I saw Niantic as one of your partners, and then I've also some some authors.
Speaker 2:Yeah. So Niantic has been a client and one of our one of our partners. So the if you if you go to techwitty.ai, you see the set of people. So my partners include a guy named Al Lindsay, who was first engineering leader in Alexa, number three person in Alexa, built the entire Alexa engineering and science team from, you know, zero to however many thousand it was, you know, and he built and led a big part of Amazon Prime on the technical side before that. Another one another ex Amazon person who built out what became Amazon's third party market, and then the you know, all their content platform ran Kindle, and Microsoft did, I mean, Amazon's digital content, and then, led, Amazon's, AI shopping effort.
Speaker 2:So what Rufus, if you're you're familiar with Rufus. Another one was, the CISO of Azure Security. So we got people from, you know, from a variety. And the one at Niantic, one of our partners as a former, senior Google product leader and got involved with Niantic to, to lean in there and help them with what became, you know, the more recent pivot from the gaming to more of essentially a data platform, an API based data platform for a very different way to do mapping, you know, based on, you know, less on street. So Niantic is one.
Speaker 2:That's not a classic client for us. The majority of our clients are non tech clients, but the bench of people who are Techquity partners are all people of that level.
Speaker 1:Yeah. And then you didn't mention it, but for folks who don't know, Niantic is the company that created Pokemon GO, which is worldwide sensation.
Speaker 3:And I think Google Maps,
Speaker 1:I think, put an April Fools thing, like, way back before Pokemon GO, and then it became a reality, which is, like, an incredible story. And I think Niantic was also a spin off off of Google, if I'm
Speaker 2:Niantic was. The founders, the founder and CEO, they started a company called Keyhole in their early 2000s, which Google bought and became Google Maps. And that person who, and then that bent became Google Earth. And then they merged Google Earth inside of Google with Maps and that whole geographic thing. And then they spun out Niantic in I think like 2015.
Speaker 2:Yeah. And so super strong technical organization, as you said. You know, most of the mapping that goes on, you know, are things that are basically based on cars driving around for the most part or, you know, or geospatial and satellites. You know, their innovation was everybody walking around with, you know, their phone, which led, you know, Pokemon GO. So the, you know, the derivative of what of what allowed Pokemon GO was a tremendous amount of mapping data.
Speaker 2:And then so, you know, they've they've now changed the company around. Think it's they renamed Niantic Spatial and sold off the gaming business.
Speaker 1:Yeah. That's right. Because they're moving more to, like, the data and API platform.
Speaker 2:Yep. They have a remarkable you know, in a world of AI, data becomes the data underlying the models is ultimately where a lot of the interesting differentiation and value creation comes from. They a great data, a great table set.
Speaker 1:Yeah, that's awesome. That's awesome. So then what does your kind of like day to day at Techwoody look like now, now that you're over helping a bunch of different clients?
Speaker 2:So our main time is spent helping clients, and clients include both investors, if you will, private equity firms and venture firms. And so the life cycle starts really with some form of an assessment. In some cases, it's due diligence before they make an investment or make an acquisition. And so we'll do a deep dive technical due diligence. I've been surprised really at the depth of technical diligence compared to financial and all the other parts of diligence, because obviously investors have to get a bunch of very critical information in order to make decisions.
Speaker 2:A lot of times the diligence isn't as strong. Think an example in the last couple of weeks was that JPMorgan acquired that company. I'm trying to remember the name, but the CEO was just convicted of fraud. JPMorgan spent $175,000,000 to buy a company that was helping essentially student loan borrowers and claimed they had 4,000,000 customers when in fact they had a fraction of that and it didn't get discovered in diligence. So there's a lot, you know, there's a lot of times that happens.
Speaker 2:And in, you know, in the case of a company itself, the company realizes they're not, things aren't being shipped on time, they're not sure if they have the right leaders, they're wondering, are they making the right tech decisions or in many cases, the company scales to the point where the people who are great at one stage may not be great at another. And so a lot of what we do is in a sense sort of like a health journey except tech, to come in and say, okay, where are the issues? Where are the challenges? And before you buy, it's one, after is that, and then working with the CEO on whatever the issues are. So in some cases, it's helping them hire a strong leadership team, helping them build that team, in other it's helping them make architectural decisions, helping figure out spend, it's a big issue right now, is how much money is being spent and on vendors as well.
Speaker 2:You know, AI, the spike in cost is huge. And then more recently, you know, helping companies make informed decisions about AI. A substack called Techquity Takes, and we've written a few pieces really, you know, around this. And our our our main focus again is on, I would say, not companies building building AI tech per se, but on companies trying to understand, which is really the vast majority of us all, you know, what do I do? Yeah.
Speaker 2:And how how do I think properly? And so one of our first pieces was really around the idea of, you know, how do you get started? You know, and this this idea of how do you use AI to enhance what you do and then ultimately figure out where you can do products or services that are AI first. So one of the discussions was really that. And then another one on really starting to understand building your own, digging your data wells, figuring out the data.
Speaker 2:Most companies do not have their data well structured, well organized, and so for most companies one of your core assets, your core IP is the data you have. The data you have that over many years that's developed in some cases around the projects and the customers and the business and the deliverables and so figuring out how to organize that, they're certainly using the classic models and the tools that are out there which are helpful in a lot of ways, so they're gonna augment and there's so many ways you could do that, But when it comes to companies building their own products and services, really for the most part needs to start with what are your core assets. So we help people start to think about that.
Speaker 1:Awesome. So much to dig into. I'm curious to hear more about what do you think or what have you seen as successful in terms of using AI to enhance what people are currently doing in existing organizations?
Speaker 2:Look I see.
Speaker 1:There tools as well that like you've seen had been successfully adopted?
Speaker 2:Well you know I think for most, if you sit on X'd or LinkedIn, there's a bunch of people posting here's all the really cool things and here's a lot of things you can do and those are all true. I think the interesting question becomes kind of on a more substantive sense for most companies, all right that's interesting how do I apply those things?
Speaker 1:Totally totally. And the
Speaker 2:main thing, and I don't think it's a surprise, is you've got to get on the learning curve. You have to start using these tools and and start learning. You know, whether it's programming, you know, there's all sorts of there. You know, there's all sorts of Yeah. Streams about whether people use a cursor or Copilot or, you know, there's there's dozens.
Speaker 2:And the I think the great news if you're a user, if you're a if you're a customer of these things is the pace of innovation is in sync.
Speaker 1:It really is. Yeah.
Speaker 2:I'm old enough to have been through a few of these, and where these revolutions, if you will, drive sea change. Starting back with sort of PCs, and internet and then mobile and then probably cloud computing if you look at these, and AI is a version of that. Others haven't had that kind of impact, but this is one of those ones where the the the the fundamental sea changes are ones that I think are, you know, are less obvious in the, you know, in the beginning. It's like what turned out to matter in the Internet was less pets.com, you know, and more companies, you know, figuring out how do they reorient their business around using those technologies. You know, in in cloud computing,
Speaker 1:it changed the fundamental set of infrastructure you needed to have.
Speaker 2:You know, I ran a music company. We had to have our own data centers. You don't even think about that anymore. And, I mean, for the most part, people don't. And so I think the thing with AI is the good news is these insane amounts of
Speaker 1:money are being spent on on both on compute and then on the evolution of the models and the tools on top of that.
Speaker 2:And and so experimenting with those, I would say kinda two things. Number one is experimenting
Speaker 1:with the tools that are there and looking at your own business.
Speaker 2:I would put them in kind of two buckets when you talk about kind of enhanced. There's there's enhanced around actually people creating, you know, creating use cases and applications. You know, the the fact that you don't need to be a programmer anymore, really to be able to get, to get impact. I mean, that's life changing. David Sachs was on you know, said
Speaker 1:the other day and again whatever you
Speaker 2:whatever people's opinions about David Sachs politically, he you know, what he said was, you know, in his entire career that the limiting factor on innovation was the number of good developers. That innovation is going And in the same way the ability to build big applications, CapEx was a limitation, it's not there. Needing to think differently. In our world, a lot of the applications around, if you will, enhancing are using tools like deep research from whichever platform. It's it's it's remarkable.
Speaker 2:I mean, the the value of these tools and again, you have to be careful that, you know, you you use them as assistance and not you know not to just produce the the output of work because they're not good enough yet. But you know in the world we see which is a lot of white collar oriented work, you know those tools are remarkably impactful. And Brad Smith, one of the I guess the president of Microsoft said the other day, he said, when people are worried about losing their job to AI, it's more losing your job to a human using AI. Know? The Totally.
Speaker 2:You know, the the the imperative is to start learning how to use these, and that's been true in every, you know, in every generation of tech. And then, you know, you see people reinventing, deeper use cases and deeper applications, and I think those are emerging. I think it's still super early. I think the the transformational use cases around AI and new products were very early. It's hard to see.
Speaker 2:What I think you're seeing is businesses shifting in their priorities. You use Niantic who is saying, look, our core asset turns out to be the data and there's these applications that you build. And so that I think will be one of the biggest shifts and you see most companies aren't prepared for that. A lot of the people we talk with are you have to think about data governance and data structure. What do you actually have the rights to?
Speaker 2:You see a lot of these battles happening now where it's protecting your data as IP is important, you have to be very careful about that, and then knowing what you do and don't have the rights to. So I think you're gonna see more of that emerging, which is really trying to understand what you're gonna build applications on in getting that foundation is a key. And then, you know, classic, if you will, Amazon, it's just working backwards from where you know, who are your customers? Where's the value that you can create? And then you work backwards to you know iterating and testing.
Speaker 2:So long winded answer.
Speaker 1:Yeah. Yeah. Yeah. That that was awesome. That was that was incredible.
Speaker 1:I definitely have used working backwards in various roles and various jobs in my life as well.
Speaker 2:There's a great book, by the way, who a good friend Bill Carr wrote called Working Backwards.
Speaker 1:Yeah. Yeah. Yeah.
Speaker 2:If she's not read that book, would highly recommend it.
Speaker 1:I have not fully read it. I have read snippets of it, had him on a pod or not not me interviewing him, but, like, listened to him on a podcast as well and, like, got the kind of, like, core ideas of it. Right? And I think that's been super helpful for sure. For sure.
Speaker 2:Great model. You know, one of the things that Amazon has been really, really good at and I appreciate it a lot is Amazon has built a a way of a way of doing things and an approach that's incredibly systematized so they can the breadth of things that Amazon does is remarkable, and so the the approach they use, have to every company has to use it themselves, but it is highly relevant and applicable. The thing I would just say before we go off that is the mistake I think we see a lot is companies don't spend enough time on what and jump immediately to how. And the process of deciding what to do, where are there things that you can move the needle and create real value versus jumping into, hey we need to have an AI project, know, I gotta be doing something. But what you do, you know, deserves a lot of time.
Speaker 1:Yeah totally. I definitely can resonate with sometimes you're like, I just need to feed the engineer some tickets, some work, and you're like, just go do this. And then you get too lost into doing that thing, and then you end up, like, spending so much time that you have to maintain it as opposed to taking some time to, like, hey. Actually, engineers, maybe go tackle some tech debt while we go spend a little bit more time talking to customers and try to identify the right thing to go build. Yep.
Speaker 1:Take that upfront time to think about and talk to the customers instead of committing to start doing something and then you realize it was the wrong thing like two years later. Right?
Speaker 2:Yes. That and you're just creating more debt for yourself.
Speaker 1:Yeah. A %. So I'm super curious. You you mentioned about just trying out all these different tools. How have you seen companies assess what tools to allow them to use?
Speaker 1:So for example, some companies are like, you are only allowed to use AI tools if the IT team or legal team reviews and approves it. And so some companies are like, oh, I can't even use Cursor as an engineer, even though I want to use Cursor because I've used it in my personal projects. Do you have any recommendations around how companies should think about assessing and evaluating and rolling out those tools that sometimes the individual people are like, I need this tool. This tool is incredible. But then they need to get past the IT and legal approvals and things like that.
Speaker 2:Look, that is a theme that has existed way back when, I'm dating myself, but people bought personal computers. They bought an IBM PC or they bought an Apple or they bought a Mac when it wasn't allowed because it made their life better. So you always have that classic tension between and in some cases, there are certain parts of it that are well founded. The biggest issues are security related and provenance of data. You have to be very careful that you don't accidentally upload confidential proprietary data into something where you basically, you're handing over the rights to that.
Speaker 2:So governance, my experience has been you have to focus on rules that are not about control but rules that are about guardrails and some degree of safety. But other than that you have to let people experiment because first of all it's important otherwise you are a laggard and the other thing is they will just do it. They'll do it at home, they'll do it outside, they'll find ways to experiment with these things and so, and I think security is one that is more understood although it's not necessarily well implemented, mean we're moving to a zero trust world which I think is probably the appropriate model for thinking about these, but the biggest gap is in this whole issue of data governance and that field is growing very quickly for exactly this reason. You could accidentally use a model for research, This is less on coding, the coding one has a different set of risks but in both of these cases you have to be very careful about, I'll call it leakage of confidential information and important stuff. So those are the two places to focus and I think done properly you know you'll get people to cooperate but you know if it's too strict people will just bypass.
Speaker 2:Mean that's you know how many people were told you know you can't have an iPhone. Yeah. I mean and how well did that work?
Speaker 1:Yeah yeah yeah. You also touched on the topic of like having really high quality data, right? You know I think a lot of people say that, but I'd love to learn from you around like what does good data actually look like?
Speaker 2:Yeah, look, it's a big question. I think the first place to start is I'll go back to that thing I mentioned earlier, is the deciding what matters. Most companies, any company of any size is generating huge amounts of data and a lot of it doesn't matter. The first part is trying to really get your hands around what is it that's important and what is it that you have that's unique. And then it's a data management and a data how do you organize that?
Speaker 2:How do you think about how to organize that data using the tools that are out there? I think one of the challenges that we see happen a lot, we actually wrote a sub stack about this, it was called Does AI Spell the End of SaaS? And it was, I think more, you know, one of our guys wrote it and it was a bit provocative, for many many many companies a lot of your data is living in someone else's SaaS application and you know, you you don't necessarily and if you look at you look at the number of SaaS apps that, you know, people are running, each of them is its own little, you know, data universe. You know, your data around customers is over here, and your data around money is over here and your data around manufacturing is over there and your HR data is over here. Really getting an understanding of where our data is and what systems it's in and ensuring that you can get access to your data.
Speaker 2:So I think one of the things you're gonna see, one of the big pressures you'll see on SaaS apps, I think this was if somebody has the time to go read our Substack on that, is you really start rethinking differently about decisions you make on SaaS applications and ensuring that you have the access. It may be that data lives within there for that particular application purpose, but you need to be able to consolidate that data yourself exactly, and you think about your own data lakes. I think that's the first place to start is what matters, what are the sources you have, what is it that you have that's gonna be interesting and unique when you start putting it together, and then figuring out how to assemble that because the good news is the pace of evolution in the AI tools, and the good news also is the cost of running these models is also gonna decline and so it's biggest questions I think are the ones I just described.
Speaker 1:Yeah, it's actually been really incredible how many people were worried about the cost of the LLL models and we were as well. And then as we started building, every few months it'd be like, oh, cheaper and better. And then it's like, it's cheaper and better I get. And I think somebody mentioned that it's like the fastest depreciating technology that we've ever seen. And yeah, it's been at an incredible pace.
Speaker 1:And then you mentioned before, you were around during the adoption of personal computers and also the internet. Do you believe that this current wave in technology innovation or revolution is quicker than either of those?
Speaker 2:Yes. I mean I think there's no doubt. The pace of change and the speed of reduction in cost. Mean I don't know what law they're, I don't know whose law it's gonna get named It's
Speaker 1:Moore's law anymore. No it's orders
Speaker 2:of magnitude, I mean it's crazy but it's crazy good from the point of view of and it's far faster than anything happened in cloud computing where costs come down, but nothing like this. From a price and capability point of view. It's great, and the good news is I think given that you can assume that at some point cost is not the primary thing to design around. You gotta be smart, but the fact that a small number of companies are spending hundreds and hundreds of billions of dollars essentially for our benefit is a great thing. Whatever their return will be is unknown.
Speaker 2:You also see this playing out with the whole debate about open source models versus closed versus these hybrid. The biggest impact I think of open source in these models is it's accelerating this cost curve. At some point, to oversimplify, think it becomes another service that use just like any of the other services that are out there when running in a cloud but it's also the applications on the edge are amazing as well and so it's just again if you're a tech person, it's super cool. This is one of those moments, way faster pace of change than any of the other ones.
Speaker 1:Got it. Good to hear that I feel like everybody that I've talked to has echoed that sentiment as well.
Speaker 2:I wanna say one thing is it is a step change in a way those others weren't. And all of those other waves expanded the capability of the kinds of things you could build and the scale and the number of people you could reach, but AI is AI is augmenting people in a way that, you know, that, you know, it is this this is one of those moments in history. So
Speaker 1:Yeah. A %. So before we jumped onto this podcast, I was told that I should definitely ask you about orgoneering and what it is and why it's important. So can you tell us a little bit more about orgoneering and what that is?
Speaker 2:The biggest I'm trying to think of how best to do this. The biggest issue for most companies that we come across is less about which tech to use, and more about how to organize yourself and how to create a culture that knows how to build things, because for most companies, they're not used to building software capabilities. They are users, and of course there are lots of tech companies that build tech, but for the most part most companies are users of tech in their own business. They apply the tech in whatever they are, and so what's happening in AI is it's happening in a variety of areas accelerated by AI is you need to learn how it is that tech companies design and build software products. What's the role of product?
Speaker 2:How do you think about engineering? How do you think about those two work together? What are the processes you put in place? What are the tools? What are the metrics?
Speaker 2:What we found is, and if you don't have this, nothing else really matters. People get very focused on which AI tool I'm gonna use and all these things, but it's how you build. Elon Musk said something, he said a lot of things, but this one thing, nothing related to that, but he said Tesla's real product is its manufacturing, and that how we manufacture is the magic and that, yes, they make cars and they're making batteries and they're gonna make robots, but the how is where the innovation and so when it comes to building software or building products that are AI based and data that are intended to be a product in a sense that other people use, those same kind of things apply. I mentioned a little bit about how Amazon has built its organization. Every successful tech company, big or small, has its own version of this.
Speaker 2:They're not all the same, but the themes are the same. So orgoneering is about helping non tech companies primarily understand how do you get the right tech culture and tech leaders and processes to be able to build stuff. So that's what it means.
Speaker 1:Gotcha, awesome. Yeah, so getting the right people in place, but also the right processes and like how to think about building products as well. That reminds me
Speaker 2:of I'm iterating, know, you're never done. I mean, if you look at any successful company whose product you use, tech company, you know, and you just described, you know, the the AI models and iterations. I mean, these things it it's the pace of iteration. Know? It's you're not gonna your your your main thing is to get out there and then get really good at learning and iterating and testing.
Speaker 2:And those are skills, those are just skills that need to be developed for most companies.
Speaker 1:Yeah, absolutely. So then what's on roadmap for the vision for Techriti in 2025? And if you could wave a magic wand on a block, one major roadblock or challenge to get to that vision for 2025 for TechCritty, what would it be?
Speaker 2:Well look, first of all thanks for the question. We are not a product company. So our real star and our mission is helping companies in the ways that we've been talking about here, and so we found a couple of things. What we found is that what we do and who we are is relatively unique. The idea that you could have a person that helped you build Alexa or Google Maps or one of a dozen other things that you could have that person kind of help you and be a guide and a mentor in a sense a copilot is pretty rare.
Speaker 2:You couldn't hire these people and you can't go take a course to learn about this. And again the metaphor is not the same, but if you could have Troy Aikman or Tom Brady or somebody like that, you know, around as a coach, that would be very valuable. And so, you know, our mission is to provide a platform for that kind of person to to be able to engage with clients and help. We're a collective of people who got to that place in our careers where we want to work on different projects and help people and help companies who are trying to do important things. So the biggest thing for us is awareness, is, you know, having companies, you know, know that we're out there when they're, you know, when they're struggling with these decisions, you know, whether it's diligence or how do I scale, how do I build this culture, do I have the right leaders that there's somebody who can help.
Speaker 2:So our biggest breakthrough I would say is probably that awareness and getting out there and helping.
Speaker 1:Yeah, just getting your name out there, getting your awareness out there so that you can help more companies. It sounds like an exciting future though where you can come in and help a lot of multiple companies, whether it's primarily technology or non technology, but it sounds like you love and love focusing on like non technology companies as well at TechBuddy.
Speaker 2:Yeah. That's where in a lot of cases you can have the biggest impact. And again I would say a non technology company today, really every company needs to be a tech company in some degree, it's kind of where your core product isn't tech, where you're using tech you know to to deliver value in one form or another, but your people aren't you know people aren't buying you, you're not they're not buying you as you know they're not buying you as a software tool, you know you're using there, and that's a that's you know we sort of say every every company that wants to be a great company needs to understand how also to be a great tech company.
Speaker 1:Yeah. And Absolutely.
Speaker 2:So that's true.
Speaker 1:Absolutely. Alright. I have a last few questions. One is, what are you most proud of and why? And it can be professional or it can be personal.
Speaker 2:I'm most proud of my family and my kids.
Speaker 1:That's awesome. That's awesome.
Speaker 2:That's like, I I I made a decision which thankfully with a lot of help from my wife about what to prioritize and it's very easy to over index on your career and under index on your family, and I had some of those moments, but someone told me once, no kid, if you ask someone when they grow up, do you wish your dad worked more? No kid would say, yeah I wish my dad worked more. And so family figuring out, it's not work life balance, but it's how you stay alive and energized and feeling like you're making a difference. But I think your family to some degree is your most important legacy.
Speaker 1:Yeah I think I heard somewhere that the folks that get most impacted of your absence is your kids. And then the other thing that I learned like a few years ago was the longest running study in human happiness and health, the number one determining factor is the number of high quality relationships, whether it's like friends or Yep.
Speaker 2:And I think that's absolutely true. And it's easy to forget that.
Speaker 1:Yeah absolutely. All right lastly, where can people find you online where they can learn about you, Entekwity, and then how can listeners be useful to you?
Speaker 2:Sure, well that's a great question, thanks. So the simplest thing is on LinkedIn, know Anthony Bay, I don't think there's a lot of Anthony Bays but you know Anthony Bay Techwity, would probably be the easiest way. And then our company is called Techquity, and the URL is .ai, so if you want to learn more about Techquity, what know what we do, who we are, there's 18 or 19 of us now. It's a collective of pretty interesting people who work together. So those would be the best ways to find me and
Speaker 1:us. Awesome. Awesome. Well, thanks for coming on, Anthony. Thank you so much.
Speaker 2:Thank you so much.
Speaker 3:Thank you for tuning into the very first episode of Beyond The Prompt. If you enjoyed this discussion, please subscribe to the podcast so you don't miss future episodes. Also, if you could take a moment to review and rate the podcast, it would help me tremendously in reaching more listeners and bringing you more great content. Until next time, keep going beyond the prompt.