Restructuring Your Team in the Age of AI with Ryan Graciano, CTO & Cofounder at Credit Karma

(Intro Narrator at 00:00:00) Today, we're talking to Ryan Graciano, CTO and cofounder at Credit Karma, about how they're restructuring their organization in the age of AI. You're listening to Joel Beasley, Modern CTO.

(Joel Beasley at 00:00:17) Three years since we last spoke. Time goes by quickly, doesn't it?

(Ryan Graciano at 00:00:21) Yeah. Three years since 2021 is a lot of time. That was right off of COVID. It was different times now.

(Joel Beasley at 00:00:28) And your company is growing rapidly. What's been going on over the past three years?

(Ryan Graciano at 00:00:32) Well, in three years, right around we last spoke was the Intuit acquisition. So that's very different. You know, we're three years into that now, realizing some of the platform and ecosystem benefits. Intuit Assist is one of those things. It's our generative AI assistant that lives across all Intuit products. And so we've really been focused on how do we transform the product to be more active and have the AI take a more central position in the product experience.

(Joel Beasley at 00:01:03) Oh, that's pretty cool. And so how have things been for you personally with the acquisition?

(Ryan Graciano at 00:01:10) I actually really like Intuit. I like a lot of the people at Intuit. And the thing about Credit Karma is, you know, when we started, we just had your credit. You know, it's a pretty straightforward set of data assets that we had. With Intuit, we have your income through the tax situation. We know a lot more about your broader situation, especially if you're a QuickBooks customer. And then we can do more for you because a lot of your financial needs are cross cutting. You know, they straddle your taxes and your income and, if you're a small business owner, all those things are kind of intertwined. And to realize the full vision, which is that we make all of your financial management easy and automated, you know, we just needed to be able to do more. And so that's actually nice to be a part of a company that has more capabilities and gives us some advantage to dream a little bigger.

(Joel Beasley at 00:02:03) From an end user perspective, because I meet those customer requirements across multiple products, do am I just signing away data to be integrated across any subsidiary in the end user license agreement? Or can I pick and choose, like, okay, if I'm an Intuit customer, I don't want their subsidiary Credit Karma to have this access? How's that set up?

(Ryan Graciano at 00:02:23) Yeah. It gets complicated. There's different permissions for different things. Depends on the purpose and, you know, there's consents and so on. We're really just using it to try to make things easier. So, you know, for example, I want to be able to, if I see a mortgage on your credit report, automatically put that in your tax return for you so that you don't forget that that deduction is there. You know, this is how I work with my own financial manager that I worked with for years. They make sure that when something happens, it's represented everywhere. It's not like I have to collect the document. I have to remember where it goes. I have to put it on the tax return. You know, that's all stuff that software could do for you. Make sure no money is just left on the table sitting around.

(Joel Beasley at 00:03:06) Yeah. I mean, the government could technically just do the return mostly for us. They have all of our data anyways.

(Ryan Graciano at 00:03:12) Well, they have some of it. They don't know a lot of the things that you actually do.

(Joel Beasley at 00:03:18) Oh, they know.

(Ryan Graciano at 00:03:21) The government knows and doesn't know. You know, the car that you buy is in one system, and then the deductions that you take are in another system. Everything is less interconnected than you might think it is.

(Joel Beasley at 00:03:37) Yeah. I've been a user since very early days when you could only do the one thing, when you could only just pull the credit report. Right? And it's been cool to watch you and the company grow. You know, at first, you start out. You're like startup. We got startup energy, and then you grow and you scale, and now you're acquired by, you know, multibillion dollar company. Do you try to maintain that startup energy, or do you just try to, you know, say, hey. We're no longer a startup. We're now this mature mid-market company, and we need to act like it. How do you think about that?

(Ryan Graciano at 00:04:13) Yeah. It's an interesting question because I feel like bigger companies get a bad rap in a way, you know, mature companies, for good reason. Right? Like, there's a lot of stuff that people don't like about big companies. They don't like TPS reports, process, politics. There's a million things that we all hate about big companies and they're deserved. And then startups, you know, they're scrappy, they're exciting. But the problem with the startup is that there is no process. You know, there's no defined way of doing anything. A lot of things are dependent on the personalities of the people involved, what's in your head. And they are limited in what they can accomplish. You know, there's only so much that one small company can do. And so I do think it's important for us to make this transition to, hey, we're growing up. We need to have real processes. You shouldn't have to know the person that does the thing to get something done. You should be able to actually just do it on your own, which means there's a certain level of maturity in documenting and making things self-serve. And, you know, at the startup, you can go to the person's desk and say, hey, can you write that, can you write a hook for me to do this thing? That's not possible at a tens of thousands of person company. And so I push us more towards, like, hey, let's actually operationally think like a big company. But when we envision new products, you have to be as creative, as excited as if you're trying to disrupt yourself, you know, coming at it from the startup perspective. And I could go on and on about that, but I think a lot of that is organizational. How do you set up the organization so that those different sets of people have different operating functions and objectives that they're trying to accomplish?

(Joel Beasley at 00:06:02) So what do you do? You build teams that are small and insulated that can have that startup energy for a new product?

(Ryan Graciano at 00:06:10) Yeah. That's essentially kind of what I'm getting at. So when you're, and you have to make that mission clear. Right? So there's different types of product development, I would say. You know, when you have a really mature product, you know, say, like, our credit, the credit part of our product. It's very mature. Right? So you can do that in two ways. You can come at it with a mission of, like, let's just completely reinvent this from the ground up. You know, something new happened, something crazy, like mobile came along. What's the mobile version of this thing? That's one way to do it. Another way to do it is you're more incremental. That's a more natural position to be in. I'm the market leader. I need to squeeze out X points of growth. I'm going to take an approach to figure out what the customers want and kind of tackle these individual smaller items and you get a gradually maturing product. You need to actually do both. Right? So what I have tried to do is just separate those acts. So you have one group of people that's working on the mature thing, and then you have another group of people that's trying to innovate on what is coming next. What is going to be distinct and different? What would you invent from the ground up if you could? And, yeah, mobile was a really good example of that. I think Gen AI is going to be the next big transformation in how a lot of software works, and we've structured that in kind of a similar way. We've set some people aside to go and say, like, hey. If you were to do this from the ground up, if you were to build a new app, what would that look like? How would that work? And that's a tricky thing to do, you know, in a big company.

(Joel Beasley at 00:07:43) What's going on then right now with the next, well, you guys already handled mobile. How are you approaching Gen AI is the next big thing? And then how are you approaching? Are you building APIs and toolsets so people can integrate intelligent large language models that are aware of my credit scores into chats? Like, what are you doing with that?

(Ryan Graciano at 00:08:04) Yeah. I use mobile as an analogy since I mentioned it. You know, when the iPhone came out, I like to ask people who actually had one when the iPhone came out because it wasn't actually amazing. You know, you could see that it was going to be amazing. You could see the potential. And some of the technology was really cool, but I couldn't get an AT&T signal in the city. It was actually very slow. There's no multitasking. Apps weren't really a thing yet. There are a handful of apps. But you could see it was going to be great. And then what was interesting is different companies kind of moved at a different pace at recognizing how big a deal this was going to be, and they took really different approaches to how fundamentally they would change their product experiences. And what we did at first was, I thought, not dramatic enough. You know, we kind of took these incremental steps, and we ended up with basically our web app in a phone. And it wasn't very disruptive, and it wasn't really what people expected. And when we took the approach of, like, let's actually just take a team, build it from the ground up, you know, re-envision the whole experience for this new platform. And then at the same time, kind of separate and say, okay. And then operationally, everyone's going to need to do this. So how are we going to make sure that they can deploy software and write code and go different languages? And we have different ways of moving assets into production. And, you know, there's just this whole long tail of stuff you have to do in training. And so we try to create this central team that's going to come up with this completely new experience, the center of excellence approach, and then at the same time had platform folks trying to then take the things that they were doing and then make them consumable by everybody else. That actually then worked. That's how we really eventually made the big transition. And I think that this is similar. Right? Because it's a completely new, it's a new technology. It's a new skill set for most people. Writing software around it and with it is very different. I think conceptually, even people kind of have a hard time understanding what makes these experiences work. And if you were to envision your product from the very start with AI in mind, you might come with something very, very different. You know? And I always say that this is kind of like the difference between, you know, Facebook, I think, did a good job in going mobile first, but Instagram was the real mobile native company. Right? Like, they came out with the product that was truly designed around this new way of thinking and being, whereas there's only so far Facebook could go. And so what I really want to do is make sure that we have a team of people that's trying to come up with the Instagram experience. Like, what would this actually be if you could do anything?

(Joel Beasley at 00:11:08) What are they coming up with right now?

(Ryan Graciano at 00:11:10) Well, I can't talk about it too much.

(Joel Beasley at 00:11:13) Oh, come on.

(Ryan Graciano at 00:11:14) What's interesting about our product now is, yeah, there's checking and savings. There's I can see into what loans you'll be approved for. We can file your taxes. You know, we can start to pull together all these different elements of your finances in a pretty coordinated way, and we know you. You know? If you go to Google and ask a question, you'll get an answer that kind of knows you. It's not that they don't know you. They know you a little bit. They can say, like, you know, hey, Joel likes marble or bicycling or whatever your hobbies are and then kind of try to veer the subject in that direction. But they don't really know all of the little details that make you you and what make an answer to a question really seem like it's directed to you. That's hard because it's hard to do unless you're in a specific domain. And in the financial domain, we know a lot. You know? We know you really well. If you ask us a question like, hey, how should I think about home buying? Or, you know, what's the best path towards retirement? We should be the best.

(Joel Beasley at 00:12:28) Can you ask questions like that yet inside of the app?

(Ryan Graciano at 00:12:32) We just now have, we recently started rolling out, ramping an experience on iOS where you can ask those types of questions. And right now, it's like the earliest version of this experience, but you can see where it's going because you can see, oh, it does know, it knows me. It, especially if you connected your accounts, it knows what you spend on, what your transactions look like. It knows your debt profile, knows your income. And so, you know, most people just go to YouTube or Reddit for financial advice, places where there's no information about you. You're just kind of digesting information and trying to apply it to your life. Only ironically, only the absolute wealthiest people actually have a person that they can go to and ask a question and get a, hey, before you do these things type of answer. We could definitely do that. And we could actually be more proactive about it. You know, we could go to you and say, hey, we think you should be doing these things. You should be on this track. And that's a pretty cool product, especially once you start to think, oh, and then it could actually do stuff for me. You know? It could actually take some of the actions on my behalf. Right? Which it could if you had your checking account and your savings account there and your tax filing. So there's a lot of potential in that world. So that's really exciting.

(Joel Beasley at 00:14:07) But it can't have the discipline for you.

(Ryan Graciano at 00:14:11) Well, you know, if you want to spend a thousand dollars a day gambling or whatever, you know, you're going to do that. Yeah. It's, you know, it definitely cannot tell you, hey, stop doing that. And if you ignore it, you know, can't do anything about that. But, you know, it can help you do whatever your thing is or whatever your advice is more efficiently than you do it now.

(Joel Beasley at 00:14:35) Yeah. For people who have, if they have the will to want to change and it's just a lack of knowledge, then that's an excellent technology to cover the gap. It's like, I want to be better, but I don't know how, and then they can tell you how you could be better, but it can't necessarily do it for you. You still have to choose to do it. Right?

(Ryan Graciano at 00:14:55) Yeah. You still have to do the thing. There's just a lot of nuance in finance. There's a lot of gotchas and little bits of information that people just aren't aware of. You know, I remember one year, I can't remember, it was years ago when I was in my twenties. Someone was just randomly, oh, you should open up an IRA. And, you know, I didn't really know why. But at the time, you know, there's a big tax benefit to doing that. There's no real reason that I would have Googled for it or learned about it other than just someone just randomly was like, hey, you should do this thing. That's just wasteful that people don't know that these avenues of advancement are available to them. And they're, you know, these things are out there. It's just that you need to know a lot to take advantage. And as you get more sophisticated, you start learning about instruments like, oh, what's a home equity loan?

(Ryan Graciano at 00:15:53) Does that make sense? What's an interest-only mortgage? You know, I took advantage of that when rates were really low at the perfect time and had a 2% rate for many years, which is wild, right? And it's not the type of thing that is obvious or natural.

(Ryan Graciano at 00:16:15) And so that's the kind of stuff that AI can help you with. I can't tell you to not blow money every day on whatever thing you blow money on. That's more of a—that's up for all of us to figure out. But we shouldn't feel like we're missing out just because we didn't—we don't spend four hours a day reading about finance. You know? Nobody wants to do that. I don't wanna do that, and I'm in this industry.

(Joel Beasley at 00:16:39) No, I don't wanna do it. I hire people and pay them to tell me what to do. And so it sounds like this is just doing that, but I mean, it's not cheap at all to do that. But this just sounds like a way to bring that knowledge to a larger set of people in a more streamlined fashion.

(Ryan Graciano at 00:16:59) Yeah. And prior to AI, you know, there were just so many permutations. There's just too much going on to write traditional software that does a good job. And many people have tried to figure that out. You know, there's all kinds of rules engines and stuff that people put together, but ultimately it's just too complex a problem.

(Ryan Graciano at 00:17:17) What what is really cool and interesting is that with generative AI, you can actually have a lot of the data points and then have it digest, you know, elements of the tax code, your transactions, all kinds of information and say, "Hey, you know, draw connections. What should I do based on this information?" And it can do a lot. And this is, you know, I think early days where I think we're in the first inning of this.

(Joel Beasley at 00:17:40) Yeah. Well, I mean, I did about 10 years ago—I was working on a project for lawyers that would analyze documents for legal proceedings and draw those connections, right, through discovery and all of that. And it was super early days. And so what I've seen happen in 10 years from those language models to what's happened today—oh, man. It's gonna be a really exciting 20 or 30 years here. It's gonna do some really cool stuff. So, hey, I saw that Credit Karma was doing some restructuring. How's that going?

(Ryan Graciano at 00:18:18) Yeah. We have rethought some of the organizational boundaries, you know, in this new era. I think it's important to recognize—you know, mobile was a big deal. You know, when mobile came around, we really rethought how we were organized and how we're gonna operate and all of that. And I thought we're actually kinda slow to do it. We eventually did it. This time, we're trying to be a lot faster. And so, you know, we're much quicker about making sure we had a set of people who are really just working on, you know, kind of a new AI experience and people who are working on how do we extend the existing experience with AI. And then there's a lot of capabilities and things that you need under the hood. So how do we make sure that those people are building the stuff that it needs?

(Ryan Graciano at 00:19:04) Like, AI can—you've probably seen things like AI can mail a letter. It can't physically mail the letter, but it can call an API to mail a letter. So if you, you know, if you dream up what those capabilities are and build them in advance, you're gonna give it access to those things later when it needs them. You just have to have some careful planning. So we've done a lot in terms of restructuring the organization to make sure that those lanes are clearly established. And it's not more like we're all just kind of looking at each other saying, "Oh, AI is a big deal. I hope everyone's working on it," because I feel like that was a lot of people's mobile strategy for, you know, two or three years when the iPhone came out, and we all know how that went. It didn't result in a lot of great apps. It resulted in a lot of people being disrupted by folks building great apps. And, you know, I think that this is more transformational. You know, like you said, right, the next 10 years are gonna be pretty interesting.

(Ryan Graciano at 00:20:05) And so, yeah, I feel like changing the organization is the least people should be doing to get ahead.

(Joel Beasley at 00:20:14) Do you see other companies being complacent?

(Ryan Graciano at 00:20:18) Yeah. I do think that different companies are moving at different paces. I think some of the big, you know, the big platform guys obviously seem pretty aggressive about it. They're moving pretty fast. I do talk to friends where I feel like their strategy is a little bit more of a bolt-on, you know, kind of a wait-and-see approach. You know, they're not ignoring it. No one's ignoring it. Everyone understands it's important. But I don't know that people are all moving as, you know, as quickly as they could be. And, you know, we all have our reasons, right? If you believe that, "Oh, well, you know, I have a few years before this is crazy, and maybe I should let, you know, some things shake out," then, you know, that's not—it's not unreasonable. But I think particularly this time around, the change is coming pretty fast.

(Joel Beasley at 00:21:15) Yeah. I had an experience a while back when the augmented reality came out for the phone, right? And I had a friend who owned a furniture company, and so I messaged around to a couple different technology leaders at furniture companies. And I was like, "Hey, how is this gonna impact, you know, your business now that I can place the couch in my room, and it's pretty photorealistic?" And when I started to have those conversations—you know, just they were offline things before I did the podcast—with some of the larger furniture companies, they really took a, "We'll just wait, see what happens, and whatever cool technology comes out, we'll just buy them."

(Ryan Graciano at 00:21:57) Sure.

(Joel Beasley at 00:21:57) I was like, "Well, that is a strategy." That is when you're the billion-dollar monkey in the room, right, you can—you can, you know, try really hard and innovate. You can just watch what happens in the marketplace and buy them up.

(Ryan Graciano at 00:22:11) Yeah. It's absolutely a strategy. It's not even a crazy one. It depends on where you are. It's a bit of dice rolling because you have to assume that you will be able to buy them, that they will sell, and that that will be allowed to happen. But on paper, if you're—and especially—and I know some companies like this that I won't name that are, you know, their strategy is more, you know, we don't really—they wouldn't probably say we don't innovate, but they don't, you know, make a meaningful effort to build new products themselves. They have more of an acquisition strategy, and then they grow the acquired product. You know, companies like that exist. And if you're one of those companies, it's not crazy.

(Joel Beasley at 00:22:53) Try to get the fledglings. There are companies that—that's their stated model too, right? There are companies that—that's their model, and they don't say it, and they just wanna be seen as a—but then there's ones that are just like, "Hey, we just roll stuff up. Like, we're private equity. We roll up in this category. Let's go."

(Ryan Graciano at 00:23:12) Yeah. Well, if you're PE, that's definitely, you know, you're not—that's a different ballgame. But I think there are tech companies that, you know, will do that. And that, yeah, it's not crazy.

(Joel Beasley at 00:23:26) Is there anything coming that we can talk about that's coming from Credit Karma soon that's public knowledge or that can be public?

(Ryan Graciano at 00:23:33) Yeah. I mean, what we're—what I'm excited about is this first set of AI tools that we're releasing. You know, Intuit Assist is pretty cool. It is—sometimes it does really surprise me with how effectively it uses my information or what, you know, what the LLM actually comes back with when you give it some context. We have some really interesting technology behind that, you know, combination of prompt engineering and retrieval-augmented generation that we've experimented with a lot. And, you know, it's really—I could go on and on about that, but it's really been a fun process to—it's almost like training a, you know, an employee kind of in how they talk to the customer and what kind of information they should have and how they should work and, you know, what's like the best employee guidebook in a way, you know, if you're bringing on someone new.

(Ryan Graciano at 00:24:28) And we have made huge strides in improving the way that our AI personalizes its responses and the data that it uses. So I'm excited to see that roll out. That's actually—a lot of that is rolling out this month in May. And so a lot of that is gonna be available to people to play with for the first time.

(Joel Beasley at 00:24:50) Oh, nice. So I've made it a full-time job keeping up with technology, and AI is just one sector, and I still can't even come close to—there's niche influencers. One guy called AI Daddy, and all he does is look at all the hundreds of new AI things come out. But he's going crazy on TikTok too. But one thing that caught my attention recently was autonomous AI agents where these—there's these networks of these agents that have these narrow specialties, and you can dispatch a task to one of them that will then work with the other specialists to achieve an outcome and bring it back to you. Have you seen this yet? Because I just saw this two weeks ago.

(Ryan Graciano at 00:25:25) Yeah. I've heard a little bit about it. I like the concept a lot because that's, I think, how these—how we're not stitching these things together as much as we could be yet. And that's kind of what I'm alluding to a little bit with when you build a lot of the capabilities and then you put agents in front of the capabilities, yeah, you can start to chain them together in really unique ways.

(Joel Beasley at 00:25:46) Have you seen these operate, though? Like, have you actually seen a task run through and be completed by one of them?

(Ryan Graciano at 00:25:52) I haven't seen the one you're referring to. I've seen another prototype that's very much like what you're describing, which is really neat.

(Joel Beasley at 00:25:59) Yeah. I think they'll end up being an inference layer, like, the layer that's just trying to understand and dispatch, and then there'll be the individual intelligence layers that can actually perform the functions.

(Ryan Graciano at 00:26:11) Yeah. I think that's exactly right. Well, I mean, you look at how—just look at our society, right? So you have a person that's trained essentially to do each task. You have a lawyer and a doctor, and you have different specializations of doctors, and you have your accountant and so on. And so, yeah, it doesn't really make a ton of sense to try to have one AI that does all of it. And it also doesn't really make sense to have them be all completely disparate. So, you know, for medicine, I think, is the best analogy because I wanna be able to talk to one, you know, kinda GP type of interface. But then, yeah, it's gonna need to actually kinda leverage all these other different specializations. You know, these other agents, if you will, that can do each one of these, you know, thousand different things, and then it's coordinating and essentially orchestrating tasks between them. And you could imagine sort of a super assistant that's even sitting on top of that and saying, "Well, I see that this thing's happening. I'm gonna just send a note to your doctor thing for you."

(Joel Beasley at 00:27:12) Well, you see that in people, right? There are these project manager-type people or these high-powered executive assistant-type people where you can just give them something, and they can do this incredibly valuable task of figuring out how to get it done.

(Ryan Graciano at 00:27:29) Yes, exactly. And we are thinking a lot about our own platform in this way. And so, you know, there's a lot that Karma can do for you. And so it's like, well, is there kind of an accountant agent that knows about, you know, how taxes are filed and what you should be doing there and so on. And then there's kind of an agent that's helping you manage your cash flow and another agent that's looking at your transactions and figuring out sort of day-to-day stuff. And then there's something that sits on top of all of it—and Intuit Assist, it's coordinating all of these things and interacting with you in a way that, you know, it doesn't—you don't have to see all that complexity that's happening behind the scenes, but you know that, you know, you can kinda know that it exists, but not have to go that deep.

(Joel Beasley at 00:28:15) Yeah. There was one feature in QuickBooks I liked where it said to me, you know, "Based off of your cash flow, we can offer you a loan for X at this rate." And it was a really good rate. And it was kinda cool because you already know that they already have essentially underwrote it because they have access to all the data that you would give them access to if you were to go get a third-party loan anyways.

(Ryan Graciano at 00:28:37) Yeah. It just makes everything a bit easier and a bit more seamless. And, yeah, you could imagine that—you know, we've built a lot of capabilities to facilitate those kinds of transactions, right, make them fast, seamless, that kind of stuff. But for those we don't have, you know, partnerships with, you could imagine actually having, you know, an agent that applies on your behalf. And, you know, you could have agents that do a lot of previously very manual human tasks. Finance is very manual currently.

(Joel Beasley at 00:29:11) Oh, yeah. As we start to wrap up, I've got a question—a fun question about your red line. Like, where do you draw the line at what you refuse to use AI for? Like, for example, I'll use AI for assistance, for booking, for finance, but I'm not gonna let my digital twin host the show. Where do you draw that line?

(Ryan Graciano at 00:29:32) Yeah. I mean, I think I—in my personal life, there's my personal life and my professional life. In my personal life, I don't like using it for anything interpersonal. Like, if I'm gonna write a thank-you note to someone, I'm gonna write it myself. And if I'm gonna write a joke, I'm gonna write it myself. And if I'm writing something for my kids, I'm gonna write it myself. You know, I feel like one of the most annoying things about AI—you know, we'll have a party where everyone has to author something or write a little thing, and then everyone comes with an AI-generated version. It's like, "Well, you know, did I even need to have the—you know, did they even need to have this little task, or is that now just a hurdle that people are using computers for?"

(Ryan Graciano at 00:30:14) And it's a bit of a digression, but then professionally, the key thing to me—you know, I don't think anyone wants to do anything for themselves in finance if they can avoid it. It just has to be operating to your benefit. That's the most important thing, is it has to try to figure out what—what is it that is most important to you, which is not always obvious, and then be operating to that end.

(Joel Beasley at 00:30:41) But if you had a digital twin that was more intelligent than you and could pass off as you, would you just let it roll and just go to the beach?

(Ryan Graciano at 00:30:50) No. Well, it sounds boring, one.

(Joel Beasley at 00:30:57) Alright. Just go to—sorry. Let it roll and start a rock band? I don't know.

(Ryan Graciano at 00:31:01) I like working. That's why I'm still here. But, you know, I—I wanna do something. I think it's important that we still give people some agency, you know, handing everything over to the robots. I can't imagine that that is the future that we actually want. But there's many things I would like to really hand over to the robots. I don't like filing TPS reports. The robots can do that part for me. The tedious stuff, certainly.

(Joel Beasley at 00:31:34) Yeah. One of the things that's been interesting since the growth of the show and the production company and all of that is this idea of personal financial freedom. And I'm actually surprised at how much I still want to work. That's where I derive a lot of my dopamine, I guess. It makes me feel good to contribute to others in a meaningful way.

(Ryan Graciano at 00:31:57) Yeah. I think this is the most important thing. And it could be work. It could be really anything productive. You know, it's that autonomy, mastery, and purpose.

(Ryan Graciano at 00:32:06) I think we've all heard those three things. Those matter. And to me, the danger of not working is if you're not the type of person who will find another activity. It doesn't really matter what it is, but another activity that will give you that feeling of, "I'm mastering something. There's a purpose behind this."

(Ryan Graciano at 00:32:26) It could be for the advancement of society. It could be that you just really like, you know, whatever the thing is and you feel that it matters. But it's gotta be something. And what I think many of us in tech are fortunate to have is we are doing something that we enjoy. And it feels like there is a true purpose behind it, and it matters.

(Ryan Graciano at 00:32:49) And it gives us, you know, something to really care about mastering. And that's valuable.

(Joel Beasley at 00:32:57) Where do these principles come from? Autonomy, mastery, and purpose?

(Ryan Graciano at 00:33:01) I have heard those things for many years. I think that they are listed in some book, and I can't remember which one. It might be Drive or something. I can't remember which book. But ages ago, those were essentially laid out as the keys to motivation.

(Joel Beasley at 00:33:22) What do we need?

(Ryan Graciano at 00:33:23) Yeah. I think it was that. Yeah. Drive. Those three items. And I always thought it was a very succinct way of putting it. You know, what do we need to each feel fulfilled, like we're on a path towards something that matters? Autonomy, mastery, and purpose. Yeah. I really believe in that.

(Joel Beasley at 00:33:47) Nice. And what do you have mastery of?

(Ryan Graciano at 00:33:51) That's so hard. It feels so arrogant to say I'm mastery of anything.

(Joel Beasley at 00:33:55) You've put 10,000 hours into something, man. And you should recognize too when you achieve mastery of a specific topic because then you understand in context the amount of effort it takes to achieve mastery.

(Ryan Graciano at 00:34:09) Yeah. And, you know, I think in the tech world, building this company is something that, you know, certainly there's so much that goes into that that I feel like it's hard to even articulate how many things in the stages and so on. But I do feel, you know, certainly, I feel mastery over that. And then there are hobbies and things where, you know, I'd like to say I'm a master, but—

(Joel Beasley at 00:34:32) Like what?

(Ryan Graciano at 00:34:32) You know, I'm working on it. Well, I've recently taken up some new stuff. I've been snowboarding and skateboarding. That's what I've been doing. Board sports. And then I've weight lifted for many years. So I hit the 1,000-pound club, actually, which was kind of my big milestone just back in October. So I was feeling mastery then.

(Joel Beasley at 00:34:55) What's the 1,000-pound club? Is that leg press? Or—

(Ryan Graciano at 00:34:58) It's when your squat, your deadlift, and your bench add up to 1,000 pounds. Oh. It's a fun goal. It's a good one to set. I love one rep max. It's fun.

(Joel Beasley at 00:35:09) Awesome. Well, Ryan, man, this is great. We did it. We made a podcast. How do you feel?

(Ryan Graciano at 00:35:15) Yeah. It's fun. It's good to see you again, Joel.

(Joel Beasley at 00:35:18) Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it. And if you have topics that you'd like to hear discussed on the podcast, either add me on LinkedIn or send me an email: [email protected]. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.