Interview with Dr. Subhan Ali

You're listening to One Sharp Sword cutting through to what Matters Most with outfluencer Dr. Wayne Purnell. Welcome to One Sharp Sword Cutting through to what Matters Most. I'm your host, Dr. P. Dr. Wayne Purnell, your host, guide and coach on this journey. And today I have with me Dr. Subhan Ali and he is based in the Silicon Valley though he didn't start there. He's the co founder of data and AI company called Geckit. We'll get into more of what that is, but you don't just land in the founding role or co founding role of a new data organization, a new AI organization in the Silicon Valley. So the journey there is is fascinating and I had the opportunity to talk a little bit with Saban before we did this podcast and I am really excited about the journey. Saban, welcome to One Sharp Sword. Glad you're here.

Thanks for having me, Wayne. Excited to be here.

Yeah. Good. So. So let's talk a little bit about your journey. Let's talk first, let's talk about what geck it is, what you, what you're doing these days and why that's important because there's so much data stuff out there. There's so much new stuff with AI and, and some people, you know, you say AI these days and a lot of people freeze, like not more. And it's like, yeah, it's, it's what we do. It's kind of the wave of, of now. I wouldn't even say it's the wave of the future, it's the wave of now. So talk a little bit about what Geckit does and then we'll back up a little bit and we'll get into your journey because that was fascinating too.

Sure. So I think I'd kind of first start by describing the field that we're working in, which is really this intersection of data infrastructure and AI. And that's kind of my background, that's really my co founder's background since this is where we're really focused. And I think in general the way that we think about this is that data, data infrastructure is nothing new, but it's taken a bit of a backseat lately when it comes to LLMs and AI really being kind of all the rage. And it turns out that the data that you feed into LLMs is quite similar to what was happening even before LLMs pre chatgpt existed. And data actually really does matter. And so building a data data platform that's geared towards this LLM or AI first world is kind of over really focused and thinking deeply about.

So I want to, I just want to make sure that people understand LLM When Saban says it is language learning model and that's large language model. I'm sorry. Wow. Large learning model. And so really it's how the initial AI models or apps like ChatGPT have gathered all the information to be able to bring back to us. So the large learning model is, it scrapes all kinds of data from all kinds of places and, and brings it together. And so as you're talking, I just want to make sure that, that, you know, what is just kind of what you do is, is clear for everybody because we get, we have, you know, the audience is broad, so breaking it up.

Yeah, exactly. I mean really, the ChatGPT is the most prominent, almost well known LLM or large language model. And, and so, but there's a number of these that are available commercially now that people use. That includes Claw, that includes Gemini, that includes, so there's a long list of them. But yes, that's what I'm referring to. Great.

And, and so what, when you talk about data infrastructure and the intersection of data infrastructure and AI, what does that mean to us who aren't in the, in the tech world? Like we touch it, we use it, but what does that mean? Like who are your end users and what do they do with it?

Yeah, so when you think about the tech world or, or the process of creating anything, there's really three parts of the flow. The first part is data. The second part is what's called model training. And the third part is what's called inference. I'll explain what each of these are. So the first part is the data component where you have to create a data set upon which you're going to go ahead and train them all. So let's say that I have a very simple model I want to do that predicts people's heights and you know, and it predicts them based on weight. So you could have two axes of information, weight and height. And that's it. And that's your data. And then what you do is something called model training. And in model training what you're doing is you're taking the data that you did in that first step and you're now creating a model that describes that data in some way, shape or form. So in the example I just gave, okay, well, let's say you've got an X axis of, you know, weight, and then you've got a Y axis of height. You can draw a line between Those two things. So that line is your mark now. Now the third part is what's called inference. And in inference what are you doing? You're predicting new data points that you don't actually know right now. So in other words, you take that model and you plug in a new weight and you predict a new height that that model did not know previously. And so, so this three part stage data training and inferencing is really kind of the crux of how all machine learning AI are built and developed. And so that's the kind of thing to keep in mind I'd say in terms of what we're doing is it turns out a lot of people have been very focused on the second part of the equation lately, which is called model training and even inferencing. But, but for really the last 20 years, data has been a lot of where the attention in mind share has been at. And it turns out that that data that you're feeding into the model has a disproportionate and extremely large impact on how useful the model actually is. So in other words, if you're giving it just kind of whatever data, it's not going to be 10x better. And feeding the model the right data is actually just as important as properly training that model to begin with.

That's amazing. That's the, the most people I would assume are familiar with geo, right. Garbage in, garbage out. So depending on what data you're after at the, as a result, like what do you want to have happen? You have to give it clean, it's like a body, right. You have to give it clean fuel in order to come out with something that's, that's at a high performance level. So, so you talked about data infrastructure meets AI. What is that? Who are your end users right now? Who are you targeting, marketing to serving? Like how are you using what you do to help others?

Yeah, so I mean the people that we're really most entirely focused on, on helping are really the technical practitioners. So who are these people? They're data scientists or machine learning engineers, they're software engineers who are actually really building and deploying these models in what I would call production environments, enterprise settings. And so they have actual users, they have a large language model they may have from somewhere else and they are faced with the task of putting data or feeding their custom own data into a large language model to make an enterprise workflow work well. And so that's, that's really who we're focused on.

That's awesome. It's amazing. Because it's. What you do is so behind the scenes that it's sort of like you're, you're building the, the water dam that allows the electrical turbines to turn so that eventually if I hit a light sw, my light comes on automatically. Right. You're doing the equivalent with, with data sets so that when somebody needs information, bang, it's there right now. And that's because you're, you're busy creating the engine that makes that happen. This is, this is a world that is. Obviously it's not my world. And so the questions that I have around the tech part of are maybe a little awkward. So, you know, I know that you have an engineering background, but you also chose then to not leverage that or you leveraged it in a very different direction. Do you want to talk about some of the other places you've been? Like you. The way you grew up was very different than the way most people grew up. And yet the decision making and some of the obstacles I think are hugely relatable. Can you talk a little bit about that?

Yeah, I mean, so I, I grew up in the, in the Southern California area, specifically in Orange County. My parents are both from Pakistan and moved here from Pakistan before I was born. And then, and I think growing up, obviously I had an affinity towards math and physics and kind of the hard sciences. That was pretty obvious. And then I think what happened was when I was applying to college, I knew I wanted to do engineering. I think that was quite clear to me. But what wasn't clear to me was which specific field or domain of engineering I should pursue. And this was, you know, a while ago. And at that time, one of the things that that's. This is 2002, 2003 time frame is that, you know, the computer science, like, era that we're kind of in now wasn't actually so obvious yet. And I kind of narrowed down my choice to two different fields. One was electrical engineering, the other was civil engineering. And I had thought about civil engineering mainly because my father was actually a civil engineer and has his own company as a civil engineer. And then two, electrical engineering I was really thinking about because it appeared to kind of be the more attractive or hot area at that point in time. But I also didn't feel like I had a natural gravitation towards electrical engineering. It can get pretty in the weeds very quickly. And so I was trying to figure out what to do. I did civil engineering. I realized that, you know, the part of civil engineering that I like the most was structural engineering, which is really, physics. At the end of the day, it's. It's. It's designing buildings, but designing the. The support systems that underpin the building, actually. And so I think, like, the thing that's perhaps the most interesting about what I did as a structural engineer is that it's like a building can't stand without the structure behind it, but you don't see the structure. And. And so it's like. It's there. It's, like, super critical, but you don't see it. Right.

And, like, that's a great analogy.

It is. It is, yeah. Because. Because it's like, it's. It's. When you think about what I do now with infrastructure or data infrastructure, it's like the same thing. It's like you. It's there, it matters tremendously, but you don't see it. And so people kind of forget about it until everything breaks, and you're like, oh, holy crap. Like, what do I. What do I got to do to fix this? Yeah. And so. So. So for me. Yeah, so. So that. That's kind of what happened. And I ended up coming to Stanford. I had a plan to get just a master's degree in this field. I started my degree in 2008. And then very quickly, the US economy really went into a downward spiral, and the job market became really tough. And so I. I realized that, you know, there was an option to get a PhD, which I took and did that and definitely had a lot of fun. But I also knew that I didn't. I wasn't completely clear if I wanted to be a civil engineer, if that was kind of where the biggest challenges were for me in the future. And so I figured that pretty quickly, a mentor of mine who knew me quite well told me, hey, you should really think about data science. Because it turns out I'm on the boards of a lot of these companies, and they have all this data, and they, you know, people who know how to understand that data have, I think, a lot of. A lot of say at the table. And so I listened to him.

This was a friend that advised you?

A mentor. A mentor of mine.

A mentor of mine.

He's a venture capitalist. Yeah.

Okay.

Yeah.

D.C. good.

Yes, yes. And so. Yeah, so. So I took his advice, began to, like, really look at it with a fresh set of eyes, and realized that there was something there. And then I did a bunch of things. I did this boot camp that helps people with PhDs become data scientists. I ultimately worked at a company called Symantec, which is a cyber security company as a data scientist, and then I worked at ebay as a product manager. And then six years ago, I joined Nvidia and had an amazing ride there. And you talk more about that, but that's. That's kind of what my path has been.

Just small companies like, you know, Semantech and ebay and Nvidia just, you know, companies no one's heard of.

Well, one of them is the biggest company in the world now, so.

Right, yes.

Most valuable company in the world. Most valuable company in the world. Yeah.

Yeah, that's. That's great. And so along the way, you know, what I, What I like to do is I like to inject sort of the. The human part of the bullet points. Right? So I did this and I did that, and I did this. You had decision making along the way, and that's why I asked you about your mentor, right? That's why I asked you, how'd you decide? And. And it's like, sometimes you get really good advice from people that see things that you can't see, and then, you know, you go in and you decide, wow, you know what? Cement tech is a pretty darn good company. Right? And then it's time for you to grow. What was, what was the decision making for you to move from Symantec to ebay and then from ebay to Nvidia? And I assume from Nvidia you springboarded and said, you know what? I'll do something fun for myself. I'll start my own company. Well, can you talk about kind of the decision making, the challenges, all the things that showed up for you along the way at each of those points?

Yeah, for sure. I mean, I think I'll talk to the, The. Each of the. Each of the transitions, but I'll start with maybe that. I mean, I have a deep belief that if I'm not working on the absolute best thing I think I can be working on, I go figure out what that best thing is. And so I asked myself that question constantly.

Okay, so that's where I want to put, like, that is. That's huge. Because if. If we each took that on, right? If that's a learning lesson, it's like, am I working at the best? Am I. Am I creating the best? Am I serving at the highest level? Am I working at the best company to provide the best thing for others? Yeah, that's huge, right? Yeah, that in itself is huge.

Yeah, for sure. I mean, it's. It's. It's something that is, you know, it's. It's Easy to say. It's hard to do.

Yes.

Yeah, yeah. And very easy to say, but. But ultimately extremely hard to do because I see. I see, you know, all the time people settle for the second best, third best, or whatever, or, you know, just. Just keep things on the path that they're on. And I often think that staying on the path that you're on is actually the most dangerous thing, because if I had not. If I just stayed on the path I've been on before, Nvidia, I would have missed Nvidia, and my life would be totally different.

So let's. So let's talk about that for a second, because the. That obviously sparks me. That's the. That's the kind of stuff that lights me up. People settle, and the. The challenge is, like, you're in a place where you go, okay, you know what? I don't settle. I want the best part of who I am is creating a new me every day. Right? That's kind of where you live in that. In that headspace of what was good enough yesterday isn't good enough today. Let's go. Right. That's. There's this. Without it being right, you're not. You're not out there trying to, you know, tear things up. It's just like, I know there's more. Most people get stuck because they get scared. And that's a subconscious process that says, hey, your identity's changing. It's. It's. You know, you're gonna move on from who you were. And for you, you're like, of course I am. That's exactly what I want. And for most people, it's like, oh, yeah, that's not safe, is it? It's like, well, the safety comes from within. Your safety is, I know I can create bigger, better, and come out okay. Most people don't have that consciousness. And that's why I want to spend. I wanted to just kind of interrupt you and spend some time there to say, you could choose this. You could. Like, this is. Savon, you completely chose this path of. I want to be at my best. That's not easy. And that's. It's.

It.

It's as easy as a decision, and for most people, it's just not easy. So I. I love that.

Yeah. I mean, I think. I think. I think it's. It's like the hard. You know, it's a really hard thing to do because I think at a certain point, you have to get to this point of conviction and belief in yourself, and until you get there, you Know, you kind of are really at someone else's whim in terms of what your options are, what, what your, your day to day is, what your life might be. And I think for me, I, I've, I think I've always kind of believed in myself. I think that's always been really clear. But I had to also take steps to get like a, a solid footing kind of in the new field that I was in. And that took time. And that's okay because like, I, I, I was convinced that I was on the right path. And, and um, you know, this is 10 years ago that I was, I started the journey of, of like really making that decision happen. And I, I just had this belief that like, okay, this is going to be really big, it's going to go somewhere and if you know you're a part of that, it's going to have a much better outcome for you than, you know, just taking the safe option. And I have zero regrets about doing all the things that I did because I had a belief that. All right, you know what, you know, it's, it's like people always think that the, that the small decisions really matter, but they actually don't, they don't matter as much as you think that they do. It's actually the big ones, the big decisions that compound over years that, that matter a lot. And once you realize that, you realize that like, you know, what you can accomplish in this is the saying I think is what you accomplish in 10 years is way more than you than what you realize. But what you accomplish in a year is way less than you, than you hope for something like that. And so for me, I was just very, I would say deliberate, intentional, but also open minded. Like when something came along that didn't fit a paradigm or a model that I had in my mind, I was still open to it. And like, I think maybe the best example of that, quite frankly, is actually joining Nvidia. Because when I joined Nvidia, it's not the company it was today. It was six years ago. It was before ChatGPT, it was, most people thought of it as just a semiconductor company, a chip company is what I was told, or a video game company, even, even more so shocking. Like people said that to me all the time. And, and at that time, six years ago, it was risky joining Nvidia, quite frankly. But, but it, it, there was, there were things about it that I saw that were like, oh wait, I think this is a much better place than anywhere else I worked. And, and I was right that was clearly the case. But you have to have this openness to new experiences that, like, most people may not have. And if you recognize them for what they are when they present themselves to you, you won't have any regrets. And so for me, it was really about, like, having an open mind and being like, you know, what Can I learn something here that I don't know right now? And the answer to that was obviously, yes. I realized that then it took a little bit longer for it to play out than I probably hoped, but I, I stuck around and, and benefited from it completely. So. So no regrets there.

This is awesome. I'm making notes real fast. Here's. Here's what I want to point out. Okay. You had a conviction and a belief in yourself. That's first, right? That, that a lot of people are looking to others for that approval for that safety. Um, there's so much around personal safety, psychological safety right now. And what I see when I'm consulting in organizations where, where there's this sense of lack of psychological safety when I talk to people, it's. It's actually a matter of they don't feel like they're getting enough information and they're also not asking. It's like, so have the dialogue, right? And that's, that's first for you. Your belief in self is. It comes down to this. Something as simple as I believe in my ability to figure it out. And so if it was wrong, if it was a wrong path, if something happened, you'd pivot. And that's your ability to figure it out. It's not like, oh, I went down this wrong path, everything's lost. It's. I went down this path, didn't turn out the way I thought it would. Let me find a different path. Awesome. It's Right. So you, you had this belief in a process, also belief in an outcome. And this is. I just, I wanted to point this out, Subhan, because a lot of people will put their faith in a belief in fear. Oh, if I do this, everything will go wrong. And that's where like, a belief is, is like faith. It's. It's putting energy into something you don't know. And it could be, wow, this is going to be the positive outcome that I'm after. Or it could be, this is going to be the most horrible thing. And I don't dare. And so you talk yourself into it one way or the other. And the choice is, I believe in pursuing something that's a positive outcome. And what you did along the way was I'm going to pursue something that I believe has a positive outcome, and I'm going to be taking in information along the way and I'm going to stay open to opportunities. And a lot of people don't do any of those things. So I just, I really wanted to call that out, openness to new experiences. And what you said is, and what can I learn? Which means that you have a different idea about failure than most people as well. Since you're asking the what did I learn question, did you fail along the way?

For sure. 100. I mean, I think, I think like, you know, I think your ability to fail and your ability to learn from outsiders is probably the thing that defines the most successful people in the world. By far. Far. What you begin to realize is, you know, I, I've obviously been very fortunate to spend time around a lot of incredible leaders, including Jensen Wong, CEO of Nvidia, amongst many others. But what I began to realize is that the thing that defines those people, yes, there is intellectual rigor to what they do and whatnot. Yes, there is. But more than anything, it's actually not that. It's this kind of like, ability to show up every single day and learn quickly, take in the ground, root, ground truth and keep on pushing no matter what's coming at them. That's actually the defining characteristic of them. It's not like all the other things that you may associate with them or think about them. Yeah, they matter. They do, but they just don't quit. They just don't stop. And I think that's really the key thing, which is like, you know, the people who really succeed are. They're up at the end of the day, you know, term learning machine. They are learning machines because like, reality may change, the, the ground truth in their company may change, the whole market may, but it doesn't matter. Like, they, they have a viewpoint, they sharpen that viewpoint through trial and error. But then eventually it becomes so sharp that they, they have no kind of way they break through.

And yeah, they're so good at it that they, they can't fail, right? It's like there is no failure. There's only learning at a certain point. I, I love everything you just said. And that's just to clarify. It's like, well, the environment may change. The, it's like, oh, it will, right? That. I mean, if you look back, how long has it been since we've had a phone, right? An iPhone, a handheld computing device? It's, it's not been that long. The regular telephone, the Wired telephone that used to be on the wall. That hasn't been that long. Electric lights hasn't been that long. The environment changes, and so our demand changes as well. Which means that, that every single company that we deal with, whether we're in one or own one or we interact with one, we're changing. We're all changing in response to what we want, what we as a global society wants. So I think, I think that's great in terms of the other two things you said that I want to bring up. One is show up and don't stop.

Right?

It's like, just understand the environment is changing. Keep showing up. Keep being that learning machine. Don't stop. And whatever you learned yesterday, there's more. There's got to be more. The other thing, I want to wind back just a second. You said, you know, it took a little bit longer. It took a little bit longer. Your journey took a little bit longer than you thought. Great. Right? A lot of people will be go, well, what is taking so long? Or if I don't have X by this time, I must be a failure and becomes a personal thing. It's like, no, no. Things take time. You build skills that get you to the next skills. There were opportunities. You talked about being open to new experiences, new and new opportunities. There were opportunities that were in front of you that had you not been showing up, had you not been learning, you wouldn't have seen them. It's not that they weren't there. It's that you wouldn't have seen them. They. Right. And so that's why we need to keep showing up. It's why we need to be open to new experiences. I am fond of saying that each of us is a leader because we are being watched. We are modeling for each other, for. And we never know who we're leading. When you show up and you seek new opportunities and new experiences, and when you show up in that place of confidence, where you go, I'll figure it out. You know, it's like, that's part of what makes for an exceptional leader as well. I love going down this path with you because you've done so much of it. This is awesome. So I paused you. Where. Where are you headed? What's next?

Yeah, I mean, I think. I mean, I think maybe something else I can. I can talk about is kind of some of the transitions that I went through.

Yes.

I can even talk about some of the things I did in Nvidia and kind of what I'm doing now. I mean, I think one Thing I'd point out is, you know, I always tell people that, like, when you're making transitions, transitions are painful. They actually are. There's no, there's no easy transition, so be ready for that. But two, be strategic and thoughtful about what you're doing. Because, you know, like, at one point I was like, I think when I wanted to get in the tech industry, I was like, I think I said, I want to work in business development or product management. Now, with a PhD in civil engineering, that story made no sense, to be honest. It just made no sense.

Right.

And, and so it's like, well, okay, hold on. Like, even if that's where you want to get to, like, how do you get there? Because what you can't do is be like, oh, I want the perfect job, you know, from where I am today. No, you have to work there gradually, which is what I did. And so what I did was having a PhD in engineering. Becoming a data scientist was a very credible transition. And so that's what I did. That's the first thing that I did, actually. Um, I just became a data scientist. I'm like, great, awesome, wonderful. So I, I love that.

That's the first thing I did. So I had this PhD. So the first thing I did was just become a data scientist. I love that.

So, so I, I made that first transition happen.

Yeah.

And then when I moved to ebay, what happened was they, they had a product manager role and it turned out that particular product manager role, they wanted somebody who had a data background. And I was like, perfect, great. I'm your data guy who knows data, but I don't have product management yet. And I got the job at ebay. And so again, I used my previous thing to leverage myself into the next moment. And then when Nvidia came around, what happened was I knew very little about Nvidia when I joined the company. And in fact, I knew very little about AI and deep learning and GPUs and large language models didn't even exist at the time. And what happened was the, the reason that that transition made sense was I was joining a kind of a, a go to market team and one of their customers was ebay. Guess what? I worked at ebay. So in all three of the transitions that I made, I like, leverage what I had previously to catapult myself into a better position on the other side of it. And that's kind of the magic that you have to do when you're trying to like, make something really incredible happen. And so, so that's Something else to keep in mind.

Always leverage. Right. And. And what you did was. It wasn't, oh, I have these skills and it's a perfect match. It's. I have these skills. I don't have that. I think that's perfect. And they said that's probably perfect because you can learn this. We needed this. Welcome. And that's how you leverage. I. Again, amazing. Transitions are painful, is what you said. Transitions are. That's. That's. I would say uncomfortable versus painful. There's painful as you want them to be or. Or need them to be. Sure they are uncomfortable. And what I've found is that the amazing leaders, the top performers, live in this place of slight discomfort. They're at this place of, yeah, if I'm not. If I'm not growing past where I was, I'm probably declining, so I need to push to. So they live in this place of discomfort, constant. Of new identity almost every day. But it's not. It's not a completely new. It's not like they're throwing out who they were. They're carving off who doesn't belong anymore so that they can operate at a higher level, which is fantastic. Also be strategic and thoughtful. Sometimes those are two different things.

Right.

Sometimes the while strategically it would look like this thoughtfully, you know, coming in with a different perspective or with a different thought, you know, when you've managed to align them. Because a lot of times people's thoughts, again, will hold them back versus, you know, what strategically, I want after this. I want to go after this target for myself. Fantastic. So. So how'd you decide to move from Nvidia to go. You know what? It's time for me to start my own company because that, like the entrepreneurial seizure, as it's been called. Like, I'm good at this. I think I'll start a company. Doing it is. It's a ridiculous thing that causes so much stress and so many people have great ideas and then turn it into mush. So you had the fortitude and the strategy, the strategic thought, the like, all of it. So how did you know? How did you know it was time to start your own company? Yeah. Talk about that for a little bit. Yeah.

Let me actually discuss maybe a little more what I did in Nvidia and my journey within the company, and then I'll tell how that leads into exactly what I'm doing now. Perfect. They're highly connected, so I think I. Nvidia was. It was obviously an incredible experience to be at the company, particularly the time that I was there. And I think I put my time in Nvidia down to two halves. One was before ChatGPT and other was after ChatGPT and for a few reasons, but before ChatGPT, you know, I definitely had, I would say, you know, a great job. I really enjoyed everything that I was doing. But at the end of the day, you know, for me, particularly being in the world of both product management and business development Nvidia, like I had to really engage with the external world to make progress. What I was doing and, you know, the awareness just wasn't quite what it was after came out. And that, that tailwind really propelled my ability to execute and do exceptional work. And I took advantage of that completely. And so that shows up in one particular way for me. I worked with a company called Snowflake and I worked with them for almost the entire time I was in Nvidia. But there was a pretty big shift in engagement after ChatGPT came out. And so their enthusiasm and even product development come around. AI went as with a lot of companies went kind of through the roof after ChatGPT. And so for me, the last two years of 2023 and 2024 were really just laser focused on, on, on Snowflake, but also on the broader area of data infrastructure and AI. And I just had kind of a demand for my skill set and my work that went really through the roof in that point in time. And because I had that, I really just, you know, it's. It's one of those things where it's like, it's like. I don't know the best way to describe it, like the hero's journey or something along those lines. But like, but it kind of really felt like that to me and it felt really clear. And what I realized was like, was I wanted to start a company for a really long time, but I knew that I still hadn't quite developed that completely intrinsic sense of motivation that one needs to have to start a company. And the thing that was stopping me was like, hey, I haven't. I don't feel like I half professionally push myself to a level where I know that I'm going to have to when I start a company. And the last years Nvidia did that to me. Like, it's. And it's like one of those things. It's like, you know, whether it's like the Matrix or whatever, it's like once you see it, you can, you can never go back to what you were before. For me, that's what happened in those last Two years at Nvidia and I had this, like, unshakable conviction in myself that I was like, you know what? I almost kind of don't care what anybody says or thinks about me. I'm going to do exceptional work anyways because I know that that's what I have to do. That's who I am now. And so, so I think, I think for me, like, you know, there is no, like the, the, you know, Nvidia was an incredible company. It was incredible, big company. But, you know, to. To work in another big company right now, I don't think it's a thing for me. I think the thing for me is to go and pave a new path that doesn't exist right now. And, and, and being an entrepreneur starting a company, that. That really was the only option for me.

So that's fabulous. That, like, again, I mean, this is, this is why I have you here. This is really what one sharp sword is, is cutting through. And this is the way you cut through, is to keep your blade sharp. Right. And what you did is. I don't care what other people say about me. And so many people live their lives in a place of approval seeking. Like, I would branch out. Do you think it's a good idea, like, I could start my own company? And you're like, no, I see a path. I see something that doesn't exist. You talked about the hero's journey, right? You dared to enter the cave of fear, like, the place that was the most scary for you. You're like, well, I need the skills to be able to do the thing that I don't know how to do yet. I'm gonna go get them. Fantastic. Most people are like, I don't know, you know, so this is, this is huge. And the. It was Emerson who said, you know, the mind once stretched to a different proportion can never return to its original state. And. And so that's the matrix, you know, once you see something, you can't unsee it. And, and you see this path. You see this path that's going to affect possibly all of us, which, which is great. Hopefully you're the next Nvidia. Right? So it's like that. That would be amazing. And for you to be able to describe sort of your path at this point with me, that's also amazing.

Yeah. If.

So. Do you want to talk about Gecko? I'm. Yeah. Keep a note of our time together. I want to make sure that you do two things. Talk about Gecko, your next steps in the world.

Yeah.

And if there are three things and if there are kind of leadership lessons that sort of jump out at you as. You know what? I just want to make sure people get this.

Yeah, I think so. So get. So, yeah, we. We actually started our company about, whatever, two and a half months ago. I met my co founders earlier this year, and they're both exceptional. One is, his name is Mike Caffarella. He created the data processing library called Hadoop, which was a very big deal. I mean, really, during the last 20 years, it's been quite the thing. And then Mike is also, you know, a principal research scientist at MIT. And then Shiva Sedir, who was doing his PhD under Mike, but finished his PhD. So I started Gatekeep with both Mike and Shiva, and really excited to be working with both of them on what we're doing. And I think generally what we're thinking about is, again, this intersection of data infrastructure and AI. More specifically, the way that we're thinking about this is it's like, really that level of how do you begin to put data into the large language models and give it the data that helps it be most effective? And what does this show up? This shows up in, like, something called context. What is the context? It's like when you use a ChatGPT or you use a large language model, you're giving it a prompt, and it turns out you put a lot of things in that prompt. So, for example, when you ask a question, you could say, like, let's say we're asking some medical advice or something. Oh, you know, I went to the doctor three times in the last year, and they said this, this, and this. And my readings are this, this, and this. And now you ask it a question, what should I do next next time I go to the doctor? Or how should I change my exercise routine to account for all this information? So the process of optimizing that context becomes really, really important because it turns out that what you don't want to do is dump a bunch of stuff into the context that is actually garbage, because you won't get garbage out. But it turns out giving it just the right context at the right time with the right prompts can dramatically improve your performance. And so process of context optimization, that's really what we're focused on and trying to build a much brighter future around.

That's. Thank you. That's helpful. You talked about inference also as part of this, and I don't know enough about that part of the tech world to know if there's overlap with context. Like, are you are you creating the process by which inference can happen and therefore context is created.

We're, we're actually a step right before inference happens. So when you ask the, when you actually prompt the model, like you ask it a question, at that point in time, inference occurs. And so in this case, we're giving the model additional context in the prompt before it does inference. But inference is an iterative process, so each time that you ask it a question, the model, the inferences occurring.

Great, thank you, that's helpful. I know I'm not the only one that doesn't know all this stuff. And yet, you know, maybe there are people that I'm certain there are others that are listening and know much more than I. Fantastic. So what's. What's next for you? What's. What big challenges are you facing? What do you, what are you hoping for? And these are a bunch of questions all at once. And if people wanted to know more about you or about Geckit, where would they find that out?

Sure. I mean, what's next for me is really building getget into, I'd say, you know, a world class company. That's what we're really focused on. And so in our case, you know, really getting a great technology, a great product to market and putting that into the hands of a lot of customers, that's something that, that's pretty much what we're laser focused on right now. So that's what we're hoping to do. In terms of myself, I mean, the best way to learn about me is through LinkedIn. I actively post on LinkedIn pretty frequently and so I've always got the latest in terms of what I want to there. So can definitely show them my profile link and people feel free to follow me there.

Great. Okay, that's perfect. Any last, Lenny, last words, any kind of final comments that you want to make for our podcast time together?

No, I think, I think this is great. I mean, I think like, you know, ultimately everyone has to figure out the path for themselves that makes the most sense. And that's just something to always keep in mind. It's so easy to get caught in this world of comparisons and depression to this depression did that. But ultimately the people who really make the difference are the people who forgot their own path because it's no one else's. And so the more that, that, that you can do that, I think the better for, for everyone.

So well said. Thank you for that. That was, that's so good. Remember, comparison is the thief of joy. And you've got to find your own path, the thing that makes you happy, that's like. That's golden. I always ask, what would you treasure? Right? Instead of what do you want? And knowing what you want, knowing what you would treasure, what would you trade a part of your life for? To. To give right. To learn to step into even more. That's huge. Good for you for. Good for you for following that. That's awesome.

Yeah, for sure.

All right, Dr. Saban Ali. Thank you, Ali. Just I'm put the emphasis on. That. Anyway, Dr. Saban Ali, thank you. And. And you can find him on LinkedIn. S u b H A N A L I that's where you'll find him on LinkedIn. Thank you again for being here. Appreciate you.

Perfect. Thanks, Wayne.

Alrighty. This is one sharp sword cutting through to what matters Most. I've had Dr. Sabana Lee with me. I am Dr. Wayne Purnell, your host Kaid and coach on this journey and we'll see you again next time. Thanks for being here. Thanks for listening to one sharp sword cutting through to what matters Most with outfluencer Dr. Wayne Purnell. For more information please go to onesharpsword.com.