Jake Aaron Villarreal: I'm Jake Aaron Villarreal, born and raised in Silicon Valley, and here to take you behind the scenes to share what it's like to be a startup founder, the journey they're on, the problems they face, and the products they build in an effort to transform industries. I'm excited to have with us today David Smith, co-founder and CEO of MLtwist. David, welcome to the show.
David Smith: Hey, Jake. Great to be here. Thanks for having me here.
Jake Aaron Villarreal: We have a little bit of a background here. We're both from the Bay Area and we'll talk a little bit more about that, but we'll also get into your company MLtwist. Before we do that, a little bit more about David. He is um not just the founder, but also has held leadership roles at companies like Google, Oracle, DoubleClick, Neustar, and has had... has been through four acquisitions. He is focused on enabling strategic data for AI and launched first-of-its-kind data partnerships with companies like Oracle, Google, J.D. Power, Twitter, etc. David holds a bachelor of science degree in computer science and engineering from UC Davis and happy to have him here with us today. Before we jump in here, David, where are you calling from today?
David Smith: San Jose, California.
Jake Aaron Villarreal: Hundreds of AI startups are launching every month, battling to build their founding teams. As a leader, your job is to get results. When it comes to hiring, that's where it gets tough. So, you go out and you try a recruitment firm, but they don't understand your story. They're off target. And when they send you candidates, it's a waste of time. We believe you should never have your time wasted. That's why we launch Match Relevant, because your story is more than just an open role. It's your founders' journey, the problem you're solving, the product you're building, and why it matters. When we work with companies, we make sure we understand your whole story. So, when we go out and do a search, we're on target. It's worth their time. They're interested. And more importantly, it's worth yours. And when it comes to hiring engineers, we work to make sure we get it right. By deploying a team of seasoned CTOs that have built some of Silicon Valley's best companies that can collaborate with you in the technical interviewing process, they can be a sounding board or they can run it for you. When it comes to building teams, there's no time to waste. Let's make it count. If you have a role that needs to be filled, book a time with a hiring guide at matchrelevant.com and learn how we do it.
Great. We're both from there. So, it's surprising we don't have more of a connected network at some level. Maybe we do. Probably that'd be whole other podcast.
David Smith: Yeah, exactly. Both been in Oracle and I'm sure there is some lines that cross at some point.
Jake Aaron Villarreal: But anyway, um yeah, want to um just kind of walk through um your background a little bit. Why don't you kind of give us um how did you get into technology and in the, into the startup ecosystem?
David Smith: Yeah. So, I guess technology I, I've always been, you know, that, that kind of stereotypical Dungeons and Dragons and into computers at a young age, all that stuff. Magic the Gathering in high school. And I ended up um getting a computer engineering degree from UC Davis, but I graduated during the dot-com bust. So, I couldn't get a job right away uh out of college. And I actually ended up in Europe uh doing different things. I was a bartender at one point uh in Paris, which was pretty cool. And uh ended up working as a sales engineer for uh, at the time what I didn't know but uh was NLP, uh an early iteration. And I ended up at, eventually joining a company that got bought by DoubleClick. That company got bought by Google. Got to, got to be a part of that. And, and then from then on ended up working at some other companies like MarketShare that got bought by Neustar, that got bought by Golden Gate Capital. And I left them to join Oracle just before Golden Gate sold that business to, to TransUnion. So, it's been, it's been a, a crazy ride and I'm back in, back in the Silicon Valley.
Jake Aaron Villarreal: Yeah, it's great. Yeah, there's a lot of tis- twists and turns in your, in your career. I like the title of your company, MLtwist. Um, you know, there's so much conversation happening around AI and data and what it all means. Um, talk to us a little bit about what inspired you to start your current company with the background you have. And what was the problem you saw that you thought, "You know what, there's an opportunity here to, to dig in and build something."
David Smith: I've been dealing with data for a long time. Like at all those companies that talking about, even the one in Europe, data has always been kind of front and center. And AI sort of added a whole new dimension to everything. So data's always been interesting to work with. "Where did you get it? Who's touched it? Where has it been? What rights do you have?" And that's before you even start talking about the quality, right? So what I thought was really interesting was when I, AI... so back in 2021 or 2020, when AI was becoming more and more prominent, and even before then I'd been heavily involved in, in making sure data was ready for, for models whether they were machine learning or some of the early Bayesian u- models. It dawned on me that there's a lot more to be done around, around working with data specifically for AI that some of the more traditional ETL (extract, transform, load) technologies didn't really deal with. So that for me was kind of an opportunity to say, "Hey, I love working with data. There's something, some good I think I can do out there. Let's, let's give this a shot and, and see where it takes us."
Jake Aaron Villarreal: Yeah, you know, for engineers, this is probably very easy to understand. But for the layman that is not a data engineer or really isn't technical, when we talk about LLMs, large language models, and ingesting data, you know, it's bringing data into systems and models that you can then take and, and hopefully make better decisions with that data, create some sort of opportunity out of it. Maybe it's helping optimize how you run your business, or maybe revenue opportunities. But you talk about strategic data that you provide for companies. Kind of, let's take a step back and, and t- talk to us about in the data space, what, what are you solving for the companies that might already have LLM or they might already be, you know, pretty deep into trying to make use of their data? Where do you fit there?
David Smith: Yeah. So a lot of our customers, they actually have already built the AI, the first, the first iteration of it. And a lot of them go, "Oh great, this is... we, we can do this. We need it to be better," right? So a lot of our customers are trying to solve some problem. That problem might be "I need to identify a fault in a, in some material coming down a manufacturing line." It might be uh "I have a news article and I want to automatically classify all of the, you know, all of the references to buildings." It might be an investor who's like, "I want to take uh the transcript from an earnings report and automatically, you know, extract all the, all the information or look, look for certain signals." So, companies, the, the, the thing that's tempting and the thing that's also you got to be careful with is AI is... it can kind of let your imagination run wild. So, a lot of these companies are doing different things. We'll talk about some of the specifics later, but what you'll do is you'll typically take some thing that you have, throw it at AI, get AI to do something, to, to make a very basic understanding of what, what you're throwing at it. And then you'll go, "Great, I need you to do that every single time."
And that's when it gets tricky because what you'll find is that the model will do pretty well on like a very small data set or a data set that you threw at it. But to get it to do better and better on more and more versions of that data, let's say it's, you know, back to the cracking, the, the cracking, some products coming down, the cracks might look different. There's, there's always going to be like an exception to the rule. And what you end up with is you end up with a need to throw more and more data at your model. And that's kind of when you start to decide, "Okay, am I, am I, am I gonna do this myself? Is this, is this my thing? I'm gonna, you know, or, or do I partner? Do I use other technologies that are more into getting the data ready for my model and then I can just focus on building the model itself?"
Jake Aaron Villarreal: So when you look at a company for example, I don't know, the US government, or one of your clients by the way, or you know an organization that's got tons of data, they've already put an LLM together. They've got the data in there and then it starts to crack or break or just isn't functioning when they start layering more data in there. What does your platform do or what's, how do you help solve that problem?
David Smith: So, if you look at the AI data space right now, you're going to see hundreds of companies that are all building technologies focused on AI data. And what MLtwist is more focused on is the, the process of getting that data ready. So my co-founder Audrey, she's, she's in Data Operations. It's a, it's a fairly, it's a role that's becoming more and more prominent. It's effectively the people like who get the data ready for the AI models. And they're not necessarily engineers or data scientists. It's like they can be lawyers looking through client briefs and trying to tell a model what, what they're looking for. Or it can be, you know, we were talking about the government, could be a security specialist that's like looking for something uh that somebody should not have on their body. So these people they kind of need to be set up in an environment where they can easily identify, use their human you know their, their intelligence, their expertise to, to signal stuff to a model and then have the model pick up on it. And MLtwist is focused on making that happen at a super high level.
So what you end up with, if you go into the weeds, is you kind of end up with a hundred things that need to happen. And if any one of those things doesn't go the way it should, it can actually ruin the, the other pieces. So MLtwist kind of facilitates all that by making those hundred things more automated and giving people the flexibility to use different data tools to work on the data that they need. And then change the data tool or keep that data tool and add a new one for some new data that they're working on in, in a, in an environment that's kind of like "no code." So you don't need to write code to, to get this to work. And, and it's giving these data operations people the ability to have the flexibility to work on data without needing to, to, to be data scientists or, or uh engineers themselves.
Jake Aaron Villarreal: That's great. You know, creating a product or a platform or a solution is one thing, but actually getting it to the right people is another. When you're out there presenting or pitching or getting new customers, who are you selling to? Is it the people you talked about that don't have to be engineers that are going to use this no code platform to do their job better? And if it is them, how are you getting to them? Like what's, what's your go to market strategy that's working for you there?
David Smith: Yeah. So, it's uh surprisingly it's actually, well surprisingly, it's, it's product managers. So, oftentimes product managers have an AI remit. They are mandated to either build AI or integrate AI into a product that they're managing. So they typically collaborate with a team of data scientists to make that happen. And eventually those data scientists will either say "we need help" or "there's some sort of model drift." So the model, you know, was doing great and now the model's not doing great anymore, and we think we have a data problem. So we'll typically talk with them to, to talk through, "Okay, hey, like what... talk us through your process. What tools are you using? How are you going about it? What are you trying to do with the data? What kind of data do you have?" Those are all things that are, are, are organic to the conversation. Um Data Operations people are typically within, somewhere within those teams. They're influencers and, and, and they're very critical to that conversation as well. Um, so it's kind of like, when you look at AI, it's actually, it's a massive team effort across engineering, operations, experts, and, and, and typically product management is one of the roles that leads that.
Jake Aaron Villarreal: Yeah, that's great. So, just walk me through the product itself. So, if you're listening and you're a product manager and maybe you're involved in the data strategy for your company and maybe you're hitting some walls or just in general trying to figure out like, "Hey, what are the tools out there that can really make my job better?" What do they get? Are they logging into a system with a dashboard? Are they seeing like data buckets that they need to connect? Like what's, what's it look like? Walk us through that a little bit.
David Smith: Yeah, that's right. So if you, to, to kind of like in an ideal world, if you could wave a magic wand and just have your data ready for AI, that you would, you would probably do that, right? Like if, if that option existed, that's what you would do because you're not actually focused, like your business is not going to, to really make its margin by getting the data ready. Your business is going to make its margin by actually building good AI that works and then is, is correctly deployed and, and, and continues to advance. Usually those are part and parcel. So the AI is, is linked to the data prep.
Our platform is: someone logs into a dashboard, points it to unstructured data where it lives, can decide, "Hey, this unstructured data is going to go through this type of AI to take a first pass at it." It's what we call pre-labeling. Then that data then goes into an AI data tool. Again, this is stuff that um the, the customer can select or if they don't, they, they, they have options if they're not, they're not sure what to select. They assign it to experts. So either within their own company or people that they approved outside of their company to work on that data. And then after that the data goes through a semi-automated quality control process. There's like... MLtwist looks at the raw JSON, the, the files that get created from that whole process that, that is effectively the work that the person did. And creates a report. We call it an HRR, human readable report. Um, that takes all this, this, this language called JSON, turns it into something that is easier to understand. And then when that's all said and done, they hit the "go" button, and then the data takes it from whatever tool they were using and whatever format it spits out data in and changes it into the format that the data scientists actually use for their own models. So, as a product manager or as a user, you don't really have to know all of that. All you really are is like, "Hey, I want to work on some data today. I want to process this data. I wanna, you know," so, so you don't, you don't really need to know behind the scenes what's going on. You're more like point it at data, visualize it, work on it, and then, and then eventually get it to the next stop.
Jake Aaron Villarreal: Got it. So what's the benefit to the company or to the product manager by using your platform?
David Smith: So these are all things that they need to figure out, right? So, so most of the industry today in our experience actually are not like using... there's a lot of tools. There's billions of dollars that have been spent on different tools out there for AI data. Is still interesting to me that oftentimes a lot of the companies we talk to do open source stuff. They're like, "No, this, this tool is unique to us. It does the thing that we need to do." But eventually what data scientists want to get out of their data continues to evolve and, and outpaces the tooling that's available in the market today. So these product managers will probably need to sign up with, either, either build their own tools themselves and hire a bunch of engineers and to maintain, build, maintain, and then continue to upgrade. Or they'll take a third party tool, and even then the third party tool has an API. Every single tool worth its salt has, has an API integration because they know things need to be prepared in a certain way before it can be pushed to their tool and then worked on with- within their tool. So those product managers also need to get engineering resources to do the API integration.
Um and then what happens is typically they'll have an update where they need to keep that tool, but there's like a new data file. So maybe you were working on video and now you need to do text or audio. And now you need another tool. And you kind of need to like continue to, to build this... these integrations to push your data into these tools, pull them from these tools. And effectively that, that's your world if you're a, a product manager. MLtwist kind of comes in goes, "Hey, you, you don't need to do that. Like you, you can just use the platform. It'll push the data where it needs to go and then it'll, it'll pull the data. And then you still get to leverage all these amazing tools that are out there in the ecosystem."
Jake Aaron Villarreal: That's great. Sounds like it solves a lot of problems and complexity where everyone's still trying to figure out what to do with the data. And I think the biggest question is if you already set it up and, and your, your system starts to break down, like who do you go to? It sounds like you can help with that aspect of it. Um, you know, when you talk about AI and there's so many different routes to go in it, it's I think the next transformation that we're hitting. I- we've both through been through the dot-com boom and bust and so you know it's we've gone through different transformations from cloud to mobile and now AI uh I think is probably going to be bigger than any of them except for the internet obviously. But when you look at it, um you know you were, you were sharing that the we've already ingested the world's data on the internet in, within you know a year, maybe a couple years. Um kind of where do we go from here? I mean, yeah. What's... Give us your perspective there.
David Smith: Yeah, it's pretty crazy. I mean, even a year or two ago, this idea that like um... so there's reports out there that OpenAI effectively have like gone through all of the world's... all the data they could get their hands on to train their LLMs. Um I don't know if that's correct or not. They have, they haven't commented on it, but to me is an astounding concept. And there's two, there's kind of like three... it's, it's sort of like pick a path, right? Um and there's different camps on what to do. So one camp is saying, "Well, we're generating new data at a rate that is unprecedented. So like just wait a year and then you'll have more data to feed your LLM." Um there's another camp that's like, "Oh well, we should create data, you know, we should do augmented data, synthetic data, these other concepts. We should, we should like create data, train them all." Um, but I think the camp that is really has kind of made the most headway in terms of ability to pursue that and get results is going back to the data that you ingested and then improving the quality. There's a lot to be talked about in terms of what does that actually mean. But I believe that's what we're seeing. So we are now seeing an uptick in people...
First off like, there's Gartner reported that 4% of companies in a, in one of the reports that they ran said that their data is AI ready. So yes, there are some companies that are ahead of the curve, but the average company probably does not have its own data ready for AI. And for those companies that are ahead of the curve and kind of like gone through everything and the kitchen sink, what we're seeing is that those companies are going back to the data and going, "Oh, we need to, we need to make this data better to improve our AI." In fact, arguably that's better than throwing more data at it. There's a research by Andrew Ng, professor over at Stanford, who says that... and he, you know, one of the thought leaders in AI. He says he, he had a presentation that showed that you need to throw roughly two to three times the amount of data to your model to get the same type of performance as if that data was, was of good quality versus like okay quality. And there's other research that shows if you throw bad data at your model and quality that's lower, the model actually degrades and, and goes backwards in performance. So I think you're going to see a lot of people going to their data sets and then going, "Okay, let's, let's make this data better."
Jake Aaron Villarreal: Yeah. Well, you know, we're hearing from almost every CEO of Fortune 500 companies that we talk to that they need a strategy in place for AI and they're going to their CTO and their data teams and saying, "What are we going to do with AI? What's our strategy? How can we become a better company, generate more revenue," whatever the strategy is that it's all centered around the data. So, there's this huge gap of what providers can do for these companies that are still trying to figure out their own strategy. And then once they do have a strategy, what tools they're going to use that are going to help them maximize their people and their time to, to get the value out of it. So I, I really, I like the space you're in. I know there's a lot of competition out there. What's the biggest challenge you face today as a company?
David Smith: I would go back to what I mentioned before. A lot of people are still uh... so one of the things I get told all the time is, "Hey, I'm using open source. It's free." And what you tend to find is you tend to find, "Well, you still do need to pay for the engineers who are supporting the open, open source tooling and, and, and being able to, to continue to build on that." So, one of the, one of the concepts is just companies getting further along the AI curve is kind of the way I think about it. Because eventually there, there's a reason why these other platforms, the other, other data tools exist, these other companies exist, and it's because eventually things, things kind of hit a breaking point and it makes sense to, to, to take advantage of some of the, the third party technology that's out there. Um, so I, I guess what I'm really saying is it's a long road. We're at, we're all at the beginning of it when it comes to AI. A lot of companies have a lot of initiatives and as they move from kind of this "let's experiment and get some ideas down" to, "Hey, we really need to improve the performance of, of the models that we're building or the models that we're leveraging," that's kind of going to naturally and organically push teams to see if there are other companies who've built solutions that can help them navigate some of the craziness that's going on in the world of...
Jake Aaron Villarreal: Makes sense. I, I want to talk a little bit about data pipelines, you know, they've been around for a long time. Why did data pipelines for AI need to be different?
David Smith: Yeah, data pipeline. So, so data pipelines have always been like, it's, it's kind of notorious. They've always been around. They've always been these things that have been, they are, they are special. You, you typically every company has a team of data engineers supporting data pipelines. AI data is weird. What happened with AI data is you had this idea of quality. So back in the day uh let's say that you were in, you, you need to export your customer list from Salesforce to, to, to Google Ad Manager. Well, you have a list of people and the QA is pretty simple. Did that list make it over to the other side? Right? It was like a checkbox, a 0, 1. There's a way to know if you, you did the job. The data was good. Was there any corruption? Did some the names not fully make it across? Things like that.
What AI did was it introduced this concept of quality in a different sense where it was more based on a human's, on a person's perception. So you could have done everything right. The format's right, the, the, the, the data, you know, the data made its way across. But if the thing that the, that is being described was wrong, like you say it's a cat and it's a dog or things like that, um it can look right, but it can still be wrong. And that's where AI data started to add complexity. It's this idea that we're now beyond data engineers and data scientists and, and, and computer engineers. And that AI is actually like it's a human, it's a human concept. And with that you need subject matter experts to effectively get involved in the data pipeline process and say, "Yeah, that, that looks right. That, that looks wrong." Um and that has caused all sorts of differences in the way that data needs to be worked on that are sort of very off the beaten path of like "I've got a database here. I'm trying to get the data over to that, that side and you know and, and then we're, we're good." So for me that was an opportunity to say, "Hey, like let's, let's start to create technology that is very, very tuned to this the concept of AI data versus some of the other ETL that's out there."
Jake Aaron Villarreal: Got it. You know, there's a lot of buzz about AI. And transparently, you know, all of our customers today are AI startups. And that wasn't the case, you know, a couple years ago. We know there's real opportunities and innovation happening and a lot of money being thrown at that innovation. There's also a lot of buzz on social media. What do you think the current state of AI is from your perspective?
David Smith: So, I mean, MLtwist started in Jan 2021. I think AI didn't really get its, its "Oh my gosh, this is real" moment until, you know, when, when OpenAI did their thing and, and, and that was several years later. And it's really impressive, it... On the flip side, a lot of our customers are still interested in the good old-fashioned, image recognition, other parts of uh of other types of AI that are out there, machine learning. So, I'm not trying to say that there's a lot of companies raising money because everyone now has always understood, I think now, now really understands that this is game changer. This is like you said it's, you know, h- how do you compare it to, to the the to the internet? Um on the flip side, there's going to be a curve of companies. There's a difference between B2C and B2B. B2C, you can have a large language model hallucinate. And it's like, it's like, you know, with search, like you can have for search results that are not very good. That's, that's okay. In the B2B world, you're going to find a lot of use cases where that's not okay. And what you're seeing is you're going to see, I think, a lot of businesses try to figure out, "How do we take um these people who, who are very, very good at their, at what they do, and then use AI to help them." That is still a, a thing that's progressing. So, I think it's a long-winded answer to say there is a lot of opportunity, and at the same time I think that there are, there is still a long way to go before we, we really start to see I think what, what AI is going to, h- how it's going to transform businesses.
Jake Aaron Villarreal: Yeah. Well, I, I think that makes a lot of sense. I know we're kind of in the early stages. It seems like we're progressing very quickly. You know, as a company, you oftentimes have a North Star and you shoot for it, but you make a lot of left and right decisions. Has there been any major pivots or shifts you've had to make when you started with your concept and where you're at today as a company?
David Smith: Yeah. So, one of the things that happened to us fairly early was we won an award from the US government on that was more focused on building technology to extract, to interpret data. And from that we were like, "Oh, okay. We can do this." So, not only do you have to grab the data and, and kind... you also have to figure out the AI piece of, of, of interpreting it. Um what we noticed was that we were spending a ton of our time working out the, the, the data flow piece and not as much time in the modeling piece. So is this program that the Department of Energy awarded us called an SBIR, Small Business Innovation, I believe, Research Award. From that, we kind of did away with, with the idea of, "Okay, we should be building the, the, the AI thing." And we should instead be far more focused on just getting the data ready and then allow our customers to focus on building the models. With the idea that eventually the models can be plugged into the data so that they can assist with making, with the data preparation process. Um but let customers like focus at what they're good at and then let us... and then there's enough work to do on the data prep side.
So I would say the reason why kind of a failure is we did not proceed to like the phase two of that award. Uh but in doing the phase one and building what we had uh built, we realized there were applications for other, other entities, other companies. So for example that we recently did a, a webinar with Sandia National Laboratory that uses MLtwist to process data for the TSA and get that data ready. We're not building the threat detection algorithms. We're fully focused on just the data processing, the data transformation, the data labeling, getting everything ready for those companies. So in a way, by not advancing to the phase two and by having built in this, this data processing piece within the phase one that we were able to, to kind of continue going forward with. It was a kind of a, a major transformation for the company of "Okay, this, this is something that people want and people, and people need and, and, and we took the business that way."
Jake Aaron Villarreal: Yeah that's great. You know, every company goes through breakthroughs personally as well as sometimes technically or otherwise. What's been a breakthrough for you that you felt has really been an accelerator for the company?
David Smith: I would say there's, there's a couple things. Um the biggest is just the advancement of the... so, so in that story I just said at the time the base models were still not that good. Nowadays you plug into, you know, Google... So, we're, we're, we're on all the different clouds, uh, GCP, Azure, uh, AWS. They all have these base models that are getting better and better and better. So, for me, a breakthrough... like Facebook just launched their Segment Anything model version two, which allows you to throw an image at it and then it does a better job of like trying, trying to identify what's going on. So the idea of you, that you can, that the world is multi-model and that you can throw a lot of these models to data and then get your data the quality up by, by still integrating expertise I think for us has been uh has been a big thing.
The other one was, and this is going to sound silly, but the HRRs, the human readable reports, uh, were kind of transformational. That was sort of inspired by Audrey, co-founder, who ended up saying, "Listen, like that's great that you've got all these JSONs, but like me and the rest of the team don't understand what the heck's going on in these things." Like, like you could, but they're, they're a nightmare to comb through. Uh so the other, the other breakthrough was um taking, not the... it was effectively taking that and turning it into something that people could actually look at and derive insights from, which then influenced uh them going back to the data and fixing things that, that looked off or, or checking things that looked off and validating "No, they, they look off but that, that's actually correct." Um so those are two things that I think were, were fairly uh big for MLtwist.
Jake Aaron Villarreal: Yeah that's great. Well, we're heading into 2025 already, you know, a quarter, a little over a quarter away. What, what are you excited about? What's on the road map for MLtwist?
David Smith: The, the goal for MLtwist was always to take what we're doing and then uh make it so that a, a person, a data operations person doesn't actually need to know what's happening behind the scenes. So today if you look at the platform uh customers still fairly aware of "Hey, we need to use this technology and that technology" or they, they'll use some of the defaults that we have. But the ability to effectively start to automate those concepts. Like I was asked by, by someone close to me who's a stamp collector. They said, "Hey, I need to get these I, I have like a, a poster of all these stamps and I need to effectively visualize stamp one by one and then I need to identify which stamp belongs to where it is on that poster." And I was thinking to myself, this is, you know, as, as AI starts to advance, we're going to get more and more people who are not data scientists, who are not engineers, who want to do cool stuff, but need help like organizing and, and...
And if you're going to build AI, you actually do have to start, you, you do have to use the data. You have to, you're going to have to do that yourself and apply your own intelligence, your own magic. But then once you apply it, you can then fairly easily port that to, to some of the base models and, and train them and then off to, you're off to the races. So in terms of where we're, where we're working to go, it's important that we get the core right and then after that, can we take the, the, the understanding people have to have about their AI data pipelines and kind of make it so that they do not need to know those details and they can just focus on, on what they're trying to do. So that's, that is kind of our take of where AI would fit in our world for, for ourselves on, on things that we can build and develop.
Jake Aaron Villarreal: Yeah, that's great. Well, you know, every company that starts has a journey and a path. It sounds like you're on a good one. What's something you wish you would have known before you started the company? Was that on the list of questions? I'm kidding.
David Smith: So, I think what would have been good to know was when you, when you raise, like how important raising is. Um, I kind of, I think a little bit naively thought, "Hey, I'm a coder. I'm going to build something that's useful and then we're going to get lots [of] customers and continue to, to do that." What I, I've learned is that there are people who, who, who can do that. Um but in today's world getting investors on board who believe in, in what you're doing, believe in you know, like, like what you're doing and then want to be a part of it has been really, really powerful. Um so I think before a lot of times you're like just, just, just very focused on the product and then you realize "No, like you need to have backers."
And then that translates into the team. You need to make sure that the people who are also... especially when you're a smaller team, are, are on board. Also believe in, in what's happening and, and are aware of the problems that are out there. So it, it can start, if you're a solo founder, it can start with an investor, but then it very quickly translates once you use that capital to bring on engineers, to bring on, to bring on teams to, to the people around you. So I think another, it's just the, the old adage: "If you want to go fast, go alone. If you want to go far, go together." I would say that that's probably one of the biggest things that I um, I have a new appreciation for uh now, now that we're a few years down the road.
Jake Aaron Villarreal: Yeah. Well, that's great. I love that. Hopefully others will learn from that, too. Um as we wrap up here, David, I want to thank you for your time and really having the courage to come on and tell your story. And for all the listeners spending your time with us today, it means a lot to me that you've made it to the show. Um, I'm the host Jake Aaron Villarreal signing off for now, but can't wait to catch up with you all on the next episode. Until then, David, everyone else, take care. If you like what we're doing, don't forget to subscribe. Leave a review on Apple Podcast or wherever you listen and follow us on YouTube where we go behind the scenes to learn what it takes to be a startup founder.
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