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- [00:00]
David
A lot of these big data players who have scaled are now going public. But that doesn't mean that the war has been won. In fact, it's only the early days, because my thesis is just how we looked at the 2010s where every company is a software or technology company. Now it's the world where every company is a data company.
- [00:24]
Joel Palo Thinkle
Welcome to the Investor, a podcast where I, Joel Palo Thinkle, your host, dives deep into the minds of the world's most influential institutional investors. In each episode, we sit down with an investor to hear about their journeys and how global markets are driving capital allocation. So join us on this journey as we explore these insights.
- [00:48]
David
PhD, that works for me.
- [00:54]
Joel Palo Thinkle
Less syllables. Cool. I think we are live now. So. Hey, David, really excited to have you on the show. David and I have just become friends over the last probably six, seven months and excited to have him in our fund accelerator as well. But really just it's interesting to see so many different funds now, from my experience, break out into specific niches. You know, we've had psychedelics funds, we've had health care funds, climate change funds. You're the first fund that I've met that has a strict focus on AI, ML and data science. And I would say, you know, this is something that you could probably educate us on, really, just the trends and the exits. Because I feel like some of these large data science companies are the ones that are really getting acquired by massive, you know, probably institutions and then larger conglomerates like Amazon. Right. So I'm excited to kind of learn a little bit about that as far as the trends and exits and then just a space. And I think another big thing that would be helpful, even for me and the people here is to just unpack data science. So, you know, there's AI ML, there's explainable AI, that's another hot topic. But before we nerd out on all that stuff, why don't we talk about you, David? I know you're from Florida like myself, so let's go back to there. Let's go back to where you grew up, where you studied, and how that helped you pivot into bc.
- [02:22]
David
Yeah, thanks, Joel. Thanks so much for having me on the show. And proud Floridian here. I like to always say, go Gators. The University of Florida alumni. And for me, it does go back to the college days. In fact, I was studying actuarial science, finance and information systems, wanted to go hardcore into data. And I discovered in my first internship where I worked for AFLAC doing loss experience monitoring and that means to teach me like I'm five. That means actually predicting what the price should be for your insurance, whether that's car insurance or health insurance. And I discovered at that time that the cloud was emerging and that no longer did working with algorithms have to be in Excel spreadsheets, but could be in large distributed systems and with programming languages like SQL, Python and R, which tapped me into wanting to evolve into more of the data field. And so I've spent now over the past decade working for both the big Fortune 5000 as well as startups in all things data. I led and scaled data analytics and dashboards and products beyond Aflac to Deutsche Bank, Citigroup and adp. And that was all in Florida. I worked for some great organizations there and then of course decided to take my startup plunge into the startup ecosystem when I moved to New York City about eight years ago.
- [03:55]
Joel Palo Thinkle
And tell us about the. Tell us about moving to New York, because I can probably really relate. It's a different ecosystem, it's a different environment as a whole. People in general might, you know, call us too laid back or just say that we're a little more, you know, less. Less hyperactive as the typical New Yorkers. So tell me about just your thoughts on New York and what made you think about New York, and then, you know, just what you experienced when you first moved there from Miami.
- [04:33]
David
Sure. So I did the reverse migration. You see many people, especially in the Pandemic or later in their life where they moved to Florid. And I decided that moved to New York. And for me it was fascinating because all the companies I worked for, Deutsche Bank, Citigroup, adp, all have New Jersey and New York offices. So over my first part of my career, I was going twice a year up to the offices and saying, what is this ecosystem? What am I missing out on? In fact, I also considered moving to Silicon Valley, and I spent a few months living in the Bay Area saying I'm thinking about SF or New York. And I landed on New York for a few prime reasons. The first is the diversity of the. You have every industry imaginable in New York, from business, technology, fashion and finance. And that creates a wealth of new startup ecosystem innovation and ideas. And you also have a wealth of individuals working in different careers. You have the sales, development reps and account executives. You have the engineers, the CEOs and the finance team members. In Silicon Valley, I only saw engineers. And for me, it was important to align close to the business, which led me to make it to New York and I think one of the best things about being in New York is the fast moving pace of the lifestyle. I've traveled to different cities around the world like Singapore, Taipei, Paris, London, and nowhere is it as fast paced in New York. So today when I work with founders and investors, I say there's nothing like having a New Yorker on your cap table.
- [06:05]
Joel Palo Thinkle
Yeah, no, I totally agree. And then tell me about the startup you're working for and what your role was there.
- [06:12]
David
So today I work, I continue going into the startup ecosystem once I moved to New York and two of the startups I helped scale were General assembly and Galvanize, which were leaders in the edtech ecosystem. And I was scaling their enterprise data science teams, which meant I was supporting Fortune 500 clients like Charles Schwab, Bloomberg Refinitiv, Invesco, USAA. And after both of those companies were acquired, I joined a little over a year and a half ago, Single Store. And Single Store is a database startup. It was y Combinator 2011. We're backed by Google Ventures, Insight, Cosa, Dell and HPE. And today, 11 years into its journey, in the last 18 months, single store raised a Series E and Series F finance and is continuing to scale on its pathway to being beyond a unicorn. We're on that territory, but primarily having the passion and purpose to creating an industry where any company can be a data company, where companies no longer have to think about where their data is warehoused or what data lake the data is being managed on, but that everyone can build for real time applications, for data intensive applications and for fast analytics. And so my role being there has been scaling our technical enablement that's been with clients, partners and employees as well as looking for strategic initiatives, for partnerships.
- [07:40]
Joel Palo Thinkle
You make a, you know, you're a perfect example. I mean there's a lot of people that have become an investor with the industry experience that they've had. So do you think that's a good starting point for a lot of people, if they work in healthcare, if they work in like healthcare, it to maybe join like a digital health fund? Or do you think it's also good for people to just kind of jump in to other industries as well? And it sounds like for you, I mean that's kind of been your niche, right? You, you really built a lot of great enterprise and industry experience and data. So it just kind of makes it a natural fit to just, you know, you've already understood all the problems in the models to just jump in and directly be on the investor side.
- [08:17]
David
Yeah, I really appreciate what you shared earlier, Joel, which is about niching down into a vertical. And for me it's just, well, my whole career has been around data ML, AI. So it made sense, take what I know and apply that to diligence and to mentorship and advisory for startups, which led into investments. So I do think if you're someone who's emerging as a fund manager, if you're getting into the angel investing industry as well, start with what you know. It is a vast industry. When we started, our thesis was about being data driven, but we realized very quickly that was too general. And so then we narrowed down into data intensive apps, real time insights, data developer tools by focusing on data power, those infrastructure layers, those insight layers and those prediction layers of the economy. I think that's really helpful when you're a new investor because otherwise you're seeing things, so many opportunities and it's tough for you to build your mental models. You want to build the best mental models so you can decide how they make investments. And I think that's based on either the knowledge you know, or the team you surround yourself with.
- [09:28]
Joel Palo Thinkle
I've also heard, you know, I just want to make a comment to your earlier point. So with the niche funds, I've, I've also seen that be really attractive to LPs because LPs do have different interests, right? They're interested in Web3, they're interested in, you know, quantum computing, they're interested in psychedelics, but they don't have the time to source these deals and they don't have the network and they may not know, they may not have the technical chops. Some of the, a lot of them do because they're past entrepreneurs, but a lot of them just may not have the technical chops to due diligence. And one thing that you and I talked about, which, which helped you, I think, form a really interesting partnership with someone was, you know, your ability to kind of do technical diligence with someone else. Right. So I think that's an interesting point. You know, LPs want exposure to an exciting sector that is really hot, but they just don't have the bandwidth to go out and source all those deals individually. And my favorite analogy is you can either buy Tesla ad hoc or you could just buy an index with a bunch of the faang companies in a certain theme or something like that. So I just wanted to make that comment. But you started doing this. I want you to go a little deeper. Can you just unpack the whole universe for us for just data science and how the investment activity is broken out. So you know, we talked a couple of things we talked about, right, explainable AI, AI, ML, can you just kind of give us a high level overview of just the whole landscape and then, and then where you play you've talked about like the data power. So maybe just kind of elaborate a little more for our education.
- [10:58]
David
So when we think about software and data, the big trend to look at is the emergence of the software engineering industry. And so software engineering had its rise to fam in the early 2000s. I would say that even went to like 2010 and 2015 with software, automation and all the tools so that every engineer has a toolkit where they can build the best world class systems. What we're seeing with data is that has not existed until now. The 2020s are known as the decade of Data. This is where the modern data stack is being built. New developer tools, new infrastructure, new systems so that data scientists, ML engineers and AI specialists alike have their own toolkit to also build and scale enterprise grade data systems. And to do that, it doesn't only require the tools, it requires the systems and having them talk to each other. So we've also been seeing as a result of that, some of the early players, like the single stores who've been around since 2011 to 2015 focused on data, are now going through M and A, getting acquired and or IPOing. We've seen companies like Datadog go on to the public markets. MongoDB, Snowflake, a lot of these big data players who have scaled are now going public. But that doesn't mean that the war has been won. In fact, it's only the early days because my thesis is just how we looked at the 2010s where every company is a software or technology company. Now it's the world where every company is a data company.
- [12:38]
Joel Palo Thinkle
That's exciting. And what are some of the segments that you're most excited about? Kind of unpacking that with the data focus, I guess.
- [12:50]
David
So I look at the data industry as four layers. The first layer is data collection. And data collection has typically been a very manual process with labeling and aggregating the data sets and then storing them. So that's beginning to automate. That's a very exciting space. It's growing a lot. We've seen label, box, scale, AI and other players become unicorns in that space. We even see newer companies like Roboflow which are helping automate some of the labeling for computer vision and NLP systems. Then we have layer two and Layer two is really about the real time insights. It's having tools that are dashboards or specific for different industries, whether it's healthcare, biomedical, or looking at security and the cyber industry and having these tools so a company can say I can buy this tool and have data layer integrate with the APIs and get real time insights without having an army of data scientists and data analysts to generate these insights. So that's layer two, which we're seeing a lot of platforms emerge and scale in that space. Layer three is where we start seeing the predictive insights. That's the traditional machine learning that's been told of if I apply for a loan today, do I get approved or do I get rejected? And looking at those insights in real time is beneficial for industries to not have to have a delay of taking a few days for results, but instead having instantaneous data intensive apps. We're seeing that today in underwriting, we're seeing that in credit reporting, we're seeing that in these new ML powered apps that support you and me to be more successful at enterprises. And then the fourth layer is about AI. So most companies today talk about we're AI first, but if you don't get the other three layers right, then the fourth layer won't get there just yet. And so the fourth layer of being AI powered means you're using this modern AI stack, which could be like OpenAI's GPT3, to generate natural language, processing text, or create generative images, or create systems that self learn and heal on themselves. The challenge when companies talk about being AI powered is it's the whole ecosystem of data, ML and AI, not AI alone. And so I'm really excited about all four of those layers and we look at all of them with our investment thesis and mathematics.
- [15:22]
Joel Palo Thinkle
That's really exciting and that was really good learning for me. Where do you think Quantum will play in all of this? I guess will Quantum have a. Because from my learnings and I'd love for you to clarify or help me with this, some of the bigger applications with Quantum are like material sciences, you know, security infrastructure. There's also some chemical applications as well. So I guess there's applications for all of these that you mentioned, right? The collection, the automation, the predictive and AI power stacks. But how will Quantum evolve into kind of this whole ecosystem?
- [16:03]
David
I'm very excited for Quantum and I think it is still the early days of Quantum. I do have several good friends who work in that space. So we routinely chat about the trends and changes that we're seeing in the Quantum industry. Just this month I've looked at this really exciting quantum startup that's using quantum entanglement to replace global GPS with satellites to get better real time data for clocks and movement of trains around the world. And when you hear this story for the first time, most people say is this really a problem? But the truth is there is. There was cases where 13 microseconds were off between some of these travels which caused flight airline disruptions and interruptions. You even have trading Systems where there's 13 microseconds that can mean the difference between a profitable or a loss driven month. So I think there's a lot of opportunity in Quantum and we've seen that with the qubits we see now, systems scale to be on 100 qubits. I think we need to get to that thousand mile marker to start seeing some benefits. Just like we saw in the AI space with the scale of GPUs and now with Asics in the blockchain space. So Quantum will have its day and there have been some unicorn IPOs there as well. I just think it's still the early days and we need to find the commercially viable business cases to work with. Quantum.
- [17:28]
Joel Palo Thinkle
Yeah, no, it's really exciting. What are some pieces of advice that you would have for people if they're interested in being data science investors for sourcing and screening? Are there, you know, a lot of these, Are they still in the academic, academic ecosystem? Because I know a lot of the quantum deals and the technology still in like tech transformation. Some of the events that I went to in the past at a lot of university presence, kind of doing a lot of research. But is that the similar space to kind of meet a lot of these data startups or are there great accelerators that are incubating these, these startups and technologies? I guess what are the best avenues to kind of learn about these opportunities?
- [18:10]
David
Yeah, so I do partner with a lot of accelerators and some of my favorite ones of course are techstars. Being in New York City, I've seen a lot of companies emerge that are building developer tools around data and ML in the techstars ecosystem. Of course there's a lot of ways that you can discover the market because there are so many data startups today. In fact, my mission is over the next 10 years to accelerate a thousand data powered startups. So I think we're going to continue to see more founders and more executives and engineers alike move into the data ecosystem. But when all things are considered, if you're looking to invest I think it goes back to your point earlier, Joel, that there's a couple ways you can go deal by deal or you can do that index portfolio. And that's what we see with a fund like Data Power Ventures that we've had. LPs come in really for a couple reasons. First, they're either the engineers or business executives who build or scale technology and they understand that data is the next wave that we're seeing in Industrial Revolution. Or secondly, they want that exposure, they want that education. They're a finance executive who doesn't know much about data ML AI. But they also believe that, hey, this is something that I don't want to miss out on. I want the data education, information rights and to have also some upside in my portfolio.
- [19:28]
Joel Palo Thinkle
Yeah, no, I totally agree. I think those are all, you know, good reasons to become LPs and you know, it's, it's also just, it's great too if you can build a relationship with LPs and you know, have these focus groups and kind of keep them involved because a lot of them have that industry experience as well. Some of them are really well known data scientists. So they may actually help you de risk some of the investments as well. So I think if you keep them engaged and kind of seek their feedback, I mean, that also helps to build that long term relationship as well. That's just kind of what I've seen and I can probably assume you agree too.
- [20:07]
David
Community is everything. Absolutely.
- [20:09]
Joel Palo Thinkle
Yeah. And David, David's got a really great. I wanted to plug this too. You've got a really amazing community of technologists as well. You have a, a tech dinner that you host every once in a while. Is there a website where we can go to sign up for that or is that just more, more off the record kind of connecting with you?
- [20:28]
David
Yeah. So there's a few community driven events that I do which I can share. You know, number one with the podcast. I do have an AI data science podcast been running for over three years called Humane with AI in the middle. So that's H M A I N and we have Humane podcasts where you can listen to great episodes with founders from Pre seed to IPO ventures, DataPowers@DataPower VC. So you can of course learn more about our portfolio and how we're investing and everything around there. And the tech dinners. This is an invite only curated community around investors, founders, developers in New York City. But to get involved with the Tech Dinner series, you can find me on LinkedIn and you'll see my most recent Saved item on my profile has an airtable link so I'm making it really hard to get to. But if you're interested and excited you can check it out. We run these events in New York City. We've actually brought multiple portfolio companies in person with LPs they've directly invested. After meeting the founders, we've brought in near introductions who've been hired by the portfolio companies. So they're very strategic, community driven events.
- [21:42]
Joel Palo Thinkle
Yeah, yeah. I mean I've been seeing a lot of people use NFTs now to kind of have utility in the community and also get access to, you know, to events as well. So you know, I wouldn't be surprised if you had a data Data Power NFT launching at some point in the future. Let me think, what are some other hot trends? So I think, you know one thing that I've seen too, and this probably falls into the tools, I've seen some platforms that help to just provide cost savings. So like you know, I think, I think cloud admin is one of them where you know, they help to kind of help you manage your costs. So do you think that's probably a huge application and you know, very, very high value to, to data scientists but do you think that's a hot industry or. There are a lot of people doing that now.
- [22:25]
David
So we've seen a few startups in this DevOps automation space in the last couple years and I agree that this is very important. Even at single store we face these costs every single day because we're a cloud first company and our bill is minimum into the hundreds of thousands of dollars a month on cloud cost Just by using some automations that we set up recently, we're now saving at least $100,000 a month. This is no joke, right? And this is just by an engineer writing scripts. So there are startups that also are focused on this business where they can plug in to your cloud aws, gcp, Azure and then help understand, think about these systems turning off, turning on or using more reserved or dedicated instances and you can see those changes. I think that's very important because one of the underlying factors that we've seen as a result of the pandemic is that every company is digital first. You no longer have a choice. And even in the hybrid world that means cloud is here to stay and a cloud first strategy is required.
- [23:31]
Joel Palo Thinkle
Yeah, no, I totally agree. Where, where do you see data? You know, so we've seen a lot of these companies do like UI automation. So what, what's kind of like super deep tech for you. Like, you know, like do you feel I'm trying to think of like super sci fi stuff with data. So I think when I can think about it, I can think about like just real time movements of your body and like maybe predicting, you know what I think could be really interesting is if you could. And they're probably already doing this but, but you know, based on your bio, based on your family history and based on kind of your eating habits in real time, like imagine if they could just detect your lifespan if you still eat pizza like and fried chicken like four to five times a week. They could probably already predict some outcomes because there's so much data that's been collected. There's probably like some parts of it too tied to like your ethnicity and your cultural background too. But I feel like that would be a really hot space for just like real time data and then predicting kind of what your future would be. If you're continuing to do what you're doing, there could be results. There was a company that I, that I met three or four years ago and they were trying to do that. I don't know if they got the traction but they claim to be able to detect within high accuracy, like when you would get a heart attack based on. But I don't know if you've been seeing anything like that in the healthcare space. But that's gotta be a huge insurance play as well with you coming from like Aflac as well maybe.
- [25:01]
David
There's so much opportunity in the deep tech sci fi space that is really fascinating and companies we've looked at that we haven't participated in, but we've explored. One includes a satellite company that's producing satellites at 1/10 the cost. But what's great is the satellites are tracking data about where objects are in space to then provide the insights for space trash removal and cleanup. So that's a really deep tech play. Another one is a sensor company that installs in warehouses these special sensors to see where any movement occurs anytime based on vibrations and waves occurring in those warehouses. And this is a great company because Boeing and Maersk and others have already committed MOUs to them. So we're really looking at that kind of deep tech to see all these motions. So I think those are a couple really unique ones. One that's more down to earth on literally speaking on the health side that we're finishing an investment in is focused around accelerating research and development for biopharmaceutical companies. And that company is called Applied xl. And so Applied xl is led by Francesco, who's the former head of R and D at the Wall Street Journal, a really exciting startup that we're finishing to round out with Data Power Ventures. That company actually is already being led by some really exciting investors, all in New York City. We do love to support New York founders. We actually have 5 of our portfolio companies are based out of New York. There's, I think, a lot of movement we're seeing today in the entire data ecosystem.
- [26:41]
Joel Palo Thinkle
Yeah. Then I think one thing that I've seen in the last probably five or six years is just advances in how you're housing the data and storing them and categorizing them. So, you know, maybe you can share a little bit of insight on like just some of the enterprise workflows. Like, you know, now they have the concept of a data lake where, you know, you're kind of taking the data and you're categorizing in this lake where you can kind of use it whenever you need. But can you educate us a little more on kind of some terminology that like if you work at a big data company, some things to think about. If you're like a data scientist on the enterprise side, you may, you know, people may not know about it because they don't work at these companies. So. But I think you might have a little more insight into just kind of the industry because there's just a lot of internal workflows that people need to think about.
- [27:28]
David
At single store. So we call it actually three tiered storage. So this is our name of the technology which is similar to data Lake. It means that one, you actually have the data structures that you house the data in. For us, that's row stores and column stores. For other companies, those are data frames, those are CSV spreadsheets, those are SQL tables. Then below that, we actually have the storage on your hardware that could be, in this case for us. These are the compute units which are Linux AMI machines and they have their own database that's sharded or distributed into storage on these machines. Then where the data lake appears, we see with databricks where they have their universal data lake and their delta lake system. And then for single store, it's where we have what's called bottomless or unlimited storage. So pretty much it's like you have this ocean of data and you can dip into it with a fishing net and grab the data that you need when you need it without searching for all the data, which means you can search quicker and speed up your time to real time insights that's why a lot of companies have been moving into data lakes, because storage is getting much more. In fact, today we're seeing over 98% of the cost to run an application is the compute of running the insights, not the fetching, transmission or storage of the data. That's less than 2% of the cost. And that goes back, Joel, to your point earlier, that managing those cloud compute costs is the most important way for startups to scale effectively, especially with their venture capital dollars.
- [29:08]
Joel Palo Thinkle
Yeah, no, it's really helpful. We got around 10 minutes and you got to run soon, so maybe you can, you know, we'll take a few questions if people have it, but maybe you can tell us a little bit about, you know, maybe you could just share some advice that you have for people that are looking to pivot into VC and, you know, what worked for you and what you would advise people to do if they're trying to, trying to break it in, if they're having some trouble doing that.
- [29:34]
David
So for me, what I saw getting into the venture capital industry was first you want to get your hands and your feet wet, which is by exploring what does VC mean. So if you're someone who comes from finance, it is different than other parts of the industry like private equity and other areas. You do want to make sure you can try a couple investments and start getting involved with diligence and get involved with different angel networks when you get started. Also, if it's your first time in industry, I say start slow, put in some small checks or wait a while, sit the sideline, see what the deals look like. There's going to be plenty of opportunities coming ahead. As we're seeing in 2022, there's no sign of venture slowing down as family offices, endowments and fund of funds are continuing to double down on their investments in the venture capital space. If you're someone who doesn't come from VC yet, the best way is to start getting involved with a network. Roll up your sleeves. Hey, I'll help you with technical due diligence. If you're someone who's focused on healthcare or finance or on data or infrastructure, lend that to a team. And you can see a lot of benefits by partnering with those venture networks. And when you get started, it's also important to identify what are the goals you want to do for being in the venture industry. Does that mean you want to launch your own syndicator fund? Do you want to join a. You want to be part of a university network of investors? There's a lot of opportunities to consider. So you want to make sure it aligns with your mission and purpose and goals.
- [31:11]
Joel Palo Thinkle
Yeah, and I think a syndicate is a great way to start as well. You know, you can kind of go on angel list and also just get some early stage signs of a track record from sourcing deals and then getting insights from the community as well. And I think to your point, and I've done this too, I built the community first. Right. So I think building, building a really strong, high quality community and that means vetting is involved, that keeps a tight circle of just high quality people. And I feel that's for both of us. I feel like that's helped our networks compound with just other great people to add to the circle. So I think that's a huge thing too. One thing that I would also say is just building in public. So if you're working on a really cool project or prototype, maybe just share that with people and see what people think. Would you recommend writing content and blogs? I've seen that as a pretty common piece of advice too. Some people kind of write an investment thesis if they want to. You know, if they wanted to eventually be like an intern for you or something or someone else, they could probably write some type of blog piece that's really, really tied to what that investor is kind of investing in and maybe even surface some interesting deals too, I'd say.
- [32:22]
David
Right. I absolutely encourage you to build in public. One thing that we've done in public is launch the Data rider, which is now becoming the Data Pledge. You can think, for example of Melinda's giving pledge. And the same thing here with the Data Pledge is committing to ethical and responsible use of data on the cap table with investors and the community. When I first launched the working document, I shared this with over 50 experts and luminaries in the field to say, hey, what's your thoughts? Do you have draft edits? Do you want to contribute or collaborate here? And it evolved, it changed at least five times to its current steady state and will continue over time. So I do encourage you to enlist the community. And with all the projects I do, I do enlist our LPs because I think the best LPs on the cap table are smart LPs who provide value add. And that can mean customer introduction, hiring trends on the industry, that they're seeing different ways to partner together. And we see that, that when you ask questions, often you'll get back feedback, you'll get advice, you'll get answers, which is very helpful when you scale.
- [33:35]
Joel Palo Thinkle
Yeah, totally agree. Well, I want to get you out of here for your hard stop. So if nobody else has any questions, feel free to chime in if you have a last minute question. The question I always ask at the end is if you have any advice from maybe a friend or a mentor just on just life as a whole. So any feedback, it could be about your career, it could be about relationships. Anything that you have that kind of sticks out from a mentor or a friend or a family member.
- [34:03]
David
I think the biggest takeaway I got from one of my mentors who's an advisor to Data Power Ventures Fund, Chris Sanchez. He also launched the Data os and he partnered with me for launching a Data Writer data pledge. And what he told me was very interesting. He said, consider that what you're building is best in class. Consider you're building the next Blackstone and that what you're building is ready and that you're good enough to launch and scale what you want to do and that you don't need to be an expert in everything. Right. You can align yourself with other advisors and venture partners to help you scale and see the industry as a whole. And when you share that advice, to me, that was very thoughtful because it helped me accelerate my growth, be willing to not be a perfectionist and to continue making updates to our product offerings to our platform. And by doing that a lot faster, it's going back to your point, Joel. Build in public and then when you're seen, you also get this great feedback. So great minds think alike.
- [35:06]
Joel Palo Thinkle
Yeah. Well, thank you so much. Well, guys, if you have a question, feel free to yell it out now within the next five seconds. If not, and feel free to jump in if you do. If not, hey, David, I want to let you go because I know you got a hard stop, so shoot me a note later. And hey, thanks again for popping in at such last minute and good luck with everything. Catch up soon.
- [35:30]
David
Thanks everyone. Thanks for having me. Happy to answer any questions and make it the greatest.