Statsig Founder Vijaye Raji on Product Building, Launching a Startup, and AI

Statsig's Vijaye Raji on Product Building, Launching a Startup, AI

Today, Madrona Managing Director S. Somasegar talks with Statsig founder and CEO Vijaye Raji, who spent 20 years at Microsoft and Facebook before launching Statsig as a way to bring the powerful tools he was used to using inside Facebook to all builders.

Statsig experimentation platform and automated AB testing helps companies make product decisions in a data-driven way at scale, which means shipping the right products faster. Vijaye and Soma go way back and take a little trip down memory lane, talking about Microsoft’s Small Basic. But then they dive into what it’s like transitioning from big tech to a founder, the importance of data-driven decision-making, integrating AI into those decisions, identifying the ideal customer profile, and learning how to sell your product. Something that did not come naturally to Vijaye. He and Soma talk about all of this and so much more.

The 2023 Intelligent Applications Summit is happening on October 10th and 11th. If you’re interested, request an invite here.

This transcript was automatically generated and edited for clarity.

Soma: Hello, everybody. I’m Soma, one of the managing directors here at Madrona, and today, I’m very excited to have Vijaye Raji, the founder and CEO of Statsig, here with me. Before I get started, I want to sort of go down the memory lane a little bit. I remember meeting Vijaye for the first time when both of us were at Microsoft. He was working on this tool called Small Basic, and he was really passionate and focused on figuring out like, Hey, how do I make programming a whole lot easier and simpler to get started for the next generation of developers? And since then, throughout whatever Vijaye has done, there’s always been a focus on how do I make developers and development teams successful and effective, and how do I make their jobs easier, and make them more agile in the process. Vijaye, you were at Microsoft for a while and then you went to Meta and now at Statsig. Can you shed light a little bit on the journey that you’ve taken so far leading up to Statsig?

Vijaye Raji: Yeah, absolutely, Soma. And thanks for having me on your podcast. Really excited to be here. You touched on Microsoft’s Small Basic, which is something near and dear to my heart, primarily because I was so passionate about building a tool for kids and early programmers to get their programming teeth cut. And particularly so right now because my 10-year-old son is also learning programming right now, and he’s using Small Basic to program, and it’s really awesome because on the weekends, we sit down and we jam together, come up with games, and then he codes it up and then shows it off. So really, really relevant. Thanks for bringing that up.

Yeah, so we met, I think, sometime in 2005 or so. I was working on Small Basic and then we were talking about how you wrote the first blog post of releasing Small Basic from Microsoft. I remember that. Thank you for doing that. I’ve always been passionate about the developer space and how we can get more productive tools out to the developers. And then, in 2011, I left Microsoft when I was working on Windows, actually developer tools on Windows 8. I joined Facebook, which at that time was still called a startup when it was only about 1300 people. Then, as an engineer, I continued to build various different products and, throughout the process, was also enamored with the tools that Facebook had invested in that helped all the engineers and the product builders inside Facebook to build and release products really fast and precisely.

And those are all the formative years. And I think about what I learned there, what perspective I gained there, and what I took away from that all led me to leave Facebook in 2021 to start Statsig, which is basically a culmination of the tools that I was so excited about that I wanted to bring and build for everyone outside. And so that’s the journey of back in the day, as you remember, Microsoft, Small Basic all the way down to Statsig.

Soma: Thanks Vijaye for sharing that. We at Madrona have been talking about intelligent applications for many, many, many years now. In fact, I would go so forth to say that we are strong believers in saying that every application that gets built today is an intelligent application. And by intelligent application, what I mean is it takes into account the data that the application has access to and uses AI and ML in some fundamental ways to be able to build a continuous learning system that, in turn, helps you deliver a better service to your customers.

So if I look at the world through that lens, I look at Statsig and say, Hey, Statsig is a classic example of what I would call an intelligent application enabler that allows product teams to experiment with different features and ensure that they create best-in-class experiences for their end users. But before we talk about Statsig and what Statsig is doing, particularly in the world of generative AI today, which you can’t have a conversation anymore without talking about generative AI in some way, shape, or form. Tell me the origin story of Statsig. How did you come up with this idea and how did you get together with your founding team?

Vijaye Raji: So if you trace back software development, all the way back to the ’80s and the ’90s, a lot of what the development process that led to software being shipped came from hardware. So you would have this waterfall model where you’ll go talk to your customers, come up with a set of requirements, and then that goes into a design document. The design document gets reviewed and then engineers pick up that design document, turn it into an architecture document, and then write code. And then, the coding is done during milestones, which will then be packaged and sent off to the QA team. The QA team will run the test and then validate it, it’ll get released, it’ll go to, I don’t know, back in the day, best Buy Circuit City and sit in boxes, and people will go buy that and take the CD and insert it.

And that’s how software development happened. Then V2 will happen, V3 will happen. And then over time, with the internet, a lot of that process got faster and faster. So people got into this build measure learn loop where the continuous deployment of new features enabled a lot of this fast iteration. And along with that came the data aspect of it. So, where you were instrumenting your product, your understanding of what users were using, which products or features they did not care about, and how much they used. And then it subsequently led to this really fast iteration that also led to this experimentation-based software development, which is, I think, the latest trend, where every product that you build, every feature that you release to your users, you want to understand the impact of that. Without understanding the impact of it, you don’t always get your product intuition right.

And that is an interesting progression over the last couple of decades. And so, I was in the middle of all of that when I went from Microsoft, which at that time in Windows was primarily following a waterfall model. When I went to Facebook, in my first six weeks of boot camp, I got to release a bunch of features that actually got to reach 600 million people at that time within days. And that was fascinating. First of all, I have to say that when I first got that experience, it was a little scary. I thought something was going to break all the time, and just knowing all my software engineering, this is not how software should be built. But over time, I started to understand and appreciate that kind of fast iteration and when I dug deeper, the tools that were underlying all of that software development process were fundamental to keeping everything going in the right direction.

They were fundamental in capturing data. They were also instrumental in understanding the impact of every single feature. Now what was interesting was that the tools shape the culture that came down. So the downstream culture of product development where everyone felt empowered to look at the data and make product decisions, both good and bad. If the numbers were not looking good, they decided to cut the feature. And there was no personal attachment. And so, the downstream culture was one of the very objective data-oriented decision-making, which was distributed among all teams.

That was fascinating. That was really, really powerful. And so the journey of Statsig is basically that so how do we bring that kind of a product-building culture to everyone? When I looked outside from Facebook, not every company has the luxury of building these tools or the team sizes or the engineers to go build all of these tools by themselves. So that was the opportunity that I thought would be useful to go and solve for the world. The mission really is like how do we improve software development using data-driven decision-making and bringing that to the masses.

So in 2021, I left Facebook. At that time, I was the head of Seattle, head of the entertainment division at Facebook, and I felt so strongly that this was an important problem to go solve, and so did seven other people; they all came with me on day one, we started building, and since then we’ve been building. It’s been two and a half years. It’s been a fascinating journey. I’ve learned a lot. So happy to share.

Soma: That’s fantastic, Vijaye. Hearing you tell that story reminded me of why I was so excited to be a part of the developer division at Microsoft for many years. Because when you end up building products for people like yourselves, there is a special excitement around that. And I always wondered for the last many years, as companies and developers have been building what I call analytic tools and AI tools for the rest of the world, I was wondering when are developers going to start building these tools for themselves. And I look at Statsig and Statsig is doing just that, right? Building tools for people like us who are building next-generation products, and that’s a whole lot of fun. But Vijaye, as you mentioned earlier, you were at Facebook for 10 years and at Microsoft for 10 years prior to that. Having worked at two large companies, two large technology companies, because though when you started Facebook, it might have still been called a startup, by the time you left, it was a fairly large company. But what was it that made you decide to leave a large company environment and move to start an 8-person startup? And related to that, what experiences, whether it’s the experience that you got at Microsoft or the experience that you got at Facebook, do you feel prepared you to launch your own company?

Vijaye Raji: I have to say that anyone who is starting a startup from scratch has to be a little bit crazy. Some amount of irrational decision-making has to be there because, look, if you are completely rational, every formula, every expected outcome that you calculate should indicate that you go join a large company. The only reason why you would start a company from scratch is something you feel so strongly about solving a particular problem in the world so that you’re willing to leave behind all the comforts and go do this thing.

So for me, that happened about three or four years before I started Statsig. For me, the journey was more around what are the necessary conditions for me to be able to be successful in a startup environment. What kind of skills would I need? Am I good at hiring people, coaching people, and growing people? Am I good at understanding business, the profit and loss for a business, how we sell, and how to market the product? Am I good at finding product market fit for a new idea? Am I good at getting it to market and actually having people understand the problems that the product is solving?

So those are all the kinds of criteria in my mind, I was like, okay, well, how do I learn all of that? And so the last three years that I spent at Facebook was basically figuring out or putting myself in positions where I would learn a bunch of that stuff. And then finally comes the point of, okay, who would want to leave great jobs and want to pursue this journey? A great entrepreneur would have to have a set of followers, people who are crazy enough to follow an entrepreneur who’s about to jump off a cliff. And so that’s the image that you want to think about. And if you can convince a set of folks to come and join you, that is already a validation. And those are the kinds of stuff that I prepared myself for.

Now, there’s another aspect of it — I am married, and we have two kids, a 10-year-old and a 7-year-old. Family is every single bit involved in your startup journey as you are. And so a lot of the commitment has to also come from the family side. So if I think about my wife and my kids — every day, we talk about the startup. I feel like they’re also bought into the idea of this startup — Statsig. Without their help, I wouldn’t be here.

Soma: So Vijaye, I know that you’ve been asked this question a ton by other people, whether it’s from Facebook or from other companies, but if I have to take a step back and ask you, Hey, what advice do you have for someone who’s thinking of maybe making a similar transition to what you did from a larger company environment to move off and say, Hey, I’m going to launch my own startup. Are there any pulls of wisdom or key learnings that you want to share?

Vijaye Raji: So I’ve only done this one time, and so take this with a huge grain of salt because there are a lot of biases here. The one thing that has helped me quite a bit is once I understood that my next journey is going to be a startup, it really comes down to what kind of skills can I pick up. What kind of connections can I create now? What kind of relationships could I build right now? And then what kind of team can I bring on day one to the startup? And if you walk backward from it, then it becomes very clear what all should be done in order for the startup to just get up and going as quickly as you can and start building.

So most companies won’t let you write code for your startup as you’re continuing to work in the other company, but you can always think about the problem. You can talk to other people about the problem. So there’s a lot of validation that you can do even before you start the startup, even before you embark on the journey. And so, that should all inform you on what is day 1, day 90, and day 180 going to look like.

And then the other one that most people don’t really talk about is your financial safety net. Because when you’re going into a startup, you’re kind of committing several years of your life to the startup, and during those periods, there’s going to be a lot of instability and a lot of ups and downs, primarily on the financial side. And if you have a family, you want to make sure that you build a good financial nest that supports you during that journey. And so those are all necessary components as you think about starting a startup. For me, it took me three years or so to ready myself to leave behind a comfortable job and get into Statsig. And sometimes, it could take longer.

Soma: In looking back at your origin story for Statsig, I think you were lucky enough to start with a founding team of eight people, all of whom you had known from before as you all had worked together in one way, shape, or form at Facebook. But some people have the luxury, some people don’t have the luxury, but every entrepreneur thinks about, Hey, I want to make sure that the founding team is the best team that I can put together, and I need to start thinking about culture from day one or all that fun stuff.

From your vantage point, how did you decide, hey, what your founding team should be and then, more importantly, what kind of a culture you want to set? On the one hand, you all had what I call the fortune to work together in a company like Meta with a particular culture, but here you are starting off on your own, and I’m sure you had some ideas of what you wanted the culture to be, and what you want to prioritize and what you wanted to be below the line. Talk to us a little bit about how you found the right people to instill the right set of culture from day one.

Vijaye Raji: I think culture is a very, very important aspect of a company, especially as you’re starting from scratch, because there’s only a few things that you can hold strongly as the company grows and as you add more and more people. So you want to hold onto the things that you really care about. And those could come in the form of what is the environment that you want to build, that you want to continue working on 10 years from now? And if you put that in that kind of lens, then that becomes really clear. What I’m doing is building a place that becomes exciting for me to come in every single day and work every day.

So early on, we set out to pick a set of values, and when we said we were going to pick values, we decided we’re going to pick a handful of values. Those are the ones that we really care about that we don’t want to compromise ever on. You don’t want to have 15 or 20 values because nobody can even remember that many. And so we picked four, and even when it came to the set of values, we decided these have to be trade-offs. These cannot be truisms like motherhood and apple pie. So you can’t say things like, oh, be honest or be transparent or be happy or be friendly because those things are all given — those are all table stakes. A value should have a downside and a trade-off. Every day you should use them in debates and conversations and decision-making processes, thereby doing the right trade-offs, and that sets the tone for the whole company.

I’ll give you one example. We have one value that says, “No sacred cats.” No sacred cats is actually a way to say, do not take anything for a given unless it’s logical, unless it’s reasonable. If you have a set of processes or traditions that people are following, and if nobody can explain why, then it’s okay to change it. But the trade-off here is there’s a lot of overhead in changing things, but the outcome is that you will never end up in a position where things are illogical. Nobody would ever come in, like why the heck are you doing this thing where it doesn’t make any sense? So that’s the trade-off. And so those are the kinds of values we ended up picking early on.

And I remember having exercises with the early teams of seven or eight people where we all sat down together, we all agreed, and these are the values that we’re going to adhere to the company, and we have continued to maintain that.

Now over time, that becomes your culture, that becomes your identity, and when you hire more people, they opt into the culture, and so you want to make sure that something that is attracting people or attracting good people. So yeah, I think those are all important stuff. Like I said, you want to pick a handful of things that you can hold onto because every time the company grows, culture evolves, culture shifts, and if you try to control too much, it doesn’t work.

Soma: If I take a look at the progress that you made, first of all, Vijaye, you guys have made some great progress in the last two and a half years that you’ve been around. More than anything else now, I continue to be impressed with the velocity of product development. It’s one of these things where you’re building a set of tools to enable the rest of the world to do that. And by virtue of that, being able to be in a position where I’m leading by example is a fantastic place to be, and that warms my heart when I think about Statsig. But in the last couple of years, you’ve sort of gone through a phase of learning about what does it mean to go identify the right customer, or people call it, ICP or the ideal customer profile. You now have what I call small companies and large enterprises as your customers. You’ve got companies that are delivering consumer-facing services. You’ve got companies that are delivering B2B services or enterprise-facing services. So you’ve got a wide array of customers today.

Can you share a little bit of your perspective on how has your thinking evolved in terms of who’s your ideal customer and anything that you’ve learned in the process of deciding, Hey, this one works well, that one doesn’t work well? How do you define yourself in terms of going after customers and landing customers for Statsig?

Vijaye Raji: This is a really, really good question because it was a journey. It wasn’t intuitive or it wasn’t very clear on day one. I come from an engineering background, and I had lots of exposure to product roles, but I had very little exposure to sales or marketing. So I don’t have intuition for sales or marketing, something that I had to learn during Statsig in the early days and something that I actually made a lot of mistakes in the early days of trying to sell a product and then learning how not to sell. So when it comes to product building, now you said the velocity, we take pride in the fact that we help other companies build fast, and it is important for us to actually be an example for that. If we cannot even build fast, then we can’t claim that we can help other people build fast.

So a part of that is what are the set of conditions that would make engineers, product managers and designers and all the creative people blossom and flourish? And so removing all kinds of friction, removing overhead, removing ability, one of the key elements is transparency, radical transparency. Everything is available for everyone. All the code is transparent, all the data points are transparent, so people don’t have to get stuck in the process in order to move fast. So creative people should be allowed to move as fast as they can. And so we’ve established an environment where product building can happen really fast. Now what happens after you build the product, you have to go and convince a set of customers to try and use the product. That took a long time.

So I think the first eight months, we couldn’t even convince people to use our product for free. How do we sell this product? We know there’s value in the product. We believe very strongly that this is an important platform that others should use, but we were not having very much success convincing people to try and use it. One day, we saw an ex-Facebook engineer start using our product and started sending a lot of events, and those events came from Singapore, Malaysia, and Indonesia. At first, we thought, oh, somebody’s DDoSing us from various different countries by sending lots and lots of events. And then later on, we realized nobody cares about Statsig, so they probably are not DDoSing. And then we figured out it’s the next Facebook engineer that is using the product.

That was when it hit us like, okay, there’s a set of people that left Facebook that all had access to the tools inside Facebook. They are probably all misusing those tools, and that should be the people that we should be talking to. Then we started talking to, I think Headspace was our first customer. We talked to an ex-Facebook product manager at Headspace, and it resonated very, very strongly, much stronger than what we were trying to do before.

And so that was our first ICP, really not the big companies or the small companies and not the stage of the company or what series they’re funded. It was really like the ex-Facebook folks that missed the tools that they had access to. And that’s how we figured our way, almost like in a stumbled out on our ICP. And then since then, we’ve had plenty of traction, and that was, for me, a pivotal moment in understanding the value of our own product.

Soma: That’s super, Vijaye. Two and a half years since you started the company, since you became an entrepreneur since you sort of went from zero to wherever you are today, I’m sure not everything has been fine and everything going well. Can you sort of highlight one or two unexpected challenges that you face? The things that you did not expect that you said, oh my God, what is this new thing? And more importantly, how did you deal with them?

Vijaye Raji: Every single day is a learning. There are hundreds of things that happen during the day, and for me, there are a bunch of new things that I learn and take away. If I were to think about the big ones, understanding how sales teams operate was key for me to be able to unlock. So one of the earliest hires we made, Sam, who came from Segment, who leads our sales — he’s the head of sales for us. He knows sales like I don’t. I learn a lot from Sam every single day. The way he thinks about problems. The way he thinks about customer’s problems. This is one of the things where founders make a mistake — customers don’t care about your vision. They care about their problems.

And so when I sit down and talk about it, I get so excited about sharing Statsig’s vision to this poor customer who’s like, I have a problem to solve and I’m not here to listen to your vision. And then you hear how Sam talks to the customer and there’s so much to be learned from there. And so that was a pretty large area, I would say, a huge area of figuring out, slowly making some mistakes, and then learning from the people around me who are so much better than me at what they do.

Then the next one was marketing. That’s another one of those things. So our tool is used generally by data scientists and engineers, and those are the two groups of people that don’t want to be sold to, don’t want to be marketed to, and how do you make sure that they understand the value of Statsig? And so the way you approach that is through communities or content where you actually make our product useful for them and then have them pick it up, learn what they can do with it, versus actually being out there and shouting.

And so those are nuances that I had to learn about marketing. And then here’s another one. It’s so naive at this point when I think about this, when everyone says marketing, they combine about 10 different types of marketing in one bucket. That’s another one of those revelations like, oh, there’s content marketing, performance marketing, demand gen marketing, product marketing, events marketing. Whenever I bucket everything into marketing, it actually turns out, there are like 10 different ways of marketing stuff. Even that aspect I didn’t know, and I had to learn. And so once I hire the marketing folks around me, now they’re doing a great job and I learn from them every single day. So yeah, it’s like every day is a day of learning and it’s been great.

Soma: That’s awesome, Vijaye. You’ve been working in the technology industry for a couple of decades now. As you know, we are in the midst of a huge platform wave. People call it AI, people call it generative AI, and there seems to be an announcement about a new generative model, a new AI application, or a new piece of innovation, literally every other day. It’s almost like a tsunami is over us in terms of the rate of innovation as far as generative AI goes. With the speed the market is evolving, how do you decide or how do you know what is the right set of features that you want to prioritize and focus on? And then, more importantly, what would you tell your customers? How should they prioritize?

Vijaye Raji: One of the things that Statsig was fortunate about is we built a set of tools, the primitives of experimentation, that was applicable for AI tools. So we have some of the biggest AI companies that use Statsig for optimizing their user experience that are based on AI. So the application of AI, when you present the experience to your users, you want to understand if it’s actually improving and if it is improving by how much that impacts quantification. And so, Statsig became a tool for AI producers and AI consumers to validate some of the AI applications. So when they bring a new model, when they bring new prompts, or when they tweak the parameters like temperature or frequency penalty, they use Statsig to validate whether this is positive or detrimental to their product. And so we found ourselves in that space and we started doubling down on it.

So we have partnered with companies like LangChain, where we provide tooling for everyone using AI for their product to just start running experiments. In the world of experiments for AI/ML, there are offline experiments and then there are online experiments. In offline experiments, you take a set of training data and you verify and you validate, okay, I’ve gotten it to a pretty good place. Now I want to put it in production. But production is an entirely different story. How the model operates at scale, and how it actually improves your business-level metrics is something that you need to measure, that you need to validate, and that’s where Statsig’s abilities are really shining.

And then the second aspect of that is like, okay, how can Statsig help using AI within our own product to maybe even bring a lot of insights to the customer? So, for example, one of the things that the Statsig experimentation platform provides is the new feature that you launched yesterday — maybe it’s a registration feature that is improving your conversion metrics by 4%. With AI/Ml, it’ll be easy for us to actually even dive deeper and tell you, okay, on average, you’re seeing a 4% improvement in your conversion. However, on Android phones that are in non-English speaking countries, you’re seeing a 20% drop. And that’s a question that you wouldn’t even ask in the beginning.

So, if AI is able to identify these anomalies and patterns and bring it back to you and provide insights, you can go fix that problem or the bug that is affecting all the Android, non-English speaking phones. Then after that, your 4% improvement can actually become a 5% improvement, which is a huge, huge win. And that’s the area that we’re kind of starting to investigate and invest in.

Soma: When I think about Statsig, Vijaye, I think about Statsig as sort of a product experimentation platform, a product observability platform, but really something that is going to be helping you build the right set of features and the right set of capabilities irrespective of whether you are building a generative AI application, you’re building a large language model, or you’re building what I call an intelligent application kind thing. So I think about Statsig as, Hey, you are an enabler for generative AI. Is there a good way to think about it, or do you think about how you position Statsig in a world of generative AI differently than that?

Vijaye Raji: So anybody who is using AI or ML today will need a set of tools to understand the impact of that. And so Statsig being an enabler of, in general, better products, better features, or even knowing which of the features or the products that we launched to the customers is doing well. So that, to me, is an enabler of better product building. So one of the things that at Facebook, at least we understood was unless you’re a Steve Jobs, you’re going to be about 33% correct in your product intuitions. It’s kind of like a humbling fact and brings a lot of humility in, like when we have product intuitions, it’s been validated 33% of the time you’re going to be absolutely right and about 33% of the time you’re neutral, and then the rest of the time you’re actually hurting the product. You may not think that is the case, but the data shows that.

But just by knowing which 33% of the products are actually hurting your company’s business metrics, and by turning those off, you’re going to get a bump up, but being able to know that is how you’re going to first start by measuring and then understanding and then actually removing your personal attachment to those features. Those are all the ways in which you end up building better and better features, and Statsig is one of those enablers of building better features.

Soma: In my conversations with you, particularly about generative AI and talking about large language morals and the like, at least it feels to me that hey, you have a little bit of a contrarian view on what many people think of as a hallucination problem that exists with generative models and large language models. Can you share your thoughts on that or your perspective on that? Because I think it’s a fascinating line of thinking.

Vijaye Raji: Yeah, absolutely. At first, it started out as, like, let me take a contrarian view, and then I started doing some research and then I started actually believing in it. When people talk about hallucinations as a big problem, I think it’s a little bit overblown and it’s actually not that bad of a problem. In fact, if you think about it, it’s a creative way that whenever there’s stuff that is made up when you give two inputs and then there’s stuff that is filled, the gap is filled. That’s what we do all the time. Humans do this all the time, and our brain does this all the time. When we do it, we call it creativity. So when AI does it, we call it hallucination, which is, over time, it’s going to get better and better.

If I already think about how AI is generating art, how AI is generating music, those are non-deterministic. So every time you run the same model, you don’t get the same output, you don’t get the same image, you don’t get the same music. It’s a little bit different. I think it’s a form of creativity, and a lot of times what happens is these creative ways, sometimes based on if we think about evolutionary practices — over time, these mutations start to get better results. And so if those are able to be absorbed back into the system and the system gets overall better, I think there’s a potential upside to all of these hallucinations. And so that’s my take on it. We’ll see where things go. Hallucinations are not that bad, and over time, they will get better. That’s my feeling.

Soma: No, I agree that they’re going to get better over time. I know that we are coming up on time here, Vijaye, but before we wrap up, I want to ask you about your own usage of generative AI, whether it is for personal reasons or for professional reasons. Do you use generative AI on a day-to-day basis? And if so, what do you use or how do you use that?

Vijaye Raji: I use it all the time now. In fact, if there are no temporal data points, I prefer using AI to ask questions, and over going and searching on Google or Bing. Just last week, one of the things that I was doing is I was trying to remember the name of this electrical thing and I couldn’t express what that is. And so I went to OpenAI ChatGPT and started typing like, Hey, look, I’m looking at this thing. It’s an aluminum box. It’s rectangular in shape, and it’s got conduits coming into it. I see electrical wires. I don’t know what it’s called. What is it called? And it said, “Oh, it’s a junction box.” Oh, thank you. And then I went to Home Depot. I searched for Junction Box and I found it and I bought it. So I think it’s really cool. It’s just crept into everyday usage. I love it.

Soma: Vijaye, thank you so much for spending the time with us today on this podcast session. I do want to take this opportunity to congratulate you again on all the success that you’ve seen so far, particularly the progress that you and the Statsig team have made in the last couple of years. And looking forward to seeing what is possible and potential with what you’re building. Thank you so much.

Vijaye Raji: Thanks, Soma. Thanks for having me here.

Coral: Thank you for listening to this week’s episode of Founded & Funded. If you want to learn more about Statsig, visit If you’re interested in attending our intelligent applications summit, visit to request an invite. Thank you again for listening to Founded & Funded, please rate and review us wherever you get your podcasts, and tune in in a couple of weeks for a conversation with Troop Travel’s Co-founders

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Related Insights

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