The DTC Enterprise Tech Podcast

Ankit Gordhandas (00:00):
I think the founders and the executive team at Zapier early this year. So around January or early February, so this was two months after Chat GPT came out, we decided that AI was here to stay. AI is powerful; it's only going to get more powerful, and we better embrace AI. So we had what we called Code Red, which was a directive for everyone at the company to start using AI to make their workday lives easier, faster, and better. That was one director, and the other director was to quickly figure out how we can use AI to deliver a better experience to users.

Ronda Scott (00:32):
Welcome to the DTC podcast, a series of conversations between the investors and operators at Dell Technologies Capital and the people who are building what's next in enterprise technologies. In this episode recorded at the end of 2023, DTC Investor Radhika Malik talks with Ankit Gordhandas about his career transformation from being a biomedical engineer by training to an exited tech startup founder, and then now his role as a staff engineer leading AI related projects at Zapier.

Radhika Malik (01:03):
Hey, Ankit, great to have you here. Thanks for spending time with the DTC team today. Maybe just kick us off by sharing who you are, a little bit of your background and we can take it from there.

Ankit Gordhandas (01:17):
Hey, thanks Radhika. Thanks for having me on this podcast. Really excited to share some of my journey with you and the rest of the DTC listeners. My background, so my name is Ankit. I'm a staff engineer at Zapier, which is a workflow automation company. The company's completely remote, and the one unique thing about Zapier is that it's been completely remote since 2012 since it was founded, and I used to live in the Bay Area. I now live in Denver. We took advantage of our work and my wife's job opportunity and relocated here and love it. Most recently, before Zapier, I was a startup founder. I founded a company called Intersect Labs, and eventually Intersect Labs got folded into Zapier.

Radhika Malik (01:54):
For people's context, Ankit that I have known each other almost 15 years now. Spent several nights in college dealing with problem sets, and we were similar majors, electrical engineering, computer science. He was a couple of years ahead of me, so gave me some words of wisdom right from 15 years ago and where we are at today from those days in college over at MIT. At that point in time, I think the idea was to go into research related roles and that's what you did post school, right? So what took you from that initial thinking to becoming a founder and now doing the things that you're doing today?

Ankit Gordhandas (02:33):
Yeah, for sure. Yeah, so I was really gunning to go into the academia, but more as a researcher. And so, right after school, I joined a lab at MIT. I did some algorithm development for medical devices there, and then after that, I had a stint at Mass General Hospital, one of the largest hospitals in the New England area where I worked as a researcher at the sleep lab and wrote algorithms to process EEG data, which is data recorded from the brain surface while patients are sleeping. And soon after that I felt this urge to move into the industry because I wanted something more faster based. The output of academia was mostly papers and I wanted to have something that I could build that user studios. And so then I joined a medical device startup called Cibiem, and that job brought me out to California, worked there for almost two and a half years and really enjoyed it.

Ankit Gordhandas (03:30):
I got to dabble in everything from designing the electrodes that would deliver a small current to this set of organs called carotid body in the neck to actually working on clinical trials and overseeing them in Poland. So I had a blast working on that. And then I had the itch to switch again. I wanted to work in something that was not as regulated because even though I was building something that user studios, the timelines were long, there was a lot of paperwork, there was a lot of approvals needed, which is a good thing obviously, but I decided I was not at the stage in my career where I wanted to have that big of a feedback loop. I wanted tighter feedback loops. And so I sought to work at a more traditional tech company, which is still somewhat related to the human body. So I joined a company called Athos.

Ankit Gordhandas (04:22):
Athos was a wearable technology company. We made garments that measured muscle activity in real-time. So it was mostly used by athletes, by physical therapists, by people who worked out at the gym regularly and again, had a blast there. Worked on everything from the actual design of the garment electrodes to writing algorithms that ran on the firmware to writing algorithms that ran in the cloud. And then until then, all my job changes were mostly aspirational. I wanted to switch from academia to industry. I wanted to switch from regulated to non-regulated. The next switch was more practical or logistical in nature. My wife was starting a residency program at UCSD in San Diego, and so we were going to San Diego from the Bay Area, and I decided it was time for a fresh start. So I ended up consulting for about a year. I consulted mostly for small companies that needed help with their algorithm development or data processing.

Ankit Gordhandas (05:20):
Then, in working with these companies, I found the need for some platform that allowed nonpractical users to easily analyze their data and, more specifically, make predictions or forecasts using their data. And so that's when I started this company called Intersect Labs. Our first product was, it was called Machine Learning. In three clicks, it enabled users to upload their historical data, select which column they wanted to be able to predict, and then hit create model. And based on that, our software would create a machine learning model for them, deploy it on the cloud, and then from there on users could make predictions both using the web UI or using the API took that company through Y Combinator. We raised about $1.6 million in the seed round, pivoted a couple times, but that was my journey from college academia to starting a company.

Radhika Malik (06:14):
Fascinating story. I guess one of the audiences we are hoping this podcast caters to is the founder Curious, we call it people who are either in academia or in startups or scale-ups who are thinking about starting their own company at some point as well. And obviously you had a meandering path, but I'd like to pretty interesting directions for those people. What part of the founding experience really called out to you and what has really resonated with you the most?

Ankit Gordhandas (06:41):
Yeah, I think it's the impact, right? And that was a common story, rod. If you tease apart the story, you'll realize that when I wanted to switch from academia to industry was because I wanted to have a particular kind of impact. When I wanted to switch from regulated devices to unregulated devices, it was again, the kind of impact that I was hoping for. And similarly founding a startup was all about that impact. You can make a ton of impact, obviously working at a smaller company or a bigger company, but that just that impact is 10 x or a hundred x, or even a thousand x when you start your own company because the company lives or dies by you, the code you write, the conversations you have with customers, how you run the company internally, the conversations you have with your employees, with your investors, they all shaped the future of the company. And so I really wanted to have that kind of an impact. And because I had an interesting idea that seemed to cater to a need in the market, I decided to act on it.

Radhika Malik (07:39):
It seems like it was both the impact and then you were just looking for a faster paced environment at every point in time as well. And how was the Y Combinator experience for you as a first-time founder?

Ankit Gordhandas (07:50):
It was amazing. I really enjoyed it. I would definitely do it again if I were a first-time founder. Until then, I was really a novice at running companies, at raising money, or even at talking to customers. Until then, I'd been an engineer. I had not really been exposed to talking to users or figuring out what the business side of things is going to be, and Y Combinator really helped shape some of that aspect for me. So I'm really grateful for that 10 week journey, had a really good time.

Radhika Malik (08:20):
That makes a ton of sense. And then you obviously went through a few pivots as well through that journey at Intersect Labs. So what was the journey through some of those pivots? And obviously you're talking to a ton of users, you're building fast. One of Y Combinators real advantages is really their focus on making you shift and iterate pretty quickly. We see that in a lot of IC founders that emphasis on doing that. So did that help in that process? And then how did you think about the Pivots and how was that journey for you as you tried to find product market fit and that early product development validation?

Ankit Gordhandas (08:56):
The whole YC ethos of shipping fast building fast was incredibly useful, and that's something I carry to this day, even now that I'm not at my own startup. I think in terms of pivots, we started the company around middle to end of 2018, went through YC in summer of 2019, and then started sales earnestly around that time, basically summer of 2019. And we were doing pretty well. We were growing. And then six months later, the pandemic hit February of 2020 was the pandemic time. And suddenly all discretionary budgets were just canceled at every single of our customers. And we were squarely in the enterprise sales game. Our starting price at the time were about $25,000, and then most contracts were a little bit higher, and so it required some amount of approvals so users couldn't just come in, log in, put down the credit cards and start using it.

Ankit Gordhandas (09:50):
Unfortunately, and because of the nature of those kinds of sales cycles, we either saw a lot of those potential deals just get lost or people start telling us, okay, you know what? Let's talk again in a few months. A good example of that is the US Tennis Association. We had a signed contract with 'em; they'd use it for a month, or they'd just onboarded in early February of 2020. And then when the pandemic hit, people stopped playing tennis. Eventually people realized that you could go out and exercise, but early on in the pandemic, it was all about just sitting at home. And so we were getting hit pretty hard, and so we decided, Hey, can we build something that let's users just come into the upside and start using it and pay small amounts of money for? And the product we found the need for was a more data analytics product.

Ankit Gordhandas (10:38):
So think about an operations person who wants to upload their spreadsheet and have some sort of a join of that spreadsheet with some data in Salesforce, and then they want to do some analysis, like maybe some pivot tables, maybe some aggregations, maybe some filtering, and then output the final report into Google Sheet. To do that is cumbersome, and to do that repeatedly is extremely hard. And so we basically built a product that allowed non-technical users, mostly business users to build and automate these data processes. And we started working on it in, I would say, October of 2020 and came out with it soon after. I think we were out with it by the end of the year of 2020. That was our main pivot, and the rationale for that was a lot of business-driven decisions, but the actual pivot and what we built came from users. We talked to a lot of users, and we just decided, okay, this is what users wanted.

Radhika Malik (11:37):
Yeah, that's also interesting. We are also in a similar macro environment today with enterprise budgets being a lot slower, especially for non-core projects, and I'm pretty sure entrepreneurs are going through a similar journey right now, which is, Hey, should we continue to believe in our product and this is what we want to build, and this is what we want to execute, or is it time to pivot and think about a new product? It's also interesting that by pivoting the product, you also probably had to think about a different go-to-market motion. I guess that was the ethos of doing that as well, trying to move away from top-down enterprise sales to more bottom-up product-led related stuff. So how was that retooling your go-to- market motion as well in addition to pivoting the product to do that?

Ankit Gordhandas (12:26):
Yeah, it was fine. We were a small team at the time, those three or four of us, and I was the only go-to-market salesperson at the company. listeners cannot see this, but I just put air quotes up there next to me, saying I was the salesperson or the GTM person. It was not that hard. I think at earlier stages, when there are few, a lot of the G TM just comes from how the product is, and so we were able to quickly adapt to it. We built a product that was geared towards bottom self-serve adoption, and we were able to execute on that pretty well.

Radhika Malik (12:58):
Yeah, I'm guessing that was a nice segue into the next chapter of your life because Zapier, the company that ended up acquiring Intersect Labs, was all about product-led growth, right? That's

Ankit Gordhandas (13:08):
Right.

Radhika Malik (13:09):
Product sub stuff. So how is that journey also deciding, alright, I'm going to now do this at part of a bigger platform versus trying to go the startup route.

Ankit Gordhandas (13:20):
And a lot of that was governed by both or alter some of the macro conditions that were prevailing at the time and then how the company was doing at the time. And then things that were happening in my personal life in terms of the macro, the economy was definitely recovering. Companies were raising money, budgets were still not completely loose. We're talking about middle to end of 2021, so we were still facing some headwinds in terms of adoption and sales. The company was not yet on a path of becoming a unicorn in two or three years by any means. We were growing, we were growing pretty well, but we were not three xing year over year, which is what we should be doing if you want to become a unicorn. On the personal side, we were expecting a baby, and so we had a huddle with the rest of our team and said, okay, let's see if we can get acquired by a bigger company and continue our journey there and have a shot at this again.

Ankit Gordhandas (14:18):
And fortunately, the team responded pretty favorably, and actually, around that time, we got some inbound from another company. And so that's when we started having these conversations internally and eventually we ended up talking to Zapier, which is also a YC company and really enjoyed talking to them. There was one other company that was in the mix until the very end but ultimately decided to go with Zapier because Zapier seemed more, they had this division called Zapier Labs at the time, and it was definitely more focused on innovation, on building new products. And I really enjoyed talking to the founders, specifically Mike Kop and Brian Beck. Mike was the president at the time. Brian is the CTO. And so eventually, we ended up joining Zapier, and it's a decision that I am really grateful for. It's been an amazing journey at Zapier.

Radhika Malik (15:10):
Yeah, no, that's great. And it's also a decision that founders go through after they decide, all right, we're going to try to go do this in a bigger platform. And finding that fit, as you said, is also a hard process. It's critical. And I'm glad you did that intelligence, and you've been here for a bit, so it seems like this is the right decision, but tell us more about your role at Zapier Labs and now where you're at today and what you're doing a little bit more.

Ankit Gordhandas (15:36):
So I joined Zapier Labs in March of 2022, so it's been about a year and eight months, year nine months. Our charter was very simple, go find a need for something that users have that specifically our users have or zapper users, and then build it. And the only requirement is that it should have the capability of becoming a big business in five years. And so we tinkered quite a bit, talked to a lot of users, and that's something by the way, I really respect about the Zapier founders and how that's trickled down to the rest of the company. Talking to users is highly encouraged and not just highly encouraged. It's required. So if you show up to meetings and say, Hey, I think we should build this, or Hey, I think this is how we should design this feature, if you don't have evidence backing, this is what the users want.

Ankit Gordhandas (16:27):
And not through surveys, not through analyzing look data on Looker, purely through conversations you had with customers. If you don't have that kind of data, then you don't have any down-to-stand downs. And that's something I really, really admire about the company. And I started at Zapper Labs. We talked to a bunch of users, we found the need for a couple different things. So started prototyping it, presented it to the company. They were all AI-related. So this was March of 2020 was about six or seven months before, about nine months before chat GPT came out. So AI was not as ubiquitous in the world. It was getting ubiquitous in the tech world. I think we had GPT-3 at the time, and the model was great. We mostly had to use it using APIs. It was a playground, but it was not as good as it is today.

Ankit Gordhandas (17:17):
Definitely not as functional. And so we started building some products using the AI capabilities of OpenAI, got a lot of supporters from OpenAI who at the time were really encouraging companies to adopt LS. Around October, which was about a month before Chad GPT came out, Zapier decided to invest a little bit more in AI stuff. And so two of the founders and a bunch of us came together to start an AI team at Zapier. And we ended up again building a lot of different products. Some of those products got folded into other products. So, for example, there was a bot builder that enabled you to turn your prompt into a user-facing app that was a RAG-related product that allowed you to query or ask questions about your data. And since then, I now work in this division called New products, which is the national evolution of what Labs was. And I specifically work on the newest product in new products. It's not been launched yet. We're trying to figure out if we can build something that's more developer facing. So traditionally, Zapier has been used by non-developers, mostly our business users, and now we're at a point where we're trying to figure out if we can build something that's more developer-facing.

Radhika Malik (18:32):
Yeah, that's some very interesting projects. I guess on the AI side, you said Zapier's ethos is all about talking to users and using those insights to inform the products. And again, as you said, March 2022 ai, it was a buzzword, but it wasn't as buzzy as it is today. Happy after a year of chat, GPT. So were users asking you for AI-related stuff, or was it they were asking you for stuff, and you're like, Hey, this technology actually might be ready enough now that we could use it to solve some of the things that users are asking us for?

Ankit Gordhandas (19:05):
Yeah, the latter. I mean, most of our users had barely heard of ai, I assume. So they'd heard of AI in as much as they maybe read a few tweets here and there, or read a few headlines here and there. Most people didn't know the powers of AI or LLMs at the time. LMS was definitely not a word that most people, Neil, I'd say users were not asking us for AI things, right? Users were giving us their problems, and we decided that AI was a good technology to solve those problems. And if I can have a minor segue here, if this is where that famous quote by Henry Ford comes in where he says, if I asked users what I wanted, I'd be breeding faster horses not building cars. A lot of entrepreneurs and a lot of bigger companies use that quote as evidence to suggest that you should not be talking to your users because you know exactly what they want.

Ankit Gordhandas (19:54):
But in reality, if Henry Ford asked as users and they said they wanted faster horses, their problem was that it was taking them a long time to get from point A to point B. And sure, the solution they were asking for was faster horses, but the problem was getting from point A to point B faster, and Henry Ford figured out that a way to solve that problem was to build cars. And so I think that holds true for almost everything. Users will tell you what their problems are and you got to figure out how to build those problems, but the problems have to come from users.

Radhika Malik (20:27):
Yep, absolutely. And it's also interesting, as you said, the source, the company's also been to embrace this new technology, and enterprises are still trying to figure out how to do that at this point. But it looks like Zapier's, the folks, everybody at Zapier has gone headfirst into trying to say, can we use it internally for our stuff? Can we use it in some of our products for our users also? So tell us about some of the projects going on for both internal and external folks within Zapier and what the different pillars there are of the AI stuff going on.

Ankit Gordhandas (21:04):
Yeah, for sure. I think the founders and the executive team at Zapier early this year, so around January or February, late January, early February, so this was two months after Chad G PT came out, decided that AI was here to stay. AI is powerful, it's only going to get more powerful and we better embrace ai. So we had what we called Code Red, which was a directive for everyone at the company to start using AI to make their workday lives easier, faster, better. This goes back to one of the core tenets of Zapier, which is called Build the Robot, not be their robot. We're always encouraged to automate stuff. So yeah, that was one director and the other director was to quickly figure out how we can use AI to deliver a better experience to users. And out of that came a few different products or features.

Ankit Gordhandas (21:55):
One of them is this way to go from Idea to Zapper in minutes, you basically go to Zapier and traditionally you build Zapier a Zap, which is workflow. Things like Get Triggered every time there's a new email, and then that's a building block, and then you connect the next building block, which is populate a row in a spreadsheet. So with AI, we have a feature that lets you describe that workflow in Words, and Zapper will build a zap for you that you can then edit. Similarly, we have a bunch of our other products like the AI chat bot, which allows users to build user facing bots. A lot of our small business users are building these bots to enable them to embed some sort of q and a bot on their website that users can come in and ask questions of, and it just gives the answer.

Ankit Gordhandas (22:44):
We have other products like newer products like tables and interfaces; tables is our superpower spreadsheet product, and then Interfaces is a website builder that users can use to build user-facing sites, and both of them have a lot of superpowers baked in. As Zapier has embraced AI really well, everyone has encouraged to get chat GPT accounts. I think last I checked the usage and I checked a few months ago, so it's higher since. But the usage of AI and specifically GPT at Zapier is over 50%. And that's across the board, not just engineering products, design teams, but also our support teams heavily use AI to make their lives easier. Our sales teams use AI, and basically everyone uses and is highly encouraged to use AI. Unlike some companies where the director early on was, oh, don't use AI because if you find out, you use AI to automate your jobs, we will fire you because that's cheating, right? Zapier was quite the opposite, and we were industry leaders there. And then the other thing that happened from this embracing AI was our partnership with OpenAI. We were the launch partners for a couple of OpenAI products.

Radhika Malik (23:58):
So it's great to see Zapier going really heads first into it. And you've hinted the fact that Zapier is a very customer-centric company, very user-focused. And so now that fast forward a year and a half year eight months, AI has become ubiquitous and everybody's talking about LLMs and foundation models. How has that changed today? How are people asking you for AI-related stuff today, and has it lived up to people's expectations so far?

Ankit Gordhandas (24:30):
Now that users know what's possible, especially users that play around with chat, GPT, and a lot of our users, while they're not technical, they're very tech-forward. So chat GPT is the perfect product for these kinds of people because they don't know how to write code, but they know how to embrace technology. And so a lot of our users started asking for a way to build chatbots, and initially that was a feature in interfaces, and then it got so successful that they decided to split the feature, turn it into a product, dedicate a full team behind it. And so that's the one example of what users were asking for that we were able to deliver. Another is being able to build workflows or apps from natural languages. That's also something that users were wanting and we built that in terms of whether we're meeting customer demands or we're satisfying customers. I think tech will never do that. I think aspirationally you can only get asymptotically closer and closer, but I think we're pretty close both things to some amazing work by the folks at Zapier and then also the underlying technology itself. So we're getting super close, but there's always edge cases where things don't work as well, but fortunately, users are very understanding.

Radhika Malik (25:49):
And then we need entrepreneurs and innovators like you to think about newer use cases. That's right. Users don't directly ask for as well.

Ankit Gordhandas (25:58):
That's right.

Radhika Malik (25:59):
But how do you also think about success in an AI project? Also, there's a lot of enterprises going through POCs at this point trying to figure out what the ROI is going to be. So whether conversations within the company to say, Hey, is this project is success? What should we really move forward versus not going that route?

Ankit Gordhandas (26:20):
Yeah, I mean, for user-facing things, it's very simple. It's all about adoption and revenue. If users like it early on, it's not even about adoption or revenue; it's more art than science. At very early stages, it's all about, Hey, are we building something that customers tell us? They like Going back to Chatbot, that was a great example. Early on, it was a feature that customers really liked, and then eventually in terms of usage metrics, it got up there, which is when the team decided to split it out and turn it into its own product. I think it really depends. I think the ROC calculations come in, especially when you're spending a lot of money to build something small that moves the needle a little bit. I think in Zapier's case, we had some projects like that, but a lot of our stuff was user-facing. That was going to be a stop change in how users interact with Zapier. And so there's always RSC calculations, but especially this year, I think the team has been pretty good about saying, okay, experiment, experiment, experiment. Don't worry about costs right now because whatever you learn will be great. And I think it's been paying off really handsomely for us.

Radhika Malik (27:33):
Yeah, that's great. And for you, how do you think about making sure your entrepreneurial scratch keeps, you keep scratching that itch, I guess, even at a 10-year-old company and the parallels between innovating at a startup, sub 10 people to a large company, but still trying to be at the forefront of innovation?

Ankit Gordhandas (27:54):
Yeah, I mean, in many ways, I still get to scratch that itch on a daily basis. Like I said, I'm working on this newer product, so for about a month, month and a half, all we were doing, and when I say it was a team of two of us, so all we were doing was talking to users, talking to customers, prototyping things and Figma prototyping things. And then we got to a point where we were reasonably confident, we knew what we wanted to build, and so now we're at a point where we're building it, we're putting in front of users very soon I get to write code, I get to talk to users. I got to think a lot about what the business would look like in one year or five years. And so, in many ways, I do get to scratch that itch on a day-to-day basis, and I am extremely grateful for that. The one great thing about this is that while I do get to scratch that itch, I also don't have to stay up all night trying to figure out if we'll meet payroll next month or if we'll hit our short-term targets. We always have targets, obviously, for any project, but we're not on a very tight timeline right now.

Radhika Malik (28:57):
Are there parts of being a founder that you actively miss?

Ankit Gordhandas (29:02):
Yeah, definitely. I think that whole impact thing is definitely something I miss. I do get to have a large impact at Zapier, and then hopefully, with the product that I'm building right now if it takes off, then it'll be an amazingly large impact because Zapier has a lot of brand recognition, but having that agency, having that impact is something I missed. And then I think there's something to be said about that pressure. I just said it's good not to have that pressure, but I do miss that pressure as well. It does bring out the past in some of us.

Radhika Malik (29:39):
You're obviously enjoying at Zapier, but if the stars align personally, would you consider starting another company?

Ankit Gordhandas (29:48):
Yeah, I think so. I think in a few years I would definitely consider starting a new company, especially if the right idea came by. If the right business need came by, I would definitely consider it. I think I'll be banking on a lot of experience that I had from starting Intersect Labs and then seeing how a well run startup like Zapier runs. I'll be using a lot of that.

Radhika Malik (30:11):
Yeah, those experiences, again, going back to the founder, curious folks, many of whom would be first time founders as now or a few years from now, a second time founder, what would be some lessons that you want to share with folks?

Ankit Gordhandas (30:28):
Yeah, I think this has been something that's been said a million times by other people and probably about 10 times by me on this podcast, but just leaning into that user discovery, I did a lot of that at Intersect, and then when I came to Zapier, I realized that I could have done so much more. And then if a 10 year company can keep contingent do that, a startup founder should always bank on users, not their knowledge of users. So that's number one. I think the other thing is you're a little more mindful of how the company needs to grow, especially around funding and stuff like that. I don't regret any of the funds or any of the money I raised during the first journey, but I think from that experience, I'll be a little more mindful of raising when I absolutely need to, right?

Ankit Gordhandas (31:16):
I'm in a fortunate position now. We built some savings, so if I were to quit my job and start a startup in a couple of years, we probably wouldn't have to rely on VC money to pay my salary for at least a few months, and I wouldn't rush into raising money right away. I would first talk to users, build something, try to make money out of it, and then when it got to a point where more money would fuel more growth, that's when I would raise VC money because as great as VC money is, it does put you on a timeline and it does put you on this path still not allowing you to start from scratch if you wanted to. It does limit you in how much you can experiment. And so that's what I would do for myself. Obviously, every founder is different; every founder's life is different. Every founder's company is different. If you're building a large language model yourself and need to hire a bunch of GPUs for a few months, yeah, sure, go ahead and raise some leasing money. You need that. But for a lot of problems out there, maybe you can just experiment without raising leasing money and actually raise revenue, which is the best signal in terms of the philosophy business. So yeah, that's what I would do.

Radhika Malik (32:29):
So, raise money to service demand that you already have versus trying to figure out if there's actual demand or not. Would that be one way to say,

Ankit Gordhandas (32:38):
Well, kind, not just demand, so raise money when money is a limiting factor. So in my example of building lms, if you need to raise GPUs, money is kind of a limiting factor and you don't know whether there's a demand for it yet. You think you do, but there's no real way to get there until you build those models, and there's no real way to get there until you raise money. So sure, go raise money, right? So yeah, I think if I had to put it ally, raise money when money is a limiting factor. Yeah,

Radhika Malik (33:07):
This is a great conversation. Ankit. I think we talked about a lot of different stuff, so really appreciate your time and also really appreciate the candid insights, and I'm sure the people who are listening as well hopefully got some interesting nuggets out of it. So thanks so much again.

Ankit Gordhandas (33:22):
Yeah, thanks for having me. I had a blast. I'll always have a blast when I talk to you and had a blast again.

Radhika Malik (33:27):
Awesome.

Ronda Scott (33:28):
Thanks for listening to the DTC podcast. If you like what you heard, you know what to do, like, share, and subscribe wherever you get your podcasts.