How do you build products in the middle of an AI tornado, stay ahead of rapidly evolving technologies, and lead teams through uncertainty? Aparna Sinha, SVP of Product at Vercel, shares her playbook for navigating the future of product development in the AI era.
How do you build products in the middle of an AI tornado, stay ahead of rapidly evolving technologies, and lead teams through uncertainty? Aparna Sinha, SVP of Product at Vercel, shares her playbook for navigating the future of product development in the AI era.
Host Francois Ajenstat kicks off the conversation by exploring how Aparna’s unconventional journey has shaped the leadership approach she brings to her daily work at Vercel. Aparna also reflects on the early days of Kubernetes, her time at McKinsey, and what it means to go from building for users to building with users.
You’ll hear how Vercel is redefining developer experience with frameworks like Next.js and tools like v0, and why product agility, not long-term roadmaps, is now the cornerstone of innovation. Francois and Aparna also cover future challenges and opportunities enterprises are facing as a result of unfaltering AI usage.
Whether you’re shipping code in the AI era or leading platform strategy at scale, this episode delivers hard-won lessons on product velocity, strategic clarity, and building what’s next.
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Guest Bio
Aparna Sinha is Senior Vice President of Product at Vercel, where she leads product strategy across performance, developer experience, and AI-driven tooling. With over a decade of experience building developer platforms, she is known for bridging cutting-edge infrastructure with intuitive product design.
Previously, Aparna spent nearly ten years at Google, leading teams behind Google Kubernetes Engine (GKE) and developer tools for Google Cloud. She holds a PhD in Electrical Engineering from Stanford University and advises startups through Pear VC, with a focus on AI/ML, cloud, and dev tools.
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Guest Quote
“My passion is technology, and that often starts with understanding what the fundamental breakthrough is. Once I understand that, it becomes a lot easier to talk to a customer about what it could mean for them. That requires sitting in their shoes, really knowing their environment, their goals, and the pressures they face. Then you marry that technical insight to user need, and that’s where the real value gets created.” – Aparna Sinha
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Time Stamps
00:00 Episode Start
01:50 Aparna's unconventional career path
06:20 Applying lessons from the cloud revolution to today's world
11:45 Finding your passion
16:20 Vercel's journey from Next.js to V0
19:10 Staying in touch with what is now
20:45 Roadmaps are no longer written in pen
23:00 Leapfrogging of models
28:00 Aparna's "Oh Sh*t Moment"
34:00 Advice for building in the AI era
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Links
0:00:00.2 Aparna Sinha: And so we had all started building. And I think over time, we just got used to, okay, every day there's something new. The eyes are always open, the ears are always to the ground. And I think being here in San Francisco in the middle of things, it helps a lot because you are sort of in the middle of shaping the revolution. That's kind of what Vercel's role is.
0:00:27.1 Francois Anjestat: This is Next Gen Builders, the show for the growth and product leaders of tomorrow. Cloud, mobile, SaaS. We've seen the big shifts before, but this one, it's deja vu on fast forward. Of course, we're talking about AI. AI is moving faster than any technology wave we've ever seen. If you blink, you're already behind. On today's episode, we'll be talking about building in the middle of the AI tornado, how you can stay ahead when everything keeps changing, and what it takes to lead through tech's most chaotic moments. Today, we're going to step inside the eye of the storm with someone who's been at the center of multiple tech revolutions. Aparna Sinha is SVP of Product at Vercel. Welcome.
0:01:21.2 Aparna Sinha: Thank you, Francois. I'm really happy to be here. Very excited to speak with you and to talk to other product marketing and business leaders.
0:01:32.0 Francois Anjestat: Awesome. Well, let's start to talk about you. Let's talk about your career because you haven't had the most traditional career. Started, I think, at Stanford, and now you're the SVP of Product at Vercel. Walk us through your journey. How'd you get to where you are today?
0:01:50.3 Aparna Sinha: Yeah. You know, I don't know that there is a traditional career, especially for product managers. Product managers and product leaders come from all kinds of different paths to product management. But I think something that we share in common is a real love for users and for customers, an ability to identify patterns and see what customer needs are, as well as like a business acumen. And so, yeah, along the path of my career, I discovered that those are the things that I was most passionate about, was really strategy, understanding customer segments. I'm really, really deeply driven by technology, particularly cutting edge technology. I tend to run towards the latest deep technical changes, not sort of like small ones, especially high risk ones. So that's just, I guess, how I came to this career.
0:02:54.0 Aparna Sinha: I started at Stanford as an undergrad studying physics, which maybe it's an unusual thing, but I like to understand how things work, particularly in science and engineering. And I found during my studies that actually the most interesting part was how computers work. And so I got involved and I got interested in computer architecture and realized that I guess it's not exactly physics. I moved to electrical engineering. I don't really care what the titles of these things are. And I did my PhD in electrical engineering. I like building things. I actually really like building things with my hands and creating things for people, things that other people will use, which I guess is the definition of an engineer.
0:03:44.7 Francois Anjestat: Absolutely. And is that how you ended up eventually going to NetApp?
0:03:49.4 Aparna Sinha: Yeah. Eventually... Well, actually, after my PhD I worked as a research staff member of technical staff, briefly at Intel and briefly at Agilent. Those were very brief stints, but then I went to McKinsey. McKinsey at the time was hiring what they called advanced PhD candidates or advanced degree candidates into these technical disciplines in their high-tech practice. So, that's how my career started. I, again, wanted to understand not just how products are built, but how do the products, how do they get shaped and how do they make it into the hands of the user and which ones become successful? So, that's just kind of going from building things myself to wanting to build something useful for other people. Realized during my time at Stanford that that is what I wanted to devote my life to, building things that are useful for other people. So, I went to McKinsey to kind of really learn how big companies do that and how do they get these products into the hands of people. Five years at McKinsey, I learned a lot. And I think particularly about myself that I love working with customers. I am an abstract thinker.
0:05:01.1 Aparna Sinha: I like to connect the dots, see the patterns. What are the uses? And McKinsey in general, you want to be a few years ahead of the game. You know, you're doing strategy. You're looking out several years ahead of the game. So at McKinsey, myself and a few others, we started and became part of the cloud computing practice, which at the time was a very emerging area. Most of our clients, which were large enterprises, were not going to any kind of cloud. They had servers, they had data centers, and no one was going to go to the cloud. And so we started this thing called the cloud computing practice, which is like, not only will your software be in the cloud, but your servers will be in the cloud, your entire everything, infrastructure will be in the cloud. And I kind of focused on this interesting layer called platform as a service, or PaaS, which is that you don't need to be, you the user, the customer, will not need to be aware of the servers, you will focus on doing your work, which will be whatever it is, you know, maybe you're a developer and you're trying to deploy an app. So that became my platform, if you will, platform as a service became my platform as a consultant at McKinsey.
0:06:09.0 Francois Anjestat: Are there some frameworks that you learned at McKinsey that you're still applying today? You know, you're in this transition phase where the cloud was being born. And now we're in this new phase where AI is being born. Are the lessons of then of how you approach problems, how you think about problems, still relevant today and still something that guides you today?
0:06:30.5 Aparna Sinha: Yeah. And, you know, Francois, many of your viewers probably have an MBA. I have no MBA. I don't have any business training, actually. I have a PhD in engineering. And so McKinsey was my training ground for it. We call it like an express or working MBA. You know, I learned all my corporate finance, M&A, also strategy, marketing, everything having to do with how to run a company and how to execute a strategy, a long term strategy for a company or even how to create one and how to get alignment and buy in. And most importantly, I think the thing that I learned at McKinsey, which obviously builds on my own talent or my own natural kind of tendencies, is understanding the market, understanding customers, analyzing, finding those trends. And then the analyzing part is obviously very mathematical. And, you know, McKinsey typically people create these models of the future, you know, with sensitivities. And you can do that for pricing. You can do it for market entry, for growth. There's so many different ways in which that applies. It also applies to infrastructure. Like if you're analyzing a trend like movement to cloud there you'll generally analyze the supply chain.
0:07:56.4 Aparna Sinha: So, you'll think about who are the providers and who are the consumers? What is the economics for the consumer that would make them move to this? What are the forces that work for the supplier? How will they need to change both their offering as well as their pricing and packaging, as well as their cost structure in order to go into this new industry structure? And we're, of course, now in the midst of a similar change with AI, where the industry structure is changing and the value structure is changing and the pricing structure is changing. And how, you know, where is the value to the user? Who are the users? What are they going to consume? And who are going to be the players? We're seeing it sort of like play out. And I would say every shift is different, but there are certainly things that you can learn. And there's certainly a toolkit that can be useful.
0:08:44.2 Francois Anjestat: That's fascinating. And when you were in those days of trying to get customers to move to the cloud and deal with an uncertain market or unknown market, they had to take a leap and a leap of faith. I think about those lessons, especially today about AI and, you know, we're building an unknown future which has a lot of possibilities but unclear outcomes yet. How do you drive that change with clients, to adopt these new technologies, to adopt a new way of thinking, right, a new way of operating?
0:09:22.4 Aparna Sinha: Yeah. And for me, because I am a technology leader and my passion is in the technology, and that goes back to sort of like, you know, just my roots, it often starts with understanding the technology first. And so that involves, of course, playing around with the technology and understanding what is the fundamental difference? What is the fundamental breakthrough? And then where is this going to go? Typically, when you're in the early stages of a technological change, you want to assess, like, how big of a shift is this? How long is this going to take? How real is it? What are the big problems that still need to be solved? What's my prognosis for when these problems will be solved and why? Like, why do I think this will happen?
0:10:11.1 Aparna Sinha: Once you understand for yourself, like, what that innovation is, then it's a lot easier to talk to a customer or potential user about what it could mean for them. And of course, that requires having sat in their shoes. And I think my early career at McKinsey and then also my most recent work at Capital One, which I would consider sitting in the user's or customer's shoes, I think it gives me a good sense for the types of stresses, pressures, constraints, and goals that our customers have. So that's really important. I think in any kind of business role is people call it user empathy, but it's really knowledge of the environment of your users and what are their goals, what are they really trying to accomplish. So, then you marry that technical breakthrough and what it could be to the needs of the user.
0:11:12.4 Francois Anjestat: That makes sense. And I love the expression of being in a customer's shoes. And it's one thing to say it, it's a different thing to live it. And you've lived it. And you've also lived different experiences. I know we jumped from McKinsey to Vercel, but in between you've had NetApp, you had Google, Capital One. Tell us maybe a little bit of why you made those changes in your career, what were you trying to learn, experience? And what are some of the big learnings you had through those different experiences?
0:11:43.8 Aparna Sinha: Yeah. So I knew that I didn't want to be an advisor, that my personality is very much a doer, builder, seller, all of those things, an operator. I would call myself an operator. So after five years at McKinsey, I felt like I had gotten all the tooling as well as a good network, as well as some sense of business judgment and all of that. And I knew at that point that I wanted to do product because I love technology and I love customers and I like to build. So it was just the right role for me. And I had never done product management. So people were like, well, do you want to run corporate strategy? I was like, no, I do not want to run strategy. I want to build and ship product and I want to sell it and I want to make a lot of money for the company. That is what I'm driven by. I'm driven by P&L ownership and user satisfaction and getting value to customers. So, it became very clear to me at that time. And yeah, I went to NetApp because it was actually a good company at the time.
0:12:45.7 Aparna Sinha: They were working on private cloud. They wanted me to lead the private cloud manageability, private cloud software. And then I went to, pretty quickly went to Google from there. Google was interesting because they didn't have, or they had a very nascent enterprise offering and I had mostly worked in enterprise. I went to Google also because I didn't want to only do enterprise. I wanted to try consumer and Google was a very good fit for me because it had that deeply technical, probably even more so than NetApp, deeply technical product in engineering culture, which was a super, super good fit for me. And so I stayed there for 10 years. I definitely really fit in, enjoyed. I would say I'm a Googler at heart, now a Xoogler. But yeah, and I've worked in different parts of Google. I started in the consumer group, actually on Android, which is an operating system. I'm still a deeply loyal Android user. And that is just, the whole sort of operating system area is an area that I feel aligned to. It's kind of related to my PhD, how computers work.
0:14:01.3 Aparna Sinha: After a couple of years at Google, I found this other open source project, which was more enterprise oriented, or at the time it was just developer oriented, called Kubernetes, which is also open source. It was a very small team and a very technical team, entirely engineering, but very technical. Most product managers at Google who were maybe consumer product managers would not want to join the Kubernetes team at that time. It's like 2015, 2016, would not want to join the Kubernetes team because it's so hard to pronounce. What is this thing?
0:14:35.1 Francois Anjestat: And Kubernetes at the time was purely open source. There was no commercial model yet for it.
0:14:39.0 Aparna Sinha: It was purely open source. We had started hosting it on GCE, which is the VM-based offering, more for test purposes, more to see like, okay, this thing that we're putting out in the world, does it also work? But not really a commercial angle, not really with the goal of building a business. And so it was the perfect opportunity for me, an operating system, not for your mobile phone, but for the cloud, for everybody, for all enterprises. I was initially the open source product manager, which is like open source product manager. But yeah, I did help to set the open source roadmap, working with many of the community members. I certainly gained from working in an open community, a global community. But my contribution was really to build Google Kubernetes Engine, which is a hosted service of Kubernetes on Google Cloud and have a focused team, both from engineering. I built the product team, built a business, became the P&L owner, built that service into a multi-billion dollar service, the third largest revenue generator for Google Cloud over several years. Yeah. And it was a fantastic experience for me, growth experience for me.
0:15:56.0 Francois Anjestat: So, let's talk about this transition to Vercel and your role there, and maybe stepping back. Can you explain what is Vercel? And some people may have heard of V0, but not really know what V0 is. Give us the overview, give us the pitch of Vercel.
0:16:12.5 Aparna Sinha: Yeah. So Vercel, I first heard of Vercel when I was at Google because if you remember talking about, you know, thinking about platform as a service or PaaS, the idea is that you can just build your application and the infrastructure will just take care of it and you can host it at scale. That's what Vercel is. Vercel is basically framework-defined infrastructure, which is that you have an application, you define what you want the application to do, and we, you know, you see this next logo behind me, Vercel is the creator of Next, which is an open source framework for web applications. It's one of the most popular, it's a standard for web applications that was pioneered by Vercel. It is fully open source, similar to Kubernetes, has an open source community, but a lot of the maintainers are here at Vercel. And the infrastructure that Vercel hosts is in service of this framework and other frameworks. So, Vercel is now a much more general purpose cloud. It is optimized for many different types of web applications, front-end and also back-end.
0:17:27.4 Aparna Sinha: And over the years, so Vercel's been around for, I think, 10 years, although really has been in sort of explosive growth mode for the last five years with the success of Next. And then with Vercel's expansion to cover all frameworks for web applications. And now over the last couple of years, Vercel has expanded into providing the primitives for creating and hosting AI applications, starting with, again, an open source project called the AI SDK, which is an open source SDK for developers to create AI applications, AI applications like AI chatbots and also multi-agent applications. And Vercel excels at easy developer experience, going back to you can just imagine an application and you can just build it. And that's also the heritage from which V0 comes. For those of you that have heard of V0, it's a vibe coding solution also created at Vercel and hosted on this cloud that we're talking about.
0:18:32.5 Francois Anjestat: It's an amazing solution and the unlock that it provides in terms of reducing complexity but also empowering creativity is just incredible. You're building AI solutions. You're building these new frameworks, but you're also building on an environment where it's constantly changing. Models are changing, expectations are changing. The art of the possible seems to be changing every six weeks with new capabilities that are coming up. How do you build in a rapidly changing environment like that?
0:19:11.3 Aparna Sinha: Yeah. And I think that's why you need to partner with a company like Vercel and certainly why I am at Vercel. Actually, when Vercel was founded, I think it was called ZEIT and then maybe now because it's a company that's very much in touch with the zeitgeist or with the current, with what is now, what is coming and what is now. Vercel is also sort of a hotbed of activity for startups, both literally, physically, there's a lot of startups that are in our office day in and day out, but also technically. There's a lot of startups that are hosted on Vercel and that start their life on Vercel. Like with V0, six and a half apps are created every second with V0.
0:20:07.0 Francois Anjestat: Every second?
0:20:07.8 Aparna Sinha: Every second.
0:20:08.8 Francois Anjestat: That's unbelievable.
0:20:11.8 Aparna Sinha: So yeah, and that is a pace that I don't think really existed before vibe coding. And now you don't have to be a developer. You don't have to be on GitHub at all.
0:20:21.9 Francois Anjestat: That's great. So when you're, as a builder and as a leader of teams, does that force you to rethink how you build? Like, can you even do 12 month roadmaps? Can you even plan when everything is moving so fast all around you? Like, how do you manage your teams and innovation in that pipeline?
0:20:45.0 Aparna Sinha: Yeah. There are multiple loops. I mean, and actually I don't think it's any different than even if I think back to my career at McKinsey, you know, you're typically trying to make very, very long term changes, but you are doing it every day. So you're doing small things every day. And it is in an environment that's constantly changing. So you're actually responding to the environment as well. And you're very much aware of what's going on. And I think in AI, that's I certainly have gotten accustomed to it over the last two years. So I left Google in 2022. And I went to a startup accelerator. I started an AI accelerator at Pear VC. And I started working with like 15 different teams of founders, like straight out of college founders, you know, and they had no notion of like 12 year 12 month roadmap or plan or like large teams. You know, you have like teams of two people that would just like overnight build something the next day build something else. And so my whole everything got reset. And it was an AI accelerator at the time when I think GPT 3.5 if you can, if you remember that. So long ago.
0:22:01.8 Francois Anjestat: It's an eternity ago now.
0:22:03.4 Aparna Sinha: Yeah. End of 2022. That's when that had come out. And so we had all started building. And I think over time, we just got used to, okay, every day, there's something new. And we just, the eyes are always open, the ears are always to the ground. And I think being here in San Francisco in the middle of things, working with all the startups, it's where I want to be. And I think it helps a lot because you know, you are sort of in the middle of shaping the revolution. That's kind of what Marcel's role is.
0:22:39.0 Francois Anjestat: It's great. So you mean you brought up GPT 3.5, which wasn't that long ago, but feels like an eternity in this world. Just recently as we're recording this GPT-5 just came out. What's your view on that? Like, how does it change what you build? Does it unlock new possibilities? Or does it make you rethink assumptions of what you've already done?
0:23:03.3 Aparna Sinha: Yeah. Well, I mean, along the way, there were a lot of other things, you know, there were a number of innovations in image models and voice and speech to text. I mean, all of that has gotten very good. But also in code generation, which is perhaps the area of most maximum interest to me, given my background in the developer space at Google and the work that I do here at Vercel, you know, Anthropic has had just the leading model in this space for at least the last year with Sonnet, Claude Sonnet 3.5. And that has just been an enabler for so much of the ecosystem. And that, I think, is what has led to sort of this huge revolution in how engineering is done and how AI assists with engineering, everything from correcting code to writing new code to planning new code to reviewing code to any number of engineering tasks. So, I think that that is the first thing to mention is, and that is also what led to all of the vibe coding and so on. And there are other models, you know, there's models from Gemini. Gemini Code Assist has become like really good at code generation.
0:24:33.8 Aparna Sinha: And so you see it like every few months, you know, there's a model that sort of leapfrogs the previous model. I think it's great. It's like amazing innovation. At Vercel, we have the AI cloud. A big part of that is our AI gateway. So our AI gateway is this service that allows users to connect to over 100 models, including, of course, the Claude Sonnet models, the Gemini. Gemini has a whole family of many different models, Pro and Mini and Nano. There's so many different models. The same with OpenAI. And so the gateway is a way to connect to any of these models. And in fact, one of the value propositions of the gateway is that allows you to connect to the same model from different providers, so that you can have a consistent level of service and you can sort of switch between providers to maintain that consistent level of service. And because we are the creators of V0, we ourselves use these models, you know, Claude, Gemini, OpenAI. We use these models in a vibe coding context, which is it, like I said, six and a half applications per second means it's very scaled usage. And so we have good volume with these providers, with the model providers, which, you know, gives us access to a higher rate limit, which we are able to provide to our users through the gateway.
0:25:57.1 Aparna Sinha: So, the higher SLA through balancing across providers and then the higher rate limits with some of these frontier models. GPT-5 in particular- There were two things that happened. Was it last week or the week before? I can't keep track.
0:26:13.7 Francois Anjestat: We're in a time warp.
0:26:14.8 Aparna Sinha: Yeah. One of the big things that happened was that OpenAI open sourced state-of-the-art cutting edge models, two of them, 20 billion parameter and 120 billion parameter. I think now it's just like, oh yeah, that also happened, but that was like a big deal because they had not open sourced and they are one of the leading, if not the leading provider of these models. So, I think that's a big boon for many enterprises who want to have a model that they host themselves. Of course, we made that available in our gateway within, I think we were already working, OpenAI provided us a little bit of early access there and we had several providers that were part of the gateway, so we were able to launch that. And then a few days, or I think two days later, GPT-5, the three GPT-5 models came out and we were a launch partner with OpenAI. So, Vercel was a launch partner partly because of our expertise with Next. When you are that involved with a framework, these models, they want to be optimized for a framework like Next, and so we've developed evals for Next.js and also for AI SDK, and so we're able to provide feedback to the model providers, any and all of them, whoever wants it, on how well the model performs against these evals.
0:27:45.6 Francois Anjestat: That's great. Looking back at your career, you know, I love asking the Oh shit question or the Oh shit moment in your career. What's an example of one of your experiences where you had this Oh shit moment?
0:28:00.5 Aparna Sinha: Well, I think nowadays it's pretty frequent. And I would say that it's in a positive way, like really good. When you work with good engineers in a highly productive culture, which is what I would say we have at Vercel, we've got a small team relative to someone like a Google or even some of the AI labs. We're about 600 people, growing very fast, but I would consider that still a smaller team where everyone knows each other, and we have this culture that is about shipping. So, if you ever see the logos and the signs on the 101 about Vercel, if it's fast, it must be on Vercel, and you can just ship things. That is not just a logo. That is the way that we live, and I think one of the kind of key moments for me in joining the company was realizing that you can just ship things and that you can work extremely fast and that you can partner with really large companies like OpenAI and Anthropic, and you can enable your users to have access to this technology and that you can actually translate your expertise in, in this case, it's in developer experience and it's in the web.
0:29:41.0 Aparna Sinha: You can actually parlay that into sort of the next frontier of what AI can do for the web. For me, that's a huge, I think, moment of change and moment of excitement, and so I think we're living it right now, and the biggest sort of key moments these days are in being able to enable some of those dreams. A lot of things, at least in my career, are coming together. When I first started in open source, maybe one moment for me was like, oh my God, open source, how am I going to monetize this? Is this going to be possible? Or maybe I will not be able to monetize. Maybe we will not be able to. Maybe, and if we can't, then it won't be a sustainable business, but then we did, and I think I did certainly contribute to how to monetize something that's open source and still keep the soul, still keep the community, still keep it open source, and so now I feel like you can certainly do that in AI. I think I maybe have learned from that a little bit and feel like that that's doable.
0:30:54.6 Aparna Sinha: Same thing with PaaS. It's just been a long journey. Understanding how to get to that point where you make the developer experience really delightful, and now we're here. I don't know. Maybe it's nirvana. Now we have vibe coding. Another thing is, but if we have vibe coding, and this is the new thing. This is the new, actually, maybe another moment is like, oh my God, how do we make this secure? Wow, there's AI, and AI can be really insecure. And that's the benefit of, I think, working with a world-class team here, a small team, but a world-class, tight-knit team that has this mentality of ship and make secure, is some of the work that we've done on security I'm really, really excited about, meaning there are a lot more threats on the web now because of AI. There's a lot more bots on the web, and there's a lot more agents on the web. And so one of the things we're doing is just recognizing what is an agent and recognizing what is a bot and what type of bot is it. Is this a bot that's scraping your website for a particular purpose, like, for example, to allow ChatGPT or Claude to provide answers? Or is this a bot that is scraping your site in order to be able to sell your wares?
0:32:18.6 Aparna Sinha: And again, I love working with customers. We talked about that. That goes back forever. I'm learning from all of the customers about what they want. They want certain bots. They don't want certain bots. And so we're now creating a bot protection service, bot management service, that enables you to know which bots are coming to you and which ones you want, which ones you don't want, which I think is critical, and also recognizing agents. And there are good agents and agents that you may not want. A big question is that if your customers are coming and they're talking to your agent and your agent is using something expensive like OpenAI tokens or Claude tokens, well, if you get hit by a bot that's then using your agent, that could really run up your bill. So, how do we protect our customers from those kinds of bots? And we've got a new technology, new capability called Bot ID that is able to recognize what's a human and what's not a human, which is amazing.
0:33:15.5 Aparna Sinha: How to recognize what's a human, what's not a human. It's very sophisticated. It's a lot of machine learning. And so I'm sort of like in this rebirth, maybe, phase of my life where all this new technology is needed to make the new technology safe and possible. And I feel like a child again, being able to be part of this and study it and understand it. And again, like find where it could fit with customer needs.
0:33:44.2 Francois Anjestat: I love it. I mean, you hear it in your voice, the passion, the excitement. You're making me dream and you're making me inspired, which is fantastic.
0:33:51.9 Aparna Sinha: It's a good time.
0:33:53.4 Francois Anjestat: Absolutely.
0:33:53.9 Aparna Sinha: I think it's a good time for everyone.
0:33:55.9 Francois Anjestat: So, just as we wrap up, what is one piece of advice you can give to aspiring builders on how to succeed in the AI era?
0:34:06.8 Aparna Sinha: One piece of advice. There is so much new enabling technology that is so accessible in the AI era. I think it is important for builders to use this technology and also find ways of building with it. I think it has never been easier to be a builder because you can literally have the AI teach you how to build in whatever language or whatever format you may want. Beyond that, I would just say follow your heart and follow your curiosity. Whatever speaks to you, that's probably the right direction to pursue.
0:34:55.3 Francois Anjestat: Oh, that is great advice. And Aparna, thank you so much for joining us. And thank you all for listening to NextGen Builders and look out for our next episode wherever you get your podcasts. And please don't forget to subscribe.