Dhruv Sharma: Hey there, listeners. It's Wednesday, October 7. My name is Dhruv. This is TON 148. We're streaming live. Our guest is here already, and today we're gonna dive straight in. Our first guest today is Swapnil Vats, who's, who's working on something called Luna. Swapnil, welcome to the show.

Swapnil Vats (Luna): Thank you so much, sir. Thanks for inviting. Very excited to be here.

Dhruv Sharma: Likewise. Likewise. Let's get things going. So, before we talk about Luna, Swapnil, what was this thing called SocialBoat, and how did it turn into, you know, what you're doing right now?

Swapnil Vats (Luna): So, back in COVID, you know, I was... There was something that I was very passionate about for some time. I was building time to understand how do we make the conversations around menstrual health more, dinner table conversations. I think that's something that we've always avoided. So my sister's a gynecologist. My cofounder's mother was a yoga trainer. So it's something we've, you know, we were interacting about. We started building, solutions around this, and we started building an AI model that helped women predict their next menstrual event better and also to understand. And this is... And we're talking about the pre-GPT era. So that is what SocialBoat was. SocialBoat was an, AI assistant that helped women manage the menstrual conditions better. That company got acquired by Noise, and, and then with Amit Khatri, who's the cofounder at Noise, we... Who want... Was trying to get into a space, into deep health tech. We collaborated, and we started what we now know as Luna.

Dhruv Sharma: Fantastic. We haven't hosted Amit yet, but his brother Gaurav was, a guest in, I think, one of our first 10 streams. He's also an offline member. Great. So so that establishes the the connection between Noise and Luna.

Swapnil Vats (Luna): Yes.

Dhruv Sharma: What is Luna doing today?

Swapnil Vats (Luna): So Luna is building an operating system for your body. What that essentially means is we're building a health intelligence that reads every possible parameter that is important for your body and then converts them into actionable guidance for you. So we we are able to read information about your body by variables that we're making ourselves. This is the Luna Band and the Luna Ring. And we've got these health models that run on top of that that merge with every other possible data source and then give you that advice. So if I have to, you know, maybe just state in a single line, Luna is trying to be the calendar for your body. So if you if you had to have a Google Calendar for your own body, that's what Luna would look like.

Dhruv Sharma: Love it. I'm assuming you're wearing your ring and you're wearing a band.

Swapnil Vats (Luna): Yes. Yes. Can we get a smoother look? Maybe just Yes. Yes. Yeah. So that's a brand. Yeah.

Dhruv Sharma: That's a fancy color too.

Swapnil Vats (Luna): And that's how the ring looks like. Not my wedding ring, but the other ring.

Dhruv Sharma: That was amazing. Yeah. The the one that's black and in

Swapnil Vats (Luna): the red.

Dhruv Sharma: The one that's......

Swapnil Vats (Luna): The casting. Yes.

Dhruv Sharma: Tell us more about what's in the inside of these, these devices, like the hardware. Let's not get too technical, but, you know, for those of us who are curious, like, what's inside of them?

Swapnil Vats (Luna): So, any body measurement device, largely. This could be... I mean, this could be different form factors that you could be wearing them on the wrist, on the finger, maybe on any other part of the body. And we've seen a couple of these devices would typically have a PPG sensor, which is essentially a light sensor that would emit light and get light back. And then algorithms would help you identify what is heart rate, what is blood oxygen, those measurements out of those readings. Now these lights and diodes essentially would be custom to each product that is making it, what is the layout of these products. Then they would have accelerometers. Accelerometers and, typically, sensor that help identify motion. Now they could be... These could be of different access types. Now there's a three axis versus a six axis versus a nine axis... Accelerometer. It would essentially help you measure more, you know, axis of motion and then essentially trying to identify what we understand as steps, what we understand as a fast motion, what we derive as a running or, you know, something... Some of the workout largely. So everything else is a derivation of these core metrics, and then some variables would have temperature sensors, which are specialized sensors in nature. So device like ours would have these temperature sensors, skin temperature sensors, or core body temperature sensors largely. And then anything else beyond it would be custom to a device. Like, you could be possibly adding a microphone to it or, you know, whatever. Yes.

Dhruv Sharma: And so... But but as you said, you guys are building pretty much a calendar for the body. And so that would require some sense of data, which, of course, the hardware fixes. But so lifestyle data. So how are people putting their lifestyle data into their calendar?

Swapnil Vats (Luna): Perfect. So that's the interesting bit because that's what... So the first part, the device, I think, variables have been doing it for some time now. We've... We have that information around us. But the real intelligence would be... Or the real value would be, what do we do with that data? And that's what... That's a core problem Luna looked out to solve. So, the entire Luna experience says that I want to be... Luna needs to be able to tell you what is it that you need to do today to achieve whatever your health goals are, and this could be personalized to all of us. Now what LifeOS is essentially doing is trying to understand other data parameters about your body. We are doing integrations with every possible data. That could be medical data. That could be your hospital records. That could be your period tracking app. So we're building a connection layer between each of these applications. This is similar to how you today see a cloud or a GPT integrating to different data sources. We're building that connection layer, and we're also building a layer for the user to talk to it too much. So we call ourselves a voice first health intelligence, which means we're building methods for user to talk throughout the day to your device. You could simply say, I just had a cup of coffee and your product and your... Or sensor would know that this heart rate reading is is essentially associated with a certain cup of caffeine that we have done. And That makes sense. All the compounding happens once that information comes into a single source.

Dhruv Sharma: Fantastic. And, do you typically recommend that, Luna users wear both of, both of the variables, or what do you call them, by the way? What are the... What is it?

Swapnil Vats (Luna): We we we still... I think that's that's something we're still figuring out. We could call them Lunars. We call them, you know, Luna users. So I think that's a marketing speaker still figuring out.

Dhruv Sharma: Maybe we can just put a word out there into you and this now will also help us with... Would love that.... Recall if they're they're getting Luna. But but, no, the question was, do you recommend that they get both or either one of them?

Swapnil Vats (Luna): Either one of them. Either one of them is right. We, of course, designed the system for both devices to work as well because there could be people who, you know, at different times of the day, prefer different variable. But for us, the device is a personal preference of how a user wants to wear, and it is the intelligence that is the more critical part. So it is okay you can use any of these devices, lastly. I I... I'm a tester, so I possibly... There are days when I move out of limbs and hands to wear as much as I can, but, the typical user should wear only one. Mhmm.

Dhruv Sharma: Alright. So, Swapnil, as I was telling you before we went live, I'm not much of a variable user myself. In fact, I've only ever had two in life, for anyone who cares to know. The first one was, you know, when when when we still had iPods, there used to be a version of it called the iPods Mini, which had a Nike plus app. And Nike would give you the sensor, which I think was also a gyro that you could insert in your, you know, your shoe. And you'd go for a run and the data would show up and it was half accurate, half inaccurate. But those were really early days. The only other one I had was, I think, a modest Fitbit Fitbit. I'm sorry. Yes. 2016, '17, etcetera. So it's been a while. But what I hear from my friends, including Otsev, you know, people who love variables and variables are very much part of their lives is that, dude, sometimes you just get too much information. Like, there's clutter. There's all of this information. You don't even know what to do with it.

Swapnil Vats (Luna): Yes.

Dhruv Sharma: And so what are you doing? So the sense I got from you is, like, LifeOS is kind of like your personalized coach. Yes. What are you guys doing to kind of, you know, to to work around that overwhelming the user with unnecessary data and

Swapnil Vats (Luna): clutter kind of... Perfect. Thanks. You asked this, Dhruv, because, this has been one of the most critical pieces we knew we wanted to solve. You know? There's just so much data today that these wearables capturing. Honestly, we don't even show half of the data that the device is possible capturing because there's not much you do with that quantified, volume. What Luna is trying to do is Luna is trying to become, as you rightly said, a coach in your life, largely. And a coach is supposed to do all the complex computation behind the scenes and then give you those finite actionables to do. And that is what Luna is trying to get towards. We believe, intelligence has reached a space where that compounding of information is possible. And that's what... So we are we are running models on the device. We're running models on the cloud. We take a lot of users' real time context. And the and the purpose that LifeOS solves on the mobile application is a simple set of three to four actionable set you need to follow in a day, and you should be on your path largely. So even if somebody, like you rightly said... And I'd love to, you know, maybe send you a product of ours and then see if we are able to, convert you to somebody who appreciates or... And loves these products. But, but the idea is that even our devices, if you look at the choice of the form factors also we are doing, are not interactive in nature, largely. They don't have screens. They don't have anything else because the core idea is that we have enough information about you. Can we just give you three or four simple steps that help you, just achieve what you're looking to achieve?

Dhruv Sharma: Yeah. There's some questions coming in. Unfortunately, they name name other people in the business. We don't typically Okay. Bring bring those kind of questions up. But I'm gonna maybe paraphrase one of those questions, Swapnil, which is what can loo... Luna help users understand about their health that they don't know already? And if you've anecdotes from, you know, speaking with, users, I think that would be just lovely.

Swapnil Vats (Luna): I'll give you a very relevant example. I think before we started, we're talking about, do I still have jet lag from the travel that we did last week? Right? So that's... These are kind of things that Luna has already evolved into words, which is how do we make an understanding that, hey, Dhruv, Your supplements or your magnesium or your... Whatever you're eating is not impacting your recovery as much as it should because you're probably having a caffeine for 6PM in the evening. So how do we create first these correlations between different activities that you're doing as an individual and then move towards creating positions between these, different variables. So that's a code that Luna is doing different. And an example of this could be, we should be, after fourteen to fifteen days of usage of of usage by a user, be able to tell the user that, hey. You know, the caffeine that you're having is probably negating the impact of the magnesium supplement you're having in your body. So we should be able to do these correlations after fifteen days of usage. And, of course, provided the user gives us this information. And, so that's where the magic of Luna would come in. In a longer term in a longer term, we see ourselves being able to tell you in a very, very quantified manner that if you choose to achieve a, b, and c goal in your life... Now this goal could be losing weight, sleeping better, reducing anxiety, training for a HYROX. This could be any of these goals. Right? This is exactly the probability and the three or four things you need to do to achieve this, lastly. And we should be able to do it in a very, very quantified level.

Dhruv Sharma: Tell me, has HYROX been good for your business?

Swapnil Vats (Luna): It has been good. Amit, is, is what we internally call a HYROX. Both, He competes? Yes. He does. He does. I I... As in... I... I've also competed in HYROX last, but Amit has been now on his second HYROX and coming to his third. So... Yes.

Dhruv Sharma: Yeah. Fantastic. No. No. I I get it. Like, I think this cause and effect attribution is so important because very often, you know, you'll you'll actually be under some sort of physiological strain and think it's and think it's emotional stress or the other way around. And, and, you know, one of the other things I hear from friends who who are big into variables, Swapnil, is that, you know, like, I think I slept okay, but my variable says that I did not. And so, again, there's that gap between objective sensor data and then subjective feeling. And

Swapnil Vats (Luna): Sure.

Dhruv Sharma: How do you guys think about, you know, that gap and closing that gap?

Swapnil Vats (Luna): So I think that gap is also because we've made variables to data first. We've ended up showing the user that, hey. Your recovery is low, which is a derived algorithm somebody has made. Right? Your recovery is low. And I might not feel that because there could be 100 more variables impacting our field right now, touch shield. Right? And it could be the opposite as well, not just when a wearable is saying low versus... It could even be when somebody is saying high. So what we are moving towards, as a product is that lesser of your recovery sales low, do this. More of, hey, Swapnil. We've understood your body. The... Try to do these two things today. Try not to do these two things today, which in my understanding are more actionable for an end user and lesser about, you know, oh my god. There's a there's a statistic called HRV, and it is five points down. Right? What do I do about it today? So lesser about those. And maybe maybe more correlation saying you had a couple of drinks last night, maybe increase your hydration today. Yeah. Which is a which is a little bit more actionable which is a more actionable, aspect today. And it does not need to be the end of the world today. Yes.

Dhruv Sharma: Yeah. Yeah. Great. Tell us more about the team, Swapnil, besides you. Who there is? I don't know. Who's there in Luna? Like Perfect.

Swapnil Vats (Luna): So you already know, Amit, who's who's sort of a living legend in the variable space. So, Amit is there, of course. We've built a a strong team of ex founders. We have... I'll start... Our hardware and R&D team come from ex-Suunto. We've acquired them from, you know, sort of, you know... The whole team comes from the background of Suunto. Suunto is a Finnish wearable, very well known among the swimming and the Olympian community. Mhmm. So that's where our R&D comes from. We've got founders who are... Who've scaled companies throughout who now are leading our marketing functions. So there's Ashna, who's the next founder, successful founder. Now it's a partnership in marketing functions. There's, there's folks like Lusifo who've led R&D at Suunto. There's folks like Hamish who are, you know, running our product and R&D. So we've built, there's folks like Karan who's essentially sold a company previously, and has now joined us to lead our engineering. So, a strong bunch of folks. Then we're also working with a lot of these athletes across the world. We've got a community of close to 400 athletes who, out of whom, they could be... The 30 or 35 of them are ex Olympic athletes who across the world today are testing our products and giving us that feedback. So that's that's how the team looks like.

Dhruv Sharma: Do you guys go after big, big name celebrity athletes only or or, or do you go after, you know, more nuts and bolts, meats and meat and potatoes kind of athletes as well? Like

Swapnil Vats (Luna): I think I think the, the latter because, we are at a stage where we don't really... Are not really seeking that name credibility. We're seeking active feedback and participation. So I think athletes who would value, give us feedback, become a part of a team. So, I mean, in a nutshell, if they're part of a Slack channels, they are the athletes that we're working with.

Dhruv Sharma: Yeah. That's that's great. I... You know, we both live in, Gurugram, Swapnil. I I know a place or two where we might go find the athletes. Let's, pick up some samples one day and and work with them.

Swapnil Vats (Luna): Absolutely. Absolutely.

Dhruv Sharma: Absolutely. Preparing for events and get get their feedback. Thank you so much for joining us, Swapnil. This was a very, very cool, session. Thank you.

Swapnil Vats (Luna): Thank you so much. Thank you for your time. Thanks. Hey,. All the best.

Dhruv Sharma: Alright. Listeners, that was Swapnil from, Luna, the wearables company. I'm very excited for our next guest who is Mona, who is... Who... Who's building Kello now. Let's welcome Mona to The Offline Network. Mona, it's, great to have you with us.

Mona Gandhi (Kello): Thank you for having me. Super excited to be here.

Dhruv Sharma: No. Absolutely our pleasure. So, Mona, we're gonna be talking a lot about Kello, but you've also had a very interesting journey that's led up to this point. We wanna hear it in your words.

Mona Gandhi (Kello): Sure. My name is Mona, and I am the founder of Kello. Like Dhruv introduced me. Prior to this, I also founded another company called Upraised. Upraised was basically an upskilling platform to help people make career transitions. So think of it as like a MBA alternative without having to not have a paycheck for two years, but finding a career that your strengths naturally lie in rather than just whatever your degree, made you have your first job. You know, so that's basically what Upraised did. And then prior to that, I actually worked at Airbnb for a little over, seven and a half years. I started, at Airbnb, as the first female engineer when the company was, you know, double digits. And then I kind of, saw the journey of, scaling Airbnb from that to 3,000 plus folks, and becoming sort of a global brand. I'd say a lot of, what I learned in terms of, product design, culture, it all kinda comes from this Airbnb school of, thought. And then before that, I worked at a few other, startups. So I've kinda worked in Silicon Valley for about ten years, and I moved, to India in 2015. So I've had ten years of experience in the valley, and then now ten years in in India. So, it's it's it's been a fun ride.

Dhruv Sharma: I'm sure it has. And we we... K. We... We're gonna talk to you also at Airbnb and, you know, your time living in the valley, but perhaps towards towards the end of the segment, tell us more about Kello. What is Kello? How does it work?

Mona Gandhi (Kello): Sure. Kello helps companies to start interviewing great people within seven days. That's what we focus on. And, we wanna help you, do that no matter how niche the road is. And our secret sauce is intelligence that we built, to understand people's spikes, their capability, their motivation, their career trajectory, and then understanding where is five. Right? And we build this intelligence for, over 850,000,000 people globally. So we actually support, about 80 companies, across the globe, to help them hire everything from a, head of applied AI for a foundation lab to, head of product at at a telecom company, to a country manager for an FMCG product, to sales and GTM talent, to even a junior front end engineer, to an intern. Right? So we've done across the board. And the reason we've been able to do that is because, you know, LLMs for the very first time give us the ability to contextualize information at scale. And so we wanna kinda use the power of AI to enable, in the world... In in this doomsday scenario that everyone's talking about AI taking jobs, we actually wanna make, you know, leverage AI to build more fulfilling careers, to find you the job that you would love, the work that you actually deserve, and and where you'd scale. So that's what Kello does. And even... You know, in in short, you can think of Kello as sort of a Claude Code for recruiters. Right? If it's like a copilot, it works at your command. So you kind of give it a JD, and Kello does all the leg work. So it, it... You tell them, like, what to hire for. Kello goes and does the diligence across eight, 15,000,000 people, finds you the relevant 20 you should speak to so that you can spend all your time and your human judgment on people that matter. Once you say yes to a few folks that you wanna interview, Kello goes ahead and schedules it. And then if you give feedback that out of these 20, I don't like these six, seven folks, because of x, y, z reason. Kello kinda learns and gets better. So it kinda works with you. So, yeah, that's what Kello does.

Dhruv Sharma: Yeah. Wow. That was, that was quite the explanation, Mona. And I wanna ask you. So, Brian at Airbnb talks about magical trips that are 10 on 10 user experience. Right? So what is the 10 on 10 experience for the candidate, and what is the 10 on 10 experience for the recruiter?

Mona Gandhi (Kello): Yeah. It's a great question. I think a 10 on 10 experience, for a candidate is, to... You know, there is... I think for the very first time, at least in my professional career, I feel more people, more companies in respect to whether they are early stage startups or their MNCs, or even government organizations or sovereign AI companies are, looking for people with capability, and agency and drive more than credentials. And I think the 10 on 10 experience for a company is to be able to find people that, that match that capability, that agency, that AI nativeness, without having to rely on, you know, tools that are tied to keywords and Boolean searches. Right? That's where credentials basically shine. So the 10 or 10 experience on the company is to find really that hungry talent that can... You know, that that 100 x person that has the potential to become thousand x. And on the candidate side, it's actually the same, which is, you know, everyone... For everyone, there's a relevant opportunity. How do we help you discover that? And, unfortunately, in the past, it's always kind of been, oh, I didn't go to this college, so I never got the opportunity. My campus placement was x, y, and z. But now, AI is a great equalizer. If you have the drive, if you have the capability, Kello can help you get discovered.

Dhruv Sharma: Yeah. I agree. I mean, for anyone who doesn't wanna be beholden to their past, like, proof of work is is the one thing they have going for

Mona Gandhi (Kello): their day. Absolutely.

Dhruv Sharma: How do you bring that into the context of the work you're doing at Kello?

Mona Gandhi (Kello): Yeah. So, we essentially, one of the core, things that we focus on is to, use a lot of elements to contextualize everything, about your career and about yourself. So we want to understand three sixty about you. We use all the public information and the breadcrumbs you've left over the Internet, over the years to kinda stitch this together. So for example, let's take me as an example. I worked at Airbnb and that's interesting. But actually, what's more interesting is overlaying my experience with the context of the company. So what stage was Airbnb at? How was the... How big was the team size? Who did I work with? What kind of problems did I solve? What kind of geography did I focus on? And how did I scale as I went through that career journey of seven years at Airbnb? Right? And so, now, you can understand me far more deeply than, just my resume stating that this was my title at Airbnb. And, and I think that is truly what Kello focuses on. We care a lot about, making sure that, you know, whatever we understand about people across the Internet, we are 100% sure about it. So, for example, you know, Mona on Twitter is very different than, you know, the username is different than Mona on LinkedIn is different than Mona on GitHub is different than Mona on Hugging Face, for example. We care a lot about identity resolution, so we will not... It's okay to restart on a few signals, but whatever we know about a person, we are a 100% sure that this is the same individual and we are kind of, you know, highlighting their strengths. So Yeah. So that's the first thing that kinda Kello does, the magic of. And I think the second thing that that Kello does is at the intersection of understanding the person and understanding the company. So every company has, you you know, it's important to understand what great looks like at that company and whether you would be a fit there. This has nothing to do with your capabilities. It's actually a matchmaking process. Right? And so, understanding what great looks like at that company is also what Kello spends a lot of time, in. And it can do that because it has a talent graph to understand the trajectories of people that have scaled at these companies, people that have gotten, you know, thrived versus people that really struggled. What kind of, you know, patterns can be observed? What kind of behaviors can we can we can we think about? What does a company, you know, actually, celebrate versus what does it not celebrate, things like that. Right? The softer things. And so, essentially, if I were to kinda summarize in one line, I think what is a 10 on 10 experience for a candidate is, you know, imagine, Kello discovering jobs for you even without you applying. What is a 10 on 10 experience for a company is I tell you this is my requirement, and you do the job of... Imagine having a recruiter that has been with the company from the very beginning till today and understands the entirety of the context and can help you find your bar raisers without having to deal with a lot of other noise. So I'm only focusing on the people that truly can convert and and kind of build a higher talent bar inside my company.

Dhruv Sharma: Got it. Yeah. You know, one of the ironies of of the hiring process, Mona, is that very often the most, highly sought after candidates aren't even on the job market, and you have to passively

Mona Gandhi (Kello): That's right. Team.

Dhruv Sharma: And you guys have a massive, you know, I don't wanna call it a database because it's a lot more... But they're just... You've seen a lot of people, and I'm sure you've screened a lot of them passively. How have you gone about the...

Mona Gandhi (Kello): So I think, look, you know, you're absolutely right. The best people are, at least, everybody is trying to strive to, you know, punch about their weight. Right? So whoever you are looking for is probably not the one that's deeply interested in working with you. You have to kind of pitch it to them, and you're gonna have to get them interested. But I think the most important thing that Kello does is is personalization because of the understanding we have on both sides. We understand the person really well. We understand what motivates them. For some people, it is the size of the company. For some people, it's who they're gonna work with. For some people, it's the problem statement. For some people, it's, the timing and and salary and other kind of preferences. Because we understand this deeply, we know, if that company is going to be able to fulfill that need or not. And more likely, and if it's going to be able to fulfill, more likely you are open to at least exploring that opportunity. And so because we've taken this kind of personalized point of view on both the company side and with... And the candidate side, we actually have a pretty high kind of success rate in connecting folks. So, we first, share a list of 20 folks with you that would be good fits for your for your role. Let's say you pick 10. We've, on average, been able to connect about six people to move to the interview process from those 10, which is a very, very high percentage if you consider that these are people that have not actively applied for the job. These are people that we've pitched and gotten them interested in having a conversation with you.

Dhruv Sharma: Yeah. Can you also maybe just say a word about the impact AI has had on recruitment economics, Mona? Because on your website, I I I saw that you guys are also being very creative about that whole thing.

Mona Gandhi (Kello): Yeah. Actually, you know, one of the things that has led to, to, adoption of our product, at the speed, at which it has happened is, is also the innovation on the business side. Because if you're gonna think about the the the current models of, you know, kind of, recruiting agencies. Right? I think the challenge is there's a lot of misaligned incentives. Firstly, like, you know, your payout only happens if the candidate gets hired. So there's overselling of candidates sometimes. They don't tend to work out, and then there's all these other guarantees of replacement, etcetera, etcetera. But then on the other side, because it's a percentage of CTC, they also end up pushing you, candidates which may or may not kind of be the right, budget range for yourself. Right? And so what we've kind of said is, actually, AI affords us, efficiency, and we're gonna pass that efficiency, that cost efficiency onto the customer. So we actually have a flat pricing, which is, you... We think of us as, your co pilot. Like I said, it's... You know, think of us as your, as your recruiting partner, not a replacement to your recruiter. And so the most important thing to internalize is that recruiting is still a very human process. You need humans to complete the process. It cannot be finished through AI from start to end. But there are parts of the recruiting process that can be delegated to AI, and AI can do a far better job far more diligently. That's where Kello comes in. So, we still work recruiting... Recruiters are needed to kinda close the role. What we do is we help you source, which is where recruiters spend a lot of time. But once we... Because we do the sourcing, the recruiter can actually spend time on on on having conversations with meaningful people and take them to the interview process. And so, because of this efficiency created by AI, we're able to offer a fairly low cost. Like, it's like, you know, for India, for example, today, at an introductory price, we are doing 50,000 rupees, a role. Irrespective of whether you're hiring for a 10 lakh, a year salary or you're hiring for a 5 crore a year salary. Right? And so if you think about that in the context of a recruiting agency, which costs you anywhere between eight to 25% of your CTC, that's that's a significant, you know, benefit in terms of cost for companies.

Dhruv Sharma: Yeah. You've got to think of thirteen months and not twelve months, of of phases. And I'm so glad you, you got to the second part of your answer, Mona, because if I were a professional recruiter and only heard the first part of I would have freaked out. Like, I'm totally out of a job. But glad to hear that no human...

Mona Gandhi (Kello): No. Actually, no. I actually think that recruiters are absolutely near because, you know, this is a this is a this is a human process. It's... Recruiting is actually uniquely human. Yeah. And, there is no candidate who's going to just talk to a bot and say I'm gonna join this company.

Dhruv Sharma: Yeah. Honestly, I'm a recruitment agency for agents, which is also work that some other people might be doing. Yes. Yes. Can you tell us more about what you're seeing? And and now I'm actually asking not just here in India, but even in the Valley. How are the recruiter or recruitment suburbs within the large tech companies changing from the, you know, from the inside?

Mona Gandhi (Kello): I think, I think there are a few trends that we're starting to see. I think, you know, all of us are kind of... People who are doing startups, I think the common fear everyone has is, you know, at some point, Anthropic and OpenAI are going to eat up their business. Right? They'll start doing what you're doing. And I honestly think that the own... That one of the most, important defensible mode, is actually your team. So the... And the the quality of that, quality of, talent bar that you have. Right? And talent acquisition is actually a real exist strategy these days as much as an IPOs. Like, if you think about a cursor or a or a poolside, you know, these are billion... Multibillion dollar acquisitions just for, like, you know, pretty pretty small tight teams. So as a consequence, what has happened is I think, company, companies are spending a lot more time, in understanding the capability and the agencies of the people that they want to hire because they wanna hire less. They wanna do more with less. They're picky. They're selective. And the way we're seeing recruiting teams change is, it's actually changing... Shifting from generalized skill to specialized skill. So if a company, let's say, is in deep tech, I think they want recruiters who have experience just hiring for deep tech talent rather than just being an overall general generalist recruiter. And so I think... And and we're seeing this specialized talent need not just for recruiting. It's actually across the board. More and more companies want experts because they assume that experts will bring the thought process and the creativity and the problem solving, But the execution can still happen with all the AI tools that we have. Right? So we kinda delegate that. So I think one is specialized talent is getting appreciated a lot more across the board. Recruiting is definitely one of those key ones, because the the delta between a hiring manager telling you the requirements and, as a recruiter, you're understanding the domain expertise is, like, I think that needs to be super tight. I think the second thing, very interesting, kind of thing that we've noticed, is, the number of people who label themselves as having, currently working at a stealth company, that number has actually doubled every year since 2023. Yeah. So, and I I......

Dhruv Sharma: Something great or just terrible? Very often that's

Mona Gandhi (Kello): Yeah. I don't... I I I think it's, I I think time will tell. But I think what is happening is I think the the underlying, my my thesis on the underlying, reason on why this is happening is because I think there's no place left that offers stability, including governments. Right? Like, everyone is kinda churning low performing talent. And so this concept that there is lower risk if you go to a larger company has actually shifted. There is risk everywhere if you don't adapt and learn. And so, people who have high agency are able to learn really fast are becoming extremely confident to be able to actually start something of their own and kind of, you know, and and and solve the problems that they've experienced themselves. And so the number of people who are in... Like, if you go to LinkedIn and search for stealth, that number has actually doubled every single year since 2023. India is at seven x of what it used to be in '24. So, so, you know, that's another interesting trend, which is, I think, smaller teams, more hands on teams, people going deep on specific problems is what we're seeing across the board. I think the third thing that we're also kind of, seeing is, I think you touched upon it earlier, is I think every single company that is hiring cares way, way, way, way more about proof of work and way, way, way more about the thought process behind the proof of work rather than the solution itself compared to anything I have seen in my professional career. So Yeah. It's very, you know, for the very first time, I feel like people with years of experience actually have a, you know, they're actually, they're more at risk and have lower leverage because they actually have to unlearn and then learn.

Dhruv Sharma: Yeah.

Mona Gandhi (Kello): The younger folks are just learning, so they have no cycle of unlearning. And so, they're also less fearless. They put their work out. They don't think too much. They... They're able to communicate way better. And as a consequence, that discovery of talent happens so much faster than folks in their forties trying to do, like, a side project or, like, you know, things like that.

Dhruv Sharma: I I wanted to ask another... For quick follow-up, with respect to proof of work, and I'll ask this on behalf of technical talent. What if my best work is sitting inside of a private repo and Kello doesn't even have access to it? Then what happens?

Mona Gandhi (Kello): Yeah. So look, you know, it's a challenge. We can't analyze the the content of the repos, but we can still understand the commit frequency. We can understand, you know, what is your consistency, what kind of problem are you solving. Typically, a readme is public. We can try to understand, you know, aspects of it, but we obviously cannot understand, like, the quality of code, for example. So so that is a limitation we have. But we try to we try to cover that up by looking for other signals like, you know, are you actively contributing to, let's say, open source? Are you, contributing to, you know, are you contributing to things like Hugging Face? Have you, you know, done any work there? Have you written any research papers? Have you shared any thought leadership material on Substack or Medium or things like that? Ultimately, look, the primary goal is not to invade anybody's privacy and kinda go sneaking on you. The goal is, if you had the intention of putting it out in the public, we wanna make sure that we make it shine. And we we we include that in understanding who you are as a person and what you what you desire rather than just kind of relying on a one pager that you wrote at some point in time for a specific job. Yeah.

Dhruv Sharma: You know, I wanna turn attention to your cofounder also who's not with us on on stream, but, I believe when you were building up praise, he was building Ambition Box. And...

Mona Gandhi (Kello): Yeah. He actually built Ambition Box, sooner. So he, built Ambition Box, right after, right, he actually dropped out of Adi Madras and kinda built that out. That got acquired by Noffi, and then he and I worked together at Upraised for three years.

Dhruv Sharma: Right.

Mona Gandhi (Kello): And that's how we got to know each other. But, yeah. I mean, this is a testament to our obsession about this problem. We've both been thinking about this for a little over a decade. It is not an overnight thought. I don't think recruiting is a new problem. I think the only reason, it's exciting at this moment in time in history is because of the power of LLMs. We didn't have LLMs to be able to contextualize information and understand people at scale. We can do that now. And so a company as small as ours, can understand and build, intelligence on eight, 15,000,000 people globally. That is not... That that is the power of elements. And we wanna use AI for making sure that it's actually, you know, getting you the best opportunity where you would love to work rather than kind of, you know, always be scared that the scared and stress that he has on their placement.

Dhruv Sharma: Yeah. One one of the questions I was gonna ask you though is, you know, as successful returning founders, is there something that's gotten easier? And, you know, what's what's always stuff when you're doing these things, these new founder journeys?

Mona Gandhi (Kello): This may, this may kinda sound better, but I think the hardest thing is to build a great team. And I don't think it's ever gotten easier. I think as repeat founders, you obviously, make fewer mistakes because you kind of seen it play out in some places, but, irrespective that the journey of building a great team doesn't get any easier. I think the things that have gotten easier is, I think, maybe the ability to, not get distracted by a lot of, shiny objects because you kind of, you know, it's it's it's just the process of sort of maturing and going through that that journey multiple times. So I think couple of things. And and maybe I think, you know, you... I think as a second time founder, I feel like, to be very honest, personally for me, I think, I have very deeply internalized that I'm only here to win. I only wanna play to win. Otherwise, you know, the the the default is that it is going to die. So every choice you make has to kind of increase your odds of success and and the probability of kind of winning. So, there's no point in kinda just surviving unless you're thriving. You... There's, like, a large opportunity cost for yourself, your cofounder, and then also your team. Right? So so it's... It better be worth it.

Dhruv Sharma: Yeah. And I believe you guys went through surge 12.

Mona Gandhi (Kello): We just did. Yes.

Dhruv Sharma: Yeah. We're likely to host Rajan on Friday, by the way. I I hope for us. What was the search experience like?

Mona Gandhi (Kello): It's great. It's great. I... Look. I think, it always helps to surround yourself with people who have diverse ideas, because, you know, that's how you discover, interesting parallels. You discover agencies. You discover, you know, things that are relevant in another industry, how do you gonna apply it to yours? And that's where sort of the creative juices start to flow. I think the second thing that it really helps is, you know, the founder's journey is pretty lonely, and it just helps to have people who are going through the same journey. You can ask questions and kind of, you know, learn from each other. And I think that Surgeons has done an exceptional job of sort of, you know, the the caliber of founders. Right? So it's very inspiring to be in that room and kind of, you know, people are solving very, very big problems. And so you kind of... You know, your your own size of ambition also grows as a process. And I think that's super critical if you wanna be a founder of a company, and, you know, at a global scale. You don't want to kind of limit it to a certain geography. So, you know, just around yourself, if there's gonna be... It just helps you kind of keep that goal.

Dhruv Sharma: Yeah. And I'm sure with, like, 12 cohorts, even the ecosystem's gotten pretty, large.

Mona Gandhi (Kello): Absolutely. Absolutely.

Dhruv Sharma: Yeah. I I guess my final questions are going to be about those early days at Airbnb. Mona, what what was the Airbnb of the early twenty tens like?

Mona Gandhi (Kello): Oh my god. It's been a while, but I'll tell you, those those are the most memorable days. I can I can honestly very vividly remember, all the things that happened? You know, the team was very small. I, you know, everyone kind of, you know, took a problem and ran with it. And, you know, these were all large problems. There was one engineer that was just purely focused on doing all payments at Airbnb. Can you imagine? Across countries, across processes, you know, I focused, all my energy on growth. So So there was just one person kinda doing that. And that meant anything and everything. That meant supporting performance marketing team. That meant creating landing pages. That meant creating copies. That meant thinking about alternate platforms from where you could acquire users. It was just a bunch of, like, you know, ideation, trying, testing, stick... Seeing what sticks. Right? And at the... And at the same time, it was it was such a memorable experience because you actually got to work with founders one on one. And for me, I think that was that was sort of my peak, because, you know, Nate, who's the cofounder at Airbnb, used to be my boss then. And, just, like, his sheer smartness and kind of the ability to kinda learn from him was such a privilege. He was also actually one of my first angel investors at Upraised. Yeah. And so, you know, yeah. And I think, I think the the the other thing that kinda just stood out from the very first day that I mean, we just felt like a place where you could build build deep corrections. It very much stood for what it actually, ultimately became the brand of belonging. The place in itself was very belonging. You know, everybody felt very comfortable. We spent nights and weekends, but we never felt like we were burning out. It was always having fun with, you know, with with your colleagues, and and it was just, like, work, dinner, fun, work. It just kinda all blended, and it was just super fun. And I think it was also, you know, travel as a category just by nature and global. So that, came with a lot of interesting learnings. Because from day one, you have to think about scale, in a different way. And you have to think about cultural nuances in a different way. You have to think about integrations a different way. You have to think about localization a different way. Most of our travel was, people from San Francisco traveling to Europe. And so we suddenly went from, you know, we suddenly went from, being, you know, having 100 hosts but being open in, like, 20 countries. So it's... It was this... It was it was a it was a fascinating time. But, yeah, it was not all rosy. There were all these bumps of, you know, challenges with strangers entering your homes, vandalism, etcetera etcetera. But, but I think the one thing Airbnb always stood for and that probably is, like, one of my, you know, proudest things is the founders always did right. Like, they believed in a certain way of dealing with every problem, and they... Even if though... Even if it was hard to deal with that... Deal with it that way, they just stuck to it. And so it was a good role model example to kind of how do you build a long lasting, you know, multi decade company, with a culture that continues to stay very true to what it was on day one.

Dhruv Sharma: Yeah. And I'm sure you had a remarkable life there, and then you you guys came back and built a beautiful life over here. And now we're again in this phase where a lot of friends are heading out and, you know, going to the valley. Like, what do you typically tell people who come to you looking for advice and going over there, coming back? And maybe not everyone has the benefit of, you know, reaching out to your director. So maybe share... Say a thing or two on on the stream.

Mona Gandhi (Kello): I think, look, you know, there's obviously benefits of being in a being in a culture that doesn't afford you comfort because it just it just instills this this habit of agency and kind of having to figure it out yourself. And that discomfort is actually a great learning experience. And, that was actually... There was discomfort for us on both sides because we had never professionally worked here. Both me and my husband, went to study, abroad and, as a consequence, just started our professional careers there. So, you know, in that regard, we had the discomfort of figuring out the h one b life, and kind of, you know, where we wanna be, what we wanna do. But we always worked at startups, which is always kinda slightly risky. And I started my professional career in 2007, which was great, and then the two thousand eight crisis happened. So it was, like, the whole shebang. And then on the other side, also, there was discomfort because we had never professionally worked in India, so we had to kinda find our footing. But, but but but I'd say that there is definitely a lot of value in having multicultural exposure. It just... It it gives you a different perspective. I actually wrote wrote a long tweet about, like, you know, differences between value and and culture in in in India and sort of the benefits of what one can can can get from both. Mhmm. Having said that, I will... I'm actually very pleasantly surprised, at how many, you know, inbounds I get on understanding about the journey of moving back. Mhmm. I think a lot of people are looking to move back. Actually, maybe because I'm also much older. I think there's fewer people asking me how do I get there. There are more people asking me how do I come back. And I think it's an interesting time to be in India, actually. It's, you know, we were just discussing internally today. If you actually think about sort of, the the the kind of opportunities in India today, they are no longer, like, outsourced opportunities. They're true global capability centers. It's actually just capability shifting from some of the center of the universe to here because there's, you know, cost optimization. There's other kinds of skill sets available, more tech talent available, more freely available. You can build at scale. So I think that, it's actually, honestly, a very interesting time to be alive. And, for what it's worth, you know, everyone around the world, at least today, has access to the same cloud and the same OpenAI, but you do miss the the energy and the infection of the... You know, there's something about SF that just is contagious. But I think you can make up for it by just being there every now and then, if you wanted to. But if you've never lived abroad, I would highly recommend getting that experience. It just... It changes your perspective. It makes you more confident. It just increases the size of your ambition. You feel like you can solve any problem in the world because you've kind of been forced to live in discomfort and kinda figure it out yourself. So I think, I'd say there's pros and cons to both.

Dhruv Sharma: Yeah. Vice wise words, Mona and listeners, you heard it directly from us. Thank you so much for coming, on our show and, sharing what you're doing at Kello. Wish you all the very best.

Mona Gandhi (Kello): Thank you, Dhruv. Thanks so much. Thanks for having me. Thanks, everyone. Bye.

Dhruv Sharma: Alright, listeners. That was Mona of Kello.ai. That's it from us today. We're now gonna see you on Friday, and Utsev is gonna be back as well. See you, guys.