The Secret to Scaling Teams from 37 to 1000 Engineers with Ameya Kanitkar

Transcript

[00:00:01] Ameya:  We were building systems at Groupon, and Groupon was in hyper scale phase. I think we went from 37 engineers to a thousand engineers, and we went from the fastest company to reach a billion dollars in revenue. There might be more companies now, but at the time, it was the fastest. And when we’re building systems, there came a time when one of my managers came and told me, hey, Amir. You’re good at building systems, but you’re yet to get to a point where you can build teams that make systems. So, essentially, create a factory that builds more systems.

[00:00:30] Intro:  Welcome to Signal to Noise by Riviera Partners, the podcast where leading executives share how they cut through the noise and act on what matters most. We go beyond the headlines to explore the pivotal decisions, opportunities, and inflection points that define their careers and shape the future of the companies they let. It’s time to cut through the noise and get to the signal.

[00:00:52] Eoin:  Welcome. We’re sitting down today with Maya Kanetkar. He’s the cofounder and CTO of Laradyn. Laradyn’s based in the Bay Area, backed by some pretty heavyweight investors. You’ve got Andreessen Horowitz, Bloomberg, Gradient Ventures, and they’re building what it calls an organizational fitness platform. So think real time AI powered health metrics for enterprise productivity, and it generates like a FICO style scorecard and intelligent chat insights for operations leadership. This isn’t Maya’s first dance with AI at scale game, previously at Coinbase. He led engineering for the consumer trading business, the financial engine that really was the company’s top line revenue. And then prior to that, super scaled growth systems, including, uh, NFT discovery, global expansion, messaging, incentive platforms. And then prior to that, Amaya was at LinkedIn where he built the unified data analytics powerhouse behind the company wide AI, ML, AB testing, and executive analytics. Very similar role to which he had at Groupon prior to that during their hyperscale phase. So, like I said, not Amaya’s first radio. So So we’re looking forward to chatting today about what leadership looks like and particularly leadership across these different and dynamic landscapes that he’s participated in and been a catalyst in, how AI systems that are there’s a lot of talk could actually be implemented and move business needles, and then how we could define leadership at this stage of an organization. So, Emaia, welcome. Great to chat. I’m Owen O’Toole, lead the venture practice at Riviera Partners here. I’ve spoken with, I would guess, 5,000 founders, uh, over the years, and we’ve helped out on searches with hundreds, probably close to a thousand of those. And so, you know, very excited to chat with you. You’ve got a fantastic background and one that, you know, candidly, if I was working with you again, we’re very excited to be able to represent in the market as a founder given your breadth of expertise. So thank you for joining us.

[00:02:43] Ameya:  Thanks for having me. Really excited to chat with you.

[00:02:45] Eoin:  Likewise. Yeah. So, look, we’ve got a lot to cover. I’m, uh, I’m excited to hear your thoughts on a number of different areas. Real, first of all, this is the signal to noise podcast. So the opening question I’d love to start with is, what’s the biggest signal that you’re paying attention to right now, and what noise are you tuning at?

[00:03:03] Ameya:  It’s interesting because, you know, my answer to both the noise and the signal is actually the same. So, obviously, AI is the biggest thing everybody’s working on, everybody’s focused on. So that’s the signal that you’re obviously looking for, but it’s also the noise at the same time. So what I mean by that is if you take any idea in the world today, you can argue for it and against it at the same time with the same lens. So you can say, hey. AI can solve this without actually having to do anything, so there’s no point building anything. So you obviously want to leverage AI, but if you believe that AI is going to solve everything in one shot, then what’s the point in building it? So on one hand, you want to pay attention to it, but at the same time, you also want to kind of tune it out. Because the fundamental idea is that the reason you want to tune it out is not because you don’t want to accept the fact that it might actually not be important, but the way world operates is they operate at a different speed. So, ultimately, that the top velocity will take a long time to reach at the most bottom layers. And sometimes people just don’t want that velocity. So whatever you’re building would still be valuable, um, but obviously you want to keep paying attention. So it’s the same same answer from both sides.

[00:04:07] Eoin:  Yeah. It’s something I’m sure we’ll come back to later in the podcast where I’m curious to hear particularly given what you’re building, how enterprises are, you know, looking at what to pay attention to there, what the barriers for adoption are. That’s gonna be a a key component, I’m sure, to your success as well. So, yeah, fascinating. It’s, uh, I’m curious. Where do you think we are in the AI life cycle, if you like? There’s a lot of different opinions out there about how evolved and advanced we are compared to where we can’t be.

[00:04:32] Ameya:  You know, my view on this is that I don’t wanna jump ahead, but before starting Ladder than, you know, when when TragerBrady came out, it was kind of obvious that this is going to be a next big thing, but it was very hard to tell how big it is going to be, sort of where that value will accrue in the larger scope of things. And if you just looked at last year, most of the value was largely accrued at only at the initial phase of the supply chain, essentially, Nvidia and maybe TSMC. But somewhere around last year, around once the four o was launched, it started to become a little bit more clear, at least to me, that what else can you build and how we can build it to potentially create the value for your customers and for your shareholders. And you can actually see that play out in the last year or so where lot of rappers have become like, what they call initially just the rappers have become now a multibillion dollar companies. Right? Whether it’s Cursor, whether it’s Perplexity. And I think we are in that second phase now where now people have figured out these models are good enough that now there are so many applications you can apply. So I don’t know exactly where we are, but now we are definitely at a phase where you can start to see the path to value very clearly in more and more areas.

[00:05:36] Eoin:  Yeah. It’s funny. We’re seeing that centrally here reflected in the types of roles people are hiring for, whereas to your point two years ago, it was a little bit more about AI strategy, philosophy, and and really thought leadership. Now it’s high velocity, rubber meets the road. There is explicit value to be derived from what we can ship today, and it’s changed the dynamic of the talent that’s in demand as well in the market, which I’m sure you’re you’re experiencing as well for

[00:06:00] Ameya:  yourself. Absolutely.

[00:06:02] Eoin:  And so, you know, just to zoom out a little bit and just go through a little bit of your journey with me if you could, is there any particular point in your career that you found pivotal, whether on your leadership journey or your technical journey? You know, what changed and how you navigated it?

[00:06:18] Ameya:  In hindsight, it was it’s not surprising. But at that time, it was, uh, sort of it took more time for me to fully realize one of my mentors’ advice was at the time. So sometimes, you know, all all these advice in hindsight are, like, plain plain text. Right? But to give you make it more concrete, we were building systems at Groupon, and Groupon was in hyper scale phase. I think we went from, like, 37 engineers to a thousand engineers, and we went from I think it was the fastest, uh, company to reach a billion dollars in revenue. There might be more companies now, but at the time, it was the fastest. And when we’re building systems, there came a time when one of my managers came and told me, hey, Amir. You’re good at building systems engineering systems, but you’re yet to get to a point where you can build teams that make systems. So, essentially, you create a factory that builds more systems. And what that essentially meant was that how do you create an environment and a team that produces lot more than just what you can or as just as a leader and, uh, just as an individual contributor. And in hindsight, it’s it’s, uh, it’s kind of obvious. But looking at it from a sort of systems perspective was a key differentiator. So instead of just approaching it as, okay, now I’m just a leader, and, you know, we’re gonna follow it versus, hey. How can we do it in a way that, you know, that can sustain itself and that flywheel sort of keeps going? And then, of course, it took a long time to do it, but I think that mindset change was was important.

[00:07:38] Eoin:  Yeah. That’s a great insight and a great observation. You know, when we sit back and look at the data in the world of technology leadership, there are less than 3% of people in that entire population who can take something and stay with the company from as early as you did, like you said, 37 people to thousands and continue to scale their leadership. Most people have much more finite life cycles. I’m good for this phase or that phase, and then they kinda are are scaled out. Right? So, uh, it seems like that served you well. Obviously, you were able to stay stay the course. So fantastic. So from beyond, obviously, the systems of scaling technology is the people. And when we think about how to build or scale teams, do you have a particular belief or a strategy? Particularly, I’m interested if you have one that you don’t think is considered conventional wisdom right now.

[00:08:25] Ameya:  We believe the last topic. I actually have a system that that kind of allows you to think about a specific how you approach the problem. So once you start with people, you know, it really is the question is really there’s sort of three things to it. Do you have the right set of people on the team? What is missing? And what can you do to achieve your goals with what existing set of people you have or whatever the talent you have on the team at a given point? And now we have an addition of that, which is now you have AI. How do you mix the AI with the people and what that means, uh, for your teams and so on and so forth? So first, you always start with the right composition of the team. You want to make sure you have the number of times that has happened that you go into the team and you just figure out, okay, there’s actually just one or two missing components. And you once you bring that in, you know, the team kind of gels together and operates at a different capacity. Uh, and sometimes you just have to identify that what is that missing piece. Sometimes it’s a senior engineer. Sometimes it’s actually junior engineers, depending on the phase of the project, the technology where you are at. And once you bring the right set of people, that that’s the, essentially, I would say, the ingredients of the recipe that you want to build out. Then you go from there in the next phase. I’ll give you specific examples. 

We were building different sort of teams in Coinbase, and we were building essentially a a good growth org. And we had set of teams that were building things like SEO and growth acquisition pieces, marketing tech. And there were also other set of teams which are essentially building our core notifications engine that would send millions of notifications. ML component was involved. AI components was involved. And the way you build these two teams is completely fundamentally different. Right? If you bring a very senior set of folks at least what we found out is that if you bring senior set of folks building this kind of agile SEO, you know, growth marketing kind of teams, you actually slow things down in terms of you might have a higher quality engineering output, but it doesn’t matter. The business requires you to move much faster. And the kind of team we build there was, like, hungry engineers who were ready to break things, go into different sort of code bases. They’re not afraid to pick on different technology. Because by design, you sometimes would be working in maybe Go, maybe Python, maybe TypeScript. You know? It will keep changing because you had to make little changes in lots of different places. But our notifications team, you know, we wanted a solid infrastructure, you know, a solid place where we could send millions of notifications at the right time to the right person with the right message. And if we send too many, you will lose the trust of the customer. If you send too less, you it will impact your revenue, and so on and so forth. So you need a very different sort of mindset on building that team versus building, say, an SEO team or or or some of the growth teams we build. That’s fundamentally a question of, okay, what kind of people you want on the those teams. There are some people who can scale up and down, but, uh, sometimes you just have the right mix.

[00:11:08] Eoin:  I love that. I guess the fundamental anchor, I’m sure, to all of that is just hiring for great talent, right, whether junior or senior engineer. So I’m curious, how do you identify or distinguish great as distinct from good when you’re looking in your recruiting processes?

[00:11:23] Ameya:  There are so many answers, and there’s no one single answer. And, uh, in LinkedIn, we look for certain things. And we had all these questions, how your career path was, how the growth trajectory was. And there are few tenets that we used to high one is that, you know, your past is not a guarantee for future, but it’s a pretty good guidance that it will likely be true. Right? If some person has kind of failed again and again, that doesn’t mean they will fail again, but there’s a higher probability that they would. I was just a certain person has consistently succeeded and grown in their career ladder. That’s again a high high signal for it. But though those are all good indications for hiring good engineers. Right? Like, for me, the difference between good and great is actually very simple, which is ownership. Are there cases where in their career or ideally in their career, but if not in their personal lives, the cases where, you know, they’ve taken an ownership of something and did it on their own. And the ownership comes in different forms and different sort of things. So, for example, sometimes some they would be a founder previously. I mean, that’s quite obvious signal that, you know, they they have ownership. You know, they probably raised money. They’ve they’ve done something. Everybody doesn’t need to be a founder. It cannot be a founder. So the ownership can come in other forms. Maybe they saw a problem and took initiative to do it. Right? Maybe they they learned something new because they looked at the market, thought about what would happen, and sort of, you know, responded to that. It could be something as simple as, you know, just owning the complete problem beyond what their, technically, what their on paper job discussion looks like. You would identify that in your in your conversations. But for me, that’s a very good signal that, you know, the higher the ownership, you know, the higher the signal.

[00:13:00] Eoin:  Yeah. I think you’ve hit on something really, really important there. And I think particularly at a start up, but I think for any organization, when you hear people looking for talent, they will churn through a lot of interviews. And what it feels like to your point is very reactive talent that will wait to be told what to do next. And I think what you’re talking about is ownership meets critical thinking, right, ultimately, uh, who can really assess their situation and think how to advance the organization to whatever it might be. It’s a great insight. Yeah. I love that. Fantastic. Changing gears a little bit here to your own leadership and styles. Right? You’ve done major engineering teams, huge tech companies at LinkedIn and Coinbase. You’ve gone from small to big at Groupon, and now you’re right at the start again at at Ariden. So, you know, what was the most challenging part, or is the most challenging part for you transitioning now to something as early and as at a start up level as as Laradine?

[00:13:50] Ameya:  Yeah. I have actually a very I would say I don’t know if it’s contrarian or whatever, but, you know, uh, a take, which is actually, the the big companies and start ups are not that different. And let’s me explain how. I think in good companies, if you’re in the right place, at least all the companies I have worked in, you know, most people actually want to move fast, and most people want to build a value to your customers, and most people want to build really good engineering systems. That doesn’t change whether you’re a large company or a very small company. What changes is the amount of scaffolding you have around you. And the scaffolding, I’m I’m talking about primarily processes. And you can look at the processes from two lenses. One process is that one lens is that the process exists to align everybody so that you are all rowing into the same direction. And the other lens is that you have processes because you have lack of trust. So the process is essentially filling in for a lack of trust in it. I commit to doing this OKR this quarter because, you know, I want to hold you accountable for that. You know? That’s probably a lack

[00:14:48] Eoin:  of trust. But I want

[00:14:49] Ameya:  to do this OKR because, you know, you depend on me, I depend on you, and we build together. So we achieve something in, whatever, three months instead of so that’s a communication thing. And once you sort of double click and find out which is which, right, if you have processes because it’s a lack of trust, then it feels like a large company. If you have processes because you want to just align people, align, you know, your goals, and align your value that building, then it doesn’t matter whether you’re a startup or a large company. Now I would say, though, that, you know, in most valley companies, the number of processes and sort of the communication over there you have in large and medium teams is very large. So I wouldn’t sort of tune that out and exist, but I call it, like imagine you have, like, a a browser thread always running in your machine and just slowing it down 30%. And it’s just basically an idle CPU that they’re just a busy CPU that you just don’t have even if you have on your system. And as an engineering leader, you can actually create an environment where that extra CPU cycles that you need to just keep running systems can be low lower and lower. In, it can be very low. In large companies, it can get very high, sometimes as high as 50%. But I would argue that, you know, some of the best engineering leaders I worked with, they managed to figure out how to keep that over at much smaller, not necessarily 2%, but maybe at 15% and still allow these teams to move very fast. So I think, fundamentally, people are still the same, and they still want to deliver the value. And as a leadership team, if you can create an environment where you tune out the noise, you cannot remove all of the noise. But if you can minimize it, then you can actually operate like a start up even in large companies.

[00:16:20] Eoin:  That’s fantastic. A very, very sophisticated insight, I think, as to what what people will inherit as a big company feeling, and I think you’ve just nailed exactly why and where that comes from. I think the other big area I would contend there is at a start up at your stage, most decisions can feel and can be critical, at times existential, which maybe isn’t always the case when you work in a larger organization. So how do you solve for, you know, ultimately, when you think about for yourself, even adjusting that mindset of what failure tolerance you have and don’t have?

[00:16:52] Ameya:  I think it also helps with sort of setting up the expectations. I’ll give you a specific example. If you’re joining Coinbase or LinkedIn or, you know, even Groupon, you’re joining a team and team had certain goals, you know, whatever, building notifications, you know, building trading systems. And you as an engineer are bought into that mission. You’re bought into that idea. You’re bought into and there is a very high chance, I would say, more than 50% chance that what you have been promised, you will work on it a year from now. In a very large company, it might be true for three years from now. But this was a very volatile because of the way crypto is, but there is very high chance you will work on it. So if you are joining the team because of that reason, that’s actually a problem because in startups. Now when we are hiring, we tell people that, hey. Yes. This is what as Aladdin, we are solving today, but there’s no guarantee that’s what we’ll be doing three months from now, six months from now. And if you are like, hey. I’ll tell you right now that we are always going to be in AI, and we are always going to solve, and we are going to be here to solve an enterprise, solving the problems for enterprises. Those are the two things which are not going to change, but a specific aspect of the problem is very likely to change. So if you’re joining the company because, you know, the pitch I gave you, you have really resonated with it. And if we pivot in three months, you know, you’re gonna be disappointed. Then don’t join. Alright? So I think setting that expectation is very important because, you know, you don’t want to otherwise, it will feel like bit and switch. So that goes back to sort of building your team. You know? If you are all aligned on what you’re building and why you’re here, then it should work. But even a large companies can change. We used to tell in Coinbase, you know, we are probably always been crypto, but there’s no guarantee, you know, we’ll be doing exactly this thing. Market just changes so much.

[00:18:27] Eoin:  Chaos is a feature, not a bug. Right?

[00:18:29] Ameya:  I tell this to people. You’re like, I was in Coinbase for four and a half years, and within four and a half years, forget about the headcount. The revenue went from 800,000,000 to 8,000,000,000 back to about a billion and back to 6,000,000,000. So when you have a apex growth on the revenue on both sides in billions of dollars, right, at that scale, things are going to I mean, like, somebody’s surprised that things are changing. I mean, they’re killing

[00:18:53] Eoin:  I think that back to your earlier point then about how you hire and looking for people who take ownership in what they do, like you said, then it becomes about how do I solve problems for the business as opposed to complaining about what’s happening to them. And, uh, you know, for you personally, did you find any difference in your leadership approach now to your point about that the process implementation can’t be all at once in a start up, then it can’t be zero either. So have you had to, again, toggle your leadership style in order to meet the needs of the company at this stage?

[00:19:22] Ameya:  I mean, I love it now because, you know, I don’t have to worry about that extra overhead that I have to pay, that extra tax I have to pay in larger companies. So I’ll give you some couple of specific examples. We all are all adopted performance evaluations for individuals from Google into most of the value companies. And some some companies are breaking breaking out, but, you know, most of it came from, essentially, Google. LinkedIn kind of adopted it. Coinbase also had adopted it, which means that, hey. From a senior engineer engineer to senior engineer promotions, senior engineer to staff in your promotions, A certain rubric had to be followed. You know? If you’re a staff engineer, you’re kind of independently led assistance. You have aligned teams. Blah blah blah. There are bunch of things that you had to do. And, unfortunately, in a company like LinkedIn or Coinbase or Google or whatever, you had to meet that rubric before you can go for a promotion. Like, unless you have obvious outside success, which is very hard to do, uh, in large company because the business is not changing that quickly, you just had to check all these boxes. So So as engineering leader, you’re not only just figuring out when you’re working with people, you want to take care of their careers and make sure that they have a path to that next level. On one side, you yes. You want to deliver the value, but you also want to make sure the team is motivated. You create a path for them and you sort of, which is all important because not because, you know, um, I mean, ultimately, everybody wants to grow in their career. You know, you you have hired ambitious people in the first place. In start up, you don’t have to worry about that. Right? You’re you’re primarily worried about it doesn’t matter whether you’re staffing it or not if you’re not alive as a company. So the first goal is to build value, and we’ll figure those things out. So it’s just much easier to align everybody because everybody’s rowing in the same direction, which is build a a sustainable company, which brings revenue, delivers value for your customers. In a large company I mean, think about in LinkedIn. You know? We were in LinkedIn. We were maybe, like, four or 5% of Microsoft’s revenue. Even if LinkedIn grew 50% a year, it was, like, 2% for Microsoft growth. It didn’t matter. You know? And LinkedIn itself was very large. So if you’re in a small team in LinkedIn, even if it’s, like, a 50 people team, you know, your impact in Microsoft is nothing. You know? Basically, nothing. Right? So it was very hard to align where the company is to where you are. So everybody’s obviously incentivized to optimize for their VP or their SVP or, you know, their engineering leaders, and that’s just being human. So as a leader, you have you you have to recognize the reality of that and then make sure you align the incentives in every aspects, uh, of that chain so that you ultimately deliver the value that you’re looking for. But you have to play kind of play that. It would be ignorant if you’re not looking at this reality because that’s how people will respond to those incentives.

[00:21:53] Eoin:  Yeah. It’ll feel like you’re trying to sell them something instead of, you know, really dealing with how you can advance them inside the construct.

[00:21:58] Ameya:  Yeah. I mean, you you can’t you have to find that balance. Right? In a very small like, in start up, you probably don’t have to do that because you don’t know if your company will be aligned in a year. If you don’t deliver the value, it doesn’t matter. Talking about the promotion.

[00:22:08] Eoin:  That’s a good way to put it. And if we we change to a kinda little bit more of the contemporaneous you, right, in terms of Ed Lawarden. Right? You’re focused on transforming how organizations measure your productivity through AI. How do you think your background at LinkedIn or at Coinbase or those helped you shape the vision and the direction here, especially because you’re pioneering, you know, new AI technology in the workplace?

[00:22:31] Ameya:  Both working in my previous Coinbase or LinkedIn, you know, there were few observations which really resonated with us. One was that in in Coinbase, especially, we were really focused on security and making sure that, you know, as a company, we are probably Coinbase was best in the like, every hacker wanted to hack Coinbase. Right? Where $203,100,000,000,000 dollars of crypto is sitting there. And, you know, if you hack it, nobody can recover it. That’s great. Like, if you hack a bank, probably you can get it back through whatever ACH or whatever banking system. In crypto, you’re gone. It’s gone. So we were essentially the target for pretty much everybody. So security was kind of built into us, but that also means that, you know, every new thing that comes to us, we were looking at it from that lens. So AI was no exception. Today, for example, whether it’s open AI or, you know, perplexity or whatnot, sometimes employees are uploading their customer’s data or whatnot. You just don’t know whatever you upload into ChargeGPT unless you have a specific agreements done with the with the LLM providers. You just don’t know where it’s going to show up because even they don’t know where it’s going to show up. Right? It’s all going into training. Like, nobody actually knows, you know, how this model actually do it. You know, there’s no e finance clause that actually so this is this becomes a problem for many organizations and many enterprises. Right? Like, how do you safely adopt AI and in a way that, you know, you want to enable productivity of your employees, but in a safe compliant manner. So that that’s problem number one. We clearly saw it firsthand in Coinbase. The second is that we are in a unprecedented times as in no matter how how sophisticated or unsuspensive you are as a company, everybody wants to actually be on this AI way. That also means that there are basically a very large AI experimental budgets in pretty much every company. VCs are telling us that they have never seen companies grow this fast before like this.

[00:24:16] Eoin:  They they said that about crypto at that time, and now they’re saying AI is out pricing crypto. You know? So it’s that’s crazy.

[00:24:22] Ameya:  I think AI is much bigger than crypto, but it’s just like I I was I mean, the Replit’s uh, numbers came out. I think it was leaked or whatever. I think I saw the graph is, like, 1,000,000 revenue in November ARR, and now they’re 150,000,000 in now, what, August, like, ten months. The cursor is growing. Like like, it’s it’s not a curve. It’s a straight line. It’s not it’s it’s not orchestral. I don’t know what to call it. But but, you know, the what I’m trying to say is that, you know, these enterprises are adopting all these AI tools. And if you’re a CFO or a CIO, you just don’t know which tools are actually being used, how much you’re paying for it, and which you would keep. Because there’s also high churn, remember. You know? They these are not getting renewed. So this is actually a, uh, significant problem for enterprises. Like, how do you measure it? How do you go with your AI strategy? First, you need to measure it, then you need to safely enable it and then measure the impact of AI on your productivity. And at, we are trying to solve these three fundamental problems. That’s our kind of, uh, uh, stepping function.

[00:25:15] Eoin:  I’m curious if you’re seeing you know, a trend that we saw during crypto was everyone loved the idea of the fundamental concepts of what blockchain could do, but wanted then, particularly enterprises because of security and otherwise said, well, I wanna build my own private blockchain. I’m starting to hear that same trend with AI. Can I build my own private LLM to really just train on our own data, mitigate some of those security concerns? Are you feeling any of that sentiment from our particularly larger enterprises or those in regulated industries like banking?

[00:25:42] Ameya:  There’s definitely that, but not as much because so let me be very a little bit more clear. So if you think about the core LLM, it’s kind of a generic brain, which which is very smart. Right? And just your company’s data will not get to this level of brain. Right? You just need basically world knowledge to do it. But then you want to bring sort of a layer on top that is specific to your company, to your organization, and you you want that to be airtight. And what we are seeing is that companies using lots of open source models to create that tight layer, but then you also take on a significant operational burden of, you know, running these GPUs and and things like that. So I think we’ll see where it goes. I think that’s TBD. You know? It’s it’s yet unknown where things will go, but but that’s where these open source models will, uh, fill the role.

[00:26:26] Eoin:  And do you think that’s predominantly done through Agentic AI or, you know, in ultimately to deliver those personalized, actual insights for organizations at an employee level, at an organizational level? Is is agentic the way, or how how do you see that playing out?

[00:26:40] Ameya:  My controversial take on this is that agentic doesn’t exist yet. So let me explain what I mean by that. So, yes, you can see that as a future. Right? Um, just like how LLMs were, by the way, two years ago. Like, when Chargebee first came out, it was all about LLMs and AI. Right? But we didn’t actually see that being implemented in real life in a meaningful way. But now we are seeing it two and a half years later. Right? Like, the number of tools that are now that actually generating value for your customers and and, you know, for business enterprises. Agentic AI is where Chargebee launch was. Right? This thinking modes and this kind of doing on your own. Yeah. There are some areas, you know, Cloud Code does whatever it does is pretty impressive, especially in coding and some of these things. But we are not seeing somebody like, I’m an, uh, AI auditor. You just hand it over to me all the documents, and here’s your final thing. You know? You don’t have to talk talk to me ever again. You know? Like, we have not seen this level of sort of purely agent take, you know, sort of completely hands off kind of operations, uh, in in practice, in production. But that doesn’t mean it won’t happen, but it’s not there today. And, um, it’s gonna take some time to to build out those things. This is where we think Gladwell is thinking about this as a company because one of the estimate they have is that today, the total IT spend, the technology spend is about a trillion dollars. And they’re they’re expecting this to go to 10,000,000,000,000. I don’t know the exact number, but, you know, by I don’t know. In ten years or something. If we have a $10,000,000,000,000 of IT spend, you’re essentially spending same amount of capital human on your technology. And at that time, it becomes really important that, you know, how do you measure the output of it? Are you really how do you measure the productivity of your workforce, which is now both humans and AI at the same time? Because you’re not spending $100 a month on AI seed. You’re gonna spend $10,000 a month on an expert per month. Right? That’s that’s a very different value proposition, and you you have to make sure that you actually can measure that and measure the productivity of it.

[00:28:34] Eoin:  What do you think that the barrier is? What will be the breakthrough moment for AgenTic AI? Is it is it just time and and learning of these models, or is there something holding it up?

[00:28:42] Ameya:  I mean, I’m sure there’s there will be a time when somebody just drops a model that does everything, and I don’t know what to do at that point. But

[00:28:50] Eoin:  You’ll be both.

[00:28:53] Ameya:  But if I have to guess, I think it’s just going to take time because, you know, people don’t change that quickly. Like, organizations take time to change and for a reason. You know? Like, the habits are hard to change. So I I think people will look at it one by one and, you know, sort of will start to do a little bit more and then a little bit more and then and you can see that behavior already in many cases. You know? I think in our company, you know, CloudCore is now standard. Was standard six months ago, but it still took six months. You know? Like, CloudCore was launched in February. Now it’s August. Right? So and it’s changing, and I’m pretty sure that some of the features they have launched in the last three months, we have not fully adopted it. So it’s gonna take some time.

[00:29:33] Eoin:  It’s so exponential the way that it’s compounding at this point that it becomes hard to, like, say, keep up, but we are seeing the rates of adoption and change increase. But, again, depending on where you’re looking, early stage start ups to mid stage start ups are the rate of adoption is far higher, and there’s an increased pressure to do that. But you’re still talking all the way down to large organizations, and then there are non tech native organizations who are miles away from this. I mean, the world won’t all change all at once for sure. But, uh

[00:29:57] Ameya:  And and this is where, you know, we help companies, enterprises. And if you think about forget about the Silicon Valley engineers or whatever VCs or, you know, people like you. If you think about sort of a common person, 40 year old person with three kids in general mills really wishes AI to go away because there’s more likelihood that AI will take his job than make him more productive in the workforce. And this is most of the workforce, honestly. Right? Like, uh, when I I I was traveling in India, and I explained to some of these things to my my mother and sort sort of some back population, and they were like and I showed them some demo. They’re like, okay. Then what will people do? That’s I showed them the demo. I just okay. Then what will people do? That’s a good question. But let’s not go there. But the point I’m trying to make is that, you know, on one hand, you know, you have this kind of anxiety in the larger set of workforce. And on the other hand, you have organizations who clearly understand that AI is going to make them more productive, and they want employees to use more AI in a safe and compliant manner. And there is this kind of attention. Right? And I think picking any one side is extreme. Right? You need to find a balance in terms of how do you safely enable this technology to your organization in a way that you can truly enhance the productivity of your workforce in in a way that, you know, that that drives value for your your business. Um, and, uh, we we we are very focused on that, you know, as a company.

[00:31:16] Eoin:  Yeah. And change is hard. And, fundamentally, you you hit the nail on the head, which is the only problem with the AI adoption is is the humans. Right? It’s it’s gonna take us time to adjust to new workflows, new processes, new job descriptions candidly about letting go of the things that we’re used to doing. I think you have to embrace it and treat it, like you say, as a friend, and look answer that question yourself. What does this free me up to do versus what’s it taking away from me? Right? And I think that’s the way that people need to assess this, but I very understandable human nature to react the way a lot of people are reacting defensively. So fantastic. What advice would you give to other leaders who are making the leap from larger enterprises to a start up environment?

[00:31:54] Ameya:  Focus on what matters most, ignore everything else, uh, signal to noise. And, oh, and one more thing. You know, Brian Armstrong, the CEO of Coinbase, used to say, you know, action produces information, which I found very, very interesting. If you are worried about what you what happens if you do, just do it. It will give you some information, then you it will inform you the next step. You know? If you’re working on marketing, you know, should I do this or that? Okay. Try something. You’ll have some information. You know? Action.

[00:32:17] Eoin:  It seems like you’ve benefited along the years from some really great leaders and and, obviously, a lot of great advice. What do you think is the best piece of leadership advice or advice in general you’ve ever received?

[00:32:27] Ameya:  One of my managers used to say, know, if you look at the circuit board, right, like the the chip the the circuit board, you have, like, transistors, you have resistors, you have battery, you have, you know, all these things. You know, you as a leader, your role is actually the battery. Your role is actually providing the source of energy. The rest of the team is actually doing the actual work. You know, they’re writing code. They’re shipping. They’re making deals and stuff like that. But your role as a leader is actually really be the battery in that circuit. And I’ve seen that firsthand. If you are putting energy into the system, the output is actually more, and the same system will produce less output if your input is less. And all high caliber leaders I have seen, they have just just tremendous energy. There’s basically unlimited source of energy, uh, and that just energizes the whole organization. They’re next layers. They’re next layers and so and so.

[00:33:13] Eoin:  That’s infectious. Right? And it and it catches on. It’s very infectious. Yeah. People talk about it as as a gravitas or an executive presence or anything else, but it fundamentally comes down to, I think and you tell me if you agree, but it’s it’s an intentional choice to show up that way every day. I don’t think that natural at some of the energy levels that I witnessed to just be that way. I think you have to continue to choose that every day and in every interaction of how you show up.

[00:33:35] Ameya:  Maybe they just have it. I don’t know.

[00:33:36] Eoin:  Maybe. Yeah. But, uh, I’m a big, uh, big book nerd. Tend to be a bit of a voracious reader. I’m curious either if you’ve got a book you read lately or just a book that you hold up as one that you think everybody should read that you’d recommend.

[00:33:48] Ameya:  I love history books. I had nothing to do with, uh, leadership or anything like that, but I’m just a big nerd on history. And I read this book called Silk Roads, which is amazing, and this gives you understanding of the world. And even in, you know, it’s it doesn’t repeat it rhymes or something like that. You just see so many patterns that you can just apply in anything, really. Beyond that, it’s just fascinating.

[00:34:08] Eoin:  Yeah. I mean, you end up being less surprised by events when you know that they’ve happened to our version of them as happened before. It’s you point to try to be like, okay. This is this is kinda predictable at some point.

[00:34:17] Ameya:  Yeah. I’ve never seen COVID like this.

[00:34:19] Eoin:  It’s it’s happened the future. Yeah. So maybe a too loaded a question. We won’t get into the solution, but let me ask the question nonetheless. If you could instantly solve one major problem in technology today, what would that be?

[00:34:32] Ameya:  I think we test upon this, which is, you know, I’m pretty sure we’ll get to AGI sooner or later or ASI, whatever they call it. We need to still figure out how to do the change on the people side of the business. So the technology may be there, but, you know, we still need to figure out the people side. So if there’s a magic wand, I don’t need to do it on the technology side. I will have to do it on people side because, you know, technology will happen no matter what. I think it’ll happen.

[00:34:54] Eoin:  Those pesky humans keep getting in the way of this whole fix. And last question, uh, if you don’t mind. In one word, what does great leadership mean to you? I own the Thought that might be the answer. They still there. Owning the failings, owning the successes, stepping out in front of it. Do that do you inherently come with vulnerability, or how do you think about it? When there are failures that you have to put your name to or you have forks and roads? You know? How how do you treat being vulnerable in those moments?

[00:35:23] Ameya:  You know, my family everybody in my family is is is an entrepreneur. My my my my mother, my dad, my uncles, my my basically, everybody. For a long time, I was a w two employee, and they used to ridicule me, then I’m a w two employee. So I think it’s in it just comes naturally to me that, you know, there is a problem. You don’t look at it because you, uh, are part of some cog in the wheel, but because you wanna solve the problem. And that’s just me, but, you know, that is super valuable. So and it is what it is. Right? You cannot fake it. Right? Like, I mean, as in if you don’t have money, you don’t have money. You know? You cannot fake it. Right? So maybe that’s the vulnerability. But, you know, if you have that, you know, or even if you don’t have it, you have if you cultivate it, you know, that that that would be very valuable.

[00:36:00] Eoin:  Thank you. That wraps us up. Love your philosophies and how your mind works on the leadership and systems. That was great. Thank you.

[00:36:06] Ameya:  Awesome. Thank you, Owen. Really appreciate. Thanks for having me.

[00:35:35] Eoin: I think that wraps up the episode for today, and check out what Toufic and the team at cat.io are doing. I think there’s a lot of great to be had there as well.

Outro: Signal to Noise is brought to you by Riviera Partners, leaders in executive search and the premier choice for tech talent. To learn more about how Riviera helps people and companies reach their full potential, visit rivierapartners.com. And don’t forget to search for Signal to Noise by Riviera Partners on Apple Podcasts, Spotify, or anywhere you listen to podcasts.