Radical Thinking: Why "Fail Fast" Is Failing You with Radhika Dutt: täielik transkriptsioon

know, ⁓ ⁓ that we needed to see world differently from the Silicon Valley approach just because of my own personal experience. So let share a little bit about, you know, the product diseases I've run into. So back in 2000, I was graduating from you know, I just electrical engineering MIT and right out of our dorm rooms, we starting our startup. We had a grand vision. Sam Sivarajan: Hypothesize, learn, adapt, starts in the problem space before jumping to solutions. By asking, how well did it work? What did we learn? And what will we try next? Leaders invite honest reflection rather than performance. That shift from proving to learning is what doubled revenues for one of our clients two years running. Radhika Dutt: our vision was ⁓ wireless. And had caught the disease I now call HERO syndrome, where our was on being big, on scaling. The way we were measuring success was by the amount of fundraising that we had done, the big client that we had on our website. It all sounds very right? And this was, Sam Sivarajan: Radhika reminds us that the most dangerous assumption in leadership is that more speed and more building will solve a problem you haven't fully understood yet. Advisors and leaders who slow down to set the puzzle right will consistently outperform those who sprint towards the wrong answer. Radhika Dutt: 25 years ago and yet we see the same sort of product diseases that are just as prevalent today in startups. what I've realized was, this whole Silicon idea of ⁓ you just start startup, give it funding, ⁓ just keep iterating, product market fit. It doesn't really work way. the reality is you have to three pivots before you run out of either money or That is reality. just the pretty picture we have or the survivor bias that happens is ⁓ there are a unicorns that make it by ⁓ just iterating. And come to believe that, ⁓ that's how you build products. It works for the VC business model. The VC business model I'm going to invest in 10 startups. I just need ⁓ one of these to it big. Everything else I wanted to Sam Sivarajan: Mm-hmm. Radhika Dutt: either fail quickly or make it big. I don't want to invest in middling startup. ⁓ And from a VC perspective, that's really where these mantras come from. This idea go big or go home, fail fast, learn fast. This idea of just keep iterating, ⁓ keep pivoting till find product market fit. Sam Sivarajan: Right. Radhika Dutt: It all works for the VC because that's their business model. I invest in 10, just one needs to make it big. from an entrepreneur's perspective, you have two to three pivots before you run out of money and momentum. ⁓ the way we need to think differently, what truly works is we have to treat every like a silver bullet. You we have to treat pivots with gravitas. We need clarity of vision and strategy, even as a startup. And you might have to change your way, but how do you pivot with gravitas? is key to succeeding. This is why I realized from personal experience that we really need to think differently to succeed. Find is right for the entrepreneur, not necessarily for the VC. ⁓ Sam Sivarajan: No, that's very profound, Radhika. And I think you're right. think nothing has changed dramatically in 25 years. I think that VCs were throwing money at startups in the dot com era. They're throwing money at startups, ⁓ or least were in the age of AI that we have right now. I like positioning. I think it is very profound when you say that model of fail fast or iterate works. very, very well for VCs, but perhaps less so for entrepreneurs or startup founders. It seems to me that the mantra of VCs is almost, if you build it, they will come for a select few of the So whether there is actually a demand for it or not. If you build it, if you throw enough money at it, if you market it, if you tell people that this is going to solve some problem that people don't even know that they had or wanted to solve, eventually becomes a fait accompli. I mean, it sounds like, and I'd love your take on it. It sounds like a little bit of the template that was used with Theranos, with WeWork, with perhaps with OpenAI that we keep hearing about gobs of money that is being sucked up into cetera, without there being corresponding evidence that they're either pivoting, as you point out, ⁓ that ⁓ market that they are targeting and that the problem that they're ostensibly solving is that people actually are willing to pay to solve. Radhika Dutt: It's so true. think increasingly, this is the game where you a lot of money against or towards startup, right. And it's become a ⁓ of who can the money and treasure chest, a war chest so that you can wage wars, right. This is how Uber and Lyft ⁓ sort of have gotten their share of the market. Sam Sivarajan: Mm-hmm. Mm-hmm. Radhika Dutt: because they just had a huge war chest and could just flaunt whatever laws or go against whatever laws there were ⁓ say, well, we'll just fight those battles in court. Right? Like what startup ever had that take back in the day? Sam Sivarajan: Right, exactly. Yeah. think so in this kind of a world that you so paint, clearly ⁓ the VCs with the right kind of clout win. Who loses? Radhika Dutt: Oh, it's a huge loss to society, right? If you think about Uber and what it's done to society, it's not necessarily good. We paint it as progress and technological progress. But if you talk to an Uber driver, it's not progress for them. I was talking to an Uber driver the other day. He was saying how, when Uber started out, they used to... Sam Sivarajan: Mm-hmm. Mm-hmm. Radhika Dutt: take 20 % of the cut and 80 % would go to drivers. it's more like a 50-50 split. And on top of the 50-50 split, ⁓ the Uber driver who is paying for his car, gas, repairs, et cetera. It's a fair system at all. And there's an algorithm that figures out what is the lowest I can pay Uber drivers. And it's not good for consumers either, because for consumers, what it does is, ⁓ well, things. ⁓ One, Sam Sivarajan: ⁓ wow. Radhika Dutt:

There's of course surge pricing and my ride to the airport used to be much cheaper many years ago than it is now. But there's second element to it. In New York, it now takes 17 % more time for ⁓ anyone to around the city and it's because of Uber. ⁓ Exactly. And it's made public transportation that much worse because anyone who afford it then takes an Uber Sam Sivarajan: of the congestion. Mm-hmm. Radhika Dutt: And the city has fewer people taking public transportation. So it's the people who are income that'll take public transportation, less of an incentive to invest in it. And continues to spiral, right? ⁓ So are few, so ⁓ this whole model a few companies taking up all of the funding, having a war chest, then having the money lobby, to fight the ⁓ legal battles, et cetera, building a user base or a marketplace, by force, and imposing that on society. ⁓ And think it's progress, but it turns out not to be over time. But we don't know that early on. This is of a recipe that we see over and over. ⁓ And now seeing this with AI as well. Sam Sivarajan: Mm-hmm. Mm-hmm. No, I think that's very profound. think, and beyond all of the factors that you talked about, ⁓ greater higher pricing, because there's no competition, you've got, worsening climate because of all of the ⁓ Uber drivers that are to and idling in order to wait for their next fare. I think to your point, and this is a topic for an entirely different conversation, but we're... ⁓ we've gotten to the point where monopolies were bad, right? I I remember the time I'm a corporate lawyer by background. I remember a time when monopolies were bad and we could trust all of the forces of government and regulators to do their best to kind of fight monopolies from existing. And it seems to me that the models that you're talking about is very much dependent on that old paradigm being washed away and that we need monopolies, need, one or two category killers, or dominators that are going to define what that industry is going to be. And yes, as you say, it is a fortune for the VCs that back it from the beginning. It is a fortune for the lucky startup, one or two startups that fit that mold, but seems like everybody else pays for it, right? Radhika Dutt: Yeah, exactly. there's a there's a book titled One We Will Have All Been Against This. I love even the title of the book. But in book, the author talks about how, we think about as progress, but it's more an acceptance of mediocrity. That is what it turns out become over time. There are so many examples of this I think are coming up for me, especially now. ⁓ we're in the AI era. have thought the AI era as something that is going to bring so much technological progress. And yes, there is for that. But, increasingly, what I see of are the dangers, not necessarily the promise of potential. ⁓ Yes, there is potential that, we'll build stuff much faster. ⁓ But what I increasingly see will do is Sam Sivarajan: Mm-hmm. Radhika Dutt: will create a bunch of AI product slop because it was easy to create. And everyone else will have to just accept that mediocrity. here's an example. It's not just that I'm making this up. Coca-Cola, last year, they created their holiday ads using generative AI. And they a ton of backlash for it because people would look forward to their holiday ads, of course. And last year, ads were just so crappy. ⁓ Sam Sivarajan: Mm-hmm. Radhika Dutt: Coca-Cola got a ton of backlash. So then you would think, okay, well, so what did they do this year in 2025? Well, they doubled down on AI. So this year they just created more AI generated ads. And what? Their chief marketing officer, he didn't even defend it. He just said, guys, It took us one month to make this as opposed to having to plan a year ahead. We're going to do more of this. this is their plan. And so as consumers, the message is, accept it. Mediocrity is what's coming. Just go with it. Sam Sivarajan: right. That's scary. And, to put it in a more vulgar term, I've heard it, I'm sure you have, I think there's actually been books, it's now become a is enshittification of the, world, right? I mean, this idea of mediocrity that has not just acceptable, but it's almost as aspirational that, you, blanket the market with a mediocre product and you will make lots of money, not because it's good, but because you've crowded out anything else that could even hope to compete, right? Radhika Dutt: Exactly. so herein lies the challenge, right? Because a lot of people will say, well, the free market will address that, that people will vote with their dollar. But exactly to your point, when there's only one or two companies that flood the market, or if everything is mediocre because everything is AI generated in this way, there isn't really consumer choice for something better. So you can't really vote with your dollar. so you do end up accepting this end stratification is how it goes. And what I talk about, whether in radical product thinking or in a lot of my work, we can't ⁓ that the has a choice to vote with their dollar. So where is the power? ⁓ And the to that for me lies with employees. For every listener, Sam Sivarajan: Mm. Radhika Dutt: Realization we have to come to is that you're voting with your labor for the world that you want to create. You're voting with your labor you have a choice. You can choose to just create things with AI and throw out slop without truly reflecting on it is you're creating in the world, how it affects society. Or, and is what I advocate for, you can think about your product as Sam Sivarajan: Okay. Radhika Dutt: your mechanism for creating change in the world. by creating this mechanism in a way that you very systematically engineer that change you want to bring about, this how you vote for the world that ⁓ want create. Sam Sivarajan: So I think that's very powerful. Are you talking about and file employees? Are you talking about startup founders? mean, cause the argument I suppose one could use is that

if I'm worried about getting paid and putting food on the table, how much power do I really have to kind of put those, that kind of thinking, that kind of ethos into my work. Radhika Dutt: It's such a great question, right? this is where maybe I'll talk about the framework that I think about where in corporate world, you think about how do you balance long-term vision against short-term survival? But in the same as an employee, it's the same sort of question that have to ask yourself. What is your vision? What is the change that you want to see the world, right? ⁓ What happens often is we don't reflect on is the world that I want to vote Sam Sivarajan: Mm-hmm. Radhika Dutt: Without that reflection, whatever vote that you're casting, we aren't even giving it a second thought, right? have a friend who used to work at Google. And for the longest time, he would complain about the change that Google was bringing to the world, how ⁓ Google to have this approach of do no evil, how they remove that. ⁓ Yeah. Sam Sivarajan: Mm-hmm. Right, right, very quickly. Radhika Dutt: And he felt like, I want to do something better. Like I want to create change that I care about, et cetera. ⁓ But he saying, well, but I really appreciate Google's and being well paid by Google. The thing is though, I often said, it bothers you, then why don't you look for another role? And he was always like, he never took that time to do that, right? ⁓ So here's the I agree with you. We sometimes have to balance this long-term versus short-term. You have to feed the family, pay the mortgage bills, et cetera. if you have a vision for what is the change you want to bring, ⁓ you have to weigh that and think about how balancing the two. If you draw that up on an X and a Y axis, your Y axis is, is this good for the vision or not? Is this good for the long-term vision? And the X axis is, is this good for survival or not? Things are good for vision and survival. Those are the easy decisions. But, we don't always hang out always in the easy decisions, right? Often we're what we call vision debt. This is good for survival. Maybe it brings in the paycheck or it's great for bringing in revenues, but it's not good for long-term vision. So that's vision debt. For some people, right, like the Sphrenomine, he felt like he was taking on vision debt. Sam Sivarajan: I love that. Radhika Dutt: And the opposite of that is when you're taking on investing in the vision. This is where it's good for the long-term vision, but it's not good for short-term survival. Example of this might be you've decided to take some time off to decide what is it that you really want to work on. Or you've decided you really want a career change. This requires going back into... ⁓ a step backwards in your career to do this career change. And there's the danger zone, of course, which is it's bad for vision, bad for survival. Nobody is really going after opportunities there. So my point is, you can the same matrix, even if you're

balancing opportunities at work. You have a sales deal, you can think about is this vision debt, are you investing in the vision? ⁓ But you look at this from a personal lens as well. What is your vision for change? Sam Sivarajan: Right. Radhika Dutt: wherever you're working, if you're feeling like this is truly not satisfying, or if it goes against my values, ⁓ maybe taking on vision debt. ⁓ But we never acknowledge that, you're never ever going to change where you're investing your time. Sam Sivarajan: I love that. I boil it down, saying look, can make a decision of whether you want be in vision debt or invest in your vision. But fundamentally, you have to make it as an explicit choice and decision that you're making in the moment, right? That you have to think about, is this something that I'm doing for immediate survival? And what am I giving up in the process? Or this the time I'm going to invest in the future? And it's similar to a part my framework, which I talk about being spanned, that you have to make decisions that balance time horizons, both today and in the future. And I think a very hard thing to do, but I also think that we have to do it. The analogy I always use is that companies that talk about how innovation is critical and then yet cut their R &D budget, well, that doesn't compute, right? I mean, you can't sit there and say something is important, but you're not ready to invest in it. And I think that's you're getting at is whether it's at on your personal level, as employee, or whether you're a company, you have to balance short term survival with who you want to be or what you want to be somewhere down in the future, And ⁓ if your isn't, explicitly making that trade off, the reality is you're implicitly making that trade off and you may not like it, a year, two years, five years into the future. Radhika Dutt: Yes, exactly. And I love what you said. that example that you gave ⁓ of that want to innovative, but then you cut R &D budget. In a sense, if you look at that vision versus survival, what you've just said is, ⁓ I'm going to investing in the vision. ⁓ All of that &D budget I just slashed, I'm not investing in the vision. And on the other when you take on all these custom features, because, we want to win big deal. This customer will buy if we this feature. Sam Sivarajan: Okay, right. Radhika Dutt: All of that is adding vision debt, for example. so at some point you have to be able to look at the opportunities on this two by two that you're taking on ⁓ you're always just taking easy decisions and taking on vision debt. You're not innovative because innovation very often lies in investing in the vision. It's being able to think ahead that longer time horizon and being able to say that I'm doing that are good for that long-term vision. Sam Sivarajan: Mm-hmm. Hmm. Radhika Dutt: but it's not gonna sell tomorrow, right? And that's where you're investing in the vision. Sam Sivarajan: That's a great example of framework that you talk about Radhika, this radical product thinking. Can you share an example of how you've ⁓ this framework to help an organization break through a strategic logjam or perhaps avoid a costly mistake they were about to make? Radhika Dutt: I find question so interesting by the way. one thing that it highlights for me is we often think about costly mistake or one, big deal or a problem saving someone from something big. What I think happens, the way we build great product is not by that one decision that Sam Sivarajan: Thanks. Radhika Dutt: you made that was a good decision or averted a bad decision. a series of small things that you do that you in the right direction. Right. So when I think about building a product, right. And when make decisions about features or whatever we're building, when we say, know, what am I going build this next month? What are the features I'm going to take on? Sam Sivarajan: Mm-hmm. Radhika Dutt: It's not necessarily about that one feature that I said no to or that one feature I said yes to that made all the difference. really a series of small decisions ⁓ what happens, is very often you take on small decisions in that vision debt quadrant ⁓ or don't invest adequately in the vision. And over it starts to piss off customers. Sam Sivarajan: Mm-hmm. accumulate. ⁓ Radhika Dutt: And I'll give you an example of this. was a company where, you know, there was a, the whole engineering team had thought ahead and knew where the body was going five years ahead. They even participated in the standards body ⁓ and they the standards body to approve a particular standard ⁓ that they that they could be first to market with and that this they would lock in their market position. So the engineers were thinking so far ahead, brilliant. they came back to Homebase headquarters and they said to the product manager, we need to build this feature. And product manager goes, but nobody's for this feature. We're not going to build it right now. We'll wait. ⁓ We have So they kept pushing it out, kept pushing it out. They never invested that vision in terms of building out the standard. And in the meanwhile, everyone else went and built the standard and this big company that was the behemoth that had set the standard in the standards body were behind. And then finally they implemented it and they sort of caught up. But it was sort of a me too solution, half-assed. But this is what happens. We often don't invest in the vision and then we end up taking on vision debt. This is how this was implemented. So my point is that it's never just one big decision. Sam Sivarajan: more behind. Radhika Dutt: It's constantly observing where you are in terms of, we taking on too much vision debt? Are we not investing in the vision enough? Like, how are we balancing these quadrants? that constant reflection. That the power of this approach, being able to reflect as a team. So the biggest thing I'll say is when I work with organizations, the biggest of this view is

instead of just imposing a view, Sam Sivarajan: Hmm. Radhika Dutt: or instead of me imposing a view and saying, this is our priority, we need to do one, two and three. draw this up for the team where I talk about, here's vision, here's what survival means. How are balancing these quadrants? So ⁓ whether I'm about sales opportunities, initiatives for the quarter, ⁓ or even a plan for this next sprint coming up, ⁓ we look at we're balancing these quadrants, right? And this means I'm ⁓ Sam Sivarajan: Mm-hmm. Radhika Dutt: my influence, my way of thinking and how I'm balancing long-term against short-term the whole team, the company, so people are thinking like me even when I'm not in that room. that, even when making decisions about how do I code this up or how do I sell this product, they can think about the vision versus survival balance and make the right choices when I'm not in the room. Sam Sivarajan: So love your characterization of this as balance. I would call it trade-offs, ⁓ cause that's just language that I'm used to, but I think it's important. And I've always said, whether it's in a financial context or in a business context or in a personal context, every decision that we make has a trade-off. Whether you choose to spend versus whether you choose to... eat that piece of cheesecake instead of salad, etc. It's a trade off. And I think that we've gotten to some extent to this view, or we're being marketed a view that we don't have to make trade offs that we can have our cake and eat it too. And that's to a sort of a lazy behavior that says, well, you know, we don't have to make this choice, this this product manager that you're talking about, for example, right. And I think ⁓ What you're arguing with this vision debt versus vision investing in the vision is making it crystal clear to people that this balance is about where does that trade off for you as an individual or for you as an organization? And there's no right answer. It's ⁓ your or my job or anyone else's job to tell a company or an individual how they should make that trade off decision. only that they recognize that they will have to make a trade-off and they just should do it methodically and it well thought out as opposed to having it made for them implicitly. Radhika Dutt: And I love your point. And that's exactly right. Very often when I share this two by two in these four quadrants, people ask me, what the right balance between across these quadrants? And exactly your point, right? There's no one can tell you what is the right balance for you. ⁓ If you're a startup, you might find that you end up taking on more vision debt. you might find that take on one custom feature after another because you got to ⁓ Sam Sivarajan: Right. Right. Right. Radhika Dutt: win that deal and that's how you're going to survive. might be a much bigger company and ⁓ you have a whole war chest and then you can do more of investing in the vision. whatever that balance is, you have to talk about this within your team and the between leadership and all the employees in understanding what is the right trade-off for you. I think that's where the power of this lies. And to your point also, with AI just making it so easy to stuff, whether it's ⁓ content ⁓ creating apps vibe coding, et cetera, ⁓ we to think that, there are no trade-offs anymore, that I can just build all of these things with AI. But the is, ⁓ yeah, sure, could vibe code all of these things, but someone still has to put it together. Sam Sivarajan: Hmm. Right, right. Radhika Dutt: There is someone who still has to make sure that of this code, it's someone is to have to end up refactoring it. It all has to fit together. You can't just throw these disparate apps together. It to fit in. There has to be a cohesive backend, et cetera. There is still work to be There are always inherently trade-offs and we can't forget that. Sam Sivarajan: think I to switch gears a little bit. When we talked earlier, you talked about your upcoming work it challenges this entire premise. We talked about alignment within an organization, but your work is challenging this premise of goal setting and OKRs. listeners who are not aware, it's the objectives and key results frameworks that had been Like gospel in corporate America for decades. it is the playbook that all ⁓ and all big consultants companies, ⁓ consulting companies want to ensure that, that, companies are following you argue, quite convincingly that these approaches were designed for repetitive assembly line work from the 1940s and doesn't translate well today's knowledge economy. Now that's a very powerful and provocative argument. So But I'd like you to expand on that for our audience. So what should leaders be doing instead? And how does your puzzle setting, puzzle solving type of approach differ fundamentally from goal setting? Radhika Dutt: Yeah. So let's take a step back even in terms of why I say goals and targets don't work. And we can call it OKR, smart goals, whatever the framework for goal setting. it always seems like goal setting frameworks that this is the new and best way of doing it. OKR is being the latest version. It was evangelized in 2018 by John Doar, the famous VC billionaire. Sam Sivarajan: right. Radhika Dutt: And it it seem like this is a new idea. Google says that's its secret success, et cetera. So clearly it must be working well. The reality is goals and targets, they have a fundamental flaw to them. And the fundamental issue goals and targets. When you set a goal or a target, the incentive that you create for your team is to prove to you, goals and they have a fundamental flaw ⁓ to them. here's the fundamental issue with and targets. When you set a goal or a target, the incentive that you create for your team ⁓ is to prove to you, I've hit that target. It's to prove that whatever incentive, whatever initiative it was, whatever it was that we were trying, that strategy, et cetera, to prove that we've hit those

numbers and therefore it's working. Whereas the reality is what you need someone to do is just pick out the good numbers and say, ta-da, but to look at the bad numbers and play detective, ⁓ to say, actually going on? Why didn't that work? Sam Sivarajan: Mm-hmm. Radhika Dutt: It's to prove that whatever incentive, whatever initiative it was, whatever it was that we were trying, that strategy, et cetera, ⁓ is to prove we've hit those numbers and therefore it's working. Whereas the reality is what you need someone to do ⁓ is not just out the good numbers and say, ta-da, ⁓ but to actually at the bad numbers and play detective, ⁓ to say, what's going on? Why didn't that work? Sam Sivarajan: Mm-hmm. Mm-hmm. Radhika Dutt: what are the bad numbers telling me? what are the bad numbers telling me? when you set a target, everyone wants to show, look, I'm a high performer, I achieved targets. So there isn't an incentive to really reflect on the bad numbers, this is why Andy Grove, legendary Intel CEO, he said, leaders are always the last to know. You don't get real information from the trenches in terms of what's actually happening. What you get is ⁓ information in of what your what you expect to hear the incentives that you're giving people in terms of what you want to hear. And so what do I do instead? So although in organizations very often we work based on goals, targets, et cetera, I work teams from this lens of puzzles to set and puzzles. So I'll give you example of a company I've been working with. It's called Signal Ocean and it's in the maritime industry. Sam Sivarajan: Mm-hmm. Radhika Dutt: And basically what Signal Ocean does is their data platform. And if you, in the simplest terms, look at it, it's basically helping people in the maritime industry figure out what is the best vessel, shipping vessel to match to which cargo so that I can optimize profits. Now, Signal's data platform, it's brilliantly accurate. But when I joined the company in 2023 as a consultant, know, sales had stopped. And, looking at the technology, go like, I don't understand, why isn't everyone using this in the, ⁓ in the market the technology is revolutionary. So why has adoption stalled? And so when I joined, looked at the data and what we saw was a was forming for me, which was only tech savvy brokers in the maritime were using the platform. So you go, huh. Sam Sivarajan: Mm-hmm. Hmm. Radhika Dutt: Interesting. Why are only tech savvy brokers using it? Why aren't the others using it? then we started framing the puzzle by asking questions because, a mindset means there are, there are questions that don't know answers to. Cause if you knew everything, it's not a puzzle. Right. So, so we felt free to a few questions. So here are a few questions. What do people who are not brokers, how do they work? How do they think? Sam Sivarajan: Mm-hmm. Right. Radhika Dutt: And how is it different from how brokers think? Then there's this next question. It's the tech savvy users who using

it. What about the tech averse users? How do those people think? And how are they different from these other users? these are questions we genuinely didn't know answers to. Like, how do these people operate? And we started doing some observations so that we started to frame this puzzle for ourselves. And The way you can visualize all of this approach until now is imagine you have a Rubik's Cube. You can't start trying to solve a Rubik's Cube until you have a really good sense of how these pieces are moving. And so this was our framing the puzzle and observing users. Now we understood how the pieces were moving. Then you start to solve the puzzle. And when you solve the puzzle, now you're ready to take your first attempt because you've understood the puzzle. as you take your first attempt, Sam Sivarajan: Mm-hmm. Radhika Dutt: you ask three questions. So the first question is, how well did it work? And notice how different it is from goals and targets. I'm not asking for just the good numbers. I'm not asking you, did you or didn't you hit this target? I'm inviting the good and the bad. me how well did it work, right? Then the second question is, what did you learn? This is where I say to teams, Sam Sivarajan: Mm-hmm. Mm-hmm. Radhika Dutt: Don't just spit out lots of numbers at me. Don't just tell me weekly active users and time spent on site, blah, me what's going on. What have you actually figured out? And then the third question is, what will you try next? So if I gave you a magic wand based on how well it worked and what you learned, what will you try next? So here's what we tried at Signal. So saw tech savvy brokers were using it. And we observed all these different users, those who had cargos, those who owned vessels, how do they behave, what do they do? And then we came up with a hypothesis. We had all these workflows we figured out. we figured out what ⁓ we needed to add. And the second thing was we said, aha, not only are we going to add these workflows, we're going to automate it so that those who are tech averse, if we do some automation, that things will be much easier. Sam Sivarajan: Mm-hmm. Radhika Dutt: So now we can ask those three questions, right? How well did it work? So here's the first answer. Well, it turned out people liked the workflows that we'd figured out. They hated the automation, absolutely hated the automation. Question number two, what did we learn? So we learned that yes, we should implement the workflows, but why did the automation fail so badly? Why did they hate the automation? And what we learned was, that these tech averse people, don't trust technology, that our technology felt like magic to them. So when we automated, they were like, this is just ⁓ Yeah, exactly. How did you get these numbers? This is like magic. I trust this, right? So that's what we learned, that magic doesn't work. So the third question, what will we try next? This is where we said, OK, so we'll add the workflows, but instead of automation,

Sam Sivarajan: I don't understand it. Mm. Radhika Dutt: We will guide them step by step at every point. We'll make them feel like they are in control. No magic. so this is what we did, right? And so we to puzzle set and puzzle solve like this. And we revenues in 2024, doubled it again in 2025. And this is power of puzzle setting over goal setting. Sam Sivarajan: Hmm. I love it. I think that's powerful. I love the metaphor of puzzles. And ⁓ I can encapsulate what you the the the approach, I think you're you're if go with the puzzle analogy, you're talking about first looking at the box, right at the picture on the box, etc, to get an outside perspective of how others are seeing it and where you're trying to get to. And so that's asking the questions. What are we trying to do and and how and all of those things? I think in a puzzle together, you try to get the pieces that you know. So ⁓ way do it, I put the corner pieces, the edge pieces, because you sort of know that they frame the picture. And I think you're iterating, right? You keep experimenting, you try. You say, does this piece look like it goes here? There's reasons to believe that this could work. we're going to try it and when it doesn't, you don't give up, which is, the problem that you talk about, I is the inherent problem and goal setting. It's a binary outcome, either you've succeeded or you failed. Whereas this puzzle setting, puzzle solving approach is that it's inherent there that there is going to be iterations that you're going to fail, but you're going to learn from it. And as a result that you're going to finally come. still closer and closer to the outcome that you're looking for, which is to match the picture that you have on the outside of the box or in this case to solve the problem of what is it Signal is trying to achieve with its target consumer base, right? Radhika Dutt: Yeah, so much to unpack in what you said, right? So in the mantra of just keep iterating, iterate quickly, et cetera, right? And if you look at Lean Startup and what it says, in Lean Startup, it does say you have to identify the problem. But if you look at how it is summarized, ⁓ the of Lean Startup is build, test, learn, scale. Think about where the emphasis is. The emphasis is on build. Just start by building, trying something, putting it in the market. So in fact, the puzzle setting and puzzle solving approach, starting point is different. So the framework that I call puzzle setting, puzzle solving framework, I call it OLA, ⁓ which for Observe, Hypothesize, Learn, Adapt. But observe is the first step. What happens we don't stay in the problem space long enough. Sam Sivarajan: Right, right. Radhika Dutt: What happens today is we want to jump to solutions. So in fact, when I started working with Signal, at first, whole team, when they were looking at data, the data was telling us that the biggest pain point for users was filtering. So basically ⁓ there's a functionality for lists in this tool. The biggest problem was filtering. This is what data was telling us. Sam Sivarajan: you Radhika Dutt: If you're focusing on goals and targets and improving user engagement, et cetera, look at data and you jump to the first solution. And this whole iteration mindset tells you, it says filtering is the problem. Let's jump to fixing filtering. I'm going to improve filtering and launch the solution. And hopefully, moved the needle in terms of goals and the targets user engagement, right? But instead, what we said was, is the puzzle? Sam Sivarajan: Right, right. Radhika Dutt: We took some time to define the puzzle, to observe, to ask questions. We didn't at it from this angle of, ⁓ the solution. ⁓ first said, what is the problem? And then observing, then we decided to take our first hypothesis. That a key difference. And in what happens in organizations, and I was talking to someone who was heading up user experience at Morgan Stanley, and they were how Sam Sivarajan: Right. Radhika Dutt: even being a user experience researcher, et cetera, UX researcher, know all of these principles. They know that they have to stay in the problem space, except do they ever get to do that? No, because you might do user research and say, we need to do this, but everyone pushes you towards, okay, yes, but we have these goals to hit. What's the quick win that we can do? Sam Sivarajan: these quarterly goals exactly. Interesting. So on that note, in our earlier conversation, you said that one of the key traits that you look for now, whether in founders or team members or people that you're working with is coachability. And that's an interesting concept because venture capital is historically rewarded confidence and perhaps even arrogance. So how do you assess whether someone is truly coachable versus just agreeable? And why does this matter so much in navigating the uncertainty of solving for what the market needs? Radhika Dutt: Yeah. I think this is such an interesting question because I've also had this debate with investors, ⁓ sometimes I've to them about ⁓ why, why investors pick someone with so much arrogance, right? Because on the hand, you need arrogance and to say, I'm going to doggedly pursue this idea. ⁓ But at the same you have to be not so in love with your own idea that Sam Sivarajan: Right. Right. Radhika Dutt: you're unwilling to learn and adapt. in the whole OLA format, of observe, learn, adapt, what makes realize if someone is coachable or not is how flexible they in terms of wanting to learn and adapt the level of curiosity that they have. You truly have to openness and curiosity to be able to look at a puzzle. not just try to, so if you look at the example of the puzzle that I was setting, right, we talked about tech savvy using it, you have have genuine curiosity to say, how are the other people behaving today? What do they do? Not just approach from how do they use signal? So you have to approach it with genuine curiosity. You have to be able to say, why

should those users behave exactly the way they are behaving today? Like, why is it in their right? are they exactly right to behave the way they are? Maybe can change the world for them, ⁓ but have to approach it that level of curiosity so that you can then understand them, empathize, and then solve the problem. I think this the key piece of coachability that I look for, that openness and curiosity, then ⁓ the willingness to learn and adapt from it. not enough just to have openness, but a big piece of it for me is to see what do they do then afterwards, right? Like sometimes one of my biggest tests says, I give feedback on their pitch deck and then what do they do with it? Do they then adapt it or do they sort of just discard the feedback and it's back to business as usual? Sam Sivarajan: Right. And what have you found? Can you those kind of instances? I guess there's a bit of both, right? You've got, people that do take that feedback and others that don't, etc. So do you do in those instances where, they ask you for feedback, you give the time and the effort, and you give them this coaching, if you will, they ignore it ⁓ and and they come back next time, what do you say to them at that stage? Radhika Dutt: It is funny, this has happened to me where, a startup I was advising, I was sharing feedback in terms of what I was seeing in terms of issues with co-founders. And I was saying, beware of this issue coming up in six months. And, six months later, he was yeah, you were right. How did you know? It's like, yeah, I've been through founder divorce. It's painful. So, and then I was giving him some other advice and then, months later, he was like, how did you know? ⁓ Sam Sivarajan: Ha ha ha ha ha. Radhika Dutt: But I think this is where for me, learned two One is I don't just do advice anymore. Like I am very happy to share information or advice on a passion project, but it's very rare now that it's really about being an advisor. know, if someone really my advice, if they it enough, then there's compensation either in terms of equity or pay because otherwise I find exactly what you're describing. it up time, when there isn't anything, when there is no investment on the part of the founder, like it just doesn't go anywhere. Sam Sivarajan: There's no skin in the game, right? Yeah. So Radhika, looking at high profile failures, we started this conversation with that, with the failures like WeWork, Theranos, or even the current AI hype cycle. do you think enables these companies to raise billions of dollars, attract top talent, despite, arguably fundamental flaws in their vision or their strategy? And what do you think investors and advisors can learn from these patterns? Radhika Dutt: I think one the things that I see as a pattern, is ⁓ that feels like there is strength in numbers. So whenever startup has momentum, everyone wants to invest in it and it feels like there's safety in numbers. And so there lots of exactly. And I think this both with WeWork,

⁓ but also with Theranos, there are so many other examples. So one thing that I find Sam Sivarajan: herd mentality, basically. Radhika Dutt: very helpful clarity of vision, right? ⁓ instead finding safety in numbers, asking or expecting clarity in vision. So as an investor, what we really need to look for is, are you seeing that sort clarity in terms of vision in that startup? And so, this is the big mistake that I had made when I started out, right? I was telling you about my first startup, our vision was revolutionizing wireless. In hindsight, we needed was a radical vision statement. When you have vision like revolutionizing wireless or, Snapchat, for example, has this vision of contributing to human progress by empowering people to express themselves, right? What does that even mean? When you such broad vision statements, basically anything goes, just build anything. ⁓ And so if you go back to also what I was saying about the vision versus survival, the X and Y axis, you have such a broad vision like democratizing justice or revolutionizing wireless, that two two matrix becomes meaningless because everything feels like a vision fit. And so, Sam Sivarajan: Right. Right. Right. I love that. Radhika Dutt: What you need as an investor, but even as a founder, so this is where you can be aligned, is have a detailed radical vision statement. So here's an example of a vision statement written in the radical product thinking format. It's a fill in the blank statement so that you can focus on what is the problem statement and then talk about how you'll change the world with your product. So here's an example, right? This was a startup that I founded in 2011. sold in 2014. Today when amateur wine drinkers want to find wines that they are likely to like learn about wine, they have to find attractive looking wine labels or wines that are on sale. This is unacceptable because it leads to so many disappointments and it's hard to learn about wine in this way by picking nice looking bottles. We envision a where Finding wines you like is as easy as finding movies you like on Netflix. We are bringing about this world through a recommendations algorithm that matches wines to your taste an operational setup that delivers these wines to your door. So this is a radical vision, right? Every time I share this fill in the blank statement, maybe some words might change here and there, but the point is not about memorizing the It's about having detailed vision that tells you exactly what is the problem, who has this problem, are they solving it and then what is the end you want to bring about and how you'll bring it about. it's only after you finally define the problem and why the world is absolutely unacceptable without this being solved, ⁓ that finally earned the right to talk about the change you can bring with your product. Sam Sivarajan: Right. No, and I love it. And I think to your point, it becomes then your North Star that allows you to make each of those decisions, those hard decisions along the way along that X and Y axis, those quadrants, whether it's a vision debt or investing in your vision, because have clearly articulated the problem is, why you are the one to solve it. ⁓ And, you can you you can evaluate decisions accordingly. Rathika, we're coming to the end of our podcast. So have a few final rapid fire questions for you. So number one, professionally, what is the most important lesson you've learned over the years? Radhika Dutt: most important lesson. I will say that my background is as an engineer. And so, ⁓ a long time in my career, I was very task oriented and getting things done. And it was really all about, I know what is what it is I need to get done. And this whole idea of consensus, buy in It all felt like fluff that I just have to waste time on. get with it, people. But there's a whole different side that I've learned time and the power of bringing everyone with on the journey and how, as a leader, we delegate more because people really get it, get our way of thinking. We've been able to not just transmit information in terms of Sam Sivarajan: Thank Radhika Dutt: These are my priorities this is the vision. But details like, getting people to understand what are right trade-offs to make. What is the end state this vision so that they can also pursue it? How to make your vision a shared vision, not just your own that others chasing because you're telling them to, right? All of this and the soft skills. has been a huge part of my learning experience. Sam Sivarajan: Mm-hmm. I mean, I couldn't agree with you more. think, I think what you're describing is the, the learning curves that every subject matter expert or individual individual contributor goes through to, as they grow in their careers and in their, in their lives to lead a business or run a team, et cetera. You have to kind of bring your technical expertise to, to map it along with human behavior to say, how do you make this as you so well put it, shared vision, a shared mission that the others around you want to embrace, not because you tell them to or that you're paying them to, but because they truly believe it. Right. And it is, I think it's a lesson that is it applies to everyone. And I think arguably perhaps even more so in today's world, when people are looking and companies are arguing about purpose and not just profit. Right? So how do you bring people along on that vision? Radhika, what is number two? What is one practical tip you would offer listeners keen on applying your insights? Radhika Dutt: One thing I'd say is, you're using radical product thinking ⁓ this framework that I talk about for puzzle setting and puzzle solving, a great to start is just to start in the of while you're sitting at desk and just try out this vision statement. Think about a puzzle also that you're working about a past initiative and then think about how you would set the puzzle And solve that puzzle, doing all of this by yourself having this time to is such ⁓ a powerful way of learning there was quote I came recently which was you learn not through experience, ⁓ but you learn by on experience and so Being able to write such a vision statement, work on puzzle setting, puzzle solving, et cetera, all this is a way also reflecting. It helps us learn from experience. And then it us become much better leaders so that we can communicate much better with our teams. Sam Sivarajan: I love it. Radhika, an amazing discussion. If listeners want to learn more about you or find out about your work, where do they go? Radhika Dutt: of all, they're welcome to reach out to me on LinkedIn. I always love to hear how people are creating change. They also get the Radical Product Thinking book, which is on Amazon and in bookstores. lastly, I'm on my second book, and it's about why goals and targets don't work and how to use this puzzle setting and puzzle solving. So people can download free Ola toolkit, and I'll share with you link for our show notes. So people can use the OLA toolkit to be able to use the puzzle setting puzzle solving framework. And as you use it, you're welcome to reach out to me on LinkedIn share with me your experiences, which might make it into the book as a case study while I'm on the second book. Sam Sivarajan: Awesome. And as you say, this will all be in the show notes. Radhika, thank you for joining us today on the Uncertainty Edge podcast. Radhika Dutt: Thanks so much for having me on. This was such a fun discussion. Sam Sivarajan: Likewise.