Superhuman Founder Rahul Vohra On Reverse Engineering Product Market Fit

Email productivity tool Superhuman has been a buzzy startup since its launch and was last valued at $825 million in 2021. In early July, Superhuman dropped the news that it had been acquired by Grammarly‬ for an undisclosed sum. The deal follows Grammarly's $1 billion funding round this year as it seeks to transform itself into a platform of AI workplace tools.

Just a few weeks before the deal, I had the chance to interview Superhuman CEO and Founder Rahul Vohra on stage at ‪Viva Technology‬ 2025. Beyond the company, Vohru has emerged as a guru of sorts on the topic of product-market fit.

In our conversation, Vohra reveals the exact methodology he used to build one of the fastest-growing productivity startups. Instead of relying on vague definitions of product-market fit (PMF), Rahul shares a data-driven framework called the "PMF Engine," including a 4-question user survey and a repeatable process to go from struggling to thriving.

The following is a slightly edited transcript of the conversation:

CO: Hello again, Viva Tech, and thank you for joining us. I'm Chris O'Brien, your interviewer, moderator for this session, and the founder and editor of the French Tech Journal. And we have a special treat today, because with us all the way from San Francisco, we have Rahul Vohra, CEO and founder of SuperHuman. So welcome and thank you for coming all this way.

RV: Absolutely, thank you for having me.

CO: I'm super excited because this is a company I've followed as a tech journalist for a long time from a distance. So it's great to have a chance to have this conversation with you. Let's just start with the basics. For anyone who doesn't know: What is Superhuman?

RV: Superhuman is the most productive email experience ever made. Imagine getting through your inbox twice as fast as before, replying to important messages one to two days sooner, and saving four hours or more every single week. We're also reinventing the future of productivity with AI. Imagine waking up to an inbox where every email already had a draft reply you would simply edit and then send, and sometimes you wouldn't even have to edit.

CO: Yeah, that is the dream. So there are a number of things we could talk about in the next 25 minutes, but we're here to talk about product market fit, which is really something that you have become a notable thinker and leader on over the years. So let's just start with that. What does Product Market Fit feel like?

RV: Product market fit is the number one reason why startups succeed, and the lack of product market fit is the number one reason why startups fail. What does it feel like? Paul Graham [Y Combinator founder] would say you have product market fit when your users, or rather, when you've made something simply that people want. Sam Altman [former Y Combinator CEO, current OpenAI CEO] would say that you have product-market fit when your users spontaneously tell other people to use your product. But it is perhaps Marc Andreessen who has the most vivid definition of product-market fit. He would say, you can almost always feel it when product-market fit is not happening. Customers aren't quite getting value. Users aren't growing that fast. Word of mouth isn't spreading. The press reviews are kind of blah, and the sales cycle takes too damn long. But he also says you can almost always feel it when product market fit is happening, customers are buying as fast as you can add servers, you're hiring sales and support as fast as you can. Reporters are constantly calling you about your hot new thing. Money is piling up in your checking account, and investors are hanging out outside of your house. And that's the most vivid definition I found. And by the way, a definition I was staring at through tears in the summer of 2017 because it seemed so subjective, so inactionable. What do you do when, by that definition, you don't have product-market fit? So I began to wonder, can you measure product market fit? Because if you could, then maybe you could systematically optimize it.

CO: Yeah, I mean, that's my thought. You have this new generation of entrepreneurs, especially in the GenAI age, and product market fit is now certainly a centerpiece of the startup lingo anywhere you go. Yet it can be vague. What do we really mean when we say something like this? And then you can have the written definition to say, Okay, here's what it means. Here's how people define it. You guys, you personally went a step further. You actually have a quantifiable model called "PMF Engine" that other entrepreneurs can sort of take and use as a template. And one of the things that is interesting, and I wrote this down to make sure I get it right, one of the central questions is, "How would you feel if you could no longer use this product?" So, of all the aspects when you think about that user interaction, what made that the most powerful question?

RV: The journey to this question started with an experience I had in 2015. We started the company in 2015, and we started much like any other company by writing code. And in the summer of 2016, we were still writing code. And in the summer of 2017, we were still writing code. I felt this intense, incredible pressure to launch both from the team, but also from deep down within. Myself, after all, my last company had launched, scaled, and been acquired in less time. And here we were, two years in, and we still had not launched. But no matter how intensely I felt that pressure, I knew that a launch would go really badly. I did not believe that we had product-market fit. But I couldn't just say that to the team. These are super ambitious, hyper-intelligent engineers. They poured their hearts and souls into the product. So I needed a plan. I started my search for the Holy Grail. I spoke to everybody I could find, read everything written on the topic, searched high and low, and then I met this guy called Sean Ellis. Now Sean ran early growth at companies like Dropbox, LogMeIn, and Eventbrite. Even coined the term "growth hacker." And Sean found a benchmark that is predictive of success, one that is even more predictive than Net Promoter Score. Simply ask your users that question, "How would you feel if you could no longer use the product?" Give them three possible answers: very disappointed, somewhat disappointed, and not disappointed. And so very disappointed means they'd be very disappointed without the product, they love it, and measure the percentage that answer very disappointed. What Sean found is that the companies that struggled to grow almost always had less than 40% very disappointed, whereas the companies that grew the fastest, well, those companies almost always had more than 40% very disappointed. So this metric is more objective than a feeling. It predicts success better than Net Promoter Score. And with this metric, you can actually construct what we call a "Product Market Fit Engine." That engine can generate a roadmap that is essentially guaranteed to take you from where you are today to product-market fit.

CO: Okay, so let's break down the PMF engine a bit. I know it can be complicated, but there are four primary questions. So what are those? Can you kind of walk us through the framework?

RV: Now you're testing me. Okay, so the four questions. Email these four questions to every user. Number one, like we just said, "How would you feel if you could no longer use the product?" Three possible answers: very disappointed, somewhat disappointed, not disappointed. Number two, "Who do you think this product is best for?" This is a very powerful question, because happy users will almost always describe themselves, but using the words that matter most to them. So this is the perfect source for product marketing copy that will speak directly to your happiest users. Number three, "What is the main benefit of the products for you?" One of the most important questions, I'm sure we'll circle back to that. And number four, "How can we improve the product for you?" So very simple, four questions. Now, timing is key, and also, you shouldn't repeat the survey in terms of timing. I would send it as soon as the user has had the chance to experience the core value proposition of whatever it is that you do. So, for example, let's say that it's Uber or Lyft, just after you've experienced your first ride, and that frictionless payment. That would be the perfect time to survey the user. If it was Airbnb, just after your first vacation stay. At Superhuman, we wait until you've sent a minimum number of emails, and after about two or three weeks, that's when we survey the users.

CO: So that leads to my next question. So in my career as a journalist and the turmoil in the media industry, I've been part of a number of projects over the decades where we've gone out and surveyed customers, we've hired consultants, we've gathered all this data about changing behaviors, media consumption, et cetera. So there's the part of that process that's framing the questions, gathering the data. And then at some point, you sit down and you have this stuff in front of you, and then there's a whole other element of, well, what do you do with that when you have it? And how do you interpret that, and how do you get the right insights and move forward? So in your case, one of the elements, one of the bold decisions you made, was, we're going to ignore the feedback or the people who said they weren't disappointed. So, how did you get there? How did that seem like the right call in terms of how to use that data?

RV: I'm going to neatly sidestep the philosophical discussion that naturally comes up here about causality and correlation. Put that point aside. Let's say that product market fit is when you have more than 40% of folks very disappointed without your product. So we want to increase the size of that pie, the number of people who'd be very disappointed without your product. And there are two other segments, somewhat disappointed and not disappointed. In order to increase the size of that pie, we need to understand two things. Number one, why do people love your product? And number two, what holds people back from falling in love with your product? And that's where we go to the other questions, and we start analyzing the answers. So, to figure out why people love your product, we go back to question number three, which is, "What is the main benefit of the product for you?" Take all the survey results, filter them down to just those survey results from the people who were very disappointed. Remember, these are the people who love it and look at their answers only to question number three. This is where I like to take those answers and put them onto our gigantic word cloud. So, for Superhuman, the answers are going to be things like the product is really fast. I end up saving so much time. I love the keyboard shortcuts. I really like the design or the aesthetics. It makes me so much more efficient, and so on and so on. And so you'll probably have hundreds of those responses. Stick them on a word cloud. This is why the people who really love your product. So now we understand why people love the product. Then we need to figure out what holds people back from falling in love with the product. First of all, you have the set of people who were not disappointed if the products were to go away. Now this is to the point of your question. There is almost no point acting on their feedback. Because even if you built all the things that they asked for, they didn't even like your products for the same reason why the people who fall in love with it do so. Chances are you'll spend all of this time building all of that stuff, and it won't make a difference. So, as hard as this is to do, and as weird as it is to have a founder sitting on stage saying this, just ignore their feedback. Similarly, for the crowd that was somewhat disappointed without your product. I cannot stress this enough: Do not simply act on their feedback. Because again, if you just implement all the stuff that they ask for, there'll be a large contingent of those who still will not fall in love with your product. The question then becomes, how do you figure out who to listen to? And this is the magic part. Take the somewhat disappointed users. We're going to segment them into two parts. The first part will be the set of people for whom the main benefit was the same as the very disappointed users. The second part will be those for whom the main benefit was different from the very disappointed users. That second group, again, we're going to politely disregard all of their feedback because they don't love the product for the right reasons. They don't love the product in the same way that the people who really love your products do. So just ignore all of their feedback. Then there's the specific segment of the somewhat disappointed users who love the products for the right reasons, but it's something small. And I would wager something very small is holding them back. Implement all of those things. And if you do the math, you can write this down on a piece of paper. It's pretty simple. You'll see that you're incrementally increasing the number of people who would be very disappointed without your product.

CO: And if I may, just to ask a follow-up on that, that's a very precise, refined equation, framework for getting there. Was this a process of trial and error? Were there sort of references where you could say, oh, aha, this will guide us toward making those slicing up those different tranches of users, which one to favor, which one not. Or as you work through it, you realize, "Oh, this is a wrong path. We have to go back and focus on these people."

RV: Well, I blame the precision aspects of it on my background as a trained computer scientist. It's just my engineering brain being applied to it. But I was really trying to solve the problem of you are going to get so many different users and so many different customers coming into your product. How do you figure out who to listen to? If you just act on everybody's feedback, you're going to end up with a muddled, confusing, incoherent product and a product that, sadly, won't have product-market fit. We all know that you have to focus. We all know that you have to pick a direction. The question is, "How do you pick that direction?" And this is a way that marries, in my opinion, vision with data, and has very successfully, in our case, and with dozens of other companies I've helped do this.

CO: You mentioned a second ago this notion, or the concept of "something's holding people back." There's that layer of people. So you get your super fans, you figure out who's really passionate. But there might be some blocks to them really, really adopting, really feeling, that next level of passion. So when you're looking at the various things that might be holding them back, like lack of integrations or mobile, how do you start to address that without then veering off that sort of core focus?

RV: Great question. So let's go back, for example, in our case, to 2017, a long time ago. The obvious thing at the top of that list was the lack of a mobile app. And when you have obvious things like that, you should just build them. So, of course, we, a long time ago, built them. But then the list became less obvious and more interesting things, like we need more integrations, or better search, or attachment handling, or calendaring, or all of the things you might imagine people wanting in an email application. And if I understand the question correctly, it's, "How do you prevent yourself just kind of veering off in this direction?" So here's how we do this in every planning cycle. We do this quarterly. But as a smaller company, you might do it more frequently. We aim to spend 50% of our time doubling down on the things that people really love about Superhuman. So that's stuff like the productivity, the efficiency, the shortcuts, the speed, the design. And the other half of our time systematically addressing that list of objections. Remember, these objections came from the somewhat disappointed users for whom the main benefit resonated. Now you might be wondering, why is this 50-50 split important? Well, imagine you spent all of your time simply doubling down on the things that people love about your product. After all, that's what made you successful. Why not do more of that? Well, then the answer, and hopefully this will feel obvious now that I've laid it out, is that by doing that, you won't increase the size of the pie that is very disappointed. You won't get more people to fall in love with your products. The only way you'll do that is by converting some of the somewhat disappointed people. Okay, that's why you should do at least some of that. But why not spend all of your time running down that list, the mobile app, the search, the calendar, and so on? Well, if you spend all of your time doing that, chances are somebody will come along and do your special sauce better than you even do it. Someone will do the speed and the shortcuts and the design and all of that good stuff, better, faster, harder than you. So you don't want that to happen either, which is why we go into every planning cycle with a roughly equal balance between doubling down on what people like and systematically addressing objections from the user base.

CO: So you get through this process, at least the initial turn of it, you've got what you feel very confident about is real product-market fit validated by this framework. As the founder and the CEO, then, how do you begin to actually apply that in the way you're managing, hiring, setting, OKRs, all the things that you actually have to do to start to build the business?

RV: In the very early days of the company, as we were working on this metric. We had one core OKR, one core goal for the company, which was to get to 40% plus, initially. When I came up with this framework, and I was kind of developing it at the same time as applying it to Superhuman, the very first result for that number was, I think, 22%. You may notice that 22% is not 40% it's not even close. But it was close enough that I thought by systematically applying this and by working on the framework, perhaps over the course of a year, we could get that number to where it needed to be. So in quarter number one, it was 22%. There's a re-segmentation trick that you can do that we didn't get into. But if you're interested, you can search for my name, Superhuman, Product Market Fit Engine, I go into it in detail online how that got that number from 22% to 32%. And then we just systematically ran this process every single quarter. We got from 22 to 32 to 48 to 58%. So, after a year of running this process, we got to the point where 58% of our users would have been very disappointed without Superhuman. Now, the thing about metrics like this, and any business metric, is that they all come down over time. Your churn rates will go up, your marketing channels will become less efficient, and yes, even your product market fit score, if left to its own devices, is going to fall over time. Why does this happen? Because as you're growing, you're going to be encountering new personas, new user types, new people coming in via new channels, people who are maybe lower intent than those early adopters who sought you out more proactively. And so this number, it's going to come down. That's natural. It's normal. That means that the work is never done. And so even for the last seven, eight years that we've been running the company in this fashion, we still keep on working on this number, and we still keep on driving it.

CO: One of the reasons I think the timing of this conversation is interesting is we're in the middle of this hype cycle around GenAI startups, and again, it's extraordinary for me. 30 years following this stuff, seeing these stories about two guys sit down, they code something, they release it. We have 10 million ARR. We don't even need venture capital. We're fine. And as a journalist, of course, I'm skeptical. I look at that and I say, Okay, well, are they deluding themselves? How real is that growth? We're all trying this stuff. How much are people churning? So, has the current dynamic lowered the bar for PMF? Has it kind of caused you to rethink some of the assumptions that you make? Or is it, well, you know, are some of these people potentially deluding themselves just because they see that revenue coming and think, "We've got PMF"?

RV: I think that there are two fundamental things. To your point, AI has really changed. Number one, you can, with a much smaller team, much faster, build an application or a user experience that has a chance of hitting product-market fit. And number two, you can do so with far fewer people. So what I'm seeing from my vantage point, also as an angel investor, I've invested in over 120 companies at this point, is that it is easier than ever to get to product-market fit. But I don't think that's widely known yet. So we're in the early days of this phenomenon. Everyone knows how easy it is compared to how difficult it used to be. And to put some numbers on that, it used to be the case in Silicon Valley that good for a pre-seed or a seed stage startup is a company getting from zero, let's say, to a million dollars of annual recurring revenue in about a year. That used to be good in a year. It's now normal to see companies go from zero to 4-5-6-7, all the way up to $10 million of annual recurring revenue in one year. Or to give you another example, the average company in Y Combinator today, the average is growing at 10% week over week. If you go back two years, it was only the top quartile of companies that were growing at 10% week over week. So hopefully you can see just how much AI has changed the game. It's changed demand. It's changed how easy it is to build. It's reduced the number of people you need in some of the obvious ways, like you need fewer customer support people. But also in less obvious ways, as well, the very best founders are busy automating every single aspect of their business. So it's an entirely new game, and I'm learning as much as everyone else is.

CO: Okay, I'm going to try to squeeze in one more question here. So you mentioned at the beginning, there was a journey at Superhuman to create this framework. Now you have it. If you could go back to day one, obviously, you'd go faster. But how would you proceed differently if you had the PMF engine in your hand day one?

RV: Well, I think I would apply it from day one, as opposed to just kind of doing stuff for two years. Although I suppose the phrase du jour is you can just do stuff. Doing stuff with a plan is better than just doing stuff.

CO: Great. Well, we'll stop there. Perfect. Thank you all for joining us, and please give a warm round of applause.