How Do I Stand Out in the AI-Era PM Job Market?

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Shobhit: Ready to move to the next level in your product career? I'm Shobhit from Intentional Product Manager. Join me as we discuss ways to help you stand out in your job search and your career so you can have more impact and make more money. Hey everyone, welcome to the Intentional Product Manager podcast. Today I have a very special guest, and I say that many times, but really I've followed Ravi Mehta's career for a long, long time. Ravi is a senior product executive — I've known him since the days he was a VP at Trip Advisor, then he went on to be a director at Facebook, then Chief Product Officer at Tinder, and recently he's been Entrepreneur in Residence at Reforge. Ravi is also a very well-respected thought leader, and he's written on product strategy, leadership, product careers — and look, even in our program, Intentional Job Search, we often refer people to some of Ravi's thought leadership articles on product management careers, defining your shape, and whatnot. We'll get into all of that, but Ravi, welcome to our podcast, really great to have you here.

Ravi: Yeah, thanks so much for having me. I'm excited for the conversation.

Shobhit: So Ravi, I told the audience a bit about you, but I would love to hear your product journey — how did you get into product management? I'll ask you a bunch of questions, but let's get started there.

Ravi: Yeah, it's an interesting story — I actually got into product management by happy accident. I've loved technology almost my whole life. My dad, in the early days of personal computers, was working with Apple on some things for his work, and decided to bring home an Apple IIc computer — this little tiny computer that almost looked like a laptop — and I was just fascinated by it. I started to play around with it, started to learn to code, and that kicked off a lifelong passion for technology. I went to undergrad for computer engineering, dropped out for a little bit to run a game company full time, then went back and finished my degree, and I was like, I should probably get a real job. I graduated at a really fortunate time — it was the middle of the Web 1.0 bubble, so there were lots of engineering opportunities. I talked to a bunch of companies, thinking I should get something B2B, and one of the interviews I had was with Microsoft. At the time Microsoft said, "we're making a really big investment in games, we like that you came from a game company," so there was an interesting overlap. I went in to interview at Microsoft, and I found the interviews very different from all the other engineering interviews I'd been in — a lot more about users and specs and requirements and strategy, not getting up on the whiteboard and coding, which is what I was expecting. About halfway through the day, the recruiter came in and said, "I'm really sorry, but we put you in the wrong track — we actually put you in product management interviews, which at Microsoft was called program management, rather than engineering interviews. Would you like to finish the day in the program management track, or come back tomorrow for engineering interviews?" At this point I already had a couple offers. I thought this was fascinating — a whole set of things I hadn't really been thinking about but had been doing hands-on running a game company — so I decided to finish the day with program management interviews, eventually got an offer to join Microsoft, and decided that was the one I wanted to pursue. I haven't looked back since. I spent a little time outside of product management — when I went to business school I did a summer internship in management consulting — but I always come back to product and program management. I love working with customers, working with teams to build products, and over and over throughout my career I've come back to it.

Shobhit: Almost everyone tells me they got into product management by accident, there's no defined path, but this was probably the wildest story I've ever heard — being put in the wrong track and still landing that job you were after.

Ravi: Literally an accident, and it was really fortunate, because at the time there weren't a lot of people talking about product management. Microsoft had a growing product management discipline, a couple of other larger companies did too, but this triad of PMs, engineers, designers working closely together was really in its infancy.

Shobhit: Very cool — so tell me more, what happened next? I assume this Microsoft program manager role was pre-MBA?

Ravi: Yes, I went right out of undergrad into Microsoft, into the games group, but shortly after I joined, the Xbox initiative got funded — there's a whole story about a Valentine's Day dinner being interrupted with Bill Gates and his wife to get approval for the Xbox — and ultimately Microsoft approved I think one or two billion dollars, a massive investment, to take Microsoft's success on the desktop and move it into the living room. As that team formed, I got to be one of the first 20 or so people on the Xbox team, initially working on Xbox Live, thinking about platform capabilities. Over my time at Microsoft I spent time on the platform side, but most of my time was on the product and game side, thinking about how to build massively multiplayer games — Microsoft's critical advantage in the console wars, traditionally dominated by Sony and Nintendo, was coming in with a really deep understanding of the internet and how networked multiplayer gaming was going to change how people buy and play games. I worked on that for about six years, on both the content and the platform side. After about six years I felt like I'd gotten into product management by accident, and there was a whole set of things I didn't have a deep enough foundation in — strategy, finance, marketing, the pieces that go into being a good business thinker — so I decided to go to business school. I did my MBA at MIT, and coming out, rather than go into management consulting, which I'd experimented with over the summer, I joined an early-stage startup as basically founder and employee number one. Since then I've had roles at all sorts of stages, from big tech all the way down to a handful of people working in a room on a really tough problem. The common thread throughout my career has been, one, I love building products, and two, almost everything I've worked on has been consumer-oriented and had a social element to it. Ultimately what drives me is how you use technology to put people together in a way that creates value for them — whether that's utility, like finding the right hotel on vacation, or entertainment, which is something I worked on at Meta.

Shobhit: Love it — I also did management consulting in my summer, and I unfortunately gave in to the peer pressure and went into it later, and a lot of people do that after the MBA. So let's go now and explore a bit of your career — you were at Trip Advisor, then Meta, then Tinder. I'd love to know more about the day-to-day and the core challenges you were tackling, maybe the core skill sets you were building in all these different roles — these might seem similar, but I'm sure there are massive differences in how each company works and the skill sets you need to succeed.

Ravi: Really different, and I think as I look back at my career, the five years I spent at Trip Advisor were the most formative — they really pushed me on some of the hardest challenges and helped me refine my take on how to approach product management and product leadership. I joined Trip Advisor in 2012. I'd been at a startup started by Brian Balfour, who's now the CEO of Reforge — the company was called Viximo, building a platform for people to add microtransactions and virtual goods to their products, in the very early days of in-app purchases. We sold that company to Tapjoy, a mobile advertising company, and I was looking for what was next. I'd been a fan of Trip Advisor for a long time — it would never steer us wrong. It just so happened Trip Advisor was looking to build out its product organization, in part because the company had just been spun out from Expedia. Trip Advisor started as a startup, got product-market fit early, and was really optimized for the growing distribution channel of SEO — search engine optimization is hungry for unique and valuable content, and Trip Advisor was one of the first user-generated-content companies, so it got this great match between what users want and a fast-growing distribution channel. Shortly after it was founded it got acquired by Expedia, and was part of Expedia for many years, but Expedia's leadership realized Trip Advisor and Expedia were solving different things, and the enterprise value of the company separately could be much greater than combined — so they decided to IPO Trip Advisor out of Expedia, and I joined right around that time. Strategically we could start doing things that didn't make sense as part of Expedia — like adding the ability to book directly on Trip Advisor, which would have cannibalized Expedia's business but made total sense now that Trip Advisor was independent. I started on a small team called the core product team, with a really ambitious goal to make it easier for people to price-shop and book on Trip Advisor — hotels as well as restaurants and attractions. Over the time I was there, my team grew considerably, the company grew considerably, and it was a moment in my career where I felt like I had a lot of opportunity that was disproportionate to my experience, and I had to grow really quickly. By taking advantage of that opportunity, I was able to learn a lot, hopefully create a lot of value along the way, and it set me up for what I'd do later at Meta and Tinder, leading product teams in different product areas.

Shobhit: I want to double-click into one of the things you said — opportunity disproportionate to your experience. What are the characteristics one would look at if they want such an amazing opportunity?

Ravi: I think there's two things to look for, one of which is immediately obvious, the second is something people don't talk enough about. The first is you want a company that's growing quickly — if you're on a rocket ship, your career goes up with the rocket ship, a rising tide raises all boats — really important to identify fast-growing companies, especially if you want to accelerate your career past the average pace of growth. The second thing people don't talk about is you want to look for a shallow bench. If you're at a fast-growing company and there isn't a deep bench of talent, as opportunities come up you're going to get plucked for them and get the chance to grow. At Trip Advisor I got that because the company had just been spun out from Expedia, it was growing quickly, the team was really small, and we knew we wanted to grow really quickly. I had this nice combination of fast-growing opportunity plus shallow bench, and got a lot of opportunity to grow disproportionately quickly. If you look at every very successful product leader or executive, you'll see there was a time in their career where both of these things were true.

You're exactly right, not enough people talk about the second point — the deep career bench, and not having one, as an opportunity. What's interesting is if you look at big tech, especially as these companies saturate in their growth, you have a company that isn't growing as quickly, that's been recruiting really high-powered talent, so you have an incredibly deep bench and a shrinking slate of opportunities, and it becomes much harder to grow your career — your career grows along a predefined path of a couple of years between every rung of the ladder, versus a company that's growing fast and doesn't have that deep talent bench yet, where you're going to grow much faster. So the road from senior PM to director might be three years or five years at a bigger company — it might be twelve months, six months, depending on where you are, if it's a smaller company that's growing quickly. Perfect — so let's move forward to Meta. First of all, how did you think about the decision to leave Trip Advisor and go to Meta as a director?

Ravi: Yeah, so I'd been working at Trip Advisor for about five years, we'd shipped a number of really important initiatives, I'd gotten pretty deep in the travel space, but I was also feeling like there was something happening around the internet that I didn't fully appreciate — this was in the early days of the shift toward visual, video-oriented content, Instagram was growing quickly, Snap was growing quickly, there was a lot more focus on social media, and I felt like at Trip Advisor I was really focused on SEO and the travel use case, but I wanted to understand more deeply what's happening with younger users, what's happening on the front lines of how people connect online. I had an opportunity to go to Meta, to join a team focused on Gen Z, specifically trying to understand how Gen Z engages with entertainment on the internet, both from a product strategy standpoint and a corporate development standpoint. We shipped a couple of initiatives, I led the acquisition of TBH, Nikita's company, looking at how we could build up the talent base of people who understand Gen Z at Facebook, and make sure we had a clear understanding of what those needs were and what's important for Facebook to build versus okay to yield to Instagram or others. We also did a lot of work around short-format video and what was happening with what would eventually become TikTok in the US — there was a lot of growth around Musical.ly at the time. That was a period of my career where I had a much smaller team, and it was really about getting familiar with a new area I didn't have a lot of experience in. That highlights something important for people to think about from a career standpoint — career ladders are not necessarily linear. Several times in my career I've gone from a bigger title to a lower title, from a bigger team to a smaller team, and the important thing is not to optimize for the number or the shiny object, but really for the learning you're getting along the way. I don't feel like I'd have as deep an understanding of consumer social, of entertainment, of where consumer is going overall, without that time at Meta as well as at Tinder.

Shobhit: You made that decision — what specific thing were you looking to learn more about, the space or something else?

Ravi: I was really looking to learn at the time about where consumer was going generally, and where consumer social was going, because my entire career had been about consumer — Trip Advisor is a consumer company, but the work we were doing day-to-day was really focused on the user-generated-content model, which had been incredibly successful for Trip Advisor but was more text-based, more Web 2.0, than thinking about where things are going. The video sharing, the visual sharing happening on Instagram and Snap — I always thought would be interesting to bring to the travel space, but I didn't feel like I understood it well enough, and going to Meta and being part of that team was a great way to get immersed in it. That dovetailed nicely with Tinder — Tinder was pulling on two threads, the scale of team I'd led at Trip Advisor, plus some of the Gen Z-specific work I'd done at Meta, and a lot of my work at Tinder was focused on essentially Tinder 2.0 — how to think about where dating and social discovery are going for Gen Z moving forward, because Tinder had found product-market fit very early and stayed largely committed to that early product vision, and yet the users were changing, their needs were changing, and Tinder needed to change with it. A lot of my work there was around enhancing the product-market fit that was already there, improving monetization, but also thinking about where the next sources of product-market fit would be.

Shobhit: These three roles — how did the challenges you faced and the skill sets you needed change as you went from Trip Advisor to Meta to Tinder?

Ravi: At Trip Advisor, the skills I needed were, one, to move quickly — Trip Advisor's motto was "speed wins," very much geared toward figuring out the shortest path forward and making sure we weren't building Rome before we'd figured out whether that's really the thing to build. How to move quickly and do that at scale — scale in terms of product reach, hundreds of millions of people using Trip Advisor every month, as well as scale in terms of team, how to organize a team of product and engineering folks so they can work quickly toward objectives that are clear and iterative but also strategic. At peak, my team at Trip Advisor was about 70 people, which across design, engineering, encompassed a product development headcount of several hundred, so we were working at scale, across a large scope of responsibility, with the goal of being really fast — so it was important to understand how to set strategy, define goals, coach and hire great product managers, and do that at scale.

Shobhit: I love it — and how did that change at Meta?

Ravi: At Meta my team was much smaller, and it was really about understanding the problem — getting to the root cause of why Gen Z was moving away from Facebook, because it's hard to remember now, but it used to be the cool place to be, it started on college campuses, every high schooler wanted a Facebook account, and that was changing rapidly in the 2017, 2018 timeframe. We wanted to understand why, and based on that understanding, articulate it clearly and figure out strategically what to do about it — so it was much more user research, corporate development, and strategy work than team leadership work. And I missed the team leadership — that was one of the things that brought me to Tinder, where I got to combine the two, the strategic thinking about where the product could go, plus the large team that could have a large scope of responsibility and move quickly against those goals.

Shobhit: That's awesome, great to see how at senior roles the skill sets you need are very situational — dependent on the challenges the company's going through at that point. Now, since then you've written a lot — especially the thing we refer to often is your thoughts on product management careers. When you think of today's market, where you see a fair number of layoffs, it's a tight market, it's an employer-favored market, companies are hiring but maybe want something more specific — what are your observations of product managers who succeed in this market, both in the search and in their career, and what are they doing that might be different than before, and what are things that are just evergreen, that always work?

Ravi: Yeah, so post-Tinder I made a decision to go earlier stage, to be closer to the building, for a few reasons. One of the key ones is that product management as a discipline — especially now, and I think this is accelerating with AI — is one where you can have PMs really focused on the stuff around the building of the product, but every great product leader I've worked with doesn't go too high-level or too abstract, they know that details matter, and as you get more senior it's about having the dynamic range to both be thoughtful and opinionated about the details, as well as think in a grand and aspirational way about the strategy. So for me, I wanted to get closer to the building, work earlier-stage. I think now in the job market that's what we're seeing — the last couple of years have been really challenging in terms of companies realizing they had layers of management they didn't necessarily need, and could remove those layers without losing anything in terms of pace of execution or speed. That doesn't mean product management as a discipline is less important — if you think about it from first principles, it's about understanding what a customer needs and figuring out how to use that understanding to create value for the business, which is central to everything a company does. But the way you do that has changed, and I think the PMs who've been most successful in this new era are the ones who aren't afraid to build, aren't afraid to get their hands dirty, aren't afraid to talk to customers, aren't afraid to dive into the data or wireframe things out or even prototype things, because they're honing their builder skills so there's nothing standing in the way of getting to that deep understanding and executing on what the customer wants. The PMs who were more focused on — I don't say this pejoratively, but the political aspect of product, managing stakeholders, managing up, leading your team, there's nothing wrong with that, working with people is an essential part of being a successful product manager — but the PMs who can both be hands-on and build with the product, as well as work with people effectively, are the ones best prepared to succeed in this new reality, where there's less management layers, and PMs need to do more work they might have thought of as IC work, but is really critical to delivering a great product.

Shobhit: Let's get one level deeper on the hands-on work — do you mean coding, UX design? What are the kinds of hands-on work a product manager should be doing?

Ravi: I've been doing everything because I love this stuff — coding, getting better at Figma, talking to customers. I like the details, and I think details are really important — at the end of the day, people experience our products through the pixels on the screen, and it's important for PMs to have opinions on what those pixels are, what the copy says, what the UX looks like, what the label on the button is. These might seem like trivial implementation details, but they're actually strategic questions, and often they don't get the attention they need. You can tell the difference between a product that's the result of a craftsperson who cared about every aspect of it versus a product created on an assembly line, where someone wrote a spec without thinking about how it's going to get built, someone else built it, someone else launched it, someone else is doing user research on it. It doesn't mean every person needs to be good at all of these things — I'm certainly not good at all of them, or very many — but being able to get my hands on these tools and think about product in a more detailed way has helped me figure out what's the best thing to do from a product strategy standpoint. I think this is even more true with AI — right now AI is this grand thing impacting the product function, and I think the people who are going to be most successful with it are the ones who understand how it works and think about it not as "I'm going to drop a chatbot into my product," but as a set of really interesting Lego blocks, a set of interesting machine learning capabilities you can snap together in different ways to create experiences that just couldn't exist before. One of my favorite examples is a product we're using right now, Descript — Descript used the idea of being able to reliably transcribe voice into text to create an entirely new way of editing video, where you edit video as if it's a script, rather than editing on a timeline. For 30 years Adobe Premiere looked the same, because the timeline they created 30 years ago is the same timeline we're using today, and Descript used a completely different, learning-oriented model to rethink how videos are edited through a script. I think we'll see, just like we can now edit a video as if it were still a script, in the future with video generation we'll be able to edit video as if we were still behind the camera, and being able to think about how learning capabilities change these fundamental user interactions is going to be a really important part of the product manager job.

Shobhit: I love Descript, we were using it, and when I started I was like, that's not going to work, but when I started editing my videos as a document I was like, wow, I can do so many things myself, it makes so many things easy for customers — it's truly a wonderful application of AI into a very real-world problem, not just AI for AI's sake, like people often just throw it in, but this was a true, amazing application. Now Ravi, what else is going to change from a perspective of AI — how do you think it's going to affect product management careers?

Ravi: I think in terms of product management careers there's this existential dread that somehow AI is going to put us out of business, and I don't think that's the case. Everything is going to move faster — Steve Jobs had this analogy, he said a computer is like a bicycle for the mind, and the analogy is that through your own energy, if you give yourself a bit of machinery and gearing, you can move much faster. In the same way, I think machine learning is a motorcycle for the mind — much faster than computers, you can do a lot more with it, but it's also not as safe, there's risk associated with it. I think the fact that this technology feels so fast and risky is giving people this existential dread, which makes them pull back on how to use it in a meaningful way. If you pull the thread on it, it means things are going to move faster — engineers will write more code, designers will create faster designs, people will write copy faster, write content faster — and moving faster doesn't mean that work goes away, it just means you move faster. There's not a single PM in the world who feels like they'd run out of good ideas if they could get through the roadmap faster — we all have an infinite set of things we want to do better. Jeff Bezos has a great quote — the thing he loves about customers is no matter what you do for them, they're always dissatisfied — so it's not like we're going to run out of things to do because things are moving faster, we're just going to move faster on them. I think what that means is the PM-to-engineering ratio is going to change — a PM might have been able to work with ten engineers effectively before, now a PM might only be able to work with five engineers, because those five engineers are moving two or three times faster. So I actually think it's going to be an advantage to PMs — the intuition, the strategic decision-making, the alignment, the coordination, all the things product managers and product leaders do, are just as important, if not more important, in an AI-driven world, and if everyone in the organization is moving faster on execution, it puts extra emphasis on the strategic pieces, the alignment, the communication to move quickly as well. I think that's job security for PMs rather than a job risk.

Shobhit: I love it — and Ravi, you said one of the things AI is going to enable is more PMs, but the layers might be reduced — so what about the people right on the cusp of group PM sort of levels, where they're partially individual contributor, partially starting to manage people? How should they think about their careers?

Ravi: This is a really interesting question — I think it largely depends on what's going to make them fulfilled and where they'll do their best work. If someone is looking at moving into a director level and what they love is having a large scope of responsibility, working with a team, setting strategy across that scope, I think in this AI world there's going to be more opportunities than ever to do that, and opportunities to grow into them. If, on the other hand, someone says "I really love to stay close to the product, I want to be deep in this particular feature area or domain," I think there's going to be more opportunities to do that too. Over my career I've had different opinions about whether a two-track product management model makes sense — like engineering, where you have people who become more and more senior individual contributors, and others who become more senior in scope of responsibility and team. In the past I felt that probably doesn't make sense for product, but I think it's going to make more and more sense as AI becomes a bigger part of how we build products, because there will be really deep, technical, domain-oriented things we need to do, where it makes sense to have a very senior principal product manager responsible for them, and there will also be things where a relatively small number of people are moving quickly across a broad scope of initiatives, and you need a great director- or VP-level person to help and support them in creating value for the company. So understanding where you want to be in that two-track model, understanding where you do your best work, and plotting your career based on that is really important.

Shobhit: I'm thinking back to earlier, when you talked about your career — I loved when you said you weren't optimizing for team size, you went from VP to director, smaller team, but the opportunity was for learning and deepening your understanding of the sector trends, Gen Z, and then continuing at Tinder. I think that's a very important thing — for people to not be obsessed with, "hey, I used to manage five product managers, now I'm an individual contributor, is that a demotion?" I think people are a little over-obsessed with that.

Ravi: Yeah, it's really hard to say, "okay, I've gotten into a particular title and worked really hard for it," or "I've grown my team to a certain size and worked really hard for it," and then feel like you're taking a step backward by having some of those external aspects change — but at the same time, your personal fulfillment, your career growth, is going to be directly proportional to the impact you can make. If your career is on the right track and you can see the long term, growing in terms of "I want to go more senior, I want a larger team" makes total sense. But if you're finding success in a different way, want to move quickly, want to be on the frontier of tech, want to solve tough problems with a small, really smart team, then you're defining success in a different way, and it's okay to move in a different direction. I think particularly within PMs there's this battle between how we internally define success and how the outside world defines success, and often we get pulled into defining our own success based on what the outside world expects, which is a recipe for getting into a position where you may be ostensibly successful but not feeling fulfilled every day. The wonderful thing about being in tech, being in product, is it's moving so quickly that if you're growth-oriented and love to learn and focus on new problems, you can find different pockets to be successful, and you don't have to follow any specific path.

Shobhit: In this new AI world, what would you say are some of the skills product managers should really equip themselves with to succeed and do really well?

Ravi: I think the first one is to really familiarize yourself with the technology. In the past, I don't think it's been that necessary to have a really deep technical understanding of how the web works or how mobile apps work — it helps, but it's a different skill set. Now with AI, I think that's changed — it's going to be hard to know what's possible and how the pieces fit together without becoming somewhat fluent in what the different pieces are. That doesn't mean you need to be able to build a large language model from scratch, although you can if you want, but it does mean you need to know what a large language model is, what the mechanics of the architecture driving it are, why it's good at certain things and bad at others. When OpenAI releases something new, like the Strawberry model with advanced reasoning, it's helpful to understand what Chain of Thought prompting is and how that might have influenced how OpenAI approached creating an AI that's starting to look like it can reason. Having that detailed technical understanding is important to know what all the pieces are, how they fit together, what the capabilities are, and then when you're creating a product you can think about whether you need a more traditional approach or a more machine-learning approach. Right now we're at kind of peak hype around AI — everyone is seeing problems as a nail and they've got a hammer, and they're willing to hit those problems with it. AI is the wrong solution for the majority of the product we need to build, the majority of features we need to enable, but it's the very right solution for a small set of very valuable problems. Being able to understand the difference between traditional computational systems and learning systems, and when to use which, is a really important core capability that requires some technical fluency to do well.

Shobhit: Back to what we were talking about earlier — it almost seems like some of the best applications of AI need a paradigm shift, or what we called at Google a "technical insight" — okay, what if video editing was like editing a Word document? That goes right back to the exact product we both love, Descript.

Ravi: Absolutely — and you know, before, you might have asked that question five years ago and everyone would have looked at you cross-eyed, like that's not possible, but now you can ask that question and those things are possible and reliable, they just work in very different ways. I had a post recently where I talked about the difference between learning systems and computation systems — pretty much all the innovation we've seen over the last 50 years in computing comes from the idea that you have an input, a set of rules, and you can very quickly, with a microprocessor, create a bunch of outputs. That simple rule has scaled to the point where you can get video games running at 60 frames per second, doing trillions of lighting and shadow and physics and collision and narrative-based computations to create an experience that feels real — those systems are incredibly flexible and powerful. But the thing we couldn't do until recently is solve a problem where we couldn't express the rules — if you wanted to say "is there a bird or a cat in this photo," you couldn't solve that computationally, because the rules we as humans use to figure out what animal is in a photo aren't possible for us to express clearly in code. That's where machine learning comes in — it can actually figure out the rules, rather than us having to explicitly state them, and that gives rise to two very different types of systems that act in different ways. A computational system is deterministic, it does the same thing every single time; a learning system is probabilistic, it does a different thing every time. As a product manager, that difference between determinism and a system that's more probabilistic is really key to how you create a feature that works or doesn't work for people, because an output being different every time might defy expectations — if you're calculating the solution to a math problem, you don't want the answer to be different every time, but it might be a feature if you're creating image generation and want to create a lot of different variants. These different properties between learning systems and computational systems are important to understand and factor into how you approach product development.

Shobhit: I did an experiment with an AI system — I was like, let's test it out. There's a product called photo.ai where I uploaded a few images and created a virtual model of myself, then had it generate an image of me speaking at a conference, and I sent it to my family WhatsApp channel, saying "this is from a recent conference." Everybody was like, "we're proud of you, that's amazing, you're doing so well," full of messages. I woke up in the morning and the messages were there, and I was like, oh crap — I told them, look, I've been doing a lot of conferences, podcasts, all these other things, but just so you know, this was completely fake. A picture of me with three kittens, here's a picture of me on my jet — people were like, the only difference they could really tell was they thought maybe I'd gotten Botox on my forehead because it looked incredibly smooth, but other than that they couldn't tell anything. It's amazing what these systems can do, what they're capable of — really cool stuff. Awesome, one last topic, one last main question — when you think of product managers in this market of 2024, 2025, looking to grow up a level in their career, and you've thought a lot about AI as well, what are the things they should be doing?

Ravi: I think the biggest advice is to assess yourself and understand where you do your best work. Product management is an impossibly broad discipline now — not unlike engineering in the early days, when everyone was a full-stack engineer because you could know all the things about engineering and build workable software. Today specialization is incredibly important in engineering — front-end specialists, back-end specialists, machine learning specialists, people who work on game engines, embedded systems — there's way too much scope in engineering for any one person to know all the things equally well. The same is true of product, it's just that some of the skills are a little softer, so it's harder to wrap your hands around it, but it's true, and the degree to which you can pin down where you're strong and where you want to focus is really helpful — then you can figure out what set of opportunities you want to explore. I worked with someone at Tinder who was a fantastic customer service rep, and her dream was to become a product manager, but she only wanted to become a PM for the trust and safety team — no desire to work on user-facing features or any other part of the Tinder product, she wanted to work on trust and safety. The fact that she had that clarity was great, because it dovetailed nicely with her talent, and the fact that she knew where she wanted to focus was also great, because that was an often-overlooked area — so many PMs at Tinder wanted to work on the front-end product, not the backend systems, but from a business standpoint the backend systems are often the most important driver of business results and customer satisfaction. That focus gave her a leg up. It's similar for anyone thinking about their product management career — if you know where you're strong, where you do your best work, and what you want to focus on, that can give you an unfair advantage when you're looking for your next role.

Shobhit: I was thinking, in our program we refer to it as the Angle of Mastery — recently I started working with someone discovering this, and their standard pitch was, "oh, I'm an accomplished product leader with so many years of experience across these sectors." The more we dug in, it was like, what you're really good at is you can take products with at least some product-market fit and get them to scale to 10 million, 100 million users in the B2C space — you've done it many times, that's your skill set, you love doing it, let's put that as your positioning, rather than "accomplished product leader with many years of experience." That's exactly what people should be doing now — that's so different, so much more different than another "accomplished product leader with many years of experience."

Ravi: Absolutely — and if that thing you're strong at is overlooked in an organization or in the industry, even better, because you can come in and create value. As an industry we flock to a lot of things — a while ago it was Web3, now it's AI — so going back to what we were talking about earlier, if you can go into a space that's growing quickly, where there's not a deep bench, that's a great place to grow, and that doesn't necessarily need to be an entire company, it could be a subset of a company going through significant growth, where they just don't have enough people who are really deeply thoughtful working on that particular area.

Shobhit: Where can people find out more and learn from you — where do you put out your amazing content?

Ravi: Yeah, absolutely — you can go to ravimehta.com, and I have some articles there. I also have a Substack, if you want to search that out — I'm starting to write more frequently, so would love for you to follow that. And feel free, I'm pretty accessible, comment on the post or reach out to me on LinkedIn — if I can be helpful at all, please reach out.

Shobhit: Please keep writing, Ravi, because we've always been big fans of it — I really appreciate you sharing your thoughts with my awesome audience.

Ravi: Yeah, absolutely, thanks for having me.

Shobhit: Hey, be sure to check out our website at intentionalproductmanager.com to see how you can level up in your career.