Lenny's Podcast: Product | Career | Growth
01:00:48 · Elena Verna
[01:00:48] S1: So I'll first start with what I've felt at least before when people were talking about product market fit that, yeah, obviously always product market fit is an evolving thing. But the rate of that evolution was measured in years. What is it that you need the next product market fit step function change, which often was called second horizon or third horizon, sometimes five, ten years, sometimes even longer that you'd need to that you depending on how good you are and how your initial product market fit was. But you'd spend years scaling the original product market fit. It was like blitz growth stage, marketing sales growth was very important that you just try to get it to as many people as possible. And then once you have saturation or the cost to getting the marginal people becomes too high, you start thinking, okay, what else can I offer to help me reach additional people or sell more to existing users that I already have? And again, the main point here is it would take years to get to that stage where it became a question that you had to face really hard face to face. Now it's three months and all of a sudden you have to face that question again. And it's happening because of two things, in my opinion. Number one, in AI technology of what LLM is capable of doing changes still very rapidly with new model release, with each new model release. So I think we'll stabilize at some point and it was going to become more marginal, but we're not there yet. So every three months or so, every single AI LLM provider creates a step function change in what is possible with that LLM. And when you have this new possibility in just the underlying technology that opens up in front of you, then it creates another ceiling of what is possible to build on top of it. And the tricky piece here is that it's not enough to just wait for that technology to get better and then start building on top. You have to build beforehand to like make a bet and then it's the LLM to catch up because when that model releases, you already need to have
01:02:51 · Lenny Rachitsky
[01:02:51] S0: that
01:02:51 · Elena Verna
[01:02:51] S1: functionality available. So that piece is I've never been in a company where the fundamental capabilities are still changing so rapidly. And that's the product part. So the product can leap to the new expectations. But let's not talk about the market part as well. Consumer expectations have never changed this fast before. What we expected to chat GPT to be able to do an answer and how we wanted it to talk to us eight months ago versus now is night and day. And like the deep thinking mode and how how how deeply you can go into answering questions and what is capable of being building on top of it. So consumer perception has never changed this fast too. It's this unprecedented time of a consumer's all stand like in a month saying, oh, it's not doing this yet. Like, I'm bouncing before. Again, consumer perceptions would be years to take. It's actually technology would sometimes be able to already address it, but consumer perception has not been changed yet. So it would take a long time. So we're like in this really weird part where both product and market is shifting so rapidly that every three months, I feel like we have to recapture our product market fit. And not just recapture on the same technology and with same customers. It's both of those pieces of the equation change every three months. And it's terrifying in a way. It's also very confusing in a way because we're $200,000,000 company and we're not solely focused on marketing and sales because we still have to recapture our product market fit. And you know that the team that finds your product market fit is very different than the team that usually scales your company. Yet we have to find the team that is capable of doing both on ongoing basis. Now, I think every AI company is on this product market fit treadmill. Hopefully that treadmill speed slows down. If not, I think we're going to come up with like crazy things of what this LLM and AI will be able to do if it's going to continue at this cusp. But it's a weird place to be in because every three months we have to throttle on our scaling efforts and just reinvent and then scale again. But it's like short blitz of growth, not these long year long commitments.
01:05:07 · Lenny Rachitsky, Elena Verna
[01:05:07] S0: What makes this very real is just this week, apparently, OpenAI had this whole Code Red moment where even though OpenAI, by far, the leading AI assistant over almost a billion, I think, monthly active users, like, basically synonymous with AI around the world, With Gemini three launching, their market share just started to dip really quickly. I think they lost, like, six something percent in, a week. And so even OpenAI, like ChatGPT, the original, the one that everyone uses constantly is is is in danger. [01:05:40] S1: It's like nobody's nobody's future is bulletproof yet. And ten years ago, if you asked me if a $200,000,000 company was at risk in losing product market fit in the next three months, it's experiencing 10% month over month growth, I would have said you're crazy. And now that's the reality that we live in. And I I don't know. I... It's fascinating to world and what a time to be alive. [01:06:04] S0: What a time to be alive. And very stressful, but the prize at the end is massive. That's the, you know, that's why this is worth doing. Not just, you know, monetarily, but just the impact it's gonna have on the world, the way we people build and ship [01:06:16] S1: and live. Exactly. The ceiling of what is possible has been raised so massively that we haven't even became too closest to even see it, I believe. So I think that that's the exciting part of it.
01:06:16 · Elena Verna, Lenny Rachitsky
[01:06:16] S1: and live. Exactly. The ceiling of what is possible has been raised so massively that we haven't even became too closest to even see it, I believe. So I think that that's the exciting part of it. [01:06:29] S0: The way I've seen you write about this product market fit challenge is the traditional approaches. You have these like core users that are using it, happy with it, and then you expand to the adjacent users and expand to the next. And you're basically just trying to recapture that same core constantly and don't even have time to go adjacent.
01:06:44 · Elena Verna
[01:06:44] S1: Yeah. I... Bangaly wrote a really wonderful article. It was many years ago at this point on adjacent user theory and that your product market fit expansion, when you're in the growth stages, the biggest opportunity for you to go after is this what he called adjacent user, which are just outside of your core user. They have somewhat similar needs, but maybe they're in different geo, maybe they have slightly different use case, slightly different needs. And your biggest way to continue growing your product market fit without having to go to next horizon is to capture those, that next group of users. The interesting piece here of how I relate to it, we still have the core users. And by the way, those core users are mostly pioneers right now that are excited by the capabilities. Then there's latent majority that is filled with adjacent users. And the issue right now, which I'm actually quite worried about us like as a category, is that we're constantly focusing on recapturing the pioneers. We don't have time to go after adjacent users. And I'm worried of whether there's going to be a gap in the space where we actually going to alienate the latent majority because we're so hyper focused on just staying top of mind and top of capabilities on the pioneers. But I don't know the right answer here because without the pioneers, they'll like you need pioneers for a latent majority to follow. But if you take pioneers and you take them too far into capabilities, will latent majority never be able to catch up? Maybe this is a fruitless concern, but it's just like something that I think about because at this stage we should be working on adjacent users. I would argue maybe OpenAI definitely started hard to do that with many people they have on their platform, but not most of the other AI companies.