--:-- Host: Consumer activity typically lags by 6 to 9 to 12 months, what's happening on the research side. So many of these assumptions, and that's why their assumptions, they seem intuitively correct, are going to turn out to be incorrect.
--:-- Olivia Moore: We are finally on the verge of AI video starting to really work.
--:-- Anish Acharya: It sort of follows the trend of AI decreasing the cost of creation in every way.
--:-- Olivia Moore: 95% of YC companies or something are now building using those tools.
--:-- Anish Acharya: I think compared to where we're going to be, we're still incredibly early.
--:-- Host: This month, our consumer team at A16Z dropped our fourth installment of the Gen AI 100 list, a list of the top 50 AI-first web products and mobile apps based on unique monthly visits and active users. And as our consumer team said themselves, in just six months, the consumer AI landscape has been redrawn. Some products surged, others stalled, and a few unexpected players rewrote the leaderboard overnight.
--:-- In today's episode, we explore the latest rankings and the pivotal AI moments over the last few years. Mid-Journey and Character AI both came out before ChatGPT. Remember Snapchat's MyAI? The Balenciaga Pope? Coke did their Christmas ad. Each one of those unlocks broke down the assumptions that many of us held prior, and have helped culminate hundreds, if not thousands, of AI applications that are now vying for our attention.
--:-- The App Store is going to be chaos.
--:-- Anish Acharya: Yeah.
--:-- Host: It is going to be chaos.
--:-- So which applications top the charts this time around? Whether household brand names or tools that you may have never heard of. Plus, where does this flurry of activity place us on the adoption curve? And what trends stood out, like AI video or vibe coding, that give us a window into what's to come? Finally, this fourth edition of the list is actually the first time that we broke out what was actually making money.
--:-- And today we have A16Z consumer partner Olivia Moore and general partner Anish Acharya to break down all of the above. Of course, if you'd like to see the full list of the top 100 GenAI apps, head on over to A16Z.com slash GenAI100-4, or you can check the link in our show notes. Okay, let's get started.
--:-- [Disclaimer: The content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com slash disclosures.]
--:-- We're back for the fourth edition of the Gen AI 100 list. You guys have been working hard and tracking the consumer landscape for years now, but specifically for the last two and a half years since we really had that ChatGPT moment. Tell me more about how you're tracking that ecosystem and how that comes through in this list.
--:-- Olivia Moore: Yeah, it's super fun. This is one of my favorite reports that we put together a couple of times a year. We track the consumer AI landscape through what we do every day, which is meeting with consumer AI startups that come to pitch us, seeing what goes viral on Twitter. But actually, there's a whole separate set of companies and products that might be reaching the true mainstream consumer that might not even be marketing themselves as AI products, but they're powered by and made possible by AI. The whole original purpose of this report was to see how much overlap is there between those two categories and what is the actual everyday person who might not know that they care about AI using in their day-to-day.
--:-- Host: That's great. Talk about the methodology, like what makes it onto this list or not? Because to your point, there are certain household names that you might see on Twitter or have that viral moment. But I think some people might be surprised to see what made it onto this list.
--:-- Olivia Moore: It's entirely based on data. We have two lists here: the top 50 on web and the top 50 on mobile. For the top 50 on web, we use a data provider called SimilarWeb, which tracks every single website globally. We essentially go down in descending order of how many visits they get each month. For this report, it was January 2025. We pick the first 50 of those that have the most monthly visits that are GenAI-first products. We do something similar on mobile, but with a different data set from Sensor Tower. For mobile, we look at monthly active users on the app. Then again, we pick the top 50 that are GenAI products. For the first time ever, we actually looked at the top 50 on mobile by revenue, which we hadn't done before. It was a really interesting experiment because the lists were pretty non-overlapping.
--:-- Host: Totally. And we'll get to that. Yes. We've been in this AI ecosystem for a few years now. In your eyes, what were the pivotal moments that led up to this point in time where we have, like you said, 50 on mobile, 50 on desktop, and a whole lot more in the wider ecosystem?
--:-- Anish Acharya: You often say, actually, it's usually like the papers are written, then the models are developed, then applications are built on top of it. So the consumer activity typically lags by six to nine to 12 months behind what's happening on the research side. From a consumer awareness or behavioral perspective, there are a couple moments for me. Actually, Midjourney and Character AI both came out before ChatGPT, which I think a lot of people don't know. There were these early niche communities of early adopters using both of those products in the summer and fall of 2022 leading up to ChatGPT.
--:-- Then post-ChatGPT, things that brought AI to consumer consciousness. Remember Snapchat's MyAI with that little bot that appeared at the very top of your feed? 150 million people used it. For a lot of younger consumers, that was probably their first real chance having a conversation with an LLM. On the image side, I think of the Balenciaga Pope, which was spring 2023.
--:-- Host: Such a cultural moment.
--:-- Anish Acharya: It was. I think it made a lot of people realize for the first time that they should even be interested in AI images because they could be that good and that convincing. The first big AI music moment for me was the BBL Drizzy song, which was spring of 2024.
--:-- Host: That was huge.
--:-- Anish Acharya: That also went mega viral. Notebook LM was another one. One of the moments where creative AI really shifted into almost enterprise consciousness was the end of last year when Coke did their Christmas ad. A lot of that was generated by AI. Then, of course, the DeepSeek launch earlier this year. DeepSeek was so interesting because I think it had become settled wisdom that it would be very hard for a horizontal model to get to mass consumer scale quickly again. ChatGPT had done it and ChatGPT had become a verb, and that opportunity had already been explored. Now we see DeepSeek growing as quickly as it did.
--:-- There are a couple of interesting nuances to DeepSeek. One important nuance is that they released their reasoning model for free at scale. Previously, you had to use O1 Pro and pay ChatGPT's premium subscription to get access to it. The other thing was the product execution around chain of thought, which we've talked about a lot. The fact that it showed you its thought process in real time was super captivating and now something that's become a step that every model takes. I think it just really illustrates how early we are. We as sophisticated users and investors are looking for further refinements. Once in a while, something like DeepSeek comes out of the clear blue sky and blows away all assumptions.
--:-- Host: Totally. That word specifically, assumptions, is so key when you talk about these pivotal moments. I feel like you could match each pivotal moment with an assumption being broken. Like, "Oh, AI could never trick me into thinking a picture is real when it's not," or "I would never actually listen to a top 100 song that's generated by AI," or "ChatGPT has cornered the market and no one else can penetrate it." All of these assumptions that people are like, "Okay, sure, I was wrong about that prior one, but this one I'm pretty sure about." We're seeing months being the delta between assumptions being broken.
--:-- To your point on the arc of the market or the industry, I could see an argument where people are like, "We're actually pretty far along because we've already slashed all of those assumptions." But on the other hand, I'm hearing we still have a long way to go. So put us along that arc if we were to compare to the mobile era or the cloud era or previous technology eras. Are we in that early innovator stage still, or are we somewhere else?
--:-- Anish Acharya: I think we're still very much in the early adopter phase. In many of these categories, we're arguably still in the infrastructure building era and moving into the application building era. It depends on the modality. People thought LLMs were a solved problem, but then DeepSeek came in and upended all of that. There are a lot of things that are definitely not fully solved. AI video right now can generate great three, five, or six-second clips, but hopefully years from now, we have AI video that can generate minutes-long or even hours-long movies. Compared to where we're going to be, we're still incredibly early.
--:-- Here are two assumptions that I think are interesting because it may turn out the reality is the exact opposite. One is that AI will be very good at transactional interactions, but humans will still be the ones to build relationships and connection. An example would be what kind of phone calls AI will be best at. The assumption was they'll be great at scheduling and logistics and the exchanging of information and facts, but we've heard over and over that in many cases the AIs are more human than humans. They just have more patience, more nuance, they're never having a bad day, they're never hungover. That's an interesting area of exploration.
--:-- The other one is the idea that humans will delegate work to the AIs and the AIs will do it. What if the AIs are the ones delegating the work to us? Perhaps AI is really good at organizing work, and we're really good and also get a lot of joy out of doing it. So many of these assumptions, and that's why they're assumptions, they seem intuitively correct, are going to turn out to be incorrect.
--:-- Host: Totally. If we think about the report, maybe one important data point is the fact that we see so many newcomers still. If we were in that later part of the innovation curve, you might expect more stagnancy, you might expect to see the same players. But every time you guys build out this report, we're seeing all of these newcomers. In this particular time, the fourth report, we saw 17 new companies on the web rankings in particular. You actually have this quote where you say, "a few unexpected players rewrote the leaderboard overnight." Can you speak to that and the movement that we're seeing?
--:-- Olivia Moore: One of the biggest trends among the newcomers is we are finally on the verge of AI video starting to really work. Not just for people who are enamored by AI and willing to generate 100 times to get a good clip, but for people who actually want to make something creatively in a condensed time period. We had three new video models on the list this time: Hailuo and Kling, which are both Chinese models, and then Sora, which was OpenAI's model that was announced more than a year ago and finally was released. I think we'll see even more of a shakeup here because Veo 2 is the new Google model that is even next level beyond that from what we've seen in testing. That will probably finally come out in the next three or six months.
--:-- The other big category of newcomers were these vibe coding products. Cursor made the list. It's more of an agentic IDE for a technical audience. Then Bolt made the list, which is for a non-technical audience where you basically go from a text prompt to a fully functioning web app. Even though they made the list, I think there's still a really significant portion of their users that are people who are in tech and are actually technical. They might be using something like a Bolt or a Lovable, which made our Brink List, to prototype something easier and then export the code and play with it themselves. I don't think we've quite seen the vibe coding products hit the true mainstream user in terms of someone who's never worked in tech or developed an app.
--:-- Anish Acharya: I love this category. It's so fun and it's so satisfying to actually see your ideas come to life. In the case of Bolt and Lovable, sometimes they are just compelling interactive prototypes more than full-fledged products. But that's usually enough to get a feel for whether this is something you want to invest deeper in. It follows the trend of AI decreasing the cost of creation in every way and people just trying more ideas.
--:-- Olivia Moore: Just think about what that says about the untapped market of people who wanted to build things with code, that this is on the top 50 list. Honestly, both of them haven't had many apps built on them yet that have gone super viral. When that happens, and I'm sure it will, those will become stories of their own, which will increase awareness of the products to the true mainstream audience. I think we're going to see a really interesting diversity of products built on these. It might just be, "This is my app that I used for my very specific niche pain point." Or there might be people who never learned how to code who want to build a venture-scale product on something like a Bolt or a Lovable. Seeing how that plays out will be very cool.
--:-- Anish Acharya: There are two phrases I've heard that I like. One is DIY or personal software, but it never made economic sense to design software for one person. The other is disposable software. Just as Suno and Udio made it possible to make a song just to capture a joke that would be irrelevant the next day, these products make it possible to create a product or an experience that may have an extremely short shelf life, like 20 minutes or a week.
--:-- Host: Let's talk about the Brink List because that's completely new to this year's fourth generation. What is the Brink List and why add it?
--:-- Olivia Moore: The Brink List is essentially the five companies that almost made the list and were right below the cutoff, again purely based on the data. We pulled the five websites and the five mobile apps. Honestly, we were just curious to see what it would capture. We didn't quite know. The takeaway for me is that it reflects how fast things are changing. There were a couple companies on the list, like Runway, Otter, and UMax, across web and mobile, that have been on the core top 50 ranks in the past, but they got edged out by something like DeepSeek launching this time. They lost their spot for this ranking, but might be on there the next one. They still have massive usage. The other trend it caught was a rise in more recent products like Crea and Lovable that are very much on a consistent upswing. If it continues, we might see them on the main ranks.
--:-- Host: What did you predict that you would see on the list that you didn't really see there? Were there any surprises on that end?
--:-- Anish Acharya: One thing I thought we'd see more of is style transfer as an approach to scalable video, because style transfer is a much more tractable problem and has a lot lower cost of inference versus raw text to video. But researchers and product developers seem to be really going for it on text to video. We've seen more of that than I would have expected. The other things we didn't see on this list that we have seen at the model level, so maybe they'll be on the next list, are consumer voice products. There are a few of them, but not a ton. Some of the new models, like the Gemini Flash model that can see what's going on on your screen and interact with you.
--:-- Host: I built something to yell at me if I go on Netflix or something. I need that. It's a BS detector.
--:-- Anish Acharya: Yeah. It starts screaming at you like, "No, get back to work." Or the new OpenAI Operator model, which can actually interact with things on the browser level on your computer and get tasks done for you, like pay a bill or make a graphic design or hire someone to landscape your yard. There's always a lag because the models have to be released to developers and they have to be tuned by the developers. It takes a while, but I would expect to see an explosion of fun and unique products built on models like that on hopefully the next list or two.
--:-- Host: It feels like we really have seen an explosion on the model side, and it is right there in terms of manifesting at the apps level too.
--:-- Anish Acharya: One example is Deep Research. If you've played with it, it's completely magical. But it's a primitive, not a product. It's something to build other things with. It's really unclear if Deep Research is going to be used to write college theses or to find the perfect meme to match a joke you want to make. That's all going to be up to the app developers.
--:-- Host: To double click on that, you could see a world where Deep Research is just this more broad, horizontal application, or you could see what you just described, where developers are tailoring that to specific end use cases. Are you saying that you think the latter is more likely in terms of the progression of these models and apps?
--:-- Anish Acharya: Not more likely, but I think it's underexplored. If you come to Deep Research today, you have the blank page problem. I'd love to see developers create some constraints that lead to unexpected outcomes. The known or prescribed use of Deep Research right now is basically market research reports. It's amazing for that. I've used it for that a lot. But if you try other things, like one day we were trying to trace the origin of a meme. Deep Research is a 100x better version of that Know Your Meme website that goes through the history.
--:-- Olivia Moore: Yes. The etymology of how a meme comes to be.
--:-- Anish Acharya: That should be an app. There are lots of other use cases that aren't market research reports that could really benefit from an incredibly obsessive, compelling model that will go and read every website on the internet until it finds the answer.
--:-- Host: I love that. I've always wanted that for creators. You know how there's the whole "success overnight" phenomenon that everyone else thinks happens, but it's not true for most creators. Mr. Beast is like, "I literally counted to 100,000 and then did thousands of more videos until something started to work." I wish you could actually see the history of a meme, but also the history of a creator. When did the unlock happen? Those are the things that you thought might be on the list, but you didn't actually see there. What about the opposite?
--:-- Olivia Moore: The fact that the vibe coding products, like Bolt, Cursor, and Lovable, made the mainstream consumer list is a testament to how widely they're used by the technical audience. They have gotten to saturation so quickly. I think Gary Tan had a tweet that 95% of YC companies or something are now building using those tools. It's something that nearly every developer now is probably using. That was maybe a surprise to me, how quickly we reached saturation.
--:-- We talked about this, but a continuing surprise is how many companion products are on the list and how many of them rank so high. We had three companion products in the top 10. Two of them were NSFW oriented. Maybe not surprising when you think about traffic on the internet in general outside of AI, but a lot of people are using them as interactive fan fiction. Some of the biggest fan fiction sites in the world are also top 100, top 200 global sites. So it makes sense in that way.
--:-- My last surprise would be there's actually quite a bit of consistency in the list over the past four versions. There are always new entrants, which is exciting. But across the four lists, there are now 16 companies on the web ranks who have made it every single time and have kept the streak going. That's pretty remarkable when you think of how early we are in AI, but it's a testament to how those companies have cemented their brands, their products, and their status in consumer consciousness. It's also a testament to the fact that real businesses have been built in consumer AI already.
--:-- Anish Acharya: To add to that, one of the surprises for me on companion was not seeing more multimodality. The first glimmers of that at scale were Grok. Grok added a bunch of voices with some real aesthetics and points of view. It's just interesting that character and companionship is such a horizontal category. There's so much latent demand, and it'll really increase once you have multimodality.
--:-- The other interesting thing is that a lot of the text-to-code work, my assumption was that there was a small number of people creating sites that were heavily trafficked, and that explained the rise of them. But actually, the majority of the traffic, correct me if I'm wrong here, Olivia, is from people doing creation, not just consuming other people's creations. It really shows how much demand there is to make things, even if people are not that interested in consuming them.
--:-- Olivia Moore: Yeah. You can track the traffic of apps that people have launched on lovable.app versus visits to lovable.dev, which is where people go to make a Lovable product. Lovable.dev has significantly more usage or visits than traffic to lovable.app. That gets back to what I was saying before. We have not even seen the first wave of viral products built on top of Lovable and Bolt. When that happens, the awareness of these types of platforms is going to go significantly up too.
--:-- Host: The App Store is going to be chaos.
--:-- Anish Acharya: Yeah.
--:-- Host: It is going to be chaos. We're going to need an AI just to solve the AI app management problem.
--:-- Anish Acharya: Completely.
--:-- Host: To that end, you talked about the fact that there are some consistent players. One of those players is ChatGPT, which we've talked about as the starting gun of some of this application development. ChatGPT has been at the very top of the list. Has it been that way for every single iteration of this?
--:-- Olivia Moore: On web and mobile. But what might surprise people is that the traffic to ChatGPT hasn't always been the same trajectory. It was basically flat for a while. Between February 2023 and February 2024, it was essentially flat in monthly visits to the website. At that point, from the data I've seen, 50% plus of the traffic was students using it for essays or homework problems. The vast majority of other people, me included, had not maybe found a daily active use case for ChatGPT yet.
--:-- It has completely resurged more recently. They doubled the number of visits on web since then. They made their own announcement counting across web and mobile. In the past six months, they grew from 200 million to 400 million weekly active users.
--:-- Olivia Moore: That's especially surprising because it took them nine months to double before that. It usually gets way harder to double at scale, not easier. If you plot it on the graph, you can track the increases to the release of new models that unlock new use cases. The new O1 reasoning models, the 4.0 models which were multimodal for the first time, and then advanced voice mode. They've also launched new products like Operator that can perform tasks on your computer, and Canvas where you can write more naturally. It's both bringing in new users who never tried it, and taking people like me who were maybe weekly or less. Did you find your daily active use case?
--:-- Olivia Moore: Now I'm daily active across several use cases. Some days I'm driving and talking to it in voice mode. Some days I'm working on a memo and generating something with Deep Research. Some days I'm doing some random other project and brainstorming ideas with it. I would expect that to continue as they release new models.
--:-- Host: Have you heard from the ecosystem in terms of what more frequent use cases have emerged like yours? If before it was a lot of students writing research reports, is there a sense of understanding of what those newer use cases are?
--:-- Olivia Moore: It's gotten better at some things related to coding. It's gotten better at data analysis. The reasoning models are hard to overestimate because in the past, you couldn't even rely on ChatGPT to tell you how many R's were in strawberry accurately. It was hard to feel good about tasking any sort of delicate or serious work to it. There are probably a long tail of use cases that people have migrated over now that they have more confidence in the models.
--:-- Anish Acharya: What's interesting to add is that Claude is not a traditional number two player. Typically the number two player has 10% of the market share and 10% of the product quality. Instead, Claude sits in this very interesting place where it seems more beloved by a smaller number of people. It's better at creative writing. It seems to have more of a personality, which is interesting because I think it's designed to be more constrained. It's also strangely much better at coding. Why? I don't know. But it's very interesting to see there's a place for both ChatGPT and Claude and Mistral and potentially other models to augment each other.
--:-- Olivia Moore: The really interesting thing about this list when it came to general LLM assistant usage was that we only had 10 days of data for DeepSeek for January because it launched at the end of the month. It shot up from literally nothing to number two on the list, 10% of ChatGPT scale on web within a week. On mobile, it had even less than that, five days, and it was number 14. If it had had five more days, it would have been number two. The gap is even narrower there between DeepSeek and ChatGPT. To Anish's point, that was a surprise in that we could see a broad-based product go so viral still and capture so many users.
--:-- Host: DeepSeek was obviously the story when it came out. What have we learned about retention since then? Is that learning specific to DeepSeek or are we seeing that learning applied across the ecosystem?
--:-- Olivia Moore: It's a little early to call on retention, and also because they're giving away so much for free right now, it's somewhat easy to retain. The mobile data is fairly conclusive. You can look at sessions per week and time per week for any app. We looked at Perplexity, Claude, DeepSeek, and ChatGPT. DeepSeek is already at the levels of Perplexity and Claude, which is interesting. Users are spending about 20 minutes a week across 10 sessions. That still significantly lags ChatGPT, which has about 45 minutes a week. It's already at the level, if not actually slightly better, versus Perplexity and Claude in terms of engagement. On a retention basis, how many users are coming back to the app exactly 30 days, exactly 7 days, exactly 60 days, it's just slightly below ChatGPT. We're looking at 7% day 30 for DeepSeek and 9% day 30 for ChatGPT. It's too early to call on web because it's hard to track usage.
--:-- Anish Acharya: Part of my theory here is that if you look at DeepSeek usage, a lot of it is the U.S., but a lot of it is China and other countries where you can't use it, or they try to make you not use it and you can only get by with a VPN. In those markets, it's not ChatGPT versus DeepSeek versus Perplexity. It's DeepSeek versus nothing. In those markets, they have a structural advantage from the retention side that might skew the overall sample.
--:-- Host: Totally. Next time we should add a different cut for DeepSeek USA.
--:-- Anish Acharya: Yes, exactly. Geographic breakdown.
--:-- Host: Talking about trends that we're seeing on the list, you mentioned AI video before. Anything else you want to call out there in terms of its presence on the report?
--:-- Host: Two of the video models were Chinese video models, which is super interesting. The models are less copyright sensitive in their training data.
--:-- Olivia Moore: That's a great euphemism.
--:-- Host: Yeah, they're maybe more realistic and more prompted here and in the outputs as a result. But also just in China, it's easier to hire people to caption videos. They have maybe a greater volume of researchers doing image and video stuff versus other stuff. I think Sora in some ways was a little bit disappointing for some people, whereas the Chinese video models were maybe better than a lot of people expected, given the relative lack of capital that they raised.
--:-- Anish Acharya: That's right. I think an interesting trend is just seeing CREA on the brink list.
--:-- Host: Yes. CREA is the single best place to access all the models and all the tools. And the nice thing that they do is stitch all of these things together to make them greater than some of their parts. So insofar as we live in this sort of multipolar world of models — image models, video models, language models — there'll be a role for aggregators like CREA to put them all together in a thoughtful way.
--:-- Anish Acharya: Totally. Especially because people who are deep in AI video understand this, but each model is known for being good at specific things: shots of people, shots of landscapes, anime, hyper-realistic. And so it can rack up very quickly on $20 a month subscriptions if you're paying for 10 or 15 different models independently versus having one canvas to work with all of them. You also typically use the products together. You usually generate an image in Midjourney or Flux, and then you take that image and upscale it, and then you put it as the beginning frame in a video. So you really want to not have the seams between all those products.
--:-- Host: Completely. Are we seeing these video models in particular become more opinionated? And what I mean by that is we see that in image models, right, where Midjourney might be good at this, and then you might see another model better at something else. The users will gravitate towards either models or applications that provide them with that specificity or opinion. Are we seeing that in video yet?
--:-- Anish Acharya: I'd say they're both becoming more opinionated on the model level, but also the application choices that they're making — even the model companies are making — are becoming more opinionated. If you've used Runway or Kling or something, you can now prompt basically the camera angles or the wideness of the shot or all of these things a human cinematographer would do. You can prompt how the video sweeps over the surface of the screen. And so that's also a big factor in what you use for maybe even different parts of one video, which is interesting.
--:-- Olivia Moore: I mean, the comments specifically, I still think Ideogram is one of the most unique models for what it does, what it's great at, which is text generation and the aesthetic that it has. It just sits in a very unique place in the ecosystem.
--:-- Host: Yeah, we did an internal competition where we had to generate a bunch of video — 30 second video — and Ideogram was amazing for that because you could not get that layer of specificity anywhere else. And then you could then take what was generated in Ideogram and put it into another model to animate it or to swerve or to do whatever you needed to do with it.
--:-- Olivia Moore: Well, they also have a fun feature which essentially is image to text. So if you have a meme or a copyrighted image that you want to replicate or at least be inspired by, you can use their image to text and then use that text as the prompt to create a new image. I also found that fascinating because I would prompt something, and as you learn when you're prompting with AI in general, you learn that you don't know what you're looking for. And so when I would prompt, Ideogram would modify your prompt before generating the image. And then you could actually go interrogate that and be like, "Oh, that's why I'm getting X, Y, or Z."
--:-- Host: So video actually in general, it tends to be more of a mobile first phenomenon, right? We see tons of — even before AI — tons of applications that focus on creators being able to edit and splice video. What are we seeing in terms of the difference between what's working on mobile and what's working on desktop?
--:-- Anish Acharya: It's somewhat obvious, but a lot of the things that are working on mobile are either things you want to use on the go or where the underlying asset you're working with is easily captured by the phone. So all of the avatar apps blew up on mobile because you have 10 selfies of yourself sitting on your phone. A lot of the voice-first consumer products that we are seeing working actually are on mobile versus web because it's easier and more natural to talk into your phone for language learning or for companionship or other use cases than it is to maybe talk into your laptop. And same with homework helper apps. Those are really blowing up on mobile as compared to web.
--:-- Host: So maybe another interesting breakdown that kind of represents where we are in the innovation curve is not just what is getting views, but what's actually making money and how those aren't always one-to-one mirrored. What is making money today? What are we learning there? And is that the same as what's getting traffic?
--:-- Olivia Moore: So for the first time, we actually ranked the top 50 by what Sensor Tower can measure as mobile revenue, which is typically in-app purchases and subscriptions — so probably not ads — and we ranked those separately from what has the most monthly active users. And there was only 40% overlap between the two lists.
--:-- Host: Interesting. So a lot of difference.
--:-- Olivia Moore: The surprise to me actually was the main categories are the same in terms of what's making money versus what people are using. So photo and video generators, photo and video editors, beauty filters and beauty enhancers — massive standalone category. And then the realm of ChatGPT copycat apps — both making a ton of money, getting a lot of users. But the companies within those categories are very different in terms of who's making money and who has the most usage. We actually plotted revenue per user versus number of users. And we found the apps that had smaller user bases out of this sample set were much more likely to be making significantly more money on a per user basis. So apps like Speak, apps like Otter, captions and video editing. There's a lot of reasons for this. One is that if you are making a lot of money per user, you're probably more of a serious prosumer app. And so you've probably actually gated the usage pretty significantly. Like you have to subscribe to use the product. And so there are companies on here that might be making $50, $100 million in ARR off of only a million users, 2 million users. So they wouldn't make the ranks, ironically enough, for monthly active users, but they rank really, really high on a revenue basis, which is exciting.
--:-- Anish Acharya: And then as anyone who looks at the mobile list knows, there's a lot of — maybe for the tech audience — seemingly random products on there of, "I've never heard of this. Is this from a startup?" And on mobile especially, there is a very precise game that you can play with app store ads and other kind of paid but fairly low cost acquisition channels. And if you're doing this as an indie developer or maybe an app studio running internationally, you're not looking for the 10x payback of acquisition costs that we might be looking for as venture investors. So if you make back 1 or 2x your money on a user, that's amazing. So you can get to 10 million users mostly by paying for them, but you're probably not going to make as much revenue or ultimately as much profit maybe as some of the companies that are lower usage but higher revenue.
--:-- Host: And is there a learning there in terms of — you mentioned how by nature, if you start getting certain features or an application entirely, you are potentially stifling growth of the overall user base. Is there a learning in terms of how AI founders should be thinking about that tradeoff today?
--:-- Anish Acharya: I think it depends. Some of these markets are naturally maybe not mainstream behavior. Like one example of a category that did appear on the mobile revenue list but was not on mobile usage was several plant identification apps.
--:-- Host: I love those.
--:-- Anish Acharya: Yeah, where you take a picture, you save down the plant, it tells you exactly what it is if you've seen that plant before. Is that an app that 100 million people will have on their phones? Maybe not. But if you're one of the — like I can think of a few relatives who love plants or love birds — totally they'll pay $100 a year for that and they'll use it every day or every other day. So I think it's more for founders optimizing for the type of product you have and how mainstream it can be.
--:-- Host: All right. So there's a lot of information here that we've covered. We've covered desktop. We've covered mobile. We've covered revenue versus users.
--:-- Anish Acharya: Yeah.
--:-- Host: And then we've also talked about the stickiness of some of these players, right? You said there was 16 that have showed up every single list.
--:-- Anish Acharya: Yes.
--:-- Host: So what can we learn from the last few lists?
--:-- Olivia Moore: I feel like the biggest thing now — being a consumer investor for close to a decade now — it's almost like the more you know, the less you know in some cases, because it all just comes back to the product at the end of the day. Like technologists or investors can have opinions on the best monetization strategy or the best growth hacks. But in the end, if the product isn't capturing users' attention and isn't retaining them, the business is just going to be a completely leaky bucket of users in and users out. Often we meet with these amazing PhD researchers, best in class in the whole world in terms of their technical understanding of a model or capability. And they can struggle building in consumer sometimes because often the more complicated thing is not actually the thing that is highest utility, most delightful, most helpful to a consumer user. So we never like to be prescriptive on consumer products, but in general, we see when teams focus on either the pain point they're trying to solve or the unique experience they're trying to create and build towards that. And if that means you're actually using the old model because it's better than the new model, use that. If that means it's just one AI feature instead of the whole product being built on AI because it's not stable enough, do that. I think in consumer, you really have to let the data be your guide there.
--:-- Host: All right, that is all for today. If you did make it this far, first of all, thank you. We put a lot of thought into each of these episodes, whether it's guests, the calendar Tetris, the cycles with our amazing editor, Tommy, until the music is just right. So if you like what we've put together, consider dropping us a line at ratethispodcast.com slash A16Z. And let us know what your favorite episode is. It'll make my day, and I'm sure Tommy's too. We'll catch you on the flip side. Bye.
