The Current State of Consumer AI

Key Takeaway

Use AI today to turn one idea into a finished artifact instead of merely saving time. Pick a small project—a drafted email, a one-page plan, a short video, or a simple prototype—and give an AI tool clear context, examples of your style, and permission to iterate. Evaluate the result by how much it helps you create or decide, not by whether it produces a perfect first draft. Reusable context and feedback compound into a more personal, useful assistant over time.

Episode Overview

Elena Berger speaks with A16Z partners Olivia Moore and Josh Elman about the seventh edition of A16Z’s Top 100 Consumer AI Apps report. They examine the gap between AI traffic and consumer spending, the rise of personal agents, evolving models for subscriptions and advertising, and the large consumer categories—from shopping to social and entertainment—still open for AI-native products.

Full Transcript

Transcript of The Current State of Consumer AI from A16Z. Auto-generated from episode audio; may contain minor errors.

The most interesting big new trend is in personal agents. The reality is people are now building software. They're building these really rich products. They may be using their agents to write some of that code, but when they're delivering this whole experience to a user, it is much more than just a shim on the model. The run rate that OpenAI has reached through advertising, what does that tell us? That would previously take a company years and years and years to get to, even after launching an ads product.

What I'm fascinated to see in terms of the evolution of the ads product is half of Americans now report using AI, but only a small fraction are actually paying for it, and the people who do are spending a lot. In this episode, A16Z's Elena Berger sits down with partners Olivia Moore and Josh Elman to unpack the 7th edition of our Top 100 Consumer AI Ad Support. For the first time, the report includes consumer spending data, revealing a very different picture from traffic alone. The biggest spenders are developers, creators, and other power users, with the top 1% spending more than $900 a month on AI tools.

They also get into the rise of personal agents, how ChatGPT, Claude, and Gemini are diverging, why advertising could unlock new consumer AI business models, and the categories that remain surprisingly wide open, from shopping and entertainment to social and dating. Welcome to the A16Z podcast. I'm Elena Berger, and I'm joined by Olivia Moore, who's the author of our ongoing Top 100 Consumer AI Apps report, and Josh Elman on our consumer team. Today, we're digging into the 7th edition of this report, who's paying for AI, why the big assistants are heading in different directions, and whether personal agents can turn everyday errands into the next big consumer business.

Team, thanks so much for joining. Thanks so much for having us. Olivia, let's just get right into it. I would love to hear your top takeaways from this report. Yeah, so this is the 7th time we've done this list. The very first time, we only ranked all global websites that were AI native by just traffic, like hits on the website. And that worked then, because the way that almost everyone was using AI was like a prompt box product in a browser. We've had to evolve that a lot over the last few years as AI expands.

So this list is our most expansive yet. We added revenue data for the first time, so we worked with the EBIT data to get specifically consumer card spend data, so not accounting for enterprises or SMBs. And we ranked products by what consumers are actually paying for. I think for me, the biggest takeaways from this report is on a traffic basis, it looks like a lot of these products are kind of settling. We only had 11 new products across the web and mobile list combined, which is the least we've had in any of the prior editions of the series.

But the spend data really introduced a lot of new variables because of those 50 that ranked for spend, 29 of them were not on either traffic list. And that also, I think, kind of works towards or connects into the other big conclusion of the report, which is just how much of a power user game consumer AI is right now. Yeah, that's something that's funny. We also recently released our state of markets and something that Sarah Wang actually commented directly on is she said, hey, there is such a power law game happening now.

It's happening at the enterprise level and it's happening at the consumer level. So one thing that's probably notable is, yeah, we have the fewest number of newcomers on this list and it seems like the space is ossifying. But I think the past few months, things have actually been picking up a little bit more. I think the most interesting big new trend is in personal agents. So what are you seeing? What are the big shifts that are happening? Yeah, it's funny. The last report that we did, it ended right before the cutoff of when OpenClaw traffic would come in.

So we had done kind of like a retro before we published the report and we had found that it would have been very high up in the rankings. And this time, actually, OpenClaw is nowhere in the rankings because the traffic has completely declined. I think that the team was acquired by OpenAI, so they're probably working on the agent assistant products there. But also it's kind of been overtaken, I think, by some of the both other attempts at prosumer agents, like Crock-Bot, Town has some fantastic agents, DOTS now by OpenAI, which is new.

But also on the consumer side, we're finally seeing things that I would feel comfortable giving to a family member and telling them that they can safely use. Most of those also, because to your point, this consumer assistant trend has literally been the last three weeks to a month. A lot of those didn't appear on the ranks yet, but we did pull the data to see what's happening with them. And it's been very, very fast growth, and yet still not quite reaching the mainstream users, is what I would say.

I think we're in this really interesting era that did really change at the end of last year, beginning of this year with OpenClaw, that AI went from something that we were using as a tool to enhance our productivity, to maybe search for us faster, something that would actually get things done for us. And I think OpenClaw was an incredible pioneer to do this. And I know they're still doing a lot of work through their open source foundation to continue to advance that. But it really turned a lot of heads on, wait, what can these products actually be?

And we were seeing people buying Mac minis. We were seeing people run all their own data with their own local OpenClaw. And there was a huge run on that. But it was really inspiring to kind of feel this new energy of what is possible. And like most trends, that spark happens. It takes a little while for those first products to get built. It takes a little while for the momentum to then start getting those even released in the market for users to see them. And so in some ways, though, this report is showing, like, that's what we're talking a lot about on our podcast in Silicon Valley.

But this really does a great job representing what's happening in the broader world. And I do think the truth is AI really is showing up everywhere. Even if there's not a lot of new entrants, all the entrants are all getting so much bigger. Yeah. I think probably the biggest things on when we talk about pure consumer assistance, the biggest developments have been Muse and Instinct, I would say, although there's many other fantastic products that we kind of list in the report that have also, you know, Polk was a very early pioneer here.

They were a pioneer of this, yeah. Exactly. Tomo has a very big audience and is doing incredibly well. Instinct had announced, like, 100,000 users growing 10% day over day. They'd announced 40% of users connected a credit card in the first three weeks, and they were spending over $1,000 on average in their first month. Muse is interesting because in many ways, very successful launch, especially within the tech community. I think the number was like 500,000 downloads and 250,000 active users in the first 12 days or so.

But if you look at it versus Threads, which was another somewhat recent big, splashy meta launch, it still pales in comparison in terms of appeal to the average person who doesn't work in tech. Yeah, so if you look at the downloads, Muse is still only U.S. and Canada, so we grabbed the Threads U.S. and Canada downloads only. In the first 22 days or so, Threads had about 16 million downloads and Muse is still sitting around 5 million over that same time period. Is the distribution mechanism different?

Like, I remember with Threads, it was already tied to your Instagram account and there was kind of this very natural distribution. Are they trying a similar strategy with Muse or is it just a different distribution strategy too? I'd be curious what Josh thinks, but from my point of view, it's not as aggressive for sure. And also part of that is because there's a much higher cost to serve these users, so they don't want it to get too big too fast. And I also think Threads very much had a critical mass effect, which is if they didn't get a lot of people on it communicating quickly, you don't actually get to that leverage of a new network starting to form.

Whereas Muse is still such a personal tool that they're pushing it and it's very exciting to kind of even see this early just adoption and people having these experiences. And again, if you're using OpenClaw in January, you were having an incredible experience. If you're using CrockBot when that first launched earlier this summer or spring, you were having these incredible experiences. And now we're seeing, as I think Olivia said, our moms, our families, people who don't just live on the tech, what's next in AI cycle. And I think that's what's really exciting about this consumer.

I assume when we do this again next time, we're going to see massive adoption. But I think it hasn't needed to push as hard to have that early network effect. But then I also think about what's going to make these things stick around for a long time. And it is going to be things like network effects and marketplaces and all the businesses you can interact with those agents. That's going to be really transformative too. Yeah, this is one of the things we talk about a lot when it comes to consumer agents and assistants, which is I think they are getting kind of the compounding effects of building out a platform, or at least it seems like they are in terms of the ecosystem of other apps that are available and more easily usable by the agent.

We've seen that cut both ways. Like Amazon is like, we're not going to allow MUSE to kind of browse and purchase products on our site. But also MUSE has already hundreds of other partnerships with different products. Shopify, to be able to buy things on Shopify. Exactly, yes. I still don't feel like any of the assistant products have unlocked person-to-person network effects. And part of this for me is this software knows us more intimately than any other product in the past. Based on email, but also I find myself telling extremely personal things to my AI.

And so there's kind of this natural tension between the more useful the product is to you as the consumer, the more it knows about you. And then do you really want to introduce that into other contexts with other people? It's the same thing as many people have a separate Chachabiti work account and personal account. And no one has quite cracked that yet. No, and I think as we talk about consumers and AI privacy, safety, security are becoming more and more important. I mean, there's obviously the overall trends of trust in AI and this kind of belief that AI is good for the world, that is in some ways affecting adoption and affecting some of the excitement around these products.

But on a very personal level, if you don't trust the thing that you're giving it, your most intimate access to your email or your life or your credit card, and it might do rogue things that you wouldn't expect or share things with other people you wouldn't expect, that's going to be the biggest impingement on these things really growing as massive consumer apps. So we're in a fascinating time where we're all learning these new norms. And these are norms that like as people, we're actually very good at it.

We're very good at knowing what information we're comfortable sharing, what information if somebody gives it to us, what information that we're trusted to be able to pass on to somebody or should be protecting and keeping ourselves. And we're just like all of a sudden expecting software to do this too. It's a very interesting new set of things we're learning. Yeah. Well, you both just brought up like two interesting sort of facets of this. One is like, there's this endogenous force, which is all of the platforms and how they allow agents to interact with them.

And then there's more like this exogenous force of like how comfortable am I using this and maybe I mixed up endogenous and exogenous. One is on the outside and one is on the inside. We knew what you meant. What the platforms are doing and what is how you feel about it or how you feel about the agent. And I'm curious, which do you think is going to get resolved sooner and whose onus is that? Like whose responsibility is resolving those kinds of questions? I think what the platforms are capable of is improving faster than true consumer willingness to use them.

Which we've seen kind of throughout AI so far. It's interesting because a lot of people are like seeing these numbers that founders of these assistant products are posting that it costs like hundreds or thousands of dollars a month to serve a user and like, how is that possible? And I was stumped as well, but I have been working with David Paulan who runs Assistant Benchmark, who's fantastic. There's a recent podcast with him. Yeah, he's great. So basically what he's done is he's created a website where they track, I think over 170 agents.

I checked it last night and it's probably like 200 something this morning. And he has users kind of use them and then rank them on success on different tasks. But the other really interesting thing that he does is he hosts kind of group chats and communities of early adopters of these agents. So he has a group of, I think it's 1500 plus users. And he pulled some cool data for us, which is like, what are they doing with agents? What are they talking about doing with agents?

And the vast majority still, the number one use case was coding and technical automation, even for these consumer assistant products like Muse and Instinct. And so under that framing, it's like, oh, of course it costs that much. This is like when we talk to some agent or assistant products that are aimed towards the more mainstream consumer, they're seeing costs in the tens of dollars, not the thousands of dollars a month. So I think that is maybe a representation of the fact that we have a very long way to go in terms of consumers getting comfortable with these products.

And I think people who've been coding and have been working with AI, I mean, it's been such an incredible enhancer and that shows up in a lot of the data on the top 100 here. Like, it's incredible. And then people who've been able to do that have had these great ideas for all the other things in their life. But when you just hand this to somebody new, it feels like a blank box. And it feels like, what should I do with this? And maybe there's examples of getting a reservation or booking a flight, but if you're not actively seeking a reservation or booking a flight, it's hard to figure out how to personalize it.

And so much of what we do as people is we learn from others and we get these great ideas and we try the other things people tell us. So much of how social media has worked is people put things on social media and other people see that and they try the same thing. And then it kind of spawns these effects. They make a little riff on it and then those riffs become cascading snowballs and that's how great ideas flourish. And with AI, because so much of it's so personal, we haven't even seen any of that flourishing yet outside of areas like coding and productivity, all of the creative work.

I created this incredible video and how'd you do that? I use AI. There's so many tools that do that that have been growing and are here. But it's so interesting that now we have to figure out how to mainstream this for not the millions and millions of people who are using these and paying and everything, but for the... tens, hundreds, and hundreds and hundreds of millions. If you look at broad usage data now, about half of Americans report using AI, and about 25 percent of Americans think they're interacting with AI probably on close to a daily basis.

But the spending data is much more concentrated, so it depends on the source. Our IP panel shows probably sub 5 percent, around 4.5 percent of US consumers are paying a subscription to an AI product. Some other sources say as low as two or two and a half or maybe a little bit higher. It is expanding, so that's doubled from a year ago, but that spend is still extremely concentrated. Even within that top 4.5 percent of people who do spend, the top 10 percent of those are more than half of the revenue, and the top 1 percent are 20 percent of the revenue, whereas the bottom 50 percent are like 16 percent.

So the top 10 percent of users are driving this market. This was the other interesting thing about revenue data is we were able to look at it on a almost person-by-person basis, what it looks like, who are these people who are spending on AI. The top 1 percent user is spending $903 per month personally on their personal credit cards on AI, and the median is spending $25. Again, all of this is within only the 4.5 percent that are spending at all. Yibit also was helpful for us because we could see what products are they actually spending on, like on a personal basis.

Probably unsurprisingly, it was mostly developer tools, productivity tools, creative tools, things used to build, make, sell. Things like N8N, Granola, Higgsfield, Manus, those all were way over-represented amongst the top spenders. In a way, this is actually really cool. Even though these numbers aren't everybody yet, we're enabling this whole new generation of makers of people who are not just using this for work, but actually using their personal credit cards to code things. They never coded before to make art, make videos, make content, and really have this power of making.

It's actually in some ways, while it's all still so early, it's actually really cool how much making is going on, that people are actually so excited, they're willing to spend. If you're spending $900 a month on a set of AI tools, you're really using that to hopefully make things faster, better, have more ideas in your head coming to life than ever before. That's when I was looking at these categories between creativity and coding, that's actually hopefully really, really powerful. I think this is a point that's really important, which is, I think developers and coders are people who are naturally attuned to ask, how do I get leverage over this thing?

How do I build a product that allows me to get leverage over my time and AI obviously supercharges that. But it is a specific mindset, I think, and it's a specific world view. I think part of my understanding of these numbers is, it's still a world view that gets reflected only among a smaller group of people, and the question might be just like, how do you expand that world view? How do you help people understand, here are all the ways you can get leverage over your time and over your life?

We talked about this a little bit in this report in terms of areas where we have not yet seen AI native startups pop up or categories where we haven't. Eugenia, who's one of our portfolio CEOs, runs a company called Wabi, which is fantastic. She's this great quote, I'm probably going to botch it, but it's something like, most people aren't looking to save time, they're looking for ways to spend their time. This is why social media, entertainment, Netflix, TikTok, all of these, YouTube are the most used consumer products that we have.

I think that much of what we've seen in AI thus far, to your point, is like, how do I do this thing a little bit faster, a little bit easier, a little bit more impressively, and that's not an incredibly compelling daily or hourly active value proposition for most people. No, I think this saving time is still very hyper-productivity, people who are focused on work or being creative, but I'm also seeing this next wave of people who are, maybe used to be coders, they're no longer maybe people who want to make videos, but never quite mastered the Pro Tools and Adobe Premiere or something else, where they're all of a sudden able to sign up for these things and take the ideas in their head and make them happen.

So I do think we're starting to see these rise up. Again, I look at all these numbers as we're still so early. Yes. The fact that we're even here and that we're talking about how big some of these companies are and that one percent of people are spending $900 a month. I see these as harbingers of what's to come as more people feel empowered, as more people feel like they can create, where it's not necessarily about saving time, it's about spending the joy of actually willing this thing into reality.

There were all these jokes of when people were in this the peak phase of everybody's using their open cloud for the first time, their agents, they'd walk around with their computers and their laptops, and they had to keep them open, and they were like, I'm not sleeping, I'm coding and I'm making things more than I ever have. Again, it shows up at the top of the data, but I do think it's starting to show up everywhere across this list. I actually don't necessarily want to see that 4.5 percent of people paying for AI products directly expand, which is maybe controversial because I'm like invested in consumer AI products and I'm a consumer AI maximalist.

But the reason I say that is because substantially all of the consumer AI revenue thus far has come from direct subscriptions. On this list, we looked and it was like 85 percent or something of our web list monetized via subscriptions, another 62 percent monetized via credits or token extra usage payments. Only 13 percent had ads or other options where you are the product instead of paying for the product. If you look back at the history of the consumer Internet, that's a pretty unnatural inversion. Almost all of the really big consumer technology companies that we have now make the vast majority of their revenue from ads or from transaction fees, not from subscriptions.

Because I think the truth of it is, most similar to the fact that not everyone is looking for a coding tool to help them start a business or create and sell a product. Most people don't have the funds or don't want to spend their funds on software. If you look at the top 20 consumer subscription products globally, ChatGPT is already on there, which is crazy in like three and a half years of growth. Almost all the other ones are media companies or big platforms like Amazon, or even Uber, things like that, where you have a very frequent purchase behavior.

So I actually am a big believer that we need to move. We have transcended the need for everyone to buy a subscription to AI, and we need to see these other business models come back. I think we're still at the early days of inference costs coming down. We're still at the early days of people finding ways to run lower cost models, to build great products. And as they do that, a lot of the other business models that have always been on the Internet from transaction fees and ads will finally start working.

But we need that to happen. And that's, I think, where we're still, there's so much learning and so much that like, well, if it's going to cost me a lot to serve, at least let me just charge money so that people pay for it, became sort of an early default. Yes. I mean, you can probably speak to this from seeing many prior eras of the consumer Internet. But like, it used to be, at least for me as a consumer investor, if a company was making money in the first five years, it was like, whoa, what's happening?

The rule was like, let's build density of users. And because they cost near zero to serve, we can then wait and make money via ads or start charging for transactions once we have liquidity and density. And that just isn't the case given how high COGS are. No, I think this cost thing is a really interesting challenge. And companies get afraid of growing too fast. Yeah. Like they actually, if they grow too fast and they don't have meters on how they're charging for it, you can actually have out of control spend that can be very, very hard to manage.

So, you know, it's created this very different shape of what we see in the top apps, in the top 100, you know, traffic and everything right now. But again, this is, again, we're all still so early. There's so much to build and learn and bring costs down and bring value up and introduce advertising and introduce other ways to spend money. And by the way, if it is we subscribe to these services we love and that's actually what we get continual value from, that's amazing, too. I think this is a great transition to talk about ads.

Yeah. Your Consumer Top 100 report had a really, really interesting fact in there, which was the run rate that OpenAI has reached through advertising. Yeah. They're at a billion dollars now in annual run rate on advertising. And what does that tell us? You know, again, the number was from August. And so there's probably a chance that it's reasonably higher, even a month or so later. Yeah. But they have been very slowly, I would say, rolling out ads with kind of a network of partners. And they're already at, you know, a billion.

Billion in annualized run rate like that would previously take a company years and years and years to get to even after launching an ads product. I think there's two interesting things there. One, they have real density now. So I think the last reported number a few days ago was one point two billion weekly active users, which is like a real density. And then the second thing and what I'm fascinated to see in terms of the evolution of the ads product is hypothetically they should be able to charge more and have higher conversion rates because they just know so much more about you as a user.

And Chachibiti has launched things like login with Chachibiti. I think a wallet is coming, things like that. And so you could imagine that it becomes a much more compelling value prop for the advertisers. And then that just like juices the ad business even more. But we'll see. It's very early days there. It's early days. And I think, you know, there have been some really funny conversations about ads and AI if you are talking to this incredibly personal thing. And then it's like, let me tell you about health and let me introduce you to this like crazy product that can help you you know, lose weight fast.

Like that's not building trust with AI. And so I think the other really interesting thing is like, how do we as an ecosystem, these products are so different. There's so much more personal than ever before. They're giving you answers that are custom tailored to what you're doing and what you're asking about and what it knows about you. They're actually introducing ads has been done very deftly. I've actually been really impressed with the open AI roll out of ads. When I've actually ever seen an ad, it's it's very clearly labeled.

And it actually feels like a natural ad to the conversation that I'm having. I think we're also, you know, one of the other shifts is people are starting to rely on, you know, Chachapiti a lot more for a lot more queries. It might have used to been like, look up a fact or tell me the history of this thing or help me with my homework, not necessarily problem solving and things were advertising. Now people are going to it for real advice or shopping or trying to figure out exactly, you know, where they want to go or what they want to do.

And those are great opportunities when you're in a discovery mode where ads are actually often a great answer to help you solve that. And so a lot more of these commercial queries and these commercial intent creates that, too. But again, it has to be done very, very deftly, not this weird, interruptive, very personal experience I'm having that has just get shoved into the conversation. I'd be I'd be super curious to how the unit economics on ads work. Like presumably it's a it's a lower cost question or lower cost compared to, you know, the coding or something like that or image gen.

So so for OpenAI, you know, what what kinds of margins are they seeing on ads is a super interesting question. Yeah, I'm curious also, Josh, but what kind of ads do you see? Because I don't get any ads. I guess I guess you're on a pro plan. Yeah, enterprise. So, you know, yeah. You know, I was doing a bunch of travel research recently and was was going to be in New York for a few days. And I just started seeing some ads for a couple of things that were going on in New York that I might have wanted to do.

And I just found that really useful. And like I started using it more for commercial shopping. I was looking for a special key chain that would have a really easy separation for a valet key. And I was having trouble finding this actually on Google. And I finally was like, let me just go ask at GBT. And I went back and forth and I was describing what I wanted and start giving me some really good examples. And at the end, I was like, great. And I'd also love one that's made in America.

And I actually found this incredible like option from a factory in Detroit that often makes a lot of other metal work and other stuff that had this like really cool key for me. And it was one of those things that if I had never had that chance to really go back and forth and actually have that conversation with GBT and if there had been other ads that no ads were presented in that conversation, but it was entirely open for ads that they would have helped me find stuff.

And eventually I went to this website. I'd never heard of that company before. And it was a great experience. So interesting. I feel like we may see as they lean into it, open AI being able to do crazy good targeting on like even better than I think Meta, which previously was like best in class for this, just because they knew so much about you. But like you're having this live conversation with them. I do think that it'll help chat GBT, but also I think it will also support a lot of these more focused, almost verticalized to use the enterprise term consumer AI products.

So another company that has seen early success in ads is Open Evidence, which is an AI product for doctors. And it already has 50. I think the most recent numbers were 50 to 60 percent density of U.S. all of all U.S. physicians on the product. And so when you have that kind of density of a really valuable audience and it's like a targeted audience because, you know, they're physicians, you can, I think, advertise really effectively. And there will be and should be more of those, I think.

Is it is it worth talking and taking a step back and talking about open AI, Anthropic and Gemini and sort of these of these are companies, these are labs that have been on the list pretty much since. the beginning. Are we seeing any kind of evolution in the way people are paying for these products? What can we surmise about the different kinds of users of all three? Super, super curious what you're what you're seeing. Yeah, absolutely. From the very first time we did this report, the main conclusion was like, ChatGBT is the dominant global user by both usage and revenue on the consumer side.

That continues to be true. Everything below ChatGBT has seen, I think, more movements and more shakeups. So ChatGBT is now ahead of Claude around 6x on the web, around 2x ahead of Gemini on the web. Probably going forward, given a lot of the usage is concentrated in these power users who are paying, the revenue numbers could be even more useful here. And probably that's one of the biggest surprises to me in this report was we got a panel of US spenders. And Claude has actually passed Gemini in terms of number of paid subscribers, which is kind of crazy given Gemini is a much bigger install base and user base overall.

And it also has like the natural distribution mechanism of something like a Google. I think that's a big, you know, accomplishment, of course, for Anthropic. I think it's come on the wave of a bunch of very successful products that they've launched over the last few months, like Claude Design. It's also come from a lot more press that they've had, starting with the Department of War stuff all the way back in, I think it was February. The other main difference, I would say, in how these products are monetizing, and again, ChatGBT is still far in the lead by monetization.

They have around three times more paid consumer subscribers in the US than both Gemini and Claude. But Anthropic has said, very famously, no ads. So they're much more aggressive about subscription. They have around seven and a half percent of subscribers on the $100 plus per month, like the max plan. And that's like 1% for both ChatGBT and Gemini. And so those users naturally look different and are doing different things on the product, even in that paid user base. Should we talk about creative tools? And I think one thing that's interesting is actually all of the big labs have gotten really good at creative tools.

And one thing that actually Anthropic famously abstained from, but then somehow achieves anyway, was creating an image model or video model. But now people are making images and videos because coding is so good at creating images and videos. So it's interesting that they didn't build a diffusion model. Yeah, they're still capable of doing image gen. But about the pure creative tools, the companies that have kind of like verticalized in that category, what are we seeing? Creative tools is very interestingly distributed in terms of who is succeeding where.

So there's a few categories of creator tools, or maybe modalities is the better term, where the labs have not focused energy and attention for many reasons we could discuss. A lot of those around audio, so like text to speech, music, 11 labs in Suno are now very reliably near the top of our traffic lists and are also ranked very highly in our spend list. The labs have so many things to do. They're building coding agents, they're building AGI. Does it make sense at this point for them to deal with all of the IP headaches that Suno has had to go through and launch a competing music model?

I don't know. And in the meantime, Suno has been able to kind of run away with a really strong lead. And, you know, I'm a big believer that, you know, the labs are going to do what they're going to do, and they're going to keep growing. And they obviously are great baselines for everything. But in so many of these markets, you're seeing really specialized products that are really focused, knowing who their audience is and what they're doing, that's able to build a really custom things. You know, if you want to make music just for fun, or for something that you're really producing, Suno is an incredible tool.

And it speaks to you from the moment you start using it through the end, you know, 11 labs, if you need voice and audio within the product that you're making, it just has a way of working and, and interacting with it, that feels completely different. When you look at a bunch of the video tools, you know, when you look at design tools, even like Figma, like they're so designed to, to use deeply for the thing you're trying to express yourself. And it speaks to designers, not to people who, you know, want to just skip that step.

And I think we're going to see so much more value from these really bespoke interfaces, where the software layer of all the things you can do and the things you save and the way you interact with it become really where the value sits, not just the models. Yes. So this is the second category, I think, of creative tools, which is images and video that looks different from audio. On images, I think to your point, like OpenAI has really leaned into images quite successfully, like images 2.0 is very, very good, both for image creation and editing.

And then of course, Google with the various iterations of Nano, Banana and Veo, I think they have candidly taken away a lot of the high level consumer traffic that was previously going to these standalone image generators. So like Midjourney was fairly high on our first ever rankings, it has fallen off the traffic rankings completely. But to your point in power users, if you look at the revenue rankings, Midjourney is back. So people, the power users still want the model that they feel has taste, has an aesthetic sense, and that they're able to tune in a really specific way.

And then that brings us to video, which to me is kind of like a wild frontier right now, because Chinese companies that can train on any data and do have a real advantage. Video is just so complex to generate with like the visual, the sound, the visual and sound synced, and looking a specific way that I think like the more data you have to train, the better. They've been successful there thus far. I think more broadly in creative tools, I'd be curious if either of you have experienced this, but I have recently been asking Astra and other really powerful models to generate or create, generate or edit creative for me.

And it does a very, very good job and it will go tool call to elsewhere on the internet. And so I think that creative tools that are agent friendly, and agent accessible are going to increasingly see really compelling tailwinds. Yeah, yeah, it's actually crazy what you can do if you just like empower an agent and just say like, yeah, install whatever you want to install. I had it, I actually had Astra and 5.5 go like head to head and animating some stuff for me. And like, it was just like it knew exactly what to do.

It's like, yeah, so And what I love about this is, you know, this is the tinkerers that people at the edge are figuring out these capabilities. And we're going to build products over the next year that are going to show up on these lists, because they're going to take all of this complexity of what you can do and package it up in a way that makes it really easy to use. And like, that's this, you know, when I think of this gap of like, all AI versus consumer AI, it's exactly this, this movement of packaging these up and building great product experiences.

Yeah, that really turns it into into these things that sustain. Yeah, yeah. And I guess it has to start with the tinkerers, and then they post on X. And then, you know, six months later, we get a real like product and startup out of it, or like, you know, 12 different startups all competing. Outside of the web window, there have been a big proliferation of mature products. So there's granola, there's whisper flow, they're super human. What what are you kind of seeing there? And then obviously the ones that exist in chat and messaging, so an instinct.

What are you seeing when it comes to distribution? And what are you seeing just when it comes to the kinds of products and users that they're approaching? Yeah, I think there's two maybe interesting threads to pull on here. One, I wrote this piece called maybe six, nine months ago called The Great Expansion, which is basically like, why are consumer companies becoming enterprise companies so quickly? Like things like CREA, you know, Repl.it, 11 Labs, Gamma, these products previously like Canva took six plus years to have real team and enterprise revenue.

And what's happening is that they're very successful and growing via PLG, whisper flow and granola are another great examples there. And then because they are kind of work productivity related, they get pulled into enterprises and then the founders are like, oh, we got to add privacy and security and team plan, but it's very effective, very low cost distribution. And again, it works really well for these tools that can be used personally, but are like work adjacent. I think things like whisper flow and granola and superhuman are another good example of this trend we've seen in general, which is incumbents being unwilling or unable to cannibalize their existing interfaces.

The classic example of this is Google, like we haven't really seen them reinvent Docs or Gmail or Calendar for the AI age. I think it's even happening with OpenAI and Anthropic, where they have been much less successful in anything that is not a launch within their existing ChachiBT or Codex or Cloud or Cloud Code interfaces. So and I know many of them have, you know, attempted to incubate killers for these many startups, but I think it is difficult from a product perspective for them to innovate beyond that, especially when they have so many other things on their plates.

I mean, I think all that's true. And I think we now as we get used to some interfaces, we're just still in such a period of incredible reinvention. Like a lot of the reason we've seen so much growth over the past year, year and a half is the models got so much better. And the models got so much better. So the people who had been spending time creating these incredible interfaces that were easy to use that were that were natural, that were, you know, easy to share and get a bunch of your team on and, you know, WhisperFlow and Granola were just so easy to get started with, you then would say, Oh my gosh, now they've gotten so much better as the models have gotten better that the products utility gets so much better too.

And so we're kind of this, it's incredible, you build something great, but it may not do everything you want, because the model capabilities weren't quite there, then models get better and your product gets to explode in demand and utility. And this kind of rising tide lifts everything. And so to think that, you know, we only have one or two companies that can build that experience layer, I think is what this sort of report sort of debunks. Now, those are certainly some of the top companies in these very general use cases.

But it's, you know, the reason that so many other things are growing is there's so many interfaces that get to be reinvented. And, and the sort of rising tide lifts everything. And then, you know, the infra value may still go to the model providers or may go to the other things that are serving inference. And there's a whole different conversation there. But what consumers are adopting are things that work for them and are easy to use and bring into their lives. Yeah, maybe to also make the, to continue to make the case for startups here, OpenAI and Anthropic in many ways are startups, but they have been so successful that they have become big companies with thousands of people.

And there are just so many extra considerate, like a larger ship is harder to kind of steer to turn around. And so I think that's like one of the companies on our revenue list this time is Plod, which is an AI notetaker device that started kind of in China in the East and now has come to the US. And you buy the device, you can also buy a subscription. OpenAI can and will do a hardware device. But as we've seen with them, it just everything takes longer.

When you're a big company, you have to integrate with maybe other existing products, there's more checks and balances it has to go through. So I do think that the big labs will continue to be very, very successful. And there's a lot more for them to do. And I think that startups will continue to be able to find these quite big windows of opportunity. Yeah, and well, it also sounds like the the question for startups is how do you build a product that as you said, Josh gets better as models get better, but doesn't get cannibalized as models get better?

Yes. Which is, you know, what is like, like, an interesting kind of sweet spot to exist in? I mean, part of the reason that I was so excited to join Andreessen Horowitz a couple months ago, was this belief that the value really now is moving back to the software layer, that now that we have these incredible models at our, you know, capabilities, we can access them, you can build really rich products that leverage the models to do it, you know, and there's new innovations like, you know, Jeff from type safe that allows even developers to build things in even new ways.

And we haven't even seen all the incredible new ideas that can be done when you have access to a model like that that's fast, that's easy to integrate that, you know, gives you code, you know, are very structured answers back. And so because of that, now the creativity is back to the experience, the network, you can build the ways that you can serve a lot of people and bring them together, using the models to be something powerful, but the context and the community really becomes the asset.

And there's so much in invention. And I think almost every category has a chance to be reinvented. And you know, some big companies are going to be able to make the leap and become the relevant company for their sector in the AI era. But not all of them will. And that's where startups come in to kind of beat them to that punch and really be the best new possible experience. Yeah, yeah, I think to that point, I think a lot about compounding personal value, and hopefully eventually compounding multiplayer value that becomes like a lock in to some of these startup products.

One example I would give is town, which is more on the prosumer end of the agent assistant, it can connect into all your systems, Slack, email, Google Drive, understand kind of the context of you and your org, and then do things for you. Like I use it to write a lot of my emails, helped with a lot of this report. And I know it's easy for people to dismiss things like this is like, oh, it's just a harness, which is now the slightly less derogatory term for rapper, I guess.

But town has built these playbooks of who I am, what I sound like, that no other product has, and that I cannot easily take and migrate over to something that comes along tomorrow. Because the difference between an email that is 99.9% sounds like me and an even an email that's 85% sounds like me is the difference between spending like 10 seconds to fix it and like 10 plus minutes. And so I think that we'll see more products like town that do a really good job of collecting context and acting on that context to build playbooks around you continue to be successful.

No, I think that's totally right. I try to avoid using words like harness and rap. Yes. Because because I think the reality is people are now building software, and they're building these really rich products, and they may be using their agents to write some of that code, but when they're delivering this whole experience to a user, it is much more than just a shim on the model or a harness that brings some context to the model. It's actually a really rich product and an experience that happens to now use a model as part of what makes them able to deliver that.

I think that's really the excitement of what we're going to see on this list. Many of these already are doing that, and we think really the next wave, they're all going to be these companies that have built so much more value, and the model is something that is just one piece. Olivia, I think you referenced this chart that you made, but maybe we can bring it up, is all of the white space that still exists for just all the product categories that exist in Web 2 and the pre-AI age.

Maybe you can just talk through some of these categories and where are the big opportunities? Yeah. The vast majority of pure consumers are basically using AI still as a replacement for search products, things like Google. Maybe especially as you get into education use cases, kids are using it to write essays, little bit more of an expansive use case. Homework help. Homework help, exactly. Yes. Or people in the workplace to write emails. But if you look at this chart, which will show almost all of the areas where we've seen startups succeed thus far have been in and around that theme of getting things done.

Like productivity, photo and video design and editing, search and answers. So many categories are wide open. Many of those categories are, I would call them network categories where you need a multiplayer product. Dating is one we haven't seen. There are certainly startups there that I hope and believe will make future additions of the list, but there's no one on the list doing dating now. Recruiting is another one. I think of dating and recruiting as somewhat similar networks, actually, in terms of trying to make matches. Social AI, we really haven't seen anything take off.

Most of social AI is people posting AI-generated content on existing social platforms. And then I think there's all of these other categories that look maybe more like marketplaces did, like shopping, home buying, retail. We'll have to see if those end up living in agents like Instinct or Muse or living as standalone products or the next ones, which there will be many, or living as standalone products or perhaps a mix of both. Perhaps your agent is calling the AI native marketplace. So I'm very excited about those things.

I think also categories like gaming and entertainment, the models just have not been good enough to produce content that the average person likes to watch, which may be the exception of AI microdramas, which are blowing up. But I think that is coming soon too. No, I think that's exactly right. And I just think we're in this world where we're kind of like so excited by what Chachapiti did with turning things into conversation and now these agents we can get in our messaging apps to turn them into more conversation.

It's an incredible paradigm. But when you're shopping, you wanna visually see options and explore and have things customized for you and be able to say, I really like that, but I wish it had some piping on the collar or something and actually have AI helped you both realize that and maybe even get it made for you. Like shopping can have so much invention there. Entertainment, I think we've barely started scratching the surface of the ability to tell better stories of the way to empower people to be better storytellers that then creates these incredible networks of people consuming stories.

You know, TikTok was this incredible place where people came together and just create little videos. You see people around the world that you're now watching, but there's still so much capturing people's heads that if only they could express their ideas even better, AI is gonna help them. We're gonna have an incredible new network. So I feel like we are just at the cusp of all these categories and we've needed the models to get better so that you could start to dabble in some of these ideas and invent them.

But as that happens over the coming years and with the new model capabilities, I still think the value is gonna end up in this software layer in the products. Yeah. To me, this gets back to what we were talking about of products that help people save time versus spend time. Almost everything we've seen in consumer AI is save time. And that spend time, those end up often actually being amongst the biggest companies, if not the biggest ones. And so we need that to exist in many, many builders, I'm sure are tinkering away on that right now.

Absolutely. Absolutely. I think that's a great note to end on. Thank you both so much for joining and absolutely make sure to check out the top 100 consumer AI apps, seventh edition. Thank you. Thanks for having us. We will see you for edition eight. Yeah. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify.

Follow us on X, A16Z and subscribe to our sub stack at A16Z.substack.com. Thanks again for listening and I'll see you in the next episode. As a reminder, 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 forward slash disclosures.

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