Pod Insights a16z Podcast Inside a16z’s Top 100 AI Apps Report with Olivia Moore About this episode Anish Acharya speaks with Olivia Moore about the latest edition of The Top 100 Gen AI Consumer Apps report. They cover why ChatGPT is still 30 times bigger than Claude on web, how the three major platforms are specializing for different users, what global adoption data reveals about cultural attitudes toward AI, and why agents, memory, and voice are about to change everything.
Timestamps:
0:00—Introduction
4:18—The App Store Dynamic and Monetization Strategies
9:12—Google's Gemini Comeback and the DeepMind Creative Push
11:33—Global AI Adoption: Russia, China, and the Per Capita Heat Map
17:55—The Evolution of Creative Tools
20:51—Sora's Social Experiment: A Million Users Faster Than ChatGPT
24:53—OpenAI Operator: Number One GitHub Stars of All Time
32:27—How Teenagers Are Actually Using AI
36:37—Memory as a Core Advantage for AI Products
Read the full transcript here: https://www.a16z.news/s/podcast
Resources:
Follow Anish Acharya on X: https://twitter.com/illscience
Follow Olivia Moore on X: https://twitter.com/omooretweets
The Top 100 Gen AI Consumer Apps — 6th Edition: https://a16z.com/100-gen-ai-apps-6/
Stay Updated:
If you enjoyed this episode, be sure to like, subscribe, and share with your friends!
Find a16z on X: https://twitter.com/a16z
Find a16z on LinkedIn: https://www.linkedin.com/company/a16z
Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX
Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711
Follow our host: https://x.com/eriktorenberg
Please note that 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. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Olivia Moore? The cultural change and the cultural adoption will be slower than the technology change in what's actually possible. If you actually look at the app stores that are emerging on Claude and ChatGPT, they both have 200-plus apps, but there's only 11% overlap. Essentially, you'd be able to log in with your ChatGPT account and take like your memory and your tokens with you, and then that other product would be able to kind of borrow those things to be even more powerful and helpful for you. I don't think we've seen a social product yet succeed that's like entirely AI content. The emotional stakes just feel lower in some ways.
0:39 Erik Torenberg? ChatGPT is the biggest AI product in the world. It's also only reaching 10% of the global population on a weekly basis. We're still early. Six editions into the A16Z Top 100 AI Action Report, the platforms are diverging. Claude is doubling down on prosumer tools. Gemini's traction tracks almost perfectly to creative model releases. And ChatGPT is building towards something that looks more like Google and everything at that monetizes through ads, transactions, and subscriptions. The global data is surprising. Singapore, Hong Kong, and the UAE lead per capita AI usage. The US sits at number 20.
1:24 Erik Torenberg? Trust in AI varies wildly, from 32% in the US to 80% in China. Anisha Charia, General Partner at A16Z, speaks with Olivia Moore, Partner at A16Z.
1:37 Anish Acharya? Olivia, welcome.
1:40 Olivia Moore? Thanks for having me.
1:42 Anish Acharya? It's the most exciting time of the year, which is the Top 100 Report is coming out today, I think, is that right?
1:47 Olivia Moore? Yep.
1:48 Anish Acharya? It's been six editions over three years. Talk to us about what's the same, what's changed, what's your excitement level, what's up with the report?
1:56 Olivia Moore? Yeah, in many ways, like so much has changed and there's been just an incredible amount of growth since the first time we put out this list in 2023. On the other hand, from like a macro level, we're still so early. ChatGPT is by far the biggest global AI product and still only 10% of the global population is using it on a weekly active basis. So there's like a lot more to come. I do think this past six months has been maybe my favorite time and the most exciting time because of the shifts that we've seen. One of them has been that the race for the consumer is really heating up. So ChatGPT, of course, but also Gemini and Claude are kind of doubling down on their own ICP within consumer and prosumer.
2:38 Olivia Moore? And I think we're starting to see how these platforms might have compounding advantages over time. And so that makes it especially kind of existential or interesting of who is acquiring the most users. And then on a related note, this was actually the first issue that we included products that were non-AI native but are now majority AI-enabled. So things like Canva, Notion, Free Pick. Notion actually announced that now they think half of their new ARR is driven by AI-first features, which is very cool. And then lastly, I think we've seen a big expansion of AI outside of just like the website or app prompt box. So we have all of the browsers that have come out like DIA, Comet, Atlas.
3:20 Olivia Moore? We have Claude in Excel, PowerPoint in Chrome, and then we have desktop apps like Cursor, Whisper Flow, Granola. And so there's been just a really kind of exciting explosion in the ways that people are using AI.
3:32 Anish Acharya? So exciting. There's a ton to cover here. So let's start with the big foundation models. Can you talk a bit about what you think are the respective areas of specialization for Gemini, Claude, and then of course ChatGPT? Because it feels like it's been a rising tide story more than these models trading off with each other.
3:47 Olivia Moore? Yes, I agree. Despite the drama maybe of the past week where we have Katy Perry taking sides on Twitter in the LLM war, which is something that I didn't ever see coming. I think at a base level still, if you look at AI usage, like ChatGPT is a very, very clear winner. So on web, they're 2.7 times bigger than Gemini. On mobile, they're 2.5 times bigger than Gemini. And then despite, again, like the kind of tech Twitter discourse, Claude, they're almost 30 times bigger than Claude on web and almost 80 times bigger than Claude on mobile. So we had seen that Sam Altman tweet back in the Super Bowl ad wars era.
4:25 Anish Acharya? The Texas tweet.
4:26 Olivia Moore? Yes, he was like, we have more people using ChatGPT free version in Texas than Claude has all users globally, which is true. That being said, I think we are seeing I don't think bifurcation is the right word, but maybe expansion in the number of products people are using and what they're using different products for, which has kind of changed the market share a little bit. Claude in particular has really doubled down on prosumer with things like cowork, Claude Code, Claude in Excel and PowerPoint. If you actually look at the app stores that are emerging on Claude and ChatGPT, they both have 200-plus apps, but there's only 11% overlap. Like Claude is very much doubling down on like premium data sources, research tools, science tools, financial data.
5:10 Olivia Moore? And ChatGPT is really doubling down on like consumer marketplaces, travel, nutrition, consumer finance, things like that. And then Gemini is kind of in its own little corner as well. And the traction has largely been driven by creative tools there. So if you look at their kind of active users and paying users, it's nearly perfectly correlated to releases of like VO3, Nano Banana 1, Nano Banana Pro, Nano Banana 2. They're doing a little more on prosumer. They're adding AI to Gmail, Sheets, Calendar. But that's all being captured by like their existing products versus like a net new experience.
5:47 Anish Acharya? Maybe let's dig into the app store dynamic a little bit because that's so fascinating. Can you talk about the bull case for ChatGPT with I think what they call the apps directory?
5:56 Olivia Moore? Yeah, yeah. I think the approach we're seeing with ChatGPT, and Sam said this himself on Twitter, is we want to be the AI for everyone. And that means that they're trying to acquire every consumer and they'll monetize them in different ways. So like I think Claude has been very clear that they're just going to monetize via subscriptions, which is great for people and companies who can pay for subscriptions, but it won't be everyone. I think you see that with the plugins that they're leaning into, which are like paid, high ACV, like work data tools.
6:24 Anish Acharya? Similar web, things like that.
6:25 Olivia Moore? Yeah, things like that, like PitchBook, like things that you would use if you're an investor or a scientist, a mathematician. And ChatGPT, I think, is going more of the Somewhat of a Google-type approach in that they're building things that like the average person will want to use. And maybe a smaller percentage of those convert to subscriptions right now, but they will be able to monetize those people through ads and probably also, I would guess, through transactions. Like if they're building the gateway to book a trip or do all of these other kind of long-tail consumer purchases. Hypothetically, they should eventually be able to take some kind of cut of that, at least for the traffic that they're driving.
7:06 Olivia Moore? And so I think that that is the bull case for the ChatGPT app store that isn't yet showing up in the data that will probably become like even more evident in the next year or two.
7:17 Anish Acharya? Yeah, it's really interesting because it touches on your point in the report about compounding advantages and how context compounds. Can you talk a little bit about that concept? And then what's your proxy in terms of a metric for it? Is it session time? Is it number of sessions? Is it the amount of data you've provided or is there something else?
7:32 Olivia Moore? Yeah, this is a really exciting question to me because I think thus far with these horizontal LLMs like ChatGPT, Claude, Gemini, Perplexity, we've kind of lived in a world where the context and the memory is somewhat easily exportable. Claude ran a campaign around this recently. But I think there's going to be increasing lock-in and I do think that probably actually benefits the broader, more horizontal tools like ChatGPT for a few reasons. So I think, one, we've already seen ChatGPT focus on or start to build out products where you interact with other people on them through the platform. So the group chats. Like imagine if there's an even more successful version of ChatGPT group chats and all of
8:14 Olivia Moore? your friends are on there, then if you wanted to churn from ChatGPT, you'd also have to convince them all to go do another product.
8:20 Anish Acharya? Classic network effect.
8:21 Olivia Moore? Exactly. I would say the second one is kind of also like an Apple-Google comparison in that as these app stores emerge, it is likely that developers might start to concentrate. Their time and effort in who they build for in the most sophisticated way, who they ship to first, depending on who has the most users. Or maybe in some cases, who's the most willing to pay. But for a lot of these consumer tools, it'll be who has the most users. So I think that also benefits ChatGPT. And then the other thing probably that I'm most excited for this year that Sam Altman had kind of hinted at is this like authentication with ChatGPT layer.
8:57 Anish Acharya? Yeah.
8:58 Olivia Moore? So essentially you'd be able to log in with your ChatGPT account and take like your memory and your tokens with you. And then that other product would be able to kind of borrow those things to be even more powerful and helpful for you. And if that's the case, then you're wanting to have more of your core identity live on ChatGPT, because then it can lend it to these other tools that are even better for you.
9:22 Anish Acharya? It's so smart and it really plays to their advantages in that they have signups for 900 million people. And then the third party developer ideally would not want to pay for the inference.
9:31 Olivia Moore? Yes.
9:32 Anish Acharya? So the user can bring their inference capacity with them. There's an advantage for the developer. ChatGPT gets the lock in. The user gets the benefit of personalization and it all kind of works.
9:41 Olivia Moore? Yes, I totally agree. The one question mark I still have on this that I think could play both positive and negative in terms of increasing lock in for the consumer product is what your work goes with, like what your enterprise contract is. So for example, in some ways, it's good for me if my company uses ChatGPT for work, because then I know how to use the product. And as a normal consumer, they might have tried one or two AI products. So they're more likely to be comfortable and keep using something that they've already used. On the other hand, some people might not want to mix identity and mix memory across their personal and work use cases.
10:18 Anish Acharya? True.
10:18 Olivia Moore? And so I'm really interested, I think OpenAI hinted at this recently, but I'm really interested in how we kind of segment memory across different personas that are within yourself that are using these products.
10:30 Anish Acharya? Don't cross the streams.
10:31 Olivia Moore? Yeah, exactly, exactly.
10:33 Anish Acharya? Well, maybe actually switching gears to Gemini for a moment. You know, I think about the kind of, just the vibes around Google with their early AI products, Bard, which they'll never live down.
10:44 Olivia Moore? Some tough times there.
10:45 Anish Acharya? To where we are today with products like Nano Banana, like even naming it Nano Banana is such a perfect microcosm for how far Google has come.
10:52 Olivia Moore? Yeah.
10:53 Anish Acharya? And it seems like they have a lot of intentions around multimodality.
10:56 Olivia Moore? Yeah.
10:56 Anish Acharya? What's your assessment of their approach?
10:58 Olivia Moore? I've been impressed. I think they have been hesitant, maybe in some ways more hesitant, in same ways as exactly what we would expect, so to kind of bake AI into the core features. Because there's a risk of either cannibalizing their own product, or like, there are so many people who are, have used these tools for 10, 20, 30, 40 years. And so, the switching cost there is like, a little bit high. They don't want to scare users when AI is suddenly popping up in everything, which I understand. But, what they've done a really, really good job at is these new creative products that are basically very model-driven, from the DeepMind team, who I think is generally fantastic.
11:40 Olivia Moore? I think Notebook LM was actually the first look at this, and that was something truly new in like, consumer AI audio. And now we have the image and video models. So in some ways with a big company like this, they kind of have to get over, get out of their own way, in terms of being able to actually innovate.
11:58 Anish Acharya? Yeah.
11:58 Olivia Moore? And it seems like they are, but you also worked at Google, so I'd be curious for your take.
12:02 Anish Acharya? Well, it's interesting to just, I'm glad you brought up Notebook, because Notebook is sort of this greenfield area within, product area within the company, so you don't have 10 VPs fighting over it.
12:10 Olivia Moore? Yeah.
12:11 Anish Acharya? And as a result, I think just the progress at Notebook has been tremendous. You know, they just launched a video generation feature.
12:16 Olivia Moore? Yeah.
12:16 Anish Acharya? That helps visually demonstrate all the concepts in your sort of workspace, which is cool. Conversely, when you look at the existing product surfaces, like Sheets or Docs, there's just so much one sort of momentum and inertia from the past, but then management overhead.
12:32 Olivia Moore? Yes.
12:33 Anish Acharya? Around those, it's harder for them to do anything other than the most obvious incremental thing.
12:36 Olivia Moore? Yes, I agree. We'll see what happens there in the, in the next few years. I feel like they're gonna put up a fight on some of those products, because they don't want to lose that user base. But to your point, they're already locked in with so many enterprises, that they might not have to do that much, at least in the near term, to kind of keep up.
12:53 Anish Acharya? You know, implicit in this conversation is we experience and talk a lot about AI in the West.
12:59 Olivia Moore? Yeah.
12:59 Anish Acharya? Talk a bit about the sort of global AI trends. There was a few surprising things I saw in there.
13:03 Olivia Moore? We kind of expanded our scope in terms of what we looked at for this report, which ended up being like very fun and interesting. Two things that are probably obvious in terms of how they differ from the rest of the world would be Russia and China. So Russia, I think China, everyone knows, like a ton of AI products are kind of censored or banned. And so almost all of the usage, they actually have the lowest combined ChatGPT and Gemini usage of any country. It's only 15%. So they're mostly using like Daobao, which is made by ByteDance, DeepSeek, Quen, Kimi, those kinds of models.
13:41 Olivia Moore? The somewhat of a surprise to me was that Russia actually is a very, very similar story where they have also their own kind of parallel AI ecosystem out of necessity because they have some level of sanctions and things like that that prevent them from using all the US-based tools. So we've seen products like GigaChat and Yandex, which are Russia-specific, built by Russian, often state-affiliated companies, have big, big usage there. And then DeepSeek. So Russia is the number two market for DeepSeek after China. And so if you look at the kind of like per-country adoption data, like yes, there's some blips where like this country uses Claude a little bit more. This country uses Gemini a little bit more, but the two huge outliers are Russia and China.
14:26 Olivia Moore? And those are like big, big markets. And so I think it's worth watching what's going on there.
14:30 Anish Acharya? It's interesting though, because both Russia and China, they're outliers because of restrictions around how models can be used and maybe cultural preferences. Are there any other countries that have geo-specific trends or is this a sort of global AI behavior set?
14:45 Olivia Moore? I would say in terms of like model development, proprietary model development that allows you to deploy proprietary AI products. Most of that research is coming out of the US and China, maybe a little bit out of Russia.
14:59 Anish Acharya? Okay.
14:59 Olivia Moore? I think we are seeing a few kind of native ecosystems in other places. I would, Korea has a couple of their own products like Naver and Kakao that have built out nice kind of LLM interfaces. India is probably the other one that I watch really closely just because there are so many people that you can have standalone big companies focus on India. The other interesting thing about India is there are so many different languages, like such a range that both LLM products and even voice products don't necessarily support very well. Like it's a worse experience if you're a primary user of one of those languages and you're trying to use something like a ChatGPT. So so far, we haven't seen a huge amount of variance there yet,
15:43 Olivia Moore? but I would not be surprised maybe to see more founders even from the US like targeting the Indian market for AI.
15:50 Olivia Moore? And then the other thing I wanted to mention, we did for the first time also kind of what like a heat map essentially of which countries are adopting AI the most and the least on a on a per capita basis. So we looked across like the 10 biggest LLM products to see on web and mobile to see what this might look like. So Singapore is number one.
16:10 Anish Acharya? Crazy.
16:10 Olivia Moore? Yes, then Hong Kong, then the UAE, then South Korea. The US is down at number 20, so not super low, not incredibly high. Russia and China are like very far down the list, like sub 50. And there's a lot of interesting stories, I think, that live in that data.
16:28 Anish Acharya? Yeah.
16:29 Olivia Moore? The first one is if you think about those top five, like Singapore, South Korea, Hong Kong, it's a very, like the demographics of the workforce are very like tech-first, white collar, high skill. And the US has a giant chunk of jobs where AI hasn't really touched them yet, like retail and transportation and some of these other things.
16:51 Olivia Moore? I think also the cultural norms around AI are shockingly diverse. If you're in the US, you have probably internalized this ongoing angst and questioning around.
17:01 Anish Acharya? Yeah, I was gonna ask about this.
17:03 Olivia Moore? Is AI gonna take my job?
17:04 Anish Acharya? A hundred percent.
17:04 Olivia Moore? Or, you know, AI is terrible for artists, or all of these other things that make people pick up or not pick up AI.
17:11 Anish Acharya? Yes.
17:12 Olivia Moore? There was actually a big survey last year from Edelman, the global media company. And the US had a fairly low rate of trust in AI. It was like thirty-two percent. And most of these other countries that are high on the list are like fifty, sixty, seventy percent. So that, I think, has also held the US back. Despite the fact that we are where the biggest products come from, our per capita usage is lower than a lot of these other markets that have maybe smaller populations, but have been, have embraced it more.
17:40 Anish Acharya? I think that's exactly right. You know, I was reading that in China, the sort of favorability views on AI are eighty percent.
17:46 Olivia Moore? Yeah.
17:46 Anish Acharya? Eighty percent hold a favorable view. And I know UAE and Singapore, I think they've sort of culturally wired to be tech optimistic.
17:54 Olivia Moore? Yes.
17:55 Anish Acharya? Which is an advantage, you know?
17:56 Olivia Moore? Yes, definitely. It's interesting to see some of these smaller countries, like the, the per capita adoption rate. Like in the US, it's around, probably a third of people are monthly active users of something like a ChatGPT.
18:09 Anish Acharya? Yeah.
18:09 Olivia Moore? And some, even some of like the European countries, or Eastern Europe, it's like fifty, forty, forty-five, sixty percent on, on smaller bases, but they've kind of embraced it more quickly than we have here.
18:22 Anish Acharya? Yeah, really interesting. You know, one, one thing that I'm sort of watching and I'm interested in is, as you look at the spectrum of AI from the most functional, almost like a Google search replacement, to the most cultural, creative, personal, we should see more divergence country by country. Because obviously the culture, the movies they make in India couldn't be more different than the movies they make in China or the US.
18:43 Olivia Moore? Yeah.
18:43 Anish Acharya? So why wouldn't their use of creative tools be different?
18:45 Olivia Moore? Yeah, and this is honestly part of the reason why we started looking at the geographic segmentation in this report, is because for the first two and a half, three years of generative AI, the vast majority of consumers were maybe interacting with one product. And now it's broadening quite a bit, and I think that we will see more of these market specific tools. And if they are, if they capture enough of that market, like some of these Russian companies or Chinese companies, they can actually surface up to the global list if, if kind of the market is big enough.
19:16 Anish Acharya? Talk a bit about the evolution of creative tools and how much you think that that is that a reflection of culture? Is that driving culture? When do we cross that threshold?
19:25 Olivia Moore? The creative tools trend has been fascinating. I mean, obviously, the first big generative AI product was actually Mid Journey, which came out before ChatGPT.
19:33 Anish Acharya? True, that's right, yeah.
19:34 Olivia Moore? And in our first few editions of the list, it was very much dominated by creative tools. And the, I've said this before, but the creative tools benefit from kind of hallucination of the early models because they produce things that are more kind of surprising or beautiful or original. And so for a while, that, those were the only things working in consumer AI, really. Now it shifted a lot. Creative tools are still a huge chunk of the list, but like the type of creative tool that is a standalone big business has changed. I would say the biggest change is we're seeing fewer standalone image generators. A lot of this activity, if you're making like a basic commodity image, like, you know, a meme or a basic marketing image or an infographic,
20:19 Olivia Moore? like, the core models in ChatGPT and Gemini are quite good at those things now.
20:25 Anish Acharya? Yeah.
20:25 Olivia Moore? So the products that are still surfacing on the list, like an ideogram or a Mid Journey, are either very aesthetically opinionated or they have very more sophisticated workflows that you can't get on something like a, like a ChatGPT.
20:41 Olivia Moore? Contrasting that, I would say like music, voice, video all seem to be things that the model, the biggest model companies have maybe invested less in. And so we've seen players like Suno in music and Eleven Labs in voice kind of completely break out and rise to top 20, top 15 on the list and then like hold their spot there over time. And then there's like a compounding lock-in from like the community and, you know, the big base of enterprise customers and all of that.
21:11 Olivia Moore? Video is where I have the most questions. OpenAI has been investing in it with Sora and of course, Google with VO. But the Chinese models are so good because they can train on any data. So C-dance 2 is probably the best example of this, where it's just kind of in some ways head and shoulders above what the US companies have thus far been able to do. So I think we'll see. I think this actually benefits platforms like a Kria, where you can use all the models in one place. Because my sister Justine wrote an article about this, but the way video is shaping out, there's it's unlikely to be like one model to rule them all. And so you kind of need to be able to switch between them.
21:52 Anish Acharya? That seems true of most of the model spaces, you know? Chat models, creative models, even code models have their areas of specialization. You know, people talk about kind of ergonomics of Opus.
22:03 Olivia Moore? Yes.
22:04 Anish Acharya? Versus the accuracy of Codex.
22:05 Olivia Moore? Yes.
22:06 Anish Acharya? And that's just, that's a trade off, you know? And you have to choose what tool you want to use for which problem.
22:10 Olivia Moore? Yeah, absolutely.
22:13 Anish Acharya? Sora is really interesting to me because it represented both a sort of a big step forward in the model, but also a really ambitious experiment around social. And there was data in the early days of Sora, like the percentage of people that were creating, which was dramatically, 10x higher than we'd seen before.
22:28 Olivia Moore? Yeah.
22:28 Anish Acharya? You know, what's your kind of assessment of the Sora social effort versus the model effort? And where do you see that going?
22:34 Olivia Moore? Sora is so fascinating and I think was a very interesting early experiment that I think taught us all a lot about kind of both creative tools, but also maybe more importantly, what consumer social in the AI era might look like. So by the numbers, they had a massive launch. They were number one on the App Store, the US App Store for 20 consecutive days, which is very hard to do. It means you're probably getting, to be number one on the App Store, you probably have to get these days 150,000 daily downloads. So it's like a high download volume. And they actually hit a million users faster than ChatGPT itself. So like huge launch. And actually, I think what a lot of people underestimate is it still has very significant usage. So 3 million dowls per sensor tower, which is not bad at all.
23:20 Olivia Moore? What has dropped off about Sora is the new downloads. So there may be, they peaked at like 6 million a month in November. It's looking like a million and a half now. I think that what has really worked about Sora is that it's a very good video model. And they kind of innovated and introduced this concept of cameos, which is where a real person can grant their likeness to Sora so that they and others can generate videos of them. So like a lot of people in the early days were doing like meme videos of their friends. Like Jake Paul went viral because he was the first big celebrity to like lean into Sora. So you were seeing like insane Jake Paul videos everywhere. Yeah, I mean, honestly, good for him.
23:57 Anish Acharya? Yes, yes.
23:59 Olivia Moore? I think what worked less about Sora is that because the content was exportable, people would take it to TikTok, they would take it to Instagram reels, they would take it to YouTube. And there it competed against the best human-made content.
24:14 Anish Acharya? Right.
24:15 Olivia Moore? And so the overall feed experience was just better because you were seeing the best of both, not just like the best of Sora.
24:21 Anish Acharya? Right.
24:22 Olivia Moore? I don't think we've seen a social product yet succeed that's like entirely AI content. The emotional stakes are just feel lower in some ways.
24:31 Anish Acharya? Right.
24:32 Olivia Moore? And so I would imagine we'll see more examples like these where Sora still has clearly very, very significant usage and revenue as a creative tool, but not so much as a social app.
24:42 Anish Acharya? Right.
24:43 Olivia Moore? And I don't know if there'll be, there probably will be a massive AI-native social network, but we haven't seen what it looks like just yet, I would say.
24:51 Anish Acharya? It'll be interesting, you know, we discuss this frequently, but every social product has a status game.
24:55 Olivia Moore? Yes.
24:56 Anish Acharya? You know, and on Insta, it's maybe be the hottest and on X, it's be the most interesting. And it felt like the emerging status game on Sora was be the funniest.
25:02 Olivia Moore? Yes.
25:03 Anish Acharya? I think this is one of the reasons why it's hard for the content to cross over.
25:06 Olivia Moore? Yeah.
25:07 Anish Acharya? Because it's just two different ways of judging what is interesting and great.
25:11 Olivia Moore? I agree. What they might do, if I had to imagine where they might find more of a niche, they have now inked a bunch of deals with big media companies like Disney.
25:19 Anish Acharya? Right.
25:20 Olivia Moore? And so if Sora is the only place where you can make like licensed like fan videos of like beloved kind of characters and entertainment figures, then like that's very interesting.
25:30 Anish Acharya? Totally.
25:30 Olivia Moore? But we're early, I think, in how that rules out.
25:32 Anish Acharya? It's so early.
25:33 Olivia Moore? I know. We keep saying it. Yeah.
25:36 Anish Acharya? Okay, let's switch gears. We can't have this conversation without talking about Agents, OpenClaw, Manus, GenSpark, Moldbook. Give us an overview of what has happened in the last 60 days in the world of Agents, and what does a report tell us?
25:49 Olivia Moore? I think this is mostly why I say the last, you know, even six months, but actually even two months of this report have been like the most interesting that I think we've seen. So OpenClaw actually, as you'll see, is not on our rankings because it blew up in February. Our data ends in January. But we did pull the data for February, and if it had been eligible, it would have been number 30 on our web list, which is a pretty big debut. I think the really interesting thing about OpenClaw is the usage has just continued to accelerate in the technical community. So now it's, I think, number one GitHub stars of all time. It passed React, it passed Linux. It's really, really impressive.
26:27 Anish Acharya? Passed Linux?
26:27 Olivia Moore? Yes.
26:28 Anish Acharya? Holy cow.
26:28 Olivia Moore? Very impressive.
26:29 Anish Acharya? Yeah.
26:29 Olivia Moore? But in terms of overall new users, it's kind of plateaued. So we looked at kind of visits to the Get Started or Sign Up page, and that is kind of flat week over week since early February, which I think indicates that like it is an amazing product if you're technical. It has not yet fully escaped containment to non-technical people, which of course is like a bigger population. They were acquired by OpenAI, so if I had to guess, or what I'd love to see OpenAI do is build like product ties OpenClaw into something that is usable for a mainstream consumer. And I think we've also just seen the ideas behind the OpenClaw architecture inspire so many other founders.
27:12 Olivia Moore? Like, how many pitches do we take a day where the founder is like, "I want to be OpenClaw for this," or "OpenClaw made me realize this was possible."
27:20 Anish Acharya? Yes.
27:20 Olivia Moore? And so I think we're going to see, OpenClaw itself will continue to succeed and be a massive product, and I'm guessing we'll see more kind of like verticalized, focused versions of OpenClaw for different use cases.
27:30 Anish Acharya? Yeah, it's so interesting because it feels like one of the things that makes OpenClaw work so well is it can operate across all models in all directions.
27:37 Olivia Moore? Yes.
27:38 Anish Acharya? And I sort of, you know, I sort of wonder if it dilutes the value of OpenClaw to have it be sole model provided, and therefore it's sort of counter positioned against labs.
27:45 Olivia Moore? Totally, yeah. They've kept it, I think, multimodal for now, at least in my usage.
27:49 Anish Acharya? They have, yeah.
27:50 Olivia Moore? So we'll see how it trends. I think it would be smart to keep it that way for usage, but...
27:53 Anish Acharya? Yeah. Is Manus the consumer-grade OpenClaw, or how do you distinguish the two?
27:58 Olivia Moore? Yes, some might say that. And I do actually think, so Manus made our web list. And of course, they had a $2 billion plus acquisition by Meta,
28:08 Anish Acharya? Amazing.
28:08 Olivia Moore? also in the course of the list. Incredible growth, like the ramp that they reported from like zero to 100 million, 200 million ARR in the span of like, honestly, six, nine months is really kind of best in class. My view on why Manus was so successful was it was really the first consumer-grade agent that could actually operate fairly autonomously across products and platforms. So you could connect email, you could have it browse the web, and it could make slides, it could make spreadsheets. I spent a lot of time in the early days trying like, this was a year ago, ChatGPT Operator or Google's Project Mariner.
28:46 Anish Acharya? Yeah.
28:46 Olivia Moore? And none of them were reliable. And Manus was a breakthrough in kind of agent reliability and agent accessibility for the consumer. I think the fact that they did the acquisition is interesting in terms of where this is going in that, once everyone has that agentic capability, and you might imagine they will if it's kind of based on the core underlying models, then it's actually, if you're such a horizontal product, you may be better off with the distribution forces of a Meta or a Google or something like that versus a standalone company. That's definitely not true if you're building something more vertical. But if you imagine that like, Google now has the resources to create a Manus,
29:31 Olivia Moore? then that's a really hard thing to keep fighting against as a startup, I think. And obviously, the big companies have a billion different priorities, so they're not going to do everything best in class. But it's why I've generally been a little more cautious about the very, very horizontal consumer AI apps, just because it's probably both in scope for the bigger companies and they have the advantage of already having IT approval and enterprise contracts and all of that.
29:56 Anish Acharya? Right, right. It is interesting that we sort of cross this cultural threshold, though.
30:00 Olivia Moore? Yes.
30:01 Anish Acharya? Where Manus seemed like a non-obvious bet in terms of just the breadth of the offering.
30:07 Olivia Moore? Yeah.
30:07 Anish Acharya? And now it seems like they're living in the future a little bit.
30:10 Olivia Moore? Yes, absolutely. They were. It's obviously an incredible engineering team. Like, the quality of the product was like three, six months ahead of the rest of the market, which is not easy to do when you're competing with teams of like thousands of researchers.
30:22 Anish Acharya? Totally. Let's use this to kind of leg into a conversation about other horizontal AI products, things that live beyond the sort of web window.
30:30 Olivia Moore? Yes.
30:31 Anish Acharya? What are you seeing there?
30:32 Olivia Moore? Yeah, that has been a massive theme. When I think about the products that I interact with on a daily basis in the AI world, quite a few of them are actually desktop apps, like things like granola, voice dictation tools, Claude Co-Work, those kinds of things. And it does become a methodology problem for our report because we can track website visits very well, and so we can track the first time that they download that desktop app. We can track mobile app usage very well. We cannot track desktop usage that closely. And I think that is increasingly, as AI products become more sophisticated, having them live in their own dedicated application, much of which will run on desktop because it can interact with your files and it can be more ambient.
31:15 Olivia Moore? I think that's going to happen more and more. And so I think moving forward, us finding ways to parallel-track ranking these products by web and mobile usage, but also by revenue, is going to be a pretty good idea because if you think about things like Cursor, some of the consumer, prosumer AI apps that are generating the most revenue have very few, very little usage on web. It's almost all in kind of a dedicated app.
31:40 Anish Acharya? Yeah, it's really interesting. It also feels like the fact that OpenAI released Atlas and Anthropic released Co-Work shows you where their priorities are.
31:49 Olivia Moore? Yes, definitely. I fully agree. The AI browser debate is its own interesting thing. I feel like we're still in the early to mid phases of how that's going to play out. And I think the instinct behind an AI-native browser is right in that if you can have AI be Kind of always on, always available, ambient in where you're spending a lot of your time online, like that's a good opportunity. Perplexity Comet, I think, actually led the way there.
32:20 Anish Acharya? It's a great product.
32:21 Olivia Moore? It's a great product. And the interesting thing is if you look at kind of the highest spike for Comet and Atlas in terms of visits to the download page, Comet is five times ahead of Atlas, which is wild because ChatGPT's audience is like so massive.
32:37 Anish Acharya? Enormous, yeah.
32:38 Olivia Moore? And I think what we've seen is like Comet and Atlas still have very dedicated, excited user bases, but for the average consumer, the switching cost of a browser is non-trivial, just because like you have workflows set up, you naturally just open this one app.
32:56 Anish Acharya? Yeah.
32:57 Olivia Moore? And so it not only has to be like feature parity, there has to be one or two features of the AI browser that are really killer and that are easy enough for the average person to set up and access. And I don't think that we've seen that quite yet.
33:09 Anish Acharya? You know, it's really interesting because Sam said, I think, six months ago on a pod, you know, somebody was asking him, what has surprised you the most? And he said, it's that the world hasn't changed more.
33:18 Olivia Moore? Yeah.
33:19 Anish Acharya? And if you look at the trends around how people are using ChatGPT at scale, it's still, you know, homework and Google-like queries and a little bit of companionship. In a sense, something like a browser gives you an opportunity to point the user in a different direction. What's your view on how the average person is using AI today?
33:35 Olivia Moore? Yeah. I think a couple of things. So one, I feel like teenage girls are like the best source of what is happening in consumer and what will be happening in consumer. If you look at all of the biggest consumer outcomes, like they were the early adopters of all of these products.
33:51 Anish Acharya? Incredible.
33:52 Olivia Moore? And so there was actually a Pew Research study fairly recently on how teenagers are using AI. Now, finally, I think for the first time, over half of them are admitting to using it for their homework. So the real number is probably like 99.999%. But some of them didn't want to get in trouble with their parents.
34:08 Anish Acharya? Uh huh.
34:09 Olivia Moore? Thirty-eight percent are now using it for a creative tool. So editing images, editing video, generating images and video. And then this like emerging slightly longer tail, but I think will ultimately be amongst the biggest behaviors. Sixteen percent are using it for just like casual conversation, like not the intense companion products, but like just having someone to talk to. And then 12% are using it for like emotional support and advice. I think all of these use cases will like asymptote around probably 100% ultimately. And so those are behaviors that maybe have been less well-served by products so far and will be going forward, whether it's on a chat GPT or whether it's on like a standalone product.
34:53 Olivia Moore? And then the other big thing that I'm looking out for is agents. Like I think basically.
34:58 Anish Acharya? Are teenage girls gonna use agents? Come on.
35:00 Olivia Moore? So here's the thing. I think that agents, similar to like how in 1990, an internet company was like a dot com company, right? Or a tech company like dot com was its own, its own designator.
35:12 Anish Acharya? Right.
35:12 Olivia Moore? I think that this is what's gonna happen with agents. Where ultimately like every tech company was a dot com company. Like I think ultimately every AI company and then every tech company is going to be an agenda company. Because that's just where the models are headed. And if you can deliver outcomes and not just kind of inputs to your users as a software product, that's so much more compelling. So yes, I think 13-year-old girls will be using agents, but they will not think of them as agents. But I think it does unlock a lot of these other consumer use cases of AI, like finance, healthcare, travel planning, complex shopping even, where pre-agents, there was just so much data you had to go out and grab.
35:55 Anish Acharya? Right.
35:55 Olivia Moore? And do it reliably and do it across systems that it like wasn't really possible. And now it is. And so I think we're gonna see an explosion of those other use cases in the next few months.
36:04 Anish Acharya? How long do you think it takes to play out? I mean, is everybody using their own open claw in 12 months? Is that five years away? Is that the wrong mental model? Like where, when we have this conversation.
36:13 Olivia Moore? Yeah.
36:13 Anish Acharya? Perhaps in six months at the next top 100? What does the world look like?
36:19 Olivia Moore? I feel like every time I predict something, it happens much more quickly than I would have thought, which I think is what we're seeing every day. And that startups are growing faster than they ever have. I think people, there's still, the cultural change and the cultural adoption will be slower than the technology change and what's actually possible. And so I think what we'll continue to see is this early wave of often technical, sometimes not technical, AI adopters like lead the charge on a behavior that then, six months later, everyone else is doing. One good example of this that I'm very, very excited about is voice, which we've talked about a lot.
36:58 Anish Acharya? We have talked about voice.
37:00 Olivia Moore? To me, it's like the most information-dense, high-quality source of basically media that we have. Like, so much of what you do every day is actually downstream or upstream of like what you say. And we're, I think for the first time in the past six months, have seen first engineers and now other people within tech companies adopt things like voice dictation. Now it's kind of almost a norm at many companies that your meetings are going to be kind of recorded and transcribed by an AI. Whether that's voice dictation, whether that's like a voice pin that answers questions or does tasks for you, I think that is going to spread to the mainstream consumer in the next six to nine months.
37:40 Anish Acharya? Really, really interesting. Maybe to close, can you talk a little bit about memory?
37:45 Olivia Moore? Yes.
37:46 Anish Acharya? And where you see that going?
37:46 Olivia Moore? Yes. Memories, as we mentioned early right now, and it can be a little bit jarring in that Claude and ChatGPT in particular are very good at this. Even Gemini, Google has launched something called personal intelligence, where it now can pull information it knows about you from your Doc's email, et cetera, to like serve you better with AI across all of the apps. And like I said, it can be a little bit jarring now because... Many people are talking to AI about everything, personal and professional. And so it can sometimes inadvertently cross the line of what it knows about you to try to help you better, but in the wrong context. So I think there's a lot of work to do, kind of on the infrastructure side, almost,
38:29 Olivia Moore? of how we sort out who someone is in every context. Once that is settled, I think that memory will be one of the core advantages for AI products. Whether it's their own memory or ChatGPT lending memory, any product that you start to use two years from now, if it doesn't immediately feel like it knows you, it will feel broken. The concept of onboarding to a product should not be something that exists in a couple of years. And I think that that is something that memory is really going to enable. I see it personally for myself, where I talk to AI all day. I've talked to several AIs all day. And the way that they interact with me and the kind of value that they're able to provide
39:12 Olivia Moore? has been so much higher two or three months in than it is when you start using it.
39:16 Anish Acharya? Incredible. Well, I don't know what the future holds, but it's going to be weird and wonderful.
39:20 Olivia Moore? I'm excited for it. Yes.
39:21 Anish Acharya? Olivia, thank you so much. It was super fun to actually have this conversation today and go through the report. Any closing comments?
39:27 Olivia Moore? No, I'm just excited for people to read it. There's a lot of interesting data in there next time. And I'm sure it will look wildly different six months from now. So we'll be back then.
39:36 Anish Acharya? Really exciting. Well, tell us what you think and thanks for checking us out.
39:38 Olivia Moore? Thank you.
39:42 Erik Torenberg? 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 at A16Z and subscribe to our Substack at A16Z.substack.com. Thanks again for listening and I'll see you in the next episode.
40:07 Erik Torenberg? 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/disclosures.
Previous Andrew Huberman: Peptides, Sleep Tech, and the End of Obesity Next What It Takes to Clear a Million Crimes a Year with Flock Safety's CEO Page updated October 3, 2026 · transcript and summary are machine-generated
