The Top 100 Consumer AI Apps | The a16z Show

Olivia, welcome. Olivia, welcome. >> Thanks for having me. >> Thanks for having me. >> Thanks for having me. >> It's the most exciting time of the year, >> It's the most exciting time of the year, >> It's the most exciting time of the year, which is the top 100 report is coming which is the top 100 report is coming which is the top 100 report is coming out today, I think. Is that right? out today, I think. Is that right? out today, I think. Is that right? >> Yep. >> Yep. >> Yep. >> It's been six editions over three years. >> It's been six editions over three years. >> It's been six editions over three years. >> Talk to us about what's the same, what's >> Talk to us about what's the same, what's >> Talk to us about what's the same, what's changed, what's your excitement level, changed, what's your excitement level, changed, what's your excitement level, what's up with the report? what's up with the report? what's up with the report? >> Yeah, I in many ways like so much has >> Yeah, I in many ways like so much has >> Yeah, I in many ways like so much has changed and there's been just an changed and there's been just an changed and there's been just an incredible amount of growth since the incredible amount of growth since the incredible amount of growth since the first time we put out this list in 2023. first time we put out this list in 2023. first time we put out this list in 2023. On the other hand, from like a macro On the other hand, from like a macro On the other hand, from like a macro level, we're still so early. Like ChatBT level, we're still so early. Like ChatBT level, we're still so early. Like ChatBT is by far the biggest global AI product is by far the biggest global AI product is by far the biggest global AI product and still only 10% of the global and still only 10% of the global and still only 10% of the global population is using it on a weekly population is using it on a weekly population is using it on a weekly active basis. So there's like a lot more active basis. So there's like a lot more active basis. So there's like a lot more to come. I do think this past 6 months to come. I do think this past 6 months to come. I do think this past 6 months has been maybe my favorite time and the has been maybe my favorite time and the has been maybe my favorite time and the most exciting time because of the shifts most exciting time because of the shifts most exciting time because of the shifts that we've seen. Um, one of them has that we've seen. Um, one of them has that we've seen. Um, one of them has been that the race for the consumer is been that the race for the consumer is been that the race for the consumer is really heating up. So ChateBT of course, really heating up. So ChateBT of course, really heating up. So ChateBT of course, but also Gemini and Claude are kind of but also Gemini and Claude are kind of but also Gemini and Claude are kind of doubling down on their own ICP within doubling down on their own ICP within doubling down on their own ICP within consumer and proumer. And I think we're consumer and proumer. And I think we're consumer and proumer. And I think we're starting to see how these platforms starting to see how these platforms starting to see how these platforms might have compounding advantages over might have compounding advantages over might have compounding advantages over time. And so that makes it especially time. And so that makes it especially time. And so that makes it especially kind of existential or interesting of kind of existential or interesting of kind of existential or interesting of who is acquiring the most users. And who is acquiring the most users. And who is acquiring the most users. And then on a related note, this was then on a related note, this was then on a related note, this was actually the first issue that we actually the first issue that we actually the first issue that we included products that were nonAI native included products that were nonAI native included products that were nonAI native but are now majority AI enabled. So but are now majority AI enabled. So but are now majority AI enabled. So things like Canva, Notion, Freepick, things like Canva, Notion, Freepick, things like Canva, Notion, Freepick, Notion actually announced that now they Notion actually announced that now they Notion actually announced that now they think half of their new ARR is driven by think half of their new ARR is driven by think half of their new ARR is driven by AI first features, which is very cool. AI first features, which is very cool. AI first features, which is very cool. And then lastly, I think we've seen a And then lastly, I think we've seen a And then lastly, I think we've seen a big expansion of AI outside of just like big expansion of AI outside of just like big expansion of AI outside of just like the website or app prompt box. So we the website or app prompt box. So we the website or app prompt box. So we have all of the browsers that have come have all of the browsers that have come have all of the browsers that have come out like, you know, Dia, comet, Atlas. out like, you know, Dia, comet, Atlas. out like, you know, Dia, comet, Atlas. We have Claude and Excel, PowerPoint, We have Claude and Excel, PowerPoint, We have Claude and Excel, PowerPoint, and Chrome. And then we have desktop and Chrome. And then we have desktop and Chrome. And then we have desktop apps like Cursor, Whisper Flow, Granola. apps like Cursor, Whisper Flow, Granola. apps like Cursor, Whisper Flow, Granola. Um, and so there's been just a really Um, and so there's been just a really Um, and so there's been just a really kind of exciting explosion in the ways kind of exciting explosion in the ways kind of exciting explosion in the ways that people are using AI. that people are using AI. that people are using AI. >> So exciting. There's a ton to cover >> So exciting. There's a ton to cover >> So exciting. There's a ton to cover here. So let's start with the big here. So let's start with the big here. So let's start with the big foundation models. Can you talk a bit foundation models. Can you talk a bit foundation models. Can you talk a bit about what you think are the respective about what you think are the respective about what you think are the respective areas of specialization for Gemini Quad areas of specialization for Gemini Quad areas of specialization for Gemini Quad and then of course Chhat GPT because it and then of course Chhat GPT because it and then of course Chhat GPT because it feels like it's been a rising tide story feels like it's been a rising tide story feels like it's been a rising tide story more than these models trading off with more than these models trading off with more than these models trading off with each other. each other. each other. >> Yes, I agree. despite despite the drama >> Yes, I agree. despite despite the drama >> Yes, I agree. despite despite the drama maybe of the past week where we have maybe of the past week where we have maybe of the past week where we have Katy Perry taking sides on Twitter in Katy Perry taking sides on Twitter in Katy Perry taking sides on Twitter in the yellow war which is something that I the yellow war which is something that I the yellow war which is something that I didn't ever see coming. didn't ever see coming. didn't ever see coming. >> I think at a base level still if you >> I think at a base level still if you >> I think at a base level still if you look at AI usage like chat GBT is a very look at AI usage like chat GBT is a very look at AI usage like chat GBT is a very very clear winner. So on web they're 2.7 very clear winner. So on web they're 2.7 very clear winner. So on web they're 2.7 times bigger than Gemini. On mobile times bigger than Gemini. On mobile times bigger than Gemini. On mobile they're 2.5 times bigger than Gemini. they're 2.5 times bigger than Gemini. they're 2.5 times bigger than Gemini. And then despite again like the kind of And then despite again like the kind of And then despite again like the kind of tech Twitter discourse um Claude they're tech Twitter discourse um Claude they're tech Twitter discourse um Claude they're almost 30 times bigger than Claude on almost 30 times bigger than Claude on almost 30 times bigger than Claude on web and almost 80 times bigger than web and almost 80 times bigger than web and almost 80 times bigger than Claude on mobile. So we had seen that Claude on mobile. So we had seen that Claude on mobile. So we had seen that Sam Alman tweet back in the Super Bowl Sam Alman tweet back in the Super Bowl Sam Alman tweet back in the Super Bowl ad wars era. The ad wars era. The ad wars era. The >> Texas tweet. >> Texas tweet. >> Texas tweet. >> Yes. He was like we have more people >> Yes. He was like we have more people >> Yes. He was like we have more people using chatbt free version in Texas than using chatbt free version in Texas than using chatbt free version in Texas than Claude has like all users globally which Claude has like all users globally which Claude has like all users globally which is true. is true. is true. >> Um that being said I think we are seeing >> Um that being said I think we are seeing >> Um that being said I think we are seeing I don't think bifurcation is the is the I don't think bifurcation is the is the I don't think bifurcation is the is the right word but maybe expansion in the right word but maybe expansion in the right word but maybe expansion in the number of products people are using and number of products people are using and number of products people are using and what they're using different products what they're using different products what they're using different products for. Uh which has kind of changed the for. Uh which has kind of changed the for. Uh which has kind of changed the market share a little bit. Claude in market share a little bit. Claude in market share a little bit. Claude in particular has really doubled down on particular has really doubled down on particular has really doubled down on proumer with things like co-work cla proumer with things like co-work cla proumer with things like co-work cla code claude and excel and powerpoint. If code claude and excel and powerpoint. If code claude and excel and powerpoint. If you actually look at the app stores that you actually look at the app stores that you actually look at the app stores that are emerging on claude and chatbt they are emerging on claude and chatbt they are emerging on claude and chatbt they both have 200 plus apps but there's only both have 200 plus apps but there's only both have 200 plus apps but there's only 11% overlap. Like Claude is very much 11% overlap. Like Claude is very much 11% overlap. Like Claude is very much doubling down on like premium data doubling down on like premium data doubling down on like premium data sources, research tools, science tools, sources, research tools, science tools, sources, research tools, science tools, financial data, and chatbt is really financial data, and chatbt is really financial data, and chatbt is really doubling down on like consumer doubling down on like consumer doubling down on like consumer marketplaces, travel, nutrition, marketplaces, travel, nutrition, marketplaces, travel, nutrition, consumer finance, things like that. Um, consumer finance, things like that. Um, consumer finance, things like that. Um, and then Gemini is kind of in its own and then Gemini is kind of in its own and then Gemini is kind of in its own little corner as [laughter] well, and little corner as [laughter] well, and little corner as [laughter] well, and the traction has largely been driven by the traction has largely been driven by the traction has largely been driven by creative tools there. So if you look at creative tools there. So if you look at creative tools there. So if you look at their kind of active users and paying their kind of active users and paying their kind of active users and paying users, it's nearly perfectly correlated users, it's nearly perfectly correlated users, it's nearly perfectly correlated to releases of like V3, Nano Banana 1, to releases of like V3, Nano Banana 1, to releases of like V3, Nano Banana 1, Nano Banana Pro, Nano Banana 2. Um Nano Banana Pro, Nano Banana 2. Um Nano Banana Pro, Nano Banana 2. Um they're doing a little more on proumer. they're doing a little more on proumer. they're doing a little more on proumer. They're adding AI to uh Gmail, Sheets, They're adding AI to uh Gmail, Sheets, They're adding AI to uh Gmail, Sheets, Calendar. Um but but that's all being Calendar. Um but but that's all being Calendar. Um but but that's all being captured by like their existing products captured by like their existing products captured by like their existing products versus like a net new experience. versus like a net new experience. versus like a net new experience. >> Maybe let's dig into the app store >> Maybe let's dig into the app store >> Maybe let's dig into the app store dynamic a little bit because that's so dynamic a little bit because that's so dynamic a little bit because that's so fascinating. Can you talk about the fascinating. Can you talk about the fascinating. Can you talk about the bullcase for chat GBT with I think what bullcase for chat GBT with I think what bullcase for chat GBT with I think what they call the apps directory. they call the apps directory. they call the apps directory. >> Yeah. Yeah. I think the approach we're >> Yeah. Yeah. I think the approach we're >> Yeah. Yeah. I think the approach we're seeing with Chat GBT and and Sam said seeing with Chat GBT and and Sam said seeing with Chat GBT and and Sam said this himself on Twitter is like we want this himself on Twitter is like we want this himself on Twitter is like we want to be the AI for everyone and that means to be the AI for everyone and that means to be the AI for everyone and that means that they're trying to acquire every that they're trying to acquire every that they're trying to acquire every consumer and they'll monetize them in consumer and they'll monetize them in consumer and they'll monetize them in different ways. So like I think Claude different ways. So like I think Claude different ways. So like I think Claude has been very clear that they're just has been very clear that they're just has been very clear that they're just going to monetize via subscriptions going to monetize via subscriptions going to monetize via subscriptions which is great for people in companies which is great for people in companies which is great for people in companies who can pay for subscriptions but it who can pay for subscriptions but it who can pay for subscriptions but it won't be everyone. I think you see that won't be everyone. I think you see that won't be everyone. I think you see that with the plugins that they're leaning with the plugins that they're leaning with the plugins that they're leaning into which are like paid high ACV like into which are like paid high ACV like into which are like paid high ACV like work data tools work data tools work data tools that things like that like pitchbook that things like that like pitchbook that things like that like pitchbook like things that you would use if you're like things that you would use if you're like things that you would use if you're an investor or a scientist a an investor or a scientist a an investor or a scientist a mathematician. Um, and Chad GBT, I mathematician. Um, and Chad GBT, I mathematician. Um, and Chad GBT, I think, is going more of the somewhat of think, is going more of the somewhat of think, is going more of the somewhat of a Google type approach in that, um, a Google type approach in that, um, a Google type approach in that, um, they're building things that like the they're building things that like the they're building things that like the average person will want to use average person will want to use average person will want to use >> and maybe a smaller percentage of those >> and maybe a smaller percentage of those >> and maybe a smaller percentage of those convert to subscriptions right now, but convert to subscriptions right now, but convert to subscriptions right now, but they will be able to monetize those they will be able to monetize those they will be able to monetize those people through ads and probably also, I people through ads and probably also, I people through ads and probably also, I would guess, through transactions. like would guess, through transactions. like would guess, through transactions. like if they're building the gateway to like if they're building the gateway to like if they're building the gateway to like um book a trip or do all of these other um book a trip or do all of these other um book a trip or do all of these other kind of longtail consumer purchases, kind of longtail consumer purchases, kind of longtail consumer purchases, >> hypothetically, they should eventually >> hypothetically, they should eventually >> hypothetically, they should eventually be able to take some kind of cut of that be able to take some kind of cut of that be able to take some kind of cut of that at least for for the traffic that at least for for the traffic that at least for for the traffic that they're driving. And so I think that they're driving. And so I think that they're driving. And so I think that that is the the bull case for the chat that is the the bull case for the chat that is the the bull case for the chat GBT app store that isn't yet showing up GBT app store that isn't yet showing up GBT app store that isn't yet showing up in the data that will probably become in the data that will probably become in the data that will probably become like even more evident in the next year like even more evident in the next year like even more evident in the next year or two. Yeah, it's really interesting or two. Yeah, it's really interesting or two. Yeah, it's really interesting because it touches on your point in the because it touches on your point in the because it touches on your point in the report about compounding advantages and report about compounding advantages and report about compounding advantages and how context compounds. Can you talk a how context compounds. Can you talk a how context compounds. Can you talk a little bit about that concept and then little bit about that concept and then little bit about that concept and then what's your proxy in terms of a metric what's your proxy in terms of a metric what's your proxy in terms of a metric for it? Is it session time? Is it number for it? Is it session time? Is it number for it? Is it session time? Is it number of sessions? Is there is it the amount of sessions? Is there is it the amount of sessions? Is there is it the amount of data you've provided or is there of data you've provided or is there of data you've provided or is there something else? Yeah, this is a really something else? Yeah, this is a really something else? Yeah, this is a really exciting question to me because I think exciting question to me because I think exciting question to me because I think thus far with these horizontal LLMs like thus far with these horizontal LLMs like thus far with these horizontal LLMs like ChachiBT, Claude, Gemini, Perplexity, ChachiBT, Claude, Gemini, Perplexity, ChachiBT, Claude, Gemini, Perplexity, we've kind of lived in a world where the we've kind of lived in a world where the we've kind of lived in a world where the context and the memory is somewhat context and the memory is somewhat context and the memory is somewhat easily exportable. Like Claude ran a easily exportable. Like Claude ran a easily exportable. Like Claude ran a campaign around this recently, but I campaign around this recently, but I campaign around this recently, but I think there's going to be increasing think there's going to be increasing think there's going to be increasing lock in. And I do think that probably lock in. And I do think that probably lock in. And I do think that probably actually benefits the broader more actually benefits the broader more actually benefits the broader more horizontal tools like chatbt for a few horizontal tools like chatbt for a few horizontal tools like chatbt for a few reasons. So I think one we've already reasons. So I think one we've already reasons. So I think one we've already seen chat GBT focus on or start to build seen chat GBT focus on or start to build seen chat GBT focus on or start to build out products where you interact with out products where you interact with out products where you interact with other people on them through the other people on them through the other people on them through the platform. So the group chats like platform. So the group chats like platform. So the group chats like imagine if you if there's an even more imagine if you if there's an even more imagine if you if there's an even more successful version of chatbt group chats successful version of chatbt group chats successful version of chatbt group chats >> and all of your friends are on there >> and all of your friends are on there >> and all of your friends are on there then if you wanted to turn from chatbt then if you wanted to turn from chatbt then if you wanted to turn from chatbt you'd also have to convince them all to you'd also have to convince them all to you'd also have to convince them all to go effect. go effect. go effect. >> Exactly. I would say the second one is >> Exactly. I would say the second one is >> Exactly. I would say the second one is kind of also like an Apple Google kind of also like an Apple Google kind of also like an Apple Google comparison in that as these app stores comparison in that as these app stores comparison in that as these app stores emerge, it is likely that developers emerge, it is likely that developers emerge, it is likely that developers might start to concentrate their time might start to concentrate their time might start to concentrate their time and effort in who they build for in the and effort in who they build for in the and effort in who they build for in the most sophisticated way, who they ship to most sophisticated way, who they ship to most sophisticated way, who they ship to first um depending on who has the most first um depending on who has the most first um depending on who has the most users or maybe in some cases who's the users or maybe in some cases who's the users or maybe in some cases who's the most willing to pay. But for a lot of most willing to pay. But for a lot of most willing to pay. But for a lot of these consumer tools, it'll be who has these consumer tools, it'll be who has these consumer tools, it'll be who has the most users. So I think that also the most users. So I think that also the most users. So I think that also benefits JBT. And then the other thing benefits JBT. And then the other thing benefits JBT. And then the other thing probably that I'm most excited for this probably that I'm most excited for this probably that I'm most excited for this year that Sam uh Alman had kind of year that Sam uh Alman had kind of year that Sam uh Alman had kind of hinted at is this like authentication hinted at is this like authentication hinted at is this like authentication with chatbt layer. Yeah. So essentially with chatbt layer. Yeah. So essentially with chatbt layer. Yeah. So essentially you'd be able to log in with your chatbt you'd be able to log in with your chatbt you'd be able to log in with your chatbt account and take like your memory and account and take like your memory and account and take like your memory and your tokens with you and then that other your tokens with you and then that other your tokens with you and then that other product would be able to kind of borrow product would be able to kind of borrow product would be able to kind of borrow those things to be even more powerful those things to be even more powerful those things to be even more powerful and helpful for you. And if that's the and helpful for you. And if that's the and helpful for you. And if that's the case, then you're wanting to have more case, then you're wanting to have more case, then you're wanting to have more of your core identity live on chatbt of your core identity live on chatbt of your core identity live on chatbt because then it can lend it to these because then it can lend it to these because then it can lend it to these other tools that are that are even other tools that are that are even other tools that are that are even better for you. better for you. better for you. >> It's so smart and it really plays to >> It's so smart and it really plays to >> It's so smart and it really plays to their advantages in that they have their advantages in that they have their advantages in that they have signups for 900 million people and then signups for 900 million people and then signups for 900 million people and then the third party developer ideally would the third party developer ideally would the third party developer ideally would not want to pay for the inference. Yes. not want to pay for the inference. Yes. not want to pay for the inference. Yes. >> So the user can bring their inference >> So the user can bring their inference >> So the user can bring their inference capacity with them. There's an advantage capacity with them. There's an advantage capacity with them. There's an advantage for the developer. Chat GBT gets the for the developer. Chat GBT gets the for the developer. Chat GBT gets the lock in. the user gets the benefit of lock in. the user gets the benefit of lock in. the user gets the benefit of personalization and it all kind of personalization and it all kind of personalization and it all kind of works. works. works. >> Yes, I totally agree. The one question >> Yes, I totally agree. The one question >> Yes, I totally agree. The one question mark I still have on this that I think mark I still have on this that I think mark I still have on this that I think could play both positive and negative in could play both positive and negative in could play both positive and negative in terms of increasing lock in terms of increasing lock in terms of increasing lock in >> uh for the consumer product is what your >> uh for the consumer product is what your >> uh for the consumer product is what your work goes with like what your enterprise work goes with like what your enterprise work goes with like what your enterprise contract is. So for example, in some contract is. So for example, in some contract is. So for example, in some ways it's good for me if we if my ways it's good for me if we if my ways it's good for me if we if my company uses chatbt for work company uses chatbt for work company uses chatbt for work >> because then I know how to use the >> because then I know how to use the >> because then I know how to use the product and as a normal consumer they product and as a normal consumer they product and as a normal consumer they might have tried one or two AI products might have tried one or two AI products might have tried one or two AI products so they're more likely to be comfortable so they're more likely to be comfortable so they're more likely to be comfortable and keep using something that they've and keep using something that they've and keep using something that they've already used. already used. already used. >> On the other hand, some people might not >> On the other hand, some people might not >> On the other hand, some people might not want to mix identity and mix memory want to mix identity and mix memory want to mix identity and mix memory across their personal and work use across their personal and work use across their personal and work use cases. True. And so I'm really cases. True. And so I'm really cases. True. And so I'm really interested I I think OpenAI hinted at interested I I think OpenAI hinted at interested I I think OpenAI hinted at this recently, but I'm really interested this recently, but I'm really interested this recently, but I'm really interested in how we kind of segment memory across in how we kind of segment memory across in how we kind of segment memory across different um personas that are within different um personas that are within different um personas that are within yourself that are that are using these yourself that are that are using these yourself that are that are using these products. products. products. >> Don't cross the streams. >> Don't cross the streams. >> Don't cross the streams. >> Yeah. Exactly. Exactly. >> Yeah. Exactly. Exactly. >> Yeah. Exactly. Exactly. >> Well, maybe actually switching gears to >> Well, maybe actually switching gears to >> Well, maybe actually switching gears to Gemini for a moment. You know, I think Gemini for a moment. You know, I think Gemini for a moment. You know, I think about the kind of just the vibes around about the kind of just the vibes around about the kind of just the vibes around Google with their early AI products, Google with their early AI products, Google with their early AI products, Bard, which they'll never live down. um Bard, which they'll never live down. um Bard, which they'll never live down. um >> some tough times there >> some tough times there >> some tough times there >> to where we are today with products like >> to where we are today with products like >> to where we are today with products like nano banana like even naming it nano banana like even naming it nano banana like even naming it nanobanana is such a perfect microcosm nanobanana is such a perfect microcosm nanobanana is such a perfect microcosm for how far Google has come for how far Google has come for how far Google has come >> and it seems like they have a lot of >> and it seems like they have a lot of >> and it seems like they have a lot of intentions around multimodality intentions around multimodality intentions around multimodality >> what's your assessment of their approach >> what's your assessment of their approach >> what's your assessment of their approach >> I've been impressed um I think they have >> I've been impressed um I think they have >> I've been impressed um I think they have been hesitant maybe in some ways more been hesitant maybe in some ways more been hesitant maybe in some ways more hesitant in same ways exactly what we hesitant in same ways exactly what we hesitant in same ways exactly what we would expect so to to kind of bake AI would expect so to to kind of bake AI would expect so to to kind of bake AI into the core features because there's a into the core features because there's a into the core features because there's a risk of either like cannibalizing their risk of either like cannibalizing their risk of either like cannibalizing their own product or like there are so many own product or like there are so many own product or like there are so many people who are have used these tools for people who are have used these tools for people who are have used these tools for 10 20 30 40 years and so the the 10 20 30 40 years and so the the 10 20 30 40 years and so the the switching cost there is like a little switching cost there is like a little switching cost there is like a little bit high. They don't want to scare users bit high. They don't want to scare users bit high. They don't want to scare users when AI is suddenly popping up and when AI is suddenly popping up and when AI is suddenly popping up and everything which I understand. Um but everything which I understand. Um but everything which I understand. Um but what they've done a really really good what they've done a really really good what they've done a really really good job at is these new creative products job at is these new creative products job at is these new creative products that are basically very model driven that are basically very model driven that are basically very model driven from the the deep mind team who I think from the the deep mind team who I think from the the deep mind team who I think is generally fantastic. I think Notebook is generally fantastic. I think Notebook is generally fantastic. I think Notebook LM was actually the first look at this LM was actually the first look at this LM was actually the first look at this and that was something truly new in like and that was something truly new in like and that was something truly new in like consumer AI audio. Um, and now we have consumer AI audio. Um, and now we have consumer AI audio. Um, and now we have the image and video models. Um, so in the image and video models. Um, so in the image and video models. Um, so in some ways with a big company like this, some ways with a big company like this, some ways with a big company like this, they kind of have to get over get out of they kind of have to get over get out of they kind of have to get over get out of their own way in terms of being able to their own way in terms of being able to their own way in terms of being able to actually innovate and it seems like they actually innovate and it seems like they actually innovate and it seems like they are but you also worked at Google. So are but you also worked at Google. So are but you also worked at Google. So I'd be curious for your it. It's I'd be curious for your it. It's I'd be curious for your it. It's interesting to just I'm glad you brought interesting to just I'm glad you brought interesting to just I'm glad you brought up notebook because notebook is sort of up notebook because notebook is sort of up notebook because notebook is sort of this green field area within product this green field area within product this green field area within product area within the company. So you don't area within the company. So you don't area within the company. So you don't have 10 VPs fighting over it. And as a have 10 VPs fighting over it. And as a have 10 VPs fighting over it. And as a result, I think just the progress on result, I think just the progress on result, I think just the progress on notebook has been tremendous. You know, notebook has been tremendous. You know, notebook has been tremendous. You know, they just launched a video generation they just launched a video generation they just launched a video generation feature that helps visually demonstrate feature that helps visually demonstrate feature that helps visually demonstrate all the concepts in your sort of all the concepts in your sort of all the concepts in your sort of workspace, which is cool. Conversely, workspace, which is cool. Conversely, workspace, which is cool. Conversely, when you look at the existing product when you look at the existing product when you look at the existing product services like sheets or docs, there's services like sheets or docs, there's services like sheets or docs, there's just so much one sort of momentum and just so much one sort of momentum and just so much one sort of momentum and inertia from the past, but then inertia from the past, but then inertia from the past, but then management overhead. Yes. Around those, management overhead. Yes. Around those, management overhead. Yes. Around those, it's harder for them to do anything it's harder for them to do anything it's harder for them to do anything other than the most obvious incremental other than the most obvious incremental other than the most obvious incremental thing. thing. thing. >> Yes, I agree. We'll see what happens >> Yes, I agree. We'll see what happens >> Yes, I agree. We'll see what happens there in the in the next few years. I there in the in the next few years. I there in the in the next few years. I feel like they're going to put up a feel like they're going to put up a feel like they're going to put up a fight on some of those products because fight on some of those products because fight on some of those products because they don't want to lose that user base, they don't want to lose that user base, they don't want to lose that user base, but to your point, they're already but to your point, they're already but to your point, they're already locked in with so many enterprises that locked in with so many enterprises that locked in with so many enterprises that they might not have to do that much at they might not have to do that much at they might not have to do that much at least in the near term to kind of keep least in the near term to kind of keep least in the near term to kind of keep up. up. up. >> You know, implicit in this conversation >> You know, implicit in this conversation >> You know, implicit in this conversation is we experience and talk a lot about AI is we experience and talk a lot about AI is we experience and talk a lot about AI in the west. in the west. in the west. >> Yeah. >> Yeah. >> Yeah. >> Talk a bit about the sort of global AI >> Talk a bit about the sort of global AI >> Talk a bit about the sort of global AI trends. There was a few surprising trends. There was a few surprising trends. There was a few surprising things I saw in there. we uh kind of things I saw in there. we uh kind of things I saw in there. we uh kind of expanded our scope in terms of what we expanded our scope in terms of what we expanded our scope in terms of what we looked at for this report which ended up looked at for this report which ended up looked at for this report which ended up being like very fun and interesting. Um being like very fun and interesting. Um being like very fun and interesting. Um two things that are are are probably two things that are are are probably two things that are are are probably obvious uh in terms of how they differ obvious uh in terms of how they differ obvious uh in terms of how they differ from the rest of the world uh would be from the rest of the world uh would be from the rest of the world uh would be Russia and China. So Russia I think Russia and China. So Russia I think Russia and China. So Russia I think China everyone knows like a ton of AI China everyone knows like a ton of AI China everyone knows like a ton of AI products are kind of censored or banned products are kind of censored or banned products are kind of censored or banned and so almost all of the usage they and so almost all of the usage they and so almost all of the usage they actually have the lowest combined chatbt actually have the lowest combined chatbt actually have the lowest combined chatbt and Gemini usage of any country. it's and Gemini usage of any country. it's and Gemini usage of any country. it's only 15%. So they're mostly using like only 15%. So they're mostly using like only 15%. So they're mostly using like Dow Bow which is made by Bite Dance, Dow Bow which is made by Bite Dance, Dow Bow which is made by Bite Dance, Deep Seek, Quen, Kimmy, those kinds of Deep Seek, Quen, Kimmy, those kinds of Deep Seek, Quen, Kimmy, those kinds of models. models. models. >> The somewhat of a surprise to me was >> The somewhat of a surprise to me was >> The somewhat of a surprise to me was that Russia actually is a very very that Russia actually is a very very that Russia actually is a very very similar story where they have also their similar story where they have also their similar story where they have also their own kind of parallel AI ecosystem out of own kind of parallel AI ecosystem out of own kind of parallel AI ecosystem out of necessity because they have some level necessity because they have some level necessity because they have some level of sanctions and and things like that of sanctions and and things like that of sanctions and and things like that that prevent them from using all the that prevent them from using all the that prevent them from using all the US-based tools. Um so we've seen US-based tools. Um so we've seen US-based tools. Um so we've seen products like Giga Chat and Yandex which products like Giga Chat and Yandex which products like Giga Chat and Yandex which are Russia specific built by Russian are Russia specific built by Russian are Russia specific built by Russian often state affiliated companies have often state affiliated companies have often state affiliated companies have big big usage there and then DeepSeek. big big usage there and then DeepSeek. big big usage there and then DeepSeek. So Russia is the number two market for So Russia is the number two market for So Russia is the number two market for deepseek after after China. And so if deepseek after after China. And so if deepseek after after China. And so if you look at the kind of like per country you look at the kind of like per country you look at the kind of like per country adoption data, like yes, there's some adoption data, like yes, there's some adoption data, like yes, there's some blips where like this country uses blips where like this country uses blips where like this country uses Claude a little bit more, this country Claude a little bit more, this country Claude a little bit more, this country uses Gemini a little bit more, but the uses Gemini a little bit more, but the uses Gemini a little bit more, but the two huge outliers are Russia and China, two huge outliers are Russia and China, two huge outliers are Russia and China, and those are like big big markets. And and those are like big big markets. And and those are like big big markets. And so I think it's worth watching what's so I think it's worth watching what's so I think it's worth watching what's going on there. going on there. going on there. >> It's [snorts] interesting though because >> It's [snorts] interesting though because >> It's [snorts] interesting though because both Russia and China, they're outliers both Russia and China, they're outliers both Russia and China, they're outliers because of restrictions around how because of restrictions around how because of restrictions around how models can be used and and maybe models can be used and and maybe models can be used and and maybe cultural preferences. Are there any cultural preferences. Are there any cultural preferences. Are there any other countries that have geospecific other countries that have geospecific other countries that have geospecific trends or is this a sort of global AI trends or is this a sort of global AI trends or is this a sort of global AI behavior set? behavior set? behavior set? >> Yeah, I would say in terms of like model >> Yeah, I would say in terms of like model >> Yeah, I would say in terms of like model development, proprietary model development, proprietary model development, proprietary model development that allows you to to deploy development that allows you to to deploy development that allows you to to deploy proprietary AI products, proprietary AI products, proprietary AI products, >> most of that research is coming out of >> most of that research is coming out of >> most of that research is coming out of the US and China, maybe a little bit out the US and China, maybe a little bit out the US and China, maybe a little bit out of Russia. Okay. of Russia. Okay. of Russia. Okay. >> I think we are seeing a few kind of >> I think we are seeing a few kind of >> I think we are seeing a few kind of native ecosystems in other places. I native ecosystems in other places. I native ecosystems in other places. I would Korea has a couple of their own would Korea has a couple of their own would Korea has a couple of their own products like neighbor and cacao that products like neighbor and cacao that products like neighbor and cacao that have built out nice kind of LLM have built out nice kind of LLM have built out nice kind of LLM interfaces. interfaces. interfaces. >> India is probably the other one that I >> India is probably the other one that I >> India is probably the other one that I watch really closely just because watch really closely just because watch really closely just because there's so many people that you can have there's so many people that you can have there's so many people that you can have standalone big companies focus on India. standalone big companies focus on India. standalone big companies focus on India. The other interesting thing about India The other interesting thing about India The other interesting thing about India is there's so many different languages is there's so many different languages is there's so many different languages like such a range that both LLM products like such a range that both LLM products like such a range that both LLM products and even voice products don't and even voice products don't and even voice products don't necessarily support very well. like it's necessarily support very well. like it's necessarily support very well. like it's a worse experience if you're a primary a worse experience if you're a primary a worse experience if you're a primary user of one of those languages and user of one of those languages and user of one of those languages and you're trying to use something like a you're trying to use something like a you're trying to use something like a chatbt. Um so so far we haven't seen a chatbt. Um so so far we haven't seen a chatbt. Um so so far we haven't seen a huge amount of variance there yet but I huge amount of variance there yet but I huge amount of variance there yet but I would not be surprised maybe to see more would not be surprised maybe to see more would not be surprised maybe to see more founders even from the US like targeting founders even from the US like targeting founders even from the US like targeting the Indian market for AI. Um and then the Indian market for AI. Um and then the Indian market for AI. Um and then the other thing I wanted to mention we the other thing I wanted to mention we the other thing I wanted to mention we did for the first time also kind of what did for the first time also kind of what did for the first time also kind of what like a heat map essentially of which like a heat map essentially of which like a heat map essentially of which countries are adopting AI the most and countries are adopting AI the most and countries are adopting AI the most and the least on a on a per capita basis. So the least on a on a per capita basis. So the least on a on a per capita basis. So we looked across like the 10 biggest LLM we looked across like the 10 biggest LLM we looked across like the 10 biggest LLM products products products >> to see on web and mobile to see what >> to see on web and mobile to see what >> to see on web and mobile to see what this might look like. this might look like. this might look like. >> So Singapore is number one. >> So Singapore is number one. >> So Singapore is number one. >> Crazy. >> Crazy. >> Crazy. >> Um yes then Hong Kong then the UAE then >> Um yes then Hong Kong then the UAE then >> Um yes then Hong Kong then the UAE then South Korea. Uh the US is down at number South Korea. Uh the US is down at number South Korea. Uh the US is down at number 20. So, not super low, not incredibly 20. So, not super low, not incredibly 20. So, not super low, not incredibly high. Russia and China are like very far high. Russia and China are like very far high. Russia and China are like very far down the list, like sub 50. down the list, like sub 50. down the list, like sub 50. >> Um, and there's a lot of interesting >> Um, and there's a lot of interesting >> Um, and there's a lot of interesting stories I think that live in that data. stories I think that live in that data. stories I think that live in that data. >> The first one is if you think about >> The first one is if you think about >> The first one is if you think about those top five like Singapore, South those top five like Singapore, South those top five like Singapore, South Korea, Hong Kong, it's a very like the Korea, Hong Kong, it's a very like the Korea, Hong Kong, it's a very like the demographics of the workforce are very demographics of the workforce are very demographics of the workforce are very like tech first, white collar, high like tech first, white collar, high like tech first, white collar, high skill. and the US has a giant chunk of skill. and the US has a giant chunk of skill. and the US has a giant chunk of jobs where AI hasn't really touched them jobs where AI hasn't really touched them jobs where AI hasn't really touched them yet, like retail and transportation and yet, like retail and transportation and yet, like retail and transportation and and some of these other things. Um, and some of these other things. Um, and some of these other things. Um, >> I think also the cultural norms around >> I think also the cultural norms around >> I think also the cultural norms around AI are shockingly diverse. If you're in AI are shockingly diverse. If you're in AI are shockingly diverse. If you're in the US, you have probably internalized the US, you have probably internalized the US, you have probably internalized this ongoing angst and questioning this ongoing angst and questioning this ongoing angst and questioning around around around my job. or um you know AI is terrible my job. or um you know AI is terrible my job. or um you know AI is terrible for artists or all of these other things for artists or all of these other things for artists or all of these other things that make people pick up or not pick up that make people pick up or not pick up that make people pick up or not pick up AI. AI. AI. >> Yes, >> Yes, >> Yes, >> there was actually a big survey last >> there was actually a big survey last >> there was actually a big survey last year from Edelman um the global media year from Edelman um the global media year from Edelman um the global media company company company >> and the US had a fairly low rate of >> and the US had a fairly low rate of >> and the US had a fairly low rate of trust in AI. It was like 32%. And most trust in AI. It was like 32%. And most trust in AI. It was like 32%. And most of these other countries that are high of these other countries that are high of these other countries that are high on the list are like 50 60 70%. So that on the list are like 50 60 70%. So that on the list are like 50 60 70%. So that I think has also held the US back I think has also held the US back I think has also held the US back despite the fact that we are where the despite the fact that we are where the despite the fact that we are where the biggest products come from. our per biggest products come from. our per biggest products come from. our per capita usage is lower than a lot of capita usage is lower than a lot of capita usage is lower than a lot of these other markets that have maybe these other markets that have maybe these other markets that have maybe smaller populations but have been uh smaller populations but have been uh smaller populations but have been uh have embraced it more. have embraced it more. have embraced it more. >> I think that's exactly right. You know, >> I think that's exactly right. You know, >> I think that's exactly right. You know, I was reading that in China the sort of I was reading that in China the sort of I was reading that in China the sort of favorability views on AI or 80% 80% hold favorability views on AI or 80% 80% hold favorability views on AI or 80% 80% hold a favorable view and I know UAE and a favorable view and I know UAE and a favorable view and I know UAE and Singapore I think they've sort of Singapore I think they've sort of Singapore I think they've sort of culturally wired to be tech optimistic. culturally wired to be tech optimistic. culturally wired to be tech optimistic. >> Yes. >> Yes. >> Yes. >> Um which is an advantage, you know. >> Um which is an advantage, you know. >> Um which is an advantage, you know. >> Yes. Yes. Definitely. It's interesting >> Yes. Yes. Definitely. It's interesting >> Yes. Yes. Definitely. It's interesting to see some of these smaller countries to see some of these smaller countries to see some of these smaller countries like the the per capita adoption rate like the the per capita adoption rate like the the per capita adoption rate like in the US it's around probably a like in the US it's around probably a like in the US it's around probably a third of people are monthly active users third of people are monthly active users third of people are monthly active users of something like a chatbt. Yeah. of something like a chatbt. Yeah. of something like a chatbt. Yeah. >> In some even some of like the European >> In some even some of like the European >> In some even some of like the European countries um or Eastern Europe it's like countries um or Eastern Europe it's like countries um or Eastern Europe it's like 50 40 45 60% on on smaller bases but 50 40 45 60% on on smaller bases but 50 40 45 60% on on smaller bases but they've kind of embraced it more quickly they've kind of embraced it more quickly they've kind of embraced it more quickly than we have here. than we have here. than we have here. >> Yeah. Really interesting. You know, one >> Yeah. Really interesting. You know, one >> Yeah. Really interesting. You know, one one thing that I'm sort of watching and one thing that I'm sort of watching and one thing that I'm sort of watching and I'm interested in is as you look at the I'm interested in is as you look at the I'm interested in is as you look at the spectrum of AI from the most functional spectrum of AI from the most functional spectrum of AI from the most functional almost like a Google search replacement almost like a Google search replacement almost like a Google search replacement to the most cultural, creative, to the most cultural, creative, to the most cultural, creative, personal, we should see more divergence personal, we should see more divergence personal, we should see more divergence country by country because obviously the country by country because obviously the country by country because obviously the culture the movies they make in India culture the movies they make in India culture the movies they make in India couldn't be more different than the couldn't be more different than the couldn't be more different than the movies they make in China or the US. movies they make in China or the US. movies they make in China or the US. >> So why wouldn't their use of creative >> So why wouldn't their use of creative >> So why wouldn't their use of creative tools be different? tools be different? tools be different? >> Yeah. And this is honestly part of the >> Yeah. And this is honestly part of the >> Yeah. And this is honestly part of the reason why we started looking at the reason why we started looking at the reason why we started looking at the geographic segmentation in this report geographic segmentation in this report geographic segmentation in this report is because for the first two and a half is because for the first two and a half is because for the first two and a half three years of generative AI the vast three years of generative AI the vast three years of generative AI the vast majority of consumers were maybe majority of consumers were maybe majority of consumers were maybe interacting with one product and now interacting with one product and now interacting with one product and now it's broadening quite a bit and I think it's broadening quite a bit and I think it's broadening quite a bit and I think that we will see more of these market that we will see more of these market that we will see more of these market specific tools specific tools specific tools >> and if they are if they capture enough >> and if they are if they capture enough >> and if they are if they capture enough of that market like some of these of that market like some of these of that market like some of these Russian companies or Chinese companies Russian companies or Chinese companies Russian companies or Chinese companies they can actually surface up to the they can actually surface up to the they can actually surface up to the global list if if kind of the market is global list if if kind of the market is global list if if kind of the market is big big big talk a bit about the evolution of talk a bit about the evolution of talk a bit about the evolution of creative tools and how much you think creative tools and how much you think creative tools and how much you think that that is that a reflection of that that is that a reflection of that that is that a reflection of culture is that driving culture when do culture is that driving culture when do culture is that driving culture when do we cross that threshold we cross that threshold we cross that threshold >> the creative tools trend has been >> the creative tools trend has been >> the creative tools trend has been fascinating um I mean obviously the fascinating um I mean obviously the fascinating um I mean obviously the first big generative AI product was first big generative AI product was first big generative AI product was actually midjourney which came out actually midjourney which came out actually midjourney which came out before chat that's right before chat that's right before chat that's right >> and in our first few editions of the >> and in our first few editions of the >> and in our first few editions of the list it was very much dominated by list it was very much dominated by list it was very much dominated by creative tools um and creative tools um and creative tools um and >> the I've said this before but the >> the I've said this before but the >> the I've said this before but the creative tools benefit from kind of creative tools benefit from kind of creative tools benefit from kind of hallucination of the early models um hallucination of the early models um hallucination of the early models um because they produce things that are because they produce things that are because they produce things that are more kind of surprising or beautiful or more kind of surprising or beautiful or more kind of surprising or beautiful or original. Um and so for a while that original. Um and so for a while that original. Um and so for a while that those were the only things working in those were the only things working in those were the only things working in consumer AI really. Um now it's shifted consumer AI really. Um now it's shifted consumer AI really. Um now it's shifted a lot. Creative tools are still a huge a lot. Creative tools are still a huge a lot. Creative tools are still a huge chunk of the list, but like the type of chunk of the list, but like the type of chunk of the list, but like the type of creative tool that is a standalone big creative tool that is a standalone big creative tool that is a standalone big business has changed. Um I would say the business has changed. Um I would say the business has changed. Um I would say the biggest change is we're seeing fewer biggest change is we're seeing fewer biggest change is we're seeing fewer standalone image generators. Mhm. standalone image generators. Mhm. standalone image generators. Mhm. >> A lot of this activity if you're making >> A lot of this activity if you're making >> A lot of this activity if you're making like a basic commodity image like you like a basic commodity image like you like a basic commodity image like you know a meme or a basic marketing image know a meme or a basic marketing image know a meme or a basic marketing image or an infographic like the core models or an infographic like the core models or an infographic like the core models in ChachiBT and Gemini are quite good at in ChachiBT and Gemini are quite good at in ChachiBT and Gemini are quite good at those things now. those things now. those things now. >> So the products that are still surfacing >> So the products that are still surfacing >> So the products that are still surfacing on the list like an ideoggram um or a on the list like an ideoggram um or a on the list like an ideoggram um or a midjourney are either very aesthetically midjourney are either very aesthetically midjourney are either very aesthetically opinionated or they have very more opinionated or they have very more opinionated or they have very more sophisticated workflows that you can't sophisticated workflows that you can't sophisticated workflows that you can't get on something like a like a chatbt. get on something like a like a chatbt. get on something like a like a chatbt. Um, Um, Um, contrasting that, I would say like contrasting that, I would say like contrasting that, I would say like music, voice, video all seem to be music, voice, video all seem to be music, voice, video all seem to be things that the model the biggest model things that the model the biggest model things that the model the biggest model companies have maybe invested less in. companies have maybe invested less in. companies have maybe invested less in. And so we've seen players like Suno and And so we've seen players like Suno and And so we've seen players like Suno and music and 11 Labs in voice music and 11 Labs in voice music and 11 Labs in voice >> kind of completely break out and rise to >> kind of completely break out and rise to >> kind of completely break out and rise to top 20, top 15 on the list and then like top 20, top 15 on the list and then like top 20, top 15 on the list and then like hold their spot there over time. And hold their spot there over time. And hold their spot there over time. And then there's like a compounding lock in then there's like a compounding lock in then there's like a compounding lock in from like the community and you know the from like the community and you know the from like the community and you know the big base of enterprise customers and all big base of enterprise customers and all big base of enterprise customers and all of that. Um video is where I have the of that. Um video is where I have the of that. Um video is where I have the most questions. most questions. most questions. OpenAI has been investing in it with OpenAI has been investing in it with OpenAI has been investing in it with Sora and of course Google with VO but Sora and of course Google with VO but Sora and of course Google with VO but >> the Chinese models are so good because >> the Chinese models are so good because >> the Chinese models are so good because they can train on any data. [laughter] they can train on any data. [laughter] they can train on any data. [laughter] So Cance 2 is probably the best example So Cance 2 is probably the best example So Cance 2 is probably the best example of this where it's just kind of in some of this where it's just kind of in some of this where it's just kind of in some ways head and shoulders above what the ways head and shoulders above what the ways head and shoulders above what the US companies have thus far been able to US companies have thus far been able to US companies have thus far been able to do. Um so I think we'll see. I think do. Um so I think we'll see. I think do. Um so I think we'll see. I think this actually benefits platforms like a this actually benefits platforms like a this actually benefits platforms like a Korea where you can use all the models Korea where you can use all the models Korea where you can use all the models in one place because my sister Justine in one place because my sister Justine in one place because my sister Justine wrote an article about this but the way wrote an article about this but the way wrote an article about this but the way video is shaping out there's it's video is shaping out there's it's video is shaping out there's it's unlikely to be like one model to rule unlikely to be like one model to rule unlikely to be like one model to rule them all and so you kind of need to be them all and so you kind of need to be them all and so you kind of need to be able to switch between them. That seems able to switch between them. That seems able to switch between them. That seems true of most of the model spaces. You true of most of the model spaces. You true of most of the model spaces. You know, chat models, creative models, even know, chat models, creative models, even know, chat models, creative models, even code models have their areas of code models have their areas of code models have their areas of specialization. You know, people talk specialization. You know, people talk specialization. You know, people talk about kind of ergonomics of Opus. Yes. about kind of ergonomics of Opus. Yes. about kind of ergonomics of Opus. Yes. >> Versus the accuracy of codeex. Yes. And >> Versus the accuracy of codeex. Yes. And >> Versus the accuracy of codeex. Yes. And that's just that's a trade-off, you that's just that's a trade-off, you that's just that's a trade-off, you know, and you have to choose what tool know, and you have to choose what tool know, and you have to choose what tool you want to use for which problem. you want to use for which problem. you want to use for which problem. >> Yeah. Absolutely. Um, Sora is really >> Yeah. Absolutely. Um, Sora is really >> Yeah. Absolutely. Um, Sora is really interesting to me because it represented interesting to me because it represented interesting to me because it represented both a sort of a big step forward in the both a sort of a big step forward in the both a sort of a big step forward in the model but also a really ambitious model but also a really ambitious model but also a really ambitious experiment around social and there was experiment around social and there was experiment around social and there was data in the early days of Sora like the data in the early days of Sora like the data in the early days of Sora like the percentage of people that were creating percentage of people that were creating percentage of people that were creating which was dramatically 10x higher than which was dramatically 10x higher than which was dramatically 10x higher than we'd seen before. we'd seen before. we'd seen before. >> You know what's your kind of assessment >> You know what's your kind of assessment >> You know what's your kind of assessment of the Sora social effort versus the of the Sora social effort versus the of the Sora social effort versus the model effort and where do you see that model effort and where do you see that model effort and where do you see that going? store is so fascinating and I going? store is so fascinating and I going? store is so fascinating and I think was a very um interesting early think was a very um interesting early think was a very um interesting early experiment that I think taught us all a experiment that I think taught us all a experiment that I think taught us all a lot about kind of both creative tools lot about kind of both creative tools lot about kind of both creative tools but also maybe more importantly what but also maybe more importantly what but also maybe more importantly what consumer social in the AI era might look consumer social in the AI era might look consumer social in the AI era might look like. like. like. >> So by the numbers they had a massive >> So by the numbers they had a massive >> So by the numbers they had a massive launch. They were number one on the app launch. They were number one on the app launch. They were number one on the app store the US app store for 20 store the US app store for 20 store the US app store for 20 consecutive days which is very hard to consecutive days which is very hard to consecutive days which is very hard to do. It means you're probably getting do. It means you're probably getting do. It means you're probably getting >> to be number one on the app store you >> to be number one on the app store you >> to be number one on the app store you probably have to get these days 150,000 probably have to get these days 150,000 probably have to get these days 150,000 daily downloads. So it's like a high daily downloads. So it's like a high daily downloads. So it's like a high download volume. Um, and they actually download volume. Um, and they actually download volume. Um, and they actually hit a million users faster than chatbt hit a million users faster than chatbt hit a million users faster than chatbt itself. So like huge launch itself. So like huge launch itself. So like huge launch >> and actually I think what a lot of >> and actually I think what a lot of >> and actually I think what a lot of people underestimate is it still is very people underestimate is it still is very people underestimate is it still is very significant usage. So 3 million Dows per significant usage. So 3 million Dows per significant usage. So 3 million Dows per sensor tower. sensor tower. sensor tower. >> Um, which is not bad at all. What has >> Um, which is not bad at all. What has >> Um, which is not bad at all. What has dropped off about Sora is the new dropped off about Sora is the new dropped off about Sora is the new downloads. So they're maybe they peaked downloads. So they're maybe they peaked downloads. So they're maybe they peaked at like 6 million a month in November. at like 6 million a month in November. at like 6 million a month in November. It's looking like a million and a half It's looking like a million and a half It's looking like a million and a half now. Um, I think the what has really now. Um, I think the what has really now. Um, I think the what has really worked about Sora is that it's a very worked about Sora is that it's a very worked about Sora is that it's a very good video model good video model good video model >> and they kind of innovated and >> and they kind of innovated and >> and they kind of innovated and introduced this concept of cameos which introduced this concept of cameos which introduced this concept of cameos which is where a real person can grant their is where a real person can grant their is where a real person can grant their likeness to Sora so that they and others likeness to Sora so that they and others likeness to Sora so that they and others can generate videos of them. can generate videos of them. can generate videos of them. >> So like a lot of people in the early >> So like a lot of people in the early >> So like a lot of people in the early days were doing like meme videos of days were doing like meme videos of days were doing like meme videos of their friends like Jake Paul went viral their friends like Jake Paul went viral their friends like Jake Paul went viral cuz he was the first big celebrity to cuz he was the first big celebrity to cuz he was the first big celebrity to like lean into Sora. So you were seeing like lean into Sora. So you were seeing like lean into Sora. So you were seeing like insane Jake Paul videos everywhere. like insane Jake Paul videos everywhere. like insane Jake Paul videos everywhere. Yeah. I mean honestly good for him. Yeah. I mean honestly good for him. Yeah. I mean honestly good for him. >> Yes. Yes. >> Yes. Yes. >> Yes. Yes. >> Um I think what worked less about Sora >> Um I think what worked less about Sora >> Um I think what worked less about Sora is that because the content was is that because the content was is that because the content was exportable, exportable, exportable, >> um people would take it to Tik Tok, they >> um people would take it to Tik Tok, they >> um people would take it to Tik Tok, they would take it to Instagram reels, they would take it to Instagram reels, they would take it to Instagram reels, they would take it to YouTube and there it would take it to YouTube and there it would take it to YouTube and there it competed against the best humanmade competed against the best humanmade competed against the best humanmade content content content >> and so the overall feed experience was >> and so the overall feed experience was >> and so the overall feed experience was just better because you were seeing the just better because you were seeing the just better because you were seeing the best of both, not just like the best of best of both, not just like the best of best of both, not just like the best of Sora, Sora, Sora, >> right? Um, I don't think we've seen a >> right? Um, I don't think we've seen a >> right? Um, I don't think we've seen a social product yet succeed that's like social product yet succeed that's like social product yet succeed that's like entirely AI content. The emotional entirely AI content. The emotional entirely AI content. The emotional stakes are just feel lower in some ways. stakes are just feel lower in some ways. stakes are just feel lower in some ways. >> And so I would imagine we'll see more >> And so I would imagine we'll see more >> And so I would imagine we'll see more examples like these where Soros still examples like these where Soros still examples like these where Soros still has clearly very very significant usage has clearly very very significant usage has clearly very very significant usage and revenue as a creative tool, but not and revenue as a creative tool, but not and revenue as a creative tool, but not so much as a social app, so much as a social app, so much as a social app, >> right? Um, and I don't know if there'll >> right? Um, and I don't know if there'll >> right? Um, and I don't know if there'll be there probably will be an a massive be there probably will be an a massive be there probably will be an a massive AI native social network, but we haven't AI native social network, but we haven't AI native social network, but we haven't seen what it looks like just yet. I seen what it looks like just yet. I seen what it looks like just yet. I would say would say would say >> it'll be interesting. You know, we >> it'll be interesting. You know, we >> it'll be interesting. You know, we discuss this frequently, but every discuss this frequently, but every discuss this frequently, but every social product has a status game. Yes. social product has a status game. Yes. social product has a status game. Yes. You know, and on Insta it's maybe be the You know, and on Insta it's maybe be the You know, and on Insta it's maybe be the hottest and on X it's be the most hottest and on X it's be the most hottest and on X it's be the most interesting. And it felt like the interesting. And it felt like the interesting. And it felt like the emerging status game on Soro was be the emerging status game on Soro was be the emerging status game on Soro was be the funniest. And I think this is one of the funniest. And I think this is one of the funniest. And I think this is one of the reasons why it's hard for the content to reasons why it's hard for the content to reasons why it's hard for the content to cross over cross over cross over >> because it's just two different ways of >> because it's just two different ways of >> because it's just two different ways of judging what is interesting and and judging what is interesting and and judging what is interesting and and great. I agree what they might do if I great. I agree what they might do if I great. I agree what they might do if I had to imagine where they might find had to imagine where they might find had to imagine where they might find more of a niche. They have now inked a more of a niche. They have now inked a more of a niche. They have now inked a bunch of deals with big media companies bunch of deals with big media companies bunch of deals with big media companies like Disney. like Disney. like Disney. >> And so if Sora is the only place where >> And so if Sora is the only place where >> And so if Sora is the only place where you can make like licensed like fan you can make like licensed like fan you can make like licensed like fan videos of like beloved kind of videos of like beloved kind of videos of like beloved kind of characters and entertainment figures, characters and entertainment figures, characters and entertainment figures, then like that's very interesting. then like that's very interesting. then like that's very interesting. >> Totally. >> Totally. >> Totally. >> Um but we're early I think in how that >> Um but we're early I think in how that >> Um but we're early I think in how that rules out. rules out. rules out. >> It's so early. >> It's so early. >> It's so early. >> I know we keep saying it. [laughter] >> I know we keep saying it. [laughter] >> I know we keep saying it. [laughter] >> Yeah. >> Yeah. >> Yeah. >> We can't have this conversation without >> We can't have this conversation without >> We can't have this conversation without talking about agents. Open claw. Manis, talking about agents. Open claw. Manis, talking about agents. Open claw. Manis, Gen Spark, Moldbook. Gen Spark, Moldbook. Gen Spark, Moldbook. >> Give us an overview of what has happened >> Give us an overview of what has happened >> Give us an overview of what has happened in the last 60 days in the world of in the last 60 days in the world of in the last 60 days in the world of agents and what does a report tell us? agents and what does a report tell us? agents and what does a report tell us? >> I think this is mostly why I say the >> I think this is mostly why I say the >> I think this is mostly why I say the last, you know, even 6 months, but last, you know, even 6 months, but last, you know, even 6 months, but actually even two months of this report actually even two months of this report actually even two months of this report have been like the most interesting that have been like the most interesting that have been like the most interesting that I think we've seen. So, OpenClaw I think we've seen. So, OpenClaw I think we've seen. So, OpenClaw actually, as you'll see, is not on our actually, as you'll see, is not on our actually, as you'll see, is not on our rankings because it blew up in February. rankings because it blew up in February. rankings because it blew up in February. Our data ends in January, but we did Our data ends in January, but we did Our data ends in January, but we did pull the data uh for February. And if it pull the data uh for February. And if it pull the data uh for February. And if it had been eligible, it would have been had been eligible, it would have been had been eligible, it would have been number 30 on our web list, which is a number 30 on our web list, which is a number 30 on our web list, which is a pretty big debut. I think the really pretty big debut. I think the really pretty big debut. I think the really interesting thing about OpenClaw is the interesting thing about OpenClaw is the interesting thing about OpenClaw is the usage has just continued to accelerate usage has just continued to accelerate usage has just continued to accelerate in the technical community. So now it's in the technical community. So now it's in the technical community. So now it's I think number one GitHub stars of all I think number one GitHub stars of all I think number one GitHub stars of all time. It passed React, it passed Linux. time. It passed React, it passed Linux. time. It passed React, it passed Linux. It's really, really It's really, really It's really, really >> Linux. Holy cow. >> Linux. Holy cow. >> Linux. Holy cow. >> Very impressive. But in terms of overall >> Very impressive. But in terms of overall >> Very impressive. But in terms of overall new users, it's kind of plateaued. So we new users, it's kind of plateaued. So we new users, it's kind of plateaued. So we looked at kind of visits to the get looked at kind of visits to the get looked at kind of visits to the get started or signup page and that has kind started or signup page and that has kind started or signup page and that has kind of flat week over week since early of flat week over week since early of flat week over week since early February, which I think indicates that February, which I think indicates that February, which I think indicates that like it is an amazing product if you're like it is an amazing product if you're like it is an amazing product if you're technical. It has not yet fully escaped technical. It has not yet fully escaped technical. It has not yet fully escaped containment to non-technical people, containment to non-technical people, containment to non-technical people, which of course is like a bigger which of course is like a bigger which of course is like a bigger population. population. population. >> They were acquired by OpenAI. So, if I >> They were acquired by OpenAI. So, if I >> They were acquired by OpenAI. So, if I had to guess, um, or what I'd love to had to guess, um, or what I'd love to had to guess, um, or what I'd love to see OpenAI do is build like productize see OpenAI do is build like productize see OpenAI do is build like productize OpenClaw into something that is usable OpenClaw into something that is usable OpenClaw into something that is usable for a mainstream consumer. And I think for a mainstream consumer. And I think for a mainstream consumer. And I think we've also just seen the ideas behind we've also just seen the ideas behind we've also just seen the ideas behind the OpenClaw architecture inspire so the OpenClaw architecture inspire so the OpenClaw architecture inspire so many other founders. Like, how many many other founders. Like, how many many other founders. Like, how many pitches do we take a day where the pitches do we take a day where the pitches do we take a day where the founder is like, I want to be open Claw founder is like, I want to be open Claw founder is like, I want to be open Claw for this, or OpenClaw made me realize for this, or OpenClaw made me realize for this, or OpenClaw made me realize this was possible. And so I think we're this was possible. And so I think we're this was possible. And so I think we're going to see OpenClaw itself will going to see OpenClaw itself will going to see OpenClaw itself will continue to succeed and be a massive continue to succeed and be a massive continue to succeed and be a massive product. And I'm guessing we'll see more product. And I'm guessing we'll see more product. And I'm guessing we'll see more kind of like verticalized focused kind of like verticalized focused kind of like verticalized focused versions of OpenClaw for different use versions of OpenClaw for different use versions of OpenClaw for different use cases. cases. cases. >> Yeah. It's so interesting because it >> Yeah. It's so interesting because it >> Yeah. It's so interesting because it feels like one of the things that makes feels like one of the things that makes feels like one of the things that makes OpenClaw work so well is it can operate OpenClaw work so well is it can operate OpenClaw work so well is it can operate across all models in all directions. across all models in all directions. across all models in all directions. >> Yes. >> Yes. >> Yes. >> And I s you know I sort of wonder if it >> And I s you know I sort of wonder if it >> And I s you know I sort of wonder if it dilutes the value of open claw to have dilutes the value of open claw to have dilutes the value of open claw to have it be sole model provided and therefore it be sole model provided and therefore it be sole model provided and therefore it's sort of counterpositioned against it's sort of counterpositioned against it's sort of counterpositioned against labs. labs. labs. >> Totally. Yeah. They've kept it I think >> Totally. Yeah. They've kept it I think >> Totally. Yeah. They've kept it I think multimodel for now at least in my usage. multimodel for now at least in my usage. multimodel for now at least in my usage. So, we'll see how it trends. I think it So, we'll see how it trends. I think it So, we'll see how it trends. I think it would be smart to keep it that way for would be smart to keep it that way for would be smart to keep it that way for us. But us. But us. But >> yeah, is Manis the consumer grade open >> yeah, is Manis the consumer grade open >> yeah, is Manis the consumer grade open claw or how do you distinguish the two? claw or how do you distinguish the two? claw or how do you distinguish the two? >> Some some might say that and and I do >> Some some might say that and and I do >> Some some might say that and and I do actually think so. So So Manis made our actually think so. So So Manis made our actually think so. So So Manis made our web list. Um and of course they had a $2 web list. Um and of course they had a $2 web list. Um and of course they had a $2 billion plus acquisition by Meta also in billion plus acquisition by Meta also in billion plus acquisition by Meta also in the course of the list. incredible the course of the list. incredible the course of the list. incredible growth like the the ramp that they growth like the the ramp that they growth like the the ramp that they reported from like zero to 100 million reported from like zero to 100 million reported from like zero to 100 million 200 million or ARR in the span of like 200 million or ARR in the span of like 200 million or ARR in the span of like honestly six nine months is is really honestly six nine months is is really honestly six nine months is is really kind of bestin-class. My view on why kind of bestin-class. My view on why kind of bestin-class. My view on why Manis was so successful was it was Manis was so successful was it was Manis was so successful was it was really the first consumer-grade agent really the first consumer-grade agent really the first consumer-grade agent that could actually operate fairly that could actually operate fairly that could actually operate fairly autonomously across uh products and autonomously across uh products and autonomously across uh products and platforms. So you could connect email, platforms. So you could connect email, platforms. So you could connect email, you could have it browse the web you could have it browse the web you could have it browse the web >> um and it would it could make slides, it >> um and it would it could make slides, it >> um and it would it could make slides, it could make spreadsheets. I spent a lot could make spreadsheets. I spent a lot could make spreadsheets. I spent a lot of time in the early days trying like of time in the early days trying like of time in the early days trying like this was a year ago Chad GPT operator or this was a year ago Chad GPT operator or this was a year ago Chad GPT operator or Google's project mariner and none of Google's project mariner and none of Google's project mariner and none of them were reliable and Manis was a them were reliable and Manis was a them were reliable and Manis was a breakthrough in kind of agent breakthrough in kind of agent breakthrough in kind of agent reliability and agent accessibility for reliability and agent accessibility for reliability and agent accessibility for the consumer. I think the fact that they the consumer. I think the fact that they the consumer. I think the fact that they did the acquisition is interesting in did the acquisition is interesting in did the acquisition is interesting in terms of where this is going in that terms of where this is going in that terms of where this is going in that once everyone has that agentic once everyone has that agentic once everyone has that agentic capability and you might imagine they capability and you might imagine they capability and you might imagine they will if it's kind of based on the core will if it's kind of based on the core will if it's kind of based on the core underlying models underlying models underlying models >> um then it's actually if you're such a >> um then it's actually if you're such a >> um then it's actually if you're such a horizontal product you may be better off horizontal product you may be better off horizontal product you may be better off with the distribution forces of a meta with the distribution forces of a meta with the distribution forces of a meta or a Google or something like that or a Google or something like that or a Google or something like that versus a standalone company that's versus a standalone company that's versus a standalone company that's definitely not true if you're building definitely not true if you're building definitely not true if you're building something more vertical something more vertical something more vertical But if you imagine that like Google now But if you imagine that like Google now But if you imagine that like Google now has the resources to create a manace, has the resources to create a manace, has the resources to create a manace, then that's a really hard thing to keep then that's a really hard thing to keep then that's a really hard thing to keep fighting against as a startup, I think. fighting against as a startup, I think. fighting against as a startup, I think. And obviously the big companies have a And obviously the big companies have a And obviously the big companies have a billion different priorities. So they're billion different priorities. So they're billion different priorities. So they're not going to do everything bestin-class. not going to do everything bestin-class. not going to do everything bestin-class. But it's why I've generally been a But it's why I've generally been a But it's why I've generally been a little more cautious about the very very little more cautious about the very very little more cautious about the very very horizontal consumer AI apps just because horizontal consumer AI apps just because horizontal consumer AI apps just because it's probably both in scope for the for it's probably both in scope for the for it's probably both in scope for the for the bigger companies and they have the the bigger companies and they have the the bigger companies and they have the advantage of already having it approval advantage of already having it approval advantage of already having it approval and enterprise contracts and all of and enterprise contracts and all of and enterprise contracts and all of that. that. that. >> Right. Right. It is interesting that we >> Right. Right. It is interesting that we >> Right. Right. It is interesting that we sort of cross this cultural threshold sort of cross this cultural threshold sort of cross this cultural threshold though. Yes. though. Yes. though. Yes. >> Where man seemed like a nonobvious bet >> Where man seemed like a nonobvious bet >> Where man seemed like a nonobvious bet in terms of just the breadth of the in terms of just the breadth of the in terms of just the breadth of the offering and now it seems like they're offering and now it seems like they're offering and now it seems like they're living in the future a little bit. Yes, living in the future a little bit. Yes, living in the future a little bit. Yes, absolutely. They were It's obviously an absolutely. They were It's obviously an absolutely. They were It's obviously an incredible engineering team. Like the incredible engineering team. Like the incredible engineering team. Like the quality of the product was like three, quality of the product was like three, quality of the product was like three, six months ahead of the rest of the six months ahead of the rest of the six months ahead of the rest of the market, which is not easy to do when market, which is not easy to do when market, which is not easy to do when you're competing with teams of like you're competing with teams of like you're competing with teams of like thousands of researchers. thousands of researchers. thousands of researchers. >> Totally. >> Totally. >> Totally. >> Let's use this to kind of leg into a >> Let's use this to kind of leg into a >> Let's use this to kind of leg into a conversation about other horizontal AI conversation about other horizontal AI conversation about other horizontal AI products, things that live beyond the products, things that live beyond the products, things that live beyond the sort of web window. sort of web window. sort of web window. >> Yes. >> Yes. >> Yes. >> What are you seeing there? >> What are you seeing there? >> What are you seeing there? >> Yeah, that has been a massive theme. Um, >> Yeah, that has been a massive theme. Um, >> Yeah, that has been a massive theme. Um, when I think about the products that I when I think about the products that I when I think about the products that I interact with on a daily basis in the AI interact with on a daily basis in the AI interact with on a daily basis in the AI world, quite a few of them are actually world, quite a few of them are actually world, quite a few of them are actually desktop apps like things like granola, desktop apps like things like granola, desktop apps like things like granola, voice dictation tools, cloud co-work, voice dictation tools, cloud co-work, voice dictation tools, cloud co-work, those kinds of things. Um, and it and it those kinds of things. Um, and it and it those kinds of things. Um, and it and it does become a methodology problem for does become a methodology problem for does become a methodology problem for our report because we can track website our report because we can track website our report because we can track website visits very well and so we can track the visits very well and so we can track the visits very well and so we can track the first time that they download that first time that they download that first time that they download that desktop app. We can track mobile app desktop app. We can track mobile app desktop app. We can track mobile app usage very well. We cannot track desktop usage very well. We cannot track desktop usage very well. We cannot track desktop usage that closely. And I think that is usage that closely. And I think that is usage that closely. And I think that is increasingly as AI products become more increasingly as AI products become more increasingly as AI products become more sophisticated, sophisticated, sophisticated, having them live in their own dedicated having them live in their own dedicated having them live in their own dedicated application, much of which will run on application, much of which will run on application, much of which will run on desktop because it can interact with desktop because it can interact with desktop because it can interact with your files and it can be more ambient. I your files and it can be more ambient. I your files and it can be more ambient. I think that's going to happen more and think that's going to happen more and think that's going to happen more and more. Um, and so I think moving forward, more. Um, and so I think moving forward, more. Um, and so I think moving forward, finding us finding ways to parallel finding us finding ways to parallel finding us finding ways to parallel track ranking these products by web and track ranking these products by web and track ranking these products by web and mo mobile usage, but also by revenue is mo mobile usage, but also by revenue is mo mobile usage, but also by revenue is going to be a a pretty good idea because going to be a a pretty good idea because going to be a a pretty good idea because if you think about things like cursor, if you think about things like cursor, if you think about things like cursor, some of the consumer proumer AI apps some of the consumer proumer AI apps some of the consumer proumer AI apps that are generating the most revenue that are generating the most revenue that are generating the most revenue have very few very little usage on web. have very few very little usage on web. have very few very little usage on web. It's almost all in kind of a dedicated It's almost all in kind of a dedicated It's almost all in kind of a dedicated app. app. app. >> Yeah, it's really interesting. It also >> Yeah, it's really interesting. It also >> Yeah, it's really interesting. It also feels like the fact that OpenAI released feels like the fact that OpenAI released feels like the fact that OpenAI released Atlas and Anthropic released co-work Atlas and Anthropic released co-work Atlas and Anthropic released co-work >> shows you where their priorities are. >> shows you where their priorities are. >> shows you where their priorities are. >> Yes, definitely. I I fully agree. Um the >> Yes, definitely. I I fully agree. Um the >> Yes, definitely. I I fully agree. Um the AI browser debate is is own interesting AI browser debate is is own interesting AI browser debate is is own interesting thing. I feel like we're still in the thing. I feel like we're still in the thing. I feel like we're still in the early to mid phases of how that's going early to mid phases of how that's going early to mid phases of how that's going to play out. Yeah. And I think the to play out. Yeah. And I think the to play out. Yeah. And I think the instinct behind an AI native browser is instinct behind an AI native browser is instinct behind an AI native browser is right in that if you can have AI be um right in that if you can have AI be um right in that if you can have AI be um kind of always on, always available, kind of always on, always available, kind of always on, always available, ambient in where you're spending a lot ambient in where you're spending a lot ambient in where you're spending a lot of your time online, like that's a a of your time online, like that's a a of your time online, like that's a a good opportunity. good opportunity. good opportunity. >> Perplexity Comet I think actually led >> Perplexity Comet I think actually led >> Perplexity Comet I think actually led the way there. the way there. the way there. >> It's a great product. >> It's a great product. >> It's a great product. >> It's a great product. And the >> It's a great product. And the >> It's a great product. And the interesting thing is if you look at kind interesting thing is if you look at kind interesting thing is if you look at kind of the highest spike for Comet and Atlas of the highest spike for Comet and Atlas of the highest spike for Comet and Atlas in terms of visits to the download page, in terms of visits to the download page, in terms of visits to the download page, Comet is five times ahead of Atlas, Comet is five times ahead of Atlas, Comet is five times ahead of Atlas, which is wild because ChachiBT's which is wild because ChachiBT's which is wild because ChachiBT's audience is like so massive. audience is like so massive. audience is like so massive. >> Yeah. I and I think what we've seen is >> Yeah. I and I think what we've seen is >> Yeah. I and I think what we've seen is like Comet and Atlas still have very like Comet and Atlas still have very like Comet and Atlas still have very dedicated dedicated dedicated excited user bases, but for the average excited user bases, but for the average excited user bases, but for the average consumer, the switching cost of a consumer, the switching cost of a consumer, the switching cost of a browser is non-trivial just because like browser is non-trivial just because like browser is non-trivial just because like you have workflows set up, you naturally you have workflows set up, you naturally you have workflows set up, you naturally just open this one app. just open this one app. just open this one app. >> And so it not only has to be like >> And so it not only has to be like >> And so it not only has to be like feature par, there has to be one or two feature par, there has to be one or two feature par, there has to be one or two features of the AI browser that are features of the AI browser that are features of the AI browser that are really killer and that are easy enough really killer and that are easy enough really killer and that are easy enough for the average person to set up and for the average person to set up and for the average person to set up and access. And I don't think that we've access. And I don't think that we've access. And I don't think that we've seen that quite yet. seen that quite yet. seen that quite yet. >> You know, it's really interesting >> You know, it's really interesting >> You know, it's really interesting because Sam said, I think six months ago because Sam said, I think six months ago because Sam said, I think six months ago in a pod, you know, somebody was asking in a pod, you know, somebody was asking in a pod, you know, somebody was asking him what has surprised you the most and him what has surprised you the most and him what has surprised you the most and he said it's that the world hasn't he said it's that the world hasn't he said it's that the world hasn't changed more. Yeah. changed more. Yeah. changed more. Yeah. >> And if you look at the trends around how >> And if you look at the trends around how >> And if you look at the trends around how people are using chat GBT at scale, it's people are using chat GBT at scale, it's people are using chat GBT at scale, it's still, you know, homework and Google still, you know, homework and Google still, you know, homework and Google like queries and a little bit of like queries and a little bit of like queries and a little bit of companionship. companionship. companionship. >> In a sense, something like a browser >> In a sense, something like a browser >> In a sense, something like a browser gives you an opportunity to point the gives you an opportunity to point the gives you an opportunity to point the user in a different direction. What's user in a different direction. What's user in a different direction. What's your view on how the average person is your view on how the average person is your view on how the average person is using AI today? using AI today? using AI today? >> Yeah. Um, I think a couple things. So, >> Yeah. Um, I think a couple things. So, >> Yeah. Um, I think a couple things. So, one, I feel like, uh, teenage girls are one, I feel like, uh, teenage girls are one, I feel like, uh, teenage girls are like the best source of what is like the best source of what is like the best source of what is happening in consumer, what will be happening in consumer, what will be happening in consumer, what will be happening in consumer. If you look at happening in consumer. If you look at happening in consumer. If you look at all of the biggest consumer outcomes, all of the biggest consumer outcomes, all of the biggest consumer outcomes, like they were the early adopters of all like they were the early adopters of all like they were the early adopters of all of these products. Um, and so there was of these products. Um, and so there was of these products. Um, and so there was actually a Pew Research study fairly actually a Pew Research study fairly actually a Pew Research study fairly recently on how teenagers are using AI. recently on how teenagers are using AI. recently on how teenagers are using AI. Now, finally, I think for the first Now, finally, I think for the first Now, finally, I think for the first time, over half of them are admitting to time, over half of them are admitting to time, over half of them are admitting to using it for their homework. So the real using it for their homework. So the real using it for their homework. So the real number is probably like 99.999% number is probably like 99.999% number is probably like 99.999% like some of them didn't want to get in like some of them didn't want to get in like some of them didn't want to get in trouble with their parents. trouble with their parents. trouble with their parents. >> Uhhuh. >> Uhhuh. >> Uhhuh. >> Um 38% are now using it for creative >> Um 38% are now using it for creative >> Um 38% are now using it for creative tools. So editing images, editing video, tools. So editing images, editing video, tools. So editing images, editing video, generating images and video. And then generating images and video. And then generating images and video. And then this like emerging slightly longer tail, this like emerging slightly longer tail, this like emerging slightly longer tail, but I think will ultimately be amongst but I think will ultimately be amongst but I think will ultimately be amongst the biggest behaviors. 16% are using it the biggest behaviors. 16% are using it the biggest behaviors. 16% are using it for just like casual conversation like for just like casual conversation like for just like casual conversation like not the intense companion products but not the intense companion products but not the intense companion products but like just having someone to talk to. and like just having someone to talk to. and like just having someone to talk to. and then 12% are using it for like emotional then 12% are using it for like emotional then 12% are using it for like emotional support and advice. I think all of these support and advice. I think all of these support and advice. I think all of these use cases will like asmtote around use cases will like asmtote around use cases will like asmtote around probably 100% ultimately probably 100% ultimately probably 100% ultimately >> and so those are behaviors that maybe >> and so those are behaviors that maybe >> and so those are behaviors that maybe have been less well served by products have been less well served by products have been less well served by products so far and will be going forward whether so far and will be going forward whether so far and will be going forward whether it's on a chachi BT or whether it's on it's on a chachi BT or whether it's on it's on a chachi BT or whether it's on like a standalone product. Um and then like a standalone product. Um and then like a standalone product. Um and then the other big thing that I'm looking out the other big thing that I'm looking out the other big thing that I'm looking out for is agents. Like I think basically for is agents. Like I think basically for is agents. Like I think basically >> are teenage girls going to use agents? >> are teenage girls going to use agents? >> are teenage girls going to use agents? Come on. So here's the thing. I think Come on. So here's the thing. I think Come on. So here's the thing. I think that agents similar to like how in 1990 that agents similar to like how in 1990 that agents similar to like how in 1990 an internet company was like a dot an internet company was like a dot an internet company was like a dot company, right? Or a tech company like company, right? Or a tech company like company, right? Or a tech company like do was its own its own designator. do was its own its own designator. do was its own its own designator. >> I think that this is what's going to >> I think that this is what's going to >> I think that this is what's going to happen with agents where ultimately like happen with agents where ultimately like happen with agents where ultimately like every tech company was a dot company. every tech company was a dot company. every tech company was a dot company. Like I think ultimately every AI company Like I think ultimately every AI company Like I think ultimately every AI company and then every tech company is going to and then every tech company is going to and then every tech company is going to be an agentic company because that's be an agentic company because that's be an agentic company because that's just where the models are headed. And if just where the models are headed. And if just where the models are headed. And if you can deliver outcomes and and not you can deliver outcomes and and not you can deliver outcomes and and not just kind of inputs to your users as a just kind of inputs to your users as a just kind of inputs to your users as a software product, that's so much more software product, that's so much more software product, that's so much more compelling. So yes, I think 13-year-old compelling. So yes, I think 13-year-old compelling. So yes, I think 13-year-old girls will be using agents, but they girls will be using agents, but they girls will be using agents, but they will not think of them as agents. But I will not think of them as agents. But I will not think of them as agents. But I think it does unlock a lot of these think it does unlock a lot of these think it does unlock a lot of these other consumer use cases of AI like other consumer use cases of AI like other consumer use cases of AI like finance, healthcare, uh travel planning, finance, healthcare, uh travel planning, finance, healthcare, uh travel planning, complex shopping even where pre- agents complex shopping even where pre- agents complex shopping even where pre- agents there was just so much data you had to there was just so much data you had to there was just so much data you had to go out and grab and do it reliably and go out and grab and do it reliably and go out and grab and do it reliably and do it across systems that it like wasn't do it across systems that it like wasn't do it across systems that it like wasn't really possible and now it is. And so I really possible and now it is. And so I really possible and now it is. And so I think we're going to see an explosion of think we're going to see an explosion of think we're going to see an explosion of those other use cases in the next few those other use cases in the next few those other use cases in the next few months. months. months. >> How long do you think it takes to play >> How long do you think it takes to play >> How long do you think it takes to play out? I mean, is everybody using their out? I mean, is everybody using their out? I mean, is everybody using their own open claw in 12 months? Is that five own open claw in 12 months? Is that five own open claw in 12 months? Is that five years away? Is that the wrong mental years away? Is that the wrong mental years away? Is that the wrong mental model? Like where when we have this model? Like where when we have this model? Like where when we have this conversation, yeah, perhaps in six conversation, yeah, perhaps in six conversation, yeah, perhaps in six months at the next um top 100, what does months at the next um top 100, what does months at the next um top 100, what does the world look like? the world look like? the world look like? >> I feel like every time I predict >> I feel like every time I predict >> I feel like every time I predict something, it happens much more quickly something, it happens much more quickly something, it happens much more quickly than I would have thought, which I think than I would have thought, which I think than I would have thought, which I think is what we're seeing every day. And that is what we're seeing every day. And that is what we're seeing every day. And that startups are growing faster than they startups are growing faster than they startups are growing faster than they ever have. ever have. ever have. I think people there's still the I think people there's still the I think people there's still the cultural change and the cultural cultural change and the cultural cultural change and the cultural adoption will be slower than the techn adoption will be slower than the techn adoption will be slower than the techn uh the technology change and what's uh the technology change and what's uh the technology change and what's actually possible. And so I think what actually possible. And so I think what actually possible. And so I think what we'll continue to see is this early wave we'll continue to see is this early wave we'll continue to see is this early wave of um often technical sometimes not of um often technical sometimes not of um often technical sometimes not technical AI adopters like lead the technical AI adopters like lead the technical AI adopters like lead the charge on a behavior that then 6 months charge on a behavior that then 6 months charge on a behavior that then 6 months later everyone else is doing. later everyone else is doing. later everyone else is doing. >> One good example of this that I'm very >> One good example of this that I'm very >> One good example of this that I'm very very excited about is voice which we've very excited about is voice which we've very excited about is voice which we've talked about a lot. We have talked about talked about a lot. We have talked about talked about a lot. We have talked about voice. voice. voice. >> Um to me it's like the most information >> Um to me it's like the most information >> Um to me it's like the most information dense high quality source of uh dense high quality source of uh dense high quality source of uh basically media that we have. basically media that we have. basically media that we have. >> Um like so much of what you do every day >> Um like so much of what you do every day >> Um like so much of what you do every day is actually downstream or upstream of is actually downstream or upstream of is actually downstream or upstream of like what you say. like what you say. like what you say. >> Um and we're I think for the first time >> Um and we're I think for the first time >> Um and we're I think for the first time in the past 6 months have seen first in the past 6 months have seen first in the past 6 months have seen first engineers and now other people within engineers and now other people within engineers and now other people within tech companies adopt things like voice tech companies adopt things like voice tech companies adopt things like voice dictation. Now, it's kind of almost a dictation. Now, it's kind of almost a dictation. Now, it's kind of almost a norm at that meetings are going to be norm at that meetings are going to be norm at that meetings are going to be kind of recorded and transcribed by an kind of recorded and transcribed by an kind of recorded and transcribed by an AI. AI. AI. >> Um, whether that's voice dictation, >> Um, whether that's voice dictation, >> Um, whether that's voice dictation, whether that's like a voice pin that whether that's like a voice pin that whether that's like a voice pin that answers questions or does tasks for you, answers questions or does tasks for you, answers questions or does tasks for you, I think that is going to spread to the I think that is going to spread to the I think that is going to spread to the mainstream consumer in the next 6 to9 mainstream consumer in the next 6 to9 mainstream consumer in the next 6 to9 months. months. months. >> Really, really interesting. Maybe to >> Really, really interesting. Maybe to >> Really, really interesting. Maybe to close, can you talk a little bit about close, can you talk a little bit about close, can you talk a little bit about memory? memory? memory? >> Yes. >> Yes. >> Yes. >> And where you see that going? >> And where you see that going? >> And where you see that going? >> Yes. >> Yes. >> Yes. Um, memories as we mentioned early right Um, memories as we mentioned early right Um, memories as we mentioned early right now and it can be a little bit jarring now and it can be a little bit jarring now and it can be a little bit jarring in that um, Claude and Chat PT in in that um, Claude and Chat PT in in that um, Claude and Chat PT in particular are very good at this. Even particular are very good at this. Even particular are very good at this. Even Gemini, Google has launched something Gemini, Google has launched something Gemini, Google has launched something called personal intelligence where it called personal intelligence where it called personal intelligence where it now can pull information it knows about now can pull information it knows about now can pull information it knows about you from your docs, email, etc. to like you from your docs, email, etc. to like you from your docs, email, etc. to like serve you better with AI across all of serve you better with AI across all of serve you better with AI across all of the apps. And like I said, it can be a the apps. And like I said, it can be a the apps. And like I said, it can be a little bit jarring now because many little bit jarring now because many little bit jarring now because many people are talking to AI about people are talking to AI about people are talking to AI about everything personal and professional. everything personal and professional. everything personal and professional. And so it can sometimes kind of And so it can sometimes kind of And so it can sometimes kind of inadvertently cross the line of like inadvertently cross the line of like inadvertently cross the line of like what it knows about you to try to help what it knows about you to try to help what it knows about you to try to help you better but in the wrong context. So you better but in the wrong context. So you better but in the wrong context. So I think there's a lot of work to do kind I think there's a lot of work to do kind I think there's a lot of work to do kind of on like the infrastructure side of on like the infrastructure side of on like the infrastructure side almost of like how we sort out who almost of like how we sort out who almost of like how we sort out who someone is in every context. Mhm. someone is in every context. Mhm. someone is in every context. Mhm. >> Once that is settled, I think that >> Once that is settled, I think that >> Once that is settled, I think that memory will be one of the core memory will be one of the core memory will be one of the core advantages for AI products, whether it's advantages for AI products, whether it's advantages for AI products, whether it's their own memory or like chatbt lending their own memory or like chatbt lending their own memory or like chatbt lending memory. memory. memory. >> Any product that you start to use 2 >> Any product that you start to use 2 >> Any product that you start to use 2 years from now, if it doesn't years from now, if it doesn't years from now, if it doesn't immediately feel like it knows you, immediately feel like it knows you, immediately feel like it knows you, >> it will feel broken. >> it will feel broken. >> it will feel broken. >> Like the concept of like onboarding to a >> Like the concept of like onboarding to a >> Like the concept of like onboarding to a product should not be something that product should not be something that product should not be something that exists in a couple years. And I think exists in a couple years. And I think exists in a couple years. And I think that that is something that memory is that that is something that memory is that that is something that memory is really going to enable. I see it really going to enable. I see it really going to enable. I see it personally for myself where I talk to AI personally for myself where I talk to AI personally for myself where I talk to AI all day. I talked to several AIs all day all day. I talked to several AIs all day all day. I talked to several AIs all day and the way that they interact with me and the way that they interact with me and the way that they interact with me and the kind of value that they're able and the kind of value that they're able and the kind of value that they're able to provide has been so much higher 2 or to provide has been so much higher 2 or to provide has been so much higher 2 or 3 months in than it is when you start 3 months in than it is when you start 3 months in than it is when you start using it. using it. using it. >> Incredible. Well, I don't know what the >> Incredible. Well, I don't know what the >> Incredible. Well, I don't know what the future holds, but it's going to be weird future holds, but it's going to be weird future holds, but it's going to be weird and wonderful. and wonderful. and wonderful. >> Excited for it. Yes. >> Excited for it. Yes. >> Excited for it. Yes. >> Olivia, thank you so much. It was super >> Olivia, thank you so much. It was super >> Olivia, thank you so much. It was super fun to actually have this conversation fun to actually have this conversation fun to actually have this conversation today and go through the report. Any today and go through the report. Any today and go through the report. Any closing comments? closing comments? closing comments? >> No, I'm just excited for people to read >> No, I'm just excited for people to read >> No, I'm just excited for people to read it. There's a lot of interesting data in it. There's a lot of interesting data in it. There's a lot of interesting data in there next time and I'm sure it will there next time and I'm sure it will there next time and I'm sure it will look wildly different 6 months from now. look wildly different 6 months from now. look wildly different 6 months from now. So, we'll be back then. So, we'll be back then. So, we'll be back then. >> Really exciting. Well, tell us what you >> Really exciting. Well, tell us what you >> Really exciting. Well, tell us what you think and thanks for checking us out. think and thanks for checking us out. think and thanks for checking us out. >> Thank you.