2025年消费级AI市场:巨头竞争与“赢者通吃”趋势
2025年是消费级AI领域竞争白热化的一年,以OpenAI和Google为代表的大型模型提供商,均在消费者市场投入了巨大精力,推出了新模型、新产品、新功能及用户界面,旨在触达更广泛的主流用户。当前市场呈现出“赢者通吃”或“赢者通大部分”的趋势,因为数据显示,仅有9%的消费者会同时使用ChatGPT、Gemini、Claude和Cursor等多个大型语言模型(LLM)助手。在过去一年中的大部分时间里,甚至不到10%的ChatGPT用户会访问其他大型LLM提供商,例如Gemini。
从市场份额来看,ChatGPT目前以每周8亿至9亿的活跃用户遥遥领先。Gemini在网页端的用户规模约为ChatGPT的35%,移动端约为40%。其他竞争者如Claude、Grock和Perplexity的使用率则显著落后,大约只占ChatGPT的8%到10%。然而,在过去3到6个月内,市场格局正迅速变化。随着Nano Banana等新型病毒式模型的推出,Gemini的桌面用户同比增长了155%,并且在规模扩大的同时仍在加速增长,这令人瞩目。相比之下,ChatGPT的年增长率仅为23%。此外,我们开始看到像Anthropic这样的公司在消费级AI领域进行专业化,例如专注于服务超技术用户(hyper-technical user)。
Original English Source
For most the year, less than 10% of chatbt users even visited another one of the big LLM providers. When you open Gemini, it has a popup says, "We got nano banana. Would you like to do something with it?" The little pain where you have to type something. Yeah. I don't I don't know what to do. These are product nuances that I I think makes people actually take the first step. The models have gotten to the level of quality that you can build a real scalable app on top of them. And so the hope is 2026 will be a huge year for consumer builders. Today we're talking about who won consumer AI in 2025. This was arguably the year that we saw the big model providers OpenAI and Google most out of everyone make a major push of their own into consumer. Both in terms of new models they release but also in terms of new products, features and interfaces that target the mainstream user. You might wonder why does it matter who is in the lead here? There's some early signs that the general LLM assistant space might be trending towards winner take all or at least winner take most. So only 9% of consumers are paying for more than one out of the group of chatbt, Gemini, Claude and Cursor. And for most of the year, less than 10% of ChateBT users even visited another one of the big LLM providers like Gemini. If we had to call it now, chatbt is currently in the lead by far at 800 to 900 million weekly active users. Gemini's at an estimated 35% of their scale on web and about 40% on mobile. And everyone else significantly trails this. So Claude, Grock, Perplexity are all about 8 to 10% of the usage. But especially in the last 3 to 6 months, things are changing very quickly with the launch of new viral models like Nano Banana. Gemini is now growing desktop users 155% year-over-year, which is actually accelerating even as they reach more scale, which is pretty crazy to see. And ChatCBT is only growing 23% year-over-year. And we're starting to see players like Anthropic almost specialize within consumer owning different verticals like the hyper techchnical user. So today we've brought together the A16Z consumer team to recap what we saw this year from the big model companies and consumer and also to predict what might be ahead of us in 2026. Cool. Well, thank you Olivia. It's it's been a super fun year. If we kind of wind the timeline back to last January, maybe we should start with what we saw, launches, products, what worked, what didn't. So Justine, tell us what you saw this year. Openai, Google, what are you paying attention to? What have you changed your mind on?
多模态AI的爆发式增长与产品策略的演进
2025年,图像和视频生成模型成为消费级AI领域最具病毒式传播潜力的产品。OpenAI推出了ChatGPT4图像功能,其吉卜力时刻(Ghibli moment: 指AI生成图像达到吉卜力动画般艺术水准的时刻)令人惊叹,随后又发布了Sora 2视频生成模型。Google则推出了VO、V3和VO3.1视频模型,以及在图像模型领域的Nano Banana和Nano Banana Pro,后者以惊人的速度病毒式传播,其影响力甚至超越了OpenAI的吉卜力时刻。
在产品层面上,OpenAI倾向于将新功能集成到ChatGPT的核心界面中,例如Pulse、群聊、购物研究任务等,唯一的例外是作为独立视频应用的Sora。而Google则更倾向于将新功能作为独立产品发布,通过Google AI Studio、Google Labs、Gemini以及各种Google平台推出,甚至以独立网站的形式提供,这使得每个产品都能拥有更定制化的界面,而非仅仅局限于聊天或图像/视频输出的单一模式。
今年的多模态模型在真实感(realism)和推理能力(reasoning)方面取得了显著进步。过去,人们关注Midjourney等模型的美学(aesthetics)和逼真度(realism),但现在,AI模型能够处理图像和视频中的微小细节,使其看起来更真实。例如,在视频中,背景车辆能以正确的方向移动,而非变形或出现奇怪的现象。在图像方面,模型可以接受多张输入图像和文本,并进行跨模态推理(cross-modal reasoning),从而生成具有连贯性设计(cohesive design)的复杂图像。
值得一提的是,Nano Banana Pro甚至能够生成市场地图(market maps),并结合网络搜索功能,获取正确的公司列表及其图片,这在过去是难以想象的。尽管如此,推理图像模型在处理多步骤推理任务时仍面临挑战。例如,要求模型移除垄断棋盘上的所有地名,并替换为AI实验室和初创公司的名称,同时确保新名称的正确位置和无重叠,这对于目前的模型来说仍非常困难,尽管GPT Image 1.5在这方面已是表现最好的模型之一。此外,ChatGPT的图像模型在角色一致性(character consistency)和风格保持(style preservation)方面也展现出令人惊喜的能力,这对于故事板创作(storyboarding)等应用具有巨大潜力。
Nano Banana最被低估的方面之一是其与搜索功能的集成。在生成产品照片或历史准确的图像时,搜索集成至关重要,它弥补了模型在准确性(accuracy)方面的不足,使其能够生成符合用户要求的、基于真实世界信息的图像。这类似于VO3将音频与视频结合,从而使AI视频迅速走红的时刻,它揭示了非直觉但极其有用的能力组合。
Original English Source
Yeah, those two in particular had a ton of consumer launches like Olivia mentioned. From a model perspective, I would say I would argue their most viral models this year, at least among consumers, were in image and video. So for OpenAI, it was the chat GBT4 image, the Giblly moment, which is crazy that that was this year. It's insane that this is this year. It feels like it was years ago. And then Sora obviously Sora 2. And then for Google, it's VO, V3, and VO3.1. And then Nano Banana and Nano Banana Pro in image models which went insanely viral probably comparable to if not beyond the the Giblly moment for OpenAI. I think in terms of the product layer what we saw was OpenAI tended to keep more things in the chat GBT interface. So like pulse group chats shopping research tasks all of these features launched inside chat GBT as the core. The exception there is obviously Sora as a standalone video app whereas Google tended to launch more things as standalone products. So they did ship a lot through like Google AI Studio and Google Labs and Gemini and the plethora of Google surfaces there are to launch a product. But they would also ship things as standalone websites that you could go to and visit, which basically allowed for a more custom interface for each type of product, not just the kind of chat entry, chat exit, or image video exit. Well, so Justine, I have a question for you on that. So, it felt like 18 months ago we were talking about midjourney and most of the multimodal models were defined by aesthetics and realism. Is that still true? Like what changed this year? Yeah, I think there's definitely different styles still and I think midjourney when you talk to people really deep in image and video, it still kind of stands apart for this like aesthetic sensibility that a lot of the models don't have if you don't know how to prompt for it. But I would say this year in particular, we made a lot more strides on realism and also on reasoning within both image and video. Like all of the little details that make an image or a video actually seem real. For example, if you have a person walking and talking, the people in the cars in the background, if they're on a street, should be moving in the correct direction, like they shouldn't be morphing and looking strange. An image, we were able to, you know, have multiple input images and text and sort of reason across all of those uploads to create like a cohesive design or something like that. Which was not something we saw happening last year for sure. Yeah. I remember when we were excited about having a letter show up correctly in images and now we have insane infographics. Yes, we can just put up amazing YouTube video and say give me an image image that explains us. That's incredibly different. Nano Banana Pro can even generate like market maps like I generate generated a market map. It's incredible. And it will like it either has or will go do the web research within the image model which is crazy to get the correct list of companies and then pull their photos which is insane. I know there's there's one benchmark left that the reasoning image models have not cracked. Not I tested GT image 1.5 yesterday. They sometimes struggle with like both reasoning and multi-step reasoning. So what I've been testing is you upload a picture of a monopoly board and you say remove the names of all the properties and replace them with names of AI labs and startups. And GPT image 1.5 is actually the closest, but it's very hard for them to do all of those steps. Remove it, come up with the new names, put all of the new names in the correct places, make sure there's not overlaps or one thing you mentioned three times and another big player you never mention. So there's still some room to go on the imag. It's interesting that especially from the image model from ChachiPT where you can actually see preser perseverance of like it it carries a character over into multiple image generation the same style and I thought that was like oh like this is actually very interesting where storyboarding totally wants want makes you want to generate more. You know for me it felt like the most underhyped aspect of Nano Banana was the integration with search because it feels like there's realism you know which is physics and sort of other things that feel, you know, like we're on Kenny Valley. There is reasoning, which is apply modifications that are adherent to what the user asked for. But then there's also sort of accuracy. And for me, a good example of this is product photography. If you say, "Hey, generate a photo of this album cover or a historically accurate photo of this moment in time," you have to actually have the search integration. And that was sort of non-intuitive, but it's actually very useful. Totally. Yeah. It's kind of like the VO3 moment when I don't think it was intuitive to people that video would be cracked necessarily by bringing audio together with video in the same place. And that ended up being the thing that made AI video go viral. Like since V3 and and now Sora maybe dominates, but like since V3, my social feeds have been like full of really realistic looking about oneif of my feeds are AI generated. Amazing. Yeah.
被低估的AI生产力工具:从个人助理到“一切应用”的潜力
在众多AI产品发布中,一些被低估但潜力巨大的工具正逐渐浮现。OpenAI的Pulse和连接器(Connectors: 允许AI模型连接用户的日历、电子邮件和文档等个人数据)是两个值得关注的例子。尽管Pulse的执行效果尚不完美,导致用户流失,但它代表了一种新型的AI原生(AI-native)交互模式。连接器则允许ChatGPT访问用户的个人上下文,例如总结过去六个月的备忘录,这在模型能力提升后,有望使OpenAI在专业消费者(Proumer: 介于专业人士和普通消费者之间的用户群体)工作空间占据主导地位。
ChatGPT每周高达24-25次的使用频率,使其成为提供主动式提醒(proactive nudges)和总结的理想平台,有望成为西方世界长期以来的“一切应用”(everything app)的现实版本。通过不断摄取用户数据并提供有用的主动建议,ChatGPT正在朝着这个方向发展。
除了OpenAI,Perplexity的Comet浏览器也展现了强大的潜力。尽管Perplexity并非主流LLM助手,但其Comet浏览器在代理模型(agentic model: 能够自主执行复杂任务的AI模型)和工作流设置(workflow setup: 允许用户设置重复任务或在特定网页触发任务的功能)方面表现出色。数据显示,Comet在发布后的流量峰值和持续流量都远高于ChatGPT自家的Atlas浏览器,这表明其在专业消费者界面(proumer interfaces)方面具有巨大潜力。Perplexity还推出了邮件助手,并收购了一些强大的代理型初创公司(agentic startups),预示着其未来将进一步深耕这一领域。
这些被低估的产品和功能,虽然在初期可能因执行问题未能获得广泛关注,但它们代表了AI应用发展的新方向:从简单的问答工具,转向能够深入个人工作流、提供主动帮助、甚至整合多模态能力的智能代理(intelligent agents)。
Original English Source
What do you guys do? There's so many launches this year and many of them went well like VO and Nano. What do you think is underhyped or products that you think didn't get enough attention? Brian, it's a good question. I think underhyped pulse of the world is probably still underhyped and you know we're talking about open AAI Google which to me fall under productivity category. So if you actually think about if you go to app store today, top five out of top 10 productivity apps are all Google. It's insane. And and Tachibb is number one. So we're talking about a productivity category where it's like helps you do things. And I feel like a lot of people are trying this from a different angle like how do I actually ingest your data or your schedule your email to make it more helpful and give more proactive you notification to you. I think a lot of people are working on it. Given the frequency of people using chat GPT, which I think is what 25 uh times a week, pretty good. Pretty good. Three to four times a day. It feels like it's a really good position to actually give you proactive nudges and summary and help your life in general. So feel like the everything app was always this myth in in in the western world. I think OpenAI is trying to move in that direction where it's ingesting enough. People are going there enough to start giving really useful proactive nudges and I think that's a space that I'm excited about. It's interesting. But are you a DAOU? Because I am not a DAU. A pulse not not a false. Similarly, I tried pulse for a while and have kind of largely turned off of it. But I would agree with you that I feel like pulse and a couple other examples that OpenAI launched this year are kind of new primitives or ideas that feel underhyped but because the execution is a little off. I think it's the execution the usage is off. Another example that I would give which is similarly like personal context would be their connectors. So now you can and you can do this on cloud as well. You can connect your calendar, your email, your documents. And so hypothetically, you could say to chatbt, you know, read all of my memos over the past 6 months and like summarize what's most interesting, least interesting. I think when that works, it's really exciting. I have found it to be a little bit unreliable so far, but I think as the models get better, they have a real chance to kind of own the proumer workspace if they get that right. Proumer is perfect category because like we we talk about it sometimes, but 99% of people don't run their lives on calendar. Yeah, we do. But so that that's when I'm thinking about the actual average frequency of using Chachi PT and if it's look if it's 24 times a week that's pretty good place to start. Yeah Olivia I feel like you're the ultimate power user. You know what are you still using? What's your stack? It's a great question. From all of the larger model companies actually I would have to say the thing that I'm still using the most and was maybe the most impressed by this year was the Perplexity Comet browser. And I don't and was not using perplexity as my core general LLM assistant. I use chatbt and claude much more. But I think they really executed on it in a first class way in terms of both the agentic model within the browser, but also perhaps more importantly all the workflows that you can set up that allow you to basically run the same task over and over either at a preset time or when you trigger it on a certain web page. So that to me was a really exciting watch. And if you look at the data, like the spike at launch and the sustained traffic for Comet was actually much higher than for ChateBT's own browser launch, Atlas, which is kind of crazy given how much more distribution ChatBT has than Perplexity. But I think they also launched an email assistant this year, Perplexity did, and they made a couple acquisitions of really strong agentic startups. And so what I would love to see from them next year is like more of these dedicated proumer interfaces. cuz I feel like that would be an awesome direction for them to kind of double down in. They do feel like the startup that has the biggest breadth of ambition, you know, alongside the labs and sort of big tech. Like it's very very impressive just the number of things they've shipped this year. Yes, definitely.
挑战者们的突围之路:产品理念与用户心智的博弈
在ChatGPT占据主导地位的市场中,其他挑战者如Google的Gemini、Anthropic的Claude、xAI的Grock以及Meta正在寻求突围。Gemini凭借其病毒式传播的图像和视频模型,以及Google庞大的分发优势(例如在Android设备上,Gemini的移动端规模已达ChatGPT的50%,而iOS上仅为17%),正试图迎头赶上。Google通过在Chrome、Google Docs和Gmail中集成Gemini小部件,积极鼓励用户使用。然而,ChatGPT已成为AI领域的“Kleenex”(舒洁纸巾:指代某个品类中占据主导地位的品牌),其品牌认知度构成了Gemini需要跨越的巨大障碍。
产品设计理念上的差异也影响着用户体验。例如,当打开Gemini的图像功能时,用户会看到一个空白屏幕,并弹出一个提示说“我们有Nano Banana,你想用它做什么?”然后是一个需要用户输入内容的文本框,这让许多用户感到无所适从。相比之下,ChatGPT的界面更像TikTok,提供热门主题和模板,引导用户轻松生成内容,并主动推荐后续操作(如制作节日贺卡)。这种更深层次的产品敏感性(product sensibility)是OpenAI及其ChatGPT团队能够不断创新的关键。
有趣的是,ChatGPT的许多产品创意似乎来自应用层公司(application layer companies),例如Krea、Hedra、Higsfield等初创公司率先普及了模板格式,ChatGPT则将其整合到自己的产品中,形成了一种“产品创意供应链”(supply chain of product ideas)。
在社交功能方面,OpenAI的尝试目前看来并不成功。ChatGPT的群聊功能和Sora 2(一个AI生成视频的TikTok式信息流,允许朋友客串)都未能像TikTok那样成为成功的社交应用。分析认为,ChatGPT的核心价值在于“帮助我变得更好”,属于生产力类别(productivity category),而社交应用的核心在于“娱乐我”和“让我被看见”。当内容由AI生成而非真实的人类表达时,社交应用赖以生存的“地位游戏”(status game)便会失效,因为AI生成的内容无法真实代表用户,从而削弱了用户参与的动力。因此,Sora更像是一个创作工具(creative tool),类似于CapCut,而非一个社交平台,其生成的病毒式视频更多是在TikTok、Instagram、X等现有平台上进行传播。
Claude作为另一个挑战者,在技术圈内备受推崇,尤其是在AI工作流(AI workflows)构建方面,其Artifacts和Skills功能强大,能够设置任务和工作流。Claude也是首批推出文件创建、幻灯片制作和编辑功能的大型参与者之一。然而,Claude的产品设计更倾向于技术用户(technical user)或工程师,其复杂的用户界面和功能(如命令行界面(command line interface)和Claude Code)使其难以被主流消费者接受。一项针对美国青少年的调查显示,使用过Character AI的青少年数量是使用过Claude的三倍,这凸显了Claude在主流市场普及度上的挑战。
Meta的AI战略则主要集中在非消费者模型(non-consumer facing models),例如其SAM 3系列(Segment Anything Model: 能够识别并分割图像、视频、音频中任何对象的模型),这些模型更多是为开发者提供实验平台(playground for developers)。尽管如此,Meta也推出了一些优秀的消费者功能,如Instagram的AI翻译(AI translations),它能够克隆用户声音,将其翻译成五种不同语言,并进行唇形同步(lip-sync),让用户听起来像母语者。
Grock在过去一年中也取得了惊人的进展,尤其是在图像和视频生成方面。从最初不具备图像和视频模型,到迅速推出图像转视频、文本转视频、添加音频、唇形同步、15秒视频等功能,其发展速度令人印象深刻。Elon Musk对Grock寄予厚望,希望它能生成更多互动式视频游戏(interactive video game type content)和电影。Grock通过提供流行模板,也成功在TikTok等平台制造了病毒式传播内容。
Original English Source
What um you know, one one thing I wanted to ask you Justine was sort of Gemini feels like it's having a real moment because of all the image and video models. Do you think it can overtake Chat GPT? Is there truly that much demand for these types of models? I think yeah. So, what we what I've seen basically is um there is always nearly infinite demand for like the best-in-class image or video model. Um because then you have a mix of tons of different people seeing it and wanting to use it. You have like if you're using it professionally, if you're marketing or in entertainment or storyboarding or whatever, um you always want to be using what's at the forefront of the field. And so, you're totally fine to go somewhere other than chat GPT and Sora to to get access to VO. Um even if you're an everyday consumer, so many new viral trends are created around new capabilities of the best-in-class image and video models. And so that ends up driving users into different products that they may have never tried before. Like you might be downloading the Gemini app or accidentally ending up on Google AI Studio, which I know they're trying to make be more for developers to use Nano Banana Pro, which a lot of users I think experienced in the past couple of months. Yeah. The interesting thing about Gemini to me is like hypothetically they benefit from the massive Google distribution advantage. Like if you look at Android, Gemini is at like 50% of Chat GBT scale on mobile, whereas on iOS it's like 17%. So like clearly something is working there. They launched a little Gemini widget within Chrome recently that encourages you to use it. They're launching it within Google Docs and Gmail and other things. But I think that most the average person is still just using one AI product and SHABT is like the Kleenex of AI. Like it is the brand that has become Exactly. And so I think that Gemini still has a pretty big hurdle to overcome just in terms of that. Um but if they keep doing what they're doing on these amazing um viral consumer creative tool launches and model launches, like they could get there next year. I was thinking about this it's uh really interesting when you look at Gemini which is everywhere. Yeah. But yet nowhere to some extent right you don't like you know when you look at the actual usage people still think of the Kleenex um and they go to chatbt but the interesting thing also is on the product sensibility. So this morning I had like two panes open Open AI's image model and um and Google's uh Gemini and basically use a image functionality. When you open Gemini Mhm. it's a blank screen. It has a popup says we got nano banana. Would you like to do something with it and a little pane where you have to type something? Yeah. I don't I don't know what to do. Yeah. GPT. You go in and it has a very Tik Tok like style of like here's a trending themes that you might want to generate and you click on I want a sketch pen or whatever and then just like use one other picture and it creates something amazing and then it says would you like a holiday card? Would you like a blah blah blah blah. These are product nuances that I I think makes people actually take the first step to generate it and then once you have it you have character consistency so you keep going right. So that's interesting in that I think OpenAI and Chachib team has proven that there's deeper product sensibility. Y but then I this is a funny thing maybe maybe a little non-coosher thing to say but you know I I worked at Snap. So when you look at Meta versus Snap famously uh Evans Spiegel was chief product officer of Meta. Yeah. Yeah. Um I wonder if there's a world where the chatbt team that innovates on the product front again and again. Google with distribution looks at him like that's cool. Let's just let's just integrate it and keep going and actually play that game. The interesting thing there is that images pane just launched yesterday when we're filming this in chatbot brand new and it took them like they had image models for years and it took them that long to come up with a separate relatively basic interface for generating images. I would almost argue the application layer companies like the the Creas, the Hedraas, the Higsfields of the world popularized that template format and did it first needed it better and they are chatbt product people and then maybe the chatbt supply chain of product ideas. Exactly. Always. Always. Well, well, maybe going in a slightly different direction, VK, I'm very curious for your take on OpenAI's social features because it does feel like that's something that you really have to get product execution right on, but also network design. You know, there's some efforts around Sora, too. We should talk about that. There's also group chats within chat GPT. You're our sort of social guy or have been historically bullish, bearish. Where's your head at? Uh bearish for now. Okay. Uh and the reason to me is twofold. Uh historically, we we look at sort of it's funny. I I look at products based on what I call inception theory. You go like three to four layers down to figure out what the oneliner is, which is like I want my dad to love me. And so, you know, when they think of a product for you or for the world, that's for me as well as for a walk. Yes. And so I I looked at some of the you know um products like like Chachi PT ultimately when you peel the onion five times I think essentially is help me be better like help me get that information help me be more productive help me be more efficient and then when I think about social features meta Instagram what have you or even Tik Tok the two layers of information or the you know emotion that is trying to address to me is for Tik Tok entertain me I want my clown entertain me. Yeah. And then the other layer is I'm lonely. I want to be seen. I want to connect with people. And to me, these are pretty two different parallels in the product direction. And OpenAI's product is incredible. It's magic. It's amazing. But it's ultimately a see me or or help me category, which essentially is why it's number one in productivity category. Yeah. Now we're trying to take this and shove it in people's life and say, "Guys, connect connect better." and like actually feel like you're being seen. And even the group chat function, which I love, it'll be so good to plan a trip and like actually have that common pain. Um, but I think it still stops at probably end count of two to three people planning something in a help me way. Yeah. Versus, oh, I feel like I understand a niche so much better because I I've sort of done that. So largely over time I think that's the reason of that division but that is not to say you can build a separate product that completely sort of addresses that I think Sora so we talked about group chat Sora 2 was the other big I think social push this year from all the consumer AI which was basically like a Tik Tok feed but all AI generated video and you can make cameos of your friends the cameos was a very good bet. It was a strong bet. Yeah. And I but I think what we've seen is like in the retention data and how we're seeing it used is it was massively successful as a creator tool like now my feed is probably twothirds AI slop if not more and I over 50% of it is now Sora whereas before it was like all VO and some cling um but it has not been as successful as like a social app consumption. Uh yeah, people are like a small number of creators are creating a ton of content and then bringing it out to like Tik Tok, Instagram X, Reddit where it's going massively viral, but it doesn't seem like there's a as much um consumption happening in the app, as much remixing, as much commenting, especially as there was initially. You know, in a funny way, the way I think about it is like Sora's competition or analogy isn't actually Tik Tok. Mhm. It's actually Capka. It's like a funny way. is almost like a creative tool. Interesting. Yes. Olivia, what's your I think well I was going to say like I think it goes back to your earlier point which is like the kind of motion that drives social apps is both these like positive and negative feelings of like oh I'm publishing this thing of myself that's kind of sensitive or that I want people to think it's this or that or this other thing. And so that's kind of what drives participation on the app. The status game. Exactly. The status game. And when it's AI generated content and people know it's not real, like a real representation of you as a human being, the status game is lost a little bit. Absolutely lost. Yeah. I think the status game comes then with um can you prompt something very cool, but that's a different type of product. And that's why I think it goes viral on like Twitter and all these other existing platforms. I mean my my sort of counterpoint or bullcase for Sora 2 is actually think the status game was about humor more than anything else. And humor is the intersection of knowing how to prompt and sort of being culturally aware. So I think that if they iterated on that, that's like a direction that nobody has captured before. Yeah. Yes. But if you can export those videos, isn't it true that like Tik Tok with Sora videos on it is strictly better than Sora? We talked about it so much where like the ultimate social product is where consumption and creation both live together and that the output of it is not native to other platforms like Tik Tok, like YouTube shorts. So what do folks think of the challengers? You know, we're talking about sort of to I mean Meta, it's crazy to talk about Meta as a challenger. I guess in this context they are, but I think Claude, Perplexity, Grock are the more obvious names for challengers. Olivia, what's your take? I love Claude. I talk to Claude all the time. Claude is somewhat replaced ChachiBT for me as my general LLM. I think Claude is opinionated in an interesting way. Um, I also love Claude because I'm willing to invest time into building out AI workflows. I think Claude actually launched a lot of really powerful things this year around like artifacts and skills where you can essentially set up tasks or workflows to run um over time. I do think the reason it hasn't hit the mainstream yet is even the way they built those things is geared towards a technical user or an engineer. It's I think they tried to make skills as easy as they could to create and it still was not anywhere near easy enough for the mainstream consumer. Um, another example would be they were actually the first of the big players to kind of launch file creation, slide deck creation, editing and they branded it as like file generation and analysis or something and it was like a toggle feature within a setting bar of a setting bar or something. So like very few people used it and yet to me it's still the best product across all of them at doing that kind of complex work. So, I love Claude, but I think if they want to be a true mainstream consumer product, they need to dumb it down even more in terms of accessibility. There was that survey you found recently of US teens. Yeah, there's um I think it was three times more US teens have ever used character AI than have used Claude. Yeah. So, I think that shows that like research a pretty broad thing. Yeah. Cloud is beloved amongst tech people but outside of tech people I think they are maybe struggling to pick up relevance. It is interesting though like if you look at the sort of aesthetics the product design the craft like three things that anthropic did were MCP skills and command line interface quad code like those are three surprising bets especially cloud code. I would have said command line interface really like is this the way that people want to air I thought you were going to talk about taking over air mail and the thinking cap yeah that too consumer so three think where's the thinking cap but it's sort of very highminded design it's sort of like versus or maybe that's apologetic on their behalf but I think it is it is that it's opinionated and it's great you know I do need to hear Justine's take on both Meta and Grock as I feel like they both had fascinating years in different ways. Yes. So, Meta um hired all those researchers. I think their strongest models are actually not consumerf facing models. It's their SAM 3 series. So, like the segment anything for video, for image, and for audio. And basically like for video for example, you can upload a video and you can describe in natural language like find the kid in the red t-shirt and it will find and track that person across every the entire video even if they're coming in and out of the frame. It will let you apply effects like blurring them out or removing them or whatever. And you can imagine a similar thing with audio um with different stems and then with image with different objects in an image. I think we're going to see next year hopefully some incredible consumer products built on top of those models, but today they're more of a playground for developers than they are a consumer, which is surprising given just like the DNA of the company. Yeah. There. So, the one good consumer feature I think they've launched this year with AI is the Instagram AI translations where um when you're uploading a real now, you can opt in to enable translations and it will clone your voice, translate it into five different languages, um apply the translation with your voice, so you know, and then redub with with the lip sync. Wow. And so, it basically makes it seem like you're a native speaker in whatever language. So, I would love to see more of that stuff come to to the meta products. Um, Grock, I think, has had So, Grock had a crazy year with like the companions with all of the LLM progress and the coding progress. Um, I think their image and video progress is probably the steepest slope I've seen of any of the companies. like um it was probably like 6 months ago they didn't even like have image and video models and they're shipping so fast to launch new features. Like it was initially just image to video. They added text to video. They added audio. Then they added lip sync with speech. Then they added 15-second videos. Like they are just not slowing down the speed of progress. And Elon has made a bunch of statements about like wanting more interactive video game type content out of Grock and wanting movies out of Grock by the end of next year. So, um, let's hope it continues to go at that pace. Do you feel like it's a pinser movement where like on one hand there's like a very, uh, infrastructural model layer of like let's get to the let's top the Ella Marina charts and then the other one is like let's go Annie. I think it's like a little bit of like a bifurcated move, right? Like the entertainment and the like Absolutely. but entertainment in a way that like we're talking about, you know, anthropic and chat general population, but you just said character AI is way more popular. Yes. So then like how do we think about that? And I think, you know, it's it's a very interesting strategy in my mind. and Grock like in the image and video app um since pretty early on they've had templates of popular things like you're standing somewhere and suddenly like a thing drops a rope drops from the ceiling and you grab onto it and it like swings you out of the scene like some really good ones that go viral regularly on Tik Tok and other places.
展望2026:多模态融合、企业级应用与创业新机遇
展望2026年,AI消费市场将呈现以下几个关键趋势:
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多模态AI的全面融合:未来AI将实现“任意输入,任意输出”(anything in to anything out)。最初,图像和视频模型主要接受文本提示并输出图像或视频。现在,我们已经看到图像编辑模型(如Nano Banana和Flux)可以接受图像输入并输出另一张图像,或者结合文本和参考图像进行编辑。未来,视频输入输出图像、视频输入文本提示进行编辑等功能将成为常态。各大实验室正努力将文本推理、图像和视频生成等独立能力整合到巨型模型(mega model)中,以处理更多样化的内容形式。这将对设计领域(design field)产生巨大影响,因为设计本质上就是以有趣的方式组合图像、文本、视频和各种元素。
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企业级AI的消费者转化:ChatGPT正在大力进军企业市场,提供企业许可证并为特定公司训练定制模型。鉴于大多数消费者仅使用一款通用LLM产品,ChatGPT在企业领域的使用量同比增长了8到9倍,这意味着企业用户在工作中习惯使用ChatGPT后,很可能将其应用延伸到个人消费场景。此外,ChatGPT的应用SDK(Apps SDK)和应用目录(Apps Directory)不仅是消费者市场的新渠道,对企业市场也具有重要意义。它能让ChatGPT在一个工作流中协调多个工具,从而提升企业日常工作的效率,对SaaS生态系统(SaaS ecosystem)产生深远影响。
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应用生成与模板化:应用生成(app generation)是2025年的一大趋势。未来,大型AI实验室可能会利用其分发优势和用户使用频率,帮助用户在其产品内部生成常见的产品和应用。这表明模板(templates)和风格(styles)的重要性日益凸显,就像TikTok通过不断变化的趋势和格式保持内容新鲜感一样。将视频优先产品(video-first products)内置到模型中,随着成本下降,将激发更多用户尝试。
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初创公司的独特机遇:尽管大型实验室在模型和核心体验的增量改进方面表现出色,但它们在构建有主见(opinionated)的独立消费者UI方面仍面临挑战。许多大型公司推出的新消费产品或界面(如ChatGPT的群聊、Pulse、Atlas、Sora,以及Google的Stitch、Gems、Opal、Doppel等)都未能取得成功。这主要是因为大型公司的晋升机制(promo committee)往往鼓励PM(产品经理)构建安全、能扩展核心指标的功能,而非冒险开发可能失败的有主见产品,这使得它们在法律合规和高层审查方面面临更多风险。
相比之下,初创公司在应用层(app layer)具有天然优势,它们可以利用不断改进的模型,专注于构建有主见的消费者产品。此外,大型实验室受限于计算资源瓶颈(compute bottleneck),需要在模型训练和推理之间进行权衡,以及在娱乐和编码用例之间分配资源,而专注于应用层的初创公司则没有这个困扰。
AI时代,多模态(multimodal)能力能够为客户提供更好的价值主张。虽然单一模型可能满足80%的需求,但对于超级用户(power users)而言,整合多个最佳模型的能力至关重要。AI的价值和变现深度远超以往,使得AI产品成为一个“超级用户故事”(power user story),甚至出现了收入留存率超过100%的消费产品,这通常通过订阅加使用量付费(subscription plus usage-based pricing)实现。
产品推荐:
- Pamelli:Google Labs的产品,通过代理(agent)访问商业网站,分析品牌美学和目标客户,然后生成包含文本和视觉素材的广告活动。
- Krea:一个优秀的创意工具平台,允许用户在不同模态下使用各种高质量模型,并保存元素(elements: 角色、风格或对象),以便重复使用。
- 11Labs Reader:将书面材料转化为听力内容,适应现代人碎片化阅读习惯的文本转语音(text-to-speech)工具。
- Gamma:用于生成幻灯片,支持文本或文档输入,并提供灵活的幻灯片尺寸。
- Granola:会议笔记工具,通过记录更多会议,提供更丰富的上下文。
- Perplexity Comet浏览器:AI原生工作空间,提供代理模型和可定制的工作流。
- Wabby:在应用生成方面表现出色,通过设置适当的约束,提供令人满意的功能性结果。
- GPT-52在Codeex或Cursor中的应用:不仅适用于技术人员,非技术人员也能利用其进行知识工作和论文写作。
总而言之,2026年将是消费级AI构建者(consumer builders)的爆发之年,模型质量的提升将为创新应用提供坚实基础。
Original English Source
Yeah, really really interesting. Well, so maybe switching gears from 25 to 26, what are some of all your predictions for next year? But what do you think we'll see? Hardware models, commerce we haven't spoken about yet. So what do we think will play out? I think I know this is we're talking about consumer, but one of the things that's been really maybe underrated for me about chatbt that we might see more of next year is they've really made a push into the enterprise both with the traditional enterprise licenses and then working with specific companies to even like train models for them. And I think when we think about the fact that most consumers only use one general LLM product, um, ChatBT enterprise usage, they published a big study, but it's up somewhat like eight or 9x year over year. And so if we're entering a world now where people have to use chatbt for their company or as part of their work, that could really translate into consumer usage. Um, or maybe they become the the workspace with the connectors and some of the other things that they're investing in. um and someone else owns the consumer consumer use cases. I think to that end we have to talk about their push into apps and I think whether or not that works is going to be kind of the defining question for them next year. Yeah. And I think that the we've all discussed the importance of the apps SDK and the apps directory as they're calling it and it's going to be a huge new channel for consumer. I think what's less discussed is it's hyper relevant to enterprise. So I think where Chad GPT shines is where it's able to operate across a number of tools for one workflow. And if you think about the number of things you do in your sort of business dayto-day that operates across many tools, it's it's most of those things. Yeah. So I think that will have very interesting implications for the SAS ecosystem and it's a part of the app store we're not talking about as much. Yeah. Yeah. um maybe less of a prediction but um thinking through 2025 and we talked about all the big moves from big labs and from the startup point um I think one of the biggest trend we've seen is app generation and I think uh there's a real world where we see the big labs uh with the distribution and the frequency of usage of people coming in to start saying look like maybe there's a common type of product and apps that we could actually help you generate within the confines of the big lab products. Yeah, I think that's like one of the interesting thing which you know again going back to the supply chain of ideas and research maybe that's one thing and again nothing groundbreaking but as we know um the Gibli broke the internet my cousin who who knows nothing about uh tech sent me a Giblly photo. Um well let's send this to your cousin then. Yeah. Uh, and I think that goes to show that templates matter. Yeah. That style matters. Yeah. And I think about video and like it's pretty freaking good. Yeah. And it's possible that we're already at a point that it's not necessarily just about the capability of models of the big labs, but the stylistic things, a template. Think of Tik Tok. The large capability is largely still the same. Music trend, dance, go. except the trend and format keeps on changing and keeps it extremely fresh. So I feel like there's a real world where um the repurpos have you can start thinking about ways to actually really build in video first products into these lab models and I think the cost will go down enough for people to try it out and I'm excited to see that. Yeah, I think what I'm most excited about is sort of along those lines, basically everything becoming multimodal. Like I call it like anything in to anything out, which is basically initially, especially with these image and video models, um it was you put in a text prompt and you get an image out or a video out. You couldn't really do much with it. And now um we've started to see this with the image edit models with like nano banana and with flux um and and with the new openi model where you can put an image in now and get an im another image out. You can put an image in with a text pair and a direction or put an image with a template another reference image and get another image out. um what happens when you can put a video in and get images out uh that are related to or the the next iteration of the video or you can put a video in and a text prompt about what you want to edit and get the edited video out. Um, from my conversations with the labs, a lot of them are trying to basically combine all of these largely separate efforts they've had across like text reasoning and intelligence, the LLM space and image and video into like what if we can put merge those all into like a mega model that can take a lot different forms of content and and produce much more. I think it's also going to have huge implications for like design. Um because if you think about it, a lot of design is combining images with text, with video, with different elements in kind of interesting ways. Yeah, I guess if I think about like a macro level prediction, I think it's actually going to be more of the same in that when we talk about what all of the cons the labs have launched in consumer, they've done a great job with models and they've done a great job with incremental things that improve the core experience of using like a chatebt or a Gemini. Yeah. In my opinion, we've gone through dozens of things that they've launched or tried as new consumer products or new consumer interfaces like group chat, like pulse, like atlas, like Sora, Google has had a long tail, like Stitch, Gems, Opal, Doppel, tons. None of those are really working. And I think it's because it's not the core competency of these companies anymore to build opinionated standalone consumer UI. Out of all of those, I think the product that's working the most is like Notebook LM. And that's one of like maybe 20 things that Google has tried or experimented with. So I think it's actually very positive for startups in that consumer startups in that the models would keep getting better which the startups can use and they'll keep you know they'll make Chat GBT better and better but I don't necessarily think that ChatBT like verticalizes into all of these other amazing use cases or products and there's still room for startups to be building there. I have a yes and to that. Okay. Where absolutely but however when the input and the output is text where Chachi PT and Gemini of the world shine the most. No matter how deeper you go no matter how specific you think your text output is going to be essentially given the frequency of usage of the main big lab products I think it's going to be really hard to stitch that and get that away from that usage if your product is mainly text and text out. Yeah. So I I do think you you have to be creative around what is the angle that you can like go steal people away from. You know, I love that you use the word opinionated because I think that for labs um certainly for big tech and perhaps increasingly for labs, the priorities get set in the promo committee always. And if you're a PM and it's always the sort of mid-career PMs and I've been one of these and you like the incentives are always to get promoted and the way to get promoted is to build something safe that extends a core metric and a core feature. So building opinionated products is a very risky way to manage your career, you know, because they're probably not going to work. They're probably going to have a bunch of implications for legal and compliance and the CEO might yell at you. So I just think that they are so structured to do incremental things. The more founders do opinionated things, the more advantaged they are. Yeah. I think honestly the big thing we haven't discussed here too is compute which is the labs have this inherent tension between um there's a limited amount of compute and they either spend it on like training models or they spend it on inference and even with inference there's this split between like the entertainment Giblly use cases and the like coding intelligence use cases. I think XAI is probably the only model company that is not bottlenecked on compute from my understanding. Whereas the others have to make really like serious and significant calls of like if we let if we release Nano Banana and go super viral like it may slow down the next like big LLM we're trying to push forward. Whereas startups who focus on the app layer don't have that problem because there's there's no tension there. Absolutely. Yeah. I look we've talked about this before. But I also think that there are categories in which being multimodel is just allows you to deliver a better proposition to the customer and the labs and big tech are always going to be sort of definitionally first party model only. So I think as all the models get better perhaps 80% of what you need can be received from a single model but for the power users and so much of AI is a power user story. You know, you always said that like, well, power users are just power users. And I think that's true in a preAI world, but now the kind of depth of value and the depth of monetization is so much higher that maybe all of AI is actually a power user story, you know, and everyone else is just traffic. Yes. Yeah. Which is why we're also seeing like consumer products for the first time ever have more than 100% revenue retention. Yes. And that's separating the good from the great from the exceptional in consumer AI world. And to be clear, how that happens is um they charge for usage often in addition to a subscription. So you can use beyond whatever your quota is for the month given your subscription and pay more. It's either upgrade of the tier or actually buying tokens or more more usage. Yeah. It's that's what differentiates it. Like you know if you told me preai we see a consumer company with 100 plus retention and money. I'm like that that doesn't make any sense. Doesn't that compute? Yeah. Yeah. No pun intended. Exactly. Exactly. Well guys, okay, maybe let's talk about start specific recommendations like after this pod, what are the products people should download or the features or the models? What should folks be using today? I I guess on the multimmoal point, I think one really um underhyped product that people should check out, not because they'll use it every day, but because it shows sort of what is possible when you combine like an agent with image with text is Pamelli. So, this is like the Google Labs product where you put in the URL of your business, and it has an agent go to the website, pull all of the product and brand photos, summarize what it thinks your brand's um aesthetic is, what it stands for, what kind of customers it's targeting, and then it will generate three different ad campaigns for you. And it will generate not only the text but it will generate like the Instagram post. It will generate the flyer. It will generate like the the photo of your product and this you know whatever wherever it thinks it should be based on your customer. Um and very cool product would be hard to become a giant standalone product within Google I think but um shows sort of the future of what happens if we combine agents with generation models that have sort of really deep understanding of context that an image model or a video model normally wouldn't have. Startup products though. Do you have a favorite startup product creative tool? Yes. In creative tools. I think I I mean we're investors in Korea, so this is bias, but I think they they've they've really done an exceptional job of um being the best place to use e every model or every quality model across every modality and also building more of the interface on top of these models. Like I now prefer to use Nano Banana Pro on Korea because Korea allows you to save elements which are essentially characters or styles or objects that you can like at tag to reprompt versus having to drag the same image reference into Nano Banana over and over again. That's a good one. Um I suppose it falls under startup category again shilling companies but you know um the one that I use the most is actually 11 laps reader and the reason is uh we've seen an explosion in podcasts and there's I think a reason for that right people are a lot more on the go the reading uh capability of us reading I think is going down over time and so you know let's not fight the reality let's embrace it and okay so then like let's actually find the written material translate into listening and and do that. And I used to be a power user of tools like Pocket. You know, I didn't have time to read everything that I wanted to read. Um, and it's a saving behavior, right? You're you're going around and saving all the things you eventually want to consume. But I think what I do now is similar where I go get all the things I want to read and I just put it I like either PDF it or put it on 11 reader and just like once in a while when I'm on a walk and I have like 3 4 minute, you know, 1.5x speed or 2x speed and just listen to one of these and get the gist of it. So I think that's been a good way to use a little bit of time as a sort of semi-normal person. Well, first of all, I love this question because I am strongly opinionated that by far the best way to get up to speed on AI is just to try a ton of products and you get opinionated really quickly. Um, Justine and I actually for the whole month of December are on Twitter publishing one new consumer product a day for people to check out. So, that's one way. Um, I'll name three others that I think are super maybe relevant or interesting that people can plug into their workflows. So, one would be Gamma for slide deck generations. You can go text prompt to slide deck. You can go document to slide deck. I use it for everything. Also, the slides are flexible sizes, so you're no longer like editing every little pixel in your Google Slides to get it to fit into one, which is great. Um, granola for note-taking. You might not have any meetings over the holidays, but in the new year, um, and it just gets better and better the more meetings you have on it because it has the context of what you talked about before. And then lastly, I'm still going to plug try the comet browser if you want to try kind of an AI native workspace. I think that's one of the most accessible ones to start with. I mean, for me, I've spent my whole year obsessed with coding and AI code. Um, it's just been so tremendously fun. I, by the way, Brian would take the other side of your argument that the big labs or big tech will win in app generation. I think they just lack the focus. Products like Opal have been, you know, released with a wimper and they're one model only. So I I didn't think they will win it. I think that we will see them doing it. Yes. Yes. I I think that's true. But I think for the pure consumer side, of course, Wabby is is really fun and really capable and I think they are they're creating the right sort of um uh constraints on app generation so that you can get a really satisfying functional result. And I think so far there's been a lot of overpromising in app generation which has discouraged the early users. I also think if you haven't tried you know GBT 52 in codeex or in cursor it's worth trying even for nontechnical people it's just amazing. I think almost being technical is sort of a constraint because you have a pre-existing idea for what these models can do and they can do a lot more and I'm hearing increasingly about people doing knowledge work and writing essays in cursor instead of just writing code. Wow. Just one thing I'm going to do at the end year year end is just to plug in like a popular trend I seeing on Tik Tok where there are people who said what is a most unhinged thing I said this year. Okay. And it actually does a review of of of all the things that you said. But I think similarly it'll be a good thing. I'm going to do this at the year end. Tell me how to live a better life next year. Yeah. Yeah, give me give me actual unvarnished uh opinions and and some direction that I think it'll be helpful. I love that idea. I'm going for a worse life next year. Fantastic. Let's go full DGN, guys. Any closing thoughts? That wasn't I mean the obvious one is we are very actively investing in consumer companies and and I genu I think a lot of people say this. I genuinely believe that the models have gotten to the level of quality that you can build a real scalable app on top of them. Wobbby is a great example of this. Um and so the hope is 2026 will be a huge year for consumer builders. Um not just like consumers as um like consumers being consumers of a product. Yeah. Yes. Well, thank you all for a super fun year in consumer in AI. We'll be back with more next year and merry Christmas, guys. This is a wrap. Yeah. Happy holidays. Happy holidays.
📌 文中提及的人物和组织
公司/组织: OpenAI, Google, Anthropic, Perplexity, Meta, xAI
产品/模型: ChatGPT, Gemini, Claude, Sora, Nano Banana, Nano Banana Pro, Perplexity Comet browser, Grock, SAM 3 series, VO, Character AI, 11Labs Reader, Gamma, Granola, Notebook LM, Wabby, Cursor