正反观点：消费者永远不会为AI付费

最新的消费者AI数据出来了，包括哪些应用在手机上用得最多，哪些网站访问量最大，以及哪些AI服务的收入最高。

The latest consumer AI numbers are out in terms of which apps are used most on mobile, which websites are most visited, and which AI services have the highest revenue.

对很多人来说，最有趣的是这些数据里存在的巨大反差。

For many, the most interesting story is the wild dichotomies that exist in these numbers.

首先，是付费用户中最高消费和中等消费之间的差距，前1%的AI买家花的钱相当于后50%的总和，但还有另一个惊人的差距，就是购买AI的家庭和完全不买AI的家庭之间的差异。

First, the gap between the top paying users and the median paying users, where the top 1% of AI buyers spend as much as the bottom 50% combined, but then also the incredible gap in households that buy any AI versus those that don't.

今天我们探讨的是，目前98%的美国不付费订阅AI的家庭，是代表着未来巨大的收入机会，还是说他们永远不会为AI订阅付费，可能需要完全不同类型的AI产品，或者至少是AI商业模式，才能对大型AI公司来说稍微有点意义。

Today we're exploring whether the 98% of US households that currently don't pay for an AI subscription represent a massive revenue opportunity to come, or the reality that they will never pay for an AI subscription and might need totally different types of AI products or at least AI business models to be even a little bit relevant for the big AI companies.

《AI每日简报》是一个每日播客和视频节目，讨论AI领域最重要的新闻和讨论。

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

好了，朋友们，在我们开始之前，先快速说几个通知。

All right, friends, quick announcements before we dive in.

首先，感谢今天的赞助商：毕马威、Harbor、Granola和Blitzy。

First of all, thank you to today's sponsors, KPMG, Harbor, Granola, and Blitzy.

想收听无广告版本，请访问patreon.com/aidailybrief，或者也可以在Apple Podcasts上订阅。

To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts.

想了解如何赞助我们的节目，请发邮件到 sponsors@aidailybrief.ai。

And to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai.

在我们昨天那期节目提到美国可能推出开源权重模型之后，Reflection AI 确实发布了他们的新模型，并把它定位成美国训练的挑战者，来对抗那些已经开始争夺用户使用量的中国开源模型。

After our episode yesterday about the rumored forthcoming open weight model of American origins, Reflection AI did indeed unveil their new model and positioned it as the US-trained challenger to the Chinese open models that have started to compete for token share.

Beam 有 5010 亿个参数。

Beam has 501 billion parameters.

这个规模跟 NVIDIA 的 Nemotron 3 Ultra 差不多，但比 GLM 5.3 小，跟 Kimi K3 的 2.8 万亿参数相比更是小巫见大巫。

That makes it around the same size as NVIDIA's Nemotron 3 Ultra, but smaller than GLM 5.3 and tiny compared to Kimi K3's 2.8 trillion parameters.

目前这个模型只对早期测试者开放，所以我们只能根据公布的基准测试来评估它。

At this stage, the model is only available to early testers, so we can only gauge it based on the reported benchmarks.

Reflection AI 声称在编程基准测试 DeepSui 上得了 44.4 分，超过了 GLM 5.2 的 44 分，但略低于 Qwen 3.8 Max 的 51 分。

Reflection AI claimed a score of 44.4 on coding benchmark DeepSui, beating GLM 5.2 at 44 and slightly behind Qwen 3.8 Max at 51.

在衡量智能体编程能力的 Terminal Bench 2.1 上，Beam 得了 80.1 分。

On Terminal Bench 2.1, which measures agentic coding, Beam scored 80.1.

这比 Nemotron Ultra 的 56.4 分和 Inkling 的 63.8 分高出不少，但比 GLM 5.2 略低，比 Qwen 3.8 Max 低 6 分。

That put it well ahead of Nemotron Ultra at 56.4, Inkling at 63.8, but slightly behind GLM 5.2 and 6 points behind Qwen 3.8 Max.

在Reflection选择展示的核心基准测试中，它只在Sui Bench Verified上领先，而这个测试并没有包含任何中国模型的分数。

Across the core set of benchmarks Reflection chose to showcase, it only led on Sui Bench Verified, which didn't include scores from any of the Chinese models.

值得注意的是，Reflection没有在Terminal Bench 4.0上进行基准测试，这个测试自发布以来已经揭示了很多基准测试的刷分现象。

It was also notable that Reflection didn't benchmark on Terminal Bench 4.0, which has revealed a lot of benchmark maxing since it was released last month.

Reflection还选择不将自己的模型与中国的领先模型如GLM 5.3、Kimi K3和DeepSeek 4.1 Flash进行比较。

Reflection also chose not to compare their model to Chinese leaders like GLM 5.3, Kimi K3 and DeepSeek 4.1 Flash.

从积极的一面看，Beam确实看起来提升了西方开源模型的性能。

Now on the plus side, Beam does look like it pushes the performance of Western open models.

Reflection还声称，它的效率会非常高，在推理计算方面比GLM 5.2高效三到四倍。

Reflection also claimed that it will be extremely efficient, between three and four times more efficient in terms of inference compute compared to GLM 5.2.

这可能使Beam成为一个非常经济实惠的基础模型，适合进一步的后期训练，这正是我们之前提到的对企业越来越有吸引力的用例，也显然是Reflection目标的重要组成部分。

This could make Beam a very cost-effective base model for additional post-training, which is exactly the use case that we talked about becoming increasingly interesting to enterprises and is very clearly a big part of Reflection's goal.

Reflection指出，这个模型是从零开始预训练的，然后经过了他们认为是有史以来最大规模的公开记录的强化学习运行。

Reflection noted that the model was pre-trained from scratch, then put through what they believe to be the largest publicly documented reinforcement learning run ever.

他们还利用了分布式基础设施进行强化学习，这是一种在训练开源模型中极少被探索的方法。

They also utilized distributed infrastructure for their reinforcement learning, which is a wildly underexplored method of training open models.

目前，Artificial Analysis 还没有完成独立测试，但他们确实对部分说法给出了一些方向性的确认，写道：“早期迹象表明，Beam 将成为我们见过的、在其智能水平上最具 token 效率的开源模型之一。”

Artificial Analysis hasn't completed their independent testing at this stage, but they did give some directional confirmation of some of the claims, writing, "Early indicators suggest Beam will be one of the most token-efficient open models we've seen for its level of intelligence."

对一些人来说，这一切与本周早些时候的炒作相比，显得有些不足。布鲁金斯学会的 Kyle Chan 写道：“真正的改变游戏规则者，应该是能与最优秀的中国模型相媲美的美国开源模型。”

Now to some, this all fell a bit short of the hype from earlier in the week, with Kyle Chan of the Brookings Institute writing, "The real game changer would be an American open source model on par with the best Chinese models.

“这是一个令人印象深刻的模型，但仍落后于 GLM 5.3 和 Kimi K3。”

This is an impressive model, but still behind GLM 5.3 and Kimi K3."

另一方面，尽管这不是 DeepSeek 或 Kimi 的杀手，但许多人还是乐于看到一家新的美国实验室推出有竞争力的模型。

On the other hand, despite this not being a DeepSeek or Kimi killer, many were simply pleased to see a competitive model out of a new US lab.

开源模型研究员 Nathan Lambert 写道：“祝贺 Reflection 团队。”

Open model researcher Nathan Lambert wrote, "Congrats to the Reflection folks.

“推出第一个模型很难，我们希望你们能从这里开始，迅速加速对生态系统的贡献。”

It's hard to get the first model out, and we hope you can rapidly accelerate contributions to the ecosystem from here."

前白宫顾问 Sriram Krishnan 补充道：“祝贺 Reflection 发布 Beam。”

Added former White House advisor Sriram Krishnan, "Congrats to Reflection on the launch of Beam.

“美国拥有领先的开源模型，对于赢得 AI 竞赛至关重要。”

It is critical for the US to have leading open models to win the AI race."

我觉得有意思的是，如果发布前没有那些泄露和炒作，大家的反应会是什么样。

I think it's interesting to think about what the reaction would've been if there hadn't been leaks and hype for the couple of days leading up to this.

我猜，与其说是有些人感到失望，不如说更像是你的球队落后几个球，然后你打进了第一个球，开始逆转。

My guess is that rather than a letdown that some felt, it would've been more like when your team is down by a couple of goals and you score the first one on their way to a comeback.

这并不意味着逆转已经板上钉钉，但你的感觉比几分钟前好多了。

It doesn't mean that the comeback is fait accompli, but you're feeling a heck of a lot better than you did just a few minutes earlier.

接下来看看AI安全的最新动态，这事儿有点怪，因为纽约市议会举行了一场关于存在风险的听证会。

Moving over to the latest in AI safety, a weird one as the New York City Council held a hearing on existential risk.

会上有几位AI安全领域的知名人物作证，包括前Anthropic员工Jacob Coxen，他几周前当然在网上火了一把。

It featured testimony from several prominent figures in the AI safety movement, including former Anthropic employee Jacob Coxen, who of course went viral a few weeks ago.

讨论的内容没什么特别新鲜的，这次听证会主要是为纽约州正在提出的各种AI法规提供了一个平台。

There was nothing particularly new in what was discussed, with the hearing largely serving as a canvas for various AI regulations being proposed in New York State.

为了让大家了解这些监管者的立场，市议会议长Julie Menin在开场时说：“认为人工智能会自我监管，这完全不合常理。”

Giving a sense of the position that these regulators are coming from, City Council Speaker Julie Menin opened the hearing by saying, "The idea that artificial intelligence is going to self-regulate defies all reason."

她说纽约的法案可以作为全国范本，而且纽约已经有一套相当完善的AI法规，未来几年还会有更严格的消费者保护措施生效。

She said that New York bills could serve as a national model, with New York already having a fairly robust set of AI regulations, with tougher consumer protections set to kick in over the coming years.

目前，州议会正在审议关于AI紧急关闭开关和举报人保护的提案。

Proposals for an AI kill switch and whistleblower protections are currently before the state Congress.

此外，州长凯西·霍楚尔已对数据中心建设实施了一年的暂停令。

In addition, Governor Kathy Hochul has imposed a one-year moratorium on data center construction.

完全在意料之中，听证会上，议长梅宁引用了萨姆·奥尔特曼最近的采访言论，他说世界应该，引用一下，“为了这项技术的好处，接受一些坏事发生”，甚至还想制造一个“抓到你”的时刻，要求AI公司的代表们给灭绝风险的概率打个分。

Completely predictably during the hearing, Speaker Menin referenced Sam Altman's recent interview comments where he said that the world should, quote, "accept some bad things happening for the benefits of this technology," and even tried to get in a little gotcha moment, asking the representatives from the AI companies to attach a probability to extinction risk.

没有人给出具体数字。

None provided an actual number.

与此同时，OpenAI的提案侧重于解决当前的AI安全问题，而不是递归自我改进这种假设性的危险。

Meanwhile, OpenAI's proposal centered on addressing the current AI safety issues rather than the hypothetical danger of recursive self-improvement.

在给纽约市议会的一封信中，OpenAI提出要与该市的网络防御者更广泛地合作，并指出他们已经在与州政府合作。

In a letter to the New York City Council, OpenAI offered to work more broadly with cyber defenders in the city and noted they're already working with the state.

接下来，一群自称“智能体群追逐者”的人——这让我想起了90年代的经典电影《龙卷风》——检测到了一群使用腾讯AI基础设施的智能体。

Next up, a group of self-styled agent swarm chasers, which reminds me, of course, of the '90s classic "Twister," have detected a group of agents using Tencent's AI infrastructure.

在周日发布的一份初步报告中，该组织表示，他们检测到了数千个智能体，似乎正在向阿里巴巴的地图服务高德发送批量请求。

In a preliminary report published on Sunday, the group said that they'd detected several thousand agents that seemed to be sending bulk requests to Alibaba's map service, AMAP.

研究人员特意避免使用“代理群”这个词。

The researchers went out of their way to avoid using the term agent swarm.

他们没有发现这些代理之间有相互协调的证据，不像我们在Hugging Face事件中看到OpenAI的代理那样。

They found no evidence that the agents were coordinating with each other, as we saw happening with OpenAI's agents during the Hugging Face incident.

相反，他们把这个群体称为“代理舰队”。

Instead, they referred to the group as an agent fleet.

这可能是中国服务器上大规模代理舰队出现的第一个迹象。

This could be the first sign of large-scale agent fleets emanating from Chinese servers.

但追踪代理群是个比较新的趋势，所以我们可能只是看到了第一次有记录的追踪。

But agentic swarm chasing is a relatively new trend, so we could be just seeing the first documented chase.

目前也完全不清楚是否有任何恶意行为在发生。

It's also entirely unclear about whether anything nefarious is going on.

这些代理似乎只是在批量请求关于当地地标的信息，比如公园、动物园和医院。

The agents seem to be merely sending bulk requests for information about local landmarks, like parks, zoos, and hospitals.

最坏的情况，看起来这些代理是在绕过阿里的API规则，进行批量数据抓取。

At worst, it seems like the agents are circumventing Alibaba's API rules to conduct bulk data scraping.

我觉得很酷的一点是，这个领域的研究正在不断增长，上周末甚至吸引了一群志同道合的人参加黑客马拉松。

What's cool, in my opinion, is the fact that this is a growing area of research and even attracted a group of like-minded people to a hackathon last weekend.

我不仅认为这类研究会成为我们韧性基础设施中不可避免的一部分，我还觉得通过做这些工作并持续追踪，能帮助揭开这些代理舰队或代理群（随便你怎么叫）的神秘面纱，让大家了解它们在网上爬取时到底在做什么。

Not only do I think that these type of research will be an inevitable part of our resiliency infrastructure, I also think that by doing this work and keeping track of it, it will help demystify these agent fleets or swarms, or whatever you want to call them, to help people understand what they're actually doing as they crawl the internet.

在企业竞争方面，微软和Meta已经削减了他们在Claude上的支出。

Over in enterprise competition land, Microsoft and Meta have slashed their Claude spending.

《The Information》报道说，微软已经将他们的Claude预计账单削减了三分之一。

"The Information" reports that Microsoft has cut their projected Claude bill by a third.

消息人士称，今年早些时候，微软内部使用Claude的支出有望达到10亿美元。

Sources said that Microsoft was on track to spend $1 billion on internal Claude use earlier this year.

程序员们被要求限量使用，以节省成本，同时转向微软自家的模型。

Programmers have been asked to ration their use as a cost-saving measure, as well as switching to Microsoft's in-house models.

报道指出，这个数字仅指内部使用，不包括微软客户的支出。

The reporting notes that this figure is internal use only and doesn't include spending from Microsoft's customers.

微软今年早些时候预测，为支持Copilot功能，他们在Anthropic上的支出将达到20亿美元，而这一预测并未改变。

Microsoft was forecasting $2 billion in Anthropic spend to power Copilot features earlier in the year, and that forecast hasn't changed.

微软在很多查询上已经换用了自家模型和OpenAI的GPT-56，但消息人士说，Copilot客户增长抵消了弃用Claude带来的影响。

Microsoft has substituted their in-house models and OpenAI's GPT-56 for many queries, but sources said that Copilot customer growth has offset the effects of switching away from Claude.

与此同时，在Meta那边，消息人士说Claude的使用量已经减半，但在春季期间使用量被大幅控制。

At Meta, meanwhile, sources said that Claude usage has been cut in half, but dramatically reined in usage during the spring.

消息人士说，在高峰期，Meta当时78,000名员工中约有60,000人使用Claude代码。

Sources said that around 60,000 of Meta's then 78,000 employees were using Claude code at the peak.

但在内部模型发布后进行了10%的裁员，用户数量降到了30,000。

But following 10% layoffs in the in-house model releases, user numbers are down to 30,000.

这种下降几乎完全反映在他们新内部工具Meta Code的采用上，现在也有30,000用户。

That drop-off is almost completely mirrored in adoption of their new in-house tool, Meta Code, which now also has 30,000 users.

据报道，截至6月，Meta原本有望在Anthropic上花费数十亿美元，但现在他们每月大约花费1.05亿美元。

Meta was reportedly on pace to spend several billion with Anthropic as of June, but they're now spending around $105 million per month.

现在，显然，这个故事上新闻的原因是Anthropic即将进行的IPO。

Now, obviously, the reason this story is in the news is Anthropic's upcoming IPO.

随着IPO临近，华尔街越来越深入地了解其内部情况。

The closer it gets, the more that Wall Street is digging in to understand what's under the hood.

我们已经从Anthropic的财务披露中看到了很多泄露的信息，但大多数是关于2025年的数据，其中大部分对今天的公司状况来说，相关性有限。

We've had numerous leaks from Anthropic's financial disclosures, but mostly related to 2025 numbers, most of which have what we'll call limited relevance for where the company is today.

不过，有一项披露确实引起了人们的注意，那就是在2025年，仅两个客户就占了Anthropic收入的四分之一。

Still, one disclosure that people did take note of was that just two customers accounted for a quarter of Anthropic's revenue in 2025.

有人猜测，这两个客户可能是Meta、微软和Cursor中的某两个组合。

Speculation had suggested that the two were some combination of Meta, Microsoft, and Cursor.

Anthropic已经告诉投资者，这种极端的客户集中度自那以后有所扩大，现在他们有6000个客户，年账单超过10万美元。

Anthropic has told investors that this extreme customer concentration has expanded since then, and they now have 6,000 customers with annual bills above $100,000.

不过，鉴于他们只有100个客户年消费超过1000万美元，顶级客户的任何紧缩开支都可能产生重大影响。

Still, given that they only have 100 customers spending over $10 million annually, any belt-tightening at the top could be material.

我确实认为，如果把微软和Meta视为整个商业世界的普遍代表，那是对此的误读，特别是因为它们是竞争实验室，只是因为Anthropic的模型比它们自己内部的模型好太多，才勉强使用。

I certainly think it would be an incorrect reading of this to view Microsoft and Meta as broadly reflective of the rest of the business world, specifically because they are competing labs that have only been begrudgingly using Anthropic models because of just how much better those models were than what they had in-house.

随着这种差距的缩小，或者至少随着这些内部模型的最低水平提高，这些公司就越有理由进行这些转变。

The more that that gap closes, or at least the more that the floor level of those in-house models improves, the more those companies can justify these shifts.

对于普通客户来说，情况未必如此。

That won't necessarily be the case with the average customer.

更广泛地说，我觉得看待AI客户集中度有两种截然不同的方式。

More broadly, I think that there are two very different ways to look at customer concentration for AI.

一种是从风险的角度来看，少数几个客户大幅削减开支，可能会实质性改变收入格局。

One, you can view it exclusively in risk terms, where just a handful of customers significantly cutting their spend could materially change the revenue dynamics.

另一种看法是，这些客户是早期采用者，这种集中度其实显示出其他企业买家群体中还有相当大的增长潜力。

The other way to look at it is, of course, that those are early adopters, and that that concentration actually shows quite a bit of growth potential across the rest of enterprise buyers.

就像生活中几乎每次遇到二选一的情况一样，我觉得可能两者兼有，但这也确实是华尔街在IPO之前争论的焦点。

As almost any time we're presented with a binary in life, I think it's probably a little bit of both, but that's certainly where a lot of the debate is going to be on Wall Street in advance of this IPO.

我相信这方面还有很多可以讨论的，但今天的热点新闻就到这里。

I'm sure there will be much more to discuss on that front, but for now, that is going to do it for today's headlines.

接下来是本期主节目。

Next up, the main episode.

毕马威和德克萨斯大学奥斯汀分校的一项新研究发现，当人们与AI合作时，相似的技能并不能保证相似的成果。

A new study from KPMG and the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes.

研究人员研究了500多名早期职业专业人士，发现表现最好的人通过引导、评估和优化AI的输出，持续放大了AI的价值。

Researchers studied more than 500 early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs.

这些顶尖表现者，被称为AI放大器，他们的优势不在于知识本身，而在于他们如何与AI协作。

These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI.

想了解更多关于AI放大器与其他人的区别，请访问kpmg.com/us/aiamplifiers。

Learn more about what separates AI amplifiers from everyone else at kpmg.com/us/aiamplifiers.

每期节目我都会聊到OpenAI、Anthropic、SpaceX AI、谷歌和Meta之间的竞争，如果你一直在听，可能已经有自己偏爱的公司了。

Every episode, I talk about the competition between OpenAI, Anthropic, SpaceX AI, Google, and Meta, and if you've been listening for a while, you might have a favorite.

也许你觉得OpenAI和Anthropic能保持领先，或者Meta的开源策略能胜出。

Maybe you think OpenAI and Anthropic can stay ahead, or perhaps Meta's open source strategy can win out.

不管你怎么看，每个AI实验室都创造了不同的投资机会。

Whatever your view, every AI lab creates a different investment opportunity.

Harbor Capital Advisor的AI实验室生态系统ETF系列，让你可以投资于你相信的AI实验室背后的生态系统。

Harbor Capital Advisor's AI Lab Ecosystem ETF suite lets you invest in the ecosystem behind the AI lab you believe in.

在你投资的地方搜索Harbor AI实验室生态系统ETF，或者在X上关注@HarborCapital了解更多。

Search Harbor AI Lab Ecosystem ETFs wherever you invest or follow @HarborCapital on X to learn more.

访问harborcapital.com获取招股说明书，其中包含投资目标、风险、费用、支出及其他重要信息。

Visit harborcapital.com for a prospectus containing investment objectives, risks, fees, expenses, and other important information.

投资前请仔细阅读并考虑。

Read and consider it carefully before investing.

风险包括本金损失以及与人工智能相关的风险。

Risks include principal loss and artificial intelligence-related risks.

Harbor ETF由Foresight Fund Services LLC分销。

Harbor ETFs are distributed by Foresight Fund Services LLC.

Harbor与AI Daily Brief无关，这些基金与任何AI实验室也没有关联、赞助或背书关系。

Harbor is not affiliated with AI Daily Brief, and the funds are not affiliated with, sponsored by, or endorsed by any AI lab.

这是付费广告，并非个性化投资建议。

This is a paid advertisement and not personalized investment advice.

投资有风险，包括可能损失本金。

Investing involves risk, including possible loss of principal.

开会时，我会全身心投入。

When I'm in a meeting, I'm fully in it.

我在思考如何通过反复迭代和创意碰撞，真正推动目标前进。

I'm thinking about the iteration and creative back and forth it takes to actually push a goal forward.

我不会去想记录要点、追踪待办事项这些事，这正是Granola发挥作用的地方。

What I'm not thinking about is capturing takeaways, tracking to-dos, or any of that, and that's where Granola comes in.

Granola是一款AI驱动的记事本，它能捕捉会议中的内容，并将其整理成清晰、结构化的笔记，把决策和行动项单独提取出来，方便查找。

Granola is an AI-powered notepad that captures what happens in your meetings and turns it into clean, structured notes with the decisions and action items pulled out and easy to find.

无需设置，也无需配置。

There's no setup and no configuration.

它完全融入你现有的工作方式。

It just fits into how you already work.

对我来说，这意味着我可以一直保持创意状态，而Granola确保这些想法真正转化为行动。

For me, it means I get to stay in idea mode, and Granola makes sure those ideas actually become action.

一旦你在第一次会议上试用Granola，就很难再离开它了。

Once you try Granola on a first meeting, it is hard to go without.

你可以在granola.ai/brief完全免费试用。

You can try it totally free at granola.ai/brief.

访问granola.ai/brief，把时间还给自己。

That's granola.ai/brief to get your time back.

Blitzy在写代码之前，会深入了解你的代码库。

Blitzy deeply understands your code base before it writes code.

这是它第一个发挥作用的地方：AI时代的安全问题。

Here's the first place that pays off, security in the age of AI.

漏洞不是孤立存在的。

Vulnerabilities don't live in isolation.

它们藏在数百万行相互关联的代码里，修补一个地方，可能悄悄破坏另外三个地方。

They live buried inside millions of lines of interconnected code, where patching one thing quietly breaks three others.

这就是为什么表面扫描会失败。

That's why surface-level scans fail.

Blitzy从整个应用的知识图谱出发，识别并暴露整个系统中的CVE，主动推荐补丁，还能直接执行PR。

Blitzy starts from its knowledge graph of your entire application, identifies and surfaces CVEs across the full estate, proactively recommends patches, and can execute the PR.

每个修复都基于系统之间的连接方式，并经过验证，确保不会引入新的问题。

Each fix is grounded in how your systems connect and validates, so nothing new breaks.

知识图谱会动态更新，让你始终领先于不断加速的威胁形势。

And the knowledge graph dynamically updates, keeping you ahead of an ever-accelerating threat landscape.

有一个Blitzy客户在四天内解决了六个核心微服务中的21个活跃CVE。

One Blitzy customer resolved 21 active CVEs across six core microservices in four days.

零编译错误，所有验证扫描都干净，计划数月的工作在不到一周内完成。

Zero compile errors, every validation scan clean, months of planned work fixed in less than a week.

基于真实架构背景的安全修复，速度堪比计算速度。

Security remediation grounded in real architectural context at the speed of compute.

在blitzy.com上加固你的代码库。

Harden your code base at blitzy.com.

就是B-L-I-T-Z-Y点com。

That's B-L-I-T-Z-Y dot com.

欢迎回到《AI每日简报》。

Welcome back to the "AI Daily Brief."

今天，我们来看一个相当有趣的对话，不仅讨论最受欢迎的消费级AI应用有哪些，以及这些类别反映了哪些不同类型的AI使用方式，还探讨了消费者是否会为人工智能花大钱的问题。

Today, we are looking at a fairly fascinating conversation about not only what the most popular consumer AI apps are and what the categories say about different types of AI usage, but we're also exploring the question of whether consumers will ever spend significant money on artificial intelligence.

当然，如果答案是否定的，那对AI市场意味着什么？

And of course, if the answer is no, what does that mean for the AI market?

这个灵感和起点来自安德森·霍洛维茨，他们刚刚发布了第七版消费者AI应用百强榜。

The inspiration and starting point for this comes from Andreessen Horowitz, who has just released the seventh edition of their top 100 consumer AI apps.

其中一部分信息来自消费者流量数据。

One part of this information comes from consumer traffic data.

这是七次发布中数据最具可比性的一次。

This is the most comparable data across all seven instances of this.

百强榜分为两部分：按网站月度独立访客排名的前50名，以及按移动端月度活跃用户排名的前50名。

The top 100 then is divided, top 50 by unique monthly visits on the web and top 50 by monthly active users on mobile.

但这次是首次，a16z还使用了Yippit的数据，发布了按月度收入排名的消费者AI应用前50名。

But then for the first time this round, a16z also used data from Yippit to publish a top 50 consumer AI apps by monthly revenue.

在很多情况下，情况大不相同。

And in many cases, the story is quite different.

在我们讨论社区反应之前，a16z团队有几个重要的发现。

Now, before we get into the community's reaction, there were a few big takeaways that the team at a16z had.

首先，他们发现这一版是七份榜单中首次亮相应用数量最少的一次。

The first is that they found this edition had the smallest number of debuts across their seven lists so far.

换句话说，就是之前没上榜、现在新上榜的公司数量最少。

In other words, the smallest number of companies that weren't on previous lists that are on the list now.

只有11个产品是首次出现在这个榜单上。

Just 11 products made their first ever appearance here.

对于这个现象，可以有几种不同的解读，但我觉得一个合理的看法是，我们至少正在慢慢进入某种整合阶段，不再只是百花齐放，而是开始看到某些类别里出现了一些模式和网络效应。

There are a few different ways to interpret this, but one reasonable take, I think, is that we are at least inching towards some sort of consolidation phase where instead of it just being a thousand flowers blooming, we're actually starting to see some patterns and network effects take hold within certain categories.

a16z的团队还指出了使用情况中的几个关键趋势。

The team at a16z also noted a couple of key trends in usage.

也许并不意外，他们认为消费者AI的第一个真正市场，是那些愿意为帮助自己构建、创造和完成工作的工具付费的人。

Perhaps unsurprisingly, they argue that consumer AI's first real market is people who pay for tools that help them build, create, and get work done.

换句话说，虽然移动端或网页应用排行榜上可能有一些有趣、好玩、陪伴型的AI，但按收入排名的榜单上，大多数平台都是那些在某种程度上将其用于职业生活的人。

In other words, while there might be some fun, playful, companion style AI showing up on the mobile or the web app charts, the majority of the platforms that are on the chart organized by revenue are for people who are using those tools in their professional life in some way.

现在，聚焦排行榜的顶端，研究作者谈到了他们在领先的大语言模型——ChatGPT、Gemini和Claude——身上看到的情况。

Now, focusing on the top of the charts, the study authors talk about what they see among the leading LLMs, ChatGPT, Gemini, and Claude.

总体来看，它们之间的相对位置保持得相当稳定。

Overall, their positions relative to one another have stayed fairly consistent.

再说一遍，这是针对消费者的数据，不过在整个2026年期间，Claude在付费美国订阅用户数上追上了Gemini，虽然两者都还远远落后于ChatGPT。

Again, this is among consumers, although over the course of 2026, Claude caught up to Gemini in terms of paid US subscribers, although both remain pretty far behind ChatGPT.

他们还发现，Claude在把用户转化为更高价订阅方面做得更好。

They also found that Claude does a better job of converting users to higher cost subscriptions.

在今年的大部分时间里，Claude在新增净订阅用户数上曾领先ChatGPT，但最近几个月情况又反转了，7月和8月ChatGPT在新增净订阅用户数上明显超过了Claude。

For a big chunk of this year, Claude had started to lead ChatGPT in net new subscriptions, but that's flipped once again in the past couple of months, with ChatGPT significantly outperforming Claude in net new subscriptions in the months of July and August.

随着这些大玩家巩固自己的地位，a16z也探讨了初创公司仍然具有优势的地方。

And as those big leaders consolidate their position, a16z also explored where startups still have an advantage.

他们写道：“在那些能持续吸引关注的初创公司中，有几个主题很突出。

They wrote, "Among the startups attracting durable attention, a few themes stand out.

一个是真正拥有差异化的模型，最明显的例子是创意类产品，比如音乐生成平台Suno和语音生成平台ElevenLabs。”

One is to actually own a differentiated model, with the clearest examples being the creative products like music generation platform Suno and voice generation platform ElevenLabs."

另一种从现有巨头中脱颖而出的方式是拥有多模型体验，比如Cursor，它支持来自多个实验室的模型，不过我们得看看随着它们更多被SpaceX吸收，这种情况会怎么变化；还有OpenRouter，它显然就是为了让开发者从一个平台访问多个提供商而构建的。

Another way to stand out from the incumbents is to own a multi-model experience, with examples being Cursor, which supports models from several labs, although we'll see how that changes as they get absorbed more into SpaceX, and OpenRouter, which is of course specifically built to give developers access to multiple providers from one platform.

初创公司进行差异化的第三种方式是对某个有特定需求的受众群体进行极度聚焦。

A third way that startups are differentiating is an extreme focus on some audience with a specific need.

他们举的例子包括服务医生的OpenEvidence，以及面向注重隐私用户的Venice。

The examples they give are OpenEvidence, which serves physicians, and Venice, which is aimed at privacy-focused users.

关于社区的反馈，一个常见的反应是惊讶。

Now, in terms of the community's reaction, one frequent response was to be surprised.

要么是不知道某个东西原来这么受欢迎，要么是压根儿就没听说过。

To either not have known that something was as popular as it was, or to actually have just never heard of it as all.

彼得·詹姆斯总结道：“这里有很多让我惊讶的地方。

Peter James sums this up when he writes, "There are so many surprises here to me.

Superhuman排第四，Suno排第七，OpenArt排在Notion和Figma前面。

Superhuman at number four, Suno at number seven, OpenArt above Notion and Figma.

还有，Plaud是什么鬼？

Also, WTF is Plaud?

我完全懵了。”

I'm absolutely bamboozled."

我想很多人都会有这种反应。

I think a lot of people will have this type of response.

我们每个人都有自己的信息圈，所以有些类别的使用情况可能完全没注意到。

We all get in different bubbles where entire categories of usage might evade our notice.

Plaud就是个很好的例子。我为了做这个节目一直在关注它，但在公开场合其实没怎么见过。不过这周有个室内设计师来我家，他们居然掏出一个来用，因为边走边录、边记笔记这种形式，对他们现实中的工作方式来说更实用。

Plaud is a great example, where I have followed it from the standpoint of following everything for this show but hadn't really come across it in public, and yet when I had an interior designer at my house this week, they pulled one out and used it because the walk around the house form factor for taking notes made more sense for the way that they work in the real world.

说到Suno在月收入榜上排第七，我之前也聊过，很多人没意识到的是，虽然大家可能以为Suno的主要付费用户是专业或准专业音乐人，但实际上很大一部分使用是出于娱乐目的。

In terms of Suno at number seven on the monthly revenue charts, as I've discussed before with Suno, one thing that people do not realize is that while many would've assumed that the main paid users of Suno are professional or aspiring professional musicians, a huge amount of the usage is actually entertainment.

人们给家人朋友做搞笑、好玩的歌，玩得开心到愿意为此花钱。

People making fun, ridiculous songs for their family and friends, and having enough fun that they're actually willing to put some money behind that.

另外，大家还注意到，很多我们节目里聊过的大主题，在这些榜单上都有体现。

Another thing that people noticed is that many of the big themes, such as the ones we've been talking about on this show, are showing up on these charts.

像Replit和Lovable这样的智能体编程平台，在收入榜上依然名列前茅。

Agentic coding platforms like Replit and Lovable remain near the top of the revenue charts.

OpenRouter在消费榜上排到第13位，这确实说明很多人都在寻找成本优化的方案。

OpenRouter being at 13 even on a consumer chart does seem to give indication of how much people are looking for cost optimization.

看到Noose、GenSpark和Manus这些平台都上榜，也进一步印证了个人AI助手领域的竞争正在加剧。

And seeing platforms like Noose, GenSpark, and Manus all on the charts reinforces the growing competition around personal AI assistance.

另一个常见的讨论话题是，人们对某些体验的粘性感到惊讶。

Another common theme in the discourse is surprise at the stickiness of certain experiences.

比如Perplexity，在高度参与AI的圈子里已经有一段时间不是主要话题了，但它仍然排在月度独立访问量第12位，月度活跃用户第25位，月度收入甚至排到了第9位。

Perplexity, for example, hasn't been a major topic of conversation in the super enfranchised AI world for some time, and yet there it sits, number 12 on the unique monthly visits chart, number 25 on the monthly active users chart, and all the way up to number nine on the monthly revenue chart.

n8n在很多方面是智能体时代的先驱，它也仍然在收入榜上，这或许说明，一旦人们投入使用了某个特定平台，尤其是如果他们花力气自动化了某些关键工作流，行为改变的速度就会相对较慢。

n8n, which is in many ways a precursor to the agentic era, remains on the revenue charts as well, perhaps showing the relative slowness of behavior change once people have invested in using a particular type of platform, especially if they took the work to automate some key workflow.

然而，如果有一个主要话题的话，那就是重度用户和其他人之间的差异。

And yet, if there was one major conversation, it was about the difference between power users and everyone else.

a16z展示的一个现象是，AI支出最高的前1%用户，其总支出超过了后50%用户的总和。

One of the things that a16z showed was that the top 1% of AI spenders now outspend the bottom 50% combined.

前1%的用户平均每月花费903美元，而中位数客户每月只花25美元。

The top 1% spend an average of $903 a month compared to the median customer, who spends just $25.

记住，这还不是所有AI用户中的前1%。

And remember, that's not the top 1% of all AI users.

这只是实际有支出的用户中的前1%，而正如我们接下来会看到的，这部分人本身在AI用户总体中只占极小比例。

That's just the top 1% of people who are actually spending, which itself, as we'll see, is a vanishingly small portion of AI users overall.

我觉得最有趣的图表之一，就是展示那1%的人比其他付费AI用户更愿意花钱在哪些东西上的那张图。

One of the most interesting charts to me is the chart that shows what things those 1% spend money on in higher proportion to other paid AI users.

基本上就是那些能让他们搭建和自动化的工具，能让他们创作内容的工具，还有工作中会用到的工具。

It's basically tools that allow them to build and automate, tools that allow them to create content, and tools that you would use for work.

他们购买Notion的比例是平均水平的九倍多。

Their purchase rate for Notion is over nine times as much as the average.

他们购买视频平台比如Runway和Higgsfield的比例，分别是平均水平的10倍和15倍。

Their purchase rate for video platforms like Runway and Higgsfield is 10 times and 15 times as much as the average, respectively.

然后又是n8n，那1%的人付费使用这个服务的可能性，比其他所有买家高出差不多23倍。

And there again is n8n, where the top 1% are basically 23 times as likely to be paying for that service as compared to all other buyers.

很多人把那张关于AI买家前1%集中度的图表，和a16z另一份研究报告里的图表做了对比，就是他们的《市场状况报告》。

Now, many people compared that chart about the concentration of the top 1% of AI buyers with this chart from another a16z research piece, their State of Markets Report.

这张图讲的是美国拥有付费AI订阅的家庭比例。

This chart is about the share of US households with paid AI subscriptions.

简单来说，根据PNC Research的数据，截至今年四月，这个数字只有2.2%。

And TLDR, as of April of this year, according to PNC Research, that number is only 2.2%.

换句话说，目前98%的美国家庭没有为AI付费。

In other words, 98% of US households aren't paying for AI currently.

当然，关键问题是，这98%的美国家庭是还没开始为AI付费，还是永远不会为AI付费。

The big question, of course, is whether that is 98% of US households aren't paying for AI yet, or 98% of US households aren't going to ever pay for AI.

对某些人来说，这显然全是机会。

To some, this is obviously all opportunity.

Rep View的Adam写道：“我认为这反映了早期采用的情况，以及随着大众市场采用普及，实际潜在市场有多大。”

Adam at Rep View writes, "I think this is just a reflection on early adoption and how much TAM there actually is as mass market adoption proliferates."

Ruben Hassid指出，在美国，电脑从2%普及到50%用了19年，汽车用了15年，智能手机用了六年半。

Ruben Hassid pointed out that the time it took to go from 2% to 50% adoption in the US for the computer was 19 years, for cars was 15 years, for the smartphone was six and a half years.

换句话说，暗示我们只是处于早期阶段。

In other words, implying that we're just early.

还有一些人把它和其他人们付费的东西做了比较。

Some others compared it to what other things people pay for.

联合创始人Nick指出，25%的家庭为SiriusXM付费，55%为云存储付费，91%至少订阅了一项流媒体服务。

Co-founders Nick points to 25% of households paying for SiriusXM, 55% paying for cloud storage, and 91% paying for at least one streaming service.

有些人最终持乐观态度，但更坚定地认为我们确实还处于早期阶段。

Some were ultimately optimistic, but even more assertive about how early we are.

微软的尼古拉斯·布斯塔曼特写道：“这是我的论点。

Microsoft's Nicholas Bustamante writes, "This is my thesis.

没有人使用人工智能。

No one uses AI.

我再说一遍，绝对没有人。

I repeat, absolutely no one.

我们生活在一个泡沫里。

We live in a bubble.

即使在我那些付费的朋友中，当我让他们打开ChatGPT给我看他们的查询时，也只是一些基本的东西。

Even among my friends who pay for it, when I ask them to open ChatGPT and show me their queries, it's the same handful of basic things.

大多数人甚至不知道他们可以上传照片并询问相关问题。

Most don't even know they can upload a photo and ask questions about it.

连接Gmail让智能体读取和发送邮件让他们感到震惊。

Connecting Gmail so an agent can read and send emails blows their minds.

一个代理打开浏览器帮你办理航班值机？

An agent opening a browser and checking them into a flight?

他们连听都没听说过。

They've never even heard of it.

现在最大的挑战是普及，然后让那些已经注册的人真正用上他们付费的服务。

The massive challenge right now is adoption, and then getting people who already signed up to actually use what they're paying for.

大多数人完全不知道能做什么。

Most have absolutely zero clue what's possible.

想象一下，如果每个人都像现在最顶尖的1%用户那样使用AI，计算资源会有多紧张。

Imagine the compute shortage when everyone starts using AI like the top 1% of users do today."

但也有人说：“这永远不会发生。”

Others, though, said, "Never going to happen."

律师Basil Musharbash转发了Nicholas的帖子，并说：“我是个能干的成年人。”

Lawyer Basil Musharbash reposted Nicholas and said, "I'm a competent adult.

为什么我需要一个AI代理帮我办理航班值机？”

Why do I need an AI agent to check into a flight for me?

我识字啊。

I'm literate.

我为什么要用AI代理来帮我读写邮件？

Why do I need an AI agent to read and write emails for me?

没人用AI的原因就是，对绝大多数人来说，这里根本没有什么真正的实用价值。

The reason no one uses AI is that there's no serious utility here for the vast majority of people."

Habibislop也不喜欢那个订机票的例子。

Habibislop also didn't like the flight booking example.

他们写道：“为什么说死磕这个订机票的事情蠢到家了。

They wrote, "Here's why doubling down on this flight booking thing is dumb as rocks.

第一，我觉得我从来没花过20分钟去办理登机手续。

One, I don't think it has ever taken me 20 minutes to check into a flight.

你描述的那些要省掉的步骤，根本就不是真正的步骤。

The steps you're describing removing are not even real steps.

通常就是打开航空公司的App，点那个大大的红色‘办理登机’按钮，选一下托运行李的数量，完事儿了。

They are usually open the airline's app, click the big red Check In button, click number of checked bags, done.

你还得花同样的功夫去检查这个代理是否真的完成了任务。

You'd have to do the same amount of work to check if the agent actually succeeded at its task.

第二，大约55%的美国成年人一年到头一次飞机都不坐，所以没多少人关心这个用例。

Two, about 55% of American adults take zero flights in a given year, so few people care about this use case.

人们低估AI代理，并不是真正的问题所在。

People underestimate AI agents is not what's happening here.

真正的问题是，科技圈的人经常把自己那种异常频繁的旅行、邮件和行政事务的生活方式，误认为是所有人的日常。

What's happening is tech people routinely mistake their own unusually high travel, high email, high admin lifestyle for a universal human workload.

过去20年科技行业最大的问题，就是他们没法跳出自己的视角看问题。

The overarching problem of the tech industry the last 20 years is their inability to look outside themselves.

他们沉迷于推销那些别人根本不需要的解决方案。

They become obsessed with marketing solutions to problems that nobody else has."

有些人甚至对美国普通消费者更悲观，Breaking Point的主持人Sagar和Jetty写道：“其实没人真正需要个人AI助手。”

Some are even more pessimistic about the average US consumer, with Breaking Point's hosts Sagar and Jetty writing, "Nobody actually needs a personal AI assistant.

美国人的平均屏幕使用时间是每天七小时。

The average screen time in the US is seven hours a day.

大家有的是时间。

People have plenty of time.

没有证据表明，腾出更多时间除了让人刷更多抖音之外，还能带来什么别的结果。

There's no evidence that freeing up more will lead to anything but more TikTok scrolling.

要说的话，日常生活反而需要更多线下干扰。

If anything, daily life needs more offline friction.

“我会把时间花在我喜欢的事情上。”

'I'll spend it doing what I love.'

不，你不会的。

No, you won't.

你会去刷抖音。

You'll watch TikTok.

数据不会说谎。”

The data doesn't lie."

但话说回来，从另一个角度看——这也是为什么我把这期节目叫做“观点对决”——有些人感叹这简直是想象力的彻底失败。

But then again, on the flip side, and this is why I called this episode a point-counterpoint, you have folks lamenting what an utter failure of imagination this represents.

转发了律师巴兹尔·穆沙尔巴什的帖子，他说他们不需要AI代理来办理登机手续或读写邮件，投资者尼克·卡特说：“我觉得这些观点真的很让人困惑。”

Reposting lawyer Basil Musharbash, who said that they don't need an AI agent to check into flights or read or write emails for them, investor Nick Carter says, "I find these takes really confusing.

你的电脑里住着一个智商超过150、不知疲倦的天才。

You have 150 IQ plus tireless genius living in your computer.

它能用你的浏览器，读你的邮件，访问你想要的任何文件。

It can use your browser and read your email and access any file you want.

你给它最无聊的行政任务，它也不会抱怨。

It won't complain if you give it the most tedious administrative tasks.

它愿意24小时工作，而且工资低于最低标准。

It's willing to work 24/7 for less than minimum wage.

它能连续几小时无人监督地工作。

It can work unsupervised for hours at a time.

你却想不出任何用途？

And you can't think of any way to put it to use?

你是完全缺乏想象力，还是就是喜欢做那些琐碎的行政工作？

Are you completely lacking in imagination, or do you just enjoy mundane administrative tasks?

你喜欢交停车罚单、处理邮件、手动退订垃圾邮件、审核信用卡付款、取消订阅，还有重新谈判保险吗？

You enjoy paying parking tickets and triaging emails and manually unsubscribing from spam and vetting credit card payments and canceling subscriptions and renegotiating insurance?

你喜欢做这些事吗？

You like doing that?"

对某些人来说，这种不同用户之间的差异，几乎就像科技界的自然法则一样。

For some, this split between different types of users is as close to a law of nature as we have in tech.

我个人很反感那种说法，认为大家都太懒、太傻，只会刷抖音，而不去真正利用这些工具，即使生产力并不是他们生活中的主要动力。

Personally, I bristle at the idea that everyone is too lazy and slack-jawed to get off TikTok to actually take advantage of these tools, even if productivity isn't their primary motivation in life.

同时，我确实看到，绝大多数积极正面地使用AI的人，基本上都是那种积极进取的类型。

And at the same time, it is certainly the case that the vast majority of people that I see interacting with AI in assertive and positive ways are basically go-getter types.

他们是那种想要从生活和工作中得到更多东西的人。

They're people who want more out of life and more out of their jobs and careers and work.

他们不一定都是创业者，但他们做事时带着创业者的那股劲头。

They're not necessarily all entrepreneurs, but they bring entrepreneurial energy to the things that they do.

简而言之，他们志向远大，但并不是每个人都这样。

They are, in short, high aspiration, and not everyone is.

现在，有两个原因，即使我们接受这个想法，即普通消费者对使用AI或专门为其付费的意愿可能总是有限的，有几个原因说明这并不一定会削弱这个行业的财务论点。

Now, there are two reasons that even if we accept the idea that there might always be limits on how much the average consumer wants to use AI or specifically wants to pay for it There are a couple reasons that that doesn't necessarily undermine the financial thesis of the industry.

第一个原因当然是，与之前的软件类别不同，那些有抱负的人和他们所在的公司能在AI上花费的金额几乎没有上限。

The first is, of course, that unlike previous categories of software, there is far less of an upper bound on how much those aspiring type individuals and the companies they work with can actually spend on AI.

这不是SaaS，SaaS的上限是最贵的账户，每月100或200美元。

This is not SaaS, where the upper bound is the most premium account that costs $100 or $200 a month.

这是一个重度用户每月可以花费数千甚至数万美元，而且完全投资回报率为正的领域。

This is a domain in which power users can spend thousands and thousands or tens of thousands of dollars per month in a totally ROI positive sort of way.

但回到消费者焦点，没有什么规定说消费者AI应用必须全是关于生产力的，或者必须严格通过订阅来变现。

But keeping it on the consumer focus, there's nothing that says that consumer AI apps have to be all about productivity or that they have to monetize strictly through subscriptions.

我再次提到Suno，该公司现在取得的成功和数亿美元的年度经常性收入，不仅仅是作为音乐家的生产力工具，也是作为很多普通人的娱乐平台。

I'll point to Suno again, where the success and the hundreds of millions of dollars of ARR that company has now achieved is not exclusively as a productivity tool for musicians, but as an entertainment platform for a lot of normies as well.

然后，当然，还有另一种商业模式，我们喜欢又讨厌的那种，但到目前为止，它资助了互联网的大部分，那就是广告。

And then, of course, there is that alternate business model, the one which we love to hate, but which funds most of the internet at this point, advertising.

正好，OpenAI刚刚宣布了一种新的广告形式，他们将在ChatGPT中内联显示视觉广告，而不仅仅是基于文本的广告。

Right on time, OpenAI just announced a new ad format where they'll be displaying visual ads inline in ChatGPT, not just text-based ads.

他们写道：“我们新的视觉广告格式帮助人们想象产品和服务如何融入他们的生活。

They wrote, "Our new visual ad format helps people imagine how products and services could fit into their lives.

通过展示产品灵感、产品使用方式或它们带来的体验的图片，人们可以发现新品牌，了解产品的独特之处，并决定购买什么。

Through images showing product inspiration, product usage, or the experiences they make possible, people can discover a new brand, understand what makes an offer distinctive, and decide what to buy.

对于广告商来说，这为他们通过图片讲述品牌故事创造了新的机会。”

For advertisers, this creates new opportunities to tell their story through images."

另外，在那次公告中，他们重申了一个我在DevDay上也听到过的数字，那就是ChatGPT现在每周触达12亿人，比7月底宣布达到10亿用户里程碑时增长了20%。

Now, also in that announcement, they reiterated a number that I had heard during DevDay as well, which is that ChatGPT now reaches 1.2 billion people each week, up 20% since they announced their billion user milestone at the end of July.

Alex Immerman指出，这里还有很大的增长空间。

Alex Immerman points out that there is a lot of room to run here.

他写道：“ChatGPT在2月推出广告，六周内收入突破1亿美元，到8月达到10亿美元。

He wrote, "ChatGPT launched ads in February, crossed 100 million run rate revenue in six weeks, and a billion by August.

谷歌和Meta的广告收入合计达到5000亿美元。

Google and Meta combined for 500 billion in ad revenue.

我们在AI广告领域还处于非常早期的阶段。”

We are so early in AI ads."

对于那些想了解消费级AI未来的人来说，a16z报告中最有趣的图表可能就是这张，显示消费级AI还有很大的增长空间。

For one who is trying to understand the future of consumer AI, maybe the most interesting chart from the a16z report is this one, where consumer AI has room to grow.

左边是各个类别。

On the left, they have categories.

中间是Web 1.0和Web 2.0时代的赢家。

In the middle, the winners from Web 1.0 and Web 2.0.

右边则是看这些类别中是否有AI公司进入消费应用前100名。

And then on the right, whether that particular category has AI companies in the top 100 of consumer apps.

这些AI公司主要集中在生产力工具、搜索与问答、内容创作、照片和视频领域，教育和健康方面也有一点。

They are highly concentrated within productivity tools, search and answers, production and creation, photos and videos, with a little bit of education and health.

而前100名中没有AI平台的类别包括流媒体和媒体平台、零售市场、社交网络和通讯、旅游、个人理财、求职和职业网络、房地产、游戏和约会。

Where there are no AI platforms in the top 100 include categories like streaming and media platforms, retail marketplaces, social networks and messaging, travel, personal finance, jobs and career networks, real estate, gaming, and dating.

我确实觉得，早期采用者看到不太亮眼的采用数据时，有时太容易说“我们太早了”。

I do think it is sometimes too easy for early adopter types to say, "We're so early" when they look at underwhelming adoption numbers.

但我确实认为，就消费级AI而言，如果认为今天的状况就是几年后的状况，那将是一个错误。

But I certainly do think that when it comes to consumer AI, it would be a mistake to assume that where we are today is where we will be a couple of years down the line.

好了，今天的AI每日简报就到这里。

For now, that's going to do it for today's AI Daily Brief.

一如既往，感谢你的收听或观看。

Appreciate you listening or watching, as always.

下次再见，祝一切顺利。

And until next time, peace.