The Most Important Trends Showing Up in New AI Products — 文稿

M10:00

这周我们又看到了一些超棒的新AI产品和功能。

Once again this week, we got some awesome new AI products and features.

ChatGPT中出现了一种新型的生成式UI，Claude发布了一个新模型，表明Anthropic不仅仅要在高端模型上竞争，还有Elon的一个大宣布，说GrokBot将使用任何能够提供最佳体验的模型，而不只是Grok模型。

We got a new type of generative UI in ChatGPT, a new model from Claude that shows that Anthropic is not going to compete just at the high end of models, and a big announcement from Elon that GrokBot will use whatever models it takes to have the best experience, not just Grok models.

这些公告以不同的方式分别代表了当前塑造AI的一些关键趋势。

In different ways, each of these announcements represents some key trends shaping AI right now.

AI Daily Brief是一个每日播客和视频节目，内容涵盖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.

好的朋友们，在深入之前先快速说几个公告。

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

M10:54

我通常不会在头条开头讲融资故事。事实上，大多数融资故事根本进不了头条。

I don't normally start with fundraising stories at the top of the headlines, In fact, most fundraising stories don't actually make it.

然而，我觉得Hermes的创造者Nous Research在新一轮融资后估值达到15亿美元，这件事值得讨论。

And yet, I think that Hermes creator Nous Research reaching a valuation of $1.5 billion after a new funding round is worthy of discussion.

在OpenAI之后，一群人从OpenAI所做的事情中汲取灵感，决定以稍微不同的方式做自己的版本。

In the wake of OpenAI, a group of people took inspiration from what OpenAI was doing and decided to do their own version in a slightly different way.

说实话，这个团队的早期工作感觉像是AI研究和行为艺术的结合，因为他们试图构建第一个LLM，这个LLM是在互联网上陌生人贡献的匿名GPU组成的分布式网络上训练的。

In all honesty, the group's early work felt like a mix of AI research and performance art, given that they were trying to build the first LLM trained across a distributed network of anonymous GPUs contributed by strangers on the internet.

然而，他们真正跃入顶尖行列，是在发布Hermes agent的时候。

Where they vaulted into the big leagues, however, was when they released the Hermes agent.

Hermes拥有那种吸引人们关注OpenAI的极致用户控制和开放性，而且它动作非常快。

Hermes has the same sort of extreme user control and openness that attracted people to OpenAI, but has been moving incredibly quickly.

事实上，几乎每当某个封闭实验室发布一些很酷的新功能或产品时，你可以打赌Hermes很快也会为用户提供类似的版本。

In fact, pretty much any time that one of the closed labs releases some cool new feature or product, you can bet that Hermes is going to have some version of it available for their users quickly thereafter.

用数字来说，据《华尔街日报》报道，Hermes已经被安装了2200万次，更令人印象深刻的是，它大约占了全球token使用总量的2.5%。

Putting some numbers on this, the Wall Street Journal reports that Hermes has now been installed 22 million times, and even more impressively, accounts for roughly 2.5% of global token usage overall.

有了这笔资金，Nous Research打算从个人爱好者群体进军企业级顶尖领域，这意味着这笔新资本将用于推出Hermes for Business。

With this funding, Noose intends to move from individual enthusiasts to the enterprise big leagues, suggesting that the fresh capital will fund the launch of Hermes for Business.

在进军企业的同时，这轮融资也印证了开源在AI行业是有可行商业模式的。

Alongside the jump to enterprise, this fundraising reinforces that open source has a viable business case in the AI industry.

Nous Research免费提供Hermes，用户可以自带模型。

Noose offers Hermes for free, with users able to bring their own models.

但他们也提供一系列不同的订阅选项，这些选项捆绑了前沿模型的使用和托管实例。

But they also have a range of different subscription options, which bundle frontier model usage and hosted instances.

这种商业模式让Nous Research今年到目前为止的年化收入达到了3600万美元，目标是在年底达到1亿美元。

The business model allowed Noose to grow to $36 million in annualized revenue so far this year, with a target of $100 million by year-end.

在宣布融资时，CEO Dylan Rolnick发了一条帖子，既像庆祝，又像开源宣言。

Announcing the fundraise, CEO Dylan Rolnick posted a note that was halfway between a celebration and an open-source manifesto.

他写道，复制一份软件的边际成本已经大幅下降。

He wrote, The marginal cost to reproduce a piece of software has plummeted.

像Hermes Agent这样的技术，封锁起来几乎没什么好处，而推广它却有很多好处。

There are few benefits to keeping a technology like Hermes Agent locked down, and many to proliferating it.

用户不想要锁定，而且他们理应能够观察这个他们允许进入生活诸多方面的东西。

Users don't want lock-in, and they deserve observability into the thing they will allow into so many aspects of their lives.

Hermes作为一个开放生态系统，给了他们一个信任的基础。

Hermes as an open ecosystem gives them a foundation of trust.

我们相信，各种规模的企业都想要同样的自由。

We believe businesses of all size want the same liberties.

在X上另一篇独立但至少在哲学上相关的帖子中，Nous Research官方账号发布了一份相当于他们AI不可妥协原则的清单。

In a separate but at least philosophically related post on X, the official News Research account declared what amounted to their list of non-negotiables for AI.

你应该能够在你的agent中使用更智能的模型。

You should be able to use smarter models in your agent.

你应该能够在你的agent中使用更便宜的模型。

You should be able to use cheaper models in your agent.

你应该能够在你的agent中使用本地模型。

You should be able to use local models in your agent.

你应该能够为每项任务使用不同的模型。

You should be able to use a different model for every job.

你应该能够在对话中途切换模型。

You should be able to switch models in the middle of a conversation.

你应该能选择你的 agent 有哪些数据。

You should be able to choose what data your agent has.

你应该能选择它记住什么、忘掉什么。

You should be able to choose what it remembers and what it forgets.

你应该能选择你的 agent 跑在哪台电脑上。

You should be able to choose what computer your agent runs on.

你应该能把它运行在一台永不联网的机器上。

You should be able to run it on a machine that never touches the internet.

你应该能在合上笔记本电脑的时候运行它。

You should be able to run it while your laptop is closed.

你应该能从你已经在用的应用里访问它。

You should be able to reach it from apps you already use.

你应该能选择你的 agent 怎么思考。

You should be able to choose how your agent thinks.

你应该能读懂它运行的 prompt。

You should be able to read the prompt it runs on.

你应该能看到它拥有的每一个工具，然后把不想要的关掉。

You should be able to see every tool it has and turn off the ones you don't want.

你应该能教它一次，之后就不用再解释。

You should be able to teach it something once and never explain it again.

你应该能知道它做了什么、什么时候做的、为什么这么做。

You should be able to know what it did and when and why.

你应该能导出你的 agent。

You should be able to export your agent.

你应该能阅读代码。

You should be able to read the code.

你应该能修改代码。

You should be able to change the code.

你的 agent 应该属于你自己。

Your agent should be yours.

恭喜那边的团队。

Congrats to the team there.

我很兴奋看到那股力量和能量背后有了更多资源支持。

I'm excited to see that force and that energy have more resources behind it.

接下来，是一种不太一样的融资方式。

Next up, a bit of a different type of fundraising.

大型玩家们正在寻求下一轮融资，与此同时，数百亿美元的芯片融资交易也正在安排中。

Tens of billions of dollars of chip financing deals are being arranged as the big players seek their next round of funding.

据《华尔街日报》报道，目前有三笔大交易正在敲定。

According to the Wall Street Journal, 3 big deals are being hashed out at the moment.

据报道，Oracle 正在向 Apollo 和 Goldman Sachs 寻求资金，用于消息人士所说的一笔大规模芯片采购。

Oracle is reportedly seeking funding from Apollo and Goldman Sachs for what sources called a big purchase of chips.

交易规模没有披露，但 Oracle 已经面临信用评级下调的风险，这可能意味着他们转向私人市场，而不是在投资级债券市场碰运气。

The size of the deal wasn't disclosed, but with Oracle already at risk of a credit downgrade, this could mean they're turning to private markets in lieu of trying their luck in the investment-grade bond market.

Broadcom 也在与私人信贷机构洽谈，Apollo 和 Blackstone 参与了一笔 500 亿美元的交易，为 OpenAI 定制芯片的生产提供融资。

Broadcom is also in talks with private credit, with Apollo and Blackstone attached to a $50 billion deal to finance the production of OpenAI's custom chips.

这笔交易看起来像是 Broadcom 把自己的信用评级借给 OpenAI，以获得更好的利率，这也表明 Frontier Labs 一旦上市，可能很难按投资级评级借到钱。

That deal looks like Broadcom lending their credit rating to OpenAI to access a better interest rate, suggesting that Frontier Labs could struggle to borrow at investment grade once they go public.

第三笔交易是 SpaceX 正在接触多家贷款机构，借款 400 亿美元用于购买 Nvidia 芯片。

The third deal has SpaceX approaching various lenders to borrow $40 billion to pay for Nvidia chips.

据 Financial Times 报道，Apollo 正在主导这笔交易，这也是历史上最大规模的交易之一。

The Financial Times reports that Apollo is leading the deal, which is among the largest in history.

一些贷款机构在收到一份只能说是 SoftBank 风格的投资者演示材料之后，已经拒绝了这笔交易。

A few lenders have already passed after receiving what can only be described as a SoftBank-style investor deck.

据 FT 报道，他们说，原话是，只收到了一份简短的两页交易备忘录，上面有外太空的照片，还有一个箭头，指出公司要在——原话是——‘宇宙中的某个地方’建数据中心。

Per the FT, they said they, quote, only received a short 2-page deal memo with pictures of outer space and an arrow pointing out that the company was going to build data centers, quote, somewhere in the universe.

其中一位放弃这笔交易的投资者评论说：我们怎么把这个拿去给投资委员会看？

One of the investors that passed on the deal commented, How are we supposed to take that to the investor committee?

然而，似乎仍有一些投资公司愿意给 Elon 开支票，Bloomberg 报道称 PIMCO 正在考虑这笔交易。

And yet it seems like some investment firms are still willing to cut Elon a check, with Bloomberg reporting that PIMCO is looking at the deal.

下一阶段AI基础设施建设的融资开始在华尔街受到更多批评，因为这些巨额交易正受到严格审查。

The financing for the next leg of the AI buildout is starting to draw more criticism on Wall Street as these mega deals come under scrutiny.

SpaceX的信用违约互换价格因此消息飙升，目前市场预测其到2031年底违约的概率为15%。

SpaceX credit default swaps spiked on the news, now pricing in a 15% chance of default by the end of 2031.

不过，有些分析师认为，几张太空照片应该就够了。

Still, some analysts are arguing that a few pictures of space should be enough.

Citrini写道，公司早就该反抗投资委员会为了投资而索要的荒谬信息量了。

Citrini wrote, it's about time that companies stood up to the ridiculous amount of information that investment committees demand in order to invest.

除了知道会有数据中心，而且这些数据中心位于地球之外、宇宙之内，你还想知道什么？

What more could you possibly need to know other than there will be data centers and they will be extraplanetary yet intra-universal?

说到底，可能那份备忘录的逻辑就是：要么你想给Elon Musk 400亿美元建太空数据中心，要么你不想。

Ultimately, and this is probably the logic behind that memo, you either want to give Elon Musk $40 billion to build space data centers or you don't.

一大摞投资者材料大概也改变不了你的想法。

A giant folio of investor materials probably isn't going to change your mind.

转到行业里一个截然不同的领域，Mark Zuckerberg的Biohub与Google和美国政府合作，启动了一项价值18亿美元的生物学数据计划。

Moving over to a very different area of the industry, Mark Zuckerberg's Biohub has launched a new $1.8 billion biological data initiative in collaboration with Google and the US government.

该团队的目标是构建有史以来规模最大的公开生物学数据集。

The group aims to build the largest open biological dataset ever assembled.

这些数据随后可用于训练AI模型，以研究更复杂的生物系统。

This data can then be used to train AI models on more complex biological systems.

像Google的AlphaFold这样的模型已经描绘了蛋白质生物学，但Biohub相信，整个细胞也可以做到同样的事情。

Models like Google's AlphaFold have mapped out protein biology, but Biohub believes the same can be done for entire cells.

Biohub的科学负责人Alex Rivez表示，目标是帮助科学家进行虚拟实验，这样只有最有前景的实验才会进入实体实验室。

Biohub head of science Alex Rivez said that the goal is to help scientists conduct virtual experiments, so only the most promising ones progress to a physical lab.

他说，如果我们能在实验室提出的每一个问题中注入越来越多的推理和智能，那么这些实证结果的价值将大得多。

He said, if we can put more and more reasoning and intelligence into every single question we ask in the lab, the value of those empirical results will be far greater.

我们正处于AI带来的新科学范式的开端。

We're at the beginning of a new scientific paradigm with AI.

Sorcery!的主持人Molly O'Shea说，这实际上是在为AI生物学建设基础设施。

Said Molly O'Shea, host of Sorcery!, this is effectively an infrastructure buildout for AI biology.

他们不只是构建更大的模型，而是正在攻克当今生物学AI最大的瓶颈之一：大规模生成标准化、高质量的生物学数据集，并向研究人员开放。

Instead of just building bigger models, they're attacking one of the biggest bottlenecks in biology AI today: generating standardized, high-quality biological datasets at massive scale, and making it openly available to researchers.

在模型方面，Anthropic通过一项新的网络安全验证计划扩大了对Mythos的访问权限。

On the model front, Anthropic has expanded access to Mythos under a new cyber verification program.

Mythos自4月发布以来基本处于保密状态，最初只有几十家被选中参与Project Glasswing的公司可以使用。

Mythos has mostly remained under lock and key since its launch in April, initially available to a few dozen companies selected to participate in Project Glasswing.

Anthropic在6月将Project Glasswing扩展到15个国家的国际公司时，甚至引发了一场政治事件。

Anthropic even triggered a political incident when they expanded Project Glasswing to international firms across 15 countries in June.

一周后，政府命令Anthropic完全关闭Mythos和Fable。

A week later, the government ordered Anthropic to shut down Mythos and Fable entirely.

Fable的全球访问权限最终恢复了，但政府确保Mythos只保留给一小部分美国公司和政府部门。

Global access to Fable was eventually returned, but the government ensured that Mythos was kept to a small group of US companies and government departments.

Anthropic新的网络安全验证计划似乎是自发布以来对Mythos访问权限的首次重大扩展。

Anthropic's new Cyber Verification Program looks to be the first major expansion of access to Mythos since launch.

安全专业人士现在可以申请访问权限，包括使用Mythos以及安全护栏减少的Opus和Sonnet版本。

Security professionals can now apply for access, which includes the use of Mythos as well as versions of Opus and Sonnet with reduced safety guardrails.

Anthropic提供了三个层级，每个层级有不同的安全护栏设置。

Anthropic is offering 3 tiers providing different guardrail settings.

防御访问权限将支持以防御为主的网络安全任务。

Defense access will enable defense-focused cybersecurity tasks.

比如事件响应、恶意软件逆向工程和分析漏洞。

Like incident response, reverse engineering malware, and analyzing vulnerabilities.

红队访问权限将允许有限的攻击性网络活动，比如渗透测试。

Red Team access will allow for limited offensive cyber activities like penetration testing.

Anthropic表示，红队访问权限仍会受到实时拦截，阻止可能导致物理伤害或大规模破坏的行为。

Anthropic says Red Team access will still be subject to real-time blocks for actions that could cause physical harm or mass disruption.

访问这一层级需要经过更严格的审查，并且只对企业客户开放，而非个人研究人员。

Access to this tier will require enhanced vetting and is only available to enterprise customers rather than individual researchers.

最后，Anthropic提供了一个名为Specialized Access的层级，该层级的安全护栏最少。

Finally, Anthropic is offering a tier called Specialized Access, which has the fewest guardrails.

进入这个层级需要政府批准，而且只面向那些参与测试高风险系统的组织，比如飞控、电网和金融基础设施。

Access to this tier involves government approval and is reserved for organizations involved in testing high-risk systems like flight control, power grids, and financial plumbing.

这个层级将取代并扩展 Project Glasswing。

This tier will replace and expand Project Glasswing.

有意思的是，Specialized Access 是唯一一个强制要求数据保留的层级，其他所有层级都遵循 Anthropic 的零数据保留政策。

Interestingly, the Specialized Access tier is the only one with mandatory data retention, with everything else being available under Anthropic's zero data retention policies.

现在，除了 X 上满是网络安全专业人士在炫耀他们获得了 Mythos 的新访问权限之外，更有意思的讨论之一是，自从四月份宣布 Mythos 级别的模型以来，我们对其潜在风险的看法发生了怎样的变化。

Now outside of X being filled with cyber professionals showing off their new access to Mythos, one of the more interesting conversations has been how our thoughts about the potential risks of a Mythos-class model have changed since it was announced back in April.

开源模型研究员 Nathan Lambert 本周早些时候在一篇文章里说，如果 Claude Opus 不小心以开放权重的方式发布，世界大概也不会有太大问题。

Open model researcher Nathan Lambert argued in a post earlier this week, If Claude Opus was accidentally released as open weight, it seems like the world would have been more or less fine.

没错，网络安全事件肯定会明显增加，如果模型泄露了，对社会来说也不是好事，但这种情景看起来更像是风险的加速，而不是风险的阶梯式跃升。

Yes, there would be a clear increase in cybersecurity incident, and it would be bad for society if the model leaked, but it would be a scenario that looks more like an acceleration than a step change in risk.

摩根大通的 CEO Jamie Dimon 不同意这种看法，他声称在 Opus 之后，网络安全风险上升了十倍。

JPMorgan CEO Jamie Dimon disagrees, claiming that cybersecurity risks went up tenfold after Opus.

这是他的原话。

That's his phrase.

在周二的一次采访中，Dimon 说，AI 制造了我们之前不知道的漏洞，而在这些事情出现之前，我们一直都在担心网络安全问题。

In an interview on Tuesday, Dimon said, AI created vulnerabilities that we didn't know about, and we always worried about cyber before these things.

这跟传统的说法不太一样，Dimon 似乎在说 AI 的能力创造了新的风险，而不是暴露了已有的风险。

Now, this is different than the conventional framing, with Dimon seeming to claim that AI capabilities created new risks rather than exposed existing ones.

尽管如此，Dimon 并没有呼吁采取激烈措施来防范 AI 黑客攻击，他说，我不会因为这事是否关乎存亡而变得歇斯底里。

And yet, in spite of this, Dimon is not calling for drastic measures to protect against AI hacking, commenting, I'm not going to get hysterical over whether it's existential or not.

我们要做的是撸起袖子，开始干活，把问题解决掉。

What we're going to do is roll up our sleeves and go to work to fix it.

最后，今天还有一个给各位 Muse 用户的小功能。

Lastly today, a little feature for you Musers out there.

Muse 现在可以在 iPad 上使用了，这样一来，苹果设备的三件套就齐了。

Muse is now available on iPads, completing the trifecta of Apple devices.

新的 iPad 版本跟 iPhone 版本非常相似，但充分利用了更大的屏幕空间和更强的多任务处理能力。

The new iPad version is very similar to the iPhone version but takes advantage of having more screen real estate and enhanced multitasking capabilities.

除了支持 iPad 之外，Meta 还增加了对 GitHub、Asana、QuickBooks 和 Canva 这些软件工具的连接器。

Alongside iPad support, Meta has added connectors for software tools including GitHub, Asana, QuickBooks, and Canva.

这次更新主要聚焦于小企业的使用场景，Meta 引入了给 Muse 设定一个高层级业务目标的能力，让它自主朝着这个目标去工作。

This update is focused on use cases for small business, with Meta introducing the ability to give Muse a high-level business goal and allowing it to work autonomously toward it.

路线图上的下一步是 Muse 的 Windows 版本，Windows 部门的执行副总裁在周三的硬件发布会上公布了这一移植计划。

Next up on the roadmap is Muse for Windows, with a Windows EVP announcing the port at a hardware launch event on Wednesday.

当然，新功能也是我们本期主节目的主题。

New features are, of course, the subject of our main episode.

那么接下来，我们就从头条新闻切换到主节目部分。

So with that, we will switch from the headlines and move on over into the main.

如果你在企业里负责 AI 相关的工作，你肯定已经知道，现在的差距不是能力，而是执行。

If you're leading AI inside an enterprise, you already know that the gap right now isn't capability, but execution.

M110:51

这里有个残酷的事实。

Here's a harsh truth.

M111:41

如果你听这个节目，你很可能有一个自己的观点。

If you listen to this show, you likely have a thesis.

可能是关于企业采用，可能是关于算力，也可能是关于某个具体的实验室。

Maybe it's enterprise adoption, maybe it's compute, maybe it's a specific lab.

M113:28

欢迎回到 AI Daily Brief。

Welcome back to the AI Daily Brief.

我们现在绝对处于一个令人兴奋的初秋时节，各家公司都在拼命地推出产品。

We are definitely in the exciting early fall time when companies are just absolutely pumping out products.

当然，AI 行业比其他行业季节性更弱，因为一切都在疯狂冲刺。但尽管如此，现在也是你看到特别多产品集中发布的时候，因为大家都从暑假回来，回到了学校或工作，而且还没进入假期模式。

With AI, of course, there is less seasonality than other industries because everything is such a mad dash, But still, this is one of those moments where you see an extra big concentration because everyone's back to school or back to work from the summer and not yet in holiday mode.

而且他们都在为 2027 年做规划，尤其是如果你要处理企业预算的话。

Plus they're all planning for 2027, especially if you're dealing with enterprise budgets.

所以现在简直就是新产品和新功能的全面爆发。

And so it is just an absolute explosion of new products and features.

除了这对我们作为消费者、用户和开发者来说非常酷之外，现在特别有意思的是，所有新发布的产品和功能在多大程度上表达和体现了那些正在塑造 AI 整体发展方向的关键趋势。

In addition to that being very cool for us as consumers and users and builders, what's especially interesting right now is how much all of the new products and features being released express and embody key trends that are shaping the direction of AI overall.

所以这就是我们今天要聊的内容。

So that's what we're going to be looking at today.

为了开场，给我们一个参考框架来理解这些趋势，我想先从Ramp最近公布的数据说起。

And to kick it off, to give us a frame of reference for some of those trends, I want to start with Ramp's most recent data drop.

可能有些人不了解，Ramp其实是为那些技术驱动的现代化企业提供的新一代银行和财务管理解决方案。

For those of you who don't know, Ramp is effectively a next-generation banking and money-type solution for dynamic tech-first enterprises.

他们做账单支付、费用管理、公司卡，现在也越来越多地涉及传统银行业务。

They do bill payments, expenses, company cards, increasingly more traditional banking as well.

每个月他们都会发布一份增长最快的软件供应商榜单，衡量指标是市场份额和相对于规模的收入增长。

Each month they published a list of their fastest-growing software vendors in terms of market share and revenue growth relative to size.

需要说明的是，使用Ramp的公司本身就是走在最前沿的，所以这份数据不一定能反映整个企业级市场的情况，但了解最前沿在哪里还是非常有价值的，因为那些早期采用者往往会设定模式，后来者最终都会跟上。

There is always an important grain of salt that the companies that use Ramp are themselves on the bleeding edge, and so this isn't going to necessarily reflect the entire enterprise sector, but there's still obviously a ton of value in understanding where the bleeding edge is because those early adopters tend to set the patterns that everyone catches up to eventually.

这份数据集通常挺有趣，但一般不会让人太惊讶，也不是特别有启发性。

The dataset is always interesting but isn't necessarily usually surprising or all that enlightening.

它通常会确认几家公司把热度转化成了付费客户，还会显示在基础模型这块，Anthropic和OpenAI谁在企业市场领先。

It typically confirms a few companies that are converting buzz into paying customers, and shows whether Anthropic or OpenAI are currently leading in the market share war for foundation models with businesses.

但十月份的榜单特别引人注目，因为它捕捉到了很多新兴趋势。

October's list, however, was remarkable for just how many emerging trends it captured.

首先，我们看到Jev在数据中崛起。

First of all, we have the ascendance of Jev showing up in the numbers.

TypeSafe AI，也就是那个决策模型的创造者，在相对规模的增长上排名第一。顺便说一句，相对于我之前用的“判断模型”，这个术语现在似乎越来越流行了。

TypeSafe AI, who are the creators of that decision model, ranked first in growth relative to size, which by the way seems to be the terminology that's becoming normalized versus the judgment model language that I was using before.

不管怎样，Jev和TypeSafe在相对规模增长上排名第一，在市场份额增长上排名第二。

In any case, Jev and TypeSafe ranked first in growth relative to size, and second in growth in market share terms.

请注意，Ramp把Jev归在了基础LLM类别里，这意味着它的市场份额增长比除了Anthropic之外的所有前沿实验室都快。

Keep in mind that Ramp placed Jev in the foundation LLM category, meaning it's gaining market share faster than all the other frontier labs besides Anthropic.

事实上，Jev在一个月内就增长了整整一个百分点的市场份额。

Jev in fact gained a full point of market share within a month.

Jev不仅仅是在X上获得关注。

Jev is not just capturing mindshare on X.

初创公司和一些先进企业确实在大量注册并使用这个模型。

Startups and some advanced enterprises are actually signing up and using the model in a significant way.

这个月其他前沿模型的排名也比平时更有意思，Anthropic、xAI和OpenAI都进入了市场份额增长的前五名。

The rest of the Frontier Model Rankings were more interesting than usual this month as well, with Anthropic, xAI, and OpenAI all ranking in the top 5 for capturing market share.

尤其是xAI，之前很少出现在这些排名里，这说明GrokBot带来了不少新业务。

xAI in particular has rarely shown up in these rankings, suggesting that GrokBot is driving a significant amount of new business.

DeepSeek也在两个榜单上都出现了，像OpenRouter和Featherless这样的模型路由器也上榜了。

DeepSeek also featured on both lists, and model routers like OpenRouter and Featherless also showed up.

Ramp经济学家Eric Karazian写道：“竞争因素，比如更便宜模型的引入，正在推动AI支出的下降。”

Ramp economist Eric Karazian wrote, Competitive factors, like the introduction of cheaper models, are pushing AI spend down.

引入高性能的标准和轻量模型，以及为特定企业用例（比如开发）设计的高效令牌模型，将会威胁到OpenAI和Anthropic在企业市场的收入增长。

The introduction of highly performant standard and light models and highly token-efficient models designed for specific enterprise use cases like Dev will threaten OpenAI and Anthropic's revenue growth among businesses.

换句话说，这些变化不仅仅是企业在模型之间跳来跳去那么简单。

In other words, these shifts are about more than just companies bouncing from one model to another.

随着企业构建我们过去几个月一直在讨论的那些先进模型架构，一场更深层次的结构性调整正在进行中。

There's a more structural realignment going on as companies build out these advanced model architectures that we've been talking about for the last several months.

值得一提的是，Scale AI几个月来首次出现在相对规模增长的图表上。

For honorable mentions, we also have Scale AI showing up on the growth relative to size chart for the first time in months.

这表明用于强化学习的数据供应正在重新升温。

Suggesting that data provision for reinforcement learning is heating back up.

这可能反映了前沿模型实验室最近完成了大型训练任务，也可能暗示着对开源模型的微调开始加快。

This could either be reflective of the big training runs recently completed by each of the frontier model labs, or it could be a hint that fine-tuning of open models is starting to pick up.

Runway和Higgsfield也进入了市场份额增长榜单，Karazian写道：“昂贵图像和视频生成模型的采用增加将推动AI支出上升。”

Runway and Higgsfield also both ranked on the market share growth list, with Karazian writing, the increased adoption of expensive image and video generation models will push AI spend up.

相对于主导企业AI采用的编码和语言模型用例，这些用例的成本相对较高。

These use cases are comparatively expensive relative to the coding and language model use cases that have dominated enterprise adoption of AI.

在推测强大的新图像和视频模型会如何影响行业时，他继续说道：“目前它们的使用主要局限于社交媒体营销和广告，不过你可以想象AI的一个乐观情景是，在建筑和制造自动化中更多地采用图像和视频模型。”

Speculating about where strong new image and video models will take the industry, he continued, today their use is largely limited to social media marketing and advertising, though you could imagine the bull case for AI being greater adoption of image and video models for construction and manufacturing automation.

有迹象表明未来可能如此，但我们还没到那一步。

Signs of what could come, but we're not there yet.

所以对我们今天节目接下来的内容来说，核心观点是，即便是数据本身，也都在呈现一系列关键趋势，而这些趋势同样体现在那些产品里。

So for our purposes for the rest of the episode today, the key idea is that even when it comes to the data, everything is showing a key set of trends which are also showing up in the products themselves.

首先，本周最有趣的公告之一来自Elon Musk。

So first up, one of the most interesting announcements of the week came from Elon Musk.

周三一大早，他发帖说：“关于GrokBot的重要说明。”

Early on Wednesday morning, he posted, important note regarding GrokBot.

今后，SpaceX将为任何给定的任务使用最佳的后端模型，包括Claude Opus 5.5、Midjourney、Suno以及其他领先的API。

Going forward, SpaceX will use the best backend model for any given task, including Claude Opus 5.5, Midjourney, Suno, and other leading APIs.

也就是最有可能给你最佳结果的那个模型。

Whatever is most likely to give you the best outcome.

这是重磅消息。

This is massive news.

我们今年一整年都在讨论harness的重要性，以及护城河到底会落在harness这一层，还是模型这一层。

We have been talking all year about the importance of harnesses and whether moats are going to be in the harness or the model domain.

而Elon基本上就是在表态立旗，说Grok愿意在这两个领域都展开竞争。

And Elon is basically planting a flag here and saying that Grok is willing to compete on both fronts.

他后来澄清说，原话是：“大多数bot请求都非常简单，等Grok 4.8的超快版本推出后，这些请求会由它来处理。”

He did clarify later on that, quote, most bot requests are pretty simple and will be handled by a lightning-fast version of Grok 4.8 when that comes out.

我们的运营原则是，给Grok bot用户提供速度和智能的最佳组合。

Our operating principle is to give Grok bot users the best possible combination of speed and intelligence.

换句话说，这并不意味着xAI不再重视自己的模型。

In other words, this does not mean that xAI is looking away from their own models.

他们只是愿意承认，在某些领域，自己的模型和最先进的模型之间确实存在差距。

They're just willing to acknowledge that there is a difference between their models and the most advanced in certain areas.

而且对于某些使用场景，他们不愿意因为这种差距而让Grok bot的体验打折扣。

And for some use cases, they're not willing to let the experience of Grok bot be compromised because of that differential.

至于人们对这件事的负面反应，要么是第一种：他们已经为Grok付费了，所以不希望那些其他模型的费用通过API成本转嫁到他们头上。

Now, to the extent that people had negative responses to this, it was either that one, they already pay for Grok, so they don't want the fees for those other models to be passed on to them via those API costs.

第二种，对一些人来说，还存在一个原则性问题，因为Grok的一部分核心受众在理念上反对其他一些实验室。

And 2, for some, there is also a principled question, given that some amount of Grok's core audience are philosophically against some of the other labs.

我确实看到有些人要求能够选择退出，只用Grok的模型，即使能力上存在差距。

I did see some people requesting the ability to opt out and only use Grok models, even if there is a difference in capability.

对一些人来说，有趣的是这件事反映出xAI在整个竞争格局中的位置。

For some, the interesting thing is what this said about xAI's place in the overall race dynamics.

投资人Anish Asharya称这是SpaceX鱼和熊掌兼得。

Investor Anish Asharya called it SpaceX getting to have their cake and eat it too.

他说，引用：“他们既得到了作为实验室的好处，又能垂直整合推理能力，同时还像一个Cursor风格的路由器。”

Quote, they get the benefits of being a lab, vertical integration into inference, and being a cursor-style router.

他们拥有最先进的模型，所以其他实验室没法欺负他们。

They have a state-of-the-art model so labs can't bully them.

他们可以通过推理服务，在第三方模型上获取利润空间。

They can capture margin on third-party models through inference.

而且不知怎么的，他们还能用到Suno和Midjourney。

And somehow, they have access to Suno and Midjourney as well.

他们能提供整体最优、帕累托最优，或者长尾模态下的最优选择，而庄家永远是赢家。

They can offer the overall best, the Pareto best, or the best for a tail modality, and the house always wins.

我同意，从竞争动态的角度看，这很有意思。

And I agree, I find that interesting from a competitive dynamics perspective.

然而，对绝大多数人来说——甚至对我自己对这个事情的大部分关注点来说——这件事主要在用户体验层面令人兴奋。

However, for the vast majority of people, and even the vast majority of my own interest in this, this is exciting primarily on a user experience basis.

整个行业的趋势和发展方向，是公司在模型方面想要更多选择，而不是更少选择。

The whole trend and trajectory of the industry is companies wanting more choice, not less choice when it comes to their models.

当然，那些愿意自己处理上下文的人，早就已经做到了。

Certainly individuals who are willing to deal with context have been there already.

但随着企业用户和高级用户要求不被锁定进单一生态系统，他们不再把harness、产品以及随之而来的上下文当作锁定手段，而是让模型变得可移动、可迁移，这给用户带来了巨大的价值。

But as business and power users demand the ability to not be locked into one ecosystem, using the harness and product and the context that comes with it as the lock-in, but making the models mobile and transportable adds just a ton of value for users.

值得一提的是，这仍然不同于像使用Hermes Research那样，真正可以访问任何模型。

It is worth noting that it is still not the same as something like using Hermes Research where you can really get access to any model.

这些都是与Elon或Grok有某种关系的公司，例如Anthropic在SpaceX的AI数据中心运行模型或训练模型，但你仍然无法通过这种方式访问OpenAI的模型。

These are all companies that have some relationship with Elon or Grok, for example, Anthropic running their models or training their models in SpaceX AI data centers, and you're still not going to get access to the OpenAI models through this.

但很明显，这展示了多模型化的趋势，我认为这个需求只会越来越旺盛。

But still, it clearly shows the trend of multi-modelness that I think is going to do nothing but get more in demand over time.

没有哪家公司能提供一个适用于所有场景的最佳模型。

No company can provide the best model for every use case.

在商品化市场里，最大化用户的关注和留存是最有价值的资产。

Maximizing customer attention and retention is the most valuable asset in a commodity market.

另外，如果还需要更多证据证明这不仅仅是Elon在做的事情，而是一个趋势，那么还记得我提过吗？OpenAI Dev Day上一个最重要但被忽略的公告是，他们现在通过B2B市场销售开放模型推理服务。

And by the way, if we need more evidence that this is a trend and not just something that Elon is doing, remember that I argued that one of the most important, if under-the-radar, announcements from the OpenAI Dev Day was the fact that they're now selling open model inference through their B2B marketplace.

也就是说，当你的企业在OpenAI那里做了一大笔消费承诺后，你可以把其中相当一部分额度用来购买非OpenAI的模型——也就是通过他们的市场购买其他开放模型。

That means that when your enterprise makes a big spending commit with OpenAI, you can use a meaningful portion of that on not OpenAI models, but on other open models that are bought through their marketplace in exchange.

即使是那些花最多钱来拥有最前沿、最先进模型的公司，也意识到自己不可能为所有人解决所有问题。

Even the companies that are spending the most to have the state-of-the-art, best, most frontier models realize that they're not going to be able to solve everything for everyone.

还有一个额外功能，对某些人来说无关紧要，但对另一些人（比如我）却非常重要：GrokBot现在可以直接访问、搜索、读取和监控X平台，这确实打开了大量关于社交媒体监控和其他场景的用例。

One other feature bonus, which will be irrelevant to some and hugely relevant to others, i.e., me, GrokBot now has direct access to Search, Read, and Monitor X, which really does open up a ton of use cases around social media monitoring and other things.

我认为这反映出一种趋势，即智能体产品通过简化用户体验来竞争，但我们待会再聊用户体验。

I think it reflects a push for agentic offerings to compete on UX streamlining, but we'll come back to UX in a minute.

现在我想聊聊这个观点：那些基础模型实验室意识到他们自家的模型无法满足所有人的需求。

For now, I want to talk about this idea of the Foundation Labs realizing that their owned models can't be everything to everyone.

然而下一个公告似乎有点跟这个观点唱反调——Anthropic发布了Claude 3.5 Haiku，这本身就是个很强的信号：这家公司也要在市场的低端价位上竞争，提供便宜又快速的模型。

Our next announcement seems to fly a little bit in the face of that, with Anthropic announcing Claude 3.5 Haiku, which is itself a very strong statement that this company is going to compete to provide cheap and fast models to compete at the bottom end of the market as well.

Anthropic称这是他们发布过的成本最低、速度最快、能力最强的小模型。

Anthropic called this the cheapest, fastest, and most capable small model they've ever released.

根据Anthropic展示的一系列基准测试，它的性能表现确实相当惊人。

And performance looks pretty incredible across the suite of benchmarks showcased by Anthropic.

Claude 3.5 Haiku比4.5版本领先了好几个量级，几乎没法直接比较。

Claude 3.5 Haiku is light-years ahead of version 4.5 to the point that there's no real comparison.

更重要的是，与GPT-4o Luna相比，Haiku也是全面胜出。

More importantly, against GPT-4o Luna, Haiku also wins across the board.

它在TerminalBench 4.0上的智能体编程得分超过Luna一倍多，在Frontier Code 1.1上也有微弱优势。

It scored more than double on TerminalBench 4.0 for agentic coding, with a smaller edge on Frontier Code 1.1.

在计算机使用方面，Haiku在OSWorld上得分72.4%，而Luna是48.9%。

In computer use, Haiku scored 72.4% on OSWorld compared to 48.9% for Luna.

在知识工作方面，Haiku在GDPval和AA Briefcase两项上都超过了Luna。

And on knowledge work, Haiku beat Luna on both GDPval and AA Briefcase.

Artificial Analysis给Haiku的智能指数打了43分，这个分数恰好让它排在Kimi K3和GLM 5.3 Flash之间，挺有意思的。

Artificial analysis scored Haiku at 43 on the intelligence index, which amazingly puts it between Kimi K3 and GLM 5.3 Flash.

但重头戏是成本。

But the big story is cost.

Anthropic给这个模型的定价是Sonnet 5.5的四分之一，以及Haiku 4.5的一半，按token算。

Anthropic is offering this model at a quarter of the cost of Sonnet 5.5 and half the cost of Haiku 4.5 per token.

为了进一步强调这个模型适用于快速、小型查询以及子智能体场景，Anthropic对低于10万token的小型提示词提供了80%的折扣。

And to reinforce that the model is intended to be used for quick, small queries and for use on subagents, Anthropic is discounting tokens by 80% for smaller prompts below 100,000 tokens.

Artificial Analysis发现，这种定价结构下，Haiku在最大推理模式下每个任务的花费是0.21美元，在超高速模式下是0.12美元。

Artificial Analysis found that this pricing structure meant Haiku cost $0.21 per task on max inference, or 12 cents per task on Extra High.

这与GPT-6 Luna基本持平，比GLM-5-3 Flash和DeepSeek V4-1 Flash便宜几美分，只有GPT-6-1 Sol价格的六分之一。

That's right in line with GPT-6 Luna, a few cents cheaper than GLM-5-3 Flash and DeepSeek V4-1 Flash, and 1/6th the price of GPT-6-1 Sol.

Haiku是Artificial Analysis测试过的模型中对token消耗最贪婪的之一，但它胜在极其便宜，完全弥补了这一点。

Haiku was one of the most token-hungry models Artificial Analysis has ever tested, but it more than made up for it by being extremely cheap.

从趋势上看，投资人Haseeb Qureshi一句话总结：叙事颠覆了——如果你想要最便宜的LLM，现在应该买美国货。

Now in terms of the trend that this one represents, investor Haseeb Qureshi sums it up simply: narrative violation, if you want the cheapest LLMs, you should now buy American.

今年六月的时候，GLM和DeepSeek还同时处在成本和智能的帕累托前沿上。

Back in June, both GLM and DeepSeek were on the cost and intelligence Pareto frontier.

在那个价格点上，没有模型能击败它们。

At their price points, nothing could beat them.

然后成本危机来了，过去几个月里，美国实验室通过大幅降价和效率提升来应对。

Then the cost crisis hit, and in the last few months, the US labs have responded with aggressive price cuts and efficiency improvements.

现在，随着新Haiku的发布，整个前沿格局已经完全重塑。

Now, with the new Haiku, the frontier is fully reshaped.

整个帕累托前沿现在都被美国实验室占领了：Anthropic、OpenAI，以及Typesafe的Jev。

The entire Pareto frontier is now owned by the US labs: Anthropic, OpenAI, and Typesafe's Jev.

轮到你了，中国！

Your move, China!

在这一点上——真正值得关注的是，到了紧要关头，企业对来自中国的开源模型的兴趣，到底有多少是出于成本考量，又有多少是出于主权考量，也就是他们能否在自己的硬件上运行这些模型，并确保自己的数据永远不会被任何其他人拿去训练。

At this point— What will be interesting to watch is when push comes to shove, how much of enterprises' interest in the open models from China has to do with cost versus how much has to do with sovereignty and their ability to run those models on their own hardware and ensure that their data isn't ever used by anyone else for training.

美国的实验室已经越来越多地解决了成本这一侧的问题，但在主权方面的担忧上，显然还没有做出回应。

The American labs have increasingly solved the cost side of that equation, but obviously haven't addressed the issue when it comes to concerns around sovereignty.

从 OpenAI 那里，我们看到了两个公告，以及它们所代表的两个趋势。

From OpenAI, we got 2 announcements and 2 trends they represent.

第一个趋势是，即使是免费账户，也能用到更好、更强大的模型。

The first trend is better, more powerful models even in free accounts.

OpenAI 写道，上个月我们为付费客户推出了首批 GPT-6 模型，今天我们带来一个新的 GPT-6 模型，将下一代智能带给更多人——这个模型是为每周使用 ChatGPT 的超过 12 亿人打造的。

OpenAI writes, last month we introduced the first GPT-6 models for paid customers, and today we're bringing that next generation of intelligence to more people with a new GPT-6 model built for more than 1.2 billion people who use ChatGPT each week.

考虑到即便在今天，人们对 AI 的体验和认知依然很大程度上由核心的免费体验决定，这绝对是一次不小的升级。

Given how much even today people's experience with AI and their perception of AI is dictated by the core free experience, this is a non-trivial upgrade.

不过，很明显，它是两个重要公告中的第二个，而第一个是 OpenAI 所称的 Intelligent UI。

Still, it was very clearly the second in a list of 2 important announcements, with the first being what OpenAI is calling Intelligent UI.

Intelligent UI 基本上就是说，文本并不是我们接收信息的唯一方式。

Intelligent UI basically says text is not the only way we want to receive information.

相反，ChatGPT 新的 Intelligent UI 会使用各种各样的其他方式来呈现信息，让结果变得更好、更贴合具体问题。

Instead, ChatGPT's new Intelligent UI will use all sorts of other ways of presenting information to make it better and more contextual for whatever the query is.

可以是交互式可视化，可以是图表，也可以是菜谱旁边的图片。

This can be interactive visualizations, it can be charts, it can be images alongside recipes.

目标基本上就是，在给出尽可能好的答案的同时，也给出尽可能好的信息呈现方式。

The goal is basically to give people the best possible answers by also giving them the best possible presentation of the information.

而且你可以从 OpenAI 各位研究人员的纷纷发言中看出，这是一次不小的升级。

And you can see from all of the different OpenAI researchers chiming in that this was a non-trivial upgrade.

这是我们团队和许多出色的合作者好几个月工作的成果。

This is the result of many months of work from our team and so many amazing collaborators.

我们构建了一个原生可流式组件库，外加一个编译器，能在模型生成界面的同时把界面渲染出来。

We built a library of native streamable components and a compiler that renders the interface as the model generates it.

我们训练模型用这些组件做出深思熟虑的设计决策，包括什么时候该用交互式元素，什么时候纯文本依然是最佳选择。

We trained the model to make thoughtful design decisions with these components, including when to use something interactive and when plain text still works best.

OpenAI 的 Andrew Chen 补充说，事实证明，生成式 UI 最难的部分并不是生成 UI 本身——模型早就能生成好看的 HTML 了。

OpenAI's Andrew Chen added, it turns out the hardest part of generative UI isn't generating the UI— models have been able to generate good-looking HTML for a while.

更有趣的挑战是，让它感觉起来真的属于 ChatGPT。

The more interesting challenge is getting it to feel like it actually belongs in ChatGPT.

我们在 intelligent UI 上下的一大赌注是，你不需要做取舍——你既可以保留 HTML 的所有能力，同时又能让它感觉起来很快、很原生，就像一直是聊天的一部分。

One of our big bets with intelligent UI was that you didn't have to choose— that you could keep all the power of HTML and still have it feel fast and native like it was always part of chat.

问题的另一半是模型的判断力——我们如何对 GPT-6 进行后训练，让它知道什么时候界面真的有用？

The other half of the problem is model judgment— how do we post-train GPT-6 to know when an interface would actually help?

它应该给你展示什么，或者让你做什么？

What should it show you or let you do?

我们怎么让它既实用又好看，又不会喧宾夺主、接管整个对话？

How do we make it useful and beautiful without taking over the conversation?

这些在我们刚开始的时候都不是显而易见的，而且还有很多东西我们仍在摸索。

None of this was obvious when we started, and there's plenty we're still figuring out.

确实，有些人的第一印象是，它让信息变得不太清晰，或者感觉信息太多了。

And indeed, some folks' first experience was that it felt to them like it made the information a bit less clear or like there was too much.

但总的来说，我觉得可以用 Ethan Mollick 的话来概括第一印象。他写道，我提前体验了 intelligent UI，跟大段大段的文字相比，这是一个很不错的改变。

But overall, I think first impressions can be summed up by Ethan Mollick, who wrote, had early access to the intelligent UI experience, and it was a nice change from walls of text.

这同时也表明，我们会越来越多地看到针对你面临的具体问题而即时生成的界面。

It also suggests that increasingly we are going to see interfaces built on demand for the problem that you have.

所以这里值得关注的两个趋势——更长远一点的那个，正如 Ethan 所说，是生成式即时界面这个整体方向；但更直接、更广泛的趋势是，你首次看到这些基础模型实验室真的开始在乎产品体验了。

So the 2 trends to keep track of here, the one that's farther out, like Ethan said, is generative just-in-time interfaces in general, but the more immediate and broader is just the idea that for the first time, you're seeing these foundation labs actually care about the product experience.

到目前为止，他们一直能靠相当糟糕、不够直观的产品混过去，因为你通过这些产品获得的智能实在太强大了，完全弥补了产品上的不足。

So far, they've gotten away with having fairly bad, unintuitive products because the intelligence you get access to through them is so powerful that it more than makes up for it.

既然现在每个人都能随手获得这种智能，产品差异化就真的至关重要了。

Now that everyone has that sort of intelligence on tap, product differentiation is going to really matter.

总的来说，我觉得这是新功能和产品发布超级有趣的一周，这些趋势对消费者而言总体上都非常积极正面，无论个人还是企业。

Overall, I think a super interesting week for new features and product announcements, all with trends that are generally very net positive for consumers, be they individuals or businesses.

不过，今天的 AI Daily Brief 就到这里。

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

一如既往，感谢您的收听或收看。

Appreciate you listening or watching as always.

下次见，peace。

And until next time, peace.

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