The AI Challenges Businesses Are Actually Focused On Right Now — 文稿

M10:00

就AI在社会上的讨论而言，过去这几周真的是非常特别的一段时间。

It has been a heck of a last couple of weeks when it comes to the AI discussion in society.

然而在这一切之中，有一群人还在努力搞清楚：对他们来说，事情到底有没有真正改变，还是说他们只是在走原来的路。这群人就是多年来一直在想办法最大化AI对自身价值的企业和组织。

And yet in all of that, one group that's left trying to figure out if anything has actually changed for them or if they are just on the same path that they were before is the businesses and enterprises that have been trying to figure out how to maximize AI for their own value for at this point a number of years.

今天我们会讨论企业如何看待这个AI安全的新时代——如果他们真的有在看待的话。同时我们也会更深入地去挖掘，企业真正的担忧是什么，以及他们现在真正面临的AI挑战是什么。

Today we're discussing both how enterprises are thinking about, if at all, this new era of AI safety, And also digging a little bit deeper to find out what the real concerns that businesses have and the real AI challenges they're facing right now.

和往常一样，我们现在正处在这样一个时刻：新的挑战也在创造新的机会。

As always, we're in a moment where new challenges are creating new opportunities as well.

《AI Daily Brief》是一档每日播客和视频节目，专门讲述AI领域最重要的新闻和讨论。

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

好的，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.

M11:10

现在，AI安全讨论已经完全主导了对话，这种情况已经持续两周了。

We are now 2 weeks into the AI safety discourse absolutely dominating the conversation.

对于那些认为这些问题过去严重缺乏关注的人来说，这段时间可以说是非常好。

For those who think that awareness of these issues had been sorely lacking, it has been a very good period.

不过，现在看到民调数字显示，大约有17%的美国人完全相信AI会终结人类，我们确实有理由担心，是不是我们把警报调得太高了。

Although now seeing polling numbers that suggest that something like 17% of Americans are completely convinced that AI is going to end humanity, there is certainly some reasonable concern that we might have over-calibrated.

我们先暂时把那个讨论放一边。我觉得几乎所有人都在寻求的是越来越具体的东西。

Holding that discussion aside for a moment, one thing that I think almost everyone is looking for is increasing specificity.

也就是说，政策上要具体，监控上也要具体。而Anthropic正是针对这一点，提出了一套新的衡量指标，来帮助公众理解先进AI到底在以多快的速度发展。

That is specificity of policy, but also specificity of monitoring, and it's to that that Anthropic speaks with their new proposed set of measurements to help the public understand just how quickly advanced AI is developing.

当你越过那些最吓人的头条新闻，这个月的安全争论很大程度上是围绕着递归式自我改进，以及这样一个观点：第一，AI发展得太快；第二，它马上就要变得更快得多。

Once you move past the scariest headlines, this month's safety debate has been largely about recursive self-improvement and the idea that AI development is A) moving too fast, but B) really about to move much more quickly.

社会毁灭的威胁确实能成为很好的头条，但发出这些警告的AI研究人员却很难准确描述出他们在实验室里看到的东西。

The threat of societal destruction makes for a good headline, but the AI researchers issuing the warnings have had a difficult time describing exactly what they've seen inside the labs.

为了更好地让公众了解情况，Anthropic提出了一个三维衡量框架，用来理解AI发展的当前速度。

To better inform the public, Anthropic has proposed a 3-axis measurement to understand the current pace of AI development.

第一个维度，是AI构建自身下一个版本的能力。

The first axis is AI's ability to build the next version of itself.

第二个，是Anthropic监督并干预agents行为的能力。

The second is Anthropic's ability to oversee and intervene in the actions of agents.

第三个，是投入到AI模型开发中的资源规模。

And the third is the scale of resources that go into AI model development.

换句话说，他们只衡量模型开发的输入端，但他们认为这些输入与模型能力的增长是相关的。

Now, Anthropic explicitly notes that these measurements only look at how models are built.

第一个衡量指标是AI模型在未来的模型训练中的使用。

In other words, they are measuring only the inputs to model development, but they argue that these inputs are correlated to the growth in model capabilities.

关于第一个衡量标准，也就是AI模型在将来模型训练中的使用情况。

On the first measure, The use of AI models in future model training.

Anthropic提出了一项叫做R&D Automation Index的衡量指标。

Anthropic put together a measure they called the R&D Automation Index.

它试图衡量模型训练的每一个方面，评估每个任务当前的自动化程度，并把这些评分汇总成一个数字。

It seeks to measure every aspect of model training, rate how automated each task currently is, and aggregate those rankings to express them as a single number.

按照Anthropic的衡量，Claude现在，用他们的话来说，可以说‘主导’了26%的研发工作，并且‘协作’参与了超过90%。

By Anthropic's measure, Claude now quote unquote leads 26% of their R&D work and quote unquote collaborates on more than 90%.

Anthropic对AI主导研发的定义是：人类提供高层级目标，AI在人类监督下端到端地完成整个过程。

Anthropic's definition of AI-led R&D was tasks where a human provides a high-level goal and the AI completes it end-to-end with human oversight.

而在这里，你就能看到挑战所在，即便他们试图做到精确。

And here you see the challenge even as they are trying to be precise.

他们说的是AI主导研发，但那个‘主导’的定义里，仍然包括人类提供高层级目标，而不是AI自己决定目标。而我觉得，有些人听到‘AI主导研发’时，可能以为的是这种情况。

They are talking about AIs leading R&D, but that definition of leading still includes the human providing a high-level goal rather than the AI determining the goal, which is what I think some might assume when they hear AI-led R&D.

就他们而言，Anthropic谨慎地指出，在任何一个被衡量的AI研发工作子集中，Claude都不是完全自主运行的。

For their part, Anthropic was careful to note that Claude is not operating fully autonomously for any measured subset of AI R&D work.

不过他们指出，自Mythos完成训练以来，研究自动化已经大幅提升，而且最近几个月还在继续加快。

Still, they noted that research automation has massively increased since Mythos finished its training run and has continued to ramp in recent months.

在三月份，也就是Mythos出现之前，Anthropic的研发工作中只有1%由AI主导。

In March, prior to Mythos, only 1% of Anthropic's R&D was led by AI.

Mythos在五月份把这个比例提升到了12%，而之后又翻了一番。

Mythos boosted this to 12% in May, and it has doubled since then.

关于对智能体的监管，Anthropic衡量了其智能体监管系统中的覆盖率、审查延迟和升级率。

Regarding oversight of agents, Anthropic measured coverage, review latency, and escalation rates within their agent oversight systems.

他们提到，目前大约有3万个智能体在同时进行研究和工程工作。

They said that around 30,000 agents are currently doing research and engineering work at any particular time.

Anthropic声称，对这些工作的智能体行为实现了100%的覆盖率，任何被标记的行为都能得到即时的人工智能审查，而需要实时审查的升级率大约为0.002%，也就是说，每47000个行为中大约有1个会被监控系统拦截。

Anthropic claims to have 100% coverage of agentic actions for this work, instant AI review of any flagged actions, and around a 0.002% escalation rate for real-time review, meaning around 1 in 47,000 actions are blocked by the monitoring system.

Anthropic还维持了一个事后审查系统，覆盖率达到100%。

Anthropic also maintains an after-the-fact review system with 100% coverage.

该系统每周会标记大约10万份智能体转录记录，然后从中筛选误报，最终大约有50份会提交给人工审查。

This system flags around 100,000 agentic transcripts per week that are then parsed for false positives with around 50 escalated to human review.

换句话说，每1000份转录记录中，大约有1到2份会引起实质性的担忧。

In other words, around 1 or 2 transcripts per 1,000 cause any material concern.

最后，关于资源分配，Anthropic用分配给安全系统的总计算资源的百分比来衡量。

Finally, on resource allocation, Anthropic expressed their measure in terms of a percentage of overall compute dedicated to safety systems.

在AI辅助研发中，大约6%的计算资源用于安全，而在AI主导的研发中，这个比例是12%。

Around 6% of the compute allocated to AI-assisted R&D went towards safety, and around 12% of the compute allocated to AI-led R&D went towards safety.

Anthropic指出，这个衡量指标并没有包括那些持续监控智能体活动的分类器，而且它只是公司对安全重视程度的一个不太完美的代理指标。

Anthropic noted that this measure doesn't include classifiers that constantly monitor agentic activity, and is an imperfect proxy for how much a company focuses on safety.

因为安全研究是由单个研究人员设计实验，虽然运行实验本身并不消耗太多计算资源，但设计过程很耗时。

This is because safety research consists of individual researchers designing experiments, which is time-consuming even though running the experiments is not particularly compute-intensive.

总的来说，Anthropic的目标并不是提供完美的衡量标准。

Overall, Anthropic's goal was not to provide perfect measures.

而是定义并提出一套任何AI实验室都可以在监管体系中报告的指标。

Instead, it was to define and propose a set of metrics that any AI lab could report as part of a regulatory system.

他们总结道，当世界在考虑如何把握前沿发展节奏时，我们应该尽一切可能缩小前沿实验室所知与公众所知之间的差距。

They conclude, as the world considers pacing the frontier, we should do everything possible to minimize the gap between what frontier labs know and what the public knows.

这意味着要更好地衡量AI的发展，公开报告这些信息，并让社会有机会决定如何使用这些信息。

This means better measuring the development of AI, reporting on it publicly, and giving society an opportunity to decide how to use this information.

我们希望以发布这些衡量数据的方式示范透明度，并且我们会继续这样做。

We hope to model that transparency by releasing these measurements, and we'll continue to do so.

对于这个公告的反应，我认为展现了当下这个时刻的好、坏和困难。

Now, the response to this announcement, I think, shows the good, bad, and difficult of this particular moment.

一方面，这是一个罕见的举动，让AI安全辩论中不同立场的人大都认为，有这个东西总比没有好。

On the one hand, it is the rare move where people on all different sides of the AI safety debate largely think that this existing is better than it not existing.

AI安全研究员Jeffrey LeDish写道：很高兴看到Anthropic这样做。

AI safety researcher Jeffrey LeDish writes, Great to see this from Anthropic.

几周前，我写过这家公司可以更透明，我很感激他们这次真的改进了，无论是这份报告还是最近的事故调查报告和滥用报告。

A few weeks ago, I wrote that the company could be a lot more transparent, and I appreciate how they've stepped up, both with this and the recent incident investigation report and misuse report.

与此同时，也有很多人希望看到更多内容。

At the same time, there are plenty of people who also wanted to see more.

Prime Intellect的Elie Bikausch写道：有趣的是，OpenAI给出了每个研发任务的用量细分，而Anthropic给出了自动化水平。

Prime Intellect's Elie Bikausch writes, Interesting that OpenAI gives us the breakdown of usage per R&D task and Anthropic gives us automation levels.

如果能把两者结合起来，得到每个研发任务自动化的演变过程，那就太好了。

Would be great to combine both and get the evolution of automation per R&D task.

也有人认为，自我报告虽然是个好的开始，但还不够。

There are also some who argue that the self-reporting, while a fine start, isn't enough.

Arun Rao写道：我们需要所有实验室都采用统一标准来衡量递归自我改进的进展，并每周到每月公开报告。

Arun Rao writes, we need standard measures across all labs to measure progress to RSI and publicly report on it weekly to monthly.

随着市场消化这些信息，我们将会看到更多这样的讨论：用某种方式——可能是愤世嫉俗的方式，也可能是现实的方式——来看待安全监控的增加，这本质上是一种非常昂贵的研发开销，也就是说，安全投入占比实际上就是衡量这种开销的指标。

Now, one discussion that we'll see a lot more of, especially as markets digest all of this, is that one way, perhaps a cynical way, perhaps a realistic way, to look at an increase in safety monitoring is to view it as really expensive overhead for R&D, meaning that these measures of percentage of spend on safety is basically a measurement of that overhead.

市场会奖励那些在安全上投入更多的公司吗？

Will markets reward companies that spend more on safety?

还是会因为它们压缩了自己的利润而惩罚它们？

Or will they punish them for cutting into their own margins?

这显示出让这些公司在公开市场环境中运营的另一个复杂之处。

This shows another complication for having these companies operate in a public market environment.

值得一提的是，这周也提醒了我们，递归自我改进并不只是美国实验室的专利。

For what it's worth, this week also reminded that recursive self-improvement is not just the province of the US labs.

ZAI在周四发布了一篇博客文章，标题是《走向递归自我改进：GLM如何构建自己的推理基础设施》。

ZAI released a blog post on Thursday called Towards Recursive Self-Improvement: How GLM Built Its Own Inference Infrastructure.

第一句话写道，随着我们开发GLM，模型有时会展现出让我们惊讶甚至不安的能力。

The first sentence reads, as we develop GLM, the model sometimes exhibits capabilities that surprise us and even unsettle us.

最近让我们震撼的时刻是：GLM越来越多地帮助构建AI本身。

The most recent moment that shook us: GLM is increasingly helping build AI itself.

我们看着模型完成了一项基础设施任务，这项任务以前需要一支经验丰富的基础设施工程师团队花费数周时间。

We watched the model complete an infrastructure task that would have previously taken a team of experienced infrastructure engineers weeks.

当我们意识到这项工作将直接改变下一代模型的训练方式时，我们变得更加确信。

When we realized that this work would directly change how the next generation of models is trained, we became even more convinced.

我们的接班人是我们在创造的AI系统。

Our successors are the AI systems we are creating ourselves.

这当然超出了头条新闻的范畴，但它提醒我们，任何不包括中国的放缓讨论基本上都谈不上是讨论。

It's certainly beyond the scope of the headlines, but it is a reminder that any slowdown discourse that does not include China is basically no discourse at all.

与此同时，如果我说得对，我们现在已经进入了下一代AI的谈判期，不再只是由AI实验室决定我们的节奏，那么很多人现在正站出来提出新整体系统的方案。

Meanwhile, if I am correct that we have now entered this new negotiation period for the next generation of AI, where it is no longer just the AI lab that determines our pacing, lots of people are now stepping in with proposals for what the new overall system might look like.

Bridgewater的首席投资官Greg Jensen本周登上了头条，他建议权力集中是主要的AI风险，需要进行监管。

Bridgewater CIO Greg Jensen made headlines this week by suggesting that concentration of power is the major AI risk to be regulated.

Bridgewater是世界上最成功的对冲基金之一，近年来广泛使用AI。

Bridgewater is one of the world's most successful hedge funds and has used AI extensively over recent years.

在接受The Information采访时，Jensen讨论了他对AI风险真正所在之处的看法，以及他会如何处理监管。

In an interview with The Information, Jensen discussed his views on where AI risk actually resides, and how he would approach regulation.

他说，开源模型存在一个大问题，因为你可以训练它们。

He said, There's a huge problem with open-source models because you can train them.

我们在Bridgewater就是这么做的。

We do this at Bridgewater.

在强大的开源模型上进行强化学习训练非常有效。

It's extremely effective reinforcement learning training on powerful open-source models.

这非常强大，是一项伟大的技术，但同时显然也是非常危险的。

It's extremely powerful, a great technology, and at the same time clearly a very dangerous one.

你不知道人们在用它们做什么。

You don't know what people are doing with them.

这根本无法追踪。

There's no way to track that.

所以从这个角度来看，这是危险的。

So it's dangerous from that perspective.

我们连开始都还没开始。

We haven't even begun.

关于如何处理这个问题的讨论。

The conversation on how to deal with that issue.

然而，在Jensen看来，这个风险与前沿实验室本身带来的风险相比，显得微不足道。

And yet, in Jensen's view, that risk pales in comparison to the risk posed by the frontier labs themselves.

他继续说，我们必须处理权力集中的问题。

He continued, we have to deal with the concentration of power.

两年内，OpenAI和Anthropic将控制全球35%到50%的计算资源。

In 2 years, OpenAI and Anthropic are going to control 35% to 50% of the world's compute.

对于一个社会来说，允许在像计算资源这样强大的东西上发生这种情况，是一个疯狂的结果。

That's a crazy outcome for a society to allow on something as powerful as compute.

我们会让一个实体控制其他某种能源或商品的那么多吗？

Would we let one entity control that much of some other form of energy or commodity?

现在，过去一个月里常见的观察之一是，Hugging Face事件和其他类似的安全漏洞需要巨大的资源来驱动代理群。

Now, one of the common observations over the past month has been that the Hugging Face incident and other security breaches like it require immense amounts of resources to power agent swarms.

目前尚不清楚前沿实验室之外的威胁行为者能否复制这些事件，至少在当前模型下，因为它们在计算资源访问上的限制。

It's unclear that they could be replicated by threat actors outside of the frontier labs, at least with current models, because of limits in their access to compute.

Jensen认为，解决方案的一部分需要是对AI采取行动的问责制提供明确指导，以便公司和个人了解他们承担的风险。

Jensen argued that part of the solution needs to be clear guidance on liability for actions taken by AI so that companies and individuals understand the risks they're taking.

当被问及他会如何处理权力集中时，Jensen回答说，我们应该把任何拥有超过X%比例的人，比如说世界或美国计算资源的5%，定为系统重要性机构。

When asked how he would deal with the concentration of power, Jensen responded, we should take anybody that has more than X percent, let's say 5% of the world or US's compute resources, say, okay, those are systemically important institutions.

我们会把它们放在类似系统重要性银行的机构中，并说会有监管。

We're going to put them into some sort of thing like we do with systemically important banks and say there's going to be regulation.

我还认为，说我们对你能拥有多少设置上限也是可以的。

I also think it's fine to say we're going to have caps on how much you can own.

对于你能持有多少大宗商品期货，是有限额的。

There's a cap on how many commodity futures you can own.

对于远没那么重要的事情，也是有限额的。

There's a cap on much less important things.

也许我们可以在现行法律下做到这一点，也许需要新的法律。

We may be able to do that with current law, you may need new laws.

不管怎样，我们得开始从法律上解决这个问题。

Either way, let's get going on sorting that out legally.

两党应该都有共识，那就是不应该让垄断控制发生在——我认为大多数人都会认同的——世界上最重要的资源之一上。

There should be a bipartisan recognition that you shouldn't want monopolistic control on what I think most people will agree is one of the most important resources in the world.

我本可以花上一周的节目来讨论对现有算力实施类似 G-SIB 监管的影响和挑战，但我喜欢这个讨论的原因，不只是因为它抛开了过于简化的二元对立，还在于它开始发问：真正的权力中心到底在哪里。

Now I could spend the next week's worth of episodes discussing the implications and challenges of trying to apply G-SIB-style regulation to existing compute, but what I like about this discourse is that not only does it leave behind overly simplistic binaries, it starts to ask questions about where the actual locus of power is.

问题在于模型本身，还是在于运行这些模型所需的算力？

Is the problem in the models, or is the problem in the power to run the models?

如果我们试图阻止不良后果，这两者对应的干预点是非常不同的，而这正是我们所需要的对话。

Those have very different intervention points if we're trying to stop bad outcomes, and that's exactly the sort of conversation we need to be having.

现在，安全问题中另一个正在展开的动态，是一个法律问题：各实验室能否真正在安全问题上协调，还是这涉及反垄断问题。

Now, one additional dynamic of the safety question that has been playing out is a legal question of whether the labs can actually coordinate on safety, or whether that implicates antitrust issues.

据报道，特朗普政府正在考虑反垄断豁免，允许前沿实验室在安全问题上进行协同。

The Trump administration is reportedly considering an antitrust carve-out to allow frontier labs to collude on safety.

这里涉及的理论基本上是，AI 放缓可能意味着研究支出协调减少，从而以牺牲消费者为代价提高盈利能力。

Now, the theory that this deals with is basically that an AI slowdown could mean a coordinated reduction in research spending and therefore an increase in profitability at the expense of the consumer.

从抽象层面，用个比较宽泛的类比来看，这就像苹果和三星约定停止研发新手机一样。

In the abstract, and in very loose analogy, it would not be dissimilar to if Apple and Samsung agreed to stop working on new phones.

美国代理司法部副部长斯坦利·伍德沃德表示，政府正在考虑更新跨部门反垄断指南，为 AI 安全协调提供豁免。

Associate U.S. Attorney General Stanley Woodward said the administration is considering an update to interagency antitrust guidance to provide a carve-out for AI safety coordination.

现有指南已经允许在网络安全风险方面进行协调，因此这一变更将把该指南扩展到 AI 安全领域。

The guidelines already allow for coordination on cybersecurity risks, so a change would extend that guidance to AI safety.

有趣的是，伍德沃德表示，尽管多位 AI 高管公开呼吁反垄断豁免，但截至目前，还没有人就此事联系过他的办公室。

Interestingly, Woodward said that although multiple AI executives have publicly called for an antitrust carve-out, no one has contacted his office on the matter as of yet.

不过，即使没有指南，伍德沃德也表示，司法部不反对协调放缓，他评论道：“在我看来，在网络安全或安全方面进行协调并不违反竞争。”

Even without guidance, though, Woodward has indicated that the DOJ has no objections to a coordinated slowdown, commenting, it doesn't occur to me that coordinating on cybersecurity or security is anti-competitive.

欧洲反垄断负责人特蕾莎·里贝拉也同意这一看法。

Europe's antitrust chief, Teresa Ribera, agreed.

她在同一活动中表示：“这是一个经典的博弈论问题。”

Speaking at the same event, she said, this is a classic game theory problem.

当风险是共享的，合作符合每个人的利益。

When the risks are shared, cooperation is in everyone's interest.

在这一切发生的同时，肯定还有很多人在试图总体上缓和这种讨论的论调。

At the same time all of this is happening, there are certainly still many out there who are trying to tone down the tenor of the conversation in general.

传奇 AI 研究员、Coursera 联合创始人吴恩达在接受彭博电视台采访时，将灭绝风险斥为科幻小说，并警告说，AI 末日论者在过去十年中多次炒作这一说法，目的是为其事业博取关注并影响监管。

Legendary AI researcher and Coursera co-founder Andrew Ng, in an interview with Bloomberg TV, dismissed extinction risk as science fiction and warned that AI doomers have pushed this narrative numerous times over the past decade in a bid to gain publicity for their cause and help shape regulation.

他说：“过去两周我感到相当沮丧。”

He said, I was quite dismayed over the past 2 weeks.

这波公关潮再次掀起，可能也是出于类似目的。

This wave of PR has kicked up again for probably similar purposes.

吴恩达承认 AI 确实存在风险，主要围绕网络安全，但他表示：“最近这种关于 AI 导致人类灭绝的恐慌，更像是科幻而非科学。”

Andrew acknowledged that there are genuine risks associated with AI, largely around cybersecurity, but said, this recent fear about AI leading to human extinction and so on is much more science fiction than science.

这非常有害。

It's very damaging.

在他看来，AI 构成的危险是“实际的工程问题”，而行业需要继续专注于“它能做的许多奇妙事情”。

In his view, the dangers posed by AI are, quote, practical engineering problems, and the industry needs to continue focusing on, quote, many of the wonderful things it can do.

说到当下真实存在的网络安全问题，Mistral 第二次遭到黑客攻击，其知识产权如今在暗网上待价而沽。

And speaking of real here and now cybersecurity issues, Mistral has been hacked for the second time with their intellectual property now available for purchase on the dark web.

五月份，大约 5 GB 的内部源代码和约 450 个私有仓库被窃取并提供出售。

In May, around 5 gigabytes of internal source code and around 450 private repos were exfiltrated and offered for sale.

现在，这件事再次发生，黑客提供了完整源代码、内部开发文件、Web 应用代码以及其他专有信息。

And now it's happened again with hackers offering full source code, internal development files, web app code, and additional proprietary information.

一位名叫 Benny 的前用户表示，他们联系了卖家，并确认这批文件包含模型权重、后训练流水线和数据集构建信息。

An ex-user called Benny said that they contacted the seller and confirmed the file dump included model weights, post-training pipelines, and dataset construction.

黑客要价两万五千美元，但不久后就删除了帖子，很可能要么是找到了独家买家，要么是操作安全出了纰漏，不得不销声匿迹。

The hacker was asking $25,000 but deleted their post shortly afterwards, likely either because they found an exclusive buyer or got sloppy with their OPSEC and needed to disappear.

当天晚些时候，Mistral表示经过彻底调查，没有发现未经授权的访问证据，暗示这可能又是五月份那次入侵的资料，现在再次拿出来卖。

Later in the day, Mistral said that they found no evidence of unauthorized access after a thorough investigation, suggesting perhaps that this is the same material from the May break-in coming up for sale again.

最后今天还有一个发布，就是本周相对低调的Gemini 3.8 Live Extended Thinking。

Lastly today, one model release that went a little under the radar this week was Gemini 3.8 Live Extended Thinking.

这个模型是另一个实时语音模型，也就是说它可以处理连续的对话，而不是基于轮次的结构。

The model is another live speech model, meaning it can process continuous conversations rather than using a turn-based structure.

它在Artificial Analysis语音到语音指数中排名第一，超过了GPT Live 1 Astra和Groq Voice ThinkFast 2.0。

It topped the Artificial Analysis Speech-to-Speech Index, beating out GPT Live 1 Astra and Groq Voice ThinkFast 2.0.

除了流畅的对话风格，该模型还支持近实时的视觉输入、自动检测97种语言，以及工具调用的交接功能，让它在后台完成任务。

Aside from the smooth conversation style, the model also supports near-real-time visual inputs, automatic detection for 97 languages, and handoff for tool calls to allow it to complete tasks in the background.

Google的开发关系主管Tim Messerschmidt发布了一个很酷的技术演示，展示该模型驱动一个Reachy迷你机器人。

Tim Messerschmidt, a Developer Relations Lead at Google, published a cool tech demo showing the model powering a Reachy mini robot.

Tim展示了该模型在英语和德语之间无缝切换对话的能力。

Tim demonstrated the model's ability to keep up a seamless conversation that weaves between English and German.

在推测影响时，Greg Eisenberg写道：无形的界面要来了吗？

Speculating about the implications, Greg Eisenberg wrote, are invisible interfaces coming?

Google刚刚发布了Gemini 3.8 Live。

Google just announced Gemini 3.8 Live.

它可以跟你边聊边完成任务，然后在对话结束后继续工作。

It can talk through a task with you and then keep working after the conversation ends.

我认为90%以上的垂直SaaS都需要一个语音门户。

I think 90%+ of vertical SaaS will need a voice front door.

我的意思是，你使用软件的方式变成跟它说话，而打字、点击和填表单都在另一边完成，你不需要做这些。

By that, I mean the way you use the software becomes talking to it, and the typing, clicking, and form filling happens on the other side without you.

比如，一个站在工地的承包商只要大声说出出了什么问题，等他回到卡车上时，报价已经发出，库存已检查，CRM已更新，客户收到了短信，任何风险项都已标记出来。

So a contractor standing on a job site just says what went wrong out loud, and by the time he's back in the truck, the quote is sent, inventory is checked, the CRM is updated, the customer got a text, and anything risky is flagged for him.

有点梦想成真的感觉，对吧？

Kinda the dream, right?

同样适用于护士、调度员、招聘人员、经纪人、保险代理人等等。

The same thing works for nurses, dispatchers, recruiters, brokers, insurance agents, etc.

人说话，AI代理完成行政工作。

The person talks and the agent finishes the admin.

这里有很多打造语音优先业务的机会。

Lots of opportunities here to build voice-first businesses.

我认为这就是垂直软件变得无形的方式。

I think this is how vertical software becomes invisible.

没有人登录，没有人填写表单。

Nobody logs in, nobody fills out a form.

也没有人学习你的界面。

And nobody learns your interface.

你只需要说话，工作就在你身后完成了。

You just talk and the work gets done behind you.

这是SaaS未来走向的一瞥。

This is a glimpse of where SaaS is going.

还没完全实现，但快了。

Not fully there yet, but it's coming.

在本周末的长篇/深度思考节目中，我会更深入探讨这个转变，以及我们在使用AI方面的其他一系列变化。

In this weekend's Long Read/Big Think episode, I get a little bit more into this particular shift as well as a bunch of other shifts in how we use AI.

但现在，新闻头条就到这里。

But for now, that's gonna do it for the headlines.

让我们进入主节目，聊聊在经历了这几周疯狂的AI安全讨论之后，大型企业对AI下一步的优先事项有何看法。

Let's move over on into the main episode where we talk about what, after this couple weeks of crazy AI safety discussions, big enterprises are thinking about their priorities with AI next.

KPMG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结果。

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、评估和完善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，他们的定义不是仅仅看他们知道什么，而是看他们如何与AI合作。

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

M117:06

到这个时候，问题已经不再是公司是否在积极使用AI了。

At this point, it's no longer a question of whether companies are actively using AI.

但是，能否把AI用好，那就完全是另一回事了。

Using it well, on the other hand, is a whole different story.

M118:04

是时候拥有像队友一样的智能体了。

It's time you had agents that feel like teammates.

M118:16

欢迎回到AI每日简报！

Welcome back to the AI Daily Brief!

今天这期节目的背景，自然是过去几周已经完全失控的AI安全讨论。

The background context for today's episode is, of course, the AI safety debate which has completely broken containment over the last couple of weeks.

然而，尽管我们看到这个问题在政治层面引起了巨大反响，民调也显示人们的看法在快速变化，但各种规模和类型的企业仍然卡在这里，琢磨着这事到底对它们有没有实际影响，还是说只需要按部就班继续干下去。

And yet, even as we have seen the political resonance of this issue absolutely explode and polling showing very dynamic, fast-shifting attitudes around it, enterprises and businesses of all shapes and sizes are still stuck out here figuring out whether it has any real implications for them or whether they just need to keep on keeping on.

今天，我们来探讨这个问题的一些具体答案，以及企业目前真正关注的一些更广泛的AI挑战。

Today, we're looking at some specific answers to that question, as well as a few of the broader AI challenges that enterprises and businesses are actually focused on right now.

当然，安全讨论已经进入了企业领导人的谈话中。

Certainly the safety debate has found its way to the business leader conversation.

周一在《华尔街日报》科技理事会峰会上，当《华尔街日报》要求举手示意时，只有少数人表示担心AI可能会毁灭人类。

When the Wall Street Journal asked for a show of hands at the WSJ Technology Council Summit on Monday, only a few people said that they were worried that AI might kill us all.

同时，大约一半的与会者表示，他们赞成放缓前沿AI研究，优先建立更好的护栏。

At the same time, around half of attendees said that they were in favor of slowing down frontier AI research and prioritizing better guardrails.

这似乎印证了我在上周四那期大型AI安全节目中分享的观点：尽管我认为市场起初会将任何形式的放缓视为AI的重大风险，但我实际上认为，有一个非常有力的经济反驳论点——新开发速度放缓反而可能增加公司在AI上的支出。

This sort of comports with my thesis that I shared, I think, in last Thursday's big AI safety episode, that while I believe that the markets would view any sort of slowdown as initially a big risk for AI, I actually think that there is a very compelling economic counterargument that a slower pace of new development might actually increase the amount that companies were spending on AI.

这个论点基本上是：变化速度之快，实际上在某种程度上削弱了企业进行全面转型的动力，理由是它们会把所有时间和精力花在转型上，结果转型完成时，目标已经过时了。

The thesis was basically that the incredible speed and pace of change actually in some ways creates a disincentive for companies to try to do comprehensive transformation on the logic that they're gonna spend all this time and energy on transforming into something that isn't even relevant anymore by the time the transformation is complete.

确实，回到科技理事会峰会，在小组讨论中，大家得出的主要结论是，AI放缓实际上对企业目前使用AI的方式并没有太大影响。

Indeed, moving back to the Technology Council Summit, on the panels, the big takeaway was that an AI slowdown actually doesn't really have that many implications for the way enterprises are using AI right now.

FedEx DataWorks的总裁Vishal Talwar表示，这掌握在我们手中，取决于我们如何将AI用于善途，我认为它可以为社会带来很多好处。

Vishal Talwar, the president of FedEx DataWorks, said, it's in our hands and it's up to us to apply AI for good, and I think it can do a lot of good for society.

讨论基本上仍然围绕着需要对正常的商业风险进行审慎的AI治理，而不是那些主导媒体话语的生存风险。

The discussion basically still centered around the need for prudent AI governance around normal business risks rather than the existential risks that are dominating the media discourse.

在北方举行的加拿大投资峰会上，贝莱德CEO Larry Fink更担心的是数据中心引发的反弹，而不是生存风险。

Up north at the Canada Investment Summit, BlackRock CEO Larry Fink was far more worried about the data center backlash than x-risk.

他警告说，建设延迟可能会使AI成为“大公司的专属领域”。

He warned that construction delays could make AI the, quote, domain of large firms.

Fink补充道，我们越快地建设更多容量，就能越快地让它民主化，让所有人都能用上。

Fink added, the faster we can build out more capacity, the more we can democratize and make it available for everyone.

与此同时，在创业领域，创始人开始思考随着智能体变得更强大，企业将需要的治理和监控工具。

In the startup world, meanwhile, founders are starting to think about the governance and monitoring tools that businesses will need as agents get more powerful.

一位风险投资家告诉《The Information》，他们开始关注那些构建提升模型安全和基础设施技术的初创公司。

One venture investor told The Information that they're beginning to focus on startups building tech that improves model security and infrastructure.

换句话说，私营市场正在回应所有这些担忧，通过资助那些能够解决具体问题的初创公司，在我看来，这正是我们希望看到的部分。

In other words, private markets are responding to all of this concern by funding startups that can address specifics around this concern, which to me is part of exactly what you wanna see.

微软也试图将AI安全讨论拉回到他们过去几个月一直在强调的更广泛的AI业务主权问题上。

Microsoft also tried to bring the AI safety discussion back to some of the broader AI business sovereignty points that they'd been trying to make for the past several months.

本周，该公司发布了一份长达15000字的详尽行为准则文件，按照他们的说法，只有一个压倒一切的目标：人类必须对AI保持有意义的控制，以便AI能够帮助人们过上更健康、更幸福、更有成效的生活。

This week, the company dropped a very extensive 15,000-word code of conduct document that has, as they put it, a single overriding objective, that humans must retain meaningful control over AI so that it can help people live healthier, happier, and more productive lives.

围绕这份文件的很多讨论都集中在它明确拒绝AI意识，并禁止设计AI去模仿意识。

A lot of the discourse around this document was its outright rejection of AI consciousness and a prohibition of designing AI to even imitate consciousness.

但微软CEO Satya Nadella在X上关于此事的帖子中也提到了对企业自身的影响，他写道，对于企业来说，必须保持对其独特隐性知识的完全控制。

But in his post about this on X, Microsoft CEO Satya Nadella also brought up the implications for businesses themselves, writing, for firms, it's imperative that they retain full control over their unique and tacit knowledge.

每个组织都应该能够建立自己的持续学习循环和爬山机器，而不依赖任何一个模型提供商，并且有能力将自身知识嵌入到它们控制的模型和权重中。

Every organization should be able to build its own continuous learning loop and hill-climbing machine without becoming dependent on any one model provider, and have the ability to embed its own knowledge into models and weights they control.

基本上，这部分是关于权力集中在企业手中，而应对企业权力集中的方法之一，就是不要以你的独特专有数据的形式向企业拱手让出权力。

Basically, part of this is power concentration in the firms, and a way to deal with power concentration in the firms is to not surrender power to the firms in the form of your unique and proprietary data.

同时，在AI安全讨论之外，已经开始出现一些关于以企业支出形式体现的AI市场信号的辩论。

Now meanwhile, outside of the AI safety conversation, there had started to be some debate about AI market signals in the form of enterprise spend.

9月9日，Ramp发布了最新的AI指数，发现AI支出最高的前1%企业的支出有所下降。

On September 9th, Ramp released its latest AI index that found that AI spend declined among the top 1% of businesses spending on AI.

Ramp首席经济学家Eric Karazian写道，8月份，前1%的企业每月每个员工的支出为7200美元，比7月份8000美元的峰值下降了10%。

In August, wrote Ramp lead economist Eric Karazian, the top 1% of businesses spent $7,200 per employee per month, which was down 10% from a July peak of $8,000.

现在，我们聊了很多关于Ramp数据的局限性。

Now, we've talked a lot about the limits of Ramp data.

Ramp的受众非常非常集中在那些技术前沿的早期采用者身上。

Ramp has a very, very highly concentrated tech-forward early adopter type of audience.

而且它主推的产品也专门针对成本效率。

It is also pushing products that are specifically about cost efficiency.

但当然，考虑到所有这些前提，它仍然提供了一个有趣且重要的信号。

But of course, when you take all those caveats, it still provides an interesting and important signal.

现在，说到他们在这一领域的分析，我个人认为他们低估了夏季季节性作为驱动因素的作用。

Now, when it comes to their analysis in this particular area, I personally think that they are underestimating summer seasonality as a driving force.

但他们的观点是，这是因为最精通的AI用户越来越擅长于那些不总是依赖最昂贵模型的复杂模型架构。

But their take is that this is about the most sophisticated AI users getting more adept at complex model architectures that don't rely on the most expensive models at all times.

Era写道，我的看法是，这与中国开源模型无关，而是模型之战。

Era wrote, my take is that it's not because of Chinese open models, it's model wars.

降价加上越来越多的支出正转向Standard和Lite模型，这些模型本来就比Frontier便宜。

Price cuts plus a growing share of spend is shifting to Standard and Lite models, which are already cheaper over the Frontier.

说到Ramp，他们的分析并不总是完全准确，尽管他们的数据非常有价值。之前他们发布报告称企业没有采用Fable，引发了一堆恐慌性的头条新闻，还找了很多其他原因，却没提到最明显的一个——数据保留政策。但值得肯定的是，在最近的报告中，他们发现Fable 5.1取消了数据保留要求后，已经占到企业支出的22.5%，而且增长非常迅速。

Now, speaking of Ramp not necessarily having exactly the right analysis all the time, even though their data is really valuable, while they had previously caused a bunch of frantic headlines when they showed that businesses weren't adopting Fable, arguing all sorts of reasoning for that besides the obvious one, which was data retention policies, but to their credit, in more recent reporting, they found that Fable 5.1, which got rid of the data retention requirements, had started to make up 22.5% of enterprise spend and was rising very quickly.

但整体情况如何呢？

But what about overall?

9月10日，Box的Aaron Levy在X上发表了一篇长文，谈到了他在银行、媒体、信息服务、保险等行业的高管那里听到的关于AI和企业级Agent的看法。

On September 10th, Box's Aaron Levy wrote a long post on X about the issues that he was hearing about from executives across industries including banking, media, information services, and insurance when thinking about AI and agents in the enterprise.

Aaron听到的主要趋势包括网络安全、模型竞争、Agent安全与身份、流程再造、架构调整、评估以及遗留系统的障碍。

The big trends that Aaron heard about were cyber, model battles, agent security and identity, process re-engineering, architecture adjustment, evals, and the hurdle of legacy systems.

关于网络安全，他写道，每个人都对AI带来的日益增长的漏洞数量以及OpenAI Hugging Face事件的后果感到紧张。

On cyber and security, he wrote, everyone is nervous about the growing rate of vulnerabilities coming at them from AI and the implications of the OpenAI Hugging Face incident.

这种讨论不像硅谷那样关乎生存，但仍然非常关切且务实，关注如何在他们的环境中从操作层面应对。

The conversation is not as existential as it is in Silicon Valley, but still highly concerned and pragmatic about what to do about it operationally in their environments.

由于AI，出现了很多新发现，但跟上他们现在必须执行的所有变化仍然很困难。

Lots of new discoveries due to AI and still hard to keep up with all the changes they have to execute now.

关于Agent安全，他继续写道，与Hugging Face事件有些关联，大家越来越意识到Agent安全和身份管理方面的新挑战，因为Agent试图进入它们能接触到的每一个系统。

Adding in what he wrote about agent security, he continues, somewhat tied to Hugging Face, there's much more awareness to the new challenges around agent security and identity management, in a world where agents are trying to get into every system they can.

在理想情况下，企业可以为所有Agent设置身份，并控制它们的所作所为。

In a perfect world, enterprises could set up identities for all their agents and control what they're doing.

但当然，有时Agent也需要完全像用户一样行动。

But of course, sometimes the agent needs to act exactly as the user as well.

这确实是我们之前在播客和Superintelligent节目中经常听到的。

This is certainly something that we've seen a lot in our conversations within the podcast context as well as Superintelligent.

有意思的是，很多安全担忧并不仅仅来自恶意行为者。

And interestingly, a lot of the security concerns aren't just about malicious actors.

在某种程度上，这源于这些系统本身强大的能力。

It is to some extent rooted in just the general power of these systems.

现在，不仅仅是工程师能接触Agent，许多公司发现，即使非工程师用户没有试图做任何坏事，Agent的强大能力也足以脱离他们的控制。

Now that it's not just engineers who have access to agents, many companies are finding that the agents are powerful enough that they escape the containment of the non-engineers that are using them even if those non-engineers aren't trying to do anything problematic.

这一点也反映在数据上。

This is also showing up in the numbers.

再次看Ramp的数据，首席经济学家Eric Karazian写道，企业增加支出的一个领域是AI安全软件。

Once again looking at Ramp data, lead economist Eric Karazian writes, one area companies are increasing their spend is AI security software.

在Hugging Face黑客事件之后，我们追踪的三个热门软件供应商专门提供用于监控生产环境中Agent的软件。

In the wake of the Hugging Face hack, 3 of our trending software vendors make software specifically designed to monitor agents in production.

他指出，这些特定供应商可能无法阻止Hugging Face黑客事件，但这显然是商业买家以及初创公司关注的领域。

Now he did point out that these specific vendors might not have done anything to stop the Hugging Face hack, but it's clearly a category of focus for business buyers as well as startup builders.

Aaron谈到的另一个有趣领域是开源和替代模型架构的初步探索。

Another interesting area that Aaron talks about is the nascent exploration of open source and alternative model architectures.

他写道，大多数公司在企业内部部署了多个前沿模型，很难统一标准，而且不同团队和用例有不同的偏好。

He wrote, most companies are deploying multiple frontier models within their enterprise, too hard to standardize on anything, and seeing different preferences across their teams and use cases.

但资金仍然集中在少数几个供应商上。

But the dollars are still concentrated on just a few vendors.

开源权重在这些组织中大规模使用仍处于初级阶段，通常是因为缺乏国内前沿的开源选择。

Open weights still in infancy at scale in most of these organizations, often due to lack of domestic frontier open source options.

大家对更多选择很感兴趣，但目前可选的还很少。

Plenty of appetite for more options here, but so far, few places to go.

与此同时，我认为Aaron观察到一个现象，就是大家在疯狂调整自己的架构。

Now paired with that, I think, is Aaron's observation that there is a ruthless adjusting of architectures.

他写道，大多数公司在过去一两年里，都有过多次更换不同供应商系统的案例。

Most companies, he wrote, had examples of changing systems out multiple times just in the past year or 2 with different vendors.

我大概没听说过比现在更频繁的情况了，我们试过X没用，所以我换成了Y。

I probably haven't heard, we tried X and it didn't work, so I've gone with Y, more than in today's environment.

这里的教训是，因为创新太快了，没人会等着某个供应商把东西做对。

The lesson here is that because innovation is happening so fast, no one hangs around until a vendor gets something right.

他们就直接跳到下一个去了。

They just move on to the next one.

我觉得这为观察现在的竞争动态提供了很有意思的背景，看看不同类型的玩家是怎么吸引不同类型的企业的。

Now I think this sets really interesting context for watching some of the competitive dynamics right now around how different types of actors are trying to appeal to different types of businesses.

像OpenAI和Anthropic这样的实验室，明显是想把所有东西都整合在自己的环境里。

Labs like OpenAI and Anthropic are clearly trying to keep everything consolidated in their own environment.

尤其是OpenAI，他们的重点之一是做更便宜的模型，尽可能把模型价格压到最低。

Part of that, especially for OpenAI, has been a real focus on cheaper models and pushing the price of their models down as far as they possibly can.

过去几周我们看到了不少这方面的动向。

That's something we've seen a lot over the past couple of weeks.

但他们也像今年一直做的那样，专注于垂直解决方案，比如OpenAI刚刚推出的Astra for Law。

But they also are, as they have been all year, focused on vertical solutions such as the newly launched Astra for Law from OpenAI.

除了Astra for Law，OpenAI和律师事务所Cooley还联合推出了一款叫Go Public的产品，用来起草S-1文件，就是公司上市前必须提交给SEC的那些文件。

Alongside the Astra for Law launch, OpenAI and law firm Cooley also co-launched a product that they called Go Public that's designed to draft S-1 filings, which are the documents that companies must submit to the SEC before they can go public.

然而，仿佛是要给我们一个完美的对比，看看不同公司采取的不同选择，另一家律师事务所Latham & Watkins最近被报道购买NVIDIA服务器来搭建自己的内部系统，特别是作为OpenAI和Anthropic模型的替代方案。

And yet, as if to give us a perfect comparison of the different types of options that different companies are taking, another law firm, Latham & Watkins, was recently reported to be buying NVIDIA servers to set up their own in-house systems, specifically as an alternative to models from OpenAI and Anthropic.

Latham，美国第二大律师事务所，表示，引用说，有时候我们可能有非常敏感的信息，客户的信息我们真的想保护，不想把它放在任何云供应商上。

Latham, which is the US's second-largest law firm, said, quote, sometimes we may have information that is so sensitive, client information that we really want to protect, we don't want to put it on any cloud vendor.

似乎是想说明企业可以通过拥有自己的模型来避开AI安全讨论的纷争，Mistral的CEO Arthur Mensch发帖说，企业不要放缓构建和拥有自己的AI模型和系统的步伐，这样就不会有末日降临到你头上。

Seeming to make the point that one of the ways that enterprises can stay out of the fray of the AI safety discourse is to own their own models, Mistral CEO Arthur Mensch posted, don't pace building and owning your own AI models and systems as an enterprise, and there will be no doomsday for you.

Foundation Capital的Jaya Gupta也认为，这种放缓前沿发展的时机对现有的软件公司来说可能是个好机会。

Foundation Capital's Jaya Gupta also thinks that this pacing the frontier moment could be a good one for the software incumbents.

她写道，如果你是任何一家软件公司的CEO，现在还不提供开源模型作为产品线的话，那你就是睡过头了。

She wrote, if you're the CEO of any software company and you're not offering open-weight models as a SKU right now, you're asleep.

放慢前沿发展可能是现有软件公司获得的最大机遇。

Pace the frontier may be the greatest invitation software incumbents have ever gotten.

当实验室在争论智能应该发展得多快时，软件公司应该竞相把我们已有的智能商品化。

While the labs debate how quickly intelligence should advance, software companies should be racing to commoditize the intelligence we already have.

制药公司和银行已经开始采用开源模型，部分是因为利润率，部分是因为那些可撤销的实验室API是他们越来越不想要的依赖。

Pharma and banks are already picking up open-weight models partly for margins, partly because a revocable lab API is a dependency they increasingly don't want.

关注销售成本的AI原生公司和科技公司也在做同样的事。

AI natives and tech companies that care about cost of goods sold are doing the same.

大多数尝试过的软件公司都失败了，因为开源模型之前很烂。

Most software companies that tried had failed attempts because the open-weight models sucked.

但现在开源模型已经足够好了。

But now open-weight models are good enough.

我相信每家主要的软件公司都应该成为自己垂直领域的模型工厂。

I believe that every major software company should become a model factory for its own vertical.

自己掌握评估标准，用自己独有的工作负载来后训练开源模型，把这些模型提供给客户，并用生产反馈来持续改进它们。

Own the evals, post-train open weights on the workload it uniquely sees, serve those models to its customers, and use production feedback to continuously improve them.

所以一方面，企业对AI安全讨论的回应，在某种程度上似乎一切照旧。

And so on the one hand, to some extent, the response from businesses to the AI safety discussion seems to be business as usual.

企业内部的采用节奏和挑战一直都是独特且与众不同的。

The pace and challenges of adoption within the enterprise were always unique and distinct to them.

还有伴随而来的大型机构惯性带来的挑战。

And the challenges of big institutional inertia that comes with them.

但另一方面，这些讨论也在某种程度上强化了本来就已经开始的一些趋势。

But on the other, there are some ways that the conversation is reinforcing trend lines that were already starting.

公司需要在网络安全和安全问题上花费更多时间和资源。

Companies will need to spend more time and resources on cyber and security issues.

这本来就是迟早的事，但现在这一点变得更加明确了。

That was always coming, but the point has been made even more crisply now.

而且，虽然之前已经有了一些令人信服的理由去探索和考虑开放权重或更自主的模型替代方案，而不是仅仅与大型供应商合作，但现在有更多的理由让我们愿意走这条路。

And while already there were some compelling reasons to explore and consider open weights or more owned model alternatives to just getting in bed with the big vendors, there are now even more reasons to be willing to walk down that path.

尤其是监管干预的可能性在不断增加。

Not least of which is the increasing likelihood of regulatory disruption.

我觉得如果非要用一句话来总结我的建议，那就是虽然整体上AI安全讨论的变化对企业的短期影响不大，但确实强化了一个事实：那些愿意尝试最困难的事情，比如真正投资于自己的自有架构的公司，比以往任何时候都更有潜力在同行和竞争对手中脱颖而出。

I think that if I had to summarize my advice in a single thought, it's that while overall the changing AI safety discourse doesn't really impact the short term for enterprises, it certainly reinforces the fact that the companies that are willing to try the hardest things, like actually investing in their own owned architectures, have even more potential to differentiate from their peers and competitors than they did before.

当然，我会继续关注这些趋势的发展。

I will, of course, continue to watch these trends as they evolve.

但现在，这就是今天的AI每日简报的全部内容。

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

一如既往，感谢你的收听或收看。

Appreciate you listening or watching as always.

下次再见，祝平安。

And until next time, peace.

平安。

Peace.

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