当AI解决你的毕生事业时，会发生什么

到目前为止，AI会颠覆和破坏整个行业的想法还没有真正实现。

So far, the idea that AI would upend and undermine entire industries hasn't really come to fruition.

当然，我们看到人们做事的方式几乎都在改变。

Certainly, we're seeing how people do pretty much everything change.

编程可能是变化最大的，但程序员的需求似乎还在增加。

Coding is perhaps the most changed, and yet demand for coders seems to be going up.

然而，随着OpenAI发布的一系列数学成果，有人开始问：这是否是AI在某个领域首次真正引发实际颠覆？

With a new set of mathematics results from OpenAI, however, some are asking: Is this the first field-level AI disruption actually happening in practice?

《AI每日简报》是一档每日更新的播客和视频节目，聚焦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，在深入话题之前，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、Robots and Pencils和Blitzy。

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

想要收听无广告版本，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.

如果你想了解更多关于赞助节目的信息，请发邮件到sponsors@aidailybrief.ai。

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

哎呀。

Uh-oh.

AI行业的财务数据正受到质疑，因为OpenAI的营收数字被曝出远低于此前报道的水平。

Financial figures for the AI industry are being called into question as OpenAI's revenue numbers are revealed to be significantly lower than previously reported.

《金融时报》报道称，OpenAI已告知投资者，他们在9月底实现了约500亿美元的年化营收。

The "Financial Times" reports that OpenAI has told investors that they hit roughly 50 billion in annualized revenue at the end of September.

这与上个月广泛报道的680亿美元相比，差距相当大。

That's a fairly big gap from the 68 billion that was widely reported last month.

那么，这仅仅是报道有误，还是另有隐情？

So is this just misreporting or something else going on?

据消息人士称，680亿美元这个传闻数字并非来自OpenAI本身，而是来自OpenAI的一位投资者。

Well, sources said that the $68 billion whisper number came not from OpenAI itself, but from an OpenAI investor.

显然，当时的情况是，那位投资者试图与Anthropic进行同类比较。

Apparently, what was going on is that that investor was trying to get an apples to apples comparison with Anthropic.

Anthropic的数字总是按收入分成前的总营收来报的。

Anthropic numbers are always quoted as gross revenue before revenue sharing.

换句话说，当Anthropic的代币通过亚马逊或微软这样的第三方销售时，Anthropic会把那部分也算进总收入里，尽管按照他们的协议，其中很大一部分收入直接进了那些第三方的口袋。

In other words, when Anthropic tokens are sold through a third party like Amazon or Microsoft, Anthropic is including that in the total revenue, even though by the terms of their deal, some big chunk of that revenue is going straight to the pockets of those third parties.

我想他们的逻辑是想给出一个总需求量的数字，也就是以Anthropic代币销售形式体现的总需求，这个数字确实有参考价值。

The logic I imagine is to try to provide an overall number of the total expressed demand in the form of Anthropic tokens sold, and that is a useful number to know.

不过，现在这些公司要上市了，这种算法在公开市场上恐怕行不通。

However, now that these companies are going public, that's not really a convention that's likely to fly in public markets.

与此同时，OpenAI这边呢，一直用的是收入分成后的净收入。

Meanwhile, OpenAI has, for their part, always used net revenue after those revenue splits.

更糟的是，《金融时报》指出，那位OpenAI的投资者是根据一个很短的时间段来算总营收的。

To make matters worse, the "FT" noted that the OpenAI investor calculated gross revenue based on a short timeframe.

不清楚他们是从一个月、一周，甚至一天的数据来推算年化收入的。

It's unclear whether they extrapolated annualized revenue from a month, a week, or even a day.

不过也不全是坏消息。

The news was not all bad here.

OpenAI还告诉投资者，他们在第三季度实现了77%的运营率增长，企业业务增长了107%，但即便是这样惊人的增长，也完全被200亿美元的收入差距所掩盖。

OpenAI also told investors that they achieved 77% run rate growth in the third quarter and 107% growth in their enterprise business, but even that incredible growth was completely overshadowed by the $20 billion gap in revenue.

从很多方面来看，华尔街的反应就像OpenAI在财报中收入未达预期一样。

In many ways, Wall Street reacted as if OpenAI had missed on revenue during an earnings report.

在《金融时报》发布报道后，半导体股票大幅下跌，英伟达下跌3%，甲骨文下跌5.5%，而众多新云股票跌幅更大。

Semiconductor stocks were down significantly after the "FT" published their report, with Nvidia down 3%, Oracle down 5.5%, and the long tail of neocloud stocks seeing even larger drops.

然而，即使我们承认OpenAI还不是一家上市公司，没有分享过误报的数字，实际上也没有在财报中未达预期，这一反应仍然引发了围绕Anthropic IPO的一些重大问题。

And yet, even if we recognize that OpenAI is not yet a public company, had not shared the misreported number, and did not in fact miss projections on an earnings report, the reaction still brings up some big questions around the Anthropic IPO.

一旦Anthropic的审计财务数据公布，这些数据很可能会远低于Anthropic向私募市场投资者展示的数字，因为后者使用了非标准会计方法。

Once Anthropic's audited financials are available, they are presumably going to be much lower than the numbers Anthropic has been sharing with private market investors by the nature of using non-standard accounting.

最终，随着Anthropic走向IPO，叙事之争变得越来越激烈。

Ultimately, the narrative battle is growing more intense as Anthropic heads towards IPO.

Ahmed Is Investing写道：“我不了解OpenAI收入故事的所有细节，但再次显而易见的是，整个半导体交易都依赖于OpenAI和Anthropic，而且它脆弱得很。

Ahmed Is Investing wrote, "I don't know all the facts about this OpenAI revenue story, but what's becoming apparent once again is that this entire semi trade is levered against OpenAI and Anthropic, and it's fragile AF.

任何关于增长放缓的说法都会让华尔街抓狂。”

Any narrative on slowing growth and the street loses their mind."

接下来，我们来看看《The Information》最新订阅用户调查的一些有趣结果。

Next up, some interesting results from "The Information's" latest subscriber survey.

从受众来看，《The Information》的读者群体非常偏向科技前沿和科技行业。

In terms of audience, "The Information" has a very tech-forward, tech industry type of reader base.

他们发现，三分之二的订阅用户所在的组织正在开发AI应用。

They found that two-thirds of their subscribers work at an organization that develops AI applications.

感知到的投资回报率似乎在快速上升，35%的人表示他们的组织在AI投入上获得了多倍回报。

Perceived return on investment seems to be rising rapidly, with 35% saying their organization is returning multiples on their AI spend.

另外17%的人表示回报是正面的，但低于预期。

Another 17% said the returns are positive, but less than hoped.

30%的人说现在判断还为时过早，8%的人认为回报是中性的，只有8%的人认为AI投入是净负面的。

30% said it was too early to tell, and 8% said returns were neutral, leaving just 8% who said AI spend had been a net negative.

AI服务的市场份额排名再次发生变化，OpenAI重新回到了首位。

The split between AI services has shaken up again, with OpenAI back on top.

在夏季进行的上一次调查中，Claude曾超越ChatGPT，Google Gemini也在上升。

During the last survey conducted during the summer, Claude had overtaken ChatGPT, and Google Gemini was also gaining.

这一期里，Claude和Gemini的使用率都下降了，因为《The Information》的读者又回到了OpenAI的怀抱。

In this edition, usage of Claude and Gemini both fell as "The Information's" readers signed back up with OpenAI.

这三家供应商仍然遥遥领先，有65%到78%的读者在使用他们的服务。

These three vendors are still in the clear lead, with between 65% and 78% of readers using their services.

接下来的梯队包括Groq、Microsoft Copilot和Perplexity，各自的使用率在25%到29%之间。

The next grouping includes Groq, Microsoft Copilot, and Perplexity, each between 25% and 29%.

这次调查没有专门针对最近发布的个人代理工具设置板块，但《The Information》确实发现OpenClaw的使用率仍然保持在12%。

The survey didn't include a specific section on recently released personal agent use, but "The Information" did find that OpenClaw use is still at 12%.

有意思的是，这是今年到目前为止《The Information》订阅者中OpenClaw使用率的最高点，也就是说，尽管那股炒作热潮早就过去了，但产品本身依然有吸引力。

Fascinatingly, this is the high watermark for OpenClaw among subscribers of "The Information" so far this year, which means despite that hype train having died down quite a long time ago, the product itself continues to resonate.

在其他代理编程工具中，45%的人使用Claude Code，33%的人使用Codex，而Cursor的使用率在最近几个月翻了一番，达到了23%。

Among other agentic coding tools, 45% use Claude Code, 33% use Codex, and Cursor usage has doubled in recent months to reach 23%.

《The Information》指出，和一年前相比，情况发生了巨大变化。

"The Information" noted huge changes from a year ago.

去年这个时候，59%的读者表示他们从未使用过代理工具。

This time last year, 59% of readers said that they had never used an agent.

开源权重模型的使用率也在上升，现在有72%的受访者会定期使用开源权重模型。

The use of open weight models is also climbing, with 72% of survey respondents now using open weight models with some regularity.

20%的人表示，开源权重模型现在承担了他们组织大部分的工作负载，10%的人说使用情况与闭源模型差不多持平，还有42%的人表示开源权重模型只部署在少数工作负载中。

20% said that open weight models now drive the majority of their organization's workload, 10% said usage was about even with closed models, and 42% said open weight models are deployed to a minority of workloads.

有意思的是，虽然46%的受访者表示他们在工作中使用开源权重模型，但只有21%的人说他们使用的是中国的开源权重模型，这表明大多数组织，即使是那些非常前沿的初创型组织，仍然对外国模型有些犹豫。

Interestingly, although 46% of respondents said that they were using open weight models for their work, only 21% said that they were using Chinese open weight models, suggesting that most organizations, even these very forward startup type organizations, are still a little hesitant to rely on foreign models.

最后，《信息报》几乎没有发现AI的采用改变了招聘模式的证据。

Finally, "The Information" found little evidence that AI adoption has shifted hiring patterns.

只有22%的人说他们在减少招聘，10%的人说他们在增加招聘，还有21%的人说现在说这个还为时过早。

Only 22% said that they were hiring less, 10% said that they were hiring more, and 21% said that this was too early to tell.

这导致近一半的人表示，AI完全没有改变他们的招聘做法。

This left almost half saying that AI hasn't altered their hiring practices at all.

当然，这只是对一群高度参与AI的用户的一个小缩影，但仍然提供了一些非常有趣的模式。

Of course, this is all just a small snapshot of a highly enfranchised set of AI users, but still provides some really interesting patterns nonetheless.

有一个更新在社交媒体上引起了很多讨论，Anthropic在服务条款中做了修改，现在将禁止用户对Claude太刻薄。

One update that is getting a lot of chatter on social media, in a change to their terms of service, Anthropic will now ban users for being too mean to Claude.

根据新政策，用户被禁止进行所谓的“持续且无谓的辱骂或残忍行为”。

According to the new policy, users are prohibited from, quote, "sustained and needless abusive or cruel behavior."

Anthropic表示，该政策只适用于极端情况，不应干涉所谓的“常见的用户不满和反驳”。

Anthropic said that the policy is only meant to apply in extreme cases and should not interfere with, quote, "common versions of user frustration and pushback."

谢天谢地，因为我经常用更粗鲁的语言问Claude或GPT到底出了什么问题，这肯定会让我被禁。

Thank goodness, because the number of times I find myself asking Claude or GPT what the heck is wrong with it in much more colorful language would most certainly get me banned.

接下来的讨论大多转向了AI模型福利和AI模型意识，这远远超出了头条新闻的范围。

Now, the discussion from there mostly went into AI model welfare and AI model consciousness, which is way beyond the scope of the headlines.

但从AI现实主义者的角度来看，Box的Aaron Levie写道：“这听起来很奇怪，但实际上可能是个好政策。”

But for a takeaway from the AI realist perspective, Box's Aaron Levie writes, "This sounds weird, but it is actually probably a good policy.

即使你不相信AI有意识，我也不相信，但按理说，你不想让未来的模型在人类对模型无休止的粗鲁内容上训练。

Even if you don't believe AI is conscious, I don't, it stands to reason that you don't want future models trained on endless content of humans being rude to models.

模型只能理解它们训练过的数据，或者它们在交互中遇到的内容。

The models only understand the data they've been trained on, or what they run into in their interactions.

所以，如果你想要安全且对齐的模型，我们可能希望AI在训练数据中有很多良好的互动。”

So if you want safe and aligned models, we probably want AI to have lots of good interactions in their training data."

对于那些只关注功能的人来说，Claude 更值得关注的更新是推出了 Claude 仪表盘和 Claude Motion。

For those just here for the features, a more interesting update for Claude was the introduction of Claude Dashboards and Claude Motion.

仪表盘功能让用户可以把 Claude 连接到数据集，比如 CRM 或会计软件，然后生成自定义仪表盘来可视化这些数据。

Dashboards allow users to connect Claude to a data set, such as a CRM or accounting software, and generate a custom dashboard for visualization of the data.

用户还可以用自然语言查询数据，获取更多洞察。

Users can also query the data in natural language to gather further insights.

这些仪表盘还会配置成在新数据进来时自动更新。

The dashboards are also configured to automatically update as new data comes in.

其实这种功能在技术上已经可以实现一段时间了，但 Anthropic 把它打包成了一个简化功能，通过简单的提示就能使用。

Now, this kind of functionality has been technically possible for a while, but Anthropic is packaging it as a simplified feature accessible through simple prompts.

而 Motion 则允许用户把数据转换成适合演示的短动画。

Motion, meanwhile, allows users to transform the data into short animations suitable for presentations.

这些动画是用代码生成的，而不是视频帧，所以你可以轻松修改文字和数字，不用每次编辑都从头开始。

The animations are written in code rather than video frames, so you can easily modify words and numbers without needing to start over with each edit.

这看起来就是 Opus 5.5 发布时那些炫酷动画的实际应用场景。

This seems to be the practical use case for the flashy animations highlighted in the release of Opus 5.5.

再加上OpenAI这周早些时候推出的智能界面，我们看到AI在沟通和数据交互方面的应用正在大规模扩展。

Along with OpenAI's introduction of intelligent UI earlier this week, we're seeing a massive expansion in how AI can be used to communicate and interact with data.

与此同时，Anthropic的人建议你尽量多试试这个功能，Robert Bai发帖说：“给它比你认为它能处理的更大的任务。”

The folks at Anthropic, meanwhile, suggest pushing this feature as hard as you can, with Robert Bai posting, "Try giving it bigger briefs than you think it can handle.

它会让你大吃一惊的。”

It will seriously impress you."

确实，早期用户对所见到的效果印象深刻。

And indeed, early users are certainly impressed by what they see.

Iris Finance的Drew Fallon制作了一个动画产品演示，并评论说：“一年前，这要花好几万美元。”

Drew Fallon of Iris Finance made an animated product demo commenting, "A year ago, this would cost tens of thousands of dollars.

太疯狂了。”

Insane."

如果你还没决定这周末要花时间在黑客马拉松上做什么，Claude Motion似乎是个不错的玩处。

If you haven't yet decided what you're going to spend your hacking time on this weekend, Claude Motion seems like a pretty good place to play.

不过，今天就到这里，以上就是今天的头条新闻。

For now, though, that is going to do it for today's headlines.

接下来是本期的主要内容。

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”，他们的定义不仅仅在于他们知道什么，而在于他们如何与AI合作。

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

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，我都会谈论OpenAI、Anthropic、SpaceX AI、谷歌和Meta之间的竞争，如果你一直在听，你可能会有自己喜欢的公司。

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.

也许你认为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.

无论你的观点如何，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 Advisors的AI实验室生态系统ETF系列，让你投资于你相信的AI实验室背后的生态系统。

Harbor Capital Advisors AI Lab Ecosystem ETF Suite lets you invest in the ecosystem behind the AI lab you believe in.

在你投资的任何地方搜索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获取包含投资目标、风险、费用、支出及其他重要信息的招股说明书。

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这些信息。

Read and consider it carefully before investing.

风险包括本金损失和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.

这是一则付费广告，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.

最好的团队不是靠一个人扛着所有人。

The best teams don't have a single star carrying everyone else.

他们清楚自己的长处，也了解彼此的短板，并且会利用这两点。

They know their own strengths and each other's weaknesses and play to both.

这正是Robots and Pencils团队刻意打造的。

That's the team Robots and Pencils has built on purpose.

在那里，没有人为了凑人头数而埋头做琐碎的工作。

Nobody there is grinding through busy work to pad a headcount number.

人们为了挑战难题而来，留下是因为周围的每个人都在同步成长。

People come for the hard problems, and they stay because everyone around them is leveling up at the same time.

在一个满是只想快速招人的公司市场里，这值得一看。

In a market full of companies that are just trying to hire fast, that's worth a look.

去 robotsandpencils.com/careers 看看吧。

Check out robotsandpencils.com/careers.

Blitzy 对庞大代码库的理解，让它能自主完成安全修复、现代化改造和新功能开发。

Blitzy's understanding of massive code bases unlocks autonomous security fixes, modernization, and new features.

那么，如果完全没有遗留代码，会发生什么呢？

So what happens when there's no legacy code at all?

绿地项目本该是最容易的部分。

Greenfield is supposed to be the easy part.

从零开始，没有技术债务。

Clean slate, no technical debt.

但即便是绿地项目，也只能按人类的速度推进，一个冲刺接一个冲刺。

But even greenfield moves at human speed, one sprint at a time.

Blitzy 把工作单元从开发者变成了整个项目，自主规划、构建、测试并验证整个应用，从零开始。

Blitzy changes the unit of work from the developer to the project, autonomously planning, building, testing, and validating entire applications from scratch.

几十万行可直接投入生产的代码。

Hundreds of thousands of lines of production-ready code.

有一位 Blitzy 客户搭建了一个全新应用，共 53.4 万行代码，把原本 65 周的路线图压缩到了两周。

One Blitzy customer stood up a brand-new application, 534,000 lines of code, compressing a 65-week roadmap into two weeks.

还有一家公司，在没有前端工程师的情况下，就交付了整个应用。

Another shipped an entire application with no front-end engineer.

不管是老系统还是全新项目，答案都一样：软件要以计算的速度来构建。

Legacy or greenfield, the answer is the same: software at the speed of compute.

去 blitzy.com 打造你的下一个项目吧。

Build what's next at blitzy.com.

网址是 B-L-I-T-Z-Y.com。

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

欢迎回到《AI 每日简报》。

Welcome back to the AI Daily Brief.

本周早些时候，OpenAI 发布了一大批开创性的数学证明，彻底颠覆了学术界。

Earlier this week, OpenAI released a tome of pioneering mathematical proofs that completely upended the academic field.

这些成果发布在 GitHub 仓库里，包含 372 项新发现和 722 篇支持论文。

The results were published on a GitHub repo and included 372 novel results and 722 supporting papers.

OpenAI 表示，这些成果是由一个尚未公开的内部前沿模型生成的，很可能就是上个月解决纳维-斯托克斯方程的那个模型，当时引发了大量讨论。

OpenAI said that the results were generated by an internal frontier model that hasn't been released to the public, likely the same model that produced the Navier-Stokes solution last month, which kicked off a massive round of discussion.

这次，OpenAI与刚成立的数学咨询委员会协调发布了这些成果。

This time around, OpenAI coordinated the release with their newly appointed mathematics advisory committee.

委员会的一些建议包括随证明一起发布推理过程，OpenAI已经对10个问题的样本这样做了。

Some of their suggestions included releasing reasoning traces alongside the proofs, which OpenAI has done for a sample of 10 problems.

委员会还建议，了解每个问题所投入的资源会很有用。

The committee also suggested it would be useful to know the resources committed to each problem.

在这方面，OpenAI透露，平均每个结果使用的计算量相当于ChatGPT Pro思考约三个小时。

In that regard, OpenAI disclosed that the average result used compute equivalent to roughly three hours of ChatGPT Pro thinking.

但真正重要的是这次发布如何改变数学领域。

But the real story here is how this release changes the field of mathematics.

OpenAI一举解决了该领域许多最棘手的未解问题。

In one fell swoop, OpenAI has solved a significant chunk of the most difficult outstanding problems in the field.

过去一年里，我们确实看到了一些渐进式的警示信号。

We did have a few progressive warning shots over the past year.

去年夏天，谷歌和OpenAI的模型在国际数学奥林匹克竞赛中达到了金牌水平，这是高中生数学家的顶级赛事。

Last summer, models from Google and OpenAI achieved gold-level performance in the International Math Olympiad, the top contest for high school mathematicians.

今年早些时候，OpenAI和Anthropic的模型推翻了好几个埃尔德什猜想，这些是几何学里非常有名且长期未解的问题。

Then earlier this year, models from OpenAI and Anthropic disproved several Erdős conjectures, which are famous and longstanding problems in geometry.

然后上个月，OpenAI发布了纳维-斯托克斯方程的证明，这是流体力学里一个将近200年的难题。

Then last month, OpenAI released a proof for the Navier-Stokes equation, an almost 200-year-old problem in fluid dynamics.

纳维-斯托克斯是七个千禧年大奖难题之一，每个都有一百万美元的奖金。

Navier-Stokes is one of seven Millennium Prize Problems, which have a million-dollar prize attached.

人类只解出了其中一个，而且只要在这个问题上取得重大进展，就足以保证获得菲尔兹奖，这个奖经常被比作诺贝尔奖。

Only one has been solved by a human, and merely making significant progress on one is enough to guarantee a Fields Medal, often compared to a Nobel Prize.

如果你现在脑子里正浮现出罗宾·威廉姆斯和斯特兰·斯卡斯加德在《心灵捕手》里争论菲尔兹奖的场景，那你不是一个人。

If you are currently in your head hearing Robin Williams and Stellan Skarsgard argue about a Fields Medal in "Good Will Hunting," you are not alone.

总之，纳维-斯托克斯问题是AI模型产生的最重要的科学成果之一，但它只是OpenAI这周发布内容的一个小小前奏。

In any case, the Navier-Stokes problem was one of the most significant scientific results produced by an AI model, and yet it was just a tiny precursor for what OpenAI released this week.

要理解OpenAI这些证明的规模，最好的办法是参考Proof Atlas列出的数学领域前500个未解难题清单。

The best way to understand the scope of OpenAI's proofs is to refer to a list of the top 500 open problems in mathematics produced by Proof Atlas.

这份清单是AI生成的，但它大致能代表这个领域里最著名的问题。

This is an AI-generated list, but it's a decent representation of the most well-known problems in the field.

OpenAI 完全解决了前500个问题中的90个。

OpenAI fully solved 90 of the top 500 problems.

还有几十个部分证明，这些也足以算作对该领域的重大贡献。

There's also dozens of partial proofs that would qualify as significant contributions to the field.

其中包括另外两个千禧年大奖问题的部分解答：黎曼猜想和霍奇猜想。

Among them were partial solutions to two additional Millennium Prize problems, the Riemann hypothesis and the Hodge conjecture.

今天这期节目，我不打算深入探讨这些问题的具体内容，因为它们都涉及研究生级别的理论数学，普通人基本上看不懂。

For the purposes of this episode today, I'm not going to get deep into what these problems actually are, as they all involve post-graduate level theoretical mathematics that is basically impenetrable to a layman.

举个例子，黎曼猜想可以说是数论中最重要的一个问题。

Just to make that point, the Riemann hypothesis is arguably the most important problem in number theory.

它说的是，黎曼ζ函数——这个函数与素数的分布有关——的零点只出现在负偶整数和实部为二分之一的复数上。

It states that the Riemann zeta function, which concerns the distribution of prime numbers, has zeros only at even integers and complex numbers with a real component of one-half.

不过，要衡量这件事有多重大，更简单的办法是看看专业数学家们的反应。

Instead, it's easier to gauge how big a moment this is by observing how professional mathematicians reacted.

罗格斯大学教授亚历克斯·孔托罗维奇写道：“准黎曼猜想？

Alex Kontorovich, a Rutgers professor, wrote, "Quasi-Riemann hypothesis?

你在开玩笑吧？

Are you kidding me?

如果是人类做到这一点，那绝对是菲尔兹奖，毫无疑问。

If a human did this, it would be an instant Fields Medal, no questions asked."

确实，最初的反应大多来自那些对AI在该领域的贡献持相对积极态度的数学家。

Indeed, most of the initial takes came from mathematicians that have been relatively positive about AI's contribution to the field.

多伦多大学的达伦·利特将这一时刻描述为数学家从花几十年研究问题转向验证AI生成的证明。

Darren Litt from the University of Toronto framed this moment as being about mathematicians shifting from working on problems for decades to verifying proofs produced by AI.

他评论说：“有趣。

He commented, "Fun.

看来数学家们有很多令人兴奋的工作要做。

Looks like mathematicians have a lot of exciting work to do.

如果我理解正确的话，其中一个结果是我一个猜想的一个非常特殊的案例，这也是我一个学生正在进行的一项更强工作的结果。”

If I understand correctly, one of the results is a very special case of a conjecture of mine, which is also a consequence of stronger work in progress by a student of mine."

数学家迈克尔·沃伦写道：“我没有看到什么特别出乎意料的东西。

Mathematician Michael Warren wrote, "I didn't see anything particularly unexpected.

现在实验室能解数学题了，所以他们花了一大笔钱，解了一堆问题。

Labs can solve math problems now, so they just spent a bunch of money and solved a bunch of problems.

从历史角度看，用棒球术语来说，今天就是巴里·邦兹打出第756支全垒打的那一天。

Historically speaking, in baseball terms, today would be the day that Barry Bonds blasted his 756th home run.

如果你在1987年告诉邦兹这件事，那简直难以置信。

If you would have told Bonds this in 1987, it would have been unbelievable.

但到了2007年呢？

But in 2007?"

不过，近年来在人工智能和数学交叉领域工作的人，还是感到了一种敬畏。

Still, there was a sense of awe from those who had worked at the intersection of AI and mathematics over recent years.

Anthropic的研究员莱文·达尔帕吉，在OpenAI发布结果之前一直在研究纳维-斯托克斯方程，他称这是“显然是数学史上最重要的时刻”。

Anthropic researcher Levin Dalpagi, who had been working on Navier-Stokes before OpenAI released their result, called this "obviously the most significant moment in mathematical history."

前OpenAI研究员阿瑟说，这个准黎曼结果是他在数学领域的个人“第37步”时刻。

Former OpenAI researcher Acer said the quasi-Riemann result was his personal Move 37 moment for mathematics.

第37步指的是AlphaGo在2016年击败围棋世界冠军李世石时，那步出人意料的棋。

Move 37 refers to AlphaGo's unintuitive move that helped it defeat Go World Champion Lee Sedol in 2016.

即使是没有数学背景的人，看到这些结果也会觉得相当震撼。

Even for those without mathematical training, the results were fairly mind-blowing.

Chubby 在 X 上第一次读完这些结果后写道：“这份清单简直离谱。

Responding to his first read through the results, Chubby on X wrote, "The list is absurd.

zeta 函数的零点半平面，这是百年来首个同类结果。

A zero-free half plane for the zeta function, which is the first result of its kind in over a century.

有理数上的希尔伯特第十问题。

Hilbert's 10th problem over the rationals.

CM 阿贝尔簇的霍奇猜想。

The Hodge conjecture for CM abelian varieties.

卡塔兰常数的无理性，还有几十个其他结果。

Irrationality at Catalan's constant and dozens more.

其中任何一个单独拿出来都足以成就一番事业，但很多人没注意到的一个数字是——

Any one of these would normally be a career, but the number that many aren't seeing is the following.

三。

It's three.

这是每个结果平均消耗的ChatGPT Pro计算时长。

That's the average hours of ChatGPT Pro compute per result.

一个月前，解决纳维-斯托克斯方程大约需要一万个代理和88小时。

A month ago, Navier-Stokes took them around 10,000 agents and 88 hours.

这种效率提升仅仅发生在几周之内。

That efficiency gain is within just a few weeks.

数学圈的人显然都震惊了。

Math Twitter obviously is shocked.

再说一次，这真的是智能爆炸正在发生。

Again, this is literally the intelligence explosion happening right now.

2027年将是超级智能的一年。

2027 will be the year of super intelligence.

我现在确信这一点了。

I'm now convinced of that."

不过，这次发布的一个大问题是，很难确切理解到底发布了什么内容。

Still, one big wrinkle in the release is the difficulty in understanding exactly what's been published.

上个月发布的纳维-斯托克斯结果还在等待数学家验证，而现在又有数百个新结果需要仔细研究。

The Navier-Stokes result published last month is still in the process of being verified by mathematicians, and this is hundreds of new results to pore over.

数学家弗朗切斯科·马吉指出了高产量AI数学的明显问题，他写道：“数学并不是简单地通过积累正确的陈述而增长的。

Mathematician Francesco Maggi pointed out the obvious issue with high-volume AI mathematics, writing, "Mathematics does not simply grow by accumulating correct statements.

结果必须被理解、联系、解释、挑战和重用。

Results have to be understood, connected, explained, challenged, reused.

在那之前，它们可能只是死文字。

Until that happens, they risk remaining lettera morta.

现在想象一下，再增加400个AI生成的证明。

Now imagine adding 400 more AI-generated proofs.

对于每一个证明，除非有数学家去研究它、理解它，并将其与现有的数学文化联系起来，否则它们本质上不也是同样的处境吗？

For each of them, until some human mathematician picks it up, studies it, and connects it to the existing mathematical culture, isn't it in essentially the same position?

所以我的问题很真诚：一次性发布数百个证明，其预期的数学价值到底是什么？

So my question is genuinely, what is the intended mathematical value of releasing hundreds of proofs at once?

如果没有足够的人愿意或能够阅读它们，会发生什么？

What happens if there simply aren't enough people willing or able to read them?

说清楚一点，我觉得数学家应该用一切必要的手段去发现数学，包括在那些看起来像外星人搞出来的数学材料里翻找，因为那里可能藏着特别了不起的东西。

To be clear, I think mathematicians should discover mathematics by any means necessary, including scavenging through artificial alien-looking mathematical material, since there may be extraordinary things to find there.

但人不是机器，因为注意力、理解力、品味和数学文化都是稀缺资源。

But humans are not machines, as attention, understanding, taste, and mathematical culture are scarce resources.

如果数学产生的速度远远超过数学家能消化的速度，那会怎么样？

What happens if the rate at which mathematics is generated becomes much greater than the rate at which mathematicians can absorb it?"

物理学家史蒂夫·苏更进一步问道：“如果AI一天的研究产出的数学比人类一个世纪能消化的还多，会发生什么？

Physicist Steve Hsu took it a step further, asking, "What happens when a single day of AI research produces more mathematics than humanity can absorb in a century?

想象一下，数百万个超人级别的研究智能体在同时工作。

Imagine millions of superhuman research agents at work.

形式系统也许能验证这些证明，但人类没有足够的背景知识去理解那些机器发明的概念背后的整个网络。”

Formal systems may verify the proofs, but humans won't have enough context to understand the underlying web of machine-invented concepts."

在他看来，我们可能很快就会进入一个阶段，数学和物理这些领域会失去可解释性。

In his view, we could quickly reach a period where fields like mathematics and physics lose interpretability.

他接着说，到那时，“AI就不再是科学工具了。

At that point, he continued, "AI is no longer a scientific tool.

人类接收到的，是从一个他们无法独立理解的更庞大的智能文明中挑选出来的解释。

Humans are receiving selected explanations from a much larger intellectual civilization they cannot independently grasp."

对话中另一个重要话题，是猜测这对数学作为一门学科意味着什么。

Another big strand of the conversation was speculation on what this could mean for mathematics as a field of academic study.

丹·麦卡迪写道：“OpenAI出于公关考虑在淡化这件事。

Dan McAdie wrote, "OpenAI is downplaying this for PR reasons.

如果你是数学家，你肯定感觉像核弹爆炸了，而你就站在震中。

If you're a mathematician, you must feel like a nuclear bomb hit and you're at ground zero.

推理模型才出现两年。

Reasoning models are two years old.

在这段时间里，它们从连基本算术都不会，发展到能解决人类几十年都解不出的问题。

In that time, they went from incapable of basic arithmetic to solving problems humans couldn't solve for decades.

数学只是开始。

Math is only the beginning.

AI将彻底改变整个人类科学事业。

AI will revolutionize the entirety of the human scientific endeavor.

我们大多数人并不真正理解这意味着什么。

Most of us don't appreciate what that means."

你能感受到丹所说的那种冲击，尤其是那些还在寻找自己位置的数学系学生。

And you can feel a bit of that shell shock that Dan is talking about, especially among mathematics students still trying to find their place in the world.

一个叫“基本无害的研究生”的人问道：“既然AI已经抢走了数学，我现在该转行做什么？”

Mostly Harmless Grad Student asked, "What can I pivot to now that AI has taken away math?"

他们后来解释说，这次发布“基本上把我项目里所有想参与或研究的问题和项目都炸没了。”

They later explained that this release, quote, "kind of nukes basically all the problems and projects I'd want to contribute to or work on in my program."

然而，虽然有很多人在哀叹一个领域的终结，但有些数学家却异常乐观。

And yet, if there were many lamenting over the apparent end of a field, some mathematicians were incredibly optimistic.

前华盛顿大学数学教授斯里拉姆·坎南写道：“对人类和数学来说，这是伟大的一天。”

Sriram Kannan, a former math professor at the University of Washington, wrote, "It's a great day for humanity and math.

如果你问公元前5000年到2026年1月之间的任何一位数学家，是否希望有一个数学精灵存在，100%的数学家都会说愿意。

If you asked a mathematician anywhere from 5000 BC to January 2026 whether they would like a math genie to exist, 100% of mathematicians would have said yes.

数学家们为了推动人类知识的进步，付出了巨大的机会成本。

Mathematicians give up significant opportunity costs to help advance human knowledge.

这毫无疑问，他们最终会适应这个新世界，并且会庆幸这一切发生了。

It's a no-brainer that they will eventually adjust to this new world and be glad this happened.

这只是一个极端但暂时的冲击，对‘我在数学上世界一流’这种身份认同的冲击。

It's just an extreme but temporary shock to 'I am world-class at math' as an identity."

另一个完全不同的话题是，这些理论数学问题的结果如何应用到现实世界中。

Another very different topic of conversation was how results in any of these theoretical mathematics problems apply to the real world.

埃尔德什问题大多只是智力练习，没有明确的应用。

The Erdős problems are mostly just intellectual exercises with no clear application.

即使是纳维-斯托克斯方程的证明，虽然非常令人印象深刻，但在现实世界中并没有明显的影响。

Even the Navier-Stokes proof, while very impressive, doesn't really have an obvious impact in the real world.

从事流体动力学问题（如飞机设计）的工程师们，使用简化版的纳维-斯托克斯方程就足够了。

Engineers working on fluid dynamics problems like aircraft design get by just fine using a simplified version of Navier-Stokes.

OpenAI 在这一批中解决的多数问题都类似：非常令人印象深刻，长期悬而未决，但最终并不那么实用。

Most of the problems that OpenAI solved in this batch are similar: very impressive, long-standing, but ultimately not all that practical.

不过，还是有一些例子可能对现实世界产生影响。

Still, there were a few examples that could make an impact in the real world.

其中一个是对矩阵乘法理论效率的显著提升，而矩阵乘法正是支撑AI推理的数学基础。

One was a dramatic improvement to the theoretical efficiency of matrix multiplication, the math that underpins AI inference.

康奈尔大学数学教授史蒂文·斯特罗加茨评论说：“这里有很多令人震惊的成果，但这一项尤其惊人。

Cornell math professor Steven Strogatz commented, "Many staggering results here, but this is a particularly amazing one.

矩阵乘法的指数已经不超过2.25了。

The exponent for matrix multiplication is no more than 2.25.

之前的世界纪录大概是2.37。

The previous world record had been something like 2.37.

这种进步就像鲍勃·比蒙的跳远一样，是个巨大的飞跃。”

This leap in progress is like Bob Beamon's long jump."

对一些人来说，最有趣的不是里面有什么，而是缺了什么。

For some, what was most interesting was not what was there, but what was missing.

比特币安全研究员贾斯汀·德雷克指出，在OpenAI发布的722项数学成果中，密码学突破的数量“明显不足”。

Bitcoin security researcher Justin Drake noted a, quote, "striking under-representation of cryptographic breakthroughs among the 722 mathematical results OpenAI published.

我亲眼目睹过美国政府审查学术量子密码分析成果的情况。

I've witnessed firsthand the US government censoring academic quantum cryptanalysis results.

幕后干预是我最可能的情况。

Backroom interventionism is my base case."

德克萨斯大学计算机科学教授斯科特·阿伦森在他的博客上补充说：“我的消息来源告诉我，AI公司现在已经开始谨慎而低调地调查他们最新的内部模型是否能破解重要的加密协议和原语。”

University of Texas computer science professor Scott Aaronson added on his blog, "My sources tell me that AI companies have now started, gingerly and discreetly, investigating whether their latest internal models can break important cryptographic protocols and primitives.

如果它们能，那么在全世界其他人发现同样的事情之前，提前做好准备当然会很好。”

If they can, then it would certainly be nice to get ahead of things before the rest of the world figures out the same."

换句话说，如果OpenAI的内部模型能破解主要的加密方案，那不仅仅是比特币的问题，而是几乎所有数字世界所依赖的加密技术的问题。

In other words, if OpenAI's internal model can break major cryptographic schemes, that's not just a problem for Bitcoin, it's a problem for the cryptography that underpins pretty much all of the digital world.

尽管如此，在所有这一切中，也许最引人共鸣的讨论是我们是否能期待其他领域也发生同样的事情。

Still, with all of this, maybe the most resonant conversation was whether we can expect the same thing to happen to other fields.

纽约大学斯特恩分校金融学副教授阿尔皮特·古普塔写道：“如果你认为像数学这样的AI进步不会延伸到经济学和其他社会科学，那你就是在自欺欺人。”

Arpit Gupta, an associate professor of finance at NYU Stern, wrote, "You're deluding yourself if you think AI advances like in math aren't coming to economics and other social sciences."

沃顿商学院经济学教授伊桑·莫利克已经开始看到这一点，他评论说：“在经历了一个短暂但侵蚀机构的水货科学时代之后，我们越来越有可能看到AI带来的两场革命。”

Wharton economics professor Ethan Mollick is already starting to see it, commenting, "After a brief but institution-eroding slop-science era, it increasingly looks like we are going to have two revolutions from AI.

第一，所有已发表的内容都将以人类科学家从未预料到的方式被重新阅读和重新评判。

One, everything ever published will be reread and rejudged in ways that human scientists never anticipated.

第二，新的发现会开始快速涌现。

Two, novel discoveries will start to come fast.

我看到AI在我所在的经济社会学领域做新颖工作的能力在快速提升，几乎自主的研究已经能达到顶级期刊的水平。

I am seeing rapid increases in the ability of AI to do novel work in my field of economic sociology, with nearly autonomous research getting to top journal level."

不过，一个关键问题是，其他所有领域是否都能像数学那样被处理。

Still, a big question is whether all other fields are able to be approached in the same way as mathematics.

总的来说，这些数学证明并不是通过推理产生的新想法。

By and large, these math proofs were not generated by reasoning into novel ideas.

它们大多属于可以通过大数据方法和模式识别来解决的问题范畴。

They were largely within the domain of problems that can be solved with big data approaches coupled to pattern recognition.

把它们称为蛮力可能有点简单化，但这些确实是可验证的问题，非常适合强化学习的范式。

It would be reductive to call them brute force, but these are all verifiable problems that fit neatly into the reinforcement learning paradigm.

华盛顿大学的计算机科学教授佩德罗·多明戈斯总结道：“数学是莫拉维克悖论的经典例子。”

Pedro Domingos, a computer science professor at the University of Washington, summed it up by commenting, "Mathematics is a classic example of Moravec's paradox.

对人类来说很难，对机器来说却很容易。”

Hard for humans, easy for machines."

谷歌杰出科学家佩蒙·米兰法尔在阐述他的发展路线图时写道：“自动化的速度与领域熵成反比。”

Unpacking his roadmap for where this is all going, Google distinguished scientist Paymon Milanfar wrote, "Speed of automation is in inverse proportion to the entropy of the subject domain.

数学和编程结构清晰、条理分明，所以AI能做得这么好。

Math and coding are structured and orderly, which is why AI is able to do them so well.

按难度排序，接下来是硬科学。

In order of difficulty, the hard sciences are next.

它们可能受严格的物理定律支配，但依赖嘈杂的测量和实验室实验，这让事情变得复杂。

They may be governed by strict physical laws, but are complicated by dependence on noisy measurements and laboratory experimentation.

金融市场则更难。

Financial markets are more difficult still.

它们是复杂的自适应系统，受人类心理影响。

They are complex, adaptive systems subject to human psychology.

虽然历史数据丰富，但底层统计是非平稳的，变化很大。

While past data is abundant, the underlying statistics are non-stationary and highly variable.

临床诊断和医学决策的难度远超大多数人的想象。

Clinical diagnostics and decision-making in medicine are far more difficult than most people realize.

这风险极高，需要综合各种零散的信息，还要应对各种古怪的人类行为和生理特征。

It's very high stakes, it requires synthesizing very fragmented signals, and dealing with idiosyncratic human behavior and biology.

这简直是信息不全的噩梦。

It's a nightmare of incomplete information.

法律、法理学和法律解释大多时候基于结构化的文本和先例，但很大程度上依赖模糊的语言、不断变化的社会规范、有说服力的修辞和人的判断。

The law, jurisprudence, and legal interpretation operate on structured text and precedent most of the time, but rely heavily on ambiguous language, evolving societal norms, persuasive rhetoric, and human judgment.

它们短期内不会实现自动化。

They won't be automatic anytime soon.

文化、艺术和战略领导力是个奇怪的组合，但我觉得它们都很难被自动化取代。

Culture, art, and strategic leadership are an odd mix, but I think all of them are quite safe from automation.

它们是混乱和人类创造力的巅峰。

They are the pinnacle of disorder and human creativity.

它们依赖隐性知识、对世界的直觉感知、情商、对文化共识的理解，以及处理复杂的道德权衡。

They rely on tacit knowledge, intuitive perceptions of the world, emotional intelligence, understanding of cultural consensus, and navigating complex moral trade-offs.

所以，高兴吧。

So rejoice.

艺术家、律师、医生和CEO，我们还会拥有很长时间。

We'll have artists, lawyers, doctors, and CEOs for a long time yet."

事实上，我觉得从现在开始，有两条非常引人入胜的路径值得关注。

In fact, I think from here, there are two incredibly fascinating paths to watch.

一条是Paymon猜测的，这种颠覆会以什么方式进入哪些领域，或者反过来，向我们展示人类或系统的惯性障碍，使得这种颠覆在其他领域不那么不可避免。

One is what Paymon was speculating on, which fields this sort of disruption comes to in what ways, or alternatively shows us that there are human or systemic inertia road bumps that make this disruption not as inevitable in other areas.

但第二件我觉得值得关注的事情是，数学家们会怎么利用这一切。

But the second thing that I think will be interesting to watch is what mathematicians do with all of this.

用之前那位评论者的话说，当每个数学家都有一个数学精灵时，会发生什么？

To use the words of the previous commenter, what happens when every mathematician has a mathematical genie?

这会否定他们所有的努力和学习，还是让他们能够做到至今无法想象的事情？

Does it negate all of their hard work and learning, or does it allow them to do things that are undreamed of as of yet?

在接下来的几个月里，我们就会开始看到答案。

Over the coming months, that is what we will begin to see.

今天就到这里，这就是今天的AI每日简报。

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

感谢你的收听或观看，一如既往。我们下次再见，祝一切安好。

Appreciate you listening or watching, as always, and until next time, peace.