人工智能如何改变工作的意义

现在基本上没人还需要被说服，AI正在改变工作的方式。

Pretty much no one at this point needs to be convinced that AI is changing how work works.

确实，它改变我们执行现有任务的速度和效率，而且这些变化的规模和种类似乎还在不断增加。

Certainly, it changes the speed and efficiency with which we do existing tasks, and the magnitude and kind of those changes seems to grow over time.

比如，虽然几年前我们有些人可能用AI来写邮件，但现在很多人都有个人代理常驻在邮箱里，当有真正需要回复的邮件时，它会通知我们，而且往往已经帮你起草好了回复。

For example, while some of us may have used AI to write emails a couple of years ago, now many of us have personal agents sitting in our email perpetually, notifying us when there's something we actually have to respond to, often after having drafted the response itself.

但是，正如我一直说的，这只是AI真正改变工作方式的一个冰山一角。

And yet, as I've always argued, that is just the tip of the iceberg when it comes to how AI will actually change work.

今天的故事就是一个很好的例子，一家知名的金融研究公司临时变成了生物研究实验室，与Anthropic合作，为蝎子毒液寻找一些可能的解决方案。

A great example of that comes in today's story, where a well-known financial research firm turned itself temporarily into a bio-research lab and collaborated with Anthropic to come up with some potential answers to scorpion venom.

这不仅仅是一个新奇事，也不仅仅是标题党，我认为这能让我们看到AI在职场中的下一步发展方向。

More than a novelty and more than a clickbait headline, I actually think that this can tell us quite a bit about where AI at work is headed next.

AI日报是一个关于AI领域最重要新闻和讨论的每日播客和视频节目。

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

好了，朋友们，在我们深入之前，先快速说一些通知。

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

首先，感谢今天的赞助商：KPMG、CData、Granola 和 Blitzy。

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

想收听无广告版本，可以去 patreon.com/aidailybrief，或者在 Apple Podcasts 订阅。

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

如果想了解如何赞助这个节目，请发邮件到 sponsors@aidailybrief.ai。

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

顺便说一下，如果你想找到我每天提到的所有链接，有两种方式。

And by the way, if you are just looking for all the links that I talk over on any given day, there are two ways you can do it.

第一种，可以去网站上任何一集的配套页面。

First, you can go into the episode companion page on the website for any episode.

你会看到所有内容按关键数字、关键话题、关键引用分类。

You're going to see everything split up by key numbers, key topics, key quotes.

另外，你也可以订阅我们的通讯，网站上也提供订阅链接，里面会有我讨论的所有内容的摘要，以及关键链接。

But you can also just subscribe to our newsletter, which is also linked from the website, which is going to have summaries of everything I discuss, as well as the key links.

所有信息都在 aidailybrief.ai 上。

Check it all out at aidailybrief.ai.

好了，废话不多说，我们直接开始吧。

But with that out of the way, let's dive in.

今天一开始，我们先聊聊个人助理这个主题的最新动态。Meta 的 Muse 在短短 40 天内就达到了美国 1000 万次的下载量。

We kick off today with an update on the personal assistant theme, where Meta's Muse has reached 10 million US downloads in just 40 days.

这个速度非常快。

That is a very quick pace.

实际上，这让 Muse 成为仅有的约十几个在类似时间内达成这一里程碑的应用之一。

In fact, it makes Muse one of only about a dozen apps to have achieved that milestone in a similar period of time.

其他 11 个几乎都是手机游戏，特别是像《精灵宝可梦GO》、《堡垒之夜》和《绝地求生》这类爆款手游。

The other 11 are almost exclusively mobile games, specifically the smash hits of mobile gaming like "Pokémon Go," "Fortnite," and "PUBG."

只有两个非游戏应用增长得这么快，那就是 Disney+ 和 Meta 自家的 Threads。

Only two non-gaming apps have grown this quickly, which is Disney+ and Meta's own Threads.

有趣的是，ChatGPT、Claude 和 Gemini 作为手机应用都没有这么快地增长，虽然它们最终都登上了下载榜的榜首。

Curiously, none of ChatGPT, Claude, or Gemini grew that quickly as a mobile app, even though they eventually made it to the top of the download charts.

每个聊天机器人都是先推出网页版，手机应用后来才上线，错过了早期爆发式增长的阶段。

Each chatbot was introduced as a web app first, with the mobile app coming later and missing the early phases of explosive growth.

第一个月的数据已经出来了，Sensor Tower 还公布了关于 Muse 发布的其他几个有趣数据点。

With its first month in the books, Sensor Tower had several other interesting data points about the Muse rollout as well.

Meta 将十月总广告支出的 39% 分配给了 Muse，比九月的 23% 有所上升。

Meta has allocated 39% of their total advertising spend for October to Muse, which is up from 23% in September.

这使得 Muse 在本月美国广告印象量排名中升至第 21 位。

That has boosted Muse to become the 21st largest brand by impressions for this month in the United States.

Sensor Tower 的数据显示，Muse 的七天留存率为 30%，十四天留存率为 23%，而 ChatGPT 的七天和十四天留存率大约在 25% 左右，iOS 应用的平均留存率在 30 天后大约为 5%。

Sensor Tower data showed that Muse has a 30% retention rate after seven days and a 23% retention rate after 14 days, which compares to ChatGPT's seven- and 14-day retention rates at around 25%, and the average retention rates for an iOS app at around 5% after 30 days.

有意思的是，关于 Muse 的留存率也有一些批评的声音。

Interestingly, there has been some criticism around Muse's retention rate.

不过，那些对比是将 Muse 与 Instagram、Facebook 和 Google 应用进行比较，而这些应用是有史以来用户粘性最高的消费类应用。

However, that analysis compares Muse unfavorably against Instagram, Facebook, and the Google app, which are some of the stickiest consumer apps ever created.

看起来 Muse 仍然处于增长初期，上周日活跃用户达到 250 万，环比增长了 15%。

It also looks like Muse is still early in its growth phase, with daily active users reaching 2.5 million last week, which was up 15% week over week.

简单来说，是的，他们确实在花大量广告费推广它，但 Meta 似乎确实有一款热门产品在手。

TLDR is, yes, they are spending a ton of advertising to push it, but Meta does seem to have a hit on their hands.

接下来的问题不是关于短期留存，而是最终人们会在生活中给这个应用找到什么位置，如果真的有这样的位置的话。

The next question isn't going to be about short-term retention, but about where people ultimately find the app fitting into their life, if it does.

X平台上的Signal写道，“大多数消费产品通常有两种留存类型之一。

Signal on X writes, "Most consumer products typically have one of two types of retention.

一种是欲望驱动型，纯粹的多巴胺，比如Instagram、TikTok和游戏。

One is desire driven, which is just pure dopamine, like Instagram, TikTok, and games.

它们往往连通知都不需要，因为你知道总有奖励在等着满足你的欲望。

They often don't even need notifications because you know there is a reward for your desire always waiting.

第二种是依赖驱动型，比如电子邮件、日历，甚至信用卡和银行业务。

The second is dependency driven, like emails, calendars, even credit cards and banking.

大多数人必须用这些。

Most people have to do this.

这不一定是因为他们想要，而是不得不。

It's not something they necessarily desire.

现在个人AI助手面临的挑战是，它们占据了一个奇怪的中间地带：大多数时候它们不能像第一类那样提供情感回报，让人上瘾，但又不够可靠或不可或缺，无法成为纯粹的基础设施，也就是说，它们会出错，而且使用起来有很多摩擦。

The challenge with personal AI agents right now is that they occupy a weird third space where they aren't emotionally rewarding most of the time to become entertainment like the first category, and they're not reliable or indispensable enough to become pure infrastructure, i.e. they fail and there's a lot of friction.

除了纯粹的研究型任务，哪怕只出一次错，也常常足以让人不再回头，因为它的实用价值就打了折扣。

Outside of pure research-driven tasks, even one failure is often enough to not come back for the utility value proposition."

然后他接着推测了不少由此带来的机会，但我觉得，尽管我们现在看到了一些早期的积极信号，但要记住，在理解个人代理相关行为方面，我们还处在非常早期的阶段。

Now, he goes on to speculate a bunch about what opportunities that opens up, but I think even though we're seeing early positive signals, it is worth remembering that we are still very early days in terms of understanding the behavior surrounding personal agents.

Muse 本身也面临着一些挑战。

Muse also is not without its challenges.

这周早些时候，研究人员报告说，Muse 允许用户分享搜索、对话和图片的功能，存在一个特殊问题，那就是会让其他人也能看到这些对话。

Earlier this week, researchers reported that the feature by which Muse allows users to share searches, conversations, and images had a particular challenge that we're allowing other people to see those conversations.

一般来说，这类分享功能通常会生成一个独特的链接，任何拿到这个链接的人都能用它打开一个网页，看到上面的内容。

In general, those types of share features usually involve creating a unique URL that anyone who has that URL can use to load a page hosting the content.

而防止敏感信息被访问的方式，就是靠链接的复杂性，理论上这些链接不容易被猜到。

The way that sensitive information is kept from being accessed is the complicatedness of the URLs that theoretically can't easily be guessed.

然而，Meta 的工程师在设计这些链接时做了一个特别的决定，使得这些链接很容易被发现。

However, a particular design decision that Meta's engineers made around the construction of those URLs has made them easy to discover.

据 Ars Technica 报道，安全公司 Clarrock 的研究人员表示，他们成功列举出了 6,800 个 Muse 用户为了互相分享对话而创建的链接。

As a result, wrote Ars Technica, researchers from security firm Clarrock said they were able to enumerate 6,800 links Muse users created for sharing conversations with each other.

他们打开了几百个链接之后，发现了一些明显不打算公开的信息，比如一个小企业主的贷款申请、一个房地产投资者的内部业绩报告，还有一家全国性营销公司的内部收入审查。

After opening a few hundred of them, they continue, the researchers found information that was clearly not intended to be publicly available, including things like a small business owner's loan application, a real estate investor's internal performance report, and a national marketing company's internal revenue review.

Meta 表示他们已经暂时关闭了这个功能，并且正在加强安全措施，确保类似的事情不再发生。

Meta has said that they've temporarily taken down the feature and are working to harden it to make sure that doesn't happen.

下一个要聊的是另一个你会和智能代理打交道的地方——你的汽车。

Next up, is another place that you're going to interact with agents your car?

差不多一年来，Android Auto 里一直有一个基础版的 Gemini 助手。

For almost a year, a basic version of the Gemini assistant has been available in Android Auto.

这个代理主要负责地图和音响系统的语音控制，但也能在你开车时发短信，并在网上查找信息。

The agent mostly functioned as a voice control for maps and the stereo system, but could also send text messages while you were driving and find information on the web.

不过，它并没有连接到你的个人数据，而人们已经开始期望个人代理能做到这一点。

It was not, however, connected to your personal context as users have begun to expect from a personal agent.

用户反馈也表明，它其实不太好用，经常连基本的任务都完不成。

User feedback also suggested that it didn't really work that well and often failed at basic tasks.

新版本增加了从你其他 Google 服务中读取数据的能力。

The new version adds the ability to access data from the rest of your Google-based services.

谷歌说，现在你可以在路上问助手自己会不会错过航班，它会翻遍你的收件箱找到必要的信息，同时还能在谷歌地图里查看交通状况。

Google says you can now do things like asking the assistant whether you're going to miss your flight while en route, and the agent can rifle through your inbox to find the necessary details while also checking traffic conditions in Google Maps.

另一个例子是，仅凭一张照片就能问路。

Another example was the ability to ask for directions based solely on a photo.

谷歌表示，现在Gemini可以仅凭一张你在卡座里喝浓缩咖啡的照片，就指引你到上周朋友带你去的那家咖啡店。

Google said Gemini could now direct you to a coffee place your friend took you to last week based only on a picture of you enjoying your espresso in a booth.

不过，我目前还是不太确定这些功能最终到底有多重要。

Now, I'm still quite unsure about how important these features are ultimately going to be.

一方面，我觉得这种跟汽车互动的功能可能会变得非常普遍，以至于我们以后都难以想象没有它们会怎样；但另一方面，我也看到有很多人可能会觉得，与其费劲去设置和集成到另一个设备上，不如直接拿起就放在中控台旁边的手机，一样能获得这些信息。

On the one hand, I could see this type of interaction with your car becoming something that is so common that we have a hard time imagining that we didn't always have those features, and yet at the same time, I can see a lot of people deciding that the setup and integration into another device isn't worth all the hassle when they can already just pick up their phone that's probably already sitting right there on their console and get the same information.

无论如何，看起来公司们会在这一领域继续探索，因为特斯拉也已经把Grok Bot集成到了他们的车里，你可以想象其他公司也会跟进。

Whatever the case, it seems that companies are going to continue to explore that, as Tesla has also integrated Grok Bot into their vehicles, and you have to imagine everyone else is going to follow.

转到市场方面，Anthropic的一份更新版财务披露文件被泄露了，引发了金融媒体的又一轮焦虑。

Moving over to markets, an updated financial disclosure from Anthropic has leaked, sparking another round of hand-wringing in the financial press.

《华尔街日报》拿到了Anthropic更新后的招股书草稿，其中包含了今年上半年的完整审计财务数据。

The Wall Street Journal got their hands on Anthropic's updated prospectus draft, which included fully audited financials for the first half of the year.

他们的报道聚焦于两个吓人的大数字：上半年净亏损800亿美元，以及未来几年高达7500亿美元的计算支出。

Their reporting focused on two big, scary numbers, an $80 billion net loss for the first half of the year, and up to $750 billion in compute spending over the coming years.

800亿美元的净亏损明显大于之前版本招股书中报告的2025年420亿美元亏损。

The $80 billion net loss is substantially larger than the $42 billion loss for 2025 reported from the previous edition of their prospectus.

但这两个数字都源于对股票授予的相同会计处理方式。

But both numbers stem from the same accounting treatment of stock grants.

其中约720亿美元的亏损是账面会计变动，实际上半年运营亏损为87亿美元。

Around $72 billion of the loss was an on-paper accounting change, leaving $8.7 billion as the actual operating loss for the first half.

针对声称这是任何寻求上市的公司中最大亏损的说法，财经播客主持人杰克·法利评论道：“如果这800亿美元的会计亏损在经济上真是如此，那才值得担忧。”

Responding to the claim that this is the biggest loss of any company seeking to go public, financial podcaster Jack Farley commented, "This would be worrisome if the $80 billion accounting loss by Anthropic were economically real in any way.

好消息是，事实并非如此。

Good news, it isn't.

当一家公司发行可转换票据和认股权证，而市值增长了1000倍时，做空看涨期权的账面亏损是巨大的，但并非根本性的。”

When a company issues convertible notes and warrants and the market cap 1,000Xs, the on-paper loss from being short calls is massive, not fundamental."

对其他人来说，更大的问题是7500亿美元的计算支出。

Now for others, the bigger deal is the $750 billion in compute spending.

报道提到，其中大约4500亿美元是确定不可取消的承诺，将在未来七到十年内到期。

The reporting suggested that of that, firm uncancellable commitments were around $450 billion and come due over the next seven to 10 years.

换句话说，这是一个很高的支出水平，但分摊到十年时间里就可行多了。

In other words, it's a very high level of spending, but one that's far more tenable spread out over a decade.

在更大的数字里可能还有重复下单的成分，Anthropic 对新兴云服务商做出软承诺，是希望他们真能建成新的设施。

There also could be an element of double ordering in the larger number, with Anthropic making soft commitments to neoclouds in hopes they can actually complete new facilities.

《华尔街日报》评论说，引用原文：“Anthropic 需要比市场能提供的更强大的计算能力。”

The Journal commented that, quote, "Anthropic needs more computing power than the market can provide."

人们特别关注的一个数字是上半年的营收，达到163亿美元。

The one other number that people are laser focused on is the revenue number of $16.3 billion for the first half.

Anthropic 曾声称截至7月的年化营收率为650亿美元，虽然这两个数字可能同时成立，但实际入账的营收比预期要低一些。

Anthropic had claimed a revenue run rate of $65 billion for the year up to July, and while both of those things can be true, actual booked revenue yields a lower number than might've been expected.

在上周的投资者日上，Anthropic 表示第三季度又增加了168亿美元的营收，环比增长45%，但这个未经审计的小道数字不会包含在IPO的招股说明书中。

At last week's Investor Day, Anthropic said that they added another $16.8 billion in revenue in Q3 for a 45% quarter-over-quarter growth rate, but that unaudited whisper number won't be included in the prospectus when they IPO.

其余报道内容主要涉及之前媒体已经报道过的风险披露，比如对齐问题、数据中心遭到抵制，以及客户集中度风险。

The rest of the reporting largely covers risk disclosures that were already in the press, things like alignment issues, the data center backlash, and customer concentration.

说实话，这些泄露的信息基本上只是印证了大家原本就有的看法。

Realistically, these leaks mostly have served to confirm preexisting biases.

800亿美元的净亏损给AI批评者提供了他们下一期播客的新素材，但在我看来，很难想象有人会感到惊讶，毕竟Anthropic发行了大量股票期权，而且最终会在算力上花费超过五千亿美元。

The $80 billion net loss gives AI critics some new material for their next podcast, but it strikes me as fairly difficult to imagine that anyone is surprised that Anthropic issued a ton of stock options and will spend more than a half trillion on compute when all is said and done.

和SpaceX的IPO一样，这次股票发行关注的重点不是过去几个季度，而是人们对未来十年的预期。

Like the SpaceX IPO, this stock offering is not going to be about the past few quarters, it's going to be about what people believe about the next decade.

不过，市场的紧张情绪还是很重要，或许这也就解释了为什么一些投资者在推动Anthropic调整他们的目标。

Still, market skittishness matters, and perhaps explains why some investors are pushing Anthropic to moderate their targets.

上周，路透社报道称，一些投资者对据传的2万亿美元估值表示反对，认为1.5万亿美元更合适。

Last week, Reuters reported that some investors are pushing back on the rumored $2 trillion valuation, and would be more comfortable at $1.5 trillion.

要知道，这两个数字都来自投资者，我们还没有听到Anthropic或其投行方面的任何表态。

Keep in mind, both numbers are coming from investors, and we've heard nothing attributed to Anthropic or their investment bankers.

与此同时，OpenAI正在加紧完成最新一轮融资，抢在Anthropic IPO之前。

Meanwhile, OpenAI is rushing to complete their latest round of fundraising ahead of the Anthropic IPO.

关于这轮融资的报道最早在上月底出现，暗示OpenAI寻求以1.4万亿美元的估值融资300亿美元。

Reports of the round first circulated towards the end of last month, suggesting OpenAI was seeking $30 billion at a valuation of $1.4 trillion.

现在，消息人士告诉路透社，OpenAI 正试图在几周内完成这笔交易，以赶在 Anthropic 的 IPO 之前，并应对随之而来的市场条件。

Now, sources tell Reuters that OpenAI is trying to close the deal in weeks to get ahead of the Anthropic IPO and whatever conditions that brings to market.

消息人士称，OpenAI 还希望利用 GPT-6 Astra 发布后最近的增长势头，但即使在这个疯狂的行业里，这笔交易的速度也异常快。

The sources said that OpenAI is also keen to capitalize on recent growth after the release of GPT-6 Astra, but this is an unusually fast deal even for this crazy sector.

路透社写道：“这种加速凸显了这两家 AI 开发商在争夺资本方面的激烈程度，因为双方都在追求万亿美元的估值，同时市场对利率上升、科技股大幅波动以及 AI 估值审查日益严格的担忧也在加剧。”

"The acceleration," writes Reuters, "underscores how closely the two AI developers are competing for capital as both pursue trillion dollar price tags and growing concern about rising interest rates, significant volatility in technology stocks, and rising scrutiny of AI valuations."

这些关于资金的讨论到年底只会变得更加激烈。

These money conversations are going to get nothing but more frenetic to end the year.

不过，今天的 AI 每日简报就到这里。

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

一如既往，感谢你的收听或观看。

Appreciate you listening or watching, as always.

下次再见，祝一切安好。

And until next time, peace.

如果你在企业中负责 AI 项目，你已经知道，现在的差距不在于能力，而在于执行。

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

这就是为什么毕马威的“你可以与AI同行”节目回归了，新一季邀请了像Emma公司的Sarojit Chatterjee、Writer公司的Muhibbib、McKesson的CIO Ellery Fisher等领导者，一起讨论实际执行中的问题，哪些有效，哪些无效，以及如何从试点项目真正走向大规模影响，涵盖战略、数据准备、治理、员工和价值等方面。

That's why KPMG's You Can with AI is back with a new season featuring conversations with leaders like Sarojit Chatterjee of Emma, Muhibbib of Writer, McKesson CIO Ellery Fisher, and others focused on practical execution, what's working, what's not, and what it actually takes to move from pilots to real scaled impact across strategy, data readiness, governance, workforce, and value.

当然，这个节目由我，纳撒尼尔·惠特莫尔，共同主持。

And of course, it's co-hosted by me, Nathaniel Whittemore.

去www.kpmg.us/aipodcasts收听和订阅吧。

Go listen and subscribe at www.kpmg.us/aipodcasts.

就是www.kpmg.us/aipodcasts。

That's www.kpmg.us/aipodcasts.

本期节目由CData赞助。

This episode is brought to you by CData.

推出AI的公司总是碰到同一个难题。

Companies rolling out AI keep hitting the same wall.

Token花费不断攀升，安全问题阻碍了AI代理的发展，而且没人真正知道AI是如何被使用的。

Token spend is climbing, security is holding agents back, and no one knows how AI is truly being used.

根本原因在于，AI与业务运行所依赖的系统和上下文脱节了。

The root cause, AI is cut off from the systems and context your business runs on.

所以CData就打造了Connect AI Gateway，这个网关就是你的AI和你的系统之间的一个控制点。

That's why CData built Connect AI Gateway, one control point between your AI and your systems.

它基于那套支持Palantir、谷歌、Salesforce以及数百家企业的连接层构建，每个AI代理都能获得它所需的数据和工具。

Built on the connectivity layer powering Palantir, Google, Salesforce, and hundreds more, each agent gets exactly the data and tools it needs.

它能把治理细化到每一条记录，而且让你终于能在同一个地方看到整个组织在AI使用上的全貌。

It governs down to the record and lets you finally see your org's complete AI usage in one place.

更准确的答案，大幅降低的令牌成本，再加上一个每次交互都越来越懂你业务的网关。

Accurate answers, a fraction of the token cost, and a gateway that learns your business with every interaction.

更多详情，请访问cdata.ai。

See how at cdata.ai.

我开会的时候，就完全沉浸在其中。

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

我脑子里想的是如何通过反复迭代和创造性的交流，真正推动目标前进。

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

我不会去想的，是怎么记录要点、追踪待办事项，或者那些琐碎的事。

What I'm not thinking about is capturing takeaways, tracking to-dos, or any of that.

而这时，Granola就派上用场了。

And that's where Granola comes in.

Granola是一款AI驱动的记事本，能捕捉你会议中的内容，并将其转换成干净、结构清晰的笔记，把决策和行动事项都提取出来，方便查找。

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

无需任何设置或配置。

There's no setup and no configuration.

它自然会融入你现有的工作方式。

It just fits into how you already work.

对我而言，这意味着我可以一直保持创意状态，而Granola确保这些创意真正转化为行动。

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

一旦你在第一次会议中尝试了Granola，就很难再离开它了。

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

你可以在granola.ai/brief上完全免费试用。

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

就是granola.ai/brief，帮你把时间找回来。

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

这就是大多数传统系统现代化项目失败的原因。

Here's why most legacy modernization projects fail.

因为做这些工作的AI无法理解大规模代码库。

The AI doing the work can't understand code bases at scale.

它只能看到一小段上下文，检查语法，却错过了遍布整个全球应用生态系统中多年积累的决策。

It sees a small slice of context, examines syntax, and misses years of decisions distributed across the global application ecosystem.

Blitzy用一种方法解决了所有问题，就是它一贯的方式。

Blitzy solves this the way it solves everything.

在任何迁移开始之前，Blitzy的智能体就扎根于你的代码，将整个遗留系统逆向工程成一个持久的知识图谱。

Grounded in your code before any migration begins, Blitzy's agents reverse engineer the entire legacy system into a persistent knowledge graph.

每一个依赖、每一个约束、每一个过去只存在于某个工程师脑子里的隐性知识。

Every dependency, every constraint, every piece of tribal knowledge that used to live in one engineer's head.

基于这种理解，Blitzy自主执行语言迁移、框架升级以及从单体架构到微服务的转型，并全程进行端到端验证。

From that understanding, Blitzy autonomously executes language migrations, framework upgrades, and monolith to microservices transformations, all validated end to end.

Blitzy的一位客户在16周内用编码智能体将一套耗资1000万美元的单体保险系统进行了现代化改造，而传统方式需要137周。

One Blitzy customer modernized a $10 million monolithic insurance stack in 16 weeks against a 137-week baseline with coding agents.

这就是9倍的压缩效率。

That's 9X compression.

一边消除技术债务，一边加速产品路线图。

Retire technical debt while accelerating your roadmap.

详情请访问 blitzy.com。

See how at blitzy.com.

网址是 B-L-I-T-Z-Y 点 com。

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

欢迎回到AI日报。

Welcome back to the AI Daily Brief.

今天我们来聊聊蝎子抗毒血清能告诉我们AI的未来是什么样子。

Today we're discussing what scorpion antivenom tells us about the future of AI.

真的，这就是本期节目的主题，或者说至少是那些与这个吸引眼球的标题相关的故事，而这些故事所代表的主题才是节目更广泛的话题。

Seriously, that's actually the subject of the show, or at least a set of stories that relate to that attention-grabbing headline, and the themes that they represent are the broader subject of the show.

AI新闻聚合者安德鲁·柯伦本周早些时候发帖说：“药物发现、新疾病疗法、制药和生物医学的进展，以及目前正在讨论的直接分红或股票。”

AI news aggregator Andrew Curran posted earlier this week, "Drug discovery, new disease cures, advancements in pharma and biomedicine, along with direct dividends or shares as is currently being discussed.

这些是扭转公众看法的唯一可行办法。

These are the only viable ways to turn public sentiment around."

这个观点你经常听到，就是说要让人们不再讨厌AI，我们得赶紧拿出那些承诺过的疾病疗法。

And this is an idea that you see bandied about a lot, that to actually get people to stop disliking AI, we got to hop to and give them some of those disease cures that they've been promised.

我其实觉得，生物学和AI结合带来的新动向，引发的问题远不止那一个，不过那个问题当然也很重要。

I actually think that the new activities around biology and AI bring up a much larger set of questions than just that one, although that one certainly matters as well.

所以，我们先聊聊那些涉及生物和AI交叉领域的故事，特别是那个疯狂的关于金融公司Citrini Research研究蝎子蜇伤疗法的消息。

So first, let's talk about a couple of the stories at the intersection of bio and AI, particularly this crazy one with financial firm Citrini Research working on scorpion sting cures.

不过第一个消息其实来自OpenAI。

But the first actually comes from OpenAI.

这家公司宣布收购了一家AI药物发现初创公司，以扩展其生物技术业务。

The company has announced that they have acquired an AI-powered drug discovery startup to build out their biotech offering.

OpenAI在周二宣布，他们收购了成立四年的AI生物科技初创公司Orchestra Bio。

OpenAI announced on Tuesday that they'd acquired four-year-old AI biotech startup Orchestra Bio.

交易条款没有披露，Orchestra去年最后一次融资2000万美元。

Terms of the deal were not disclosed, and Orchestra last raised 20 million in venture funding last year.

Orchestra开发的人工智能代理，旨在协助药物发现的整个流程。

Orchestra develops AI agents designed to assist in drug discovery workflows.

他们并没有去探索新的候选化合物，而是主要专注于简化实验室任务，比如监控化合物生产过程，以及追踪药物试验的进展。

Rather than exploring new candidate compounds, Orchestra has largely focused on streamlining lab tasks like monitoring the compound manufacturing process and tracking drug trials as they progress.

Orchestra团队将参与GPT-Rosalind的工作，这是OpenAI为生命科学领域设计的模型版本。

The Orchestra team will work on GPT-Rosalind, OpenAI's model variant designed for life sciences work.

这个模型最初是在四月份作为研究预览版发布的，但现在已经作为完整的商业产品推出了。

The model was first released in April as a research preview but has now been released as a full commercial product.

除了这个模型，OpenAI还提供了Rosalind Workbench，这是一套代理工具，帮助模型连接科学仪器并分析数据。

Alongside the model, OpenAI offers Rosalind Workbench, which is a suite of agents to help the model connect to scientific instruments and analyze data.

在新闻稿中，OpenAI写道：“我们的愿景是让人工智能在发现和开发新药的整个过程中帮助科学家，包括探索想法、设计实验、解读结果，以及协调推进有前景的治疗方案所需的工作。”

In their press release, OpenAI wrote, "Our vision is for AI to help scientists across the full process of discovering and developing new medicines, exploring ideas, designing experiments, interpreting results, and coordinating the work needed to advance promising treatments.

把Orchestra Bio和Rosalind结合起来，就是朝着这个愿景迈出的一步。

Bringing Orchestra Bio together with Rosalind is a step toward that vision.

我们希望能与研究人员和合作伙伴一起，促成更多发现，让新药更快地送到患者手中。”

Alongside researchers and our partners, we aim to make more discoveries possible and help new medicines reach patients sooner."

需要说明的是，和稍后我们会讨论的一些事情不同，这不是OpenAI要自己开生物科技公司。

Now, to be clear, in contrast to some things we're going to discuss a little bit later, this is not about OpenAI building their own biotech firm.

而是为了打造更好的工具来帮助现有的制药行业。

It's about building better tools to help the existing pharma industry.

同样在新闻稿里，他们写道：“要释放AI在生命科学领域的潜力，就必须与那些处于药物发现和开发前沿的组织紧密合作。

Also from the press release, they wrote, "Realizing the potential of AI in life sciences requires close collaboration with the organizations working at the forefront of drug discovery and development.

我们与诺和诺德的合作，旨在将其规模和科学专长转化为更快的研发和商业执行，同时保持强有力的治理。

Our partnership with Novo Nordisk aims to translate its scale and scientific expertise into faster R&D and commercial execution while maintaining strong governance.

在莫德纳，AI正在帮助团队将复杂数据的洞察转化为可能加速研发的实验工作流程。”

At Moderna, AI is helping teams translate insights from complex data into experimental workflows with the potential to accelerate R&D."

换句话说，OpenAI想传达的信息是，他们希望更好地支持这些行业的AI应用，而不是自己用AI去做那些具体的工作。

In other words, OpenAI is giving you the idea that they want to better support those industries' use cases of AI, not do that particular work with AI.

很多人对这种工作方式感到兴奋。

Many were enthusiastic about this type of work.

Rand Longevity在X上写道：“我真的希望那些大型实验室继续专注于药物开发。”

Rand Longevity on X writes, "I really hope the big labs keep focusing on drug development.

这才是我们真正能改变世界的地方。

That is where we can really change the world."

而Presa Genetics的Parmida Mistra认为这是趋势的一部分，分享这条新闻时还加了句“生物热潮开始了”。

And Presa Genetics' Parmida Mistra thinks this is part of a trend, sharing the news with the added caption, "The bio hype begins."

然而，有些人认为这只是渐进的一步。

However, some think that this is just an incremental step.

Tanev Vashista写道：“前沿实验室实现盈利的唯一途径，最终是成为一家生物技术公司。”

Tanev Vashista writes, "The only path to profitability for the frontier labs is becoming a biotech company eventually."

而上个月路透社报道称Anthropic悄悄建立了自己的生物学实验室，这让那个观点更有了可信度。

And that position got a bit more credence last month when Reuters reported that Anthropic had quietly set up its own biology lab.

所以需要注意，这与OpenAI为现有实验室打造更好系统不同，Anthropic是建立自己的实验室来做实际的生物学研究工作。

So to be clear, in contrast to OpenAI building better systems for existing labs to use, this is Anthropic setting up their own lab to do physical biology work.

路透社消息传出后，Anthropic生命科学部门的负责人证实了该实验室的存在。

After the Reuters news broke, the lab's existence was confirmed by Anthropic's head of life sciences.

Eric Kuderer Abrams说：“我们认为，生物学研究的最终检验标准，现在以及未来一段时间内，仍将是真实的实验室工作。”

Eric Kuderer Abrams said, "We believe that to do biology, the final test is still and will be for a while in real lab work.

我们确实正在这样做，我会把我的方法描述为大多数生物技术公司里常见的做法，就是部分在我们自己的设施里进行，部分与外部合作伙伴合作。

We absolutely are doing that today, and I would describe our approach as being typical of what you would see in most biotech companies, where there's some amount of that that we're doing in our own facilities and some amount of that that we're working with external partners on."

这个消息传出来的时候，正值关于X风险的讨论再次升温，很多人不禁怀疑，Anthropic在那个时候建湿实验室是否合适。

Now, this news broke right at the time that the X risk discussion was surging once again, leaving many to wonder if it was exactly the right time for Anthropic to be building a wet lab.

然而，随着更多报道的出现，大家发现这个实验室的权限比较有限，在做研究的类型上有很多具体的限制。

However, with further reporting, it became clear that it had a relatively limited remit that had pretty specific limitations in the types of research it could do.

不过，就在那时候也有报道说，Anthropic内部的一些领导认为，将来某一天，他们可能会是剩下的唯一一家公司。

Still, this was also the time that there were reports that leaders inside Anthropic believed that at some point in the future, they would be the only company left.

考虑到这感觉像是朝一个全新领域迈进，并且是一种根本不同的人工智能发展模式——也就是说，不仅仅是提供智能，而是用那种智能去做实际的事情——大家对它的走向有很大的兴趣，可以说是拭目以待。

And given how much this felt like a move into a totally different area and a fundamentally different model for AI, i.e., not just providing the intelligence, but using that intelligence to do things, there was a lot of interest, let's say, to see how it would evolve.

不过，这就把我们带到了Citrini的故事。

That brings us, however, to the story of Citrini.

Anthropic写道：“七月的时候，Citrini Research的一个团队带着一个有趣的问题来找我们。”

"Back in July," Anthropic writes, "a team from Citrini Research had approached us with an interesting question.

“Claude以及更广泛的人工智能药物发现，能否让一群有激情的非专业人士去解决一个人体健康方面的真实问题？”

Could Claude and AI drug discovery more generally enable a group of motivated non-experts to tackle a genuine problem in human health?"

现在，关于Citrini Research，你可能会想，我是不是在关于金融问题的通讯里听说过他们？

Now, Citrini Research, you might be saying to yourself, haven't I heard of them in the context of newsletters about financial issues?

简而言之，是的。

And the short answer is yes.

当你访问citriniresearch.com时，它会直接链接到他们的Substack，网站将其概括为提供关于主题股票投资和全球宏观交易的见解。

When you go to citriniresearch.com, it links directly to their Substack, which it sums up as providing insights on thematic equity investing in global macro trading.

那他们到底在搞什么，居然想设计新药？

So what the heck are they doing trying to design new drugs?

根据Citrini关于这项工作的长博文，这个实验的起源来自于他们所描述的“在AI基础设施之外寻找机会，因为他们担心市场会变得拥挤。”

According to Citrini's long blog post about this work, the origins of the experiment came from, as they put it, "Searching for opportunities outside of AI infrastructure where they were concerned about markets crowding."

在他们看来，生命科学和医疗保健是一个具有不对称机会的领域。

One area that seemed to them to have asymmetric opportunity was life sciences and healthcare.

他们写道：“该行业经历了疫情繁荣的逆转，加上融资环境艰难，因为投资者找到了更令人兴奋的投资去处。”

They wrote, "The sector had endured the reversal of the pandemic boom in a difficult funding environment as investors found more exciting places for their money.

在过去的五年里，它一直是回报的墓地。

Over the past half decade, it has been a graveyard for returns.

当大家还在关注哪家日本半导体小零件厂商会下一个被收购时，周期似乎已经悄然转变了。

While everyone focused on which Japanese semi doodad producer would sell out next, the cycle had seemingly turned.

然而，这些股票还没有真正意义上的上涨，而我们也一直听到关于AI在药物研发上的潜力。

However, the stocks had yet to climb in any meaningful way, and we kept hearing about AI's promise in drug discovery.

一个被忽视的行业，又有AI带来的增长潜力，这正像是我们最喜欢的那种布局——在周期底部有着长远的故事。

A neglected sector with the chance for AI upside, it had the markings of our favorite type of setup, a secular story at cyclical lows."

而且他们还提到，鉴于他们上一篇关于AI的重磅文章实际上是在讲AI发展得太好甚至可能摧毁经济，这次有机会讲一个乐观的故事，他们也觉得挺兴奋的。

And also, they said, given that their last big AI post had been effectively about how AI doing too well even could also destroy the economy, the opportunity to potentially tell an optimistic story also seemed exciting to them as well.

他们半开玩笑地写道：“终于，我们有機會写一篇让大家暂时摆脱反乌托邦AI未来威胁的新闻的文章了。”

With tongue firmly in cheek, they wrote, "Finally, it was our chance to write an article offering a reprieve from the threats of a dystopian AI future dominating the news.

当然，我们从来都没在助长对AI未来的恐惧。

Not that we have ever been guilty of fueling fears about the future of AI, of course."

他们接着说，唯一的问题是：“我们当中没有人敢说自己了解AI在生物学中的应用。”

The one problem, they continued, "None of us could claim to know anything about AI as it related to biology.

他们写道：“我们通常的做法是进行深入研究和进行数十次专家访谈。”

Our usual process," they wrote, "would involve deep research and dozens of calls with experts.

但你要是去问十几个生物学专家，肯定会听到两种截然不同的说法：一种是‘AI没造出过一种重要的药，以后也不会’；另一种是‘AI五年内能治好所有病’。其余的各种观点，就是看每个人对AI的痴迷程度了。

But survey a dozen experts in biology, and you're going to hear both, 'AI has not created a single important drug, and it won't in the future either,' two, 'AI will cure all diseases in the next five years,' and everything in between, depending on each individual's degree of AI pilledness.

光靠问是得不到明确答案的。

We weren't going to get a clear answer by simply asking.

我们得亲自试试才行。

We needed to try it for ourselves.

于是我们就试了。

So we did.

我们用模型来设计蛋白质，找实验室帮忙合成，然后做实验验证，再用模型来解释结果。”

We used the model to design proteins, contracted a lab to synthesize them, and conduct experimental validation, and again, using the model to interpret the results."

这种疯狂的生物技术尝试，似乎确实搞出了一些名堂。

Now, this gonzo approach to biotech seems to have indeed yielded some results.

Citrini团队的人用Claude设计出了一些蛋白质结合剂，用来对付蝎子神经毒素。

The Citrini folks were able to use Claude to design protein binders for use against scorpion neurotoxins.

他们一开始用了35页的提示词，这个提示词是Anthropic在之前一项研究里已经测试过的，他们简单改了一下，然后就让Claude开始干活了。

The group started with a 35-page prompt that Anthropic had already tested in a previous study, modified it to taste, and set Claude on its drug discovery work.

300个子代理负责蛋白质设计，生成了总共318,000个候选方案，并挑选出211个有希望的选项进行进一步测试。

300 sub-agents worked on the protein design, generating 318,000 total candidates and selecting 211 promising options for further testing.

在另一个实验室独立测试后，有17个候选方案能够与四种不同的蝎子毒素结合。

17 were found to bind to four different scorpion toxins after independent testing in another lab.

目前还没有一种蛋白质被证明能阻止神经毒素发挥作用，但这已经是一个很有希望的初步结果。

None of the proteins has yet been shown to stop the neurotoxin from working, but it's a promising initial result.

而且，所有这些工作的成本不到几十万美元，虽然不算便宜，但对于研究一个每年影响一百万人、实际上导致三千人死亡的缺乏关注的问题来说，这个价格也不算高得离谱。

Also, all this work came in under a couple of hundred thousand dollars, which while not cheap, also isn't prohibitively expensive for trying to go after an under-focused on issue that affects a million people a year and actually kills 3,000.

有趣的是，Citrini团队的做法是，他们这群杂牌股票分析师一下载Claude for Science，就立刻撞上了生物安全方面的限制。

Now, what was interesting about the way that Citrini did this is that almost immediately after their ragtag group of stock analysts downloaded Claude for Science, they ran headfirst into the biological guardrails.

他们没有去走繁琐的申请流程，而是直接联系了Anthropic，问能不能在公司的办公室里进行操作。

Rather than trying to do some extensive application process, they just reached out to Anthropic and asked if they could do it sitting in the company's offices.

Anthropic立刻产生了兴趣，因为这正是他们认为AI有机会特别受益的被忽视的疾病类型。

Anthropic was interested right away because this is exactly the type of neglected disease that they think AI has a particular opportunity to benefit.

蝎子每年大约蜇伤120万人，导致约3200人死亡，其中大多数是儿童，而且他们大多生活在医疗基础设施极其有限的地区。

Scorpions sting about 1.2 million people per year, which leads to around 3,200 deaths, and most of those come from children in parts of the world that have extremely limited access to quality medical infrastructure.

Anthropic指出，问题在于新疗法研发成本高昂，而且很难收回研发投入，而AI可能带来的新疗法研发成本的大幅下降，正是可以发挥作用的地方。

The problem, Anthropic writes, is that new treatments are expensive to make and also unlikely to earn back the cost of developing them, and this is where the potential step change in the cost of developing new treatments that AI enables could come in.

当然，Citrini发布这份报告时，招来了不少质疑。

There was, of course, plenty of skepticism when Citrini dropped the report.

X平台上的WisPlum写道：“所以你做到的，其实是文献里15多年前就展示过的东西——能结合蝎子毒素的蛋白质，但你没有展示真正重要的东西，也就是中和毒素的作用。”

WisPlum on X writes, "So you achieved something demonstrated in the literature 15 plus years ago, proteins that bind scorpion toxins, but didn't demonstrate the thing that actually matters, neutralizing the venom.

“也许应该把这个作为标题，而不是把它埋在Claude写的一万字长篇大论里。”

Maybe make that the headline instead of burying it in 10,000 words of Claude prose."

Citrini对此回应道：“哈哈，参与这件事的所有人都说过，‘我们一发布这个，不管怎么做都会有一堆黑子。’”

Citrini responded to that one saying, "LMAO, everyone involved in this said, 'Well, as soon as we publish this, there will be a handful of haters no matter what we do.

“我们可不想再等三个月才发布。”

We weren't going to wait another three months to publish.'

“不过，我们还是会继续推进中和测试的。”

Still want to pursue the neutralization testing, though."

Google DeepMind的AGI经济学主管Alex Imas写道：“Citrini有一种非凡的天赋，能让公众关注到那些被忽视但又重要的问题。”

Google DeepMind's director of AGI economics, Alex Imas, wrote, "Citrini has an incredible talent for getting the public to pay attention to overlooked but important issues.

去年冬天，那篇关于AI驱动的生产力如何导致贫困化的著名文章。

Last winter, it was the famous essay on how AI-driven productivity could lead to immiseration.

我虽然不同意其中某些部分，但觉得它在引发关于经济风险的必要讨论方面做得非常出色。

I disagreed with parts but thought it did an excellent job of starting a much-needed conversation about economic risks.

今天，Citrini让大家开始关注AI在科学领域的应用前景。

Today, Citrini got everyone talking about the promise of AI for science.

在假设生成和计算方面大幅提速，但也揭示了瓶颈、验证和测试等问题。

Huge speed-ups in hypothesis generation and computation, but also illustrates the bottlenecks, verification, and testing.

我们需要清醒地看待这两方面，才能知道该往哪里投资。“

We need to be clear-eyed about both in order to know where to invest."

实际上，Citrini的主要结论之一就是，即使AI加速了部分流程，物理世界的惯性依然很大。

And in fact, one of Citrini's major conclusions was just how much physical world inertia there is even when AI speeds up part of the process.

他们写道：“我们的虚拟生物学家可能把一年的工作压缩到几天内完成，但生物学的速度是生命本身的速度。

They wrote, "Our virtual biologists may have compressed a year's worth of work into a couple of days, but biology works at the speed of life.

DNA必须合成，蛋白质要生产和纯化，毒素要以可用的形式获取，而且要建立适当的对照组。

DNA had to be synthesized, proteins produced and purified, toxins obtained in usable form, and appropriate controls established.

我们的数字发明现在反而给人类科学家创造了工作。

Our digital invention was now set to create work for human scientists."

前OpenAI研究员威尔·德普把西特里尼研究中的两张图表放在一起，然后说：“天哪，这两张图真让我心痛。”

Former OpenAI researcher Will Deppu put two of the charts from Citrini research next to one another and said, "God, these two charts really hurt my soul.

干实验室设计一种多价抗蛇毒血清需要64小时，等结果要39天，而真正上市则需要4到7年。”

64 hours to dry lab design a multi-binding antivenom, 39 days for results, and 4 to 7 years to release."

我觉得西特里尼和Anthropic的合作，再加上这次OpenAI的新协议，引出了三个非常有意思的问题。

I think that the combination of the Citrini-Anthropic collaboration and this new OpenAI deal bring up three really interesting questions.

第一个问题是，AI实验室会不会开始把科研成果内部消化，而不是为别人开发技术，以及如果真的这样做，会意味着什么。

The first is whether AI labs will start internalizing scientific results rather than building technology for others to use, and what it means if they do.

这个问题我觉得几个月前就已经开始引起关注了。

This is a question I said really picked up steam a couple of months ago.

首先，当时有报道称，Anthropic的领导层认为他们最终会成为世界上唯一一家私营公司，这暗示着他们将来会包揽一切，包括现在所有客户在做的事情。当然，这些报道还没有被证实。而一个月后，Anthropic宣布建立湿实验室，这个话题就更火了。

First, when reports came out, which by the way, are unverified, that Anthropic leadership thought that they would be ultimately the only private company left in the world, implying that at some point, they do everything, including all the things that their customers do currently, and which picked up more steam a month later when the Anthropic wet lab was announced.

然而，西特里尼的研究表明，在这个过程中有很多环节，即使增加一个湿实验室，让理论上的AI发起的实验再往前推进一步，也改变不了一个事实，那就是要真正把某个东西变成现实，还需要大量其他步骤，以及人和机构的参与。

Yet what the Citrini research shows is that there are so many parts of this process that even adding a wet lab to take theoretical AI-initiated experiments a little bit farther doesn't change the fact that there is an immense number of other steps in people and institutions involved in actually bringing something real into the world.

我完全不认为这能解决那个问题，即某一天AI客户是否会发现自己正在与AI实验室竞争。

I don't think this at all settles the question of whether at some point AI customers will find themselves competing with the AI labs.

事实上，这种竞争绝对不可避免，但认为它会一夜之间发生或波及整个行业，可能不符合现实。

In fact, that is going to be absolutely inevitable, but thinking it's going to happen all at once or across entire industries probably doesn't reflect the reality.

第二个问题是关于公众舆论中的发现。

The second question is this one about discoveries in public opinion.

如果AI真的治愈了癌症，那会改变人们对AI的看法吗？

If AI actually does cure cancer, does that change people's perspective on AI?

首先，至少就Anthropic目前的实验而言，他们似乎更多是在处理那些过去因为市场激励机制不足而被忽视的医学项目，而不是像治愈癌症这样的终极目标。但现在有了AI，这些项目不再需要被忽视了。

First of all, at least when it comes to Anthropic experiments right now, they seem to be a lot less about these holy grails like curing cancer, and more about addressing these medical projects which previously fell through the cracks of market incentives, but which don't have to anymore thanks to AI.

Anthropic可能完全正确，认为这些项目比那些大而亮眼的东西有更多的杠杆作用，但像“Anthropic治愈蝎子蜇伤”这样的新闻标题——需要说明的是，这甚至还不算接近，只是某些可能有助于此的解决方案的最初一步——仍然与“Anthropic治愈癌症”给人的感觉大不相同。

Anthropic might absolutely be correct that there is a lot more leverage there than the big flashy stuff, but a news headline like "Anthropic Cures Scorpion Stings," which to be clear, this isn't even really close to, it's just the very, very first step in some solutions that could help this, still hits very different to "Anthropic Cures Cancer."

其次，即使AI在治愈重大疾病中起了关键作用，它也不可能只是发现一种神奇的化合物，然后一下子解决所有问题。

Second, even if AI is totally integral to curing major diseases, it seems unlikely that the way that that works is it just discovers a miracle compound that all of a sudden fixes everything in one fell swoop.

实际上，更可能的情况是，AI加速的发现会成为常态。

In fact, it seems far more likely that AI-accelerated discoveries just become the norm.

大家都会习惯这些的，而且这些对人们是否喜欢AI影响并不大。

Everyone gets used to them, and they don't really have all that much bearing on whether they like AI or not.

威尔·马尼迪斯写道：“如果AI行业认为治愈癌症就足以扭转公众舆论，那他们应该看看大型制药公司的民调结果。”

Will Manidis writes, "If the AI industry thinks curing cancer is enough to turn public sentiment around, they should look at opinion polling on big pharma."

需要说明的是，这绝不意味着我认为AI实验室不应该在这些问题上努力。

To be clear, this does not in any way mean that I think that AI labs shouldn't be working on these issues.

事实上，在我看来，我相信他们能做的事情在短期内对公众舆论影响很小，这意味着他们应该专注于他们认为能产生最大影响的领域，不管会带来什么样的头条新闻。

In fact, to me, my belief that very little they can do can, in the short term at least, actually really impact public opinion means they should be focused on working on the areas that they think are going to have the biggest impact, regardless of the headlines they create.

虽然我很希望有一个万能的解决办法，但我就是不相信AI在认知问题上有什么神奇的解药，而且这种问题还会持续一段时间。

As much as I would like there to be, I just simply don't believe that there is any magic bullet to the perception problems that AI has and is going to continue to have for some time.

第三个问题最微妙，但在某种程度上对我来说也最有趣，那就是Citrini实验具体展示了AI不仅会改变我们工作的方式，还会改变我们工作的内容。

The third and most subtle, but in some ways most interesting question to me is how the Citrini experiment specifically demonstrates the way in which AI will change not only how we work, but what we work on.

记住，Citrini实验一开始是从他们通常的做法出发的，也就是从投资主题的角度寻找市场中被忽视的机会。

Remember, the Citrini experiment started from them doing what they normally do, which is look for under-recognized opportunities in the markets from an investment thesis perspective.

他们按照正常的工作流程，考察了不同行业在回报、公众认知、当前市场价值等方面的表现，基本上得出结论，认为生命科学被低估了，尤其是在AI可能改变这些领域的背景下。

They went through their normal work process of examining different sectors in the context of returns, public perception, current market values, and basically came away believing that life sciences were undervalued, especially in the context of how AI could change those areas.

他们遇到的问题就是，光靠问一群专家，他们没法对AI在生命科学领域的真正潜力有一个全面准确的了解，正是在这种情况下，他们自己动手做实验才变得经济上可行。

The problem they ran into is that they didn't think that they could get a robust enough picture about the real potential of AI in life sciences just by asking a bunch of experts, and it was in that context that it became economically viable for them to just experiment for themselves.

不过，Citrini在结尾说得很清楚，他们不打算变成生物实验室，他们依然是、并且将继续是一家金融研究公司。

Now, Citrini was very clear at the end of this that they are not about to become a biolab, that they are and remain a financial research firm.

但我认为，我们会看到更多这种所谓的“边缘化”工作，人们和公司能做到以前做不到的事情，这些工作实际上能提升他们核心业务的表现。

But I think that you're going to see a lot more of this type of orthogonal work, let's call it, where people and firms can do things that they couldn't do before that actually change how well they do their core work stream.

这正是我一直说的AI效率与机会之分的另一种体现，即深入到生物学和生命科学过程中的原始一手研究。

This is another version of the efficiency versus opportunity AI divide that I always talk about, to go to original hands-on research deep in the biology and life science process.

即便没有其他收获，这也是个很有意思的故事，我建议大家去读一下完整的Citrini Therapeutics研究报告。

If nothing else, it is a very entertaining story, and I suggest you go read the full Citrini Therapeutics research report.

现在，我很期待能继续和你们一起探讨这些话题，但今天AI日报就到这里。

For now, I'm excited to keep exploring these themes with you, but that's going to do it for today's AI Daily Brief.

感谢你们的收听或观看。

Appreciate you listening or watching.

一如既往，下次再见，祝你们平安。

As always, and until next time, peace.