95%的企业在人工智能投资上真的毫无回报吗？——与Aaron Levie对话

为什么头条新闻告诉我们企业在人工智能投资上毫无回报，而AI智能体是否终于准备好投入工作？广告之后，我们将与Box公司CEO Aaron Levy一起全面探讨。Oktane是顶级的身份认证盛会，汇聚全球顶尖思想者探讨安全访问的未来。与其将安全整合到单一平台，现代身份安全架构才是统一防御的关键。在Oktane，您将学习如何将该架构扩展到所有类型的身份，包括新兴的AI智能体威胁。

Why are the headlines telling us that businesses are getting no return on AI investment, and are AI agents finally ready to get to work? We'll cover it all with Box CEO, Aaron Levy, right after this. Oktane is the premier identity event, bringing together the world's leading minds to discuss the future of secure access. Instead of consolidating security into a single platform, a modern identity security fabric is the key to unifying your defenses. At Oktane, you'll learn how to extend that fabric across all types of identities, including the emerging threat of AI agents.

九月亲临拉斯维加斯参会，或在线观看主题演讲和分会场。注册并查看完整议程，请访问okta.com/oktane。网址是0kta.com/0ktane。

Join in person in Las Vegas from September, or catch the keynotes and sessions online. To register and see the full agenda, visit okta.com/oktane. That's 0kta.com/0ktane.

您已习惯通过《世界》节目听到我的声音，收听来自全球各地的访谈。

You're used to hearing my voice on the world, bringing you interviews from around the globe.

而您听到我报道环境与气候新闻。我是Carolyn Beeler。

And you hear me reporting environment and climate news. I'm Carolyn Beeler.

我是Marco Wurman。我们现在共同为您主持《世界》节目。以焕然一新的声音呈现更多全球新闻报道。

And I'm Marco Wurman. We're now with you hosting the world together. More global journalism with a fresh new sound.

请在您当地的公共广播电台以及任何播客平台收听《世界》节目。

Listen to the world on your local public radio station and wherever you find your podcasts.

欢迎收听《大科技播客》，一档冷静深入探讨科技界及更广阔天地的对话节目。今天，我们将讨论人工智能及其在商业中的应用，它是否真正产生影响，以及AI智能体是否真实可行。今天我们请到了最合适的嘉宾，因为Aaron Levy刚参加完Box的BoxWorks AI活动后再次做客。Aaron，一如既往很高兴见到您。

Welcome to Big Technology Podcast, a show for cool headed and nuanced conversation of the tech world and beyond. Well, today, we're gonna talk about AI and its application in business, whether it's actually making a difference, and whether AI agents are a real thing. We have the perfect guest to do it today because we have Aaron Levy back with us fresh off the Box BoxWorks AI event. And, Aaron, it's great to see you as always.

谢谢。谢谢你，Alex。很高兴能来到这里。

Thank you. Thank you, Alex. Good to do good to be here.

那么我是否应该把BoxWorks AI活动称为AI活动，还是就叫BoxWorks？

So did I did I add box BoxWorks AI event as or is it just called BoxWorks?

我正在确认——我喜欢你称之为AI活动。它确实就叫BoxWorks，但任何时候你想加入AI元素，我们都非常欢迎。

And I'm just checking the I I like you I like you call it an AI event. It it is just called BoxWorks, but but any anytime you wanna jam an AI in there, we're good.

好的。听起来不错。你们有很多AI相关的新闻，我们稍后会深入讨论。但既然你正在与很多人探讨AI在商业中的应用，我想和你分享这项MIT研究，听听你对其中虚实之分的看法。

Okay. Sounds good. You had a lot of AI news. We'll get into that in a moment. But since you are talking with a lot of folks about AI applications in business, I wanna run this MIT study by you and get your perspective on what's real and what's not.

这是几周前来自Maxios的报道：MIT关于AI盈利的研究震惊了科技投资者。华尔街最大的担忧被一项最近的MIT研究证实，该研究表明95%的被调查组织在AI投资上获得零回报。他们研究了300个公共AI项目，试图揭示AI对商业影响的真实情况（去除炒作）。尽管企业在生成式AI上投入了300亿到400亿美元，95%的组织表示他们发现零回报。

So this is from Maxios a couple weeks ago. MIT study on AI profits rattles tech investors. Wall Street's biggest fear was validated by a recent MIT study indicating that 95% of organizations studied gets zero get zero return on their AI investment. They studied 300 public AI initiatives trying to suss out the no hype reality on AI's impact on business. 95% of organizations said they found zero return despite despite enterprise investment of 30,000,000,000 to 40,000,000,000 into generative AI.

这项研究已经成为商界人人都在讨论的话题。你认为它有任何有效性吗？我看你已经在摇头了。

This has been a study that everybody in the business world is talking about. Do you think there's any validity to it? You're already shaking your head.

我确实在摇头，而且实际上是从七个维度来思考。我们可以逐一解析。

I'm shaking my head on actually like seven dimensions. We can parse each one.

我们开始吧。

Let's do it.

我的意思是，实际上，也许第一个最有趣的是华尔街的那部分。实际上，华尔街在这个维度上完全是精神分裂的。你知道，显然，像那样的报告在一个维度上吓到了他们，但实际上，华尔街同样存在着一种疯狂的精力，认为AI会如此强大以至于所有软件都将消亡。所以这是一种非常两极分化的状态：我们到底处于AI应用的哪个阶段，还是AI将如此强大以至于连软件商业模式都不复存在，因为一切都将由AI直接交付。就像大多数具有这种极端两极分化特征的事物一样，我认为现实情况要微妙得多。

I mean, actually, maybe the first one that is maybe the most funny is the kind of Wall Street element. Actually, Wall Street is is completely schizophrenic on this dimension. You know, obviously, a report like that, you know, scares them on one dimension, but, actually, there's an equal amount of of kind of Wall Street maybe, you know, kind of frenetic energy around the idea that AI will be so good that all of software is dead. So this very kind of bipolar state of where are we in AI adoption versus AI is going be so powerful that there's not even going to be software business models because everything will just be delivered by AI. And as with most things that have these kind of extreme polarization elements, I think the reality is just way more nuanced.

我们仍处于AI应用曲线的早期阶段。在所有这类技术的早期曲线中，会有大量概念验证，有各种技术的试验，人们正在尝试弄清楚哪种工具适用于哪种用例。因此，从定义上来说，你正处于一种'狂野西部'的状态，有很多尝试使用不同供应商和技术栈来试验这些技术。其中许多项目和试点绝对会失败，因为从定义上来说，它们就是试点。而且我们仍处于早期阶段。

We are still early in the adoption curve of AI. In the early curve of all of these types of technologies, have lots and lots of proof of concepts, you have lots of trials of different technologies, people are trying to figure out which tool works for which use case. So by definition, you're kind of in the Wild West where there's lots of attempts at trying these technologies with various vendors and and technology stacks. And and many of those projects and pilots will absolutely fail because by definition, they're pilots. And they're and we're we're still in the early phases.

这项研究的一个有趣之处在于，他们发现试图有效DIY自己的AI技术栈的公司与选择应用解决方案和用例的公司之间存在显著差距。这也是我们在客户群中经常发现的情况。所以，我认为最初可能有一种理论认为，AI相对容易掌握，我们可以构建自己的AI应用，自己完成所有数据的向量嵌入。

One one interesting thing about this study was they they saw a significant delta between companies that tried to effectively DIY their AI stack versus, you know, going with really kind of applied solutions and use cases. And this is what we tend to find in our customer base. So, you know, I think there was maybe an initial theory of, well, AI will be relatively easy to kinda get our arms around. We could build our own AI application. We'll do all of the vector embeddings of our data ourselves.

我们会把它放入向量数据库，自己管理数据访问的安全和权限。在你意识到之前，一个想要在企业中特定工作流程部署AI的公司，可能需要在单个用户真正能与组织内的AI交互之前，运行和管理10或15个不同的软件组件。所以这很可能是一种行不通的架构。你需要有专门构建的解决方案来解决特定的用例。

We'll put it into a vector database. We'll have we'll manage the security and permissions of data access ourselves. And, you know, before you know it, a company that wanted to deploy AI in in a particular workflow in their enterprise, they might have 10 or 15 different pieces of software that they have to run and manage just before, you know, before a single user could actually interact with with with AI within that organization. So that's that's probably an architecture that's not going to work. You need to you need to have purpose built solutions that that solve sort of tailored use cases.

这些可以是很大的用例，比如所有的AI编程，但你很可能不想处于需要自己从头开始构建这一切的境地。这是调查中的一种认识。但我显然完全不同意任何可能的结论，除了你必须找准用例这一点。你必须瞄准AI最有效的领域，而且很可能不应该自己构建这项技术。所以这在我们的角度上是经验性的。

Those can be very big use cases like all of AI coding, but you probably don't wanna be in a position where you have to kinda bootstrap this or or build it all out yourselves. And that was one of the kind of recognitions in the survey. But I obviously wholeheartedly disagree with any of the maybe conclusions other than just you have to get your use cases right. You have to kind of target the most effective areas for AI and you probably shouldn't be building this technology yourselves. So the but it's sort of empirical on our end.

我们每天都能与看到即时收益的客户交流。我们曾与一些客户交谈，他们的同事无法向董事会展示实际的ROI节省，预期的ROI节省，因为董事会不会相信这些数字有多好。所以他们实际上不得不淡化这些数字，使其基于他们所看到的情况更加务实和可信。

We get to talk to customers every single day that are seeing the immediate gains. We've talked to customers where they have had colleagues that can't actually they can't present the actual ROI savings to their board, The actual kind of expected ROI savings to the board because the board won't believe how how they won't believe the numbers based on how good they are. So they actually have to water them down. So it's actually more pragmatic and and believable based on what they're seeing.

所以这难道不是一个糟糕的董事会吗？我的意思是，董事会听不进真话。好吧，谢谢。

So Isn't that a terrible board? I mean, the board can't hear the truth Well, thank you.

但事实是如此之好，以至于听起来不可信。所以当投资回报率如此之高时，当你实际解释这个东西将如何运作时，你反而不会被相信。我们在各个领域都看到这样的例子，至少对我们的客户来说是这样。你知道，我们有一个非常实用的用例优势，就是我们处理文档和非结构化数据，然后我们有AI代理可以操作这些数据，比如从你的文档中提取结构化数据。所以给我们10万份合同，我们就能从中提取出结构化数据字段。

But the truth is so good that that it's it doesn't sound credible. So so that that that is the like, when when the the ROI is so good that you actually don't you you aren't gonna be believed when you actually explain how this thing's going to work. So we're we're seeing examples all across the board, at least for our customers. You know, we have we have the benefit of a a very applied use case, which is we we take documents and unstructured data, and then we have AI agents that can operate on that data to do things like extract structured data from your documents. So give us, you know, a 100,000 contracts, and we'll pull out the structured data fields in those contracts.

或者给我们发票，我们就能提取出发票中的关键细节，这样我们就能帮助自动化工作流程。这些用例往往具有很高的投资回报率，因为要么你以前无法获取这些数据，要么过去这样做成本很高。而AI在执行这类任务方面正变得越来越好。因此客户能立即获得收益，你可以因此更容易地自动化工作流程。

Or give us invoices, and we'll pull out the key details in an invoice so we can help automate a workflow. Those use cases tend to be very high ROI because either you weren't getting that data before or it used to be very expensive to do so. And AI is getting increasingly good at being able to execute that kind of task. And so there's immediate benefit to customers. You can automate workflows much more easily as a result.

你可以在某些领域降低运营成本。因此，根据客户群体中AI采用情况的不同，我们倾向于看到不同的结果。但如果你放大视角，思考过去几年的所有项目，我认为你会得到一个混合的结果，这反映了我们在这个领域还处于早期阶段这一现实。

You can lower the cost of operations in some areas. So we tend to see a different set of outcomes based on the AI adoption within our customer base. But if you zoom out and you kind of think about all projects across the past couple of years, I do think you're going get a mixed bag just as a reality of how early we are on this space.

是的。报告显示内部构建的失败率是外部合作的两倍。这一点说得太准了。人们试图自己拼凑解决方案，而不是通过外部合作，确实遇到了困难，这与一些传统观念相悖。我认为传统观念是希望能够在内部构建，也许使用开源技术，以便根据你的用例进行定制，但事实证明一些现成的解决方案实际上效果相当不错。

Yeah. And it says internal builds fail at double the rate of external partnerships. So spot on there. People trying to parse this together on their own versus doing it externally are having a tough time, which sort of flies in the face of, like, some of the conventional wisdom. I think the conventional wisdom was you wanted to be able to build internally maybe with open source so you could customize to your use case, but it turns out some of the off the shelf stuff is actually working quite well.

是的。我认为你必须明白，无论是这类调查还是讨论架构，很多挑战在于你需要将科技行业与非科技行业分开。非科技行业是这类技术的消费者，而科技行业是构建者。开源非常有价值，但这并不意味着律师事务所应该去用开源模型构建自己的AI项目。如果我们认为地球上每家公司都要构建自己的技术来自动化工作流程，那简直就是灾难的配方。

Yeah. I think you have to know, a lot of the the challenge with with either these types of surveys or even talking about architectures is you have to kinda separate the tech industry from the non tech industry. The nontech industry being the kind of consumers of these types of technologies and the tech industry being the builders. So open source is insanely valuable, but not in the sense where a law firm should go off and build their own AI project using an open source model. Like, that that is just a recipe for disaster if if, you know, we think that that every single company on the planet is gonna go build their own technology to go automate their workflows.

实际上很多试点项目就是这种情况，因为我们处于技术早期阶段，还没有现成的应用解决方案可以部署。但开源对像Box这样的公司来说确实非常有价值，因为我们为12万客户提供技术支持。所以我们内部确实有专业知识来利用这些能力。因此，从开源这个维度得出的结论是，你不应该期望计划中的每家公司都DIY自己的AI战略，那样很难从AI采用中获得回报和收益。最后我想指出的另一点是，要真正从AI中获得收益，确实需要相当多的变革管理。

And that that has been actually the case for a lot of pilots because we've been early in the technology and you haven't had applied solutions that you could go deploy. But open source is actually extremely valuable for a company like Box because it it you know, we're we're con you know, we're powering technology for a 120,000 customers. And so we actually do have the expertise internally to leverage those kinds of capabilities. And so I would say the conclusion from the dimension of open source as an example is just you probably shouldn't expect that every company in the plan is going to DIY their own AI strategy, and that's a recipe for not getting the returns and gains from an AI adoption standpoint. And then maybe the other final point, the thing I'd kind of point out is just there really is a decent amount of change management required to getting real gains from AI.

这并非一种万能解决方案，你不能简单地将AI直接嵌入现有工作流程，然后突然之间该流程就会变得高效三倍。通常你必须重新设计工作流程才能充分利用AI的优势。我最近越来越得出的结论是：两三年前我们可能有过这样的感觉，认为AI将学会我们所有的工作方式，能够适应我们的工作流程并为其带来自动化。但现实地看，我们很可能需要调整我们的工作方式——希望是渐进式的，但在某些情况下需要做出实质性改变，才能充分发挥AI的潜力。

This is not a panacea type of of of solution where you could take an existing workflow, drop AI directly into it, and then all of a sudden that workflow will be, you know, three x better. You usually do have to reengineer the work to take advantage of AI. And the conclusion I've recently come to more and more is, I think we had this feeling maybe two or three years ago where AI was going to learn everything about how we work. It would be able to adapt to our workflows and then bring automation to our workflows. And I think realistically, increasingly, we probably will have to modify our work, hopefully incrementally, but in some cases meaningfully to fully take advantage of AI.

这听起来可能有些困难，但对于那些这样做的公司来说，投资回报率将会非常可观。以AI编程为例，这可能是当前最明显的生产力提升领域。AI工程师的工作方式与两三年前已经大不相同。工程师实际上更像管理者——你部署智能代理去处理代码库的大部分工作，然后它们会带着大量成果回来供你审阅。如果你不改变作为工程师的工作流程来充分利用后台代理，不学会如何给它们提供正确的提示来执行任务，不重新思考如何有效管理代码库以及处理AI代理应遵循的规范和规则...

And that sounds maybe hard on one hand, but for the companies that do that, the ROI is going to be fairly massive. So if you think about AI coding as as maybe the the, you know, most obvious example right now where you're seeing productivity gains, the way that AI kind of first engineers tend to work is pretty different than how you engineered two or three years ago. The engineer really becomes more of a manager. You're deploying agents to go off and work on large parts of the code base, and then it's coming back with a a bunch of work that you go and review. So if you don't change your workflow as an engineer to take advantage of background agents and how you give them the right kinds of prompts to actually execute on their task and the new ways you should effectively think about your code base and, handling the specifications and rules of what the AI agent should do.

如果你不做所有这些工作，你可能无法从AI中获得两倍或五倍的收益。因此我们实际上需要重新设计一些业务流程来让代理发挥作用，而不是指望代理能直接融入我们的流程并自动化我们正在做的一切。

If you don't do all of that work, you're probably not gonna get a two x or five x gain from AI. And so we will actually have to reengineer some of our business processes to make agents effective as opposed to thinking agents will just drop into our processes and automate everything that we're doing.

顺便说一下，你多次提到了试点项目。我认为这很重要，因为这项研究不仅涉及试点——它显示95%的组织在AI投资上获得零回报。试点项目很有意思，因为试点失败是很自然的。事实上，有些听众给我反馈说，因为我经常谈到只有20%或10-20%的AI试点项目能够真正投入生产。

By the way, you've brought up pilots a couple times. And I think it's important to talk about because this study was not just pilots. It was 95% of organizations get zero return on AI investment. So I think the pilot thing is interesting because it's natural that pilots are gonna fail. And in fact, we've had some listeners who've given me some feedback that said because I talk often about how like only 20% of AI pilots or 10 to 20% of AI pilots get out the door into production.

这个比例可能还算不错，因为在早期阶段显然需要一些试错过程。

And that might be a good number because you're you're gonna obviously, you know, have some trial and error in the early days.

是的。需要说明的是，我是在通俗意义上使用'试点'这个词——因为这项技术还处于非常早期的阶段，当我们与客户交流时，很多时候他们迄今为止部署的实际上就相当于试点项目，这纯粹是因为...

Yeah. And and and to be clear, I I'm using pilots colloquially in the sense that we're just so early in the technology that when we talk to customers, a lot of times they have so far deployed is the equivalent of a pilot just because of literally how

组织范围内的部署。

Organization wide.

是的。嗯，范围太广，一个集中的调查者很难代表整个组织。这就像...这就是为什么，再次强调，这就是为什么我不想...调查本身很好，它是一个有趣的对话启动器。但如果你真的试图去评估他们如何回答这个问题，他们衡量生产力的方式是什么，以及他们是否真的调查了所有未经授权使用ChatGPT的终端用户及其行为，那是不可能的。

Yes. Well, wide is hard for one centralized survey taker to represent an organization It's like like the that that's why again, that's why I don't wanna like the survey is great. It's an interesting, you know, kinda conversation starter. But, like, if you actually tried to go assess how is the answer you know, answering this question and what is their way of measuring that productivity and have they actually surveyed all of the end users that are just using ChatGPT in an unsanctioned way and what they're doing. It's not possible to capture all of that.

所以它更倾向于代表那些集中化的、我认为更可能是试点导向型的项目，因为毕竟我们还处于早期阶段。'智能体'这个词出现还不到一年。我们在这些领域都还很早期。但我认为这是一个极好的调查，因为它引发了对话。但如果得出的结论是放缓使用AI，而不是从风险角度认识到应该规避什么，那么问题就在于这只会让一些公司行动更慢，而其他公司则会超越它们。

So it tends to more represent the the centralized, you know, heavily sort of, you know, again, kind of, I think more more likely pilot oriented type projects because of just, again, how early we are. You know, the word agents just came onto the scene less than a year ago. So we're we're just early in a lot of these spaces. But again, I think it's a fantastic survey because it gets a conversation going. But I think if the takeaway was to slow down using AI or to do anything other than realize what you should mitigate from a risk standpoint, then actually the failure would just be or the problem with that would just be all it's gonna do is cause some companies to move even more slowly and then you'll have other companies just outrun them.

所以这某种程度上取决于...可以说，风险在于听众需要决定他们想如何对待那个调查结果。

So it's kind of up to the it's sort of, you know, at the risk of, you know, you know, the the the risk is now on the listener to decide what they wanna do about that that survey.

是的。我还可以告诉你这项研究中另一个我觉得超级有趣但未被充分重视的发现。它说官方的大语言模型采购仅覆盖40%的企业，但90%的员工每天使用个人AI工具——至少受访者如此。这太有趣了，因为它意味着个人使用和个人对将AI投入生产的兴趣实际上超过了公司层面的意愿。

Yeah. And I can tell you one more thing that I found super interesting about this study, which has sort of been underappreciated. So it says official LLM purchases cover only 40% of firms, yet 90% of employees use personal AI daily. At least those surveyed. But that's just it's so interesting because it means that, yeah, there's there's more personal use and more interest among individuals than companies to get this stuff into production.

是的。我显然对此有反应，所以让我们听听看。

Yeah. I obviously have reaction here, so let's hear it.

是的。我知道。我只是觉得这就像是实证显示出的偏好。一旦你知道这一点，甚至不需要调查。为什么人们会以那样的频率私下使用AI来提升个人生产力？

Yeah. Well, I know. I just think that's that's like empirical revealed preference. So so, you even have to survey once you know that. Why are people going off and using AI a personal productivity sense at that rate?

这是因为他们从中获得了价值。所以这几乎已经成为人们工作的基础方式。毫无疑问，如果你今天突然取消AI，你会发现：哇，好吧，我实际上必须去做那三个小时的研究，而以前我只需将其作为一个深度研究项目启动，五分钟后回来查看即可。

It's because they're getting value from it. So you almost that is sort of now in the baseline of how people are working. It's it's unquestionable that if you just sort of eliminated AI just today, let's just say, you you would just notice, wow. Okay. I I actually have to go and do that three hours of research that I used to be able to go and kick off as a deep research project and go and check back in on it after five minutes.

因此，我们选择每天使用这些技术是经验性的，因为它们能提升生产力。我认为，迄今为止我们所看到的人工智能应用，仅仅触及了部署这些技术后即将发生变化的表面。

And so it's empirical that we're choosing to use these technologies on a daily basis because adding that productivity. And I would argue that what we've seen with AI thus far is barely scratching the surface of what is going to start to happen as you start to deploy these technologies.

但你认为在商业应用方面，未来可能主要是个人独立使用ChatGPT这类工具，还是企业规模化使用大型语言模型？或者你认为会是某种混合模式？你显然在关注未来发展趋势，因为你在从另一个角度观察这一进程。

But do you think the use in business, could it potentially be just individuals using, let's say, ChatGPT on their own versus scaled enterprise use of large language models? Or because or or or do you think it will be some blend? In the future? You're obviously watching in the future because you're obviously watching this happen on on the other side of things.

不。我认为未来是这样的：我们甚至正处于技术扩散的最初阶段，包括基础应用场景。比如当你要调研客户时，为什么不让人工智能系统生成完整的客户计划，而不仅仅是知道这个人在哪家公司工作、对什么感兴趣以及行业趋势？

No. The the future is I I think that that we are in the earliest phases of just even the diffusion of the the technology itself of of the the basic use cases of, hey. When you're gonna go research a customer, you know, why don't why don't you get a full account plan, you know, instead of just saying, okay. This person works at this company and they're interested in these things and this is these are the trends of that industry. Why not ask a an AI system to to generate the full plan?

这非常强大，但如果你考虑人们的工作方式和完整工作流程，这还相对基础。有个很有趣的例子能说明我们处于多早期的阶段：Claude本周宣布了新功能，可以为你生成文件。尽管我们进入'聊天机器人时代'已两年半近三年，这是首次有AI系统能可靠地生成高质量Word文档或PowerPoint演示文稿。近三年时间才首次能生成让人看了会说'这像份不错演示稿'的内容。我们仅仅处于非常非常初级的阶段。

That that's super powerful, but also relatively basic if you think about about how people work and the full scope of workflows that people do. One one really interesting example of of, again, how early we are, Claude this week announced new capability that will generate files for you. And even though we're two and a half years, you know, nearly three years into the chatty bitty moment, it's the first time where an AI system can, I believe, generate reliably a kind of high quality document in the form of a Word document or a PowerPoint presentation? So we're nearly three years in and it's the first time ever that you could generate something that you would sort of look at and say, oh, that looks like a good presentation. We are only at the very, very beginning stages.

想象一下还需要几年时间。想象这样的技术开始在企业中扩散。未来在你向客户展示产品前，不用花一两小时调研制作PPT，而是对AI助手说'我要向这个客户销售，为我生成演示稿'，三分钟后它就为你完成了。这将融入我们每天几乎所有工作流程中。

Now imagine it'll still take a couple of years. Now imagine a technology like that begins to ripple through corporations. And in the future, before you go and present whatever product you're selling to a customer, instead of spending one or two hours of doing a bunch of research and making your PowerPoint file that's your presentation, you go to an AI agent, you say, I'm about to go sell to this customer, generate this presentation for me. You kick that off and again, three minutes later, it's sort of done for you. That this is going to just show up in all of our workflows every single day in in almost everything that we're doing.

所以程序员最先窥见未来图景，因为他们天生擅长利用这些工具，AI编程已成为首个突破性应用案例。但同样的模式——通过界面与助手对话，让它为你执行多步工作——将在未来几年逐渐出现在所有知识工作中。我对此持务实态度：这不会一夜之间彻底改变工作方式，需要多年的变革管理。正如你提到的，我们本周刚举办会议，与会者自然是技术前瞻者和早期采用者。

So coders are getting the first lens into what the future looks like, you know, earliest because, you know, they're they're sort of wired to take advantage of these tools, and AI coding has been the kind of first breakout use case. But that same dynamic of you're gonna go to an interface, you're gonna talk to an agent, it's gonna go and execute kind of multiple steps of work for you, that will start to emerge within, you know, all of knowledge work over the coming years. I actually am am probably a pragmatist on this sense that it will not be like this instant overnight, you know, transformation of work. It will take years of change management. We just hosted our conference this week as you as you noted, and it happens to be a a crowd, obviously, by definition that is sort of forward leaning and and kind of early adopters of technology.

但这只是整体经济的一小部分。还需要多年时间，所有银行、制药公司、律师事务所才会以AI优先的方式全面接入。毫无疑问这必将发生，没有什么能阻挡这个趋势。

But that represents a small fraction of the total economy. It will take years before, again, all of the banks, all of the pharma companies, all of the law firms start to get wired up in this AI first way. Unequivocally, it's going to happen. And there's nothing that will kind of slow that train down.

好的。让我们再详细谈谈这个利用云生成文档的用例。我的意思是，我想象中你提到的例子是使用这些工具去面向客户销售。现在，我认为大多数组织都有他们自己的PowerPoint模板和内置数据。所以即使我进入云端，上传我的定价电子表格、库存电子表格、关于定位的文档，然后说基于这些生成一个PowerPoint，我相信它会做得很好。

All right. Let's talk a little bit more about this using cloud to generate documents use case. I mean, I would imagine so you talked the example that you gave was using one of these to go in and sell into a client. Now, I would imagine most organizations, they have, like, their PowerPoint templates and the data baked in. So even if I were to go into Cloud and, like, upload my pricing spreadsheet, my inventory spreadsheet, a document about positioning and say make a PowerPoint based off of this, I'm sure it would do a good job.

但要说这将成为人们工作的方式，而不是看起来像某种派对把戏——当你真正要进入市场时，你仍然会使用已有的其他文档——这在实际中有多实用呢？

But how practical is it to then say this is gonna be a way that people do their work versus something that might look like a party trick where you're gonna use the other documents that you have already when you actually are gonna go out into market?

哦，是的。不，这种方式实际上会这样呈现——我无法确切说出这会在什么时候发生，但在Box中，你只需进入Box然后说：这是我的销售演示模板，这是新客户信息，请基于这些生成一个PowerPoint演示文稿。

Oh, yeah. No. The the way that this will actually show up and and I I, you know, I can't represent the exact date that this will happen, but Box you you'll just go to Box and you'll say, here's my sales presentation template. Here's the new client information. Please generate a PowerPoint presentation with that.

就像这样，然后你就可以用现有数据直接完成。这不是某种一次性的临时编码文档。你将使用现有资产作为生成下一个文档的源材料，然后去审查它的工作，这只需要你三分钟时间，但却能为你节省一两个小时——原本需要花时间进行客户研究、调整所有图形布局和放置相关信息的时间。这些都将为你自动完成。在经济体的某个领域，每天有上百万人这样做，你会看到这将为经济带来数千万小时的生产力增益。

Like and then you'll just do that with your existing data. This is not some kind of one off vibe coded document. You will use your existing assets as the source material for the next document that you'll generate and you'll go and review its work and that'll take you three minutes but it will have saved you an hour or two hours of all of the time that it took to do the customer research and move around all the graphics and put the relevant information in place. That will just be done for you. Multiply that over a million people that do that per day in some sector of the economy and you'll just see that's how you'll get tens of millions of hours of productivity gained within the economy.

你对这些模型的可信度感觉如何？因为你已经多次谈到，你可以通过深度研究来为某事做准备，或者使用这些模型生成PowerPoint，然后花几分钟检查一下。你现在是否认为这些模型及其输出已经足够可信，只需要这样做就足够了？

And how are you feeling about the trustworthiness of these models? Because you've talked a couple times now about how you could use deep research to prepare you for something or you could use these models to generate a PowerPoint and then spend a couple minutes checking them over. Are you at the point now where you think these and the outputs of these models are trustworthy enough that that's all it takes?

我认为只要——这正是让我对当前显然处于时代精神中的语境工程感到非常兴奋的地方——只要你非常清楚给AI提供什么语境，如何通过正确的提示和足够高质量的模型有效地将AI基于可信数据，你几乎可以消除所有（如果不是绝大多数的话）幻觉或准确性问题。所以在我们的案例中，Box所做的一切都是将您的现有数据视为AI代理的源材料。这是AI代理有效工作的源语境。因此，如果我拿一个现有的PowerPoint文档（这是我们的销售演示），然后说为一位新客户修改这个文档，并且你使用一个前沿模型（一个具有某种程度思维模式的推理模型）来完成，我认为99%的情况下它只会产生极其微小的错误或失误。

I think as long as and this is where I get very excited about about now that obviously what's in the zeitgeist is context engineering. As long as you are really good about what context you're giving the AI and how you are effectively grounding AI in trustworthy data with the right kinds of prompts and a high enough quality model, you can nearly eradicate all of, if not the vast majority of hallucinations or accuracy issues. So in our case, everything that we do at Box is we think about your existing data as the source material for for the AI agent. So it's the source context for the AI agent to be effective. And so if I take an existing PowerPoint document that's our sales presentation and I say modify this for a new customer and and you do that with a, you know, a frontier model that is a a reasoning model, you know, with some degree of kind of thinking mode, I I would I would posit that 99% of the time it's going to make, you know, infinitesimally fall the small kind of errors or failures on that.

这在现在基本上已经是一个解决了的问题。所以，用五分钟的检查换取节省的几个小时仍然是完全值得的。这实际上让我们拥有了一个不可思议的前排座位，可以观察编程未来的模样。如果你和那些全新的初创公司交流——我不知道你是否这样做，但我知道你有机会与世界上最聪明的人相处等等——去和一个全新的五人初创公司聊聊吧。

That's just like a a solved problem at this point. And so and and it is still easily worth the the kind of five minute trade off for the couple hours you save to go and review its work. And this is the we actually have this incredible front row seat in watching what the future looks like with coding. So if you talk to the new like the brand new startups, and I don't know if you do this, but I know that you get to spend your time with the dumbests of the world and whatnot. Go talk to a five person startup that's brand new.

令人兴奋的是，他们正以我一生中见过的最疯狂的方式工作。前几天我和一个9人初创公司聊天，他们估计自己至少达到了约100人规模公司的执行能力。而且当你仔细计算时，这个估计可能还偏保守。这是因为他们每位工程师现在都能产出相当于5个、10个甚至20个工程师的工作量，但他们的工作方式完全不同。他们是AI智能体的管理者。

And what's exciting is they are working in the craziest ways that I've ever seen in my entire life. I was talking to a nine person startup the other day that estimates that they're, at a minimum, executing at the size of about a 100 person company. And that that was, again, kind of conservative probably when when you, you know, do the underlying math. And it's because each of their engineers has the capacity output now of five or 10 or 20 engineers worth of work, but they are working in a completely different way. They managers of AI agents.

他们把时间花在编写想要构建的功能的详细规格说明上。他们花大量时间在软件的设计架构上，然后花很多时间审查智能体的输出。当然，并非所有知识工作领域都会完全如此。但想象一下在销售、市场营销、法律工作中，如果你的角色是管理那些负责底层数据准备、研究和创作类工作的智能体，然后你的工作是审查这些工作并将其整合到更广泛的业务流程中。这实际上将是未来许多工作的形态。

They spend their time on writing really good specs for what they wanna build. They spend really good time on on the design architecture of their software, and then they spend a lot of time on reviewing the output of the agent. So, not every area of knowledge work will look exactly like that. But if you imagine in sales, if you imagine in marketing, if you imagine in legal work, and your role is to manage agents that are doing a lot of the underlying data preparation, research, creation type of work and then your job is to go review that work and put it together in a broader business process. That will actually be what a lot of work looks like in the future.

这种幻觉或错误的概念，将与我有时需要审查他人工作、他人审查我的工作没有什么不同。我制作的演示文稿中会有错误，有人会发现拼写错误，或者发现我错误地更改了客户名称并予以修正。我们将对AI智能体做同样的事情。所以这是模式的翻转——我们原以为AI智能体会审查我们的工作并逐步提高我们的生产力，但实际上我们将成为AI智能体工作的审查者。

And this idea of hallucinations or errors will be no different than the fact that I have to sometimes review other people's work and other people review my work. And I have errors in the presentations that I create that somebody catches and they see a misspelling or they they see that I changed the name of a customer in the wrong way and they they changed that. We will be doing that for AI agents. So it's this it's this flip of the model where we thought AI agents were going to review our work and kind of incrementally make us more productive. We will be the reviewers of the AI agents work.

我们将成为编辑者。我们将成为管理者。我们将成为协调者。而这正是获得生产力提升的方式。所以我建议关注AI编程领域，观察初创公司如何获取杠杆效应，然后思考这对更广泛经济的影响。

We will be the editors. We will be the managers. We'll be the orchestrators. And that's actually how you then get the productivity gains. So I'd say watch the AI coding space, watch what startups are doing to get leverage, and then think about that against the broader economy.

你知道这真的很有趣，Aaron，因为我们上次交谈时，你告诉我你认识一个人基本上独自使用AI编程工具创建公司。当时我正在撰写Anthropic的Dario简介（其中引用了你的观点），于是我找到了一位使用Cloud Code独自进行类似开发的程序员。是的，这很明显——以至于Anthropic现在不得不设置一些速率限制，但这确实正在发生。

You know, it's really interesting, Aaron, because the last time we spoke, you told me about this person that you knew who was basically building a company on their own using AI coding tools. And so I was in the process of writing this profile of Dario at Anthropic, which you're quoted in, and I went out and found a developer doing something quite similar using Cloud Code to build on their own. So Yep. This is clearly I mean, to the point where like Anthropic now has to put some rate limits on, but this is clearly a thing that's happening.

没错。这正是关键所在——我真的很喜欢MIT的调查，它很棒，是个有趣的话题。但最大的遗憾可能是人们忽略了你刚才说的正在实际发生的情况，然后没有开始关注这将在企业中产生怎样的连锁反应，以及人们应该如何开始为AI智能体世界重新设计工作流程。

Right. And and this is the this is the thing that again, I I'm I love the MIT survey. I think it's great. It's it's it's a fun conversation topic. But the the the the one travesty would be if people miss that what you just said is actually happening on the ground and then not starting to pay attention to what that's going to mean as that ripples through corporations and how people should probably start to think about reengineering workflows for a world of AI agents.

这在每一波技术浪潮中都会发生，这实际上就是为什么会有早期采用者和创新者，以及为什么会有落后者。早期采用者和创新者会阅读你的Anthropic文章并意识到：哦，这确实是一个真实趋势。而落后者会阅读MIT的文章说：哦，我被证实了。一些公司将以更快的速度获得早期回报，其他公司可以等待。有时这意味着你的公司会被颠覆，有时不会，因为你的组织确实拥有一些专有能力。如果辉瑞或礼来公司因为想要更务实而在采用AI上稍慢一些，那也完全没问题。

And this happens in every single technology wave, which is actually why you have early adopters and early innovators and why you have laggards is because the early adopters and innovators are gonna read, you know, your anthropic piece and see, oh, this actually is a real trend. And the laggards are gonna read the MIT piece saying, oh, I've been vindicated. And some companies will then get those early returns at a much faster rate and other companies can wait. And sometimes that means that your company gets disrupted and sometimes it doesn't because you actually have some proprietary capability as an organization. If Pfizer or Eli Lilly took a little bit longer to adopt AI as a result of wanting to be more pragmatic, that'll be totally fine.

它们不会被颠覆。它们拥有足够的市场地位，拥有足够的分销渠道，完全可以等待这项技术更加成熟。但如果我现在是一家初创公司，我可能会尽可能利用这一点作为优势，试图在更大的现有企业周围绕圈子。正是这种市场中的良性张力，在每一波技术变革中创造了创造性破坏。

They're not gonna get disrupted. They have enough market position, they have enough distribution, they can afford to kind of wait for this technology to be more baked. But if I'm a startup right now, I'm probably gonna use that as my advantage as much as possible to try and run circles around maybe a larger incumbent. And this is what kind of creates this nice tension in the market that creates creative destruction in every wave of technological change.

好的。我肯定想多谈谈代理的定义是什么，以及你们在Box是如何部署它们的，还有听听你对GPT-5的看法。我们马上就来讨论这些

Okay. I definitely wanna speak a little bit more about what the definition of an agent is and how you're rolling them out at Box and also get your reaction about GPT-five. So let's do that right after

你已经习惯了在世界节目中听到我的声音，为你带来全球各地的访谈。

You're used to hearing my voice on the world, bringing you interviews from around the globe.

你会听到我报道环境和气候新闻。我是卡罗琳·比勒。

And you hear me reporting environment and climate news. I'm Carolyn Beeler.

我是马可·乌尔曼。我们现在一起为你主持《世界》节目，以全新的声音带来更多全球新闻报道。

And I'm Marco Wurman. We're now with you hosting the world together, More global journalism with a fresh new sound.

请在当地公共广播电台以及你收听播客的任何地方收听《世界》节目。

Listen to the world on your local public radio station and wherever you find your podcasts.

欢迎回到《大科技播客》，今天我们邀请到Box首席执行官亚伦·列维。亚伦，在我们深入讨论代理和GPT-5之前，让我先问一个基本问题：既然这已经在商业中发生了，也就是你们找到了让AI自主工作、从不同数据源提取信息并连贯呈现的方法，为什么你认为消费类公司——比如亚马逊的Alexa Plus和苹果的Apple Intelligence——却难以将这些功能整合到设备上或做成类似的消费产品？它们都承诺过，但至今尚未完全实现。

And we're back here on big technology podcast with Box CEO, Aaron Levy. Aaron, let me start before we get into agents and before we get into, g p t five, let me just start with a basic question, which is if this is already happening in business, which is basically, like, you're finding ways to get the AI to do work on its own and pull information from different data sources and present it coherently, why do you think it's been so difficult for consumer companies, like, let's say, Amazon with Alexa Plus and Apple with Apple Intelligence to put this together some as something on device or a consumer product that does similar activities? Because they've all promised it, but it's not quite there yet.

是的。我认为技术能够存在的事实与将其实现所需的执行要求是不同的。因此我们都能坐在前排观看前沿模型能做什么，而有些公司能够将这些技术打包用于这些应用场景。但如果你是一家拥有数千万或数亿用户产品的公司，消费者有着特定期望。从前沿模型到如何以可靠、可信且经济的方式将其交付给最终客户，这中间存在着巨大的执行鸿沟。

Yeah. I think there's fact that the technology can exist is different from still the execution requirements to bring it to life. And so we get to all have a front row seat on what the frontier models can do, and you have companies that can package those up in a way for these applied use cases. But if you're a company with tens of millions or hundreds of millions of users of your product and consumers that have a certain expectation. And that is a lot of execution gap required to go from the frontier model to how do you deliver that to your end customer in a reliable way that is trustworthy, that is affordable.

所以我认为大公司都在经历各自版本的这一过程。我也想象到，鉴于这个领域发展如此迅速，我能够理解某种程度上可能存在犹豫不决——今天某个模型领先，第二天另一个模型领先，然后又有一天另一个模型突破。因此你可能希望确保当你确定最终架构时，那是一个可持续的长期架构。所以在某种程度上，时间对你有利，因为你可能想等待看看哪些被淘汰，哪些继续前进。以你刚才提到的公司为例，我认为这些领域还没有被彻底颠覆到一旦确定最终架构就无法追赶的程度。

And and so I I think that the bigger companies are all going through their own version of that motion. I'd also imagine that given this given the space is moving so fast, I can sympathize for probably some degree of indecision maybe where one day a model is on top and then the next day a different model is on top and then another day, another model kind of breaks through. And so you probably wanna make sure that by the time that you land on a final architecture, you want that to be the sustainable long term architecture. And so to some extent, time is on your side up to a point because you might wanna wait to see who falls out and who keeps going. I think as an example, the companies you just mentioned, I don't think those spaces have been so utterly disrupted that they can't catch up once they land on a final architecture.

但我们需要看他们如何执行这个过程。

But we'll have to see how they execute through this.

那么对于企业来说，更多的是有更多预设的使用场景。我认为如果是手机，如果你试图获取这些主动通知，你需要处理海量数据，而在企业环境中更加集中？或者有什么区别？

And so for business, it's more that there are more prescribed use cases. And I think with a phone maybe if you're trying to get these proactive notifications, you're looking at a massive universe of data, whereas you're more concentrated in business? Or what's the difference?

实际上，我不会说有什么不同。我认为即使在企业领域，我们也处于非常早期的阶段。我们必须认识到我们有多早期。迄今为止的突破是面向消费者的Chat GPT，以及为非常精通网络工程的工程师们打造的编程助手。

Well, actually, wouldn't say there's a difference. I would say even in business, we're insanely early. We have to process how early we are. The breakout so far have been chat GBT for consumers. The breakouts have been coding agents for very, very wired in engineers that are very online.

他们关注着一切动态。然后是整个经济中的早期采用者。大多数在企业中部署的智能体可能是通过快速部署等方式实现的。杰弗里·摩尔提出了技术采用曲线的概念，或者至少普及了它。这个曲线包含公司或个人群体可能处于的多个类别。

They're paying attention to everything going on. And then early adopters across the economy. Most of the agents that are being deployed in the enterprise are being done by maybe you can flash it up or something. Jeffrey Moore came up with this idea of the technology adoption curve or at least popularized it. It has multiple categories of where a company or a group of individuals will be.

有这些早期创新者和早期采用者，然后存在一个鸿沟，接着基本上是实用主义者和早期大众，最后是落后者。我们在某些应用场景中正处于早期采用者跨越鸿沟的最初阶段。但我们必须想象存在这样一个鸿沟：早期采用者，也就是我们整天交流和相处的人，他们会尝试一切。我们会尝试这些疯狂的眼镜，会在头上贴磁铁，会做最疯狂的事情。我们会戴Google Glass。

Have these early innovators and early adopters, then you have a chasm, then you basically have kind of pragmatists and early majority, and then you have laggards. We are in the early adopter kind of the earliest phase of jumping over the chasm on some use cases. But we have to imagine there's this chasm where what happens is the early adopters, the people that we all hang out with and talk to all day, they're gonna try everything. We're we're gonna try these crazy goggles and we're gonna put, you know, magnets on our head and we're gonna do the craziest things. We're gonna wear Google Glass.

而这实际上几乎无法告诉你这个东西是否会跨越鸿沟。你必须真正看到哪些技术能够触达早期大众或那些真正大规模采用技术的实用主义者。像ChatGPT、Cursor这类产品，以及类似新一代研究代理工具的产品，Perplexity在这方面已经在早期大众中表现良好。但就AI代理跨越鸿沟而言，我们仍处于非常早期的阶段。所以有些会成功，有些则不会。

And that that actually tells you almost nothing about whether the thing will jump over the chasm. You have to actually see what makes it to the early majority or those pragmatists that really adopt things at scale. And so the kind of technologies that have clearly broken through are ChatGPT, products like Cursor, products like, let's say, a bunch of these kind of next gen research agent type things, Perplexity has done well in that kind of early majority. But we are so early in terms of AI agents jumping over now the chasm. So some won't make it, some will.

但我认为商业领域的进展并不比你刚才举的例子快多少。我只是觉得我们可以看到很多实例，但它们通常都属于那种早期采用者的范畴。

But I would say that business is not particularly moving faster than the examples you just gave. I just think we can see lots of examples of it, but they're usually in that kind of early adopter type category.

没错。在我们讨论的这一周，你们Box正在发布多个不同的代理。是的。但让我先从这个讨论开始，问你什么是代理？因为这似乎是一个被过度使用的术语，甚至像我这样一直沉浸在这个领域的人，也不确定这个词的确切含义。

Right. And so the week we're talking, you at Box are releasing a number of different agents. Yep. But let me start this discussion by just asking you what is an agent? Because it does seem like it's an overused term and even myself who I'm in this all the time, don't have on what that word actually means.

我认为我们应该预料到它会被完全过度使用。它现在是描述为你工作的AI系统的新术语。所以这将成为我们行业向前发展的主要术语。不是因为它是流行词，而是因为它实际上是一个有用的术语。它是一个可定义的对象，正在为你执行自动化工作。

I think we should anticipate that it's fully overused. It is now the new term of art for talking to an AI system that is doing work for you. So just we will hear this will be the main term that we use going forward as an industry. And not because it's a buzzword, but actually it's a it's a useful term. It's a it's a definable object that is doing automated work for you.

在某些情况下，这可能简单到只是回答问题。但我认为科技行业的大多数人通常会主张，它应该完成一定程度的工作，并多次循环通过AI模型来完成这项工作。这可能包括像Claude代码或Cursor拥有代理，或者Replit拥有代理，你给它一个任务，比如构建一个具有这些特性的网站，它会在十分钟内完成相当于人类数周的工作。这就是一个管理整个过程的代理，多次循环通过模型，跟踪它在做什么，在此过程中更新其记忆。这实际上就是一个代理。

That could be, in some cases, as simple as answering a question. But I think most people in in the tech industry would generally argue that it should be doing some degree of of work and looping through the AI model multiple times to do that work. And so that could be everything from, you know, very clearly something like Claude code or Cursor has an agent or Replit has an agent where you give it a task like build me a website that has these qualities and it will go off and do, you know, weeks worth of human work in ten minutes. And that's an agent that is managing that whole process, looping through the model multiple times, keeping track of what it's doing, updating its memory in the process. And that's effectively an agent.

所以这是一种代理编码。我们将在法律、医疗、金融、教育领域看到相同类型的代理架构出现，你可以部署代理去为你完成工作。并且会有一个关键的可访问性指标，即代理在你必须干预、修改并重新指引正确方向之前能完成多少工作。目前很多这类工作可能只能持续几分钟，但我们看到了一些例子，代理可以运行几十分钟甚至几个小时，有效地驱动越来越好的高质量输出。所以我认为这是思考代理的一种方式，它们在未来几年将会非常普及。

So that's an agent encoding. And we're going to see that same kind of agent architecture emerge in law, in healthcare, in finance, in education, where you can deploy agents to go off and do work for you. And and there will be, you know, a critical access which is how much work can the agent do before you have to intervene and modify and kind of repoint it in the right direction. And so a lot of that work right now can be maybe a couple minutes long, but we're seeing examples where agents could be running for tens of minutes or maybe even hours and effectively drive better and better and more high quality output. So I think that's a way to think about agents and these are gonna be very pervasive in the coming years.

但这确实是第一年——2025年是我们可以真正认真讨论它的第一年。我认为安德烈·卡帕西可能将其表述为：我们不应该认为这是代理之年，而应该认为是代理的十年。这可能是正确的思考方式。这就是

But this is really the first year 2025 is the first year where we could even really be talking about it seriously. And I think Andre, you know, Carpathi had a had a you know, probably phrased it as we shouldn't think about this as the year of agents. We should think about it as the decade of agents. That's probably the right way to think about it. This is this

可以说是移动时代。成为了移动的十年，但最终我们开始使用移动设备。

is sort mobile. Became the decade of mobile, but then eventually we started using mobile.

是的。但是但是但是，而且再次强调，当你刚才说移动元年很重要时。对吧？人们是否说过，有些人说那是在2022年，但可能第一次真正现实的是2000年，抱歉，不是2022年。

Yeah. But but but the and and again, when you just said the year of mobile mattered. Right? Did did people say know, so some people said that was in 2022, but probably the first time it could have been realistic was 2000 sorry. Not 2022.

02/2002年。但有些人认为直到2006年和2007年iPhone出现时才真正现实。所以，你知道，我和我认为相当多的其他许多人实际上相信我们已经有了我们的iPhone用于代理。我们不需要任何新的突破性架构。我们已经有了一种架构，它已经可以作为代理的核心脚手架工作。

02/2002. Some people but but it wasn't really realistic until 2006 and 2007 when when you had the iPhone. So, you know, I and I think fairly other many many other people are actually convinced we we already have our iPhone for agents. We don't need don't need any kind of new breakthrough architecture. We have an architecture that already kind of works as the core scaffolding for agents.

所以我们现在可以开始这个十年的时钟了。但这将是一个完全自动驾驶类型的问题。你知道，显然，Waymo在大概十年前、十五年前就开始了。而直到今年，它才在硅谷郊区可用。所以是什么花了十年或十五年时间？

So we can start the decade kind of clock now. But it will be a full self driving type problem. You know, obviously, Waymo, you know, got kicked off, I don't know, a decade, decade and a half ago. And only this year is it, you know, accessible in suburban Silicon Valley. So what what took a decade or a decade and a half?

这只是大量的工程工作，大量的道路测试里程，不断改进系统的每一个维度，你知道，提高系统的准确性和智能性。我们将在知识工作中看到同样的情况。这将需要数年时间。早期采用者将获得早期回报。实用主义者将在它基本无需大量手动干预时使用它，而每个人都会落在这个光谱的中间某处。

It was just lots of engineering work, lots of miles on the road, lots of improving every single dimension of, you know, of of of the accuracy of and the intelligence of of the system. We're going to see the same thing for knowledge work. It's going take years. The early adopters will get the early returns. The pragmatists will use it once it sort of works without a lot of hand holding and everybody will land somewhere in the middle of that spectrum.

好的。所以我本周观看了你演讲的一部分，你谈到的一些让公司能够部署的代理将是，例如，查看申请以参与可能租出公寓或哦，是的。或者查看一些财产记录然后在那里执行任务，或者创建报告，查看临床测试并尝试找出问题。所以请谈谈创建这些的过程是如何工作的。这还处于演示阶段，还是已经实际可用？

Okay. And so I watched a chunk of your presentation this week, and some of the agents that you're talking about enabling companies to deploy will be things that will, for instance, take a look at a application to be involved to maybe take an apartment out or oh, yeah. Or to look at some property records and then do tasks there or to create reports, looking at clinical tests and trying to pull out issues. So talk a little bit about how the process to create these works. And is this still in the demo phase, or is this actually real?

所以也许先回答第二个问题。我们本周发布了一些重大公告。我们宣布的一些产品和功能现在已经全面上市（GA），所以客户已经可以开始使用。其中一些，我们稍微展望了一下未来几个季度的产品路线。举个例子，我们现在有一个AI代理，任何客户都可以去使用，它是一个数据提取代理。

So maybe second question first. So so we we made a a number of big announcements this week. Some of the the the product and capabilities that we announced are fully GA right now, so customers can already start to use it. Some of it, we we kinda give a little bit of a of a crystal ball view into the next couple of quarters of of the product that we're getting out there. As an example, we have an AI agent right now that any customer can go and use, which is an a data extraction agent.

所以你可以提供给我们合同、发票或医疗数据。然后我们有一个AI代理来处理这些内容，从这些文档中提取关键数据，并让你围绕这些数据自动化工作流程。我们在Boxworks大会上宣布了一个名为Box Automate的新功能。Box Automate的理念是，拥有能够帮助你审查文档、生成提案或基于数据为客户制定销售计划的独立代理非常强大。

So you can give us, again, contracts or invoices or medical data. And then we have an AI agent that that that works through that content, pulls out the critical data from those documents, and then lets you go and automate a workflow around that. What we announced at Boxworks was a a new capability called Box Automate. And what the idea of Box Automate is is it's very, very powerful to have one off agents that can help you, you know, review a document or generate a proposal or generate a sales plan for a client based on data. That's super powerful.

但更强大的是，我可以将许多这样的代理整合到一个完整的业务流程中。Box Automate让你可以在Box内定义你的业务流程。这可以是客户入职流程、并购尽职审查流程，也可以是医疗保健患者审查流程。

But what's even more powerful is that I can drop many of those agents into a full business process. So what Box Automate lets you do is actually define your business process within Box. It could be a client onboarding workflow. It could be an M and A due diligence review process. It could be a health core a health care patient, review process.

你可以用Box Automate定义这个工作流程。它是一个拖放式的工作流构建器。然后在流程的任何节点，你都可以引入AI代理来执行工作。对于AI代理来说，获得正确的上下文非常重要，这样才能有效工作。我们的系统允许你从企业内容中为代理提供这种上下文。

And you define that workflow with Inbox Automate. It's a drag and drop, kind of workflow builder. And then at any point in the process, you can bring in an AI agent to do work within that process. And so one one thing that that that is very important with AI agents is they need the right context to be effective. So our system allows you to get that context to agents from your enterprise content.

所以你的营销资产、研究数据、合同、发票都成为代理的重要上下文。Box Automate让你能够在工作流中按需或即时构建这些代理，利用你现有的内容。然后我们可以开始帮助你自动化企业中的一系列知识工作任务。

So your marketing assets, your research data, your contracts, your invoices, that becomes very important context for agents. So Box Automate lets you basically build these agents on demand or on the fly in workflow that leverages your existing content. And then we can start to help you automate a bunch of knowledge work tasks around the enterprise.

很多早期关于GPT-5的评论都认为它是为这类任务构建的，或者作为这类工作的基础层。对吗？

Now a lot of the early reviews around GPT five was it was sort of built to do these type of things or, like, as a foundational layer for this type of work. Right?

是的。

The Yep.

我们早期读到的评论是说它就是在做事，而且有人注意到，当你在ChatGPT中使用GPT-5时，几乎每个回答都会问'我能为你做些什么吗？'。所以我很好奇，Aaron，你的反应是什么。我们上次交谈是在GPT-5发布之前。你对这套新模型有什么感觉？实际上这是一套模型。我很好奇你对早期很多人感到失望这一事实怎么看。

The reviews we read early on was that it just does stuff, and there have been people that have noticed that, like, when you're in ChatGPT using GPT five, you, like, literally can't have an answer where it doesn't say, can I do something for you? So I'm actually curious, Aaron, what your response has been. We last time we spoke was pre GPT five. What your what your feeling has been about this new set of models? Really, it's a set of And and I'm curious, like, what you make of the fact that so many people were disappointed early on.

嗯，是的。关于失望情绪或者说网络上的时代思潮，有趣的是它其实已经发生了很大转变，我认为很多人已经更新了对GPT-5的看法。而且我觉得Codex最近在代码代理方面表现非常出色。你知道，如果说我们已经习惯了并且沉迷于过去一年左右这些不可思议的飞跃和突破。我们经历了——如果你仔细想想——我们从GPT-4到GPT-4.01、0.3，再到GPT-4.1。

Well, yeah. So on disappointment or kind of online zeitgeist, which actually interestingly has already shifted, I think, quite a bit where a lot of folks have kind of updated their views on GBD5. And I think Codex has come out very strong recently on the coding agentic side. You know, if I think we have gotten used to and we've been hooked on these incredible kind of jumps and breakthroughs over the past year or so. We went from if you think about it, we went from GPD four to GPD four point zero to one, zero three, and then GPD 4.1.

每一个版本在不同维度上都实现了相当有意义的阶梯式进步。所以如果你直接从GPT-4跳到GPT-5，那看起来会是惊人的指数级增长。但我们通过这些中间版本，实际上提前预览了GPT-5最终会成为的样子：一个思维模型，一个思维链，拥有更高质量的编程能力和在工作关键维度上的一系列能力。我认为这主要是因为我们获得了许多通向GPT-5的渐进式进步或阶梯式进步，而GPT-5正是这些突破的集大成者。

And each of those on a different axis was actually a pretty meaningful step function. So if you had just taken GPT four and then you jumped to GPT five, it would have looked insanely exponential. But we got these points along the way that effectively, you know, kinda gave us an early preview into what GPT-five would ultimately become, which is a thinking model, a chain of thought, with with a a way higher quality of coding skills and a bunch of capability on capabilities on critical dimensions of work. And so I think it was mostly just driven by the fact that we got lots of incremental steps or step function steps on the path to GPT-five. And then GPT-five was just the culmination of a lot of those breakthroughs.

所以我认为这更多是心理层面的而非实证层面的。如果我们直接从三代跳到四代再跳到五代，那将会是我们见过的最陡峭的增长曲线。但事实上正是那些中间步骤可能引起了一些这样的反应。在我们的领域，我们会在多个评估中测试每个模型，给模型提供不同类型的企业数据：合同、财务文件、研究材料、内部备忘录等这类资料。然后我们会就这些文档或数据向模型提出一系列问题。

So again, I think it's probably more psychological than kind of empirical. Like I think if we had gone from three to four to five, it would be the most vertical axis we've ever seen. But it was really again those steps along the way that maybe caused a little bit of that kind of reaction. In our world, we test every single model on a number of evaluations where we give the model different types of enterprise data, contracts, financial documents, research materials, internal memos, those types of things. And we ask the model a series of questions about that document or data.

在我们的评估中，我们看到GPT-5相比GPT-4.1取得了显著改进。对我们来说，在多个关键测试中都看到了多方面的提升。这些改进随后转化为客户实际使用中的提升，比如当医疗保健提供商在非结构化医疗数据上使用GPT-5时，会获得比之前更好的结果。或者在处理合同时，也会得到更好的结果。在需要专业分析的领域——无论是医疗、法律还是金融服务——我们都看到了改进。

And we saw meaningful improvements from GPT-five versus GPT-four-one as an example on our eval. So for us it was multiple points of improvement on a number of our key tests. And those improvements then translate into real life improvements for customers where they all of a sudden will it'll mean that when you're a healthcare provider using GPT-five on unstructured healthcare data, you're going get better results than you got before. Or when you're using it on your contracts, you're going to get better results. And so on a number of spaces where either it was kind of expert analysis required in healthcare or law or financial services, we saw improvements.

或者更普遍地说，如果你需要逻辑推理或数学能力，在这些维度上也都有所提升。

Or in more a general sense, if you needed logic or reasoning or math, it was also an improvement on those dimensions as well.

我能快速请教一下您对当前AI行业经济状况的直观看法吗？我的意思是，我们刚刚在周五的节目中和Ranjan讨论过这个问题——OpenAI的亏损到2029年预计将达到1150亿美元。哦抱歉，是现金消耗。到2029年将达到1150亿美元，比之前预期高出800亿美元。

Can I get a quick gut check from you on the economics of the AI industry right now? Mean, are talking at a moment where we just talked about this on the Friday show with Ranjan that OpenAI's losses are now gonna total a 115,000,000,000 through 2029. Oh, sorry. It's cash burn. A 115,000,000,000 through 2029, 80,000,000,000 higher than it previously expected.

预计今年能赚100亿美元，但它刚刚与甲骨文签署了3000亿美元的协议，这笔交易几乎一夜之间让甲骨文成为近万亿美元的公司，并使拉里·埃里森超越埃隆·马斯克成为世界首富。这...这怎么说得通呢？

It's it's expected to make, like, 10,000,000,000 this year, but it just signed a $300,000,000,000 deal with Oracle that, like, turned Oracle into a nearly $1,000,000,000,000 company almost overnight and made Larry Ellison the richest person in the world above Elon Musk. How does this how does this make sense?

嗯，我认为如果你像我一样相信——当然还有其他人，Jensen、显然Sam，甚至Elon，我想他们都会相信这是我们可能接触过的最大技术。所以如果你把这看作是第三次工业革命，有史以来第一次，我们可以将自动化引入知识工作。稍微想一想这一点。我们将自动化引入知识工作。知识工作世界的一切基本上一直受限于我们人类能够工作的速度。

Well, I think it makes sense if you believe like I do and certainly others, Jensen, clearly Sam, even Elon, I think would believe that this is the single biggest technology that we've probably ever had access to. So if you think about this as sort of a third industrial revolution where for the first time ever, we can bring automation to knowledge work. Just think about that for a second. We bring automation to knowledge work. Everything about the world of knowledge work was always basically limited by how fast we as humans could work.

我们可以往电脑里输入，将数据放入系统，其他人读取这些数据，它在某种流程中移动。这就是知识工作的速度——我们打字或阅读信息的速度，然后用这些数据在现实世界中做些什么。这就是知识工作能够发生的节奏。因此，我们所知的每一个知识工作领域，医疗专家阅读医学诊断，生命科学专家进行临床研究，律师试图寻找案件事实或处理知识产权，工程师试图生成代码和阅读产品规格——所有这些工作一直受限于我们作为个体能够独立完成这些工作的速度。

We could type into a computer, put data into a system, somebody else reads that data, it moves along in some kind of process. That was about the speed of knowledge work was how quickly we could type or read information and then do something in the real world with that data. That was the pace that knowledge work could happen at. And so every field that we know of in kind of knowledge work, healthcare experts reading medical diagnoses, life sciences experts that are doing research on clinical studies, lawyers that are trying to find facts about a case or working through intellectual property, an engineer trying to generate code and read product specifications. All of that work has always been constrained by how fast we as individuals can do that work individually ourselves.

有史以来第一次，借助人工智能，我们可以将自动化有效地引入所有这些工作。而这种自动化可以根据我们投入多少计算能力来调整。当然，还有我们的数据质量以及我们的系统在将这些数据提供给AI方面的效率。但在一个你可以调节计算能力，然后获得不同水平的自动化和有效工作产出，以远低于人工成本完成工作的世界里，这是我们经济和后工业世界有史以来最大的突破。因此，比如说1000亿美元的损失，以达到该技术普及的程度，当你考虑到整个经济以及所有医疗保健、所有法律、所有生命科学、所有金融服务、所有工程的规模时，这实际上是一个非常小的数字。

For the first time ever, with AI, we can bring automation to effectively all of that work. And that automation can kind of be tuned based on just how much compute we throw at the problem. And then, of course, how good our data is and how effective our systems are in getting that data to the AI. But in a world where you can toggle compute and then get different levels of automation and effective output in work to get done at a way lower cost than what people can do, that is the biggest breakthrough we've ever had in the economy and in the post industrial world. And so a $100,000,000,000 of loss, let's say, to get to that point of saturation where that technology is out there, that's a very it's actually a very small number when you think about the economy and the size of the economy for all of health care, all of law, all of life sciences, all of financial services, all of engineering.

所以我认为这就是这些科技公司正在为此背书的方式。而且损失是一种选择，明确地说。就像，那一点非常明显。他们选择损失那笔钱。他们是出于战略原因这样做的。

So I think that's how that's how these technology companies are underwriting this. And the losses are a choice, to be clear. Like, that and it's very that's very obvious. Like, they're choosing to lose that money. They're doing it for a strategic reason.

你知道，那至少是他们的决定。战略原因是这个市场非常有价值，值得拥有和主导，以至于他们宁愿建立产能，并在许多情况下补贴使用，比如在ChatGPT的免费消费者层级，而不是按今天的成本费率对所有东西收费并确保一切盈利。这是一种选择。他们本可以决定对所有东西收费。那样今天的采用率就会降低。

You know, that that's at least their decision. The strategic reason is is that this is such a valuable market to own and to dominate in that they would rather build up capacity and in many cases subsidize usage, let's say in free consumer tiers of Chatuchu BT, then charge everything at today's kind of rate of cost and then make sure everything is profitable. That's a choice. They could decide to charge for everything. They would get less adoption today.

他们会立刻成为一个更可持续的企业。但足够多的人相信这个奖赏足够大，值得投入所有的研究费用、所有的数据中心费用，以及在必要时进行补贴以推动采用和需求。这是一种要么做大要么回家的赌注。你知道，显然非常非常聪明、非常经济理性的公司、个人、主权财富基金都认为这个赌注是值得的。我可能也站在认为这个赌注值得的一边，因为再次强调，这项技术可能产生的经济影响有多么重大。

They would be instantly a more sustainable business. But enough people believe that the prize is big enough that it's worth actually doing all of the research expenses, all of the data center expenses, and the subsidization where necessary to drive that adoption and demand. And it's a go big or go home type of bet. You know, clearly very very smart, very economically rational firms, individuals, sovereign wealth funds believe that that bet is worth it. I'm probably on the side that that the bet is worth it because of, again, how material of an economic impact this technology can have.

然后我们显然会看到它如何与领域内的任何个别参与者展开。

And then we'll obviously how how we'll see how it plays out with any kind of individual player in the in the space.

各位，您可以在box.com了解更多关于Box的产品服务。首页现在正在播放一个视频，更详细地介绍了今天亚伦和我在节目中讨论的内容。亚伦，见到你真是太棒了。再次感谢你参加

Folks, you can learn more about Box's offerings at box.com. There's a video playing on the home page right now that talks a lot more about things that Aaron and I have discussed here today. Aaron, so great to see you. Thanks again for coming on the

节目。谢谢，亚历克斯。

show. Thanks, Alex.

好了各位。非常感谢大家的观看。我们下次在《大科技播客》中再见。

Alright, everybody. Thank you so much for watching. We'll see you next time on big technology podcast.