DeepSeek新模型发布，TikTok美国CEO潜在人选，微软CEO动态，纽约AI议程直播 | 2025年10月1日

欢迎各位收看Informations TI TV。我是Akash Basricha。今天是10月1日星期三。我们为您准备了精彩的节目内容。我们将带您回顾昨天举行的AI Agenda Live大会，呈现所有精彩亮点。

Welcome everyone to the Informations TI TV. My name is Akash Basricha. It is Wednesday, October 1. We have got a great show planned for you today. We are recapping our AI Agenda Live conference from yesterday with all of the highlights for you.

我们还邀请了ClickUp的CEO来分享他对企业数据战争的看法，并将与Futurum集团共同评估当前交易环境。此外还有大量新闻要报道——我们将深入解析一则关于TikTok的重大新闻，但我想先从微软今天的消息开始。微软今早宣布，CEO萨提亚·纳德拉将把部分职责移交给公司首席商务官Judson Althoff。Althoff将成为微软商业业务CEO，而纳德拉将更专注于微软的核心技术。

We've also got the CEO of ClickUp coming on to get his view on the corporate data wars, and we are taking stock of the current deal making environment with the Futurum Group. We also have a ton of news to get to. We've got a big TikTok story that we are going to break down, but I want to start with the news from Microsoft today. Microsoft announced this morning that CEO Satya Nadella will offload some of his duties to the company's Chief Commercial Officer, Judson Althoff. Althoff will become the CEO of Microsoft's commercial business, and Nadella will start focusing his time more on Microsoft's core technology.

这是个重大公告，因此我想请我们的微软记者Aaron Holmes来详细解读他的看法。Aaron，欢迎回到节目，很高兴你能来。

It is a big announcement, and so I want to bring on our Microsoft reporter, Aaron Holmes, to tell us more about what he thinks of all this. Aaron, welcome back to the show. It's great to have you.

很荣幸参与节目。

Happy to be here.

今天我们收到重磅消息——天啊，这完全出乎意料。你认为微软为何做出这个决定？

So we got big news today. Gosh, I wasn't expecting this one. Why do you think Microsoft is making this move?

这确实很有意思。萨提亚·纳德拉在今早给员工的备忘录中提到，他希望集中精力领导微软面临的工程任务，比如开发尖端AI技术（这是他的原话），或是研究系统架构和数据中心，同时将更多销售和市场推广职责委派给Judson Althoff。不过要判断这个变化的实际影响还为时过早，毕竟萨提亚在组织架构上仍高于Judson，实质上仍是统管全局的CEO。我认为需要观察这是否会真正改变他的职能定位。

You know, it's really interesting. I think that what Satya Nadella said in his memo to staff this morning is that he wants to be laser focused on leading, you know, some of the engineering tasks that Microsoft has in front of it, like, you know, developing cutting edge AI, as he put it, or, you know, working on systems architecture and data centers, and is somewhat, you know, delegating more of the sales and go to market role to Judson Altoff. At the same time, you know, it's hard to judge exactly how big of a change this is just because Satya is still, you know, above Judson in the org chart and is effectively still the CEO overseeing everything. So I think we're going to have to wait and see whether this actually, you know, meaningfully changes his role in that regard.

值得一提的是，微软内部其实有众多CEO头衔的高管，对吧？

And we should say, I mean, Microsoft has a ton of CEOs inside the org, right?

是的。我是说，这家公司某种程度上以职位膨胀著称，可能有几十个执行副总裁和超过100个企业副总裁，更不用说好几位CEO了，包括AI领域的CEO穆斯塔法·苏莱曼。所以在这家公司，人们拥有这些听起来很高大上的头衔，但在组织架构中实际层级较低并不罕见。

Yeah. I mean, this is a company that is kind of known for, you know, for role inflation, it has probably like dozens of executive vice presidents and over 100 corporate vice presidents, not to mention several CEOs, including, you know, Mustafa Suleyman, who's the CEO of AI. So it's not uncommon for people to have these, you know, really lofty titles at the company while still being somewhat lower on the org chart.

没错。那么，我想谈谈萨提亚·纳德拉现在要扮演的这个角色。要理解他将更密切监管业务的哪些方面，我认为需要先盘点微软核心技术的现状，以及它与谷歌、AWS等其他大型云服务商的对比——Meta也正在加入竞争。跟我们聊聊他们目前的模型发展吧，虽然我们常将其与OpenAI划等号，但显然他们也在自主研发。

Right. Well, look, I want to talk about this role that Satya Nadella will now play. You know, I think to understand what sorts of elements of the business he's now going to oversee a little more closely, I kind of want to take stock of where Microsoft's core technology is and how it stacks up against other big cloud players like Google, like AWS, Meta's getting into the game. I mean, talk to us about where their models sit right now, because I think we think of it as synonymous with OpenAI, and yet they're obviously developing their own stuff too.

是的，我们看到微软过去两年多试图开发自己的AI模型。但迄今为止进展有些断断续续——上个月才首次亮相其AI模型，却仍未完全发布。早期这些模型的表现略逊于OpenAI和谷歌的模型，所以我认为微软在这方面仍处于追赶状态。

Yeah, I mean, so we've seen Microsoft attempt to develop its own AI models over the past two plus years. And so far, you know, the progress on that has been somewhat halting. They finally just debuted their AI models for the first time last month, but they still haven't done a full release of those models. And, you know, they early on stacked up a bit lower than than the models from OpenAI and Google. So I think, you know, the company is still somewhat playing catch up there.

与此同时，正如我们几个月前报道的，他们自主研发芯片的进度因落后于计划而被迫缩减。因此我认为有几个领域纳德拉可能会全力投入，让微软重新回到这些领域的前五名甚至榜首位置。

And at the same time, you know, their efforts to develop their own chips, as we reported, you know, a couple months ago had to be scaled back because they were behind schedule. So I think there are a few realms where, you know, Nadella might really want to put his head down and work on catching up and getting back Microsoft back into, you know, the top five or first place in those fields.

那么芯片和模型之外，是否有迹象表明他也会重点关注数据中心和量子计算？

So we've got chips, we've got models. Any sort of signal from them as to how much he's going to be focused on data centers and quantum as well?

确实。近年来微软发布了大量量子计算研究并强调其重要性，但尚不清楚何时能实际应用于其系统。同时，它在数据中心上的投入不逊于其他超大规模厂商——去年支出超过800亿美元，新财年资本支出预计更高。萨提亚在今天的备忘录中明确表示，他希望将数据中心这类最具雄心的技术工作作为其职责重点，预计他会更深度介入该领域。

Yeah, I mean, the company has put out a lot of research about quantum computing in recent years and signaled that it's important, but it's still not clear when that will become something that's actually usable as part of its systems. At the same time, it is spending as much as many of the other hyperscalers on data centers. The company spent more than 80,000,000,000 last year and is on track to spend even more than that in the coming year on CapEx. And, you know, Satya said in his memo today that he essentially wants to focus on the highest ambition technical work like those data centers as part of his role. So I think we'll see, you know, him get even more hands on in that realm.

明白了。亚伦，看来这只是我们探索微软新运营方式的开始，期待再次邀请你讨论。以上就是《The Information》微软领域记者亚伦·霍姆斯的分享。

Right. Great. Well, Aaron, I take it this is just the start of what we have to find out about the new ways that Microsoft plans to operate. And so I look forward to having you on again. That was Aaron Holmes, our Microsoft reporter here at The Information.

好的。昨天我们在纽约举办了AI Agenda Live大会。我们邀请了许多出色的演讲嘉宾，包括Conviction的Sarah Guo，以及来自Reflection AI、WUKA和ONE X的其他演讲者。现在我想请出主持这次活动的AI记者Stephanie Palazzolo，来分享她昨天讨论中的主要收获。Stephanie，欢迎回到节目。

Okay. Well, yesterday was our AI Agenda Live conference in New York. We had a number of great speakers, including Sarah Guo from Conviction and other speakers from Reflection AI, WUKA and ONE X. I want to bring on our AI reporter, Stephanie Palazzolo, who hosted the event to share more about her big takeaways from the discussions yesterday. Stephanie, welcome back to the show.

很高兴你能来。

It's great to have you.

谢谢，谢谢邀请我。

Thanks. Thanks for having me.

那么，我想详细聊聊你最大的收获是什么，但在开始之前，我想先播放一段视频。我们有一段Conviction的Sarah Guo的发言片段，让我们来看一下。

So look, I want to talk all about what your biggest key takeaways were, but before we do it, do want to play a clip. We've got a clip from Sarah Guo at Conviction. Let's take a look.

我认为大多数工具的采用周期都远不止几年。我们最近花了很多时间与一些大型私募股权公司合作，他们正在研究如何改造他们拥有的公司。他们说，好吧，我们有一个五年计划，包括教育、工具采用、培训、技能再培训和绩效管理。我认为这才是人们应该预期的时间线，因为习惯了长期做某事的人需要时间学会使用这些工具。这需要大量的教育工作。

I think most tools take a cycle of adoption that is a lot longer than a few years. We spent a bunch of time recently with really large private equity firms that are figuring out how to transform companies they own. And they're like, okay, we have a five year plan that involves education, adoption of tools, training, reskilling, performance management. And I think that is more of the timeline that people should expect of just real human beings who are used to doing something for a long time are gonna need to figure out how to use these tools. And that is a lot of education.

这个五年时间线的观点，我觉得Sarah说得很好。在某些情况下，等待投资回报确实需要很长时间。你对那次讨论的主要收获是什么？稍后我们再谈谈会议的其他部分。

So this idea here of a five year timeline, I mean, that's the I I thought it was a good point that Sarah made. You know, it's also a long time to, you know, sort of wait for a return on your investment in some cases. What were your big key takeaways from that discussion? Then we'll get into later parts of the conference in a second.

是的，完全同意。Sarah是在回答我提出的一个问题，关于我们在AI领域经常写到的这种脱节现象。一方面，有些公司显然正在投入数十亿甚至数百亿美元建设基础设施数据中心；另一方面，像Sam Altman这样的人说AI很快就能治愈癌症、改革教育系统。但同时我们也写了很多关于企业在可靠地运行AI或为其找到有用用例（如Salesforce的Agent Force或微软的Copilot）时遇到的困难。所以我认为Sarah的观点是，这些事情从来不像人们想象的那么容易。

Yeah, totally. I mean, so Sarah was responding to a question that I'd asked her around kind of this disconnect that we've written a lot about in the world of AI, where on one hand you have companies that are obviously investing tens, if not hundreds of billions of dollars into this infrastructure data centers. And then we have people like Sam Altman who are saying, you know, AI will soon be able to cure cancer and fix our education system. But then on the other hand, we've also written a lot about businesses that are just having a lot of trouble getting AI up and running in a reliable way or finding useful use cases for it with products like Agent Force from Salesforce or Copilot from Microsoft. And so I think Sarah's point here is that these things are never as easy as what people think.

而我们预期这些产品开始以可靠方式运作的时间线，更多是在五年以上的范围，而非短短几个月内。

And the timeline that we should expect to kind of see some of these products start working in a reliable way is more on the, you know, more in the scope of, you know, five plus years versus a couple months.

没错。我想补充两点很有意思的观察：昨天我们还有来自SAP和AMD的演讲嘉宾。他们提出的观点让我深有共鸣——很多公司目前正在试点项目上投入资金，虽然初期可能看不到投资回报，但必须坚持。试点不顺不意味着停止投资，反而更应该持续寻找能带来巨大收益的方向。

Right. And I should say, you know, two of the other points that I thought were really interesting is we also had speakers from SAP and from AMD on the stage yesterday. And some of the points that they raised, which resonated with me, are this idea that, hey, you know, a lot of companies are spending on pilot projects right now, and they might not be seeing the the ROI initially from these pilot projects, but you just kind of got to stick with it. And, you know, just because a pilot doesn't go well doesn't mean you stop investing. It's actually more reason to keep finding the thing that can make you a lot of money.

他们提到的另一点是，当前AI技术最实用的反而是那些看似枯燥的应用场景，不是宣传视频里的炫酷功能，而是像快速阅读文档、PDF这类基础工作。

And the other point that they raised was that it's really the boring use cases of AI that are being the most practical uses right now for the technology. It's not the stuff in the promo videos. It's like reading through documents quicker and PDFs and stuff like that.

确实。与我们交流的客户普遍如此。问题可能在于人们对AI的过高期待与现实能力之间存在巨大落差——实际应用往往只是搜索PDF或把图片文字转成报销单。进展缓慢并不意外，但理想与现实的鸿沟确实相当显著。

Yeah. No, I mean, I think that's definitely very true with the customers that we talk to. And I think maybe what the issue is, is just how big that disconnect is between, again, the very high expectations and the promises of AI and what they're able to do in real life, which a lot of the times is just, you know, searching through PDF or taking an image and taking that text to put into an expense report. So, think the fact that it's taking a while is not a surprise, but just reality versus expectations. That gap, I think, is just quite large.

你当天最后那场小组讨论怎么样？Databricks和Anthropic的专家们讨论核心技术时——比如强化学习、通用人工智能——有什么洞见？他们似乎在某些观点上存在分歧？

What about the panel you had at the end of the day? You had some folks on from Databricks and from Anthropic. You were talking about the core technology, right? I mean, reinforcement learning, AGI, what were some of the reflections you had from that? Because they didn't necessarily agree on some of the points that they were both talking about.

那场讨论非常精彩。我试图通过他们了解AI研究领域的真实氛围。要知道去年十一月我们报道过实验室面临的困境：即便投入更多算力和数据训练模型，效果提升仍不及预期。

Yeah. I mean, I think that was a fascinating panel. So I think part of what I wanted to get from both of them is a bit of a kind of vibe check of sorts on the AI research world. Because again, you know, last year there have been a lot of stories kicked off by one that we wrote in November around, you know, the issues the labs are running into whenever they just simply try to train models on more compute and more data. They weren't getting the improvements that they had expected.

有趣的是，昨天Anthropic的Sholto Douglas等人展现出某种谨慎乐观的态度，这似乎标志着某种转变。

And so interestingly, I I think yesterday was a bit of a shift from that with people like Sholto Douglas from Anthropic showing some kind of cautious optimism there.

嗯。

Mhmm.

他当时发表的观点大意是说，他认为仅凭现有技术——比如我们已基本掌握如何运用的强化学习——就能实现AGI，或者说在某些领域达到人类专家水平的人工智能。这个说法显然相当惊人，毕竟AGI向来被视为遥不可及的终极目标。

He kind of made the comment where he said, you know, I really think we can get to AGI or like AI that's on the level of human experts in certain domains just by using techniques that we already have today and we understand largely how to use like reinforcement learning. And so that's obviously a pretty striking point just because AGI is kind of, you know, it's like the pie in the sky and the gold.

没错。就像现在微软和OpenAI正在争论的——到底什么是AGI？我们可能永远无法真正确定何时实现了它。

Right. It's like, what is AGI? You know, this is the thing that Microsoft and OpenAI are debating right now. It's like, we're never going to know when we get there really.

是啊。AGI确实很难定义，而且这个标准似乎总在变化。但令人意外又振奋的是，他如此乐观地认为我们不需要新型模型或全新科学发现就能实现AGI——无论你如何定义它。

Yeah. Yeah. I mean, it's very hard to define and it kind of feels like the goalposts keep on moving a little bit. But I mean, I think it was surprising and promising that he was so optimistic on our chances of reaching AGI, however you want to define it, without needing new types of models or brand new kind of scientific discoveries.

是的。Stephanie，昨天的活动非常精彩，我们还讨论了机器人技术和AI科学应用等更多话题，我迫不及待想在本周后续节目中分享给观众。我们一定会邀请你回来深入探讨。这位是Stephanie Palazzolo，我们的AI记者，也是昨天纽约AI议程直播大会的主持人。

Right. Well, Stephanie, it was a great event yesterday. There was so much more, including topics about robotics and also some of the more scientific applications of AI that I'm excited to bring to our audience later on throughout the week. And we'll be sure to have you on more to talk more about it. That is Stephanie Palazzolo, our AI reporter and host of yesterday's AI Agenda Live Conference here in New York City.

说到Anthropic，本周发布新模型的公司不止他们。深度求索也推出了暂称为实验性的新模型。鉴于深度求索历来能撼动全球市场，AI界对其每个更新都高度关注。现在有请Futurium集团AI副总裁兼实践负责人Nick Patience，来谈谈他对这次发布的看法。Nick，欢迎来到TI电视台。

Okay, well, speaking of Anthropic, Anthropic wasn't the only company to launch a new model this week. DeepSeek also released a new model, which it is calling experimental right now. But given the way that DeepSeek has a track record of moving markets around the world, everyone in AI plays close attention to every update that they put out. I want to bring on Nick Patience, VP and Practice Lead of AI at the Futurium Group, to talk to us about how he is thinking about this new model release. Nick, welcome to TI TV.

很高兴你能来。

It's great to have you.

谢谢，阿卡什。感谢邀请我。

Thanks, Akash. Thanks for having me.

那么跟我们详细说说这个新模型到底是什么，我有点搞不清楚。里面有小数点标注，又说是实验性质。他们这周到底发布了什么？

So talk to us about what exactly this new model is, because I get kind of confused. You've got the decimals in there. You've got the experimental nature of it. What exactly did they release this week?

他们发布了名为DeepSeek 3.2实验版的模型。正如你暗示的，'实验'这个词很关键，这是基于今年早些时候发布的第三代模型，但并非第四代。他们毫不讳言这就是个实验项目。他们重点介绍的创新是所谓的深度稀疏注意力机制。

So they released what they call DeepSeek version 3.2 experimental. So given, as you kind of hinted, the word experimental is in there, Anna 3.2. This is based on the version three of the model they released earlier this year, but it's not version four. So, this is very much and they made no bones about it, this is an experiment. So, key innovation that they talked about was what they call deep seek sparse attention.

这种机制能在不明显降低性能的前提下，让模型运行更高效、成本更低。这显然意义重大，因为我们刚讨论过，运行大型AI模型的成本可能高得令人却步。任何能提升模型效率的改进都会增强其实用性。

So, it makes the model more efficient and more cost effective to use without a noticeable drop in performance. And so, this is obviously a big deal because as we were just talking about, the cost of running large AI models can be cost prohibitive. And so, anything that can be done to make models more efficient would make them more useful.

所以当我们听到稀疏注意力时，基本可以理解为：嘿，我们减少了计算量，成本更低了。是这个意思吗？

And so, when we hear sparse attention, basically, we are just to think that, hey, we use less compute, and it's cheaper. Is that the idea?

没错，正是如此。还有个有趣的关联性事件——他们同时宣布将API价格砍半。

Yeah, and that's exactly the idea. And as a kind of whether was a correlation causation here thing, they also announced at the same time they cut their API prices in half.

哇。

Oh.

所以，你知道，他们是在暗示

So, you know, they're implying

所以他们在这里的每个方面都追求价格领先。

So they're going for the price leader in every respect here.

没错，正是如此。我猜他们暗示的是，因为他们有一个更高效的模型，可以降低收费，通过API调用的量来弥补，我猜。虽然很难确切知道，但这就是他们的言外之意。

Yeah, exactly. And I guess they were implying that because they've got a more efficient model, they can charge less for it and make it up on volume of API calls, I guess. It's always hard to know exactly, but that's the implication.

提醒我们一下，我们听说有几个模型特别适合某些特定的工具和应用。这个模型，有没有什么共识，它适合编码、文本生成吗？它擅长什么领域？是一个通用模型吗？

And remind us, we've heard that several models are good for several specific tools and applications, I guess. This model, is there any sort of consensus on, is it good for coding, for text generation? Where does it specialize? Is it a broad model?

它相当通用。有一点，他们没有透露太多，但他们确实提到了长上下文窗口。所以这基本上是指处理大量信息或文本的文档处理。这可能是它擅长的。他们并没有特别指出它在代码生成或其他类似任务上表现突出。

It's fairly broad. One thing, they didn't give a lot away, but the one thing they did talk about was long context windows. So you're talking there about document processing of large volumes of information, of text, essentially. That's what it's probably good at. They haven't really indicated that it's particularly good at code generation or anything like that.

更像是，它可以输入大量信息然后处理这些信息，进行分析。对吧，大概就是这样。

It's more the, it can input a large volume of information and then process that, do analysis on that Right. Kind of

从你交谈过的人那里，你觉得它有多好？

And from the people you talk to, how good is it?

目前还很难详细评估其优劣，毕竟为时尚早。但初步迹象显示表现不错。不过我对基准测试竞赛持保留态度，因为不确定这些指标是否具有实际应用场景。当前显然还未投入真实场景使用，因此很难下定论。

Well, it's hard to get much details yet on how good it is, because it's extremely early. But yeah, early indications, it's pretty good. You know, it's difficult to I'm slightly skeptical of the benchmark race, because I'm not sure some of these things have real world use cases. So it's not obviously being used in a real world use case at the moment, so it's pretty hard to determine.

你追踪了这么多不同公司发布的各类模型。我想了解你对中国当前推出模型的看法，毕竟深度求索初亮相时震惊业界。此后我们看到字节跳动、阿里巴巴等中国企业也在自研模型上取得进展。能否分析下深度求索技术目前在中国这些大公司中的相对位置？

You follow so many of these different models that are being released and the different companies that are behind it. I kind of want to take a little bit of your pulse on the models coming out of China right now, because a lot was made of DeepSeek when it first came out and sort of shocked the world. Since then, we've seen a lot of other companies in China, not the least of which are ByteDance and Alibaba also making progress with their own models. Give us a little bit of the lay of the land in terms of how DeepSeek's technology now stacks up against some of those other big companies in China that are releasing their own tools.

我认为深度求索与阿里等企业相比毫不逊色。中国似乎对此采取相当务实的态度——这不是通往AGI的征途，而更聚焦于商业应用和学术研究等实际场景，而非宣称'距AGI只差一个模型改进'的华丽宣言。这种根本性态度与欧美模型开发者不同，同时中国厂商也积极拥抱开源，不仅是深度求索，其他企业也发布大量开源项目——这点Mistral和Cohere有所体现，而OpenAI最近也意识到必须跟进。

I think it does stack up pretty well, with Alibaba, as you say, and the others. I think China is appearing to take a fairly pragmatic attitude towards this. This is not a kind of march towards AGI, a quest for AGI. This does seem to be more focused on practical applications in business and in academic research and things like that, rather than big flashy announcements and of great statements that we're just one model improvement away from AGI. So I think they have a kind of fundamental different attitude to Western Europe and US are certainly model makers, and also embracing open source, and releasing a lot of not just DeepSeek, but other Chinese vendors releasing a lot of things open source, which is something a lot of the you have some elements of it with Mistral and Cohere, and then obviously more recently OpenAI, realized they had to go and do that as well.

你提到的那些中国公司，在计算效率等方面是否正在追赶深度求索？目前进展到什么程度了？

Are those other companies, the companies I mentioned in China, for example, are they catching up to DeepSeek in terms of you know, compute efficiency, you know, not being able to have to use as much? Where where are we there?

我对深度求索一月份最初宣称的效率持怀疑态度。他们在论文中暗示已完成90%前期工作，仅花费数十万美元完成最后5%训练。考虑到之前多次训练运行，我不完全认同他们宣称的超级效率——认为所有成本仅六位数而非数百万美元。虽然他们的营销非常出色，但在高效模型构建方面，他们与其他顶尖团队实力相当。

I'd be slightly I'm slightly skeptical about the the claims that DeepSeek made in the first place back in January, because obviously they were basically implying, and they said so in a paper then and a later paper, that they actually done most of the work in advance, like 90% of the work, and then basically spent a few $100,000 on the last 5% of training. So, there's multiple training runs that went on before that, so I don't quite buy the notion that they are so super efficient, and everything just costs 6 figures versus millions. I just don't really think that's the case. So, I think they have done an incredibly good job of pitching that, but there's obviously some elements of it that they are. They are as good as anybody else at efficient model making.

这取决于具体情况。模型本身并非应用或平台，而是需要开发环境整合使用的工具。深度求索与中国同行相比表现优异——尤其考虑到其对冲基金出身的背景。但我认为其技术底层比表面呈现的更复杂，且他们未披露太多细节。

Depends, but there's a whole load of other things around Models are not applications. Models are not platforms. They're things you can use and then get a development environment and bring them in, and things like that. I think they stack up pretty well against their Chinese counterparts, which is not bad considering where they came from, spun out of a hedge fund. But there's a lot more going on there, I think, under the hood than they sometimes make out, and they haven't really released a lot of detail on this one.

综合这些因素，你认为深度求索的长期前景如何？随着其他公司入场和模型开发细节的澄清，深度求索会成为AI长河中的昙花一现，还是发展成被全球广泛采用的持久性技术与商业实体？

And so, taken together with all this into consideration then, the question I have for you is what you see the long term future of DeepSeek specifically being. You talked about now how the other companies are coming into the picture, you talked about how there have been some clarifications about how the model was actually developed. Is DeepSeek a flash in the pan in the long run of AI, or do you see it as an enduring business and an enduring technology that actually gets adopted very widely around the world?

我认为它们在中国被广泛采用，尤其是在庞大的电动汽车市场。关于一个市场需要多少家电动汽车厂商还有待讨论，但它们在那里确实非常普及。老实说，我对它们的长期前景持谨慎态度，因为一切似乎都集中在模型本身，而我认为OpenAI早先就意识到，其他公司也逐渐明白，生活中不只有模型。举例来说，如果你现在是一家大银行的首席信息官，你是更关注DeepSeek的实验性模型，还是更关注将AI应用投入生产所面临的挑战？我猜是后者。

I think they're getting adopted pretty widely in China, and especially in the large EV market there. There's a whole other discussion about how many EV vendors you need in one market, but they're very widely adopted there. I'm slightly skeptical about a long term future, I'll be honest, of them, because they say it does all seem to be focused on the model itself, and I think OpenAI has realized early on and the others are realizing early on, there is more to life than models. And for instance, if you're a CIO of a large bank at the moment, are you focused on DeepSeek's experimental model, or are you focused more on the challenges of getting your AI applications into production? Suspect it's the latter.

因此，我认为在这个市场中能够成功的公司，是那些能够构建更完整技术栈的公司。

And so I think the companies that will succeed in this market are the ones that build out more of a stack,

这样的话，也许它们会成为很好的收购目标。

and they're Maybe that makes them a good acquisition candidate in that case.

是的，很有可能，确实如此。它们显然擅长自己所做的事情。它们以模型为核心的工作方式，做得很好，这一点毋庸置疑。而且它们显然吸引了许多优秀的研究人员，这也是AI领域的另一场竞争。

Yes, possibly, yeah, exactly. They're obviously good at what they do. The model centric nature of what they do, they're good at it. There's no two ways about it. And they're obviously attracting good researchers, and that's another battle that goes on in the AI world.

所以，你说得对，如果所有者有意向的话，它们很可能是一个很好的收购目标。

And so, you're right, I think they probably are a good target, if that's what the owners want to do, of course.

没错。尼克，非常感谢你参加我们的节目。我知道这些模型层出不穷，我们甚至没来得及讨论本周Anthropic发布的新模型，但下次有重大发布时，我们一定再请你来。这位是来自Future Room Group的尼克·佩兴斯。

Right. Well, Nick, thank you so much for coming on the show. I know that these models are coming fast and furious. We didn't even get to talk about Anthropics' new model this week, but we'll have to have you back on next time we get a new big release. That was Nick Patience from the Future Room Group.

本周早些时候，我们邀请了Snowflake的一位高管来讨论他们对AI领域企业数据之争的回应。Snowflake并非唯一对此问题直言不讳的软件公司。现在我想请ClickUp的首席执行官泽布·埃文斯来分享他的看法。ClickUp是一家项目管理和产品开发软件公司，最新估值为40亿美元。泽布，欢迎来到节目，很高兴你能来。

Well, earlier this week, we had an executive from Snowflake on the show talking about their answer to the corporate data wars that are shaking out in AI. And Snowflake is not the only software company that has been outspoken on that issue. I want to bring on Zeb Evans, the CEO of ClickUp, to give us some of his thoughts. ClickUp is a project management and product development software company that was last valued at $4,000,000,000 Zeb, welcome to the show. It's great to have you.

非常感谢邀请我。很高兴来到这里。

Thanks so much for having me. Happy to be here.

老兄，你穿的这件衬衫太棒了。天哪。

It's a great shirt you're wearing, man. Holy moly.

我总是穿些疯狂的衬衫。你知道吗？不幸的是，我已经给自己打上了这个标签。所以如果我不穿点疯狂的，人们就会问，Zeb怎么了？

I always have crazy shirts. You know? Unfortunately, I I branded myself as this. So if I don't wear something crazy, then I'm like, what's wrong, Zeb?

这很有趣。我们团队给我的指示是尽量穿最低调的衬衫。也许我现在得告诉他们，看看Zeb穿的是什么。我是说，你看，他玩得多开心啊。

Well, it's funny. I the instruction I get from our team is they say wear the quietest shirts as possible. So maybe I'll have to I'll have to tell them now. I say, know, look at what Zeb was wearing. I mean, you know, he looks you know, he's having fun out there.

总之，我们来谈谈企业数据战争的乐趣吧，这是我们经常讨论的话题。简单用二十秒告诉我们，你的业务是做什么的，你们销售的软件是什么，以及你们与当前这个问题的关系。

Anyway, let's talk about the fun of corporate data wars because it's something that we've written about a lot of the information. Very quickly, just in twenty seconds, tell us about what your business does and the software that you sell and sort of your relationship to this issue at hand.

好的。ClickUp构建的是我们所说的聚合软件，这是一个单一应用平台基础，你可以在我们的平台上构建几乎任何软件。我们认为未来你会从一个供应商那里购买软件和AI。我们预见未来会出现超级软件供应商，而这就是我们目前正在努力实现的目标。

Yeah. So ClickUp builds what we call converged software, which is single application platform primitives where you can build pretty much any software on top of our platform. And so we see the future as you purchasing software and AI from one vendor. And we see this as like super software providers that occur in the future, and that is what we are building towards today.

明白了。那么告诉我，企业数据战争在AI领域对你们造成了多大影响？我是说，在尝试构建AI工具时，你们是否遇到过数据获取困难的问题？

Got it. And so tell me, how how big of an issue has the corporate data wars in AI been for you? I mean, have you had trouble accessing data, you know, as you try to build out AI tools and stuff like that?

我们有两个例子就是Slack和Figma。没错。这两家公司都开始根据API权限范围来限制数据访问。关键在于，我认为大多数客户和人们尚未意识到上下文的重要性，因为AI在企业对企业（B2B）领域的价值在绝大多数公司和应用场景中还未充分体现——除了工程和文案写作等领域。随着时间的推移，这些领域的价值将大幅提升。

We have so with two examples would be Slack and with Figma. Right. Both of those have started locking down data access depending on which scope you have with with their APIs. And, you know, the the big thing here is is that I think most customers and most people don't understand how important that context is yet because the b to b value has yet to be realized in in AI in in in the vast majority of companies and the vast majority of use cases, you know, outside of engineering, let's let's say, and and copywriting. And those things will become so much more valuable over time.

这再次说明为什么我们将世界视为融合的软件。拥有100%的上下文，AI才能真正发挥最佳效能。

And that's, again, why we see the world as converged software. You have a 100% context, and you'll need 100% context for AI to actually do its best job.

你们是否尝试量化过这个问题的影响？比如‘这导致我们损失了多少收入’或‘如果有这个功能，我们的用户增长率本可以提高10个百分点’？你们做过相关数据测算吗？

Do you I mean, have you gone about quantifying this issue at all in terms of like, Hey, you know, like this has costed us this much revenue. You know, we could have grown our user base, you know, I don't know, 10 percentage points faster if had this. Have you put any numbers around it?

价值损失更多体现在客户层面。确实有些公司因此受损——比如当它们的商业模式依赖于此。以Glean为例，我们早就预言过：首先企业搜索会变成其他平台的标配功能。

The value and the loss of value is more for the customer. It's really in certain companies, for sure, when their business model is based off this, they're hurt. Like Glean, for example. Right? That's something that we predicted what would happen is that these other companies first of all, enterprise search would become a feature on everybody else's platform.

我认为未来一两年开发的几乎所有软件都会如此。如果你相信工程效率将提升生产力，就必须承认每家公司都会开发更多而非更少的软件。所以真正吃亏的是客户，除非这家公司本身就是企业搜索工具的提供商。

I think that's what will happen with nearly all software that is built in the next year or two years. I mean, if you believe that engineering, what efficiency will will gain in productivity, you have to believe that every company will build more software, not less software. So it's it's really the customer that that gets screwed in these situations. Not not the company unless that company is is one of those providers of that actual enterprise search tool.

明白。我想说的就是这个——那些销售企业搜索工具的公司，或者像你们这样销售AI工具的公司？

Right. And I I guess that's what I was getting at. Presumably, companies that that sell enterprise search tools or or, you you know, your company that sells You guys sell these types of AI tools as well?

确实如此。但这次我们反而看到聊天产品用户激增。因为我们的聊天产品免费且具备与Slack或Microsoft Teams相同的功能。当原有平台切断数据访问时，那些已从AI访问团队通讯中获益的客户，很大一部分就直接转投ClickUp了。

We do. We do. But in this case, we've actually just seen a surge in customers adopting our chat product because of this. Our chat product is is free and it is the same feature parity of something like Slack or or Microsoft Teams. So in many of these cases where their data access was cut off and that customer was already realizing value from AI having access to their team communication, they a big portion of them would just switch to ClickUp.

这样一来，他们就能获得100%的上下文信息，完全不用担心数据是否会被再次切断？他们究竟会允许访问多少数据？因此我们实际上认为这对我们的平台是有利的。

And then that it gives them 100% context, and there is no worry about are they gonna cut off data again? How much how much to access are they going to allow? So we've actually seen it as a benefit for for our platform.

我相信你一定关注了Snowflake的新闻，以及他们本周早些时候联合多家公司成立的联盟，这些公司共同签署声明表示：'我们需要开放数据访问，这是这个行业的未来。'这是你们期待采取的那种举措吗？当Snowflake做客我们节目时，他明确表示这有点像君子协议，但理念是大家都要遵守。这是你们寻求的解决方案吗？

So you I'm sure you followed the news of Snowflake and the consortium that they put together earlier this week with a bunch of companies signing on to say, hey, you know, we need open data access. You know, that is the future of this sector. Was that sort of the step that you were looking to take? And when we had Snowflake on the show, you know, he sort of made clear, it's a bit of a handshake agreement, but, you know, the idea is everyone abides by it. Was that the solution that you were looking for?

你们只是希望所有人都加入那个承诺吗？解决方案到底是什么？

Are you just looking for everyone to join that pledge? Like, what is the solution here?

我认为从商业角度和利益考量来看，唯一现实的发展方向是：随着时间的推移，将会出现超级软件供应商。会有几家软件供应商为企业提供所有软件和AI服务。所以你只需要选择其中一家。

I believe that the only thing that is realistic that will happen when you think about business perspectives and and interest is that over time, you will have super software providers. You will have several software providers that provide all software and all AI for for companies. So you only go to you only go to to one. So, you know, we So

你是说行业整合会进一步加剧？

you're you're saying we're gonna get even there's it's gonna be consolidation even more.

没错，百分之百。所以我觉得所有人都可以坐下来讨论这些事。

Yep. 100%. 100%. And so I think everybody can sit there and and say those those things.

等等，我想确认自己理解正确。你是说像Salesforce这样拥有Slack的公司——我们报道过，正如你所说，他们设置了数据访问壁垒——你认为这样的公司会变得更大，最终吞并所有工具成为垄断者？

So so so are you are you saying that some you know, like, I I I just wanna make sure I understand. So you you know, you're looking at a company like Salesforce, the owner of Slack, right? We've written about, and as you said, right, they've put up some of these walls for data access. You're saying that a company like Salesforce is going to get even bigger and sort of engulf all of the tools at one?

他们将建立更多壁垒。随着时间的推移，确实如此。而且他们会试图为你的企业提供所有软件。我认为这就是B2B软件的未来。

They're going to put up more walls. And over time, yes. And they will attempt to provide all of your software for your business. And I think that's the future of software in B2B.

好的。那么我只是想弄明白，初创企业的未来会怎样？

Okay. So I'm just trying so then so what is the future of the startups then?

是的。听着，我有一份40页的备忘录专门讨论这个，很乐意分享。但我认为对初创企业来说非常困难。我不认为你能——

Yeah. Look, I have a 40 page memo on this. I'm happy to share. But I think it's very difficult for startups. I don't think you can

真的，就像你自己一样。

really It's like yourself.

我认为，早期阶段的初创企业在未来几乎不可能在B2B软件领域创建上市公司。但有些平台已经发展到足够规模，具备上市和与巨头竞争的能力，它们能以'后发优势'提供全套软件——不是简单捆绑地球上所有软件产品却不实现良好互通、用户体验割裂。新入场者仍有空间，但最终所有软件都会趋于整合。软件将与AI融合，AI实验室自身也将开始涉足生产力工具领域。

I think it it I think at early stage startups, it is it is going to be impossible to create a public company and b to b software in in the future. But I think that there are some platforms that have have built large enough already and have the scale to be able to go public and to be able to compete against against the giants and that can provide all of that software with last mover advantage, not just bundling every single software product under the planet and it not actually talking well together and not actually connecting together and having totally different user experiences. I think that there is there is room for new entrants. But, ultimately, yeah, all software will will converge. I think software will converge with AI, and AI labs themselves will start building into productivity Okay.

软件

Software

可能我没完全理解你的观点。你知道，我只是想确认自己理解正确。因为我读过你的专栏文章，你谈到这些数据壁垒对新兴初创企业的限制，对吧？现在我听你说大公司确实要建立这些壁垒，这将成为必然趋势。

So maybe I'm just not following the point. You know, I just want to make sure I get it here. Because, you know, I read your op ed, you know, and you talked about how these data walls are sort of a limitation for up and coming startups, right? And so I read that, and now I'm hearing you say that the big companies are really going to They're going to put up these walls. It's going to be inevitable.

它们会变得更大。所以我试图理解你在这个问题上的立场，因为听起来你在说，嘿，壁垒将会形成。大公司会赢，我们对此无能为力。

They're going to get bigger. And so I'm sort of trying to understand where you stand on the issue then, because sounds like you're saying, Hey, the walls are going to happen. The big guys are gonna win, and there's nothing we could do about it.

我认为我并不是说只有大公司会赢，但大公司会受到如此大的威胁，以至于他们会筑起更高的壁垒。他们会受到新增到他们类别或周边类似类别中的新软件的威胁。他们会自己开发这些软件。他们会尝试自己提供这些服务并维持他们的壁垒。因此，试图向他们的客户销售所有软件。

I think that I'm not saying only the big guys will will win, but the big guys will be so threatened that they will put up more walls. And they will be threatened by new software that gets added to their category or to similar categories around them. Will build that themselves. They will try to provide that themselves and keep their walls up. And so trying to to put to sell all software to their their customers.

而且会有新的进入者，这一点要明确。我认为我们就是其中之一，在这个水平软件的灵活性世界里，你可以在水平软件平台上构建垂直软件。我认为那里肯定会有一些新的进入者。没错。但是，是的，这会非常困难。

And there will be new entrants to to be clear. And I consider us us one of those in this horizontal software world of of flexibility where you can build vertical software on horizontal software plat platforms. I think I think there will be some some new entrants there for sure. Right. But, yeah, it will be very difficult.

好的。在我让你走之前，最后一个问题。你计划上市。时间表是怎样的？

Okay. Last question for me before I let you go. You are planning to go public. What's the timeline for that?

希望如此，而且你知道，我们还没有确定日期。一旦我们知道了，我很乐意尽快告诉你。但是

Hopefully and and I you know, we don't have a date yet. I'm happy to get back to you as as soon as we we know it. But in the

是2025年吗？是今年吗？

Is it is it 2025? Is it is it this year?

很可能不是今年。大概

Most likely not this not this year. Probably

明年。明年。好的。那么，我们必须再次邀请你上节目，因为我对你在这方面的观点非常感兴趣。非常感谢你参加我们的节目。

next year. Next year. Okay. Well, we'll have to have you back on because I, you know, I am very interested in your perspective on this. Thank you so much for coming on the show.

这位是ClickUp的首席执行官。这是他第一次在TI TV亮相。

That is the CEO of ClickUp. It is his first time here on TI TV.

谢谢，非常感谢邀请我。

Thanks Thanks a lot for having me.

好的。那么，在我们今天的最后一个环节中，随着关于TikTok美国业务结构的细节逐渐清晰，一个仍然悬而未决的大问题是谁将实际运营这个新实体？一个可能的候选人是公司的一位名叫Adam Presser的领导者。为了让我们更了解为什么这可能是个合理的选择，我想请出我们在华盛顿特区的记者Sylvia Varnum O'Reagan。Sylvia，欢迎回到节目。

Okay. Well, for our final segment today, as details start to get clearer about what TikTok's US business structure will look like, one big question that still remains is who actually is going to run the new entity? One possible candidate is a leader at the company named Adam Presser. And to tell us more about why that could make sense, I want to bring on our DC correspondent, Sylvia Varnum O'Reagan. Sylvia, welcome back to the show.

很高兴你能来。

It's great to have you.

嗨，Akash。一如既往，很高兴见到你。

Hi, Akash. Great to see you as always.

好的。让我们来谈谈Presser先生。为什么我们应该关注Presser先生？

Okay. Let's talk about Mr. Pressure. Why should we be paying attention to Mr. Pressure?

亚当·普雷瑟。我不知道为什么。我们这周已经够长了。我们来聊聊亚当吧。

Adam Pressure. I don't know why. We've had a long week already. Let's talk about Adam.

当然。亚当·普雷瑟是TikTok的一位非常资深的高管。事实上，他是该公司中职位最高的美国人之一，与现任TikTok CEO关系非常密切。他的背景也相当有趣，曾在耶鲁大学学习中国语言与文学。

Sure. Adam Pressure is a very senior executive at TikTok. He's in fact one of the most high ranking Americans at the company, and he's very closely aligned with the current CEO of TikTok. He also has quite an interesting background. He studied Chinese languages and literature at Yale.

他后来进入法学院，并获得了哈佛大学的MBA学位。他还曾在中国多家公司工作多年，尤其深耕娱乐产业。2022年，他以CEO幕僚长的身份加入TikTok，此后迅速晋升——我们《The Information》对此有过报道。他参与了许多不同团队和关键决策。最近几个月，他刚被提拔领导名为USDS的部门，这个部门在当前交易（据我们所知尚未最终敲定）中似乎处于核心位置。

He went to law school and got an MBA from Harvard. And he also spent many years working in China for various companies and working in the entertainment industry in particular. Now he started at TikTok in 2022 as chief of staff to the CEO, and since then he's really risen through the ranks, and we've covered this at The Information. He's been involved in a lot of different teams, a lot of different critical decisions. And recently, in the past couple of months, he was actually promoted to lead a unit called USDS, which is looking to be quite central to this deal, or what we know of this deal, which of course is still very much in motion and not yet finalized from our understanding.

我想重点讨论USDS这个部门。在我们追踪TikTok事件的漫长过程中，曾听说过'得州计划'——这是TikTok试图将其部分美国业务分离出来的一个尝试。USDS与之有关吗？我们该如何理解USDS的定位？

So I do want to talk about this group, USDS, because it feels like for the length that we've been covering this TikTok saga, I mean, we heard about Project Texas was one sort of iteration of TikTok's effort to sort of separate some of its operations from The US. Is that related to USDS, or how should we think about what USDS is?

这是个很好的问题。众所周知，国家安全关切多年来始终是美国政府对TikTok采取行动的核心。2022年TikTok创建USDS这个独立部门，正是为了应对这些担忧。它确实与你提到的'得州计划'有关，该计划旨在解决政府对美国用户数据安全的顾虑。不过需要说明，'得州计划'并未完全实施，政府也未接受该方案。

Right, it's a really good question. So as we know, national security concerns have been central to a lot of different government moves over the years with regards to TikTok in The US. And back in 2022, TikTok created USDS, this separate unit within the company, to address some of those concerns. And it was part of what you mentioned, Project Texas, and this was a proposal to address those concerns from the government around the security of US users' data. Project Texas wasn't fully implemented, I should say, and the government didn't accept it as a solution.

但TikTok确实建立了USDS，其宗旨如我所说，是保护平台上美国用户的资料。根据我们目前了解（关于当前将TikTok美国业务出售给投资集团的交易仍有许多未知），USDS很可能成为交易核心。因为请记住，这笔交易的关键焦点正是数据——特别是美国用户的数据。

However, TikTok did create USDS, and the purpose of it was, as I said, to safeguard the user data of Americans on the platform. And from what we understand, and again, there's so much that we're still trying to learn about this deal now in the present day, which is involving selling TikTok's US operations to a group of investors, but from what we understand about what is actually going to be handed over in this deal, it's looking like USDS could be central to that. Because remember, the deal is really focused on data, user data, and on American users' data in particular.

没错。还需要指出的是，关于广告收入归属等问题仍存在疑问——是流向中国实体还是美国实体？因此你认为USDS可能成为'TikTok美国'的主体，这个观点很有见地。

Right. And what we should say, I mean, there's still sort of some questions around advertising revenue, for example, who does that go to? Who earns that? Go back to the China entity, The US entity. So I think this idea of it being you know, USDS possibly as the thing that becomes TikTok US is a good point.

我确实想谈谈，无论谁接任TikTok美国CEO这个职位，对这个人来说都如同攀登一座大山，对吧？我的意思是，既要维系那些为平台带来巨大流量的用户基础，又可能无法像在中国那样拥有丰富的初始资源。你认为这位CEO将面临哪些必须克服的挑战，无论是广告压力还是其他问题？

I do want to talk about, you know, whoever comes into this role as CEO of TikTok US, this is like a huge mountain to climb for the person, right? I mean, you have to sort of maintain the customer base that has really driven so much traffic on the platform, and yet you have to do it without a lot of the, you know, resources conceivably that you initially had in China. How do you think about the challenges that this person will have to overcome in the role as CEO, whether it's ad impresser or someone else?

没错。我认为这将是一份极其艰巨的工作，因为此人需要监督一项复杂的业务剥离过渡——美国业务与TikTok中国母公司字节跳动的分离程度尚不明确，可能大也可能小。甚至可能基本维持现状。但无论如何，算法授权和组建新合资公司都是确定的，这家拥有美国TikTok的公司将设立独立董事会。

Right. I think it will be a really tough job because this person will be overseeing a really complicated transition, some type of separation of The US business. Now, that could be a big or small separation from TikTok's Chinese parent company ByteDance. We don't really know, and it could be the case that in fact things stay somewhat similar, right? But regardless, there is going to be this licensing of the algorithm, and there is going to be some kind of separation insofar as the new joint venture company that will own the American TikTok will have its own board.

这位CEO需要与董事会打交道。而目前已知参与交易的投资者背景复杂，其中不少亿万富翁或其掌舵的公司与特朗普关系密切。白宫在此交易中异常活跃，因此白宫或特朗普总统会多大程度干预运营仍是疑问。另一个关键问题是字节跳动——TikTok中国母公司——的参与度，毕竟至今TikTok仍高度依赖字节跳动。

So the CEO will be interacting with that board. And of course, the investors involved in this deal that we know of are a real mixed bag, but a lot of them are billionaires, or there are billionaires at the head of these companies who are aligned with Trump. The White House has taken a very active role in this deal, so I think there's a question about how much is the White House or President Trump going to seek to be involved in the operations. And there's also this big question of how much ByteDance, the Chinese parent company of TikTok, will be involved also. Because to date TikTok is very reliant on ByteDance.

两家公司联系紧密，TikTok大量依赖字节跳动的工程人才。因此剥离过程绝非易事，在多年动荡后带领员工度过转型期，对管理者将是巨大挑战。

The companies are very connected, and TikTok draws on a lot of engineering talent from ByteDance. So separating these companies will not be easy, and overseeing a group of employees through this transition after many years of tumult as it is could be quite a challenge.

确实。Sylvia，每次聊到这个话题我都觉得——今天才周三对吧？我们等到周末肯定会有更多进展可以讨论。感谢你一如既往的分享。

Right. Well, Sylvia, I suspect that this story, I tell you every time, it's like, it's only Wednesday, okay? So let's wait till the end of the week. We'll probably have more updates to talk with you about by then. Thank you, Sylvia, for coming on as always.

这位是我们The Information的华盛顿特派记者Sylvia Varnum O'Reagan。今天的节目就到这里，提醒大家我们每周一至周五太平洋时间上午10点/东部时间下午1点播出，您可通过本流媒体收看。特别感谢本节目首席赞助商亚马逊云服务。

Is Sylvia Varnum O'Reagan, our DC correspondent here at The Information. Well, that does it for today's show. A reminder that our show airs Monday through Friday at 10AM Pacific, 1PM Eastern. You can find us on this stream. I want to thank Amazon Web Services, who is our presenting sponsor for this production.

最后感谢各位的观看，我们衷心珍惜每位观众。我已经开始期待明天的节目了，我们下次见！

And I want to thank you for tuning in. We really do appreciate your viewership. I am already excited for our next show tomorrow. And so until then, bye bye for now.