人工智能将如何变革客户体验：Cresta CEO吴平与红杉资本Doug Leone的对话

如今，如果你思考一家企业，它们给顾客的感觉就像多重人格分裂。在销售阶段，他们会非常激进地联系你。而一旦你签约成为客户，你面对的却是完全不同的另一副面孔，需要与客服部门打交道。感觉这些环节是完全割裂的，对吧？我认为AI助手能让整个客户旅程变成一场持续连贯的长期对话。

Today, if you think about a business, they feel like most multiple personality to the customer. So in the sales phase, they call you very, very aggressively. And once you sign up and become a customer, you dealing with entire different personality, And you're dealing with service departments. Feel like these are really disconnected, right? And I do feel like AI agents can make this entire experience a continuous, long going conversation throughout the entire customer journey.

而大语言模型正是实现这一目标的完美工具。这将真正带来前所未有的个性化水平和客户体验层级。

And LLM is a perfect tool to do that. And that will really bring the level of personalization, the level of customer experience that wasn't possible before.

大家好，欢迎收听《训练数据》节目。今天我们邀请到Cresta首席执行官吴平和红杉资本董事会成员Doug Leone。本期节目将深入探讨联络中心这个充满挑战的传统行业——这个由行动迟缓的老牌企业主导的巨大产业，承载着企业绝大部分客户对话。吴平对此领域有着深刻理解，他最初创建了谷歌的联络中心业务，后成为Cresta产品负责人直至CEO。他将与我们探讨冲击呼叫中心的多轮技术浪潮，如何看待大语言模型推动客户体验向丰饶未来演进，以及为何他的策略是'客户在哪里就去哪里'，将人工坐席辅助与自主数字助手相融合。

Hi, and welcome to Training Data. Today, we're joined by Cresta CEO, Ping Wu, and Sequoia's Doug Leone, who sits on the Cresta board. Today's episode dives into the gnarly world of the contact center, a giant legacy industry filled with slow moving incumbents that is responsible for driving the vast majority of company customer conversations. Ping understands this world deeply, having first built Google's contact center business before becoming product leader and then CEO of Cresta. Ping joins to talk about the different waves of technology that have hit the call center, how he sees the future of customer experience evolving with LLMs towards an abundance future, and why his playbook is to meet customers where they are, blending human agent assist with autonomous digital agents.

Doug Leone也分享了他数十年来投资企业建设的洞见，以及对当前是否处于AI泡沫期的犀利观点。他还揭示了AI价值将沉淀在何处——提示：就在应用层这艰难的最后一公里。请享受本期节目。吴平，欢迎做客，也感谢你邀请董事会特别嘉宾Doug Leone一同参与。

Doug Leone also shares his perspectives from several decades of investing in company building and his hot takes on whether we're in an AI bubble. He also shares where he believes the value will accrue in AI. Hint, it's in the application layer in this gnarly last mile. Enjoy the show. Ping, welcome to the show, and thank you for bringing me bringing along our special guest, Doug Leone, as well on your board.

我的荣幸，谢谢。

My pleasure. Thank you.

感谢二位参与。吴平，我想先问一个关于AI核心命题的问题：AI将在全球范围内取代劳动力，其潜在市场规模达数十万亿美元。显然，联络中心作为庞大的劳动力池，正亟待自动化。如果要预测，你认为AI最终能完全自动化多少比例的呼叫中心人力成本？

Thank you both for joining. Ping, I wanna start by asking a big part of the AI thesis is that AI is going to replace labor globally, and that the TAM is in the tens of trillions of dollars. Obviously, the contact center, the call center is a big pool of labor that, you know, is just begging to be automated. If you had to guess, how much of call center labor spend will actually be automated fully by AI?

是的。我们内部对此进行过大量讨论。但现实是，没人能确切知道答案。如果你询问不同的人——这实际上取决于他们销售的产品——你会得到不同答案。

Yeah. So we internally, we have spent a lot of time debating about this. But the reality is, don't think anyone knows for sure. And if you ask, depends on really what they're selling. And you ask different people and they give you different answers.

有人认为联络中心将完全不再需要人类员工。而高德纳的研究显示，未来五年内没有任何一家财富500强企业会实现联络中心完全无人化。所以实际情况很可能介于两者之间。事实上，两年前GPT-4刚问世时我们就讨论过这个问题，当时很多人预测两三年后联络中心将不再需要人类。但我们始终认为，特别是对现有财富500强企业而言，这种转型需要的时间远比人们想象的更久。

Some people will say that a 100% human will be gone in contact center. And some Gartner's research actually shows that none of the Fortune 500 over the next five years will have contact center gone entirely humanless. So, you know, it's also probably fall into somewhere in the middle. And in fact, we got this asked this question two years ago when GPT-four first came out, and a lot of people will say that maybe in two or three years, there will no longer be humans in the contact center. So, at that time, our belief was that, you know, probably the transformation, especially for existing Fortune 500 companies, will probably take them way longer than a lot of people think.

Doug，你怎么看？你的预测是什么？

Doug, what do you think? What's your bet?

从极限来看，最终会是100%自动化。但我注意到美国银行系统至今仍在使用IBM大型机和COBOL语言。所以对我来说关键不是百分比，而是变革发生的速度——究竟是在10年、20年、25年还是30年内实现？因为无论是30%还是60%的自动化程度，若需50年实现，这对Cresta这类公司意味着一种情况。

At the limit, it's a 100%. But I'm mindful that there are still IBM mainframes and Cobalt being used in America in a banking system. So to me, it's not really what percent, to me is the speed at which this is going happen. Is it going to happen within ten, twenty, twenty five years, thirty years? Because whether the answer is 30% or 60%, if it happens in fifty years, that means one thing for companies like Cresta.

若在三年内实现，则意味着完全不同的局面。所以最终数字对我而言并非关键指标，我更关注技术落地的速度。

If it happens in three years, it means something else. So the end number is not the relevant metric for me. To me is the speed of adoption.

这个区分很有见地。Ping，你在联络中心AI领域深耕超过十年，在出任Cresta CEO之前还负责过谷歌相关业务。能否为在场不太了解联络中心市场的观众简单介绍下这个行业的规模，以及技术迄今带来的改变？

Great distinction. Ping, you've been working in the contact center AI space for well over a decade. Prior to becoming CEO at Cresta, you ran the equivalent function over at Google. And so maybe for those of us in the audience that don't know the contact center market, can you tell us a little bit about what it is, how big it is, and how technology has served it so far?

是的。提到联络中心，很多人会自然联想到呼叫中心——大量人工坐席接听电话的场景。但实际上联络中心涵盖更广，包括邮件、数字聊天、网站/应用交互等全渠道沟通，当然也包含电话服务。

Yeah. When we first talk about contact center, a lot of people will naturally think about call centers. A lot of humans sitting there, listening, answering calls. Right? But contact center really is a broader category that including the omni channel interactions from emails to digital chats and, you know, on website and in apps and also including calls, of course.

这个市场规模非常庞大，历史上约有1700万至2000万人工坐席从事联络中心工作。软件市场规模可能达数百亿美元，而根据研究，AI相关市场将高达数百亿美元。

And the overall market is quite big and there are historically there are around 17 to 20,000,000 agents, human agents actually work in the contact center. For the software market, it's probably, in the tens of billions. And, for AI market, according to some research, it will be in the high tens of billions of dollars.

那么主要使用场景是客户来电投诉或寻求客服支持吗？这些联络中心主要就是用于这些吗？

And is the use case mostly, you know, customers calling in to complain, customer support? Is that what these these contact centers are mostly used for?

哦，是的。客户来电原因多种多样，对吧？有投诉解决问题的，但很多人可能没意识到，联络中心约25%的业务其实是创收性质的——包括销售产品、收款或客户挽留这类对话。所以不全是客服支持。

Oh, so yeah. So customer call in, there are all kind of reasons they call in, right? Complaints are fixing the issue, But also, think a lot of people may not realize that there are probably a quarter of the contact center, 25%, is actually revenue generating. They're including selling stuff or collecting money or retaining customers and that kind of conversations. So it's not 100% customer support.

我有个一直没问过你的问题。回顾联络中心发展史——我年纪够大可以说这个——三十年前是Avaya等濒临破产的老牌企业，十五年前是行业萌芽期。是什么让年轻的吴平工程师在十五年前被这个市场吸引？按理说这向来是个沉闷的市场。

So I have a question for you that I never asked you. If you look at the contact center, and I'm old enough to date myself, you go back thirty years, you heard names Avaya and God knows whoever else that's barely living in and out of bankruptcy. You go back fifteen years, you see Genesis of the world. What caused a bright young engineer called Ping Wu fifteen years ago to be attracted with this market? And one could have said, it's always been a stodgy market.

这个市场历来关注度低，催生的都是增长缓慢的公司。当时吸引你的是什么？当然现在我们知道这是个充满机遇的活跃市场。但倒退回十年前，这个市场哪点吸引了你？

It's always been of low interest. It always created these slow growing companies. What is it that interested you now? Of course, now we understand it's a vibrant market with lots of opportunity. But turning the clock back ten years ago, what attracted you to this market?

首先十五年前我根本没意识到这是个长期缓慢增长的市场，否则可能想法就不同了。其次当时正值对话式AI技术热潮，尤其是面向消费者的智能音箱领域。我当时认为这会颠覆谷歌，成为所有消费者交互的入口。而我坚信联络中心将是对话式AI最具变革潜力的领域。

First of all, fifteen years ago, I didn't even realize that's a long history of slow growth of the market. Otherwise, maybe I would think differently. And second, at that time, I just Do you remember there's a period of time there's a lot of excitement in conversation, AI technology, and especially around consumer facing speakers? And at that time, think that that would disrupt Google, would become the entry point for all the consumer interactions. And I happen to really believe that the contact center will probably be the most exciting opportunity for conversation AI to transform.

因为它具备所有传统上令人兴奋的要素：巨大市场规模、大量人力投入、处于企业与客户交互枢纽位置。而且联络中心里没人开心——这里'没人'指三方：客户因漫长等待不满；客服人员方面很多人可能没意识到，这个行业的人员流失率高得惊人。

And it's because it has all the issues that traditionally people get excited about, we see get excited about. It's a massive market, a lot of humans working there, and it's in the middle between businesses and customers, right? It's all interaction going through, and also no one's happy in contact center. So, if you By no one, I mean there's three different parties. There are customers that call in that most of us may not be too happy because the wait time is very long, And agents, by the way, I think a lot of people two: may not realize, the agent, the workforce attrition in contact center is massive.

平均达到35%到40%。疫情期间有些公司甚至超过100%

It's on average of thirty five to forty percent. In some cases during COVID, some company have more than 100%

他们整天都在挨骂。

They just get yelled at all day long.

没错。所以这份工作压力非常大，而且并不是一份很有趣的工作。此外，企业也总觉得有机会用更少的资源做更多的事。似乎没有人感到满意，尽管这是一个巨大的市场，但我认为这正是AI和技术带来丰裕的绝佳机会，而丰裕在我看来是解决所有这些问题的答案。

Right. So it's very high stress and it's not a very, very fun job. Also the business also feel like there are always the opportunity to do more with less. It seems no one is happy and it's a massive market, but I think that's the great opportunity for AI and technology to bring abundance and to and then the abundance is the answer, in in my opinion, to solve all these issues.

所以你十年前在谷歌就从事这方面的工作。我想那应该是小语言模型浪潮，BERT时代。当时技术准备好了吗？也许你能带我们回顾一下影响客服中心的不同技术浪潮。

So you you were working on this at Google ten years ago. I I imagine this is the small language model wave in the the the BERT days. Was the technology ready at that point? And maybe walk us through the different waves of technology that have hit the contact center.

是的。这是个很好的问题。在那之前很久就有一种叫IVR的技术，你可以按+1、23等不同按键来转接不同服务。之后围绕输入方式出现了创新，你不再需要按键，可以直接用自然语言说话，这得益于自然语言处理、TTS和文本转语音技术的进步，体验越来越好。我们最初在谷歌从事AI语境理解时，甚至早于BERT，是在Transformer出现之前。

Yeah. So that's a great question. Even long before that, there's technology called IVR, that you press +1, 23 for different routes and through for different core reasons. And then since then there are innovation around the input, You can instead of pressing, you can directly speak natural language, and that's with advance of natural language processing and TTS and text to speech generation, that experience getting better and better. For when we first started in context under AI Google, it's even before BERT actually, it's before transformers.

当时主要使用AI（那时还叫AI）进行分类，用前Transformer模型进行意图分类和实体提取。但对话体验仍然是人工设计的，对吧？那是上一代技术。之后当然Transformer出现了，但最初也是用于分类目的。

It's mainly using AI or at that time using AI to do classification, intent classification and entity extraction using pre transformer models. And then but the conversation experience is still manually crafted. Right? That's the last generation of technology. Then after that, of course, the transformer came along, but initially it's also for classification purposes.

体验仍然是人工设计的，但后来语言模型彻底改变了一切。不仅自动化端的对话体验变了，而且你现在能以前所未有的方式理解对话。

Still the experience is manually crafted, but then the LMs entirely changed the whole thing. Not only the conversation experience on automation side, but also just you can understand conversation in a way that never was able before.

这项技术在客服中心实际部署意味着什么？是否意味着IVR时代客户非常不满，当你们开始引入更多Transformer时客户稍微不那么不满，而现在客户非常乐意与基于NLM的客服交谈？还是说技术演变如何改变了客户体验？

And what does that mean practically in terms of the rollout of this technology inside contact centers? Does it mean that customers were just extremely unhappy when it was IVR, and then they were slightly less unhappy when you started to have kind of more transformers in the flow, and now now customers are very happy to be talking to NLM based agents? Or how has the evolution of technology changed the customer experience?

是的。我认为我们更倾向于从第一性原理来思考这个问题。对吧？而且，在我们看来，很多对话本就不该发生。要知道，这些对话之所以出现，是因为客户不满意。

Yeah. I think the way we would like to think about it is really from the first principle. Right? And, you know, the there are a lot of the conversations shouldn't even happen in in our view. And, you know, the fact that it happens because the customer's not happy.

我认为解决方案是利用AI真正理解问题，实现当前联络中心所有交互的100%可视化，用AI进行分析，然后深入研究，找出根本原因。这通常反映出某些流程故障、或网站更新让人困惑、或固件更新导致网络中断等问题。所以你需要先解决这些问题。对吧？首先，如果没必要，就避免交互。对吧？除此之外，我认为AI可以自动化处理很多没人愿意进行的交互。

I think the solution for that is to use the AI to really understand, to bring a 100% visibility into all interaction in the contact center today, and using AI to analyze it, and then to do deep research, and then find out the root cause, and then, and then that usually reflects some process broken, or website updates that freak out people, or, you know, firmware update that bring down network and all that kind of stuff, So you need to fix that first. Right? And first, you know, avoid interaction if it's not necessary. Right? And, you know, beyond that, I do feel like, you know, their AI can automate a lot of interactions that no one wants to have.

比如，无论是企业还是客户都不想要的那些交互。这些我们称之为低情感价值的交互，本应通过自助服务完成。在此之上，我认为联络中心AI将促成新型交互，那些目前你无法承担的交互。

Like, know, neither the business nor the customer want to have those interactions. Those are what we call low emotion value interactions that should be self self served. And then on top of that, I do think that contact center AI will enable new interactions. Interactions. That's the ones that you cannot afford to do that today.

所以所有这些都在提升客户体验。

So all these are improving customer experience.

你认为终端客户会愿意与AI客服交谈胜过人工客服吗？我们现在达到这个阶段了吗？

Do you think end customers will ever prefer talking to an AI agent over a human agent? And have we reached that point yet?

这个问题很有意思。我来这里的路上一直在思考。我从没见过有人在与电话客服沟通后说'我太沮丧了，请给我换成AI吧'。事实上，我建议大家去搜索一些公司的客服评价。

So, look, I mean, that's a really interesting question. So, I've been thinking about this, you know, on my way here. So, I never met anyone that have this experience of talking to a customer support agent on the phone and go, I'm really frustrated, send me your AI please. And we never had that experience, in fact, that I would encourage people to look up some of the companies in a search for their custom service. Right?

人们在谷歌上最常问的第一个问题是什么？总是'如何联系到某某服务的真人客服？'。所以我认为这个时代可能还未完全到来。当然，这也取决于具体是什么类型的交互。

The first question that people ask on Google, and Google will surface, what is the most popular question? The first question is always, how do I talk to a live person for this type of, you know, custom service? So so I think that that time probably hasn't arrived fully yet. It depends on what kind of interactions again.

或许我过于技术乐观或对AGI充满信心，但我现在确实看到一些记录显示AI可以具备情感智能。它拥有无限的耐心，不是吗？它不会追求解决时间的指标。比如当有人来电时正经历糟糕的一天，AI可以比人类客服更耐心、更有同理心。因此我对机器人这一方持乐观态度。

I've I'm maybe too techno optimistic or AGI pilled here, but I've I feel like I've seen some recordings now where the AI can be emotionally intelligent. It has infinite patience, right? It's not trying to hit some metric on time to resolution. And so, for example, if somebody calls in and they're having a really bad day, for example, your AI can be a lot more patient and empathetic than a human agent even could. And so I'm sort of optimistic on the side of the bots here.

我同意。人类有耐心的特质和人性的微妙之处，但还有客服培训与AI训练的差异。三年后，谁会更擅长回答问题？显然AI是答案。我无法将黄金与比特币相提并论。

Well, I agree. There's the human component of patience or the subtleties of humanity, but there's also the training of the agent versus the training of the AI. Three years from now, who's going to be much more equipped to answer a question? It's clear that AI is the answer. I cannot think of gold versus Bitcoin.

听你这么说我突然想到这个类比。显然比特币会胜出。显然比特币价值将超越黄金。

Somehow the analogy came to my mind as you said that. It is clear that Bitcoin is going to win. It is clear that Bitcoin is going to be worth more than gold.

非投资建议。

Not investment advice.

非投资建议。但显然从定义上看，很多客服甚至不在美国，存在语言障碍。我并非贬低客服，但存在语言因素、培训因素和人性因素。我认为在这些维度上，AI将在未来两三年内胜出。

Not investment advice. But it is clear that the agents, by definition, and a lot of which don't even reside in America, there's a language component. I'm not saying anything bad about the agents, but there's a language component, there's a training component, there's the human component. And I think in all those dimensions, I think AI is going to win in the next two to three years.

比特币作为数字黄金的比喻，与数字客服对抗人类客服的问题形成了有趣的类比。

Bitcoin as digital gold is a really interesting analogy to the agent, the digital agent versus the human agent question.

是的。从我们角度看，我们真正希望满足客户当下的需求。与自动驾驶汽车不同——必须实现100%全自动化才能产生经济效益——而呼叫中心的工作具有高度可分割性，这很独特。

Yeah. From our perspective, we really want to meet customer where they are today. So unlike self driving cars, you really have to automate the entire thing a 100% of time. Otherwise, you do not have the economic impact. For contact center, would find is very unique is that the work is very divisible.

首先，对话是独立单元，可以自动化其中X%已准备就绪的部分。具体原因我们稍后详谈。对于剩余部分，仍可利用AI辅助人类，处理诸如身份验证、信息收集或潜在客户筛选等初期约10%的交互，并接管所有通话后工作。AI助手还能在对话过程中协助知识检索、数据录入等操作。这些方式并非互斥。

So first the conversation is, you know, those are every conversations, independent unit, and you can automate X percent of conversation that's ready to be automated. And for a lot of reason, we can get into details. Then for the remaining ones, you can still use AI to assist humans and to take away the initial, maybe 10% of the interactions like authentication or intake or lead qualification, and then take away all the after call work. And also have AI agents to help humans in the middle of the conversation to do knowledge retrieval, to do data entries, all that stuff. So that's not mutually exclusive.

只要客户尚未准备好全面转向AI呼叫中心，我们认为转型周期会因业务类型和IT基础设施差异而不同。我们的核心目标是因地制宜满足客户需求。

And as long as we feel like customer not ready to say that we just need to turn on our call center today and then go full AI, we feel like there's a long depending again, what kind of business and what kind of technical, the IT infrastructure. So I think the journey will probably take different timeframe, but our goal is really meet the customer where they are.

确实。Krausz的独特之处在于同时拥有提升现有客服效率的辅助产品和直面客户的自主AI客服产品。您认为当前客户大多处于哪个阶段？他们准备好全面部署了吗？

Yeah. So Krausz is in an interesting position because you both have the agent assist product that helps make existing contact center agents more productive. And then you have the actual AI agent product that is directly customer facing, autonomous agents. Where do you think most customers are today? Are they ready to go full force?

是否已有客户直接将AI客服部署到网站任其自由运作？客户现阶段更多是在尝试性探索吗？

Just put the agent on my website, let it go crazy. Are they are they, you know, experimenting with that? Where where is the customer today?

这取决于客户类型。如果我们今天在Shopify开电动自行车店，完全可以实现100%自动化——产品复杂度是关键。电动自行车这类简单产品与跨国覆盖数千万用户的业务存在数量级差异，这直接影响客服中心需处理的对话复杂度。另一变量是IT基础设施。

It depends on the customer. If you and I start a e bike store today on Shopify, we can automate a 100%, I'm sure, because it really depends on how complex is our product. It can be order or magnitude difference between like a simple product like e bike or versus a real world touching many different countries and then millions of tens of millions of people. So it's very different, and then that impacts the the complexity of the conversation handled by the contact center. And then the other part is the IT infrastructure.

许多人可能没意识到：在接入客服中心前，看似容易自动化的环节，实则因现有系统数十年来一直为人类操作优化——比如工单系统大多缺乏API接口，只能通过图形界面人工操作。

A lot of people may actually realize that before you actually enter the contact center, will feel like, oh, this should be easily very easy to automate. The reality is a lot of those things that human do in the contact center today is optimized for humans. So those system record or the system action ticketing system, these has been around for decades. A lot of them just simply do not have APIs, right? So the only thing that to make changes is through a graphic user interface that optimize for humans.

缺乏实时API接口并非AI技术问题。我们认为这正是与客户共同开发实时API的机遇所在，因此企业转型周期会因其业务特性而异。

Without a real time API, you know, again, these are not AI problems. And we believe that, you know, these are the opportunity that we work with our customer to develop those real time APIs. And then so that's why we feel like those transformation depends on the nature of the business would take different time frame.

是的。你之前用自动驾驶汽车做类比很有意思，因为今天早上我就在思考你们的业务。想想特斯拉，他们实现完全自动驾驶的美妙之处在于，即便处于L2级别，也能从车辆获取海量数据。而你们作为呼叫中心和座席助手，实际上能获取对话的完整数据，无论是语音还是数字化的对话交流，这些都能成为训练基础，帮助客户逐步将更多对话自动化转移给座席。

Yeah. It's interesting you made the self driving car analogy earlier because I was thinking about your business earlier this morning, and if you think about Tesla, part of the beauty of them getting to full autonomy is that they have so much data coming in from their cars even when they're on l l two. Right? For you guys, because you are the call center, you're the agent's assist, you actually get full data of the conversation, whether it's voice, whether conversational based digitally, and that can become a training base for customers to automate more and more of their conversations over to the agent over time.

没错，百分之百同意。事实上，当我们七八年前刚开始这段旅程时，完全是自动化导向。我坚信应该只做自动化。但随着时间的推移，我们在实际部署中遇到各种情况，这真正拓宽了我的视野。于是我意识到，要做出最好的自动化系统，反直觉的是——

Yes, 100%. In fact, when we first my first the journey when it first started seven, eight years ago, it's really automation only. I really believe it should be automation only. Fast forward, we run into all kind of real deployments and then we really actually brought in my own horizon. Then I believe that in order to really do the best possible automation, it's counterintuitively.

你必须了解联络中心实际发生的情况。人类座席究竟在做什么？所以不仅要关注对话内容，还要看他们在屏幕上看到的信息。这对构建最佳自动化系统至关重要。

You need to know what actually happened in the contact center. What are humans actually doing? So not only just the conversations, but also what they are seeing on the screen. That's super important to actually build the best automation possible.

其中一个是性感吸引力。是那种让人津津乐道的闪光点。没有这个，你的公司就会显得陈旧乏味。另一个则是我们业务运作的实际需求和它们真正需要的东西。

One of them is the sex appeal. It's the sizzle. It's what everybody wants to talk about. What you have to have, otherwise you're a tired old company. The other is the realities of our business are run and what they need.

因此，如果你属于这类新兴企业，很快就会碰壁，因为你既缺乏数据，也没有真正运营客服中心所需的系统。但如果你具备前者而不具备后者，就会被归类为线上公司。我们很早就意识到这一点，并确保投入资源。我们不仅加倍强化了客服辅助操作系统，还开发了极具吸引力的产品，因为这才是客户第一天就想谈论的。

And so if you are one of these new age companies, you're quickly gonna hit a wall because you don't have the data and you don't have the systems that you really need to run a contact center. But if you're at the former, don't have the latter, then you're labeled as as an online company. So here in our case, we understood this a while back and we make sure we invested. We not only we doubled down on the operational system for agent assist, but we also developed the sex appeal product because that's what all the customers wanna talk about day one.

是的。另一个相关方面与我早先的观点一致：很多这类成本本不该发生。客户来电投诉却无法让他们满意，根源在于他们最初就不满意。

Yeah. And another aspect of it is really just tied to the the point I make earlier is that a lot of those costs shouldn't really happen. People call in. There's no way to make them happy. It's because they're not happy to begin with.

对吧？要知道，如果你的产品正常、流程顺畅，这些问题本不该出现。就像这个房间如果冷得刺骨，解决方案可能不是开暖气，而是先检查是否有窗户破损或露台门大开。正确做法是开灯找出根本原因，修复问题后再考虑开暖气。

Right? And, you know, if your product works, if your process works, they shouldn't really happen. So if look, if this room we feel really, really cold, maybe the answer is not a heater. Maybe maybe there's a broken window or there is a patio door wide open. The solution is turn on the light and see the root cause and then fix that first before you turn on the heater.

非常赞同。客户支持正是人们认为大语言模型最具变革性的典型应用场景之一。你知道，这几乎已成为风险投资初创企业的共识领域。你们如何竞争？当所有人都能使用相同的大语言模型，并着眼于相同的宏大愿景时，竞争态势是怎样的？

Love that. Customer support is one of those canonical examples of where people think large language models will be most transformative. And you know, it's almost a consensus category for venture startups at this point. How do you compete? What is it like to compete when everyone has access to the same LLMs and, you know, latching on to the same big picture vision?

没错。要真正实现联络中心转型的价值，关键不仅在于模型本身。模型只是一堆权重和数据，其本身并不能创造价值，对吧？

Yeah. So again, in order to really deliver value in the contact center transformation, it's not just the models. It's just not a model. Model is a bunch of weights and the data, and in itself is not gonna provide a value. Right?

问题在于需要在其基础上构建多少才能释放价值。如果这个附加层非常薄弱，那么我们认为企业很难积累价值优势。而且如果这个附加层会随着模型改进而消失，那就不可能建立持久的业务。但联络中心领域并非如此——大多数机构仍在使用本地化系统，根据我们对《财富》500强企业的调研，客服人员平均需要操作8到10个不同系统。要知道，这些公司经过多年甚至数十年的并购，后台系统可能根本互不联通。比如预订航班和酒店的渠道可能就不同。

And now the question is how much you need to build on top of it to deliver that value. If that layer is very, very thin, then our argument probably is you don't have much opportunity to accrue value. Right? And then also, if that layer will be gone with the model gets better, there's no way you have a durable business, but that is not a case for contact centers and where majority of the agencies are still on premise and where a lot of there are so many Look, on average agents in the Fortune 500, we look at some surveys, they interact with eight to 10 different systems. Remember, these companies also apply other companies over years, over decades, those backend system may not even talk to each other, You know, it depends on where you book the flights or depends on where you book the hotel.

客服人员可能需要登录不同系统，这就是现实情况。因此我们的战略是立足客户现状，在首日就实现价值交付。

They may need to log into different systems. Right? So that's the reality we're talking about. So that's why, you know, we believe is our strategy is meeting customer where they are and then drive value on day one.

从基础设施到用户体验的垂直整合——这就是制胜之道。你认为当前联络中心AI领域哪些方面被过度炒作，哪些又被低估了？

Vertical integration from the stake to the sizzle. That's how you win. What do you think is overhyped and what's underhyped in the kind of contact center AI space right now?

关于过度炒作，我认为是稀缺性思维。短期内对岗位替代的担忧可能被夸大了。而被低估的是丰饶思维——AI能实现哪些新体验？比如直接与网站对话，

Yeah, for overhyped, I think it's the mindset of scarcity. Is the job displacement, I think, the short term is probably a little overhyped. And what's underhyped is the mindset of abundance. You know, think about new experience that AI can enable. For example, can you talk to a website?

或直接对APP说话？能否将同步交互转为异步？比如对航空APP说'帮我处理XZ事项，完成后回电'？能否配备精通多语言的AI坐席来处理这些对话？现在有多少交互因人力不足而根本无法实现？

Can you directly talk to the app? And can you turn a synchronous interaction into a asynchronous interaction? Can you talk to the airline app and say that, I want you to do this X, Z and then call me back when you get it done, right? And then can you have that super multi language AI agents to have those conversations? Or there are so many interactions that today you just cannot happen just simply because you do not have the staff.

对吧？所以，我觉得另一个被严重低估的方面是，人们似乎只痴迷于讨论问题的一个侧面——劳动力。大家总在问有多少劳动力会被AI取代？但没人问过有多少来电咨询会被AI取代？我认为未来几年，我们将看到一场竞赛，看谁能率先在消费者聚合平台上部署AI助手。

Right? So, and then the other thing actually I feel is really underhyped is people really seems obsessed with one side of the conversation, which is the workforce. And then people ask, you know, how many of the workforce were replaced by AI? But no one ever asked the question is how many inbound calls will be replaced by AI? So my belief is that there will be, over the next few years, you will probably see a race to getting the AI assistant on the consumer aggregators.

而且，消费者可能会将许多事务委托给AI助手处理，包括拨打电话。所以我觉得这是个值得关注的有趣现象。

And, and then a lot of things that consumer probably will dedicate to the AI assistant, including making the phone calls. So I think that's maybe an interesting thing to pay attention to.

这真的很酷。也就是说，你可以直接和联合航空的应用程序对话，让它异步处理问题并回电给你。这是你们正在研发的功能吗？

That's really cool. Okay. So you could talk to the United Airlines app and have it, you know, asynchronously go figure something out for you and call you back. Is that something that you're working on?

呃，对此不予置评。

Well, no comment on that.

好的，非常酷。我想稍微转换话题聊聊公司建设。Doug，你在这个行业摸爬滚打多年，算是经验丰富的老手了。

Okay. Very cool. K. I wanna transition to talk a little bit about company building. Doug, you've been around the block for a while, seen seen the movie a few times.

你这是在委婉地说我老。

Means I'm old. That's what you just said.

我...我本来是想说得委婉些的。

I I was trying to say it nicely.

是的。

Yes.

现在创办公司的情况如何？你正通过Ping实时见证这一切。在AI领域创办公司与你过去几十年打造传奇企业的经历有何不同？

How is building a company right now? You're you're seeing this live with Ping. How how is building a company in AI different from from your, you know, your last few decades of building legendary companies?

其实差别不大。我的意思是，你需要一位出色的创始人——稍后我们可能会讨论Cresta的情况。一开始就必须引入世界级的工程师团队。如果起点不是A+水平，你永远无法向上突破，只会不断下滑。

It's not very different. What I mean by that is you need a terrific founder, and we'll talk about the Cresta situation a little later, hopefully. You need to plug in world class engineers at the very start. Unless you start with a pluses, you'll never move up. You'll only be moving down.

你需要引进的销售团队不能是行政型人才，而要有冲劲。早期可能只能找到区域销售经理级别的人选，因为一方面难以吸引顶尖人才，另一方面即便招到，他们也可能与公司规模不匹配。你必须明确愿意投入的成长曲线，厘清市场营销的职能定位，还要解决我称之为'商品化循环'的问题——这个理论最近在网上很火——即从产品营销到业务开发代表再到营收的完整链条。

You have to plug in salespeople that are not administrators, that are fresh. Maybe they were regional sales manager early on because one, you can't get the world class people, and two, if you get them, they're too big for the company. You have to figure out what the ramp is that you're willing to fund. You have to figure out what the role of marketing is. You have to solve this thing that I call the merchandising cycle that's been getting some play online, which is from product marketing to BDRs to revenue.

这个链条任何环节出问题，表面看像是销售员或销售副总裁能力不足，但你必须确保整个体系运转顺畅。所以我认为商业底层逻辑其实非常相似。

Wherever that's broken, it looks like a bad sales guy, bad VP of sales, but you have to get that right. And so I think the business fundamentals are very are very similar.

我确实认为当前在AI领域表现最出色的公司都有一个共同特征：它们以极快的速度推进。也许历来如此，但现在的竞争强度更甚。你如何向合作企业乃至红杉内部灌输这种速度至上的理念？

I do think one of the characteristics of the companies that are doing the best in AI right now is they just move with extreme speed. And maybe that's always been the case, but I I think it's it's even more intense right now. How do think about instilling the need for speed in the companies you work with, and even at Sequoia?

我本想把这点纳入之前的回答。之所以没提，是因为我参与的所有董事会都以极速运转。我会向创始人们描绘这样一幅图景：一条布满礁石的河流。创始人和CEO的职责就是清除这些障碍。所以当你提交明年计划时，我不在乎那150%的净新增ARR增长率，我要知道制定这个计划的依据，并要质问为什么不能做到三倍于此。

So I thought of answering that as part of my answer, and the reason I left it out, all the boards I'm on move with extreme speed. And that's because I paint a picture for the founders of a river, a river with rocks. And the founders and the CEO's job is to remove those rocks. So when you give me next year's plan, I don't care that's a 150% net new ARR growth. I wanna know why the plan is the plan, and I want to challenge you why it's not three x that.

也许答案是资金，在这个市场上我们能获得资金。也许答案是管理经验，这通常是个好答案。有人会说市场，但绝不可能是市场。

And maybe the answer is funding, where we can get funding in this market. Maybe the answer is management experience. Well, that's often a good answer. Some people will say market. Well, no way that's market.

我们是一家小公司。所以在我看来，这迫使人们理解这些公司有能力做他们自认为还做不到的事情，并移除那些障碍。我不断推动、推动、推动，我说为什么我们不能更快地以线性方式推进？因为天知道会不会出问题？如果你在第一季度雇佣250名销售人员，然后在第三季度发现产品有问题，你就会陷入资金消耗的困境。所以我坚信这一点，尽管我听到的都是‘不’。

We're a little company that is. And so in my mind, it's forcing the understanding that these companies are capable of doing things which they don't believe they are capable of doing yet, and to remove those rocks. And I push and I push and I push and I said, why can't we go faster and do it in a linear fashion because God forbid something isn't gonna happen? If you hire 250 salespeople in q one, and then you realize in q three something's wrong in q three something's wrong with the product, then you're stuck with a burn. So I'm a believer, and I hear, no.

我们得用同样的陈词滥调培训他们。请给我们一个线性的收入增长曲线，这样我们就能在中途进行上下调整。别被这些数字束缚。我们有十根手指，100%的增长，全是胡扯。

We gotta train them all the same baloney. Give us, please, a revenue ramp that's linear so we can make mid course corrections up and down. And let's not be stuck by these numbers. We have 10 fingers, a 100% growth. That's all bullshit.

我们最快能增长多快？这是我参与的所有董事会中一直奉行的信条。AI领域也不例外。

How fast can we possibly grow? That's always been the mantra in all the boards that that I've served on. AI is not different.

Cresta接下来需要做什么？在未来五年甚至更长时间里，Cresta需要采取哪些措施才能成为一家伟大的、传奇的公司？

What does Cresta need to do next? So what does Cresta need to do over the next five plus years in order to become a great company, a legendary company?

首先，它必须继续开发产品。必须一步一个脚印地前进。必须时刻注意当某些人达到彼得原理的职位极限时，要相对积极地确保雇佣能带领公司继续前进的人才，避开那些所谓的‘经验丰富’却开始变得官僚化的管理者。这是最重要的一点。但Cresta还需要提升营销水平。

So, well, first of all, it has to continue to develop product. It has to continue to put one foot in front of the other. It has to always see whenever some people reach a Peter principle of their role, it has to be relatively aggressive in making sure it hires people that are capable of taking it from that point on and forward, staying away from these quote very experienced people that start feeling a bit like suits and administrators. Point one, that's the most important thing. But the other thing that Cresta has to do, it is to up its game in marketing.

有很多公司——我用‘花哨’这个词形容。很多公司徒有其表。而我们有很多实质内容。我们是现代化企业，在某个领域是顶尖的。

There's a lot of companies, I use the word the sizzle. There's a lot of company with a lot of sizzle and no steak. We have a whole bunch of steak. We're a modern company. We're best in class in one category.

我们将在其他类别中做到行业最佳。我们的代理辅助和产品AI自动化部分都保持着漂亮的增长速率。我认为只需要附加一个营销覆盖层，就能让我们在市场上家喻户晓。

We're gonna be best in class in the other category. We have beautiful growing run rate in both the agent assist and in the AI part of the product, in the automated part of the product. I just think we need to attach a marketing overlay so we become a household name out of the market.

太棒了。很高兴你能来参加播客。道格，退一步说，你经历过几个市场周期，你认为我们现在处于AI泡沫中吗？

Wonderful. Well, glad you're on the podcast then. Maybe stepping back, Doug, you've seen some market cycles. Are we in an AI bubble?

泡沫这个词意味着你投入资金后会亏损，原因要么是公司供给不足，要么是资本过剩。目前资本确实过剩。但我注意到过去两个周期——95年网景上市时的互联网周期，90年代末谷歌亚马逊等伟大公司的崛起，随后出现短暂停滞，甚至有人声称互联网是骗局。但三年后世界就疯狂了。

The word bubble implies you invest money in and you lose money because either due to lack of supply of companies or abundance of capital. There's certainly an abundance of capital. But I've noticed over the last two cycles, the Internet cycle when Netskip going public in '95, two great companies being built in the late '90s in Google and Amazon, a few others names that came to me. Then a bit of a pause, even the words I heard, the internet is a fraud, it's not gonna do anything. And then three years later, the world went crazy.

移动互联网的这个延迟期短得多。记得我们第一次看这些应用时，前合伙人吉姆·盖茨曾问：怎么从1.99美元的应用赚钱？如何打造百亿公司？当时根本没想到Airbnb和DoorDash，一两年后它们就出现了。

That latency was a lot less in mobile. I remember when we first looked at these apps and Jim Getzon, our former partner said, How do you make money from a 19 app? How do you build a multi billion dollar company? Never thinking of Airbnb, never thinking of DoorDash. A year or two later, we saw a Airbnb and DoorDash.

从初始诞生到真正市场化，这个周期比互联网时代更短了。我认为AI已经到来，你必须投资。虽然处于周期前端，但并不意味着要投资所有项目。

Again, that from initial birth to real market shrunk from the internet. I think this has shrunk even further. I think AI is here. I think you have to invest. I think you're at the front end of a cycle, which doesn't mean you have to invest in everything.

红杉犯过的错误是：每当看到收入增长势头，总有合伙人在会议上天才般预言‘这随时会停止或被替代’。其实很简单——在细分垂直领域（不是2021年的SaaS市场），看到小公司展现出市场前端势头时...

But one of the mistakes that we made at Sequoia is whenever we see a bit of revenue momentum, we have some geniuses around the partners meeting that say, oh, it can stop. It can be substituted. Keep it very easy. You see a small company with very momentum in a front end of the market. I'm not talking about the SaaS market in 2021, you're down to niche verticals.

在市场前端，只要看到微弱的收入增长势头，你就该果断投入并暂时忽略估值问题。

At the front end of the market, you start seeing the modicum or revenue momentum you lean in and you hold your nose on price.

我很赞同。当你在思考市场价值流向时，有算力、基础设施、基础模型、应用层等环节。你认为价值会流向哪里？向上？向上？

I love that. As you think about where value accrues in the market, there's compute, there's other infrastructure, there's the foundation models, there's the application layer. Where do think value accrues? Up. Up?

价值总是向上聚集。看看随着市场层级上升的毛利率就知道了。看看芯片公司的毛利率，系统公司的毛利率，再看看这个领域的毛利率。

It always accrues up. Just look at the gross margins as you move up markets. Look at the gross margin of chip companies. Look at the gross margins of the system companies. Look at the gross margin of this.

以英伟达为例——我们是其首位投资人——它是一家伟大的公司。黄仁勋多年前就能预见未来，他完成了硅谷史上最漂亮的战略转型之一，堪称传奇。但长期来看，我认为价值终将流向所谓的应用层——无论最终形态如何。它会靠近客户、靠近资金、靠近商业用户聚集。

Well, that's NVIDIA, of which we were the first investor, is a great company. Jensen was able to see the future many years ahead, and he pulled one of the great probably the greatest coup in Silicon Valley, what he did. It's just spectacular. But if we're looking over time, I think value is going to accrue to quote the application layer, what that ever looks like. You know, it's going to accrue up near the customer, near the money, near the business user.

我

I

同意。你认为AI浪潮与互联网或移动革命有何不同？

agree. How do you think the AI wave is different than Internet or mobile?

我认为其他技术都是提升效率的工具，让我们实现网络化和移动化。而AI浪潮是工业革命2.0，其影响深远得多。记得五年前我们见证过最大市值时，我就思考过：为什么？

I thought of everything else being tools to make us more productive, meaning we all became networked and we all became network and mobile. I view the iWave as the industrial revolution two point o. I think this is much, much larger. I remember thinking, boy, we have just seen the biggest market caps five years ago. Why is it?

因为当时是互联性创造了收入增长。但从未想过会出现比互联性和移动性更宏大的事物——这将彻底重构人类的生存方式、工作模式、生活形态和娱乐体验。我认为AI在未来十到二十年既会是人类的福音，也可能成为致命威胁。

Because it was connectivity that created this revenue growth. Never imagined that there was this thing that was gonna be much bigger than connectivity and the mobility. It was a complete redoing of humanity, of how humanity exists, works, lives, enjoys. And I think AI is both going to be a wonderful thing for us and maybe even a kiss of death for us over the next ten, twenty years.

是的，Tore，我同意Doug的观点。我认为人工智能非常独特的一点在于它带来了太多惊喜，这些底层能力的涌现是互联网或移动时代从未见过的。试想一下，如果你带着2015年的世界观乘坐时光机回到2007年——史蒂夫·乔布斯刚发布iPhone的时候——人们是能够理解这种变化的。互联网时代也是如此。

Yeah, Tore, I agree with what Doug said. And I think one thing AI is very unique is that there are so many surprises. There are surprises of underlying capabilities that you never seen before in Internet or mobile age. If you take, you know, you if you take the world view in 2015 and take a time machine to give that to someone in 2007 when Steve Jobs first introduced iPhone, I think someone can resonate with that. And then same for Internet.

我觉得人们多少能预见互联网的发展趋势。但对于AI，随着底层模型不断进步，惊喜层出不穷。即便是Transformer论文的作者们，也未曾预料到大语言模型会展现某些能力，这些突破不断改变着我们的认知。所以我认为很多进步是非线性的，是从0到1的质变，持续发生在技术底层。

I think people can kind of foresee what's coming. But for AI, I feel there's so many surprises as the underlying model gets better. There are things that even the authors for transformer paper will not have imagined some of the capability that just came after the large language models, and then that can change or surprise us. So, I do think that, you know, a lot of the improvements is nonlinear. It's really from zero to one, continued happening at the bottom layer.

因此，我觉得这正是AI令人更加兴奋的地方。

So, I think that's something that make it even more exciting.

我要提醒大家一件事。2022年3月——现在听起来恍如隔世——那是我最后一次参加全体投资者年会，算是告别演讲，当时我展示了业绩报告等内容。其中有张幻灯片列出了历次技术浪潮：从芯片浪潮、系统浪潮、局域网/广域网浪潮到互联网、移动互联网。而在三年前的幻灯片上，下一个技术浪潮的标注还是个问号。

You know, I'm gonna remind you something. In March 2022, which now sounds like an eternity, it was my last annual meeting where we meet with all the investors. And it was a goodbye kind of thing, where I presented performance and everything. And I had a slide that talked about all the waves back from the chip wave to the systems wave to the LANWAN wave to internet to mobile. And the next box, a short three and a half years ago, was a question mark.

即便作为最前沿的种子投资者，我们合伙人也未能预见这波浪潮的到来。而如今这波AI浪潮已如海啸般席卷而来，我看不到它的尽头在哪里。

We did not know as a partnership, and we are as advanced as anybody. We are the bleeding edge investor, right, in seed. We did not see the wave coming. And this wave has been a tsunami, and I don't think there's any end in sight.

谢谢分享这些深刻见解。您想谈谈Cresta的技术架构吗？还是我们应该去请教Ping？

Thank you. Thank you for sharing those insights. Do you want to talk about Cresta's technical stack, or should we should we bug Ping on that?

事实上我几分钟后得先离开，因为正在重新录制部分...

I'd like to well, in fact, I'm gonna have to go in a few minutes because I'm in a process of recoding some of the some of

你是在用氛围编程吗？

Are you vibes coding the

是Cresta吗？对，对。我所有东西都在用氛围编程。

the Cresta? Yes. Yes. I'm vibes coding everything.

Ping，给我们讲讲技术栈吧。

Ping, tell us about the tech stack.

好的。我们有一个覆盖面很广的产品，我可以重点说说语音AI代理。我们实现了端到端的双向音频流传输，并协调多个不同模型运作。包括语音转文本模型、提升音频质量的降噪模型，还有检测术语和语音活动以处理打断的模型。

Yeah. So, we have a pretty broad, surface or product, and, I can maybe talk about the voice AI agent. We, we streaming end to end audio bi directional, and we orchestrate multiple different models. There are speech to text model, and then noise cancellation model to improve the audio. There are models that detect the terms and the speech activities and to handle interruptions.

当然还包括处理对话的基础模型，另一侧是TTS文本生成模型。同时我们还并行运行多个小型模型进行护栏检查，确保一切正常运转，这些模型还会执行公司特定的合规检查。

And then, of course, there's a foundation model and to handle the conversation. And the other side is the TTS text generation model. Right? And then in parallel, we also run multiple smaller models to do guardrail checking and to make sure that nothing has gone crazy. And as well as those models will do company specific kind of checks.

比如绝不提供税务建议或财务承诺之类的。这就是语音AI代理的运行时部分。设计时部分还包括运行大规模模拟测试的组件，全面压测AI代理覆盖所有边缘情况，以及测试用例管理组件。

For example, never give out tax advice or never give out financial promises, things like that. Right? And then that's the runtime of voice AI agent. And also there's design time, there's components like running large scale simulation to really stress test the AI agent to cover all the edge cases. There's test case management components.

同理，我们的语音助手也是音频流式传输，虽然底层基础设施有很多相似之处，但这次是单向的——监听通话并理解两个人类对话的实际内容。

And then similarly, if you think about our voice AI assistant, so it's also streaming, audio, but again, so there's a lot of similarity between the infrastructure, but, it's now bidirectional. Right? It's one direction. And in listening to the call and then understanding what's actually happening in the call with two humans. Right?

实际上，我们协调了超过10个模型。事实上，类似于Vertex的AutoML，我们有一个平台，能让客户构建自己的定制模型，检测对话中有趣的事件。然后将其与工作流结合，人们用它来检测欺诈，甚至包括呼叫中心的欺诈，并培训客服如何处理异议。借助我们称为Opera的工具，现在有如此多的应用场景可以表达并触发工作流。底层采用师生蒸馏技术，将模型压缩到非常小的规模，以便实时运行并理解人类对话。

And then orchestrating 10 plus more models, actually. And and, you know, in fact, we similar similar to Vertex, you know, AutoML, we have a platform that can allow customers to build their custom models and to detect interesting events in the conversation. So and then marry that with with workflows, and people use that to and and to detect fraud for even used to detect fraud, call center fraud, and to train agents to how to handle objections. There are so many use cases that now with that tool we call Opera, they can express and trigger workflows. And underneath is teacher student distillation to distill into very small models that we can run-in real time and to understand to human conversations.

当我与你们的客服交谈时，延迟是多少？

What's the latency when I talk to to one of your agents?

大约在800毫秒以下。

So it's around below eight hundred milliseconds.

哇，那感觉就像在和真人对话一样？

Wow. So it feels like talking to a human?

是的。

Yes.

所以你们是在近乎实时地运行所有这些模型？

Yes. So so you're running all these models in in near real time then?

是的。

Yes.

你们是在运行开源模型，还是在运行类似11 labs这样的服务？

Are you running open source models, or are you running, you know, 11 labs in the equivalent?

整个平台上有20种不同的模型。有些是开源并经过微调的。比如有些小模型专门用于聊天或邮件辅助，能自动补全人类代理的句子并预测输入。这些模型非常非常小。至于TTS（文本转语音），是的，我们确实使用。

So across the platform, are 20 different models. Some are open source and fine fine tunes. There are small models that, for example, we only do chat or email for human agents, and we auto complete their sentences and type ahead. Those are very, very small models. And for TTS, we we yes.

我们使用Eleven Lab。他们是很棒的合作伙伴。我们也使用其他供应商，并持续比较它们的性能。

We use eleven Lab. There's a great they're a great partner. We also use other vendors, and we constantly compare the performance.

非常酷。不过对话的实际核心部分——即对话内容或交流流程，你们如何控制它既不像过去的IVR系统那样僵化，又不会过于自由放任，导致客户可能胡来，比如要求机票退款，或者让机器人说出离谱的话让客户难堪？你们如何平衡流程控制，实现两全其美？

Really cool. And then the actual meat of the conversation, though, the the the dialogue or the conversational flow, how do you how do you control that in a way that's not so rigid that it's like the IVR systems of yesterday, but not so free form that, you know, customers can go crazy and get their refunds on airline tickets and, have the bots say crazy things and embarrass the customers. How do you control the flow and get the best of both worlds?

是的。这其实就像培训人类员工一样。你给他们明确目标和工具规范，然后——这正是大语言模型处理复杂工作流的优势所在。关于什么是工作流、什么是代理行为的讨论很多。

Yeah. So it's really just how you train humans. You give them the specification about what's the goal and these are the tools. And then have the That's the beauty of large language models to handle those messy kind of workflows. So there's a lot of discussion about what's workflow, what's agentic.

工作流是任何可以用代码写出来的步骤化流程。比如洗车——洗车其实是典型的工作流。想想珍珠奶茶的制作，那些是实体工作流，但它们无法处理其他事务。而人类对话则非常杂乱无章。

Workflow is anything you can write it down in code. That's step by step, that's workflow. And car wash, Car wash is actually workflow. You can think about boba tea, milk tea, know, those are physical workflows, but they cannot do other things. For for human conversation, it's very messy.

它是非线性的，对吧？所以这就是代理工作流的用武之地，也是LLM真正擅长的领域。在此基础上，你需要确定性——为此我们引入了测试、模拟以及防护机制，确保系统任何部分变更后，其行为仍符合预期。

It's non linear, right? So it's like, you know, so that's how the agentic workflow come in, that's where LLM is really good at. And then on top of those, so you want to determinism, right? And that's how we're introduced the testing, the simulation, and then the guardrails to make sure that whenever you have a change in any part of the system, the behavior is still expected.

你们也会调整客户模型吗？因为你们还有这个代理辅助产品，所以你们能接触到所有客户对话流。你们是根据这些训练数据调整代理，还是完全由前线工程师在现场重新绘制对话流程？

Do you tune your customers' models too? Because you also have this agent assist product, so you're in the flow of all these customer conversations. Do you tune the agent to that training data or is it completely net new forward deployed engineers on-site mapping out conversations?

是的，我们有一个工具可以从实际的人类对话中提取出对话的蓝图。我认为这样做的好处在于能发现许多未知的未知因素。人们打电话的原因涉及很多话题，有些甚至可能占据了大量通话量却不为人知。一旦掌握了这些，就能深入分析，用ILM识别出57种表达相同意图的不同方式，以及通话流程的各种可能路径。

Yeah, so we have a tool that can map from, you know, what's actually in the human conversation to extract the blueprint of the conversation. Right? So, you know, I think the beauty of that, again, is to discover a lot of unknown unknowns. So there are a lot of topics and there's lot of things that reason people call in, may not even know that may actually contain the call volume, a very large call volume. And then once you have that, you can now look deeper and you can use ILM to do analysis and extract what are 57 different ways that people express the same intent and what are the different ways that the call flow will go.

对吧？然后我们可以进行总结提炼。所有这些都在构建产品，实际上工具会变得更好，前线工程师的工作效率也会大幅提升。此外我们还利用对话中的人为因素。

Right? And then we can summarize and extract that. Right? So all these are building the products and then they in fact, the tooling gets better, the four deployed engineers will just be a lot more efficient. And then there are also other ways we use the human side of conversations.

比如我们会提取访客模型，这是构建模拟系统的基础。模拟是改进AI代理的重要环节，我们相信通过获取真实客户以各种方式（有时很混乱）描述问题的原始数据，提取模型后就能对AI代理进行更好的模拟训练。

For example, we extract the model for the visitors. So that's how you build your simulation, and the simulation is a huge part of improving the AI agent, and we believe that having access to exactly how your real customer humans come in and describe ways in different ways, sometimes it's very messy, You can extract the model and then do better simulation on your AI agent as well.

那么你们用什么方法让LLM真正适应客户环境？是用RAG？提示工程？微调？还是包括强化学习在内的所有方法？

And then what methods do you use to make LLMs really bespoke for customer environments? Like, is it RAG? Is it prompt engineering? Is it fine tuning? Is it all of the above reinforcement learning?

在这些技术中，你们最看好哪种？

Like, are you most optimistic on in terms of techniques?

没错。我们几乎使用所有方法——肯定包括提示工程和RAG（针对简单代理）。但我们仍在探索如何通过观察人类行为和结果，用强化学习提升端到端性能。不过就AI代理本身而言，基础模型已经相当不错了，至少在数字聊天渠道上，只需要充分发挥它的潜力。

Yeah. We use almost everything and to so definitely prompting and then RAG and for for those simpler agents. But we're still exploring, you know, by looking at the human behavior and then the outcomes, how do you use RL to improve this end to end performance? And but, you know, for AI agent by itself, I think the foundation model itself is already pretty pretty good. You just need to get the best out of it, at least for a digital channel for chat.

但对于其他应用场景，微调模型有很大空间，比如摘要生成、句子自动补全这类任务，我认为开源模型的微调领域还有很多潜力可挖。

But for other use cases, there's a lot of opportunity to fine tune the models and to make them, you know, for tasks like summarization, for task like auto completion sentences, and that kind of stuff, I feel like there's a lot of room to extract from the fine tuning open source models.

没错。构建一个成功的演示原型与打造生产就绪的AI系统之间有哪些关键差异？

Yeah. What goes into building a successful fleshy demo versus production ready AI systems?

这是个非常有趣的问题，因为AI的独特之处在于演示与生产环境之间存在巨大鸿沟。以火箭发射为例，演示即生产，生产即演示，根本无法作假。

Yeah. So that's a really interesting question because I think one thing unique about AI is that there's a huge gap between the demo and production. And on one end of spectrum, you have rocket launches. The rocket launches, the demo is the production, and the production is the demo. You cannot fake it.

对吧？但AI就有所不同。我可以举个自动摘要的例子说明。

Right? But for AI, it's it's little different. And, you know, I can just give you example. Right? So auto summary.

自动摘要看似是标配功能，谁都能用ChatGPT生成摘要。但要部署在横跨多洲、拥有2万坐席的呼叫中心，就面临巨大挑战：如何获取实时音频？演示时用Twilio云端方案很简单，但实际50%通话发生在本地环境。

Auto summary feels like a commodity capability that, you know, anyone can use ChatGPT to create auto summary. But in order to deploy in some call centers that today that 20,000 people across multiple continents, call centers, and the challenge huge list of challenges. You know, first, how do you get the real time audio? In in the demo, you can, you know, demo very easy on on Twilio in the cloud, but remember 50% of the conversation happened on premise. Right?

而且这些数据的获取方式可能耗费巨额成本，如何解决？真实呼叫中2万坐席存在大量转接，还有第三方医疗专家加入，所有内容都需要转录和摘要。

And then and then sometime, you know, the how they access that will cause you a lot of money as well. And then how do you go around that? And then in the real call, 20,000 agent calls, there are transfers. There are lot of transfers, and then there are third party third callers that come in as health care specialists. All that need to be transcribed and summarized.

有时通话长达三四小时超出服务窗口，如何处理？背景噪音怎么办？不同核心原因需要不同模板，这些关键信息必须准确提取，不容遗漏。

And sometimes the conversation goes so long, how do you handle like three hour, four hour calls that go beyond the contact window, right? And then things like, is there a background noise? And the thing things like, you know, for different core reasons, there can be different templates. You really, really want to extract these type of information. You cannot miss that.

你如何确保几乎100%的时间都能做到这一点？顺便问一下，你们如何处理个人信息？不能让个人信息处于不安全状态。另外，如果与跨洲跨国银行或医疗保健提供商合作，你们如何处理数据驻留问题？所有这些都变成了额外要求，使得我们原本认为高度标准化的事情——比如呼叫中心业务——在实际操作中变得异常复杂。

How do you make sure you do that almost a 100% of time? And by the way, how do you handle PIs? And then you cannot have the personal information unrest. And then by the way, how do you handle data residency if you're talking to a multi continental, multinational bank or a healthcare provider. So all these have become additional requirements that make something that we feel very commoditized, like, know, out of summary, very, very much harder to do in actually a contact center.

这就是为什么这类公司需要一位具有产品思维的CEO。

And that's why you need a product minded chief executive officer for one of these companies.

完全同意。这也正是所有痛苦和价值都存在于最后一公里的原因。价值就在于应用层。

Absolutely. And this is also why all the pain and all the value is in the last mile. This why the value is in the application layer.

是的，我倾向于同意这个观点。

Yeah. Tend to agree with that.

好的。和我们聊聊未来吧。如果一切顺利会怎样？这对Creston意味着什么？对世界又意味着什么？

Yeah. Talk to us about the future. What happens if everything goes right? What does that mean for Creston? What does that mean for the world?

我认为AI会像之前的任何技术（比如电力）一样消失。它会融入工作流程中，二三十年后，人们甚至不会意识到自己可能正在与AI对话，或是得到AI辅助的人类服务。让我特别兴奋的是：如今企业面对客户时就像有多重人格——销售或营销阶段他们会非常积极地联系你。

I think that AI will just like any technology before it, like electricity, it would disappear. It would disappear into workflows, and I think, you know, twenty, thirty years later, no one will realize that they may actually talking to AI or is a human assisted by AI. There's one thing I'm really excited about is that today, if you think about a business, right, and they feel like multiple personality to the customer. So in the sales phase or the marketing phase, they really, really want to talk to you. They call you very, very aggressively.

但当你签约成为客户后，面对的却是完全不同的态度。你要和客服部门打交道，他们常用'分级防御''问题转移'这类术语，而几天前他们还在热情争取你这个客户。更讽刺的是，即使你在客服热线长谈并提供了大量反馈，两周后其他部门还会来问：'您有什么反馈？'

And once you sign up and become a customer, you dealing with entire different personality. Right? And you're dealing with service departments and they use tend to use the terms like tier defense, deflection, and to just handle, you know, to refer to the exact person that they were courting just a few days ago. And then even if you have a long conversation on the customer support line and share a lot of feedback, two weeks later, another departments will come in. What's your feedback?

你对这次调查有什么看法？对我们的业务呢？感觉这些内容之间缺乏连贯性，对吧？而我认为AI代理可以让整个体验变成贯穿客户旅程的持续、长期的对话。大语言模型正是实现这一点的完美工具。

How about you feel about this survey? To our business? Feel like these are really disconnected, right? And I do feel like AI agents can make this entire experience a continuous, long going conversation throughout the entire customer journey. And LLM is a perfect tool to do that.

这将真正带来前所未有的个性化水平和客户体验水平。

And that will really bring the level of personalization, the level of customer experience that wasn't possible before.

是的。你之前提到的稀缺思维与丰盈思维的对比让我印象深刻。如果将丰盈思维应用于将大语言模型引入这一领域，企业客户沟通和应用程序体验能实现多大的进化。谢谢Ping，也谢谢Doug今天参与我们的讨论。

Yeah. The point that really stuck with me that you said earlier was about kind of the scarcity versus the abundance mindset. And how much can business to customer communications really evolve and app experiences really evolve if you take the abundance mindset to bringing LLMs into this field. Thank you, Ping. Thank you, Doug, for joining us today.

我非常喜欢这次对话。

I love this conversation.

谢谢。感谢邀请我们参加。

Thank you. Thank you for having us.