AI模型与实际价值的鸿沟
中文翻译： GPT 曾是一个非常流行的术语，用来描述许多早期的AI应用公司。我认为人们不理解的是，与这些模型合作非常困难。出厂能力与为最终企业买家展示价值所需的实际能力是不同的。在基础模型和最终客户之间存在着太多需要完成的工作。而且我认为很多人低估了需要完成的工作量。人们脑海中存在着许多知识，这些知识并非写在纸上，它们无法被模型消除。你只需要有人坐下来与客户交谈，理解他们想做什么，然后将这些需求映射出来。只有初创公司的创始人或员工会去客户现场，花时间真正学习这些东西。因此，我强烈鼓励那些正在构建企业级AI的早期创始人飞到客户那里，坐在他们身边，真正深入理解，因为你能够提供一种高度聚焦的解决方案，这为你带来了巨大的杠杆作用，让你确切知道需要为他们构建什么。能够以他们完全符合他们需求的非常具体的方式构建你的产品。我认为这非常有价值。
Original English
GPT was a very porative term used to describe a lot of the early AI application companies. I think what people don't understand is that it's very difficult to work with these models. The capabilities out of the box are not the same as the capabilities needed to actually show value to an end enterprise buyer. And there's so much work between the base model and the end client that needs to get done. And I think a lot of people underestimated just how much work there needed to be done. There's so much knowledge that exists in people's heads that are not on paper. they're not ingestible by the models that you just need someone to sit down and talk to the customer and understand what it is they're trying to do and then map that out and the only people who will understand it are the founders or the employees of startups who go to the customer site and take the time to actually learn these things and so I highly encourage early stage founders who are building an enterprise AI to fly to their customer to sit next to them to really understand to the depth that you can being the industry focused solution gives you a ton of leverage and knowing exactly what you need to build for them. Being able to build your product in a very specific way that works exactly for their needs. I think there's just a lot of value in doing.投资经验与行业洞察
中文翻译： 我是 Kimberly Tan，我在 Andrea Horowitz 担任投资合伙人。我在这家公司已经超过六年了。所有早期软件投资今天都非常关注应用AI，我有幸与许多公司合作，包括 Decagon, Preparedmem 和 Solar。我23岁时加入时对科技世界不太熟悉，所以头几个月非常困难，我只是试着学习我能学到的所有关于风险投资的一切。在行业经验非常有限的情况下，我阅读了所有经典书籍，在每一次会议上我都会写下他们说的我没理解的每一句话，然后在晚上查阅，并从地面上的创始人告诉我哪些趋势可能在未来，因为他们最了解，他们看到了别人看不到的东西。在2020、2021年，许多这些创始人兼建设者告诉我，你不能理解像AI这样会渗透到一切的东西，你必须理解AI。我认为我们只是有机地看到了AI将改变我们已经花费时间投入的许多领域，并真正决定投入更多时间作为结果。
Original English
I'm Kimberly Tan. I'm an investing partner here at Andrea Horowitz. I've been at the firm now for over 6 years. all early stage BB software investing today focused a lot on applied AI and I've had the privilege of working with a lot of companies including decagon prepared mem and solar I joined in when I was 23 years old and not being very familiar with the world of tech so it's actually very hard in the first couple months I just tried to learn everything I could about venture and having had very little exposure to the industry I read all the classic books people tell you to read about venture in every single meeting I would write down every single thing that they said that I didn't understand and then look it up that night and I draw a lot of inspiration about what trends might be on the horizon from what founders on the ground are telling me cuz they know best um they're seeing things that nobody else is seeing. In 2020 2021 a lot of these founders and builders are telling me you don't understand like AI is going to be in everything like you guys have to understand AI. I think we just saw very organically how AI was going to change a lot of the fields that we were already spending time in and really decided to spend more time as a result.投资AI的核心逻辑
中文翻译： 我投资AI的一个非常坚定的信念是，一个花哨的演示和一个真正的生产应用之间存在巨大的鸿沟。这对于许多原因都是如此。主要原因在于，构建一个真正好的AI产品非常困难。因此，当你确切知道底层数据源是什么时，展示一个有趣的演示会更容易。你知道你想要产生的结果。但构建AI产品实际上非常困难，因为AI本质上是非确定性的。所以，知道95%的时间它可能会做某事，但5%的时间我做的是完全不同的事情。这与100%确定性的企业软件的构建范式完全不同。你点击这个按钮，这就会发生。因此，它需要一种非常不同的肌肉和非常不同的构建方式。这有几个影响。首先，我认为这意味着看到产品在生产中实际运行非常非常重要，这个产品将真正做成什么，解决你打算解决的问题，以一种演示无法展示的方式解决它，因为它是非确定性的。这也意味着需要更多的教育、入职培训和实施，才能确保这个产品能做他们想要的东西。所以我们今天看到的一个非常大的趋势，在AI浪潮之前并非必然存在，是前瞻性部署。我认为很多人开始谈论的。你将有某人去客户那边，实际帮助设置这些AI产品。这确保了它能集成到正确的解决方案中，它具有正确的商业背景。它理解你做什么以及不做什么的护栏，并非常认真地对待最后一步，以确保他们的产品能提供价值。我认为这比以往任何时候都更重要。垂直AI或应用AI服务于非常特定的行业，无论是物流还是医疗保健等，我认为是今天构建最有前景的领域。它需要大量工作，将行业的规则、法规、合规性、一般商业逻辑、文化转化为AI代理可以与之对抗的方式。
Original English
One of my like very strong beliefs about investing in AI is that there's a huge gap between a fancy demo and real production application. And that's true for a number of reasons. Primary of which is that it's really hard to build a really good AI product. And so it's easier to show an interesting demo when you know exactly the underlying data source is. You know exactly what the outcome you want to produce. But it's actually very hard to build AI products because AI is non-deterministic by nature. And so knowing something that 95% of the time might do this thing, but 5% of the time I do something totally different. It's just a totally different paradigm of building than enterprise software which is 100% deterministic. You click this button, this thing will happen. And so it requires a very different muscle and a very different way of building products. That has several implications. First, I think it means that seeing a product actually work in production is very very important that this product will actually do and solve the problem that you're intending it to solve in a way that a demo just can't really show anymore because it's nondeterministic. And it also means that there's a lot more education and onboarding and implementation that is needed to actually be able to make sure that this product will do what they want. And so what we're seeing today is a really big trend that wasn't necessarily true prior to this wave of AI is the forward deploy motion that I think a lot of people have started talking about. Well, you'll have somebody who will go to the client side and actually help set up these AI products. And that makes sure that it it integrates into the right solutions. It has the right business context. it understands the guardrails of what you do and do not want to do and just take that last mile very very seriously to actually make sure that their product delivers value and I think that's become more important than ever today vertical AI or applied AI that serves a very specific industry whether it's logistics healthcare etc I think are one of the most fruitful areas to build in today it requires a lot of work to translate an industry's rules regulations compliance just general business logic culture into code in a way that an AI agent can actually do something against.复杂业务背景的理解
中文翻译： 我认为第二个原因是企业拥有很多业务背景，这无法通过一个提示词直接体现出来。不仅是因为人们在提示模型方面很差，而且我认为写一个好的提示词实际上非常困难，还因为人们脑海中存在着许多书面上的知识，这些知识无法被模型消除。你只需要有人坐下来与客户交谈，理解他们想做什么，然后将这些需求映射出来。那种工作不是模型可以开箱即用的。只有初创公司的创始人或员工会去客户现场，花时间真正学习这些东西。
Original English
I think what people don't understand is that it's very difficult to work with these models. The capabilities out of the box are not the same as the capabilities needed to actually show value to an end enterprise buyer. And so there's so much work between the base model and the end client that needs to get done. And I think a lot of people underestimated just how much work there needed to be done. You don't just call the most advanced model to solve everything because there's obviously it's probably has more latency. It's probably much more expensive. It's probably overkill for what you actually need. And so a more sophisticated agent query understands the intent of a query that comes in and knows which model to route it to. It knows which models are best at which things, which has the best latency, cost effectiveness trade-off and it's actually a very sophisticated chaining of many models together in order to get to your actual output. I think a second reason is enterprises have a lot of business context that is not immediately apparent through one prompt to a model. Not only because people are pretty bad at prompting models and I think it's actually very difficult to write a good prompt but also because there's so much knowledge that exists in people's heads that are not on paper. They're not ingestible by the models that you just need someone to sit down and talk to the customer and understand what it is they're trying to do and then map that out. That sort of work is not something that a model can do out of the box. The only people who will understand it are the founders or the employees of startups who go to the customer site and take the time to actually learn these things.垂直AI的护城河
中文翻译： 我认为 Prepared 是一个不可思议的故事。它是一个用于紧急响应的AI助手平台。所以，对于911中心来说，它是一个垂直应用AI公司的完美例子。他们比任何人都更了解911市场。CEO认识这个行业的每个人，每个人都认识他。他去参加所有会议。人们喜欢他。他有大量的客户同理心，所以他以一种其他人没有的方式了解这些客户的需求，并可以构建专门为该市场设计的AI解决方案。我并不认为911中心在历史上以成为不可思议的软件买家而闻名，但AI提供了如此差异化的价值，人们可以理解，如果你能帮助分流这些电话，了解什么是紧急的，什么不是紧急的。所有这些都汇集起来，就是能够提供你的成果，即让需要帮助的人更快得到帮助。他们理解价值提示，因此“Prepared”的市场需求很大，而垂直行业解决方案为你提供了巨大的杠杆作用，让你确切知道需要为他们构建什么。这对我们来说是一种持久的护城河，我认为很多其他行业可能没有这么多。我们许多表现得非常好的投资，都选择了一个非常具体的行业，成为该行业的首要AI解决方案。
Original English
I think Prepared is such an incredible story. They're an AI assistant platform for emergency response. So, literally for 911 centers, it's a perfect example of like a vertical applied AI company. They knew the 911 one market better than anybody. The CEO knows everybody in this industry and everybody knows him. He goes to all the conferences. People love him. He has a ton of customer empathy and so he knew the needs of these customers in a way that a lot of other people didn't and could build purpose-built AI solutions that worked just for that market. I don't think 911 centers were particularly known for being incredible software buyers historically, but AI just provides such differentiated value and that people can understand, you know, if if you can help triage these calls to know what is emergency and what is not emergency. All those things add up to being able to deliver your outcome which is getting a person help who needs help faster. They understand that value prompt and so there was a lot of market pull for prepared and being the industry focused solution gives you a ton of leverage in knowing exactly what you need to build for them. To us that's an enduring moat in a way that I think a lot of other sectors potentially don't have as much. A lot of our investments who have been doing incredibly well have taken a very specific industry and just been the premier AI solution for that industry.投资的风险管理
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Decagon的创始故事是我认为作为投资者见证的最幸运的事情之一。我们在他们有 Decagon 这个名字之前就一直在那里，在他们有想法之前就一直在那里，看着他们从零开始建立公司。他们从一开始就非常明确。他们想构建企业级AI，因为他们认为企业有明确的需求，但很多企业不知道如何真正获得那种价值，他们认为他们的技能集非常适合构建非常好的AI产品，但他们的商业本能告诉他们他们非常擅长做这种企业销售，所以他们会从一家公司到另一家公司谈，在当时很多技术驱动的公司那里，只是问他们他们最大的痛点是什么，从某种意义上说非常直接，就像你期望人们会做什么一样，他们会问他们最大的问题>>他们会问他们ROI是多少，而且客户一直告诉他们支持是他们最大的问题，如果你能解决它，他们会为它支付很多钱。他们就像我们有很多做这件事的人。消费者总是对支持渠道很生气。你知道，想象一下你每次给某人咨询客户支持问题的时候。人们总是很生气。所以他们只是知道AI将是解决这个问题的非常好的解决方案，而且他们可以构建一个解决它的方案。所以看着他们从只是迭代，没有确切知道自己想做什么，非常快地找到了这个世界级的产品，然后几乎是瞬间完成了产品市场契合。你可以构建一个很棒的解决方案，但你仍然需要找到有人购买它。我认为很多技术人员理解技术的价值，他们理解为什么它很棒，为什么会有ROI。但从商业角度来看，如果你不能向客户解释它，他们看不到价值，你又不能以他们满意的方式实施它。人们需要非常聪明地设计他们的试点，使其能相对快速进入生产并展示一些明确的ROI指标。我认为大家今天谈论的很多企业AI的大事是交付AI解决方案的实际ROI是多少。在编码和支持等某些类别中，我认为人们可以直观地理解ROI，因为你看到你的编码员生产得更多，或者定量地，因为你可以看到对于支持来说，你的解决率提高了，你的NPS分数提高了。Decagon最惊人的事情之一是，它是今天少数我认为有非常明确可量化ROI用例之一。市场清楚地知道AI将24/7响应。不再有等待时间，不再有等待时间。你的seesat分数会提高。你会回答更多的工单。它会更便宜。所以，它就像一个清晰的、高ROI用例。但我想在很多其他类别中，人们正在构建AI解决方案，自动化流程的一部分，但不是整个流程，或者它们更多是增强解决方案，展示你提供的明确价值很困难。这一直是许多AI公司面临的挑战。所以我强烈敦促每个人不仅要确保你拥有组织中实际需要的全部赞助，以便你为成功做好准备，然后也要有一个非常清晰、可识别的ROI指标，你计划在试点结束时展示，以便向人们解释为什么他们应该采用你的解决方案。
Original English
Enjoying this one? It's already an article meet EO magazine where you can save quote and share your favorite lines with others. We publish every story on this channel in writing, plus deep dives you won't find here. EO stories now in your inbox. Subscribe at eomag.io. The founding story of Decagon is I think one of the most fortunate things as an investor to have gotten to witness. We were there from the early days before they had the name of DecaGon, before they even had an idea and watched them build the company from the ground up. They were very clear from the beginning. they wanted to build in enterprise AI because they thought there was clear demand in the enterprise but a lot of those enterprises don't know how to actually get that value and they thought that their skill sets were particularly suited to building very good AI products but also their commercial instincts told them that they would be very good at actually doing this enterprise sale and so they would go company to company talk to at the time a lot of technative companies and just ask them what their biggest pain points were in some sense very straightforward like exactly what you would expect people to do where they would just ask them for their biggest problems >> They would ask them what is the ROI and customers consistently told them that support was their biggest problem and that if you could solve that they would pay a lot of money for it. They were like we have so many people who do this. Consumers are always mad at support channels. You know like imagine all the times that you call somebody for some customer support query. People were always angry. And so they just knew that AI would be a very good solution to this problem and that they could build a solution that solved it. And so it's been a really incredible journey to watch them from just iterating around having no idea exactly what they wanted to do to very quickly landing on this very quickly defining a worldclass product and then getting product market fit after they did that almost instantaneously. You can really build an excellent solution but you still need to find someone who will buy it. And I think a lot of people who are technologists, they understand the value of the technology and they understand why it's amazing and why there's going to be ROI. But that doesn't matter from a business standpoint if you can't explain it to a customer and they can't see the value and you can't implement it in a way where they are happy about the product. People need to be very smart about how they design their pilot such that it can get into production relatively quickly and show some clear ROI metric. And I think one of the big things that everyone's talking about enterprise AI today is what's the actual ROI on delivering an AI solution. And in certain categories like in coding and support, I think people understand the ROI either intuitively cuz you see your coders producing more or quantitatively because you can see for support that your resolution rate has gone up and your NPS score has gone up. One of the most amazing things about Deacon is it's one of the only use cases today where I think there's actually very clear quantifiable ROI. The market clearly understands that AI will respond 24/7. No more hold times, no more wait times. Your seesat score will go up. You'll answer more tickets. It'll be cheaper. So, it's just like a clear clear high ROI use case. But I think in a lot of other categories where people are building AI solutions that automate one part of a flow but not the whole flow or they're more of an augmentation solution, it's hard to really show what the clear value you're delivering is. And I think that's been a challenge for a lot of AI companies today. And so I would strongly urge everyone to not only make sure you have all the sponsorship you actually need in an organization so that you are actually set up for success, then to also have a very clear identifiable ROI metric that you're you plan to point to at the end of the pilot to actually be able to explain to people why they should adopt your solution.自动化与人类角色的平衡
中文翻译： 在许多大型企业的后办公室中存在大量的日常手动工作，比如数据录入、索赔处理等。所以我们认为有巨大的机会自动化大量的这些后办公室的日常琐事。所以我们非常兴奋地投资于一家名为 Solo 的公司。他们本质上允许那些对流程有背景的业务用户，这些背景在他们脑海中很难写下来。它允许他们记录自己的流程，然后 Sol 的代理自动化框架将以上下文理解该流程。它会理解这是登录步骤，它会理解这是数据提取步骤。然后我们可以构建一个可以动态处理这些解决方案的机器人。那些以前人们必须做的事情，看起来很琐碎和乏味，希望很快AI就能接管，然后人们可以去做更多更具战略性的工作，这为他们工作的地方带来了更长期的价值。
Original English
There's a lot of mundane manual work that happens in back offices of many large enterprises today. to think data entry, claims processing, things like that. And so we thought there was a huge opportunity to actually be able to automate a lot of this back office mundane work. And so we were very excited to invest in a company called Solo. They essentially allow the business users who have the context on the process, which is in their heads very hard to get out on paper. It allows them to record their own process and then Sol's agentic automation framework contextually will understand the process. It'll understand that this is a login step. It'll understand that this is a data extraction step. And we'll be able to build a bot that can dynamically handle those solutions. Things that seem mundane and tedious that people had to do before, hopefully soon AI will be able to take that over and then those people can work on much more strategic work um that is much more long-term value recreative to the businesses they work at.自动化深度的维度
中文翻译： 自动化一个支持工单与完全自动化之间存在一个很大的区别，这取决于职业的复杂性。例如，完全自动化一个律师，完全自动化一个医生，完全自动化一个工程师。所以思考它的一个维度是，你正在投入的时间领域是什么，以及从技术角度能将解决方案自动化到什么程度。我认为第二个维度是，存在很多文化、社会、监管的动态，允许你完全自动化某事或不完全自动化。例如，也许律师可以处理他们今天做的大部分相对直接的法律工作。但到最后，你仍然需要律师来签署，因为这与责任相关。所以今天很多工作，我认为你仍然需要一个人类在那里。而且我认为对于许多人来说，这是一个重要的步骤，并且实际上获得信任AI正在做它想做的事情。所以在大多数情况下，我认为一定程度的人类监督很重要。即使在支持案例中，我也会说它是更直接的完全替代解决方案之一，你仍然有可以升级到人类代理的途径，当你需要时，而且仍然有人类经理在监督AI代理，以确保它正在做它所预期的工作。我认为这会长期保持动态。
Original English
There's a big difference between automating a support ticket which I think can be done relatively end to end today depending on the complexity of the career versus fully automating let's say a lawyer fully automating a doctor fully automating an engineer. So one dimension to think about it is just what is the actual domain that you're spending time in and how fully can you actually automate a solution from the technical standpoint. I think the second dimension then to look at is there's a lot of cultural, social, regulatory dynamics that allow you to fully automate something or not. So for example, maybe a lawyer, you can do a bulk of the work they do today for relatively straightforward legal work. You still need a lawyer to sign off on it at the end of the day because there's liability associated with it. And so a lot of work today, I think you still want a human there. And I also think that for many people is an important step. and actually gaining trust that the AI is doing what it intends to do. So in most cases, I think some level of human oversight is important. Even in the support case, which I would say is one of the more straightforward fully replacing solutions, there are still paths to be able to escalate to a human agent when you need and there are still human managers who are overseeing the AI agents to make sure that it's doing what is intended. And I think that dynamic will probably be true for quite a long time.风险投资的长期视角
中文翻译： 在风险投资中，你必须明白工作是有风险的。你正在投资非常早期的公司，在很多时候市场仍然与创始人处于不确定的状态，创始人有漫长的建设之路。所以存在风险，LP在管理这笔资本时负有很大责任，确保你能做出最好的决定，然后引导你合作的公司走向它们能达到的最佳结果。但在日常生活中，你不会想太多，因为日常就是你和创始人一起工作。你遇到你想要见的人，然后你知道当你进行投资时，很多时候结果的分布可能不是最好的结果，但你只是必须接受这一点。而且与那些表现得非常好的公司和创始人合作，以及与那些可能正在经历艰难时刻的创始人合作，确保你也在他们身边，是有很多价值的。所以日常上，我不会太在意数字。我只关注你与一家公司合作时做出的每一个个人决策，然后尽你所能帮助他们引导到他们期望的结果。
Original English
In venture you have to understand that the job has risks. You're investing in very early stage companies in often times markets that are still nent with founders who have a long road to build. And so there is risk involved and there's a lot of responsibility in managing that amount of capital for LPs and making sure that you can make the best decisions you can and then steer the companies you partner with to the best outcomes they can reach. But you know on a day-to-day you try not to think about it too much cuz on a day-to-day it's just you work with the founders you work with. You meet the folks that you want to meet and then you know that when you make an investment many times the distribution of outcomes it may not be the best outcome possible but you just have to know that you have to make peace with that. And there's a lot of value in not only working with the companies and the founders that are doing incredibly well like beyond what you could have even hoped but also working with the founders who maybe are going through a rough patch which everyone goes through at one point and making sure that you're there for them too. So on a day-to-day, I would say I don't think about the number as much. I just think about each individual decision that you make when you partner with a company and then helping them the best you can to steer them to whatever outcome they're hoping for.竞争中的专注力
中文翻译： 公司需要很长时间。有些公司立即开始工作，然后他们会遇到挑战。有些公司需要很长时间才能找到火花，然后变得非常出色。还有一些公司则需要多年的挣扎。在那些时刻，我认为很多创始人可能需要他们投资者的战术帮助，我们也会提供。你知道，我一直在为人才做客户介绍。我们帮助他们思考他们的跑道和现金余额等，但我想很多时候他们真正需要的是一位有耐心的投资者，一位理解这是一段漫长旅程的人。所以我们经常有公司在任何时候都经历困难时期。而仅仅知道他们可以打电话给我们，可以诚实，可以告诉我们公司里到底发生了什么，知道我们站在同一战线上，我们会尽力帮助他们。我们不会指责他们。显然，这些创始人正在尽他们所能。我花了很多时间希望并试图与我许多投资组合创始人建立友谊，而且我们仍然是投资者，但就你与他们之间有真正的个人关系，他们知道他们可以给你打电话，而且我许多投资组合公司在他们公司建设过程中曾在我非常紧张的时刻给我打电话。有时在业务中会有一些重大的转折点，你知道，所以桌上会有一些提议，也许有一个非常重要的执行层正在经历一些事情，也许一轮融资没有按预期进行。我认为在那些时刻，有时间采取很多行动并制定策略，思考什么才是正确的。但我认为很多时候，像那些创始人，在那些时刻，你不想做出任何在当下热度下过于鲁莽的决定。你不想做出任何不可逆转的决定。所以我们经常会要求创始人思考一下那一天。很多时候，比如我亲自去见过创始人。我有一家投资组合公司在某个时间经历了一轮相对困难的融资。所以我们在周五早上6点就在办公室做最后一分钟的推介准备。我认为在那些时刻，仅仅知道你将为他们到场并关注公司的最佳利益，这真的很有意义。我们现在处于一个非常激烈的生态系统中。每个人都知道AI提供了很多价值，每个人都知道有很多机会可以去追逐，你可能会有先发优势。也许你更早，也许你更早，但如果你不踩油门，而是继续保持势头并继续构建，世界可能会移动得太快。所以我真的认为现在是真正需要找到更好的术语来锁定并保持超级专注的时候，因为我认为在这一时期会建立起很多伟大的公司。但那些真正成功的公司将拥有对客户非常有同理心的创始人，他们将是超级技术性和AI原生，并且也会比任何其他人更快、更努力地工作。