构建开源AI革命（与Hugging Face CEO克莱姆·德朗格对话）

克莱姆·德隆，欢迎来到ACQ2。

Clem DeLong, welcome to ACQ2.

感谢邀请。

Thanks for having me.

很荣幸能邀请您来。过去几年我们听闻了许多关于Hugging Face的事迹，此刻能直接与您探讨这家公司再合适不过了。

It's a pleasure to have you here. We have heard so much about Hugging Face over the last few years. It just feels appropriate in this moment to talk to you about the company directly.

我认为这是人工智能的关键时刻，而Hugging Face有幸能站在这个浪潮的中心。非常兴奋能与大家分享我们的所见所闻。

I feel like at the very critical time for AI, and with Fucking Face, we have the pleasure and the honor to be at the center of it. So excited to be able to share some of the things that we're seeing.

我想听众们会好奇本期节目的主题——我们将以零门槛的方式展开：无需AI背景知识，听完后您将清晰理解开源AI与封闭生态的区别、各自的权衡取舍及优势，并通过Hugging Face的故事呈现。那么您在生态中扮演什么角色？合作对象有哪些？又有哪些不合作的？

I think the listeners who are tuning into this and saying, what is this episode going to be about? We want to frame it as you should come in and you don't need to know anything about AI, and you should walk out with a pretty clear understanding of open source AI, the more closed ecosystem, what is the difference between the two, what are the trade offs, what are the virtues of each one, and we're gonna tell it through the Hugging Face story. So what role do you play in the ecosystem? Who do you work with? Who do you not?

考虑到贵公司名称的特殊性，这个项目是如何从看似不可能的地方崛起的？我们将逆向追溯。就当下而言，您如何定义Hugging Face？

How did this thing spring up out of quite an unlikely place, given the name of your company? We'll kind of work our way backwards. At this moment in time today, how do you describe what Hugging Face is?

Hugging Face很幸运地成为了AI开发者的首选平台。这些开发者某种程度上像是新时代的软件工程师——过去的技术范式是通过编写代码（比如百万行代码打造出Facebook、Google等日常产品）来构建技术，而如今则是通过训练模型、使用数据集和开发AI应用来创造技术。

So Hugging Face has been lucky to become the number one platform for AI builders. AI builders are kind of like the new software engineers, in a way. Like in the previous paradigm of technology, the way you would build technology was by writing code. You would write like a million lines of code and that would create a product like a Facebook, like a Google or all the products that we use in our day to day life. Now, today, the way that you create technology is by training models, using data sets and building AI apps.

如今大多数人都在使用Hugging Face平台寻找模型、数据集并构建应用。我们每天有超过500万AI开发者使用该平台进行这些工作。

Most of the people that do that today are using the Hugging Face platform to find models, find data sets and build apps. We have over 5,000,000 AI builders that are using the platform every day to do that.

Hugging Face周围的生态系统在很多方面让我想起2010年2月Web2.0时代，那时大家都在发布RESTful API，突然就能把无数公司的服务像雏菊花环一样串联起来

The ecosystem around Hugging Face in many ways reminds me of the 02/2010 era of the Web two point o sort of RESTful APIs that everybody was publishing, and you could suddenly daisy chain together a million different companies

混搭应用。

Mashups.

对，就是这类API混搭。感觉你们推动的运动至少与那个时代存在某种松散的类比。用这些更开放、灵活的构建模块，我们能创造出什么？

Services into yeah. That this sort of like API mashups. It kind of feels like there's a loose analogy to at least the movement that you're on is similar to that one. What can we create with a bunch of these sort of more open, flexible building blocks?

没错，这非常令人兴奋，因为它正在取代部分旧有功能。现在你开始看到用AI构建的搜索网络。但同时它也在赋能新的应用场景，释放了过去在某些极端情况下不可能实现的新能力。

Yeah. It's super exciting because it's replacing some of the previous capabilities. Now you're starting to see search networks being built with AI. But at the same time, it's empowering new use cases. It's unlocking new capabilities that weren't possible before to some extremes, right?

比如有人在讨论超级智能、通用人工智能这些我们过去根本未曾设想过的全新事物。所以我们正处在这个技术开始追赶应用场景的奇妙时刻，目睹着无数前所未有的新事物涌现。

Like some people are talking about superintelligence, AGI, completely new things that we weren't even thinking about in the past. So we're at this kind of like very interesting time where the technology is starting to catch up to the use cases and we're seeing the emergence of a million new things that weren't possible before.

很酷。为了让听众理解你们的运营规模，据录制时数据，Hugging Face当前估值45亿美元。投资者包括英伟达、Salesforce、谷歌、亚马逊、英特尔、AMD、高通和IBM，阵容相当豪华。作为公司，你们关注哪些指标来描述开发者当前的使用规模？

That's cool. And just so listeners understand the scale at which you're operating, Hugging Face is currently valued as of recording at 4 and a half billion dollars. Investors include NVIDIA, Salesforce, Google, Amazon, Intel, AMD, Qualcomm, IBM. It's a pretty wild set. What are some metrics that you care about as a company that you can sort of use to describe the scale at which developers are using it today?

我刚才提到，我们有500万AI开发者在使用这个平台，但更有趣的是他们在平台上的使用频率和规模。总体而言，他们在平台上共享了超过300万个模型、数据集和应用程序。其中一些模型你可能知道或听说过，比如LAMMA 3.1，可能听说过用于图像的稳定扩散模型，或者用于音频的Whisper，以及用于图像的Flux。

So I was saying that we have 5,000,000 AI builders using the platform, but more interestingly, I think it's the frequency and volume of usage that they have on the platform. So collectively, they shared over 3,000,000 models, datasets and apps on the platform. So some of these models, you might know them, might have heard of them, like LAMMA 3.1. Maybe you've heard of stable diffusion for image. Maybe you've heard of Whisper for audio or Flux for image.

我们即将突破100万个在平台上共享的公开模型，还有几乎同样数量的未共享模型，这些是企业内部为自身用例私有使用的。

We're going to cross soon 1,000,000 public models that have been shared on the platform and almost as many that have not been shared and that companies are using internally, privately for their use cases.

所以对你们来说，这个类比和模式就像GitHub，只不过针对的是AI模型，对吧？可以公开、开源、对所有人开放，企业也可以使用内部闭源代码库供自己使用，对吗？

So the analogy and model for you guys really is just like GitHub, except for AI models. Right? You can public, open source, open to everybody, and companies can also use internal closed source repositories for their own use, right?

是的。这是一种新范式。AI与传统软件有很大不同，所以不会完全一样。但相似之处在于，我们是这类新技术构建者使用最多的平台。GitHub服务于软件工程师，而我们服务于AI构建者。

Yeah. It's a new paradigm. AI is quite different than traditional software, so it's not going to be exactly the same. But we're similar in the sense that we're the most used platform for this new class of technology builders. For GitHub, was software engineers and for us, it's AI builders.

再补充一点关于使用情况的有趣数据：现在Hugging Face平台上每十秒就有一个模型、数据集或应用程序被创建。我不知道这期播客会持续多久，但等到结束时，平台上又会多出几百个模型、数据集和应用程序。

And to add to kind of like the usage side of things, One interesting metric is now that a model, a dataset, or an app is built every ten seconds on the Hugging Face platform. So I don't know how long this podcast is going to last, but by the end of this podcast, we're going to have a few 100 more models, datasets and apps built on the Hugging Face platform.

继续延伸这个代码库的比喻，除了上传一堆可供所有人查看并可能尝试修改的代码外，还需要存在的东西还包括数据集本身、实际运行应用程序的平台，以及如果你想训练模型也能在Hugging Face上实现的算力平台，对吧？

And to continue to maybe torture the repository comparison, the set of things that need to exist besides I'm gonna upload a pile of code that everyone can see and potentially, you know, attempt to modify. It's also the datasets themselves. It's also a platform to actually run applications, and also a compute platform where if you want to train a model, that is also possible on Hugging Face. Right?

没错。还有一个常被低估的方面是围绕AI构建的协作功能。事实上，AI不是单打独斗能完成的，你需要团队中每个人的帮助，有时还需要公司其他部门甚至领域内其他人的协助。比如对模型、数据集或应用程序进行评论的功能，代码、模型和数据集的版本控制，报告错误，对代码、模型和数据集进行评论和审核。这些是平台上最常用的功能，因为它们能让越来越大的团队共同构建AI。

Yeah. And one additional aspect that sometimes people underestimate is a lot of features around collaboration for building AI. The truth is that you don't build AI by yourself as a single individual, you need the help of everybody in your team, but also sometimes people in other teams in your company, or even people in the field. Things things like the ability to comment on a model, on a dataset, on an app, to version your code, your models, your datasets, to report bugs, to comment and add reviews about your code, your models, your datasets. These are kind of like some of the most used features on the platform because it enables bigger and bigger teams to build AI together.

这正是我们在企业中所观察到的现象。几年前，可能只有一个小团队或五到十人领导公司的AI团队。而现在团队规模已大幅扩张。例如在微软、英伟达和Salesforce，我们已有成千上万的用户同时通过私有和公开渠道使用Hugging Face平台。

And that's something we're seeing at companies, is that a few years ago, maybe there was a small team or five, ten people leading the AI teams at companies. Now it's much bigger teams. So for example, at Microsoft, at NVIDIA, at Salesforce, we have thousands of users using the Hugging Face platform altogether, privately and publicly.

我有许多问题，其中有些是哲学层面的——关于AI未来发展方向，以及AI生态系统的思维模式与以往技术世代有何不同。但要探讨这些，我认为有必要先了解你们的发展历程。2016年，你们共同创立了以Unicode字符'拥抱表情'命名的公司。据我所知，当时这是个主要面向青少年的可对话聊天机器人表情。

So I have a whole bunch of questions, kind of philosophical ones, where AI goes from here, and sort of how the mental model for the AI ecosystem is different than previous generations. But to get there, think it's helpful to understand how you arrived here. So in 2016, you co founded a company named after the Unicode code point hugging face, the emoji. And as far as I can tell, it was an emoji that you could talk to as a chatbot aimed primarily at teenagers. Is

是这样吗？

that right?

没错，完全正确。这是个漫长的旅程。

Yes. Yes. Absolutely correct. It was a long journey.

也就是说，你们最初创立的既不是AI基础设施公司，甚至没有赶上当前这波AI浪潮。

So you started neither an AI infrastructure company nor did you even start in the current era of AI.

是的。但我们确实是基于对AI的热情和激情起步的，尽管当时我们甚至没称之为AI。更多是用机器学习、深度学习这些说法。大约十五年前——现在想来都快这么久了——我很幸运能在巴黎一家名为Mootstocks的初创公司工作，从事计算机视觉的机器学习研究。

No. But we did start based on our excitement and passion for AI, even if we weren't even calling it AI at the time. Right? We were saying more machine learning, deep learning. I was lucky enough, I think it's now almost fifteen years ago, a few years more, to work at a startup in Paris that was called Mootstocks, where we're doing machine learning for computer vision.

那远在AI成为热议话题之前，这段经历让我深刻认识到新技术的潜力，以及我们如何通过AI改变现状。所以当我和联合创始人Julian、Thomas创立Hugging Face时，我们对这个领域充满热情，认为它将开启无数新可能。于是我们选择从兼具科学挑战性和趣味性的对话AI切入，那时正值Siri、Alexa等语音助手兴起之际。

So much before a lot of people were talking about AI, and it kind of made me realize some sort of the potential for the new technology and the way we could change things with AI. So when we started Hugging Face with my co founders, Julian and Thomas, we were super excited about the topic and we were like, Okay, it's going to enable a lot of new things. So let's start with a topic that is both scientifically challenging and fun. And so we started with conversational AI. Were at the time, okay, Siri, Alexa, Daesok.

我们记得电子宠物机Tamagotchi，那是一种有趣的虚拟宠物，你可以和它玩耍。于是我们决定打造一个AI版的电子宠物，就像一个人工智能对话伙伴，能带来愉快的交流体验。这就是我们的初衷。我们为此投入了三年时间，并基于这个想法完成了前两轮融资。

We remember our Tamagotchi, which were these kind of fun virtual pets that you would play with. So let's build an AI Tamagotchi, like an AI conversational AI that would be fun to talk to. And that's what we did. We worked on it for three years. We raised our first two rounds of funding on this idea.

在此特别感谢我们的首批投资人，他们投资的理念与我们如今的形态截然不同。

So shout out to our first investors who invested in a very different idea than what we are today.

你们的早期投资人有哪些？

Who were your early investors?

我们最早的投资方是纽约的Betaworks。

So our earliest investor was Betaworks in New York.

我完全不知道这件事。

I had no idea.

是的。John Borswicke和Matt Hartmann作为我们的首批支持者，在我们还是英语蹩脚、毫无专业背景的法国无名小卒时，就坚定地支持了我们。

Yes. With John Borswicke and Matt Hartmann, who were our first supporters, really backed us when we were like random French dudes with like no specific background or credentials with a broken English.

我猜你们现在是Betaworks投资过的最具价值的公司了吧？或者说...

I assume you're now the most valuable company Betaworks has ever invested in or

是的，更让我们自豪的是，如今这些公司已将最多资金投入我们。因此我们是他们押下的最大赌注。他们给予了极大支持。此外，还有多位极具影响力的天使投资人鼎力相助，比如Richard Socher——you.com创始人，当时还是Salesforce的首席科学家。还有Conways家族通过A Capital（由Ronnie Conway运营）的支持，他们领投了我们后续轮次，以及Ron Conway，他在Fucking Face早期阶段就一直支持我们。

Yes, and more proud of the fact that now with the companies that they invested the most money in. So we're the biggest bets that they've made. They've been extremely supportive. But the support from a bunch of very important, impactful angel investors for us, like Richard Socher, who's the founder of you.com, who was the chief scientist at Salesforce at the time. And then the support of the Conways family with A Capital, run by Ronnie Conway, that led our next rounds, and Ron Conway, who also kind of like supported us throughout the early days of Fucking Face.

太棒了。所以这一切仍然是为了那个'我要和表情符号聊天'的创意？

That's awesome. And so this was all still for the I'm going to chat with an emoji idea.

对，没错。

Yes, Yes.

更准确地说，你们公司成立于2016年。2017年谷歌才发布Transformer论文。当时甚至AI圈内人士都还未预见到LLMs即将成为前沿技术——OpenAI尚未完成重大转型，自然语言处理的最新技术仍局限于基于特定清洗数据集训练的小型模型。是这样吧？

And to put a finer point on it, you started the company in 2016. 2017 is when the transformer paper gets released from Google. So we are not yet to the era of even people in the AI community really knowing LLMs are close to on the forefront. Like, OpenAI hadn't made their big pivot yet, and so the state of the art for natural language processing is still like pretty limited, small models trained on, you know, very particular, well cleaned datasets. Is that right?

没错。但出人意料或幸运的是，这恰恰造就了今天的Hugging Face。因为当时构建对话AI的方式是拼接多个功能各异的模型——需要用一个模型提取文本信息，另一个检测句子意图，再一个生成回答，还有理解情绪关联的模型。因此在Thinking Phase早期，我们就开始思考如何构建一个平台抽象层，能兼容多模型多数据集——毕竟我们希望聊天机器人能谈论天气、体育等海量话题，这需要整合大量不同数据集。

Yeah. And surprisingly or luckily, that's what led to what Hugging Face is today. Because at the time, the way you were doing conversational AI is by stitching a bunch of different models, which would do very different tasks. So you would need one model to extract information from the text, one model to detect the intent of the sentence, one model to generate the answer, one model to understand the emotion linked with the model. And so very early on in the journey of Thinking Phase, we started to think about how do you build a layer, a platform, an abstraction layer that allows you to have multiple models with multiple datasets because we wanted the chatbot to be able to talk about the weather, talk about sports, talk about so many different topics that you needed a bunch of different datasets.

这某种程度上奠定了如今Hugging Face作为海量模型/数据集托管平台的基础。命运真是奇妙——这对听众们也是个绝佳启示：保持灵活、把握机遇的重要性。即便创业三年后（当时我们融资约600万美元），我们仍能彻底转向全新方向。当然我们毫不后悔，但对每位创业者都是宝贵经验：即便融资600万美元三年后，你依然可以转型并找到更优发展路径。

And that was kind of like the foundation to what Hugging Face is today, like this platform to host so many models, so many datasets. So it's a very interesting fate, a very interesting thing. Obviously, it reinforces for people who are listening the importance of being flexible, being opportunistic, and being able to seize kind of like new opportunities even three years in, right? For us it was three years in with maybe $6,000,000 raised, completely changing what we're doing, what we're going after, what we're building. Obviously we don't regret at all, but it's a good good learning for everyone listening that even, like, with $6,000,000 raised three years in, you can still pivot and find kind of, like, a a new direction for your company, and this is for the best.

这些转型讨论是如何开始的？过程怎样？从酝酿到实施花了多长时间？

How did those conversations start? How did they go? How much time did it take to go from talking about it to doing it?

是的。出乎意料的是，转型并没有我们想象的那么困难。这一切始于我们的第三位联合创始人兼首席科学家托马斯的一个倡议。我记得非常清楚，那是在BERT——第一个非常流行的Transformer模型——刚发布的时候。

Yeah. Surprisingly, the transition wasn't as hard as we thought. It all started from an initiative from Thomas, who is our third co founder and our chief scientist. I think it's right at the time when BERT, so the first very popular Transformers models, came out.

那是谷歌的模型吗？

That's Google's model?

是谷歌开源的那个模型。我记得那是个周五，托马斯告诉我们说，谷歌发布了一个新的Transformer模型，非常棒，但可惜是用TensorFlow写的。当时AI领域最流行（现在仍然是）的语言其实是PyTorch。他就说，'我打算这个周末把这个模型移植到PyTorch上。'

Google's model that they open sourced. I think on a Friday, that day, I remember really vividly, Thomas told us like, oh, there's this new transformer model that came out from Google. It's amazing, but it sucks because it's in TensorFlow. And at the time, the most popular language for AI was and still is actually PyTorch. And it was like, Oh, I think I'm going to spend the weekends porting this model into PyTorch.

我和朱利安当时就说，'好吧，如果你周末没别的事可做，那就去折腾吧，玩得开心。'结果周一他就发布了PyTorch版的BERT，还发了推特。我记得那条推文大概获得了一千个赞。我们当时都懵了，心想这是怎么回事？我们这是把互联网搞崩了吗？

And Julian and I were like, okay, yeah, if you don't have anything better to do during your weekend, just have fun, do it. And on Monday, he released PyTorch version of BERT, tweeted about it. And I think his tweet got maybe like a thousand likes. And for us at the time, we're like, what is happening here? We we broke the Internet.

推特一千个赞？太疯狂了。

Thousand Twitter likes? That's insane.

开发者需求在那时候显然更偏向PyTorch。但既然这个模型出自谷歌，他们当然会用TensorFlow实现。他们必须使用自家支持的技术栈。就等着第一个意识到'天啊这东西需要PyTorch版本'的人去收割全网热度了。

The the developer demand is so obviously at that point in time, PyTorch. But since it was born out of Google, of course, we're gonna implement it in TensorFlow. They had to use their own sort of endorsed stack. It's just waiting there for the first person to realize, oh my god, this thing needs to exist in PyTorch to like go and get all the Internet points by doing that.

没错。我想这算是命运或宇宙给我们的又一个礼物，多亏了汤姆的工作我们才能抓住这个机会。之后我们看到市场反响很好，就全力投入其中。大概六个月后，我们告诉投资人：'看这个新平台的采用率和用户量。'

Yeah. Yeah, I guess it's another gift from fate or from the universe to us that we managed to seize, thanks to the work of Tom. And after that, we kind of saw the interest, doubled down on it. And I think six months later, we told our investors, look, this is the adoption. This is the usage that we're getting on on this new new platform.

我们认为需要从一个方向转向另一个方向。幸运的是，他们都给予了极大的支持，正是这种支持促成了我们的转型和最终选择的方向。

We think we need to pivot from one to another. And luckily, they were all super supportive, and that's what led to the pivot and to the direction that we took.

哇。你们是如何从Thomas将BERT从TensorFlow移植到PyTorch这件事，想到‘哦，其实应该为此建立一个平台’这个主意的？

Wow. How did you take Thomas porting BERT from TensorFlow into PyTorch into the idea of, Oh, there should actually be a platform for this?

这个过程非常自然。我们真正遵循了社区的反馈。在首次模型发布后，我们开始收到其他科学家的消息，他们正在构建其他模型，并表示有兴趣将他们的模型加入我们的库。如果我没记错的话，当时有比如ExcelNet，来自Guillaume Lamp——他现在是Mistral的创始人，还有当时来自OpenAI团队的开源GPT-2。

It was very organic. What we did is really followed community feedback. So what happened is after this first model release, we just started to hear from other scientists building other models who expressed interest in adding their models to our library. So I think at the time, it was things like ExcelNet actually coming, if I'm not mistaken, from Guillaume Lamp, who is, like, the founder of Mistral now. There was I think it was GPT two from the OpenAI team at the time, which was open source.

没错，那时候还是OpenAI。

That's right. It used to be OpenAI.

是的。他们告诉我们想加入他们的模型，因为我们确实遵循了社区的反馈。这促使项目从一个单一模型库——最初名为‘Pre-trained PyTorch BERTs’，后来改为‘PyTorch transformers’，再到‘transformers’，最终扩展成了我们现在所见的Hugging Face平台。

Yes. And they told us that they wanted to add their model since we really followed the community feedback on it. And that's what kind of like took it from a single model repository. I think the first name was Pre trained PyTorch BERTs to I think it was PyTorch transformers to transformers, and then it expanded to the Hugging Face platform as we see it now.

这就是你们因此成名的关键——那个Transformers库。你某种程度上成为了这个开源项目的守护者，并围绕它构建了Hugging Face平台，用于托管和促进围绕Transformers的所有社区互动。结果发现，天哪，还有很多人在构建类似我们Transformers库的东西，他们也需要同样的基础设施。

That's the thing you kind of got famous for, that Transformers library. And you were sort of the steward of that open source project, and you sort of constructed the Hugging Face platform around it to sort of host and facilitate all the community interaction on Transformers. And it turned out, oh my gosh, there's a lot of other people who are building something that looks like our Transformers libraries that also want a place for that same infrastructure.

正是如此。同样的过程再次发生。某个时刻，社区用户开始告诉我们：‘我有更大的模型，已经无法继续托管在GitHub上了。’

Exactly. It was the same process. At some point, users in the community started to tell us, oh, I have bigger models. I can't host them on GitHub anymore. Alright.

让我们为此构建一个平台。或者说，我想托管我的数据集，但希望能够搜索这些数据集，看看数据质量好坏？如何过滤数据等等？于是我们开始构建这个平台。几个月后，我们意识到，我们基本上打造了一个类似AI领域的新GitHub。

Let's build a platform for that. Or I want to host my datasets, but I want to be able to search in my datasets to see, you know, is a good data, bad data? How can I filter my data and things like that? So we started to build that. And a few months later, we realized that basically we built kind of like a new GitHub for AI.

因此我们的发展始终以社区驱动为核心，真正遵循社区的反馈。我认为这是我们多年来如此成功的重要原因，也是社区为我们的平台和成功做出巨大贡献的原因。如果没有数百万AI建设者、贡献者分享开源模型、开放数据集、开放应用，通过评论和漏洞修复等方式参与，我们绝不可能取得今天的成就。这是当前成功的主要因素。

So our development has always been very community driven, really following the feedback from the community. And I think that's a big part of the reason why we've been so successful over the years and why the community has contributed so much to our platform and to our success. We couldn't be anywhere close to where we are without the millions of AI builders, contributors that are sharing open models, open datasets, open apps that are contributing with comments, with bug fixes. It's the main reason for success today.

你们以开放著称。我的意思是，你们真正践行这一点。我们实际上会构建社区告诉我们他们想要的产品。在内部，你们有非常开放的政策。推特账号、社交媒体账号，我想所有员工都可以访问，对吧？

You're sort of famously open. I mean, you really embrace this. We literally will build the product that the community tells us they want. Internally, you have a very open policy. The Twitter account, your social media accounts are actually accessible, I think, by all employees, right?

是的。

Yes.

是的。作为开源倡导者，你认为多少开放度是过度的？比如，你们不是DAO组织，不会公开每个人的薪资，我想。你们倾向于开放哪些内容，又认为哪些应该保持专有？

Yes. As someone who is a champion of open source, how much openness is too much openness? Like, you're not a DAO, you don't do the thing where you publish everyone's salaries, I don't think. What do you like to be open versus what do you feel is good that it's proprietary?

我们喜欢做的是为企业提供工具，让他们比没有我们时更开放，但绝不强迫他们。我刚才提到平台上构建的模型、数据集和应用数量。人们不太了解的是，其中一半实际上是私有的，对吧？公司只是内部使用，不分享给他人。我们完全理解这一点，因为不同公司的开放程度不同，我们想提供工具让他们开放愿意开放的部分。

What we like to do is to give tools for companies to be more open than they would be without us, but without forcing them in any way. So I was mentioning the number of models, data sets, and apps that are built on the platform. Something that people don't know as well is that half of them are actually private, right? That companies are just using internally for their own AI systems that they're not sharing. And we're completely fine with that because we understand that some companies build more openly than others, but we want to kind of like provide them tools to open what they feel comfortable opening.

所以有时候分享的可能不是大型模型或庞大数据集，而是一篇研究论文——毕竟对科学而言，开放性比AI领域更为重要。渐进式开放让他们能分享更多，为世界贡献更多，因为我们坚信开放和开源AI、开放科学如同水涨船高，让所有人能构建、理解、获得AI运作的透明度，最终导向更安全的未来。现在很多人讨论通用人工智能（AGI），我对非去中心化的AGI感到极度担忧。

So sometimes it's not, you know, like a big model, it's not big data sets, they can share kind of like a research paper on the platform because obviously openness is even more important for science than it is for AI in general. And progressively it allows them to share more and contribute more to the world because ultimately we believe that openness and open source AI, open science is really kind of like the tides that lift all boats, right? That enable everyone to build, that enable everyone to understand, to get transparency on how AI is working, not working, and ultimately leads to a safer future. It's like a lot of people right now are talking about AGI. I'm incredibly scared of a non decentralized AGI.

要知道，如果只有一家公司或组织能实现通用人工智能（AGI），我认为那才是风险最高的时候。相反，如果我们能让所有人——不仅是私营企业，还包括政策制定者、非营利组织和公民社会——都能接触这项技术，我认为这将创造一个更安全的未来，也是让我更期待的未来。

You know, like if only one company, one organization gets to AGI, I think that's when the risk is the highest. Versus if we can give access to the technology to everyone, not only private companies, but also policymakers, nonprofits, civil society. I think it creates a much safer future and a future I'm much more excited about.

我本来不想问这个，因为这问题听起来太炫酷了，但既然我们聊到AGI就不得不提。你觉得现在的模型正在通往AGI的路上吗？还是说AGI是完全不同的东西，这些模型并非它的垫脚石？

I was going to not go here because it's almost like too much of a shiny question to ask, but we're talking AGI, we have to do it. Do you feel that the models today are on a path to AGI, or do you feel like AGI is something completely separate and these are not stepping stones to it?

我认为它们肯定是AGI的基础构件，毕竟我们正在学习如何打造更好的技术。但与此同时，这项技术本身的名称也造成了某种误解。我们不能因为它叫'人工智能'就让人们联想到科幻、技术奇点之类的东西。实际上在我看来，这只是构建技术的新范式，我宁愿称它为'软件2.0'。

Well, I think they're building blocks for AGI, surely, in the sense that we're learning how to build some better technologies. But I think at the same time, there's some sort of a misconception based on the name of the technology itself. We can't like call it AI and so in people's minds it brings association with sci fi, with like acceleration with singularity. Whereas for me, what I'm seeing on the ground is that it's just a new paradigm to build technology. So I prefer to call it almost software two point zero, right?

就像过去有软件，现在有了软件2.0，我认为未来几年它会持续进步，就像过去几年软件的演进一样。但不会因为我们称之为'人工智能'，就真的会接近《机械战警》那种统治世界的AI系统场景。

Like you had software before, you have software two point zero and I think it will keep improving in the next few years, the way software has kept improving in the past few years. But it's not because we call it AI that it makes it kind of like closer to some sort of Robocop scenario of kind of like an all dominating AI system that is going to take over the world.

感觉确实有两种不同的东西都顶着'AI'的名号。其一是你提到的软件2.0——传统软件让人类能用更少人力实现更大规模，而这个新时代的软件就像打了兴奋剂的版本。现在能快速开发出的应用丰富度令人震惊，是在原有惊人软件范式上的十倍提升。但另一类是通过图灵测试的东西...

It does feel like there's kind of these two different things that masquerade under the same name as AI. One of them is I kinda like software two point o because software gave humans leverage to do more and to scale more with a small set of humans. And this new era of software really feels like it's just that on steroids. The the richness of applications that you can build very quickly is astonishing and is, you know, another 10 x improvement on top of the amazing software paradigms that we had until now. There is a completely separate thing, which is things that pass the Turing test.

当我与某个对象对话时，我确信它是人类，结果却不是。有趣的是这两者都被称为AI，其中一种其实只是给开发者提供的生产力杠杆。

I'm talking to something, and I'm pretty convinced that thing is a human, but it's not. And it is a little bit funny to me that these are both sort of referred to as AI, where one is really just like leverage for builders on how much they can make.

没错。或许也因为我们对第二类技术的预期过高。在我看来，我们终于能造出聊天机器人这件事，并没有那么困难或震撼。

Yes. It's also maybe because we overestimate the second field that you're talking about. To me, it doesn't feel incredibly difficult and incredibly mind blowing that we finally managed to build a chatbot.

你曾以为2016年就能做到，对吧？

You thought you could do it in 2016, right?

是啊。要说有什么让我惊讶的，反而是我们之前居然没能开发出优秀的聊天机器人。对我而言，这甚至可视为过去几十年技术发展的必然结果。有时我们容易遗忘这点，因为过度沉浸于当下——我们对今日进步的惊叹往往胜过对往昔成就的认知。试想首批比人类奔跑更快的车辆。

Yeah. If anything, I'm surprised that we didn't manage to build a good chatbots before. So to me, even that kind of falls into development of the technology for the past few decades. And I think sometimes we forget because we're so entrenched about on today and we are more impressed with progress of today than progress in the past. But imagine the first vehicles that were going faster than humans.

试想第一台信息检索能力远超人类的计算机。试想首次使用谷歌在数秒内获取任何信息的体验。这些都是令人震撼的进步。如今我们视之为理所当然，但它们当年确是划时代的突破。因此我认为，技术仍将延续近年来的发展轨迹持续演进。

Imagine the first computer that can retrieve information much better than humans. Imagine the first time you would go on Google and find any information in a matter of a few seconds. These are all impressive progress. Now we take them for granted, but they were impressive progress. So I think technology continues to progress the way it's been progressing for the past few years.

当然，这些技术的构建者难免会过度宣传并为之兴奋，这很正常。但作为社会整体，保持适度理性很重要——我们要明白技术将持续改进，需要引导其向对人类和社会有益的方向发展。无需担忧聊天机器人会在数月内导致世界末日，一切终将向好。

Obviously, of the builders of these technologies are hyping it, right, and are excited about it, which is normal. But as a society, I think it's good to keep some moderation and understand that the technology will keep improving, that we need to take it into the direction that is positive for us, for society, for humans, and that everything is going to be fine, that we're not going to fall into a doomsday scenario in a few months because of the chatbot.

真有意思。你刚才提到自行车让我想起乔布斯的名言：'计算机是思维的自行车'。这本质上在说技术如同杠杆——就像自行车扩展了人的行走能力，计算机让思维能输出远超本能的内容。而现在的软件2.0版本，简直像是'思维自行车的自行车'。

Fascinating. It's funny, as you were talking, you linked it to the bicycle. I always think back to the Steve Jobs quote, a computer is a bicycle for the mind, which is in many ways saying it's leverage. It's a way for the mind to output way more than it otherwise could have, the way that a bicycle does to someone walking. And it's almost like this software two point o is a bicycle for the bicycle for the mind.

就像是复合型自行车。好了听众们，现在要隆重感谢节目新合作伙伴Koyfin。有趣的是，虽然说是新朋友，其实我使用他们的产品已有多年。

It's like a compounded bicycle. Alright, listeners. Now is a great time to thank a new friend of the show, Koyfin. And it's funny. They're new, but actually I've been using their product for years.

我每期新节目的研究项目都离不开Koyfin。所以当他们提出赞助时，我觉得这简直是天作之合。Koyfin是深受个人投资者和理财顾问喜爱的金融研究工具：个人用户用它进行股票研究、绘制财务图表和追踪投资组合；理财顾问则用它构建模型组合和制作客户提案。

My research project for every single new acquired episode involves Koyfin. So when they reached out to sponsor the show, I thought, well, this is convenient. Indeed. So Koyfin is a financial research tool loved by both individual investors and financial advisers. Individuals use it for stock research, graphing financials, and portfolio tracking, and financial advisers use it to build model portfolios and create client proposals.

他们提供实时市场数据和强大的分析工具。

They have live market data and powerful analytics tools.

所以它有点像彭博终端，只是没有那个天价标签。对吧？

So it's kinda like a Bloomberg terminal except without the huge price tag. Right?

是的，本质上如此。这是个网页应用，完全自助服务。实际上我使用它的头几年里，都没和公司任何人交流过。所以Koyfin是面向更广泛市场的产品，比如所有Acquired节目的听众都会用，而不仅仅是华尔街的投资银行家。

Yes. Essentially. It's a web app, and it's totally self serve. I've actually not talked to anyone at the company for the first few years that I used it. So Koyfin is a product that the broader market, like all acquired listeners, would use, not just Wall Street investment bankers.

我在这里获取我们研究的每家公司的增长率、毛利率、市盈率或收入倍数等数据。你可以通过历史图表或与其他公司对比来观察这些指标的变化。研究像劳力士、玛氏或宜家这样的非上市公司时，我也常用它来查看可比公司，估算这些公司如果上市会值多少钱。他们还有个筛选器，能让你从数千只股票中快速筛选出投资点子。

It's where I pull things like growth rate or gross margins or the PE ratio or revenue multiples for every company we study. And you can compare these things over time with historical graphs or against other companies. It's often what I use when we're studying private companies too, like Rolex or Mars or IKEA, to look at the comparables to estimate what these companies would be worth if they were public. They also have a screener that lets you filter across thousands of stocks so you can quickly surface investment ideas.

没错。总的来说，如果你习惯与数据为伴，在思考投资时就应该能随时调用这些数据。

Yep. So the general idea is if you're someone who's used to living in data, you should have that at your fingertips as you think about investing.

正是如此。它将专业级数据包裹在这些出色的数据可视化图表中。所以如果你想了解当前股价反映了哪些假设，Koyfin就是为你准备的。

Exactly. It's got these great graphs for data visualization wrapped around institutional grade data. So if you wanna understand what assumptions are baked into the stock price today, Koyfin is for you.

我正想说Acquired听众有个很棒的优惠，但其实Koyfin的免费版本已经非常强大了。

I was about to say that acquired listeners have a great offer, but Koyfin's free product is actually already really robust.

这是我多年来一直在使用的。

Is what I was using for years.

我知道。我知道。但对于《Acquired》的听众们，还有你，本，如果你访问koyfin.com/acquired并升级到付费版，首年可享8折优惠。

I know. I know. But indeed, for Acquired listeners and also for you, Ben, if you go to koyfin.com/acquired and you end up upgrading to paid, you'll get 20% off your first year.

感谢Koyfin的支持。网址是k0yfin.com/acquired，或点击节目简介中的链接。

Our thanks to Koyfin. That's k0yfin.com/acquired, or click the link in the show notes.

你几乎见证了AI现代发展的整个历程。过去六七年里，你如何看待开源与闭源的演变？这段时期是否感觉天平明显倾斜了？还是说，哦不，开源与闭源本就一直并存？

You've kinda been there for this whole arc of the modern development of AI. How would you characterize open versus closed over the last, call it, six, seven years that you've been in this? Does it feel like the pendulum has shifted significantly during that time? Or is it like, oh, no. Well, there was always open and closed.

回溯最初，Facebook和谷歌是闭源的，而学术研究社区是开放的。你如何看待这种对比？

You know, you go back to the beginning and, like, well, Facebook and Google were closed, and the academic research community was open. How do you view it?

首先，这场辩论本身有些误导性，因为事实上开源是所有AI的基础。人们常忽略的是，即便是闭源公司也在大量使用开源技术。比如OpenAI、Entropic，它们都在利用开放研究和开源资源。这就像是技术栈的两个不同层级——开源科学是底层基础，而闭源可以构建在这个开源基础之上。但总体来看，这个领域确实比过去更封闭了。

So first, the debate itself is a bit misleading because the truth is that open source is kind of like the foundation to all AI. Something that people forget is even the closed source companies are using open source quite a lot. So like if you think about OpenAI, if you think about Entropic, they're using open research, they're using open source quite a lot. So it's almost kind of like two different layers of the stack, where open source, open science is here, and then you can build kind of like closed source on top of this open source foundation. But I do think if you look at the field in general, that it has become less open than it used to be.

我们讨论过2017到2019年。那时大多数研究都是由学术界公开分享的，对吧？Transformer架构是这样诞生的，BERT也是这样问世的。当时谷歌、OpenAI等机构都公开了大部分AI研究和模型。我认为正是这种开放性和跨领域协作，才造就了我们今天的成就——如果当初一切闭源，进展速度绝不会如此迅猛。

We talked about 2017, 2018, 2019. At that time, most of the research was shared publicly by the research community, right? That's how transformers emerged, that's how BERT emerged. Players like Google, OpenAI at the time were sharing most of their AI research and their models, which in my opinion led to the time that we are now. It's all this openness and this collaborativeness between the fields that led to much faster progress than we would have had if everything was closed source, right?

OpenAI采用Transformer架构，推出了GPT-2、GPT-3，这才有了我们今天的局面。过去几年，也许是两三年间，它变得不那么开放了，或者说更开放了，这取决于你的视角，很可能是因为商业考量开始产生影响。同时我认为，围绕开放与封闭安全性的某些误导性论点，导致了一种奇怪现象——开源和开放科学不再像过去那样受到推崇。

OpenAI took transformers, did GPT-two, GPT-three, and that led to where we are today. For the past few years, maybe two, three years, it became a bit less open, or a lot more open, depending on your point of view, probably because more commercial considerations are starting to play a factor. Also because I think there has been some misleading arguments around the safety of openness against closeness, which leads to something weird where open source and open science is not as celebrated as it used to be.

对，可以展开说说。这些论点是什么？为什么你觉得它们具有误导性？

Yeah, maybe talk about that. What is the argument and why do you feel it is misleading?

很多人强调AI的生存风险，以此论证其不应保持当前的开放程度，声称不分享研究更安全，因为技术具有危险性。

There are a lot of people emphasizing the existential risk of AI to justify the fact that it shouldn't be as open as it is, right, saying that it's better not to share research because it's dangerous.

恶意行为者掌握技术后可能造成危害。

A bad actor gets a hold of this and could do bad things.

正是。这种论调并非首次出现。实际上每个技术周期都是如此。就像书籍曾被视作危险品——不该让所有人接触，对吧？

Exactly. That's not the first time that such things have been used. Actually, in every technology cycle, if you look at it, it's kind of the same. Know, like books are dangerous. They shouldn't be given to everyone, right?

仿佛书籍应该由少数机构管控，需要许可证才能撰写或传播书籍。

Like they should be controlled just by a few organizations. You need a license to write a book, to share a book.

但软件行业似乎从未如此。核能时代确实有过类似情况，但我不记得有人惊呼'软件即服务太危险了'，或是'严防国家行为体获取移动应用'这类论调。

It feels like that's never happened, though, in the, like, software industry. Yes, that happened in the nuclear era, but, like, I don't remember any of this around, like, Oh my god, software as a service, that's terribly dangerous. Or the mobile apps, Ah, make sure state actors don't get ahold of that.

确实如此。AI的发展周期可能更快，从人们完全不了解这项技术到人人皆知。因此它引发了更多恐惧，也给了人们更多操纵和误导的空间。也许名称起了很大作用，对吧？当你称之为‘人工智能’时，远比叫它‘软件’要吓人得多。

Yeah, it's true. Maybe the cycle has been faster with AI between people not knowing about the technology at all to everyone knowing. And so it creates more fears, more ability for people to manipulate and people to kind of like mislead. Maybe the name played a big factor, right? When you call it artificial intelligence, it's much more scary than when you call it software.

即使在过去，世界看待这件事的态度也是‘哦，不过是一群书呆子’。它有自己的社区规范，围绕着开放性，坦率地说，这源自湾区60、70年代的嬉皮士运动。但如今赌注要高得多。

Even back in the day, it was the world viewed what was happening as like, oh, it's a bunch of nerds. Like, there was its own community, and it was the norms of the community were around openness and, you know, really just coming out of the hippie movement in the Bay Area, frankly, in the, you know, 60s and 70s. But now the stakes are way higher.

是的。竞争环境也大不相同。我觉得软件早期时，新公司、新参与者更容易涌现，而现在权力更集中在少数大型科技公司手中。这可能是个因素。对我来说，支持开放最重要的意义在于，它有望赋能成千上万新AI公司的诞生，这非常令人振奋。

Yeah. The competitive environment is quite different too. I feel like the early days of software, I think it was easier for new companies, new actors to emerge than now, where you have much more concentration of power in the hands of a few big technology companies. So that might play a role. For me, one of the most important things in support to openness is that hopefully it's going to empower thousands of new AI companies to be built, which is incredibly exciting.

大公司做了很多好事，在许多方面表现出色。但如果我们能利用软件与AI之间的范式转变来重新洗牌，赋能新一代公司、创始人、CEO和团队成员，让他们在世界舞台上扮演更重要角色，那就太棒了。这将更好地让企业构建的内容与社会面临的挑战和关切保持一致。我对此充满期待。

Big companies are doing a lot of good and they're doing a great job in many aspects. But I think if we can use this changing paradigm between software and AI as a way to redistribute the cards and change things and empower a new generation of companies, of founders, of CEOs, of team members to play a bigger role in the world, it would be great. I think it would align in a way more the challenges and the preoccupations of society with what companies are actually building. So I'm excited to try to do that.

对于未曾亲历的听众，我上周末和一位非技术背景的初创公司创始人朋友在一起。他的小公司自筹资金，十天前决定围绕AI打造产品（可能一个月前决定的）。仅用几周时间就完成了开发——我确信用了Hugging Face。

For listeners who haven't seen this firsthand, I was over the weekend with a good friend of mine who is a startup founder, nontechnical, has a small bootstrapped company, decided to essentially build an AI product around it ten days ago. You know? Built it well, probably decided a month ago. Built it over the course of a couple weeks being nontechnical. I'm sure using Hugging Face.

产品上线后彻底改变了他的业务。作为产品的产出效果令人震撼，堪称世界级——这都归功于AI工具。

Launched it, and it's, like, completely transformed his business. And, like, the output of it as a product is, like, mind blowing and world class thanks to these AI tools.

确实令人振奋。这也是为什么我觉得我们不需要AI末日场景或通用人工智能的超级智能讨论——仅凭它构建OTEC的全新范式就足够激动人心。想想它将赋能多少人、创造多少新能力、催生多少初创企业，对我（和许多人）而言已经足够兴奋。它将彻底改变公司创立、初创企业建设以及投资方式，就像你提到的。

Yeah. It's incredibly exciting. That's one of the reasons why I feel like we we don't need the doomsday scenario of AI or, like, the AGI super intelligence talks about AI because just the fact that it's a completely new paradigm to build OTEC is exciting enough. It's already kind of like thinking about how many people it will empower, how many new capabilities, how many new startups, companies it's going to create is exciting enough for me and for a lot of people. It's going to change a lot of things in the ways that you build companies, you build startups, as you mentioned, the way you invest in startups.

我知道很多投资者在收听这个播客。我认为这将彻底改变你们投资初创企业的方式。我涉足投资领域不久，过去两年里已做了数百笔天使投资，主要集中在Hugging Face社区。我们开始发现，构建AI初创公司与软件公司在许多方面截然不同。

I know a lot of investors are listening to this podcast. I think it's going to completely change the way you invest in startups. I've played a little bit with investments. At this point, I've done hundreds of angel investments in the past two years, mostly in the community around Hugging Face. And I think we're starting to see that building an AI startup is very different than building a software startup in many ways.

我认为这对投资思维和基金回报模式具有颠覆性影响。比如，这是首次出现如此多需要巨额资本和算力的初创公司，就像我们熟知的Mistral和OpenAI。这改变了人们对投资回报率、初创企业烧钱速度的传统认知。

That is, I think, impactful for the way you think about investing and returns for funds. Like, for example, it seems like it's the first time that you're seeing so many of these startups with very heavy need for capital, for compute, like a Mistral that we know with like an OpenAI. So I think it changed a little bit the way you think about investment, returns on investments, burn for startups.

这类公司需要的资金量级大得多，但真正的底层模型公司其实并不多。

That category of companies requires way, way more capital, but there's not that many foundational model companies.

我认为潜在数量可能很庞大。当前投资主要流向大型语言模型，但这只是文本单模态。视频领域的底层模型呢？生物、化学、音频、图像的基础模型呢？

I think there could be. There could be. If you think of it, most of the investment now is going towards foundational LLMs, but it's just one modality, text, right? What about foundational models for video, right? What about foundational models for biology, for chemistry, for audio, for image?

或许基础模型公司最终会像当年软件公司那样，成为AI时代的默认形态？现实是我们尚无定论。现在判断AI初创的成功公式为时尚早，这也正是投资的魅力所在——传统软件行业的经验法则已不再适用。过去那套创始人配置（CTO+CEO）、小团队精益创业、分轮融资的成功路径...

What if actually foundational model companies are actually just normal AI companies, the same way software companies were like the new type, the new default for companies in the software paradigm? The truth is that we don't know yet, right? I think it's still too early to tell exactly what are the recipes for AI startups. And so that's why it's super exciting as an investor too, because the truth is you can't apply the same playbook that you used to in software, right? In software, it was so mature that you had the playbooks, You needed like a co founder, CTO, CEO, small team, and then you do the lean startup and then you follow your rounds and then you get to the highest probability of success.

AI领域可能完全颠覆这套逻辑。比如多数创始人不再是软件工程师，而是科学家。精益模式失效，因为他们需要先获得巨额资本投入才可能产生回报。

What if in AI it's completely different? For example, most of the founders actually are not software engineers anymore. They're scientists, right? It's a totally, completely different game. The lean startup doesn't work anymore because they need heavy capital investment before any sort of return.

我的观点是：游戏规则已彻底改变，必须摒弃所有固有认知，从零开始重构投资思维。

So what I'm saying is that it just completely changes the game and you have to forget everything that you've learned, everything that you've internalized, and start from scratch.

有趣的是，我原以为你会谈到AI公司或使用AI的企业，它们可能只需少数人就能通过调用基础模型公司提供的API实现巨大产出，这种杠杆效应能以极少员工为客户创造巨大价值。但你完全反其道而行，提出了我认为相当反主流的观点：大多数AI公司——或者说投入AI的大部分资金——将需要新的基础模型。因此，这些领域要实现阶跃式进步，就需要难以置信的大规模投资。我理解得对吗？

It's funny. Where I thought you were gonna go with this was AI companies or companies that use AI can be just a few people and get huge output because they're just using the APIs provided by these foundational model companies, and there's an extreme amount of leverage to to produce great value for customers with few employees. You took it completely the other direction, which I think is quite contrarian and said, most AI companies, or perhaps you were saying most dollars deployed into AI, will require new foundational models. And therefore, they're gonna be these unbelievably large investments to get these step function advancements in a lot of different fields. Am I hearing you right?

没错。我认为真相是现阶段无人能确定。我并非百分百确信会朝这个方向发展，但认为存在这种可能性。正因如此，观察未来几年如何演变才令人兴奋。

Yeah. Yeah. And I think the truth is that nobody knows yet. So I'm not saying that I'm a 100% sure that it's gonna go that way, but I'm saying that it's possible. And so that's why it's exciting to see how how it's going to evolve in the next few years.

有个简单论据能支持你的观点：基础模型公司的资金消耗如此庞大，即便存在成千上万家普通初创公司调用AI企业提供的API，大部分投资仍将流向基础模型和大规模训练任务。

One easy way you win that argument is that the dollars consumed by foundational model companies are so large that even if there's a thousand times more regular startups consuming APIs provided by AI companies, it's still the case that most investment dollars will actually go to foundational model and and large training runs.

看看目前成功的公司——比如Hugging Face和OpenAI——它们的行事方式完全不同于传统软件公司的预期。OpenAI最初融资十亿美元，却做了六七年开源开放科学研究，然后彻底转向新模型。Hugging Face多年完全开源运营，真正由社区驱动，这种组织形式与主流建议背道而驰。我认为这启示我们要彻底抛弃软件时代的经验手册，从第一性原理出发，为AI建立全新的发展范式。

I mean, if you look at some of the successful companies so far, if you look at Hugging Face, if you look at OpenAI, companies like that, I don't think they acted in the traditional way you would expect a software company to act, right? And maybe on OpenAI, they started with a billion dollar raise, did open source, open science for six, seven years, and then started a completely new model. For Hugging Face, we operated in like fully open source for many years, really community driven, very different kind of organization than what everyone was telling us to do. So I think there's something to be said about really throwing away the playbooks, throwing away the learnings from the software paradigm, and really start from scratch, maybe start from first principles, and build a new model, a new playbook for AI.

Hugging Face作为公司是否特别消耗资本？如果是，原因是什么？

Has Hugging Face as a company been particularly capital intensive? And if so, why?

其实没有。七年来我们累计融资超5亿美元，实际支出不到半数，幸运的是目前已实现盈利。

We haven't. So we raised a bit more than $500,000,000 so far over the course of seven years. We actually spent less than half of that, and we're lucky enough to be profitable.

恭喜。

Congratulations.

这对大多数AI初创企业来说相当不寻常。我们拥有与其他AI公司不同的商业模式。

Which is quite unusual for most AI startups. We have a different kind of model than some other AI companies.

我猜你们在计算资源和训练方面的资本支出需求远不及OpenAI这类公司吧？

I assume you all don't have nearly the same kind of capital expenditure requirements that, say, an OpenAI does in terms of compute and training.

没错。而且我们现有的免费用户规模，配合简单宽松的免费增值模式，已经能轻松达到可观的收入水平。我们确实有些独特优势来实现这点。这也是我们的战略选择——作为社区平台，我们必须确保不会只运营一两年就消失。

Yeah. Yeah. And we have enough usage already that is free, that with quite straightforward and quite permissive freemium model, we can easily get to a level of revenue that is meaningful. We have some specificities for sure that allows us to do that. And it was also an intentional decision for us because as a community platform, we want to make sure that we're not going to be here just for a year or two years.

当用户在平台上构建应用或贡献内容时，我认为你有责任长期维护这个平台。因此找到既能盈利又可持续的商业模式，同时不阻碍我们开源和免费共享大部分平台功能，这对服务好我们的目标社区至关重要。

When people build on top of you, when they contribute to the platform, I think you have some sort of a responsibility towards them to be here for the long term. And so finding kind of like a profitable, sustainable business model that doesn't prevent us from doing open source and sharing most of the platform for free was important for us to be able to to deliver to the community that we're catering to.

你们的客户确实用Hugging Face做资本密集型的事（比如训练模型），但这不会体现在你们的财报上——不像某些公司需要砸十亿美元搞训练。你们后端与云服务商合作，把成本转嫁给训练方，对吧？

Your customers do use Hugging Face for very capital intensive things, training these models, but that doesn't show up in your financials as, oh my god, we had to sink a billion dollars into a training run. You partner with a cloud provider on the back end and pass it along to whoever is doing the training run. Right?

是的。我们通过三种可持续方式实现：与云厂商合作；提供足够价值让客户愿意支付高利润的计算资源溢价；或推出利润率100%的付费功能。比如现在很多企业订阅我们的企业版Hub，这种软件服务的商业逻辑与出售计算资源完全不同。

Yeah. We try to find sustainable ways to do that, either by partnering with the cloud providers, by providing enough value so that the companies that are buying the compute are okay with paying a markup to the compute that makes it high margin for us or providing paid features that are basically like a 100% margins. Like for example, a lot of companies are now subscribed to our enterprise hub offering, which is an enterprise version of the hub, which is obviously kind of like a different kind of economics than selling compute.

很成熟的商业模式。你们可以自主选择盈利方式——是通过计算资源加价？还是销售SaaS服务？

Yep. Very proven business model. You get to choose how you make money. Are you marking up compute? Are you selling SaaS?

你们是否走企业路线，为每个项目定制开发这个软件包？我很好奇你们选择在计算成本上加成或加价的策略。显然你们对此并不避讳，我认为这是很好的商业模式。Hugging Face能提供什么，让客户觉得‘没错，我宁愿通过Hugging Face来做，而不是自己去云服务商那里折腾’？

Are you going the enterprise route and developing this custom package for, you know, every engagement? I'm very curious on the routes where you choose to apply a margin or a markup on top of compute. What is it because clearly you're not like ashamed of this, and I think it's a great business model. What is it that Hugging Face can provide where a customer goes, yeah, I'll do it through Hugging Face instead of going and figuring out how to do it myself directly on a cloud provider?

我们从未有兴趣参与计算资源的低价竞争。这个商业模式比许多人想象的更具挑战性，特别是超大规模云服务商在服务能力和现金流方面都占据绝对优势，这使他们能做出其他机构无法企及的事情。因此我们的思路是：与其卷入价格战，不如通过平台功能与计算服务的整合，让企业愿意为可持续的价值付费。比如使用推理终端或平台上的spaCy GPU服务时，其与平台功能的深度集成能让企业使用效率提升十倍，这比单独使用平台再自行对接云服务商要便捷得多。

We've never been so interested in taking part of the race to the bottom on compute. It's a much more challenging business model than a lot of people think, especially with the hyperscaler being in such a position of strength, both in terms of offering, but also in terms of cash flow, right? Giving them the ability to do a lot of things that other organizations wouldn't be able to do. And so the way we think about it is instead of taking part of this race to the bottom, we're trying to provide enough value both with the platform, the features and the compute so that companies are comfortable paying a sustainable amount of money for it. So, when you use, for example, the platform, the idea is, and when you use offering like the inference endpoints or spaCy GPUs on the platform, the idea is that it's so integrated with the feature of the platform that it actually makes it 10 times easier for you as a company to use that as a bundle versus using just the platform and then going for cloud provider for the compute.

我称之为‘锁定式计算’。这不像可以在AWS、谷歌云或其他供应商之间随意切换的通用计算服务。我们致力于打造无缝衔接的体验，极大降低复杂度——毕竟AI对多数企业仍很复杂。最终企业确实需要支付更多费用，但...

So it's what I call a locked in compute. It's almost kind of like not the compute that you can trade in and it doesn't really matter to you if you switch from AWS, Google Cloud or another provider. It's more we make the experience so much more seamless, so much less complex, which is the name of the game for AI, right? AI is still complex for most companies. That at the end of the day, yes, companies are paying more for it.

...他们可能只需要1-2名ML工程师，而不需要10人团队。

But instead of having 10 ML engineers, maybe they're going to have one or two.

如果不通过Hugging Face，当AI研究人员完成模型开发后想要训练或部署时，就不得不组建另一支AI基础设施部署工程师团队，对吧？

The alternative to this would be you have your AI researchers working on models. Then when you want to go train or deploy it, not through Hugging Face, you basically need a whole another team of AI infrastructure deployment engineers, right?

没错。正如之前提到的，我们正处于AI商业化的早期阶段。目前没人知道什么是真正可持续盈利的AI商业模式，即便是行业巨头也...

Yeah. Yeah. So we mentioned before, I think we're in the early days of AI, we're in the early days of AI monetization. Today, no one knows what is like a profitable, sustainable business model for AI, right? Like even the big players.

虽然OpenAI营收可观，但这些收入的盈利可持续性仍是未知数。我相信他们会找到答案，但整个AI商业模式的探索才刚起步，这令人无比振奋。

I mean, OpenAI is of course generating a lot of revenue, but the question of profitability and sustainability of this revenue is still an open question. And I think they're going to figure it out, and I hope they're going to figure it out. But we're so early in figuring out business models for AI that there's a lot to build. And so that is extremely exciting.

我认为你并没有在探索任何商业模式。你只是在使用经过时间检验、已被证明有效的赚钱方式，即在价值链中占据特定位置，为开发者提供丰富的体验。他们愿意直接为此付费，也愿意通过支付略高的计算费用来间接买单。最妙的是，你可以在所有AI领域进行创新，而无需从零开始构建商业模式。

And I would argue you're not figuring out any business model. You are using time tested, proven ways to make money where, like, you occupy a particular part in the value chain where you're providing a rich set of experiences to developers. They're willing to pay for that directly. They're willing to pay for it in the chain of slightly more expensive compute. The nice thing is you get to innovate on all the AI things without having to build a business model from scratch.

这些基础模型公司正面临商业模式的核心难题——当消费者期望与各类AI聊天代理互动时，大部分功能都应该是免费的，这尤其令人困扰。

These foundational model companies, that is where there's this big oaf of question of what exactly is the business model, especially when the consumer expectation with interacting with all these AI chat style agents is that that is free for a huge set of functionality.

没错。我们当前处境的美妙之处在于：如果成为AI开发者首选的第一平台，且AI成为技术构建的默认方式，那么围绕它形成可持续的巨大商业模式是显而易见的，否则就是我们战略失误了。这就是为什么我们如此专注用户增长和社区建设——只要持续引领工具使用与普及，赋能社区通过我们的工具取得成功，Hugging Face和整个社区必将迎来美好未来。

Yeah. The beauty of the position we're in is that if you're the number one platform that AI builders are using, And if AI becomes the default to build or tech, it's pretty obvious that there's kind of like a sustainable massive business model around it, right? Otherwise, we would be doing something wrong. That's why we're so much focused on the usage, on the community, because we believe if we keep figuring that out, if we keep leading on the usage and the adoption, we keep kind of like empowering the community to use our tools and be successful with our tools, there's going to be good things in the future for us for Hugging Face and hopefully for the community.

有些商业模式堪称完美。比如你分析Visa就会感叹：这个模式几乎无懈可击，作为股东简直处处都是亮点。

There are some businesses that are just perfect. Like, you sort of analyze them. Visa's a good example. And you're like, man, there's basically nothing wrong with this business model. Everything about it is just glorious if you are a shareholder of Visa.

而所有不及Visa的企业都存在这样的矛盾：既有卓越之处，又伴随着如鲠在喉的运营痛点。你们在AI生态中占据了非凡位置，那么那个让你们忍不住抱怨'这真是心头刺'的问题是什么？

And every business shy of Visa has these things where you're like, that's an exceptional thing about that business. And here's the thorn in my side, that as I'm operating this business, I just can't escape this thing that kinda sucks. We've talked a lot about all the ways in which you've positioned yourself in a remarkable place in the emerging AI ecosystem. What's the thing that you have to deal with where you're like, Ugh, it is such a thorn in my side?

对我们而言，平台型企业的宿命就是必须与赋能对象保持一定距离。就像GitHub——过去二十年它彻底改变了技术构建方式，几乎所有工程师都使用过其协作功能，但人们很少讨论这个产品本身。

For us, inherently, we have to almost take a step back from the communities that we're empowering. That's kind of like a little bit the curse of the platforms. So like if you think, for example, of GitHub, it's probably the company in the past twenty years that has empowered the most the way you build technology, right? Because visually all software engineers have used GitHub as their way of kind of like collaboratively building. And yet people don't talk about them, Like don't talk about the product.

我们注定无法像Facebook或谷歌那样耀眼，也永远不会像OpenAI那样充满话题性。这种在能见度和吸引力方面的'诅咒'，意味着我们将长期保持幕后英雄的角色。

It's not as visible as Facebook, Google or these companies can be. So we have some sort of a curse around, I would say, visibility, maybe sexiness. We'll never be kind of like an open AI in terms of sexiness and hotness and people talking about us and always kind of like stayed a little bit in the background.

不过回溯当年，GitHub还处于初创阶段时，情况非常

Back in the day though, when GitHub was in its earlier years and was a startup, it was very

那笔1亿美元的A轮融资。我至今

The $100,000,000 Series A. I still

记得。没错，我肯定记得这件事。当时引起了很大轰动。但正如你所说，作为基础设施公司，或者更广泛地说，在你们案例中是AI构建平台，你们更多是幕后角色。

remember Yeah. I remember that for sure. It was plenty buzzy. But to your point of as a infrastructure company or a well, developer writ large, in your case, AI builder platform, you're more behind the scenes.

我们面临的另一个挑战是，虽然AI在应用层面正逐渐成为主流，但若仔细观察，底层技术基础仍在快速演进。因此我们始终面临这样的矛盾：既要构建成熟稳定的平台和解决方案，又要保持足够快的创新迭代速度以免错过下一波浪潮。作为公司建设层面，这是我们一直担忧的问题。公司250名员工时我们就强调，团队规模要始终比同行小一个数量级——我们本可以扩张到2000人，但宁愿保持200人规模。这是为了在极速开发与构建可扩展工具之间找到平衡。

And then another challenge for us is that, yes, AI is starting to be mainstream in terms of usage. But if you really look at it, the underlying technology foundations are still evolving really fast. And so there's this constant battle between building mature, stable platforms and solutions but at the same time innovating, iterating fast enough so that you don't miss the next wave. So for us, more like as a company building aspect, it's something that we always worried about. With two fifty team members in the company, we say that we always want to stay an order of magnitude less team members than our peers, like we could be 2,000 people but we prefer to be 200 than 2,000, as a way to reconcile this difficult challenge between building really, really fast, but really building tools that scale.

这确实是我们面临的重要挑战。

That's an important challenge for us for sure.

这个观点非常精辟。你刚才提到时我还没完全意识到——我们可能仍处于基础模型公司的雅虎、AltaVista时代。虽然其中许多公司非常成功，你也了解它们。

That's such a good point. And you made it a minute ago. I hadn't really considered, you know, we might still be in the sorta, you know, Yahoo, AltaVista era of foundational model companies. Many of them are very successful. You know about them.

正如你所说，它们营收可观，但从根本上说，这些业务真的实现盈利了吗？恐怕还没有。

As you're saying, they make a lot of revenue, but, like, are they fundamentally profitable endeavors yet? Probably not.

我认为确实如此。即便你思考企业如何利用AI进行建设，对我来说，一家依赖API的AI公司听起来非常反直觉，或者说这不是构建AI的最佳方式，更像是一个过渡阶段——当前技术对大多数公司而言自主开发AI仍过于困难。但如果这种情况不发生，我反而会感到惊讶。这有点像软件早期的情形，当时人们不得不使用类似Squarespace的无代码平台（虽然我记不清具体名称）

I think we are. Even when you think about how companies are building with AI, To me, an AI company using an API sounds very unintuitive, or it doesn't sound like the optimal way to build AI, and more almost kind of like a transitional time where the technology is still a bit too hard for all companies to build AI themselves. But I would be surprised if it didn't happen. It's almost kind of like the early days of software where you had to use, I don't remember what they were at the time, but like a Squarespace, you had to use kind of like a no code

来搭建网站。比如Dreamweaver和Microsoft FrontPage。

platform to build the websites. Dreamweaver and Microsoft FrontPage.

没错。在技术公司掌握自主编码能力之前，在软件工程师学会独立编写代码之前，AI领域可能正处于相同阶段——企业因尚未建立相关能力、信任和自主开发AI的实力而使用API。但终有一天他们会具备这些。他们了解自己的客户、业务限制和提供的价值。历史上所有科技公司终将成为AI公司。

Yeah. Before technology companies could learn, before software engineers could learn to build code themselves, We might be at the same time in AI where companies are using API because they haven't built yet the capabilities, the trust, the ability to do AI themselves. At some point they will. They know their customers, they know their constraints, they know the value that they're providing. At some point in history, all tech companies will be AI companies.

这意味着所有企业都将针对自身用例、业务限制和专业领域，构建专属模型、优化模型并进行微调。

And that means that all companies, all these technologies, they're going to build their own models, optimize their own models, fine tune their own models for their own use case, for their own constraints, for their own domains.

这个观点也相当反主流。在开始对话前，我原本持相反立场——认为未来将由五到八个巨头（数量可能进一步减少）垄断市场，它们需要每几年投入100亿至1000亿美元，其他企业既无财力也吸引不到这类研究人才，最终我们都将使用它们的API。而你描绘的未来图景截然不同。

I think this is pretty contrarian too. I, coming into this conversation, would have fallen in the opposite camp of there are going to be five to eight players, maybe even consolidating more from there, that need to spend 10 to a $100,000,000,000 every couple years, and no one else has that ability to spend or attract that sort of research talent, and so we all consume their APIs. And you're you're proposing a very opposite future.

是的。显然，我的观点受到了我们实际使用情况的某些偏见影响。

Yeah. I mean, I'm a bit biased, obviously, by the usage that we see.

毕竟你比我们更贴近这个领域。

Well, you're a lot closer to it than we are.

正如我所说，现在每十秒钟就有一个基于Hugging Face构建的新模型数据集或应用诞生。我无法相信这些新模型仅仅是为了存在而被创造。我认为关键在于，你需要新模型是因为它们针对特定领域、特定延迟要求、特定硬件或特定用例进行了优化。因此它们更小、更高效、更经济实惠，运行成本更低。最终，我相信未来世界的模型数量会接近如今的代码仓库数量。

As I was saying, there's a new model dataset or app that is built on Hugging Face every ten seconds. So I can't believe that these new models are just created for the sake of new models. I think what we're saying is that you need new models because they're like optimized for a specific domain, they're optimized for specific latency, for specific hardware, for specific use case. And so they're smaller, more efficient, cheaper, cheaper to run. So ultimately, I believe in a world where there's almost as many models as code repositories today.

其实，如果你仔细想想，模型某种程度上类似于代码仓库，对吧？它就像技术栈。一个模型就是一个技术栈。我难以想象只有少数几家公司会构建这些技术栈，而其他所有人都只能通过API调用来使用它们。所以我预见的未来图景会有些不同。

And actually, if you think about models, they're somehow similar to code repositories, right? It's a tech stack. A model is like a tech stack. So I can't imagine that only a few players are going to build the tech stacks and that everyone else is just going to try to ping them through APIs to use their tech stacks. So I envision a bit of a different world.

是的，这很合理。你话中隐含的意思是99.9%的模型训练和推理成本都很低，它们体积小且专为特定目的打造。过去三年出现的那种'全能模型'似乎比人们花费十年构建的专业模型表现更好，但这只是短暂现象。随着技术进步，我们将回归由专业化廉价模型承担大部分工作的状态。

Yeah, it makes sense. And implicit in your comment is that 99.9 something percent of models are inexpensive to train and do inference on, and they're small and they're purpose built. And, you know, it's nice that this thing happened in the last three years where these sort of god models seem to be able to do everything better than all the specialized models that people spent ten years building before, but that's a that's a blip in time, and we're gonna kind of shift back to specialized cheap models handling a lot of the labor as everyone gets better at the state of the art.

没错。或者某种中间状态。这始终是个渐变的过程。我认为某些企业、某些场景和用例仍需要非常庞大的通用模型。

Yeah. Or something in between. Right? It's always kind of like a gradient. And I think some companies, some context, some use cases will require very large generalist models.

比如开发ChatGPT时，你当然需要大型通用模型，因为用户会询问各种问题。但如果是构建银行客服聊天机器人，它并不需要回答生命的意义，对吧？这样你就可以精简参数，让聊天机器人更小巧，更专注于相关数据的训练，从而降低成本、加快响应速度。这完全取决于你计划将AI应用于什么场景。

So like when you're doing a ChatGPT, yes, of course, you need kind of like a big generalist models because your users are asking everything. But when you're building banking customer support chatbots, you don't really need it to tell you the meaning of life, right? So you can save some of the parameters to make sure that your chatbot is smaller, has been trained more on the data that is relevant to you, that is going to cost you less, that is going to reply faster. So that's kind of like, of course, also very depending on the use cases that you plan to use AI for.

我很好奇，如果你正在收听并考虑创业，特别是AI创业，也许你已锁定某个垂直领域的用例。假设你认同应该自建模型而非依赖API，那么组建团队需要哪些核心能力和技术储备？要打造出优秀的自有模型，关键要素是什么？

I'm curious if you're listening to this and, you know, thinking about starting a company, thinking about starting an AI company, maybe you have a use case or a, you know, vertical use case knowledge that you want to go after. What are the ingredients and skill sets that you need on your team if you buy what you're saying of, you could use APIs, but really, ultimately, you want to build your own model? What do you need to build your own model and build a great one?

在我看来，软件范式与AI范式的核心区别在于：AI更具科学驱动特性。这有点讽刺，因为在软件领域我们常称从业者为计算机科学家，但实际上他们并非真正意义上的科学家，对吧？

So for me, the main difference between the software paradigm and the AI paradigm is that AI is much more science driven than software. It's a bit of a paradox because in software, sometimes we call people computer scientists, right? But the reality is that they're not really scientists in the true sense of it, right?

这真是个误称。他们是工程师。是的，这总是困扰我在大学学习计算机科学的时候。就像，所有其他科学都是关于自然界中存在的事物，生物学、化学、物理学。

Such a misnomer. They're engineers. Yeah. This always bothered me studying computer science in college. Like, all of the other sciences are things that occur in our natural world, biology, chemistry, physics.

而计算机科学则是，不，你在学习如何理解和操作一个人造物。

And computer science is like, no, you're learning how a thing that is man made works and how to operate it.

没错。对我来说，这就是软件范式与AI范式的主要区别。所以在创始团队和能力方面，我认为拥有更多科学背景实际上是必须的。有一位科学家作为联合创始人，我认为是一个巨大的加分项。如果你看看大多数成功的AI公司，他们确实有一位科学联合创始人。

Yeah. So to me, that's the main difference between the software paradigm and the AI paradigm. So When it comes to founding teams and capabilities, I think having more science backgrounds are actually kind of like a must. Having one co founder who is a scientist, I think is a big, big plus. If you look at most of the successful AI companies, they actually have a science co founder.

我们在Hugging Face就有，OpenAI当然也有，比如Vidya。这是一个很大的因素。

We do at Hugging Face, I think OpenAI has, of course, Vidya. That's one big thing.

你会如何描述传统软件创业公司所需的心态和技能集与AI领域所需的科学家技能集和研究技能集之间的区别？

How would you describe the difference in mindset and skill set between a traditional software startup and the engineering skill set you need for that versus the scientist skill set and the research skill set?

在看待构建和发布速度上，时间观念非常不同。当我在软件初创公司工作时，我们有一种快速发布的文化。这在AI领域可能不太适用。虽然你希望尽可能快地发布，但实际上，训练和优化一个模型至少需要几个月而不是几天。所以你可能需要以不同的方式看待发布的速度和迭代的方式。

Timing is very different in the way you look at how fast to build something, ship something. When I was more working at software startups, we have the cult of like shipping really fast. This might not be as true for AI. I think you want to ship as fast as you can, but realistically, to train a model and optimize a model is more, at best, a matter of months than a matter of days. So you probably want to look differently at how you're shipping, how fast you're shipping, how you're iterating on things.

技能也有很大不同。我认为AI科学家在数学、纯数学方面可能比工程师更擅长。他们更多思考的是如何在现有技术基础上取得基础性或重大进展，关注的是更大规模的改进。而在软件范式中，你几乎可以认为，如果我的产品比别人好5%，那就足够了，因为我现在让它好5%，两周后再好5%，再过两周再5%，最终你会在价值增值上有足够的差异来吸引和留住用户。

The skills are quite different too. I think an AI scientist has the potential to be more skilled at math, pure math, than kind of like an engineer. I think thinking more in terms of like, how can I make foundational or meaningful progress compared to the state of the art? And you're kind of like looking at bigger scales of improvement. I think in the software paradigm, you can almost think like, okay, if I make my product 5% better than others, it's going to be enough because I'm going to make it 5% better now and then in two weeks 5% more and in two weeks 5% more and at some point you'll have enough differential in terms of value adds to get users and convince and retain users.

对科学而言，这几乎就像你并未创造任何价值，你花六个月时间研究某件事，然后六个月后，你得到了比现有技术好十倍的东西。对吧？某种程度上，OpenAI就是这么做的，不是吗？他们埋头苦干了六年，几乎没有发布任何成果或成功产品。但在某个时刻，他们终于推出了可能比其他技术领先十倍的东西。

For science, it's almost like you don't create any value, you work on something for six months, and then after six months, you have something 10 times better than the existing. Right? Like in a way, that's what OpenAI did, right? They worked for six years, barely releasing anything or anything successful. But at some point, they were able to release something that was probably 10 times better than than others.

所以这也是一种不同的思考方式。

So that's got like a different way of looking at it too.

我对此持不同看法。我认为这种说法有点修正历史的意味。我确信你一直在密切关注OpenAI。感觉他们当时发布了各种各样的东西——虽然都没有商业价值，全都显得非常学术化。但比如他们在《侠盗猎车手》上训练Universe系统，还有GPT和GPT-2，虽然主流市场不熟悉，但当时看着确实相当了不起。

I'd push back on that. I I think that's a little bit revisionist history. I'm sure you were watching OpenAI very closely. It felt like they were releasing all sorts of stuff. None of it had any commercial value, and all of it felt super research But that thing where they trained Universe on Grand Theft Auto, I mean, the GPT and GPT two, they weren't known in the mainstream, but it was like pretty remarkable watching that.

我认为他们全力投入Transformer架构并决定'我们需要彻底改变研究方向'这个决策很关键。这家公司行动极其迅速，产品迭代很快，现在由于身处这场技术军备竞赛，他们的发布速度比以往任何时候都快。我完全不认为他们是那种闭门造车十年才发布产品的类型。

I think them going all in on the transformer and deciding, hey, we need to fundamentally change the set of things that we're working on. I think that company has worked incredibly fast, shipped pretty fast, and now they're shipping faster than ever because they're actually in this arms race. Like, I I definitely don't think of them as a go away, think and build for ten years and then finally release something.

他们确实发布了不少成果，但考虑到公司规模和最初10亿美元的启动资金，可能每三个月或半年才发布一个成果。相对于其规模、体量和融资额度，我认为他们比同等预算的典型软件公司发布的产品要少得多。不过我同意你的观点，这是个渐进式的过程。

They did release a lot of things, but compared to their size and their scale, knowing that they started with a $1,000,000,000 investment, maybe they were releasing one thing every three months or like one thing every six months. So relatively to their size and their scale and the amount of money that they raised, I think they were shipping and releasing way less things than the typical software company would have with their budget. But I agree with you that it was an iterative way.

我想说的是，如果你有个大模型，你不可能做持续部署——至少得重新训练模型对吧？

I guess to the point too, like if you have a large model, you're not going to do continuous deployment because you got to retrain the model if nothing else, right?

这完全是另一种方法论。我给创业者最好的建议就是：创立AI公司时先把《精益创业》那本书扔掉。因为这类理念已经深深植根于我们软件创业者的思维模式中，以至于我们很容易不自觉地陷入固有套路，而不是彻底改变范式、重构创业者的操作系统——在我看来后者才能带来更好的结果。

It's just a different approach. The best advice I give to people is to trash their Lean Startup book when they're starting an AI company, because I think these kind of things have been so ingrained into our minds, into our way of building as kind of like software entrepreneurs, that it's really easy to fall into the trap of doing it without even realizing we do it, instead of completely changing the paradigm, changing the operating system of the startup builder, which in my opinion leads to much better results.

克莱姆，这引出了我一直想请教你的话题，我认为这将是今天我们讨论的最后一个主要议题。在开源与闭源AI哪种方式将在市场中胜出的讨论中，有个颇具说服力的观点：随着更多实时训练数据的需求增加，用户与应用程序的互动对微调或训练下一代模型变得极其宝贵。某种程度上，闭源AI会胜出的论点很吸引人，因为它们能直接从用户那里获取这些数据——当你拥有模型和应用程序，并将所有环节紧密整合时。相比之下，在开源世界，虽然你发布了某个项目，但人们会分叉它，构建自己的应用，而应用程序的实时交互数据却无法完整回传到上游让模型变得更智能。

Well, Clem, this brings us to a topic that I've been wanting to ask you about, which I think will be kind of our last major topic for today. In the discussion of which approach will sort of win in the marketplace of open source versus closed source AI, there's a pretty compelling argument, which is as more training data is real time training data is required, people's interactions with an application will become incredibly valuable to fine tune or train the next version of the model. There's sort of a a compelling argument that as closed source AI will win because they're just gonna get all of that directly from users when you own the model and the application, and you sort of have tightly integrated everything. Versus in the open source world, like, great. You publish something, and then a bunch of people fork it, they build their own applications, and then the real time interaction data with the application doesn't make its way all the way back upstream to make the model smarter.

你对此怎么看？

How do you think about that?

我认为很多人正在思考和讨论AI的护城河与规模经济效应。目前这些都还是开放性问题，实际上没人真正知道如何为AI建立护城河或实现规模经济。我的直觉是，它们不会与软件范式有太大差异，你会看到相似的护城河类型——可能应用方式不同，但成本规模经济会存在，类似于云服务商或硬件供应商通过扩大规模降低价格的优势。社交护城河或网络效应也会出现，对吧？

Well, I think a lot of people are are thinking and talking about moats and economies of scale for AI. I think that all of that is is kind of like open questions at at this point. I think nobody really knows how to create a moat or like how to generate economies of scale for for AI. My intuition is that they're not going to be so different than the software paradigm, and that you're going to find the same kind of modes, maybe applied differently, but you're going to have the cost economies of scale, similar maybe to a cloud provider or a hardware provider who can get an advantage from larger scale to reduce prices. I think you're going to have the social moats or the network effect, right?

这更像我们所在的游戏规则：当协作使用发生时，平台用户越多就越有价值，使得竞争者难以匹敌。这就是GitHub从未真正被挑战，或社交网络难以竞争的原因。这些效应可能比软件范式更强烈，比如计算成本护城河会更极端。但这是开放问题，因为当前的一些赢家最初并未拥有这些优势。比如OpenAI最初并不比其他公司获得更多数据，他们最终是通过爬取公开网络获取了大家都能得到的数据。

That's more like the game that we play in, where when you have collaborative usage, in a way your platform becomes more and more useful the more users you have, and so it makes it difficult for anyone to compete with you. That's why GitHub has never really been challenged or that's why social networks are arguably very hard to compete with. They're going to maybe be more intense than in the software paradigm, so maybe the cost mode of compute will be more extreme. But it's an open question because, you know, if you think about the current winners, some of the current winners, they didn't have so much of these advantages from the get go. Like if you look at OpenAI, they didn't really have more access to data than most companies, right?

以Hugging Face为例，我们起步时也没有特殊优势，除了尽可能以社区驱动的方式发展出社交网络效应。这仍是未解之谜。我会警惕某些人或公司过度夸大某种护城河。从伦理和世界需求角度，我希望不会只有少数公司胜出。

They ended up scrapping the web and getting data that everyone else could get. If you look at Hugging Face, I don't think going in, we had any specific advantage that allowed us, except being kind of like as community driven as we were, that enabled us to develop the social network effects. It's still an open question. I would be careful of people and companies kind of like overplaying and overhyping one sort of moat compared to others. And even if you think of ethically and the kind of world that we need, I hope that we're not going to have just a few companies winning.

那将是遗憾的。如果最终只有五家AI公司成功，会非常可悲。这很危险——想象如果只有少数公司能开发软件，世界将完全不同。我期待更多公司能成功。

It would be a shame. It would be quite sad if we ended up with just five companies winning in AI. I think it would be dangerous, right? Imagine if only a few companies were able to do software, we would be in a very different world than we are today. I hope many companies win.

这项技术的影响力足以容纳比过去软件行业更多的成功AI企业，这让我非常兴奋。

I think the technology is impactful enough so that there can be almost more AI companies winning than software companies in the past, that'd be very exciting to me.

你提出了一个极具说服力的观点，即这将比以往任何时候都赋能更多人打造产品。因此可以合理推断，这一代人中应该会有比以往任何时代更多的公司，或至少更多的创业尝试，来满足特定客户需求。

And you make a very credible argument that it's going to empower more people than ever to build products. And so it stands to reason that there should be more companies or or at least more attempts to start companies that can serve a particular customer need in this generation than any previous generation before.

人工智能是本世纪打破僵局、瓦解垄断、颠覆既有格局并开创新局面的绝佳机遇。

AI is the opportunity of the century to shake things up, break the monopolies and break the established positions and do something a bit new.

我很想了解这点。你认为我们是需要培养更多掌握AI构建与科研能力的人才，还是需要让工具和基础设施变得更易用，抑或两者兼需？

I'm curious to get there. Do you think that we need just a lot more people getting trained in how to be AI builders and AI scientists? Or do we need the tools and infrastructure to get a lot easier to use or both?

两者都需要，但我认为培养更多AI构建者远比现状重要得多。以Hugging Face为例，正如我刚才所说，我们有500万AI构建者，可以推测全球现有约500万AI构建者。而软件工程师或软件开发者大约有5000万，具体取决于定义标准。

Both, but I think it's much more important that we get many more AI builders than we do today. If you're looking at Hugging Face, as I was saying, we have 5,000,000 AI builders, right? So we can assume, like, most AI builders are using Hugging Face one way or another. So you can estimate that there's around 5,000,000 AI builders in the world today. There's probably around 50,000,000 software engineers or, like, software builders, depending on how you set this definition.

GitHub用户超1亿，虽然其中很多并非软件工程师，但估计半数都是。所以我们仍处于早期阶段。几年后AI构建者数量超过软件开发者也毫不奇怪，对吧？

I think GitHub has over 100,000,000 users. A lot of them, obviously, are not software engineers, but probably half of them. So we're still at the early innings. Right? It wouldn't be surprising that if in a few years you would have more AI builders than software builders, right?

或许几年后AI构建者将达到5000万甚至1亿。由于AI的美妙之处在于其限制比软件开发少——贡献方式更灵活。成为软件开发者需要学习编程语言和写代码，门槛较高；而AI构建者可以通过贡献专业知识或数据来优化模型。未来AI构建者可能是软件开发者的10倍，这对世界有益，意味着更多人能参与技术塑造，使其更符合大众需求。硅谷和科技界常忽略的是：少数人在为多数人设计产品。

So maybe in a few years, you're going to have 50,000,000, 100,000,000 AI builders. Even more because the beauty of AI is that it's a bit less constrained than software in the way that people can contribute to it. In a way, to be a software building, you have to learn a programming language and write lines of code, which is a pretty high barrier to entry. Versus for AI, you can be considered an AI builder if you contribute expertise, if you contribute data to a model that improves the model, maybe we're going to have like 10 times more AI builders than software builders, which would be also good for the world because it would mean that more people could contribute, could understand, and could kind of like shape the technology more aligned with what they want. I think sometimes in San Francisco, in Silicon Valley, or in tech in general, we forget that it's a very small number of people shaping products for a much bigger number of people.

如果让更多人参与构建过程，不仅能打造更优质的产品，还能创造更具包容性的解决方案，甚至解决更多社会问题。这无疑是令人振奋的未来图景。

Whereas if you maybe include more people in the building process, you can not only build better products, but more inclusive products, maybe products that can solve more social issues than we've been solving. And so that's quite an exciting future for sure.

嗯，克莱姆，我想不出比这更好的地方来结束对话了。听众们应该去哪里了解更多关于你或Hugging Face的信息，或者参与其中呢？

Well, Clem, I can't imagine a better place to leave it. Where should listeners go to learn more about you or Hugging Face or get involved?

Huggingface.co。其实是.com。我们几天前才拿到.com域名。

Huggingface.co. Actually, .com. We just got the.com a few days ago.

嘿，恭喜啊。

Hey. Congratulations.

是的。这是个很好的例子，说明早期不必为小事过分纠结，对吧？我们的名字Hugging Face显然与我们做的事情很不寻常。七年来我们一直用huggingface.co这个域名，但这并没有给我们带来太多问题。我在Twitter上。

Yes. It's a good example that you shouldn't sweat the small things early on, right? Our name hugging Face is obviously very unusual for the kind of things we do. Our domain name for like seven years, we kept huggingface.co, but it didn't create too many problems for us. I'm on Twitter.

我在X和LinkedIn上分享很多内容，你可以在那里关注我或提问，我很乐意回答。

I share a lot on X and on LinkedIn, so you can follow me there or ask me questions there and happy to answer.

太棒了。非常感谢你。听众们，我们下次见。

Awesome. Well, you so much. And listeners, we'll see you next time.

我们下次见。

We'll see you next time.