斯特凡诺·埃尔蒙谈筹集5000万美元，助力企业打造快10倍的实时AI应用

欢迎回到《交易者播客》节目，本期嘉宾是连续创业者亚历杭德罗·克雷蒙纳斯，他是畅销书《初创融资艺术》的作者，同时也是Panthera Advisors的联合创始人。

Welcome back to The Dealmakers Podcast Show with serial entrepreneur Alejandro Cremonas, bestselling author of The Art of Startup Fundraising and co founder at Panthera Advisors.

在本节目中，我们会邀请嘉宾分享他们成功的收购和融资经历。

In this podcast, we ask our guests about their successful acquisitions and financing rounds.

好的。

Alrighty.

大家好，欢迎来到《交易者秀》。

Hello everyone and welcome to The Deal Maker Show.

今天，我们有一位出色的创始人加入我们。

So today we have an amazing founder joining us.

请做好准备迎接口音大碰撞，因为一会儿我们会听到西班牙语和英语混杂的表达，还有意大利口音，所以请准备好。

Be prepared to have the battle of accents because we're going to have the Spanglish and also the Italian kicking in booze, so be ready.

但同样，我们会学到很多关于创建、扩展和融资的知识。

But again, we're going to be learning a lot on the building, the scaling, financing.

他们今天要宣布一些令人兴奋的消息。

Mean, they have something exciting to announce today.

现在关于生成式AI的讨论很多。

There's a lot of generative AI talk going on.

我们今天的嘉宾，你知道的，他早就投身其中了。

Our guest today, you know, he's been at it.

在它还没像今天这样流行之前，他就已经非常了解了。

He knows it very well before it even became trendy as it is today.

但你今天会发现这场对话非常鼓舞人心。

But you're going to find the conversation today quite inspiring.

那么，不多说了，让我们欢迎今天的嘉宾，斯特凡诺·埃尔曼。

So without further ado, let's welcome our guest today, Stefano Erman.

欢迎来到节目。

Welcome to the show.

非常感谢你邀请我，亚历杭德罗。

Thank you so much for having me, Alejandro.

你最初来自意大利。

So originally from Italy.

给我们讲讲你的回忆吧。

So give us a walk through memory lane.

斯蒂法诺，你小时候的生活是怎样的？

How was life growing up for you, Stefano?

是的。

Yeah.

我出生在意大利东北部阿尔卑斯山区的一个小村庄。

So I grew up in a small village in the Northeastern Italy in the Alps.

那是一个只有几百人的小社区。

So it was a very small community of a few 100 people.

我在那里上小学，后来一直读到高中，都是在很小的学校，但非常有趣，我花了很多时间在户外，从小就滑雪。

I went to elementary school there and, yeah, I went all the way through, through high school in very small schools, but you know, a lot of fun, spend a lot of time outdoors, you know, grew up skiing.

所以那是我的爱好之一。

So that's one of my passions.

是的，我那时在学习人工智能。

Yeah, I was studying and, artificial intelligence.

我一直对电脑、编程和动手制作东西充满热情。

You know, I was always very excited about computers and coding and building stuff.

我大学其实学了电气工程，因为我真的很想亲手制造电脑。

I actually studied electrical engineering in college because I really wanted to build the, computers.

你必须真正动手，从零开始构建一切。

You got to have to actually work, build everything from scratch.

这一直是我热爱的事情。

It's always been my thing.

之后，我去了美国攻读博士学位。

And, yeah, then after that I went to, came to The States for my PhD.

在那之前，你是怎么对解决问题、工程和你 geek 的一面产生兴趣的？

And before that, before that, how did you get into the whole problem solving engineering and the geeky side of you?

这种兴趣是如何发展起来的？

How, how did that flourish?

你知道，这种兴趣是从哪里来的？

You know, where did that come from?

我不太确定。

I don't know exactly.

我爸爸我觉得也很喜欢科学，特别喜欢做实验，我们经常一起做化学实验和动手制作东西，特别有趣。

My dad, I think was also, you know, very into science, very much into like doing experiments and we would have a lot of fun and like doing chemistry stuff and building things.

所以我想这应该是我从小耳濡目染的，一直让我对动手制作、从零开始建造东西充满兴趣。

So I think it's just something that I grew up in and, you know, always got me interested, like building with my hands, building stuff from scratch.

我不清楚这到底是从哪儿来的。

I don't know where it came from.

我觉得是来自我爸爸，但我也不太确定。

I think from my dad, but I'm not really sure.

所以你在那儿上大学了。

So you studied the college there.

我的意思是，你在意大利学的工程。

I mean, you did engineering in Italy.

这很有趣，因为意大利和我家乡西班牙很相似，在那里去美国是一件非常了不起的事。

And it's interesting because Italy is very similar to Spain where I'm from, where ultimately coming to The US is a really big deal.

我的意思是，就像在西班牙一样，意大利人也通常会和父母一起住很长时间。

I mean, just like in Spain, people in Italy, they live with their parents for quite a while too.

那你是什么时候决定，嘿，也许我该去看看美国的情况，去那里读博士的呢？

At what point do you decide, Hey, I think maybe it makes sense for me to take a look at what's going on in The US and go to do my PhD there?

是的，这其实是碰巧发生的。

Yeah, that was actually by luck.

所以当我还在意大利做硕士论文时，我跟一位叫詹弗兰科·贝拉尔迪的教授合作，他本人曾在美國学习过，还曾在康奈尔大学当过教授。

So when I did my master thesis in Italy, I worked with a professor, Gianfranco Bellardi, and, he had actually studied in The U S he had been a professor in The U S for a while at Cornell University.

正是他让我开阔了眼界，告诉我，真正最激动人心的研究都在美国，如果你真想站在某个领域的前沿，就必须去美国。我以前从未去过美国，也从未旅行到过那里，但我还是申请了好几个博士项目。

And, he was the one that kind like opened my eyes and told me that, you know, the, the, the, the most exciting research is happening in The U S like, if you really want to be at the forefront of something, you've got to go to The U S and, I had never been in The U S I had never traveled to The U S before, but I just applied for a bunch of PhD programs.

我参加了托福考试。

I did my TOEFL.

我做了那个英语测试。

I did the kind of like the English test.

我没考GRE。

I didn't my GRE.

我做了所有必须做的事，然后申请了多个地方。

I did all the things that I had to do and just applied for a bunch of places.

而且，幸运的是，我被录取了。

And, yeah, luckily I was admitted.

我不知道今天是否还能被录取。

I don't know if I would be admitted again today.

现在读博士项目变得这么难，但我觉得那时候要容易一点。

Like now that now it's so hard to go into a PhD program, but I think back then it was a little bit easier.

最后，我收到了几个学校的录取通知，决定去康奈尔大学，因为我的导师曾在那儿任教。

And, eventually, yeah, I got into a few places and I decided to go to Cornell just because my advisor had been a professor there.

他跟我讲了这个项目的很多优点。

He was telling me good things about the program.

而且，当时我坐飞机去美国，那是我第一次去那个国家。

And, yeah, at the time I took the plane to The U S that was my first time in the, in the, in the country.

然后我抵达了纽约州伊萨卡，开始了我的博士生涯。

And then I landed in Ithaca, New York and, you know, I started my PhD.

所以，我们现在自然要聊聊你目前在商业上做什么。

So obviously we're going to talk about now what you're up to with your business.

但我想问你，显然在完成博士之后，你走上了学术这条路。

But I wanted to ask you, obviously after doing the PhD, you got into this academia path.

你的求知欲驱使你前往斯坦福，并一直沿着这条路径前进。

I mean, really got your curiosity wanting to go after that you know, to Stanford, and really following that track.

是什么真正激发了你开启这一段旅程的呢？

What really sparked that, that, that chapter for you?

是的，我一直以来都非常好奇，也始终渴望学习。

Yeah, I've always been very curious and I know I always want to learn.

我想学得更多。

I want to learn more.

我想试着去弄明白。

I want to try to figure out.

我想学会自己动手做事，解决那些前所未有人解决过的问题，无论是从拼图，到电子游戏，再到数学难题。

You know how to do things myself, how to solve problems that nobody has ever solved before, whether it's, you know, like from puzzles all the way to computer games, all the way to math problems.

我总是想要解决它们。

Like I always want to solve them.

我喜欢挑战，研究对我来说非常棒，因为根本不用担心缺少那些前所未有的难题。

I want I like challenges and the research has been amazing for me because, you know, there's no shortage of, you know, very hard problems that nobody has ever solved before.

这一直是我真正的热情所在。

And, that has always been my, my passion.

这让我感到兴奋。

That's what gets me excited.

特别是，我一直认为人工智能可能是人类迄今为止所面临的最大问题。

And specifically, I've always thought that artificial intelligence is probably the biggest problem that humanity has ever worked on.

我的意思是，还有什么比创造一台能够模拟人类智能的机器更重要的呢？

I mean, what's more important than building a machine that effectively imitates human intelligence.

这可能是终极的研究问题。

That is probably the ultimate of research question.

因此，我一直以来都关注神经科学，研究大脑如何运作，理解什么是智能，并探索如何构建出像人一样聪明的系统。

So that's, I've always gravitated around neuroscience, understanding how the brain works, understanding what is intelligence, figuring out how to build something that is as intelligent as a person.

从高中开始，这一直是我着迷的事情。

That's always been kind of like what has fascinated me since basically high school.

出于好奇，基于你所了解和接触的一切，你刚才提到过，什么是智能？

Out of curiosity, based on everything that you know and that you've been exposed to, I mean, you were alluding to it, What is intelligence?

你能用非技术性的语言，让我们所有人都能理解的方式描述一下吗？

Like, how would you describe it in a nontechnical way for all of us in a way that we get it?

是的，这很难定义。

Yeah, it's very hard to define.

这就是问题所在。

That's the thing.

而且，这个领域里有句说法：如果它能工作，那就不算人工智能。

And that's, you know, there is the saying in, in the field where we say, if it works, it's not AI.

对。

Right.

所以，你几乎总是在不断移动目标。

So it's, it's almost like you always keep shifting the goalpost then.

我认为，最自然的定义可能是图灵测试。

And I think, you know, probably the, the, the most natural definition is, is kind of like the imitation game.

如果你根本无法区分这台机器、这个人工制品、这个人造智能和一个人，那么你可能已经达成了目标。

So if you basically cannot distinguish this machine, this artifact, this artificial intelligence from a person, then you've probably achieved your goal.

无论是通过交流，还是让这个实体像人一样工作或创造经济价值，都有很多不同的方式来理解它，但归根结底，我认为这与构建一个在行为、表现和价值创造方面与人类非常相似的东西有关。

So whether it's communicating it with entity or whether it's getting this entity to do work, the way a person would do it or create economic value, there's many different kind of like ways to think about it, but ultimately I think it has something to do with building something that is quite similar in terms of the way it behaves, way it performs, the way it generates value compared to a human.

我在斯坦福待了十年。

And in Stanford, were there for ten years.

我的意思是，那可是相当长的一段时间，对吧？

I mean, that's a hell of a lot of time, right?

我敢肯定你在那里过得非常开心，所以你才待了这么久。

And I'm sure that you had a blast, so that's why you stayed there for quite a bit.

但我们现在谈的是，你作为教授身处全球最负盛名的大学之一，而且这所大学正是许多重大创新和公司诞生的地方。

But we're talking about being there a professor in one of the most renowned universities and also in one of the universities where some of the biggest innovations and companies have been built.

对你来说，置身于这一切之中，亲身体验这一切，是一种怎样的经历？

How was that experience for you as well of being exposed to all of that and just being in it?

这太棒了。

It's been amazing.

你知道吗，在来湾区之前，我从未体验过这种文化，比如创业精神，这在康奈尔根本不是什么大事。

You know, before coming to the, to, to the, to the Bay Area, I had not kind of like experienced this, this culture, like the, the startup, the entrepreneurship, like it was not really a thing at Cornell.

我觉得康奈尔更偏学术，人们并不一定思考这些技术可能带来的影响、作用或商业机会。

I think Cornell was much more academic and people were not necessarily thinking about the implications, about the impact, about the business opportunities that all these technologies could lead to.

斯坦福则完全不同。

Stanford has been completely different.

每个人都在思考影响，思考如何改变世界，无论是本科生、研究生还是教授，这已经成为一种文化。

Everybody is kind of thinking about the impact, how they can change the world, whether it's undergrads or grad students or professors, it's kind of like the culture.

你一踏上校园就能感受到这种氛围。

It's what you feel the moment you're on campus.

这真的太棒了。

And it's just, it's been, it's been amazing.

你每天都能感受到的那种能量，简直不可思议。

Like Like the energy that you see every day, it's just incredible.

起初我并没有想过要创业或创办一家公司，但过了一段时间，你就不知不觉被卷入其中，这种氛围极具感染力。

And I think, initially I was not thinking about starting a business, about doing a startup, but after a little bit, you just get into it and it just, it just contagious.

你根本躲不开。

You can't avoid it.

每个人在斯坦福待久了，最终都会融入其中。

Everybody spends enough time at Stanford eventually.

对你来说，这是一种很大的文化冲击吗？

Was it for you like a big of a culture shock?

因为显然，在意大利，你通常要么当律师，要么当医生，虽然现在情况正在变化，但突然间你来到这样一个校园，人们走进咖啡馆就开始一起头脑风暴，讨论如何改变未来。

Because obviously being used to Italy where you're either a lawyer or a doctor, obviously things are changing nowadays, but then all of a sudden you're like in this campus where people just get into a cafe and they start brainstorming the future together how to change things.

我的意思是，我肯定这也很令人兴奋。

I mean, I'm sure that that was quite exciting too.

是的。

Yeah.

非常不同，但我真的很喜欢这种活力。

Very different, but I mean, I love the energy.

我喜欢人们思考问题的方式，比如敢于冒险、志存高远，他们不怕尝试新事物、打破常规。

Love the way people think about, you know, like taking risks and, and thinking big, like they're not scared about, you know, taking risks and trying new things and breaking things.

我真的非常喜欢这一点。

Like, I really love that.

这种心态让我特别欣赏，人们做事效率真高。

The mindset, I love how people move quickly.

他们没有繁文缛节，就像我之前说的那样。

They, they, there's no red tape like the way I, yeah.

我的成长背景在意大利。

I mean, I grew up in Italy.

那里一切都太慢了，官僚主义严重，做任何事都困难重重。

Everything is so slow, so bureaucratic, so difficult to do everything.

这完全是两个不同的世界。

And it's just a different world.

你能明白为什么这里的商业、公司、初创企业，甚至研究人员都如此成功。

You can see why businesses and companies and startups and even researchers are so successful here.

这里简直是创新蓬勃发展的完美环境。

It's just like perfect environment for innovation to flourish.

你刚才谈到的是承担风险。

And you're talking there about taking risks.

那我们来谈谈承担风险吧。

So let's talk about taking risks.

我们来谈谈开创性的风险，对吧？

Let's talk about the risk of inception, right?

因为对你现在来说，你已经在那里待了大约十年，这段时间足够让你对日常事务和你所做的事情感到舒适。

Because for you now at this time, you are about ten years there, And that's a significant amount of time where you start to get comfortable with just the routine and what you're doing.

那么，开创性的想法是如何出现在你脑海中的？在这个过程中，发生了哪些事件，让你真正想要采取行动，并在职业生涯中做出如此巨大的转变？

So how did the whole idea of inception come to mind or come across you're this, and also that process or the sequence of events that needed to happen for you really to want to take action and to make such an incredible shift in your career.

是的。

Yeah.

正如我提到的，我在斯坦福已经待了大约十年，我一直从事人工智能研究，特别是从一开始我就在研究我们过去称为生成模型、现在称为生成式人工智能的领域。

So as I mentioned, I I've been at Stanford for about a decade and I've always been doing research in AI and most specifically, I've been working since the very beginning on what we used to call generative models, which is now called generative AI.

在它还没成为热门之前，我就已经在做这方面的研究了。

And I was working on that before, before it was school.

我还记得当年，想为这类研究争取资金非常困难。

And I still remember back in the day, like it was hard to get funding for this kind of research.

人们不理解为什么你会想要开发一种能生成图像或类似内容的软件。

People would not understand why you would ever want to model or some kind of software that can generate images or comprise that.

根本没人明白这有什么意义。

Like nobody understood why that even made sense.

你知道，发表论文也很困难。

You know, it was hard to publish papers.

那是不一样的时代。

It was a, it was a, different times.

是的。

Right.

但我一直对它充满热情。

But I've always been passionate about it.

我一直觉得，这才是迈向下一步的正确方式。

I always felt that that is the way to, to, to take the next step.

当你思考构建现实世界的AI系统时，你需要尽可能多地利用数据，并且必须在无监督的情况下进行，因为这才是实现可扩展性的唯一途径。

When you think about building real world AI systems, like you need to leverage as much data as possible and you need to do it without any supervision because that's the only way to make things scalable.

我很幸运，因为我在这个领域起步很早，我在斯坦福的研究团队做出了一些关键的技术贡献。

And so I was lucky enough that because I was so early in this field, my research group at Stanford made a few key contributions, technical contributions.

比如，我们开发了一系列被业界广泛部署和使用的算法与模型。

Like we developed a bunch of algorithms and models that have been widely deployed and used in industry.

更具体地说，我在斯坦福的团队基本发明了所谓的扩散模型，这项技术如今被几乎所有生成图像和视频的系统所采用，比如AI的Sora模型、MidJourney等，这些生成图像和视频的系统都基于我们在2019年于斯坦福实验室开发的核心技术。

More specifically, my group at Stanford basically invented this thing called diffusion models, which is the technology that is now used in pretty much every, system that generates images, video, like the, the Sora models from AI, which is sort of mid journey is like all these systems that generate images and video, they're based on that core technology that we developed in my lab at Stanford in 2019.

从那以后，我一直在努力让扩散模型不仅用于图像和视频生成，还能应用于文本和代码生成等通常由大型语言模型（如GPT-3、Gemini或Claude）处理的任务。

And, since then I've been trying very hard to get this diffusion models to work, not just on image generation and video generation, but also on text and code generation kind of things that you would normally do with a typical large language model, like tragic D or Gemini or Claude.

去年，我们的实验室取得了重大突破，首次找到了让未来模型在文本生成上发挥作用的方法，而且是在相对较小的规模上实现的。

And what happened is that, last year we had this big breakthrough in the lab where for the first time we were able to figure out a way to get, the future models to work on text generation and at relatively small scales.

在GPT-2规模上，也就是参数少于十亿的模型中，我们在生成质量上达到了与主流模型相当的水平，但我们的模型速度要快得多，比其他人普遍构建的模型快了一个数量级。

So at the GPT-two scale, so less than a billion parameter models, we were able to match the performance in terms of quality, but our models were way faster, like an order of magnitude faster than the typical model that everybody else is kind of like building on.

原因是，如果你想想GPT、Gemini或Claude，它们都是一种叫做自回归模型的东西。

And the reason is that if you think about the touch GPT or Gemini or Claude, they're all, something called an outdoor aggressive model.

这意味着，如果你问我一个问题，它会一个字一个字地生成答案。

And what this means is that if you ask me the question, it will generate the answer, one word at a time.

对。

Right.

这就像一种你无法避免的瓶颈结构。

And that's kind of like a structure of bottleneck that you can't avoid.

它非常慢。

It's just very slow.

它是完全顺序的。

It's very sequential.

这就是为什么这些模型如此昂贵且缓慢。

And that's why these models are so expensive and so slow.

而我们有一种完全不同的方法，本质上是为并行设计的，以利用GPU、TPU或类似的并行硬件。

And we have this completely different approach that is basically built to be parallel to take advantage of GPU who is on TPU or like parallel hardware.

神经网络并不是一个字一个字地生成内容。

The neural network is not generating one word at a time.

相反，它更像是先猜测答案可能是什么。

Instead, it's kind of like starting with a guess of what the answer should be.

然后通过同时修改多个词的方式并行地进行优化。

And then it's refining it, in parallel by essentially being able to modify multiple words at the same time.

而这正是让这些模型变得快这么多的原因。

And, this is what enables these models to be so much faster.

我对这些结果感到非常兴奋，以至于我必须去看看我们能把它推到多远。

And I was so excited by the kind of results that I, that I had to, see, you know, how far we can push it.

于是我很快意识到，在学术界做这件事非常困难，因为训练更大模型、组建工程团队以及获取足够数据来构建一个成熟的生产系统所需的资源实在太过庞大。

And so I quickly realized that it was very hard to do, in academia just because of the resources that were going to be needed to train bigger models and get the kind of engineering teams and the kind of data to really build up production with that ready system.

因此，我决定创办Inception，因为我对这些技术成果感到无比兴奋，并看到了巨大的机遇。

And so that's why I decided to start inception because I was so excited by the technical results and I saw a huge opportunity.

否则，我的意思是，LAMP才是这场革命的核心。

Else, I mean, lamps are at the core of the revolution.

大家都在谈论其他大模型，但幕后其实大家都心知肚明，这些其他大模型本质上都是彼此的复制品。

Everybody's talking about other lamps, but under the hood, everybody's kind of like, all these other lamps are kind of like clones of each other.

它们都建立在相同的技术基础上，而我们却做出了显著更好的成果。

They're all building on the same technology and we got something significantly better.

没错。

Right.

所以我看到了巨大的机会，决定以斯坦福的方式去做——承担风险，全力以赴，亲自把它做出来。

And so I just saw a huge opportunity and I decided to, yeah, do it the Stanford way, take the risk and, and, you know, go all in and build it out myself.

嘿，各位，你们现在正在融资吗？

Hey guys, are you fundraising right now?

那你们一定要听一下这个。

Then you need to hear this.

Startupfundraising.com 是创业者用来将融资速度提升三倍的AI平台。

Startupfundraising.com is the AI platform founders are using to raise money three times faster.

你只需上传你的路演材料，它就会立刻用AI告诉你哪里有问题以及如何改进。

You plug in your deck and boom, it tells you the AI what's wrong and how to fix it.

你会被匹配到真正投资类似你这样的初创公司的投资人，并在一个统一的指挥中心内完成路演、跟进，直至最终成交。

You get matched with real investors actually funding startups like yours and you run your race inside one command center pitch track and follow-up all the way to close.

已有获得顶级基金支持的创始人在使用它。

Founders backed by actually top tier funds are already using it.

如果你没有使用，那你就会处于劣势。

If you're not, you're at a disadvantage.

所以现在就去 startupfundraising.com 免费注册吧，因为靠猜测不是一种融资策略。

So go to startupfundraising.com now and get free access because guessing is not a fundraising strategy.

所以对于正在听的朋友们来说，Stefano，Inception 公司最终的商业模式是什么？

So I guess for the people that are listening then, Stefano, what ended up being the business model of basically the company of inception?

我的意思是，你们赚钱吗？

I mean, do you guys make money?

是的。

Yeah.

我们花了公司最初大约六到九个月的时间，纯粹专注于技术研发。

So we spent the first, probably six to nine months of the company just developing the technology, purely

R

R

以及扩展我们最初的想法，实际构建出具有商业规模的融合语言模型。

and D scaling up the kind of like ideas that we had and actually building, commercial scale, the fusion language model.

因此，今年早些时候，我们成为全球首个发布商业规模扩散语言模型的公司，我们称这个模型为Mercury。

So earlier this year, we were the first in the world to release a commercial scale diffusion language model that we call this model Mercury.

你可以把它看作是ChatGPT、Gemini和Claude的竞争对手，但它基于完全不同的技术栈。

And you can think of it as competitor to the Charge GPT, to Gemini, to Claude that is based on a completely different stack, completely different technology.

我们将以这个扩散语言模型进入市场。

And we're going to market with this, diffusion language model.

我们的模型通过一个API平台提供服务。

We, our model is available through, an API platform.

我们的商业模式是向全球的企业客户和开发者提供该模型的访问权限，他们正在基于这些扩散语言模型构建生成式AI应用，这些模型速度更快、成本更低，从而帮助他们为应用提供更出色的体验，因为模型速度极快。

And our business model is we are providing access to this model to enterprise customers and developers around the world who are building Gen AI applications on top of these diffusion language models that are much faster, much cheaper, and that's enabling them to deliver much better experiences in their apps because the models are so fast.

你不必等待太久就能获得高质量的结果。

You don't have to wait so long to get high quality results.

我们的模型已经部署在多种场景中。

And so our models are already deployed in a variety of settings.

我们拥有多家客户，包括财富500强公司。

We have a number of customers, including fortune 500 companies.

一个可能引起您许多观众共鸣的用例是代码自动补全和氛围编码。

One use case that probably would resonate with a lot of your viewers is for example, code autocomplete and vibe coding.

每个人都使用其他工具来实现更快速、更高效的编程。

Like everybody's using other lens to get better and faster coding.

我们的模型在建议代码补全和需要对代码库进行的修改方面表现非常出色。

Our models are really good at suggesting code completions, suggesting changes that you need to do to a code base.

它们已经被部署在多种代码编辑器中作为默认模型，因为其性能更优越。

And they're deployed already in a variety of coding IDs as default model because they deliver superior performance.

如果您需要快速向开发者提供答案，扩散大语言模型是最佳选择。

If you need to be able to give an answer to a developer very quickly, diffusion LLMs are the way to go.

它们比其他竞争性的自回归模型更快、质量更高。

They're much faster, higher quality than competing autoregressive models.

在真正开发出合适的技术这一过程中，感觉如何？

How has it been to the journey of really landing developing the right technology here?

因为归根结底，对于这样的项目来说，这才是最重要的。

Because, I mean, in the end, that's everything for something like this.

是的，这段旅程很有趣。

Yeah, it's been a fun journey.

这其实是我之前在斯坦福就一直在做的事情。

It's something that I had been doing at Stanford for a while.

从这个角度看，本质上并没有太大不同，因为很多工作都涉及研发，以及尝试不同的方法、不同的GPU和不同的算法。

So from that perspective, not too different in the sense that a lot of it involves R and D and experimenting with different approaches, different kinds of GPUs and different kinds of algorithms.

所以，很多内容和我们在斯坦福之前做过的事情类似，但规模不同了。

So a lot of that was similar to things we had done before at Stanford, but the scale was different.

你知道，运行实验的成本变得高得多，而且需要交付这类成果的工程团队也更大了。

Like, you know, it became much more expensive to run experiments, just like the engineering teams that are needed to deliver these kind of results are bigger.

因此，需要更多的协调，不再只是单个或几个研究生写博士级别的、经常出错的研究型代码了；在代码质量以及系统开发方式上的期望也完全不同。

And so it requires more coordination and, you know, it's no longer just a single or a couple of grad students kind of like writing PhD level code, research grade code that breaks all the time and like the expectations in terms of like quality of the code and the and the the way we develop the systems are are are very different.

这真是一段有趣的经历，能够有一支庞大的团队朝着同一个方向努力，而不是像典型的学术实验室那样，每个学生都各自为政、互不相干。

So it's been a it's been a it's been a fun experience to to actually have a a big team of people all working in the same in the same direction as opposed to the more typical academic, a lab structure where, you know, each student is kinda like working on their own thing and they're all kinda like separate.

能够看到所有人都齐心协力、专注同一个目标，并拥有合适的资源和团队，这真的非常棒。

It's been it's been really great to to sort of like everybody's pushing in one direction to be laser focused on on this one goal, with the with the right resources, the right team.

这确实很令人兴奋。

It's been, yeah, it's been exciting.

那资本方面呢？

What about the capital side of things?

因为显然，开发这样的东西成本非常高。

Because obviously to develop something like this is very expensive.

事实上，你们今天要向我们宣布一些令人兴奋的消息。

And in fact, you have something to announce today with us, to share with us, which is quite exciting.

但在这个过程中，获得合适类型的资源、资金和人才，感觉如何？

But how has it been that journey to getting the right type of resources and capital and people for this?

是的，这并不太难，因为我在斯坦福时就有很多风险投资人的联系，而且大家对人工智能和生成式人工智能都非常感兴趣，而我们又非常独特。

Yeah, so it's been not too difficult in the sense that I think being at Stanford, I had a lot of connections with venture capitalists and there's a lot of appetite for AI and generative AI, and we're very unique.

我们拥有独特的优势。

Like we have this unique value proposition.

这是我们依然热爱的事情。

It's the kind of thing that we still love.

这件事有很小的可能性会变得非常巨大。

There's some small probability that this thing will become huge.

因此，我们很幸运地看到了很多人对我们所构建的东西感兴趣。

And so we were lucky to see a lot of interest in what we're building.

我们很早就与梅菲尔德的纳维·查达合作，我们很熟悉他，他也曾就读于斯坦福。

We partner early on with Navi Chadda at Mayfield, who we knew well and was also at Stanford before.

因此，我们与他们和梅菲尔德有着深厚的联系，并从他们那里获得了启动资金。

So we had a deep connection with them and Mayfield, and, we got some initial capital from them to get started.

然后，是的，我们刚刚宣布了新一轮更大的种子轮融资。

And then, yeah, we, we just announced, a bigger seed round.

我们刚刚完成了5000万美元的种子轮融资，由梅隆风投领投。

We just raised $50,000,000, from, this around seed round, led by Mellon Ventures.

此外，还有其他几家重要的风险投资机构和战略合作伙伴参与，包括微软、英伟达、Databricks 和 Snowflake。

And, with, with participation from few other key, VCs and strategics, including Microsoft, the Nvidia, Databricks, Snowflake.

许多顶级风投和战略合作伙伴都清楚地看到，我们所构建的技术具有巨大潜力，他们预见未来扩散式语言模型将成为大语言模型和生成式AI解决方案的标准。

So a lot of top VCs and strategic partners are really seeing, are, you know, seeing that there is a lot of potential in the technology that we're building and they see a future where diffusional language models will become the standard for LLMs and generative AI solutions.

这太棒了。

That's amazing.

你知道，对于正在听的听众来说，纳文绝对是个传奇人物，他也上过我们的播客。

You know, well, obviously for the people that are listening, Navin, an absolute legend, you know, he's been on the podcast too.

来自 Databricks 的阿里·戈特西，他也上过我们的播客。

Ali Gottsi from Databricks, you know, also on the podcast.

你们身边聚集了这么多优秀的人才，真了不起。

Great people that you guys are surrounding yourselves with, so well done.

我想，当你吸引到这种级别的顶尖人才——无论是员工还是目标客户——归根结底，他们都是在押注你所坚信的未来愿景。

I guess when you're getting this type of high caliber individuals whether it's also employees or even the customers that you're going after, in the end, they're all betting on a vision on the future that you're living in.

如果今晚你睡一觉，明天醒来时，Inception 的愿景已经完全实现，那样的世界会是什么样子？

And I think that in that regard, if you were to go to sleep tonight, Stefano, and you wake up in a world where the vision of inception is fully realized, what does that world look like?

是的，这是一个AI普及并赋能每一家企业的世界，AI解决方案运行迅速。

Yeah, it's a world where AI is available to and empowering every business and AI solutions are fast.

它们高效。

They're efficient.

它们快如人类的思维。

They are as fast as human thought.

你无需等待就能得到答案。

You don't have to wait to get an answer.

一切都是即时的。

Everything is immediate.

一切都是高质量、稳健且可靠的。

Everything is high quality, is robust, reliable.

它几乎让人感觉像魔法，归根结底，任何技术的终极目标都是让人感觉像魔法。

And it almost feels like magic At the end of the day, that's always the goal of any technology that it has to feel like magic.

而我认为，这一点目前仍然缺失。

And that I think is still missing.

事情进展缓慢，效率低下，不可靠。

Things are slow, they're inefficient, they're unreliable.

我们还没有达到那个水平。

We're just not there.

我看到一个未来，扩散语言模型将使我们能够 everywhere 拥有这类人工智能解决方案。

And I see a future where diffusion language models will enable us to have those kinds of AI powered solutions everywhere.

这太棒了。

That's amazing.

现在我们展望未来。

Now we're looking forward.

我想回顾过去，但带着反思的视角。

I want to look back, but doing so with a lens of reflection.

假设你回到了斯坦福，正处在那个时刻，想着：啊，我要离开学术界了吗？

If let's say you're back at Stanford and you're there at that moment where you're like, Ah, am I leaving academia?

我要成为创业者了吗？

Am I going into becoming a founder?

我在这里做什么？

What am I doing here?

假设你能回到那个时刻，站在年轻的斯特凡诺面前，给他一条建议，然后再开始你的创业之旅。

Let's say you're able to show up right there in front of that Stefano you're able to give that younger self one piece of advice before going at it with inception.

基于你现在所知道的一切，你会对大约一年前的年轻斯特凡诺说些什么？为什么？

What would you tell that younger Stefano about a year ago or so, and why, given what you know now?

我认为，我会说，人是最重要的，你要多和优秀的人在一起，组建正确的团队，这样一切皆有可能。

I think, I would, I would say that, you know, people are the most important thing, you know, just, just surround yourself with great people and, and, and the right team and then everything is possible.

我喜欢这一点。

I love that.

我还想问你一个问题，对于那些想了解更多、或者想来打个招呼的听众来说，斯特凡诺，他们最好的联系方式是什么？

One thing that I like to ask you too is for the people that are listening that would really love to be able to learn more or to reach out and say hi, what will be the best way for them, Stefano, to do so?

是的，请访问我们的网站 inceptionlabs.ai。

Yeah, please come to our website, inceptionlabs.ai.

你可以在那里找到我们平台的链接，如果你想使用我们的模型，或者想构建由顶尖扩散语言模型驱动的下一代AI解决方案，请访问我们的平台。

You'll find links to our platform if you wanna use your models, you wanna build next generation AI solutions powered by the best in class diffusion language models, please come to our platform.

如果你想要和我们的模型聊天，有一个互动平台。

There's a playground if you wanna chat with our model.

我们正在招聘，公司正在积极扩张。

And we are hiring, we're actively growing the company.

所以请访问我们的招聘页面，申请加入我们，一起打造生成式AI的未来。

And so please visit our job section and apply to come and work with us on what's going to be the future of generative AI.

太棒了。

Amazing.

好了，Stefano，非常感谢你今天做客《交易者秀》。

Well, Stefano, thank you so much for being on The Dealmaker Show today with us.

能邀请到你，我们感到无比荣幸。

It has been an absolute honor to have you.

非常感谢你们邀请我。

Thank you so much for having me.

这真是一场非常愉快的对话。

It's been a really fun conversation.

如果你喜欢这个节目，请记得点击订阅按钮。

If you like the show, make sure that you hit that subscribe button.

如果你能留下评论，那就太好了。

If you could leave a review as well, that will be fantastic.

如果本集或整个节目对你有任何帮助，请分享给朋友。

And if you got any value either from this episode or from the show itself, share it with a friend.

也许他们也会欣赏这些内容。

Perhaps they also appreciate it.

所以请记住，如果你在融资或出售企业方面需要任何帮助，可以联系我，邮箱是 AlejandroPantheradvisors dot com。

So also remember that if you need any help, whether it is with your fundraising efforts or with selling your business, you can reach me at AlejandroPantheradvisors dot com.

你已经听完了《The Deal Makers Podcast》的另一期节目。

You've reached the end of another episode of The Deal Makers Podcast.

如需免费资源和资料，请访问 alejandrocremotes.com。

For free resources and materials, head over to alejandrocremotes.com.

感谢收听，我们下期节目再见。

Thank you for listening and see you at the next episode.