#863：埃拉德·吉尔——帝国缔造者的顾问：如何在他人之前发现十亿美元级公司、迷雾般的AI前沿、可口可乐如何击败百事、共识何时带来回报，以及更多内容

你知道，每个应用场景都有不同的公司。

You know, there's these different companies per application.

你可以从三个或四个角度来分析。

There's three or four lenses that you can look at.

如果底层模型变强了，你的产品或服务会不会因此大幅提升，让用户还愿意继续用你？

One is if the underlying model gets better, does your product or service get dramatically better for your customers in a way that they still wanna keep using you?

第二，从产品角度看，你的布局有多深、多广？

Second, how deep and broad are you going from a product perspective?

你是不是在开发多个产品？

Are you building out multiple products?

这些产品有没有整合成一个有机的整体？

Are they all integrated in an cohesive whole?

它是不是已经深度嵌入到企业的流程里，让人根本离不开？

Is it really being built directly into the processes in a company in a way that it's hard to pull out?

你知道，很多公司用AI时，问题往往不在于AI本身够不够好。

You know, often the issue for companies in adoption of AI isn't how good is the AI.

关键是你得花多大劲去改流程，改员工做事的方式，才能用上它。

It's how much do I have to change the workflows and the ways that my people do things in order to adopt it.

这通常其实是变革管理的问题。

It's about change management usually.

根本不是技术问题。

It's not about technology.

只要你能深深嵌入到工作流程里，融入人们做事的方式，把方方面面都串起来，那你的东西就特别难被取代。

And so if you've been able to embed yourself enough into workflows and how people do business and how they they work and how everything else kind of ties together, that tends to be quite durable.

你有没有在收集、存储和利用专有数据？

Are you capturing and storing and using proprietary data?

有时候确实挺有帮助的。

Sometimes it's useful.

我觉得数据优势这事儿整体被夸大了，但有时候真能派上大用场，通常就是那种‘系统记录’的视角。

I think data modes in general are overstated, but I think sometimes it can be actually quite useful, and that's usually the system of record view of the world.

所以，归根结底，就那么几条标准：这东西能不能长期站得住脚？

So, you know, there's a handful of criteria around, like, will this thing be long term defensible or not?

而在应用层面上，这通常也是一个值得一看的角度。

And the application level, that's often, you know, one potential lens on it.

所以有个问题，如果有人在听这个，而且他们正好是创始人，可能需要考虑在估值高峰期赶紧退出，那他们有哪些选择呢？

So question, if people are listening to this and they are in the position of perhaps a founder who should consider identifying their kind of short period of maximum valuation and perhaps hitting the parachute in some way.

有哪些路可以走？

What are the options?

我想起一些公司，我不点名了，但确实有不少公司估值都到了几十亿美元。

Because I think of some of these companies, I'm not gonna name them, but there are multiple companies that have multibillion dollar valuations.

从一个外行——比如我——的角度来看，这些实验室其实要复刻他们现在卖的东西，应该也不算太难。

There's seems to be, again, from a mostly layperson perspective, I e me, that the labs probably can build what they are currently selling without too much trouble.

他们是想被实验室收购吗？

Do they aim to be acquired by a lab?

如果是这样，那实验室自己就得在‘自建’和‘收购’之间做选择了。

In which case, there's sort of a build versus buy decision for the lab itself.

他们瞄准的会不会是那些不想只当旁观者、想真正下场的玩家？比如亚马逊，或者别的谁？

Are they aiming for one of not the OpenAI's or Anthropics, but maybe somebody who's trying to get more skin in the game, like Amazon or fill in the blank?

那退出的途径有哪些？

What are the exit options?

我觉得退出的路子多得很。

I think there's a lot of exit options.

现在最疯狂的是，你要是回看十年前、十五年前，全球市值最高的公司也就三千亿美元左右。

And the thing that's crazy right now is if you go back ten or fifteen years, the biggest market cap in the world was, like, 300,000,000,000.

当时科技公司里市值最高的，大概也就两千亿美元上下吧。

The biggest tech market cap was, I don't know, 200 ish or something.

那时候最大的应该是埃克森美孚之类的公司，对吧？十五年前。

I think the biggest one at the time was Exxon or somebody, right, like, fifteen years ago.

但过去这十几年，我们突然冒出一堆万亿美金市值的公司，当时人人都觉得疯了，可现在看来，未来只会更大。

And over the last ten or fifteen years, what happens is we suddenly ended up with these multi trillion dollar market caps, which everybody thought was nuts at the time, but things will probably only get bigger.

资源会越来越往头部玩家手里集中，而不是分散。

There'll probably be more aggregation versus less into the biggest winners.

现在有越来越多公司，市值在一千亿到几万亿之间，这种规模以前根本没见过。

And there's more and more companies who have these market counts between, say, 100,000,000,000 and a few trillion in a way that's just unprecedented.

这意味着巨大的购买力，因为3万亿美元的1%就是300亿美元。

And that means there's enormous buying power because 1% of 3,000,000,000,000 is 30,000,000,000.

对吧？

Right?

所以你随便花300亿美元买下一家公司，这简直太疯狂了。

So you can put 1% and pay $30,000,000,000 for something, which is insane.

对吧？

Right?

这种事以前真没见过。

That's that's really unprecedented.

这也意味着，这种超大规模的收购真的可能发生。

And that means that these really big acquisitions can happen.

就我想到的那些公司来说，我就不点名了。

For the companies that I'm imagining, again, I don't wanna name names

是的。

Yeah.

这可能看起来没多久就过时了。

That may have seem to have a limited lifespan.

我经常跟一群朋友在小群聊里，也不总是，但确实挺多的。

When I'm in these small group threads with friends of mine who are often time, not always, but I'm in a bunch of them.

当他们都是特别成功的科技投资人时，我就说：好，你们这五家公司，手上有十艘船。

And when they're tech investors, very successful tech investors, and I'm like, okay, these five companies, you've got 10 ships.

你会怎么分配这十艘船？

How would you allocate your 10 ships?

对吧？

Right?

有些公司，虽然挺有名的，却总是拿不到任何投资。

There are certain companies that can consistently get zero even though they're reasonably well known.

那为什么某个实验室会去收购它们呢？

Why would one of the labs buy one of those?

得看是什么东西，也可能是实验室自己在搞。

Depends on what it is, and it may be a lab.

可能是苹果、亚马逊、谷歌这类大型科技巨头。

It may be one of the big tech incumbents in Apple, Amazon, Google's kind of the things.

还有甲骨文。

There's Oracle.

还有三星。

There's Samsung.

还有特斯拉。

There's Tesla.

现在连SpaceX也入局了，干着各种事儿，买家类型可多了。

There's SpaceX now in the market doing things that you know, there's a bunch of different buyers of different types.

还有Snowflake和Databricks。

There's Snowflake and Databricks.

还有Stripe。

There's Stripe.

对。

Right.

Coinbase这种公司搞的是金融领域，其实有很多规模很大的企业。

Coinbase, you're doing financial sort of there's just a ton of companies that actually are quite large.

这正是我想说的重点。

That's kind of the point.

对。

Mhmm.

所以你很多时候最终都是卖给这四类人之一。

And so often you end up selling to one of four things.

对吧？

Right?

你可以卖给那些大型实验室、超大规模云服务商，或者巨头科技公司。

You can sell to one of the big labs or hyperscalers or giant tech companies.

也可以卖给对你所在行业特别关注的客户。

You can sell to somebody who cares a lot about your vertical.

比如你做法律或会计相关业务，那就可以卖给汤森路透这样的公司。

So for example, a Thomson Reuters if you're doing legal or accounting or things that are kind of related to that.

我觉得其实少有人做的一件事，就是竞争对手之间的合并，尤其是私企，这种事其实完全可以做。

I think actually one thing that doesn't happen enough is merger of competitors, particularly private companies where you can do that.

毕竟，如果你的目标就是赢，而你和对手势均力敌，每单生意都要抢，还把价格打到地板上，那还不如直接合并算了。

Because ultimately, if your primary vector is winning and you're neck and neck with somebody and you're competing on every deal and you're destroying pricing for each other, like, maybe it's better to just merge.

对吧？

Right?

这其实就发生在九十年代的X.com和PayPal身上。

That actually was x.com and PayPal in the nineties.

对吧？

Right?

当时埃隆·马斯克、彼得和皮埃尔各自管着不同的公司，最后选择合并了。

Elon Musk, Peter and Pierre were running different companies, and they merged.

因为他们说，咱们都是干同一件事的人。

Because they said, we're people doing this.

何必互相较劲呢？

Why fight?

是的

Yeah.

就像当年的优步和Lyft。

Or Uber, Lyft way back in the day.

对吧？

Right?

那可能不是合并。

That might not have been a merger.

更可能是被收购了。

It might have been an acquisition.

对。

Yeah.

据说当时差点就谈成了，结果优步那边临阵退出了。

And the rumor is that that almost happened, and then, you know, the Uber side walked away from it.

优步这些年花在和Lyft打价格战上的钱，说不定还不如直接把对方买下来。

But all the money that Uber spent on fighting Lyft for all those years maybe would have been better spent just buying them.

也不一定。

Maybe not.

对吧？

Right?

我也不清楚具体数字。

I don't know the exact math on it.

但很多时候，真不如干脆说：算了。

But often, it actually does make sense to say, you know what?

咱们别打了，直接合并，一起赢就是了。

We'll just stop fighting it out, and we'll just combine and just go win.

你懂的。

You know?

因为既然目标是拿下市场，你本来就得跟那些老牌巨头死磕。

Because if the primary purpose is to win the market, you're already fighting all these big incumbents that already exist anyhow.

那为啥还要给自己添堵呢？

So why why make it even harder?

你也知道，我们平时经常聊这类话题，但今天咱们换个角度，来聊聊你作为投资人的经历。

As you know, we talk about this a lot, but we'll talk about you with your investing hat on.

不过在聊全职投资的事之前，先说说你早年的那些经历吧，它们多多少少都对你后来的路有帮助。

But before you even put that, let's call it full time investing hat on, you had a lot in your background that may or may not have helped you.

我很好奇，你自己觉得学生物、学数学的这些背景，有没有从根本上影响了你做投资的思路？这些积累有没有帮你建立起优势？要知道抢项目分很多阶段，有的时候赛道还没那么挤，但先不说别的，

And I'm curious if you look at your biology background, the math background, do you think any of those things or other elements materially contributed to how you think about investing that has given you an advantage in, I suppose, there are different stages to kind of winning deals, but sometimes they're not crowded.

咱们先从项目筛选的环节聊起吧。

But let's just talk about the selection process.

我觉得数学知识在两个方面帮到了我。

The math stuff helped me, I think, in two ways.

一方面是它帮我搞定了计算机科学里一些技术、算法相关的内容，能把这些知识学明白。有时候研究AI的工作原理之类的场景里这些就很有用，说白了就是对数字、数据很敏感，用行话讲就是圈内人能聊到一块儿去。

One is it's helped me with certain aspects of, like, technical or algorithmic CS and understanding it, and sometimes that's useful in the context of how certain things work in AI or things like that or just fluency of numbers and data and, I don't what to call it, nerd language or something.

说实话我读数学专业纯粹是因为喜欢，而这份热爱反而成了最有用的东西。

And I did the math degree honestly just for fun, and I think that's actually the thing that was helpful.

我也就只读到了数学本科，没往深了继续研究。

You I know, only did an undergrad degree in math, so didn't go that far with it.

但我做的都是特别抽象的纯数学，这反而逼着我一步步严谨地逻辑思考问题。

But I did the very sort of abstract pure math stuff, and I think that was a good forcing function of how to really think logically step by step about things.

因为至少我学证明的方式是：先按逻辑一步步推，但有时候也会突然来个直觉跳跃，然后再回头去验证它，把直觉背后的道理补全。

Because, you know, roughly the way that at least I learned how to do proofs was you do the logical sequence, but then sometimes you do these intuitive leaps and then go back and try and prove it to yourself or flesh out the reasoning behind that intuitive leap.

我觉得投资有时候也挺像这么回事的。

And I think sometimes investing is a little bit like that.

你是什么时候第一次觉得，自己可能在投资上能做得不错？

When did you first have the inkling that you could be good at investing?

这里的投资，可以是广义的。

And that could be investing writ large.

也可以是我们对话里提到的创业和天使投资这个范围。

It could be maybe within the context of our conversations, startups and angel investing.

你是什么时候第一次心里冒出个念头：这事儿我或许真能行？

When did you first kind of go, Yeah.

也许我真能在这行干出点名堂。

Maybe I could be good at this.

有没有哪个瞬间、哪笔投资，让你突然想起来的？

Was there a moment or a deal or anything like that that comes to mind?

还真没有。

Not really.

我对自己的要求特别严。

I'm really hard on myself.

到现在，我还是老爱怀疑自己。

So, you know, even now, I second guess myself a lot.

有个人跟我说，最会事后自责的两个人，一个是我，另一个是位挺有名的创始人兼投资人。

Somebody was telling me that the two people that always beat themselves up the most in hindsight is me and this one other person who's another well known founder slash investor.

所以我觉得，压根儿没有哪个瞬间让我突然觉得：哇，这事儿我真适合干。

And so I think, you know, I don't think there's a single moment where I'm like, wow.

我就是觉得，这事儿好像挺顺其自然的。

This makes sense for me to do.

因为我投的公司一个比一个牛，就这么一路做下来了。

I think it just kind of organically kept going because I was getting into some very strong companies, and then that allowed me to sort of continue what I'm doing.

是的

Yeah.

我真希望自己当初没那样做。

I wish I hadn't done that like that.

真该死。

Goddamn it.

你得像所有优秀的创始人那样，重新梳理一下你的创业故事。

You need to revise your Genesis story like every every good founder.

所以

So

对啊

Yeah.

从七岁起，我就一直在想怎么投资科技领域。

I mean, ever since I was seven, I've been thinking about investing in technology.

没错。

Right.

那你是怎么拿到这些投资机会的？

So getting into those deals, what allowed you to get into those deals?

对吧？

Right?

有些人就是信息占优势，他们故意把自己放到能获取信息的位置上。

Because some people have an informational advantage, and they put themselves in a position to have an informational advantage.

对吧？

Right?

要不是我一开始就没觉得这是个引导性问题，我得说，要不是我2000年那时候搬去硅谷，后来还留在那儿、专门搬到旧金山，我根本不可能做成后来那些天使投资。

And I think that had I not thought this to be a leading question, it's like, had I not moved to Silicon Valley when I did, like 2000, and then subsequently stayed there, moved to San Francisco specifically, like nothing that I was able to do in angel investing would have been possible.

但你的故事不止这么简单，因为很多人搬去那儿，都是抱着赚 startup 横财的指望。

But there's more to your story because a lot of people move there with hopes of startup riches in whatever capacity.

我不是说你搬去就是为了这个。

Not saying that that's why you moved there.

但到底是什么让你拿到了这些投资机会？

But what was it that allowed you to get into those deals?

对吧？

Right?

我脑子里其实有几件事，是根据咱们之前聊的想到的，但我就先不展开说了。

Because there are certain things that come to mind based on our prior conversations, but I'll just leave it at that.

那你到底是怎么选中或接触到这些项目的？

Like, why were you able to get into or select those deals?

我觉得是早期发生的事和现在发生的事，这两者其实不一样。

I think it was what happened early and what happens now, and I think those two things are different.

你说得对，想进入任何一个行业，最重要的一点就是去那个行业的总部或者聚集地，直接搬过去。

I think to your point, the single most important thing for anybody wanting to break into any industry is go to the headquarters or cluster of that industry, like move to wherever that thing is.

说什么都能远程、哪儿都能干，全是扯淡。

And all the advice that you can do anything from anywhere and everything's remote is all BS.

不只是科技行业，每个行业都这样。

And you see that for every industry, not just tech.

比如说你想进电影圈，没人会跟你说：嘿，

You know, if you wanted to get into the movie business, people wouldn't say, you know, hey.

电影剧本哪儿都能写。

You can write a film script from anywhere.

配乐也能在网上搞定。

You can digitally score from anywhere.

剪辑更是哪儿都能做。

You can edit it from anywhere.

拍摄地点更随便了。

You can film it anywhere.

你要是说去达拉斯吧。

Like, go to Dallas.

人家肯定说：去好莱坞。

They'd say, go to Hollywood.

搞金融的人老说，融资哪儿不能融？交易策略、对冲基金方案哪儿不能想？哪儿都能干。

And if you wanna do something in finance, you're like, well, you could raise money from anywhere and come up with trading strategies and a hedge fund strategy from anywhere, and you could do it from anywhere.

但你要是说，嘿——

You know, people wouldn't say, hey.

去那儿，随便吧，西雅图也行。

Go to, you know, whatever, Seattle.

他们肯定会说，去纽约，或者去那个啥金融中心。

They'd like, go to New York or go to x y z financial center.

科技领域也是同样的道理。

So the same is true for tech.

莎伦和我团队一直在做一种独角兽式分析，看私人市场估值在科技领域都集中在哪儿。

Sharon and my team has been performing this sort of unicorn analysis of where is all the private market cap aggregating for technology.

以前差不多一半在美国，而美国那半边又有一半集中在湾区。

And traditionally, about half of it's been The US, and then half of that has been the Bay Area.

但有了AI之后，91%的私人科技公司估值都集中在湾区。

But with AI, 91% of private technology market cap is the Bay Area.

全球91%的AI市场估值，全挤在这么一小块地方，就十公里见方那么大。

91% of the entire global set of AI market cap is all in one, you know, 10 by 10 area.

所以你要搞AI，最好还是待在湾区。

So if you wanna do stuff in AI, you should probably be in the Bay Area.

大概第二大城市是纽约，再往后就急剧下滑了。

Probably the secondary place is New York, and then after that, it drops off a cliff.

对吧？

Right?

说到底，还是湾区。

And, really, it's the Bay Area.

如果你想搞国防科技，最好还是待在南加州，比如SpaceX和Anduril所在的尔湾、橙县那一带，或者埃尔塞贡多。

If you wanna do defense tech, you probably should be in, you know, Southern California, close to where SpaceX and Anduril are in sort of Irvine, Orange County, etcetera, or El Segundo.

那儿初创公司可多了。

There's a lot of startups there.

如果你想做金融科技或者加密货币，那可能纽约更合适。

If you wanna do fintech and crypto, maybe it's New York.

但现实是，这些地方都是高度集中的产业集群。

But the reality is these are very strong clusters.

所以说到你刚才那点，第一，我正好在对的地方。

So to your point, number one is I was just in the right location.

我当时处在对的圈子里，而且我自己也在创业，自然而然就有人来找我讨教。

I was in the right networks, and I default was you know, I was running a startup myself.

我在谷歌待了很多年，后来离开去创办了自己的公司，之后就不断有人来找我提建议。

I was at Google for many years, and then I left to start a company, and people just started coming to me for advice.

我投资Airbnb的契机是，他们刚八个人的时候，我帮他们对接了一些人，稍微给了一点战略上的建议，帮他们完成A轮融资。

And the way I ended up investing in Airbnb is I was helping them when they were eight people or something raise their series a and introduce them to a bunch of people and help with some of the strategy there in very light ways.

对吧？

Right?

就算没我，他们也能做成。

They would have done it without me.

他们后来跟我说：

And they said, hey.

事情搞定了，你要不要也投一点？

At the end of it, do you wanna invest a little bit?

我说：好啊。

I said, great.

听起来真不错。

That sounds wonderful.

所以这完全是顺其自然的。

So it's very organic.

我投资Stripe的方式是，我之前把一家做底层API的公司卖给了Twitter。

Or the way I invested in Stripe is I'd sold a sort of infrastructure early API company to Twitter.

当时Twitter大概有90号人左右，我就给Stripe的CEO帕特里克发了封邮件，说：嘿。

And when Twitter was, say, 90 people or so, and I sent an email to Patrick, the CEO of Stripe, just saying, hey.

我听说你特别棒，Stripe做的事我也特别认同，要是我自己创业，肯定用这个产品。

I've heard great things about you, and I really like what Stripe is doing, and I would use it for my own startup.

我自己刚把那家API公司卖了。

And I sold this API company myself.

要不要聊聊这事儿？

Do wanna just talk about this stuff?

后来我们一块儿散了几次步，大概一两周后，他给我发了条消息：嘿。

And so I went on a couple walks, and then a week or two later, he texted me, and he's like, hey.

我们现在在搞一轮融资。

We're doing a round.

要不要一起投？

Do wanna invest?

我最早做的那几笔投资都很自然，创始人自己就主动说：‘我想让你加入。’

So the first few things that I did were very organic, where the founders were like, oh, I want you on board.

我根本没想过：‘我得当个投资人，得去追项目。’

I didn't think, oh, I should be an investor, and I'm gonna chase things.

我只是特别喜欢跟聪明人聊天，对某些商业问题感兴趣，也特别热爱科技以及它对世界的重塑。

I'm gonna I just, like, really like talking to smart people, and I liked working on certain business problems, and I love technology and its translation of the world.

所以其实就是，我就是一个技术宅，认识了其他技术宅，一聊就特别投缘。

And so it was very, you know, I was just a nerd, and I I met other nerds, and we get hit it off.

我突然想到，你肯定听过这话，或者别人跟你说过：如果你想搞钱，就去讨教建议。

It just struck me that I'm sure people have heard or I'm sure you've heard this before, but, you know, if you want money, ask for advice.

如果你想听建议，那就去要钱。

If you want advice, ask for money.

我突然想到，反过来也是一样。

It just struck me that it kinda goes the other way around too.

你要是主动给很多建议，往往人家就会给你投资。

It's like, if you offer a bunch of advice, oftentimes you get to give money.

如果你想着要投钱，嗯——

And if you try to give money Yeah.

人家反而会来找你讨教。

You might get solicited for advice.

对。

Yeah.

对。

Yeah.

这话说得在理。

That's a good point.

你是什么时候写完《高增长手册》的？

When did you write the High Growth Handbook?

那是什么时候出版的？

When was that published?

已经好一阵子了。

It's a while ago now.

大概七年前吧，差不多是那时候。

It's probably like seven ish years ago, something like that.

七年前啊。

Seven years ago.

明白了。

Alright.

我们待会儿再回来说这个。

We're gonna come back to that in a minute.

从地理位置上来说，你确实占了便宜。

You were in the right place geographically speaking.

对吧？

Right?

你当时正好处在交换机的核心位置。

You were in the center of the switchboard.

就像你自己说的，那些最初特别亮眼的投资，都是很自然地冒出来的。

And like you said, these some of these initial kind of standout investments came about very organically.

我特别想知道的是，你不久前还说过，‘我以前怎么做，现在怎么做’，

And what I'd be curious to hear because you also said yourself not too long ago that there's what I did then, there's what I did now.

但中间那段路呢？你那时候是怎么走过来的？

There's also what you did in between, right, along the way.

我想问问，你现在还会坚持当年那个看法吗？

And I'm wondering, for instance, if you would still stand by this.

这是之前我提到的那次初访时说的话，

This is from that first round interview I was mentioning.

我投资一般优先看市场，团队实力排第二，当然还有别的考量，

As a general rule, when I make investments, it's market first and the strength of the team second, and there's more to it.

但你现在还认同这个观点吗？

But would you still agree with that?

百分之九十。

90%.

对。

Yes.

偶尔你会碰到特别出众的人，哪怕时机还特别早，你也想支持他们。

Every once in while you meet somebody exceptional and you just back them or something maybe so early.

比如我领投Perplexity的首轮投资时，那真的是最早期的阶段。

Like, when I I led the first round of Perplexity, like, very, very first round.

当时的情况是，创始人阿尔文直接在LinkedIn上给我发了条消息，真的就是随手一发。

And the way that came about was Arvind, the CEO, just I think he, like, pinged me on LinkedIn, literally.

那时候根本没人关注AI，而他当时是OpenAI的工程师或研究员。

And this was when nobody was doing anything in AI, and he was like an OpenAI engineer or researcher.

他跟我说：嘿。

And he's like, hey.

他在OpenAI工作，可那时候根本没人把OpenAI当回事。

I'm at OpenAI, which nobody cares about at the time.

我当时正想搞点AI相关的项目，听说你在聊这事儿，可别人都不提这个。

And I'm thinking of doing something in AI, and I heard that you're talking about this stuff, and nobody else is talking about it.

咱们能见个面吗？

And can we meet up?

于是我们就每两周见一次，一起头脑风暴，后来就干脆投了他们。

And so we just started meeting every two weeks and brainstorming, and then that led to, like, investing in that.

这事儿本质上是看人，他真的特别出色。

And that was kind of a people first thing where he was just so good.

每次聊完，他总能隔一周就把我们讨论的东西做出来。

And every time we talk, he'd show up a week later with the thing that we discussed built.

谁家干这事啊？

Like, who does that?

是的。

Yeah.

对。

Yeah.

这可是个好兆头。

That's a good sign.

太棒了。

So good.

我后来投资Anduril，是因为谷歌把他们的国防项目Maven给关了。

Or, you know, the way I ended up investing in Anduril was, you know, Google shuts down Maven, which was their sort of defense project.

我就想，要是大公司都干这个，那初创公司岂不是正好有空间切入？

And so I think, well, if if the incumbents are gonna do it, what a great place for startups to play.

硅谷和国防工业之间一直有很深的渊源。

Because there's been a long history of the, you know, Silicon Valley and the defense industry.

惠普就是例子，还有好多早期的公司都是这么来的。

That's HP, and that's a lot of the, you know, early brands.

所以我当时就在找人、找项目做这个领域，那时候根本没人看好。

And so I was just looking for something or somebody to work on this area, it was very unpopular at the time.

后来我在一次午餐会上碰上了Trey Stevens——Anduril的联合创始人之一，他也是创始人基金的，那会儿正好在正确的地方。

And I ran into I think it was Trey Stevens, who's one of the cofounders of Anduril, who's also a founder's fund at some lunch or something else again, right city to be in.

他说他在搞一个新国防项目，我一听就说：太棒了。

And he said, oh, I'm working on this new defense thing, and I said, amazing.

咱们聊聊吧。

Let's talk about it.

有时候是找市场，有时候是找人，两者都重要。

Sometimes it's just looking for these things too in a market, and sometimes it's people.

Anduril 就是先找市场，再找到牛人。

So Anduril was looking for a market and then finding amazing people.

Perplexity 那会儿算是中间状态——我当时在全面关注 AI，觉得这玩意儿肯定特别重要，但那时候真没几个人这么想。

Perplexity was kind of in between where it was like, I was looking at everything in AI because I thought it was gonna be incredibly important, but not very many people were.

后来我偶然遇到了一个特别出色的人。

And then I just ran across an exceptional individual.

就是那时候，我投了 OpenAI。

And that's when I funded OpenAI.

也是那时候，我投了 Harvey，那是早期的法律 AI 公司。我投了一堆特别早期的项目，因为当时就他们真在干我觉得未来会特别重要的事。

That's when I funded Harvey, which is the early legal I funded a lot of really early stuff because they were the only people doing anything in this market that I thought would be really important.

我再回过头聊聊你刚才说的几件事。

Let me come back to a few things you said.

你提到过Perplexity的创始人，或者后来那个说你在讨论这些事的人。

So you mentioned the Perplexity founder or later the founder who said you're talking about this stuff.

对吧？

Right?

他是听到、看到，还是怎么发现你在聊这些的？具体在哪？

Or he heard or read or found you talking about this Where was that?

是你博客上的那篇帖子吗？

Was that post on your blog?

还是别的地方？

Was it somewhere else?

他到底是怎么找到你谈论这些内容的？

How did he actually find you talking about anything?

是的。

Yeah.

我是觉得，他找我 partly 是因为我和之前那批科技公司关系挺深的，比如Airbnb、Stripe、Coinbase、Instacart、Square这些。

I mean, I think he pinged me in part because I was involved with a bunch of the prior wave of technology companies, Airbnb, Stripe, Coinbase, Instacart, Square, a bunch of stuff like that.

所以那时候，我已经算是个创始人兼投资人了。

And so I think at that point, I was already known as a founder and investor.

但除此之外，我还老去跟AI研究员们抬杠，就爱问他们最近在搞啥，特别有意思。

But then on top of that, I was just trolling AI researchers and just asking them about what's going on because it was so interesting.

那时候正好有人用一种叫GAN的东西做艺术创作，就是生成对抗网络。

There's a bunch of art that was being done with these things called GANs at the time, these generative adversarial networks.

我就自己瞎玩这些玩意儿。

And so I was playing around with that.

我还想雇工程师帮我搞个工具，因为我真觉得，要是能轻松做出AI艺术，那也太酷了。

I tried to hire engineers to build me effectively with my journey because I just thought it'd be really cool to make it easy to make AI art.

我先打断一下，这正好是我第二个问题，现在问挺合适。

Let me pause for a second because this is my second question, and it's a good time.

你提到AI的时候，我就觉得这事儿绝对关键。

When you mentioned, you know, AI, I thought it would be incredibly important.

是的

Yeah.

那有哪些迹象呢？

What were the indicators of that?

你当时看到哪些苗头，觉得这方向挺有意思？

What was the smoke in the distance where you're like, oh, that's an interesting direction?

我觉得大概有两三个吧。

I think there was two or three things.

AI 就是其中之一，大家一直都在聊这个。

AI was one of those things that people have always talked about.

我读数学的时候，选了很多理论计算机科学的课，那时候就有早期的神经网络课程，还有背后的数学原理。

So when I was doing my math degree, I took a lot of kind of theoretical CS classes, and there there were the early neural network classes and things like that and the math behind it.

所以一直以来，人们都期待着能造出各种各样的人工智能。

And and so there's always this promise of building these artificial intelligences of different forms.

有人甚至说，谷歌其实是第一家真正以 AI 为核心的公司。

And one could argue Google was a first AI first company.

那时候叫机器学习，技术基础跟现在也不太一样。

And back then, was called machine learning, and it was, you know, different technology basis in some sense.

我觉得2012年AlexNet出来的时候，大家才真正看到，原来AI系统能通过规模扩张展现出这么惊人的能力。

And I think 2012 was when AlexNet came out, and there was this proof that you can start scaling things and have really interesting characteristics in terms of how AI systems work.

到了2017年，谷歌的一个团队发明了Transformer架构，现在几乎所有东西都是基于这个的。

And then 2017 is when a team at Google invented the transformer architecture, which everything is based on now or roughly everything.

比如GPT，包括ChatGPT里的那个‘T’，指的就是Transformer。

And so for example, if you look at GPT for chat GPT, the t stands for transformer.

大概2020年左右，GPT-3发布了，跟GPT-2比简直是飞跃。

And around twenty ish, I think, was when GPT-three came out, and that was such a big step from GPT-two.

虽然那时候还远远谈不上能实际用，但你心里立马就咯噔一下：卧槽。

And it still wasn't good enough to really do stuff with, but you're like, oh, shit.

规模效应的威力已经挡不住了。

The scaling wallpapers are out.

能力的跃升完全是断崖式的。

The step function and capabilities was huge.

突然间，你就能通过API调用一个通用模型，谁都能随时调用。

You suddenly have a generalizable model available via an API that anybody can ping.

再往前推一步，这事儿可就太关键了。

And so just extrapolate that out to the next step, and this is gonna be really important.

其实就是盯着这个能力跃迁，自己动手折腾技术，再看看那些scaling wallpapers。

So it's basically looking at that capability step and playing around with the technology and then reading the scaling wallpapers.

或者说，总的来说，这种规模效应好像对啥都管用。

Or just in general, the scaling walls seem to work for everything.

你就会说，哇塞。

And you're like, wow.

这事儿绝对非同小可，我得赶紧搭上这班车。

This is gonna be really, really important, so let me start getting involved with it.

你觉得，要是没有数学背景，你还能做到这些吗？

Do you think you would have or could have done that without a mathematics background?

我猜肯定还有其他人也是一样。

I'm guessing there were probably some other folks.

但这让我想知道，你是怎么发现并吸收这些信息的呢？

But that leads me to the question of, like, how are you ingesting finding and ingesting that?

当时是不是大家都在热议这个？

Was it the talk of the town?

所以是说，在你身边的圈子和社交网络里，大家经常讨论，你自然就参与进来了？还是说你本来就在大量吸收不同领域的信息，恰好这个引起了你的注意？

So it was in a sense, like, within your social circles and the networks that you're a part of, it was open discussions, so you're engaged with it, or are you ingesting vast quantities of information from different fields, and this happened to be something that really caught your attention?

我觉得大概是三方面吧。

I guess it's three things.

我一直都喜欢从各种领域吸收信息，就是单纯喜欢学新东西。

I mean, I've always ingested a lot of information from a lot of different fields just because I like learning about stuff.

我向来就是数学、生物、动漫、艺术，各种东西混在一起。

And I was always this mix of, like, math and biology and, you know, anime and art and other things.

所以一直都是这种杂糅的状态。

So, you know, it was always kind of a mix.

而且那时候朋友也在聊，但感觉还比较像玩票性质。

And then it was something that my friends were talking about, but it was a bit more, like, toy like.

哦，这个太酷了，你看都出啥了。

Oh, this is cool, and look at what came out.

但那时候大多数人没往深了想。

And but most people didn't then extrapolate.

就像早期的加密货币或者比特币一样。

It's kinda like early crypto or Bitcoin.

人人都在聊，可真买的人寥寥无几。

Like, everybody was talking about it, but very few people bought it.

所以我觉得这也是原因之一。

And so I think that was part of it.

第三，说实话，我就觉得这玩意儿特别有意思，一直忍不住去捣鼓。

And then third, honestly, I just thought it was really neat stuff that I kept playing around with.

这又回到GAN和艺术那块了，每次新模型一出来，你就能随便玩。

This is back to the GAN stuff and the art where these different models would come out, and you could mess around with them.

其实有一件事，跟现在这场基础模型和AI浪潮比起来，却很少人提到，那就是以前做AI或机器学习的时候，你所在的公司或团队里，总得有个叫ML运维的小组，专门帮你搭数据、建流水线，把模型训练起来。

And, you know, one of the things that's really under discussed in terms of the importance of it relative to this wave of foundation models and AI and everything else is the way AI or machine learning used to work is your team at a company or wherever else would go, and there'd be what's known as an ML ops team, operations team whose whole thing was helping you set up all the data and the pipelines and everything to train a model.

你要训练一个专门为你的需求量身定制的模型。

And you train a model that was custom to your use case and what you're trying to accomplish.

然后你还得自己搭一堆内部服务来跟这个模型打交道。

And then it was you had to build a bunch of internal services to interact with that model.

所以想把一个能跑起来的机器学习系统上线，简直太折腾了。

So it's a huge pain to get to the point where you had a working ML system up and running in production.

但突然间，你只要调个 API 就行了。

Then And suddenly, have a thing where you just do an API call.

写一两行代码，全世界任何地方的人都能调用它，还不止这样。

So with a line of code or a few lines of code, anybody anywhere in the world can ping it, but not just that.

这玩意儿是通用的。

It's generalizable.

它不是只针对某一个场景，比如拼写检查之类的。

So it's not just specialized to one use case, like spell correction or whatever.

你可以拿它干任何事，它本质上相当于把整个互联网的知识都装进去了。

You can use it for anything, and it has all of the Internet embedded in it in some sense in terms of the knowledge base.

他们就能开始拥有这些高级的推理能力了。

And they can start having these advanced reasoning capabilities.

所以最重要的一点是，嘿。

And so one of the most important things is, hey.

你几行代码就能搞定。

You can get it with a couple lines of code.

不用再专门组个MLOps团队了。

You don't have to go and build an MLOps team.

不用自己去部署。

You have to host it.

也不用去跟它打交道。

You have to interact with it.

那些麻烦事全都不用管。

You don't have to do all this extra stuff.

它开箱即用。

It just works.

这真的特别重要。

That's really important.

太关键了。

It's huge.

没错。

Yeah.

怎么强调都不为过。

It's hard kinda hard to overstate.

先简单感谢一下我们的赞助商，马上回来继续节目。

Just a quick thanks to our sponsors, and we'll be right back to the show.

像很多朋友知道的，过去几年我一直睡的是今天赞助商Helix Sleep家的Midnight Luxe床垫。

As many of you know, for the last few years, I've been sleeping on a Midnight Luxe mattress from today's sponsor, Helix Sleep.

楼下客房里我也放了一张，朋友来住都夸个不停。

I also have one in the guest bedroom downstairs, and feedback from friends has always been fantastic.

他们都是自己主动提到的，我根本没提过。

It's something they comment on without any prompting from me whatsoever.

我最近还试了试Helix的Sunset Elite床垫。

I also recently had a chance to test the Helix Sunset Elite.

Sunset Elite不仅舒适度一流，还能在关键部位提供精准支撑。

The Sunset Elite delivers exceptional comfort while putting the right support in the right spots.

它有五层定制泡沫，底层是全周缘分区腰椎支撑，正好贴合我的需求，中间层则用了高级泡沫和微型弹簧，带来柔软贴合的包裹感。

It is made with five tailored foam layers, including a base layer with full perimeter zoned lumbar support right where I need it, and middle layers with premium foam and micro coils that create a soft contouring feel.

今年春天，如果你打算换张床垫，Helix在美国免费送货上门，还给你120天试睡期。

This spring, if you're thinking about upgrading your sleep, Helix will ship it to your door for free in The US, let you sleep on it for one hundred and twenty nights.

要是不满意，他们家的Helix保证让你无后顾之忧。

And if you don't love it, they're happy with Helix Guarantee.

退货？一点不麻烦。

It makes returns completely painless.

赶紧去看看吧。

So check it out.

去helixsleep.com/tim，全场立减20%。

Go to helixsleep.com/tim for 20% off-site wide.

就是 helixsleep.com/tim，全场八折。

That's helixsleep.com/tim for 20% off-site wide.

那是2004年左右的事了，我在咖啡馆里被人搭讪，说：‘嘿，老兄，你好啊！’然后自我介绍。

Back in the day, this was 2004 maybe, I had someone approach me in a coffee shop and say, good day, mate, and introduce himself.

那人是谁？

Who was that?

你敢信吗？居然是AG1的创始人，那时候他还刚起步呢。

It turned out to be founder of AG1, believe it or not, way back in the day.

很多人问我，二十多年了，试过那么多补剂，最后还留着没换的是哪个？

And people often ask me, what has survived after twenty plus years of testing every supplement under the sun?

差不多就这一个。

Just about.

真正一直用着、离不开的是什么？

What actually has stayed in the rotation, in the toolbox?

今天节目的赞助商AG1，就在这份极短名单的榜首。

This episode sponsor, AG one, is at the top of that very, very short list.

我大概十五年前就开始用了，那时候还叫Athletic Greens。

I started using it close to fifteen years ago when it was still called Athletic Greens.

我当初在《四小时身体》里推荐它，完全没拿报酬，结果它比其他我试过的所有东西都撑得久。

I put it in the four hour body, didn't get paid to put it in there, and it's outlasted almost everything else that I've tried.

一勺就能补上你饮食里缺的那些营养。

One scoop covers your nutritional bases.

对吧？

Right?

填补那些空缺。

Fills the gaps.

当然你得吃好东西，但它有七十五种以上成分，包括益生菌、B族维生素和全食物营养素，在我看来，这简直就是超值的营养保险。

You wanna eat good food, of course, but 75 plus ingredients, including probiotics, b vitamins, and whole food nutrients act as, in my opinion, pretty cheap nutritional insurance.

我每天早上一睁眼就用冷水冲着喝。

I take it first thing every morning with cold water.

现在这都成习惯了，跟刷牙一样自然。

And at this point, it's automatic like brushing my teeth.

如果你想找一个简单又日常的习惯，来改善肠道健康、补充常见营养缺口，我建议你就从这个开始。

If you're looking for one simple daily habit that supports gut health and fills common nutrient gaps, this is where I'd start.

去官网看看吧。

So check them out.

现在订阅，就能体验AG One的下一代产品。

Subscribe today to try the next gen of AG one.

首次下单的听众还能免费获得一瓶D3K2、AG One入门套装和随身旅行装。

Listeners will also get a free bottle of d three k two and AG one welcome kit and AG one travel packs with your first order.

赶紧用上AG One的下一代产品，亲自感受一下差别。

So start your journey with AG one's next gen and experience the difference firsthand.

直接去 drinkag1.com/tim 就行。

Simply go to drinkag1.com/tim.

就是 drinkag1.com/tim。

That's drinkag1.com/tim.

我这儿有一大堆问题想问你。

So I have a million questions for you.

问题是，可选的方向太多了，反而让人不知道从哪儿下手。

The problem with this is like the embarrassment of riches of directions that we could go.

我现在团队里正在用Claude代码和一堆工具做各种事情。

So I am using in my team Claude code and assorted tools for all sorts of stuff right now.

其中有一项，刚好跟你擅长且经验丰富的领域重合了——天使投资。

And one of them, it just so happens, overlaps with an area of great skill for you and experience, which is angel investing.

这还是我第一次觉得真能干成这事，当然得花不少人工，但我想回头好好分析一下我过去二十年的天使投资记录，对，就是这个。

So this is the first time where I feel really enabled to do and there is some manual effort involved as you might imagine, but to go back and do an analysis of twenty years of angel investing Mhmm.

想尝试做各种分析。

To try to do any number of things.

我猜我感兴趣的很多东西其实没什么用，比如搞一些假设性推演。

And I suspect that a lot of what interests me is not particularly useful, like doing some counterfactuals.

要是我当初每笔投资都持三年、五年、或者更久，会怎么样？

What if I had held each of these for three years, for five years, for whatever?

说白了，这基本就是自己跟自己较劲，瞎折腾罢了。

I mean, that's kind of like just opus day whipping myself in the back for the most part.

但做这种分析的时候，我马上想到一些可能有意思的问题。

But in doing an analysis like that, there are certain things that immediately come to mind for me that might be of interest.

我想听听你会怎么处理，你甚至会不会做这种事。

And I wanna hear what you would do, if you would even do this.

说白了，这部分纯粹是出于好奇。

I mean, part of it is, frankly, just curiosity.

我给自己编的那些故事，到底是不是真的？

Are the stories I tell myself about this true or not?

所以我特别想知道，是谁引荐了那些人？

So I'm interested, like, who made certain introductions?

有没有人根本就是把快不行的项目扔给我，当最后一搏？

Are there certain people who just took me their basically people in hospice care and, like, shipped them over as, like, a last ditch effort?

有没有谁一直给我靠谱的项目，等等这些？

Are there people who actually sent me good stuff consistently, etcetera, etcetera?

我能从各种角度去挖掘数据、丰富分析，方法多得数不清。

So there are a million and one ways I could try to interrogate the data and enrich it.

我们其实已经把数据丰富得不错了。

We're doing a pretty good job of enriching it.

我的意思是，Claude 和其他工具，还有 OpenAI，在这方面都做得挺厉害的。

I mean, Claude and other tools, you know, OpenAI is very good at this.

回头看看这些数据，你觉得哪些问题或者方向最值得深挖？

What are some of the more interesting questions or lines of examination, you think, looking back whatever it is?

就我来说，大概有二十年的资料。

In my case, it's about roughly twenty years of stuff.

是啊。

Yeah.

我最近干了件挺怪的事儿：把创始人的照片上传上去，让模型猜他们会不会是成功的创业者。

You know, the weird thing I've been doing is uploading pictures of founders and asking the models to predict if they'd be good founders.

哇，真的吗？

Oh, wow.

你想想，我们平时见人的时候，不都在做同样的事吗？

Because if you think about it, we do this all the time when we meet people.

我们都会迅速对一个人的性格和为人做个判断，里面包含好多细微的线索。

We quickly try to create an assessment of that person and their personality and what they're like, and there's all these micro features.

比如，你眼角有没有鱼尾纹？这可能说明你的笑是发自内心的。

Like, do you have crow's feet by your eyes, which suggests that your smiles are genuine?

那这又反映出你是个什么样的幽默感呢？

And what does that imply about the sense of humor you have?

对啊。

Or Mhmm.

还有，你眉头是不是经常皱着？这又意味着什么？

Furrowed your brow over time, and what does that mean?

你懂的。

You know?

所以，这些小细节其实都挺重要的。

So there's all these, like, micro features.

其实你一见到人，很快就能对他有个大致的印象。

And when you meet people, you actually can get a pretty quick impression of them pretty fast.

但这并不意味着就对了。

It doesn't mean it's correct.

对吧？

Right?

但我们人类确实能飞快地做出这种判断。

But we actually do this really fast as people.

对。

Mhmm.

所以我弄了一大堆提示语，纯属好玩，就想看看能不能根据几张照片，推断出一个人的性格。

So I have this whole, like, set of prompts that I've been messing around with just for fun around, can you extrapolate, like, a person's personality based off of a few images?

进而，能不能预测他们的行为？

And therefore, can you be predictive about their behavior in any way?

我觉得这挺有意思的。

I think that's fun.

对吧？

Right?

是的。

Yeah.

你有没有发现什么规律？

Are you finding any any signal there?

对。

Yeah.

效果还挺好的。

It works pretty well.

哇哦。

Wow.

我最近干了件挺怪的事。

So I've been doing a weird shit.

对吧？

Right?

比如，练习微笑，大家。

Like Practice smiling, people.

不是。

No.

我开玩笑的。

I'm just kidding.

是的。

Yeah.

我知道。

I know.

但我觉得这挺有意思的。

But I think it's interesting.

对吧？

Right?

因为我们平时老这么做，读人，这本身就是提示的一部分。

Because we do this all the time where we read people, and that's part of the prompt.

你特别擅长通过微表情这些细节来读人，说白了就是这么回事。

It's like, you're a very good cold reader of people based on micro features and etcetera, etcetera, you know, kind of spell it out.

然后基于这个，别光说你对这人的判断，还得把每一条判断背后的具体微表情或小细节都给我讲清楚。

And then based on that, you know, not only give me your interpretation of this person, but explain the specific micro features for each thing that you're stating about the person.

它会帮你一条一条拆开分析。

And it'll break it down for you.

太神奇了。

It's amazing.

你想想，这技术到底有多厉害。

Like, imagine what this technology is.

简直离谱。

It's crazy.

当然，我不是说它百分百准。

And, again, I'm not saying it's fully accurate.

我也没说它能预测未来，但确实挺准的，特别擅长看透人。

I'm not saying, you know, it'll be predictive, but it's done pretty well in terms of nailing people.

它甚至能猜出：这人可能幽默感很特别，或者平时在社交场合总憋着不说，但一开口就是句出人意料的冷笑话——说得特具体，真服了。

It's even done things like, oh, this person probably has this type of sense of humor, or this person probably holds themselves back in most social settings and then chimes in with a witty, wry thing that nobody expects or what I mean, it's very specific.

特别精准。

Very specific.

哇塞。

Wow.

太厉害了。

That's amazing.

对吧？

Right?

所以我一直在做这类事儿，可能不完全答你问题，但我真觉得特别有意思。

And so I've been doing stuff like that, which may not be that your question, but I've been finding it really fun.

你懂的。

You know?

其实挺相关的。

Well, it's related.

对吧？

Right?

我的意思是，我肯定漏掉了一些步骤。

In the sense that and I'm sure I'm missing some steps.

但我真的很喜欢天使投资。

But I love angel investing.

剂量决定毒性，所以当我达到某个临界点时，我就会觉得该收手了。

The dose makes the poison, so there's usually a case to be made when I get to a certain threshold.

我就想：行了，这事儿不好玩了。

I'm like, okay, this isn't fun anymore.

我也爱吃黑巧克力，但总不能一天到晚硬逼着我吃吧。

Like, love dark chocolate too, but I don't want just to be force fed dark chocolate all day.

不过，咱们之前也聊过，我确实享受这个过程里的学习和博弈，说实话，还能跟一群特别聪明的人打交道。

But, and you and I have talked about this, I really do enjoy the learning and the sport of it, frankly, and interacting with some very, very smart people.

不是每个创业者都能成功，但归根结底，我就是在努力分辨哪些是真信号，哪些是噪音。

Not all of them work out as far as founders of companies, but ultimately, I'm trying to figure out how to separate signal from noise.

而且，用投资来锻炼自己的思维，这本身就很有趣——能检验你的观点，挑战你那些支撑预测的底层假设。

And also, it's fun to try to use anything, but in this case, investing to sharpen your own thinking, right, and to stress test your own beliefs and the assumptions that undergird some of your predictions.

对吧？

Right?

就是这类事情。

Things like that.

是的。

Yeah.

我就想知道，你有没有做过类似的投资复盘，比如回过头看自己投过的初创公司？还是说你根本不想这么做？

I'm just wondering if you've ever done, like, sort of a retrospective analysis of your startup investing or if you're like, no.

像马克·安德森那样，只向前看，不回头。

More Marc Andreessen style, only forward.

你知道吗，我刚开始投资的时候，会列一个很长的评分表，给每家公司打分，然后回头看看预测准不准。

You know, early on when I was first starting to invest, I would have this long grid of things by which I would score each company, and then I'd go back and see if it was correct.

大致上是准的。

It was roughly correct.

我觉得难点在于，结果里头有太多随机性了。

I think the hard part is there's a lot of, like, randomness in outcomes.

你懂的。

You know?

就是那种你本来觉得没戏了，结果却卖了几十亿美元的公司。

There's the the company that sells for a few billion dollars that you thought was dead or whatever it is.

对吧？

Right?

没错。

Sure.

那这种该怎么评分呢？

So How do you score things like that?

现在这个市场特别诡异，几万亿美元都在争抢同一个目标，干出的各种事儿在平时根本不可能发生。

Right now, we're in this really weird market moment where trillions of dollars of market cap are all chasing the same prize, and so they're gonna do all sorts of stuff that wouldn't happen normally.

在我看来，这些行为其实挺理性的，只是换在别的时候，压根不会出现。

And it's rational stuff, in my opinion, but it's just stuff that in any other time would never happen.

所以要把这些因素纳入考量，真的特别难。

So it's really hard to account for that kind of thing relative to all this.

我更偏向马克·安德森那种态度——根本不太在意过去。

I'm much more in the Marc Andreessen camp of, like, I think very little about the past.

至于我自己的过去，我几乎完全不回头看。

I think close to zero about my own past.

你懂的吧？

You know?

我就想着，别停，继续往前走。

I just am like, let's keep going.

也许这不太对，确实该多反思自己。

And maybe that's bad, and there should be dramatically more self reflection.

我倒是会在当下反思，但不会去倒推、细究自己一辈子的决定。

And I try to self reflect in the moment, but I don't try to re extrapolate and examine my entire life and decisions.

说真的，我最懊悔的，往往是那些没敢放手一搏的决定。

And, if anything, most of the decisions have been ones where I'm really upset with myself for not being more aggressive on something.

比如我投了那家公司，但就算我已经拼了命去投了，其实还是该再拼一把，因为真正重要的公司，就那么几个。

In other words, I invested in the company, but I should have tried even harder to invest more even if I tried really, really hard because there's a handful of companies that really matter.

作为投资者，这才是唯一重要的事。

That's all that kind of matters as an investor.

当然，作为个人，我喜欢参与不同的公司和创始人，帮他们一把，不管项目最后成不成功，或者我觉得技术是不是有意思。

Obviously, as a person, I enjoy getting involved with different companies and different founders and helping them whether the thing works or not, or I think the technology is interesting or whatever.

但从回报的角度来看，确实有个很明显的幂律分布，大家常说的，一点不假。

But the reality is from a returns perspective, there's a very clear power law that people talk about, and it's true.

我记得有个朋友做过这个分析。

And I remember a friend of mine did this analysis.

好像是杰里·米尔纳，或者谁吧，他说你看看从2000年或者2004年到现在，整个科技领域所有公司的情况。

I think it may have been Jerry Milner or someone where it's like, look at all the companies from, like, I don't remember the exact dates, 2000 or 2004 until today in technology.

结果发现，大概一百家公司贡献了九成以上的回报，而总共就十家公司，就撑起了二十年来八成的回报。

And it was something like a 100 companies drove, like, 90 something percent of all the returns, and 10 companies total drove, like, 80% of all returns over a two decade period in technology.

对吧？

Right?

没错。

Yeah.

如果你没投中那十家公司，那你就是个失败的投资者。

If you weren't if you weren't in that 10 companies, you were a bad investor.

对。

Mhmm.

一旦你开始面对这种幂律、这种极端回报，你该怎么衡量呢？

Once you start dealing with these power laws and these outsized outcomes and all you know, how can you rate that?

对吧？

Right?

说白了，你就看有没有中那十个项目中的一个。

Basically, did you hit one of 10 things or not?

这才是真正的评判标准。

That's really the rating.

这大概才是投资该有的正确评估方式。

That's probably the correct rate for investment.

所以我特别想在这档播客里聊聊那些早期的决策，因为就像你说的，早期做过的那些选择，决定了你现在是怎么做事的——不是说早期的就一定更好，但过去的做法确实会影响你现在能做什么、怎么做。

So I'd love to try to focus on some early ish decisions on this podcast because, like you said, the earlier decisions is how you did things then, they're how you're doing things now, which isn't to say that one is better than the other, but certainly what you do in the past tends to inform what you're able to do and what you do in the present.

我还有个好奇的点，咱们不会花太多时间聊这个，但对听众来说可能挺有意思的：就是想问问你是从什么时候开始，从纯粹自己做天使投资，变成会拉其他投资人一起参与项目的？

And what I'm curious about, we won't spend a ton of time on this, but it might be interesting to folks, is to discuss when you moved from purely doing angel investing yourself to involving other investors in your deals.

这件事其实有很多种操作方式，我之所以想问这个，是因为你当时做了好几个特殊目的载体，也就是SPV。

And there are multiple ways to do this, but the reason I want to ask this is because you did a number of SPVs.

我先给大家解释下这个词，就是专门为某个项目设立的特殊目的实体。

I'll explain what that is, special purpose vehicle.

可能大家都熟悉风投机构，但这个工具很多人不一定了解。

But for folks, you might be familiar with venture capital firm.

这些机构掌管着基金，举个例子，他们会筹集大约一亿美元作为一支基金的规模。

They have funds, and they raise, let's just call it, a $100,000,000 for a fund.

当然实际金额可能多一点也可能少一点。

That can be more or less, of course.

之后他们会把钱投去一大堆不同的公司，最后看哪些成了、哪些败了。如果赚钱了，按照行业里的常规玩法，也就是教科书里说的标准模式是这样的。

Then they invest in a bunch of different companies, and then you sort of see who wins, who lose, and then if there are profits, I guess, conventionally, let's just use the textbook example.

风投机构会分走20%的投资收益，剩下80%归LP也就是出资的有限合伙人所有。除此之外风投还会收取管理费来维持机构运营，不过这笔管理费往往远不止 cover 基本运营成本那么简单。

The venture capital firm takes 20 of the upside, and then the LPs, the investors get 80% and the venture capital firm takes a management fee to keep the lights on, although it usually does a lot more than keep the lights on.