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你好，欢迎来到玻璃银行频道。
Hey, Bank of Glass Nation.
在这一期节目中，我们将采访来自Lighter的弗拉德·诺瓦科夫斯基。
In this episode, we're going to talk to Vlad Novakovski from Lighter.
弗拉德是个很有意思的人，他经常参加各种播客，聊Lighter这个平台，它和Hyperliquid以及其他衍生品平台有什么不同，以及他的目标是什么。
Vlad is a pretty interesting character, and he's been around the podcast circuit quite a lot talking about Lighter as a platform, how it's different from Hyperliquid and other perk platforms, and what his goals are.
但我觉得很少有人真正深入了解过弗拉德的早期经历——从他青少年时代说起，那时他还在参加数学奥林匹克竞赛，和我们这个时代最顶尖的头脑们同台竞技，比如Anthropic的达里奥、Robinhood的弗拉德，还有其他许多后来创办了大型公司的名人。
But I don't think too many people have really explored the beginnings of Vlad, going all the way back to his teenage years when he was just a a math Olympiad competing with alongside some of the brightest minds of our age, including Dario from Anthropic and Vlad from Robinhood and a bunch of other names that have gone on to raise massive companies.
所以，弗拉德这个人到底是谁，我觉得非常独特，也很有意思。
And so the who Vlad is, I think, is a pretty unique and interesting character.
我特别想问一问，也试着回答一个问题：为什么投资者们都这么喜欢弗拉德？
And I just really wanted to ask and answer the question, why do investors love Vlad?
接下来你将听到的，是一个你可能从未见过的、来自Lighter的弗拉德。
And so this is what you're going to hear, a different side of Vlad from Lighter.
那我们这就开始吧。
So let's go ahead and get right into it.
我们是在曼哈顿的一个录音棚里面对面录制的。
We record this in person in a studio in Manhattan.
这期节目的开头，我们就直接开始录制了。
And the beginning of this episode, we just kinda start rolling.
所以你可能觉得像是突然插进了对话的中间，确实是这样。
So maybe it feels like you started the middle of the conversation, because you kind of do.
那我们现在就听听他怎么说。
So let's go hear from him right now.
Vlad，我想多了解一下你的背景。大家知道你是Lighter的创始人，做永续合约平台的，但你的经历其实可以追溯到很早期的童年时期。
Vlad, I want to know a little bit more about your background, because people know you as the founder of letter, the perp decks, theorem layer to perp decks, but your background goes pretty far pretty far back into your childhood.
说说你最早掌握的那些核心技能吧。
Think with your first like relevant set of skills.
对，具体讲讲那些技能。
Talk to me about particular set of skills, right?
这些技能。
Set of skills.
是的
Yeah.
跟我说说数学吧。
Talk to me about math.
你拿过数学和物理奥林匹克竞赛的奖。
You won a math and physics Olympiad.
我和大多数听众可能都不太清楚那是什么。
Me and most of my listeners probably don't know what that means.
那到底是什么意思？
What does that mean?
对
Yeah.
嗯，我
Well, I
开始参加各种学术竞赛。
started competing in, you know, academic competitions.
我们就说那时候我大概12岁吧。
Let's just call that when I was around 12.
那段时间的亮点包括入选美国队参加国际物理奥林匹克竞赛和国际信息学奥林匹克竞赛。
And I guess some of the highlights of that were making the US team to the International Physics Olympiad, and the International Olympiad at Informatics.
数学这边是信息学。
On the math side Informatics.
对，就是编程那个比赛。
Yeah, that's the programming one.
好吧，我
Okay, I
我是想争取包揽这三个比赛的入选资格。
mean I tried to do a hat trick as far as making all three.
我应该是美国最早入选两个国家队的人之一。
I was actually the first or one of the I think one one of the first people in The US ever to make two teams.
虽然没凑齐三个，但已经很不错了。
Didn't quite make three, but, you know, but it was good.
参加数学和物理竞赛到底是什么意思？
What what does a what does it mean to compete in math and physics?
就是说，具体是怎么比的？比赛内容到底是什么？
Like, how does one actually what what what it is to compete over?
奥赛各个项目的赛制都不一样。
Well, so the Olympiads all have a different format.
编程竞赛可能是最值得一看的，特别精彩。
The programming one is maybe the most would be, like, the most exciting to watch.
现在还有不少主播在Twitch上直播这个，选手得现场写代码，解决题目，比如……
There's actually are, like, like, these days, like, Twitch streamers and whatnot who do that, where it's like you're you're writing code, basically have a problem solved, like I don't know.
题目可能是这样的：纽约有两个披萨店，给你交通状况，
The problem could be, like, here, you know, here's a location of two pizza places in New York.
算出从A点到B点的最短路线。
Find the shortest path given traffic how to get from Adrian.
这可不是纸上谈兵，得真刀真枪地写程序解决。
You you have to actually, like it's it's not theoretical.
你得真写个程序来实际完成这个任务。
You actually have to write a program that would actually do it.
对。
Right.
这是确定性的。
It's deterministic.
所以答案非常明确。
So there's a very clear correct answer.
是的。
Yes.
明白了。
Okay.
是的。
Yes.
你得在两天里，每天五小时，解决三个这样的问题。
And so, you have, you know, three problems like that five hours, two days of of that.
所以这就是编程那部分。
So that's the that's the programming one.
然后是数学，我指的是最高难度的那种，对吧？
Math at the and I'm talking about the highest levels, right?
通往那个目标的路径上，你知道的，并不是五个小时就能搞定的，对吧？
And the ways leading up to that, it's, you know, it's not five hours, right?
强度没那么大，不过数学那块看起来也得花两天时间，你有三道题需要给出带证明的解法。
There's like it's less intense, but and then the math, that one looks like it's it's also two days, and you have three problems where you have to find a solution with proof.
不过你可能听说过那个四色定理，就是说你可以用四种颜色给地图上色，对吧。
But you've probably heard of, like, the four color theorem, like, you can, like, paint, like, a map with, like, four colors Right.
而你，是的。
And you, Yeah.
不管它是州还是国家都没关系。
Doesn't matter if it's states, countries.
对。
Right.
所以这个题特别难，但假设问题是：证明用六种颜色就能做到。
So that that one's very hard, but let's say the problem is, like, prove that you can do it with, six colors.
只是假设而已，对吧？
Hypothetically, right?
光举一个例子可不够，你得证明无论什么情况都能成立。
And it's like, it's not enough just to show one example, have to like prove that you can always do it.
然后你得在五个小时内，连续两天，写出这三个问题的完整证明。
And then you have to write proofs like that for like three problems over five hours also I think, two days.
物理这门，分理论和实验两部分。
Physics one, there's a theory part and an experiment part.
理论部分也差不多，比如一块冰从山坡上滑下来，得假设一些条件，像它融化的时候速度会怎么变化之类的。
So the theory part is also similar, it's like, you know, like a block of ice is coming down the hill, know, with, you know, with kind of assumptions like, you know, how fast is it gonna get there while it melts, you know.
这可不是基础物理那种只算石头下滑的题，因为是冰，还在融化，你得把好几个概念结合起来分析。
So it's not just like basic it's not like physics 101 where it's like just like a rock, but like because it's ice and melting you have to like do something bring in a bunch of different concepts together.
实验部分就更不一样了，你得亲手操作，测量数据，或者设计、搭建一些装置。
But then there's also the experiment part, and that that's probably the most different of all these, you you like, you actually have to like do stuff with your hands and like, you know, measure something or like build something, design something.
你觉得这些都很有趣吗？
And this was all fun for you?
我猜你是发自内心地享受做这些数学题，参加这些比赛吧？
You you this I'm I'm assuming you were like intrinsically just thrilled to do this math, to to be in these competitions, right?
数学本身对你来说就很有趣吗？
Like, math is entertaining entertaining for you?
是的。
Yeah.
其实这是一个良性循环，因为我还没参赛时就已经挺擅长了。
So the thing was that, like, it was like a virtuous cycle, because I was pretty good at it, even before competing.
后来开始参赛，我发现赢比赛真的很有意思。
And then once I started competing, I saw that, like, winning is fun.
明白了。
Okay.
没错。
Then Yes.
而且赢了不仅有趣，还能认识其他志同道合的人，对吧。
And not only is winning fun, meeting other like minded people Mhmm.
长远来看，这才是这一切最重要的部分——你认识的那些人。
And in the long run, that's actually the most important part of all this, is the people you meet.
没错。
Right.
因为这可不是一个人的活动。
Because this wasn't solo activity.
你是有团队的。
This you were on a team.
没错，不过很多比赛其实都是个人成绩，到了国际层面，国家才会组队，但平时训练营里，你得等着成绩出来，闲着没事就和很多人打交道。
Well, yes, but even the I mean, a lot of the the competitions are individually measured, and then the country you know, once you get the international level, the the countries have teams, but you just, you know, because there's training camps, like, as it was, you know, you sit around, wait for results, like, meet a lot of the the people.
其实就是有机会认识其他有共同兴趣的学生，说白了，就是全国各地的同龄人。
It's like, you know, you the opportunity to meet other like minded, you know, students, but really just kids around the country.
对。
Right.
你遇到了谁？
Who did you meet?
挺有意思的。说实话，那时候在训练营里认识的人，当时就觉得特别厉害，毕竟技术过硬嘛。但现在有些人已经成了行业里的关键人物。比如在美国物理奥赛的训练营里，Anthropic（一家AI公司）的两位联合创始人都在那儿，我有幸认识了他们，其中一位我还挺熟的。还有在信息学竞赛那边，Facebook的首席技术官（CTO）也出现过——当然，那是他还没当上CTO的时候。对。
It's funny because a lot of the I mean, back then, these are people that I just met at the camps, mean, were impressive people then, right, just because of their skills, but some of them now are, like, shaping what we see in industry, like, for example, at The US the training camp for the US Physics Olympiad, two of the co founders of Anthropic were there, I got to meet them, got to know one of them quite well actually, and then at the Intronics one, you know, first CTO of Facebook was I mean, they weren't Right, the future CTO, Uh-huh.
所以有些人，你知道的，现在也在OpenAI的董事会里。这确实挺有意思的。
So some people, you know, who's also on the board of OpenAI now, right, so like, that's pretty interesting.
而且这只是那些最有名、成就最高的人的例子。其实还有很多其他例子，比如你去看看那些大型量化交易公司的负责人，你会发现里面有很多来自这些竞赛的人，诸如此类。
And and that that's that's just, like, people who've achieved, you know, the kind of the most well known outcomes, but there are a bunch of others who write, like, for example, like, I think if you look at, people who run some of the big quant trading shops, you know, you see a lot of people from those competitions there, etcetera.
从这些数学、物理和信息学奥林匹克竞赛走出来的学生，你们那一届是不是比较特别？其中有一小部分人后来成了非常厉害的创业者，正在塑造世界的未来。
People that come out of these, like, math and physics and informatics Olympiads, does it was your cohort unique in that a handful of them grew up to be incredible founders shaping the future of the world?
还是说这种情况一直都有？
Or like, is it always like this?
对，这是个很好的问题。
Yeah, it's a good question.
我觉得这其实是分阶段的，会有爆发期。
I mean, I think it goes there are bursts.
对。
Mhmm.
我觉得大概是2000年代初吧，不管什么原因，也许是因为我们是第一批在互联网环境中长大的人。
Like, think the early two thousands for whatever reason, maybe it's because we were the first to grow up with the Internet.
是的。
Mhmm.
没错。
Mhmm.
那届学生，尤其是他们，不仅在数学和物理方面表现突出，整体来说成就也非常大。
That that cohort in particular, not and not just on the math and physics side, like, that cohort, you know, like, achieved a lot of success.
你知道，副总统也是那一届的，对吧？
Mean, I you know, the vice president is part of that cohort too, right?
比如那些在政界、商界都取得巨大成功的人。我想在2000年代初，从那里涌现出了很多了不起的成就。
Like, you know, people who've come who've done very well in politics, who've done very well in other forms of business, like that early two thousands, I think there was a lot a lot of great achievement that came out of that.
不过话说回来，如果你回顾90年代，有个很有意思的例子：1993年哈佛大学派出的ICPC（国际大学生程序设计竞赛）队伍。这相当于大学级别的信息学奥林匹克竞赛，同样是三人组队。
But but but that being said, like, you look back even to the nineties, like, one interesting thing was the '93 team that Harvard sent to the ICPC, is like the equivalent of the Informatics Olympiad at the college level, same competition, team of three.
那支三人队伍里，有两个人成了亿万富翁。
So out of that three person team, not one but two became billionaires.
是在二月，不是现在。
And like in the February, not not not now.
是的。
Yeah.
2000年的时候，那个标准已经非常高了。
When when 2000 was when that was a very high bar.
对。
Yeah.
我觉得竞争强度肯定有很大关系，我一直说物理、数学和信息学之间有很多共通点。
I would imagine the competition has plenty to do with it because I always say common denominator between physics, math, and informatics.
它们确实有很多相似之处。
You know, there's a lot in common there.
但关键还是在于竞争本身。
But really, it's the competition side of things.
如果你擅长这个，就像你自己说的，你可是完成了三冠王。
If you're good at you, right, you were almost as you said, did the hat trick.
不管是数学、物理还是信息学，你当时喜欢做哪一类题目？
What kind of problems whether whether it was math, physics or informatics, what kind of problems?
你最喜欢哪种类型的题目？
What, type of problems did you really enjoy the most?
这里面有什么值得聊聊的吗？
Is there is there something to discuss there?
是的，没错。
Yeah, yeah.
每次比赛结束后我们聚在一起聊天，总会谈到这个话题。那些表现突出的人，每个人都有自己特别偏爱的题型，觉得自己特别拿手的领域。
This was always like, you know, when we would hang out after the competitions and talk, this would always come up like a different even of the people who did well, We all had, like, our own, like, favorites or, like, the kinds of things that, like, we thought we grok better than others.
你特别擅长哪一类？
You were particularly strong at?
对。
Yeah.
这一直是个争论的话题。
And and and this was always a debate.
有人就会说，哎呀，我特别喜欢几何，太酷了。
Like, some guy would be like, oh, like, I love like, geometry is really cool.
我就擅长几何。
I have geometry.
但另一个人可能会说，不对。
And then, like, maybe somebody else will, like, oh, no.
他们觉得三角学才真正厉害。
Like, that like like, trigonometry is really cool.
对吧？
Right?
诸如此类。
Etcetera.
但对我来说，我最喜欢那些需要独特洞察力才能解决的问题。
But, like, for me, I really like the stuff where you needed, like, an unusual insight to solve it.
有时候你从一个角度看问题，觉得根本无解，或者路特别难走，但要是换个角度一想，哎，原来两笔就能解出来。
Or if you look at the problem one way, it looks like there's, like, no solution or very difficult path to solution, and then if you look at it like sideways, like, oh, actually, like, you can solve this in two lines.
我不是总能想到这些解法，但每当我想到的时候，我觉得自己学得最多，也最满足。
And I mean, I didn't always find those, but when I did, I think that's when I learned the most, and that's why I got the most satisfaction out of it.
那种灵光一现的题目。
The stroke of genius problems.
我觉得吧，与其说是天赋，不如说是换个思路，不拘泥于常规。
Well, I guess gene, you know, I I think it's more of just Or thinking outside of
跳出框架。
the box.
用创意去思考。
Being thinking creatively.
没错。
Right.
对。
Yeah.
没错。
Exactly.
对。
Yeah.
是的。
Yeah.
解决这些问题，你不需要像爱因斯坦那么聪明。
You didn't, know, were these problems solving, you don't have to be, you know, Einstein.
你并不是在构思一个前人从未想过的点子，但你确实需要跳出固有思维。
You're not thinking of something that no one's ever thought of before, but you do have to think outside the box.
我交易加密货币资产快十年了，用过无数的钱包、交易所、聚合器和前端界面，但我一直在寻找同样的东西。
I've been trading crypto assets for almost a decade, and I've used so many wallets, exchanges, aggregators, front ends, and I'm kinda always looking for the same thing.
就是一个统一的界面，能在多条链和各种资产上提供深度流动性，同时支持隐私操作，而且资金始终由我自己掌控。
One interface with deep liquidity across a bunch of chains and assets and the ability to act in private, and I still control my own funds.
但我一直没找到这样的平台，总是得换来换去，还要应付各种Gas费，或者被滑点吃掉利润。
And I've never really found it, and I'm always switching wallets, juggling gas fees, or getting eaten by slippage.
对我来说，Near.com 完全不一样。
Near.com feels fundamentally different to me.
我的账户用起来特别顺手，不管在哪个链上操作，都能保持私密性。
My account is easy to use, and I can take all the actions I want from any chain while my activity remains confidential.
它基于 NEAR 网络，这个网络已经通过后量子签名技术完成了超过230亿美元的跨链交易，而且五年来一直稳定运行。
It runs on NEAR, which has moved over $23,000,000,000 cross chain using post quantum signing and has over five years of up time.
Near.com 是你掌控链上资产的最佳选择。
Near.com is the best way to be on chain and be in control.
通过银行列表链接，你能拿回20%的交易手续费。
Get 20 of your trading fees back through the bank list link.
链接在节目笔记里，这不是投资建议。
It's in the show notes, not investment advice.
市场从来不是只动一个资产。
Markets don't move one asset at a time.
今天可能是比特币，明天是英伟达，接着是黄金，然后是标普500。
One day it's Bitcoin, the next Nvidia, then gold, then and the S and P.
但大多数交易者仍然要在不同的平台、不同的账户和不同的资金池之间管理自己的投资组合。
But most traders are still managing their portfolio across different platforms, different accounts, and different pools of capital.
BITGET 正好改变了这一点。
BITGET just changed that.
他们新推出的 Stocks 2.0 产品让你可以直接用 USDT 交易代币化股票，而且全部都在你平时用的同一个 App 里完成。
Their new stocks two point o product lets you trade tokenized equities directly with USDT, all inside the same app you already use for crypto.
这可不是又一个普通的代币化股票产品。
This is not just another tokenized stock product.
Stocks 2.0 的设计聚焦于更深的流动性、更快的成交速度，以及市场上最低的手续费——仅 0.04%，而且完全一对一追踪标的股票的走势。
Stocks two point o is designed around deeper liquidity, faster execution, and the lowest fees on the market at just point zero four percent and one to one economic exposure to the underlying stock.
股息、拆股和其他公司行为都会自动同步，确保你的持仓始终与你真正想投资的资产保持一致。
Dividends, stock splits, and other corporate actions are reflected automatically, helping your position stay aligned with the asset you actually want exposure to.
一个平台，一个账户，覆盖多个市场：加密货币、股票、大宗商品等等，全部都能用 USDT 交易。
One platform, one account, multiple markets, crypto equities, commodities, and more, all accessible with USDT.
BITGET。
BITGET.
更聪明地交易。
Trade smarter.
点击节目笔记中的链接，立即开始交易。
Start trading today through the link in the show notes.
这不是投资建议。
This is not investment advice.
交易正在改变，不是慢慢变，就是现在。
Trading is changing, not gradually, right now.
OKX刚刚在OKX应用内推出了交易机器人。
OKX just launched TradingBots directly inside the OKX app.
网格交易、定投、套利，你设置一次策略，它就会全天候自动执行。
Grid trading, DCA, arbitrage, you set your strategy once and it executes around the clock.
不用整天盯着图表，不用手动下单，也不用睡觉时错失机会。
No staring at charts all day, no manual entries, no missing moves while you're asleep.
而且，首次让自动化交易变得真正简单。
And for the first time, automated trading actually feels simple.
但OKX的愿景不止于交易。
But OKX is thinking bigger than just trading.
他们还推出了代理支付协议，这是一个开放标准，让AI代理能够在链上完成完整的商业交易。
They also launched the agent payments protocol, an open standard that lets AI agents execute full commercial transactions on chain.
以太坊基金会、Uniswap和AWS都已经在使用它，现在它已经在美国正式上线。
The Ethereum Foundation, Uniswap, and AWS are already building on it, and now it's live inside The United States.
新用户只要存入资金并进行交易，就能通过Bankless链接获得最高500美元的比特币。
And new users who deposit and trade can get up to $500 in Bitcoin through the bankless link.
更多详情请查看节目说明中的链接。
The link is in the show notes to learn more.
这不是投资建议。
Not investment advice.
纽约和德克萨斯州的用户无法使用。
Not available in New York or Texas.
你青少年时期数学课的压力可真不小，而且
Your teenage years were all super heavy in math, and
也就是在那儿，你认识了罗宾汉的那位伙伴。
this is also where you also met Robin Hood's lad.
对吧？
Correct?
我们当时在同一所高中，叫托马斯·杰斐逊高中，是弗吉尼亚北部一所数理特色高中。
So we were in the same high school, school called Thomas Jefferson, which was a math and science high school in Northern Virginia.
对。
Mhmm.
我们确实。
We yeah.
我们俩都叫弗拉德。
We were the two Vlads.
就是那个俄罗斯名字弗拉德。
Was, you know, Russian Vlad.
是的。
Yeah.
保加利亚移民。
Bulgarian immigrant.
后来我们的人生又有了交集。
Our paths crossed more later in life.
我的意思是，我们当时确实在同一所高中。
Like, I mean, we, you know, we were at the high school.
当然，他大学时就开始更深入地研究数学，读研时又跟着特里·陶做博士研究，陶教授可以说是世界上最好的数学家之一，在加州大学洛杉矶分校。后来他也进入了高频交易领域，和我一样，所以我们的路其实是在后来才越走越近的。
Of course, I mean, he started doing math actually more deeply in undergrad and then graduate he he was a PhD student under Terry Tau who is probably like the best mathematician in the world you know UCLA and so so anyway so then he got into high frequency trading later as I did So we our paths actually got closer kind of later on.
你在参加奥赛、和那些同龄人相处的时候，有没有觉得大家明显都会在将来做出一番大事业？
Was it obvious to you being in these Olympiads and just in the these cohorts of people was it kind of just obvious amongst you and and your peers that everyone was going to go and apply themselves in some great way?
还是说你们当时只是几个爱玩数字的孩子？
Or were you guys just kids playing with numbers?
对我来说，我倒是隐约感觉到了一点。
For me, I saw it a little bit.
因为我和
Because I
我以前觉得吧，就像如果给所有人排个序，你知道的，比如按某种标准把大家从好到差列出来，假设是全国范围。
used to think of it like, okay, if you if you like, stack rank everybody, like, you know, like the people like, well, let's say you were like, whatever metric you come up with, like, you stack rank everyone, let's say, the country.
对吧？
Right?
就像头号种子、二号种子、三号种子那样。
Like, whoever the top seed is, second seed, third seed.
而且这种排名通常每年变化都不大。
If you, like and and and that that ranking doesn't change that much from one year to the next.
我当时想，好吧，如果把时间线拉长到二十年后，情况可能就不一样了。
If you I thought, okay, well, if you project that out twenty years, it's probably gonna be not about the same then.
所以，这些人就是会做出贡献的那一批。
So, like, these are the people who will contribute.
现在我觉得不一样的地方在于，组织这些比赛的都是学术界的人。
And now the I think the the the thing that was different is like, I think because because these competitions, the people who organize them are academics.
所以他们的默认职业路径就是留在学术界。
So your the default career path is academia.
所以我觉得，我们当时根本没想过Facebook、Anthropic或者OpenAI这些公司。
So I think, like, we weren't obviously, we weren't thinking about, like, Facebook and Anthropic and, you know, OpenAI.
我们想的是，这些人以后大概会成为他们领域里的哈佛或麻省理工教授。
We were thinking more, okay, these people are probably gonna become, like, like Harvard and MIT professors down the road in their field.
对。
Mhmm.
至于他们具体会怎么运用自己的技能，那时候没人真正在意。
So how exactly they applied their skills was not something anyone was really thinking about.
但确实如此，
But But yeah,
你从小长大，慢慢步入成年的时候，有没有某个方向突然变得清晰起来？
when did as you're growing up, and just kind of getting into your adult years, did a particular direction for you open up and become clear?
你是怎么从一个爱玩数字、跟其他孩子比赛的小孩，一步步想到要走职业道路、甚至当企业家的？
Or, or how did you how did you go from, know, a kid playing with numbers and competing with other kids into like, thinking about a career thinking about being an entrepreneur?
有一件事，我后来才意识到，就是我真的很喜欢数据。
Well, one one thing that I realized, at some point, around then is like, I really like data.
关键是，你可能会觉得，嗯，数字、数据，当然重要，但其实那些比赛根本用不上数据。
And that's the the thing is that you would think, well, yeah, like numbers data, of course, but actually the competitions don't really involve data.
我刚才说的那些比赛里，唯一用到真实数据的，就是实验物理那场，那时候你真正在跟现实世界的数据打交道。
The only out of the competitions I described, the only one that does is the experimental physics one, where you're actually dealing with the real world data.
对吧？
Right?
有意思的是，训练的时候，我实验部分表现很差，总是在美国集训营里理论部分第一个出成绩，可实验部分老是拖后腿，只能指望靠理论拉回来，勉强进前五。
And the funny thing about that was like, when I was training I didn't do that well on the experiment part, I would always like, be the first at the training camp in The US on the theory, and then I would like not do that well in the experiment I would hope that I would still make the top five even with that.
但到了国际比赛，我理论部分状态特别差，可能是时差没倒过来，可实验部分却发挥得特别好，世界排名第二，最后总分还是拿了个银牌。
But the actual international one like I actually had like a pretty bad day on theory jet lag would I don't know but in the experimental one, I was ranked second in the world, and so overall, I still got a silver.
所以，那其实算是我意识到的一件事——好吧，
So that was kind of the start of like, I mean, okay.
我真的很喜欢这种跟真实世界数据打交道的东西。
I actually really like this, like, data real world stuff.
对。
Mhmm.
我觉得这也让我后来在应用科学方面投入了更多精力，比如经济学、金融、交易之类的。
And that I think led to doing more with, applied sciences later on, like economics, finance, trading, and kind of so on and so forth.
你
You
你在十几岁的时候见过肯·格里芬，对吧？
met Ken Griffin sometime in your teenage years, correct?
是的，我16岁高中毕业，18岁从哈佛毕业。
Yes, I finished high school at 16 and Harvard at 18.
所以是的，当时
So yeah, was
你18岁就毕业了。
you finished finished Harvard at 18.
对。
Yeah.
怎么能在两年内完成哈佛的学业呢？
How does one do to Harvard in two years?
嗯，两年半。
Well, two and a half.
两年
Two and a
零半年。
half years.
对。
Yeah.
这怎么做到的？
How does one do that?
你知道，其实你可以因为修了很多课程而获得一年的学分，我当时选了一大堆，我们那群人之间竞争很激烈，比谁修的AP课程多。
You know, there there's like you you can get credit for one year from because I took a bunch, you know, I like, we were competing on a lot of things, right, including how many APs you took.
是的。
Mhmm.
我说的是我们那个竞争圈子里的情况。
Like, I'm talking about it among that competitive cohort.
有个孩子选了十七门大学先修课程。
So, like, I mean, there's one kid doing 17 APs.
我记得我选了十二门左右。
I think I did, like, 12.
这样就能在哈佛换一年学分，然后我就没像别人那样每学期选五门课，而是选了六门，多修了一个学期。
So that's enough to get one year of credit at Harvard, and then and then I just like, instead of taking five classes a semester, took six to get another semester.
你是为了早点毕业才这么做的吗？
Was it about finishing Harvard?
这又是另一种竞争吗？
Like, that was it another competition?
就是看谁能更快读完哈佛？
It was just, like, who can finish Harvard quicker?
其实这更多是
Well, that was more of
我和自己的较量。
a competition between me and myself.
其实没人真想这么做。
No one else was really trying to do that.
我是说，咱们不是总在讲要跳出框框吗？
The I mean, I looked at it I like, talked about thinking outside of the box, right?
我当时的想法是，现在要是有我这样的人，大概早就退学去搞协议或者开发AI模型了，但那时候这种事特别少。
Like the way I looked at it was you know assuming I now these days probably someone like me would have dropped out and gone to like you know start you know build a protocol or build an AI model or something but back then that was rare.
我们当时只知道比尔·盖茨是这么干的。
Like, the only case we really knew about who did that was Bill Gates.
对吧？
Right?
当然，我们那届确实有个人后来用零知识证明做了类似的事，不过呢——
So the I mean, and then of course, one of us in that cohort did it, you know, with with with ZK, but, like
没错。
Right.
ZK那时候还没这么做，因为当时还没到那一步。
ZK hadn't done it yet because Yeah.
对。
Right.
好吧。
Okay.
是的。
Yeah.
但说实话，我那时候根本没考虑过这条路。
But, like, but anyway, like so that wasn't really a path I was considering.
对吧？
Right?
所以我就想，行吧。
And so I was like, okay.
我觉得，如果非要留在学校，与其当本科生，不如直接读研究生，这样明显更划算。
Well, like, I might do something in the industry, but if I'm gonna stay in school, the way I look at it is, like, it's strictly better to be a grad student than an undergrad.
因为当本科生还得自己掏钱，而研究生反而有补贴。
Because you get undergrad, you have to pay them.
研究生是学校给你发钱。
Grad student, they pay you.
对。
Right.
所以我心想，不如赶紧把本科读完，这样就能跨选课了。
So I was like, I should finish the undergrad as quickly as possible, and then I can actually can cross register.
我可以选和研究生完全一样的课，也在同一个校园里上课。
I can literally take the same exact classes as graduate student and be on the same exact campus.
我觉得，既然这样，不如就这么干。
And it's it's like, I was like, I might as well do that.
所以你所谓的退学，其实就是加速完成。
So your form of dropping out was just doing it faster.
是的。
Yeah.
干嘛要把时间卡得这么紧呢？
Why get so aggressive and competitive with the timelines?
那为什么不在四年里读完哈佛呢？
Why not just do Harvard in four years?
因为有这个原因，对吧？
Because of this thing, right?
因为当研究生绝对比当本科生更划算。
Because because it's strictly better to be a grad student than an undergrad.
所以你就把它当成一个优化问题了。
So just a use as an optimization problem.
假设我想待七年，那我会花两年半当本科生，四年半当研究生，而不是反过来。
Let's say I wanted to stay for seven years, I would have spent two and a half as an undergrad and four and a half as a grad student versus the other way around.
对吧？
Right?
当然。
Sure.
明显更划算。
Okay, strictly better.
那是一个体系。
Was a system.
你可以选和以前完全一样的课。
You can take the same exact class.
是的。
Yeah.
这简直是个没人想到的生活窍门。
And this is just like a life hack that no one really thought about.
嗯哼。
Uh-huh.
我的意思是，你在哈佛确实接受了教育，但除此之外，你还从哈佛得到了什么？
I mean, so obviously, you got an education out of Harvard, but what did you get out of Harvard beyond that?
学会怎么去思考，你知道的，他们说过这么一句话，听起来挺老生常谈的，但后来我发现确实是对的——就是学会用不同的方式思考。
Learning how to you know, the like, there was this thing they said, which sounded, like, cliche, but I think ended up being true, which is like learning to think in different ways.
比如说，在某个领域里，像数学，你可以用两种不同的数学方法来看同一个问题。
So, you know, it was one thing in, like, within a certain field, like, math to look at a problem within two different mathematical approaches.
我们其实已经在这么做了，比如用数学家的眼光，或者哲学家的视角，去看待一个现实中的问题。
Like, we're already doing that, but, like, to think about a real world problem from like, imagine thinking about a real world problem as a mathematician, as a philosopher Mhmm.
还有生物学家那种完全不同的思维方式。我觉得在学术上，我学到的就是这些；至于个人层面，就是认识了很多很棒的人。
As, you know, biologist, you know, like, thinking about problems in completely different ways, I think, was, like on the academic side, what I learned, I think on the personal side, just, like, met a lot of great people.
我真正学会的，说起来挺老生常谈的，但确实是怎么去建立人脉。
Like, I think I really learned, like again, this is cliche, but, like, how to network.
对。
Mhmm.
怎么走进一个房间，跟不同的人开启对话，慢慢了解他们。
Like, how do you, like, go into a room and, like, start conversations with different kinds of people, get to know them.
就像你以前参加物理、数学和信息学奥赛那样，但这些比赛的共同点其实就是竞争。
Kind of in the same way that you you were competing in the the physics and the math and the informatics Olympiads, but really the common denominator of all those is competition.
而在哈佛，你学的是如何用各种不同的方式去思考。
At Harvard, you're learning all these different ways to think.
但说到底，哈佛最核心的特点就是社交氛围特别强，那里的人际互动非常密集。
But really, common denominator of Harvard is you're hyper social at Harvard, like Harvard Harvard is a super social environment.
没错。
That's
对。
right.
要想真正出类拔萃，就得融入这种环境，我觉得。
And need to be in in that game to really excel, I think, coming
尤其是从哈佛出来的。
out of Harvard.
这得看具体领域，对吧？
Well, it depends on the field, right?
比如，大多数科学领域都讲究合作，但也有不少领域更推崇独来独往的类型。
If you're like I mean, I guess, like, most scientific fields have some collaboration, obviously, there there are also others, like, that reward more like the lone wolf archetype too.
关键还是看领域本身。
It really depends on the field.
不过，对很多领域来说，你在哈佛最大的收获，一方面是合作，另一方面是结识不同领域的人。
But, yeah, for for a lot of the fields, the value you get out of Harvard is through, like, a, collaboration, but b, meeting people from other fields.
你离开哈佛后的第一站是和肯·格里菲斯一起工作。
So your first step out of Harvard was working with Ken Griffith.
是的。
Yes.
我想这正是最初的问题。
I guess that was the original question.
我们加入了城堡资本。
We got Citadel.
等等。
Wait.
对。
Yeah.
肯这个人，你知道的，他当年也是从哈佛创办了城堡资本。
So Ken, you know, like he has he himself had started Citadel out of Harvard.
我记得他是毕业了的，并不是退学。
I believe he did he graduate he wasn't a dropout.
我觉得他是毕业了，但他在1988年、1989年左右是在宿舍里管理基金的，那大概是我去那里之前的十五年了。
I think he he graduated, but he was running the fund out of his dorm room in, like, 1988, 1989, like, fifteen years before I was there.
所以我想，当他了解我的背景后——我是说，我和其他人一样先去公司面试了，但后来他确实了解了我的情况，花了很多时间跟我聊，最后说服我加入。
And so I think he once he got to know my background I mean, I first interviewed with the firm like everybody else, but then, you know, they kind of you know, he got to know my background and spent a lot of time, and he convinced me to join.
那是你第一次把对数字、工程和编程的热爱应用到金融领域吗？
Was that your first time applying your love of, like, numbers and engineering and and programming to finance?
嗯，做交易是从
Well, was doing trading from
我的宿舍。
my dorm room.
我是说，当时我在那儿的时候，一直想成为像肯或者吉姆·西蒙斯那样的人。
Like, I was trying to be like someone like Ken or Jim Simons, you know, when I was there.
结果嘛，其实没太如愿。
So, I mean, it didn't work out that well.
对。
Yeah.
你那时候做得怎么样？
Were were you good at it?
就是，作为一个大学生做交易？
Like, trading as a college student?
是的。
Yeah.
我算不上特别厉害，不过确实找到一些还不错的策略。
I'm not particularly I mean, I have found some strategies that worked okay.
后来肯告诉我，这些策略其实没那么好，以前有些方法确实管用。
And then Ken actually told me, like, yeah, these strategies aren't as good as, like, like, some of these strategies used to work Mhmm.
但那些方法现在不管用了。
Back to some, but they don't work anymore.
对。
Mhmm.
因为我把套利空间都抹平了。
Because I'm arbitraging them away.
所以你和肯可能同时发现了这些策略，但肯早就用上了，我觉得我才是后来才找到的。
So you maybe maybe you and Ken found these same strategies at the same time, but Ken already had I think I found
我晚了五六年才接触到，不过没错。
them, like, five, six years too late, but yes.
因为我之前回测效果不错，但实际交易时收益几乎为零。
Because I because I had some good back tests, but in in real trading, was like close to zero.
明白了。
Okay.
所以你确实发现了一些东西，但肯说他早就抢先一步了。
So you did find something, but Ken was like, NEAR NEAR NEAR NEAR, I got there first.
是的。
Yes.
对。
Yes.
是
Is
展开剩余字幕（还有 480 条）
就是另一个领域吗？
that is that one other area?
那就行。
Then that's fine.
我的意思是，他其实，我不是故意的，我只是想
I mean, the he like, I wasn't trying to I I was just, like
你在
You were
找些有意思的交易标的。
trying to find interesting stuff to trade.
对吧？
Right?
对。
Right.
没错。
Right.
没错。
Right.
所以他雇你是因为这个吗？
Is that why he hired you?
我觉得，他的理由之一是，团队合作发现阿尔法收益比单打独斗强得多，我觉得这话没错。
Well, I I think that, you know, he part of, like, the his rationale for why I should join is that, like, it's much better to find these sources of Alpha on a team versus doing it on your own, which I think is correct.
为什么这么说？
Why is that correct?
你知道，当你在寻找阿尔法时，
You know, when
得把各种不同的想法整合起来，一个人不太可能一下子看到所有这些因素。
you're finding Alpha, have to bring a lot of different ideas together, and it's like unlikely that one person's gonna see all of that at once.
而且，如果团队里的人背景和方法各有不同，因为市场已经非常接近有效了。
And or or if you have like a team of people with somewhat different backgrounds and approaches like because like like for like the markets are very close to being efficient.
要发现套利机会，得很多条件同时成立，而团队合作更容易找到这些组合。
And so like to find inefficiencies like a bunch of different things all have to be true, and it's, like, easier to find those combinations in a group.
对。
Right.
当然。
Sure.
没错。
Yeah.
把各种不同的视角都整合起来，就像哈佛教的那样。
Add add all the different perspectives together kind of the with the same lesson from Harvard.
比如说，如果你告诉我你对以太坊有个很有趣的观点，然后我碰巧跟汤姆·莱恩聊了，他分享了他的看法，我把这两者结合起来。
Just like, for example, if you were to tell me that, like, you have a really interesting thesis on, you know, Ethereum, and then, you know, I don't know if I talked to Tom Lean, he gives me his thesis, then I put that together.
现在你就有了两个观点。
Now you have two thesis.
是的。
Yeah.
对。
Right.
对。
Right.
你在Citadel工作了多久？
How long did you work at at Citadel?
是的。
Yeah.
我在那儿待了大概一年，后来另一家公司挖我过去，让我去搭建一个交易团队。
So I I was there for around a year, and then another firm recruited me to kind of come in and continue, you know, to kind of actually build a trading desk
对。
Mhmm.
就是。
There.
所以我其实一直想，怎么说呢，我想做点更有创业精神的事情。
So I kind of because I wanna ultimately, like, I wanted to do something more entrepreneurial.
就像我在大学的时候就在尝试那样。
Like, I was trying to do that in college.
你早就知道自己想当企业家吗？
Did you know that you wanted to be an entrepreneur?
是的。
Yes.
对啊？
Yeah?
对。
Yes.
你是什么时候第一次意识到的？
When did you first know that?
我八岁的时候。
When I was eight.
真的吗？
Oh, really?
是啊。
Yeah.
你八岁那年发生了什么？
What what happened when you were eight?
我爸爸和我一起看了关于比尔·盖茨的电视节目。
My dad and I watched the TV program about Bill Gates.
是啊。
Uh-huh.
那时候我就觉得特别酷，居然能写软件，还能围绕它做成一家了不起的公司。
And, you know, that was I thought that was pretty cool that you could, like, you know, build software and, you know, create a great business around that.
对。
Mhmm.
所以，创业一直都是你想做的事，
So building a company has always been something that you wanted to
对。
do.
没错。
Yes.
是的
Yeah.
好的
Okay.
后来你在哈佛到处摸索，跟肯一起做项目，然后接下来那个公司
And then you were kinda, like, searching around throughout Harvard and working with Ken, and then the next Well, I firm
我不确定。
don't know.
我的意思是，没错。
I mean right.
我的意思是，那从来不是最终目标。
I mean, it's not like that was the goal ultimately.
但这并不意味着我得马上去做。
That doesn't mean I had to do it right away.
对。
Right.
是的
Yeah.
加入一家公司，深入了解整个行业的运作方式，这一点很重要。
And then so, like, joining a firm and learning a lot of the lay of the land of an industry is important.
说到和名人打交道，还有一件挺有意思的事，就是我从金融圈转到硅谷的时候，见过彼得·蒂尔，他当时建议我，别急着创业。
I mean, I guess, like, if we're talking about interactions with, like, famous people, one other interesting thing was, like, when I moved from finance to Silicon Valley, like, I met with Peter Thiel at that point, and his advice was, like, don't start a company yet.
先弄清楚初创公司是怎么运作的。
Like, get to know how startups work first.
对
Right.
这建议也特别好。
And that was great advice too.
对
Right.
是的
Yeah.
我猜作为创业者，你一直都在留意有没有什么好点子。
And I'm assuming you were you as as an entrepreneur, you were always kinda like looking around for an idea.
但你不能硬憋出一个点子。
But you don't you don't wanna force an idea.
得让点子自己找上门来。
You want the idea to come to you.
对吧？
Correct?
没错。
That's right.
没错。
That's right.
是的。
Uh-huh.
说到底，你确实会留意周围，但当你在交易台上，尤其是经历过2008年、2010年那些闪崩事件的时候，你根本没空想四年后该创业什么。
Well, it's I mean, you're looking around at some level, but I mean, when you're, like, on a trading desk and the markets, especially, like, we lived through 2008, 2010, flash crash, all of that stuff, like, when you're in the thick of it there, you're not thinking about, like, what's my startup idea four years from now gonna be.
现在你在市场上看到的一些信息，你的大脑可能之后才会处理，比如在洗澡的时候突然想到，哇，这个点子真不错。
And now some of the information that you're seeing in the markets may end up, like, you know, your brain may may may process that later, like, the shower and it's like, actually, like, this is pretty cool.
你可能会想，也许应该做一个更好的风险管理平台之类的，但这可能要过几年才会发生。
You can, like maybe there should be, like, a better risk management platform or something, but that may happen years later.
对。
Yeah.
你是怎么认识彼得·蒂尔的？
How did you meet Peter Thiel?
我是通过我朋友认识的彼得，你知道的，他当时是Facebook的首席技术官。
Well, Peter, I met through, you know, through my friend who was CTO of Facebook.
对吧？
Right?
所以……
So
啊，原来是这样。
Ah, okay.
彼得是他们那边的首席技术官。
Peter was their first
你当时是。
You were
在那家公司担任首席技术官。
the CTO there.
你们现在还保持联系吗？
Do you have an ongoing relationship with him?
或者嗯，
Or Well,
他们，你知道的，在我们推出代币之前最后一轮融资，他们参与了，就是那次。
they they they, you know, the last venture round we we did before launching a token, you know, they call it that one.
那是创始人基金吗？
That was founders fund?
对。
Yes.
好的。
Okay.
好的。
Okay.
好的。
Okay.
后来我去了一家叫Citadel的公司，之后又被一家新公司招去搭建交易团队。
So then worked at Citadel, got picked up by a new firm to build out the trading desk.
我把在Citadel学到的东西用到了新地方，那感觉有点像——
So took what you learned as Citadel, applied it to the And it was a little
现在大公司常讲的一个词，叫‘公司内的创业团队’之类的。
bit like they, you know, nowadays, you know, big companies use the terminology, like, start up within a start up or something.
在大公司里从零搭建一个交易团队，对我来说就是这么回事。
Start up within a big company, and, like, for me, building a trading desk within a larger firm was literally that.
对吧？
Right?
那是你第一次真正组建团队并担任领导。
That was your first experience actually building a squad and being a leader.
没错。
That's right.
就像创业一样，你知道的，确实如此。
And and just like building a business in the sense that like yeah.
我们那时候，说实话，不用操心人力资源、发工资这些琐事。
I mean, we we were like, you know, I didn't have to set up HR or payroll or like The silly stuff.
是的。
Yeah.
对。
Right.
所以，其实这些事情都有不少有意思的地方。
So well, yeah, I mean, it's actually all of the stuff has some interesting aspects of it.
没错。
Yes.
比如，我之前并没有跟那些主要经纪商有过直接往来。
Like the or like I didn't like, had obviously relationships with, like, prime brokers.
对的。
Mhmm.
但我记得第一天做这事的时候，还得亲自打电话联系数据供应商。
But I remember, like, on my first day doing that, like, calling, like, data vendors.
明白。
Okay.
谁家有这个市场的订单簿二层数据啊？
Like, who has data for, you know, order, like, l two order book data for, like, this market?
我当时就在干这些事。
Like, I was, like, doing that.
对吧？
Right?
可不是什么现成的资源都摆在那里等着你。
It's not like it was all there.
你当时待的那家公司是格雷厄姆资本吗？
What firm was this that you were Graham Capital.
格雷厄姆资本？
Graham Capital?
是的。
Yeah.
明白了。
Okay.
所以离我们现在所在的位置在纽约并不远。
So not far from where we are right now in New York.
哦，对。
Oh, yeah.
对。
Yeah.
没错。
Yeah.
而且这也是，就像
And that's also, like,
高频交易之类的事情。
high frequency trading stuff.
好吧，那是他们想让我去做的。
Well, that's what they wanted me to build.
他们没有
They didn't have
我明白了。
it I see.
所以是你搭建的
And so you built
那个吗？
that?
对。
Yes.
是的。
Yeah.
你是什么时候达到平价的？
When did you end up at at a par?
对，没错。
So right.
所以2012年的时候，我决定从华尔街转向硅谷。
So then 2012 is when I kind of took the swing to go from, you know, Wall Street to Silicon Valley.
一开始，我在Quora待了一年半，因为我对那里的问题领域特别感兴趣，说白了，我本来就喜欢做匹配类的问题，对吧？
So initially, I was at Quora for a year and a half because I was really interested in in the problem space there of kind of, like, I guess I'm attracted to like matching problems in general, right?
本质上就是把用户和他们该看的问题、答案匹配起来。
Like, it's in the core we're like matching users with questions and answers that to show them.
Adapar当时是一家快速增长的金融科技公司，主要做的是从各种来源收集数据，用来分析投资组合。
Adapar was a really, you know, at the time like fast growing fintech company, right, kind of building actually like aggregating data from various sources to build ways to analyze portfolios like.
Adapar的创始人之一是乔尔·隆斯代尔，他也是Palantir的联合创始人，他确实把一些类似的想法应用到了金融领域。
And like a lot of AATAPAR was started by Joel Lonsdale, right, who co founded Palantir, and he's like actually like applying some of these ideas to finance.
你看2008年的时候，风险之所以被误判，很大程度上是因为本该集中在一起的数据却分散了。
Like if you look at 2008, lot of the reason why risk was miscalculated is because a lot of the data that should have been there in one place wasn't.
所以你在Quora时是负责机器学习的负责人。
And so the you were head of machine learning at Quora.
没错。
That's correct.
对。
Yeah.
后来在Adapar也是。
And then also at Adapar.
Adapar的话，我是负责整个工程团队的。
Well, Adapar was head of all of engineering.
对。
Yeah.
负责整个工程团队。
Head of all of engineering.
这就回到了你之前提到的，你在奥赛时期特别擅长的技能。那时候打数学竞赛，你是不是特别喜欢去接触和处理数据？
So this kind of goes back to what you were talking about with like the skills that you were uniquely good at, in your Olympiad days when your math competition days were like, you really liked getting your hands on data.
没错，那就是你特别拿手的地方。
And that's what that's where you were particularly good at.
到了Quora的时候，情况就像是，嘿，我们这儿有用户在找问题答案。
And then Quora is like, hey, we have all the we have users looking for questions.
同时也有人在提供回答。
We have people providing answers.
我们怎么把这些东西匹配起来？
How do we match these things?
没错。
That's right.
这也是当时在Quora需要解决的大问题。
And that was the that was the big question to answer at Quora.
对。
Yeah.
而且你
And, you
说起来，机器学习其实挺有意思的，因为我早在二十五年前就开始做现在被称为人工智能的项目了。
know, machine learning so interesting enough, right, like, I actually worked on AI, what's now called AI, going back to twenty five years ago.
除了参加奥赛，我还参加过一个叫英特尔科学人才搜索的科研竞赛。
So because I did, like in addition to the Olympiads, I also did a research competition called the Intel Science Talent Search.
那个比赛的声望其实比奥赛还高。
That was actually a more prestigious competition than the Olympiads.
你还能见到总统之类的名人。
Like that's where you get to meet the president and
是的。
Yeah.
还有各种这样的事情。
And stuff like that.
我当时做的项目，是用光学技术代替芯片，来更快地运行神经网络。
And so the project I did for that was essentially, like, using optics to instead of chips to, like, run neural networks faster.
所以那个领域其实是深度神经网络的前身，后来就发展成了大家说的深度学习。
And but and so that that's like the, you know, that whole field was like the precursor to to deep neural networks, which then, you know, called deep learning.
再后来才有了Transformer，以及现在的AI技术。
And then that led let's like transformers and kind of modern AI.
那部分确实是
Was part
真的跟物理有关吗？
of that actually physical?
就是用光学来做？
So using optics?
我的项目是实实在在的物理装置。
So my project was physical.
对吧？
Right?
是的。
Yeah.
所以比赛的一部分是考察数学和编程能力，但同时也要求动手搭建
So part of the competition was a hybrid of being able to do math and coding, but then also build
实物。你想怎么弄都行，你可以提交任何你喜欢的研究项目。
physical, do whatever you want, you can submit any research project you want.
我的那个项目……我想大概有一半的参赛者确实做了些实验室里的实际操作，但其他人呢，你知道，他们可能只是写写东西。我那次拿了第十二名，但第二名那位选手提交的几乎就是一篇纯理论的数学论文。
So my project, like, think probably at least half of the kids who competed in that did do some actual, like, hands on stuff in the lab, but the others, you know, you could just write, like, you know, like I play placed twelfth in that one, but, like, the guy who placed second was, like, basically just sort of complete theoretical math paper.
原来如此。
Okay.
我明白了。
I see.
但你的工作有一部分是跟光学有关的物理实验。
But what you did was one part physical with the optics.
所以你是说，
And so you had like
那时候这玩意儿还不叫光子学呢。
it was now called photonics that wasn't a turn back then.
但其实就是光学和神经网络的结合。
But it was just like optics and neural neural networks.
是的。
Yeah.
对。
Yeah.
没错。
Right.
明白了。
Okay.
所以你主要是想平衡光学工程和背后的数学运算，来开发一个机器学习程序？
And it was just really trying to balance the actual optics engineering and, like, all the math behind it to come up with a machine learning program?
其实是为了实现神经网络。
Well, it's to implement neural networks.
你知道吗，说实话，这个项目现在来看依然很有意义，因为与其一味依赖硅芯片做大量计算，不如用光子技术。
Like, you know how mean, to be honest, like, project would be relevant today too because, like, instead of throwing all this compute at silicon chips, like, using photonics.
这确实是个实际存在的方向。
Like, that is a thing.
我的意思是，这个想法根本不是我想出来的。
I mean, I mean, I didn't come up with that whole idea.
我只是在优化其中某个具体的环节。
I was, like, optimizing a particular aspect of it.
对。
Right.
对。
Right.
用光子技术替代硅芯片，这本身就是一个方向，就像量子计算一样，是种全新的计算方式。
But the idea of using optics instead of silicon is a thing, I think is, like, that is a new form of computing as is, you know, quantum.
对。
Right.
对。
Right.
对。
Right.
对。
Right.
那时候你做这个，还是机器学习的早期阶段吧？
And this this was early stage machine learning when you were
是吗？
doing this?
其实也不是。
Well, not really.
机器学习从80年代就存在了。
I mean, machine learning was around since the 80s.
没错。
Sure.
是的，是的。
Yeah, yeah.
嗯，以我们现在对它的理解来看，那时候还算早期吧，就是说——
Well, I guess early stage by how we kind of know it today, which is just like,
你知道的，人工智能。
you know, AI.
对。
Yes.
但有意思的是当时的那种竞争氛围。
But what's interesting is like, that competition.
那时候，虽然现在这些奥赛因为出了一些成功人士而更出名了，但当年，这个科研竞赛可是美国最重要的科学赛事，我们还见过总统、副总统，以及一堆诺贝尔奖得主。
Back then, I think now these Olympiads have become more famous because of, you know, success of some of the people who've done them, but like back then, this research competition was considered like the main science competition in in The US, and we got to meet the president, the vice president, a bunch of Nobel laureates.
那些诺贝尔奖得主，你知道的，他们代表了当时科学界普遍的看法。
And the Nobel laureates, you know, and they were, I think, expressing the opinion of the scientific community at the time.
他们说，所有这些机器学习的东西，根本就是条死胡同。
They were like, yeah, all this, like, machine learning stuff, like, that's a dead end.
他们觉得，你小时候做这个挺可爱的，但你要真想搞科研，就该去搞量子物理。
Like, you like, that's kinda it's cute that you did this work as a kid, but, like, if you wanna do science, like, do quantum.
这种神经网络的东西根本行不通。
Like, this neural network stuff is not gonna work.
哇哦。
Wow.
所以是啊。
And so yeah.
他们这么说的时候，你有什么反应吗？
Did you did you have a reaction to that when they said that?
有啊。
Yeah.
我当时就在想，我到底是谁？
I mean, I I at the time, I mean, I was like, who am I?
就是觉得，嗯，
Was like, yeah.
咱们还是多关注量子方向吧。
Like, let's let's let's focus more on the quantum stuff.
当然。
Sure.
说实话，我当时并没有完全放弃，而是十二年后，当我开始在Quora工作时，又重新捡了起来。
Like, you know, I was I kind of I didn't move away from it, then I came back to it twelve years after when kind of in in my role at Quora.
是的。
Yeah.
哦，我明白了。
Oh, you know, okay.
我觉得，大数据的出现彻底改变了局面。
And I I guess the the, like, the big data kind of changed the game.
对。
Mhmm.
因为我觉得，他们当时说的也没错，毕竟那会儿还没达到互联网规模的数据量。
Because I think they I mean, they weren't wrong what they were saying given what exists at the time because this was pre internet scale data.
没错。
Right.
是的。
Yeah.
那时候他们根本没足够的数据来做机器学习，更别提把这些数据变成有用的东西了。
They didn't have the you don't have the clay to do the machine learning to actually manipulate and stuff into being useful.
对。
Yes.
好的。
Okay.
所以跟我说说吧，我这儿有个假设。
And so tell me, I'm assuming I'll make an assumption here.
我猜你在Quora工作的时候，某个时刻突然灵光一现，意识到：等等，我这儿真有大数据啊。
But I'm assuming there was like an moment or like a neuron neural connection coming together in your time at Quora, where you realize that, you know, you actually I do have big data here.
你当时在做匹配问题，比如把Quora上的人连接起来，现在你既有数据，又有机器学习的经验。
And you're doing the the matching the matching problem, like connecting people at Quora, and now you have the data and you have your experience with machine learning.
对。
Yes.
是不是有什么东西突然触发了你的灵感？
Did some something collide there?
当你意识到从机器学习的角度来看，这里有大量可用的数据时，脑子里是不是像粒子碰撞一样迸发出火花？
Did particles collide there in your head when you realize that, oh, there's actually a lot clay here to work with from the machine learning perspective.
机器学习嘛，你知道的，我们之前在量化金融领域也用过了。
Machine learning, you know, we were using it in quant finance too.
对。
Mhmm.
因为2008年之后，跟以前不太一样，交易方式变得相对简单，重点更多在于实现快速执行。
Because after 2008, a lot like before that, you could trade in fairly simple ways, and it was more around implementing, like, fast execution.
对。
Mhmm.
也就是现在大家所说的高频交易。
You know, what's now known as high frequency trading.
对吧？
Right?
但那时候，简单的策略其实挺管用的。
But, like, pretty simple strategies worked.
2008年之后，这些简单策略就不灵了，你得去发现市场里更复杂的模式。
Then, after 2008, those simple strategies stopped working, and you had to find more complicated patterns in the market.
所以机器学习也被用在这上面，只不过区别在于：在科技行业，只要你有点新东西，就恨不得到处宣传造势。
So machine learning was used for that too, it just wasn't See the difference is like, in the tech industry, if you come up with anything interesting, you wanna create buzz around it.
对。
Mhmm.
全都这样。
All that.
但在量化交易里，恰恰相反，对吧？
In quant trading, it's the opposite, right?
如果你真有个特别厉害的数学模型能用来交易，你根本不想让人知道。
Like you don't like, if you actually have really interesting math model to trade
没错，得藏着。
Yeah, you keep it secret.
你希望竞争对手以为你做的只是些很基础的东西。
And you want the competitors to think that you're doing something very basic.
对。
Yeah.
即使你根本不是这么做的，对吧？
Even if you're not, right?
没错。
Right.
所以我们当时用了很多机器学习技术，不管是我的团队、公司里其他团队，还是竞争对手，都在大量使用这些方法。我早就接触过一些，包括小时候做的项目。但像Quora这样的互联网公司，我们肯定不是唯一这么做的人——Spotify就在用类似方法为用户匹配音乐推荐。
So like, so we were using a lot of this machine learning stuff, whether it's my team or other teams at my firm or our competitors, like we're using a lot this stuff, so I knew some of that already including from like the work I did as a kid, but like yeah with with with Quora you know the internet companies we certainly weren't the only ones to do it I mean Spotify was matching users and music recommendations.
那个做这个的人，是我以前参加奥赛时认识的一个年轻人，现在他正在经营一家叫Modal的公司。
The guy worked on that is another kid I knew from Olympiads, now he's running a company called Modal.
你记得Netflix吧？2010年的时候，他们搞过一个Netflix大奖，悬赏一百万美元，征集更好的电影推荐算法。
You know the Netflix you know this is you know if you recall there's a Netflix prize back in 2010, that was when there was like a million dollar prize to come up with a better algorithm to rank movies.
这类技术，其实就是通过结构化学习用户数据、偏好和产品，来做个性化推荐。
So this kind of stuff like you know structured learning of like user data and user preferences and products to recommend.
对，就是那回事。
Like that was the thing.
我是说，在知识问答这个领域里，Quora是第一个这么做的。
I mean, we were the first to do it in the space of knowledge at Quora.
但有趣的是，Quora其实已经具备了构建大语言模型所需的大量要素。
But what was funny is that Quora actually had a lot of the things you need to build LLMs.
对吧？
Right?
因为那是文本数据，跟 Spotify 或者 Netflix 不一样。
Because it was text data, unlike Spotify or Netflix.
那是文本数据，而且信息密度特别高。
It was text data, and it was also super information dense.
对。
Yes.
因为里面全是问题和回答。
Because it was all questions and answers.
没错。
That's right.
对。
Yeah.
理论上，我们本来可以做出第一个像样的大语言模型。
Like in principle, we could have built the first good LLM.
我的意思是，我们的创始人最近刚加入了OpenAI的董事会。
Mean, like, our our founder be you know, joined the board of OpenAI for recently.
对吧？
Right?
不过那时候，语言模型其实还很不成熟。
But like, you know, we I mean, at the time, language models were pretty bad, though.
所以我们早在十二年前就试过用它们。
And so the we tried using them even, like, twelve years ago.
但它们对预测模型的提升微乎其微。
They they didn't add much alpha to the prediction model.
是的。
Yeah.
对。
Yeah.
没错。
Yeah.
所以你当年在奥赛时期就认识了Anthropic的两位创始人。
So you also had the connections to the two Anthropic founders from the Olympiads days.
是这样吗？
Correct?
是的。
Yes.
我不太清楚你是怎么跟他们保持联系的，或者这些年有没有一直保持联系，但你很早就跟他们有交集，很早就开始做机器学习，还在Quora待过。
And I don't know if how, where how you kept up with them, or if you kept up with them throughout the years, but you had those connections early on, you worked with machine learning relatively early on, you had your time at Quora.
当AI真正兴起的时候，你觉得奇怪吗？
Was it odd when when AI finally happened?
就像2023年那个ChatGPT爆发的时刻吗？
Like we had the chat GBT moment in 2023?
就是那时候吧？
Or whenever that was?
对你来说，是那样吗？
Yeah, for you?
突然觉得，哎，终于等到这一天了？
That like, Oh, finally, like, we've got this thing now?
或者，当你手握那些信息的时候，感觉怎么样？
Or like, what was it like being on having the kind of like the information that
在ChatGPT出现之前？
you had?
那些公司都有挺有意思的背景。
Pre ChatGET?
这些公司的发展历程都很特别。
These companies had, like, interesting histories.
对吧？
Right?
OpenAI 是当初由一群联合创始人创办的，你知道的。
So, like, OpenAI was started, and, you know, they had, like, a bunch of cofounders.
对吧？
Right?
埃隆提供了资金支持。
Like, Elon funded it.
我觉得他们当时搞的是个挺开放的实验室，做了不少方向。
You know, it was, like I think they're working on a bunch of it was it was, like, pretty open ended lab.
对吧？
Right?
他们同时在推进好多不同的项目。
Like, they're working on a bunch of different things.
那时候还有 DeepMind。
I mean, there was DeepMind back then.
那时候还有谷歌大脑。
There was Google Brain.
Anthropic 的团队挺特别的，因为直到创业前三四年，他们中不少人还是学者。
Anthropic was actually its team was interesting because some of them were actually academics up until, like, three years before starting a company.
对。
Mhmm.
从学者直接转身创办公司，这速度可能是最快的了。
So that's probably the fastest anyone's gone from, being an academic to Yeah.
把一家出色的公司做起来。
Building a great business.
所以我当时大概看明白了，AI 一定会火，
So I think I got a lot of the big picture, Like, that AI was gonna be big Mhmm.
投资早期 AI 公司会是个大趋势。
And investing in AI first companies was gonna be a thing.
但他们后来具体走的路，我真没料到，估计也没人猜到——就连在 OpenAI 内部，当年大语言模型也不是重点。我记得 2018 年他们给初创公司做过一次演示。
And so, like the exact path they took, I don't think I would have predicted it or I don't know if anyone predicted Like the fact that because even within OpenAI, like the LLM stuff was not Like I remember they gave a presentation in 2018 to the startup.
我们当时属于一个叫南公园公社的群体，正在用AI做我们手头的东西，对吧？
We were part of this group called South Park Commons, and we were using AI for what we were building at the time, right?
那个项目叫Lunchclub，也是做匹配的，把人和人连接起来，主要是为了职业上的交流。
Which was Lunchclub, like matching again, matching problem, kind of matching people with other people for professional connection.
那时候，我们创业圈里的人，或者请来的外部嘉宾，经常做分享。
And anyway, there were all these like presentations people gave either people who were part of our startup community or external guests.
对吧？
Right?
OpenAI那帮人来做过一次分享，其实是Dario主讲的。
So like the OpenAI guys gave a presentation was actually Dario who did it.
那时候他还在OpenAI。
He was still at OpenAI back then.
对吧？
Right?
他们聊的是当时跟DeepMind竞争的项目，比如用AI玩电子游戏那些事。
And so they were talking about the stuff that were they were competing with DeepMind on like playing video games.
我不是个玩游戏的人，你知道的。
And you know like I'm not a video game person.
所以我觉得这挺有意思，但说实话，这并不是让我特别兴奋的AI应用场景。
And so I thought okay this is interesting but like This is not exactly the application of AI that I'm excited about.
事实上，他们当时确实在做这类东西。
The reality is that they they were working on that kind stuff.
他们团队里有两个人专门搞大语言模型相关的工作。
Had like two guys on their team who were doing this LLM stuff.
就算他们开了个公开应用，那也不是他们的核心方向。
Like even with an open app, that wasn't their main thing.
对。
Right.
后来他们找到了一个真正不一样的应用场景，就像GPT从一代升级到二代时那样，那种转变是质的飞跃——在业内看来可能都是渐进的，对吧？
And then they found some application that like was phase shift different than, like, I think GP when they went from, like, GPT-one to GPT-two, it was pretty big, like, like, if you're in the field, it all feels iterative, right?
技术确实在一点一点变好，但对普通用户来说，真正让人觉得惊艳的，往往是那种跃迁式的突破。
It's like the the stuff gets better and better, but for the for it to be interesting to the consumer was more of a step function.
对。
Right.
因为说实话，我最早意识到这会成为一款主流消费产品，大概是在两年前。
Because, like, the con like, I I guess when I first got a sense that this was gonna be a big consumer product was in was probably, like, two years.
我记得在2021年底，我们的一些实习生说，我们其实都不写作业了。
I had like in late twenty twenty one, I remember some of our interns were like, you know, we don't really do homework anymore.
我当时就问，什么意思？
Was like, what do mean?
他们说，这个GPT-2已经足够好，能帮我们写作业了。
Like, this this like GPT two is actually like good enough to do our homework.
我当时就想，光这一项用途，就已经是个大市场了。
I was like, that's even just that is a big market.
对吧？
Right?
就光是写作业这一件事。
Just just like homework.
写作业。
Doing homework.
是的。
Yeah.
对，没错。
Like, yeah.
我当时就想，哪怕它只能干这个，也说明这产品和市场是匹配的。
I was like, I would even if that's all it is, I was like, this is like, there there's some product market fit there.
这东西已经不只是纯科研了。
Like, this stuff is not just pure research anymore.
那就是我第一次觉得，这东西真能成。
But, like, that's like when I first thought, this stuff was gonna work out.
因为就算在AI领域内部，像生成视频、生成图像这些，也都是完全不同的方向。
Because there's a lot of even within AI, Like, even within what's used on there's like generative video images, like all these things are quite different.
对吧？
Right?
是的，人工智能这个概念现在已经渗透到方方面面了。
Yeah, the whole kind of concept of AI has blossomed into basically everything.
对，我想聊聊你午餐俱乐部那段经历。
Yeah, I want to I want to get to your time at lunch.
去说说你在Adapar的工作经历吧。
Go look for someone to talk about your time at Adapar.
是的，据我了解，
Yeah, which is the as I understand it,
就是你加入午餐俱乐部之前待的那家公司。
the last company you're at before before lunch club.
Adapar是做什么的？
What was Adapar?
Adapar呢，就像我之前说的，是Joe Lanza共同创办的一家金融科技公司，整合了大量金融数据。
So Adapar, as I was saying, was like the FinTech company that Joe Lanza co founded, brought together a lot of financial data.
你可以用它来了解自己的投资组合，做分析、出报告。
And like, you can understand your portfolio, like run analytics, run reports.
我当时还想用人工智能来帮人思考，你的投资组合里到底该有什么。
What what what I wanted to also do there is to use AI to think about like what should be in your portfolio.
对。
Mhmm.
那时候技术还没成熟到能做这个。
At the time, the tech wasn't ready for that.
所以当时更多是搞清楚你现在拥有的到底是什么，跟你应该拥有什么之间的差距，但这一点特别重要——如果你连自己手里有什么都不知道，那岂不是更奇怪？
So it was more around like understanding the what it is now versus what it should be, but also very very important function because if you don't know what's in your portfolio now, you think why would you not know?
那是因为你可能有好几层结构，比如有人直接持有苹果股票，同时又通过一个特殊目的实体持有。
Well, because if you have many layers of like like, maybe somebody owns Apple stock directly, but they also own an SPV.
他还可能持有某只共同基金，而那只基金里也包含了苹果股票，这些关联其实并不明显。
They may also own a mutual fund that has Apple like under, like, the correlations are actually non obvious.
你那时候在Adapar搞过实习项目，招实习生来工作，是吧？
And when you ran an internship program there, so you hired interns to work at a par.
没错。
Correct?
其实这主意也不是我想的。
Well, the you know, it wasn't my idea.
乔从一开始就在招实习生。
You know, Joe was hiring interns from day one.
Palantir 那会儿就有实习生了。
I mean, they had interns at Palantir.
招聪明的实习生，这想法一点都不新鲜。
So the idea to hire smart interns was not new.
我带来的新点子是，我们应该从奥赛里找人。
What was new that I brought to the table was that we should source them from the Olympiads.
明白了。
Okay.
嗯，懂了。
Okay.
所以你是挑了自己熟悉的领域来招人？
So you you picked your an arena that you were familiar with to to hire talent?
我们其实对来自任何地方的人才都持开放态度，当然了。
Well, we, you know, we we're open to talent from anywhere, but Right.
我们特别关注这个，因为这不光是我一个人的想法。
Like, we're specifically you know, that was a, you know, because we it was like it wasn't just me.
我们有个招聘团队，是的。
We had a recruiting team Yeah.
我当时让招聘团队去了解的是，这些奥林匹克竞赛的选手，其实17岁就能合法在美国工作。
And and what I kind of was getting the recruiting team to learn about is like, hey, these Olympiads, so you can actually, like you know, it's legal to work in The US, you know, at 17 years old.
只要参加过奥林匹克竞赛，不管多大年纪，我们都能雇。
Like, we we can hire people at any age who did who did these Olympiads.
这种招聘方式的信号特别强。
It's gonna be high signal hiring.
这个想法对你们有用吗？
Did that thesis work out for you?
确实很成功。
At It did work out.
是的。
Yeah.
你该怎么衡量这种成功呢？
How how would you gauge that success?
对Adapar来说，结果相当不错，但对那些孩子来说，效果特别好。
Well, certainly, it worked out fairly well for Adapar, but it worked out really well for those kids.
对。
Yeah.
而且我觉得，对我们这些早期投资了他们未来项目的天使投资人来说，结果也很理想。
And, I mean, I think, you know, it worked out for those of us who angel invested in their future projects.
是的。
Yeah.
你和哪些孩子合作过？
Who who were some of those kids that you that you worked with?
其中一个，我和他一起创办了Lunchclub。
Well, one of them I cofounded Lunchclub with.
对吧？
Right?
斯科特。
Scott.
斯科特·吴，他现在是人形智能公司 Cognition 的联合创始人兼 CEO。
Scott Wu, who is now co founder CEO of Cognition.
不过，说实话，从阿塔帕尔实习项目到核心实习项目，我认识的那些联合创始人，比如 Scale、Perplexity、Modal 这些公司的创始人，还有刚才提到的那些，他们现在都在做 AI 相关的事。
But, yeah, you know, like, between the Atapar intern program and, you know, the the core intern program, I mean, like, I think, like, so, you know, co founders of companies like Scale, Perplexity, Modal, as I mentioned, you know, nothing and that's just that's just an AI.
对吧？
Right?
还有一些是加密货币项目的创始人，你知道的，他们当中好多人去了交易公司。
Also, founders of some crypto projects too, you know, a bunch of them went to trading firms.
我是说，我觉得……其实吧，我们就是把那些早期阶段的人才都聚集在了一起，然后他们……
I mean, I I think it was, you know, very, I mean, I I think, but essentially we just, like, got a lot of the the early stage talent in one place, and they they
简直就像瓮中捉鳖一样，太容易了。
just This like like shooting fish in a bar, Merrill.
他们确实从全职员工身上学到了很多，彼此之间也建立了联系。
They they they yeah, and then, like, they learn a lot from the full time folks, but they also, like, form connections with each other.
有些人就是因为在那儿结识了伙伴，后来才一起创业的。
And some of them, like, started companies as a result of connections there.
对。
Mhmm.
我们稍微回退一下。
This is we're going backwards a little bit.
你觉得奥赛人才产出率这么高的原因到底有多大程度是源于这些因素？
But to what degree do you ascribe the reasons why there was such a strong hit rate of talent coming out of the Olympiads?
更像是
It was more of like
纯粹是还没被发现的潜力。
just like undiscovered alpha.
对吧？
Right?
你知道吗，从我的角度看，一直以来都应该是这样。
You know, because I think from my perspective, it it always would have been like that.
当然，不同阶段可能会有起落，但举个例子吧——我2004、2005年在城堡投资的时候，那时候他们招聘团队很强，算是业内顶尖的，可当我跟他们聊起奥赛的时候，他们根本不知道这是什么。
I mean, maybe some ebbs and flows depending on specific cycles, but, like, like, for one one piece of context, right, like, so when I was at at Citadel in the early two thousands or the mid two thousands, I guess, I should say, like 2004, 2005, like, you know, and Citadel had a great recruiting team, one of, you know, top firms in the industry, but, like, when I talked to the recruiting team, like, they didn't know what the Olympiads were.
现在想想真不可思议，因为如今这些公司全都盯着这个人才池，别的都不管了。
Like, that's crazy to think now because now all these firms, like that's, like, all they do is, like, focus on this talent pool.
可那时候，我得从最基础的层面跟他们解释，这些比赛到底是什么。
But back then, like, I had told them from, like, from first principles, like, what these competitions even were.
所以你之所以清楚，是因为你自己亲身经历过，知道这种人才确实存在。
And so it's just you knew you had the experience because you went through it, and so you knew that it was
我知道他们就在那里。
out there.
我敢说，肯定还有别的渠道。
I mean, I'm sure there are other Yeah.
早年在简街资本，就有个奥赛出身的人，HRT，还有其他对冲基金和初创公司，也都一样。
You know, there was a guy at at Early Jane Street who was an Olympiad guy, like HRT, like all these trading firms and and startups too.
对吧？
Right?
你知道吗，Facebook 的 CTO 就是参加过奥赛的。
Like, you know, CTO of Facebook did Olympiads.
谷歌的首任 CTO 也是奥赛出身。
Google, their first CTO actually also did Olympiads.
这些人慢慢把这种理念带给了同事，但花了好多年。
Like, so they they those people kind of slowly but surely educated their colleagues about this stuff, but it took years.
后来套利空间就被压缩了。
And and then the Alpha got got squeezed out.
是的。
Yeah.
对。
Yeah.
咱们聊聊 Lunchclub 吧。
Let's talk about let's talk about Lunchclub.
所以Lunchclub是你创办的第一家公司，对吧？
So Lunchclub was the first company that you founded.
没错。
Correct?
是的。
That's correct.
对。
Yeah.
能说说你是怎么想到要做Lunchclub的吗？当时是什么让你觉得这个点子不错？
Tell me about just how the the spark came to you about, like, why Lunchclub was a good idea.
你想啊，就拿我们现在这场对话来说，很明显，人脉关系特别重要。
Well, think a lot of the if you like, if you just if you think about our conversation right now, I think it's it's pretty clear that connections are very important.
对吧？
Right?
没错。
Mhmm.
有时候，仅仅一个连接就可能改变一个人的人生或职业轨迹，甚至影响整个行业。
Like, sometimes it's just like even a single connection can change the trajectory of someone's life or career, in some cases an industry.
对吧？
Right?
你想想，1997年彼得·蒂尔和埃隆·马斯克那次见面，创造了多大的价值？
Like, you know, like what how much value was created from Peter Thiel meeting Elon Musk in '97?
没错。
Right.
我们熟悉的这些行业，这种事天天都在发生。
The industries we know, th this happens all the time.
对吧？
Right?
没错。
Right.
所以当时的想法就是，好吧，但这些事都太随机了。
So the idea was like, okay, but this stuff is all random.
人们偶然相遇，网上有海量的数据。
People just happen to meet like, there's all this data on the Internet.
我们现在用这些数据购物、听音乐、找电影和阅读内容的推荐。
Now we use this data to shop, we use it to listen to music, to find recommendations for movies, for content to read.
那为什么不用它来建立真正有价值的社交联系呢？
Why not use it as a way to, like, do really valuable networking, right?
对。
Mhmm.
建立对的连接。
Making the right connections.
这个想法本身没错，但真正让它成功、或者说成功到这个程度的，是具体的执行方式。
And but the the so that that idea was kind of that was the the mission, but the implementation detail that made it work, or at least made it work as well as it did.
之前也有人试过类似的做法，但都没什么起色。
Because there there have been others that have tried this kind of stuff, right, that didn't really go anywhere.
但对我们来说，效果挺好的，尤其是在疫情期间。
But I mean for us it worked pretty well especially during COVID.
从那以后，增长就停滞了，但没消失。关键在于怎么避免逆向选择。
And even since then it's like plateaued, it didn't die, but the key insight on the implementation side was, like, do we avoid adverse selection, right?
如果你照搬约会网站的模式，搞什么双向确认，那在社交网络上根本行不通。因为约会是人人有伴，可社交不一样——如果你随便谁都能发请求，那情况就乱了，你也懂的。
Because if you have a system where you use like the the model from, you know, dating sites, where you're like, okay, like like double opt in, that doesn't really work too well for networking, because unlike dating where there's, like, somebody for everybody, like, in networking, it's like if you see if you could send requests to anybody, like, it's like, you know, I don't know.
马克·安德森这种人，一天能收到上百万条请求。
Mark Andreessen would get, like, a million requests.
对。
Right.
是啊。
And Yeah.
可一个大学生可能一条回复都没有。
Someone who's, like, a college kid might not get any.
对。
Right.
其实该发生的是，一个想搞生物的大学生，应该和一个想搞人工智能的大学生配对，一起做出下一代医疗领域的AI工具。
Even though what should happen is the college kid who's thinking about biology maybe should be matched with a college kid who's thinking about AI and they can make the next great AI agent for health care.
当然。
Sure.
但真正该发生的情况是，这两个学生不该都去联系马克·安德森，而马克根本没时间回复。
But like that's the kind of condition that should happen instead of both of those kids being matched with Marc Andreessen and Marc never having the time to respond.
对。
Right.
是的。
Yeah.
对。
Right.
对。
Right.
对。
Right.
没错。
Yeah.
去掉中间环节，还有那些无关紧要的部分。
Cut out cut out the intermediary or the the indifferent and irrelevant point.
实际上，Marc 很擅长牵线搭桥，这点得承认。
Actually, Marc, be fair to Marc actually is very good at connecting people.
所以我并不想拿他当反例，但我想你明白我的意思，就是要避免逆向选择。
So I don't wanna use that, know, you may be a bad example of, you know, but but I think you get the point right to avoid adverse selection.
你其实不需要主动去匹配某个特定的人。
So you actually don't opt into a particular connection.
让系统来帮你做匹配就好。
You let the system make the match.
对，没错。
Right, right.
这是你创办的第一家公司吧。
So this was your first company.
在我看来，这简直是顺理成章的延续。
And it seems to me just like a pretty logical continuation.
因为你一开始在 Quora 工作，负责匹配问题和回答。
Because first, you were working at Quora, where you are matching between queries and answers.
没错，就是把人和他们的提问、回答对应起来。
Yes, people and their queries and their answers.
然后你又去做了另一个项目，当时你在搞
And then and then you were working at a par working on, like, you were working on your
自己的事，同时也涉及数据聚合和数据匹配。
own thing, but also about aggregating data than matching data.
不过，更多时候是偏向人事方面，比如组建团队这类事。
But but yes, and then I guess a lot of the like, I guess less on the product side, more on the people side, that part of, like, building these teams.
我觉得那部分其实没那么重要。
I guess a lot of that was kind of irrelevant.
是的。
Yeah.
对。
Yeah.
后来就出现了Lunchclub，其实就是把这两件事结合起来。
And and then so then came Lunchclub, which is just kind of just the you smash those two things together.
我得提一下，这部分工作很大程度上也是斯科特的功劳。
And, know, I should point out, like, a large part of that was also the efforts of Scott too.
对吧？
Right?
所以这可不是我一个人的功劳，斯科特也出了很多力。
So it wasn't, you know, it was only Scott.
是的。
Yeah.
别管用什么办法，都来找我。
Come for me by any means.
对。
Yeah.
没错。
Yeah.
但感觉就像我们的故事线正在向弗拉迪斯拉夫的高潮推进，他的这段经历正逐渐推向顶点。
But it just seems kinda kinda like we're kinda your arc to Vlad of the arcs, the arc of Vlad is kind of crescendoing here.
这个时候的感觉是，你似乎正在从积累技能和参与竞争，转向实际应用这些技能。
At this time, was like, it's feel like you're going you're kind of shifting from, you know, acquiring skills and competing in skills to applying skills.
在我看来，这就是 Lunchclub 的一个关键转折点。
That's kinda like the inflection point that I see with with Lunchclub.
好像弗拉迪斯拉夫开始真正找到自己想成为企业家的方向了。
Was like, okay, Vlad, Vlad is like kinda starting to find where he wants to really be an entrepreneur.
对。
Yes.
是的。
Yeah.
来聊聊从 Lunchclub 到 Lighter 的转变吧，因为这是同一家公司，对吧？
Talk about the transition from Lunchclub to Lighter because this is the same company, correct?
没错。
Yeah.
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