克利夫·索辛 - 拥有智力的补充 - [像最佳投资者一样投资，第497集]

你可以把现在的美国社会想象成一个正在为买房存钱的家庭。

You could think about American society right now as a household that's saving money for a house.

我们在盖房子，大家都觉得手头有点紧，因为所有资源都投在建设这个庞大的AI基础设施上。

We're building a house, and we're all kind of feeling pinched because all these resources are dedicated to building out this big AI infrastructure.

那些电工、水管工还有其他工人都去干这些活了。

All these electricians and plumbers and everything else are going to do these tasks.

结果就是，经济其他部分的成本就上去了。

And as a consequence, there's higher costs elsewhere in the economy.

毫无疑问，我们会从中获得很多价值，我们会继续推进，因为没人真正知道结果会怎样。

And undoubtedly, we're going to get a lot of value for it, and we're going to keep pushing it because no one really knows.

我们会一直推，直到最后做过头。

We're going to keep pushing it until eventually we overdo it.

在这个过程中，人们必须赚到很多钱，不然没人会继续推下去。

And along the way, people have to make tons of money because otherwise people won't keep pushing it.

而且因为事情有滞后性，也因为这是赢家的诅咒，最后停手的那个人肯定会后悔。

And because things have lead times and because it's a winner's curse, invariably, the last person to stop will regret it.

而且这真的太难了。

And it's just so hard.

真的太难了。

It's just so hard.

再想想，这里技术的变化太快了，我觉得这在以前的历史性基础设施建设中不太常见。

And then you combine that with the fact that there's just so much dynamism in the technology here, which I think was less true of other historic infrastructure build-outs.

做这些事的方式变化太多了。

There's just so much change in terms of how you do these things.

其他人也说过，我觉得这不一定错，我们谈到了模型，但即使在芯片层面或者别的什么层面，AI也有可能让像英伟达这样的公司也变得大众化，甚至……

Other people have made the point, I don't think this is necessarily wrong, that we talked about the models, but even at the chips level or whatever, the potential for AI to commoditize even things like Nvidia or-

到处都是递归自我改进，在……

Recursive self-improvement everywhere at the-

到处都是，而且很多最终都变成了智能。

Everywhere, and a lot of it ends up being just intelligence.

比如英伟达其实就是把平面文件发送到台湾。

Like Nvidia quite literally makes flat files that they send to Taiwan.

他们的那些文件很难做，但他们有很多人在上面费尽心思。

Their flat files are very hard to make, but what they have is a lot of people who think really hard about it.

所以理论上讲，这些文件还是能造出来的。

And so in theory, you could make them.

然后问题就来了：如果你稍微退一步，不做最前沿的东西呢？

And also then the question becomes, what if you step back a little from the frontier?

那样的话，制造成本就会大幅下降。

Then the cost of making something comes way down.

在那种情况下，你会不会认为物理和规模是你在供应链这边投资时想要的东西，比如台积电？

In that world, would your model be that physical and scale are the things that you'd want in a business that you were to back on the supply side of this equation, TSMC?

我可不想把自己当成什么台积电专家。

I don't want to carry myself around as some sort of TSMC expert.

当然，我觉得他们做的事情在这方面确实有一些更好的特性。

Certainly, what they do has better attributes, I think, in that regard.

他们也有一个有意思的问题，就是他们做的那件事特别难，需要很多年积累起来的所有工艺能力，而且极难复制，也不容易被AI替代，因为这件事太依赖物理实体，有很多细节AI甚至都看不明白。

They've also got this sort of interesting problem where they did this super hard thing that requires all this process power built up for many years, and it's super hard to replicate and much less amenable to an AI just because it is so physical and because it's all these little things, many of which aren't even legible to an AI.

就像是系统里的某个人知道你必须这么做。

It's like some guy who is in the system kind of knows that you have to do this.

因为需求太大，而他们一直没法满足，基本上就逼得全世界的人都想模仿他们的护城河。

Because there's so much demand and because they haven't been able to meet it, they've basically caused everyone in the world to seek to copy their moat.

这不算好事，但我觉得更普遍地讲，跳出AI建设本身，想想企业的话——我们聊过Meta的例子——那些有地理邻近性或规模优势的企业，如果能用智能更好地服务客户或降低成本，可能会受益。

That's not a great fact, but I think more generally, stepping outside the AI build-out, if you think about businesses, we talked about the Meta example, just the idea of businesses that have physical proximity or physical scale might benefit if they can use intelligence to serve their customers better or have lower costs as a consequence.

我想回到智能的自然互补品上，请你解释一下为什么它们是互补的，这样大家就能去思考：到底哪些公司具备这种特征，可能会很有趣？

I want to go back through the natural complements to intelligence and just have you explain why it's a complement to intelligence so then people can go away and think about, okay, what companies have this feature that might be interesting?

好例子，聊一聊地理邻近性吧。

Good example to go talk about physical proximity.

你怎么看DoorDash、Uber这些企业，它们的本地密度效应很重要？

How would you think about DoorDash or Uber or these businesses that have local density effects that matter a lot?

如果智能变得无处不在，是不是会自然放大所有这类有规模赢家的优势，其中地理邻近性是某种关键优势？

Does intelligence being ubiquitous sort of naturally magnify all of the scale winners of this type where physical proximity is some sort of key advantage?

嗯，如果你是最低成本的某个东西的生产者，我给你举个比较典型的例子。

Well, so if you're the low-cost producer of something, I'll give a sort of stylized example.

如果每个人都有AI代理，那么很多决策都会被外包给AI代理，因为它能帮你优化，帮你把那些丢在地上的零钱、美元、镍币之类的都捡起来。

So if everyone has an AI agent, then what happens is many decisions get offloaded to the AI agent because the AI agent helps you optimize, helps you pick up all the pennies and dollars and nickels and stuff that you were leaving on the ground.

所以我觉得消费者的选择可以看作一个光谱。

So I think about consumer choices in a spectrum.

一端是最大化我的银行存款。

On the one hand is maximizing my bank deposit.

这个我觉得我几乎可以完全交给AI来判断。

This is something where I think I can almost entirely defer to the AI's judgment.

只要有FDIC保险，我就只要最高利率。

As long as it's FDIC insured, I just want the highest rate.

另一端可能是选我最喜欢的毛衣。

On the other hand, this end of the spectrum might be picking my favorite sweater.

这个就全靠我自己的判断了。

This is something where it's just all of my judgment.

AI可以给我找一些选项来看，但最终还是要我自己决定。

The AI can source me things to look at, but in the end, I need to decide.

这个判断我可真没法交给它。

I can't really delegate that judgment to it.

而且这里有个范围。

And there's a spectrum.

所以咱们先假设一下，垃圾回收这件事，我可以把判断交给AI。

And so let's just pretend for a moment that garbage collection is something where I can defer the judgment.

你可能会说，“不，我真的很在乎那个人，我信任那个服务，他开车来我家，”之类的，但咱们先把这个放一边。

You might say, "No, I really care about the guy, and I trust the service, and he's driving to my house," or whatever, but let's just set all that aside for a second.

现在，如果我做垃圾回收这一行，我可能做得很好，但要让人们费心去换服务商还是很难。

So right now, if I'm in the waste collection business, I might be great at it, but it's still really hard to get people to bother to switch.

我怀疑你未必能脱口而出你每月垃圾回收的费用是多少。

I doubt you know what you pay for garbage collection off the top of your head.

很多人可能知道，但我觉得你不知道。

Many people do, but I doubt you do.

所以，即使你提供更便宜更好的服务，你也得费很大劲才能让人们注意到你，愿意换垃圾回收公司。

And so what happens is you have to do all this work to get people to pay attention, to bother to switch garbage collection companies, even if you're offering a better service at a lower price.

你可以想象，他们的代理能跟我的代理说：‘嘿，我每个月能帮你省50美分。’

You could imagine that their agent could talk to my agent and be like, "Hey, I'll save you 50 cents a month."

然后那个代理会问：‘垃圾会被收走吗？’

And the agent will say, "Will the garbage go away?"

然后我说：‘是的，垃圾会被收走，没问题，我就换。’

And I'm like, "Yes, the garbage will go away, and no problem, and I'll change."

所以第一层效应是，你会转向低成本的服务商。

So the first order effect is that you move to the low-cost provider.

但更广泛的市场效应其实也挺有意思的，因为现在假设有一条街，如果街上的所有人都用同一个垃圾回收公司，那对社会来说会更高效，因为那个人可以沿街开过去收垃圾，然后他给我们所有人一个更低的价格，自己也能赚更多。

But the general market effects are actually sort of interesting too because now let's say that there's a street, and before it would be more efficient for society if all of the people on my street had the same garbage collector because the guy could drive up the street and pick up the trash, and he could offer all of us a lower price and make more money.

但我们很难协调这个事，因为大家都懒得换垃圾回收公司。所以每个人都有自己的服务商，而那个街上已经有两家客户的人，没法打电话给其他人说：‘嘿，我这条街上已经有两家了。’

But it's really hard for us to coordinate that because what happens is all of us are just lazy about switching garbage collectors, and so we all have one, and the guy with two guys on the street can't call everyone else up and say, "Hey, I've got two guys on the street.

其他人只有一家。

Everyone else has just one.

我是低成本的服务商。

I'm the low-cost producer.

让我把这些汇总起来。

Let me roll this up."

所以第二个层面的影响可能是，那条街上已经有两家客户的收垃圾的人可以去找其他住户说：‘嘿，我可以给你提供更低的垃圾处理价格。’然后所有人都会换过去。

So a second order effect might be that the guy who has two people on my street can go to everyone else on the street and say, "Hey, I'll offer you a lower price on the garbage," and everyone will switch.

于是大家都转向低成本的服务提供商，而这个人也因此变成了成本更低的提供商。

And so everyone moves to the low-cost producer, who in turn gets to be the lower-cost producer.

这实际上在某种程度上创造了一个更好的商业模式，低成本的人以更低的利润率赢得更多的业务量。

That actually creates, in some sense, a better business, where the low-cost guy wins a bunch more volume at much lower margins.

当你研究这些现象时，你会发现很多公司因为这些事情，其实受到的影响是混合的，而且方向各不相同。

As you study them, a lot of companies have mixed effects that point in different directions because of these things.

你觉得智能代理会对你的世界产生什么影响？

What do you think the impact of agents is going to be on your world?

你的角色会是什么？

What will be your role?

五年后会有怎样的不同？

How will it be different in five years?

最好的智能体能不能像你那样，因为你训练了它按照你的思路思考，从而构建一个包含Carvana、希尔顿分时度假和Meta的投资组合呢？

Will the best agent be able to build a portfolio of Carvana and Hilton Grand Vacations and Meta in the same way that you would because you trained it how you think?

嗯，希望它做得更好。

Well, hopefully better.

就我的工作而言，我觉得长期来看，我们可能会被淘汰出局。

In terms of what I do, I think that in the long term, we'll probably get competed out.

你做的就是基本的定价风险吗？

Basic pricing risk as the thing you do?

当电脑辅助国际象棋出现时，有一段很长的时间，自由式国际象棋更好，也就是人类与电脑程序合作，能够胜过单独的人类或单独的电脑。

When computer-aided chess came along, there was this long span of time where freestyle chess was better, where the human working with the computer program was able to outcompete either the human or the computer by itself.

这只是因为人类和电脑的思考方式不同，它们之间产生了协同效应。

And this is just because humans thought about things differently, and computers thought about things differently, and they were synergistic.

我觉得在投资领域，有一段时间——

And I think in investing, for a while-

我们现在就处于那个时代。

We're in that era now

...我们就处在这个时代。

... we're in that era.

这是因为，作为一个人，经历过生活，我在世界上有些事情的理解方式是模型不具备的。

And that's because there are things that I understand in the world in ways that the model just doesn't, by virtue of being a person and having lived a life.

而且我掌握的一些世界信息，模型里根本没有，因为尽管互联网上信息很多，但并不是所有事情都在网上。

And there's information I have about the world that just isn't in models, because as much information is on the internet, not everything is actually on the internet.

虽然这是事实，但我觉得这种情况还会持续一段时间，而我能利用模型辅助我，然后做出判断，这样合作会比单独判断更好。

While that's true, which is going to, I think, be a while, the ability of me to use models to assist me and then to make judgments will make better judgments working together.

不过你可以想象未来有一种情况，这种差距会缩小到不再重要的地步。

You could imagine a circumstance, though, in the future where that gap diminishes to the point where it doesn't matter anymore.

到那时，我不认为模型还能预测未来。

And then I don't think models will be able to predict the future.

就像我们聊过的，未来在某些方面本质上就是难以预测的。

As we talked about, the future is in some ways very intrinsically unpredictable.

但模型给出的可能结果分布可能是你能做的最好的了，而加入一个人来预测，就像让我来判断七天后会不会下雨一样。

But the model's distribution of outcomes may be the best that you can do, and that adding a human to the forecast would be kind of like adding my judgment to whether or not it's going to rain in seven days.

你就会说，"克里夫，我真的不在乎你怎么想。

You just kind of would be like, "Cliff, I don't really care what you think.

我用的是预测。

I'm using the forecast.

这个会更好。

This is going to be better.

不管你怎么调整，只会让结果更糟。"

However you fudge it, it's just going to make it worse."

那一天可能会到来。

That day may come.

那是个挺有意思的时刻。

It's sort of an interesting moment there.

有很多值得投入的地方。

There's a lot to invest in.

想想评估管理层之类的事情。

Think about judging management or something.

也许这些模型将来能判断管理层的能力。

Maybe the models will be able to judge management.

它们能去面试管理层。

They'll be able to interview them.

它们能搞清楚这些。

They'll be able to figure it out.

然后事情就变得非常有意思了。

And then it gets really interesting.

那你怎么筹集资金呢？

How do you raise capital?

你会说：“我想开一个游乐园。”

You'll be like, "I'd like to run an amusement park."

而模型会说：“行，

And the model will say, "Sure.

这里有六个地方，有51%的概率适合开游乐园。”

Here are six locations that have a 51% chance of an amusement park working.

只要你真的下定决心要做，而且我也评估过你，你成功的概率是51.2%，因为你其实略低于平均水平。

And as long as you're really committed to it, and I've assessed you, and you have a 51.2% chance of succeeding because you're actually slightly below average."

你会说，好。

You're going to be like, "Okay.

那我有什么能做得比平均水平更好呢？

Well, what can I do better than average?"

他们就会说，那你想过开卡丁车场吗？

And they'll say, "Well, have you thought about running a go-karting park?"

幸运的是，我觉得这不会对我职业生涯有太大影响，因为我觉得这得花上一段时间。

Fortunately, I don't think it's going to affect my career that much because I think it'll take a while.

那用价格作为协调经济活动的组织原则和信号，如果被替换掉会怎么样？

What about replacing price as the organizing principle and signal that coordinates economic activity?

价格是个奇迹，因为它既是信号，又带着激励。

Price is a miracle because it's a signal wrapped in an incentive.

那是什么意思？

What does that mean?

价格是一种激励。

Price is an incentive.

它会因为上涨或下跌而让你想多买或少买某个东西。

It causes you to want to buy more or less of something because it goes up or down.

它同时也是一个信号，因为它反映了某样东西的相对价值、稀缺程度以及生产成本。

And it's also a signal because it says something about the relative value of something and the scarcity of something and the cost to produce something.

这种协调机制简直是奇迹，我觉得即使拥有无限的智慧，也很难找到比它更好的系统。

It's a miracle of a mechanism to coordinate things, and I think you'd be really hard-pressed to find a better system, even with unlimited intelligence.

那会相当困难。

It'd be pretty hard.

也许换个问法，这有点像投资。

Maybe another way of asking the question is kind of like investing.

如果无处不在的智能变得更强大、更便宜，而且能在某种程度上替代你的工作，而你又在定价风险方面特别擅长。

If ubiquitous intelligence gets so much better and so much cheaper, and in some way it can replicate what you do, and you're really good at pricing risk.

我有个朋友叫丹，他说这件事分三个层次。

A friend of mine, Dan, was saying there's three levels to this.

有一种是我输入一个提示词，然后得到一些结果。

There's I put in a prompt and I get something back.

这是第一层。

That's level one.

第二层是我设置一个瞭望塔。

Level two is I set up a watchtower.

我每天晚上的程序就像运行一组任务，在后台按计划自动进行。

My instinct every night runs like a set of tasks, and it happens in the background on a schedule.

而第三层，最充分利用智能的是，它总是开机，主动去自己想办法利用自身的智能，而且这样一直持续下去，能做的事情没有上限。

And level three, which produces the most use of intelligence, is it's just always on, proactive, figuring out its own way to use its own intelligence, and that just goes on forever, and it's unlimited in how much it can do.

如果我想想这第三层应用到所有事情上，哪些事情应该被定价——

If I think about that level three applying to everything, what things should be priced-

对

Sure

……效率提升、优化——

... efficiencies, optimization-

当然。

Sure

……所有这类东西。

... all this kind of stuff.

那会不会给价格带来一些有趣的变化？

Does that add something interesting to the price-

会让市场更敏锐。

sharpens markets.

价格有很多粘性。

There's a lot of stickiness to prices.

价格粘性是经济学里一个很著名的现象。

It's a well-known macroeconomic phenomenon, this price stickiness.

很多东西的定价都很随意。

There's a lot of lazily priced stuff.

比如存款之类的。

Think about deposits and such.

外面竞争很激烈，但其实并没有理论上那么激烈。

It's a really competitive world out there, but it's actually not as competitive as in theory it could be.

所以我觉得，如果每个人都有代理人替自己工作，不断优化，那垃圾收集的价格波动会大得多。

So I think if everyone has agents who are working on their behalf and constantly optimizing, I think the price of garbage collection will move around a lot more.

随着垃圾收集的所有成本变动，每一家垃圾收集公司都在不断重新核算财务状况，试图找出真正的边际成本，然后以高于边际成本的价格去竞标每个客户，而每个代理人都转向成本最低的供应商。

As all the input costs of garbage collection evolve, and every garbage-collecting company is constantly rerunning its financials and trying to figure out its true marginal cost and bid every customer where it could serve for more than marginal cost, and every agent is switching to the lowest-cost provider.

你不会只是每月付60美元付上12个月，然后对方回来跟你说：“涨价5%。”

You won't just pay $60 a month for 12 months and then have the guy come back to you and say, "It's a 5% increase.

明年就63美元了”，而你甚至都没注意到，因为他们只是来收垃圾而已。

It's going to be 63 next year," and you don't even notice because they're just picking up your garbage.

你的垃圾收集费用天天变，某周可能就不同了。

Your garbage collection will vary by day, and one week it'll be whatever.

我觉得会出现这种更有趣、更准确、变动更快的价格。

There will be all this interesting, much more accurate, much more rapidly moving prices, I would imagine.

你在资本市场创新方面看到什么有趣的东西了吗？

Have you seen anything interesting in capital markets innovation?

《经济学人》有一篇很棒的文章，讲的是美国资本市场这个庞然大物，以及它多么擅长适应新事物、创造新工具和新的融资方式，而且这在美国故事中与科技创新同等重要。

There was this awesome Economist story talking about the behemoth that is American capital markets and how good it is at adapting to new stuff and creating new instruments, new ways of financing things, and that that's been as much a part of the American story as the technology innovations.

确实。

Sure.

华尔街和硅谷之间有着惊人的相互关系，这可以作为一种思考方式。

There's this amazing interrelationship between Wall Street and Silicon Valley, would be one way of thinking about it.

在资本市场、融资方式等方面，你有什么感兴趣的新动向吗？

Anything interesting percolating for you in markets, in capital markets, in ways of financing things, things like this?

嗯，停下来想想，或许我们应该为资本市场在人工智能方面的作为庆祝一下。

Well, it's fun to take a moment to pause and maybe congratulate ourselves as capital markets for what we're doing with AI.

人们曾担心美国活力衰退。

People were worried about this, a decline in American dynamism.

我想我们刚刚把大约3%的GDP重新部署到这个高度投机的领域，几乎是瞬间就完成了。

I think we just redeployed, what is it, 3% of GDP right into this highly speculative venture, basically on the turn of a dime.

这真是太了不起了。

It's just this remarkable thing.

当然，谁知道呢？

Of course, who knows?

也许应该只有两个百分点，也许应该是六个百分点。

Maybe it should be two, maybe it should be six.

但令人惊叹的是，我们能以这么快的速度、这么大的规模重新配置资源，而世界上没有其他国家能做到这一点。

But it is amazing that we can redeploy resources at this scale this quickly, and there's no one else in the world that's been able to do it.

而且，无法将资源投入到生产力领域，这确实会制约生产力的增长，即使在技术快速进步的情况下也是如此，甚至正是因为技术进步才显得更加明显。

And certainly, the inability to move investment in productive resources can be a binding constraint on productivity growth, even with, and especially with, rapid technological progress.

所以，至少就资本而言，——虽然我们主要谈的是资本——但资本的投入是伴随着人们去从事不同的事情的。

And so, at least with regards to capital, and obviously we talk about capital, but the capital investments are tied to people doing different things.

美国确实在每天都做着不同的事情。

Americans are doing different things today, every day.

人们早上醒来，去宾夕法尼亚州的某个地方建造数据中心。

People are waking up and going to someplace in, I don't know, Pennsylvania and building a data center.

如果没有我们的整个基础设施，如果没有这种能够调动巨额资本的能力，这一切都不会发生。

And that wouldn't be happening if it wasn't for this whole infrastructure we have that's somehow been able to move this enormous amount of capital.

真是太不可思议了。

It's incredible.

从深层创新来看，我认为人们冒险投入投机性项目并以不同方式分配收益，这个基本理念是不可或缺的。

As deep innovation, I think the fundamental idea that people are taking risks on speculative ventures and slicing up the pie in different ways is irreducible.

我觉得随着时间的推移，人们会给本质上相同的东西起很多不同的名字。

I think people will come up with lots of different names for what are essentially the same things over time.

我认为这并没有本质上的不同，但确实令人惊叹。

I don't think that's deeply different, but it is incredible.

我还应该指出，我确信当潮水退去时，我们最终会发现各种各样的欺诈和不良行为。

I should also note that I'm sure we'll discover all kinds of fraud and bad behavior and stuff eventually when the water goes out.

但鉴于规模如此之大，我怀疑这将会非常干净。

But I think given the magnitude of this, I suspect it's so clean.

太棒了。

It's amazing.

如果你读过铁路建设之类的历史，你会发现浪费和欺诈总是存在的，但这里有很多人基本上在努力做正确的事情，而治理方面我们习以为常。

If you read about the railroad build-out or something, the amount of waste and fraud, and invariably there is some, but there's a lot of people trying to basically do the right thing here, and the governance parts that we just take for granted.

这真是太不可思议了。

It's incredible.

我觉得美国的资本市场，以及整个资本市场，其实都被严重低估了。

America's capital markets, and the capital markets in general, I think, are just incredibly underappreciated.

任何人都可以花十万美元买股票，获得一个由管理良好、积极性高的团队组成的投资组合，这些团队在经营公司，做的大多是正确的事情，而且几乎可以即时、零成本地完成，之后想卖就卖，这真的很了不起。

The idea that any person can buy $100,000 of stock and get a portfolio of well-run, well-motivated teams building companies that are doing mostly the right things and do it basically instantly and at basically no cost, and then sell it as soon as they're done is amazing.

而且，所有这些公司以及其他投资方式，最终都能在整体上很好地分配社会资源，这也非常了不起。

And then the fact that all those companies and other ways people invest can then allocate society's resources pretty well in the scheme of things is just incredible.

整个体系都被低估了。

The whole system is just very underrated.

你觉得为什么美国在这方面如此占优势？

Why has America so dominated this, do you think?

我个人的看法是，这跟基因、文化演进和制度演进之间的相互作用有关。每个人都有自己的理论。

My pet theory, and everyone has their own theories, is some sort of interplay between genetics, cultural evolution, and institutional evolution.

基本上，来到美国的人是那些最愿意冒险的人群，因为去美国本身就是一件高风险的事情。

So basically, the people who came to the United States were the subset of people who were most interested in taking risks because going to the United States was very risky.

而且我觉得，爱冒险这件事，很大程度上是基因决定的。

And I think that risk-taking has a meaningful genetic component.

所以我觉得，这种倾向在美国人里更常见。

And so I think that tendency is just more common amongst Americans.

在一个把冒险视为常态的地方，自然也会衍生出很多其他相关的东西。

When you're in a place where risk-taking is more the norm, there's also a whole bunch of other things that come from that.

因为冒险是常态，所以文化上、制度上会不断积累，让这种风气越来越强。

It's more the norm, and then that leads to buildups of cultural and institutional things that reinforce that.

大家都明白，冒险是允许失败的。

It's understood that people can take risks and fail.

这就催生了合理的破产法。

That leads to bankruptcy laws that make sense.

还有相应的劳动法。

That leads to labor laws that support that.

还有支持创业的风投。

That leads to venture capital that supports that.

而这一切又相互强化。

And all of those things reinforce that.

然后就会带来成功的故事。

And then that leads to success stories.

整个就是个大杂烩。

You get this whole soup.

不过这只是我的个人猜测。

But that's my pet theory.

所以如果将来月球上建了基地，你很看好那个社区的经济能发展得很好，对吧？

So if there's a moon base at some point, you're going to be bullish on that community's ability to do really well economically.

没错，这么说很到位。

Actually, that's a great way to put it.

美国一直以非常惊人的方式持续创新。

The United States has been persistently innovative in a very amazing way.

所以如果月球上建基地，即使中国人聪明而且人多，欧洲人也存在，我也不惊讶会是我们的。

So if there's a moon base, I wouldn't be shocked if it's ours, even though the Chinese are brilliant and numerous, and the Europeans exist.

当你看到利率上升时，你会看到那张美国10年期利率的长期图表，它基本上在我们的一生中都是这样一直走高的。

When you see rates go up, you see that great long-term chart of rates, let's say the 10-year rate in the US, and it just looks like this forever, basically our whole lives.

现在我们在想，“哎呀。”

And now we're going, "Ah."

你对此图有什么看法？

Comment on that chart.

这个话题太多了，我肯定不是宏观经济学专家，也不花太多时间去想它，但我可以提几个观察。

Well, there's so much, and I'm certainly no macroeconomics expert, nor I spend too much time thinking about it, but I'll make a couple of observations.

第一，很明显的是，当一个社会在为未来做巨大投资时，就意味着你今天必须少消费一些。

One, obviously, is just in general, when you're a society and you're making a giant investment in the future, it means you're going to have to consume less today.

基本上，所有投入AI建设的资源——

Fundamentally, all of the resources going into this AI build-out-

都是关于未来的。

Are about the future

……都是关于未来的。

... are about the future.

从根本上说，今天干活的水管工并不是在创造今天的消费。

And fundamentally, the plumber building that today is not making consumption today.

他本可以创造今天的消费。

He could be making today's present consumption.

但他现在建的是数据中心，这属于未来的消费。

Instead, he's making a data center, which is future consumption.

所以，这个复杂难题的一个方面——我也想过一些——就是利率之所以更高，是因为我们在激励人们为未来储蓄。

So one piece of a very complex puzzle that I've thought a bit about is this idea that rates are higher because we're inducing people to save for the future.

而激励的方式就是付给他们钱。

And the way you do that is you pay them.

如果我不稍微问问你的投资组合，人们会杀了我的，这对我来说太有意思了。

People would kill me if I didn't ask about your portfolio a little bit, which is so interesting to me.

你80%的仓位是Carvana，还有度假——

It's 80% Carvana, vacations-

当然

Sure

...5%是Meta，基本上这就是你的投资组合。

... 5% Meta is basically your portfolio.

差不多就是这样，大概就五样东西。

Rough justice, it's like five things.

当然了。

Sure.

而其中一样占了80%。

And one thing is 80%.

所以，如果你想想你的投资组合和那些公司——那些你显然特别了解的公司——如果你在给他们提建议，告诉他们怎么用AI来改变他们的业务——

So if you think about your portfolio and those companies, which obviously you know extremely well, and you were advising them on how to think about using AI to transform their businesses-

——那你会怎么跟他们说呢？

... what's that speech look like?

嗯，首先得确保每个人都在用“vibe coding”这种方式编程。

Well, it starts by making sure that everyone is vibe coding.

对。

Yep.

从董事会和CEO开始，所有人都需要参加黑客马拉松。

Starting at the board and the CEO, everybody needs to have hackathons.

当然，我把这个带到了Carvana，他们说，‘对，我们叫它披萨派对。’

And of course, I brought this to Carvana and they said, "Yeah, we call them pizza parties."

我不是说开发人员，我是说所有人都需要参加黑客马拉松。

I don't mean the developers, I mean everybody needs to have hackathons.

其实，这些黑客马拉松不一定能开发出多么有用的软件，重要的是让每个人了解到什么是可行的，这将会改变他们对可能性的期望。

And it's not so much that you're going to build hugely useful software necessarily that comes out of these hackathons, but it's that you're going to teach everybody what can be done, and that's going to change their expectations, I think, for what can be done.

然后，我要告诉他们的第二件事是，CEO基本上需要转身对IT负责人说，‘我希望我的Grok机器人、Meta Muse代理之类的，能够以只读方式访问公司的每个系统。’

And then the second thing which I tell them is the CEO needs to basically turn to the head of IT and say something to the effect of, "So I want my Grok bot, Meta Muse agent, whatever, to be able to access every system in the company on a read-only basis.

所有我如果问别人就能查到的信息，我都希望通过一个提示就能获取。”

Everything I could look up if I asked somebody, I want to be able to get that in a prompt."

然后IT负责人会说，‘这太难了。’

And the guy's going to say, "That's really hard.

我们在这方面有一大堆问题。’

We have umpteen trillion problems with that."

然后他会说，"这正是我的意思。"

And he's going to say, "That's my point.

我就想要那样。"

I want that."

这确实是个大工程，要一个系统一个系统地搞，但如果你能做到那一步，你就能获得只读权限。

And that's a big process, and it's system by system and all the rest, but if you can get to that, then that gets you read-only access.

到起跑线了。

To the starting line.

对，到起跑线了。

Yeah, to the starting line.

那样你就有只读权限了。下一步自然而然就是，CEO 到处都用这个工具，然后其他人都会眼红。

That gets you read-only access, and then the next logical thing is the CEO needs to use this everywhere, and everyone is going to get jealous.

然后你会说，"哦，你也可以用，但我们需要解决你的权限问题。"

And you're going to say, "Oh, you can have it too, but we need to work out your permissions."

然后理论上，这会一级一级往下传。现在大家都有了只读权限，接下来必然有人会说，"我真的很想把数据录入系统里。"

And then that, in theory, percolates down, and now you've got permission to read-only access, and then invariably someone is going to say something like, "I'd really like to put data into the system."

然后你会说：“那太好了。”

And you're going to say, "That's fantastic.

我们得解决写权限的问题。”

We've got to work out write permissions."

然后有人会说：“我很想改一下系统，因为我们刚搞了个 vibe coding 的东西，我觉得我们完全能做到。”

And then someone's going to say, "I'd love to change the system because we just did this vibe coding thing, and I'm confident we could do it."

然后你会说：“那也非常棒。”

And you're going to say, "That's also fantastic.

我们需要制定一套流程来管理系统变更。”

We need to work out a process by which we're going to manage changes to the system."

我刚才说的这些，听起来就四个要点，但那其实就像——

And what I just described, sure, it's four bullet points, but that's like-

好几年的工作量。

Years of work

——简直难如登天。

... it's like impossibly hard.

所以问题是，你的业务有没有时间等这个过程发生，而不会被一个从零开始的人抢先？

So the question is, are you in a business where you have time for that to happen before someone who starts-

在跳球之前，别人从起步就超过了你。

Before the jump ball, someone else that starts from go beats you.

没错。

Exactly.

所以拥有一个好业务是很重要的。

And so it's good to have a really good business.

从中得出的一个结论似乎是，现在是创办公司的最佳时机，不仅因为资本市场的条件——人们愿意以较低的资金成本资助高实验性的项目——而且因为有了这些工具，如果你没有历史包袱，你可以走得又快又远。

It seems like one of the conclusions from that is it's the best time ever to start companies, both because of capital market conditions that people will fund at fairly low cost of capital, highly experimental things, but also just because with these tools, if you start without the baggage, you can go so far so fast.

你觉得这大致对吗？

Do you think that's roughly right?

是的，现在是创办公司的最佳时机，直到明天。

I mean, yeah, it's the best time to start a company until tomorrow.

所以我同意。

So I agree.

真是太厉害了。

It's amazing.

我还想说说，从我的角度看，人真是一种特别麻烦的动物。

I'd also just say that, just thinking from my perspective, people are such a pain in the butt.

你要是经营一家公司，就会觉得人特别烦人。有了各种智能体之类的，你就能用更少的人做更多的事情。

If you're running a company, they're such a pain in the butt, and the idea that you have all these agents and stuff, it makes it so you can do so much more with so many fewer people.

当然，有一个老问题就是，如果你能轻松做到某件事，那别人也能一样做到。

Of course, one of the challenges always is if you can easily do something, then so can everybody else.

所以，你得有某种独特的优势，让你做出来的东西能产生持续的价值。

So there has to be some wedge by which the thing you build produces enduring value.

你有没有一条路，能让你最终获得某种锁定效应或专有数据呢？

Does it have a path where you end up with some sort of lock-in or proprietary data?

在这个智能体都每天可以随时换服务商的世界上，什么能给你带来持续的经济收益呢？

In this world where all the agents are happily changing providers every day, what gives you any enduring economics?

但接下来还有一点，就是如果你做的事情看起来有一个非常明显的通往持续经济收益的路径，那这件事其实并不好，因为所有人都会去做，竞争会非常激烈。

But then, of course, then there's the further thing is that obviously if you do the thing where it seems like there's an obvious path to enduring economics, then that's a bad thing to do because everyone is trying to do that, so it's very competitive.

真正出色的成果往往是那些看似不合常理的。

And the really great outcomes are the ones that make no sense.

你本不该去做，但结果却找到了出路，或者世事转变了。

You shouldn't do it at all, but then it turns out there is a path, or the world turns.

你的财务团队并不是因为大的失误而亏钱。

Your finance team isn't losing money on big mistakes.

钱是从没人注意的无数小决定中一点点漏掉的。

It's leaking through a thousand tiny decisions nobody's watching.

Ramp在支出发生之前就设置了管控措施。

Ramp puts guardrails on spending before it happens.

实时限制、自动规则、零救火。

Real-time limits, automatic rules, zero firefighting.

到ramp.com/invest试试吧。

Try it at ramp.com/invest.

随着你的业务增长，Vanta也会随之扩展，自动化合规流程，为安全和风险提供统一的信息源。

As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk.

更多信息请访问 vanta.com/invest。

Learn more at vanta.com/invest.

Ridgeline 提供统一平台，自动化处理投资组合会计、对账、报告、交易、合规等所有复杂工作，并且能够全面扩展。

Ridgeline offers one unified platform that automates away the complexity across portfolio accounting, reconciliation, reporting, trading, compliance, and more, all at scale.

在 ridgeline.ai 预约演示。

Schedule a demo at ridgeline.ai.

每家公司都独特，通用 AI 聊天机器人并不了解你的业务流程。

Every investment firm is unique, and generic AI chatbots don't understand your process.

Rogo 是由金融专业人士打造的。

Rogo is built by finance professionals.

它连接到你的数据，学习你的流程，并能快速生成可直接交付给客户的成果。

It's connected to your data, learns your process, and quickly produces client-ready outputs.

访问 rogo.com/invest 了解一下。

Check them out at rogo.com/invest.

从 OpenAI 到 Cursor 到 Perplexity，领先的 AI 和软件公司都使用 WorkOS，以便在一夜之间达到企业级标准，而不是几个月。

The best AI and software companies, from OpenAI to Cursor to Perplexity, use WorkOS to become enterprise-ready overnight, not in months.

去 workos.com 看看吧，可以省掉那些不讨好的基础设施建设工作，专心做好你的产品。

Visit workos.com to skip the unglamorous infrastructure work and focus on your product.

那你觉得，未来竞争优势里，最被低估的来源有哪些？

What do you think are the most underrated sources of future competitive advantage, then?

如果最终好企业都需要可持续的优势，也就是可持续的竞争优势，才能获得高利润，那有什么是被低估的呢？

If ultimately good businesses need sustainable edge, sustainable competitive advantage to have great profits, anything underrated?

被低估的。

Underrated.

我们之前聊过注意力、数据和物理接近性。

We talked about attention and data and physical proximity.

这些例子都是因为智能变得便宜，未来可能比现在更有价值的东西。

These are examples of things that might be super or more valuable in the future than they are today because of cheap intelligence.

有没有哪类，可能像是监管护城河，你觉得有哪些类型的优势是被低估的？

Is there any type of, maybe it's regulatory capture, are there any types of edge that you think are underrated?

我觉得很多这类优势其实都被合理评估了。

I get the sense that a lot of these are appropriately weighted.

我觉得有个有趣的问题，其实有两个。

I guess one interesting question I've had, two of these actually.

一个是，如果看企业整体利润率，是上升还是下降？

One is, if you think of corporate margins in aggregate, do they go up or down?

像这样下降？

Down like this?

是上升还是下降？

Do they go up or down?

我觉得我们讨论的这些趋势都指向下降。

And I think everything we're talking about would argue down.

很多企业能赚取利润，是因为——

There's many businesses where they make margin because-

更多的消费者剩余

More consumer surplus

……更多的消费者剩余。

... more consumer surplus.

到处都是。

Everywhere.

对，到处都是。

Everywhere.

有意思的是，如果你回去翻翻90年代互联网刚兴起时的研究报告——

What's interesting is if you go back and read a lot of the research reports from the '90s when the internet was coming along-

差不多也是这么说的。

Same thing

——他们当时也是这么说的。

... they said the same thing.

所以这次可能也未必准。

So the base rate here might not be great.

也许我们错了，但我觉得他们当时经常出错，主要原因就是这种聚合经济，也就是本·汤普森说的聚合经济。

Maybe we're wrong, but I think the main reason they were often wrong was this aggregation economics, the Ben Thompson aggregation economics.

所以也许没有那个因素的话，他们当初的预测也不至于错得那么离谱。

So maybe absent that, maybe it wouldn't have been so wrong.

而且我觉得聚合理论有很多地方是站不住脚的，因为它基本上依赖于消费者只看谷歌上第一个结果这个假设。

And I do think that a lot of aggregation theory breaks down because that basically relies on the idea that consumers only look at the first thing on Google.

但事实证明，如果你的智能体看了所有未被搜索过的内容，那么排名第一就不重要了，而像质量、价格以及服务这些核心因素就会变得更加突出。

But it turns out if your agent looks at everything unsearched, then being ranked first doesn't matter, and all the core things like quality and price and everything else, service, become more salient.

另外，这可能带来的另一个下游效应是，你能建立更复杂的业务。

But the other thing is that maybe another downstream effect of this is you'll be able to build more complicated businesses.

现在很多行业的结构更偏向横向，因为经营你那一块的复杂性很高，这让你很难兼顾上下游，而且不会带来过度的复杂性。

Right now, a lot of industries are more horizontal in structure because the complexity of running your slice is very high, and that makes it hard to do what's above and below you and not have undue complexity.

有了AI智能体，以及基本上便宜又无处不在的代码和智能，你就能构建整个垂直链条。

With AI agents and basically cheap and ubiquitous code and intelligence, you could build the whole vertical stack.

我觉得在其他国家已经有人在尝试这样做，你可以拥有一个全栈的宠物护理业务，从制作宠物食品一直到宠物保险、宠物医院和宠物兽医，所有这些在历史上都是分层的。

People are trying to do this, I think, in other countries, but you can have a full-stack pet care business that does everything from make pet food all the way up through pet insurance and pet hospitals and pet vets, and all these things that historically were multiple layers.

但现在，因为能用AI解决协调问题并减少委托代理问题，也许这类业务就变得可行了，而且最终这些业务的利润率可能更高、效率更高，还能给消费者带来更多实惠。

But now, because you can solve the coordination and reduce the principal-agent problems by having AI do it, maybe those sorts of businesses are possible, and then maybe those businesses are higher margin in the end, more efficient, and more consumer surplus.

Carvana就是一个例子，它在做这个还不太容易的时候就已经这么干了，但也许人们能在其他垂直领域这样做，这样的话利润率可能会上升，消费者也能得到实惠。

Carvana is an example of that built before this was easy to do, but maybe people can do it in other verticals, in which case maybe margins go up and there's consumer surplus.

另一个方面是，这如何影响财富和收入分配。

The other one is one of the things where it's just how this affects wealth and income distribution.

我可以讲两个故事。

I can tell two stories.

一方面，我想很多人都讨论过这种观点，认为它会让收入分配更加集中。

On the one hand, I think a lot of people have talked about this idea that it concentrates income distribution.

你会看到那些在实验室、数据、英伟达这些上面赚钱的人，还有那个观点说回报越来越集中在少数人手里。

You've got all these people who made money on various labs and whatever data, NVIDIA, and this idea that there are increasing returns to small groups of people.

但反过来说，如果我们有便宜又普及的智能，会发生几件事。

But the counterargument is if we have cheap and ubiquitous intelligence, a couple of things happen.

一是经济中有很多收租的人。

One is there's all these rent-takers in the economy.

比如说你是给保龄球馆收费的软件公司，那些人全都垮了，这对他们来说很糟，但确实降低了打保龄球的成本。

Think you're the software company that charges bowling alleys, and those guys all get smoked, which sucks for them, but it does lower the cost of bowling.

这样一来，所有打保龄球的人的生活水平就提高了，这有助于拉平收入，因为我在保龄球上的花费占我收入的比例很小，但收入低的人花在保龄球上的比例更大，所以这有拉平收入的效果。

And so then that increases living standards for everyone who bowls, and that's flattening because my spending on bowling is small proportion to my income, but people who make less money spend more proportion to their income on bowling, and so that's a flattening effect on income.

如果这些小的租金收入都没了，企业利润下降，普通美国人交给美国银行的钱就不再归美国银行了。

If all these little rent pieces go away, the decline in corporate profits, all the money that the average American is giving to Bank of America goes away from Bank of America.

资本家会受损。

The capitalists lose.

普通消费者会受益。

The average consumer wins.

所以这其实是拉平差距的。

So that's leveling.

另一部分呢，是有一整个专业阶层，他们的角色基本上可以定义为——

The other piece is there's a whole professional class who basically you could define their role as-

智力。

Intelligence

——智力。

... intelligence.

他们就是把大量信息应用到特定情境中，而且方式相当直接。

They take big corpuses of information and apply them to certain circumstances in a fairly straightforward way.

如果所有这些都被商品化了，第一，每个人都能得到最好的普遍服务，这对每个人都有好处。

And if those all get commoditized, one, everyone gets the best ubiquitous, which is, one, good for everybody.

第二，尤其是对那些现在付不起最好服务或任何服务的人有利。

But two, particularly good for people who can't afford the best right now or any.

但第三，那些个体，他们之所以能获得这些额外的租金，是因为他们更聪明。

But then three, those individuals, they earned these extra rents because they were smarter.

然后他们必须退而求其次，理论上，这可能会使曲线变得平缓。

They then have to drop to their next best alternative, and in theory, then that might flatten the curve.

现在，对此的反驳可能是，是的，但主动性和雄心以及智慧带来的回报可能会上升，因为现在我能做更多事情，因为这些模型就像给思维装的超级自行车。

Now, the counterargument to that might be something like, well, yes, but returns to initiative and ambition and intelligence could go up because now I can do so much more because these models are like a bicycle for the mind on steroids.

它们让那些聪明且有主动性的人能够完成更多事情。

They give people who are smart and have initiative the ability to accomplish so much more.

也许这实际上会导致那一小部分人的回报递增，从而使曲线变得更陡峭。

Maybe that actually causes increasing returns to that subset of people, and it steepens the curve.

杜鲁门想要一个只用一个手的经济学家。

Truman asked for a one-handed economist.

我好像对每件事都特别明显地各执一词。

I seem to very distinctly have two hands on everything.

我觉得这就是——

I think that's-

这些都是重要且有意思的问题。

They're big, interesting questions.

这些问题特别有意思，而且我发现很多我看到的情况里，都有很多暗流涌动。

They're super interesting questions, and I find that a lot of situations I look at, there's a lot of crosscurrents.

我们已经聊到了AI建设的话题。

We've started the conversation about the AI build-out.

一方面，我能看到所有那些利用AI的绝妙方式，我自己每天都在用海量token，而且我还能想到更多用法。

On the one hand, I can see all these incredible ways to use AI, and I use a gajubillion tokens, and I can see how I could use a gajillion more.

显然，我是属于那最极端的一小撮人。

And obviously, I am in the far right end of the distribution.

如果每个人都像我一样用这么多token，我们资源会严重不足。

And if everyone uses as many tokens as me, we are so short.

这会是个持续很久的大项目，参与建设的人都会赚大钱。

It's going to be this huge thing that's gone on forever, and everyone who's in the build-out's going to make a fortune.

但另一方面，每用一次token我得到的收益简直像登月一样多，而且很多方面我都挺特别的，别人适应得会更慢，但token的成本会大幅下降。

On the other hand, the amount I get per token is like going to the moon, and it turns out I'm a pretty unusual guy in a lot of senses, and people are going to adapt more slowly, and the cost per token is going to collapse.

所以也许我们实际上是过度建设了。

And so maybe actually we're totally overbuilt.

这两种说法我都能讲得头头是道。

I can tell both stories with equal certainty.

我有一个好朋友一直在做一个很有意思的实验，他把投资拆成选择买什么和每种买多少，虽然有点简化，但想法挺有意思的。

I have a good friend who has been running this very cool experiment where if you break investing down into picking what to own and then how much to own of each thing that you've picked, overly simplified, but interesting way of thinking about it.

他一直在做这个实验：他还是自己选要买的东西，但第一组用他以前的方式自己做组合构建，第二组则完全交给他自己搞的一个AI工具，让它全权负责组合构建，但必须限于他挑选的那些资产。

He's been running this experiment where he continues to pick the things to own, but then runs his own portfolio construction as he would've in the past in one lane, and in a second lane is just completely handing that off to an AI harness thing that he's built and says, "You do all the portfolio construction, but stick with the stuff that I've picked."

到目前为止的结果是，AI那组的表现大幅超越他自己的组合构建，而他自己的方法本身已经相当复杂了。

And what he's found so far is that that is drastically outperforming his version of portfolio construction, which itself is quite sophisticated.

他的简单说法是，AI特别擅长优化，而且它们读遍了所有关于组合构建、仓位大小和凯利公式的论文，什么都懂。

And the simple argument that he would make is while AIs are just really good at optimization, and they've read every paper on portfolio construction and all this kind of stuff, and bet sizing and Kelly criterion, they know it all.

而在未来，也许我们会转向那个模式，投资者的工作只是决定买什么，而不是每样买多少。

And that in the future, perhaps we make that transition where the investor's job is just picking what to own and not how much of each to own.

我想问问你，你对这个想法和未来怎么看，因为你的投资组合非常特别，80%都集中在一种特定的资产上。

And I ask you this question, what you think about this idea and the future, because you have such an unusual portfolio where, again, 80% of it is in one specific asset.

我猜如果你是走第二条路，AI给你的资产配置会和你自己选择的方式大不一样。

And my guess is if you were in lane two, AI would weight your portfolio very differently than you have chosen to do it.

我仓位偏低。

I'm underweight.

我特别想知道你对这个想法的看法。

So I'm dying to know what you think about this idea.

这很聪明。

That's clever.

Carvana仓位偏低。

Underweight Carvana.

应该到90%。

Should be 90.

时间尺度这方面，想想也挺有意思的。

Time scales probably are interesting on that to think about, too.

我在想人工智能会怎么渗透进这个行业，我觉得它会先从较短的时间尺度开始，然后逐步延伸到较长的时间尺度。

As I think about how AI is going to encroach into this business, I think it's going to start at shorter time scales and work its way up to longer time scales.

因为其中一个挑战是没有验证回路。

Because one of the challenges is that there's no verification loop.

你朋友能这么说，是基于结果，当然不是二十年的结果。

Your friend's ability to say that is based on results over, certainly not 20-year results.

我强烈怀疑AI进入市场的过程，可能已经开始了。

I strongly suspect that the march of AI into markets, and it may have already started.

我不知道文艺复兴科技公司是怎么做到的，但我猜——

I don't know how Renaissance Technologies did it, but I assume-

看起来他们可能比任何人都早发现了Transformer。

Seems like they might've discovered a transformer before anybody else.

我没开玩笑。

I'm not kidding.

如果那些做短线投资的人，从几秒钟开始，慢慢扩展到几分钟、几小时、几周、几个月，然后发现自己在越来越多地把决策交给模型，我一点也不会觉得奇怪。

It wouldn't shock me if people who invest over shorter timescales, starting with seconds and working their way out to minutes, hours, weeks, months, find themselves delegating more and more to models.

这完全说得通。你可以想象把这类事情分成两部分，一部分是让模型变得更智能。

Makes perfect sense in that you could definitely imagine bifurcating that between Trying to enrich the model.

我觉得其他人已经在做类似的事情了，这只是一个更复杂的版本。你可以有一套完全量化的策略，然后结合你认为是定性的、由人类设计的分析来补充它。

I think other people do things like this already, and this is just a more sophisticated version, but you can have a fully quantitative strategy, and then you can enrich it with what you think is qualitative human-designed analysis.

然后你可以用量化策略来融合那些定性的因素。

And then you can have a quantitative strategy to incorporate the qualitative.

我觉得很多多策略基金就是这么做的，他做的只是稍微变了个花样而已。

I think many of these multi-strats do these sorts of things, and what he's doing is just a slightly different flavor of that.

完全合理。

Makes perfect sense.

成功的路径有很多，而模型只是增加了更多的自由度。

There are so many ways to be successful, and models have just added more dimensions of freedom.

那你自己的投资组合呢？

But what about your own portfolio?

再多说一点，你怎么会觉得持有一个仓位很安心，先不管那家公司是谁——

A word more about how you feel comfortable having a position, forget the company that it is-

行

Sure

……仓位那么大，而那么多研究却表明，那样构建投资组合简直是疯了。

... that's that big, in the face of so much research that would suggest that's a crazy way to build a portfolio.

我觉得，那些研究大多是在看证券价格的涨跌——

A lot of that research, I think, is more looking at price changes in terms of securities prices-

……而不是从拥有这家公司的角度去想问题。

... and less focused on thinking about it the way someone who owns a company might think about it.

我们说的是Carvana。

We're talking about Carvana.

如果不是因为我觉得Carvana的基础设施、品牌以及其他各方面都太独特了，几乎没什么情况会让它彻底完蛋，我也不会放心持有这么多。

Were it not for my sense that Carvana's infrastructure and brand and everything else is just so differentiated that there are few scenarios where it works out really badly, then I wouldn't feel so comfortable owning so much of it.

可能就这么简单。

It might just be as simple as that.

那种投资组合的方式确实会奏效。

That portfolio approach would have worked absolutely.

当然，如果你早就发现了沃尔玛、家得宝或者快扣那样的公司，你也不会充分利用它的。

Certainly, if you discovered Walmart or Home Depot or Fastenal or something back in the day, you wouldn't have taken full advantage of it.

我对Carvana的感觉就是这样。

And that's kind of my sense with Carvana.

如果你读历史，当然，所有真正做得很好的人往往是高度集中的，但历史也充满了失败者的例子。

If you read history, of course, all the people who did really great tended to be very concentrated, but of course, the history is littered-

分散投资能抵消这个风险。

Diversify buys that

或者历史上有那些不这样做的人。

Or the history of people who didn't.

我还认为，很多这类文献是针对那些追求平均或略高于平均结果的人的建议，那完全是对的，但那不是我所追求的。

I also think that a lot of that literature is what most people who are looking for average or slightly better than average results should do, and it's totally the right thing to do, but that's not what I'm looking for.

我非常喜欢Carvana。

I like Carvana a lot.

如果股票涨了很多，我觉得有必要分散投资，那也不会让我不开心。

It wouldn't upset me any if the stock went up a bunch and I felt compelled to diversify.

那样也挺好的。

That would also be okay.

这只是在预期回报和明知异常风险之间做权衡。

It's just more of a trade-off between expected returns and knowingly taking an unusual risk.

你对教育怎么看？

What do you think about education?

你和我孩子的年龄差不多。

You and I have kids of roughly similar ages.

谈谈你如何看待他们的教育，以及推动这个国家教育的机构，这些在你描述的整个路径依赖故事中非常重要。

Comment a little bit on how you're thinking about their educations, the institutions that have driven education in this country that have been really important in that whole path-dependent story you've described.

你认为教育的现在和未来是什么样的，或者应该是什么样的？

What do you think the present and future of education looks like or should look like?

我一直在和我孩子学校的校长谈这件事，越来越觉得，他的想法和我的想法都趋向于这样一个方向：教育孩子的方式在你要寻找的东西上变得越来越双峰分布。

I've been talking to the head of my kids' school about this, and more and more, it seems to be that his thinking and my thinking are heading in this direction of the way you're educating kids is getting more bimodal in terms of what you're looking for.

让我退一步来看这个问题。

Let me step way back.

在一个模型拥有无限智能的世界里，个人的智能，你自己的智能，还有什么用呢？

What is the benefit of intelligence, personal intelligence, like your own intelligence, in a world where there's infinite intelligence for models?

你可能会说，"哦，那没什么用。"

You might say, "Oh, it's useless.

不重要。

Doesn't matter.

智商50的人能和智商150的人一样自如地应对世界。"

Someone with a 50 IQ can navigate the world just as well as someone with a 150 IQ."

但我认为这不太对，直觉上也感觉不对。

And I don't think that's quite right, and intuitively doesn't feel right.

但我觉得关键在于，你越聪明，越有结晶化的智能，也就是教育带来的那种，你就越擅长和模型交流，能让模型明白你想要什么。

But I think to nail down where it is, is that the more intelligence you have, and the more crystallized intelligence you have, which would be sort of education, the better you are at communicating with a model and telling a model what you want.

比如，要我和一个模型深入探讨，设计前沿的量子物理实验，那会非常困难，因为我对量子物理一无所知。模型可以很努力地跟我解释，但最终还是会用简单浅显的方式跟我讲，很难真正触及前沿，也没法充分利用模型的潜力。

I would have a very tough time having a deep conversation with a model to create leading quantum physics experiments because I don't know anything about quantum physics, and the model could try very hard to explain things to me, but in the end, it would be explaining things in crayon and nerf, and it would just be very hard to get to the leading edge and get the best out of what the model has to offer.

而在我有一定专业知识的领域，我和模型就能有效沟通，我更能让模型按照我的意愿行事。

Whereas in an area where I do have some expertise, the model and I can communicate effectively, and I can direct the model to my will much better.

所以，要让模型按照你的意愿行事——我认为这会是未来的一个重要技能——你确实需要对世界有所了解，聪明才智和受教育程度对此都有帮助。

And so to direct the model to do your will, which I think is going to be an important skill in the future, you do have to understand something about the world, and smarts help for that, and education helps for that.

如果你不知道什么是随机过程，你就不能让它假设这是一个随机过程。

You can't ask it to assume it's a stochastic process unless you know what a stochastic process is.

它可以教你什么是随机过程，但要弄清楚这一切会非常费劲。

It could teach you what one is, but that's a lot of friction in figuring this all out.

你不必去推导那些数学，但你必须知道它是什么。

You don't have to work out the math, but you have to know what it is.

所以当我思考教育时，有这样一种观念：你教给孩子一系列基础技能，这些技能相互叠加，我认为最终所有这些加起来，他们必须能在没有模型的情况下完成这些，并不是因为他们未来会不用模型，而是因为如果他们不会做乘法，就很难理解矩阵乘法。

And so when I've been thinking about education, there's this idea that you're teaching your kids a bunch of foundational skills that build on one another, where I think in the end, the sum total of those, they have to be able to do them without models, not because they're going to do them without models in the future, but because if they don't know how to do multiplication, it's going to be really hard to understand matrix multiplication.

而如果他们不能足够深入地理解什么是矩阵乘法，他们不一定非要会做得很熟练，但他们必须能理解模型在做什么。

And if they don't know what matrix multiplication is at a high enough level, they don't have to be able to do it really well, but they have to be able to understand what the model is doing when it's doing it.

这样他们才能指导模型。

And that's so that they can direct the model.

所以我越来越觉得，我们应该花大量时间让孩子们基本脱离这些模型，教他们我们认为重要的基础技能，而这些技能具体是什么，确实是个很难的问题。

And so increasingly, I'm of the view that we should spend a bunch of time with kids basically separated from these models, teaching them basic skills that we think are important, and what those are is a really hard thing.

但核心想法是，模型确实能做，但你得懂这些知识，才能告诉模型该怎么做。

But the idea being that, yes, the model can do it, but you need to know it so that you can tell the model what to do.

然后反过来，有些英语老师说：‘我不喜欢模型，因为孩子们写莎士比亚论文的时候直接作弊。’

And then on the flip side, some English teacher is like, "I don't like models because the kids just cheat on their Shakespeare essays."

我就想：‘你们目标定得太低了。’

I'm like, "You're not being ambitious enough.

如果孩子们在莎士比亚论文上作弊——

If the kids are cheating on a Shakespeare essay-

那就给他们更难的任务。

Give them something harder to do

……给他们难一百倍、更有挑战性的任务，让他们最大限度地去利用模型。

... give them something 100 times harder and more ambitious and have them use models to their fullest extent.

让他们把成果带回来，然后确保他们不是简单地给模型一个提示，做完作业，而是确保他们真正理解和掌握了这个内容。

Have them bring the work product back and then make sure that they didn't just give a prompt to the model, do the homework, and make sure they own it.

问问他们为什么会这样。

Ask them why it's like this.

逼他们说明理由。

Force them to justify it."

我觉得这又是一件非常重要的事，因为孩子们需要擅长使用这些工具，而且说实话，我觉得他们也需要被教育，让他们知道自己什么都能做到。

And I think that's another really important thing because kids need to be good at using them, and they also, I think, frankly, just need to be taught that they can do anything.

你和我偶尔会通个电话。

You and I get to talk every so often on the phone.

每次通话，我都希望被录下来，让其他人也能听到，因为我学到太多了。

Every time, I wish it was recorded so that other people could hear it because I learn so much.

而且我觉得今天这次对话，算是我们几个月前那次电话的延续，真的提供了很多关于智能未来及其对市场和公司影响的有价值的思考。这和你经常听到的那种主流观点不太一样，那种观点是无限扩张、永远做空、对智能的需求没有上限。

And I think in this particular conversation today, which is sort of a version of a call we had several months ago, it's just such a valuable set of food for thought about the future of intelligence and what that means for markets and companies, and is a little bit, not in opposition to, but a different perspective than the most common one that you're hearing, which is unlimited build-out forever, short everything forever, no cap on the demand for intelligence.

无论是在电话里还是今天，和你聊这些我都非常享受。

And I've loved talking to you about it both on the phone and today.

所以非常感谢你抽时间。

So thank you so much for your time.

谢谢。

Thank you.

如果你喜欢这一集，请访问 colossus.com。

If you enjoyed this episode, visit colossus.com.

你会找到这个播客的每一集，都配有手工编辑的文稿。

You'll find every episode of this podcast complete with hand-edited transcripts.

你还可以订阅 Colossus，这是我们每季度出版的印刷版、数字版和私人音频刊物，包含我们最欣赏的创始人、投资者和公司的深度介绍。

You can also subscribe to Colossus, our quarterly print, digital, and private audio publication featuring in-depth profiles of the founders, investors, and companies that we admire most.

更多信息请访问 colossus.com/subscribe。

Learn more at colossus.com/subscribe.

Patrick O'Shaughnessy 是 Positive Sum 的首席执行官。

Patrick O'Shaughnessy is the CEO of Positive Sum.

Patrick 和播客嘉宾所表达的所有观点仅代表他们个人，不代表 Positive Sum 的立场。

All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum.

这个播客仅供参考，不应作为投资决策的依据。

This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.

Positive Sum 的客户可能持有本播客中讨论的证券头寸。

Clients of Positive Sum may maintain positions in the securities discussed in this podcast.

想了解更多，请访问 psum.vc。

To learn more, visit psum.vc.

你知道小的优势会随着时间不断累积吗？

You know how small advantages compound over time?

这在投资中如此，在经营公司时也是如此。

That's true in investing and just as true in how you run your company.

你的支出体系就是你的资本配置策略。

Your spending system is your capital allocation strategy.

Ramp 让它默认更智能，更好的数据、更好的决策、长期更好的经济效益。

Ramp makes it smarter by default, better data, better decisions, better economics over time.

访问 ramp.com/invest 了解详情。

See how at ramp.com/invest.

随着你的业务增长，Vanta 与你一起扩展，自动化合规，并为安全和风险管理提供单一事实来源。

As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk.

更多信息请访问vanta.com/invest。

Learn more at vanta.com/invest.

从OpenAI到Cursor再到Perplexity，最好的AI和软件公司都用WorkOS，一夜之间就能准备好服务企业客户，而不是要花几个月。

The best AI and software companies from OpenAI to Cursor to Perplexity use WorkOS to become enterprise-ready overnight, not in months.

访问workos.com，跳过那些不起眼的基础设施工作，专注你的产品。

Visit workos.com to skip the unglamorous infrastructure work and focus on your product.

Ridgeline正在重新定义资产管理技术，它不只是软件供应商，更是真正的合作伙伴。

Ridgeline is redefining asset management technology as a true partner, not just a software vendor.

他们帮助公司规模扩大5倍，实现更快的增长、更智能的运营，并获得竞争优势。

They've helped firms 5X in scale, enabling faster growth, smarter operations, and a competitive edge.

访问ridgelineapps.com，看看他们能为你的公司解锁什么。

Visit ridgelineapps.com to see what they can unlock for your firm.

每家投资公司都独一无二，通用AI不懂你的流程。

Every investment firm is unique, and generic AI doesn't understand your process.

Rogo懂。

Rogo does.

这是一个专为华尔街打造的AI平台，它连接你的数据，理解你的运作流程，并能产出实际成果。

It's an AI platform built specifically for Wall Street, connected to your data, understanding your process, and producing real outputs.

快去rogo.ai/invest看看吧。

Check them out at rogo.ai/invest.