马克·安德森2026展望：AI时间线、中美之争与人工智能的代价

顺便说一下，这个论点的另一部分是，中国的基本治理模式建立在高就业率之上，因为至少所有地缘政治人士都说，如果中国失业率达到25%或50%，就会引发社会动荡，而这正是中共最不想看到的。

By the way, the other part of that argument is that the Chinese basically, the Chinese, you know, the Chinese governance model is based on high employment, you know, because, you know, if if if, you know, at least all the geopolitical people say if China ended up with, like, 25 or 50% unemployment, that would cause civil unrest, which is the one thing that the CCP doesn't want.

因此，相应的贸易压力就是中国需要美国的出口市场。

And so the corresponding part of the trade pressure is China needs the American export market.

你知道，美国消费者占全球经济的三分之一，占全球消费需求的三分之一。

You know, the the American consumer is, a third of the global economy, a third of global consumer demand.

所以，中国需要美国的出口市场，否则突然间，大量工厂会立即破产，导致中国出现大规模失业和动荡。

And so, you know, China needs The US export market or it has high all of a sudden, lot of its factories would go kind of instantly bankrupt and, you know, would cause mass unemployment and unrest in China.

所以，总之，这是一种复杂且相互交织的关系。

So so anyway, like, know, we there is this complicated it's a it's a complicated intertwined relationship.

话虽如此，过去十年来，华盛顿的主流情绪，无论两党，都认为我们需要更认真地对待中国作为地缘政治对手。

Having said that, you know, the the mood in DC basically for the last ten years on a bipartisan basis, has been that we need to take we, The US, need to take China more seriously as a geopolitical foe.

在这种观点下，存在一种军事维度，即南海爆发战争的风险，以及围绕台湾发生战争的风险。

And, you know, under under under that school of thought, there's sort of this sort of you know, there's there's the military dimension, which is, you know, this sort of the, you know, the the the risk of some kind of war in the South China Sea, the risk of some kind of war around around Taiwan.

因此，这让华盛顿的每个人都处于高度戒备状态。

And so that, you know, that that has everybody in Washington on high alert.

你知道，还有一个关于美国去工业化以及潜在再工业化的经济问题，这关系到我们对中国的依赖。

You know, there's also this this economic question around the kind of deindustrialization of The US and the potential reindustrialization and what that means about, you know, the dependence on China.

然后呢，还有一个关于人工智能的问题。

And then and then there's and then there's this this this AI question.

人工智能问题虽然是一个经济问题，但也像是一个地缘政治问题，也就是说，好吧。

And and the AI question is an economic question, but it's also like a geopolitical question, which is, okay.

基本上，人工智能目前只在美国和中国研发。

You know, basically, AI is essentially only being built in The US and in China.

世界上其他地方要么无法研发，要么不想研发，这一点我们以后可以讨论。

You know, the rest of the world either, you know, can't build it or doesn't want to, which we which we could talk about.

所以本质上就是美国与中国之间的竞争。

So it's basically US versus China.

而人工智能将会在全球范围内普及。

And then AI is going to proliferate all over the world.

到时候，普及全球的是美国的人工智能，还是中国的人工智能呢？

And is it going to be American AI that proliferates all over the world, or is it gonna be Chinese AI that proliferates all over the world?

所以，我认为在华盛顿，无论哪个政党，大家基本上都是这样看待我刚才提到的这些事情的。

And so and I would say just generally across party lines in DC, this you know, the the the things I just went through are kinda how they look at it.

而且中国也参与其中。

And and and and the Chinese are in the game.

毫无疑问，中国已经参与其中，尤其是在软件领域，比如DeepSeek，它可以说是打响了软件竞赛的第一枪。

And so the, you know, the Chinese are in the game for sure, you know, with software, you know, deep deep seek, you know, was kind of the big, you know, kind of fired the starting gun of the software race.

现在你有了像DeepSeek这样的模型，它实际上是一家中国对冲基金开发的AI模型。

And now you've got I think it's I think you've got sports like DeepSeek, which is a deep so DeepSeek is an AI model from actually a hedge fund in in China.

这让很多人感到意外。

It's a little bit kinda took a lot of people by surprise.

然后Qwen是阿里巴巴推出的模型。

Then Quinn is the model from Alibaba.

Kimmy来自另一家初创公司，叫做月之暗面。

Kimmy is from another startup, oh, called Moonshot.

这家公司名叫月之暗面。

This the company is called Moonshot.

还有腾讯、百度和字节跳动，这些公司都在人工智能领域投入了大量工作。

And then there's, you know, and then, you know, there's also Tencent and Baidu, and Bydance, you know, that are all primary, you know, companies doing a lot of work in AI.

所以，大概有三到六家主要的人工智能公司，此外还有数量庞大的初创企业。

And so, you know, there's somewhere between three to six, you know, kind of primary AI companies, and then there's, know, tremendous numbers of, of startups.

它们在软件领域也参与了这场竞赛。

And so, you know, they're in the race on you know, they're in the race on on on software.

它们正在努力追赶芯片技术。

They are, you know, working to catch up on chips.

虽然还没达到目标，但它们正极其努力地追赶。

They're not there yet, but they're working incredibly hard to catch up.

举个例子，美国普遍认为，DeepSeek新版尚未发布的原因是，中国政府要求它们只能使用国产芯片来研发，以此推动中国芯片生态的发展。

And just as an example of that, you know, the at least a common understanding, you know, in The US is that the reason you haven't seen the new version of DeepSeek yet is that basically the Chinese government has instructed them to build it only on Chinese chips as a as a motivator to get the Chinese ship ecosystem up and running.

而国内主要的芯片公司是华为，未来可能还会有更多企业加入。

And and the then the main chip company there is Huawei, although there could be more in the future.

所以，情况就是这样。

And then there's so, you know, so so so so there's that.

然后，接下来还有所有这些以机器人形式呈现的AI应用。

And then and then there's everything to follow, which is basically AI in kind of robotic form.

对吧？

Right?

因此，一场全球性的科技经济机器人竞赛正在启动，而中国在机器人领域起步时就占据优势，因为他们在机器人所需的众多组件上都领先，正如我所说，整个机电产品的供应链早在三十年前就从美国转移到了中国，而且再也没有回去过。

And so there there's this basically global technological economic robotics competition that's kicking off, And, you know, China kinda starts out ahead on robotics because they're just ahead on so many of the, so many of the components that go into robots, because the, you know, this sort of like I said, this the kind of entire supply chain of, like, electromechanical things, you know, basically moved from The US to China thirty years ago and has and has never come back.

所以，这就是从DC视角来看的情况。

So so so that's kind of the the the the the DC lens on it.

我认为，DC正在非常密切关注这一点。

And and I would say, you know, DC is watching it, you know, quite carefully.

今年最大的爆发性事件就是DeepSeek的发布。

The the the big kind of supernova moment this year was the DeepSeek release.

DeepSeek的发布在多个方面都令人惊讶。

The DeepSeek release was surprising on a number of fronts.

其中之一就是它的表现竟然如此出色。

One was just how good it was.

而且，沿着这条思路，他们把我们在云端运行大型模型所需的能力集，缩小并压缩到一种更小的版本上，使其能在少量本地硬件上运行，同时保持大致相当的能力。

And again, along this line of it took the capability set that we're running in large models in the cloud and kinda shrunk it onto a, you know, into into a into a a sort of a a a reduced size, you know, a smaller version of sort of equivalent capabilities that you could run on small amounts of local hardware.

于是就有了这一点。

And so there was that.

然后，它被作为开源项目发布，这同样令人惊讶，尤其是它来自中国，因为中国长期以来并不以开源著称。

And then it was also a surprise that it was released as open source, and particularly open source from China, because China China does not have a long history of open source.

接着，更令人惊讶的是，它竟然来自一家对冲基金。

And then it was also a surprise that it actually came from a hedge fund.

它并不是来自大型研发机构，比如大学研究实验室。

So it didn't come from a big r and d, know, sort of university research lab.

它也不是来自大型科技公司。

It didn't come from you know, from a big tech company.

它来自一家对冲基金，据我们所知，这是一种相当独特的状况：一家极其成功的量化对冲基金，拥有众多超级天才，而这家基金的创始人决定去打造人工智能。

It it came from a hedge fund, and it it like, as as far as we can tell, it it basically is this somewhat idiosyncratic situation where you just have this incredibly successful quant hedge fund with all these, you know, super geniuses, and the the founder of that hedge fund, you know, basically decided to build AI.

至少从外部迹象来看，甚至连中国政府都对此感到意外。

And, you know, at least external indications are this was a surprise to even even the Chinese government.

这不可能被证实，你知道中国政府究竟对什么感到惊讶，但至少从氛围上看，这并不是完全计划好的。

It's it's impossible to prove, you know, what the Chinese government was surprised by or not, but, you know, there's at least the atmospherics are that this was not exactly planned.

在DeepSeek发布时，它还不是一家国家级的科技巨头。

This was not a national champion tech company at the time that DeepSeek was released.

它就像是从天而降，顺便说一句，这为整个领域带来了极大的鼓舞，因为一个无名之辈也能做到这种事。

It was it sort of came out of left field, which, by the way, is very encouraging for the field that it was possible for somebody to do that kind of who was unknown.

对吧？

Right?

因为这意味着你或许并不需要那些超级天才、明星级的研究人员。

Because it kinda means that maybe you don't need all these, you know, super genius, superstar researchers.

也许真正聪明的年轻人就能自己搭建这些东西，我认为这正是未来的发展方向。

Maybe actually smart kids can just build this stuff, which I think is is the direction things are headed.

因此，我认为这开启了一种趋势——我不知道‘模仿’这个词是否准确，但DeepSeek的成功，尤其是它作为中国开源项目取得的成功，似乎引发了一股中国发布开源模型的潮流。

And so that kicked off, I would say, like, this kind of I I don't know if copycat's the wrong word, but that that was sort of it feels like the success of DeepSeek and the success of DeepSeek from China as open source kinda kicked off a a sort of trend in China releasing these open source models.

你知道，华盛顿的愤世嫉俗者会说，嗯。

You know, look, the cynics, you know, in DC would say, you know, yeah.

他们在大量抛售。

Like, they're dumping.

对吧？

Right?

他们明显在大量抛售。

They're they're they're obviously dumping.

他们试图让你知道，他们看到西方有机会建立一个庞大的产业。

They're they're trying to you know, they see that the West has this opportunity to build this giant industry.

他们正试图从一开始就将其商品化。

You know, they're trying to commoditize it right out of the gate.

这可能有一定道理。

You know, there's probably something to that.

你知道，中国工业经济历来有补贴生产的历史，有时甚至以低于成本的价格销售产品。

You know, the the Chinese industrial economy does have a history of, you know, sort of, let's say, subsidized production that leads to selling, you know, selling things below cost in some cases.

但我认为，这种看法也过于愤世嫉俗了，因为事实就是如此。

But I think also it's it like I think that's almost too cynical of a view also because it's just like, alright.

哇。

Wow.

他们真的在全力追赶。

Like, they're really in the race.

开源、闭源，都无所谓。

Like, open source, closed source, whatever.

他们真的在全力追赶。

Like, they're, you know, they're actually really in the race.

过去，我想我们在一些理性的讨论中，谈过过去两年里我们在华盛顿一直进行的这些政策之争。

You know, we've in the past, I think, on on healthy calls about, you know, these policy fights that, you know, we've been having in DC for the last two years.

而且，两年前，美国政府内部掀起了一场相当大的运动，试图限制甚至 outright 禁止许多人工智能技术。

And, you know, there's a big pretty pretty big push within the US government, you know, two years ago to basically, you know, restrict, you know, or outright ban, you know, a lot of AI.

对于一个独占鳌头的国家来说，进行这样的讨论很容易。

And, you know, it's very easy for a country that is the only game in town to have those conversations.

但如果你真的在和中国赛跑，那就是另一回事了。

It's quite another thing if you're actually in a foot race with China.

所以我认为，实际上，由于现在大家意识到这是一场双马赛跑，而不是单马赛跑，华盛顿的政策环境已经显著改善了。

And so I think, actually, the the the the policy landscape in DC has, I would say, has improved dramatically as a consequence of sort of an awareness now that this is actually a two horse race, not a one horse race.

当然。

For sure.

没错。

Yeah.

实际上，关于这一点，我想提前谈谈政策和监管，因为目前各州有五十套不同的AI法律，这似乎是一种灾难性的做法，会让我们在AI竞赛中实际上束手束脚。

Actually, on on the point, I'll I'll jump ahead here to policy and regulation just because it seems like the current stance on on 50 different set of AI laws by state seems like a catastrophic way to to put us effectively with a or one of our our hands tied behind our our back here in terms of the the AI race.

人们是否意识到这对进步和发展将是灾难性的？目前的应对计划是什么？

What what's the state of plan that are folks recognizing that that would be catastrophic for progress and development?

今天大多数人对这个话题至少持什么立场？

Where do most people at least stand on that topic today?

是的，这有点复杂。

Yeah, so it's a little bit complicated.

所以让我倒回两年前，那时我非常担心会出现破坏性的联邦AI立法，当时我们在这方面投入了大量精力，我们之前也提到过。

So I rewind to say like two years ago, I was very worried about like really ruinous federal legislation on AI and there was, we engaged kind of very heavily at that point, which we talked about in past.

我认为，好消息是，就今天的情况来看，这种风险非常低。

And I think the good news on that is I think the risk of that sitting here today is very low.

在华盛顿，无论是哪一派，都几乎没有意愿去做任何会阻碍我们超越中国的事情。

I there's very little mood in DC on either side of the aisle to really, you know essentially, there's very little there's very little interest in doing anything that would prevent us from beating China.

所以，在联邦层面，现在情况好多了。

So so, you know, I on on the federal side, things things are much better now.

系统中当然仍会有问题和紧张局势，但总体来看，形势相当不错。

There there will there will be issues and there are tensions in the system, but, like, things things are looking looking pretty good.

正如珍你提到的，这已经将大量关注转向了各州。

That has translated, Jen, to your point, that's translated a lot of the attention to the states.

基本上，在我们的联邦制体系下，各州有权就许多事务制定自己的法律。

And basically what's happened is, you know, under our system of federalism, you know, the states get to pass their own laws on a lot of things.

因此，确实，这些事情总是多种因素的结合。

And so, yeah, basically, you know, a lot of, you know, and and, you know, with these things, it's always a combination.

许多出于善意的人正在努力思考各州层面该怎么做。

A lot of well meaning people are trying to figure out what to do at the state level.

当然，还有很多投机行为，人工智能现在是个热门话题。

And then, of course, there's a lot of opportunism AI is just the hot topic.

所以，如果你是一个有野心的、正在崛起的州议员，无论在哪个州，想竞选州长甚至总统，你都想蹭一蹭这股热度。

And so if you're a, you know, aggressive up and coming state legislator, whatever, in some state and you wanna run for governor and then president, you know, you wanna kind of attach yourself to the heat.

因此，推动州级事务背后有政治动机。

And so there's, a political motivation to to do state level stuff.

没错。

Yeah.

而你今天坐在这里，我们正在追踪全美50个州大约1200项法案。

And, you sitting here today, like, we're tracking on the order of 1,200 bills across the 50 states.

顺便说一句，不只是蓝州，红州也是如此。

And by the way, not just the blue states, also the red states.

所以，过去五年左右，我一直花很多时间抱怨民主党政治人物对科技行业可能采取的威胁性举措。

And so, you know, I've I've you know, for the last, five years or whatever, I spent a lot of time complaining about, you know, kinda what Democratic politicians are threatening to do to tech.

但共和党人也不都是反对者，许多地方的共和党官员同样持有错误或不明智的观点，正在推动一些糟糕的法案。

There's also a lot of Republicans that that like, Republicans are not a block on this, and there are quite a few, like, local Republican officials in different states, that that also I think have, you know, let's say, you know, misinformed or ill advised, views and are trying to put together, put out bad bills.

这有点奇怪，因为联邦政府本应监管州际贸易，而人工智能从定义上来说就是跨州的。

You know, it's a little bit weird that this is happening and that, you know, the federal government does have regulation of interstate commerce, and, you know, technology AI kinda by definition is interstate.

你知道，没有哪家AI公司只在加利福尼亚、科罗拉多或德克萨斯运营。

Like, you know, there's there's no AI company that just operates in California or just operates in, you know, Colorado or Texas.

在所有技术中，人工智能显然具有全国性的范围。

You know, AI of all technologies, AI is obviously something that's that's sort of national in scope.

显然，联邦政府才应该是监管者，而不是各州。

You know, it's sort it's sort of obvious that the federal government should be the regulator, not not not the states.

但联邦政府必须挺身而出，必须介入。

But but the federal government knee needs to assert itself, needs to step in.

事实上，曾经有过这样的尝试。

There there was actually an attempt to do that.

曾有人试图加入一项暂停各州人工智能监管的条款，本质上是将人工智能的监管权保留给联邦政府，阻止各州推进这些法案。

There was a there was an attempt to add a moratorium on state level AI regulation that basically would would reserve the right of the federal government to regulate AI and sort of prevent the states from moving forward with these bills.

我认为，这曾是所谓‘一项宏伟法案’谈判的一部分。

That was, I think, part of the negotiation for the, quote, one big beautiful bill.

而当时背后其实有个协议，但这个协议在最后一刻破裂了，因此这项暂停措施未能实现。

And then that that there was a deal behind that, and that deal kinda blew up at the at the last minute, and that moratorium didn't happen.

而且，公平地说，那些反对这项暂停的人认为，这可能太过牵强了。

And and, you know, in fairness, the critics of that moratorium, it probably was a was it it was probably too much of a stretch.

哦，对不起。

Oh, it was I'm sorry.

要获得足够支持通过这项措施确实太过牵强，但同时，从限制各州进行某些真正应该有权实施的监管来看，这也可能太过越界了。

It was definitely too much of a stretch to get enough support to pass, but it was also probably too much of a stretch in terms of restricting the states from certain kinds of regulation that they really should be able to do.

所以，它就是没能顺利成形。

So so it just it didn't quite come together.

目前在华盛顿，我们正在就下一步该如何推进进行非常活跃的讨论。

There's a very active we're having very active discussions in DC right now about kind of the next you know, the the kind of the next turn on that.

你知道，政府方面可以说非常支持由联邦政府来主导这件事。

You know, the administration is I would say the administration is very supportive of of the idea of of the federal government being in charge of this.

这正是因为它是一个涉及50个州的实际问题，也是一个具有全国重要性的问题。

That's part of it being an actual, you know, 50 state issue and and and an issue of national importance.

然后，我想说，两党大多数国会议员都明白这一点。

And then, you know, I'd say most most congresspeople on both sides of the aisle, you know, kinda get this.

所以我们只需要想办法妥善解决这个问题，但我认为这会发生。

So we just we we kinda have to figure out a way to, you know, to land this, but but I think that'll happen.

一些州级的法案非常极端。

Some of the state level bills are wild.

科罗拉多州去年通过了一项非常严苛的监管法案，尽管丹佛和博尔德地区的初创企业生态系统提出了强烈反对。

The the Colorado passed a very draconian regulation bill, last year, and against, like, serious objections from the local startup ecosystem in in in and around Denver and Boulder.

实际上，一年后，他们现在正试图撤销这项法案。

And, actually, they're they're now actually trying to reverse their way out of that bill, you know, a year later.

也许其中一些细微之处，比如算法歧视，以及如何界定——他们当初提出的极端版本具体是什么？

Maybe some of the the nuance of it, like, the algorithmic discrimination and, like, how to make it like, what were some of the the extreme versions of what they they had proposed?

是的。

Yeah.

真正非常严苛的是我们在加州激烈反对的那项法案，即SB 1047，它基本上是以欧盟的《人工智能法案》为蓝本制定的。

So the really draconian one was the the one that we really fought hard was the one in California, which is called SB ten forty seven, and it wasn't a it it it was basically it was modeled basically after the it was called the EU AI act, so the European Union's AI act.

哦，好吧。

Oh, okay.

所以，这就是美国所有事情的背景：欧盟两年前通过了一项名为《人工智能法案》的法案，我不确定具体细节，但这项法案在很大程度上扼杀了欧洲的人工智能发展。

So this is the backdrop to all The US stuff, which is the EU passed this bill called the AI act, don't I know, whatever, two years ago, and it basically has killed AI development in well, it's actually killed AI development in Europe to a large extent.

这项法案如此严苛，以至于像苹果和Meta这样的美国大公司都不敢在欧洲的产品中推出前沿的人工智能功能。

And then it even it is so draconian that even even big American companies like Apple and Meta are not launching leading edge AI capabilities in their products in Europe.

这足以说明这项法案有多么严苛。

Like, that that's how that's how, like, draconian that bill was.

这其实是一种典型的欧洲做法——他们认为，如果我们不能成为创新的领导者，至少我们可以成为监管的领导者。

And it's it's sort of a classic it's it's a classic kinda European thing where they, like, you know, like, they just thought that you know, they they have this kind of view that it's just like, well, you know, we if we can't be the leader they literally say this, by the way.

如果我们不能在创新上领先，至少我们可以在监管上领先。

If we can't be the leaders in innovation, at least we can be the leaders in regulation.

然后他们通过了这种极具破坏性、自伤性的规定。

And then they pass this incredibly kind of ruinous self harm kind of thing.

几年后，他们才惊呼：天啊，我们到底干了什么？

And then a few years pass, and they're like, oh my god, what have we done?

所以他们正在经历自己版本的这种情况。

And so they're kind of going through their own version of that.

是的。

Yeah.

顺便说一下，当我谈到欧洲时，我往往对整个事情持非常悲观的态度。

By the way, you know, I I you know, when I talk about Europe, I I tend to be very dark about the whole thing.

我要告诉你，我对欧洲最悲观的看法，来自于那些移居美国的欧洲创业者，他们对欧洲在这件事上的状况简直愤怒至极。

I will tell you the darkest people I know about Europe are the European entrepreneurs who moved to The US, are just, like, absolutely furious about what's happening in in in Europe on this stuff.

但即便如此，欧洲的情况也糟糕透顶。

But but even there, like, it it's it's so bad in Europe.

他们把自己脚给打穿了，现在欧盟内部甚至已经开始尝试扭转这一局面。

Like, they they shot themselves in the foot so badly that there's actually a process now at the at the EU to try to unwind that.

他们正试图放宽GDPR。

They're trying to unwind the GDPR.

不过，总之，对于关注欧洲的人来说，马里奥·德拉吉——前意大利总理——一年前发布了一份名为《德拉吉报告》的文件，这份报告是关于欧洲竞争力的。

So, but, anyway, for people tracking Europe, Mario Draghi, is the former, I guess, prime minister of Italy, did this thing about a year ago called the Draghi report, which is the report on European competitiveness.

他详细地阐述了欧洲如何自我束缚，其中一部分原因在于人工智能等领域的过度监管。

And he kind of outlined kind of in great detail all the ways that Europe was holding itself back, and part of it was overregulation areas like AI.

所以他们正试图摆脱这种状况，或至少做出一些姿态。

So so they're trying to reverse out of that or making gestures.

你知道，我们拭目以待吧。

You know, we'll we'll see what happens.

就在这一切发生之际，加州莫名其妙地决定照搬欧盟的AI法案，并试图将其应用于加州，这可能让你觉得完全疯狂——而我会说，没错。

In the middle of all that, California sort of inexplicably decided to basically copycat the EU AI act and try to apply it to California, which might strike you as completely insane to which I would say, yes.

欢迎来到加州。

Welcome to California.

这基本上是萨克拉门托的政治动态变得疯狂的结果。

And, you know, it was this basically this, like, Sacramento political dynamic that kinda got got got crazy.

它本会彻底扼杀加州的人工智能发展。

It would have, you know, completely killed, you know, AI development in California.

不幸的是，我们的州长在最后一刻否决了这项法案。

Unfortunately, our our governor vetoed it at the last minute.

该法案确实已通过两院立法，但他最后时刻否决了。

It did pass both houses legislature that he vetoed at the last minute.

简，正如你所说，这项法案会带来一系列灾难性后果，其中之一就是将下游责任归咎于开源开发者。

It Jen, to your point, it would have done for it would have done a whole bunch of things that were ruinously bad, but one of the things it would have done is it would have assigned downstream liability to open source developers.

我们之前谈过中国的开源项目。

And so, you know, we talked about, you know, the Chinese open source thing.

好的。

Okay.

你看，中国那边有开源项目。

So you got Chinese out there with open source.

现在，美国公司也要推出开源AI了。

Now you're gonna have American companies that have open source AI.

顺便说一句，美国的学者、还有那些在业余时间搞开发的普通人，也会参与开源，而这正是技术传播的关键途径。

And by the way, you're also gonna have American academics and just like independent people in their nights and weekends developing open source, you know, which is a key way that all this technology proliferates.

因此，这项法律会将任何开源项目被滥用的下游责任，归咎于最初的开源开发者。

And and so this this law would have assigned downstream liability to any misuse of open source to the original developer in the open source.

所以，你知道，你是一个独立开发者，或者是个学者，或者是个初创公司。

And so, you know, you're an independent developer or you're an academic or you're a startup.

你开发并发布了一个AI模型。

You develop and release an AI model.

这个AI模型运行良好。

The AI model works fine.

你发布它的那天，一切都很棒。

The day you release it, it's great.

但五年后，它被集成到了核电站中，然后核电站发生了熔毁。

But, like, five years later, it gets built into a nuclear power plant, and then there's a meltdown at the nuclear power plant.

然后有人会说，哦，这是AI的错。

And then somebody says, oh, it's the fault of the AI.

核熔毁或其他任何多年后可能出现的实际问题的法律责任，都会追溯到那个开源开发者身上。

The the the the legal liability for nuclear meltdown or for anything any other practical real world thing that would follow in the out years would then be assigned back to that open source developer.

当然，这完全是荒谬的。

Of course, this is completely insane.

这将彻底扼杀开源。

It would completely kill open source.

这将彻底扼杀从事开源的初创公司。

It would completely kill startups doing open source.

这将彻底扼杀学术研究，整个领域都会受到影响，你知道的。

It would completely kill academic research, like, in its entirety, you know, anything in the field.

所以，你知道，这就是玩火的级别，那些州级政客们对此着迷不已。

And so, you know, that like, that's the level of playing with fire, you know, kind of that these state level politicians have become enamored with.

正如我所说，好消息是联邦政府理解这一点。

Like I said, I think the good news is the feds understand this.

我怀疑这个问题最终会得到解决，但它确实需要被解决，因为作为一个国家，让各州这样自杀式地运作根本说不通。

I suspect that this is gonna get resolved, but it but it does need to get resolved because, you know, just as a country, it just doesn't make any sense to let let the states kind of operate suicidally like this.

所以我们正在做这件事。

And so that that that's what we're doing.

你知道，我们一直在讨论这个问题。

You know, we we talk about this.

我们称这是我们的科技议程。

We call this our little tech agenda.

我们非常关注初创企业创新的自由。

We're extremely focused on on on the freedom of startups to innovate.

我们并不想争论，你知道的，许多其他问题。

We are not trying to argue, you know, many, many other issues.

我们以完全两党合作的方式运作。

We operate in a completely bipartisan fashion.

我们在两党内部都获得了广泛支持。

We have extensive support, you know, on both sides of the aisle and for both sides of the aisle.

因此，这是一次真正两党合作的行动，基于政策，并且我认为与国家的广泛利益高度一致。

So it's it's a truly bipartisan effort, very policy based, and, you know, I think very much aligned with the interest of the country broadly.

因此，这就是我们正在做的。

And so that is what we're doing.

然后我们经常被问到的另一个问题是，实际上在某些情况下，但在很多情况下，员工们会说：好吧。

And then and then the other question we get, we we get actually, know, in some cases, but in a lot of cases, actually, employees is like, okay.

为什么是我们？

Why us?

对吧？

Right?

就像你知道的，对于任何这样的政策问题，总有一个集体行动的问题，也就是所谓的公地悲剧——理论上，每个风险投资公司、每家科技公司都应该对此发声，但现实中并非如此。

Like, you know, you know, with any sort of, you know, policy question like this, there's always this collective action question, which is this like, you know, tragedy of the commons, which is in theory, like everybody, every venture firm, every tech company, whatever should be weighing in on these things in practice.

结果是，大多数公司根本不会参与。

What happens is most of them just simply don't.

因此，最终总得有人站出来为这些事奋斗。

And so at some point, it falls on somebody's shoulders to fight these things.

我和本最终认为，这里的利害关系实在太高了。

And we we we Ben and I just basically concluded that the stakes here were just way too high.

如果你想要成为行业领导者，就必须为自己的命运负责。

You know, if if we're gonna be the industry leader, we just have to take responsibility for our own destiny.

不管好坏，我认为这正是当前身为行业领导者所必须付出的代价。

You know, for better or for worse, I think that's the cost of doing business, for being the leader in the field right now.

在我们离开AI这个话题之前，我想回过头来回答一个提交进来的问题。

Before we get off the topic of AI, I wanna go back to one question that that, was submitted in.

那么，你认为基于使用量或实用性的定价方式，相比按座位收费，是AI领域的正确方式吗？

So do you think usage based or utility is the right way to price in AI compared to seats?

啊，这是个绝佳的问题。

Ah, that is a fantastic question.

这属于我称之为‘万亿美元级问题’中的一项，这个问题的答案将决定数万亿美元的市场价值。

So this is one of these giant This is in my list of what I call the trillion dollar questions, where depending on how this is answered, we'll drive trillions of dollars of market value.

所以，是的。

So, yeah.

基于使用量的定价方式，非常了不起。

So usage based pricing, fairly amazing.

如果你从初创公司或风险投资的角度来看，这实际上发生了非常了不起的变化。

If you think about this from a startup standpoint, from a venture standpoint, it's actually fairly amazing what's happened.

我之所以不在公开场合谈论这个，是因为我不想让它停下来。

And I'm trying I'm not really talking about this in public because I don't really guess I don't want it to stop.

我觉得这实际上非常惊人，这些科技公司，你知道的，这些拥有强大研发能力的大型科技公司，正在构建这些大型模型、这些拥有全新类型智能的大型人工智能模型。

I think it's actually quite amazing, which is you have these technology companies, you know, these big tech companies with these, like, incredible r and d capabilities that are building these big models, these big AI models with this incredible, you know, new new kind of new new new kind of intelligence.

结果发现，他们其实早已身处一场战争之中。

And then it it turns out that they were already in a war.

他们早就卷入了云服务战争。

They were already in the cloud war.

对吧？

Right?

所以他们原本就在争夺云服务市场，比如AWS、Azure和谷歌云，还有其他各种云服务项目。

And so they were already in the war for kinda cloud services, and this is like AWS versus Azure versus Google Cloud, you know, and then all the all these other all these other cloud efforts.

但实际上发生的是，在另一个平行宇宙中，他们本可以将所有这些神奇的AI技术保密并牢牢掌控，只用于自己的业务，或者用它来在更多领域与更多公司竞争。

And so what what what what actually happened was they sort of, like, there's an alternate universe in which they basically just kept all of their magic AI secret and captive and just used it in their own business or used it to just compete with more companies, you know, in more in more categories.

但事实上，他们所做的，如果说‘商品化’这个词太强烈的话，至少他们通过自己的云业务，广泛传播了这些全新的魔法技术——而这个云业务本身就具有惊人的规模和组件，以及提供商之间激烈的竞争和迅速下降的价格。

But instead, what they've done is they basically, you know, if commoditize is too strong a word, but they they have they have proliferated their magic new technologies through their cloud business, which is which is this business that just has these, like, incredible scale, you know, kind of kind of components to it, you know, and sort of this hyper competition between the providers and these, you know, these these prices that that come down very fast.

于是，你拥有了世界上最具魔力的全新技术，而这些公司却通过云服务将其提供出来，让地球上几乎每个人都能轻松点击使用，而且只需支付相对低廉的费用。

And so you've got, like, the most magic new technology in the world, and then it's basically being served up by those companies in in in as a cloud business and made made basically available to everybody in the planet to just click and use and for, like, relatively small amounts of money.

从使用角度来看，这对初创公司来说非常有利，因为你能够轻松起步。

And and then on a on a usage basis, which means and usage is great for startups because you it means you can start easily.

对吧？

Right?

你知道，几乎没有任何固定成本。

You the the the you know, there's very you know, there's basically no fixed cost.

对于开发AI应用的初创公司来说，他们不需要承担巨额固定成本，因为他们可以直接接入OpenAI、Anthropic、谷歌、微软等公司的云服务，按需获取智能资源，随时启动。

For a startup building an AI app, they don't have giant fixed costs because they can just tap into the OpenAI or Anthropic or Google or Microsoft or whatever, you know, cloud, you know, tokens by the drink, you know, intelligence tokens by the drink offering and just get going.

因此，从初创公司的角度来看，这简直太棒了——世界上最具魔力的技术竟然可以按需使用。

And so it's it's kinda this this from this from the startup standpoint, it's like this marvelous thing where, like, the most magical thing in the world is available by the drink.

这简直太惊人了。

You know, it's absolutely amazing.

你知道吗？

I you know?

而且，那个模式，你知道吧？

And and, you know, that model you know?

顺便说一下，这个模式是有效的，这些公司也很满意，它们增长得非常快，并且愉快地报告了巨大的云收入增长，它们对利润率等都很满意。

By the way, that model's working, and those companies are happy, and they're growing really fast, and they're, you know, happily reporting massive cloud revenue growth, you know, they they're happy with the margins and so forth.

所以，我认为总体上这个模式是有效的。

And so, you know, I think generally it's working.

而且这些企业我认为很可能会变得更大。

And those businesses are, I think, likely to get much larger.

因此，我认为总体上这个模式会奏效。

And so I I think, you know, generally, that's gonna work.

但问题是，这并不意味着最优的定价模式，或者所有应用都应该是按使用量计费。

But but to to to the question like that doesn't mean that the optimal pricing models or, for example, all of the applications should be tokens by the drink.

事实上，我认为恰恰相反。

And in fact, very much, I think, not the case.

我们知道，我们花了很多时间在研究上。

You know, we spend a lot of time working.

实际上，我们公司有专门的定价专家。

We actually have, you know, dedicated, you know, experts on on on pricing in our firm.

我们花大量时间与客户一起研究定价，因为这确实是一门许多公司不够重视的神奇艺术与科学。

We spend a lot of time with our companies working on pricing because it's, you know, it's really this magical art and science that that a lot of companies don't take don't take seriously enough.

因此，我们花大量时间与客户探讨这个问题。

So we spend a lot of time with our companies on this.

当然，定价的一个核心原则是尽量不要按成本定价。

And, course, you know, a core principle of pricing is you don't wanna price by cost.

如果可能的话，你应该按价值定价。

If you can avoid it, you wanna price by value.

对吧？

Right?

比如，你希望定价能反映你所创造的业务价值的一定比例，尤其是在向企业销售时，你希望按你带来的业务价值的百分比来定价。

Like, you wanna price you have a price where you're getting a percentage of the business value, of, you know, especially when you're selling two businesses, you wanna price as a percentage of the business value that you're getting.

因此，确实有一些人工智能初创公司对某些服务采用按使用量计费的模式，但也有许多其他公司正在探索其他定价模式。

And so so you do have some AI startups that are that are pricing by the drink for certain things that they're doing, but you have many others that are exploring other pricing models.

有些公司只是复制了SaaS的定价模式，但也有其他公司正在探索新的定价模型，例如：如果AI能真正胜任程序员的工作，或者能替代医生、护士、放射科医师、律师或律师助理的工作，对吧？

You know, some that are just like replications of SaaS pricing models, but you also have other companies who are exploring pricing models, for example, of, well, if the AI can actually do the job of a coder or the AI could do the job of a doctor or a nurse or a radiologist or a lawyer or a paralegal, right?

或者老师，基本上，你是根据价值来定价，能否获得原本由人来完成的工作所产生的价值的一定比例？

Or whatever, or a teacher, basically, you price by value and can you get a percentage of the value of what otherwise would have been literally a person?

你知道吗？

You know?

或者，顺便说一下，等价地，你能根据边际生产力来定价吗？

Or or by the way, equivalently, can you price by marginal productivity?

所以，如果你能通过给医生提供AI，使他们变得高效得多，你能否将价格设定为生产力提升的一定比例，也就是人类与AI之间这种协同关系所带来的提升？

So if you can take a a human doctor and make them much more productive because you give them AI, you know, can you price as a percentage of kind of the productivity uplift, you know, from the from from the augment you know, the comb the symbiotic relationship between the the human being and and the AI.

因此，我认为在初创企业领域，正发生着大量关于这些定价模式的实验。

And so I I think what we see in startup land is like a lot of experimentation happening on on these pricing models.

而且我认为，这真的非常健康。

And I and I and I think, again, I I think that's, like, super healthy.

我刚才那番话就是关于这个的。

I I you know, it's was in this little speech on this.

高价其实被严重低估了。

It's like high prices are really underappreciated.

高价常常是客户所青睐的。

High prices are often a favorite of the customer.

这其实真的很有趣。

It's actually really funny.

很多天真的人对定价的看法是，价格越低对客户越有利。

A lot of like, the naive view on pricing is the lower the price, the better is for the customer.

更明智的看待方式是，高价往往对客户有利，因为高价意味着供应商能更快地改进产品。

The more sophisticated way of looking at it is higher prices are often good for the customer because a higher price means that the vendor can make the product better faster.

对吧？

Right?

实际上，定价更高、利润率更高的公司能够投入更多资金用于研发，从而真正提升产品品质。

Like you can actually, companies with higher prices, higher margins can actually invest more in R and D, and they can actually make the product better.

大多数购买产品的人并不仅仅追求最低的价格，他们想要的是能出色发挥作用的东西。

And most people who buy things aren't just looking for the cheapest price, they want something that's gonna work really well.

因此，高价时，客户通常不会直接这么说。

And so often high prices, the customer doesn't ever say this.

它永远不会出现在调查中。

It'll never show up in a survey.

但高价实际上可能是给客户的礼物，因为它能让供应商变得更好，让产品更优，最终让客户受益。

But but the high price can actually be a gift to the customer because it can make the vendor better, can make the product better, and ultimately make the customer better off.

因此，我对人工智能创业者愿意进行这些实验的程度感到非常鼓舞。

And so I I'm I'm very encouraged by the degree to which the AI entrepreneurs are willing to run these experiments.

我知道，我们还得看看最终结果如何，但至少到目前为止，我对这个行业对此的态度感到乐观。

And I I know, we'll have to see where it pans out, but at least so far, I feel I feel good about the the, you know, at least the attitude in the industry about it.

太棒了。

Awesome.

实际上，当你在讲述时，我可能有十几个后续问题，但我现在想回到你之前 briefly 提到的一个话题——万亿美元的问题。

I actually as as you were going through it, I had probably 10 more follow-up questions, but I'm actually gonna go back to a topic you had briefly, the trillion dollar questions.

开源还是闭源会胜出？

Will open source or closed source win?

感觉我们在这个争论上已经有所结论了，或者你怎么看这个问题？

Feels like we we've come out on this this debate, or where do you where do you put that?

不。

No.

我认为这仍然未定。

I think this is still open.

我认为这仍然非常开放。

I I think this is still very open.

你知道，那些闭源模型一直在变得更好。

You know, the, like, the the the closed source models keep getting better.

顺便说一下，通常如果你去问问那些在大型实验室从事大型专有模型工作的人员，他们会告诉你，进展仍在以非常快的速度进行。

By the way, generally, if you just take the temperature of the people working at the big labs who work on the big proprietary models, generally what they'll tell you is progress is continuing at a very rapid pace.

网上或市场上时不时会出现一种担忧，即这些模型的能力可能已经达到上限。

There's this periodic concern that kinda shows up online, or in the market, which is like, maybe the capabilities these models are topping out.

在某些领域，人们正在努力，但大型实验室的工作人员会说：不，没有。

And there's certain areas in which people are working, But like, the people working at the big labs are like, oh, no.

我们有大约800个新想法。

We have like 800 new idea.

我们有大量的新点子。

Like, we have tons of new ideas.

我们有很多新的做事方式。

We have tons of new ways of doing things.

我们可能需要找到新的扩展方法，但我们在如何做到这一点上有不少想法。

We might need to find new ways to scale, but like, we have a lot of ideas on how to do that.

我们知道很多让这些东西变得更好的方法，而且我们基本上一直在不断取得新发现。

We know a lot of ways to make these things better, and, you know, we're basically making new discoveries all the time.

所以，总的来说，所有大型实验室的工作人员都相当乐观。

So like I would say, generally, the people working across all the big labs are pretty optimistic.

因此，我认为大型模型在这里将继续迅速进步，总体上也是如此。

And so, think the big models are gonna continue to get better very quickly here, and then overall.

而开源模型也在持续改进。

And then the open source models continue to better.

正如我所说，每隔一个月左右，就会有类似的东西发布，比如这种新东西。

And like I said, you know, every, every, every, I don't know, every month or something, there's like another big release of like something like this, give me a thing.

就是那种感觉，哇，你知道，这太棒了。

Where it's just like, wow, like, you know, that's amazing.

而且，你知道，他们真的把尺寸缩小了，却在这么小的设备上实现了这样的能力。

And, you know, wow, they really like shrunk that down and got that capability on a very small form factor.

所以，情况就是这样。

And so, that's the case.

然后，我想提到的第三点是，开源的另一个巨大好处是，它很容易学习。

And then, you know, maybe just the third kind of thing to bring up is the other really nice benefit of open source is that open source is the thing that's easy to learn from.

对吧？

Right?

所以，如果你是一位想教授人工智能课程的计算机科学教授，或者是一位想学习这门技术的计算机科学学生，或者只是一个在普通公司里想了解这项新技术的普通工程师，又或者只是你家地下室里晚上有个创业点子的普通人。

And so if you're a, you know, computer science if you're a computer science professor who wants to teach a class on on CS on AI, or if you're a computer science student that's trying to learn about it, or if you're just like a normal engineer in a normal company trying to learn this new thing, or it's just somebody in your, you know by the way, somebody in your basement at night with a startup idea.

这些前沿的开源模型的存在太棒了，因为这就是你需要的教育资源。

The existence of these of these state of the art open source models is amazing because that's the education that you need.

实际上，这些开源模型直接展示了如何做所有事情。

Like, actually these open source models actually show you how to do everything.

对吧？

Right?

所以，这正导致关于如何构建AI的知识正在迅速扩散。

And so, like and and that what that's leading to, right, is the proliferation of the knowledge about how to build AI is like expanding very fast.

与那种技术完全被两三家大公司垄断的反事实世界相比。

Again, as compared to a counterfactual world in which it was all basically bottled up in two or three big companies.

因此，开源不仅在传播知识，而且这些知识正在催生大量新人。

And so, you know, the open source thing is also just proliferating knowledge, and then that knowledge is generating a lot of new people.

所以，正如你们今天坐在这里所看到的，AI研究人员极为稀缺。

And so I I know, say as you guys have all seen sitting here today, AI researchers are at an enormous premium.

如今，AI研究人员的薪酬比职业运动员还高。

You know, AI researchers today are getting paid more than professional athletes.

对吧？

Right?

是的，确实如此。

Like, you know, and that's right.

这是供需失衡。

That's a supply demand imbalance.

他们的人数不够分配，但你知道，短缺会催生过剩。

There there aren't enough of them to go around, but, you know, again, shortages create gluts.

世界上有越来越多聪明的人正在快速掌握如何构建这些技术。

The the number of the number of smart people in the world who are coming up to speed very quickly on how to build these things.

我的意思是，世界上一些最优秀的AI人才才22、23、24岁。

I mean, some of the best AI people in the world are like 22, 23, 24.

他们，怎么说呢，按定义来说，入行时间并不长。

They, know, kinda by definition, they haven't been in the field that long.

你知道吗？

You know?

他们不可能一辈子都是专家。

You know, they can't have been experts their whole lives.

对吧？

Right?

所以，他们基本上是在过去四五年里迅速跟上来的。

So, you know, they kinda have to have come up to speed over the course of the last four or five years.

如果他们能做到这一点，那么未来会有更多人做到这一点。

And and if if they if they've been able to do that, then then there's gonna be a lot more in the future that are gonna do that.

因此，这项技术的专业知识正在迅速普及开来。

And so just the the the sort of spread of the level of expertise on this technology is happening now very quickly.

所以，是的。

So I yeah.

我的意思是，我认为它仍然像我说的那样，这仍然是一场竞赛。

I mean, I think it's still like I said, I think it's I think it's still a race.

顺便说一句，长远来看，答案很可能是两者兼有。

And and by the way, you know, like the the long term answer may well just be both.

正如我所说，如果你相信我的金字塔行业结构，那么无论成本多高，最聪明的那个东西都会有一个庞大的市场。

You know, like I said, if if you if you believe my pyramid industry structure, then there will then there will certainly be a large business of whatever is the smartest thing, almost regardless of how of how much it costs.

但同时，也会出现一个巨大的低端模型市场，到处都是小型模型，而这正是我们目前所看到的。

And then there but there will also be this just giant volume market of of smaller models everywhere, which which is what we're also seeing.

嗯。

Yep.

嗯。

Yep.

你当时提出的另一个问题是，现有企业与初创公司谁会胜出？

The another question you had posed at at that point in time was will incumbents versus startups win?

当时，我认为现有企业在应对人工智能方面的情况参差不齐。

And at that point in time, I think there was a mixed bag of where the incumbents were approaching AI.

我认为过去两年里，这种情况已经发生了根本性变化。

I think that's radically changed in the last two years.

而另一方面，初创公司正越来越多地涌现，有些可能正逐渐转变为现有企业。

And then on the counterexample, the the blossoming of startups increasingly now might maybe migrating into the incumbent category.

自从那时以来，它们的规模已经变得如此之大。

Just how big they've been since that time.

你想就这个问题谈谈，对当前世界状况做一下评估吗？

You you wanna take that question and and give your assessment of where where the state of the world is?

嗯。

Yeah.

所以，我的意思是，你看，那些大公司确实很拼命。

So, I mean, look, you know, big companies that are definitely, you know, hard.

谷歌在拼命竞争。

You know, Google's playing hard.

Meta也在拼命竞争。

Meta's playing hard.

亚马逊、微软，还有好多这样的公司，都在非常积极地投入。

Amazon, Microsoft, you know, there's a bunch of these companies that are, you know, that are kind of in in in their, you know, very aggressively.

然后你还有我们所谓的这些新巨头，比如Anthropic和OpenAI。

And then you've got these, you know, what we call the new incumbents, like Anthropic and and and OpenAI.

但你也看到，就在过去两年里，突然冒出了一批全新的公司，几乎瞬间就成了巨头，XAI就是其中之一。

But you also have, like you know, even in the last two years, you've had this birth of all of a sudden, like, brand new companies that are almost instant incumbents, and you you could say XAI is one of those.

顺便说一句，Mistral是我之前提到的欧洲情况中的一个例外。

Mistral by the way, Mistral is the great outlier to my Europe thing from earlier.

比如，Mistral 作为欧洲的、法国的、欧洲大陆的AI代表，表现得非常出色。

Like, Mistral is actually doing very well as sort of the European kind of, you know, French national, European continental, you know, kind of AI champion.

可以说是例外证明了规则。

Sort of the, know, the exception that proves the rule.

但现在有很多这样的公司，表现相当不错，正在成为新的行业巨头。

But, know, there's there's a bunch of these now that are, like, you know, doing quite well and are kinda becoming new incumbents.

当然，还有大量的初创公司。

And then, of course, there's tons of startups.

顺便说一下，还有真正专注于基础模型的初创公司。

By the way, there's and then there's there's actual foundation model startups.

对吧？

Right?

所以我们投资了来自OpenAI的伊利亚·萨茨克弗，让他创办一家新的基础模型公司。

And so, you know, we funded, you know, we funded, Ilya Seskever out of OpenAI to do a new foundation model company.

我们也投资了来自OpenAI的米拉马拉迪。

We funded Miramaradi also out of OpenAI.

我们资助了来自斯坦福的费·李，成立一家世界模型基础模型公司。

We funded, say, Faye Lee out of Stanford to do a world model foundation model company.

所以，你知道，现在出现了很多新的尝试，虽然都还处于早期阶段，但非常有希望快速建立起新的行业巨头。

And so, you know, there there, you know, there's there are new swings all all, you know, all early, but very promising for to kinda build, you know, new incumbents quickly.

所以，这一切都在发生。

And so, you know, that's all happening.

此外，还有人工智能应用公司的巨大爆发。

And then and then, you know, what's and then on top of that, there's just this giant explosion of AI application companies.

对吧？

Right?

因此，有很多基础公司，通常是初创企业，它们将这项技术应用于特定领域，比如法律、医疗、教育、创意或其他任何领域。

And so there there's basic companies that then usually start ups that basically take the technology and then, you know, seal that in a specific domain, whether that's law or medicine or education or, you know, creativity or or or whatever.

但同样，这里令人惊叹的是，事物正以惊人的速度变得越来越复杂。

But, again, here, it's just like it's it's amazing kinda how how sophisticated things are getting very quickly.

我刚才谈了一下应用公司。

So I was just talking about the application companies for a moment.

比如，应用公司的一个经典例子就是 Cursor，它就是一个应用公司。

So, like, an application company, classic examples, like, a cursor is like an application company.

它们从 Anthropic、OpenAI 或 Google 等公司按需购买核心 AI 能力，也就是按 token 计费。

So they take the core AI capability, which they purchase by the drink from, you know, Anthropic or OpenAI or Google, you know, tokens by the drink.

然后它们构建了一个代码编辑器，也就是我们过去所说的集成开发环境（IDE），或者说是某种软件创作系统。

And then they they they build a code, basically, a code editor, what we used to call an IDE, integrated development environment, or basically like a software creation system.

因此，它们在 Anthropic、OpenAI 或其他大型模型之上，构建了一个 AI 编码系统。

So they build like an AI coding system on on top of that, Anthropica or OpenAI or whatever, you know, kind of kind of big models.

感受一下吧。

So feel that.

业内对这些公司的批评是，它们不过是 GPT 搭配者。

And the the the critique of those companies in the industry has been, oh, those are what GPT rappers.

这是一种带有贬义的说法。

It's kind of the pejorative.

其核心观点是，它们实际上并没有做任何能持久创造价值的事情，因为它们所做的一切本质上只是把 AI 展示出来，但这些 AI 并不是它们自己的。

And the idea basically being as, well, they're not actually like they're not actually doing anything that's gonna preserve value because the the actual the the whole point of what they're doing is they're surfacing AI, but it's not their AI.

所展示的AI来自其他人。

The the AI that's being surfaced is from somebody else.

因此，这些本质上是传递型的壳公司，最终不会产生价值。

And so these are kind of these pass pass through shell things that ultimately won't have value.

但实际上发生的情况恰恰相反，领先的AI应用公司，比如Cursor。

It actually turns out what's happening is kind of the opposite of that, which is the the leading, AI application companies like Cursor.

我的意思是，首先，他们发现并不仅仅使用单一的AI模型。

I mean, first of all, what they're discovering is they they're not just using a single AI model.

随着这些产品变得越来越复杂，它们实际上会使用多种不同的模型，这些模型专门针对产品运作的各个具体方面进行定制。

They're actually they actually as these products get more sophisticated, they actually end up using many different kinds of models that are kind of custom tailored to the specific aspects of how these products work.

因此，它们可能一开始使用一个模型，但最终会使用十几个模型。

And so they may start out using one model, but they end up using a dozen models.

随着时间推移，可能会使用五六十甚至上百个不同的模型，以应对产品的不同方面。

And then in the fullness of time, it might 50 or a 100 different models for different aspects of the product, a.

其次，它们最终会构建大量自己的模型。

And then, b, they end up building a lot of their own models.

因此，许多前沿应用公司实际上正在进行逆向整合，自行构建自己的AI模型。

And so they they a lot of these the the leading edge application companies are actually backward integrating and actually building their own AI models.

模型。

Models.

因为它们对自身领域有最深入的理解，所以能够构建出最适合该领域的模型。

Because because they have the deepest understanding of their domain, they're able to build the model that's best suited to that.

顺便说一下，借助AI开源技术，它们也能采用并运行开源模型。

And and then by the way, also AI open source, they're also able to pick up and run on open source models.

因此，如果它们不喜欢从云服务提供商那里按需购买智能的商业模式，就可以选择这些开源模型并自行实现——这些公司也在这么做。

And so if they don't like the economics of buying intelligence by the drink from a cloud service provider, they can pick up one of these open source models and implement it instead, these companies are also doing.

因此，最优秀的AI应用公司实际上是成熟的深度技术公司，正在自行构建自己的AI。

And so the best of the AI application companies, they are actually full fledged deep technology companies actually building their own AI.

所以，我认为，

So that, you know, that's I think

小模型。

Small models.

不。

No.

对吧，马克？

Right, Mark?

但当你思考你刚才描述的大型模型和小型模型时，那会是小型模型吗？

But when you think about god models versus small models as you were describing that, would that be small?

你会把这归类为小型模型吗？

Would you categorize that as a small?

关于它们，我的意思是，我应该让它们自己宣布，你知道，它们在做什么，只要时机合适。

Of them, I mean, we should I will let them I will let them announce, you know, whatever they're doing, whenever it's appropriate.

但其中一些公司现在也在进行大型模型的开发。

But some of them are now also doing big model development.

而且，同样地，这也是过去两年中学到的内容的一部分。

And and again, this this is also part of what this is also part of learning just the last two years.

好吧，举个例子，过去两年里一个非常有趣的深刻教训是：两年前或三年前，我们肯定会说，哇，OpenAI 真的是遥遥领先。

Well, so, like, here's a big learning just from the last two years, which is very interesting, which is two years ago or three years ago, for sure, would have said, wow, OpenAI is like way out ahead.

而且，很可能所有人都无法追上。

And, like, it's probably gonna be impossible for everybody to catch up.

然后呢，好吧。

And then it's like, okay.

但Anthropic追上了，不过你知道，他们是从OpenAI出来的，所以掌握了所有秘密，等等。

Well, Anthropic caught up and so but, know, they came out of OpenAI, so they had all the secrets, know, whatever.

因此知道该如何做。

And so knew how to do it.

所以，好吧，他们追上了。

And so, okay, they caught up.

但肯定没人能在他们之后追上了。

But surely nobody can catch up after them.

就在那之后不久，又有一大批公司迅速赶了上来。

And then very quickly after that, there were a raft of other companies that caught up very fast.

XAI可能是最好的例子，你知道，XAI是埃隆的公司，XAI是公司名称，Brock是它的消费产品版本。

And and XAI is maybe the best example of that, which is like, you know, XAI, you know, Elon's company, XAI is the company name, Brock is the consumer product version of it.

XAI在不到十二个月的时间里，从零开始迅速追上了OpenAI和Anthropic的顶尖水平。

XAI basically caught up to state of the art OpenAI anthropic level in less than twelve months from a standing start.

而且，这再次表明，任何一家现有巨头都不可能长期垄断市场，如果别人能这么快就追上来的话。

And again, that kind of argues against any kind of permanent lead by any one incumbent that's just gonna basically be able to lock the entire market down If you can catch up like that.

正如我们之前讨论的，中国部分的发展是过去一年才出现的新情况。

And then as we've discussed, the China part is all new in the last year.

我认为DeepSeek的突破发生在今年一月或二月。

The DeepSeek moment, I think was in January or February this year.

所以还不到十二个月前。

So less than twelve months ago.

而现在，已经有四家中国公司实际上追上了领先水平。

And so and now you've got like four Chinese companies that have effectively caught up.

所以，好吧，事情就是这样。

And so, you know, so it's like, alright.

我的意思是，这些问题都是涉及万亿美元级别的，没有确切答案，但真的让人惊叹。

I mean, again, this is these are these are trillion dollar questions, not answers, but it's just like, wow.

好的。

Okay.

就像这样，一旦有人证明了这种能力，其他人即使资源少得多，似乎也不难赶上。

Like, it's it's one of these things where once somebody proves that capable, it seems to not be that hard for other people to be able to catch up, even people with far less resources.

所以，我不知道这会带来什么影响。

And so, you know, I don't know what that does.

也许这会让你对大型玩家的长期经济模式持更怀疑的态度。

Maybe it makes you slightly more skeptical in the long run economics of of the big players.

另一方面，也许这会让你对初创企业生态系统更加乐观。

On the other hand, maybe it makes you like more bullish about the startup ecosystem.

它无疑应该让你对初创应用公司更加乐观，因为它们能够做出有趣的东西，这正是我们对此如此兴奋的原因。

It certainly should make you more bullish about startup application companies, right, being able to do interesting things, which is why we're so excited about that.

它应该会让你对中国的前景感到更兴奋一些。

You know, it should make you probably, you know, a bit more excited about about certainly about China.

是的。

Yeah.

另一方面，中国竞争对手给美国体系带来的压力，促使美国不要自我搞砸，这非常积极，因此可能会让你对美国稍微更乐观一些。

On the other hand, the Chinese competition putting pressure on the American system to not screw itself up is very positive, so it should probably make you a little bit more bullish on The US.

所以，是的。

And so I yeah.

我认为，这些，嗯。

I think, you know, the these are yeah.

这些，嗯。

These are yeah.

这些都是动态变化，我认为我们还需要更多时间来等待确切的答案。

These are our live dynamics, and I I think we still need more time to pass before we know the exact answer.

我应该说，有时候我说这些都是开放性问题时，会让人感到恐慌。

I should say this, I don't know, sometimes they freak people out when I say these are open questions.

当一家公司面临根本性的战略或经济问题时，这通常是个大问题。

When a company is confronted with fundamentally open strategic or economic questions, it's often a big problem.

因为公司需要有明确的战略，而且这个战略必须非常具体。

Because a company needs to have a strategy and the strategy needs to be very specific.

公司必须做出非常具体、切实的决策，决定将资金和人力投入到哪些领域。

And a company has to make like very specific concrete choices about where it like deploys investment dollars and personnel.

而且战略必须逻辑清晰、前后一致，否则公司就会陷入混乱。

And like the strategy has to be like logical and coherent, or the company kind of collapses into chaos.

所以公司必须回答这些问题。

And so like companies like need to answer these questions.

如果他们答错了，就会陷入极大的困境。

And if they get the answers wrong, they're really in trouble.

风险投资领域，我们也有自己的问题，但我们的巨大优势在于，我们不必非得选择一种策略，而是可以同时押注多种策略。

Venture, we have our issues in venture, but a huge advantage that we have is we don't have to we we can bet on multiple strategies at the same time.

对吧？

Right?

而且我们确实在这么做。

And and we are doing this.

因此，我们同时押注大模型、小模型、专有模型和开源模型。

So we are betting on big models and small models and proprietary models and open source models.

对吧？

Right?

还有基础模型和应用。

And and and, you know, and foundation models and applications.

对吧？

Right?

还有消费端和企业端。

And consumer and enterprise.

所以，这种投资组合策略的本质就是，我们正在积极投资于每一个我们认为有可行前景的策略，即使这些策略彼此之间存在矛盾。

And so the the portfolio approach, the nature of it is, like, we we are aggressively basically, we we are aggressively investing behind every strategy that we've identified that we think has a plausible chance of working even when that those even when that's contradictory to another strategy that we're investing in.

而且世界本来就很复杂，很可能很多方向都会成功。

And and one is just like the world's messy and probably a bunch of things are gonna work.

所以，很多问题并没有非黑即白的明确答案。

And so, like, there's not gonna be clean yes or no answers to a bunch of this.

我认为，这些问题的很多答案，其实都是‘兼而有之’的答案。

Like, a lot a lot of the answers to this, I think, are just gonna be and answers.

但另一方面，如果这些策略中的一个不奏效，你知道，我们并不是在刻意对冲，但我们的投资组合中会包含替代策略的布局。

But the other is, like, if one of these strategies doesn't work, like, you know, we're not we're not trying to hedge per se, but, you know, we're gonna have representation in the portfolio of the alternate strategy.

所以我们会有多种获胜的方式。

And and so we're gonna have multiple ways to win.

所以，总之，这就是我们的目标。

So anyway, that's that's the goal.

这就是为什么我们采取当前这种做法的理论依据。

That's the theory of why we are, you know, kind of taking the approach in the space that we're taking.

因此，当我提到这些重大的开放性问题时，我会笑得那么开心，因为我认为这实际上对我们有利。

And and that's why I have a big smile on my face when I say that there are these big open questions because I think that actually works to our advantage.

这正好引出了16个问题，因为我们已经收到一些提问了，还有一些是提前提交的。

It's a good segue to a 16 z questions because we we've gotten a few in so far, and and we have few that were were sent in ahead as well.

所以我会从一个广泛的话题开始。

So I'll start one with a with a broad topic.

你和本在哪些事情上意见不同但仍然决定共同推进？

What is something you and Ben disagree and commit on?

不同意但要执行。

Disagree and commit.

你知道，我们达成一致了。

You know, we agree.

我的意思是，我和本——本来想说，我们就像一对老夫妻，总是争吵，但我们一直在一起。

I mean, we as Ben and I was gonna say, you know, we're an old married couple, we argue argue argue but we've been We're the girl

男人的父亲。

mans's dad.

女孩的长久父亲。

The girl mans's long dad.

是的。

Yes.

是的。

Yes.

是的。

Yes.

是的。

Yes.

火早就熄灭了。

The fire has long since gone out.

但是，是的。

But, yes.

是的。

Yes.

我们在公园里总是争吵。

We're in the park squabbling all the time.

是啊，我的意思是，我们对每件事都争论。

Yeah, mean, so look, we debate everything.

我们为每件事争吵。

We argue about everything.

尽管如此，让我们的合作关系得以维持的一点是，我们往往能得出相同的结论。

That said, one of the things that's made our partnership work is like, do tend to come to the same conclusion.

我们彼此都愿意被对方说服。

Like each of us is open to being persuaded by the other one.

所以我们大多数时候都会得出相同的结论。

So we end coming to the same conclusion most of the time.

所以我要说，就像我今天坐在这里，实际上没有任何问题让我觉得：‘我简直不敢相信我居然要忍受他做的这种疯狂事，我强烈反对，却还得被迫支持’，反过来也一样。

So I would say there aren't like are, as I said, specifically sitting here today, there are like zero issues where I'm sitting here and I'm like, I can't believe I just I can't believe I'm putting up with this crazy thing on his part that he's doing that I really disagree with, but I feel like I have to commit to, or I don't think vice versa.

所以我们没有这类问题。

And so we don't have any of those.

老实说，我和他讨论的最大问题——顺便说一句，这并不是我们正在做的最重要的事，但既然有人问了，我就提一下。

Quite honestly, biggest thing, I gotta say, biggest that he and I discuss, by the way, this is not the most important thing we're doing, but it is a topic, since somebody asked the question.

我和他讨论的最大问题，说实话，我也不知道。

The biggest thing he and I discuss where I I don't know.

也许我总是怀疑自己，或者从不确定自己该站在哪一边，而我和他经常讨论的就是公司对外的公众形象。

Maybe I'm always like second guessing myself or I never quite know where I should come out on it, that he and I talked about a lot is just like basically the public footprint of the company.

比如我们在世界上的存在感，包括公开声明、争议，以及我们如何发声和表达对各种事情的看法。

So like our presence in the world in terms of like public statements, controversy, how we vocalize and express our views on things.

我想说的是，这里存在一种关注，一种真实且可能显而易见但极其重要的张力。

And I would just say there, there's attention, there's a real, maybe obvious, but like a very important tension.

一般来说，我们越公开、越直言不讳、越具争议性，业务就越兴旺，因为创业者们非常喜欢这一点。

Generally speaking, the more out there we are and the more outspoken we are and the more controversial we are, the better the business in the sense of the entrepreneurs love it.

创始人明确希望与我们合作。

The founders want to work with this is very clear at this point.

创始人希望与那些勇敢、有争议、敢于表达鲜明立场并清晰阐述观点的人合作。

The founders want to work with people who basically are brave and controversial and take controversial stands and articulate things clearly.

他们有诸多原因如此希望。

And they and they want that for a bunch of reasons.

一是因为这展现了勇气，而他们非常欣赏这一点；二是因为这在他们与我们见面之前，就向他们展示了我们的本质。

One is because it's a demonstration of courage, which they appreciate, but the other is because it it it it teaches them who we are before they even meet us.

这一点已被证明是一种巨大的竞争优势。

And and and that has just proven to be just like this incredible competitive advantage.

长期有限合伙人会明白：这就是为什么我们从一开始就采取了非常积极的营销策略。

Long term LPs will know like this is why we started with a very active marketing strategy since the very beginning.

而且这完全奏效了。

And like it completely worked.

整个事情就是，如果我们能够传达我们的观点，并且能够非常清晰地表达我们的信念，即使这意味着引发争议。

Like the whole thing was if we're able to broadcast our message and we're able to basically be very clear in what we believe, even to the point where it's controversial.

世界上最好的创始人会在他们踏进门槛之前就理解我们。

Like, the best founders in the world are gonna understand us before they even walk in the door.

对吧？

Right?

他们甚至在见到我们之前就了解我们，而其他风险投资机构至少在当时基本上都是保持沉默，创始人根本不知道这些人是谁、他们相信什么。

And they're gonna they're gonna know us even before they've met us as opposed to everybody else in venture, at least at the time that was basically just like keeping everything quiet, where they do you know, the founder just has no idea who these people are and what they believe.

所以，这真的非常有效。

And so that that, like, worked incredibly well.

它至今仍然非常有效。

It continues to work incredibly well.

顺便说一句，这在行业内普遍适用。

It's by the way, it's, you know, it it it's generally true across the industry.

这通常是普遍情况。

It's it's it's like generally the case.

另一方面，公开露面并在多个方面具有争议性也会带来外部影响。

On the other hand, there are externalities to being publicly visible and to being controversial on many fronts.

我想说的是，我们正在努力在这两者之间找到平衡。

I would say this, much we're trying very hard to thread this needle.

所以，我们并没有退缩，仍然坚持做一家积极主动对外发声的公司。

So like, we're not backing off of generally being a a company that does a lot of outbound.

我们知道，埃里克·索恩伯格和他的团队——我们过去曾向你们提到过的这个团队——已经全面启动了。

We know, we've Eric Thornburg and the team that he's built, know, that we've talked to you guys about in the past, you know, is I is already off to the races.

我们会进一步加大投入，致力于成为引领者，清晰阐述那些重要的技术和商业议题。

You know, we're we're gonna you know, we're tripling down on the idea of basically being the leaders and articulating the tech and business issues that matter.

当然，关键在于人们必须能够理解，而这已经被证明非常有效。

You know, the the, you know, the issue is for sure that people need to be able to understand, and and that's proven to be very effective.

顺便说一句，我们相当一部分传播工作实际上是针对华盛顿的，因为如果你是华盛顿的政策制定者，坐在三千英里之外，而你获取信息的唯一来源是那些敌视硅谷的东海岸报纸，那可就糟了。

By the way, a fair amount of our comms are actually aimed at Washington because, again, it's like if you're a policymaker in Washington and you're sitting there 3,000 miles away and your entire information source is like East Coast newspapers that hate Silicon Valley, like that's bad.

所以，你知道，我们传播技术观点的能力，我们经常在华盛顿遇到的人会说，没错。

And so, you know, our ability to like broadcast, you know, inform points of view on technology, we just we meet people in DC all the time who say, yeah.

我知道，我关于这个话题的大部分知识都是从你们这里学到的，因为我听你们的播客。

I you know, most of what I know about this topic, learned from you guys because I listened to podcast.

我读你们的文章。

I read the articles.

我观看你们的YouTube频道。

I watched the YouTube channel.

所以，我们会继续这样做。

And so, you know, we're we're gonna continue to do that.

总的来说，我们在这件事上一直处于积极主动的地位。

And so we, you know, over over over overall, we have a you know, we're we're kind of on our front foot on that stuff.

但他和我确实会时不时地讨论，到底应该触碰多少个敏感话题，以及频率如何。

But, he he and I do he and I do go back and forth a bit on exactly how, yeah, how many third rail topics should we touch and and how frequently.

我想说，我们正在努力适度控制这一点。

And I I would say we're we're we we are trying to we are trying to moderate that.

正如伊丽莎白·泰勒所说，只要我把我们的名字拼对了，在大多数情况下通常都有效，尤其是在科技领域。

As Elizabeth Taylor said, as long as I spell our name right, it's oftentimes could be good in most scenarios, particularly when it comes to level tech.

双e双s。

Double e double s.

而且，我认为这个问题背后还隐含着你和本之间的关系，如今这段关系已经持续了三十多年。

And also, I think embedded in that question is probably some degree of of the relationship that you and Ben have, which is now going on thirty plus years at this point.

以至于马克已经成了代表你们两人的一个人。

So much so that that Mark has become one person representing both.

有些人把马克称为安德里森·霍罗威茨。

Some people refer to Mark as Andreessen Horowitz.

不。

No.

马克被搞混了，被合并成了一个人。

Lost the Mark, have combined just into one person.

是的。

Yes.