The State of Startups in 2026

Frankly, some of the most powerful and badass founders that we've been seeing lately, there might be in their late 30s, 40s, even 50s. I mean, there's a sort of resurgence of the experienced founder. A lot of people seem to say that they want to be uh YC for solo founders, but it turns out YC is the YC for solo founders. What a weird moment we are in history where you wake up in the morning, you like wire up a new

坦率地说，我们最近见到的一些最强大的创始人，可能已经年近30岁、40岁，甚至50岁。经验丰富的创始人正以某种方式重新崛起。很多人似乎都表示，希望成为面向单人创始人的 YC，但事实证明，YC 本身就是面向单人创始人的 YC。我们正处于一个奇妙的历史时刻：早上醒来，接入一个新模型，那些哪怕在

model and then these things that even a month ago you're just like why isn't it working? It just starts working.

一个月前还会让人疑惑“为什么它运行不起来”的东西，突然就能运行了。

Welcome back to another episode of the light cone. At YC, we work with thousands of founders per year, which means we start to see things before they're obvious. So, we wanted to share some of that with you today. What's the state of the art and what's coming next?

欢迎回来收看新一期的光锥。在 YC，我们与数千位创始人合作，因此能够在一些事情变得显而易见之前，提前看到端倪。我们想分享其中的一些观察：当前的前沿水平如何，接下来又会发生什么？

What should you, the builder, know?

作为产品构建者，你应该了解哪些事情？

Let's get started. Diana, you have a few things to share with us. So we did a bit of an analysis for all the companies we accepted in the last 18 12 months and we have some pretty shocking stats to share with everyone. So one of the big ones is the number of heart tech companies that are in the bat. It has gone from 8% to 20%. There's a lot of uh underlying reasons why that has happened. We we will go deeper into that. The other one

我们开始吧。Diana 有几件事想与我们分享。我们对过去18 12个月里录取的所有公司进行了一些分析，得到了一些非常惊人的数据。其中一项重要数据是，每期硬科技公司的占比已经从8%增长到20%。这背后有很多深层原因，我们稍后会深入探讨。另一项数据

is uh the rate of growth of companies and what YC does to the companies has accelerated. So the median YC company when it gets accepted is at zero in revenue is pre- revenue pre-product and by the end of the batch in the past companies would get to about 8K median revenue and now the companies in median are getting to 20,000 monthly revenue as opposed to 8K. So those are the top two that we can dive

涉及公司的增长速度，以及 YC 对公司发展的推动作用，两者都在加快。一般来说，YC 公司刚被录取时营收为零，也没有产品；过去到了每期结束时，这些公司的营收中位数约为8K，而现在，公司的月营收中位数已经达到20,000，而非8K。这是最值得我们深入

deeper into.

探讨的两项数据。

Yeah. Let's dig into hard tech first.

我们先深入讨论硬科技。

Like what are these hard tech companies and what's what's driving this things that actually touch atoms and not just bits.

这些硬科技公司具体在做什么？又是什么在推动这一趋势？这里所说的是那些真正涉及原子，而不只是涉及比特的事物。

Yeah. And I I I think you have the the the category breakdown of the heart tech companies, right Diana?

Diana，你还整理了硬科技公司的类别明细。

Yeah. So specifically robotics has been a big one. It has gone from 1% of the batch to about six 7% of the batch.

具体而言，机器人技术一直是一大热门，其占比已经从这一期的1%增长到约6%至7%。

Industrial manufacturing building things back in the US has been a huge trend. It has it has gone from about 4% to 10% of the batch. The other one is defense is a big one. We all have been working with a lot of uh defense startup. It has gone from about 1.5% to about 5% of the batch. The [snorts] other big one is there's this uh compute need that the world is getting into with AI. So there's a lot of companies building the

工业制造，也就是把制造业带回美国，已经成为一大趋势，其占比已从约4%增长到这一期的10%。另一个热门领域是国防。我们一直在与许多国防初创公司合作，这类公司的占比已从约1.5%增长到这一期的约5%。另一个热门领域源于 AI 的发展：全球正产生大量算力需求。因此，许多公司都在构建

semiconductor stack or photonix. It has gone from about 1% of the batch from a year ago to about close to 4% of the batch. And the other one even below the stack of compute is power. So there's a lot of power infrastructure as well has gone from also 1% to about close to 3% of the batch. So all these numbers across the physical atom stacks have somewhere triple or quintupled. Yeah, this is the uh age of the machine, I

半导体技术栈或光子技术。这类公司的占比已从一年前当期的约1%增长到如今这一期的接近4%。另一个领域甚至位于算力技术栈的更底层，那就是电力。因此，电力基础设施公司也有很多，其占比同样从1%增长到接近3%。在横跨实体原子技术栈的所有领域中，这些数字几乎都增长到了原来的三倍或五倍。现在正处于机器时代。

think. And that's I mean, as the world goes, you know, our our motto um the t-shirt says, "Make something people want." And people sure do want those things right now.

这也顺应了世界的发展。我们的口号印在 T 恤上：“做人们想要的东西。”现在，人们确实非常需要这些东西。

And the other interesting factor about all these companies that are going deep into Adams is that we've been funding more technical founders and with more expertise than ever. Right Jared? We have this fun stat about the current summer batch with in the current summer batch, one in six of the founders actually has a PhD. It's way more than that's been historically and it's because yeah, if you're doing,

这些深入原子领域的公司还有一个有趣之处：我们资助的技术型创始人比以往更多，他们的专业能力也比以往更强。Jared，关于当前的夏季期，我们有一项很有意思的统计数据：当前夏季期每六位创始人中，就有一位拥有博士学位。这个比例远高于历史水平。原因在于，如果你从事

you know, something with like silicon photonics, you're probably going to need a pretty strong research background in that. And so we've been funding a lot more of those founders and those founders, I think, have disproportionately been doing like especially well. I think AGI compounds this in a really fascinating and awesome way in that like you might think in the past you actually like hard techch was

硅光子技术之类的工作，很可能需要具备相当扎实的相关研究背景。因此，我们资助了更多这样的创始人，而且这些创始人取得亮眼成绩的比例尤其高。通用人工智能又以一种非常有趣且卓有成效的方式，进一步放大了这一点。过去，硬科技之所以困难，

hard because you had supply chains you had uh an incredible software component of often uh I think Palmer Lucky talked about this a lot when it came to and it's like having codegen means that suddenly even all the things that they do at Anderil uh can happen much much faster right even three or four years ago you would talk about software engineering and like the top tier software engineers as one of one of the

是因为创始人必须处理供应链，同时往往还需要非常出色的软件组件。我记得帕尔默·拉基多次谈到这个问题：对 Anduril 而言，代码生成的出现，意味着他们在 Anduril 开展的所有工作突然都能快得多。即使在三四年前，谈到软件工程和最顶尖的软件工程师时，人们还会将其视为

limiting reagents to being able to do really really top tier full stack uh hardware and that's less and less true.

实现真正顶尖的全栈硬件的关键制约因素之一，但现在，这种情况正变得越来越少。

I mean you still need one or two of them or you need like a small team but you don't need to hire a thousand great engineers versus Google or Meta or whoever else and that really changes the economics.

公司仍然需要一两位这样的人，或者一支小型团队，但不再需要招聘一千名优秀工程师去与 Google、Meta 或其他公司抗衡。这确实改变了其中的经济账。

I mean that's the that's the true bullcase for heart. It's that it's not just that people are shying away from funding software businesses, but it's actually that the super smart models that we have now are actually accelerating scientific research and making it possible for startups to have bigger research breakthroughs earlier and that therefore these deep tech companies will actually work better. I

这才是真正看好硬科技的逻辑。原因并不只是人们开始避开对软件企业的投资，而是我们现在拥有的超级智能模型正在加速科学研究，并让初创公司能够更早取得更大的研究突破。因此，这些深科技公司实际上会发展得更好。我

think the other factor is um there's basically three macro trends that are also driving all this all this growth on atoms and is seeing huge companies like SpaceX have such a successful IPO has created a generation of founders wanting to build in space. There lots of these companies that are building across the whole stack. So there's been companies uh in in the current batch in summer 26.

认为还有三大宏观趋势推动着原子领域的全面增长。其中之一，是 SpaceX 这样的巨头如此成功地完成首次公开募股，由此催生了一代希望在太空领域创业的创始人。许多这样的公司正在构建整个技术栈。现在这一期，也就是26 年夏季这一期，也有一些这样的公司。

This is company that we work with called Exosat that's trying to build basically a sovereign Starlink solution. There's other company that I work with uh in winter 26 called Beyond Reach Labs that's building solar panels for satellites in space. If you imagine companies like StarCloud wanting to have all these data center in space, they will need to have power. So this is a obvious solution. Now the other macro

其中一家与我们合作的公司叫Exosat，基本上是在尝试打造主权版 Starlink 解决方案。另一家与我合作的公司来自26 年冬季这一期，叫 Beyond Reach Labs，正在制造供太空卫星使用的太阳能电池板。可以想象，StarCloud 这样的公司希望在太空中部署这些数据中心，就需要电力。因此，这是一个显而易见的解决方案。另一个宏观

trend is um I think we have a generation of current founders right now that have uh grown with the war that's been very front and center and spoken a lot in social media and they want to do something like two two of the companies I'm most excited about that I funded the last couple batches one was Icarus last fall and then nine mothers this last spring and both of them were defense Icorus is doing like a solarp powered U2 spy plane

趋势是，当下有一代创始人在成长过程中，战争始终处于最受关注的中心位置，社交媒体上也存在大量相关讨论，而他们希望采取行动。我最感兴趣的两家公司，都是我在最近几期投资的：一家是去年秋天的 Icarus，另一家是今年春天的 Nine Mothers。它们都从事国防业务。Icarus 正在制造一种太阳能驱动的 U2 侦察机

that gives overwatch and can also do comms, which is actually really important. The future of drone war is being able to actually communicate with your drones on the ground and see what's going on. They've been able to get to seven figure contracts with the new department of war. Um, and then likewise with drone war, um, special forces has been buying, uh, nine mothers anti- drone defense. So, it's basically a

，既可以提供空中监视，也可以用于通信，而这实际上非常重要。未来无人机战争的关键，在于能否真正与地面的无人机通信，并看清那里正在发生什么。他们已经获得了新成立的战争部价值七位数的合同。同样是在无人机战争领域，特种部队一直在采购 Nine Mothers 的反无人机防御系统。它基本上是一座

shotgun turret with CV. Uh, but it's actually almost the only way that you could protect special forces uh, deep behind en enemy lines. I mean these are people who have been training for years and years uh you know in a very elite special force that um you know America doesn't have uh you know thousands of these people you know we we have a very very small set and so protecting them from uh what could be like a commodity

搭载计算机视觉的霰弹枪炮塔，但实际上，这几乎是保护深入敌后的特种部队的唯一办法。这些人员都接受了经年累月的训练，隶属于极其精锐的特种部队。美国并没有数千名这样的人员，实际人数非常少，因此必须保护他们免受那种可能由廉价通用型

drone attack is actually really existential for the department of war.

无人机发起的攻击，对战争部而言确实事关生死存亡。

So just really cool to see this new administration actually uh approach defense in a very different way. like it, you know, classically there was just a lot of frankly capture from the big defense primes that are just doing sort of um cost plus. They think of themselves as consultants and you know to be able to see new startups that can actually take advantage of all of the AI, all of the tech, all of the new ways

因此，看到这个新政府以截然不同的方式对待国防，确实令人振奋。过去，国防领域一直存在大量来自大型国防主承包商的利益俘获。坦率地说，这些承包商基本采用成本加成模式，并将自己视为顾问。如今，新的初创公司正在充分利用 AI、各类技术以及全新的

of building things to build things that frankly uh the defense primes can't build. You know, that's a really powerful mega trend right now. Now the thing about defense is not just those full solutions that get sold to the government. There's also a lot of category of startups that are dual use that they sell both to the private sector and to the government and that have to do with everything down the

制造方式，造出大型国防主承包商无法制造的产品。这确实是当下一个非常强劲的大趋势。国防领域并不只有向政府出售完整解决方案的企业，还有很大一类初创公司从事军民两用产品，同时向私营部门和政府销售，而且业务遍及整个

supply chain. So things like manufacturing things back in America, building custom um I think you had this company Nox Metal. Yeah, they're bringing metal manufacturing back to America. America has like largely lost its metal industry. It got hollowed out over the last few decades and can't build stuff without metal. And so Knox Metals is like rebuilding America's metal supply chain. And they're doing it

供应链。例如，将制造业带回美国，并生产定制产品。我记得你投资了一家名为 Nox Metal 的公司。对，他们正在把金属制造业带回美国。美国基本上已经失去了自己的金属产业。在过去几十年间，这个产业逐渐被掏空，但没有金属就无法制造产品。因此，Nox Metal 相当于是在重建美国的金属供应链。他们正在

in in the heartland of America in in in Detroit where there's all these like empty factories that have basically just been like sitting there.

美国腹地的底特律开展这项工作。当地有许多空置工厂，长期处于闲置状态。

And they're an example of like the trend where the it's not just people are doing hardware companies, but the hardware companies themselves are growing faster than ever. Like I think I saw a PG tweet that Nox Metals is growing like at software growth rates. Do do you understand that? Like kind of what how are they growing so fast?

他们也体现了这样一种趋势：不仅越来越多的人开始创办硬件公司，这些硬件公司的发展速度也比以往任何时候都快。我似乎看到过 PG 发推称，Nox Metal 正以软件公司的速度增长。你如何理解这种现象？他们为什么能增长得这么快？

So one reason is that a lot of their customers are these new defense tech startups that have sprung up and need metal to build all their all their stuff and the existing suppliers that are these sort of like sleepy old businesses mostly run by old people just like can't keep up with the pace that the new defense tech startups want to build at.

其中一个原因是，他们的许多客户都是新兴的国防科技初创公司。这些公司如雨后春笋般涌现，需要金属来制造自己的产品。现有供应商往往是缺乏活力的老企业，大多由年长者经营，根本无法跟上这些新兴国防科技初创公司所要求的研发和制造节奏。

And it reminds me a bit of like when the web 2.0 boom happened early in in the YC days. We would have these new startups, but then they would prefer to buy from new startups that could sort of like move at their speed and like work well with them. Like Stripe, for example, you could use a legacy credit card vendor, but like it's just like way better to work with Stripe. And so I feel like they're sort of becoming that for the

这让我想起 YC 早期 Web 2.0 浪潮兴起时的情形。当时不断有新的初创公司出现，它们也更愿意向其他新兴初创公司采购，因为后者能够跟上它们的节奏，并与它们顺畅合作。以 Stripe 为例，企业当然可以使用传统的信用卡服务商，但与 Stripe 合作要好得多。因此，我认为对于整个

whole defense tech ecosystem. Now the third trend is um basically compute is a very heavy physical atoms process to get all these data centers live very quickly because a lot of the demand for AI that we've been talking has been skyrocketing and there's a very interesting stat where GPUs from Nvidia let's say like a A100 GPU per hour is actually appreciating in cost which is unusual in the past when you get a A100

国防科技生态系统而言，他们正在成为这样的公司。第三个趋势是，计算本质上是一项高度依赖物理原子的过程。为了满足持续飙升的 AI 需求，必须迅速建成并启用这些数据中心。还有一项很有意思的数据：Nvidia 的 GPU，例如一块 A100 GPU，每小时的价格实际上仍在上涨，这种情况很不寻常。过去购买的一块 A100，

by now sort of old.

到现在无论如何也该算旧型号了。

They're pretty old. Yeah.

确实已经相当旧了。

The price is going up because there's just too much demand and not enough supply and compute. So there's a lot of uh startups that are now working on bringing data centers live and you have everything from the construction of the sites to the software to planet to actually doing the data center buildout to interesting solutions that have to do with how to power them and combination of energy, battery. So there's all these

价格之所以上涨，是因为需求极大，而供给和算力都不足。因此，现在有许多初创公司正在努力推动数据中心上线，业务范围涵盖场地建设、规划软件以及数据中心的实际建设。此外，还有一些有意思的解决方案，涉及如何为数据中心供电，以及如何组合使用能源和电池。因此，这一领域存在各种各样的

category of startups and even to the point of uh going down to the core compute silicons. There's a number of uh startups that are building new new silicon for an alternative to to to Nvidia. There's this company that Tyler worked with called Lamb Labs that's building new processors for compute.

初创公司，甚至已经深入到最核心的计算芯片层面。目前，不少初创公司正在开发新芯片，试图成为 Nvidia 的替代方案。Tyler 合作过的一家公司名为 Lamb Labs，他们正在开发新的计算处理器。

There's another one that I'm working on this batch called bot that is trying to build basically new custom hardware architecture that's using turnary representation for models because what it turns out which is a funny trend right now if you look at all the Nvidia architectures from A100s to H100s and now the B B300s each of these generations they're actually going down in floating point precision in terms of

这一期还有一家与我合作的公司，名为 bot。他们正尝试打造一种全新的定制硬件架构，采用三值表示法运行模型。目前出现了一个很有意思的趋势：纵观 Nvidia 的所有架构，从 A100s 到 H100s，再到现在的 B B300s，每一代产品的浮点精度实际上都在下降。也就是说，

uh what they were they're going from FP3 32 168 etc. And it turns out that the LLM architecture doesn't need the full precision floating.

它们从 FP3 32、168 等一路降下来。事实证明，LLM 架构并不需要完整的浮点精度。

FP2 is even somewhat usable.

FP2 甚至也勉强可用。

Right. So this is what bot is trying to do. And I think you have an interesting one that's doing um the interconnect with uh with photonics.

对，这正是 bot 想做的事情。我认为你那边也有一家很有意思的公司，正在使用光子技术实现互连。

Yeah, there's a company called Dipole Labs in the current batch that is replacing the switches that are in data centers which are essentially the like routing systems between different GPUs.

对，这一期有一家名为 Dipole Labs 的公司，正在替换数据中心里的交换机。这些交换机本质上是不同 GPU 之间的路由系统。

If like GPU want a wants to talk to GPU B, they talk to each other through this device. It's called a switch. And these switches right now are electronic. And so there's actually an issue which is like the switches are not keeping up with the GPUs. The the speed of the GPUs keeps going up. And the switches are actually the bottleneck for many data centers in many different workloads. And so Dipole Labs is building the first

例如，当 GPU A 想与 GPU B 通信时，它们会通过这种设备相互传输数据。这种设备称为交换机，目前采用的都是电子式交换机。由此产生了一个问题：交换机的速度跟不上 GPU。GPU 的速度不断提升，交换机却成为许多数据中心处理各种工作负载时的瓶颈。因此，Dipole Labs 正在打造首款

fully optical switch where it's like all photons from GPU A all the way to GPU B.

全光交换机，也就是说，从 GPU A 到 GPU B 的整个过程都使用光子。

And so it will actually be much faster than the electronic switches that we use now. And now the last one that's driving all this move to atoms is this uh aspect where robotics is going to happen. So there's a lot of companies building the stack around that and everything from vertical robotics in specific industries to the infrastructure to deploy robots to uh data selling to the new robotics labs

因此，它实际上会比目前使用的电子交换机快得多。推动这一切转向原子的最后一个因素，是机器人技术即将真正落地。现在，许多公司都在围绕机器人构建完整的技术栈，涵盖面向特定行业的垂直机器人技术、部署机器人的基础设施，以及向新的机器人实验室出售数据

because there's this moment that everyone in in the industry is feeling that we're going to get to the ChatGPT moment. just not quite there yet. And I think we're figuring it out and new a scaling law around it. So there's a lot of that and we had Quan here uh a couple episodes ago and we're believers that's that's going to happen. I mean robotics from Pi from Pi, right? Half is AI and half is

。目前正处在这样一个时刻：业内所有人都认为，机器人领域即将迎来 ChatGPT 时刻，只是还差一点。我们正在逐步摸索清楚，并找到一条新的扩展定律。因此，这一领域正在取得大量进展。几期之前，我们还邀请了 Quan。我们相信这件事一定会发生。机器人技术来自 Pi来自 Pi，对吗？其中一半是 AI，另一半是

uh hardware. I was hearing this reading this morning that even Astra is like a big leap forward for for robotics like on I forget the benchmark but there's a benchmark where like Fable was maybe at 10% and um Astra is showing like you can do like 60 to 70% of the tasks.

硬件。我今天早上还看到消息称，就连 Astra 也实现了机器人技术上的一次巨大飞跃。我不记得具体是哪项基准测试，但在其中一项基准测试中，Fable 似乎只能达到 10%，而 Astra 显示可以完成约 60% 到 70% 的任务。

So data to just you know wake up in you know another couple weeks and another breakthrough happens and we're a little bit closer.

因此，每隔几周醒来时，就会发现又出现了新的突破，让我们离目标更近了一步。

It's been really cool for me to see the resurgence of heart because you know YC we've been funding hard companies since 2014. That's really when we [snorts] we started. But it was like pretty hard to get these companies funded before. Like I remember like pre pre this recent resurgence, we would like fund awesome stuff that we were super excited like rockets and planes and chips and data centers and stuff like that. And then

对我而言，看到硬科技重新兴起确实令人振奋，因为 YC 从 2014 年起就在资助硬科技公司。我们正是从那时开始的。但过去这些公司很难获得融资。例如，在最近这轮复兴之前，我们会资助一些非常优秀的项目，并对此感到十分兴奋，包括火箭、飞机、芯片和数据中心等。随后

like VCs would just be like ah we only do B2B SAS. Um [laughter] and it's hard to bootstrap a company like this. So you really do need like downstream investors who can fund lots of who who who can who can fund you know a full a full capital buildout. And so it's cool that like it seems like Silicon Valley which historically like venture was set up to fund hard techch but it like drifted away from it for a

风投往往会说：“我们只投 B2B SaaS。”而这类公司很难依靠自身实现滚动发展，因此确实需要后续投资者投入大量资金，为完整的资本建设提供支持。值得注意的是，回顾硅谷历史，风险投资原本就是为硬科技而设的，但此后有一二十年偏离了这一方向，

decade or two because it was so profitable to just fund SAS companies and so it's cool to have it coming back to its roots.

原因在于投资 SaaS 公司实在太赚钱了。如今看到风险投资回归本源，确实令人振奋。

Yeah. On the point about like the the investors wanting to do hard tech again it does seem like that I've never seen that happen so quickly. I mean it seemed like it happened pretty immediately when like SAS stocks were down earlier this year. Claude code was surging and that just became like the the I mean I feel like even at demo day it literally happened I feel like that happened probably mid the winter batch at the

谈到投资人重新希望投资硬科技这件事，情况似乎确实如此。我从未见过这种转变发生得如此迅速，几乎是在一夜之间完成的。今年早些时候，SaaS 股票下跌，Claude Code 又迅速崛起。到了 Demo Day，这种转变已经明显发生。我认为，它大概始于今年年初冬季那一期的中段，

start of this year and it seemed by even demo day that investors were starting to be a lot more interested in hard tech companies and that's just extrapolated.

甚至到了 Demo Day 时，投资人似乎已经对硬科技公司产生了浓厚得多的兴趣，此后这一趋势更是愈演愈烈。

I mean it is worth knowing though on the other side like since that a bunch of the SAS stocks have actually recovered and are doing better than ever. Like Salesforce is like the prime example of that. I think Snowflake recently had like like two days ago had these like blowout earnings and so it's possible that it all hits.

不过，另一方面也值得注意：自那以后，许多 SaaS 股票其实已经回升，而且表现比以往任何时候都好。Salesforce 就是最典型的例子。我记得 Snowflake 最近似乎就在两天前公布了远超预期的财报，因此这些公司也有可能全都会成功。

Yeah, maybe. I mean that would be the dream [laughter] case. Um I mean I still think we're seeing real stuff though.

或许如此，那当然是最理想的情况。不过，我仍然认为我们确实看到了实质性的变化。

Like it clearly the software that gets built in the future and what's valuable is different. like it just has to be.

很明显，未来构建的软件以及价值判断标准都会有所不同，不可能维持不变。

And so partly it seems like what we're seeing with Salesforce is the classic the system of record argument is actually playing out.

因此，从某种程度上看，我们在 Salesforce 身上看到的，正是经典的“记录系统”论点正在得到印证。

The Moes are intact for now.

目前来看，其护城河依然完好。

Yeah. Like if you have a thing that agents can use um that is actually valuable and if anything you'll just like agents will use software a lot more than humans will and that seems to be driving Salesforce growth. And so kind of takes us back to the other trend that we've talked a little bit about is if you think of agents as your customers and you make things that agents want and your software as something that agents

例如，如果你拥有一种可供智能体使用且确有价值的产品，那么智能体使用软件的频率只会远高于人类，而这似乎正在推动 Salesforce 的增长。这在某种程度上又把我们带回了此前讨论过的另一个趋势：如果把智能体视为客户，打造智能体需要的产品，并让你的软件成为智能体

want to use, then that seems like the right type of software.

想要使用的产品，那么这似乎就是正确的软件类型。

Yeah, Salesforce is super interesting because uh I think they started releasing their own Slack harness, Slack AI harness. And so I think we're right at the beginning of like the next AI harness wars. It's like Codex wants to be it, Cloud Code wants to be it. Um Open Cloud could be it. Hermes, open code. It seems like there are going to be a bunch of them and it's not going to be quite like the browser wars and that

Salesforce 尤其有意思，因为我认为他们已经开始推出自己的 Slack 框架，也就是 Slack AI 框架。我们可能正处于下一场 AI 框架大战的开端。Codex 和 Claude Code 都想成为这样的框架，OpenClaw 也可能成为这样的框架，此外还有 Hermes、OpenCode。未来似乎会涌现大量此类框架。这场竞争不会完全像浏览器大战，因为浏览器大战往往会走向

the browser wars tend toward like one winner, but you know, I guess it's anyone's guess. And then um yeah, Ben off has a pretty big advantage in that you a lot of the most AI pill and companies in the world still use Slack.

一家独大的局面，但目前谁也无法确定最终结果。Benioff拥有一个相当大的优势：全球许多最信奉 AI 的公司仍在使用 Slack。如果框架就在 Slack 之中，

And you know, if the harness is in there and it's your system of record for how people collaborate, then you have like this mega data mode. And then you know SAS can still be as valuable as it's ever been valued if those modes hold.

而 Slack 又是记录人们如何协作的系统，那么它就拥有了一条巨大的数据护城河。只要这些护城河能够守住，SaaS 的价值仍然可以达到过去的估值水平。我记得你大约一周前发过一条

Yeah. I thought you had a really interesting tweet maybe a week or so ago about how the software or system of record companies will have to become like harnesses.

特别有意思的推文，谈到软件或记录系统类公司将不得不转型，成为类似框架的产品。你所指的基本上就是

I mean and that's bas that was about slack I would say. It's like basically if you are a system of record you either will be prayed upon like you'll release an MCP and then maybe like the data you know you lose your moat around the data the data goes elsewhere like becomes very trivial to switch or you kind of have to be a harness you have to be the way [clears throat] people not just read and write but actually do their work

Slack。基本而言，如果你是一套记录系统，就只有两种选择：要么成为别人的猎物，例如发布一个 MCP，随后数据可能流向别处，导致围绕数据构筑的护城河消失，系统切换也变得轻而易举；要么成为一个框架，成为人们不只进行读写、而是真正在其中完成工作的方式，也就是直接在你的记录系统里

inside you know your system of record and get the most value out of it I mean it's a it's a little bit like the model companies like was the um was it RKGI was the benchmark Mark where there was a benchmark where of the RPGI V3.

完成工作，并从中获得最大价值。这有点像模型公司。Mark，当时是不是有一个叫 RKGI 的基准测试？回答是：“RPGI V3。”

Yeah. Where the pre or pre-Astra ChachiBT model didn't do as well, but then they said, "Well, that's just cuz it was plugged into the wrong harness."

对。在 Astra 之前，或者说前 Astra 的 ChachiBT 模型表现并不理想，但后来他们表示：“那只是因为测试框架接错了。”

So, it's like the model plus the harness gets you the output.

因此，模型与测试框架共同决定了输出结果。

Oh, yeah. I mean, uh, with a custom harness, they claim Astra got to n north of 90% on RKGI 3.

对。他们声称，采用定制的测试框架后，Astra 在 RKGI 3 上达到了 90% 以上。

Yes. I think a couple months ago, this was in the low two digits, right? Which is an impressive leap.

我记得就在几个月前，这个数字还只有百分之十几。这是一次非常惊人的飞跃。

And I think you have a very good point around software. It's not that software and SAS is dead what people claim on the internet. It's just that is has transformed. We actually seen this in the batch. The percentage of companies we accepted that do sort of full stack end to end work or a task has gone from just 10% to over 25% of the batch. This has to do with actually doing the job.

我认为你对软件的看法非常准确。软件和 SaaS 并没有像网上一些人所说的那样消亡，而是已经发生了转型。事实上，我们在这一期中已经看到了这一点。获录取的公司中，从事某种全栈式端到端工作或任务的公司占比，已经从仅仅 10% 上升到这一期的 25% 以上。原因就在于，它们确实完成了实际工作。

the agent does the job, not just like a point solution, which old SAS in five, eight years ago was just like a point solution and you needed a you needed someone to operate the SAS software.

智能体会完成工作，而不只是提供一个单点解决方案。五年或八年前，旧式 SaaS 只是一个单点解决方案，需要有人操作 SaaS 软件。

Right now, it just runs by itself and actually in the batch, this is where we're seeing a lot of the growth in revenue. I think I gave that stat of the median uh startup when it gets into YC is at zero in revenue and it has gone from by the end of the batch it was about 8K in MR now it's like about 20K in MR and a lot of these that's a huge jump that's like non-trivially big jump for the median

如今，软件可以自行运行。事实上，这正是我们在这一期中看到大量营收增长的领域。我曾提到过这一数据：一家初创公司刚进入 YC 时，营收中位数为零；到这一期结束时，这一数字过去大约是 8K 的 MR，现在则大约是 20K 的 MR，而且其中很多公司都取得了这样的增长。对于中位数而言，这绝对是一次相当大的跃升，

right the average is even higher and it has to do with doing the full end toend job with for example doing insurance broker actually doing the clinical intake doing the full end to end workflow of I don't know medical billing etc and these are the ones that are growing a lot and I think there's another factor where that's happened I think we talked about this in couple episode ago we right now are about

平均值甚至更高。这与完成完整的端到端工作有关，例如真正开展保险经纪业务、完成临床接诊，或完成医疗账单之类的完整端到端工作流。这些公司的增长速度非常快。我认为背后还有另一个因素，记得我们在前几期节目中讨论过这一点。现在距离

almost a year since agentic coding started to work since opus 4.5 that we're seeing these workflows fully blossom and the result are basically people want their job just be done and are willing to buy software that just gets the job done.

智能体编程真正奏效已经快一年了。从 opus 4.5 开始，我们看到这些工作流全面开花结果。最终，人们希望工作能够直接完成，也愿意为能够直接完成工作的软件付费。

I think when people hear these revenue numbers growing so fast, an easy knock on it is like maybe it's just AI hype and these companies are just like shelling out money for AI products because it's like the cool thing to do.

人们听到这些收入数字增长得如此迅速时，很容易产生质疑：这可能只是 AI 炒作，这些公司只是在大手笔购买 AI 产品，因为这样做看起来很酷。

And to be fair, that's probably some of that. But I think like the bullcase is actually something that we said in an episode like 2 years ago when agents were really just beginning to be a thing where we were like actually the products are just going to be more valuable. Like if they automate the whole job, they will actually just be more valuable than some like system of record that tracks the job but doesn't do the job and

公平地说，可能确实有一部分属于这种情况。但看多的逻辑，其实来自我们大约 2 年前某期节目中讨论过的一件事。当时，智能体刚刚开始成为一种趋势，我们就提出，产品其实会变得更有价值。例如，如果产品能够将整项工作自动化，它的价值确实会高于某种只追踪工作进度、却不实际完成工作的记录系统。

therefore companies will just like pay more money for the for for the product.

因此，公司自然愿意为这种产品支付更高的费用。

I definitely see that in companies that I work with where yeah, they just go to a company and like the value proposition is so great that like large enterprises are willing to write big checks very very early.

我合作的公司中确实存在这种情况。它们找到一家公司，提出的价值主张极具吸引力，以至于大型企业在很早的阶段就愿意开出大额支票。

Yeah. A company that I'm seeing um having this effect is Juicebox, an AI recruiting tool. Like it's been on incredible growth rate for like the last couple of years now, but they started out as I mean I would say it was essentially sort of LLM powered people search. Like the thing that they did was you could type in sort of the spec of the type of person you wanted to hire and it did a really good job of pulling

我看到具有这种效应的一家公司是 Juicebox，这是一款 AI招聘工具。它在过去几年中一直保持着惊人的增长速度。最初，他们所做的本质上是一种由 LLM 驱动的人才搜索：用户输入希望招聘的人才类型要求，它就能非常准确地筛选出

the um good profiles of people that you may want to contact. Um but then you still have to go and contact the people.

用户可能希望联系的优秀人才资料。不过，接下来用户仍然需要自行联系这些人。

And recently they've launched an agent product which is really taking off and the agent like doesn't just search for the people. It like contacts the people.

最近，他们推出了一款发展势头非常强劲的智能体产品。这个智能体不仅能帮助用户搜索人才，还会直接联系这些人才。

It'll be able to schedule the interview, do a bunch of things. That's awesome.

它可以安排面试，还能完成许多其他工作，效果非常出色。

Yeah. and they're seeing that that's going to just on a like per account basis I think is going to double or triple like the revenue they make from a single customer cuz customers want more and more of these agents. I don't think it's fair to say that it's like it's not like it's like automating the job of the recruiter at all. It's just that it's just changing it like it like the recruiters didn't necessarily want to be

他们发现，仅按单个账户计算，这款产品就能让他们从单个客户那里获得的收入翻一番或两番，因为客户希望使用越来越多这样的智能体。这绝不是在将招聘人员的工作完全自动化，而是在改变这份工作，因为招聘人员原本就不一定愿意从事机械式的外联工作。

doing like that sort of wrote reach out to like 500 people anyway. Like the thing that makes the recruiter job I would say like more skilled and interesting is like there's like culture fit. that's just going to be really hard for like an AI to do a phone screen that assesses like how well someone's going to be like a culture fit and um and the human element of it. And so I think they're finding that the recruiters

例如，一次性联系 500 个人。招聘工作中更需要专业能力、也更有意思的部分，其实是判断文化契合度。对 AI 来说，这会非常困难：它很难通过电话初筛判断一个人在文化上有多契合，也很难处理其中涉及的人性因素。因此，他们发现招聘人员其实非常期待

themselves are actually really excited to use the agents because it frees them up to do the work that they feel is like unique and interesting.

使用这些智能体，因为这能让招聘人员腾出时间，去完成他们认为真正独特且有意思的工作。另一个惊人的数据是：

The other shocking stat is that the companies that really accelerate during the batch, they really start taking off.

本期 YC 中真正开始加速的公司，确实很快就起飞了。今年，我们开始看到一件

One of the things that we started experiencing this year that we never experienced in the past is we have companies breaking from zero to seven figures in revenue during the batch. And that is in a span of 3 months.

过去从未见过的事：有些公司能在一期 YC 期间从零做到七位数营收，而这只用了 3 个月。这非常惊人。过去，一家公司要达到

And that's shocking. In the past, that would have taken for a company to get to that about 18 months or more. And they're doing it in that amount of time.

这个水平，大概要花费 18 个月甚至更久。但现在，它们在如此短的时间内就做到了。部分原因在于，它们解决的是真实

Part of it is they're solving real problems. And because of agentic coding, they're actually building products that are a lot more mature as well. And they're able these founders that are super AID run, I don't know, 20 coding agent sessions to get to that product maturity.

问题。此外，得益于智能体编程，它们开发出的产品也成熟得多。这些极度依赖 AI 的创始人能够同时运行大约 20 个编程智能体会话，从而让产品达到那样的成熟度。还有一类

And there's another category of companies that's also been growing super fast recently, which is companies that sell data or RL environments to the labs.

公司最近也增长得非常快，那就是向实验室出售数据或 RL 环境的公司。

This one might be interesting to talk about because a lot of these companies are pretty stealthy. they tend to have a disincentive to talk about how well they're doing instead, you know, compared to most companies that like to talk about how well they're doing. And so I think people out there might not realize how big a category this has become. When YC funded scale back in 2016, this was like a tiny little niche

这个话题很值得讨论，因为这些公司中有许多都相当隐秘。它们往往不太愿意谈论自己取得了多好的成绩，不像大多数公司那样热衷于宣传自身表现。因此，外界可能并未意识到这个品类已经发展到多么庞大的规模。YC 在 2016 年投资 Scale 时，这还是一个非常小众的领域。

thing. It wasn't even a category. There was initially it was basically just scale who was doing it and then Meror began to do it and then like a couple other companies, but the last couple years has become a big category. We pulled the data recently and um just in the last two years, YC has funded more than a dozen companies that are each making more than $10 million a year selling data or RL environments to the

当时，它甚至算不上一个品类。最初基本上只有 Scale 在做，后来 Meror 也开始涉足，再后来又有几家其他公司加入。但在过去几年中，它已经发展成一个大品类。我们最近整理了相关数据：仅在过去两年中，YC 就投资了超过 12 家公司，其中每一家每年都能赚到超过 $10 million，业务正是向实验室出售数据或 RL 环境。

labs and in many cases hundreds of millions of dollars.

而且，很多公司的收入往往达到数亿美元。

It makes hundreds of millions of dollars and these are companies that were just just a couple of years old.

这些公司能够赚到数亿美元，而它们才刚成立短短几年。

That's pretty fast to revenue honestly.

坦率地说，这样的营收增长速度相当快。

Yeah, it's like pretty bananas. Do you want to talk about any of them Gary?

确实快得惊人。Gary，你想谈谈其中的哪几家公司吗？

Uh I mean the big ones I mean I think After Corey and data curve both really really great. I mean there probably too many to name that are honestly like maybe don't even want to be mentioned because well you know once you have something that's working you almost don't want people to know. I think that it's kind of natural to understand this though. I mean data is one of the legs of the scaling law and you know much has

在规模较大的公司中，After Corey 和 data curve 都非常出色。这样的公司可能多到无法一一列举，而且它们甚至可能不愿被提及，因为一旦找到了行之有效的方法，就几乎不想让别人知道。这其实很容易理解。数据是规模定律的支柱之一。人们已经大量讨论了

been made of compute but without the data how are you going to make these models that much better? Um the RL environment thing is interesting. I mean there's a lot there. I mean there's a lot of like pure customization that's happening for specific use cases like you you'll have like RL environments for finance for instance and someone can go very very infinitely deep with that and it's like a little bit of expertise it's

算力，但如果没有数据，又怎么能让这些模型得到如此显著的提升？RL 环境也很有意思，其中包含许多不同内容。例如，大量工作都是针对特定用例进行纯定制化开发的。可能会有专门面向金融领域的 RL 环境，有人可以在这个方向上不断深入钻研。这既需要一定的专业知识，还需要

a bunch of computer science it's some systems work but RL seems to be I mean one of the big engines for how I mean people are maybe bench benchmark maxing a little bit more than they should but uh it costs money to do it and it's seemingly here to stay in terms of how big model companies are going to approach it.

这需要大量计算机科学知识，也需要完成一些系统方面的工作。强化学习似乎是其中一个重要引擎。人们对于刷满基准测试的追求可能有些过度，但这样做成本高昂，而且从大模型公司的应对方式来看，这种做法似乎会长期存在。

Reportedly, the big labs are spending about a billion dollars on this. It's not a very known fact, but there's there's actually a real business to be built around this and a real environment is the current flavor of it and there's things with long-term horizon tasks that are getting built up and I think that is starting to also emerge in robotics. The labs also want to solve the problem of getting AI to work on the physical

据报道，大型实验室正在为此投入大约 10 亿美元。这并不是一个广为人知的事实，但围绕这件事确实可以打造一门真正的生意。目前，真实环境是最流行的方向，一些时间跨度较长的任务也在逐步搭建。我认为，这种趋势也开始在机器人技术领域涌现。这些实验室同样希望解决 AI 如何在物理环境中发挥作用的问题。

world. So they need a lot of the environments in the real world. So things with egocentric data, tea optic task starting to emerge as a big data category where labs are spending eight nine figure deals with these companies.

为了让 AI 在现实世界中发挥作用，它们需要大量现实世界的环境。因此，第一视角数据、遥操作任务之类的内容开始成为重要的数据类别，实验室也在与这些公司签订金额达八位数或九位数的合同。

We had a number of companies in the batch that work on that and been able to close close revenues in that in that space. companies like um in the current bachelor of summer 26 there's practis robotics there's one that I'm working with that has like a network of places across the world where industrial moduction gets done they collect data from that there's this other company that Brad work with called deep reach

我们这一期有好几家公司在从事这方面的业务，而且已经能够在这个领域获得营收。例如，目前 2026 年夏季这一期有 Practis Robotics。还有一家我正在合作的公司，它在全球各地建立了一个专门用于开展工业生产的场所网络，并从中收集数据。另一家公司由 Brad 合作，名为 Deep Reach。

that also has data that local entrepreneurs in across the world do and human archive and winter 26 yeah there have been a bunch of these companies recently right I think like if I were going prognosticate like one of the things going back to the uh you know all systems of record need to be AI harnesses they might also need to start training their own models and that's

它也拥有由当地创业者在世界各地采集的数据还有 2026 年冬季期的 Human Archive，对最近冒出了很多这样的公司对我觉得，如果让我来预测的话，其中一件事还是回到，呃，你知道，所有记录系统都需要成为 AI的载体，它们可能还得开始训练自己的模型，而这正是

where things like river AI or tinker start becoming really interesting out of the box like you can sit there in cloud code or even open I use openclaw to train my own models which is very fun it'll do its own data cleaning and everything but to date like that hasn't been a huge factor but I can see that becoming a much much bigger factor I And when you have proprietary data and you can train I mean the open open weight

River AI 或 Tinker 之类的工具正因此变得非常有意思。在这类框架中，你可以使用云端代码，甚至使用开源工具。我会用 openclaw 训练自己的模型，过程非常有趣。它会自行清洗数据并处理其他一切事务。到目前为止，这还不是一个特别重要的因素，但我认为它会变得重要得多。尤其是在拥有专有数据并能够训练开放权重模型的情况下，这一点更为明显。

models are uh really nearly frontier if you can like sort of special purpose train these things to do um even better than what the frontier can do like that that's going to be really really powerful.

这些模型其实已经非常接近前沿水平。如果能以某种方式针对特定用途训练这些模型，让它们在相应任务上的表现甚至超过前沿模型，效果将非常强大。

I think this is actually going to be even bigger in robotics. I mean this is a hypothesis is not proven yet but robotic foundation models in robotics I think uh have a very different characteristics versus LLMs. LM is the whole thing is you model reality as language and for robotics you model reality in the physical 3D space which has way more degrees of freedom and perhaps in order to get robots to work

我认为，这实际上会在机器人技术领域产生更大的影响。这只是一个尚未得到证实的假设，但与 LLM 相比，机器人技术中的机器人基础模型具有非常不同的特性。LM 的核心是用语言建模现实，而机器人技术是在物理 3D 空间中建模现实。这个空间拥有多得多的自由度，也可能更适合让机器人在特定环境中工作。

in a specific vertical like let's say robots that do operations in data centers. I have this company called Boost Robotic that build robots for data centers like doing the cabling. It is possible for these robots to work. It's better to get a model that's fine-tuned and trained on custom data that just works on that environment because the the thing that's also challenging for robotics, they need to be in real time

例如，机器人可以在数据中心执行操作。我有一家名为 Boost Robotic 的公司，他们为数据中心制造机器人，例如负责布线的机器人。这些机器人能够正常工作。最理想的方式是使用经过微调、以定制数据训练的模型，让它专门在相应环境中工作。机器人技术还有一个棘手之处：机器人需要实时运行。

and respond very quickly to to the stimuli and have an action plan which is different than LMS. LM you can have this this feature where you can just let it go and come back. But for Borax, you can't be because if if I don't know, let's say you connect a cable to the data center and then someone comes in and like knocks a robot out and things could get connected to the wrong plug, let's say.

机器人必须对刺激作出非常快速的响应，还要制定行动方案，这一点与 LM 不同。LM 可以自行运行，用户稍后再回来查看结果，但机器人不行。例如，机器人正在数据中心连接一根线缆时，如果有人走进来把它撞偏，线缆就可能被接到错误的插口上。

Yeah. My understanding is that all the YC companies that are using physical intelligence as models to deploy robotics, they're all fine-tuning the PI models. I don't think any of them are able to use the PI models out of the box. Even though it's a great like starting point, you have to actually fine-tune it for like your specific case like data center cables in order for it to work.

据我了解，所有使用 Physical Intelligence 模型部署机器人的 YC 公司都在微调 PI 模型。我认为，没有任何一家公司能够直接使用 PI 模型。尽管它是一个很好的起点，但仍然必须针对具体应用场景进行微调，例如数据中心线缆连接，才能正常工作。

Mhm. You work with this company uh Ultra, right?

你和 Ultra 这家公司合作过，对吗？

Yeah. You start with the PI model, but then they have like thousands of hours of footage of like putting things in boxes that makes it really good at putting things in boxes. I've heard the argument basically that you know you could look at claude code like claude code can use its mo its uh code transcripts to figure out who the top coders are and you can take that and turn it around and you know basically

对。你可以从 PI 模型起步。此后，他们积累了数千小时将各种物品装进箱子的视频素材，这使模型非常擅长把物品装进箱子。我听过一种说法：以 Claude Code 为例，Claude Code 可以利用自己的代码记录，找出最顶尖的程序员，然后再利用这些数据。

train the next coding model to be even better. If you happen to own TikTok you happen to have all of the data on uh what people watch and click on and what's compelling and you can use that to make much more compelling videos in seed dance. So, you know, that's already been happening. I just, you know, I think that that that trend is going to continue in a fairly spectacular way from here. So, one of the things that

这些数据可以反过来用于把下一个编程模型训练得更加出色。如果你恰好拥有 TikTok，也就掌握了关于人们观看什么、点击什么，以及哪些内容更具吸引力的全部数据。随后，你可以使用这些数据，在 Seed Dance 中制作吸引力强得多的视频。这种情况其实已经发生了。我认为，这一趋势将以相当惊人的方式延续下去。

we've been noticing, I think all of us have, is that frankly some of the most powerful and badass founders that we've been seeing lately, they're might be in their late 30s, 40s, even 50s. I mean, there's a sort of resurgence of the experienced founder. A lot of people seem to say that they want to be uh YC for solo founders, but it turns out YC is the YC for solo founders. Diana, you have a few stats that uh you found

我们所有人最近都注意到，一些最强大、最出色的创始人可能已经接近 40 岁，或处于 40 多岁，甚至 50 多岁。资深创始人正在以某种方式重新崛起。许多人似乎都宣称自己想成为独立创始人的 YC，但事实证明，YC 才是独立创始人的 YC。Diana，你发现了一些令人意外的统计数据。

surprising.

这些统计数据确实出乎意料。

One of the shocking stats from analyzing the septic companies from a year ago, we used to only have about 5% of the companies accepted be solid founders and now we're over 18 19%. Which is a huge This is the highest spike that we've seen almost 1/5if of the batch. So, and it seems like it's going to keep going. You know, before you had you had to have like, you know, so many different skills. You had to be a great hustler.

分析一年前获录取的公司后，我们得出了一个惊人的统计数据：过去录取的公司中，只有大约 5% 由独立创始人创办，如今这一比例已经超过 18%、19%。这是一个巨大的跃升，也是我们见过的最高涨幅，几乎占整期公司的 1/5。这个比例看起来还会继续增长。过去，创始人必须具备多种不同的能力，既要特别善于解决问题，也要能够推动事情落地。

You know, you had to be able to explain and, you know, we would say like they have to be good talkers, right? Like you need someone who can, you know, uh be a hot person. You need someone who can actually come in and convince someone of something. Uh and then if you paired that with someone who is a world-class technologist, that's sort of the combo that is so ideal. Um, and so classically you would need co-founders to do that.

创始人还必须能够把事情讲清楚。按照我们的说法，他们必须非常善于表达，能够成为有号召力的人，真正走进一个房间并说服别人接受某件事。如果再为这样的人搭配一位世界顶尖的技术专家，基本上就是最理想的组合。因此，传统上通常需要联合创始人才能做到这一点。

Like you know any it you didn't necessarily need one but like it would increase your chances by so so much. I feel like a lot of that is like changing to this degree. It's becoming such that like knowing what to prompt and knowing what to build is so much more difficult and valuable than just knowing you know the CTO being able to code the thing. I think what's going on is that we've always actually had um hugely successful

并不是说创业者必须拥有联合创始人，但有联合创始人确实会大幅提高成功概率。我认为，现在很多事情都在发生巨大变化。知道应该输入什么提示词、应该做什么，远比仅仅知道如何让 CTO 把它写出来更困难，也更有价值。事实上，我们一直都有极其成功的独立创始人。

single founders. I think people don't realize this about YC. There's different sort of definitions of it, but for all intents and purposes, our Puver with Instacart, Brian Armstrong with Coinbase, at least when the batch started, were single founders.

我认为，许多人没有意识到 YC 的这一点。虽然对此存在不同的定义方式，但无论采用哪种定义，Instacart 的 Apoorva 和 Coinbase 的 Brian Armstrong 至少在当期项目刚开始时都是独立创始人。

Yeah, Parker Conrad got into YC as a single founder and then I uh interviewed Lakshiny his who ended up being his CTO.

Parker Conrad 也是以独立创始人的身份进入 YC 的。后来，我面试了 Lakshiny，后者最终成为他的 CTO。

the bar for being able to like have the idea, be able to sell it, and be able to build it all by yourself was just really, really high. Um, and that's actually totally doable.

一个人既能想出创意，又能把它卖出去，还能独自把它做出来，门槛非常高。而现在，这些事情已经完全可以由一个人完成。

Yeah, I think that's what's going on like in that case like those three are just like incredibly exceptional people.

我认为，现在发生的正是这种变化。在刚才提到的例子中，那三个人都是极其出众的人。

And so there's like very very few people who are capable of that. And now you can actually like get going and so I think you just don't have to be quite that like exceptional at least on one of those dimensions, the building part to be able to get going. But net net, it's still valuable to have co-founders. It's still a measure of like, you know, if your co-founders are super elite, like that means you're probably

过去，具备这种能力的人少之又少。现在，一个人确实可以先把事情做起来。至少在把产品做出来这个维度上，创始人不必再那么出类拔萃，也能开始创业。但总体而言，拥有联合创始人依然很有价值。这仍然是一项重要的衡量标准：如果你的联合创始人都极其出色，那么你很可能也同样出众。

super elite and it just increases the chance of success by a lot. And in each of those cases, they did bring on co-founders. I think in each of those cases, you just get going and they got traction and they added on co-founders sort of at a certain point. And so maybe like the um the equity ownership is different or maybe the dynamic is just slightly different to the traditional hey like you're you start out and like

这种顶尖程度会大幅提高成功概率。而且在这些案例中，他们后来确实都找到了联合创始人。这些人都是先直接开始创业，取得一些进展之后，再在某个时间点邀请联合创始人加入。因此，他们的股权结构或相处模式可能与传统方式略有不同。传统方式则是双方从一开始就共同创业。

the two of you in a room and uh and you're completely 50/50. I don't know if you want to put stuff in but yeah I don't have the stats but I have definitely seen a greater trend towards that. People adding co-founders later in the company life cycle after the thing has already like gotten off the ground.

两个人待在一个房间里，股权完全五五开。我不知道你是否也想补充几句。——我没有具体数据，但确实看到这种趋势越来越明显。越来越多的人会在公司的生命周期后期，也就是项目真正起步之后，再邀请联合创始人加入。

I think that will be the trend. I think we'll see a like more single founders in the batch which we're already seeing like starting the batch but at least of the things that succeed I still expect that they're going to be adding co-founders um as the company progresses.

我认为这会成为一种趋势。每期开始时，独立创始人的数量会越来越多，我们已经看到了这种情况。不过，至少在最终成功的项目中，我仍然认为，随着公司的发展，这些创始人会陆续邀请联合创始人加入。

Gary, do you also want to talk about the trend towards like more experienced people starting companies?

Gary，你是否也愿意谈谈越来越多经验丰富的人开始创业这一趋势？

So it does seem like uh people who have been around the block a few times are doing much much better. I think of Peter Steinberger as like sort of the canonical example like you know he's uh I believe in his early 40s and he'd been a dev manager. He'd worked on startups before and then you know he sort of uniquely got extremely AI pill with the clankers early but then he just tried a lot of stuff and then he knows what to

情况看起来确实如此。那些已经在这一行业摸爬滚打过几轮的人，如今的表现要好得多。我认为彼得·施泰因贝格就是一个很典型的例子。我记得他四十出头，之前担任过开发经理，也创办过创业公司。后来，他很早就独树一帜，完全相信 AI 和那些clankers。此后，他尝试了很多东西，而且知道应该

build and so that's one thing that I think is actually really encouraging.

做什么。因此，我认为这一点尤其令人振奋。

Like basically if you've been around the block, you know where the you know where the dragons are. You sort of uh have taste and then those people in particular are like unusually powerful right now. Yeah. I mean there just so many uh classic gatekept things that happen. It's like oh you know you have to have a co-founder, you need like a certain set of you know cool investors to be into you. And like now it's just

基本上，如果你已经在这一行业摸爬滚打过，就会知道哪些地方藏着恶龙。你多少会形成自己的品味和判断力，而这类人在当下尤其强大。过去有太多由传统守门人设置的门槛。例如，你必须有联合创始人，还必须得到某一群厉害投资人的青睐。如今，这些条件正变得

less and less true. It's actually like you need to know what to build. That's like the high order bit now is you need to know what to build. And if you've lived a little bit and you've been in places and you're very opinionated, like actually now you might not have an excuse. Like what's your excuse? Like you've been this loudmouth on the internet for so long. Like you know, why are you not building something? Like

越来越不成立。事实上，你必须知道自己应该做什么。这才是如今最重要的事情。如果你已经有了一些人生阅历，去过一些地方，而且非常有主见，那么现在可能真的已经没有借口了。你还能有什么借口？你已经在网上高谈阔论了这么久，为什么还不动手做点东西？

just pop open open code and just go do it. You know, like put your money where your mouth is.

直接打开 OpenCode，开始动手即可。不要只说不做，要拿出真金白银。

I also wonder if managing coding agents is actually like in some ways not that different from managing people. Oh yes.

我也在思考，从某些方面来看，管理编程智能体是否真的与管理人没有太大区别。——确实如此。

And so like people like Peter or you or or Boris Churnney and like Toby from Shopify who who have had whole careers like managing teams of engineers actually like take to this super well and can like spin up huge teams of coding agents and manage them maybe more effectively than even like a really smart 19-year-old who hasn't had those years of experience.

因此，像彼得、你、鲍里斯·切尔尼，以及 Shopify 的 Toby 这样的人，整个职业生涯都在管理工程师团队。他们实际上非常擅长上手这项工作，可以迅速组建并管理庞大的编程智能体团队，效率甚至可能高于一个非常聪明、但没有多年经验的 19 岁年轻人。

Yeah. We can be a little bit less abusive to our agents, try to understand where they're coming from. uh you have to catch their emotions like 99.9% less. [laughter] So yeah, it's pretty helpful.

我们可以少虐待智能体一点，尝试理解它们为什么会这样思考。需要照顾它们情绪的次数要少 99.9%。[笑声]所以，这一点确实很有帮助。

I wonder what's the concrete advice for uh someone that wants to get started and want to build a company like right now in the current era.

我想知道，对于希望起步、希望现在就创办公司的人，有什么具体建议。

I mean just start prompting. I mean opening up GPT6 today was pretty wild. I mean just that moment where your agents are, you know, palpably smarter. they, you know, a bunch of things that you've been annoyed about, like these bugs that, you know, you haven't had time to deep deep dive yourself. You just be like, "Actually, could you just go back to the list of things that you couldn't figure out?" Like, look at your, you

直接开始写提示词即可。今天打开 GPT-6 时，那种感觉十分疯狂。就在那一刻，你会明显感觉到自己的智能体变聪明了。那些一直令你烦恼的问题，例如你始终没有时间亲自深入研究的 bug，现在只要对它说：“你能否回头查看那份之前未能解决的问题清单？例如，查阅

know, all of our last chats and, you know, anything that looks like you didn't figure out like try to figure it out now and it'll do it like every single time. Like you know it's what a weird moment we are in history where you wake up in the morning you like wire up a new model and then these things that even a month ago you're just like why isn't it working it just starts working and like you know to think that that might be

我们此前的所有聊天记录，只要发现尚未解决的问题，就尝试将其解决。”现在就可以这样使用它，而且它每次都能做到。这确实是一个奇特的历史时刻：早上醒来，接入一个新模型，那些哪怕在一个月前还让你觉得无论如何也无法运行的东西，突然就可以运行了。一想到这可能就是

this thing that we get to do for the next 18 24 months 36 months like who you know I don't know where when it ends but um that's coding in the time of AGI I guess. Well, that's all we have time for for today, but um if you can't tell, we're all pretty excited about what's going on right now, and you should be, too. So, we can't wait to see what you build.

我们未来18个月、24个月、36个月都能做的事情，我也不知道这种情况何时会结束，但我想，这就是通用人工智能时代的编程。今天的时间就到这里。如果你还没有听出来，我们都对眼下正在发生的事情无比兴奋，你也应该如此。我们迫不及待地想看到你会创造出什么。