创业基础设施：深度科技的经验教训

我叫 Max Hodak，是一家名为 Science 的公司的 CEO。我们接下来会聊一聊初创公司的基础设施。我一生大部分时间都在研究脑机接口，这差不多是 20 年前的事了。我的职业生涯始于杜克大学的一个实验室，当时我还是本科生。这是我们第一次参加神经科学学会会议时的照片。我当时做的实验是：把电极植入猴子的大脑，给猴子一个操纵杆，在它玩游戏时记录神经活动。如果你让操纵杆在猴子前推时让光标侧移，大脑会怎么做？大脑是表征操纵杆、屏幕，还是别的东西？事实证明，有些神经元两者都表征。

My name is Max Hodak. I'm the CEO of a company called Science. We're going to talk a little bit about infrastructure at startups. I've spent most of my life working on brain-computer interfaces. This is almost 20 years ago now. I started my career as an undergrad working in a lab at Duke. This is from our very first Society for Neuroscience conference. The experiment I was working on back then was: if you put electrodes in the brain of a monkey, then give the monkey a joystick, and record the neural activity as it's playing a game. If you make the joystick move the cursor sideways when the monkey pushes forward, what does the brain do? Does the brain represent the joystick, or the screen, or something else? It turns out there are neurons that do both.

在我们公司 Science，我们的主要产品是视网膜假体。这是一种植入眼球后部视网膜下方的芯片，用于恢复因视网膜杆状细胞和锥状细胞丧失而失明的患者的视力。左边这张是去年 11 月我们一位患者登上《时代》杂志封面的照片。右边你可以看到植入物和眼镜的图片。植入物上看到的每一个六边形网格本质上都是一个太阳能电池。当它被植入视网膜下方时，患者佩戴的眼镜上有一个摄像头，可以感知世界，还有一个激光投影仪，可以将图像投射到植入物上。无论光线在植入物上的哪个位置被吸收，它都会产生一个微小的电场来刺激视网膜，从而直接绕过死亡的杆状细胞和锥状细胞，在最早的可能机会将视觉信号重新刺激回视网膜。这是一个非常酷的产品。它去年完成了主要的临床试验，现在已经进行了三项临床试验。去年秋天 BBC 报道了它。我们的一位患者用这个设备读完了一本 300 页的小说，并把书寄给了我们。

At our company Science, our main product is a retinal prosthesis. It's a chip implanted under the retina at the back of the eye to restore vision to patients who have gone blind due to loss of the rods and cones in their eye. On the left, this is one of our patients on the cover of Time last November. On the right, you can see a picture of the implant with the glasses. Every little one of those hex grids you see on the implant is essentially a solar cell. When this is implanted under the retina, the patient wears glasses that have a camera that sees the world and a laser projector that projects onto the implant. Wherever the light is absorbed on the implant, it creates a little electric field to excite the retina, thereby directly bypassing the dead rods and cones to stimulate the visual signal back into the retina at the first possible opportunity. This is a pretty cool product. It finished major clinical trials last year. It's been in three clinical trials now. It was covered by the BBC last fall. One of our patients finished a 300-page novel with the device and mailed us the book.

但在接下来的 30 分钟里，我大部分时间不会谈论这项工作。我们要谈的是基础设施和经验教训。我是说，这里是创业学校，也许有些东西你会觉得对你的公司有用。毕加索曾说过，当艺术评论家聚在一起时，他们谈论形式、结构和意义；而当艺术家聚在一起时，他们谈论去哪里买便宜的松节油。这句话也常被表述为：业余人士谈战略，专业人士谈物流。这句话出自一位美国以他的名字命名坦克的人。

But I'm not going to talk about this work for the most part for the next 30 minutes. We're going to talk about infrastructure and lessons. I mean, this is Startup School. Maybe there are some things that you'll find useful in your company. Picasso was noted for saying that when art critics get together, they talk about form and structure and meaning. And when artists get together, they talk about where to buy cheap turpentine. This is also often phrased as: amateurs talk strategy, professionals talk logistics. A quote from a guy that the United States named a tank after.

真正能跨公司通用的经验教训少得惊人。通常，经营初创公司的体验是：你每天看着源源不断的事实涌到你的办公桌上，然后你试图为这些事实做出最好的局部决策。如果几周后看起来不一致，那通常就是正确的做法。但有几个话题会反复出现，至少对深科技公司来说是普遍的经历，而我的大部分经验正是在深科技领域，不仅仅是纯软件。有些话题会不断出现，比如购买东西。

There are surprisingly few lessons that are really broad across companies. Typically, the experience of running a startup is: you're just looking at a continual stream of facts that hit your desk every day, and you're trying to make the best local decision you can for those facts. If it looks inconsistent over weeks, that's usually the way to go. But there are a couple of topics that keep very repeatedly coming up that are kind of universal experiences, at least for deep tech companies, which is the thing that I know most of my experiences in, not just pure software. There are things that keep coming up, like buying things.

你的第一反应可能是，如果你做软件，你不需要买东西。那就是我一个人在一个空房间里，用一些电脑写软件，这就是我们建立公司的方式。如果这是你，是的，你已经找到了风投喜欢投资软件的原因，以及为什么他们在过去 25 年里投资了这么多软件。但如果你做的不是纯软件，你将购买成千上万的东西。这是我们，我想大概是公司成立六个月的时候。有点难以辨认。有很多电脑，还有一堆显微镜、其他电子设备、3D 打印机、树脂和 PCB。你确实在持续不断地买东西。

Your first reaction might be that if you do software, you don't need to buy things. It will be me alone in an empty room with some computers writing software, and this is going to be how we build a company. If this is you, yes, you have figured out a reason why VCs love funding software and why they've done so much of it for the last 25 years. But if you do anything other than pure software, you will be buying many, many thousands of things. This is us, I think about six months into the company. It's a little tough to make out. There are a lot of computers. There's also a bunch of microscopes and other electronics and 3D printers and resin and PCBs. You're buying things really continuously.

所以这听起来可能很明显，像一个非常基本的问题，比如你肯定就是买东西。作为创始人，你可以使用信用卡。信用卡很好用。你可以用信用卡买很多东西。你也可以电汇。问题是，你的第 17 位员工怎么买东西？他们有信用卡吗？假设你把信用卡发给所有员工，告诉他们买他们需要的东西。你开始收到这样的消息。然后你想，你知道，我在意烧钱速度。我们必须高效支出。我要批准所有购买。然后你收到这样的消息。然后你想，3000 美元买一个电源听起来有点贵。我们需要一个 3000 美元的电源吗？如果我们从拍卖会上买一个呢？比如，3 天后有个拍卖会。也许我们能半价买到。我们可以在两周内拿到。

So it might sound really obvious, like a really basic question, like you surely just buy things. For you as the founder, you can use a credit card. Credit cards work great. You can buy lots of things with credit cards. You can also send a wire transfer. The question is, how does your 17th employee buy things? Do they have a credit card? Let's say you hand out credit cards to all of your employees and tell them to buy what they need. You start getting messages like this. And you think, you know, I care about burn. We have to spend efficiently. I'm going to approve all the purchases as they happen. And you get a message like this. And then you think, $3,000 sounds like a lot for a power supply. Do we need a $3,000 power supply? What if we get one from an auction? Like, in 3 days there's an auction. Maybe we'll get it for half off. We can get it in two weeks.

但随后你也会想起，你雇佣了一些薪酬很高、才华横溢的员工。你能说他们不能得到他们需要的工具吗？你每周花费 10 万美元。如果你等一周半价买电源，你肯定已经让任何可能的收益相形见绌了。而且，如果他们在 Anthropic，他们不会因为 3000 美元的购买而受到刁难。他们只会有一个电源。所以你意识到，这不仅很难控制烧钱速度，而且这不是进行支出审查的合适地方。支出审查必须更早进行。你必须有一些预算的概念。

But then you also remember that you've hired some very highly paid and talented employees. Are you saying they can't get the tools that they need? That you're spending $100,000 a week. If you wait a week to get a power supply half off, you have certainly dwarfed any possible benefit from getting it. And if they were at Anthropic, they're not going to be hassled over a $3,000 purchase. They're just going to have a power supply. And so what you realize is not only is this very hard to keep burn under control, but also this is the inappropriate place to exercise spending review. Spending review has to come earlier. You have to have some concept of budgeting.

这甚至不仅仅是购买东西的支付渠道问题。而是你如何理解你所拥有的资金池。我不想在 1500 美元的电源和 3000 美元的电源之间做权衡。他们需要理解他们拥有的资源，这样他们才能在可用资源内进行权衡。所以你建立了一个采购系统，现在你高薪的员工整天在 B2B 企业软件里点击。结果发现，从他们下电源订单到到货需要两周时间，因为你不能用信用卡买那个。你必须与供应商建立账户，处理保险和认证文件，并建立账户。他们必须生成报价，这样你才能生成采购订单，然后你才能生成发票。现在每个人都因为订购东西花费太长时间而感到不满。

It's not really about even just the payment rail of buying a thing. It's how do you understand the bucket of money that you have. And I don't want to be making the $1,500 power supply versus $3,000 power supply trade-off. They need to understand the resources that they have so they can make trade-offs within those available resources. So you set up a procurement system, and now your highly paid employees are spending their days clicking around B2B enterprise ass. And it turns out that from the time that they place an order for a power supply, it takes two weeks to arrive because you can't actually buy that with a credit card. You have to set up an account with the vendor and deal with insurance and certification paperwork and get an account set up. And they have to generate a quote so that you can generate a purchase order so that you can generate an invoice. And now everyone's upset that things are taking super long to get ordered.

这实际上真的需要一个活的有机体。我喜欢当人们从学术界转到初创公司时，我认为人们经常有的一个反应是：为什么会有人的工作就是购买东西？我肯定可以直接买东西。但绝对有人的工作就是购买东西。

This actually really requires like a living organism. I like when people move from academia to startups, I think one of the reactions that people often have is: why are there people whose job it is to purchase things? Surely I can just buy things. But absolutely there are people whose job is to buy things.

从你提交订单开始，和供应商来回沟通、把所有事情安排好，这个过程非常耗时，而且很容易拖得很长。需要主动管理才能让这些事情快速推进。我觉得当我们思考这个问题时，我们之所以有行动迅速的名声，人们不明白这是怎么做到的，其实主要不是因为我们更聪明，而是靠这样的基础设施。速度就是这样建立起来的。

From the time that you submit the order, going back and forth with the vendor to set all of this up is very time-consuming and it can easily stretch out. It takes active management to get these things to go fast. And I think part of why, when we think about this, we have a reputation I think of often being very quick, and people are unsure how that happens. It is mostly not that we are smarter. It is infrastructure like this. That is how speed is built.

所以现在人们可以买东西，至少你可以控制整体烧钱速度。现在你知道每个月不会超出某个大额支出。然后你意识到那其实不是问题，你可以算出你的跑道。问题在于归因。

So now people can buy things, at least you can keep overall burn under control. Now you know that you're not going to exceed some large amount of spending every month. And then you realize that that wasn't really the problem. You could figure out your runway. The problem is attribution.

无论你是在做火箭、汽车、药物还是脑机接口，任何涉及现实世界的事情，你都会意识到另一个问题是你在批量购买东西。比如我们购买从氩气到硅烷到氮气的气体，还有树脂、培养基，我们批量购买这些东西，然后分给很多不同的实验。

When you're doing whether you're working on rockets or cars or drugs or brain computer interfaces, anything that involves dealing with the real world, you realize that one of your other problems is that you're buying stuff in bulk. Like we buy gases from argon to silane to nitrogen, resins to media, and we buy these things in bulk and we part them out to lots of different experiments.

当你这样做时，归因就被破坏了，而且相当困难。所以如果没人知道一个实验的成本——比如每次你培养一个新的细胞系，或者每次我们在晶圆厂做一个新的探针，那个循环要花多少钱？没人知道。因此，实验是免费的。它不花美元，它花培养基，而培养基来自冰箱。

Now when you do this, it breaks attribution, and it's pretty difficult. And so if nobody knows how much an experiment costs—like every time you grow up a new cell line or every time we make a new probe in the fab, how much does that loop cost? Nobody knows. Therefore, experiments are free. It doesn't cost dollars. It costs media. And media comes from the fridge.

我们想知道如何为我们制造的东西定价？比如我们在晶圆厂批量制造很多东西，我们想知道那能卖多少钱？这需要所有这些电子表格来估算价格。而且这里面有观点，这些不全是事实。比如你包括多少租金？你包括多少工具折旧？这涉及到你对未来产量的看法。

And we want to know how do we price a thing that we make? Like we make a bunch of things in volume in the foundry. We want to know what can we sell that for? That requires all of these spreadsheets to get an estimate of the pricing. And there's opinions in here. These are not all facts. Like how much do you include rent? How much do you include depreciation of the tools? This comes with opinions about your future volume.

所有这些不仅是为了了解你应该收取多少费用，也是为了了解你在花多少钱以及你的跑道有多长。为了处理这个问题，我们在公司内部建立了大量的内部软件来管理这一切。我们做的第一件事之一就是，公司里几乎所有你能做的事情都是软件里的一个按钮。我们称之为 Helix，包括采购之类的事情。但因为这延伸到了制造环节，实验室里发生的每一步都在数据库中，我们可以把所有这些关联起来，得到这些信息。

All of this is required to understand not just what you should charge but also what you're spending and what your runway is. And so to deal with this, we've built a huge amount of internal software at the company for managing this. One of the first things that we did is almost everything that you can do in the company is a button somewhere in the software. We call it Helix, including stuff like purchasing. But because this extends all the way through to manufacturing where we have every step that happens in the lab in the database, we can correlate all of this through and get this information.

而且这些，事实证明，我们每迭代一次晶圆，在这种情况下，对于这个协议，成本是 4 万美元。这是一大笔钱。你可能已经筹集了，比如说你 A 轮融了 2000 万美元。你以为你需要四年，需要 20 个人。根据我的经验，大约一半的烧钱是人力成本。所以假设 20 个人，那可能每年 300 万到 350 万美元，那是你烧钱的一半。你需要 2 万平方英尺，每平方英尺约 4 美元，那又是每年 75 万到 100 万美元。所以现在你突然有了每年 300 万美元的研究预算，持续三四年。那比你想的要快得多。

And these again, so it turns out that for every iteration of a wafer that we make, in this case, for this protocol, it cost $40,000. This is like a lot of money. And you might have raised, let's say you raised $20 million in a Series A. You think you need four years, you need 20 people. In my experience, about half the burn is headcount. So let's say 20 people, that's probably three to three and a half million a year in revenue, that's half of your burn. You need 20,000 square feet, about $4 a square foot, that's another $750,000 to a million a year. So now suddenly you've got really a $3 million a year research budget for three or four years. That goes way faster than you think.

但同样，你的团队看到你筹集了他们一生中从未见过的巨额资金，他们认为 3000 美元的电源是免费的。这非常重要。实际上，这种基础设施在很多公司里决定了成败。

But again, your team sees that you raised a larger amount of money than they've ever seen in their lives and they think that the $3,000 power supplies are free. This is pretty important. This is a thing that actually this infrastructure actually determines success or failure in many companies.

另一个普遍的经历是招聘。我认为招聘也真正区分了成功与失败。初创公司通常不会凭空出现。根据我的经验，最好的公司来自可能被称为“场景”的东西。有一个时刻让新公司得以诞生，有一群人聚集在一起，真正为新公司创造了独特的成核点。一旦那个时刻过去了——因为某家公司已经执行了它，或者只是时间已经过去——就很难再回来了。

Another universal experience is hiring. Hiring also, I think, really separates the successes from the failures. Startups usually don't come out of nowhere. I think the best companies in my experience come from what might be characterized as scenes. There's a moment that enables a new company to be born, and there's a bunch that comes together that really creates this unique nucleation for the new company. And once that moment is passed—because some company has executed on it or just the time has gone—it's kind of tough to get back.

所以最好的招聘来自你的人脉，那些你以前共事过、你知道他们很好的人。扩展版本是从产生初创公司的网络中招聘。通常有一些扩展的场景，公司从中诞生。有一群联合创始人从中结晶出来，但还有一个扩展的社区，那应该是你初期营销的真正目标。这些是已经说你的语言、已经熟悉它的人，但永远不够填满整个公司。你必须从大众中招聘。

And so the best hiring comes from within your network, people that you've worked with before, you know are good. And the extended version of that is to hire from the network that produced the startup. There's usually some extended scene that the thing came out of. There's a bunch of co-founders that crystallize out of that, but then there's an extended community, and that should really be the target of your initial marketing. These are the people that already speak your language, already familiar with it, but there's never enough of them to really fill an entire company. You have to hire from the general public.

所以不同的公司以不同的方式招聘。不同的流程对不同创始人来说有意义。这确实是要与创始人是谁以及他们如何看待世界相匹配的。没有唯一正确的答案，但这是一个你需要真正明确流程的事情。没有正确的答案，但一个错误的答案肯定是作为公司你没有非常虔诚地执行的事情。

So there's different companies hire in different ways. There's different processes that make sense to different founders. This is the thing that really is going to be matched to who the founders are and how they view the world. And there's no one right answer, but this is a thing where you need a really defined process. There is no right answer, but a wrong answer for sure is not having something that you do very religiously as a company.

这是一个现实有惊人细节的领域。看起来很简单，比如，哦，你会有一个招聘板，你会收到申请，你会审查它们。这很快成为对你团队其他成员的巨大拖累。如果你不高效地做，你很容易几乎把所有时间都花在招聘上，却得到次优的结果。

And this is an area where reality has a surprising amount of detail. It seems really straightforward like, oh, you'll have a job board, you'll get applications, you'll review them. This very quickly becomes a huge drag on the rest of your team. You could easily spend almost all of your time recruiting if you're not doing it efficiently to get a suboptimal outcome.

所以对我们来说，我们建立了大量软件来做这件事。我们的流程有四个步骤。第一步是我们建立了一个软件界面，让申请人在线申请，我们可以预先从他们那里捕获一些结构化信息，包括并行申请多个职位的能力。所以我们最初使用了商业申请人跟踪系统。我们已经把它移到了我们的内部工具。我们这样做的原因之一是这让我们能够做一些在任何商业 ATS 中都找不到的事情。

And so for us again, we've built a lot of software to do this. There are four steps to our process. The first is that we've built a software interface for applicants to apply online where we can capture some structured information from them upfront, including the ability to apply to multiple jobs in parallel. And so we originally used a commercial applicant tracking system. We've moved this to our internal tools. And one of the reasons we did that is because this allowed us to do something that we couldn't find in any of the commercial ATS's.

所以，当我们流程的第一步，用户申请时，它会进入全公司投票。这是内部界面的一个经过大量编辑的版本，但希望你能看出这里发生了什么。所以在中间的申请人简历中，我们收集一些其他结构化信息，但最重要的是在最右边你看到这个问题，比如你会如何投票给这个候选人？他们是已知优秀、强烈赞成、赞成、反对、强烈反对。

So, when the first step of our process when users apply, it goes to company-wide voting. This is a heavily redacted version of the internal interface, but hopefully you can make out the idea of what's going on here. So in the applicant's resume in the middle, we collect a little bit of other structured information, but the most important thing is on the far right you see this question like how would you vote for this candidate? Are they known good, strong yes, yes, no, strong no.

所以当有人申请时，系统会挑出七八个背景看起来相似的现有员工，然后向他们所有人发送投票请求。这样我们就可以把初始筛选的投票分散到公司很多人身上，这一点至关重要，因为如果你在做任何酷的事情，几年之后，漏斗顶端的申请量会非常庞大。如果你让任何一小群员工或任何一个人成为瓶颈，他们绝对会拖累整个组织的其他部分。

And so when a person applies, the system picks out seven or eight current employees that it thinks look something like their backgrounds and it pings them all for votes. And so we can distribute the voting across a lot of the company for this initial review, which is essential because if you're doing anything cool, by the time you get a couple years into it, that top of funnel is overwhelming. And if you place any small group of employees or any one person in the way as a bottleneck on this, they will absolutely bottleneck the whole rest of the organization.

而且我认为你还想对判断进行平均。我觉得不同的人在招聘方面有好有坏，对那个阶段你该找什么样的人也有不同的看法。所以一开始作为创始人，你可以和每个人见面，也应该这么做，这会让你走得很远。你绝对应该花相当长的时间面试每个人，但即使在那之后，你仍然需要一些方法来平均团队其他成员的判断，而投票机制通常是一个非常好的方法。

And it's also you want to, I think, average over judgment. I think there are different people that are better or worse at hiring and have different perspectives on what you're looking for at that stage. And so in the beginning as the founder you can meet with everybody and you should, that will take you quite far. You should definitely interview everybody for quite a while, but even beyond that you still want ways to average over the judgment of the rest of your team, and voting mechanisms are usually a really good way to do that.

这些是我们过去几年的实际数据。我们收到的漏斗顶端申请中，有 17% 会进入电话筛选。再说一次，最初的申请投票阶段是从全公司范围内抽取人员，这样我们通常能在 24 到 48 小时内快速完成投票，而不会让任何一小群人成为瓶颈。

So these are our actual statistics over the last couple years. So 17% of the top of funnel applications that we get go to a phone screen. Again, that first initial app voting stage is drawn from a companywide pool so that we can get the voting done quickly within usually 24 to 48 hours and not bottleneck that on any small group of people.

电话筛选同样是从全公司范围内抽取人员。这不是针对特定团队的，而是全公司的标准，真正寻找三样东西：判断力、动力和自主性。比如，如果我们把你扔进一个复杂且定义模糊的情境，你是倾向于做出好的决策，还是会制造外交事件？你是否达到技术能力的基本门槛，并展现出学习能力？你是否能有效地让世界变成你想要的样子？比如你的生活是否如你所愿，或者说你是否实现了你的抱负？你对自己的生活有具体的抱负吗？

The phone screen is again drawn from a companywide pool of people. This is not team specific. This is a companywide bar really looking for three things: judgment, horsepower, and agency. Like if we throw you into a complex vaguely defined situation, will you tend to make good decisions or will you create diplomatic incidents? Do you meet a basic hurdle for technical competence and demonstrated ability to learn things? And are you effective at causing the world to look like you wish it were? Like how does your life look or not like whatever ambitions you had? And do you have specific ambitions for your life?

这样我们就可以把这项工作分散到整个公司，然后其中一半通常会进入家庭作业阶段。理想情况下，我们现在会完全使用抗 AI 的家庭作业。所以我们最喜欢的家庭作业类型是那些不会饱和、有非常高的上限、并且自然可以量化为两三个数字放到图表上的东西，这样当我们收到作业回复时，可以把它们全部画出来，当有人真正突破了前沿时，会非常明显，而我们不关心他们用了什么 AI 模型，因为那可以让你变得更好。

And so this we can distribute over the entire company, and then half of those tended to go to homework. Ideally we'd be using entirely AI-resistant homeworks now. So our favorite types of homeworks are things that don't saturate, have a very high ceiling, and are naturally scorable to two or three numbers that we can put on a plot, so that when we get responses to homeworks, we can just plot them all and it's very obvious when someone has really beaten the PTO frontier, and we otherwise don't care whatever AI models they use, like that can make you better.

在目前无法做到这一点的家庭作业中，我们正在增加越来越多的技术电话面试或现场实践测试，但理想情况下，我们希望能为每一项都设计抗 AI 的带回家作业。我们有一个非常有趣的抗 AI 家庭作业思路，其中包含几个任务，比如 GPU 内核优化：你能把它降到的最小周期数是多少？这是自然调整的。比如有一段时间的障碍是，我认为是 Sonnet 的表现。如果你能超过它，就能获得面试机会。我认为有很多方法可以构建抗 AI 的家庭作业。

In cases where that's not possible for homework right now, we're doing an increasing number of technical phone calls or practical on-site tests, but ideally we would have an AI-resistant take-home for each of these. We had a really interesting take on the AI-resistant homework where they've had a couple tasks where it's like GPU kernel optimization: what is the minimum number of cycles you can get it down to? And this is naturally adjusting. Like the hurdle for a while was, I think, Sonnet's performance. If you could beat that, then you could get an interview. I think there are a bunch of ways to construct resistant homeworks.

然后当你进入面试阶段时，重要的是你要有相当高的转化率，至少 25% 能拿到 offer，否则你会浪费太多时间在那些最终不会录用的员工身上。你不能降低这个标准。所以这里有四个步骤：初始投票、电话筛选、家庭作业和完整面试。根据我的经验，这是我们做出完整决策所需的最少信息集。我不认为有更高效的方式来获取这些信息。我不认为我们可以用更少的步骤。所以这已经成为我们的流程。

And then by the time you get to the interview, it is important that you have a reasonably high conversion rate, at least 25% to an offer, because otherwise you're going to waste too much of your time doing on-sites for employees that don't convert. You can't get that down. And so there are four steps to this: initial voting, the phone screen, homework, and a full interview. And this is, as far as from my experience, this is the minimum set of information that we need to make a full decision. And I don't think that there's a more efficient way to elicit this. Like I don't think there's a smaller number of steps that we could use. So this has become our process.

所以你招人，他们来上班，开始工作，你开始支付工资。但你怎么知道你擅长这个？最终你会从市场得到关于你招聘能力的反馈，因为公司要么成功要么失败。你的团队会有能力完成你设定的目标，他们会帮你在这个过程中纠正方向。但这是一个非常非常长的反馈回路，而且它是一个表现很差的损失函数。

So you're hiring people, they're coming into work, they're starting, you're incurring payroll. But how do you know that you're good at this? Like eventually you'll get feedback from the market on how good you are at hiring because the company will work or it won't. Like your team will be capable of accomplishing the stuff that you've set out and they'll help you course correct through that. But this is a very, very long feedback and it's a very poorly behaved loss function.

所以作为管理层，你的工作就是设计合成梯度，让你能更早地、在过程中发现招聘进展如何，以及是否需要纠正方向。一个传统的答案是 360 度评估流程。每年一次，你发出很多表格，收集每个员工的反馈，和 HR 以及各个经理开很多会议，然后进行传统的绩效评估周期。

And so it's kind of your job as management to design synthetic gradients that allow you to find out earlier and along the way how recruiting is going and if you need a course correction. A conventional answer to this is the 360 review process. So once a year you send out a lot of forms, you gather up a bunch of feedback around each employee, you set up a bunch of meetings with HR and with the various managers, and you can do the conventional performance review cycle.

根据我的经验，这是一个非常具有破坏性的过程，它往往不会暴露那些你本来就知道但尚未采取行动的问题，因为你已经知道问题的存在。但解雇人很难，所以人们会拖延，这只是在强化你已经知道的事情，而且一年只发生一次。如果你把公司分成几个组，也许一年两次。

Which, based on my experience, is a very disruptive process that doesn't tend to surface issues that you don't already know about but haven't acted on because you knew that thing was there. But firing people is hard, so people drag their feet on it, and this is kind of reinforcing things you already knew and it only happens once a year. Maybe twice a year if you split up the company into cohorts.

但我的意思是，我认为真正理想的是有一个信号，能给你来自全公司的自然反馈，关于谁好谁不好，什么有效什么无效，这种方式在很大程度上是无偏的，而且是更连续的。想象一下，如果你能每隔几周就得到关于哪里有问题、哪里进展顺利的反馈。

But I mean, I think really what would be nice to have is a signal that gives you this kind of natural feedback from across the company about who's good and who isn't and what's working and what's not in a way that is largely unbiased and is more continuous. Imagine if you could get feedback kind of every few weeks on where there are issues and where things are going well.

所以我开发的这个过程，我现在已经用了六七年了，就是每隔几周，每四到六周，不是那么频繁，公司里的人会通过软件 Helix 收到一个问题，这是一个表单，但实际上真正重要的只有一个问题：知道了这个人的表现后，你今天还会投票录用他们吗？这和我们在初始投票时用的问题是一样的。

And so the process that I had developed, which I've now used for the last really six or seven years, is every few weeks, every four to six weeks, it's not that often, people around the company get pinged with a question through the software, through Helix, and it's a form, but really there's only one question that really matters, which is: knowing how this person turned out, would you vote again today for their hire? It's the same question we use on the initial voting.

所以你会收到一个提示，比如：这个和你一起工作的人，你今天会怎么投票录用他们？然后我们能做的就是构建一个覆盖全公司所有反馈的图。基本直觉是，如果其他人都给你打高分，那么你的投票应该被赋予更高的权重。敏锐的人可能会注意到，这很像最初的 Google 算法 PageRank，这是一个叫做特征向量中心性的概念，通过观察图如何相互指向来创建权重。

And so you'll get a prompt to say like, this person you work with, how would you vote for their hire today? And then what we can do is we construct a graph over the company of all of the feedback. And so the basic intuition is that your vote should be weighted more highly if everybody else has rated you highly. And the astute may notice that this looks a lot like the original Google algorithm, PageRank, which is an idea called eigenvector centrality, where you can create a weight over the graph by looking at how the graph points together.

这跟字面意义上的特征向量中心性略有不同，但非常相似，我们称这种技术为 IGEN 评审。我已经确信这大致上是做绩效评估的正确方式。要让这个方法真正奏效，还需要一些其他技巧。比如，我们应用 dropout，运行一千次迭代，每次随机移除一定比例的边。然后观察得到的分数分布，如果出现额外的峰值，就提示可能存在需要进一步调查的投票小团体。但总的来说，这种方法把判断权分散到全公司，大约以一个月为周期持续更新，能让你对公司实际情况有更好的洞察。它也彻底摆脱了那种一年一次、由 HR 主导的沉重且令人痛苦的绩效评估流程。

This is a little bit different than literally eigenvector centrality, but it's very similar, and we call this technique IGEN reviews. I've become convinced that this is more or less the right way to do performance reviews. There are some other tricks you have to apply to get this to work really well. For example, we apply dropout, where we run a thousand iterations and randomly remove some percentage of the edges each iteration. Then, when you look at the distribution of scores you get out of that, if you see additional peaks, that's a clue that there could be voting cliques that need further investigation. But as a whole, this distributes the judgment across the company, updates more or less continuously with about a month lag, and gives you much better insight into what's going on around the company. It also totally gets rid of that traumatic, super heavy, once-a-year HR-driven performance review process.

这次演讲的重点不是说你一定要用这个方法，尽管你应该考虑一下。如果你真的在公司推行了，可以给我发邮件，我会发给你一份文档，里面有更具体的技巧，教你如何让它运转良好。但演讲的真正主题是：迭代速度决定了成败。如果你能有一个快速的迭代循环，那就能克服很多其他问题。这个效应非常显著。如果你每周能学到一件事，而竞争对手每月才学一件事，那他们永远不会构成威胁。绝大多数情况下，如果你在比较两种解决问题的方法，其中一种能让你在更短的时间内实现复利增长，即使另一种方法有显著的优点，你也应该认真考虑选择迭代周期更短的那个，因为复利效应实在太惊人了。

The point of this talk is not that you should use this in particular, although you should consider it. If you actually roll this out at your company, you can email me and I'll send you a doc with more specific tricks on how to get this to work well. But the real theme of the talk is that rate of iteration separates success from failure. If you can get a fast iteration loop, that really overcomes many other things you're going to run into. This effect is so severe. If you can learn one thing every week and there's a competitor learning a thing every month, they will never matter. Overwhelmingly, if you're looking at two different approaches to solve a problem, and one allows you to compound in a much shorter amount of time than the other, even if the other approach has significant redeeming characteristics, you should really consider going with the shorter iteration cycle because the compounding effect is just so dramatic.

速度决定成败，而速度由基础设施决定。这取决于一些听起来很无聊的事情，比如你的采购、招聘和支出流程运转得如何。这跟你对正在构建的东西在对象层面的技术内容理解得有多深同样重要。我经常看到由顶尖科学家和工程师创立的公司，因为执行难以坚持而中途夭折。你的工作是组织。深科技公司很少因为技术不行而失败。它们失败是因为一旦你有了一个数百人的组织和数十万平方英尺的物理基础设施，却没有建立管理这些的系统。一切变得难以驾驭，然后你就无法将战略与执行连接起来。

Speed determines success and failure, and speed is determined by infrastructure. This is driven by really boring-sounding things like how well your purchasing, recruiting, and spending processes work. This is as important as how well you understand the object-level technical content of what you're building. I see companies founded by stellar pedigree scientists and engineers all the time that die on the vine because execution is tough to follow through. Your job is to organize. It's uncommon that deep tech companies fail because the technology doesn't work. They fail because once you end up with an organization of hundreds of people and hundreds of thousands of square feet of physical infrastructure, you haven't built the systems to manage that. It becomes unwieldy, and then you can't connect strategy to execution.

在其他条件相同的情况下，我们强烈倾向于迭代周期更短的事物。但这不是一条笼统的规则——创业公司里没有笼统的规则。你要把每一个新的事实模式当作独特的情况来对待，然后做出对你有意义的决定。作为创业公司创始人，最难的教训之一就是你无法委托你的判断。作为 CEO，你必须始终做出对你有意义的决定，无论其他选择看起来有多大的势头或惯性。在学校里，如果旁边有人，你偷看他们的答案作弊，你的成绩会被拉向班级平均分。这在创业中不足以成功。你必须做那些处于长尾的事情。成功的公司是例外；变成平庸是不够的。为了成功，你的判断必须与众不同地出色。

We heavily lean towards things that have shorter iteration cycles, all else equal. But that's not a blanket rule—there are no blanket rules in startups. You look at each new fact pattern as its own unique thing and then make decisions that make sense to you. One of the harder lessons as a startup founder is that you cannot delegate your judgment. As the CEO, you must always make decisions that make sense to you, no matter how much momentum or inertia alternatives seem to have. In school, if somebody sitting next to you and you cheat on the test by looking over at them, your grade will be dragged towards the average of the class. That is not good enough to succeed in startups. You have to do things that are at the long tail. Successful companies are the exceptions; becoming an average is not good enough. In order to succeed, your judgment has to be differentiatedly good.

现实可能是你还不知道自己的判断是否出色。所以无论如何，你必须去发现。这意味着即使你在这个认识上完全孤立，也要做出对你有意义的决定。这是通往真正巨大成果的唯一途径。不经常发生的情况是，其他所有人都认为一件事，而你觉得“你们都完全错了”。但这确实是一种非常诡异的感觉。在公司成立四五年后，你会遇到一个时刻，有数亿美元的利益攸关，有一个真正高风险的决策，只有你能做出。然后你会四处寻找建议，因为一开始你会得到很多容易建议或容易弄清楚的事情，但几年后你会到达一个关键点，你寻找建议时却发现无人可问。在那一刻，你必须非常清楚自己判断的局限和边界。那是一种非常诡异的感觉，你必须无论如何都要坚持到底。

The reality might be that you don't know if your judgment is good yet. So one way or another, you will have to find out. That means making decisions that make sense to you even when you are totally alone in that realization. That is the only way to get to the really big outcomes. It's not often that everyone else will think one thing and you'll be like, 'You're all totally wrong.' But it is a really eerie feeling. You'll get to a point four or five years into the company when there's hundreds of millions of dollars on the line and there's some really high-stakes decision and only you can make it. Then you will look around for advice because in the beginning you'll get lots of things that are easily advised or figured out, but you'll get to a key point years in and you'll look for advice and there is nobody to ask. At that point, you must have a really good sense of the limits and boundaries of your judgment. That is a very eerie feeling, and you have to be able to commit to it regardless.

好消息是，根据我的经验，真正陷入困境是非常困难的。你可能会给自己惹上麻烦，但行动空间总是比看起来要大。无论发生什么，你很容易去预想各种你永远不会真正遇到的问题。然后你去做，然后你到达一个点，遇到一些真正的限制。总有上百个关于如何改进的想法。这有时被表述为“行动产生信息”。这个想法比听起来要深刻得多。在物理学中，有一个量叫做“作用量”。如果我扔一个球，它沿着抛物线轨迹运动，那个轨迹在它离开我手的那一刻就完全确定了，除非有风或其他作用力施加在它上面。它会沿着这条弹道轨迹运动，从某种意义上说，这是一条信息最小化的轨迹。我可以说它只是沿着弹道运动；这完全决定了它。如果发生了其他事情，你必须花费能量和时间来促成那件事。所以每当你向宇宙施加作用时，在非常根本的意义上，这就创造了信息。每当你陷入困境，你必须开始注入行动，产生熵。这会产生一些相当反直觉的效果。

The good news is that in my experience, it's very difficult to actually get stuck. You can get yourself into trouble, but the action space is always larger than it appears. No matter what happens, it's very easy to try and anticipate all kinds of problems that you'll never actually run into. Then you go and do it, and you get to a point where you run into some real limitation. There's always a hundred ideas about how to make it better. This is sometimes phrased as 'action produces information.' This idea is much deeper than it sounds. In physics, there's a quantity called action. If I throw a ball and it follows a parabolic trajectory, that trajectory is totally set when it leaves my hand unless it's blown by wind or some other action is exerted on it. It will follow this ballistic trajectory, which is in a sense an information-minimizing trajectory. I can say it just followed a ballistic path; that totally determines it. If something else happens, you had to spend energy and time to cause that to happen. So whenever you exert action into the universe, that creates information in a very fundamental sense. Whenever you get stuck, you have to start injecting action, producing entropy. This produces some fairly counterintuitive effects.

比如我见过一些情况，公司陷入很深的局部最小值，有个人在很多方面都很出色，但就是不适合公司当时的需求，即使他们个人能力很强，但移除他们反而能解锁公司，让它进入新阶段。嗯，当你陷入困境时，你不得不开始做事。所以，在你构建的产品这个对象层内容之下，你有所有这些支持系统，比如公司怎么做采购、会计、招聘、绩效评估、预算、安全和质量，这就是公司的操作系统，它对你能把公司带多远有巨大影响。所以速度由基础设施决定。速度决定成败。你需要在这些基础建设上投入更多思考。如果一开始就做对了，其他一切都会容易得多。如果做错了，你最终可能会每月花掉 500 万美元，却感觉几乎无法控制。嗯，然后你被迫采用更粗糙的手段和更艰难的决定。嗯，谢谢你们来听我的 TED 演讲。

Like I've seen situations where the company is stuck in a deep local minimum and there's someone who is great in many ways but it's just the wrong fit for what that company needs at the time and removing them even though they individually are very strong unblocks the company and allows it to kind of enter a new phase. Um when you're when you get stuck you have to start doing things. And so all the thing underneath the the object level content of what the product you are building is you've got this you have all these support systems kind of the company like how the company does purchasing and accounting and recruiting and performance reviews and budgeting and safety and quality is the operating system of the company and that has a huge impact on how far you can take it. So speed is determined by infrastructure. Speed determines success and failure. You need to put more thought into these into getting these foundations right. If you do them right at the beginning, everything else is much easier. If you get them wrong, you'll end up like spending $5 million a month and feel like you have very little control over it. Um and then you're forced into into coarser levers and harder decisions. Um thank you for coming to my TED talk.

好的。对于在工业界和学术界之间做选择的人，比如现在创业还是先读博士，你有什么建议？

Okay. Do you have advice for people trying to choose between industry and academia, starting a company now versus getting a PhD first?

所以，这真的取决于你具体在做什么。如果你的领域只存在于基础研究中，那么读博士可能非常合理。嗯，所以当事情真正开始奏效时，嗯，比如 20 年前最好的计算机科学家在卡内基梅隆大学和哈佛，50 年前最好的火箭科学家，如果你想研究火箭发动机，你会在 NASA，在大学，在马里兰大学或其他地方，而现在他们，现在最好的计算机科学家在谷歌、苹果，嗯，还有 OpenAI，最好的火箭科学家在 SpaceX、蓝色起源等公司。所以当一个领域真正开始奏效时，工业界能调动更大量的资源，而且能快得多。嗯，所以我想一个问题一直是为什么学术界在生命科学领域仍然如此重要。现实是，对大多数事情来说，它并没有那么有效。比如人类就是不擅长药物发现。所以如果你的领域真的只在学术界，那么读博士完全合理。但嗯，我想你知道，很多情况下，创业公司不常见的是技术无法实现。更常见的是他们无法组织人类组织来实现目标，而学习这一点也是一种技能。我认为唯一的办法是口传心授。你必须去做。所以如果选择是在一个你想要的领域附近的高绩效公司工作，还是读博士，我可能会推荐公司。但这不是绝对规则，真的取决于领域。

So, it really it depends on specifically what you're doing. If if your field only exists in basic research, then getting a PhD might be very reasonable. Um the so when things really start to work um like if 20 years ago the best computer scientists were at CMU and Harvard and 50 years ago the best rock like if you wanted to work on rocket engines you were at NASA you were at a university you're at University of Maryland or somewhere and now they're at now the best computer scientists are at Google and Apple and um and OpenAI And the best rocket scientists are at SpaceX and Blue Origin and others. So when a field really starts to work, industry can just marshall such larger levels of resources and can just move so much faster. Um, and so I think a question has been why has academia stayed so relevant in the life sciences. And it's just the reality is that it doesn't work that well for most things. Like humans just aren't that good at drug discovery. And so if your if your field is really only in academia, then it can make total sense to get a PhD. But um I think you know a lot of it is uncommon that startups don't get the technology to work. It is more common that they can't organize the human organizations to accomplish their goals and learning that is also a skill set. The only way to learn it. I think it's an oral tradition. You have to do it. And so if the choice is working at a really high performing company adjacent to where you want to be versus getting a PhD, I'd probably recommend the company. But it's not an absolute rule and it really depends on the field.

对你来说，什么算作卓越能力的证据？

What counts as evidence of exceptional ability to you?

嗯，任何你能具体指出的、能把这个人和他的高中同学区分开来的东西。嗯，比如，如果你有一个普通的高中生，我们只是想要一些具体的事实，嗯，我的意思是，理想情况下，卓越能力的最佳证据是在清晰的竞争性比赛中获胜。所以这可能是成为国际象棋特级大师，可能是赢得设计、建造、飞行或大学生方程式赛车比赛。嗯，硅谷有很多深度科技公司基本上是由大学里的大学生方程式赛车冠军创办的。嗯，那些人把整个大学时光都用来造东西、比赛、发现。我认为你必须要有那种竞争性反馈。如果没有某种清晰的竞争性比赛，很难知道自己是否卓越。

Um, anything that concretely you can put your finger on that separates that person from their high school class. Um, like what is you like if you have your average high school student? We just like want some concrete fact that is that um I mean ideally the the best evidence of exceptional ability is are winning at legible competitive games. So this could be being a like a chess grandmaster. It could be winning design, build, fly or formula SAE competitions. Um there's a bunch of Silicon Valley deep tech companies that are basically built out of Formula SAE winners from college. Um people that just spent their college experience building things and racing them and finding out. I think you have to have that type of competitive feedback. It is tough to know if you're exceptional um without having some legible competitive game.

我们现在怎么招聘工程师？我们还在用 LeetCode 吗，还是有更好的方法？如果我们允许使用 AI，你怎么了解申请人的技能？

How do we hire engineers now? Do we still use LeetCode or do we have better ways? If we allow AI use, how do you understand the skills of the applicant?

嗯，所以我们从来没有，我觉得我们真的没用过 LeetCode。也许团队里其他人偷偷用，但我从没问过。嗯，所以特别是软件，它对算力的回报如此之大，以至于它真的是一个吸引非常聪明的人的领域，因为它给你非常快速的反馈。比如你想，生物学里有很多非常聪明的人，但当你有一个生物学想法时，你可能要花好几个月才能知道它好不好。在软件里，如果你有一个想法，你通常可以在几个小时内构建出来，或者几天内得到反馈。所以它有这种非常上瘾的反馈循环，有点像高频交易，吸引着非常聪明的人。嗯，所以我们寻找的是，在你的一生中，我们有什么信号表明你做过一些有趣的事情，比如一个人到了 20 多岁中期，背景中却没有一些他们主动去寻找并做过的事情，这是不常见的。嗯，但这可以，但这是一个如此开放的标准。嗯，它真的可以是任何东西。我们越来越直接地回答你的问题，我们越来越不直接评估编程。我们评估，试图评估思维。所以这是设计问题，比如如果我们给你一个领域，你怎么分解它？你能清楚地理解问题的分解吗？嗯，它真正衡量的是你能不能清晰思考，而不是你能不能写代码。

Um, so we've never I don't think we've ever really used LeetCode. Maybe some other people on the team do it in secret, but I've never asked it. Um, so software in particular, it the rewards to horsepower are so great that it really is just it's a field that attracts really smart people because it gives you this very rapid feedback. Like if you think about like there's a lot of really smart people in biology, but when you have a biological idea, it can take you many months to find out if it's a good one. In software, if you have an idea, you can often build it in a couple hours or you can get feedback within days. And so it has this really addictive feedback loop kind of like high frequency trading that just draws in really smart people. Um and uh and so we look for kind of over your life what signals do we have that you have done something interesting like it's it's uncommon for someone to get into their mid20s without having without there being some thing in their background that they went out and sought out and did. Um but it can but it this is like such an open-ended criteria. Um it can really be anything. The we don't we increasingly more directly to the question we increasingly don't directly evaluate programming. We evaluate try to evaluate thinking. So this is design questions like if we give you a domain how do you break it down? Can you understand the decomposition of the problem clearly? Um it's really measures of like can you think clearly rather than can you write code?

你在 Neuralink 的经历中得到了什么？

What did you take away from your experience at Neuralink?

所以关于你应该去读博士的问题？我没有博士学位。我在 Neuralink 为我的 CEO 花了五年时间经营一家公司。嗯，那是，我的意思是，我认为最大的教训之一是，很少有真正通用的，没有通用的算法能让你在创业公司成功。没有一套五个要点可以传达，只要你按部就班，你的公司就会成功。这是一长串的判断决策。所以最重要的是那些过滤器调得很好。所以我认为在 Neuralink 对我来说最有价值的事情之一是我和一个经验上判断力极佳的人一起工作。比如我们可以一起陷入麻烦，然后会发生一些事情，有两个可能的解决方案都说得通，我会去找他问，是选项 A 还是选项 B？他会看一眼说，“哦，绝对是选项 B。这个问题永远不会再发生。”而经历过那些我试图下注、带着赌注、向前看、直到后来才得到反馈的情况，加上那个建议，对训练和调整那些过滤器极其有用。而且我不知道是否真的有捷径。

So the question of like should you go get a PhD? I don't have a PhD. I spent five years running a company for my CEO at Neuralink. Um that was I mean there's one of the biggest lessons I think is that there are few really generic there's no generic algorithm for how to succeed at a startup. There's no like set of like five bullet points that can be conveyed that if you just like turn the crank your company will be successful. It is a long series of judgment calls. And so the most important thing is that those filters are tuned really well. And so the I think the most one of the most valuable things for me at Neuralink was I was working with someone who has empirically excellent judgment. Like we could get into trouble together and there'd be all like something would happen and there'd be two possible solutions that would make sense and I'd go to him and say like is it option A or is it option B? you'd look at and be like, "Oh, it's definitely option B. The problem would never recur." And having been in those situations where I was trying to make these bets kind of with stakes attached, looking forward in time, not getting feedback until later with that advice was incredibly useful for train for fitting those filters. And I don't know that there was really a shortcut.

而且我认为，当你不在场时听到这些故事，真正去思考，因为其中有真实的利害关系，然后得到反馈——这是创业者教育中至关重要的一部分，我认为很多人都低估了它。我认为，在跳进自己的创业公司之前，先在一家文化优秀、你尊重的公司工作是非常值得的。创业文化很少能从第一性原理完全重新发现。通常它们是作为口述传统传承下来的，因为创始团队曾在另一家公司工作，而那家公司又曾在另一家公司工作，所以他们继承了这种文化。或者在某些情况下，确实有突破，比如市场错位让一个团队凭空崛起，他们往往会从风投那里学到这些。但关键是与那些有判断力的人一起工作，这样你就能获得那种强化学习，作为一系列长期事实，这非常重要。

And I think that just hearing the stories when you're not there, really thinking about it because there are real stakes, and then getting that feedback—that is an essential part of the education of an entrepreneur that I think many people underrate. I think it is really worth working for a company that has an excellent culture that you respect before jumping right into your own startup. It is relatively uncommon that startup cultures get rediscovered entirely from first principles. Usually they're passed down as oral traditions because there's a founding team that worked at another company which worked at another company, and so they inherited it. Or in some cases where there's really a breakout, where there's just some market dislocation that really enables a team out of nowhere to build it, they'll often get it from the VCs. But it's working with the people that have that judgment so that you can get that reinforcement learning as a long series of facts is really important.

脑机接口或神经接口能帮助我们弄清楚意识到底是什么吗？怎么帮？

Could BCIs or neural interfaces help us figure out what consciousness actually is? How?

绝对可以。所以，如果人工智能探索的终点是超级智能机器，我认为脑机接口探索的终点是意识机器。大脑是由普通物质按照化学规则排列而成的，只包含元素周期表上的东西。很难相信那里有什么新物理学。所以我们正在寻找基质活动与现象内容之间的某种映射。现在，如果我们有一个神奇的脑机接口，能让我看到大脑中每个神经元的即时状态并驱动它们，我想我们会很快弄清楚意识。我认为这是一个实际问题，而不是哲学问题。但要证明这一点，首先那个实际问题是真实的，我们必须在人类身上做这些事。要证明任何这一点，我们必须在人类身上做。我认为有可能用脑机接口来证明它。我们有一些关于如何做这些实验的想法，但它们还需要几年时间。现在进入人体的东西并不是为了研究意识而设计的，但我确实认为那是这条道路的下一步。

Absolutely. So if the end of the artificial intelligence quest is super intelligent machines, I think the end of the BCI quest is conscious machines. The brain is composed of ordinary matter arranged according to the rules of chemistry, only things found on the periodic table. It seems tough to believe that there's some new physics going on in there. And so we're looking for some mapping between the substrate activity and the phenomenal content. Now, if we had a magical BCI that allowed me to see the instant state of every neuron in the brain and drive them, I think we'd figure out consciousness pretty fast. I think this is a practical problem, not a philosophical problem. But to prove it, first of all that practical problem is real, and we'll have to do the stuff in humans. To prove any of this, we'll have to do it in humans. I think it is possible that you could use a BCI to prove it. We have some ideas about how to do those experiments, but they are still a few years off. Things going into humans now are not designed to study consciousness, but I do think that that is further down this path.

我应该学什么才能为脑机接口做出贡献？

What should I study to contribute to BCIs?

这真的取决于你的背景。神经接口是一个非常跨学科的问题。它涉及从干细胞生物学到材料学和微加工，再到软件、动物行为、外科手术等方方面面。所以有很多不同的切入点。我们发现的一件事是，最好有一个较小的团队，能把更多问题装进脑子里，然后一起压缩。这与学术界通常处理跨学科问题的方式形成对比，他们往往会建立一个跨学科中心，吸引非常深入的垂直化专家，他们在中心会面。问题在于他们说着不同的语言。所以即使能沟通，通常最终你交付的是那些部门之间的接口。而对我们来说，如果我们能让少数人把问题装进脑子里，我们就能调整瓶颈所在。一个具体的例子是，我们的蛋白质工程团队已经能够为我们需要的一些事情开发出更敏感、更好的蛋白质，这让我们放宽了一些电子学要求。具体来说，我们有叫做视蛋白的蛋白质，能让神经元对光敏感，这样如果我们用光照射它们，就能触发一个神经元。问题在于你需要用大量光照射神经元才能触发它，这意味着你不能有太多光源，因为会过热。所以我们让蛋白质更敏感，这意味着我们可以有更多 LED，因为每个都可以更暗。这样我们就把这个电子学问题变成了生物学问题，从而放宽了那些约束。在那些跨学科中心，你不会得到这么多，因为一个小组专注于一件事，另一个小组专注于另一件事。所以我想说，能够对更多问题有更广阔的视角是非常有价值的。然后，尽可能深入清晰地理解系统。我认为动手实践是无可替代的。具体学什么并不重要——你需要一些硬技能来入门：软件、电子、机械、材料，什么都行。然后从那里，我会尽量多学。

This really depends on your background. Neural interfaces are a very interdisciplinary problem. It uses everything from stem cell biology to materials and microfabrication, to software, to animal behavior, to surgery. So there are many different entry points. One of the things that we found is that it's better to have a smaller team that can fit more of the problem in their heads and then compress it together. Contrast this to how academia usually handles interdisciplinary problems, where they'll have an interdisciplinary center that pulls in very deep verticalized experts who kind of meet at the center. The problem is that they're all speaking different languages. So it's often hard to really communicate; typically you end up shipping the interfaces of those departments. Whereas for us, if we can kind of hold the problem in the head of a smaller number of people, we can shift around where the bottlenecks are. A specific example of this is our protein engineering group has been able to develop much more sensitive, much better proteins for some things that we need to do, which has allowed us to relax some electronics requirements. So specifically, we have proteins called opsins that allow us to make neurons light-sensitive, so that if we shine light on them, we can fire a neuron. The problem was that you needed to hit a neuron with a lot of light to fire it, which means that you can't have that many light sources because it gets too hot. So we've been able to make the protein more sensitive, which means that we can have more LEDs because each one can be dimmer. And so we've turned this electronics problem into a biology problem that allowed us to relax those constraints. You don't get that as much when you have these interdisciplinary centers where there's one group focused on one thing, another group focused on another thing. So I would say being able to have a broader perspective of more of the problem is really valuable. And then just have really as deep and clear an understanding of the system as you can get. I think there's no substitute for being hands-on. It doesn't really matter—you want some hard skill to get you in the door: software, electronics, mechanical, materials, something. And then from there, I would try to learn as much of it as you can.

在科学研究中，AI 不能取代什么？如果还有的话，人类在哪里仍然是必要的？

What doesn't AI replace in scientific research? Where are humans still necessary, if anywhere?

我们肯定仍然需要人类。尤其是在科学研究中，我的意思是，很难预测——AI 显然在飞速发展。我确实认为你需要考虑如何让你的公司成为 AI 原生的，也就是说，你想收集所有的上下文，公司里发生的一切，并能够高效地提供给智能体，因为这些显然是未来的重要部分。所以对我们 Helix 来说，一切都在里面。我们这样做的一个原因，不仅仅是因为把所有东西放在一个数据库里，把采购、质量、批次记录和制造联系起来，以便更高效地追踪，而且也是为了能把所有东西交给智能体。我们发现它们是团队的倍增器，而不是替代品。到目前为止，AI 对我们产生影响的三大领域是：首先是编码——我的意思是，现在基本上都是这样。我一生写过很多代码；我觉得过去六个月我很少看源码。这变得非常好。第二是法规。所以如果你在做任何真正有趣的事情，你最终会受到监管，然后你可能会处理这些叫做质量体系的东西。我认为质量体系会给人留下很多创伤，因为它是典型的重官僚主义，拖慢一切。但质量本身的想法其实不是问题。问题在于人类不擅长阅读和解释这些东西。所以当我们制造产品时，我们必须做的一件事是识别所有可能适用的标准。每件事都有标准。有关于锂离子电池如何插入 PCB 的标准，有关于电路板电气绝缘的标准。

We still definitely need humans. And in scientific research in particular, I mean, it's tough to predict—AI is clearly advancing very rapidly. I do think that you need to think about how to have your company be AI-native in the sense that you want to gather all of the context, all the stuff happening in your company, and be able to make that available efficiently to agents, because those are clearly a big part of the future. So for us in Helix, really everything goes in there. One of the reasons that we did that was not just because it's powerful to have everything in one database to link together purchasing, quality, batch records, and manufacturing so that we can trace stuff more efficiently, but also so that we could give it all to agents. And so we found them to be a multiplier for the team, not a replacement. The three biggest areas that AI has had an impact for us so far are: first of all, coding—I mean, that's now basically all this. I've written a lot of code in my life; I don't think I've looked at the source very much the last six months. That is getting really good. Second, regulations. So if you're doing anything really interesting, you're going to end up regulated, and then you'll probably end up dealing with these things called quality systems. A quality system, I think, triggers a lot of scar tissue for people because it's the quintessential heavy bureaucracy, slows everything down. But the idea of quality itself is actually not a problem. The problem is that humans are bad at reading and interpreting these things. So when we make a product, one of the things we have to do is identify all of the standards that might apply. And there are standards for everything. There are standards for how the lithium-ion batteries plug into a PCB, standards for electrical insulation of the boards.

关于运输标签是有标准的，比如某个时候你得把运输包装拿过来，在上面打印一个标签，然后放进一个振动箱里，证明标签的边角不会翘起来导致脱落。所以你要雇监管专家去找所有可能适用的标准，列一个清单，然后做一个电子表格，里面是所有你符合这些标准的证据。这可能需要很多很多个月。历史上，AI 已经完全改变了这一点。我们可以很快地查出所有标准。我们可以很快地生成证据表。而且我认为，在某种程度上存在过度监管——我的意思是，我们确实需要放松一些监管——但我认为 AI 和监管的结合比人们想象的要更契合，你可以用它来理顺很多事情。那些用鲜血写成的监管规定，大体上都是好主意，只是对人类来说很难执行。

There are standards for shipping labels, like at some point you'll have to take your shipping packaging, print a label on it, and put it in a vibe box and show that the corners of the label don't curl in a way that might cause it to detach. So you hire regulatory experts to go find all the standards that might apply, make a list of them, and then have a spreadsheet with all the evidence that you comply with all the standards. This can take many, many months. Historically, AI has totally transformed it. We can very quickly look up all the standards. We can very quickly generate the evidence tables. And I think that to the degree that there's kind of overregulation—I mean, we definitely need to deregulate some things—but I think that the combination of AI and regulation is a better fit than people think, and you can use it to smooth a lot of stuff. Where the regulations are written in blood and largely good ideas, it's just hard for humans to do it.

为什么要构建自己的基础设施平台，而不是直接购买呢？

Why build your own infrastructure platforms rather than just buying them?

这些东西你真的买不到。市面上确实有 ERP 系统，但没有哪家公司喜欢自己的 ERP 系统。我不知道有谁真的想花更多时间在 NetSuite 上。相反，现在有很多例子，公司围绕着一套真正适合自己的软件成长起来。YC 以拥有大量内部软件而闻名，我认为正是这些软件让 YC 运转得很好。Facebook 也非常著名地大力投资内部工具，现在从中获得了大量效率。SpaceX 和特斯拉内部有一套相当大的软件叫 Warp Speed，运行着他们很多制造和研发流程。所以当一家公司围绕着一套适合它的工具成长起来时，它可以非常强大。它的强大方式是你能买到的软件所不具备的。但这需要你真正放眼未来，因为当然，尤其是在种子阶段，这不是你会认为应该关注的事情，而且历史上也确实不是。我认为这是随着智能体而改变的事情。现在你可以用 vibe coding 来做这件事，这使它成为一个合理的考虑。历史上，软件如此昂贵，你不得不购买，而且很长一段时间每个人都是这么做的。我认为那是一个更糟的世界，而那个世界已经改变了，所以现在有更好的选择。但就像我说的，我们之前用过 Greenhouse。Greenhouse 要求我们在第一个漏斗阶段有少数人作为瓶颈。用软件取代它，我们得以探索投票机制，相当详细的投票机制，可以对谁会了解某个申请人做出智能推断。这些是商业软件做不到的。所以对于很多这样的流程，你应该思考你希望它如何为你工作。这些是人的组织，这些人的流程需要有人来运作，如果不经常做，它们就会萎缩。不同的团队和创始人对世界的看法和思考方式不同，适合他们的东西也不同——确实非常不同。但如果你构建了一个适合你的东西，然后把它融入公司，这样当你把某样东西放进去，它就会留在那里，这可能非常非常有用。

You can't really buy these things. There are ERP systems out there, but there's no company that loves their ERP system. I don't know anyone who really wants to spend more time in NetSuite. On the contrary, there are a bunch of examples now of companies that grow up around a piece of software that's really fit just for them. YC famously has a lot of internal software that I think really makes YC work. Facebook also very famously invested heavily in internal tools and now gets a lot of efficiency from that. SpaceX and Tesla internally have a pretty giant piece of software called Warp Speed that runs a lot of their manufacturing and R&D processes. So when one company grows up around a harness fit to it, it can be very powerful. It is powerful in a way that the software you can buy isn't. But this requires you to really look into the future, because certainly, especially at the seed stage, this is not the thing that you would think you should be focusing on, and historically it has not been. I think this is a thing that has changed with agents. The fact that you can vibe code this now makes it a reasonable thing to think about. Historically, software has been so expensive that you would have had to buy it, and that's what everybody did for a long time. That was, I think, a worse world, and that world has changed, so now there are better options available. But like I said, we previously used Greenhouse. Greenhouse required us to have a small number of people as a bottleneck at that first funnel stage. Replacing that with software, we were able to explore voting mechanisms and fairly detailed voting mechanisms that can make smart inferences about who would know about an applicant. Things that you can't really do with commercial software. So for a lot of these processes, you should think about how you want it to work for you. These are human organizations, these human processes that have to be staffed, and if they aren't done routinely, they will atrophy. There are things that make sense for different teams and founders in the way they view the world and think about it—it really is all very different. But if you build a thing that works for you and then bake that into the company, so when you put something there, it stays there, it can be very, very useful.

随着脑机接口开始工作并被广泛采用，我们应该期待哪些变化？智力差异不再重要了吗？

What changes should we expect as BCIs start to work and get widely adopted? Do intelligence differences no longer matter?

有一种说法是脑机接口是人工智能的相邻故事，确实有些道理。最终，如果 AI 在构建超级智能机器，而脑机接口实验室在构建有意识的机器，并且我们在构建脑对脑的连接，使得这些事物之间的界限变得不那么有意义——在某个时刻，你会想要一个我们能参与其中的超级智能有意识机器。但这对我来说其实感觉更遥远。我认为在短期内，脑机接口确实是一个长寿故事。而我把长寿看作就是医疗保健，就是生物技术。只是——我认为不能说制药公司或任何过去的医疗公司对治愈不感兴趣。我认为那是他们所有人都想要的。只是那超出了我们的能力。在神经工程领域，人们听到脑机接口会想到运动解码——比如我在运动皮层放一些电极，现在他们可以像玩电子游戏一样控制它。我认为神经工程远不止于此。我们把视网膜假体也包括在内。我们把人工耳蜗也包括在内。我认为这是对所有医疗保健的一个逆向观点。它给你带来了你在医学中通常看不到的效应量。比如，如果一位帕金森患者服用多巴胺能药物，能起效一段时间，但过一会儿效果就相对较小。你打开脑深部刺激器，患者从无法握住一杯水，到大约 10 秒内就能写连笔字。你打开——如果你想谈强烈的患者证言，你应该看看新生儿的人工耳蜗被打开时的情景。这些就是——当你直接把大脑当作计算机来处理时，你不仅不必解决一些真正困难、超出人类能力的生物学问题，而且你能很容易地得到这些结果，再次是——你能得到一个工程梯度，你能更可靠地得到它们，而且它们就是很大的效应。所以我认为这是延长和改善每个人生命的一种方式。大脑是让你成为你的东西。它是原则上你无法移植的唯一东西。你可以得到一个新的心脏或新的肝脏。你甚至原则上不能得到一个新的大脑。而且大脑通常不是衰竭的那个。所以如果你能直接处理大脑，我认为这与其说是一个 AI 故事，不如说是一个激进的长寿故事，就目前而言。尽管所有这些事情会在某段时间内汇聚在一起。

There's this meme that BCI is an artificial intelligence adjacent story, and there's some of that. Eventually, if AI is building super intelligent machines and BCI labs are building conscious machines and we're building brain-to-brain connections so that the boundaries between those things become less meaningful—at some point you want a super intelligent conscious machine that we can participate in. But that actually feels further away to me. I think in the near term, BCI is really a longevity story. And I view longevity as really just healthcare, just biotech. It's just that—I think it is not right to say that the pharma companies or any of these past healthcare companies are not interested in cures. I think that is what all of them want. It's just that that's been beyond our capabilities. In neural engineering, people hear BCI and think of motor decoding—like I put some electrodes in motor cortex and now they can control it like a video game. I think neural engineering is much broader than that. We include our retinal prosthesis in that. We include cochlear implants in that. This, I think, is a contrarian take on all of healthcare. It gives you these effect sizes that you just don't really see in medicine. Like if you have a patient on a dopaminergic drug for Parkinson's that works for some period of time, but it's a relatively small effect after a little while. You turn on a deep brain stimulator and a patient goes from not being able to hold a cup of water to being able to write cursive in like 10 seconds. You turn on—if you want to talk about strong patient testimonials, you should see a newborn having their cochlear implant turned on. These are just—when you deal directly with the brain as a computer, not only do you not have to solve some of these really hard biology problems that are just beyond humanity's capabilities, but you get these results very readily that again are just—you can get an engineering gradient, you can get them more reliably, and they're just large effects. So I see this as a way to extend and improve the life of everybody. The brain is the thing that makes you you. It's the only thing that in principle you can't transplant. You can get a new heart or a new liver. You cannot even in principle get a new brain. And the brain is usually not the thing that fails. So if you can deal with the brain directly, I think this is more of a radical longevity story than it is an AI one for the moment. Although all of these things will come together over some period of time.

对于你的 IEN 审查绩效系统，你如何防止员工在投票上串通，或者故意给某人投反对票？

For your IEN review performance system, how do you prevent employees from colluding on their votes or downvoting somebody on purpose?

正如我提到的，有一些技巧。例如，应用马尔可夫链蒙特卡洛 dropout 让我们能够检测到投票点击之类的东西，因为现在不是看到一个峰，而是会看到两个峰。这是一个线索，可以去调查。它的设计是为了容忍这些事情。我认为它确实相当透明。它也不是我们唯一的信号。

As I mentioned, there are some tricks. For example, applying Markov chain Monte Carlo dropout allows us to detect things like voting clicks, because now instead of seeing one peak, you'll see two peaks. That is a clue to look into that. It's designed to be tolerant of these things. I think it is really fairly transparent. It's also not our only signal.

在构建像神经科技这样长周期的事情时，这其中的一些是技巧。你如何确定维持公司生存实际需要多少资金跑道，又如何让投资者为这么多资金买单？

Some of this is trade craft when you're building something as long horizon as Neurotech. How do you figure out how much runway you actually need to keep the company alive and how do you get investors to fund that much?

所以有时候，我的意思是，你经常看到创始人，尤其是更有经验的那些，向风投推销他们认为合理的要价，而不是他们实际运行实验所需的资金。你筹集一定数额的钱是为了去找到某个答案。那个答案可能是“不”。投资者理解这一点，取决于你所在的行业，但你必须真正去运行实验。而且，有些事情确实值得用 5000 万美元或零美元来资助，但不是 500 万美元。你不会运行实验。那会是一次非常令人沮丧的经历。你会得到一个模棱两可的结果。所以我的第一条建议是，你应该弄清楚你认为实际运行实验需要什么，这不是整个公司。那是你的下一个价值拐点。无论你的计划多么雄心勃勃、多么开放，你都应该对下一个关键价值拐点有所感知。需要哪些实验来支撑它？把它定价出来，然后筹集两倍的钱。所以我会，我的意思是，会有一些浪费。我认为如果你能把浪费降到 20% 或 30%，那就相当不错了。总之，建议是弄清楚实际运行实验的成本，筹集两倍于此的钱，然后要么筹集要么不筹集。除此之外，你总会发现新事物。通常有一条穿过混乱的路。但同样，当你创办公司时，你不会得到保证，说你不会走上一条通往虚无的桥梁，或者说它会在你现有的资金上成功。你必须深入其中，在半路上摸索出来。我认为人们应该比他们通常认为的更需要尽早推动盈利。对我们来说，即使我们被视为一家非常开放、路线图很长的深科技公司，这是事实，但我们在这一点上也毫不留情地专注于收入。我们正努力实现可持续性。感觉公司就像是在慢慢死于这种“金钱癌症”，我们每隔几年就能通过融资把它缓解到缓解期，但它最终会卷土重来。我希望那种感觉结束。所以无论问题有多大、愿景有多大，你确实需要考虑如何实现收入，这样不仅你能永远做下去，而且你会根据你的长期路线图被估值，而不是根据你死亡的概率。而且这真的会打开另一批投资者的大门，否则他们不会相关。

So sometimes, I mean, you often see founders, especially more experienced ones, pitching VCs for what they think is reasonable to ask for rather than what they need to run the experiment. You're raising some amount of money to go find out some answer. The answer to that might be no. The investors understand this, depending on what business you're in, but you have to actually run the experiment. And one of the things, there are definitely some ideas that are worth funding with $50 million or zero dollars, but not $5 million. You won't run the experiment. It'll be a really frustrating experience. You'll get an ambiguous outcome. And so my first piece of advice is you should figure out what you think it's going to take to actually run the experiment, which is not the whole company. That is what is your next value inflection. You should have, no matter how ambitious and open-ended your plan is, some sense of what is your next key value inflection point. What are the experiments that need to go into that? Price that out and then raise twice the money. So I would, I mean, there's some amount of waste. I think if you can get waste down to 20 or 30%, that's pretty good. And anyway, the advice is figure out what it costs to actually run the experiment, raise twice that, and raise that or not. Beyond that, you'll always discover new things. There's usually some path through the mess. But also, when you start the company, you're not going to get a guarantee that you won't be on a bridge to nowhere or that it will work on the funding that you have. You're going to have to get in there and figure it out halfway through. I think that people should push for profitability sooner than they often think they need to. For us, even though we are seen as this very open-ended deep tech company with a very long roadmap, which is true, we are also relentlessly focused on revenue at this point. We are trying to get to sustainability. It feels like the company is kind of constantly dying slowly of this money cancer that we can beat into remission every couple years with the fundraising, but then it eventually comes back. And I want that feeling to be over. So no matter how big of a problem or big of a vision it feels, you do need to think about how do you get to revenue so that not just you can do it forever, but then you'll be valued on your long-term road map, not valued on your probability of dying. And it really opens up another set of investors that wouldn't be relevant otherwise.

你收到过的最好的建议是什么？

What is the best piece of advice you've received?

我不知道。在过去 20 年里，我积累了太多的脑损伤，以至于我的记忆无法挑出那一条。我的意思是，我认为，除了速度是成功的基础、基础设施决定你的速度之外，重要的是要认识到没有普遍原则。我认为人们在寻找捷径。人们在寻找一套现成的指令，就像“哦，我搞明白了”，但那并不存在。每一件事都是不同的。当你到达历史的那一刻，你在做新的事情。我的意思是，我们可以反思一下，这一切成为可能有多么疯狂。在人类历史的大部分时间里，如果你是一个聪明的 20 岁年轻人，有一个让社会变得更好的想法，并且你向有资本的人提出这个想法，反应会是“你应该关注收成”。事实上，这并不广泛可用，也不是普遍可用，但如果你是一个真正聪明的 20 岁年轻人，你可以来到旧金山提出你的理由，如果这是一个有趣的想法，你会得到数百万美元去验证。这在当今世界大部分地区都不是这样，而且在历史上任何地方的大多数时候肯定也不是这样。但这不应该让人觉得正常。这是为了推动前沿。当你在前沿时，你是一边走一边摸索。这就是工作。所以我会尝试少依赖那些感觉像创业建议的东西，更多地依赖你的判断力有多好，你在你的领域里如何精炼它，并记住你必须自己思考。

I don't know. I've acquired way too much brain damage over the last 20 years to have a memory capable of picking that out. I mean, I think if other than speed being the basis of success and infrastructure determines your speed, it is important to appreciate that there are no general principles. I think people are looking for shortcuts. People are looking for a pat set of instructions that are like, oh I figured it out, and that doesn't exist. Every one of these things is different. When you get to that moment in history, you're doing something new. And I mean, we can reflect for a second on how crazy it is that all this is possible. For the vast majority of human history, if you were a smart 20-year-old that had an idea to make your society better and you raised this to the people with capital, the reaction was like, you should pay attention to the harvest. The fact that it is not widely available, it's not universally available, but it's not widely available that if you're a really smart 20-year-old, you can come to San Francisco and make the case and if it's an interesting idea, you'll get millions of dollars to find out. This is not the case for most of the world today and it's certainly not the case for most of history anywhere. But that shouldn't feel normal. This is given to push the frontier out. And when you're on the frontier, you're figuring it out as you go. That is the job. And so I would try to rely less on things that feel like startup advice and more on how good is your judgment, how well is that refined in your domain, and remembering that you have to think for yourself.

让脑机接口（BCI）发挥作用所涉及的一些最困难的剩余工程挑战是什么？

What are some of the hardest remaining engineering challenges involved in getting BCIs to work?

所以在脑机接口中，我们常常感到受到植入物功率和热约束的极大限制。这造成了强烈的压力，要求尽可能少地植入，其余部分在体外完成。你不能让电线穿过皮肤，因为皮肤是一个非常重要的免疫屏障。如果你有任何连接器穿过头皮，皮肤不会完全愈合，而且你不断面临细菌沿着它爬进大脑的风险，然后病人就会非常糟糕。所以你真的必须能够闭合皮肤。这要求你植入某种无线电或收发器，并让电力传输下去。低功耗电子学是一个前沿，这非常重要。更多像我之前提到的，随着我们的生物工程能力增强，很多这方面正变得越来越生物学化。但讽刺的是，在这些植入物上，更困难、更开放的问题之一是我们所谓的封装。我们的欧洲同事称之为“热带化”。这是你保持设备在体内、身体不进入植入物的能力。身体里没有真正被动的表面，即使是骨头也在不断重塑。所以如果我放入一个设备，它会受到身体的攻击，而它不会自我再生。所以你需要一种能够长期存活的材料。经典的例子是激光焊接的钛罐。如果你见过心脏起搏器或脑深部刺激器，它们有一个大钛盒。显然我们不能在眼睛里放一个大钛盒。

So in BCIs, we often feel very limited by power and thermal constraints on the implants. And so this creates a strong pressure to implant as little as possible and do the rest off the body. You can't pass a wire through the skin because the skin is a very important immune barrier. And if you have any connector through the scalp, the skin won't fully heal around it, and you're constantly at risk of a bacteria crawling down that and into the brain, and then the patient's going to have a really bad time. And so you really have to be able to close the skin. That requires you to have implanted a radio or transceiver of some sort, and getting the power on that down. There's a frontier at low power electronics, which is really important. More as I mentioned earlier, a lot of this is now becoming increasingly biology as our biological engineering capabilities increase. But then on those implants, ironically, one of the harder, more open problems is what we call packaging. Our colleagues in Europe call it tropicalization. This is your ability to keep your device in and the body out of an implant that you put in the body. There are no truly passive surfaces anywhere in the body, even bone is constantly getting remolded. And so if I put a device in, it's going to be getting attacked by the body and it's not regenerating itself. And so you need a material that is going to survive that for an extended period of time. The classic example of this is the laser welded titanium can. If you've seen a pacemaker or deep brain stimulator, they've got this big titanium box. Obviously we can't put a big titanium box in the eye.

有趣的是，在我们之前大约 10 年，有一种早期的视网膜假体，是一个把钛盒附着在眼球上的装置。手术需要四个半小时。他们有一条小带子绕着眼球，眼睛侧面有个钛盒，里面装着电池和一块小 PCB。这根本不行，不够好。他们必须以某种方式去掉那个盒子。在我们的案例中，我们通过激光投影技术解决了这个问题，实现了无线供电。但拥有这种下一代封装，某种保形涂层，既能保护植入物不被身体降解，又对身体无害，还能抵抗身体试图摧毁它的各种方式——这种材料科学是一个非常开放的领域。如果你对材料科学感兴趣，那是我们需要取得进展的地方。

Interestingly, one of the earlier retinal prostheses before us, about 10 years ago, was a device that had a titanium box attached to the eyeball. It required a four-and-a-half-hour surgery. They had a little belt that went around the eyeball with a titanium box on the side, containing a battery and a small PCB. This didn't work; it wasn't good enough. They needed to get rid of that somehow. In our case, we've solved this with the laser projection trick, where we power it wirelessly. But having this next-generation packaging, some type of conformal coating that we can use to protect the implant—that is not degraded by the body, is also not harmful to the body, and is resistant to all the ways the body will try to kill it—that material science is a very open-ended field. If you're interested in material science, that is a thing we need progress in.

你是如何与医学领域互动来构建你的视网膜植入物的？

How did you approach interacting with the medical field to build your retinal implant?

商业只是与人交谈和做事的花哨说法。你给他们发邮件。我们的视网膜植入物最初是由斯坦福的一位教授在将近 15 年前发明的，我想。它被授权给了一家我们一直在追踪的欧洲公司。让我先退一步。当我们创办公司时，我来自 Neuralink，我的五位联合创始人中有四位来自 Neuralink。我们在 2021 年初环顾世界，问：我们能做的最有价值的事情是什么，既可能在不久的将来成功，又能对患者产生重大影响，还能让我们成为想建立的那种可扩展医疗设备公司的基础？我们得出的结论是，通过刺激视网膜来恢复盲人视力就是那件事。视网膜中有两种细胞可以选择刺激：双极细胞或视神经。你可以用电或光来刺激。我们探索了所有四个象限。我们在内部开发了一种最先进的基因疗法，用光刺激其中一种细胞。我们认定这家法国公司是电刺激领域最先进的。这是一个小圈子；你可以见到人，和他们交谈。最终，收购他们对我们来说是有意义的。我们最终获得了许可和技术，并且我们一直与外科医生和医生合作。如果你想开发一种新的手术，最好通过人脉网络，这样人们更有可能回复你的邮件，但我们经常给外科医生发冷邮件，说：‘嘿，我们有一种奇怪的手术要开发。你想当顾问吗？’人们会回复。

Business is just a fancy word for talking to people and doing things. You send them emails. For our retinal implant, it was originally invented by a professor at Stanford almost 15 years ago, I think. It was licensed to a European company that we were tracking. Let me back up a second. When we started the company, I came from Neuralink, and four of my five co-founders came from Neuralink. We looked around the world in early 2021 and asked: what is the most valuable thing we can do that would likely work in the near future, have a big impact on patients, and allow us to be the foundation for the type of scalable medical device company we wanted to build? We concluded that restoring vision to the blind by stimulating the retina was the thing. You have a choice of two types of cells in the retina to stimulate: bipolar cells or the optic nerve. You could do that electrically or optically. We explored all four quadrants. We developed internally a state-of-the-art gene therapy that optically stimulated one of those cells. And we identified this French company as being the state-of-the-art in electrical stimulation. It's a small community; you can meet people and talk to them. Eventually, it made sense for us to acquire them. We ended up with the license and the technology, and we work with surgeons and doctors all the time. If there's a new surgery you want to figure out, it's best to go through networks so people are more likely to respond to your email, but we cold email surgeons all the time, saying, 'Hey, we have a weird surgery to develop. Do you want to be a consultant?' and people reply.

在我进入下一个问题之前，这里有一个真正的文化现象。我在 SpaceX 边缘徘徊的时候观察到，至少在七八年前，这家公司大约 20% 的人可以说是坚定的火星殖民者，80% 是严肃的工程师。我觉得那些人是疯子，他们只想研究世界上最高性能的甲烷氧发动机。要长期真正成功，你需要这两种文化。这在医学领域尤其棘手，对吧？因为那是一个非常保守、可以说是非常专制的文化。同样，在我们公司，我们大约有 30%——这是一个公开的超人类主义使命——然后 70% 是严肃的临床医生、科学家和研究人员，他们认为那些人是疯子，但我们会在这个过程中为关键的未满足需求制造一些真正有价值的医疗设备。我认为让 Science Corporation 非常特别的一点是，它同时拥有这两种文化，并且能够将它们整合。我们能够同时进行一些我认为处于 Overton 窗口边缘的非常酷的研究，同时现在在六个国家进行临床试验，在欧洲有获批的医疗设备，临床试验结果发表在《新英格兰医学杂志》上。你必须能够驾驭这两件事，才能真正重塑未来。

Before I get to the next question, there is a real cultural thing here. In my time hanging around the periphery of SpaceX, I observed that at least circa seven or eight years ago, probably 20% of that company is what you might characterize as committed Martian colonists, and 80% are serious engineers. I think those people are lunatics, and they just want to work on the highest performance methalox engines in the world. You need both of those cultures to be really successful long term. That's especially tricky in medicine, right? Because that's a very conservative, arguably very authoritarian culture for the most part. Similarly, at our company, we have I'd say 30%—it's an overtly transhumanist mission—and then 70% serious clinicians, scientists, and researchers who think those guys are crazy, but we're going to build some really valuable medical devices for critical unmet needs in the process. I think one of the things that makes Science Corporation very special is that it has both of those cultures and is able to integrate them. We're able to simultaneously do some really cool research that I think is at the edge of the Overton window, while simultaneously running clinical trials in six countries now, with an approved medical device in Europe and clinical trial results in the New England Journal of Medicine. You have to be able to navigate both of those things to really reshape the future.

对于早期创始人来说，生物技术是否变得更容易进入了？

Has biotech gotten easier to break into for earlier stage founders?

生物技术仍然是资本密集型的。如果你有其他选择，我不确定我会推荐生物技术。对我来说，我几乎在 30 年前就意识到，如果你能改变大脑，你就能改变现实。这是未来 30 或 40 年最大的使命之一：建造这些东西。我时不时会想，如果我当初选择 AI 而不是 BCI，我的生活会轻松得多。但总得有人去做。生物技术很难；它比你能做的许多其他事情要艰难得多。但当你成功时，它会对你在其他地方看到的东西产生影响。我越来越觉得——保罗·格雷厄姆很久以前写过，你在不同的城市会感受到不同的氛围：马萨诸塞州剑桥的氛围是你应该更聪明；纽约的氛围是你应该更富有；旧金山的氛围是你应该更强大。尤其是随着人工智能的兴起，我认为人们意识到这不仅仅是钱的问题。对许多最有效的初创公司创始人来说，这不是钱的问题；而是以某种方式改变世界。结果发现，对于那个项目，营利性公司是一种极其强大的方式，可以调动所需的资源，让世界以那种方式改变。这不是关于钱，而是关于权力。权力有很多种：经济权力、军事权力。但治愈病人的权力是非常戏剧性的一种。当你获得这种权力时，它不仅是一种重塑世界的真正力量，而且是一种可以很容易分享的力量。你不能分享军事权力或经济权力，但你可以分享恢复盲人视力或给癌症患者生命的权力。世界变得越来越复杂，在这个房间里的人们所从事的所有事情都会产生重大影响。生物技术很难，非常资本密集，而且道路漫长。

Biotech remains capital intensive. I don't know that I'd recommend biotech if you have a choice of other stuff to do. For me, I realized almost 30 years ago that if you could alter the brain, you could alter reality. This was one of the biggest missions of the next 30 or 40 years: building these things. Every now and then I think my life would be way easier if I'd just gone into AI instead of BCI. But somebody has to do it. Biotech is hard; it's a much harder path than many other things you can do. But when you're successful, it has an impact on what you see elsewhere. Increasingly, I think—it was Paul Graham who wrote a long time ago that you get vibes in different cities: the vibe in Cambridge, Massachusetts, is you should be smarter; the vibe in New York is you should be wealthier; the vibe in San Francisco is you should be more powerful. Especially with the rise of artificial intelligence, I think people realize this isn't just about money. For many of the most effective startup founders, it's not about the money; it's about changing the world in some way. It just turns out that for that project, the for-profit company is an incredibly powerful way to marshal the resources required to cause the world to be different in that way. This is not about money; this is about power. There are many different types of power: economic power, military power. But the power to heal the sick is a very dramatic one. When you get that, not only is it a real force to reshape the world, it's one that can be shared very readily. You can't share military power or economic power, but you can share the power of restoring sight to the blind or giving life to the cancer patient. The world is getting more complicated, and there are big impacts of all the things being worked on by the people in this room. Biotech is hard, very capital intensive, and a long road.

当你在这个领域创办一家公司时，你是在投入生命中无法挽回的十年，无论结果如何。但当它成功时，对患者及其家庭的影响确实是任何其他行业都无法比拟的。

When you start a company in this space, you're committing to a decade of your life that you will never get back no matter how it turns out. But the results of that when it works, the impact that this has on patients and their families is really unlike any other sector.

最后一个问题，你认为科技界有哪些流行的看法是错误的？

So the last question, what's a popular belief in tech that you think is wrong?

我甚至不知道现在科技界流行的看法是什么。嗯，我是说，好吧，就连构建 Helix 的整个基础都是反传统的。比如，我认为如果你完成了 A 轮融资，然后告诉你的投资者你要用 vibe coding 来开发一个采购系统，我觉得任何理性的董事会都会问你到底在想什么。而我们之所以能做到这一点，是因为我从未收到过这些问题，因为我控制着公司。但这只是一个狭隘的例子，我想。

And I don't even know what the popular beliefs in tech are now. Well, I mean, okay, even the whole basis of building Helix is contrarian. Like, I think that if you raise a series A and then you tell your investors that you're going to vibe code a purchasing system, I think that any reasonable board is going to ask you what you're thinking. And that we were able to do that because I never got those questions because I control the company. But that's one narrow example, I guess.