Debating Immigration Policy for Tech Talent
Interviewer: I want to get into the immigration news. What were your reactions to what you think of the policy?
采访者: 我想谈谈关于移民的新闻。你对这项政策有什么看法？
Interviewer: Well, it was interesting because it seems like anytime the administration touches immigration, there's a huge outcry, knee-jerk outcry, and we saw a lot of that from VCs even. But it's also very interesting that Reed Hastings, who is a classic lefty, was like I've been doing policy for immigration for 30 years and this is the right approach. And this is very much my thought which is this system has been gamed for a very long time. It's very hard for startups to hire because of the lottery system. And it's locked up by the large companies, the consultants, Amazon and Google. And that has to change and I think a very reasonable way to do it is to set price because you've got a market and you need to allocate supply, price is a great way to do it. So I'm very very positive on it.
采访者: 嗯，这很有趣，因为似乎每当政府触及移民问题时，都会有巨大的、本能的抗议，我们甚至在风险投资人中也看到了很多这种情况。但同样有趣的是，像里德·哈斯廷斯（Reed Hastings）这样典型的左派人士却说，他从事移民政策工作30年了，认为这是正确的方法。这和我的想法非常一致，即这个系统已经被滥用了很长时间。由于抽签制度，初创公司很难招聘人才。而这个市场被大公司、咨询公司、亚马逊和谷歌所垄断。这种情况必须改变，我认为一个非常合理的方法就是设定价格，因为你有市场，需要分配供给，价格是一个很好的分配方式。所以我对此非常非常看好。
Aaron Levie: I wouldn't conclude the same outcome that you just concluded. I think that you'd have a situation where the Amazons and Googles would probably actually capture the vast portion of the talent in this situation. So it's not clear to me that startups sort of come out ahead or better off from this particular implementation.
亚伦·列维: 我得出的结论可能和你的不一样。我认为在这种情况下，像亚马逊和谷歌这样的公司实际上可能会攫取绝大部分的人才。所以，我不确定初创公司是否能从这个具体的实施方案中获益或处于更有利的位置。
Aaron Levie: My strategy would be we want the absolute best in the world here. There's not exactly clear that there's a fixed number on that. Some years there might be 5,000, some years there might be 50,000, some years there might be 80,000. We probably want them to be net positive to wages. So let's agree that in any given industry or local, wages should go up with this talent pool as opposed to down. I think you could build a system that basically meets all of those goals while still ensuring that you can get somebody that goes to their master's program and they come out of it they're an AI engineer, they're not yet at the sort of Meta is going to pay them $100 million, but they are going to be totally valuable contributors to our economy.
亚伦·列维: 我的策略是，我们希望世界上最顶尖的人才来到这里。这个数量并没有一个明确的定数，某一年可能是5000人，另一年可能是5万人，再一年可能是8万人。我们可能希望他们对工资水平有净积极影响。所以，我们应该达成共识，在任何特定行业或地区，随着这个人才库的引入，工资水平应该上升而不是下降。我认为你可以建立一个系统，基本满足所有这些目标，同时还能确保一个读完硕士项目出来的人工智能工程师能够留下来。他们或许还达不到Meta愿意支付他们一亿美元的水平，但他们将对我们的经济做出非常有价值的贡献。
AI's Impact on Developer Productivity
Interviewer: Aaron, we were talking about the Meta paper and it was the paper suggested that their developers were actually less productive with AI, but that doesn't square with your experience talking to a lot of different startups, seeing a lot of different startups and how they're so much more productive. So, why don't you talk about where you're seeing startups say they're more productive and why is that happening?
采访者: 亚伦，我们之前在谈论Meta的那篇论文，论文指出他们的开发者在使用AI后生产力实际上降低了，但这与你和许多不同初创公司交流、观察他们如何变得更高效的经验不符。所以，你能谈谈你在哪些方面看到初创公司表示他们生产力更高了，以及为什么会这样吗？
Aaron Levie: So our own case study is we've adopted a few different kind of AI coding tools, cursor being a super popular one internally. The qualitative answers I get from people, some people will say I'm getting a 20 to 30% productivity gain. Other people will say 75%. Interestingly, I have not been able to pinpoint the demographic difference on the answers.
亚伦·列维: 就我们自己的案例研究而言，我们采用了一些不同的人工智能编码工具，其中Cursor内部非常受欢迎。我从员工那里得到的定性反馈是，一些人说他们的生产力提升了20%到30%，另一些人则说提升了75%。有趣的是，我还没能找出这些不同回答背后的人群特征差异。
Aaron Levie: This is self-reporting. But we have internal metrics as well. So about 30% of our code right now is coming from AI. I have a 2 by two of I have senior people that are saying that they're getting 75% productivity. I have junior people that are saying they're getting 75% and then vice versa on the 25%. And I haven't been able to quite figure out a pattern. Maybe except for the biggest criteria is just the people that actually push the AI to do more, which is sort of this other new kind of psychographic, which is just like who is willing to just be like, "you know what, I'm going to yolo this this task and just see what the AI comes up with."
亚伦·列维: 这是自我报告的数据。但我们也有内部指标。目前，我们大约30%的代码是由AI生成的。我有一个二维矩阵，既有资深员工说他们生产力提升了75%，也有初级员工说提升了75%，反之亦然，也有人说只提升了25%。我还没能完全找出其中的规律。或许最大的标准就是那些真正去推动AI做更多事情的人，这是一种新的心理特质，就是看谁愿意抱着“管他呢，我把这个任务交给AI试试，看它能搞出什么名堂”的态度。
Aaron Levie: What's crazy and this is the thing that just blows my mind. I will regularly talk to three, five, 10 person startup founders that self-report they might be getting somewhere on the order of like three to five to 10x productivity improvements. And the big difference is that a year ago, the conversation would be about AI sort of doing type ahead and it can add maybe a few lines of code. And then now obviously the big phenomenon is background agents where I give it a very detailed prompt, I send it off, it comes back. The startups that are getting real multiples of productivity gain are fundamentally engineering in a different way. They're sending off a task, the task goes off, comes back in 20 minutes and then they're really in the business of doing code review, not code writing.
亚伦·列维: 让我感到疯狂和震惊的是，我经常和那些只有三、五、十个人的初创公司创始人交谈，他们自己报告说，他们的生产力提升可能达到了三倍、五倍甚至十倍。最大的不同在于，一年前，我们谈论的AI还只是做一些类似自动补全的功能，可能帮你增加几行代码。而现在，最显著的现象是后台代理（background agents），我给它一个非常详细的指令，把它派出去，然后它带着结果回来。那些生产力实现倍数增长的初创公司，正在从根本上以一种不同的方式进行工程开发。他们派发一个任务，任务执行20分钟后返回结果，然后他们真正的工作就变成了代码审查（code review），而不是代码编写。
The Early Adopter Mindset and AI's Forgiveness Factor
Interviewer: This is all early adopters and early adopters are very forgiving of mistakes on purpose. And it's a super interesting dynamic where when something is brand new a culture around it develops which just lets anything happen. The early internet people didn't complain that the internet was slow, right?
采访者: 这些都是早期采用者，而早期采用者对错误是有意地非常宽容的。这是一个非常有趣的动态，当一个东西是全新的，围绕它会形成一种文化，允许任何事情发生。早期的互联网用户并没有抱怨网速慢，对吧？
Interviewer: I think that people use these AI tools today and they assume like, "Well I'm not a doctor. I don't know anything about being a doctor. Let me ask you to cure me right and diagnose me." And it's like whoa that is the worst. And all the people who see failure, they weren't great programmers to begin with and they didn't know how to ask, they didn't want to review it. Whereas great programmers or just professional programmers know that code review is really important and that's just how you do it.
采访者: 我认为现在人们使用这些AI工具时，他们会假设：“嗯，我不是医生，我对医学一无所知，让我来请求你治愈我并诊断我吧。”这简直是太糟糕了。所有看到失败案例的人，他们本身就不是优秀的程序员，他们不知道如何提问，也不想去审查结果。而优秀的程序员或者说专业的程序员知道代码审查非常重要，这是工作的标准流程。
Interviewer: I think there's an aspect that makes it very difficult to measure. One of them is these models are so magic that you get dazzled. So even if it's not what you want, you're like it was great. I think it's very easy to conflate that with being productive. Like it's not what I wanted, but it was amazing, therefore it must be great. I see this a lot, people are so enthusiastic about using AI, but it really hasn't impacted their output. They're just enthusiastic.
采访者: 我认为有一个方面使得AI的生产力很难衡量。其中之一是，这些模型太神奇了，以至于你会被迷住。所以即使它给出的不是你想要的，你也会觉得“太棒了”。我认为人们很容易将这种感觉与生产力混为一谈。就像，“这不是我想要的，但它太惊人了，所以一定很棒”。我经常看到这种情况，人们对使用AI充满热情，但这并没有真正影响他们的产出，他们只是热情高涨而已。
AI as a Platform Shift: Advantage Startups
Aaron Levie: This is why I'm so unbelievably pumped up. And it's because you have a complete reset of the landscape where there's incumbent advantage in distribution but that is it. There's no other real advantages. And then there's a bunch of disadvantages. You have the exact makings of a landscape where new startups can come in and do things that incumbents either can't or there's no obvious incumbent to even do that thing because you're taking maybe services and turning them into AI labor and there was no software incumbent previously to even attempt to do that.
亚伦·列维: 这就是我为什么感到如此难以置信地兴奋。因为你看到了整个格局的完全重置，现有的大公司在分销渠道上还有优势，但仅此而已。没有其他真正的优势了，反而有一堆劣势。你看到的正是一个让新初创公司能够进入并做那些现有公司要么做不了、要么根本没有明显对应公司去做的市场格局。因为你可能正在将服务转化为人工智能劳动力，而以前根本没有软件公司尝试过这样做。
Aaron Levie: It's the first time in history where you have none of the disadvantages of a big company. And the traditional advantage you have as a big company is you have scale and you have distribution. Scale because you can look at a feature and you say we're going to go build that next month. Now as a startup you instantly have scale because you know background agents etc. So we've kind of neutralized a lot of the incumbent advantages. And so thus it's a ripe opportunity for brand new startups.
亚伦·列维: 这是历史上第一次，你作为一家初创公司，却没有任何大公司的劣势。传统上，大公司的优势在于规模和分销渠道。有规模是因为你可以看到一个功能，然后说“我们下个月就把它做出来”。而现在，作为一家初创公司，你瞬间就拥有了规模，因为有后台代理等等。所以我们在某种程度上已经抵消了许多现有大公司的优势。因此，这对全新的初创公司来说是一个成熟的机遇。
Interviewer: This is so critical because what's really happening is this is why it's an actual platform shift. So Silicon Valley has seen this movie many times before and that's why often there's a lot of this "is this crying wolf or not" because everybody knows that when there's a platform shift that's the moment in time that startups are at an advantage.
采访者: 这太关键了，因为正在发生的事情就是为什么这是一个真正的平台转移。硅谷已经多次看过这部电影了，所以常常会有很多关于“这是不是狼来了”的讨论，因为大家都知道，当平台转移发生时，就是初创公司占优势的时刻。
Software Finally Eats Professional Services
Aaron Levie: The other thing that I just don't think we've had a modern case study for is this opening up of non-software TAM for software. So there's not even incumbents in the classic sense. The incumbents are really just professional services categories of work. And so it's really for the first time ever you're packaging up intelligence for a particular domain and workflow. And so there's no software company you're competing against.
亚伦·列维: 另一件我认为我们还没有现代案例可以研究的事情是，软件正在为非软件的TAM（潜在市场总额：如果一个产品或服务达到100%的市场份额，可能获得的总收入机会）开辟新天地。所以，这里甚至没有传统意义上的现有竞争者。真正的“现有者”其实只是专业服务领域的工作类别。因此，这真的是有史以来第一次，你将某个特定领域和工作流程的智能打包成产品。所以，你并没有在和另一家软件公司竞争。
Interviewer: The people you're probably disrupting on paper are actually the primary users of your technology.
采访者: 那些在纸面上你可能正在颠覆的人，实际上是你技术的主要用户。
Aaron Levie: And so then it's like there's really no inherent competition until eventually more companies flood that space to do that idea.
亚伦·列维: 所以，这就好像在最终有更多公司涌入这个领域来实现同样的想法之前，基本上不存在固有的竞争。
Interviewer: There's a whole world. The earliest PC software was extremely vertical. If you actually look at the TRS-80 catalog from the early 1980s, it would be like this is crop rotation software. There was like I run a dentist's office and this is scheduling for a dentist's office. And what happened was, these professional services organizations are going to be some that are computer savvy. They're just going to go, "you know what, we should just build a company." Huge numbers of these verticals are just going to be existing pro-serve that turn into...
采访者: 这里有整个一个世界。最早的个人电脑软件是极其垂直化的。如果你看80年代初的TRS-80电脑目录，你会看到像“作物轮作软件”这样的东西。还有，比如“我开牙医诊所，这是牙医诊所的排班软件”。将要发生的是，这些专业服务机构中，会有一些对计算机很精通的。他们会想，“你知道吗，我们干脆自己成立一家公司吧。”大量这样的垂直领域，将会由现有的专业服务公司转型而来。
Aaron Levie: It's an incredible time where if you're just committed to not building a software company. So you're going to build a real world company. It's an incredible time if you started from scratch with now AI as your foundation. If you wanted to be a new systems integrator and your whole point is that we are a systems integrator but we use Claude Code or we use Cognition or we use Cursor to get the output, you will have such an advantage over any incumbent. I've seen examples of people building new ad agencies because obviously if you can do literally a million dollar ad video campaign for $5,000, somewhere between those two numbers, you can charge the customer. So there's this incredible time where you can just be building all new kinds of companies from the ground up leveraging the breakthroughs that we've now seen in AI.
亚伦·列维: 这是一个不可思议的时代，即使你决定不创建一家软件公司，而是要建立一家实体公司。如果你现在以AI为基础从零开始，这会是一个绝佳的时机。如果你想成为一个新的系统集成商，你的核心理念是“我们是系统集成商，但我们使用Claude Code、Cognition或Cursor来获得产出”，你将比任何现有公司都有巨大的优势。我见过有人建立新的广告公司的例子，因为很明显，如果你能用5000美元做出一个价值百万美元的广告视频宣传活动，你就可以在两者之间的某个价位向客户收费。所以，这是一个可以利用我们在AI领域看到的突破，从零开始建立各种全新公司的绝佳时机。