RIP to RPA: How AI Makes Operations Work

人们总说AI，好像一切都要天翻地覆了似的。

People use the term AI, and they're like, oh, everything's gonna change something.

可这到底是什么意思呢？

Like, what does that mean?

没人愿意干数据录入这种活儿。

Nobody wants to do data entry.

没人愿意，真的。

Nobody wants to Yeah.

坐在后面，对着一百份传真一个字一个字往系统里输。

Sit in the back and read a 100 faxes and try to input that into a system.

现在正是切入这些传统市场最让人兴奋的时机。

This is one of the most exciting times to actually actually be going after some of these legacy markets.

以前技术够不着的地方，现在全有大把机会等着挖。

There's just so much untapped opportunity that technology just wasn't able to penetrate before.

有了智能AI代理、语音代理这些工具，现在终于能啃下这些硬骨头了。

Then now with Intelligent AI agents, with voice agents, etcetera, you can now tackle.

互联网给了我们太多东西——随时随地获取全球信息、展示新艺术和表达的平台、几乎所有的电影和歌曲都能装进手机，还有只要有Wi-Fi，哪儿都能上班。

The Internet has given us so much, whether it's instant access to the world's information, platforms for new art and expression, nearly every feature film or song in your pocket, or the ability to work from just about anywhere with a Wi Fi connection.

好处当然远不止这些。

The list, of course, goes on.

但还是有些承诺，到现在也没真正兑现。

But there are still some promises that haven't quite hit their mark yet.

比如机器人流程自动化，也就是RPA，这词儿都喊好多年了。

One example, Robotic Process Automation, otherwise known as RPA, has been a buzzword for years.

它当初承诺要彻底改变重复性工作的自动化方式。

A buzzword that promised a revolution in automating repetitive tasks.

像UiPath这种2005年就成立的公司，甚至宣称要实现‘全自动化企业’。

Companies like UiPath, founded in 2005, even promised to enable the, quote, fully automated enterprise.

RPA起步时确实挺火，但问题很快暴露了——系统太死板，连点击哪个按钮都得硬编码，可现实中的工作流程哪有那么规整？

And while RPA did start strong, its limitations became evident due to its rigid systems, sometimes even hard coding specific processes down to the button click, meeting with the unpredictability of most workflows.

当然，UiPath现在市值七百亿美金，整个RPA行业也没失败，只是现在该换新面貌了。

Of course, UiPath, with a market cap of $7,000,000,000 as of this recording, and the RPA industry at large are no failure, but they are poised for a facelift.

现在出现了智能自动化，这是一种由人工智能驱动的新模式，能够处理几乎所有组织中那些杂乱无章、非结构化的流程——而这正是以前RPA完全搞不定的任务。

Enter intelligent automation, a new paradigm powered by AI that can handle messy, unstructured workflows that exist in just about every organization, precisely the types of tasks that RPA previously could not handle.

所以今天这期节目，我们和a16z的合伙人Tan一起聊聊RPA的第二春，话题基于她那篇爆火的文章《RPA再见，智能自动化崛起》，当然，文章链接我们会放在节目笔记里。

So in today's episode, we discuss RPA's second leg together with a16z partner, Tan, based on her viral article, RIP to RPA, the rise of intelligent automation, which, of course, we'll link in the show notes.

那到底是什么让AI今天能做成这些事呢？

So what specifically about AI makes this possible today?

初创公司该怎么看待这个机会？为什么这个机会比上一轮大得多？

How should startups be thinking about this opportunity, and why is this opportunity so much larger than the last era?

不过在开始之前，我想让你想想，你生活中最烦人、最重复的那个事儿是什么。

But before we begin, I want you to think about the most annoying and repetitive thing that you need to do.

现在想象一下，要是这个事儿能自动完成。

Now imagine that task automated.

这样的未来，可能没你想象的那么远。

That future may not actually be so far away.

赶紧听下去，看看你怎么能参与到这场变革里来。

So listen in to see how you can get involved.

提醒一下，本内容仅作信息参考，不构成法律、商业、税务或投资建议，也不应用于评估任何投资或证券，且并非针对任何a16z基金的投资者或潜在投资者。

As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a sixteen z fund.

请注意，a16z及其关联方可能仍持有本节目讨论的公司的投资。

Please note that a sixteen z and its affiliates may also maintain investments in the companies discussed in this podcast.

更多详情，包括链接

For more details, including a link

关于我们投资的信息

to our investments,

请访问a16c.com/disclosures查看。

please see a 16c.com/disclosures.

Kimberly，你写了一篇挺有意思的标题的文章——RIP RPA。

Kimberly, you wrote an article with a pretty fun title, RIP to RPA.

那咱们直接切入正题吧。

So let's jump into that.

不过在那之前，先说说，什么是RPA？

But first, what is RPA?

RPA是机器人流程自动化，简单说就是帮公司把那些特别繁琐的手动工作自动化掉。

RPA stands for Robotic Process Automation, and it's a way of basically automating very manual tasks within an organization.

比如数据录入、发票处理这类每个企业都得干，但又没人觉得这是核心本事的事儿。

So things like data entry or invoice processing that basically every business has to do, but it's nobody's core competency.

就是那种公司里脏活累活、乱七八糟的内部琐事。

It's just one of the, like, dirty, messy internal things within an organization.

过去呢，全靠人肉一点一点干。

So historically, it's been done very manually.

你只能雇个数据分析师，或者招个后台运营的人来搞定。

Like, you would just hire a data analyst or you would hire a back office operations person.

过去二十年左右，开始有人琢磨：这些事儿能不能自动化？

And there was this, I would say, innovation in the last twenty years where people were like, is it possible to automate these tasks?

以前的做法是搞机器人流程自动化，就是写个小软件机器人，模仿人平时点鼠标的操作。

And so the historical way people have done it is through robotic process automation where you basically build like a little software bot that mimics the actual clicks that somebody would be doing.

特别死板，就是完完全全复制人类点那些框框按钮的动作。

It's very deterministic, meaning they're literally clicking the different boxes that I would be clicking as a human.

但企业内部的运作往往很混乱，我们实际要做的工作并不总是能被清晰、固定的过程界定清楚。

But organizations are messy, and the work we actually have to do is not perfectly delineated by a very specific process.

所以很多时候，只要稍微出点岔子——比如名字拼错了，或者网页上登录框的位置变了——以前这种小变动就会让整个RPA流程直接崩掉。

So oftentimes, if something veers a little bit off course, like maybe someone misspelled a name or maybe a website changed where the sign in box physically is on a page, then historically, that would break the RPA process.

你可以想象，这种小问题多得数不清，随时都可能发生。

And as you can imagine, there's an infinite number of small little things that could happen like that.

所以RPA确实能搞定80%的任务，但剩下的20%出问题的时候，还是得靠人来手动补救。

So RPA is often very good for doing 80 of the task, but then, like, 20% of the time that it fails, it's still a manual person who has to come in.

也就是说，它根本不够稳定，没法真正独立完成整个流程。

So it's just not reliable enough to actually do the full task.

结果呢？你还是得留着那些最早干这些活儿的后台人员。

And so you're still left with having the back office people that were the first generation doing these sorts of tasks there.

所以我现在觉得，有了AI和大语言模型，它们能处理这么乱七八糟的数据，还能自己理解上下文、判断最优做法。

So I just think, like, with AI and LLMs now, because they're able to process such unstructured data and they're able to intelligently collect context and then figure out what the best course of action is.

所以下一代自动化后台任务的方式，应该是智能AI代理才对。

The next generation of actually automating these back office tasks should be, like, intelligent AI agents instead.

智能自动化，或者你所说的这些正在运行的LLM，它们能做什么是RPA做不到的？

What can intelligent automation or what you refer to as these LLMs in action, what can they do that RPA couldn't?

我们拿一家我们实际投资的公司举例，叫Tenor。

Let's use the example of a company that we are actually invested in called Tenor.

Tenor是做医疗转诊管理的。

Tenor does referral management for health practices.

比如我是一名全科医生，需要把病人转给专科医生，过去的做法是我得亲手在纸上写下来。

So if I'm a primary physician and I need to refer a patient to a specialist, historically, the way that that would be done is I would literally write something out on a piece of paper.

然后传真给专科医生。

I would fax it to the specialist.

专科医生的前台人员收到传真后，得逐项查看上面的信息，再手动录入自己的系统，核对保险政策、既往病史之类的，最后决定要不要接这个病人。

The specialist front desk person would take the fax, look at it, look at all the information on it, and then input it into my own database, check, you know, like the insurance policies, check prior history, etcetera, and then decide whether to accept the patient or not.

这个活儿太繁琐了，流程太复杂，RPA根本搞不定。

And that was a very manual task that there's just a little bit too much complexity in the way that it's done for RPA to be able to handle.

所以必须得靠一个真人行政人员来处理。

So it had to be some sort of administrative person, like human who was gonna do it.

现在有了智能自动化，Tenor 提出了一套特别简洁的解决方案，能完全自动化整个流程。

And with now, like, Intelligent automation, Tenors come up with a very sleek solution that is basically able to automate that whole process.

而且它更偏向自助服务，因为传统 RPA 的做法是你得请个实施顾问，让他坐在做这项工作的人旁边。

And it's much more self serve because the way that RPA would historically work is you would have to hire an implementation consultant, and they would sit next to whoever was doing the task.

然后盯着对方每一步操作，看清楚点的是哪些。

And they would basically just watch what are the clicks that you are doing

对。

Right.

再把这些操作步骤编程进去。

And then program those clicks.

但像 Tenor 这样的系统，根本不需要有人盯着前台行政人员的一举一动。

But someone like a tenor, for example, you're not gonna have somebody sitting there watching what the front office admin is doing.

他们做了一个特别简洁的界面，是的。

Rather, they've created a really sleek UI Mhmm.

界面看起来就像拖拽式流程设计一样。

Where it looks very much like a drag and drop different process flows.

没错。

Yep.

他们可以自己创建自动化流程，感觉特别直观，自己就能搞定，背后其实藏着一大堆复杂逻辑，全都被系统默默处理了。

And they're able to create their own automation process, which to them feels very intuitive so they can set it up themselves, but actually has a ton of complexity under the hood that's being handled.

我猜很多人，尤其是想到幻觉这些问题时，都会自然问一句：这技术现在发展到哪一步了？

I mean, one natural question that comes up, I think, for many people, especially as they think about things like hallucinations, is where is the technology in this arc?

我们今天真能实现所谓的智能自动化吗？

Are we able to really achieve this idea of intelligent automation today?

有什么障碍吗？

Are there barriers?

换句话说，我们现在到底处在哪个阶段？

Like, where do we sit in that trajectory?

我们看到最有效的方式是，先从一个非常具体的自动化流程入手，至少刚开始时是这样。

The way that we've seen it work best is when there's one very specific automation flow, at least to start Mhmm.

让公司先把它彻底做透。

That a company can just nail.

也就是说，这通常是行业特有的，你可以直接对接那些核心系统。

Meaning, it's often industry specific, So you can integrate into all the core systems there.

还能理解这个行业的具体背景。

You can understand the context for that industry.

而且是一个重复率高、但完全靠人工操作的流程。

And it's one very repeated, but very manual flow.

比如数据录入这种事。

So for example, like data entry.

我打个电话。

I get on a phone call.

听对方说说订单现在到哪儿了。

I hear the update on where an order is.

整个通话里的信息都能被自动提取，然后录入到我的主系统里。

All the information from that order can be parsed through that call and inputted into my main system.

对大公司来说，这种事每天可能要重复几千次，全靠人工干。

That probably happens, like, thousands of times a day for the largest organizations, all manually done.

是的

Yep.

这只是一个非常具体的流程。

And that is one very specific flow.

这还只是开始。

And that's just to start.

等你搞定了这个，就可以进一步深入其他流程了。

And then once you get there, you can build deeper into other flows.

但我认为，更靠谱的做法是先搞懂这些限制，围绕它们来设计，确保代理能正确运行，而不是一上来就试图搞定医疗、法律和物流所有领域。

But I think that is a much more successful path where you can actually understand the constraints and build around them, make sure that the agent performs correctly versus tackling, let's say, like, everything within health care, everything within legal and logistics to start.

通常大家会问，为什么是现在？

Normally, ask the question why now?

但我觉得听众都明白，AI已经来了。

But I feel like listeners know that AI is coming.

它就在那儿了。

It's here.

很多人现在都用‘大语言模型’这个词。

LLMs are maybe the term that a lot of people use.

但除了这个词，是不是还有更深层的原因？或者在大语言模型这个领域里，有哪些具体的技术突破真正让这一切成为可能？

But is there a deeper why now or specific technological advances within the sphere of LLMs that you can point to that actually make this possible?

是的。

Yeah.

我觉得我们特别兴奋的一点是，大家一说‘AI’，就觉得所有东西都要变了，全因为AI。

I think one thing that we're really excited about is people use the term AI, and they're like, oh, everything's gonna change now because of AI.

但‘AI’到底意味着什么？

But, like, what does that mean?

其实有很多截然不同的技术突破，才让各种应用场景成为可能。

There's a lot of very distinct technological breakthroughs that make different applications possible.

就拿智能自动化来说，我觉得比以前更可行的一个关键，是那些大实验室出来的一系列基础研究。

And specific to intelligent automation, I think one of the things that makes it much more possible than before is a lot of the fundamental research coming out of the large labs.

比如最近，Anthropic 发布了‘电脑使用’功能，本质上就是一个浏览器代理，能智能理解电脑屏幕上任何操作界面发生了什么，并据此做出相应动作。

So for example, recently, Anthropic announced computer use, which is basically a browser agent that is able to intelligently understand what is happening on the browser level of any sort of desktop and be able to take actions accordingly.

我们之前说过，传统RPA本质上是按像素来理解界面的。

So we talked about how historically RPA basically understood at a pixel level.

嘿。

Hey.

我得点这个，然后点那个。

I should click this thing, then I should click that.

但像计算机使用这种功能，或者OpenAI即将推出的Operator，未来的智能代理能更聪明地浏览网页，这会大大拓展智能代理能做的事。

But with something like computer use or I think OpenAI has something called operator that they're gonna release soon, agents are gonna be able to browse the Internet and browse the web in a much more sophisticated way, which is gonna open up a lot of possibilities for what intelligent agents can do before.

我们觉得，很多做智能自动化的初创公司，其实不会自己搞底层研究。

We So think a lot of these intelligent automation startups, they're not gonna be doing fundamental research on their own.

要让浏览器代理真正大规模跑起来，技术上还有很多工作要做。

There's still tech that needs to be done to make a browser agent fully work at scale.

但让我们特别兴奋的是，大实验室已经在认真做这事，而且清楚地看到了其中的机会。

But what's really exciting for us is that the large labs are clearly working on this and clearly understand the opportunity.

所以随着这些技术不断进步，我们相信会涌现出一大批初创公司，去把它们用在各大行业里——而这些领域，大实验室自己根本不会去碰。

And so as that tech gets better, we think there's gonna be a whole world of startups who are able to leverage it for all the different industries out there that the large labs themselves are not gonna tackle.

而且随着

And as

你提到这个机会时，在你的文章里说有两条不同的路可走。

you think about the opportunity, you framed it in your article as two different paths that people might take.

一条是横向的AI赋能平台，另一条是垂直领域的自动化解决方案。

So one of them was the horizontal AI enabler, and the other was a vertical automation solution.

那你详细说说，如果有人想在这个领域创业，你看到的这两条路具体是什么？

So tell us about that, the two different paths that you see if people want to build in this space.

第一条是横向的AI赋能平台，我们认为，任何做智能自动化的企业都绕不开这一点。

So the first is the horizontal AI enabler, and that's something that we think any company who's doing any sort of automation, intelligent automation, is gonna have to do.

一个很常见的例子，我之前也提过，就是数据提取。

One very common example, which I've touched upon a little bit already, is data extraction.

几乎每一条智能自动化路径，第一步都是要从一堆杂乱无章的非结构化数据里提取关键信息。

Almost every intelligent automation path starts with some messy unstructured data that you need to pull key outputs from.

但现在，很多人还是靠人工手动来做这件事。

And today, a lot of people are just building that manually.

但我们现在看到一些公司专门走这条路，就是把非结构化数据里的关键信息提取出来，转成结构化数据。

But we've started to see companies emerge that are purely doing that path, which is taking unstructured data and pulling out the key pieces to turn it into structured data.

我们认为这会是一个非常有意思的机会。

And we think that could be one really interesting opportunity.

所以，不管是企业自己内部搭建自动化系统，还是做外部解决方案，都可以把这种能力当成核心组件来用。

So anyone who is either building their own automation in house can leverage that as a key component.

如果你在打造一个端到端的完整方案，也可以把这一步作为其中一个环节集成进去。

Or if you're building like a full end to end solution, maybe you input that as one of your components as well.

我个人特别兴奋的是垂直自动化这条路。

One thing that I'm personally really excited about is the vertical automation path.

我觉得要想让一个智能AI代理真正成功，一开始最好把它限制在一个非常具体的领域里。

I think to make an intelligent AI agent very successful, it often is helpful in the beginning to have it be in a very constrained domain.

比如物流、医疗或者法律这些领域，它们的上下文都很明确，AI能完全理解。

For example, like, logistics or in health care and legal, it is a domain that they can understand all the context for.

这些领域有完整的数据输入、系统对接等等，能专门搞定某一个具体的流程。

They have all the necessary inputs, integrations, etcetera, and they're able to automate one specific flow.

所以我们特别感兴趣的是，找一些人工操作特别多的行业，比如规模很大的后台办公室。

So what we're really excited about there is, let's take an industry that does have a lot of manual work that needs to be done, like a very large back office.

想想看，哪些工作其实特别需要自动化，但以前RPA搞不定，因为客户体量不够大？

If you think about what are the things there that actually have to be automated that maybe RPA wasn't able to tackle before because it just wasn't, like, a large enough individual customer?

就是说，不是那种财富500强的大客户。

Like, it wasn't one of the Fortune 500 customers.

这是一方面。

That's one thing.

那什么样的行业符合这种特点呢？

It's like, what sort of industries fit that criteria?

然后我们再想，到底哪个流程最适合先拿来做自动化？

And then thinking about what is an actual automatable flow to start with?

真正让我们兴奋的是那些能直接带来收入的流程——以前客户因为这个流程卡着，没法接更多生意。

And ones that get us really excited are flows that are actually revenue generating, where the customer that you would sell to was previously constrained on the amount of business that they could handle Mhmm.

就是因为这个流程限制了他们。

Because of this flow.

比如，现在你可以用语音来接收客户订单，这以前根本做不到。

So that could be taking customer orders by voice that was maybe not possible before that now you could do.

或者像我之前说的，客户推荐管理，以前根本处理不了那么多数据，但现在轻松搞定。

Or it could be like a referral management, like I said before, where you just couldn't process that amount of data quickly enough, but now you can.

说到底，当你考虑市场规模时，其实就是看你能多高效地替代原本靠人力完成的工作。

I mean, when you think about the market size as well, you're just talking about how effectively you're targeting what was previously done by labor.

这说明了这个机会有多大、多可观？

What does that say about the opportunity and the scale of it?

规模大太多了。

It's just so much larger.

有太多这样的市场了。

There's so many markets.

你看看劳工统计局的数据，就知道这市场有多大了。

You look at the market from just like Bureau of Labor Statistics data, and you're like, this is an enormous market.

再看看现在市面上的软件厂商，根本没法跟这个机会的规模相提并论。

And then you look at who the software incumbents are, and you're like, they just don't match up to the size of the opportunity.

这在过去是因为，我之前说过，软件根本处理不了这些事。

And that was historically because, as I said before, software could not handle it.

对。

Yeah.

就是那些公司实际操作中五花八门的边缘情况，要么就是他们压根没多少软件预算。

Like, long tail of edge cases of what these companies were actually doing, or they just didn't have large software budgets.

但这些公司都有大笔的人力预算。

But all these companies have large labor budgets.

没错。

Mhmm.

他们其实有大量机会等着被梳理，而技术正好能帮上忙。

And they do have a lot of opportunity that obviously they do wanna wrangle and technology can empower.

我们认为，借助智能自动化，现在正是切入这些传统市场的绝佳时机，看看能不能用AI代理真正服务好他们——这是传统工作流或软件做不到的。

And we think with intelligent automation, this is one of the most exciting times to actually be going after some of these legacy markets, seeing whether or not you can actually serve them through AI agents in a way that maybe traditional workflow or software couldn't.

所以，拿历史上的软件巨头来对比，说‘这就是公司能发展的上限了’，这其实是个错误的思路。

So I think it's actually like a false comparison to look at the historical software incumbents and say, oh, this is the cap on what a company could become.

我觉得以前技术根本触碰不到的潜在机会多得惊人。

Think there's just so much untapped opportunity that technology just wasn't able to penetrate before.

现在有了智能AI代理、语音代理这些工具，终于能搞定这些事了。

Then now with Intelligent AI agents, with voice agents, etcetera, you can now tackle.

是的。

Yeah.

我完全同意，以前之所以有这么多没被开发的潜力，就是因为技术本身有局限。

I think you're absolutely right that we were there was all this untapped potential because the technology only went so far.

但既然现在到了这一步，你觉得未来五到十年会怎么发展？

But now that we're here, how do you see the next five, ten years evolving?

因为现在大家不仅得重新考虑软件预算和人力预算的分配，还得从思维上彻底转变——原来我们真能做自动化，以前根本不敢想。

Because there is a shift that people have to do intellectually as well as they're thinking about their software budget to labor budget, and they almost have to regear their brain to say, oh, we actually can do this automation, which we previously couldn't.

没错。

So Yeah.

你怎么看这条发展路径？

How do you see that trajectory?

我 definitely 认为这将会是

I definitely think it's gonna be

一个逐步演进的过程，而且我觉得这取决于各个行业的技术成熟度，看他们是不是走在技术前沿。

an evolution, and I think it'll depend on the technology spectrum, like how technology savvy or at the forefront that industry is.

但对于我们刚才说的那些传统行业，尤其是那些更依赖本地部署、更扎根于实体世界的大型企业，我觉得转变需要时间，这正是为什么端到端的垂直自动化解决方案这么有吸引力——因为你能打造出完全贴合他们工作流程的系统，用起来几乎是顺理成章的事。

But for a lot of these older industries that we're talking about, like the larger ones that are a little bit more on prem, a little bit more based in the physical world, I think it will take time, which is why I think doing the vertical end to end automation solution is so exciting because you can actually build something that is very tailored for their specific workflow or it's almost a no brainer to use it.

没人喜欢做数据录入。

Like, nobody wants to do data entry.

没人喜欢，对。

Nobody wants to Yeah.

坐在后面一页页翻一百份传真，再手动输进系统里。

Sit in the back and read a 100 faxes and try to input that into a system.

而且这也不是任何一家公司的核心业务。

And that's no no company's core competency either.

所以，如果你能为这个行业专门打造一个智能AI代理，精准匹配他们的实际业务流程，那这事儿根本不用犹豫，直接上就行。

So if you're able to build an intelligent AI agent specifically for that industry that is tailored to exactly how they do their business, it's almost a no brainer to do it.

之前做这些事的人，现在就能腾出手来，专注更有价值的工作，比如直接面对客户，或者处理更复杂的问题。

And then the folks who were doing that before can now focus on much higher value, either customer facing tasks or much more complex tasks.

再过几年，比如未来五到十年，这项技术会越来越普及，更多公司会逐渐了解这些AI代理能做什么、不能做什么，也会越来越习惯用它们。

And then over time, let's say in the next five to ten years, the technology wave will continue to get adopted by more and more companies, people that will become more knowledgeable about what these agents can and cannot do, more comfortable with the technology.

而且因为你已经深度接入了客户的系统，你就能逐步接手更多原本由人工完成、或传统系统能处理的核心任务。

And then because you've integrated yourself with that customer base with their core systems, you'll have the opportunity to take on more and more human labor or core tasks that their traditional systems record could do.

所以现在入局真的特别棒，因为机会明明白白——能做出真正提升ROI、直接拉动公司营收的产品。

So it's a really exciting time, I think, to wedge in now because there's a clear opportunity to build something that is ROI generating and just an obvious boost to the company's top line.

但你又不至于太晚，还能赶在这些公司逐渐成熟、全面拥抱技术之前，抢占未来的主导权。

But you'll still get in early enough that you'll have the right to win in the future as these companies get more and more mature on the adoption curve.

完全对。

Totally.

就像你说的，我们确实还处在早期阶段，但接下来会有很多有意思、让人兴奋的发展。

And so obviously, we're early in this arc, as you mentioned, but there's a lot of interesting, exciting things to come.

你希望开发者们现在最该聚焦在哪些地方？

What would you like to see builders focus on?

你还想听到哪类建设者的声音？

What kind of builders would you like to hear from as well?

我特别想听听那些思考过去不可能做到的事情的人。

I would be really excited about people who are thinking about what was not possible before.

对。

Yeah.

我们已经聊了很多关于RPA现在能做什么、服务哪些客户。

We've talked a lot about what RPA does today and the types of customers it's able to target today.

当你想象一下未来可能被智能自动化取代的工作场景，员工和公司能节省的时间和成本，那规模比现在大了不止一个数量级。

When you think about the world of work that could be intelligently automated away and the amount of time and savings both employees and companies can get, it's just like an order of magnitude larger than what what is currently possible.

所以我特别期待那些在思考：哪些任务过去RPA根本搞不定，但现在可以了；哪些行业以前碰不了，现在能切入的人。

And so I'd be really excited about people who are thinking about the bucket of types of tasks that were automatable that RPA historically could not handle Mhmm.

还有，去想清楚这些行业里第一批能自动化的流程是什么，以及能为这些解决方案设计出怎样的简洁界面和用户体验。

And types of industries that it currently was not able to tackle, and really thinking about what are those first flows or first automations within those industries that are possible and really thinking about what are the clean UI or UX paradigms that you could bring to bear for their solutions.

太棒了。

I love that.

我很喜欢你不仅想听金融或医疗领域的开发者分享，还关注这些这么小众的市场。

I love hearing that you're not just interested in hearing from builders in finance or health care, but some of these really niche markets.

我觉得这真是个观念上的转变。

I think that's a paradigm shift.

是的。

Yeah.

要是十年后，再也没人需要手动录入数据，也没人再在客服电话那头被客户骂得狗血淋头，那对所有人来说都是个大胜利。

And if let tell let's say ten years from now, no one has to do manual data entry again or no one has to get yelled at on the other side of the line for an angry person in customer service, I think that'll be a win for everybody.

对。

Yeah.

这样一来，大家都能去干点更有创意、更高效的事，说不定还更开心呢。

And then all these folks can then focus on much more creative, productive tasks that probably make them happier too.

终于可以把传真机送进博物馆了。

Finally sunset the fax machine.

是的。

Yeah.

好嘞。

Alright.

今天就到这里了。

That is all for today.

如果你坚持听到了这儿，首先真的谢谢你。

If you did make it this far, first of all, thank you.

我们为每一期节目都花了很多心思——无论是嘉宾的选择、日程的排布，还是和我们超棒的剪辑师Tommy一起反复打磨，直到音乐刚好到位。

We put a lot of thought into each of these episodes, whether it's guests, the calendar Tetris, the cycles with our amazing editor, Tommy, until the music is just right.

如果你喜欢我们做的内容，欢迎去 ratethispodcast.com/a16z 留个言，告诉我们你最爱的是哪一期。

So if you like what we put together, consider dropping us a line at ratethispodcast.com/a16z, and let us know what your favorite episode is.

这绝对能让我开心一整天，Tommy肯定也一样。

It'll make my day, and I'm sure Tommy's too.

咱们下期再见。

We'll catch you on the flip side.