如何运用人工智能：与三位专家进行的实用商业问答

不。

No.

我们可以单独讨论这家公司的其他行为，这些行为违背了我认为是基本隐私的原则。

We can have a whole separate conversation about other ways that that company is is breaching what I would consider to be basic privacy.

但同样，有时候当工具太容易使用时，我们更容易直接使用它们，而不是停下来思考使用它们可能带来的影响。

But but again, like, sometimes when tools are so easy, it's easier for us to use them than to stop and ask a question about what the potential implications are of using them.

然后你就放弃了。

And then you give up.

唉，我就直接告诉他们吧。

Like, ugh, I'll just tell them.

嗯，没错。

Well, yeah.

而且已经有很多实例了，尤其是在新闻和媒体领域，档案被出售了，但没人认真考虑过这将来会意味着什么。

And there's there's a lot of already instances, especially in in the world of news and media where archives have been sold and nobody really thought about what that would mean going forward.

而这正是收入来源。

And that's revenue.

未来，这将成为公司的收入问题。

It's gonna be a revenue problem for companies in the future.

现在正是时候，利用关于未来的资讯进行思考并做出决策。

So now is the time to be thinking and and making some decisions using information about the future.

我们一分钟后再回来。

We'll be back in a minute.

本节目由Odoo赞助。

Support for this show comes from Odoo.

经营企业已经够难了，为什么还要用十几个互不相通的应用程序来让事情更复杂呢？

Running a business is hard enough, so why make it harder with a dozen different apps that don't talk to each other?

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It's an all in one, fully integrated platform that makes your work easier.

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And the best part?

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Odoo replaces multiple expensive platforms for a fraction of the cost.

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So why not you?

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Try Odoo for free at odoo.com.

就是 odoo.com。

That's odoo.com.

好的。

Okay.

我们下一位听众的问题来自宾夕法尼亚州斯克兰顿的卡莉·托勒里科。

Our next listener question comes from Carrie Tolerico in Scranton, Pennsylvania.

她给我们发送了一段语音备忘录。

She sent us this voice memo.

让我们来听一下。

Let's hear it.

嗨，卡拉。

Hi, Kara.

我想知道，你们的专家小组是否同意我的观点，即人工智能的广泛采用将受到普通公司员工技术能力不足的限制。

I'm curious if your panel would agree with my opinion that the broad adoption of AI is going to

会被普通公司员工缺乏技术素养所限制。

be limited by the lack of tech savvy among average employees at average companies.

我们公司一直在测试这些工具，我注意到在演示或培训期间，我们的中层管理者需要大量的指导。

My company has been testing some of these tools and I've been struck by the amount of hand holding our middle managers have needed during demos or training.

这些并不是刻板印象中不懂技术的婴儿潮一代，他们大多是X世代和年长的千禧一代，像我这样的人。

These aren't stereotypical tech illiterate boomers, They're mostly Gen Xers and older millennials like me.

我认为，在普通小型或中型公司的员工与直接从事科技行业或科技相关、科技密集型行业的员工之间，存在着真正的技能差距，这种差距将对这些工具的广泛采用产生重大影响。

I think there's a real skill gap between the people working at your average small or mid sized company, and those working directly in tech or industries that are tech adjacent or particularly tech heavy, and this gap will have a big effect on the broad adoption of these tools.

非常想听听大家的看法。

Would love to hear everyone's thoughts.

谢谢。

Thanks.

Syash，你怎么看？

Syash, what do you think?

我完全同意。

I absolutely agree.

嗯。

Mhmm.

我的意思是，正如艾米所说，我们已经看到过类似的过程，过去的技术也是如此，技术会经历发明和创新阶段，但广泛采用却要漫长得多。

I mean, I think we've seen this process once again, as Amy said, with past technologies as well, where technologies go through phases of invention and innovation on top of it, but then the broad adoption takes much, much longer.

因此，这就是我认为人工智能的真正影响甚至不会在本十年内实现的原因之一。

And so this is one reason why I think the true impact of AI will actually not even be realized this decade.

我们要花远远超过未来五年的时间，才能真正认识到人工智能的影响，并确定可以将其应用于哪些高效用户。

It'll take us much more than the next five years to actually realize the impact of AI to figure out what productive users we can put it towards.

在某些情况下，现有的商业结构可能根本无法很好地利用人工智能。

And in some cases, it might not even be the case that, you know, existing business structures can make use of AI very well.

我想起，在我们发明电力之后，花了四十年才弄清楚如何为工厂实现电气化。

I mean, I'm reminded of how after we invented electricity, it took forty years for us to figure out how to electrify factories.

而且，仅仅在现有工厂里安装电力是不够的。

And, you know, just putting in electricity in the existing factories was not enough.

我们需要从根本上重新规划整个工厂的布局，围绕电气化流程进行重组，才能使其发挥作用。

We need to basically reorient the entire factory layout around the process of electrification to make it work.

因此，在某些情况下，最显著的变革和最深入的人工智能应用，只有在人们学会如何使用它、我们发现新的应用场景，以及在某些情况下围绕人工智能重新设计商业模式时，才会发生。

And so in some cases, the most dramatic transformations and the sort of most in-depth adoption of AI will only take place as people learn how to use it, as we figure out what new use cases there are, and in some cases, reinvent how we run businesses around AI.

那么，针对你的问题，公司在将人工智能应用于业务时，通常在哪些方面会犯错？

So to your question, where do companies get it wrong when it comes to them rolling out AI for their business?

我看到的一个主要问题是，没有认识到能力与可靠性之间的差距。

One of the main things I've seen is not recognizing the gap between capability and reliability.

嗯。

Mhmm.

所以聊天机器人非常擅长帮你完成80%的工作。

So chatbots are very good at taking you 80% of the way there.

如果你要求生成某个内容的初稿，你可以得到一个大约80%合格的版本。

If you ask for a first draft of something, you can get something that's about 80% good.

如果你让它在网上做点什么，你可能会得到一个大约80%完成的版本。

If you ask it to do something on the Internet, maybe you will get, like, an 80% version of it back.

但要真正达到通常所说的九个九的可靠性——即99.999%的可靠性——要困难得多，尤其是对于聊天机器人这类随机或随机性系统。

But actually getting to, like, what is often called the five nines of reliability, that something is 99.999 reliable, is much, much harder, especially with random systems or stochastic systems like chatbots.

因此，我认为许多公司都未能意识到这一点。

And so I think that's what many companies have failed to realize.

结果我们已经看到了一些灾难性的产品失败。

And we've seen catastrophic product failures as a result.

因此，我想起了这两款产品：Humane Tech针

So I'm reminded of these two products, the Humane Tech pin

嗯。

Mhmm.

还有RabbitR1。

And the RabbitR1.

这两者都是通用型AI助手。

These were both general purpose AI assistants.

这两者都相当有能力。

Both of these were pretty capable.

它们可以帮你下单DoorDash送到你家，但大约有10%的时间会把食物送到错误的地址。

They could, like, order DoorDash to your home address, but maybe 10% of the times, they would order your food to the wrong address.

当你在实验室里使用这个产品时，这可能还能接受，但从消费者实际使用的现实产品角度来看，这是一次灾难性的失败。

And, you know, that might be alright when you're using this product in a lab, but from the perspective of a real world actual product that consumers can use, it's a catastrophic failure.

所以，艾米和拉吉夫，我想让你们举一个例子，说明一家鼓励员工使用AI的公司可能会发生什么情景。

So both Amy and Rajeev, I want you to give an example of a scenario that might unfold at a company that's encouraging its employees to use AI.

假设你雇了一名员工，让他成为某个特定主题或项目的专家，而他遇到的情况是：比他职位高的人用AI解决了他正在处理的问题，AI生成的解决方案是错误的，而这名员工知道这是错的。

Say you have an employee who's hired to be a subject matter expert on a given topic or project, and they're put in a situation where someone higher up than them has used AI to solve a problem they're working on, the solution generated is wrong, and the employee knows it's wrong.

上级并不知道这一点，却仍然以AI作为他们正确的证据。

The higher up doesn't know that but keeps pointing to AI as evidence that they're right.

例如，这种情况很容易发生。

For example, that's something that could easily happen.

公司如何才能提前防范这类情况？需要制定哪些政策？

How can companies get ahead of situations like that, and what policies need to be in place?

艾米，然后是拉吉夫。

Amy and then Rajeev.

嗯。

Yeah.

我的意思是，过去两年我接触过的几乎所有公司，现实中都在发生这样的情况：嗯。

I mean, look, the the real world scenario that is happening in just about every company that I've been inside of for the past two years is Mhmm.

很可能企业内部正在使用某种AI系统，而且几乎都与纸质表格有关。

There is very likely an enterprise level AI something or another going on, and almost exclusively that has to do with paper forms.

比如金融服务、医疗保健、保险行业。

So financial services, health care, insurance.

它正在自动化手动输入这些数据或将数据整合成一套标准格式的过程。

It is automating the process of manually entering those data or synthesizing data into one standard set.

但以前这是一项非常依赖人力资本的工作。

But that used to be a very capital like, human capital intensive job.

这种情况已经发生，并且正在统一这些数据集。

That's already happening, and unifying those data sets.

所以，这种类型的AI正在发挥作用。

So there is that kind of AI going on.

目前大部分情况是，个人在未经授权的情况下自行使用AI，无论是撰写报告，还是胡乱尝试提出一些 brilliant 的大点子，带到会议上，而这些都超出了高管层或管理团队的职权范围。

Most of what's happening is individuals are are sort of going rogue and doing their own things on AI, whether that's writing their reports or, I don't know, screwing around and trying to come up with idea brilliant big ideas to bring into meetings outside the purview of the executive leadership or any management team.

我还有些轶事证据：我的岳父今年82岁，每次发短信都手笨打错字，但现在他主要用ChatGPT的手机应用来代替搜索。

And I also have anecdotal evidence because my father-in-law, who's, like, 82 years old and fat fingers every text message he's ever sent, is now primarily using I think it's Chatchipitiana's mobile phone in replace of of instead of search.

嗯。

Mhmm.

对吧？

Right?

因为这样更简单，能更快地帮他达到目的。

Because it's just easier and gets him to where he needs to go faster.

好的。

Okay.

这就是当前的状况。

That is the present day scenario.

问题是，大多数公司还没有建立相应的法律条款。

The problem with that is there are not yet legal provisions in place in most companies.

数据保护方面，你看，仅在美国，各州之间的监管体系就是零散的。

The data protection I mean, look, in The United States alone, there's a patchwork regulatory situation between states.

所以，在某些州，比如保险领域，我认为可能是康涅狄格州。

So in some states, with some insurance, for example, I think in the state of it might be Connecticut.

我可能会说错。

I'm gonna get this wrong possibly.

但在大多数情况下，那里不允许使用人工智能。

But, like, I don't think you can use AI in most circumstances there.

德克萨斯州则完全不同。

Totally different in Texas.

所以这一部分非常真实且极具挑战性。

So that part is very real and very challenging.

我认为我们需要做的是停止疯狂地幻想人工智能的末日或完全乌托邦式的场景，而应深入细节，关注当下实际正在发生的事情，以及为什么发生、如何在每家公司中将其引向正轨。

I think what we need to do is stop going crazy and wild and thinking about all of these doomsday or totally utopian scenarios for AI and just get very much into the weeds on what's actually happening right now and why and how to put it on the right path in every company.

实际上，里希，我来重新表述一下。

Actually, Rishi, I'm gonna restate that.

你之前说过，数据就是石油，你也说过，商业领袖需要理解生成式人工智能和机器学习之间的区别，才能取得成功，需要专注于机器学习并投资于数据。

You said earlier that data is oil, and you've also said that business leaders need to understand the difference between generative AI and machine learning in order for them to be successful and need to focus on their machine learning and invest in their data.

详细解释一下，因为艾米和沙什之前提到，很多人擅自行动，做其他事情。

Flesh that out for us because the you know, what both Amy and Syash had been saying is that, you know, a lot of people go rogue, they're doing other things.

你如何在所做的事情中保持自律，明确什么对你重要，而不是像艾米刚才说的那样，随便去用ChatGPT获取一个不安全的答案。

How do you become disciplined in what you're doing and what's important for you versus what Amy was just talking about, like, hey, jump on ChatGPT and get a very unsafe answer.

是的。

Yeah.

我的意思是，首先，艾米的回答完全正确。

I mean, first of all, Amy's answer was dead on right.

关于机器学习和数据的问题，听好了，这是关键点，对吧？

And in terms of the issue of machine learning and data, look, here's the bottom line, right?

也就是说，公司每天、每分钟都在生成数据。

Which is companies create data every single day, every single minute.

但如果你去看看这些组织，过去两年里，我跟大约2500位中小型企业CEO聊过，卡拉，我能用两只手和两只脚的指头数得过来，有多少人觉得他们拥有优质数据。

But if you start looking at organizations, I've spoken to probably about 2,500 CEOs in that SMB space in the last two years, Kara, and I can count on two hands and my two feet, how many of them feel like they have good data.

他们就是没有优质数据。

They just don't have good data.

原因在于，当他们想到数据时，只想到花钱，却不理解，他们从未把理解和认识到数据对他们的极端重要性当作优先事项。

And the reason why is because when they think about data, they think spend, they don't understand, it was never a priority for them to understand and realize how data becomes so important to them.

因此，当你开始看到一些项目失败，看到麻省理工学院关于项目失败的报告时，这正是部分原因——因为他们对数据缺乏掌控。

And so that's when you start to see some of the projects start to fail and you start to see some of the report from MIT about projects failing and this and that, and that's part of the reason why is because they just don't have good command of their data.

他们根本不理解。

They don't understand.

尽管我们生活在这个科技的泡泡中，但外面还有一个世界，他们根本不了解数据仓库，不懂数据管理，不理解数据分析，也不知道如何提取数据并用它来讲故事，无论具体情况如何。

As much as we live in this bubble of tech, there's a whole world out there that doesn't understand a data warehouse, that doesn't understand data management, doesn't understand data analytics, doesn't understand how to pull data and use it to have it tell a story, whatever the case might be.

所以我常和那些追逐每天层出不穷的新潮技术的人争论，尽管这些公司推出的新产品很棒、很出色，但它们发展得太快了，不如专注于核心执行：先雇一名数据科学家，就从这一点开始，找个人来看看你的数据，开始理解并发现你能接触到的基本应用，就从这里起步，把它当作一个成功案例来逐步扩展。

So I argue with folks that don't chase all the shiny new objects that are coming out every single day that all these companies are putting out, which is great and it's awesome, but it's moving so fast, just focus on your core execution of, let's just get a data scientist on board, just start with that, just somebody to look at your data, start understanding and finding basic implementations of what you have access to, and just start there and use that as a win to start building off of.

把它作为你的基础。

Use that as your foundation.

如果我可以插一句，因为

If if I may Because

你什么也做不了

you can't do anything

请说。

Go ahead.

说吧，艾米。

Ahead, Amy.

找一个数据科学家，就像告诉洛杉矶一位优雅的女士去随便买个爱马仕包一样。

Just get a data scientist is is like telling a a fancy lady in Los Angeles to just go get a Birkin.

对吧？

Right?

也就是说，非常非常稀缺。

Which is to say like very, very scarce.

所以我希望每个中小企业，或者任何企业，都能像去数据科学家商店一样雇到人。

So I would love for every small and medium business or like any business to be able to go to the the data scientist store and hire people.

但现实是，他们根本不存在。

The reality is they aren't there.

这是另一个大问题。

And this is another big problem.

我们根本没有认真思考过未来需要什么样的劳动力。

We we didn't really think through the future of the workforce we would need.

所以我们的人才储备根本不够。

So we just we don't have enough people in the pipeline.

我只是想给大家设定一些预期，因为你们确实需要对这些系统和工具的工作原理有一定了解。

And I just I just wanna set expectations for everybody because you you do need to have some understanding of how these systems and tools work.

在某些情况下，你确实需要数据科学家，但目前市场上可供选择的人才非常有限。

And in some cases, you do need a data scientist, but there's not a lot out there to choose from right now.

对。

Right.

让我来指出这一点。

So let me point to that.

你刚才提到了这一点，我们现在看到越来越多的公司鼓励员工使用大语言模型，这甚至更糟，因为员工不仅自己随意使用，而且很多人在不告知雇主的情况下偷偷使用。

You you you touched on this right now, but we're seeing more companies encourage their employees to use LLMs, which is even worse, right, just on their own, but also more employees using without telling their employers.

几个月前，安全与软件公司Ivanti发布了一份报告，显示超过40%的办公室职员在使用像JETGPT这样的生成式AI工具，其中三分之一的人表示他们是在秘密使用。

A few months ago, the security and software company, Ivanti, put out a report showing that a little more than 40% of office workers are using generative AI tools like JETGPT, and one in three said they're doing it in secret.

如果AI的普及不可避免，我想请你们每位谈谈，领导层最明智的做法是什么？是主动引导而非被动追赶？

So if AI adoption is inevitable, I'd like each of you, what is the smartest way for leadership to shape it rather than chase it?

艾米，然后是赛什和拉吉。

Amy and then Saish and Rajee.

我觉得这听起来可能有点模糊，但关键在于多提问。

I think the this is gonna be sound a little ambiguous, but I think the bottom line is ask questions.

现在有太多领导者正在向各大专业服务公司砸钱，投入巨额资金，而这些公司都争相上门，寻求资助，帮助他们构建庞大复杂的AI系统，但这些系统要么表现不佳，要么很快就需要改造。

There are too many there are too many leaders out there throwing cash, obscene amounts of money at every big professional services firm, and they're all knocking down the door, looking for handouts to help them build huge enormous AI systems that are either gonna underperform or gonna need to change.

因此，你必须稍微保持冷静，首先弄清楚：你到底想解决什么问题？

So there is some amount of, like, you have to be a little bit more level headed and understand, first of all, what problem are you trying to solve?

这是最重要的事情。

That's the most important thing.

你想要做什么？为什么？

What are you trying to do and why?

如果你无法回答这个问题，AI也无法替你回答。

And if you can't answer that question, AI is not gonna answer it for you.

它只会成为一个极其复杂、极其昂贵且耗时的解决方案。

It's just gonna be a very, very complicated, very expensive, time intensive solution.

所以，这是第一点。

So that's the first thing.

我确实担心对顾问的过度依赖。

And I do worry about overreliance on consultants.

我给你一个快速的案例研究，一个小例子。

I'll give you a quick case study, quick little example.

我见过的最聪明的CEO之一，有一个非常明智的想法，不仅想推动自己的公司，还想推动整个行业。

One of the smartest CEOs I've ever met, had a very smart idea for how to advance not just his company, but the industry.

一家大型咨询公司前来表示：‘我们懂了。’

And a one of the the big consulting houses came in and said, well, we got it.

我们会为你把它建出来。

You know, we'll build build it for you.

他们过度建设到了我前所未见的程度，结果表现远低于预期。

And they overbuilt something to a degree that I had just never seen before, and it drastically underperformed.

现在他们离目标还差得远，却已经花掉了一大笔钱。

And now they're nowhere closer to where they needed to be, but they're out a significant amount of of money.

这是一家上市公司，因此他们的资产负债表受到了影响。

And this was a publicly traded company, so they took a hit on the balance sheet.

你首先得弄清楚自己到底想做什么，然后找出谁在你的价值网络中能帮助你实现目标。

You first of all have to figure out what it is that you're trying to do and then figure out who's in your value network that's gonna help you get there.

这可能是一个顾问，也可能不是。

And it may or may not be a consultant.

可能是与一小群人合作。

It might be partnering with some smaller group of people.

我不知道。

I don't know.

但这已经不再是那种一站式解决方案了。

But this is not a, like, a one stop shop thing anymore.

对。

Correct.

好的。

Alright.

特吉？

Tej?

我非常乐观的一点是，实际上可以在内部开展小型实验和试点。

One of the things I'm quite optimistic about, is actually running experiments, small pilots in house.

我看到过很多企业取得了成功，员工们从副项目开始，尝试使用人工智能来加速或提升某个业务流程的效率。

And I've seen, like, many businesses have had successes where, you know, employees start out with a side project, like, trying to use AI to sort of speed up or improve the productivity of a certain business process.

这就是你如何区分精华与糟粕的方法，对吧？

And that's how you figure out, like, do you separate the wheat from the chaff, basically?

而且，在可能的范围内，我认为公司也应当积极支持这类做法。

And, you know, to some extent, to the extent that it is possible, I think it's also good for companies to lean into this type of thing.

例如，我记得所有大型科技公司都会给员工每周留出几个小时去从事副项目，其中一些最终发展成了重要的核心业务创意，如今已拥有数亿用户。

For example, I remember all of the big tech companies giving people, like, a few hours of their week off to pursue side projects, and some of them went on to become these really important crucial business ideas that that have now grown to, like, hundreds of millions of users.

因此，这类情况中，公司内部已经自发进行的实验，实际上可以用来增强公司对人工智能的应对能力。

And so this is the type of thing where, you know, the experiments that are already happening organically within a company can actually be used to sort of shore up the company's responsiveness to AI.

嗯。

Mhmm.

拉吉夫？

Rajiv?

是的。

Yeah.

不。

No.

我的意思是，我同意。

I mean, agree.

听我说，我们公司的一个部门主要从事数据分析的教育和培训项目。

Look, we at my company, one of my divisions, what we do is we do a lot of data analytics education and training programs.

让我感到鼓舞的是，也许我对艾米之前所说的观点有些不同的看法：如今，首席执行官和高管们会带着他们的IT人员来参加这些活动。

And the thing that I'm encouraged by, and maybe a little bit of a different take on what Amy said earlier, which is CEOs and executives are now coming with their IT people to these events.

嗯。

Mhmm.

因为他们意识到这是一种合作关系。

Because they realize it's a partnership.

他们开始意识到自己在哪些方面存在不足。

They re they're they're starting to understand and realize where they have gaps.

所以我感到鼓舞，因为以前从未发生过这种情况。

So I'm encouraged because this hasn't happened before.

我的意思是，我们已经做了这么多年甚至几十年了，但如今第一次看到C级高管中的一些人带着他们的首席信息官、首席技术官一起来参加。

I mean, we've been doing this now for years and decades, and now for the first time, you're actually seeing the the the c suite, some of the members of the c suite actually coming with their CIO, their CTO.

所以看到这一点真的令人振奋。

So that that's really exciting to see.

因此，我对未来感到乐观。

So I'm I'm encouraged for that, for the future.

嗯。

Mhmm.

好的。

Okay.

现在我有一个关于AI使用透明度的双重问题。

So I've got a kind of two in one question now about transparency around the use of AI.

这两个问题都来自Blue Sky。

Both of these come from Blue Sky.

Tav问：企业领导者是如何应对（或没有应对）在内部流程和面向客户的输出中标记AI生成内容这一迫切需求的？

Tav asks, how are business leaders addressing or not the imperative of labeling AI generated content in internal workflows and client facing outputs?

谢恩·斯派瑟问：如果你从事的是涉及人际关系建立、教育、心理健康等领域的工作，那么在人们阅读或看到的内容由AI生成时，你应该多透明地告知他们呢？

And Shane Spicer asks, if you work in a field that involves relationship and relation building, education, mental health, etcetera, how transparent should one be with people that they're reading or seeing is generated by AI?

我认为这实际上是在探讨标准的问题。

I think this is sort of getting the idea of standards.

我们先请艾米谈谈你的看法，然后是萨什，你们怎么看这个问题？

Why don't we start with you, Amy, and then Saesh on this one?

我认为这需要根据行业而定。

I think this is industry by industry.

到目前为止，我看到在创意行业，尤其是新闻和媒体领域，透明度的重视程度要高得多。

And what I've seen so far is that in the creative industry, certainly news and journalism, I think there's a much bigger emphasis on being transparent.

嗯。

Mhmm.

而在医疗和医药行业，由于监管非常严格，我不确定在其他许多行业——比如大型工程与建筑或零售业——是否也看到了同样的情况。

And I think in the health and medicine industry, because, again, very heavily regulated, I don't know that I've seen the same in many other industries, whether that's like big engineering and construction or retail.

而且我认为目前大多数国家还没有相关的法规。

And I I don't know that there are any current regulations around that in most countries.

在欧盟就不同，在日本也是如此。

That's different in the EU, and it's different in, like, Japan.

所以这真的取决于具体情况。

So really it really depends.

但如果你真正想问的是，是否应该保持透明？

But if the question you're trying to get at is, should there be transparency?

答案是肯定的。

The answer is yes.

但要想出有效的执行机制可不容易，因为目前的经济激励是

But good luck figuring out mechanisms to enforce that because right now, the financial incentive is

也是这样。

That too.

直接用就行了。

Just use it.

这完全正确。

That's absolutely right.

把它当作

Use it as a

工具并藏起来。

tool and hide it away.

你说什么，你

What do you Saesh, what do

觉得呢？

you think?

我的意思是，我大致同意。

I mean, I I broadly agree.

我想说，归根结底，最有可能实际奏效的解决方案是仅仅对最终结果进行问责。

I I would say, like, at the end of the day, the solution that is most likely pragmatically to work is to just enforce accountability on the final outcomes.

嗯。

Mhmm.

所以，无论员工或这些行业中的其他人使用什么工具来达到最终结果，他们都必须为此负责。

So irrespective of what tools an employee or someone in these industries uses to get to the sort of final point, they're the ones who have to stand behind it.

我认为我们正看到一些不错的例子，也就是说，取决于你怎么看，有些律师因为使用ChatGPT生成的引文而被追责。

And I think we're seeing some good examples, I mean, like, depending on how you look at it, of lawyers actually being held to account for using ChatGPT generated citations.

所以我认为，在过去几年里，我们已经看到了超过一百起案件，其中律师将AI生成的幻觉内容引入了他们的法律简报中。

So I think over the last couple of years, we've seen over a 100 cases where lawyers have introduced AI generated hallucinations into their legal briefings.

他们甚至真的提交了这些内容。

They've actually even presented it.

我们稍后回来。

We'll be back in a minute.

好的。

Okay.

所以你可能听说了，纽约市本周迎来了一位新市长，34岁的民主社会主义者扎兰·马曼尼。

So you may have heard, New York City gets a new mayor this week, 34 year old Democratic socialist, Zoran Mamdani.

马曼尼的当选是2025年左翼取得的最大胜利之一。

Mamdani's election was one of the biggest wins for the left in 2025.

但自那以后，他一直在默默推进一项新任务，努力确保他宏大的竞选承诺能够真正实现。

But since then, he's been quietly going about a new task, trying to make sure his sweeping campaign promises can actually happen.

一项将冻结超过200万租金管制租户租金的议程。

An agenda that will freeze the rents for more than 2,000,000 rent stabilized tenants.

让公交车快速且免费，并在全市提供普惠型托儿服务。

Make buses fast and free and deliver universal childcare across our city.

我对他如何能完成所有这些事情有点怀疑。

I'm a little skeptical about how he's gonna get everything done.

我认为很多人都是这么想的。

I think that's what a lot

很多人都是。

of people are.

许下这么多承诺。

Promise so many things.

是啊。

Yeah.

比如免费公交，你知道的，还有住房等等。

Like free buses, you know, housing and all that.

承诺就是承诺。

Promise is promises.

这种新的政治方式能成功吗？

Can this new kind of politics succeed?

还是这是马曼尼的巅峰时刻，即他上任前的日子？

Or is this Mamdani's high point, the days before he gets into office?

在今日解析（Today Explained）这期节目中，我们与纽约市当选市长深入交谈，直接问他：他是否言出必行？

On this episode of Today Explained from Vox, we sit down with New York City's mayor elect and ask him directly, is he for real?

本周《今日解析》敬请关注。

That's this week on Today Explained.

让我们来总结一下关于标准、伦理及其相关规则的讨论。

So let's wrap up talking about standards, ethics, and the rules around it.

萨尤什，你共同撰写了一份名为《AI作为普通技术》的Substack文章，你试图在AI的反乌托邦和乌托邦版本之间寻找中间地带。

Sayush, you co write a a substack that you recently retitled AI as normal technology where you try to find the middle ground between dystopian and utopian versions of what AI can do.

那么，阐明你的立场吧。

So stake out your claim here.

称AI为普通技术，是否意味着它并不会真正改变公司的运营方式？

Does calling AI normal technology mean it's not really gonna change how companies operate?

AI与互联网相比如何？互联网显然是一种真正具有革命性的技术。

And how does AI compare to, say, the Internet, which clearly has been a truly revolutionary technology?

我的意思是，称AI为普通技术，是反对那种认为我们正在创造新物种，或可能在2027年、2030年或更早实现超级智能的观点。

I mean, so the claim that AI is normal technology is in opposition to claims that we are inventing a new species or we might potentially reach superintelligence by 2027 or 2030 or what have you.

我认为，如果没有这种常识性的默认思维方式来思考技术的未来影响，很多人开始怀疑是否应该为这样一个世界做准备：五年后公司可能不再有任何员工。

I think in the absence of this kind of common sense default way of thinking about the future impacts of the technology, I think lots of people were starting to think about whether they should prepare for a world where the company has no employees in the next five years or Mhmm.

超级智能是否会真正接管一切。

Whether superintelligence is actually going to take over.

因此，我们‘AI作为普通技术’项目的一个重点，就是反驳这种观点，指出AI实际上可能与之前的通用技术非常相似，比如互联网，甚至电力。

And so a lot of our emphasis with the AI as normal technology project is to kind of push back against that to outline that AI actually might well be very similar to previous general purpose technologies like the Internet or perhaps even like electricity.

这些技术显然具有变革性，但标题中的‘普通’并不是说AI没有变革性，而是说AI的影响不会在接下来两年内显现，而可能需要十年甚至二十年才能逐步展开。

Now these technologies were clearly transformative, and the normal in the title is not to say that AI is not transformative, but just that the impacts of AI will play out not over the next two years, but perhaps over the next decade or two.

但现在，拉吉夫，你对AI更快地改变事物持更乐观的态度。

But now, Rajiv, you're more bullish on AI shifting things rather quickly.

请说明为什么它从根本上改变了经济和社会，以及政府领导人应该如何应对这种革命性技术带来的意外后果。

Make the case for why it fundamentally transformed the economy and and society presumably, and how government leaders should deal with the unintended consequences of a revolutionary technology.

而且，企业使用人工智能是否应该受到限制？

And again, should there be limits on how businesses can use AI?

嗯。

Yeah.

听好了。

Look.

我觉得回答这些问题的关键在于，我为什么这么乐观？

I mean, I guess the couple way to answer these questions, why am I bullish?

我乐观是因为我开始看到它真正改变了很多人的人生。

I'm bullish because I'm starting to see it really make a difference in a lot of people's lives.

我看到它在个人层面上发挥作用。

I'm seeing it happen with individuals.

我看到它带来了巨大的变化和转型，帮助组织和公司思考未来的方向以及如何竞争。

I'm seeing it provide major change and transformation and helping organizations and companies really think about their company going forward, how to compete.

我看涨是因为我认为我们将进入一个全球范围内的创业扩张新纪元。

I'm bullish because I think we're going to enter into a new world of entrepreneurial expansion around the world.

当你让这些工具普及化，让世界各地的人们都能接触到这些工具时，我认为会出现更多创业点子。

And when you democratize these tools and people have access to these tools from around the world, I think you're going see a lot more entrepreneurial ideas coming on board.

那么，人们是否应该毫无约束地使用呢？

Now, in terms of should people just be able to run hog wild?

不，我的意思是，可能不行，但归根结底，这里可能需要一些监管。

No, I mean, probably not, but look, at the end of the day, there's some regulation that probably should happen here.

我意识到‘人工智能’这个词对一些人来说很难接受和理解。

And I realized that our word is really difficult for some people to embrace and understand.

我不认为大公司会愿意接受它。

I don't think the big companies are going to want it.

除非所有人都这么做，否则这里的政府也不会愿意接受它。

The governments here are not gonna want it unless everybody does it.

但话虽如此，我认为在应对深度伪造等类似问题上，还是存在一些共同点的。

But with that being said, I think there are some common common grounds to really address areas such as deep fakes and and, you know, those kinds of things.

所以我认为在这方面我们有共同点可以合作。

So I think there's some common ground there that we could do.

但我说看好的原因是因为我亲眼看到了，卡拉。

But in terms of why I'm bullish is because I'm seeing it, Kara.

我看到了。

I'm seeing it.

我每天都能看到。

I'm seeing it every day.

我认为这是一场不同类型的革命。

And and I think this is a different type of revolution.

所以，我记得电商时代，卡拉。

So, look, I remember the ecommerce time, Kara.

你知道，我曾经为迈克尔·戴尔工作，记得走进他的办公室。

You know, I used to work for Michael Dell and I remember walking into his office.

我想我跟你说过这个故事，他说：‘伙计们，去想办法通过互联网卖电脑吧。’

I think I told you the story when he said, Hey guys, go figure out how to sell computers on the internet.

我们都看着他，觉得他疯了，谁会在网上买东西呢？

We all looked at him and he thought he was crazy, like, Who's going buy anything on the internet?

对吧？

Right?

而看看我们现在到了哪里。

And they look where we are right now.

所以，保持好奇而非评判的态度，我认为将变得至关重要。

And so this idea of being curious, not judgmental, I think it's going be really important.

我真的认为，这场革命更多地发生在个体、中小企业的层面。

And I really think that this is a revolution that's happening more at that individual small, medium business level.

我认为这将真正提升这里每一个人，这就是我对它持乐观态度的原因。

I think that's gonna really rise everybody up here, and that's why I'm bullish about it.

艾米，你曾认为隐私已经死亡，而现在有了大语言模型，知识产权似乎也岌岌可危。

Amy, now you've argued that privacy is dead, and now with LLMs, it seems like intellectual property is also on shaky ground.

那么，AI还会侵蚀哪些其他权利？对企业和公民来说，这种权衡值得吗？

What other rights will AI erode then, and is the trade off worth it both for businesses and citizens?

是的。

Yeah.

所以隐私已经死了。

So privacy is dead.

嗯。

Mhmm.

我认为知识产权，即使还没有完全断气，也肯定正在走向终结。

I think intellectual property, if not already breathe having breathed its last breath, is certainly on its way.

问题是，我们必须停止从个人和商业角度将数据仅仅视为写在纸上的内容或电子表格中的数字。

The the problem is that we have to stop thinking personally and in business as of data as just things that are written or numbers that are in spreadsheets.

数据以多种不同的方式存在。

Data exist in many different ways.

你敲击键盘的方式、你走路的姿势、你独特的步态、你的心跳。

The way that you type on your keyboard, the way that you you walk, your unique gait, your heartbeat.

我们正迅速进入一个机器人时代，我知道，一百年来，每个人都在承诺机器人。

And we are very quickly entering an era of robotics, which I know I know for a hundred years, everybody's been promising robots.

我认为它们实际上正在到来，但并不是我们所有人预期的那种形式。

I think they're actually coming now, but not in the form that we all expected.

原因在于，要实现人工智能的下一代，我们需要具身化，也就是说，AI系统需要获取比可抓取数据更多的信息。

And the reason for that is to achieve the next iteration of AI, we need embodiment, which is to say AI systems need to to pick up more data than exists that's scrapable

嗯。

Mhmm.

以物理形式。

In physical form.

因此，说到这里，你的个人数据不仅仅是你的外貌，还包括你具体的动作、身体移动的方式，以及所有这些不同因素的组合，这对员工、团队、企业都适用，我还能继续说下去。

So, with that being said, you know, your personal data is not just a way that you look, but literally the the gestures, the the way that you move your body around, and the combinations of all of these different points, which is true for employees, which is true of teams, which is true of businesses, and I could go on and on.

嗯。

Mhmm.

挑战在于，监管本质上是对已经发生事情的反应。

The challenge is regulation is inherently a reaction to something that's already happened.

它并不是通往

It's not a pathway to to

为了未来。

To the future.

嗯。

Yeah.

为了未来。

To the future.

而这最终会带来的问题是，当我们开始看到真正的进步时，我可以另开一个话题讨论AGI以及我们实际所处的位置和这一切意味着什么。

And the problem that's that that will eventually create is that as we start to see true advancements and I can have a whole separate conversation about AGI and where we're actually at and what all of that means.

但如果我们正朝着这个方向前进，因为我们将会拥有更多类型的数据、更丰富的上下文数据，这意味着未来将会有大量的诉讼，因为我们目前并没有为一个将数据定义得更广泛的世界做好准备。

But in the event that we're headed in that direction, because we're gonna have more types of data, more contextual data, what that implies is lots and lots of lawsuits ahead because we're not currently preparing for a world in which we define data much more broadly.

我们正在以不同的方式抓取数据，并开始用这些数据支持各种类型的企业，而你可能并不会在另一方面成为受益者。

We're scraping data in different ways, and we're starting to use those data in ways to to support different types of businesses where you may not become a a beneficiary on the other side personally.

那么，你认为未来有哪些人工智能趋势是我们尚未涉及但我们的听众应该关注的呢？

So what are some of the AI trends you see on the horizon, ones that we didn't get to cover but that our listeners should be thinking about?

萨沙，我们先从你开始，然后是拉吉夫，再是艾米。

Sayash, let's start with you, and then Rajiv and then Amy.

我认为，最大的变化之一将发生在组织层面。

So I think I continue to think that one of the biggest changes will happen organizationally.

我认为，迄今为止我们所见的组织结构，可能需要根据我们如何使用人工智能技术进行彻底的变革。

Like, I think the organizational structure that we've seen so far might need to be radically, like, changed in response to how we can use AI as a technology.

举个例子，让我们来看看软件工程。

And to give you an example, I think, let's turn to software engineering.

长期以来，我们一直有开发软件的公司和使用软件的公司。

So for the longest time, we've had companies that build software and companies that use software.

我们只有几百家公司开发了世界上大部分其他公司所使用的软件。

We have had, like, a few 100 companies that build the majority of the software that the rest of the world uses.

嗯。

Mhmm.

但随着人工智能的发展，软件开发成本持续下降，我认为这可能需要一种根本性的变革：我们不再依赖少数几家公司的技术，而是每个公司都可能拥有一个小型软件工程团队，能够处理该公司所有的业务需求。

But with AI, as the cost of developing software keeps going down, I think we might need this fundamental change in the sense that rather than sort of us all relying on tech from a few companies, it might turn out that it's more efficient for each company to have this small software engineering team that can actually handle all of the sort of business needs that this company has.

因此，我认为这是一种通常需要十年甚至二十年才能显现的颠覆性变革。

And so I think this is the sort of seismic shift that usually takes a decade or two to unfold.

正如拉吉夫所言，互联网花了这么长时间才达到今天的水平，是因为我们需要整个商业模式的变革。

Like, to Rajiv's point, the reason it took the Internet so long to get to the point where it is today was because we needed shifts in entire business models.

我们需要像亚马逊这样的电子商务公司，或者其他的类似企业出现，才能真正推动这一转变。

We needed ecommerce companies like Amazon or perhaps like even others to sort of come about to really make this move possible.

因此，我认为对于那些试图展望未来的人来说，问题可能在于组织层面：组织的未来会是什么样子？无论是因为AI在软件开发中的应用，还是在营销或其他业务流程中的应用。

And so I think for people trying to look ahead, maybe the questions are at the level of organizations and what does the future of the organization look like, whether it's just because of AI for developing software or for any other application, for marketing, for any other business process.

好的，拉吉夫？

Okay, Rajiv?

是的，

Yeah,

塞莎，你提出了一个很好的观点。

was a great point, Seysha.

而且在艾米关于机器人技术的观点和塞莎的观点基础上，卡拉，我们这四个人，是最后一辈能够单独管理人类的世代。

And to build on kind of Amy's point about the robotics and Seysha's point there, like, Kara, we are the last generation, like right here, the four of us, that's ever gonna manage humans alone.

我们必须管理人类、AI代理，或者 whatever 这种形式最终会变成什么，以及在某个时候还要管理机器人。

We're gonna have to manage humans, AI agents, or whatever that format takes, and at some point robotics.

所以这完全是另一种技能组合。

So that's a whole different type of skill set.

所以如果你回看之前我们讨论过的播客内容，

So if you look back earlier in pod where we were talking about

拉吉夫，我连管理人类都不想，但你继续说吧。

Rajeev, I don't even wanna manage humans, but go ahead.

是的，但如果你回溯到我们之前讨论领导力和首席执行官被取代的那期播客，你会发现你根本无法取代这种同理心。

Yeah, so, but if you go back earlier to the podcast where we were talking about leadership and CEOs being replaced, you just can't because you can't replace that empathy.

所以，简而言之，火花已经点燃，精灵已经出瓶，最大的挑战 frankly 是它发展得太快了。

And so look, the bottom line here is the spark's been lit, the genie's out of the bottle and the biggest challenge, quite frankly, it's moving so fast.

就像我接触的那些人，我所交谈的那些首席执行官们，他们感到晕头转向，因为一切都变得太快了。

Like people I speak to, again, the CEOs I'm speaking to, is that they're getting whiplash because it's just moving too fast.

他们根本不知道该用哪个工具，该选哪个。

Like they just don't know which tool to use, which one to go to.

未来会如何发展，真值得期待。

It's going be interesting what the future unfolds.

我很兴奋，也很高兴有艾米、扎亚什这样的人，还有你，能够真正帮助我们揭示这一点。

I'm excited and glad that there's folks like Amy and Zayash and yourself who can really help bring, you know, light on this.

好的。

All right.

艾米，最后请你发言。

Amy, last word.

当然。

Sure.

在我的世界里，趋势并不是一时的潮流。

So in my world, trends are are not trendy.

它们是长期变化的迹象。

They're they're long term indications of change.

我认为在讨论人工智能时，更重要的是思考其融合趋势，我这里有三个。

I think it's more important to think about convergences when it comes to AI, so I've got three.

第一个是人工智能与生物学的融合，但不是用于医学，而是用于建筑等其他领域。

The first is the convergence of AI in biology, but not for medicine, for other things like construction.

嗯。

Mhmm.

因此，正在生成和创造一些超材料。

So there are metamaterials that are being generated, created.

想象一下，能够随地震活动移动的砖块，或者由不同类型的木材制成的东西，这些木材通常不会被使用，或者包装材料。

So think of bricks that can move with seismic activity or things that are made out of wood, a different type of wood that that normally wouldn't be, or packaging.

还有工程领域，比如大豆和甘蔗，各种不同的东西，用于在气候带来巨大挑战的环境中生长。

Also engineering, soybeans and cane sugar, all different types of things to grow in environments where climate, has presented a huge challenge.

这已经是我们正在看到的，将对经济和社会产生持久影响的事情。

So that's already something that we're seeing that'll have long lasting impact on the economy and society.

回到人工智能和机器人技术，暂时忘掉拟人化的机器人。

AI and robotics, going back to that for a moment, forget anthropomorphized humans.

想象一下，不需要人去建筑物上搭脚手架，在纽约，脚手架是无处不在且长期存在的。

This is stuff like, imagine instead of a person having to put up scaffolding on a building, which in New York is, you know, persistent and everywhere

嗯。

Mhmm.

机器人来做这件事会更快、更高效，而且肯定更安全，等等。

A robot doing that instead faster, more efficient, and definitely safer among other things.

在中国，现在已经被用来停车了。

In China, they're already using to park cars these days.

没错。

That's That's right.

第三个领域，因为我想我们现在快说到结尾了，是人工智能在太空中的应用。

And then the third area, because I guess we're at the end now, is, AI in space.

嗯。

Mhmm.

我认为我们将看到太空探索的更快进展。

I think we're gonna see a faster advancement, space exploration.

这其实不是我研究的领域，但我女儿想学习航空航天工程，成为一名月球建筑师。

And this is not actually an area that I research, but my daughter wants to study aerospace engineering and become a lunar, architect.

所以她正在学习如何利用人工智能进行一种完全不同的设计和模拟，以便在月球上建造栖息地。

So part of what she is learning how to do with AI is a totally different type of design and simulation so that she can build habitats on the moon.

回到之前的问题，她15岁。

And going back to that earlier question, she's 15.

她上九年级。

She's in ninth grade.

对。

Right.

这就是她已经开始思考如何推动人类文明进步的内容。

And this is the kind of stuff that she's already starting to think about how to advance human civilization.

不仅仅是因为埃隆·马斯克觉得离开地球很酷，而是为了。

Not just to be off planet because Elon Musk thinks it's cool, but Mhmm.

为了探索关于我们自身的全新知识。

For the purpose of learning new things about ourselves.

是的。

Yeah.

你知道，我之前对天体生物学家很感兴趣，因为我是……哦，是的。

You know, I I was just interested in astrobiologists because I'm Oh, yeah.

谈论这个。

Talking about this.

他说，地球上最糟糕的一天，也比火星上最好的一天要好。

And he said the worst day on Earth is better than the best day on Mars.

100%。

100%.

100%。

100%.

但你

But you

在月球上仍然可能有一些很棒的日子。

could still have some cool days on the moon.

你

You

知道吗？

know?

月球。

The moon.

月球。

The moon.

是啊。

Yeah.

是啊。

Yeah.

月球吧，我想。

The moon, I guess.

没错。

That's right.

它是个

It's a

对我来说有点尘土飞扬。

little dusty for me.

这就像火人节。

It's like Burning Man.

总之，我非常感谢这一切，我认为随着时代变迁，保持这种对话对人们来说真的很有帮助。

Anyway, I really appreciate all this, and and I think it's really helpful for people to keep this dialogue up as things change over time.

我认为你们所有人传达的一个关键信息是：不要逃避这个问题，因为这就像逃避电力或互联网一样。

One of the key messages I think all of you are saying is do not run away from this because it's like running away from electricity or the Internet or something.

这是不可避免的。

It's inevitable.

如果你不参与其中，肯定会落后。

And if you're not part of it, you will be definitely left behind.

总之，我们非常感谢。

Anyway, we appreciate it.

非常感谢。

Thank you so much.

谢谢。

Thank you.

非常感谢你们邀请我们。

Thank you so much for having us.

谢谢。

Thanks.

今天的节目由克里斯蒂安·卡斯特罗塞尔、卡特里·奥卡姆、米歇尔·阿尔洛、梅根·伯尼和凯特琳·林奇制作。

Today's show was produced by Christian Castrocelle, Kateri Okam, Michelle Alloy, Megan Bernie, and Kaitlyn Lynch.

特别感谢布拉德利·西尔维斯特。

Special thanks to Bradley Sylvester.

我们的工程师是费尔南多·阿鲁达和关理，主题音乐由Trackademics提供。

Our engineers are Fernando Arruda and Rick Kwan, and our theme music is by Trackademics.

如果你已经关注本节目，那你就是正常的科技使用者。

If you're already following the show, you're normal technology.

如果没有，机器人就要来找你了。

If not, robots are coming for you.

请前往你收听播客的平台，搜索《On with Kara Swisher》并点击关注。

Go wherever you listen to podcasts, search for On with Kara Swisher and hit follow.

感谢您收听由Podium Media、《纽约杂志》、Vox Media播客网络和我们共同制作的《与Kara Swisher同行》。

Thanks for listening to on with Kara Swisher from Podium Media, New York Magazine, the Vox Media Podcast Network, and us.

我们周四再见，带来更多内容。

We'll be back on Thursday with more.