And that other tech company leaders should be looking at what's happening with NVIDIA and wincing over how rapidly it's taken the AI world.

其他科技公司的領導者也應該關注英偉達的發展，併為英偉達在人工智能領域的迅速崛起而扼腕嘆息。

Parmi, good timing to have you on the program.

帕爾米，你來得正是時候。

Explain your thesis to me.

向我解釋一下你的論文。

Well, I think a lot of people look at NVIDIA as this kind of bellwether for AI's success.

我認為很多人都把英偉達視為人工智能成功的風向標。

But it's also a reflection of a lot of the hype in AI.

但這也反映了人工智能領域的許多炒作行為。

You know, it's kind of profiting from the short term success of businesses who are buying its chips for their servers, cloud companies like Microsoft and Amazon.

要知道，微軟和亞馬遜等雲計算公司購買它的芯片用於服務器，它也算是從這些企業的短期成功中獲利。

But if you look further downstream at the actual businesses who are buying the generative AI tools from the likes of OpenAI or Microsoft Azure, we're seeing signs of discontent.

但是，如果你再往下游看看那些從 OpenAI 或微軟 Azure 等公司購買生成式人工智能工具的實際企業，我們就會看到不滿的跡象。

There are businesses who are saying, and I've seen multiple surveys coming out just in the last few months, saying that they are not really getting the productivity they were hoping for.

有企業表示，就在過去的幾個月裡，我看到多項調查顯示，他們並沒有真正獲得他們所期望的生產力。

They're not quite sure how to use it.

他們不知道如何使用它。

There's been a decline in plans to this year, in spite of the fact that these tools are getting better.

儘管這些工具越來越好，但今年的計劃卻有所減少。

So I think a big reason for this is that these generative AI tools from the likes of Microsoft OpenAI and Google have been marketed as general purpose tools, like a Swiss army knife of technology that's going to make your workforce more productive.

是以，我認為造成這種情況的一個重要原因是，微軟 OpenAI 和谷歌等公司推出的這些生成式人工智能工具被當作通用工具來推銷，就像一把瑞士軍刀，能讓你的員工提高工作效率。

But of course, these tools aren't necessarily general purposes.

當然，這些工具並不一定是通用的。

They're certainly capable in some areas, but they make mistakes.

他們在某些方面確實有能力，但也會犯錯。

They make hallucinations.

他們會產生幻覺。

There are issues with data security.

數據安全存在問題。

Strangely, our whole perception of computers and AI and how it was kind of marketed to us by science fiction as being these kind of robotic fact-based machines isn't really how it is in reality.

奇怪的是，我們對計算機和人工智能的整體認知，以及科幻小說是如何把它推銷給我們的，把它說成是一種基於事實的機器人機器，但現實並非如此。

They're actually very good at artistic endeavors and generating images and poetry and stories.

實際上，他們非常擅長藝術創作，擅長創作影像、詩歌和故事。

They're actually really not so good at generating facts that you can rely on.

實際上，他們並不擅長提供可以信賴的事實。

And this is something that businesses are realizing, sometimes to their detriment.

企業也意識到了這一點，但有時這對它們是不利的。

And so I think there's been this kind of raining in of spending.

是以，我認為現在的支出是雨後春筍。

And I think we're starting to see that reflected potentially in the decline in shares in Nvidia.

我認為，我們已經開始看到這一點在 Nvidia 股價下跌中的潛在反映。

A lot of investors, even if they can't do the math on valuation or correctly forecasting or predicting sales, they'll say, oh, Nvidia's supply constrained.

很多投資者即使無法計算估值，也無法正確預測銷售額，但他們會說，哦，Nvidia 供應緊張。

Demand right now for AI accelerators greatly exceeds their ability to supply.

目前，人工智能加速器的需求大大超過了供應能力。

There was one other piece of your column that I found so interesting.

在您的專欄中，還有一篇文章讓我覺得非常有趣。

We've both covered technology for quite a long time.

我們都長期從事科技報道。

And you make the point that historically, in phases of technological progress, all companies have a very clear North Star, something to work toward.

你還指出，從歷史上看，在技術進步的各個階段，所有公司都有一個非常明確的 "北極星"，即努力的方向。

But you lost all meaning.

但你失去了一切意義。

What do you mean by that?

你這是什麼意思？

Well, this whole race, this arms race for AI was sparked by two men, Sam Altman of OpenAI and Demis Hassabis, the founder of DeepMind, Google DeepMind now.

這場人工智能的軍備競賽是由兩個人引發的：OpenAI 的薩姆-阿爾特曼（Sam Altman）和 DeepMind（谷歌 DeepMind）的創始人德米斯-哈薩比斯（Demis Hassabis）。

And both guys were trying to create artificial general intelligence, which is this hugely ambitious goal to make AI as more knowledgeable than humans.

這兩個人都在努力創造人工通用智能，這是一個雄心勃勃的目標，要讓人工智能比人類更博學。

It can surpass our own cognitive abilities, and it has general knowledge, meaning it can do creative things, but it can also do mathematical calculations very well and better than humans.

它可以超越我們自己的認知能力，它擁有常識，這意味著它可以做創造性的事情，但它也可以做非常好的數學計算，而且比人類做得更好。

And their objectives were nothing less than curing cancer and solving climate change.

他們的目標不亞於治癒癌症和解決氣候變化問題。

There was a sense that if you had this machine, this almost godlike machine that could solve everything in a general capacity, that it could fix all problems.

有一種感覺是，如果你有了這臺機器，這臺幾乎像神一樣的機器，它可以解決所有的問題。

And I think when you have a vision like that, that's just so grand, and then it trickles down into the marketing and sales channels of your tech company, and then they're going out and trying to sell this to businesses, then your end customers have this kind of sense that they're getting this tool that has this general purpose ability, they're almost left with a sense of paralysis.

我認為，當你有了這樣一個宏偉的願景，並將其滲透到科技公司的營銷和銷售管道中，然後他們走出去，試圖將其銷售給企業，那麼你的最終客戶就會有這樣一種感覺，他們正在獲得這樣一個具有通用能力的工具，他們幾乎會有一種癱瘓的感覺。

I mean, what do you do with technology that can do everything?

我的意思是，面對無所不能的技術，你該怎麼辦？

Where do you even start?

從何說起呢？

So I think the mistake that some tech companies have made in marketing AI has been not necessarily in saying that the capabilities are too high, because they're very capable, but it's been in marketing them as being general purpose, because they can't do everything everywhere all at once.

是以，我認為一些科技公司在營銷人工智能時犯的錯誤並不一定是說它們的能力太高，因為它們的能力很強，而是把它們當作通用型產品來營銷，因為它們不可能同時在所有地方做所有事情。

They can only do a few things very well.

他們只能做好幾件事。

And that's why I think businesses need time when they buy these tools to experiment with them.

這就是為什麼我認為企業在購買這些工具時需要時間進行試驗。

You know, it's a little bit like with the mobile revolution, people, individuals who worked for companies brought in their smartphones, and they told the IT people, can you just set up my corporate email to my BlackBerry to my iPhone?

你知道，這有點像移動革命，人們，為公司工作的個人，帶來了他們的智能手機，他們告訴IT人員，你能不能把我的公司電子郵件設置到我的黑莓手機上，再設置到我的iPhone上？

And they were sort of experimenting with them and using them as productivity tools for themselves.

他們將其作為一種實驗，並將其作為自己的生產力工具。

And I think right now this kind of top down approach to let's force the entire staff to use these tools is just a recipe for failure.

我認為，現在這種自上而下的方式，即強迫全體員工使用這些工具，只會導致失敗。

Because when technology is so new and cutting edge, it really needs time to percolate and for individuals to just sort of find the way and how these tools will work for everybody else.

因為當技術如此嶄新和前沿時，它確實需要時間來滲透，需要個人來找到方法，以及這些工具將如何為其他人服務。