Name: 山姆·奧特曼看到的 2030 年「一人獨角獸時代」！ (샘 올트먼이 본 2030년 '1인 유니콘 시대')
Uploaded: 2026-01-03T21:49:34.000Z
Duration: 8 min 55 s

未來簡單式

There are some leaders in the AI space that have said that in five years half of the entry level white collar workforce will be replaced by AI.

AI 領域裡有些領導者說，五年內入門級白領勞動力將有一半被 AI 取代。

So we're college graduates in five years.

所以我們是五年後的應屆大學畢業生。

What do you hope the world looks like for us?

你希望我們未來看到的世界是什麼樣子的？

I think there's been a lot of talk about how AI might cause job displacement.

我覺得大家已經討論了很多關於 AI 可能會造成工作職位流失的議題。

I have a job that nobody would have thought we could have, you know, a decade ago.

我現在有份工作，是十年前大家絕對想不到我們能做到的。

What are the things that we could look ahead if we're thinking about in 2035 that like graduating college student, if they still go to college at all, could very well be like leaving on a mission to explore the solar system on a spaceship in some kind of completely new, exciting, super well paid, super interesting job.

如果我們想像一下 2035 年，大學畢業生（如果他們還會去唸大學的話），很有可能會肩負任務，搭乘太空梭探索太陽系，從事一種全新、令人興奮、薪水超高、超級有趣的工作。

And feeling so bad for you and I that like we had to do this kind of like really boring old kind of work and everything is just better.

而覺得我們過去做的那些無聊老派工作，現在一切都變得更好了。

10 years feels very hard to imagine at this point because it's too far.

十年對現在來說真的太難想像了，因為太遙遠了。

If you compound the current rate of change for 10 more years, it's probably something we can't even time travel trips.

如果我們把現在的變化速度再乘以十年，那大概是我們連時間旅行都無法想像的。

I mean, I think now would be really hard to imagine 10 years ago.

我是說，我覺得現在回頭看十年前，那時候真的很難想像。

But I think 10 years forward will be even much harder, much more different.

但我認為十年後會更加難以想像，更加不同。

I'm curious what you think the pretty short term impacts of this will be for young people.

我很好奇你認為這對年輕人來說，短期內會有什麼影響。

I mean these like half of entry level jobs replaced by AI makes it sound like a very different world that they would be entering than the one that I did.

我是說，像是有一半的入門級白領工作被 AI 取代，聽起來他們將進入的世界，跟我當年進入的世界非常不一樣。

I think it's totally true that some classes of jobs will totally go away.

我認為某些類型的職位確實會完全消失，這完全是真的。

This always happens and young people are the best at adapting to this.

這總是會發生，而年輕人最擅長適應。

I'm more worried about what it means not for the 22 year old, but

for the 62 year old that doesn't want to go retrain or reskill or whatever the politicians call it that no one actually wants, but

62 歲不想再培訓或再培訓的人意味著什麼，或是政客們稱之為大家都不想做的事，但

If I were 22 right now and graduating college, I would feel like the luckiest kid in all of history.

如果我現在 22 歲大學畢業，我會覺得自己是史上最幸運的孩子。

Because there's never been a more amazing time to go create something totally new, to go invent something to start a company, whatever it is.

因為現在是創造全新事物、發明東西、創辦公司，無論是什麼，最棒的時機。

I think it is probably possible now to start a company that is a one person company that will go on to be worth like more than a billion dollars.

我認為現在有可能創辦一家個人公司，最終價值超過十億美元。

And more importantly than that deliver an amazing product and service to the world.

更重要的是，能為世界提供令人驚豔的產品和服務。

You have access to tools that can let you do what used to take teams of hundreds and you just have to like, you know, learn how to use these tools and come up with a great idea.

你擁有各種工具，可以讓你一個人完成過去需要數百人團隊才能做到的事，你只需要學會如何使用這些工具，並想出一個很棒的點子。

If we take a step back, I think the most important thing that this audience could hear from you on this optimistic show is in two parts.

如果我們退一步來看，我認為在這個樂觀的節目中，這個觀眾最想聽到的，有兩個重點。

First there's tactically, how are you actually trying to build the world's most powerful intelligence and what are the rate limiting factors to doing that?

首先，戰術層面來說，你實際上是如何努力打造世界上最強大的智能，以及有哪些速率限制因素？

And then philosophically, how are you and others working on building that technology in a way that really helps and not hurts people?

然後，哲學層面來說，你和其他人是如何以一種真正幫助而非傷害人們的方式，來建構這項技術的？

So just taking the tactical part right now, my understanding is that there are three big categories that have been limiting factors for AI.

所以，我們現在先來談戰術層面，我的理解是，有三大類別一直是 AI 的限制因素。

The first is compute, the second is data and the third is algorithmic design.

第一是運算能力，第二是資料，第三是演算法設計。

How do you think about each of those three categories right now?

你現在是如何看待這三個類別的？

And if you were to help someone understand the next headlines that they might see, how would you help them make sense of all of this?

如果你要幫助某人理解他們接下來可能會看到的新聞標題，你會如何幫助他們理解這一切？

I would say there's a fourth too which is figuring out the products to build like scientific progress on its own, not put into the hands of people, is of limited utility and doesn't sort of co evolve with society in the same way.

我會說還有第四個，那就是找出要開發的產品，因為科學進步本身，如果沒有被應用到人們手中，其效用是有限的，而且不會以相同的方式與社會共同演進。

So on the compute side, yeah, this is like the biggest infrastructure project certainly that I've ever seen.

所以，在運算能力方面，這確實是我見過最大的基礎設施專案。

Possibly it will become the, I think it will maybe already is the biggest and most expensive one in human history.

這可能已經是人類史上最大、最昂貴的專案了。

But the whole supply chain from making the chips and the memory and the networking gear, racking them up in servers, doing a giant construction project to build like a mega, mega data center, putting the, you know, finding a way to get the energy which is often a limiting factor piece of this and all the other components together.

從製造晶片、記憶體和網路設備，將它們組裝到伺服器，進行大規模建設來打造超級資料中心，找到能源供應（這常常是限制因素），以及所有其他零組件。

This is hugely complex and expensive and we are, we're still doing this in like a sort of bespoke one off way, although it's getting better.

這非常複雜且昂貴，我們仍然是以一種客製化、一次性的方式進行，雖然情況正在改善。

Like eventually we will just design a whole kind of like mega factory that takes, you know, I mean spiritually it will be melting sand on one end and putting out fully built AI compute on the other.

最終，我們將設計一個大型工廠，從某種意義上說，它會將沙子融化，然後產出完全建置好的 AI 運算設備。

但我們離那一步還有很長的路要走。

And it's a, it's an enormously complex and expensive process.

這是一個極其複雜且昂貴的過程。

We are putting a huge amount of work into building out as much compute as we can and to do it fast.

我們投入了巨大的努力來建置盡可能多的運算能力，並且要快速完成。

And you know, it's going to be like sad because GPT5 is going to launch and there's going to be another big spike in demand and we're not going to be able to serve it.

而且，這會有點令人難過，因為 GPT5 將會推出，需求又會激增，而我們將無法滿足。

And it's going to be like those early GPT four days.

而且這會跟早期 GPT 四的時候一樣。

And the world just wants much more AI than we can currently deliver.

全世界對 AI 的需求遠遠超過我們目前的供應能力。

And building more compute is an important part of doing that.

建造更多的運算能力是達成這個目標的重要一環。

That's actually this is what I expect to turn the majority of my attention to, is how we build compute at much greater scales.

這其實是我預計會將大部分精力投入的方向，也就是我們如何以更大的規模建造運算能力。

So how we go from millions to tens of millions and hundreds of millions and eventually hopefully billions of GPUs that are sort of in service of what people want to do with this.

所以我們要如何從數百萬、數千萬、數億，最終希望是數十億的 GPU，來服務人們想做的事情。

When you're thinking about it, what are the big challenges here in this category that you're going to be thinking about?

當你思考這件事時，這個類別裡你將會思考的主要挑戰有哪些？

You know, like if you're going to run a gigawatt scale data center, it's like, oh, a gigawatt, how hard can that be to find?

你知道嗎，像是如果你要建造一個百萬瓦等級的資料中心，你會想說，喔，百萬瓦，找起來有多難？

It's really hard to find a gigawatt of power available in the short term.

短期內要找到可用的百萬瓦電力，其實非常困難。

We're also very much limited by the processing chips and the memory chips, how you package these all together, how you build the racks.

我們也非常受限於處理器晶片和記憶體晶片，如何將這些全部整合在一起，如何建造機架。

And then there's like a list of other things that are, you know, there's like permits, there's construction work.

還有像是許可證、施工這些事情。

But again, the goal here will be to really automate this.

但我們的目標是要把這件事完全自動化。

Once we get some of those robots built, they can help us automate it even more.

一旦我們建好了一些機器人，牠們就能幫我們進一步自動化。

But just, you know, like a world where you can basically pour in money and get out a pre built data center.

就像是，你可以投入金錢，然後就得到一個預先建好的資料中心。

So that'll be a huge unlock if we can get it to work.

所以如果我們能做到，那將是一個巨大的突破。

是啊，這些模型變得越來越聰明瞭。

There was a time when we could just feed it another physics textbook and got a little bit smarter at physics.

以前我們只要再給它一本物理課本，它在物理方面的知識就會進步一點。

But now, honestly, GPT5 understands everything in a physics textbook pretty well.

但現在，說實話，GPT5 對物理課本裡的內容已經理解得很好了。

We're very excited about our users helping us create harder and harder tasks and environments to go off and have the system solved.

我們很期待使用者能幫我們創造越來越難的任務和環境，讓系統去解決。

But I think data will always be important.

But we're entering a realm where the models need to learn things that don't exist in any data set yet.

但我們正進入一個模型需要學習目前資料集中不存在的新事物的領域。

How do you teach a model to discover new things?

你該怎麼教模型去發現新事物呢？

Like we can go off and come up with hypotheses and test them and get experimental results and update on what we learn.

就像我們可以提出假設、測試它們、獲得實驗結果，並根據我們學到的東西進行更新。

Yeah, we've made huge progress on algorithmic design.

是的，我們在演算法設計上取得了巨大的進展。

The thing that OpenAI does best in the world is we have built this culture of repeated and big algorithmic research gains.

OpenAI 在世界上做得最好的事情是，我們建立了一種文化，能夠持續進行大規模的演算法研究。

So we kind of figured out what became the GPT paradigm, we figured out what became the reasoning paradigm.

所以我們大概弄清楚了什麼是 GPT 範式，什麼是推理範式。

But it is very exciting to me to think that there are still many more orders of magnitudes of algorithmic gains ahead of us.

但對我來說，想到還有許多數量級的演算法進步在我們前面，這真的令人興奮。

We just yesterday released a model called GPT OSS Open Source Models, a model that is as smart as 04 Mini, which is a very smart model that runs locally on a laptop.

我們昨天剛發布了一個叫做 GPT OSS Open Source Models 的模型，這個模型跟 04 Mini 一樣聰明，它是一個非常聰明的模型，可以在筆記型電腦上在地運行。

If you had asked me a few years ago when we would have a model of that intelligence running on a laptop, I would have said many, many years in the future.

如果幾年前你問我，我們什麼時候會有一個這麼聰明的模型能在筆記型電腦上運行，我會說還要很多很多年。

But then we found some algorithmic gains, particularly around reasoning, but

但後來我們發現了一些演算法上的進步，特別是在推理方面，但

also some other things that let us do a tiny model that can do this amazing thing.

還有一些其他的事情，讓我們能夠做出一個能做這件驚人事情的小型模型。

And, you know, those are the most fun things.

而且，你知道的，這些是最有趣的事情。

That's like kind of the coolest part of the job.

這大概是這份工作最酷的部分了。

I can see you really enjoying thinking about this.

我看得出來你真的很享受思考這件事。

I'm curious, for people who don't quite know what you're talking about, who aren't familiar with how an algorithmic design would lead to a better experience that they actually use, could you summarize the state of things right now?

我很好奇，對於那些不太清楚你在說什麼，不熟悉演算法設計如何能帶來更好的使用體驗的人，你能不能總結一下目前的狀況？

What is it that you're thinking about when you're thinking about how fun this problem is?

你想到這個問題的樂趣時，你都在想些什麼呢？

Let me start back in history and then I'll get to some things from.

讓我先從歷史講起，然後再講一些後續的事情。