Name: 샘 올트먼이 본 2030년 '1인 유니콘 시대'
Uploaded: 2026-01-03T21:47:26.000Z
Duration: 8 min 55 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Future simple

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.

So we're college graduates in five years.

What do you hope the world looks like for us?

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

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

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.

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.

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

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

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

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

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

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.

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

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

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

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

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.

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.

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

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.

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.

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?

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

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

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

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

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?

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.

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

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

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.

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.

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.

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

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

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.

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

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

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

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.

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.

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

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?

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

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.

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

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

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

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

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

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

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

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

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.

How do you teach a model to discover new things?

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

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

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

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

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

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.

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.

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

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

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

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

I can see you really enjoying thinking about this.

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?

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

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