20 Surprising Things You MISSED From SAM Altman's New Interview (Q-Star,GPT-5,AGI)

So here are 20 revelations from Sam Altman's interview with Lex Friedman. I thought it was a very insightful interview, and I've collected 20 of the most interesting things. So let's not waste any time and get into what there is. So one of the first things that he actually said was he said that things are very intense at OpenAI. And I thought that this was rather fascinating because it goes to show that the scale that OpenAI are operating on in terms of the products they're trying.

To develop, you know, like something that was in the past that was really unpleasant and really difficult and painful. But we're back to work, and things are so busy and so intense that I don't spend a lot of time thinking about it. There was a time after there was like this fugue state for kind of like the month after, maybe 45 days after that was I was just sort of like drifting through the days. I was so out of it. I was feeling so down.

Just on a personal psychological level.

Yeah, really painful and hard to have to keep running OpenAI in the middle of that. I just wanted to crawl into a cave and kind of recover for a while. But now it's like we're just back to working on the mission.

The next quick point that Sam Altman did actually make was he did talk about why they changed the board. He also spoke essentially about the fact that the new board is going to have a lot more experience. And something that he did actually also talk about was he did actually talk about. You know, it is pretty hard to say that OpenAI's governance did work because essentially it did fail, because when they did fire him, Sam Altman was immediately replaced. But he said, that is going to be something that they do work on in the future, I think.

Didn't have a lot of experienced board members, and a lot of the new board members at OpenAI have just have more experience as board members. I think that'll help.

Now, here's the point that a lot of people wanted to see. Point number three is that they did actually speak about Ilya Sutskova, which is one of the most renowned scientists in terms of artificial intelligence. And of course, there has been many speculation in regards to what Ilya Sutskova saw at OpenAI that prompted him to oust this Altman from the board. And here they kind of discuss what Ilya, quote, unquote, saw. And Sam Altman reveals that Ilya didn't see anything.

He's a little bit younger than me. Maybe he works a little bit longer.

You know, there's a meme that he saw something like he maybe saw AGI, and that gave him a lot of worry internally. What did Ilya see?

Ilya has not seen AGI. None of us have seen AGI. We've not built AGI. I do think one of the many things that I really love about Ilya is he takes AGI and the safety concerns, broadly speaking, including things like the impact this is going to have on society very seriously and as we continue to make significant progress. Ilya is one of the people that I've spent the most time over the last couple of years talking about what this is going to mean, what we need to do to ensure we get it right, to ensure that we succeed at the mission. So Ilya did not see AGI, but Ilya is a credit to humanity in terms of how much he thinks and worries about making sure we get this right.

Then, of course, here we do have another point, which is, why is Ilya Sutskova remaining silent? I thought it was rather fascinating that I guess Ilya just wants to be out of the spotlight for some time. I think another thing as well is that maybe with all of the board drama, like Sam Altman previously talked about the, you know, mental health impacts of that, just wanting to stay out of the spotlight for quite some time, just wanting to focus on mainly the work and just kind of think about the future of AI development rather than being in the entire spotlight. And maybe that's just what Ilya Satskov is doing, just rather being a bit quiet.

I kind of think Evilia is always on the soul search in a really good way. Yes. Yeah.

Also, he appreciates the power of silence.

Then we had a rather fascinating point, and that was actually Sam Altman talking about OpenAI and what the Open stands for. So this is where he actually refers to Elon Musk's lawsuit, where he's stating that OpenAI is no longer open. And he actually pretty much says that, you know, the Open in OpenAI, the transformation of what that means. And essentially, pretty much now it just means, so OpenAI's, I guess you could say, tools are going to be open and available to the public for use, but not open in terms of actual research. So I think this is a pretty interesting shift because now people are going to be looking at this statement and, you know, I guess you could say, seeing that, you know, of course, OpenAI has changed in its nature, but I wonder what people's opinions on this open nature now are.

Is the word open and OpenAI mean to Elon at the time? Ilya has talked about this in the email exchanges and all this kind of stuff. What does it mean to you at the time? What does it mean to you now?

I would definitely pick a different. Speaking of going back with an Oracle, I'd pick a different name. One of the things that I think OpenAI is doing that is the most important of everything that we're doing is putting powerful technology in the hands of people for free as a public good. We don't run ads on our free version, we don't monetize it in other ways. We just say it's part of our mission. We want to put increasingly powerful tools in the hands of people for free and get them to use them. I think that kind of open is really important to our mission. I think if you give people great tools and teach them to use them or don't even teach them, they'll figure it out and let them go build an incredible future for each other with that. That's a big deal. So if we can keep putting free or low cost, or free and low cost powerful AI tools out in the world, I think it's a huge deal for how we fulfill the mission. Open source or not. Yeah, I think we should open source some stuff and not other stuff. It does become this religious battle line where nuance is hard to have. But I think nuance is the right answer.

Now here's where Sam Altman actually also did talk about Sauron needing to be better for its release. And this was mainly due to computing power Restrict. So essentially one of the things that with these advanced AI systems and one of the constant, constant thing that we're going to keep seeing is that there is a real need, in fact a dire need for more compute. Because the problem that we do have is that there just isn't enough compute to go around. We know that GPT4 is still very limited in terms of what we can access on a regular user. And the fact that we're building more and more capable systems, they're going to need more and more compute. And this doesn't seem to be a trend that is going away anytime soon. And it seems like that is of course the case with Sora. And I think it was, you know, the estimated times for Sora video generation are between five to 10 minutes. That is of course pure speculation, just based on the fact that they said you could go ahead and make a cup of coffee whilst you wait for the video to be generated. But essentially he's stating that, you know, we still need to make this more efficient and have more compute available before it is widely released to the public.

Can this, can the Same magic of LLMs now start moving towards visual data? And what does that take to do that?

I mean, it looks to me like yes, but we have more work to do. Sure.

What are the dangers? Why are you concerned about releasing the system? What are some possible dangers of this?

Frankly speaking, one thing we have to do before releasing the system is just get it to work at a level of efficiency that will deliver the scale people are going to want from this. So I don't want to downplay that. And there's still a ton, ton of work to do there. But you can imagine issues with deep fakes, misinformation. We try to be a thoughtful company about what we put out into the world and it doesn't take much thought to think about the ways this can go badly.

Now, here's something that I did actually find rather fascinating and this is where he talked about the job market changes. This is something that is rather concerning. It's a concerning point because of course, as you know, as technology develops, develops, it becomes more and more capable and that means it's capable of more and more tasks, which means it's more and more capable of replacing your job. So that is, of course, that not most people. In fact, I don't think anyone is going to be excited about, especially if they are someone who's been in their field for a while. So he kind of, you know, talks.

About this AI generated content. Do you think in the next five.

Years people talk about, like, how many jobs is AI going to do in five years? And the framework that people have is what percentage of current jobs are just going to be totally replaced by some AI doing the job? The way I think about it is not what percent of jobs AI will do, but what percent of tasks will AI do and over what time horizon. So if you think of all of the five second tasks in the economy, five minute tasks, the five hour tasks, maybe even the five day tasks, how many of those can AI do? And I think that's a way more interesting, impactful, important question than how many jobs AI can do. Because it is a tool that will work at increasing levels of sophistication over longer and longer time horizons for more and more tasks, and let people operate at a higher level of abstraction. So maybe people are way more efficient at the job they do. And at some point that's not just a quantitative change, but it's a qualitative one. Too about the kinds of problems you can keep in your head. I think that for videos on YouTube it'll be the same. Many videos, maybe most of them will use AI tools in the production, but they'll still be fundamentally driven by a person thinking about it, putting it together, doing parts of it, sort of directing it and running it.

Yeah, it's so interesting. I mean, it's scary, but it's interesting to think about. I tend to believe that humans like to watch other humans or other human.

Humans really care about other humans a lot.

Yeah. If there's a cooler thing that's more, that's better than a human. Humans care about that for like two days and then they go back to humans.

That seems very deeply wired.

It's the whole chess thing. Yeah. But now let's everybody keep playing chess and let's ignore the alpha in the room that humans are really bad at chess.

Relative to AI systems, we still run races and cars are much faster. I mean there's like a lot of examples.

Yeah. And maybe it'll just be tooling.

Now this is one of the craziest statements and I think it was quite eye opening because Sam Altman said that GPT4 sucks. Now for those of you who use GPT4 on a day to day basis, I don't think it sucks. I think it's something that's really, really cool. But if Sam Altman says that GPT4 sucks, I think that this potentially means that from his point of view, based on the technology that he's seeing and what he knows is coming from the future systems, maybe he just believes that, you know, GPT4 and you know, the systems beforehand are so terrible in comparison to the new systems that they are working on. You know, so I'm guessing that potentially they are, they are so far ahead that they know that the newer systems are really, really going to shatter our expectations to the point where he can say that GPT4 sucks. And that was something that definitely actually did shock me. So it was quite surprising.

But for Me Looking back, GPT4ChatGPT is pretty damn impressive, like historically impressive. So allow me to ask, what's been the most impressive capabilities of GPT4 to you and GPT4 Turbo?

I think it kind of sucks.

Typical human also gotten used to an awesome thing?

No, I think it is an amazing thing. But relative to where we need to get to and where I believe we will get to, at the time of GPT3, people were like, oh, this is amazing. This is this marvel of technology and it is, it was. But now we have GPT4 and look at GPT3 and you're like, that's unimaginably horrible. I expect that the delta between 5 and 4 will be the same as between 4 and 3. And I think it is our job to live a few years in the future and remember that the tools we have now are going to kind of suck looking backwards at them. And that's how we make sure the future is better.

Another point that Sam Altman did make was he did talk about the future of long context Windows and he did state that, you know, potentially not unlimited, but really, really long context windows are probably going to be somewhere in the distant future. And he also did talk about the fact that, you know, previously when, you know, Bill Gates was you know, developing, you know, memory and computers and that stuff, he couldn't really conceptualize a really lot that like the need for, you know, huge gigabytes of storage. But of course, as we know, that is something that is really, really, you know, easy to do in today's day and age. We have terabytes and terabytes of storage. But something like the future long context Windows is going to be something that is something that they're thinking about from.

GPT4 to GPT4 turbo.

Most people don't need all the way to 128 most of the time. Although if we dream into the distant future, we'll have way distant future, we'll have context length of several billion. You will feed in all of your information, all of your history over time and it'll just get to know you better and better and that'll be great for now. The way people use these models, they're not doing that. And people sometimes post in a paper or a significant fraction of a code repository, whatever, but most usage of the models is not using the long context most of the time.

I like that. This is your I have a dream speech. One day you'll be judged by the full context of your character or of your whole lifetime. That's interesting. So that's part of the expansion that you're hoping for is a greater and greater context.

I saw this Internet clip once, I'm going to get the numbers wrong. But it was like Bill Gates talking about the amount of memory on some early computer. Maybe 64K, maybe 640K, something like that. And most of it was used for the screen buffer. And he just couldn't seem genuine this couldn't imagine that the world would eventually need gigabytes of memory in a computer or terabytes of memory in a computer. And you always do, or you always do just need to follow the exponential of technology and we're going to like, we will find out how to use better technology. So I can't really imagine what it's like right now for context links to go out to the billions someday. And they might not literally go there, but effectively it'll feel like that.

But.

I know we'll use it now.

Here's an interesting point, is that memory. Remember how a lot of people were talking about memory and that kind of stuff? Memory is something that is of course, as you know, OpenAI said they are working on it, but interestingly enough, it seems like OpenAI have shifted their focus away from memory. And I think that's because maybe they've just reached, I wouldn't say a dead end, but maybe they've realized that, you know, reasoning is more reasoning and you know, agent capabilities are stuff that, you know, can be applied to more things. And of course personalization and memory is going to be something that they've said that they've worked on, but from this kind of interview statement, it didn't seem like they were too, too more.

You've given ChatGPT the ability to have memories, you've been playing with that about previous conversations and also the ability to turn off memory. I wish I could do that sometimes, just turn on and off depending, I guess sometimes alcohol can do that, but not, not, not optimally, I suppose. What have you seen through that? Like playing around with that idea of remembering conversations and not.

We're very early in our explorations here, but I think what people want, or at least what I want for myself, is a model that gets to know me and gets more useful to me over time. This is an early exploration. I think there's like a lot of other things to do, but that's where we'd like to head. You'd like to use a model and over the course of your life, or use a system, there'll be many models and over the course of your life it gets better and better.

Yeah.

How hard is that problem? Because right now it's more like remembering little factoids and preferences and so on. What about remembering, like, don't you want GPT to remember all the shit you went through in November and all the drama and then you can, because right now you're clearly blocking it out a little bit.

It's not just that I want it to remember that. I want it to integrate the lessons of that and remind me in the future, what to do differently or what to watch out for. And we all gain from experience over the course of our lives, varying degrees. And I'd like my AI agent to gain with that experience too. So if we go back and let ourselves imagine that trillions and trillions of context length, if I can put every conversation I've ever had with anybody in my life in there, if I can have all of my emails input out, like all of my input output in the context window every time I ask a question, that'd be pretty cool, I think.

Yeah.

Now, this is something that I also did find rather fascinating was the fact that he also talked about different levels of compute. And one of the things that he made clear was that, you know, in the future, I think it's going to be, of course, more effective. Is that like, let's say, for example, I want to boil an egg and I want to ask an LLM or an AI system, how do I boil an egg in a specific type of way? I could easily use some kind of very, very basic system like ChatGPT 3.5 to answer this kind of question. But of course, if we have, you know, a super advanced AI that is definitely requiring a lot of compute, we wouldn't route this question to that kind of AI. So there's going to be different levels of compute for different levels of AI systems to be able to tackle easier and harder problems. And I think that that is something that is rather important.

So it's allocating approximately the same amount of compute for each token it generates. Is there room there in this kind of approach to slower thinking, sequential thinking?

I think there will be a new paradigm for that kind of thinking.

Will it be similar, like architecturally as what we're seeing now with LLMs? Is it a layer on top of the LLMs?

I can imagine many ways to implement that. I think that's less important than the question you were getting at, which is do we need a way to do a slower kind of thinking where the answer doesn't have to get. I guess spiritually you could say that you want an AI to be able to think harder about a harder problem and answer more quickly about an easier problem? And I think that will be important. It seems to me like you want to be able to allocate more compute to harder problems. It seems to me that a system knowing if you ask a system like that prove Fermat's last theorem versus what's today's date, unless it already knew and had memorized the answer to the proof Assuming it's got to go figure that out. Seems like that will take more compute now.

This is some very, very juicy information. Point number 12 is the Q Star leaks. This is some very, very juicy stuff because Q Star was something that many people didn't think was real. Of course, there were many different articles floating about. But Q Star was something that of course, as you know, was in the speculation area for quite some time. And now Sam Altman has confirmed that, you know, these were some unfortunate leaks, as he previously stated in the past. But now it's a situation where the fact that he's openly stating that they can't talk about it, it means that we are looking at something that probably is going to come in the Future and maybe GPT5 and future AI systems. They really have discovered something amazing.

One can dream.

OpenAI is not a good company at keeping secrets. It would be nice. We're been plagued by a lot of leaks. It would be nice if we were able to have something like that.

Can you speak to what Q is?

We are not ready to talk about that.

See, but an answer like that means there's something to talk about. It's very mysterious, Sam.

I mean, we work on all kinds of research. We have said for a while that we think better reasoning in these systems is an important direction that we'd like to pursue. We haven't cracked the code yet. We're very interested in it.

Point number 13 is, of course, as you all know, Sam Altman's iterative deployment. This is where they do not want AI systems to be shocking individuals. They actually don't want that because that just isn't something that the public is going to take very well. This can cause problems like mass hysteria and other issues in society, which they actually do talk about later on in the interview. But the point is, is that they are trying to, you know, be even more iterative with their launches because they feel like they're not shocking people with their updates. And, you know, Alex Friedman basically says that, you know, I actually did feel that these were quite surprising. But essentially right here he states that, you know, they're going to try and release updates even more iteratively because that's not their intended goal.

It's interesting to me, it all feels pretty continuous. Right.

This is kind of a theme that you're saying is there's a gradual. You're basically gradually going up an exponential slope. But from an outsider perspective, for me just watching it, it does feel like there's leaps, but to you there isn't.

I Do wonder if we should have. So part of the reason that we deploy the way we do is that we think we call it iterative deployment. Rather than go build in secret until we got all the way to GPT 5, we decided to talk about GPT 1, 2, 3 and 4. And part of the reason there is, I think AI and surprise don't go together. And also the world, people, institutions, whatever you want to call it, need time to adapt and think about these things. And I think one of the best things that OpenAI has done is this strategy. And we get the world to pay attention to the progress, to take AGI seriously, to think about what systems and structures and governance we want in place before we're under the gun and have to make a rush decision. I think that's really good. But the fact that people like you and others say you still feel like there are these leaps makes me think that maybe we should be doing our releasing even more iteratively. I don't know what that would mean. I don't have an answer ready to go. But our goal is not to have shock updates to the world. The opposite.

Yeah, for sure. More iterative would be amazing. I think that's just beautiful for everybody.

But that's what we're trying to do. That's our state of the strategy and I think we're somehow missing the mark. So maybe we should think about releasing GPT5 in a different way or something like that.

Yeah, 4.71, 4.72. But people tend to like to celebrate. People celebrate birthdays. I don't know if you know humans, but they kind of have these milestones and all.

I do know some humans. People do like milestones. I totally get that right here.

He actually does talk about the GPT5 release date.

So when is GPT5 coming out again?

I don't know. That's an honest answer.

Oh, that's the honest answer, is it? Blink twice if it's this year.

I also. We will release an amazing model this year. I don't know what we'll call it.

So that goes to the question of like, what's the way we release this.

Thing Will release over in the coming months, many different things. I think they'll be very cool. I think before we Talk about a GPT5 like model called that or not called that, or a little bit worse or a little bit better than what you'd expect from a GPT5, I know we have a lot of other important things to release first.

And right here, this is where previously I've stated as well, more compute is of course needed for the future because of course they're trying to build AGI. I think that most people did miss the mark with this because Sam Altman stating that a few different models are going to be coming out this year means that if we look back on some of the stuff that was talked about earlier, Sam Altman actually did state. Well, he didn't actually state, you know, they did speak about some of the models called like Arrakis and Gobi. That kind of means that potentially we do have maybe other models coming before GPT5. Some people have speculated that the models coming before GPT5 are an audio model. We know that that is something that is penciled in for GPT6. But I think it's going to be kind of interesting to see what other models they do drop because whilst of course they have been working on GPT5 and we do know that is the main focus because everyone's like, okay, GPT4, GPT5, you know, with like Sora, we didn't really predict that Sora was going to be here. So them working on some other stuff. I'm not surprised if that does happen. So it will be, it will be interesting to see if in the future this kind of thing does happen because I mean for the next couple of months I don't think we're going to be in GPT5. It seems that we're getting a couple of different models and potentially, like I said before, a couple of new models that we didn't even predict that we were going to get. That OpenAI have somehow figured out, learned how to crack the code and then done some really good stuff of it. So that should be something that's really interesting because I think that kind of changes the concept and the scope of what people think is going on this year. And when you combine that with the fact that they said that this year is going to move really, really quickly, I think that we're going to start to see this kind of landscape.

I think the world is going to want a tremendous amount of computer and there's a lot of parts of that that are hard. Energy is the hardest part. Building data centers is also hard. The supply chain is harder than of course fabricating enough chips is hard. But this seems to me where things are going, we're going to want an amount of compute that's just hard to reason about right now.

Now point number 16 is one of the most interesting things I actually did see because this was where Sam Altman actually talks about the risks of artificial intelligence in the mainstream and the kind of shift in societal opinions regarding how AI is viewed by consumers. And I think this is one of the main issues, and I didn't see anyone really talk about this, but Sam Altman actually did express his concerns of his own safety. And he said the percentage of me getting shot is not zero. Which means that I've been saying this for quite some time, that I do think that in the future, OpenAI people that work there are going to need some kind of security personnel at all times. You know, maybe at the facility or whatever. Because I think as tensions mount between people who are losing their jobs and livelihoods and as these capabilities grow, I think people potentially are going to have a lot of anger and a lot of rage. And of course, we don't want innocent people that work at the company to get harmed. So I think him expressing that is definitely an important point, because this transition from an economy that's based on labor to one that isn't is going to be fascinating because with millions of people displaced, it's a real mystery on how we're going to make that transition smooth and effective for everyone.

I don't know if you know humans, but that's one of the dangers. That's one of the security threats for nuclear fission is humans seem to be really afraid of it. And that's something we have to incorporate into the calculus of it. So we have to kind of win people over and to show how safe it is.

I worry about that for AI I think some things are going to go theatrically wrong with AI I don't know what the percent chance is that I eventually get shot, but it's not zero.

Oh, we want to stop this problem.

Maybe.

How do you decrease this?

What I meant more about theatrical risks is AI is going to have, I believe, tremendously more good consequences than bad ones. But it is going to have bad ones, and there will be some bad ones that are. Bad but not theatrical. A lot more people have died of air pollution than nuclear reactors, for example. But most people worry more about living next to a nuclear reactor than a coal plant. But something about the way we're wired is that although there's many different kinds of risks we have to confront, the ones that make a good climax scene of a movie carry much more weight with us than the ones that are very bad over a long period of time, but on a slow burn.

Well, that's why truth matters. And hopefully AI can help us see the truth of things, to have balance to understand what are the actual risks, what are the actual dangers of things in the world.

Now here's where Sam Altman in point 17 talks about the AI arms race and why it is a little bit of an issue.

The con is that I think if we're not careful, it could lead to an increase in sort of an arms race that I'm nervous about.

And this is probably one of the most interesting points is where Sam Altman talks about the leap from gpt4 to gpt5. And I think the leap from gpt4 to gPT5, as we previously discussed, is going to be rather stunning. So that was one as well.

What aspect of the leap? And sorry to linger on this, even though you can't quite say details yet, but what aspects of the leap from GPT4 to GPT5 are you excited about?

I'm excited about being smarter. And I know that sounds like a glib answer, but I think the really special thing happening is that it's not like it gets better in this one area and worse at others. It's getting better across the board. That's, I think, super cool.

Yeah. There's this magic.

Now here was something that I think most people from yesterday's video would want to see. And this is, of course, the future of software engineering. As I previously discussed, the future of work is changing. It's on a very, very rapidly changing scale. And the thing is here is that we don't know how the future is going to work. But Sam Altman kinds of quells anyone's fears by stating that the future of software engineering won't be one that is entirely automated, but rather augmented.

Looking out into the future, how much programming do you think humans will be doing five, ten years from now?

I mean, a lot, but I think it'll be in a very different shape. Like, you know, maybe some people program entirely in natural language.

Entirely natural language.

I mean, no one programs like writing bytecode. I'm some people, no one programs the punch cards anymore. I'm sure you can find someone who does, but you know what I mean.

Yeah. You're going to get a lot of angry comments. No. Yeah, there's very few. I've been looking for people who program Fortran. It's hard to find even Fortran. I hear you, but that changes the nature of what? The skill set or the predisposition for the kind of people we call programmers.

Then changes the skill set. How much it changes the predisposition, I'm not sure.

Oh, same kind of puzzle solving, all that Kind of stuff program is hard, that last 1% to close the gap. How hard is that?

Yeah, I think with most other cases, the best practitioners of the craft will use multiple tools and they'll do some work in natural language, and when they need to go write C for something, they'll do that.

Point number 20 is Sam Altman actually touches on the humanoid robots. And essentially right here he discusses how OpenAI, of course, if you didn't know, OpenAI previously did work on robotics, but stopped because it was too difficult at that time. And they discussed that in the future they will return to robotics at some point. So I'm not sure if it's just going to be through a collaboration with figure 1 or 1 extra robotics, or if OpenAI are going to build the humanoid robotics themselves with their amazing team.

How important is embodied AI to you?

I think it's sort of depressing if we have AGI and the only way to get things done in the physical world is to make a human go do it. So I really hope that as part of this transition, as this phase change, we also get humanoid robots or some sort of physical world robots.

I mean, OpenAI has some history, quite a bit of history, working in robotics, but it hasn't quite done in terms.

Of we're like a small company, we have to really focus. And also, robots were hard for the wrong reason at the time, but we will return to robots in some way at some point.

Now, here's where I think this is one of the most important points, because this is where Sam Altman talks about the AGI threshold. Now, the threshold for AGI for many different people has been different. But I think this is the important point that you should take away from the video at point 21, is because if Sam Altman is essentially declaring that the AGI threshold is essentially the point at which AI systems are effectively going to be producing scientific research, significantly impacting the economy, I think that that means his definition of AGI is one that is truly, truly advanced, like an AI, that can advance scientific research in terms of being able to, you know, publish research papers and discover new things. I think that is going to be something that is truly, truly far in terms of the sliding scale in terms of abilities. And I think that Sam Altman stating that that is where his definition of AGI is, means that we are truly, truly far ahead in terms of, you know, standard AI systems. Because most people think of AGI as a system that can just do standard things on a computer at the level of any human. But I Think him stating that it's not just that. It's the cognitive ability to be able to not only reason things together and get simple tasks done, but to be able to actually have an impact on the world in terms of research and not just automating work and discover new knowledge. I think that is really important.

You and we, as humanity, will build AGI.

I used to love to speculate on that question. I have realized since that I think it's very poorly formed and that people use extremely different definitions for what AGI is. And so I think it makes more sense to talk about when we'll build systems that can do capability X or Y or Z, rather than when we kind of fuzzily cross this one mile marker. It's not like AGI is also not an ending. It's much more of a. It's closer to a beginning, but it's much more of a mile marker than either of those things. But what I would say, in the interest of not trying to dodge a question, is I expect that by the end of this decade, and possibly somewhat sooner than that, we will have quite capable systems that we look at and say, wow, that's really remarkable. If we could look at it now, maybe we've adjusted by the time we get there.

Yeah. But if you look at ChatGPT, even 1, 3 5, and you show that to Alan Turing, or not even Alan Turing, people in the 90s, they would be like, this is definitely AGI. Well, not definitely, but there's a lot of experts that would say, this is AGI.

Yeah, but I don't think 35 changed the world. It maybe changed the world's expectations for the future, and that's actually really important. And it did kind of get more people to take this seriously and put us on this new trajectory. And that's really important, too.

Now, what did you all think about this Lex Friedman Sam Altman interview? It was really, really fascinating. I hope Sam Altman does more interviews, and I'm glad he didn't avoid a lot of the questions because a lot of the times we do have vague answers to questions. So I'm glad he was able to, you know, state a lot more than he usually does, because he doesn't usually state that, you know, he's been deliberately vague and it's completely understandable. But, yeah, it was definitely a very entertaining interview. And let me know, what point was your favorite from this video?