How GPT-3 Is Shaping Our AI Future

Hbr presents.

Hi there. I'm as a bizarre and you're listening to the first episode in season 5 of the exponential view podcast. This is the place where every week I come together with a brilliant mind to discuss the forces that are shaping our near future. For those of you new to this podcast, let me introduce myself. I'm a Serial founder and investor in Tech and sustainability, startups, and an advisor to some amazing companies. I am fascinated by the

Exponential Technologies on our society politics and business. Now, I profoundly believe that if we're to enjoy the benefits of these Technologies and to mitigate their worst effects conversations, like the one you're about to hear are key this clear respectful and thoughtful communication does become the Bedrock for our civilization who put simply technology is too important to be left to technologists. The impact it has on society is pretty wide-ranging. Broad conversations will help. And if you want more than this,

Cast. Please do find my newsletter at exponential view dot Co today. I'm thrilled to be kicking off this season with an entrepreneur investor. A thinker who is truly of the exponential age. Sam Altman is a prolific investor and the previous president of Y combinator the world's most

successful Tech accelerator. It

helped launch air B&B Dropbox Reddit and over

2,000 other companies into our lives today, Sam is the

CEO of open Ai and organization created, to research and develop.

It's first AGI or artificial general intelligence a technology. So powerful that it could outperform humans across a wide range of tasks, at least in theory in the summer of twenty twenty open. A i released GPT three, a small Milestone perhaps in the journey towards a GI. It is by far the most effective and sophisticated language generating software ever created. But like any powerful technology, it does come with profound risks. And in the case of this one, this includes the danger for

I love amplifying racial or gender bias or becoming a tool for malicious disinformation, Sam. And I touch on how you manage risks like this when developing these kind of breakthrough Technologies in our conversations. But we did record this discussion before. Open a I announced its exclusive distribution deal with Microsoft so that is something that we couldn't talk about. Now if as Sam predicts, the AI Revolution will be bigger than the agricultural industrial and computer revolutions. Put together these new technologies must be harnessed with sensitivity.

And deep reflection Sam. Welcome to exponential

view. Thanks for having me.

Now, do you still believe a few years on from setting up open AI? That the AI Revolution is going to be more significant than the Agricultural Revolution, which created civilization

itself on a long enough time Horizon. I definitely do. I think that Technologies get sort of to stand on the shoulders of the technologies that come before and we needed civilization itself to be able to have the industrial.

Lucian. And then the computer Revolution. Certainly we need those to be able to build AI, but everything just compounds at this phenomenal rate. And so in this next level of a merchant power I do think is going to transform everything.

So this summer, we had G PT 3, which was a really powerful milestone in a number of ways, how would you characterize G, PT 3, as a technology on that continuous curve

on the curve of a

I think it is a baby baby step. I think that it's it's still quite weak and important ways but it is a preview of what is to come. It is definitely not a GI in the sense that people mean when they use that word, but it is like it is a model that has General capabilities and some degree of what feels like intelligence or at least, what can be used in an intelligent way for lots of interesting applications.

And I think it's also sort of a preview of how these Technologies are going to be, as you mentioned, both good and bad or they have the at least the potential to be significantly helpful or harmful. And I think it as important as the technological Milestone is the sort of societal Milestone of saying, okay, this is going to happen. How do we as a society, decide what the rules should be and how we're going to use this? I don't think Silicon Valley has a perfect track record here and so, hopefully we can do it a little bit differently this time.

What was the dissonance that you were experiencing between what you thought of G, PT 3 and what you were sort of reading in the frenzied excitement around

it? Yeah. I think what you just said is exactly right. I think it just got sort of frenzied. I think I think it is if you're not looking at this every day and sort of watching the exponential curve, I see why it looked like a discontinuity. I can understand that because people have been talking about AI for so long and there's been no there's been no publicly available system that feels really General.

And so the first one of those is in some sense, a big deal, but but I think people got ahead of themselves and, you know, this like often happens with the new technology and then you use it for, I only get used to it. So, what we, what we were trying to do there is just like, remind people that look. This is a moment in time. This is a, an interesting checkpoint. This is not a GI, this is not mean, AGI is around the corner, but it does mean that like something

Powerful in this direction is going to happen and we should have a conversation about how that's going to go.

Let's talk a little bit about G, PT 3 and what it actually does. I mean if you're explaining it to somebody who is not involved in the field, what does this tool that you've built actually

do? I would say that it is a general-purpose language tool. You can have it respond to a text in any format you'd like and it's pretty good in many cases. Not all at understanding. What should come next?

But to do that. Well, it has to sort of understand what what a human would think, would come next. It's trained on looking at lots of text and predicting, what would come next and to the surprise of a lot of other people? It can do that. It can use that same model to have a chat in a answer questions. Write code summarize documents translate between languages and I think that's what's powerful about it. Is that this one model just by predicting the words that should come next.

Can do so many powerful applications,

right? The the point being that when you think about where we were say, three years ago with text based systems, you'd have had to have one system that could do customer service chat support for a mortgage company. Another to do customer service for an insurance company. A completely different one to help you with writing software code and the, and a fourth model to translate between Mandarin in French, right? These would all be distinct separate.

Protools. And what we've started to see across the industry in the last few years and GPT 3 is a very good example of this is ones that are more General, that can be used for many, many different

applications. Yeah. And that's what I think is really exciting about this little stepping stone on the path. Is this, this is the first really general-purpose AI, that were aware of that has been that has been released when these general purpose. Things come along like the iPhone spawned.

Huge number of new companies before the internet, the computer the combustion engine. They spawned a lot of new derivative Technologies and companies. And why I think this matters is we are now at the beginning of what I think will be fairly rapid development of sufficiently General AI that it counts as one of these general-purpose Technologies and you know, like obviously I'm biased here but my guess is that this will be the next technological platform that we've

All sort of been waiting for in the industry for some time to enable a lot of new Incredible services, that would even be difficult to imagine today,

as adults sort of observing this from afar, I can say, oh, this thing looks quite General. But do you have a formal way of saying, this particular model is actually General? I mean, what is the test for something to be

General? The best way is to just evaluate it all across. Lots of different sorts of tasks, and lots of different metrics. I'm a big believer in you. You make what you

So if you measure some narrow thing, you'll do well that not everything else and we really try to hold ourselves to evaluate and its performance across many, many different paths. And, you know, we kind of know the areas where it's week and then we know the areas where it's strong and some it's really strong but we're going to just keep expanding the number of tasks. We evaluate these systems on and try to do better on all of them simultaneously. So there's like mathematical definitions that I think we're kind of like reaching the edge of the utility of and

At this point, like our evaluation metric will just be like, how useful is it to the world.

And you'll be able to measure that by the amount of views. People make a vault in the number of different applications.

You know, one of the things that we learned at YC was that if you just set a growth goal and a very aggressive growth goal for yourself like 10% a week, or something on your key metric. You can't hide from that very long. And it's either like a good enough product that people really love it and base there.

Company off of it and tell their friends. It's amazing or it's not. And if you just sort of hold yourself to a growth metric, that will eventually

be an acid test of whether what you're doing is good enough or not,

you keep referring to GPT 3 as a small step on this this curve, you know, when I look at it, I think of it as one of the most complex machines that has been built in the world, it has a hundred and seventy five billion parameters, which if you're not a computer scientist, think of a parameter as a dial that you can turn like a volume button or the base or the treble or it's a it's a slider on a graphic equalizer that's a lot of

Parameters. And the previous baby step that you took, which was G Pt 2, which was a year and a half ago was 1.5 billion parameters so it was a hundred times less. And the one before that was tenth, the size of GPT to. So for you as insiders, you see these as sort of incremental steps that you're taking along the curve for people, observing from the outside these look like really big steps. I mean, they're kind of order of magnitudes is increasing each time.

Sure. But if you think on a lot,

Log scale it's like,

right? So do you think on a log scale? Is that how it's done with an opening I

do? Yeah, I think that is that's basically like been the story of the field for for some time

now. So in a product like G PT 3 which is quite complicated. What have you had to learn in order to build something like G PT 3. So why is it May 20 2010? Not May 2017 to bring

Sout. One of the things about opening I that is unusual is that we are a combination of a, of a research lab of like, a very good system. Scaling startup Engineering Group, and sort of a policy and safety something pink. And the thing that we've learned is, you have to execute as well as anybody in each of those three areas the same time. So there's like a lot of complicated research. There's very complicated system scaling the that the

The policy issues around how we allow access to this technology who gets to use it for what how we make? Sure people are not misusing. It are very complicated how you have the model itself, help with that and we have had to learn how to do well in all of those axes and I think the thing that is tricky is they're very different cultures. And so getting one group of people to execute is challenging but that's why we've been to whatever degree. We've been successful so far. It's not that we just get the system up. It's not that

Just that research is not that. We just figured out sort of the policy rules here. It's doing all of them at the same time.

So what would you say is culturally different about the way that open AI runs relative to the many hundreds of startups that you will have seen and experienced through your time of Running, Y combinator,

actually feels quite different. I had a steep learning curve here and I'm thankful for people's Indulgence with me but I think research culture is like quite different than than startup culture and

Like, even how like, what the company values? How you set the goals? How you measure? Progress, what motivates people. It's a very different world. Like an example of something that was hard for me to understand is how important publication is two people, right? Like I just didn't really have a mental framework for that and it took me a while to understand. Now I think I get it but how you get that to coexist with people who are like, we're going to measure ourselves off of a product growth goal and people say we're going to measure ourselves off of like the

Number of citations, we get, it sounds like those should be complementary and I'd say, it, turns out you have to do more work than you think to get those to co-exist getting the company to exist, happily in the middle of these three, sort of Clans, or cultures has been, has been an interesting challenge. I think we're doing pretty well on it now though,

but you've also discussed this idea that, you know, you have to now start to measure, open my eyes value in terms of the usage,

right? I certainly don't think that's the only way to

To evaluate our progress, in addition to the academic benchmarks. We now have a new one, which is the real world. We will continue to measure ourselves on lots of metrics. I think that's a key to getting something general. But but like, what we really care about, are we, what I think most people doing this kind of work, she really care about is like, how do you do something useful? Do you make people's lives better? And now that we can directly measure sort of impact in the world? I think that's like a wonderful new Avenue. That's

Exposed itself to us

as they suck entity. That is part research and development and also putting products out there. It seems like the the way in which you need to make decisions about what projects to go forward with and what Avenues you don't is perhaps different to what it might be in a traditional product based company. So, what is the framework that you might use to internally to Desert to decide whether to initiate an internal project and whether to progress it?

So we're like one thing that helps with that is we it's a difficult Mission but we have like a very clear Mission which is to build safe AGI and maximally share the benefits of humanity and we basically evaluate everything we do on that question. If we should do a piece of research at all, if we should scale it up, if we should release it and we have a small enough company where we can even though we're doing something new and hard and sort of with this kind of quirky structure. And as far as I know, no other way.

Organization in the world runs quite like this, but we have to make these hard decisions. We have a small enough team that we can get everybody in the room or now on the zoom call and or the Google Hangouts call and talk through it. And, you know, I think it's like it's that's a that's a real advantage of being a being small. If we also realized that we don't have enough input from broader Society,

how do you know that you don't have?

Have the right external

input. I think there's like a hundred and fifty people. That work at open II. There's no way we have enough input from Ryan sense of the world as a whole.

Okay, fair enough. Then I'm curious about one of the things that you learned with GPT to which was a precursor to the latest model. I think there was there was a particular statement around not wanting to release this into the into the wild into the open because you felt it was too risky. There were a whole set of risks with the with my

To risk that it might be too risky, right? And when you got to GPT 3 which was a much more sophisticated, more powerful technology, you did open it up. If there's an access program with a big long queue of people where researchers and developers can access this through an API and build their own applications. So I'm curious about what you learnt such that, the decision was different for the more powerful technology.

So one thing that we learned is that you just need to like stick with your convictions, even if you get panned on Twitter and like

Lately ignore it, which, you know, everyone knows. But it's good to be reminded like when we did that for GPT to, we were just sort of continuously in ruthlessly mocked saying, like, oh, this is so silly. Like this is never going to be dangerous, blah, blah, blah, and like then. G PT 3 comes along those same people are like, I can't believe I open my eyes releasing this at all. It's so dangerous blah blah blah. So like it was a good moment for the company to like really stick with the courage of our convictions and that we would, you know, the I think the general mistake Silicon Valley has made has been be cautious.

Too late and we'd rather err on being cautious too early. So that was like a good overall learning. One specific learning is that, if you, if you just release the model way, it's like we did eventually with GPT to on the stage process, it's out there and that's that you can't do anything about it and if you instead release things via an API which we did with G, PT 3, you can turn people off. You can turn the whole thing off. You can change the model, you can improve it to continually like do less bad things you

An rate limit it you can you can do a lot of things, you can do a lot of things. So this idea that we're going to have to like have some Access Control to this Technologies seems very clear and this current method may not be the best but it's a start. This is like a way where we can enforce some usage rules and continue to improve the model so that it does more of a good and less of the bad. And I think that's going to be some something like

God is going to be a framework that people want. Is these Technologies get really powerful? I mean, again I think we're like the baby baby

stage. I think what we've seen with G PT 3 of course, is that the first thing that happens with any new piece of software that is that is programmatic, is people try to push it to its limits, right? That's what you, you know, you get access to the Twitter API in 2009 2010 and the first thing you try to do is what can I break down. So you must have seen people trying to break G PT 3 in different ways.

Ways to what extent is that helpful for you and other surprising ways in which people have tried to stress the system. It's

totally helpful. Look at some level. You could say that. It's like unfair that most of the negative stuff you see on Twitter as like, people turning off the content toxicity filter really trying hard to beat it into giving offense of outputs, but then on another level like,

that's the kind of stuff that can happen with technology like this and it is important for us to get that input in that feedback so that we can build

Models that do much less of that or someday just don't do it. And I think it's important with a technology like this to get enough exposure to the real world that you find some of the misuse cases you wouldn't have thought of so that you can build better tools. There's a whole set of hard questions about. Like if someone really deliberately wants to use the model to say offensive things in a context where that might be okay, or even good. Should that be allowed? And my personal belief is yes but it's a tricky.

Action but I think we can all agree. On is no one who is like people developers and users should have controls on these models such that they behave in a way that the actual user using them once and I think seen where that doesn't happen and getting the feedback is what lets us get there. I think

this is a this is a challenging set of questions here though that relate in general to the question of governance of powerful

Knowledge. He's one of the things I think that's tricky with AI and software Technologies in general, is it? They get progressively easier to develop and deliver, right? It's very different to nuclear power, right? Which is still a state level entity and you can be a big country like Iran and still struggle you know militarize in those Technologies. But with software every two years it gets ten times cheaper and the breakthroughs that you've made this year will be you know quotidian within three or four years. So the the sort of March of wine.

Law and Moore's Law. So how do you think about whether we need some kind of broader governance framework for these Technologies? I mean I think Silicon Valley has been brilliant at many things but it hasn't been brilliant around. The questions of ought there, be some sort of governance around the Technologies in the way in which they get used. Have you thought through that problem with, with, with AI in general? I

think we do need new governance. I think that more generally

I would say our existing governance models did not account for the idea that technology would eventually become so powerful in such that companies like open a, I could eventually do. What only nation-state actors could do? You know, if we go back and think about your example, of, let's say, nuclear weapons first, and then power or something like the Apollo program, those required an amount of capital, and just resources that.

You know, like a private company just wasn't gonna do it. I forget the exact number for the amount of electricity in 1943 that went to enriching uranium but it was something like 23 percent of all of the US has electricity. Like just took massive resources and so I think we need new governance models. And we also need new economic models.

And part of what I think opening II is good about, is that we are able because of our sort of unique structure and this capped profit model that we came up with to

Advocate loudly for those. I think your economic model is super interesting and I would love us to spend a bit of time explaining it. Let's talk about the governance question though because I think that seems to me to be a really difficult question. So the way that I look at, you know, technology governance at the moment is that especially internet technologies have not.

He had a governance framework, they emerged from a west coast of the free addressable end-to-end internet which in a way is a governance model of of its own. And recently as you rightly point out, these technologies have got more and more powerful and the biggest companies like the Microsoft and the Google's have state like capabilities in certain areas. You know, when there's a Cyber attack, the local military can't do anything about it. And then the trouble with this this governance

Model is that these large companies don't have any accountability to Citizens and we are ultimately at the whim of the senior Executives and the board of directors to make good decisions about the chain of accountability has been broken. So I'm curious about what you think Global compact might look like, in the next few years where we feel, we can get these Technologies working for Humanity but in a way that as well governed,

it's a super important question.

We can study history and see how that goes when there is no public oversight. You know, when you have these companies that are as powerful or more powerful than most Nations and are sort of governed by unelected and to be perfectly honest like fairly unaccountable leaders

I think that should give all of us. Pause as these Technologies, get more and more powerful. And I think we need we need to figure out some way. Not just that economics, get shared and that we deal with going to Quality problems. That Society is facing. But that governance about these very big decisions. You know what do we want our future to look like in the war with very powerful? A I did that is not a decision that a hundred and fifty people sitting in San Francisco, make collectively like that.

Not going to be

good. I haven't found good models right now that do this. I mean, I think that people talk about the idea of mini lateral is MM, which is that if you can just bring enough people to talk often enough, perhaps you create the kernel of a framework that other people can buy into, and then people copy that.

Yeah, I think that is helpful for sure. And I think, I think, but then the glares, like, always a question of how much teeth does it really have?

Let's say we really do. Create a GI mom like em shirt. Like right now if I'm doing a bad job with that, our board of directors which is also not publicly accountable can fire me and say, let's try somebody else but doesn't it feels to me like at that point at some level of power like the world as a whole should be able to say like hey maybe we need a new leader of his company just like we are able to vote on you know the person who runs the country and so this question of what democratic process for companies that

Have sufficient impact looks like I am very far from an expert here and much smarter people than me have been thinking about it. But it occurs to me that we've got these models and we can look at the different ones in the around the world and talk about some countries, where it works better, and where it doesn't work as

well. Is that one that Springs to mind for you?

We are working on this actively right now, I wouldn't say we're at a point where were like, okay, this is like, definitely the right model for us or for the industry as a whole.

But it's something that we're trying to turn our Focus to

the other one. The other piece that's interesting is that you know, historically technologies have always created inequality. That's why we see the arrival of status and hierarchy and private property. You're tend to have 15,000 years ago. This technology is going to be no different but it's clearly been something on your mind as you've gone through to design with your colleagues open. A I one of the particular things

You've built within open AI that start to address that.

So I think the biggest thing that we did is this idea of a cap, The Profit structure. So we said like look we started as a non-profit, we are hoping that we could make it work there. Making a GI looks like it's just going to take so much Capital that we thought we couldn't do that as a non-profit. But we didn't want to be like a full for probably either because of the potential to drive like truly, massive inequality. And so we said, is there some new structure?

Where we can be, you know, we can have the benefits of capitalism and all of the wonderful aligning power that produces attract the capital and talent we need in Fairly reward them, but then figure out a way to mostly share the benefits here. And I think that as technology keeps going, these companies get bigger and bigger and more powerful and have more leverage. We will need something new as a more General structure. And I think like, either companies need to somehow either.

Make less money because they pay people more or like somehow there's got to be some degree of societal ownership of Corporations or like a direct sharing in the equity value. Otherwise inequality will just run away and open my eyes, like one way of sharing the equity value

and I think just maybe put some historical framing on some of those observations. I mean, the the labor share of income in most developed countries has declined by about 10 points over the last

80 years with most of that gain going back to companies themselves. In fact, not even to shareholders. And the other thing is that within an AI, kind of product, you have a rich get richer phenomenon because your you've got the data Network effect that is making your model better than the next person's. And every query that comes into G. PT 3 is a not new form of data that you can use to improve the next the next model, right? So you have these Dynamics and you know, I think it's fantastic that you've identified that this is one of the

The challenges you talked about there being a problem with the inequality that would cause. So do you have an internal mental model about how much inequality is too much and how much inequality is kind of just enough to keep people

motivated. That is such a hard question. You some degree of inequality is the price of capitalism. I still think capitalism has like, these wonderful benefits that have been replicated by no other system. And if you stop rewarding people for innovating or even,

Officially allocating Capital, that'd be really bad. So, perfect. Equality is wrong. I think but this the current level of inequality we have is like way too much and in terms of like the multiple of how much should the average person have relative to the riches or whatever else? I don't really know. What I will say is that I think people are most sensitive to just being on a very steep curve of their lives. Improving and the fundamental thing that has gone most wrong with inequality in the u.s. is not the spread between like the rich.

Person and the median person. But it is that all of the gains, all of the relative progress has gone to the people at the top. And that I think is the most toxic of all. So, rather than a spread, I would say, everybody needs to participate in a massive updraft year-over-year. I do think AG, I will do that.

So, let's talk about the mechanism by which a GI then helps address the inequality

problem, the cost of the

Most critical goods and services should in in like some sort of, you know, real dollars sense. Just go down and down and down as technology can do more and more of what human labor has done, which drives the cost of most goods and services. And that means that like what you really want is not a certain number of dollars but a certain amount of wealth and we're saying an increasing amount of wealth. And as a g, I can drive the cost of goods and services down dramatically if we even hope.

Just redistribute a little bit of

money, I hope we do more than that. Everybody should be able to afford what they need for, really good life and more of it. Every year,

one of the problems that we've seen in the u.s. in particular, is that the price of things that I suppose a kind of consumer-oriented maybe a little bit trivial, whether it's, you know, it's televisions or games, and so on has gone down. But the things that you actually need to have a fulfilling flourishing, Safe, Life, Health and education has has shot through the roof.

Oof,

it's really disappointing to see. I think this is like a huge problem and not well understood. But basically, we're technology and more Technologies been able to do its thing and where people have had to have like a competitive market. It's been great and then where you've had these problems where there's not very good Market competition. And particularly where services are sort of in some way or other government subsidized, or government protected, but not government-run, you

These like wild and efficiencies. So I think it like it clearly works and other countries that besides the us where the government just provides the healthcare, but they also pay for it and they make the decisions about it. But what we have in the u.s. is an absolute disaster and if you look at kind of like the three Horsemen of the cost apocalypse housing, health care, and higher education for slightly different reasons. And all three cases, we are just facing an absolute catastrophe and that, you know, technology can help those somewhat for sure.

So I think those are policy failures more than anything else and until we address, those can do a basic income, you can build a GI, you can do a lot of other things, it's still going to be bad and I think those are so critical to. As you said, a good life, a fair life and the American dream that if we can't fix those basically as a society. Very it would be very limited impact on everything else that we can

do. So you talked about being a non-profit and the cost of doing a GI

I was clearly going to be yeah. Hi. I mean to the to the average listener I think your initial funding was around a billion dollars is what's been put in the Press. So how much will a GI actually cost a

lot more than that? I don't know the exact number, we're going to spend as responsibly as we can and as effect efficiently as we can but we'll also spend whatever it takes. So I don't know what that number is, but it's going to be vastly more than a billion

dollars. So somebody with a cheeky

Sheet might try and run some numbers. They might say well you've got a hundred and fifty headcount, we know that g PT 3 cost x million dollars of computer time to train the model and there are some numbers that are floating out there. We know that the price per compute cycle is declining at this rate because of the great work of you know Nvidia or graphic or whoever's doing it. So they're going to put that in very quickly and say well if it's more than a billion dollars and we've got these declining

Kind of price curves, we have a sense of the amount of compute that is going to be needed in order to build a GI. So I feel like I have to ask you this question, which is how much

computers me. Not even Oracle could tell me exactly how much compute we're going to need. It would save me like a lot of hand-wringing and I'd love to know. So if you figure that out, please tell me it would be really useful information. I, first of all, I the estimate that I've seen for how much it costs to train gbt three were all way under, but I'm sure I haven't seen all of

Them. None of this stuff is cheap. When people have run those spreadsheets, they come up with wildly different numbers. I have seen people say 10 billion, I've seen people say a hundred billion, I have seen people say a trillion I have seen people say a hundred million we just need the right algorithm and the true answer of course is no one knows and everyone spreadsheet is wrong in some important way. We ourselves, I think we do some of the best work in the world trying to project this and we ourselves have huge error bars.

But the more we learn, the more confident, we are, and then we can go often raise more specific amounts of capital, and make more specific computer orders. But all I can say with confidence, it's going to be a lot.

Yeah, but I think the important thing as well as it's not just a dollar. Some there's something that you said earlier in our conversation about you have these different teams who are have competing interests and competing constraints. And essentially, the capabilities of each one has to have to grow like an ecosystem with the other teams.

So it's not as if you could get someone to write you a huge check today and that would solve the problem tomorrow. It's more that there's some undiscovered capabilities that need to be learned. For

sure. One thing that I certainly believe is that if you do you could throw like a trillion dollars at the problem without any new ideas and you still wouldn't get to where we'd like to get. I'm dying. I'm very confident that we're making Fast progress on the new ideas and certainly on kind of the policy and safety work. Question that like we get a lot is

wat, you know, open II is

Smaller. It has less compute than some of these other organizations in labs and like, how are you outperforming them or why? You are performing them. And I think one of the answers that sounds a little bit silly, but if somehow true is, it's not in spite of that. It's because of it and because we can stay so small and focused and only do a few things at a time. And really have to align a lot of people with these different cultures into thinking about, you know, where are we going to bet are very extremely Limited.

Horses and talent. That's actually the best way to make progress towards these

problems with that approach. I mean, it sounds to me, like, it's the, it's the startup approach, right or sure. So it's come out of your experience of not having been a researcher, but having nurtured, a thousand startups or more. Absolutely, it's a very distinctly different way that our lab would work, but then I'm curious also about whether you can do basic science in that way, right? Sometimes basic

It's just requires someone to be able to ask the question and just spend their time scratching their forehead and throwing things up on a

whiteboard. Well, it seems like we're doing. Okay at that. It we really try to build an organization that supports that and I view a big part of my job is not letting all of the pressures that come with success and offering a product to get in the way of what got us here in the first place, which is someone scratching their head in front of a white board.

I still think we have a long, a lot of that, that we need to do, obviously some of the most talented people in the world to do it. And we've got to make sure that we keep a culture where that used to happen. I

want to zoom back out a little bit. We talked about the importance of AI and AGI as heralding, a new epochal revolution in human development. And when we look at these revolutions, one of the things that's interesting is I think of it as