Hi guys. Happy South by. I feel like what a way to kick this off. One of the things I love about South by Southwest is I’ve been coming for the last decade, and we’re always talking about what’s the next big thing in tech, and I would say like artificial intelligence and chat GPT is like couldn’t be more relevant. So glad to be sitting here with you. How many folks in the audience have used chat GPT?
Okay, so it feels like this is an audience that like we can. That’s good. I can be very specific on this stuff, and remember you guys ask questions. I’m going to leave 15 minutes at the end to get to it, so I want to get to open AI, and I want to talk about the company behind chat GPT, but I would love to start with chat GPT, so let’s go. It’s November 22nd. You guys released chat GPT.
This is an AI chatbot that’s developed by open AI. It’s built on top of large language models, a large language model called GPT-3. You release it November 2022. Over 100 million users in two months, this becomes the fastest growing application in history.
Just for some perspective, it took Facebook meta 4.5 years to reach 100 million users, took TikTok nine months. Like why was chat GPT the killer app?
Yeah, I actually think about this question a lot because for us, you know, we actually had the technology behind it, the model behind it created almost a year prior, so it wasn’t new technology, but the thing that we really did differently is that we did a little bit of extra work to make it more aligned, so it really you could talk to it. It would do what you wanted, but secondly, we made it accessible, right?
We built an interface that was super simple. It was kind of the simplest interface we could think of. We made it available for free to anyone, and I think that the thing that was very interesting was as this app really took off and people started using it, we could see the gap between what people thought was possible and what actually had been possible for quite some time.
And I think to me, this is actually maybe the biggest takeaway is that I really want us as a company and as a field to be informing people to make sure that they know what’s possible, what’s not, kind of what the forefront is going to look like and where things are going because I think that’s actually really important to figure out how to absorb this in society, like how do we actually get all the positives and how do we mitigate the negatives?
Like in the past, I mean, should we talk about Tay? We won’t talk too much about Tay, but like chatbots are hard to put out there, but there was something about what you put out there, and you talk about that gap, right, that it didn’t implode, right? It learned a lot, and all of a sudden, it’s almost spurred this whole new era of everyone saying, could we do this? Could we do this? Could we do this? Why now?
Yes, so I, as we were preparing Chachapiti for release, the thing I kept telling to the team was the most important thing, we can be overly conservative in terms of like refusing to do anything that seems even a little bit sketchy, that’s fine. Most important thing is that we don’t have to like turn it off in three days.
Yeah, are you worried when you kind of like pressed publish on this?
You have to be worried. How could you not, right?
Yeah.
Right, so we’ve been doing lots of testing, right? We have our own internal red teams. We’ve had beta testers on it, hundreds of beta testers for many, many months, but it’s very different from kind of exposing it to kind of the full diversity and adversarial and sort of beautiful force of the world and where people are going to apply it.
And so for us, I think that, you know, we have been doing iterative deployment for a very long time, right? We’ve been, you know, ever since, you know, 2020, June or so is when we first released a product, you know, an API so people could use these language models.
We’ve been making them more capable, getting them into more people’s hands, but we kind of knew this was going to be just a different dimension.
Yeah.
And it was our first time building a consumer facing app. And so we definitely were nervous, but I think that the team really rose to the occasion.
Yeah. Well, I want to look, I definitely want to talk about the future of ChatGPT because I know a lot of folks, especially we have a lot of users in the audience are curious about it.
But let’s look, I want to start at the, I want to go to the past, right? Because the company behind ChatGPT, Dali, is OpenAI.
And this is, it’s interesting because in the Silicon Valley world, you have like a sexy company comes out, everyone’s talking about it. OpenAI was just kind of the opposite.
It just was kind of like hanging out in the background until this thing came out. Until you, you know, you put out these products that could shift culture and start all these questions.
And so let’s go back. It’s 2015, July, and you’re in Menlo Park at a fancy hotel called the Rosewood. I don’t know if anyone here has been to the Rosewood. It’s certainly a scene.
You’re sitting there. Who’s there? What are we eating? Why are we there? What’s the topic of conversation? And I promise I’m going somewhere with this.
Well, I couldn’t tell you what was on the menu that night.
Right.
But yeah, we were.
I just want to know what Elon Musk was eating during this conversation.
Uh-huh, yeah.
Okay, sorry, I got ahead of it. Go ahead.
So we were having a dinner to discuss AI in the future and kind of just what might be possible and whether we could do something positive to affect it.
And so my co-founders at OpenAI, so that’s Elon, Sam, Ilya, and other people were all there.
And kind of the question was, is it too late to start a lab with a bunch of the best people at it?
Right, we all kind of saw that like AI feels like it’s going to happen.
It feels like AGI, really building human level machines, will be achievable.
And what can we do as technologists, as just people who care about this problem, to try to steer in a positive direction?
And kind of the conclusion from the dinner was, it’s not obviously impossible to do something here.
You felt a sense of urgency.
I did.
Why?
For sure.
The moment, I think the thing that is easy to miss here, right, is I think now people see ChatGPT and they say, wow, like suddenly you feel the possibilities.
Right.
And you both see what’s possible, like not science fiction anymore, right, actually usable today.
But it’s still hard to kind of extrapolate, to really follow the exponential, to think what might be possible tomorrow.
And I think that the mode that I have been in for a long time has been really thinking about that exponential.
Like I remember reading Alan Turing’s 1950 paper on the Turing test.
And the thing that really stuck out to me, and this was right after high school, was he said, look, you’re never going to program a machine to solve this problem.
Instead, you need a machine that can actually learn how to do it.
And that for me was the aha moment.
The idea that you could have a machine that could solve problems that I could not, that no human could figure out how to solve.
Like that so clearly could be so transformational, right?
There’s all these challenges, global warming, you know, just like medicine for everyone, like all these things that are kind of out of reach.
Yeah.
I don’t know how we’re going to do it.
But if you could use machines to aid in that process, we want to.
And so I think we all kind of felt like, OK, the technology is starting to happen.
You know, deep learning is an overnight success that took 70 years, right?
It’s like, you know, 2012, there was a big breakthrough on image recognition.
But it really took another decade to start to get to the point that we’re at now.
But we could all see that exponential.
And I think we really wanted to really push it along and really steer it.
And I mean, you at the time.
So you before you were the CTO of Stripe, this little company called Stripe.
And you really felt that you, Sam Altman at the time, Elon Musk.
We can get into all this later, but that you guys could build something better and you guys could build something that was pro-humanity and not anti-humanity, which is always that fine line in technology, which I think the last decade has kind of taught us.
Yeah.
And I would I would quibble a little bit with, you know, I don’t know that at least for me personally, that I viewed it as we would build something better.
You know, in the sense of like, you know, there’s lots of other people who are in this field doing great work, too.
But I wanted to contribute, you know, and I think it’s one thing that’s actually very important about AI and something that’s very core to our values and our mission is that we think this really should be an endeavor of humanity.
Right.
If we’re all thinking about, well, what’s my part of it?
You know, like, what do I get to own?
I think that is actually one place where the danger really lies.
And so so tell me about how the company was and is structured, because now that was seven years ago.
So take us behind the curtain.
I saw something Sam Altman wrote.
He said, we’ve attempted to set up our structure in a way that aligns our incentives with a good outcome.
What does that even mean?
Yeah.
So we are a weird looking company.
In what sense?
So we started as a nonprofit because we had this grand mission, but we did not know how to operationalize it.
Right.
We know that we want to have AGI benefit all of humanity.
But what is what does that mean?
What are you supposed to do?
And so we started as a research lab.
We hired some PhDs.
We did some research.
We open source some code.
And our original plan was open source everything.
Right.
You think about how you can have a good impact.
Maybe if you just make everything available to anyone that can make any changes they want, then, you know, if there’s one bad actor, well, you’ve got seven billion good actors who can keep them in check.
And, you know, I think that this plan was a good place to start.
But, you know, Ilya and I, we were really the ones running the company in the early days, spent a lot of time really thinking about how do you turn this into the kind of impact that we think is possible and to something that really can make a difference in terms of just how beneficial AGI ends up being.
And I think that we found kind of two important pieces.
One was simply a question of scale.
Right.
You know, all the results that we were getting that were impressive and really pushing things forward were requiring bigger and bigger computers.
And we kind of realized that, OK, well, you’re just going to need to raise billions of dollars to build these supercomputers.
And we actually tried really hard to raise that money as a nonprofit.
Like I remember sitting in a room during one of these fundraisers and looking in the eyes of a well-known Silicon Valley investor.
Who is that?
I wouldn’t share the name, but he was like, $100 million, which is what we’re trying to raise.
He’s like, that’s a staggering amount for a nonprofit.
Right.
And we looked at each other, we were like, it is.
Yeah.
And we actually succeeded.
We actually raised the money.
But we realized that 10x that.
That was not going to happen.
I mean, if anyone in this audience knows how to do that as a nonprofit, like, please, we will hire you in an instant.
But we realized that, you know, that if we wanted to actually achieve the mission, that we needed a vehicle that could get us there.
And, you know, we’re not anti-capitalist.
Like, that’s not why we started a nonprofit the way, opening as a nonprofit.
Actually, capitalism is a very good mechanism within the bounds that it’s designed for.
But if you do build sort of the most powerful technology ever in a single company, and that thing becomes just like way more valuable or powerful than any company we have today.
Capitalism is not really designed for that.
So we ended up sort of designing this custom bespoke structure.
It’s super weird.
Like, we have this limited partnership with all custom docs.
You know, if you’re if you’re a legal nerd, like, it’s the kind of thing that, like, you know, is like actually really, really fun to dig into.
But the way we design things is that we actually have the nonprofit is the governing body.
So there’s a board of a nonprofit that kind of owns everything.
It owns this limited partnership that actually has profit interest, but they’re capped.
So there’s only a fixed amount that investors and shareholders are able to get.
And that there’s a very careful balance in a lot of these details in terms of, like, you know, having the board have a majority of people who don’t have profit interest.
All these things in order to really try to change the incentive and make it so that, you know, that the way that we operate the company is it comports with the mission.
And so I think that, you know, this kind of approach of, like, really trying to figure out how do you balance?
How do you approach the mission?
But how do you make it practical?
How do you operationalize it?
That is something that has come up again and again in our history.
And if we look back at the history of, I mean, artificial intelligence like this is nothing new, obviously.
So, like, what is it about now that feels like a watershed moment?
Why now are all companies putting money into this?
Why now is this the thing that we all are talking about?
What is it about the technology now?
Yeah, well, I think the fundamental thing here is really about exponentials, right?
It’s like no matter how many times you hear it, it is still hard to impossible to internalize.
And when I look back, like, we’ve done these studies on the growth of compute power in the field.
And we see this nice exponential with a doubling period of, like, every 3.5 months, you know, as opposed to 18 months for Moore’s Law.
It’s been going on for the past 10 years or so.
But we actually extrapolated back even further.
And you can see that this exponential continues all the way.
Slightly smaller slope.
It used to be Moore’s Law.
But over the past 10 years, basically, people have been being like, well, you can go faster than Moore’s Law by just spending more money.
And I think that what’s been happening is we’ve been having this accumulated value, this slow roll.
Rather than trying to do a flash in the pan, like, just get rich quick kind of thing that maybe other fields have been accused of.
AI, I think, has been a much more steady incremental build of value.
And I think that the thing that’s so interesting is normally if you have a technology in search of a problem, adoption is hard.
It’s a new technology.
Everyone has to change their business.
They don’t know where it fits in.
But for language in particular, every business is already a language business.
Every flow is a language flow.
And so if you can add a little bit of value, then everyone wants it.
And I think that is the fundamental thing that really has driven the adoption and the excitement.
Is that it just fits into what everyone already wants to do.
Well, and also in 2017, you know, a model called Transformers, right?
These large language models and this idea that you could treat everything as a language.
Music and code and speech and image.
The entire world almost looks like a sequence of tokens, right?
If we could put a language behind it.
That was really an accelerant for a lot of what you’re building, too.
Yeah, I think that it’s, you know, the way they think about the progress, like the technological driver behind this,
is that it’s very easy to latch on to any one piece of it, right?
Transformer, definitely a really important thing.
But where the Transformer came from was really trying to figure out how do you get good compute utilization out of the compute hardware that we use?
The GPUs, right?
And the GPUs themselves are really impressive feat of engineering that has required just huge amounts of investment to get there.
And the software stack on top of them.
And so it’s kind of each of these pieces.
And each one kind of has its time.
Like one thing that’s super interesting to me, looking from the inside,
was that we were working on language models that look very similar to what we do today.
Starting 2016, you know, we had one person, Alec Radford, who was really excited about language.
And, you know, like he just was kind of working on building these little chatbots.
And like, we really liked Alec.
And so we were just like very supportive of him, doing whatever he wanted.
And meanwhile, we were off like investing in serious projects and stuff.
And we’re just like, you know, whatever Alec needs, like we’ll make sure he gets.
And 2017, you know, we had a first really interesting result,
which was that we had a model that was trained on Amazon reviews.
And that it was just predicting the next character, the next character, just what letter comes next.
And it actually learned a state-of-the-art sentiment analysis classifier.
You could give it a sentence and it would say like, this is positive or negative.
May not sound very impressive, but this was the moment where we kind of knew it was going to work.
It’s so clear that you would transcend it, just syntax, where the commas go.
And you’d move to semantics.
And so we just knew we had to push and push and push.
I mean, it always comes to Amazon reviews.
Who knew that this is the real story behind it?
Exactly, exactly. You always start small.
You know, every day there’s a new headline on how this technology is being adapted.
I just literally was Googling it yesterday.
It’s like the latest headlines are companies are harnessing the power of a chatbot
to write and automate emails with a little bit of personalization.
Another headline, how Chachapiti can help abuse survivors represent themselves in court if they can’t afford.
Otherwise, we obviously know about Microsoft’s being and disrupting search.
From the seat that you’re sitting in, what for you, and if you could be as specific as possible,
what do you think are the most interesting and disruptive use cases for generative AI?
Yeah, well, you know, I actually first want to just tell a personal anecdote
of the kind of thing that I am very hopeful for.
So, you know, medicine is definitely a very high stakes area.
We’re very cautious with, you know, how people should use this kind of technology there.
But even today, I want to talk about a place where I have just been like,
I really want for my own use.
So, you know, my wife, a number of years ago, had a mysterious ailment.
That she had this pulsating pain right here on her abdomen, bottom right side.
And it wasn’t appendicitis.
You know, we went to the first doctor and the doctor was like, oh, I know what this is.
And prescribes some antibiotic.
Nothing happened.
Went to a second doctor who said, oh, it’s a super rare disease.
Went to a second doctor who said, oh, it’s a super rare bacterial infection.
You need this other super powerful antibiotic.
Took that.
And over the course of three months, we went to four different doctors.
Until finally someone just like did an ultrasound and found what it was.
And I kid you not, I just typed in, you know, a couple sentences of description
that I just gave here into chat GPT.
Said number one, make sure it’s not appendicitis.
Number two, ruptured ovarian cyst.
And that is in fact what it was.
Wow.
And so the kind of thing that I want is I personally in the medical field
want something that I don’t rely on.
I don’t want it to replace a doctor.
I don’t want it to tell me like, oh, go take this super rare antibiotic.
I don’t want a doctor telling me that either.
And also chat GPT sometimes confidently says the exact wrong thing.
It’s kind of like a drunk frat guy every so often.
Exactly.
So you got to be a little bit careful.
You got to be careful.
Something we’re working on.
Yeah, right.
And I think that our models actually are much more calibrated than we realize
and can say when they’re right or wrong.
But we currently destroy that information in some of the training processes we do.
So more to say there.
But yeah, I think this suggests, give you ideas really, you know, in writing.
It’s like the blank page problem.
But I think this for me is where generative AI can really shine.
Right.
It’s really about sort of unblocking you, giving you ideas,
and just giving you an assistant that is willing to do whatever you want 24
7.
And so let’s you’ve now the chat GPT has been deployed to millions.
Has there been anything that’s really shocked you or surprised you and how
people have been utilizing it?
I mean, of course.
Yeah.
I mean, I do think that for me, the overall most interesting thing has just
been seeing just how many people engage with it for so many just sort of
surprising aspects of life.
Right.
Like what?
I think knowledge work is maybe the area that I kind of see as most
important for us to really focus on.
And, you know, we see people within OpenAI who don’t have who aren’t native
English speakers use it to improve their writing.
And that you know, at first that there was someone with OpenAI who is
suddenly his you could just tell it the writing style of everything changed.
And it was just like way more fluid and just also just like honestly just
like way more understandable.
And at first, what just happened?
And he literally at one point had hired someone to to do the writing for him.
But that was actually really hard.
It was just like a lot of overhead and he wasn’t able to get the points
across.
But with chat GPT, he really was able to.
And I think that that for me is just like so interesting to see that people
just use it as a cognitive aid to think just more clearly and to
communicate with others.
Well, you always know you have disruptive technology when you put it out
there and people misuse it.
I remember a decade ago doing like a story on pimps recruiting women on
Which is like, OK, you know, if someone’s using your technology in a bad
way, like you have something that’s hitting mainstream.
So like, can you tell us like what how are people using it in ways that
Have you what have you learned from putting this out there?
And what have you learned from how people are misusing it?
Well, misuse is definitely also very core to what we think about.
Part of why we wanted to put this out there was to get feedback to see
how people use it for good and for bad and to continually tune.
And honestly, one of the biggest things that we’ve seen, you know,
we always anticipate all the different things that might go wrong for GPT-3.
We really focused on misinformation and that actually the most common
people, the most common abuse vector was generating spam for drugs,
you know, for various medicines.
And so you don’t necessarily see the problems around the corner.
For Chachapiti, one thing we’ve just seen is people just creating
thousands or hundreds of thousands of accounts in order to just be able
to use it much more.
Some people generating lots of spam.
It’s clear that people are using it for all sorts of different things.
I think for individuals, there’s definitely, I think, actually,
I would say this is an interesting category of, you know, to your point
where it says something that is confidently wrong.
My drunk frat guy point.
Exactly. Yeah. Over reliance. Right.
And thinking, oh, because it said that, it must be true.
Yeah. And that’s not true for humans.
It’s not quite true for AIs.
Yeah. I think we will get there at one point,
but I think that it’s going to be a process and something we all need
to participate in. Right.
And so, I mean, I would love to get into kind of what we can predict
in the future with AI, but before we leave Chachapiti,
this isn’t really Chachapiti, but I feel like we have to talk
about Sydney for a moment.
People in the audience, people who heard of,
who read Kevin Roose’s article in the New York Times?
Right. So just a little background.
You know, you guys put Chachapiti out there,
Microsoft, Google, racing to get search products out there.
Microsoft releases its own AI-powered search,
a Bing chatbot, and all of a sudden, Kevin Roose,
great writer at the New York Times, is playing with it,
with the Bing chatbot.
It reveals that its name is, the shadow name is Sydney,
and also tries and tells Kevin when prompted a certain way,
I want to be alive, and tried to persuade him to leave his wife.
So obviously, that’s like an awkward conversation.
So what are the guardrails?
And to be clear, Microsoft’s an investor and partner.
This isn’t something that OpenAI specifically put out there,
but I do think it’s an interesting point of saying,
you put this stuff out there, the next thing you know,
like, I don’t know, Sydney’s trying to make you leave your wife.
So like, what are the guardrails that need to be put in?
Like, what have you learned over the last couple months
where you’ve seen the misuse, and what can you put in
to make sure that we’re not all, you know,
trying to leave our significant others
that bots are telling us to?
I mean, look, like, there’s, I think that even the,
I think this is actually a great question, right?
And I think that even the most high-order bit, right,
the most important thing in my mind,
is this question of when.
When do you want to release?
And my point earlier of, well, there was this overhang
in terms of this gap between people’s expectations,
what they were prepared for,
and what was actually possible.
And I think that’s actually where a lot of the danger lies.
You know, we can kind of joke about or laugh about this article
because it wasn’t very convincing.
You know, just like chatbot saying, you know, leave your wife.
Sydney was pretty spicy, I don’t know.
Yeah, it was very spicy, right?
But did not actually have an impact, you know?
And I think that is actually, in my mind,
the most important thing, is trying to surface these things
as early in the process as possible, right?
Before you have some system that is much more persuasive
or capable or able to operate in more subtle ways.
Because we want to build trust
and figure out where we can’t trust yet.
You know, figure out where we put the guardrails in.
So that, to me, this is the process, right?
This is the pain of the learning.
And that we’ve seen this across the board, right?
We’ve seen places where people try really hard
to get the model to do something,
and it says, sorry, nope, can’t do that.
We’ve seen places where people use it for positive things,
and we’ve seen cases where people have outcomes like this.
And so, I think that my answer is that, you know,
we have a team that works really hard on these problems.
You know, that we have people who build on top of us,
who customize the technology in different ways.
But fundamentally, I think that we’re all very aligned
in terms of trying to make this technology
more trustworthy and usable.
And, you know, we do a lot of red teaming internally.
And so that’s, you know, we hire experts in different domains.
We hire lots of people to try to break the models.
You know, when we actually released it,
we knew, like, we’d kind of cleared a bar, we felt,
in terms of just how hard it was
to get it to go off the rails.
But we knew it wasn’t perfect.
We knew that we had come up with some ways
to get around it with sufficient effort.
And we knew that other people would find more, too.
But we’ve been feeding all that back in.
We’ve been learning from what we see in practice.
And so I think that this sort of loop
of there being failures, I think that’s important.
Because if not, it means you’re kind of holding it too long
because you’re being too conservative.
And then when you do release it,
now you actually are taking on much more risk
and much more danger.
It’s not 100% true in all cases,
but I think that that heuristic, I think, is important.
Well, I think it’s also, we’ll get to it a little bit later,
but an important segue, too,
to talk about the future of misinformation
and how we can prep now for what’s coming
with this innovation.
Before we get to it, I mean,
I think one of the most interesting things to me
is the ability for this technology
to synthesize information and make predictions
and identify patterns.
So can you tell me what you think
the most interesting future use cases
of what artificial intelligence will be able to predict
will be, like predict disease,
predict stock market,
predict if you’re going to get a, not you,
if someone’s going to get a divorce?
What could this predict?
Paint the image of the future.
Well, I think that the real story here in my mind
is amplification of what humans can do.
And I think that that will be true on knowledge work.
I think that it will just be that we’re all,
it’s kind of like if you hire six assistants
who are all like, you know, they’re not perfect.
They need to be trained up a little bit.
They don’t quite know exactly what you want always,
but they’re so eager, they never sleep.
They’re there to help you.
They’re willing to do the drudge work.
And you get to be the director.
And I think that that is going to be
what writing will look like.
I think that’s what sort of, you know,
business communication will look like.
But I also think that is what entertainment will look like.
You think about today
where everyone watches the same TV show.
And, you know, maybe people are still upset
about the last season of Game of Thrones.
But imagine if you could ask your AI
to make a new ending that goes a different way.
And maybe even put yourself in there
as a main character or something.
Having interactive experiences.
And so I think it’s just going to be
every aspect of life
is going to be sort of amplified by this technology.
And I’m sure there are some aspects,
people or companies that will say,
I don’t want that.
And that’s okay.
Like, I think it’s really going to be a tool
just like the cell phone in your pocket
that is going to be available when it makes sense.
I think, we think a lot at my company
about we’re knee-deep in exploring
how artificial intelligence can personalize content,
develop closer relationships with the audience,
which is a wide open space
and an interesting space.
But also there’s so many ethics that come up with that.
So we’re developing a lot of
these ethical frameworks around it.
I’m curious, when you talk about Game of Thrones
and personalized media
and being able to put yourself in it,
when we look at the future of media and entertainment,
would you say this is a new frontier
for personalized media?
Yeah, I think for sure.
And I kind of think it’s a new frontier
for most areas.
You know, it may not be great yet
at some domains,
but I think that we are just going to see
just like way more creative action happening.
And to me, actually, the thing that’s
I think most sort of encouraging
is I think it will be
the barriers to entry decrease.
And this is, by the way,
how we thought about things at Stripe.
Decrease the barrier to people
making payments online,
to people making services.
Way more activity happens,
things you would never think of.
And I think we’ll see this in content.
Individuals who have a creative idea
that they want to see realized,
they now have a whole creative studio
at their disposal.
But also the pros,
the people who really want to make something good
or make something way better
than any of the amateurs could.
And we’ve seen this with Dolly.
There’s literally these 100-page books
that people write on how to prompt Dolly.
And there’s all these murky questions
around identity and attribution
as these models go mainstream.
So it’s not perfectly clear
what the data sets are used to train.
So when we take a step back,
and this is a more fundamental question,
should an artist style
with models trained on their work,
should it be available to folks,
to anyone without use of attribution?
What are you guys thinking about
when it comes to these ethical?
Yeah, so we engage very closely
with policymakers,
and I think that’s something that we have.
Fundamentally, we as a company
want to provide information
and to show just what’s possible
and let there be a public conversation
about these topics.
I don’t think that we have all the answers,
but we think it’s really important
to be talking about.
So take me for example.
I’m like the beta test.
I’ll put myself in the driver’s seat.
So let’s say someone took all the footage
of me interviewing folks like you,
Zuckerberg, whatever,
and they trained this as a Lori model.
I’ve already named it.
I don’t know.
Please don’t do it, guys.
And then, I don’t know why I’m inviting this,
but then they launched a podcast
using my likeness, my style, my voice.
Hopefully you’d have fabulous style.
That would be all I’d ask.
But could they do it?
Should they get a cut?
Should I get a say in it?
As a content creator,
as someone who’s sat at the center
of the conversation about the future,
what does that look like?
Yeah.
Again, I think this is a great question.
I think it would be kind of
futuristic of me to say
that I have all the answers,
but I can tell you a little bit
about how we think about it.
As a company, our mission to build AGI
that benefits all of humanity.
We’ve kind of built with this
cap profit structure.
I really think that an answer
on this question, but more broadly,
all of humanity are kind of
stakeholders in what gets built.
Everyone benefits if it’s
access to these services,
if it’s that you’re able to
have your AI personality
or this AI that you build up
that represents you
and build a business with that.
I think all of this is on the table.
I do think that we need
some sort of,
I think that society as a whole
needs to adapt here.
There’s no question that
we need to think done
to get a little black mirror,
but why not?
Do you see a future where
we verify our own AI identities
and we can license them out
so I could license out
my likeness to some degree?
Yeah.
Again, I think kind of
everything is on the table.
I think actually this,
to your earlier question too
of why now, what’s happening now,
is I think everyone kind of
agrees that I think
where content comes from,
in good and bad ways,
how it’s created,
what an application is.
There’s Web 1.0 and 2.0
or something,
and I’m not going to talk
about Web 3.
Is it too soon?
There you go.
More to say there.
I think that where we’re going
is what an application is
will be very different.
It’s static.
You can’t really interact with it,
but we’re clearly moving
to a world where it’s alive.
You can talk to it
and it understands you
and helps you.
Honestly, every time I go
through some menu
and I keep trying to find
where I’m supposed to click,
I’m like,
why is this still here?
I think in the future
it will not.
How much powerful
is the current technology
you’re building?
We are continuing
to make, I say,
significant progress.
Blink twice if it’s
ten times more powerful.
Or, okay,
three times.
There we go.
I guess all I can say
is that I can’t comment
on unreleased work,
but I can say that
there’s been a lot of rumors
swirling around about
what we’re going to be releasing
and what’s coming out.
What I can definitely say
is that we do not release
until we feel good
about the safety
and the risk mitigations.
You guys have the ability
to turn up the dial,
turn down the dial.
I joke about ChatGPT
confidently.
It does so many
things, right?
Can you give any insight,
maybe speaking,
I don’t know,
we could speak around it
about what future versions
are going to look like?
Will it be more cautious,
more creative?
Let me give you a mental
model for how we build
these systems.
There’s a first step
in the process
of training what we
do.
It sees all the bad stuff.
It sees true facts.
It sees math problems
with good answers
and incorrect answers.
No one tells its incorrect
answers.
It sees everything.
It learns to predict.
It learns to,
given some document,
it’s supposed to predict
what comes next.
It has to think
about what’s the next word.
That model,
it has every bias,
it has every ideology,
it has every idea
that has been almost
expressed in this system,
compressed and learned
in a real way.
Then we do a second step
of reinforcement learning
from human preferences,
of what we call post-training.
Here you move from this
giant sea of data of
what’s the next word,
what’s the next thing.
Here I think there’s
something that’s very
important, very fraught.
This question of,
what should the AI do?
Who should pick that?
That is also a whole
different conversation.
That second step is
where these behaviors come
from.
I alluded to earlier
that we found that the
base model itself is
actually right with
quite good precision.
But our current
post-training process,
this next step that we
do to really say,
no, no, no, this is
what you’re supposed to do.
We don’t really include
any of that calibration
in there.
That the model really
learns, you know what,
just go for it.
That I think is a
engineering challenge
for us to address.
You should expect that
even with the current
chatGB team, we’ve
released four or five
versions since December
and they’ve gotten
a lot better.
Factuality improves,
that hallucinations
are a problem.
People talk about those
have improved.
A lot of the jailbreaks
that used to work
don’t work anymore.
And that is because
of the post-training
process.
And so I would expect
that we will have
systems that are much
more calibrated,
that are able to
sort of check their
own work,
that are able to
be much more calibrated
on when they should
refuse, when they
should help you,
but also that are
able to help you
solve more ambitious
tasks.
Like what?
Well, you know,
I think that the
kinds of things that
I want as a programmer
is that, you know,
right now, we started
with a program called
Copilot, which can do
sort of, you know,
just like autocomplete
a line.
And it was very useful
if you don’t really
know the programming
language that you’re in
or you don’t know
specific library functions,
that kind of stuff.
So it’s basically like,
you know, being able
to get, skip the
dictionary and look up
and it just does it
for you right there
in your text editor.
With chatGPT, you
can start being more
ambitious, you can
start asking to write
whole functions for you
or like, oh, like you
write the skeleton of
writing a bot in this
way.
And I think that where
we’re going to go is
towards systems that
could help you be much
more like a manager,
right?
Where you can really
be like, okay, I want
a software system that’s
architected in this way
and the system goes
and it writes a lot
of the pieces and
actually tests them
and runs them.
And I think this
kind of like moving,
you know, giving
everyone a promotion,
right?
Like making you into
more of the, you know,
the CEO of a company
and making you more
of the person who
does the whole pay
grades.
I think literally
and figuratively, I
think that’s like the
kind of thing that
they would do.
So the future of
chatGPT is we’re all
getting a promotion.
I think so.
It’s not too bad
if we achieve it.
Interesting.
I think there’s
obviously a lot of
fear around the
future of artificial
intelligence, right?
People say AI’s coming
for our jobs.
Be honest with all
of our friends here.
What jobs are most
at risk?
And one of the
things that I think
about this, certainly
that I did, was it’s
very clear that AI’s
coming for the jobs.
Just a question of
what order?
And clearly the like,
you know, ones that
don’t, you know, that
are like menial or,
you know, just like
require physical work
or something like
that, oh, the robots
will come for that
first.
And in reality, it’s
been very different,
right?
That actually we’ve
made great strides on
cognitive labor,
right?
On, you know, think
about writing poems or,
you know, anything like
that.
And we have not made
very much progress on
physical things.
And I think that this
amplification is kind of
showing a very different
character from what was
expected.
But it’s also the case
that we haven’t really
automated a whole job,
right?
That you think about, I
think the lesson from
that is that humans, I
think, are much more
capable than we give
ourselves credit for,
right?
To actually, you know,
do your job, to do
what you’re doing right
now.
It’s not just-
Well, I asked
ChatGPT.
These aren’t the
ChatGPT questions.
These are the
ChatGPT questions.
I had to follow up and
say, can you be more
hard-hitting?
There you go.
Well, thank you.
Are these the hard-
hitting ones?
No, they’re coming.
Here we go.
We’re about to go into
the future of truth right
after this.
There we go.
Perfect.
But ChatGPT, it’s not up
here on stage with me.
You know, there’s the
personal relationship
aspect.
There’s this judgment
aspect.
There’s so many details
that are what you want
from the person in
charge.
But the, like, writing
of the actual copy, I
mean, who cares about
that?
But the, like,
writing of the actual
copy, I mean, who cares
about that?
Maybe we’ll do the
follow-up.
Well, probably we’ll
do the follow-up
question.
My follow-up
question is, so give
us a couple jobs
most at risk.
Yeah, well, I’ll tell
you, the one that I
think is is actually
content moderator.
So jobs, what I’ve
really seen is jobs
that you kind of didn’t
want human judgment
there in the first
place, right?
You really just wanted
a set of rules that
could be followed, and
you kind of wanted a
computer to do it.
And then you had
to decide, is this
thing sufficiently
horrible or just
like slightly not
sufficiently horrible
to be disallowed?
And that’s
something I already
see this technology
impacting.
So that might be a
good segue into the
future of truth,
right?
Because I think
we’re entering this
really fascinating,
exciting, and scary
era of you have the
rise of deep fakes,
these automated
chatbots that could
have the ability to
persuade someone one
way or the other.
What happens to truth
in an era where AI
just makes fiction so
believable?
Well, I have a
slightly spicy take
here, which is that I
think technology has
not been kind in a lot
of ways to journalism.
And I think that AI
and this particular
problem might actually
be something that is
quite kind and actually
really reinforces the
need for authoritative
sources that can tell
you this is real,
right?
We actually went out,
had humans investigate
this, that we looked at
all the different sides
of this thing, and this
is actually user
authenticated videos or
whatever it is that can
tell you what happened
and what the facts are.
And so I think that
where we’re going to go
is away from a world
where because certainly
you saw some text
somewhere that you can
trust it’s true, it’s
never really been the
case.
Humans have always been
very good at writing
fake text.
Images,
pictures,
images,
images,
doctored images,
those have existed since
the invention of
photography.
But this gives us the
ability to do this at
viral speed.
100%.
Right?
All the bad things that
happened over the last
decade, if we’re not
careful, this will
amplify.
Yes.
And I think this is,
to me, I agree with
this, right?
I think this is kind of
the crux is that the
fact of being able to do
these things at all,
not new.
The fact of being able
to do it with a much
lower barrier to entry,
that’s new.
And I think that’s
what sparked the need
for new solutions.
We’ve never had real
answers for sort of
chain of custody of
information online.
We’ve never really had
verified identities.
All these things people
talked about since the
beginning of the
internet.
But I think there was
never really a need for
it.
And I think the need
will come.
Yeah.
I, the folks, I was at
an event for the folks
for the Center for
Humane Technology.
They’re the folks who
did also like the
Social Dilemma, which
in my opinion, Social
Dilemma’s great, but
it’s like we’ve been
having these conversations
for 10 years before
Netflix puts out a
doc and asks these
questions, right?
So we’re at the
beginning of an
interesting era.
We should ask these
questions, you know,
before like we have to
do a sexy doc on it in
10 years.
So there was something
that was said there that
I thought was really
important.
They said that 2024
will be the last human
election, meaning by
2028, we will see
synthesized ads, viral
information powered by
artificial intelligence.
Someone releases a
Biden-Trump filter,
tens of millions of
videos are going out
there.
How do we build now?
Like what has to
happen now in your
opinion to get ahead of
what will be the
inevitable downside of
this?
Yeah.
So I think this is a
great question and I
think this is like maybe
also going to be a tip
of an iceberg kind of
problem where it’s like
it’s the most visible
one.
It’s clearly extremely
impactful.
It’s one that, you
know, has been very
topical for a long
time.
But I think that we’re
going to see the same
questions appearing
across all sorts of
human endeavor of just
as there’s more access
to creation, how do
you sift through for
good creation?
How do you actually,
you know, find what is
true or find what is
high quality or, you
know, how do you make
sense of it?
And I think some of
this is really going to
be about what people
building good tools.
Like we’ve seen this
within, I think, the
social media space.
Like even, for example,
you know, people
building tools for
cyber harassment, you
know, to make it so
that people can easily
block, you know,
various efforts and
things like that.
And I think that we
need lots of tools to
be able to do that.
And I think that we
need lots of tools to
be built here that are
really tackling this
problem.
And so that’s one reason
that we, you know, we
don’t just build chat
GPT, the app.
Actually, our main focus
is building a platform.
So we release an API.
Anyone can use this to
build applications.
And I think that you
have an opportunity,
some using traditional
technology, some using,
you know, the AI
technology itself in
order to actually sift
through and figure out
like what is high
quality curated and
people want to play
with it.
And so I think that
that’s one of the
things that we need
to do.
I think that’s one of the
things that we need
to do is to create
an API that’s
high quality curated and
people want to put
their stamp of
approval on it.
I remember the
move fast and
break things era of
meta-Facebook.
Remember they used
to have the sign that
said move fast and
break things.
I know Open AI put
these things out there
in an iterative way and
has a philosophy about
limiting growth to
some degree and
getting feedback.
But now I would say
because of what’s
launched, there’s this
AI race with the
biggest companies
around the world
that is like what
is the best for
society?
What do you think
we’ve learned from
the last decade of
tech innovation that
we must use as we
enter into this new
era where the stakes
you could argue are
even higher?
Yeah.
We think about this a
lot.
Like I have spent a
lot of time really
trying to understand
for each of the big
tech companies, you
know, what did they
do wrong?
And right.
But to the extent
that things, that
mistakes were made,
like what are they,
what can we learn?
And actually one thing
I found very
interesting is that
there’s not really
consensus on that
answer.
Like I wish there was
a clean narrative that
everyone knew and it’s
just like just don’t
do this thing.
Well, I could give an
opinion.
Please.
I would love it.
I’ve interviewed Mark
Zuckerberg many times
and I would say just
having been across
from some of those
folks, I think the
biggest mistake is
not understanding
humans.
In a nutshell, right?
So how I think like,
you know.
We’ve got the stamp
of approval on this.
Great.
And the audience.
So I think it’s, I
mean, it sounds like
you’ve done a lot of,
you guys have done a
lot of thinking into
how you put this out
there and how you
build out these APIs
that other people can
build on.
Who are the people
that need to build
out for these
solutions?
Like who can you
guys, now that you
have a seat in Silicon
Valley and you’re at
this really powerful
place, like who do
you guys bring in
that’s different,
diverse and interesting?
Yeah.
So we do quite a lot
of outreach and I
actually think this is
a really good example
on how we make
decisions on the
limits of what the
AI should say.
We’ve written a blog
post about this, but
we think that this is
something that really
needs legitimacy.
It can’t just be a
company in Silicon
Valley.
It can’t just be us
who’s making these
decisions.
It has to be
collective.
And so we’re
actually, and we’ll
have more to share
soon in terms of
exactly what we’re
doing, but we’re
really trying to
scale up efforts to
get input to actually
be able to help
make collective
decisions.
And so I think
that it’s just so
clear that you do
need everyone to
have a seat at the
table here and
that’s something we’re
very committed to.
And then talking
like regulation, I
think it’s, Open
AI talks about moving
at a bit of a slower
pace, but these tools
are being deployed to
millions.
So the FDA doesn’t
allow a drug to go
out to the market
unless it’s safe.
So what is the
right regulation look
like for artificial
intelligence and what’s
happening?
So yeah, this is
again something we’ve
been engaging with
Congressional
testimonies back in
like 2016, 2017.
It was so
interesting to see
that policymakers
were already quite
smart on these issues
and already starting
to engage.
And I think that,
you know, one thing
we think is really
important is really
about focusing
regulation on
regulating harms,
right?
That it’s very
tempting to regulate
the means.
And we’re actually
seeing this right now
with like the EU AI
Act that’s kind of a
question of exactly
how to sort of
operationalize some
of these issues.
And the thing you
really want is to
really say like,
let’s think about
the stakes and
really parse apart
what are high-stakes
areas, what are
low-stakes areas,
what does it mean
to do a good job,
how do you know?
And these sort of
measurements and
evaluations, like
those are really,
really critical.
And so we think
the government,
it’s a key part
of the issue,
right?
Like this question
of how do you
get everyone
involved?
The answer is we
have institutions
that are meant
to be able to
regulate these
issues.
I’m sorry, I
have Facebook made
money and the
answer was like
we sell ads.
So really
understanding because
it certainly seems
like there’s going
to be all these
new issues.
Should there be
a new regulatory
body for this?
Again, I think
it’s on the table.
I think more
likely what I see
happening is like
I think that AI
is just going to
be so baked into
so many different
pieces and honestly
so helpful in so
many different areas
and it’s a good
strategy.
But I think that
every organization,
government or
otherwise, is going
to have to understand
AI and really
figure it out.
I know we have to
wrap soon because
I want to get to
questions but I
thought we could
do a little
lightning round.
I love a good
lightning round.
Okay.
AI will be
sentient when?
Long time from
now.
Like how long?
This kind of
question I prefer
not to comment on.
It’s hard to answer.
Most interesting
question.
I think it’s
going to be just
making your dreams
come to life.
Oh.
Huh.
In what sense?
Sorry.
It’s not part of
the lightning round.
You hook up your
brain machine interface
and then you do a
nice rendering and
you’ll get great
visions of your
dreams.
Wow.
Spiciest take on
the future of AI
that you’re
generally not
allowed to say
publicly.
Oh, man.
I think it’s
going to be
making your
dreams come to
life.
I think we’re
going to figure it
out.
I think it’s
going to go well.
You’re optimistic.
I’m optimistic.
I consider myself
an optimistic realist.
I think it’s not
going to go well by
default, but I
think humanity can
rise to this challenge.
Elon Musk no longer
really deeply involved
with open AI,
building potentially
what’s called
anti-woke AI.
Success or failure?
Well, I think a
failure on our part
for sure.
In what sense?
Well, I think we
were not fast enough
with our biases in
chat GPT, and we
did not intend them
to be there, that
our goal really was
to have a system that
would kind of, you
know, be sort of
egalitarian, sort of
treat all the sort
of mainstream sides
equally, and we
actually made a lot
of improvements on
this over the past
month, and we’ll have
more to share soon,
but, yeah, I think
that people were
right to criticize us,
and I think that we
really sort of, you
know, responded to
that.
It’s one of the
pieces of feedback
that I think is most
valuable.
Fill in the blank.
What do you think
the future of AI in
2050 is?
Unimaginable.
Okay.
I like that.
The single most
important ethical issue
we’re facing when it
comes to the future
of AI in humans.
This one’s hard.
I think it’s the
whole package,
honestly.
I think it’s this
question of how the
values get in there,
whose values get in
there, who’s values
get in there, and
who’s values don’t
get in there, and
who’s values don’t
get in there, and
who’s values don’t
get in there, and
who’s values don’t
get in there, and
who’s benefits get
in there, and who’s
benefits get in
there, and who’s
benefits get in
there, and who’s
benefits get
distributed.
How do you make
sure the technology
is safe and used in
the right ways and
the emergent risks
that are going to
appear at some point
with very capable
systems don’t end up
overwhelming the
positives we’re going
to get.
So I
think it’s the
whole thing.
At some point to
your first question,
the sentience
question, at what
point do the
systems have moral
philosophers to
help answer some
of these questions?
Are you going to
hire philosophers?
We’re going to
hire I think everyone
across the board.
I think this is
one key thing to get
across.
I think that within
AI I’ve definitely
seen this fallacy of
people thinking this
is a technology
problem or just
saying look,
there’s the
alignment problem
of how do you
make the AI not
go off the rails,
but the society
thing, that’s the
hard part.
And I think
that’s the
hard part.
I’m not going to
worry about that.
And I think you
can’t do that.
I think that it
really has to be that
you engage with the
whole package and
that I think is going
to require everyone.
I like the
understanding of
understanding the
people behind the
code that transforms
society.
And so I’ve just
met you in person
today, but we’ve
spoken a little bit
about some of the
ethical stuff too.
You’re at the helm
of one of the most
important technological
advances of our time.
What do you want
people here to know
about you?
Well, I love my
wife.
I’m not going to
listen to the chat
bot.
I bet she is
fabulous.
She’s not being
replaced.
Sidney cannot
break up that
marriage.
Exactly.
And we were
talking about this
last night.
She was asking
why do I do it?
Because I work a
lot.
I think we give
up a lot of time
together as a result
of just how much I
really try to focus
on the work and
trying to kind of
move the company
forward.
And, you know, I
hadn’t really thought
about that question
for a while.
And I thought
about it, and my
true answer was
because it’s the
right thing to do.
Like, I just think
that this technology
really can help
everyone, can help
the world.
I think it’s, you
know, these problems
that we just see
coming down the
pipe, you know,
climate change again
being one of them,
I think we have a
way out.
And if I can
move the needle on
that, and, you
know, I’m grateful
to be in the
position that I am,
but honestly, when
we started the
company, what I
cared about most
was I was just like,
I’m happy to do
anything.
You know, like,
first day two people
were arguing about
something, they
didn’t have a white
board, I was like,
great, I’ll go get
the white board.
And I think that
this problem is just
so important.
It transcends
each of us
individually.
It transcends our
own position in it.
And I think it is
really about trying
to get to that
good future.
Great.
Well, thank you.
So, I think we’re
talking about the
decline in human
intelligence as we
start to outsource
our cognition to
AI.
Yeah, this is
definitely something
that keeps me up at
night.
Although, it’s
interesting to see
this trend across
all previous
technologies, you
know, radio,
television, you
know, I’ve talked
to some esteemed
states people who
have said, like,
the politicians
these days, nothing
compared to
Teddy Roosevelt.
Like, read all of
Teddy Roosevelt’s
great thoughts, and
it’s so unclear to
me.
Like, you know, I
feel, like, is this
true or is it not?
But I think that
what is definitely
important as we see
this new technology
coming is figuring
out how to have it
be an intelligence
multiplier, right?
So that, you know,
sometimes, yeah, you
do need to solve the
problem yourself, but
what you really want
is you want a great
tutor.
You want someone who
breaks down the
problem to you,
really understands
what motivates you,
and if you have a
different learning
style.
And so I think
that’s important.
But if you have
something that
actually is figuring
out the how do I
help you fish, or
how do I help you
learn to fish, I
think you can go way
further.
What is your opinion
on, this one was
upvoted a lot, so
I’m being true to
the audience.
They have a good
question.
All right.
What is your opinion
on intellectual
property rights for
AI-generated content
trained on the work
of a particular
artist?
We talked a little
bit about this, but
The people want more.
I think this is like
asking a question
about exactly how
copyright should work
right at the
creation of the
Gutenberg press,
right, where it’s
like we are going to
need to have an
answer.
We’re engaging with
the copyright office.
We’re engaging with
lots of different
areas, and I don’t
personally know what
exactly the answer
should be, but I do
think that, like, one
thing that I do want
to say, not to kind
of hedge everything
here, is that I do
think that content
creators should be
sort of, you know,
it should be a more
prestigious, a more
compensated, a more
just, like, good thing
for people to pursue
now than ever, and I
think if we don’t
achieve that in some
way, then I think
that something has
gone wrong.
Will there be new
laws that didn’t
exist?
Oh, for sure.
I mean, there should
be.
What do you think
they will be?
Well, again, I don’t
want to speak out of
turn.
I don’t want to be
too, yeah, I just
don’t want to speak
out of turn on
these issues, but I
think that, to me,
the process that’s
happening right now
is really important.
I think that, you
know, it’s really
important that, you
know, when we’re
talking about
things, people really
care, and they should,
and that we are
trying to figure out
mechanisms just
within our own, you
know, sort of, you
know, slice of how we
implement things and
how we sort of work
with different
partners.
You know, for
Dolly, for example,
the very first people
that we invited to
use it were artists,
right, because we
really wanted to figure
out how do we make
this be a tool that
you are excited about
and that you feel
like, yes, like, I
think that the most
important thing is
really going to be
these higher-level
skills, right,
judgment, really
figuring out is this
good, is this bad, do
I like this, do I
not, knowing when to
sort of, you know,
sort of dig more into
the details, and
really, I think today
just even playing with
these systems, like, I
think that it will be
the case that we’re
going to make, you
know, the next
generations of the
Dolly’s and these
other systems just be,
you don’t even have
to, no language,
you know, you don’t
even have to know
the language, you
don’t even have to
know the language,
you don’t even have to
know the language,
right, they should
become much more
child-accessible, and I
think that children
being sort of AI native
users, I think you’re
going to find that
you’re going to figure
out how to just use
these in totally
unimaginable ways.
Let’s see.
Sorry, this one’s not
working, I’m going to
this one.
Okay, how can we
maintain the integrity
of AI models like
ChatGPT when capital
from corporates has
entered the space
monetizing a tool run
by a nonprofit, and
you’ve, I mean, a lot
of folks, this is,
actually, this is what
I was going to ask
you, but ChatGPT
also asked me to ask
you, which is
interesting.
It’s very topical.
I like that.
This is good.
And so if you could
give us a little more
insight, because
obviously we’re very
far from when you
guys sat at that
dinner and said we
want to change
things, and now
there’s money, there’s
profit, there’s all
these other things, so
how do you guys
maintain that?
Yep.
Well, I think that
our answer to this
question, and you
should hold us
accountable, by the
way, is really about
the fact that we
really set up our
structure in a very
specific way, which,
by the way, has
turned off a lot of
investors.
We have this big
purple box at the
top of all of our
investment docs that
say the mission comes
first, that we may
have to, you know,
if there’s a conflict
with achieving the
mission, cancel all
of your profit
interests, which,
yeah, you know,
sends many traditional
investors running for
the hills, and I
think that, you know,
like, there’s a part
of the frame of the
question that I, you
know, sort of don’t
agree with, which is
that the existence of
capital is itself a
problem.
Like, I think that,
you know, we’re all
using iPhones, we’re
using TVs created by
companies.
There’s a lot of good
things, but I do think
it comes with great
incentives, right?
It comes with this
pressure to, you
know, sort of do
what’s good for you
specifically, but not
necessarily for the
rest of society, not
to internalize those
externalities.
And so I think that
the important thing for
us has been to really
figure out how do you
set up the incentives
that are on yourself so
that you do, as much
as possible, get
the, you know, the
best people to join,
you can build the
massive supercomputers,
you can actually build
these tools and get
them out there.
But at the same time,
if you do succeed
massively and wildly
beyond anything that’s
happened, how do you
make sure that you
don’t, you know, once
you’ve kind of gotten
to everything, you
don’t have to then 2x
everything, you know?
And I think that these
kinds of very subtle
choices make a huge
difference in terms of
outcome.
I want to end with a
quote from your
co-founder Sam
Altman.
He wrote, a misaligned
super intelligent AGI
could lead to
serious harm to the
world.
An autocratic regime
with a super intelligence
could lead to that,
too.
Successfully
transitioning to a world
where super intelligence
is perhaps the most
important and hopeful
and scary project in
human, is perhaps the
most, sorry, I’m really
messing this up, is the
most important, hopeful
and scary project in
human history.
Success is far from
guaranteed and the
stakes, boundless
downside and boundless
upside, are there to
hopefully unite us
all.
I want to end
with a quote from
the great guy Sam
Altman, who’s an
artist, a philosopher,
a 봉을 탄생하고
40허리 열풍을
I think that this is
the key.
And I think by engaging
in these technologies
we have to study
these questions
and not know the
answers yet.
That’s the
responsibility not just
of us but of everyone
It’s going to be a
project of decades
to really go from
where we are to the
kinds of systems that
we’re talking about
there.
And all along the way
there’s going to be
surprising things.
There’s going to be
great things that
happen.
There’s going to be
causes for joy,
causes for grief.
And I think that they
all happen in small
ways now.
And I think in the
future maybe they’ll
happen in bigger and
bigger ways.
And I think that just
really engaging with
this process, just
really everyone
educating themselves
as much as possible
and figuring out how
can this fit in.
I love the question
about what should I
teach my one-year-old
because that is a hope
for the future kind of
question.
And I think that I am
very optimistic.
Again, I think I
consider myself this
realistic optimist that
you really have to be
calibrated.
You can’t just blindly
think it’s all going to
work out but you have
to engage with all the
problems.
But I think it is
possible that we will
end up in this world
of abundance and sort
of the real good
future.
And I think it’s
possible that we
will end up in this
world of abundance and
sort of the real good
future.
I think it won’t be
perfect.
I think there will be
problems.
And there will certainly
be many problems along
the way.
But I think we can
rise to the occasion.
You have children?
Not yet.
Working on convincing
my wife though.
Okay.
So I was going to
say, do you believe
that kids of your
friends, if you end up
having children, will
grow up in a better
world?
I do think so.
I think we have a
shot at it.
And I think the moment
you think that it is,
that’s when things go
wrong.
And so I think we all
have to be sort of
constantly asking what
can go wrong and what
can we do to prevent
that.
Great.
Greg Ruckman, thank
you so much.
Thank you.
Thanks, guys.
Appreciate it