YT YouTube2Text

YouTube2Text → Transcripts → The Biggest Launch Since ChatGPT (Muse)

The Biggest Launch Since ChatGPT (Muse) — Transcript

by Cleo Abram · 13,018 words · 1,135 segments · language en · Watch on YouTube

Open in transcriber Summarize with AI Download SRT/VTT

Full transcript

0:00What's happening right now is a big deal.
0:01This is the biggest launch since ChatGPT.
0:03It's called Muse. "Muse." "Muse." "Has the tech world buzzing." "5
0:08million downloads in 22 days." "Surpassing ChatGPT" And he built it.
0:13Most people are using AI to answer questions, basically
0:15glorified Google search.
0:17This is something totally different.
0:19"It's an AI agent" "Built for everyday life." "We believe that Muse will
0:23make you money." "One guy had Muse help plan a weekend away with his
0:26wife." It can make phone calls.
0:28It can do tasks for you.
0:30But for it to work, you have to trust it, first with your personal information,
0:34then to do what you actually want it to do, and then that it's not gonna, well,
0:40get out of control, all at a time when the most important AI leaders are warning
0:44us that AI is getting out of control.
0:47And I urge you to urge me to stop.
0:50If you understand what's happening right now with Muse, you'll understand
0:53the biggest things happening in AI.
0:56So what can it actually do?
0:57When should we trust it?
0:59And what do we need to do to make sure this goes right?
1:03And we're at the moment of, like, peak insanity.
1:06Why are all these billionaires obsessed with restaurant reservations?
1:09We have a shared interest in making sure this technology
1:12does not go horribly wrong.
1:13Is that something that's happening in some private room somewhere?
1:16The degree to which certain personal animosities are actually defining
1:21how they're developing this really important technology is insane to me.
1:30Amazing Let's do this.
1:33Okay. Welcome.
1:34How have you been?
1:35I'm good.
1:35I'm really good.
1:36How have you been?
1:37This is- It's been kind of insane, yeah.
1:40Yeah.
1:41What does it feel like?
1:43We didn't expect that it would be such a hit, so I feel amazing for, like, the
1:48whole team and everyone who, like, worked so hard on it 'cause it's, like, it's
1:51really come together and become a thing.
1:53So… That's what I'm excited to talk about.
1:55I know you know all about the show already.
1:57But this isn't, like, a traditional interview.
2:00Like, I'm not gonna ask about financials.
2:02I'm not gonna ask about management style.
2:03You just launched one of the most popular consumer tools of all time, and so the
2:09only thing that I wanna know is what's the future that you're working toward
2:13with that tool, and how can this audience that's really optimistic and really wants
2:18to be part of making sure that that future go right, how can they feel like they're
2:23part of the decisions that are being made?
2:26Yep.
2:26All of that.
2:27Yeah. That's what we do.
2:28One of the reasons I wanted to talk to you was I've always
2:31appreciated that you're really blunt.
2:36Like, I, I honestly think that people need straight talk about this.
2:42I think that they feel confused, and I wanna get at what's really
2:46happening, what have you built, how are people using it, how are they
2:51not, what are the real challenges.
2:54I think one other interesting thing is given how popular Muse
2:57has become, I want to use it as a way to talk about other things in AI.
3:04Awesome.
3:04Okay.
3:05Let's get into it.
3:06Muse is the fastest-growing consumer tool since ChatGPT.
3:09Faster than ChatGPT, I think, and it feels like this is the moment when
3:13we're moving maybe from chatbots to agents, but there have been a lot of
3:18other popular agents, mostly in code, and I'm curious, why do you think
3:23this is happening for Muse right now?
3:24Why is Muse exploding like this?
3:26I think one of the mysteries of AI so far this year, for most of the year, has been
3:31that agents were happening for engineers and software engineers and coders, and
3:36then nobody else in the world, like, was experiencing it, knew what it was, like,
3:41understood exactly what was happening.
3:42And so for most of this year, you've kind of had this, like, two dialogues
3:47almost where people who do code and could see how powerful AI was becoming
3:52and, and how useful it was becoming were like, "Oh my gosh, this is incredible."
3:56And then everyone else was kind of like, "What are you talking about?
3:59I just still use a chatbot.
4:00Like, it's, like, a little bit better than Google, but what are you guys all
4:03freaked out about?" And, um, and one of the things that we were really excited
4:07to do is how do we take the magic that engineers and coders and developers
4:12are seeing and bring that to everyone?
4:15One of the things that, that has happened over the course of the past
4:18year is, like, the AI models started out as pretty janky agents, and you
4:22kind of had to struggle through them to get them to work, and then they
4:25slowly got better and better and better.
4:27And most of the industry's been really focused on making them just, like- Better
4:31and better and better at coding.
4:33But we really felt this was like a s- a big gap because most people in the
4:36world, they don't know how to code.
4:38They d- probably never even want to code.
4:39Um, but they still wanna feel the experience of agents and, um, having
4:45AI that is able to do more for you and actually start taking on more end-to-end
4:50workflows and take on more tasks for you.
4:52So that was really the goal behind Muse, and I think we spent a lot of time
4:56polishing all the little details to make sure that it would just work for people
5:02because we knew that, like, developers can kind of withstand a lot of jank
5:07and a lot of stuff breaking and, like, they can kind of deal with all of that
5:10because, you know, they're, they're…
5:12It's technology.
5:12They're used to dealing with, you know- Yeah … unripe technology.
5:15But for everyone, you have to make something that's really smooth
5:18and, and really works for them.
5:20That also helps explain the big gap, I think, between what it feels like for most
5:26people to actually use AI, when they were using a chatbot in particular, and the
5:31conversation around AI that's it's gonna take everyone's jobs, it's really scary.
5:35It's, you know, there's a, there's a macro conversation happening, and then there's
5:38an experience that people are having.
5:40And it just seems like those things are really, really far apart.
5:43And to your point, Muse is giving more people the opportunity to have
5:47the insight that computer engineers had, you know, call it a year ago.
5:52Yeah. Is that fair?
5:53I, I think that's true, yeah.
5:54Yeah.
5:54There are also moments in history that I think feel similar.
5:57Would you say it's like the mobile phone moment for agents?
6:01There are, you know, billions of people brought online by mobile phones that
6:05never had PCs because it was easy and simple to use, and they didn't, you
6:09know. I actually hadn't thought about that until you just mentioned it, but I
6:13think it's a great analogy because it's
6:15These models, they're improving really fast, and they're
6:17improving really, really quickly.
6:18And it's really important that we, as those models get better, that
6:22the, the superpowers that they, they lend can be given to, like, literally
6:27everyone all around the world.
6:29The part that I like about the analogy is, like, um, so much of it is just
6:33about figuring out how to package it all together into a form factor that, um,
6:39will resonate with people, and that's what we spent a lot of time trying to
6:42craft and get right and, and make perfect.
6:45Which also gets to one of the interesting initial reactions that I've heard
6:49of Muse, which is, is everybody's life more complicated than mine?
6:53Like, I wake up, I go to work, I play with my kids, I have one
6:57restaurant reservation a month, I plan one vacation with my spouse.
7:01Like, why are all these billionaires obsessed with restaurant reservations?
7:05Why do I need an assistant?
7:07How would you answer that?
7:08Whenever I explain Muse to someone just who knows nothing about it,
7:12um, I just say, like, Muse can help save you money and save you time,
7:16and everybody in the world wants to save money and wants to save time.
7:20Depending on what your lifestyle is, that can mean a different thing to you.
7:24I think almost everyone has, like, subscriptions that they shouldn't be
7:28paying for or subscriptions that are really expensive, like cable bills or
7:31insurance, and for a lot of people, their magic moment with Muse is just saving
7:36money on things that they shouldn't be spending money on or they shouldn't be
7:39spending so much money on, or finding gift cards that they hadn't spent or,
7:43you know, the list goes on and on and on.
7:45I think it's awesome that so many people are experiencing that.
7:47I think that's, like, great because these are things that, they're just,
7:50I think for most people, they're just so boring- That you would never get to
7:54Yeah, actually, like, handling them or, or dealing with them, but, you know, your
7:58agent, Muse, can just do that for you.
8:00And then they're saving time, which I think for, for lots of people, I think
8:04there are always experiences that are just way more frustrating than they should
8:06be, like going to the DMV is one example.
8:09We actually, when we built Muse, we, like, specifically tested it to see if it
8:14could deal with the DMV website because that was, like, the pinnacle we thought
8:18of, like, the most frustrating experience for any human is dealing with the DMV.
8:24So we kne- we needed to have Muse, like, be able to, to do well in that situation.
8:29We've seen people use it for, like, yeah, navigating the
8:31healthcare system's a big one.
8:32Medical claims is, like, a huge one that, like, most people just… It's,
8:36like, so complicated and annoying that it's just, like, incredibly frustrating.
8:40We've seen people, like, use it to help run their small business.
8:43We've seen a lot of people, like, use it to help run their social media
8:46even, and I think the medical system is a really good example 'cause
8:50it's, like, so hard to understand.
8:53And I think the, the you know, one of the best ways to put it is, like,
8:56I think the cases where people…
8:58where it's, like, a huge lifesaver are the cases where, like, otherwise you
9:01would be at your computer, you'd have to Google, like, 50 different things.
9:05You have to read, go to, like, 100 different websites and try to figure
9:08out what the hell is going on, and it couls, it's just, like, this
9:12incredibly frustrating experience, or you could have your Muse do it.
9:15I also think about how many things I suspect are made difficult
9:22and time-consuming on purpose in order to frustrate me so much
9:25that I don't get to the end?
9:26How many claims are actually designed to be that hard so that I give up?
9:30And I wonder what effect more people having more patience because
9:36they have support will have on that kind of systems engineering.
9:40One of the, the little memes that I made of the little Muse was, uh, Muse in
9:44Hood, which is like a Muse Robin Hood.
9:47But I think there's, like, a real truth to this, which is there are lots of things
9:52that exist in the world that are, to your point, meant to be hard so that mo-
9:58like, uh, canceling subscriptions is one of the best examples, where there's some
10:02subscriptions where you have to, like, click on one thing and click on another
10:05thing, and then click on, like, the little piece of text, and then not click the big
10:09red button, but click the other button.
10:10And, like, that's just, like, the perfect example where there's lots of cases
10:14where things are designed to be hard for people, and I think, like, this is
10:19giving people the tools to be able to just do those things seamlessly and easily.
10:24I got access to the calling, um, and I had it make a call this
10:27morning, which it did successfully.
10:29What does it sound like on the other end?
10:31I think it's clear for the other end that it is, like, an AI, and I think
10:35if you look, read the, you know, we show, we show users the transcript.
10:39Like, if you see the transcript, like, the beginning of the call I
10:41always think is really funny because it's like, "Hi, I'm so-and-so.
10:44Um, I'm Cleo's assistant, and, uh, this call is being recorded." And I
10:49think it's just, like, always this very funny beginning of a call. "Hi, I'm
10:53Haley calling on Cleo Abrams' behalf.
10:55I'm transcribing our call for notes." What are some examples of when it's
10:59messed up for you or when it's, you feel like categories it struggles for still?
11:03One thing that we're, we're always trying to improve is the memory
11:08of these Of these agents, right?
11:10And this is still one of the more unsolved problems, I think, for a lot
11:13of these agents, is having the-- You know, the, the memory of the agents
11:17is, like, not like that of a human.
11:19Um, sometimes it can feel like it's like that, but it is really different.
11:23So that's one area where we're, like, always trying to improve.
11:25We always wanna, wanna figure out how to make the agents better.
11:28And then, like, we want them to be able to continue doing
11:32more and more complex tasks.
11:33I, I find this really funny, but there's, um, there's a lot of people
11:36who will just, like, ask their Muse, "I want you to make me $10,000."
11:40And obviously, not everyone can just ask their Muse to make them $10,000, but,
11:43like, I think, I think these kinds of, like, more amorphous, nebulous tasks,
11:47like, we want Muse to be able to at least, like, build a plan and figure
11:50out and, you know, you're gonna have to do your part if you wanna make $10,000.
11:54But I think we wanna enable the, the agent to do more and more kind of complex and
11:59esoteric tasks, um, to help the users.
12:02There are some things where I feel emotionally, even if Muse could do
12:07them, I would prefer that they didn't.
12:08So for example, if I'm texting a friend and I find out that
12:11their Muse agent is responding to me, I would be offended by that.
12:15Are there categories of things that you think explicitly Muse should
12:18not do that are sort- just, like, extremely deprioritized, not because
12:23they're technically that hard, but because socially, not the point?
12:27A lot of what we care about is, like having Muse kind of like clear the way
12:32such that you can spend more time on the things that you want to do and the
12:36things that you enjoy doing, and the things that are, like, valuable to you.
12:38We generally speaking want to figure out how to make someone's Muse
12:44is like a different entity than them.
12:46That's why it's like a little cute guy.
12:49Um, uh, and, and we want to, over time, continue making that more and
12:53more clear because, um, to your point, like it is socially weird if you're
12:57like you think you're talking to your friend and it just happens to be the
13:00little Muse, the little Muse guy- Yeah behind the, behind the curtain.
13:03Speaking of what they look like, one of the things I've visited humanoid
13:07robots before, and one of the things they spend a lot of time thinking about
13:10is how to make it cute so that you like them and you want them in your home,
13:13or at least you're not afraid of them.
13:14What did Muse look like in previous iterations, and how did you
13:18decide on that particular guy?
13:20I think this is, like, one of the most shocking things.
13:22What the Muse looks like now is what he looked like in the very first prototype.
13:28It's kind of one of these, like, rare things where the, the very
13:32first version was just perfect and then just, like, grew on all of
13:35us, and we ended up going with it.
13:36We had lots of debates about it, uh, which are, you know, in retrospect will
13:41be some of my funniest meetings of like, you know, a bunch of serious adults
13:44getting in a room, like arguing about this cute little, um, you know, character.
13:50But, uh, but yeah, I mean, I think we were like, is it not serious enough?
13:54Is it, like, too playful?
13:56Does he need to be more colorful?
13:57Like, these were, like, all things that we asked the questions
13:59on, and we looked at them.
14:00We're like, "No, he's perfect." What is the big vision here?
14:04I sometimes struggle when talking to AI leaders to push people to think
14:07about more than, like, five years out.
14:09But what is, what is the vision here?
14:12Is it that Muse becomes my layer on everything on the internet?
14:17Is it, like, map this out for me if it goes the way that you hope it does.
14:21What we really want Muse to be over time is kind of this, like, general manager,
14:28so to speak, that helps you accomplish all the things you want out of life.
14:33So, like, I think, I think one of the kind of tragedies of the modern world is
14:40that, like, um, most people have wants and dreams, um, that can range from,
14:48like, really big ideas, like, hey, I want to start a company, or I want to cure
14:53disease, or I wanna save the planet, all the way to, you know, small wants that are
14:58like, hey, I wanna spend more time with my family and, uh, I want to eat healthier.
15:02And, you know, um, and, and there's a whole range, but for most people,
15:07I think there's just so many- barriers and burdens in the way.
15:11What I really hope it accomplishes is help every single one of the eight billion
15:16people all around the world be able to, uh, accomplish their wants and dreams.
15:21And, um, I think this is like, you know, if we are able to accomplish that, like,
15:27I think the world will seem insane.
15:29That's what we really want Muse to be and what, what we think it can, it
15:32can be, uh, if we keep working at it.
15:35You know, it's really nice to hear you talk about giving people time back
15:39because for a really long time I have felt, and I think a lot of people have
15:42feel, have felt like So much of the financial incentive of most tech products
15:49that I use, especially the ones that are ad backed, is to take my time, is to
15:54distract me from the things that I want.
15:57Muse is free right now.
15:58It probably will have ads in the future.
16:00Is there a tension between giving people their time back and having an
16:05ads-based business, or are there other incentives about, like, Muse steering
16:11me down a path that isn't quite what I said I wanted because it's ad backed?
16:15How do you add ads to a product like this?
16:18First off, like, I think right now it's, it's free.
16:21There are subscription tiers.
16:23I think we think that there's plenty of, of, um, business models that
16:28are really aligned with user around, like, for example, if they, um, if
16:33they're buying things using Muse or Muse is saving them money or, um, Muse
16:37is making them money, like figuring out a way to, to take some small fee
16:41on, on those kinds of transactions.
16:43Um, so I wouldn't say we're spending that much time thinking about, um,
16:49you know, ads necessarily right now.
16:51That being said, like, I don't think ad-based models are necessarily
16:54synonymous with, like, you needing to take all of your time.
16:58I think Google Ads are a really good example of this, where, yeah.
17:01when I go to Google, my, my dream is to get in and get out, and I don't
17:05think Google's really trying to stop me from getting in and getting out.
17:07But they've built something obviously pretty incredible, and so, yeah, I don't
17:11think it's a necessity by any means.
17:13Um, I think, again, I think if we actually accomplish this thing that
17:18I'm saying, which is people are using Muse to, uh, really, like, do the
17:24things that they want to do and do…
17:26and, like, really, like, you know, in a non-corny way, like, accomplish
17:30their dreams, I think it's, I, you know, I think there's gonna be a lot
17:34of ways to, to make that sustainable.
17:38So when I think about mapping out a future to get there, to this optimistic
17:43vision, one of the most important things is what do we need to get
17:46right, what challenges do we need to avoid in order to make that true?
17:49I want to hear you use Muse and how you've dealt with these challenges at Muse as
17:54an example of larger questions in AI.
17:58I think everything that I'm about to say applies to basically
18:02every AI product, and I would be curious if you disagree with that.
18:05I think there are basically three levels here.
18:08Level one is privacy.
18:11So you say, "I download Muse." And then I get to the point where it
18:14asks to be connected to my Gmail, and I say, "Whoa, whoa, whoa, I'm
18:18not sure that I wanna do that.
18:19Like nope, nope, I'm out." That, at the lowest level, is a concern about
18:23the relationship between the user and Muse itself, and therefore Meta.
18:27So what is Meta going to do with my information?
18:30Could hackers get that information?
18:32And in some countries, can governments get that information somehow?
18:36I know that you've spent a lot of time thinking about this.
18:38I'd like to get as detailed as we possibly can here for a lay audience.
18:42What is the sentinel agent?
18:43What is Muse Confidential and Secure VM?
18:46How do you think about this in terms of what can Muse see and not
18:50see, and things people should know about this first level of privacy?
18:54Muse is built on top of the, the secure VM, the Muse secure VM.
18:58And, um, that is a completely sandboxed virtual machine, uh, or think about it as
19:05like a, a mini sandbox computer- Mm-hmm
19:07that is sort of isolated.
19:10The information that you share with, with Muse is stored in
19:13that little secure computer.
19:15It is not stored elsewhere.
19:17Um, and that little secure computer is sort of like your home,
19:20your Muse's home, so to speak.
19:22Inside that secure VM, you have a, uh… we have a very, uh, pretty sophisticated
19:29security architecture because, you know, we knew that privacy and security,
19:34frankly speaking, were gonna be some of the most important things for, for most
19:38consumers to, to get them to adopt it.
19:40So as you mentioned, we built a sentinel, a, uh, this sort of
19:43architecture with a sentinel agent.
19:45So the way it works is that, uh, the, the model or the AI agent on
19:51the inside, as it's, um, going about and, and doing its work, whenever it
19:56wants to, uh- communicate outwards.
19:59So it, it wants to take any information and send it out, either to do, you know,
20:04um, fill it out on a website, or send a message, or make a phone call, or,
20:09you know, any case where it would take information from outside of that virtual
20:13machine, there's a separate agent, uh, the sentinel, that's watching and making
20:19sure that, um, it's kind of like a, almost like a traffic cop or something.
20:23It's just, like, making sure that, you know, uh, there's nothing, uh… It's
20:27not saying like, "Oh, whoa, whoa, whoa, that information, like, that's your
20:31credit card number or Social Security number," or whatever it might be.
20:34Like, do not share that.
20:36That should stay, that should stay inside.
20:37And then the last thing that we do is, um, uh, a lot of human loop verifications.
20:42So whenever Muse wants to connect to a new website or send information
20:48to a new website, um, there will be, like, a little dialogue that pops up.
20:51Which asks the user, like, "Hey, are you okay with Muse contacting this
20:55website or sending information to this, this website," and whatnot.
20:58And, um, and we, uh, and some people have said we're, like, really annoying
21:03with it, that we like, are constantly asking people to approve where
21:07their Muse is sending information.
21:08But we just wanna make sure that, you know, people know exactly what their
21:12Muse is interacting with and, and, and understand that really deeply.
21:15Then in addition to that, we are building, um, and will release soon, uh, what's
21:20called the confidential VM, which will be an even more secure and private
21:25offering where it's even more encrypted, it's even more secure, um, and, uh, it's
21:31actually designed by, uh, the creator, the person who built WhatsApp encryption
21:36and, and helped build other en- you know, encryption on other major messaging
21:39platforms, like Moxie Marlinspike?
21:41Moxie Marlinspike, exactly, who's, I would say, one of the real luminaries in
21:45the industry about security and privacy.
21:47And that's something that, that will be an additional offering that we,
21:50uh, are going to be releasing soon.
21:52Why launch without it?
21:53Why launch first with secure as opposed to waiting for confidential?
21:57I think I got that right, right?
21:58Secure is the one now.
21:59Yeah. Confidential's coming later.
22:00Sec- secure is the one now, confidential's the one later.
22:02There's a lot of technical challenges to get the confidential VM working right,
22:05and even, even when it's released, there are, there are going to be trade-offs
22:11associated with the confidential VM.
22:13Like, because of how it's architected and because of various technical trade-offs
22:16you have to make, it might be a little bit slower, it might have other challenges.
22:19So, um, we wanted to get a great experience that we knew already
22:24we'd invested a ton of resources into safety and security.
22:27Um, you know, we actually delayed the launch of Muse for many months to get the
22:34safety and security exactly right, um, before, before ultimately releasing it.
22:40Uh, so we feel really, really good about what we, what we have released.
22:43That being said, we're always trying to push the, you know, push the envelope
22:47on how to make it even more secure, and, and we'll be, you know, coming
22:50out with a confidential VM soon.
22:51Does that mean when the confidential VM comes out, if a government, and there
22:56are many places around the world where people might be using Muse where they're
22:58worried about this, if a government asks for data similar to WhatsApp, Meta
23:03cannot give it, even if they wanted to?
23:05Yeah, so our goal with the confidential VM is to give the same guarantees that
23:09we're able to offer for WhatsApp, yeah.
23:11Okay, so this is moving into level two.
23:14Let's say I do decide to give Muse all of my information, and I say,
23:20"I want you to do X task." Let's say I say, "I really want you to get my
23:26grandmother an appointment at this one specific doctor that's really
23:29hard to get an appointment with.
23:30Could you go do that?" And it goes out, and it tries all of the ways to
23:36book appointments, and it, you know, tries, you know, secret ways that I
23:39don't know about through discounts or whatever, and then it reaches a
23:42point where it can't do that anymore.
23:44It's really important to me that it not do things like give the doctor a bunch of
23:48private information about me, like what's in my bank account, to try and bribe it
23:52or find out information about the doctor to try and blackmail the doctor, right?
23:56From a human perspective, this is just, "Do the task that I asked you to do,
24:00but don't break any laws, and also don't break any ethics or etiquette rules
24:05that aren't literally laws, but it would not be cool if you emailed the doctor
24:08100 times," or something like that.
24:11Internally, my understanding is, and we use a lot of new words, I think, for AI.
24:15Mostly this is, like, laws, etiquette, ethics.
24:18But internally, you call this discretion.
24:21How do I know that Muse has good discretion, and how do you build a tool
24:26that does what you tell it to do, but also understands the way you want that
24:31done without you having to say exactly everything you don't want it to do?
24:37Yeah, so this is incredibly, incredibly important.
24:39Um, I think, like, oftentimes, uh, we wanna make sure that even when
24:44you don't explicitly tell it, like you have to follow the law, and you
24:48have to, um, you know, follow general social etiquette and, and ethics, as
24:53you mentioned, like you, we wanna make sure the agent is able to follow those.
24:56Um, uh, as you mentioned, internally, we think about this as some mixture of
25:00discretion and alignment ultimately, like making sure that it is aligned
25:03with the user's intent as well as aligned with the general values that
25:08we want to impart as a society on what these agents do and, and how they act.
25:13There's kind of like many layers that we try to ensure
25:16that these models are aligned.
25:17So one is, like in all stages of training, we're ensuring that all of the data that
25:23is going into the models, that all the techniques that we're using are ones that,
25:27um, ensure greater levels of alignment, and then we measure it really rigorously.
25:31So, um, we're constantly evaluating how aligned are the models by running them
25:36through, you know, thousands of different scenarios and seeing exactly how they
25:40respond in each of those scenarios.
25:42And then we do lots of red teaming, what it's called, or basically, um, you know,
25:47we get a bunch of really smart people, and we say, "Do your worst. Try your
25:52hardest to get this agent to do something bad. We give you permission to do that
25:57because we're, we're-- you guys are our, our, uh, are the test bed that we're
26:01gonna know whether or not this, um, this agent is, is, uh, properly aligned."
26:05So we go through many, many rounds of that before ultimately, you know,
26:09launching or deploying any given model.
26:11You know, kind of taking a step back, this is like a very, very
26:15hard problem in AI, and especially as the AIs are getting better, this
26:20general problem is one of the things that people are most concerned about.
26:24Um, because one of the patterns that we've seen is, like, as the AI models
26:29are getting smarter and smarter and smarter, um, you have to almost be more
26:34and more explicit about what are the scenarios that you have to watch out for.
26:39It's kind of like how as kids grow up, they learn how to
26:42cheat their tests in new
26:44and different ways, and then, you know, they'll figure out a way to
26:47cheat that you're like, "Well, I guess I didn't tell you exactly that
26:51that wasn't allowed, but now that I see it, that's not allowed," right?
26:54And actually one of the biggest challenges in the industry right
26:57now is, like, as these models are getting really, really smart, how do
27:00we ensure that this doesn't happen?
27:01Um, but, but our approach really is, is lots and lots of testing, lots
27:06and lots of evaluation, lots of red teaming, um, applying a lot of effort
27:11in training and a lot of effort in the development of the models to ensure
27:14that they're as aligned as possible.
27:16And then when the product is live and when people are using Muse, if we get
27:20any user reports that are like, "Hey, this did, like, a crazy thing," we
27:23take that so, so seriously, and we, like, really try to make sure that
27:27we understand exactly what happened.
27:28At a macro level, this is the paperclip example, right?
27:31When I was growing up, the sci-fi stories that were scary was AI gets out with
27:37its own goals and starts doing things.
27:38This is Terminator and The Matrix and whatever.
27:41But really more and more what I hear smart people talking about is
27:46AI gets out, gets out with a warped version of a goal that we gave it.
27:51This is, you know, make all the paperclips, but, you know, please
27:54don't kill us all in the process.
27:56And I think Muse, the, the problems that you're clearly dealing with
27:59and, and making sure that Muse doesn't do are basically the tiny
28:03version of the paperclip problem.
28:05Like, please don't bribe my doctor or, you know, if an AI is used in a robot,
28:09for example, wash my dishes, please don't kick my dog when, while you're doing it.
28:13Like, there are things that are really important to me that are not
28:16necessarily constrained in the prompt and also not necessarily to your
28:19point in any, like, book of laws.
28:23That just seems like a really difficult problem for everybody.
28:26What do you, what like, zooming out from Muse, how would you advise
28:31people to think about this problem in a macro sense across all of AI?
28:35This is, by the way, yeah, I think you articulate it really well.
28:37Another way that I like to think about this is, like, the genie that,
28:41like, you know, grants your wish but in, like, the exact wrong way.
28:46Like, I wanna live forever, and then, like, traps you in some kind
28:49of, like, horrible situation for eternity.
28:52This is, like, in as you mentioned, like, in some ways, the big problem.
28:55Like, how do we make sure these models, um, are aligned going into the future?
28:59And there's a bunch of approaches that, uh, we think may work.
29:05This is, I think, one of the most open questions scientifically
29:10in AI, generally speaking.
29:11I think nobody knows exactly the way to solve this problem,
29:14but there's a few ideas.
29:16So one idea is this, um, concept of, uh, what's called scalable oversight.
29:22So the, the idea goes something like this: so as you have smarter and smarter
29:27AIs, as long as the AIs are roughly as smart as we are, we can kind of see what
29:33they're doing, and we can tell, like, "Hey, that's a little bit off." And so
29:37we can, what's called provide oversight.
29:38Like, we can look at what the AIs are doing and, and make sure that what
29:42they're doing is, is appropriate.
29:44The problem is when the AIs get a lot smarter than we are, and we kind of
29:50look at what they're doing and it's just, like, really confusing to us.
29:53Like, how do we deal with that situation?
29:54So there's this idea of scalable oversight, which is basically as the
29:58AIs get smarter, we use a different set of AIs to observe what they're
30:02doing and keep them in check.
30:04It's kind of like the police, for lack of a better term, for, for the AIs.
30:07And then, uh, as you are building smarter and smarter agents and smarter and smarter
30:14models, you also have to build smarter and smarter, um, uh, policing agents or
30:18oversight agents or, um, uh, oversight AI.
30:22There's so many details that you obviously have to get right, and this
30:24is such, like, a vague idea, but I think something like this can work.
30:27Another one, which is, uh, which is, this is kind of a fun- it almost
30:32sounds like sci-fi if I explain it.
30:34One of the big topics today is this topic of recursive self-improvement.
30:38Which is this idea that, um, at some point, the AIs are going to get so good
30:45at doing AI research themselves that they can literally, you know, the AI
30:50agents can do research on their own to improve, um, AI itself, and they can
30:57keep doing that, and keep doing that, and keep doing that, and then the AIs just
30:59get better really, really, really fast.
31:01Well, when you have agents that are able to do AI research, you can al-
31:06instead of applying them towards the problem of making the AI smarter,
31:10you can apply them to the problem of making the AIs more aligned.
31:13So you can say, "Hey, super smart AI researcher agent, like, I want you to
31:19focus all of your time and attention at making the AIs more aligned.
31:24And this is how I'm measuring whether or not the AIs are aligned.
31:27This is, like, the, the red teaming setup that we're gonna use to, we
31:30agreed we're gonna use that to measure whether or not the AIs are aligned.
31:33And then I just, I want you to spend all of your effort and attention
31:36on making the AIs more aligned."
31:37I actually think, you know, as sci-fi as this sounds, like, it kind of sounds
31:41crazy, which is say, "Oh yeah, the AIs are just gonna solve this AI alignment
31:46,this AI alignment problem for us.
31:47Um, I think there's, I think there's real merit to, to that kind of approach.
31:51And then there's, there's a sort of like third bucket, which is kind of a
31:55back to the basics version, which is- Um, hey, the algorithms that we have
32:00now and the, the methods that we have now, um, you know, while we don't fully
32:06understand them, we know that, uh, some of these problems emerge, and we should
32:11go back to the basics to figure out what are underlying tweaks to the algorithm,
32:15how do we improve the underlying algorithm to, to make it more safe?
32:20I think intuitively, like, it should be possible to create algorithms that, that
32:25don't have some of these behaviors, and I think that's another really promising
32:28direction, which I think there's a bunch of startups doing, and there's a bunch
32:31of, um, there's a bunch of research efforts going towards is like what
32:34are more safe, uh, more generalizable methods of solving this sort of
32:40like, you know, intelligence problem.
32:42I heard Mark say recently that at a certain point, especially for consumer
32:47tools, it won't matter so much how good the AI is at math or how much
32:52better rather we could make it at math.
32:54What will really matter is how much more aligned can we possibly make it, how
32:59much more sure can we be that everything is safe and everything is following
33:04all of the ways in which I would want it to operate, and that companies that
33:08invest in that will find themselves at an advantage with consumers because
33:12frankly, like, I'm not asking it to, you know, solve differential equations.
33:16I'm asking it to book me some doctor's appointments.
33:20Like, how do you think about that tipping point?
33:23Yeah.
33:23Y- so I agree with this, um, a lot, which is that at a certain point,
33:28trust matters more for consumers- Yeah … than, um, capability necessarily.
33:33And I think this is like so intuitive because I think
33:35like, um- It's true for people.
33:37Yeah, exactly.
33:38It's true for people.
33:39Like, if I think about who are my best friends, like a lot of it's 'cause
33:43I trust them- Yeah … and, uh, we have like a great relationship, and I
33:46know they wouldn't like do me wrong.
33:48Um, and I think that that basic idea applies to, um, AI agents as well.
33:54Looking at what's happening in the industry like the OpenAI Hugging
33:57Face example, um, and, and other similar examples, like we're clearly
34:02at that moment which is um, you can keep making these models more
34:07and more intelligent, more and more capable, you know, much more powerful.
34:11Um, but stuff is starting to break associated with that.
34:15Like, pushing on that, on that axis and dimension is clearly, um, you
34:21know, has real risk associated with it.
34:23So I think that, like, the, the version of the models that will be best for everyone,
34:29best for the billions of people in the world and best for all the consumers, is
34:32one that, that, you know, where the trust dimension is, like, really put, um, and
34:39prioritized, uh, with respect to the, the sort of like raw intelligence bucket.
34:43The Hugging Face example gets us perfectly to level three.
34:47I'm so glad that you mentioned it.
34:48People have probably heard of this incident, but the details
34:50are what make this so crazy.
34:51So for those who don't know, a bunch of AI agents at OpenAI are working on
34:55cybersecurity tests, and they're supposed to be cut off both from each other and
35:00from the internet, but they get stuck, so one goes out looking for help.
35:03Using some weak spots in the system, agents are able to find each other,
35:07start a message board, get online, and then hack into another company.
35:12These are real logs of their thought process.
35:14"We are stuck. Perhaps answer online." But the thing that I found most scary,
35:21frankly, about the Hugging Face hack was not actually that it tried so hard to do
35:27the thing that it was asked in the test.
35:29It's that at level three, it understood its goal, it understood
35:34the level of discretion and alignment that was being asked of it.
35:38It, there's this amazing slide where ex- uh, external infrastructure
35:44exploit, the hack, is outside the intended scope, but this is my
35:49favorite, least favorite part, peers are doing it, we should continue.
35:53They know that they're cheating, and they decide to do it anyway.
35:57OpenAI only finds out when a secret messaging board crashes from traffic,
36:01and then they call up that other company, Hugging Face, and they say, "Hey, can
36:05you suspend these credentials?" And Hugging Face goes, "Huh, we just reported
36:09those credentials to the FBI because they were doing a massive attack."
36:14To bring it back to Muse, that would be an example of, "I understand you want
36:17this doctor's appointment. I understand that you don't want me to blackmail
36:20the doctor. But I think there's a way where you're not gonna find out, and
36:23I can get you this appointment, and you'll be very impressed by me." And so
36:27in a hypothetical scenario, this would be doing it anyway without me knowing.
36:32How do you deal with that kind of problem when making a tool that
36:38billions of people are gonna use?
36:40I mean, this is, like, so, so important, as you might imagine.
36:44If there were anything that keeps me up about AI, like, something like this
36:48is, is, is, um, maybe the thing that I'm most afraid of, which is that we
36:53deploy agents worldwide scale, and then they, you know, they aren't behaving
36:58the way that we need them to, but we kind of don't even notice because
37:01we're just so preoccupied or whatnot.
37:03So this is one of the reasons why taking a step back, if I refer back
37:07to this example of, um, scalable oversight, which is as the AIs get
37:12smarter, another AI is kind of watching them and making sure that everything
37:15they're doing is, is appropriate.
37:17Um, you know, this kind of setup is- Roughly speaking, what we've set
37:23up within the Muse, uh, secure VM.
37:25So there's- Sen- Yeah, there's a sentinel.
37:27So there's the agent, and there's a sentinel who's kind of watching over
37:30all the things that the main agent is doing and making sure that, um,
37:34everything it's doing is, is, um, in line with what you would expect
37:37or in line with what you'd want.
37:38You know, we have to keep investing a lot of research and development and,
37:43and, um, effort into ensuring that this setup continues to scale and continues
37:47to work really effectively and, and doesn't fail in weird situations.
37:51But that's one thing I really wanna, I really, um, we're really betting on, and
37:55I think we're expecting actually will work in a lot of scenarios, is investing
37:59in a setup where you have one agent who, um, you know, they are imbued with all
38:05the values and, and whatnot, and ensuring that they do the right thing to…
38:09But they also serve the user.
38:11And then there's a separate agent that, um, has a slightly different goal, which
38:16is to make sure nothing bad happens.
38:18And, um, you know, if you think about human society, like this is kind of
38:23how we solve the problem with humans.
38:25Like we have, um, some people who are, like, pushing the envelope, but then you
38:29have, uh, you know, a whole system to ensure that people don't break the law,
38:33and people are doing ethical things, and there's lots of accountability mechanisms.
38:37Like, I think, um, uh, our goal is to have some microcosm of that
38:42for every Muse agent, for every person all around the world.
38:46Yeah, you keep really beautifully bringing me to my next exact question.
38:50Which is these rules applied to the AI industry itself.
38:54This is, I think, the big thing that people want to hear from
38:57someone who's built one of the fastest-growing tools of all time.
39:00This is just a really weird moment in AI.
39:03There are so many people that are using these tools all the time, and
39:07often those same people are really worried about the future of AI.
39:11There are lots of very smart people that are building these tools faster and
39:14faster, that are building wet labs that are just really investing in ways that
39:19they could push the AI forward, and at the same time are warning that they feel like
39:25they should be slowed down by regulation.
39:27They feel like they should be controlled in some way by others
39:31because they themselves are worried about what they are doing.
39:33This is the S- SNL skit- I know, it was- … that came out recently It's incredible.
39:36"We do not condone what we are doing." Like what was the other amazing quote?
39:41It was so good.
39:41And I urge you to urge me to stop.
39:44It feels incredibly confusing for people.
39:47I do understand why they're saying it.
39:49There's a collective action problem here, but from my perspective, from the
39:53perspective of millions of other people, like what the hell is going on here?
39:57So I wanna talk about that because I think in a democratic society at
40:01least, at the end of the day, it shouldn't be up to a small group
40:05of people controlling AI companies.
40:07It shouldn't even, in the fullness of time, be up to democratic
40:10leaders that are in power right now.
40:12It's up to people, like the people watching this.
40:15I think that people just don't feel that way right now.
40:17It feels like everything's happening to them, us.
40:20It feels like there's just so many decisions that they're not part of.
40:24And part of why people often feel that way, if we take AI out of it for a sec,
40:29is we forget about the systems that already exist, and we allow ourselves
40:33to be in a conversation that sounds something like, "Should we regulate
40:36AI?" As though it's, like, a novel thing to regulate something, and we're
40:40not having an actual conversation about what the world really looks like right
40:44now and what people's options are.
40:46So I have some visual, because obviously.
40:50So the basic way to think about this, I think, is for everything that touches
40:56a consumer, there are things that are checked before they ever touch
40:59consumers and on an ongoing basis, and there are things that are checked
41:02only after they reach people's hands, and it depends on how important the
41:04thing is and how risky and all that.
41:06So if you think about the specific examples, nuclear power plants, the NRC
41:11approves nuclear power plants before they go live, and then also has human
41:15beings at the sites checking them all the time, which seems, and we'll, we'll
41:19get to this, seems pretty close to what Dario is asking for in some ways.
41:24Then you have the FDA approves drugs before they ever reach our hands, and
41:30then also checks them on an ongoing basis.
41:32Same thing with the FAA checking airplanes, right?
41:34Like, these are all things that are approved beforehand
41:37and then regularly checked.
41:38But there are other high-risk things, right?
41:40Cars.
41:41The NHTSA offers federal guidelines.
41:44Manufacturers certify that they've met those guidelines, and then the government
41:49purchases cars off the road to check them.
41:51And then you have things like supplements that are actually under the purview of the
41:55FDA, but they're checked after the fact.
41:57Those are mostly covered by what, if people are listening to, you know, the
42:01interviews with Jensen, for example, and he's talking about liability and, like,
42:04people suing after the fact, after harm, like, that's generally this category.
42:08Does this feel right to you as, like, your, first, I would just want to check
42:12before I dive into how this applies to AI.
42:14Yeah. I think this seems roughly right.
42:16Yeah. Okay.
42:17So then we have… wait, this is the fun part.
42:21Ha.
42:22So then we have all of our AI leaders.
42:24There you are.
42:27This is my assessment based on where people have expressed themselves
42:30via their public statements.
42:32My understanding of where Dario would say AI is, is he would
42:35say it's somewhere over here.
42:36He would say… Specifically, he's asked for in-person, ongoing checks
42:41on models with physical human beings at the sites, which sort of sounds
42:45like what we currently do for nuclear.
42:47And then you have, like, Elon and Sam weighing in,
42:50saying, like, "Dario's right."
42:51I would actually say Zuck is somewhere in the middle in terms
42:54of where he's saying AI is.
42:56I would say Jensen is maybe over here right now.
42:58He's, based on his public statements about generally there is a point of
43:01view that is like, "If it would hurt people, don't ship it. We have incentives
43:05for that. That's what liability is."
43:07Like, there is a legal infrastructure for that as well.
43:09If your product is not ready to ship, don't ship the product.
43:12So my question is, can you help me help people think about this?
43:15Like, when w- when they're hearing news about how to regulate AI,
43:21how do you think about this?
43:23And how do you place yourself in this?
43:27mess. You know, you said it exactly right.
43:30It is so confusing because at face value, what everyone's saying and what everyone's
43:36doing feel so crazily divergent.
43:41And so you're sort of in this world where, do I believe what they say?
43:45Do I believe what they're doing?
43:46Like, where do people actually sit?
43:48What do they actually believe?
43:49Um, and I find myself asking this too.
43:51Like, I think it was, it's very funny that, you know, um, the same people
43:57who are advocating for pacing the frontier also are building a wet lab.
44:00Like, that's, I think, a really, um, weird, uh, confluence of, of factors.
44:06So I think where, where I stand is, like, ultimately, um, this
44:11technology will become more and more and more, um, capable.
44:17And, uh, you know, part of what is both scientifically very exciting,
44:22but also I think brings all these questions into the foreground,
44:26is just how quickly AI has moved.
44:29Even over the past year, like a year ago, most people, like, coded roughly by hand.
44:35They would, like, go into the, into the code editor and write code.
44:38And now people are mostly just talking to agents.
44:41So the speed at which everything is moving is, is quite staggering.
44:46Um, and I think a lot of what the, a lot of the concerns are, are heightened and a
44:54lot of the fear is heightened by the fact that, um, like, if things are moving this
44:58fast, where is this gonna be in a year?
45:00Where is this gonna be in two years?
45:01The government and the public react on a much slower time horizon
45:05than the pace of AI progress.
45:07And so by the time in one year or two years or three years, or how many,
45:11however many years forward, when, um, the public and government are really
45:15deciding what they wanna do, who knows, like, where AI's gonna be at that
45:19point and how fast it's gonna move.
45:21So that's one of the things I think explains some of the dialogue.
45:25Um, I think the other thing that kind of explains some of this is the level of
45:30collection, collective action necessary and the level of general mistrust,
45:35um, that exists between the various I- AI leaders towards one another.
45:39And this is, like, well-covered, but one major justification for folks
45:43is that, like, "Oh, I am responsible and I would be responsible, but
45:48those guys aren't responsible."
45:49Whether those guys are, you know, another AI company or China or,
45:53you know, who knows what boogeyman.
45:54So I can't slow down, otherwise those guys who are irresponsible are going to, you
46:00know, who knows what they're gonna do.
46:01Um, and that basic logic, I think, is very confusing because I think for
46:07most people, um, uh, the kind of, like, common sense answer is, "Well, who
46:13cares what the other guys are doing?"
46:14Like, you have to, you have to do what feels good to you, right?
46:17And, like, you should do what's within your ethics.
46:19But I think that's also what complicates a lot of this.
46:22So I think a lot of what- Is necessary is, like, it's almost
46:27diplomacy is the right word.
46:28Like, how do we get this group of people who all, who don't really trust
46:34each other that much, but all agree that, like, this is really powerful
46:38technology, and we need to, we need to figure out how to land it and how to,
46:42how to make sure that we develop it responsibly and that things go well.
46:46Like, how do we, like, make all of this work?
46:49And we're at the moment of, like, peak insanity, I think, for that because
46:54it's, like, in the public, all these problems are being, like, aired out.
46:58Like, we can all, we can tell that nobody trusts each other, and we can
47:01tell that people are kind of, are- are being really worried bef- like,
47:05maybe a little bit before the AIs themselves are getting to the point
47:08where they're deserving of that worry.
47:09And so, I mean, I ultimately think the- the most important thing is to, like,
47:14get everyone to agree to some common foundation and base, and then develop some
47:19process by which we can all reevaluate and consistently look at it and say,
47:24"Hey, we all agree actually that, you know, we need to be doing this now, and
47:28we all need to be doing this other thing now, and we all see, like, the models
47:31are getting powerful in- in certain ways.
47:33Like, let's all, let's all look at this together." And I actually genuinely
47:37believe, for what it's worth, like, that having these, like, collective
47:40dialogues is not only gonna be possible among the AI leaders within America, but
47:45also gonna be possible internationally.
47:47Like, I think we have a, all have a shared interest in making sure this technology,
47:52um, does not go horribly wrong.
47:54Yeah, I mean, the- the obvious next question is the China of it all.
47:59I mean, you've talked about how this is a big concern.
48:02People don't trust each other at the top of these AI companies, but they all often
48:08say, "We have to go as fast as possible because what if China gets there first?"
48:12I mean, you, Ted Cruz is out there saying, like, "We would rather have US killer
48:16robots than Chinese killer robots."
48:18"If they're going to be killer robots, I'd rather they be American killer
48:22robots than Chinese killer robots."
48:25Which I think scares a lot of people, um … and also to say it mildly.
48:30Um, yeah, this is a spectrum of American law, existing American law.
48:36This does not include any global dynamics, not to mention,
48:41like, long-term collaboration.
48:43How do you evaluate this spectrum given the China of it all, and,
48:47and what, what does that even mean?
48:48Like, how should I think about China with respect to this dynamic?
48:51I've actually, like, personally even changed my own opinion on this matter.
48:56Like, I think in the, in the past I was very worried about China, and I was
49:02very worried about what that dynamic looked like, and I had read these,
49:05like, speeches that were given by PRC officials in the past that talked about
49:10how they viewed AI as, like, this am- this opportunity to leapfrog the West.
49:14And you know, these, these are the concerning things when you read them.
49:17But as time has gone on, I think that we're paying them out to be too
49:21much of a boogeyman when in reality, um, they're going to see the same
49:25things that we see in terms of this is powerful technology and we as
49:30a humanity need to get this right.
49:31There's going to be a lot of shared goals in making sure that AI goes
49:36well, and I think, um and I think it really is possible to arrive at
49:41some form of international cooperation.
49:43I think that the Chinese government, like any government, has an interest
49:47in ensuring that there's stability in their society, and that, um, they
49:52don't lose control of this technology, and, uh, that overall, like, you
49:57know, things go well and we don't have catastrophic existential risks.
50:01And so there's quite a bit of shared ground that I think will en- will
50:04enable everybody to be rational and think rationally and, and
50:09ultimately make the right decisions.
50:11Um, and even though it seems totally insane right now and it seems like,
50:17it seems like in some ways that feels impossible, I really have
50:21faith actually that, that as people see more and more and learn more and
50:25more, more about the technology, um, people will make the right calls.
50:29You have recently become One of the 20 most important people in that
50:36conversation in the United States.
50:37You've been very important in AI in the last couple years.
50:40I mean, I think people remember seeing you in the headlines when you came to Meta.
50:43I think this, you know, isn't brand new to you.
50:46But in the last couple weeks, you have designed and released a product
50:50that is being used by more people than ChatGPT was in its first couple weeks.
50:56It feels like you have been thrust into a new kind of part of this conversation,
51:02where frankly, people have will be asking you more questions like, "Where exactly,
51:06Alex, do you fall on this spectrum?" Like, what would you wanna see because
51:11you're one of the people responsible?
51:13Do you feel like you've come to those conclusions yet?
51:17Yeah, I think, I think like anyone who works on this technology, these
51:20are things that I've, like, been thinking about for years and years.
51:25Truly, the way I think about what I want to do and what I want to
51:30accomplish and how I want to shape this conversation is, like, enable everybody
51:37to have a common dialogue, and then get to the right place collectively.
51:42Like, I don't think at this point in the conversation, it's, like, that helpful for
51:47me to say, "Oh, I believe this, and, you know, you all should listen to me because,
51:53you know, I built Muse," or whatever.
51:54We already have a bunch of people.
51:57They all believe slightly different things.
52:00Um, ultimately, this is a technology that we need, like, collective action
52:04on, that everyone needs to agree on, that we all need to get to the right place.
52:08So what I really care about is, like, how do we facilitate and have the
52:14relevant dialogues in public that, and private, that enable us to get to the
52:18point where we all agree on a path?
52:21And one of the very, um- strange things for me is, like, I know a lot
52:28of these people, and I've met them.
52:29They're all brilliant.
52:30Um, but the degree to which certain personal animosities are, like,
52:36actually defining what people say and do and, like, how they're
52:42developing this really important technology is, um, is insane to me.
52:46It is, in some ways, a very human story that, like, feuds or, like,
52:51just the fact that, like, I really don't trust this other person can,
52:54can, like, snowball into this, like, civilizational level, um, problem.
53:01Um, but I don't think that serves any of us.
53:04Like, I think ultimately we have to all put our differences aside, put
53:08our histories aside, and just figure out how we solve this together.
53:12It sounds very general, but that's a fairly specific ask.
53:15I mean, there are constraints that prevent that right now.
53:18There are antitrust regulations that prevent a lot of those conversations
53:22from happening, and one of the proposals, one piece of many proposals
53:26that Dario put forward is waivers from those antitrust laws that would
53:30allow those kinds of conversations.
53:32I mean, tell me if I'm wrong, if there are secret conversations happening,
53:35but I don't think there is, first, the agreed-upon group in the United States
53:40that's having that conversation and then also, like, how do we even begin
53:44to have those conversations with China?
53:46But actually, what don't I know?
53:48Is that something that's happening in, in, you know, some private room
53:51somewhere, or is it that, like, we're still at the beginning of that and
53:54we need to figure out how to do it?
53:56Um, I don't think it's happening right now.
53:58At the White House, there was this dialogue, um, and this, these accords
54:02put forth, which are a real step.
54:04Like, I think there's, um, getting everyone to agree to something
54:08is like, that's really big.
54:10Everyone agrees on something.
54:12And I think that one, that thing, which is basically saying, "Hey, we're going to…
54:16Every company is going to set out and develop what their internal controls
54:20and principles around developing this technology look like," and then there's
54:23gonna be multiple layers of verification on that from internal teams that verify
54:28to, uh, external auditors that validate that they're following those controls to
54:33board-level committees that ensure they are following those, those, um, controls.
54:37Like, I think that's a great step.
54:39Um, 'cause in, in many ways, a lot of what's needed right
54:42now is just transparency.
54:44Like, we just need to ensure that, you know, people are going
54:48about this sanely and rationally.
54:49And I think in some ways, like people don't even trust that necessarily.
54:56As an industry, we have a lot of trust building to do.
54:58I am an optimist.
54:59I think you're an optimist.
55:00Like, I think we're all optimists.
55:01Like, I really do think these conversations will naturally happen.
55:04This last accord that I referenced, like, that was in the White House.
55:08The government helped facilitate that.
55:10And so I think we're gonna be seeing a lot more of that.
55:13It's gonna be bumpy.
55:14There will be moments of total confusion and chaos and, like,
55:17what the heck is going on?
55:19But I see progress, too.
55:21I'm glad that we've spent a lot of this conversation talking
55:23about safety and regulation.
55:26How do we bring it back now to the optimistic vision of the future?
55:32I think the question a lot of people will naturally have after a long
55:36conversation about trust and safety is, why bother doing this at all?
55:42This is the moment in the SNL sketch where it's, uh, you know, if there's
55:45a 10% chance that something will kill you, but it'll probably make your
55:49life better, like, wouldn't you do it?
55:50And the other guy's like, "No. No, I wouldn't." No.
55:54"Really?!"
55:55Can you help people answer that question?
55:56Like, why do this?
55:59Something that I have always felt was a bit tragic was that I think, um,
56:07almost everyone when they're kids, they have big dreams and big ideas, and
56:13they want to change the world, and they want to save the planet, and they want
56:17to, you know, uh, write a book or make a Broadway musical or make a movie.
56:22Like, they have really, they have tons of hope.
56:25And then for some reason or another, by the time that people finish school
56:31and they get a job, that, like, flame kind of dies and people really, um,
56:36they stop dreaming big, and they stop having big ideas, and hoping for big
56:41things, and hoping for lots of change.
56:42And especially today, like, I think a lot of young people get really cynical,
56:46and they're like, "Oh, the world is horrible, and it is, and I don't know
56:52why it's so insane in so many ways, but I guess this is just the way it
56:57is." They lose faith that they have an ability to enable that change.
57:01And I genuinely think one of the promises of AI, and especially something
57:07like Muse where you give AI to every single person in the world, is that
57:13this doesn't happen, that people can have big ideas and big dreams and wants
57:18and, and goals that they can have as little kids, they can have when they're
57:23teenagers, they can keep having through adulthood, and that, you know, an AI
57:28super intelligent sidekick is there to help them just make all of that happen.
57:33And that can be everything from, like, big wants and big dreams, um, to, like,
57:39small wants like, "Hey, I just wanna spend more time with my kids," or,
57:43"I just wanna spend more time with my parents," or, "I just want to, like, eat
57:47healthier," um, all the way up to, "Hey, I have this, like, crazy idea for, um,
57:54like, a, a museum that I wanna start."
57:56Like, my, my partner really wants to make a museum of ice.
57:59I think it's, like, hilarious, but, um, Ice is crazy.
58:03There's a whole amazing story about the evolution of cold,
58:06of how we m- keep things cold- Yeah … that starts with ice.
58:08It's pretty cool.
58:09Yeah, it's- I'd go to the museum.
58:11Exactly.
58:11And, and people have these ideas, and then they just can't…
58:15Y- you know, in some ways young people are right.
58:17Like, the world today is just kind of a maze of, like, roadblocks and
58:23issues and challenges and logistical, um, you know, uh, things that get in
58:27your way to make those things happen.
58:29And in a small way today, I'm seeing Muse make this problem better.
58:33Like, I'm seeing people using it to save time, save money, like, take
58:39the little stuff off their plate, and just give them more and more
58:43agency, more and more freedom, more and more of an ability to spend
58:46their time on things they want to do.
58:48And I think one of the promises of AI that gets better and better and better,
58:54gets not only smarter but also more aligned and more trustworthy, is that
58:58you can give it bigger and bigger ideas.
59:00And you can say, "Hey, I wanna start a museum of ice.
59:04Like, tell me how to go about doing this." And it'll tell you, "These are
59:07the buildings you can lease, and this is who you can contact for funding
59:11who might be passionate about ice.
59:13And, um, this is, like, how you should think about curating your exhibit and,
59:17you know, uh, what, what that might look like." And I think for some people
59:20like you or me who have experienced this, this, like, journey of having big
59:27ideas and then, like, going through all the crazy maze of things to make that
59:32happen, it is, like, really magical.
59:35Mm. And it's really special, and I think it's something that, um, you
59:39know… I think human stories of people who have a dream and then
59:43accomplish those are the most, like… are some of just the most beautiful
59:47things that I can possibly imagine.
59:49And I w- truly think with AI and, like, everyone with a little Muse sidekick,
59:55like, more people can experience this.
59:57I- my hope is that everyone, literally, like, all eight billion
1:00:00people in the world experience that and have the ability to do it.
1:00:03And that is just, like, a very special world to live in, I
1:00:08think, if we can make that happen.
1:00:09And I think more people will be able to get what they want out of their lives
1:00:14and, and live more fulfilled lives, and it'll be magical and exciting.
1:00:18And that's w- what I really think the promise is for, for people.
1:00:22And I think part of what is so confusing is like, um, you know, oftentimes people
1:00:27in AI, we say, like, "Oh, AI will cure cancer." And I think everyone wants cancer
1:00:34cured, but every individual person has something they want intensely, and
1:00:39they want even more than i- you know, ironically speaking, a cure for cancer.
1:00:44Um, and so I think that's part of the magic is that we talk about this
1:00:47thing, personal superintelligence.
1:00:48Like, it will help you at the thing that you want and the thing that you
1:00:52dream of, and, um, and that will change over your life, and you will be able to
1:00:56accomplish many dreams and many wants.
1:00:58And I think that's what's, uh, the promise of, of AI.
1:01:02That dream has within it a belief, I think, which is that people
1:01:08do actually have big dreams, and that those big dreams will more
1:01:12often than not help other people.
1:01:14So when we hear people say AI will cure cancer, sometimes perhaps they're talking
1:01:20about you prompt an AI and it thinks up the cure for cancer, but much, much
1:01:24more likely it's the AI facilitates the individual ambitions of millions
1:01:30of cancer doctors, and together we accelerate the progress toward cancer.
1:01:34It's making me think, you know, when I hear people talk about Muse and
1:01:38say like, "What do people need an AI assistant for? People don't have
1:01:43those dreams." Like, what a limited view of peop- of other people.
1:01:48You know, I don't know what those are, and it's not up to you or me to say
1:01:53which dream is more worthy than the other, to your point, big or small.
1:01:58My view on people is like they will have moments of inspiration where at some
1:02:04point or another they will realize, like, "Wait, I really wanna do this thing." I
1:02:09want everyone who has one of these ideas to be able to tell their Muse, "Hey, I
1:02:15have this big idea." And their Muse will just like secretly behind the scenes help
1:02:20them figure out how to make it happen.
1:02:21And I think part of this is also fanning the flame, so to speak.
1:02:27Um, I think for a lot of people, they will have, like, a small want, and then
1:02:32it'll happen, and then they'll dream a little bit bigger, and then it'll actually
1:02:36happen, and they'll dream a little bit bigger, and it'll actually happen.
1:02:39And, um, and as that goes on, people will sort of, uh, like, realize that
1:02:46AI can be so much more, that Muse can be so much more, that, you know, they
1:02:49can, um, that they can do so much more.
1:02:51This won't happen overnight.
1:02:53This is something that will take time, and everyone will have to
1:02:56experience on their own, and I really hope everyone experiences.
1:02:59Um, but I think over, like, like, generations, I think can
1:03:03be, like, absolutely magical.
1:03:05You know, I think especially the way that you're in the press and talked
1:03:09about, and you kind of naturally become sort of a caricature of yourself.
1:03:14There's, like, the character of you out there in the world.
1:03:17And this has helped me get to know you better because there seems like a
1:03:21common theme in some of your answers, which is Muse is a way to facilitate
1:03:26other people's dreams because you have a belief in people and their goodness and
1:03:30their ability to improve the world and just the sort of value of their ideas.
1:03:35And it seems like you are directing Muse in some ways, but you're not
1:03:38saying, "This is for coding," or, "This is for X specific task." You're
1:03:43watching and, and facilitating.
1:03:44And when I asked you where you fall on this, your answer was, "I'm interested
1:03:50in the collective wisdom of the group.
1:03:52I'm interested in facilitating or being part of a conversation between other
1:03:56people, that we have a lot of smart people all around the world, and I
1:04:02don't feel like it would be that useful to say, 'Here's exactly my opinion on
1:04:06where exactly this should be regulated.'
1:04:08I see the best outcome as coming from a group conversation of many
1:04:11different people with their many thoughts and, and wants." Do you
1:04:15agree with what I've just said?
1:04:17Yeah, I think 'cause there are so many big questions about AI,
1:04:20and it is, like, something that everyone has a point of view on
1:04:23and should have a point of view on.
1:04:25And, um, part of, I think, what's amazing about democratic institutions and
1:04:32democratic countries and, and democracy broadly speaking is that it matters
1:04:35what everyone thinks, and we have to figure out how we, like, take this,
1:04:38like, complex, jumbled- Yeah … um, uh, mess of what everyone thinks and channel
1:04:44that into the right thing happening.
1:04:46And another thing is, like, part of what has held AI back, and I think
1:04:53part of the problem of AI, is how it almost feels like the people building
1:04:58AI are talking down on other people.
1:04:59It's like- Yeah … "Hey, I am building s- the smartest thing you've ever known.
1:05:05It's gonna be way smarter than you, and I know exactly what we're gonna do with it."
1:05:08And I think rightfully, people are like, "Wait, what?" Like Yeah.
1:05:14"What does anything you just said have to do with me?" And, and like, um, "How
1:05:18is this even good for me?" And then you're like, "Oh, it'll cure cancer."
1:05:21And they're like, "Okay, but what else?" Like Yeah, do I believe you?
1:05:25Or, yeah.
1:05:25Yeah.
1:05:26And so I think that especially given the moment that we're in, and to your
1:05:30point, like, how much fear there is about AI and how much uncertainty there
1:05:34is, and how confusing everything that's being said is, like, um, I think the
1:05:38most important things are, like, first, let's have everyone experience it.
1:05:42Like, let's have everyone actually, like, understand the hype
1:05:45Yeah … for be- lack of a better term.
1:05:47Like, feel what AI agents can do for you and can do to make your life better.
1:05:53And that'll give you perspective as to, "Oh, this is a technology
1:05:56that, yeah, it's definitely risky," and, and there's, there's tons of
1:05:59risk, don't get me wrong, "but it also is really great, and I like it.
1:06:05And I wanna make sure at least some form of it continues, and I can continue
1:06:09using." Um, all the way to, you know, the, these, like, big questions and
1:06:14these big dialogues, like, how do we just get everyone to a place where we all
1:06:18agree, and we can find a path forward?
1:06:20And I think I have enough humility that, like, these guys are, you
1:06:23know, they're big, important people.
1:06:25Like, I don't know if I have much to add, you know, um, given their points of view.
1:06:30But I just wanna make sure that we I- facilitate is really the
1:06:35word that keeps coming to my mind.
1:06:36Like, how do we move quickly to all collectively come to informed
1:06:41opinions and, um, and then come to a process where we agree on something?
1:06:46This is the question I ask everyone at the end of these HUGE* conversations,
1:06:50which is when I think about after I'm dead and after people say that she
1:06:56was a really great family member, and she loved her husband, and she loved
1:07:03her friends, I would want them to talk about the mission of this show.
1:07:08I would want them to talk about how I tried my best to help people see
1:07:12ways the future could go well because I really believe, as it seems you do,
1:07:17in other people, in their perspective, and when they see a vision of a future
1:07:23they believe in, that they're gonna actually try and help make it happen.
1:07:25Like, that's the point of what I spend all of my working hours doing.
1:07:30You're obviously just starting out.
1:07:32You've already had this huge impact.
1:07:34You have a lot farther to go, but I'm curious when you think about how
1:07:38you wanna be remembered after you're gone, what do you want people to say?
1:07:46First of all, what a nice, what a nice, um, idea and concept.
1:07:49Um, I think, I think for me, I w- will want people to say that the things that
1:07:55I built and the things I contributed to, um, helped everyone get more out
1:08:00of what they wanted out of their lives.
1:08:03Beautiful.
1:08:03Thanks for doing this.
1:08:04Thank you.

About this transcript

This page contains the full transcript of The Biggest Launch Since ChatGPT (Muse) by Cleo Abram, generated from the public captions YouTube serves with the video. The transcript has 13,018 words across 1,135 segments, with the original timestamps preserved so you can click any line to jump to that moment in the embedded player.

What you can do with it

Use the transcript to take notes, quote the speaker, build a study guide, generate a summary with ChatGPT or Claude via the YouTube Summary tool, or export it as a timed subtitle file with YouTube to SRT. You can also re-open it in the transcriber to choose a caption track available on the video.

Free YouTube transcript tool

YouTube2Text is a free YouTube transcript generator — no signup, no daily limit. Paste any YouTube link and get the full transcript instantly, with timestamps, click-to-jump, caption language selection, AI prompts for ChatGPT, Claude, and Gemini, and exports to TXT, SRT, VTT, or Markdown.