Pod Insights No Priors Your AI Friends Have Awoken, With Noam Shazeer About this episode Noam Shazeer played a key role in developing key foundations of modern AI - including co-inventing Transformers at Google, as well as pioneering AI chat pre-chatGPT. These are the foundations supporting today’s AI revolution. On this episode of No Priors, Noam discusses his work as an AI researcher, engineer, inventor, and now CEO.
Noam Shazeer is currently the CEO and Co-founder of Character AI, a service that allows users to design and interact with their own personal bots that take on the personalities of well-known individuals or archetypes. You could have a socratic conversation with Socrates. You could pretend you’re being interviewed by Oprah. Or you could work through a life decision with a therapist bot. Character recently raised $150M from A16Z, Elad Gil, and others. Noam talks about his early AI adventures at Google, why he started Character, and what he sees on the horizon of AI development.
No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode.
Show Links:
Noam Shazeer - Google Scholar
Noam Shazeer - Chief Executive Officer - Character. AI | LinkedIn
Character. AI
Sign up for new podcasts every week. Email feedback to [email protected]
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Character_ai
Show Notes:
[1:50] - Noam’s early AI projects at Google
[7:13] - Noam’s focus on language models and AI applications
[11:13] - Character’s co-founder Daniel de Freitas Adiwardana work on Google’s Lambda
[13:53] - The origin story of Character. AI
[18:47] - How AI can express emotions
[26:51] - What Noam looks for in new hires
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:05 Speaker 00 Do you view all this as a path to AGI or sort of superintelligence?
0:09 Speaker 01 Sure, yeah.
0:10 Speaker 00 And is that part of the goal? For some companies, it seems like it's part of the goal. And for some companies, it seems like it's either not explicitly an anti-goal or if it happens, it happens. And you know, the thing people are trying to build is just something useful for people.
0:22 Sarah Guo? What a flex. AGI is a side effect.
0:25 Speaker 01 Yeah, well, I mean, that was a lot of the motivations here. Cause like, I mean, my main motivation for working on AI, other than that it's fun. Well, I mean, fun is secondary. Like the real thing is like, I want to drive technology forward.
0:42 Sarah Guo? This is the No Priors podcast. I'm Sarah Guo.
0:45 Speaker 00 I'm Alad Gil.
0:46 Sarah Guo? We invest, advise and help start technology companies.
0:49 Speaker 00 In this podcast, we're talking with the leading founders and researchers in AI about the biggest questions.
1:01 Speaker 00 Transformers, large language models, AI chat. These are the foundations supporting today's AI revolution. And this week on No Priors, we have AI researcher, engineer and inventor, who is a key part to these innovations and is considered one of the smartest people in AI. Noam Shazeer is the CEO and co-founder of Character AI, a service that allows users to design and interact with their own personal bots that take on the personalities of well-known individuals or archetypes. You could have a Socratic conversation with Socrates, or you could pretend you're being interviewed by Oprah. Or you could work through a life decision with a therapist bot. Character recently raised $150 million from Andreessen Horowitz, myself and others. We talk about how Noam got his start at Google, his groundbreaking AI discoveries and what he's doing at Character. So Noam, welcome to No Priors.
1:45 Speaker 00 Hey, Alad, thanks for having me on. Hi, Sarah.
1:48 Sarah Guo? Good to see you.
1:49 Speaker 00 Yeah, thanks for joining. So you've been working on NLP and AI for a long time. So I think you were at Google for something like 17 years off and on. And I think even your Google interview question was something around spell checking, an approach that eventually got implemented there. And when I joined Google, one of the main systems being used at the time for ads targeting was like Phil and Phil clusters and all this stuff, which I think you wrote with George Herrick. And so it'd just be great to get kind of your history in terms of working on AI, NLP, language models, how this all evolved, what you got started on, and what sparked your interest.
2:16 Speaker 01 Oh, thanks a lot. Yeah, just always naturally drawn to AI. Wanted to make the computer do something smart. Seems like pretty much the most fun game around. Was lucky to find Google early on and really is an AI company. So yeah, got involved in a lot of the early projects there that maybe you wouldn't call AI now, but seemed pretty smart at the time. And then more recently was on the Google Brain team, starting in 2012. It looked like a really smart group of people doing something interesting. I had never done deep learning before, or neural networks, I guess, as it was called then, or whatever. I forget when the rebrand happened. But yeah, it turned out to be really fun.
2:57 Speaker 00 That's cool. And then, you know, you were one of the main people working on the transformer paper and design in 2017, and then you worked on mesh TensorFlow, I think, sometime within the following year. Could you talk a little bit about how all that got going?
3:10 Speaker 01 Yeah, I mean, I messed around a few years on the Google Brain team and like utterly failed at a bunch of stuff till I kind of got the hang of it. Really, the key insight is that what makes deep learning work is that it is really well-suited to modern hardware, where, you know, you have the current generation of chips that are great at matrix multiplies and, you know, other forms of things that require large amounts of computation relative to communication. So basically, deep learning like really took off because, you know, it runs thousands of times faster than anything else. And as soon as I got the hang of that, started designing things that actually were smart and ran fast.
3:51 Speaker 01 But, you know, the most exciting problem out there is language modeling. It's like the best problem ever because there's like an infinite amount of data, you know, just scrape the web and you've got all the training data you could ever hope for. And like the problem- Problem is super simple to define. It's predict the next word. The fat cat sat on the, you know, what comes next? Like it's extremely easy to define. And if you can do a great job of it, then you get everything that you're seeing right now and more. You can just talk to the thing and it's really AI complete. And so got started around like 2015 or so, working on language modeling and messing with the recurrent neural networks, which was what was great then.
4:33 Speaker 01 And then transformer kind of came about as someone had the bright idea with Jacob was great. The, that, hey, these R N Ns are just annoying. Let's try to replace them with something better, uh, you know, in attention. And overheard a couple of colleagues talking about it in the next cube over. I was like, that sounds great. Let me help you guys. You know, these R N Ns are annoying. It's going to be so much more fun.
4:55 Speaker 00 Can you quickly describe sort of the difference between an R N N and a transformer based or attention based model?
4:59 Speaker 01 Yeah, sure. Um, okay. So like the recurrent neural network is the sequential computation where every word you read the next word and you kind of compute your current, you know, state of your brain based on the old state of your brain and what this next word is. And then you predict the next word. So you have this very long sequence of computations that has to be executed in order. So that, you know, the magic of transformer, kind of like convolutions, is that you get to process the whole sequence at once. I mean, it's still a function of the predictions for the later words are dependent on what the earlier words are.
5:39 Speaker 01 But it happens in like a constant number of steps where you get to take advantage of this parallelism of you can look at like the whole thing at once. And like, that's what modern hardware is good at is parallelism. And now you can use the length of the sequence as your parallelism and everything works super well. Attention itself, it's kind of like you're creating this big key value associative memory where you're like building this big table, like with one entry for every word in the sequence. And then you're kind of looking things up in that table. It's all like fuzzy and differentiable and a big difference. Function that you can backprop through. And people have been using this for problems where there are two sequences where you've got machine translation. You're like translating English to French.
6:24 Speaker 01 And so while you're producing the French sequence, you are like looking over the English sequence and trying to pay attention to the right place in that sequence. But the insight here was, hey, you can use the same attention thing to like, look back at the past of the sequence that you're trying to produce. And the beauty is that it runs great on GPUs and TPUs. And it's kind of parallel to like how deep learning is taking off because it's great on the hardware that exists. And this sort of brought the same thing to sequences.
6:57 Sarah Guo? Yeah, I think the classic example to help people picture it is like, you know, saying the same sentence in French and English. The ordering of the words is different is you're not mapping like one to one in that sequence. And to figure out how to do that with parallel computation without information loss is like a really elegant thing.
7:12 Speaker 01 Yep.
7:13 Speaker 00 Yeah, it seems like the technology has also been applied in a variety of different areas. The obvious ones are these multimodal language models. So it's things like chat GPT or what you're doing at Character. I've also been surprised by some of the applications into things like AlphaFold, the protein folding efforts that Google did where it actually worked in an enormously performant way. Are there any application areas that you've found really unexpected relative to how transformers work and relative to what they can do?
7:36 Speaker 01 Oh, um, I've just had my head down in language. Like here you have a problem that can do like anything. I want this thing to be good enough. So I just ask it, how do you cure cancer? And it like invents a solution. So I've been totally ignoring what everybody's been doing in all these other modalities where I think a lot of the early successes in deep learning have been like in images. And people are like all excited about images and kind of completely ignored it because, you know, an image is worth a thousand words, but it's a million pixels. So the text is like a thousand times as dense. So kind of big text nerd here, but very exciting to see it take off in, you know, in all these other modalities as well. And, you know, those things are going to be great. It's super useful for building products that people want to use.
8:18 Speaker 01 But I think a lot of the core intelligence is going to come from these text models.
8:22 Speaker 00 Where do you think the limitations for these models? What do you think creates the asymptote that all this is being built against? Because people often talk about just scale, like you just throw more compute and this thing will scale further. There's data and different types of data that may or may not be available. There's algorithmic tweaks. There's adding new things like memory or loop backs or things like that. What do you think are the big things that people still need to build against? And where do you think this sort of taps out as an architecture?
8:45 Speaker 01 Yeah, I don't know that it taps out. I mean, we haven't seen the tap out yet. The amount of work that has gone into it is probably nothing compared to the amount of work that will go into it. So quite possibly, there will be all kinds of like factors of two and efficiency that people are going to get through better training algorithms, better model architectures, better ways of building chips and using quantization and like all of that. And then there are going to be factors of 10 and 100 and 1,000 of just like scaling and money that people are just going to throw into the thing because, hey, everyone just realized this thing is phenomenally valuable. At the same time, I don't think anyone's seen a wall in terms of how good this stuff is.
9:28 Speaker 01 So I think it will just, it's just going to keep getting better. And I don't know what stops it.
9:34 Sarah Guo? What do you think about this sort of idea that we can increase compute, but the largest models are under trained? We've used all the text data on the internet that's easily accessible. We have to go improve the quality. We have to go do human feedback. Like, how do you think about that?
9:49 Speaker 01 Yeah, I mean, in terms of getting some more data, like there are a lot of people talking all the time. I mean.
9:55 Sarah Guo? Why do you think we do this podcast?
9:57 Speaker 01 Right, like there's like order 10 billion people like producing a thousand, you know, like, I don't know, 10,000 words a day. I mean, that's like a lot of words that, you know, and pretty soon many of those people will be doing a lot of that talking to AI systems. So I have a feeling like a lot of data is going to find its way into some AI systems. I mean, in privacy preserving ways, I would hope. And then the data requirements tend to go up like with the square root of the amount of computation because you're going to train a bigger model and then you're going to throw more data at it. I'm not that worried about coming up with data and I feel like we could probably like just generate some more with the AI.
10:37 Speaker 00 And then what do you think are the main things to solve for these models going forward? Is it hallucinations? Is it memory? Is it something else?
10:43 Speaker 01 I don't know, I kind of like hallucinations.
10:46 Sarah Guo? It's also a feature. Yeah.
10:47 Speaker 01 They're fun. Yeah, I'll call it a feature. Yeah, some of the things we want to work on the most are memory because our users definitely want their virtual friends to remember them. There's so much you can do with personalization and you want to dump in a lot of data and use it efficiently. Yeah, there's a ton of great work going on and trying to figure out what's real and what's hallucinated, of course. I think we'll solve this.
11:12 Speaker 00 And then do you want to talk a little bit about Lambda and your role with it and how that led eventually to character?
11:17 Speaker 01 Yeah, my co-founder, Daniel De Freitas, he's like the scrappiest, most hardworking, really, you know, smartest guy. He's kind of been on this lifelong mission to build chatbots. Like since he was like a kid in Brazil, he's like always been trying to build chatbots. So he came to join us at Google Brain because I think he had read some papers and figured that this neural language model technology would be like, you know, something that could actually generalize and build something truly open domain. So and he did not get a lot of headcount. He started the thing as a 20% project where like people are encouraged to spend 20% of their time like doing whatever they want. And then he just recruited like an army of 20% helpers who were like ignoring their day jobs and like actually just,
12:02 Speaker 01 you know, helping him with the system. And he went as far as going around and panhandling people's TPU quota. Like he called his project Mina because he, I guess it came to him in a dream. And like at some point I'm looking at the scoreboard and what is this thing called Mina? And why does it have 30 TPU credits? And it was just gotten a bunch of people to contribute. And then he was really successful at this because, you know, in building something really cool that actually worked where like a lot of other systems were just like totally failing either because people were not, you know, just weren't scrappy enough or were going for like rule based systems that were just never going to generalize. So at some point I was like, okay, there are so many ways we can make this technology better by like factors of two.
12:47 Speaker 01 But the biggest thing is just convince everyone that this is like worth trillions of dollars by demonstrating some application that is clearly super valuable to like billions of people.
12:59 Speaker 00 And Lambda was this, I believe, is the internal chatbot pre-GPT at Google that was famously in the news because an engineer thought it'd become sentient, right?
13:07 Speaker 01 Yeah, yeah, yeah. So that was like a renaming of Mina. So I guess I went and helped Daniel on Mina. We got it on some giant language models and then kind of became like an internal viral sensation and then got renamed to Lambda. And yeah, we had left before the business about somebody thought it was sentient. I'm flattered.
13:26 Sarah Guo? Can you talk a little bit about just why it wasn't released, what some of the concerns were?
13:31 Speaker 01 I think just large companies have concerns around launching products that can say anything. I would, I would guess it's just like a matter of risk versus, you know, how much you're risking versus how much you have to gain from it. So figured, hey, startup seems like the right idea and that you can kind of just move faster.
13:52 Sarah Guo? Yeah. So tell us about character. What's the origin story there? Did you and Daniel look at each other one day and were just like, we have to get it out there?
13:59 Speaker 01 Yeah, pretty much. We're like, yeah, then kind of noticed, hey, there are people who like just go out and get some investors and start doing something. So we were just like, okay, let's just like build this thing and launch it as fast as we can. So hired a total rock star team of engineer researchers and got some compute.
14:18 Speaker 00 One thing that comes up a lot is people say that you all have one of the truly extraordinary teams in the AI world. Are there specific things that you recruited against or how did you actually go about finding these people?
14:28 Speaker 01 You know, some people we knew from Google happened to get introduced to Mileout formerly from Meta, who's had launched a lot of, well, built a lot of their large language model stuff and their neural language model infrastructure and a bunch of other Meta people followed him and, you know, they were great.
14:47 Speaker 00 Is there anything specific that you would like look for in people or ways to test for it? Or was it just standard interviewing approaches?
14:53 Speaker 01 I mean, a lot of it was just kind of motivation. I think Daniel tends to very, very highly value motivation. I think he's looking for something between burning desire and childhood dream. So like, there were a lot of great people that, that we did not hire because they didn't quite meet that bar. But then we got a bunch of people who were kind of up for joining a startup and really talented and highly motivated.
15:18 Sarah Guo? I mean, speaking of childhood dreams, it's like, you want to describe the product a little bit? Like you have these bots, they can be user created, they can be character created. You can be public figures, fictional figures, anybody with like a corpus that you can make up or historic figures. How'd you even arrive there as the right form for this?
15:35 Speaker 01 Yeah, I mean, like, basically, this is kind of a technology that's so accessible that billions of people can just invent use cases, you know, and it's so flexible that you really just want to put the user in control because often they know way better than you do what what they want to use the thing for. And I guess we had kind of seen some of the assistants from sort of assistant bots from large companies. You know, you've got Siri and Alexa and Google Assistant. And and like some of the problems there are that when you're just projecting one persona to the world, people will A, expect you to like be very consistent in say your likes and dislikes,
16:20 Speaker 01 and B, just not be offensive to anyone and not really have an opinion. It's kind of like, you know, like you're the Queen of England and you can't say something that's going to disappoint someone. Or I don't know, like, I remember, like, I think it was George H. W. Bush said he didn't like broccoli. And then like the broccoli farmers were like all mad at him or something. So if you're like such a public, trying to present like one public persona that everyone likes, you're going to end up just being boring, essentially, and people just don't want boring. You know, people want to interact with something that feels human, you know. So basically, you need to go for multiple personas, you know, like, let people invent personas as much as they want. And kind of, I like the name character
17:01 Speaker 01 because it's got a few different meanings. You know, it was character like, you know, ASCII character. It's like unit of text, character like a persona, or character like good morals. But anyway, so it's, I think that's just how people like to relate to this stuff. It's okay, I kind of know what to expect from an experience if I can kind of define it as a person or a character. Maybe it's someone I know, maybe it's just something I invent. But it kind of helps people like kind of use their imagination.
17:29 Sarah Guo? So what do people want? Like, do they do their friends? Do they do fiction? Do they do entirely new things?
17:34 Speaker 01 Yeah, I mean, there's like a lot of Role playing, role playing games are big, you know, like text adventure where it's just making it up as it goes. There's a lot of like video game characters and anime and there's, you know, some amount of people talking to public figures and influencers and like, I think a lot of people have these existing parasocial relationships where there's, they've got characters they're following like on TV or some, you know, or internet or influencers or, or whatever. And so far, they just have not had the experience of, okay, the, now this character responds. Cause like, it's always something you can watch, or maybe you're in like a thousand on one fan chat or something where like this VTuber will write back to you like
18:15 Speaker 01 once in an hour or something. But now they get the experience of, oh, like I can just create a version of this privately and just talk to it. And it's pretty fun. We also see like a lot of people using it because they're lonely or troubled and need someone to talk to. Like so many people just don't have someone to talk to. And a lot of use kind of crosses all of these boundaries. Like somebody will post, okay, this video game character is my new therapist or something. So like, it's a huge mix of fun and people who need a friend and connecting with, you know, game playing, all, all kinds of stuff.
18:46 Sarah Guo? How do you think about emotion both ways, right? Like people's relationships with characters or like what level we are at in expressing coherent emotion and how important that is?
18:56 Speaker 01 Oh yeah. I mean, probably you don't need that high end level of intelligence to do emotion. I mean, emotion is great and is, is super important, but like a dog probably does emotion pretty well, right? I mean, I don't have a dog, but I've heard that people will, like a dog is great for like emotional support and it's got pretty lousy linguistic capabilities. But, um, but the emotional use case is huge and people are using the stuff for all kinds of emotional support or relationships or, or whatever, which is just terrific.
19:30 Speaker 00 How do you think the behavior of the system will change as you kind of scale things up? Because I think the original model was trained on not a ton of money. Like on a relative basis, you fixed were incredibly frugal.
19:40 Speaker 01 Yeah, I think we should be able to make it smarter in all kinds of ways, both algorithmically and scaling, you know, get more compute and train a bigger model and train it for longer. Should just get more brilliant and more knowledgeable and better attuned to what people want, what people are looking for.
19:57 Sarah Guo? You have some users that are on the service like many hours a day. Like, how do you think about your target user over time? And what the usage patterns you expect to be are?
20:06 Speaker 01 We're gonna just leave that up to the user. Our aim has always been like, get something out there and let users decide what they think it's good for. And, you know, we see like, somebody who's on the site today is active for about two hours on average today. That's of people who send a message today, which is, which is pretty wild. But it's a great metric that people are finding some sort of value. And as I said, it's really hard to pin down exactly what that value is. Because it's, it's really like a big mix of things. But our goal is like, make this thing more useful to people and let people kind of customize it and decide what they want to use it for. If it's brainstorming or help or information or fun or like emotional support, let's get it into users' hands and see what happens.
20:51 Sarah Guo? How do you think about commercialization?
20:53 Speaker 01 We're just going to lose money on every user and make it up in volume.
20:58 Sarah Guo? Oh, good. That's a good strategy.
20:59 Speaker 01 No, I'm joking. No.
21:01 Speaker 00 The traditional 1990s business model. So that's good.
21:05 Sarah Guo? It's kind of a 2022 business model, too.
21:07 Speaker 00 You should issue a token and then just make it a crypto thing.
21:10 Speaker 01 I know. We're going to monetize at some point pretty soon. Because, again, this, this is the kind of thing that benefits from having a lot of compute and, you know, rather than burn investor money. The most scalable way to fund something is actually provide a lot of value to a huge number of people. So probably try some premium subscription type of service where, you know, we can, as we develop some new capabilities that might be a little more expensive to serve, then start charging for them. I really like that anyone can use it now for free because it's, you know, there are so many people that it's, it's providing so much value.
21:47 Speaker 00 I mean, it's really taken off as a consumer service in a really striking way. If you look at the numbers of users and the number of hours of usage per user, which is insane. Are there any scenarios where you think it's likely to go down like a commercial setting where you have like customer service bots who provide like a brand identity or run support? Or is that just not that interesting right now as a direction?
22:08 Speaker 01 I mean, right now, we have 22 employees, so we need to prioritize. We are hiring definitely enough work for way, way more people. Priority number one is just, just get it available to the general public. It would be fun to like, uh, launch it as customer service bots when we're able. People would just stay on customer service all day.
22:28 Speaker 00 They're like chatting with a friend effectively. So.
22:31 Speaker 01 Yeah.
22:31 Speaker 00 Start with the customer support. And that actually happened apparently on some old ecommerce sites like eBay. Apparently it was effectively a social network really early on as people were buying and selling things and just kind of hanging out because there weren't that many places to hang out online. So I always think it's kind of interesting to see these emergent social behaviors on different types of almost like commercial products or sites. But it makes a lot of sense.
22:51 Sarah Guo? So you said one of the obvious reasons Lambda didn't ship immediately at Google was safety. Like, how you guys think about that? Like, remember, everything characters say is made up.
23:00 Speaker 01 Exactly. Right. Make sure the users are aware that this is fiction. There's anything factual that you're trying to extract from it. At this point, it's best to go look it up somewhere that you find reliable. You know, I mean, there are other things that it's types of filters we've got there. Like, you know, we don't want to encourage people to hurt themselves or hurt other people or we're blocking porn. There's been a bit of protest around that.
23:27 Speaker 00 Yeah. And do you view all this as a path to AGI or sort of superintelligence?
23:32 Speaker 01 Sure. Yeah.
23:32 Speaker 00 And is that part of the goal? For some companies, it seems like it's part of the goal. And for some companies, it seems like it's either not explicitly an anti-goal, or if it happens, it happens. And the thing people are trying to build is just something useful for people.
23:44 Sarah Guo? What a flex. AGI is a side effect.
23:46 Speaker 01 Yeah, well, I mean, that was a lot of the motivations here because like, I mean, my main motivation for working on AI other than that it's fun. Well, I mean, fun is secondary. Like, the real thing is like, I want to drive technology forward. There are just so many technological problems in the world that could be solved. For example, like all of medicine, like there are all these people who die from all kinds of things that we could come up with technological solutions for. I would like that to happen like as soon as possible, which is why I've been working on AI because, okay, rather than working on, say, medicine directly, let's work on AI and then AI can be used to accelerate some of these other things. So basically, that's why I'm working so hard on the AI stuff.
24:30 Speaker 01 And I wanted to have a company that was both AGI first and product first. Because product is great, it lets you build a company and like motivates you. And so like the way you have a company that's both AGI first and product first is that you make your product depend entirely on the quality of the AI. Like, the biggest determining factor in the quality of our product is how smart the thing's going to be. So now we're like fully motivated, A, to make the AI better and to make the product better.
25:03 Speaker 00 Yeah, it's a really nice sort of virtuous feedback loop because to your point, as you make the product better, more people interact with it. And that helps make it a better product over time. So it's a really smart approach. How far away do you think we are from AIs that are smarter than people? And obviously, they're smarter than people on certain dimensions already. But I'm just thinking of something that would be sort of equivalent.
25:24 Speaker 01 Yeah, I guess we just always get surprised at like what dimensions the AI gets better than people. It's pretty cool that some of these things can now like do your homework for you. I wish I had that as a kid.
25:37 Speaker 00 What advice would you give to people starting companies now who come from backgrounds similar to yours? Like, what are things that you learned as a founder that you didn't necessarily learn while working at Google or other places?
25:47 Speaker 01 Oh, good question. Basically, like, you learn from horrible mistakes, but.
25:53 Speaker 01 I don't feel like we've made really, really bad ones so far, or at least we've kind of recovered. But I guess, yeah, just build the thing you want really fast. Hire people who are just like really motivated to do it.
26:07 Speaker 00 Yeah.
26:08 Sarah Guo? So one, one quick question, just for users, like, what's the secret to making a good character? Like, if I'm gonna go make a copy of a lot instead of rubber ducking with myself, like, what do I need?
26:17 Speaker 01 Oh.
26:17 Sarah Guo? Just like my text chat with a lot? Yeah, stop disappearing the chat a lot.
26:21 Speaker 00 I'm just trying to protect myself from becoming a character.
26:25 Speaker 01 I mean, so you can do it just as simply as like, put in a greeting, a name in the greeting is all you need. Typically for famous characters or famous people, because the model probably already knows what they're supposed to be like. If it's, you know, something that the model is not going to know about because it's a little less famous, then you can create an example conversation to like, show it how the character is supposed to act.
26:51 Sarah Guo? It's insane that characters only 22 people, like, you're hiring, what are you hiring for? What are you looking for?
26:56 Speaker 01 So far, 21 of the 22 are engineers, so we're gonna hire more engineers. No, I'm joking, we are, we are gonna hire more engineers.
27:05 Sarah Guo? Shocked.
27:06 Speaker 01 Both in the deep learning, but also like, you know, front and back end, definitely hire more people on like, the business and product side. Yeah, we've got a recruiter starting on Monday.
27:17 Sarah Guo? Hard requirement, burning desire or childhood dream to bring characters to life?
27:22 Speaker 00 Yeah.
27:22 Speaker 01 Yeah.
27:22 Speaker 00 Yeah, an exceptional person.
27:24 Speaker 01 Yep.
27:24 Speaker 00 Do you mind if I ask you like two or three quickfire questions and then we'll wrap up?
27:27 Speaker 01 Sure.
27:27 Speaker 00 Okay. Who's your favorite mathematician or computer scientist?
27:31 Speaker 01 Oh, um, huh, that's a good one. They were all standing on the shoulders of giants. Yeah, it's hard to pick out in this big tower of mathematicians and computer scientists. I got to work with Jeff Dean a lot at Google. He's really nice, fun to work with. I guess he's now running their large language model stuff. It's a little bit of a regret of having left Google, but hopefully collaborate in the future.
27:56 Speaker 00 Yeah. Do you think math is invented or discovered?
27:59 Speaker 01 Oh, that's interesting. Okay, I guess discovered, maybe, maybe all of it's discovered. Everything and we're just discovering it.
28:07 Speaker 00 And then last question, what do you think is something you wish you'd invented?
28:10 Speaker 01 Uh, let's see.
28:13 Sarah Guo? Teleportation.
28:13 Speaker 01 Ooh, that.
28:14 Speaker 00 It seems hard.
28:15 Speaker 01 That sounds like a good one. I'm not, I'm not going to step into a teleporter.
28:19 Sarah Guo? Some physics involved here.
28:20 Speaker 01 Yeah, I do not want to be like disassembled or anything like no beaming. I'm, I'll walk. I don't want like.
28:27 Speaker 00 Take the elevator. We didn't need to teleport.
28:29 Speaker 01 My brain upload into a computer. Like I think I would like to keep my physical body. Please, thank you.
28:36 Sarah Guo? Oh, I don't care. Let me out of the meat box. What do you wish you'd invented?
28:39 Speaker 01 Oh, what we wish we'd invented. Sorry, I was dodging the question.
28:44 Speaker 01 Just focused on, on inventing AI that, you know, that can push the technology forward.
28:50 Sarah Guo? Such a good founder answer.
28:51 Speaker 00 Makes sense.
28:52 Speaker 01 Working on it.
28:52 Speaker 00 Very focused. That's great. Well, no, this was an incredible conversation. So thank you so much for joining us today on the podcast.
28:58 Speaker 01 Thank you a lot. Thank you, Sarah. Good to see you too.
29:01 Sarah Guo? Yeah, good to see you. Thanks, Noam.
29:03 Speaker 00 All right. Thanks for the time.
29:04 Speaker 01 Bye.
29:05 Sarah Guo? Thank you for listening to this week's episode of No Priors.
29:09 Speaker 00 Follow No Priors for new guests each week and let us know online what you think and who in AI you want to hear from.
29:14 Sarah Guo? You can keep in touch with me and Conviction by following at Sarah Normas.
29:18 Speaker 00 You can follow me on Twitter at ALODgill. Thanks for listening.
29:23 Sarah Guo? No Priors is produced in partnership with Pod People. Special thanks to our team, Sinphil Galdea and Pranav Ruddy, and the production team at Pod People. Alex Vicmanis, Matt Saab, Amy Machado, Ashton Carter, Danielle Roth, Carter Wogan, and Billy Libby. Also, our parents, our children, the Academy, GovGPT, and our future AGI overlords.
Previous The Future is Small Models, with Matei Zaharia, CTO of Databricks Next The Computing Platform Underlying AI, with Jensen Huang, Founder & CEO of NVIDIA Page updated October 26, 2026 · transcript and summary are machine-generated
