No Priors Ep. 124 | With SurgeAI Founder and CEO Edwin Chen

0:00 [Music] Hi listeners, welcome back to No Priors. Today Allad and I are here with Edwin Chen, the founder and CEO of Surge, the bootstrapped human data startup that surpassed a billion in revenue last year and serves top tier clients like Google, OpenAI, and Anthropic. We talk about what highquality human data means, the role of humans as models become superhuman, benchmark hacking, why he believes in a diversity of frontier models, the scale meta not M&A deal, and why there's no ceiling on environment quality for RL or the simulated worlds that labs want to train agents in. Edwin, thanks for joining us. Great. Great seeing you guys today. Serge has been really under the radar until just about now. So um can you give

0:45 us a little bit of uh color on sort of scale of the company and what the original founding thesis was? So we hit over a billion written revenue last year. We are kind of like the biggest human data player in the space and we're about 100 a little over 100 people. And our original thesis was we just really believed in the power of human data to advance AI and we just had this really big focus from the start of making sure that we we had the highest quality data possible. Can you give people context for how long you've been around, how you got going, etc. I think again you all have accomplished an enormous amount in a short period of time and I think you know you've been very quiet about some of the things you've been doing. So it be great to just get a little bit of history and you know when you started, how you got started and how long you've been around. Uh yeah, so we've been

1:32 around for five years. I think we just hit our 5year anniversary. So we started in 2020. So before that, so I can give some of the context. So before that, I used to work at Google, Facebook, and Twitter. And one of the like basically the reason we started Surge was I just used to work on ML at a bunch of these big companies. And just the problem I kept running into over and over again was that it really was impossible getting the data that we needed to train our models. So it was just this big blocker that we faced over and over again. And there was just like so much more that we wanted to do like even just the basic things that we wanted to do. We struggled so hard to get the data. It was really just the the big blocker. But then simultaneously there were all these more futuristic things that we wanted to build. Like if we thought of the next generation AI systems if we could barely get the data that we needed at the time to solve like just building a simple analysis classifier. We if we could

2:18 barely do that then like how would we ever advance advance beyond that? So that that really was the the biggest problem. I can go into more of that, but that that that was what we faced. And then you guys are also known for having bootstrapped the company versus raising a lot of external venture money or things like that. Do you want to talk about that choice in terms of going profitable early and then scaling off of that? In terms of why we didn't raise so big part of it was obviously just that we didn't need a money. I think we were very very lucky to be profitable from from the start. So we didn't need a money. It always felt weird to give up control and like one of the things I've always hated about Silicon Valley is that you see so many people raising for the sake of racing. Like I think one of the things that I often see is that a lot of founders that I know they don't they don't have some big dream of building a product that solves some idea that they really believe in. Like if you

3:04 talk to a bunch of YC founders or whoever it is, like what what is their goal? It really is to tell all their friends that they raised $10 million and to show their parents they got a headline on Techrunch. Like that is their goal. Like I I think of like my friends at Google, they they often tell me, "Oh yeah, I you know, I've been at Google or Facebook for 10 years and I want to start a company." I'm like, "Okay, so what problem do you want to solve?" And they don't know. They're like, "Yeah, I just want to start something new. I'm bored." And it's weird because they can like pay their own salaries for a couple months. Again, they've been at Google and Facebook for 10 years. They're not just like fresh out of school. They they can pay their own salaries, but the first thing they think about is just going out and raising money. And I've always thought it weird because they like might try talking to some users and they might try building an MVP, but they kind of just do it in this throwaway manner where the only reason they do it is to check off a box on a startup accelerate application and then they'll just pay right around

3:49 these random product ideas and they happen to get a little bit of traction so that the VC DMs them. And so they spend all their time tweeting and they go to these VC dinners and it's all just so that they can show the world that they raised a big amount of money. And so I think raising immediately always felt silly to me like everybody's default is to just immediately raise. But if you were to think about it from first principles, like if you didn't know how Silicon Valley worked, if you didn't know that raising was a thing, like why why would you do that? Like what is money really going to solve for 90% of these startups where the founders are lucky to have some savings? I really really think that your first instinct should be to go out and build whatever you're dreaming of. And sure, if you ever run into financial problems, then sure, think about raising money then, but don't waste all this effort and time up front when you don't even know what you do with it. Yeah, it's funny. I feel like I'm one of the few investors that actually tries to talk people out of fundraising often, right? Like I actually had a conversation today where the founder was talking about doing a raise and I'm like

4:35 why you know you don't have to you can maintain control etc. And then the flip side of it is I would actually argue outside of Silicon Valley too few people raise venture capital when the money can actually help them scale. And so I feel like in Silicon Valley there's too much and outside of Silicon Valley there's too little. So it's this interesting you know spread of uh different models that sort of stick. Edwin, what would you um what would you say to founders who uh feel like there's some external validation necessary to uh especially like a hire a team or scale their team? This is a very like common complaint or rationale for going and raising more capital. I think about it in a couple ways. So I guess it depends on what you mean by external validation. Like in my mind again like I often think about things from a p perspective of are you trying

5:21 to build a startup that's actually going to change the world like do you have this big thing that you're dreaming of and if you have this big thing that you're dreaming of you like why do you care? Maybe the way to to think about it is in in Sarah's context like if you haven't say you're a YC founder, you haven't been at Google, you haven't been at Meta, you haven't been at Twitter, you don't have this network of engineers, you're a complete unknown, you haven't worked with very many people, you're straight out of school, how do you then attract that talent? And to your point, you can tell a story of how you're going to build things or what you're going to do or but it is a harder um obstacle to basically convince others to join you or for others to come on board or to have money to pay them if you haven't if you don't have long work history. So I think maybe that's the point Sarah is making. Uh yeah. So I mean I think I would differentiate between maybe two two things like one is do you need the money? So first of all

6:06 like there's a difference between people who are yeah like literally fresh out of school or maybe you know never gone to school for first place and so maybe they don't have any savings and so they literally need some money in order in order to live and then there's others who okay like let's assume that you don't necessarily need money because again you've been working at Google or Facebook for 10 years like or you know 5 years whatever it is you have some savings. So I would say one of the questions is again like it kind of the the path kind of differs depending on depending on those those two those two choices or those two um scenarios but I think one of the questions is well do you really need to go out and hire all these people? Like one of the things I often see again like I'm curious what you guys see but one of the things I often see is founders will tell me like uh okay so I'm trying I'm trying to think about the first few hires I'm going to make and they're like yeah I'm going to hire a PM. I'm going to hire a data scientist. Yeah, these are one of

6:52 my first five to 10 hires. I'm like what? Like this is just wild to me. Like I would never hire data scientists one of the first few people in a company. And and I say this because I used to be a data scientist. Like data scientists are great when you want to optimize your product by 2% or 5%. But that's definitely not what you want to be doing when you start a company. You you're trying to swing for 10x or 100x changes, not worrying and nitpicking about small percentage points that are just noise anyways. And to someone like product managers, like product managers are great when your company gets big enough, but at the beginning you you should be thinking about yourself about what product you want to build. And your engineer should be hands-on. they should be having great ideas as well. And so product management is kind of this weird conception that big companies have when your engineers don't have time to be in the weeds on the on the details and drive things themselves. It's not a role that you come up with before. So I guess um with the initial surge team, it sounds like you had sort of a

7:38 small initial tight engineering team. You guys started building product. You were bootstrapping off of revenue. You know, at this point you're at over a billion dollars in revenue, which is amazing. Um, how do you think about the future of how you want to shape the organization, how big you want to get, the different products you're you're launching and introducing? Like what what do you view sort of the future of Surge and how that's all going to evolve? Before we do that, can you just explain like what the at whatever level of detail makes sense here? Like what the billion dollars of revenue is? Maybe like how product supports the company, who your data, who your humans are, because I think there's just very little visibility into into all of that. So in terms of what our product is, I mean at the end of the day our product is our data. Um like we literally deliver data to companies and that is what they use to train and evaluate their models. So

8:24 imagine you know when you're one of those these frontier labs and you want to improve your model uh your model's coding abilities. What we will do on our end is we will gather a lot of coding data. And so this coding data may come in different forms and maybe SFT data. We are literally writing out coding solutions or maybe unit tests like these are the tests that a good uh that a good piece of code must pass. Maybe it's preference data where it's okay like here are two pieces of code or here are two coding explanations which one's better or these might be like verifiers like okay um here's a web app that I created I want to make sure that in the top right hand of the screen there's like a there's a login button or I want to make sure that when you click this button something else happens like there's a bunch of different forms that this data may take but at the end of the day what we're doing is we're delivering data that'll basically help the models improve on these capabilities very very

9:11 related to that is this notion of evaluating the models like you also want to know yeah is this good coding model uh is it better than this other one what are the errors in which this model is weak and this this model is worse like what insights can we get from that and so in addition to the data often times we're delivering insights to our customers we're delivering loss patterns we're delivering failure modes so there may be a lot of other things um like related to the data but at the end it's like this this universe of uh of like applications or this like just universe around the data that we deliver and that that is our product yeah and maybe going back to all's question um uh maybe like product isn't actually the right word here, but what's what's like repeatable about the company or what are like core capabilities that you guys have that you would say your competitors you know um fail to meet the mark? The way we think about a company is that

9:57 and the way we differentiate from others is that a lot of other companies in this space they are essentially just body shops. What they are delivering is not data. They are literally just delivering warm bodies to um to uh to companies. And so what that means is like at the end of the day they don't have any technology and one of our fundamental beliefs is that again quality is the most important thing at the end of the day like is this high quality data is this a good coding solution is this a good unit test is this mathematical problem solved correctly is this a great poem and basically a lot of companies in this space like just just as of like of how things have worked out historically it's that like historically a lot of companies they uh uh they've treated quality and data as commodity like one of the ways We often think about it is imagine you were trying to draw a bounding box around a car. Like Sarah,

10:45 you and I, we're probably going to draw the same bounding box. Like ask Hemingway and ask a second grader. Well, at the end of the day, we're all going to draw the same bounding box. There's not much difference that we can do. So, there's a very very low ceiling on the bar of quality, but then take something like writing poetry. Well, I I suck at writing poetry. Hemingway is definitely going to write a much better poem than I am. Or imagine a I don't know, a VC pitch deck. you're going to write a much better you're going to create a much better pitch deck than I will. And so there's almost an unlimited ceiling in this Genai world on the type of quality that you can that you can build. And so the way we think of our product is like we have a platform. We have actual technology that we're using to measure the quality that or workers or annotators are generating. If you don't have that technology, if you don't have any way of measuring it, is the measurement through human evaluation? Is

11:30 it through modelbased evaluation? And I'm a little bit curious like how you create that feedback loop since to some extent it's a little bit this question of how do you have enough evaluators to evaluate the output relative to the people generating the output or do you use models or how how do you approach it like I think one analogy that we often make is think about something like Google search or think about something like YouTube like you have you know millions of search results you have millions of web pages you have millions of videos how do you evaluate the qualities of these videos like is this a high quality like is this a high quality web page is it informative or is it really spammy like in the way you do this is like you just need I mean you gather so many signals you gather like page dependent signals you gather like user dependent signals you gather activity based signals and all these feed into you know a giant ML at the end of the day and so in the same way we we gather all these signals about our

12:15 annotators about the work that they're performing about like their activity on the site and we just feed it into um a lot of these different uh like we basically have an ML team in TR that builds a lot of these algorithms to measure all of this what is changing or breaking as you are like scaling increasingly sophisticated like annotations, right? Like if you know model quality baseline is going up um every couple of months, then the expectation is it like exceeds you know what might have been um a random human at some point as you said like can draw a bounding box into all of these different fields um where uh you know we we have modeled better than the 90th percentile at some point. So this is actually something that we do a lot of internal research on ourselves as well. So there's basically this field of uh AI alignment called scalable

13:02 oversight which is basically this question of how do you how do you like have models and humans working together hand in hand to produce data that is better than either one of them can achieve on their own and so even like even today something like writing an SAT story from scratch even today like sure a couple years ago we might have written that story completely from scratch ourselves today it's just like not very efficient right like you might start with a story that a model model created and then you would edit it you might edit did it in a very substantial way. Like maybe just the core of it is very vanilla, very generic, but there's just so much like kind of like croft that is just inefficient for a human to do and doesn't really benefit from like the human creativity and human ingenuity that we're trying to add into the response. And so you can just start with like this barebones uh structure that you're basically just layering on top of. And so again, like there's like more sophisticated ways of thinking about

13:48 scalable oversight, but just this question of how do you build the right interfaces, how do you build the right tools, how do you just combine people with with with AI in the right ways to like make them u make them more efficient. Um is something that we build a build a lot of technology for. A lot of the discussion in terms of what human data the labs want has moved to um RL environments and reward models in you know recent months. um what is hard about this or you know what are you guys working on here? So we do a lot of work building our environments and I think one of the things that people really underestimate is how it is how complicated it is that you can't just synthetically generate it like for example you think you need a lot of tools because these are massive environments that people want. Can you give an example of

14:34 like just to make it more real like imagine you are a salesperson and when you are a salesperson you need to be interacting with Salesforce you need to be getting leads through Gmail you're going to be talking to customers in Slack you're going to be creating Excel sheets tracking your leads you're going to be uh I don't know writing Google Docs and making PowerPoint presentations to present things to customers and so you want the basically these are very rich environments that are literally simulating your entire world as a salesperson just like imagine like your entire world. So everything in the future is not on your desktop as well. Maybe you have a calendar to a meeting to meet a customer and then you want to simulate a car accident happening and you're getting notified of that. So you need to like leave a little

15:20 bit earlier. Like all these things are things that we actually want to model in these very very rich RO environments. And so the question is how do you generate all the data that goes into this? like, okay, you're going to need to generate like thousands of Slack messages, hundreds of emails. You need to make sure that these are all consistent with each other. You need to make sure that like going back to like my core example, you need to make sure that time is evolving in these environments and like certain like external events happen. Like how do you do all of this? And then um like in a way that's actually kind of like interesting and creative but also realistic and not like in congruent with each other. uh like there's just like a lot of thought that needs to go into these environments um to to make sure that they're again like rich creative environments that the models can learn interesting things from and so uh yeah you basically need like a lot of tools

16:05 and balance sophistication for for great use. Is there any intuition for like how real or how complex is enough or is it just like you know there's a there's no ceiling on the um realism that is useful here or the complexity of environment that is useful here. I think there's no ceiling like at the end of the day you just want as as like much diversity and richness as you can get because the more richness that you have yeah the more the models can learn from like the longer the time horizons the more that the models can learn on and improve on. So I think there's almost almost an unlimited ceiling here. If you were to make a five or 10 year bet on like what scales most in terms of demand from people training AI models and and types of data, is it RL environments or is it traces on types

16:51 of like expert reasoning or or what other areas do you think there's going to be a really large demand for? I mean I think it will be all of the above like I don't think our environments alone will suffice just because I mean it depends on how you think but there are our environments but oftentimes these are very very rich trajectories are very very long and so it's almost like inconceivable that a single reward um I mean I think even today we often think about things in terms of multiple rewards not just a single reward but a thing like a single reward just may not be like rich enough to capture um all the all the work that goes into like the model solving some like very very complicated goal Um so I think it'll probably be a combination of of all those. If you assume eventually um some form of superhuman performance across different model types relative to human experts,

17:38 how do you think about the role of humans relative to data and data generation versus synthetic data or other approaches like at what point does human input sort of run out as a as a useful um point of either feedback or data data generation? So I think human feedback will never run out and that's for a couple reasons. So I mean even if I think about the landscape today I think people often overestimate the role of synthetic data like I personally I think synthetic data actually is very very useful like we use it um like a ton ourselves in order to supplement what the humans do like again like like I said earlier there's like a lot of croft that simply isn't worth a human's time but what we often find is that like for example a lot of times where customers will come to us and they'll be like yeah for this past 6 months I've been experimenting with with synthetic data I've gathered 10 to 20 million pieces of synthetic data

18:25 Actually, we finally realized that 99% of it just wasn't useful. And so, we're trying to find right now, we're trying to curate the 5% that is useful, but we are literally going to throw out 9 million of it. And often times, you'll find out that yeah, like actually a thousand even a thousand pieces of high quality human data, highly cured, really really high quality human data is actually more valuable than those 10 mill than those 10 million points. So, that is one thing I'll say. Another thing I'll say is that it's almost like sometimes you need an external signal to the models. like the models just think so differently from humans that you always need to make sure that they're kind of aligned with the actual objectives that you want. Let me give two examples. So one example is that it's kind of funny if sometimes if you try so one of the frontier models let me just say that one of them if you go use

19:11 the frontier model it's like one of the top models or one of the models everybody think is one of the top if you go use it today like maybe 10% of the time when I use it will just output random Hindi characters and random Russian characters into one of my responses so I be like tell me about Donald Trump tell me about Barack Obama and just like in the middle of it will just output Hindi and Russian it's like what is this and the model just isn't like self-consistent enough to be aware of this. It's almost like you need a like an external human to tell the model that yeah this is wrong. One of the things I think is a giant plague on AI is almsus alam marina and I'll skip the details for now but I think right now people will often it's like if you train your model on the wrong objectives so like the the mental model that you should have of LMS marina is that

19:57 people are writing prompts they'll get two responses and they'll spend like 5 10 seconds looking their responses and they'll just pick whichever one looks better to them. So they're not evaluating whether or not the model hallucinated. They're not evaluating the factual accuracy and whether it followed any instructions. They're literally just vibing with the model and like, okay, yeah, like this one seemed better because it had a bunch of formatting. It had a bunch of emojis. It just looks more impressive. And people will train on like basically an elements of subjective and they won't realize all the consequences of it. And again, like the model itself doesn't doesn't like know what its objective is. It's like you almost need like an external like quality signal in order to tell it what the right objective should be. And if you don't have that, then the model will just go in all these crazy directions. Again, like you may you may have seen some of the results with like the with law before, but just go in all these

20:42 crazy directions that um kind of kind of mean you need these external external validators. This also happens actually when you do different forms of like protein evolution or things like that where you select a protein against a catalytic function or something else and you just kind of randomize it and have like a giant library of them and you end up with the same thing where you have these really weird activities that you didn't anticipate actually happening. And so I sometimes think of model training as almost this odd evolutionary landscape that you're effectively evolving and selecting against and you're kind of shaping the model into that local maxima or something. And so it's kind of this really interesting output of anything where you're effectively evolving against a feedback uh signal and depending what that fe feedback signal is, you just end up with these odd results. So it's interesting to see how it kind of transfers across domains. these you know course uh as you said

21:30 5-second reaction academic benchmarks or even non-academic industrial benchmarks are uh easily hacked or like not the right gauge of performance against any given task. They are very popular. What is the alternative um for somebody who's trying to like choose the right model or understand model capability? So the alternative that I think all the frontier labs view as a gold standards is basically human evaluation. So again proper human evaluation where you're actually taking the time to look at the response. You're going to fact check it. You're going to see whether or not it followed all the instructions. You have good pace so you know whether or not the the model has good writing quality. Like this concept of like doing all that and spending all of time to do that as opposed to just vibing for 5 seconds I think actually is really really important because if you don't do this

22:17 you basically you're basically just training your models on the analog of clickbait. Um, so I think it actually is really really important for model progress. If it's not LMIS, like how should people um actually evaluate model capability for any given task? What all differential labs find is that human evals really are the gold standard? Like you really need to take a lot of time to fact check these responses to verify the following instructions. You need people with good taste to evaluate the writing quality uh and so on and so on. And if you don't do this, you're basically training your models on the analy clickbait. And so I think I think that really really harms model progress. Is there work that surge is doing in this domain of like trying to standardize human eval or make it more transparent to end consumers of the API or even users? So internally we do a lot of work

23:04 actually today with working with all the frontier labs to help them understand our models. So again we're constantly evaluating them. Um we're constantly surfacing loss areas for them to improve on and so on and so on. And so right now a lot of this is internal but one of the things we actually want to do is sort of external forms of this as well where we're helping educate people on yeah like these are the different capabilities of all these models here these models are better at coding here these models are better at instruction following here these models are actually hallucinating a lot so you trust trust them as much so we we actually do want to start a lot of external work to to help educate the the broader landscape on this. If we can zoom out and talk just about the um the larger like competitive landscape and what happens with frontier models over time. What does a meta scale deal mean for you guys or what do you make of it? So, I think it's kind of interesting in

23:49 that. So, we were already the number one player in the space. It's been beneficial because yeah, there were still some legacy teams using scale like they just didn't know about us because we were still pretty under the radar. I think it's been beneficial because one of the things that we've always believed is that sometimes when you use these lowquality data solutions, people kind of get burned on human data and so they had this negative experience and so then they don't want to use human data again. And so to try these other methods that are honestly just a lot slower and don't come with the right objectives and so I think it just harms model progress overall. And so like just like the more and more we can get all these frontier labs using high quality data I think it actually really really is beneficial for an industry as a whole. So I I am like I I think overall it was a like a good

24:35 thing to happen. If you were to um make a bet that an underdog catches up to open athropic and deep mind who would it be? So, I would bet on XAI. I think they're just very hungry and missionoriented in a way that gives them a lot of really unique advantages. I guess maybe another um sort of broader question is do you think there's three competitive frontier models, 10 competitive frontier models uh a couple years from now? And is any of those open source? Yeah. So, I actually see more and more frontier models opening up over time because I actually don't think that the models will be commodities. Like I think one of the things that we've I mean I think one of the things that has actually been surprising the past couple of years is that you actually see all of their models have their own focuses that give them unique strengths. Like for example I think obviously been really

25:22 really amazing at coding and enterprise and open has this big consumer focus because of chat activityd like I actually really love it. It's models personality and then grock you know just has a different set of things that's willing to say and to build. And so it's almost like every company has it's almost like a different set of principles that they care about. Like they're like some will just never do one thing. Others are totally willing to do it. Others just have different like models will just have so many different facets to their personality, so many different facets to the type of skills that they will be good at. And sure, like eventually AGI will maybe encompass this this all, but in the meantime, you just kind of need to focus. Like there's only so many focuses that you can have as a company. And so I think that just will lead to uh like different strengths for all the model providers. So I mean I

26:08 think today you know we already see like a lot of people including me we will switch between all the different uh models just depending on what we're doing. And so in the future I think that will um just happen even more as as people are just using more and more models for or using models for different aspects of their lives like both their personal and their in their like professional lives. Going back to something Elad mentioned, like where should we expect to see like a surge investing over time? Like what do you think you guys will do a few years from now that you don't do today? Again, I think I'm really excited about this more kind of like public research push that we're we're starting to have like I think it is really interesting in that a lot of the like for obvious reasons a lot of the frontier labs they're just not publishing anymore. And as a result of that, I think it's almost

26:54 like the the industry has fallen into kind of a trap that I worry about. So like maybe to dig into some of some of the things I said earlier um with some of the negative incentives of the industry and some of the kind of concerning trends that that that we've seen. So like going back to LMS, one of the things that we'll see is like a lot of researchers, they'll tell us that their VPs make them focus on increasing their rake on LM LMS. And so I've had researchers explicitly tell me that they're okay with making their models worse at factuality, worse at following instructions as long as it improves their ranking because their leadership just wants to see these metrics go up. And again, that is like something that literally literally happens because the people ranking these things analysis. They don't care whether the models are good at following. They don't care whether the models are um are emitting

27:40 like factual responses. They what they care about is okay, did this model emit a lot of emojis? Did it emit a lot of bold words? Did it have really long responses? Because that's just going to look more impressive to them. Like one of the things that we found is that the easiest way to improve your rank on Elm Marina is literally to make your make your model response longer. And so what happens is like there are a lot of companies who are trying to improve their leaderboard rank. So they'll see progress for 6 months because all they're doing is unwillingly making their model responses longer and adding more emojis and they don't realize that all they're doing is training their models to produce better clickbait. and they might finally realize six months or a year later like again you may have seen some of these things in industry but it basically means that they spent the past six months making zero progress and in a similar way I think you know besides LMS you have all these academic benchmarks

28:26 and they're completely diverse in the real world like a lot of teams are focused on improving these SAT style scores instead of real world progress like like I'll give an example there's a benchmark benchmark called if eval and if you look at if eval so it stands for instruction following eval if you look at if eval like some of the instructions it's trying to check whe our models can do It's like, "Hey, can you uh write an essay about Abraham Lincoln?" And every time you uh like mention the word Abraham Lincoln, make sure that five of the letters are capitalized and all the other letters are uncalized. It's like what is this? And sometimes we'll get customers telling us like yeah like we really really need to improve our like our score on on if val and what this means is again like you have all these companies or all these researchers who instead of focused on real world progress they're just like optimizing for these silly SAT style benchmarks.

29:13 And so one of the things that we we really want to do is just think about ways to educate the industry think about ways of publishing on our own just like think about ways of steering the industry into like hopefully a better direction. And so I think that's just one one big thing that we're we're really excited about and could be could be really big in the next five years. Okay. Yeah. I mean, so Sarah brought up earlier um how everybody kind of wants high quality data. What does that mean? How do you think about that? How do you generate it? Can you tell us a little bit more about your thoughts on that? So let's let's say you wanted to train a model to write an eight line poem about the moon. And so the way most companies think about it is well let's just hire a bunch of people from Craigslist or through some recruiting agency and let's ask them to write poems. And then the way they think about quality is well is this a poem? Is it eight lines? Does it contain the word moon? If so, like, okay, yeah, I hit these three check

29:58 boxes. So, yeah, sure, this is a great poem because it follows all these instructions. But if you think about it, like the reality is you get these terrible poems. Like, sure, it's eight lines that has the word moon, but they feel like they're written by kids from high school. And so, other companies be like, okay, sure, these people on Craigslist don't have any poetry experience. So, I'm going to do instead is hire a bunch of people with PhDs in English literature. But this is also terrible. Like, a lot of PhDs, they are actually not good writers or poets. Like if you think like think of Hemingway or Emily Dickinson, they definitely didn't have a PhD. I don't think they even completed college. And like one of the things I will say is like yeah, I I went to MIT. I think Eli you went you went there too. And a lot of people I knew from MIT who graduate with a CS degree, they're terrible coders. And so we think about quality completely differently. Like what we want isn't poetry that checks some boxes and like okay yeah check the street boxes and uses some

30:44 complicated language. We want the type of poetry that Nobel Prize laurates would write. So what you want is like okay we want to recognize that poetry is actually really subjective and rich like maybe one poem it's a haik coup about moonlight on water and there's another poem that's like it has a lot of internal rhyme meter and another one that I don't know focus on the emotions behind the moon rising at night and so you actually want to capture that there's thousands of ways to write a poem about the moon there isn't a single correct way and each one gives you all these different insights into language and imagery and poetry and if you think about it's not just poetry it's like math there's a thousand ways probably to to prove the pythagorian theorem. And so I think the the difference is that when you think about quality the wrong way, you kind of get commodity data that optimizes for things like inner radar agreement and again checking boxes off

31:29 of some list. But one of the things that we try to teach all of our customers is that high quality data actually really embraces human intelligence creativity. And when you train the models on this like richer data, they don't just learn to follow instructions. They really learn all these deeper patterns about all the stuff that makes language in the world really compelling and meaningful. And so I think a lot of companies they just throw humans at the problem and they think that you can get good data that way. But I think you really need to think about quality from first principles and what it means and you need a lot of technology to identify yeah that these are amazing programs and these are creative math problems and these are games and web apps that are beautiful and fun to play and these ones are terrible to use. So I think you you really need to build a lot of technology and think about quality in the right way. Otherwise you're basically just like scaling up mediocrity. That sounds very domain specific. So do you like in every domain are you

32:15 building a lens of what quality looks like along with your partners? Yeah, I mean I think we have kind of like holistic quality principles but then often times there are differences per domain. So it's like a combination of both. I think we got all the uh core topics. Nice work on podcast number two Edwin and thanks for doing this. Congrats on all the progress with the business. Yeah. No, thanks so much for joining us. Yeah, it was great great meeting you guys. Find us on Twitter at no prior pod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way, you get a new episode every week. And sign up for emails or find transcripts for every episode at no-bers.com. [Music]