# Scale AI & the Startup Hunger Games with Alexandr Wang

Channel: South Park Commons
Video: https://www.youtube.com/watch?v=CEebe451GZs
Duration: 56 min
Language: English
Words: 10758
Transcript page: https://viewrankai.com/tools/youtube-transcript/CEebe451GZs

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[0:00] [Music] so everyone please join me in welcoming Alex [Music] tobc we are very excited to host you Alice says you know SBC really specializes in the minus one part of the founder Journey uh and that really means you know in in that in that squiggle in that kind of soup if you will of kind of like trying out different ideas stress testing them talking them talking to people about it um so take us back to the minus one days of your own Journey um like did you try out different ideas before you kind of converged on scale uh what what were the early days of ideation and kind of like like for you

[0:52] yeah so um we did uh we did YC and uh the first half of our batch I would say was probably you know we refer to as a squiggle was sort of like highly there's a lot of existential angst you don't really know what you're doing with your life um and uh and I remember it was kind of like yeah I remember this time you like you know you like you have like these Google Docs of startup ideas and you like you sort of like keep trying to come up with more startup ideas I remember I read Paul Graham's essay um how to come up with startup ideas um and

[1:28] that was actually a really good FOC focusing framework which was I think the basic ideas you like live in the future what are the things that exist in the future and sort of like build backwards um but uh but yeah it was like I it was very unclear I think when you're in certainly when I was in YC and probably a community like this like there's just there's a lot of people working a lot of things around you and so it's very hard to have any sense of you know what is a good idea what's an idea that's worthwhile you sort of also feel like hey any new idea that I start working on

[2:00] everybody around me has already been working on something for a while so I'm already like you know behind the eightball just by like starting a new idea and so there's a lot of like I think there's a lot of like um you know uh turmoil associated with all that but ultimately um we built scale um it was kind of like it was kind of a perfect storm I think how it all came together and I don't think there was sort of there's just like a lot of serendipity so I think uh I think the idea more or less was like you know per the per the

[2:29] hram framework it's like okay in the future clearly we're going to orchestrate um human compute uh much more dynamically and much more like we orchestrate any other resource like we orchestrate compute today and so you're going to get to a state where human computation so to speak is uh is as easy to use an API as anything else and that API doesn't exist and so you know that was sort of the original idea for scale and then um and then after we had the idea then I spent like you know a night just looking for a domain um scale ai.com was available so uh so bought it

[3:08] um that turned out to be unusually good decision I think um scale uh has been turned out to be a really good name uh and and then we launched on product ton and it generated like enough traction on product ton and then there were probably you know there's probably like after that maybe like four to six months of just sort of like you're kind of in this like wandering mode where you know have a few customers you're trying to get more customers I remember um I remember that like I would I would personally talk to anybody who'd come into our our company's intercom so if you were a

[3:42] visitor on scale ai.com and uh and you click the little chat bubble you'd end up talking to me um and uh and so I remembered like there's like you know it was this period where it was like very still very unclear if it was going to work and then it wasn't until you know 6 months later where we started getting there was one client who wanted to be really big and then sort of like the idea started come into its own but um but yeah there's probably you know a year or so of of what I generally call wandering and uh and I think that's on

[4:12] the low end honestly of of most startups so so um let's focus a little bit on that angsty part um how did you deal with it did you just because you know it's easy to it's easy to look back on it and to kind of like acknowledge it as like a right of Passage and it's kind of like a learning process but when you're in it it [ __ ] sucks so like did you did you at that point were you able to in some ways like stare the an in the face or were you just kind of like I am along for the ride and it just up and

[4:45] down every single day every single hour you know one thing I'll say I don't know if it sucked more so than other periods in the future of the company so I think there's like plenty of times that suck um when building a company but I think that the um I think the main thing that sort of got me through all of it was sort of um I think it was I think I think once we arrived on the idea for scale that I did really believe like I get a very high conviction that in the future this thing will exist and it doesn't exist right now so then you have like you have

[5:21] enough of a gradient to go off of at that point and then you can I think have a reasonable confidence in the pathway and like hey at some point I'm gonna like figure out how like we go from point A to point B and that's going to be like this long journey um I think that uh I think that so that that I think was like pretty um comforting but I think in the in the in the process itself I think um I remember one of the things that like kept getting in my head was just like the survival odds in YC like The Hunger Games like you know 90%

[5:55] of the companies just die and um but they don't die for years so you're just like you know unlike the Hunger Games where people dying the first day immediately um for wiv if you're going to die anyway than five days you might as well die now yeah exactly well that was that was kind of the that was the most terrifying thing I think the most terrifying idea was that like you could already be dead but you might only find out in three years um which uh but but I think that like I a as I'm a pretty anxious person and I think I think I channeled all of the uncertainty into

[6:27] just you know anxiously doing things and I think if you if you do that I think a lot of Founders do that but I think if you do that then at the very least like you feel like you're ruding and you're making progress in a particular direction do you ever worry I mean coming back to your point about um for any idea that you have you know you can kind of squint and see like 10 people ostensibly doing something similar um and I always say that it's if you tend to get easily psyched out by competition in the early days then you then you're in a hard spot

[7:00] because if you pair that with like extreme paranoia which a lot of Founders have that's a deadly combination because you're never actually going to get over it so I guess my question to you here is that mechanical tur kind of existed and there were some other I guess players did you did you worry about them or you like no that that is just not a good quality product so I know I can do better yeah um yeah there were a bunch of things that existed and I think [Music] um I think the actual answer here is the right the right attitude is kind of this like blustery confidence um and it's

[7:34] actually pretty interesting I see this uh I just this conference with uh with this guy from one of the one of the key Executives at paler and um he uh he we were both talking to this government customer and he said this right in front of me and he was like you know it's okay that you guys didn't pick paler but um you know you're going to come to regret it and you know it would have been nicer if you just pick pal from the start and you'd be way happier and we do a much better job for you than anybody else possibly could but um but it's cool that

[8:04] you guys didn't and uh and you know you'll come around eventually and eventually we'll be friends again but for right now you know it was it was it was I'm not even exaggerating it was like a ridiculously aggressive talk track and um and that's actually a part of the success uh recipe for paler has been this sort of like the this like deep fundamental not particularly ground I would say but this deep fundamental belief that you know any software they build is God's greatest software God's greatest gift to Earth and uh and nobody else can build software as good and you know there's some of that that is a self

[8:43] reinforcing Loop and so I I mention this because obviously you know if you're sort of like a rational third party bystander you can be like oh that seems ridiculous and that seems kind of you know obnoxious but um but I think it's actually part of the success story which is I think you need some kind kind of you know irrational self-belief to be to believe that you will be able to compete in the marketplace and over the long run you know what end what ends up happening is you need to believe that you can recruit better than everybody else and you need to believe you'll make better

[9:13] product decisions and you need to believe you'll go the extra mile and maybe other people don't care as much and then over the long term those things will ultimately accumulate to the the irrational self-belief that you had and so one of the things that I believed or that we believed at scale is like I did a lot of competitive programming competitions and competitive math competitions and so I felt you know if I have to end up competing with people I'll do just fine you know I've done plenty of competitions to date and so you know bring it we're just we're just going to build the better thing and uh I

[9:45] remember there was like an 18-year-old YC founder I talked to um maybe six months ago and I asked her the same question I was like oh you know how do you think about competition and she was like I think for competition just be better just do better just be better um and I think that is you know it's kind of it's kind of blustery but I think that really is the answer you just have to like really believe you're going to be better um you know that speaking of that talenter uh anecdote they're just really good this is they're actually very good at making the customers feel as though

[10:22] that without their software their business is actually going to be literally an order of magnitude worse off right um and this is something I struggled with at Dropbox because you know at Dropbox we were like we would build good software and the software would speak for itself and every time you would launch something somebody else would launch the S launch the same thing two days later and it took us a while to understand it's like oh they weren't actually launching it but they were very good at talking about it so which philosophy do you follow at skip do you like to build the thing or do you like

[10:50] to sell the thing or do you find a happy medium I think you have you to both well at least for what we do so at scale we have like small number of customers um they're very large customer and um and in that world you have to I think it's like um one way to think about it is is uh perception is reality a lot of times and I think you know probably a lot of the companies that you all have worked with they very data driven companies it feels like you know the truth makes its way um uh the truth you know everybody serves a shared sense

[11:22] of reality and whatnot that's not true at most large companies and also not true within the government unfortunately but um at a lot of large customers uh perception is more real than reality like the reality is just so ugly most of the time that very rarely do people actually confront reality and most of the time they just sort of choose to believe the perceptions that they that they live in so um so what that means is if you end up doing Enterprise sales or you end up building Enterprise products that you have to you know just as much as your job is to improve the reality it

[11:54] is to shape the perception and this is I think uh in some ways pal um uh one of palente superpowers is that they shape the perception better than most other uh technology companies do uh because they think they view themselves it's like a combination of a sort of acting troop combined with a software company um and I'm not even saying that you know they they literally give an acting book to all of the new hir so they did for a very long time so so I think what you I think there's something profound in what you just said because what if I'm if I'm understanding you

[12:28] correctly it's that most companies including the government they're already living in some perception because the reality is so painful so your job is not to actually outsell reality it's to outsell shitty perception with an even better one okay all right sorry you also should produce value I this is I just this is like this is like dark arts on top of if you're build a good product I think that like fundamentally you also need to build a good product maybe switching to you personally uh a little bit what would I mean you're clearly very competitive which is great uh and you have extreme self-belief which is

[13:06] amazing but apart from those do you had when you what else would you self identify as kind of being your 10x Spike um I think for us the the thing I think my personal one as well as I think it's exhibited in a lot of the the key people at scale it's really sort of creative problem solving but in the face of kind of like the least sexy problems you can imagine so you know a lot of our a lot of what we have to do is uh is large scale operations we have you know hundreds of thousands of contributors all around the world we have to

[13:39] coordinate this process of them producing high quality data for models and it's a very very unsexy problem but I think we treat that problem with the same level of respect that we would you know let's say a math Olympiad problem and we apply the same kinds of techniques we're like okay like what are the most creative ways that we could solve this problem how could you break it down let's like apply a scientific method towards solving it I think something a lot of great tech companies do um but we we hold no sort of uh we hold no sort of um elevated sense of purpose of like oh we'll only solve

[14:12] certain problems you know that we think about the impact of the problems and so I think with and then and then we work like hell you know we work very very hard and the combination of these things means that um that's ultimately I think the recipe that allows us to out compete all of our um all the competition which is that we we're we're smart you know we're good at problem solving and we also will treat the sort of like most lowly problems with the utmost respect um and uh what that means is over time I think we've devised some pretty clever operational Solutions pretty clever technology

[14:47] pretty clever products um and we all these things have sort of come together in a way that it's be very hard you know I think a lot of times when I look at other companies or or other startups you know you have one or the other either it's an exceptional team but they they limit themselves artificially towards you know esoteric problems that are intellectually interesting um this is a very common pattern there's a lot of companies where you have a brilliant team but they work on um the most useless problems and then you have and then you have teams which are like Scrappy and they'll work on uh they'll

[15:22] work on really um any problem that that is given to them but um but they just don't uh they don't manage to solve the problems in scalable ways or they don't figure out how to like abstract the problems into a way that um enables them to solve it effectively so the sort of combination of of you know extremely strong powerful problem solving or extremely good sort of scientific process and techn ability married with sort of um you know uh full humility about the problems to solve how did you I mean that's a that that those unsexy problems and ones that are operationally heavy those are often just the ones that are hard to

[16:00] kind of motivate people in particularly in the Bay area but I would say largely in Silicon Valley to get excited about so have there been cultural things that you have tried to Institute at scale either in hiring or maybe onboarding or kind of just like the things you talk about how have you managed to make that a core part of your of your DNA yeah so um well I think the main thing that we encourage people to think about is you know you know uh we want we want all our employees to think about their sort of like their their net economic impact much more so than they're sort of like

[16:38] you know the mo the difficulty of their technical problems and so I think a lot of times like in school or um yeah particularly in school you sort of like you know you feel like you gain Mastery by solving more and more technical problems and that's that's fine but I think that um uh there's an in correlation between technical difficulty and economic value um and it turns out like a lot of uh a lot of very economic economically valuable problems are not super technically difficult but they're just like there's just like a lot of hair and you just have to like sift through a lot of mess and that's I think

[17:18] where most of the good startup um opportunities are and um and so for us I think what what we really motivated um our staff on is like you know if you solve this problem effectively than the net economic impact you just think about like net economic impact of problems that you're solving relative to just technical difficulty um that's the ladder you want to be climbing and that that means that you know the unsexy but very valuable problems end up getting solved very effectively cool I think I think Facebook did something pretty similar as far as I can understand yeah I mean I think that's it's a good

[17:50] analogy to Facebook at you know Facebook we took great pains that Mo in most companies like especially on the engineering ladder you tend to get promoted if you tackle the harder problems right like the harder the problem you kind of in some ways there's a lot of implicit status and respect accorded to it and at Facebook we would often kind of be like no we would actually want to promote the most productive engineer right like simply the person who actually tranked out the most Cod maybe worked on like the least kind of like sexy problems but maybe like you know so I think it was a very

[18:24] conscious like archetype that we promoted um and lot of the senior Engineers the more traditional senior Engineers I think didn't get it they're like why is this person getting promoted they worked on like you know just like kind of you know reducing the number of errors in our logs by like an order of magnitude but that shit's easy could I mean like yeah but somebody's got to do it you know well um yeah that's a that's it's a good lesson from Facebook um let's maybe shifting gears a second let's talk about geopolitics um so what are uh tell us some predictions that you might have for like

[18:59] next year as in how you know obviously the along multiple fronts but certainly on kind of the technology and the hardware and chips kind of like I would say the the cold and the the the warm war between kind of like the US and China is definitely heating up so how's you know do you have any thoughts on kind of like how that plays out over the next 12 months and how it might affect everyone in Silicon Valley yeah so I think the first thing um to really observe you know one of the most meaningful things from a geopolitical standpoint happened a few weeks ago which is that um you know opening ey

[19:35] obviously released 0an some number of months ago or all preview and the first replication of the thinking Loop uh so to speak where the sort of like test time compute scaling uh came out of China from Deep seek uh as a deep seek R1 model and this is like a very surprising result that the first replication is not from you know anthropic or Google or any of the American compan compes it was an open source model released out of a Chinese lab uh what this tells you is is a few things first I think um there's no Gap in in research there's basically no research gap between the leading us labs

[20:13] and the Chinese Labs so you know they're basically caught up in terms of performance and um and it has like pretty far-reaching implications for how this plays out going forward and so um so that was that was kind of like a a a landmark result probably a few things to watch I think the first is um which AI technology gets exported uh more between the US versus China there will be this race to see which AI stack so to speak becomes more globally uh adopted so um you know uh in the first wave of Technology the US came out on top so Google search is is basically globally

[20:56] dominant except for China um uh social networking American social networking is basically dominant everywhere except for China um then in sort of like the the second leg of this race when it was more about hardware and Telco um China actually came out on top Huawei technology became pretty widely exported globally um it was sort of packaged in with the belt and Road initiatives where China sort of pretty quickly became the partner of choice for uh the majority of countries around the world actually and now I think AI is kind of the third phase so you can see that the US Force the UAE to decide if they want to be on

[21:31] the sort of like China Huawei stack or they want to be on the US stack the UAE for now is picking the US um I think we're going to see a lot more decisions um where you know countries are going to decide are they on the usai stack or the the um on the Chinese AI stack I don't think the US even the US from a foreign policy standpoint doesn't even really care about being the I stack globally I think we most we care about being the I stack to some of our partners but not all the partners that'll be one war that that happens and the other one that

[22:02] happens is you know the thing to really watch is the uh export controls so one of the most important things that the Biden ad men did um is they launched very restrictive export controls on chips so there's maybe like one 100th the number of highend G uh highend gpus in China versus the United States because we don't let them to we don't let them buy them and they also don't have ASM Asm all machines and they don't have all the sort of precursors to enable them to actually build the build the chip industry um with President Trump coming in and and with uh with sort of new players in the field I think

[22:41] one of the questions I think one of the biggest questions is um are we going to maintain a hard line on the export controls um I think we certainly should that's one of the biggest things that's enabling us to maintain a strong advantage and um what is that negotiation going to look like because there's the looming threat is by 2027 president XI and the CCP have said they want to take Taiwan so and they they've asked the military to prepare um the people Li Liberation Army to prepare to take Taiwan by 2027 so there's this looming date three years from now um uh by which you know China's at least the

[23:22] CCP is at Le saying if they'll take um they'll take Taiwan and uh as soon as president Trump gets into seege and gets inaugurated January 20th that's going to start the clock on the negotiations which is can can we prevent China from taking that action or prevent China from blowing out PSNC or you know prevent the sort of catastrophic scenario and what do we need to give an exchange and I think that will be you know whether it's the headline or it's like a lowgrade kind of um negotiation over time that is the big picture thing that we'll watch over the next few years and ultimately I think there's like too much

[24:00] incentive in the world like World War II will be a averted I don't think there's any interest to get into a hot War but I think we need to watch like um what are we GNA have to give in exchange for them not invading and not taking over the the the semiconductor industry maybe coming back to your point about the AI stack and what um you know whether essentially like sovereign states or companies around the world will pick a western kind of AI stack versus kind of a Chinese stack um curious like do you think it's actually possible for us to affect that by anything other than actually innovating

[24:36] in our Labs I will admit though to your point it has been a little surprising that after the inference time compute it's kind of been what like eight weeks 8 10 weeks that we haven't seen an equivalent release by some of the other major Labs which has actually really surprised me because my initial take was that this feels somewhat somewhat like easy to replicate just like hey just throw a bunch of like more INF time essentially like loops on kind of like um on the various techniques and essentially like you know do some tree climbing and figure out the end result but I haven't seen that be

[25:07] replicated which is odd uh and your deep seek point is a good one so do you think we can we have to just innovate right or do you think we can actually enforce it through policy um well I think the first step is to not squander our open source industry which I think you know at this point I we're through that but definitely there was like there's a lot of conversation at one point of whether or not the US is going to more actively regulate open source models um now that we're through that I think that there's you know we do have United States has interesting levers at

[25:38] our disposal most notably it's the export control so I think for a lot of countries we can tell that we can negotiate and say hey do you want clusters of Nvidia gpus well if you want those you probably have to you know you have to build on top of our stack and that stack can include the gpus it can include the open source models it include sort of um a broad package um I don't know if that's going to be a priority for the new Administration I don't know this is a priority from a foreign policy standpoint but I think we have levers to their disposal just in

[26:09] the same way that on the flip side China has their levers you know they can offer large infrastructure build outs they can offer debt um they can offer you know a lot of free technology um and that's not stuff that we can match so it's a you know this kind of like give and take of you know a few decades ago China was like or the US was indisputably the sort of like provider of infrastructure and Technology of the world um hopefully that continues being the case in the next next Z yeah I mean I think to your point about the open source models I think we're going to look back on the

[26:40] decision by by Zak to actually have like a frontier scale open source model as being pretty critical in that kind of like Evolution very American decision for sure I know absolutely very patriotic absolutely um coming back to one of your maybe your other thoughts I mean obviously we have seen a lot of um kind like excitement about agentic AI agents kind of means everything and also means nothing right now um what what do you make of it like what do you how do you think in 2025 that kind of plays out both from a consumer perspective but then maybe also I think it's a little bit clearer what it means

[27:18] maybe for companies but like for consumers I I would still argue that I I mean I haven't used an agent to kind of do anything interesting for me yet uh well I haven't used one in the company and Thea but I'm curious what what do you make of that yeah I think there's I think where we are with agents so where we are with models overall is that they're um they're quite good in within one turn you know with one promp response they perform pretty well and then the performance just goes down a cliff as you increase the number of turns um and this is kind of like I mean

[27:54] nobody will really go out and say it but this is this is the reality of where we are so where they work really well is kind of like the the Google use case where you you ask a query and kind of look at the answer um where they work really poorly is if you actually need to like work with the model and do something more complicated you know they just they just um kind of fall off a cliff and so um I think that I think there's kind of two things I think the first is um the the industry is gonna like the the model companies are going

[28:24] to have to work very hard to improve the reliability um with greater numbers of turns and ultimately get these models to have some greater level of internal coherence and and ultimately sort of you know be more like entities right now models don't even know what they don't know and so they kind of [ __ ] and you know in general you're sort of like you know in interacting with the model on multiple terms you're sort of like it's like this [ __ ] machine that you know is statistically more likely to be correct than incorrect but it's sort of it's not like a it's not like an entity

[28:52] with any theory of mind that you're interacting with um so I think that'll that'll have to change but I actually think that like for a lot of for for this like Watershed agents moment um the biggest blocker is just um the product design yeah and I think the like you know there is the models are good enough already for there to be some agent product or agent kind of um product experience that actually will will be pretty mind-blowing and pretty great for a lot of people um I think for the people in this room it's probably not obvious because you know maybe we play with models all the time and so we know

[29:29] that they're actually really good at a lot of things but most people don't even know that the models are that good and so cursor is like a good example of this like I think most Engineers didn't actually know that the models were super good and you put it into cursor and all of a sudden it's like a part of their workflow it's much easier to use so I think that kind of moment will happen for consumers um and I think it's it really is just breaking the models out of the chat Paradigm into something that's a little bit more um baked into baked into the fundamental workflows I

[29:58] think this is one of the big this is like the biggest startup opportunity in 2025 if I guess is like actually iterating to find the right agent modality um I agree um maybe let's move on to talking about obviously you know everybody is talking about one of the biggest limitations to building the next kind of generation of Frontier models is simply accessibility of like data um how real do you think the data wall is um what do your take what's your take on kind of essentially synthetic data for training models um and obviously I think in this overall conversation scale is a huge kind of like you know uh huge part

[30:41] in the conversation so curious to get your thoughts there yeah I think like I think the the um the frenzy of the last you know 12 to 18 months of conversation was just like who has more computed who has more chips you know the more chips you have you know you're going to win and so it was just this like very unnuanced conversation around who has the biggest cluster and whoever has the biggest cluster is going to win and then I think what's like coming to light is you know even with a much bigger cluster you we hitting some data limits like you know we've we're hitting the limits of all

[31:15] publicly available data and um you know we need a tandem approach of you know production you know what are the specialized data sets in addition to the the computational power that's going to yield much greater performance and so I think I mean the this the uh the the data wall and the sort of like the wall on progress that we've hit right now is like certainly real know I think we've hit some kind of pre-training limits um and a lot of the progress now is coming from post training that post trainining is much more bottleneck by specialized data sets and and uh and sort of high

[31:51] quality data sets that don't look like the ones that we have on the internet so data is really critical I think there was a sort of like there was a belief um there were the research one of the research beliefs was that oh the models will just generate or long large scale reasoning traces or other kind of data that we'll just train pre-train the models on that hasn't really panned out I think a lot of the experiments on synthetic data um it just turns out you you like you lose so much of the real richness in the data distribution if you use synthetic data so I think the

[32:23] reality of what's going to happen going forward is like you know we're going to need to rely on new forms of human generated data to get to where we want to go with the models um but if we scale data in addition to scaling compute we can keep making progress we may not it may not be necessary to have the sort of like you know trillion dollar5 trillion doll 10 trillion dollar clusters that we were talking about a year ago but uh but we still probably need the hundred billion doll clusters so I think it's kind of I think there's some kind of normalization to the conversation that's

[32:56] happening yeah it's kind of like it was actually all about who had the biggest cluster and then will capitalism work and then we actually have a lot more chips which is great but I do think that the the new bottleneck is on the data side so I mean I'm curious like what how do you think what scales role in that like obviously you guys play a big role uh in the post training part of the world but do you think that are there opportunities for scale to even ramp up it's kind of its presence on on that front yeah I mean we're having to ramp up data production

[33:26] across the board so I think that like I think that frankly what's going to have to happen is like if you if we if you zoomed all the way out on the like path to AGI so to speak I think we're going to need you know the computer is going have to scale exponentially and then you'll have this like smaller curve but this equivalent curve of like data production needing to scale up exponentially and so I think we just need to sort of like keep scaling up on that curve and it needs to scale with everything else so you can't just scale the data ahead of the computer or you

[33:54] know you can't get one can't get too far of the other but um yeah I think our role is really our key role to play is in producing all the data to actually enables to get to that um get to you know get to data scaling in addition to compute scaling um maybe shifting a little bit to like you know a lot of folks out there in the audience are founders of AI companies um what do you think is different about starting a company in the AI Space versus you know uh all the companies that have been created kind of in in the prior generation like what

[34:28] what are some of the notable differences in in your mind so I think I think there's a few so um one big thing in any new technologically new space um there's just a lot of uncertainty and the flip side of that is a lot of [ __ ] so um so AI is no different as an industry um and I think a long time ago probably software of service was the same where there was you know a lot of a lot of [ __ ] but but AI is certainly to a point right now where like I don't know 80 to 90% of what is out there so to

[35:04] speak is [ __ ] and uh and a lot of like what people will say or what investors believe or what like other people if you go to a party will tell you like nobody knows what the [ __ ] is actually going on truly TR nobody knows what is actually going on and so um but there's a lot of people who be confident and wrong and so I think one of the things that's like hard about I'll get I'll tell this funny anecdote um so now we're getting into yeah okay so uh so when um when scale when we were raising our series a so was like 208 27

[35:43] no 2018 early 2018 um one of our first slides in our uh in Our Deck said something like um data is the lifeblood of AI systems or something like that and then um and then uh for we were pitching Sequoia it was a whole partner pitch I get to this slide and one of the Sequoia Partners I'm not going to say exactly who um very loudly and confidently says that's not true anding just told me that you know we no longer need more data for AI and so uh actually we're going to be fine without more data this is like two minutes into my presentation um and I'm

[36:23] just like uh I don't know what and I don't know I was I was 20 20 at the time so I was a little shell shocked and I was like uh I don't know what Andrew is talking about I'm sure I could talk to him about it but you obviously need way more data for AI and then you base was like no I don't think so and then um uh and then he didn't pay attention he was pretty senior so he didn't pay attention to the rest of the presentation soone the junior people paid any attention so I just went through the motions and then it would like you know obviously they

[36:51] said no and that's an example of the kind of thing like there's there's people who are like very very confident about some things which will just prove to be wrong um and that's true in any early industry it's certainly true right now because you know so many people in the tech ecosystem are paid to have thesis to have a thesis or to have thesis so it's like if you're like an investor you're paid to have a thesis in a point of view you can tell your LPS about your thesis or if you're like a if you're a product leader in a big tech company you're paid to have a thesis and a point

[37:24] of view and so so many people have these like beliefs that are not actually truly grounded in that much reality and so you're just like I think if you're building in this space um there's a large premium in you having the ability to figure out what you actually believe and then executing a against the flow to to accomplish that and I think that's probably the that's probably the biggest thing I think that like where you're going to get [ __ ] is if you follow the trends and like you could you could even raise a lot of money following trends like there's tons of companies if I look

[37:58] at the history of AI there's tons of companies that raised like $50 million series A's or100 million series A's by like doing something that was really like Vogue and in Trend but then it turns out that that was just a stupid idea so um I think that's like the that's like the Hallmark and AI obviously has gone through many ups and downs and you know um it'll there will be many more ups and downs in the future but um but the biggest challenge I think for Founders is to develop a sense of what you actually believe and then also having a mechanism by which you're constantly learning from the ecosystem

[38:35] versus just digging your heels in an A belief and then you get like washed out in the tide um that that'd be my the most important thing yeah you know I'd say that uh so I mean kind of it helps to be right um but i' say I mean there's actually a very interesting takeway particularly for the I I think the founders at spbc see which is that I think that there are very few uh categories or verticals that are actually have like any kind of incumbent or even kind of an an emerging kind of like winner that's actually winning right now I think a lot of the

[39:13] [ __ ] is still actually up for like grabs uh because I think that and it's not actually an indicator of like how much money has been raised um or even honestly like their usage metrics because everyone's usage metrics are kind of low right now um so I think that you just have to not be psyched out well you also have to be right which is I think a good filter uh but I also just don't think that there's it's I would not get SED out about essentially like you know the competition at this stage because there's still a ton of opportunity uh and if anything um you

[39:46] know if you have a bunch of people all kind of harping about the same in some Way's approach towards a particular idea it's pretty interesting to kind of take a slightly contrarian take on that particular subdomain um yeah yeah I think that I think in general you know most people in San Francisco uh don't really believe anything and they like get all their ideas off of Twitter and parties so um so I think your mandate is to not be one of those people and to think independently um and I think that's critical for for being able start a company because at various points whatever you build will be popular or

[40:26] unpopular popular unpopular um and uh and your job is to weather that crazy storm and that's true at the at the at the highest levels like Nvidia you know has been at various points very unsexy and then all of a sudden very sexy um and they'll go through those waves again and so um that's kind of the uh I think that's the Mandate yeah something that you know um something that Mark always used to tell us at Facebook is that you never add as bad as kind of the world tells you but you're Never As Good As the World tells you right and everybody always takes the like oh you know when I'm

[41:06] [ __ ] struggling then like I need to have belief in myself but there's a flip side to it is when the world is telling you that what everything you're doing is right that's also kind of like a sign that like maybe you're not actually thinking critically enough maybe you're not actually being opinionated enough maybe like you're actually converging to like you know that midwit mean you know so um so I I do think that there's something to be said about that uh it's easier said than done like the echo Chambers are real we're all pretty susceptible to it I'm curious if you agree with this Alex you I I think that

[41:37] one of the mythologies in Silicon Valley has been that in order to compete in AI you need a lot of money I would probably like take the opposite point of view on it I think there's a ton more opportunity either if you're actually doing on the foundation model space I think the T of opportunity in terms of being creative with like algorithmic changes maybe even like in your data sources and certainly like if you're building up and down the AI stack I think you can get a lot further with having like contrarian creative strategies than simply going out and raising boatloads of money um I I'm

[42:10] curious is does that resonate so um if your business plan is that you need to raise a lot of money because you need to spend a lot of money that is in business terms a bad business because um because it uh it um yeah obviously you want to build a profitable company you want to build something that makes money so if you have to spend a lot of money you're just deeper in the hole um I think that like I think that often times um Silicon Valley gets caught up in certain uh sort of uh um races for these grandiose ambitious objectives and so you know if you want

[42:50] to be emperor of the World by controlling the foundation model that is God then I think yeah you probably lots of money but um that's probably not the goal of most people here most companies the goal of most companies is to you know build a profitable business that uh has the ability to keep compounding for a long time period And so you know we we watch this pretty closely in the self-driving car um debacle where you know there were um there were hundreds of billions of dollars raised I think close to1 billion do raised to build uh large scale autonomous vehicles I think like now with the whole Cruise thing I

[43:30] think actually like basic like exactly zero of those companies ended up succeeding um so all the money that went into the self-driving cars like you have one winner which is wh Mo uh and it turns out they have an infinite bank account so they're not really a company you can compete with and I think there's a real analogy to where we are today which is there's like you know if you want to get on the treadmill and compete on trading Foundation models you're competing with companies with more money than than you know all but 10 country like truly like yeah um the the richest entities that Humanity has ever known

[44:07] that Microsoft should be in the G8 yeah exactly yeah um and that's not a good strategy I don't think you should try to compete with uh with an infinite balance sheet and so um I think that like for sure right now uh the the the imperative is like what is a creative way but what is a what is the what is a way in which you can develop something that is genuinely differentiated in the ecosystem and also something that you can keep investing in for a long time Horizon like I think for at least right now in AI like the the premium is actually just picking a

[44:42] certain problem to care about for a really long time um you know openi and anthropic and Google these are like very fearsome companies but they also have like literally a bajillion like 10,000 things to care about and worry about and optimize for and um it's very hard for any of them to focus on a particular area perplexity I think is the best example of this like it's still none of the other products are as good as perplexity in just simple search-based llm output yep and so um and they're just they haven't spent that much money they're just they're just like way more focused on that problem so Focus always

[45:20] has a big premium okay um we'll open it up to the audience uh in a couple of minutes so for start thinking of your questions um but before I open it up do you still write any code no no um no I used cursor a little bit the other day just to kind of like what's your hot see what the Hub up is about um I think it's a great product uh I think it's what I don't know is like it's pretty hard to fully monopolize a workflow yeah um in this way so I think that like if you look at the history of of of com businesses like

[46:04] like just owning a workflow is very hard to hold on to over long periods of time because like developers like anybody you know they're fickle and they're going to try something new and eventually something new will have like one B of whle that's cooler so um I think it'll be interesting to see how they choose to like sync roots and how they try to lay roots to try to like solidify their their position but uh we both a great thing yeah know I think developers are super picky and fickle my my take on installing cursor was also like oh I don't like these three small things and

[46:35] ignore kind of the 10 great things you know um who is one person that you think Silicon Valley should pay more attention to I think in general uh Silicon Valley is very politically int so to speak like or like politically I would say like un investigated and so um the uh the broad Strokes I mean I think there's this great um email that leaked a long time ago where it was like an email thread is the all this demographic information of the Baby Boomers and like that's actually the major thread to pay attention to and then I think that in many ways that's like if you get the

[47:17] like broad strr political threads right then those can be like huge trends for uh for growth and so obviously the major kind of the the major political threat right now is is you know populism and trumpism and all that kind of stuff and um it's one of these things where like I think you know hey I don't think I Tech compies mostly are apolitical and so shouldn't be that shouldn't be that focus on this stuff but um if you were to think like globally what's happening politically uh in terms of Greater isolationism greater nationalism greater populism etc etc um there's there's good takeaways for like what are the you know

[47:58] 20-year um Tailwinds that might that might be embedded in all the things that we see and so I think the sort of like greater political literacy in Silicon Valley is underrated all open it up to the audience um I'm Ryan I just wanted to ask you about your position on synthetic data so you didn't see very bullish on synthetic data for post training uh I wanted to hear more about that position and do you think that is indicative of where we are right now and just need to find the right tricks or is this more of a fundamental thing also understanding that like you said people

[48:32] get paid to have DCS you're also paid to have a thesis and this thesis is important for scale oh sorry I think sythetic data works for post training obviously but it's like I guess like synthetic data it's kind of it's not a philosopher stone it's not like it's not like this like thing that you just like I'm going to press the button and keep getting more data out of it it's like you need to come up with all these tricks to get like each trick gets you a little more synthetic data and you need to like fundamentally What synthetic data is is like you're leveraging um the

[49:05] structure of the data or you're leveraging all these priors that you can or these sort of like structural you're you're leveraging underlying structures in the data to get you know squeeze out some amount of synthetic data to improve your model like that obviously works as a paradigm but it's not like a infinite Pursuit it's it's not something that like work to infinity and so I guess my overall thought is like it obviously works and it's obviously part of a world class Post Trading effort it's just not the like it's not the like solution to overscaling WS Hi how are you my name is Lorena and um yeah I would like to um

[49:43] understand you know most entrepreneurs uh at the beginning they're told you know you should Target small to mediumsized companies you shouldn't go for an Enterprise uh you know you're likely to fail uh so what what would you say especially if I understand correctly from a early from an early stage you focus on Enterprise why did you choose Enterprise despite the advice that said even from why combinator and um yeah just any advice for for for entrepreneurs who want to go straight you know cater to Enterprise and not small to medium uh businesses so the issue with most Enterprises is that you can't even believe what they tell you

[50:24] because um because there's like then I mean it's just like this it's like this most large Enterprises are these cancers and most of the people you'll talk to um have nothing to do with the success of the company but they have some job where they're sort of like you know responsible for a thing and and so you're like it's it's like this whole uh rats nest that you have to learn to navigate and figure out exactly how you leverage it to to work and uh but if it works then obviously you know it can be incredibly it can work incredibly well all the largest um all

[51:04] the largest companies in the world are either Enterprise companies or consumer companies so um the sort of like small this is like a really common about like large very large scale businesses but small medium businesses you know you tap out at some point and this is not really a statement for startups um so I think I think for like I think for a startup if you want there are some startups that focus on Enterprises we were one of them there's there's some others that I think have been successful but if you want to do it you have to know that you'll have to spend let's say 30% of your time

[51:36] navigating the bureaucracy and [ __ ] of an organization um whereas if you work with small medium businesses then they don't even have time to lie to you so they're just going to you know whatever they tell you you can take it face value which I think is a great benefit for um for startups um yeah I mean I think I think the general advice probably is that like focus on small and medium businesses I of you want to like get lift off and success as a company small medium businesses is definitely the the obvious gradient um but I think depending on whatever the ecosystem is like like if

[52:13] you're in a startup ecosystem where 99% of companies are focus on small to medium businesses then maybe there's less opportunity there and I think this was kind of the this is the thing that I notice um if you think I think if you look in longer themes with startups the kind of this is what kind of ends up happening which is that um so a bunch of companies start uh half of them are trying to model themselves against the the companies that are successful at that time they all end up being in like perfect competition with one another so none of them get traction then some

[52:47] company those focus on some Fringe idea ends up having no competition but that idea ends up working and so then two years later that's the hot company and then all the startups try to copy that try to model themselves over that company and so on and so forth and so you never want to be like if you look at like all the companies around you you never want to be exactly where all the other companies are focused you want to try doing something that's a little bit Fringe because um it's kind of you avoid the the perfect competition so to speak sure hey Alex um just a quick

[53:23] question on um your thought on people's opinions and kind of having a specific opinion I think people's opinions are often a combination of their life experiences Force principles thinking and your own inputs um now it's difficult to be right if most of your inputs are [ __ ] so what are your inputs and how do you filter for your inputs so um what what I choose to like um I try really hard to focus on get it on talking to so the best inputs for me personally are um is like writing on the Internet or like talking to specific experts and I try really hard to talk to

[54:04] people who like truly do not care about what other people think right um and you'll you'll find these people because they just really seem to beat to the you know you know go to the beat of Their Own Drum and you know they like they'll seem very strange and weird but like try to go to try to get the advice of people who like genuinely are uh yeah are are independent thinkers and um and also look at the writings of people who are independent thinkers um I think that's that's another thing I think there's a there's a one of the great benefits of I think the modern

[54:41] internet ecosystem is there's there's so much high quality writing on substack that like you can actually learn so much about any individual thing from like experts who post about on substack it's actually like crazy um and so the those I think were the the key inputs and then your customers and I think that like um you just have to uh I adopt a mindset where like customers are just always right and like um some people are successful in in various ways without needing this but um you just have to like like you have to be super super sub subservient to your customers cool uh I think that's uh

[55:21] that's think that's all the time that we have uh thank you so much Alex for coming by and sharing get talk today yeah thanks thank you [Music]

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