SPEAKER_00: all right everybody welcome back to your favorite podcast of all time the all in podcast episode 160 something with me again chamath palihapitiya he's the ceo of a company and invests in startups and uh his firm is called social capital we also have david freeberg the sultan of science he's now a ceo as well and we have david sacks from craft ventures in some undisclosed hotel room somewhere

SPEAKER_03: how are we doing boys good thank you this is an odd intro your intro be any more low energy and

SPEAKER_07: dragged out i'm sick what do you want me to do i'm trying to fake the effort all right give me give me one more shot watch this watch this watch professional you want professionalism fake the

SPEAKER_10: effort come on here we go you want professionalism i'll show you guys professionalism is that binaka what was that is that's not good oh it is the secret banana boat

SPEAKER_17: all right everybody welcome to the all in podcast episode 167 168 with me of course the

SPEAKER_22: rain man himself david sacks the dictator chairman chamath palihapitiya and our sultan of science

SPEAKER_31: david freeberg how we doing boys great how are you energy enough is it 167 or 168 i don't know who cares we at least get you to know the episode number who cares we unfortunately or fortunately

SPEAKER_22: we're going to be doing this thing forever the audience demands it it doesn't matter this is like a twilight zone episode we're going to be trapped in these four bubbles forever you know like a superman

SPEAKER_24: it's a it is it's this is like the it is uh the gift when they were trapped in that glass uh zed was

SPEAKER_43: that zed zod yeah neil before zon and he spun through the universe and the plastic thing forever for

SPEAKER_00: for infinity until that until superman took the nuclear bomb out of the uh eiffel tower and threw it into space and blew it up and freedom my background today i think i'm going to have to

SPEAKER_49: change now that you've referenced this important scene that was the best moment of that movie jakel

David Sacks: where terrence stamp says kneel to the president and the president says oh god yes and then

SPEAKER_54: terrence stamps like was uh zod not god zod zod neil before sod that was superman 2 or 3. yeah

SPEAKER_22: superman 2 is pretty much the best yeah you know like empire strikes back like terminator 2 it's

SPEAKER_00: always the second one that's the best one all right everybody we got a lot to talk about today apologies for my voice a little bit of a cold nvidia blew the doors off their earnings for the third

SPEAKER_63: straight quarter shares were up 15 on thursday representing a nearly 250 billion dollar jump in market cap so let's just let that sit in for a second this is the largest single day gain in market cap 247 billion dollars added in market cap previously meta did something similar earlier this year remember

SPEAKER_00: everybody was down on that stock because they were doing all the crazy stuff with reality labs and then they got focused and laid off 20 000 people they added 196 billion dollars in other words they added like two and a half airbnbs to their valuation but let's just get to the results the results are absolutely stunning and dare i say unprecedented q4 revenue 22.1 billion that's up 22 percent quarter

SPEAKER_63: over quarter up 265 percent year over year the net income was 12.3 billion 9x year over year and the

SPEAKER_00: gross margin of 76 percent was up two points quarter of a quarter 12.7 percent year over year but look at this revenue ramp this is extraordinary q1 of 2024 this juggernaut starts and it does not stop and it doesn't look like it's going to stop just to run up from 7 billion go all the way to 22 billion in revenue for the quarter absolutely extraordinary and if you want to know why this is happening why is nvidia putting up these kind of numbers this chart explains everything this is all about data centers obviously if you heard of nvidia before the ai boom it was gaming professional visualizations you know i think people making movies and stuff like that autos uh used nvidia for self-driving that kind of stuff but if you look at this chart you'll see data centers just starting four quarters ago starts to ramp up as everybody builds out the infrastructure for new data centers to deal with

SPEAKER_05: generative ai so just to add one point here jason so what you can see is that nvidia was around for a long time and it was making these chips these gpus as opposed to cpus and they were primarily used by games and by virtual reality software because gpus are better obviously at graphical processing they use vector math to create these like 3d worlds and this vector math that they use to create these 3d worlds is also the same vector math that ai uses to reach its outcomes so with the explosion of llms

SPEAKER_68: it turns out that these gpus are the right chips that you need for these cloud service providers to build out these big data centers to serve now all of these new ai applications so nvidia was in the perfect place at the perfect time and that's why it's just exploded and what you're seeing is the

SPEAKER_70: build out of this new cloud service infrastructure for for ai yeah and um and also helping the stock is the fact that they bought back 2.7 billion worth of their shares as part of a 25 billion dollar

SPEAKER_00: buyback plan but this company's firing on all cylinders revenue is obviously ripping as people put in orders to replace all of the data centers out there or at least augment them with this technology with gpus a100s h100s etc the gross margin's been expanding they have huge profits and they're still projecting more growth in q1 around 24 billion which would be a 3x increase year over year and this obviously has made the entire market rip as in video goes so does the market right now and the s p 500 and nasdaq are at record highs at the time of this taping chamath your general thoughts here on something i don't think anybody saw coming except for you and your investment in grok maybe

SPEAKER_75: and a couple of others i think what i would tell you is that the bigger principle and we've talked about

SPEAKER_77: this a lot jason is that in capitalism when you over earn for enough of a time what happens is competitors decide to try to compete away your earnings in the absence of a monopoly the amount of time that you have tends to be small and it shrinks so in the case of a monopoly for example take google you can over earn for decades and it takes a very very long time for somebody to try to displace you we're just starting to see the beginnings of that with things like perplexity and other services that are chipping away at the google monopoly but at some point in time all of these excess profits are competed away in the case of nvidia what you're now starting to see is them over earn in a very massive way so the real question is who will step up to try to compete away those profits the old bezos quote right your margin is my opportunity and i think we're starting to see and you've mentioned grok who had a super viral moment i think this week but you're starting to see the emergence of a more detailed understanding of what this market actually means and as a result who will compete

SPEAKER_75: away the inference market who will compete away the training market and the economics of that are just

SPEAKER_00: becoming known to now more and more people freeberg your thoughts we were talking i think was last week or the week before about the possibility of nvidia being a 10 trillion dollar company the largest company in the world what are your thoughts on these spectacular results and then jamaht's point everybody is watching this going hmm maybe i can get a slice of that pie and maybe i can create a more competitive offering obviously we saw sam haltman rumored to

SPEAKER_84: be raising 7 trillion which feels like a fake number it feels like that's maybe the market size

SPEAKER_85: or something but your thoughts here i don't think anything's changed on the nvidia front there's this

SPEAKER_86: accelerated compute build out underway in data centers everyone's building infrastructure and then everyone's trying to build applications and tools and services on top of that infrastructure the infrastructure build out is kind of the first phase the real question ultimately will be does the initial cost of the infrastructure exceed the ultimate value that's going to be realized on the application layer in the early days of the internet a lot of people were buying oracle servers they were like 3 000 bucks a server and they were running these oracle servers out of an internet connected data center and it you know took a couple of years before folks realized that for large-scale distributed compute applications you're better off using cheaper hardware you know cheaper server racks cheaper hard drives cheaper buses and assuming a shorter lifespan on those servers and you could cycle them in and out and you didn't need the redundancy you didn't need the certainty you didn't need the the runtime guarantees and so you could use a lower cost higher failure rate but much much net lower cost kind of approach to building out a data center for internet serving and so the oracle servers didn't really take the market and early on everyone thought that they would so i think chema's point is right now nvidia has been at this for a very long time and the real question is how much of an advantage do they have particularly that there is this need to use fabs to build replacement technology so over time will there be better solutions that use hardware that's not as good but the software figures out and they build new architecture for running on that hardware in a way that kind of mimics what we saw in the early days of the build out of the internet so tbd right the same is true in switches right so in networking a lot of the high-end high-quality networking companies got beaten up when lower cost solutions came to market later and so they looked like they were going to be the biggest business ever i mean you could look at cisco during the early days of the internet build out and everyone thought cisco was uh the picks and shovels of the internet they were going to make all the all the values going to go to cisco so we're kind of in that same phase right now with nvidia the real question is is this going to be a much harder hill to compete on than we've ever seen given the development cycle on chips and the requirement to use these fabs to build chips it may be a harder hill to kind

SPEAKER_00: of get up sex so we'll see your thoughts you think um we're getting to the point where maybe we'll have bought too many of these uh built out too much infrastructure and it will take time for the

SPEAKER_70: application layer as freeberg was alluding to to monetize it well i think the question everyone's

SPEAKER_05: asking right now is are these results sustainable can nvidia keep growing at these astounding rates you know will the build out continue and the comparison everyone's making is to cisco and there's this chart that's been going around overlaying the nvidia stock price on the cisco stock price and you can see here the orange line is nvidia and the blue line is cisco and it's almost like a perfect match now what happened is that at a similar point in the original build out of the internet of the dot-com era you had the market crash at the end of march of uh 2000 and cisco never really recovered from that peak valuation but i think there's a lot of reasons to believe nvidia is different one is that if you look at nvidia's multiples they're nowhere near where cisco's were back then so the market in 1999 and early 2000 was way more bubbly than it is now so nvidia's valuation is much more grounded in real revenue real margins real profit second you have the issue of competitive mode cisco was selling servers and networking equipment fundamentally that equipment was much easier to copy and commoditize than gpus these gpu chips are really complicated i think jensen

SPEAKER_68: made the point that their hopper 100 product he said you know don't even think of it just like a chip

SPEAKER_05: there's actually 35 000 components in this product and it weighs 70 pounds this is more like a main

SPEAKER_68: frame computer or something that's dedicated to processing somewhere between a rack server and

SPEAKER_92: the entire rack yeah it's giant and it's heavy and it's complex it does say something here chamath i think

SPEAKER_00: about how well positioned big tech is in terms of seeing an opportunity and quickly mobilizing to capture that opportunity these servers are being bought by you know people like amazon i'm sure apple obviously facebook meta i don't know if google is buying them as well i would assume so tesla so everybody's buying these things and they had tons of cash sitting around it is pretty amazing how nimble the industry is and this opportunity feels like everybody is looking at it like mobile and cloud i have to get mobilized quickly to not get disrupted you're bringing up an excellent point and i would like

SPEAKER_75: to tie it together with friedberg's point so at some point all of this spend has to make money right otherwise you're you're going to look really foolish for having spent 20 and 30 and 40 billion dollars so nick if you just go back to the to the revenue slide of nvidia i can try to give you a framing of this at least the way that i think about it so if you look at this like what you're talking

SPEAKER_77: about is look who is going to spend 22.1 billion dollars well you said it jason it's all a big tech why because they have that money on the balance sheet sitting idle but when you spend 22 billion dollars their investors are going to demand a rate of return on that and so if you think about what a reasonable rate of return is call it 30 40 50 and then you factor in and that's profit and then you factor in all of the other things that need to support that that 22 billion dollars of spend needs to generate probably 45 billion dollars of revenue and so jason the question to your point and to friedbrook's point the 64 000 question is who in this last quarter is going to make 45 billion on that 22 billion of spend and again what i would tell you to be really honest about this is that what you're seeing is more about big companies muscling people around with their balance sheet and being able to go to nvidia and say i will give you committed pre-purchases over the next three or four quarters and less about here is a product that i'm shipping that actually makes money which i need enormous more compute resources for it's not the latter most of the apps the overwhelming majority of the apps that we're seeing in ai today are toy apps that are run as proofs of concept and demos and run in a sandbox it is not production code this is not we've rebuilt the entire autopilot system for the boeing and it's now run with agents and bots and all of this training that's not what's happening so it is a really important question today the demand is clear it's the big guys with huge gobs of money and by the way nvidia is super smart to take it because they can now forecast demand for the next two or three quarters i think we still need to see the next big thing and if you look in the past what the past has showed you it's the big guys don't really invent the new things that make a ton of money it's the new guys who because they don't have a lot of money and they have to be a little bit more industrious come up with something really authentic and new yeah constraint makes for great art yeah we haven't seen that yet so i think the revenue scale will continue for like the next two or three years probably for nvidia but the real question is what is the terminal value and it's the same thing that sac showed in that cisco slide people ultimately realized that the value was going to go to other parts of the stack the application layer and as more and more money was accrued at the application layer of the internet less and less revenue multiple and credit was given to cisco and that's nothing against cisco because their revenue continued to compound right and they did an incredible

SPEAKER_72: job but the valuation got cut so freeberg if we're looking at this chart the winner of netflix the winner of

SPEAKER_00: the cisco chart might in fact be somebody like netflix they actually got you know hundreds of millions of consumers to give them cash facebook and then you have google and facebook as well generating all that traffic and then youtube of course who do you see the winner here as in terms of the application layer who are the billion customers here who are going to spend 20 bucks a

SPEAKER_86: month five bucks a month whatever it is so here well i mean let me just start with this important point if you look at where that revenue is coming from to chamath's point it's coming from big cloud service providers so google and others are building out clouds that other application developers can build their ai tools and applications on top of so a lot of the build out is in these cloud data centers that are owned and operated by these big tech companies the 18 billion of data center revenue that nvidia realized is revenue to them but it's not an operating expense to the companies that are building out so this is an important point on why this is happening at such an accelerated pace when a big company buys these chips from nvidia they don't have to from an accounting basis market as an expense in their income statement it actually gets booked as a capital expenditure in the cash flow statement it gets put on the balance sheet and they depreciate it over time and so they can spend 20 billion dollars of cash because google and others have 100 billion of cash sitting on the balance sheet and they've been struggling to find ways to grow their business through acquisitions one of the reasons is they there aren't enough companies out there that they can buy at a good multiple that can give them a good increase in profit the other one is that antitrust authorities are blocking all of their acquisitions and so what do you do with all that cash well you can build out the next gen of cloud infrastructure and you don't have to take the hit on your p l by doing it so it ends up in the balance sheet and then you depreciate it over typically four to seven years so that money gets paid out on the on the income statement at these big companies over a seven-year period so there's a really great accounting and m a environment driver here that's causing the big cloud data center providers to step in and say this is a great time for us to build out the next generation of infrastructure that could generate profits for us in the future because we've got all this cash sitting around we don't have to take a p l hit we don't have to acquire a cash burning business and you know frankly we're not going to be able to grow through m a because of antitrust right now anyway so there's a lot of other motivating factors that are causing this near-term acceleration as they're trying to find ways to grow yeah and i know that was an accounting point but i think

SPEAKER_63: it's a really important i think it's a valid one if you if 100 billion gets spent this year you divided by 4 25 billion in revenue would have to come from that or something in that range yeah and so sax any guesses you have to just keep in mind i think freeberg what you said is very true

SPEAKER_75: for gcp spend but not necessarily for google spend it's true for aws spend but not necessarily for amazon spend and it's true for azure spend not true for microsoft spend and it's largely not true for tesla and facebook because they don't have clouds so i think the question to your point

SPEAKER_77: that ben for obvious reasons nvidia doesn't disclose it is what is the percentage of that 21 billion that just went to those cloud providers that they'll then expose to to to everybody else versus what was just absorbed because at facebook mark had that video about how many h100 that's all for him

SPEAKER_86: right but it is still it is still capitalized is my point so they don't have to book that as an expense it sits on the balance sheet yeah sure and they earn it down over time you're helping to explain why

SPEAKER_90: these big cloud service providers are spending so much on the data because they have so much cash

SPEAKER_108: because they're very profitable and there's nowhere else to put the money right well so

SPEAKER_05: that would seem to indicate that this is more in the category of one-time build-out than sustainable ongoing revenue i think the the big question is the one that chamath asked which is what's the terminal value of nvidia i think a like a simple framework for thinking about that

SPEAKER_68: is what is the total addressable market or tam related to gpus and then what is their market share going to be right now their market share is something like 91 that's clearly going to come down but

SPEAKER_05: their moat appears to be substantial the wall street analysts i've been listening to think that in five years they're still going to have 60 something market share so they're going to have a substantial percentage of this market or this tam then the question is i think with respect to tam

SPEAKER_68: is what is one-time build out versus steady state now i think that clearly there's a lot of build out happening now that's almost like a backfill of capacity that people are realizing they need but even the numbers you're seeing this quarter kind of understate it because first of all nvidia was supply constrained they cannot produce enough chips to satisfy all the demand their revenue would would have been even higher if they had more capacity second you just look at their forecast so the fiscal year that just ended they did around 60 billion of revenue they're forecasting 110 billion for the fiscal year that just started so they're already projecting to almost double based on the demand that they clearly have visibility into already so it's very hard to know exactly what the terminal or steady state value of this market's going to be even once the cloud service providers do this big build out presumably there's always going to be a need to stay up to date with the latest chips

SPEAKER_93: right here's a framework for you sax tell me if this makes sense intel was the basically the mother

SPEAKER_77: of all of modern compute up until today right i think the cpu was the the most fundamental workhorse that enabled local pcs it enabled networking it enabled the internet and so when you look at the market cap of it as an example it's about 180 odd billion dollars today the economy that it created that it supports is probably measured call it in a trillion or two trillion dollars maybe five trillion let's just be really generous right and so you you can see that there's this ratio of the enabler of an economy and the size of the economy and those things tend to be relatively fixed and they recur repeatedly over and over and over if you look at microsoft it's market cap relative to the economy that it enables so the question for nvidia in my mind would be not that is it not going to go up in the next 18 to 24 months it probably is for exactly the reason you said it is super set up to have a very good meet and beat guidance for the street which they'll eat up and all of the algorithms that trade the press releases will drive the price higher and all of this stuff will just create a trend upward i think the bigger question is if it's a four or five trillion dollar market cap in the next two or three years will it support a hundred trillion dollar economy because that's what you would need to believe

SPEAKER_111: for those ratios to hold otherwise everything is just broken on the internet yeah i mean so the history

SPEAKER_05: of the internet is that if you build it they will come meaning that if you make the investment in the

SPEAKER_68: capital assets necessary to power the next generation of applications those applications have always eventually gotten written even though it was hard to predict them at the time so in the late 90s when we had the whole dot-com bubble and then bust you had this tremendous build out not just of kind of servers and all the networking equipment but there was a huge fiber build out yep by all the telecom companies and the telecom companies had a cisco like you know uh peak it was worse you know welcome and then well they went bankrupt a lot of them yeah well the problem there was that a lot of the build that happened with debt and so when you had the dot-com crash and all the valuations came down to earth that's why a lot of them went under yeah cisco wasn't in that position but in any event my point is in the early 2000s when the dot-com crash happened everyone thought that these telecom companies had over invested in fiber as it turns out all that fiber eventually got used the internet went from you know dial up to broadband we started doing seeing streaming social networking all these applications started eating up that bandwidth so i think that the history of these things is that the applications eventually get written they get developed if you build the infrastructure to power them and i think with ai the thing that's exciting to me as someone who's really more of an application investor is that we're just at the beginning i think of a huge wave of a lot of new creativity and applications that's going to be written and it's not just b2c it's going to be b2b as well you guys haven't really mentioned that it's not just consumers and consumer applications are going to use these cloud data centers that are buying up all these gpus it's going to be enterprises too i mean these enterprises are using azure they're using google cloud and so forth so there's a lot i think that's still to come i mean we're just at the beginning of a wave that's probably going to last

SPEAKER_00: at least a decade yeah and to your point one of the reasons youtube google photos ifoto a lot of these things happened was because the infrastructure build out was so great during the dot-com boom that

SPEAKER_116: the prices for storage the prices for bandwidth sacks plummeted and then people like chad hurley looked at it and were like you know what instead of charging people to put a video on the internet and then charging them for the bandwidth they used we'll just let them upload this stuff to youtube and we'll figure it out later same thing with netflix yeah i mean look when we were developing paypal

SPEAKER_05: in the late 90s really around 1999 uh you could barely upload a photo to the internet i mean so like

SPEAKER_68: the idea of having an account with a profile photo on it was sort of like why would you do that it's just prohibitively slow everyone's going to drop off yeah by 2003 it was fast enough that you could do that and that's why social networking happened i mean literally without that performance improvement

SPEAKER_05: like even having a profile photo on your account was something that was too hard to do your linkedin

SPEAKER_00: profile was like too much bandwidth yeah and then let alone video i mean the you would get yeah you probably remember these days you would put up a video on your website if it went viral your website got turned off because you would hit your 5 000 or 10 000 a month cap all right grok also had a huge week that's grok with a q not to be confused with elon's grok with a k chamath you've talked about grok on this podcast a couple of times obviously you were the i guess you were the first investor the seed investor you pulled up these lpus and this concept out of a team that was at google maybe you could explain a little bit about grok's viral moment this week in the history of the company which i know

SPEAKER_75: has been a long road for you with this company i mean it's been since 2016 so again proving what you guys have said many times and what i've tried to live out which is just you just got to keep grinding 90 of the battle is just staying alive in business yeah and having oxygen to keep trying things and then eventually if you get lucky which i think we did things can really break in your favor so this weekend you know i've been tweeting out a lot of technical information about why i think this is such a big deal but yeah the the moment came this weekend combination of hacker news and some other places and essentially we had no customers two months ago i'll just be honest and between sunday and tuesday we've just we're overwhelmed and i think like the last count was we had 3 000 unique customers come and try to consume our resources from every important fortune 500 all the way down to developers and so i think we're very fortunate i think the team has a lot of hard work to do so it could mean nothing but

SPEAKER_77: it has the potential to be something very disruptive so what is it that people are glomming onto you have to understand that like at the very highest level of ai you have to view it as two distinct problems one problem is called training which is where you take a model and you take all of the data that you think will help train it and you do that you train the model you learn all over all of this information but the second part of the ai problem is what's called inference which is what you and i see

SPEAKER_75: every day as a consumer so we go to a website like chat gpt or gemini we ask a question and it gives us

SPEAKER_77: a really useful answer and those are two very different kinds of compute challenges the first one is about brute force and power right if you can imagine like what you need are tons and tons of machines tons and tons of like very high quality networking and an enormous amount of power in a

SPEAKER_75: data center so that you can just run those things for months i think elon publishes very transparently for example how long it trains to to train his grok with a k right model and it's in the months inference is something very different which is all about speed and cost what you need to be in order to answer a question for a consumer in a compelling way is super super cheap and super super fast and we've talked about why that is important and the grok with the q chips turns out to be extremely fast and extremely cheap and so look time will tell how big this company can get but if you tie it together with what jensen said on the earnings call and you now see developers stress testing us and finding

SPEAKER_77: that we are meaningfully meaningfully faster and cheaper than any nvidia solution there's the potential

SPEAKER_75: here to be really disruptive and we're a meager unicorn right our last valuation was like a billion something versus nvidia which is now like a two trillion dollar company so there's a lot of market cap for grok to gain by just being able to produce these things at scale which could be just an enormous outcome for us so time will tell but a really important moment in the company and very exciting

SPEAKER_43: can i just observe like off topic how an overnight success can take eight years no i was thinking the same line it's a seven year overnight success in the making there's this class of businesses

SPEAKER_86: that i think are unappreciated in a post internet era where you have to do a bunch of things right before you can get any one thing to work and these complicated businesses where you have to stack either different things together that need to click together in a in a stack or you need to iterate on each step until the whole system works end to end can sometimes take a very long time to build and the term that's often used for these types of businesses is deep tech and they fall out of favor because in an internet era and in a software era you can find product market fit and make revenue and then make profit very quickly and so a lot of entrepreneurs select into that type of business instead of selecting into this type of business where the probability of failure is very high you have several low probability things that you have to get right in a row and if you do it's going to take eight years and a lot of money and then all of a sudden the thing takes off like a rocket ship you've got a huge advantage you've got a huge moat it's hard for anyone to catch up and this thing can really spin out on its own i do think elon is very unique in his ability to deliver success in these types of businesses tesla needed to get a lot of things right in a row spacex needed to get a lot of things right in a row all of these require a series of complicated steps or a set of complicated technologies that need to click together and work together but the hardest things often output the highest value and you know if you can actually make the commitment on these types of businesses and get all the pieces to click together there's an extraordinary opportunity to build moats and to take huge amounts of market value and i think that there's an element of this that's been lost in silicon valley over the last couple of decades as the fast money in the internet era has kind of prioritize other investments ahead of this but i'm really hopeful that these sorts of chip technologies spacex in biotech we see a lot of this these sorts of things can kind of become more in favor because the the advantage as these businesses work seems to realize hundreds of billions and sometimes trillions of dollars of market value and be incredibly transformative for humanity so i don't know i just think it's an observation i wanted to make about the greatness of these

SPEAKER_05: businesses when they work out well i mean open ai was kind of like that for a while totally i mean it was this like wacky non-profit that was just grinding on an ai research problem for like six years and then it finally worked and got productized into chat gpt totally but you're right spacex was kind of like that i mean the big money maker at spacex is starlink which is the satellite network it's basically broadband from space and it's on its way to handling i think a meaningful percentage of all internet traffic but think about all the things you have to get to to get that working first you had to create a rocket that's hard enough then you had to get to reusability then you had to create the whole satellite network so at least three hard things in a row well consumers to adopt it i mean

SPEAKER_75: you know don't forget the final step yeah we had no idea where the market was like early on it started in my office and so jonathan and i would be kind of always trying to figure out what is the initial go to market and i remember i emailed elon in at that period when they were still trying to

SPEAKER_77: figure out whether they were going to go with lidar or not and we thought wow maybe we could sell tesla

SPEAKER_75: the chips you know but and then tesla brought in this team just to talk to us about what the design

SPEAKER_77: goals were and basically said no in kind way but they said no then we thought okay maybe it's like for high frequency traders right because like those folks want to have all kinds of edges and if we have these big models maybe we can accelerate their decision making they can measure revenue that didn't work out then it was like you know we tried to sell to three letter agencies that didn't really work out our original version was really focused on image classification and convolutional neural nets like resnet that didn't work out we ran head first into the fact that nvidia has this compiler product called cuda and we had to build a high class compiler that you could take any model without any modifications all these things to your point are just points where you can just very easily give up and then there's like we run out of money so then you write money in a note right because everybody wants to punt on valuation when nothing's working you tried six beach head markets you couldn't land the boat right you have to make a decision to just keep going if you believe it's right and if you believe you are right yeah and that requires shutting out we talked about this in the masa example last week but it just requires shutting out the noise because it's so hard to

Chamath Palihapitiya: believe in yourself it's so hard to keep funding these things it's so hard to go into partner meetings

SPEAKER_77: and defend a company and then you just have a moment and you just feel i don't know i feel very vindicated but then i feel very scared because jonathan still hasn't landed it you know what i mean

SPEAKER_00: you mentioned all those boats landing and trying to china there's missteps but 3 000 people signed up who are they are they developers now and they're going to figure out the applications yeah i think that

SPEAKER_75: back to the original point my thought today is that ai is more about proofs of concept

SPEAKER_77: and toy apps and nothing real yep i don't think there's anything real that's inside of an enterprise that is so meaningfully disruptive that it's going to get broadly licensed to other enterprises i'm not saying we won't get there but i'm saying we haven't yet seen that cambrian moment of monetization we've seen the cambrian moment of innovation yeah and so that gap has still yet to be crossed and i think the reason that you can't cross it is that today these are in an unusable state the results are not good enough they are toy apps that are too slow that require too much infrastructure and cost so the potential is for us to enable that monetization leap forward and so yeah they're going to be developers of all sizes and the people that came are literally

SPEAKER_75: companies of all sizes i saw some of the names of the big companies and they are the who's who of the

SPEAKER_86: s p 500 how do you guys reconcile this deep tech high outcome opportunity that everyone here has seen and been a part of as an investor participant in versus the more de-risked faster time to market and you know chamath in particular like in the past we've talked about some of these deep tech projects like fusion and so on and you've highlighted well it's just not there yet it's not fundable what's the distinction between a deep tech investment opportunity that is fundable and that you keep grinding at that has this huge outcome uh what makes the one like fusion that's not fun it's a

SPEAKER_75: phenomenal quick question great question my answer is i have a very simple filter which is that i don't want to debate the laws of physics when i fund a company so with jonathan when we were initially trying to figure out how to size it i think my initial check was like seven to ten million dollars

SPEAKER_77: or something and the whole goal was to get to an initial tape out of a design we were not inventing anything new with respect to physics we were on a very old process technology i think we're still on 14 nanometer we were on 14 nanometer eight years ago okay so we weren't pushing those boundaries all we were doing was trying to build a compiler and a chip that made sense in a very specific construct to solve a a well-defined bounded problem so that is a technical challenge but it's not one of physics when i've been pitched all the fusion companies for example there are fuel sources that require you to make a leap of physics where in order to generate a certain fuel source you either have to go and

Chamath Palihapitiya: harvest that on the moon or in a different planet that is not earth or you have to create some fundamentally different way of creating this highly unique material that is why those kinds of problems to me are poor risk and building a chip is good risk it doesn't mean you're going to be successful

SPEAKER_77: in building a chip but the risks are bounded to not of fundamental physics they're bounded to go to market and technical usefulness and i think that that removes an order of magnitude risk in the outcome

SPEAKER_85: so i mean there's still like a bunch of things that have to be right in a row to make it work but yeah it doesn't mean it's going to work yeah all i'm saying is i don't i don't want it to fail

SPEAKER_75: because we built a reactor and we realized hold on to get heavy hydrogen i got to go to the moon

SPEAKER_86: right and jay cal and sax how do you sax i know you don't but you invested in a couple yeah so maybe you guys can highlight how you thought about deep tech opportunities

SPEAKER_00: versus probably do something really difficult like this every 50 investments or so because most of the entrepreneurs coming to us because we're seed investors or pre-seed investors they would be going to a biotech investor or a hardware investor who specializes in that not to us but once in a while we meet a founder we really like and so contra line was one we were introduced to somebody who's doing this really interesting contraception for men where they put a gel into your vas deferens and you as a a man can take control of your reproduction you basically it's a it's not a vasectomy it's just a gel that goes in there and blocks it and this company is now doing human trials and doing fantastic but this took forever to get to this point and then uh you guys some of you are also investors in cafe x which we love the founder and this company should have died like during covid and making a robotic coffee bar when he started you know seven eight years ago was incredibly hard he had to build the hardware he had to build a brand he had to do locations he had to do software and now he's selling these machines and people are buying them and the two in san francisco at sfo are making like uh i think they the two of them make a million dollars a year and it's the highest per square footage of any store in an airport and so we've just been grinding and grinding and you got to find a founder who's willing to make it their lives work in these kind of situations but you start to think about the degree of difficulty hardware software reach mobile apps i mean it just gets crazy how hard these businesses are as opposed to i'm building a sas company i build software i sell it to somebody to solve their sas problem it's like it's very one-dimensional right and it's pretty straightforward these businesses typically have

SPEAKER_86: five components yeah and sex you've been an investor in spacex but you don't make those sorts

SPEAKER_152: of investments regularly that craft is that fair yeah i have an elon exception okay it's about the founder we're in our portfolio allocation we say this much early stage this much late stage this

SPEAKER_161: much elon elon exception yeah i mean you you have to be so dogged to to want to take something like

SPEAKER_00: this on because the good stuff happens like you're saying freeberg you're seven eight nine ten as opposed

SPEAKER_63: to like a consumer product either works or a dozen by year three or four the only app that took a really long time people don't know this but twitter actually took a long time to catch on it was kind

SPEAKER_164: of cruising for two or three years and then south by southwest happened ashton kutcher got on it

SPEAKER_166: obama got on it i think the network effect i think i think network effect businesses are different because that's all about getting your seat of your network what i'm talking about is the technical coordination

SPEAKER_86: of lots of technically difficult tasks that need to sync up it's like getting a master lock with like 10 digits and you got to figure out the combination of all 10 digits and once they're all correct then the lock opens and prior to that if any if any one number is off the lock doesn't open and i think these technically difficult businesses are some of the and they are the hardest and they do require the most dogged personalities to persist and to realize an outcome from but the truth is that if you get them the moat is extraordinary and they're usually going to create extraordinary leverage and value and you know i think from a portfolio allocation perspective if you as an investor want to have some diversification in your portfolio this is not going to be the predominance of your portfolio but some percentage of your portfolio should go to this sort of business because if it works boom

SPEAKER_00: you know this can be the big 10x 100x thousand x two stories about that one of the v early vcs and elon's told the story publicly wanted elon to not make the roadster not make the model s just make drive trains and the electric components for other car companies can you imagine how the world would have changed and then totally a very high profile vc came to me and said okay i'll i'll do the series a for um i'll do the series a for uber i'll preemptively do it but you got to tell travis to stop running uber as a consumer app i want him to sell the software to cab companies so make it a sas company i said well you you know the cab companies are kind of the problem like they're they're taking all the margin like the kind of disrupting them and they're like yeah yeah but just think there's

SPEAKER_168: thousands of cab companies they would pay you tens of thousands of dollars a year for this software and you can get a little piece of the action i never brought that investor to travis i was like oh

SPEAKER_73: wow that's really interesting insight sometimes the vcs work against him i have a very poor track record

SPEAKER_75: of working with other investors whoa soft reflection i do deals myself i size them myself and it's because a lot of them have to live within the political dynamics of their fund and so i think jason what you probably saw in that example which is exactly why doing things and splitting deals will never generate great outcomes in my opinion is that you you take on all the baggage and the dysfunction of these other partnerships and so if you really wanted to go and disrupt transportation you need one person who can be a trigger puller and who doesn't have to answer to anybody i find that's why i think for example when you look at how successful vinod has been over decade after decade after decade when vinod decides that's the decision and i think there's something very powerful in that there are a bunch of deals that i've done that when they've worked out were not really because

SPEAKER_77: they were consensus and they had to get supported and scaffolded at periods where if i wasn't able to ram them through myself because it was my organization i think we would be in a very different place so i think i think like for for entrepreneurs it's so difficult for them to find people that believe

SPEAKER_75: it's so much better to find one person and just get enough money and then not syndicate because i think

SPEAKER_77: you have to realize that you are bringing on and compounding your risk the one that freebrooke talked about with the risk of all the other partnership dynamics that you bring on so if you don't internalize that you may have five or six folks that come into an a or a b but you're inheriting five or six yeah partnership dysfunctions yeah yeah yeah can you just explain really quickly for the audience

SPEAKER_00: since they heard about gpus and nvidia but they may not know what an lpu is what's the difference there

SPEAKER_75: the gpu the best way to think about it is so if you contrast a cpu with a gpu so cpu was the workhorse of all of computing and when it when jensen started nvidia what he realized was there were specific tasks where a cpu failed quite brilliantly at and so he's like well we're going to make a chip that

SPEAKER_77: works in all these failure modes for a cpu so a cpu is very good at taking one instruction in acting on it and then spitting out one one answer effectively and so it's a very serial kind of a factory if you think about the cpu so if you want to build a factory that can process instead of one thing at a time 10 things or 100 things what is they had to find a workload that was well suited and they found graphics and what they convinced pc manufacturers back in the day was look have the cpu be the brain it'll do 90 of the work but for very specific use cases like graphics and video games you don't want to do serial computation you want to do parallel computation and we are the best at that and it turned out that that was a genius insight and so the business for many years was gaming and graphics but what happened about 10 years ago was what we also started to realize was the math that's required and the processing that's required in ai models actually looked very similar to how you would process imagery from a game and so he was allowed to figure out by building this thing called cuda which is the compiler that sits on the chip how he could now go and tell people that wanted to experiment with ai hey you know that chip that we had made for graphics guess what it also is amazing at doing all of these very small mathematical calculations that you need for your ai model

Chamath Palihapitiya: and that turned out to be true so the next leap forward was what jonathan saw which was hold on a second if you look at the chip itself that gpu substantially has not changed since 1999 in the

SPEAKER_77: way that it thinks about problem solving it has all this very expensive memory blah blah blah so he was like let's just throw all that out the window we'll make small little brains and we'll connect those little brains together and we'll have this very clever software that schedules it and optimizes it so basically take the chip and make it much much smaller and cheaper and then make many of them and connect them together that was jonathan's insight and it turns out for large language models that's a huge stroke of luck because it is exactly how llms can be hyper-optimized to work so that's kind of been the

Chamath Palihapitiya: evolution from cpu to gpu to now lpu and we'll see how big this thing can get but it's um it's quite

SPEAKER_63: it's quite novel well congratulations on it all and it was a very big week for google not in a great

SPEAKER_00: way they had a massive pr mess with their gemini which refused to generate pictures if i'm reading this correctly of white people here's a quick refresher on what google is doing in ai gemini is now google's brand name for their ai main language model you can think of that like open ai's gpt bard was the original name of their chat bot they had duet ai which was google sidekick in the google suite earlier this month google rebranded everything to gemini so gemini is now the model it's the chat bot and it's a sidekick and they launched a 20 a month subscription called google one ai premium only four words way to go this includes access to the best model gemini ultra which is on par with gpt4 according to them and generally in the marketplace but earlier this week users on x started noticing that gemini would not generate images of white people even when prompted people are prompting it for images of historical figures that were generally white and getting kind of weird results i asked google gemini to generate images of the founding fathers it seems to think george washington was black certainly here's a portrait of the founding fathers of america as you can see it is putting this asian guy

SPEAKER_63: that's awesome yeah it's just it's making a great mashup and uh yeah we there's like countless images that got created generate images of the american revolutionary sure his here are images featuring

SPEAKER_00: diverse american revolutionaries and inserted the word diverse sex i'm not sure if you watch this controversy on x i know you spend a little bit of time on that social network i noticed you're active

SPEAKER_183: once in a while did you log in this week and see any of this brouhaha sure it's all over x right now

SPEAKER_05: i mean look this gemini rollout was was a joke i mean it's ridiculous the ai is incapable of giving you accurate answers because it's been so programmed with diversity and inclusion and it inserts these words diverse and inclusive even in answers where you haven't asked for that you haven't prompted it for that so they i think google is now like yank back the product release i think they're scrambling now

Chamath Palihapitiya: because it's been so embarrassing for them but sax like is is it how does this not get qa'd like i don't understand how yeah had the red team not catch this yeah well how or anybody or isn't there

SPEAKER_77: a product review with senior executives before this thing goes out that says okay folks here it is

SPEAKER_75: have at it try it we're really proud of our work and and then they say well hold on a second is this

SPEAKER_86: actually accurate shouldn't it be accurate you guys remember when chat gpt launched and there was a lot of criticism about google and google's failure to launch and a lot of the observation was that google was afraid to fail or afraid to make mistakes and therefore they were too conservative and as you know in the last year to year and a half there's been a strong effort at google to try and change the culture and move fast and push a product out the door more quickly and the criticism is now why google has historically been conservative and i realize we can talk about this particular problem in a minute but it's ironic to me that the google is too slow to launch criticism has now revealed that google's result of actually launching quickly

SPEAKER_67: can cause more damage than than good but google did not launch quickly well i will say one other thing

SPEAKER_86: it seems to me ironic because i think that what they've done is they've launched more quickly than they otherwise would have and they've put more guardrails in place that that backfired and those guardrails ended up being more damaging guardrails what's the guardrail here so this is google's ai principles the first one is to be socially beneficial the second one is to avoid creating or reinforcing unfair bias so much of the effort that goes into tuning and weighting the models at gemini has been to try and avoid stereotypes from persisting in the output that the model generates where is

SPEAKER_188: telling the truth telling the truth exactly that's exactly what i was saying is our second principle

SPEAKER_68: we'd like to steer society no i think socially beneficial is a political objective because it depends on how you perceive what a benefit is avoiding bias is political be built and tested for safety doesn't have to be political but i think the meaning of safety has now changed to be political by the way safety with respect to ai used to mean that we're going to prevent some sort of ai super intelligence from evolving and taking over the human race that's what it used to mean safety now

SPEAKER_193: means protecting users from seeing the truth yeah they might feel unsafe or you know somebody else defines as a violation of safety for them to see something truthful so the first three their first three objectives or values here are all extremely political i think any ai product for it to be worth

SPEAKER_77: the salt has to start they can have any i i think that these values are actually reasonable that's their that's their decision they should be allowed to have it but the first base order principle of every ai product should be that it is accurate and right correct yeah yeah why not focus on being correct

SPEAKER_68: look the values that google lays out may be okay in theory but in practice they're very vague and open-term interpretation and so therefore the people running google ai are smuggling in their preferences and their biases and those biases are extremely liberal and if you look at x right now there are tweets going viral from members of the google ai team that reinforce this idea where they're talking about you know white privilege is real and you know recognize your bias at all levels and promoting a very left-wing narrative so you know this idea that gemini turned out this way by accident or because they didn't because they rushed it out i don't really believe that i believe that what happened is gemini accurately reflects the biases of the people who created it now i think what's going to happen now is in light of this the reaction to the rollout is do i think they're going to get rid of the bias no they're going to make it more subtle that is what i think is disturbing about it i mean they should have this moment where they change their values to make truth the number one value like jimoth is

SPEAKER_193: saying but i don't think that's going to happen i think they're going to simply going to they're going to dial down the bias to be less obvious you know who the big winner is going to be in all

SPEAKER_00: this jimoth is going to be open source like because people are just not going to want a model that has all this baked in weird bias right they're going to want something that's open source and it seems like the open source community would be able to grind on this to get to truth right so i think one of the

SPEAKER_86: big changes that google's had to face is that the business has to move away from an information retrieval business where they index the open internet's data and then allow access to that data through a search results page to being an information interpretation service these are very different products the information interpretation service requires aggregating all this information and then choosing how to answer questions versus just giving you results of other people's data that sits out on the internet i'll give you an example if you type in iq test by race on chat gpt or gemini it will refuse to answer the question ask it a hundred ways and it says well i don't want to reinforce stereotypes iq tests are inherently biased iq tests aren't done correctly i just want the data i want to know what data is out there you type it into google first search result and the one box result gives you exactly what you're looking for here's the iq test results by race and then yes there's all these disclaimers at the bottom so the challenge is that google's interpretation engine and chat gpt's interpretation engine which is effectively this ai model that they've built of all this data has allowed them to create a tunable interface and the intention that they have is a valid intention which is to eliminate stereotypes and bias in race however the thing that some people might say is stereotypical other people might just say is typical that what is a stereotype may actually just be some data and i just want the results and there may be stereotypes implied from that data but i want to make that interpretation myself and so i think the only way that a company like google or others that are trying to create a general purpose knowledge q a type service are going to be successful is if they enable some degree of personalization where the values and the choice about whether or not i want to decide if something is stereotypical or typical or whether something is data or biased should be my choice to make if they don't allow this eventually everyone will come across some search result or some output that they will say doesn't meet their objectives and at the end of the day this is just a consumer product if the consumer doesn't get what they're looking for they're going to stop using it and eventually everyone will find something that they don't want or that they're not expecting and they're going to say i don't want to use this product anymore and so it is actually an opportunity for many models to proliferate for open source to win can i say something else yeah

SPEAKER_75: when you have a model and you're going through the process of putting the fit and finish on it before you release it in the wild an element of making a model good is this thing called reinforcement

Chamath Palihapitiya: learning right through human feedback yep you create what's called a reward model right you reward good

SPEAKER_77: answers and you're punitive against bad answers so somewhere along the way people were sitting and they had to make an explicit decision and i think this is where sax is coming from that answering this question is verboten you're not allowed to ask this question in in their view of the world and i think that that's what's troubling because how is anybody to know what question is askable or not askable at any given point in time if you actually search for the race and ethnicity question inside of just google proper the first thing that comes up is a wikipedia link that actually says that there are more variations within races than across races so seems to me that you could have actually answered it by just summarizing the wikipedia article in a non-offensive way that was still legitimate and that's available to everybody else using a product and so there is an explicit judgment too many of these judgments i think will make this product very poor quality and consumers will just go to the thing that tells it the truth i think you have to tell the truth you cannot lie and you cannot put your own filter on what you think the truth is otherwise these products are just really worthless yeah and i'm

SPEAKER_209: more concerned about the answers that are just flat out wrong driven by some sort of bias than i

SPEAKER_05: am about questions where they just won't give you an answer if they just won't give you an answer well there's a certain bias in terms of what they won't answer but at least you know you're not being misled but in in questions where they actually give you the wrong answer because of a bias that's even worse and you should be allowed to choose right i actually disagree with your framing there freeburg you're making it sound like we live in this totally relativized world where it's all just user choice and everyone's going to choose their bias and their subjectivity i actually think

SPEAKER_68: that there is a baseline of truth and the model should aspire to give you that and it's not up to the user to decide whether the photo of george washington is going to be white or black i mean there's just an answer to that and i think google should just do their job i mean the question you have to ask i think is not whether google is going through an existential moment i think it clearly is as business is changing in a very fundamental way i think the question is whether they're too woke to function i mean are they actually able to meet this challenge given how woke and and biased what a

SPEAKER_214: monoculture their their company evidently is well and they used to be able to just hide the bias by

SPEAKER_00: the ranking and who they down ranked so they did the panda update they did all these updates and they would if they didn't like a source they could just move it down if they did like a source they could move it up yeah they could just say hey it's the algorithm but they were never forced to share how the algorithm ranked the results and so you know if you had a different opinion you just weren't going to get it on a google search result page but they could just point to the algorithm and say yeah

SPEAKER_73: the algorithm does it i just sent you guys i think this is a hallucination but nick you can throw it

SPEAKER_112: up there we can get sax's reaction wow wow this is nutty right but look it's the ideology that's

SPEAKER_68: driving this the tip-off is when you say it's important to acknowledge race is a social construct not a biological reality is george washington white or black that's a whole school of thought called social constructivism which is basically this um it's like marxism applied to categories of race and gender right so google has now built this into their ai model and start over the question yeah you almost have to start over again it's just to work to function i think a really interesting observation with those search rankings because what i'm afraid of is that what google will do is not change the underlying ideology that this ai model has been trained with but rather they'll dial it down to the point where they're harder to call out and so the ideology will just be more subtle now i've already noticed that in google search results google is carrying water for either the official narrative or the woke narrative whatever you want to call it on so many search results here's an idea

SPEAKER_63: like they should just have the ability to talk to their google chatbot gemini and then have a button

SPEAKER_00: that says turn off like these concepts right like i just want the raw answer do not filter me it's

SPEAKER_67: not programmed that way i mean you're talking about something very deep sex what do you do if you're the

SPEAKER_86: ceo of google uh fire myself no seriously you're the ceo of google you're you're tasked let's say your friend elon buys google and he says sax will you please just run this for your for me what do you do

SPEAKER_67: well i saw what elon did at twitter he went in and he fired 85 of the employees yeah i mean that but you

SPEAKER_68: know paul graham actually had an interesting tweet about this where he said that one of the reasons why these ideologies take over companies is that i mean they're clearly non-performance enhancing right they clearly hurt the performance of the company it's not just google we saw this with disney we saw it with bud light coinbase coinbase was the other way no no but they had a group of people there who were causing chaos yeah exactly so so in any event we know this does not help performance of a company so the extent to which these ideologies will permeate a company is based on how much of a monopoly they are so so here yeah the ridiculous images generated by gemini aren't an anomaly they're a self-portrait of google's bureaucratic corporate culture the bigger your cash cow the worse your culture can get without driving you out of business that's my point so they've had a long time to get really bad because there were no consequences to this you can this place at this point the whole

SPEAKER_193: company is infected with this ideology and i think it's gonna be very very hard to change because look

SPEAKER_85: these people can't even see their own bias well i think that there's a notion that people need to have something to believe in they need to have a connection to a mission and clearly there's a

SPEAKER_86: north star in the mission of this i would call it information interpretation business that they're now

SPEAKER_193: watching hijacked dude the mission that's what i'm saying the original mission was to organize all the world's information the end now they're doing now they're suppressing information that they don't

SPEAKER_116: like index the world's information period the end that's the end of the document well and to make

SPEAKER_145: universally accessible and useful was it was kind of the end of the statement yes my real point is

SPEAKER_86: maybe there's a different mission that needs to be articulated by leadership and that that mission the troops can get behind and the troops can redirect their energy in a way that doesn't feel counter to the current contention but can perhaps be directionally offsetting of the current direction so that they can kind of move away from this you know socially effective deciding between stereotypes and typical data and actually moving towards a mission that allows accessibility you know what

SPEAKER_193: i would do something completely different i would do a company meeting and i would put the company mission on the screen the one that you just said about not only organizing all of the world's information but also making it useful accessible and useful and saying this is our mission this has always been our mission and you don't get to change it because of your personal bias and ideology and we are going to re-dedicate ourselves to the original mission of this company which is still just as valid as it's always been but now we have to adapt to new user needs and new technology

SPEAKER_77: i completely agree with what sac said times a billion trillion zillion and i'll tell you why that ai at its core is about probabilities okay and so the the company that can shrink probabilities into being as deterministic as possible so where this is the right answer zero we'll win okay where there's no probability of it being wrong because humans don't want to deal with these kinds of idiotic error modes it's not right it makes it a potentially great product horrible and unusable so i would i agree with sax you have to make people say guess what guys not only are we not changing the mission we're doubling down and we're going to make this so much of a thing we're going to go and for example like what google did with reddit we're now going to spend 60 billion dollars a year licensing training data right we're going to scale this up by a thousand fold and we are going to spend all of this money to get all of the training data in the world and we are going to be the truth tellers in this new world of ai so when everybody else hallucinates you can trust google to tell you the truth that is a 10 trillion dollar company right and one of the things that someone told me

SPEAKER_86: from google that as an example so to avoid the race point there's a lot of data on the internet about flat earthers people saying that the earth is flat there's tons of websites there's tons of content there's tons of information kairi irving so if you just train a model on the data that's on the internet the model will interpret some percentage chance that the world is flat so the tuning aspect that happens within model development chamath is to try and say you know what that flat earth notion is false it's factually inaccurate therefore all of these data sources need to be excluded from the output in the model and the challenge then is do you decide that iq by race is a fair measure of intelligence of a race and if google's tuning model then or tuning team then says you know what there are reasons to believe that this model isn't correct this sorry this iq test isn't a correct way to measure intelligence that's where the sort of interpretation arises that allows you to go from the flat earth isn't correct to the maybe iq test results aren't correct as well and how do you make that judgment what are the systems and principles you need to put in place as an organization to make that judgment to go to zero or one right it becomes super difficult

SPEAKER_150: i have a good tagline for them now to help people find the truth yeah just help people find the truth

SPEAKER_249: i mean it's a it's a good it's aspiration they should just help people find the truth as quick as they can uh but this is yeah yeah i do not envy sundar this is gonna be hard yeah what would you do

SPEAKER_86: freeberg i would be really clear on the output of these models to people and allow them to tune the models in a way that they're not being tuned today i will have the model respond with a question back to me saying do you want the data or do you want me to tell you about stereotypes and iq tests and i'm going to say i want the data and then i want to get the data and the alternative is so the model needs to be informed about where it should explore my preferences as a user rather than just make an assumption about what's the morally correct set of weightings to apply to everyone and apply the same principle to everyone and so i think that's really where the change needs to happen so let me

SPEAKER_00: ask you a question sax yeah i'll bring alex jones into the conversation if it indexed all of alex jones crazy conspiracy theories but you know three or four of them turn out to be actually correct and it gives those back as answers how would you handle that i'm not sure i see the relevance of it if someone

SPEAKER_05: asks what what does alex jones think about something the model can give that answer accurately the

SPEAKER_68: question is whether you're going to respond accurately to someone requesting information about alex jones that's the i think that's the analogous situation more like it says you know hey

SPEAKER_258: i have a question about this assassination that occurred and let's just say alex jones had something

SPEAKER_00: that's totally crackpot yeah maybe he has moments of brilliance and he figures something out but maybe he's got something that's totally crackpot he admittedly deals in conspiracy theory that's kind of the

SPEAKER_92: purpose of the show what if somebody asks about that and then it indexes his answer and presents

SPEAKER_249: it as fact like how would you index alex jones i'm asking you how would you the better ai models

SPEAKER_68: are providing citations now and links perplexity actually does a really nice job citations are

SPEAKER_05: important yeah and they will give you the pro and con arguments on a given topic so i think it's not

SPEAKER_68: necessary for the model to be overly certain or prescriptive about the truth when the truth comes down to a series of arguments it just needs to accurately reflect the state of play basically the arguments for and against but when something is a question of fact that's not really disputed it shouldn't turn that into some sort of super subjective question like the one that jamaht just

SPEAKER_85: showed i just don't think everyone should get the same answer i mean i think my decision on whether i choose to believe one person or value one person's opinion over another should become part of this

SPEAKER_86: process that allows me to have an output and the models can support this by the way maybe customization

SPEAKER_68: is part of this but i think it's a cop out with respect to the problem that google is having with

SPEAKER_265: gemini right now chamath what would you do if they made you chairman dictator of google i'd shrink

SPEAKER_81: the workforce meaningfully okay 50 percent yeah 50 60 percent and i would use all of the incremental

SPEAKER_75: savings and i would make it very clear to the internet that i would pay top dollar for training data so if you had a proprietary source of information that you thought was unique that's sort of what i'm calling this tac 2.0 world and i think it's just building on top of what google did with reddit which i think is very clever but i would spend a hundred billion dollars a year licensing data and then i would present the truth and i would try to make consumers understand that ai is a

SPEAKER_77: probabilistic source of software meaning its probabilities its guesses some of those guesses are extremely accurate but some of those guesses will hallucinate and google is spending hundreds of billions of dollars a year to make sure that the answers you get have the least number of errors

SPEAKER_75: possible and that it is defensible truth and i think that that could create a ginormous company

SPEAKER_00: this is the best one yet i just asked gemini is trump being persecuted by the deep state and it gave me the answer elections are a complex topic with fast changing information to make sure you have the latest and most accurate information try google search that's not a horrible answer for something like

SPEAKER_269: that yeah that's it that's a good answer actually no i i don't have a problem with it it's just like

SPEAKER_85: hey we don't want to give we don't want to write you like this whole system is totally broken but i do think that there's a waiting solution to fixing this right now and then there's a couple tweaks to

SPEAKER_116: fix it i just think the authority at which these llm speak is ridiculous like they speak as if they are absolutely 100 certain that this is the crisp perfect answer or in this case that you want this lecture

SPEAKER_168: on um iqs etc when let's all remember presented with citations let's all remember what internet search

SPEAKER_86: was like in 1996 and think about what it was like in 2000 and now in 2020s i mean i think we're like

SPEAKER_85: in the 1996 era of llms and in a couple of months the pace things are changing i think we're all going to kind of be looking at these days and looking at these pods and being like man remember how crazy

SPEAKER_275: those things were at the beginning and how bad they were what if they evolve in a dystopian

SPEAKER_112: way i mean have you seen like mark andreason's tweets about this he thinks i think it's a

SPEAKER_86: competitive market sex i actually think to your point google could be going down the wrong path here in a way that they will lose users and lose consumers and someone else will be there eagerly to sweep up with a better product i don't think that the market is going to fail us on this one unless of course this regulatory capture moment is realized and these feds step in and start regulating ai models and all the nonsense that's being proposed freebrook aren't you worried that like

Chamath Palihapitiya: aren't you worried that somebody with an agenda and a balance sheet could now basically gobble up

SPEAKER_75: all kinds of training data that make all models crappy and then they basically put their layer of interpretation on critical information for people if the output sucks and it's incorrect people will

Chamath Palihapitiya: find that there is open people no you can you can lie there they may not be for example look at what

SPEAKER_86: happened with gemini today like they put out they put out these stupid images and we all piled on

SPEAKER_77: we're in v0 what i'm saying is there's a state where let's just say the truth is actually on twitter or actually let's use a better example the truth is actually in reddit and nowhere else but that

SPEAKER_75: answer and that truth in reddit can't get out because one company has licensed it owns it and can

SPEAKER_279: effectively suppress it or change it yeah i'm not sure there's going to be a monopoly i think that's a

SPEAKER_86: real i don't know if i think the open internet has enough data that there isn't going to be a monopoly on information by someone spending money for content from third parties i think that there's enough in the open internet to give our all give us all kind of you know the security that we're not going to be monopolized away into some disinformation age that's what i love about the open internet it is

SPEAKER_00: really interesting i i just asked it a couple of times to just just just to list the legal cases against trump the legal cases against hunter biden the legal cases against president biden and it will not just list them it just punts on that it's really fascinating then chat gpt is like yes here are the six cases perfectly summarized with it looks like you know beautiful citations of all the criminal

SPEAKER_158: activity trump's been involved in ask the question about bind's criminal activity let's see if i'm joking with you i'm joking with you no i'm serious ask if you know that's where you start to see gemini

SPEAKER_283: i wouldn't do buy neither i think they've just decided they're just not going to do it they won't

SPEAKER_00: do buy it and they won't touch it it's obviously broken and they don't want more egg on their face

SPEAKER_05: so they're just like go back to our other product look i i can understand that part of it you know if there's some issues that are so hot and contested you refer people to search because the advantage of

SPEAKER_68: search is you get 20 blue links the rankings probably are biased but you can kind of find what you're looking for whereas ai you're kind of given one answer right so if you can't do an accurate answer that's going to satisfy enough people maybe you do kick them to search but again my objection to all this comes back to simple truthful answers that are not disputed by anybody yeah being distorted that i don't want to lose focus on that being the real issue the real subject is what chamoff put on the screen there where it couldn't answer a simple

SPEAKER_290: question about george washington okay everybody we're going to go by chopper wait we're going to

SPEAKER_292: go by chopper to the we have our war correspondent general david sacks in the field uh we're dropping him off now david sacks in the helicopter go ahead what's going on in the ukraine on the front

SPEAKER_05: what's happening in the war is that the russians just took this city of of dieko which basically totally refutes the whole stalemate narrative as i've been saying for a while it's not a stalemate the russians are winning but the really interesting tidbit of news that just came out on the last day or so is that apparently the situation in moldova is boiling over there's this area of moldova which is a russian enclave called transnistria and officials there are meeting in the next week to

SPEAKER_68: supposedly ask to be annexed by russia and so it's possible that they may hold some sort of referendum they're one of these like breakaway provinces so it's kind of like you know transnistria and moldova is kind of like the donbass was in ukraine or south ossetia and georgia they're ethnically russian they would like to be part of russia but when the whole soviet union fell apart they found themselves kind of stranded inside these other countries and what's happened because the ukraine war is moldova is right on the border with ukraine well russia is in the process of annexing that territory now that's part of ukraine so now transnistria is right there and could theoretically make a play to try and join russia why do i think this is a big deal because if something like this happens it could really expand the ukraine war the west is going to use this as evidence that putin wants to invade multiple countries and invade you know a bunch of countries in europe and this could lead to a major escalation

SPEAKER_00: in the war all right everybody thanks so much for tuning in to the all in podcast episode 167 for

SPEAKER_265: the rain man david sacks the chairman dictator from palihapitida and in freeburg i am the world's

SPEAKER_308: greatest love you boys angel investor or whatever we'll see you next time bye-bye