Secrets Of Investing Early – Lessons From A Billion-Dollar Investor

Summary

On the 100th episode of American Optimist, Joe Lonsdale hosts Elad Gil, a prominent investor with a keen eye on AI's potential. They dive deep into Gil's journey from Google to launching successful startups like Color, and investing in unicorns such as Stripe and Airbnb. The conversation veers into AI's transformative power, suggesting it's currently underhyped despite its massive potential to revolutionize sectors through 'units of cognition.' Gil emphasizes the importance of resilience and knowledge adaptation for the future, alongside supporting innovative education and health solutions through AI.

Highlights

• AI's revolutionary potential is currently underhyped, despite its immense revenue impact and transformative power. 🔥
• The significance of strategic, resilient investment in successful startups, witnessed by Elad's early support of Stripe, Airbnb, and more. 💼
• The role of resilience and foresight in entrepreneurship, highlighted through Elad's journey through Silicon Valley's dot-com bust. 🌐
• How AI could redefine industries, providing custom education and enhanced healthcare universally. 📚
• The vital role of youth and young entrepreneurs in driving future innovation and success. 🌱

Key Takeaways

• Elad Gil discusses the pivotal, often underestimated role of AI and its potential to transform industries. 🤖
• Insights into the dynamics of building and scaling successful startups, with learnings from industry giants like Google and Twitter. 🚀
• The importance of resilience and adaptability in entrepreneurship, especially in volatile environments. 💪
• AI's future role in education and healthcare, making personalized learning and healthcare more accessible. 🏥
• Vibrant discussions on maintaining entrepreneurial spirit and encouraging youth involvement in tech and innovation. 🌟

Overview

Elad Gil joins Joe Lonsdale to discuss AI's underestimated potential. Emphasizing that AI is presently underhyped, Gil forecasts it will revolutionize sectors by creating 'units of cognition,' indicating a future where AI can handle diverse tasks typically managed by humans. This could unlock unprecedented efficiency and innovation across industries.

Their dialogue further explores the nuances of startup success, drawing from Gil's extensive experience with pivotal tech companies. From surviving Silicon Valley’s early turmoil to thriving in major tech firms like Google, and his venture into entrepreneurship with startups like Color, Gil's journey illuminates the crucial role of resilience and strategic investment in building successful enterprises.

Additionally, the potential of AI in education and healthcare is underscored. Progressive ideas like personalized education and AI-driven healthcare innovations suggest a transformative future. Gil also contemplates ways to encourage youthful engagement in tech, thereby sustaining innovation and entrepreneurial vigor across generations.

Secrets Of Investing Early – Lessons From A Billion-Dollar Investor Transcription

• 00:00 - 00:30 AI is in my opinion dramatically underhyped right now we're seeing massive actual revenue and impact without that much adoption how are you able to keep winning again and again and again with with these early stage Investments they just happen to be some of the biggest companies Airbnb at stripe base and figma and instacar I think where people misunderstand AI right now really this is a revolution in terms of units of cognition when you had GPT 3.5 and you went to four suddenly legal opened up as a vertical eventually you're going to hit a point where it can do all of Human Services CEO and coer
• 00:30 - 01:00 [Music] we have something special for episode 100 of American Optimist elod Gil has been a friend for a long time he's one of the great thinkers great investors in Silicon Valley we go really deep on AI on history of the tech World on what's coming up next in the next 10 20 years you know El od's funds is one of the
• 01:00 - 01:30 funds I am the most bullish on he's just involved in so many of the best companies over the last 20 years and already probably involved and things that will become the most famous companies the next decade excited for you to meet him I'm Joe Lonzo welcome to American Optimist have my friend elad Gil with us here today I'm really glad you're here thanks for coming a thanks for having it's great to see you so elad want to start with your background tell us a little bit about your upbringing how'd you make it to Silicon Valley sure yeah I moved out um to the Bay Area originally for a job I joined a startup um sort of right at the end of the Telecom or theom boom and sort of bust
• 01:30 - 02:00 and unfortunately I had perfectly bad timing and I showed up right as everything was collapsing and I worked at a telecom equipment startup and that got um that went through a series of layoffs I was kind of laid off in the fifth round this is like right at the end of the bubble then it was right at the end of the bubble and We Grew From 120 to 150 people and then shrank to 13 people wow and you were still there you were one of the 13 they kept no I I got cut around 50 or so so I was in the third round there you go so I made it through two rounds Inta um but it was a pretty brutal environment everything was kind of collapsing it actually very good
• 02:00 - 02:30 very formative right cuz if you've been through a layoff and a really tough time like that and you know I was straight out of school so I didn't have any money and so I remember um even as that company was ramping up I was like this is not sustainable like I you know I don't know how this thing is going to survive and so I remember me and a friend of mine used to go to the grocery store um every week once a week and we buy like a loaf of bread and a thing of cheese and that's what and I just eat cheese sandwiches for every meal like all week because it was like I was saving money because I thought this layoff was coming and I'm like how am I going to live right wow so you knew it was coming and you saving as much cash did Ma just looked at their burn and you
• 02:30 - 03:00 looked at their cash and you're like they have to do layout how do you know most Engineers aren't like aware of those financial things how' you know to'd be aware uh I don't it just felt like math to me honestly like it's just you start thinking about it and you're like huh these things seem off and then you do the math and you're like okay we're going to run out of money in 12 months or whatever if they don't do something so they have to do something and you know it's it's kind of funny how if you just look at certain things from first principles or just look at data you're like oh of course this thing is this way did you become an entrepreneur after you got laid off no I ended up um eventually making my way to Google Google at the time was for of scaling
• 03:00 - 03:30 and it was soaking in all the talent um in Silicon Valley or at least all the entrepreneurial talent and so um I joined there and um I I ended up working on two things one is I helped Buy in the Android team and start a lot of the early mobile efforts and then second I worked on some early um Ai and machine learning for ads targeting related products I worked more on the product side um and then after that I left to start a company and um uh sold that to Twitter and then I worked at Twitter for a bunch of years and then started another company so what were the lessons
• 03:30 - 04:00 from building the team at Google on the mobile sign like what do you guys that was obviously a pretty important team for Google what did you do yeah you know at the time um uh Larry Page um was still very active at Google and he decided to get rid of all middle managers and so when I joined he removed like three management layers and suddenly every director or VP level person had 50 to 100 people working directly for them and so they had no ability to actually meet with the the team that that worked for them right because how can you meet with 100 people would you br them together or or what did you do no basically you had this giant gray market for talent where you
• 04:00 - 04:30 could go do whatever you want cuz nobody's paying attention right so it was actually for me it was great and so um I recruited a lot of that early mobile Team without their managers knowing because their managers didn't know what they were doing so they just come and start working with me on mobile and so we kind of organically built this I don't what to call it like um this this team of pirates or something this is kind of insane is this because the Google is making so much I like to think of Google as like this like this gusher gusher of money or and then there's people like all around it and it's just kind of a mess and you're able to do stuff I mean it was basically a golden era where um Silicon Valley had just
• 04:30 - 05:00 gone through a major depression huge layoffs had happened a bunch of companies had blown up Founders were sort of out in the street and Google just soaked in all the very very best talent of that era but then also just let it do whatever it wanted um up to a point yeah I mean eventually they put back in middle management and you know they did a bunch of reorgs and realigned everything but it was a very um if you want to go try start something if you can convince one of a handful of people then you could go do it so you're kind of like an entrepreneur inside of Google basically yeah and I you know I never
• 05:00 - 05:30 think that you see how hard it is to really be an entrepreneur and you're like well no big company really has entrepreneurs but um but yeah it was basically you had this gray market for talent people could just start all sorts of things and that's when you had this big expansion and Google used to have this uh web page up that was like here's all the things we're never going to do and it said we're never going to do IM or chat we're never going to do a browser we're never GNA and it it ended up doing all those things right we're never going to do satellites it was supposed to be like this exaggerated funny thing that's funny so I had this thing we're never going to do and they did all of them they ended up doing
• 05:30 - 06:00 basically everything even the Dopey evil I'm just kidding you can't say that yeah so it was a very Dynamic time and I feel like um at any given moment in time there's one or two companies that are going through this kind of golden age period where you're expanding rapidly you're soaking in all the best talent you're launching multiple product lines you have this diversity of stuff happening and then often those cohorts of people go off to then do amazing things in Silicon Valley later and so maybe right now that's open AI maybe that's stripe you know there's a handful of companies that are probably these ramp seems a little bit like that too in some ways but going back again so why
• 06:00 - 06:30 did you leave to start a company after Google yeah I always wanted to start a company it's just because I had worked at a startup that failed I had no money right I was just out of school so so you made enough money to Google go do it yeah and I think before before the current ERA it actually really hard to start companies right there weren't that many people funding it there wasn't white combinator these accelerator programs where you could just go and people would give you some money to get going there weren't very many Angel Investors there wasn't information online about how to do it you had to eat cheap sandwiches for months money no seriously right like I was literally paying off school debt yeah and so
• 06:30 - 07:00 you're very constrained and I we're in a much lined environment now because seed capital is EAS to raise which means that you know really smart people can just show up get some money and start doing things at a very young age in some ways though if to be an entrepreneur back then there was a higher bar wasn't there for most for a lot of people it was it was it definitely seems like it was harder and I don't know if that's just me being an old person now and saying you know it's always harder when I was young or what we had to walk both ways uphill in the snow Yeah but it it was dramatically um it was it was really hard to get something up in back then or
• 07:00 - 07:30 even convince people to quit and go do it because there weren't that many startup successes until the the you know um especially after the Doom bubble where everything collapsed people took away the lesson of oh all these things are kind of fake companies and I want to go somewhere that's stable yeah no totally I remember when hiring for paler like a lot higher bar to convince people for a quite a while what was the company you started when you first yeah the first company I started was called mixer Labs um it basically ended up being a data infra platform company before there was a lot of those for mainly for developers to use or so for Engineers to
• 07:30 - 08:00 basically build different applications we ended up selling it to Twitter when Twitter was about 90 people Twitter really needed some help back then is my recollection Twitter was um a mess right and it's kind of funny because as you see multiple companies go from you know 50 or 100 people to thousands of people you see that almost all of them have these phases where they where they were a mess or something really bad was happening or you know serial things or problems had to be dealt with Twitter was especially bad yeah Twitter seems uniquely bad for me the thing I've heard you can tell me maybe it's too mean
• 08:00 - 08:30 but but it's like I mean I think Mark Zuckerberg by the way called it a clown car that that fell into a gold mine which is a little that's pretty mean but it's pretty funny the thing I heard is that I like to say that you don't get billion dollar companies Without Really top Tech cultures except for Twitter's like the exception that proves a rule I can think of a couple others so I think um my big lesson over the last couple years is that product Market fit matters above everything else in other words if there's an active Market that really wants to use what you're doing it'll put up with a lot of stuff unless there's a very clear alternative the fail whale the fail well was up constantly and the
• 08:30 - 09:00 thing kept growing right and there'd be articles about the fail well and that would just boost growth and so Twitter was in This Magic Moment where what was doing is very unique and defensible and honestly I mean it survived to this day as now X under musk but you know it survived for 20 years is something that wasn't very well run for much of that history when you came in did you guys fix some things there at least we fixed so much stuff so Twitter was in a state where um you mentioned the fawell days basically what that was is a site kept going down and they put up a picture of a very cute whale like a graphic and that was their product cuz you couldn't
• 09:00 - 09:30 log in or anything cuz the site was down cuz they couldn't deal with the traffic at the same time when we were bought um they couldn't um what's known as deploy code they couldn't update the website and they hadn't been updating it for weeks they just didn't know how to update the code and so we went in and we fixed that deploy queue and that was the first my team was supposed to go and build all the stuff for the ecosystem and developers you had to fix the basics first we just had to come in and fix the basics yeah wow how long did you stay there I was there uh two and a half years full-time and then another year as an adviser and my job was basically eventually I became one of the fixers in
• 09:30 - 10:00 the company um what year was that we got bought in 2009 and I started off running like search and goo and some other kind of AI Centric teams and then I morphed into this fixer role um we basically had a big um reorg internally and um basically what happened is we did kind of a slow mo reorg which you usually don't want to do right so it took like a month to reorg the product org and Dick asked each person what they want to be doing and most people said I I want this job or I want that job and I showed up and I said well just tell me what you
• 10:00 - 10:30 need me to do right like it's not about me it's about helping this company Thrive and so half the team got fired I got promoted for saying that SL acting that way and then I got kind of pulled into this fixer role and so I ended up getting involved with m&a with internationalization a problem we need a smart guy to work hard and help yeah just jump in help fix something maybe help hire in the exact and then go on and do the next thing and I want to ask just because it's going to be a lot of people's minds um and I don't want to focus too much on this culture War stuff but like when Elon bought Twitter and became X my friends went in there and
• 10:30 - 11:00 there were accounts like mine that were like turned down and it was all this crazy politics and it was very clear it was very politicized and it felt like that might have started more 2014 2015 was that going on a little bit when you were there or no when I was there it was actually viewed more as a platform for helping disseminate information for Freedom right so it's like free speech was more on the ascent even yeah because it was um the Arab Spring was happening on top of Twitter yep um it was starting to be used as like a news media where I don't know if you remember there was that famous Landing of the plane in the in the River yeah yeah that was a cool
• 11:00 - 11:30 Captain the first photo for that ended up on Twitter and so it was just emerging as a sort of real time news media Captain Su I think he was called yeah yeah that's right and so um in that era there's also a lot of EX Google people at Twitter particularly on the legal team and they came from this mindset of what's fair use and how do you create more access to information it's very much the old kind of like classical liberal Silicon Valley freedom and free free speech and everything so that's fascinating so you never experienced any of that other mess just all aligned yeah what did you leave
• 11:30 - 12:00 there would you want to leave to build something yeah I wanted to start a company um so I started a company called uh color which you you uh thankfully help back um so really appreciate all your support over years on that and um it was basically a really early cancer genomics and digital Health company that was focused on helping people get really key information about their health particularly around genetic risk of cancer and related areas you actually have a background both in math and biology so this is an area you you know really well I guess obviously right around 2011 2012 there were a lot of new possibilities is coming up with with
• 12:00 - 12:30 genomics and whatnot what inspired you to do it yeah there was sort of three three points of inspiration I think the biggest one was just my co-founder umman who's now is still running the company um he had a familial history of breast cancer his mother had breast cancer twice he had multiple family members die of it and so the real driving force for us was was just how do we help people get really important information that's key to their health yeah and so that's the reason we started the company is we want to help people and you probably know this story it was something really inspiring for us is my my my now wife we were dating at the time and and I think I somehow I like
• 12:30 - 13:00 bought a couple extra tests that you guys giving us like like low cost and she took one and all of a sudden she had this like crazy risk and 23 and Mia told her she had no risk but now of a sudden she knows she has this crazy cancer risk so just in case uh her family took the test and her mother was 61 at the time and it came back and it said 80% chance of cancer by the time you're 60 based on the based on the results and then so the doctor said oh don't worry about it but that's really strange you would know that I've never seen that before just in case probably at the age we should do an E rectomy take out your ovaries and so three or four months later no rush they
• 13:00 - 13:30 took him out and they found stage three cancer wow yeah and so fortunately they were able to treat it and help her she she probably would have only love lived a couple years because they found it she lived like six or seven more years she got to meet a couple few of her grandkids yeah uh which you know which is really wonderful that at least she gave her another five years of life thanks to a $100 test it's shocking to me Healthcare is like that where a $100 thing could just like prolong someone's life a lot yeah it's amazing um how little actual preventative care helps and by the way the the reason like what you just said is the reason we started the company no thank you right so it's it you know it's um it's one of those
• 13:30 - 14:00 things that I'm never going to regret anything about color simply because we were able to help people in those ways what didn't go well J lot of lessons learned from it I mean it's just a uh healthc care is really hard as an industry and um there's a regulatory part of it honestly people just don't want to pay out a pocket for their health so to your point you could pay $100 $200 and get information that may be lifesaving people don't want to do that right they're used to their employer paying for it or the government paying for it and uh most of healthcare is chronic versus preventative in other words you wait for somebody to get really sick and then you try and treat them versus saying how do we just
• 14:00 - 14:30 prevent all these things from happening to begin with 100% one of the one of my neighbors in uh who I've had on here in know back in Texas is Peter AA the who's the healthcare guy and the he gave me these Frameworks where it's like the average doctor will say your cholesterol is like you know 60th percentile like bad but like it's not 90 percentile there no need to do anything whereas a really good doctor is like let's get it to be the very best let's not just leave it like okay to mediocre like keep pushing your health which is I think it's a better framework right keep there's very basic stuff you can be
• 14:30 - 15:00 doing and um people just don't know about or don't know know to do um I mean the other part of it is when you look at it a lot of our Focus from a biopharmaceutical perspective like the new drugs we develop and things like that often aren't going to move the needle that much actually yeah they're very important for specific diseases but if you got rid of all of cancer and all of heart disease I think you add something like five to seven years to the average person's is it really only five to seven years it's very small amount on a relative basis and you're like okay what's everything else and how should we actually be thinking about extending life span and health span how
• 15:00 - 15:30 do you keep people healthy for longer and we're not really doing much to develop drugs in that direction and so I think I think there's big gaps in the market other the things you're doing today actually in BIO since we're on the topic that you're passionate about like what are types of things uh there's one company that I backed uh called bio age that just went public which I'm very excited about I've been involved with them since the very early days I think they may still be in their quiet period so I don't know that there's much I can say about it um I think at a very high level they're working on um uh different types of drugs to help with aspects of Aging I think that's a very exciting area overall uh and
• 15:30 - 16:00 they've already announced certain things so it's worth you know I think checking them out totally while I'm in town I'm seeing my friend Rick clausner who's build a bunch of these companies and the latest stuff they're doing with epigenetics and aging it's just just really it's like sci-fi really helpful for helping people it's very cool stuff I want to ask in general uh about your success as an investor so you you obviously we talked a little bit about these companies you built and you did really well at them but I think you're probably most famous uh these days is for someone who's invested in I think maybe dozens of unicorns you've you've backed some really top companies from the uh you mentioned stripe earlier I one of
• 16:00 - 16:30 one of the famous ones you kind of invested them really early on and had been a mentor to those guys like like like how did you find guys like that how how are you able to keep winning again and again and again with with these early stage Investments yeah I think I've been super lucky on that stuff um you know I think a lot of the companies that I got involved with quite early uh was just because I was helping out people in the ecosystem like I was I was starting a company myself and people just started coming to me either for advice or I was a couple months ahead of them or we would help each other you just happened to help the really talented ones huh yeah just fell into it
• 16:30 - 17:00 you know so I just fell into the uh the clown car and the gold mine or whatever I I I don't think that's the right an I got very lucky so you know I helped out um uh a lot of these early teams and they just happen to be some of the biggest companies you know it's Airbnb it's stripe you know eventually ended up helping out coinbase and figma and instacart and all and you know it's been a great ride like what is it about teams for example like stripe that to differentiate them from others like what what about what guys running that that company like made them special yeah you know I've been kind of noodling on this
• 17:00 - 17:30 I had a conversation with some folks on my team uh yesterday where I was just trying to brainstorm on what are there specific signs of truly outside successful outcomes in terms of the types of Founders and I think a lot of it is just like do you end up in the right product Market is the market big enough is it growing is it dynamic in the right way and so I think half of it is just you find the right market and some of that could be luck and some of that couldn't but those guys are like I mean obviously I know you agree they're special but they seem like they're like some of the leading kind ofs our matter what you need the market right now within that there's tons and tons of people who enter these markets and do it
• 17:30 - 18:00 horribly right and so what's different and so I I almost view it as there being three types of Founders that become hyper successful and I'm just yeah testing this out or spitballing it right I don't know this correct the first one is kind of the polyic hyper intellectual yet very competitive person and that's probably Patrick and John from stripe honestly that was Larry and Sergey when I worked at Google they were very polymathic very deep on everything they'd go down into the Weeds on the data yeah and so I think one almost like super founder Arch type is that I think
• 18:00 - 18:30 the second one is the super hardcore extremely focused really really driven overdrive founder that may be you know Travis from Uber that reminds me of like Peter at wish back in the day but when he was doing well yeah Peter was was was just super focused and I think there's almost signals of those because a lot of people in our ecosystem now for example are doing Angel Investing or they're involved with lots of other companies while they're running their company and that second class of Founders don't do any of that they're just they actually say no to everything and they're just all in on one thing and a really Travis is definitely that way yeah yeah so I think that's another Arch type and
• 18:30 - 19:00 there's a few people I can think of who just had massive success that I just view is incredibly focused right I feel like Elon used to be more that way cuz he really would say no to everything yeah yeah some of the best Founders are just um like Parker ripling is incredibly judicious with this time and he's just like this is you know he's really focused uh and then I think there's a third type of founder which is just like the network effect business and it's just you have something that has Network effects you're going to do well even if even if you're a clown car you could be well you could be Zuck and then you take it to the next level you start things like llama and um things
• 19:00 - 19:30 like that Zu maybe has Network effect plus maybe some of the polymath side or something like that yeah or just I I I don't know him so I don't know if he's on the intense Focus or the polymathic side or whatever but I'm just saying like um maybe he's sort of an overlapping Arch type of like one of those things plus you now have a network effect business so it's a default defensible have you seen a lot of more Network effect businesses recently because Network effect is thing we always talked about a lot in consumer obviously those are giant companies in our world and there there's some Enterprise uh what are you seeing in network effects these days that are
• 19:30 - 20:00 exciting yeah I'm seeing less Network effects and more scale effects which are related or overlapping where as you get more scale you end up with cost advantages that then allow you to get more scale You could argue maybe stripe is in that basket but there's also more modern versions of that particularly in energy and other related markets um so I you know it's back to a broader question of how do you create defensibility in a business and there's almost like five or six common patterns of defensibility scale effects as one network effects as another um creating an ecosystem on top of your platform like what sales force is done as one um there may be long-term
• 20:00 - 20:30 contracts and durability of those contracts which happens for example in the medical distribution world so you know there's there's in the in the world of business there's like five ways or six ways to create defensibility and roughly everything falls into those yeah pter we always try to create network effects and we did get a few of them going so we got a lot of countries working over it with dozens of countries we kind of had to be on it I think you know we're really proud to sell to to Israel and it was a it was a big deal that they bought it CU never buy anything from anyone but I think it was probably that Network effect that you know to think we we're just good enough but I think that kind of helps you know
• 20:30 - 21:00 and then and then we had like Airbus using with all his suppliers and everyone had to be on it so so I think I think there are Enterprise Network effects you can do but but they're they do seem to be more rare these days yeah I think so and then there's also just the the scale effects we mentioned like I know we were both involved with Ander really early and that's a good example where they now have such a broad portfolio of products they have the Deep customer relationships and that allows a flywheel in terms of the ability to continue to cross- sell different types of products over I think you get big enough too that you get a reputation in DC and you can like do things in DC you know what's crazy on andral one so we
• 21:00 - 21:30 have this company Sonic I think you're also just invested in that we help help build and it's growing really fast for the Navy and and they people they asked us to weaponize some of the autonomous vessels and and so we reached out to a few different primes and Ander roll was so good that like within three days literally three days we had the NDA sign we all the models all the data exactly how it works send it back to the Navy and it was it was like it took multiple weeks even to get the NDA sign with like the other guys and so it's kind of cool because androll is now this thing that you can use as a prime it's then making it grow even faster because everyone wants to work with it because it's the
• 21:30 - 22:00 best yeah yeah no you that that is that is one of those sort of feedback loops that's very positive for some of these businesses and so I think everybody always underestimates the degree to which these feedback loops end up mattering and the way that it changes the velocity and trajectory of a business and if you end up on a positive track it keeps going and that's also true for hiring right there's certain networks that you tap into that keep the flight wheeel going um somebody on my team actually went back and asked what are all are there common schools that all the founders of the biggest companies went to over the last decade for example right and there's basically three schools that
• 22:00 - 22:30 are like a huge chunk of the overall value created and then there's maybe five more and that's very disperse what are the schols kind of what you'd expect at least for entrepreneurship um it's Stanford um it's MIT it's Harvard yep and then there's sort of the next set which is like Duke it's Carnegie melon you know it's a handful of places did Berkeley make it uh Berkeley's on there particularly for AI and blockchain I'm Stanford but my my rival school so I'm curious every yeah know Berkeley actually it's really interesting Berkeley has a lot of um PhD and AI whove started interesting companies I I
• 22:30 - 23:00 I actually have hired quite a few phds in AI from Berkeley they're very good and data bricks so all the data AI Etc is like basically PhD is from Berkeley and so there these weird clusters or pockets of talent that go on to do interesting things and that's actually been true throughout history right you go back yeah and you look at the Renaissance or you look at Paris uh in the late 1800s groups from the small some small town in Hungary will have like 10 of the most important maeci it's crazy how it works yeah exactly and that that's that's true for art movements that's true for um literary movements and it's for technology movements that's
• 23:00 - 23:30 amazing you know a lot a lot of the success you talked about just right now is is still in your you have a really popular high growth handbook right so this you published this uh when that came out like in the last year or two uh it came out a couple years ago yeah what are a few lessons and Frameworks from that could you tell us what do you remember that I mean that the he handook is basically a book um that says say that your company starts working how do you scale it I saw this New York Times article today where Colin Kaepernick listed the books that is by his bedside right now and my book was the first one listed and I was like wow that's amazing that it's it's kind of spreading more broadly Beyond just the tech ecosystem
• 23:30 - 24:00 that's really cool so that was really neat yeah are some of the high growth like strategies relevant for other areas maybe as well yeah it's all about just scaling organizations dealing with um uh change in your organization and then it has a lot of very tactical stuff like how to raise money or how to deal with a bad board member or you know different aspects of really building out an organization and their organization could be uh a technology company it could be a nonprofit it could be a variety of things it's funny when the book came out I actually had people from all over the world reach out around different types of organizations they were scaling I had a nonprofit and
• 24:00 - 24:30 Indonesia reach out to me I had you know different technology companies so it's been a really fun ride that's really cool so I guess a lot of what we're doing does apply to a lot of other fields is there definitely common patterns and then I think um you know the only good generic startup advice is that there's no good generic startup advice and so I think a lot of things for any organization is situational and who's involved and who are the people and how do you want to structure it and everything else that makes sense another thing I want to ask you about is I think recently you interviewed Lena Khan yeah she's not my favorite government leader uh I think you were very polite to her I think you asked her about a little bit
• 24:30 - 25:00 about like really aggressive m&a enforcements and in my experience there's been things in biotech that are being really like hurt and set back and things are not being cured because she's being so aggressive and blocking things um like what do you think of her answer about the about when you talked about that I think you talked about like blocking meta from acquiring a 30 person company which seemed a little crazy to me you wish you'd push her harder like where are you on this yeah so we had um a reasonably short interview I mean she it was generous with her time to do this right we had about 30 minutes and um I think there was a lot of standard questions that we asked around m&a and
• 25:00 - 25:30 Ai and open source and all these topics and we just didn't have a lot of time to go deep on a lot of things and honestly the things I was most interested in because I actually feel that her stance on small m&a by big Tech is very well understood and known right it's not like we would have uncovered something new and I almost viewed those things as the warm-up the things I wanted to ask about were things like why aren't there young people in government anymore right Lena khong got appointed at I don't remember 31 32 yeah um JFK I think was what was he 27 29 when he first became a senator
• 25:30 - 26:00 late 20s yeah late 20s um the Hoover dam was built by a 25-year-old like throughout history America was founded by a bunch of people mostly in their 20s or 30s so where have all the young people in government gone right and why is there no longer any sort of artment of you think about that I I asked her in the in the podcast and um I think I caught her a little bit off guard because that's the type of question nobody ever asked but I think it's super interesting right the other question I wanted to ask was um and she she had an answer around Millennials I mean about generational change and all this stuff but like I would have loved to keep digging on that I would love to keep digging on um and I didn't have a chance
• 26:00 - 26:30 to ask these questions right but I think they're really interesting like how do you run a government agency like what's important what do you think about what do you consider what would you do differently next time and so to me the really interesting stuff is a stuff that she and others like her are uniquely positioned to provide insights on that just nobody asks right because everybody keeps asking her about m&a so that's a stuff I wanted to get to and if I ever have a chance to talk with her more I would love to get into those sortes of topics because I think they're fascinating yeah the young versus old thing is really interesting to me I one of my favorite cancer nonprofits you might know Damon runan which what they
• 26:30 - 27:00 do is they take the people who made the breakthroughs when they were young and then they let them help choose the new young people who should get a lot of money and by all accounts it's like a 100 times more effective per dollar than what the NH does in these areas and I know you're not supposed to say that it's like it's like sacred cow you're not allowed to attack in government but the NIH as you know is packing more and more and more older and older people sure and I think that means it just I think it's tied to unfortunately it's not having as many breakthroughs because you're and by the way there are Labs run by 40 56 year olds that that deserve to be back that are amazing but we need to have a little bit more m
• 27:00 - 27:30 going towards people in their ands it seems like maybe govern in I think becom more intialized more credential is just like more careful I think there's two things going on um the by the way the first set of the NH was in his late 20s when he so again when was that that must have been like uh 1800 late 1800s yeah that's great but again like it's this point of what why societally has that changed and actually if you look at startup Founders the same thing has happened right Patrick Collison has this good point of like we're all the young startup Founders and there's there's some again and Alexander scale maybe but
• 27:30 - 28:00 there's yeah he's the only one right like generationally you go back 10 years and it was Patrick and it was Zach and it was Dylan from figma and you can kind of come up with the list today there's many fewer now I'm starting to see some of them in AI emerge again right which I think is interesting Scott woo I guess is pretty young still Scott Woo is is is pretty young but it's kind of like where are the people who have very clear outside success really early and those are the people that do amazing things for 20 years after that right that's actually a platform then to have enormous impact it was very useful to Bill pal here as a when I was 21 get my 20s and then yeah we can build lots of
• 28:00 - 28:30 other things yeah exactly right and so where where what where' all those people go it it is interesting I'm seeing a lot more of my favorite companies built by people who are second third fourth time entrepreneurs are you seeing that too I'm seeing a bunch of that um and that's almost a negative sign right it's always been that case but we're all the young people for the first time who are really succeeding and it's not about starting it's about succeeding and I think those are different things you think it's a cultural thing where there's like different level of risk aversion or something in the culture I have like three or four theories I don't think any of them are right do you know what I mean it's kind of like one Theory could
• 28:30 - 29:00 be it's a cultural shift where we have so much helicopter parenting that too cautious people are less resilient or they don't take risks or you could make an argument around how suddenly there's all these um jobs at Big Tech that are incredibly lucrative when you're really young and so that distracts you yeah um you it would have been tough if someone offered me half a million dollars a year when I was 21 I probably wouldn't have gone to start pounder I have to be honest like and so that's maybe you would have ended up at Google or meta whatever least for a few years or something who knows yeah yeah and then it puts you
• 29:00 - 29:30 on a very different trajectory you think about the world differently right and so there's this really interesting question of missing young people um societally and I just I don't know what the driver is and again Patrick I think has brought this up in a really crisp way and he he's probably thought about it much more probably correlate with progress in important ways you probably need to figure it out yeah it's corate with progress important ways and again I think it's about this Arc of impact that some people have over their life time like look at what musk has done but his first success was zip to in the 90s right and that money then went into x.com PayPal and then that money went
• 29:30 - 30:00 into rockets and cars and you know compounds saving civilization with politics just kidding me you guess yeah but but so it's it's stuff like that right and so um and you see these other arcs right Mark andreon was a Phenom right with Netscape and he was on the cover of Time Magazine he was what 22 23 yeah and then he had two decades to do more stuff and so the question is you need you need those early successes I think to have massive societal change because then you can stick around long enough to do stuff no it's true it's true a lot of the impact that we were having right now it's cuz we were able to do stuff when we were younger I want
• 30:00 - 30:30 to talk about AI with you I know it's like the most TR thing but but uh you've been involved I guess in machine learning in generative AI since it's very very early on you doing AI Google early on um first of all like give us some perspective on like the investment landscape in AI right now I'm still seeing I'm still seeing like lots of Mega rounds in a hyped bubble I hear the cost of H 100s or 200s or whatever are coming coming way down right now so like like what's what's going on where are we in this in this trend yeah I mean is um in my opinion dramatically underhyped
• 30:30 - 31:00 right now and um I I think part of that is number one we're seeing massive actual revenue and impact without that much adoption right so um azures last quarter they did like $28 billion in the quarter I think they said 10 to 15% of that lift or growth was from AI products so that would be what two and a half billion or something quarterly right amazing but that's the infrastructure people are are building on people are paying them so are they making people the things on top of it profitable or those things just raise money and build well some things on top of it are doing extremely well right mid journey is rumored to be doing extremely well financially there's a variety of
• 31:00 - 31:30 companies that are scaling very rapidly but it's very early days right chat GPT came out less than two years ago and that was a starting gun for a lot of people to realize gpt3 was only two years huh yeah it's amazing chat GPT GPT 4 came out 18 months ago gpt3 came out in um can't was 21 yeah that feels more like about this three years I think 20 or something yeah yeah okay years that three three you could kind of see things kind of working but three and a half four was when you really saw the C change in terms of capabilities and then chat GPT got slapped on top of for that is sort of a a post training thing um so
• 31:30 - 32:00 on the one hand I think it's dramatically underhyped because very little adoption is still creating these massive um revenue streams but also Clara the fintech company mentioned that they reduced uh their customer success team by 700 people Yeah by adopting Ai and they suddenly had 247 availability in 30 languages with a higher net promoter score faster response time higher customer success we've seen already like a lot more money go into this wave than anything else in a very long time used to but like how much money is right like what would be not underhyped yeah I think what people are
• 32:00 - 32:30 really misunderstanding is what is going to be the end product and I think the end product is units of cognition right you're going to be effectively paying for or renting um something that's going to think or do things on your behalf and that could be legal documents that could be a customer success team effectively it could be a software engineering team which is basically Bots writing code for you and so really this is a revolution in terms of units of cognition right and I think that's very under thought is actually what the product is going to it's interesting because deploying it
• 32:30 - 33:00 right now for example in the services area million bunch of companies in that area and it's not pure cognition it's like right now it's like doing it's doing things to save people tons of time and make each person able to do like three times or four times as much work to so it's like so it's like for example like an auditor like you can bring up right away there 10 screens are most likely to look at all around and that way they go right away they see it and that way they saves them each like five minutes each time you know so it's saving them like it's making them three times as fast so that so it's interesting because right now that's more M machine symbiosis do you see that
• 33:00 - 33:30 yeah that staying is M machine symbiosis or do you see the audit eventually pretty run I I you know a couple years ago um I wrote this blog post that I never published um I just never got around to polishing I should put it out I wrote this maybe five six years ago which is basically positing that there'd be like three eras of mankind in terms of or three eras of intelligence right and the first era is basically people and the second era is some hybrid era where it's a uh you know if you look at the number of intelligence units or whatever you want to call it the relative brain power you'd have a mix of
• 33:30 - 34:00 you know humankind and machines and then eventually it's going to be mainly dominated by machines in terms just sheer number of brain equivalents that'll exist out there because it's just easy to build like so many more of these things you can scale it fast with um with a bunch of uh chips and software and but the idea is these are like serving us and they like work for us or is the idea that at some point like they're not doing that I I I don't know what the very long run looks like but in the short run um you know my team actually looked at this if you look at for example the services world in the US uh or if you look at SAS and enterprise
• 34:00 - 34:30 software in the US is about half trillion dollars a year y um if you look at the areas of AI or the areas of services that AI can transform it's probably $5 trillion doar in headcount right costs it's just paying employees well five trillion of all services in the US our best estimates about 40% of that right now could be transformed by AI but and then you take a cut of that and you take 10 to 20% of that but our math was basically if you say take a 10% cut and transform um employee headcount costs into software you're back to half a trillion and you've recreated the
• 34:30 - 35:00 entire enterprise software market cap no it's true I think in the 2.1 trillion that we think could be addressed right now stuff like Healthcare billing Logistics billing auditing whatever legal some legal stuff we think already you can at least double the productivity which would be kind of a trillion dollars pull out right which is which is huge yeah and then you capture some subset of that right so people should definitely give money to lot to all our funds that's that's just uh it's just it's just again the the unit of work that we're selling is different from we're we're selling well eventually be selling units of productive intelligence
• 35:00 - 35:30 and that's a new type of skew that nobody's ever thought thought about or had before because the prior waves of machine learning I think we're people misunderstand AI right now is we've been talking about AI for 20 years and that's because we had these prior waves of machine learning and convolutional learn networks and you know rnns and ganss and all these other types of networks um but they were a very different fundamental both architecture but also type of product really what those things used to be good at or still are good at is pulling out statistical um associations between large data sets
• 35:30 - 36:00 Y and so they're really good at at Stats is basically that prior wave the current wave is really powerful and that it's actually understanding and generating language and images and other things associated with that it's basically um and this is based on this new architecture called the Transformer architecture which was invented at Google in 2017 and that's been the big transformation so it's actually a different technology curve from what we had before but people call both of them AI but I think we need almost like a segmentation of these things cuz it's we're on a different trajectory right
• 36:00 - 36:30 now when you say curve like this to me it seems like the curve is more like this but I could be totally wrong you actually you think it's gonna I mean is there going to be a gbt 5 and six or whatever from someone else too it's gonna just be exponentially better than what we have right now still it's not only exponentially better it's impacting other fields so you look at what wh is doing for self-driving or for self-driving they've moved over to a Transformer backbone and suddenly they see dramatic improvements in what no I mean El City got rid of all the lines of AI just by using by using AI got rid of 300,000 lines of Juris sticks basically in Tesla's AI for just go to end to end
• 36:30 - 37:00 AI instead of having all these little edge cases hardcoded in and that's happening in every single field I want to push back because I agree it's like dramatically affecting all these fields I agree like I said we're using it for services already tripling the productivity and many cases but that's stuff you could do with like where llms are today sure and so the question is when you go like this the question is like are llms going to exponentially keep getting better to the point that it like totally changes and improves it or are they hitting some kind of yeah they're not even close to an ASM toote as far as I can tell and
• 37:00 - 37:30 I've talked to a lot of the main researchers in the field the way I think about it is almost like a um llm steps right and when you had GPT 3.5 and you went to four suddenly legal opened up as a vertical um I backed a company called Harvey and they showed me side by side 3.5 versus 4 3.5 doesn't work otherwise just doesn't work right you had a model update something you could do legal what's the next step podcast H yeah I think that one you could do with like gpt1 oh but you know you have these steps and so it's this ladder that you're climbing and eventually you're
• 37:30 - 38:00 going to hit a point where it can do all of Human Services right and I don't know if that's gpt7 equivalent or eight or six or 10 or whatever but we're going to get there so you really don't think of it in terms of man machine symbiosis stuff the stuff we're building now to do these services and whenn will those companies still be valuable in five years because they can replace it with AI as goes along or is it going to be some new is it gonna be open AI itself that just owns all of those things when I um when I dced Harvey I called a bunch of big law firms and I asked them how do you think about this and law firms traditionally are of the worst software buyers in the world right they don't really innovate they're locked down
• 38:00 - 38:30 because of security and privacy and other very legitimate reasons um but they tend to be very bad buyers but they were adopting Harvey really quickly and so I asked them like how do you view the future of AI or what do you think is going to happen and I was surprised by how thoughtful and forward thinking they were on this where they said look we think the nature of a law firm will change yeah because right now we hire I'm making it up 50 Associates every year and five of them will make it to partner but we now think that in a couple years we can just hire five Associates to with but then who becomes our partners what do they learn along
• 38:30 - 39:00 the way how do we teach them how do we screen them how do we have a portfolio of people so that some work out and some don't and so they're thinking about the legacy of their law firms and they're like we don't know how to think about this future world but it also means maybe you have one senior part partner and two Associates and 30 Bots yeah you know and so the the whole thing kind of shifts but it's it's also interesting so with Harvey you have the model where you're selling to a law firm a lot of my companies when we call them AI Services we're literally replacing like the Healthcare billing firm like like we're competing and we're doing it all ourselves but we're doing it way better
• 39:00 - 39:30 for a fraction of the costs like are you seeing a lot of those companies right nowt yeah it's both and and and I guess what's your intuition let's say let's just say for the sake of argument to scare people that that you're right and it's going to get way better with GPT 5 67 or their equivalence yeah and is it the case that by gpt7 maybe they just like do it their company themselves even better than our services company and all of a sudden they're competing with us directly or if they want if they wanted to it's always possible but I think it it comes down to like um what is the other
• 39:30 - 40:00 tooling that's needed around what you're doing or what you're providing and so the things that I'm bullish on are where you own a workflow for a vertical yep and so everybody in that vertical is using that workflow workflow directly yeah and so if you upgrade the model it just makes that workflow better so it doesn't matter what the model that's how I see it maybe challenge this model for me just really briefly um I we I talk about five layers for the AI Value stock so the bottom layer is like Nvidia and chips and everything the next one's the data centers that all our friends offices putting tons of money into and then it's like the models whether it's
• 40:00 - 40:30 elon's X or anthropic then it's like level four would be the tools for deploying AI which there's a bunch of interesting ones data infrastructure pter trying to do things there a lot of interesting things there and then level five is actually owning the workflow services company or this that's where I'm building like probably a dozen things right now uh which I'm sure you're doing a lot too um so which so so so which of those five are your most bullish on which are you spending time on you know yeah I'm mainly spending time on the top three and is that the right way to talk about it I mean I have the I have a slide that literally has that in sort of um uh presentations I
• 40:30 - 41:00 give and stuff um the bottom side of it the chip side actually invested in two companies seven eight years ago thinking that there would be Nvidia competitors and I was totally wrong right hard that was really hard was just so good um they're just they're just very good um uh and there's still room for other players I'm just saying like they they've really executed well um but yeah you know I've mainly been focused on the top three of those layers the other thing I've been doing is actually been backing I've now backed two um AI driven buyouts where the idea is you buy the
• 41:00 - 41:30 asset itself and you can radically change the cost structure or increase the leverage of our organization um by putting an AI in a deep way and then that entity can go and roll up other companies in its vertical and so I think that's very exciting this is what we're doing too like with the Healthcare billing and the logistics billing and other ones like that is we'll grow organically at first but then we go inorganically and there's principles for in organic growth with AI that are pretty crazy right now which is what buying um couple more things while we have you on on AI uh and uh you know you have
• 41:30 - 42:00 children as as do I um the world's going to probably be very different in 10 or 20 years like like what are you paying attention to for them what do we do differently with education with with you know how's this affect how we raise our kids yeah you know I wonder about that a lot and I don't have a good answer and I've asked some of the world's top researchers about this right because IAL I talked to many of them with some regularity and I'm like what like what should my kids study you know like what's a what's a good thing to know in the future um so that that and you know the
• 42:00 - 42:30 positive of this AI way for this kind of stuff is that eventually each person will have a custom tutor which is helping them learn really deeply at their own pace right and I think AI is perfectly suited for that and it's going to be a very exciting world where you have ai really going deep with your kid on different topics and there's all sorts of research from the 80s that shows that um kids that receive one-on-one tutoring uh learn dramatically faster the aristot Alexander the Great framework it really is I mean a lot of things that Happ thousands of ago turns out make a lot of
• 42:30 - 43:00 sense and um teaching kids uh oneon-one um increases their performance by I think I C has one or two orders of magnitude right um I mean one or two uh uh standard deviations yeah I think it's one or two standard deviations no I agree a lot a lot of the great minds of the Enlightenment and of the Scientific Revolution did get private tutoring something maybe we should be doing more of today which is kind of interesting because back then I was very aristocratic now should make it democratized anybody should be able to get it because it should be so cheap because it's just a machine Learning System running in the background or an AI system running in the background so I
• 43:00 - 43:30 think that part of it is extremely exciting um you know the other question I've been thinking about is just um is there anything that you know if you can afford it that you can buy for your kids now so that it's durable into the future because if you imagine that AI is going to upend a lot of Industries and a lot type of jobs and is it like land that is useful like what's what's actually worth something in the future elod Gill is fun come on it's like dud yeah it's it's crazy to think like what what's valuable 20 years from now we do have a lot of land around
• 43:30 - 44:00 Austin Bitcoin just buy Bitcoin that's what it all comes down to I think it's Bitcoin I think it's like high growth areas land is not so bad I think I think I think top companies that are attracting Talent right I don't know I feel like I don't know I feel like companies with great talent that are good at AI has to be like I have no predicted value of what companies will exist in 20 years maybe railroads right you could say okay the railroads are basically localized monopolies right they own the tracks you're not going to build out the new bu agrees with you yeah you know seriously stuff like that that's really durable so maybe buy railroad stocks just go along America
• 44:00 - 44:30 that's right American Optimist exactly on the but but nothing in now to teach kids in particular like should they is it anything in particular I think it's math computer science resilience um physical fitness like I feel like it's kind of that same that same uh you know how to write how to think um how to express oneself creatively philosophy maybe a little bit some bit yeah yeah depending on which part which part yeah there's a lot of philosophy out there so
• 44:30 - 45:00 teaching my kids the values of Liberty and Order and all this stuff yeah I know it's like what is the moral Fabric and framework that you want your kids to be part of and then also I feel that some form of um religious upbringing is useful if only to protect them from fake religions sort of coming in or modern religions we need to inoculate them against the modern fake religious stuff you need to you need to fill up that whole right part we're reasing our kids Jewish we think the tradition is really important to have that that's important so you know you're doing obviously really important work on AI and biotech and other Frontiers one of the things
• 45:00 - 45:30 you're passionate about I believe is building inspiring new monuments right you think you invested in I'm just starting on that um and I'm looking to bring on somebody to to help me drive that I don't have time to do it but I think um if you look at every society at its peak or at least on its way up they you'd always have these large scale inspiring monuments you celebrate wins and You' celebrate you celebrate but you also Inspire the future you inspire the next generation of people it's like watching a SpaceX rocket launch right it's so inspiring my friend Rodney cook in Georgia is the is the head of the
• 45:30 - 46:00 National Monument foundation and and I've helped him build a bunch of monuments I agree it's really important it's something it's that he's more he's a neoc classical architect which is like I love that style and there's a lot of lot of lot of beauty and Truth in that but is is this something where you'd want to do it in traditional ways or would you want to do it in entirely new ways or both or how do you think about it yeah it's a little bit of both I mean if you look at um a lot of the really important historical ones or you know there's obviously the seven wonders of the ancient world and all this stuff but also more in more recent times you look at things like the statue of libert and people coming into Ellis Island as sort of a major entry point for immigration
• 46:00 - 46:30 and you'd see this inspiring statue right and the inscriptions and everything else around it that that helps motivate people to do amazing things right or you look at um the Eiffel Tower and that was built for the world fair that was held in Paris in the late 1800s and it was supposed to be a testament to French steel making I love right and so that was supposed to show technology progress it was look at how we can do stuff with steel isn't that amazing you're showing it off do you believe in these things like the golden ratio and like ideas of beauty and stuff there's other other those things Ian
• 46:30 - 47:00 we've kind of lost some form of beauty or sense of Beauty in society right we don't aspire to make giant beautiful artifacts I think our culture rejects it as well it's like it mocks it right it mocks it and the question is why and why would you mock something that's inspiring and creates a sense of light a sense of hope a sense of purpose um and I think that large scale monuments do that right and so um and that that's true in every society throughout history well I love that monumentals do a lot of things around like neoc classical and traditions as well is trying to be
• 47:00 - 47:30 inspiring I'm totally with you on that you know we we actually star American Optimist to try to push back allot the pessimism in our country what areas of innovation are you most excited about right now in terms of like positive outcomes for the world what could the world look like in 10 or 20 years with with these positive you know te I think there's so much exciting stuff coming um uh by the way one could argue that the sphere in Las Vegas is a good example of almost like a really interesting modern artifact right it's beautiful to look at when you're there it's amazing it's so even stuff like that right could be really neat um if you think about how do you make that a public artwork or
• 47:30 - 48:00 something else let's build Inspire more people I agreee um and then in terms of the future I mean obviously there's so much coming that can be incredibly positive for the world um there's sort of stuff in AI stuff in health stuff in education um you know there's a lot of different um areas that we can transform Society in a really positive way I think fundamentally um we already talked about education side and how I think AI is actually going to have huge impact there I think from a Health Equity perspective AI can be transformative there um Google released a model I think two years ago
• 48:00 - 48:30 called Med pom 2 where um if you compared the answers from that model relative to physician experts it outperform them in other words if you use Physicians to train the model the model will get worse the Physicians are way behind are way behind now what you can do with AI which they don't seem to acknowledge when they do their work though well I think it can be very additive to Physicians right it can be a tool for them but also imagine if anywhere in the world if you had a device right and you know billions of people now have smartphones or equivalent if you could take a photo of something upload it add some text or speak into it um and then get out um uh
• 48:30 - 49:00 a medical answer that potentially is actionable and it's the equivalent of you know Stanford Medical Care or MD Anderson or whatever yeah uh that's incredibly empowering for the world right if anybody anywhere in the world can do that so I think that's very exciting if you don't have scoper practice slids you could do a lot of cool things with that yeah yeah so one side project that I'm working on um with Stan who's who's on my team is um we're we're going to take a thousand great works that are off copyright and we're
• 49:00 - 49:30 going to translate them into 100 languages using Ai and then we're going to do an audio book uh for all of them in Ai and then we're actually going to build a module where you can interact with and chat with the book in a way where it has the intelligence of the full Foundation model but it sort of represents the book and its Persona that's cool and so um you know we think it's almost like a modern Library of Alexandria right because if you look at throughout history public libraries have been massive public goods and there's two threads of that one is how do you create access for anywhere in the anyone in the world through any modality they
• 49:30 - 50:00 should be able to hear it they can read it whatever it is um they can interact with it um to gain new knowledge right help help summarize this chapter for me and let's talk about it right this would be cool for for kids and everyone though I just think it's really exciting um so that's one thing and it could be religious works like the babag Gita or the Bible or whatever it could be philosophical treaties it could be works of mathematics it could be great books Shakespeare Etc and so we think there's a broad swath of stuff that you know we can incorporate as part of so um that's another example of where I just think we
• 50:00 - 50:30 have these amazing waves of Technology coming and they can be used in all sorts of ways that people aren't doing yet and they can be incredibly powerful and empowering so I think there's a lot to do Health art inspiration and we got we we have a we have a bright fure on there's a lot to do all right well Ela thanks for joining us ah thanks for having me