I always remembered when I joined Google Larry Page came and talked to us and he said the ultimate search engine should be one where you ask a question and get a single correct answer you’ve been at Apple at Microsoft uh president of Google China I love Google but they’re an engine that has been powered by advertising how long is that going to last do you think that’s going to survive I think there needs to be a business model flip at some point and Google will fail to do that just as any innovator facing innovator dilemma in your last book but you said something like the US will lead in breakthrough Innovations but China is better in execution what does that mean the major technology breakthroughs were almost invariably invented by Americans now when it comes to execution it requires additional capabilities before we get started I want to share with you the fact that they incredible break bre throughs
[00:01:00] coming on the health span and Longevity front these Technologies are enabling us to extend how long we live how long we’re healthy the truth is a lot of the approaches are in fact cheap or even free and I want to share this with you I just wrote a book called Longevity guide book that outlines what I’ve been doing to reverse my biological age what I’ve been doing to increase my health my strength my energy and I want to make this available to my community at cost so longevity guidebook.com you can get information or check out the link below all right let’s jump into this episode hey Kaiu uh good morning to you hi Peter good to be back yeah yeah it’s great to see you my friend we’re on flip sides of the planet I can’t wait till we’re having this podcast and we’re in in different parts of the solar system that’ll be that’ll be fun but we need faster than light travel um you know I I have the Fondest Memories of coming and visiting you in in China um
[00:02:02] in your different uh locations and I have to say you know my takeaway from I used to come to China every year you would host a number of the abundance 360 members I’d bring with me super gracious and I remember my takeaways were were that number one there was an incredible work ethic um from Chinese entrepreneurs um right no and I remember you describing at the wor work ethic as 996 uh that a good is that still is that still the saying there yes yes definitely yeah a good a good job was 9:00 am to 9900 p.m six days a week that was a good balance of life um and the second thing I remember uh as a as a key takeaway was at least this was you know I don’t know decade ago and it’s been
[00:03:01] some time but that you know in the US entrepreneurs see their the marketplace as the us maybe Europe in in China the entrepreneurs saw the marketplace as China and Europe and the US it was a much more global view and I am curious if that’s still the uh the view in in entrepreneur World in China today um because I’ve heard you know and I’m seeing comments where you know we’re sort of like going into two parallel universes where products developed in China or staying in China and products developed in the US or staying in the US how do you see that I’m curious uh yeah I think a lot of the B2B is becoming very much a parallel universe it’s hard to sell B2B especially given uh export control and geopolitical issues uh
[00:04:01] especially in the Deep Tech areas which you and I care deeply about uh b2c areas are much easier you know Americans use um shien and temu and um Tik Tock and of course Chinese use a lot of American Products mac Apple windows and so on so that hasn’t been as affected I would also say the um in pursuit of um scaling law AGI gen while the efforts are separate uh the uh collaboration or at least the sharing of ideas are pretty strong in paper publishing open source of course with the notable exception of open Ai and now Google who don’t publish but they don’t do it for geopolitical reasons they don’t want the competitor to see yeah um that is fascinating we’ll get we’ll get into that because you’ve taken very much an open source uh
[00:05:00] focused mindset um and there have been many many that do I I I have to ask a question so you’ve seen you’ve been at Apple at Microsoft uh president of Google China you’ve seen so much and in Innovations I mean uh you’re managing what like three billion in Investments thereabouts so you’ve seen it all I mean uh and of course here two excellent books which we’ve we’ve discussed on my state ages before um I am curious about something uh and I I’d love your opinion if you’re willing which is I love Google um I love Google for many reasons what they’ve done their Investments their mindset of driving breakthroughs but they’re an engine that has been powered by advertising um and they’ve been able to reinvest that but what happens now when geni is giving single Solutions and the
[00:06:02] ad powered models um is you I how long is that going to last do you think that’s going to survive um or is there going to have to be a business model flip for Google I think there needs to be a business model flip at some point and Google will fail to do that just as any innovator facing innovator dilemma because Google is critically dependent on the advertising revenue and to do the flip would require going away losing all the revenue coming in going to an at best break even value proposition of a single answer search engine and then building rebuilding up the new business model whether it’s subscription or advertising and that’s going to cause a uh roller coaster ride mostly downwards for the stock price and that’s not something that a publicly listed company can do it’s kind of sad to see because
[00:07:00] Google is clearly in the best position to reinvent handcuffs right it’s it’s handcuffs yeah your quarterly earnings reports are handcuffs your your stock market your stock holders aren’t going to let you uh sacrifice or take the risks and and that’s I mean that’s why a lot of companies that should have jumped to the next generation of Technology never made it right right it’s it’s Such a Pity because Google clearly has uh one of the world’s top two AI Eng and by far the world’s number one search engine and now we’re talking about merging two areas in which they’re the best yet they can’t win because of this innovators dilemma it’s unfortunate yeah um I want to dive in during our conversation into what you’re doing now so for the better part of 30 years you were one of the lead investors in technology in in China um uh but you also in Ed across around the world but
[00:08:02] typically in in in Chinese markets and you you flipped over from being an investor now to being an entrepreneur um right and and and building uh 01 a what was the positive moment I mean because I am curious I mean you’ve seen so many so many entrepreneurs and so many deals I mean just a you know what I wrote down here was you know you’ve been investing in NLP Tech Enterprise AI AI driven Financial Solutions autonomous vehicles autonomous software um a lot but there was a moment in which you said okay I need to go and build a company why yeah right by the way let’s call it Z1 thatai we were flexible before but now that open AI has taken the 01 name we’ll let him have it we’ll just be01 01 a it is yeah yeah it it it really means recreating uh the world with 01 using AI
[00:09:02] Technologies so yeah I I was content doing investment in the early days of AI in the days of deep learning computer vision convolution neuron networks I was super excited because in my uh 40-year career in AI I basically saw two AI Winters and even the non-winter days were not that shiny uh so finally I saw wow this AI is becoming mature so I was very exited it’s not a fad no no right and I was uh in the position of being a venture capitalist uh so I figure the role I should play is invest because I’m you know older hopefully in some ways wiser and experience and knows technology and knows business so I uh invested in about 50 AI companies uh mostly in China but some in the US and um they did well we now have 12 AI
[00:10:00] unicorns uh we soon have half a dozen IPOs from just from the AI uh companies being the first investor which is pretty rare so I kind of got on the ride and enjoyed watching from the back seat uh the excitement that my uh entrepreneurs went through so that was good but then uh jna came about uh we all saw and understood gen but we didn’t see how big it would be until open AI showed us with Chachi and at that moment I realized that uh I could invest in the area um in China and elsewhere I looked at a bunch of J companies but then I realized that uh to start one that late to start a gen company after Chad gbd had taken over World by storm would really be very very hard for any entrepreneur because you you you have a you you’re behind by six or seven years and if you don’t already have a team or
[00:11:02] products or Technologies how could I fund these people because China did not have really a lot of genni companies there was one or two at most and I just thought hey if anyone could do it maybe I could do it it would still be a long shot but given my uh years of experience and people Network and understanding of the technology and business let’s give it a shot it may be a long shot but you know I feel that you know when I’m really really old I’m old now but when I’m really really old and look don’t call your don’t call yourself old my friend you’re still young and vibrant yeah okay I I would you know when I’m 80 I would look I would not want to look back and say hey I just had a Cod fee and I decided to invest and even if I won with a great investor building China’s open Ai and I were an investor I would still have regrets because because how could I my my love of my life not to
[00:12:02] participate in it this time and also I saw that um if I did it it really could work I would have a shot others may have a shot too but I thought I would have a better shot because I could pull pull a great team um that have the uh right ideas and and also I saw the world kind of dividing up into parallel universes and that someone needed to do a j for China otherwise the Chinese businesses and people will fall way behind and all the work that D shaing did to bring China forward um could be lost if um uh the world had gen but China didn’t so I thought I would do it it’s interesting right because um if well here’s the question right if open Ai and all the other llm systems had been equally available in China as they are in other parts of the world
[00:13:02] would you still have done it uh good chance I might not I would then have to uh think about the likelihood of success right right uh that that’s the main becomes the main factor not as much as to uh helping the Chinese people in business to have a solution even if it’s not as good as open AI so it might be 5050 in that case but open a I decided not to make it available to China so that was tough I mean I I think everyone would agree every country and every human is going to need to have access to this infrastructure called gen it’s going to be your it’s going to be your um consultant your doctor your educator your everything and it would be like it be like denying a country access to oxygen or electricity yeah that’s why when we found that 01 that our vision
[00:14:00] statement was to make AGI beneficial and accessible and that’s very similar to open ai’s um Vision make make gen AI uh beneficial to humans but we add it accessible we wanted to stress the point that we want everyone to access it no matter where they live uh what their nationality their uh income level Etc it it is quite interesting that you’ve gone The open- Source Road um can you speak to that a little bit I mean I just had Ry on the podcast and we were talking about open source and I had the mo the Mozilla Foundation um CEO on the podcast talking about open source um why why aren’t all companies going open source and what was your motivation for open source right well um I think a smaller company a newcomer really needs
[00:15:02] the open source Community because having a 100 people trying to compete with a thousand people at you know Google and uh starting 10 years late is a loose definitely lose proposition if you don’t somehow work with the open- source Community to help each other make progress so that’s just out of a practical consideration uh secondly we saw a lot of good work in in open source um from universities from meta um from Microsoft from Nvidia and we couldn’t start our company without these especially Nvidia Megatron um Microsoft steep speed uh without these it would have taken us much longer to start the company so um so we said well if we’re going to take from the open source Community well we should rightfully give back every model we make except the most fr model so we
[00:16:00] would keep close Source the very very best model that we make everything else would become open source and that is a way of giving back I know some companies open source everything but we uh we do we can’t do that we do need a business model and some commercial advantage and also we decided the way we uh would do open source is through the Apache license we would not be asking people you know to get our approval for commercialization nor would we put a limit that if you started making too much money or have too many users we have to sit down come talk commercial terms we want everyone to have what we have just like we took from um Nvidia and um um Microsoft their open source they didn’t ask for anything back and we thought we also should not so you’re putting everything up on and hugging phas um yes for for people access and GitHub um did you see the movie Oppenheimer if you did did you know that
[00:17:02] besides building the atomic bomb at Los Alamos National Labs that they spent billions on biod defense weapons the ability to accurately detect viruses and microbes by reading their RNA well a company called viome exclusively licensed the technology from Los Alamos labs to build a platform that can measure your microbiome and the RNA in your blood now viome has a product that I’ve personally used for years called full body intelligence which collects a few drops of your blood spit and stool and can tell you so much about your health they’ve tested over 700,000 individuals and used their AI models to deliver members critical Health guidance like what foods you should eat what foods you shouldn’t eat as well as your supplements and probiotics your biological age and other deep Health insights and the results of the recommendations are nothing short of Stellar you know as reported in the American Journal of Lifestyle medicine after just 6 months of following vom’s
[00:18:01] recommendations members reported the following a 36% reduction in depression a 40% reduction in anxiety a 30% reduction in diabetes and a 48% reduction in IBS listen I’ve been using viome for 3 years I know that my oral and gut health is one of my highest priorities best of all viome is Affordable which is part of my mission to democratize health if you want to join me on this journey go to vi.com Peter I’ve asked naen Jane a friend of mine who’s the founder and CEO of viome to give my listeners a special discount you’ll find it at vom.com Peter I’d like to you provide me a few charts I want to share uh one or two of these with the audience at this point um there’s one in particular that talks about the impact of GDP of the PC era the mobile era and the AI era if we can put that up um let’s talk about uh what
[00:19:03] that means how do you interpret this yeah I think you know if we look at the global GDP it’s interesting to note that the PC era brought about an uplift of the global GDP then it kind of saturated then mobile brought another then it kind of saturated now there are many factors to the GDP I don’t claim PC and mobile were the only factors but they were clearly a major factors that greatly enhance productivity and changed the way we worked we as humans do more or less the same things for thousands of years we work we play uh we communicate we learn um but the way in which we do them change from PC to mobile and I would say with AI it would be in some sense a similar change it would be a new platform that rather than infusing a computer on every desktop or allowing anywhere I need time uh mobile access we
[00:20:01] would make super intelligent AI in every app and we would have apps that are super intelligent that could do work for us that could give us answers and I think that is clear that this is not only the third um platform Revolution third um productivity Revolution but by far the largest one because of how much value uh it adds there there was something you said uh that I that you wrote about in your last book I thought was fascinating it’s an approximate quote but you said something like the US will lead in breakthrough Innovations but China is better in execution um yes fascinated about that please elaborate what is that what does that mean for entrepreneurs here in the US entrepreneurs in China yeah I think you know we’ve seen this through mobile Revolution and through the uh early days of deep learning and computer vision AI Revolution that um the the major technology breakthroughs were almost
[00:21:02] invariably invented by Americans and that’s because of the great University system research labs and a culture that um that encourages and rewards risk-taking and Innovation and and a amazing early stage Venture community that allows new ideas to be funded um U and also the patent system all of that basically started in the US and no no wonder that us is best at discovering new technologies in the phases where new ideas were coming out now when it comes to execution um it requires a additional capabilities I think the Breakthrough Innovation is less important but more important would be uh figuring out what to build and being focused on building it and ask no questions and execute and work incredibly hard in particular
[00:22:02] asking really really smart people to say well you’re not writing papers you’re writing code to get this out there and to view success as um a success of a product or a business not a success of a paper or an award so it’s it’s the notion that a lot of AI researchers today are more concerned about the sightings they get on their paper um versus making something that is generating revenue and users um and I I see that it was it was fascinating of course that uh you know open AI turned on chat GPT um and made a very successful first user product but I’ve criticized and many have Google for not having an app right you have to do a lot of steps between something you’re doing to get to a a gem I um you know
[00:23:00] Gemini search so you think that CH the Chinese entrepreneurs are better at execution and better creating uh something that’s a a beautiful user interface is that is that the primary uh yeah but that interface is uh not just the artistic Beauty but rather uh using uh all the principles um again invented in Silicon Valley The Lean Startup uh 0er to one that is building them uh MVP do do ab tests and tweaking and really it’s the availability of the internet as an instrumentation that allows entrepreneurs to no longer have to be Steve Jobs you don’t have to know what the user is thinking you just test it and tweak it and if you work hard around the clock and measure you measure the right things improve the right things you will evolve to the right user interface and that’s where hard work becomes the um the um uh the oil right that makes the uh engine work and building a good app
[00:24:01] it’s not just Brilliance and insight and Brilliance Insight would have favored the American entrepreneur I mean there’s another thing going on with a lot of us AI companies which I you know call the race to AGI um and I I I’d like to show a short video of a statement by Sam Alman um that’s going and pull that up and what you think about this whether we burn 500 million a year or 5 billion or 50 billion a year I don’t care I genuinely don’t as long as we can I think stay on a trajectory where eventually we create way more value for society than that and as long as we can figure out a way to pay the bills like we’re making AGI it’s going to be expensive it’s totally worth it so what’s your reaction to that um how do you think about that well I I think we all aspire to build AGI uh I
[00:25:01] know you do I’ve wanted I’ve wanted it for 40 plus years that I’ve been in AI um and we’re very lucky to be at the point where scaling law appears to still be working meaning that if you throw 10 times more Computing at the AGI problem it gets smarter so it’s tempting and logical to want to keep throwing 10 times more compute every one and a half years or so of course where this run runs into some issues is you know is it a good investment once you’re putting $50 billion into it are you sure there would not be diminishing returns um and also are you too focused on the Breakthrough AGI and not enough on the application ecosystem yeah is it a bunch of researchers geeking out uh versus people building building businesses yeah you know um there’s a there’s a another chart here I want to bring up and the question is is as I watch the cost of uh
[00:26:02] of models uh in particular uh inference models and such plummeting in price um and the question is is it a race to the bottom is there I mean it’s fascinating that the single most powerful technology in the world is effectively free so this is your chart Kaiu tell me what this means for you yeah uh actually I wouldn’t quite draw that conclusion about effectively free it’s eventually free so given a particular technology let’s say GPD 4 in this chart um it started it was launched in May 2023 at $75 per million tokens and today it’s at only $440 and it’s using a better version GPD 40 which is smaller faster better and much cheaper roughly coming down 10 times a year and and this is a good thing this is the market leader reducing
[00:27:02] price um and it’s reflecting the lower cost that they’ve accomplished because GPU costs have come down it’s reflecting better Technologies because you can get better performance with a smaller model and just as we had mors law um I think the scaling law is a law because we do seem to see every year and a half or so it gets better and also the cost comes down 10x per year so so I I I would um see a conclusion of wow this is going great uh we just can sit around and then um all the things we want that are too expensive will become cheap uh but I would also have a word of caution because we are basically in the stratosphere going at turbo speed so one year is a really really long time just think two one years ago we had no idea
[00:28:00] any of this was happening right one year ago we were still complaining chpt was hallucinating didn’t know anything that’s recent and all that’s been changed so this industry is moving in one year what mobile probably would have taken seven or 10 years to do so a year is a long time and I would also argue that even at $440 GPD 40 is way too expensive for applications um for example let’s take a look at let’s say AI search the example we talked about earlier yeah I think if you took gp40 and used it to build an AI search uh you would end up basically uh paying um something like 10 cents or more uh per search query and Google only makes 1.6 cents of Revenue per search period so you’d be on a fast road to bankrupt due to the cost of GPD 40 and that’s not
[00:29:01] even counting you have to build a search infrastructure that’s just the llm costs it’s just way way way way too high um I think more more precisely um it is um yeah around 10 cents so to summarize you find yourself a situation where chat GPT is not available to China and and the people of China need generative Ai and you look around and say who better to do this than me um and you’ve got a huge amount of experience so you jump in and you create 01 a uh so what’s the background there and I one of the products you showed me was bego which is beautiful and fast and apparently cheap uh so let’s talk about the history real quick of 01 a and then let’s jump into bego yeah in 0 1. a we realized we were way behind open AI we were perhaps seven years behind when we founded it 17 months ago it was only 17
[00:30:02] months ago and I didn’t have a team of Engineers I had to use the first four or five months to hire people um but but even with that um basically the Playbook that I took was from my own book AI superpowers we said we’re not going to beat open AI at their own game can we build things very quickly um sometimes the most um challenging part of building something is proving a an unknown idea to be feasible which Chad gbd had done which gbd4 and gbd4 and now gb01 have done and that with the the the leaders in research demonstrating that something is feasible that is all we need to know because when someone uh builds a nuclear bomb or puts a man on the moon for others to do it is much much easier because empirically it had been demonstrated so we were just saying now we just have to be more
[00:31:00] diligent read more papers um and work harder around the clock and leverage the strength that we have as Chinese entrepreneurs and engineers and just go 996 or longer if needed until we get to products that are competitive and and and efficient right because we probably can’t win on accuracy but can we make the equally accurate product much cheaper cheaper to train cheaper to inference cheaper to train because we’re poor we don’t have the 50 billion or5 billion dollar um uh Sam Alman talked about and cheap to inference because we want apps to run lightening fast and and that is what it would take for adoption because you want fast and lowcost of inference and in the last 17 months we achieved all that so you just said something that’s fascinating which is access to compute I I think
[00:32:00] everybody uh imagines China has huge infinite resources but it hasn’t been the case in terms of uh of of gpus and does that does that scarcity of resources cause you to think differently and not be lazy or to be more Innovative uh yes um one is just a difficulty of acquiring gpus given the US restrictions but also we only raised the small amount of money so we couldn’t afford uh uh you know 10,000 gpus anyway and basically everything we’ve done um we did production runs on only 2,000 gpus which is um a small fraction of uh what the US companies are using Elon Musk just put together 100,000 h100s and um open the ey impressive have even more it’s impressive but we have basically you you know less than 2% of their compute but I am a deep believer
[00:33:02] in efficiency power of engineering small teams working together vertical integration and we uh I also I’m strong I’m a strong believer that necessity is the mother innov of innovation so I have a team I told them all we got is 2,000 gpus we don’t have 100,000 I don’t need you to you know invent the GPT 5 I want you to take a look at gp4 gbd 40 and can we match that in the in 12 months in in five months and and can in the process of making it all we have is 2,000 gpus you don’t have a lot of compute and when you make it by the way if you train it in in very efficiently we can also can we also have an inference that’s very efficient calling costing only a few perent to run it in apps so uh talk to me about your product beo uh by the way I asked you earlier how to pronounce it
[00:34:02] and where the name come came from and I think it’s worth repeating so people remember it better uh beagle and golden retriever right it’s a dog name yeah yeah yeah I mean we all know the name beagle B A GLE e it’s a cute little dog and golden retriever but when the two of them make a little puppy that puppy is called B go b a g o and okay B ble ble is very good at hunting and uh golden retriever is good at retrieving so it’s an apt name for an AI search engine but I should also point out that eagle is not 01 a product um it is a product that I did um Venture build and it’s actually an American company and it uses a model very similar to the model that 01 thatai has built super fast super cheap thereby thinking that um AI search could be reinvented so do you
[00:35:01] want to talk about 01 or you want to jump into a little bit about bigo which you prefer well yeah let me start with 01 then we go into bigo so um yeah 01 I think you know uh this may we came up with a very good model uh called e- llarge and e- llarge was a bit behind GPD 40 which came out one day later and um uh we we had the time where ranked number seven and which is great number seven model number four company just behind open AI Google and anthropics something to be really let’s pull up that chart one second go ahead so that’s the May chart uh but I want to talk about what just happened um in October because over between May and October lots of models emerged e large was no longer competitive and but we had been working based on what I describ as uh working super hard building to match GPD 40 and
[00:36:01] maybe even be faster and that was accomplished in October that we kind of took revenge on the GPT 40 uh May version which you can now see on this chart uh as just below us as number seven in the world we just beat them by a little bit so this is a casing point where we saw GPD 40 we saw what it could do and we knew it could be done and we said let’s go do it and basically with no hint on how it’s done we figured out how how to do it ourselves I’m sure the methods are different but we did match their performance in just five months of course in these past five months other great models came out including new version of GPD 40 gr and Gro and others so we came out in October with a tiny model called e lightning because we wanted it to be lightning fast this a much smaller much faster model but it
[00:37:00] became number six model in the world in number three company and we also surpassed anthropic this time how big was your team building this uh the pre-training team is basically three or four people uh the Post train team was maybe uh 10 people the INF sorry the infrastructure team maybe another 10 people so it’s a 20 to 30 person project uh it’s pretty small and um the thing where most excited about and proud and unique about is that we trained this model the pre-train um only cost a little over $3 million and this is uh this is 3% of what gbd4 cost to train and we actually beat gbd4 in performance and the inference cost is very very low it’s around 10 cents per million tokens and let’s go to that let’s go to that chart there’s a chart here it looks at inference cost over time um which is
[00:38:00] super impressive as well so explain this is uh explain this chart here please uh yeah so back in June we were a140 per million tokens costs which is which was a lot lower than GPD 40 at the time which was I think about $10 uh price and then by September we came up with a number of breakthroughs including new ways of doing mixture of experts and uh better inference and ideas of uh KV cash management Etc so we we had really a big breakthrough not in in launching the e-l lightning uh because e-l lightning was 1114th the cost of the previous e- llarge model and at 10 cents per million tokens and and GPD 40 had also come down in price but it was $440 cents so to a developer yeah just to just to give folks who are just listening and watching this um back in June um e
[00:39:02] lightning was a buck 40 per million tokens today it’s at 10 cents per million tokens and next June it’s expected to be 3 cents per million tokens it’s a 50-fold decrease and comparing to GPT 40 which uh was at you know 4 and a half bucks per million token so I mean we are seeing this precip um efficiency gains over time yes so you know another way to look at it is gbt 40 dropped 10x in one year our we we actually succeeded in dropping 50x in the past um in the past year so so we we feel we now have the most competitive lowest pric um lightning engine our cost is 10 cents per million tokens our price is only 14 cents per million tokens so we’re also not taking a big margin so if you look at the
[00:40:01] performance of GPD 40 and E lightning uh their new version is a little better not a lot only a little better but they’re 440 and we’re 14 cents incredible um is it all algorithmic gains uh the um there are a number of differences I think we actually I’m sure we use different algorithms because we don’t know what they use uh we came up with our own but I’m saying the improvements you’re the improvements you’re making over time are they algorithmic gains there yeah our per our performance gains going from e- llarge to e-l lightning are using a new mixture of experts model and um new ways of of modeling uh and also getting more high quality uh diverse data um and also having super fast infrastructure so we can train multiple times to learn more and to do research the
[00:41:00] efficiency gains were also mostly by the Superfast mixture of expert model but also by some inference advancements in terms of uh KV cach man memory management um as an example you know the way we do our nextg model design is not go invent a bunch of new things and go make them fast but it’s from the GetGo they have to be fast so we would first ask the question where do we project in in in four months which is our product cycle uh the best chips might be and and how do we get those chips to inference really fast and oh these chips with a lot of um hbm which is high bandwidth memories coming out and can we turn the inference problem from a compute problem to more of um a memory bound problem then should we rewrite our inference engine then how much um Ram can we put on as a second layer memory how much SSD
[00:42:01] can we put on can we construct a computer four months from now that is super fast I mean it’s not a it’s made out of standard Parts but still it has a lot of memory then we put the memory bound inference engine on top then we asked the modeling team in four months what model can you build that fits perfectly into this box not too large not too small use up all the memory but don’t go too far and use power of two so a lot of constraints for the researchers which some companies um might might face reluctant researchers but in our case we are all building a product we’re one team uh in One Direction so we all took each team took the order and then marched ahead and out came a very accurate and a super fast model thanks to this vertical integration from model to inference engine down to the hardware and memory I I love the old saying um you know Innovation comes from thinking
[00:43:00] in a smaller and smaller box when you put constraints on on yourself right everybody I want to take a short break from our episode to talk about a company that’s very important to me and could actually save your life or the life of someone that you love company is called Fountain life and it’s a company I started years ago with Tony Robbins and a group of very talented Physicians you know most of us don’t actually know what’s going on inside our body we’re all Optimus until that day when you have a pain in your side you go to the physician or the emergency room and they say listen I’m sorry to tell you this but you have this stage three or four going on and you know it didn’t start that morning it probably was a problem that’s been going on for some time but because we never look we don’t find out so what we built at Fountain life was the world’s most advanced diagnostic Centers we have four across the us today and we’re building 20 around the world these centers give you a full body MRI a
[00:44:01] brain a brain vasculature an AI enabled coronary CT looking for soft plaque dexa scan a Grail blood cancer test a full executive blood workup it’s the most advanced workup you’ll ever receive 150 gigabyt of data that then go to our AIS and our physicians to find any disease at the very beginning when it’s solvable you’re going to find out eventually might as well find out when you can take action Fountain life also has an entire side of Therapeutics we look around the world for the most Advanced Therapeutics that can add 10 20 healthy years to your life and we provide them to you at our centers so if this is of interest to you please go and check it out go to fountainlife decomp when Tony and I wrote Our New York Times bestseller life force we had 30,000 people reached out to us for Fountain life memberships if you go to Fountain life.com back/ Peter will put
[00:45:01] you to the top of the list really it’s something that is um for me one of the most important things I offer my entire family the CEOs of my companies my friends it’s a chance to really add decades onto our healthy lifespans go to fountainlife decomp it’s one of the most important things I can offer to you as one of my listeners all right let’s go back to our episode um you know one of the conversations over the last year is we’re running out of data to really improve the models what do you think about that do you believe that to be the case I believe it has a bit of a dampening effect on how much we can expect scaling law to continue but I do think we have ways of getting more data just not as more as easily as it used to be because the fact is that humans were smart to create language as something that could be passed on over Millennia
[00:46:01] and we have so once we start doing gen we took all the language data and put it on now every year we’re generating more language data but clearly way less than the total collection so that is incrementally much much slower but on the other hand we have video data we have audio data um and also uh we’re going to have embodied AI Gathering spatial data so those and also Al we have ways of creating synthetic data which is not as good but better than not using it so these are the ways I think we’re trying to uh compensate for the fact that most textual data has been using used if the outcome is I think we will still get more data benefit just not as much as we used to get y let’s jump over to to uh toigo so it’s a US company um right and uh why was it started in the US uh is it something
[00:47:00] that was funded out of Innovations uh um what’s its Mission talk to us about it right uh as I was building up 01 a I ran into a lot of brilliant American engineers and researchers uh they want to stay in America but they liked my vision so I said why don’t I help you guys Venture build a company so they built a company called Rhymes Technologies and uh they build an excellent model um very similar in approach to to the model in 01 and on that model they added a lot of their unique multimodal and launched ARA which is an open- Source multimodal um engine which is one of the best in the world but only 3.5 B so continuing the tradition that companies that I uh help build are very committed to open source and on on an advanced version of that area they built an AI search engine so I was pleasantly surprised when they showed it to me in fact I was blown away
[00:48:01] by uh how good it was already um and also how really really fast it was what’s your hope with bego uh to I mean to come in and through an app become the dominant uh search player yeah I always remembered when I joined Google um Larry Page came and talked to us and he said Google in this current form is not the ultimate form the ultimate search engine should be one where you ask a question and get a single correct answer that always kind of stuck with me and when I Venture build the the the the Rhymes and Beagle team in the US we talked about it and we feel that the time has come um and in building such an engine we also consider well first on a on a mobile phone is a very small screen so you can’t have all the tabs so doing a research oriented m multi-link uh Search exploration is very very uh awkward and
[00:49:03] a single answer just makes so much sense but of course the first issue with a single answer is what if it’s not correct what if there’s hallucination or some errors so we work very hard to maximize factuality and beo is actually better in factuality than a lot of the other um AI engines measured by objective third-party um queries so I think I think those really bringing us one step closer to Larry Page’s dream I think right now the team just wants more people to try it and they want you know really knowledgeable caring smart people to try it first and giving them the most feedback and it’s great that I can be on your abundance program because um those are the types of users your uh readers are yeah I I’m and how do you possibly compete against you know companies who’ve got billions of dollars in this
[00:50:00] field um is it just that much better in implementation that much better and Alternate uh yeah it’s a tough challenge that’s why very few companies go after the space the fact perplexity gain some ground is an indication people want something refreshing um and also I think uh we’re confident about beagle’s um factuality and and engage it also has pictures inside the search making it more engaging and entertaining um but also I think just the search players particularly Google and to some extent being will be hesitant to uh replace their search engine with a one answer engine because with one answer people don’t don’t look at ads and the ad Revenue will permit yeah they they will be I have to ask the question that probably a lot of people are thinking is this another Tik Tok where it’s a Chinese own company and it’s a way for you know what
[00:51:03] people’s imaginations that it’s just a way to get um us data into this is a US based company a us-owned company yes uh it’s actually both us-based and us-owned so it’s not um uh its employees are Americans singaporeans Taiwanese I myself I’m Taiwanese so it’s it’s not a Chinese only quite different from Tik Tok yeah I I I get that um I’ve been a fan of your work uh Kaiu for a decade now and I’ve had the pleasure of calling you a friend um I you know I had Elon Musk uh and Jeffrey Hinton and Ray KW you know them all on my stage at abundance 360 last year and there was a fascinating question that came up the probability that AI will be the Great invention versus the probability that it will destroy Humanity to put it
[00:52:01] very bluntly and I think uh Hinton and congrats to him on his Nobel uh and and Elon said yeah 80% it’s good 20% were screwed um where where do you come out on that do you have a do you have an opinion uh on this and then how do if how do we protect the downside in your mind yeah so well if we assume it’s like a 10-year Horizon is that reasonable yeah I think I think all of it’s going to play out in the next five to 10 years I think if we get through the next my belief I don’t know if you agree with me if we get through the next 10 years we’re fine I totally agree that’s why I ask the question okay so in a 10-year Horizon I would say we have a uh 5% chance of a disaster caused by Ai and uh 35% chance of a dis caused by humans using Ai and 60% were
[00:53:02] good okay so uh now you’ve written an entire book on this but I’m going to ask you to provide some some summarization what do we do uh how do we how do we you know if we protect our downside the upside is fantastic um do you have any advice for for parents entrepreneurs leaders here how should they think about protecting our downside what would you if you’re head of uh you know head of the world here what what do you do what do you think well I think a lot of technological risks are best um addressed by Technologies like when electricity went out the invention of circuit breakers when this internet went out the antivirus so Technologies are the best likely savior to technological problems so I would encourage more computer scientists AI people to not just work on the biggest next big model
[00:54:00] or AI applications or AI inference or whatever but some percentage of them the ones who feel a responsibility and uh their conscience asking them questions then they should jump into AI safety to find the various types of um safeguards and and and and guide rails that will protect us I think to me that’s the most important thing regulation comes second um I would actually feel General AI only regulation to be in unchartered territory and potentially not constructive I think it would be better to take existing laws let’s say laws about fraud the laws about um other blackmail and then and then apply it apply the use of AI to achieve those things laws about slander laws about theft so we have lots of those laws it’s those laws are effective understood so
[00:55:00] apply them to people who use AI to break those laws make sure the punishments are equally if not more severe uh that would create some deterrence um to start to regulate AI before it matures and while it’s changing uh by governments that are slow moving seems like a futile exercise yeah governments are linear or sublinear at best um you’re going to be joining me on stage in one of my panels in Saudi Arabia in just uh 10 days or so excited to see you there at the fii summit yeah um yeah great and one of the conversations we’re going to have is around the potential dangers of uh ASI uh artificial superintelligence um but before I go there you know I would argue that we passed the touring test many years ago and no one really noticed it just just it’s come and gone um will we know
[00:56:03] when we get to AGI I don’t know that there’s a good definition of AGI and I don’t even know if there’s a good definition of digital super intelligence I mean these are these are challenges when we talk about these words do you agree with that uh yeah I think AGI was created to me that AI could do absolutely everything humans do and that may not be the right definition because we can’t yet project when AI will have love or uh or even when AI will be viewed as having love those are still some distance away um but I think thinking generally that AGI just means something overwhelms us that does almost everything we do so much better even the most challenging intellectual tasks like uh inventing a new um theorem or something in physics or chemistry so if we kind of extrapolate that to be the ASI or AGI uh then uh then I think it’s highly
[00:57:04] likely that it will arrive in the next 5 to 10 years and we do need to P put in the uh the safeguards I imagine you we just saw a a number of Nobel prizes related to AI I have to imagine that in the very near future every single breakthrough in physics and math and chemistry is going to be uh enabled or driven or connected to some AI models doing the work yes absolutely yeah yeah I I met an um econom economic Professor on my recent trip to the Bay Area and he said he already treats um gpt1 as a graduate student um one that’s able to challenge him and find mistakes and he will teach and guide the student and the student learns the two of them are uh great Partners in inventing new things so it’s already happening um and
[00:58:02] so I would add also economics to be perhaps another area to what you listed do you say please and thank you to your AI when you’re speaking to it I do say please I don’t say thank you I’m not sure why uh it’s interesting right I I find myself saying please sometimes thank you uh for but it’s it is interesting to to get how uh it’s just a very small step away for being a part of every aspect of Our Lives you know people are worried deeply about jobs you’ve made some prediction about loss of White Collar jobs um and of course all the multimodal AI systems that you’re speaking about are being embodied in robots uh there is a number of uh fantastic uh humanoid robot companies coming out of China China needs robotics uh for its aging population right the one child policy
[00:59:02] has significant um implications for an aging population um so uh talk to me about about uh your prediction about jobs your advice I know you wrote about this um as well in your previous uh best-selling books but if you just have a a few you know how should people think about jobs what color jobs and then labor with humanoid robots coming and what should what do they tell their kids what do they what do they do for themselves how do you think about that well I think the fact is that the um White Collar jobs are going to be the first set most challenged by AI because just software can replace a lot of the routine and even non-routine work work and they will do so very rapidly in the next 5 years
[01:00:00] um that’s I think now universally believed when I wrote about it in my earlier books it was um um met with a mixed reaction so the people everybody expected everybody expected it was going to be blue collar work leaving first yes yes right because it seems you know uh having intelligence in a white collar job is harder to replace but it turns out dexterity is harder to replace because that’s not necessarily solved by the Gen um Technologies so blue collor work I think going from Factory to the type of carrying work you talked about for elderly is um is going to happen as the next wave I’m among the more conservative on how fast that will happen because I think these Technologies are very expensive not only do you need the llm expense but also these robots um that Elon Musk has shown are way out of any consumer’s price
[01:01:01] range so they’re kind of um going to be a while because before the The Kinks are worked out before people accept them into their families and lives and offices and the costs have come have to come down so I would project that you say that but you know I would I’m an investor in figure AI uh Brett atc’s company and figure yeah and Tesla both are projecting around a $30,000 price tag let’s say it’s $440,000 price tag if you can lease that right and you lease it at $300 or $400 per month having a 247 um employee for uh $400 a month is is pretty affordable um at least here yeah I can I can see your point I can see your point I I’m still a little more cautious because you know especially used around the home you know the uh the just clean my room is one thing that I would
[01:02:00] predict in three years this robot cannot even begin to do because every room is different every definition of clean is different and every family home is different um but there are many other things you know like um uh talking to the kids or uh doing more household repetitive work that that can be done um 30,000 I think is probably a reasonable price point for um middle class America but for China India other countries is still way too expensive real quick I’ve been getting the most unusual compliments lately on my skin truth is I use a lotion every morning and every night religiously called one skin it was developed by four PhD women who determined a 10 amino acid sequence that is a cytic that kills scile cells in your skin and this literally revers versus the age of your skin and I think it’s one of the most incredible products I use it all the time uh if you’re
[01:03:02] interested check out the show notes I’ve asked my team to link to it below all right let’s get back to the episode Kaiu looking forward into 2025 2026 um would you mind sharing some predictions of what you might see in the AI world coming that would be surprising um sure um I think from the um product side we will see basically every category of mobile app uh be incorporate AI with many of them showing AI first disruptive types of um changes so in other words in about two years every app we use will be replaced by another app or super upgraded by the same app I think agents will be major technology where we delegate what we want rather than just get an answer I
[01:04:00] think multimodal will not only be dramatically better because a lot of the super smart people are working on it and we’re seeing text to video something that would be fairy tale in my youth is now starting to happen and I would also project that these great technology advancements will find real uses in applications so it’s not just demonstrating hey look what the AI Drew for me in a video today but while I created this marketing video for $10 those are the kinds of advances I would uh definitely anticipate to see from a business products and um some of the known technology side you know we just uh I don’t know if you saw this we just mapped the connectome of the dropa did you see that there was a um uh they were able to map 50 million synaptic connections and it’s a step a step away
[01:05:01] from a mammal let’s see the mouse I think we’re going to probably see the connect him of a mouse done in the next year or two um where do you come out on the whole uh you know brain computer interface world I I’m still a little bit more uh I would say I’m a bit more cautious about it um I I think this is one of the areas where there’s major disagreement um how fast this is moving and what dangers it might provide um and I think we need to be cautious because it is intrusive to our bodies and it’s a kind of um potential um a potentially SL slippery slope right I think people can all get on board with uh treatments using interfaces and get on board with non-intrusive uh kinds of BCI but as we go deeper and
[01:06:00] deeper into reading our mind creating star tissues and I just I think we just have to make sure that people who are uh being experimented with are aware what kind of risks they have and that um and that the downside doesn’t outweigh the upside um let’s wrap up with a a a quick look at something I’ve heard you speak about which is uh you were there at the you know the PC Revolution the mobile phone Revolution and the AI Revolution and you’ve seen those progressions and I think you’ve modeled what the progression will be for AI um can you can you give me that um that summary because I think it’s super useful for entrepreneurs listening if you’re looking at starting a company in the AI world I there’s a lot of lessons
[01:07:01] learn from the PC and the mobile phone world yes uh yeah I I think applications always follow a reasonable pattern of being replaced because when a new technology new form of content comes out you you you as users have to first browse them then you make the content then you search and organize the content then the content gets richer into multimedia multimodal then you can trans transact on the content whether it’s by payment advertising um e-commerce or online to offline because these are the fundamental needs of people and the progression of apps that I talked about uh go from fewer users to more users small amount of usage to more usage simple usage to complex usage so it’s a really exciting um iteration of better technology enables the next step on the trajectory more people use it more money’s made more entrepreneurs more funding more gpus more products more
[01:08:00] models so The Virtuous cycle uh goes on and and the most most exciting thing is uh it took PC ecosystem easily 30 years to play out uh it took mobile maybe 15 but we’re going to see AI play out in the next three years or so so if you jump in to start the company this is the biggest roller coaster ride you can ever imagine what’s your advice to the entrepreneur jumping in to start a company in the AI space what would what should they do what should they not do right yeah I use the roller coaster as a metaphor because I don’t see it as a rocket ship purely upside there are a lot of a lot of challenges and traps um I would be cautious to uh probably look at an app company um because that’s the biggest space with the most entrepreneurs and the inference costs are coming down but when you think about starting an AI app company be cautious first about can you handle the inference costs because those are
[01:09:00] too expensive don’t run out of money before the uh because inference cost is coming down so time your launches time your product design according to the technology you need and when that technology will be low enough in inference costs secondly be careful of the modeling companies because we’ve seen companies like Jasper who buil great apps but then the model sucked all their knowhow because they saw the data so find ensure that you don’t do that the last advice is uh all the models are getting better one day they’ll be close to AGI does that mean my app will be eventually um limited to a veneer and with very limited value I don’t necessarily think so because historically we’ve seen great platforms emerge but other apps can often build a modes the mode that Tik Tok Instagram and and others have they’re value were not taken away by the lower level uh transaction layers or operating system
[01:10:01] layers or uh uh browser layers so the key is when you build an app and gain some Edge and don’t sit on your laurels think about how to build a mo that Mo could be your brand um your user loyalty user data or social graph things that we have seen or maybe new things in the AI era yeah I like to think about it and I’m curious if you agree um when I’m evaluating an AI company to invest in it uh you know the I’m looking at what dat what unique data do they have and what customers do they have a very close relationship with and everything else in the middle will get demonetized and replaced over time yeah yeah I think your advice is great for B2B I was thinking more B thec the two are definitely in concert yeah what should people know about you know let’s let’s turn to the last question which is
[01:11:00] we live on one planet um and we’ve got uh sort of this bipolar element of China versus the US and Ai and we have this split universe um it would always be better to have alignment and everybody working well together but um what advice do you have there how should people think about this I mean because it’s a complicated uh way above my pay grade um and I want I don’t want to put you in a situation where you’re talking about anything that you don’t feel comfortable about but I can’t not have the conversation of you know I I see a lot of people um feeling like China is the enemy there or us is being monopolistic there how do how do we you know navigate the next 5 10 years which are the most critical yeah there are some things that we’re just not able to change they are
[01:12:00] what they are and but I think each of us can make our own judgments and decide where we can reduce the impact of this unfortunate geopolitical situation for example you know open source is one area where all the countries collaborate equally and generously um academic collaborations continue on um areas of collaborations not involved in the sensitive model or semiconductor can still go on and I think you know connectivity in the world working People to People business to to business needs to go on it has to be good globalism has to be right uh differences between governments is kind of like you know when our parents have fights with another parents we kids can still get along and and do something interesting and fun right agreed you know I like to say we all have the same biology so a breakthrough in medicine in China is the equivalent of a
[01:13:01] breakthrough in the Bronx um and we all we all share 24 hours in a day and seven days in a week that’s a it’s something every single human has and so anything that gains As Time efficiency in one place gains time efficiency in another yeah and we share the same planets yeah is facing his own challenges right yes thank you for sharing time uh uh super excited about um their performance I’ve seen in bigo and look forward to to playing with it and thank you for joining me on moonshots to talk about your passion your vision and congrats on going from the guy behind the curtain to the guy in front of the company thank you thank you so much be well see you soon my friend bye [Music]