SPEAKER_00: apparently if i fancy getting married anytime soon you're available for that too right so

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SPEAKER_06: married to your startup you raised a billion dollars i can tell you who you're married to for the next 10 years absolutely i'm selecting ai and you're 40 people over there this week in

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SPEAKER_11: promo code twist all right we got a big treat for you today on this week in start us musafa soliman is here he's with inflection ai but uh very famous for having been the co-founder of deep

SPEAKER_17: mind welcome to the program mustafa great to be here thank you jt thanks for having me of course

SPEAKER_18: of course uh you know i wanted to start with the origins of deep mind because it seems like so much of what we're seeing in ai stands on the shoulders uh of that organization and i don't think most people know the history of it i happen to know a little bit of the history of it because i remember when peter tl and elon i think were two of the early funders of it and we're talking about it and we met i think at a couple of different industry events over time tell me what what was the origin of deep mind uh and then how did it uh you know originate and start to tackle ai general ai vertical ai all these different

SPEAKER_19: um things that are coming to fruition and i guess that was 2010 right 2011 it was 2010 that we started

SPEAKER_21: the company yeah exactly which seems kind of insane like almost 15 years ago and it it's just quite

SPEAKER_23: surreal to see because in the last sort of what is it nine to 12 months it feels like the kind of large language model revolution has come out of nowhere um and exploded onto the scene but in fact there's been the kind of steady march of many many years and a huge amount of failure and a lot of risk and a lot of persistence that i often think gets slightly neglected in the story of the perfect explosion of a new technology um you know in fact um you know for most of the last decade we didn't have language models i mean the transformer was really only popularized in 2017 i mean people often say that it was invented then it was certainly not invented then it was invented a good 15 or 20 years earlier by osho benjo and then many other people developed the ideas but um it was really only uh you know four or five years ago that the idea started to get traction again and then it wasn't until gbt3 that people started to get a glimpse of you know what it looked like at scale instead of just

SPEAKER_26: in a test environment so yeah it's been a crazy journey um how do we start the company so 2010

SPEAKER_23: um i was actually playing poker with demis uh hasabis who was my long time friend since we were uh

SPEAKER_28: quite a bit younger um in london i assume at those high-rate casinos that's right it was at the victoria

SPEAKER_23: casino in london which is on edgeware road um not the biggest game in the world i i seem to remember it was probably a 250 pound tournament only 120 people but you know um so we would play at these things regularly um both of us were very passionate about poker i was playing i was i was i was one of these people that was doing like eight table poker stars yeah back in the day um my friends were doing 16 table but i didn't have the uh actions per minute speed to be able to manage that you're

SPEAKER_18: you're on a clock yeah it's it's not easy to multi-table uh although it's something about multi-tabling becomes like a flow experience and you start to see patterns right because you're playing so fast that you have no choice but to kind of play instinct right um and now it seems like gto and all these

SPEAKER_38: theories are people are able to really deploy it very quickly i i hate online poker i like in person

SPEAKER_39: because i think the only edge i have is my ability to read people which is such a critical part of the

SPEAKER_42: game and it's so hard for me to read people online it's also the fun part of the game right like pushing

SPEAKER_23: people off pots and teasing people for their losses i mean that's that's the fun part like so yeah but i mean getting through a lot of hands is also a very great way to practice i mean because you end up developing heuristics and so you just see that's the problem is it's such a high variance game in your career if you only ever play live you never get to see the volume which gives you the range of experiences so the good thing about really having a short stint of abusing online poker is that

SPEAKER_44: you just get to see depth and breadth which which is which is cool but you can pick up bad habits because

SPEAKER_46: it can make you too cautious ah interesting uh i haven't heard that before so you don't is that is

SPEAKER_18: the reason you get too cautious is because everybody's reading each other's like statistics and you're

SPEAKER_11: just like i'm going to be too easy to read here i can't make a non-traditional play i'm going to get

SPEAKER_23: caught yeah because because everybody sees so much more volume then then they play in a much more predictable and structured way so you learn to predict everybody else's moves and you know also they end up being because they see more volume they are more deliberate with their hands and more cautious whereas in a home game you may only see a couple hundred hands even in a six to eight hour yeah right and so you you your range is clearly much lower you you're playing cards that you would otherwise leave behind because you're seeing more throughput online right so you know the classic is the knit you know we used to call them the knits when they would come to the live tables and they had like clearly just playing this robotic game and driving themselves nuts because they weren't

SPEAKER_53: seeing enough volume just pretty funny yeah it's it's it's it's such a fascinating game

SPEAKER_55: and playing live in a casino you get to see like a real broad spectrum of humanity i was just

SPEAKER_18: talking to somebody about my friend sky date and i used to play at hollywood park and commerce uh in la and we would play at the lowest tables and at one point i was trying to figure out how to read people better and i came up with the idea of jedi poker uh where i would pull my cards up and put my thumb on it but i'd make a bit of a show of looking at my cards but i would have them covered so i didn't know what cards i had mustafa that's the best way i would only play the person and i'd be like this person seems very strong this person seems pretty scared let me see i can get this person off the hand let me and then i get to the river and i would literally if somebody called me down i would turn

SPEAKER_06: over my cards and be embarrassed like oh i have a set i didn't know it or i had bottom pair and they'd

SPEAKER_62: be like how would you bet like that it makes no sense and making no sense is part of poker because you have to break the ability for people to be able to read you a hundred percent this is my one of

SPEAKER_23: my favorite ways of doing it the other way i like to do it is to represent a hand off the flop that i don't have assuming that it is the opposite hand or a better hand and the whatever i place that person on so you know that that's actually a very good way of doing it because then you bet

SPEAKER_65: consistently across the three you know uh steps streets but you um but you know so you're not being ridiculous and wacky you but you're telling a story you're representing yourself that you have 10 jack

SPEAKER_60: and when the board comes down you know nine king queen you're like i've i'm playing 10 jack and i'm

SPEAKER_67: gonna play it like 10 jack would play this yeah you just got to make sure you know how to lay down if

SPEAKER_50: your opponent actually ends up having the hand that you're trying to represent that can get pretty sticky

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SPEAKER_55: so you're playing cards and uh you get bounced out of this tournament and you're you're you're sitting

SPEAKER_60: there uh doing your post bounce uh or did you make it to the final table and you're just going like

SPEAKER_75: early you nailed it so now you're trying to explain your bad luck and how bad everybody else is to each

SPEAKER_23: other right we've we've gone over the whinging about our bad beats right now that took up the first half an hour running through our knockout hands and we're sitting there eating chocolate cake and vanilla ice cream and diet cokes because obviously we're super cool and we you know we're we're not getting pissed we're talking about the future of the world and you know both of us have always been interested in like how do we impact the world how you know what does the future look like we've both been very very long-term thinkers and just instinctively that is just one of our kind of gifts i think and i i was particularly interested in you know how you do good in the world and how you know politics shapes our future and stuff like that we were both talking about robotics and you know is now the time for you know robots to come on and automate everything and and i think we both agreed that actually that was way further away than people realized but the thing that was likely to be more prescient is teaching machines to learn their own representations of what is valuable in a space like surely a machine could learn to play poker a machine could learn a set of heuristics and then reproduce those patterns and um at the time demis was just finishing up his phd and postdoctoral work in neuroscience at ucl at the computational neuroscience unit and so he invited me to join the lunch and learns uh which i did for almost six months i think pretty much every day went down to you know basically smuggled in the back door of the gatsby computational neuroscience unit and just listened to the lunch and learn so that's

SPEAKER_84: where we met shane leg our third co-founder and then we all went for lunch what is this lunch and learn i mean i can there's lunch and then somebody speaks and you learn uh yeah it's like a brown bag

SPEAKER_23: lunch you know like where where you know it'll be like at the lab so there's 40 or 50 people at the lab and you and people invite different people and so on and so there'll be speakers or there'll be postdocs or every lunch basically someone gives a talk about their work and takes questions and it's a bit of a bear pit i mean you know they don't take prisoners if you if you're not on your toes then you get some pretty rough questions like and it was just an amazing way to learn and be thrown in the deep end and really experience it firsthand i i was only 24 at the time um so then basically a few months after that um shane leg got invited to the singularity summit um in uh 2010 to be a speaker because he was on the less wrong forums back in the day uh and is was a bit of a transhumanist to be honest with you at that time uh and then you know we we decided to go because peter was one of the sponsors i think was the main sponsor of the summit and then we got invited to the drinks afterwards and we used that as an opportunity to pitch pitch peter on on agi you know he was the only person in the valley to his credit talking about agi or even ai in any form to everybody else ai was a weird taboo word and everyone was sort of talking about machine learning but even not really like it was mostly in the labs in the in academic labs that people talk about machine learning um yeah and then we went to his office in uh in the big park is the presidero yeah the presidium yeah presidio presidio yeah we went there and to the founders fund office and yeah he made a decision on the spot i was pretty easy i think he gave us like two million dollars yeah ten million dollar valuation

David Friedberg: not even dude it was like when it was like half that we was really well because we were like randos

SPEAKER_00: from london i mean it might like he he's he he joked that it might as well be somalia that was his view he was literally saying what you might as well be investing in somalia i was like london's a serious

SPEAKER_38: place but apparently not to peter well you've got to also put in context he had just done the he had done the facebook investments probably feeling pretty good about himself that was going well and uh you know five to eight million dollars was what a seed round would evaluation would be um and ai at the time to be honest as you said nobody thought there was a commercial application or or that it was going to work right like that was kind of the big question is this actually going to come up with an answer that is going to have some application in the real world because you had deep blue right we had kasparov got beat and so narrow ai had proven itself but ibm had spent hundreds

SPEAKER_51: of millions of dollars and they had no product i mean that was the playing field right it was like

SPEAKER_23: this is a money pit right and of course that was a decade before us as well you know so that that that had proven to not have serious commercial applications so that was that was actually a kind of non-goal in our pitching is to not bring up don't bring it up because it was like a cool research thing but never quite had it had had the impact that we hoped and yeah you know for the first two or three years it was it was very tough going because you know deep learning just didn't seem to be you know catching on and and then all of a sudden um you know we we had the cat classification paper from uh alex krasinski alex net in 2012 and then in 2013 we had the atari game player um dqn which we published and that was really the thing that changed everything for us because you know larry page had seen uh the demo and just emailed us cold page google.com and was like you know you guys should come and come and be part of

SPEAKER_101: us i've spent my entire career building the infrastructure to enable a company like you guys to come and work on on agi stepping back what was the pitch to peter we're going to build

SPEAKER_55: reinforcement learning we don't know if there's an application it's a science project your two million

SPEAKER_67: is going to be gone in three years like was there any path to commercialization that you pitched him on or was it let's see what we can do in the lab there was yeah so i mean we actually didn't pitch him on

SPEAKER_104: reinforcement learning because at that point that was really early we pitched him on deep learning

SPEAKER_23: and um what we were working on was a visual image search uh tool for uh fashion and furniture and and clothing and so on and we actually i actually i held the first pattern um for deep learning in this area

SPEAKER_107: which actually takes the shape and the texture and the color of one item of clothing like ideally a

SPEAKER_23: more affordable high street version and then uses that to find the more expensive uh you know equivalent that you could then you know you know go on you know find find a comparison for and um you know that that was a big moment actually because it was it was the beginnings of you know the generative ai movement i mean you know it's now called gen ai but it was never called that at the time it was really

SPEAKER_109: just deep learning classification yeah at that time forget about generating something you were trying

SPEAKER_60: to identify something this is a hot dog this is a dog these are two different things and that hadn't

SPEAKER_18: that framework hadn't actually happened yet what google was doing at the time would they put two or three low wage people on a group of images and they would say there is it you know describe five tags for

SPEAKER_67: this image and then whichever three or four came you know in common with two different people that was what the image was about right that was the state of the google index at the time right spot on and they

SPEAKER_23: would have them draw bounding boxes around certain parts of the image so this area of the image contains a penguin this one contains an iceberg and you know uh it turned out that was exactly the kind of thing that this hierarchical neural network representation was pretty good at doing like it would certainly it would essentially cluster together pixels which were correlated around a particular region and then then where there was a sharp distinction like an edge or a line or uh you know a break in a cluster then that would that would end up being a sub representation and then if then the next layer would absorb that sub representation and increasingly build more and more symbolically representative ideas like it would go from you know basically you know a tiny little area of the iris to a wider eye to an eyebrow to the side of the face to the full face to the background and you could kind of think of that as a way of understanding how the hierarchical neural network representation was was formed and that and obviously now that you know we we had made so much progress over the last 10 years on the classification side you then use those classifications to generate novel predictions and that's that's basically what image generation is doing is saying given this sentence find the sort of optimal representation of all the competing points in this

SPEAKER_107: big space that best represents this long sentence as a new image and that's the transform a model that we

SPEAKER_55: hear about in the that 2017 paper from google yeah exactly yeah yeah and that's deep learning but then

SPEAKER_115: there's lots of other generative ai components that were you know pushing it that direction but they did

SPEAKER_23: it on they they made it work first for the language side of things that was really the big deal

SPEAKER_18: so you get a couple years into this you figured a couple of things out uh and you start getting into reinforcement learning so and that's when larry page was larry on the board or just elon on the board at

SPEAKER_107: that time at peter no so so we had uh first peter invest uh then elon then we were the first check

SPEAKER_23: out of um sorry no we were the third check out of the first fund of mark stads dragoneer oh wow in

SPEAKER_11: 2012 i think it was it was such a great time period to be an investor because only lunatics were starting

SPEAKER_18: companies after the great financial crisis it was like this five-year period where if you started a

SPEAKER_60: company you you had no choice because you were a lunatic who had to start that company because

SPEAKER_38: everything in the world was telling you don't start a company right it's going to be pain and suffering so you raised this money what was the first project that you guys started to work on how did you pick it and then you know what clicked because there were i remember alpha go was one and then there was this clock that became sentient there were just all these like little projects that we would hear about inside of deep mind but deep mind kind of kept a lot close to the vest i think we i mean we operated in

SPEAKER_47: stealth for most of our entire period and we actually didn't even announce our investors i mean there was a bunch of other like we we had selena chow as another investor from horizons lee cushing's fund and

SPEAKER_23: you know there was a we had a very good group of people we're lucky i think we raised 45 million dollars in the end so we every each year we went back i think we raised like

SPEAKER_18: we raised two or three and then 10 and then 30. um and what did you show each time to keep people investing in the vision during a time when people didn't believe in the vision yeah most people didn't

SPEAKER_23: yeah yeah i mean so we showed in the second time that we raised we showed uh flatland which was our little agent-based environment like a 2d grid world where you know the uh the model had kind of learned a way to navigate through the environment um using purely the pixels and we then said okay for our next you know milestone we're going to basically teach the the model to learn um arbitrary games of atari and in the end we we played 56 games um at you know which is pretty incredible is this 24 frames per second and it's learning to basically correlate actions where it can basically

SPEAKER_65: go up down left right or shoot um the original atari 2600 controller yeah exactly which had five

SPEAKER_23: actions exactly exactly and and you know so it's basically got to figure out which of those actions is randomly kind of moving them around at the beginning and then it stumbles on a rewarding you know moment it luckily gets some you know gets some score and then it realizes okay that's a useful thing to do next time i see the ball bouncing towards me in that position i'll move the paddle left or right and it's just kind of incredible that purely through self-play and reinforcement learning just very simple heuristic exploration and then exploit the strategy that turns out to be uh useful for generating score and suddenly you can learn to play all the games to basically

SPEAKER_114: superhuman performance i mean that was mind-blowing to me and that was all done in atari 2600 emulator

SPEAKER_55: obviously you're not taking a physical joystick and putting a robot on it it's able to run very quickly in the cloud right you figured out a way to accelerate it so that it could just be playing whatever pong or tank whatever those early games were adventure play them you know millions of them

SPEAKER_60: right how many how many runs did it have to do to be to perfect them did you did you track that like

SPEAKER_140: how many how many how many quarters until you perfect the game and you get a high score i mean it's

SPEAKER_23: interesting that you mentioned cloud right because this was 2012-13 so there wasn't really any cloud to speak of we actually ran on-prem uh we had our own little cluster in the office and it used to train atari dqn we it used um two peta flops of computation so so a flop is a floating point operation this is a unit of computation it's like one calculation think of it and obviously peta is a million billion so it's two million billion calculations to train the entire model over the course of about two weeks um so put that into perspective like and then obviously at the time that was you know one of the largest i mean we don't know for sure but there weren't any other big training runs of those kinds of things at that time so it's fair to say it was probably the largest um that was a decade ago and you know you roll forward the models that we train today at inflection uh and you know the other

SPEAKER_21: frontier model companies use 10 billion petaflops wow 10 billion million billion

SPEAKER_143: floating operations which is insane it's it's you you're a human brain cannot even conceive of what

SPEAKER_30: that is uh it's kind of like when we start talking about there's a billion suns in our galaxy and right that's and there's billions of galaxies the human mind is not designed to even comprehend

SPEAKER_146: millions of billions of millions billions of millions of billions it's just not even possible

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SPEAKER_152: the hardware did start to catch up here and and in hardware seems to have been part of the enabling here maybe you could talk a little bit about what the infrastructure looked like at that time

SPEAKER_154: the hardware footprint versus what we see today and what you're doing in inflection and the hardware

SPEAKER_107: footprint it's a great point i mean it's it's really the hardware revolution rather than the ai revolution i mean the it is it's funny because people fixate on the algorithms um obviously the algorithms are

SPEAKER_23: critical but they they really have not evolved at the exponential rate that computing has evolved at right so those 10 billion million billion petaflops i described that's a that that is the equivalent of one order of magnitude so 10x increase in the total amount of compute used for the cutting edge models every year for 10 years 10 to the power of 10 i mean it's insane um it's truly insane so so yeah that is basically about hardware and i that's why i think actually this revolution is has been easier to predict than i think people people realize i mean this trajectory has been continuing for a long time and we can look out at what the next three four five doublings look like um sorry three or four

SPEAKER_107: five ten x's look like they're not doublings anymore like moore's law that their orders of magnitude

SPEAKER_23: increase in compute and that's a very predictable trajectory i mean obviously it's unclear exactly what the what what capabilities emerge from that but you know you can certainly predict what we're

SPEAKER_55: going to be able to build steve jervison has a lot of charts on this where he's been tracking i don't know if you've seen steve's charts on just you know um the amount of computing power and you know the this sort of tipping point is somewhat predictable and now we've got heat and power friction i guess is

SPEAKER_18: the the limit right now or how much we can connect these super computers together what what's the

SPEAKER_107: gating factor now it's a good point yeah it's a good point that that is going to become the constraint

SPEAKER_23: so the the a100 uses 700 watt uh per chip the h 100 is twice that like 1200 watt um wow so the next per chip right so obviously you have eight of these on a node then the chassis and the node itself has some additional power constraints you know so they're actually it's a different data center design to

SPEAKER_107: you know what it was two or three years ago where you know there's actually spaces in between racks they're not like completely stacked up they have to be like really large gaps in between and i

SPEAKER_23: and some of the designs i've seen for for new cooling systems are that they'll actually be fans

SPEAKER_167: in between the node layers um so and all fiber optics all glass photonic computing to to transfer

SPEAKER_55: data from one to the other because the amount of data being moved now can't be moved over copper it

SPEAKER_126: can't be moved over ethernet cables it's just too much right being moved on for sure for sure all of

SPEAKER_107: it is is a fiber optic cable it's actually called infiniband the um melanox um nvidia cabling and that

SPEAKER_23: that's like 900 gigabyte a second um which is pretty nuts for you know direct chip to chip connections um you know so it it is really driven by all the hardware innovations and those hardware innovations are very predictable because you know they're they're actually laid out three years in advance uh yeah

SPEAKER_55: because they're planning on building they're building those schematics and getting the fabs and the factories ready to actually build them right um so there's starts to be a little controversy inside of deep mind i guess at a certain point uh larry page is like we need this team inside of google maybe peter tl elon wants you to stay independent maybe you could explain that moment in time and

SPEAKER_107: and the decision making there yeah i mean i think this was way back in 2014 that we were acquired um and you know i think that elon and peter all of our investors you know wanted us to stay independent and i

SPEAKER_23: think that um the challenging decision for us was just the scale of investment that we could see that would be required um going forward i mean we'd raised 40 million dollars and you know we could see a path to spending 500 million dollars in three to five years and in fact that's what we ended up doing exactly that um you know deep mind now has i think 12 1300 people and spends over a billion dollars on compute but yeah so i mean that's that's public information so you know the the it's it's pretty remarkable the trajectory and um so one of the things that we were focused on you know larry made us an incredible offer to be able to do that we were acquired for 650 million dollars um free revenue

SPEAKER_28: obviously yeah it's a pretty great deal especially at the time i mean the world has changed dramatically

SPEAKER_18: in the last decade but at the time this was people were shaking their heads like what did they buy i mean in fact the conversation was i think you had maybe 100 people at the time uh less yeah yeah yeah exactly the conversation was is larry lost his mind he just paid 10 million dollars per engineer and then that became well engineers in silicon valley are worth 10 million each it's like well these are different types of engineers you hired a very elite group of people maybe you could talk about the

SPEAKER_55: recruiting of bringing together the deep mind team at the time because it was a lot of phds a lot of people who had some you had a pretty deep bench there yeah we were extremely focused on hiring the

SPEAKER_23: best phds and postdocs actually and i've carried that through to how i hire inflection i mean you know you talent is the differentiator at the end of the day i mean you could be first to get access to compute you can have the most amount of capital but selecting a very very high quality team is really the only thing that makes the real difference and that means you have to be very deliberate about who you don't hire um you know it was actually amazing at that time how many people who were fundamental to the deep learning revolution we had around us right so you know um jeff hinton was uh one of our consultants uh for two years before he set up his company that he then sold to google so it was ilia satskiva the chief scientist of um open ai now um voy check was an intern at deep mind who was one of the co-founders of uh open ai it's gonna be like the paypal mafia

SPEAKER_11: it's gonna be the deep mind it's already turned out to be the deep mind mafia basically you got a whole group of alumni who are just creating the future here yeah was it uh looking back on it was

SPEAKER_19: it a mistake to sell you regret selling to google should you have taken elon's advice and stayed

SPEAKER_196: independent or anything about it elon was certainly keen for us to come and be do the tesla thing be

SPEAKER_00: part of his ecosystem yeah but you know i'll be honest i was a bit i mean back then you know

SPEAKER_196: he's an incredible person but sure i mean it was a very uncertain uh bet in 2014 would be the

SPEAKER_72: definition of uncertain i mean model three almost killed him almost killed the company i mean that

SPEAKER_18: companies had a near-death experience with each launch of a product um i mean you want to talk about hard hardware plus software and manufacturing at scale and building a public brand i mean the

SPEAKER_38: degree of difficulty is absurd inside of google to the extent you can't talk about it uh you guys worked on a lot of theoretical things but you also worked on a lot of practical stuff what were the

SPEAKER_85: big wins inside of google that you can talk about that deep mine participated in yeah i mean we uh

SPEAKER_107: deployed deep mine technologies on all of the main products other than search actually and youtube

SPEAKER_23: um so i think we did seven pas in the end on everything from um data centers to healthcare to play store to android battery optimization to android operating system i mean we we reduced the amount of energy you needed to call the google data center fleet by 30 percent um that was a three-year

SPEAKER_47: collaboration it's a huge project we made the google wind turbines 20 percent more efficient which google

SPEAKER_23: has the largest winter the largest wind turbine farm in the world it's pretty crazy um yeah we designed the activity classification uh algorithms for uh all the wearable devices that would basically tell

SPEAKER_204: whether you're sleeping or running the two biggest franchises they wouldn't let you touch search

SPEAKER_18: they wouldn't touch youtube why would they you got this incredible thousand folks and you don't let them touch the two biggest franchises why well we politics no i mean we tried and we actually tried

SPEAKER_47: youtube in 2015 and we failed it was too early and it was it was just super hard we were trying to

SPEAKER_23: optimize watch next time actually yeah um and we were trying to use reinforcement learning for it and it was just too it was too early we we didn't succeed um search is a different story i mean search is just so difficult to ship anything and they're super conservative they also they like the fact that all of the rules are very transparent so they can see exactly why a page is being recommended and really have much more transparency on the algorithm which is very understandable so in fact there were some you know deployments of deep learning systems which ended up causing regressions over time because of drift um you know over over a six month period and in other words quality would go

SPEAKER_50: down well it would go up initially at the beginning and then come down and then come down exactly why

SPEAKER_38: why does that why does that drift happen people are talking about that with chat gpt4 that results have deprecated well i i didn't understand why that would occur is it's garbage in garbage out kind

SPEAKER_67: of situation what's what's happening well and something like that happens i think there's slightly

SPEAKER_107: different problems i think with the chat gpt thing it's probably that they basically serve their best

SPEAKER_23: model which is expensive to serve right because it's the biggest and best and uses the most number of gpus and then once people are coming back frequently they'll use they'll serve a smaller model which is cheaper it'd be a less well trained model quality is basically as as always the case quality is cost right so we can serve a cheaper model for uh you know quicker um but it won't be as good

SPEAKER_55: so that's probably what's going on i think ah i've never heard that theory but that would track and make

SPEAKER_38: sense um and as more people use it they they may have no choice but to give everybody a little bit

SPEAKER_11: of an easier model to use or a more basic model because they don't they don't have a choice

SPEAKER_23: when the other variable would be speed so if you want it really fast then you have to get a smaller model or you have to use more chips to serve a super large model so you you can't have all three and so if you want a super high quality one you could have it really slow and cheap but that would be really slow like 20 seconds or something for a response listen if you're in the tech industry you

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SPEAKER_38: they never launched any of this stuff until open ai did uh but they clearly had it sitting there right um it makes sense that google has more responsibility with their brand name and they

SPEAKER_67: they can't put stuff out there that's schlocky or confusing under the google brand name but eventually i guess open ai and microsoft forced their hands right why did it go down that way yeah i mean people say

SPEAKER_23: google was asleep at the wheel and all the rest of it but you know it's not quite true i think um so i i was there at google and working on the lambda team right so that i spent a year and a half working on that team and we um you know basically had chat gpt before chat gpt it was incredible i mean summer of 2020 and we had it it was working it was amazing and was that what we saw on the google in gmail autocomplete was that model it wasn't gmail autocomplete but it was featured by sundar in may at io the annual development conference at um yeah in 2020 so and he actually it was actually featured as lambda you can see it up there now and yeah he he actually had a conversation we designed it was so stupid we he had a conversation with a paper airplane about what what it's like to be a paper airplane and then he had a conversation with pluto and then the language model pretended it was pluto and like you know talked about the weather and stuff well you know what we always say examples

SPEAKER_11: matter and they pick terrible examples exactly literally you know when you're when you're pitching

SPEAKER_60: your startup you're pitching a new product you want the most evocative interesting applicable example

SPEAKER_237: well it's two inane ones and and i can tell you it was deliberate because we didn't want it to look

SPEAKER_23: like a person or sound like a person who wanted it to be kind of like uh you know sharing the cool technology but it was just you know the first small step in that direction don't be scared it's not

SPEAKER_140: taking your job it's just pluto i mean if you make it a doctor or you make it a librarian or you make it a copy editor or you know all of a sudden it's like huh and and that's what's happened today which i

SPEAKER_38: think is a good pivot point here so anyway suffice it to say google is a large organization they're conservative and so they just took a measured approach and they have the goods right i think

SPEAKER_31: there was just a confidence that you know we don't have to go first on this and we could take more time

SPEAKER_23: to get it right and that you know search is just this phenomenal lock-in in distribution and data

SPEAKER_250: and i i think that's going to pay dividends because i i per you know i think google's going to be just

SPEAKER_252: fine i mean google google's gonna be fine i agree i bought google shares when i saw this going down because i was like i looked at bard me too and i'm watching bard and i'm like you've got so much click

SPEAKER_38: stream data and you got so much local data that it's all of a sudden doing links tables it's putting in photos i mean i've seen this movie before i watched google go from 10 blue links to you know comprehensive search content shopping maps everything and that happened over a decade or two and it's obviously going to happen there and i also think the ad model you know there is a theory like the more confusing it is the more you click on ads but if you do a search for travel there's no reason that links inside the bard result cannot be monetizable in fact they will right i think

SPEAKER_107: that's true i think i think where google is going to struggle is that google has developed an incredible

SPEAKER_23: expertise for getting in its own way right it's just almost like the master of like internal chaos and so there's loads of you know amazing teams and projects which just block each other because there's huge amounts of duplication it's a very chaotic place it really is and so i think that's that's

SPEAKER_107: going to be challenging for them i think the second thing is the ad model may not be the model of the future right it may be the case that people cannot tolerate having a you know an ai in your pocket that

SPEAKER_23: is funded by whoever is the highest bidder trying to sell you something because these models are so persuasive because they're so personal because they'll get to know you because you end up having you know conversations with them and sharing information that you wouldn't normally type in a regular search query where it's just like you might say like you know something sensitive about your cancer or your you know whatever your heartbreak you know but it's not the same as having a fluent continuous natural language conversation as though you and just like you and i are now right and so i think

SPEAKER_107: people are not going to want you know your ai to suddenly turn around and say by the way tada like

SPEAKER_122: i'm you know so we'll see how that turns out and i think google's going to struggle with that one

SPEAKER_18: yeah it could be affiliate links you know if i was taught i was talking to my ai and i'm suffering i'm

SPEAKER_55: melancholy i got depression i'm feeling sad and it knows my my ai knows i'm sad uh it could be like

SPEAKER_18: you know maybe exercise cold plunge bath go see a psychiatrist all of those things are monetizable links in some way um and so you know if it gives you the perfect answer the question is is is it possible to monetize if you just got the answer and larry always said like eventually we're going

SPEAKER_67: to give you the answer we're just going to give you the answer and so it the mind does wonder if that

SPEAKER_107: screws up the ad auction in a major way well and and that's precisely the problem number three for google which is that if google always gives you the answer then what is the you know future for

SPEAKER_23: the open web because google is going to disintermediate the third car the third party content creator like if you're a regular mom and pop shop with your bakery on a website or you have a blog post and you rely on that display ad income well google's just going to give you the perfect recipe so why would you ever go to that kind of third party blog post and that's actually a problem for google and the regulator because google has been telling the regulator for the best part of 15 years that the reason it can crawl all of these websites is because it's only indexing so that it can redirect the

SPEAKER_107: user to the third party page it feels fair it feels fair right it's a yellow pages they always used to

SPEAKER_23: say it's a lookup table whereas if it's now cutting out that source of information and giving you the perfect answer that's a big problem with the regulators certainly in the european context because

SPEAKER_190: many google execs have been on the witness stand claiming that they will never do that right so now the

SPEAKER_55: models are doing that they've been trained on the web it's obvious it's been proven you can you used to be able to ask openai chat gpt like hey what where's this answer trained from it would actually tell you um some of the training data i think it doesn't do that now what's the fair outcome here

SPEAKER_38: for pools of data lakes oceans of data and who gets to leverage them to build these models but what do you think is the outcome here because we're starting to see the lawsuits pile up we're starting

SPEAKER_55: to see you know elon say hey twitter data is not available reddit saying it's available at a price core saying it's available at a price or maybe with a link back stack overflow built their own language

SPEAKER_38: model this new ceo just emailed me to say like look i know keeps stack overflow keeps coming up we're building our own co-pilot nobody else can use our data set so talk to me a little bit about what you think will happen in the industry because i feel like it's tremendously unfair to take gourmet or whatever

SPEAKER_67: recipe database and then just give the answer and not give a citation at least what's going to happen

SPEAKER_23: here's the tricky thing i mean the reality is that the information was placed on the open web and the open source crawling engines have gathered up their information um under perfectly legal uh you know acceptable terms and that crawler you know the common crawl crawler you know collects the information and clearly says that it'll be used for um you know research and development purposes and you know

SPEAKER_107: be used for experimentation by other you know people trying to build other products off the on top of the open source search engine so the crawler the crawling data that everyone's collected is is just a

SPEAKER_23: well-established status quo so i don't think that is going to be undone or there's going to be any compensation you know people sometimes talk about this data trusts idea where you know each individual data contributor gets like one cent or something i mean this is not going to happen i think why not

SPEAKER_47: too hard to execute on i think it's impossible to generate sufficient revenue used to make the payment

SPEAKER_23: to the end you know producer of data material right so maybe in the case of a very large data owner like you know the opening i just didn't deal with associated press right but that's actually not for historic data that's actually for fresh uh real-time news see that's where i think there is a possibility

SPEAKER_18: of this if we think as an industry collectively that this could actually be a benefit you remember

SPEAKER_55: minitel in france used to charge a certain amount per hour and they would share that with the data

SPEAKER_38: sources uh aol used to charge three four or five bucks an hour compu serve and they would share that with the data provider so if you were on some data site that had to do with weddings or whatever they would just give them 50 cents of the hour right you actually had a model there i think if we took

SPEAKER_145: robots.txt and we put in a license and said hey listen these are my recipes i'm gordon ramsey

SPEAKER_38: if you want them in your index mazel tov there's a thousand recipes it's a minimum payment each year of ten dollars a recipe it's ten thousand dollars a year to put it into your index plus i want something on top of it whatever it is and that might be enough to incentivize people to start putting more

SPEAKER_107: recipes online it's possible it's possible i mean i think the the challenge of these things is that the creative tools are now going to be so widely available that the models are going to be you

SPEAKER_23: know better at generating new recipes um so the cat's out of the bag i mean in a way it's true yeah

SPEAKER_289: you uh you leave google uh and you start inflection with uh reid hoffman who's just on the pod um you

SPEAKER_18: raised a bunch of money what is inflection ai what what what is the goal here you obviously uh got to um see everything up close and personal that's happened uh with open ai and with deep mind uh where do you sit uh in that sort of pantheon of you know elite uh ai offerings you got barred over here you

SPEAKER_154: got open air over here where where are you going to sit and and what market are you going to try to

SPEAKER_23: carve out so we're developing a personal ai um i believe there are going to be lots of different types of ais there'll be business ais you know there'll be medical legal you know every digital influencer will be an ai every brand and big platform that's trying to sell stuff will have their own ai that you know that is more than marketing ai i think you know wherever you see a website or an app expect that in the next five years that's going to become a conversational interface that you might as well just call an ai right it's it'll be able to produce video and text and audio and talk to you just as i'm talking to you now in that world where everything becomes an ai i think you as an individual consumer want to have a personal ai that is on your team right it is fiduciary aligned to your interests in your corner helping you find information identify credible sources negotiate with other ais for the best bargains plan and prioritize your day and you know your thoughts your ideas follow up on your research interests find you entertaining information and it is super important that it's personalized to you because you're going to end up sharing a lot of sensitive personal intimate information in order that it can then go out and be your representative right whether it's in gaming environment and it's kind of in the metaverse or whether it is you know looking for sports news you know on your behalf and coming back to you and talking about it like the way i think about it is is kind of like imagine if everybody had a chief of staff right a digital chief of staff that was a coordinator scheduler prioritizer summarizer you know you wake up in the morning and it gives you the perfect briefing of everything you've got on in your day what's happened with the news what's happened with the sports the companies that you're tracking um that is what i think it reminds me of uh remember general magic

SPEAKER_18: uh and there yeah this is we're dating ourselves but sony and a company called general magic made a pda personal digital assistant device uh long before palm i think there was a documentary on it um but they had a concept of agents and the this is before search really on the internet uh search

SPEAKER_55: engines even existed and the agent would go on your behalf and go find your flights or go find your

SPEAKER_18: reservations go do tasks for you and so you see this ai as being autonomous in some ways and being able to put it on repeat tasks hey i'm trying to lose weight i want to be 165 pounds what should

SPEAKER_55: i be doing and then it's going to counsel me every day about that right and if if you're going to put

SPEAKER_23: an ai in that kind of position which it will be incredibly effective at doing because it's not going to nag you and moan it's going to you know be inspiring it's going to be reassuring it's going to be gentle and polite and respectful i mean it's it's not going to be an arsehole about it unless that's obviously what you want whatever you're into whatever you're into please fat shame me i am not

SPEAKER_309: worthy yeah it could get weird it could get weird but to your point it's going to be personalized and

SPEAKER_152: it's going to be your agent and so this framework is critically important in terms of your vision is that it's your agent it's working on your behalf not the corporation's behalf not open ais not bing search results or google search results this is your ai and whatever you talk to it about we don't

SPEAKER_38: have any insights into and if you put data into it we're not sharing that with advertisers or anybody else so that means i have to pay you 100 bucks a year for this yeah i mean at the end of the day

SPEAKER_23: if you want to have full trust you need to not be the product and if you're not paying for it somebody else is paying for it and if you're putting that amount of attention and sensitive information into a place the only way to make sure that it's on your team is for you to pay for it in some way like you wouldn't rock up and be like oh my accountant is actually being funded by you know

SPEAKER_00: this insurance company yeah and so i'm going to go and speak to my accountant and you're trying to get you know your your tax return done or you know you're trying to decide on how to make some investment

SPEAKER_107: and you're like well are you working for me or are you trying to sell some insurance product or whatever

SPEAKER_55: right yeah i think understanding the intent and the business model is so critical and consumers are super savvy now like they understand it you you can't get over on customers now they they expect that like alexa is listening to them even you know in serving them up ads even when that's not what's

SPEAKER_18: actually happening but you know they they are pretty empowered and and they understand this concept of

SPEAKER_38: you are the um if you're not paying you're the product so i've used it a bit quite delightful

SPEAKER_55: beautifully designed um what can we expect to be the beachhead markets or tasks that you think it's going to

SPEAKER_126: to delight people with in the early days here well so far you know we've we've actually only got a small

SPEAKER_107: model that's shipped in production right so you know it only founded the company a short while ago

SPEAKER_23: sort of 15 months ago now and we're just bringing up our super uh cluster um so you know just a few months ago we raised a pretty large round and you know we've we're building out the largest cluster of h100s that's in operation in the world today so today we have the largest cluster of the largest operational cluster of h100s by the end of the year we will have 22 000 h100s which is equivalent of

SPEAKER_190: about 80 000 a100s um in a single cluster and nvidia uh you had to go wait on their doorstep and beg them

SPEAKER_67: to buy these i mean maybe you talk a little bit about the scarcity of these h100s uh yeah they're

SPEAKER_23: extremely scarce i mean nvidia is one of our investors um that works so is microsoft um uh yeah i mean you know i i think that we we were just very lucky to get to the top of the supply chain with them and they've they've been great to us so it's been incredible we we've also helped them optimize their cluster for ml perf so they have an open source benchmark that stress test is stress tests their cluster so we've invested a huge amount over the last six months to optimize their cluster um so it was kind of a good quid pro quo that we were both for guinea pigs um and also the beneficiaries

SPEAKER_145: of the first big shipment when this 1.3 billion dollar uh raise was announced it was a little confusing to

SPEAKER_327: people because microsoft you know has this big bet on uh open ai and then you know they're making this

SPEAKER_67: big bet here what what what should we take away from microsoft's behavior here investing in you and open ai it was kind of confusing for folks i think the way to think about it is that microsoft is a

SPEAKER_23: platform of platforms you know it's traditionally been very good at doing deals with lots and lots of third parties interacting with a whole range of different suppliers and i think um you know that's probably how they're going to continue they they want to back lots of the best teams and um you know we we have one of the strongest teams in the world right now if not the second best team in the

SPEAKER_330: world and we have the co-creators of gpt2 gpt3 llama chinchilla gopher palm lambda how much of the 1.3

SPEAKER_46: billion goes to hardware just to out of curiosity most entirely really so i just ship it right to

SPEAKER_55: nvidia and build out this gigantic data center to do that and then that becomes a massive competitive

SPEAKER_23: advantage yeah it's a it will be a huge advantage because we will train models that are very very

SPEAKER_65: much larger than gpt4 uh before anybody else in the world so um you know by by the spring for sure maybe even a little bit earlier so um you know all of it goes to compute basically we're only 40 people

SPEAKER_55: oh wow and so did you consider using google cloud or amazon web services or azure or do you need to

SPEAKER_67: control the hardware in order to get the gains that you need to see no we we wouldn't use tpus

SPEAKER_107: they're they're difficult for other reasons um but we you know so we certainly wanted nvidia so we did look at aws and oracle and stuff and you know um we actually do use azure for some workloads but we we

SPEAKER_23: wanted to make sure we designed the the architecture for the h100s and we've we've really optimized

SPEAKER_137: everything you know down to the lowest levels in terms of how that operates and try and get maximum

SPEAKER_340: performance out of it how long does it take to build out this cluster it's gonna take a year or two

SPEAKER_23: it takes a while so i mean we we're we're currently uh operational with 7 000 uh h100s um uh we'll be 22 000 fully operational by the beginning of this effort so it's pretty quick yeah that's unbelievable

SPEAKER_55: and this is just in just different data centers around the world you co-locate in and you just start

SPEAKER_65: racking them no it's just one data center because we need it all to be in the same place so it's actually

SPEAKER_342: the size of like three football pitches where is it where's the data center i'm curious uh we it's

SPEAKER_01: in the us oh it's in the us i don't want to say okay we can't say yeah gotta be near something that's got hydroelectric or some nuclear power plant exactly nailed it that's exactly what it is or solar or

SPEAKER_55: solar you gotta be near something so tell me when we look at um the downside to ai obviously this has been a big debate and you know there's job compression does seem to me i asked a lot of smart people on the program from brian chesky at airbnb to aaron levy up box i asked everybody like what kind of gains are you seeing internally on your team almost universally people say 30 everybody's 30 percent more effective whether it's a developer copywriter customer support whatever uh which means every two years people become twice as good at their job or efficient rule 72 ish there's job compression and then there's like scary scenarios people are going to use this to hack things or you know build super biological weapons how concerned are you about each of those or those two specific scenarios and how do you think society should think about them terrorism crazy people and then just job loss or maybe displacement

SPEAKER_23: it's a it's a good question i mean i'm very concerned about it i've um it's something that i've worked on my entire career um the ethics and safety of of ai um in fact our business plan back in 2010 which i wrote was had the strapline building safe and ethical agi so i think we saw a lot of these risks right from the outset and i'm still i think it's appropriate to be pretty concerned around them um i don't agree with a lot of the timelines i think people are very anxious that we're about to have this intelligence explosion and somehow i'm going to present an existential risk to our world but i've actually just written a book uh called the coming wave um and it basically looks at um all of the threats basically uh that ai might create over the next 10 to 15 years uh as well as the synthetic biology threats and i think the labor market risk is a real one i think for the next 10 years people will get more productive um but the challenge is that the increases in that productivity are going to generate surplus value which will be captured by capital and not labor which means that we probably won't see you know an average increase in wages uh certainly for the middle um those who are doing you can think of it as like cognitive manual labor back office administration basic telephone calls new factory

SPEAKER_314: workers right like they become the and so the steam engine the factory the robots can replace them we were

SPEAKER_252: very dismissive i think about factory workers losing their jobs but now that it's white collar

SPEAKER_67: yeah it's uh i think people are like wait a second you could make a logo better than the designer i mean we're kind of there right now that you can create a logo or a tagline as good or better than a marketing agency and i think when people see that it doesn't take a genius to say not going to need as many marketing agencies we're not going to need as many logo creators and the question is less yeah

SPEAKER_23: that's exactly right i mean everything is going to cost less which is amazing that is going to drive the biggest productivity explosion we have seen in the history of our species right it is truly going to be an incredible couple of decades but the reality is that those who have their jobs displaced are not going to be able to retrain adapt their role and then compete against man plus machine in the labor market in good enough time like if you're a designer there's only x number of design slots in the world right jobs right and if suddenly the work of that x number is being done by 70 of the humans because they're aided and augmented and accelerated by you know good ai then there's going to be people who are basically graphic designers who are squeezed out and they'll have to then do the next tier down

SPEAKER_107: of work and that will squeeze out the next tier below that so you're going to get this tiering um where the bottom is squeezed out more and more and i don't see how those bottom are going to be

SPEAKER_23: able to adapt quickly enough and that's why there's a tough remedy which a lot of people don't like but you've got to face the facts which is if you don't want there to be really significant structural disemployment where people cannot compete in the labor market but they want to then there has to be some kind of subsidization for retraining ubi retraining something and before you get to full ubi there's there's obviously you don't have to go as far as that to begin with but that is the direction

SPEAKER_279: of travel over a 20-year period one of the great things is we create new jobs when all jobs get retired

SPEAKER_18: and it really is the pace at which that happens it's kind of sad that cashiers have lost their jobs over the last 10 years but i remember when they went on strike and mcdonald's cashiers were like we need to make 20 bucks an hour to make this job work and then mcdonald's was like that's interesting because we have a company that wants to build registers that are touchscreens and they're getting cheaper and the cost curve at some point panera bread and mcdonald's were like why do we need cashiers put one and then everything else is going to be ordering on a chaos and that job has been eliminated those people can go find other jobs podcasting's a job now it's the speed and how we

SPEAKER_23: manage the transition because we will create new work there will be new demand people will have new you know income because of this productivity boost and so people will have money to spend and people will be more efficient so they can deliver the same output with less work so the question is how you manage the transition for this period of you know the next couple decades where people who get pushed out of the workforce have to somehow retrain and adapt i mean even you know it is pretty clear that there's a retraining and adaptation requirement and that many people are

SPEAKER_11: just not going to be able to keep up we started with factories we started with coal workers you know if you're a coal worker and that's all you've done for 20 years the idea that at 45 years old you're

SPEAKER_18: going to just magically learn the code or become a blogger kind of hard to think but then again with ai tutoring maybe there's an opportunity that the ai tutoring will get so good that people can actually learn skills faster with customized education yeah right right i mean people this is

SPEAKER_23: the incredible thing is that it will be a very meritocratic moment because a lot of people who have

SPEAKER_47: had safe and steady families for two three four generations have inherited peace and stability in

SPEAKER_23: their life and that has turbocharged their education it's given them confidence it's given them emotional support it's given them you know access to education access to opportunities what's going to happen now is that those people who've been on a comfortable trajectory are going to face the competition by

SPEAKER_107: people who are hungrier and who now have access to personalized ai tutors yeah that are going to teach you anything that you're obsessed by anything that you want to go deep on it's infinitely patient it's infinitely smart it knows exactly how you like to learn and it's free and it's going to

SPEAKER_23: basically be free or free or close to free yeah close to free i mean compared to a college education

SPEAKER_55: it's going to be free for sure i mean compared and it's just basically getting an internet connection and

SPEAKER_18: you know then you are going towards the third rail which is motivation and drive and this is a very

SPEAKER_38: hard conversation for people to have but it might be the case that there's somebody in sri lanka pakistan sam paulo who wants it more than somebody in san diego or brooklyn and they're just going to work harder and they're going to spend more time on that ai and now it's a global that ai tutor and it's a global marketplace and and that's i think going to be very scary for people is oh my god i'm competing against you know the top five percent on a global basis who now have starlink have a internet high speed connection and they've got the ai tutor in the cloud the khan academy teacher that is infinitely

SPEAKER_363: patient and yeah that's your privilege in the west that you were born in london or new york means

SPEAKER_23: nothing in that scenario right yeah i mean i have a whole section about that in the book which i really enjoyed writing i mean it's is about exactly that story because the costs of production are going through the floor and everything is now going to be zero marginal cost so knowledge is widely available right and now not just knowledge but intelligence right intelligence being the mode of synthesizing knowledge and turning it into new strategies or insights or action plans that if that goes to zero marginal cost then why shouldn't anybody be able to be super creative and it really is going to be about how hungry and dynamic you are as an individual which i think is going to really displace or you know it's going to undermine or put some pressure on that you know the complacency class

SPEAKER_18: that has kind of like taken over us a little bit in in the west it's the group of elites who get into their college because their legacy and if you're a legacy person and you get into harvard guess what it may not mean as much as the person who's motivated and becomes a neurosurgeon or a developer and they're from like i said you know bangalore and they just wanted it more than you and now they're going to be society is going to be super useful for them uh listen this has been great thank you for giving me over an hour of your time gotta have you come back everybody should try um uh it's pi right

SPEAKER_11: is the the name of the personal assistant pai is the uh short uh pi pi pi pi pi dot ai so pi stands

SPEAKER_44: for personal intelligence yeah pi dot ai and the book's called the coming wave which is available

SPEAKER_18: now i didn't realize you had the book i'm going to read it this week and i'm going to order the audio book now uh when did the book come out uh it's actually available for pre-order now comes out september the fifth oh fantastic so perfect well after i read i'll have to have you come back on and we'll talk all about it and uh hopefully we have a book party or something for you here in the valley uh if

SPEAKER_87: you need if you need a if you need the world's greatest moderator uh to interview you at any book parties or something let me know i'm available well and also apparently if i fancy getting married

SPEAKER_00: anytime soon you're available for that too right so currently the world's greatest uh officiant is

SPEAKER_01: available if you can find a woman who will marry you must have a uh but you got a startup oh you

SPEAKER_04: already got that accomplished no no i'm struggling with that i'm very much single so i mean if you want to marry me to my single collection you you you are married to your startup you raised a billion

SPEAKER_06: dollars i could tell you who you're married to for the next 10 years absolutely absolutely ai and

SPEAKER_279: you're 40 people over there also you're hiring so if you want to join uh inflection go to inflection ai and um listen it's an elite group uh they're in the bay area palo alto london you

SPEAKER_30: believe in people working out of an office or you think uh remote work is fine what's your what's your

SPEAKER_23: take on all this i i have an interesting we have an interesting balance actually so i think that you need the best of both worlds so the way that we operate is that we run the entire company on a six week cycle right so when you when you join the company you sign up to traveling to be in person for a full week wherever you are in the world for our seventh week meetups and that's a key part of the schedule because then for that for that one week in our seventh week meetup we have a very intense hackathon style meetup where it's you know the classic 14 16 hours a day in the same room really going out hardcore and the rest of the six weeks we recognize that people need to work in a flexible way so i am personally in every day and so is probably about a third of the company uh i would say another third come in tuesday wednesday thursday uh and then some people are actually fully remote

SPEAKER_18: so that's the right hybrid structure i think i agree with you you know if you have my belief is a third of people are more productive as remote workers and over the last two years i figured out who they are and then there's another two-thirds that do better work when they're in an office with other people just like some people are better runners alone and then other people when they run with the group the majority of people when you run with the group you will perform uh better when you're running with runners who are faster than you it's that simple or you play with poker players you're better than you you're going to get better quicker and so i think there might be on the margins 25

SPEAKER_19: a third who are better remote but i think two-thirds are better in person and that's for sure

SPEAKER_280: true and there's no way that those people can stay completely remote forever i i personally am

SPEAKER_44: not a believer in these fully remote environments so that's why we do this six one rhythm i think it's

SPEAKER_55: the right amount basically it's the right amount of sacrifice it creates a certain um esprit de corpse you know like i could see it being super motivating to like get together and then yeah some people got kids they got family you're hiring people who are uber successful and have many options so it's not like you can always dictate you know you might have somebody who's just a genius who wants to live at lake tahoe and you may be able to break her off for a week to come

SPEAKER_126: but you might not get her for the seven weeks so you don't want to lose that person right i think

SPEAKER_67: that's the weird standoff we now have or maybe it's a settlement um amongst workers and corporations right because you don't want to lose a high performer right right exactly so i mean getting the

SPEAKER_23: flexibility is the right way to do it and having the kind of peace during the cycle that some people need not everyone but some people need to do their own thing and then have the super intense meet

SPEAKER_67: up which i think is a good rhythm so yeah i mean it's also sounds sustainable you know i was thinking

SPEAKER_252: about the early days of our industry and like just everybody at work six days a week 12 hours a day 14 hour days it led to a lot of incredible outcomes so i don't think anybody who does it is

SPEAKER_67: making a mistake necessarily but it can break people it it can exclude certain people from the team

SPEAKER_252: that might be high performer so it's really the job of management to just figure out a cadence it sounds like you found the cadence that works for you sounds kind of exciting actually it's working

SPEAKER_17: right now and we're having a great time so yeah if any of your listeners want to come and uh yeah

SPEAKER_196: stuck in we're having a great time i mean you that's the other thing is people have choice amongst

SPEAKER_11: elite folks you got to make it fun and it's got to be purpose right and it sounds like a lot of fun to go to these remote locations and do a week so all right listen great job uh look forward to reading

SPEAKER_18: the book uh comes out on september 5th everybody pre-order it tell me the name one more time of the book uh it's the coming wave the coming wave so go look for that on amazon or audible and pre-order right now if you hear my voice please pre-order so he gets that big first week bump you need a 10 000

SPEAKER_396: in order to make the new york times bestseller list that's true i'll see you all next week on this week start-ups bye-bye