No Priors Ep. 34 | With Ginkgo Bioworks Co-Founder and CEO Jason Kelly

Key Takeaways

biology is undergoing a digital Revolution as we build developer tools and production infrastructure for synthetic biology how will it change Industries how does it intersect with AI and how do we rethink biosecurity this week Sarah and I are joined by Jason Kelly co-founder and CEO of Genco bioworks to discuss their goal of making cells as easy to work with as computers their data strategy and the tech keeping the next pandemic at Bay and in general what cell programming will do for the future of food medicine and agriculture Jason thanks so much for joining us today yeah thanks for having me on so I think there's a lot of talk about synthetic biology and how biology and DNA and proteins are effectively just code and you can manipulate them in different ways now and things like that I'd love to just get your review of both what Ginkgo does as well as what a synthetic biology actually mean so the I think the idea of synthetic biology is that DNA is code right and inside of cells are atcs and G's essentially on like a tape and it is very like surprisingly analogous to zeros and ones and you know inside memory in a computer that's roughly where the similarities end okay like once you get to the next step of what the cell does with that code we are in a totally different world it is not virtual is the first thing right it is a physical thing the code itself is literally physical right it is a polymer uh and it is going to use that to make proteins which are basically little pieces of nanotechnology and they're all going to be bumping into each other and it's all crazy it's not physically isolated like you would imagine with a semiconductor chip it's not built by humans so you have this really interesting thing where the hook is there for people in Tech to engage with Biology but then once they get in they're like what the uh and so and so like that I'm happy to talk about those pieces but I think you're right the core idea of Symbio is that it runs on code and then what can we bring over from programming into this world that actually sticks and so I think what's in that biology has been you know really since it got going I met um you know the founders of Ginkgo back we met at MIT in 2002 that was like early days of symbios about 20 years now it's basically Engineers asking the question of what can they bring over into biology that's actually going to work and some stuff has been left by the wayside and some things do work and the latest technology that's being tried now is AI can you walk us through what you actually think does transfer over and then where are there one or two unique challenges then how does AI help to solve for some of those things I'll tell you like a funny story right so um one of the fellas I started the company was this guy Tom Knight right and Tom Knight started on the faculty at MIT in 1972. okay right like mainframe computers punch card computers he was a computer architect for a very famous mini computer which was like the size refrigerator called the list machine okay like symbolics that company's one of the founders of like old school classic Steven Levy hackers in the book kind of guy right mid 90s he realizes this thing about DNA is code and basically it's like forget computers I'm moving into programming DNA he's still Tom right he's been like teaching the semiconductor course for 20 years at MIT at this point opens a wet lab in the MIT computer science building starts growing bacteria freaking everybody out right and he puts up this flag and he's like hey computer scientist like DNA is code if you're interested in in this thing like come over and try it out right some of us came over we're like all right cool we got there got our hands wet and we're okay with it a lot of computer scientists they get there Tom's like okay here's the lab bench remember this code is physical so if you want to compile it I'm going to have to teach you how to do molecular cloning and here is a pipette and you're going to sit at this bench and you're going to do these steps okay and and the person would do them and then get a result the next day they're like well that's really interesting and then and then they do the same thing again the next day and they would get a different result and they'd be like Tom I just did the exact same thing and I got two different results like what's going on and he was like you're never gonna know you're leaving a world in computer science of like pure logic right at the end of the day if there's a bug you can always run at the ground and and that's because a these are systems that run like clocks B we design them we design them right uh and so like you at the end of the day can go in and figure it out and in biology it's like sometimes you can right and sometimes it's just a part of the biology that we just frankly don't understand well enough that's broken and like tough luck and and you and you gotta like stomach that and one of the things my favorite things about AI is like how does that neural network nobody knows yeah like you're about to experience like like like the analysis of these neural Nets is going to look like systems biology right it's gonna be like go in and like try to back figure out a thing that you didn't design my friends and so like that that'll be your first taste of really feeling like biological engineer right but like why bother like why work with these neural Nets that like God you actually can't easily debug and understand why it's hallucinating and all this stuff and the answer is because they're powerful it's worth it and that's the same reason you want to do biological engineering like even though it's unpredictable even though you're gonna it's gonna be so frustrating it's not gonna do what you want and blah blah blah people are gonna like it's because the substrate is incredible right it self-replicates itself assembles we have nothing else like it in the physical world so you want to work with it even though it's hard that you know that that's that's what that's what ultimately it gets people passionate about this stuff yeah totally makes sense and I think one could argue that neural networks are actually heading even more in that direction because as people build systems that can code themselves we're going to end up with evolutionary systems that are completely non-designed yeah and I think then we're truly in the world of biology where you have Evolution kicking in and you know to your point evolution is really messy right it's always optimizing for the utility of something versus the form of it it reuses Parts aggressively it creates enormous redundancies in weird ways that you don't know that there's a perturbation here and it propagates across like in weird ways and so I think people are really underestimating what happens once we have self-evolving neural Nets which I think is coming quite soon be still my heart it's it's gonna be great it's so cool I mean this is so cool it's so worth it yeah I mean it's neat because there's a magic to it right like again people people like different stuff and like I get I think this would be like one of the big cultural divides there's a certain kind of mind that really likes things to be predictable but some people who just like the magic right like some of what's cool about biology is the is how hard it is to understand so how does how does Ginkgo go about harvesting all this you know this shift in biology in terms of the ability to manipulate these systems using molecular biology and molecular cloning techniques and then software tooling and other things so can you tell us a bit more about the company and where you focus and what you've done with it all today so one of the ideas that we've tried to bring over from computer science was abstraction right and what is abstraction well in Tom's era of computing in order to be a computer scientist you had to be an electrical engineer because how do you program a computer you don't know how computer works now obviously today like eight-year-old is able to program a thing on their iPad by drawing boxes around it's like what happened right like well Assembly Language operating system programming language graphical programming language we built all these abstraction layers to to split the disciplines of electrical engineering and computer science into their own paths both of which had very long roads right and so one of the big things we did at Ginkgo we started the company you know 15 years ago like unimaginably a long time here was to do that split from the get-go so we have we have part of our infrastructure we call it a Foundry taking a page from semis that is basically a group whose whole job is automate and scale the lab work okay and move away from a system where that lab work is being done by hand by a scientist and then the DNA programmers who are really typically scientifically trained and PhD biology types they order from that system to get their work done that is actually very difficult to pull off it's culturally difficult because like a scientist does not want somebody else to do their experiments and like they're good scientists you know like this a whole long laundry list of why it's hard not to mention that when you first try to build the infrastructure it sucks okay and so The Foundry team has been able to drive enormous scale economics in doing the lab work which gives us the data of lots of different genetic designs that we've tested which is exactly going to be useful for the AI stuff but it's also just generally useful right because you're you got to try a lot of designs to get the cell to do the thing you wanted to do and so that's that's been probably like one of the biggest activities the last decade I can go it's been like driving that scale Jason what's the right way to think about the abstraction between like your customers and then your DNA programmers like what's the spec that gets passed over or how should we understand the signs they do versus you so today the way that it works is basically a customer of ginkgo's would be like you know um like a recent customer's Merc okay like Mercedes Biogen on the Pharma side Bears agenta corteva the biggest ad companies in the world all customers not a lot of startups right and the way they interface with us is that we basically are agreeing on a spec we're like okay here's what I would like to sell to do they tell us and we agree on like a timeline to develop it and we're kind of like a prop software development shop like we're gonna like make it for the customer and then license it to them and they'll take and they'll own it to go develop their product in exchange for that we'll get a royalty and we'll also get some payment along the way all right that's the business model today they're they're scientists don't use our infrastructure my scientists yeah our scientists here at Ginkgo they do the they use the infrastructure and they have this interface with the customer about hitting goals that's mostly a technical limitation I think would be very cool ultimately to have scientists at all these companies accessing our infrastructure directly it's just too early that's the problem and then how does AI come into the picture or where when when and how did you start using it and has this current wave of AI impacted you or how much do diffusion models llms et cetera matter relative to what you're doing so the short answer is like we have you we do a lot of protein engineering so you know you want to program a bacteria right okay so bacteria has a three million letter genome and like a customer has asked you to express a protein and remember a protein are basically like the little pieces of nanotechnology inside the cell that bump into each other and like do all the things like you're sitting there you're like a big giant bag of proteins right uh and so and so like they want to make a lot of this protein because it's going to go into cold water laundry detergent okay so people don't realize this but like the reason cold water laundry detergent doesn't need hot water is because there's enzymes in there okay proteins and so they want to make a lot of this protein and by the way if they could make it more active like break up dirt faster like whatever reaction it is catalyzing so if you remember chemistry class like a catalyst makes a certain chemical reaction happen faster than if you don't have the Catalyst okay so this enzyme is a catalyst and I want to make it also just better like so I want to improve the quality of the enzyme and I want to make a ton of it all right that's the spec and so how do we do that today well we would have for example a host strain that's really good at producing a lot of protein to begin with okay so so think of that more like an existing software Library so that's one form of Leverage from existing data assets is literally like a hard physical asset an actual microbe with a genome that I engineered in a project previously that is useful for your project so now now we're starting from a good place we're already starting to make a lot of protein but you want to make it more active you want to make more of the cap catalysis okay how do you do that well remember that protein is encoded in DNA and the sequence of DNA determines the effectively everything about that protein but in this case what you care about is how good of a catalyst is it and so you go in there and you have certain tools to try to model the protein this that the other thing and you make some choices and along with the software tools and you say I want to try these 1000 designs of the of the DNA in the lab and see how they do all right and you then get that data back on how they perform you use that to update these design tools you have and you do it again and that's what we've been doing at Ginkgo for a long time including with neural Nets and all this stuff the latest and greatest you know all that but just on that like data asset I think the new idea is on the back and this is we just announced a deal with Google um a couple weeks ago the new idea is can I make a foundation model that will be additive to what I've previously been doing just with the data I was getting on enzymes and so now I have a foundation model that really is is not specific to catalysis or anything else it like speaks protein you know right like just like gpt4 speaks English right that's what we're going to try out with Google that's what we think it's like a really uh new new idea and people you know there's people obviously working on it and obviously Google themselves with Alpha fold was like one of the first generations of this but we see a lot of ways to make it better and make it bigger and all the things and so we'll see uh we'll see how it goes let me just also give you like one other thing why I think bio is particularly interesting for folks that are interested in AI in general so this whole idea of like a foundation model plus fine tuning with specialized data right like we all like all those people that pay attention to AI like understand this idea right so let's let's just take it in one of the categories of English like legal right like Lexus Nexus they have all this data we're going to fine tune GPT also okay the that thing has to compete with like a lawyer at ropes and gray right and light up rub some gray has trained for 15 years uh you know being taught by other humans how to do law they are writing contracts that were designed to be understood by human brains they work the way we think uh they're writing that contract in English a language that co-evolved with our brains you know just language in general with our brains uh to also uh give us leverage from how our brains work and we're asking this computer brain a neural net to compete with us on our turf but it's a pretty high bar that it's got to compete with now let's go over into biology I remind you it runs on code sequential letters feels a lot like language it ain't our language right we did not invent it we do not understand it we do not speak it we not read it write it and so I feel like these computer brains are going to kick our ass a lot faster in this domain than they do in English right like you you know like I think the applications in AI are all going to be like replace the intern uh not not the partner at Rose and gray at best for a while whereas over in bio it should be it could quickly become the best if you're looking to understand like where AI is really going to flip the script and not be like kind of a low level clay Christensen style disruption which I think is sort of what's happening in English but rather be a like splitting the atom it's bio when people talk about protein folding related models you know into your point there's things like Alpha fold and there's a few new companies that have been set up to basically focus on protein folding models because of the breakthroughs in AI they kind of divide it into a few markets right they're sort of the Pharma Market which is better designed for pharmaceuticals and biologics that are used as drugs and then there's more of the industrial the catalysts the AG those sorts of things I'm sort of curious like how do you think about those relative markets just in terms of sheer Market size because if you look at the cost of developing a drug it's like 1.5 billion per drug but very little of it actually goes into the underlying molecule that's being used relative most of its clinical trials but for AG or for catalystra for other things a lot of it could actually go into the molecule so I'm just sort of curious how you view those as relative markets for this kind of stuff so gingo has like a animal right like you need compute I don't really care if it's you know if your AWS whether it's medicine or their startup or you're video streaming like rock and roll right so so we have an attitude that like we're supporting cell engineering wherever it is it is definitely different by market like pretty substantially right both the assets you need the Enterprise sales everything is different um and the biotech you know it's not it's not a small industry but it's it depends like which side of the house you're looking at if it's fees it's probably a little more the the more valuable markets that you could get more research fees but royalty is a different game right like certain things go to market a lot faster that's how I see it but the real problem is like there aren't any platform services so like the other thing that's just wild to people in the tech industry is like where's the fast platforms like where is all the horizontal stuff where's the operating systems and it's like nowhere like like vertical integration vertical integration vertical integration Merc Pfizer bear syngenta like every one of these companies is like its own top to bottom you know and the closest thing they do to anything vertical is buying equipment from the same people uh but like it's really fascinating like totally different industry structures and so that surprises people right Jason what's the most rational way to understand that because you you look at that as a non-bio person and you're just yeah it doesn't make any sense right so the rational a is the work being done cross product is too dissimilar to support common platforms I don't agree with this obviously my entire business model is predicated on that statement being false but that that is the reason you're like oh well because the the platform you build for customer a will never read on customer B and so now now you're just bad right like you're you're having to build a new platform for every customer you've got you know leverage you're getting none of the reasons that like operating systems and and data like why does data centers work because compute's really generic right and like you can use software to make it different but the hardware underneath is all common and now we're seeing a little Edge case difference here right like actually the CPUs aren't that useful for the AI now everyone's freaking out about the gpus so we're having like an instance of like Hardware variability but the argument would be that that type of Hardware variability you're seeing is sort of like per company okay right like you know or at least per modality and Pharma which modality is like a fancy word for type of drug right like gene therapy is going to have very different stuff than you know whatever and it's true to that right like it's not that's not a false statement it's just a question of degree and we happen to believe that when it comes to the engineering of organisms that that is at Parts common but plenty of people think we're wrong yeah it seems like if you just go back to this analogy of like human design computation where you're Building Systems from the ground up that you can understand but you're all discovering the same set of systems with common building blocks and the need for data analysis it would it would shock me if that would not eventually be true and it's a like a temporal cultural figment of these companies okay maybe one more general question about how to think about AI in in Pharma writ large why do you think we haven't seen AI discovered drugs yet because people have been talking about it for a long time will we and will we see it soon well and so first off I would say like the people been talking about for a long time is sort of like saying three years ago like why haven't we seen good natural language processing and AI people have been talking about it forever right so I think there is an element of like you need the Breakthrough right how's the neural Nets been big enough the big limitation in BIOS the availability of data to train these things right and so you have this tough situation where like everyone is doing these models of training on the same data right and so one of our advantages have a ton of data that's a real Gap that I think is I think partially it's like have people gone big enough for it to have happened yeah and I think I think it now people are trying like we're gonna try recursion is another great example and like yeah it might still not be big enough or more likely it's not enough data right and like there's nothing stopping you from making a giant neural net at this point you know like the tech industry is gonna commoditize that infrastructure but like you might not have enough data to give it to solve the problem you're asking Sarah right where does ginkgo's data come from is it like your own experimental data yeah we have a 300 000 square foot robotic lab that they uh haven't built in the last 10 years and so we generate that and we do it in service of our customer project we can do our own data generation but yeah that's where it comes from I want to talk a little bit about one area that Ginkgo has been an expert in which is some infectious disease right can you talk a little bit about the work you guys do here and I think a question everybody cares about is like are we prepared for another Global pandemic what has changed since covid-19 yeah I think like the reality is infectious disease is is really scary and bad right but like the the big the big lesson of covid is Modern Health Care Systems and our current infrastructure does not render us immune even in the developed World from pandemic scale infectious disease period we don't just allow ourselves to not have defenses against things that are like Society killers that are known to exist you know right like this is not like like you know a fanciful idea like it freaking happened two years ago right like you know and so the so what should we build right and and the answer is like a lot of different things right like we should build rapid vaccine response which is really good through operation warp speed and kind of what we figured out with mRNA vaccines and just like I got a Target I got vaccines for the entire country in three months not every version of this thing is vaccinatable the other one when we've been big Believers in is like monitoring like radar I grew up in Florida right like we have radar systems that warn us for hurricanes okay my co-founder Tom Knight obviously a lot older than me was explaining to me that when he was a kid they would get three hours warning for a hurricane currently we find out about hurricane kovid after it has landed in New York City a week ago okay like unacceptable right so so one of the things we're doing is like with the CDC we run programs we collect Wastewater from inbound airplanes and we sequence the DNA and we look for pathogens we look we monitor variants and all this both for flu and covet and I can add other things to that list we have a similar program actually in Doha airport and Qatar we've got a program in Ukraine and think of these like bio radar stations and that's get you Baseline because you also want to you look for anomalies right so like that whole thing has been missing and so we think that's like where you start and then you want rapid responsibility like basically patch think like cyber security like it should feel like that's your answer right like like cyber security I think is a bit the mental model for like what the future of infectious disease response looks like persistent monitoring rapid response kill it and the Beautiful Thing is and it's scary and beautiful remember these things replicate so if you can snuff it out at the beginning you win like speed will matter right I'm chair of a National Security Commission down DC um emerging attack and like the dod just put out their biosecurity it's like basically biodefense posture review and like the dod maintains like millisecond preparedness in this country right like I know that seems crazy but like that's kind of like how you ought to treat these things yeah you actually saw that with SARS right because SARS they both snuffed out with the original form but then it leaked four times in the first two years after it was cultured in a lab it kept leaking from what eventually became the Wuhan Institute of biology when they moved it from Beijing to one and it was really rapid response in terms of shutting down SARS outbreaks it really helped prevent it from spreading and so I think to your point there's good precedent in terms of trying to prevent spread and having it be effective assuming that the coefficients on the disease spread are reasonable yeah exactly certain ones are gonna be harder than others right but like it's not going to impressive you just need logic if you could get it early you win right now there's a question of how hard is that and like covid was a lot harder than MERS right because for a variety of reasons but there's probably a level of tooling that could even have stopped that could even like stop a covid since we had nothing when that happened sure I guess one of the things that people in the AI safety Community bring up quite a bit is that one of the big risks that are associated with the use of AI and llms and these Foundation models for biology is that there's some risk of alone actors somewhere deciding to to build a virus that is infectious and deadly and can sort of run through the population rapidly how much of a risk do you think that really is the idea that like we know how to like exactly like design for that sort of thing is low you could try something it's not like oh I know for sure it's just someone's waiting to do it remember you need data it's hard to accumulate that yeah it's easy for me to accumulate data on on enzymatic catalysis it is a little bit hard to accumulate data on case fatality right and it's very hard to do because the argument I always hear from the safety Community is oh the loan actor Will of course be somebody who isn't that well-versed in biology anyhow because the people who all versus biology are unlikely to do these types of attacks so it's it's kind of this really weird needle that's threaded in the community to try and make arguments that to your points seem to not really hold up relative to the reality of what's needed to actually pull something like that off at least today I would basically agree with that today the only thing I would say though is like we are unacceptably exposed to these things like we would not tolerate like in our computers our human defenses against viruses like we would not allow our computers to be as exposed to viruses as we allow ourselves to be okay I'm talking about like technological solution things in the background right and there's an entire edifice handling that right including like detection letting other nodes know all around the world all this stuff like like but all happening in the background that's what this should feel like if it's done properly so whether you wear it alone after not you don't have to worry about that Nature's going to toss it out at us again we should we should have it ready for that yeah I think you laid out like a really rational program around pandemic response and I think most of the the people lay out things that are I feel in some cases actually subtractive so I think your points on global monitoring makes a ton of sense your points on having rapid response vaccine generation makes a ton of sense so I think those are like really smart grounded approaches it's kind of interesting because one of my big lessons from covid when I looked at the biosafety levels that were actually enacted at some of these labs and you're collecting large masses of bat viruses right and I remember when I used to work in a lab at MIT I'd be working with different viruses and different agents and things like that for gene therapy purposes and you look at the biosafety level and there'd be somebody in a hood and then kind of rub their shirt and they'd walk out the door and you know one of the things that I almost get comfort in is I'm like wow there's been so few actual lab leaks over time relative to the poor behavior in Labs themselves that it's really hard to actually have something jumping humans well I mean to some degree We Are The evolutionary product of being able to defend against that stuff right like you know like if it was easy like we wouldn't have made it right like you know like there's other species that like didn't get our I mean didn't get the immune system we got and what you have in every organism on the planet is the integration of four billion years of incident solar radiation on the entire planet they've been trained over evolutionary time yeah a time that we have a really hard time comprehending how much energy that is because of the time scale yeah one of the things that I think is really unique about Ginkgo is some of the decisions you've made as you've built a startup so for example I believe you have super voting shares for all the employees as like a public company could you tell us a little bit more about ideas like that that you've enacted and how you thought about them it's really cool stuff yeah I mean we're kind of a weird bug right like we started out of grad school in 2008 right so like straight out of school total common attack not at all common in biotech okay right so we were like unfundable couldn't raise money it was 2008 and we weren't developing a drug and so like biotech people don't back that we're developing like a platform for programming cells and the tech people wouldn't back it because like what are you kidding me a wet lab and so we were like not fun well we five years of government grants DARPA arpa e and sfsbir so we were the first biotech to do YC because Sam had just taken over from Paul and he wanted to do nuclear biotech all these things right I do think like the entrepreneur energy is right is common so even if you're a hard tech company I don't think there's like a different you don't need a different entrepreneurial training than what like YC has perfected you know right like that that's a good thing so I think there you're okay I think we were like finding out too late that things like social networks and large scale Tech platforms also have enormous real world consequences but they were sort of getting a pass because they were in the world of bits and people are like yeah bits you know right like whatever it doesn't feel that dangerous right but once you talk about a drug you know you're putting a thing in a kid's body you know like like it's like a medicine you know like there's just stuff that's like you know you can't around with it right and so we're building a powerful platform again so one of the questions was who should control it how do you make these decisions about like who can use it like platform ethics stuff all the stuff that's now being talked about in AI because AI is finally making people be like a little bit like ooh bits you know right like maybe it is scary uh and so like it's just a little more at the front for those of us that have already been hanging out in Adam's World right and this is why people like I think there's like a hard time between Pharma and Tech in terms of cultural like not overlap because like the tech people feel like the Therapeutics people are like losers and slow because they're lame and not ambitious but they're all but these guys have clinical trials where people die right and so like it's being at the coal face of actually building things that really inflect on on people and the world in a way that's not second order like information technology creates a different kind of culture okay and so who should control that a platform that in if we're right and is it successful will powerfully read on people's lives right one answers like the founders right that's Facebook right like Mark's got super voting and then his kids do or some Insanity the other option is Capital markets right you know like BlackRock you know right Melody like like that's just every normal corporation that isn't found or controlled in Silicon Valley where like the voting Shares are majority held by arms length capital and they if the CEO is not doing anything they want they bring in a different board and fire the person but like it comes down to the control is is who has the share votes what is it in a public company ultimately hires a fire SEO ultimately then sets the control of the platform okay and so at Ginkgo we took the Silicon Valley idea of founder super voting shares and extended it to the entire employee base so it's not just us anyone who's at the company and it goes away if you leave uh gets 10x voting for their B shares versus the a shares which have one exploiting and so the way the math works is that the employees own more than 9.1 collectively multiply that by 10 and it outvotes the remainder and so the theory was who should control the platform humans okay not not divorced Capital because that's not their priority they're kind of like my job is to get a high return at the end of the day it's your job company leadership to like decide how to do it but in reality what that means is company leadership Primacy is the return okay and so that seems ah and then what we've decided is a persistent thing is the employees okay the workers because they are humans and they actually work there and they go home to their families on Thanksgiving and have to explain like why they work at this company and are proud of it and that that may be long term this is a theory uh it is a is a good group to give governance to yeah it's a it's a really cool approach yeah and I think a lot of the early sort of super voting share stuff was pioneered in the media world right so the New York Times the same family still controls it you know 100 years later because of super voting shares in the family and that's why it's the New York Times sure only reason that place is the New York Times is because like like humans have control not Capital so I guess one potential question about the model because I think it's a really smart and unique model is sometimes CEOs have to do really unpopular things and if you don't have the founder Authority and you come in and you do something really unpopular maybe you do a big riff and the people who are left are really upset about it but you really have to do it for the business to survive or you make tough choices that may be at odds with the employee base how does that impact governance later where to some extent You could argue the motivation if you're not really answering to your board you're answering to your employees purely you may fall into more Dynamics around popularity contests or trying to appease people around tough decisions to make and as a Foundry of the moral authority to do those as a Hired Gun CEO I think it's much harder I agree completely yep I I don't dispute any of that so and the answer is share voting okay so it's not like one person one vote sure so how do you accumulate more shares at a company work there longer okay to build the value like work there when it was cheaper and and grow the value so there will be a waiting against all Theory I don't know but like like what I'm imagining yeah yeah is you'll have you know employees who own a lot and are like yeah that's a hardest thing but it's right for the organization right and like I I don't think that that's Auto out of school I think you can see that happen but we'll see I don't know we're trying it yeah now it's a very exciting experiment that's cool but it originates from the platform governance that that's like what we're actually doing and I think some of these other reasons I think are interesting and I like them because I'm like generally likely in worker ownership but I think the real point is like at the end of the day someone has to have platform governance Jason you've expressed like this awe around the result of humans you know and four four billion years of evolution we're incredibly energy efficient versus neural networks everybody knows that you now have the very largest Labs talking about spending literally a trillion dollars in compute over the next decade if you think of that as maybe half energy and then you have to make assumptions about energy prices but now you're talking trillions of kilowatt hours and maybe you're off by magnitude but like where's that money going to come from becomes a big question these things have to get more efficient if you compare that to like humans like maybe we spend 5 000 kilowatt hours before we learn to read right do you think we get more biological inspiration AI do you have any point of view on the intersection of this from an architecture perspective I I don't have a great intuition um other than I think you know the other option is we just do giant braining of that and we throw that up against gpt4 you know like why are why are we just limiting ourselves to a brain that fits in our head why don't we grow a room-sized brain and just go straight biological yeah have you thought about that Eli I mean I think that what's cool about um neural Nets is that like brains basically allowed uh computer scientists to like Escape their world of like logic like back to the beginning of our conversation it was an excuse for them to basically build a piece of software that was gonna like they weren't going to understand how it worked there's probably a lot more things like that because the community that builds software wants to understand it because that's the kind of people that historically have been good being good at building software right so like open your minds right like like I'm sure the neural net is not the best architecture right but like you know and I know people are working on it but like really Go in different directions right like do something crazier you're raising a really key point which I think was back to part of the conversation around Evolution as the driver for all sorts of optimizations that you don't expect and if you are a rational person and you look at biological system right there's the gene that can be coded that you can produce RNA in either direction and it produce two different proteins and why would you ever do that and one of them is actually duplication of this thing that got repositioned for catalysis for this other thing and so it's really messy weird systems that evolved and I think the second you have self-replicating systems where you have code writing its own code and you start going down that evolutionary path you should have hyper optimization for energetics and for all sorts of other things because it's just going to be part of the utility function that gets selected for by that system and I think that's when you get out of the realm of you know hand-picked design and logic and laying it down and just like an explosion of stuff right it's kind of the Cambrian explosion happened for a reason and that reason was Evolution and resources right yeah there's a very good book um if you'd like to nerd out on this particular line of stuff uh called the plausibility of life um it's awesome and I personally think instead of learning from brains I agree with you a lot you want to learn from evolution like because Evolution itself evolved things to become more evolvable right like the example they give in the book is really cool I'll just saying it's like uh skeletal system so your skeletal system like you can have a person you've seen like that can have like a six finger like that happens sometimes right have you noticed it's not just like a bone jutting out of their hand it's like wrapped in skin and nerves well that's because your skin and nerves are adaptive to bones and that as you can imagine for exploring body plans is a much more efficient way to explore the space and body plan so if you jut out a bone and maybe it's going to be better but it's definitely not going to be better if it's not wrapped in skin okay and so like that for what however it happened there was like this layering in evolution where like we created the the system created skin and nerves to be adaptive and the exploratory part of it is the bones and again I'll emphasize four billion years it's a long time a lot of energy that has been spent I know it was a random walk but like Evolution figured a lot of cool stuff out and I I think that is like totally untapped for any of the scientists exploring alternative architectures out there if you're like gonna do any sort of crazy mixture of exports routing like wrap the skin around the bones is the advice we have from Jason thank you so much for doing this this is really fun yeah super fun thanks Sarah thanks Elon find us on Twitter at no Pryor's pod subscribe to our YouTube channel if you want to see our faces follow the show on Apple podcast Spotify or wherever you listen that way you get a new episode every week and sign up for emails or find transcripts for every episode at no 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