Season 3 - Ep. 3 - Productizing R&D w Juan Benet

Transcript

[Music] welcome to the green pill. network podcast if you are just joining us we are building a coordination a network Society of thousands of hackers dreamers and doers focused on using crypto to bring positive some digital systems to the world this podcast features the people who are doing it and we publish a season of five to 10 episodes every couple months this season is about impact how can we manifest the meme of impact equals profit how can we Elevate the most interesting experiments and ideas in Impact tracking how can we build cryptoeconomic systems that elevate impactful Builders and how do we move from virtue signaling to cryptoeconomic proof of virtue these are the themes of green pill impact season 2024 another experiment that we are

doing for impact season is allowing you to collect this episode so this episode with Juan Benet and all other episodes from this season will be available at pods. medgreen pill and you will and if you go there you will find the episodes and be able to start collecting them as nfts if you want to learn more about green pill. Network visit our website at Green pill. Network where you can download the green pill book for free join the Discord and become a member of your local green pill. Network chapter okay with that intro out of the way I am very excited to introduce today's guest who is Juan Benet the founder of filecoin and the creator of ipfs we are talking about productionizing R&D so

basically how do we get through The Uncanny uncanny valley between research and actually productionizing and scaling new technologies onean is a builder who has felt this acutely through his creation of ipfs and as the founder of protocol abs and filecoin we talk about Juan's early journey in starting ipfs and trying to figure out how he was going to fund it he looked at tech companies he looked at government grants and we talked about The Uncanny Valley in between creating ipfs and actually productionizing it and we talk about how the incentive field is just sort of broken in between the creation of new technology and the producation of productization of of new technology I saw Juan talk about this at devc connect

and I knew that during that time I had to have him on green pill impact season to talk about this problem we talk about how the incentives are broken we talk about how the existing models like IP uh do not work and how New onchain Primitives like hypers certs and and other things could be used in order to bottle up those Innovations and help them cross this Chasm a little bit more reliably then we talk about the upside for Humanity of of that happening what if uh crypto grows to be as large as nation states like the United States and is is funding public goods and is funding research at the scale that nation states like the United States or China are doing and what would that mean for the creation of new technologies if

we're able to solve this problem so onean is someone that I've long looked up to he's long been a thought partner for me in creating more regen protop and world and uh I can't I think it can't be a mistake that we're doing impact season here at uh at green pill and that Juan has been thinking about the exact same themes so I think you're gonna enjoy this episode without further Ado I give you Juan Benet so great to have you again Juan um so we wanted to talk about the R&D pipeline uh between research in science and productization and can you tell us a little bit about the work that you've been doing there and the thinking that you've been doing there yeah so um probably it's what it's maybe the thing

that I've been most obsessed by in the last 10 10 12 years um trying to understand why even though we have like this amazingly Advanced civilization and we are doing so many amazing things all over the place why R&D is proceeding so slowly um like are a lot of ways that you could measure this but and there's a lot of people that have talked a lot about this um and you know and and at the same time like we're can look around and be impressed by all the new things that are coming out but um by a lot of measures you can you can see that um humanity is just not innovating nearly as fast as we could be um and things are getting stuck along the way in the in

the process and so um took me a long time to kind of like PE like understand all the different layers and piece together like the um issues and and kind of and so on I used to kind of think that the big bottleneck was in science funding where um hey there were like these massive corporations that were able to to develop a lot of Technologies and build a lot of products and had a lot of capital um but a lot of the funding wasn't kind of like flowing all the way into into science funding and basic research and so on um and after like you know that deepen into into this and um and so on I realized that wasn't quite the case like the the bottleneck is not in science funding it's actually a little bit further Downstream of that in what um the people at the labs used to call

fundamental development which is when you take some um early uh research uh on some kind of basic fundamental concepts and you then start Translating that into what are the potential possible applications that you could that you could develop um what kind of things could eventually turn into some Innovation that could go into a device and then you know build a product and so on um and it's in that area that I think like the biggest bottleneck is uh today um like if you I've since kind of structured this in terms of a of a of a pipeline like a like broader R&D pipeline um and you know this is of course like many institutions have modeled it this way for for a very long time um and I kind of landed on maybe five stages um kind

of like early basic research um fundamental development which is kind of what I was describing is where the bottleneck is um productionizing and this is a word that I borrowed from B and and any other groups which is kind of like where you go from like concrete devices that um might work but like you're you're trying to refine them into something that could eventually be put into a product um and and and kind of figuring out what the product is going to look like and what what is the product that you're going to be uh manufacturing at scale or something like that or delivering to the world at scale um and then after that like you go into production which is like okay you you sort of like know what the product is now you need to kind of figure out how to produce it at scale um

and and give it out to the to the whole world uh and then after that there's kind of this broad word that I uh that I describ as scaling which is like you now have a product in the market you're uh delivering it to users and you're now kind of like trying to compound that and and grow into a pretty large uh scale of benefit for for everybody um and so this whole pipeline people tend to think of R&D as maybe just the basic research part um but in reality um if you think of most successful R&D institutions they tend to think about R&D as the whole thing like uh R&D is the process of of um going from extending what you know and doing like the basic fundamental research to

understand more about the world like the science part and the translation of that science part into concrete devices and technologies that you can then put into products and then sell into the world at scale there was this old description from the labs that if you hadn't sold a product at scale you hadn't yet innovated there was a very strong uh connection between um actually delivering the products of some some new uh scientific breakthrough uh into the hands of people as the measure of whether or not you actually had to come up with something significant um because there's so many like easy ways of figuring out new discoveries and and new con conceptual advancements but if you

don't kind of connect them all the way to something that is actually tangibly getting better around you um then then you know you haven't quite realized the impact of that Discovery and you haven't been able to like compound the return of that of that Discovery meaning you I tend to think of Science and Technology as two sides of the same kind of integrated process which is not one part is extending what you know and the other part is extending what you can do um and of course you one feeds into the like they they feed into each other U but you kind of need them both and they're both parts of of the same broader thing which is kind of um as a species we're getting to know more and extending what we know and so on so this kind of like R&D pipeline that I've been um I guess very

obsessed by in the last uh 10 12 years um I've kind of like more recently started to uh think through each of these stages and like what are the bottom legs in each one and how how could we measure how well each of these are working um what are the blockers in each one of these and and whatnot and um you know I could probably you know uh spend a long time talking about this but the the kind of like uh summary uh statement is that we have like this huge funding Gap uh in the fundamental development stage where uh down like further Downstream of that stage when you get into like productionizing and production and scaling um you have the broad modern Capital uh structures with large

technology companies and you know and the startup environment and um and venture capital and um and so on like all the systems are like working pretty well like of course there's problems but like they're working pretty well at um generating new uh Innovative products and and generating new technologies and developing them into the market and scaling them and though of course there's bottlenecks and it could all be working better there it's working well way better than the fundamental development piece and likewise in the earlier basic research stage um though of course like there's huge you know funding problems and so on science in general tends to tends to get a lot of funding from a lot of the world uh there there are large

amounts of money flowing into scientific research in a bunch of different area so there is a lot of basic research being done um there's lots of lots of scientists um producing really good good things and in reality my view is like right now we're getting this pileup this like buildup of basic research discoveries that aren't translating because we don't have a good incentive model in the fundamental development part and it's leading to this like um you know kind of what people in in in deep Tech startups send to call like the value of death where um there's basically like zero funding like it's way too early for VC to invest in so like kind of like the traditional Capital structures don't work there um

and it is way too late for any kind of scientific funding to to um you know kind of like scientific public goods Government funding to um uh to support you because you're not going to come up with some conceptual Discovery so so it's not there's no like Grant money for you and so that value of death is like where most things get stuck um and so that's the area that I think is like most important to uh fix and I think you know there's lots of different ways of doing this but um one of the basic areas is like figuring out better ways to Route Capital into those areas in a way that could be that could be compounded right so I think um uh I just there mentioned kind of two incentive Fields like one is the the capital incentive

field which is how companies and and you know how startups and tech companies operate where um they kind of hook into like the rest of capitalism and they create a you you create products that um build on some technology and those products get sold into the world um and they're able to kind of operate in that that broad incentive field of uh if you produce a good product and you can sell it at scale and you can make some profit then you can compound that um and I mentioned this other incenter field which is kind of um scientific funding like public good scientific funding coming from governments or philanthropists and you know Brad donors and that was kind of you know invented in the early 20th century um in order to I of course there were like

other versions of this and scientific grants going back you know hundreds to potentially thousands of years depending on how you want to look at it um but for the most part like the modern version of this was really built in like the in the um early 20th century um going and really kind of got redefining the around the world around world to and you know the the set of like scientific funding institutions that came out of that especially in the US but also in Europe and a few other places um uh that entire system got designed as governments need to um deeply invest in basic research and funding of science so that society and civilization can advance um and so that that created this

additional um incentive field it's not connected to Capital in the same way it's connected to capital capital in maybe like a second or third order um link where if the entire country is doing better and like the whole GDP of the country is doing better then maybe the budget of the country will be uh higher and then maybe then the funding flow to science can can be larger um but it's not kind of like directly connected in terms of like Capital instruments um but then kind of fundamental development doesn't have any of these like uh it's way too too early for the traditional normal Capital incentive field to work and it's way too late for the for most of the government funding to to hit and so this is where like we really need new incentive structures we need new instruments to um that could be used

here um and there's lots of different ways that you could you could approach this like one is by dragging the the kind of like Capital incentive field earlier by creating new um investable assets that might um might work uh the other path is you could try to stretch the fund scientific funding field by trying to give scientific credit for the fundament fundamental development piece and creating like a very strong um you know Association of like uh rewards there um or you can come up with like a totally new incentive field in place where like you could generate other kind of ways of rewarding this uh traditionally the intellectual property um incentive field is supposed to be there but like it it doesn't work very well um sorry I just kind of like uh

went super deep and uh talked for a long time so I can pause here that's great maybe I'll try to say some of that back to you just so the the audience can hear it twice so um there's kind of like a inverse bell curve uh you if you look at the amount of funding as the Y AIS and then the stage of production as the x-axis uh stage of production going from R&D to productionizing to production and scaling um you've got a lot of funding from governments and nonprofits to incentivize research in the research stage at the end stage you've got VC funding you've got tech companies you've got the private sector which is really good at using markets in order to fund stuff but there's kind of an uncanny

value I think you called it a a valley of death in between those and and and it seems like uh the translation between government funding and and private funding like those two things are a little bit incompatible with each other we don't have good structures for bringing things across that uncanny valley and uh there's another challenge presented by the fact that there's just not a lot of funding for those in between stages so we we can either come up with new vehicles to help people cross that Chasm we can stretch govern or nonprofit funding out or we can bring uh Market funding earlier that's maybe reductionist but that's that's a lot of what I heard there yeah totally and and

I think um uh it's kind of like interesting why each each of these fields doesn't extend all the way so like the The Venture Capital field doesn't extend all the way because um that earlier part in fundamental development is to too early by by kind of VC standards too risky and what that means is like it there's probably a host of different reasons like one is this area is like so early and and removed from a product that usually the work done here tends to be pretty different from the work done in defining the product and defining what eventually turns into a startup so the people might actually be different meaning like in

the traditional way that startups get built which is kind of like you have an early founding team that's gonna go build the whole thing and scale it um this is probably the area where it's you're going to see like the like most more turnover in terms of like the people that are going to be really good at fundamental development might not be that good at producing the actual products and scaling them another reason is the funds that the typical VC fund has like a 10 year 10 to 15 year time Horizon um and that is basically just enough to get you through like the productionizing pH like productionizing production and then scaling um and then going all the way back to fundamental development might require VC funds that that last like 20 to 25 years so if we

had if the Norman VC was to have 25y year funds then we might actually kind of cover this whole field and um not just the length of time but the size so if you want want to um in kind of like dealing with VC portfolio math like if you want to invest at that early stage you um you have a much higher likelihood of failure like another order magnitude or more so that means like you need a portfolio that's a order magnitude larger and so that means you need that many more Investments and that many that much more funding so you deal with like then VC funds that are like in the single to 10 billion dollar scale and we just don't have VC funds like that we have single billion dollar funds and

those are about as like you know maybe the largest one that I know of is like four billion five billion or so um but you you know it's not the norm to have like 10 billion to 15 billion do scale BC funds and so you the VC in general can't quite stretch all the way to the there even though some of the best startups in history have been founded in that part right so when you think about like maybe apple or um SpaceX or um you things like Genentech and um maybe Fair than Intel and and so on like some of the most important technology companies ever built had to go through that fundamental de development stage and if you kind of like were a a an investor that could invest in them at that stage then you could

get some of the best returns ever to to Really you know create the this this structure um but in general today the the VC kind of model and the VC game as as it's kind of built out can't really afford to invest in that stage even though some of the best ARS will come out of that um and so you know the typical description here that you know many people have probably heard is like VC can't invest in research projects and really what they mean is like you know um get through the research phase get through the fundamental development phase come to us once you can kind of configure out the product and and you're there to scale it because that's really like what VCS can't afford like they can't really afford to um to invest earlier and then kind of likewise in the in the scientific credit side um

scientific credit tends to follow very significant conceptual breakthroughs that are going to be field redefining that are going to enable a lot of people to look at a a a at things you know significantly differently things that maybe generating an entire New Field sometimes or that just become these like Breakthrough ideas that kind of move the thinking forward for everybody in a particular area and they scientists in general tend to kind of disregard or undervalue um like derivative work work that is kind of already building on important conceptual breakthroughs that already happened and where you're really just kind of optimizing something where you're just kind of changing it a bit

and and and reshaping it so putting it in R&D terms this means like the the the conceptual breakthrough of making the transistor from like the very first one like the actual like first physical device was an amazing achievement that you know scientist around the world were really Amazed by but doing the work of like deciding whether you should use like one material or another or like whether you could like make it twice as small or 10 times as small or whether you should like use this particular refinement process versus another one like that's just not that interesting to most scientists and so it tends to not carry the level of um scientific credit or scientific reputation and so that means that in general the scientific um reputation world is not going to care

about those kinds of Innovations nearly as much and so you end up with this like huge drop off in the in the um in the scientific credit field and so that means like and the weird thing here is that by my accounting like the fundamental development phase takes about 10 to 20 times more work from from scientists and Engineers than than the basic research part and so we have an area that like takes way more time and effort that is way less funded and so this is kind of the big bottleneck that is holding back the just our civilization ju I'm curious if you could pinch and zoom a little bit as to how that would apply to protocol development and maybe even kind of looking at

specific examples of ipfs of the PDP and how you might P that forward so in software we are very lucky that you know experimentation is extremely cheap relative to most other areas right so already we're in a we're in a in a landscape where the entire R&D pipeline is way shorter you can develop new ideas in in software and get to you know do do the research the development the productionizing production and scaling in like a few years relative to you know in Hardware like if you were trying to make a fusion reactor or you were trying to do like create a a uh find a new drug to cure can a particular strand of cancer or something like that you're looking at

like 10 to 20 years um or more and uh and so in software already like you're you have like you're play playing on easy mode you're able to like go much faster and even then um when I was first making IPS and and um I like wasn't trying to decide like should I go do this in like a tech company should I make a startup should I like make it as an open source project uh kind of like work nights and weekends and you know have a day job somewhere else um or should I go to like University and do it as a PhD project and like the Outlook was Bleak even for an open source project like the Outlook was terrible like in the in the science side I went to talk to a few profs and like the the overwhelming response was like uh kind

of interesting pretty derivative like maybe this part is interesting and maybe you could write a paper about that but like not quite enough for a PhD thesis so like go do something else um and then on kind of like the company side like just so difficult to try and convince the large company that to go and invest in something like this to like develop it as a as an open source project um and you you kind of like would give all the autonomy of the project over to you know kind of like whatever is like the corly uh benefit for that particular team in that particular company so that means like most likely to be cancelled um right and so um that really left like trying to create a startup or R ipfs um and that requires finding a monetization path and this is kind of like where f

one came from um and which I kind of like wanted to have a separate project for a bunch of reasons won't get into into that now um or to then do it as like nice and weekend a separate project and doing it as a side project um that just kind of consigns Open Source to go a lot slower it it just means that like you're you might it might take 20 years to get somewhere so like this was what uh many gr open source projects have done that and have become really successful but have taking a lot longer to get developed and so I knew that something like IP Fest was going to require 10 to hundreds of people working on it uh to really get somewhere and so that just really left the route of like okay great I just have to go and figure

out how to build a start up but even then um the like I had to do a bunch of work individually to find like a really solid monetization path and something that was going to be much closer to um production and scaling and something that like um was going to make a lot more sense than say something like ipfs that is much more fundamental as a publica could um that is was kind of very far away from from um for monetizing right and so that and and kind of like I think like I was already playing on easy mode relative to like many other worlds um and even then there was just like there there's no no no group that you could go to and try and get like some easy funding quickly um even kind of like some very enlightened

Grant institutions that could have given money for this like their application processes last months to you know quarters and so you know it's just completely different than the start startup landscape where you can raise funding in a week or somewhere between a week and four weeks um and so that's just kind of a totally different different landscape and so I think if we want to get really serious about funding massive scale public goods we need to have vast amounts of capital to flowing in very short time scales and we need uh and and that means like days to weeks not you know months to years um and we need extremely good measurement systems to figure at like where are we doing a good job uh and so that we can kind of

like do the capital allocation correctly um and that's one of the key things with all of these systems like the systems that succeed have an evolutionary structure to them where you can measure their success rate and you can um over time like different rounds um re allocate better meaning the VC system works because it's an evolutionary structure where um if the VC fund is getting kind of bloated and making bad decisions it's going to get replaced by another VC fund that does better that does a better job um and you have like you know thousands of VC uh institutions competing with each other trying to find the signal as best as possible and so in general as a as a distributed algorithm it like works pretty well at finding the

signal um in Government funding it's like not as clean as that and not as good as that um there are all kinds of like problems in Government funding that stem from the fact that that you have like these Mega institutions that are that are allocating grants individually uh you do have a lot more visibility and control and ability to invest in long-term projects that maybe short-term oriented profit-seeking entities just can't um but you have like all kinds of bias structures that end up preferring to fund mainstream type ideas and tend to underfund like really amazing breakthrough work that just isn't popular at the time um examples of this might be like nanotech or you know the

fact that Fusion like was was had such a big price stag that nobody wanted to do it for for so long um or where like if something is kind of politically difficult like biotech or longevity or cryo then you get like zero funding um and and so it just becomes a huge bottom L to fundamentally break through work and so you you know you get this problem there uh it's gotten better over the last 20 years and that now we have many more governments around the world taking the R&D uh funding R&D much more seriously um but it used to be like primarily the US right so in in the latter half of the 20th century most of the scientific funding was the US and a few of the European countries since then

we and Japan Japan and and South Korea um uh as well but like for the most part um not that much in the rest of the world and um and now we have like a much better spot where uh there just really are many more countries doing massive scale R&D uh especially China China become like one of the largest funders of Rd in the world um and so that's I think extremely good for for the world because you you have many more institutions trying to do many more kind of this kind of funding and and they themselves are embedded in this evolutionary structure where like the better they do the more they're going to succeed the more they're um they're going to be able to compound those returns into into funding more um

ideally we would love to I would love to be able to like do this kind of thing in a in a broad International like you know not kind of associated with a flag type of um uh public goods oriented funding structures for for Humanity in general um and I think this is where like I find crypto just so exciting because like we we could generate new funding structures new instruments new mechanisms for deploying massive scale funding into these areas at the scale of nation states it's one of the things that I find just so amazingly um optimistic about this industry is like when you look at crypto networks they're within like some of them are already about as valuable in terms of the the total like market cap

and so on um and just the value they you create as small country uh economies and they're like within two or three orders of magnitude of being the scale of large country economies and so that means that like if crypto just Grows by two to three ERS of magnitude in terms of the value it it organizes um we would be able to then fund R&D at the scale of nation states and like that's enormous like that we're talking about like a a world shifting moment like if if a crypto Network like ethereum or fcoin or or whatever is now suddenly funding R&D at the scale of the US like that that changes the entire you know kind of infrastructure of the world

um and especially if we can kind of like do it really well across all these stages and not end up with these like huge bottlenecks in in the middle that are hard to kind of work through and so this is why why I find things like all of the work that we're doing around public goods um whether it's like the broad field building or individual instruments like hypers certs or you know credit attribution or or or other specific like components um just so exciting because like if we can get all these um public goods money Legos figured out well and we know how they work and we can measure them and we can scale them um then as crypto broadly gets more and more successful ful then we can route vast amounts of resource allocation budget into provisioning like massive scale public goods around the

world which I think is like a super optimistic vision of the future yes so I'm wondering one what Primitives you're really excited about um it seems like the the job to be done is sort of to connect the impact or potential impact of the research stage all the way through to the pro producti productization and scaling stage although it's very reduction what what Primitives are you excited about how does crypto change the game in the broken incentive fields in order to uh to change this for the better if if it does at all yeah so I I think it totally can and I think um maybe I'll mention a few that I'm pretty excited about so uh maybe I'll start with like the more um simple ones and then scale to like the weirder and more interesting

ones so the simpler ones that like just straight up better grant funding structure so first off what gatecoin has been able to do um and in just figuring out uh ways of getting a lot more participants uh work deploying um Capital together um in a way that more closely aligns with the with the goals of communities is like super super valuable making Grand processes just work more efficiently work faster um reach larger scale already massively valuable um I think there's a bunch of interesting work to do there on like um how do we couple Grant processes to some kind of sustainability structure in mind so um I

think there's some groups that are starting to think about like Grant programs coupled to investing funds where you have a lot of public goods grant funding happening but then if any one of those projects ends up generating some value that could be captured in some way then some amount of that value should flow back into the pool uh anything those kinds of instruments uh will be pretty interesting I think there's um the there's like the this concept that we're kind of experimenting with called like um we call like impact funds or or Brew funds where um it's kind of the analogous structure to a VC fund but oriented for public goods funding where instead of kind of creating large Grant

making agencies where like you know you have a large treasury allocated by a small group or or even by a large community voting through you know qf or something like that that um what if you could have like an evolutionary structure in there where you had a like a a much larger number of smaller Grant makers that were competing with each other um and I think that that is like pretty promising as a as a mechanism to be able to like have some evolutionary Dynamics here of of of getting better and better signal of like really making sure that people are are uh competing with each other to do the best job possible um then uh I'm really excited about hypers search specifically um because

they give you a way of uh describing the credit of having done a bunch of the work which gives you a composable structure that works across all the stages right so you can use hypers Sears for any part of the stage and so for fundamental development you could like use hypers for that work being done and you can retroactively reward and and buy up those those uh those units what we need in order to make this kind of thing work is we need like large volume hyper SS buyers on the other side right so um the the uh uh we we can scale this if we can get large amounts of capital flowing into buying the hypers source on the other side of a particular stage um I think here here's where I think it'll be

harder to convince people to buy hypers search for fundamental development in the early stages I think it'll be easier to convince people to buy hypers search for things like green um refi oriented um uh like uh so work in for example like carbon credits are are a special case of of a hypers cert um and so I think things like that that are more tangibly connected to um some broad-based impact on something that a lot of people recognize are going to be easier um and so I think simultaneously that hypers Sears are going to be super valuable in this to solve this problem but it might be harder to convince a lot of people unless maybe there's some governments that are that kind of get it or some corporations that sort of get it

um hypers have the ability to connect conect IP to them so maybe and this is one of the things that um that one of the yeah the person that invented hypers Sears um uh thought was going to be like a like a really good component here is like if you can associate the IPO generating the invention in the fundament fundal development part then maybe hypers Source can be like a really successful piece here um I worri that that um that IP in general just isn't a good mechanism today for small scale Innovation like if you're kind of coming up with some improvements along the way you can't really use patents or IP as a way to like um generate significant income as an individual like you know how many inventors do you know that whose like

main full-time job is inventing and they kind of like file patents and so on um I think maybe we could like fix this if we focus on fixing the IP field itself so you know one another idea here is like let's go and rethink the IP transaction and come up with better markets and better mechanisms for selling them I tend to think of this as like the the type of innovation that Airbnb did so Airbnb was like a a a Marketplace Innovation that recast and reshaped a particular transaction um so there was like there were people's rooms around you could go and stay in people's houses the way that that transaction worked was like fairly difficult you had to know somebody you had to like know enough about their place um you had to like figure out if they were in town at the

right time and so on and they kind of like just reshaped that entire transaction by enabling you enabling you to tap the entire network the entire Community um they they just kind of like reshaped that how the transaction was meant to work and then they opened up like an amazing new Marketplace uh this kind of thing could happen to IP by removing some of like the big toxic Elements which is like you know it shouldn't be possible for the owner of Ip to kind of prevent the entire world from using this and like you know from saving like thousands of lives or millions of lives which happens today right like today as an IP owner you could prevent theoretically legally you know governments from using this to like save lives which is like crazy to me like that's that just should not be a

thing that you could do um you do need like some Fair compensation uh in order to make the entire R&D process work but instead of that being like a an asking price maybe IP can turn into you have to describe the value of the thing similar to real estate maybe and you have to pay tax on it or something like if you want to have IP maybe you have to declare what the value you think it is and maybe you have to pay taxes on it and um maybe like harborer saxs type structures like anybody could buy the IP from you for that price at any moment in time and maybe that kind of thing can make IP as a system like work better so I think like this an area where if we got like a 100 to a couple hundred Builders kind of

experimenting in IP like maybe we can generate a dramatically better better structure um one other thing that I think is like heavily undervalued is just basic credit attribution linking the thread of invention so this is something that we explored in The Source C project um in 2018 2019 which is like um and I think there's a lot of good ideas there that that it's probably time to like revisit that and try try it again um this idea is like if you can describe the credit that went into producing something and you can figure out the transitive dependencies there like you know what scientific results built on what other scientific results which usually gets tracked in the scientific literature um reference graph right like one paper sites another paper

or you know one paper sites 10 papers those 10 papers site you know 100 papers whatever you can figure out like that chain of of Discovery but you also crucially U map it to the development part you know like this GitHub commit um Builds on the work this other important work um and or this GitHub project has this other set of dependencies and these other GitHub projects or these GitHub projects like depend on this set of scientific results like if you can kind of Trace that credit attribution across the through line of all of this work then that gives you the ability to do funding a totally different way which is like you can just pour money through that entire credit attribution graph and reward all of the participants that were

involved in producing that work um which is gives just gives you a very different way of doing resource allocation right at the end of the day like transactions today and in you know whatever economy you want to think of are just inventions and there are technology that we put in place and an operating system that we put in place because it like works well enough and so like this kind of different way of doing it could you know do dramatically better in certain areas um and I think like that you know I think there's like low hanging fruit there of experiments that somebody could go do that could like try it out so for example today you could take the entire scientific graph um and all the references and instantiate a um website where you for each of these you create

like the credit attribution graph for each of the papers uh linking to every other paper or whatever and right away like you could have a a little credit attribution network of all the scientific literature in the world and you could start flowing funding through it like no there's nothing preventing you from doing that today that could be huge be so simple too yeah so powerful so yeah these are the kinds of things that I think like are pretty promising in the in the R&D R&D space um amazing hey real quick admin note Know Carl you have to run in two minutes Juan are you good to keep going or if you have a hard stop also we can we can head for the landing yeah I'm gonna to keep going but I also um I've talking a lot

so and I haven't been self-reflecting on what the how the no it's great you're you're dropping knowledge bomb good uh Carl you want to maybe lead the episode for three more minutes while you're still here yeah I have um one thing that's been in my mind hearing you talk about the importance of creating composable funding is how do you generate the initial demand signal for these things like where where is that going to come from and if you think about like bootstrapping any of these ecosystems and creating an impact market around a new type of behavior that didn't exist before there need to be some buyers that are going to lead and create that very strong initial demand signal so where do you see that coming from yeah and in this are we just specifically talking about hypers Sears

or just more generally uh um I guess more generally but we can use the example of hypers certs yes I think I think in the hypers case um the problem gets easier because um hypers have this really really nice property which is like it what I think is just so fundamentally amazing about this Innovation is like um it lets you bottle impact the same way that currency bottles utility right so so if you think about utility uh it it's just like the ability for somebody to get some benefit over something right like that's very vague what the hell does that mean well it means if I am thirsty and I can get a glass of water and I can satisfy some

need then I got some utility out of that water or if I'm hungry I can get some food or if I'm cold I can get some clothes or whatever um and and that utility is not the same all over the place right like different parties have different needs at different moments in time and even though those needs are somewhat similar in some cases and there are broad clusters of activity um we found a really neat way of like making the whole system work by bottling the utility into this like um corresponding interchangeable thing of like the currency units uh which is I think like pretty pretty um um pretty pretty neat impact Sears uh or you know hypers Sears give you the same structure but with um

with uh impact where like you can now bottle the credit of having done something and even though there are there's different clusters of parties that care about different kinds of impact you can take those clusters of people that care about that kind of impact and and create specific markets for those people where they can transact in that in that work um so for example with like the green type credits where you have um where you have communities that really care about um improving the environment in some significant way whether it's by um fixing some carbon pulling some carbon out of the atmosphere atmosphere and fixing it or whether it's by preventing carbon emissions in the first place or whether it's by planting more trees or whether it's by um reducing the um you know extension of of certain species like you

can group and cluster people and organizations and and institutions and governments into parties that care about those things and generate a market around that area and so for example like today we have already impact certificates for fixing carbon known as carbon credits and there's very large very robust markets already working there um and likewise we can generate other markets in other specific areas so I would like really think through the how to construct that market from the GetGo like how many institutions and groups and people both are there to do the impact and the work required but are also care so deeply about that work happening that they're very willing to

buy the but buy the impact certificates I would stay away from markets where there aren't very clear buyers yet you can maybe tackle that but if you like really really care about that work but you you you will have to do a bunch of demand generation initially and and I think that's going to be harder than instead finding some area where there is already a lot of interest and a lot of demand and you just have to change the the the structure meaning you have to convince them to use a new transaction type in order to get their the work done and in order to achieve the impact that they care about but they already care about that um I think it's going to be harder to get people to care about something new that they didn't previously care about and and try to then also in addition to that get them

to spend money trying to cause that impact and so I would first start by like broadly looking at Broad categories of impact where there's already massive amount of interest in getting the work done but there there are in deep inefficiencies in how that work is being done today where we could show up with like a concrete hypers Market that could solve that problem for them and and yield a higher efficiency system that makes it all work better and I think like I would kind of prioritize the the I would kind of like approach that that way I think more generally for other mechanisms I would kind of like still follow the same idea that like you want to go for um groups and groups and of entities where whether it's individuals

or or institutions already care about that work being done and you know create that mechanism for them as opposed to try and to draw attention to something new that they didn't previously care about um maybe an example of doing this really well is patreon so I think like patreon not is is like interesting problem in the internet age where we already have the coordination technology to be able to like allocate and group together a lot of funding to be able to fund causes um and you had already deep interest in a lot of like nuance and Niche uh areas of of uh art creation and you just didn't have a good way of

connecting the people that wanted the art to the people producing the art and they all had to go through these like Mega um you know labels like Mega processes like in music for example it would be like the record labels so that means that the record labels tend to prioritize what what is broadly interesting to you know the whole world and like you know things that are like heavily marketable and instead patreon kind of like created this environment where a community that really cares about some particular type of art can just fund that art period like they don't have to go through any any other party um and that was like a like a really really good Innovation but it but it really already it was built for communities that already existed meaning there were already artists trying to create that

work there were already people consuming that art that cared about that work and once the system was put in place then of course it could like extend and support new kinds of Art and new new kinds of artists that maybe otherwise wouldn't have ever considered um taking that career uh or taking that path um and it definitely helped Maybe a lot of people discover art that they didn't know about but I think broadly the the the kind of structure of the market was already in place it just needed a new transaction type and so likewise here I would like encourage finding other structures like that where like there's already you're really just finding two sides of a market that are not well connected and connecting them better um and creating new markets from scratch I think is much harder um it can also be done to maybe

like to offer some more thoughts to people trying to do that I would maybe start by figuring out like who stands to benefit from something happening um who are all the parties that like really really stand to benefit from it from it and figuring out how much do they know about that benefit like do they really do they understand that they sent to benefit a lot from it do they understand that they um uh that they need to take part in provisioning for that benefit to happen and then at that point you can kind of like um propose a transaction structure that they then can fund the thing so an example of doing this is like all of the work from climate scientists and policy makers in the you know 80s

90s 2000s in telling the world about climate change and global warming and um the massive disasters coming ahead in order to get everybody to understand the problem scale um so that then later all of these groups could actually do the work of you know kind of unwinding the the the climate problems and so like you couldn't have a carbon emissions Market before doing that work work and so that was that's an example of a market that like had to generate all of that awareness and interest first to then be able to like create a transaction type but that takes a long time um you know and and even today like there's still an enormous amount of you know crazy there's still an enormous amount of

controversy over this even though there's like incontrovertible evidence on on like all kinds of measurement instruments um you don't have to like try very hard to like convince yourself um and but but it's just kind of like an example of like how difficult it can be to to coordinate a lot to to cause the world to care about something that the world didn't previously care about um and so I think it's easier and faster to go after things that the world already cares about and help them structure it differently and better and then later maybe you can go after like the things that are that people don't yet care about right I'm wondering um you know if if we're maximally successful in solving this problem in 10 year in in 10 years what does the world look

like maximumly successful in 10 years um I think if we can be maximally successful in say like the next two three years will then um have pretty well structured pretty robust um mechanism for for Capital allocation uh and C you thinking about like the R&D pipeline specifically or just broadly other public goods yeah let's let's stick with the R&D pipeline specifically yes I think in 10 years um if we had a much better way of flowing value to the fundament fundamental development part I think in 10 years realistically we could scale crypto by I

want to say like two magnitude maybe three but but it but it so that means like that would put crypto in the mediumsized medium to large siiz country scale of economies um and at that point we could be looking at funding fundamental development in the scale of like single billions a year maybe 5 billion a year um I think five more years after that we could scale to the scale of major nation states and think at that point we could be we could be realistically looking at funding R&D at the scale of like you know 50 to 100 billion a year um and that that's the size of the US just for reference like the US spends about somewhere between 60 and 100

billion a year um in non-defense R&D the defense R&D is more than that of course like you know another crazy fact um you know spend vastly more money in in uh producing all kinds of Defense Technologies than uh than in like saving lives and whatnot but um or it's saving lives in a different way but like um you know things like the the the broadest dangers to the world include like you know all the diseases cancer and you know all kinds of things like that and they get like a fraction of the of the funding scale that that um that they should be getting and so I think I think if you know in 10 15 years we could be funding R&D at the scale of nation states and if we can

then especially fix this bottleneck problem then we would have a pipeline that works dramatically better and faster and we should be able to like shave years um off of like the time it takes to go from a concept into a technology into a product into people's hands and so we might shrink the time it takes to go from a fundamental Discovery into a product from what it is now like 20 you know 20 to 25 years for like a lot of tech and maybe in software like 10 years um and we can probably shave that down by like 50% maybe more uh I think AI tools are going to help a lot here AI tools might shave that even more

um and so if you maybe couple these like better funding structures better Capital allocation and better AI tools we could be looking at like really short circuiting the this entire pipeline down to like single years in a lot of cases uh or maybe like five to 10 years for other Tech and that would be just amazingly faster right like that that would like just double the rate of improvement in the world or or more um and so you could both like paralyze and do a lot more and you would be doing all of those things faster so you you might like um you might do like exponentially more um uh you get to like accelerate the entire you know exponential that we're on um and I think that that could be like tremendously successful and

broadly beneficial for the whole world like we could be um you know putting into tangible terms like this would mean like making solar cells that are much more efficient and and better at like um capturing sunlight or or uh better batteries to store that energy or um better uh networks to that you know like have give people more bandwidth or better water treatment structures to like be able to like give everybody in the world like better water um faster time to um Cancer Treatments or any other kind of like um broad disease um or you know even specific like you drug development for specific um diseases like personalized

medicine like just broadly any branch of R&D any branch of technology that you care about um you could get the benefit and the fruits of that entire branch of of R&D much faster and at much higher scale if we can get this stuff right so like this is kind of one of those root of all problems type of solutions like if you can kind of greatly accelerate the R&D pipeline you then get all this broad Downstream Improvement globally um and I think like this is among the most important problems to to solve in the in the world yeah plus one um let's see I guess we're getting towards the bottom of the hour so we got to wrap up soon but before we do is there anything I didn't ask that you want to

say um yeah I think maybe like kind of a lot of people right now are broadly interested in this work but they don't know kind of like where to get started and I think like what I would recommend to a lot of people is um go find a lot of the communities that are experimenting with these uh new mechanisms and so you know go look at the the improvements in in like grants and structures like from from getcoin go look at hypers go look at um credit attribution tools go look at go go look through the you know libraries of

content from shelling point and fun in the Commons like there's a lot of good ideas that have been talked about there go look at like the dite Community um and then once you find kind of like a set of projects that you're like really interested in or you find like potentially like very exciting go go join those teams or like go contribute and collaborate with those groups like all of them are working openly in various discords or telegram groups or GitHub and help figure out like the next range of experiments and the next range of things to do um I think right now I think we were like very we were needing a lot more ideas two years ago and that's kind of like why why a lot of us started working on kind of getting the word out and getting a lot more people thinking about

mechanisms and I think a lot of great ideas have been proposed now and where we're where the bottl link is today is in experimenting more so I think we need more experiments more um development of more mechanisms we need more data Gathering of how well those mechanisms are working we need more um yeah we just need a lot more experimentation at this point and so I think like trying out a lot of projects and collecting data on what's working and what's not working and kind of feeding into the hole I think could be extremely extremely successful um and so I think if we can like over the next couple years experiment a lot um I think we can do can do better um one other plug I I'll give now is like Capital location is a High um it's an intensive signal

processing problem like it's really fundamentally about Gathering a lot of information of making some predictions and this is something that like ml models are extremely good at and I think one of the things that has not been tried enough is to do better Capital allocation using using ml models and so I think that today you could probably take your favorite llm and give it the task of like coming up with good signal for better Capital allocation um and it could probably do pretty well um I think you might have to work hard to like get it to work just right like you might you know a lot of the early answers might be bad or like hallucinations or whatever um but I do think that like we could

solve some really large Problems by by throwing AI at it um and I think this is like way underexplored and I think we might see like some great improvements on this in the next two to two to four years yeah for sure um well Juan thanks so much for being on the green pill podcast have uh long been inspired by your work and I when I saw your talks at zalo I was like oh man we got to do a green pill episode about this so thanks for coming on riffin for an hour with us yeah thank you so much and thank you I just want to reflect a ton of appreciation for all of the work that you're doing um get you know talking through all of these um projects and mechanisms and and helping lead the community in such a an awesome

way like I'm always super inspired by your GitHub threads and the memes and the podcasts and I really think that as a um you know kind of like culture Shapers have such an like a high leverage way of uh you know solving problems broadly um and I think like you know thank you for doing that and thanks a lot of people listening um today for like doing a lot of that that work broadly and like for sure you know coordinating us into into a better better future yeah I mean I think that like about two years ago when you and I started working together a little bit more I think we're reflecting back that the Community Builders the culture Shapers The Mists have a really symbiotic role to play with the Deep researchers the big brains at the table and I'm just honored to be at the table

with you all and and trying to create a movement here so it's been a lot of fun awesome cool all right well uh we'll see you next time thanks one take care bye Al righty well you just heard from Juan Benet I'm very excited to talk to him he has been a thought partner for me a long time with building the regen crypto movement protocol labs and gitcoin have worked together a lot and I think some of the things that we talk about in this episode hypers Sears productionizing R&D are really gerine to what we're trying to do with green pill impact season try to figure out how do we actualize the vision of impact equals profit so hope you enjoyed the episode and thank you for listening to the green pill. network podcast again we are featuring people who are building a

coordination a network Society of thousands of hackers dreamers and doers focused on using crypto to bring positive sum digital systems to the world like Juan Benet like like protocol Labs like hypers certs if you value our work then you can collect this episode at pods. medgreen pill if you go to that URL you'll be able to collect this episode and others from green pill impact season if you're not into doing something on chain please subscribe and review the podcast it really helps the algorithm surface this podcast to other people who are interested in the green pill movement thanks again see you next time peace and [Music] love