Are We Overhyping AI in Crypto? | Casey Caruso & Mohamed Baioumy

Transcript

we are hitting our limit in terms of what can be publicly scraped to create these models and because of that there's a lot of talk around where crypto could come into play to incentivize the collection of different data sources more funding does not necessarily make it for you easier to find pro market fit but there's a big caveat here which is many things in AI are closer to horor and to software hey everyone we all know blockchain is changing the world but who's actually driving it I'm excited to invite you to the sixth edition of Meridian a web 3 conference hosted by the Stellar development foundation in London from October 15th through 17th get early bird tickets at meridian.

stellar.org with code block Works pod special thanks to Stellar for sponsoring us now let's get into today's episode hey everyone the polygon Community grants program was launched with 1 billion tokens All For polygon builders really excited to share that on today's episode of Empire season 1 of the polygon Community grants program is now live it features 35 million in mtic to support the next generation of polygon projects join the aggregated future today by applying at polygon.com partner Harpy is the best

tool to prevent your wallet from theft in real time Harpy is not just a security solution they are a peace of mind solution but don't just take our word for it Harpy is the only wallet security solution that protected 100% of its users from attacks like The Ledger one and Q4 which was an offchain signature attack to learn more about Harpy click the link in the show notes or visit [Music] welcome back to Empire I'm really excited to do this sequel to the first uh AI crypto pod that we did back in December uh Casey you were on it with Daniel from modulus people should go listen to that it was very well received

the space has moved quite a bit the attention in AI um seems like it's alltime high so I'm really excited to host Mo welcome um I think uh you're the co-founder of exol laabs doing some really interesting things I'll pass it on to you for intro and then Casey uh for people not familiar living under a rock you could also give an intro yeah thanks so much for having me I guess Daniel was too busy this time so I all uh my name is Muhammad I work on a company called EXO essentially you can like use Edge Edge devices like phones and laptops run AI models like locally and privately on there uh my I guess the two things I know about AI I learned

mostly during my studies at Oxford I did a PhD in Ai and Robotics at the Oxford robotics Institute and I've done a few things in web 3 um over the years and now I'm working fulltime on EXO like cago said so great to be here I should say full disclosure I am an investor in exol laabs and I'm also an investor in Casey's fun topology so if I I'll try to remove my bias from this episode I mostly will be in the background because these two folks are gonna have a great conversation but full disclosure I can give a quick bio too so my name is Casey I'm the found ing GP of topology which is an early stage Frontier Tech firm we do not just crypto but crypto is one of our Focus areas and prior to starting topology I was an

engineer at Google for a long time and then most recently was an investment partner at Paradigm amazing I think um I'd like to start this conversation uh with just highle framework you know I think it's um you know people come to crypto and they expect like grandiose Innovations and they may be too impatient or may not be paying close attention to what's happening at the ground level and they miss uh perhaps really profound but maybe subtle um Innovations um and or also they expect many Innovations uh and particularly overnight and I I think there's some interesting stuff happening in at this intersection of crypto and AI um you guys are probably um the closest to it uh so I'd like to start that the

conversation just maybe starting off like what you're seeing what's really exciting and and and um and then we can go from there what we're seeing is changing I'll start there so when we did this podcast back in December we were seeing a lot of foundation model companies and that was probably the most popular pitch I got was we're going to create a foundation model for crypto and crypto is an incredible playground that's Co like contained that we can train data on and that was a very very common thing and that has died down and what has come up is more like agents applications and infrastructure and I think that's only been fortified by the fact that llms are being commoditized across web 2 as well and so the pitch around foundation models is just getting weaker and weaker

by the day um but to Santiago's point I will say that I deeply agree that there's probably 10 to 15 topics right now that are popular that I have seen maybe more than five pitches in each and I would be excited if just three things worked uh I will say right now very little is working I think the thing that is working the most is from a technical lens there are some breakthroughs in local compute so like what EXO is doing there's some breakthroughs potentially in distributed training uh in inference but then from a product standpoint the thing that probably has the most product Market fit is just distributed uh either computer imprints where basically you can get

cheaper compute by hooking into a crowdsource network instead of just using centralized players and like a very concrete example of this is that you can go to brev dodev which was recently acquired by Nvidia it's a just regular web 2 company AI company and you can just rent aosh servers for a fraction of the cost of what you would rent them on AWS and again like we we don't we haven't really pressure tested totally over a long period of Time how long this uh price arbit charge will last AWS could easily come in and maybe say screw it we want we want to eat their lunch so I don't want to say this is a differentiator that's forever lasting it could be more Emeral than we hope but that's just like a very I think

concrete example of where we're seeing some sense of product Market fit and then before we go on from this topic I just want to say one thing which is that um at topology we're basically taking a very engineering Centric approach to this entire category or entire space um and we're looking for a technical investor and so if you're listening to this and you're interested DM me I always get a plug the chill well Casey you mentioned uh local training so Mo I think this would be a great opportunity to explain kind of what EXO is doing and you know a lot of the Innovation that's happening there yeah just just before I go there just to comment on what you were both saying about like the a lot of hype and a lot of the stuff that Ray promised and not much work yet today um

like in February when I was before I start working on EXO directly like when I was looking what to work on I just wrote this report I just like tried to like see what's happening in the space all the different like problems and people are working on and I think it was we called like the I crypto primer was like this like very long report and I I had a few Tak takeways from that takeway one is I'm never writing reports again this is like the worst thing ever like it's just so timeconsuming to write a good report good report you meant did you actually write it yourself or did you had like a you know an AI we actually like most of it most of the substance the first few sections were all like written and iterated on like crazy like um uh it was a very brutal

month to just write the report but the second thing is like a lot of the stuff um didn't quite uh make sense to me and I couldn't wrap my head around around it uh because I couldn't often pinpoint what is the problem being solved here like what problem is this thing addressing I would see a project I'm like oh this looks very cool and I'm just trying to understand like what is it actually solving which is like quite um like non-trivial to me to just to be able to say and um and I think that's like a general um challenge in the space in general is like you have a lot of stuff it's like Tech first we have this thing that exists and it's cool and let's try to apply it with this AI context in mind but like it's not like oh here's a real problem that we

just like trying to address and the report basically starts by like making this high level distinction about like when you think about like these two spaces like Ai and web 3 I try to use AI crypto less and say more AI web 3 it makes me feel less that it's a casino it's more like uh just technology and it's really like if you think about intersection it's like either using something in AI some AI tool to solve a problem in web 3 for example um you could say a problem in web 3 is the IFI hacks stuff gets hacked all the time and that's a problem web 3 people web 3 understand it and I'm going to go take tools from AI like I don't know reinforcement learning whatever I'm GNA build a product that makes it less like for stuff to get hacked for example like

an auditing tool that is like AI powered and that's type one so I'm using something in AI to Sol web three problem and the second thing is like I'm using something in web 3 uh like token incentives or maybe cryptography or something like that to solve an AI problem let's say I don't actually have a specific example in mine but let's say data labeling is a problem that's hard in this setting um and we need more data to drain this model label data and I'm going to use incentives to get people to label that data whether that makes sense or not I'm not sure but this is an AI problem so for someone to solve that problem you need to understand you know the AI aspect of it uh and kind of like you need to go talk to AI practitioners to understand how that part works so

what we do in EXO it just starts with the premise of like you want to run a workload for example you have a model that you want to run yourself because you care about privacy let's say you're a law firm or a bio company or a business or anyone who cares about privacy you want to run something and you care about privacy so what do you do just run on premise just like run it in your own um hardware and your own Hardware in this case means a GPU but what if you don't have a GPU well we can just allow you to do an edge devices so that's kind of like just the TLD of what we do yeah Casey I'm curious what you think of that like as as you think from an investing lens you know how much of what is talked at you know this crypto

AI intersection is like crypto enhancing what is possible with AI or AI enhancing just crypto applications and use cases or just something completely new that is like possible at both this intersection of crypto and AI yeah yeah I so I I used to use that mental framework a lot of AI meets crypto crypto meets Ai and then net new I've actually walked back from that mental model a little bit because I think the lines are starting to get a more blurred but I agree with you that is one popular mental model and to answer Santiago's question we see a lot of crypto helping AI in Venture scale problems because a lot of the AI helping

crypto are so profitable and I'll give you an example of like an AI trading bot where they don't really need Venture money and even if they did need Venture money it's more of a lifestyle business and I don't I'm not interested in the funding those businesses um because they're just the from day one and so that's like more of like AI ubiquitously helping crypto in like a lot of defi projects which I think are great businesses but again I don't think they're Venture scale and and so the things that we venture firms are getting a lot of is more of the latter um and then to sano's point there are there is kind of this like new category of like the what is uniquely enabled by both and that starting to take form but there's there's not much product Market fit there but that's why maybe this is a a

good segue into the map when I was thinking more of like 30,000 foot views how to really index the space and make sure I have a pulse on it what I ended up deciding was that I wanted to build some software that enabled the community to manage it themselves and so we have this if you go to like topology dovc D- map it is a crowdsourced market map where you actually give submissions of what category specific projects live in and then I have an agent that actually goes through and opens the Twitter link and sees how many people in my network follow them and if it's over a threshold then it sends it to me on slack and then I can decide if we add it to the map

actually somebody else decides if it gets added to the map on a weekly basis but the point is that like most Market maps are really static and biased and they don't scale time and the purpose of this map is that it actually will update continuously forever and um it hopefully gives an overview of not what I think the categories are but actually what people think the categories are and if it would be helpful I'm happy to take like a minute or two and walk through what those categories are today so we have the map pulled up so Casey continue okay great yeah so starting at the bottom of the stack the first thing we have is compute and these are just raw processors and servers that you have access to and then within compute there's a handful of categories this is

where I put Edge so what Muhammad is working on also just like aggregated gpus at scale which is something we've already talked about um and then on top of that we have inference networks which is kind of a subset of compute but it's really focused on just inference not training at all and we have some image specific inference networks already in crypto we also have llm specific inference networks like Cusco uh and then on top of that category I have coordination layers this is really a catchall uh for things that there's just so many categories that this is more of just layers between things that don't fit within one category so things like uh let's see would be a good example like

near near is doing a lot in Ai and I think of it as a maybe a general orchestration layer or coordination layer on top of that we have privacy which I did not actually want this to be its own category but because this is crowd source and I listen to the people it's its own category now because uh this will come out later but I'm pretty I have pretty strong views on privacy I'll say and then there's agents which is a really big promising exciting category so just using agentic workflows with non-deterministic models like llms um and then on top of that we finally have some applications so this is where like consumer and business B2B use cases next to that which really maybe

should be below but like are the actual model creation and fine-tuning so building the base models as well as doing the fine tuning on top so people like new or Pond are living within this category and then there's I guess the most the biggest category we see and I put it at the top just because of the raw number of companies we're seeing is data and the idea here is that we are hitting our limit in terms of what can be publicly scraped to create these models and because of that there's a lot of talk around where crypto could come into play to incentivize the collection of different data sources and this is just a known thing you can Google around in

the fact that we really are hitting this ceiling and so there's kind of two options from here it's either people start coordinating to create more data or there's like there's synthetic data which is I guess another option and then the third option is we're creating more data moving forward that's used but as it goes right now we are just like tapping out and so crypto introduces a totally new option for how to de deal with and I guess like both interact and generate this data so that's the that's the map as it is today and then like I said if you want to add to it you basically just hit add new projects and then you can choose a category or say a new category and then once we hit a quorum with enough requests for a new

category it gets created Mo what are your thoughts when you look at this um obviously you're there at the very bottom Edge compute uh but what's your overall reaction or thoughts when you look at the map um yeah I was actually curious like why Casey why don't you think privacy should be a category and if if not like what what is the space for things like Z or FHA like where does that fit in the stack yeah I just think that privacy is a feature not a category and I feel that it should be with embedded in each of these so I think there's for example nilon is compute and I think it's privacy Centric compute and so I think that it's just a secondary or tary classification in the taxonomy that

I would prefer but again I mean people are so obviously it's so part of the ethos of this space and people want that to be a differentiating factor so my hands are tied yeah because I find privacy a really tricky one to think about in general because well first of all um retail caring about privacy is a bit of a meme like very few retail users in the world actually care about privacy and we've seen this like in like web to social media in particular uh but like for example businesses care about privacy a lot and I was just always curious like uh I guess like if let's say I'm a business and I care about privacy like I'm a bio company whatever or uh what are my options it's either like on premise or Cloud plus something on it so I let's say

FH and when I when I personally like try to think if I'm a business what is the tradeoff here like I guess if I'm a law firm doing stuff on premise means I have to not buy and maintain hardware and like that's quite a hard thing but if I'm doing FH I'm paying like I don't know 25,000 x overhead or if I want like proper privacy uh so I wonder like what what do you think that space looks like and for who it makes sense to just like you know just do stuff on premise versus use some of these protocols that are like maybe doing something with NPC or FH and these kind of Technologies yeah the problem is the trade-off between price and Trust yeah over and over again I just hear that from businesses like any any of my portfolio companies that

have B2B customers I like to talk to them and they'll just tell me that you know we're willing to try it but but we don't really know this company we don't really want to trust it and then the more they say they're decentralized the less they're interested because what business wants to work with a decentralized protocol like that that just introduces a huge moreal laundry list of concerns and there are like cases where that makes sense but I think that that's why Edge compute is going to make there there's the consumer case which we can talk about but for the business case it's so obvious it's so strong for them to be able to be doing these computations locally on their own machines that they trust and they don't have the price Arbitrage or like the price uh Delta so it's a win-win because

they don't have to trust anybody and they get the the price benefits yeah I guess what the reason I'm asking is like obviously can Edge compute and one of the things I believe is like oh with EXO these businesses who wants to care about privacy they can run stuff on premise without having to buy or maintain Hardware yeah but like I always think about their risks like am I mid curving or does I don't know a huge data center in the cloud FHA in five years make much more sense yeah I mean I don't think anybody like so I personally have stayed away stayed away from investing in homomorphic encryption for now just because my smartest research friends say we're not there yet and I think you know in the game Adventure timing is really important you just you don't need to just be right you need to be right on

the right time Horizon and that's a totally different algorithm and so like do I think it's eventually going to work wouldn't had against it do I think that's like where the best Capital could be deployed today [ __ ] no like no but um so I I don't have an answer to a question but I think like today I'll say like it's that's not really what businesses are using out of everyone I've talked to and they still are looking for a solution and I just want to comment on something you said which is that like businesses really care about privacy I don't really know if businesses care about privacy I think they care about regulation and following the rules and I think that there's a lot of uh let's see like Downstream pressure from a lot of regulatory bodies and and that's good for them to follow certain

protocols and so that's kind of why it's like they're not actually concerned about the Privacy they're concerned about checking the chat boxes most of the time I feel yeah from our chats like we've seen for example like law firms it just well well legally I'm supposed to not reveal data so I just you know regulations so that's why I care about privacy only people who seem to care a lot about like privacy are in the more competitive settings like for example if you're a hedge fund you're not allowed to use chbt because like you just like there's a chance you leak something through these clouds right and the same for like bio companies if you're using like a bio model since as a drug that output is like your IP so like that get Le is really dangerous so there very few cases where they actually care about it

because of competitive market reasons and in the other case just regulations and uh but yeah but I guess that is very real demand if someone has to comply with regulations that as a business is a very attractive to go after yeah well I'll actually turn it back i' would love to know from your perspective so yeah I mean I'm very familiar with what x is doing from a product standpoint but from a usage standpoint and a customer standpoint what are you seeing as the greatest demand for this right now I can tell you after when we're off the air we haven't announced yet I can tell you this after oh you you can share the alpha in the pot you know no problem you know for now okay okay I'll DM you later well I mean candidly Mo when I when I um

evaluated EXO initially it was like this is really cool uh being able to you know run a model on the Ed like with your any device you know hook up an iPad a your MacBook idle machines whatever really cool um I have asked you time and time again and during the process like what does the actual product look like how big of an opportunity this is um or is it just more for hobbyists um so without without going into product announcements or whatnot but like I'm curious someone like you can build so many different things and you can get pulled in so many different directions which is very common in the space how do you think about like killer vertical use cases that you're going out after um and if you can comment on that

that'd be great yeah so um the the on a high level essentially we do at XO is we allow to use these Edge devices to run useful models so essentially it goes down to a tradeoff of like local versus cloud in some capacity if like why run stuff on your own device if you can just like you know go in AWS and host it there and there are some cases where it becomes relevant so like case one is what we just discussed which is privacy if I'm a business and I have to comply privacy or I'm competitive business like a hedge fund or a buy company I need to be private so on premise makes sense it's very economic way to comply with my privacy requirements and then in that in that case like the reason uh you might want to use EXO is you don't have to buy

or maintain Hardware so uh instead of like going to buy and maintain gpus which is a very hard thing to do unless you're like super technical just like buy these fre gpus and run a models on them and maintain that and rack up servers you can just like use the existing devices you have so let's say in the context of a hypothetical like Law Firm you have 50 laptops in The Firm you can just download an app and there you go you have an air cluster you can use air models on premise fully private so that's like one clear on that point with the law firm say is that going to compromise my performance in other applications how do we think about like load balancing all that kind of stuff like these are all things we can solve in the back end and right now there's

fairly abstracted from them you just like download the app and then it just kind of work the app open it use LM and then literally the only thing you would notice is with the current version of EXO that we have internally is if there are more people uh doing work let's say playing games and the M1 chips and the laptops really busy like rendering Graphics it'll be a little bit slower but if people are available like two three of your colleagues are available it would be a lot faster so essentially I have stuff on my own device laptop and then like the late spare compute of my colleagues who are in the same organization uh is being used to speed up my inference and like run these large models they just like uh one use case to think about here from a performance standpoint it's parody

um like I guess depends how you what you compare it against uh like um you can get like really high performance like more tokens per second it would just be like less efficient so like you're paying more um it's like when when you this think called like tensor parallelism when I try to like paralyze certain things I can speed up the inference to some extent but that comes at the cost of efficiency I do more than in computes um so it depends what you compare against if you think about compar this against the GPU the math is very different depending whether you have the GPU you have to buy it if you have to buy the GPU then the cost the tradeoff is like electricity cost versus buying a GPU but if I have a GPU the tra is very different so then you have to look into

like memory and stuff like that so it it comes down to unit economics theend of the day but like pretty much everything well to some exent to to some extent most things are possible so you can look large models very fast locally if you have enough compute um so that's one way to think about it one other way to think about like why you might want to do stuff on edge is cost like let's say you just don't give a [ __ ] about um like privacy at all I just want I just care about like optimizing cost and you have spare compute available you might be able to use that spare compute versus spending more money and more gpus um and yeah cost is one of those things that's like kind of interesting uh I don't quite uh know how to think about this in

the long term because like I think we're going to be in a transient phase people going invest a lot of infrastructure and like gpus and so on but might probably plau but still to this day I think open AI is going to lose like four billion dollars this year because like just they subsidize the INF cost and so on um so that's the other thing where you might think about the tra between like Edge and like Cloud when you think about like whether to do stuff local or in the cloud it just depends like what kind of use case you're in again something might be privacy some things might be more about cost something more about latency and you have the more ideological stuff about for example owning your model if you can just like have your model just locally on your device they private you

and you have access to them that is one way to like think about like democratizing access to Ai and that kind of comes in two layers like you have these incredible open source models that someone like meta creates these llama models and they release them and that's great for the space but once the model is released like llama 400 like 405b 400 billion parameters um and fine tun version of that model are like like comparative and in some cases better than CHP performance these are really really good models freely available but how do you run it like you can't run llama 4 OB on like an you know a phone it's like too big that's where for example XO comes in just allows you to like have access and use these models and you know have them available and not have to rely on like

AWS or someone else to just like give you access to these models like kind of democratize the access to it so if you have your phone and your laptop and maybe your family now you guys can have your own llama at home and you just own it it's just your model so different ways in which you can think about like where this Tech could be useful what we are doing from a business Point as a company I can tell you after the call but that's just like the higher level overview over time do you see this more case you kind of alluded to this earlier but like more consumer focused or uh equally you know focused on like Enterprise and you know B2B well I one thing just quickly on Mo the I love this spectrum of latency sensitivity and how it unlocks different Technologies I think it's spoton and I

could I could draw it and share my screen or I could do it in the the space but basically it's like if your latency sensitivity is super super high the most promising technology is doing it as local as possible and I yeah cursor is a great example that every Dev is using um notion I think is another great example then if it's like middleof the road latency sensitivity you know you're probably going to go with closed Source open AI CH chbt or CLA on it you know normal models that are close SES and then if you're really latency insensitive so things like with my dii map it only needs to run once a week I really don't care when it runs uh

definitely not don't care in like 20 minutes 10 minutes and so I could use geographically distributed compute and drive down my cost and that's like on the other end of the spectrum and I think that spectrum is probably not spoken about enough when it comes to Consumers making decisions of what technologies to be using and I think these things are going to become a lot more like stuff like latency matters once there are more apps because you only have to think about this once this an app you know like cursor notion and so on when we still think about like just like base infra like these things are not quite obvious but it makes a huge difference to user experience uh in many cases like gaming is a great example where like like a little bit of latency is really annoying when you're playing a game and you notice it yeah

it's like this it's like if you're uh you have like low latency you're doing something like EXO Medium you're working with open Ai and then over here it's like a distributed like a Cusco hyperbolic all of that um but I think that of course there's other there's other things that you need to consider with this like privacy and other requirements but I do think that is spot on as these consumer applications come to Market and we're actually forced to make these trade-offs in real time we'll see these materialize in aifi uh but then Santiago your question about B2B and consumer is it just well I mean maybe more of a general question both of you talk to businesses uh you also talk to portfolio companies or other people in the space um there's a lot that I want to unpack there one

how much interest there is from just general businesses and people to use open decentralized protocols um and also you know how much of the AI community that has been dismissive or just not paying attention to crypto uh is actually inclined or interested in using some of the tech that is being built by you know crypto people I I I think High I think high but I think that actually consumer AI is severely underrated I think that the non-deterministic oh go ahead well how would you define consumer AI I guess yeah just not Enterprise use cases

you're not dealing with another business on the other end and the reason is because basically I think AI forget geni just AI in general has always had a if you break up AI into what's production grade and what's research a lot of it is research and this is just like throughout my own career I've noticed this is that just a lot of the AI that we think's ready for production it's not yet and it's just it's a highly technical field and that's like that's true across a lot of technical fields and so what happens is you think something's production grade you try to sell it and then you realize it there's the non-deterministic nature or there's all these you know false positives and it's not it's not um it's not ready and so

we're seeing this now with geni this similar thing where I see a lot of like this is one of the biggest mistakes I see investors making is that they're pouring money into research Labs thinking that they're going to be commercialized but they're never they're not going to be commercialized because that technology actually isn't ready for production at all and so the path to monetization is a lot hairier Than People realize and so to that end consumer use cases that are being that are willing to deal with maybe false positives or the non-deterministic nature is actually feature not a bug like that's stuff is really really interesting and ready for businesses today and I see a lot of people obviously focused on B2B because I think vertical B2B AI is also really interesting but I I just think if we're going to talk about like underrated

overrated consumer AI underrated hey everyone Jason here just wanted to take a a quick break to talk about something exciting in the world of blockchain the Stellar development foundation is hosting their sixth annual Meridian conference this October 15th through 17th in London it's a 3-day event where the brightest Minds in web 3 come together to discuss everything from tokenization to defi if you're a developer Builder policy maker or business leader looking to make an impact in transforming Global Systems through blockchain this is your chance to join the conversation you'll get to network with the forward thinkers who

are defining the future of this space head over to meridian. stellar.org and use the code block Works pod for early bird pricing now let's get back to today's episode this episode is brought to you by polygon polygon Labs is developing the next generation of open-source zero knowledge Tech to aggregate crypto liquidity and user bases empowering developers to grow in a unified web of interoperable chains with the agir that was a big mouthful so I'm going to tell you what it means in my in my words there's all these things popping up l2s and l3s and it's chaos right if you are building something you no longer have to worry

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about areas that are overhyped and underestimated in this map okay Mo you want to kick us off um yeah I I'm gonna try and be very without pissing off 99% of the people in the space no hard feelings um I will I'll say a few general ones so um in the bottom right corner you have like RAR compute and there just you know zkl uh I think zkl is really interesting thing where a lot of the chat about it is scaling zml and like making it more efficient and reading the overheads I think zkl will scale but I think one

thing that's like overhyped is like the software aspect of it one thing that's like underhyped is the hardware aspect I think like the biggest uh um reasons for speed up will come from like hard acceleration like companies like you know in gy or fabric cryptography and so on I think those will be a little bit more important for like actually scaling these things up than like the software companies and the reason I believe that I can go on a whole Spiel uh is this thing called The Bitter lesson in AI from famous block Stu and and we can um go into that but most breakthroughs in AI over time came from like just scale and like Hardware is like Hardware optimized some extent for these cryptographic operations I

think is going to be like a bigger driver than the software aspect I think it's going to be like specialized Hardware generic soft like more general purpose software not like for example ZK CNN improver and stuff like that I think it's going to be a bit more General and Hardware is going to scale it up so I think Hardware is a bit underrated and it's not underrated in terms of funding these companies have raised like a lot of capital u I believe fabric like announced around recently uh but they're underrated in terms of like just talking about them they don't come up as often when you think about you know zkl um the other thing I guess tangent maybe make make a pause there um when you think about Casey to your point around you know being too early on some of this stuff and being you know sort of

indistinguishable from being wrong Hardware takes time uh most people tend to underestimate that how do you think about like the time that it will take for these like Hardware to be available and ready production ready is it are we talking a year five years 10 years I have no idea uh how long the har would take um I guess like in AI there's like a lot of new like Harbor specific companies like for example chips for Transformers and those have done quite well in Fairly short Cycles uh so there's a chance this goes well um I think like in the long run the hardware companies going to like contribut more I'm not sure if that means you should invest in it now uh and this is general like there's a very interesting

category um in in like just crypto in general of like this stuff doesn't make sense but I would invest in it like this this stuff is going to pump so investing is a good idea but doesn't quite make sense and stuff that might make sense of the long run but it's probably not good investment I'm not an investor I'm not a professional investor so I don't think about these things often just what I think from a technical standpoint what is underrated I think Hardware acceleration for like this refi compute is underrated compared to software uh I will say though a lot of stuff around like ver compute zml I'm not sure like why we need it a lot of the use cases are a bit shaky in my head uh but it is going to scale eventually and I think Hardware is gonna play a bigger role so

that's one thing I would say is like overrated underrated notice it Mo's being very conservative just talking about underrated not overrated overhype stuff yeah polit very politically correct you're lucky I'm not a harder uh podcaster here I can try in I okay is gonna go for the Hammers he's gonna go [ __ ] over I already I already gave my overhyped which I think is privacy generally I I'm really not that interested in that as a primary value proposition and I think that okay let me think what's underrated I think specific things in agents are underrated like I

think I genuinely believe agents are going to use crypto infrastructure for payments in stable coins I just think that's what's going to happen and it's I I I like to say that I'm not a psychic I'm an investor and I think a lot of investors try to be psychics and it messes up the returns and so you know you always need to be open-minded to where the puck is headed and be nimble enough to to be there and so I don't say that as like I have a crystal ball but I just think from like first principles it makes a ton of sense and so I think that infrastructure is underrated and then I think what's overrated in agent is this like General development framework stuff which is like we're creating a new L1 to build to be the home of Agents I I have

seen so many of those pitches I just don't think the value proposition is concrete enough and I think you run into a common Pitfall of crypto or rep three which is like the devs will come mentality and then what else do I think I think maybe one more thing that's underrated on the data side is that because I already mentioned that we're hitting this wall in terms of like the amount of data that we can publicly scrape and that there's things like fine web which is I think it's like 14 trillion tokens which is like the cap of where we're at maybe it's 15 I I think protocols like van which like discloser I am an investor in but I really do think that this idea of coordinating people to pull more

information and now leverage that in a way that's financialized for new models to use is like is underrated to to me that feels like the huge unlock that crypto crypto's always better at coordination yeah and getting people to do stuff and so if you overlay that with the data problem like again like in Investment Banking and finance it's like you're only as good as your the inputs the model's only as good as the inputs like that that's something that you know it's true in finance is true in Ai and so if you want to really do like fine-tuning specialized data if you're a law firm if you're you take a generalized model you need either synthetic data or like um like reinforced with human learning you you need a like if you overlay like a coordination layer uh with crypto it can

be really powerful you can do it at like it's like deepen right it's you can do it like uh far more Capital efficiently um and that's the area that I'm most excited about um but yeah I'm sort of a lizard brain I know I agree with you I I mention two they're not on the map but two other things that I think are underrated or underappreciated or talked about is that AI is generally commoditizing software development and it's really hard for me to say that given my background but I'm coming to accept it and it really is going to be a class change of it's been the rise of the Nerds in Silicon Valley for so long and having a technical degree was your you know wiie woner golden ticket situation and that's just not going to

be the case in 10 not even 10 five years and so with that there's a lot of Downstream effects of how Venture will change how Innovation will change how yeah like class structure in terms of who's Talent where to Talent webs move and what's valuable than Talent webs and so that is like I just think not explored enough when it comes to crypto and then to Mo's point which is semi-related which is that some of these businesses that are AI meets crypto because they're profitable so quickly and because crypto is so overloaded with capital because of these multi-billion dollar funds there's actually a new category I think that could become real which are like micro loans or micro

Investments for these nearly profitable businesses where because now software development is so cheap people around the world can make these businesses and maybe they need like a thousand dollars or $10,000 to get to a point where they don't need additional Venture and and these larger funds obviously aren't positioned to do that because it wouldn't impact their p&l it wouldn't it wouldn't make sense for them incentive wise yeah any how do you think um I mean I guess this map is the real estate is not to scale in terms of the amount of money that's been poured in it's just sort of like random I right yeah well yeah okay H how do you think about the evolution of this map going forward um

perhaps what are what are some of the areas that you think perhaps new categories that might emerge or if you were to like do a kind of morphing overtime map in terms of actual traction actual usage however you want to Define that I I don't I care less about Capital more so actual usage like number of users or adoption how would you you know how would you think that this map evolves over time okay two new categories so one's on here one isn't even on here yet because we need one more for the quorum to threshold to be hit but it's robotics data because there's this huge thing right now with humanoids and and now people are thinking crypto could be the

missing incentive as Santiago talked about already for getting the robotics data and so there's a lot of companies going after that all of a sudden it's like overnight too and I think the what I'll say is I know from a traction standpoint the Lois that they've been able to secure and it's pretty impressive again not sure how it scales but I will say at least from an Loi and pocc standpoint I'm intrigued and then the other thing is World models which are if people aren't familiar with that term are basically more generalized models that know about our world and can do variety of things from bioinformatics chemistry all over and Eric Schmidt famously started a company in this category also like a very well-known Stanford Professor fify Lee started a company in this category and that I'm

I'm starting to see companies go after as well um and so those are like two new categories and then to quickly answer like how I expect the usage or product Market fit to evolve with this I do think some applications are going to start to have pmf like the durability I'm not sure but I think something in consumer will obviously take off or maybe that's not obvious but to me I think it it is and then I think the agentic is also going to start working so agents probably in 2025 are going to start to work in a way that we can actually use them on a day-to-day basis is there normal example be like you want an agent to do certain tasks that you want to like you know uh and it would use stable coins um because obviously you can't kyc you can't use

bank account but it naturally can use a stable coin to pay certain vendors to you know know build your website and pay and do everything in the background and and you kind of need the crypto rails for that yeah exactly I I think it's more likely to be an actual ecosystem like Amazon like I think Amazon is actually going to launch an agent in 2025 and it's going to be really good because they have the contained they they know their software and so they can create guardrails for it not to go off the deep end and so I think we're going to start to see major organizations launch like their versions of agentic flows and work maybe with maybe with CTO companies to do some like payment integration possible possible not but I just think it could it could make sense and but I'm curious if Mo I yeah do you

disagree mo mo before I go to you on this point of guard rails I mean obviously we had Dan for modulus and verifiability being a big important aspect how do you think about um the traction that you may or may not get from these decentralized protocols because of this particular issue like uh maybe it's just a trust issue it may or may not be true but like you know what there's just too much there's no one to call and there's no like red button to stop yeah exactly people pause of like if this [ __ ] goes Haywire I'd rather be using like open AI that I can call Sam and just it may or may not be truth this is more of a matter of perception that can hold back the space a bit I mean do you think that's real and actually a

credible like threat to crypto AI like getting any sense of adoption I think that's a broad stroke but yeah in general do I think it's real that people trust open AI more than they want to admit yes yeah I do I think you want to say you don't trust it but I think the you know there were a small set of people in this community and I think most people do trust open Ai and I think they're moving in that direction to to get to Garner more trust I also think people trust Apple I think those are the and I think apple is really well positioned to actually break into this category but but I still think that there's a lot of there's a lot of thing we don't know how the story ends yet especially with regulation and I think that there's just like a few things that

could happen over the next few years that bring decentralized AI more to the Forefront and I think there's still certain categories where even if you trust open ai decentralized ai still makes sense um so I don't think I guess I want to say it's like I think there's Nuance to that answer and I think the devil's is pro the devil is probably in the details there well one thing that is definitely like um going to change hopefully soon is like you know a lot of people right now build like fixing troubles like really like low level infra and for some reason in general like crypto space loves to fund infra uh versus just like apps um and uh a lot of this lowle infra I think will be just comod very quickly and because like um

if if I'm like a successful app developer and I'm looking at like which GPU Marketplace run stuff on it's probably at some point going to be like a very like hard calculation maybe I care about privacy so I'll go for that but if I if I don't probably just cost like whatever is cheapest it'll be commoditized very quickly uh so I think that's something that right now like there's so many projects like doing GP Market places and probably this will like get commoditized and then like you know be the the distinction will be a more on the app player like the useful stuff there um well I guess people have been saying this in like General crypto for a while but it's still like infra is still a very popular um thing the other thing that's like very interesting to think about I guess is a lot of things like get

accelerated or decelerated a little bit based on funding I think it's a little bit of a fallacy in general like more funding does not necessarily make it for you easier to find market fit but there's a big caveat here which is many things in AI are closer to Hardware than they to software like many thing AI companies are very Capital intensive if you think about I don't know the foundational model companies like you can't just be two kids in a dorm room to build mistol you need all the compute so it's like a little bit more like you need this up front capital investment and all that stuff that you don't necessarily have in the computer software company and but again the foundational model is probably gonna get like very quid like Mark is gon keep funding like a few these open source

ones and then like a lot of value will AC to the app ler um so a few things there is one thing I think about a lot is when you you just Qui quick like um step back first over time most progress in AI history came from like scaling up there's this famous blog post that I really like to quote called The Bitter lesson uh and basically throughout history specialized models that were handcrafted don't perform as well just like people who leverage scale I have a personal experience with this where in the start of my PhD in Oxford we had a talk and uh open I was mentioned at the time at the time we still had gpd2 so it still trash and I remember the lecture uh was

talking about open Ai and how they're just doing this like next world prediction very simple structure just trying to scale it up to build these good models and he called opening ey [ __ ] and he said that we should look into like how kids learn humans learn language and encode all this stuff into language models but the thing that worked well was just like you know it's fairly simple models somewhat simple and i' like handcrafted for like English language or whatever it's like fairly General and we just scale them up like crazy just like more data more compute just bigger better and this is like a common Trend and the reason it's called The Bitter lesson is like as a scientist it's not very sad satisfying to just think oh skill is just going to beat my intellect of how to cleverly construct the models but this happen and over

again like in speech recognition yeah had all these people tried to craft models for speech recognition but like h mark of models were the best thing just like statistical model back in the 90s um you had all these chess researchers who built chess computers based on like you know how humans play chess but then like uh IBM came along with a fairly Brute Force thing and like beat everyone out and I think Hallmark moment where like the the model like did a move that most chess like Savant were like this doesn't make any sense and then that's how would actually beat Kasparov I think I don't quite remember but I think a lot of this stuff happened also like later on with the game of Go a lot of these go particular as well yeah where the model just created a totally new move and and

and experts are like this doesn't make any sense and then they're like holy [ __ ] no it actually does and that's how you beat the human and yeah that that's just Nam trending General that most Brokers just came from like scaling uh scaling up these models and actually in the case of like Chess at the time when like the best chess AI like beat Kasparov loads of the researchers said oh this is just brute force and this is not going to win in the long run this is how humans do it but like machines are not humans and they're very different in nature so they can afford to just like scale up and like plug these gpus together so I think one General thing is um you go through these Cycles where we discover some specific architecture we scale up and it gets like passes

everything else and then we somewhat some point we plateau and then a new architecture come along that allows us to scale better U so like things that allow you to scale are like historically a good bet to make that's how like just the space is going to progress like just by scaling things up over time I just think about like how Stuff evolved I think soon someone come up with a new architecture isn't a Transformer like some new model some different thing and then like a lot of funding would have to move and like go to that but scale that up very quickly one thing I'm very curious about is like this model called an X lstm it's thing like a Transformer it's like a different architecture and if someone like scales it up like crazy and like makes a really big version is that going to do much better because

these models perform much better on a smaller scale and um what that means is like some investment become quite tricky so let's say I want to invest in like etched like this like hardware company that's like optimized for Transformers um if you believe Transformers of the future this architecture and scaling is just better and we need to keep scaling for a long time then that's a pretty good bet to make because we can like just do a lot more with Transformers but like if a new architecture comes along then youve specialized Transformers and now it's kind of like not very useful for this other setting uh so that's another thing that I think is interesting around like the hardware layer and allow like influence the whole stack depending on like what the model looks like the whole stack changes one big change that happened over time is previously you had

to train every model from scratch but then foundational models came along and foundational model is this big thing that you train ones and then you train small things on it and you need a lot less data to do it um so that will change a lot how the data layer looks like for example the other thing is like if synthetic data somehow becomes useful with skeptical synthetic data in general but historically it has worked in multiple instances I remember like when I was taking a deep learning class in in college you had like this thing to like classify dogs and cats like or like cat or not a cat and then like the research is like oh yeah we're GNA augment this data we're GNA take this exact same C picture and like for example crop it and then or take this C picture and like

give it like a different shade of color and then you have like more fake data and that helps the model do better always a bit confused listeners that's synthetic data right yeah that's that's somewhat synthetic but now like synthetic just literally mean like just you know AI generated if that works there's there's like a interesting thing called Model collapse where if you train these models based on synthetic data from feature models so like I guess the idea here is a lot of people now use chat GPT and Cloud so a lot of the content on the internet that's going to be used for the future version of them is generated by like gbt and CLA so if you have a lot of like synthetic data and use it to train a new model what happens and that paper just shows like these model just digate over time and

become gibberish if you keep training on data but maybe someone solves that so here are some of the things that I think are interesting and like the general space going forward uh one thing to note is for most stuff we talked about these are like AI problems like these are things to think about them productively you have to like understand like machine learning uh at some depth um so like for a lot of this stuff to progress in decentralized AI you would need also a lot of AI Talent machine learning talent to come and look at these things and that's I guess like why I have people like KC who like have you know traditional like machine learning experience is very important because these are not crypto problems like defi hacks this is like you know very deep uh

Technical Machine learning problems and we're trying to like let's say for example to scale up compute leverage all these like lated comput that's cool but that's not the hard bit the hard bit is like actually on the machine learning side and um yeah the other way around I think is lot more interesting like leveraging AI in crypto is like security and like trading Bots and all that stuff that's like there you the defi expertise to defi hacks and for that you don't need crazy AI talent but for the other aspect like web three helping AI you need like well a deep understanding of like machine learning yeah you go to an AI conference you go talk to your colleagues in Oxford or Google Casey and you you tell hey what are the biggest problems bottlenecks

that you're facing today and they list out top 10 you know you survey 100 people are any of those bottlenecks or problems constraints that they have today can be unlocked by crypto do you want me to ask researchers like if I put hypothe or conversation yeah like you go talk engineers and they're like do they see value I guess is the question in things that are unlocked by crypto the large the the only thing that I really feel would be the consensus is that people want AI to stay open they

really believe that they really want AI to be democratized and accessible and that is something that web 3 and web two are aligned on and I think the web 2 world sees crypto or decentralization as one Contender technology to keep that future protected other than that no but that is like a big thing and then there's all these like technical things like reducing hallucinations scaling as as Mo talked about um alternatives to Transformer models that are getting there's a few that are getting really popular um there there's like a lot of technical problems that we're still overcoming and things like mexture of experts are really popular right now but in terms of just like large vision and an ethical standpoint it's how do we

keep this technology accelerating and democratized and free and open and maybe maybe the web three Community could help there Mo do you agree with that you're it's the third time uh there's definitely like a lot of like chat about these things like safety alignment and so on there was this really interesting quote I'm paraphrasing here that I think George har tweeted he said something along lines of like when llama 400b came out he said an aligned model or Gro like when Gro came out he said an aligned model is a model that does what you tell to do H and is not give you a moral lecture based on like the values of people created it like when you go to J you TR to ask certain things just give

you like a lecture about like I don't know political views or whatever and he said like an onlined model is a model that does what he's to do and that's these are like a lot of interesting discussions about what does it mean for a model to be like quotequote aligned what does alignment actually mean what does safety actually mean should you open source stuff should not open source so on uh one other is interesting discussion is around How Stuff how these foundational models and all these tools impacting like job markets so there's this interesting thing about oh now with Claude and like let's say with Claud like people would that aren't very technical can now like for example create a game and all this stuff so like oh this enables a lot of people to come into AI this does not necessarily like uh help everyone uh because these AI

tools like amplify the best people to like a disproportionate degree like like normal people can now like come in and use CLA to create a game but the really best devs are like maybe like 100 De better with like a really good setup like that's like enhanced so like uh and you could argue then that the best thing to do in that setting is to open these models to everyone because you don't want like the most powerful tools that make very small people like a thousand X better only available to like a very small subset of people because that like puts them so far ahead so like democratizing like Ai and access to it and is something that's talked talk a lot and I think this this kind of stuff around tools like cursor and cloud is really interesting because it does make like a normal Dev like a lot better

maybe like 23x or whatever but I think the really best devs like are steroids once they have access to thei tools so it actually like creates more inequality in a sense I Haven I don't yeah sorry that will okay so I completely agree this is another Nuance which is that when I said software development is going to be Comm monetized actually today all it's doing is making good Engineers great Engineers it's not making no Engineers Engineers because you still need to understand how it all works and so it's like the everybody says oh it's the worst time to be an engineer it's the best time to be an engineer because you move faster but that will change like in two years I think it will help people go from zero to one yeah and that's why it's quite important for these things to be open so

as many people as possible come in and like try to do up yeah one other interesting Trend in general is around um video models and it's something particularly around my work X I'm very excited about because these like video language models are like so much bigger and like the stuff you sending over the internet are not like streaming text you're streaming video now and that is the kind of stuff where a lot of things around Edge become really interesting to me one is uh streaming videoos really hard because there's so much data like if if it was possible to stream video you probably would have Netflix trying to like stream stuff to like Airlines or something like on a plane St was pre-downloaded cuz like sending data over a bad Wi-Fi is like terrible and like these like video language models

could become like really powerful uh in the future and it's quite important for them to be local and one interesting thing that I learned about recently is like moving data around is extremely expensive so you have a lot of like climate considerations there I I don't remember the numbers up in my head but uh I think it was something like um in 2023 like the the cost of like moving data around across the world like uh D transmission was like more than of Spain which reminded me of a lot of these like stats around Bitcoin I remember like you say oh Bitcoin network has more energy than like Ireland so I remember like I compared like energy transmission data transmission cost in energy and like B the Bitcoin Network and it was more I think it was like

almost like 2X or something like moving data around is a very expensive thing and that's one of the things I'm excited about Edge Computing like doing stuff where the data is is like also a very important thing from an energy standpoint and I think these like video models like really useful uh in the future and for that like streaming is like really hard anyway streaming video over internet is like really hard and also like the environmental considerations there are like like massive and um yeah that's another thing that is interesting and I've seen some people in like web 2 AI talk about like the climate considerations of like developing these like large models uh but it's it's not very popular discourse yet but I I really I'm interested in that

yeah I guess I mean this has been a fascinating discussion I think we can go keep going on and on and on we should definitely do a followup and because I I would suspect that this map is going to continue to grow um and evolve and it's just such a good Central Point to to Anchor the discussion around uh I want to maybe end with something that I've thought a lot about which is this centralizing um force of Technology um it's incredibly powerful and abundant at the same time it is also very centralizing and unequal has been um how do you think how do you think AI um do you think AI I guess exponentiate that or it does not and does crypto play a role in the outcome I think that

AI is yes a naturally centralizing Force as most Technologies are and I do think that crypto is going to play a role or I wouldn't be here I think that it's very promising what the fundamental principles of crypto are and how it can play into the vision of of AI but I think we just need to be very careful in how we construct the future because I think there's also a lot of negative that crypto can bring to AI to be honest um if we move too fast and loose yeah I think it's good that this isn't near the end because I'm going to say some stuff that might be better to

cut out uh uh I think like yes I I do agree is like naturally a AI is naturally a centralizing force and there is some role of decentralization that could happen and help with that uh but I definitely like Casey's point just now I think it's very important like how this works like the ethos of decentralizing stuff is really good and this this actually like just brought it in AI like I assume like back in 201 like 13 2014 when people like working like the first version of ethereum and so on the ethos of crypto is like fairly admirable but then like quickly what happens is like you know like a lot of malicious stuff happens in the space a lot of you know scams and grifts and so on which you things like damage progress to some

extent and they take resources away from like good projects and there are all types of dynamics that are very hard to like reason about like you know maybe like um one interesting thing is like you know with a lot of projects you're using something from web 3 to help solve an AI problem but to assess something is an AI problem or not you need to understand that space and if you're like let's say a crypto investor there's a bit of a fog of War because you don't spend your days like looking into machine learning so like whether this makes sense or not is not immediately obvious so I guess you just go with the narratives and if there's like overfunding and a lot of like you know people need to deploy their funds and ownership targets you might have like a lot of capital B deploy like stuff just

like nonsense which like distracts resources for actually Mak sense which is like not just AI crypto but just like a general thing in the space of like um uh and yeah and yeah these kind of things like the deceleration the diesel and like the grips and stuff like that can definitely be damaging if we move too quickly and then the other type of moving too quickly which is um like poorly designed systems can also be very dangerous I'm not sure if you've seen this but uh something scared me last week was this guy wrote like a Twitter Thread about he used claw to create like a nuclear fusion at home which is great uh like you know just like you know open sze and all that stuff but it's also a little bit terrifying that you can like

someone in a dorm room with no har experience like made the N nuclear fusion and he had like you know like the metal thing and a protein Shaker that was part of the fusure I was like wait and that that is like these be quite dangerous you know like you don't want to put like nukes in everyone's hand so there is an argument around safety I'm just not sure what best the difference between nuclear fusion and then an actual nuke is is quite large but I take your point yeah also the counter argument of that is most uh nuclear uh accidents have been human error and so if the AI can correct that assuming he doesn't want to destroy Humanity then um you know it's a good thing because you would avoid Fukushima and all the other like three mile and all the other

nuclear accidents actually which have been human error and the other argument you make is like you know a big reason why many people leaked nuclear stuff in the Cold War to Russia was like you know if two people have nukes no one's going to use it yeah deterr mutually assure deter exactly like the best way could just be like you know the open everything Geor like you know alignment just like it should what you thought to do it should be open should be access to everyone uh but yeah so like that's just like a comment in general about like how like the ideas here I think are good but it doesn't mean we should just like you know go ahead and like everything and stuff like that and there's like real challenges like the fog of War if you're saying I'm gonna use like incentives or

like cryptography or something like that to go solve this like ANL problem like the people assessing this if they don't know enough machine learning is like very hard to do and there's no like easy way out of this and again like as an investor you don't want to like you know under invest is very dangerous because you might miss like like early project and so on so there's a lot of nuances that could like fuel what we've seen like you know in like normal crypto like the negative sentiment about crypto is like the public stim crypto is very negative because of all the the scams and griffs that happen over the years if you go to SF like you mentioned crypto a l people like really turned off immediately uh and I the dependent has swung in the other direction too hard crypto was really cool and that was like

really not cool I think on a podcast recently tun from the golet was saying that he was talking to some people and he was didn't want to say that he works in like crypto he just said I work in cyber security he's just like yeah notal thing at all and that could happen again in I crypto in particular by just like you know funding like all these right things I think that line will blur over time we make this distinction AI crypto and non- crypto that goes away I think for me my my central point around Ai and how it fits is I actually think that if there's a shot that we have to making it more equal and not as centralizing is with crypto so that so it's it's AI with crypto properties attached to it that

that kind of like acts as a counter Force to this Central natural like gravitational pool to centralization and I think it's begins and ends with why I think crypto is so powerful it's just a better coordination layer to whatever it is that you're doing whether it's Finance whether it's deep in or in AI it's just a better coordinate it's a it's a kind of Novel system to coordinate and I think it's really interesting for some of these like use cases like data labeling um I mean I've always felt like there are data Gatekeepers and that has facilitated centralization you think about Google meta Amazon um as more data gets created and gets created on chain um and it's open then and you can coordinate and

incentivize people more cost-efficiently it you're never going to win with ideology I think you win with practical um like Roi kind of stuff and I think crypto can be really good at that um I don't think there's any problem with that I think uh you know ideology is great but if it's not backed by real like tangible higher more efficient Roi better parody like we're never going to win and but actually now when you have like really opinionated models from centralized companies if you're a developer then maybe you would want to use open model um you know because you have to uh you can't really use a model that's really opinionated and tells you not to eat red meat or something like that it's kind of

bizarre um anyway really fascinating discussion both of you I know you're incredibly busy um so thank you for for coming on um we've linked a we've talked about a lot of uh different things you know blog post and this website with a map Casey people can contribute we'll link those in the show notes but I guess any parting thoughts um as we you know look to close the pod my one parting thought is that there's a dynamic in crypto where investors have made the most money on infrastructure to mohammad's point and specifically l1s and so a lot of Builders are pushed to build in that category because it's where the money lies and where

historical returns lie my push for the space is just to not fall into the fallacy that the future will be replicated by historical performance and just do the thing that you think is most technically interesting and makes the most sense from first principles instead of doing any copy pasta because I know it can be tempting but it's just it's not actually what's going to lead the entire space to the the place that we want it to be yeah I guess my parting thought which is how I think about this in general is like just try to solve a useful problem and if if it happens to e like crypto use crypto if the blockchain use the blockchain but just try to solve problem and like um I guess like even if

hypothetically you raise from some crypto investors but end up like being a very valuable ey company I don't think anyone's gonna mind like if you accidentally become open a ey or something uh so just try to solve a useful problem and this is like very hard or at least very rare to see like when you ask someone what does this problem solve what is this what problem is the solving for some reason like it's not clear to the people working on it which is like a bit dangerous so I don't get too caught up in like Ai and crypto and like all these different things just solve a problem and then like I love that yeah parting parting joint word solve problems yeah go go go to an AI conference and then ask people what their bottl links and problems are to that inti prior question and then I

think maybe in in that list of problems you'll find something that can be unlocked with AI with crypto or if not to solve the problem still or maybe there's a problem in crypto that would maybe like you you should build the like you know that problem with like pump. fun desability buildt pump. AI like a recommendation engine for pump. fun or something maybe that would be very useful like or I don't know I'm just saying stuff here don't actually build this like just someone's gonna go and build it for sure um if you do build if you build reach out to me there you go uh amazing well uh Casey MO thanks so much for coming on I mean I really enjoyed this discussion uh it was great learning for me and I'm sure our

audience will appreciate it as well so uh yeah thanks so much for for coming on and uh we'll we'll have to do a part three soon sounds good thank you Santiago thanks hey everyone big thanks for watching today's episode wanted to just quickly remind you about the Stellar development foundation's sixth annual Meridian conference this October 15th through 17th in London this is an incredible opportunity to network with the forward thinkers who are defining the future of this space head over to meridian. stellar .org and use the code blockworks pod for early bird pricing [Music]