Everything Bagel: Open Source AI, Security, and Decentralization with Greg Osuri, Akash Founder

Today , we're going to explore the future of open source AI with our special guest , greg Osuri , who's the founder of Akash , which is a decentralized compute marketplace . Greg , thanks so much for being here today . I appreciate you joining us . Great to be here , alex . Thank you so much for having me .

So for some context , I've known Greg for a while since , I think , before Akash actually launched , because we were pretty early investors back in the day , and one of the things I love about both Greg and Bidon is they're like open source purists , which is , you know , I got red pilled by open source by my partner , brian Fox , who's the , you know , the author

of the Baschel Rola GPL licenses , and both Greg and Bidon have been massive advocates of open source and are an inspiration to me in that realm .

Greg , I know you've been following Bagel , but I'm sure one of the things you love about them is like they ship really innovative software technology around AI open to everybody and , like most recently they figured out how to use ZKP zero knowledge proofs to prove who contributed what to an AI model , and that is what enables them to power monetizable open source AI

, which is huge , and they open source the white paper and all the code that shows it actually working and they like just to give you like an idea of the order of magnitude here , they reduce the time that it takes researchers to do the same thing from like 1000 thousands of hours to like two seconds with a team of like three researchers Like there are nine

people on their team total . So I've been like , yeah , really inspired about that . We've been talking a lot about that on the show , Just like our last episode that we just recorded was a decent . Part of the episode was like what's the insight ?

Like how do you get to those insights that lead to these kinds of big innovations when there's teams that have , you know , 10 times the amount of funding and 10 times the headcount ? But then this little team , this like underdog , the Dave and Goliath kind of story , ships open source , kind of like the deep seek thing that just happened .

I really feel like that what they did is very similar to that where it's way cheaper , way faster and came from this like way less funded , smaller team .

I wrote something in 2019 or I think 2018 , a post that went a little viral , that talked about how the amount of funding is inversely correlated to progress in early stage , not later stage , especially seed stage , and I wrote this saying I mean , there has historically hasn't been a single success story that raised or over fund , that's overfunded , that actually

innovated and I don't want to pick names in crypto , because if I say something it's going to cause a lot of scare , but classic . Do you remember ? There used to be an app called Color .

There hasn't been a single success story that came out with companies that raised millions and millions of dollars In crypto very classic . Now . Solana is a classic story . Ethereum is a classic story . Pretty much any teams I mean that are considered top in terms of usage .

Forget the market cap , because market cap can be may not be the actual way to estimate progress , but in terms of actual usage , solana raised what ? 30 million before they launched . I mean it's significantly lower to actually build . I remember because I was there the first , like the first round , ethereum raised what ?

18 million in their ico significantly lower to actually get started . Atom similar right and 15 million or so just enough to get you started . And most of the successful teams always have significantly lower funding . Because my thesis is once you have money , you have distractions .

Now you're under the radar to spend the money because people are not going to give you money without you know either , having conviction that you're going to spend the money , because people are not going to give you money without you know either , having conviction , that you're going to deploy the capital because you know the opportunity .

Cost of money is fairly high , right ? So you can't just like sit and have money waiting in the bank . You get a lot of pressure from investors and usually they have controlling . You know authority to a degree and then you're screwed . So screwed . But teams that have less funding , smaller sizes , your coordination cost is much lower .

Similar to how I believe Jeff Bezos has a saying that apparently in Amazon there is no single team that is too big for a pizza to share . If you cannot share a pizza with a team , that team is too big for a pizza to share , and if you cannot share a pizza with a team , that team is too big to work .

So the pizza team size is an optimal way of thinking like small teams actually make a lot of progress versus large , humongous teams with overfunding usually typically don't make progress because they have too much distraction . That's a thesis . But here's a very classic example here of how Bagel is innovating with less funding .

Because they're more resourceful , they're more ruthless when it comes to focus on the user and the market In a cash ride . We raised $1.8 million to launch what is a billion-dollar chain . Right , but a lot of that comes from our hyper-market focus and hyper-ruthless execution , and that comes with less money .

My first shock in terms of how to build this company came when talking to lawyers think it was cooley which was the innovator .

When it comes to marco centauri , I think he created this like thing called , say , saft right , the , the saft document based from safe and we were like , okay , we're just going to build a product and you know it's going to be a web-based product , and people are how do we get these tokens for two people to use ?

Uh , we're just going to charge them with a credit card . I was told that I'll go to jail if I do that . So first shock in terms because I'm grew up , you know , raised the whole notion that credit cards are the default ways you purchase something on the internet .

When I was told that you cannot use credit cards to sell , I was like , okay , can we throw an ad ? How do you get users ? Can we do google ads ? Like , no , crypto is banned from google . You cannot use crypto , cannot do anything . So the whole notion of user engagement and demand generation and payments that I knew was wrong in crypto .

So a lot of things , things are very different now , right , I mean you can launch a meme coin and go to billions of dollars of value and you still are okay . In fact , you didn't get presidential immunity . So lessons are different , right , and we have to go and do quite a lot of work to even get to using credit cards on Akash .

Now we have credit cards on Akash legally , without going through all the money services , license and all that stuff . And also what's the definition of a security and what's a commodity .

I mean , these days it's a lot more relaxed , right , I mean it got to a point where truly decentralized companies are getting like investigated , were getting investigated at least , but current administration has taken a very different policy position when it comes to crypto . So , like I almost have to go back to 2017 and rethink what I could have done .

You know that I was not allowed to from a legal standpoint , you know , and that's why we survived eight years , right , without getting in trouble in any way because we went by the book . So some of the things we did right , that's across the board was building a large community based on users and not based on speculators .

So even before we had a token in the market , we had a series of events we call them testnets where I mean remember , alex , I think your company was involved too , so you can . Actually we ran , we ran it out for years yeah , it was 2018 . We started testings from 2018 , 2019 , 2020 and we had , um , all kinds of cool things .

We had like challenges and whatnot . Everyone's . To complete a challenge by using the product , you get some , some , some credits . You can exchange the credits for tokens that do not exist yet .

So we were able to bootstrap our community pretty selective community , because they have to do be technical and they have to do a deployment , they use a command line , they have to be a provider , they have to be a validator , they have to do things on the network and you get some rewards right .

And when we launched , we had a community that you know , we airdropped tokens to that bootstrap our community and that I think , in my definition , one of the best things . We kickstarted this whole notion of Airdrops in a very different way .

Things are different now in terms of how people are launching Airdrops , but I think incentivizing your early users , not speculators , is a very , very good way of launching things . We went open source first , like day one , even before we launched . When you see my background , I'm very open source . I open source from license to and readmemd .

That's how I open source .

Would I do it from launch or would I not ? If you ask me , I would still do open source , but it comes down to the comfort level of the team . Right , because I've been doing open source for a long time . I'm comfortable doing open source . I'm not embarrassed of my code and no , it is code because I understand there's quite a lot of like restrictions .

When you go to someone else in my team being like , hey , we want to open source , they get very , very , very , you know , uncomfortable . And that I think you got to make sure you have an entire team buy-in , not a founder buy-in when it comes to open source .

And it's very , very important because that might impede your progress because people will be afraid to ship code to open because they feel judgmental . So there's all kinds of emotional aspects that you've got to deal with open source .

And third , I think like if I were to redo things right , we obviously launched the sovereign state chain and cosmos and akash is the first cosmos chain because there were there was a little option . The other option was ethereum , which is unusable till date . It's still unusable .

Like you can't expect people to pay 30 gas fees to make a deployment that's , you know that's way more expensive than actual deployment . You're paying with gpus . So from from a shade state chain ecosystem , it was non-existent beyond Ethereum . If I were to do today , I would most likely do it on Solana or one of these shade state chain systems .

When you do sovereign , yes , you have the benefits of control , but if you ask yourself deeply , deeply , do you really need that control in the early stages or can that control come later ? I would most likely would choose a more modular system where a mechanism that lets me start off with a shared state but I can transition to a sovereign state if I need to .

Classic example is SQL right and the reason why you want to do SQL , because SQL is a standard that can be used in MySQL or Postgres . All you need to do is SQL dump if you're using a shared database , and SQL import to a sovereign , a completely controlled database . If you need to right Similar analogies . I mean blockchain , I think should be .

It's a key value pair system . You should be able to interoperate technically from one key value pair to another key value pair . I mean there are nuances in terms of transactions and block space and whatnot , but ultimately , if you remove all the wrappers , it's just a key value pair state system . That's your . You know it's immutable key value pair .

It's it's immutable key value based . Yes , it says right . So some of the lessons like yes , that would save you quite a lot of security budget that you can repurpose for incentives . And one more thing we did absolutely right , absolutely right . I think a lot of them don't get it get , get these incentives .

So people think of using incentives to bootstrap a network . We did the opposite . We're using incentives to grow the network . Now , is it the right way , the wrong way ? That really depends on what you're trying to do . Right , it's . There's no silver bullet . You know that answer is not that helpful .

But in a scenario like , uh , let's say helium , helium , you need the network even before you get product market fit . So there's no way in hell you have to bootstrap the network because it doesn't exist the resources , but something like Akash , where there's abundance of compute everywhere . They've got 7 million data centers .

11 million of them are over 1 megawatt data centers . There's abundance of compute everywhere , so you don't need to bootstrap . People don't need money to go buy compute units , they already have . So bootstrap people don't need money to go buy compute units , they already have .

So now the question is do you want to incentivize early for that computer to come on board or do you want to experiment with the understand the behavior before you incentivize ?

I think we chose the latter and that's working out really well because you know like most compute networks today , straight up pay , you know , for talking in tokens to have their compute on the network . Like I would literally give you block rewards , every block for you to go put your compute on the network .

The problem is you know you don't know the quality of the compute , how good enough it is to your users . Does that fit your that compute , fit your market right ? How would you know ? By measuring utilization rates .

So if you have high utilization rates for a certain type of compute that obviously is in high demand , on akash , for example , h200s , which are which are best gpus to run deep seek , are 100 utilized . That tells you that h200s are high demand . H100s were pretty high utilization . Now they're at 70 a100 , similar right .

So we know that h100s , a100s , h200s , 49s have high demand . We know that we be 100s . The older chips have low demand . Now the question is how do you incentivize each chip ? So our incentive model now is , instead of straight up paying for the compute , we guarantee utilization because we know that if you have h200s you don't need incentives .

I mean , utilization is 100 , you don't need to be incentivized . But we do know that if you have h100s , where you , there's volatility in utilization . Sometimes it's high , sometimes it's lower . If you go to a provider and be like , I can guarantee you 80% utilization , like if you get under .

I mean , provided that you have high quality compute and the provider you pass all these marks . If you're underutilized , we'll make sure we increase utilization . How Well ? Just lower the price so we can subsidize the cost to the tenant . Because we know for sure from our data that if we lower the price for H100s to like 99 cents , they're gone .

So we have the product market fit . Now can we throw in a discount ? Everybody has discounts , right ? Cloud providers do provide discounts . So if we throw an extra discount they're gone . Know that for sure . So incentivizing after understanding we came to this conclusion after we saw how our cash works . There's no way in hell we would .

I mean we could draw math , model all that stuff in a in a room , but ultimately the customer's behavior is what's going to be the most valuable , most accurate inputs . You need to design incentives . So designing incentives post-launch , in growth phase , post-pmf , I believe , is working out really well for Akash . The numbers are very clear .

Our growth is very clear . Our utilization Not only revenue growth , but we also measure earnings per gpu and we also measure utilization per gpu right . So across the board utilization right now is 70 and that was what 10 when we began . Over the last 12 months it came really high . On per gpu right now is about 20 per day compared to 10 roughly .

That was about 12 months ago . So we clearly see an upper trend right . So some of the ways we look at how we design incentives , instead of blindly following and I think like Filecoin , I mean I can comment on incentive structures based on the outcomes of each deep end .

I can tell you with a degree of confidence what went wrong , what came right , what went wrong . A lot of opinions on like different incentive models at this point now .

For me , investing is all about pattern recognition . Right , it's like seeing these patterns and feeling that feeling and knowing to have conviction and take action on that conviction . It's a very like it's as much as a science and an art . I get that same feeling with bid on .

What strikes me of what you just said is you guys both approach these problems from first principles and with certain constraints in mind . Right , and it goes back to the earlier conversation we were having about like being very capital efficient . Right , like super capital efficient than open source from day one .

It's like these three things are very common between both of you . Like the last conversation that Don and I had last week was I asked him like what was the insight that led you guys to uncover this zero knowledge proof , innovation and the whole market ? All these researchers that were looking at how to execute this , were focused on burning compute .

Measuring was compute used , but really their insight was , instead of looking at the burn , like at the burning of the compute we're going to look at , did the model actually get improved ? And when they switched to like measuring that that's what the zero knowledge proof is proving is did the model improve by a certain amount . It changed everything .

Right , that was the big , the big difference .

So if a network is not working and you just put more money into it , that actually makes it worse . So if a network is not working and you just put more incentive into it , that makes it worse .

That's when you know what's useful in terms of the for the customer customer and then you can supercharge that with the incentives . That's how I see it as well . So totally , I see eye to eye with you on that . And now going back to the protocol verification protocol that alex was mentioning . So it's basically what we have done .

We figured out this like a modular structure for models where each contributor contributing they're building a model together , but they are not training this monolithic dense transformer together .

Instead , like they're providing this modular contributions , which are called like adapter layers , like lauras , they're providing that and they stack on top of each other like Lego pieces and all those Lego pieces come together and build a model . So it's a fundamentally different approach of seeing this thing .

Like a lot of people , a lot of teams , very talented teams , are trying to reduce the communication overhead over data centers to be able to train a dense transformer , monolithic model . We looked at it a different way .

We looked at it in a way like why can't we just make the architecture itself modular , which is very much in line with the industry trend at the moment , because MOEs are a rage right now ? Deepseq is a mixture of expert and this is a modular architecture , so they keep the transformer core and modularize it for efficiency .

We did the same and then what that enabled us is that the contributions are , first of all , they can come from one data center each contribution , so you don't need that much of a communication to begin with . And second , the contributions are small enough that you can do zk verification of that .

So when the person or developer or the data center developing this modular adapter , they run a zk verification on top and it's way less overhead .

But then even that was like higher than what would be acceptable in a production environment , because in production environment you want seconds , not minutes , not hours or not days , and the previous example of verified training were weeks . So what we did ?

We looked into how this works and we saw that the previous attempts of ZK verifying these contributions were tied to compute . They were trying to verify . If compute was burned Then , like we , I personally have been in machine learning for more than 10 years and my team has like more than 40 papers published in machine learning together .

So we have like extensive experience of training models in the traditional ML and we know that's not how it works In traditional machine learning . You do these massive training runs , you get the result and you look at the evals and if they don't match up to your standard you just throw them out . So they don't really count how much compute they burnt .

They count how much contribution , how much improvement to the model that was done for this specific training graph . So that was kind of the mismatch between the traditional ML and the Web3 AI side . In Web3 AI we were just incentivizing compute , which makes sense to some extent .

But if we don't actually look at the quality of those compute usage then we get lower quality supply on our network and that does not incentivize the consumer set to come in and put their capital in and use the resources . That's there , yeah .

I think Moose is doing a similar approach . If I'm not wrong , in the distro they did present a mechanism where they're reducing the communications overhead for distributed training , but also for the verification . I believe they took a similar approach . But it's fascinating , very cool .

And we believe this is the first and we open sourced it the both the paper and and the code , and it has been peer-reviewed by people at Stanford , U of T and whatnot . It works and we believe we have solved the ZK verifiable training .

We are using that framework initially for fine-tuning only , but this is the stepping stone of going towards actually having fully ZK verified training in a distributed setup . So that's what we are up to with this .

When token , we have launched the first version of our product and we are planning on going to mainnet very soon as well , and token , as we already discussed , like a kind of similar thought process as you , we believe that we want to do the product market fit and feed in the token into that .

But going back to what I was saying , is that , like that's how we see it , like incentivizing actual equality , not incentivizing , you know , just whatever contribution , and that's that was the gap in all of these machine learning related resource networks . Right , and this is so versatile , the framework we have built .

It can snap on top of any compute aggregated network , like Akash , and instantly convert it to a verifiable training network . So the compute can just feed into that , and that has a lot of value as well , because it increases the value of this like compute aggregated networks at the same time .

And the last point I want to make about open source we love open source . I believe in open source as well . There are lots of upsides to that , but there are some downsides . We have noticed as well I'm sure you have as well .

I was discussing that with Alex a couple of episodes ago that we open sourced our research on a very fast model verification algorithm watermarking and fingerprinting early last year and that has been adopted by actually quite a few well-known Web3 AI projects in the space and they're using that , which I'm happy about .

But the downside of this is that we were not credited when that algorithm became the core part of the protocol . So sometimes that happens right Like you open source it , people like it , but they just use it for their own use .

Again , I'm kind of biased because of Brian who helped write those licenses , but for me , if it's my company and my team making it , I like it to be a GPL or GPL . Just because open source software for me is my legacy , I want it to live forever .

I want it to go to Mars , like bash Bash is literally powering quadcopter on Mars , and I want it to be there for everybody , for the world . Right , and I feel super strongly about that . It's a personal choice for other teams , like I don't judge other people for choosing different licenses and whatnot , I get it , but for me that's it's .

I don't know again , it's my legacy etched in stone , etched in bare metal on the internet .

A lot of the deep end networks that are launching just with Akash straight up code base , you know , change the names . There was even instances where the founders were like doing demos and I could clearly see AKT in there as a payment mechanism . They haven't even bothered to change the currency . But yeah , but it is a problem . I mean , crypto is sadly .

I think a more restrictive license is definitely an answer , which I hate to be saying that , but I bet the ones that copied you have enormous funding .

I don't know what the right phrase here is , but the Apple doesn't fall from far from the tree or whatever . I'd be curious to hear what you think about this , Greg , but I was like ready to go to war . I was like dude , let's call this out Like it's , it's bullshit , you know , and I would go to war . You know what I mean ?

Yeah , Like right , Like it's it's , it's complete BS . Maybe there's still time for us to do that bit . Back to where we started this conversation .

It's about execution , thinking about things from first , principles and the people involved , and even if it was closed source software , you know it's easy to copy people and we've seen that happen in SaaS all the time .

Smaller team comes by , build the exact same product out , executes the well-funded Goliath , and then they've got a huge company and the other company ends up dying at some point .

That's just been a repeated story that we see , and I think this same thing applies from open source and the thing that happens when you don't open source is you lose all those network effects , you lose all that community . And you know , one of the reasons why I joined Solana is because of that pattern recognition we're talking about .

I saw the same thing that I saw in you , Greg , with Tully and Raj , and they were shipping day one open source . And I even asked Tully as kind of a test , like , hey , what happens if someone right whatever left click copy or right whatever the right click ? You got to do copies , your code base ?

And this is back when they had that office on Howard street . They were just starting out , they hadn't even launched main yet , yet , I don't think .

And they and they and they were shipping a really good idea in public and he was like please fork it , go for it , Cause the other part of it about the execution , especially in crypto , is getting to where you are , Greg , with a real community , a real customer base , actual people providing node services .

You know , leasing , leasing and registering like providers , dude , that is a whole ball of wax , it's a it's . It's hard to replicate that , Um , and you're only stronger because of the open source community . I just , I just believe that you know to my core and ultimately , you know , karma is a bitch for the people who don't respect that .

You know , I was there literally two weeks ago . I did the first video and they needed testimonials . If you go to Solana's YouTube , I'm like one of the first videos they did In that office . I had a photo shoot too . I took like black and white photos of them , literally like one week from shipping . So funny .

That was like right before covid hit and they hit . Right when covid hit they launched and I was concerned because we were like planning to launch . A few months from there they crashed and it was crazy . Unfortunately , I was able to like rake up some solana tokens at like one dollar , two dollar range or less than that .

I think it's significantly less than that , but they were . So I mean totally . I remember totally was like the software was not ready to get shipped , but totally was like we're gonna ship and that's that's execution right , because you're never ready . It's like being a dad you're never ready . It's like being a dad You're never ready .

You just got to go for it . It's never going to be perfect , but I think it takes the pragmatism that tells you quite a lot on execution Out-execute , you know , people are going to copy you . People are going to copy you because they're good at it , but they cannot execute you right . Out-execute you right . So I would execute ship as aggressively as possible .

Build that community based on truth . People like drama , you know , people like the fact that you know somebody copied you . That actually builds a stronger community in my opinion . It did for me , for us , right , and you get more sticky community .

But focus on the community very important , I think , one of the best community builders I see in the space and , of course , like and toli and they're very , very good at it . But I think in jacob const reborn jacob from bit tensor or even shaw walters from from ai 16z .

I mean , it's more modern times , but but I I admire community builders and I studied them . Um , I think Jacob has quite a lot of admiration . I mean really think about how he builds BitTensor . Go to the discourse and understand how the discourse works and how the communications are working there . Spend a day in BitTensor discourse .

Spend a day in AI16 discourse . I was so impressed by Shawaw's gtm in terms of like they had this eliza framework open source from day one of the top trending repo in in the women , it's like for a month or something .

Like it's crazy , and I was training on github like 10 years ago , so I understand what it means to be trending and what that brings you in terms of like inbounds and it's just a crazy phase you go through .

And the reason why I think he was so successful in shipping software is you know his he would do like this , like massive three hour long videos on explaining the software and you would think like this video has to be succinct and like to the point because nobody has time , like no .

He proved that if you explain things well , people will sit and listen to you and that's what Joe Rogan did and a lot of the new podcasters long-form podcasts are about . If the content is interesting enough , people will listen to you and think you know . Shaw definitely understood that and his videos are like I watch a three-hour

video on friday night to understand eliza and I sat through it , right , you know , and I was surprised that I could spend . I mean , I don't watch tv that much , I mean especially youtube and whatnot , not for that long but but there's something to be understood about these founders and how they build communities around open source systems . Same thing with Jacob .

Jacob would spend hours on out . Jacob's weapon is Discord right , and everybody has their own choice of platforms , right . That is something that I think cannot be forked , no matter how hard you try , and I think that's one of the reasons why I think I'm not endorsing ai16z as a token or any of that sort .

I'm just observing community builders and seeing their patterns . Uh , like you say , it's pattern recognition . Right now , I can recognize these patterns among successful founders , successful builders . They all have one thing in common is cut the bullshit , be pragmatic . Don't worry about getting judged .

If you have to go on a three-hour podcast , three-hour video , where are you going to make mistakes ? It's not going to be perfect because it's life , right . I've noticed so much , so much , I guess , like , like engagement . When I do live raw videos without filters , you know people love it .

I mean , I just randomly go on twitter and be like hey , I'm going to live stream my coding session . People love to see me code , right because , like people , love to see other people play games . You know things of that nature will give you quite a lot of like true community .

That well-funded company with big PR teams will be prohibited from going live because they have they're too afraid to lose .

He's a great product manager . The way he would approach a PRD using AI these days is just amazing because I need to check that out it's amazing .

So he starts with , like black document why and what , why they want to do something and what they're going to do Like a few lines and sentences , plain English , and he develops this PRD using different tools , using DeepSea , using , you know , chaigpt , you know O3 , and actually he does the V0 , the whole , like you know , user interface thing , and he does it

live . And that's awesome Because you see the mistakes people make and you see someone work . A great way of improving your work is to imitate somebody's work and I had that in my career for a long time . I imitated when I was learning Go . A lot of my imitation came from Mish Hashimoto , the founder of HashiCorp , developing Terraform and whatnot .

I learned from how these people code . Looking at the code . I love learning by other , watching experts or people that I consider good to work , and I think that speaks quite a lot in how these founders build communities , and we need open source . I think is a great way to imitate them .

The conversations I've had with Ben on the last couple of months have really helped me develop this thesis around open intelligence , and the term open intelligence is actually came from a brainstorming session that Markeisha and I had . She leads marketing over at Bagel , but basically it's artificial general intelligence or artificial super intelligence . On crypto rails .

It's open source , right Open source or AGI on crypto rails . I have come to believe that this is really really important for humanity and for our industry and that it really should be the North Star of every founder . That's , building critical infrastructure should be to help make this happen , to accelerate open intelligence .

Bagel is critical for it and I think a cash is critical for it . I think for obvious reasons , but I also am asking myself , like , am I crazy ? Right , like you know , can we do this ? Can we get a bunch of entrepreneurs who are building these systems to work together to create open intelligence for the sake of humanity ? Am I being like overly dramatic ?

Is it even possible ? Like , and then , if it is possible , like , what are the components ? It's like I look at it like a factory system and there's like inputs and outputs and there's a bunch of tooling that needs to be built in between to make it happen , and I I'm like trying to develop like a vision for what that is .

So that's , I know that's a ton , but generally speaking , like how am I doing ? Here are we , are we going to make it to open intelligence ?

In modern era , even , as a matter of fact , phones , I mean , without Android , you wouldn't have the same level of adoption if it just were , yeah , transformers for open source , right ? So open source is so critical for mass adoption because the network effects would not come otherwise .

And we've seen very clearly what happened with DeepSeek , right , very , very obvious . Like , until DeepSeek , the state-of-the-art model was a closed model and DeepSeek came and changed things , and this is just going to be a stepping stone in reaching ultimate intelligence . Now , have we done that before ? Yes , we have World Wide Web .

Like , without the companies that innovated early failures , successes , whatnot we wouldn't have the open internet that we so much use . Right , 20 years from now , you look back and be like we'll be asking the question . So , looking back to internet itself , right , it did not begin as open . Internet began very close , aol was literally shipping cds .

You needed aol to get on the internet , right , look where aol is today . We're still around . But it's not the internet company , right , it is the companies that actually operate the internet . So , similarly like open ai , I think would be the new aol of the companies that actually operate the internet .

So , similarly like OpenAI , I think would be the new AOL of the world . Yes , they were the first to innovate with ChatGPT . I mean not first to innovate , but at least first to get to market with ChatGPT and definitely like proved product market fit with ChatGPT . That kicked off an entire industry .

But I think the true intelligence through AGI will be open source . And if you look at how do you monetize and how do you sustain open source software , decentralization is the way . Crypto is the way .

There is no better mechanism that I can think of and I have factual evidence for this , looking at successful open source companies that open this products that could not create a successful mechanisms to sustain themselves . So , looking back , there's no doubt in my mind that crypto is the only mechanism , only framework to have monetizable .

You know machine intelligence right . So that's really comes down to it . I'm giving a Bindan's pitch right here . But open source is good as long as it can be monetizable and sustainable , and I think crypto is a great way . So , yeah , it's going to be multiple people , multiple organizations , multiple protocols , rather interoperating with each other permissionlessly .

If Bagel wants to use Akash , no one can stop Bagel from using Akash . No one can stop you from getting the H200s for $0.99 . Right , and that's what it comes down to . If I want to use Bagel , no one can stop me to use Bagel , and I can build protocols on top of .

I mean . You want to review code , you have a question ? You should be able to go read the code yourself and you have AI . Now they explain a lot of things on how things work . I mean , back in the day it's like actually read the code , figure things out . That takes a while , but I can feed that into an AI and be like explain what's going on .

The logic here , right . There are so much tools now that open source adoption is going to be accelerated with tools as well as creation as well .