Well, thank you um for the introduction and um Protocol Labs and Filecoin Foundation have been big supporters of Fluence Labs and we appreciate that. Um you know, you can't have decentralized compute without decentralized storage. Um both you need both of them and we all need both of those for this ecosystem to be truly decentralized. And so while we wait for my slides, I think the first one is going to be pretty obvious, but it's about chat GPT and about the growth of chat GPT. And I think everybody here has seen that.
The stats are interesting. It's a million users in like a week and 100 million users in a month. And this is without a doubt the most the fastest growth of any consumer related application in the in history. And that is catalyzing a tremendous amount of interest in in AI. And if we go beyond that, you know, you think about what's it used for, there are some terrifically fun uses for it.
And we've seen people, you know, I saw John Oliver did a comedy routine where he had a rap written about cats in the style of Eminem. So that is something you can do with it. But there is a terrific number of commercial uses as well. And on the slide you can't see I have a list of those uses um which go from health care to finance to accounting education media you know law um video editing developers code both writing debugging a terrific amount of potential uses but the problem is that old we're just not getting on Yeah. Heat.
I don't need. Nope. No. It's it's it's right here. PowerPoint.
Hooray. We're up. Okay. So, I'm not going to I'll just give this quick quick review. So, here we are.
This just is a graphic depiction of the scale of chat GPT's growth, which I think a picture tells a thousand words. So, you know, 100 million users in pretty much zero time scale given everything else. And catching up here, commercial uses besides the fun uses are remarkable. All right, I have a request to go back for a picture on this slide. There we go.
We're good. Um, oh, too slow. Next time. Um, and um, these are the commercial uses, but the problem with the commercial uses is we have these things called regulations and certifications. And this is just a snapshot of California as an example.
These are all the professions for which regular a certification is required which means a study, a test and a degree. And a lot of these have to do with physical activities related to people and have a health component to it. But really what these are are protectionist measures put in place by industries to protect the um earnings and make it harder for people to actually enter those industries. And these go back, you know, hundreds of years to guilds, right? This is nothing new, but you had very protectionist interests of every particular industry, particularly not just unions, but also white collar.
And so it shouldn't be a surprise that one of the first industry reactions to chat GPT entering it is the legal profession where um Bill Browder's son was going to go to a court case and have the court case against a traffic ticket argued via chat GPT prompt in an ear and the bar association found out about this because he publicized it and they threatened him with six months in jail if he went ahead and did that. Right. And this is clearly not who's harmed by have fighting a traffic ticket with with AI prompts in your ear, right? The only people harmed is the legal profession and they are very scared of this. So came down very hard.
This is the first example of an industry really recoiling against and fighting against AI and I expect we will see much much more of it. Um the other risk with a chat GPT in particular is these are centralized for-profit companies that have and Google is as well obviously for which if you try to build a business on it you are subject to their pricing their API and anything you build on they can decide to cut you off. Obviously we saw this back in for people remember the Farmville Facebook but we've also seen it happen on Twitter thirdparty applications Twitter closed those down as well LinkedIn did as well. So whenever you have a valuable platform that has power, you try to build on it, you're a real risk of being cut off. So you need an open-source alternative that you are safe to build on.
Otherwise, your business can be taken out from under you at any moment. Next risk is what happens when chat GPT scales to many hundreds of millions or billions of users. At that point, governments start to become interested. You know what's the answer? Should I wear a mask?
Should my kids be vaccinated? Is was there Armenian genocide? You know, is exgovernment um corrupt? Right? These are answers that don't that certain governments first may not like the answer.
Second of all, different governments will have different responses that they want. And you know, in Europe, as just an example, vaccines aren't offered to kids under 18 in a whole bunch of um countries. US, you get them almost mandatory from 6 months, right? So, how is this going to play out when you have a company that is creating answers to these questions that are being asked globally with very different government interests in what those answers are? Well, we have a can look to Twitter and we now have every day more and more evidence of different countries exerting different influence on Twitter and you can expect that to come from totalitarian companies like China, like Russia.
That is expected. But democracies, India in particular has had a very heavy hand with Twitter silencing and banning opposition figures and for and not allowing um protest uh uh videos and protest information to be shared a couple months ago. Um and they got very aggressive threatening jail for it. Also happened in the US obviously and let's not forget that you know you were banned for talking about a lab leak theory that now is relatively um you know mainstream. So I think you see now multiple reasons and multiple re um rationale for having an open transparent and free learning language model.
And your first response might be well listen Microsoft spent $10 billion in this. How on earth can open source compete? And the answer is first Bloom is already out there um in starting this effort. But Linux in 2008 estimated $10 billion worth of developer resources were deployed to build Linux. And if you've been starting to build Linux early on, no one or very few people would have given you a real um chance of survival versus OS um versus you know Microsoft operating systems and they did it and are dominant now.
And so I think what will happen is not a one learning language model to compete with Google AI, but it will start much more specifically and narrow. You could have a DAO raise, for example, to raise to focus perhaps just a model that's focused on pediatric medicine diagnosis, right? Super small or someone even smaller say, you know, I don't think minority children are particularly appropriately represented in broad LLM. So I want to have a DAO that's just focused on the research around minority children health issues as an example that is a type and and so it's not clouded by data that's separate or that is distinct from that particular group and so you could see those type of models start be very specialized much easier to fund and to build and also be open and transparent safe to build on. Once you have that though, you end up with a whole suite of specialized open source models.
The front end becomes very important at that point because you want to have one front end that you can access and use all these models with. And the front end is also subject to potential um censorship and vulnerability. And we've seen you know you know Bing didn't like some of the answers that its model was given after was giving after a variety of um prompts. So it's limited the front end to only ask six questions, right? So this front end is also as important as the training of the data and as the training of the model.
And so distributed serverless compute enters here because that allows you to run this model anywhere and make it make run the front end and then run the model anywhere to make it unstoppable. The other part is you can run the model and have proofs that the model was actually run and you weren't just given an answer that was already generated for somebody else for even a different question. And again coming back to health care or law, you may care a lot that the model was actually run and actually generate the question that your prompt um requested and that that model was a certified model run by the particular DAO that has been um focused on that particular issue and was credible in it. And so what's required to run this type of distributed chat GPT or learning language model and you need distributed serverless architecture is what we think is kind of the crit critical piece here. The main point of it is a fault tolerant design and so you're not you're not dependent on any network or connectivity provider or hardware and um if a host goes down the application continues to function by traveling to other hosts and distinction here what doesn't work or what's not ideal because when you think of decentralized compute there's a couple other aspects to it compute at the edge and hardware marketplaces both of those are terrific solutions that are um very usable and viable, but they don't work so well here, mainly because they don't have failover, at least in the ones that I've seen.
And so it's compute at the edge or hardware marketplaces work, but they're still isolated to the particular location they're happening, so are subject to both um uh both both censorship risk and and can easily go and much more easily go down. And so how does Fluence enter into all this? Well, it's a distributed serverless platform that's fault tolerant by default. um and has and it obviously has failover and it has a security model that verifies the computations have been completed. That's a critical piece that is you ask of any distributed um computation.
It also has a native language AUA that makes building these frontends far easier because it abstracts out all the diff all the complexity of peer-to-peer applications and distributed applications which is very difficult to build from scratch and the marketplace where you can buy and buy this compute helps drive price down. So, but if it's good for learning language models, this distributed compute actually has far broader applications um and broader use to applications generally. And so applications can live on the distributed global network with no single point of failure and no single point of censorship in a very price competitive um environment. And so it can scale up or down on demand and be free to move from any provider. And this gets to the cloud lockin.
You avoid cloud lock in. And so if you're running a serverless compute, your application is on that. Someone may be running instance at Google. Someone may be and then you decide I want to move to Amazon. You can move it to Amazon server.
Someone is hosting there to your own server to somebody else. And that level of um and that and that level of that ability to move um that ability to move from provider to provider allows you allows both price competition to be terrific. And it also allows you to be free of the cloud ecosystems. And you know why do we not like cloud ecosystems? This is an example of parlor.
But the I don't think anyone's lives here has been dramatically impacted by the shutdown of parlor. But it just gives an example of the power of clouds to turn off your application if they decide you violate their term of service. And this was a big application, right? Millions of users. And it was shut down with like a day or so's notice.
Why is that relevant for us? Well, guess what? Web 3 is really dependent on Amazon. And I don't know if anyone's heard of Gary Gensler and um the SEC, but but there is a risk here that we are all bearing. Um all these applications are using the cloud and until we free ourselves from the cloud, we are at risk.
And the cloud notice is not some white puffy benign entity. It's a storm cloud because it's dangerous and we should all be very wary of it. Not only is it expensive, but it is a risk we all bear. And there's also centralized backends to a lot of DAPs. And this again isn't anybody's fault.
It's just the infrastructure hasn't been built yet to allow these applications and these products to move off of the cloud. And that's what we are trying to do. And we think that decentralized serverless compute is the next evolution of compute. So you started with mainframes, people are drag, kicking and screaming into the cloud which is clearly won so far and is you know been an enormous success but decentralized serverless compute will be faster. It'll be more secure um and it'll be cheaper and so you get on demand scalability, higher security, competitive pricing, it's more censorship resistant um it's fault tolerant and this is you know this is what basically we have to go towards or else we are subject to oligopoly um of Google and Amazon which I don't think anyone in this ecosystem wants.
So compute is kind of the key core piece of that. And so the Fluence decentralized platform has two pieces to it. A developer platform and a computing marketplace. And the developer platform is designed to mimic the cloud stack. And so we have again no slides um which I just noticed but um it mimics the cloud stack with an a AWS kind of lambda um analogy the step function and rest API and aqua and then kind of fluent CLI which which obviously enhances building.
So there's a variety of the idea is to make the journey building very similar to a cloud analogy and if you don't as much as we don't like centralization people will still use the cloud. So we have to make it as easy and simple as we can in order to do that and that's what we're working on doing at Fluence. Um this was actually really relevant to see which we're not but um cloud native basically accesses services through an API. you cut off that API and you lose access to the microservices. With Fluence, you access the microervices directly um through marine.
And so as long as you have access to the internet, your compute travels to each of the microservices, you need to execute in order for your application to run. And that means that there's no API to cut off and there's no one place that you can be um can be shut down. Verifiability. There's two parts of this. Proof of processing.
You want to make sure that your aqua execution and that's verified by every host along the way. And then also proof of execution where you have zk proofs. Look at this for marine. Um and so we got luckily we can visualize the verifiability. Um but this was relevant.
I just would love to be able to show that which now I can't. Um it's gone. it only likes that slide. Um um and then and then the final piece is a compute marketplace which is um you know global scale. When you have a marketplace you are obviously driving pricing to um the most competitive value that you can find and you know Amazon and Google have 60% margins.
Those margins aren't coming from nowhere. They're coming from everyone that's either using the services built on the cloud or they're coming from the people building on the cloud. That is a lot of value to be distributed out to the ecosystem. And when you have a compute marketplace, that value will acrue to people building and to people and then probably goes to people using the applications as well. So the goal here is to actually compress that margin and put it and allow us to build and use it um to to to better um better our applications and better the universe versus Amazon's bottom line.
So um Fluence is moving forward. We've we've we've got the market. We've we've developed Aqua, which is the core language. Um the we've done developer workshops while we're here. I encourage you to check it out.
We have several um we have a handful of bounties that people are pursuing. The next step for us is launching a DAO which is where our governance will take place. It's going to be a Swiss association and the DAO will then um likely issue a coin which will be related to the use of the network which is important and that's part of the compute economy which will allow um host to be paid for compute and then also um uh users to actually make deals with compute providers and run their applications in a truly decentralized way. So um you know AI helps us maximize our potential but it needs to be free and compute needs to be free. Centralized compute will will kind of runs the risk of wrecking this whole ecosystem we're a part of.
So decentralized serverless compute is what we're we're we're building here. So please follow us at fluence project and here is um the website's fluence.network and you can find us um at Discord, Fluence. and Twitterfluence project. So, sorry for the technical difficulties, but thank you guys for coming and listening.
Automatic transcript — names and jargon may be misspelled.