(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

>> live from Boston, Massachusetts. It's the Cube covering A W s reinforce 2019. Brought to you by Amazon Web service is and its ecosystem partners. >> Hey, welcome back and run cubes. Live coverage of A W S Amazon Webster's reinforced their inaugural conference around security here in Boston. Messages. I'm John for a day. Volante Day we've been talking about Blockchain has been part of security, but no mention of it here. Amazon announced a Blockchain intention, but was more of a service model. Less of a pure play infrastructure or kind of a new game changes. So we thought we would get our friends to come on, the Cuban tell. Tell us about it. Val Birch, Avicii CEO and founder. A pencil day that Cube alumni formerly of NetApp, among other great companies, and Ed You, founder and CEO of Strong Salt. Welcome to the Q. Tell us why aren't we taught him a Blockchain at a security conference on cloud computing, where they always resource is different. Paradigm is decentralized. What's your take? >> So maybe having been in this world for about 18 24 months now, Enterprise lodging reinvents about six months ago and jazz he mentioned that he finally understood US enterprise an opportunity, and it was the integrity value, finest complex, even announced a specific product announced database available, >> maybe bythe on cryptographic verifiability of transactions minus the complexity of smart contract wallets. Wait, you party with Amazon way too. Versions right? One for distributed use cases. When I call, everyone rises. Never like you need to know what >> the Amazon wants to be that hard on top like complexity. But the reality is, they're they're They're world is targeting a new generation star 14 show is the new generation of developing >> a >> new generation of David. They were. Some of those are in trouble, and I'm hard core on this because it's just so obvious. >> I just can't get him behind myself if you don't >> see this out quicker. The new developers are younger and older systems people. There's a range of ages doing it. They're they're seeing the agility, and it's a cultural shift, not just the age thing. Head this. They're not here right now. This is the missing picture of this show, and my criticism of reinforces big, gaping hole around crypto and blocks, >> and I actually know that people I don't see anything here because it is difficult to currency. >> Blocking is very important that people understand way. Launch strong allows you to see the launching. I don't think that works. Basically, Just like Well, well said everything you do, you always have a single source. I think that's something that people doing this thing here. You want to get your thoughts on this because you made a comment >> about security native being the team here and security native implying that Dev ops what they did for configuration hardening the infrastructures code. You have to consider this token economic business model side of it with the apple cases, a decision application is still an application. Okay. Blockchain is still in infrastructure dynamic their software involved. I mean, we're talking about the same thing is they're lost in translation. In your opinion? >> Well, yeah, I think that you know, to your point, Val, if you can abstract that complexity away, But the fundamentals of of cryptography and software engineering and game theory coming together is what always has fascinated me about this space. And so you're right. I think certainly enterprise customers don't wanna you know, they hear crypto, though no, although it's interesting it was just a conference IBM yesterday. They talk a lot about Blockchain. Don't talk about crypto to me. They go together. Of course, IBM. They don't like to talk a lot about job loss and automation, but But the reality is it's there and it's it's it's has a lot of momentum, which is why you started the company. >> Yeah, we're actually seeing it all over right now. And again, our thing is around reducing, If not eliminating the friction towards adopting Blockchain so less is more. In our case, we're explicitly choosing not to do crypto wallets or currency transactions. It's that Andy Jassy observation the integrity value, the core integrity, value for financial reconciliation, for detecting supply chain counterfeiting for tracking assets and inventory across to your distribution. Unifying multiple source systems of record into a shared state. Those are the kinds of applications received >> culture, and there's so many different use cases, obviously, so >> an Amazon likes to use that word. Words raised the bar, which is more functionality, but on the other, phrases undifferentiated, heavy lifting. There's a lot of details involved in some of those complexity exactly what you're talking about that can be automated away. That's goodness. But you still have a security problem of mutability, which is a beautiful thing with Blockchain. >> Actually, a lot of times people actually forgot to mention one thing that blotchy and all you do that's actually different before was Actually privacy is actually not just security is also privacy, which actually is getting bigger and bigger. As we know, it's something that people feel very strongly about because it's something they feel personal about. And that's something that, in fact, took economics encourages a lot of things that enables privacy that was not able to do before. >> Well, look at Facebook. What do you think about >> face? I'm wonder that you know, I'm a public face book critic. I think they've been atrocious job on the privacy front so far in protecting our data. On the other hand, if you know it's kind of like the mullahs report, if you actually read Facebook's white paper, it's a it's not a launch. It's an announcement. That's a technical announcement. It's so well written, designed so far, and it's Facebook doesn't completely control it. They do have a vision for program ability. They're evolving it from being a permissions toe, ultimately a permission less system. So on paper, I like what I read. And I think it will start to, you know, popularizing democratize the notion of crypto amongst the broader population. I'm going to take a much more weight see approach. Just you know, >> I always love Facebook. I think the den atrocious job. But I'm addicted. I have all my stuff on there, um, centralized. They're bringing up, they bring in an education. Bitcoin is up for a reason. They're bringing the masses. They're showing that this is real market. This is kind of like when the web was still viewed as Kitty Playground for technologists say, Oh, well, it's so slow. And that was for dummies. And you had the Web World Wide Web. So when that hit, that same arguments went down right this minute, crypto things for years. But with Facebook coming, it really legitimizes that well, you bring 2,000,000,000 people to the party. Exactly a lot of good. Now the critics of Facebook is copied pass craft kind of model and there's no way they're gonna get it through because the world's not gonna let Facebook running run commerce and currents. It's like it's like and they don't do it well anyway. So I think it's gonna be a game changing market making move. I think they'll have a play in there, but I don't think that's not gonna have a global force. Says a >> lot that you get 100 companies to put up 10 >> 1,000,000 Starship is already the first accomplice. >> They don't need any more money. We have my dear to us, but >> still the power but the power of that ecosystem to me. I was a big fan of this because I think it gives credibility. So many companies get get interested in it, and I'm not sure exactly what's gonna come out of it. It's interesting that, you know, Bitcoins up. They said, Oh, cell, you're becoming like No, no, no, this is This is a very mature >> Well, I I think open is gonna always win. If you look at you know, the Web's kind of one example of kind of maturity argument. I think the rial analog for me, at least my generation value probably relate to this. David, you as well, you know, I've been born yet you are But, you know, T c p I p came after S n a which IBM on the deck net was the largest network at that time to >> not serious. Says >> mammal. Novell was land all three proprietary network operating systems. So proprietary Narcisse decimated by T c p i p. So to me, I think even their Facebook does go in there. They will recognize that unless they stay open, I think open will always win. I think I think this is the beginning of the death of the closed platform. >> Yeah, they're forced her. I think they have to open it up because if you didn't open up, people won't trust them, and people will use them. And if a Blockchain if you don't have a community behind it, there will be nothing. >> Well, so the thing about the crypto spraying everywhere with crypto winter, But but to your point d c p i p h t t p d >> N s SMTP >> Those were government funded or academic funded protocols. People stop spending money on him, and then the big Internet companies just co opted. No, no, that's what G mails built on. >> Well, I've always said >> so But when you finish the thought, is all this crypto money that came in drove innovation? Yeah, So you're seeing, you know, this new Internet emerge, and I think it's it's really think people, you know, sort of overlooked a lot of the innovation that's >> coming. I have always said, Dave, that Facebook is what the Web would look like if Tim Berners Lee took venture financing. Okay, because what they had at the time was a browser and the way that stand up websites for self service information. They kept it open and it drives. Facebook became basically the Web's version of a, well, lengthen does the same Twitter has opened. They have no developer community. So yeah, I think it is the only company in my opinion, actually does a good job opening up their data. Now they charge you for that. It brings up way still haven't encrypt those. The only community that's entire ethos is based on openness and community you mentioned. And that is a key word >> in traditional media. Of course, focus on the bad stuff that happens, but you know those of us in the business who will pay attention to it, see There's a lot of goodness to is a lot of mission driven, a lot of openness, and it's a model for innovation. What do you guys think about the narrative now to break up big tech? You know you're hearing Facebook, Amazon, Google coming under fire. What are your thoughts on that? >> So I wrote a block, maybe was ahead of its time about 18 months ago. Is coincided with Ginny Rometty, a Davos and 2018 2019 talking about data responsibility. Reason we're having this conversation is at the tech industry. By and large and especially the fang stocks or whatever we're calling them now have been irresponsible with our data. The backlash is palpable in Europe. It's law in Europe. Backlash we knew was going to start at the state level here. There's already ahead of my personal schedule. Federal discussions, FTC DOJ is in a couple weeks ago, so it's inevitable that this sort of tech reckoning is coming in. Maur responsibility is gonna have to be demonstrated by all the custodians of our data, and that's why we're positioning. Check it as a chain of custody is a service to demonstrate to the regulators your customers, your partners, suppliers, you know, transparency, irrefutable transparency, using Blockchain for how you're handling data. You know, if you don't have that, transparency can prove it. Or back to the same old discussions were back Thio Uninformed old legislators making you know Internet, his tubes type regulations. So here, here >> and DOJ, you could argue that they may be too slow to respond to Microsoft back in the nineties. I'm not sure breaking up big tech is the right thing, because I think it's almost like a t. The little Tex will become big checks again, but they should not be breaking the law. >> I think there's a reason why is there's actually a limitation off. What is possible in technology because they understand and also Facebook understands well, is that it's actually very, very hard to have data that's owned by your customers. But you are the one who's keeping track over everything, and you are the one using the data right. It's like a no win, because if you think about encryption cryptography, yes, you can make the data encrypted. That way, the customer has the key. They control it, but then Facebook can offer the service is. So now you have a Congress thinking, Well, if there's no technological way of doing this, what can you do in a legal perspective on a, you know, on the law perspective, toddy make it so that the customer actually owned the data. We actually think that is a perfect reason why you have to actually fix the book. Actually, technical should be built on our platform because we actually allow them to have a day that's encrypted and stupid able to operations holiday tha if the customer give them the permission to do so. And I think that's the perfect word way to go forward. And I think Blockchain is the fundamental thing that brings everybody together, you know, way that actually benefits everyone knows >> and take him into explain strong salt your project. What's it about? What's the mission? Where you >> so so we see strong saw as actually privacy. First, we literally are beauty, a platform where developers including Facebook linked and salesforce can't you build on top of platform, right? So what happens when you do this is that they actually give the data governess to the customers, customers Mashona data. But because our cryptography they actually can offer service is to the customers. When a customer allowed them to do so, for example, we have something. All search of encryption allows you to encrypt the data and still give the search. Aubrey on the data without decrypting the data. First, by giving the power to developers and also the community there, you can have our abstract you currently use. But they're not hard to use that frictionless and still offer the same service that Frank Facebook or sell stolen offer the favor. >> You could do some discovery on it. >> You can't do things >> some program ability around >> exactly, even though the data is encrypted. But custom owns the day. So the customer has to give them permission to do so Right this way. Actually, in fact, launched the first app that I told you it's called strong vote. You can Donald ios or Andrew it And you can't you see the Blockchain play little You can see the rocking your fingerprint. I think a fingertip to see what happens to a data. You see everything that happens when Sheriff I or you open a fire or something, I guess. >> Congratulations, Val. Give a quick plug for your project chain kid into the new branding. They're like it. Pencil data. Where are you on your project? >> So after nine months of hard selling, we're finding out what customers actually paying for right now. In our case, it's hardening their APS, their data and their logs and wrapping the chain of custody around those things. And the use case of the security conference like this is actually quite existential When you think about it, One of the things that the industry doesn't talk enough about is that every attack we read about in the headlines was three privilege escalation. So the attackers somehow hacked. Your Web server managed to get administrative credentials and network or domain administrative credentials. And here's what professional attackers do once they have godlike authority on your network. They identify all the installed security solutions, and they make themselves invisible because they can. After that, they operate with impunity. Our technology, the security use case that we're seeing a lot of traction is, is we can detect that we're applying Blockchain. We're agnostic, so bring your own Blockchain in our case. But we're able >> chain kit a product. Is it a development environment >> globally. Available service Jose on AWS rest ful AP eyes and fundamentally were enabling developers to harden their app stuff to wrap a chain of custody around key data or logs in their laps so that when the attacker's attempt a leverage at administrative authority and tamper with locks tamper >> with service, not a software, >> it's a apply. It's a developer oriented service, but >> this is one of the biggest problems and challenges security today. You see the stat after you get infiltrated. It takes 250 or 300 days to even detect, and I have not heard that number shrink. I've heard people aspire number streaking this. >> We can get it down to realize a crime tip of the spear. That's what we're excited to be here. We're excited to talk about One of the dirty secrets of the security industry is that it shouldn't take a year to detect in advance attack. >> Guys, Thanks for coming on. Cuban sharing your insight. Concussions in your head. Well, great to see you. >> Likewise. And thank you, j for having us on here, and we're looking forward to coming back and weigh. Appreciate. Absolutely >> thankful. Spj Thanks for you. >> It was always paying it forward. Of course, really the most important conversation, that security is gonna be a Blockchain type of implementation. This is a reality that's coming very soon, but we're here. They do is reinforce. I'm talking about the first conference with Amazon Web sources dedicated to sightsee. So's Cee Io's around security jumper. Develop the stables for more coverage. After this short break, >> my name is David.