(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)

(bright upbeat music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCUBE. We've got a great conversation featuring Arthur AI. I'm your host. I'm excited to have Adam Wenchel who's the Co-Founder and CEO. Thanks for joining us today, appreciate it. >> Yeah, thanks for having me on, John, looking forward to the conversation. >> I got to say, it's been an exciting world in AI or artificial intelligence. Just an explosion of interest kind of in the mainstream with the language models, which people don't really get, but they're seeing the benefits of some of the hype around OpenAI. Which kind of wakes everyone up to, "Oh, I get it now." And then of course the pessimism comes in, all the skeptics are out there. But this breakthrough in generative AI field is just awesome, it's really a shift, it's a wave. We've been calling it probably the biggest inflection point, then the others combined of what this can do from a surge standpoint, applications. I mean, all aspects of what we used to know is the computing industry, software industry, hardware, is completely going to get turbo. So we're totally obviously bullish on this thing. So, this is really interesting. So my first question is, I got to ask you, what's you guys taking? 'Cause you've been doing this, you're in it, and now all of a sudden you're at the beach where the big waves are. What's the explosion of interest is there? What are you seeing right now? >> Yeah, I mean, it's amazing, so for starters, I've been in AI for over 20 years and just seeing this amount of excitement and the growth, and like you said, the inflection point we've hit in the last six months has just been amazing. And, you know, what we're seeing is like people are getting applications into production using LLMs. I mean, really all this excitement just started a few months ago, with ChatGPT and other breakthroughs and the amount of activity and the amount of new systems that we're seeing hitting production already so soon after that is just unlike anything we've ever seen. So it's pretty awesome. And, you know, these language models are just, they could be applied in so many different business contexts and that it's just the amount of value that's being created is again, like unprecedented compared to anything. >> Adam, you know, you've been in this for a while, so it's an interesting point you're bringing up, and this is a good point. I was talking with my friend John Markoff, former New York Times journalist and he was talking about, there's been a lot of work been done on ethics. So there's been, it's not like it's new. It's like been, there's a lot of stuff that's been baking over many, many years and, you know, decades. So now everyone wakes up in the season, so I think that is a key point I want to get into some of your observations. But before we get into it, I want you to explain for the folks watching, just so we can kind of get a definition on the record. What's an LLM, what's a foundational model and what's generative ai? Can you just quickly explain the three things there? >> Yeah, absolutely. So an LLM or a large language model, it's just a large, they would imply a large language model that's been trained on a huge amount of data typically pulled from the internet. And it's a general purpose language model that can be built on top for all sorts of different things, that includes traditional NLP tasks like document classification and sentiment understanding. But the thing that's gotten people really excited is it's used for generative tasks. So, you know, asking it to summarize documents or asking it to answer questions. And these aren't new techniques, they've been around for a while, but what's changed is just this new class of models that's based on new architectures. They're just so much more capable that they've gone from sort of science projects to something that's actually incredibly useful in the real world. And there's a number of companies that are making them accessible to everyone so that you can build on top of them. So that's the other big thing is, this kind of access to these models that can power generative tasks has been democratized in the last few months and it's just opening up all these new possibilities. And then the third one you mentioned foundation models is sort of a broader term for the category that includes LLMs, but it's not just language models that are included. So we've actually seen this for a while in the computer vision world. So people have been building on top of computer vision models, pre-trained computer vision models for a while for image classification, object detection, that's something we've had customers doing for three or four years already. And so, you know, like you said, there are antecedents to like, everything that's happened, it's not entirely new, but it does feel like a step change. >> Yeah, I did ask ChatGPT to give me a riveting introduction to you and it gave me an interesting read. If we have time, I'll read it. It's kind of, it's fun, you get a kick out of it. "Ladies and gentlemen, today we're a privileged "to have Adam Wenchel, Founder of Arthur who's going to talk "about the exciting world of artificial intelligence." And then it goes on with some really riveting sentences. So if we have time, I'll share that, it's kind of funny. It was good. >> Okay. >> So anyway, this is what people see and this is why I think it's exciting 'cause I think people are going to start refactoring what they do. And I've been saying this on theCUBE now for about a couple months is that, you know, there's a scene in "Moneyball" where Billy Beane sits down with the Red Sox owner and the Red Sox owner says, "If people aren't rebuilding their teams on your model, "they're going to be dinosaurs." And it reminds me of what's happening right now. And I think everyone that I talk to in the business sphere is looking at this and they're connecting the dots and just saying, if we don't rebuild our business with this new wave, they're going to be out of business because there's so much efficiency, there's so much automation, not like DevOps automation, but like the generative tasks that will free up the intellect of people. Like just the simple things like do an intro or do this for me, write some code, write a countermeasure to a hack. I mean, this is kind of what people are doing. And you mentioned computer vision, again, another huge field where 5G things are coming on, it's going to accelerate. What do you say to people when they kind of are leaning towards that, I need to rethink my business? >> Yeah, it's 100% accurate and what's been amazing to watch the last few months is the speed at which, and the urgency that companies like Microsoft and Google or others are actually racing to, to do that rethinking of their business. And you know, those teams, those companies which are large and haven't always been the fastest moving companies are working around the clock. And the pace at which they're rolling out LLMs across their suite of products is just phenomenal to watch. And it's not just the big, the large tech companies as well, I mean, we're seeing the number of startups, like we get, every week a couple of new startups get in touch with us for help with their LLMs and you know, there's just a huge amount of venture capital flowing into it right now because everyone realizes the opportunities for transforming like legal and healthcare and content creation in all these different areas is just wide open. And so there's a massive gold rush going on right now, which is amazing. >> And the cloud scale, obviously horizontal scalability of the cloud brings us to another level. We've been seeing data infrastructure since the Hadoop days where big data was coined. Now you're seeing this kind of take fruit, now you have vertical specialization where data shines, large language models all of a set up perfectly for kind of this piece. And you know, as you mentioned, you've been doing it for a long time. Let's take a step back and I want to get into how you started the company, what drove you to start it? Because you know, as an entrepreneur you're probably saw this opportunity before other people like, "Hey, this is finally it, it's here." Can you share the origination story of what you guys came up with, how you started it, what was the motivation and take us through that origination story. >> Yeah, absolutely. So as I mentioned, I've been doing AI for many years. I started my career at DARPA, but it wasn't really until 2015, 2016, my previous company was acquired by Capital One. Then I started working there and shortly after I joined, I was asked to start their AI team and scale it up. And for the first time I was actually doing it, had production models that we were working with, that was at scale, right? And so there was hundreds of millions of dollars of business revenue and certainly a big group of customers who were impacted by the way these models acted. And so it got me hyper-aware of these issues of when you get models into production, it, you know. So I think people who are earlier in the AI maturity look at that as a finish line, but it's really just the beginning and there's this constant drive to make them better, make sure they're not degrading, make sure you can explain what they're doing, if they're impacting people, making sure they're not biased. And so at that time, there really weren't any tools to exist to do this, there wasn't open source, there wasn't anything. And so after a few years there, I really started talking to other people in the industry and there was a really clear theme that this needed to be addressed. And so, I joined with my Co-Founder John Dickerson, who was on the faculty in University of Maryland and he'd been doing a lot of research in these areas. And so we ended up joining up together and starting Arthur. >> Awesome. Well, let's get into what you guys do. Can you explain the value proposition? What are people using you for now? Where's the action? What's the customers look like? What do prospects look like? Obviously you mentioned production, this has been the theme. It's not like people woke up one day and said, "Hey, I'm going to put stuff into production." This has kind of been happening. There's been companies that have been doing this at scale and then yet there's a whole follower model coming on mainstream enterprise and businesses. So there's kind of the early adopters are there now in production. What do you guys do? I mean, 'cause I think about just driving the car off the lot is not, you got to manage operations. I mean, that's a big thing. So what do you guys do? Talk about the value proposition and how you guys make money? >> Yeah, so what we do is, listen, when you go to validate ahead of deploying these models in production, starts at that point, right? So you want to make sure that if you're going to be upgrading a model, if you're going to replacing one that's currently in production, that you've proven that it's going to perform well, that it's going to be perform ethically and that you can explain what it's doing. And then when you launch it into production, traditionally data scientists would spend 25, 30% of their time just manually checking in on their model day-to-day babysitting as we call it, just to make sure that the data hasn't drifted, the model performance hasn't degraded, that a programmer did make a change in an upstream data system. You know, there's all sorts of reasons why the world changes and that can have a real adverse effect on these models. And so what we do is bring the same kind of automation that you have for other kinds of, let's say infrastructure monitoring, application monitoring, we bring that to your AI systems. And that way if there ever is an issue, it's not like weeks or months till you find it and you find it before it has an effect on your P&L and your balance sheet, which is too often before they had tools like Arthur, that was the way they were detected. >> You know, I was talking to Swami at Amazon who I've known for a long time for 13 years and been on theCUBE multiple times and you know, I watched Amazon try to pick up that sting with stage maker about six years ago and so much has happened since then. And he and I were talking about this wave, and I kind of brought up this analogy to how when cloud started, it was, Hey, I don't need a data center. 'Cause when I did my startup that time when Amazon, one of my startups at that time, my choice was put a box in the colo, get all the configuration before I could write over the line of code. So the cloud became the benefit for that and you can stand up stuff quickly and then it grew from there. Here it's kind of the same dynamic, you don't want to have to provision a large language model or do all this heavy lifting. So that seeing companies coming out there saying, you can get started faster, there's like a new way to get it going. So it's kind of like the same vibe of limiting that heavy lifting. >> Absolutely. >> How do you look at that because this seems to be a wave that's going to be coming in and how do you guys help companies who are going to move quickly and start developing? >> Yeah, so I think in the race to this kind of gold rush mentality, race to get these models into production, there's starting to see more sort of examples and evidence that there are a lot of risks that go along with it. Either your model says things, your system says things that are just wrong, you know, whether it's hallucination or just making things up, there's lots of examples. If you go on Twitter and the news, you can read about those, as well as sort of times when there could be toxic content coming out of things like that. And so there's a lot of risks there that you need to think about and be thoughtful about when you're deploying these systems. But you know, you need to balance that with the business imperative of getting these things into production and really transforming your business. And so that's where we help people, we say go ahead, put them in production, but just make sure you have the right guardrails in place so that you can do it in a smart way that's going to reflect well on you and your company. >> Let's frame the challenge for the companies now that you have, obviously there's the people who doing large scale production and then you have companies maybe like as small as us who have large linguistic databases or transcripts for example, right? So what are customers doing and why are they deploying AI right now? And is it a speed game, is it a cost game? Why have some companies been able to deploy AI at such faster rates than others? And what's a best practice to onboard new customers? >> Yeah, absolutely. So I mean, we're seeing across a bunch of different verticals, there are leaders who have really kind of started to solve this puzzle about getting AI models into production quickly and being able to iterate on them quickly. And I think those are the ones that realize that imperative that you mentioned earlier about how transformational this technology is. And you know, a lot of times, even like the CEOs or the boards are very personally kind of driving this sense of urgency around it. And so, you know, that creates a lot of movement, right? And so those companies have put in place really smart infrastructure and rails so that people can, data scientists aren't encumbered by having to like hunt down data, get access to it. They're not encumbered by having to stand up new platforms every time they want to deploy an AI system, but that stuff is already in place. There's a really nice ecosystem of products out there, including Arthur, that you can tap into. Compared to five or six years ago when I was building at a top 10 US bank, at that point you really had to build almost everything yourself and that's not the case now. And so it's really nice to have things like, you know, you mentioned AWS SageMaker and a whole host of other tools that can really accelerate things. >> What's your profile customer? Is it someone who already has a team or can people who are learning just dial into the service? What's the persona? What's the pitch, if you will, how do you align with that customer value proposition? Do people have to be built out with a team and in play or is it pre-production or can you start with people who are just getting going? >> Yeah, people do start using it pre-production for validation, but I think a lot of our customers do have a team going and they're starting to put, either close to putting something into production or about to, it's everything from large enterprises that have really sort of complicated, they have dozens of models running all over doing all sorts of use cases to tech startups that are very focused on a single problem, but that's like the lifeblood of the company and so they need to guarantee that it works well. And you know, we make it really easy to get started, especially if you're using one of the common model development platforms, you can just kind of turn key, get going and make sure that you have a nice feedback loop. So then when your models are out there, it's pointing out, areas where it's performing well, areas where it's performing less well, giving you that feedback so that you can make improvements, whether it's in training data or futurization work or algorithm selection. There's a number of, you know, depending on the symptoms, there's a number of things you can do to increase performance over time and we help guide people on that journey. >> So Adam, I have to ask, since you have such a great customer base and they're smart and they got teams and you're on the front end, I mean, early adopters is kind of an overused word, but they're killing it. They're putting stuff in the production's, not like it's a test, it's not like it's early. So as the next wave comes of fast followers, how do you see that coming online? What's your vision for that? How do you see companies that are like just waking up out of the frozen, you know, freeze of like old IT to like, okay, they got cloud, but they're not yet there. What do you see in the market? I see you're in the front end now with the top people really nailing AI and working hard. What's the- >> Yeah, I think a lot of these tools are becoming, or every year they get easier, more accessible, easier to use. And so, you know, even for that kind of like, as the market broadens, it takes less and less of a lift to put these systems in place. And the thing is, every business is unique, they have their own kind of data and so you can use these foundation models which have just been trained on generic data. They're a great starting point, a great accelerant, but then, in most cases you're either going to want to create a model or fine tune a model using data that's really kind of comes from your particular customers, the people you serve and so that it really reflects that and takes that into account. And so I do think that these, like the size of that market is expanding and its broadening as these tools just become easier to use and also the knowledge about how to build these systems becomes more widespread. >> Talk about your customer base you have now, what's the makeup, what size are they? Give a taste a little bit of a customer base you got there, what's they look like? I'll say Capital One, we know very well while you were at there, they were large scale, lot of data from fraud detection to all kinds of cool stuff. What do your customers now look like? >> Yeah, so we have a variety, but I would say one area we're really strong, we have several of the top 10 US banks, that's not surprising, that's a strength for us, but we also have Fortune 100 customers in healthcare, in manufacturing, in retail, in semiconductor and electronics. So what we find is like in any sort of these major verticals, there's typically, you know, one, two, three kind of companies that are really leading the charge and are the ones that, you know, in our opinion, those are the ones that for the next multiple decades are going to be the leaders, the ones that really kind of lead the charge on this AI transformation. And so we're very fortunate to be working with some of those. And then we have a number of startups as well who we love working with just because they're really pushing the boundaries technologically and so they provide great feedback and make sure that we're continuing to innovate and staying abreast of everything that's going on. >> You know, these early markups, even when the hyperscalers were coming online, they had to build everything themselves. That's the new, they're like the alphas out there building it. This is going to be a big wave again as that fast follower comes in. And so when you look at the scale, what advice would you give folks out there right now who want to tee it up and what's your secret sauce that will help them get there? >> Yeah, I think that the secret to teeing it up is just dive in and start like the, I think these are, there's not really a secret. I think it's amazing how accessible these are. I mean, there's all sorts of ways to access LLMs either via either API access or downloadable in some cases. And so, you know, go ahead and get started. And then our secret sauce really is the way that we provide that performance analysis of what's going on, right? So we can tell you in a very actionable way, like, hey, here's where your model is doing good things, here's where it's doing bad things. Here's something you want to take a look at, here's some potential remedies for it. We can help guide you through that. And that way when you're putting it out there, A, you're avoiding a lot of the common pitfalls that people see and B, you're able to really kind of make it better in a much faster way with that tight feedback loop. >> It's interesting, we've been kind of riffing on this supercloud idea because it was just different name than multicloud and you see apps like Snowflake built on top of AWS without even spending any CapEx, you just ride that cloud wave. This next AI, super AI wave is coming. I don't want to call AIOps because I think there's a different distinction. If you, MLOps and AIOps seem a little bit old, almost a few years back, how do you view that because everyone's is like, "Is this AIOps?" And like, "No, not kind of, but not really." How would you, you know, when someone says, just shoots off the hip, "Hey Adam, aren't you doing AIOps?" Do you say, yes we are, do you say, yes, but we do differently because it's doesn't seem like it's the same old AIOps. What's your- >> Yeah, it's a good question. AIOps has been a term that was co-opted for other things and MLOps also has people have used it for different meanings. So I like the term just AI infrastructure, I think it kind of like describes it really well and succinctly. >> But you guys are doing the ops. I mean that's the kind of ironic thing, it's like the next level, it's like NextGen ops, but it's not, you don't want to be put in that bucket. >> Yeah, no, it's very operationally focused platform that we have, I mean, it fires alerts, people can action off them. If you're familiar with like the way people run security operations centers or network operations centers, we do that for data science, right? So think of it as a DSOC, a Data Science Operations Center where all your models, you might have hundreds of models running across your organization, you may have five, but as problems are detected, alerts can be fired and you can actually work the case, make sure they're resolved, escalate them as necessary. And so there is a very strong operational aspect to it, you're right. >> You know, one of the things I think is interesting is, is that, if you don't mind commenting on it, is that the aspect of scale is huge and it feels like that was made up and now you have scale and production. What's your reaction to that when people say, how does scale impact this? >> Yeah, scale is huge for some of, you know, I think, I think look, the highest leverage business areas to apply these to, are generally going to be the ones at the biggest scale, right? And I think that's one of the advantages we have. Several of us come from enterprise backgrounds and we're used to doing things enterprise grade at scale and so, you know, we're seeing more and more companies, I think they started out deploying AI and sort of, you know, important but not necessarily like the crown jewel area of their business, but now they're deploying AI right in the heart of things and yeah, the scale that some of our companies are operating at is pretty impressive. >> John: Well, super exciting, great to have you on and congratulations. I got a final question for you, just random. What are you most excited about right now? Because I mean, you got to be pretty pumped right now with the way the world is going and again, I think this is just the beginning. What's your personal view? How do you feel right now? >> Yeah, the thing I'm really excited about for the next couple years now, you touched on it a little bit earlier, but is a sort of convergence of AI and AI systems with sort of turning into AI native businesses. And so, as you sort of do more, get good further along this transformation curve with AI, it turns out that like the better the performance of your AI systems, the better the performance of your business. Because these models are really starting to underpin all these key areas that cumulatively drive your P&L. And so one of the things that we work a lot with our customers is to do is just understand, you know, take these really esoteric data science notions and performance and tie them to all their business KPIs so that way you really are, it's kind of like the operating system for running your AI native business. And we're starting to see more and more companies get farther along that maturity curve and starting to think that way, which is really exciting. >> I love the AI native. I haven't heard any startup yet say AI first, although we kind of use the term, but I guarantee that's going to come in all the pitch decks, we're an AI first company, it's going to be great run. Adam, congratulations on your success to you and the team. Hey, if we do a few more interviews, we'll get the linguistics down. We can have bots just interact with you directly and ask you, have an interview directly. >> That sounds good, I'm going to go hang out on the beach, right? So, sounds good. >> Thanks for coming on, really appreciate the conversation. Super exciting, really important area and you guys doing great work. Thanks for coming on. >> Adam: Yeah, thanks John. >> Again, this is Cube Conversation. I'm John Furrier here in Palo Alto, AI going next gen. This is legit, this is going to a whole nother level that's going to open up huge opportunities for startups, that's going to use opportunities for investors and the value to the users and the experience will come in, in ways I think no one will ever see. So keep an eye out for more coverage on siliconangle.com and theCUBE.net, thanks for watching. (bright upbeat music)

>> Announcer: From Miami Beach, Florida, it's The Cube, covering Acronis Global Cyber Summit 2019. Brought to you by Acronis. >> Welcome back everyone. We are here with The Cube coverage for two days. We're wrapping up, getting down on day one in the books for the Acronis Global Cyber Summit 2019. I'm John Furrier, your host of The Cube. We are in Miami Beach, the Fontainebleau Hotel. I'm personally excited for this next guest because I'm a huge Red Sox fan, even though I got moved out to California. Giants is in a different area. National League is different than American League, still my heart with the Red Sox. And we're here with an industry veteran, seasoned professional in IT and data, Brian Shield. Boston Red Sox Vice President of Technology and IT. Welcome to The Cube, thanks for joining us. >> Thank you. It's great to be here. >> John: So congratulations on the rings. Since I moved out of town, Red sox win their World Series, break the curse of the Bambino. >> Hey we appreciate that. Thank you. >> My family doesn't want me back. You got to show >> Yeah, maybe I'll put this one up for the, maybe someone can zoom in on this. Which camera is the good one? This one here? So, there ya go. So, World Series champs for at least for another week. (laughter) >> Bummer about this year. Pitching just couldn't get it done. But, good team. >> Happens. >> Again, things move on, but you know. New regime, new GM going to come on board. >> Yup. >> So, but in general, Red Sox, storied franchise. Love it there. Fenway Park, the cathedral of baseball parks. >> Brian: Defnitely. >> And you're seeing that just play out now, standard. So just a great place to go. We have tickets there. So, I got to ask you. Technology, sports, really is modernized faster than I think any category. And certainly cyber security forced to modernize because of the threats. But sports, you got a business to run, not just IT and making the planes run on time. >> Sure. >> Scouts, money, whatever. >> Fans. >> You got fan experience. >> Stadium opportunities. >> Club management, scouts are out there. So you got business, team, fans. And data's a big part of it. That's part of your career. Tell us what the cutting edge innovation is at the Red Sox these days. >> I think baseball in general, as you indicated, it's a very evolving kind of environment. I mean historically I think people really sort of relish the nostalgia of sports and Fenway Park being as historic as it is, was probably the pinnacle of that, in some respects. But Red Sox have always been leaders and baseball analytics, you know. And everyone's pretty familiar with "Moneyball" and Brad Pitt. >> John: Is that a true story, he turned down the GM job? >> I'm told it is. (laughter) I don't know if I fully vetted that question. But over the last six, seven years, you know we've really turned our attention to sort of leveraging sort of technology across the businesses, right? Not just baseball and analytics and how we do scouting, which continues to evolve at a very rapid pace. But also as you pointed out, running a better business, understanding our fans, understanding fan behavior, understanding stadiums. There's a lot of challenges around running an effective stadium. First and foremost to all of us is really ensuring it's a great fan experience. Whether it's artificial intelligence, or IoT technologies or 5G or the latest Wifi, all those things are coming up at Fenway Park. You and I talked earlier about we're about to break ground for a new theater, so a live theater on the outside, beyond the bleachers type of thing. So that'll be a 5,400-seat arena, 200 live performances a year, and with e-sports, you know, complementing it. It just gives you an example of just how fast baseball is sort of transitioning. >> And the theater, is that going to be blown out from where that parking garage is, structure and going towards >> So the corner of Landsdown and Ipswich, if you think of that sort of corner back there, for those that are familiar with the Fenway area. So it's going to be a very big change and you'll see the difference too from within the ballpark. I think we'll lose a couple of rows of the bleachers. That'll be replaced with another gathering area for fans and things like that, on the back end of that theater. So build a great experience and I think it really speaks to sort of our ability to think of Fenway as more of a destination, as a venue, as a complementary experience. We want people to come to the area to enjoy sports and to enjoy entertainment and things. >> You know Brian, the consumerization of IT has been kicked around. Last decade, that was a big buzzword. Now the blending of a physical event and digital has certainly consumed the world. >> Absolutely. >> And we're starting to see that dynamic. You speak to a theater. That's a physical space. But digital is also a big part of kind of that complementary. It's not mutually exclusive for each other. They're integrated business models. >> Absolutely. >> So therefore, the technology has to be seamless. The data has to be available. >> Yup. >> And it's got to be secure. >> Well the data's got to be ubiquitous, right? I mean you don't want to, if we're going to have fans attending theater and then you're going to go to Fenway Park or they leave a game and then go to some other event or they attend a tour of Fenway Park, and beyond maybe the traditional what people might think about, is certainly when you think about baseball and Fenway Park. You know we have ten to twelve concerts a year. We'll host Spartan games, you know. This Christmas, I'm sorry, Christmas 2020 we now have sort of the Fenway Bowl. So we'll be hosting the AAC ACC championship games there with ESPN. >> John: Hockey games? >> Hockey games. Obviously we have Liverpool soccer being held there so it's much more of a destination, a venue for us. How we leverage all the wonderful things about Fenway Park and how we modernize, how we get basically the best of what makes Fenway Park as great as it is, yet as modern as we can make it, where appropriate to create a great fan experience. >> It's a tough balance between balancing the brand and having things on brand as well. >> Sure. >> Does that come into your job a lot around IT? Saying being on brand, not kind of tearing down the old. >> Yeah absolutely. I think our CEOs and leadership team, I mean it's not success for us if you pan to the audience and everyone is looking at their phone, right? That's not what we aspire to. We aspire to leverage technology to simplify people's experience of how do you get to the ballpark, how do I park, how do I get if I want to buy concessions or merchandise, how do I do it easily and simply? How do we supplement that experience with maybe additional data that you may not have had before. Things like that, so we're doing a lot of different testing right now whether it's 4D technologies or how we can understand, watch a play from different dimensions or AI and be able to perhaps see sort of the skyline of Boston since 1912, when Fenway Park launched... And so we sort of see all these technologies as supplemental materials, really kind of making it a holistic experience for fans. >> In Las Vegas, they have a section of Las Vegas where they have all their test beds. 5G, they call it 5G, it's really, you know, evolution, fake 5G but it's a sandbox. One of the challenges that you guys have in Boston, I know from a constraint standpoint physically, you don't have a lot of space. How do you sandbox new technologies and what are some of the things that are cool that people might not know about that are being sandboxed? So, one, how do you do it? >> Yeah. >> Effectively. And then what are some of the cool things that you guys are looking at or things they might not know about that would be interesting. >> Sure. Yeah so Fenway Park, we struggle as you know, a little bit with our footprint. You know, honestly, I walk into some of these large stadiums and I get instant jealousy, relative to just the amount of space that people have to work with and things. But we have a great relationship with our partners so we really partner, I think, particularly well with key partners like Verizon and others. So we now have 5G partially implemented at Fenway Park. We expect to have it sort of fully live come opening day next year. So we're really excited about that. We hope to have a new version of Wifi, the latest version of Wifi available, for the second half of the year. After the All-Star Break, probably after the season's over. But before our bowl game hopefully. We're looking at some really interesting ways that we can tease that out. That bowl game, we're really trying to use that as an opportunity, the Fenway Bowl, as an opportunity to make it kind of a high-tech bowl. So we're looking at ways of maybe doing everything from hack-a-thons to a pre-egaming sort of event to some interesting fan experiential opportunities and things like that. >> Got a lot of nerds at MIT, Northeastern, BU, Bentley, Babson, all the schools in the area. >> Yeah, so we'll be reaching out to colleges and we'll be reaching out to our, the ACC and AACs as well, and see what we can do to kind of create sort of a really fun experience and capitalize on the evolving role of e-sports and the role that technology can play in the future. >> I want to get to the e-sports in a second but I want to just get the plug in for Acronis. We're here at their Global Cyber Summit. You flew down for it, giving some keynote speeches and talks around security. It's a security company, data protection, to cyber protection. It is a data problem, not a storage appliance problem. It's a data problem holistically. You get that. >> Sure. Sure. >> You've been in the business for a long time. What is the security kind of posture that you guys have? Obviously you want to protect the data, protect privacy. But you got to business. You have people that work with you, supply chain, complex but yet dynamic, always on environment. >> That's a great question. It's evolving as you indicated. Major League Baseball, first and foremost, does an outstanding job. So the last, probably last four plus years, Major League Baseball has had a cyber security program that all the clubs partake in. So all 30 clubs are active participants in the program. They basically help build out a suite of tools as well as the ability to kind of monitor, help participate in the monitoring, sort of a lot of our cyber security assets and logs And that's really elevated significantly our posture in terms of security. We supplement that quite a bit and a good example of that is like Acronis. Acronis, for us, represents the ability for us to be able to respond to certain potential threats like ransom-ware and other things. As well as frankly, what's wonderful about a tool like this is that it allows us to also solve other problems. Making our scouts more efficient. We've got these 125 scouts scattered around the globe. These guys are the lifeblood of our, you know, the success of our business. When they have a problem, if they're in Venezuela or the Dominican or someplace else, in southeast Asia, getting them up and running as quickly as we can, being able to consume their video assets and other things as they're scouting prospects. We use Acronis for those solutions. It's great to kind of have a partner who can both double down as a cyber partner as well as someone who helps drive a more efficient business. >> People bring their phone into the stadiums too so those are end points now connecting to your network. >> Definitely. And as you pointed out before, we've got great partnerships. We've got a great concession relationship with Aramark and they operate, in the future they'll be operating off our infrastructure. So we're in the point of rolling out all new point-of-sale terminals this off-season. We're excited about that 'cause we think for the first time it really allows us to build a very comprehensive, very secure environment for both ourselves and for all the touchpoints to fans. >> You have a very stellar career. I noticed you were at Scudder Investments back in the '80s, very cutting-edge firm. FTD that set the whole standard for connecting retailers. Again, huge scale play. Can see the data kind of coming out, they way you've been a CIO, CTO. The EVP CIO at The Weather Channel and the weather.com again, first mover, kind of pioneer. And then now the Red Sox, pioneering. So I got to ask you the modernization question. Red Sox certainly have been cutting-edge, certainly under the last few owners, and the previous Henry is a good one, doing more and more, Has the business model of baseball evolved, 'cause you guys a franchise. >> Sure. >> You operate under the franchisor, Major League Baseball, and you have jurisdictions. So has digital blurred the lines between what Advanced Media unit can do. You got communities developing outside. I watch the games in California. I'm not in there but I'm present digitally. >> Sure. Sure. >> So how has the business model flexed with the innovation of baseball? >> That's a great question. So I mean, first off, the relationship between clubs like ours and MLB continue to evolve. We have a new commissioner, relatively new commissioner, and I think the whole one-baseball model that he's been promoting I think has been great. The boundaries sometimes between digital assets and how we innovate and things like that continues to evolve. Major League Baseball and technology groups and product groups that support Major League Baseball have been a fantastic partner of ours. If you look at some of the innovations with Statcast and some of the other types of things that fans are now becoming more familiar with. And when they see how fast a runner goes or how far a home run goes and all those sort of things, these kinds of capabilities are on the surface, but even like mobile applications, to make it easy for fans to come into ballparks and things like that really. What we see is really are platforms for the future touchpoints to all of our customers. But you're right, it gets complicated. Streaming videos and people hadn't thought of before. >> Latin America, huge audience for the Red Sox. Got great players down there. That's outside the jurisdiction, I think, of the franchise agreement, isn't it? (laughs) >> Well, it's complicated. As this past summer, we played two games in England, right? So we enjoy two games in London, sadly we lost to the Yankees in both of those, but amazing experience and Major League Baseball really hats off to those guys, what they did to kind of pull that together. >> You mentioned Statcast. Every year when I meet with Andy Jassy at AWS, he's a sports fan. We love to talk sports. That's a huge, kind of shows the power of data and cloud computing. >> No doubt. >> How do you guys interface with Statcast? Is that an Amazon thing? Do they come to you? Are they leveraging dimensions, camera angles? How does that all work? Are you guys involved in that or? >> Brian: Oh yeah, yeah. >> Is that separate? >> So Statcast is just one of many data feeds as you can imagine. One of the things that Major League Baseball does is all that type of data is readily available to every club. So every club has access to the data. The real competitive differentiator, if you will, is how you use it internally. Like how your analysts can consume that data. We have a baseball system we call Beacon. We retired Carmine, if you're familiar with the old days of Carmine. So we retired Carmine a few years ago with Beacon. And Beacon for us represents sort of our opportunity to effectively collapse all this information into a decision-making environment that allows us to hopefully to kind of make the best decisions to win the most games. >> I love that you're answering all these questions. I really appreciate it. The one I really want to get into is obviously the fan experience. We talked about that. No talent on the field means no World Series so you got to always be constantly replenishing the talent pool, farm system, recruiting, scouting, all these things go on. They're instrumental. Data's a key driver. What new innovations that the casual fan or IT person might be interested in what's going on around scouting and understanding the asset of a human being? >> Right. Sure. I mean some of this gets highly confidential and things, but I think at a macro level, as you start to see both in the minor leagues and in some portions of the major leagues, wearable technologies. I think beyond just sort of player performance information that you would see traditionally with you might associate it with like Billy Beane, and things like that with "Moneyball" which is evolved obviously considerably since those days. I mean understanding sort of player wellness, understanding sort of how to get the most out of a player and understanding sort of, be able to kind of predict potential injuries and accelerate recoveries and being able to use all of this technology where appropriate to really kind of help sort of maximize the value of player performance. I mean, David Ortiz, you know, I don't know where we would have been in 2018 without, you know, David. >> John: Yeah. >> But like, you know >> Longevity of a player. >> Absolutely. >> To when they're in the zone. You wear a ring now to tell you if you're sleeping well. Will managers have a visual, in-the-zone, don't pull 'em out, he can go an extra inning? >> Well, I mean they have a lot of data. We currently don't provide all that data to the clubhouse. I mean, you know, and so If you're in the dugout, that information isn't always readily available type of thing. But players know all this information. We continue to evolve it. At the end of the day though, it's finding the balancing act between data and the aptitudes of our coaching staff and our managers to really make the wise decisions. >> Brian, final question for you. What's the coolest thing you're working on right now? Besides the fan having a great experience, 'cause that's you kind of touched on that. What's the coolest thing that you're excited about that you're working on from a tech perspective that you think is going to be game-changing or interesting? >> I think our cloud strategy coming up in the future. It's still a little bit early stage, but our hope would be to kind of have clarity about that in the next couple months. I think is going to be a game-changer for us. I think having, you know, we enjoy a great relationship with Dell EMC and yet we also do work in the cloud and so being able to leverage the best of both of those to be able to kind of create sort of a compelling experience for both fans, for both player, baseball operations as well as sort of running an efficient business, I think is really what we're all about. >> I mean you guys are the poster child for hybrid cloud because you got core, data center, IoT, and >> No doubt. So it's exciting times. And we're very fortunate that with our relationship organizations like Dell and EMC, we have leading-edge technologies. So we're excited about where that can go and kind of what that can mean. It'll be a big step. >> Okay two personal questions from me as a fan. One is there really a money-counting room like in the movie "The Town"? Where they count a big stack of dollar bills. >> Well, I'm sure there is. I personally haven't visited it. (laughs) I know it's not in the room that they would tell you it is on the movie. (laughter) >> And finally, can The Cube get press passes to cover the games, next to NESN? Talk tech. >> Yeah, we'll see what we can do. >> They can talk baseball. We can talk about bandwidth. Right now, it's the level five conductivity. We're looking good on the pipes. >> Yeah we'll give you a tech tour. And you guys can sort of help us articulate all that to the fans. >> Thank you so much. Brian Shield, Vice President of Technology of the Boston Red Sox. Here talking about security and also the complications and challenges but the mega-opportunities around what digital and fan experiences are with the physical product like baseball, encapsulates kind of the digital revolution that's happening. So keep covering it. Here in Miami, I'm John Furrier. We'll be right back after this short break. (techno music)

>> from Cambridge, Massachusetts. It's the Cube covering M. I. T. Chief Data officer and Information Quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M. I. T. In Cambridge, Massachusetts. Everybody You're watching The Cube, the Leader and Live Tech coverage. My name is Dave Volante, and it's my pleasure to introduce Matt Kobe, who's the vice president of business strategy Analytics of Chicago Bulls. We love talking sports. We love talking data. Matt. Thanks for coming on. >> No problem getting a date. So talk about >> your role. Is the head of analytics for the Bulls? >> Sure. So I work exclusively on the business side of the operation. So we have a separate team that those the basketball side, which is kind of your players stuff. But on the business side, um, what we're focused on is really two things. One is being essentially internal consultants for the rest of the customer facing functions. So we work a lot with ticketing, allow its sponsorship, um, marketing digital, all of those folks that engage with our customer base and then on the backside back end of it, we're building out the technical infrastructure for the organization right. So everything from data warehouse to C. R M to email marketing All of that sits with my team. And so we were a lot of hats, which is exciting. But at the end of the day, we're trying to use data to enhance the customer and fan experience. Um and that's our aim. And that's what we're driving towards >> success in sports. In a larger respect. It's come down to don't be offended by this. Who's got the best geeks? So now your side of the house is not about like you say, player performance about the business performances. But that's it. That's a big part of getting the best players. I mean, if it's successful and all the nuances of the N B, A salary cap and everything else, but I think there is one, and so that makes it even more important. But you're helping fund. You know that in various ways, but so are the other two teams that completely separate. Is there a Chinese wall between them? Are you part of the sort of same group? >> Um, we're pretty separate. So the basketball folks do their thing. The business folks do their thing from an analytic standpoint. We meet and we collaborate on tools and other methods of actually doing the analysis. But in terms of, um, the analysis itself, there is a little bit of separation there, and mainly that is from priority standpoint. Obviously, the basketball stuff is the most important stuff. And so if we're working on both sides that we'd always be doing the basketball stuff and the business stuff needs to get done, >> drag you into exactly okay. But which came first? The chicken or the egg was It was the sort of post Moneyball activity applied to the N B. A. And I want to ask you a question about that. And then somebody said, Hey, we should do this for the business side. Or was the business side of sort of always there? >> I think I think, the business side and probably the last 5 to 7 years you've really seen it grown. So if you look at the N. B. A. I've been with the Bulls for five years. If you look at the N. B. A. 78 years ago, there was a handful of Business analytics teams and those those teams had one or two people at him. Now every single team in the NBA has some sort of business analytics team, and the average staff is seven. So my staff is six full time folks pushed myself, so we'll write it right at the average. And I think what you've seen is everything has become more complex in sports. Right? If you look at ticketing, you've got all the secondary markets. You have all this data flowing in, and they need someone to make sense of all that data. If you look at sponsorship sponsorship, his transition from selling a sign that sits on the side of the court for these truly integrated partnerships, where our partners are coming to us and saying, What do we get out of? This was our return. And so you're seeing a lot more part lot more collaboration between analytics and sponsorship to go back to those partners and say, Hey, here's what we delivered And so I think you it started on the basketball side, certainly because that's that's where the, you know that is the most important piece. But it quickly followed on the business side because they saw the value that that type of thinking can bring in the business. >> So I know this is not, you know, your swim lane, but But, you know, the lore of Billy Beane and Moneyball and all that, a sort of the starting point for sports analytics. Is that Is that Is that a fair characterization? Yeah. I mean, was that Was that really the main spring? >> I think it It probably started even before that. I think if you have got to see Billy being at the M I t Sports Analytics conference and him thought he always references kind of Bill James is first, and so I think it started. Baseball was I wouldn't say the easiest place to start, But it was. It's a one versus one, right? It's pitcher versus batter. In a lot of cases, basketball is a little bit more fluid. It's a team. Sport is a little harder, but I think as technology has advanced, there's been more and more opportunities to do the analytics on the basketball side and on the business side. I think what you're seeing is this huge. What we've heard the first day and 1/2 here, this huge influx of data, not nearly to the levels of the MasterCard's and others of the world. But as more and more things moved to the mobile phone, I think you're going to see this huge influx of data on the business side, and you're going to need the same systems in the same sort of approach to tackle it. >> S O. Bill James is the ultimate sports geek, and he's responsible for all these stats that, no, none of us understand. He's why we don't pay attention to batting average anymore. Of course, I still do. So let's talk about the business side of things. If you think about the business of baseball, you know it's all about maximizing the gate. Yeah, there's there's some revenue, a lot of revenue course from TV. But it's not like football, which is dominated by the by the TV. Basketball, I think, is probably a mix right. You got 80 whatever 82 game season, so filling up the stadium is important. Obviously, N v A has done a great job of of really getting it right. Free agency is like, fascinating. Now >> it's 12 months a year >> scored way. Talk about the NBA all the time and of course, you know, people like celebrities like LeBron have certainly helped, and now a whole batch of others. But what's the money side of the n ba look like? Where's the money coming from? >> Yeah, I mean, I think you certainly have broadcast right, but in many ways, like national broadcast sort of takes care of it itself. In some ways, from the standpoint of my team, doesn't have a lot of control over national broadcast money. That's a league level thing. And so the things that we have control over the two big buckets are ticketing and sponsorship. Those those are the two big buckets of revenue that my team spends a lot of time on. Ticketing is, is one that is important from the standpoint, as you say, which is like, How do we fill the building right? We've got 41 home game, supposed three preseason games. We got 44 events a year. Our goal is to fill the building for all 44 of those events. We do a pretty good job of doing it, but that has cascading effects into other revenue streams. Right, As you think about concessions and merchandise and sponsorship, it's a lot easier to spell spot cell of sponsorship when you're building is full, then if you're building isn't full. And so our focus is on. How do we? How do we fill the building in the most efficient way possible? And as you have things like the secondary market and people have access to tickets in different ways than they did 10 to 15 years ago, I think that becomes increasingly complex. Um, but that's the fun area that's like, That's where we spend a lot of time. There's the pricing, There's inventory management. It's a lot of, you know, is you look a traditional cpg. There's there's some of those same principles being applied, which is how do you are you looking airline right there? They're selling a plane. It's an asset you have to fill. We have ah, building. That's an asset we have to fill, and how do we fill it in the most optimal way? >> So the idea of surge pricing demand supply, But so several years ago, the Red Sox went to a tiered pricing. You guys do the same If the Sox are playing Kansas City Royals tickets way cheaper than if they're playing the Yankees. You guys do a similar. So >> we do it for single game tickets. So far are season ticket holders. It's the same price for every game, but on the price for primary tickets for single games, right? So if we're playing, you know this year will be the Clippers and the Lakers. That price is going to be much more expensive, so we dynamically price on a game to game basis. But our season ticket holders pay this. >> Why don't you do it for the season ticket holders? Um, just haven't gone there yet. >> Yeah, I mean, there's some teams have, right, so there's a few different approaches you convey. Lovely price. Those tickets, I think, for for us, the there's in years past. In the last few years, in particular, there's been a couple of flagship games, and then every other game feels similar. I think this will be the first year where you have 8 to 10 teams that really have a shot at winning the title, and so I think you'll see a more balanced schedule. Um, and so we've We've talked about it a lot. We just haven't gone to that made that move yet? >> Well, a season ticket holder that shares his tickets with seven other guys with red sauce. You could buy a BMW. You share the tickets, so but But I would love it if they didn't do the tiered. Pricing is a season ticket holder, so hope you hold off a while, but I don't know. It could maximize revenues if the Red Sox that was probably not a stupid thing is they're smart people. What about the sponsorships? Is fascinating about the partners looking for our ally. How are you measuring that? You're building your forging a tighter relationship, obviously, with the sponsors in these partners. Yeah, what's that are? Why look like it's >> measured? A variety of relies, largely based on the assets that they deliver. But I think every single partner we talk to these days, I also leave the sponsorship team. So I oversee. It's It's rare in sports, but I stayed over business strategy and Alex and sponsorship team. Um, it's not my title, but in practice, that's what I do. And I think everyone we talked to wants digital right? They want we've got over 25,000,000 social media followers with the Bulls, right? We've got 19,000,000 on Facebook alone. And so sponsors see those numbers and they know that we can deliver impression. They know we can deliver engagement and they want access to those channels. And so, from a return on, I always call a return on objectives, right? Return on investment is a little bit tricky, but return on objectives is if we're trying to reel brand awareness, we're gonna go back to them and say, Here's how many people came to our arena and saw your logo and saw the feature that you had on the scoreboard. If you're on our social media channels or a website, here's the number of impressions you got. Here is the number of engagements you got. I think where we're at now is Maura's Bad Morris. Still better, right? Everyone wants the big numbers. I think where you're starting to see it move, though, is that more isn't always better. We want the right folks engaging with our brands, and that's really what we're starting to think about is if you get 10,000,000 impressions, but they're 10,000,000 impressions to the wrong group of potential customers, that's not terribly helpful. for a brand. We're trying to work with our brands to reach the right demographics that they want to reach in order to actually build that brand awareness they want to build. >> What, What? Your primary social channels. Twitter, Obviously. >> So every platform has a different purpose way. Have Facebook, Twitter, instagram, Snapchat. We're in a week. We bow in in China and you know, every platform has a different function. Twitter's obviously more real time news. Um, you know the timeline stuff, it falls off really quick. Instagram is really the artistic piece of it on, and then Facebook is a blend of both, and so that's kind of how we deploy our channels. We have a whole social team that generates content and pushes that content out. But those are the channels we use and those air incredibly valuable. Now what you're starting to see is those channels are changing very rapidly, based on their own set of algorithms, of how they deliver content of fans. And so we're having to continue to adapt to those changing environments in those social >> show impressions. In the term, impressions varies by various platforms. So so I know. I know I'm more familiar with Twitter impressions. They have the definition. It's not just somebody who might have seen it. It's somebody that they believe actually spent a few seconds looking at. They have some algorithm to figure that out. Yeah. Is that a metric that you finding your brands are are buying into, for example? >> Yeah. I mean, I think certainly there they view it's kind of the old, you know, when you bought TV ads, it's how many households. So my commercial right, it's It's a similar type of metric of how many eyeballs saw a piece of content that we put out. I think we're the metrics. More people are starting to care about his engagements, which is how many of you actually engaged with that piece of content, whether it's a like a common a share, because then that's actual. Yeah, you might have seen it for three seconds, but we know how things work. You're scrolling pretty fast, But if you actually stopped to engage it with something, that's where I think brands are starting to see value. And as we think about our content, we have ah framework that our digital team uses. But one of the pillars of that is thumb stopping. We want to create content that is some stopping that people actually engage with. And that's been a big focus of ours. Last couple years, >> I presume. Using video, huge >> video We've got a whole graphics team that does custom graphics for whether it's stats or for history, historical anniversaries. We have a hole in house production team that does higher end, and then our digital team does more kind of straight from the phone raw footage. So we're using a variety of different mediums toe reach our fans >> that What's your background? How'd you get into all of this? >> I spent seven years in consulting, so I worked for Deloitte on their strategy group out of Chicago, And I worked for CPG companies like at the intersection of Retailer and CPG. So a lot of in store promotional work helping brands think through just General Revenue management, pricing strategy, promotional strategy and, um stumbled upon greatness with the Bulls job. A friend gave me the heads up that they were looking to fill this type of role and I was able to get my resume in the mix and I was lucky enough to get get the job, and it's been when I started. We're single, single, single, so it's a team of one. Five years later, we're a team of six, and we'll probably keep growing. So it's been an exciting ride and >> your background is >> maths. That's eyes business. Undergrad. And then I got a went Indian undergrad business and then went to Kellogg. Northwestern got an MBA on strategy, so that's my background. But it's, you know, I've dabbled in sports. I worked for the Chicago 2016 Olympic bid back in the day when I was at Deloitte. Um, and so it's been It's always been a dream of mine. I just never knew how I get there like I was wanted to work in sports. They just don't know the path. And I'm lucky enough to find the path a lot earlier than I thought. >> How about this conference? I know you have been the other M I T. Event. How about this one? How we found some of the key takeaways. Think you >> think it's been great because a lot of the conferences we go to our really sports focus? So you've got the M. I T Sports Analytics conference. You have seat. You have n b a type, um, programming that they put on. But it's nice to get out of sports and sort of see how other bigger industries are thinking about some of the problems specifically around data management and the influx of data and how they're thinking about it. It's always nice to kind of elevated. Just have some room to breathe and think and meet people that are not in sports and start to build those, you know, relationships and with thought leaders and things like that. So it's been great. It's my first time here. What are probably back >> good that Well, hopefully get to see a game, even though that stocks are playing that well. Thanks so much for coming in Cuba. No problems here on your own. You have me. It was great to have you. All right. Keep right, everybody. I'll be back with our next guest with Paul Gill on day Volante here in the house. You're watching the cue from M I T CEO. I cube. Right back