Unofficial Partner Podcast
Unofficial Partner Podcast
UP529 ChatUP Live Pt2 - The Billion Dollar AI Race: 'Once you give away that level of knowledge, it's gone'
THE BILLION DOLLAR RACE: WHO WINS SPORTS AI?
There's a race on. Bloomberg Terminal for sport. Sports Business GPT. The industry's operating system. Who builds it first?
THE LAST FRONTIER
"Once you give away that level of knowledge, it's gone. This is the last frontier before all your intelligence is gone." — Craig Hepburn
Live from Fuse UK, featuring Craig Hepburn (ex-UEFA), Richard Ayers (Rematch), Sean Betts (Omnicom), Andy Shora & Chris Woodcock (TFG Labs and 21st Group).
BIG QUESTIONS
Will sports bodies repeat the platform mistakes? Is your archival footage the new gold? What happens when AI commoditizes entertainment?
KEY INSIGHT: Data isn't spreadsheets anymore—it's everything. Your match footage trains robotics. Your highlights feed Google's models. Are you selling or building moats?
"Don't give it away. Build your own API layer. Make them pay for your oxygen." — Craig Hepburn
The optimistic take: live experiences become priceless when AI makes content free.
Who adapts fastest wins.
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once you give away that level of knowledge, it's gone. Yeah.. literally, this is the last frontier before like. Now all of your intelligence, your knowledge, it is not just the structured information that's valuable. it's the really, it's the kind of the edge use cases. It's the nuance, it's the niche things. It's the tiny little valuable things that no one thinks about. Once it has that it can build products.
Richard Gillis, Unofficial Partner:The last frontier before all your knowledge and intelligence is gone. That was Craig Hepburn at our recent event Chat Up live held at Fuse, UK's offices on the South Bank. We collected together some interesting people to help answer a question, and this podcast is based on that conversation. There's a race going on, you know. What the race is, it's to do with ai, it's to do with the sports business's relationship to large language models. I've heard it called the Bloomberg Terminal. For sport,
or sports business, GPT. It's the sports industry's operating system.
Richard Gillis, Unofficial Partner:it's an AI system that makes real sense of sports, media betting, ticketing, fan engagement data, something that could be genuinely transformative. The prize,
if you can call it, that,
Richard Gillis, Unofficial Partner:will be substantial. We're talking about a potential billion dollar sports business, but who's gonna win? What are the characteristics of the winners in this race? That's what this episode is about.
So first of all, you're gonna hear the onstage conversation, which goes on for about 20 minutes between Craig, who you've just heard who is an AI strategist, and formerly was Chief Digital Officer for UAFA and Art Basil, Alongside Craig. Richard Ayers of Rematch, who was founder of Seven League, the digital agency, which is now part of IMG and Sean Betts, who is Chief AI and Innovation Officer at Omnicom. Then in the second part of the podcast, myself and Sean review the broader AI conversation and develop some of the themes that are discussed. On stage, and then we'll summarize at the end. The whole experiment with 21st groups, Andy Shoa and Chris Woodcock, who is the company's chief technology officer. First of all though, I have got a list of people I want thank those of you that heard Tuesday's episode, you all have heard the. The setup, if you like, in terms of how we wanted to try and disrupt the idea of a conference panel using large language models and co-pilots and ai. And I just wanna reiterate our thanks to group of people, our partners Fuse, UK and 21st Group who have been enormously helpful and generous on the event. first Of all, on the fuse side, Helen Burford. Monica Conway. Louise Johnson, Zainab Zaman and Annabel Wilson. Thanks very much for your time and all your efforts. Lucy Bason Smith, managing director of Fuse International also took part. You'll have heard her in the other episode, the first episode of this series, and our other partners 21st group. So a big thank you to Blake Worcester, his team, including Omar Chowdry, who you've heard on the previous podcast. Andy Shoa, who built the model, the co-pilot for the evening,, Connell Milligan and Dan Zelinski. Thanks to all and look forward to pursuing the conversation further in due course. Okay, so let's get into it with, first of all, Craig Hepburn.
Craig Hepburn:I think it's such a big topic, but one of the areas that I've, I've spoken about a lot and it's something we've spoken about Context. In other words, understanding the business, understanding any business, building that context layer, building that intelligence ultimately LLMs and AI is kind of, it is a general kind of language model, but actually what it starts to add values where you build really intelligent kinda conversations when you create context around the business, the processes. So I think what's really interesting, what we're trying to do here is you have a, you have a topic and you're trying to sort of extract information and insight and knowledge, and then trying to capture that into a model which can then hopefully start to extract some really interesting kind of propositions and ideas. My belief is you have kind of technology that we've used for 20 years, this kind of deterministic tech interfaces with that whole kind of platform, we're moving into something that's far more intelligent, but only when you start to train it on the actual information and the context and the knowledge in which it's able to actually work with. And that's the bit that I think everyone's caught in the models and what it can do and what we can build and all the apps and all the, the, the products, but actually where the value accrues is really at the, the edge where the, the real value lives. And that's kinda where we're starting to figure that, that that's kinda where it's been starting to develop. And I think there's interesting capturing the conversations and then seeing what intelligence it can take from that to give you ideas and things that you can actually use.
Richard Gillis, Unofficial Partner:So, Sean, what's your job, first of all, because it's sort of, it's
Craig Hepburn:what
Sean Betts, Omnicom:so hard. It's so
Richard Gillis, Unofficial Partner:it's to the conversation.
Sean Betts, Omnicom:I do? Um, so my job title is Chief AI and Innovation Officer, but what that actually means is I, I do a lot of thought leadership around the impact of the technology in marketing and for our clients' businesses.
Richard Gillis, Unofficial Partner:So you are going to clients and saying, this is what it is gonna happen to your business, or you're asking them what is happening to your business and feeding that back in.
Sean Betts, Omnicom:So, most of it's really about how consumers are using AI and how that's changing what they do. and, and then partly it is kind of sharing of like how we're seeing AI impact our business and learning from clients about how AI is impacting their business.'cause honestly, the technology's moving so fast, we've all gotta just kind of learn together right now. So it, it's a bit of both, but I'd, I'd say the, the main focus is on how AI and large language models is kind of disrupting how consumers do things online.
Richard Gillis, Unofficial Partner:Okay. Richard, just gimme a first sense
Richard Ayers:first thought, actually, I, a ridiculous note of optimism, which is rare in these conversations, in our generally fractured and problematic world, and AI is gonna take everything over. Okay. Actually, what I think is gonna happen in the longer term is that we're gonna get to a point where ai commoditizes entertainment. Okay. Because it, because if you or me or anybody in this room can write a prompt that says, create me a movie that is full of action and somebody like Bruce Willis and does whatever, and it does it really amazingly well, extremely quickly, then the, I mean, there, there's a whole bunch of the media sector that's got a serious problem, but, uh, that is gonna commoditize like crazy and the, the cost of production. I mean, you've seen, uh, that company that's doing one pound per podcast, have you seen that in, I think they called Incentive Point or something like Infect infection point. Um, and you know, it's one, and they'll create a carrot. They'll create a, a AI version of a gillis. And you'll tell it to talk about the sports business and it'll cost you$1 per episode. And it's, you know, I mean, I haven't tried the Gus one, I'm sure it's nowhere near as good, but the, you know, it's actually as a product, it's pretty good. So if you've got a, you know, I'm on table tennis, England bored, if I want to have a pretty decent podcast generated about table tennis, super easy. Right? So if the, if the cost of production drops hugely,
Richard Gillis, Unofficial Partner:it's less, it's less me, it's Sean, I'm worried about
Richard Ayers:We're, we're all, we're all worried about Sean. Yeah, absolutely. Uh, the, uh, so if cost of production goes down, the value of live experiences goes through the roof, right? So the optimistic note for sport is there is gonna be still even, it'll be even more valuable to be in the room with a real human doing a real thing. Right? That's amazing for sport. As an aside, it's also amazing for rematch, which is why I'm doing live experiences. Anyway. Yeah. That aside, just shoehorn that in. You've
Craig Hepburn:seen that coming, didn't he? Yeah. He, he played that, he played that.
Richard Ayers:I would, I also, the excellent Dan Air, no relation, but, uh, my old team who's now part of, um. IMG digital, he took a, a Craig Hepburn post about AI and he put it in Suno.'cause Dan used to work at Sony.
Craig Hepburn:me that,
Richard Ayers:He put it in Suno, the music construct and told it to do nine, uh, naughties, emo uh, rock about the thing. And it's really good.
Craig Hepburn:he sent me and I was like, wow.
Richard Ayers:it's really good. really good. So, okay, so therefore value of what we do. Amazing. That was verse reflection. Um, point of creation as in the actual players doing the actual thing is still extremely valuable. Good. But, and we didn't mention this in the earlier panel. All of the data from that, I mean, donkeys years ago, millennia ago at Citi, I took player data and I from the previous season and I did a, um, hackathon on it. And basically we didn't do a hackathon'cause they were a bit corrupt at that stage, but we put it out to a digital community and they did a bunch of work on it and came back with some products. Um.
Richard Gillis, Unofficial Partner:you say Man city? Were a bit corrupted. Sorry. I thought that's, I literally thought what you said.
Richard Ayers:I would never say. Uh, and, what you could do now is, so if I was using AI and buying club, one of the things I would say to it is, okay, can I take all the player data, not just from, uh, the, the team right now, but retro calculate all the player data from all of the matches previously, right?'cause the computer vision would be able to look at all the footage and then reprogram it and get the data of the player from, you know. 30 years ago, then you've got an amazing ability to create a, a product which is, you know, a pub conversation best all time team of best all time players created and whatever. And then if, if I can do that, and by the way, I would implore sports to pool its energies here.'cause if we just created an l and m which had all of our data in it, that would be incredibly powerful. We could ring fence it. Whereas currently what's happening is, I think you were telling me, You know, there are open AI and the others, they're going around to major clubs and picking them off and buying their, effectively buying their data. And I, I think we should avoid that and pull it. But anyway, um, so I think we could have a, a creative product.
Richard Gillis, Unofficial Partner:that what obviously you used to work at ufa that, you've got this, is this the same game again? Because this is not a new story that, you know, sports relationship, the platforms, they've given everything away. Thus far, the next iteration will be the same thing. This is another example
Craig Hepburn:Yeah, I mean, well it's, it's unintended consequences of things that you don't, you don't understand. So, um, yeah, for many years now, I mean, I remember when I first started thinking about AI kind of starting to think, where does this take us? I mean, when I was at UFA towards the end, so towards the last year, I remember we, we had meetings with Google and, and it was around how do we look at all of your data and all of your archives, all of your statistics, and all of your information. It's like, that's a valuable asset. So, I mean, just to be clear, like the, the LMS have been essentially developed on all of the open internet. It only has 1% of like your data and your company's data. So on average, like it's not got into the enterprise yet. So that's the next frontier. And that's where all the money is. So if you look at the, money in the AI world, you've seen all the crazy numbers being thrown around. that's not gonna be, gain back from consumer products. That's all gonna be taken from enterprise business. And the way to achieve that is to essentially give them the tools and the technologies to make, you know, enterprise and products and things amazing in order to get all of the data and all the product and, you know, all that insight from those companies. So, and, and actually when I left UAFI went to Art Basel. That was the point. So James Murdoch had acquired, uh, a big share in Art Basel and his view was, we need to build technology in the art world to kind of like really. Build a model, essentially a kind of art GPT model and application where we could actually bring all that information in so that we could actually manage it and, and, and take some ownership around it so artists and creatives could get rewarded for the work. So in other words, step ahead of it, actually build a moat around your own value chain and actually take ownership of it before the, the, the major LLMs come in and start training on your data. it sounds like a big, kinda crazy idea, but it's not. I mean, at the end of the day, we've spent the last 20 years collecting data from everything and, and storing it in these big, costly, expensive CRM platforms that we spent millions of pounds on. Now we have a system in which we can make value from it. It would make sense that you want to build a moat around the thing that you've spent 20 years developing. And now it's a question of be very, very smart now about. Uh, what models are A-P-N-A-P-I into so that in order to make sure that you're actually protecting that data.
Sean Betts, Omnicom:What is happening right now as well is that people are recognizing that what they've traditionally thought of as data is now so much broader. Because what's traditionally people have thought of as data is kind of like your fan data, your commercial data, your kind of operational data. But what these AI companies now looking for isn't that it's all the archival footage. They want video they wanna train their models on video'cause they wanna have an understanding of the physical world. And we've got a lot of organizations in the sporting space who are desperately trying to commercialize every asset that they have. And so we're looking at how they can create revenue streams from everything. And that definition of what is data now is gone from being this kind of neat, structured stuff that existed on a spreadsheet 10, 15 years ago to now. Anything, anything is now a data point and it can be monetized. so that's really super interesting, I think, because it opens up a lot of other commercial opportunities because where the capital is in the economy right now is with these AI companies and they're looking to acquire all of this kind of real world data that, like you say, hasn't been available on the internet, has been behind closed doors, and now has a huge value for them because that's the only thing left. They have got to train their models on next.
Richard Ayers:It's, I, I've gotta a worry. Sorry. I've gotta worry that. Um, and it's probably commercial directors. Sorry, commercial directors in the room. Uh, you know, who are under pressure and there's all the rest of it. That they aren't gonna do what they did with the NFT guys or the Web3 guys or the social media platforms beforehand. And just go like, yeah, yeah. How much, like, how much can I get for you? You know? Don't, don't mustn't jump at the first thing. This is too big. It's too fast, it's too impactful to just take the first deal that comes out. Well,
Craig Hepburn:it's also a zero sum. I mean, once you give away that level of knowledge, how do you, you, you, you, it's gone. Yeah. Like you, you literally, this is the last frontier before like. Now all of your intelligence, your knowledge, your, and I totally agree with you. It, it is not just the structured information that's valuable. it's the really, it's the kind of the edge use cases. It's the nuance, it's the niche things. It's the tiny little valuable things that no one thinks about. Once it has that it can build products. Uh, I was listening to the podcast and listening to the All In podcast, Calis was saying the other day that he does not trust, and he knows Sam Altman and he says he would never, ever use an API into open a, uh, ai because he believes that at the end of the day, they'll look at the API, they'll see where the use cases are and they'll essentially build that business product off the back. of
Speaker 46:mean they already are,
Craig Hepburn:it is happened already. It is
Speaker 46:that.
Speaker 47:Yeah.
Richard Ayers:Years ago I did a thing for Spurs with, um, uh, Amazon. It was all about, you know, doing a building and echo blah, blah, blah, you know, an app on it. And we were asking Amazon about the data they collect and they said, well, um, I said, it's on all the time, right? The microphone's on, it's listening all the time. And they were like, yes. And, uh, and I said, well, what Dod you call that piece of data? They were, we don't really have a name for it. We call it utterances.
Craig Hepburn:That's brilliant.
Richard Ayers:Okay, so you are, you are capturing the utterance. I said, what are you doing? But they said, well, at the moment we don't really know. We're just gonna sort of, we're just stashing them away, right?'cause we've got all the web servers in the world. I mean, literally all the web servers in the world. So we're just gonna capture all the utterances from all the Alexas in the world. Whenever I say I always wanna be very polite because when the war comes, I want them to remember that I was nice to the ai. Um, but uh, they get, they capture all the data, right? Now they're gonna work out because now they've got insane quantities of it. And that's the same will club from all
Speaker 21:clubs.
Richard Gillis, Unofficial Partner:bring it back to the sort of, you know, this question of what's in it for me and people in the room and who's gonna win? Who's gonna lose? Do we need to back a horse? You mentioned there about don't give stuff away. No, I, I went to, you know, Premier League just done a big deal with Microsoft in that is an AI component. So I can see the argument is, okay, it's couched, it's framed as a sponsorship deal, but they'll learn Microsoft stuff, copilot will be running fantasy or whatever it is. Is that's your saying, that's the wrong
Richard Ayers:the I, so I mean, these guys will have counterpoints, of course. Uh, the, um, the, I mean my opinion's obviously the right one, but, uh, the, the main thing
Craig Hepburn:about that Richard.
Richard Ayers:It all comes down to the digital DNA of the organization, like fundamentally that John Boyd OODA loop from the sixties, a strategic, you know, imperative to accelerate pilot round. If you've got an organization that can adapt fast, it will win, and your football club will be fine because there's gonna be a truckload of things that are gonna change in the next year, nevermind the next five. And your ability to adapt is the thing that will make you succeed in that. And, and part of that is I'm gonna do one shout out if I may. I'm gonna look at him. Am I allowed to do a shout out for you? Sort of No.
Craig Hepburn:Yeah. Do it maybe. Cool.
Richard Ayers:Uh, Dave Lip is building a, uh, AI powered intelligence system for working out sponsorships. Now, obviously, he and his merry band have a long and excellent pedigree in that kind of world, and they're taking that. And then overlaying the AI capability to be able to work out the value of everything all the time and constantly in a way that's incredibly now that use that and that that's one of those. So the digital d in your DNA in your organization needs to be able to go, Ooh, I trust them. They've got the pedigree. That's interesting. Let's go play with that. Right? And learn and learn and learn and adapt and learn. And then if you do it that way, it's smart.
Craig Hepburn:Yeah. I mean,
Richard Gillis, Unofficial Partner:are we picking winners was my question yeah.
Craig Hepburn:I think, well if we, if we pick winners in the way that it's set up, all of the odds are in favor of the tech companies winning, the 5, 6, 7, whatever. How so? And that's been happening now, right? For the last 10 years in social and now we're moving to lms. The one thing, and, and it's something I get and I know why it's difficult to move forward with it, but it's, I think most organizations, and especially in sport, and I tried to kinda like, we tried to kinda move ufa and I remember like having these conversations, how do we think more like technology companies? We had someone come to our business, um, and we talked about, how do we start building our own APIs, our own technology layers, our own stack essentially, so that all of our data and information and context can be apid into, say fans could actually, API into ufas tech stack can actually build products off the, that kind of what Google Maps do. What other, so if you think about your business, how do you become an ap? Like the world is moving to an API layer. The APIs are gonna become the transactional layers of where everything's gonna move. It's happening in currency, it's happening with agents, it's happening with all of the products and, and, and information. And I know it's a huge ask, but if companies can think more like technology companies and play them their own game. They can start to play at least on a level playing field where you want to just take my data. Actually, do you know what, we'll give you an API and we're gonna have a, a way to manage that kind of conversa, uh, that, that kind of data structure. And we're gonna put a cost against it. And guess what? The markets will decide what the value your data's worth over time. So imagine fans could build off the back, you know, your football club, give all your fans access to APIs.'cause the next generation of kids, my 16-year-old now is literally in London tonight with three friends. They're all building the next generation of like fantasy apps and stuff. And they're building with APIs because they can do it now with the ai. So you're kind of building on this stack.
Richard Gillis, Unofficial Partner:Every, day I hear stories of, someone has a, you know, a new release. Today Google has just released
Craig Hepburn:three,
Richard Gillis, Unofficial Partner:which has wiped out, several companies that whose market value was considerable in the tens and hundreds of millions. Just purely'cause it's
Craig Hepburn:only, only top layer though though. I, I think that what
Richard Gillis, Unofficial Partner:risks of building, if I go and build something on top of
Craig Hepburn:No, no, but that's not the point. So the models that no one else can build a model, like the models are billion, multi-billion dollar, like no one's getting there. Uh, the point is in which they can operate and survive, the oxygen is information and data. That's what they need In order for them to keep getting better, they may need more information. Google's done well because it's got huge amounts of data. What I'm saying is the oxygen protect your own oxygen and put a commercial ring around it and sell it so that you don't give it away. So, so that model is amazing. I'll be playing with it for the last 24 hours. Built loads of apps. It's beautiful at design, but it's basically commoditizing the interface layer, the ux, the, the application layer that's, that's gone. But they still, in order for them to be valuable in the future, they need super, incredible niche, brilliant amounts of information and context.
Okay, so that was the live panel conversation, and thanks again to Craig Hepburn, Richard Ayers and Sean Betts for their time and enthusiasm and expertise. So after doing the panels, I wanted to follow up, on some of the threads that we covered and some of the questions arising. So I sat down, first of all with Sean Bets again, who is. In charge of AI and technology across the Omnicom Group. So as you'll hear, he was able to take the conversation and point it towards his world of brands, sponsors and media. Here's me and Sean.
Richard Gillis, Unofficial Partner:So, just to start us off, what did, just give me your response from the evening, having sort of looked through the first bit and sat on the second bit. What was your. Just your general sense.
Sean Betts, Omnicom:Yeah, I think, I think it was interesting trying to meld the technology with live conversation. Um, and that like,'cause I'm a tech guy, that was the bit that really kind of grabbed me because I do think that there is gonna be a bit of a shift in how we think about using ai and it will be. what you were trying to do, which is how does it help us throughout our day and. Listening to our live conversations and helping capture and process and summarize and use that as context for everything else it does for us. So we, we are nowhere near that point yet. And the hardware needs to be developed and culturally we'd need to accept that and all sorts of other things. But I do think that that idea of having a kind of ever present AI companion is. Probably a part of our futures. And so what, what you were attempting to do at the event was kind of similar to that in a bit of a microcosm and it, and it was quite interesting because of that.
Richard Gillis, Unofficial Partner:It felt like there was a, a part of the impulse was to, first of all, it was out of boredom of the, Conferences and panels
Sean Betts, Omnicom:Yeah.
Richard Gillis, Unofficial Partner:and trying to sort of do something else. There's a theater element to it, which I quite liked that. But when you get to that sort of how we are interfacing with, the chat bot and, large language model prompting essentially, and Andy, you know, whipped this thing up and. It was for me as a complete AI dullard. I was wowed by it, but I want to know what the, is that, are we just learning? Via that method that those in, you know, my interaction with it is all about okay, prompting them, being told that I'm not prompting in the right way. And you know, the language model is hallucinates and lies and doesn't get it right and then it compounds the errors. And I'm just wondering, my little world, my little window into this is, is pretty much that describes it. I can see the opportunity without actually, you know, manifestly getting much better at anything. Um.
Sean Betts, Omnicom:Yeah. Yep.
Richard Gillis, Unofficial Partner:that where we are? And, and is that just structural and that's bigger things need to happen or are other people further along the line than that?
Sean Betts, Omnicom:So I, I think what you've captured is, is probably the experience that most people have at the moment with the technology, but I don't think that's reflective of the current state of the technology, if that makes
Richard Gillis, Unofficial Partner:Good.
Sean Betts, Omnicom:And the reason for that is because 90 to 95% of people who use chat, GBT as an example, are free users. And the difference between what you get as a free user and what you get to. If you're a paid subscriber is is quite stark. Um, not just in terms of the knowledge and the capability of the models that you have access to, but also the features that OpenAI wrap around it as well. So that's something that I think has got to change for most people to. Be able to experience what the technology is currently capable of because I think free users are probably experiencing what the technology was capable of least 12 months ago, maybe 18 months ago. And given the speed that this technology is evolving, that's quite a big gap. Um. And I think that that will come next year. Um, I think that will come when OpenAI come to market with an advertising model in chat GBT, because as soon as that happens, they can open up all the features to free users. And essentially the difference between what a free user gets and what a paid user gets is exposure to advertising rather than a deprecation of features. and I, it's, you know, it's part of my job. I
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:I'm a paid user of a lot of these platforms, and the experience that I have is. Is quite different. I, I don't feel like I have to think about my prompts too much and I don't see anywhere near as much hallucination and I can trust and rely on those models to do the things I want'em to do a lot more. and I, I'm just looking forward to a, hopefully a point next year where that is everyone's experience rather than just the five to 10% of people who are actually paying for the, the premium
Richard Gillis, Unofficial Partner:Right. That's interesting. So the, there's a, are you, are you sort of getting. To, um, more sophisticated system, so sort of complex orchestrated workflows. So this thing, rather than it making things. Getting research faster, rapid summarizations, you know, all of that. And we're talking here from an organizational perspective, that you're saying, okay, I can now see this then that and that and that and these things are going to, you know, are working and I, I dunno what to call that because obviously, I dunno if that's a agentic or, or not, but you put me right.
Sean Betts, Omnicom:I, I have a bit of a bugbear around the word agentic and agents, because I think it's used very broadly to me in lots of different
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:the, the, the pattern that you are describing is, is absolutely playing out. Um, it, it's probably worth just describing it bit. So, know, if you go to a chat GBT or a, or an Anthropics clawed model, or a Google Gemini or perplexity or any of these kind of platforms, you, you are faced with a very blank. Text box that is a chat interface. You know, it's, it's very simplistic. It's not very interactive. It's, it's very basic and it's not obvious what you should do with it for, for most people. So anyone who starts off there will start just putting stuff into the text box and kind of seeing what happens really. And that's, that's, that's kind of how we learn how to search 25 years ago with Google. we all kind of found a way to write our search queries more effectively so we'd get the results that we want. And so I think people have. Been going through that and are still going through that with, with these AI platforms right now. What I have found over the last probably year is that I've evolved how I use them from that to something that's slightly more sophisticated.'cause I just spent so much time in this technology'cause it's, it's a part of my job and the way that I use these platforms and these models now is more akin to a work companion, I don't want to use the word intern. it's not like I'm briefing models and the platforms to go off and do stuff for me, and they'll come back to me a day later with something. It's, it's not quite there yet. But the way that I work with the models right now is very iterative. if, for example, I'm exploring, you know, some. New thought leadership that I wanna do around, around AI as a technology. start off by talking to the model about the ideas that I've got and the general direction of what I think. Um, I'd ask it to give me feedback, to give me ideas. Um, I will then kind of shape it with the ai. To get it to a place where I'm comfortable with it and I think it makes sense, then I'd move to, if I'm, my ultimate endpoint is normally like a a written article or a presentation. I'll then move onto kind of writing an outline of what that would cover. That would then iterate into writing the first draft. I'd then be editing that draft, going backs and forwards with the AI model on it
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:it's in my tone of voice and my language, and it's making all the points that I wanted to make. Then when I've got a final draft, I'll get the AI to review it. Polish and any feedback and any kind of typos and things like that. And I'll get it to a kind of final state that I'm then happy with. So it's it, the way I work with it evolves through that piece of work. And it is, like I say, more like a, a work companion, um, in that sense.
Richard Gillis, Unofficial Partner:So the, the extrapolating. From that again, it's, and it, it's difficult'cause if you, you know, it's, we, we have to make it abstract, but we started off by saying that there's a, there will be organizations in sport and in the round sports, the sports business, who will rapidly benefit from this. And I can, and you hear it day in, day out, and again, it's, there's a marketing thing, you know, people are just claiming and overclaiming, but then there is reality. To that. And one of the bits of the conversation I quite enjoyed from the stage was the, okay, well what does that mean? Trying to put some, you know, reality to that, to that point. So that, you know, the billion dollar sports business idea, the conceit. One of the things that you said, which has sort of remained with me and I, I wanted to talk to you about was, um, the definition of data, and you then made the point that it's, it's moving from what we, you know. Spreadsheets and personal data and information to, to everything or anything. Can you just sort of,'cause I thought that from a sports perspective. Is incredibly important because we are at this stage, and again, this was part of the conversation, was that how we work with the big foundational models and do we give them everything in, and normally in sport it's framed as a sponsorship relationship. So we then say, right, let, we'll get into bed with Anthropic or open AI, or Microsoft or Google. It will be framed as a sponsorship deal that has a term limit, has three to five years, whatever it is. This is something that is really, really important about what the share and the value exchange is. And if I'm the Premier League or if I'm the fa, or if I'm the IOC or the NFL, I've got a lot of value, which traditionally, where I've traded and given to third parties over the years by tv, you know, monetized it, by licensing it to third parties. If I do that this time. Someone, I think Craig said, this is the last frontier of all your int intelligence. You know, once you do this, all your intelligence and knowledge as an organization has gone.
Sean Betts, Omnicom:Mm-hmm. Yep. Yep.
Richard Gillis, Unofficial Partner:Just give me a sense of that.'cause that's, I came away with that as my, quite a big takeaway and it's something that I wanna talk about in the, you know, in the follow up.
Sean Betts, Omnicom:Yeah, absolutely. So let, let's start with the data point, um, that I made. And I, I think data has always been anything that has been digitized, but because most people's exposure to data is an Excel spreadsheet or a bank statement, everyone always thought about it as it being numerical and it being tabular and structured and easy to get your head around and. be honest, that has been the story of AI development for the last 70, 80 years. It's all been about structured data, but the change that we've seen in the last 10, 15 years that's led us to this point now with with generative AI is that AI has got really good at working with unstructured data, which is anything that isn't in the table. And it could still be numerical, but often it's text, often it's images, often it's video. Often it's audio, it's, it's basically anything that's digitized now. So everyone's kind of understanding of what we mean when we say data needs to, needs to kind of broaden out. And there's kind of two bits to that. So if you, if you think about the context that we're talking about this in around the sport of football. There is a huge amount of data around football, not just in terms of, you know, tracking players and their performance and their health and all of that, but the raw footage of the games
Richard Gillis, Unofficial Partner:Hmm.
Sean Betts, Omnicom:the highlights and the broadcasts and the commentary and analysis that goes all around that is a very valuable form of data. And, and just to put into perspective in terms of why that data might be important. We're certainly not there yet. Within the next 10 years, I would say there are gonna be a lot of companies trying to build out robotics platforms that would be very interested in seeing Premier League footage to understand how people move so that they can use that to help them understand how robots can move.
Richard Gillis, Unofficial Partner:Hmm.
Sean Betts, Omnicom:And that will be a very valuable data set for, for a robotics firm. And that might be a general robotics platform, or it might even be a robotics platform that's aiming to create a robotic version of football. I mean, who
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:The world's gonna be crazy. So, um. You know, that's a very simple example of why that data would be incredibly valuable, and one of the challenges that a company like OpenAI has. the future of training their models is that they don't have access to the same kinds of video data that Google does, who owns YouTube and has got billions of hours of footage of all sorts of different things. So OpenAI, if they want to progress further and start training their models on video data in the same way that Google will be, is gonna have to be looking around for where they can buy that video data from, and they'll want that from Companies from entertainment companies from lots of different kind of verticals.'cause it'll have different uses. So
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:as, as an example, that is why you have to start thinking about data differently. And you've really gotta think about the value that you're sitting on. I think one of the points I made the panel that we did is that the best value from that data isn't always holding onto it. Sometimes data can be very valuable outside of your organization because it's can be used effectively as a marketing tool. Um, but sometimes you wanna keep that internal because you can see more value from it internally as well. So those, those are the kind of decisions that I think a lot of big sporting organizations are gonna be facing over the coming years.
Richard Gillis, Unofficial Partner:So a deal is fascinating. So a deal with open or, uh, open AI and or Anthropic or anyone who isn't Google basically is gonna be different than a Google. Relationship because actually Google, there's a need on the other side, which is much more burning, you know, much more urgent.
Sean Betts, Omnicom:Yes, but I, I think the challenge is potentially more nuanced than that as well, because, you know, the Premier League sells the rights, it sells the rights for broadcast. It sells the rights for highlights. Those highlight rights are able to go onto YouTube. The second they're onto YouTube, owns them
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:and conditions of
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:So in effect, they've already
Richard Gillis, Unofficial Partner:So they've sold'em already.
Sean Betts, Omnicom:to Google, but they've done it via the broadcaster that owns the highlight rights that publishes it on YouTube. that's something that is already in existence and, and probably needs to be looked at more thoroughly because the second highlights get posted to YouTube. Google owns it.
Richard Gillis, Unofficial Partner:And I, I had a sort of, almost in the same week or the week before we did our thing, I had a. I did a, a panel, I'm not always on panels, but, um, it was with Sportradar who are, you know, the betting sort of infrastructure.
Sean Betts, Omnicom:Yeah.
Richard Gillis, Unofficial Partner:And one of the conversations there was, you know, for your robotics, um, execution of the data, they're looking at, okay, well what does this mean for prediction and what does this mean for, you know, in terms of reinterpreting what a football match is and. Again, trying to second guess. If I am the Premier league and we always land on the Premier League'cause they're the biggest and the best, but every sports organization trying to second guess the future market for their data is a sort of impossible task I would suggest at this point. You know, it's just beyond their capability.
Sean Betts, Omnicom:Yeah.
Richard Gillis, Unofficial Partner:I dunno how. Yeah, and it's a lawyer question I guess in terms of future proofing what your data is then used for and whatever, but we see it just feels like there is a. There is jeopardy here because in the moment we're in at the mo, you know, this is the battle and I'm not sure that many organizations know they're in that fight.
Sean Betts, Omnicom:Yeah. Agreed. And I think, I think, I think there's two things that I would be thinking about if I was on, on that side of the fence. One is any rights or data licensing agreements I put in place right now, I wouldn't agree to anything longer than two years. Just because I don't know anyone can reliably predict what the world's gonna look like in two years around this technology right now. I think, you know, one year deals are probably not practical, but two years it feels about the right kind of balance. The second thing is to my point about the League highlights and the broadcasters and, and YouTube and Google. I think you have to think through a lot more carefully now what the downstream effects of that licensing deal are and what you are potentially exposing yourself to. Because think that there is unfortunately now very little chance that the Premier League could extract out of their footage and their licensing from Google, because Google have effectively already got years of it on YouTube, so why would they need anymore, whereas. If that stuff hadn't ended up on YouTube, or at least there'd been some extra remuneration because it was ending up on YouTube and therefore in Google's training data there, there was some other form of, of commercial arrangement around that.
Richard Gillis, Unofficial Partner:Yeah, no, I get that.'cause then, so strategically, YouTube is incredibly important to Google.
Sean Betts, Omnicom:Hugely important I'd, I'd say it's probably their most important asset right now, without a doubt.
Richard Gillis, Unofficial Partner:If you are not a rights holder, so you know, a lot of the focus is on the Premier League or if you are generating the source material, IE, the sports rights holders.
Sean Betts, Omnicom:Yeah.
Richard Gillis, Unofficial Partner:what are the questions on your side of the fence and from a marketing perspective? It, because again, it sort of feels like a similar conversation in terms of, well, what are we now, not talking about your own company, but I'm talking about maybe, you know, some of your clients. Everyone is then saying, well, I don't quite, I've lost my sense of gravity in terms of what this organization is going to be. Do you know what I mean? Do you get that sense from. Clients and talking to people, I think, well, if everything's content and therefore everything is, is data.
Sean Betts, Omnicom:Mm-hmm.
Richard Gillis, Unofficial Partner:What are we.
Sean Betts, Omnicom:Yes. and, and I think, I think, you know, sticking with the world of football, you can already see that there's, there's different clubs thinking about this very differently. You know, you've got some clubs who, who take that context and think, okay, our role is all about the fans. It's all about the community and it's all about the audience, and it's all about making sure that, you know, we are the most loved football team in our locality and
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:you know, everyone loves coming to watch the games and it's really accessible and all of that. Then you've got other clubs that are all about for want of a better expression. Global dominance. Like they wanna be the biggest team in the world. They want the biggest revenues, they want the, the best infrastructure, the best training grounds, the best players, et cetera. And that's a very different approach. So I, I, do think that everyone is trying to figure this out for themselves right now, and there is no kind of right or wrong answer. I think it is just asking a lot of questions about, you know, what a football club is and what does it mean as a business and what does it mean as a kind of cultural. Touchpoint in society as well. and there's different ways into that and there's different ways out of that as well.
Richard Gillis, Unofficial Partner:Yeah. Yeah. One bit that Craig mentioned. The API economy. Essentially what my headline is,
Sean Betts, Omnicom:Yep.
Richard Gillis, Unofficial Partner:one of the points he made, which I'll bounce this off, which is that sports organizations need to think, you know, there's a cliche about thinking about like a tech company and build your own a PR APIs and tech stacks so others can build on your data. Was one point. So there's that sort of the tech bit and you, you'll gather that I'm reaching the sort of ceiling of my, uh, understanding here. But the quote was, the world is moving to an API layer. API's becoming transactional layers and. His it went on to say. Right. Okay. Well that I, there's a future there where if you give fans or developers a pay API access to to build applications around and on.
Sean Betts, Omnicom:Yep.
Richard Gillis, Unofficial Partner:'cause again, it's very technical from my perspective, but I don't know what the real world application of that. I understand it and it's something I'll drop at dinner parties to make myself sound clever, but I don't want anyone question. It's like speaking, you know, saying something in Spanish and then having to answer a question, but could you explain what that means really, I suppose is the question.
Sean Betts, Omnicom:Yeah, absolutely. So I, I think this has been a bit of a slow burn the kind of development community for quite a few years now, but it's now becoming, think, a little bit more important to every organization and therefore a little bit more kind of visible and talked about. So essentially, the way I would try and. this in in kind of layperson's terms, is that we've all been used to being able to find out more and to be able to access information through a website, which has
Richard Gillis, Unofficial Partner:Great.
Sean Betts, Omnicom:visual and easy to use, and increasingly. When we look at how AI is evolving and the fact that AI will be doing things on our behalf in the future online, we want AI to be able to access that information and those services. And that means it has to be built not just for people, but for technology. And the way that you build technology access is, is via APIs. That's just the, the infrastructure that is being built out by us to, to enable that to happen. A really important change, I think.'cause it essentially does two things. One, one, it helps with the future. companions assistance, whatever you wanna call them, to be able to do the things that you would do online, because they can then access it for an API and do all of that stuff in the same way. But it also immediately does another thing, which is it opens up your data potentially to other people to allow them to do stuff with that. an example. National Rail. They sit on all of the data of what trains are running and when, whether they're delayed or not. Ticket prices, train routes, et cetera, et cetera. They built APIs out to allow developers access to all of that data. And then you saw lots of different mobile applications being built that allows people to see what times they're training running, whether there are any delays, know, book your tickets, all of that stuff. That wasn't possible until National Rail. Started building out those APIs to give that data to other people and to be able to build on top of it. So I, I think we'll increasingly see people building out applications on top of these APIs as they become more readily available. Because I think there's just a huge amount of interesting use cases for consumers for a lot of the data that is currently not available.'cause it's just not that infrastructure hasn't been built out yet.
Richard Gillis, Unofficial Partner:Yeah, that's really interesting.'cause there's a, again, one of the running themes of what we talk about a lot is where the sports body starts and stops. and what they view as a leak from their own economy versus what they can capture. Put arms around. And this requires a very different mindset. It feels like they have to have an open mindset, which is really hard because they've been trained to have a closed mindset and everything is category exclusivity. And you know, lawyered up. And if you, if you sort of advertise on one side of that line away, you go and you get a letter. So that's, it feels, it's technical, but it's also incredibly difficult cultural shift.
Sean Betts, Omnicom:and a very important strategic shift as well. And it, it comes back to the point I was making earlier about how rights holders have to be, I think, really clear about what data they want to keep and charge for versus what data they wanna let out for free, because it has some inherent value to it as a, as a marketing vehicle. So. The example I use when I talk about this is, um, from the music industry. So obviously you don't want to allow AI models to train on the music of your artists because that is the artist's IP and the record labels ip and it should be protected because that's how, you know, the artists and the labels
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:their revenue and make their money, right. there is a huge amount of value in information about an album, like names, the lyrics.
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:from the artist about what the meanings of the lyrics are and, and what mindset they're in. Then when they were writing it, and information about the, musicians and the recording process, all of that kind of metadata. Isn't valuable from an IP perspective, but it could be very valuable. From a marketing perspective,
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:and this, this is quite similar to how the music industry evolved in in Asia, where they effectively 10, 15 years ago decided that albums should be for free and they'd make all of their money in live performances. So they give albums away for free.'cause it was a marketing ploy to get more fans and they monetize those fans through live performances and merchandise and, and all of
Richard Gillis, Unofficial Partner:Yeah.
Sean Betts, Omnicom:And it's a, it is a similar thing for I think, sports right holders. There'll be some data that they have that they can use as a marketing vehicle and there's other data that they could monetize directly and they need to think about that strategically and, and figure that out.
Okay, so that was Sean Betts and myself. And now, just to finish off, you'll recall if you heard, our previous episode Where we created a, a co-pilot type language model that ran in parallel with the onstage conversation and then fed back and built on the topics and questions that we covered. That's available in a link in a Substack newsletter that went out this week. That model was created from scratch by TFG labs is Andy Shoa, and I wanted a quick debrief with Andy and Chris Woodcock, 21st group's, CTO, to see how it was from their point of view.
Richard Gillis, Unofficial Partner:Have we learned anything, Chris, about who's gonna win, who's gonna lose, just give us a sort of your summary of what we've seen
Chris Woodcock, TFG:Yeah. What, what I hear, and you know, what I've heard all night is it's, you've got this kind of dichotomy, right? Between e everything's becoming really easy. Like you've got that 10 x advantage of, um, you know, stuff that you know that you want to do, whether it's coding or getting an answer. Um, but also it's really hard. Um, you know, it's hard to build this system like I've seen behind the scenes and the number of nodes on there and the, um, you know, and the way in which you. You can build value out of it is really hard to do. Like you put that into, you put this stuff into Gemini and you don't get it out. So it's like, that's, that's a real interesting thing to tug on, right? It's like, so where have you got, where can you, where can you make things really easy? Um, you know, and get those 10 x advantages. But then also where have you got to invest time and effort into building complex systems that actually do what you want to do, um, you know, and deliver value for the clients, which, which they couldn't
Richard Gillis, Unofficial Partner:It's also, it's, it's sort of trust. I have a problem with trust in terms of with the machines I, with the machines, I sound like a grand, but the, with the, um, there's trust in terms of, you know, good actors, bad actors, but there's also right and wrong. I spend a lot of time going in and correcting at quite a deep level mistakes that they've made and then extrapolated from the mistakes. So it's the classic hallucination problem and whether or not we've act this is gonna get better or are we just gonna have to live with
Andy Shora:We, we saw just this week of the news, I think I saw on the, the, the metro on the bus down here, CEO of Google has told the world to not trust ai. And to be honest, it's probably the right time for him to project a message like this because people are blindly trusting LLMs to make and actually execute quite big decisions, which affect their lives, affects their performance at work. And there is a threshold you must pass in terms of competence and understanding what these LLMs are actually doing. If you do not pass this, this threshold, you'll not be able to amplify your productivity. You are just gonna get lazier. And, you know, the, the, the truth is, unless you point these systems at the right data, the advancement in the intelligence of these foundational models means nothing if you are not giving it the right inputs.
Richard Gillis, Unofficial Partner:Yeah. That question of who's gonna win, who's gonna lose? Are, are there any signs in terms of the characteristics of companies, organizations who are gonna prosper are in a position to
Chris Woodcock, TFG:I think, you know, something else, like we heard, you know, over the evening is like there's that cultural element, right? And you look at the role that Sean has got, for example, uh, you look at the role Andy's got in our organization and I think there's a huge cultural element to it. And, you know, I know you're a fan of the faster horses analogy, which I love as well. And I think there's a, there's a big danger here that people take this, you know, these developments and try build faster horses. And what we need is, you know, the forward model T like, um, that's what you've got to be thinking about. And that doesn't, that's a mindset shift, but it's also, it's really helped when you've got people like Sean engaging with you and you've got people like Andy in the organization. And I think if you, if you don't have that, that's the risk for me is like, you, you miss, you miss that cultural shift. Um, and then, and that's where the danger lies. I think
Richard Gillis, Unofficial Partner:the quality of, I mean there, there's a sort of shit in shit out question here in terms of if your data isn't in any position to be of use, this is not gonna help. Just putting a chat bot on top of crappy data is just gonna be a waste of time.
Andy Shora:It's true. And at TFG we face this exact situation. You know, every company has. Modern sources of new data that someone has wrestled into a good shape to build applications on top of, and probably some legacy data that relies on a human to tell you, you know, which columns in a database not to trust. And we tried to initially build on a larger data set with less reliable data and it just proved to be wasted time for very simple things pointed at, you know, by chance the right data sets, we got good results, but we very quickly plateaued. And, and the same for every organization right now that's building with AI tools are re realizing that they need to get their house in order
Richard Gillis, Unofficial Partner:so one of the, one of the questions I've got is, and you mentioned it, touched on it earlier, which is the sort of value of the data. What is the value of. What we've got now, and I was talking to Sean Betts about, Google and why Google is very well placed. To take advantage because it has YouTube and it has already got data of millions and millions and millions of hours of video. So this question which got discussed in the AI panel, which was about what is data now? Because we've all got people who've got an idea in their head about what it is. But then when everything is content, when everything is data, we are essentially feeding models. it needs to be thought of in that way. What do you think about that question?
Andy Shora:Yeah, it is a really interesting one. At at TFG. Data for us means truth and it's really important that everything we do and decisions that are made, big decisions that are made, are grounded in truth. Um, it's very easy, as we've probably all experienced, to get an LLM to hallucinate. It will invent. In order to satisfy you, the user, unless you give it really instructions to doesn't. It's really important for us internally to be trained up on the way LLMs work to be aware of the dangers. Um, and, and certainly by building a layer of in, of infrastructure that allows us to gain alignment, ask questions to large language models, which then go and interrogate the right sources of data. It unlocks amazing new avenues for us to experiment and, and it increases the amount of kind of access, the things we can access per hour from a resourcing perspective in order to achieve greater results Now. As you mentioned, Google are very well-placed. I agree. They're, they're, they're making massive strides at the moment. Um, but the data that they're training foundational models on is, is, is running out and progress is plateauing. There are many ways to invent data to gain small advancements now, but in my opinion, these, these models have reached the level of intelligence which. Will see us for a lifetime of doing kind of various office jobs. Um, and, and one of the problems I think is the things we're asking LLMs to do are not complex relative to the challenges these models are being given to verify their intelligence. And this, there seems to be this obsession with trying to one shot. Mathematical proofs in order to achieve the top of the leaderboard on all of the comparison of the new, new life language models. And, and none of us are trying to, provide proofs to mathematical theorems as part of our jobs. At least we are not quite at that level anyway. We're doing lots of complex analysis, but they don't involve, you know, achieving a, a field medal or a gold Olympiad.
Richard Gillis, Unofficial Partner:In the couple of times in the run in, and we had, group calls in the run to the event, and one of the things that you mentioned a few times was the one shot trying to do everything in one shot, which is sort of. that stayed with me. I, I dunno where we are. One of the bits of the conversation on stage, which was about, and I think it was Craig who said, about API question in terms of, you know, whether sports organizations are gonna then sort of shift into, reinventing themselves, and this is gonna be a heck of a challenge, but. They're saying, well, okay, there's an API layer that we, that that is going to then grow. And one of the phrases that you use quite often, this shift from sort of simple large language model prompting, which is what we're doing at the moment, and we're sort of being told, oh, you're not prompting it right. And it's not doing it, it's hallucinating. And so you get into that loop complex orchestrated workflows and building. Whether we call it a agentic or not, I dunno, but that feels like that. The promise of this thing is that, and I don't know how far we are from that. And the initial question we posed on the night, which is the billion dollar sports business. what that would involve and the characteristics of which, we discussed. are we in that question? And it's that sort of orchestrated I think. What's your view on that?
Andy Shora:So orchestration has been a massive unlocker for us, and, and it, if I may, explain what that means for a moment. You can think of an orchestrator as the project manager that's an expert in planning and is available, um, all the time. Has a constant view of all the resources available to them. Let's pivot to an example of something. I'm also passionate about cooking the way things used to work when we were playing around with early versions of chat GPT. Let's say I'm a chef and I'm given the instructions to, to make a bolognese. I might initially look at a carrot and start chopping it up, and then I might look at an onion and start chopping it up. I might decide to. Spoil my pasta at that point, but then I'd realize it's probably ready way too early. And you can see by looking at these tasks sequentially and just making decisions based on a simple probability distribution of what should I do next? Can yield some very unexpected results, but also expected results.'cause we know large language models are just spitting out the. You can do that for a lot of tasks and get acceptable results. But if you have a system that can break down all of those tasks, chop a vegetable, put uh, a stove on, boil pasta, and if you have an orchestrator, they can look at. The user's intentions, they can look at all of the tools available and they can form a plan, and they will often form the same plan, given the same ingredients and tools. And then they can both outsource parts of the plan to be executed by the right kind of. Agents, um, in this case it's just one agent, the chef, but it would still work and they can also keep a ledger of the work that's been done and the work left to do. And just to throw in another amazing, uh, part of these systems, they're able to course correct. If you have a manager and a chef underneath, um, based on what's happening on this ledger or whether the chef's experienced any problems, you can enter a new kind of workflow, which is something's gone wrong. I've chopped it, open the onion and it's gone bad, for example, and enter a new kind of. Problem solving workflow that could involve other agents. It could involve an agent to go out and buy another onion, for example. And ultimately, these these systems are problem solving algorithms in the hands of an orchestrator. Makes the work of the chef really easy and yields more deterministic and expected results.
Richard Gillis, Unofficial Partner:And that's possible now.
Andy Shora:It is possible now, and if I told you it's actually quite simple. I dunno if you'd believe me, but this is, this is just, uh, you know, classical programming. It's, uh, it's, it's functions being called with the use of LLMs to interpret what's happening.
Richard Gillis, Unofficial Partner:So that question of who's gonna win and who's gonna lose in the sports business race. Do you think the answers are to that? Because again, we touched on it on the night, and we are in a race, I think. I think, you know, it will go sector by sector and you've, you know, I to podcasts and read a lot of around things like healthcare or finance or education. And so it does start to then splinter. And I'm interested in the sports bit for obvious reasons. What do you, about in terms of those, the, you know, the characteristics of the winners?
Andy Shora:Well, from what we've seen, you know, it's very easy to go and browse tech news and read about success stories and, and they all seem to feature the works AI right now. And that that may be a bit of, um, how the, the search, the news algorithms are working. But when we provide solutions to clients at TFG, AI is sometimes part of that solution. The rest of it is technology. You know, we apply the right kinds of technologies to solve different tasks. I think the winners are the ones who acknowledge that. AI is part of a solution, and technology should adapt to the way your business works. Some people are extremely fluent. Let's say I'm an analyst and I'm, I'm, I'm scouting football players and I'm, I'm using some tools already and I can do a bit of programming. They're going to use ai. In a very different way to someone that's just graduated, doesn't have much domain experience and needs to learn more about how clubs work, how players develop. So the adaptability of these tools is great. That is where we have to put the work in. And it's not work that really big tech companies are doing'cause they don't really understand. How things work in our industry where decisions are often emotional, backed by data. Data is sometimes wrong or not representative of a a situation. And so it's easy to see who the losers are, the ones who aren't experimenting, who are, you know, resisting change. And right now, everybody should have kind of adopted these tools to improve their personal productivity because. That is. Absolutely critical just to, you know, your competitors are doing something, you know, your competitors have become twice as productive. Um, if you don't, you're gonna have to punch above your weight somewhere else. And for what we've seen, the winners are the people who are. Not necessarily aware of what the solutions are, what the next product they should build, or what tools they should build, but they're the ones who are having the conversations now and are starting to codify how their processes work, identify their proprietary data, and what value might exist there, and the ones that are having the open conversations with us.
Richard Gillis, Unofficial Partner:Within sport again, we, I did a, we did a betting, event. A few actually the week before we did our own event you've got betting or you've got ticketing or you've got media, you've got, the football club as a, as an entity. You've got the sort of Premier League UA for level governing body, what their relationship with these things is. So each of those poses different, similar, different questions, trying to work out what they are. of the sort of interesting bits, I think is. Again, this comes to, from my conversation with Sean, was that he was putting forward this idea that, okay, we don't, we don't know what the executional layer of data is that we possess. So it could be like the Premier League has got fantastic training data for robotics because, you for obvious reasons it's got a load of players running around and you know, in terms of there are so many. of what that could be used for just on that on a story level, I can see exactly what he's talking about. so trying to second guess what to sell now, what to, how to package it, even how to make it. Structured or to, you know, the, there's the sort of problem of structured and unstructured data in terms of just the, the level of sophistication needed to turn something into something useful. I think it's beyond the capability of any one organization, unless they're incredibly of forward thinking.
Andy Shora:Yeah, I mean, if, if you are in a position to bring structure to your data without being aware of the end use, then you are gonna be prepared for the future. Someone will find value in that data and the value of your company will increase. But that is a kind of luxury position to be in. Um, even we sometimes at TFG are. Pursuing experiments that are probably too far in the future and aren't gonna be applicable to any client in the next year, and. May go down as wasted r and d effort. You know, a lot of experiments that I've done over the years, I've found the timing just isn't right. You know, and it might mean that something doesn't sell to a client, but it, it also might mean that something's just technically not feasible. It's not gonna run on mobile devices right now. It's not going to provide value to the user in solving a problem. So we're at a really interesting point where. Sometimes we park ideas, we don't, when we fail, we kind of, we don't bend something. We just know the timing isn't right. And I've, I've experienced reactivating parked ideas because suddenly there has been a massive, say, technological advance in hardware and something has become viable. So, um, that is a kind of mode I'd encourage every company to adopt right now.
Richard Gillis, Unofficial Partner:yeah. No. Fascinating. Well, listen, I wanna just finish off by saying thank you for all the,'cause it was really great working with you guys and it was really interesting seeing how you took the initial brief and turned it into something. And again, that wowed me and I thought it was, it was a really great experiment and. Has created a whole load of forward looking questions now, which I'm very keen to go and sort of pursue. But thanks so much for all the work you did on it'cause it was, I really appreciate it.
Andy Shora:it was really enjoyable and I, I can barely remember the, the long hours. I just remember the feeling after the event, but, you know, I'm looking forward to doing the next one where I can feed in all of the code used to provide the, the infrastructure behind that event. I can feed in the feedback and I can have AI generate the next version of it.
Richard Gillis, Unofficial Partner:You can sit at the, you can sit at the side just drinking a martini. Now you don't, you've done all the, done all the work.
Andy Shora:that's always the plan.