UP544 ChatUP: Craig Hepburn on how Agentic AI changes sports consultancy

0:00If you look at the technology industry specifically, if you look at Shopify, I think Bloomberg even started to do this, Salesforce, all the big tech companies are opening up their data to AI agents because they know that's the future of their business, like that's who's coming and accessing it. So to some degree, the question is, is the sports world thinking like that, or do they have the appetite for that kind of idea? Hello, Richard Gillis here. Welcome to Unofficial Partner, the sports business podcast. Today we're talking AI and we're talking AI with Craig Hepburn because very few people know more about it than he does.

0:31He is a strategist and builder, Perplexity Fellow and former Chief Digital Officer at Art Basel and at UEFA. I think his output on LinkedIn and on Substack is pretty much unmissable if you are interested in this subject. You'll have heard him at our live event at Fuse with 21st Group before Christmas. I wanted to talk to Craig about the particular vulnerabilities of the sports marketing agency sector from AI, which is much talked about. Something that caught my eye, the advertising agency world is already feeling the pinch. 60 % of US senior marketing leaders reported spending less on agencies in 2025 as a direct result of AI.

1:12I just wanted to know what he thought the future was for the sector and in terms of what the response will be, what the particular vulnerabilities are and what the opportunities are for growing anew. It's an interesting conversation which might surprise you, certainly surprise me. Here's Craig Hepburn.

1:34I mean before we get going have you noticed on LinkedIn a sort of uptick in people because you've really done that AI thing and the sorts of things that you're talking about are beyond the chatter and the cliché stuff that you tend to find on AI. it's funny because i've learned a lot about myself over the last few years especially coming out of like full-time employment in the last year and a half because obviously sitting within a company and organizations you're kind of restricted a little bit in terms of like yeah because i've been in tech for over 30 years and i'm probably quite a deep system thinker like um from both a strategic commercial point of view all the way down to like you know work with ceos and executives but then i'm literally sitting in a room with a developer building like linux and code so i kind to feel like I spread a really broad spectrum.

2:20And it's always really interesting when people have tried to put me in a box over the years. It's like my brain racks up a lot of thinking as you start to map, I suppose, to some degree the infrastructure, but then the scaffolding around it. And also, I think being in tech for a long time, I loved it for 10, 15 years. And then I think for the last 10 years, I've just felt really frustrated by it. And I think I have a lot of opinions on what's broken about the whole technology space. and well i remember you saying i mean when we talked i remember you saying you're really enjoying building things yeah exactly again yeah again feels like there's a bit of the whole thing which not that i'm a builder of any sort but i get that bit of it is really exciting when you hear people talking about okay well you can now whip these things up and try them very quickly there is a sense of wild west to it which i really like well that was i suppose i mean when you say you're not built i mean that's the point richard technology really and considering how long we've kind of been at this game for like the last 50 60 years the idea is it should be democratized so that more people can like we said about you the technology using now if you're a podcast like the idea is probably we've made technology super complicated for companies and people and that's because the business model is to kind of make it complicated and don't get me wrong like from a deep technical architecture point of view there's a lot to unpack but i'd say yeah i think now what what people can do now is probably most people haven't haven't realized that the capabilities now not say that everyone's going to go and build their own salesforce platform tomorrow but the fact is that it's possible now for you just to build a quick product a quick app personal software that you want to use for the day and like like that kind of democratization of being able to kind of unpack that and build things is pretty profound.

4:08Like even for me and a lot of my friends that have been in this for a long time that I'm working with, like we kind of get excited, but we're also astonished by what's possible. But everyone's kind of got a framing and I think that technology is hard and it is difficult. And that's the weird thing. It is and it can be. And to some degrees, you know, there are certain industries that it should be, but there's also a lot of stuff that we pay for, we buy, we think is difficult that's actually a lot easier to access. And I think that kind of that horizon and that kind of idea of to some degree, we're making it more accessible to people and there's a lot more you can do with it.

4:42But also it also like there's a lot of complexity to unpacking it as well. So it's kind of it's weird. It's kind of a bit to some degree. It's it's quite profound. And I think that's part of the challenge that everyone's getting struggling with a little bit in terms of what models can do and what they can build in the products and things. So the question that I wanted to ask you about what this podcast is going to be about is what this moment will do to agencies, the sports marketing agency, the classic sort of could be a data agency or a research agency, whatever it is. The cliched view is what agencies are renting people out to do now can be done in-house at a fraction of the cost with a few smart people and a language model.

5:26and I just wanted to sort of interrogate that a little bit and just go a couple of stages further just to see well first of all whether you agree with that framing and that's the direction of travel a lot of people listen to this work for agencies but it's also a question of well yeah but well actually it's never as simple as that so I think that's give me your sort of sense give me your first sense to it yeah I think that is the I think that is the key point like like any business there's always more to it than just purely the deliverables. So I think what we look at, like we've always kind of, from an agency, even consulting, building agency software, SaaS, like all of it is the same.

6:05Like ultimately you're buying, paying, getting access to a group of really smart people who've built and systematically developed something using their own knowledge and intelligence that they've been able to package up and sell because you haven't had the capability to do it. And to your point now, if you have someone internally that understands how to use AI well, the models, the tools, the capabilities, they can get a lot of that, let's say, that general deliverable pretty much on serve to them through the models and the tools themselves. So, you know, if you just take something as simple as a website, so even today in a website, you still kind of want to get somebody who's a good web designer, a good developer to build your website.

6:47Like even you probably could have figured it out, but you still want to outsource it because there's a lot more to it than that, managing it, supporting it, et cetera. So that's something that now with Replit, with Lovable, with all the different, even with just the raw model now, just coding you up and deploying it, you can now build agents that will manage it for you. So there's a lot of capability. The question is just what's the appetite for a business to want to do that? Where I think it gets more complicated is where you've got a lot more value being delivered by an agency and a partner.

7:17So I think the key point really is what are you charging your time for? Like what is being delivered at the end of the day? Is it just the deliverable of a PowerPoint, a strategy document, a website, an app? Like we will pay us to build these things and implement these things. That is still not going to disappear completely. But I would say the cost and delivery of doing that is either the agency is using the model to do that quicker, faster, cheaper. And are they passing that cost on to the client? or the client's going to do that, as you said, internally, to some degree, maybe not all of it, but they might start doing 20%, 30%, 50 % of it.

7:51And so therefore, where does the agency and the partners come in and add more value on top of that? And so really, I think what it does is it kind of elevates everyone up from the pure deliverable of the asset, the artifact, where it's been delivered. And it just means that everyone needs to sit in a room and have a conversation about what that looks like now going forward. I would say that probably the biggest opportunity right now for agencies and anyone in consulting and I think that's interesting software companies consulting businesses and agencies I think are all ultimately going to kind of converge in the same space because they're all going to have to deliver something a little bit more than they did before and so the question is a consultant firm can now build software a software firm can now do consulting so see an agency can kind of do all of it and so you kind of realize like what kind of gets unpacked out of that and I think you're right it's an interesting question I would say data is going to be a big part of that and then I would say the other thing is obviously just having really good trusted relationships with people as well is still an important factor there's the sort of soft end the people end and you're right in that ever since I mean I've worked for a business in 2001 or whatever that tried to get away a sort of online marketplace this was in dial-up period the idea you sort of got there intellectually but it just never worked the tech wasn't there and whatever but the argument against it was no it's a people business it's about relationships people by people all those things so there's a sort of that's where the fuzziness comes into it and i think it's interesting we're at interesting moment in terms of where the value resides in your organization and one of the sort of things i came away with our event at fuse with 21st group was trying to sort of establish that what is the race how are we defining what the competitive set is now what are we trying to do and what can if we park agencies for a minute what is it that they're going to try and build so what where's the value in the sports data market for example because if everyone can access it via yeah large language models then I don't need someone to house that or warehouse that and is that protectable do you think so if you're selling data to marketing companies to brand clients or if you're selling it to rights holders is it defendable do you think is that as the data goes on as the sort of period goes on there's a question about what's the moat and what can you defend if you are selling information Yeah, so I think that I think go one level deeper.

10:30So I would say, if you think about what, right, okay, what are the things that stay and what are the things that change? So I think if we, again, nobody knows can predict the future, but let's just argue for the sake of it that we built all of our products, technology and systems, not just in sport, but every industry has been designed for humans. So in other words, all of our websites, apps, all of our engagement has been designed initially for people to engage with. So therefore, we built that kind of that map of all of our technology for people to engagement, views, to download your product.

11:06And also you build one product, like a gaming app or you build a fantasy app or you build some kind of product. And you've built that and you deploy that to the people that engage with your product. or you build a website and you put branded marketing on there so that your fans are engaged with that. But what happens in a world where, you know, and we're starting to see this, and again, we're super early, but, you know, where you have an agent, so there's a big kind of, like, movement at the moment of a company called Open Claw. So imagine agents, and now you're seeing Open AI, all the companies are building, like, agents.

11:39And what are they? Like, they're not, when you get to proper agents, I can then send it off and it can go and look for me for go and find me the scores for the champions league game last night so i literally have one running on my whatsapp it's called neo i built them i literally just ask him like go and find this go and do this i even could ask him to go and build me a fun app on and it will go off and build me an application and deploy it in a few minutes so agents now are the thing that every human is going to have well humans companies have an agent and they are the ones going off doing that now that might sound like 10 20 years away but i would say it's probably a lot closer in the next few two three four five years so therefore as agents start to be the propriety like the key person you engage with are you building your product not only for people today to engage with but you build you're building a product or stack or you're building data or you're building information for agents so as a simple example is you go to an agency today and you say build me the champions that you got you pay them whatever hundred grand couple hundred grand whatever they go off and build and that's a long process now internally they could do a couple of things one they could just have an app they could just build it through ai so that makes it cheaper more effective or even better why don't you build a model of all of the data or build some kind of api of all of your data all of your content all of the stuff that you have around your sport your business connect that allows some fans or people to build upon the top of that and they can let you just subscribe to your api similar to subscribing to a model and then they can a fan can build their app using the data of your product to your company so in other words if everyone can now build and use an AI or a model to build them a piece of software?

13:18Why don't I build, create and build my own version of my app using the UEFA or the Champions League or the PGA's kind of application? So that's really where companies like, if you look at the technology industry specifically, if you look at Shopify, I think Bloomberg even started to do this. A lot of the big tech companies are starting to deploy. And I noticed Box, Salesforce, all the big tech companies are opening up their data to AI agents because they know that's the future of their business like that's who's coming and accessing it so so to some degree the question is is the sports world thinking like that or do they have the appetite for that kind of idea but it means like my kids are 15 and 17 they spend a ton of time inside playing with ai with models building stuff like they're they love sport but they're engaging with it as we know through different channels and they're also wanting to build things with products and services so if you think about that generation maybe there's a whole generational change that we go through but i can see that coming pretty fast i think that's an opportunity and so if you're an agency or a partner right now it's like actually can you sit and build the agentic frameworks build all the application layers build the apis build all the data systems and actually the capability so that you can still serve people and fans directly but you're also building for the next layer of kind of the agentic ai world that's coming over the next few years let's just pursue that for a second if you get to control and releasing control like you're saying give the api to fans give it to third parties and let them develop and work on your data sport as you know well having worked for uefa yeah is about control it's about the right to do something i've sold you the rights to do this and i'm going to police those rights from everyone else because that's what i've sold you i've sold monopoly access so is it a technical question or do you think it is a cultural question for it's both i think it's both i think it's i think you end up where strangely for me i would say you by enabling by opening up a data platform that people can build upon agencies fans everyone can have access you actually take back more control because you actually build the infrastructure you build the rules, you build all of the, you can set all of the kind of the rules and regulations around how your data and APIs can be used by anyone.

15:37So you can kind of define that, you can monitor it, you can manage it, you can monetize it, you can build new products and services on top of it. So you actually got a lot more control from a technological point of view. And then purely from a philosophical point of view, it's like, we kind of, there's a kind of IP legal control framework thinking as you said like contractually what you give people contracts to do but as we know in a world of technology especially information data leakage all of that people are the models themselves are training on things so there's kind of there's a lot going on anyway so I'd say actually by building your own kind of like infrastructure and technology you're actually making a step in and playing and actually strangely making a sort of play towards we're going to play the technology companies a little bit of their own game by actually building more of the infrastructure ourselves that we can actually take back a little bit more management and control of.

16:31I mean, if we argue like the world in the next five, 10 years is not going to go backwards technically, the technology and digital is not going to go in reverse. It's just going to compound and move forward and AI is not going to just stop and kind of end. You could argue every company, no matter if you're a small independent cafe, all the way up to being a large global conglomerate, you're in the technology business because every business is being accessed to some degree through a technological platform. That's not changing. That's probably accelerating. So it might make sense to actually hire and build some of that capability in-house.

17:06It doesn't mean you do it all. You still have partners and agencies. But thinking more as a technology company, and I've said this, this is my argument for a long time has been the models that take Facebook and Google and Amazon and all the big technology companies. Look at the amount of value that they've extracted from the world. Now, go and look at how much every other company or business or vertical is extracted out of the world. Look at the difference in the gap. And then maybe the question is, should we not be maybe thinking a little bit more like a technology company if we want to get some of that value back?

17:39And also for me personally, I also think it's just about that democratization. If we don't want four or five big tech companies pretty much extracting all the value, I said this when we were together at the podcast we run live, then there's probably you would argue there's probably a bigger opportunity for everyone to build more of the technology stack themselves or at least invest in some of that capability leverage the models the ai the intelligence build the agenda infrastructure we don't know how it plays out but at least it gives you a lot more control because at some point when you realize like the your world is changing like really quickly it's it's quite hard to catch up but if you do it early at least you kind of you're kind of like you're moving towards where the puck is kind of heading towards so you're kind of like you're giving yourself a little bit more and so my argument on the control thing is the biggest opportunity to take back control of your business and your future is actually to build more of the technology and systems thinking you're not all of it necessarily but at least to start thinking about it so the big foundational models the very top end the sort of google anthropic that group open ai chat gpt all that they are thus far scraping the public internet or have scraped the public internet they are now looking at the rest so the data that resides in companies and organizations and i'm trying to what sort of proportion of that is that how much is there still for them to gather how are they going to get at it and in sport one of the conversations we've had in the past is sponsorship the route to getting at the data so when a league does a deal with google today what are they giving away what's at stake so just yeah that's i'm just trying to sort of there's a general thing let's talk about the the general question about what's still available what do they not use so when i go on chat or when i go on anthropic or on claude that's accessing the public database yeah yeah okay so to unpack that yes so all the models have been trained on the open internet so the corpus of basically all text written that's accessible online plus books and videos and images so all the models you're right is kind of being the models are being pre-trained on all of that there's also something called pre-training and kind of reinforced learning reinforcement learning so there's kind of two technical aspects so what the model is trained on as you said from that and then what it can get afterwards to add additional context and there's a whole range of technical terms rag and vector and all these other things but i think purely the models yes so they've been trained on that open content you then have the products themselves so gpt claude google gemini they are like they have both two they have two things actually which is interesting they have the raw model themselves and they have the product that you work within so when you use gpt that's a the chat gpt that's the product itself if you use a free version of that there is and there's i think there's some configurations you can turn them off for training.

20:53But ultimately, a lot of that data leakage is not into the model itself directly. When you chat to GPT, the model itself is not training directly at that moment in time on that data. What you're doing is storing that data within the product stack itself, and there's leakage that happens into the products through that, especially if you're using the free models. When you have an API, so when you API, i.e. build a product off the back of maybe a clod or GPT or Gemini, you use what's called an API and you pay for access to that API. That, all the terms of service, if you read them, they do not train on your information, they do not store your data through the API.

21:33So if we believe them or not, but that's in the terms of service. Where, when you have an enterprise version of Claude or GPT, so in other words, if you're a company right now, let's just take, I'm working with a business at the moment, we installed Claude's, Anthropics kind of Claude model as an enterprise and they paid the money to have that enterprise version. All of the training of your company information is switched off and all of the terms of service again means that we're not training on your data. What they do is they build up databases around that to organize and manage context. So ultimately the best way to leverage the models themselves is generally to the APIs and you can keep and the way that it's architecturally set up as well you can set them up in such a way that you can access the intelligent models but keep all of your data completely separate and so that's why a lot of companies are building proprietary pieces of technology around the model for companies again from an agency point of view big opportunity there to really understand data models training help companies understand exactly what that looks like there's a lot of myths as well in businesses there's a lot of misinformation and the lack of technical knowledge but i don't want to go too deep but what i would say is there's a huge opportunity for agencies and companies and every business to learn a little bit more about that but to your point then how does all that information within those business kind of get unpacked you're right i mean at last count it was like a very small percentage i don't know what the number is now but it was maybe like less than five ten percent of like internal every company towards internal intranet and all of their internal databases and knowledge none of the models have really got full access into that.

23:15And that's really where the kind of standoff is at the moment between allowing AI into businesses. I know a lot of sports organizations and a lot of any organization has put a lot of restrictions around AI within the business, and rightly so, because they want to make sure they're protecting that data. The problem is, though, the tension becomes you're going to have to go over that quickly. You're going to have to either A, understand that, understand what technically it looks like, and work with people to help you unpack that because just staying still means that someone else another business i think it was at reading fc just hired i don't know if you had reading fc hired ahead of ai yeah yeah um so but again probably again having someone inside the business really helping unpack like where we can take leverage some of the internal data to build new products and services so i would imagine he's looking at all their internal data and saying how can we build or train our own i don't know maybe even fine-tune their own models, use APIs of the major models, the frontier models, to build products and services.

24:16He, I would imagine, would have quite a good knowledge of how that works. And therefore, they're going to start to make some really interesting moves and build some products and services that probably move them forward. I think the internal, yeah, I mean, the big play for all of Anthropic and OpenAI even more so now is, if you look at the investment they've put into all of their models and their products, they're all playing for the enterprise. they're all playing for the big companies because in order to kind of get even recalibrate some of the numbers in that investment they're going to have to like play those bigger revenue numbers and that means that how much tokens are you using how many tokens are using per month i've got a couple of companies that are spending hundreds if not thousands of pounds a month on tokens and so that is money that's going directly to kind of into the models themselves and so there's a big kind of play right now to how do we become valuable for you as a business harnessing our model with your data to build new tools and systems or commercial opportunities for you and really the money is made by the API calls and the token usage that you use for building that product again that goes back to my point around understanding the technical architecture of building the right systems so that you can also potentially make some revenue on those as well in building those new products so i would say you're right the big revenue play will be um and i know gpt well i know for example i think anthropic lose money on the 20 pound a month subscription for users so they really want the enterprise play and i know gpt are now starting to move more towards building their intelligence models for enterprise so what i would imagine richard is over the next six to twelve months you're going to see more and more products come out from the companies that are specifically designed to help enterprise businesses take advantage of the models inside their companies.

26:03So the question is, do you sign up and kind of like license those products that they're building for you? But the interesting part is you don't necessarily need their products to do the things that they're offering. You can do a lot of that just with some smart developers and some APIs. So I think that's going to be where the tension lies. At the end of the day, do you want to subscribe to their product and you just want to use their api to build products on top of their stack and i guess the agency question that we started with is that there is the gap and there is the opportunity between the sort of universal and the specific isn't it so you're saying right okay here is a space that sport has or requires a particular set of i sound like liam neeson but a particular set of skills and insight and intelligence but also the knowledge of on the sports side so that's where that's what an agency is really when you boil down to it but the people will be different presumably there'll be a there'll be different types of skills that you require inside the agency but the agency as an idea will still exist because it's a different shaped thing but it's doing slightly different jobs or very different jobs but there is still a market there for advice but also building yeah so exactly that so the so if you say go back to original point about agency so before we provide strategy let's just say we create assets like websites applications we build data systems like all of the things that they build now imagine what they deliver now is agencies themselves have people who understand how to harness the models understand the architecture under a good example is i've got my son who's a who's not got a huge amount of money to spend on apis but he does a lot of building with stuff and a lot of his friends.

27:49So they don't have a huge amount of money. So they become really experts in open models. So in open weights models, open source models where it's a product, there's a platform called Open Router. And so they can go there and they can find the best models at the cheapest cost to deliver the best value and the best outcomes. So what codes the best, what can build the right tools, what products and services they can get. So they become really expert in understanding the models. I actually, I've actually, he's been advising me in some projects because he understands the capabilities of the models.

28:18He also knows how to use things like cursor, how to work with Replit. He's able to, as an individual, build full end-to-end stack products. So he can do the strategy, the research, build the product, build the PRD. So he doesn't deliver just pieces of the pie. He can do the whole thing. You start renting him out, Craig? Well, he's mine. At the moment, his mum's like, he's at college. He needs to get to do studies. But yeah, you're right. I mean, to be honest, it's like these young builders are learning what's valuable. And they're seeing that actually being able to build things, develop products, build systems, understand how to use APIs.

28:57It's kind of a skill set, as well as we've went through kind of an interesting sort of like time this year. We're trying to identify the right skills. And what we found is the people who are very kind of broad knowledge, very good problem solvers are very inquisitive. They are open minded. They are very technology, they enjoy working with technology. They really get into playing with the models and the AI tools and systems. They've started to be able to realize, to our point earlier, they've been able to build things that they didn't think they were able to do. They've kind of realized that their skill set, which was kind of generalized.

29:32And now instead of relying on developers, strategists, researchers, they can actually do a lot of the work themselves. I wouldn't say they can do all of it. And there's still a lot of edge gaps that you still need expertise in, of course. but what they're saying is that we can now do a lot more of the work and be a lot more autonomous and so if an agency model you would think they have creative designers researchers developers builders all of those skills to some degree still matter it's just they matter at the edge they matter at a smaller percentage or where the value lies and so now it becomes like one or two people can do the work of five or six but you might still need a couple of people to validate very important aspects of that deliverable.

30:14I would say the other thing is testing, validating outcomes. Probably the most amount of work we see right now is not building and developing, it's doing a lot of the evals, researching the outcome, validating that what it does is right and correct and accurate, making sure the information it's using is good, making sure that the platforms you build deliver the right outcomes. So I think a lot of the work now is shifting upstream a little bit, or downstream or however you want to put it, but whereas the work itself was where you spent all of the energy, now the work is great. You can do that a lot quicker.

30:48Now the value moves to how can we use that to be a lot more valuable? How can we evaluate? Checking that it's right. Checking that it's right, yeah. But more than that, not just checking it's right. Is this the right thing that's delivering the right value? So let's just take an example of building an app would take three months or six months to build, quite expensive. if you deploy it and realize ah we made a couple of wrong decisions our hypothesis wasn't right this doesn't work nobody's it doesn't really work the way we thought now imagine you could spin up four or five or ten versions of that app in a few hours or a few days deploy that test and learn figure out like what's working do the evaluation run lots and lots of hundreds of kind of research trials or research on that and self-improve the product you can do a lot more work on that and then what you're doing is helping the partners or you're working together to get a better outcome so whereas before once you've done all the energy put all the effort and deployed it it's quite a lot of work then to kind of change that whole thing and do it all over again so i would say there's a lot of value there moving upstream a little bit to be a little bit more valuable there I'd say that's a huge opportunity just now.

31:57On the sports side, so you look at one of the questions in the previous era, what we now call it, the sort of social media era, and how they relate to what they do with their data, all of those things. And one of the answers was always, one of the sort of questions people would put forward was pooling. So football leagues, for example, share a whole load of similarities and pooling their data when they're getting into bed with sort of various third parties whether that makes sense because actually individually you'll only get so far because it's you're small and you haven't got the capability you haven't got the money frankly so one of the questions is can they pull together that day back to the that question about the sort of open versus closed data that the the models want to get hold of you can sort of start to see well actually there's a sort of sport inc answer because of the point you put about the last generation of tech platforms has just been so extractive in terms of just taking all of the value from the marketplace i wonder if there's a way there's a sort of don't get fooled again type solution they're coming around again with a different product with the same expectations presumably but i'm wondering if you would advise sport inc to do something different this time around i think that the thesis makes sense i mean there's a lot of probably a lot to unpack in terms of different sports work.

33:21I mean, everybody's kind of... I realise, Craig, that it's almost impossible to get sports bodies around a table and to agree on anything. So this is a massive hypothetical, but I think it's worth asking that question because actually you can feel it happening again, can't you, that everything will just be given away? Yeah, I think that's because it looks like, yeah, independently, if you're kind of individually having a conversation with what we've had, again, with social media, same thing let's have independent conversations as independent sports or businesses and then you kind of like you look to Facebook, YouTube, Google and all those companies as a way to drive you traffic engagement and value and we all went through that I worked through that in all of our companies that I was involved in over the years and but I think the biggest difference now is we needed a lot of that technology company because they were building these systems And as I said, I think that to some level, that playing field is now more level.

34:19So you now have access to be able to develop and build more of the things yourself. The biggest challenge is the network effect, because I think what those big products and platforms provide you is a massive network effect. Now, your point, I think, makes sense. Now, imagine Sport Inc. got together and says, well, actually, we could start to form. We have a lot of data, we have a lot of context, and we hear this a lot in AI. Who has the context of proprietary knowledge and data that makes everything valuable in the first place? Sport has that in abundance, right? So every single event, every game, every experience provides a vast amount of incredible data.

34:56Without even getting into what every athlete has in terms of their data, what you can do with that to build products and services. So you can imagine the unbundling of data into some form of product, into some form of new value opportunity for every fan that can engage or buy or rent or subscribe to the sport. So imagine I now subscribe to the Premier League. I subscribe to the PGA, right? I pay them a subscription for their digital products and services. But the difference was they are now able to build, manage and to some degree kind of get together and build the right context. so and that's why to be fair google meta all the big kind of social networks i mean put youtube to one type of a video point of view but if you think about it like the real value is becoming the the real value is the intelligence models and products connected into your data that becomes really valuable and so the question is like what does that network effect look like what does the commercial opportunity look like and and how does that kind of like push some of those social networks to one side and again i think we're early it's really hard to kind of see how this plays out but i would imagine there's going to be a whole new there'll be a whole new operating system that will be built on top of what we have today and the question is have can sports organizations as you said get together and start to think about that more broadly and and as they get a lot more mature with technology and digital which they all have done right they've all invested a lot of money and a lot of technology, hired a lot of great talent into their businesses.

36:29Now the question is, do they just partner with third parties to build everything or do they start to think about building some of that themselves and as you said, getting together? The opportunity is great. And to be honest, it's not binary either. You can play both. That's the beauty of it right now. You can start to build things at a cost, at an ease of build now that's never been available before. And you can still play into the existing business models. And so there's a lot more leverage there and a lot more opportunity. But yeah, I think you're right, Richard. I think there's, that's the thing.

36:58The overhang is really interesting. I was chatting to somebody the other day. What people believe is possible in terms of the capabilities, if we stopped building anything today for the next five, 10 years, you would still have a huge amount of capability that people could build versus what everyone knows is available. And so I think the difference between what individuals, sports organizations, executives understand the model and the capability versus what they could be doing, there's a big gap and the big opportunity right now is to try and fill that gap with knowledge and information to make better informed decisions and I suppose that's kind of what to some degree you're doing with this podcast and we're trying to kind of unpack with people and give them that information.

37:36Yeah it's also again second guessing where the value of your organisation is not just today but further down the line and I remember talking at our conference where the question was Google and does Google have a strategic advantage because it's got YouTube and in terms of if the future of robotics is modelled on Premier League European footballers? Yes. Okay I could understand that in which case what are you selling to Google if you're getting if you're doing deals on YouTube if you're doing deals with Google today what is it that they're using how are they viewing on both sides of the table what is it that they're looking at because when i've heard people say right okay warner brothers discovery that's all about ai so again a question there i've no idea whether that's true or not but it immediately changes the lens on why companies are buying other companies what it is that they're seeing training data training data so fundamentally is that just the game that is being played at a level of billions of quid yeah yes i'd say the training yeah i mean the google thing yeah so training data yes the google thing's really interesting because i think they have they don't just have youtube and all the products they have they actually own the whole stack which is really unique so they own the tpu so nvidia sell gpus to all the other companies google have what's called tpus their own essentially their own chips their own right into the hardware right into the metal so they have the metal in their server infrastructure all the way up to the model and all the way through all the training data so all of google all of youtube all of their products all of their search information um all the way up to obviously gemini genie what they're doing with deep mind like they have an incredible stack top to bottom so i would say google are an incredibly strong position unlike open ai have to rent all their cloud, all their infrastructure, they have to find training data, they have to rent or buy all the GPUs.

39:40So I'd say there's a kind of thinking about it from a stack point of view, who owns all the layers of the stack? And to your point, how does training data become valuable where you have access to the technology, the models, the tokens, the compute? Because it comes down to really some some simple dynamics, who has the compute, the chips, the tokens, the AI models that are capable enough to ingest knowledge and information to then generate products and systems that people want or to generate some kind of outcome. So to some degree, anyone that holds huge amounts of really valuable proprietary knowledge has a lot of leverage now, because if you can figure out the right products and the right monetary or the right commercial model to build on that data then you can either partner with the right people to kind of like access that but again you can access the apis and the models to build some of that with compute power um the one you said about in hollywood i think that a lot of it was i think the vision is i think elon said it the other day in a few years you'll be able to just ask your tv to generate you a 30 second a 30 minute show on this subject with these characters but a lot of people are starting to sell I think a lot of it, a lot, but some Hollywood actors are starting to kind of, I think Matthew McConaughey and a few others are starting to license their data, their voice, their image likeness, two models who can train on it and then they get paid for that information.

41:06Again, imagine in sport, what can happen there with all of that information about being able to replay an old match, create an experience, build things. like there's so much potential there but again the question is can they get together with the right data put it in the right structure have the right teams that understand the opportunity and the value of that opportunity and then negotiate that in terms of what they can do with it but yeah i think training data is a it's a big thing yeah that to your point earlier about enterprise who can who can monetize that training data and that information and create new products and services i think that's definitely a big area and again agencies that start thinking about that and helping people figure that out is definitely an exciting space to be in.