Reviewing Our Data Trends & Predictions of 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen

That's true. I think last year we were talking a bit about have we reached a point where scaling no longer works and. Models are good enough and AI's basically done. But yeah there has been a steady stream of progress still that's been pretty exciting. Let's get on to the review of last year.

So your first prediction, and I'm gonna read this one out, was that the open AI and Google duopoly at the top of the chatbot arena, leaderboard will be broken. If you cash your mind back months ago they were basically like it was just neck and neck between open AI and Google. They would each release a model and it would top the, all the benchmarks and they would go backwards and forwards and, that was basically that was it. The question is has that changed or not? What do you think a duly there, or do you think there've been some other challenges? 

Martijn Theuwissen: I would say this is more or less correct if you purely look at the the chatbot arena leaderboard, and we can put the links in the in the show notes like it, so it's run by Berkeley, some researchers at Berkeley to be clear, and if you look at what the top is today.

So Google is still high up there, but in the top you can find the the Opus model from Tropic. You can find Grok and you can actually find both above the GPT five model By OpenAI, I would say. If you look at that ranking it's a yes. Now I think the caveat is that the strength of the models is not reflected in the market share of the models.

If you look at, okay, what are probably still the most talked about models, the most used models, you're still stuck, quote unquote with Gemini and open ai model. That said, I think tropic is a steady riser. I anecdotally hear and see it a lot more definitely in the coding environment.

But from my perspective. If I would grade it, I would say it's a yes. That's interesting as yeah. 

Richie Cotton: If we look very closely at the terms and conditions on this one, it did say Chatbot Arena. And yeah, just looking at this currently Gemini three Pro is number one and it's actually GR .

Thinking is the number two spot. So Rock is certainly broken that duopoly on the chat bot during your lead board. I was looking at data from SimilarWeb though, and yeah. Currently saying chat. GBD has a % market share, and Google's got an % market share in the LMM market. So between the two of them, they're still very much taking all the market.

Maybe I'll give you half a point for this one since

since it is right in some sense. 

Martijn Theuwissen: Now, one thing I would say is an interesting thing to look at is I think what's not covered in, in, in the simmer update is actually like how open source is doing. And I, to be clear, I don't have the answer either, but I actually wonder how many companies run open source or deployments of the open source model.

Whether it's deeps seek, whether it's the models by Alibaba it's unknown. At the moment I haven't seen good projections there. But yeah I, there's probably some hidden traction as well there. 

Jo Cornelissen: I think that's totally fair. I think the trend is clearly correctly predicted because.

If you go back a year, it's hard to underestimate like how dominant open AI and chat chip D were at the time with a bit of a, an influence of Google. And if you look at it today in terms of performance, the proprietary models are much, much closer. You can debate it depending on the benchmark.

And yes, market share, it's mostly Google and OpenAI, but the trend is quite fascinating, which is OpenAI is losing market share on a very consistent basis, month to month, even though they're still the juggernaut. If you compare that to the early days of search engines, I think once Google established its dominance it took how long?

years for that to start going down. So we're definitely in a different type of markets. That's really interesting actually. 

Richie Cotton: Yeah, there's less of a sort of network effect from Google had more data than everyone once they got big, and that just meant that they could get better search results.

And maybe there isn't that equivalent sort of effect in the LM space, or maybe it's just the competition is just so hot. But yeah. All these companies competing so hard that the the leader is bound to change at some point. Alright let's move on to the next prediction. So that was the the new AI reasoning models believed with a scientific breakthrough.

This is your prediction Jonathan. I'll let you create your homework as well. Do you think we have had scientific break for those that have been directly from artificial intelligence? I was on the 

Jo Cornelissen: fence about this one. 'cause I feel like a lot of the other predictions hit this one I think is highly debatable.

I think when we made prediction, we positioned it as AI can do this independently, which I don't think has really happens. I can't think of any examples. What is true I think is that the productivity of researchers in almost every domain is going through the roof as a result of ai.

But that would be cheating 'cause I don't think we intended to predict prediction that way. 

Richie Cotton: I think at this point, if you are a scientific researcher or almost any researcher, in fact, you are gonna be using ai in some form. Yeah, we yet to see some major scientific breakthroughs just from a bot running as they go like by themselves.

So I think, yeah we were talking about the new the then new deep research models there are promising to be able to solve really complex technical problems by themselves. And yeah, it's not quite at the point where it's done science by itself. I think it's one it's a miss, I'm afraid.

Jo Cornelissen: I think so 

Richie Cotton: virtually. Okay. Alright. We're on half a point out to two then a slow start, but let's see how we go with with the next one. This next one is still on for you, John. So is the synthetic data is going mainstream? Yeah. Do you think that's happened? I think so.

Jo Cornelissen: Yeah. I think this one actually the trend started probably more than a year ago, but I think by and large, the amount of data, is in the world is limited. And I do think there's more and more use of creative ways to create synthetic data to, to improve models. So I would say this was a correct prediction.

What do you think? 

Richie Cotton: Yeah, so I find this crazy because if Logan, when you go back like a few years, there was talk of the amount of data in the world is growing exponentially and it seemed like an almost infinite resource. We've reached a point where, certainly in the journey of AI space, all these frontier models, they've already used up all the data that's publicly available.

And yeah they've turned to synthetic data to train the models. I think particularly in finance and healthcare where you've got the sort of privacy concerns, there are there's a lot of uses synthetic data there as well. Just because if you wanna publish something make something public available, you can't make use of the real data.

Yeah. I think it has gone mainstream. I don't think it's mainstream in the sense that the man on the street knows what what synthetic data is, but I think certainly with it within our domain that one's gone. That's correct. The next prediction that is this is one from you Matt.

So this is that a video, generative AI finally takes off. What do you reckon? 

Martijn Theuwissen: Yeah, so for that one, I would definitely say it's a full point. So I think at the end of last year, like we were very much into like experimental videos were brought out like short clips of cats cooking and those kind of things.

I think if you now look back months later, you actually see a lot of those. AI videos thanks to new models like the Sora model, the new VEO model, and all the hybrid created. Like we actually see production ready videos being used. If you look at some of these shorter influencer videos, if you look at social media ads that make use of vi o, more and more of these are being created.

With ai, I see that within Data camp. I see that with other organizations. The fact that it's now in production, I think is really like the check mark on this thing took off just like. months ago, like the AI images took off and the years before that, like AI text or contents took off.

Like I think we now really have the AI video taking off. And frankly, I think it's only gonna get better as we will be able to create longer and longer videos and the use cases will grow. Absolutely.

Richie Cotton: I think for a long time you could really spot when something was AI generated. I mean start, it was like the hands, and then there's if you look at stuff in the background, there's always like some signage that was a bit wonky or something like that.

Now it's oh yeah, you. You really have to stare carefully at something to see is this AI generated or not? 

Martijn Theuwissen: And it's super accessible. Like just it's prompting. Like you, you type in the text, you tell it like, this is what it is supposed to do. And it's, and it does it. And that accessibility is really helping it in everyone in the creative sector to, to start using it.

I wouldn't underestimate the ease of use here in the capability of it taking off. 

Richie Cotton: Absolutely. The progress has been tremend for the last few months in particular. So actually I'm curious maybe a question for you, both of you. Are there any particular AI videos that you've seen that have impressed you?

Martijn Theuwissen: There was I think that went around a week or ago start of January. There was this, it was an unofficial fi was an unofficial advertising for her mass, like the back. Company. They do more than bags, but that's as far as my knowledge of ERs goes. Like they, they sell really fancy bags and there is, yeah, basically a whole ad created around that looks.

That looked amazing. Like it showed the product as it is. So they were able to use the actual product it stick to the brand guidelines. It was clearly inspired by previously, and that's all stuff that you normally like, need to build from scratch and code it. The fact that the model and you can give that through the prompts to create something in this style, is an enormously.

Powerful. And I think actually some of the ads that we created with Data Camp were not too bad either. Some of our course announcements videos which actually used your voice, Richie, not sure if you wanna speak a little bit to that on what's possible here. 

Richie Cotton: Yeah I spent a lot of time chatting to Labs.

Labs is voice ai and we've basically clone my voice. The podcast of course will always be human Richie. But yeah, for shorter advertising clips, I'm okay with my voice being used for those sort of things. Saves for me being hassled by by the marketing team. Yeah, that's pretty cool. Joe, how about yourself? Have you seen any particular videos that you, 

Jo Cornelissen: there's not one that comes to mind, but I'm a I'm a heavy consumer of AI generated meme videos. For better or worse. Interesting. 

Richie Cotton: So there was a bit of a phase a couple of months ago with like just random bits of scenery being destroyed.

I thought that was like a fun sort of trend. Just like random just AI destruction videos, which was actually. Yeah, it's one of those things with me is they tend to be like quite, quite niche at this point. There's like trends happen and maybe there's some, maybe you don't.

Alright. Let's go to onto the next prediction. That was that a product, a VEA product that takes more than an hour to respond with launch. We've gone from just trying to get text to respond as quickly as possible to sometimes you wanna wait and get a better answer. Joe, this was yours.

Do you want to. Talk about whether that's Ed or not. 

Jo Cornelissen: This feels like definitely a trend that that we correctly predicted. I think some of the new cloud models can think for hours if they need to. I think the honest take though is that the sweet spot for say coding agents is still sub minutes.

For like platforms like Lovable, I think you can get into the minutes or maybe minutes, but, they tend to get lost if they take more time. There's definitely products that do this now and I think humans are adjusting to giving higher level tasks to AI agents whether that's Claude or rep lid or lovable and just having a few of those things run in parallel where they come back after a few minutes.

And so this seems like a trend that is continuing, so I would give a full point. I think that, that, that brings us a three and a half out of five so far, if I'm not mistaken. I think like you're very focused on the points here, so Absolutely. I'm result oriented. 

Martijn Theuwissen: Sorry, one, one thing just to on this, what I think is important to reflect on like minutes is a lot of time like this whole AI wave has been going, what, for three or four years.

And like we are already at. Having an AI agent, basically doing a task for minutes without any intervention, like the high quality, I think like for everyone who's listening in, like that's really like really powerful. So early in I can imagine a future where a couple of years from now, like this is.

Seven hours. This is a full working day that you come into the office and you get your AI agent or AI agents to work and they basically execute for the full working day. Yeah. This makes me really excited. 

Richie Cotton: Yeah it's a double-edged sword though, because obviously if you're waiting for stuff then and it comes up with a bad answer, then it can be very frustrating.

I'm wondering how far companies. Can push this, would you be willing to give an AI task? And they're like, wait a month for it to think deeply and come back with something. 

Martijn Theuwissen: But it's, does it find a bit strange? Like it seems like we evaluate AI at a much stricter condition than we evaluate, for example, employees like you.

You can have the same thing with when you manage somebody, you give them a task in the morning and like at the end of the day you see the result and you think oh, this was. Probably not what I intended it to be. I think yes, that risk is there too. I think the market will fix it because like those models that will have that risk less will win.

Yeah. I think we put the bar sometimes too high. 

Jo Cornelissen: Yeah. To be honest, I have a bit of a different. O opinion here in the sense that, or a different per perception. If you anthropomorphize AI to the point where you're like, Hey, if a human would do this in minutes it's great To me.

Sometimes it feels like dial up internet where I'm like I remember the days where you would open like five websites. In parallel, and then you would wait and you would go back to the first one. 'cause by the time you typed five s, like the first one would've loaded. I catch myself.

Expecting an immediate answer, even if it's a daf, that would take a human minutes. I do, I think that the two strengths are gonna be there. On the one hand, like they're gonna reason for longer and their capabilities are gonna increase. But I think there's an enormous amount of commercial pressure to make these things faster.

If you just think about our use case of having an AI teacher, an AI tutor deliver the learning, like whenever it's more than a second or two. Humans immediately get this perception of Hey, what's wrong with this teacher? Even though a real human teacher sometimes may have to think for a few seconds when it's ai, I am immediately like, Hey, what is this thing doing?

Richie Cotton: Yeah. Not fair. Absolutely. And I, it does depend a lot on the use case. So certainly chat bots do wanna be instantaneous. Yeah, if you are crunching really difficult problems, you're like, you want to go back to the idea of AI solving a scientific problem to create some kind of breakthrough.

I feel like you're probably gonna be like, I'm okay with it. Taking a bit longer to solve a longstanding problem and write a paper. Okay. Alright. Let's move on to the next one. This is a protection me Martin on. Saying AI features will stop being labeled as AI except in tech products.

Yeah. Do you wanna explain what this one was about first? 

Martijn Theuwissen: A lot of products basically slapped on new features that they were releasing. The term ai AI for this, AI for that and so on. And if I would grade my own prediction, I was % wrong on this one. I think that it's still being used pretty much everywhere.

Al almost everything that gets gets released by company intech or. Not not on deck. So AI seems to be, seems still to be a very valuable marketing term. Hype cycles not dying and I definitely wouldn't make the same prediction for I think we're gonna be stuck to this for probably a long time.

Richie Cotton: Yeah. I like there's definitely a point where you're like. Do I really need AI in my sandwich, but apparently it works as marketing term. So I guess Okay. 

Jo Cornelissen: I have to say I do think this is a bit counterintuitive to me as well. For what it's worth, people are not tuning out the AI labeling and that it's still.

Needs to be there but I think that shows we, we live in a bubble where that's been around for a very long time. That's my guess. That's why it feels odd to us. 

Richie Cotton: Yeah. The three of us we think about AI stuff like on a daily basis, so we see it everywhere. Whereas I guess most people are.

Slightly less exposed to. Yeah, we expect it 

Jo Cornelissen: everywhere at this point. 

Richie Cotton: Yeah. Good for marketing people. We'll see how long this continues. But yeah maybe at some point we'll just go back to just selling stuff rather than saying stuff with ai. The next one this one from you Jonathan.

So saying the dominant AI conversation for boards and executives will be AI usage. I think this one needs a bit of a background explaining 

Jo Cornelissen: obviously. This is something that's top of mind for DataCamp. We help organizations with their data and AI transformations and one of the reasons we get brought in is when organizations invest heavily in data and AI infrastructure and then notice that there's a second.

A component to that, which is enabling the workforce to use that data and AI infrastructure in a very effective way. And so we've seen this firsthand in, in the past year. It's a board level conversation. It's a C-level conversation. Data camp has been preaching the importance of data literacy and AI skills for quite a while.

And it's honestly been incredibly exciting to see that. Kind of level of attention and traction in the last year accelerating our BB business very significantly. And I think that's one of the objective singles I would say that we have to say this is definitely happening.

Anything you would add there, Richard, do you agree? 

Richie Cotton: Yeah, certainly you used to speak to executives and boards much more than I do, but anytime I've run a webinar that has AI adoption in the title, it's been incredibly popular. I'm curious also as to whether the other sort of executive level conversations you've been having around ai, like beyond, adoption and usage. 

Jo Cornelissen: The initial conversation is really about AI fluency and depending on the organization, that's still a huge focus, basically about ensuring that people understand how to use ai to be more productive as well as understand what the ethical risks are. And other types of risks in, in terms of AI usage.

I think for companies that are a little bit ahead of the curve the conversation is starting to shift towards one layer higher where it's not just human with an AI copilot, but it's essentially. Humans designing AI agents and starting to collaborate with AI agents. So that's one area where we're seeing the conversation starting to shift.

We're also starting to hear more and more of that. It's not just about usage. We're, a lot of dollars have been spent in the last two years. So companies are starting to ask the ROI question where is the return on investments? And then last but not least I think as AI continues to change this fast, there's a continuous discussion around AI skills.

And that. That is obviously what DataCamp is focused on and it's, it remains very much top of mind because AI is changing so fast, right? Like where people, maybe every knowledge worker one one year ago had to learn how to use AI as a copilot for various things. Now there's all these other ways they can leverage ai all these other ways that are creating risk and blockers that need reskilling and upskilling for people in different roles.

Richie Cotton: That's very true. So certainly once you've got people to use ai, you need to make sure that they can use it well. So that's the upskilling part. And also you need to get, make sure the business is getting valued. That's the ROI part. And I think, yeah you need all three parts together in order to like save both.

Should we actually do more AI or not? That's how you're gonna get to willing. Two more predictions to go. This next one from Martin. So due to ai, sorry, due to regulation Europe will fall behind in to a point that catchup will no longer be possible this decade, both of the development of new AI models and applications and the adoption of ai.

So this is a slightly negative prediction. Talking about European regulation, martin, do you think this has happened? 

Martijn Theuwissen: So a as it happened that I think they, they fell behind. E even more in in short, I think the answer is yes. If you look at a couple of of data points.

So for example, like if you look at the investment that is dumb in the US compared to Europe, that US is doing times as much investment into ai, its infrastructure as it's happening in. Europe. Secondly, if you look for example, at the foundational models, which are pretty much the basis of this all depending on okay, who you think is willing, like underneath it, there are these foundational models in.

Eu, you have minstrel. Nothing else got added or nothing meaningful. And it seems that we also didn't catch the open source wave. So if you look at, okay what was in big narrative besides existing foundation models was the release of a couple of newer open source foundation models deep seek, and its new models grant and swamp and.

None, none of these open source models that made headwinds came out of Europe. So I think that ship has sailed and like we, we are missing the investment and we are missing. I speak, we, because I live in Europe, we're missing the investment and we are missing the foundational layer. I think there is to make it slightly positive and say okay, where just a little bit of hope.

It seems like there are a couple of very promising applications being developed. Like we were talking about labs okay, like cloning, Richie's voice which is from Europe. It seems in the vibe coding space that we being Europeans have a very good contender with with lovable.

Compared to, for example some of the US players. So it seems okay, we're making some happens there. There's talks about like the EU AI Act being softened now. From the viewpoint like any regulation is too much regulation. And let's still see what type of regulation softening they will do.

In short, I think the prediction is right. I think if you look at what I think key factors are for national models and investment we felt further behind simers of hope in the application layer. But let's see how that plays out. 

Richie Cotton: Absolutely. Certainly in terms of the amount of investment in ai yeah, you are just can't really compete at the moment like this.

I guess Silicon Valley just has such a stronghold and there's so much cash ing about that. Yeah. There's no way Europe can compete that. On the regulatory side of things I do think, things are quite promising. So there's this you said there's gonna be some simplification. So this is the EU Digital Harmonization package.

So it is gonna make the, the regulatory burden a lot easier around data and ai. Currently, it's it's basically impossible to comply with the EU AI Act and GDPR and the EU Data Act and all the cybersecurity regulations. Or once. Yeah. I think the timeline for this was like a turnaround by 

I'm hoping that the eus gonna make it. I'm not sure whether we're gonna see any sort of new startups. Jonathan, you wanted to add to that? 

Jo Cornelissen: Yeah, may maybe to add one thing, I think if you zoom out, this seems to now be a raise between kind of us and China and would continues to fascinate me, is that China is leading in the open source on the open source side which.

I would still bet on open source eventually dominating in this space. But I would also not bet against the US ultimately figuring that out. And in some ways like Europe is, with the exception of some application kind of level startups and Neal Europe is just not really part of the game.

It's really a kind of a us versus a China game. You can. Yeah, I feel like we, we got the full point here by by relying on European politicians shooting themselves in the food. 

Richie Cotton: Perhaps the European politicians were just listening to Martin's advice that less regulation was needed. And yeah, it's the start of a turnaround, but yeah I agree.

There's half a dozen, like really amazing European AI companies, but it's not at scale of US or China. Alright. I'll be generous to let you have the point for that one. The next prediction our final one from Martin. This is in we'll see that the individual, at the individual level, that proportionately teachers will be the biggest adopters of AI in their day-to-day work, not business folks.

Martin, why do you think teachers were gonna be big on ai? 

Martijn Theuwissen: So one it's accessible to them two. I think from a teaching perspective, using ai, you can do a lot of cool things to make your lessons better more interactive. I think if you approach it creatively, like it can be your new blackboard.

That you do the physical thing with the, with chalk, you have different students in front of you. And with ai you can actually tailor your lesson materials to the type of student you have in front of you two. If you think about the. Prep work that teachers need to do, like whether it's lesson plans whether it is creating the necessary presentations and so on.

Like you can do a lot of that much more effectively. And I would say third is that a lot of the information that teachers teach is actually already. Available on the internet. And an AI tool like chat, BT makes that very readily accessible in a very easy format. So you basically have alternatives to your textbooks and so on.

So that was a little bit the underlying tall experiment. In terms of where we are, it's a bit hard hard to judge because I don't think there's any clean data available. But I did look a couple of things up, which seemed to indicate that teachers are definitely ahead of the curve or very heavy users.

OpenAI I believe earlier this year released some data on top use cases for OpenAI. And what you actually see there is that teaching is pretty much up there. As a top top use case. Secondly for those listeners who know, or for those who don't know, like at Data Camp we run a project called Data Camp Classrooms.

Basically we give professors, their students free access to the whole data can platform for six months as part of the class that they're teaching or that they're attending. Now, that means that we have a large group of professors that we can survey and we surveyed them in the second part of this year on their use of ai.

In their in their teaching activities. And what we actually found was that % of these educators are saying that they use AI tools and close to % are using them daily. And anecdotally, like those are not the figures that I see when I talk to our business customers. So from that intervene okay, like teachers do seem to be very active on ai tools.

Another interesting thing that came out of that which gives there's a bit of a different view, is that actually when you. Ask educators okay, how do you think your school is doing that? They actually say that around okay, only half of these teachers get to get support, like formal training in how to use AI for their teaching activities.

So there seems to be like no centrally organized. Okay, we going to upskill all professors in how they can make best use of AI in their classes from the university or the school. Perspective. So I think that's like that's one is a missed opportunity. Two, I think it speaks a lot to the internal motivation, intrinsic motivations that teachers have to put this tool to use and the need they have to put it to use.

Given we see like over nine outta Professors are using it. I'll let you do the scoring on where this where this ends up. But from what I see, like teaching is a big use case and the educators that we surveyed, they are using it heavily. 

Richie Cotton: AI is incredibly useful for creating educational content.

Receive a data camp. I think almost everyone data camp is using AI in some form to help teach people. And so I can certainly see. The use cases are are just almost infinite for the education sector. It's interesting you're saying that, what was it, % of teachers are using some form of AI in about two thirds of 'em using it daily. This is it's not far off the sort of levels we see from our state of data and ai literacy board for people in the IT sector and data teams. So these are generally the sort of the teams that use AI the most. So that's actually, it's not far off business.

I agree some of the business teams like the sales and customer success and whatever, they use it a little bit less. So maybe they are, maybe teachers are using it more, but a bit more than, the business folk 

Jo Cornelissen: and I think it's partially because the kind of early adopters are always the youngest people, so you have a ton of students heavily using it.

So I think it's also they have no choice. They have to. Catch up because they're, the people in the classroom are adopting this and pushing them. 

Richie Cotton: Yeah. You don't wanna be outsmarted by a bunch of kids. Yeah. Gotta keep up with or at least get the good ideas from from the kids in your class.

And then yeah, you see what you can use it for. But certainly, yeah creating lessons or doing research to, to find out what's useful. These are some great use cases for for education. I thought it was last one. I missed one final prediction. I, by now, like I've got two maths degrees.

I should be able to count to Okay. So final prediction this, another one from Martin. This is the AI becomes the killer use case for blockchain. Talk us through it. What sort of use cases were you envisaging? 

Martijn Theuwissen: Yeah, I was the thesis was that because through AI you can pretty much fake everything.

You can make fake rich cheese, you can make fake Fs and so on. That there is a. There was going to be a need to actually validate that when you bring out an image, when you bring out a video, that's actually you. And the blockchain is a useful technique for that, that Id became mainstream.

I don't think so. So I think this prediction did not play out. Doesn't mean like it's not going to happen. Hard to say. One, we're gonna need to have some type of like actual, proof of identity in the future with all the capabilities that AI has today and will probably have in the future.

I think AI swap was this new term invented in the last three months. That will create a need for saying okay, this is there's an actual human being behind this, or or I. Approve this message. This is not something that was created and then wrongly attributed to me, so miss on the prediction.

But we'll need at some point in the future, some kind of like. Proof of identification or proof of identity. 

Richie Cotton: Absolutely. So certainly DeepFakes are a big problem. There only, it's only gonna get worse. And so having that kind of provenance, particularly, for political messages and things like that, or if you've got some celebrities want to be like, this is really me saying something, then yeah.

That, that seemed it's a big problem we've gotta solve. Maybe blockchain's gonna be helpful there, but Yeah. At the moment we've not seen a great deal of action in this area. 

Jo Cornelissen: Yeah, it's quite interesting. I actually I expected there to be something. In the last year or in the near future, I'm starting to shift my perspective and think maybe humans are just gonna adjust to the point where we don't need this.

'cause now when I see a video that's somewhat surprising, I just assume it's not real. And usually that's actually the correct assessment. So I don't know, may maybe humans will just adjust and not believe most things they see. It's a fascinating dynamic. 

Richie Cotton: Absolutely. So I think about my wife in these situations because she tends to believe almost anything she sees her.

And that's that's fun from my point of view sometimes. But in general, I'm like, yeah I don't know how she's gonna go with with AI everywhere where it's really believable. So yeah, hopefully people will adjust. And maybe we'll just get to the point where we are better at sussing out is this real or is this not?

Hopefully there, there will be a solution to. Credible, but incorrect things. Alright that's all predictions. I'm not sure we're up to with scoring. I think we missed two. Was it three? Joe. Yeah, I think you were keeping count better of the scoring. 

Jo Cornelissen: Six or seven? Seven. 

Richie Cotton: I think we're on seven, maybe Seven and a half.

If we give you half. Yeah, it, it was better than random anyway. We alright, wonderful. Just to wrap up, that was that was the regular last year's predictions and next episode we'll go on to making new predictions for Alright see you again both shortly, Richie.

Thank you. Thank you, Richie. This was fun.