#274 Data Trends & Predictions 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen

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There'S definitely a bet all the big players are making they're building more compute and so there will be scaling through more compute next year. And then the question is, okay, is there more data? And this is where it gets more nuanced. And I think it's a spectrum, it's not black and white.

Welcome to Data Framed. This is Ritchie. This is part two of the annual Data Framed trends and predictions. So last time we reviewed the 2024 predictions and today we're going to make some new ones for 2025. Our guests once again are Datacamp's founders. Joe Cornelissen is Datacamp's CEO and Martin Thurwissen is Datacamp's COO. Alright, let's just dive in and find out what 2025 might have in store. I think this year a lot of the generative AI hype has been centered around OpenAI and Google. So do you think they're going to continue to be leaders in 2025?

Yeah. So as you said, like I think top of the leaderboards, mainly LLMs from OpenAI, Google probably also a little bit anthropic, but they've been having a little bit of a struggle in recent months. So you could say OpenAI, Google duopoly for 2024. Now I think for 2025, like these tech leads are going to be hard to sustain. I think we're going to see a new challenger at the top of the leaderboard. So you have for example, Xai, so one from Elon Musk, which is climbing the leaderboard rankings. Also, like they're doubling the size of their GPU cluster. So if the scaling laws hold, they're going to have the infrastructure capabilities to compete at that very top level. So I think that's a possible contender, like one we're going to see like up there by the end of 2025. And then besides Croc, you also have the Llama models. So Meta's Llama Models, I think the past year has shown that they can be like a very consistent player. They regularly bring out new models that are like on the heels of these top models. And even I think like if you for example, compare like Mark Zuckerberg's talk with the other CEOs of the Magnificent Seven, you see that AI is for Meta, like this very, very important things and that tells me something. So I think it's very reasonable to make a bet that they will take one of those top spots and yeah, maybe quick shameless plug here, like Datacam has some great tracks on Llama as well. So if you want to learn more about it, definitely check those out. It's actually a very accessible model as well. And then they're like the outliers. Like, okay, how is Amazon's Nova model going to do? Which got announced in the second part of the year, like Nvidia brought out its own model. You have the Chinese players, the Quan model of Alibaba. So I think if we're going to look back 12 months from now on how the leaderboard looks compared to today where it's mainly like focused on OpenAI and Google, I think we're going to see some very big differences, including some open source at the top there.

That's cool. There's so much competition. I mean you listed a lot of models there and it seems like there's just so many people just want that glory of having the best model out there that, yeah, competition's hot and those tech leads are hard to sustain. So yeah, we might see some real challenges this year.

I think the good news about all of this is that price will continue to go down as the quality goes up. So I think it's going to be amazing for consumers and businesses built around this stuff, especially now that we have Amazon in the game, they're going to keep pushing the price down as well as the open source players like Meta.

Yeah, definitely. So I mean it's interesting like the cost per token has already sort of dramatically dropped over the last couple of years and yet Amazon is known for sort of low cost approaches to things. So that will be interesting to see if the prices drop even further. Yeah. Okay, so the prediction then, so we can remember for next year is we're going to have another challenger at the top of the sort of LLM leaderboards to challenge, challenge, sort of OpenAI and Google by the end of the year. Nice, let's see how that one pans out. So in December we've seen some big breakthroughs in LLM Reasoning. So do you think that. Well, actually, rather than be putting words in your mouth, just tell me, what are your predictions around AI reasoning for 2025?

Yeah, this is an interesting one because when we were preparing and thinking about our predictions for next year, the prediction we were going to make was we're going to see a breakthrough in reasoning. And since then that actually already happened. So maybe we should talk about what happened for a second and then how we can adjust the prediction because the reality was ahead of our podcast recording timing. So for those who don't know, there's all these benchmarks for AI models. And a consistent kind of criticism of a lot of these benchmarks is that, well, these models are essentially kind of a compression mechanism and they just remember what the answers are and maybe they generalize a little bit. And so in this context you have a benchmark called ARC Price developed by Francois Chollet and one other guy, I think. And the idea behind this benchmark is that they're really novel types of problems. So they're fairly easy, some of them to solve for humans, but they've historically been really hard to solve for AI models. This was the one benchmark that was kind of unbeaten. Most recently, OpenAI launched their O3 model where they essentially broke the record. On the ARC ARC benchmark, I think they're at 87.5%. It took them a while, it took them a lot of money, but they were able to for the first time make significant progress on that benchmark and kind of reach human level reasoning capabilities. So in some ways we were going to make the prediction. There's a significance breakthrough in reasoning that just happens basically. So I was thinking about how can we adjust the prediction, what kind of logically follows from what happened recently. And I would adjust the prediction to we're going to see a scientific breakthrough as a result of the ability of these models to reason. And my bet would be that it's somewhere in a space where there's a very kind of large search space or something like kind of biology or pharmaceutical. Because these reasoning models, while expensive, there are definitely use cases where they can, where you can pay a lot of money for them to think through things and search through potential solutions. Maybe that's more hope than like a well, well founded prediction. But yeah, I think there's going to be really interesting things happening in this space.

Yeah, absolutely. Those sort of recent breakthroughs have been pretty amazing. It does look like we're going from just models that can just memorize stuff to models that can come up with new ideas and really do reasoning well. So just to push you a bit on your prediction, so we've already had things like alphafold, which does protein folding, that has contributed to scientific research already. So what's going to be different about these models doing research and making breakthroughs in 2025?

So my expectation is that or to make the prediction more clear, I would say it's the generic models that now start to have a chance at having breakthroughs. Because you're absolutely right. There's been models that were specifically designed for a certain situation. Alphafold is probably the most exciting and the most impactful one. I think what's important to state is that O3 is a generic model. It's not optimized for the ARC prize or a certain type of reasoning. It's optimized for reasoning and that's what makes it exciting. So we could, we could see scientific breakthroughs in, in areas we really don't expect it. I think that's what's new in the prediction. Does that make sense?

It does, yeah. So we've had artificial narrow intelligence that's been sort of better performing than humans in specific tasks. But here we've got these general purpose models and they're going to be contributing to scientific research now. So. All right, yeah, I hope this one pans out.

And to add to that one is like you can think of different use cases as well. Like I recently came across a post by Ethan Mollick who basically had a scientific paper with a mathematical error in it and he basically applied one of the OpenAI models to it and asked like, hey, figure out mathematical error in this? And it could figure it out like in a matter of seconds. Like if you think about like having such a generic tool which is available to everyone, available to all researchers in the world, like pre publishing or when they evaluate their own research, that's extremely powerful and that in itself can lead to breakthroughs.

Yeah, Reviewing papers is one of the least favorite things I think of most academics. And having AI do that, that seems like a pretty amazing feat. In fact, I am now wondering how much existing research, if you go and just like ask, just loop over every single paper that's been published over the last few years, just see, are there any errors in this? I think we're going to find a lot of existing science be described.

Oh my God. Yeah, that's going to be bloodbath.

Yeah, well, it's going to be interesting times for that, I think. All right, so on subject of better reasoning, this sort of feeds into as A sort of broader conversation about AI scaling. So it seems a quite divided opinion on some people like, okay, AGI is coming soon, we're going to hit the singularity, AI overlords and all that. Other people like, well, actually, you know what, GPT is sort of plateauing. All these sort of large language models, there's only so good you can get it writing text. We're not going to see much improvement over the next few years. So do you think the AI scaling laws are going to hold?

Yeah. So I think it's a really interesting debate and a lot depends on the outcome. I don't think it's binary though, but maybe let's take a step back first. So, so what are, what are we talking about here? I think when people refer to the scaling laws, they refer to the fact that what we've observed is that if you have more compute, more data and larger models, you get significant improvements in the performance of these models. Right. And what tends to be true is that you kind of need all three to go up to get the best kind of improvements going forward. So if you kind of take them one by one, more compute there, you can just look at what's happening. And what's happening is that there's a massive amount of build out happening for data centers. There's a lot of GPUs being bought that are not yet delivered or not yet in production. So there's definitely a bet all the big players are making, they're building more compute. And so there will be scaling through more compute next year. And then the question is, okay, is there more data? And this is where it gets more nuanced. And I think it's a spectrum, it's not black and white. If you just look at text data, a lot of it has been consumed or is being consumed by these models. I'm sure there's quality improvements and so on that you can make, but that's where you're starting to hit the maximum available data for text at least. So I think we can infer a couple of predictions from this, which is like, where are we not out of data yet? I think video is the clearest example. There's, there's a lot of video that is available or can be available that hasn't been trained on yet. And then the most interesting one is like, can we generate more data to train on and is that going to be useful? I think that's a really interesting debate. My prediction for next year would be that there's kind of a comeback of synthetic data and the importance of synthetic data, especially in areas where you can validate whether or not the synthetic data makes sense and holds true. So think, for example, of code. There's only a certain amount of code on GitHub and available publicly. I'm sure most of that has been trained on. But the beautiful thing is like, you can generate code in all types of ways. You can use cursor, AI, Devin, the foundational models themselves to generate more code and you can validate whether it's doing what you're expecting it to do in various ways. So you can actually generate an infinite amount of additional code that's sensible. And it doesn't matter that there's all these hallucinations and there's a bunch of junk code that gets generated because you can just throw it away and only train on the code that makes sense. So I think there's certain areas where the scaling loss will keep going forward significantly, and that's making abstraction of any model innovation that's happening because the reasoning models seem that's kind of model innovation that's happening there more than anything else. So, yeah, in short, the prediction I would have is a resurgence of synthetic data or synthetic data going mainstream, especially where you can validate whether the data is sensible or not.

Yeah, that's interesting that with text, in a lot of cases there's only like so good. You can make it like if it just. You're asking a simple general knowledge question, there's only so good the answer can be. But for things like code, there's a divergence of like whether the model currently works well or not. So if you're writing Python, it's brilliant. If you're writing in some obscure language where there's just not that much data there, then yeah, it just doesn't work very well. And so you need to create.

And even within Python, if you're using packages that are just not that popular, you notice a huge difference as well. Right. And that's a good example of you can probably solve that by generating more code of the usage of that package for the models to train on.

So I like the idea that you're going to create synthetic code in order to train the next generation of models, and then you're going to get better performance in those areas where it doesn't work very well currently. All right, cool. Okay, so I guess it's the prediction then. We're going to continue to see scaling, but it's not necessarily going to be general purpose conversation, it's going to be in the niche areas where things don't perform very well at the moment. Is that the prediction?

I wouldn't say niche. Right. Like coding is a huge area. Video is a huge area. And we could talk about video specifically because I think it's a, it should be a prediction of its own. But yes, with the caveat that I wouldn't call those areas niche. I think they're, they're massive in potential.

Okay, all right. So yeah, I agree that coding is a fairly big topic. So yeah, I guess the prediction is just going to be we're filling out the gaps where models currently don't perform very well. So we should see consistently good performance everywhere. Is that the idea? Cool. I guess the other area that's been hyped a lot is around multimodal AI. And we talked before about how well image generation kind of works now. You said last year that video generation is going to kick off in 2025. Do you still think this is going to be the case, Martin?

Maybe. So let me clarify. So I think Vitro Genai will take off actually for social media advertising and basically everything that needs videos like below 10, 15 seconds. So we chatted already. The new models that got released in the last two months, like Sora by OpenAI, VO2 by Google, they're really impressive. The short demos they show like for the listeners who haven't like watched any of those definitely do. So like it's super impressive. So I think that these models are ready to tackle like video that's below that 10 seconds mark. Which then brings you like to okay, where are those short videos used? Like social media advertising. But to be clear, like there's been already like videos AI we just created like in a longer form, you have the indie band washed out who create like a full fledged music video. For those who are familiar with the typical Coca Cola Christmas spot. So Coca Cola made like one of their famous Christmas commercials like mid gen AI. So like it is possible. But I still think that the longer form is more B2B phenomena. Partly because of cost, partly because of, okay, the type of prompting you're going to need to do is like way more complicated. So if you think about that example I gave about this indie band that made its own music video, if you read the problems that they needed to provide, these were full blown essays with tons of jargon around, okay, how to hold your camera and so on. So definitely not something that is accessible to everyone but from a price perspective as from a technical understanding perspective. But a lot of that does not matter when you're creating your next social media video or when you're creating this short advertising. And so I think in that context we're going to see take off from videogen AI and I think it's going to be used to be honest, if you think how much, well social media content is created but if you also think about, okay, how much short videos you encounter every day in the form of ads and if that all goes to like an AI model, that's a lot that moves all of a sudden to the vegan side and I think the models like seem to be ready for that.

Okay, so that's interesting. So yeah, that does seem to make a lot of sense that it's going to be this sort of sub 10 second videos. They're going to take off so we can see like I guess infinite AI cat videos on the social media side and then I guess, I guess like cooler adverts maybe. So all that stuff popping up in your web browser, maybe on TV too, it's going to be like, yeah, a lot of AI generated stuff and maybe, maybe we'll, we'll save 2026 on the long form video. All right, so video takes off in a short form way in 2025. Now we talked a bit about the performance of AI models. What we haven't really talked about is the AI product experience. So I guess sometimes it feels like, well, chatting with whatever AI was kind of cool, but now it's like, oh, I've got to, I've got to wait three seconds for my response and that feels far too long. So yeah, talk me through. How is this experience going to change in 2025?

Yeah, I think you will have a kind of bifurcation to some extent where you'll have these in the moment interactions with AI through chat, through voice. I would expect voice to become much more prominent next year. But I think what might be more interesting is that or the prediction I would formally make is there's going to be way more AI product interactions where it takes a while for it to come back to you with the response. It might be a minute, but it could be even half an hour to an hour in some cases. Because if you see the trend of these reasoning models, they take time to think and it's expensive for them to think, but the strength of the answers is so much higher. And there's definitely economic use cases where you're willing to wait, where you're willing to spend a little bit of money to get an answer that's really good and that's well reasoned. So that's what I would predict to see more next year. I don't think we're seeing a lot of that just yet, but it goes back to all the excitement around agents. I think both agents and models will have some time. Just like you would outsource or delegate a task to a human being, you'll do that to AI models and product features and they may take half an hour or an hour to get back to you. And that will become more normalized, I think, next year.

Wow, that's the opposite of what I was expecting. I thought you're going to say, well, okay, yeah, models are going to get faster and so latency is going to.

Be a big issue.

But actually saying, I suppose this makes a lot of sense with things like O1 03, they take a long time to respond. And so actually we're going to see these high reasoning use cases where it does take a long time.

Yeah. Richie, don't get me wrong. I think what you're saying is true as well. I just think you're gonna get a bifurcation. So, for example, think about customer support. If I'm chatting with a customer support rep and it's an AI rep, like, I want an answer immediately in certain situations. But once that agent fully understands, oh, this is the problem, there's a whole series of steps that needs to happen. Or say the model that's optimized to interact with you really can't solve the problem. You can imagine a tier 2 support agent that has to think more and can take a while to then get back to the customer, if that makes sense. So I'm not saying that what you're saying is not true. I'm just saying I think there's a bifurcation and we'll get that second tier as well.

Okay, yes. There's sort of latency reasoning trade off here and you just got to pick whatever the most appropriate thing is for your use case. So going back to your idea of models that take a long time to run, what's your prediction there? Like, how long do you think it's going to be? What's the longest running model do you think is going to happen?

I think the bigger problem than time is cost because it does get really expensive after if you start thinking in hours. So I think cost is probably the real constraint less than time.

So that's interesting. Okay, so cost is going to be a big constraint there.

Maybe on, on that point, like to ask a question like, how important will the inputs be? Because like, okay, once it starts running, it's like, okay, your time is gone, your costs are going up, and if you provided the wrong prompt, well, you kind of have an issue. Like, I'm wondering when we were like, discussing this. Okay, what will that mean for like the type of input you provide the model? Like, how do you frame the question? Like, how are you hedging yourself against a bill that keeps ramping up? And at the end of the two hours you get a piece of output that you're like, oh, well, I should have put a little bit more time into my question here.

Yeah. Isn't that exactly similar, Martin, to just how companies work and delegating work to humans?

Yes, it is.

You can ask someone to do something and you can be very vague and then you get something back and you're like, oh, wait, that's not at all what I expected. And it's because the ask wasn't clear enough. I think now more and more people will have to learn that skill. It's a form of prompt engineering again, because the costs are going to be very real, at least initially. Oh, man.

So that sounds like you're going to need to learn some people management skills. I was going to say quite a.

New term, like, let's prompt management. It's going to hear first. You hear it here first.

Nice. Last year, one of your predictions was that the AI hype is going to fade. It's going to disappear. That hasn't really happened. So I'm wondering, is it going to continue into 2025? Is AI still going to be cool? Is the hype still going to persist for another year?

I do think it's going to be less prominent in product marketing. I'm not expecting every week another email where there's a subject line like new AI thing announced, AI feature announced, or something like that. So, like the time of slapping AI on everything is over, I think. I do think it made sense, like in 2024, like, just as a signal to, like, buyers who wanted to, like, know if the products they were buying were like, on top of innovation. But I also think, like in 2020, fires are going to get a bit more. And while 2024 was a lot about, like Dell in the show and tell, like, I think 2025 is going to be a lot more about the show and being able to actually display the value that adding AI features, AI functionalities through your tools have, I also think that most users are going to care less and less whether a certain functionality is going to be done with AI or not. Meaning you don't need to, like, explicitly say it. Going back to my car story, like, do I really care that AI was used somewhere in the car I'm going to buy? Like, probably not. I care about, like, can it bring me from point A to B? Can it drive fast? Does it have enough room? And so on. Something that Benedict Evans said in, like, one of his, like, newsletters, like, when it works, it's like just software. And I think we're going more and more into that direction. There's maybe like one exception here, and that's like, when the product is for technical users or like, product products that have this productivity increase based on AI as their explicit value proposition. So if you think about Copilot or if you think about AI journaling, these are a couple of products where users really love the features and want to be sure that they're AI based. So I think in those type of products, you're still going to see, okay, this is your AI software engineer or your AI performance marketeer. But at the same time, I think in other sets of products where it's more built in as a feature or functionality, it's going to be less of, like, a key message and we're going back to, like, normal there.

Yeah, I can certainly see how if you're trying to sell Copilot, you're probably going to need to mention AI at some point. But maybe for most products, people don't necessarily care whether there's AI in IT or not. We talked a lot about what's happening for sort of, I guess, users of AI. I'm curious as to what's happening at the executive level. So how do you think AI strategies are going to shift for companies in the next year?

It's a great question and it's something we obviously think about quite a bit, mostly from the upskilling angle. So let me talk a little bit about that angle. I think in the last 12 months, as Martin mentioned earlier, one of the biggest shifts we saw was especially larger organizations starting to focus not just on data literacy for their workforce, but also on AI literacy. I think that trend will 100% continue. And I think what we've seen some indications of in the last 12 months that I would predict much more strongly is AI skills for software engineers will become even more important from an upskilling perspective. Because generally in this year we've gone from a lot of prototypes to AI being part of products, AI being in production. So logically what's going to happen now is people are going to look at usage. So if you take Microsoft copilot. So now GitHub copilots. But if you take Microsoft's copilot as an example, people have actually been shocked by how little usage it gets because you have the hype and then kind of usage tapers off. And I think what's fundamentally behind that is that because Microsoft, we're not sponsored by Microsoft or anything like that, but it's very powerful what you can do with Microsoft. Microsoft Copilot and so the real problem there is that people just don't have the skills or the creativity or they're just stuck in certain ways of doing things. And it will require training, retraining for them to fully leverage the capabilities. And I think the conversation is going to shift there simply because huge investments have been made and so they need an roi. And humans are ultimately going to be the bottleneck in that ROI if they're stuck in their old ways. So I would expect the focus to shift from, well, last year we saw prototype to production. Now we're going to talk about usage and why is there not more usage. And it's ultimately going to come down to, hey, we have to upskill and reskill our work first. And that's true for all knowledge workers, for if you think about things like Microsoft Copilot. But it's going to be true for AI features, for software engineers as well for data folks. So it's going to be true across the spectrum. And that would be, I think, where the conversation shifts in the next year or so.

That's interesting that despite all the kind of the hype, the problem now is just getting people to use all these new tools that exist. But yeah, I suppose, yeah, change management is tricky. Like if you want to change your processes to using a new tool, you've got to train people, then you've got to just set down a load of time just to be like, well, this is how we should do it in a better way. So yeah, a lot of change management, a lot of upskilling in the future there. Okay, so what's the prediction going to be around this then?

So I think the prediction is that the conversations shifts towards usage and we'll see even more focus on AI upskilling. And that means AI literacy for knowledge workers and specific AI skills for STEM workers.

So lots of upskilling needed then just to make sure that usage of AI tools is going to kick in.

Nice.

So actually, while we're talking about leadership issues, one of the big sort of stories this year was like the launch of the EU AI act and there's a lot more AI regulation coming. So I'm curious as to what the impacts of AI regulation are going to be over the next year. Martin?

Yeah, this one is close to my heart, being based in Europe myself. So this is not a fun prediction. Like I actually think that the EU will only fall further behind the US in China and everything like AI related. And I kind of split into three things. So you have on the one hand the creation of new models, then you have product development based on these new models and then you have the AI literacy of your general population. So if you go one by one here, I think the first one is quite simple. Training new models requires a lot of energy. Think for example Microsoft announcing that they're going to lease nuclear plants in Europe. If you look at the policies and the regulations they did around energy, like energy is actually very expensive in Europe compared to these other regions. So like training these new models, which is very energy intensive, like nobody's going to have Europe as their location of choice just because they know like, hey, the cost I'm going to incur are going to be higher and if the scaling laws hold true, like it's even going to like go higher, higher and higher every time. So issue number one, so training new models in Europe, not that great of an idea. And the second thing is there's all these, all this regulation regarding like the deployment of AI models. So what's happening is like OpenAI Meta but Apple did like a similar announcement like they're delaying or even like canceling the rollout of their latest models in Europe. So if you as a product developer like in Europe, if you do not get access to the latest models, the products that you're going to develop will be behind. If you or Joe are based in the us can interact with better models than I can do in Europe, you're probably going to be able to deliver better products, you're going to be able to create a better agent, you're going to be able to work with better agents, which puts me at a disadvantage as a product developer. So this weakens Europe's position, like as a creator of excellent products. And then the final point on the third one, like AI literacy, like if you don't have access to the latest models or the best AI products, your population's level of AI literacy is also going to lag because you're just not working with the latest tools, you're not reading up on the latest things. So compared to your peers in again China, in the us you're going to be Lagging. So like, the AI literacy level in Europe is going to be lower than the AI literacy level in one of these other regions. So you have like three key areas where we as Europeans are behind compared to the rest of the world. And I don't see like a scenario where, or I don't see like a change in policy where we're like catching up to that. The comparison I'm thinking of is, okay, like, if you work in AI from Europe, it's like competing on the 100 meter dash. And the difference is that you actually start 50 meters behind everyone else. And like, on top of that, somebody forgot to take away like all the hurdles on your lane. So like, it's a lot of fun to go and do all that stuff. So I am afraid that Europe is today, like in a position that if you think for the next five or 10 years, it's going to be really hard to catch up and we're also always going to be like lagging there.

Okay, that's interesting. Yeah, I agreed. Not, not, not particularly fun prediction, that one. But it's interesting how.

Well, you never know if, like, if.

A policymaker listens to this podcast and decides to like, do something about it, that would be a good outcome.

Okay.

Yeah, so that's interesting that you mentioned the cost of energy there and that seems to be, well, generative. AI particularly is just incredibly energy intensive. And so places with cheap energy then I guess are going to have some sort of competitive advantage for attracting infrastructure and talent with AI. Is that about right?

Yes, it's one of the elements.

Okay. And then maybe we need some sort of softer touch regulation rather than very comprehensive regulation so as not to scare off innovation.

Yeah, I think you want to have openness towards the latest models, at least in my view. So rather than the very prudent approach that's been taken today, the fact that Apple says, like, okay, we're going to release an iPhone with less features in Europe, if you would have told that like 10 years ago, I think people would be baffled about that. It's pretty crazy that this type of bifurcation is starting to happen.

Yeah, I want to plus one that I think it's really sad. I live in the us, but I was born in Belgium. So as a fellow European, it's really sad to see kind of crippling regulation continue in the AI space. Imagine that's what Europe did at the beginning of the Internet. They sort of did, but it took them a while. I think for better or worse, they're very much on Top of their game now from a regulation perspective. So they're much faster and so the damage is much bigger, to be blunt. Especially because if you look at what's happening in France and in Paris in particular, the European Union does still have very strong engineering schools in certain areas. And so you do have a lot of really high quality research coming out of universities. And so there is potential and it's sad to then see that ultimately that gets exported or people who want to build companies in this space will ultimately emigrate, or try to emigrate in a lot of cases. So it's kind of a self inflicted problem from a European perspective. So I hope it changes. I don't think it will. So I agree with Martin's prediction, but I wish it wasn't true.

Are there any particular areas of regulation that you think are causing problems? You mentioned things like features being missing from iPhones. I'm not sure what those sort of things are. It's been a while since I compared European and US sort of product features. Are there any particular areas of regulation that you think are probably problematic?

The regulation itself, I think to me it's a very strange concept that you try to regulate something that you can't even exactly define today on what it is and what its implications are. So, like, what are you regulating?

Okay. Yeah. Certainly you can end up having too broad a regulation if you're not quite sure what you're defining there.

Okay. And just to be clear, I don't think Martin or myself are saying like, there's no risks in AI and there should be no regulation at all. I don't think that's the case. I do think ultimately there are risks with AI and there should be some sort of regulation. I just think if you're too early, you damage the ability to innovate and the fact that so many American companies are indeed saying, hey, we're just not going to launch this new version of this model or this iPhone in Europe. That's incredibly damaging. And it's actually shocking how little attention that gets in Europe itself. It gets attention outside of Europe more than inside of Europe. It feels like.

Interesting. All right, so we've got a prediction we hope is not going to come true then, that you're particularly, the European Union is going to fall behind.

Okay. Yeah. We can only win on this one.

We talked a bit before about how adoption of AI is increasing around the world. I'm curious, in 2025, do you think there are going to be any particular areas that are going to see a dramatic uptick in usage of AI.

Yeah, I can take a first one. So I think one of the things we've been discussing at datacamp is that we actually think that teachers will be one of the quickest adopters of AI, or we're going to see at least like a huge increase in AI usage from like, the education sector. And part of it is driven by the fact that like, the underlying thing is like, okay, younger people are quicker adopters of technology than older people, like we're generalizing here, but more or less that's going to be true. So there's this whole generation of AI natives being developed now. A lot of them are still at school. So that means that actually their teachers are exposed to AI through their students. They're the first ones that are exposed to this generation of this new generation of AI natives, and probably a lot more than any other profession. And so I think this is going to lead to this much higher pressure on teachers to adopt AI, to use it in school. And there's for sure going to be this ongoing battle on is AI good or a bad thing in education? I think it's mixed. Like, there are going to be many great use cases. You can get personalized help, you can get answers to your questions quicker, you can get explanations in a way that better suits you compared to your neighbor that sits next to you in the class. But I also think, like, again, there are arguments to be made going back to an earlier point where, like, the understanding, the logic behind it makes a lot of sense, where it's probably better that people or students are taught that from first level principles. Now, regardless of that, like, I think due to these dynamics, there's going to be this dramatic uptick of AI and education in school and universities. I'm also seeing like the first courses that teach teachers how to use prompt engineering to make their lessons plan and so on. So, yeah, very high hopes for some kind of evolution revolution in the education sector in the next year around AI usage.

That's interesting that basically because teachers hang around children a lot, they're much more likely to be more clued into what's going on with AI. But yeah, I think you're right about there's just so many cool things you can do with AI, AI and education that the sector's just bound to adopt it even more. Yeah, Joe, did you want to add on to that?

Yeah, I totally agree. I think the exposure teachers have to students that are all over this is really going to accelerate AI adoption within schools. The only thing maybe to add to it is do a shameless plug. So if you are a teacher, a professor, and you want to educate your students in data and AI, we have a data camp for the classrooms program that allows you to use datacamp for free for six months in a classroom setting. So definitely check that out. It can be helpful to have that for your students so you don't have to make it up all yourself. So we try to support teachers in this area as well.

Absolutely wonderful stuff. So, yeah, for any teachers listening in, please do get in touch. We've made a lot of predictions around AI. So far we've not done so much with data. Is data science still going to be relevant in 2025?

I would say yes, but again, it depends on how you define it because there's a convergence of the data and AI space and data and AI skills. So some of the skills that used to be important might not be as important anymore. There's kind of an AI layer on top of those skills. But yeah, I think if you're a data scientist and you pick up AI skills, you will truly have superpowers. Are you going to be called a data scientist or an AI engineer? Who knows? Maybe it depends on the company you work at, but in some sense there might be a bit of a rebranding of data science to things that sound sexier now, but the fundamental skills I think are similar at the end of the day.

Yeah, that's kind of interesting because, I mean, I think at one point data scientist was the coolest job role you could have and now it's all, it's become mainstream and so you need some new titles for the same important skills. Just to keep it sounding exciting, I.

Was thinking about this, like maybe AI engineer is the new data scientist in some ways. Oh man.

Okay, yeah, so that's interesting competition for the hottest sort of data related job role then. So yeah, maybe AI engineer supplants data scientists is the coolest job. It's going to be a difficult one to measure, I think at the end of the year, but yeah, that does sound like a fun prediction. All right, so just to wrap up, what are you most excited about for 2025 in the world of data and AI?

From my part, it's going to be around the. If you go back to the origins of Data camp and our mission, like, okay, a big part is around democratizing data skills, analytical skills. And I think that prompt engineering is allowing again, more and more people to become their own data analysts, become their own data scientists, interact in novel ways, very easy ways. With the data at their disposal. If I look back at the past months and there was probably like every week that I did some kind of interesting prompt engineering exercise that gave me a new insight that I could easily share with my colleagues, opening up possibilities towards them. The power of prompting with the data. They have like an ability that they not have before. Like, that really excites me because, like, there are new ways to look at your performance marketing. There are new ways to evaluate the sales calls that folks do. There are even like new ways to evaluate the UI UX of your of your website. And if you think about like having every employee in your company access to that type of availability and knowing how to use it by seeing use cases of other folks, like, that really excites me because I think a lot of it is like a creative endeavor.

That's very cool. I do like the idea of just empowering people who have less technical skills to be able to perform tasks that have traditionally been technical or just being able to do things that you've not been able to do before. Okay, Joe, how about you?

From a business perspective, I totally agree with Martin. Datakamp's mission was always about democratizing data and AI skills. And so as you get more power into the hands of people, educating them to use that power is so much more impactful. So I think it's really inspiring and it makes what we do even more relevant to a lot of people. So from a business perspective, I have the same excitement for 2025. From a personal perspective, I'm very bullish on AR glasses combined with AI, I think there's so many use cases that will be unlocked and it feels like we're very close to the inflection point. Meta has these glasses. They're building the most powerful AI models. And just think about those AI models being able to hear and see exactly what you as a human are seeing and what you can do with that. I think that's going to be incredibly exciting and, and powerful. And I think we're probably at the inflection point where in one year there's going to be so many new use cases that are really powerful and impactful to people's lives.

Yeah, certainly glasses that can tell you the name of that person sort of in front of you where you've forgotten the name. Oh, yeah, that'll be incredibly useful. Yeah, Many, many use cases for that. So, yeah, interesting idea. Let's hope it finally takes off. I mean, it's been pitched for, well, more than a decade now. This idea of like smart glasses.

Yeah, I know it's been pitched for a while. I feel like in the next two years is when it's actually going become reality. Cool.

Okay, so we got a lot of predictions. I guess I shall see you again next year for the results. I shall probably see you both again before then, but. Yeah. Thank you both for taking the time to chat.