EP 444: AI Market Forces Collide: Global Powers, Prices, and Problems (2025 AI Predictions - Vol. 2)

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Thousands of hours of studying AI, hundreds of conversations for a couple dozen AI predictions for you. I get it. You don't have three, four, ten hours a day to keep up with what's going in AI. There's probably a lot of uneasy feelings and saying, like, hey, this is going way too fast. If only I had something to help me keep up and to cut through the nonsense. Well, that's what you have in everyday AI. And today we are bringing you a special edition, volume two of our 25 biggest and boldest AI predictions for 2025. And unlike last year where I jammed them all into one episode that was entirely too long, this week we are bringing them to you five at a time. Five days. Like I said, hundreds of conversations that I had about generative AI in 2024. And, you know, going back to literally, I remember in March when I partnered up with Nvidia at their GTC conference, I was already writing notes for this very show, for this very series so you can get ahead and grow your company and grow your career. That sounds like what you're trying to do. This series and this show is for you. Welcome. This is Everyday AI. My name is Jordan Wilson and this thing's for you. It is a daily live stream podcast, free daily newsletter helping us all learn and leverage generative AI to grow our companies in Korea. This is unscripted, unedited. I'm just bringing it to you real. All right. Everyone else is, you know, trying to tell you what you need to hear or what you think you want to hear. I am telling you what you need to hear when it comes to AI. All right. Something else you need to hear. Our website, you got to go there. It's your everyday AI dot com. Sign up for the free daily newsletter. If you're looking for the daily News, technically this one's pre recorded because it's a lot of work. You know, it's technically like nighttime, but you're probably hearing it in the daytime. But live stream audience, I still want to hear from you. And let me just answer this like, okay, Jordan, why are you doing this? Why are you doing predictions? Number one? Number two, why are you drawing it out? Well, couple of reasons. Number one, why am I doing these predictions? People ask me all the time, Jordan, what's next? Jordan, you talk to, you know, people at Microsoft and IBM and Google and, right? I've great, great relationships and conversations with people. Those you know, I've interviewed hundreds of people in 2024 and I've had hundreds of conversations that you didn't hear. Right. Get things off the record, on the record, NDAs, non disclosures, right. But now I can finally give it to you all in my predictions. And I did this last year and I still had people reaching out to me like 10 months later being like, how did you know all that? Right. You were correct. I didn't say those things. But you can go listen to the old 2024 episode out there and let me know for yourself. But without further ado, let's jump back into it. Like I said, this is volume two of a five volume series. Happy Tuesday if you're listening live. But you can go listen to yesterday's episode and join us in the coming days as we go through volumes three, four and five. But to quickly set today's stage, today we're going to be covering a lot of different things, but I'm calling this group of five predictions. AI market forces collide, global powers, prices and problems. Yeah, you like that alliteration, don't you? All right, so let's jump into it. But I'll give you a quick recap of yesterday's five predictions. If you want to know more, go back, read, listen, whatever you want to do, we recap them in our newsletter Yesterday as well. So 25 agent orchestrators will be a growing position. 24 public companies will post job for AI agents. 23 company reasoning data collection. 22 high end professional services will go through a pricing crisis. And 21 UBI will become a household conversation. All right, so today's volume two. Here's our five predictions. We're going to dive in deep, but go pretty quick. I'm challenging myself to do these episodes in 25 minutes. You know, branding. 25 predictions across five days. Five predictions each for a total of 25. Trying to do these in 25 minutes. All right, here we go. Here's today's and we're going to dive into them. Ready? 20 Open source surges. Open LLMs will temporarily overtake proprietary models. 19 Chinese AI will dominate and cause confusion. 18 Perplexity will either pivot, get acquired or get squashed. Yeah, hot takes. It's Tuesday, y'. All. 17 API. API prices are gonna drop like they're hot. And 16 embodied AI will be an exploding sector. All right, live stream audience like yesterday, I want to know which one of these are the most likely and which one is the least likely. Right. I'm going to revisit this in six months, but I want to see. Do you know your stuff? Do you have a crystal ball? Right. Do you know what's going on? Let me know. You can even just put what the number and which one you think is most likely the number, what you think is least likely. Please go ahead and do that right now. I always love hearing from our live stream audience. All right, enough of the chit chat. Let's get into it and talk about our first one, open source models. Here's the reality. Open source models haven't been fully tapped into yet. They started to really trend in the LATTER Part of 2024. All right, so there are fully open source models. Let me just get this piece out of the way right now. Right. There's fully open source models that you can essentially there's an open source standard where you can download them, you can fork them, you know, you can do anything. And then there's also meta's llama models which are not technically open source. Meta calls them open source. They aren't open source by the technical open source standard. Although what meta is trying to do is they're just trying to redefine what open source is. But for a lot of intent and purposes, it is an open model, although not truly open source. But meta has been a huge driver in the movement nonetheless. Because y', all, you have big companies like Nvidia forking and building off metal llamas or sorry, meta llama models. And, and they, they are topping, not topping, but they are in the top tier of, of rankings and of these charts that we'll be talking about. So why, why the heck are open source models going to temporarily overtake proprietary. So I kind of told you what open source models are. You can for the most part download them. If you have a powerful computer, you know, there's smaller, you know, small language models that you can download. There's a lot of reasons why they're becoming very popular. Well, one is they're more secure, right? You don't have to send any of your information, you don't have to send any of your data to the cloud if you're working with a smaller, smaller model, right? Maybe something that's only a billion or two parameters. It's very fast, right? Because you don't have that kind of extra inference time that you're waiting on a cloud provider to, you know, go and crunch your prompt. So security, it's fast. It's better for the environment too, right? So there's, there's a lot of, you know, good. I, I guess kind of under the radar reasons that I think is good to root for open source models, right? Proprietary models like OpenAI GPT, Claude Anthropic, you know, there's plenty of others, right? Google, Gemini, they're, they're big models, right? These big companies, they're trying to obviously, right? They want to profit off their models. Everyone does, right. So you can't download them and fork them and build off them and distill other models with them. You can't. You have to log on to their website or use their API on the back end and pay for it. Right. So not everything is open source, but I think Open Source In 2025, this is the year I don't think an open source model is going to top the LM arena or the Chatbot arena like we say. I don't think necessarily that we are going to see an open source model with the highest, as an example, MMLU benchmark. So you know, I like to say the, and a lot of people say, but the MMLU is a benchmark. It's like the ACT or the satisfaction for large language models. So I don't think that it's. But like when I say it's going to temporarily overtake, I'm not saying it's going to be the number one model for a certain benchmark or for ELO scores on the Chatbot Arena. What I will say, what I will say, right? I'm just going to go ahead and put, put these out, put these out there, right? I do think that more than half of the top 20 models on the Chatbot arena leaderboard at some point in 2025, I'm going to say more than half of them are going to be open source. And you might be saying, okay, well it sounds like there's only, yeah, there's more than a million different open source models because people can download them, especially smaller ones. It's not terribly time consuming, right, to download one of these, fork them, do some fine tuning, you know, kind of make, make a smaller model of your own and that's what a lot of people do. And then they put it out there for everyone else to use. Proprietary models, not so much. But there are like, I don't know, like 10 different versions of Google Gemini, right? There's, there's, you know, 01 preview, 01 mini, 01 full, 01 pro. So there are dozens of very strong, very powerful proprietary models. So it's not crazy to think that open source could take half of those also recent study said 41% of enterprise companies plan to increase open source model usage. Companies know that there are huge benefits. I don't think that they were really paying attention in 2023 or the first part of 2024 in until I do think Meta made that first big open splash and said, yo, look at us. Because their models at the time, 3.1 and 3.2, I mean these were top three, top five models. They were competing with proprietary models. So that is something I think when you crowdsource these fixes, right, because if you put your model out there for everyone to, to look at, for everyone to download, for everyone to improve upon, they're going to find ways to optimize the models to make them better, right? To improve the output. So it's essentially when you put your work out there and crowdsource people to build it, to pick it apart, to tear it apart, it's going to get better in that compounding kind of crowdsourcing of compute, right? We'll say that Triple C, right? The compounding crowdsourcing of compute. It's the rising sea lift all lifts, all ships or whatever the saying is, right? All right, number 19. Chinese AI will dominate and cause confusion. So speaking of, you know, models on the leaderboard, let's look at the top 10. I do think at some point in 2025, Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on gen AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen. Five of those top 10 models are going to be from China. All right, so reportedly Alibaba's Quinn models have already been deployed by 90, 000 enterprise customers. All right. Also something that really started to cause a scene at the end of 2024. Deep seeks V3 model. All right, so, so, so, so cheap. And here's the thing. I don't think it's just large language models when it comes to, you know, Chinese AI will dominate and cause confusion. More on that part here in a second. But I think robotics China is their, their, their humanoid and their robotic production. It's scary. We've shared some of the videos. Not like, oh, this is so cool, look at this. It's nuttier than a squirrel on keto. No, it's scary, right? It's, it's warehouses of what looks like thousands of humanoid robots that are embodied AI. Right, yeah, it's kind of scary. So I think it's not just large language models, it's not just prices, it's not just benchmarks, it's robotics. I think we're going to see other things. I do think China has competed very well in the AI video sector with, with CLING and some others. But I also think that we're going to see an AI audio competitor as well. Right? So China kind of silently, right. So deep seq v3, one of the cheapest models to use via the API. Cling. The AI video models, one of the best out there, I would say. Definitely top three. You know, the Quinn models and, and you know, Quinn has a Reasoner model. They're great. They're competing all across the board. Also, here's why it might cause some confusion. There's some people rumbling about this on the Internet and I'm not a conspiracy theorist, don't think that about me. But I do think that there is an element of a kind of Trojan horse here, right? So even with, you know, some of these models that are dirt cheap, you know, companies and, and individuals, they just kind of close their eyes, right? And they just assume like, oh, okay, it's like using Google. It's like using, you know, it's like using open AI or anthropic. It's not. You should probably start reading everyone out there, business owners. If you're a decision maker making, making a big choice on what AI system to use. I'm not going to name names, read the terms of services, read the privacy policies, understand what happens with your data if you're uploading it to these companies. I'll tell you this, I've read them all. Yeah, I'm a dork. I actually read them. I read them. And I also have different AI systems. Help me read these terms and services. You don't want to know. It's. It's a Trojan horse. That's what it is, right? It's, everything's free, everything's cheap, everything's fast, everything's great. And it's like, at what cost? Well, we might find out. And I do think that there's going to be, I won't say in event, but I think there is going to be some mainstream media attention to this very fact. As AI, I think, is also going to get very political in 2025. And this is one of those things when we talk about geopolitical tensions. Yeah, AI is going to be a big part of that, right? A lot of restrictions on, you know, exporting, bans on exporting GPU chips to certain countries, you know, potential tariffs, all of these things. The AI scene is going to get very political and keep an eye out for stories. I would say probably midway through the year, once the rest of the world kind of looks and understands what's going on. Because I think these models, they're very capable, they're very popular, they're very cheap, they're very fast. People are going to use them, businesses are going to use them, and eventually some. Someone's going to be like, wait, should we have looked at this a little more closely? All right, number 18, here's a spicy hot take for hot take Tuesday, volume two of our 2025 AI predictions. Per Perplexity, everyone's favorite answer engine, will either pivot, get acquired or get squashed. Here's why I think Perplexity has lost its focus as a company, right? They're going in so many different directions. They, they started making a daily podcast and then the CEO started talking about making hardware. Right. It started going in a lot of directions, but that's fine because all other companies are exploring other verticals. Yes. Perplexity is a little smaller than, you know, Open AI. Much smaller than, obviously, you know, Google. Right? But here's the issue. When companies like OpenAI, they start competing in different spaces. Google, Claude, you know, Anthropic Claude, Their core product improves first, right? That's one thing. Especially the, the, the two startups, you know, that are building these models, Anthropic and OpenAI, right? If, if they start going in another direction, that means their core product, their core USP has improved. I'm not seeing that with perplexity. Sorry, Perplexity. At its core, it's not a large language model, right? You choose your model, although they have their sonar model. I don't really know anyone that uses that. You know, most people will use, you know, GPT4O Claud Sonnet 3. Five new or 351, whatever you want to call it. 300, you know, that's not. Perplexity is not a large language model. It is an answers engine. It is a great product. However, the core functionality. I hate to say this, it's actually gotten worse. It's gotten worse. I've showed it on the show before. What it's supposed to do is it's supposed to crawl out there, look at 10 to 20 different websites and give you answers. It's not getting better at that, in my experience. It's actually gotten a little worse. And the answers engine space has very recently got very crowded. All right, Very crowded. So Chat GPT search, which has its flaws too, right? I personally liked Browse with being better. Chat GBD Search has some problems. I've talked about that on the show many times. But then you also have these large language models are just getting better Internet connectivity. You also have Google Deep Research. Google is getting in AI mode. I mean, speaking of Deep research, it's one of the most impressive AI tools I've ever used. Right. So Chat GPT Search might look at, you know, eight to 10 websites in a single prompt. Perplexity usually does more. Maybe 12 to 20. Y', all, Google deep Research, it takes a little while, right? Perplexity, you know, you. You might ask it something, it might take, you know, 5 to 15, 20 seconds. Same thing with chat GPT, maybe a little faster because it doesn't go to. To as many sources. Deep Research, yeah, Might take one, two, three minutes. But I've had certain deep researches that it looks at 1300 websites, but I routinely get it to look at usually 60 to 200 websites. That is an agentic researcher that works for you. Okay? So Perplexity's gotta pivot because what they were doing, they didn't get better. And it's too crowded. They're not that special anymore, right? Eighteen months ago, a year ago, they were still special, not special anymore. Their core product is not getting better. The space is getting more crowded. And the interest, I don't think is there. As an example, y' all know I always bring receipts. Search volume, right? That talks about demand. Right? Now there's, you might think, I think we live in an AI bubble. So many of us out there, we might think, oh, you know, sure, Chat GPT is maybe a little bit more popular than Perplexity. Maybe twice as popular. Three times, no, 66 times as popular at least. When we talk about search demand, right, there's about450,000 people a month searching for perplexity and 30 million people searching for chat GPT. So perplexity I think has kind of lost its way. I don't use it as much as I have. I still subscribe to it. I paid the, you know, the paid plan for every single model out there because I'm pushing them every single day so I can tell you what's worth your time to grow your company and your career. Flexity might not be worth it very much anymore, right? I have paid Google Gemini, so I get deep research. Chat GPT search for very certain things is very good. Perplexities doesn't have anymore. They could get acquired. I don't know. You know, you might see something like an Amazon. I don't know, they might get Aqua hired, they might pivot or they might fade into oblivion. All right, 17, I gotta go fast now. I'm trying to keep these under 25 minutes. So large language model API prices are gonna drop like they're hot. This is not a surprise to anyone. But when I say drop, I'm saying they are going to drop. And one of the reasons why. Well, a couple of reasons why. Number one is open source, right? If there's free alternatives, the paid alternatives, if they want to stay relevant and make billions of dollars, they have to keep their prices low. Obviously, the hardware, the GPUs, the NPUs, the TPUs, right, all these AI and data center chips are, are getting more powerful, more plentiful. I think more and more things are going to go edge AI, which takes a little bit, you know, of that, some of those resources off of the cloud. But I think hardware is getting better, the AI chips are getting better. The open, competitive, the open models are getting better. All those things drive costs down, right? Inference time goes down, all these things. But I do see, and it's crazy to think about when GPT4 was first released in the API. So I'm talking about backend. These are for developers, not, you know, if you're paying $20 a month to use a product on the front end, that's all you're paying, right? Or $200 a month if you're, you know, on like Chat GPT Pro. But otherwise you're paying an API price, right? You're paying, you know, a price per token. So when GPT4 was first made available in the, in the API. So for developer, for. I'm sure there's Hundreds of thousands, if not millions, I don't know, maybe millions of businesses. These companies don't say, but it was $60 per million tokens for output. All right, so you get chat GBT to spit out 60 million tokens or sorry, a million tokens, 60 bucks. Now with GPT4O, a wildly more capable model, $2 and 50 cents went from $60 to $2 and 50 cents in about 19 months. All right, I see in 2025 leading Frontier models going even cheaper. I think, you know, for easy comparison, I think we're gonna break that 50 cent mark. Less than 50 cents per million tokens for a leading state of the art model. All right, two more here. Number 16, Embodied AI is going to be an exploding sector. All right, so what is embodied AI? Well, it's artificial intelligence systems that interact with the physical world. Whether that's, you know, robots, autonomous vehicles, drones, wearable AI, robo taxis. Right? It is, you know, we've, we've had these things for a long time, right? Those things I just mentioned, none of those are quote unquote new, you know, humanoid robotics, autonomous vehicles, drones, wearable AI, etc, none of that's new. What's new is having generative AI in large language models on board. That changes everything. That's how now you have startups as an example, like figure that can go from obscurity and in two years turn into a multiple billion dollar company in like two years. You couldn't do that pre generative AI because you had to build all that artificial intelligence. Now you can just use these companies API, use the technology that's already there and essentially start stuffing it in humanoid robots, in robo taxis, in drones, in wearables. And it changes how we interact with the world. I think I'm going to blame Apple, right? Apple came out with a, the Apple Vision Pro. And everyone's like, oh, this is going to change the world. Literally before it came out. I'm like, this thing's going to suck. No one wants it, right? But I think some of these early examples of embodied AI, especially wearable tech, they were terrible. And just so you guys know, go back and listen, I was never bullish on any of these early wearables. I'm like, nope, nope, nope, that'll flop. No one wants it. I'm very bullish. In 2025 on embodied AI, we're gonna see AI out and about, just about everywhere, right? It's, it's gonna start in some of those, some of those areas that I just talked about. So even an example, in 2024, VC funding in robotics in drones soared by more than 40%. Another additional kind of hot take on top of this prediction, I do think in some major cities where the law allows it, I think probably more than 10 to 15% of ride share rides will happen with autonomous vehicles. Right. I don't think we're gonna get the, the, the Tesla, you know, whatever they're calling it, the robo taxi AIs, you know, they're always, you know, they're like, oh, this is coming out, you know, in three months and it's going to be $30,000. Then it comes out eight years later for $100,000 like Cybertruck. So I, I don't expect Tesla to have anything in 2025, but I do think other companies with this embodied AI bringing essentially large language model technology, vision technology, I think it's going to be fairly common. I do think in big Cities, you know, one in every 10 rides potentially by the end of the year will be in these type of vehicles. So embodied AI is going to be huge. All right, I made it. I made it. I didn't accidentally talk for a couple of hours, so. Quick recap of the five bold AI predictions in this volume of our series. So open source searches, I think open source models will temporarily overtake proprietary models. I think Chinese AI will definitely dominate and cause some confusion. I think perplexity will either pivot, get maybe acquired or aqua hired or just get squashed. I think API prices are going to get ridiculously cheap. I think we're going to see like less than 50 cents per million token for big models like through OpenAI and Google Gemini. And then I think Embodied AI is going to be in exploding sector. All right, I hope that was helpful. Volume two in the books. If this is helpful for you, let me know. But probably you should go listen to yesterday's volume one. It's going to be in your show notes. Make sure to check it out. We talked about a lot of different things, but a lot of things on agents and kind of the next steps in the workplace. So make sure you join us tomorrow for volume three. We're going to be talking a lot about the kind of contents AI Revolution, videos, streams and stars. Hope to see you back tomorrow and every day for more everyday AI. Thanks y'.

All. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.