Seedance 2.0 Is Here… and It’s Better Than Sora & Veo

Hey, welcome to the Next Wave podcast. I'm back with returning guest and one of my best friends in the world, Joe Fear. He is our boots on the ground marketing business expert who's also paying attention to AI. And a good looking dude as well, I'll admit. Yeah. So we're going to do a very similar episode to what we did last week where we're going to break down the five most interesting things that happened in the world of AI last week. We're going to riff on it, we're going to talk about what we think is interesting about it, why we think you should be paying attention, test some of these tools, you know, figure out life's greatest problems, solve the three body problem, figure out the mysteries of a life. By the end of this episode, we're going to have done all of that. So stick around and let's just, you know, get into it.

Let's go. Cutting your sales cycle in half sounds pretty impossible, but that's exactly what Sandler training did with HubSpot. They use Breeze, HubSpot's AI tools to tailor every customer interaction without losing their personal touch. And the results were incredible. Click through rates jumped 25%, qualified leads quadrupled, and people spent three times longer on their landing pages. Go to HubSpot.com to see how Breeze can help your business grow.

Thanks for joining us, Joe.

Thanks, Matt. Again, good to be here.

Yeah, we got some really, really good feedback on the last episode that Joe and I and we figured, hey, let's do some more of these types of things. So here we are and let's just jump into the first story this week. So, Joe, I don't know if you came across this at all or saw it in your X feed, but There's a new AI video model this week called Seed Dance 2.0.

I did. And people Twitter X, whatever we're calling it. I'm always seeing these videos. I'm like, I don't even know what to believe anymore. But now more than ever. Yeah. These videos, they're nuts. I know. You try to get your hands on it really quickly, right?

Yeah.

But not so able to do that here, at least in the US currently, right?

Yeah, no. The Chinese company behind this is a company called ByteDance. They're also the company that makes TikTok and Cap Cut and they made it so that only non US countries got access to it early. So supposedly it's going to be accessible worldwide on February 21st is the thing that I've been hearing. I don't know where that number comes from or where that date comes from, but that's the date that's been circulating is February 24th is when we get access to it. But some people did find some loopholes and figure out how to get early access to it. Unfortunately, most of those loopholes have been closed. So we can't actually demo this on this episode. But there's a lot of videos made with it.

It's not that far from now.

They do have some on their website. I'm never a huge fan of just showing the ones they put on the website because you know there's going to be like the most cherry picked results. These are the ones that they were like, okay, that's really impressive. Let's show that. Where maybe they had 15 results that they didn't put on the website because they're like, oh yeah, let's not do that one. But there's a few that we can look at on their website.

I mean, Matt, you like analyze the AI video way more than I do personally. But like, what's your first impression on seeing these ones?

So am I like overly impressed by it? Not really. I feel like that's kind of level of like vo. The thing that I felt was most impressive by it is I feel like the lip syncing is getting better and better and better. I feel like the biggest giveaway when you're watching like a VO video or a SORA video is when you see their mouse talk and then the voice comes out of it. There's always this like uncanniness where you're like, that didn't really feel like it lined up super well. And I feel like Seed Dance is finally starting to solve for that.

Yeah.

The other thing is that you can have it generate like multiple scenes with consistent characters. So it'll show like a person walking forward and then the camera will change to like the front view of them walking towards the camera and then it might change to like a side angle and it keeps a very consistent sort of environment and person as they're walking around. And I think that's also something that's really, really impressive about it. Yeah, this is the future of product ads online. Like people aren't going to go out and shoot stuff. They're just going to take pictures of the product they're trying to sell. Have AI go and actually make a video of them using that product and we're not going to be able to tell it's AI pretty quickly here.

The lines are blurred, reality is gone.

Let me show you something real Quick too. That even hammers this home even more. So there was a post that I was looking at the other day where Google said that they're now rolling VO3 1 into Google Ads.

Oh.

So here's what I found. Google expands VO3 into Ads Asset Studio. VO3 will let advertisers generate video ads from text prompts inside of Google Ads. So, yeah, this is the VP of Ads and Commerce at Google and she posted this that said VEO is coming to Google Ads. Nano Banana Pro was a success and we're ready to bring that creativity to Google Ads. As an advertiser, it's like having a real time creative partner. Businesses will be able to produce studio quality work in minutes, accelerating creativity at scale. But if you look at the video here, you can see they're just dropping an image of a handbag in saying generate video clips and then they're actually making video ads of that handbag. Like with somebody using that bag and walking with it. Yeah. So, like, it's not theoretical that this is what ads are going to look like. Google is actively rolling this out to be how ads like move forward.

I mean, Google would be the company to do this, right?

Yeah, yeah. So we're actively seeing that happen right now. This is like a typical day at the Waffle House. Okay, here you go.

If this is your first shift at.

Waffle House, you have to fight.

Dude.

Yes.

It was like a Quentin Tarantino film or something like, but better. I don't know.

We just dropped our AI Video showdown guide. We took Runway Cling, Veo and Sora, gave them all the exact same prompts and put them head to head to see which one actually delivers. You get the full honest breakdown, pricing comparisons, and five universal prompts. You can test yourself right now. Get it right now. Click the link in the description. Now let's get back to the show. Yeah, I mean, the thing is, there's like a real sort of consistency throughout it where you can tell it's supposed to be like the same people.

Totally, man. Not only are the visuals awesome, it's like the sound. It's a full blown movie now. Now, Matt, do you know if it's stitching scenes, like when you generate these.

So it generates 15 seconds at a time. This is a 27 second video. So this has to be at least two generations. I wouldn't be surprised if they did like, you know, four or five generations and then just took like five seconds from each or something.

Yeah.

But it generates 15 seconds. So this is obviously more than one clip.

Got it. It's wild.

And here's another one. This one's coming for your old career where you used to make videos for people.

Yeah, that's gone.

Here you go. It actually does like motion graphic style as well. So yeah, motion graphic explainer style videos, it's got that covered too.

Which is interesting because I did that work a long time ago but also had a client just recently looking for this kind of stuff and I didn't have a solution literally a week ago. Yeah, that's now changed.

There are other tools that have come out recently that you can do this kind of stuff. Like Remotion is one of them where it's a skill that you can install in Claude code and then you can actually prompt it to go generate a motion graphic video for you. Yeah, but nowhere near this level. This level here is still like quite a level beyond what you can do with Remotion.

Yeah. And I'm imagining just with everything that when you start to dial in your prompts, I look at this, I'm like okay, how about like a brand style? Like I'm thinking for the businesses, entrepreneurs listening, it's like okay to get consistency that way.

Yeah.

And it's like, yeah, you could just crank out these amazing looking promo videos, explainers just like as you think. Yeah, yeah, that's what I'm seeing. Everything is just like such in time, thinking creation now it's wild.

February 24th, supposedly that's when everybody gets access to it.

Yeah.

So we'll see. It can do. Trademarked ip as we can see by Matt Vid Pro's video of SpongeBob torturing Squidward.

It's working.

He's really feeling it now. That's one of the things about the Chinese models versus like the US models. I don't want to say that over in China they don't care about IP and trademark and copyright. But they're definitely a lot more lenient on it. And I think it's a lot harder for like US based companies or companies outside of China to enforce copyright in China. So you've got these models over in China, like you know, the seed dance models where they're not as concerned about copyright. Where when you look at Google and like their VO models and you look at OpenAI and Sora and you look at all these models that are sort of US based companies, if they let you generate stuff like this, they're going to get sued up the rear end.

Oh yeah, it's going to be bad. I mean just think about like what this does for innovation too. You know, it's like they could just kind of open the net wide and start to just test like crazy, throw it out there. And in this case, it's a big tease to us wanting to use some of these models that are just, it's incredible. But yeah, I guess on the IP side, yeah, that's something that the US and a lot of the other, you know, countries of the world kind of have to tie our hands around.

But yeah, also the training data as well. Right. When I first started talking about AI, you know, whatever, five years ago, it wasn't like this controversial topic nobody was really paying attention to, like, oh, OpenAI is taking our data and training on it. And Google, I mean, okay, maybe Google people knew people were taking our data, but they didn't know they were training AI models on it. We knew they were collecting our data, but we didn't really realize the sort of AI implications of it yet. Yeah, and so it wasn't like these controversial topics. And now, you know, you fast forward to today and they're under a microscope now. They weren't under a microscope back then, but they're under a microscope now and they're. And people are saying, well, where are you getting your training data? And Now Google and OpenAI and companies like that have to be really, really careful with what they actually put into the training portion of the models. Well, if over in China they're just kind of like we're throwing it all in, well, they're going to have the better models as a result, you know.

And that's why I thought of innovation. Just like the speed of innovation, the fact that there's maybe lesser no regulations on that, that training data, it's like, who knows what's going to come? Yeah, it's hard to predict.

But yeah, all I can say about this, there's obviously pros and cons, right? Like being able to quickly make an explainer video for your company that shows off what it does. That's, you know, that's pretty cool. You know, maybe if you do have an E commerce business and you're selling physical products and you want to make a quick video of somebody using your product, okay, that's, that's starting to lean a little more into like a slightly deceptive side of like, okay, we're, we're seeing this in use, but that's not actually it in use. So how good is the quality going to be compared to what we see in the video? You know, you've got that kind of thing. But then You've also got like the whole deepfake side. We're getting to a world where we're not going to know anymore.

Dude, we're there.

I feel like, I think everybody now is going to need to default to. When I see a video, I have to assume it's fake until proven otherwise. And I think we've lived in a world where we have to assume everything is real unless, you know, there's some sort of sign that it's fake. But those signs are going to get smaller, further and far between. Right. So like now we're going to live in this world where, okay, whenever I see a video I have to ask, is this AI or assume it's AI before I assume it's real. Like, I think everybody needs to sort of flip their thinking because it's going to be so damn hard to tell.

We need to have like what X has. If you, if you see this on most videos, or at least I'm seeing it now, where it just looks too good to be true. You like, hey, Rock, is this real? Yeah, you just see that so often now. And who knows if Garrok gets it right every single time either.

But, but even that, we just need.

To have like a checker in real life.

It's this thing. Even though it's going to be a cat and mouse game, it's going to be a cat and mouse game and it's never going to stop because the videos will get better, tools will come up to detect AI, better companies will get better at making undetectable AI, New tools will pop up that will, you know, it's just going to be this circular cycle where each side of the equation is trying to outrun the other side. Yeah. And that's just, I think the world we're going to be in now where companies and people are always going to try to figure out new ways to fool the world.

Do you think China, and maybe countries that have lesser restrictions on IP like this for training data and all that will always be a step ahead in things like video, you know, like this, or maybe even music or other types of.

Hard to say. And the thing is, China doesn't not have copyright laws. In fact, they do have treaties with, you know, I think the EU and the US and stuff to sort of prevent copyright stealing over there. So Even companies like ByteDance, they're probably going to eventually like nerf this model and make it so you can't create, you know, IP eventually.

True.

Where I think they might win is they're probably caring less about what goes into the training data. Because in China they're just going to consider any data as fair use for training. They may not consider any output as fair use. Right. Like, you're probably not going to always be able to create a Super Mario video or a Disney video or, you know, whatever you want from the models. But when it comes to the training side of how they actually train these models, they're probably going to always be an open floodgate where you can train on whatever you want, where the US is constantly trying to like tighten down on what you can use as training data. So in that sense, I do think that China's probably going to stay ahead in like a lot of the visual models.

Yeah.

But Google's a wild card because Google owns YouTube and whenever you put a video on YouTube, you're essentially giving YouTube the right to train on that, you know, so it's like they do have this massive corpus of data as well, all of this video that they can train on. So I wouldn't count them out. I do think that it'll probably always sort of go back and forth between Chinese companies and Google specifically.

That's true. So VO pretty soon is going to have this and maybe some new stuff.

Yeah, yeah. I would imagine we'll see like a VO 3.5 within the next, I don't know, two or three months. And by the end of the year, probably a VO4. And I think if a VO4 comes out, it's going to blow people's minds more than Seed Dance. The next evolution of these is we're getting these models so good that like, you can't really even tell anymore. Like, you look at these videos and you're like, that looks like it came from an episode of spongebob or One Piece. You really, really can't tell anymore. So I think the next frontier is how do we make it so it generates long and longer and longer videos. I think that's where the frontier moves next. So, like this one's 15 seconds. OpenAI claims that Sora can generate 1 minute long videos, but we've never gotten access to those features. But I think that's what you're going to see. You're going to see it where you're prompting, you know, by the end of the year, like 15, 20 minute videos just from a single prompt. Right. Like, I think that's where this is all sort of heading next is how do we extend it while keeping the consistency throughout the video? How do we extend these videos to be 20 minutes long? But the character you're seeing at minute one still looks like this character you're seeing at minute 13. That makes sense, you know, So I think that's the frontier they're trying to figure out next.

And whenever I see this kind of stuff, and with AI in general, I'm always thinking, okay, where's the. Where's the human element? Where are we still relevant, Matt? And I'm not always grasping at that. A lot of this stuff we can just let go. I'm happy I'm not doing the video production stuff anymore.

Yeah, yeah.

But. Well, at least personally, I think I'm like, okay, humans can. Can probably decide what actually goes live, have a little bit more of a tap and not say that's always going to be the case either. But.

Yeah. Well, we'll talk a bit about Matt Schumer's blog post a little bit in this episode. But, you know, I do think humans still own taste. Yes, I know what looks good, what is interesting to people. I still think humans are better at storytelling. I don't know if you saw that, like, sort of viral video from the Joe Rogan show where they had Matt Damon and Ben Affleck on.

No.

And Ben Affleck, Matt Damon went on this about how they don't think AI is going to replace actors because, you know, they started giving these examples of these scenes where, like, you really feel the emotions behind the actors. You see the actors actually breaking down in tears and, you know, they're putting their brain in a space that, like, an AI can't put their brain in that space. And they have this example of, like, there's a movie called the Smashing Machine with the rock. And there's a scene in the movie where, like, he's in his hospital bed and he's, like, crying and he breaks down in tears. And it's a very emotional scene. And, like, you really, really feel the emotions because I guess, you know, the rock in that scene actually went to a dark place in his head and managed to, like, pull that out into real life. And they were saying that AI is still pretty far away from being able to do that. I think it's sooner than they think. But I do agree that that's going to be hard to replicate for a while.

Yeah, it's funny. I just think in my brain, I'm like, okay, so method acting, like what you're kind of describing there is basically a Claude project where you fill it up with another instructions in silo it to then. No, I'm like, let's hope that I don't know. I like your point of view better.

Yeah? Yeah. I mean, I don't have a crystal ball. I don't know where any of this is all going. A lot of the times my predictions are wrong, but usually when I'm wrong with my predictions, it's because the thing happened sooner than I thought it would, not because it was further out. Like, I've made predictions where I've said, like, I think we're going to be generating, you know, videos that you can't tell aren't real. I think we're going to be doing that within the next two years. And then like six months later, we got VO3, and I was like, all right. I was way off on that timing.

You know, I've known you for a long time, Matt. You've always made predictions, even before AI, and they're always pretty. They're within the flavor, you know, like the zip code.

Yeah. Well, I mean, credit to us. You and I were doing a podcast back in 2020 talking about AI and how AI was impacting marketing. And back when we were doing those videos, we were getting like 60 views on them and nobody was paying attention. So the world's changed a little bit. But we were making these predictions back then.

We were all for a rocker back then. No one wanted to talk with us anymore, but look at us. No, I'm just kidding.

Yeah. Yeah. We will be right back. But first, let me tell you about another podcast, DTC Pod. Hosted by Ramon Barrios and Blaine Bolas, the DTC Pod is a podcast about all things direct to consumer. Ramon and Blaine cover everything for starting, growing and optimizing e commerce stores and DTC or D2C brands. They talk with founders, marketers, platforms, creators and marketing and growth agencies to cover topics like brand building, social media, paid media, email marketing, and more. If you're interested in the stories behind your favorite consumer brands, this podcast is for you. They recently did an episode about what every founder needs to know about compliance, claims and scale. Listen to DTC Pod wherever you get your podcasts. The next thing that really, really sort of blew my mind this week is GPT 5.3 Codex Spark. You can't even say it in one breath. I dare you to try. Yeah. No, These companies really suck at naming this stuff. So, first off, GPT 5.3 Codex. You know, without the spark. GPT 5.3 Codex is OpenAI's newest state of the art AI coding model. The Codex means it's a coding model. It's not the model that's in chatgpt yet it's really only available to ultra plans that are. No, sorry, pro plans that are paying 200amonth. But it's a really, really good coding model. Like, this is the model I've been using lately to do sort of like vibe coding stuff behind the scenes. And this model is really good. It's been out for, like, almost two weeks now. But this GPT 5.3 Codex Spark model, it's the same model, but it's being run on different hardware. So there's this company called Cerebras. I don't know if you've ever come across them or heard of them, but Cerebras is like a company that makes inference chips. And an inference chip is a chip designed to process the information at the time you prompt it with something. Right. So you look at Nvidia. Nvidia is obviously at the center of this whole AI bubble. You know, they have their H1 hundreds and all of their big hardware that's going into the data centers. And most of what Nvidia's chips are for have been more for that training side of AI, not the inference. I mean, they're using them for inference as well, but they're really sort of optimized for training. So a lot of the big models that we've been seeing come out in the AI world have been trained on Nvidia chips. And that's why there's been so much demand for Nvidia is they want to make these models bigger, they want to put more data into them, so they need more hardware to train on. Well, there's two companies that they've been sort of making headlines recently. One is Grok. Not Grok from Elon, that's spelled G R O K. I'm talking about Gro, spelled G R O Q, which is a company that actually makes inference chips. And then there's also Cerebras. These two companies make chips that are designed for the other end, not necessarily the training, but the side where when you enter a prompt, it gives you the response back. And they're designed to do that part really, really, really fast and efficiently. And so what this GPT 5.3 Codex Spark is, is the first model that is being used with the Cerebras chips. So it makes these models insanely, insanely fast. So let me actually share my screen here and I'll fire up codecs on my computer. Yeah, Think of something you want it to build real quick.

Like you built your website recently, so some type of landing page Website, maybe something for this podcast.

Okay, so if I come down here, you can see I've got it set on 5.3 codec spark. Now, this is only available on the pro plan, so you're only going to see this if you're on their $200 a month plan. But let's go ahead and say, please design a beautiful website for the Next Wave podcast featuring Matt Wolf and Joe Fear. Make it look epic. Okay, let's just give it a simple prompt, see what it does. I'll go ahead and send it and just watch how fast this takes. It does take a minute to think. So it's thinking. This is all real time here. I've got the front end design, workflow, replacing the index HTML.

All right, it's done.

It took 18 seconds. 18 seconds. So let's see what it made for us in 18 seconds.

I'm judging the epicness of this.

Here's the website it made. Dude, 18 seconds.

Wow.

So we've got the Next Wave podcast, Matt Wolf and Joe Fear. It's all a single page. It just scrolls for us if you click on these things. But you can see. I don't think this is accurate. Seven days a week on air. So it just sort of put filler data in, right? It didn't do the research, but look. The destination for creators, operators and curious minds. Hosted by Matt Wolf and Joe Fear, we explore what's coming next and how it will reshape culture, technology, and the way we build. Listen to the podcast, meet the hosts. Here's the captains, Matt Wolf and Joe Fear. Latest episodes. Again, all filler data. Here's where you could listen. Spotify, Apple, and then even put like an opt in down at the bottom. And Again, it took 18 seconds.

18 seconds. And you gave it no context, so we got to give it a pass there. But I mean, like, that's an epic website. I can't even think of all that stuff in, I don't know, less than five minutes, ten minutes maybe.

Yeah, yeah, but you get the idea. It's really, really fast. So that's what made this Spark version so impressive to me, was that this is like basically the best coding model we've got available right now. The GPT 5.3 codecs. You know, you can argue Opus 4.6 is pretty comparable. They're pretty close to each other as far as capabilities. But now we've got GPT 5.3 that can do coding in seconds instead of, you know, minutes.

Matt, are you finding yourself? Because I have not personally used codecs like this. But I've been all over Claude and Claude code and all that stuff. Would it replace Claude code for you? Because I know you've been putting out all sorts of stuff out with that.

So I like a little more control. My impression of Codex is it's for people who just want to let AI take the wheel. Right. Like, you can see I've got my different projects on the left side here and my prompt box that looks very similar to ChatGPT here. And there's not a whole lot other stuff going on. Right. I actually tend to use Cursor because I do have more control. I can see all of the files. I can click into any individual file. I can actually tweak code myself. If I'm like, oh, this isn't working right. Let me actually look at the code and see if I can find what's going wrong with it. I also, you know, you can do this in both Codex and Cursor, where you run parallel agents, where I have multiple agents all sort of running at the same time doing different things. But to me, it's busier. There's a lot more going on inside of this. But I also feel like I have a little more control. But I have been finding myself using Codex a little bit more since Spark came out this past week because, like, it's just insane to test. So, yeah, I still use Cursor quite a bit, and I switch off between Opus 4.6 and GPT 5.3 Codex, I can use them both. And like, if they ever roll Codex Spark into Cursor, I mean, it's like, game over. Like, I don't know. Cursor for me is my comfort zone, I guess, where when I use Codex, I just feel like I have a little less control. Now you can open the terminal. There is a button to open the terminal down here if you want to type your own terminal code. And they do have an area where you can actually look at the code yourself. I mean, to me, I just feel like I have less control when I'm in Codex. But using Codex Spark, you have to use it in Codex. Right now, it's pretty much the only way to use it.

Yeah, I'm sure that'll change. I mean, it seems like they. They start to distribute to places like Cursor pretty fast. Yeah, yeah, but I like the idea. I mean, like, I just keep thinking of, like, from thought to creation, like, everything's just like that time frame, that time to something like is nothing anymore. Everything is just so fast.

It's Wild. I mean, that speed is the. I think that the thing that really blows my mind is like.

Yeah.

So when you. When you think about it, GPT 5.3 Codex is like the best coding model out there right now. And it used to, like, you'd ask it to do something and it might think for a few minutes before it finally did the thing for you. Well, now it's still thinking the exact same amount. Right. The thinking that goes into it is the same level of thinking that was going into it when it was doing it for five minutes. It's just doing that same amount of thinking now in seconds instead of minutes. So now when you let it think for, let's say, five minutes, think about how much more it can do in those five minutes at this new speed. Right. That's where like, really the next frontier of this stuff is and why this is really exciting is not that I can do stuff now in 50 seconds or less. It's just think about how much more it's going to be able to do in five minutes than it was doing in five minutes before.

For sure. I'm thinking of, like, to get the most out of Codex or probably any app. I mean, and I'm curious. I'll point it to you. It's like the bottleneck, I feel like, is more just coming up with the brief and the idea and, you know, giving it all of that prior to doing the. I know what clock code has the whole planning thing. So you can really have it question you back and forth and have this robust plan.

Yeah, I believe you can tell this to plan as well. I mean, it doesn't specifically have, like a plan mode, but if you prompt it, like, hey, help me plan this. Don't write any code yet. Like, if you put that in the prompt, it's not going to go and write the code. It'll just build out a plan for you. So it's just like a, you know, a prompting thing. I think, again, it all comes down to, like, taste. AI doesn't know what to make. Like, you can't just say, go build me an app that's going to make me a million dollars. It's like, it doesn't know what to go make. So you need taste. You're going to probably be better at figuring out user experience, getting in there, testing stuff, going, okay, this button feels like it should be over here. And when I click this, it doesn't do what I expect. There's still, like, user experience stuff that I don't feel like the AI has a grasp for yet. So letting it build a first draft, getting in there, playing around with it and going, okay, this is what I do like about it. This is what I don't like about it. And sort of iterating is where I still feel like the human touch is really, really necessary.

Got it. Yep. So I'm seeing a line from the video side. Any of the creative work all the way to here. It's like the human touch have the taste for whatever you're trying to build or the person you're building it for. Yeah, yeah, But I'm sure that's going to change pretty soon. Just keep watching.

Yeah, yeah. I mean, the Matt Schumer article we'll get into in a little bit, but he was trying to make Argum that they're getting better at. Better at like actually the taste portion as well.

The taste, yeah, exactly.

All right, third thing on to number three. So the other thing that came out and to be honest, I don't have as much to say about this one, but I do think it's really cool. So Google released a new model this week. So they updated their Gemini deepthink model. And this deepthink model is like for academic research and like trying to make scientific discoveries and things like that. So this is the newest model that they just put out and I'll try to explain some of these benchmarks a little bit as we talk through them. So ARC AGI is obviously reasoning and knowledge and this one is a pretty difficult benchmark to pass. But as you can see, they compare it to GPT 5.2, which isn't necessarily OpenAI's most state of the art model because they do have GPT 5.3 now as well. But they did compare it to Cloud Opus 4.6 and Gemini 3 Pro. And you can see that when it comes to reasoning and knowledge, this one is pretty far ahead of the rest.

Oh yeah.

Humanity's Last exam is considered to be one of the hardest tests to pass. And 48.4% makes this model the state of the art in that one as well. So this is like insane.

I just love the name. Humanity's Last Exam.

Yeah, that's a little dystopic, but yeah. MMMU Pro Multimodal understanding and reasoning. So like, you know, giving it different modalities of like media and stuff. How good does it do? It's actually pretty on par with their previous Gemini model. Beats out Opus and GPT5. And then how does it do on coding and algorithms? You can see it's pretty ahead of those. Right, so here's where this one gets really interesting. So beyond mathematics and competitive coding, Gemini 3 DeepThink now also excels across broad scientific domains such as chemistry and physics. Our updated DeepThink mode demonstrates gold medal level results on the written sections of the International Physics Olympiad and Chemistry Olympiad. It also demonstrates proficiency in advanced theoretical physics, achieving a score of 50.5% on the CMT benchmark, which is condensed matter theory.

Whoa. Okay.

And I mean, so they're 50.5% compared to Claude Opus. Claude was 17%. Like, majorly.

That's wild. Okay.

This is really interesting to me too. So they actually told it to make something that was 3D printable. With the updated DeepThink, you can turn a sketch into a 3D printable reality. DeepThink analyzes the drawing, models the complex shape, and generates a file to create the physical object with 3D printing.

Dude.

So you could give it an image and tell it to turn into a 3D printable file.

That's just nuts.

So let me go to Gemini. I have to be careful sharing this one because this is like my advanced, like, confidential version that they gave me access to. So I often have features in mine that aren't fully rolled out yet. But I can turn on the thinking model and turn on Deep Think. And this is the newest model here. So one of the things we can do is we can try to give it an image and see if it'll turn it into a 3D print file.

I think that would be interesting. And then fire up your 3D printer behind you. And then let's see what happens.

Yeah, and then three hours later, we'll see if it worked. 3D printing is not fast.

No, I know.

Let's start by creating the image with Nano Banana.

Now we're talking.

Let's use Nano Banana here. What should we have it create?

I know you have your go to wolf howling at the moon, but you've probably overplayed that one. I'm holding a squishy brain right now, so maybe there's a interesting image of a brain we can do.

Create an image of a brain, make it scientifically accurate. So let's do that. Let's have it generate an image of the brain. I'll paste it back down into our prompt box here. Let me switch this to the thinking model. Let me switch this to deepthink. Turn this image into a ready to print 3D printable STL file. This could take a minute, though, so we'll see what it does.

Hey, this is interesting.

I'm on it. All right, so why don't we get a new chat going here? So this is the one with thinking and deep think. Okay. Find a solution to the three body problem. So we'll get that going.

Watch. It's like, no problem. I'm on it.

Yeah, let's see what it says. It does say I'm on it with the deep think can take some time. So check back in a bit. This other one here, where it's trying to make the 3D print file, says check back later also.

How wild is that, though? Just being able to literally sketch something? I mean, already 3D printing's wild as it is, you know, and you can just share everything with the world, but now just sketching and now, boom, Render it printable.

Yeah, no, it's pretty crazy. So I have no idea how long these are gonna take. So maybe we just move on to the next topic and then we check back in on them.

Let's do that.

All right. So the other thing that's been all over the AI news lately is that ChatGPT starting to roll out ads. I've tried really hard to get it to show me ads. Like, I actually have a second account that's on the free plan just so I can, like, test stuff, and I cannot get it to show me ads. So I don't think it's rolled out to everybody yet. They're just sort of like, testing it in certain areas. They did announce some of the companies that are advertising, like, I think DoorDash was one of them, and I think Target was one of them. And I know Albertsons is one of them.

Adobe.

Oh, Adobe's another one. So I was. I went and asked it, like, what's the best PDF reader out there? You know, trying to get it to, like, give me an Adobe ad, or like, hey, I want burritos. Where should I go to order those burritos and have them delivered to my door? Like, I was giving it softballs to try to get it to give me ads, and it wouldn't give me ads. Yeah, I think it knew I was testing it.

I don't know. I haven't seen anybody even report that they've seen them. But no, we knew this was coming. It was interesting. Also, I know you did a whole video on this, so deep dive on that too. But anthropic, you know, Claude throwing out their super bowl commercials I thought were freaking hilarious.

Yeah.

And they don't totally paint the picture correctly, of course, but what? Yeah, it's fun, you know, you got people talking about it, right?

Yeah, yeah. Well, so in the super bowl ads, they basically paint it as like somebody asks a question, the AI sort of responds to the question, and then in the middle of their response, they start pitching them an ad.

Right.

Like, as if like the ad was part of the response, which in reality is not really what they're doing. In fact, here's like a screenshot of what the ads look like. So it gives them their answer. And then below that it's got, you know, the heirloom groceries sponsored. Right. So they make it very clear. It's got like a different background. It's got the text that says sponsor. It doesn't look like a response. The idea is that it's very clearly labeled as an ad and people aren't going to get confused on what was the ChatGPT response and what was the ad.

Yeah, and it's interesting and I know there's always different thoughts of like, where does this go? You know, is this the beginning of way more to come? And just looking at like, what Google has done over the course of time, do you think other models will be quick to adapt this type of monetization, like Claude?

I think the other models are going to sit around and wait to see how it plays out for OpenAI is what I think is going to happen. Now, Google doesn't need to. Google is able to sort of subsidize its chat because it makes all of its money from search ads, YouTube ads, cloud services. Like, Google has so many other ways of making money that they can roll out AI chatbots and not put ads in them anytime soon because all of their other business models sort of subsidize that.

Absolutely.

OpenAI, their business model has essentially become ChatGPT. Right. So they don't really have a whole lot of other revenue drivers outside of enterprises and coders using their API and people using ChatGPT, like, that's how they make their money.

It's just wild. Yeah. Like people not so much in the AI bubble, but it's called the laymans or the normal folk. Yeah. Like, you know, I talk about AI and everybody goes straight to ChatGPT, you know, and a lot of people are on the free plan. Some, I think, are now upgrading to the $8 a month plan, which I believe those are the only two plans this is showing on the ads. But I, I just start to think of like, okay, well, people are relying on those as their thinking partners. So, like, is this going to sway the responses? I think it says, like, you Know, it's not going to change how it responds to people based on, you know, if there's monetization shown right below there, it's interesting to think about. It's like, okay, so is the AI going to not give you the full picture?

Right?

Yeah, Unless you're paying more for the tier of that model. Yeah.

I don't know where they're going to take it. You know, like, they claim they'll never go down that path. However, there are talks about OpenAI ipoing, right? Like, the talks are Q4 of 2026, if I had to guess. It probably won't happen until 2027. But the talks right now are they want to IPO by the end of this year. Now, Once a company IPOs, they now have obligations to shareholders to increase revenue. OpenAI is a company that loses money every month, right? Like, they lose billions of dollars a quarter right now. It costs them more to run their company by a wide margin than what their company makes, which is why they need to do ads. Now, when this company IPOs, and you have stockholders and investors and people that want to see returns on their investment. Well, what happens when ads aren't doing the trick? Right? Like, when I look at these ads right here, I'm never clicking on one. You just gave me the answer I wanted to hear. And then you put an ad below it that is clearly labeled as an ad. Why would I click on that? Like, I. I'm probably not going to. So some people will, right? Like, they'll get some clicks on them, but they're also going to be the most expensive advertising platform out there. They want to charge $60 cpm, $60 per thousand views, right? So they're a very, very, very expensive ad platform. And sponsors are probably not going to get nearly the clicks they think they're going to get on these. And so what is OpenAI's options there? They either go, okay, this ad thing doesn't work and try to find a new model, or they go, how do we optimize this to make sure that more people are clicking on the damn ads? Well, they'll follow in the Google footsteps, most likely, if that's the path they go down. Right. This is what Google Ads looked like back in 2000-2010. Very clearly labeled as ads. They even had a yellow background says sponsored links. They had the sidebar ads. You knew those were ads, Right? This looks very similar to this right here. Like, this is just a sort of modern version of that. It's got a different colored Background, it says sponsored above it. Very clear. Google. Yeah, that's how it started for Google too. Right. So then we get to 2011, 2017, they've got this like yellow ad thing and this green text here, we can tell it still ads. You scroll a little further, still says ads. 2018, 2024.

Starting to blur a little bit.

It's starting to blur a little bit. It still says ad here, but the ad is in, you know, black text and starting to blend in a little bit more with the ad. Fast forward to where we are today. This is what they look like. They put sponsored results at the top with a line, and then everything below that just looks like every other search. Right. They've made it so you can barely tell unless you're really paying attention what an ad is and what isn't. And, you know, there's people behind the scenes at Google testing and optimizing, going, what do we got to do to get more people clicking on these ads so we can drive more ad revenue so we can make our shareholders happy. That's where ChatGPT, I think, inevitably leads to. If they go down this model, like, I don't think they have a choice if nobody's ever clicking on these ads. I think that they have to eventually figure out, how do we optimize to get more people to click. And the solution to that is make it feel more native to the rest of the sort of use case of the platform.

So, like baked into the responses, potentially.

So maybe baked into the responses. Maybe it's not even baked into the responses. Maybe it's outside, but maybe they remove the gray background. Maybe they remove the line that breaks it up between the actual response and the actual ad. They just slowly, slowly start to shift it to look more and more like it is part of the response.

Yeah, I think there's a toggle in the back. Right. You can turn off or on if you want your information to be used for essentially matching the right ads to you. Right, right. Personalization. So we have that ability. But still. Yeah, I see what you're saying. I think it's like the Google approach, you know, slowly start stripping away some of the obvious things and see where it goes from there.

Yeah, well, that switch that's in the background, all that's really going to do is make it so that the ads are based on previous conversations. Right. But like the ad will still show based on what you just asked it, you know.

True, true, true, true.

Right. It's just not going to look at like, oh, like two weeks ago, Joe was looking at Cialis. Let's put a Cialis ad in front of him today. You know, of course.

Nice.

It's going to use the history of what you were chatting with less if you toggle that box, right?

Yeah, for sure. I don't know, man. This is like, it's crazy because, you know, this is just. It'll be interesting how it plays out. Like you said, chatgpt is like kind of in a pinch, it seems like. Do you foresee any other revenue stream codecs, I guess with the integration, but I don't think that's going to do it for them. Right?

Yeah. I mean you're going to have more and more enterprises using the APIs behind the scenes. You're going to have more and more coders paying the higher tier plans to get access to more tokens to do more coding and stuff like that. So they do have other business models, but they've had those business models. Those exist. They're already making money off those business models and it's not covering their costs. I have very mixed opinions. Right. Like I do see the appeal of putting ads in there because like if you do have ads, then you can give better and better models to people. On the free tier. On the, they also have an $8 tier. A lot of people don't know this. They have an $8 tier called ChatGPT. Go.

What's the advantage of free to the 8, $8 tier?

The $8 model, I think just has better, newer models in it.

Okay.

And not quite as much usage as the 20amonth one. Right. So like the 20amonth does have usage limits. A, you're not getting the most of the art model. You can't even use 5.3 if you're on the $20 a month plan. You need to be on the pro plan to use the newest new model. The $20 a month plan, you can use pretty much their best models, but not the like top top. Like this is our experimental, like we're just kind of feeling this model out. But you do have usage limits. You will find a line if you're sitting there prompting every day, constantly, nonstop, you will find a line where it's like, you know, you're wearing me out. You can't use me this much. Right. The $8 model, those limits get hit a little bit faster. Right. But also the $8 model, they're going to put ads in that model too. So free and $8, you're going to have ads in both of those. And so it does democratize the ability for better models like ads do. Allow it to give you like better and better and better models in the lower and lower tier plans and make it accessible to anybody. And that I think is really, really valuable.

Absolutely.

I don't think only the people with money should have access to the best options to use AI. We don't want to like increase the sort of wealth and knowledge gap to only the people that already have money. And so I see the value of ads. I do. I really think that there is value to doing it. But I also see the slippery slope that Google fell down that I can also see OpenAI following in those same footsteps.

Facebook as well. They're not innocent. You know, we don't even want to go there. We're not going to go. But, but yes, I totally agree with you. It's like we don't want a knowledge gap because like everyone should have it's democratization of this information. Right. And like having a thinking partner and, and it just opens up so many more possibilities. And I think, Matt, I don't know if this cues us up nicely for that article we were going to talk about with basically the biggest shifts that are happening right now that most people don't know about.

Yeah. Well, before we actually get to that.

What you got?

Let's see if we solved the three body problem.

Ah, yes. Let's check in with our deep thinking partner.

All right, so I asked it to find a solution to the three body problem. To give you a direct answer, we first have to clarify what kind of solution you're looking for. Blah, blah, blah.

Oh boy.

So it gave us a bunch of numbers in math. Let's see, run your own solution. No, that's what I asked you to do. If you want to actually watch a solution to the three body problem play out right now, here's a working Python script.

Oh, wow.

It uses numerical integration to calculate and graph the exact figures. And then we've got a bunch of math. Let's see. Create a visualization of this that I can run in my browser.

Nice.

All right, while we're waiting on that, let's talk about Matt Schumer's article. I did a whole 30 minute rant about this article where I broke it down and he's sort of citing a bunch of sources, but doesn't link to any of his sources. So I went and hunted down all the sources and actually put the stuff on the screen and showed off what he was talking about. And so I have like a deep rant on this. So I'M curious your thoughts.

Yeah, I. I loved your rant. Other people have done the rants as well. Some similar, but yeah, yours is good. I highly recommend everybody go down. I thought the COVID comparison, like I said, it's a little far stretched there, but yeah, the point is, like, there's so much that general population. Yeah. Just really people who are not tracking everything on X or maybe watching your channel or this show have no clue. It's possible right now. Like, yeah, I think that's the biggest thing. Walking away and like, yeah, everybody's sharing this on X. Basically saying, you know, show this to your loved ones. Like, start thinking about, like, what are your plans kind of thing.

Yeah. I feel like anybody that's like actively tuning into this show or actively watching my YouTube channel, this article is not necessarily for them.

No.

Right. They're the people that are paying attention. They're the ones that are tapped in that are going, oh, man, I'm seeing this all bubble up.

Yeah.

The whole idea of this article was, hey, go and share this with other people who aren't paying attention. Let's do like a quick tldr. Because, I mean, this is a monster of an article here.

It's a Hoven article, but it's worth going through. But yeah, the tldr, he also wrote it with AI.

He admitted to that on the TBPN podcast. He's basically saying, look, I'm a coder, I have a software company. I'm noticing that I don't have to write code anymore. This is just going and writing code for me. So if it can happen to me, it can happen to you. This isn't theoretical. This is happening right now. And then he goes on and says, all the people that are saying, but I tried AI and it wasn't good. His argument is, well, if you tried AI two years ago and went, eh, it hallucinates, like, I didn't think it was that smart. Well, two years ago is like a decade ago. In the world of AI, the stuff that we were using six months ago isn't even nearly as good. We even demoed it. In this episode, we have AI solving the three body problem, writing us 3D print files, and creating apps for us in 17 seconds. It's not the same thing as it was even six months ago. Right. It's not the same thing. It was three weeks ago. Yeah. So anybody not paying attention, like, it's moving faster than you realize.

If you got on vacation, you're already way behind, so come back.

He goes into how fast Is this actually moving? In 2022, it was bad at math. In 2023, it can pass the bar exam. In 2024, it could write working software. In 2025, the best engineers in the world said they're not even coding anymore, they're just giving it to AI. And in February 2026, the new models arrived. The that are like a different era. And that's Opus 4.6 and GPT 5.3 Codex. Those are the models he's referring to. And then AI is now building the next AI. And this is the part, I think to me that is the most sort of mind boggling for people. And it was something I was talking about like three years ago of like, this is accelerating. And here's why this is accelerating is because they're using AI now to write the new AI models. So in this article about GPT 5.3. So this is the GPT 5.3 Codex launch article. GPT 5.3 Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results and evaluations. Our team was blown away by how much Codex was able to accelerate its own development. Right. And the Anthropic dude, Dario, he said the same thing about Claude. He's basically said a lot of people at Anthropic are now using AI to improve their models. So we're now in this loop where we use AI to help us write new large language models that are smarter and better than the previous large language models. And then these new models then go and help write the code for the next large language model, which is even smarter. And now this smarter model is writing code for the next smarter model. So like this flywheel that was spinning kind of slowly for the last like, like three years is now like picking up speed. And now these AI models are writing the code that's writing the next AI model, which is a better AI model, that's writing the code for the next AI model, which is now another better AI model, which is Right. And it's just like this flywheel is going now and these models are improving at a rate of like just insane exponential speed now.

Yeah, it, I mean, it just said it right there. I don't know. Is this AGI, Matt? I don't know. Are we there? No, I know we're not there. Yeah, it's interesting back to kind of like the thread I've been bringing about. The human part of this all is almost like the Human setting the constraints maybe on what these new models can or cannot do. It seems like probably is where the role of the coders or engineers are working more with these days. It's like mapping out all right. But having these models just literally build themselves is the future. Yeah.

I mean, it's still kind of the taste thing, right?

Sure.

It's still. The engineers still need to tell it where to go next. The engineers still need to tell it, here's what this model was good at, here's what it's not good at. Like, I don't think these models are self reflective enough to go like, here's what we screwed up in the last model, so let's go make it better in this model. I think it's still the engineers going in and saying like, hey, this is what you screwed up in the last model, here's what you need to fix in the next one. So there's still like, like that element. But then the point that he's also making in this article is these models are starting to feel more and more like they have a better sense of taste. Right. They have a better sense of, you know, what needs to be done next without the user prompting it of what needs to be done next. Right. They're getting a better idea of like vibes and what the visual should look like. They double check themselves on mistakes and then go back and fix their own mistakes once they've realized they've made those mistakes. And so we're starting to see like sparks of that. The thing that we might call taste, the things, the intuitions that humans have that the LLMs don't necessarily have, we're starting to see sparks of those intuition pop up.

Yeah. Line to blurring even more. Continue here on the tldr. What do we have here?

So he goes on to say, what does this mean for your job? And basically saying like, look, if it can figure out coding and it can figure out all of this stuff, it's coming for your finance job, it's coming for your medical analysis job, it's coming for your customer service job. Like, basically these models learn to get really good at code first because code was the tool that opens up everything else that's after it. Right. Like the next frontier of insane large language models that can do anything you want isn't just shoving more training data into these models and trying to say, hey, get smarter. The next sort of frontier of these large language models is getting them to be better and better at using tools on your behalf. And if it doesn't have the tools to use. Creating its own tools. So you tell it, hey, go solve this problem. And it goes and tries to solve it, and it says, hey, I don't know how to solve this, but I can make a tool that knows how to solve this or a tool that helps me solve this. And then it goes and builds that tool and then uses that tool to help get to the problem. Like. Like, AGI doesn't come from large language models alone. AGI comes from large language models that can use tools, but also create the tools that it then goes and uses.

Right. It's resourceful to figure out what the gaps are, connect all the dots, and give you the thing, whatever that is.

Yeah. It figures out its own bottlenecks and then creates tools to solve those bottlenecks. And so anybody that's in any industry going, this isn't going to impact me. Well, it probably is. I think the safest industries right now are skills like real world outside, away from a computer skill.

Be a plumber kind of thing or electrician.

Plumbers, electricians, barbers, you know, those kind of people. Like, do you want a robot with scissors to your head anytime soon? I don't. Yeah.

Do you want that shave on your neck with a robot?

Yeah, you know, athletic stuff. Like, I. I mean, a part of me does want to watch robots play baseball against each other, but I also think that would only be novel for a little while and then we'd all get bored of it because they all have the same level of skill.

Right. Like, perfect. Yeah. Ye.

Yeah. So it's like, the idea of it sounds cool, but, like, people would get over it. We want to see humans do a lot of those feats. I still think it's going to be a ways off before people really want to go and watch a movie they know was generated by AI. I still think we'd like to see actors doing their thing really, really well. I do think that gap will close over time, but I think it's still a little further off than most people realize where we're going to go. Oh, man, I really want to go see that new Star wars movie that George Lucas typed with one prompt like, no, I want to know that a human wrote it. The fact that I know that somebody put that much effort into making this thing is part of what makes it special in my mind.

The credits will just say prompted by pop pop.

Yeah, yeah.

So, yeah, and that's the point of this article, right? Is to basically shake people and say, here's what's happening. If you didn't know.

Now you know, that's pretty much the tldr. We kind of went over it. There are some analogies that I think are not great. Right. Like he says, this is like Covid, where we were seeing it overseas for like, a couple months, and we're like, oh, that's over there. That's not going to affect us. And then the next thing we know, it's here and we're shutting down and the world changed forever. And he sort of compared that to this and also said that, like, Covid was on an exponential growth curve. But no, it wasn't. You can't have an exponential growth curve when there's only 8 billion people. Like, at some point, the curve flattens out.

Unlimited people. People.

You can't give Covid to more people than exist.

Yeah.

So, like, it's not a very accurate comparison in that way. Also, AI is not sneaking up on anybody. Like, if you turn on your tv, you can't not hear about it. Right. So it's like, it's not creeping up on us. And people are going, oh, that's over there. It's never going to impact me. People know it's coming.

Right.

I do think there's a lot of people that are thinking, oh, it's not going to impact me. I do think there's a lot of people that are putting their heads in the sand and going, oh, I hate AI. It has no usefulness. I'm never going to use it. It's stealing and screw it. I'm not going to use it out of principle. I think there's a lot of people doing that, and I think those types of people are going to be in for a rough ride. But it's not sneaking up on anybody. It's not spreading like a COVID virus where one day you don't have it and then today you do. So I do think there's, like, some analogies that are like, okay, that's kind of off, but I get what you're trying to say. Like, this is coming. It's coming faster than the majority of the world realizes. And what you need to be doing is paying attention and not putting your head in the sand. Right. Like, that's the TLDR of it all.

There you go. Just become aware. Watch Matt's video on this. If you really aren't sure what, you become aware.

Like the AIs are doing right now, they're becoming aware.

Creepy. Should we check in on our experiments? Yeah.

Which one do you want to know first?

The brain. I Feel like that's the easier one.

Okay, let's see. So what do we got here? Go to image to S. It's telling me what to do.

Oh, we don't want to do. We just want to be given the thing and go print.

Okay. Okay. You know what? I'm done with this.

We're trying too hard.

Their demo basically showed them giving an image and saying, turn this into a 3D printable file. I did the same thing, and it's giving me step by step instructions on how to turn it into a 3D printable file. It's telling me to go use a background remover and then upload it to image to stl.

Oh, wow. Come on, Gemini.

Like, I knew I could have done that. I've done that kind of thing before. I wanted you to just do the whole thing for me. All right. And then our three body problem, it did write some sort of visualizer. Let's see. Copy the code below, go online, sandbox to, like, codepen, and paste it directly. Oh, okay. Let's do that. I'll open up codepen. Okay, Let me just paste it in here. What happens? Ooh, there's our visualizer. Look, it solved the three body problem.

We did.

We solved it. It's officially solved. You're welcome, science.

We're done here.

Where do I apply for the Nobel Prize?

There's written form somewhere. Where do we go?

It is a pretty cool visualization, though.

I like it. Do the colors mean something? Let's see. Does it?

I don't think so. I think it's just trying to show you that if you've got three planets, if they move in this pattern, then they never have Too negative of an impact on each other, I guess.

Yeah, it's like perfect balance.

Yeah. So there you go, man. All right, I'll have AI write up a white paper for me, and then I'll send it to archive.org and then I'll send it in for the Nobel Prize. And we did it.

Just put this episode as a credit to show our work.

We did it.

Go, team.

So here's a complete, standalone, interactive visualization of the three body problem. The stable figure 8 orbit that you can run directly in your browser. Okay, this is just telling me how to run it.

Yeah, it's not explaining it.

Okay, what's happening under the hood? The math engine. To prevent the orbit from instantly degrading into chaotic mess, the script moves the three bodies forward using an incredibly tiny slice of time, the simulation loop. To keep it from looking slow to the human eye. The script secretly runs the complex calculus function 15 times per frame before finally updating the visible image on your screen.

Oh, it's common in game development. It says.

I mean, it did a bunch of math. I just don't know how to understand the math.

Can we ask it?

Okay, here, let me take it out of deepthink so it goes faster and then say, explain this. Like, five.

Oh, man. Now we're really. I'm on it.

Check back later. I set it on fast mode, so hopefully. All right, imagine you and your best friend are holding hands and spinning around in a circle.

It's pretty easy, right?

Okay, got it. You just go round and round, and you always know exactly where your friend is. In space, this is what happens when two things like Earth and the moon pull each other with gravity. The three body problem. Now imagine a third friend runs up, grabs your hands, and tries to join the spin. Suddenly, everything gets crazy. You're pulling them, they're pulling you, and the third friend is pulling both of you at the same time. Someone gets yanked too hard, someone slips, and I'll tumble into a different wild directions. Because everyone is constantly pulling everyone else, it's completely impossible to guess exactly. So the figure eight, the magic trick. But what if you and your two friends practiced a very special dance for a really long time? You'll discover a secret trick. If you all weigh exactly the same, start the exact right spots and run at the exact perfect speed. You don't crash. Instead, you beautifully chase each other on an invisible track shaped like a giant number eight. This is what the computer animation I gave you is showing. It is a rare magic trick in space where three planets perfectly balance their gravity so nobody trips how the computer draws it. Because there's no magic math rule. To jump straight to the end of the dance, the computer has to draw the animation using tiny baby steps, et cetera. So I don't really think it solved any problem. I think it found, like, a known solution of, like, if everything was moving the same speed and everything had the exact same force of gravity and all of these things aligned perfectly, then you can get this figure eight dance thing. But in reality, isn't the three body problem much more complicated? Because there are different speeds and different forces of gravity and different, like, factors. So I don't really feel like it solved anything. It's just saying, like, there's this one scenario where if all of these being true, happen, you can get this figure eight to happen.

I think we still apply for the award.

Okay, I'll Submit.

It's gotta submit and see what they say.

All right, I got my buddy Demis, he won a Nobel Prize. So I'll just send it to him and be like, hey, can you forward this for me?

Give me it, man. Help me out.

Hook it up. Hook it up with a Nobel Prize, right?

Hook it up.

So I think we covered a lot of ground on that one. This was a different style episode. We kind of did this style of episode last week with me and Maria where we picked five interesting things and just sort of ranted on them. Let us know what you think in the comments. Do you like finding out what happened last week and hearing our little rants and rabbit holes about what we really think and what it really means for everybody? Because this sure is heck fun to make.

Yeah, there's always news. And Matt, you're on the leading edge of it. It's fun to debate and go deeper on here.

I try to stay tapped in. I like to pretend like I know what I'm talking about. I often don't, so don't fact check me on anything. But I have fun talking about it and I hope to do it again next week. So if you like an episode like this, let us know in the comments what you think about this kind of thing. And if you haven't already, make sure you like this episode and subscribe to this channel wherever you listen to podcasts, YouTube is probably the best place because then you get all the visuals. But subscribe to this podcast wherever you listen to podcast. And thanks so much for tuning in. This was a fun one.

Thank you. It.