AI Tool Better Than OpenClaw? + NVIDIA’S $1T Prediction & AI Image Wars

Hey, welcome to the Next Wave Podcast. I'm Matt Wolfe, and I'm joined once again by Joe Fear. And we've got a bit of a special episode today. This is going to be our last episode for a little bit. We're going to be taking a little bit of a break on these episodes, but nonetheless, we're going to dive into all of the crazy AI stuff that's happened in the past week, share our thoughts, break it all down, tell you what it means, and, uh, even share how you can use it in your business and for your marketing. We've actually got one really, really cool tool that I'm excited to dive into that actually kind of blew me away with like how helpful it is for businesses and marketers and things like that. So really, really fun episode, a little bit of a bittersweet episode, but fun nonetheless.

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Let's go ahead and dive right in. Joe, where do we want to start today?

I think we start with where you spent most of your week at, right? Up in San Jose at NVIDIA's GTC conference. Feels like everybody in AI was there for the most part. Wasn't a lot of Twitter action, it seemed like. It seemed quieter than normal.

Or X. Yeah, because everybody that would normally be tweeting was sitting around inside of sessions and walking the expo floor and that sort of thing. But yeah, I mean, NVIDIA's GTC conference, I've heard it described as the Super Bowl for AI. I've also heard it described as the Burning Man for AI. What you want to call it, I don't really care, but basically like everybody in the AI world is there because it revolves around the one company that AI revolves around. If we look at AI as a solar system, the sun of that solar system is pretty much NVIDIA, right? Like every company sort of rotates around that one company these days.

Seems like it. And like even in the presentation, what Jensen was saying, which I know they're hours long. So, you know, that's probably why no one was tweeting either, watching him for all day long. But it showed how implanted NVIDIA is in pretty much every part of AI. Yeah. And I know that's something we'll also talk about, like how the future of NVIDIA doesn't seem to be slowing down at all.

I don't think so. I know there's obviously bull cases, bear cases. We're not a financial show. We're not going to get into like, you know, what the stock price is going to do or anything like that. But I don't think we're as bubbly in the AI space as what a lot of people are sort of making it out to be, right? Like, I do feel like there's been this narrative for months, I mean, if not over a year now, where people have said we're in a bubble, it's about to burst. I mean, I don't know about that. I still feel really, really good about where AI is and where it's heading from like that standpoint.

So I guess I'll ask you because I wasn't at GTC and you were in there as much as, I mean, I know there's a bunch of people and you probably couldn't be everywhere, but what's your feeling walking away from there? Is there a big takeaway or something that maybe changed your perspective?

Uh, so I don't know if I had like any specific big takeaway from that event that changed my perspective. I definitely had a lot of conversations with people sort of behind the scenes and people that are deeper in the space. During the keynote, Jensen actually mentioned that like the biggest majority of people at that event were in the finance space because there's a lot of financial analysis analysts there talking about NVIDIA and trying to figure out like where NVIDIA is going from here. And so I got a chance to talk to a lot of these like people behind the scenes. And, um, a lot of them made some really, really good points for why NVIDIA is still gonna continue to grow. But there was also one point in the keynote where Jensen himself made a comment about how in the past year they've done a half a trillion dollars, $500 billion in chip sales alone, right? But between now and the end of 2027, They expect that to double to $1 trillion. And he actually said that he thinks he's being conservative on that number. He thinks it's actually going to be bigger than that. And I sat in this press Q&A session as well. There was like, I don't know, maybe 100, 150 people in the room, but it was like a press-only thing where we were able to ask Jensen questions. And somebody asked him about that trillion-dollar number and where it came from. And he said that number was purchase orders. That number is companies actually saying, we want these chips. Like, here is our sort of letter of intent to buy them when they're available. He basically said that $1 trillion number is from people saying, we're ready to buy as soon as it's available. That is insane to me. Hey, if you take a look at my web presence online, it's safe to say that I'm a bit AI obsessed. I even have a podcast all about AI that you're watching right now. I've gone down multiple rabbit holes with AI and done countless hours of research on the newest AI tools every single week. Well, I've done it again, and I just dropped my list of my favorite AI tools. I've done all the research on what's been working for me, my favorite use cases, and more. So if you want to steal my favorite tools and use them for yourself, now you can. You can get it at the link in the description below. Now back to the show.

I'm kind of assuming because of what I saw, but like, where is that growth going to come from? Like, I'm just thinking it's the whole like agent rush. It seems like, you know, and the need for more chips to supply that and probably everything else built around, right? The infrastructure.

Yeah. So there's a couple of things, right? You've got the agent stuff, like one of the big, big, big topics of this whole event. Jensen dedicated a good like 20 minutes of his keynote to OpenClaw specifically. There was a whole bunch of breakout sessions. They had a whole booth called Build a Claw where you could go and have them help you set up an OpenClaw agent. This OpenClaw thing was such a big topic of conversation during this whole event. And he basically described it as like, this is as big as like the web browser was to the internet kind of thing, right? Like, This is the thing that makes agents accessible to everybody. Throughout the last, like, year and a half, agents have kind of existed behind the scenes, but they've been, like, more enterprise, or they've been for, like, real techie people that needed to understand how to, like, do some really complicated stuff to set them up. And now with OpenClaw, it's become sort of accessible to anybody, right? You, you type one line of code in your terminal. Which, you know, some people opening up the terminal is scary, but it's really easy. You open up one app on your computer, whether it's a Windows or a Mac, you type one line and you have OpenClaw set up and it just is there and ready and it will go and do tasks on your behalf. Right. And so he sees that sort of agentic use case where everybody's going to have like this personal assistant going and doing tasks on your behalf and like double-checking their work and maybe helping manage your email and like everybody's going to have their own personal assistant as one of the big drivers of growth for AI. Another thing is we broke down last week, or maybe it was two weeks ago, but we broke down the various phases of how AI models get trained, right? You've got your pre-training. That's the really, really expensive, you know, takes months to do. Companies will spend like $100 million to do this pre-training process. But once that pre-training process is done, then they go to post-training and they do things like fine-tuning the model to get it to like answer like a chatbot. And then you have reinforcement learning with human feedback phase where now we're basically telling it what we like to hear and don't like to hear and sort of giving it feedback on its responses so that it gets closer and closer to what we're looking for. Well, going into the future, less and less of the compute power is being spent on this pre-training process, right? There's only so much data on the internet. There's only so much data that we can scrape and put into a model, right? So that pre-training process is becoming less and less important. We've kind of gotten to a point where we've shoved as much as we possibly can in it. So it understands patterns now. Like that pre-training thing is kind of like we've got that figured out. So now almost all of the AI movement that's going to happen. Most of the next advancements that we're going to see are going to come in that post-training process through fine-tuning and through reinforcement learning, but also through actual inference, right? Like when you go and type a prompt into an AI model now, you can actually see that thinking process. That's called test-time compute, where you give it a prompt and it actually thinks through the response, thinks through the response. Almost like debates itself a little bit and then finally comes out with an answer. And so because a lot of the actual like AI use is being moved to that sort of final part of the process, or would that be the front end? I don't know. The process after the pre-training, like more of the AI compute use is being moved towards that section. And that is only increasing demand for AI because now you're getting more enterprises wanting to build AI in-house, like on-premises, right? They're wanting to build their own like mini data centers in-house to run local models. They're getting more and more demand for these chips because of the agentic use case, because of the fact that everything is moving from pre-training to these post-training processes and the test-time compute processes. So there's like a whole bunch of sort of pieces coming together. That make it so like NVIDIA was super relevant during the pre-training phase. And now because we're moving into this next phase, well, they're super relevant in this phase and they're building out new infrastructure and new chips that are more optimized for this post-training phase and for this inference phase. So anyway, that was a long answer to your question.

Well, it goes back to, I think even what we talked about last week, which I forget what you called it. You might remember. I think it's 8 different layers of how computing essentially works.

And yeah, the layers of abstraction. Yeah.

Yes. And then there is something in Claude when we were going through the visualization, we're showing how AI is like getting used throughout the layers. And remember it was working from the top down and it was like 3 or 4 layers down at different percentages. But I think it's kind of like what you're talking about right now. You just know more computing power and, you know, AI technology stuff like NVIDIA is basically planted into everything affecting every layer.

Yeah, sort of, sort of. There's some layers which I don't think AI is ever going to really touch. Like it's obviously never going to touch the sort of transistor layer. It's never going to touch the binary code, right? AI itself can't just look at the ones and zeros and then figure out what all of it means, right? You need the layers of abstraction beyond that. And the AI is going to sort of understand, you know, whatever, like 3 or 4 layers deep, but won't really try to go deeper than that, at least not for a long, long time. Maybe once you get into like quantum computing and stuff, but not with like any of our current models. But yeah, it's moving further and further down those layers of abstraction. But at the end of the day, like every phase of AI buildout needs compute, right? We had the pre-training phase where we were pre-training these models on, you know, an entire scraping of the internet where we're getting like trillion parameter models and multi-trillion parameter models. Out of like scraping the internet. But now the new phase is we just need to like better fine-tune these models to respond the way we want them to. We need to do more reinforcement learning to get them to respond the way we want them to. We need to get the test time compute where you ask it a question and it thinks. Well, now they're starting to use these chips like the Cerebras and the Groq chips, which are designed for really, really, really fast inference, meaning that it does that thinking a lot faster. So right now, let's say you ask it a question and it thinks for like 15 minutes, but it gives you a really, really good answer. Well, now with these newer chips like Groq, which is a company that Nvidia kind of sort of acquired, it's a weird thing where they have a licensing deal, but they ended up hiring over the people who built it. It all looked very much like they were trying to get around regulations and antitrust laws, but that being said, basically NVIDIA owns Groq and Groq, G-R-O-Q, Groq makes these inference chips. And so it's something that thought for 15 minutes, Groq can probably think at that same level, the same amount of thinking in like 10 seconds. So now imagine if you're giving these Groq chips 15 minutes to think, now it's thinking so much more in that same amount of time. So you're going to get output that's similar to like a model that thought for 2 hours, but in 15 minutes, right? So like that, because we're speeding up the thinking, we're speeding up the inference, you can get a lot more thinking in less time. Now start to imagine where you let a model think for a whole day on like these Groq chips. That's like the equivalent of letting a model think for a month with the previous chips. So like at every phase of AI, whether it's the earlier phases where we're doing the pre-training, whether it's the post-training, whether it's the reinforcement learning, whether it's the test time compute at inference, all of these, every single phase requires compute. And who's the biggest provider of that? Who's the one who bought up all of the RAM chips so we have a shortage? Who's the company that makes all of this available to us right now? NVIDIA.

Those guys. And it seems like they are going for the whole OpenClaw thing we've been talking about for a while now. I know they released NemoClaw, or at least they talked a lot about it there. And as you're talking about all this inference, you know, basically getting shorter and quicker and able to think longer with the same, it just goes to think like now you can have your agents basically run longer in the background for cheaper to do better work on your behalf. And it goes back to your whole thing about personal agents for everybody. And now here's NVIDIA that released an option to basically make it easier and more secure. Right?

Yeah. Yeah. So real quick, like Nemo Claw is, it's basically OpenClaw, right? So before GTC, there was a lot of speculation that NVIDIA was about to launch a competitor to OpenClaw. That's not what it is. It's a wrapper. I don't know if I like that word, but it's like a sort of packaging of OpenClaw. So what they did was they took OpenClaw, they made it really easy to install, like one line of code in your terminal to install. You run that line of code and it installs Nemo Claw, which is a bundling of OpenClaw plus a bunch of extra like NVIDIA bolt-ons, right? And these NVIDIA bolt-ons are things that help make it better for security, better for privacy, make it easier to install NVIDIA's open-source models. They have a model called NemoTron 120B, which is a model that's really good for agentic use case. Like on benchmarks, it scores pretty close to Opus and GPT-5.4 and like all the state-of-the-art models. but it's an open-weight model. And so Nemo Claw makes it really easy to install that. So Nemo Claw is like this bundling of OpenClaw plus these extra NVIDIA security, privacy, LLM add-ons, right? So that's what Nemo Claw is. And Jensen and NVIDIA are really, really betting on the fact that the whole world is going to sort of jump on board this like OpenClaw, Nemo Claw, like have your own personal assistant, right? Like, They look at something like Siri or Alexa and they see like, okay, those are kind of like dumb AIs, right? They'll Google something for you or they'll look at your calendar for you real quick and tell you when something's coming up, but they won't do much else than that. And people still use these like crazy. And they're betting on the fact that in the future, your iPhone, your Android phone, like your everything around you, all of your devices are going to be attached to an OpenClaw. And you're just going to have an assistant like a Siri or Amazon Alexa, and it's going to be able to control anything in your life, right? It's going to be able to control your toaster, your refrigerator, your TV, your everything. And what does all that stuff need? Compute.

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So it needs a communication device of some sort right now. It's what Telegram and Slack and all these other places, but what's to say in the future, things like Siri or Alexa or all these other spots, shoot, someone might already be hacking that right now.

Yeah. Well, I mean, Amazon today announced that they're building an AI phone. Powered by Alexa and the newest version of Alexa. And I'm sorry to anybody who's listening and we're setting off your Alexas right now, but Amazon's actually building an Alexa-powered phone. And there's also Alexa Plus, which is the most recent version where it's got more of these extra features. And I do think Amazon in the future wants to, you know, have more agentic use cases, right? Like they already want you to control your home by saying, hey Alexa, turn on my lights. Hey Alexa, turn my TV on. Hey Alexa, whatever, right? So what enables that even better and better is something like OpenCLAW. And NVIDIA does work with Amazon. Like that's another big piece of this pie is how integrated NVIDIA is into like every company you can imagine.

Yeah. It's not really the AI side. It's literally every company out there. Yeah.

So like NVIDIA, they work with Google Cloud. NVIDIA works with Amazon AWS. NVIDIA works with Oracle. NVIDIA works with CoreWeave. NVIDIA works like You name it, Nvidia is like touching them in some way.

Nvidia gets around.

Sorry, I didn't mean it like that.

But there's a reason why, and it's wild. He said by the end of 2027, that is about a year or so, a little more, that that's doubling the company. And yeah, yeah.

The other thing they said during their Q&A, he didn't say this in the keynote, but during the Q&A, somebody actually asked, and this is going back to like why I still believe in like the Nvidia stock. Somebody asked, are you going to actually do buybacks? And, uh, Jensen basically said, you know, we've got a giant war chest. We've got just a ton of cash just sitting there that we need to deploy. And our plan over the next few years is to basically give that money back to investors through A, buybacks and B, dividends, right? And it's basically the Apple playbook. Like if you look at Apple, Apple's stock continues to do well, even though their revenue numbers haven't been growing, like the iPhone sales are not great. I don't want to say Apple hasn't been doing well, but the revenue hasn't been like on this sort of like hockey stick exponential trajectory of sales, kind of like Nvidia's has, but Apple sort of has this policy where they constantly buy back stock from the actual investors, which makes that very, very valuable because the supply of stock continuously squeezes and squeezes and squeezes, making the stock still something really, really valuable to own. Well, Nvidia is basically saying we're going to use that playbook. We're going to go and we're going to buy back stock and we're going to give out more money in dividends to our investors. And we're going to actually take all of this capital that we're sitting on. He literally said we're going to take 50% of our free cash flow and we're going to use it towards buybacks and dividends. So like they're making their stock price look more and more valuable by the day, not only in the fact that they have POs for this many chips, but in the fact that they're going to start taking all of this cash flow and giving it back to investors as well.

That's wild. Well, I know another of the big topics that was discussed, I think it was on the keynote, was the whole like space computing concept. And that's something that I know we've talked about before. You've talked about it on your YouTube channel a lot. Thoughts on that? Because I don't know what the timeline was on this specifically.

They didn't give one.

No. Okay.

No. So basically what they said was that they're working on it, right? Here's the summary. It's engineered for size, weight, and power constrained environments. NVIDIA Space One, Vera Rubin module, IGX Thor and Jetson Orin, uh, blah, blah, blah, blah, blah. AI inferencing for orbital data centers, uh, geospatial intelligence and autonomous space operations. Basically, they're saying we're building these GPUs to be able to work in space. But Jensen himself admitted the exact same thing. Like we did an episode, I don't know what, 4 or 5 weeks ago where we actually talked about these data centers in space. And I kind of went off on a rant about like, here's why we're not as close to that as everybody thinks we are. And the main issue being heat dissipation, right? Space is a vacuum. GPUs put off a lot of heat. The sun puts off a lot of heat. The whole idea of putting data centers in space is that you can kind of keep them in a spot where it's always being seen by the sun. So it's getting solar energy nonstop, 24/7, right? The satellites can sort of move and stay in contact with the sun. So they're always getting power all the time. Well, that causes additional heat problems, right? It causes problems of, well, the sun is hot. And then, you know, you've got your GPUs, which put off a lot of heat. That's why there's all sorts of discussions about how GPUs and these data centers use so much water. They use the water to cool off the data centers, to cool off the GPUs, right? So these GPUs put off a lot of heat. The sun puts off a lot of heat. And in space, it's a vacuum. Like heat doesn't just sort of like, you know, move away from them in space because it's a vacuum. There's no way for the heat to just like flow off of the GPUs, you know? So they haven't figured out that problem. And in Jensen's keynote, He said as much. He said, you know, we're trying to figure out how to do this. Like, you know, we've obviously got the rocket ships to put them there. We've got the GPUs to do it. We've got the solar panels. We've got all the pieces. The one missing piece is how do we dissipate the heat off of these GPUs? And this is a very, very small portion of the keynote. But at the very end of the keynote, he said, you know, we have our best engineers working on it and we'll update you on that soon. But he didn't give a timeline because they literally don't know a timeline because they don't have a solution yet.

Well, it seems like, you know, because I know Elon talks about the same kind of thing too. And my guess is they're probably working together somehow. I'm not sure.

Yeah, I don't know. I mean, they definitely work together in some ways. I know Elon buys chips from NVIDIA. That's definitely true. And Jensen and Elon are friends. They're not adversaries like, uh, Sam Altman and Elon are, but what's interesting is Tesla makes like their own chips. They're like one of these few companies that when it comes to like their vehicles and stuff, they're actually designing their own hardware. They're not actually using Nvidia stuff inside of like Teslas. So I don't know. I don't know if Elon is actually trying to build his own GPUs to put up into space or, you know, most likely what will end up happening is if Nvidia develops them, they're going to work with SpaceX to get them up there. Right. So they're going to be working on a partnership, at least in that sense.

We got to figure out who's going to break the code, you know, of the whole heat dissipation thing. I don't know if it's one of them or if it's some other company we haven't even heard of yet.

Yeah. Maybe they got to immerse them in giant fish tanks and then put this watery fish tank up into space with the GPUs inside of it. I've also heard like a potentially more viable route is actually putting data centers in the ocean. Because then the cooling is fairly solved, right? But that would also likely heat up the ocean and cause other environmental issues. And yeah, I don't know.

Yeah. All right.

TBD. But yeah, if I was to point out like overarching themes of GTC, I would say like I was blown away by how many companies NVIDIA sort of has their hands on, right? Like every single cloud computing company pretty much uses them. Every single robotics company pretty much uses them. Jensen actually won the Motor Trends Man of the Year award this year. I actually saw them hand them the trophy in person while I was at this private Q&A because of all the work he's doing in autonomous vehicles. You know, they're doing stuff in life science. They're doing stuff in networking and telecom. Their telecom business alone was something like $35 billion last year. Everybody looks at NVIDIA as like, they make the compute, they make the GPUs, but like, they have so many other businesses. Yes, the GPUs like overshadow that. And that's what everybody talks about because AI is the biggest thing on the planet right now. But NVIDIA is more than just GPUs. They're networking and telecom and they actually have software. They're building these NemoTron models. They have like their whole Omniverse. They do all the sort of like AI training inside of virtual environments. Like they are just like this massive, massive ecosystem, but everybody just sees them as the compute company, which, you know, it's understandable because that's like 93% of their business. But, you know, they still have like billions and billions of dollars coming in from all of these other revenue sources.

It's just wild.

Yeah.

I mean, the, the company, how it's evolved and yeah, I don't know if anyone has ever like listened about the whole history of NVIDIA and Jensen. Tons of podcasts, but I know he was even on Joe Rogan and he was breaking all this down. I thought it was super fascinating. I know the All In podcast, you know, he was on there recently, talked about stuff like SaaS too.

Well, cool.

NVIDIA GTC. Yeah, I think it's pretty cool. And all the keynotes are online too, which is awesome. So yeah. Yeah.

And like, again, if you want to play with OpenClaw, if you've seen like all the buzz about OpenClaw all over social media and YouTube and stuff, but you're like, oh, that looks really unsecure and I'm worried about it, look into the NemoClaw version because supposedly it patches a lot of the security and vulnerability issues and it handles a lot of that kind of stuff for you.

Cool.

I mean, I'm in on that because yeah, I've been a little nervous to jump in.

Yeah. Yeah.

And that's available right now. Yeah. I could see you could try it right now. So that's cool. So check this out. We have new opportunities now for work in the real world, right, Matt? We were talking about maybe this is where some new positions or new jobs will be created for better or for worse. But let's talk about what DoorDash is now doing with DoorDash Tasks.

Yeah. So with this DoorDash Tasks, basically it's like a way to just like earn some side hustle income by doing like little things. Like it seems like it was designed to like, you go to a restaurant and you take pictures of like what you ordered so that they have, you know, pictures of that food item online so people can look it up. Or like you go to a grocery store and you take pictures of like the shelves to make sure like the items are stocked or whatever, right? Like that's kind of like the initial sort of pitch of this product. Like that's how they angle it in like the first few paragraphs. But if you scroll down on this article a little bit here, this paragraph is where it gets particularly interesting. We're also piloting a new standalone app where dashers can complete activities like filming everyday tasks, or recording themselves speaking in another language. This data helps AI and robotic systems understand the physical world. Pay is shown upfront and determined based on effort and complexity of the activity. So basically DoorDash is saying we will pay you to train our AI systems. Like that to me is implicative of like where I think the world is going. And this is something that, you know, you and I, Joe, we've had conversations about this offline in the past. But there's this whole talk about like, okay, if AI does get to AGI, well then that theoretically means AI can do like all meaningful work for humans, or at least it's getting close to it. We have to obviously be able to like embody it inside of robots and stuff if we wanted to get to like plumbing and construction and stuff like that. But at least from like a knowledge work perspective, it can do like almost all knowledge work. And so where does that leave the economy if we can't make money anymore? And a lot of people speculate, well, we need some sort of like UBI, universal basic income, where the government just gives everybody a sort of stipend every month. Like, here's a check that you get and this is your spending money every month, right? Like, that's one of the theories, one of the sort of suggestions of what direction this could potentially go in. And I've always had this thought that I don't really see it going in that direction, at least not anytime really soon. I think more likely what is going to happen is you're going to see more and more stuff like this, where the big corporations are going to figure out how to pay people for their data. You know, when you think about it, humans are getting more and more sort of stingy with their data, for lack of a better word right now. We've seen all the like Cambridge Analytica stuff from Facebook, right? We've seen like, Data leaks from all sorts of companies where like your emails and passwords get leaked all the time and stuff. And so people have gotten more and more like, I don't want companies to be having my data. They're also using this data to put targeted ads in front of you, right? Like it's collecting information on you so that it can better sell you stuff. And so because people don't really like giving out their data, there's more and more sort of systems to protect your data. I actually literally use a service that goes around the internet and scrapes my data from like any list that I'm on automatically in the background so that, you know, companies stop calling my phone and stop sending me junk mail and all of that kind of stuff. There's like literally services out there that will go and try to help you remove your data from the internet. And so it's going to make it harder and harder and harder for companies to collect data to further train their models on in the future. Well, how do they get the data then? Well, they can incentivize humans by, I'll give you my data if you give me money. And so I actually think that that is a direction that a lot of these companies could end up going in, right? Like the money from AI is all going to go towards the top, right? It's all going to go to the Googles and the Microsofts and the OpenAIs, XAIs. NVIDIA is probably the very tippy top of the pyramid, right? It's all going to go up to these companies. And if these companies don't need workers anymore because AI is doing so much of the work, How does that wealth get distributed? If these companies can have less and less and less employees, the money, it's going to go to like inside the company, but not get redistributed to humans in some way. Well, this kind of thing seems like a potential way to redistribute it. Okay. Since you don't want to just freely give us your data, why don't you go out and collect the data for us that we can use and we will pay you for that. And that seems like a very incentivized way for these companies to continue to collect data, to continue to train their models, but also to get humans paid.

It's a whole industry of AI data collection, getting paid for that essentially. Uber looks like they announced in like October last year, they're doing similar stuff where it's like uploading photos, recording audios in their own language, submitting documents to train AI. Obviously that's more driving. You know, this is going to be a growing thing. Cause I'm just thinking of training data that has not reached its way online somehow, even though a lot of people have data online, probably on their websites, but it's, it's the personal stuff. It's what individuals are walking around and doing and seeing and touching and feeling, you know?

And yeah. Yeah. Well, like, I don't really think there's a data problem in the sense of text. These companies have already scraped the internet for all of the text they can get. They, They've got enough text data to pattern recognize how people speak, right? Like how people type. The text is no longer the issue. The issue now is like the next modalities other than text. It's getting these AIs to understand the physical world around them through filming videos or through your car that has its cameras and sensors all over it. You know, I just saw an AI announcement about a company that's trying to teach AI how to smell, right? Like obviously translating things into other languages. One of the things this DoorDash tasks here is like doing translations. We were even talking about how like this is a system of kind of like how CAPTCHA did it, like all over the internet. Like they have these little CAPTCHAs, right? Where it used to be like they took a picture of like a sign and then you would enter the text that was on the sign and it would let you in. Well, that served more than one purpose. The purpose wasn't just to prove that you were a human because you can read the text that was in that picture. The purpose was also you were labeling all of that data for those companies, right? Duolingo has a very similar thing. When you're actually using Duolingo, like it's training on how you're using Duolingo as well and trying to improve Duolingo's system. I'm pretty sure the people who created CAPTCHA were also the same people that created Duolingo. I could be wrong on that, but I think it's actually the same people. I know Joe's going to fact-check me now. But like these companies need data and text data isn't really all they need anymore. They need other modalities. And so that's why DoorDash is saying, look, we'll get you to translate stuff. We'll get you to, go film things in the real world. And I think that is a model that could potentially work. The only problem I have with that model is, I don't want to say only problem, there's definitely a few problems. One is like you become reliant on these companies, right? And now all of a sudden, like these companies control your income. And if you're not, you know, following their terms of service and whatever, they can just like cut off your income. That's not good either.

Privacy. How about that? Like you're literally selling.

Yeah. Privacy is also an issue. So there are a lot of problems with it. But the other problem is I also think it could be a race to the bottom. Like, because people will be willing to do this for cheaper and cheaper and cheaper and cheaper. It's not a skilled labor. So like, how much money can this really generate for people? Yeah.

I don't think it's the solution, but it's definitely something that we'll see more of to capture the data. The stuff that has not made its way into text and to LLMs already. And we know there's things out there. Just to fact-check you, there was a single person, a key founder, Louis von Ahn. So it wasn't the full team, but he came from CAPTCHA, it looks like, and was later co-founded by that same guy.

Duolingo was?

Yeah, Duolingo.

Yeah, I knew there was a tie-in between CAPTCHA and Duolingo because like the guy behind both of them a big point of what he was trying to do was like this dual-purpose system of A, data collection, and B, sort of trying to create something useful out of that data collection.

Right. Yeah. Now, this is definitely dystopian, and I don't think it would happen, at least not anytime soon. But think of Neuralink. Not everybody is having a Neuralink done. Obviously, there hasn't been that many people, but it's like, hey, you want to start tapping into the intuition like we were talking about, Jensen Huang. Last week and like, you know, the way that people are thinking without— there's all sorts of ways that maybe we can tap into data that hasn't been trained into AI yet. It's very dystopian and probably never possible.

We should have a chatbot that just taps straight into Jensen's brain. So I go to like JensenGPT and I just ask questions, but it gets the response directly from Jensen's brain.

Yeah, it's very possible.

Let's sell our brain power. Once we're out of CPUs and GPUs, we'll just sell direct access to the brain.

Someone's going to want that. We'll figure it out. You know what? I think it's time to end on the thing that we always like to bookend these episodes with, which are the weird mechanical things that are starting to look more human-like by the day. Robots. And Matt, you're queuing up a video of One that I found pretty interesting. This is a university. I have the source on the tab here, but it's called Latent Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data.

Yeah, I've actually seen some of these kinds of demos. They had like robots playing ping pong when I was at CES. I don't know if it was the same company, but like it's kind of crazy that they can do this kind of stuff.

I think this is a university that did this out of China, I believe.

Probably out of China. China is definitely the leader in robotics right now, for sure. We've talked about that in past episodes too. All right, let's go ahead and take a look.

Among all athletic scenarios, tennis is one of the most challenging tasks for humanoid robots. In fast-paced rallies, robots must respond to unpredictable landing points and constantly changing ball trajectories. Galbot introduces the world's first real-time whole-body planning and control algorithm. For athletic humanoid tennis, it enables humanoid robots to perceive dynamic inputs.

I think the human's going easy on the robot.

Autonomous decisions within milliseconds, still cool. Making a fundamental transition from mechanically imitating motions to intelligent decision-driven responses, our algorithm enables humanoid robots to sustain continuous rallies without interruption, marking a leap from reactive to individual shots to handling long-horizon interactive gameplay. The learned policy also demonstrates strong robustness, performing reliably against opponents of different ages and playing styles while maintaining high stability in real-world gameplay.

Interesting.

So Matt, I know we're going to start playing pickleball next week, so we need a pickleball robot.

Pickleball robot. No, that's pretty cool. Like, it's definitely blowing my mind where we're coming with robots. I've said this on past episodes about robots too. To me, it feels like the mechanical aspects are there, right? Like it's like these robots, we've seen videos where they're doing backflips and they're doing like dances and they look like ninjas and they can play tennis now and like, The mechanics of these robots, like it feels like we can get them to do any sort of movement you can imagine now, but the brains haven't caught up yet. Like the large language models and the AI models that are baked into these robots aren't quite at that point yet where like it can go and do your plumbing for you or, you know, whatever those physical tasks are that our chart earlier showed you're still kind of safe with, you know.

This one. Yeah, the fact that it's— but I don't know if it's learning in real time. It seems like it's collecting data is what it said of the game and like what's happening. So the backflips and all the stuff you were talking about was all pre-programmed because they're like super synchronized.

Yeah, yeah, that wasn't like an AI model like thinking, oh, I'm gonna go do a backflip and show off right now. It was like, it was either pre-programmed or somebody had a controller behind the scene, you know.

Bingo. And this one's just literally Here we go. All right. Let's play a game of tennis and see where this thing goes.

But yeah. Yeah. So that's an example of narrow AI, right? Like as an example of like they've trained it specifically on tennis, but if you took that same robot and you tried to get it to play chess with you, it'd probably really suck at that. Yeah.

So keep an eye on the robotics. It's quite interesting.

Yes, sir. Yes, sir.

Good way to wrap this episode.

This is a good way to wrap this show for a while. For a while. Yeah. So again, as we mentioned in the beginning of this show, this is going to be the last one for a bit. We're going on a bit of an indefinite hiatus. And what better way to show robots than to show robots playing tennis to wrap this up?

Great way to end cap this thing.

And so, you know, everybody who's stuck with us and hung out for this show and tuned into past episodes and nerded out with us for the last however long it's been, year and a half, 2 years that we've been doing this show, we really, really appreciate you hanging out and learning with us. It's been a super, super fun journey, and hopefully we will pop up in some other way, shape, or form in the future and continue to do some awesome long-form nerding out with you. But thank you so much, everybody, for tuning in and hanging out with us. It's been a fun ride. And Joe, thanks for being a great co-host for the last several weeks.

It's been fun, and thank you everyone for watching and having fun with us and this time with us. So, yep.

All right. Well, on that note, we're going to go ahead and sign off. Thanks again.

Adiós.