Limitless: An AI Podcast - This Week in AI: NVIDIA Wins CES With Rubin and Alpamayo, ChatGPT Health, Anthropic Raise

Episode Date: January 9, 2026

CES 2026 is here, NVIDIA's Alpamayo AI taking centerstage with its autonomous driving and its potential to challenge Tesla. We discuss NVIDIA’s unfathomable Rubin chip, OpenAI's ChatGPT Health, and Anthropic's astronomic valuation with it's recent raise.------🌌 LIMITLESS HQ: LISTEN & FOLLOW HERE ⬇️https://limitless.bankless.com/https://x.com/LimitlessFT------TIMESTAMPS0:00 CES 20266:17 Nvidia FSD9:13 Rubin GPU17:08 CES Inventions21:53 ChatGPT Health30:19 Anthropic Raise34:05 Closing------RESOURCESJosh: https://x.com/JoshKaleEjaaz: https://x.com/cryptopunk7213------Not financial or tax advice. See our investment disclosures here:https://www.bankless.com/disclosures⁠

Welcome back to the AI Roundup, the first of the year in 2026. And the first year of every year always comes with a lot of excitement for me, at least, because it is the biggest consumer electronic show in the world. In fact, that's what it's called CES 26 has come. It is on the way out. And there have been some pretty amazing announcements that have come out of it. But through all of these amazing announcements, it has become increasingly clear that almost every CES now should just be rebranded to the

NVIDIA show. and video is releasing the coolest, most badass technology every year Jensen shows up with this like really fancy, shiny alligator leather jacket and he just drops the most, honestly, the most groundbreaking new technology that we've ever seen. And this year is no different. There are two major announcements he made, one around full self driving, the other around their new chip architecture. And then also this week on the roundup, we have a few other updates, one of them being open AI's new health application. Are you going to give them all of your health data? Do you want them to know everything about you. We'll get into that, but EJas, I want to start with the autonomous car situation because

as a Tesla diehard, as someone who has been driven around autonomously for the last X amount of years, what's going on with NVIDIA and their attempt at doing this? Because it feels like if NVIDIA puts their mind to something, normally they can do it. And this is the first real attempt I've seen at someone else who doesn't actually manufacture the car, trying to build some sort of full self-driving technology. I don't think you need to start selling your Tesla shares just yet, but it's something to be aware of. So they announced this new self-driving AI model called Alpa Mayo. And in Jensen's words, himself, it's the world's first thinking, reasoning, autonomous

vehicle AI. And he's launching it on US roads as soon as Q1 of this year with Mercedes CLA cars, which is going to be like an insane turnaround time. He says, it's trained end to end, literally from camera in to actually. out. It reasons what action it's about to take, the reason by which is came about that action, and the trajectory. If this sounds similar to an end-to-end neural net, which is exactly what Tesla FSD is, you would be correct. That's basically what he's built with Alpameo. It's a vision, language action model, which takes in a lot of data via different types of sensors, such as cameras,

kind of barometers, stuff like that, and it feeds it into the neural net and judges what to do in a particular scenario. But it's not just one model, Josh. There's a few other aspects to it, such as simulation testing, which is also what Tesla FSD does, to kind of generate different types of hypothetical ways to deal with a particular scenario on a car so that it can judge and reason to kind of do the right type of action, whether to decelerate, accelerate, chain steering, etc. Now, the main core difference between what Embedia is built here versus Tesla FSD is that, he's not building the actual car. In fact, he doesn't want to go anywhere near it. He's not going to be building any of the sensors either. What he's proposing here is the end-to-end model itself that you can

retrofit into a car. And the first brand that he's working with is Mercedes to achieve this. What I like about this is that Nvidia in a way, they're kind of acting like Apple, where Apple normally kind of sits on their hands, lets the fields play out, decides what the winning strategy is, and then goes all in on that. We had really Waymo and Google and Tesla kind of competing on these different types of way that you could train it. One was hard-coded, one was neural nets. It became clear that the convergence was on neural nets. And that's what Nvidia did is they built this architecture just for that. What I find interesting is that this is kind of like Apple car play, but for full self-driving, where Apple doesn't make any cars,

and yet their software is prevalent in every single car that's basically made in the U.S. today because everyone has an iPhone. Well, Nvidia is doing the same thing but for full-self driving where they're not going to actually try to manufacture these cars. Like you said, they're just going to license them to other dealers like Mercedes using this thing called Drive Hyperion, which is this reference architecture. And it's built on the standardized sensor set. And one thing that I found interesting with the sensor set is how detailed it was, how much there was going on in this, included but not limited to 14 cameras, nine radars, one LiDAR, 12 ultrasonic sensors, and a microphone array. So this is a full stack suite.

of tools that they're giving to car companies like Mercedes to do whatever they want with it. And notably, it's funny that the ratio of LIDARs to cameras is like 0.75. There's still a lot of LIDAR built into this. So it's interesting to see someone who feels like they actually have a chance at competing with Tesla in the world of autonomy. This is the first real threat that I think exists, not to say that it's a real threat just yet because they have a long way to go before they reach the 84 billion miles or whatever it is that Tesla has of training data.

but they're on their way and this is the correct approach to do it. Yeah, it's interesting. We were watching the CES Q&A after this announcement and someone actually asked him like, is this a direct competitor to Tesla FSD? And he starts, Jensen starts off the response with saying like, hey, listen, Tesla's an amazing company, blah, blah, blah. And then he says Alpameo was designed around a different kind of idea.

The first difference being that Nvidia doesn't want to actually build a self-driving cars, which is the point that we just mentioned. but he wants to build the full stack of technology for everyone else that is trying to build self-driving cars. So reinforcing the point that he just wants to be around the software and maybe some of the sensor stack, but otherwise it's on the car producers themselves to kind of manufacture the actual vehicle itself. And this is an interesting strategy because you could argue that like Tesla has easy scalability because they've built their product end to end, right? And they're churning it out of their factories. they've kind of refacted the dye architecture

so that they can kind of pump these cars out once an hour or whatever. And Nvidia's kind of taking a different approach where they're like, okay, we'll just let every single other car manufacturer do this and we'll retrofit with our own kind of model. So I like the approach that they're taking. And I don't know if you saw this as well, Josh, but kind of within all the CES announcements, Uber and Lucid announced that they were also going to launch

an autonomous vehicle fleet or network system. So every single car company is kind of veering towards doing this. And I think Nvidia kind of pitching the software play, like the Apple CarPlay analogy that you just use, it's actually a really, really smart one. Because if this ends up kind of like, if they end up signing like all the right partnerships with the right kinds of car makers, they could end up with the same amount of data and therefore as good a model as Tesla does in fewer years that Tesla's taken to get to the standard that they are at right now. And it's funny. It's not like companies haven't tried to do this. I mean, Rivian's tried full self driving forward. Chevy, a whole bunch of companies have tried, they just can't figure it out. So Nvidia's like,

okay, well, clearly there's a huge market demand. We're going to step in to fill this. And to me, it feels like, I mean, they are the Nvidia, but of the full subdriving industry, whereas you could kind of compare Tesla to Google, where they own the entire stack, they own the chip architecture, they own the training, they own everything. Invita is just the pick and shovel salesmen. They're like, hey, if you want to sell a car, you're going to need to have it fully autonomously. You're going to want our software. And this is actually, I mean, people will view this as a threat to Tesla. but a lot of people don't know that Tesla open sourced all of their patents for any other car manufacturer to use from day one. The problem is just, it's incredibly difficult to do these things.

So it's not like Tesla has not wanted the industry to keep up because the mission is to move the whole world forward. It's just that no one actually took them up on this and used the patents to actually build something meaningful. So Nvidia came along. They're building the picks and shovels and they're doing it at scale, right? Like a significant percentage of the company is now shifted to work. 20%. Yeah, 7,000 people, so 20% of them. video employees are now going to be working, you know, Monday to Friday and probably on the weekends for the foreseeable future. And that is just like a huge signal that Jensen thinks that this is one of the most important industries to win. He said multiple times during CES that he

thinks robotics is undeniably going to be the future and the next industry to face a chat GPT like moment. And so he thinks that with this bet, it's going to be autonomous cars that kind of like flag the play first. It's interesting. Interesting, I saw a response from Elon here because someone asked him like, hey, Elon, do you see this as kind of like a threat? The way that I kind of think about this is that it's not really a threat because whilst Nvidia is kind of like creating a software that is 95% as good, the extra 5% takes years and years to actually improve. And Elon replies, you know what, you're right. The actual time from when FSD sort of works to where it is much safer than a human is several years. So maybe this is a a competitive pressure on Tesla in five or six years, but probably longer. So Elon's probably, you know, saying this for public opinion, but he doesn't see it generally as a threat. And to your point around like the Google full stack architecture, I think Tesla still very much has the moat right now. Yeah, and they'll win on the margins. I mean, no one's going to be able to

create a lower cost per kilometer for traveling. If you don't own the whole stack, then Tesla will. So it is good that I think it's a net positive for the world. It is slightly negative for Tesla, but not in a meaningful way. Both of these companies are going to do excellent. And we probably should get into the other excellent thing that was announced from Nvidia this week, which is probably even bigger than the full self. Drawing architecture. That is their new Vera Rubin chip, which, EJazz, I know you were looking at this. I was taking a peek. This chip is incredible. It's insane. It's outrageous, how meaningfully an impact this will have on the world of AI training. Some fun facts just to get started. Hopper was the first major chip that was being trained that was from

Nvidia. They went from Hopper to Blackwell. That was a 10% or sorry, a 10x energy efficiency improvement. And then from Blackwell to what we have today, Vera Rubin, is another 10x improvement. So we have two orders of magnitude energy efficiency in addition to other crazy stats. Like Rubin, the GPU, is now five times faster than just the previous generation of Blackwell. But it only has 1.6. times the amount of transistors. So they've managed to squeeze a tremendous amount of compute, energy efficiency, and also ease of integration. If you remember, the hopper chips from Hopper to Blackwell was a really nasty transition period because the whole architecture was different.

So you had to rip out the guts of the entire data center, rewire all the electricity, rewire all the cooling. What's nice about this is it's a hot swap. It's easy. You just pull one out, you pop one in, and you're on your way. And what's also interesting about this new chip is this is one of the most amazing things to me is the cooling, how they cool this thing. So with Blackwell, they had a lot of these tubes and wires that would run through that would cool it with liquid. They still have liquid cooling, but there's no more tubes or wires hanging out of this thing. And the water required to cool it can be, I think, 115 degrees Fahrenheit. It's too hot. So they're cooling these GPUs with hot water now because they're so efficient. So now, like, this is huge for cost per token. If you want to

generate more tokens at a lower cost, this is great for training. I mean, Ruben across the board seems incredible. And Josh, it's four times cheaper when it comes to inference costs as well. Wow, that's what it is four times. That's huge. It's insane. So to put this into context for listeners and viewers here, this kind of a jump between one GPU model to the next is unprecedented. Usually you'll get like a 2x better performance output. To get a 5x, you must have done something different here, right? And the answer is they did. So typically when you refactor a GPU for the next version, which is what they've done from Blackwell to Vera Rubin, you kind of have six components in your AI chip architecture, right? It's not just the GPU that you want to refactor. It's the software components. It's the CPU that helps orchestrate and help these GPUs kind of like talk to each other. It's how you stack these GPUs together. You'll notice in the tweet that we just had up here, I'll show it right here. Jensen Huang talks about a Rubin Pol. which consists of 1,152 GPUs, 16 racks of these GPUs stacked together,

which if you stack this together in this pod gives you the best performance output, the performance output and metrics that Josh and I just covered, right? What Nvidia did was they rebuilt from scratch all six of these components to build out this pod, Josh. So it wasn't just the GPU architecture. It wasn't just kind of like neatly fitting in a bunch of transistors. in a much more impactful way, they refacted the central processing unit, so the CPU, the graphics processing unit,

the MVLink 6 switch, which is what helps the software communicate between the CPU and the GPUs, and a bunch of other Ethernet cables. You'll notice the Spectrum 6 Ethernet switch as well. So the point I'm saying here is they did something unprecedented and rebuilt everything, all six components from scratch, which is what led to this massive, massive jump. And it's just like crazy to kind of see,

some of the takeaways here. So there's a summary here where Rohan over here goes. InVidio rolled out six new chips under the Rubin platform, and one highlight is the Vera Rubin super chip, which pairs one Vera CPU with two Rubin GPUs on a single processor. Now, if none of that is making sense, the summary is here where it talks about the Rubin pod, and I mentioned that. Now, they haven't released pricing for this thing, but let's conservatively take the cost of a Blackwell GPU, which was between $35,000 to $40,000. I'm going to take the high-end estimate of that. If we factor in the number of GPUs in this pop, which is $1,152, that comes out at $46 million for the cost of this entire pod. So if you're an AI lab or a startup, God forbid, that's trying to

enter this market with the best of the best GPUs, you're going to need to fork out 46 to get some of the best metrics ever. So this will continue to be a game played by very, you know, big hypers and big AI labs like Google, Open AI and XI, which are securing majority of the capacity so far. But you know why that price doesn't matter at all? Is because the constraint isn't dollars. The constraint is energy. So they could probably charge double for that rack. And there will still be no shortage of demand because, again, that energy constraint means everything. And when you look at the performance per watt of a Vera Rubin rack versus a black hole rack, it is that that multiple, would you say four times multiple? That is worth so much money because you are limited

in energy. That you cannot make more of. But now you can get four times the amount of compute for the same energy. That is worth more than any amount of dollars you can possibly spend. They will spend infinite money to get that efficiency. And that's why this ship is going to do so good. Another really interesting fact for those who just who don't really spend their life in this world, think of the internet, how much global bandwidth the entire internet uses every single day. one of these racks moves 240 terabits per second, which is equivalent to twice the entire global internet bandwidth in one rack, double.

So this is a tremendous amount of compute that we're talking about, and it is all powered by Nvidia. Now, you just, if you scroll down to the first reply here on this post, it's from none other than our good pal, Elon Musk, who says this will probably take about nine months or so before the hardware is operational at scale. And this is an important thing to note, because to move from Hopper to Blackwell, it took like 16 to 18 months. And we had this weird

lull period where there wasn't a lot of progress on the hardware front. The progress came from software. So this is when we started to get these chain of thought and these reasoning breakthroughs that kind of held us through that lull period between Hopper to Blackwell. What we're getting now is we're just starting to feel the effects of Blackwell. And then we have this nine-month period until Vera Rubin. So there should be this weird lull period where we're starting to starting to see what happens with Blackwell, but we're not actually going to feel the effects of this new chip until probably the end of this year. So I would say if we're going to get AGI, it's going to come from these chips. It's going to happen in Q4 once these things are actually online and fully operational and training things. And there's also just one random fun fact that I thought, you probably didn't know that you should know that I found out. Vera Rubin is actually the name of a person. It's an American astronomer whose work helped convince the world that dark matter was real. So I just thought that was like a fun little fun fact. It's like, okay, cool. They got some cool meaning behind it. But yeah, this ship is remarkable.

Insane. I'm super pumped about it. It is an impressive feat of engineering. I just wish it was here to play with already. You know, like Blackwell announced, what is it, like five to six months ago. And it's only now coming online. Probably the first data center is going to be XAI's Colossus 2. So we're going to see the real effects of that generation of GPUs kind of like relatively soon.

So Vera Rubin's probably not going to be seen until early. next year if we factor in pre-training and post-training for AI models once they've spun up these podcasters. So I'm excited to see that, but I hate that we have to wait so long. Another point to make, I guess, is Nvidia is pumping out these new GPU versions way more frequently than they used to be. And that just is kind of a sign of the competitive times as the likes of AMD, Intel, Google's TPU kind of breeds down their neck. They kind of want to make sure that they're top dog, and they've shown this with the 5x performance increase, I expect to see probably a new Nvidia GPU probably come out at a cadence of once every

year eventually, which gets me thinking if there are constraints around memory, we actually did an amazing episode on this earlier this week, definitely go check that out, and other types of things like energy, as you mentioned, Josh, I wonder how many of these GPUs are just going to be collecting dust. Do you remember we spoke about, there was that clip of Satchian and Adela saying, hey, I have like $300 million worth of H-100s just collecting dust because I don't have the energy to kind of like fit this out. I wonder if we'll start running into those kinds of problems going forwards. But such a, such a cool announcement. Yeah, it's exciting.

And then in the meantime, we're just going to get the software breakthroughs. So the like you were saying, the breakthroughs from Blackwell have not totally come. We have this like nine month l-l period in between where we're going to make some software innovations. And it's just this like double parallel exponential progress. They're both building on each other. They're both compounding. The line is going vertical. Progress, as fast as you think it's moving, it is moving so much faster. But I want to round out the CES segment with something a little more fun. Ejas, because it wasn't just the NVIDIA show. There was other stuff. I'm curious if you saw

anything personally that got you excited. Because for people who don't know, this is the consumer electronic show. This is where they announce all the cool new technology that, for the most part, you can actually buy sometime this year to put into your life. So is there anything that caught your eye in particular. Yeah, this is peak AI. For any of you who are just listening, I suggest you turn the screens on. The world's first robot vacuum, Josh, that has legs. With legs. So, hey, hey, hey, listen, the number one issue I had with Rumba, which, by the way, went bankrupt last week, was that, okay, you can move around my floor and vacuum and kind of get into crevices, but it can't climb stairs. So how can it, can I, I just want to

leave it alone and let it clean my entire house. This Robo Rock Saros Rover has the ability to now climb says, you have a video of this where it basically helps to put it up there. It has a rectangular surface versus a circular service. It has AI powered software and sensors, which allows it to map out your entire house in 3D so that once it's mapped the entire house out, it can just kind of run the same routine over and over again. Here is a separate angle. For those of you who are really keen to see like, hey, I don't know if it's cleaning the entire step. It is. Look at this. Look at this like flexibility and mobility on this. I just freaking love, love it. So it's going to be coming out relatively soon. They haven't kind of like gone into production yet,

but I like the fact that it's a prototype that actually works, Josh, versus something that is a kind of like graphics video demo. Very cool. That's pretty sick. Yeah. I love the robots. Look, it could even jump. It was hopping a little bit earlier. It was very impressive. Exactly. All right. What about what about you? What were you excited about? Yes, this year, this was the year of displays, man. This display technology and general was so exciting for me. First, I want to reference the Samsung folding display, because what you're seeing here isn't just a Samsung folding display. This is the display in the new iPhone that's coming later this year. And it's cool that we get to see the core technology displayed before it gets placed into products that we'll all use later in the year. And the cool

thing about this new Samsung folding display is for the first time ever, your display can fold in half and when it unfolds will not leave a seam. So if you see on the left here, there's a display that has a seam right down the middle. It doesn't look that good. You could tell that it's folded, but the display on the right looks no different than an iPad. And this is what we're going to be getting in the iPhone fold later this year. So it's cool to see Samsung, who is a supplier of Apple, showcasing their new technology that will eventually be in her hands and pockets later this year. Android users are punching the air right now, Josh. Because they've had to take for so long. Yeah, but yours had a seam. Our house doesn't have a scene. Okay, yeah. Sorry, I think you were

personally attacking me. I'm an Apple user. Come on. No, I'm attacking the, the, the Android people. Like, yeah, okay, you guys have been folding for years, but like, yeah, have fun with your seam. Anyways, the second cool technology is out micro-l-D displays. And what's cool, what we're seeing on screen is they're transparent. You can actually create these beautiful transparent displays that use micro-l-a-D. And the difference between a standard LED and a micro-l-l-ed is that micro-l-l-l-ed actually has all three color diodes. It has RGP built into each one of the tiny little pixels. So it's really high fidelity, really cool, interesting displays. And then the third and final thing in the display world is for

people who use Macs, there is no 5K 120 hertz display for a Mac. And that has driven me absolutely crazy because 120 hertz is what is, it feels very smooth. It's what the new iPhones use. And 60 Hertz just doesn't cut it, but they haven't been able to do this because they've been throwered by Thunderbolt. And long story short, if you are a MacBook user and you want the native display resolution, this is a big year for you. You will be buying a new monitor. It is very exciting. But that wraps up our CES news this week. EJez, we have a had some other big news, not CBS related, but instead from Big Dog Open AI, this is their first big announcement of the year with, yeah, chat Chabit Health. You were very excited about this.

So please explain to myself and the audience. What caught your eye with chat ChbT health? So one of the biggest ways that I use chat Chb-T is through all the research and reading that we do, including prep for the episodes that we talk about. But the other, probably second most used one, is around personal health and fitness. I would use it to kind of design gym routines for me, diet plans for myself, and in some ways, Josh, I will give it my medical labs, my reports, my records to kind of like gauge

whether I'm going to be facing some sort of issue in the future or whether the diagnosis or rather symptom that I'm feeling or getting today is actually accurate. And I've run into a few obstacles. Typically, chat GPT would give me a weird, of answer that isn't really accurate, or it'll just politely decline and say, sorry, I'm not a doctor, I can't do this until this week. OpenAIA released something called ChatchipT Health, which is a dedicated space for health conversations in ChatchipT. Okay, so what does that mean? Basically,

it's a separate space that you can connect your medical records and health fitness apps. So now Apple Health, the likes of My Fitness pal, and also Peloton and an array of other apps, I'm hoping it's 8 sleep, if you feel this into this, please connect to this. And you can feed this data into your chat GPT and it can start to identify certain patterns of health, whether you're feeling something, it can recognize whether you've consumed too much alcohol the night before. And it can give you personalized information about how you can start to improve your life. Now, it's important to stress that it's not trying to replace the role of a doctor and they've been very explicit in actually saying that, but it's meant to be an aid. It's meant to be an assistant. And it's

that jump into a personal AI assistant that extends beyond just becoming your essay writer or kind of writing up a product plan for you for your kind of nine to five knowledge worker job. This is something that actually applies to pretty much anyone that wants to live, which is every human on earth. And so I'm excited to see something like this scale. This, this to me is something useful that I would like pay an extra 10 to 20, heck, even 50 bucks to use. I'm finding it funny the roles that companies are finding themselves in. where we have in one corner Anthropic, they are the world's best at coding. They are sounding the alarm that we are building AGI very quickly.

We need to be careful. We need to make sure this is aligned. Then we have GROC, which is dead set on building the truth-seeking AI. They're building the biggest data center in the world as fast as possible. And then ChatGBT is like, we partnered with Disney to give you cute little things that you could pair with your videos. And now we have this fun little health feature where we can improve your health by ingesting your records. And it's nice. It's important. It's just the stark contrast between the other companies. And Open AI kind of made

it clear this year that they're going to be leaning more into the commercial side of the business versus the consumer side of the business. So this is interesting to me. I think the personal take was that we're reaching across roads now where you kind of have to make the personal decision of how deep do you want to go with these LLMs, with these companies. Are you ready to go all in? Because if you do, then you open yourself up to all the benefits that will come from it. You give them your health data. Well, you'll get all of the health advisory stuff. And then as the hardware comes, it'll track more of your health. It'll gain this full stack profile on you and know you better than anything else in the world. And if you're okay with the people at Open AI having that data to use at their will, then this is a great thing.

But I think it's a personal decision whether or not you want to decide to go all in on a company like this and give them all your data versus kind of reserving it and maybe giving it to someone else who you trust more or just keeping it for your same. It's this interesting world dilemma, but this is like really awesome and really impressive. And I frequently think about how Steve Jobs would think about AI and how someone like him would sell AI. And it's not the way that Open AI is doing it, but I would imagine it would follow similar principles where, listen, now you have a doctor in your pocket that knows everything about you. It can help save your life.

It can save your loved ones life. It's like this very powerful technology. And opening eyes leaning into it with this health thing. So I don't know, I'm feeling excited about it. I'm not there yet. I signed up for the wait list. I'm not sure I'm going to be user number one. But if they could prove that it really is valuable enough,

it seems like it makes sense to do it. Well, what I appreciate with Open AI from the start is that they've hyper-focused on the retail consumer, like the average show and how they can benefit from AI. Yeah, they're focused on enterprise. I'd say Anthropics actually is focused more on enterprise, but Open AI has always tried to figure out, okay, what's the best consumer product loop that I can provide them?

And I think health is a really good one to focus on. It was the, we've just obviously ended 2025. And one of the most painful tasks that I need to do at the end of every year is figure out my health insurance for the next year living in America. And being in New York on the East Coast, it is the most arduos. I absolutely hate it. I have to like sign up to like 10 different accounts. I need to evaluate a million different plans. And then even when I'm on the plan, it is super high.

hard to kind of verify whether the information about a particular doctor and their address is even correct. So it's very, very antiquated and I've been waiting for this industry to be disrupted. And then came along an app like Oscar Health. For those of you who haven't heard about this, this kind of like changed healthcare in many different ways in America in particular because you could just pick up your phone and FaceTime your doctor and get that live health care, right? ChatGPT Health to me is that next step evolution where eventually you mentioned, John, It's funny, that you'll have a doctor in your pocket. You'll have the best doctor in your pocket.

In fact, you won't just have the best specialist in your pocket. You'll have the equivalent of 10 specialists in one doctor. How much would you be willing to pay to see that doctor every single day, 24-7? These doctors don't sleep, right? And then I think about the next step evolution, which is, if you have this doctor in your pocket, it can start feeding into pharmacies, which can start delivering personalized pill packs, or maybe even personalized treatments, peptides, stuff like that going on to the future. This is the first step of many, and Sam Altman has said for a while now, I'd say, like, for the last five months, that he intends for health to be a big component for Open AI.

They have retro sciences as well, which are kind of like their lab to kind of build out future cures for diseases that chat chit helps discover. So I think this is part of a much larger puzzle that will help kind of like build this health AGI, which they're aiming for by the end of this year. Yeah, and the key piece to the health puzzle is data. And the problem is that a lot of, for a lot of people, it's just difficult to close. the data. You're not really going for MRIs and CT scans and scanning a lot of parts. It's very topical. It collects data from something like an Apple Watch or maybe some blood results you have from a doctor. One interesting thing I've seen people doing that I would advise you to possibly try if you've ever done a DNA test or you've ever done ancestry or 23M or whatever companies give you your DNA profile. It's normally a couple

hundred gigabytes of raw data, but you can actually upload this into an LLM and get customized feedback on what types of things could possibly affect you based on your genomic makeup. So you could see if you are resistant to insulin or if you are prone to these types of diseases. And these LLMs do a remarkably good job of analyzing this tremendous amount of data and kind of giving you the places that you need to focus on and be most cautious of as you move forward. So this type of health practice, as we collect more data and figure out how to parse through it, it's really exciting. And it's made me want to go get a DNA test and try to load it up to an LLM and C.

And I think it's super exciting stuff. Health is going to be a very large category this year. And Open AI is making sure of it. Going to be huge. Okay, in the final story on the docket, we have some capital raised news from two of our favorite AI labs that are out there. We got Anthropic of Claude Code. They've confirmed that they're raising $10 billion at a $350 billion valuation.

Now, this nearly doubles its previous valuation of $183 billion. Josh, you gave me the timeline of if you were an investor in August of last year, what's the return? Yeah, I was looking at like, I think it was foundry as the name of the company. There's like basically SPVs you could buy into them. Employees will sell some of their shares in the public market or private market you could buy into them. It showed the chart over time. And I think August 1st of 2025, it was trading at $69 a share. And now it's something like $260. So 4X in a couple of months. I mean, if you own any of these things, if you are an employee, any of these things, congratulations, you are getting generationally wealthy. And the train is not stopping anytime

soon. This is not the only big raise this week. We had a second one, XAI in the news, a 20 billion series E. And they were targeting 15 billion. It was oversubscribed. Five billion overtaugged. Yeah. One of the interesting stats here is that top line there, the 600 million monthly active users, that is encroaching on OpenAI. And they're getting closer and closer to that mythical 800 million weekly weekly, so it's a little bit different. But they're on a role. And I think this capital raise will be enough to, this is a crazy take. This could be enough to get them to the ADA. Could be. Could be. $20 applied to Blackwell and Vera Rubin over the course of the next 24 months leads to some pretty unbelievable progress.

And $20 billion may be enough to get there. You might have noticed, for regular listeners on this show, we've been kind of quiet about GROC. And the simple reason is, since GROC 4, we haven't really got their unofficial release of GROC 4.20 or any rumors around GROC 5. And the reason behind this is because I think Elon has gone kind of full-on founder mode, and he wants to assemble the largest armory of top-end GPUs in Colossus 1. Colossus 2 data centers. Once he's acquired these, once he's scaled these data centers quicker than any other competing AI lab, he'll be able to train the mother of all AIs. And rumor has it that he's going to amass 900,000 GPUs in Colossus 2 data center alone. And these aren't just any GPUs. These are, he's the largest acquirer of Blackwell GPUs, fun fact, the ones that are

going to go live very, very soon in this quarter. And he's also one of the largest which says of Vera Rubin GPUs. Jensen absolutely loves this guy and says he's the guy that can scale GPUs quicker than anyone. So he's aiming for 900,000 GPUs by the end, sorry, by mid-Q2. That is like astoundingly quick and will beat Open AI to the punch, even though they started well before. So if he pulls this off, Josh, I think that prediction is not too far-fetched. He will end up building the best AI model this year.

That's my bet, at least. Yeah, no, and I say this with very high levels of conviction. do not mistake their silence for weakness. XAI is building faster than anybody else. And I think they were my winner for the winner of 2026 in terms of who's going to create the best AI models. They are building at a rate faster than anybody else in the world. And in these races where the resources are the constraint,

the person to deploy them the fastest and the most efficiency, most efficiently, is the one that wins the race. And XAI right now is winning that race. If you're looking at it from a more long-term perspective, But with that, we conclude our weekly roundup, all of the topics that we wanted to get into about. I think the prompt for this week maybe is CES announcements. What's the coolest CES announcement?

There's so many fun gadgets and gizmos that I saw come out this week. And a lot of them are available to purchase right now, like even anchor the charger company that I use a lot. They put like these cute little LEDs and the charging bricks now. It's like a lot of cool stuff. So I think the prompt for today could maybe be,

what's the coolest thing you saw from CES? What are you buying from CES? What am you most excited about from CES? Does you got anything for them? I have a slightly different prompt. I know a lot of you listeners are Teslables, and even in some cases, Tesla haters. I want to hear from both of you guys

whether you think Invidia's competing Alpameo vehicle AI is actually a legitimate threat or not. Let us know in the comments. I want to hear all about this. And yeah, that is all we have for the docket today. We released two episodes earlier this week, one on the semiconductor memory squeeze as well as Open AI's new secret device.

Definitely go check those videos out. We also dropped a sick kind of thesis and essay on the memory squeeze as well. For those of you who don't want to be consuming video content all day, we've got a nice little article that were written out for you. And all this link in the description. Don't forget to click that down below. All linked in the description. And also, as you're watching this video, we dropped the five biggest highlights of this week.

So definitely go check that out. If you're wondering where the hell that is, that's on our news. newsletter, subscribe, like and subscribe on our video channels, wherever it does it do Spotify, Apple Music, RSS feeds, wherever you are. And we will see you, hopefully, on the next one. Number two of 2026. Buckle up. We are in it now.

This is going to be a huge year and we will be here to cover the entirety of it as we go through. So yeah, thanks for watching. We'll see you guys in the next one. Cheers, guys.