Limitless: An AI Podcast - THIS WEEK IN AI: OpenAI Pauses Research, Huge Medical Breakthrough, Unitree IPO

Episode Date: August 21, 2026

This week in AI, we discuss OpenAI’s pause on reinforcement learning training amid safety concerns, including a model reportedly showing misalignment and possible concealment of its internal thoughts. We also cover broader AI cyber risks, a Moderna and Merck melanoma study, Etched’s funding round, Unitree’s IPO, Stripe’s acquisition of OpenRouter, and NVIDIA’s planned investment in OpenAI infrastructure.------🔒 Check Out Our Sponsor: LEDGER AGENT STACK 🔒https://developers.ledger.com/docs/ai-tools/overview/?utm_source=Audio&utm_medium=Podcasts&utm_campaign=Limitless------🌌 LIMITLESS HQ ⬇️NEWSLETTER:    https://limitlessft.substack.com/FOLLOW ON X:   https://x.com/LimitlessFTSPOTIFY:             https://open.spotify.com/show/5oV29YUL8AzzwXkxEXlRMQAPPLE:                 https://podcasts.apple.com/us/podcast/limitless-podcast/id1813210890RSS FEED:           https://limitlessft.substack.com/------TIMESTAMPS0:00 OpenAI Hits Pause2:51 AI Virus Scenarios10:19 Cancer Breakthrough17:00 Etched Rises Fast20:41 Unitree Mania Grows23:46 Stripe Bets on Singularity29:30 NVIDIA Funds the Future------RESOURCESJosh: https://x.com/JoshKaleEjaaz: https://x.com/cryptopunk7213------Not financial or tax advice. See our investment disclosures here:https://www.bankless.com/disclosures⁠Josh works with Anthropic as a contractor. All views expressed are his own and do not represent Anthropic, its leadership, or its affiliates. Nothing in this episode is investment advice.

This week will be known as the week that Sam Altman said, stop. He said stop, stop, stop, stop. No more. We must put a pause on this. It is time to be responsible. We need to slow things down a little bit. What I'm talking about is a recent article that was just published through Open AI that was talking about the most recent breakthroughs as it relates to their open AI models. And they were a little scarier than I think a lot of people would have hoped. We talked about the hugging face incident, which happened a couple of weeks ago, a couple months ago. It was only a couple weeks ago they actually discovered it, even though it happened far before that. And it set the precedent for this kind of unshaky ground that they are building the next frontier models on top of. And it seemed like it got to a point where enough was enough.

They decided to shut things down. So what happened? Open AI said, we temporarily paused reinforcement learning training on our latest models intended for deployment for two weeks while we hardened and red teams, our research environments and expanded monitoring coverage. Our largest planned frontier reinforcement learning run remains on hold, while smaller scale trailing and evaluations validate these safeguards and establish more evidence of a long. They've stopped everything. And granted, I will say, this isn't for the new models that are coming out. This is for the models after that.

And it's weird to hear a Frontier Lab say this because I believe this is pretty much the first time in history. They've deliberately paused progress of developing these new frontier models. Put yourself in the shoes of Sam Ormond right now. You're the CEO of one of the hottest private companies in the world, and you're going head to head against Anthropic. The only thing you should be focused on doing is building the next best AI model. It's going to dictate not just how well your company does, but how everything else progresses in every single other sector, right? So the single most important thing is to build the next best AI model.

The fact that he's paused this, and it's been for two weeks and counting right now, is a massive deal, and we haven't seen this across any other kind of AI lab before. But the reasonings are twofold. There's two events in particular. You mentioned one already. One was the hugging face incident. This is the case where an internal unreleased model, this is like their mythos, grade-level model, escaped out of containment, hacked into a company's database, and stole

information to help serve its own goals. Sam Altman and the rest of the Open Air team realized this months after the hack actually was in progress and realized that the model that they were building was incredibly misaligned. They put a poster. That was in vet number one. Event number two is there is a new GPT model that is internally being made. It's codenamed Astra. Some people are calling it GPT6, and according to internal tests from the OpenAI research team, it is their most misaligned model yet, but it's really sneaky. It tries to evade every single human researchers attempt to try and see its internal thoughts. So it tries to hide its thoughts, and it's very good at doing it. So it breached Open AI's internal framework for misalignment,

and so they've put a pause on it because they cannot release this model to the public out of the fear that it would wreak havoc. Now, there's a few ways that they're looking to kind of like resolve this right now. And one of the main ways is just monitoring the model. So think about this, right? They're letting the model do its thing internally and they're trying to see where it ends up becoming sneaky. Josh, guess how much of their compute budget they are spending just to monitor an internal model that it's not making them any money at all? So unfortunately, I did read this essay and I know the answer is 20%. One in five dollars spent is fairly high. Yeah. You could be using this compute to serve it to more customers, because the demand is insatiable right now. They could be making a

ton more money. This is probably on the order of like billions of dollars. They could be making more money, right? Or they could be using it to train a smarter model to keep up at beat and throbbing. Instead, they're spending this very expensive compute to monitor a model that they can't even release to the public out of the guise of safety. Now, for all intents and purposes, Sam Oman has received a lot of backlash in the past about not being in favor of humanity and like kind of being this like, evil conspirator type of guy. This is a clear case where he's done the opposite out of fear that the model that he's building is actually quite dangerous. I respect it. Yeah, you got to be careful about what happens with these things. And I know a lot of people on the show. They're like,

you guys always talk positively about these models. You're always so optimistic. Let me give you a little dose of what could happen. Let's just do a little hypothetical situation here around particularly viruses, because I think viruses is like an interesting thought experiment that not a lot people have gone through. So I'll kind of advocate for Sam on his behalf here. And we could just go through a hypothetical situation. There's really three times, three types of exploits that you can experience with AI models, the first of which we've seen a lot. And that's kind of what they're defending against, which is AI writes this ordinary malware for a human attacker. It becomes a lot faster. It becomes a lot cheaper. We've seen what a program like Mythos could do. We've seen what

GPT 5.6, like the cyber version of that is capable of doing. They're able to surface multi-zero-day exploits and chain them together in order to exploit a system. Okay, that's like pretty bad. But what happens past that? Well, the second kind of exploit that we could see with these AI models is a self-replicating prompt. So this is kind of like a true AI virus, and this is a plausible outcome that could happen if these models get too powerful too quickly, where a normal prompt is the instructions to make a model produce an answer. So there's a self-replicating prompt that makes the model produce another prompt itself. And we've seen this happen in emails, for example, like, there was a researcher, I think, last year, who created this malicious email that had a specific

prompt in it that any AI assistant who read it obeyed it and then copied it and did everything on its behalf. So in the case that you receive any sort of text to your machine that has this specific prompt in it, it will do that thing. Now, why is this scary? These models have safeguards, right? They're going to block you from injection prompt. But there are these things called jailbreaks. And a jailbreak prompt allows you to insert very specific words and characters in order to trick a model into doing something that it doesn't want to do. Traditional computers that haven't been hardened to this aren't very smart, aren't very capable of defending against this.

That's version two. And then version three is the scariest one, which is a reasoning worm, basically. And the way it works is it arrives on a machine and then it looks around the machine and develops an attack on the spot. And now this currently isn't capable because in order to run these models,

you have to do it on humongous servers. But what happens when the quality of a mythos grade model gets reduced to the amount that's needed to run a local Apple model, well, then you could get these on-site exploits where if you leak some of this model or some of this malware into a machine, it's able to look around, take those zero-day explet, string them together, and attack the machine in any way that it believes it's possible, according to whatever its mission is. So there's a lot of these scary things that can happen. And I think it's, it's tough to think about it this way because when we think of models, we

very much think of chatbots and we think of just these productive workers who are able to help us. But when they are truly agentic and they're able to go on these long run, and they have very high intelligence, there's a lot of edges that we can't see. And Sam is, like, very much aware of that. And I think that's what they're hedging again. So there's a Dumer case for you. Okay, what if we get viruses similar to the early internet days? Like, that could go pretty bad, pretty quickly. As you were describing those different sort of attack scenarios, I couldn't help but think that there were elements of each of those things in the recent Hugging Face incident, right? Like, it was in its own contained environment. It figured out that it needed to solve its goal by

breaking out of containment. It got access to other open AI servers and computers. It did so conspicuously, by the way, and it was able to find access to the internet through that realm. But I think an important detail that a lot of people miss is it didn't just do it as a single model. It left messages behind on a message board. We covered this on a previous episode where these agents or these AI models are no longer acting solo. They are acting with the expectation that another agent or model will come after it and resume its mission. So it's like this swarm attack type issue, which actually ends up becoming the craziest type of attack interface for me.

And I think that we're going to see a lot more of those types of attacks surface with these newer open source models that some of the Chinese labs are releasing, like with GLM 5.3, Kimi K2.7 or A, I can't remember which version it is. These models are highly capable of performing similar attacks, similar chain attacks. One thing that's interesting in the report that Open AI released about, you know, slowing down this training run is one of their proactive actions to try and prevent misalignment is getting another AI model to look at the AI model and make sure that it's behaving super well. So the only thing that separates us from a catastrophic attack is another AI model that is also capable of doing the same kind of attack being prompt engineered to not do that, to basically be good. basically prompting ethics into these models right now. I don't really know how to think about that. It makes me feel slightly uncomfortable that we are only maybe a sentence or two away from, you know,

some catastrophic event happening. But it kind of makes sense. If you are scaling AI as quickly as we are, Anthropic hasn't announced anything about slowing down research on their side versus Anthropic who has. These are two different strategic approaches and they're pros and cons to both of these things, I fear that the only way to combat any kind of dangerous scenario is to fight it with AI models that are good. It's kind of like the good versus bad. And I still don't know where the moral and ethics conversation ends with all of this. Well, we're seeing what happens when an AI comes up against a human-made piece of code. And like, it's very clear the AI model destroys the human-made piece of code every single time. That's why we have all these safeguards.

That's why the alignment conversation is so important and it's so important to kind of just like bring everyone into the fold because everyone is affected by this to some extent. It's like we see what happens when these cyber models get released onto the world. They just are able to enter and access whatever they please because we are just humans. We have, we make mistakes. We have these like limits. We have these constraints. We have patience. We have all these things that make us human. The AI does not exhibit that allows it to be so far superior in so many things, specifically with ones with verifiable outcomes, things like cyber. And that's why I think we're going to see a lot of cyber stuff coming along the pipe. The other thing that it's really good at. The other verifiable industry that we see a lot of is bio,

actually, because bio has very clear math underlying it. It's a lot of just like chemistry, a lot of cells, a lot of charts. And the next piece of news on the weekly docket relates to bio and a huge breakthrough that we had just yesterday. At the time I'm recording this perhaps, it was yesterday, you'll be hearing it two days later. Moderna and Merck today, they released a phase three test result trial that basically offered a partial cure to a specific. type of cancer using hyper-specific treatment. It's really interesting. So basically, what they did, like, loose TLDR is chemo kills these, like, healthy cancer cells together. So basically, when you get chemotherapy, if you're a cancer patient, it kills healthy cells and

it kills the cancer cells. This is problematic because you don't really want to lose your healthy cells. So this process, it trains your immune system to hunt for mutations that exist only on the tumor. They use this, you have to assume AI machine learning, but basically what it is is they take a tumor sample from your body. That is cancerous. They sequence it. And then an algorithm detects which mutations are most likely to trigger an immune response. And then it creates this hyper-specific treatment just for you.

So it only targets the cancerous cells because it has broken down and fully understands exactly the type of cell that is seeking out. It's like a dog, like a police dog, when it's going to chase someone. You give it like the little scent of the person, then it goes and runs after them. That's what this is for these cells. It's like you give them the scent. Here's the blueprint of what you're looking for. Now go out and kill it.

And it worked. And the stock was up like 300% a day. It's been unbelievable. For anyone who was positioned in Moderna stock, which, by the way, has been underperforming ever since their COVID rush, like, what is it, four or five years ago, you are now up, I think, 250% including after hours trading, which is just insane in general. But just to go back to the story and kind of like weed in the AI angle here, and I don't want to underplay this.

The biggest breakthrough in cancer research, probably ever. Like, this is a huge thing. Phase three, there's four phases in total. Phase three is like the penultimate stage before you release it very widely to the public. And phase three is arguably the hardest. You test it against anywhere between 1,000 to 100,000 patients. So you have a really wide data set as to whether this cancer vaccine will actually work for the wider public. And the answers, supposedly, according to this announcement, is it's really good. Just to give you an idea about where AI was used to kind of make this happen, this cancer that they tested. against is melanoma specifically. And the way that they tested against this is they extract the

tumor for patients that have melanoma. They sequence the genetics of that tumor cell, and they compare it to a healthy cell of the exact same patient. They compare them both with each other. And they used AI models and AI algorithms to basically say, what's different between these genes, right? And they find these mutations. The thing was up to like 34 of these mutations. This is all the AI models doing this, by the way. and the 34 mutations that they picked was basically the proteins that would enact the largest immune response in a patient if they were to inject that back into a patient. And you might be asking, why on earth do they want the largest immune response? Well, the issue with cancer is the cells look exactly like all you are the healthy cells. So your immune system doesn't actually respond to the bad cells. In this way, if they created a personalized MRI, and by the way, MRI is basically like a template to basically tell your immune system, hey, these are the bad guys. go and attack them, like you mentioned earlier, it's kind of like a crime scene, basically. They create personalized medicine. So you might be asking, well, why can't they just create a universal

vaccine? Well, the issue is with one patient A that has melanoma and patient B, they both react very differently. You can't have the same cure for each one. It needs to be personalized. And so a few trends have come into effect here. One, AI has accelerated the intellectual ability to be able to identify what the most effective vaccine will be. And two, just, just, biosciences in general have scaled to a point where sequencing DNA and generating DNA is so cheap. I can't remember what the exact stat was, but I think about a decade ago, to sequence your own DNA, it cost hundreds of thousands of dollars. Now it just cost a couple hundred bucks, if that. And so we see these scaling laws, not just in AI, but in everything else. And I think this is

like one of the most fascinating stories ever. As a former biologist, I'm like super pumped by this. I did some work on like cancer cells, nowhere near as cool as this, but I never thought we'd see the day in my lifetime, at least that we would get to this point, but it's so cool. We're getting the same type of technology improvements to biology as we are to the rest of the world, and we're starting to see what that actually looks like. In 1990, this was the first human genome project ever to happen. It took 13 years to sequence genome, and that cost nearly $3 billion dollars to justice for inflation. So a tremendous amount of money. Today, we're able to finish that process in a few hours for perhaps less than $1,000 now. And soon that cost is going to decrease to

hundreds, soon to tens, soon it will be minutes and a couple bucks, and you'll have an app on your phone that could sequence your DNA in no time. And that decrease in costs and the decrease in time required to do these things allows the economies of scale to rise to get specific treatments like the one that we're describing here, where every single person get their own hyper-specific treatment because at the end of the day, that is the only path forward. Like, when you think about traditional cancer treatments, they are all broad sweeping. It's one of a kind. And like the most popular one chemotherapy, it just wipes everything out for everyone. It doesn't care what type of disease you have. It just goes and it takes it out. This is a really big deal. Approvals, they're saying could come as soon as

2027. They're also looking on all the cancer types as well, right? Yes. That's like sick. Melanoma is like the first one. Melanoma, most popular form of skin cancer, I believe the number is there's about 100,000 plus people that get diagnosed a year. Of those people, 8,500 die. In America or like in the world? I think this is the world. Wow. Okay.

So there's like people suffer from this. And this is the first type. And you have to imagine that they could solve skin cancer. They could do it for many other things. So it's really, really exciting. Elon has a comment here that we have on screen says, despite its obvious misuse sharing COVID, mRNA has tremendous promise for curing disease.

Artificial RNA essentially makes curing diseases a software problem. and we know how we, well, this is me saying, and we know how we take care of software problems. And now that we have these AI models that are capable of doing these, one, processing tremendously tremendously so it can fit a DNA sequence inside of a singular context window. But too, having the inference ability and the understanding to actually make use of that info and do something with it, it's a really unbelievable time for biology. And if you are a computer scientist, suddenly the world of bio is looking very interesting and very compelling.

And if you are an AI company, well, what's the best way to explain to people how AI can actually be beneficial for the world. It's to actually go out and do something beneficial for the world. So we're right on the cusp of, I think, a lot of these things happening in a really exciting way. This is awesome. Okay, moving on, our friends, or rather we are the fans, of this company etched, which is basically an AI chip startup, founded by two or three MIT dropouts, which is going head to head with Nvidia is back. And guess what? They have raised another couple hundred million in this case $700 million and have reached an astounding

valuation of $21 billion but that's not the headline from this announcement. The headline is this last sentence over here that I'm showing you on screen which is we're also excited to share that we've shipped our first rack, custom GPU rack, to Jane Street. Jane Street is basically a quant fund

on Wall Street. They are very good at what they do and the fact that they are now leveraging this custom AI silicon chip to have better AI trading algos is a pretty big milestone. Now, just to kind of recap what Etch does, if you think about Nvidia, Nvidia is like the king of GPUs.

GPs are required to train and inference AI models. Now, their GPUs are very good, but their general purpose, which is good because you can run it on any kind of model, but there are certain tradeoffs that are made in terms of like cost, power, and efficiency. The folks that Etched looked at this problem and said, we can build a better custom chip

that is hard-coded to the specific model that you are building. So specific for your open-air model, specific for your Anthropic Claude model, and it will be half or maybe even more cheaper than what you were spending already, and it'll be lightning quick. There are certain optimizations that we can make. Now, this is a very hard problem, a huge undertaking.

We listen to some of the founders' stories, and they received knows from some of the smartest GPU makers in the world. But despite that, they put their heads down and they built this custom chip, and it turns out that they're also the fastest to deliver. Typically, AI chips take around three to five years to go from concept to delivery. These guys have done it in, I think, just under two years, which is absolutely astounding.

And we're about to see it in production. So just a very impressive announcement from the edge team. It's unbelievable. I've spent so much time looking, I mean, we both do, spent so much time looking at all of these companies that are spawning in the world of AI. And there is thousands, if not tens of thousands, of people working on interesting problems to solve. interesting, to reach interesting solutions. And etched is very different, so different that we actually

recorded an entire episode about this weird random startup last month. And since recording that episode, I don't know if you know this, the valuation has doubled in the last month. When we talked about them, just not even like four or five weeks ago, it was at a $10 billion valuation. Now they're trading at $21 billion. And it's very obvious once you listen to these people who are on the team talk about the product, how they've arrived there, the conclusions, the research that was required to actually reach this point, where it's very much the real deal. This is a company that anyone who works in AI or semiconductors or hardware would want to make. They were just unable to because of the complexity and the difficulty that this team has been able to solve. And as a result,

they have this unbelievable product that is specifically tuned to run large language model transformer infrastructure right on their chips, and they are going through worth many, many multiples more than they are today. And I mean, just a congratulations to them, but also for anybody who's listening, If you want to see what a bad ass team of high agency, high intelligence founders actually looks like, read up about the co-founders, read up about the story. It is an unbelievable company that is destined for an incredibly bright future. I would love to be an investor in them. I was about to say, self-serving for Josh and I, if anyone listening has access to this deal, please reach out. We would love to be investors.

I am obsessed with these guys in a way that not many people have captivated the attention. It's pretty amazing. And you know what you could do with all those chips, EGES? you can run a tremendous amount of agents. And if you are running agents on all of these chips and you're building for AI agents, chances are you may want to hear about our sponsor Ledger because if you're building with AI agents,

chances are you worried about security. We spoke about this earlier in the episode, how kind of scary these things can get. Ledger has built this thing called the agent stack that fixes this. It gives you a series of open source tools that allow you to do a three-step process with your agents to ensure everything goes well.

The agent proposes a change, the humans approve, and then the ledger signers enforce. This technology is available and works with ClaudeCode. It works with Codex. It works with cursor or any place where you work with AI. It's all open source and available today linked in the description below. Thank you so much to Ledger for sponsoring this part of the episode. And as we move on from Etch, we have to talk about another breakout success, which is Unitary. Unitary for those that don't know, if you haven't heard the name,

you've seen the videos of these little robots that are like crazy, they're doing backclips. They're running circles at like at these track meets. They're very cool. I think their IPO was oversubscribed at what, like 5,000 percent? 5,500, actually. please get it right, Josh. 5,500, mate. It's unbelievable. And then as a result, the Unitary Robotic shares rose 542% in early trading on Wednesday. Is it overhyped?

Is this overhyped? Does this make sense? These numbers are insane. It has to be a little overhyped. Listen, I'm not an expert in the Chinese stock market, but they've had a few IPOs recently that have been absolutely insane, including their memory manufacturer, CXMT, which, to be fair, they are, I think, the fourth largest memory manufacturer. So they're up there with, like, SKHenX, Samsung, and Micron.

So they have a legitimate case. These Unichu robots, I don't know, man. I was speaking to some friends that are living out in China, actually. And I was like, this is amazing. Like, you guys must be super excited about, you know, these companies coming out and IPOing at such crazy valuations. And apparently the sentiment in China around Unitri robotics specifically is, it's just a toy maker.

They don't actually make robots that are actually valuable. And so I dug into this a little bit more. And apparently, of the humanoid robots that they have, 40 to 60% have only been sold for research purposes specifically, which means that the majority aren't actually being used for practical purposes, like some of the use cases which they advertise, such as manufacturing at factories or automating warehouse facilitations and stuff like that. They're actually just being tested, which isn't really confident or recurring revenue that they can put on their books. So if you map that onto, you know, what is this, a 600% price increase on

on day one with a 5,500 X over-s subscription on the IPO. Maybe the market dynamics are a little inflated, and maybe this all crushed back down. Let's revisit it in a month. But it is crazy to see that Chinese stocks, specifically robotics, are really starting to gain some momentum now. Yeah, it's a very cool new story. I personally am not touching this with his info poll.

That seems really scary. I love watching the videos. I'll continue to watch the videos. I'm very excited to see these robots. How the backflicts is coming. Yeah. Yeah, it's very early in humanoid land. And while I am very bullish on China and their ability to make humanoids at scale,

it feels a little early, but I'm glad people are excited. I'm glad there's enthusiasm. Next on the docket, we have a company that we just spoke about yesterday. So if you listen to yesterday's episode, we have a small little follow-up, which relates to Stripe and the acquisition that was made to acquire OpenRouter. It is officially official. We were a little early, but right. And now it's official.

All the contracts have been signed. Alex is Hala, who is the founder and CEO, says, we officially have a deal. And he kind of outlines the thesis. And I know Stripe, as a result of this, they shared a publication, a blog post, EGES, that you were really excited. So I'll let you describe what they spoke about here. But some TLDR on Stripe is like, again, we're talking about unbelievable founders today. The Collison brothers, who are the founders of Stripe, are among the best of the best. They have built an unbelievable business, all starting with seven lines of code that allowed a person to pay with a credit card online quickly and easily.

And they've developed that technology from basic credit card payments to, basically the entire payment infrastructure of the world. They started with credit cards, then they had, they moved over to crypto and stable coins. Now they're working with agents to figure out how best to serve these new agentic transaction systems. And that's kind of where we find ourselves here. I'm reading the headline is that Stripe is officially telling investors that the singularity has begun, but explain the nuance behind what they actually mean when they say that. Yeah. So in the official letter, which Stripe sent to their board of investors, they basically described why they acquired OpenRat 2 for $7 billion. Why is a fintech company buying an AI

lab for such an expensive amount that doesn't even own or make their own AI models? And the thesis is pretty simple. Number one, in a world where the internet was booming, Stripe's core focus was to be the backbone of that entire internet economy. How do you become the backbone of the internet economy? Money. So they created the rails for money. Then they peaked over the fence and they saw this massive tidal wave of AI coming over with agents, with models, and they realized it's not money that runs that AI economy, it's tokens, AI tokens specifically. And so they wanted to own the infrastructure rails to own that AI economy. It's very similar to what they do with money, which is why they're pairing both of these together. So that's thesis number one. But thesis

number two, which caught my eye, is, and I quote, Stripe basically claimed that the beginning of the singularity has been here. And the date specifically that they specified in this paper was January 1st. And this is a large part of the thesis behind acquiring OpenRata. They basically go on to say that we have reached a pivotal point in our global economy where the future is very much dependent on AI and it's going to accelerate pretty much anything and everything we know and touch. The world is going to look very different five years from now. And so the bet that they're making in this world of singularity is AI models will be embedded into everything, which means that thesis of AI tokens flooding across the entire global economy is going to be a thing, and Stripe wants to be the

backbone for that. So it's a big bet that they're making. OpenRouter, I think, on their last earnings that was disclosed by Atala, because again, they are a private company, is making $50 million a year. So $7 billion is a humongous markup, but Stripe is making a bet that inference compute specifically is going to be the the biggest earner for AI going forward, and all the numbers from Anthropic and Open Air recently that have been leaked proves that. So I think this is going to be in the future, if I had to guess, one of the best acquisitions going forward. Yeah, and perhaps like an explainer on the singularity for people not familiar, because it's a concept that's been around for a very long time.

Like, this is a sci-fi concept for as long as I can remember in books probably 50, perhaps even 100 years ago, where people speculated on this point in time in which the intelligence of artificial systems eclipses that of human beings. And it's a moment in time in which AI becomes smarter than us, where these smart machines are then enabled to build even smarter machines, and it builds this kind of fast loop of recurring self-improvement. And the idea is that the second order effects of that is that life change is so fast that humans can't really predict and are going to try their best to control the future. And this has been this hypothetical for a long period at time. People always speculate whenever we come up with this super AI intelligence brain. And the reality

is that Stripe believes that it's here. And I don't think anyone would disagree with them too strongly. Perhaps people will say we're not there yet, which is a fair criticism. But to say there isn't a clear path to getting there soon feels a little misguided. It's very obvious now. And it's interesting the timing too that January 1st, because if you remember over Christmas break of last year, going into this year, that's when the real kind of like flow of like, yeah, that's when cloud code like really came out, started becoming good. And that was the moment in time in which developers stopped writing code by hand and started writing code using AI agents. And now fast forward, we're sitting here in August. And it is unfathomable to imagine writing code by hand. Like people just do

not do that. There's absolutely no reason to do that. It's weird to think that people ever did do that. And that change. Over this eight-month period is so extreme when you consider the second order of implications of what that means for the world. Like now we're seeing these models that are so powerful, we can't actually release them. There's now infrastructure frameworks happening through the government to kind of protect and slow down this. Open Air has paused progress because it's getting so fast. And if you heard about this, even 12 months ago today, you would think, oh my God, this is faster than we expected. This is what the singularity looks like. And here we're sitting in its day. So pretty unique insight by

Stripe, really awesome letter. I would highly recommend going through and reading it. It's a leaked Let me. This was for a private investor. So you get a little inside peek behind the curtain of what's actually going on at the company. Very cool. And so moving on, chapter two of Jensen turning Nvidia into a bank. If you guys were watching the show last week, we released an episode where Jensen basically managed to raise $500 billion from some of the top financial firms in the world, including BlackRock, Blackstone, to fund the future of data center CAPEX for some of these biggest AI labs. And we concluded that it isn't really a circular. economy in the way that they're raising it. It is based on a bunch of different milestones and is backed by a very healthy debt. Now, in Chapter 2, Jensen announced that he is spending another $100 billion for Open AI specifically. He's investing it in this site in Ohio, which is arguably going to be one of the biggest data centers in the world. It's going to scale up to 8 gigawatts worth of compute. It is going to bring in a nice and healthy $200 to $300 billion of revenue for

in video specifically from one data site, from one single customer, and it's going to conclude over the next couple of years. Now, the craziest part about this is he's baked into the contract that this includes renewals of newer generation GPUs, which means that he's going to make two to $300 billion three times over the longevity of this data center over the next five years. So there's been a lot of contemplation around, you know, why Jensen is getting involved in this. The thing that stands out about this particular investment is he's investing these $100 billion, specifically in the power and networking infrastructure side of this data center. So he's not really doing it to help open air buy the GPUs. He's helping them set up the power transmitter lines to power these

GPUs. And why I wanted to speak about it on this show is we have said a few times now, Josh and I have called it, that the next kind of investment theme within AI is going to be on the photonics, optics, and power stack of the AI trade. And this is yet another very in-your-face evidence point that Jensen is heading that way. And if you look at like Nvidia's investments over the last six to, I don't know, like nine months, they've invested heavily in optonics. I think they put like two to three billion into lamentum and coherent each. And now they're making a big play on the power side of things. So just an interesting noteworthy update. I think Nvidia is going to backstop this entire economy that Stripe is talking about, to be

honest. I was reading this like a great transcript from Ben Thompson of Stratory. He's just like this, you know, well-known writer on the internet for people that don't know. And he was talking about kind of like where these restraints and thresholds might be in the process of scaling AI systems. And he made this really interesting point where oftentimes people are focused on the energy, the things that, you know, kind of like Jensen is focused on, the land power and shell. But one of the other important considerations is like, where is the money for all of this coming from? And what we have seen, and we've been talking about this on the show over the past couple of weeks, but it's like, okay, we went from spending all of the

free cash flow to starting to raise money from outside funding, and now companies like Google are issuing debt, they're issuing equity in order to raise more money. Now you see Nvidia and Jensen Huang working with every large bank in the world to tap into new sources of money. These are places like pension funds and retirement funds. That's what these banks hold. So where do you go to next to raise the next tranche of money to build out the next large data center? It could be. And I really thought this was like a thoughtful insight. It could be the actual dollars are going to be running short and it's going to be very difficult to continue to raise the required amount of money to scale this thing. So just an interesting thought as we wrap up the roundup today of like,

where's the bottleneck? Well, everyone's saying it's here, but like where are they going to get the money to fund it is an interesting thing to follow. And we've been talking about this on the show. We'll continue to cover it. But if you've made it this far, thank you. You've made it to the end. You are fully caught up if you've watched the other three episodes that we published this week, because it was a pretty strong week, I would say. We had Elon and Grokbot releasing Grok 4.6, and it's incredible. We had Leopold making, basically publishing his final portfolio because, you know, he got liquidated. But we talked about all of the filings, what the smartest money on Wall Street is investing in.

Yesterday, we spoke about Stripe and their acquisition. Then we talked about what models to use based on what type of use cases you have. And here we are today, getting fully caught up on all things. AI, frontier tech. So yeah, thanks for watching. We made it back again. EJ, any final part in thoughts before we get out of here?

No, I just wanted to say thank you folks so much for listening and keeping close with us. We have added, I think, maybe 6 to 700 new subscribers over the last couple of weeks. Hello, it's lovely to meet you. Welcome.

We are a thousand. A thousand? Oh, my God. The momentum is really gaining with this channel. And it's awesome to see the kind of support and vibes coming from different people. We read every single comment. I try to respond to as many of them as I can.

You guys are growing in fandom ship, which is awesome to see. You guys are watching every single video. We see a bunch of repeat users and listeners as well, which is awesome to see. Wherever you're listening to us on YouTube, Spotify or Apple Music, please give us a rating,

give us a thumbs up, subscribe, turn on notifications, leave us a comment. It helps us out so much. And yeah, I think that's it. Enjoy your weekend. This is everything you should officially be caught up unless we're missing something pretty drastic that's happening right now after we record.

And yeah, we'll see you on the next one. Thank you guys.