Limitless Podcast - Google I/O 2026: Their Next Big Thing Is Finally Here

Episode Date: May 20, 2026

🌌 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/------Google I/O 2026 was packed full of announcements, but we need to determine whether the company is keeping pace with OpenAI and Anthropic. We cover Gemini Omni, Gemini 3.5 Flash, and Gemini Spark, along with Google’s updates to Search, YouTube Search, and its new AI glasses. Let's  examine Google’s broader product strategy and see if it merits its spot as one of the largest companies in the world.------TIMESTAMPS0:00 Google i/o1:00 Omni’s World Model3:24 Gemini Flash13:48 Gemini Spark17:12 AI Search19:49 Google AI Pro21:24 AI Strategy Dilemma23:58 Glasses and Hardware26:37 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

The CEO of Google just got on stage yesterday and told everyone that there's no new frontier model. In fact, he said they've only made it 90% of the way there. So on the surface, this seems fairly disappointing given the fact that it's been many months since our last model release. And Gemini was doing pretty great. So while he didn't claim they had the lead, they did proceed to release new models in nearly every single product we use every day. And in some pretty creative ways. I mean, three of the highlights are a brand new model that's pretty prevalent throughout all the software we use. an open-claw competitor that is built right into Google,

and this new model named Omni. And that's what we're going to start today, because Omni is nothing like we've ever seen in the world of AI before. Omni is a pretty impressive model, and I think probably the highlight of this event. So, he says, what's going on with Omni? So they had this really cool moment during I.O.,

where they just started showing this announcement video, and people were kind of looking at this and were like, oh, some of this looks AI-generator, but some of this actually looks quite realistic. and the reason was they released this brand new model called Gemini Omni. Now, it's a very unique model. It's not something that we have seen before because it's a hybrid of an LLM and a world model. And world models, if you've heard that term before, is really unique because it can produce video that not only looks realistic, but is physically accurate.

So something that LLM struggle with, it's like that analogy we've used before, Josh, where you have a student in a library that's read all of the books and understands the world, but actually. actually has never stepped outside of the library and experienced the world for himself. World models are basically an upgrade from LLMs, where it can teach the LLM to understand how the world works, the consequence of an action. So say, if you hit something or if you jump, how gravity works and all those kinds of things. So the unique thing about this model is it can take any input medium. So we're talking about words. We're talking about images. We're talking about video itself, and it spits out only one output, which is video. Now, the videos that you're seeing on screen here is physically accurate. Now, obviously, it's generated by AI,

but the vortex that you're seeing on your screen now, the way that the person draws a circle, the way that the paper kind of crinkles is basically all meant to be physically accurate. And the reason why this is such a powerful model is, well, there's a few. Number one, when you're learning about something. The number one medium that people or humans ingest information is through video. Words is actually kind of slow. It takes a while to kind of like go from left to right. People don't like read all the time in today's world, but they do watch videos. There's several different sensory instruments that come in. There's sound. There's like visuals. You kind of like ingest data way quicker with video and that's the bet that Google was making with this. And the second thing I'm going to

say is this is pretty world changing for a lot of different applications. outside of just LLM. So, for example, Google has been a massive proponent of robotics. The issue with robotics is these robots don't understand the real world. And so far, all the robot models have been fed with words about how the real world works. Well, what if you could place that robot brain into a simulation that is physically accurate, like a world model, and you can start teaching people or robots specifically how to interact and they can train and learn a lot faster? So I just think there's many different applications for what this world model is. And it's the first mass scale deployed one. So I'm excited to get my hands on this personally. Yeah, and it's

available today live for basically anyone who uses Gemini. It's available in the Gemini app. And it's, pretty cool. One of the things that I've enjoyed personally is that you can create an avatar of yourself. So you can actually build a digital clone of yourself and inject it into these. You could think of this model sort of like nanobanana but for video, where for the first time ever, you can really create fully custom videos that either take base reality and build something on top of it, or just create a total AI demo in general. So we have this pretty cool demo from the CEO of Deep Mind Demasasavis which show you to kind of how this works,

where it takes his actual video that was recorded on his camera roll and then turns it into five different scenes that are all completely unique, seeming somewhat realistic. And I think this combines the fidelity of something like Nanobanana with the complexity and physics understanding of V-O-3, and that's where it really shines. What's also really fun is you have the ability to refine these things too. so you can actually tweak the model, tweak the outputs.

And the result of this is something fairly comprehensive, particularly if you're trying to learn things. I find that education is probably one of the most interesting demos and use cases of this, and we have one, which is prompted from a single question of how does photosynthesis work. And now, as a student of anything, as someone who's trying to learn, you're given this fairly comprehensive answer that starts with an image. The image is generated by nanobanana, and then it gives you the chemical recipe. And then under this How It Unfold section, we actually get a full,

generated video from the new Omni model that shows you a like actual visual representation full of graphs and charts and seemingly realistic version and an easy way to learn how this works. So I think this is probably the one of the most powerful use cases if you're a teacher, if you're a student, if you're someone who's just curious about any topic and your preferred way of learning is visually, this is a really compelling product for you. Now this also works with real videos that you could just upload to Gemini. We did this this morning. I had this video on my camera roll that was just filmed outside of a window.

and the problem was just, hey, make some meteors fall down from the sky and put the lockedest monster in the East River. And the reality was this output, which looks pretty good. I mean, if you're looking at this, you'll notice there's sound. You can hear the ambient sound of the city. There's a large splash when the dinosaur or the loctest monster hits the water. It looks pretty good, but also this is very clearly AI generated. It feels like CGI from perhaps the early 2000s. So not quite what we would expect in 2026, but very cool. the less that it can actually take one of your videos and generate something pretty powerful on top of it.

So if you listen to this, you're probably wondering, like, how can I use this? Why is this applicable to me? Well, if you're just a regular person that kind of wants to play around with video, this is plugged or integrated right into YouTube shorts. About two months ago, Google made a very important decision,

which is integrating AI into their major video platform, YouTube. A lot of people had mixed feelings about this. Artists in general in the creative world tend to hate on a lot of different AI tools. Google takes a different approach where they're saying the kind of like proactive efforts that a lot of artists are making, if they can produce more content that is applicable to various different niches of audiences, then you can kind of like create a sustainable platform.

They see the number one distribution for this being YouTube shorts. So this is plugged into YouTube shorts right now. You can go out there, create a eight second video and, you know, show us what you've got if you actually isn't of this and you have an account. Like, we would love to see it. I will say that these things, do look quite janky right now, as you mentioned, Josh, but this is also V1.

And it's important to stress that world models up until now weren't really applicable or distributed to a mass audience. If you remember, Google released, I think it was Genie 3, which is like their really powerful world model. That is what powers this model that you're seeing on your screen here today. But it's not a full implementation. Why? Because it's actually quite expensive to serve.

It requires a different. type of infrastructure to train. And so once that kind of scales up with Google, they'll be the first distributors of this. And I'm excited to see more of this, because Demis at Davos earlier this year basically said that, like, he thinks world models is the future evolution of LLMs.

He thinks that words are ultimately constraining for AI models. And in the future, if you have a model that can speak, but also understand how the world works, that will be the all-consuming model. Now, of course, that's in the future. For now, we have to focus on the major frontrunners of AI, which is Anthropic and Open AI, which have the world's leading LLM. So the question becomes, did Google release an amazing LLM? Although they did release an LLM.

It's called Gemini 3.5 Flash. And to quote Logan over here, it's their most powerful model to date. Now, it pushes the frontier in many different ways. We've got a chart here which kind of compares Gemini 3.5 Flash to the older Gemini Flash models. as well as Opus 4.7 from Anthropic and GBT 5.5.5 from Open AI. And you'll notice that a cross-coding in general, it doesn't trump or beat GBT 5.5 or Opus 4.7, which I guess some might say is a little disappointing, but also unsurprising. Google has fallen behind in coding AI for a while.

So it's not as good, but it's kind of near the marker of them. But if you look just below here, this is where this bottle actually excels. It's known as agentic tooling or agentic AI in general. What Google's been able to achieve with 3.5 Flash is the ability to spin up multiple sub-agents that work in parallel to get your prompt resolved. So if you think that having one singular brain to do all the work for you really, really well,

which is what Paul Oprah's 4.7 does, which is what GPT 5.5 does, Google took a different approach and said, let me have multiple brains, maybe even hundreds of brains working on the same problem and collectively, they can produce. a better answer. And that's what they've achieved with the Gentic tooling here, where you've got the Toolathon and the MCPP Atlas benchmarks, which absolutely crushed GPD 5.5 and Cloud Opus 4.7. So you're

probably wondering, okay, well, what tasks can I use this for? It's typically long horizon task, like long tasks that you can kind of shut your laptop down and kind of let it work away for like 10 to 20 hours. This is the model for you. So you'll note that they said that it is their best model ever, not the best model ever, and that it is fast, but it is not the fastest, and that it is affordable, but it is not the most affordable. And when you're kind of observing the parameters on the Pareto frontier, the things that we actually measure to evaluate a model, it's not really the best at anything. And in fact, we have this kind of comparison that I saw with Cerebris, which I really enjoyed, and Cerebus posts this kind of trolling them, which is Cerebris versus Gemini Flash.

And it very clearly shows where Cerebris sits versus Flash. You could see 3.5.5.5,000, which is a 3.5 flashes out pretty fast in terms of the tokens per second output. But when you're trying to optimize for one of these things along the frontier, clearly, Cerebrus has the speed thing completed. So it's not the fastest by a large margin. It's not the smartest by a pretty large margin. It sits kind of in the middle. And I think this is the most interesting part of this story today,

is that a lot of these models are not the best, but they are unique in their own way. One of the cool demos that I like that they had mentioned during the presentation is that they actually took this new Gemini 3.5 Flash model to build an operating system from scratch over the course of a series of hours. I mean, that's pretty incredible. It says it took 12 hours, 93 parallel agents, 15,000 plus model requests, and then $2.6 billion in tokens process.

So you're essentially able to build an entirely new operating system from scratch for less than $1,000 in API credits. That is pretty phenomenal. That's actually super hard to do. Do you remember we? I think who did it? I think it might have been GBT 5.4. It was GPD 5.4.

And Sam posted this video of a team building an OS from scratch. And that took them, I believe, 36 hours, like over a day. Like in that time, so I think that was like two and a half months ago, you now have Gemini Flash 3.5, which isn't as good a model as GPD 5.5, do this in a fraction of the time by creating this like novel approach of like spinning up almost 100 agents to do that. Josh, I had a comment on the speed thing here, because you're right. Like, cerebrus that runs GBT 5.5 versus Google is not just a question of speed.

It's a question of infrastructure. Now, I have a demo here which shows actually how quick Gemini Flash can be. And to be honest, I just want to start this from the start. Look at this. That's crazy. My brain can't interest that much information in real time. And I think, although it's not as fast as some of these other new chip, architectures, it's still good enough for the majority of humans, for sure, right?

The other thing I'll say is this is built off of Google's latest GPU infrastructure, known as the TPUV8, specifically the 8I, which stands for 8 inference. It's their inferencing chip, which basically is specialized around giving you quicker responses to your prompts. Now, that being said, this is incredibly expensive. Demis Sazabas dropped some stats here where he, kind of shows off the speed. He says, it's 4x faster than any of the other frontier models. And actually, if you use our IDEE environment, which is basically Google's equivalent of cursor,

it performs 12x faster. What he doesn't mention is it's four times more expensive than all the other frontier models. So there's a trade-off here, right? I think you mentioned it before we started recording, Josh, like, there's a trade-off. If speed is something that you necessarily need, which you could very well want in financial services where you're like trading algos for whatever, speed becomes a necessity, you're willing to pay that premium because you end up making more money on the top end.

That's completely fine. But for the majority of users where speed doesn't necessarily matter that much, but thought and intelligence matters more. You want to make sure that that cost per watt per intelligence or whatever that unit might be is kind of like kept at a certain bracket. Like I watched an episode where they interviewed the CFO of Anthropic

and he mentioned that these enterprises are spending like a hundred, 100% more than the initial budget that they had at the start of the year by the end of the year. So the point is like people are spending a lot of money on this and at some point we need to kind of like reduce budgets to an extent. So yeah, it's quite expensive. All right. Who's an open claw fan who's listening or who is given up on open claw? Because if you are a current open claw user, you're probably going to hate this. But if you've never used it before, this is a pretty compelling product. And it's called Gemini Spark. This is the

third major announcement that they made. And Gemini Spark is fascinating because it is that 24-7 personal agent. They're claiming that it helps you navigate your digital life, taking actions on your behalf, and under your direction. Essentially, this is Google's answer to OpenClaw. This is their version of the 24-7 agent. And the reason why I said you may hate this is because you are beholden to Gemini 3.5. Now, one of the best things about OpenClaw or if you use Hermes is it's model agnostic. You could choose models that you want to use and it will go and tackle specific tasks. In this case, on Gemini Spark, you're stuck with 3.5. So is this good? Is this bad? We'll see, but I find it really interesting because this just runs directly in your browser.

So you don't actually need to have a laptop open. You don't need to run this on a remote server. All you need to do is log into your Google account, set up a Gemini Spark instance, and then it will just run permanently in the cloud. A lot of times you'll see there's memes going around to people who are walking around with their laptops cracked open because they're running these agents that are running long-firm tasks.

This is basically your own little private server to go off and build anything that you want. And it's pretty interesting. I think for people who are less technical or for people who just don't love using OpenClaw, this is a pretty easy way of accessing a 24-7 server to just run commands, to run prompts over very long durations of time. I like this and I hate this. The reason why I like this is I use a lot of Google products to do work, Google Docs, sheets, Gmail.

So the fact that I can have an open claw that is seamlessly integrated into all of those things gives me hope. The other side of this is I just don't think it's going to be as good a quality product as OpenClaw or Clare. Claude co-work. In fact, there was news that broke, what was it, like a couple of weeks ago, that revealed that majority of Google employees were using Claude to do their work, right? And then Google kind of like put a ban on this. And the reason why they were doing this was presumably to learn how to build a better agentic or coding model that can do exactly what we're seeing on the screen here. So they're late. They're lagging the front runners. I'm glad they put something out there. Google does have the distribution. They do have the data mode. They do have all the

users for now. And so I think they have a shot at creating something good here. But I get meta vibes from this, Josh. The same reason why meta might have a good model with Muse Spark. I'm not using it, because it's not on the platforms that I actually want to use the thing. Yeah. And I think that's one of the trends of all the announcers that we're talking about today. They are all kind of existing in seemingly different places. It's not clear and obvious which features I'll want to use. We're about to rattle off maybe five or six more things that they've announced. And I think one of the themes as I, I mean, this is a common complaint that we've had with Google, or at least me personally, is I'm not really sure the best way to use all of these tools. Like with Anthropic, I go to the Claude Desktop

app and it has everything in one roof and I know exactly how it all works. With open AI, the same thing is true. And I even have a companion mobile app. With Google, there's a lot of this kind of ambient AI that exists a seemingly random series of touchpoints. Like it exists in my Gmail, but I kind of hate the AI written emails that it makes. It exists in my Google Drive, but I don't really need it there. And then you have the Gemini app, which is getting better, but it still doesn't include a lot of the functionality that we're mentioning today. I mean, the next thing we could talk about here is there a search, because for the first time in over 25 years, they're really changing how search is going to work. And again, it's just kind of like this ambient AI that you don't really

seek out. I'm not really sure where to find all of it, a little confusing. But in terms of the AI search, it's very different. Now Google Search has, Gemini 3.5 flash baked into it by default. So when you search for something, it will be routed through Gemini 3.5 instead of the traditional Google search engine. And this is a pretty profound shift in just the way the internet works, the way people get information on the internet. The first person I think about is the sites that are kind of optimizing for SEO on Google. Now it seems like the whole meta has shifted to SEO for AI models, because now when you search something, the reality is that you're going to be routed through an AI model and not the standard of Google

indexing algorithm, at least for now. So one thing a lot of critics on Wall Street said about Google three months ago was that they're worried about AI eating their dominant market share in search. And then they released their Q1 earnings about a week ago, and it revealed that not only did it not eat their search market share, it increased it. They ended up earning more money. And I think that's because Google Search has been integrating AI for a while now. This isn't the first time that I typed something in.

in Google and it gives me an AI-generated prompt. I love that we have the latest model here and that they're making it more native into the homepage. I think this is amazing. So this is my second favorite release from Google, from I.O. Hundreds of millions of people, probably even billions of people, use Google every single day. They ask a bunch of different random questions.

I would actually argue that it has more reach than your chat GBT, Home Interface or Claude Home Interface. And if Google is able to successfully pivot this into something that serves up their latest LLM prompts, and somehow get people to use or pay for it, then this could end up being a home run. Of course, as you mentioned earlier, like Google is free at this point, right?

The whole point is like distribution is demote. And so I think they're going to end up making most of their money from this particular feature through ads. Now, it's important to say that no AI company right now has figured out the SEO strategy. In fact, the leader of this is META, who has been embedding a bunch of AI agents and models into their ad services on the back end,

help advertisers reach more of a wider audience. If anyone knows the data of their users, the best is definitely matter. They've been training on this for a while. Their new model MuseSpark does exactly this. I have a feeling Google's going to go down the same group, but they're going to be a little more careful about this because they are the dominant market platform for advertising. So I'm excited to see where this goes from here.

And then there was this other post, which you were commenting on, Josh, which is basically like you get such good value for money with a Google AI Pro bundle. This is my new favorite subscription for $20. It comes with YouTube premium, Light, which doesn't include music videos, but basically an ad-free experience on YouTube. In addition to 5 terabytes of storage, you get access to all of the new Gemini Pro models. It has image, video generation, access to anti-gravity, notebook LM Pro, which is really good for deep research and long-context stuff.

It's a really compelling bundle at $20. And I think this is one of the most understated things that was released yesterday, because this is what I would probably recommend most people use. it gives you access to the entire suite, and even if you just watch YouTube videos. I mean, you get access to the ad-free version of that, and that alone makes it worth it with the 5 terabytes of storage. So very compelling value coming out of them. Speaking of YouTube, they also did the same search thing that they did with Google on YouTube

as well, where now there's AI search baked right into your search results. It turns out that Gemini has been trained on lots of YouTube data and actually has context on the types of videos that you want to watch. So if you ask a question about a specific thing that you want Troubleshot, it will not only find the right videos for you, but it will find the correct moments in the videos and serve those up to you. And I find that we've started to experience this in Google search recently where I can type something and it'll kind of timestamp me into a video. Now it's fully baked into the YouTube experience. And I think that's going to be a really compelling product as well. So Google,

in terms of upgrading their core products that everyone uses on a day-to-day basis, looking really strong. For this final section, Josh, I want to be a really important. to zoom out because the question that I have on my mind is Google has a decision to make on which strategy they want to pursue for AI. Right now, Open AI has made it very clear. They want to catch up with Anthropic on coding. And in fact, you could argue that they have done that. Anthropic focused on coding for the longest time. And the reason why both of those companies are doing that is they believe if they nail coding AI, then they can build everything else. Everything else becomes downstream of coding AI, right? There's an argument there. Now, Google, with

this latest launch or series of launches hasn't really made that explicit statement as well. In fact, I think they're making a few other statements. Number one, they're betting that video or omni models are going to be the future. That's why they spent a lot of compute and time invested in their new Gemini-emone technology model. Number two, I think that Google is realizing that the agent harness or the harness that goes around an AI model is equally as important as the fundamental foundation model itself. Now, I have proof of this. There was this tweet that I saw yesterday, which on Cursor, you can

measure like the intelligence of different models across different tasks. And it showed that the recent Gemini 3.5 Flash was worse than Cursor's own foundational model, the old version of it. Composer 2.0. And yesterday, Cursor released 2.5, which is, on par, you can see on the top here, with the other models, the frontier models. So the question I have is, like, Gemini kind of missed here. Like, they should be focusing on the agentric side. As I mentioned earlier, 3.5 Flash is great at agentic tooling. But what they've missed here is the harness. The harness is basically the wrapper, the muscle memory that goes around your model, that augustrates the model in a really ideal way. What Open AI, what Anthropica has realized,

is that that is super important. Elon Musk has figured that out, and that's why SpaceX is looking to acquire or moving to acquire a curse up for $60 billion, I think Google missed here. And what I come away feeling about the series of IO launches in general is it's good, but it's not great enough. And if you're Google, which was two weeks ago, the most valuable company in the entire world, you need to be making bigger bets. And it's a shame that they haven't made it. That's currently how I feel. Yeah, when you compare the prices too, I mean, it's four times the cost of Composer. So it's like on a price basis, it's losing on a score basis is losing. I will say this is 3.5 Flash, not 3.5 Pro, which we can expect to be released next month.

So that might change things and shake everything up in a pretty big way. It's just not currently available, so we can't judge it. We can't be critical of it. There is one final announcement. I just want to squeeze in because as a hardware guy, I get excited about anytime someone releases hardware. Google has some new hardware offerings. It comes in the form of glasses.

They are able to see the world and interpret it. Some of the examples that were used that I found pretty cool was, um, she could take a selfie, the person who was giving a demo of the crowd, and then prompt the glasses to not only capture the image, but create a variation of it using Nano Banana Pro. It gives directions. It is agentic in the sense that it can work with your phone, or do you coffee on the way to work. It's pretty cool, pretty interesting. I'm very excited to see more about glasses. They actually announced that they're planning to add a HUD, a heads-up display, basically a visual display into the glasses as soon as next year, which is exciting. So Google seems to be stepping pretty

seriously into the hardware market. I think the current versions are kind of a joke similar to all the others, but the ones with the heads up display are going to be very exciting. Next year, very much could be the year of AI glasses. But with that, I think we got it covered. Is that, is that I.O. covered? Is that all of it? I think that's just about everything. There's a bunch of other stuff, but it's kind of tertiary. There's some YouTube upgrades and stuff like that. But I think we've covered all the hot topics, Josh. I think I come away from this episode thinking Google is not out of the race, but I do think that they need to double down

on coding specifically. And the crazy part is like they have the moat. They're currently in the number one position. The only other company that could compete with them is Apple. And Apple has been doing nothing. They're getting a CEO swap. And then I think later this year with John Turner's coming in, and then I think we're going to see like them step up.

But the truth is, it's them versus these two startups, which are aggressively increasing ARR, like by the month. I don't know if you saw this, Josh, but someone projected out Anthropics' rate of revenue increase which means that by 2028, they would have surpassed Google. So Anthropics' projection for this year

was 10 billion ARR. They are about to hit 45 billion ARR this month. Yeah, that's insane. And if that projection, or if that rate of increase keeps happening and they start consuming more enterprise customers, et cetera, et cetera, you can feasibly say that they will become as large as Google, which then starts to justify all these crazy, you know,

$1.1 trillion valuations or the secondary round that they're raising right now for $1 trillion. It kind of makes sense. So I think Google still has a shot. This wasn't the killing blow, but maybe next time. Maybe when Flash, not Flash Pro, but 3.5 Pro comes out. Yeah, well, if I could give any feedback on the presentation, it would be to focus.

It feels like Google doesn't really have any focus. there, it felt like I was watching 15 separate presentations instead of one coherent one. And therefore, as someone who is an AI fan who literally follows this for a full-time job, I was unable to keep up and orient myself on where all these products fit into the ecosystem. So if I could leave one criticism for Google, it's just to focus. When I think about Anthropics offerings, I have a very clear picture in my head of what they're good at and how I want to use it. Same is true with Open AI. With Google, that's not the case. And I think if they can focus, If they can compress all of these releases down into a few key things, that would meaningfully

shift the way that people think about Gemini and engage with the products. And I hope that for their next releases, their next presentations, will get a little bit more of that focus. But that is the episode for today. That is Google I.O. You are now fully up to date and informed on all of the things Gemini, all things Google. Any final parting thoughts that you just before we head out of here? Please spin out a really good version of Google Glass.

That's one. Oh, man. I love that. I really want a good hardware device. I'm like begging for it. Josh and I saw another leak last week. We didn't mention this on last week's episode, but it was those weird open AI over earbuds with the weird disc thing.

And I was like, I need that device. I just need I need something. I realize I need hardware in an ever-increasing digital world. So Google lost, like, I almost think it's good if they would just spin it out and make it a cool consumer company. But yeah. Well, you know, no one wants hardware more than I do. dying for a good pair of glasses, just anything physical that I could touch. That's not my iPhone.

That's not made by Apple. But alas, no one has completed that yet. So we will keep our eyes peeled. We will continue to watch. If you enjoyed this video, please don't forget to share it with a friend who might also have enjoyed it. Don't forget to subscribe to the channel. Loads of new subscribers, Josh. Leave a comment. Yeah, we had, we had our biggest episode ever. Drop on Monday, the Leopold Ashenbrenner portfolio, the new 13F filing. So if you haven't seen that, I would highly advise. We had a lot of new people joining. So for the new Welcome. We publish new episodes four times a week, about 20 minutes. Sometimes we go a little bit longer when we get excited But there will be many more to come this week. So thank you guys so much for watching this one and we'll see you next time See you guys