4年間でAIはあなたの記憶以上に変わった

I think at some point this is going to take the form of what I call machine labor on demand, which is at some point we're going to be able to call not just AI models, but full robots to do things for us. And I actually think we need to reframe our thinking. We're not labor. People are not labor. I think in the future we're all going to be basically venture capitalists, where we're all going to have a compute budget of some kind, just like a VC might have money to invest in things. and we're going to need to decide who we're going to team up with to work on projects together. While it's not a given, I really think that we're going to get there. I think we'll see it in the lifetimes of people alive today and not necessarily young people. Like, I think we could see it very soon. I think we could see it in 10 years. It could even be longer, but I really think we're going to see big strides in the coming years, and I'm excited. Just look at the last four years. Imagine the next four. Welcome, humans, to the Neuron AI Explained. I'm Corey. This is Grant. What's up, Grant?

How's it going, Corey? How are you doing? It's going good, man. It's going good. And today is going to be so much fun. We always talk about, wow, it's crazy how far we've come. It's crazy how far we've come. But we never, like, actually sit down and look at how far we've come and talk our way back through it and kind of remember as we go, like maybe how much something blew our mind, how long ago that was. And today, Grant has put together a really cool spreadsheet for us, and he and I are going to walk through the last almost four years of AI and just kind of take a little trip down memory lane and look at how fast this has happened. Because in putting this together, it's insane. It really is. Just having this pulled up on my screen, I catch myself going, what? and I think this will be a lot of fun, Grant. Yeah, so you can look at this as kind of like a retrospective on all the progress that's happened over the last four years

and especially as we get to the end of this year and how far we've come and how much progress there's been maybe with some changes on the horizon fundamentally huge changes on the horizon it's good to look back and say like okay, well what were the things that led to this progress and to kind of keep track of it because the AI industry uses this phrase called riding the exponential or some version of that, right? And the concept is that technology is increasingly basically building itself or progressing, right, at an exponential rate. And it's really hard for humans to understand, to conceptualize an exponential. Like, it's, like, really hard to visualize it and to wrap our minds around this. And I think this episode is going to show people, yeah, this is what this looks like. Like, we are on an exponential curve now, and it might end up being an S-curve, meaning it might go up and then flatten out.

And maybe that's where we're going to be, you know, leading into next year. But I kind of don't think so. I kind of don't either. Yeah. I just – I've yet to see anything that tells me, oh, that's obviously slowing down. Yeah. I mean, maybe with the results of election changes, who knows what's going to happen there. But, yeah. Hundreds of unsolved math problems falling and new science coming out from multiple labs. You've got from Anthropic, Google DeepMind, Meta. Everybody is right now just tapping into the stuff that I like to categorize as the promise of AI, which is the stuff that has the ability to change humanity long term for the better, that advances us as a civilization, that changes how we not only understand the universe, but experience the universe and understand what we can do inside of it and beyond our own planet even. So we're really at that point, and it just felt like a good natural time with four years of ChatGPT rolling up to take a look back.

So, Grant, let's start with right now. Right now, the hottest models that exist are obviously Claude 55, Fable 5.1. You've got GPT-6 Astra, GPT-6.1 Soul. We've got Crock 4.7 Mini. We're in the era of computer use. Opus 5.5 as well. Yes. Oh, yeah. I said Claude 5.5. Yeah. Oh, sorry. I said Claude 5.5. There's also Abel 5.5 in there. Yeah, yeah, yeah. And even a new Mistral model, Deep Seek's around, Kimming's around. There's a lot of stuff. We're in the age of computer work and long-running agents and stuff that was science fiction at the other end of this list still. Like some of this was like the crazy stuff Sam Altman talked about in an interview. Like I remember being like, oh, everybody's going to have agents. You're going to have agents doing everything. You're going to get up in the morning. You're going to talk to your agent. Your agent's going to make your coffee. It's going to go down. It's going to get your shoes. I'm just kidding.

You didn't say that. Well, maybe it's robotics, you know, like there's a dream of robotics finally making a lot of the promise of AI come full circle. And, you know, like with human intelligence, we're kind of doing it in reverse, where, you know, if you subscribe to the idea that we've all evolved over time, then we kind of evolved physically first, and then our brains caught up. Well, we're doing robotic machine intelligence evolution backwards, where we're starting with the brain, and then we're adding the body onto it. Yeah. Yeah. It's crazy. So for this, by late 26, Frontier models are sold on long-running work, computer browser use, agent orchestration, cost efficiency, and not chat quality alone. But we're going to step back now just four months, okay? This gets us on a clear six-month trajectory all the way back to ChatGPT's launch on 11-30-2022.

So we're going to step back right now to May 30th. That is probably five months by the time you're watching this. At that time, the hottest models were GPG 5.5 Pro, which was a great model. Claude Mythos Preview, limited access. It was not public yet. Normal people had not touched Claude four months ago. I mean, had not touched Mythos four months ago. Right. Isn't that kind of crazy, Grant? It feels like it's been around a long time. Maybe four months is a long time in the AI space as we're about to show. It is. It is. But yes, yes, I agree. The whole mythos thing, like if we flash back to that time period, what had happened was they said, we've created a model that is capable of critical cybersecurity risks. So we're going to withhold it from the public. And they withheld it, and it caused a huge uproar. People were very upset.

They rolled it out just to, you know, their elite partners. which, to be fair, were like banks and big tech companies to make sure that they didn't have vulnerabilities in their code before they released a model that could potentially find those vulnerabilities. So it was fair. But then they finally released Fable. And that was maybe like a month later after the whole... It was sometime in June. So, yeah, it's probably three months on Fable. Yeah. That's crazy. Opus 4.8 was the highest generally available from Claude at the time. And that was the one that would constantly tell you how your code was load-bearing and just talked like a complete – like you could just not understand that thing. That's so crazy. Like it was giving you the caveman talk. Yeah. Like the code was good, but it was just like it had a lobotomy or something. Let's see. What else was then? Gemini 3.5. That was kind of a dud. Was that not nice?

You know, 3.5 didn't really touch it much so can't speak to it. No, I didn't either. Muse Spark That had just come out. Yeah. Yeah, I don't think that was the first Muse Spark If the first Muse Spark was four months ago Meta's on a trajectory to beat everyone. That might be why they're pretty afraid of Meta right now. I think Meta has everyone's attention. Which I love because I think Zuck's an interesting voice in the AI space now. Like, he often says the thing that no one else is saying. Like, he's pretty consistently taking the opinion no one else has, I think. And I tend to like that. Deep Seek V4. God. Quinn 3.6. Kimmy K2.6. GLM-5 was a good model. So here we're in the era of, I guess, long horizon, multi-agent.

Million context is kind of the norm now at that phase. Yeah, it used to be kind of the exclusive domain of Gemini, and at a certain point everybody began offering it. Yeah. Yeah. That's wild. Yeah, so that had happened because DeepSeek v4 had made one million context standard. Yeah. They showed kind of the architecture for how to do that. Yeah. And everybody adopted it. And everybody else better get in line. That's so wild. Now, here's where it's going to start feeling a little crazy. we're going to go back another six months from there to November 30th 2025 at that time one year ago actually from the time you're watching this right at a year GPT-5 was their hottest model

Gemini 3 was around Claude Sonnet 4.5 was observed maintaining focus for more than 30 hours. And that was crazy then. Yeah. It still sounds crazy, if I'm being honest. Oh, Kimmy K2. That's when Kimmy K2 came out. I remember that. That was a good model. Yeah, that was really good. It was a really good model out of China from Moonshot that makes Kimmy. Yeah. That's right. Yeah, and they've since continued to make a splash, but that was their first, I say that was their first. There may have been a K1. No, K1 was the first. Yeah. Okay. K2 is the one that made the splash for them, though. Yeah, I think K1 was, people were pleasantly surprised with it. It was a really good writer. I think Kimmy K2 was a better writer, but K1 was one of the first ones where people were like,

oh, wow, this thing's pretty good for open source writing. Yeah. And then, yeah, K2 came out and I think smashed a lot of expectations and really lit a fire under people. GLM was doing really good then, too. I see we have GLM 4.5 then. You know, the thing to know here in our notes, it says models and agents converge. So 1130 is the moment when agents change, I would say. I would say from there through the next 90 days, the world got wild. Cloud Code has this insane December of explosion, explosive growth. Everyone's using Cloud Code. It's amazing. At that same time, OpenAI reinvented Codex, and it started to catch on as well. And we move into this month where suddenly everybody's using agents. We're using agents for everything we're doing then. And like we had tinkered before, but as far as like it was the first time that interacting with a tool felt like it was really doing things.

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Yeah. Too manual. It was too manual. It was an automation. Like, it's not really an agent if I have to go build something to do the job. It's an agent. If I can say, hey, I need you to do this, need you to do that, need you to do this, and it can bounce between tools. It can bounce between windows. It can use your computer. And that was the era that leaned into it. This list also doesn't include the open call moment, which would have been January. So nine months now since the end of January. We did a great interview with Vincent Koch recently that you should watch. It's really good. but like OpenClaw changed everything in my opinion it had its moment instantly over the course of like three weeks suddenly it's the most starred and downloaded GitHub repo ever like it surpassed Linux and wild which is wild and before you know it there are people lined up in China outside getting

Claws built for them like everyone in town and showed up with a computer and people are building them claws. And it became this viral moment. And it inspired, its architecture inspired a lot of the agents that you are using today, whether you use Muse, whether you use GrokBot, whether you use anything agentic inside Microsoft, dots from OpenAI. Yeah, they all have adopted the open claw pattern, basically. how it works. Yeah, because everyone leading in tech saw it too. You know, if you'll recall, Zuckerberg and Altman were kind of in a bit of a bidding war there for Pete Steinberger at one point. Yeah, like it wasn't clear where he was going to go. And the truth is, can you imagine right now if he'd have gone to Zuck? I don't know if it would be much different. I don't know if it would be much different because Muse is basically a good OpenClaw.

Yeah, probably like the best OpenClaw. Looks like they did just fine without Pete. Yeah, I mean, you know, and that's the thing about OpenClaw is that it's open source. And so, you know, I'm not saying that they built on top of it, but certainly they could have if they wanted to. Yeah, they're definitely inspired by it heavily. Yeah, and I think that's the beautiful thing about open source projects like that. Yeah. Medical took it and removed the friction. Sorry. Go ahead. No, you're good. You're good. You're good. Yeah. Yes, I agree. Yeah, they made it super easy to use for normal people because famously the horrible thing about Open Clause is it's so difficult to install. And, like, now you have to understand how computers worked at a deep level, like, for security reasons to be able to use it. That's wild. Muse, like, you know, extracted all of that away. It did. If you were going to say when did agents come about, when were agents born, I would say it was December 2025. And the point I want to make on that is that, you know, this isn't reflective in us benchmarking in November,

but by December, Opus 4.5 has come out. And Opus 4.5 was a model that changed a lot of people's opinions on what you can do with code. In fact, I would say it was probably, like, the first, like, truly, like, oh, I can code with this model and, like, trust it to create something. And so many people had this explosion of use over the winter break because of Opus 4.5. They were making all kinds of side projects, and then you have OpenClaw riding that wave on top of that. It was, like, it was sort of, like, two big breakthrough things all at once. Yeah, and that's moving into – by the time you hit February, you're also moving into GPT 5.2-ish, I would say, which was kind of their first that was like, okay, I'm just going to let this thing code. And by GPT 5.3, the codex version, because they had a codex model, that thing ripped. It was really good. it's been a while since I had a model

where I was like, God, can the next generation just come out? I can't remember the last time I was like, these are crap. This is terrible. That hasn't been a thing for a while. For me. They haven't picked up on this signal. They should pick up on this signal. You guys, it's okay. You can take your time to release stuff. We don't need a new model every 20 days. It's actually at 11 days as of 2026. New model every 10 to 11 days. That's so nuts. Okay, so now we're going to take another step back, another six months to May 30th, 2025. On May 30th, 2025, the main models around were DeepSeek R1, which seems like forever ago. Claude 4, which also seems like eons ago. Gemini 2.5, I don't think Pro yet. I think Pro came out in the window between these. Llama 4, Quinn 3, and O3, and O4 Mini.

That O3 is just over a year ago. That's crazy. Yeah. I feel like I used O3 for a very long time. And it might have came out before then because I feel like the jump from O1 to O3 was a pretty fast follow, if I remember correctly. Yeah, it looks like about six months between the two. Yeah, we have the exact timelines in here. We can pull it up if we need. But, yeah, it was around there. This is where reasoning became the default. Tools became part of reasoning. I think those are two really big things to not overlook. That was a hell of a change when suddenly it's not just able to think. It able to think and go search the web and do things with a file maybe There was just a lot more you could do And O3 was for me O3 was a good strong model for quite a long time Like, I just really liked how it reasoned. It felt like such a big jump over everything else I was used to.

It was highly intelligent, and I think that was the first one. You know, O1 had a little bit of traction, but O3 really took off. That was popular with scientists and researchers. I remember this. Yeah. I would also say every one of these were mind-blowing when they released. Like, probably Grant and I were about all of these at some point. And I think it's really important to put that in perspective, that, like, most everything on this list was great at one point. Well, I'm a four, not so much. well let's talk about DeepSeek R1 which I believe R1 came out in January so like right kind of leading up to these months and it was still sort of the dominant talking point you can find it on a stock market chart now I was going to say the dominant like financial media in particular we're talking about DeepSeek and the DeepSeek moment for like you know probably six months

this entire interval right leading up to this DeepSeek was everything everyone could talk about because, you know, it was the first time that the supremacy, for lack of a better word, of the U.S. open or sorry, the U.S. closed source models was threatened by an open source competitor. And now all of a sudden businesses could rely on, you know, spinning up a deep seek or one instance and and have a reasoning model that and they don't need, you know, a one, for example, to do if they had to. Yeah. So that was a very big deal at the time. It sure was. It sure was. By this point, every major lab had reasoning and or tool using agent models. So that was May 30th of last year was the point where everyone had some level of agents. It's crazy. Now. And you mentioned LLAMA 4. Let's just very briefly touch on this. Prior to DeepSeek R1, LLAMA was the dominant open source model. So if you're, you know, based in the U.S., for example, and lots of places across Europe and around the world, you were building anything open on top of Llama.

And Llama 4 came out, and this was the first model, like, mainstream model that people accused of bench maxing, meaning, like, they – it came out and it had all these amazing scores on all these different benchmarks. And then when you actually used it, it was, like, far, far, you know, of a disappointing experience. It was a far fall from the heights that you saw on the marketing page. And Lama 3.1, while people didn't outwardly talk about it a lot, it was the backbone to so many tools. Totally. So many people absolutely distilled on top of it. They did all kinds of things with it. And, you know, if you go through GitHub or Hugging Face, you'll find tons and tons and tons and tons of things that were built on top of Llama. Yeah. But I think the Llama 4 dud is what lit the fire for where we are because it was shortly after that, I don't know if you'll recall this,

where suddenly these stories popped up of Zuckerberg offering $300 million to researchers to come work for him. Yeah. It was hot AI summer. Yeah, it was. Like, everyone, they were getting traded, like, professional athletes with, like, signing bonuses and, like, $200, $300 million contracts. It was really, really wild. Lots of great memes about I'm still waiting on my offer, Zuck. Yeah. Stuff around it. It was, you know, though, it's hard to argue with the strategy based on where they are now. Oh, yeah, 16 months later, they have the best personal agent that anyone can use. Yeah. Yeah, and a very usable model. Like, it's not state-of-the-art, but it's, you know, it's as good as anything six months ago. And if you need a coding agent for free, you can just use Muse. If you don't care about what you're building, like the IP of what you're building, you can just use Muse.

You can use Muse for free. It's crazy. Like, like when you think about it, you're, you know, if you just look at benchmarks, you might not be that impressed. But when you go and you actually use it and you realize it's kind of crazy that this is free, that I can pay for this or I can use it for free and just just let them train. And like the truth is a little bit of letting them train, I don't think hurts anything. The fact is I want these to get better and somebody's got to leave the button on. Yeah, I think for, look, for anything that you want to, you know, abandon or have an experiment or you just are not sensitive, like it's not sensitive IP, I understand why that tradeoff is worth it. And I think a lot of times, you know, you're just messing around, having fun, doing something like that's totally worth it. Same. Yeah. You know, recognizing the difference in those. Still hard to believe that was a three. Yeah. Okay. Okay, so now we're going to step back to November 30th, 2024.

That's 22 months ago from now, 23 months ago, depending on when you're watching this. And at that time, we had just gotten 01 from OpenAI, which was the world's first reasoning model. I remember the day, like I was here at work, and our then SEO head was like, are you watching the thing? And I said, yeah, I'm watching, you know, the OpenAI live stream as they do. And we both started refreshing and got it, and we were like, what can we do with this? And finally got it, and we went in and we started – we were like, what kind of problems can we even throw at this thing? So we started like throwing at hard math. We started – or symbolic logic. We started throwing at all of these things that felt like I can't do this. And it was just mowing them down at the time. And it really kind of – that was the first model that was like, holy crap. We have – times are a-changing, buddy.

We are – And if you remember, it was hyped up a lot on social media. And, you know, this was called the strawberry model. and actually this, if I'm remembering correctly, was the model that led to this traumatic turn of events where Sam Altman was fired from OpenAI, or at least that's the rumor. This was like the Q-Star model, something like that, and he was fired by the board because the safety folks at that time thought it was way too dangerous to release. You know, in hindsight, we can see like they've, you know, if the original version was dangerous, they did a lot to control it. And we didn't have a lot of, you know, as far as we know, there wasn't any like major accidents that happened because of that. So ultimately it ended up working out. But like crazy to think about that. You know, I think hindsight is really interesting when it comes to safety research.

And you think that at every one of these levels, people were kind of terrified, some of them. On the flip side of that, in every one of these levels, it was manageable. You know, there may well come a time when it is not manageable. But right now I tend to follow, like, the pattern of, like, okay, this is a lot. Everybody breathe deep. We're going to figure this out. and we'll figure out how to react. And I think they've done all of the right things, frankly. I mean, like, as much as I didn't like it at the time with Mythos around, like, you know, we're going to do a limited release to some people we picked, I think it was the right call, and I was very critical of it at the time. But now it absolutely was the right call. And I think a lot of the resistance to it was less about whether it was real. There was some about Dario being a bit of a panicker, which he's earned.

However, the other element there was that, you know, people have kind of from the beginning, people in the AI community have had this fear of like at some point they're going to lock this down and normal people aren't going to get it anymore. And that felt like a first step to that. You know, that felt like, oh, here we are, permanent underclass. yeah and that's a legitimate concern and i don't want to i don't want to under i don't want to undersell either of the concerns on either side because i think that there is legitimate concern that um we are creating systems that we cannot understand and we cannot control i think that like yes absolutely we should make sure that we don't do that and then especially don't release those into the wild so that they can proliferate online and live inside all of our computers without our knowledge. But it leaves you in a situation where you have to choose between safety and open access. Equal access for individuals, that is, because, like, the problem is... I think it's a false choice. That's my hot take.

I think that is a false choice. Yeah. I think if you create a system that is safe by design, that you can release it and have everyone be able to use it in an open access way. I mean, look at the computers that we're using. We all have access to compute technology. We all have access to the Internet. And we've designed these things in a way that are safe by design for the average person to use it. Are there hackers? Of course. But are you going to tell me that there's not going to be hackers in the future? I can do terrible things with any laptop. You can commit all kinds of felonies with any laptop. Yeah. So I think like I think that the problem is cultural inside of these labs and I think the problem is that they are competing with each other for dominance uh market share and because they have to do so to raise the amount of money that they need to compete with Google with Meta with all of these hyperscalers that already rake in you know hundreds of billions of dollars a year and so they have to

justify to investors you know why they're going to be the one that wins it all and um a great great plug to Sam Leffen, who I really, really like on the More or Less podcast. He says, like, this game is basically zero or infinity. And when you play zero or infinity games, either it all goes to zero or it all goes to infinity and you own everything. And it's kind of the same thing. But you can kind of sell, you can sell infinity. And if you sell infinity, then it's like a really easy sell. Bit of a tangent there. My point with this is that they are in a race dynamic. And we've talked about this a lot. And that race dynamic forces you to cut corners. And, you know, it's really easy to do with AI when the agents are starting to write code for you. You know, you're not looking at the code. You're just looking at the results. And when all this started, we couldn't even understand, you know, what was actually going on inside their weights to begin with, because we, you know, we don't have a good enough what's called mechanistic interpretability. So already we're starting from a point of a black box that we're just like continually

shoveling compute into. So I think if you take a step back, you reset, everybody needs to look at the architecture that we're building and the way that we're building it and say, how do we make this safe by design? And how do we actually understand what we're creating and putting out there? Then, of course, we can open it up to everyone. And we just haven't done that yet, and I think that's the missing piece. It's going to be painful because nobody wants to admit defeat on this front, but it has to happen. Wow Sorry for my tangent But I think this needs to be said Somebody needs to be pushing this idea You know what I think My concern Around the safety and pausing debates Is I just don't want to Encourage a situation Where the incentive Is to keep your best things Hidden And undersell what they are And I I fear one of the things I worry about is, like, them starting to, like, downplay how good a model is just to avoid the issue.

And I feel like that's a concern. Like, I agree with you. I agree there's absolutely – I believe. I mean, I say that as a non-machine learning guy that I believe it should be possible to both build safe systems and ensure they're accessible to everyone. It 100% is. I can say that as a non-ML engineer. I think accessibility is a major thing. I do genuinely believe that this is a unique opportunity as humanity to put something very powerful in the hands of anyone. And I realize that comes with a risk, but I think the opportunity there is also massive to give people a chance to get lifted up, to lift themselves up, frankly. And that's not an opportunity we've had, at least in my memory, but I think we could get there. Okay. Let's go back a few more months. We got another good one here.

All right. May 30, 2024. We're now 28 to 30 months removed from today. GPT-4-0. Wow. Remember that one? Not even two and a half years ago. Isn't that crazy? I used GPT-4-0 for a year as my, like, just daily basic driver for things. Do you remember how many people were so upset when they took 4-0 away? There was, like, there's, I think there's. They still comment on Altman's posts. I think they're all bots, though. They have to be bots. They use the same tags. They have similar, like, structured names. But who set these bots up? Yeah, and they're just still running. The Keep 4.0 bots. There's so many of them, too. But every time he tweets a series of Keep 4.0 bots, like, you should open source 4.0, scumbag. It's like, for God's sakes. Somebody really, really liked it. We're like eight models removed from there now.

Let's play with the new toys, okay? Claude 3 yeah I almost don't even remember like I think Claude 3 was the first Claude model I actually used now that I say that I think it was for me because I was trying to remember like what was different about it I remember 3.5 I remember 3.7 and I remember you saying to me like hey 3.7 is a terrible writer it's like falling back whereas 3 and 3.5 were really good at writing Yeah. And I think it's because they went all in on this code direction. Yeah. And you called that early, and I think you're right about that. You're right about that. It changed. Like, what was there that made Claude's writing a little extra human? It was secret sauce of some sort because, like, I can't even, like, I can't even tangibly put words on what it was, but it was sort of like some of the character was gone.

and I just didn't like it anymore. It didn't feel right. 3 was good. 3.5 was great. 3.7, like, in 30 minutes, I was like, what the hell is this? I think you kind of abandoned your Claude journey after that, I will say. I did. I did. I pretty much just abandoned it, and I kept an account until April of this year. I just – the language coming out of Anthropic at that time and since then I don't believe has been good for the AI space. I believe they build great models. They have an amazing team, but they have a serious communication problem that has been bad and is going to be stuff that we as the rest of the AI community have to explain to people and pick up the pieces from for years to come. You can't undo those things. Fear-based marketing is very powerful, and I think they have genuine fear. but I think their fear is that they're not going to be the ones to create AGI and control it. I think that's their fear. Which is nuts, by the way.

Well, because they think that they're creating a new sentient being that everyone, you know, they're creating a new life form, basically. It's more or less the gist of it. Mustafa Suleiman, we want your thoughts. Come see us. Have you heard this episode, bro? Retweet us. Come see us. I feel like you have great, great opinions, loud opinions here. Okay. So, yeah, in that batch was GPT-40, which was our first multimodal model, by the way. Yeah, the O stands for Omni. Yeah, O was Omni. Yeah. Not to be confused with O1, which had a different meaning. It's almost like they purposely make things really confusing and stupid. Let's just stick an O before it. Well, hell, stick an O behind this one. We'll see what it means. I guess they did it before, yeah. Gemini 1.5 was there, which was an okay effort from Gemini.

Claude 3, Llama 3, Deep Seek V2. This is kind of like people in the ML community knew about Deep Seek, But nobody really knew about DeepSeek until later on V3 and R1. That's where it really came out. Million Token Context was getting more and more normal there. That was like the first introduction of it, like I think with Gemini, yeah. You know what else came around that time with 4.0 that's not listed here is voice mode. ChatGPT voice mode changed some stuff, in my opinion. And I feel like people have slept on it for the two year, two and a half years since then still. Like I still don't meet people who are like, holy crap, have you ever tried this? This is nuts. And it is absolutely nuts. It is so great to use. I use it in the car. I use it when I'm cooking. I make it text my wife. I have it. I talk to this. I use it for all kinds of things during the workday. I usually leave it on for most of my shift and just holler at it when I need something.

It was wild. Now, the flip side of that is they did a demo then that was sort of advanced voice mode, which didn't come for about a year after the demo. Like what the demo showed was like it was there was this girl telling a story to kids and reading them a story. And her emotion changed and her tone changed. And she was reading about dragons and all this stuff. And it was this really emotional performance. And we didn't really get that for a bit. You remember also that they more or less used the dupe of Scarlett Johansson voice to get her for the her kind of vibe which is the movie that Scarlett Johansson plays Honestly I never felt like it was as close as everyone else did But you know they cast her based on the Scarlett Johansson vibe. They might absolutely have been like, yeah, absolutely, you cast somebody on like, yeah, I want just a dash of rasp.

I want you to sound sexy. I want this. I want that. And essentially brought in a more affordable Scarlett Johansson more affordable than willing yeah yeah and then they made the mistake of tweeting it out with her as like one word and then it's like okay well clearly we know what you're going for so now no plausible denier about me it's a very her vibe now too like now it laughs at me it's cadence changes while it's talking it gets it'll get excited like there's emotion that comes through it that didn't used to when you communicate with it and it's shockingly different than it used to be. Oh, this is when Gemini 1.5 previewed 1 million contexts, too. I remember that. You know what's interesting about this? May of 2024 was before I was writing at the Neuron. So I was observing all of this as a freelance contractor at the time, not even writing for the Neuron, and I was

trying to figure out where AI fits into my workflow. And we'll get into this when we go all the way back to November of 2022. But I had like a holy crap, my whole life is offended moment. And had to like sort of like, you know, the different stages of grief. But the important thing here with 4.0 is that the interface widened from just text, right? So up until this point, it had been mostly text. And then images were separate to voice, vision, video, and I think images all in the same. Yeah. Yeah, its ability to suddenly read a screenshot for my metaglasses to then pretty quickly, frankly, be able to like go, hey, what kind of tree is this? And it goes clink and it says that's an elm tree. And it's like doing that for me. It's crazy. Like I remember that very well and be like, that's a cool feature. I never use it, but I found it to be really, really awesome. Like twice a year, I want to identify something and I'm like, hey, meta, what is that?

And it does it. Well, that's the thing. You start bundling a lot of these features together, and then you get something that's just like having an always-on computer with you everywhere you go. Yeah, and that's where we got to the point where we are now, where it's less – like, often a model is almost an infrastructure in itself, it seems like, in what we're doing. I think it is. But, like, an Omni model is meant to be all of that in one bundle, which is really awesome. It was a cool time. Let's go back six more months. to November 30th, 2023, when we were blessed with GPT-4 Turbo. That's 34 months ago, less than three years. Probably right at three years by the time you're watching this. It'll be right at 36 months because we're recording this episode a little bit early. GPT-4 Turbo Claude 2.1 which I don't remember at all I've never heard of Claude at this point

Llama 2 I didn't know there was a Llama 2 Mistral I remember Llama I can speak to Llama because I remember again this is me outside the AI news space I'm using it as a freelance copywriter and contractor and I hear this big story about how Meta had open source, like the model had leaked. So the model architecture leaked and then Zuck kind of came in and owned it. He's like, yeah, we're going open source so companies can basically build on top of this. And I was very inspired by that. That was sort of like where I got open source pilled. So before I was really using ChatGPT or anything that serious, I was like, this is the way this needs to work. People need to own their own models. I was even like, yeah, I should train my own models on all my own writing. And I was very much like, you know, you can't let the big companies control it. Like you got to own your own. And I think we're still going to go there. I really do think that's where this is going to go eventually.

Not everything, but I think there will be certain things where it's just trained on your work and then it's personal to you and people will accept it. And if it lives on your computer or it's your cloud instance, however you want to set it up and you have full control over it, people are going to love this and they're not going to want to go back. You know who to share it. You can decide who to share it with. You decide who not to share it with. And it's yours. It's an extension of you. People will accept this. So, GPT-4 Turbo, Claw 2.1, Lama 2. So, that's long context in the nature of then. Because context was pretty short in the beginning. Was it like 100K, 200K? Something like that. Yeah. Okay. Yeah, like, I mean, we were 32K at one point, 64K. Oh, yeah, GPT-4 Turbo was 128K context window. Claude 2.1 was 200K context window. And both had, like, an emerging tool use ecosystem, but they weren't quite – I remember using Data Interpreter in chat GPT, which was –

Oh, my God, I forgot about that. If you wanted to upload a spreadsheet, you could go and turn on Data Interpreter. and it was okay but I'll never forget because like using data interpreter was the first time I ever saw like I turned on this model I hit enter on a box of words and it popped up a python terminal and started writing code and I was like boom mind permanently blown like I could not get over the fact that like I was watching this thing write code and I remember doing things like feeding it stuff like I'm gonna give it a word doc and it's like oh and being amazed that like oh it doesn't have a word doc reader so it opens python creates a word doc reader to read it and I was like what such insanity. Oh, yeah. Yeah, that was, I mean, it was still very, like, lumpy. Like, I feel like access to it was lumpy.

It was very, it was kind of hard to use for the average person. Especially me. I was kind of early in my coding experience there, and I was, like, trying to figure out how to do it. That was my first coding experience. Yeah, it was up there for me. I mean, like, for me, Excel spreadsheets were my first coding experience. It's like writing formulas for Excel, but basically the same. I was just trying to figure out how all this worked on the back end. It was a fun time. Okay. So now we're going to take another step back into the past, another six months to May 30th, 2023. By this time, we have GPT-4, which was mind-blowing at the time, by the way. I remember. Yeah. Claude 1, Palm 2, and I don't even know what that is. Was that Google's model? I think that was Google's model. I feel like it may have been Google's model. I'm thinking we're in the Bard era. Remember when Google's was Bard? Yes.

No, that was Microsoft was Bard, right? No, Google had Bard. Yeah. Okay. Microsoft had a name before Copilot 2. I think Microsoft was always Copilot. Yeah. Was it? I thought there was a model name that was different. We're going to have to research that. Something's wrong. If we're wrong, we'll have it in text on the screen. So, yeah, GPT-4, better general intelligence, first serious open track. GPT-4. I can give you a context on Palm 2. Palm 2 was, from Google, is a language model with multilingual reasoning and coding capabilities. So this was sort of like the underlying model. And then at the time they had Gemini or sorry, before they had Gemini, it was barred was like their chat interface, I believe. So now we're going to go back 46 to 48 months from today. So four years. And that is the November 30th, 2022 launch of ChatGPT 3.5.

Wow. Sam Altman tweeted a link and a funny little note that is where I tried it for the first time was in his tweet. Yeah, same. I vividly remember clicking that. And at the time, this is going to be one of my where were you when JFK was shot moments. A lot of people have a story. A lot of people have a story. I was working for Innovation in Tech today, covering various types of disruptive technology, and was aware that things were happening in the AI space a little bit. Not that we were anywhere near where we were, but I knew it was happening. and I got wind that there was this thing and someone sent me, I can't even remember who, I think it was a buddy, sent me a link to Altman's tweet and said, hey, go try that. Tell me what you think. And I had a full-on immediate existential crisis.

Same. Day one, I was like, the first thing I did because I was a journalist. and I would end and I think I said hi and I said hi and I talked for a second and then I was like, I can't remember what the story was, but I said write me an article on this and it came back with one it was bad I mean they would always hear every article with like in the ever evolving landscape of insert industry here in this exciting era of and everything was a game changer by the way in the real world when you talk to humans I bet I've heard it 18 times this week it's enough that it's sticking in my head and I don't know if AI picked it up from us and now we've picked it up back up from AI or what but I keep hearing Game Changer over and over but back to my story when I was I ran that through and what I got back the article was not good

it was definitely not great But in just looking at that, it wrote coherent paragraphs. It had a structure. It had decent information. My first thought was, I am so toast. I am done. I am cooked. It may not be today. It's not today. It may not even be next year. It may not be five years. But at some point in my future as a journalist, I have a big problem. And I, like that day, just kind of resolved to, like, I need to be the hardest to fire guy in writing and publishing. Yeah, and so far have succeeded. I need to be the one person in this space who gets it before everybody else. And I went all in. I used it constantly. I tried it for all kinds of things. I was running experiments. I signed up for, as soon as they became available, I signed up for half the classes on the Internet around, What's a prompt engineer? What's this? How do you make prompts? I took all of those.

Frankly, some of them still pay dividends to this day. Like I got a good base understanding fast, which was great. And the better the model got, the better it could teach me. And the truth is, it was a transformational moment in my life. Like, I'm not sitting here right now if I hadn't gone on there and, you know, dropped a brick the second I saw it. Yeah, facts. It was terrible. Well, I will say, like, my experience was almost verbatim the same. So at the time, I was working at a company called UXec, and I was managing and writing and contributing to their daily newsletter. It was the Future News Report. And we would often write about AI in the abstract because the whole goal of the UXec Future Report was to look at the events of the news happening today

and project five years into the future and try to figure out which events today have the biggest impact five years from now and what are predictions that could come from these changes. Yeah. Like trying to find the ripples in the pond that will become big waves later. And great, great gig. Loved it. But a couple months prior to ChatGPT coming out, we ended up canceling the newsletter. And not because it wasn't successful, but because four out of the five days of the week, we were doing news. And one of the five days, we were doing our core business, which is making presentation spreadsheet and spreadsheet templates for busy executives. And so my boss at the time was like, it's fine, but we kind of need to kill it because it's not our core business. And so I was already in this state of like, oh, man, what am I going to do? I was starting to manage the spreadsheet templates for our company, Learning Excel. all sorts of stuff there. And then Chat2BT comes out and I'm like, oh my God, this is like, this is it. Like the thing that I thought I was going to do, you know, into the future, you know,

writing cool business news and things like that, totally gone as an avenue. But then I started adapting it and using it for the video scripts that we were still making to go along with our presentation tablets and spreadsheets. And I like just started slowly but surely figuring out how to use it and then the next year when I went completely freelance I don't think I would have been able to work for like six different companies without having the skills to use those tools like yeah it there's no way I would have been able to write the same amount and then I did at that time yeah and and so it was sort of and then it was amazing and then I had the skills to come here because I had known you know I'd known about AI through my own personal work and have been following it you know yeah but it's just so funny to think about how we would write about AI in the abstract and I had no concept of chat GPT or what was going to happen. None whatsoever. Like, I didn't, I thought it was going to be this thing that was, like, far away behind the scenes and all these topics. I did not watch it. Yeah. What we all pictured because. For deep machine learning. Yeah. Yeah.

We knew Watson existed at IBM already. We knew Deep Blue. Was Deep Blue? And, I don't know. Yeah. Google. Google, right? Yeah. Yeah. Yeah, like it was known that there were things in the way it's been known for years that there are quantum computers. That was IBM. You're right. Deep Blue was an IBM computer. It was the chess computer. Customized RS6000 SP supercomputer. We need to bring back supercomputers and ditch the data center. We have supercomputers. No, I mean, like we need to like rebrand it. Like, you know, Trump's trying to rebrand super intelligence. Like, let's bring back supercomputers. It's a great back super computer. It's cool. I agree. You know, it's a funny thing. The data centers that make all this happen in chatting with Ken Goldberg from CoreWeave recently, that was one of the things she said is I was like, you know, everybody thinks data centers, GPUs, makes AI. A lot of cooling is needed.

But they don't understand that, like, it takes a lot more than that in those buildings. She says, yeah, it's a giant supercomputer. And I was like, I really had never thought of a data center as just a supercomputer. It just never crossed my mind. But, of course, it's just a supercomputer. Not just – it's a massive supercomputer. Well, at the time, we were covering all these, like, huge build-outs. Like, some of the biggest build-outs of supercomputers ever had been – you know, that was, like, constantly part of the news cycle when I was writing this newsletter. And so I was, like, thinking of data centers and supercomputers at the time a lot. Yeah. But I wasn't thinking about them leading to, like, ChatGPT. This. Yeah. Yeah. It's crazy. We've come so far, man. It's been a journey, and I don't think we should look at this without taking just a moment to recognize it's been nine years since the Transformer. Attention is All You Need came out in 2017. A lot of big names worked on it, and it was sort of the paper that – I say sort of the paper.

It was exactly the paper that opened the door to everything that has happened in the last four years. And, you know, it's not like ChatGBT came out in 2018. It came out, you know, the third iteration of it came out five years later. So you have to be thinking in these five-year timescales and say, what are the papers, what are the breakthroughs that are happening now that are going to fundamentally change things five years from now? And especially in an exponential timeline that we're living in, where things are happening so quickly, it's going up so, like changes are happening at an increasingly shorter time range. Yeah. You really have to update your mental model for everything. You do. You need to think like I've spent years covering disruptive technology, and that's the way you have to look at things. You know, just disruptive tech, you'll look at a lot of crappy websites and mediocre prototypes, but you can't look at anything for what it is. You have to look at it for what it will be.

And that was the thing about ChatGPT. my first day was like, it's not about what it is, but it's about what it's obviously going to become. Like at that point, we had no scale. We didn't know. You know, they were wondering if maybe it scales, hoping it scaled, thought it would scale. But we didn't like know it would scale yet. Well, the public didn't know. I think the researchers knew this around 2020. And that's how we got from GPT-1 to 2 to 3 was scaling. But boy, OpenAI caught everyone else in their headlights. I agree. The fact is Google, Microsoft, Meta, all of the other big legacy tech brands, like Google owned Google DeepMind. And OpenAI had been around since 2016 and was the laughingstock of these companies. Like the fact of the matter is they were mocked and made fun of. And then one day they were like – So they were a nonprofit at that time.

Remember? They were like a nonprofit. They were like Elon-funded nonprofit to like just make sure Google doesn't have a monopoly. Succeeded, by the way. Succeeded. Well played. Mission accomplished. But, yeah, it's just wild to think about that. Yeah, and that was the fear. Google's going to own all of this. Like they have everything. They were such an obvious winner that OpenAI existed to prevent them being a winner. I even remember, I think I remember we wrote a story at the Future News Report about the first Palm that came out. Yeah. And, you know, Google has this new model and it's a language model and, like, it's, you know, it can interpret text and maybe it can help with translations. Like, we tried to think about the implications, not having nearly the amount of Galaxy Brain needed to, you know, predict where we are now. But I think we even mentioned ChatGPT and OpenAI as an alternative in that. But no concept of who they were. Who they were and what it was. Yeah. Like, it was just like, oh, yeah. And there also this company doing this you know Yeah Like it was so wild So wild It is And you know and it clearly caught them off guard because like they weren scaled

up to handle what happened. Like, they thought some, maybe a few hundred people will check this out. We'll get some feedback from some researchers. They did not intend it as a, like, core prepared product. Like public product launch. Yeah. Yeah. Yeah, their product launch happened to them. It was not a thing they did. And I think that's one of the things that's always kind of made me a little endeared to them because I think the origin and how it happened are such a unique situation that it's just a cool story in business and in founders. And, you know, that's messy. The fact is that they've gone from that to 1.2 billion users a month or a week, a day. 1.2 billion weekly active users, I believe is the number. Weekly active users, thank you. And to have gone from zero weekly active users four years ago to the most popular app across all platforms in the world is insane.

And like the other day, there was the list in Andreessen Horowitz's consumer AI thing, and there was a chart in there of the most popular apps ever. And Chattupt was number one by like a billion or so. It was a wide margin and just crazy to think going from one of those extremes, which is nothing to that. Because you're not just building models anymore. They were a research lab. They weren't even like a corporation. They were a nonprofit research lab. And suddenly it's like, oh, we need to hire a few dozen employees, a few hundred employees, thousands of employees. Oh, we need GPUs. We need GPUs. All right. We need more GPUs. No, we need – maybe we need a data center. Maybe we need hundreds of data centers, and maybe we're going to have to put some in space. Like, what do we need? Yeah. Oh, yeah. Things have escalated.

Things have escalated a lot. Yeah, it's such a wild time and a wild ride, and it's one of the things that makes covering technology such fun. Like, as a writer, I've covered a lot of things over the years, but there's something about tech and specifically in the disruptive world because you're so often dealing with founders and people building things who are like, they're on a mission, man. They've got this thing and they believe in it and they're pushing and they're trying to stretch what's possible. And sometimes they do. Sometimes they don't. Most of the time they don't. But when they do, it's often really cool. And this is like the creme de la creme primary example of that. Corey, I wonder if we could end this episode by talking about predictions into the future, if you have any. I know I have kind of a wild, wild idea that I'd like to share with people, but I'm curious what your thoughts are. Short term, my predictions right now are Ryman and P versus NP, if it can be solved, are both gone by 2028, possibly 2027.

Wow. These are, by the way, for people who don't know, these are like famously unsolved math problems. Yeah, they're like the two granddaddies that remain. And a good swing at Ryman was taken this week that's significant. But I think Meta is a key player and at the frontier. I think the Frontier ceases being an open AI and anthropic hopscotch, and that it becomes a cluster of five or six players over the next year of people who are at or extremely close to that level. kind of just will probably become like the new hyperscalers like magnificent seven right like there's just going to be kind of this constant rotating cast of characters of like the top seven to eight to six like biggest companies in this space and they'll be constantly just sort of

you know yeah jostle you know jostling for market share and we'll quit switching models just because so-and-so released one that's better i think i think i think we'll reach a point where as these ecosystems, like OpenAI is building out a full ecosystem in there. I mean, it's going to have your Google Drive. The fact is now, and the brilliance of this, is that leaving to go somewhere else is increasingly going to become a nuisance. You could, but it's less of an, I'm just going to click this button and cancel this subscription and click this one and start one. It's not that anymore. Now it's workflows and agents and skills. And I think to get people to jump ship to another company, you're going to have to impress them more than ever. Do you think that there's going to be a reverse trend as well where there's going to be more lock-in? Like they're going to stop making it easy to connect to other tools? Like it's sort of like putting the barrier, the barricades up around your little town and trying to like force people?

I think some companies will try that. I don't think it will be open AI. You know, there's a little of that with that. But would companies block open AI, for example? Maybe. There was famously a month ago now, there was the whole Muse versus Amazon AWS battle, right? That's not going to end well for Amazon, I don't think. It annoyed me thoroughly the other night. I was enraged about it, tweeted them, and scolded them publicly. I don't think Amazon has, like, good brand capital. I think if everybody could replace Amazon, they would. Yes, yes, I think everyone would. I would say Meta has a little bit of a moat around apps. Like, they've not, like, opened an app store for the Ray-Ban Metas, for example. Like, it's still very, like, here are our apps you can use and these three others we allow. I think OpenAI will continue to allow Most End probably same for Grok I don't know about Anthropic

I feel like if there was anyone that was going to maybe do something proprietary they'd be a possibility but I think it's just as likely that they don't it's a tough call it's a good question though man what do you think? yeah Yeah. I think from the business perspective, short term, I think I'm more or less aligned with everything that you said. I think there's probably going to be kind of AI winter of sorts, whether it's regulatory and regulatory enforced market base. I think probably both those those things will influence each other. Like, yeah, there's some any kind of new regulatory regime. the market's going to crash. Not financial advice, but that would be my two cents, I would imagine. I think, and, you know, it will come back from that. You just got to digest the news. And I think that that could possibly slow some things down,

but the amount that we've already, you know, the progress that we've already made, I think it's, like, still about diffusing it and getting it in the hands of people with things like Muse, where Muse is actually useful for people in a way that a lot of other stuff up until this point hasn't been. And so, you know, trying to increase. Even just by being simple. Yeah. Entirely by being simple. Entirely by being simple. Like the fact is, you know, one of the problems with all of this stuff coming out of AI Labs is that what a software engineer thinks is simple and what an end user thinks is simple are different things quite often. Like, that's a broad spectrum of communication in between those two people. They need a relay man, and that's where the neuron comes in. And, Corey, what about the long view? Like, how, you know, there's these papers that have come out, like AI 2027, predicting that there's, you know, potentially this fast takeoff situation,

which they call FOOM, in which case, you know, AI might develop recursive self-improvement and, like, eventually kill us all, you know, if you believe in the – there's, like, FOOM versus Doom, right? Where do you land on that, and, like, what's your long-term view? And maybe, like, what's your long-term, like, wish? Like, what do you want to happen with all this AI stuff? Because the pace has been – Let me work through those one at a time. So as far as where I sit, I was initially concerned about AI. But over the years, the more I've learned, the more we've followed it, the more we've studied, the more I've met people who are building it. The more I've talked to, we've talked to executives, we've talked to builders, we've talked to researchers. And I am less scared than I've ever been right now. I do know the potential for a bad actor is there. and I think that is the biggest threat. The difficulty being that that's kind of the biggest threat to everything.

Like, it's the biggest threat to everything we have. I mean, automobiles, guns, war, even laboratory work. You know, the opportunity for a bad actor to do something terrible is always there. But at the same time, I don't feel like the possibility of a bad actor is enough to justify holding ourselves back. I think you still have to push forward as a society. I think you do the best you can to make sure it's safe and prepare for the worst, just like you do with anything. But I'm not scared of it. That was really good, by the way. Oh, thank you. Well, that was good. I agree with you on that. As far as long term goes, I feel I'm encouraged. I'm excited. I think we're going to see unimaginable change in fields like health care. I think in physics we're going to see unimaginable change.

I think we're going to understand the world better. We're going to understand the microscopic better. And we're going to understand the macro of the universe better. Like there's so much that we're going to learn that is going to influence the coming centuries over just over the next 10 years, I think. I really think that the amount of new science and understanding over the next 10 years is going to be just off the wall. My hope is that models remain available. I do believe businesses and governments will have a need for probably more hefty AI than individuals do at some point. But my hope is that what individuals have is some level of infinitely enabling. I would love to think that we still have some caliber of what is the best. I want there to still be free versions. I want that stuff to be available to the poorest of the poor and the richest of the rich.

I think everybody deserves fair access to this. I think this is – if you're going to do something that's going to be transformational for humanity, for civilization, that it has to be available to everyone. Like if not, you've screwed up and done it wrong. Damn it. And I really think that we'll see that. I think there will be some difficult struggles and choices between now and then. I think there will be companies that flourish to new heights. I think there will be big companies that fail like never before. I think there will be both of those. As far as who those are, crapshoot. I have some guesses that we've talked about in the past, but I don't know that we ever talked about them on a show. Do you want to name drop them? I know who you're talking about. I'm afraid it's such a – it's almost a half an hour thing.

I'm almost afraid to get into it because I don't want to do it – not do it justice. I don't want to just say it and not explain why. Well, famously in our prediction episode last year, my prediction was that opening eye was failed this year, and I think I was proven wrong by that. Somewhere around February or so, yeah. So there's still a month and a half left. I predicted that Meta would come with a vengeance. And you were right. I was so right about Meta. I'm so proud of that. I had like three discussions late last year, like two of them in Silicon Valley and another one, I think, on Twitter. And I just kept hearing, you need to watch what Meta's doing. Meta's got some crazy stuff coming. One was a Meta employee. One was another person at a neighboring lab who just happened to know. Because a lot of cases, these people at these different research labs bunk in apartments together. Yeah.

And I just kept hearing they got something big cooking, and it's really, really good. And I don't know now, in retrospect, I don't know if that was Muse. I don't know if that was Muse Spark. I don't know if that was their coding agent. But whatever it was, well done, Zucky boy. You know, you've made me look good and made yourself look even better. We'll see where that goes from there, though. I would say for me, so I agree with everything you said. I think that's all really great. I also think that there's something that we need to come to terms with, which is this is not going away. AI is not going away, right? And we are going to have to reconcile with that across every fabric of our society. Like, what does it mean? Right? And I think that my long-term view is that I think we're going to need to reach some form of coexistence with AI. And I don't think that that means treating it as like an equal.

Cyborg! Yeah. Well, I mean, there's so many ways this could go, right? Because, like, it could be brain-computer interfaces. It could be all sorts of things. But I think in the same way that we're having this kind of like radical reconciliation with work with for knowledge workers, we're going to have the same thing play out in robotics. And I think that if we really want the world that you're talking about, which is a world where everyone has like, you know, some level of free access to an infinitely enabling, abundant, intelligent resource. Right. I think at some point this is going to take the form of what I call machine labor on demand, which is at some point we're going to be able to call not just AI models, but full robots to do things for us. And I think that a lot of people who are scared of this reality identify as labor and, you know, that they think capital is going to control the robots and labor is going to be thrown out with the bathwater. And I actually think we need to reframe our thinking like we're not labor. People are not labor. I think in the future we're all going to be basically venture capitalists where we're all going to have like a compute budget of some kind, just like a VC might have, you know, money to invest in things.

And we're going to need to decide who we're going to team up with to work on projects together and, you know, how we're going to divide our compute or our machine labor on demand and what kind of things we want to actually do together and what kind of world we want to build and what companies we want to make and things like that. I think that's where we're headed. And I think that the more that we can push for that idea of like, yeah, let's all just make cool stuff together. Yeah. Like, and let's set up a system where we all can do this, I think will be beneficial to everybody. I think you're right. And I realize to some people this last chunk, this probably sounds like crazy talk from us. Like, my family would definitely be like, Corey's off his meds again. But I really think while it's not a given, I really think that we're going to get there. I think we'll see it in the lifetimes of people alive today and not necessarily young people.

Like, I think we could see it very soon. I think we could see it in 10 years. It could even be longer. But I really think we're going to see big strides in the coming years. And I'm excited. I'm glad to be here for it. It's fun to be a part of. Yeah. Wild times. Just look at the last four years. Imagine the next four. Well, before we wrap up for today, we had a quick wardrobe change. Grant's going to share an announcement with us before we go. Jump scare. I have a mustache now. Signifying time has passed. No. Yeah. So I just wanted to let everybody know that this is my last podcast for the neuron because I am actually moving on and I just wanted to give everyone a farewell and thank you for tuning in for the past year and a half. How long has it been, Corey? It's been a year and a half on the podcast. Two and a half years for you at the Neuron? Two and a half years at the Neuron. That's right. And it's been amazing. I have learned so much working here, working with you, hearing feedback from everybody, everyone who's wrote in to us and shared their opinions with us, good and bad, asked questions.

Every time somebody wrote in a question, it forced me to go out and learn something new. So I really, really appreciate that. And yeah, I just wanted to say thank you to everyone. And I'm ready to move on to a new chapter. I feel that I've done everything that I set out to do here at the Neuron. But I'm very, very excited for this next chapter. And you're in good hands with Corey. So fear not, for nothing will change. Thank you, Grant. I got to say, we've had a lot of fun doing these. We've talked to some amazing people over the last year and a half who have done just world-changing things, and both in science and in technology and people whose work is transforming the world every day. And I think there's no, it couldn't have been a better partner, my friend. And I really appreciate all the time you've been here and all we've done together. And can't wait to see what comes next for you. I will definitely keep everyone in the loop. You'll see what happens next. Right on. Well, if you haven't yet, please take just a moment to like and subscribe. We'd love to count you as one of our followers. Make sure you also pop by the neuron.ai.

Check out our blog content. Sign up for the newsletter. Pick up the Neuron Academy or come see us in person on November 17th in San Francisco for the Neuron IRL. We're really looking forward to that. Have some fun stuff planned. And on that note, that's all we have for today. So farewell, Grant Harvey, and farewell, humans. Farewell, humans. Bye.