# Ch 2: Framework 1: the 3 M's (Mindset, Method, Machine)
Creator: Nate Herk (@nateherk)
Transcript of Nate Herk's 2h33m 'Build your own AI operating system' course, split into its chapters. All content and credit: Nate Herk (@nateherk), https://x.com/nateherk/status/2101656269056610682. Transcribed locally with faster-whisper; names may be misheard.

[0:03:00] that is very future proof, because I'm gonna change the way that you think. I'm gonna change the way that you decide how to do things. I'm gonna change the way that you build this AIOS so it can survive whatever happens. Just yesterday, I took my AIOS and I moved it over to Codex just to make sure that everything was good. And it took Codex about two minutes to get adjusted. And now I have two different AIOSs, Claude Code and Codex. And if I need to put it in anti-gravity or if I need to throw it into the other new kit on the block, I can do so. So I'm gonna be teaching you the durable layer that sits underneath all these tools and all these different buzzwords. Okay, so there's two main frameworks that we're gonna be talking about today. The first one is more of a high level framework around the way that I think about AI. And it's called the three M's of AI. We've got the mindset, the method, and the machine. All right, so here is my three M's doc that I'm going to be reading over today. I'm not gonna read every single word because that would take too long, but I will drop this doc in my free Skool community. The link is down in the description. I have all of the other resources that I've ever shared on YouTube in there as well for free. So if you wanna check it out in deeper, go there. But I wanted to spend some time here because the mindset and all of these frameworks I've built out in a way that it's really important for you to think about not only when you're setting up your AI OS, but also as you use it every day and also as you scale it up. And also as you start to, you know, bring it into other areas of your business because right now we're focusing on you personally, but now that you've got this set up, your business is set up better to be more AI ready and your employees and your coworkers are as well. So it's really important. So there's three habits I wanted to touch on. The first one is the default shift. Basically the idea of, you know,

[0:04:30] before you do any task, just ask yourself, how could AI do this? Or at least how could AI do 30% of it? Because the truth is, AI is not gonna do everything 100%. It might be able to do 75 or 50%, but that's still a huge win and that is a productivity gain. So here's a real example. The other day, I needed to update over 300 YouTube video descriptions, tracking links. And the old me would have just been like, man, this sucks, but I just have to do it. And I would have manually clicked in each video, changed the link, and then clicked in the next video and just done that. Probably would have taken me an hour. But the new me, my default is okay. I don't wanna do that manually. Let me just brainstorm with Claude Code and let me figure out how do I actually make that happen? Whether that's an API or whether that's an MCP or whether that is maybe even having to just do a browser automation script. But the point is my default shift is so stubborn now where if anything sounds boring or repetitive, I'm not going to do it. I'm going to use my AI OS to do it. That's habit number one. Habit number two is the function breakdown. Understanding that your role is a set of functions. Everything you do is broken into a tiny task and you can just automate one piece of that at a time. So an example, okay, if I had to think about how can I automate an entire YouTube video? That's a very tall task, but break down the pieces that go into a YouTube video. Ideation is usually step one. And then you've got your scripting or your building or your slide creation. And then you've got packaging, titles and thumbnails and you've got descriptions and you've got comment replies. That is a huge job. But when you break that into little things, oh, I can definitely automate ideation

[0:06:00] or at least 95% of it. I can definitely automate scripting. I can definitely automate the slide deck creations for my videos. And when you have that mindset of just breaking it into little pieces, baby steps, it becomes way more achievable. So in this example, if this is the full flow of automating a YouTube video, you just have all these little chunks. And as you slowly over time automate more and more chunks, you get to a place where you're, you know, now you've automated like 80% of a YouTube video and each of these chunks is repeatable. So maybe this is like the, you know, slide deck chunk. Okay, what if we have a different process? Let's just say we're now doing like, you know, we're doing like meeting prep or something. Well, guess what? We can go ahead and grab this chunk and just move it over here. And we can just fit this into this puzzle wherever it goes. So all of these little chunks and baby steps that you're taking are reusable and that mindset shift is very important to have. And then habit number three, we've got the curiosity rule. Never accept AI output without asking why. Treat AI as a mentor, not a vending machine. There's basically this idea of dark code because so many people nowadays are using AI to write code which means they don't truly understand what each line or each block of code is doing. And not that you have to be able to read Python or whatever it is, but it is very important to at least understand why did you build this? And if X happened, what would happen to the code? You know what I mean? So the whole idea of being curious is asking those sorts of questions. Why did you design it this way? What happens if someone, you know, submits an invoice that's empty and this code is supposed to handle filled out invoices.

[0:07:30] And the other piece of curiosity is if you don't know if something's possible to be automated or automated with AI, then just ask. Okay. a method, this is really, really important, but we're gonna come back to this a little bit later once we've kind of, you know, got everything set up. But this basically just talks about, okay, how do you actually decide what's worth automating and how much of it you automate. So there's some more information here about that kind of stuff. And then the last M stands for machine. And this once again gets a little bit more technical. All of this you guys will be able to dive into later, but we will revisit some of the stuff later on in this course. So those are the three M's. A couple of the key highlights that we already hit on, it's never binary. The question is never will AI do this for me? The question is to what extent can I leverage AI here? 30%, 60%, maybe it's zero, but it's very unlikely that it's zero. Every task on your plate has a leverage percentage. You just have to find it. And mindset isn't motivation. It's the lens that finds the percentage. The other one is treat AI as a mentor, not a vending machine. Vending machines take a coin and they give you something, but mentors ask you questions. They push you back. They make you sharper. They encourage you. They give you ideas. And that is the relationship that you wanna have with AI, not a vending machine. And then of course we just talked about how your job is essentially a tree of tasks and thinking about it like that makes big, vague, overwhelming ideas more realistic. So another thing that's really important from the 3Ms is that productivity drops before it climbs. And I don't want you to get overwhelmed and just give up because you think it's too hard to build an AI OS, it is not.

[0:09:00] So think about this, a solo operator and they're trying to build an AI OS and they're doing a 30 day sprint. This bar basically represents like productivity, right? So baseline, they are not using AI hardly at all. They're being maybe 60% productive. Day three, they're slowing down. They're noticeably getting out less output because they're trying to adjust to this new way of living. And that's just how things work. Whenever you make a change, you typically are going to expect a 20% decrease in productivity. And let me actually just draw this out because I think it'll be easier to demonstrate. So if we just real quick go and draw a graph, then let me show you what this means. So let's say your business is operating like this. This is a steady line. Now, if you decide to make a change, you have to basically be okay with the fact that this change might decrease your productivity by about 20%. So this is the sort of like negative 20% gap you're seeing. The question is, is this change worth it? Will this change ultimately result in more than a 20% dip? Because up here, what you might be getting with an AIOS is a 50% gain. So is the dip in 20% worth the 50% upside? Yes, because you're getting an additional 30%. Now on top of that, you also have the idea of a learning curve. So if I can just go ahead and draw another little graph real quick. And why am I being so boring? Let's actually use a color. So the learning curve. People think that learning is linear like this

[0:10:30] and it's just really not. Learning is exponential. But what does that mean? It means that when you start to learn something new, you basically are kind of below the linear line that you would have been if you were just doing it the old way. But because of the exponentials, the exponential nature of learning, you will pass that mark. It's just that it's gonna take you a few days to get there. Maybe this is day one, maybe this is day two, and maybe on day three you break even. but by day four and five, you're way ahead of where you would have been if you just kept doing it the same way. So this gap is where people sort of tend to give up or get overwhelmed because they realize they're being less productive. So those two things are really important for you guys to understand. Don't give up. You get through it and then you start to see all the gains and now you are being 5X more productive, 10X more productive. I've seen that with myself, I've seen that with my team. It is an incredible thing to actually witness. And by the time you're two weeks in, you won't even recognize how productive you're being And you can't even imagine if someone took away your AI iOS and you had to go back to the old way, you would want to cry. Okay. So now let's get onto the second sort of framework here. And this one is called the four C's. This one is more specific to the way that we set up our operating system. And this is kind of the way that I'm calling the AIOS, the AI automation society operating system, because we've basically just been getting everyone to do it in this way. And it is the AI automation society way. So what goes into that four C's. We've got context. What AI knows about you, your team, your tools, your voice, your business, your