They're still cutting 30,000 jobs.

Not because they're struggling. Because they can.

And they're not alone.

Amazon ($77.7B in profit) cut 30,000 jobs. Meta ($60.5B) cut 6,500. Salesforce ($7.5B) cut 1,000. Block ($2.9B) cut 4,000.

Combined, these five companies banked $165.9 billion in profit and eliminated 71,500 jobs. In the same period.

This is what I call The Great Divergence. Record profits. Mass layoffs. Same companies.

So What's Actually Going On?

There are three explanations floating around:

• Efficiency gains. AI and automation are replacing roles that used to require humans.
• Opportunism. Profitable companies using a tough macro narrative as cover to cut costs.
• Softening demand. Getting lean before things get harder.

My honest take? It's probably all three. But the efficiency argument is the one people keep underestimating.

I have covered how AI tools are reshaping what's possible for businesses of every size. What's happening at Oracle and Amazon is the enterprise version of that same shift. Fewer people. Same output. Better margins.

They're Not Struggling. They're Restructuring.

That line at the bottom of the chart says it all.

These companies are not cutting because revenue is falling. They're cutting because Wall Street rewards margin expansion. Every dollar saved on headcount flows directly to the bottom line.

When you understand how cashflow drives business valuations, this math makes perfect sense. Reduce your cost base, protect your cashflow, and your valuation goes up. Simple as that.

The people losing jobs are not the problem. They're the cost.

What This Means for the Rest of Us

The uncomfortable truth is that this trend is not going away.

AI is getting better. Automation is getting cheaper. And companies have now proven, publicly, that laying off thousands of people does not hurt their stock price.

We are already seeing solo entrepreneurs and small teams replace what used to take entire departments. That pattern is playing out at scale inside big corporations right now.

If you work at a large company, the question is not whether restructuring will happen. It's whether you're on the right side of it when it does.

Conclusion

$165.9 billion in profit. 71,500 jobs cut.

This is not a warning sign. It's a strategy.

The companies doing this are not in trouble. They're optimizing. And the market is rewarding them for it.

The question everyone should be asking right now is: what happens when the next round starts?

Connect with me on LinkedIn and Instagram for more content like this.

Total Takeover

You don't realize how completely OpenClaw has taken over. This week, half of all token usage on OpenRouter was OpenClaw.

50%. Almost 3 trillion tokens. Claude Code came in at 342 billion.

The Numbers

The chart tells the whole story. OpenClaw sits at 2.89 trillion tokens, towering over every other app. Kilo Code follows at 1.18 trillion, with BLACKBOXAI at 657 billion. Claude Code, Descript, Janitor AI, Cline, ISEKAI ZERO, Roo Code, and Lemonade round out the top ten.

What's Next

OpenClaw's dominance on OpenRouter shows just how fast the AI assistant space is moving. With almost 3 trillion tokens processed in a single week, the demand for AI tools is only growing.

The AI landscape keeps shifting, and the numbers don't lie. If you're building or experimenting with AI assistants, you're not alone.

Keep an eye on these usage trends as you plan your next move in the AI space.

Elizabeth walked me through Google's newly released Nano Banana AI (their Gemini Flash 2.5 image model), demonstrated the surprisingly powerful features hiding in Google AI Studio, and showed me how she used Claude Code to build her own custom RSS feed aggregator and project management system—all without traditional coding experience.

What makes this conversation particularly valuable is that Elizabeth didn't just explain these tools theoretically; she demonstrated them live. We built a history trivia game for kids in real-time, analyzed video content to count Christmas lights, and explored how the Stream feature in Google AI Studio can provide real-time debugging feedback as you work. Perhaps most impressively, Elizabeth revealed how she replaced a $20/month subscription service by building her own solution in Claude Code, complete with AI agents that automatically manage her daily tasks and project workflows.

This article includes the main topics we covered, a link to watch the full podcast conversation, and the complete transcript below.

Key Topics Covered

In this episode, Elizabeth and I explored cutting-edge AI tools and practical applications for entrepreneurs:

• Nano Banana AI & Visual Memory Technology - Understanding Google's Gemini Flash 2.5 image model and its breakthrough "visual memory" feature that maintains consistency across multiple images (solving the long-standing problem of character/object persistence in AI-generated content), plus its superior precision in editing specific elements within images
• Google AI Studio Deep Dive - A complete walkthrough of Google's underutilized AI Studio platform, including the game-changing Stream feature that lets you share your screen or webcam for real-time debugging and learning, plus ready-made app templates and integration with Google's ecosystem (Maps, YouTube, Sheets)
• Building Custom Apps Without Coding - How Elizabeth used Claude Code to replace her $20/month RSS feed tool by building a custom aggregator, plus her sophisticated project management dashboard with AI agents that automatically pull daily todos and manage workflows—demonstrating the arbitrage opportunity available to non-technical entrepreneurs
• No-Code Platform Comparison - Practical insights on when to use Google AI Studio versus Claude Code versus Lovable/Replit, including live demonstrations of building apps directly in the browser and understanding the different workflows each platform offers

Watch Now

Watch the full conversation below, or click here to watch on YouTube.

Full Transcript

Nik Hulewsky: You’re saying, I could have just had this talk to Gemini live on and it would’ve been looking at what I was trying to build and like give me real time feedback?

Elizabeth Knopf Absolutely.

Nik Hulewsky: That’s insane. If it could generate from this one episode, then I’m gonna hang this call up and end early 'cause I’m done for the week. This is incredible. Why the f didn’t I know—this is insane. Why the freak wouldn’t I have ever done this? This is amazing.

[Intro Music]

Nik Hulewsky: Okay, Liz, I am excited to talk to you today because you are going to show me how to actually use some amazing tools, but the things we're going to talk about today are the things that are currently taking the AI world by storm. The first one is Nano Banana, which is the model that Google just released, and the second one is you're actually going to show us with Claude Code how you replaced one of your apps and created your own app using Claude Code natural language. You're not a coder, you're not a developer, but you're going to show us how we can do that ourselves. So, first question: what the freak is Nano Banana? I keep seeing this crap all over the internet.

Elizabeth Knopf: So, you know how a couple weeks ago we were talking about how Google sucks at marketing? Well, I think they were listening to our pod because someone stepped up their game and they did a great job finally releasing a product called Nano Banana. And all that is, is their Gemini Flash 2.5 image model. So, it's just another image model, but it's actually a pretty awesome image model.

Nik Hulewsky: I found this Reddit thread, I'm going to show it to you, and these are some examples of Nano Banana, of what they did. So this is a picture of Ronaldo, he's drinking a water bottle, and they've Photoshopped a Coca-Cola, they also Photoshopped a jersey on him. The face looks Photoshopped to me just because I see them next to each other, but pretty cool. Here’s one I think is nuts. They took this picture on the left, it’s two guys in the middle of the day with a Toronto skyline behind them, and then they Photoshopped that to at night. It’s literally the same guys in the same picture, you wouldn’t have known that. Incredible editing. Oh, this guy took a picture in front of his house, it looks like he’s in Nova Scotia, and then they changed his clothes and the lighting and it looks like he’s in a flea market in Morocco or something like that. So, really, really good image generation. My question is, like, what are people using this for? Is it just like a novelty trick?

Elizabeth Knopf: A couple of key challenges that have come up with a lot of the image generation models—first off, it's just like replacing things and doing simple edits can be a little bit hard to do. Even with ChatGPT in comparison, you would say make this, you know, change this one element, and sometimes it would work and sometimes it wouldn't. And same with Google's previous models. And so now, it has just become much more precise with specific edits and iteration of those edits. Changing the shirt, changing the product, like it actually is much more accurate in terms of how it takes whatever you're prompting it to change and making those changes. And I would say Adobe was still sort of the leader, you know, with Photoshop, but literally they were probably still leading the way in some of that editing versus again going to one of these image models to do your editing. But now, Nano Banana has really sort of advanced the ball in terms of doing that. The second key feature, and I think is the more exciting and really big deal, is what's called visual memory. This is essentially where you can have from one image to the next, it persists as the same person, same object, same thing. And that has been the biggest flaw in image generation because you would see these little ads or short ads and there would be a person but they'd slightly look different with different bone structure or something in the next cut. And so now, persistence has actually been improved so dramatically. And that's because they came up with some interesting technology on how they're building the model. But those are really the big deals, yeah.

Nik Hulewsky: Oh, that's really cool. So if I was starting to use these image generating models—that was always a problem, right? It’s like, cool, I generated this cool logo but how do I get it to stay the same logo across multiple iterations? Whereas this is like, well now you can do that. You can keep the same person, the same image, or the same logo. It stays persistent. Here is my question to you: we used to talk about multimodal—and maybe this is just me because I'm a caveman—but we used to talk about multimodal models where you're like, "Oh, I can upload an image, and then I could upload text, and I could upload a video." Those are all three different data types and in the past it was only text, but now you can kind of upload some images and video's getting there. However, my perception of multimodal has actually changed a little bit. I think of multimodal now as having an LLM that I use for synthesizing data, I have an AI image generator I can use to create logos, etc. I can even get these small language models that are specific use cases… maybe they're healthcare, maybe they're tech-related… or maybe there's a deep research function. So those are actually different modes of AI. I think Google has done a really good job of creating this studio… it kind of incorporates a lot of these different modes but people don't really use it or know about it. You were telling me about Google AI Lab—I can't remember exactly what it's called. What is it?

Elizabeth Knopf: Okay, so since they have so many different locations and properties, they're terrible. It's bad. aistudio.google.com. So, it's Google AI Studio is the name. One of the locations you can access Nano Banana, or of course you can go straight to Gemini and access Nano Banana there too. Google AI Studio has now started incorporating in that model into being able to build out little apps and other tools with this new model. So that's I think pretty exciting and we'll dive in here in a second just to see what's basically possible.

Nik Hulewsky: Okay, so this is it. I'm in Google's AI Studio. First of all, I don't know, it must have been two months ago or so, but you showed me a version of this and it was overwhelming. This chat interface is clean. I mean, it looks like the OpenAI chat interface. I don't get instant anxiety looking at it. Okay, so on the left side I have Chat, which looks the same as OpenAI, but then I have Stream. What is Stream?

Elizabeth Knopf: So Stream is actually a pretty cool thing. This is where you can basically use your webcam or share your screen. So like, if you are debugging something, if you want to learn something, if you want Google to watch a video with you and give you feedback or see you do something live and talk with you, it is actually doing that and capturing it in real time. This is like—people are sitting on this feature. It is pretty incredible. I use it a lot for just debugging and when I'm like doing something or following instructions and I'm like, "What am I getting wrong?" or "Look at my screen." So I don't have to take a gazillion screenshots. I'll do that, or I've tried putting like a YouTube video and having it sort of walk through it with me. So there's a lot of different cool things you can do with this feature that again is like totally slept on.

Nik Hulewsky: I did all these interviews with people. I wanted to look at all the interviews to be able to pull out information that maybe I'm not seeing. But there was a problem cleaning the data. So I had it all stored in Google and one of the coolest tools, Google Apps Script, I was writing a script. I was like, "Claude, can you help me write this script?" And so this is me just going back and forth, it's writing the script. And then every time that it would screw up, I had to take a screenshot of the execution log and then I would copy it and I would just literally paste it and it was like, "Oh, this error was because of blah blah blah." You're saying, I could have just had this talk to Gemini live on and it would’ve been looking at what I was trying to build and like give me real time feedback.

Elizabeth Knopf: Absolutely. Yes.

Nik Hulewsky: That’s insane.

Elizabeth Knopf: Exactly. I mean, I will say that I have had in the past where like I love Claude, so sometimes I think Claude is better at feedback because again, they're using the Gemini chat model, so it depends on your opinion there, but Gemini's been getting a lot better. But absolutely you can do that. So yeah, so like again, if people haven't been using Veo, pretty spectacular for like videos. They have also other sorts of media now you can produce, so whether it's like speech generation, doing audio… again you can start doing a lot of cool stuff in here. And one of the things also that I think is worth noting on Nano Banana that I also think is cool related to that persistence element is like you could do step-by-step guides. So I haven't yet tried it for software, but I have tried it for like fitness: "Show me a step-by-step guide with images of how to properly lift weights or do an exercise," and it will take the same person going through each of those steps. So you can just again get very creative and imaginative on using those step-by-step guides. Obviously they have a recipe one here, which I think that's a pretty cool feature. And as you can go in here, they have a bunch of cool examples and templates that you can be inspired by.

Nik Hulewsky: That’s insane. Yeah, yeah, yeah.

Elizabeth Knopf: Okay. So now another cool place that again people don't even know about, and this is a helpful way for beginners who are a little bit intimidated to get started in building apps. If you go to "Build," this is similar to some of the other LLMs that will have sort of like little mini apps examples. They used to call these "Gems," which was just really confusing, now they just call them apps. Basically, they have little starter apps and examples, some of which have incorporated in Nano Banana already. And there's others that have sort of showcased some of the real power of what Google and Gemini can do, which again is really on the sort of video/multimedia components that I would say the other models each sort of have elements of, but I really think that Google is excelling and doubling down on that capability of both animation, video, audio and streaming.

Nik Hulewsky: Is this kind of an answer to Claude Code?

Elizabeth Knopf: Sort of. I would say they're very different in terms of like the workflow, but everybody's trying to build out some kind of coding app because everyone sees like personal software is what's happening. That is what is being cultivated through LLMs as really I would say the biggest area of maybe annihilation or augmentation from what's happening. So, you know, everyone's building apps. And so you can do that now with ChatGPT, with Anthropic, with Claude Code, with Grok, you can all sort of build within the browser. So then still again, you can build it from here in the browser. There's still a couple steps to like get it into production, but Google has like that full workstream of getting from a little mini app into production whereas some of the other guys don't have the same kind of infrastructure that Google has.

Nik Hulewsky: So it’s probably more similar to a Replit or Lovable than it is to Claude Code.

Elizabeth Knopf: Sort of. But like again, they all have like you can start prompting to build something. This I would say is like still keeping very simple apps. I would say Claude Code is still sort of the best in going in the same environment interface from idea to production and building out sort of the infrastructure. Like again, I would say Gemini is doing well but again, there’s something about their interface that I’m just not as like thrilled about, so I think it’s personal preference.

Nik Hulewsky: I literally just put this prompt: "Build me a game I can play with my kids, it's a history trivia game with rounds of questions that is tailored to everyone's age, the app should keep score." It's been running for 75 seconds and I can see it real-time building this out. So like in this sense it reminds me of Lovable and that I'm like watching it build real time. Boom, there it is. And now I have basically an MVP to test around with. So I could say Nik, age 40, and then I’m going to add another player and we’re going to say Liz, age 29.

Elizabeth Knopf: Oh, thank you.

Nik Hulewsky: And then we’re going to start the game and we’re playing. It’s generating the questions. This is me: "Which Roman Emperor famously appointed his favorite horse, Incitatus, to the position of consul…?" I have no idea. I’m going to say Nero. Not quite! Caligula. All right, Liz, here’s your question. "Which explorer is often credited with the first circumnavigation of the Earth, despite dying halfway through the journey?"

Elizabeth Knopf: Was that Magellan?

Nik Hulewsky: Ferdinand Magellan. Correct! He died in the Philippines I think is where he died. This is freaking cool. I just built this!

Elizabeth Knopf: It's awesome. And so if you want to go back in a second, I’ll show you because I think this is cool. Cloud artifacts can do this, ChatGPT can do this, Grok can do this, but I think there's a couple pieces with the Nano Banana element and some of the features they have that's pretty cool. Scroll down… Maps. People don't talk about map making or leveraging maps. Map Explorer. You can incorporate in map elements. I started doing an exploration on the Google Maps for my kid’s game—like, "Where in the World is Carmen Sandiego?" meet something else. And maps is just like, you know, embedded and native to Google's stack, and so you can start doing cool things with maps.

Nik Hulewsky: Oh, this would be so cool. I could do a total game with my kids. Gobekli Tepe in Turkey, which was honestly just found recently. I could say, "Take me somewhere ancient." And my son… I know my oldest son's going to be like, "Let's go to Gobekli Tepe." And then all of a sudden we could just go to YouTube… like I could build this out and be like "Take me somewhere cool and then give me the top three YouTube videos to link to" just because it's in the Google ecosystem. That's awesome.

Elizabeth Knopf: Totally. All right, let’s go to another cool feature they have. It’s called Video Analyzer. We all sort of want to understand—like, you can get a transcript from a video, but like if we're doing this podcast, we want to say, "Where's our body language bad?" or "What is happening in this video?" and you can literally in their little app here add in a video.

Nik Hulewsky: I made this video a couple of months ago of people hanging Christmas lights with some music on it. I’m going to add it here. Explore this video via… let's do a chart. A chart of the total number of lightbulbs seen. I have no idea if that will work, I just thought it’d be interesting to try.

Elizabeth Knopf: Wow. That’s pretty cool. You can start imagining to like—it can actually ingest video. Wow. That’s pretty cool.

Nik Hulewsky: Now I have to go count that! But yeah, here’s the video. You can see this guy’s just setting up all the lightbulbs. I believe it. I am not going to go count them, but I believe it. How cool is that? That’s freaking cool.

Elizabeth Knopf: It's pretty cool. So again, like now you can start like ingesting video as a medium and doing more than just getting a transcript, which I just think is pretty cool.

Nik Hulewsky: I just think with this AI Studio that Google has, man, for like novices like me, I can have the video running while it's watching me go through stuff, it can tell me in real time what things I'm messing up, I can create apps… it takes the good of Lovable, Replit, and it takes Claude which helps me debug things. And then it has a good image generator. Oh, and then I can integrate my YouTube video, I can integrate my Google Sheets… like that's one thing that bugs the crap out of me… when I ask Claude to parse data and then it’s like "Sure, go ahead, just copy and paste." I'm like "No, just give me the CSV."

Elizabeth Knopf: Yeah, no, and it's interesting because right now people that are watching and paying attention, like this is a great arbitrage opportunity for any of this stuff. Like again, people don't know about Google AI Studio, so you can literally take that code, find a good URL that has some good traffic already and just put it up, like minimal changes… if people get ad revenue or whatever for a free tool.

Nik Hulewsky: That is such a freaking good point. It’s an amazing arbitrage because it used to be that software development was only for a select few who had gone through a ton of school and understood how to code. Now we can do it with natural language and something like Google’s AI Studio. You have no coding experience and you just told me you built an app recently that replaced one of your apps. What was that?

Elizabeth Knopf: Okay, let me share my screen and show you what I've been working on. I used to pay for an RSS feed tool that basically aggregates websites, keywords, different social media sources into a feed so that you can see everything in one spot versus going to a gazillion different sites or having to read through a million newsletters. So I was paying $20 a month for this.

Nik Hulewsky: And what was it going out and scraping? Was it going to Twitter, YouTube…?

Elizabeth Knopf: Yep, it was scraping specific sites like xAI news, OpenAI blogs… I input all these. I had my own Twitter feed here. I was paying 20 bucks a month for this.

Nik Hulewsky: This is incredible. Why the f didn’t I know—this is insane.

Elizabeth Knopf: So I just decided let me see if I can build this myself. This is my project management dashboard that I started building in Claude Code.

Nik Hulewsky: Of course you built a project management dashboard! How did you build this? Is it automatically pulling in new projects? Do you have to populate it every time? What am I looking at here?

Elizabeth Knopf: No, no, no. This is like a project management dashboard because as I start building projects, and me being an obsessive multitasker especially with Claude Code, I have too many projects and I need to keep track of what's what. I started building out todo lists, agents, different frameworks that I was starting to leverage. So I first went to GitHub to see if someone might have already solved this. This one was my own because again, you sort of have to decide what should you build for yourself versus what should you build for others. I just prompted Claude and told it what I want to achieve. Assume that if you haven't done it before, you're probably going to have to redo the whole project more than once until you refine what you need.

Nik Hulewsky: What were those agents doing?

Elizabeth Knopf: I have a couple different agents. I have a security agent that will help just make sure I have security best practices going. I have an agent that pulls my todo list on a daily basis and adds them to that dashboard. That runs independent of me.

Nik Hulewsky: So would it be fair to say that this is an SOP folder?

Elizabeth Knopf: It is more though, because it's both an SOP plus the execution. So I have actual agents in there, which again all I did was tell Claude "Make an agent that can do X, Y, and Z things." An agent that will get all of my todos from every project on a daily basis and aggregate them and add them to that dashboard.

Nik Hulewsky: Holy crap. This is insane. Personal Operating System—and then it has all the files.

Elizabeth Knopf: Exactly. I have a file where I have rules of what I want for that project. I tell it: "Create a todo file that breaks down every part of the project into bite-sized tasks and then update." I've had to rebuild this like a few times.

Nik Hulewsky: I know this just comes easy and naturally to you, but I think there's a huge opportunity to start businesses just around this—around helping people and companies get organized with their data infrastructure with respect to AI. You always blow my mind, Liz.

Elizabeth Knopf: You know what agent we really need to build though—or maybe I'll start working on this—is basically all the stuff we talk about from the pod that can then go and build off those little apps for us.

Nik Hulewsky: That would be awesome. All right, next week. It's a date.

Elizabeth Knopf: Sounds good.

Nik Hulewsky: All right, hopefully you liked that episode. If you've made it this far, you're either really committed or you're stuck doing yard work and you can't actually skip on your phone. So while I have you, the show is growing but I have a favor to ask of you. Will you please help me grow the show? I want to reach more people. There's a couple things you can do. Like and subscribe is the simplest thing. Obviously you want to get notifications for when the next episode is coming out. But if you go the next step, will you leave me a review? Five star on Spotify or Apple. What that does is it tells the algorithm that, oh hey, this is a high-value podcast because more people are leaving reviews for it and it then pushes it out to more people. So that's why when people are like "Will you log in, subscribe and put a five-star rating?" it's not just to make themselves feel better, it's actually to get more exposure for the show. So if you do that for me, I would greatly appreciate it and I'll see you next time.

Our conversation covered a fascinating range of topics, from the practical differences between various Large Language Models (Claude, ChatGPT, and the surprisingly powerful Gemini) to how AI is fundamentally changing web scraping, programmatic SEO, and the accessibility of building software products.

What struck me most about this conversation was Minh's hands-on approach to AI—he's not just theorizing about these tools, he's actively using them to build real businesses like Cash On, a Chrome extension that helps real estate investors identify profitable properties in a fraction of the time.

We dove deep into his AI-powered web scraping system that's gathering data on 16,000+ pickleball courts (a task that would have taken over half a working year manually), and explored how no-code platforms are empowering non-technical founders to build functional apps in days rather than months.

This article includes the main topics we covered, a link to watch the full podcast conversation, and the complete transcript below for those who prefer to read.

Topics Covered

In this episode, Minh and I explored several game-changing AI applications and frameworks:

• Choosing the Right LLM for the Job - Why Claude excels at coding, ChatGPT's deep research feature is invaluable for learning new topics (like quantitative trading), and why Gemini's massive context window and low cost make it an underrated option for 80% of use cases
• AI-Powered Web Scraping & Directory Websites - How Minh built an automated web scraper using Firecrawl to gather detailed data on pickleball courts, turning what would have been 1,300+ hours of manual work into a seven-day background process, and the business model behind programmatic SEO directory sites
• No-Code/Low-Code Revolution - A practical comparison of tools like Lovable, Bolt, Cursor, and Replit, including real examples of non-technical founders building functional products (like Minh's physical therapist friend who built an 80% complete self-diagnosis tool)
• Building Knowledge Chatbots - A framework for turning existing content (like podcast episodes or YouTube videos) into specialized chatbots that can replicate your decision-making process and provide value at scale

Watch Now

Watch the full conversation below, or click here to watch on YouTube.

Full Transcript

Nik Hulewsky: Minh, I'm very excited. I'm excited to learn some AI today. You are coming to me from the capital of the world of technology, the Bay Area. Here's my first question: what tools do you use—LLMs—and for what?

Minh Nguyen: I use all the LLMs, and they all kind of excel at different things, so I use them for different reasons. For coding, anything coding-related, I'll use Claude because it's the best coding model. For general-purpose things, but also just research, I'll use ChatGPT, especially deep research. That's been super helpful. I'm actually trying to learn a bit of quantitative trading right now as a side project with a friend. I know nothing about it, but the deep research has been super helpful with that. And then, actually, I feel like this is the one people sleep on is Gemini. For 80% of use cases, I actually feel like Gemini—for people who code—it'll get the job done just as good as the other models, but at a tenth of the cost. People, I feel like, they don't know about Gemini, they don't know how cheap it is. I was talking to this guy the other day who was doing web scraping, spent $80 with Claude in one day, and then I showed him Gemini. He switched over, did just as good of a job, but cost like five bucks to do what he had done.

Nik Hulewsky: No way! Why do you think people sleep on Gemini? Because almost everybody who I have spoken to has said Gemini is not even on their radar. Two people have said—one person said it was their main thing, and then another person was like, "I just use it for the Google Suite."

Minh Nguyen: So Gemini is actually good at two things. One is it has a large context window, which means it can consume a lot of information. For example, I think it has a million context window compared to other LLMs which only can do up to 200,000.

Nik Hulewsky: Oh, really?

Minh Nguyen: Yeah, which is—so I guess if you translated it, let's say ChatGPT can ingest 100 pages, then Gemini can ingest five times that, so 500 pages.

Nik Hulewsky: Well, I just know like Claude's context window is 200,000, ChatGPT's is around 120,000. I had no idea that Gemini's was a million.

Minh Nguyen: I think it actually can even go higher than a million, but I think the cost kind of skyrockets or it goes up. I've never needed above a million, so I haven't tried.

Nik Hulewsky: So you are an entrepreneur, you're a coder. You've built a company called Cashon. Tell me just briefly what that does.

Minh Nguyen: Sure. Cashon is a Chrome extension that hooks into Zillow. Usually what real estate investors have to do is they browse Zillow, they copy and paste the data—like the price, the mortgage, and interest—into a spreadsheet and they run their analysis. It probably takes about five minutes per property. But you can only find maybe one in a hundred properties that would be a good real estate investment. So what my app does is it will grab all that data for you, analyze it for you, and then tell you which of the properties on the map are the best ones. It takes a process that can take usually months and compresses it down into a week or two. Best success story is this guy was looking for real estate investments for like two years, couldn't find anything. I worked with him, we basically identified a market in two weeks, and then within a month, he put in and got two offers accepted on two different properties.

Nik Hulewsky: That's amazing. And it's a Chrome extension?

Minh Nguyen: Yeah, it's a Chrome extension, and I have a free tier that is pretty generous that people can try out.

Nik Hulewsky: The cool thing I didn't realize this, but there are some companies that are like eight, nine-figure companies and they're just Chrome extensions. Which is insane to me. Now I'm curious—the money question: how are you utilizing AI? Utilizing it for work? Personally? Did it build, or at least help build, Cashon? Curious to see your use cases.

Minh Nguyen: I pretty much use AI all the time now. Like I kind of mentioned in the beginning, to learn new topics, to do research, to code. I use Cursor to code. But maybe the thing that I kind of want to show you, which I'm kind of excited about, is I use AI now—I built an AI-powered web scraper. Basically, there's this concept called directories that's kind of getting popular right now. The idea is if you consolidate information and put a website up, then people will go to your website and that traffic can actually generate you quite a bit of money. Have you heard of AllTrails?

Nik Hulewsky: Yeah, the app.

Minh Nguyen: Yelp is another example. People most often use it as a directory for restaurants. I built—it's called pickleballcourtsnearme.com. People search it and then there is search volume—like 200,000 plus per month. With that kind of traffic, you can generate easily in the order of five to ten thousand a month.

Nik Hulewsky: Wait, so are you making money off of pickleballcourtsnearme.com?

Minh Nguyen: Not yet. I'm just starting it up. That's why I built this web scraper. The idea is I built a web scraper that will help me scour the web for pickleball courts and tell you information like: is it indoor? Is it outdoor? Can it be reserved? Does it have lights so you can play at night? A bunch of other stuff. I was helping a friend who was building dog parks near me, and he was basically saying that it was going to take him 80 hours to gather the data needed to build this website to hopefully make something like two, three, five thousand dollars a month. That's kind of how I got into it. I was like, "Wait, I can automate this for you, man." It took me about a day or two, but we got it running. I saved him 80 hours, he launched his website, and we'll see—SEO is kind of a long game, so we'll see the traffic that you're able to acquire. There's a lot of potential with these directories but also just LLM-based web scraping because they can browse like humans and automate what you're doing and extracting the information you need.

Nik Hulewsky: Is it similar to programmatic SEO?

Minh Nguyen: Yes, yes. Actually, let me—this will be just easier if I share my screen.

Nik Hulewsky: Yeah, I'd love to see it. I'm like really new to all this stuff. I'm trying to learn as much as I can, but programmatic SEO has been really attractive to me because, like you're saying, it's a long game. But you're also taking advantage of either different geographies or different verticals. So I've been thinking about AI use cases around programmatic SEO.

Minh Nguyen: Yeah, so this is my website, pickleballcourtsnearme.com. The programmatic is for every state and for every city—this is California, Irvine—there will be a page and this is the kind of data extracted. This is actually Denver, Colorado. You go to whatever city and then there's a URL that's already been created. This is Texas, Dallas. All the data is here. When you go to Google Search and you search "pickleball courts near me," this will say "pickleball in Dallas, Texas."

Nik Hulewsky: So how are you using AI to generate the information for these websites?

Minh Nguyen: What I first did is I used this platform called Outscraper. I scraped Google Maps for all the locations that have "pickleball" in the name or in the description. That gives me a list of all the locations and it also usually comes with a URL. Since I code, this is in my database, but usually it's given to you as a CSV that you can open up in your Excel sheet. The idea is you get the pickleball court or the location—like these sports complexes—and then you get the URL of these pickleball court locations. And then what I do is I use this open-source software called Firecrawl. I send it to this URL and I say, "Okay, I need pickleball courts and I want it to look like this." So I want the URL, I want the email, the phone number, the address. It goes to the website and then it crawls it until it finds the data needed and then it gives it back to me in the format I need to show on my website.

Nik Hulewsky: So the software you're using—is that AI-enabled?

Minh Nguyen: Yes. Previously, if you were trying to scrape, you'd have to hardcode everything. For example, you could only go to ebay.com and say, "For this product, this is where you get the email and this is where you get the address." But now with AI, no matter where it is on the page or even if it's on a different page in the website, it can decide which one to go to. Usually what happens is it goes to a website and then I get all the links on that website. "Here is the homepage and these are the 100 other links on this website."

Nik Hulewsky: Again, I'm really stupid and I'm just trying to like process through this. The reason Outscraper is able to get all the information that it does from Google is because Google probably has some standardized way of gathering and displaying information. Because Outscraper had all this information before AI, right? And they're able to do that because it was predictable—you could hardcode and pull all this stuff. Now what you can do with that website information, using context, is say like "go find me the phone number" or whatever. And you don't have to tell it where it is, but it knows what a phone number looks like so it's going to go through and make a decision. How cool!

Minh Nguyen: Exactly. You nailed it. Since businesses usually submit their business and they fill out the information, Outscraper can scrape it from Google Maps because it's always in the same place in the same format. But what happens if it's not in the same place in the same format? Then you need a human to go do that. Previously, a job like this would have taken many people many months to scrape. In this database, I have 16,000 pickleball courts. If you say 16,000 and let's say it takes five minutes per, that's 80,000 minutes, so that's 1,300 hours. That's more than half of a working year. And so with AI, I kind of just let it run in the background. It'll take me about seven days, but I don't have to do anything. And actually, what I've noticed is it actually does a lot better than a human too. Because with AI, you're basically getting like a college-level graduate—all you're doing is pulling data out, so it's not hard. If you read it and then if it exists on the page, the AI can definitely grab it, whereas a human might miss it or a human might be like, "Ah, good enough. That phone number might work," and they're just trying to get to the next thing as opposed to getting it right. Have you messed with Operator?

Nik Hulewsky: I have, I have.

Minh Nguyen: You know how slow Operator is? When it's going in and executing a task, it's slower than the human would actually do it. Is it the same with this technology? Is it just slow? Is that why it takes seven days? Or is it just a massive data set?

Nik Hulewsky: It's just a massive data set. I think with Operator, it will get faster over time. But it's still slow now because it has to be a generalized use case. My guess is it actually could run faster, but it would cost OpenAI more, so I think they purposely rate-limited it. But since for me, I just need to get the job done as fast as possible, I can let it run for as fast as possible. I'm actually more limited by the computer I run it on. And so I could technically rent a computer in the cloud.

Nik Hulewsky: How much coding does it take to do what you're doing? Like if I wanted to go—not scrape all the pickleball courts, I wanted to go scrape all of the cupcake businesses in the United States. I know the software now, Firecrawl. Could I just go do this? Or what would it actually take for me to do it?

Minh Nguyen: So Firecrawl handles the scraping for you and gives the website in a format that the LLM can do. And if you were going for like an MVP, let's say "I just want to grab the data only for cupcakes, don't need a generalized scraper," you could probably get that done if you were a software engineer in a day or two. If you didn't, it kind of depends on how good your prompting is, but I think you could still get it done in a couple days. I think the thing with prompting from a non-engineer's perspective is just you could probably try everything you could within like three days. And then either you'll get it done in three days or you're not going to be able to get it done, like you don't know how to prompt it in the correct way.

Nik Hulewsky: Yeah, I'm just thinking for a business owner who's like, "Let's say I have a PE firm and I'm rolling up cupcake shops, and I want to know who all the cupcake shops are that are owned by mom and pops." I went and got all the information from Google, but now I want you to find the specific owner—some use case where you're scraping data—how would I do that? Would I just go to Firecrawl and say this is what I want? Or do I have to build an app essentially to run Firecrawl on?

Minh Nguyen: I think specifically for this use case, there isn't a product that kind of exists yet that can do end-to-end. I am planning to release this as a paid product, but it will cost a bit of money because, just for reference, for the pickleball data set, that will cost me at least $150 and I'm only halfway done. And then it also cost me seven days of running my own computer.

Nik Hulewsky: That's nothing.

Minh Nguyen: Yeah, no, it's nothing compared to like if you had hired a VA for like 20 bucks an hour, because that would take them 55 days, which would be way more money. But yeah, I think for someone just trying to get it done, I would say give it a shot. I think with AI, you definitely have a good chance at getting it done. This is just maybe on a little bit more on the technical side. I think you get pretty far as a business owner without any coding knowledge. No, it's weird because this last week, I had never done anything even remotely technical. And in the last week, I've learned what JSON is—I know what it is, I don't necessarily know the definition behind it—but I've written a Python script in Google Colab, which I was then able to take my files and condense them and I was able to get my transcripts cleaned up. And I was like, "This is freaking amazing!" I don't know what I did, but I know that AI wrote it and it gave me the output that I wanted.

Minh Nguyen: Have you heard of bolt.new and Lovable?

Nik Hulewsky: Lovable, yes. I've heard of Lovable, but bolt.new?

Minh Nguyen: Very similar to Lovable—products that will help you build websites without any coding knowledge. My physical therapist friend, he's been trying to pitch me on this idea for a while to help like people kind of self-diagnose themselves. I kind of gave him a couple steps to do beforehand before I was like, "Okay, before we can work on this together." But he was like, "No, I don't want to do all that." So he just went on—I was like, "Okay, if you want to do it, try using Lovable." He used it and he built like a product that, without AI, probably would have taken me like a solid month. With AI, probably like a couple days. But he was able to build like 80% of a working website with just like a couple bugs to help people self-diagnose themselves like for physical therapy pains. And that blew my mind, because he's had like a bunch of ideas over the years and never been able to pursue them, and now he can.

Nik Hulewsky: Dude, I know a lot of people who are like, "Dude, I have this idea for an app." And then they tell you the app and it's like, "Yeah, you're never going to do that because you would need an engineer or coder or somebody to create an MVP for you." But you can actually—you can actually do that now.

Minh Nguyen: I know, it's crazy. Actually, it's like years beyond an MVP now. Like there's like small kinks here and there, but it's definitely working and definitely gets the job done.

Nik Hulewsky: What's the difference between Bolt and Lovable?

Minh Nguyen: I think they do the same thing. I think they're just competitors.

Nik Hulewsky: What about Replit? Replit, Bolt, and Lovable?

Minh Nguyen: I haven't actually tried Replit, I've only seen videos of it. I think it falls into a similar category. My guess though is that Replit would be a little bit more powerful.

Nik Hulewsky: What about Cursor?

Minh Nguyen: Cursor is definitely—if I put it on a spectrum for software engineers versus people that don't know how to write any code: Lovable and Bolt are on the side of people who don't know how to code. Cursor is definitely on the side for people that do know how to code. And I think Replit's in the middle. I feel like with Lovable, I kind of had to help my friend debug a bit. It didn't let me edit anything in the UI. It's really designed for people who just cannot code and just want the AI to do everything. But with Cursor, on the other hand, you need to know how to set up your environment, you need to know what to tell Cursor, but it's way more powerful.

Nik Hulewsky: If I wanted to build an app, would you recommend I try Lovable or Bolt?

Minh Nguyen: I would say Lovable because now I actually have experience with it, but from everything I've heard, they're equivalent products.

Nik Hulewsky: I'm going to try it. I'm going to try it next week. I want to try to build something.

Minh Nguyen: Yeah, dude, I think you're going to be kind of blown away. Another friend had another idea that he's been kind of trying to pitch me to build—like this Reddit analyzer. And then he just built it without me because he didn't need me anymore. It's hard to say no to your friends, but now that they can go do it themselves, it's awesome.

Nik Hulewsky: Okay, if I were to build some product for myself, are there like some parameters you would give me or some framework you would give me to identify what would be a good use case?

Minh Nguyen: The best projects to start are app ideas where you don't need to aggregate data from across the internet. Like these calorie tracker apps, for example, where like the user can input the data themselves and you can kind of like do everything within the app—those would do well. But the moment you kind of need to reference other data sources, you're kind of getting into a part of software engineering that is harder because you need to rely on other services. So anything you can build that's like completely wholly encapsulated in your app.

Nik Hulewsky: Okay, so I have like 130 podcast episodes, and let's just say that of the 130 podcast episodes, it distills my philosophy on many things. That would be a good use case for building sort of a closed system app. The "Nik Deal Analyzer" for all intents and purposes. It could be like "I'll give advice on your business if you're trying to scale" or "I want to buy this business, what do you think of the multiple?" or "I want to start a business, what do you think of this idea?" Potentially it just gives you feedback. But I guess that's just a chatbot, right?

Minh Nguyen: That would be a chatbot, but you would be surprised how helpful that is. I feel like in this kind of age where you can build anything, you still need a lot of knowledge to do things well. So I'm working with like an influencer—he has a YouTube channel and he helps with advice on programmatic SEO and trying to decide which websites to build. But he has a lot of knowledge in his head that would take a long time for you to kind of comb through YouTube videos or articles to kind of understand. And so I'm actually building something like a chatbot, but basically, you give it an idea and then it will just ask all the questions that he would ask and assess your idea. He doesn't have the time to meet with a hundred people, but now this chatbot can. I still think that's really useful.

Nik Hulewsky: So how would I do it? I want to walk through this because my problem is that the context window, even if it's a million tokens, is call it 20 podcast conversations, and I've got 130 of them.

Minh Nguyen: I would start with—and this is kind of how I'm approaching it—is you probably have a framework to analyzing if a business is going to be a good idea or not. Like: what's the monetization model? How are you going to do distribution? All these things. And then also you can then assess if that monetization model will actually work out or not. I'd say define a framework if you don't have one already, but then you take your framework and encode that into a chatbot. Or I could take 10 episodes that I really liked and clean the data—get rid of the timestamps, get rid of the "ums," "ahs"—get it down to a point where I can fit it into a context window and then just say, "Hey, help me codify my deal analysis." That's actually what I did for my friend that I'm trying to build like a chatbot for. I took about five of his YouTube videos, I threw the transcript in ChatGPT. There are these free services like YouTube to transcript. I threw it in and then I was like, "Okay, tell me all the best practices and tips that I need to follow when trying to choose a new SEO website to build." And then it spat out like 20 to 30 things he said. I then encoded those 20 to 30 things into questions and criteria to analyze, and that's how I made the chatbot. You could apply that same thinking: give an AI your audio files, say "what are the most important things to analyze and how do you analyze it? What are the criteria to make something good or not good or needs to be improved?" See what it spits out and then codify that into the app.

Nik Hulewsky: This is really interesting. This was amazing. This was my last call of the week and definitely of the 30, this is like this is a top three. Very good. Appreciate it, man.

Minh Nguyen: I'm glad, man.

Nik Hulewsky: All right, hopefully you liked that episode. If you've made it this far, you're either really committed or you're stuck doing yard work and you can't actually skip on your phone. So while I have you, will you please help me grow the show? Like and subscribe is the simplest thing. If you'll go the next step, will you leave me a review? Five-star on Spotify or Apple. What that does is it tells the algorithm that this is a high-value podcast because more people are leaving reviews for it, and it then pushes it out to more people. If you do that for me, I would greatly appreciate it, and I'll see you next time.

Not because I do not want to help, but because of the emotional tug-of-war that follows every single request.

The Guilt

I feel guilty for not making time.

I was once in the position of wanting to learn from someone 10 steps ahead.

I often find myself even still in that position.

So I understand the desire to reach out and ask.

The Anger

Then I get angry at myself for feeling guilty.

They want to pick your brain because you have high-value advice to give!

THEY should feel sheepish about asking for that for free!

The Internal Battle

Which voice wins really depends on the day.

Something I want to be better at, and I do not know why, I cannot seem to get past it.

Conclusion

The tension between wanting to help others and valuing your own expertise is real, and I am still figuring out how to navigate it.

If you struggle with this too, you are not alone.

Connect with me on LinkedIn and Instagram for more content like this.

When I mentioned how healthcare companies can lose substantial money during startup, my therapist’s dramatic reaction confirmed I was not alone in recognizing this issue.

Let me share five crucial things you need to know to avoid the costly mistakes I frequently witness in healthcare startups.

What is the game?

You need two fundamental elements to start a healthcare company:

• A license to provide care
• A business model

Who grants your license can vary significantly.

I strongly recommend conducting thorough research before pursuing any particular path.

How to Enter?

There are three main ways to obtain a healthcare license:

• Buy an existing healthcare business (with license)
• Buy an existing healthcare license (without business)
• Start from scratch

Each approach comes with its own advantages and disadvantages.

There is no universal solution that works for everyone.

Deciding

When buying an existing operation, you inherit three key elements:

• A team
• A brand
• Liability

This inheritance can be either beneficial or extremely problematic.

Starting from scratch allows you to build exactly according to your vision.

However, this control comes with significant time and monetary costs.

Liability

Payment recovery through audits is common among most payers, unless you operate on a cash-only basis.

Medicare non-compliance can result in account levies.

Poor documentation or lack of authorization can lead to payment denials.

Investment

Those attractive return models and profitability calculations you have created look promising.

Here is my professional advice: Take your budget and timeline, then double both.

I will share a personal example:

• Projected time to collections: 180 days
• Actual time to collections: 301 days

Conclusion

Healthcare can be both rewarding work and an excellent investment opportunity.

However, insufficient understanding can lead to significant challenges.

Investing time and resources in proper planning now will save you considerable heartache in the future.

Follow me on LinkedIn and Instagram for regular insights and tips.

After training and working with hundreds of leaders throughout my career, I have learned one fundamental truth: there is no excuse for being a bad leader. Leadership is a skill that can be developed, and I am excited to share the four books that have been game-changers for new leaders in my experience.

Core Foundations

I have discovered that becoming an effective leader starts with understanding ourselves and our impact on others.

The Essential Reading List

1. Leadership and Self-Deception

This book delivers a hard truth: YOU are the PROBLEM.

Want to fix others? Take a long, hard look in the mirror first.

The key question it poses: Are people merely stepping-stones for your goals?

Remember: self-improvement must come before helping others grow.

2. Crucial Conversations

Good leadership demands mastering difficult conversations.

The stakes are always high, dealing with sensitive topics like money, self-esteem, and relationships.

The guiding principle? “Start with heart.”

3. The Advantage

Once you have mastered personal growth and tough conversations, this book is your next step.

This masterpiece shows you how to build a cohesive leadership team that naturally attracts top talent.

4. Never Split the Difference

Have you ever considered negotiating like an international hostage negotiator?

This book equips you with tools to navigate any conversation successfully.

Remember: life is a constant negotiation, and this book becomes your compass.

Conclusion

These four books have been instrumental in my leadership development journey, and I am confident they can transform your leadership skills too. Leadership is not an innate talent; it’s a skill we can all develop with the right resources and dedication.

Follow me on LinkedIn and X for more insights and recommendations on becoming a better leader.

Click here to subscribe.

Most Viewed Videos

Here are my top 3 most viewed videos that helped me grow to 10,000 subscribers today.

The Most Overlooked $310K/Month Business Anyone Can Copy

With over 47,000 views is my episode with business partner Chris Koerner.

Questions This Episode Answers:

• How can a seemingly “old-fashioned” business model achieve significant financial success?
• What are the advantages of going against current trends, like favoring analog over digital experiences?
• How can AI be leveraged to create personalized content and enhance unique human experiences?
• What are some “vibe code” business ideas that tap into niche markets or combine old and new technologies?
• How can a business effectively utilize existing, often overlooked, resources for distribution and low cogs?

He Earns $10K/Month Automating Directories With AI

My second most viewed video garnering 38,000 views is my episode with Minh Nguyen.

In this episode, we dive deep into the exciting world of AI and how it’s revolutionizing the way we build businesses and gather information online.

How to Build a $250M Business ($0 Down), by Buying Premium Domains with Jesse Tinsley

22,000+ views and counting, my video with Jesse Tinsley takes 3rd place.

In this episode, Jesse talks about why premium domains like employer.com are worth millions, how to creatively finance acquisitions, and why taking the leap into entrepreneurship was his best decision.

We also dive into Jesse’s unconventional approach to buying companies, building a mini-holdco, and scaling businesses from scratch. It’s a must-listen for anyone interested in scaling, branding, and creative deal structuring.

Conclusion

We have a long way to go! But I just wanted to share this small milestone with everybody.

Click here to visit my YouTube channel (and maybe subscribe too while you’re there!).

https://www.youtube.com/@NikonomicsPodcast

We’re pulling back the curtain on the alleged “divorce” between Jony Ive and Sam Altman, a story of trademark disputes and “ruthless Machiavellian leadership” that reveals the high-stakes game being played at the top.

We also dive into the escalating “wars” between Large Language Models, exposing how giants like OpenAI and Anthropic are battling over data, acquisitions, and the very definition of fair use.

The main event? A personal walkthrough of a feature that will change how you work with AI. We get hands-on with Claude’s new “Artifacts,” a game-changing update that lets you build, customize, and share your own interactive AI tools in minutes.

Video Gallery

Watch the full episode below, or click here to watch it on YouTube.

Ive vs. Altman

What started with a friendly photo-op between Jony Ive and Sam Altman quickly soured. We unpack the lawsuit from a company named IYO, who claims OpenAI and Ive’s new venture, IO, isn’t just a naming coincidence. This conflict shines a light on Sam Altman’s aggressive leadership style and raises questions about intellectual property in a world where ideas move at the speed of light. It’s a classic Silicon Valley tale of ambition, betrayal, and big money.

LLM Wars

Beyond the personalities, a technical and ethical war is brewing. We explore how Anthropic’s Claude is making strategic moves against OpenAI, including cutting off data access for a multi-billion dollar acquisition. This is part of a larger battle over how AI models are trained. We discuss the pivotal “fair use” rulings affecting Meta and Claude, who have been using millions of copyrighted and pirated books to train their models, and what it means for the future of content and media.

Claude’s Artifacts

Here’s where theory turns into practice. We introduce you to “Artifacts,” Claude’s powerful new feature that lets you go from consumer to creator. An Artifact is any document, dashboard, or tool you generate with AI. Claude now organizes these creations, allowing you to find, publish, and share them with a single click. The real magic lies in the “Inspiration” tab, which offers pre-built templates—like a fully functional trivia game—that you can customize. You can even see the exact chat history used to create them, providing a masterclass in effective prompting.

Conclusion

This conversation is more than just a recap of the week’s news; it’s a practical guide to understanding and leveraging the power of modern AI.

Here are the key takeaways from the full episode:

• The Cutthroat Nature of AI Leadership: Get an inside look at the strategies and controversies defining figures like Sam Altman and the companies they lead.
• The High-Stakes Battle for Data: Understand the critical legal and ethical questions surrounding AI training data and what “fair use” means in the age of LLMs.
• How to Build Custom AI Tools: Receive a step-by-step walkthrough of Claude’s Artifacts, learning how to find, publish, and customize interactive tools for any need.
• The Future of AI Workflows: Discover how you can create “custom wrappers” that connect Claude to your own data in Google Drive and potentially automate entire tasks using Zapier.

Visit my podcast website for more! You can also follow me on Instagram, X, and Patron View for more.

You’re anxious to get in the game but can’t afford to miss.

You’ve heard “buy a great business at a fair price” but how do you know what “fair” is?

Here’s how I turned $90k to $7.8M in under 6 years.

Minimize Downside Risk

Right now, you only have on goal: Minimize your downside risk.

But you can’t minimize your downside risk if you don’t know the value of what you’re looking at.

The CRAFT Framework makes this easy:

C – Cashflow
R – Risk minimization
A – Analysis
F – Financing
T – The Model

Continue reading for more information.

Cashflow

Identifying cashflow is the most important step in the process.

You will use this number for your valuation so it HAS to be right.

The equation is simple but calculating it is not: CF = Cash In – Cash Out

Don’t be prideful. Use a CPA, financial whiz or investor to help.

Risk Minimization

You aren’t some deep-pocketed investor. This is your first business!

Underwrite the business based on its current state. Not what it did 3 years ago or what you think you can do.

What it’s doing right now:

• Be skeptical
• Be paranoid
• Be sober

Analysis

You need to know, post closing, how the business will perform.

Start by asking these questions:

• How would the business do if I took over today?
• With the current owner gone, what do I need to cut or add?
• How will the J-Curve look post close? (👇)

Financing

You cannot come to a valuation if you do not understand how financing works.

Gather this information:

• Loan rate
• Loan Term
• Loan Amortization period
• Your Debt Service Coverage Ratio (DSCR) range
• Cash down requirement (bank)
• Your preferred Cash down

Understanding DSCR

What is DSCR & why is it important?

Banks decide how much you can borrow based on how much debt the business can handle.

But you should be MUCH more conservative.

Why? You want a cushion

Check out this example below.

The Model

With all that work done, let’s plug it in!

Step 1. Enter Purchase Price, Cash Flow Proxy,
Step 2. Enter loan Term, Amortization Period and Rate
Step 3. Enter the your DSCR range
Step 4. Evaluate your Returns and iterate

Here is a screenshot of my tool below.

Conclusion

Buying your first business doesn’t have to be overwhelming. The CRAFT Framework gives you a clear, step-by-step process to evaluate any opportunity and protect your investment.

The tool and framework that helped me grow $90k into $7.8M can work for you too, but only if you put in the work to properly evaluate each opportunity.

Key Takeaways:

• Always start with accurate cashflow calculations—get professional help if needed
• Underwrite based on current performance, not past success or future hopes
• Be skeptical, paranoid, and conservative in your analysis
• Understand your financing options and DSCR requirements before making offers
• Use the CRAFT model to systematically evaluate every deal
• Focus on minimizing downside risk rather than chasing maximum returns
• Don’t rush—take time to properly analyze each opportunity

Start small, stay disciplined, and let the framework guide your decisions. Your first business acquisition is just the beginning.

For a copy of the tool, follow me on X, and send me a DM! You can also follow me on Instagram and Patron View for more.

1. I Spend $310/Month on AI Tools (And They're Worth It)
2. My $310/Month AI Stack: Every Tool I Use
3. The AI Tools I Actually Pay For Every Month

I spend $310 per month on AI tools, and I am not looking back.

Every single one earns its place in my workflow.

Here is exactly what I use and why.

OpenAI Tools

ChatGPT - $20/month

This is my general thought partner.

I would like to migrate away, but persistent knowledge keeps it sticky.

GPTs are underrated.

It also has top-tier image generation and a solid coding copilot.

OpenAI API - approximately $30/month

This powers the AI under the hood for any applications or automations I run.

Anthropic Tools

Claude - $100/month

Projects are most intuitive here.

Opus is outstanding.

Artifacts are incredible.

Easy to store, search, and update.

Claude Code - approximately $30/month

This is the killer feature.

It builds local with Model Context Protocol-like capabilities.

It is the smartest coding tool I have used.

It replaces Lovable, Replit, and Bolt for me.

Other Essentials

Perplexity - $20/month

Best at deep financial and market research.

Grok - $20/month

Best for real-time information and reasoning in my opinion.

Google 1 (Gemini) - $20/month

Best for automations in the Google suite.

Google Apps Script plus Claude Code equals no need for Zapier, Make, or n8n for most people.

Supporting Tools

Lovable - $20/month

I am canceling, but it is my favorite for creating simple web apps.

OtterAI - $40/month

Meeting recording, transcription, and storage.

See also: Fireflies and Fathom.

Limitless AI - $30/month

My beloved pendant.

Ever present, always on.

Recording and summarizing my interactions.

Conclusion

These tools have fundamentally changed how I work. The right AI stack is not about having everything, it is about having what actually moves the needle. I would love to hear what tools you are using.

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