Transcription
The most expensive mistake that you can make in AI isn't using the wrong tool. It's not knowing which layer the problem is on.
Here's what I see constantly among thousands and thousands of members in my community. Somebody builds an AI system, the output is wrong, the automation breaks, the agent does something unexpected, and their first instinct every single time is to change the tool or rewrite the prompt. Switch from ChatGPT to Claude. Tweak the instructions for the fifth time or Google "best AI prompt for X." And sometimes it helps, but usually it doesn't, and they can't figure out why. Be honest, it has probably happened to you as well.
You try to build an AI agent, but you don't understand tokens or context windows, so the agent forgets what it's doing, costs you $400 in API fees an hour, and you give up because AI is overhyped. It's not overhyped. You just skipped the physics of the system because the tool and the prompt are just the surface. They're what's visible. They're the thing that you're touching digitally. But AI systems aren't just one layer deep. They are, in fact, eight layers deep. And if the problem is three layers down, no amount of surface-level adjusting is going to fix it.
Often times, you get bad outputs because the model has no context to work with. That's a data layer problem. Changing models is not going to touch it. Or if your automation never triggers, it's an infrastructure problem. Better prompts are never going to help. Or maybe your agent is going off script. It's because of missing guardrails and memory. So switching the no-code tool is not going to solve your problem. You see, every one of those is a misdiagnosis. And misdiagnosis in AI does not just waste your time, but it destroys your confidence as well. People walk away thinking AI doesn't work for my business when what actually happened is they were treating a transmission problem like an engine problem.
But what I also see in my community is that the people building real AI businesses look at things differently. Not because they know more tools. I hope you see that it's not the tools, but it's because they can see the entire system. They know which layer they're working in and they know exactly where to look when something breaks. It's not because they have a specific talent or they've taken a course or, I don't know, a skill they learned somewhere. It's a framework. Okay? And if you think back in your chemistry class, Mendeleev didn't make chemists smarter by giving them more facts. He gave them a table that showed how everything was connected. And once that structure existed, chemistry became easier to diagnose. A chemist was now able to look at any reaction and know, not guess, where the problem was.
So that's what I built for AI. 48 concepts, eight groups, the periodic table of AI elements. So by the end of this video, you will not just know all the AI terms that you need to know, you will also be able to look at any AI problem, whether it's in your business or anyone else's, and immediately see which layer it's on, which group of elements is involved, and what to actually do about it. And I think what makes this different from every other AI 101 or AI explained video that you've seen is that it is built for entrepreneurs. It's not for developers. It's not for researchers. Every single element on this table is here because it shows up in real AI businesses. If it doesn't affect how you build, how you automate, and how you grow, it didn't make the cut. So, let's get into it.
And I think before we get into the table, I need to address the belief that is holding most entrepreneurs back and that I see so often. And that's the belief that you need to understand all of this before you can start doing anything. That's wrong. And I mean that in the kindest possible way, but it's wrong in a very specific way that's worth unpacking. And that's because it's not the belief itself that's the problem. It's what the belief makes you do. Okay? When you think that you need to understand everything first, you consume without direction. You watch tutorials, you read articles, you follow AI news, and none of it compounds into anything. Return on investment is still zero because information without that structure doesn't build anything. It doesn't lead anywhere and it cannot compound. It just accumulates. Okay? You end up knowing a lot of things that do not connect to each other and therefore don't connect to action. That is a problem of focus and of fear. It's not so much discipline.
So let me put it another way for you. Okay? You do not need to understand how an internal combustion engine works in order to drive a car. Correct? But you do need to know what a steering wheel is, what gas is, what brakes do, because if you have zero framework, every AI tool is a black box, and you are just crossing your fingers and hoping for the best. Understanding the map does not mean memorizing every element. You can just print it or put it as a wallpaper on your desktop. Okay? It just means knowing which group of elements matters for what you're trying to build and then start there.
Here's a very practical, simple way to understand this. A graphic designer does not need to deeply understand agent orchestration. A solopreneur automating their business doesn't really need to understand RLHF. But every entrepreneur who wants to build with AI needs to know what group they're working in and which elements in that group to pick up first. That is what this table gives you. It's not a textbook. It's a compass. It's a map. It allows you to stop needing to know everything. Because once you understand which group is relevant to what you're doing, what you're building right now, you can then go deep on those elements and move fast without the paralysis of feeling like you have to master all 48 before you're even allowed to start.
So here's the framework. I call it the eight groups of AI elements. Okay? Think of each group the way a chemist thinks about a column in the real periodic table of elements. Okay? Elements that share the same fundamental properties and play similar roles in a system.
Group number one, fundamentals. These are the atoms of everything. Nothing works without these.
Group number two, data and knowledge. How you feed intelligence into a system.
Group number three, the intelligence layer. How models get smarter and more contextual.
Group number four, models and providers. The actual AI engines that you build on.
Group number five, infrastructure and connectivity. How everything talks to everything else.
Group number six, agents and automation. Where the real leverage lives.
Group number seven, no-code builder tools. Those are the entrepreneur's toolbox.
And group eight, the business layer. Where this all becomes a business.
Okay. Now, here is a very important distinction that I really don't want you to miss. Most people think of AI as a single thing, AI, like it's one product or one tool or one skill to learn. But once you see this table, you realize that in fact, it's an ecosystem. And understanding how this ecosystem is really structured is what separates the people building real businesses from the people still watching tutorials. I mean, you can think of it this way. If you're a chef, you need to understand ingredients and cooking methods and equipment and the menu. So, this table is your kitchen blueprint for AI.
Okay, let's go through every single group because we have got a lot to cover. Okay. And before I walk you through the groups, I want to point out something that most people completely miss. So I want you to pay attention to this and not skip over it. Okay? The value in AI business is not distributed equally across these eight groups. The groups closest to infrastructure and automation, which are groups five, six, and seven. Those are where the highest leverage opportunities for entrepreneurs are hiding right now because those are the groups that deal with systems. And systems are those that compound. A good system works for you while you're asleep. A tool just does something once, right? So, most of your competitors are stuck in group four. We're going to talk about that. They're debating ChatGPT versus Claude versus Gemini. Which AI model is smarter? And that's a valid thing to understand. Don't get me wrong. But it's not where you build a business. Okay? The entrepreneurs who are in fact building the most scalable AI businesses right now, as I said, understand groups five, six, and seven at a level most people don't. They're connecting systems, building agents, using no-code tools to automate things that used to take 40 hours a week. And I'm going to flag this for you when we get to group six because that is where the game changes. But for now, put that in a file cabinet. Let's start from the beginning.
So group number one, your fundamentals. Okay, these are the cells. All right, you can think of it as the basic atoms of everything else. You're going to encounter this in literally every AI tool that you ever use.
First of all, token. This is how AI reads and processes text, not words, tokens. Roughly one token per 0.75 words. Why do you need to know this? Because it's directly what affects your cost, your speed, and how much information you can feed an AI at once. And now, when you read that an LLM has expanded their token number, you know what that means.
Next, model. The core AI engine, the brain that everything else runs on top of.
Next, we have prompt. Your instructions to the model. How you structure a prompt determines probably 80% of the quality of your output. This is the most underrated skill for non-technical entrepreneurs working with AI.
Next, context window. How much the model can see in a single conversation. You can think of it like a short-term memory with a hard limit. Okay? Once you hit it, earlier information starts to drop off. That's why ChatGPT sometimes feels like it forgets what you tell it. Okay.
Temperature. This is what controls creativity versus predictability because AI is non-deterministic. Low temperature means precise, consistent outputs, while high temperature means more creative and unpredictable. You need to know this when you're building reliable automated systems.
And last but not least, parameters. Those are the internal settings that shape how a model thinks. More parameters generally means more capability and more cost as well.
All right. So that's group number one. Six elements, the foundation of absolutely everything.
Now, group number two, this is about data and knowledge. So basically, this is about how you feed information into an AI system and make it smarter for your specific use case.
First, we've got training data. That's the information that the model learned from during its creation. Understanding this helps you know where a model is strong and where it has blind spots.
Next, embedding. How AI converts text into numbers that capture meaning. Embeddings are what allows AI to understand that "I need help with my business finances" and "I want to manage my company's money better" essentially means the exact same thing.
Next, we've got vector databases. So, basically, that's where those embeddings are stored and searched. If you're building any kind of AI that searches through information, a custom database, a document assistant, an AI that knows your product catalog, you are using a vector database.
Then you also have knowledge bases. That's a curated store of information that your AI can draw from. You can think of it as giving your AI a filing cabinet that it can search in real time.
We also have RAGs, retrieval-augmented generation. This is the combination of a knowledge base and a model where your AI answers questions using your information, not just what it was trained on. This is the backbone of most custom AI assistants being built today.
We've also got fine-tuning. Basically, that's training an existing model on your specific data to change how it behaves. Okay, more advanced and more expensive than RAGs, but incredibly powerful for specialized, consistent outputs.
Now, let's talk about the layer that sits between the raw model and what it actually produces. Okay? So group number three is the intelligence layer. All right, this is about making AI smarter, more contextual, and also safer to deploy in a real business.
First, we've got the system prompt, also sometimes called instruction. Okay, so this is the hidden instruction set that you give a model before any conversation starts. This is how you give your AI a persona, a role, rules that it must follow, and a tone of voice. Every serious AI product has one. If you're building any kind of AI assistant, this is where it gets personality.
Next, we have memory. That is the ability of an AI to remember things across separate conversations. Without memory, every interaction starts from zero. Dory-style, with memory, you have something that starts to feel like an actual, real assistant. One that knows your name, your preferences, your history.
Multimodal. Okay. AI that works with images and audio and video, not just text. You've got GPT-4 Vision, Claude's document analysis, AI that can look at a photo and describe it. I remember when GPT got this capability. It was pretty groundbreaking, and it's expanding fast, and the business applications are just getting started.
Now, I mentioned this one earlier, and maybe some of you wondered what that is. RLHF, okay, that's reinforcement learning from human feedback. That's the technique behind why modern AI sounds so natural and helpful. Models are trained with human feedback to align their outputs with what humans actually want. It's why ChatGPT doesn't sound like a search engine.
Then you have guardrails. The safety boundaries and rules that you build into an AI system to control what it will and will not do. If you're deploying AI that talks to your customers, this is not optional. This is the difference between an AI assistant and a simple library.
Okay, now we are ready to move to the group that most people spend all of their time on but probably shouldn't. Group number four is about models and providers. So this is your engine room. All right, the actual AI models you build on.
GPT from OpenAI, Claude from Anthropic, Gemini from Google, Llama from Meta, which is open source, meaning you can run it yourself. Mistral, which is lean, efficient, the European alternative. Grok from XAI.
But here is what I want you to take from this group. Do not fall in love with one model. Each has different strengths, pricing structures, context windows, and its ideal use cases. GPT is versatile. Claude is exceptional at long documents and nuanced reasoning. Gemini has deep integration with Google's ecosystem. Llama, as I said, gives you full control. The smart move is to understand what each one is good at and match the model to the job. Okay? So, you need to stop debating which AI is the best and just think about which model is right for this specific task in my specific business. And if you want to know my personal opinion about which one I would pick for which task, make sure you watch this video, or we're going to link it down below. I have done a full breakdown of all the most known models out there.
Okay, now let's get into the groups where the real money is made, starting with how everything connects. All right, so group number five is about infrastructure and connectivity. So this is where AI agents get integrated into real systems, where it stops being just a chatbot you talk to and it becomes an actual solution or business tool.
First, you've got API, which is application programming interface, the bridge between AI models and everything else. If you've ever connected two apps, your CRM to your email platform, your form to your spreadsheet, you have worked with an API concept. AI APIs are basically the same idea, a way to call on an AI model from inside another system.
But we also have webhooks. That's a way for one system to automatically notify another when something happens. For example, "a new customer just signed up." Trigger this AI workflow when that happens. That trigger is a webhook. It's what sets automation in motion.
We also have endpoints. That's the specific URL address where an API lives. When you're connecting tools like Make or Zapier, you're basically going to encounter endpoints constantly. It's just the address that your system sends requests to.
And MCP, that's Model Context Protocol. This is one of the newest and probably most important elements on this entire table. It's an emerging standard that allows AI models to connect to external tools and data sources in a structured, consistent way. Think of it as a universal power plug that lets AI agents interact with the world. This is becoming the backbone of serious AI agents infrastructure, and it's still very early on, but if you've used Claude, I'm sure you've bumped into MCPs.
Then you have function calling. That's the ability for an AI model to call external tools or APIs directly during a conversation. This is what allows AI to actually do things, not just say things. Okay? So, when you say "search the web," "check the calendar," "send the email," function calling is the mechanism behind all of that.
Last but not least, SDK, software development kit. Pre-built libraries that make it much easier to build with AI APIs. So, if you're using no-code tools, the SDK is working behind the scenes on your behalf. If you're ever dabbling into custom builds, you will for sure interact with SDKs directly.
Okay, this is where it gets real. Group number six is about agents and automation. So, I want you to pay close attention here. All right.
Agent. That's an AI system that can perceive its environment, make decisions, and take action, not just respond to prompts. This is the leap from AI tool to a digital or AI employee. An agent does not wait for you to tell it what to do next. It figures out the next step on its own.
You also have orchestration. All right? The system that manages and coordinates multiple AI agents or steps in a workflow. So, think of it this way. If an agent is a musician, orchestration is the conductor. It decides who does what, in what order, and what happens when something goes wrong.
Then you also have workflows. That's the sequence of automated steps that produces a business outcome. Build workflows, not one-off prompts. A workflow is where AI stops being a tool that you use and becomes a system that works for you.
Now, this one has become very popular recently. Multi-agent system. Multiple AI agents working together. Each one being specialized. One agent researches, one writes, one edits, one publishes, one handles the CRM update. This is how you build AI systems that replace entire job functions. Not by replacing a person, but by replacing the entire process.
Then we have human in the loop. The point in an automated workflow where a human actually reviews or approves before the system continues. This is not a weakness in your system. It is a design choice that keeps quality high and the risk low, especially when you're deploying AI that touches customers or very sensitive parts of your workflows and processes.
Next one is tool use. It's the ability of an AI agent to use external tools like search engines, calculators, databases, apps, APIs. Without tool use, an agent is just a very fast chatbot. With tool use, it's a worker. It does something.
Okay, so here's the thing about group six. This is where the entrepreneurs in our community are building businesses that run without them. Every automation that saves 10 hours a week, every agent that handles client questions at 2 in the morning, every workflow that turns one hour of work into 10, it all lives in this group. And right now, most small business owners do not even know that these elements exist, let alone how to combine them. Okay, that gap is an opportunity.
Okay, now let's move to the group that makes all of this accessible to anyone who is not a developer. Group number seven is about no-code builder tools. Okay, so this is what separates entrepreneurs from developers, in a good way, I think.
So you've got Zapier, which is the original no-code automation platform. It connects to over 6,000 apps, low learning curve, great entry point for entrepreneurs who've never automated anything before.
You also have Make, which is similar to Zapier, is good for complex multi-step workflows. It's visual. It's flexible. It's built for the kind of logic that heavy automations and real AI systems require.
Then you have N8N, open-source automation. It's more technical than Zapier or Make, but runs on your own infrastructure if you choose to, and that gives you full control and no per-task pricing. It's a growing favorite among serious AI builders, but I don't know, maybe it's had its own period of fame, let's say, and now might not be as famous or as preferred as it was 12 months ago.
Next, we have Voiceflow. That's a no-code platform specifically for building AI agents and conversational interfaces. It's drag-and-drop. If you want to build a customer-facing AI assistant without writing code, you can start there.
You also have Flowise. It's an open-source, no-code builder for LLM applications. So, you can build RAG pipelines, custom AI agents, chatbots with a visual interface. It's pretty powerful and free to self-host.
You also have Cursor. It's an AI-powered code editor, and it can sound intimidating, but even if you're not a developer, Cursor significantly lowers the barrier to building custom AI solutions because you can describe what you want, and it writes the code for you.
Now, you don't need to master all six of these, okay? But you need to know they exist because the right tool in the right situation is the difference between a three-day build and a three-hour build.
All right. Finally, the group that ties everything back to why we're actually here. Group number eight, the business layer. Okay. So, this is the noble gases of this table, if you will. They complete the picture and they're the lens through which every other group should be viewed. Okay?
You've got use case, which is the specific business problem that you're solving with AI. This should always come before picking tools. Start with the problem, not the product.
Next, you have ROI, return on investment. The only real reason to implement any of this. If you cannot articulate the time saved, the revenue added, or the cost reduced, rethink the use case before you build.
Prompt engineering. The craft of designing prompts that reliably produce the output you need. Not a developer skill, an entrepreneur skill. The highest leveraged thing that a non-technical AI builder can learn right now.
Then AI stack. Your total combination of AI tools, models, and systems working together like a tech stack but for intelligence. The best AI entrepreneurs know exactly what their stack is and why each piece is there.
You also have AI avatars. That's a digital persona or representative built on AI. It's growing rapidly in relevance for content creation, for customer service, for brand building. The economics of AI-powered avatars are changing faster than probably most people realize, and you definitely need to look into them.
And AI strategy. The deliberate, sequenced plan for how AI integrates into your business over time. This is the difference between random tool adoption and a genuine competitive advantage. Without strategy, you have a collection of tools, and with strategy, you actually have a system.
Okay. Now, let me tell you what this actually means for your business. Understanding this table is not an academic exercise. It's just a positioning exercise. Every group of this table represents a service that someone is paying for, or a system that you can build and sell, or a capability that reduces your operating costs while increasing your output.
Think about group six, for example, the agents and automation group. Entrepreneurs right now are being paid to build custom AI agents and workflows for small businesses. Why? Because most business owners don't know that agents, orchestration, workflows, and the others exist, let alone how to combine them into something useful. Okay? When you understand the full table, you can see the gaps. You can see what businesses are missing, and you can also see where value is being left on the table.
And if you look at the members of our community, the most successful ones are not the people who know the most tools. The most successful ones are the people who understand how the elements combine into systems. A token by itself does nothing. A prompt on its own does one thing. A prompt connected to a model, feeding into an agent with a vector database and a workflow, that's a business. That is what this table is pointing you towards. Results will always depend, obviously, on how you implement this, the niche you're working in, and what you put in. But understanding the map is step zero. And now you have it.
So, let's bring this home. If you implement just one thing from this video, let it be this one. The next time you feel overwhelmed by AI, come back to this table. Find the group that you're working in, focus there. And that is the antidote to AI overwhelm. Okay.
Now, here's your next move. If you want to go beyond understanding this table and actually start building with it, you can come join us inside of our community. Link is both in the description and in the QR code here. And you will be able to not only learn exactly what you need to do, we have a lot of free challenges, we have bi-weekly calls, and thousands and thousands of other people on the exact same path as you are. You came into this video with 47 browser tabs open and no idea how any of it fit together. Hopefully, you are living with a periodic table. Use it.
All right, thank you so so much for watching this until the end. Like this video if you did. Be sure to subscribe if you haven't done so. Share this with anyone in your circle of friends or family or co-workers who you think needs to really understand AI better. And until next time, I suggest you go ahead and watch this video over here, and I'll see you soon.