Your AI Isn’t Thinking. It’s Playing Tetris
Understanding how AI works helps you use it to YOUR advantage.
Your AI is playing a game of pattern completion with a limited board and no understanding of what the words mean. It’s matching shapes, not grasping concepts. You wouldn’t blame Tetris for ending the game when the blocks reach the top—that’s just how the game works. And you shouldn’t blame AI for hallucinating when you’ve filled up the context window with vague instructions, too much information, and no verification process.
TL;DR: How Does AI Think?
The simple answer is AI doesn’t “think” at all—it performs pattern matching. It uses massive datasets broken into tiny containers (known as tokens) to play a sophisticated game of pattern-matching (like Tetris) to guess which word “block” should come next.
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AI Doesn’t Get Your World
When people say “AI doesn’t get my world,” they are correct. Whether you ask AI to write a social media post, interpret a spreadsheet, or draft a nimble go-to-market plan, AI does not *understand* what you want or need.
It doesn’t understand the very human emotion of you proving to people why they should work with you. Why the Pet Rock became a commercial success when every single study would suggest that no, people will not pay $25 for a rock in a box.
But what AI does understand is data. Massive data sets filled with giant libraries of data. Think of the old kid’s question: What is infinity squared? AI is a ginormous game of autocomplete—times infinity.
So when you say “AI doesn’t get my world,” there’s a certain comfort there. My work is so wholly unique AI can never replace it. And to every extent, that’s true. Your experiences, your successes, your frustrations—those cannot be replaced.
But what AI does understand is data. It “knows” that a query with the word “bottom line” followed by a set of numbers means something. It *gets* that correlation whether it’s interpreting your industry or another industry.
This fear that AI can do your job better than you, can be paralyzing. So I thought it would be worthwhile to explain how AI works. If you’re a disbeliever (or someone who still hopes it will go away), maybe this post can help you stop fighting against AI and start using it to your benefit.
Are your marketing blind spots quietly driving away business?
– Monica Poling
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A Word on AI Fear-Mongering
The AI industry has not done a great job of creating AI content for *the rest of us.” AI execs tend to focus on enterprise solutions (where the “real” money is) and how big organizations can maximize AI. Where even the tiniest time-savers can result in millions of dollars saved.
Meanwhile, much of the rest of the world loves the narrative that AI will replace all of us. If you don’t use AI you will be left behind. If you don’t keep up, you’ll get replaced. Let me sell you my course.
Fear sells. But it’s not particularly helpful when it comes to learning how to use AI to your advantage.
Here’s the second-most popular question I get: “Where can I go to learn AI?” And nobody—not even me, really—wants you to know that the best place to learn AI is AI itself.
Seriously. Ask ChatGPT how to use ChatGPT. Ask Claude how to write better prompts. The tools will teach you if you let them.
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So How Does AI Really Work?
Remember the video game Tetris? The panic you felt when too many blocks fell at once, and you couldn’t find the right piece to clear your board? That’s very close to how AI fundamentally works.
AI is not “thinking” the way you think. It’s playing an incredibly sophisticated game of pattern-matching – Tetris with word blocks.
Tokens: Your Word Blocks
Every time you type a prompt, you’re dropping word blocks onto a game board. The AI’s job? Fit those blocks together into a coherent pattern that makes sense—just like dropping Tetris pieces into place.
AI doesn’t read “The quick brown fox” as a sentence. It reads something like “The / qu / ick / brown / fox” where each slash represents a different token. Through its training, it has seen these particular tokens often enough to *know” it needs to organize them into a familiar phrase.
But it also has enough data to consider alternatives. Maybe the user meant “the tan, foxy lady needed a quick touch-up of her brown hair.”
AI doesn’t care. It doesn’t think. It just moves around data to match what it predicts you need.
This is why better prompting is so important. Better prompts ask the AI to give your better, more relevant results, to only use *patterns* from your data set, and not from general data sets. So it knows, yes, you did mean a tan, fox lady. By refining your prompt, this saves you time and money, and helps you to make decisions faster.
Another thing to know: AI has been trained to sound extra human. “Great job, amazing thinker! Yes, the quick brown fox is a popular saying…”
But even the kudos (or the very infrequent “you got it wrong”) are learned patterns. This still not AI thinking. It’s just added value designed to give you a little dopamine hit (“Why yes AI, I am a genius thinker”) so you ‘ll keep coming back for more kudos. Don’t let the compliments distract you—stay focused on the output, not the flattery.
➤ Read More: The CRAFT Prompt Framework for better prompting
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The Game Board: Your Context Window
While tokens are how the data is moved about, the Context Window is the playing field.
When you type into ChatGPT or any other AI, you have a “chat” window open. This is known as the context window, and it’s like the Tetris game board. Eventually, it will fill up.
It’s like a messy desk. You’ll have to eventually shove papers (the original prompts) off to make room for more paper.
While Tetris has a hard limit of about 25 rows, most AI context windows have room for millions of tokens, so you have lots of space to play. But at a certain point, when your chats go long, the AI stops “remembering” the earlier content and only references the more recent exchanges.
This is why your AI:
- Forgets what you said several prompts ago
- Contradicts itself in long conversations
- Starts giving generic answers when you’ve been going back and forth for a while
- Even forgets main character names if you’re typing a novel.
At a certain point it’s better for your outputs (and slightly better for the environment) to close your current chat and open a new one.
💡 Pro Tip: Lost in the Middle
If you’ve ever asked AI to compare two long documents (like contracts or grants) and felt like it missed the most important part, you’ve experienced the “Lost in the Middle” Factor. Because the AI has a limited game board (the context window), it can’t always “see” the middle of a long document with the same clarity as the beginning or the end.
It isn’t memorizing the whole thing; it’s juggling blocks. When the board gets too crowded, the AI “squints” to make everything fit—and those tiny, nuanced details in the middle are the first things to get blurry or dropped.
If you must work with one long file, specifically ask: “What are the key points from pages 15 through 25?” This forces the AI to move its “eyes” back to the middle of the board.
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The Advantage Gap: Where Your Expertise Fills the Hole
Sometimes your AI invents a “ghost block”—a piece that doesn’t really exist, but that the AI confidently forces into place anyway.
In the tech world, they call this a hallucination. But for you, it’s actually a signal.
It happens because the AI’s pattern-matching game is trying to be helpful, but it doesn’t have the specific “blocks” of your unique business. It’s like if you dropped a circle block onto a Tetris board. The AI would think, “Hmmm, that’s interesting,” stick it in the nearest open spot, and move on.
This is where your competitive advantage lives. The AI isn’t “lying”—it’s just showing you exactly where its general knowledge ends and your specific expertise begins. When the AI gets it wrong, it’s a reminder that:
- Your niche is unique: If the AI knew the answer, so would all your competitors.
- Your data is gold: The “ghost blocks” disappear the moment you feed the AI your own proprietary information.
- You are the architect: The AI provides the speed, but you provide the “blueprints” that ensure the blocks build something valuable for your customers based on YOUR knowledge and YOUR frameworks.
🏃♀️ The “Real World” Reality Check
Here’s an example from my own work: When I talk to AI about “marketing cultural tourism,” AI has no real language model patterns for that phrase. (There are limited training models exist on cultural tourism marketing.)
Almost all of its training data has *learned* about experiencing cultural tourism as a visitor—not marketing it. No matter how many times I tell it (and update its memory), AI still acts like a super confident know-it-all), but I still have to remind it, “We are talking about MARKETING cultural tourism and NOT being a traveler.” It will keep defaulting to travel recommendations because that’s what the blocks in its memory *know*.
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Your Power-Ups: Tools That Make the Game Easier
Just like Tetris has power-ups, you have tools that give your AI an edge.
Clear Prompts
Clear prompts keep your board organized.
When you’re specific about what you need, you’re dropping perfectly shaped pieces that fit together cleanly. When you use frameworks like the CRAFT Prompt Framework, you’re basically telling the AI: “I only want L-shaped pieces from my own design studio. Please don’t match pieces from your broader, publicly-fed training data.”
RAG (Retrieval-Augmented Generation)
RAG is where things get interesting—and where a lot of YOUR real competitive advantage lives.
It sounds super techie (and a little dorky) but here’s the explainer: Standard AI pulls from its general training data, which is massive but also generic. It’s the same data everyone else has access to.
RAG changes the game by letting you connect AI to your data—your documents, your research, your proprietary information—and have it pull results from YOUR sources instead of (or in addition to) the general models.
Think of it this way:
Standard AI is like a student taking a test from general memory. RAG is like giving the AI the textbook and your notes (your data) and telling it, ‘Only use the answers from these pages.’
This is huge for a few reasons. First, it dramatically reduces hallucinations because the AI is pulling from verified sources you control rather than pattern-matching from the general internet.
Second, it gives you outputs that reflect your unique positioning, your methodology, your intellectual property.
Third, it creates a genuine competitive advantage—your AI assistant knows things that your competitors’ AI assistants don’t.
➤ Read More: Is Your Expertise Buried in an Info Dungeon?
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How to Master the Game
So if you’re trying to demonstrate YOUR unique knowledge, here’s your winning AI strategy:
Understand what AI really is. It’s pattern matching—that’s it. This means it’s excellent at analyzing large data sets (much faster than you, seeing patterns you’d miss). You may not love that, but it’s the reality. What AI is not great at is interpreting what that data means in a human context. It knows if you made less money this year than last, but it doesn’t know if that makes you feel like a failure or just a pivot-ready business.
Start fresh when the board gets messy. If your conversation has gone on for dozens of messages and the AI seems confused, start a new chat. Give yourself a clean board. This is better for the environment (because AI has to work less hard), and it keeps your ideas clean and easier to find. Most AIs can now access previous chats as part of their file system. And if they can’t, you can ask the old chat to summarize and set up a prompt for the new one.
Always verify the facts. Any time—and I mean EVERY time—the AI makes claims about real-world information, verify it. Ask the AI for its source. Then click the source and confirm. Remember: the AI is completing patterns, not accessing a database of truth.
Use your own sources for facts. If you need the AI to reference specific information from your library, give it that information directly. Upload the document. Paste the data. Don’t assume it “knows” what you’re talking about. This is your competitive edge, so don’t let AI pull from the generic information in its training data when you have better.
Keep your prompts clear and concise. Don’t drop 17 misshapen blocks onto the board at once. One well-defined request beats a rambling paragraph every time.
Define your terms upfront. Tell the AI exactly what you mean by “strategic plan” or “market analysis” or whatever terminology matters to your work. Don’t make it guess at your patterns.
Maximize your decision velocity. Don’t get bogged down in the “how.” Use AI to summarize the patterns and give you the 3 most likely paths forward. Your job isn’t to analyze the math; it’s to choose the path.
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The Bottom Line: AI Is a Pattern Player, Not a Mind Reader
Master the game by understanding its rules. Keep your board clean, your prompts clear, and your facts verified. Don’t assume AI understands your emotions—but also don’t assume you’re smarter than AI just because you know your world better.
You are smarter than AI because AI isn’t smart at all. But what AI can do, all day, every day, is outperform you on data crunching.
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Key Takeaways
- AI understands data patterns, but it doesn’t understand your human world, your emotions, or why your customers love you.
- Lack of pattern: When AI doesn’t have a pattern, it “hallucinates” to make the shapes fit anyway.
- Don’t get bogged down by mechanics: Don’t worry if AI can *think* better than you. Use AI to clear the clutter so you can make high-stakes decisions faster than your competition.
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