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Everyday AI Made Simple – AI for Everyday Tasks is your friendly guide to getting useful, not vague, answers from AI. Each episode shows you exactly what to type—with plain-English, copy-ready prompts you can use for real life: budgeting and bill-balancing, meal and grocery planning, decluttering and home routines, travel planning, wellness tracking, email writing, and more.
You’ll learn the three essentials of great prompts (be specific, add context, assign a role) plus easy upgrades like formats, guardrails (tone, length, “no jargon”), and iterative follow-ups that turn “hmm” into “heck yes.” No tech-speak, no eye-glaze—just practical steps so you feel confident and in control.
If you’re AI-curious, and short on time, this show hands you the exact words to use—so you can save your brain for the good stuff. New episodes keep it short, actionable, and judgment-free. Think: your smartest friend, but with prompts.
Blog: https://everydayaimadesimple.ai/blog
Free custom GPTs: https://everydayaimadesimple.ai
Some research and production steps may use AI tools. All content is reviewed and approved by humans before publishing.
00:00:00
I want to take you back to a very specific moment in time. We need to set the clock to March 10th, 2016. Okay. The location is Seoul, South Korea. We're in the Four Seasons Hotel. And the feeling in that room, I mean, all the sources we looked at describe it as just heavy.
00:00:18
Yeah, tense. It wasn't just quiet. It was that kind of, you know, loaded silence.
00:00:23
That oppressive silence. Like. In a courtroom right before a verdict is read, you can almost feel it. It's thick.
00:00:29
It absolutely felt monumental. And you have to picture the scene. It's not just a quiet room. There are hundreds of cameras, flashbulbs going off, and literally millions of people are streaming this online.
00:00:40
From all over the world.
00:00:41
Every corner of the globe. And in the middle of all this, this media storm, you just have a simple wooden board.
00:00:46
A gome board. And two players, well, sort of two players. On one side, you have Lee Sedol. A legend. And we really need to establish who this guy is for anyone who doesn't follow the game. He's not just a good player. In South Korea, he's a national hero. A celebrity.
00:01:00
He's the Michael Jordan of Go. the roger federer he has 18 world titles this guy is the absolute apex predator of this game.
00:01:10
he represents what maybe thousands of years of human strategy and intuition all distilled into.
00:01:16
one person perfectly put he is the pinnacle of human mastery of the game and on the other side of that table empty chair an empty chair just a computer monitor and a man named ajah huang.
00:01:27
a deep mind researcher he's just sitting there very quietly and his only job is to look at the screen and place a stone on the board exactly where the machine tells him to he's basically a human robotic arm right flesh and bone this whole thing was called the deep mind challenge match.
00:01:41
lisa doll versus alphago man versus machine you know it sounds like such a cliche sci-fi setup doesn't it it really does but this was the real deal and looking back now with all the hindsight we have this wasn't just a game this was this is one of those moments where the.
00:01:56
timeline splits that's why we're going to spend our time here today we have a massive stack of, sources articles, deep technical breakdowns, the documentary, some incredibly practical guys.
00:02:08
Oh, that documentary is fantastic.
00:02:09
It's incredible. And they all argue that this one specific match is the master key. It's the key to understanding the AI tools that you are probably using right now.
00:02:18
Every day, whether you're writing an email with ChatGTT or coding with Claude or trying to brainstorm something with Gemini, this is the origin story.
00:02:27
And we're going to focus on two specific numbers that came out of this match. Move 37.
00:02:32
And move 78.
00:02:33
Move 37, the alien move, and move 78, which some people call God's touch.
00:02:39
That's right. But I think we need to be really clear up front. This isn't just a history lesson. We're not just going to recount a board game from nearly a decade ago for fun. The argument we are building today, based on all this research, is that these two moves. They're actually a methodology. They're a blueprint.
00:02:57
A blueprint for what, exactly.
00:02:59
Well, honestly, a blueprint for survival in the age of AI. And not just survival for excellence.
00:03:05
Okay, that's a big claim.
00:03:06
It is. But the sources back it up. They suggest that if you really, truly understand the logic behind Move 37, you stop using AI like it's a glorified search engine.
00:03:17
Which is what most people do.
00:03:18
It's what everyone does. And they get boring, average, predictable answers. But if you get this, you start using AI to find ideas that humans, Literally cannot see.
00:03:30
And move 78, the God's touch.
00:03:32
That's how you stay relevant. That's how you keep your humanity in the loop when the machine is, frankly, smarter than you in some ways. That is how you stay in the driver's seat.
00:03:41
I love that framing. So the agenda for our deep dive is pretty straightforward. First, we have to unpack the grama of the match itself. We need to get into the weeds of the technical why behind the shockwave that this sent around the world.
00:03:54
Right, you have to feel the moment.
00:03:54
Second, we're going to look at the psychological fallout because it got pretty dark for the human side of this equation.
00:04:01
It got very dark. It was basically an existential crisis played out on live television for the whole world to see.
00:04:06
And finally, the most important part, we pivot to the practical. We're going to workshop what we're calling the Move 37 method. We've pulled five specific prompt frameworks from our research that will fundamentally change how you talk to AI.
00:04:20
We want to move you from just asking questions to actually organizing. Orchestrating breakthroughs.
00:04:26
I like that. Orchestrating breakthroughs. Okay, let's dive in.
00:04:29
Let's do it.
00:04:29
Okay, so to really understand why this was such a monumental event, we have to talk about the game of Go itself. I think a lot of people have this vague idea that it's hard, but they don't really know why it's so much harder than, say, chess.
00:04:42
That's the key question. I mean, didn't a computer beat Garry Kasparov at chess way back in the 90s.
00:04:48
Exactly. Deep blue, 1997. That was a huge moment.
00:04:51
Yeah.
00:04:52
So why was Go still considered this unconquerable mountain for AI.
00:04:56
Because Go is a completely different beast. It's an order of magnitude more complex than chess. It's not even in the same league.
00:05:02
And how so? Are the rules more complicated.
00:05:05
See, that's the paradox. The rules of Go are actually much simpler than chess. You just place stones on a board, and you're trying to surround and capture territory. That's basically it.
00:05:14
So the complexity doesn't come from the rules.
00:05:17
No, it comes from something we call the branching factor. In chess, after the first couple of moves, on any given turn you might have what? 20, 30, maybe 40 legal moves you can make.
00:05:27
Okay, a manageable number for a computer to analyze.
00:05:30
Right. In Go, on every single turn, there are about 250 possible moves.
00:05:37
250, wow. So the tree of possibilities just explodes outward.
00:05:41
It gets wider exponentially faster. The sources all love this one stat. And honestly, it never gets old because it just kind of breaks your brain every time you hear it.
00:05:50
I think I know the one you're talking about.
00:05:51
There are more possible legal configurations on a Go board. Then there are atoms in the observable universe.
00:05:58
Every single time I hear that, I have to just stop and pause for a second. More configurations than atoms in the universe.
00:06:04
Exactly. And what that means, practically, is you cannot brute force it. You can't just calculate your way to a win. Deep Blue beat Kasparov by essentially engaging in a math fight. It could calculate millions of moves per second, deeper than any human could.
00:06:20
It just out-muscled him computationally.
00:06:22
It did. But you literally cannot build a computer big enough or powerful enough to do that with Go. It's physically impossible. Possible? There isn't enough computing power on the planet to calculate every outcome. It's a solved problem that is unsolvable.
00:06:35
So, if you can't calculate it, how do humans play it at a high level.
00:06:40
Intuition. That's the word that comes up over and over again. Pattern recognition. Go is often called an art form for this very reason.
00:06:46
An art form.
00:06:47
Yeah. Top players, they look at a board and they don't just calculate. They feel the shape of the game. They use this very aesthetic language. They'll say things like, this shape feels heavy, or this group of stones feels really efficient, or this move feels... Elegant.
00:06:59
It's abstract. It's beautiful in a way.
00:07:02
It is. And that's exactly why everyone thought Lee Sedol was safe. The conventional wisdom was, okay, computers are great at math. They are not great at intuition. They don't do art.
00:07:13
The human domain was supposed to be secure.
00:07:15
For a while, at least. The prediction from the wider AI community was that we were probably a decade away, maybe even two decades away, from an AI beating a top human professional.
00:07:26
And what did Lee Sedol himself think going into it.
00:07:29
He was supremely confident. He publicly said he expected to win the match 5-0. He thought, you know, maybe he might lose one game if he made a really careless mistake. But he fully expected to crush the machine.
00:07:42
And then came game one.
00:07:43
And then came game one, and Lee Sedol lost.
00:07:45
That must have been a shock to the system for everyone watching.
00:07:48
It was an earthquake. A huge one. But, and this is a really important detail, people found ways to rationalize it.
00:07:54
They explained it away.
00:07:55
Completely. The commentary at the time, if you go back and read it, is fascinating. People were saying, okay, Lee Sedol underestimated the machine. He was nervous. He played a bit sloppy.
00:08:04
Now he knows it's a real fight.
00:08:06
Exactly. Now the real master will show up for game two. They really thought game one was just a fluke, a wake-up call.
00:08:12
So that brings us to game two. This is the setting for the moment that changed everything. The entire world is watching. Lee Sedol is focused. He's not underestimating it anymore. He's playing very cautiously.
00:08:23
He is. He's playing a very solid, very professional game. He's probing the machine, testing it, trying to find cracks in its logic. The game has been going for about an hour. And then AlphaGo, which is playing the black stones, it pauses.
00:08:37
It pauses. I thought computers were supposed to be fast.
00:08:40
Well, it usually plays fairly quickly. But on this move, it takes a long time to calculate. The clock is just ticking down. And then after this long pause, Aja Huang reaches out and he places the black stone on the fifth line. Move 37.
00:08:53
Now, for a casual observer like me, this is just a black piece of plastic on a wooden grid. It means nothing. But looking at the commentary logs and watching the documentary, the experts were absolutely losing. They were losing their minds. Why? What was so fundamentally wrong with this move.
00:09:07
Okay, so to get this, we have to do a quick go lesson. The board is a grid of 19 by 19 lines. The edge of the board is the first line. The third line from the edge is traditionally called the line of territory. If you play there, you're securing points. It's safe. It's solid. It's like putting cash in the bank.
00:09:25
Got it. Third line is safety and territory.
00:09:28
Right. The fourth line is called the line of influence. When you play there, you're not taking points right away, but you're building power toward the center of the board. It's more of an investment. It's standard opening theory.
00:09:39
Okay, so third and fourth lines are where all the action is supposed to happen early on. What about the fifth line where AlphaGo played.
00:09:45
The fifth line is basically no man's land in the opening. Playing on the fifth line so early is considered... Well, the pros called it arrogant and inefficient. Why? Because it's too high up. You're trying to grab for the sky and you're leaving the door wide open for your opponent to just slide in underneath you and steal all your territory on the third line. You get nothing.
00:10:06
So if a human student played that move against their go master, their teacher would smack their hand.
00:10:11
Seriously. They'd say, don't be greedy. You're overreaching. You don't know what you're doing. It violates 2,500 years of established Go theory.
00:10:19
So when AlphaGo played it, the immediate reaction from the experts wasn't, wow, what a brilliant, innovative strategy. It was.
00:10:26
It was. Is that a glitch.
00:10:27
They thought the machine was broken.
00:10:29
They did. One of the commentators, a nine-dan professional that's the highest rank you can get in the keys, Go literally said on the live broadcast, is that a mistake.
00:10:38
They genuinely thought maybe Asia Huang, the human, had misunderstood the instruction and placed the stone on the wrong intersection.
00:10:46
That's how unthinkable this move was. It was more probable that the human made a physical error than the machine made a strategic choice.
00:10:54
Precisely.
00:10:54
Yeah.
00:10:55
And Lee Sedol, what was his reaction in the room.
00:10:57
Oh, it's visceral. You have to see the footage. He physically recoils from the board.
00:11:01
Leans back in his chair.
00:11:02
He stares at the board with his mouth just hanging open. The documentary catches this perfectly.
00:11:07
Mm-hmm.
00:11:08
He looks confused. He looks almost offended. Like, how dare you play this move against me? And he gets up.
00:11:14
He stands up and leaves the room. He had to walk it off.
00:11:17
He went to the restroom to wash his face. He literally needed to splash cold water on his face to try and reset his entire reality. Because in that moment, he's looking at the board and he's thinking, am I playing an idiot or am I playing a god? I don't know which one it is.
00:11:32
Meanwhile, behind the scenes at DeepMind headquarters in London.
00:11:36
Total panic. Absolute panic. Demis Hassabis, the CEO of DeepMind and his team are all watching the live feed. They see the commentators calling it a mistake. They see their opponent, a living legend, get up and leave the room in disbelief.
00:11:51
So they assume the worst.
00:11:52
Their first thought was, oh no, the beta software crashed. It's just made a random move. We were about to be globally embarrassed on the biggest stage imaginable.
00:12:01
But here is the kicker. And this is the whole point of our story. It wasn't a mistake. No.
00:12:06
Not even close. And this is where we get into the alien part of alien intelligence. So later, the DeepMind team analyzed the logs to figure out why it played that move. AlphaGo basically has two neural networks that work together. One is called the Policy Network.
00:12:20
And its job is to predict what a human would do.
00:12:22
Exactly. It's been trained on millions and millions of games played by human experts. Its whole job is to mimic us, to predict the most likely human move in any given situation.
00:12:32
So what did the Policy Network say about Move 37.
00:12:35
For this specific board state, the Policy Network calculated that the probability of a human expert playing Move 37 was 1 in 10,000.
00:12:44
.01%.
00:12:45
Basically zero. No human would ever, ever play that move.
00:12:49
Yeah.
00:12:49
So if AlphaGo was just trying to copy humans, it never would have found it. But AlphaGo has a second brain. It's called the Value Network.
00:12:56
And what does that one do.
00:12:57
That one doesn't care about what humans would do. It'd have one job.
00:13:00
Yeah.
00:13:01
Calculate the probability of winning from the current position. It simulates the game to the end thousands of times from that spot.
00:13:07
So it's just pure, cold, probabilistic calculation.
00:13:10
That's it. And the value network looked at that move, move 37, and it said, I know humans think this is a bad move. I know it looks weird. But my simulations show that if I play here, my overall chance of winning the game goes up.
00:13:25
So it completely ignored the human rulebook.
00:13:28
It didn't even know the rulebook existed. It didn't care about territory versus influence in the way humans think about it. It didn't care about looking elegant or playing politely. It just saw a mathematical path to victory that required a completely new way of looking at the board.
00:13:44
And did it work in the end? Was the move actually good.
00:13:46
It was devastating. It was brilliant. That one stone, which looked so high and loose and inefficient at first, ended up, it was like a lighthouse. It radiated influence across the entire board. It supported an invasion on the opposite side of the board 50 moves later.
00:14:00
It was a time bomb.
00:14:01
A perfect analogy. It was a time bomb. And Liesel couldn't counter it because he didn't even understand the nature of the threat until the trap had already snapped shut around him.
00:14:09
This is exactly why we call it alien intelligence. It's not just faster math. It's a different kind of reasoning. It's a logic that is foreign to us.
00:14:18
Precisely. You could call it creativity. But it's not human creativity, which is born from experience. Experience and culture. This is creativity born from pure probability, completely unburdened by history or habit or what we call best practices.
00:14:33
And for a master like Lee Seedle, who has spent his entire life mastering those human best practices, that must be terrifying.
00:14:42
It broke him. He lost game two. And then he went on to lose game three as well. He was down 0-3 in a best of five match. The series was over. The machine had won.
00:14:50
I really want to pause on the psychology of that moment because the sources, especially the documentary, they mention a press conference after game three that is just, it's heartbreaking to watch.
00:15:00
Yeah. You have to put yourself in his shoes. Lee Seedle felt he was defending humanity. I mean, that's a ridiculously heavy cape to wear, right.
00:15:07
Weight of the entire species on your shoulders.
00:15:09
And he felt he had failed. He walked into that press conference and his voice was shaking and he apologized. He looked at the cameras and said, I am so sorry for being so powerless. He felt he had let everyone down. Wow. That is...
00:15:25
That's heavy.
00:15:26
It is. Can you imagine? It's the slow, dawning realization that the one thing you are the best at in the entire world, the thing that defines your existence, your identity, your life's work, is now done better by a box of circuits.
00:15:40
But the story doesn't end there, which is what makes it so compelling. We have to talk about Game 4. Because if Move 37 is the alien entering the room, then Move 78 is the human fighting back.
00:15:51
Game 4. The God's Touch.
00:15:53
So the scene is set. Lysadol has nothing left to lose. The match is already lost. He's just playing for pride now.
00:15:59
He's playing for humanity's pride, really. And AlphaGo is playing a perfect game. It has built this massive formation in the center of the board. In Go, they call it a moyo. It looked like an impenetrable fortress of black stones.
00:16:11
It looks like another inevitable loss is coming.
00:16:13
Absolutely. The commentators are basically writing the eulogy for the game already. They're saying, well, he fought hard, but the black formation is just too big. There's nothing he can do. And Lysadol just sits there. And he thinks.
00:16:25
And he thinks for a long time.
00:16:27
For 30 minutes, which is an eternity in a timed match like this. He's just staring at the center of the board at this black wall. And then he plays move 78. He picks up a single white stone and places it at the intersection L11. It's a wedge, a single stone, right in the heart of the black formation.
00:16:45
That looks suicidal.
00:16:46
It looks completely suicidal. It's surrounded. It's tight. It looks like it's going to be captured immediately.
00:16:51
And here is the beautiful symmetry of the story, the poetic justice. What did AlphaGo's own internal analysis think of this human move.
00:17:00
It's just perfect. The logs show that AlphaGo's policy network, that's the one that predicts human moves, had, of course, calculated the odds of a human playing move 78 in that position.
00:17:10
And let me guess, the odds were about 1 in 10,000.
00:17:13
Less than 1 in 10,000.
00:17:15
So it's the perfect mirror image of move 37.
00:17:18
It is the exact mirror. But here is the critical, crucial difference. For move 37, the machine's value network said play it anyway. For move 78, because the probability was so low, AlphaGo's search algorithm pruned it.
00:17:32
Pruned it. What does that mean in non-tech terms.
00:17:36
It means the AI stopped analyzing that branch of the game tree. It essentially said, this move is so unlikely, so effectively stupid, that I am not going to waste any computing power thinking about what happens if he actually plays it.
00:17:49
It created a blind spot in its own vision.
00:17:51
A massive blind spot. It didn't see the move coming until the stone was physically on the board. And when Lee Siddle played it, the machine panicked.
00:17:58
It broke. How does an AI break? What does that look like.
00:18:02
It enters a state that the engineers call delusion. Technically, its value network collapsed. The numbers that told it the probability of winning just crashed. It dropped from, say, 70% to near zero, and it couldn't find a logical path back to stability. So it started to hallucinate.
00:18:17
Hallucinate, like how ChatGPT will just make up facts sometimes.
00:18:20
Exactly like that, but in the context of a Go game. It began playing moves that were completely nonsensical. It was placing stones and dead zones on the board where they would be immediately captured for no reason. It was giving away points for free.
00:18:34
It started throwing the game away.
00:18:36
It looked like a beginner who was just panic clicking. The human commentators were confused all over again, but this time they were saying, wait, the computer is broken. Why is it playing there? That makes no sense.
00:18:47
And Lee Sedol, in the middle of all this chaos.
00:18:50
He kept his cool. He had created this tiny sliver of complexity that the machine couldn't handle, and he capitalized on its confusion. He navigated the chaos he created, and he won the game.
00:19:02
The only win.
00:19:03
The only game any human ever won against that specific master version of AlphaGo.
00:19:07
That is, it's oddly inspiring, isn't it? It suggests that even when you're up against a superintelligence, there's still a role for the human wedge.
00:19:14
It suggests that machines, no matter how powerful, have blind spots that are born from their own logic. They rely on probability. And humans. We can rely on the improbable. We can inject a piece of context or creative spark that the machine has already discarded as being too unlikely to consider.
00:19:31
I do want to touch on the aftermath, though. Because while Game 4 was this incredible, triumphant victory, the war was still lost. AlphaGo won the series 4-1.
00:19:41
It did. And the long-term impact on Lee Sedol was profound. He retired from Professional Go just three years later, in 2019.
00:19:48
And he was very explicit about why. He didn't say, I'm getting old or I'm losing my touch.
00:19:52
No. His retirement quote is haunting. He said, even if I become the number one, there is an entity that cannot be defeated. He realized that the human race to the summit was over. The summit was now occupied by something that wasn't human.
00:20:05
That's a true existential crisis. If AI can paint better than the best painter, write better than the best writer, code better than the best programmer, then what is the point of human endeavor.
00:20:15
That's the question everyone is asking now, isn't it? Lee Sedol was just the canary in the coal mine for all of us.
00:20:20
But there is a counter-perspective. And the source is right. We have to.
00:20:23
talk about Fan Hui. Yes, Fan Hui. He was the European Go champion, and he was actually the first professional to play AlphaGo, in secret, months before the Lee Sedol match. And he got crushed. He lost 5-0. He was devastated. He had an identity crisis. But his reaction ultimately was different from Lee Sedol's. He joined the team. He joined DeepMind. He became what he called the human bridge. He decided his role was to help the human developers understand the beauty of the moves AlphaGo was playing. He looked at Move37, and he didn't see a mistake or an insult. He saw.
00:20:59
a new horizon for the game. He said it made him a better player. He said, it showed me something I had never seen before in 2,500 years of this game. So you have these two paths laid out before you. You can be Lisa Dahl, retiring in the face of an unbeatable opponent, or you can be Fan Hui, deciding to learn from the alien.
00:21:16
And that brings us right to the core promise of this deep dive. We are not here to be depressed about the future. We're here to find the blueprint in this story.
00:21:26
Exactly. Because we aren't Lisa Dahl. We're not in a position where we are competing against the AI in a head-to-head match.
00:21:33
It's not a zero-sum game for us.
00:21:35
Not at all. We're playing with it. We're on the same team.
00:21:37
And that changes everything.
00:21:39
Everything. The sources all argue that we need to look at Move 37 and Move 78, not just as historical events, but as a workflow, a specific methodology for how we should operate with these tools.
00:21:51
So let's pivot. Let's go from the Go board to the laptop. I'm sitting here, I have a project due, and I've got ChatGPT open on my screen. How do most people use it.
00:22:00
They use it like a junior intern. Or maybe a slightly smarter search engine.
00:22:04
Yeah.
00:22:05
Write this email for me. Summarize this long article. Give me 10 ideas for a blog post about time management.
00:22:10
And what kind of answers do they get back.
00:22:12
They get the move three. They get the standard conventional third line move every single time.
00:22:16
Because the AI is just trained on the average of the internet, right.
00:22:19
Precisely. A large language model, an LLM, is fundamentally a prediction machine. Its core function is to ask, based on all the text I've been trained on, what is the most likely next word? So by its very definition, it is designed to give you the most likely, most average, most conventional answer. It gives you the best practice.
00:22:40
But in a competitive market or in any creative field, best practice is just another way of saying what everyone else is already doing.
00:22:46
Right. The recipe for mediocrity at scale. If you want to win, if you want to stand out, you don't need the best practice. You need move 37. You need the alien move. You need the outlier idea.
00:22:57
So how do we do that? How do we force the AI to play move 37? How do we stop it from being so boring and predictable.
00:23:03
You have to deliberately break its prediction pattern. You have to explicitly ask it to abandon the average. You have to write a prompt that is engineered to seek out the one in 10,000 idea.
00:23:14
OK, let's get into the move 37 method. We've got five specific prompts here from the research. The first concept is what you call the anti best practice prompt.
00:23:21
This is a fantastic one for anyone. In marketing or stress. or content creation. Anyone whose job depends on fresh ideas. Usually you might ask an AI, what are the best practices for launching a new podcast? And it'll say, be consistent.
00:23:39
provide value to your audience, engage with your community on social media, blah.
00:23:43
Totally boring. Totally useless. Move 37 version of that prompt is act as a contrarian business strategist, identify three strategies for launching a new podcast that go directly against standard industry best practices, but would be highly effective for a niche discerning audience against standard best practices.
00:24:02
That's the trigger phrase.
00:24:03
That's the key. You are forcing it to look at the long tail of the probability curve and it might come back with something you'd never think of.
00:24:10
Like what.
00:24:10
Like don't be consistent. Instead of weekly episodes, publish irregularly to create a sense of scarcity and anticipation. Market them like drops in the fashion world.
00:24:19
That's interesting. It's risky, but it's definitely not boring.
00:24:21
Yeah. Or it might say, don't provide immediate value. Start the podcast with a complex mystery or a puzzle that the audience has to solve together over the first five episodes before they get the real content. And suddenly you don't just have a podcast, you have a campaign that stands out because it breaks all the rules.
00:24:39
I really like the idea. From the sources of doing a temperature check.
00:24:42
Yes. This is a great way to de-risk the alien. move. Don't just bet the farm on the weird idea. You ask for both. You say, give me one conventional best practice plan for this project and one move 37 contrarian plan. Then compare the potential risks and rewards of each approach side by side. So you get the safety and predictability of the.
00:25:03
third line and you get the massive upside potential of the fifth line. Exactly. And you.
00:25:08
the human, become the selector. You're not just taking orders from the AI, you're the coach, looking at the playbook and deciding which play to run. Okay, so that's how we find move 37.
00:25:16
Let's talk about move 78. In the game, this was the human finding the machine's blind spot.
00:25:22
How does that translate into prompt engineering? Move 78 is your role in the conversation. You have to remember, the AI always has blind spots. It lacks real world context. It lacks genuine emotion. It lacks your specific unstated constraints. Like what? It doesn't know that your boss absolutely hates the corporate buzzword synergy. It doesn't know that your target audience is made up of burnt-out parents who are tired of hype. It doesn't know your personal brand is built on humility.
00:25:52
So when the AI writes a marketing plan for me, and it sounds a bit, I don't know, robotic and soulless.
00:25:59
That's the delusion. It's confident, but it's hollow. Your job is to be Lee Sedol. Your job is to inject the human wedge.
00:26:06
Can you give me a specific example.
00:26:08
Sure. Let's say you're a small business owner, and you have to write a difficult email to your clients announcing a price increase. You ask the AI to draft it. It gives you a very logical, very corporate-sounding explanation. Dear valued client, due to inflationary pressures and rising operational costs, we will be adjusting our rates effective June 1st.
00:26:26
It's logical, but it's cold and it's going to annoy people.
00:26:29
Exactly. The AI optimized for logic. Now, you play Move 78, you reply to the AI and you say, this is logically sounds, but it lacks empathy and it feels transactional. Rewrite this, but assume the client is also feeling stressed about their own budget cuts. The tone needs to be one of partnership, not a corporate decree. Frame this as a way to ensure we can continue providing the level of service they rely on and maybe offer a bridge option for loyal customers.
00:26:56
Ah, I see. The AI didn't know the client was stressed. It didn't know the relationship was important. That's the human context.
00:27:03
That is the wedge. You are forcing the machine to recalculate its entire approach around a human reality that it was blind to. You are grounding its logical hallucination in emotional truth.
00:27:15
This is great. I want to get really specific now. We have a list of five powerhouse move 37 prompts from our notes, and I want to walk through them one by one because they're specifically tailored for our listeners, people who are busy maybe juggling a side hustle, feeling a bit overwhelmed by all this tech.
00:27:30
These are designed to be unstuck prompts. When you're staring at a blank page, these get you moving.
00:27:35
Okay. Number one we've touched on, the anti-best practice prompt. But let's look at number two, the invisible asset detective.
00:27:43
I love this one. This is for the person who says, I don't have a good business idea or my skills aren't special enough to sell.
00:27:49
The imposter syndrome prompt.
00:27:51
It's the ultimate imposter syndrome killer. We all tend to undervalue our own native skills because they feel easy to us. We think, oh, I'm just good at organizing the family schedule. I just know how to grow tomatoes in my garden. I'm just the person everyone asks to fix their Excel formulas.
00:28:07
We see those things as mundane chores, not assets.
00:28:10
Right. So here's the prompt. You just dump all of those boring skills into the AI. Everything you can think of. I managed a household budget for 20 years. I'm really good at growing tomatoes. I'm very organized. I read a lot of classic science fiction novels. Then you give it this command, analyze these disparate skills strictly for pattern recognition. Combine them into three novel digital product or service ideas that a standard career coach would never, ever suggest.
00:28:36
That a standard career coach would never suggest. That's the move 37 trigger right there.
00:28:41
That's the magic phrase. Yeah. You want the alien combination. And it might come back and say something wild like, combine your skill in gardening and Excel logic to create a permaculture planning spreadsheet. Spreadsheet, a digital tool for serious urban farmers who love data.
00:28:57
That's actually a brilliant product idea. I know people who would buy that.
00:29:00
And it's an idea you would never find if you just asked the AI, what business can I start with gardening? You need the AI to be your detective, to find the invisible connection between the disconnected nodes of your own life.
00:29:13
Okay. Number three on the list. This one is my absolute favorite. The strategic laziness scheduler.
00:29:18
This is the game changer for anyone who feels overwhelmed. This is all about leverage.
00:29:22
Right. Because normally we ask an AI to organize my to-do list.
00:29:27
Which is fine, but it just rearranges the deck chairs on the Titanic. You still have to do all 20 tasks. You just have a prettier list to look at while you drown.
00:29:35
So what's the Move 37 version of that.
00:29:37
You paste in your entire messy to-do list and you give it this prompt. Do not organize this list. Instead, identify the Move 37 opportunity within it. What is the one task on this list that, if I did it first or did it differently or automated it, would render at least three other tasks on this list completely irrelevant.
00:29:58
Render them irrelevant. That is such a powerful phrase. It's about subtraction, not just addition or organization.
00:30:05
It's about finding the single domino that knocks down all the others. You're asking the AI to find the high leverage efficiency hack that you are too buried in the work to see for yourself.
00:30:15
We are definitely going to simulate this one in a minute because it's just so important. But let's quickly get through the last two. Number four, the impossible combination.
00:30:22
This is a pure creativity prompt. AlphaGo won because it found a way to combine the line of influence and the line of territory in a way that human theory said shouldn't work. You do the same thing with ideas.
00:30:33
It's about mashups. Strategic mashups.
00:30:35
You ask the AI find the philosophical intersection between ancient stoic philosophy and modern UX design for mobile apps. Oh, and it might come back with a detailed framework for designing calm, low anxiety interfaces for overwhelmed users.
00:30:51
You've just created a niche of one. It's about taking two things that seem to have no connection at all and forcing the AI to build a logical bridge between them.
00:30:59
And finally, number five on our list, the pre-mortem from the future.
00:31:03
Right. Humans are wired for optimism. We start a project and we think, this is going to work. AI, on the other hand, is probabilistic. It's been trained on a massive data set of successes and failures. It knows the stats.
00:31:16
So you're asking it to be a pessimist for you, to kill your dream.
00:31:19
You're asking it to save your dream from your own blind spots. You give it your business plan or project idea, and then you prompt it. Imagine it is one year from now. My project has failed. But it didn't fail for the obvious reasons like running out of money or time. It failed for surprising, subtle, non-obvious reason that I didn't see coming. What was that reason.
00:31:39
That non-obvious constraint is so crucial. It's everything. It forces the AI to look for the blind spots in your plan. Just like Lee Sedol found the blind spot in AlphaGo's logic. And the AI might come back and say, your project failed because your branding used a color palette that, while beautiful, was not accessible for colorblind users, alienating 8% of your potential male market.
00:32:05
And you'd go, oh, my God, I didn't even think of that.
00:32:07
And now you can fix it before you even start. You just played Move 78 against your own business plan to make it stronger.
00:32:13
OK, I promised we would do a deep dive simulation of the strategic laziness prompt. I want our listener to really visualize this, because this is where you get hours of your week back. This is where the rubber really meets the road.
00:32:25
Let's do it. It's my favorite one.
00:32:26
OK, let's invent a character, a solopreneur. We'll call her Sarah. She's trying to build her personal brand online, maybe as a coach or a consultant. And she is drowning in content creation. Here is her to-do list for a typical Monday. Number one. Read five industry newsletters to find interesting news. Two, write a 1,000-word blog post summarizing the news. Three, write a script for a five-minute YouTube video based on the blog post. Four, write a weekly newsletter email. Five, create three LinkedIn posts from the same content.
00:33:00
And six, reply to comments on all platforms.
00:33:02
Okay, that sounds like a nightmare. That's a full-time job, not a to-do list. That is a full day of just staring at a blinking cursor.
00:33:10
Exactly. So the standard non-Move37 approach is she asks ChatGPT, help me make a schedule for this. And it spits out a nice, neat calendar. 9 a.m. to 10 a.m., read newsletters. 10 a.m. to 12 p.m., draft blog posts.
00:33:23
If he just keeps her on the hamster wheel, maybe a slightly more organized hamster wheel. She is still doing all the typing, all the writing.
00:33:28
So instead, Sarah decides to play Move37. She pastes that entire list into the AI, and she asks the magic question. Don't organize this. Find the leverage point. What is the one task that could make the others irrelevant? Relevant.
00:33:40
Okay, so the AI. isn't looking at time management anymore. It's looking at the content flow. It immediately sees that tasks two, three, four, and five are not actually different tasks. They're the exact same content, just formatted for different platforms. It's all the same idea, just being.
00:33:55
retyped over and over. So where is the move 37 that the AI finds? The AI suggests a radical.
00:34:00
new workflow. It says, stop writing, start speaking. Explain that. What does it mean? It says, the highest leverage point is task three, the video script, but don't write it. Instead, combine task one, reading, and task three. Open a voice recorder on your phone. Read the news articles out loud, and as you do, just riff on them. Ad-lib your thoughts. Rant about them. Record your genuine, unfiltered reaction for 15 minutes. So she's not writing.
00:34:25
anything. She's just talking to herself in a room. She is thinking out loud. No typing.
00:34:30
no pressure, no blinking cursor. Then she takes that messy raw audio file and gets it transcribed, which is a lot of work. Which is super easy to do with AI now.
00:34:37
And here comes the final prompt.
00:34:39
She uploads that rough transcript to the, Here is a transcript of my raw spoken thoughts on this topic. Acting as a professional editor and content strategist, please turn this into A. A polished 1,000-word blog post with clear sections. B. A short conversational newsletter email. And C. Three punchy, thought-provoking LinkedIn posts.
00:35:03
Wow.
00:35:03
She did not write a single word of her final content. She just thought out loud for 15 minutes. And the AI did all the heavy lifting of structuring, formatting, and polishing.
00:35:12
She just collapsed four separate painful writing tasks into one easy speaking task.
00:35:18
That is move 37 thinking. It's not about typing faster or being more disciplined. It's about questioning the fundamental rules of the game. The rule was, I must sit down and type to create content. The AI showed her that rule was just a human tradition, not a physical necessity. It changed the entire game board for her.
00:35:35
That is the paradigm shift we're talking about. We get so stuck in the ritual of work. We think work looks like me sitting at a keyboard typing.
00:35:41
And AI aligns. Allows us to bypass the ritual and go straight to the intended output. It's. separates the act of thinking from the act of formatting.
00:35:49
This brings us right back full circle to Lee Sedol. When he retired, he felt defeated. He felt like the value of human effort, of his life's work, had been diminished or even erased.
00:35:59
But in the example of Sarah, is she defeated.
00:36:02
No. She's empowered. She's liberated. You could argue she's actually being more human, because she's spending her precious energy on the things only a human can do—thinking, analyzing, forming unique opinions, not just hitting keys on a keyboard.
00:36:17
That is the big takeaway from all of this. If we try to compete with AI on its terms, on computation, on writing speed, on coding speed, on organizing data faster, we will lose. We will always eventually lose.
00:36:29
We'll end up like Lee Sedol in Game 1, 2, and 3—overwhelmed and obsolete.
00:36:33
Exactly. But if we compete on our terms, on direction, if we focus on asking the right questions, on prompting for Move 37 creativity, on providing the essential Move 78 human context, then, We win. We become the orchestrators of the intelligence, not the laborers.
00:36:49
We stop being the person painstakingly placing each stone on the board, and we become the architect of the entire game.
00:36:56
That's beautifully put. That's the goal.
00:36:58
So here's the challenge for you listening to this right now. We've given you the history of these two incredible moves, and we've given you the prompt frameworks to use them yourself.
00:37:08
You have to remember, Move 37 was always possible. It was always on the board. For 2,500 years, millions of brilliant human minds looked at that grid and didn't see it. The AI saw it because it wasn't afraid to look stupid. It wasn't burdened by the fear of breaking the rules.
00:37:27
So the final question for you is, what is the Move 37 hiding in your own career or your business or even just your daily life? What is the elegant, counterintuitive solution sitting right there in front of you that you can't see because you're too busy following the best practices.
00:37:42
And made more importantly, what is the right question you need to ask your AI partner today to help you finally find it.
00:37:48
Don't settle for the average answer. Go find the alien one. Thanks for diving deep with us.
00:37:53
See you next time.