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0:00If you're worried about falling behind because of AI and are busy learning this and that, you won't need to do that after this video. Once you understand just one concept in this video, you'll end all that wasted effort and clearly know what to hold onto to survive in the AI era. Ah, it’s really driving everyone crazy these days, isn't it? Some say do this, others say do that, and they scare you by saying you'll fail if you don't know it. Trying it doesn't feel right, but not doing it makes you feel like you're falling behind. You search YouTube and they tell you to "find your humanity,"

0:28which sounds like complete nonsense. You're wondering, "How on earth am I supposed to survive in the AI era?" aren't you? The reason chasing after AI feels like there's no answer is because of the saying, "The person who survives the AI era is the one who handles AI well." That is complete nonsense. Now, you might be thinking this right about now. Who are you? Are you some kind of AI expert? No, I'm not. I'm not good at coding, and I'm definitely no AI genius . But I spent over six months digging into this one question, starting a business myself, and learning through trial and error. So, just listen to why

0:55I'm saying this from now on. Judge for yourself if it makes sense, and if you don't believe me, just try following the contents of this video. See if the results change. So, going back, who exactly is going to survive? The person who survives in the AI era isn't the one who handles AI well, but the one who handles AI insanely well. Wait,

1:10what? That's just wordplay. I'm out. Peace. No, no, wait, wait a second. Just a moment. Once you hear the reason , you'll be convinced immediately. It's because AI has raised the standard of " doing well" so high that "doing well" is no longer rare. Let me use a simple analogy with gold. Back when gold was rare, even 50%purity was considered valuable. But what happens if, one day, the supply explodes and 90%pure gold becomes abundant everywhere? Only 99% pure gold begins to be recognized as true gold. That is exactly what AI has done. In the past, solving problems itself was a rare skill. But now, thanks to AI, everyone can solve most

1:41problems quite well. Now, the act of simply solving a problem has lost its meaning. But we are still focused on just "getting gold" rather than creating 99%pure gold. What matters is solving problems insanely well with AI, but we're stuck on just solving problems with AI. But then, what does it actually mean to be "insanely good" at it? I’m not some incredible expert , so don’t you worry. What I mean by "doing it damn well" here. It’s nothing grandiose. It’s just focusing the power of AI onto a single point to make it explode, that’s all. Like using a magnifying glass to start a fire with the sun of AI. Before I

2:13explain this magnifying glass, I’ll just show you with an example right away. Let’s say you work at a cosmetics company. You need to write a proposal email to an influencer asking them to mention your product in exchange for a free sponsorship. Usually, you’d write it like this. " AI, write a sponsorship proposal email to send to this influencer." And people who have used it a bit do this. "AI, from now on, you are the best expert at writing proposal emails.""Write a sponsorship email for this person.""The goal is to create a polite and persuasive sponsorship email." It certainly comes out a bit better. But this still isn’t the magnifying glass

2:41stage yet. People who use a magnifying glass don’t start by writing the email right away. First, you have to change your goal from "polite and persuasive" to "making the other person accept the sponsorship no matter what." Because whether you write one line, a hundred lines, or write it poorly, that doesn't matter; what matters is the other person saying, "Yes, I’ll do the sponsorship," is what’s most important. After resetting the goal like this, you gather all the emails the company sent in the past. Emails that were accepted, rejected, or

3:04ignored. And then you ask the AI this. "AI, this data is a collection of emails we sent to influencers, both accepted and rejected.""Look at this data and identify the one factor so powerful that it outweighs all others in determining success or failure." Then, for example, the AI answers like this. "Unlike the rejected or ignored emails, the accepted ones had one thing in common.""They included a personal and specific expression of interest from a fan who had been supporting the influencer for a long time." Then you immediately get the idea. "Ah, that’s it.""Even for me, rather than just being told 'this product is good, try it,' if it contained something like '

3:39this person actually watched my videos and has been subscribed to me for 5 years,' I’d read it one more time too ." So now you give it a new task. "AI, use your deep research function to look into this influencer’s video style, what they work hardest on, and their interviews or history that show what makes them unique.""Then, use all that information to rewrite the proposal email from the perspective of a fan who has been supporting them since they opened their channel." See, it's completely different from the beginning , right? At first, it was just "write a good sponsorship email." But now, the real goal of writing a sponsorship

4:05email is acceptance, the data containing acceptance and rejection, and the one key point that differentiates the two. That was the pen nib. So it became, "write this sponsorship email by focusing everything on the pen nib." This is the magnifying glass. And this is also a method I actually used. Before, I made a video titled "What is Truth, as Seen Through Fullmetal Alchemist?" When this video surpassed 700,000 views and my channel was just growing, I received a copyright strike. I wondered what to do , and I thought I should ask YouTubers who do reviews and deal with copyright issues often. But they were already huge channels with over 100k, 200k, or

4:36500k subscribers. I even thought to myself, "A small 30k channel got a copyright strike, what should I do?" If I sent it like that, even I wouldn't reply. So, I had AI first research what they value, what makes their videos different, their unique traits, what they take pride in, and their values; then, I personally watched their recent videos one by one and sent an email with a story that only I could tell them. Then, thankfully, the response rate for my small 30k channel was over 95%. One major YouTuber even suggested that we talk on the phone. I was truly grateful. I'll use a short, easy story

5:11to illustrate what's different about using AI in this way. This story will help clear up our entire situation. Let's imagine the era before AI as a pitch-black age without a sun. People were cold, in the dark, and hungry. So, people who could start a fire were extremely rare. Everyone tried hard to become better at rubbing branches together to start a fire. Then one day, the sun called AI suddenly rose. The world warmed up, the wood heated up, and starting a fire that took 10 hours was reduced to one hour. People were thrilled. They studied when the sun was hottest, found sunny spots, and learned how to use the sun, reducing the time

5:47from an hour to 30 minutes to start a fire. They were clearly much better at starting fires than before. But then, someone brought a magnifying glass and gathered the sunlight into a single point. And they ignited a fire in just 10 seconds. In the end, only the person with the magnifying glass remains, and everyone else gets laid off. This is exactly what I mean by doing something damn well. It’s not about rubbing sticks together better than others, nor

6:06is it about becoming a solar expert. It’s simply focusing the power of AI on a single point to reduce the time and cost of achieving your goal to an entirely different level. Not cutting an hour down to 30 minutes, but cutting an hour down to 10 seconds. This is the meaning of "damn well" as used in this video. But in this story, the people who got fired say things like this. "Ah , this all happened because of the sun, so I should study the sun more," and they go around taking lectures on the sun. But the reason they got fired isn't because they didn't study the sun

6:31. They got fired because they didn't know how to make a magnifying glass. Do you finally see why we, including myself, have been stuck in place until now? By learning tools, taking lectures , and subscribing to multiple AIs, we were just continuously studying the sun . But what we really needed was to make a magnifying glass. So now, there is only one question. How on earth do you make that magnifying glass? Making a magnifying glass means feeding AI three specific elements before you ask your question to properly define what "damn well" means. This is just like how you need three things—a convex lens, good fuel, and a steady focus—to start a

7:00fire with a magnifying glass. I am confident that if you just pay attention to these three elements from now on, your AI results will change overwhelmingly. "Oh, so you mean 'You are now the best copywriter'?""Is that what you’re saying?""Oh, so it's just about finding success examples and training it.""Who doesn't know that?" No, that’s not it. It’s really not. That’s just doing it well. It’s not doing it damn well. If you use a magnifying glass but there’s no convex lens, or there’s a lens but no fuel, or if you have both but the focus keeps shaking, it takes 10 minutes to start a fire, not 10 seconds. AI is

7:26exactly the same. Even if just one of these conditions is missing, you end up with mediocre results instead of overwhelming ones. Then we go back to just doing it well instead of damn well , and we're back to wasting our effort. So the three conditions for the real magnifying glass I'm talking about are a clear high-level goal, an error note, and key variables. From now on, I will break down these three things one by one. Before that, if this has been helpful so far, please hit the subscribe and like buttons. It’s a huge help in making these videos. And since there are plenty of cases and

7:52examples later in this video, I believe if you watch until the end, you’ll immediately see what you need to do. Alright, let’s get started. Now, the first part of the magnifying glass: the convex lens. The convex lens is a clear high-level goal. So, what is a high-level goal? Simply put, why on earth am I doing this? It’s like this . Let’s say there is a knot. Oh, I don't have time, but the knot is tied too tight. How do I untie it? Then someone thinks of a super efficient method. Hey, just cut it. Really fast, right? So I cut the knot, and it was solved immediately. But after cutting

8:18it, the original goal was to reuse the rope after untying the knot. The knot is undone, but the real goal failed. This is one of the problems that happens so often when using AI. We just throw the problem right in front of us at the AI. Write this email for me. Tell me how to increase sales. Write a YouTube script for me. Then the AI solves that problem very quickly and efficiently. But if that problem doesn't align with my higher-level goal , the AI takes you in the wrong direction damn fast. Now, let me give you a real-life example. Let’s say you run a posture correction Pilates

8:48studio. You want to make more money than now, so you tell the AI this. AI, tell me how to get more new students for my Pilates studio. Then the AI tells you how to run ads, hold discount events, and post on Instagram more often. Things like that. And suppose you followed these methods and actually increased your customers through promotion. But here’s where the problem arises. Classes increase, there are more people to contact, and reservation management becomes tough, so you can't rest at all and just work your fingers to the bone. Now, the AI didn't give you the wrong answer. I just didn't present my higher-level goal. Actually, what I wanted wasn't to

9:17get more customers, but to earn more money and live more comfortably than I do now. This is the higher-level goal. So, you have to tell the AI this. What I ultimately want isn't to just increase customers blindly, but to work less busy while earning more money. Find me a new method based on this goal . The moment you provide this higher-level goal, the direction the AI suggests changes. When the initial goal was wanting more customers, the focus naturally shifted to promotion. Like running ads, holding discount events,

9:40and posting on Instagram more often. But when the higher-level goal changes to wanting to earn more while running the business more comfortably, the focus shifts from promotion to branding . Create a short-term intensive program that fixes tech-neck two weeks before an interview. Collect reviews and cases showing before-and-after changes and post them on Instagram Stories. Narrow your target from general students to job seekers and raise your prices accordingly. Then I won't have to say "

10:01our shop is great" every single day. You can make job seekers facing important interviews come to you first, thinking, "Oh, this place specializes in interviews, not just Pilates," without having to run around everywhere asking them to visit. But what if you do that and it doesn't work out? And do customers really just show up on their own? Of course, realistically, you'll have much more to prepare. It's the same whether you're distributing promotional materials or focusing on branding. The amount of effort required is the same either way. But just because of that single higher-level goal, the outcome changes completely— whether you're working your fingers to the bone or working with ease. So, a

10:30higher-level goal is not something difficult. Before asking AI a question, ask yourself just one more time about the work you're trying to do. Why am I trying to do this? And if possible, ask one more time. Then what's the benefit? The answer you get by digging into this until the end is the higher-level goal you truly desire. But for those who have trouble finding their higher-level goal, I have a method I use. It's to have the AI interview you in reverse about how you want your current work to change your life. You ask it in a way like this. "I want to find the higher-level goal for the work I'm

10:54trying to do.""Ask me various questions so I can figure out why I'm doing this work, what I want to gain, and what state I want to reach.""Once I answer all of them, summarize my higher-level goal into one sentence based on my responses." You create a questionnaire like this and give it to the AI. Then, if you answer all the questions and send them at once, your higher-level goal will emerge. So, instead of saying , "AI, solve this problem" based on that higher goal, say, "What I'm trying to do is A, but the real purpose of this is B.""Check if doing A is the right approach to best achieve B, and

11:19if there's a better way, suggest it." That's how you ask. Then the AI won't just quickly solve the problem in front of it, but will work to create the results you truly want. This is the first element of the magnifying glass: the convex lens, or the higher-level goal. Now, the second is the answer sheet. This is a very, very important part. Let's assume you've properly set your higher-level goal. You've even accurately told the AI what the result you truly want is. But if you just say here, "AI, this is my higher-level goal , so try to do it as well as possible," what will happen? It won't turn out

11:46well. Because the AI doesn't yet know exactly what "doing it well" means. Simply put, you gave it a problem, but you didn't tell it what the correct answer is. So, the AI gives you the most plausible answer based on the general criteria it knows. But we said earlier that what we want is to do it really, really well. To get truly great results, you have to show the AI what " truly great" actually looks like—you need to provide an answer key. This is

12:07exactly what I mean by an answer key. Let me explain this simply with a real-life example from a friend of mine . My friend was learning how to write screenplays under a film director. One day, the director gave him a mission to write a screenplay to submit for an upcoming workshop. My friend put in his own ideas and even used AI to work hard on the script. Then, he showed it to me

12:23. I read it, and it was just mediocre. So I asked him. Why are you writing this? My friend replied, "I want to hear the director say it's well-written , and I want my piece chosen for the workshop." Great. Then, the higher-level goal isn't just writing a good script, but having the director say, "Wow, this is great," and choosing it—that is the real goal. But the problem here is that we don't know what criteria the director uses to judge a script as "well-written." In this state , asking the AI to "write a script the director will like" is completely meaningless. So, I started by searching for the director's answer key. I used

12:52deep research to investigate the director's philosophy, worldview, and past interviews or works that reveal the specific criteria he uses when reviewing a script. Especially, I looked for materials where the director personally stated, "I like this kind of work." If there were any records where he evaluated something as "not so good, " I organized the results around that. Then, records of the director's own grading will emerge online. "I like things like this.""I don't like characters like this.""I liked this

13:12work because of X." Things like that. After having the AI learn all that, I gave it my friend's original script and said this: "Based on the director's criteria we just researched, rewrite my friend's script in a style that the director would absolutely love." The result was that my friend's work was actually selected. Now, the important point here is that I didn't use some genius prompt. I simply showed the AI data that already had results attached —what the director actually said was good and what he said was bad. This is an answer key. It’s data that has

13:38already been graded in the real world. "Did A, it worked.""Did B, it failed." If a result is attached to an action like this, it all becomes an answer key . In a company, for example, there are things like this. Deals won or lost, successful or failed projects, products sold or not, good or bad ad responses

13:52—all of these are answer keys. Because reality has already graded them as to what worked and what didn't. "Hey , how is this different from just training it on good references?" Well, the difference is exactly this. Simply using good references might not fit your specific situation. For example, if a small company marketing itself only looks at success stories of large corporations, that success is usually just due to their brand, recognition, or ad budget, not something you can actually replicate. Conversely, if you have data on what worked and what didn't for businesses of similar size, with similar customers and situations,

14:20you'll have a much better answer key. Looking at this part, there is one thing I can do right away. Before asking the AI to do a good job, you need to collect everything you've done so far, including what went well and what didn't. Only then will the AI begin to learn the criteria for what you consider "good." Now for the second element, the fuel: data with result values, or an answer key. But it doesn't end here. Even if you've collected a mountain of answer keys, you'll end up with nothing if you miss this final point. That is, the key variable. So, you've found the

14:45magnifying glass and prepared the fuel. But if you don't focus the lens on one spot, the fire will never start. That single point of focus is the key variable. The one factor that influences the results the most. For example, it's like being handsome. We all want to dress well, right? So, we try luxury brands, layering, color coordination, specific shoes, adding accessories, and so on. But no matter how hard I try, I can't beat Park Bo-gum wearing a plain white T-shirt and jeans. That right there is the key variable. It's the single most powerful factor that outweighs every other variable. Of course, this is an extreme example. You can't really change your

15:18face that easily. So, you have to find it among the things you actually can change. Let's take an example. Owner A is now tired of running ads. They want to create a shop that people find on their own through word-of-mouth without needing to advertise. Looking at the sales, the arugula pizza had the best response. So, "Great.""Let's make our shop famous as the best place for arugula pizza." They set this as their top-level goal. And they gathered enough answer keys by collecting Baemin reviews, repeat customer feedback, low

15:42ratings, and reviews from other shops. In other words, they have both the high-level goal and the answer keys. But if you ask, "Tell me how to increase the repurchase rate of arugula pizza," the AI will just give you a bunch of methods. "Increase your advertising.""Distribute arugula coupons.""Improve the arugula dough."" Include extra arugula in the packaging. ""Use fresher arugula.""Increase the amount of arugula." These are all correct points. But the problem is, you don't know what to do first. And you can't possibly do all of them. That's why we have to ask it this way. Analyze the data I've provided and identify the one single, most powerful way to

16:12increase the repeat purchase rate of arugula pizza that would render all other methods unnecessary. Then, the AI replies like this. Double the amount of arugula. Because the people who eat arugula pizza are eating it for the arugula. If they just wanted pizza, they would've had pineapple pizza, bacon pizza, or something else. Was mentioning pineapple too far? But the fact that they specifically chose arugula pizza means arugula is what matters, so even if you reduce other toppings, if you double the amount of arugula, these people will have no choice but to keep ordering that arugula pizza. The result is making your place remembered as the one that

16:44piles it on like a mountain. This is the core variable. But what’s important here is that it doesn’t mean you absolutely must do only one thing. That’s definitely not what this means. It means finding something so powerful that by just properly addressing this one thing, you don’t need to bother with the dozens of other methods. And a core variable must never be something abstract like "exercise hard" or "do marketing well." It needs to be specific enough that you can execute it immediately. And when you execute that core variable exactly as intended, the goal must inevitably be achieved. For example, among my videos, the one with the highest views, "What

17:09is Eloquence?" This video is similar. Eloquence involves countless factors like rhetoric, appearance, pronunciation, voice, and presence, but I didn't explain all of those and focused solely on one thing: " pre-empting the frame" by laying out the important topic to the other person before I even speak. Then, regardless of rhetoric, appearance, pronunciation, or voice, once you lay out this important topic first, you don't need to do the rest. Like this, that one thing that is specific, actionable, and drastically moves the needle—that is the core variable. "Ah, but I’m not sure what that is in my own work." In

17:37that case, I have a really great tip. There are two ways. When you want to find a truly efficient method for the work you're doing, you increase your goal tenfold. For example, if you're told to cut down ten trees, you try to sharpen your axe blade even more. But if you're told you have to cut down a hundred trees, you never bother sharpening the blade. You’ll

17:52absolutely look for a chainsaw instead. For example, let's say you want to start a YouTube channel. You want to create content about doing good deeds. But you want to reach 10,000 subscribers within half a year. Then, you can just do it in a vlog format. You can go out, say hello to people, help someone out, and upload gentle videos. But if you absolutely have to hit 100,000 in half a year, you would never upload a vlog. I ended up making content about what happens when you pick up trash all over Gwanak-gu, Seoul for 7 days. In other words, as the goal becomes significantly larger, its core

18:19variable also becomes significantly more powerful. So, when you're working on something and think, "What on earth should I do to be most effective?" When you're worried about that, try increasing your current goal tenfold. Now, if the previous method was for when you didn't know what was most efficient, this second method is for when you don't know what's important: shorten the time to achieve your goal to one-tenth. Instead of saying "I have to do this in a week," you approach it

18:40as if you have to finish it in an hour. For example, when you're preparing a PPT presentation, if you're told you have a year to do it, you think about so many different things. Like what fonts to use, how to design it, and what effects to include. But if you're told, "You're presenting in 10 minutes, so get ready," you keep only the most important message and cut out everything else. Finding that one single most important and powerful factor that determines the success or failure of the goal—that is the core variable. Now, hearing this, you might think, "Okay, sure, higher-level goals, answer keys, core variables, I get all

19:10that, but how am I actually supposed to use them?" You might find yourself thinking that. So from now on, I won't explain much; I'll just show you plenty of examples. If you watch a few of these, you'll get the hang of it right away. And if you've found this content useful so far, please hit the subscribe , like, and notification buttons. Thank you. This is a method I use every single day. I write a daily journal where I record what I did that day, along with my mood and condition. And once I have enough data, I ask an AI. " Compare my best days to my worst days

19:36and tell me the one most important action that separates the two." It turned out that in my case, it was going outside instead of going back into my room and lying down after breakfast. This one thing was the biggest factor determining my condition . So, I made it a point to schedule a walk with my grandmother right after breakfast. By doing that, I was able to identify the bottleneck in my daily routine and fix it immediately without needing various other methods. Now, what if you were a new employee? Let's say your boss says this in a meeting. " People in their 20s and 30s are really

20:02interested in blood sugar management these days.""But I don't think they'll buy it if it looks like food for diabetic patients.""Can't we make a diabetes prevention food that's just like a regular snack?" And they assigned a new employee to turn this into a report. Usually, the new employee would record the meeting minutes, feed the recording into an AI, and say, "Make a report based on these meeting minutes." You do it something like this. Then the AI brings you something like this. There’s nothing factually wrong with it. But from a boss’s perspective, it’s like, "So what?" It makes them think, "If it's going to be like this, I might as well

20:25do it myself." However, someone who uses a magnifying glass doesn't make " writing a good report" their goal; they change it to "making their boss's annoyance zero." They take all the meeting minutes from the past two or three months and dump them in. "Based on these minutes, analyze everything my boss liked, disliked, approved, rejected, or asked to revise repeatedly , identify what bothers them the most, and find the one single way to solve it ." Let’s say the result comes out that this boss likes having their rough ideas fleshed out into three concrete, visible options. Then, from that point on, you change all your reports to this

20:55format. It turns into a report in the format the boss prefers, where they can see it at a glance and immediately understand, "Oh, I see what this is about." As another example, let’s say I’m a casting director who finds good actors and introduces them to directors . In that case, I feed the profiles of all the actors who were actually cast and those who weren't into the AI. And when I look for the key variable that separated the two, it turns out it’s not about being pretty or ugly, but about whether in three seconds you think, "Oh, they'd be a perfect thug.""

21:20Oh, they’d be great as a princess." Let’s say the result is that profiles where the character comes to mind immediately were the most effective. Then you turn this into an actual tool. You could sell an "Actor Profile Success Probability Analyzer" for 9,900 won. You create an app where actors can upload their profiles and get instant feedback. You’ve taken a casting director’s intuition, built up from looking at hundreds of photos, compressed it into a single key

21:40variable, and turned it into a product. Let’s say I’m an accountant working at a cleaning company. If I’m trying to cut costs, I’d probably look at accounting ledgers for labor, detergents, and equipment. But someone using a magnifying glass doesn't just use that data; they utilize every piece of data available. They pull data from other departments, such as work hours by site, when cleaning tools are replaced, and supply usage rates. Then, they dump it all in and ask, "To achieve the high-level goal of saving the company money that’s leaking away

22:03, what is the most important thing?" And it turns out they discover a pattern: using cheap rags leads to higher material costs due to frequent replacements and longer work hours. Then you can identify those points, suggest switching to more durable products even if they cost a bit more, and present that in a report to your

22:15boss. What if I’m a dating coach? Then I collect hundreds of KakaoTalk conversations—both those that actually led to a relationship and those that just fizzled out. Compare these two and find the one key variable that best determines if the other person is interested. Let’s say the result shows that, for example, the willingness to meet in person is a much stronger signal of interest than the speed of their replies. Then you can turn this into a tool as well. You could create a "flirting probability analyzer" that analyzes text messages to identify signs of interest, red flags, and the potential for a relationship. You can then offer this

22:42for free or at a low cost to attract clients for consultations or lectures. And here is an idea I’ve thought about before. When I was taking design lessons a while back, my teacher suggested I get tested for ADHD. I actually went to get tested, and it turned out I do have ADHD. So, I had this thought. "Aren't there common sticking points for people like me with ADHD when we work?" If so, you could gather all the data, like posts from relevant communities or personal work

23:07experiences, and feed them into an AI. "Analyze cases where illustrators with ADHD fail to finish their work or take too long, and find the one biggest bottleneck preventing them from completing their tasks." Let’s say the result shows that getting too obsessed with the details of one part prevents them from moving to the next stage. Instead of creating a massive productivity app for ADHD, you would make a plugin that displays how much time you've spent on a task. "You have been editing the skin shading layer for 37 minutes.""It's time to move on to the next step." It would notify you like this. Ultimately, it’s about finding an existing answer key to

23:39achieve your higher-level goal, and using that to identify the one most important thing. This is exactly what a magnifying glass is. Now, there is one very important twist to the statement that "the person who creates this magnifying glass will survive." Before I tell you that twist, let me summarize what we've covered so far in under a minute. If you’ve watched the video up to this point, I would appreciate it if you could hit the subscribe and like buttons. We started with the question: "We’ve gotten better at using AI, so why isn't my value increasing?" Through the pure gold ratio, we saw that AI has raised the standard of "doing well" so

24:08high that "being good" is no longer enough; "being damn good" is what now holds value. Through the sun and magnifying glass analogy, we also learned that "being damn good" means focusing the power of AI onto a single point. And that magnifying glass requires three things. First, a higher-level goal. Before achieving this goal, you ask yourself once more why you are doing it. And second, an answer key. Instead of vaguely asking AI to do a good job, you need to feed it data that has actually been graded in reality. What worked and what failed . You gather records that have already been graded to show it what the correct

24:37answers are. The third is the core variable. Among the countless factors that separate the correct answers from the wrong ones in these answer sheets, you find the single most powerful one. Ultimately, if I had to boil down everything I've said so far into one sentence, it is this. What we need to grasp in the AI era is not a new AI, a new tool, or a new prompt. It is the ability to define your goal, collect real-world answer sheets that achieved that goal, find the one single factor that guarantees success based on those sheets, and pour all of AI’s power into it. This is exactly the ability to

25:05create a magnifying glass. AI will continue to change in the future. The tool you learned today might disappear next year, and a feature you struggled to learn might become just a single button a few months from now. So, trying to memorize every single sun might be madness. However, I believe that even if the sun keeps changing, those who know where to focus the light

25:22to start a fire will always survive. Not a person who learns AI, but a person who makes a magnifying glass that focuses the power of AI onto a single point. Wouldn't becoming such a person be the most important thing? But this is not the end. The people who truly make money, survive, and get recognized for their value are those who build not just the magnifying glass , but the stand for it as well. What I mean is, for example, someone who has done CS consulting for over 10 years can make a magnifying glass. They have all the know-how in their head about how to deal with difficult callers and

25:47what to say. But the person who is truly recognized and makes a lot of money isn't that CS agent, but the person who trains a system with all the standards that agent created and sells it as a complete package to companies. Because people don't ultimately want to buy our magnifying glass itself, they want to buy the fire created by that magnifying glass. So, if you have defined your own problem, accumulated answer sheets, and built a magnifying glass, don't stop at personal know-how —turn it into a complete service where someone can pay and get results immediately. From here on, this is just my own opinion. It seems that until now

26:21, humans have lived to solve the "how." How can we eat better and live better? How can we get into a better job? How can we make things faster? And those who knew that "how" well became experts and were recognized for high value. But with the advent of AI, AI is taking all of that "how" away. How to write, how to code, how to analyze data. Then, two questions remain for humans. What to do , and why to do it. So, I think this AI era is posing this question to us. "Now

26:52, you can solve most problems with AI." "Then, what kind of problem do you want to solve?" That is why I believe this question will become truly important. What is the problem I must solve before I die? This becomes your highest-level goal. And only when that high-level goal is clear can you define what is correct and what is incorrect. For example, just saying "be a good person" is too broad. But if your goal is to " create a service that keeps elderly people living alone from feeling lonely ," every action that prevents their loneliness becomes part of your answer sheet. And as those answers pile up, a

27:27single core variable that inevitably leads to achieving that goal emerges. And training an AI on that one core variable to explode your problem-solving power was the concept of the magnifying glass I’ve been talking about. Actually, I also have one problem I really want to solve before I die. Something like clearing up those ambiguous things in our daily lives. Going beyond that, I want to be someone who finds that one single thing that explains this "real-life game" most simply and expresses it in one sentence. Of course, it might be nonsensical, impossible, or just chasing clouds. But I am running this channel to solve that "chasing clouds" problem of my own. And I think the AI

28:08era has opened an age where we can actually catch those clouds. So, I want to solve those problems one by one together with you all. I hope this channel becomes a place where we find problems worth solving together and build up that answer sheet, rather than just a channel that gives out answers. I think it would be great if you could think about this question before you go to sleep tonight. What is the problem I must solve before I die? Thank you for

28:36watching the video.