https://www.youtube.com/watch?v=9q5ojtkqsBs
How to Win With AI in 2026
채널: Alex Hormozi | 길이: 24:18
자막
@loop
@[00:00] Wake up.
@[00:03] AI is here.
@[00:05] OpenAI moment happened over President's Day weekend, and it started a month earlier, and it was already acquired for a billion dollars by OpenAI.
@[00:07] If you're not paying attention to this, then you will be left behind.
@[00:10] That being said, I am not here
@[00:15] to fearmonger.
@[00:18] I'm here to try and prepare you for what I think is going to be the biggest shift that's going to happen in Main Street, not just the tech businesses.
@[00:20] If you're still even doubting this at all, this kind of like news flash for you, is that AI will
@[00:26] never be worse than it is right now.
@[00:30] And if you assume any rate of improvement over any reasonable time period, learning how to use AI should become your number one priority, your number two priority, number three priority, and your number 10 priority.
@[00:34] And so that's
@[00:40] why in this video, I'm going to share some ways to think about AI and use cases right now that you can deploy today or by the end of this video to make significant changes in your business or start one or how you work within a larger business because I'll show you how to safeguard your role
@[00:54] there too, 'cause I think it's important.
@[00:56] This also for my team.
@[01:02] So, there's never been a better time to start an AI-first business to disrupt an existing market because all the people in that existing market are so busy running their business rather than learning AI and using words like AI-first rather
@[01:11] than actually being AI-first.
@[01:14] And so, the advantage that you have if you're starting out is that you have time.
@[01:17] But the thing is that every single skill you're able to stack into your ability to use AI is going to give you disproportionate leverage over your competitors.
@[01:20] And so having started some
@[01:26] companies in this period of time that I'm not as public about, we have companies that revenue per employee, we're talking about in the millions per year per head, because day one we started that way because as much as people want to say they're AI-first, if you have a
@[01:38] big organizational chart of people, it's very hard, one, to get people to do stuff that's new and uncomfortable, which technology typically is, and then also because people aren't willing to make the hard conversations of saying, "Hey, uh, we automated away this role, now what?"
@[01:50] People then think is, "Oh well, let's find something else for Danny to do," but the thing is that I would encourage you to raise the bar for the whole company, and the people who can meet that new bar get to stay, and the people who don't.
@[01:56] And I'm sorry, and I
@[02:03] know that's ugly and that's harsh, but like, this reality, right?
@[02:07] And like there was basically, let me, I'll play a little thing by Jerome Powell.
@[02:11] He just said this I think yesterday, two days ago, um, about how there was zero jobs growth in the private sector.
@[02:18] >> Effectively, there's zero net job creation in the private sector.
@[02:21] >> Think about how weird that is.
@[02:22] It's not that the economy is not doing well.
@[02:23] It's that people are automating jobs away.
@[02:26] All right.
@[02:29] Now, again, this video is not to scare you, but this video hopefully at least motivates you to take some action because I think that it won't happen and then it will happen very quickly.
@[02:32] So, in game theory, the most flexible system survives.
@[02:35] And so, it's
@[02:40] very much like business Darwinism, if you will, which is that it's the people who can adapt are the ones who survive.
@[02:43] It's not the strongest or the smartest, people are the most adaptable.
@[02:46] And so that means that when there's a change in the environment right now, you have to change.
@[02:49] You have
@[02:54] to adapt.
@[02:57] And learning how to use the tools is the first and best way that you can do it.
@[03:00] For those of you who are scared to use AI, first off, get over it.
@[03:03] But beyond that, you often risk more by not adopting new technology than by fearing the potential downside that a
@[03:07] small percentage of people experience.
@[03:09] So some people like, "What about AI safety and all these different things, like I'm worried that AI is going to take my credit card and go spend it."
@[03:13] Of course, there's weird, you know, edge cases where, you know, someone gave too many permissions to an agent and
@[03:18] they didn't have enough guardrails installed.
@[03:21] But it's like saying, "I got hacked on the internet, so I should never use the internet again."
@[03:23] It's not good reasoning, right?
@[03:23] And so, why don't more people adopt AI?
@[03:26] Well, the reason is more like complacency, right?
@[03:29] There's
@[03:34] a short-term cost that you have to incur in order to learn a new thing.
@[03:35] Period.
@[03:38] So, it's just like training an employee.
@[03:41] If you're thinking to yourself, "Well, I don't want to train this employee because it's going to take me time to do that when I could be doing the work."
@[03:47] It's like, yeah, but as soon as you train them, then they can do the work forever, right?
@[03:48] It makes sense to do it.
@[03:51] But when you think too short-term, which most humans do, then you end up losing to people who can think even a little bit more long-term.
@[03:54] And I will, I will repeat this tweet that I've said before because I think it's so relevant right now.
@[03:57] It takes about 20 hours to become
@[04:02] proficient in any new skill.
@[04:06] But people delay the first hour decades.
@[04:09] And so I promise you, if you take a weekend and say, "Saturday, Sunday, I'm going to sit in front of this computer and I'm going to figure out how to do the—I'm going to figure out how to get an agent to do"
@[04:13] something for me.
@[04:19] All right, if by the end of the weekend you haven't completely built something, but you actually tore the, you know, you tore the wrapper off, you actually got your hands in it, your understanding of it will increase more than any amount of articles that you can read that are
@[04:25] fear-mongering and baiting you in, right?
@[04:28] That are just getting for views and impressions.
@[04:31] Let's shift to like what this actually looks like within an organization, whether you're working at one or you're leading one or you own one, is that you have to stop thinking in
@[04:36] role-based thinking and start thinking in workflow-based thinking.
@[04:40] So let's break this down tactically.
@[04:44] For every hire that you're considering, you want to write down the four to six things, or eight things, or 10 things that person actually does with their
@[04:51] hands and their eyes and like, in their mouth does stuff.
@[04:54] All right?
@[04:56] And then ask whether each of those activities could live inside of a workflow instead of headcount.
@[04:58] And so the old thinking or the old paradigm is, "I need to hire an editor."
@[05:01] The new thinking or
@[05:06] paradigm is, "What are these five things an editor actually does that creates a video?" And each one of those things should be a workflow.
@[05:09] So let me just give you a visual to kind of drive this home.
@[05:12] So let's say that you have an organization that looks like this.
@[05:16] Okay, very simple.
@[05:22] Each of these roles has tasks underneath of them, or at least they should, right?
@[05:27] Of course they should.
@[05:30] But all of these is to organize humans, not organize the inputs and outputs.
@[05:33] Because in an organization, if we were to do something perfectly, we would have it much more like a manufacturing business.
@[05:36] Now, what does that mean?
@[05:38] Every business at its
@[05:43] most basic level takes raw inputs, adds some special sauce, and then you get an output that's more valuable.
@[05:50] And so in a service business, it means you take raw talent, you add training and skills, or you put multiple of these skills together that when those skills taken in
@[05:57] aggregate are worth more than any of the individuals on their own, right?
@[06:01] So if you've got somebody who knows how to write, somebody knows how to read, somebody knows how to record, somebody knows how to edit, you can put all that together, all of a sudden you have an
@[06:06] advertising agency. And somebody else who can buy ads, great.
@[06:09] You have an advertising agency, right?
@[06:12] And so, but the thing is that this, our organizational structure is to organize communication between humans and hierarchy for decision-making.
@[06:15] But if you had the same rules from the beginning of how
@[06:20] everything should be created, then all of these little dashes that I have underneath of here, which are the tasks that someone does, should just be organized in a linear fashion that then create an output.
@[06:27] And so the key is not saying, "I'm going to, I'm going to, I'm
@[06:35] going to automate away this person."
@[06:37] You just have to look at one layer underneath of that and say, "What are the 10 things this person does? Let me see if I can just automate this one task and this next task."
@[06:43] And if you are that person, if you're not automating your
@[06:47] own job, you are missing the boat here.
@[06:49] Like, I was talking to a good friend of mine who is a very good entrepreneur last night, and he spun up a division within his company and the sole thesis or mission of that business is to put his much larger business out of business.
@[06:56] And so if you're not thinking about that same level of like, "Okay,"
@[07:04] "well, I'm going to take 20% of my time to try and put myself out of a job."
@[07:08] Because the thing is, if you don't adapt, you will eventually be out of a job.
@[07:11] The question is whether you're going to be the one who controls that automation or somebody else will.
@[07:14] And so let me talk about what the future of business is going to look like at least
@[07:19] in the medium term.
@[07:22] The medium term is going to be BYOS.
@[07:25] So what does that mean?
@[07:28] It's going to be bring your own software or BYO—bring your own agent or agents.
@[07:31] And so when you approach a business—and this means that also there's going to be tremendous earning
@[07:36] power even at the employee level, which is, you know, on by the gurus of the internet—but if I can approach a business and say, "I am your entire marketing department," and so famously, maybe we post it up here, Anthropic has one person in their marketing department.
@[07:41] How is that possible?
@[07:46] Now,
@[07:54] of course, they get tons of PR.
@[07:56] There's other things that I think help them out, but the big point here is that they've got one guy who's doing it.
@[07:58] Whether that's just marketing lingo or not, we can be sure that one person is still doing a ton of stuff.
@[08:00] All right?
@[08:04] And it's not really that person's doing a ton of stuff.
@[08:07] They have automated and created agents that do a lot of that work for them.
@[08:09] And so, all of a sudden, if you think about the marketing spend that a business would allocate for an entire department of people to get an output.
@[08:12] If you can get
@[08:16] that output because of agents that you've trained on your way of doing things, then you become very valuable.
@[08:20] Now, can you do that as a contractor and start an agency around it?
@[08:22] For sure.
@[08:23] Can you do it because you want to embed within a company and get a slice of equity?
@[08:25] Sure.
@[08:26] Can you do it just because you want to get paid more cash?
@[08:27] All of these are things that are available to
@[08:30] you, but they have never been available until now.
@[08:35] Think about what businesses need in terms of functions and outputs and just erase the title-ism that exists in the private market because I do not think it's going to survive.
@[08:39] And so if you are that employer or that
@[08:45] entrepreneur and you're like, "Okay, well, I want to, I want to do this stuff, right?"
@[08:47] "I want to actually like use AI. I want to have agents that do work for me."
@[08:49] Where people fall off is that they're not training AI the way they would train a new employee.
@[08:54] And so
@[08:58] they have an—they have the—I was going to say employee—they have the agent do something and the output goes back and it sucks and they're like, "Oh, this will never work again."
@[09:00] I will remind you: this is the worst that it will ever be.
@[09:02] Number one.
@[09:04] Number two, if you
@[09:10] had a brand new employee and then you said, "Do this task for me," and then they gave something back to you, would you immediately fire them?
@[09:12] Probably not.
@[09:17] You'd be like, "Oh, I just need to train you more."
@[09:17] And if you think, "Oh, well, uh, you know, AI can't do what humans can do," I really want to break the—I want to, I want to destroy this for the people who don't get it.
@[09:25] And if you do get it, then maybe you need to send this
@[09:30] to your employees, your team, because this is real.
@[09:31] Humans learn through reinforcement.
@[09:33] Meaning, you do a thing, you get an outcome, good or bad.
@[09:35] If it's good, you do more of it.
@[09:36] If it's bad, you do less of it.
@[09:38] That's how humans learn.
@[09:40] Period.
@[09:41] And so when someone says,
@[09:45] "Ah, but you know, this person has such good taste," it means that they recognized a pattern, they communicated that pattern, and they were rewarded for doing that.
@[09:50] And so they do more and more of it.
@[09:54] And so they get better and better at recognizing patterns.
@[10:00] Guess what's really good at recognizing patterns, even better than humans?
@[10:03] Computers, right?
@[10:05] And so fundamentally, you train a computer the way you should train a human.
@[10:08] The reality is that most people don't train humans like they should train computers.
@[10:10] And as a result, they're bad at training.
@[10:12] But one of the things that if you've ever followed my
@[10:16] channel at all, I'm a big believer in thinking through operations, thinking through observable behaviors, right?
@[10:21] And so this has actually been an amazing translation for my skill sets into training AI because if you take all of the emotional words out of it, right?
@[10:29] Take all of the ephemeral, take all of the intangible out of all of your words—charisma, make it lighter, make like all of these words that people use—and just say like, "What do you want to have happen?"
@[10:36] Which most people do not do because they do not define what good
@[10:44] looks like.
@[10:50] If you can actually take the time to define what you actually want rather than expecting the other person to guess and somehow get it right, or have the agent to guess and get it right—which is really what we're doing—then all of a sudden you'll be
@[10:57] able to be so much better as an AI trainer, which is fundamentally where you're going to be, so that they can actually do the work that you want them to do and do it at 100 times the speed, um, with no complaints and at a hundredth of the cost.
@[11:03] And so I'll give you, I'll give
@[11:11] you an example.
@[11:14] If you're like, "Hey, I want you," like this is a very simple example because most people can understand this use case, which is like, "Hey, I want you to write, uh, some copy for me," right?
@[11:17] So it's like, "Hey, write this email copy for me." If they
@[11:22] write copy and it sounds like AI slop, it's usually because you didn't give anything to it besides write words that are English and correct and make it sound like the internet, which is fundamentally what AI sounds like.
@[11:26] It was trained on the internet, right?
@[11:29] And
@[11:34] so it's not the best writing.
@[11:38] But if you say, "Hey, here is 12 rules that you can never break, and here's 16 writing samples of mine, and I want you to write only according to this," you're going to probably get an output that's probably like five times as good as that.
@[11:41] Now, if
@[11:46] you repeat that loop a hundred more times, all of a sudden, you'll have an output that's perfectly trained on the patterns.
@[11:52] Except with a person, they forget some of the things you said 16 times ago, um, and it takes them time to go type it or time to go, uh, you
@[12:00] know, learn that feedback cycle.
@[12:02] And those 100 cycles might take you a year and a half with a person, but it can take you 100 minutes with AI.
@[12:04] Some of you are not using AI at all.
@[12:05] Some of you guys are AI laggards and are like, "I don't need it."
@[12:07] "None's ever going to
@[12:10] replace humans."
@[12:12] And good for you.
@[12:13] I love that for you.
@[12:15] Um, it'll make it easier to beat you, but, you know, do you.
@[12:16] There are people today that use fax machines.
@[12:21] There are people today who still count on their fingers.
@[12:24] It doesn't mean that it makes them more likely to compete.
@[12:26] It means that they are competing and still winning with a disadvantage, which means that they have to be so much better in other arenas, right?
@[12:29] And so it would be
@[12:35] like not getting on the internet for your company.
@[12:36] Are there companies right now that do not have websites and make money?
@[12:38] Absolutely.
@[12:42] Do they make as much as they could?
@[12:43] Probably not.
@[12:46] And so this is an absolute promise to you, is that throughout all of human history, humans plus superior technology beat humans with inferior technology.
@[12:49] It worked from the Stone Age to the Bronze Age.
@[12:52] It worked from the Bronze Age to the Iron Age.
@[12:55] It worked
@[12:59] from the Iron Age to the Titanium Age, right?
@[13:03] Or the aluminium, which I think my Aussies say.
@[13:06] Um, to what you can see it very naturally.
@[13:10] And so also I want to give this as a little bit—like a tiny bit of stress relaxation for you.
@[13:13] As long as it is humans plus tools against humans with
@[13:22] other tools, then you are still competing against humans.
@[13:24] And as long as that game still goes, you should feel endlessly confident.
@[13:27] The day that you try to beat the machine, you will lose.
@[13:31] And every time we've tried to say, "We like machines will never beat us at chess, machines will never beat us at Go, machines will never beat us at insert X," they always do.
@[13:36] Uh, just like autopilot on planes, everyone is very against it.
@[13:40] Now, there's going to be pushback on a lot of AI stuff, but not
@[13:46] because of its function, but because of people's emotions around it.
@[13:50] Real quick, I'm going to show you the exact 10-stage roadmap from zero to 100 million plus that less than 1% of companies finish.
@[13:56] I've now done multiple times.
@[13:58] And so, I can say with a lot of confidence that these are the stages as headcount increases that you need to get through.
@[14:03] And I broke each of these down by eight different functions of the business.
@[14:06] What the constraint feels like, what are the symptoms of it when you're going through it, and then what steps we actually took to graduate.
@[14:09] And we've done this across software, physical products, uh, service businesses, brick and mortar, all of this, and it works.
@[14:12] And it's my gift to you.
@[14:15] It's
@[14:20] absolutely free.
@[14:21] And so the link's in the description, but you just go acquisition.com/roadmap.
@[14:24] Just enter your info and it'll spit right back to you.
@[14:28] All free.
@[14:33] Now, in a world of infinite AI, labor, and intelligence, where the cost of intelligence and labor go to functionally zero, rather the cost of energy, the last valuable thing that a human will get paid to do will be to
@[14:40] take risk.
@[14:41] And so that is something that you incur that no one else can really take away from you.
@[14:43] Which is why, like, I think like money will exist in the future.
@[14:45] It's just that labor won't have value.
@[14:47] And that's where this gets difficult.
@[14:49] So it'll be more and more difficult to provide value to a
@[14:52] marketplace when your self labor, your inherent work no longer has value when there's a robot that has infinite intelligence inside of it.
@[14:54] It's stronger, it's faster, and it works for $200.
@[14:56] It works for the price of the electricity that runs it.
@[14:59] Very hard.
@[15:01] And
@[15:06] this, again, not to scare you, but to prepare you for what is going to come.
@[15:12] And so if you're on Main Street right now and you're thinking, "Oh, like every company is going to become a technology company," you wouldn't think of yourself as a technology company today, but it's like, well, do you use social media?
@[15:15] Do you use the internet?
@[15:19] Do you use email?
@[15:22] Do
@[15:28] you use phone?
@[15:30] These are all components of technology that you integrated into your business.
@[15:33] And I would consider this the last bastion of where humans play that role.
@[15:35] Now obviously GDP is in gross domestic product, gross domestic product.
@[15:37] The amount that that companies
@[15:41] have made per headcount has continued to go up.
@[15:42] If we look at the economy, what are the two factors that drive output?
@[15:46] It's education, aka skills, and technology, right?
@[15:49] And so when you have infinite labor with infinite intelligence, there's going to be a big explosion in GDP, or gross domestic product.
@[15:53] There's going to be more companies than ever before.
@[15:56] And I also think that when many roles are going to
@[16:02] get automated away, the amount of businesses that will bloom from this will be huge.
@[16:06] Um, but I will not say that like I know, and I don't think anyone does know, but I'd like, what are the few things that I can bet on, right?
@[16:14] I would, like, I believe in a barbell strategy for approaching the future.
@[16:16] So what does that mean?
@[16:18] On one extreme, this is the high risk, high reward.
@[16:22] This is, "I'm fully incorporating AI in all my stuff."
@[16:25] All my businesses are going to be AI-first, AI-native, AI-forward.
@[16:27] I have to be willing to have the hard conversations with teams to get them to level up.
@[16:30] And if they don't, I have to have a hard
@[16:34] conversation that we no longer need them because we've automated away the role.
@[16:37] I have to be willing to do that because guess what?
@[16:40] You might not be willing to do that, but there's going to be a startup that just doesn't have to have that conversation, that is going to
@[16:44] already be automating those roles, and they will beat you.
@[16:48] That's the high risk, high reward side.
@[16:51] On the other side, it's, "What are the few bets"—and this is a Jeff Bezos frame—"which is what are the few bets that I can make that I believe won't change?"
@[16:59] What things will absolutely still exist?
@[17:02] I believe that humans will still have bodies, at least in the near, medium term.
@[17:05] So I think health-related things will absolutely exist.
@[17:10] So healthcare, fitness-related stuff, all that stuff—consumables, food, supplements—all that stuff will still exist.
@[17:13] I think in a world where there's way more robots and way more work getting done for humans,
@[17:16] what will humans have?
@[17:19] If we look at human history
@[17:24] and we have more of one thing, which is leisure or downtime.
@[17:26] So what do we fill our leisure or downtime with?
@[17:30] Entertainment.
@[17:32] I believe entertainment is going to boom.
@[17:34] It's already big.
@[17:35] It's gone—
@[17:37] it's continued to grow as a percentage of GDP, and I think it will explode because I think people have more time on their hands, and entertainment is typically very cheap.
@[17:39] And so if you want
@[17:43] to like—you can make a full motion picture now—and there's this period of time where the prices have not adjusted to the cost basis associated.
@[17:49] And so you can make some viral social media videos and get huge amount of PR for a movie that you made entirely with AI and make $100 million,
@[18:00] $200 million.
@[18:01] And the beauty of that is it's all margin, and you can absolutely do that.
@[18:03] So why don't people do it?
@[18:07] Because people are afraid to take action.
@[18:07] But is that, is that there?
@[18:09] Absolutely.
@[18:14] I'll also give you something that, uh, will make some people uncomfortable, but it's just an absolute reality of business in general, is that if you actually really want to know what the bleeding edge of like what's going to happen with anything is, in terms of
@[18:20] tech as it incorporates in business, I know this is going to sound—at least going to sound a little bit off-putting for some people—look at porn.
@[18:23] Whatever porn adopts first eventually makes its way down.
@[18:26] So, what have we already seen happen in the porn industry?
@[18:30] You've got AI avatar
@[18:36] girls who are, you know, people are spinning up these AI avatars.
@[18:38] They have an army of 100 girls or guys, I don't know, whatever your flavor is, right?
@[18:40] Or aliens.
@[18:42] I mean, again, whatever you want.
@[18:44] Um, where people—and I'm not saying good or bad, I'm just saying pure
@[18:47] economics, right?
@[18:51] Where they don't have to deal with the drama associated with— they don't have to film them.
@[18:55] They don't have to give— they just make videos, they render them, they send them out, they have chats that is really just a chatbot that's going back and forth, someone
@[19:00] that's been trained on 10,000 porn-related conversations, and people pay for them.
@[19:03] And so what I believe is going to happen in the future: I believe that humans will need a place to live.
@[19:06] I believe they will need food to eat.
@[19:10] I believe that they will have things they need to do with their time for
@[19:14] entertainment.
@[19:16] I believe those are industries that I would absolutely say like these things will not change.
@[19:18] Now, which one wins, who knows?
@[19:20] But those industries I think will exist.
@[19:21] And then I'll give you my hell in a handbasket frame.
@[19:23] So what does that mean?
@[19:28] If there's a world where everything goes to then it doesn't really matter.
@[19:33] Right.
@[19:39] And so I have to walk myself back off of the apocalyptic angle of like, "Okay, well, if all of these things eventually, you know, disrupt and there's no—and there's a permanent underclass and no one has any money, uh, and there's, you know, only a select few
@[19:47] who actually leaned into technology who acquire all the wealth in the world."
@[19:50] Um, if that were to happen, you know, it's all going to be anarchy.
@[19:53] Maybe, I don't know, but I just prefer to exist in a world where, you know, I'm hoping for the best, preparing for the worst.
@[19:57] Um,
@[20:02] but I am absolutely hoping for the best.
@[20:02] And I think in the hell in a handbasket world, none of it will matter.
@[20:04] Prepare for sunshine and rain.
@[20:07] Just be an all-weather type of person.
@[20:10] And I think if you do that, you'll give yourself the best chance at succeeding whatever the
@[20:14] next season looks like.
@[20:16] And I'll give you a final kind of thought experiment that might be helpful for you.
@[20:18] So, I read this on a Brian Johnson post from Blueprint.
@[20:21] If you've heard of him, he's the longevity guy.
@[20:23] He said, "What people do not realize
@[20:27] is that they've been training their entire lives to swim."
@[20:31] And you think that you can swim in all types of weather and you get to become a better and better swimmer, right?
@[20:34] And you know there's waves crashing and you're like, no—you go from in a pool or a lap pool to a
@[20:40] lake to eventually you're swimming off the coast to eventually you're free ocean swimming, like from border country to the next border country, right?
@[20:45] Some crazy levels.
@[20:49] He said, "But the change that's about to happen is a phase shift where you're swimming, but then all of a sudden the
@[20:55] water boils and now you're in gas because the water all evaporated and you're flapping your arms and it doesn't matter how good of a swimmer you are, the fundamental physics of the environment will have changed."
@[21:01] And so that is the change that we're on the precipice of.
@[21:01] Now, hopefully this was
@[21:09] not doom and gloom because I actually see this tremendous opportunity because humans are slow to adapt in general.
@[21:12] So what does that mean?
@[21:14] First off, if you're watching this, you're already probably ahead of most people anyways, 'cause most people just exist.
@[21:17] You
@[21:21] put their head in the dirt.
@[21:22] There's so many people over age 50 that are like, "I'm too old for this."
@[21:26] And guess what?
@[21:30] They got all the money.
@[21:30] And so, what's really interesting is that all the people got all the money are going to quickly realize that it's going to go somewhere else, which is not to
@[21:35] them, because they did not adapt.
@[21:37] So, that's a big opportunity.
@[21:40] There's also huge opportunities obviously in going to regular businesses and automating portions of their work.
@[21:42] But when I said humans are slow to adapt, it also means their price sensitivity is also slow to
@[21:46] adapt.
@[21:49] Meaning, if people are used to paying $2,000 a month for something, that embedded in that price is the typical cost of labor.
@[21:53] And so, if you can still charge that $2,000 price for whatever it is, right,
@[21:57] and instead of it costing you $500 a month, it costs you
@[22:03] $50 a month or $5 a month,
@[22:07] the margin number one that you can earn is tremendous.
@[22:11] But second, and more importantly, the amount of operational leverage you have—meaning the amount of people per dollar of additional revenue you need to create—goes down dramatically, as in one person now can
@[22:18] bring millions and millions in revenue, and it becomes much easier to scale because one of the biggest costs of scaling is just the coordination between humans.
@[22:26] And so what I would encourage you to do, so you're like, "Okay, I heard all this stuff, I feel motivated to do something."
@[22:32] "What should I do?"
@[22:34] This is what I motivate you to do.
@[22:35] Write down a list of what you do every day.
@[22:37] And I'm saying at the most granular level.
@[22:38] You respond to emails.
@[22:40] You respond to Slack messages.
@[22:41] Maybe you make content.
@[22:43] Maybe you make ads.
@[22:44] You run ads.
@[22:46] There's—you have to separate these out into
@[22:49] individual—what's the task, right?
@[22:51] Don't think it, don't think chunked up.
@[22:52] Don't think, "I make," you know, "I run ads."
@[22:54] It's like, yeah, there's a lot of stuff underneath of that, right?
@[22:57] You make campaigns, you set budgets, you analyze results, you make the creative, you write copy.
@[22:59] You test different landing pages.
@[23:01] You test different headlines on the pages.
@[23:05] There's lots of things that are underneath of that bucketed term of like, "I run ads," right?
@[23:10] Take all of the tasks and then look at those tasks and take the first one and put it into AI and say, "Help me automate this.
@[23:14] What steps would you take?"
@[23:21] It'll give you a list.
@[23:21] And then take the first thing on the list and do it.
@[23:23] And if you get stuck, here's a little pro tip.
@[23:26] Screenshot your screen and put it in and say, "What do I do now?"
@[23:28] And it'll tell you.
@[23:28] And then screenshot the next screen and say, "What do I do now?"
@[23:33] And it'll tell you.
@[23:33] Like everyone.
@[23:36] Now, here's the craziest thing of all is everyone has an AI tutor at their fingertips that you're just not using.
@[23:41] So, with that, this is the type of stuff that we, uh, that we're obviously actively working on inside of our ACQ
@[23:47] Vantage community.
@[23:49] So, if you are a million-dollar-plus business owner, we're talking about the stuff that we're doing right now every day inside of, uh, ACQ.
@[23:51] Um, and obviously we have an AI product, uh, that we've been training for years now.
@[23:53] Uh, we also have like AI
@[23:57] salesman that we've been training.
@[24:00] We've got a lot of stuff that we've been doing, um, and keeping more or less behind, you know, behind closed doors, if you will, capitalizing on opportunity, if you will.
@[24:04] Uh, but you can go check that out.
@[24:06] I'll have a link below the thing.
@[24:08] Maybe it's on the screen, whatever.
@[24:10] Um, but with that, uh, peace and blessings be upon you and your bloodline.
@[24:12] Uh, and I wish you the absolute best, and I hope you are in the permanent upper class, uh, and not
@[24:16] permanent underclass in the world that comes.
@[00:00] 일어나세요.
@[00:03] AI가 왔습니다.
@[00:05] ‘오픈AI 모먼트’가 대통령의 날 주말에 터졌고, 한 달 전부터 시작됐으며, 이미 오픈AI가 10억 달러에 인수했습니다.
@[00:07] 이걸 주의 깊게 보지 않으면 뒤처질 겁니다.
@[00:10] 그렇다고 해서 제가 겁주려고 온 건 아닙니다.
@[00:15] 저는 여러분을 준비시키려 왔습니다. 제 생각에 메인스트리트(일반 자영업/중소기업)에서, 기술 기업만이 아니라 전반에서 일어날 가장 큰 변화가 될 겁니다.
@[00:20] 아직도 의심한다면, 뉴스 플래시 하나: AI는
@[00:26] 지금보다 더 나빠지지 않습니다.
@[00:30] 합리적인 기간 동안 어떤 개선 속도든 가정하면, AI 사용법을 배우는 건 여러분의 1순위, 2순위, 3순위, 그리고 10순위 우선과제가 되어야 합니다.
@[00:34] 그래서
@[00:40] 이 영상에서 저는 AI를 바라보는 방식과, 지금 당장—아니면 영상을 다 보기 전까지라도—여러분의 비즈니스를 크게 바꾸거나 새로 시작하거나, 큰 조직 안에서 일하는 방식에 변화를 줄 수 있는 활용법들을 공유하겠습니다. 여러분이 그 안에서 역할을 지키는 방법도 보여드릴게요. 중요하다고 생각합니다.
@[00:56] 이건 제 팀에게도 해당됩니다.
@[01:02] 지금은 기존 시장을 뒤흔들 AI-퍼스트 비즈니스를 시작하기에 가장 좋은 때입니다. 기존 시장의 사람들은 AI를 배우고 쓰기보다 비즈니스 운영에 바쁘고, ‘AI-퍼스트’라는 말만 쓰지 실제로 AI-퍼스트가 아니기 때문이죠.
@[01:14] 시작하는 사람의 장점은 시간이 있다는 겁니다.
@[01:17] 그리고 AI를 다루는 능력 위에 쌓을 수 있는 모든 스킬 스택은 경쟁자 대비 불균형한 레버리지를 줍니다.
@[01:20] 저는 이 시기에 (공개적으로 말하진 않았지만) 몇몇 회사를 시작했는데, 직원 1인당 매출이 연간 수백만 달러 수준인 곳들도 있습니다. 첫날부터 그렇게 설계했기 때문이죠.
@[01:38] 조직도가 큰 회사는 실제로 AI-퍼스트가 되기 어렵습니다. (1) 사람들에게 새롭고 불편한 일을 하게 만드는 게 어렵고, 기술이란 대개 그렇고요. (2) “이 역할은 자동화됐으니 이제 어떻게 하지?” 같은 어려운 대화를 하기 싫어하기 때문입니다.
@[01:50] 사람들은 “그럼 대니에게 다른 일을 시키면 되지”라고 생각하지만, 저는 회사 전체의 기준을 올리라고 권합니다. 새 기준을 충족하는 사람은 남고, 못하는 사람은…
@[01:56] 미안하지만,
@[02:03] 이건 추하고 가혹해도 현실입니다.
@[02:07] 제롬 파월의 발언을 잠깐 들려드릴게요. 어제나 그제쯤 말한 건데, 민간 부문 순일자리 증가가 0이라는 내용입니다.
@[02:18] “사실상 민간 부문 순일자리 창출은 0입니다.”
@[02:21] 이상하죠? 경기가 나쁜 게 아니라,
@[02:23] 사람들이 일을 자동화로 없애고 있는 겁니다.
@[02:29] 다시 말하지만 겁주려는 게 아니라, 행동을 하게 만들고 싶습니다. 제 생각엔 ‘안 오다가’ 아주 빠르게 올 겁니다.
@[02:32] 게임이론에서 가장 유연한 시스템이 살아남습니다.
@[02:35] 즉 비즈니스 다윈주의죠. 살아남는 건 강하거나 똑똑한 사람이 아니라
@[02:43] 가장 잘 적응하는 사람입니다.
@[02:46] 환경이 바뀌면 여러분도 바뀌어야 합니다.
@[02:49] 적응해야 합니다.
@[02:57] 툴을 쓰는 법을 배우는 게 가장 첫 번째이자 최고의 방법입니다.
@[03:00] AI가 무서운 분들께: 일단, 극복하세요.
@[03:03] 그리고 새로운 기술을 안 쓰는 게, 일부가 겪을 수 있는 작은 확률의 다운사이드를 두려워하는 것보다 더 큰 위험일 때가 많습니다.
@[03:09] “AI 안전은요? 카드 훔쳐서 결제하면요?” 같은 얘기도 있죠.
@[03:13] 물론 엣지 케이스는 있습니다. 에이전트에 권한을 너무 많이 주고 가드레일이 부족하면 문제될 수 있죠.
@[03:21] 하지만 “인터넷에서 해킹 당했으니 인터넷을 절대 쓰지 말자”는 말과 같습니다. 좋은 추론이 아니죠.
@[03:23] 왜 사람들이 AI를 잘 안 쓰냐?
@[03:26] 더 큰 이유는 안일함(컴플레이슨시)입니다.
@[03:34] 새로운 걸 배우려면 단기 비용을 치러야 하니까요. 끝.
@[03:38] 직원 교육이랑 똑같습니다.
@[03:41] “교육하느라 시간이 드니 내가 그냥 할래”라고 생각하겠죠.
@[03:47] 그런데 교육하면 그 사람이 앞으로 계속 일을 해줍니다. 합리적이죠.
@[03:51] 하지만 대부분 인간은 단기적으로 생각해서, 조금만 더 장기적으로 생각하는 사람들에게 집니다.
@[03:54] 제가 예전에 올린 트윗을 반복하겠습니다. 지금 너무 중요합니다.
@[03:57] 어떤 새 스킬이든 숙련되려면 약 20시간이면 됩니다.
@[04:02] 그런데 사람들은 첫 1시간을 수십 년 미룹니다.
@[04:06] 주말 이틀만 잡고 “토/일에 앉아서 에이전트로 내 일을 하나 자동화해보겠다”라고 해보세요.
@[04:19] 주말 끝에 완성 못해도, 포장지를 뜯고 손을 담가보면, 공포 조장 기사 100개 읽는 것보다 이해도가 훨씬 올라갑니다.
@[04:31] 조직에서 실제로 필요한 변화는 ‘역할(직무) 중심’ 사고를 버리고 ‘워크플로우(업무 흐름) 중심’ 사고를 하는 겁니다.
@[04:44] 채용을 고려할 때, 그 사람이 실제로 하는 일을 4~6개(혹은 8~10개)로 적어보세요. 손과 눈과 입으로 실제로 하는 행위들 말입니다.
@[04:56] 그리고 그 활동 각각이 ‘인원’이 아니라 ‘워크플로우’로 들어갈 수 있는지 보세요.
@[04:58] 예전 패러다임: “편집자를 채용해야 해.”
@[05:01] 새 패러다임: “편집자가 영상을 만들기 위해 실제로 하는 5가지가 뭔데?” 그리고 각 항목은 워크플로우가 되어야 합니다.
@[05:09] 시각적으로 보여드릴게요. 어떤 조직이 이렇게 생겼다고 합시다(단순).
@[05:22] 각 역할 아래에 업무가 있죠(원래 있어야 합니다).
@[05:30] 그런데 이 구조는 ‘입출력’이 아니라 ‘사람’과 ‘소통/의사결정’ 계층을 조직하기 위한 겁니다.
@[05:33] 완벽히 한다면 제조업처럼 해야 합니다.
@[05:36] 무슨 뜻이냐: 모든 비즈니스는 원재료(입력)를 받아 특별한 무언가를 더해 더 가치 있는 출력물을 만듭니다.
@[05:50] 서비스업은 재능(원재료)에 훈련/스킬을 더하거나 여러 스킬을 결합해, 합산 가치가 개인의 합보다 커지게 만드는 겁니다.
@[06:01] 글쓰기/리딩/촬영/편집/광고집행을 모으면 광고대행사가 됩니다.
@[06:12] 하지만 조직도는 사람 간 커뮤니케이션과 의사결정 계층을 위해 존재합니다.
@[06:15] 처음부터 ‘무엇을 어떻게 만들지’ 규칙이 있었다면, 각 역할 밑의 작은 업무들은 선형으로 정리되어 출력물을 만들어야 합니다.
@[06:27] 핵심은 “이 사람을 자동화로 없애자”가 아닙니다. 한 레벨 아래로 내려가서 “이 사람이 하는 10가지 일을 봐라. 이거 하나씩 자동화해보자”입니다.
@[06:43] 그리고 여러분이 그 ‘사람’이라면: 자기 일을 자동화하지 않으면 큰 흐름을 놓치는 겁니다.
@[06:49] 제 친구(유능한 기업가)는 회사 안에 ‘더 큰 본업을 스스로 파괴하는’ 부서를 만들었습니다.
@[06:56] 여러분도 “시간의 20%를 써서 나 자신을 실직시키는(=자동화하는) 방법을 찾는다” 수준으로 생각해야 합니다.
@[07:08] 적응하지 않으면 결국 실직합니다. 문제는 그 자동화를 ‘내가’ 통제하느냐 ‘남이’ 통제하느냐입니다.
@[07:14] 중기적으로 비즈니스의 미래는 BYOS입니다.
@[07:22] Bring Your Own Software / Bring Your Own Agent(s).
@[07:31] 어떤 회사에 가서 “제가 마케팅 부서 전체입니다”라고 말할 수 있게 됩니다.
@[07:36] 예로 Anthropic은 마케팅 부서가 한 명뿐이라는 얘기가 있습니다. 어떻게 가능할까요?
@[08:04] 그 사람이 엄청 일을 하는 게 아니라, 많은 일을 대신 하는 에이전트를 자동화로 만들어둔 겁니다.
@[08:09] 기업이 마케팅 부서 전체에 배정하던 비용을, 여러분이 ‘자기 방식대로 훈련된 에이전트’로 같은 출력을 낼 수 있다면, 엄청 가치가 커집니다.
@[08:20] 계약자로 해서 에이전시를 만들 수도 있고, 회사에 들어가 지분을 받을 수도 있고, 현금 보상을 더 받을 수도 있습니다. 예전에는 불가능했지만 지금은 가능합니다.
@[08:35] 직함(타이틀) 집착을 지우고, 기업이 필요로 하는 기능과 출력을 보세요. 이 “타이틀-이즘”은 오래 못 갈 겁니다.
@[08:39] “AI로 일 시키면 결과가 별로라 포기”하는 이유는, AI를 새 직원처럼 훈련시키지 않기 때문입니다.
@[08:54] 에이전트에게 시켰더니 결과가 별로라서 “안 되네” 하고 끝내죠.
@[09:00] 다시 말합니다. 지금이 가장 구린 상태입니다.
@[09:04] 그리고 신입 직원에게 일을 시켰는데 결과가 별로라고 즉시 해고하나요? 보통은 “더 교육해야지”죠.
@[09:17] “AI는 인간이 하는 걸 못 해”라는 말을 부숴보겠습니다.
@[09:31] 인간은 강화학습으로 배웁니다. 행동→결과(좋/나쁨)→좋으면 반복, 나쁘면 줄임. 끝.
@[09:41] “저 사람은 취향이 좋아”는, 패턴을 인식하고 그걸 표현해 보상받았다는 뜻입니다. 그래서 더 반복하며 패턴 인식이 좋아집니다.
@[10:00] 패턴 인식에 인간보다 더 강한 건 뭘까요? 컴퓨터입니다.
@[10:05] 근본적으로 컴퓨터도 인간을 훈련시키듯 훈련하면 됩니다.
@[10:08] 사실은 대부분 사람이 인간을 컴퓨터처럼 훈련시키지 못해서 훈련을 못 합니다.
@[10:12] 저는 운영(ops) 관점, 관찰 가능한 행동에 대해 생각하는 걸 중요하게 봅니다. 감정적 단어를 빼고, “원하는 결과가 정확히 뭐야?”를 정의하는 겁니다.
@[10:36] 대부분 사람은 ‘좋은 결과’가 뭔지 정의하지 않습니다.
@[10:50] 원하는 걸 정의하는 데 시간을 쓰면, AI를 훨씬 잘 훈련할 수 있고(미래에는 그게 핵심 역량), 에이전트가 원하는 일을 100배 빠르게, 불평 없이, 100분의 1 비용으로 할 수 있습니다.
@[11:11] 예시: “이메일 카피 써줘.”
@[11:17] 결과가 AI 슬롭처럼 느껴진다면, 입력이 “영어로 맞게, 인터넷처럼”뿐이어서 그렇습니다. AI는 인터넷으로 학습했으니 인터넷처럼 쓰죠.
@[11:26]
@[11:34] 더 나은 방법: “절대 깨면 안 되는 규칙 12개” + “내 글 샘플 16개”를 주고 “이 기준으로만 작성해”라고 하세요. 결과가 5배는 좋아집니다.
@[11:41] 그리고 이 피드백 루프를 100번 돌리면 패턴에 완전히 맞춘 출력이 나옵니다.
@[11:52] 사람은 16번 전 피드백을 잊기도 하고, 작성/학습에 시간이 걸리지만, AI는 100사이클을 100분에 할 수 있습니다.
@[12:04] 어떤 분들은 아예 AI를 안 씁니다. “필요 없다”, “인간 대체 못 한다”.
@[12:12] 좋아요. 전 그게 좋습니다. 제가 이기기 쉬워지니까요.
@[12:16] 아직도 팩스를 쓰는 사람도 있고 손가락으로 세는 사람도 있습니다.
@[12:24] 그게 경쟁력이 있다는 뜻이 아니라, 불리한 조건에서도 억지로 이기고 있다는 뜻입니다. 다른 영역에서 훨씬 더 뛰어나야 하죠.
@[12:35] 회사에 인터넷을 안 쓰는 것과 같습니다. 웹사이트 없는 회사도 돈은 벌지만, 벌 수 있는 만큼 벌까요? 아마 아닙니다.
@[12:46] 인류 역사 내내, “인간+우월한 기술”이 “인간+열등한 기술”을 이겼습니다. 석기→청동기→철기… 항상 그랬습니다.
@[13:13] 중요한 건 “인간+도구 vs 인간+도구”인 동안은 여전히 인간과 경쟁하는 겁니다. 그래서 자신감을 가져도 됩니다.
@[13:27] 기계 자체를 이기려 들면 지죠. 체스, 바둑, 뭐든 결국 기계가 이겼습니다.
@[13:40] AI에도 반발이 있겠지만, 기능 때문이 아니라 감정 때문일 겁니다.
@[13:50] (홍보) 0에서 1억 달러+까지 10단계 로드맵을 무료로 공개했다… acquisition.com/roadmap…
@[14:33] 무한한 AI 노동과 지능으로 ‘지능/노동 비용’이 사실상 0에 수렴하는 세상에서, 인간이 마지막으로 돈을 받을 일은 ‘리스크를 지는 것’입니다.
@[14:40] 리스크는 누구도 여러분에게서 빼앗을 수 없습니다.
@[14:43] 그래서 미래에도 돈은 존재하겠지만, 노동의 가치가 사라질 수 있습니다. 여기서 어려움이 생깁니다.
@[14:49] 무한 지능을 가진 로봇이 $200(전기값 수준)으로 더 강하고 빠르게 일하는데, 내 노동으로 시장에 가치를 제공하기가 매우 어려워집니다.
@[15:06] 다시 말하지만 겁주려는 게 아니라 대비시키려는 겁니다.
@[15:12] “모든 회사가 기술 기업이 된다”는 말이 와닿지 않을 수 있지만, 지금도 여러분은 SNS, 인터넷, 이메일, 전화 등 기술을 통합해 쓰고 있습니다.
@[15:33] 이게 인간이 그 역할을 하는 마지막 보루일 수 있습니다.
@[15:35] 1인당 매출은 계속 올라왔고, 경제의 출력(산출)을 늘리는 건 교육(스킬)과 기술입니다.
@[15:49] 무한 노동+무한 지능이면 GDP가 폭발하고, 회사도 더 많아질 겁니다. 자동화로 역할이 사라지는 만큼, 새 비즈니스가 엄청나게 피어날 수도 있습니다.
@[16:06] 다만 무엇이 정확히 될지는 아무도 모릅니다. 그래서 ‘베팅’이 필요합니다.
@[16:14] 저는 미래를 ‘바벨 전략’으로 접근하라고 봅니다.
@[16:16] 한쪽 극단(고위험/고보상): AI를 전면 도입하고 AI-퍼스트/네이티브/포워드로 가며, 팀에게 기준을 올리고 못 따라오면 자동화로 역할이 없어졌다는 어려운 대화도 해야 합니다.
@[16:37] 내가 안 하면, 그런 대화가 필요 없는 스타트업이 이미 자동화해 나를 이깁니다.
@[16:51] 다른 쪽 극단(변하지 않을 것에 베팅): 제프 베이조스식으로 “절대 변하지 않을 것”에 베팅합니다.
@[16:59] 사람은 몸을 가질 겁니다(중기까지는). 그래서 건강 관련 산업(의료/피트니스/소비재/식품/보충제)은 남습니다.
@[17:13] 로봇이 더 많은 일을 하면 인간은 더 많은 여가를 갖게 되고, 여가를 채우는 건 엔터테인먼트입니다. 엔터테인먼트는 더 커질 겁니다.
@[17:39] 지금은 비용 구조 대비 가격이 아직 재조정되지 않은 구간이어서, AI로 영화급 콘텐츠를 만들어 바이럴을 내고 1~2억 달러를 버는 것도 가능하며, 그건 거의 전부 마진일 수 있습니다.
@[18:03] 왜 안 하냐? 행동이 무서워서입니다. 하지만 기회는 있습니다.
@[18:14] 불편할 수 있지만, 기술의 최전선을 보려면 ‘포르노’를 보라는 말이 있습니다. 포르노가 먼저 채택한 건 결국 대중으로 내려옵니다.
@[18:26] 이미 AI 아바타, 렌더링 콘텐츠, 챗봇 기반 대화 등이 돈을 받고 있습니다(선악이 아니라 경제 현실).
@[19:03] 미래에도 사람은 집이 필요하고, 음식이 필요하고, 시간을 보낼 엔터테인먼트가 필요합니다. 이런 산업은 사라지지 않을 겁니다.
@[19:21] 그리고 ‘헬 인 어 핸드배스킷(세상이 망한다) 프레임’도 있습니다.
@[19:28] 모든 게 망하는 세상이면 사실 뭐든 의미가 없어집니다.
@[19:39] “기술에 먼저 올라탄 소수가 부를 독점하고 영구적 하층계급이 생긴다” 같은 종말론이 떠오르지만,
@[19:50] 그럼 무정부상태일 수도 있고… 모르죠. 저는 “최선을 바라되 최악을 대비”하는 쪽입니다.
@[20:04] 맑은 날과 비 오는 날 모두 대비하세요. 사계절형 사람이 되세요. 그게 다음 시즌이 뭐든 성공 확률을 높입니다.
@[20:16] 마지막 사고실험: 브라이언 존슨(블루프린트)의 글에서 본 내용입니다.
@[20:23] 사람들은 평생 ‘수영’을 배우며, 점점 거친 환경에서도 수영을 잘하게 된다고 믿습니다.
@[20:40] 그런데 앞으로는 ‘상전이’가 와서, 수영하던 물이 갑자기 끓어 기체가 되어버립니다. 이제 아무리 수영을 잘해도 물리 법칙이 바뀌어 쓸모가 없습니다.
@[21:01] 우리가 직면한 변화가 그 정도라는 겁니다.
@[21:09] 하지만 저는 이걸 기회로 봅니다. 인간은 적응이 느리니까요.
@[21:14] 이 영상을 보는 것만으로도 이미 많은 사람보다 앞서 있습니다. 대부분은 그냥 삽니다.
@[21:21] 50대 이상 중 “난 늙어서 못 해” 하는 사람이 많고, 그런데 그들이 돈을 많이 갖고 있습니다.
@[21:30] 그 돈이 빠르게 다른 곳(적응한 사람)으로 이동할 겁니다. 큰 기회죠.
@[21:40] 일반 비즈니스의 업무 일부를 자동화해주는 기회도 큽니다.
@[21:42] 사람의 적응이 느리다는 건 ‘가격 민감도’ 적응도 느리다는 뜻입니다.
@[21:49] 예: 사람들이 월 $2,000을 내던 서비스는 그 가격에 노동비가 포함되어 있습니다.
@[21:53] 여러분이 여전히 $2,000을 받으면서, 비용을 $500→$50→$5로 낮추면 마진이 폭발합니다.
@[22:11] 더 중요한 건 운영 레버리지입니다. 추가 매출 1달러를 만들기 위해 필요한 사람이 줄어듭니다. 한 사람이 수백만 매출을 만들 수도 있고, 인간 간 조율 비용이 스케일링의 큰 비용인데 그게 줄어듭니다.
@[22:26] 그럼 뭘 하냐?
@[22:32] 매일 하는 일을 목록으로 적으세요. 최대한 쪼개서요.
@[22:38] 이메일 답장, 슬랙 답장, 콘텐츠 제작, 광고 제작/집행 등.
@[22:49] “광고 운영”처럼 뭉뚱그리지 말고, 캠페인 생성/예산 설정/결과 분석/크리에이티브 제작/카피 작성/랜딩 테스트/헤드라인 테스트처럼 분해하세요.
@[23:10] 그리고 첫 번째 작업을 AI에 넣고 “이걸 자동화하려면 어떤 단계가 필요해?”라고 물으세요.
@[23:21] 리스트가 나오면, 첫 번째부터 하세요.
@[23:23] 막히면 화면을 스크린샷으로 찍어 넣고 “이제 뭘 해?”라고 물으면 됩니다. 다음 화면도 또 찍어서 “이제 뭘 해?” 하세요.
@[23:33] 모두가요.
@[23:36] 가장 미친 점은, 모두가 손에 ‘AI 튜터’를 쥐고 있으면서도 안 쓴다는 겁니다.
@[23:41] (홍보) ACQ Vantage 커뮤니티… AI 세일즈맨… 내부에서 많은 걸 하고 있다…
@[24:10] …평화와 축복을… 여러분의 혈통에… 최고의 결과를 빌며… 다가올 세계에서 영구적 상층이 되길…