シャドーイング練習: If you’re trying to get rich with AI, you need to hear this… - 動画で英語スピーキングを学ぶ

AI is not gonna make you rich.

Not the tools, not the agents, not the prompts, none of it.

Whether you've made money with AI already or haven't made a single dollar with it, I need you to hear this.

I've skilled AI companies past a million dollars in under six months over and over and over again.

And I've seen exactly what makes people rich with AI and what keeps them broke.

So in this video, I'll be walking you through how to actually get rich with AI.

Point number one, stop selling the tool.

AI is just a tool.

It's like a database.

It's like an internet connection.

It's a thing.

Tools don't make people rich.

Think of it this way.

Selling AI is like a carpenter selling hammers.

Selling a hammer doesn't make him much more money.

The carpenter who gets rich has a full toolkit.

It's the person who can solve real problems by using all the tools.

Same with AI.

You don't need a tool.

You need a toolkit that you know how to activate.

And the first tool, the one to master right away is point Number two, the hammer.

The hammer is like an LLM, an AI, right?

You got Claude, you got Chad GPT, you got Gemini.

And it's cool, it's powerful, it can put a nail into this piece of wood, but it is just a tool.

The most basic and universal tool in your toolkit is the language model, but it requires you to actually know how to swing it.

Have you ever met somebody who can't even hold a hammer?

They're like, I'm gonna hit that nail.

It's like, you can't even get it to contact with the nail.

Amateurs with a hammer make more problems, create more holes, waste more resources than anything else.

A pro, have you ever seen these guys?

They like, it's almost like they have a nail in their teeth or behind their ear and they just go, wha-bang, and it's down.

Wha-bang, and it's down.

And you're like, how are you a real person?

Wha-bang, and it's down.

You're lucky I don't have a nail right now.

I put it right through this wood.

LLMs are the same.

They're very easy to use, like a hammer, but it requires manual input but it requires the mind, the fingertips to guide it, to make it do something useful.

Most people are using these LLMs like a fancy Google search to answer questions.

They type, they get answers, they copy, they put in another system and they move on.

And their output most of the time is shh.

The way you fix that is having a prompting framework and a strategy for using it.

And that's what I call the maps framework.

Every great AI prompt follows this process.

The M stands for mission, that's the first one.

So you start with the outcome, not the task, not the reason behind the task.

Give it direction.

Like, what are you trying to do?

The wrong way would be saying like, find me leads.

A better way would be, I need 30 new customers a month, every month to hit my revenue targets.

See how that becomes the mission?

The second one, which is the A, which stands for ask.

What is the task you needed to perform?

Now, this is where we get specific.

One clear request, not just like a bunch of ideas be clear as possible.

So bad would be help me with leads.

Good would be give me 40 qualified leads for my business and include their email and their cell number.

It turns out the LLMs love to solve problems for you if you're very specific about what you want.

The P in MAP stands for parameters.

That's the third one.

What is the context?

What do you know about the mission?

What do you know about the ask, right?

Is it your ideal customer profile?

It's what's worked before.

The The more you give it, the sharper the output.

So pro tip, if you've got a lot of stuff you wanna tell it, I like to use voice.

I like to hit there, you hit that little button to talk to it, and you just talk and talk and talk.

I can talk three times faster than I can type and so can you.

The S in maps number four is the shape.

What should the output look or sound like?

Tell it the format.

If you want it in a CSV spreadsheet, why would you copy and paste the output and go put it into a spreadsheet?

Just tell it.

Do you want bullet point?

Do you want markdown file?

If you ever copy and paste something from one system to another and the formatting goes away, get it to do an ASCII.

You're welcome.

Do you want the output to be conversational?

Do you want it formal?

How long do you want it?

All of those are specifics to the shape.

You can even give it a screenshot of what you want it to look like.

Maybe you've seen something you like from somebody else.

Just copy it, paste it into your chat, hit enter, and watch it do its magic.

Just follow the maps framework and watch that output come out dialed.

Now, most people ask at this point, well, Dan, what's the best LLM to use?

Well, if you're a business owner and you're looking to buy back your time using AI, I created a full document with my whole AI tech stack tailored specifically for business owners.

So if you want it, find me on Instagram and DM me the word YouTube stack and I'll send it right over.

So you now know how to use a hammer.

That's cool.

But not every problem is a nail.

Which leads me to point number three, the screwdriver.

The screwdriver is AI automation.

You think of like Cloud Cowork, you think of N8N, Zapier, make.com.

Essentially, anytime you have a task that has to happen over and over again, you use a screwdriver.

That is way different than using a hammer.

Instead of manually prompting AI, you can actually build a workflow that once you set it up,

the AI can be super smart and be on a schedule and repeat that task over and over again.

100

That's where you start getting massive leverage in your life.

101

And that is what people are willing to pay for because it solves a bigger problem.

102

And the reason why it's more like a screwdriver is because the solution is more permanent.

103

It's like if I have a screw and I need to put things together, once I screw it in, it's not coming apart.

104

Like the whole point is for it to stay together forever.

105

And once you set up an automation, it's set it and forget it.

106

Set it and forget it.

107

For example, I get a report sent to me every Friday in Slack

108

that analyzes every call and every company

109

that I'm involved in from a sales point of view that tells me how they're doing.

110

Happens every time like clockwork and it keeps me on the pulse of the revenue.

111

Now, how do you know if a task is worth automating?

112

Well, that's what I call the rule of R.

113

1. Is it repetitive?

114

Is it a task that you do at least once a week?

115

If you're doing it every week, then you might want to look at it as an opportunity to automate.

116

And if you're doing it every day, you're definitely needing to get yourself a screwdriver.

117

Number two, is it rule-based?

118

Does the task have the same set of inputs and outputs every single time?

119

The third is, does it generate a return?

120

Does it save you more time to automate it than it takes you to manually do it every time?

121

Because I'm telling you, some people are automating, they shouldn't automate.

122

Don't build something that takes 60 hours to build and automate only to save you two minutes a week.

123

If the answer is yes to all three of these Rs, then automate them.

124

Now you got a hammer and a screwdriver of AI.

125

Now this is where it gets really fun.

126

What if AI didn't just do tasks for you?

127

What if it took over entire workflows?

128

Point number four, the power drill.

129

The power drill is a gentic AI.

130

If you haven't heard of things like OpenClaw, Manus, if you've been watching my videos, you've heard of it.

131

Apex, one of my platforms, or even things like Perplexity Computer.

132

The hammer needs swinging.

133

The screwdriver, you have to turn it, right?

134

The power drill, you just point it, pull the trigger, and it does the work.

135

You're not doing any of the heavy lifting.

136

It's a power machine.

137

You're setting the direction and letting the tool take over.

138

That's the power, see what I did there, of agentic AI.

139

Agentic systems are supposed to just do work for you.

140

So if you wanna build an app that helps you with your nutrition, you just say, build me an app that helps me with my nutrition, hit enter, and then it's done.

141

Create a spreadsheet that models this scenario, enter, and it's done.

142

Real agentic systems can do multiple systems, multiple workflows all over for you automatically, and it's a workflow that's complete, not a step in a process.

143

So think about it.

144

You have like things that gotta get done, those are steps.

145

You have automation, those are steps within a process.

146

You still have to set up those automations.

147

With Agentic, you just say, here's the outcome I want and it just, it does it and you don't even know how it did it because it don't matter.

148

So I use Apex, one of our platforms, to build a new system called AgentForge.

149

It looks at a research body of all this knowledge about what's the future of AI

150

and Agentic platforms and operators and humans on the loop, all the crazy stuff I'm in.

151

And then identifies opportunities to build products.

152

Then it creates the website, creates the ad, tests the whole thing, gets leads, even gets people to pay to be bumped up in the

153

wait list to then coordinate the recruiting of the people who are going to build

154

that company and have them show up and the product validated so we can launch it into the world.

155

LLMs can't do that.

156

Automation can't do that.

157

You need an agentic system to go end to end.

158

If you want to start replacing your workflows with AI agents today, this is what you got to do.

159

It's called human on the loop.

160

Most people have heard human in the loop, which is kind of like the automation screwdriver side where you have like a bunch of different things going on, but the human's still like kind of pushing things forward.

161

Human on the loop is the complete loop is being done by an agent.

162

And then the human is just there to inspect what it expects.

163

No different than I have an employee that works for me.

164

And I'm just making sure that the thing that I ask them to do is being done right.

165

Because if you've ever had long running tasks with agents, it can start to have what's called context rot.

166

their brain stops working right.

167

So you need to show up, reset it, and maybe give it some guidance.

168

So this is how we do it.

169

Number one, pick a full workflow.

170

I dare you to take a full workflow.

171

Idea to a completed output and use that as your challenge workflow.

172

The second is go back and use the maps framework to prompt your agent, make sure it has the mission and the S, the parameters and the shape to actually get it to do what you need it to do.

173

The third step is once you get the output, do not jump in.

174

I dare you not to touch it.

175

Whatever your impulse is to do once the thing is done, I want you to challenge yourself to have the agent do it.

176

So if it's to share it with somebody, maybe it's to review it to see if it's any good, have the agent review its own work.

177

That is an advanced move that most people don't even consider.

178

And the fourth is you gotta guide it.

179

See, most people try to tell the AI how to do the task and it might know a way, probably a hundred ways to do it faster, better.

180

So just guide it towards the outcome and let it surprise you in its decisions to getting the thing done.

181

Some of you are too nice, that's the problem.

182

And it's like confused, it's like, it said I was doing good, but then it's upset with me.

183

No, just be like, be better.

184

I trust you, you do it.

185

And you'd be surprised half the time it actually is like, oh okay, I'll just get it done.

186

And a pro tip is I have separate agents.

187

All they do is check the work of my other agents.

188

So I have like a coding critique.

189

And every time there's code written, it goes to the critique.

190

It writes a list of things to improve, send it back to the coder.

191

And it fixed the code it wrote based on the critique.

192

Separate people, just like humans.

193

We have specialized knowledge.

194

And if you have somebody that's like really good at finding real estate deals, but somebody else that's really good at running the numbers on the real estate deals, have those as separate agents and have this agent check its work.

195

So now you got the whole toolkit, the tool for thinking, the tool for automating and the tool that runs the entire workflow.

196

Yeah.

197

But remember, AI tools are not what makes you rich.

198

So what does?

199

Point number five, the orchestrator.

200

Owning the toolkit actually isn't the win.

201

Knowing when and where to use it, that's going to make you money.

202

See, most people fail

203

because they just bounce between tools like oh my god i

204

know how to use claw code oh my gosh i know

205

how to use nano banana oh my gosh i know how

206

to use open claw cool what does it do what problem does it solve see the orchestrator

207

picks and chooses between all these three intentions and solves the right problems

208

problems make you money write it down get the tattoo problems

209

make you money the bigger the problems more money shows up in your bank account

210

and the only way you're ever gonna get rich is if that you can sell the solution to somebody.

211

How much would you pay for a hammer?

212

10, 15 bucks?

213

But how much would you pay a carpenter?

214

They would come in and fix a massive leak in your roof and change the whole roof without you lifting a finger.

215

Now we're talking thousands of dollars, 5,000, 10,000.

216

See, the carpenter doesn't sell the hammer, the screwdriver, or the power drill.

217

They fix a problem, which is a massive leak in your roof and give you a new one.

218

That's the orchestrator.

219

They use the entire toolkit to sell a solution.

220

I see this happen all day long where people are like, hey, we're selling this AI solution.

221

Normally it would cost them $5,000 for this.

222

We can do it for 500.

223

And I'm having a hard time selling it.

224

And I'm like, well, if they're already used to paying 5,000, why don't you just sell the thing for 5,000?

225

And if you can do it for 500, keep the difference.

226

I actually think most companies shouldn't even bother saying it's AI.

227

If you have a cheaper, faster, better way to do it, that's your benefit.

228

The customer doesn't care.

229

They just want their roof fixed.

230

When's the last time you asked your carpenter, your plumber, your HVAC guy, what tool he used to do the thing?

231

Now I know some of you guys said, well, just yesterday, cause you're a high fact finder.

232

Not talking to you, I'm talking to normal people.

233

And that's the thing.

234

You have to be a director, not a doer.

235

Actually teaching yourself to stop doing so much and start using the AI to get the work done.

236

That's where the power comes in.

237

And that's only when you start being rich.

238

So stop chasing trends, Stop trying to add AI to everything and start solving real problems.

239

That'll make you a lot of money.

240

AI is just like the internet.

241

It's just like mobile.

242

These are technologies.

243

These are not ways to make money.

244

Always work backwards from the customer and go get paid.

245

Drop a comment below and let me know what tool are you gonna add to your toolkit

246

so you can level up and make more money.

247

And remember, if you want a full AI tech stack, DM me YouTube stack on Instagram and I'll send it right over for free.

248

And if you wanna learn how you can get dangerously smart with AI, click here and I'll see you on another side.

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイング練習: If you’re trying to get rich with AI, you need to hear this… - 動画で英語スピーキングを学ぶ

このビデオで話す練習をする理由は？

文法と表現の文脈

一般的な発音の落とし穴

シャドーイングとは？英語上達に効果的な理由