Luyện nói tiếng Anh bằng Shadowing qua video: You’re Not Behind (Yet): Learn AI Agents in 13 Minutes

Most people think they're using AI well when they get a decent answer from ChatGPT.

⏸ Tạm dừng

Tốc độ:

241 câu

Nếu các câu quá ngắn hoặc quá dài, hãy bấm Edit để chỉnh sửa.

Most people think they're using AI well when they get a decent answer from ChatGPT.

That was enough six months ago.

It's not enough anymore.

The next shift is AI agents and the gap between people who understand them and people who don't,

it's about to get very expensive.

I've spent years in the boardrooms of billion-dollar companies

and the good news here is that agents are much simpler than most people think.

So in this video, I'll show you exactly how they work,

when to use them, and how to start before everyone else catches up.

An AI prompt and an AI agent are completely different.

But most of us are still stuck with old habits.

Let me give you the simplest way to think about this before we go any further.

This is our first framework right off the bat.

I call it ARR.

If a task is autonomous,

recurring, and reviewable, it's a strong candidate for an agent.

If it needs live judgment,

or it only happens once,

or can't be reviewed clearly, then use a prompt.

That one distinction alone will put you miles ahead of most people using AI today.

The internet gave us search, so we started googling.

AI gave us LLMs, or large language models.

But most people today still think of AI as a glorified search.

Now agents are here and we're still making the same mistake again.

We think of agents as just more capable chatbots.

They're not.

A chatbot waits for your next prompt.

An agent figures out its next move.

Prompting is like sitting next to a student driver.

You still have to guide them,

correct them, and stay very alert.

An agent, on the other hand is a hired driver you set the destination hand over the keys

and just sit in the back seat it handles the route the traffic

and all the step-by-step decisions that's the mental shift we have to make

so here's a prompt write me a linkedin post

and here's an agent watch my industry every monday find the

three most relevant stories study my previous posts draft the new post based on those stories in my voice,

revise against my style, and schedule it for Tuesday morning.

That's the power of AI agents.

And to wield that power,

you need to know what's actually running under the hood.

Everyone's talking about AI agents.

Almost nobody can tell you what's actually happening inside.

A chatbot predicts the next word.

An agent decides the next action.

Here's how a chatbot actually works.

It's a large language model.

When you type a question,

it's going to break that question into small units of words called tokens,

and it converts them into numbers.

And then it just finishes the sentence.

So if you said, Jack fell down and broke his crown,

now you know that, but LLM does not know that rhyme.

It will know words like bones and heart and crown,

and all of those words could make sense.

But based on its training,

it predicts that the most likely next word given that line is crown.

It's based on probabilities.

The agent has the same language model in the center,

but now there are four workers around it.

Analyst, planner, operator, auditor.

One finds the pattern, one decides the plan,

one does the work, one checks the results.

Let's make this real.

You can give an agent some instruction like every Monday at 7 a.m.,

review the past week's customer support tickets,

sales notes, and product feedback,

identify three biggest recurring issues,

summarize what changed, and email my leadership team a one page weekly brief.

That's it.

I mean, those are lots of steps,

but the agent will read the tickets,

notes, and feedback, and find the pattern.

Analyst.

It will then decide what matters most and what belongs in the brief.

Planner.

It will write and send the update.

Operator.

And then it'll check for weak logic,

missing context, or sloppy conclusions, and it'll refine it.

Auditor.

Now, by Monday morning, the brief is in your team's inbox.

You did not write the report.

You did not analyze it.

You just assigned the job of four people to one agent.

So here's your move.

Tonight, open ChatGPT agent mode and give it one recurring task,

but then watch what it does.

You'll see all four workers show up

in real time that's the anatomy of an ai agent now

that you understand the parts let's look at the entire loop the best thing about agents is

that they can adapt when things go wrong this is what makes agents genuinely different from everything

that came before it in the 1970s there was an air force colonel john boyd

and he studied a very intriguing puzzle from the korean war American pilots in their F-86 kept beating technically superior Soviet MiG.

Now the MiG was faster and it could climb higher.

It should have won, but it didn't.

And Boyd eventually found the difference.

The American pilots could see more from their cockpits and they could adapt faster.

So they got inside the enemy's decision cycle before the enemy could respond.

100

He called that loop, the OODA loop.

101

Observe, orient, decide, act.

102

And in the world of agents, it's the same thing.

103

That is the real test of an agent.

104

When the obvious path fails,

105

can it choose a better one?

106

Can it go through its own OODA loop?

107

So let me give you a concrete example.

108

You can build an automated workflow every Friday,

109

check this week's grocery prices,

110

build my shopping list, and place the order.

111

It works every Friday until one week your usual item is out of stock

112

and you have six friends coming for dinner on Saturday.

113

So that automated workflow is going to break.

114

Not because it's dumb, but because it's designed to be obedient.

115

It's designed to not think on its own.

116

An agent, on the other hand, does something very unique.

117

It sees the usual list.

118

It sees that that is not working, it finds substitutes.

119

It adjusts quantities for six people.

120

It checks your calendar, sees the dinner,

121

and rebuilds the entire order around it.

122

A workflow can follow the process.

123

An agent can reroute it completely.

124

That's the difference.

125

So when someone says they built an agent, ask one question.

126

When the first path breaks,

127

does the agent keep following the script or can it find a better path?

128

Can it find another way?

129

That's the agent adapting in real time.

130

So this begs the question, right?

131

If agents have autonomy, why do they still fail so often in real life?

132

That's next.

133

The most dangerous thing about AI agents is

134

that they will do wrong things faster and with more confidence than you ever could.

135

An agent is not magic, it's a multiplier.

136

I was working with a board and leadership team of a large consumer company.

137

I'm still working with them.

138

And they're profitable, well-run, great CEO.

139

And when I asked what was stopping them from using AI to drive customer acquisition,

140

for example, the CMO responded,

141

we have all the data,

142

but we'll still need to build a clean process so we can turn that into something useful, something insightful.

143

And I asked where the real challenge was.

144

And she said, you know,

145

we need the right people in the seats first.

146

That's the story everywhere.

147

Most AI problems are human problems in disguise.

148

An agent is just a mirror.

149

It reflects the quality of your thinking back at you.

150

It just amplifies it.

151

Give an agent vague goals,

152

sloppy directions, and no way to get feedback,

153

and it will drive the car straight into the tree,

154

faster and with more confidence than you ever could.

155

Here's the dangerous part.

156

An agent doesn't fix bad thinking, it formalizes it.

157

Usually, the agent fails because the human was vague,

158

not because the underlying model was bad or anything.

159

So, before you automate anything, run a GPS check.

160

Goal, proof, steps.

161

Goal.

162

Can I define the goal in one sentence very clearly?

163

Proof.

164

Can I tell what good looks like?

165

And how will I know if the agent got it right?

166

And steps.

167

Can I describe each and every step very clearly without a lot of hand-waving?

168

Unless you can do those three things very well,

169

your agent is not gonna make any difference.

170

I'll give you an example.

171

Here are two instructions for your agent.

172

First one, summarize my emails every morning.

173

It's good.

174

And here's the second one.

175

Every morning at 7 a.m.,

176

read my unread emails, categorize them by urgency,

177

draft replies to routine messages,

178

and flag anything from my top five customers.

179

So you see, there is a difference between those two instructions,

180

and that gap is exactly where the mess lives.

181

The winners who can wield the power of AI agents aren't just gonna be engineers.

182

They'll be the people who understand their work deeply enough to define it precisely.

183

Most companies want AI everywhere.

184

The ones actually winning are obsessively narrow.

185

If clarity is the bottleneck,

186

then the opportunity is not broad intelligence, it's narrow ownership.

187

I was visiting the customer conference of a construction software company

188

that I work with

189

and the product lead was on stage showing a demo of a single agent

190

that was focused on a very specific problem collecting field data

191

for a specific type of customers in a specific type of situation

192

and it was a beta launch the demo worked mostly with a few minor glitches here

193

and there but

194

when he showed the qr code at the end every hand

195

in the conference room went up with their phones everyone took a picture

196

because it solved a very specific

197

but very real pain they all had been living with for decades

198

that is where the real opportunity is in your career

199

or in your company narrow focus here's the test find a highly specific task people hate doing

200

but they have to do it repeatedly that's where the money is you know

201

we're entering an age where we'll have more agents than human beings on this planet.

202

On the business side, for every software company that exists today,

203

there will be an agent company trying to dethrone it.

204

The winners won't build the broadest agents first.

205

They'll build the one that understands one workflow,

206

one market, and one kind of user pain better than everyone else.

207

By the way, if you If you want to keep this conversation going,

208

I write a short newsletter once a week,

209

just useful ideas, tools, honest reflections.

210

You can subscribe below.

211

It's free.

212

AI will reshape almost every role,

213

but it won't replace what makes you irreplaceable.

214

You know, AI is a giant decoupling machine.

215

For most of our modern history,

216

your income was tied to your hours.

217

at the top, you're always trading time for decisions.

218

Agents are breaking that link for the first time.

219

Now they do the work and you scale your judgment in areas where it matters most.

220

And that changes what's valuable.

221

We're entering an era of infinite output,

222

content, code, and analysis all becoming super cheap.

223

When intelligence becomes that cheap,

224

judgment becomes even more expensive.

225

When output becomes infinite, taste becomes scarce.

226

Every time you define a task clearly enough for an agent to run it,

227

you're not just training the system,

228

you're clarifying your own standards.

229

You're learning what good actually looks like.

230

Sure, some roles will be reshaped.

231

The paralegal, the junior analyst, the coordinator.

232

But every disruption has always created new roles that never existed before.

233

Before the internet, nobody imagined the role of an online community manager.

234

The question is not whether the shift happens.

235

It is whether you shape it or get shaped by it.

236

The most valuable person is no longer the one who can think the fastest.

237

It's the one who can define good work,

238

spot bad work, and know when to trust an agent and when to trust a human.

239

That is where your value is going to move.

240

Ironically, AI will make human life less robotic.

241

Thank you and I love you.

TRENDING

Phổ biến

Phương Pháp Shadowing Là Gì?

Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.

ShadowingEnglish.com – Luyện Nói Tiếng Anh Bằng Shadowing

Nói tiếng Anh lưu loát với phương pháp Shadowing — nghe người bản xứ, đọc theo từng câu, rèn phản xạ ngôn ngữ thật sự. Được hàng ngàn người học IELTS tin dùng.

Luyện nói tiếng Anh bằng Shadowing qua video: You’re Not Behind (Yet): Learn AI Agents in 13 Minutes

Phổ biến

Giới thiệu về bài học này

Từ vựng và cụm từ chính

Mẹo thực hành

Phương Pháp Shadowing Là Gì?

Luyện nói tiếng Anh bằng Shadowing qua video: You’re Not Behind (Yet): Learn AI Agents in 13 Minutes

Tải Ứng Dụng

Giới thiệu về bài học này

Từ vựng và cụm từ chính

Mẹo thực hành

Phương Pháp Shadowing Là Gì?