Prática de Shadowing: You’re Not Behind (Yet): Learn AI Agents in 13 Minutes - Aprenda a falar inglês com o YouTube
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Most people think they're using AI well when they get a decent answer from ChatGPT.
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Most people think they're using AI well when they get a decent answer from ChatGPT.
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That was enough six months ago.
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It's not enough anymore.
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The next shift is AI agents and the gap between people who understand them and people who don't,
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it's about to get very expensive.
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I've spent years in the boardrooms of billion-dollar companies
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and the good news here is that agents are much simpler than most people think.
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So in this video, I'll show you exactly how they work,
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when to use them, and how to start before everyone else catches up.
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An AI prompt and an AI agent are completely different.
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But most of us are still stuck with old habits.
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Let me give you the simplest way to think about this before we go any further.
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This is our first framework right off the bat.
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I call it ARR.
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If a task is autonomous,
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recurring, and reviewable, it's a strong candidate for an agent.
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If it needs live judgment,
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or it only happens once,
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or can't be reviewed clearly, then use a prompt.
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That one distinction alone will put you miles ahead of most people using AI today.
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The internet gave us search, so we started googling.
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AI gave us LLMs, or large language models.
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But most people today still think of AI as a glorified search.
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Now agents are here and we're still making the same mistake again.
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We think of agents as just more capable chatbots.
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They're not.
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A chatbot waits for your next prompt.
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An agent figures out its next move.
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Prompting is like sitting next to a student driver.
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You still have to guide them,
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correct them, and stay very alert.
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An agent, on the other hand is a hired driver you set the destination hand over the keys
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and just sit in the back seat it handles the route the traffic
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and all the step-by-step decisions that's the mental shift we have to make
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so here's a prompt write me a linkedin post
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and here's an agent watch my industry every monday find the
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three most relevant stories study my previous posts draft the new post based on those stories in my voice,
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revise against my style, and schedule it for Tuesday morning.
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That's the power of AI agents.
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And to wield that power,
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you need to know what's actually running under the hood.
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Everyone's talking about AI agents.
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Almost nobody can tell you what's actually happening inside.
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A chatbot predicts the next word.
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An agent decides the next action.
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Here's how a chatbot actually works.
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It's a large language model.
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When you type a question,
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it's going to break that question into small units of words called tokens,
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and it converts them into numbers.
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And then it just finishes the sentence.
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So if you said, Jack fell down and broke his crown,
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now you know that, but LLM does not know that rhyme.
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It will know words like bones and heart and crown,
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and all of those words could make sense.
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But based on its training,
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it predicts that the most likely next word given that line is crown.
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It's based on probabilities.
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The agent has the same language model in the center,
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but now there are four workers around it.
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Analyst, planner, operator, auditor.
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One finds the pattern, one decides the plan,
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one does the work, one checks the results.
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Let's make this real.
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You can give an agent some instruction like every Monday at 7 a.m.,
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review the past week's customer support tickets,
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sales notes, and product feedback,
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identify three biggest recurring issues,
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summarize what changed, and email my leadership team a one page weekly brief.
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That's it.
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I mean, those are lots of steps,
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but the agent will read the tickets,
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notes, and feedback, and find the pattern.
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Analyst.
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It will then decide what matters most and what belongs in the brief.
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Planner.
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It will write and send the update.
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Operator.
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And then it'll check for weak logic,
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missing context, or sloppy conclusions, and it'll refine it.
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Auditor.
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Now, by Monday morning, the brief is in your team's inbox.
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You did not write the report.
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You did not analyze it.
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You just assigned the job of four people to one agent.
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So here's your move.
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Tonight, open ChatGPT agent mode and give it one recurring task,
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but then watch what it does.
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You'll see all four workers show up
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in real time that's the anatomy of an ai agent now
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that you understand the parts let's look at the entire loop the best thing about agents is
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that they can adapt when things go wrong this is what makes agents genuinely different from everything
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that came before it in the 1970s there was an air force colonel john boyd
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and he studied a very intriguing puzzle from the korean war American pilots in their F-86 kept beating technically superior Soviet MiG.
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Now the MiG was faster and it could climb higher.
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It should have won, but it didn't.
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And Boyd eventually found the difference.
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The American pilots could see more from their cockpits and they could adapt faster.
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So they got inside the enemy's decision cycle before the enemy could respond.
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He called that loop, the OODA loop.
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Observe, orient, decide, act.
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And in the world of agents, it's the same thing.
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That is the real test of an agent.
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When the obvious path fails,
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can it choose a better one?
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Can it go through its own OODA loop?
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So let me give you a concrete example.
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You can build an automated workflow every Friday,
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check this week's grocery prices,
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build my shopping list, and place the order.
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It works every Friday until one week your usual item is out of stock
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and you have six friends coming for dinner on Saturday.
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So that automated workflow is going to break.
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Not because it's dumb, but because it's designed to be obedient.
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It's designed to not think on its own.
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An agent, on the other hand, does something very unique.
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It sees the usual list.
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It sees that that is not working, it finds substitutes.
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It adjusts quantities for six people.
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It checks your calendar, sees the dinner,
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and rebuilds the entire order around it.
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A workflow can follow the process.
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An agent can reroute it completely.
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That's the difference.
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So when someone says they built an agent, ask one question.
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When the first path breaks,
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does the agent keep following the script or can it find a better path?
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Can it find another way?
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That's the agent adapting in real time.
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So this begs the question, right?
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If agents have autonomy, why do they still fail so often in real life?
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That's next.
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The most dangerous thing about AI agents is
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that they will do wrong things faster and with more confidence than you ever could.
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An agent is not magic, it's a multiplier.
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I was working with a board and leadership team of a large consumer company.
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I'm still working with them.
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And they're profitable, well-run, great CEO.
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And when I asked what was stopping them from using AI to drive customer acquisition,
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for example, the CMO responded,
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we have all the data,
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but we'll still need to build a clean process so we can turn that into something useful, something insightful.
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And I asked where the real challenge was.
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And she said, you know,
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we need the right people in the seats first.
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That's the story everywhere.
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Most AI problems are human problems in disguise.
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An agent is just a mirror.
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It reflects the quality of your thinking back at you.
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It just amplifies it.
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Give an agent vague goals,
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sloppy directions, and no way to get feedback,
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and it will drive the car straight into the tree,
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faster and with more confidence than you ever could.
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Here's the dangerous part.
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An agent doesn't fix bad thinking, it formalizes it.
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Usually, the agent fails because the human was vague,
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not because the underlying model was bad or anything.
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So, before you automate anything, run a GPS check.
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Goal, proof, steps.
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Goal.
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Can I define the goal in one sentence very clearly?
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Proof.
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Can I tell what good looks like?
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And how will I know if the agent got it right?
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And steps.
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Can I describe each and every step very clearly without a lot of hand-waving?
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Unless you can do those three things very well,
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your agent is not gonna make any difference.
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I'll give you an example.
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Here are two instructions for your agent.
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First one, summarize my emails every morning.
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It's good.
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And here's the second one.
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Every morning at 7 a.m.,
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read my unread emails, categorize them by urgency,
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draft replies to routine messages,
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and flag anything from my top five customers.
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So you see, there is a difference between those two instructions,
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and that gap is exactly where the mess lives.
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The winners who can wield the power of AI agents aren't just gonna be engineers.
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They'll be the people who understand their work deeply enough to define it precisely.
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Most companies want AI everywhere.
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The ones actually winning are obsessively narrow.
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If clarity is the bottleneck,
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then the opportunity is not broad intelligence, it's narrow ownership.
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I was visiting the customer conference of a construction software company
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that I work with
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and the product lead was on stage showing a demo of a single agent
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that was focused on a very specific problem collecting field data
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for a specific type of customers in a specific type of situation
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and it was a beta launch the demo worked mostly with a few minor glitches here
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and there but
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when he showed the qr code at the end every hand
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in the conference room went up with their phones everyone took a picture
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because it solved a very specific
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but very real pain they all had been living with for decades
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that is where the real opportunity is in your career
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or in your company narrow focus here's the test find a highly specific task people hate doing
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but they have to do it repeatedly that's where the money is you know
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we're entering an age where we'll have more agents than human beings on this planet.
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On the business side, for every software company that exists today,
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there will be an agent company trying to dethrone it.
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The winners won't build the broadest agents first.
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They'll build the one that understands one workflow,
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one market, and one kind of user pain better than everyone else.
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By the way, if you If you want to keep this conversation going,
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I write a short newsletter once a week,
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just useful ideas, tools, honest reflections.
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You can subscribe below.
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It's free.
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AI will reshape almost every role,
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but it won't replace what makes you irreplaceable.
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You know, AI is a giant decoupling machine.
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For most of our modern history,
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your income was tied to your hours.
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at the top, you're always trading time for decisions.
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Agents are breaking that link for the first time.
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Now they do the work and you scale your judgment in areas where it matters most.
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And that changes what's valuable.
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We're entering an era of infinite output,
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content, code, and analysis all becoming super cheap.
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When intelligence becomes that cheap,
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judgment becomes even more expensive.
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When output becomes infinite, taste becomes scarce.
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Every time you define a task clearly enough for an agent to run it,
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you're not just training the system,
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you're clarifying your own standards.
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You're learning what good actually looks like.
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Sure, some roles will be reshaped.
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The paralegal, the junior analyst, the coordinator.
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But every disruption has always created new roles that never existed before.
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Before the internet, nobody imagined the role of an online community manager.
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The question is not whether the shift happens.
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It is whether you shape it or get shaped by it.
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The most valuable person is no longer the one who can think the fastest.
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It's the one who can define good work,
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spot bad work, and know when to trust an agent and when to trust a human.
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That is where your value is going to move.
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Ironically, AI will make human life less robotic.
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Thank you and I love you.
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Por que praticar a fala com este vídeo?
Praticar a fala é essencial para se tornar um falante confiante em inglês, e este vídeo é uma excelente oportunidade para isso. Ao assistir e interagir com o conteúdo, você não só aprende sobre os novos conceitos de agentes de IA, como também tem a chance de ouvir e reproduzir a pronúncia e a intonação do falante. Essa técnica, conhecida como shadow speech, ou shadowing em inglês, permite que você desenvolva a fluência e a naturalidade na fala. Além disso, compreender as nuances da conversa sobre tecnologia pode enriquecer seu vocabulário e prepará-lo para debates e discussões sobre um tema de relevância crescente.
Gramática & Expressões em Contexto
Neste vídeo, algumas estruturas gramaticais e expressões se destacam, as quais são fundamentais para a compreensão do inglês falado. Aqui estão algumas delas:
- It’s not enough anymore – Essa expressão enfatiza a mudança nas expectativas, ideal para aprender a comunicar a ideia de que algo já não é suficiente.
- A strong candidate for an agent – Aqui aprendemos a usar "candidate" e "strong" em um contexto descritivo, algo muito útil em discussão profissional.
- I call it ARR – A forma de introduzir uma nova ideia geralmente envolve uma estrutura simples como esta, que é crucial para o discurso.
Essas expressões são valiosas; praticá-las usando shadow speak pode ajudá-lo a internalizar suas aplicações.
Traps Comuns de Pronúncia
A pronúncia pode ser desafiadora, especialmente com palavras técnicas e novas expressões. No vídeo, algumas palavras e frases podem ser particularmente traiçoeiras. Aqui estão algumas delas:
- Autonomous – A pronúncia pode ser difícil; ouça atentamente e repita em voz alta para garantir que está correto.
- Agent – A entonação pode mudar o significado em diferentes contextos, portanto, pratique-a em diferentes frases.
- Recurring – Esta palavra tem sons próximos que podem confundir, tente diferenciá-los ao praticar.
Utilizar shadowspeak neste contexto é uma maneira eficaz de superar essas barreiras e aprimorar sua autoexpressão em inglês.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.
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