WEBVTT

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I was looking at the notes for today's deep dive

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this morning, and there was this one phrase in

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the research that just stopped me cold. Oh, the

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empty company concept. Yeah, the empty company.

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It's not about the neural networks or the processing

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speed. Right. It's about us. It's a really haunting

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image, isn't it? It really is. It's this idea

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that you can look at a spreadsheet in 2026 and

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Everything looks perfect. The costs are down.

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The efficiency is through the roof. Exactly.

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Because you've fired the humans and plugged in

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the AI agents, but then you walk into the building.

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Or you log into the Slack channel. Right. And

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there is nobody home. The soul has just been

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automated right out of the building. And the

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customers feel it. That is the kicker. Yeah.

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The data we're looking at today suggests this

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massive paradox. Business owners think they are

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optimizing for profit. But they're actually optimizing

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for irrelevance. Exactly. They are building these

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incredibly efficient engines that nobody wants

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to ride in. Optimizing for irrelevance. That

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is a heavy concept. It is. So that is our mission

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today. For everyone listening, we aren't talking

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about apocalypse scenarios. No, no hype cycles

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today. Right. We are doing a deep dive into the

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12 AI traps of 2026. Basically a guide on how

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to stop tripping over our own shoelaces. Yeah,

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while trying to sprint into the future. Exactly.

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We are going to dissect why automating the wrong

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things actually breaks a business. Why AI is

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terrible at strategic decisions, despite what

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the tech trolls say. Yeah. And then we're going

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to get very tactical with the toolbox. Because

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if you're using chat GPT for everything in 2026,

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you are doing it wrong. Totally wrong. Let's

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start with that empty company concept. OK. The

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source material leads with what they call the

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big cost saving myth. Right. Trap one. And I

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have to be honest here. As someone who runs a

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business, I feel this temptation. Oh, sure. There

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is a vulnerability in leadership where you see

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the line item for salaries. And then you see

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the monthly subscription for an AI agent. Right.

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And the math is just so seductive. The math is

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seductive, but it is incomplete. The source argues

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that on paper, the AI looks like the perfect

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employee. It doesn't sleep. It doesn't ask for

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a raise. It doesn't take lunch breaks. So the

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instinct is just to replace the humans. Right.

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But the source says this kills the energy of

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the company. Now, that sounds a bit vague to

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me. What does that actually mean in a profit

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and loss statement? Think about it this way.

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Business is fundamentally about the transfer

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of emotion. Okay. You buy from people you trust.

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You work harder for leaders you respect. Humans

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read the room. And a computer doesn't. Right.

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A computer doesn't know if a client is hesitant

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or just busy. A computer creates output, but

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a human creates connection. So if you strip that

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away to save cash... You lose the mechanism that

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actually generates the revenue. You're saving

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money on the expenses, but capping your upside

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on the income. Precisely. You are efficient,

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but you're dead. Wow. And this bleeds right into

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trap three, which is customer service. This is

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where I see the most damage happening right now.

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Oh, I completely agree. Companies see a chat

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bot and think... Great. I never have to deal

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with a complaint again. I've been on the receiving

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end of that. It is infuriating. It is. You just

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want to scream representative into your phone.

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It's worse than infuriating. It's just bad strategy.

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The source calls a complaint a gift. A gift.

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That is a very generous way to frame it. When

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a customer complains, they are telling you exactly

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how to fix your product. They're giving you free

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consulting. Right. If a bot deflects that with

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a generic apology, you lose the data. But more

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importantly, you miss the emotional opportunity.

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The torn shirt analogy from the notes. Yes. The

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torn shirt analogy nails this. Walk me through

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that. So imagine you buy a shirt online, it arrives,

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and it's torn. You're annoyed. Very annoyed.

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You go to the chat support. If a bot handled

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that, what does it do? It scans your text for

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the word damage. Exactly. And it pastes a link

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to the return policy. It follows the rules. It

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follows the rules, but it fails the human. It

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is cold. It feels bureaucratic. Now imagine a

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human handles it. They see the picture and say,

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oh no, I am so sorry that happened. They validate

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the frustration. Right. They say, that looks

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awful. Tell you what, I'm sending a replacement

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overnight and I'm throwing in a discount code.

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And suddenly I am not angry. No. I am actually

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a bigger fan than I was before the shirt tour.

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Exactly. You converted a failure into loyalty.

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A bot cannot do that because a bot doesn't feel

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empathy. That makes sense. The rule of thumb

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the source gives here is super useful. Use bots

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for facts. Like opening hours or tracking numbers.

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Right. And use humans for feelings. Facts versus

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feelings. That seems like the dividing line for

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job security in 2026. Absolutely. But there is

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a flip side to this. We talk about leaders wanting

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to replace people. But the source also mentions

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the fear coming from the employees. Trap 10 touches

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on this IT silo mentality. Oh, yeah. The secret

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weapon problem. Right. Often, companies treat

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AI as this high -tech thing that only the engineering

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team or the IT guys are allowed to touch. Which

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makes everyone else feel like they are outdated.

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It creates a class system. It does. If you work

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in sales or HR, and you see the engineers playing

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with these powerful tools. We start to think,

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wait, is that thing going to take my job? And

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that fear. robs the culture. People start hiding

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information. They stop collaborating. So how

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do you break that silo? You can't just force

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people to use technology they're afraid of. The

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source suggests something surprisingly low tech.

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The Friday pizza party. Please tell me it is

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more than just free food. It's about the environment.

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You don't do a seminar. You don't do a mandatory

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training module. You get the team together, order

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pizza, and say, hey, Let's look at this one simple

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trick together. Maybe it's taking a rude email

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and asking the AI to rewrite it to be polite.

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Everyone tries it. They laugh about the results.

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So they gamify it. Exactly. You take the scary

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monster and turn it into a toy. You take the

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sci -fi out of it and make it a utility. Yes.

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So if the goal isn't replacement, but safety,

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what is the core psychological shift a leader

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needs to make? Explicitly tell your team the

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tool helps them. it does not replace them. I

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like that. Let's move to the actual mechanics

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of work. Okay. Because trap two is automating

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everything. And I think this is where the efficiency

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mindset really backfires. This is definitely

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for the speed junkies. The source drops a hard

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truth here. Yeah. Speeding up a bad process just

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means you fail faster. Wow. Speeding up a bad

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process means you fail faster. That is profound.

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It is. We often equate velocity with progress.

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If I am moving fast, I must be winning. But...

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If you are running in the wrong direction, velocity

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is your enemy. Right. The example they give is

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email outreach. Let's say you write a boring,

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ineffective sales email. OK. In the old days,

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you would send 10 of them, get no replies, and

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realize you need to rewrite it. The feedback

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loop was slow, but it was safe. Exactly. Now

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you use an AI agent to scrape 1 ,000 leads, and

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you fire off that boring email to all of them

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in three minutes. You haven't scaled your success.

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No. You have just annoyed 1 ,000 people instantly.

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You have burned your market. So the advice is

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actually counterintuitive. It's do things that

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don't scale first. Manual first. Always. Yeah.

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Write 10 personal emails. See if anyone replies.

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And once you have the words that work, then you

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automate. Exactly. This reminds me of the bakery

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story from Trap 5. Oh, that's a great one. It

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really resonated with me because it highlights

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the difference between building tools and building

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a business. It's the archetype of the engineer

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founder. Right. So this guy wants to start a

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bakery. But instead of buying flour, he spends

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three months coding a complex AI system. A system

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to predict bread sales based on local weather

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patterns. And foot traffic data. Right, right.

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He is building a prediction engine for a product

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that doesn't exist. Zero bread. He hasn't baked

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a single loaf. And what happens? When he finally

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opens... It turns out people in that neighborhood

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didn't even want bread. They wanted chocolate

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chip cookies. Exactly. His system was totally

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useless. Start with flour and water. Fix the

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bread. Sell it on the street. Use technology

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to grow, not to learn the basics. We see this

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with what the source calls shiny object syndrome.

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Oh, we are all guilty of this. We see a cool

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video of a new tool and we invent a problem just

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so we can use it. Right. But the source suggests

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using a mental model called the theory of constraints.

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The bottleneck theory. Yes. Imagine your work

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is water flowing through a pipe. OK. There's

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always one spot where the pipe is narrowest.

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That is the block. If you are great at shooting

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video, but it takes you four hours to write a

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title. The title is the block. Right. So buying

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a faster camera doesn't help. It does nothing.

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It just piles up more video footage that you

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can't title. Exactly. You need a tool specifically

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for titles. So it seems like the trap is putting

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the tool before the problem. How do we discipline

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ourselves? Identify the bottleneck first, the

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one thing taking your time, then solve only that.

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We are going to take a quick break for our sponsors,

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but when we come back, we are going to get into

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the limits of machine intelligence. Sounds good.

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And we are back. Segment 3, traps 4, 7, and 8.

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This is the philosophical part. It is. It really

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challenges the way we view intelligence itself.

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You have to be honest about what the machine

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actually is. Right. Now it is amazing that these

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models can read a thousand pages in a minute.

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Whoa. Imagine scaling to a billion queries. It's

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incredible. I definitely don't want to downplay

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that. Yeah. But the source defines AI simply

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as a guessing machine. A guessing machine? It

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is just predicting the next word. It's basically

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autocorrect on steroids. On massive steroids,

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yes. But because its goal is to predict the most

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likely next word, it wants to please you. And

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this leads to trap four, hallucinations. Right.

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We hear this term a lot. I think people misunderstand

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why it happens, though. It happens because the

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model prioritizes completion over accuracy. Let's

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define it simply. Hallucination is when the computer

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dreams up fake facts to please you. Right. Like

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if you ask it, how many cars did we sell last

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month? And it doesn't have access to your database?

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It might just say 500. Because 500 sounds like

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a plausible number for a car dealership. It sounds

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like a good sentence. It's not lying in the human

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sense. It's filling in the blank. Which is terrifying

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if you are reporting to a bank. You have to verify

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everything. This leads to the bigger issue of

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decision making. Trap seven. I see leaders who

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want the AI to make the big strategic calls.

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Oh, yeah. Should we expand to Europe? Right.

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Should I hire this VP? And that is a fatal error.

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Because remember, the AI is trained on the past.

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All of its data is historical. When you ask it

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for a decision, it gives you the average of what

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has worked before. It creates a regression to

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the meme. Exactly. Regression to the meme. Let's

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unpack that. So it pushes you toward mediocrity.

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It pushes you toward the average. Think about

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it. If you ask it to write a pop song, it will

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write a song that sounds like every top song.

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Because it's trained on all of them. Right. But

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big business success, the outliers, usually come

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from weird, brave, counterintuitive ideas. And

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data cannot predict those. If you listen to the

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AI... You will build a business that looks exactly

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like everyone else's. You will build a perfectly

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average business. The source uses a beautiful

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analogy here about creativity. Yeah. A human

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can watch a bird fly and get an idea for an airplane

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part. We connect unrelated things. But an AI

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just looks at a database of old airplane parts.

00:11:50.679 --> 00:11:53.419
Exactly. It mixes old ideas. It doesn't create

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new futures. And that links to trap sex, chasing

00:11:56.799 --> 00:12:00.220
the news. The hype cycle. With how fast this

00:12:00.220 --> 00:12:03.360
stuff moves, there is a lot of anxiety. Oh, a

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new model dropped today. I need to change my

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whole strategy. It is exhausting. The source

00:12:08.059 --> 00:12:10.019
says to just ignore it. Focus on what doesn't

00:12:10.019 --> 00:12:12.600
change. Jeff Bezos famously said this. Customers

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will always want cheap prices and fast shipping.

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No new AI model changes that. Build your business

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around those constants. not the daily software

00:12:20.429 --> 00:12:23.210
updates. If the machine effectively regresses

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to the mean, what is the human's specific role

00:12:26.730 --> 00:12:29.830
in the final decision? Humans must use gut feeling

00:12:29.830 --> 00:12:32.250
and personal risk tolerance to make the final

00:12:32.250 --> 00:12:34.509
call. Let's get practical. Segment four is the

00:12:34.509 --> 00:12:38.250
tactical toolkit. Yes. Trap 12, the one tool

00:12:38.250 --> 00:12:41.090
mistake. We want one app to rule them all. The

00:12:41.090 --> 00:12:45.049
all in one. We like simplicity. But in 2026,

00:12:45.450 --> 00:12:47.470
specialization is the reality. You wouldn't use

00:12:47.470 --> 00:12:50.210
a hammer to cut wood. But people use ChatGPT

00:12:50.210 --> 00:12:52.549
for everything. They really do. So let's break

00:12:52.549 --> 00:12:55.250
down the 2026 Toolbox as the source describes

00:12:55.250 --> 00:12:58.309
it. We have three main players, Claude, Gemini,

00:12:58.450 --> 00:13:00.450
and ChatGPT. Right. Let's start with Claude.

00:13:00.549 --> 00:13:02.750
OK. Think of Claude as your thoughtful writer

00:13:02.750 --> 00:13:05.769
or your analyst. The source describes his voice

00:13:05.769 --> 00:13:09.649
as natural, softer. It has a massive context

00:13:09.649 --> 00:13:11.809
window too. Right. Meaning you can feed it huge

00:13:11.809 --> 00:13:14.529
documents. So if I need to summarize a 50 page

00:13:14.529 --> 00:13:17.169
contract. Or write a sensitive email to a client.

00:13:17.360 --> 00:13:20.080
You use Claude. Next is Gemini? Gemini is your

00:13:20.080 --> 00:13:22.379
connection to the live internet. It's Google.

00:13:22.759 --> 00:13:25.679
So this is for facts. Checking the weather, looking

00:13:25.679 --> 00:13:28.299
up stock prices, verifying news events. Because

00:13:28.299 --> 00:13:31.080
if you ask ChatGPT for today's news, it might

00:13:31.080 --> 00:13:33.720
hallucinate. Exactly. Gemini is grounded in the

00:13:33.720 --> 00:13:36.879
live web. And then the classic, ChatGPT. What

00:13:36.879 --> 00:13:42.179
is its lane now? Logic, reasoning, coding, structure.

00:13:42.200 --> 00:13:44.639
OK. If you need to break down a complex problem

00:13:44.639 --> 00:13:48.200
into steps or write a Python script, ChatGPT

00:13:48.200 --> 00:13:50.860
is the sharpest tool in the shed. So it's a woodworker's

00:13:50.860 --> 00:13:53.220
bench. I like that. Claude is the sander, getting

00:13:53.220 --> 00:13:56.539
things smooth. Gemini is the tape measure, getting

00:13:56.539 --> 00:14:00.360
facts straight. And ChatGPT is the saw, cutting

00:14:00.360 --> 00:14:03.220
through the logic. Perfect analogy. If you try

00:14:03.220 --> 00:14:06.080
to use the logic bot to write a feeling email,

00:14:06.159 --> 00:14:08.799
it sounds robotic. And if you use the creative

00:14:08.799 --> 00:14:11.340
bot to check facts, it lies to you. You have

00:14:11.340 --> 00:14:13.539
to pick the right tool. Trap9 warns us about

00:14:13.539 --> 00:14:16.200
how we talk to these tools too. The more data

00:14:16.200 --> 00:14:19.129
myth. We tend to think if we paste a whole book

00:14:19.129 --> 00:14:21.830
into the chat, the answer will be smarter. Right.

00:14:21.830 --> 00:14:25.149
Here is everything I know. Fix my life. It's

00:14:25.149 --> 00:14:27.169
like asking for directions and getting 50 maps

00:14:27.169 --> 00:14:29.129
at once. You just get confused. And the computer

00:14:29.129 --> 00:14:32.389
gets confused too. The advice is a clean desk

00:14:32.389 --> 00:14:35.929
policy. Key prompt short. One page of notes equals

00:14:35.929 --> 00:14:39.279
one summary. If you want a clear output, you

00:14:39.279 --> 00:14:41.700
must provide a clear input. So specialization

00:14:41.700 --> 00:14:44.700
is key. What is the immediate action step regarding

00:14:44.700 --> 00:14:46.600
these different tools? Make a folder linking

00:14:46.600 --> 00:14:48.620
all three and pick the right one for the job.

00:14:48.940 --> 00:14:51.620
As we wrap up this deep dive, let's synthesize

00:14:51.620 --> 00:14:54.919
this. We have covered 12 traps today. From the

00:14:54.919 --> 00:14:57.960
empty company to the one tool mistake. But the

00:14:57.960 --> 00:15:00.559
overarching theme here seems to be about confidence.

00:15:01.159 --> 00:15:03.440
Confidence in our own humanity. That is the big

00:15:03.440 --> 00:15:06.480
idea. The narrative has been man versus machine.

00:15:06.720 --> 00:15:08.940
But the research shows it's actually man plus

00:15:08.940 --> 00:15:12.340
machine. The real winners in 2026 aren't the

00:15:12.340 --> 00:15:14.279
ones who automated everything into oblivion.

00:15:14.399 --> 00:15:17.200
They are the ones who mixed human skills like

00:15:17.200 --> 00:15:21.299
empathy and bravery with machine speed. Technology

00:15:21.299 --> 00:15:24.259
should be used to grow, not to learn. You have

00:15:24.259 --> 00:15:26.779
to learn the business manually first. So for

00:15:26.779 --> 00:15:29.059
everyone listening, what is the move for tomorrow

00:15:29.059 --> 00:15:31.000
morning? We don't want to overwhelm them. Pick

00:15:31.000 --> 00:15:34.720
one thing. Don't try to fix all 12 traps at once.

00:15:34.940 --> 00:15:37.620
Maybe tomorrow you stop using the bot for customer

00:15:37.620 --> 00:15:40.220
complaints. Or maybe you just organize your toolbar

00:15:40.220 --> 00:15:42.200
with the three different AIs. Just start with

00:15:42.200 --> 00:15:44.480
one thing. I love that. The source ends with

00:15:44.480 --> 00:15:46.220
a provocative thought that I want to leave you

00:15:46.220 --> 00:15:48.460
with. Yeah. It mentions that a machine can never

00:15:48.460 --> 00:15:51.779
understand the soul of an idea. It leads me to

00:15:51.779 --> 00:15:55.039
ask you, the listener, which parts of your work

00:15:55.039 --> 00:15:57.600
require a soul? And are you accidentally trying

00:15:57.600 --> 00:16:00.179
to automate them? That is the question. Something

00:16:00.179 --> 00:16:03.259
to think about. Thank you for joining us on this

00:16:03.259 --> 00:16:04.620
deep dive. Take care, everyone.
