I was looking at the notes for today's deep dive
this morning, and there was this one phrase in
the research that just stopped me cold. Oh, the
empty company concept. Yeah, the empty company.
It's not about the neural networks or the processing
speed. Right. It's about us. It's a really haunting
image, isn't it? It really is. It's this idea
that you can look at a spreadsheet in 2026 and
Everything looks perfect. The costs are down.
The efficiency is through the roof. Exactly.
Because you've fired the humans and plugged in
the AI agents, but then you walk into the building.
Or you log into the Slack channel. Right. And
there is nobody home. The soul has just been
automated right out of the building. And the
customers feel it. That is the kicker. Yeah.
The data we're looking at today suggests this
massive paradox. Business owners think they are
optimizing for profit. But they're actually optimizing
for irrelevance. Exactly. They are building these
incredibly efficient engines that nobody wants
to ride in. Optimizing for irrelevance. That
is a heavy concept. It is. So that is our mission
today. For everyone listening, we aren't talking
about apocalypse scenarios. No, no hype cycles
today. Right. We are doing a deep dive into the
12 AI traps of 2026. Basically a guide on how
to stop tripping over our own shoelaces. Yeah,
while trying to sprint into the future. Exactly.
We are going to dissect why automating the wrong
things actually breaks a business. Why AI is
terrible at strategic decisions, despite what
the tech trolls say. Yeah. And then we're going
to get very tactical with the toolbox. Because
if you're using chat GPT for everything in 2026,
you are doing it wrong. Totally wrong. Let's
start with that empty company concept. OK. The
source material leads with what they call the
big cost saving myth. Right. Trap one. And I
have to be honest here. As someone who runs a
business, I feel this temptation. Oh, sure. There
is a vulnerability in leadership where you see
the line item for salaries. And then you see
the monthly subscription for an AI agent. Right.
And the math is just so seductive. The math is
seductive, but it is incomplete. The source argues
that on paper, the AI looks like the perfect
employee. It doesn't sleep. It doesn't ask for
a raise. It doesn't take lunch breaks. So the
instinct is just to replace the humans. Right.
But the source says this kills the energy of
the company. Now, that sounds a bit vague to
me. What does that actually mean in a profit
and loss statement? Think about it this way.
Business is fundamentally about the transfer
of emotion. Okay. You buy from people you trust.
You work harder for leaders you respect. Humans
read the room. And a computer doesn't. Right.
A computer doesn't know if a client is hesitant
or just busy. A computer creates output, but
a human creates connection. So if you strip that
away to save cash... You lose the mechanism that
actually generates the revenue. You're saving
money on the expenses, but capping your upside
on the income. Precisely. You are efficient,
but you're dead. Wow. And this bleeds right into
trap three, which is customer service. This is
where I see the most damage happening right now.
Oh, I completely agree. Companies see a chat
bot and think... Great. I never have to deal
with a complaint again. I've been on the receiving
end of that. It is infuriating. It is. You just
want to scream representative into your phone.
It's worse than infuriating. It's just bad strategy.
The source calls a complaint a gift. A gift.
That is a very generous way to frame it. When
a customer complains, they are telling you exactly
how to fix your product. They're giving you free
consulting. Right. If a bot deflects that with
a generic apology, you lose the data. But more
importantly, you miss the emotional opportunity.
The torn shirt analogy from the notes. Yes. The
torn shirt analogy nails this. Walk me through
that. So imagine you buy a shirt online, it arrives,
and it's torn. You're annoyed. Very annoyed.
You go to the chat support. If a bot handled
that, what does it do? It scans your text for
the word damage. Exactly. And it pastes a link
to the return policy. It follows the rules. It
follows the rules, but it fails the human. It
is cold. It feels bureaucratic. Now imagine a
human handles it. They see the picture and say,
oh no, I am so sorry that happened. They validate
the frustration. Right. They say, that looks
awful. Tell you what, I'm sending a replacement
overnight and I'm throwing in a discount code.
And suddenly I am not angry. No. I am actually
a bigger fan than I was before the shirt tour.
Exactly. You converted a failure into loyalty.
A bot cannot do that because a bot doesn't feel
empathy. That makes sense. The rule of thumb
the source gives here is super useful. Use bots
for facts. Like opening hours or tracking numbers.
Right. And use humans for feelings. Facts versus
feelings. That seems like the dividing line for
job security in 2026. Absolutely. But there is
a flip side to this. We talk about leaders wanting
to replace people. But the source also mentions
the fear coming from the employees. Trap 10 touches
on this IT silo mentality. Oh, yeah. The secret
weapon problem. Right. Often, companies treat
AI as this high -tech thing that only the engineering
team or the IT guys are allowed to touch. Which
makes everyone else feel like they are outdated.
It creates a class system. It does. If you work
in sales or HR, and you see the engineers playing
with these powerful tools. We start to think,
wait, is that thing going to take my job? And
that fear. robs the culture. People start hiding
information. They stop collaborating. So how
do you break that silo? You can't just force
people to use technology they're afraid of. The
source suggests something surprisingly low tech.
The Friday pizza party. Please tell me it is
more than just free food. It's about the environment.
You don't do a seminar. You don't do a mandatory
training module. You get the team together, order
pizza, and say, hey, Let's look at this one simple
trick together. Maybe it's taking a rude email
and asking the AI to rewrite it to be polite.
Everyone tries it. They laugh about the results.
So they gamify it. Exactly. You take the scary
monster and turn it into a toy. You take the
sci -fi out of it and make it a utility. Yes.
So if the goal isn't replacement, but safety,
what is the core psychological shift a leader
needs to make? Explicitly tell your team the
tool helps them. it does not replace them. I
like that. Let's move to the actual mechanics
of work. Okay. Because trap two is automating
everything. And I think this is where the efficiency
mindset really backfires. This is definitely
for the speed junkies. The source drops a hard
truth here. Yeah. Speeding up a bad process just
means you fail faster. Wow. Speeding up a bad
process means you fail faster. That is profound.
It is. We often equate velocity with progress.
If I am moving fast, I must be winning. But...
If you are running in the wrong direction, velocity
is your enemy. Right. The example they give is
email outreach. Let's say you write a boring,
ineffective sales email. OK. In the old days,
you would send 10 of them, get no replies, and
realize you need to rewrite it. The feedback
loop was slow, but it was safe. Exactly. Now
you use an AI agent to scrape 1 ,000 leads, and
you fire off that boring email to all of them
in three minutes. You haven't scaled your success.
No. You have just annoyed 1 ,000 people instantly.
You have burned your market. So the advice is
actually counterintuitive. It's do things that
don't scale first. Manual first. Always. Yeah.
Write 10 personal emails. See if anyone replies.
And once you have the words that work, then you
automate. Exactly. This reminds me of the bakery
story from Trap 5. Oh, that's a great one. It
really resonated with me because it highlights
the difference between building tools and building
a business. It's the archetype of the engineer
founder. Right. So this guy wants to start a
bakery. But instead of buying flour, he spends
three months coding a complex AI system. A system
to predict bread sales based on local weather
patterns. And foot traffic data. Right, right.
He is building a prediction engine for a product
that doesn't exist. Zero bread. He hasn't baked
a single loaf. And what happens? When he finally
opens... It turns out people in that neighborhood
didn't even want bread. They wanted chocolate
chip cookies. Exactly. His system was totally
useless. Start with flour and water. Fix the
bread. Sell it on the street. Use technology
to grow, not to learn the basics. We see this
with what the source calls shiny object syndrome.
Oh, we are all guilty of this. We see a cool
video of a new tool and we invent a problem just
so we can use it. Right. But the source suggests
using a mental model called the theory of constraints.
The bottleneck theory. Yes. Imagine your work
is water flowing through a pipe. OK. There's
always one spot where the pipe is narrowest.
That is the block. If you are great at shooting
video, but it takes you four hours to write a
title. The title is the block. Right. So buying
a faster camera doesn't help. It does nothing.
It just piles up more video footage that you
can't title. Exactly. You need a tool specifically
for titles. So it seems like the trap is putting
the tool before the problem. How do we discipline
ourselves? Identify the bottleneck first, the
one thing taking your time, then solve only that.
We are going to take a quick break for our sponsors,
but when we come back, we are going to get into
the limits of machine intelligence. Sounds good.
And we are back. Segment 3, traps 4, 7, and 8.
This is the philosophical part. It is. It really
challenges the way we view intelligence itself.
You have to be honest about what the machine
actually is. Right. Now it is amazing that these
models can read a thousand pages in a minute.
Whoa. Imagine scaling to a billion queries. It's
incredible. I definitely don't want to downplay
that. Yeah. But the source defines AI simply
as a guessing machine. A guessing machine? It
is just predicting the next word. It's basically
autocorrect on steroids. On massive steroids,
yes. But because its goal is to predict the most
likely next word, it wants to please you. And
this leads to trap four, hallucinations. Right.
We hear this term a lot. I think people misunderstand
why it happens, though. It happens because the
model prioritizes completion over accuracy. Let's
define it simply. Hallucination is when the computer
dreams up fake facts to please you. Right. Like
if you ask it, how many cars did we sell last
month? And it doesn't have access to your database?
It might just say 500. Because 500 sounds like
a plausible number for a car dealership. It sounds
like a good sentence. It's not lying in the human
sense. It's filling in the blank. Which is terrifying
if you are reporting to a bank. You have to verify
everything. This leads to the bigger issue of
decision making. Trap seven. I see leaders who
want the AI to make the big strategic calls.
Oh, yeah. Should we expand to Europe? Right.
Should I hire this VP? And that is a fatal error.
Because remember, the AI is trained on the past.
All of its data is historical. When you ask it
for a decision, it gives you the average of what
has worked before. It creates a regression to
the meme. Exactly. Regression to the meme. Let's
unpack that. So it pushes you toward mediocrity.
It pushes you toward the average. Think about
it. If you ask it to write a pop song, it will
write a song that sounds like every top song.
Because it's trained on all of them. Right. But
big business success, the outliers, usually come
from weird, brave, counterintuitive ideas. And
data cannot predict those. If you listen to the
AI... You will build a business that looks exactly
like everyone else's. You will build a perfectly
average business. The source uses a beautiful
analogy here about creativity. Yeah. A human
can watch a bird fly and get an idea for an airplane
part. We connect unrelated things. But an AI
just looks at a database of old airplane parts.
Exactly. It mixes old ideas. It doesn't create
new futures. And that links to trap sex, chasing
the news. The hype cycle. With how fast this
stuff moves, there is a lot of anxiety. Oh, a
new model dropped today. I need to change my
whole strategy. It is exhausting. The source
says to just ignore it. Focus on what doesn't
change. Jeff Bezos famously said this. Customers
will always want cheap prices and fast shipping.
No new AI model changes that. Build your business
around those constants. not the daily software
updates. If the machine effectively regresses
to the mean, what is the human's specific role
in the final decision? Humans must use gut feeling
and personal risk tolerance to make the final
call. Let's get practical. Segment four is the
tactical toolkit. Yes. Trap 12, the one tool
mistake. We want one app to rule them all. The
all in one. We like simplicity. But in 2026,
specialization is the reality. You wouldn't use
a hammer to cut wood. But people use ChatGPT
for everything. They really do. So let's break
down the 2026 Toolbox as the source describes
it. We have three main players, Claude, Gemini,
and ChatGPT. Right. Let's start with Claude.
OK. Think of Claude as your thoughtful writer
or your analyst. The source describes his voice
as natural, softer. It has a massive context
window too. Right. Meaning you can feed it huge
documents. So if I need to summarize a 50 page
contract. Or write a sensitive email to a client.
You use Claude. Next is Gemini? Gemini is your
connection to the live internet. It's Google.
So this is for facts. Checking the weather, looking
up stock prices, verifying news events. Because
if you ask ChatGPT for today's news, it might
hallucinate. Exactly. Gemini is grounded in the
live web. And then the classic, ChatGPT. What
is its lane now? Logic, reasoning, coding, structure.
OK. If you need to break down a complex problem
into steps or write a Python script, ChatGPT
is the sharpest tool in the shed. So it's a woodworker's
bench. I like that. Claude is the sander, getting
things smooth. Gemini is the tape measure, getting
facts straight. And ChatGPT is the saw, cutting
through the logic. Perfect analogy. If you try
to use the logic bot to write a feeling email,
it sounds robotic. And if you use the creative
bot to check facts, it lies to you. You have
to pick the right tool. Trap9 warns us about
how we talk to these tools too. The more data
myth. We tend to think if we paste a whole book
into the chat, the answer will be smarter. Right.
Here is everything I know. Fix my life. It's
like asking for directions and getting 50 maps
at once. You just get confused. And the computer
gets confused too. The advice is a clean desk
policy. Key prompt short. One page of notes equals
one summary. If you want a clear output, you
must provide a clear input. So specialization
is key. What is the immediate action step regarding
these different tools? Make a folder linking
all three and pick the right one for the job.
As we wrap up this deep dive, let's synthesize
this. We have covered 12 traps today. From the
empty company to the one tool mistake. But the
overarching theme here seems to be about confidence.
Confidence in our own humanity. That is the big
idea. The narrative has been man versus machine.
But the research shows it's actually man plus
machine. The real winners in 2026 aren't the
ones who automated everything into oblivion.
They are the ones who mixed human skills like
empathy and bravery with machine speed. Technology
should be used to grow, not to learn. You have
to learn the business manually first. So for
everyone listening, what is the move for tomorrow
morning? We don't want to overwhelm them. Pick
one thing. Don't try to fix all 12 traps at once.
Maybe tomorrow you stop using the bot for customer
complaints. Or maybe you just organize your toolbar
with the three different AIs. Just start with
one thing. I love that. The source ends with
a provocative thought that I want to leave you
with. Yeah. It mentions that a machine can never
understand the soul of an idea. It leads me to
ask you, the listener, which parts of your work
require a soul? And are you accidentally trying
to automate them? That is the question. Something
to think about. Thank you for joining us on this
deep dive. Take care, everyone.