Welcome back to the Deep Dive. We're here to
cut through the noise and get you insights that,
well, really matter. And today we're tackling
something that's definitely buzzing under the
surface. Yeah, it's that feeling in offices and
maybe even in your home office right now. That
quiet, nagging question. The one everyone's asking,
even if subconsciously. Is my job. Is my role.
Secure. Especially with AI advancing so, so quickly.
It's a genuine anxiety. And our sources today
really hit this head on. The key thing. the big
insight. It's that the real fight isn't about
trying to be faster than AI. That's a losing
game for most knowledge workers. Right. That's
the trap, isn't it? This idea that we just need
to be more efficient, quicker to somehow out
-machine the machine. Exactly. But the sources
we're diving into today are pretty clear. That
is just not a winning strategy. You're never
gonna beat AI at pure computation or storing
facts or just following rules flawlessly. It's
like trying to race a car on foot. You're competing
on their turf, playing their game. Yeah, bringing
a knife to a, well, a laser fight, as you said
earlier. It's destined to fail. So why is that?
Why is it such a losing battle? Well... It'd
come down to core strengths. AI's power lies
in processing massive data sets, running complex
algorithms perfectly. Speed. Things humans just
aren't built for at that scale. It's not about
effort, then. It's about fundamental design.
Precisely. It's a subtle, but really profound
difference in how we operate versus how they
operate. OK, so if we can't win on speed and
efficiency, what's left? What's our advantage?
What can humans do that machines fundamentally
struggle with. Ah, now that's what gets really
interesting. Our source material today, drawing
from discussions around six books to outwit AI,
points to a uniquely human superpower. A superpower.
I like the sound of that. It's interdisciplinary
thinking. Interdisciplinary thinking. Okay, unpack
that a bit. Think about connecting ideas from
totally different fields, like using insights
from psychology to solve a tricky business problem,
or applying lessons from how nature builds things
biomimicry to design better systems. So it's
about bridging gaps, seeing connections others
miss. Exactly that. This ability to link distant
concepts, to see patterns across domains, that's
incredibly hard for machines. They're usually
trained on specific data sets, highly specialized.
They stay in their lane, so to speak. Right.
They lack that intuitive feel for the wider world,
that ability to make creative leaps between different
areas of knowledge. OK, so the mission for us
today, for you listening, is to figure out How
do we actually train this human skill? How do
we learn to think in ways machines just can't
copy? Think of the sources we're using, these
six books, as tools for building a kind of mental
gymnasium. A cognitive gymnasium. Okay, yeah,
each book, each thinking model, strengthens different
cognitive muscles. They aren't just abstract
theories, they're practical frameworks. They
help you build a new cognitive operating system,
one designed for connection -making and deep
human thinking. Alright, I'm ready for the workout.
Where do we start? Let's jump into the first
one, systems thinking. Ugh, systems thinking.
Okay, I think I know this feeling. You fix one
problem right and then suddenly two new ones
pop up somewhere else. Precisely, the classic
whack -a -mole scenario. We're often trained
to look at problems in neat little boxes. Like
you boost sales figures, but then customer satisfaction
takes a dive. Or you streamline a process. And
suddenly team morale just plummets. It happens
because we often treat symptoms without seeing
the underlying connections. The ripple effects.
Exactly. the feedback loops, everything's connected.
Our solutions fail when they ignore these connections.
Now, AI is great at optimizing within clear boundaries.
But throw in unpredictable system interactions.
And it struggles. Yeah. They can't easily anticipate
those unintended consequences. Danela Meadows
in Thinking in Systems peaches us to see those
invisible threads. So it's not just solving problems,
but understanding why they happen and why solutions
sometimes backfire. Right. And the real game
changer she introduces is the idea of Leverage
points. Leverage points. Okay, what are those?
They're places in a system where a small shift,
a small change, can create a really big impact.
She even ranks them. Ranks them from weak to
strong. Yeah. The weakest is often just changing
numbers, parameters, like budgets or targets.
The strongest. Changing the paradigm, the fundamental
beliefs or goals of the system itself. Wow. That
sounds incredibly powerful, like finding the
master switch. It really can be. Think about,
say, city traffic. The usual response is what?
Build more roads. Yeah, seems logical. But that's
often a low leverage action. Meadows calls it
changing a parameter. It often leads to induced
demand. The new roads just fill up. Congestion
returns. Okay, so what's the high leverage approach?
A systems thinker questions the paradigm. Is
driving a personal car the only or best way to
get around? Then you look at higher leverage
points. Investing massively in great public transport.
Designing walkable cities. Promoting remote work.
changing the goal or the mindset of the system.
That's a completely different way of looking
at it. Okay, so how can you, listening right
now, apply this? Try a mini systems audit. Pick
a recurring problem at work. A bottleneck, maybe,
or a difficult team dynamic. Okay. First, just
map it out. Who's involved, what processes, what
tools or policies. Then, and this is key, draw
the connections. How does one thing influence
another? Like, maybe constant time pressure leads
to rushed, lower quality work. Right. And that
lower quality work maybe leads to more meetings
to fix things. Which eats up time, leading to
less deep work. Which increases the time pressure
again. A vicious cycle. Exactly. A feedback loop.
Once you see that loop, you can look for leverage
points. Instead of just saying less meetings,
maybe the leverage point is a rule change, like
every meeting needs a clear agenda and objective
set beforehand. Or changing a goal, like shifting
focus from just task completion speed to, say,
long -term value creation. Precisely. It's about
finding where a small nudge can redirect the
whole system. I actually did this with my morning
routine once realized hitting snooze wasn't more
rest, it was just more stress. Seeing the system
helps. Okay, so systems thinking helps us map
the territory, but the territory is often foggy,
uncertain, right? How do we make good decisions
then? Ah, that brings us perfectly to the next
cognitive muscle, probabilistic thinking. This
draws heavily from Annie Duke's Thinking in Bats.
Annie Duke, the poker player. The very same.
And poker teaches you something crucial. The
world isn't black and white. It's shades of gray.
It's probabilities. Our brains crave certainty,
but reality rarely delivers. We want to know
if a decision was right or wrong. Exactly. But
Duke argues that's often the wrong question.
She learned the hard way at the poker table that
a good outcome doesn't automatically mean it
was a good decision. And a bad outcome doesn't
mean the decision was bad either. Precisely.
You have to separate the quality of the decision
process from the quality of the result. AI can
crunch probabilities, sure. but it struggles
with the ambiguity, the context, the hidden information
of the real world. So is less, was I right, and
more? Was my process sound, given what I knew
at the time? Duke calls the trap of judging decisions
solely by outcomes resulting. Resulting? Okay,
give us an example. Imagine your team decides,
based on solid data and research at that moment,
to cut a product feature. Six months later, oops,
a competitor launches something similar, and
it's a huge hit. The immediate reaction is, oh
no, we messed up. Terrible decision. That's resulting.
The probabilistic thinker asks, OK, hold on.
Given the information we had back then, was cutting
the feature a reasonable bet? Maybe it was. Perhaps
the market shifted unexpectedly or the competitor
had different information. So the lesson isn't
necessarily regret, but... Improvement. Exactly.
How can we improve our information gathering?
How can we refine our decision -making process
for the next bet, regardless of how this one
turned out? Okay, practical application time.
How do we practice this? Conduct a decision debrief.
After a big project, a key decision, win or lose,
get the team together, or even just reflect yourself.
What questions do you ask? What did we actually
know when we made the call? What were the unknowns?
What alternatives did we seriously consider?
Was the discussion open? Were different viewpoints
welcomed, even encouraged? And crucially, What
can we learn to make better bets next time? It's
not about assigning blame for the past. It's
about improving judgment for the future. All
right. We've mapped the system. We're learning
to navigate uncertainty. Now let's talk about
getting to the actual root of problems. This
feels like logic 101, but maybe it's trickier
than it looks. It often is. We're drawing here
from Russell Ackoff and the art of problem solving.
Logic seems basic, but it's surprising how often
even very smart people make logical errors. Confusing
correlation with causation is a classic. Jumping
to conclusions. Assuming things without checking.
Right. Ackhoff's big idea was that many, maybe
most, workplace issues aren't really technical
problems. They're problems of logic or definition
in disguise. Logic or definition. Interesting.
And his really provocative idea is that you shouldn't
just aim to solve problems, you should aim to
dissolve them. Dissolve them? Like, make them
vanish? Essentially, yes. He argued that many
problems only exist because of how we frame them
in the first place. Change the frame, change
the definition, and the problem itself might
just disappear. Wow. Can AI do that? Step back
and question the frame. That's precisely what
it struggles with. AI is brilliant at solving
clearly defined problems within given constraints.
But asking, hold on. Are we even working on the
right problem here? That's a deeply human step.
OK, I need an example of dissolving a problem.
Think about the common management question. How
do we motivate our employees? Yeah, seems like
a standard problem to solve. But notice the assumption
baked in, that employees lack motivation and
need it externally supplied. Ackhoff would flip
this. He'd dissolve the problem by reframing
it. Oh. He'd ask, What is our system, our policies,
our processes, our culture doing that demotivates
people who are likely already motivated? Ah,
that completely shifts the focus. It's not about
fixing the people. It's about fixing the environment
around them. Exactly. Suddenly you're looking
at bureaucracy, bad management, unclear goals,
lack of autonomy, entirely different avenues
for action. That's powerful. So for the listener,
how can they practice this reframing? Try ACOF's
five refrains exercise. Take a problem you're
wrestling with. Let's say it's sales for Product
X are declining. Okay, standard business problem.
Now rewrite that problem statement in five different
ways, each forcing a different perspective. Like
how? You could frame it from the customer's view.
How are the needs Product X used to meet changing?
Or the competitor's view? What alternatives are
customers choosing instead and why? Or the system
view? What internal factors might be impacting
Product X's performance? or value? Is product
X's core value still relevant? Or even the inverse?
How can we accelerate the decline of product
X to make space for something better? Each one
opens up totally different potential solutions,
doesn't it? Completely. It breaks you out of
tunnel vision and forces you to question the
initial framing of the problem itself. Okay,
this is fascinating. We're going deep. Now, let's
broaden out. You mentioned interdisciplinary
thinking earlier. This next one, broad thinking
from David Epstein's range, things right up that
alley. It absolutely is. And it pushes back against
some really common advice, doesn't it? The whole
niche down, specialize, find your lane mantra.
Epstein argues that might actually be. Counterproductive.
In many situations, yes. Especially in complex,
unpredictable fields, which is, let's face it,
most fields today. His research suggests that
generalists, people with range, often outperform
narrow specialists when faced with novel problems.
But AI is the ultimate specialist, isn't it?
It knows everything about its narrow domain.
Precisely. And that's its limitation. It lacks
that broad feel for the world, the intuition
that comes from varied experiences. Breakthroughs
often come from what Epstein calls cognitive
bees. Cognitive bees. I like that. Explain. People
who flip between different fields, different
disciplines, picking up ideas here, pollinating
concepts there, they create novel hybrids. Combinations
that someone stuck in a single silo would never
conceive of. So deep specialization might make
you efficient at known tasks, but range makes
you adaptable and innovative for unknown challenges.
That's a great way to put it. So practically
speaking, how do we cultivate this range? How
do we become cognitive bees? Start actively collecting
mental models, solutions, and ways of thinking
from fields completely unrelated to your own.
Be curious. Give me some examples. How could
that work? OK, say you're struggling with company
culture. Don't just read management books. How
do marine biologists think about complex, evolving
ecosystems? Maybe there are metaphors there.
Interesting. Or maybe leading an innovation team
under pressure. Look at how Michelin -starred
chefs manage intense creativity and execution
in a chaotic kitchen environment. Or designing
better customer experiences. How do video game
designers keep players hooked and engaged for
hundreds of hours? So you deliberately look for
analogies and frameworks in unexpected places.
Yes. Maybe commit, say, 20 % of your reading
or learning time to exploring areas totally outside
your professional domain. It builds a unique
mental toolkit. The connections might not be
immediate, but when you face a truly new challenge,
you'll have this diverse set of tools that no
specialized AI can match. All right, range gives
us diverse inputs. Now, how do we generate actual
ideas from that? Let's talk design thinking,
drawing from idea flow. All right, because it's
easy to fall into traps here, too. One common
trap is solving problems backward. Backward,
what do you mean? Starting with a solution you
already know, or like maybe a piece of technology
or a familiar process, and then looking for a
problem it can solve, or... Or just jumping on
the very first solution that comes to mind for
a problem. Exactly. We converge too quickly.
Idea Flow emphasizes a more systematic process,
drawing from design thinking principles. It's
about deliberately expanding the possibilities.
First, the divergence phase. Brainstorming widely,
no judgment. Yes, generating lots of options,
even wild ones. Then, you systematically narrow
down and refine the convergence phase. AI can
generate variations on a theme, but it struggles
with that initial, wide -open exploration and
questioning the fundamental need. It won't easily
ask, do we even need an idea here? So how do
we ensure we diverge properly? One powerful tool
they highlight is the How Might We or HMW framework.
It's a way to phrase questions that inherently
opens up possibilities. Okay, let's use an example.
Say a local bookstore struggling against online
giants. The obvious maybe backward solution is
Offer discounts. Right. A race to the bottom,
they probably can't win. But using HMW questions
reframes the challenge. How might we? What? How
might we turn the bookstore into the community's
third living room? How might we create a book
discovery experience that an algorithm simply
can't replicate? How might we become a hub for
local cultural events and book clubs? Ah, see,
each of those questions points towards completely
different types of solutions. Community, experience,
events. not just price. Exactly. They open up
the solution space dramatically, focusing on
human connection and experience, areas where
the physical store has an advantage. So the practical
application for you listening? Take a problem
you're stuck on. Instead of jumping to solutions,
try generating five or ten different how -might
-we questions about it. See how it shifts your
perspective and what new avenues it opens up.
It forces you to think wider before you narrow
down. Okay, we've covered a lot of ground. Mapping
systems, betting under uncertainty, reframing
problems, thinking broadly, generating ideas.
What's our final workout? We end with maybe the
most fundamental and perhaps challenging mode.
First, principles thinking. This is heavily inspired
by Peter Thiel's zero to one. Zero to one, meaning
creating something entirely new, not just improving
what exists. Exactly. Most people, most companies
operate in the one to end space. They take something
that already exists and make it incrementally
better. That's reasoning by analogy. Doing what
others are doing, but maybe slightly faster or
cheaper? Right. But truly disruptive innovation,
the zero to one leaps, often come from reasoning
from first principles. And AI struggles with
this. Massively. AI is, in its essence, an analogy
machine. It learns from vast amounts of existing
data and examples. It's brilliant at finding
patterns in what is. But it can't easily tear
down existing assumptions and build something
completely new from the ground up because its
entire foundation is based on precedent. So what
does thinking from first principles actually
involve? It means breaking a problem down to
its absolute most fundamental truths. The things
you know are true, like the laws of physics or
basic human needs. You strip away all the assumptions,
all the conventions, all the way things are usually
done. And then you build back up from only those
fundamental truths. Precisely. Ignoring how it's
done now. Let's take an example. Improving employee
training. Okay. The typical approach is, how
can we make our workshops better? Or should we
buy a new online course platform? That's reasoning
by analogy, improving existing models. First
principles thinking starts differently. Ask,
what is the fundamental truth here? Employees
need certain skills to do their jobs well, and
maybe. Learning is most effective when it's relevant
and applied quickly. Good. Now, what's the current
assumption? that workshops or online courses
are the best or only way to deliver that skill
transfer. Okay, now ignore that assumption. Based
only on the fundamental truths needing skills,
learning by doing, how would you design the absolute
best way to transfer skills from scratch? Maybe
it's not a course at all. Maybe it's a structured
mentorship program. Or guided real -world projects
with immediate feedback. Or an internal platform
where experts share knowledge directly as needed.
See? You've just opened the door to potentially
far superior solutions simply by questioning
the ingrained assumption and building up from
bedrock truths. That's a powerful mental reset.
So the application for listeners. Take a big
challenge you're facing. Really grill yourself.
What are the absolute undeniable truths here?
What are the assumptions I'm making just because
that's how it's always been done? And crucially,
How would I rebuild this from scratch based only
on those truths? Wow. Okay. That's six powerful
ways of thinking systems That's logic range design
first principles. So what happens when you put
them all together? What's the big picture? The
big picture is that these aren't just isolated
tools. They work together They reinforce each
other think of it as installing a new more powerful
cognitive operating system a human OS upgrade
for the AI something like that Their real power
comes from their synergy. How do they connect?
We'll think about it Systems thinking gives you
the map. It shows you the complex landscape of
the problem, all the interconnections. Okay,
I see the map. Probabilistic thinking is your
compass for navigating the inherent uncertainty
on that map. It helps you make smarter bets when
the path isn't clear. Got my map and compass.
Ackoff's logic ensures you're not just wandering,
but actually trying to get to the right destination
by dissolving misleading problems and asking
the right questions. Making sure I'm heading
the right way. Range gives you potential shortcuts
and alternative routes by letting you borrow
ideas and solutions from distant, unexpected
places on the map. Finding clever pathways. Design
thinking is like your sketchbook. It helps you
rapidly generate and explore dozens of potential
routes before committing to one. Exploring the
options. And first principles thinking. That's
the ultimate power tool. It gives you the ability
to say, this whole map is wrong. tear it up,
and draw a fundamentally new and better one from
scratch. Map, compass, destination check, shortcuts,
route sketching, and the power to redraw the
map. That's quite a toolkit. When you start combining
these modes of thought, you develop a way of
seeing and solving problems that is deeply human
and incredibly difficult for any current AI to
replicate. That's the edge. That's how you become
indispensable. Hashtag, hashtag, tag, atro. So
the message here seems pretty clear. I think
so. The future really does belong to minds that
can think in ways machines can't. If your value
is primarily in speed, efficiency, or just executing
known procedures, well, AI is getting very good
at that. Those roles are likely at risk. But
AI cannot easily replicate the ability to connect
disparate ideas, to navigate profound uncertainty
with good judgment, to reframe and dissolve complex
problems, or to imagine something truly new.
So our job isn't to become more like machines.
No. It's to become more human in our thinking,
to lean into these cognitive strengths that are
uniquely ours. And the great thing is we've outlined
a clear path to start doing that based on these
sources. Where should someone begin? Don't try
to master all six at once. That's overwhelming.
Pick the one thinking model, the one book that
resonates most with you right now, or that addresses
what you feel is your biggest current weakness.
Just to recap the titles for everyone, we talked
about Thinking in Systems by Daniella Meadows.
Thinking in Bets by Annie Duke. The Art of Problem
Solving by Russell Ackoff. Range by David Epstein.
Idea Flow by Jeremy Utley and Perry Kleban. And
Zero to One by Peter Thiel and Blake Masters.
Pick one, dive in, and start exercising that
particular cognitive muscle. Exactly. This deep
dive wasn't just about understanding the challenge.
It was about giving you practical tools, a way
forward, a way to not just survive, but actually
thrive by leveraging your unique human intelligence
in the age of AI. Don't just listen and start
thinking differently. Start practicing. Your
future thinking starts now.