Well, hello, Yvonne.
Yvonne is not in the she shed. I'm
not sure we can talk to her.
I'm not in the she shed today. For
those wondering what is going on,
my heater is broken out there, and so
we've called somebody to come fix it. But
since it is a shed and not a
house,
we are patiently waiting for our repair person
to fit us into his schedule. So I
am in our library sitting in a recliner.
With a Charlie Brown looking tree behind you.
Yeah. It is a Charlie Brown tree,
which is very true.
There's another bigger tree in this room as
well. But, yeah, it's my Charlie Brown tree.
Okay. Cool.
And Tom has not decorated the plant and
I'm disappointed.
I put my Christmas decorations up, Tom.
Mine are just in the other room. And
I'm a little thrown off my game because
in all of the hundreds of episodes we
recorded, this is the very first one in
which you were not like, Tom.
You're just like, what's going on? It's great.
It's fine. But it's, yeah. And the plant.
I think the plant needs some Christmas balls
and some, some garland.
Well, it's grown.
It looks like two, doesn't it? It does
look like two, but it's not. It's one.
It's, getting a little out of control. I
think it's, letting the, talk about it going
to its head. It might need a new
pot.
Yeah. Maybe.
It, it leaked all over the desk and
got a bunch of papers wet last night.
So it's been a bad bad plant. It's
bad plant. That's,
okay. So today we're gonna start by talking
about or we're gonna talk about
this very interesting article in the Hedgehog Review.
And I don't know if you've ever heard
of the Hedgehog Review, but it's obviously
one of my more favorite
Of course.
Journals or magazines
as the case might be. And it's called
the Amoeba and the Mathematician.
It's by Dworkin, who I think is a
philosopher
person. I'm not I don't remember his whole
he's
a physician and political scientist.
That's a real,
that's a real combination for you there. Quite
a combination
of, interests.
Yeah.
So,
anyway, he's talking about
how
we react to machines. Is that I mean,
maybe I don't know. How would you put
it, Yvonne?
Well, I think part of what he's talking
here is about how we have defined
expertise
and being the best
with becoming more
computer like, with being
behaving more like a machine or an algorithm.
Yeah. And now that we have
and we've developed machines and algorithms that are
do many, many, many things better than us,
that we might need to rephrase that a
bit. Yeah. I would actually say that's
always been true about the machines being better
than us. For instance,
there have been a lot of discussions about
why the American military succeeds in places where
other militaries don't. And part of the reason
given is because
the American the average American military member is
unafraid to disobey orders and do what needs
to be done to get the job done.
They just go do it.
And
there's a very machine like
atmosphere in the military, if you've ever been
in the military,
that you almost have to defy.
And that's been a very strong component of
our culture
here, at least in America, for many, many,
many, many, many years, is that we're not
machines.
Like, that's not what we do. We're actually
not in that category.
And I think that between, like you said,
Yvonne, like the idea that machines are taking
over more and they're more effective than us
at certain things
makes us want to trust the machine more
than the person.
Because the machine is right more often.
Well, I think one of the things that
I've heard a lot about lately
and and we don't have a word for
it other than taste.
When we talk about things like
either user interface design, you look at
software
well, that software hardware combo that Apple, you
know, when they first created the iPhone or
the early days back when, you know, Steve
Jobs was still around and in control. We
we have this concept of
taste, of design, of intuition
that we've never been able to, like, fully
define or explain
that really taps into what it is to
be human.
But I think for a lot of us
engineering types,
talking that way makes us kind of uncomfortable
because there's a component but because we can't
define it, because we can't put it in
a rubric, because we can't,
you know, develop a very specific metric to
measure it. Yeah. It's aesthetics, essentially, is what
it is, is aesthetics.
And we don't study it, and we don't
want to study it by and large because
it does. It makes us uncomfortable.
I I yeah. I get that.
One of my PhD seminars was aesthetics.
I spent, like, we've
read, like, 20 books on aesthetics and tried
to understand what this is all about and
tried to understand the concept of beauty.
And I think there's a lot more to
it than just,
I think I think it's a very real
thing.
I don't know.
Have you have you ever read
CS Lewis, The Abolition of Man?
No, neither of you. It's very I've heard
of it. Okay. Yeah. It's an interesting argument
where he's talking about how
when you replace the concept of,
sublime
and you say
this is sublime objectively
versus this is sublime because it makes me
feel that way,
you actually lose something of the humanness
and and something of the the ethical and
moral qualities of that. And
I think that's partially what we're seeing in
the computer world is that
we want to quantify the user interface to
make it,
you know, we wanna, is it more efficient?
Does it
produce more clicks? Does it produce more follow
through?
But
or does it make it faster?
But, really, there's a lot more to it
than that.
And and in this way, we're becoming more
machine like.
So I don't know. Tom, any thoughts? You
you you're kinda quiet over there
in the corner with your plant.
Well, plants are quiet. Maybe I'm taking cues
from my plant.
I,
I thought that was interesting what you said
because in some way machines have always been
better than us. That's why we have them.
That's why we, you know, we, they would
not exist if that wasn't the case. And
so I think,
I think that the expertise is in employing
the machine.
And, and I'm sure this conversation has been
had many times before, but,
the,
I almost think the aesthetics of it are,
how to employ the machine.
It's not whether or not there is a
machine or whether or not the machine is
gonna do x y and z functions because
it will. It's, you know, how and where
to employ it that is the
the thing that the human is,
suited to. Yeah. I think there's a couple
of aspects to that. The first is, what
is the machine meant to do versus what
we use it for?
I always say that once you make the
once you make the big beautiful hammer, you
can't stop people from using it to drive
nails or to close doors or right? I
mean, the hammer is designed to do a
certain thing. The other thing is that we
don't think about a lot of times is
that the hammer
actually changed construction.
Like, we used to build houses and buildings
one way, and now we build them a
different way
because of the hammer.
And, like, so this Well, I think I've
I've mentioned it here before, but there's the
great Churchill quote that first we make our
buildings and then our buildings make us. Right?
We we form these things
that have a purpose.
And then those things, whether it's
architecture, you think about, like,
The
United States interstate highway system, or you think
about cities that have
mass transit
networks and how
we build these things, but then those things
that we build shape who we become in
the future, both as individuals,
culturally, how we interact,
all of those things. And it's a
it's a very symbiotic
Yeah. Existence
that that we have even with our own
creations.
Yep. And and sometimes we unleash a thing
that that we did not anticipate in that
creation.
Yeah. And so, like, what he's talking about
in this article is interesting is about poker
players.
And, basically,
there are, he says, three different kinds of
poker players. There's the recreational player, doesn't care
about complex play, doesn't even think about winning.
They just want to enjoy
the pleasure of the game, the social aspects,
the the physical,
like, I feel the cards in my hand.
You know, it's something to do. It occupies
my mind
kind of a thing.
And then
there is
what are, like,
what he calls
GTO or elite players,
who are players who make the most who
make money. That's
That's what they do. And they guide their
play independent of the cards on the table
or even the tells. People go, oh, you
know, you have a tell. I can tell
that you're lying about what's in your deck.
And he's like, no. They they play based
on gay game theory
optimization or game theory optimal,
which is based on mathematics.
And so to be a more efficient poker
player, you have to become more like a
machine
than the recreational
player. And I think that's kind of the
point he's making is that if you want
to be, you know, a good poker player,
you need to become
more of a machine,
more of a computer that plays poker
than a human who enjoys it. I I
liked the, the so the third type that
he talked about was in between those two,
the the business minded player. Right? Yeah. The
person who's not super interested in, like,
cutting edge of the game or anything but
their boss but also they're not just there
to have a good time.
You know, he talks about how this person
is the one in the middle. That's the
one that makes money. The elite poker players
don't make money. That's not what they're trying
to do,
which I thought was really interesting.
But but the guy in or the person
in the middle is the one who's who
is playing intentionally
playing terrible terrible opponents so that he can
maximize how much how much money he makes
and is doing things that's just like, yeah
if you're trying to prove that you're the
best then you wouldn't play terrible people, you'd
play really good people. Like that's what you're
trying to do. But the person in the
middle is trying to make money and so
the way he does that is by playing
terrible opponents. Like it's very interesting.
The idea of being the best
as the you know elsewhere in the article
he talks about confusing expertise with being the
best. And I thought that was a really
interesting distinction.
And in in the machine realm, you know,
the machines are the best, because, you know,
mechanically or whatever, they're they're the standard for
what can be done.
But it's not just be the dumb meatbag
human or be the machine that's perfect at
everything. It's the it being in the middle,
you know, the the one who who has
the business minded player he calls it. And
I think that's a pretty interesting analogy for
the way that we in our in our
work,
use technology.
Cause we shouldn't be trying to be the
machine cause we're not. We'll never go fast
enough
or precise enough. But,
what's the business optimized? What's the business minded
network engineer look like?
Which which,
what kind of network person or technology person
has figured out how to make money?
You know,
I'm I'm putting quotes around that because we're
a cost center usually, but, like, has figured
out how to make money by trying to
emulate the machine but not going all the
way.
Yeah. My my other observation of those three
categories
is that we have one we prefer,
and we we make value judgments
about how folks sort themselves into these categories.
In other words, you know, the recreational player
is just wasting everybody's time and,
but but but they're doing it for the
joy of it. And then you've got the
business player and everybody's like, well, they're cutthroat
or they're, you know or then you've got
the,
what I will call the borderline rule breaker
player who's
pushing the game to its limits. And
depending on where we see ourselves and what
we deeply care about, we we make value
judgments about each of those different categories.
And and we do that in our work
as well.
We we just don't often articulate it in
our minds so clearly.
Yeah. The different categories are what we're doing.
Yeah. Oh, yeah. Yeah. Yeah. Yeah. The other
interesting thing too is what he calls this
wise player, which you were talking about there,
Tom,
is somebody who blends their knowledge of the
game,
which is not as good as the as
the elite player is,
with their understanding of the humans they are
competing against.
So there's a soft skill or what Mike
Bouchon would call super a superpower
versus
the
strictly technical skill
that's combined
in that person and being able to see
both sides of this
and use those things. And I think
this is one of the differences between sales,
per for instance,
and being a full time coder.
Right?
Although full time coders, we think of as
being, oh, they're back end. They don't ever
deal with humans. No. Go back to the
UI example.
Right? Mhmm. How how many times have you
encountered a UI
and it makes
zero sense to you? You're sitting there looking
at it going,
what was someone thinking
when they built this? Right?
The classic example when I always think about
this is in Cisco IOS. It used to
be
IP fast
to get,
Fast Cache on, and then,
or maybe it was Fast IP and then
IPCEF.
So they were backwards from each other. In
one case, IP was the noun. In the
second case, it was the object or the
verb. In the second case, it was the
object, subject or object or whatever. And I'm
like, that never made any sense to me.
But then if you go look at the
data structures
behind it,
those are directly
exposing
the way the data structure is
built. And the UI is built around the
way the data structure works.
Not As opposed to how the user experiences
it.
Exactly.
And so we don't think about this, but
that's
the elite player. I'm gonna make the I'm
gonna make that user interface
match my
match my database or my the way the
application works.
So there's a there's a paragraph in this
article I wanna read. It's actually right before
the the the poker discussion starts, but it
helps kinda frame up
the amoeba and the mathematician. And it says,
take a continuum that has an amoeba at
one end and a mathematician at the other.
The amoeba has the most interest in its
immediate environment.
It reacts strongly and quickly to the smallest
stimulus.
The mathematician
has the least interest in his immediate environment.
He's detached from temporal considerations
and lives in an insular world of numbers.
Somewhere between the amoeba and the mathematician
sits the competitive worker of the future, the
worker who reacts to a stimulus as an
amoeba does, although with more thought, but not
with so much depth of thought that he
or she ignores the stimulus altogether. And so
that's kind of the description of what he's
talking about. Hey. You gotta be aware of
your environment. You gotta send out your little
feelers and see what's going on and respond
and react and understand.
And then you gotta have enough depth of
knowledge about
the the arena so that you can make
good decisions, but you've gotta
combine the stimulus
with the data and your understanding of the
working space.
And and he says that's gonna be the
sweet spot of the future. Not the person
who can
deeply execute on a technical level
because our machines are gonna do that for
us. But it's that combining the stimulus with
all of that
to
to provide meaning and value and and all
that. Yeah. And I think there's a warning
here as well.
Don't try to beat the machine. Don't try
to become the machine. Don't shape yourself to
the machine.
Right? Yes. You have a hammer. Great. You
can build a house in a certain way.
But
don't lose your creativity
and your ability to talk to the customer
and your ability to understand the structure and
the layout
and the traffic flow in a house and
stuff like that. Like, there's more to this.
Same thing with the network. Right? Don't just
become the machine who builds the most efficient
network possible.
There are users attached to that network.
Right? Or the most efficient application possible. There
are people in there.
Russ, I have a question for you. So
when I hear the the amoeba amoeba and
the mathematician,
in my experience, I I'm seeing faces of
people that I've worked with. Mhmm.
In the IETF, do you feel like you
see both of those manifest as human personalities?
Sure. Do you see I see insular and
I think IETF, but then I but then
I see, like,
reacting to the environment and I think like
an operator, like somebody who's living in it,
you know, maybe some of those are in
the IETF.
Do you see those? Yeah. So I think
in Nanog,
for instance, in network operators groups, you'll see
more amoebas than you do mathematicians. I think
that's absolutely true.
Although, maybe there are mathematicians in a different
way in the sense that they are conforming
themselves to the machine of the global network
or the network. They're not you know, they're
all about, can I configure quickly? Can I
trouble you know, CCA the old CCA was
about troubleshooting?
I get the impression from a lot of
people now that a lot of these certifications
are about memorization and knowing the right things
to regurgitate
and stuff. That feels like a movement from
the amoeba to the machine
in that realm. And the same things can
be true like in Nanog. In the ITF,
yeah, they're actually both.
There are people who are
much more concerned
about
people
and getting work done
and just
being useful
and being peacemakers
among the people who are screaming at each
other.
And there are other people who wanna get
it exactly
technical right.
And I think part of the problem that
happens in the IETF
and other standards organizations
is the people who wanna get it perfectly
right end up paneling the process
rather than the peacemakers,
and it makes the process very precise. Did
you word this email
exactly correctly? Because if you don't word it
correctly,
then that officially did never occur. Like,
no. That's not the way things actually
work. Right? That's a machine that's a machine's
view. Right? That's a machine's view. Yes.
Interesting.
So, Yvonne, looks like you have something on
the top of your head. Well, no. I
was just thinking about, you know, I
I've had some questions in my work life
earlier this week about,
you know, every organization
has,
there may not be formal certification processes, but
conversations or or processes to validate
that people know the things that they're supposed
to know.
And and it's funny because I've always been
a very, like, spirit of the law kind
of a person. Like, people are supposed to
fill out this kind of document and build
a packet, and it's supposed to demonstrate their
knowledge and expertise and x y z. And
one of the reasons I always struggled with
certifications,
I got a few, but I was never
super passionate about it because I felt like
they were
so much they always tend toward the mathematicians
side of things.
Alright? Can you check the box? Can you
answer the question?
And and so I had a conversation spirit
of the law kind of a person. Did
the person demonstrate that they have the spirit
of the law kind of a person. Did
the person demonstrate
that they have the skills and the ability
and the knowledge that they need to be
able to execute the job? And to me,
if the answer to that is yes,
then we say yes and we move on
because I'm I don't wanna be in the
business of nitpicking
every little box and every little spot.
The problem is
on the other side of that,
people can play fast and loose with the
with that kind of approach as well,
and then you don't have any standards. Right?
And so it it
and I think some of this is personality
based and,
you know, but but I also think, like,
we're moving more toward,
and that we will have more value
for the folks
who can
figure out that kind of ambiguity
Yeah. And and make progress within it as
opposed to,
I did all the things and know all
the things just ask me, and I can
answer the most esoteric question you could possibly
ever ask me about Yeah. You know, protocol.
I'll give you an example from I don't
know.
Not too long ago, but long ago.
I once filled out an an expense report.
It's gonna sound really stupid.
And the way that
the hotel set up their
their bill,
there was 25 different taxes
on the single night because they wanna split
it all out and say, okay. You paid
the city 15¢, and you paid the county
25¢,
and you paid,
you know, the state x amount and on
and on and on. And there's this fat,
and there's this and there's that.
And so me, I'm like,
there are two slots for taxes on the
thing.
So I'm going to simply
sum all the taxes up
and put them in one of those slots.
The numbers match.
I don't really care whether it was a
city tax or a state tax or a
county tax or a VAT or I could
care less. It's a tax.
Oh, no. No. No. No. No. No. The
expense report was kicked back. You must go
in and use the additional boxes and put
in every 15¢.
Every one of those taxes has to be
to the penny.
Correct. And all of them have to be
correctly listed because,
you know, that's the way we do things.
And, like,
okay.
You know?
And
we're back to taste and judgment
and Oh, yes. Trying to understand
if, like,
is there is there actual
data
that to be gleaned from making you itemize
out each one of those?
Or
Yeah. Is it is it just a process?
Is it just toil for the sake of
toil? And I think the ability to distinguish
between those two things,
we we are not gonna be in want
of information, and that's been the case for,
you know, a couple decades at this point
with Google and the Internet and, you know,
it's it's it's it's under you know, it's
knowing what data matters in what scenario
and what to apply when and where and
then how deep to go.
Because I I see a lot of people
making mistakes because they go too deep,
not not because they don't go deep enough.
And some of that is just because the
world of which I operate these days. Yeah.
But you you I heard a very wise
man say once,
you can drive off the road into the
ditch on both sides. Mhmm.
You know?
And because the goal is to keep it
between the ditches.
Right? And you swerve too far either way,
you end up in trouble.
Yep. Yep.
Okay. Well, I don't have anything else to
put on that topic. Anything, Yvonne?
All good. Anything, Tom?
Nope. I'm good. Alright. Cool.
Alright. So thank you for listening to this
episode. Oh, I'm sorry. Let's back up. Tom,
say your word.
LinkedIn.
And Yvonne, say your words.
I have a lot of words.
You can find me on LinkedIn, Yvonne Sharp,
or you can also catch me with William
Collins over on the Cloud Gambit and sometimes
on x at Sharp Network. Alright. Cool. I'm
Russ White. You can find me here at
rule11.tech@thehedge,
and sometimes on x at routing geek
and sometimes on LinkedIn. I mean, I'm just
not a huge social media person, which is
unfortunate because that's how you get attention nowadays.
But anyway
alright. Well, thanks for listening to this episode
of The Hedge. We know that we live
in an attention driven economy and that your
attention
is
the critical thing right now. So, again, thank
you for listening all the way to the
bitter end, and we will catch you next
time.