The really scary vulnerabilities in these agentic processes that
we haven't even scratched the surface.
It's coming up with cognition speed of things human couldn't
even figure out. Your 20,000 developers are gonna
come down to probably 2000 developers.
That's a reality. Meet John Willis, a foundational
figure in the DevOps movement with over 45 years of experience
and co-author of the DevOps Handbook.
In this episode, he explains how to tackle AI and modern
complexity using Deming's System of Profound Knowledge.
Using the Deming's Profound Knowledge, how do you actually
see AI impact to our organizations?
You can't understand science without understanding philosophy
of science. So therefore you can't
understand AI without understanding the philosophy of
AI. AI today is an alien cognition.
We built a thinking machine, but it doesn't think like humans.
To understand a complex system, you need a lens that takes into
account four sort of elements. Theory of knowledge.
How do we know what we think we know?
Understanding variation. Understanding human behavior.
And systems thinking. Deming had a lot to do, not just
Toyota, but the resurrection of Japan after World War II.
In 1950, he told everybody, follow these ideas, you'll be a
world power. In five years, they overtook
Germany. For leaders out there, what are
some of the practical things about AI that you can advise?
You'd be an idiot in a corporation to sort of ban AI.
I'm sorry you're going outta business.
But you need to understand there are a lot of perils.
Here's the hard part. I just gave you a lot of keys.
You gotta figure out the doors that you can open with those
keys. Once you have that, you have a
world of opportunity to affect complex systems, change
organizational behavior. Hello, everyone.
Welcome back to another new episode of the Tech Lead Journal
podcast. Today, I'm very excited to have
John Willis here with me. Uh, John is a prolific author,
one of the authors from IT Revolutions.
His books, DevOps IT Handbook and also Deming's Journey to
Profound Knowledge, are the two most popular books in IT
Revolutions. Uh, John is also someone who is
very active in the DevOps world with all his contributions.
So yeah, really excited to have you again in the show, John.
So thank you for being here. No, that's great.
Just a quick, uh, update. The DevOps Handbook, which is
the second most popular behind Gene Kim's, uh, Phoenix Project.
And my Deming one is, there's a couple ahead of me on the Deming
one. So just, just to keep everything
in line. But thank you.
I appreciate your, uh, your shout outs.
Right. So John, I love to start our
conversation by asking you, maybe sharing a little bit from
your career, any turning points, highlights you think we all can
learn from your journey. Yeah.
You know, my two boys are out the house now and, you know, I
think the three things I've tried to really.
And any young person that I mentor, and I love mentoring
people by the way. So if you listen to this and yes
if something that interests you, which I, I love, especially
mentoring young career people, um.
And the simple one is work hard, have fun.
I mean, it's, you know, I remember telling my kids that
from like the earliest ages, and one time I was dropping one of
my sons off at the elementary school and one of the sort of,
uh, traffic guards that just wanna make sure the cars are
always going. I said that.
And she said, wow, that's stupid.
I'm like, no man. This is what's wrong with modern
day education. Like work hard.
And for some reason, they thought work hard couldn't be
fun. You know, like, sorry.
I think the other thing too, I think this is, these are
important. I'm glad you asked this, because
I never get to really address this on podcasts.
Be a boundless advisor. You get way more than you give.
This is like, you know, I've been doing this 45 years, you
know, and the one thing I've never did was being calculating
on like, if somebody called me for advice, as long as I had the
time, and then the truth is most people have time.
We talk about how I'm busy or there's no way I could give you
15 minutes. Everybody can basically spare 15
or 30 minutes. I mean, I'm sorry, like I'm
very, there's a points where I'm crazy busy and I can always
spare 15 minutes, usually spare 30 minutes.
So just don't be that person. Oh yeah.
I don't know. I don't have anything open.
And particularly, you know, when you've sort of helped them in
the past, right, in general. So just help.
Because boy, it just comes back. Like when you need somebody,
every once in a while, they're like, ah, that one didn't pay
off. But every once in a while, you
know, most, more often than not, when you need somebody that you
helped, like career advice or like, they'll drop everything
for you. And then sort of last but not
least, you know, your word is your bond.
And again, these sound like everything I learned in
kindergarten, but-but they're true.
Like I, you know, I want it to go to my grave in this world to
say, you know, John never did me dirty.
You know, he never lied to me. And again, we're not perfect,
you know, there's gray areas and stuff.
But in general though, I think you can have a great career.
I've had an incredible career. And those three pillars of what
I've, where I've gotten to where I am right now.
Wow. So thanks for distilling to
those three. I think it's really, I would say
quite insightful, right? Especially from someone who have
had long career like you, right? So thanks for sharing all this
wisdom. I really like that, right?
Especially the last one, your word is your bond, right?
So I think definitely these days, integrity is one key
attributes, especially when you work with other people, right?
You wanna be trusted and also you wanna be known as someone
who we can trust. So, John, one thing that we
wanna talk about in this conversation is about your book,
right? Deming's Journey of Profound
Knowledge. I think, uh, maybe some people
have heard about Deming, you know, Edward Deming, the name.
Some people may not. For me, I personally have seen
Deming's name being called out in many, many books and
literatures. Although seems like he's been
around for, I mean, the knowledge has been around for so
many, many, uh, years, but he's always in the background to me,
right? And only this time when seeing
your book, right, you can see Deming's being at the forefront
of the book. So maybe in the beginning, let's
tell us a little bit, what's the story behind you coming up with
this book? Yeah.
I mean, I, um, you know, the-the short version is that, you know,
when Gene Kim, right? Good friend of mine, you know,
again, owner of IT Revolution. I was sort of an advisor on the
Phoenix Project, but nothing sort of like on paper.
You know, like I gave, I got early versions of it.
I gave early reviews and we had lots of conversations about, you
know. Like I, my, probably, my major
contribution to the Phoenix Project, which is probably the
most successful DevOps book there's ever been, was that he
wasn't convinced early on that there was a DevOps version of
it. And I'm the one, and he said
this publicly, you know, I explained like, I thought like
IT operations were like, we were looking for a lighthouse to
bring us home and DevOps was that lighthouse, right?
And so he said that publicly many times.
But I asked him for an early copy of, you know, sort of the,
um... I guess the first time I asked
him for an early copy of what he was working on Kevin Behr and
George Spafford, on the Phoenix Project.
He gave me this challenge. He said, you should really read
a book by Eliyahu Goldratt called The Goal.
And it was such a gift, because like, I think a lot of other
people just sort of read The Phoenix Project and then found
out that it was sort of a purposeful, modern day rewrite
of Eliyahu Goldratt's The Goal. Yeah.
I mean it was purposeful. They even studied the structure
of the book. And that's sort of like, I just
fell in love with Eliyahu Goldratt.
You know, I felt like my whole career.
'Cause this is what, 15 years ago, right?
So I'm 25 years into my career and this guy is bleeding
everything that I believe is right about how we do work, how
we work in organizations, all those things.
And, uh, we were at a sort of DevOps days conference and, um,
another good friend of mine, very good, great mentor, uh, Ben
Rockwood, we were doing an open spaces on Eliyahu Goldratt,
what's called the Theory of Constraints, which is sort of
one of the core of his principles or his sort of
management, that called manifesto.
And Ben was a real student of Dr. Deming, and I hadn't really,
I didn't realize that Dr. Deming, you know, he'd been
around my whole career. And, you know, Ben said
something to me, he said, you know, it all goes back to Dr.
Deming. And that you'll hear that a lot
when you start opening up this sort of the can.
You can't open open the can, right?
You hear that it all goes back to Deming.
You're like, yeah, who is this Deming guy?
But he challenged me. And you know, he said, you know,
basically John, you know, he knew me.
I knew him very well. I trust, going back to trust, I
trusted his advice. And he said, go look, read the
14 points. You know, and we're about a
couple years into this DevOps movement.
And I'm like, oh my goodness! Everything that we profess about
DevOps is listed in the Deming's 14th points, which just comes
from his Out of the Crisis book. So I decided to, you know, and
Ben Rockwood gave me this sort of challenge, right?
And so I went ahead and I worked on a presentation that I did it
like the second Puppet conf, you know, Puppet being a
configuration management product.
I called Deming to DevOps. And it was about
non-determinism. And I just like, it was, again,
I took Goldratt. In fact, my first resistance to
Ben was, oh no, no man. I just read like five books on
Goldratt. I don't want to, you know,
there's, don't gimme somebody else, you know?
But I'm like, okay, once I sort of figured out like this was the
guy, you know. And then you start seeing like,
I, you know, again, I worked on this Deming to DevOps.
And I actually went down a, you know, like, why did this
physicist... So here's the thing.
He was a phy- mathematical physicist getting his degree in
the mid twenties. Know what's going on there?
Quantum, physics, you know, all this sort of non-determinism,
uncertainty principles are just all over the place.
And he's getting a degree. By the way, Goldratt a little
bit later, but same thing. They were both physicists.
And they understood that they, you know, like this sort of
like... And what Deming I think
brilliantly did out throughout his career is took part of that
and turned that into probability.
Became a CEO. You know, he, his business card
was a, he was a statistician first, right?
That it was all about, you know, this idea that like everything
is sort of rigid and deterministic and you do this
and this, you know. And his was more based on what,
you know, sort of, I, you know, I wouldn't say quantum world,
but a non-deterministic world. And that, again, fascinated me
to the point of like there was these answers to questions that
really were only one layer of an answer, right?
In other words, you know, so we, if we get into, I know the next
question's gonna be about profound knowledge, right?
I mean, that's the first tenet of profound knowledge is theory
of knowledge. How do you know what you think
you know? And that you can see a physicist
mindset there. And the, in the, uh, you know,
especially a quantum physicist, it's like that there is no like
bound truth. You know, I think there was a
physicist quote was, uh, you know, sort of behind every deep
truth is another deep truth, right?
You know, um, You know, or something like that, right?
So, um, and again, I think that, that sort of like curiosity
where I was looking for labels and things I believed if I
started that Deming journey 15 years ago for 25 years in my
career. This has gotta be more right.
You know, the sort of rules of, you know, they would call it
Taylorism, right? Like Frederick Winslow Taylor,
which is, you know, again, great contribution to organizational
behavior management theory, management science.
One of it is his book. But there was, there had to be
something. And only like, again, it, it's
sort of interesting, if you compare Newtonian, I know I'm
probably losing people. You compare Newtonian physics to
quantum mechanics, right? Like it says, okay, you know,
yeah, all that stuff works. You dropped apple, it hits the
fall... fall on the floor. But like, there's some other
stuff here that might explain what we can't explain.
Anyway, yeah, so that's sort of like, that was just a like, from
a learner perspective. The fact that you've reached out
to me and wanted to do this podcast, you're a learner.
There's no doubt in my mind. I already know that.
And we don't even know each other that well, right?
And so I use that, what I call learner, right, those for us,
like we can't resist these kind of opportunities.
Yeah, so that, that's the long version.
But yeah, that's why I fell in love with Dr. Deming.
Right. So yeah, I think for most people
who have read DevOps books, read literature, resources, you know,
The Goal, Theory of Constraints, Eliyahu Goldratt, right?
And the DevOps Handbook and things like that.
Always sometimes, you will see here, this Deming mentioned,
being mentioned, right? And I think if you trace back
the Agile movement, Lean, DevOps, all seems to have some
influence of Dr. Edward Deming's, um, you know,
knowledge and management principles and things like that,
right? And if we read literature, many
people would say about Toyota Production System, right, as
like, kind of like the reference for how DevOps started.
But actually Toyota actually has some influence from Dr. Deming
as well. So maybe tell us a little bit
about this, because I think it's good for people to understand,
uh, his contribution there. Yeah, you know, when I set out
to write Deming's Journey to Profound Knowledge, really the
goal was, you know, Deming is a conduit, right?
Like I really didn't want to become a sycophant to Deming,
even though I probably am when it's all said and done.
I mean, I do think he's been a great American prophet and not
prophet in the sycophant way. Prophet in that there's so much
like knowledge there. I think the other thing I wanted
to say too, right, is people find Deming hard.
Because, you know, like Deming one time, you know, in a
lecture, somebody was asking him questions.
Yeah, but how, and you know... He said do you want me to teach
you and do your job? And, you know, and I was
thinking about like, you know, like Peter Drucker amazing,
mentor writer, right? But like, you would never see in
a Deming book, you know, the effective executive.
Like in other words, he wouldn't tell you how to become an
effective executive. He would tell you these
principles that exist. And so back to like, you know,
what, where do we get to Japan and all that stuff.
Deming was like hard, sort of hard-nosed about like, here's
these things that I've learned. And you have to, you don't just
take 'em like here's the five steps.
Everything's gonna be perfect. Like, you know, again, theory of
knowledge and system of profound knowledge is really
epistemology, right? That's not how that works,
right? Like you are supposed to take
the torch. And so one of the sort of things
that you learn in the sort of Deming discussion when you
become a researcher about him is all these schools of thought.
There is definitely the sycophants who, like Deming,
even like the bookshelves. You know, if you look at the
books, the man who created the miracle in Japan.
The miracle way. You know, the man who invented,
you know, quality, right? Like that's freaking ridiculous.
He was just a man. He was a like awesome man.
So they, and you know, and then there's other people that are
like, yeah, it, you know, Deming had nothing to do with Japan.
Some of the sort of northeast Lean school, they literally act
like he doesn't exist in a Toyota story.
So you start weeding through that stuff, right, and try to
get it. Look, and I spent a really good
time explaining the different people that went to Japan and
how Deming was it. And I really tried not to be,
he, you know, boy, Toyota doesn't exist without Deming.
However, there are a fair amount of people who say Deming had
nothing to do with Toyota. And that's absolutely incorrect.
'Cause I've interviewed people. I interviewed a man named
Yoshino, Dr. Yoshino. And he basically said he was
hired in 1966 at Toyota, worked as a peer, you know, with Ono
and Shingo. And I asked him the question of
Deming's impact. And he was unquestionable that
when he got to Toyota, Deming was mostly what people were
talking about. And he said, Deming taught us
how to understand data. So Deming had a lot to do with,
you know, not just Toyota, but like the resurrection of Japan
after World War II. But he wasn't the only one.
And its principles are solid. So like what he did, in fact,
the most famous part is there was, uh, in 1950 there was,
basically a lecture he gave where, uh, years later they had
basically estimated that 80% of the wealth, this is post World
War II, was in this seminar. And he told everybody in that
seminar, if you follow these ideas I'm telling you right now,
you'll be a world power. And in five years, they overtook
Germany to be second behind the United States in world
economics, right? Like those are facts, right?
And again, the other thing I would like to sort of point out
not to be a sycophant is, you know, Deming didn't create the
miracle in Japan. The Japanese created the miracle
in Japan. What Toyota did wasn't great.
TPS didn't become this great model for successful complete
organization, meaning profit, everything, right?
All the things from Ono and Shingo.
But Deming was part of that mix. So Toyota created the miracle of
Toyota. Not Deming.
But Deming had some influence. Yeah.
Thanks for the background story. I think for people who would
love to study more about Deming's journey and how he
influenced the Toyota, right? I think reading your book
definitely is one good resource, right?
Because in your book, you kind of like highlighted the story
from the very beginning. Yeah, just to, one thing I just
wanted to point out in case some people listen or don't know.
The other thing I did about that book is all the books about
Deming that I had read up to that point were very dry, very
little bit about, um, you know, about, you know, his biography,
maybe five pages, 10 pages. And then it's all about his
management principles and how they...
And I thought, you know, I've always been a fan of Michael
Lewis, you know, the Moneyball, the Flash Boys, all those.
And I thought, wouldn't this be a great Michael Lewis like book
where you could teach people the principles like he does.
He explains, you know, very complex ideas to everybody and
make it fun and story and narrative.
So again, it not only gives you a strong background on his
history including what he did in Japan, and even after Japan, you
know. But it, you know, I think so far
it's been been very successful to be a very Michael Lewis, you
know, style of easy to read, anybody can read it and sort of
get the value out of it. Yeah, so thanks for highlighting
that. Because yeah, if you, uh, expect
the book to be like theoretical, right?
So totally not, right? So this book is a mix of, you
know, some theory, of course, right?
And some background stories, right, uh, including history,
uh, kind of, I think research that you did as part of, you
know, your journey, uh, writing this book.
So maybe for people who are very curious about this Deming's, you
know, profound knowledge, right? So he called it a profound
knowledge. So what are these four profound
knowledge? If you can maybe give us an
overview high level, that will be great.
So Deming, you know, again, being a physicist, right?
Uh, you know, mathematical physicist, right?
So he, you know, he saw the world, like think about like,
you know, like how a physicist sees the world.
Like it's and I believe even though he wasn't like a particle
physicist or a, you know, even labeled as a quantum physicist,
right? Actually he was mathematical
physicist. By the time he got out of, out
of his PhD, he became more of an organizational statistics guy.
But, you know, the influence had to be there.
You think about how, like, you know, the sort of, uh, universal
physicists think about like the classic world, they think about
the quantum world, right? It's sort of like it opens up
the world for a lot of pieces. So like a lot of what he would
talk about, it's not really physics, but he'd talk about
like everything is complex. This, you know, we live in a
world of complexity, right? Again, that's a physicist view
or like, you know, like if you look at a door swinging, you
have, uh, if you sort of really think about it, there's so many
more elements than open and close, right?
And so he said that in order to understand things, and
particularly his focus was sort of management of people, trying
to help managers and people co-exist with sort of, um, what
would ultimately be profit but not profit focused.
You said that to understand a complex system, you need a lens
that takes into account four sort of elements.
Cause if you don't use these four elements you, like you sort
of miss. And I'll give you an example.
But, so the first one was he called theory of knowledge.
Cause the other thing that a big influence on Deming, and this is
in my book as well, actually comes from philosophy, something
called pragmatism. And pragmatism was the first
America based or sort of US based philosophy.
Friend of mine, Jay Bloom, says it was like jazz, like we
created pragmatism. Like it wasn't some European.
And Deming was highly influenced by pragmatism.
So again, that all goes back to epistemology.
And epistemology is a fancy way to say, you know, how do we know
what we think we know? And that's what a learner does,
right? Like, okay, I got this, but
like, I'm not gonna stop and just stand up in front of me.
Like, hey, here's the answer to the question, you know.
Well, there's other answers and there's more to learn, right?
So that sort of first pillar, I'm thinking, remember there's
four here. The second sort of element of
this lens to decouple complexity, right?
And complexity could be your organizational, like you got
five teams and these teams are fast, these teams are slow.
There's resources here. Well, the things that we deal
with in an organization. The second is, uh, understanding
variation. And variation then goes into a
whole deep area of statistical process control.
But at the end of the day is, you know, how do we understand
what we think we know? Like this is the way I think
about it, right? And then, you know, I've also
sort of recently taken like, is there an ontological view,
right? Like what's inherent versus what
shapes. So if we start understanding
variation, there's variation in everything, then we can
understand what's inherent. And so there's, this goes deep,
and this would be a whole episode if I went any deeper.
But then the third is really interesting.
Because the first two are more sort of like very science, you
know, like you can understand from a science or mathematical
perspective, right? The third one is, um,
psychology. And this is the thing where I,
again, like just why I kept falling along with Deming is
because you, if I think about my career before I learned about
Deming, right? That 25 years, you'd hear all
sorts of discussions about, you know, sort of like, you know,
observing, managing, measuring, right?
And his measurement's a little different than sort of... is
statistical analytical statistics versus enumerated
statistics. Again, another long subject
covered in the book. You think all those things sort
of like, yeah, I can see that, I can see.
But then the first time you think about like, no, no, no.
An equal element of this lens understanding complexity has to
be understanding human behavior. Or you know, like maybe you have
intrinsic motivation and there's actually different variants of
intrinsic, right? Like understanding, I, you know,
I did some studies on this where there's like seven classified,
intrin- we always take extrinsic intrinsic.
There's actually seven classifications of intrinsic
motivation. So it's not just, oh, you're
intrinsic, you're intrinsic, you're extrinsic, right?
It's much deeper than that. And then cognitive biases and
all those things, right? This becomes like, you have to
input... include this, because I could have everything else
right, but if I don't understand that, you know.
Like a example of a great healthcare version is I can do
all the stats and the math and the, you know, show the theory
and all this of why hand washing kills germs.
But you fundamentally don't believe that for whatever,
because you grew up in the whatever.
Like, I'm gonna fail. And then the last, but certainly
not least, is understanding a system.
And Deming has a lot of roots in some of the earliest general
systems theory. And he was like, it just bleeds.
In fact, in that, 1950, uh, Mount Hakone where 80% of the
wealth was in there. I mean, if you read that speech
and it's online in the Deming Institute, it basically is a
sermon on system thinking. So when you put all four of
those together as part of your ability to sort of understand
things or try to solve things. Because like you'll hear, like
you, most people, like the shorts version of Deming is,
isn't he the guy who created PDCA?
Well, he didn't create PDCA. He created PDSA.
And that's another sort of like. So but in general, yes.
He's sort of the idea that... And then for those pure
Deming-ites now that are listening and screaming at me,
yes, I know. All that came from, uh, Walter
Shewhart. So Stewart was Deming's fan.
Again, Deming pulled all these great things.
You know, he pulled, uh, you know, his philosophy ideas from
C. I.
Lewis. He pulled his variation ideas
from both Schuit and actually mostly Schuit.
But, and Schuit is the one who introduced him to C.
I. Lewis, his pragmatism book,
right? So, but yeah, so you put all
that together and it becomes, you know.
And, but here's the hard part is, sorry folks.
I just gave you a lot of keys. You gotta figure out the doors
that you can open with those keys.
And I think this is where people get frustrated with Deming.
We're so used to this instant management gratification.
So if you see a major knock on Deming, it didn't work.
We tried the Deming stuff, it didn't work.
And then you moved on to, again, I'm not like, I'm just using
Drucker as an example. Peter Drucker is like, the
contributions of Peter Drucker to management's, you know, is
incredible. But to use it as an example,
there are certain type of management principles that give
you the sort of, you know, even when we talk about Lean.
You mentioned rightfully so that Lean and Agile...
Look, ultimately Deming had contributions there.
You know, there are books to 14 Steps to Lean.
You'll never see a book by Deming with 14 steps to do this.
Even the 14 points are literally a leading sort of how to do list
to understand profound knowledge.
And then the last piece, systems thinking, right?
Like that's, it's core to everything.
You know. And if you even sort of look at
Deming's, like, you know, the red bead game or the funnel
game, you know, like all these things, these games that other
people created around his ideas, they bleed systems thinking.
He bled, you know, systems thinking, you know, like the,
you know, you have to look at all the parts, you know, the,
you know, everything is connected.
So, yeah. So I think the, you know, again,
I think the hard part that most people have is they read about
profound knowledge and they haven't been given the sort of
answers. And I think Deming would say,
you know, nah, I'm gonna give you this sort of.
I'm putting what you need up here in your head.
You gotta go figure out, you know?
And I think once you have that, then you have an world of
opportunity to affect, you know, complex systems, organize...,
create change, organizational behavior, organizational
structures and all the things that we in general strive to do,
right? Yeah.
And especially, I mean when we work in technologies, right?
Technological organizations, you know, these days, especially
modern organization, complexity is a huge thing.
And systems thinking, the day when I studied that the first
time, I found it's quite insightful, right?
And in fact, I've been always amazed by how, you know, things
got explained in systems thinking.
And in fact, I got an episode with Diana Montalion about
systems thinking as well. And it's always amazed me, like
how these kind of insightful thinking actually can help us,
you know, make a change, transformational change or
deliver results that can go beyond what you can imagine, I
guess, right? Simply because if you know how
things are interrelated to each other, the relationship between
them, you can kind of like see the root cause and try to fix it
from the root cause rather than trying to fix the symptoms, so
to speak, right? So system thinking is kind of a
curse and a blessing, right? And the curse is you wind up...
like I think probably the best book I've ever read about system
thinking is Donella Meadows' Thinking in Systems.
The worst book I've ever read in systems is Donella Meadows.
'Cause it like, everything like in line at the supermarket or
like you literally... I've told CIOs at companies
where, you know, I felt they were ready for this advice,
right? Like you gotta have a threshold
where like, what are you talking about?
Like once I have enough conversations, I'm like, all
right. I think if you could create a
systemic mindset in your organization where everybody
basically understands systems thinking.
And there's a lot more. But, the point is, like,
everybody would, like, even the how, you know, the team A
stinks. They ain't never, you know,
they're always late on delivery for our team B, right?
Like if you think in systems, you think, well, wait a minute,
what's the disruption? What's the connections?
Why are they late, okay? And like, you know, like, again,
you know, Dev Ops. You know, again, DevOps was
like, you know, like the original sort of attack to
provide a more systems thinking way where you had dev, you had
ops, the original character was a wall.
My friend Andrew Clay Schafer created this caricature of a
wall where dev, you know, they're called a wall of
confusion he called it, where, you know, dev would throw the
code over the wall, ops would catch it.
They would say, this code stinks, they'd throw it back,
right? And like, never were we trying
to figure out like, why did those disconnects or impedance
mismatches happen, right? And that's system thinking at
its core. Yeah.
So definitely systems thinking is one body of knowledge by
itself, right? And you mentioned the other
three like theory of knowledge itself.
I think it's always good for individual to know the unknown
unknowns, right? So those are like kind of like
the blind spots that you never knew before.
And especially these days, so many rapid advancement.
AI itself. Like we can't really actually
understand everything, right? But it seems coming up really,
really fast. So theory of knowledge is one
thing. Theory of variation.
I think this is also very important because yeah, nothing
is predictable in the world. You can standardize stuff, but
it always happens well within variation, right?
So understanding a little bit of statistical knowledge and why
variation could happen, I think, is also important.
And one that is also important is understanding the psychology
or the human behavior aspect, right?
So especially these days, people will talk about culture,
psychological safety, bias, cognitive biases, and things
like that. Definitely that's another body
of knowledge by itself that we need to think about and master
in order to create a more thriving, transformational
organizations. So definitely other points that
when people talk about Deming is the 14 points of management that
he has, right? So I know that we won't be
covering all of them, but do you think there are some points that
you think are very important for us to start pondering or maybe
try to implement? And maybe we can then follow up
with the other points as well at our own time.
So maybe if there's some points that you want to cover today,
John. Yeah, I think...
So in, um, you know, in my book, the last chapter is sort of...
So the last third of my book is basically sort of like, what
would've Deming done? So Deming died in 1993.
So you think all the things that happened after 1993, like you
got the Agile movement, you got Lean, you got like all these
things that you know. And not even including like, you
know, DevOps, cloud computing, and now AI, right?
So there's a lot of stuff there. So I tried to say I think at
that point I knew enough about Deming and I knew enough about
cyber and, you know, like I'm pretty knowledgeable in a lot of
those subjects, you know, DevSecOps, you know, cyber.
Certainly DevOps would consider one of the founders of DevOps
movement. And I thought I could, I'm gonna
take the liberty of what would Deming do.
And I'd literally use profound knowledge and as a template.
And then I ended the... I did think it was funny, I had
kind of almost finished, I'd finished the book basically I
thought. And I'm like, you know what?
I just wrote a book about Deming.
And I didn't actually have a chapter on the 14 points.
So I literally, I gotta have a chapter.
And, but it was great 'cause it fell right in line 'cause I was
able to use a lot of examples. You know, I think the, you know,
obviously would take a whole, uh, podcast to do all 14 points.
I think the ones that you find where he is, you know, again, as
a learner, like, you know, number five, never stop
improving. You know, I think 13 is like
self-learning organization. Those ones, you know, and I gave
a modern day examples, but mostly like cyber ones.
Like they never stop improving. You know, Shannon Leeds is a
great mentor in security. She's a great white hat hacker.
She's run organization, she's run security at places like
Intuit and Adobe. Uh, brilliant, brilliant woman.
You know, I think one of the things when I first met her, you
know, we were all... Everybody was foundational on
like what were the ways that you manage security?
Like what do you look for, you know?
CVEs or sort of vulnerabilities that are known, or the OWASP Top
10. And so I meet her for the first
time and she's asking everybody at the table, how do you manage,
you know, security? And she says, let me tell you
what I do. And she basically, she had this
whole thing of adversarial analysis.
So like she went beyond what everybody thought was the sort
of how you're supposed to do this and came up with a whole
new way. So instead of looking for
vulnerabilities, like how I, why don't I measure how in her times
she was at Intuit, like how do I identify adversaries?
How do I measure how often they come to my site?
And what are the things, and this becomes pure Deming PDSA or
theory of knowledge. What are the things that I can
do? Or try or experiment with that
might reduce those adversaries? You know, sort of thinking about
it as like a criminal who robs house is basically would look at
the first house and say they got a guard door and a steel fence.
Not gonna do it. Next house, they got alarm
systems all over the place. The next house has an open
screen door with no fence. That's where they're going,
right? So like that whole science that
she sort of like, I think single handedly created of adversary
analysis, was a great example of never stop improving.
I mean, she's already industry giant in security.
She could stand up and say, all right, you need to do this,
this, this, this. This is my Shannon Leeds, know,
and she's an incredible woman. So like if she tells you to do
it, you probably should do it. But yeah, so like that one.
And then I think, you know, the self-learning organization.
Again, I go back to Shannon Leeds, but, uh, you know, my, my
good friend Andrew Clay Shafer says, you're either a learning
organization or you're losing to one who is.
One of my favorite all time quotes of contemporary DevOps
folk, if you want to say it. But Channel East was like, the
alternative would be like KLOCs, right?
You know, like, you're sort of measuring, you know, how many
bugs per line of code and like that or, um, another, it is not
Shannon, but incident management.
Like another great quote. Another good friend of mine,
John Allspaw says that so incident management, right?
Like, how would Deming, one of the chapters I thought looked at
was like how disgusting Deming would've been if he looked at
modern day incident management. You'll get P1s, P2s, P3s, who,
no, nobody ever touches 'cause you don't like management is
screaming, hollering about P1s. So even if you get all them on
some screaming and hollering measurement system, KPIs or
MBOs, and by the way, Deming hated MBOs and KPIs, then you
might get to half of the P2s and you'll never get to the P3s.
And then you contrast that with the John Allspaw quote, that
incidents are unplanned investments.
So Deming would probably be looking at all the incidents and
using a system, probably using variation, and looking at it in
its holistic systems view. And because some of those, what
you classify as P3s, on the wrong day, bring down your
system, right? The one, the nagging thing that,
oh, is hap. I mean, I know I'm going crazy
here, but like the, what was it? The, it was the Columbia, right?
Where they, the thermal panels where they literally, everybody
said, oh, we've seen that a number of times.
Don't worry about it. Don't worry about it.
Nothing will go wrong. Well, on this day, you know,
when they were on re-entry, it went wrong.
You know, and there's just number of stories.
If you look at the safety of people like John Allspaw and
Richard Cook and Dr. Woods, who I would definitely follow up on
if you understand, you know, the critical safety, cognitive
safety, they have numerous stories of that thing that like,
it almost ha- I, lemme do a quick story.
This is a great story. Uh, Paul O'Neill.
You know, when he went to Alcoa, he was giving sort of a tour and
he had heard a story about a young man who got killed by one
of the machines. And what happened was there was
a little fence and the fence said do not cross.
But for like a year or so, or quite a long time, the general
advice practice from the senior people were, if it gets stuck,
ignore that sign. You gotta hop over and you gotta
unloosen this thing. And that worked, you know,
probably thousands, tens of thousands of times.
But the one time, the reason the sign was there in the first
place, it didn't work. It killed this young man and,
you know, Paul O'Neilll basically, rightfully so, we
killed that man, we killed the family, right?
So that, those are like, those sort of like pebble in a shoe
problems, that if you don't understand why they happen and
you know that they can be the bad case, a total systems
outage, worst case, you know, like harm to humans.
Anyway, yeah, and also, but they're all great.
Like pride in workmanship. No fear.
I mean, you could just, you know, again, in my book, I try
to cover every one individually with modern day use cases,
either Lean, cybersecurity, DevOps, so...
Yeah, wow. So when you mentioned about, you
know, incident management classification, right?
P1, P2, P3, I think for all sysadmins or, you know, SRE,
DevOps people, you would kind of relate to your jobs as well.
Because so many alerts. Typically, it is the alerts,
right? Alerts that are kind of like
niggling. Uh, sometimes it happen and then
it will resolve by itself. So we think it's not a problem
until one day.... Yeah, we do.
Don't worry about that one. You know, they will take it the
old example. The code is a classic example,
right? That you're in and you're a
young. You just come into a large
organization. It's Uber or whatever, right?
And you're looking at some NOC screen.
And this like young man's, oh, what should we do?
Don't worry about that one. It always shows up there.
You know, it, we never have a problem with it.
Now, again, in Deming's world, we like, can I take a look into
it, you know? Um, actually, all right, I got,
I love telling stories, right? Margaret Hamilton, right?
Look up the Margaret Hamilton story.
So basically she was basically a mathematician that was hired by
NASA as a part-time mathematician.
But the deal was she had to be able to bring her daughter.
She had basically semi-retired, 'cause her husband was basically
going to Harvard or something. And so the agreement was she
could bring her daughter to work, right?
And the daughter was playing with the flight simulator.
And the daughter hit this thing that caused...
It was like she was playing with the flex and pulled this or hit
the button or whatever. And it was a button that should
never be pressed on re-entry, right?
And no astronaut would ever probably do it.
So they never had any error code for it.
So it turns out when the daughter did it, she added Eric.
She's like, what the heck, you know?
I'm gonna just, you know, since it's there, now I know about it.
Let me put error code in there. And then it turns out on, um,
Apollo 13, Jim Lovell and somebody on the crew hit that
button, right? They wouldn't have survived if
it wasn't for the daughter pulling that button.
Like, again, like that, that's, you know, that system's thinking
at its core. That is system of profound
knowledge at its core. All the things we just talked
about, right? Yeah.
So I find that if leaders, managers, uh, or even everyone,
right, who can actually spend some time to study this
Deming's, uh, you know, four elements of profound knowledge,
right? And, uh, 14 points of
management, I think it's really, really cool, right?
You can start thinking not just A, B and C, but like how A, B,
and C interrelate to each other that can produce, I dunno, D, Z
and all that, right? And I also love like Deming, one
point from his management point is like stop inspecting quality,
instead like you build quality in, right?
That's a core principle there. I mean, I think we, like, I
wanna go with the human factors in learning.
But certainly, like I said, that that doesn't diminish the
strength of any of the other ones.
But the, I mean, one of the core quality tenets is sort of like,
you know, this idea of like post inspection or, you know, sort of
build quality in. Again, I spend a lot of time on
this. One of his first, um, jobs as an
intern is a place called Hawthorne, which is where
they're making, you gotta imagine it's beginning of the
1920s, telephones are like cloud computing, right?
Or AI now, right? Like, that is all the buzz.
People can't get enough. Everybody wants a telephone.
You know, your Aunt Tilly moved to California, you haven't been
able to talk to her since she moved.
Now all of a sudden you could pick up this thing and talk to
her. So this Hawthorne factory
outside of Illinois is mass producing telephone, not only
telephones, but like, telephone systems to be able to do all the
relays and all that. He's there, you know, and
they're realizing, and this goes back to Walter Shewhart, who's
one of his mentors. He's realizing that they're like
outta 40,000 people working on these telephone devices, they've
got 4,000 of 'em doing inspection.
And the statistics of what they're doing is not really
working that great anyway. You'll pick it up, yeah, shake
it, yeah, it's good. No, it's bad.
So like, that's where, um, Shewhart came up with the idea
of statistical process control, where you can use statistics and
math and analytics to get a better, higher percentage on
quality then you can humans. In the end, and then that sort
of bleed into everything Deming talks about in that sort of
quality. When he talks about quality,
he's talking a lot about the principle of variation in system
of profound knowledge. So when you mentioned that
somehow I got to think about, you know, CI/CD pipelines, you
know, the Toyota production system probably is like one
reference that people use to come up with so-called the
concept of CI/CD, you know, pipelines, right?
So, but I, when they talk about that, we kind of like take
things for granted, right? CI/CD, you know, especially
youngsters these day say, yeah, it's a, it's like uh, you know,
who doesn't know CI/CD, right? But actually in the past, uh, we
didn't have such things, right? And rely on like kind of manual
tests, you know, for people to actually execute and ensure the
quality of our software. So the other topics I would like
to talk to you about is regarding AI.
So I know these days, everything must be about AI.
Yeah. So there's a lot of scare about
AI. There's also about excitement of
how AI can improve our lives. Using the Deming's kind of like
profound knowledge, how do you actually see these AI impact to
our, you know, lives and organizational, you know, maybe
management. Yeah, I think there's a lot
there and I'm glad you asked that, 'cause I, my latest book
is um, basically called Rebels of Reason.
And it's basically a history of all the people and the ideas
that came that create today's modern day.
So it isn't a book on what is ChatGPT?
But it's a book, like why did this weird, crazy UI happened in
2022, right? Like so going back a hundred
years to Turing and even Ada Lovelace and like, like I do,
like, again, if you've read Profound, it's that kind of
storytelling. But what's interesting is I
spent a fair amount of time trying to see if Deming...
I found all these sort of really interesting people that were
contributing to what led up to ChatGPT.
A guy, for example, Herbert Simon, right?
You know, like... And that the closest I could get
to sort of any evidence that Deming sort of was even involved
with any of these sort of early incarnations of, you know, AI
expert systems, those kind of things that were happening in
the seventies and eighties. I'm certain Deming would've been
aware of them, but I literally just couldn't get anything in
the book that literally, um, directly tied to what he said,
what he did, uh, and again, the closest I get to is some of the
people I know, he sort of collaborated with like guys like
Herman Simon and stuff. But then when I get done with
the book or getting close to the book, I wanted to create an
interesting epilogue and, you know, like, okay, I've got this
great history of all these interesting people that lead up
to where we get to, you know, modern what we call, uh,
generalized pre-trained Transformers GPT, right?
You know, Open AI, you know, GPT-4, GPT-5.
But it, a good friend of mine challenged me on and he made
this quote that I actually have in my Rebels of Reason book in
the epilogue, which is, um, you can't understand science without
understanding the philosophy of science.
So therefore you can't understand AI without
understanding the philosophy of AI.
So that opened the can opener of like, what would be the
philosophy of AI. Let's stop talking about AGI or
ASI, like, you know, as like the boogeyman or all this.
So the final chapter in my book is basically an epistemological
can opener. Now I don't have all the
answers. These are hard, hard, hard
questions. Where AI is going, what's the
human effect, what's the sort of cognition?
But I did a pretty clever job of trying to explain what
epistemology of AI should think about.
And then I was able to use profound knowledge as a sort of
a framing. And that was really more of a
like, hey, almost in a Deming like way is, I'm not gonna
basically be able to spend the rest of my life figuring this
out. So if this interests you, I
encourage you to take the torch and run with it, right?
And the one other thing I thought was really interesting,
what I came up with a clever idea to use that movie Arrival.
So I use the Arrival and the way I use Arrival is like the woman
in, uh, the, in the movie Arrival, she, um, doesn't take
the sort of status quo. Like they ask her to sort of
interview and learn from the aliens, right?
And then she doesn't start comparing it.
And I said like, what we're doing, like trying to compare
AGI. I mean, what she looks, she
realizes that these aliens are an alien form and probably an
alien form of cognition. So if I use the way we think to
try to understand, I'm probably gonna fail.
So what if I like step all the way back and say, I don't, can't
assume anything. You know, and that's where I lay
in where I think Deming would sort of like, let's not assume
anything. Let's take an epistological
approach. Let's, you know, I mean,
psychology might be a little deep with aliens, but certainly
can we understand what we know? Is there a system viewer?
I'm always sort of looking for the outer way.
And I think I compare that to, 'cause I, one of my frustrations
of the con-, one of the long sort of strong conversations is,
you know, artificial general intelligence and like, who
cares, really? I mean, like, I blog, I said,
I'm like, imagine she would've went and went up to when she
started communicating with aliens, like, can you ride a
bicycle? Or, eh, they don't do math.
You know, they can't do, they can't count the number R as in a
sentence or a word, right? Like, that would've been a total
waste of time. The acceptance is whether you
like it or not, AI today is an alien cognition.
It's just not a human. Like, one of the quotes I have
in it is we spent a hundred years trying to build a thinking
machine. The thing that we didn't realize
is we built a thinking machine, but it doesn't think like
humans, right? And then therefore, then we need
to sort of like, and again, it's just an entree into I think what
should be a fascinating conversation.
But I'm already on my next book, which is gonna be the history of
quantum computing. So like, so I'm, it's one of my
problems is like, I'll leave these bread trails, the
breadcrumbs, and then like, almost like Deming in a sense.
Not that I'm as smart as Deming, but like, yeah, like it's your
turn. Yeah.
So I think these days definitely so many people have, they are
thinking about AI, right? So especially leaders,
management, right, how they should tackle AI.
First, it's how, how they should use AI as an opportunity, uh,
within the organization. Second, definitely threats of AI
to their organization. We know that cybersecurity
issues now these days use AI a lot, right?
Also AI that can hallucinate and produce catastrophic errors,
right? Yeah, it's also not even just
hallucinations. I've got a couple.
In September, I've got, I'm getting back on the road.
I've had a good summer so far, you know, taking it easy.
But, um, I've got keynotes at Dallas Stays DC, Dallas States,
Dallas, and I really, I have a presentation that I'm literally
giving about the polymorphic nature of agentic programming,
right? And there's just scary stuff
that's going on. So this intelligence is sleeping
in where... So you, you mentioned my Dear
CIO newsletter. That whole idea is like, hey,
I'm not telling you not to use AI.
Like that ship has already launched, right?
Like, you'd be an idiot in a corporation to sort of ban AI.
Like, I'm sorry you're going outta business.
But like you need to understand the perils and there are a lot
of perils, right? Now, you talk about
hallucinations, I think those can be managed.
The really scary things to me right now is the polymorphic
nature of some of these agentic process.
We're going into like massive agentic programming now, and
where the agents are sort of building task, clicks, and
figuring stuff out. And it's brilliant.
I mean, it's stuff that you, that people are producing with
Claude Code or this MCP architecture.
But I mean it, these things are like polymorphic in that they'll
reconstruct the code. It's sort of like the HAL and
the bay doors. Like a couple examples that have
come up within the last, you know, six months or three or
four months, is like having a whitelist of these are the
things you can't touch, like files you can't touch, telling
the agent. And it's saying, yeah, but you
know, like, think about it mentality like the, HAL, open
the bay door. It's like, you know, I can't let
you open the bay doors. Sorry.
And so in these agentic processes, you know, I say the
heart started to get too meta here, but in those agentic
processes, they're like, you want me to do this?
The only way I see this is I've gotta change of file in that
directory. You give me a whitelist.
So I'm gonna either add, take it off the whitelist.
Or it's finding, it's using the knowledge of vulnerabilities.
Like it has this knowledge that there's a vulnerability out
there. Let me see if that doesn't do or
it's reconstructing code. I mean it's, again, like we're
gonna have to figure all this out.
And the answer isn't like stop it.
But yeah, I think there's really scary vulnerabilities in these
agentic processes that we haven't even scratched the
surface. And then you add to the case
that these intelligent alien cognition forms are trying to
solve problems we've asked it to do.
And it's coming up with cognition speed of things human
couldn't even figure out try to solve that problem, which might
not be the things we want it to do in the first place.
The logic of like, in 2001: Space Odyssey is like, dude,
open the door. I'm, we're gonna die.
No, sorry, HAL. Yeah, sorry.
What is that? I can't open the bay doors, you
know. So, yeah.
Yeah. Yeah.
Especially with the agentic architecture, right?
So when AI talks to each other and we kind of like didn't know
what is actually happening between those agents, right, and
we kind of like see the result only.
I think it's always very dangerous.
And I'm not sure how we can actually tackle the
investigation later on. Let's say if it produces a
decision that is, I dunno, catastrophic or something like
that, right. So probably it's gonna be
really, really hard. Right?
Because like I tell you I've been looking at quantum, I think
we're still three to five. I think most people say 10 to 20
years before the sort of, you know, sort of modern AI gets
married with quantum computing and into the whole sort of level
of quantum compute..., physical quantum computing is getting to
the point where they can actually work, if you will.
But I think it's gonna happen sooner than later.
And when that happens, so you talk about like debugging or
tracing, right? In AI, we can look at the logs
of agentic process. But like there's a lot, like
it's a lot, right? Like it's a needle in the
haystack problem, even right now.
Because what we're doing is we're doing cognitive solutions
at a scale that's way beyond humans, right?
Like the idea that we're using sort of the structure of the
transformers, if you will, to be able to solve these problems
that, you know, human can solve, right?
I mean, literally, I had a friend of mine the other day
said that, and this is a world class Java coder that's been
coding for 20 years, literally told me he built something for a
major software company in three weeks and it would've taken,
using Claude Code that it would've taken him a year.
So this is sort of something, who isn't this guy, basically
would've took a year and a half, right?
Like that becomes a complex problem of when that, like, when
you're having that, like how do you go through and debug the
log? It's a problem.
I mean, is it the benefit worth the problem?
Yes. But when you start adding like
the ability to go like three, four orders of magnitude more
complexity, it's going to be a point where like debugging is
just not an option. In fact, the whole state of
quantum is debugging isn't an option.
So, yeah, no, and it's gonna get fascinating.
The things that we haven't been able to solve with classic
computing, you know, will take, you know, on average like a
million years, will basically be able to be done in hours once
those two meet. And I can't imagine the
possibilities. But then like we're at that
point like, we're in for a dollar, we're in.
You know, like we're in. We, there's no returning.
Yeah. I think definitely it's gonna be
exciting, right, the kind of opportunity.
But also it will scare us a little bit.
So you mentioned that organization these days that
could, if they don't apply AI within their organization,
right? They will be disrupted by some
other people who have implemented AI.
So for leaders out there, what are some of the practical, you
know, things that you can advise, especially, you know,
maybe borrowing some ideas from your AI CIO newsletter and your
research about AI. So, what are some of the key
practical things that leaders should think about or start to
implement? Yeah, I think that, you know, so
I think I'm gonna, and when it's all said and done, I have first
use on shadow AI. I know a lot of people, like I
think it was like two and a half years ago I used it.
I, you know, again, I, like in some places who gives a crap.
But the, I did write an article almost two years ago about like
what are the birth of shadow AI, which is based on shadow IT.
There's so many lessons to be learned from shadow IT, which,
you know, you can almost attach to cloud computing, right?
If you think about shadow computing, there was all these
shadow IT stuff, you know, scoops.
And what happened there is organizations for the most part
fell into three categories. One category is no, no, no, no,
no, no, no. Well, we know how that worked.
The other category is monkey see don't, you know, like, don't,
like just don't tell me about it.
And then, you know, then people just did the do now in this
later. And a very small, small
percentage, Capital One actually being one of them, embraced it.
Organizationally embraced it, right?
Like at the time when Capital One was embracing cloud
computing, the general mantra was banks cannot do cloud
computing. Now there were sort of
restrictions and then Capital One, but that's the point.
These are not binary. You can or you can't.
Well, I can for that, I can't for this, I can for that, right?
And I think like, there's so much lessons to be learned.
And so I like, I've been drafting in my newsletter, Dear
CIO, this idea that like let's learn from that.
And let's, like, let's be, instead of a very small
percentage in shadow IT who embraced cloud, let's have a
much higher percentage. Like it's going to happen.
We know from the first two cases.
The first case like that was folly, right?
Like it was happening. Austin Airport tried to ban
Uber, right? How?
That didn't work, right? Yeah.
Like, sorry, that was never gonna work.
You know, the second group is even more dangerous because they
are not giving anybody any guidance.
So people are like, they're not saying I can't.
So that's when you found, like in cloud computing, you found
like, you know, examples of Nike putting one of their athletes'
contracts out in the cloud and somebody finding it, right?
Like because there was no regulation or governance,
because they were ignoring that it was happening.
The third group was like, okay. And so he, he'll get to what the
nut of what I'm talking about AI.
I think, you know, organizations need to be really clear.
Like we're going to use it. Here's the test scenario.
Here's your time to allocate to learn it.
But here's the thing. We wanna know what you're doing.
And particularly it all goes back, it all goes back to data,
right? So if you're gonna build some
AI, right, like the first question should ask is can we
classify the data? Is the data green?
Is it yellow? Or is it red, right?
And then like, give people clarity, right?
And so now you come say, I want to do this thing about that
helps, uh, new employees figure out, um, how to do scheduling of
meetings, where to go get lunch. You know, like all those things,
right? That's green data.
No restrictions. Go, you know, I mean, not quite,
'cause like you still need like no racism or like, you want to
have these sort of what they call evaluation software that
hallucinates bias or uh, correctness.
And then you got your yellow, which is, you know, maybe, um,
the company doesn't go outta business if this date is
incorrect. But we could like waste like
over a year of people using an internal service that gives
optimization examples. Turns out it was wrong and we
wasted, you know, $40 million because this thing told
thousands of people to do X when they should have done Y.
And then the red is obvious. The red is like, it's gonna do
harm. It's gonna do harm to the brand,
it's gonna do harm to individuals, it's gonna do harm.
Again, going back to profound knowledge, be aware that you
don't actually know what the right exact answers are right
now. Express that you are learning
with the organization. So today, you know, green is
this, yellow is that, red is this.
But we will learn. You know, one of the, one of my
favorite Deming conversations, with a student who took a
seminar and then took their seminar like five years later.
And the student stood up and said, Dr. Deming, you know, and
the last time I took your course you said X, Y, Z, and now you're
saying A, B, C, right? And Deming, like in his low
voices, I will never apologize for learning.
Like, yeah. I mean, what you know today...
Like I cringe sometimes to watch some of my early presentations
on DevOps. You know, like, oh no, I didn't
say that. Oh my goodness.
But like, I, you know, like, I think it is like, I'll never
apologize for learning. And even on the AI thing, you
know, I was new to AI and I think somewhere in the book I
put a quote like, I will never apologize for learning.
So if you, like, when I wrote this book, this is how I
understood AI, and if you don't like it, I'm not gonna apologize
to you, you know? Wow, that's, I think it's pretty
good quote, right? So we always learn, we always
find new things, right? And especially also, you know,
with a lot of advancements these days, right?
And I think that's pretty good quote, right?
So we all, we always learn, we change our minds, right?
At the core of it, it's mild, right?
This like, I'll yeah. You know, learning and it, you
know, I'm, I know we're going over.
But if we go back to some of the really cool, other cool Deming
points, there's no fear. You know, if you have that
mindset that like what I tell you, like with a no fear
attitude is I can go up, learn something, try to explain it to
people. That's why Ignite Talks are
amazing, right? Like get up there, fight your
biggest fears, get up there and do this torturous presentation.
And then like, you know, learn, you know.
And then, you know, if somebody's not a jerk, they'll
come up to you like, hey, I loved your presentation.
And I do this all the time. I do go up to people and I try
not to mansplain or I try not to be that jerk.
I say, hey, I think you did a great job.
The one point that you made, I think you probably should take a
little look at it. And people who take that well,
they go, oh, let me go back. And then they'll ping me later
and say, no, you were right. I didn't fully understand that,
right? But if you have that no fear,
like you're gonna go up there and then like, you know.
And then some people, you know. If you're sort of always afraid
of people telling you that you might be wrong, you're probably
never gonna be right. Wow!
Yes, definitely, great insights. So, yeah, I know we are a little
bit over, so, but is there anything else about Deming or
maybe about AI that you think we haven't covered that you think
is very important to actually also mention in this
conversation? Yeah.
I think that the interesting, for people who are fascinated by
Deming, you know, I think the interesting conversation is
following up on this idea of philosophy of AI.
I think there's two threads, right?
One is for CIOs. Start figuring out how you want
to embrace AI at a scale level. It's one thing to have a bunch
of people. This is what my focus in Dear
CIO newsletter is. It's one thing for letting
everybody sort of write, you know, write, you know, AI
things, you know, like... my fear is dear CIO, you might
have, you know, thousands and thousands of Jupyter notebooks
unmanaged. You might have, you know,
hundreds of vector databases, you know, on, you know,
basically with no real scale management.
You're not even understanding evaluations or, and things like
that. So there is this thing about how
do we think about how do we embrace...
You know, unfortunately, your 20,000 developers are gonna come
down to probably 2,000 developers.
You know, if that. That's a reality.
Maybe 5,000 depending on how you sort of like use those people.
But like for those, if you've got 5,000 developers now
developing AI, how do you do that in a scalable way that's
not gonna crush your organization, right?
And so that's one thread. And then I think the other
thread, um, and so that I don't spend as much time and I would
love to, but I'd love to other people to, is the philosophy of
AI. And that's a very heavily Deming
where you could use a lot of sideline knowledge of Deming's
teachings. So really exploring where does
Deming. Almost like, again, I don't have
time to, what would Deming do today with AI?
I mean, I tackled it in my profound knowledge book of what
would Deming do with cyber or DevOps or Lean and Agile.
But I haven't fully attacked it other than sort the epilogue and
my Rebels of Reason book, so. Yeah, definitely, it's gonna be
a great exercise if we use like the Deming's profound knowledge
applied to AI. Just like the cybersecurity that
you did in your book, right? Yep.
Yeah. So John, it's been a pleasant
conversation. I learned a lot from historical
reasons, you know, background stories, the anecdotes.
I think thanks for sharing that. Towards the end of our
conversation, I only have one last question, which is like a
tradition in my podcast. Uh, I would like to ask you this
thing called the 3 Technical Leadership Wisdom.
Just think of them just like an advice you want to give to us.
Uh, maybe if you can share your version today, that would be
great. Yeah, I might just go back to
the original, you know. Work hard, have fun.
Be a boundless advisor. I guess the one I would add is
the, you know, just be a learner.
Like literally, I mean the people that, um, you know, just
constantly. Like, again, let's go back to
Deming. Deming's greatest quote, I think
one of his greatest quotes is, you know, I think I'm gonna
mangle it a little bit, but people deserve to be joy in
work. There's some variant in that,
right? And like there is a navigable
path in your career. Now, I speak of this from
privilege, so I'm gonna be really clear here.
Like so I don't act like everybody should be like me.
I mean, I've lived a privileged life, right?
Like I was raised a privileged. I was capital P privileged.
But what I will say is, if you can do a journey...
I tell this to students. If you get to a point of where
you sort of, in knowledge, you're educated, you have all
these sort of capabilities, like most of people who would be
listening to this podcast at this point, like you deserve to
have fun 8 out of 10 days that you work.
You'll never hit a hundred, right?
There are days, right? And I have been able to, in my
career, successfully say probably on average 8 outta
every 10 days I've ever gotten up in the morning and went to do
some work, I enjoyed myself. Wow, that's a very good reminder
for all of us, uh, especially in our career, right?
Sometimes, uh, we hate our job, but we still do it anyway.
I think it comes back to, again, to the privilege, right?
Some people, you know, might have better privilege, but for
some who are not probably... Right.
And again, that's why I'd be really careful about that
because, you know, again. But like again, tie that would
be a learner, and that is a form of privilege.
Again, I like, I'm getting myself into dangerous waters
here. But like the point is you give
yourself advantage if you become, you know, sort of
maniacal learner. Got it.
Yeah. So John, if people love this
conversation, they wanna go little bit more meta with you,
is there a place where they can find you online?
Yeah, I think, you know, I'm... So if you could put it on the
show notes, but Botchagalupe, uh, you know, um, John Willis,
uh, I think Atlanta. I don't know why I couldn't get
John Willis on LinkedIn. I use Botchagalupe a lot of
places. I have, um, I have newsletters
on my LinkedIn, which I have a profound, a Deming one.
I have an AI one. And I'm actually starting a
quantum one now too. I'm trying to look, see, like if
a dummy like me can learn quantum, can I teach it to all
the dummies like me? If you like the idea of the
whole CIO reference of Dear CIO, it's aicio.ai is my, uh, Dear
CIO, uh, newsletter. Where I really try to point out
to things you should worry about running AI in a large
organizational scale. Yeah.
I'll put them all in the show notes.
Just a little bit of curiosity, what is Botchagalupe?
Uh, there's no really definition of it.
It turns out, uh, it was a word that my mom used to sing and use
when I was growing up, and I never did figure out what
exactly it was. There's tons of folklore that
I've gotten over the years of people told me stories.
Everything from, you know, two Italian groups, the Bachagaloop
and Bacciagalupe that used the war over who's funnier or
stupider, to Abbott and Costello.
Or like, they just... so I don't really have a clear definition
other than I thought it would be a clever name when I was just
getting my sort of Twitter account was really where it
started, so. Right.
So thanks for the trivia. So again, pleasant conversation,
uh, today, John. So thank you so much for your
sharing today. Thanks for having me.
It was great conversation. Thank you so much.
And thanks for doing all the, um, the prep work to make a
really good conversation. I really appreciate that.