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Ready, Test, Go. // Episode 15


THE ENGINE OF ORGANIZATIONAL EXCELLENCE

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29:03









Resource Library /  Podcasts /  The Engine of Organizational Excellence
Listen to this episode on:



ABOUT THIS EPISODE

Success is less a strike of lighting than a commitment to organizational
excellence. Enabling greatness means alleviating unnecessary challenges, which
sometimes means pumping the brakes to unravel tangles and inefficiencies, even —
and especially — in times of fierce competition and economic uncertainty.

Gene Kim has spent years researching the habits of successful organizations. He
joins the Ready, Test, Go. podcast to discuss some of the findings in his latest
book, including the concepts of slowification, simplification, and
amplification.



SPECIAL GUEST


GENE KIM

Gene Kim is a renowned bestselling author, researcher, speaker and CTO. His
latest book, co-written with Steven Spear called Wiring the Winning
Organization, published in November.


TRANSCRIPT

(This transcript has been edited for brevity.)


DAVID CARTY: The rise of generative AI has many of us asking it questions like,
what is the meaning of life? Or what should I get my wife for her birthday? Did
that already pass? Anyway, count Gene Kim among the many early adopters who find
themselves fascinated by the technology's capabilities.

GENE KIM: I've been a developer for my entire career, even though I've mostly
self-identified as an ops person. But I cannot tell you how much fun I've been
having with generative AI these days. Whether it's the copilots, whether it's
all these-- whether it's this ChatGPT, I cannot believe how much easier it makes
things-- to build things they actually want. I have been having outrageous
amounts of fun building things with my kids, building tools that I think would
have been a little bit out of reach, either because I didn't have enough time or
the skill. So that's something I'm just having so much fun and getting endless
amounts of just reward and satisfaction doing.

CARTY: Gene has found generative AI to be a helpful tool, not only in coding and
sharpening the language of his writing, but also in learning on the go.

KIM: I'm not really a JavaScript person. So I do a lot of programming Clojure
script. But there's all these things I don't know how to do like what is the
event model, like what events do I need to send to a thing. So I've been writing
some Tampermonkey scripts for some sites for messaging where instead of using
their terrible interface, I get to scrape all of the messages. I have my own
little edit box. I can hit Submit, and it will populate the reply form. I would
have not-- given my lack of experience with JavaScript, I could not be doing
that without all this help. So I mean, it's been so rewarding to solve my own
problems in a way that I think before it was just taking too much effort and
learning too much of stuff that I really don't want to learn. Show me code
samples. Why does that work? What does x, y, or z actually do? It's like your
private little tutor.

CARTY: Of course, generative AI is not without its risks. So that means
vigilance is key to avoid costly mishaps.

KIM: I think we're still learning how do we use this at a scale. So I mean, I
think a lot of us were having so much fun using it for our individual hobby
projects. And things that we'll notice are that sometimes the answers of how it
solves problems are wildly incorrect. And so a friend of mine, they actually did
a pilot at a large financial organization. And they called the pilot
inconclusive, because one of the things they found was that junior developers
were committing code. And it was actually the reviewer who was catching those
errors. And so it was actually putting this enormous burden on the reviewer and
the more senior people. And so I think, just maybe to my interpretation of that
is that I think for senior developers, people with a lot of experience, this can
be a fantastic multiplier for them. And people with less experience, I think,
who can't distinguish even an obviously incorrect solution, it might be less
useful. It might be not only consumes their time, but also consumes the person
who's actually supposed to be reviewing it and helping ensure the accuracy of
code they're putting potentially in production.

CARTY: This is the Ready, Test, Go. podcast brought to you by Applause. I'm
David Carty. Today's guest is generative AI whisperer and author, Gene Kim. Gene
is a multiple award-winning CTO and Wall Street Journal best-selling author.
Gene is a renowned researcher of high-performing technology organizations. And
in the DevOps community, Gene is basically a household name. His latest book,
which he co-authored with Steven Spear, called Wiring the Winning Organization,
published a few weeks ago. The way we do work is changing, or at least it
should. The organizations that can open up the flow of ideas and ease problem
solving are more likely to achieve success than those who cannot. Let's learn
from Gene Kim and the decades of research that the authors synthesize in his
latest book, how dev and QA organizations can move from the danger zone to the
winning zone.

Gene, congratulations on the new book. It's been great reading through it. And
you use some case studies from both the public and the private sector in this
book. Everything from NASA missions and plane crashes to optimizing software
design at Amazon and the release of the iPhone at Apple. Talk about running the
gamut. You know you're in serious territory when you're talking about asteroid
deflection, for example, right? So how difficult was it for both of you to
synthesize these successes and failures? And did you come to any surprising
conclusions in your research?

KIM: Yeah, for sure. And yeah, thanks for bringing up the case studies. Yeah, so
maybe I can start about what was the goal of writing this book. And just to
answer that part of your question, it was the most intellectually challenging
thing I've ever worked on in my career, but also one of the most rewarding.

And I think the reason is that what the goal of writing the book was really to
answer the question, why do organizations work in the way they do? Both in the
ideal and not ideal, and answer the question of what is in common between Agile,
DevOps, and which has been my area of passion studying high-performing
technology organizations for 24 years. But there seems to be a lot common
between those fields and also the Toyota production system, Lean, safety
culture, resilience engineering, which has been so much of Steve's area of
study.

So, Dr. Steven Spear is at MIT Sloan School of Business. And he's famous for
writing the 1999 Harvard Business Review article called, Decoding the DNA of the
Toyota Production System, which is actually based on his doctoral dissertation
at the Harvard Business School. And in part of that, he actually worked on the
plant floor of a tier one Toyota supplier for six months. And so he extended the
work beyond just the high repetition work of manufacturing to engine design at
Pratt & Whitney to helping build a safety culture at Alcoa.

And so our conclusion after working for-- I met him in 2014, we've been working
on this for over three years, and our conclusion is that all of these things,
whether it's Agile, DevOps, Toyota production system, Team Topologies, Westrum
Organizational Typology model, safety culture, is that they are all incomplete
expressions of a far greater whole. And to find that there are these common
mechanisms that span all of those domains has been just so illuminating, so
dazzling. And which explains like why we chose the case studies that we did is
to show that to the technology leader that the same principles that work in
DevOps actually work in manufacturing, in team of teams, and so forth.

And so that's been so, so fun. And so I think the goal is to show both
technology leaders and their bosses that there are things that we, as leaders,
can do to make it amazing to liberate the full creative activity and
problem-solving capabilities of the organization. And similarly, there are
things that we can do to constrain or extinguish entirely the full capability of
the organization, like dividing dev and ops, making sure that they can only talk
to each other through work tickets, and once a year, when we do a deployment. So
it's been so satisfying for it to work on this.

CARTY: I can still feel the stress of reading The Phoenix Project and The
Unicorn Project, the examples that you use in there when you talk about
separating organizations like that. So I know exactly what you're talking about.

This latest book revolves around three core concepts of successful
organizations-- you mentioned them already, slowification, simplification, and
amplification. I want to ask you about each of these individually as those are
the core concepts of the book. But on a high level, can you explain what these
ideas are in the context of a software development organization?

KIM: Absolutely. And so what we're saying in any domain of work, in every stage
of value creation in any industry, there's basically-- imagine there's two ways
of doing work. We can do it in the danger zone or the winning zone. Work in the
danger zone sounds like this. We have no time for planning and preparation, so
we have to do all our learning in dangerous production environments that are
highly complex, intertwined, highly coupled, where small problems cause global
catastrophic failures we have no ability to undo.

And those are all characteristics of danger zone properties. And so in The
Phoenix Project, when they were trying to learn about the application while it's
being deployed in front of real-life customers, that's dangerous
characteristics. The winning zone is exact opposite of that is that we're able
to do our most dangerous work not in production, but in planning and
preparation. We're able to work on not the entire entangled whole, but in
smaller components that are contained so that small failures don't ripple out
and cause global chaos and disruption.

And more importantly, we can do our work independently. In microservices, we
know that the is easier to work in smaller services where we can test and deploy
values to our users independently without having to communicate, coordinate,
prioritize, synchronize, deconflict with thousands of other people in the
organization. And so our conclusion is that whenever you see an organization
transform, there are only three mechanisms that work. So the first is we have to
slowify.

So, whenever you see people doing brilliant dangerous work in highly
consequential environments, logic says that for them to be doing that, they have
to have invested time in planning and practice. And so we see this across,
whether it's Amazon gamedays, whether it's the disaster recovery planning at
Google, whether it's special forces in hostage rescue operations. Navy Top Gun
program was essentially one around slowification.

In the Vietnam War, they found that they had unacceptable pilot casualties
because there was too much on the job training. And so the result was this
high-intensity school to simulate combat-like situations where they could
develop the habits and routines and playbooks before they entered combat. And so
the second mechanism is about simplification. How do you make the problem
simpler to solve? So instead of the wildly complex and entwined, highly-coupled
system, how do you partition it?

So, either by making it more modular for parallel processes. Or things linear
processes, like the Toyota production system or CI/CD, how do we sequentialize
it so that we can do work in parallel to safer. And then the third mechanism is
we have to amplify signals, so that even weak signals of failures can be
amplified and then acted upon, so that we can prevent disasters from happening
or enable quicker detection and recovery.

CARTY: Right. I'm with you. And you know what? Two thoughts. Slowification,
first of all, it sounds like something I'd really like to teach my two children.
But that's not quite what it means.

What it really reminds me of is it also reminds me of paying down technical
debt, which is something we've also studied and spoken about. I mean, you've
talked about this at the DevOps Enterprise Summit, except that it's applied to
the actual practice of problem solving and completing work. So what I wanted to
ask you is--

KIM: Actually, can I just interject there. So yeah, slowification, many people
will note is not a real word in English. I mean, but so we made one up, because
it was so important to us is that there has to be some word that signals we're
going to slow down to speed up. We're going to make a short-term investment for
a longer-term gain. And so there are a lot of adages for this. We've got a stop
sign to "sharpen the saw," "slow is smooth, smooth is fast." So the goal was to
actually give it a word for this very important concept. And so with technical
debt, r the whole idea is that you can't spend all your time doing. You have to
spend some time improving. The notion of improving daily work is as improvement
as daily work itself. So just to-- sorry, you brought such a good point up I
just want to make sure that we didn't lose it.

CARTY: No, absolutely. And what I wanted to ask is given our current economic
situation, you might have some teams that are short staffed or overworked. Is it
harder to win over hearts and minds to this idea right now? I mean, I know
that's the area that you speak about all the time is trying to influence change
and things of that nature. Is it especially hard to try to get that concept into
the minds of technology leaders today?

KIM: Yeah, I think it's absolutely, probably right. I mean, I think when it
comes down to it, most software teams, the job is to deliver features that users
need to either grow market share, to increase profitability, to save costs, et
cetera. And so I think that is a fact of life.

And so often, we find ourselves in a mode where we just got to focus on
delivering that feature, that functionality to our customers, whether it's
internal or external.

But at a certain point in time, we have to invest in our own productivity and
our own efficiencies, and that is often lost. And I think many people would find
it surprising, who were in the field 20 years ago, about how difficult it is to
justify doing basic things like unit testing, like testing in general. And I
think I was talking to a person who wrote the— Don Eyles, he wrote the lunar
landing control software. And I mentioned this concept to him. And he was
aghast. He was like, what do you mean when testing is not someone else's job?

It's not something that you can delegate. It's not something that you can
prioritize away. It is a core job of us as an engineering community. And so I
think he would-- people like him would find it surprising just how often we
deprioritize things like that. Does that resonate with you?

CARTY: Oh, absolutely, yeah. I mean, we talk about matters like that all the
time on the podcast, the digital quality and trying to be more efficient and
trying to win over hearts and minds on the idea of investing in your quality, in
testing, and that kind of thing.

To move on to the next point -- I want to be respectful of your time here --
simplification involves making the work a little bit more incremental, modular,
linear. And I can feel DevOps in this particular section. Can you tell us what
this approach consists of? Again, particularly in a dev-QA organization
perspective.

KIM: So the point of simplifying is to change the nature of the problem so they
are easier to solve. And so one of my favorite case studies is the Amazon API
project, where they went from a situation where they were doing, say in 1999,
hundreds of deployments per year. And it just got harder and harder. In fact,
most deployments didn't finish to the point where they were only doing tens of
deployments per year.

And there was this sort of outrageous-- and the reason for that is that the
number of product lines kept growing. It was involving hundreds, then thousands
of engineers. There was this ridiculous situation where Werner Vogels, then and
current CTO of Amazon, described in 2001 the Amazon digital team. So I think
that was Kindle video. In order for them to fulfill an order, they had to
provide a physical shipping address, [LAUGHS] which is definitely introduces
friction into the ordering process. So they had to go to 60 plus teams and ask
them, can we please reduce the need or eliminate the need to provide a shipping
address? To which they respond, we haven't budgeted for it, so go pound sand,
right? And so everyone was stuck.

And so this is what led to the Two-Pizza Team memo, that the notion that every
team the only way they could communicate and coordinate was through APIs. And
what that allowed was-- what that resulted was in the creating of hard modular
boundaries between teams. So they could regain independence of action, so they
could all work independently of each other. And so the outcome of that was they
were able to do, in 2011, 15,000 deployments a day, by 2014, they were doing
136,000 deployments per day, just showing that we're really unleashing the
productivity of developers to do their work well. And then, I think we can see
this other orthogonal technique, which is a linearization.

So, modularization is so that people can work in parallel for parallel
processes. Linearization is what we do for inherently sequential work, like
manufacturing or automated build, test, and deploy processes. So in the Phoenix
Project, we described these high stakes releases where everyone depends on
everyone else. And if anyone gets there is late, they delay the entire
deployment.

And so the idea is, how do we automate those processes and make it again enable
independence of action? So that the build engineers can work over here
independently of the deployment engineers over there, independently of the QE
quality engineering people in between them. So I just find that just amazing
because it shows that there are very small number of mechanisms that explain so
much of the techniques we use in transformation.

CARTY: Oh, that's spot on. The third component, amplification, helps bring
problems to light, as you mentioned. How does amplification make use of feedback
to help organizations stabilize and improve reliability?

KIM: Yeah, so amplification is all about signals getting to where they're
generated to where they need to go. And so in the introduction, we're talking
about generative AI where some people are finding that in these early
experiments junior engineers are generating code that was assisted by AI but
they can't recognize that it's actually incorrect. And so the people who have to
fix it are the senior developers, which is not tenable. And ideally, what you
want is-- actually what we need is the feedback to go to where it's needed,
which is a person who wrote the code. Similarly, we find that we can solve so
many of these DevOps-like problems if developers build the code and run the code
themselves and deploy themselves. It can't go to an operations team that's
distant in the organization because the signals are lost. And so I think really
amplification is about making sure that the signals go to where they need to go
and that leaders are picking up even these weaker signals of failure. So that
they can be acted upon before they cause something genuinely horrible to happen.

I think one of my favorite examples of this is the blameless post mortem, the
notion that we have a blameless postmortem that we publish to everybody for
every customer impacting outage at Google, for example. My friend Randy Shoup
talked about this. And he said something really magical happens, is that
eventually, you run out of customer impacting outages to have blameless
postmortems on.

And so what do they do next? They had these postmortems for not just customer
impacting incidents but team impacting incidents or near misses. Of the seven
safeguards that exist to prevent a live site outage, six of them failed. Let's
dig into that so that we can prevent a near miss like that from happening again.
I think all of these are just examples of amplification at work. Oh, can I tell
you one of my favorite stories on this?

CARTY: Absolutely. Yeah.

KIM: So one of the case studies we have is on the Apple iPhone versus Nokia. By
all rights, Nokia should have won. They had dominant market share, they had tens
of thousands of engineers, they had the relationships with the suppliers and
telcos. What I found so surprising in researching the book was that the Apple
iPhone initially started with a team of 10 software engineers. And so even
though when they shipped, it was now dozens of engineers, it wasn't a large
team. One, that was amazing, but, two is, one of my favorite books is called
Transforming Nokia by Risto Siilasmaa, and he described how one of the pivotal
moments for him as a board member of Nokia was when he learned that the build
times for the Symbian OS, which Nokia was depending upon to compete against the
Apple iPhone, was two days. [LAUGHTER] He said it felt like being hit in the
head with a sledge hammer because he knew that if it took two days for an
engineer to learn whether the change worked or would have to be redone, all the
hopes, dreams, and aspirations that resided on Symbian OS were an illusion.

And so I got to ask him in an interview how did he take something like a build
time of two days to be an important signal that seems like not something that
most boards talk about, and his answer just blew me away. He said, one, it
wasn't that the build times took two days; he said it was the compile times that
took two days. The build time was actually two weeks because they had
distributed R&D centers. But he said in any organization, you have the most
important work being done, and you have to ask, is that work easy or difficult
for the person doing it? And he said when he learned that the compile times were
it took two days, as a former developer, he said it was impossible for anyone to
do their work easily and well.

And so he said there's really two possibilities here: one is that the senior
leaders didn't know, and that's a huge problem; or it was that the senior
leaders did know but didn't do anything about it. And he said that is an even
bigger problem. That's actually what led to the firing of the CEO and for him to
become the CEO of Nokia. Anyway, I just thought that was an amazing example of
amplification of what we need from our leaders to help support the work, the
daily work of developers, and for that matter, everyone in the engineering value
stream.

CARTY: Gene, I saw yesterday that the DevOps Enterprise Summit is evolving a
little bit, right? And it's now going to be called the Enterprise Tech Summit.
Do I have that correct?

KIM: Yeah, the Enterprise Technology Leadership Summit. Yeah, exactly.

CARTY: Technology Leadership Summit. Yes, yes. Can you explain this change a
bit? And also what does this mean for DevOps moving forward? Is it becoming more
about influencing tech leaders in a direct way or is it just a different way of
looking at the conference specifically?

KIM: Oh, make no mistake, the conference is the same. And I think this is
actually for years, people have been saying the conference seems to be a lot
more than just DevOps. And I think DevOps, if you interpret it in a very narrow
way, you can say it's just about deployment. So I think many of us say, no,
that's a very narrow interpretation of what DevOps really is. But with the
conference, we've done 19 of them. We've had over 100 talks, 1,600 leaders
sharing their experience reports about adopting technology transformation in
large, complex organizations. And I'm so proud that we've had some of the best
known brands across every industry vertical present amazing stories of how
they're helping their organizations win. And the talks are not just about Dev
and Ops; they're about Dev, QA, Ops; it's about security. It's about regulators.
We have auditors, we have business leaders.

And so I think the aperture of the conference has certainly grown since we did
the first one in 2014. Many people have said when I tell people to go, I don't
call it a DevOps Enterprise Summit. I say it's to talk about culture,
organizational design, business technology working together, information
security, and compliance. And so after a couple of years of hearing that, I
think I think we finally said, all right, let's bite the bullet and call it what
it really is. It's a conference for technology leaders to help them and their
organizations win.

CARTY: Right and explores everything to psychological safety and burnout and
things like that impact the individual too, right? So that makes total sense.

KIM: Yeah, I'm so delighted to hear that people are positive about it. [LAUGHS]

CARTY: Yeah. Gene, you don't strike me as a guy who likes to remain
intellectually idle, and I know you just wrapped up this book, and you're still
discussing it. But I'm curious if you have anything on the horizon that you plan
to research next?

KIM: Yeah. The reason we wrote this book is, I think, one is to create this
mental model of what is it that we're actually doing? When we talk about DevOps
transformations or high performing technology organizations, and similarly, what
is it that we're doing when we talk about Toyota production system building
safety cultures and so forth.

And I feel like the mission ahead really is how do we create the right
conditions. So that business leadership and technology leadership are working
hand in hand with a sense of mutual trust, that they're working towards common
objectives because I've seen so many situations where if that top leader changes
in the technology organization, the transformation dies. Or maybe their business
counterpart changes over, and suddenly the level of support and the amount of
air cover disappears.

And so I'm hoping that by creating a common language of what does it take to
create outstanding performance that this will help accelerate technology
organizations to reach their highest potential. So that's my personal, selfish
objective. And so I think that creates a roadmap of years of work to see if we
can expand the reach, so that we're DevOpsing in more places and with higher
level support than ever.

CARTY: OK, Gene. Lightning round questions here. First one: what is your
definition of digital quality?

KIM: Definition of digital quality is we are doing all the things in our work so
that we delight the customer regardless of who the customer is internal or
external.

CARTY: Gene, what is one software testing trend that you find promising?

KIM: Oh, my gosh. I love the shift from highly expensive integration testing to
shifting all the way left so that developers are getting daily feedback, daily
minute by minute feedback on their work, and they're getting feedback within
seconds, worst case minutes, but not hours, weeks, months, or quarters.

CARTY: What is your favorite app to use in your downtime?

KIM: Oh, [LAUGHS] yeah, I love this app called Feedly. There's certain writers
and sites that I just love reading so much I don't want to miss any one of them,
and it's just my favorite app for just reading and learning. I've used it for
almost a decade.

CARTY: And what is something that you are hopeful for?

KIM: Oh, I love-- I'm actually loving generative AI. In fact, something I'm
actually using a lot is the ChatGPT app with the voice interface. Someone said
people are spending like hours talking to it, I'm like, what? But I found
myself, in a while picking up my kids, I'm like, can you teach me about parsing
CSS selectors in JavaScript? And next thing I know, I'm 25 minutes in because my
questions are like, show me how to do that in JavaScript, teach me what a CSS
selector is, all these things. It's been super, a very fun learning tool.




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