Speaker 4: Good morning, good
afternoon, good evening, time
appropriate greetings.
My name is Dewan Ahmed, host of
Ship Talk Podcast.
And this is not just any
episode, this is the finale of
season four.
And with me, I have Thomas
Dockstader, the man in the
middle of all the madness of
reInvent.
And we can't wait to hear from
Thomas what was the discussion
around uh AI meeting software
delivery, which is the theme of
season four.
But this time, this time we
have a spin with engineering
excellence.
Welcome, Thomas.
Thanks for having me.
Thank you.
Tell me what was what was the
vibe like?
Like I heard people were saying
like they had like 20,000,
30,000 steps uh during reInvent,
uh, with with all the the
energy or the innovations.
Uh what was it like around the
booth?
Speaker 7: Yeah, I mean, I think
reInvent is, I've been going to
um trade shows for years and
years, and I think reInvent has
just been one of the ones that
has astonished me the last
couple of years.
The number of people that are
there, the um the lengths at
which AWS goes to in order to
put the show on is incredible.
Uh it spans across many hotels
and just the uh the the trade
show floor in and of itself.
I've just never been at a show
where there's so many people.
And when you throw on the um
the buzz of AI right now in the
tech industry uh is just uh a
formula for uh a lot of
excitement and confusion and uh
questions.
Speaker 4: So yeah, and one of
the key questions, I guess, is
engineering excellence.
So is this a goal or a
struggle?
How do you see customers uh
checking engineering excellence
as their as their goal or
struggle?
Speaker 7: Yeah, I would say um,
you know, I think really it
comes up in in two different
areas, and and I feel like it's
becoming somewhat of a pressure
point.
Um, you know, the pulse at
reInvent was not so much in the
in the realm of is AI coming or
when is it going to come?
It's it's here.
And um I think one of the
things that's really being
magnified is when we talk about
code assists and code assist
being really, in my opinion, the
first real big insertion of AI
into engineering.
Um, the amount of work that's
coming out of code assist, the
amount of code that's being
pushed is accelerated by a
massive amount.
And what we're seeing really is
that um engineering excellence,
you know, uh what I heard was,
you know, people weren't
describing it as this nice to
have aspiration.
It's really kind of exposing um
some of the areas in the SDLC
that are not prepared for the
kind of influx in code that's
happening.
And so because of AI, you know,
you can absolutely uh it can
help teams move faster, by no
doubt.
However, um, if the system
underneath is weak and if the
governance is weak and if the
standards are inconsistent, um,
you know, AI doesn't solve for
that.
It's it's really not that
simple.
And we we we don't have AI
inserted into all of the SDLC.
And so I would say engineering
excellence was showing up really
both as a goal and a struggle.
Um uh it's really becoming the
difference between teams that
actually can turn AI AI on and
into a reliable outcome versus
the ones that are creating um
more more risk and uh you know
uh more challenges.
Speaker 4: Couldn't agree more.
And you interviewed five uh
partners, customers, engineering
leaders in the space.
Was there a common thread,
Thomas, around how teams are
using AI to transform their
software delivery?
Speaker 7: Yeah, I would say
that um there, you know, the
common thread was honestly a
little bit more grounded than um
a lot of the market hype.
Uh I think that really the
shared theme was AI is not
really um, at least at this
point, replacing engineers, and
more as it's kind of removing
friction around engineering.
And uh it is helping teams move
faster, especially with
repetitive work, test creations
and pipeline configurations um
that can drag.
And really, I mean, when you
talk about coil in engineering,
um, AI is really great at
reducing some of those um uh
monotonous tasks that we have in
engineering.
However, um, you know, I think
one one of the more important
things was the common thread was
that um the the the output is
increasing and it is creating a
tremendous pressure downstream
uh in the SDLC.
And so um I think the real
transformation is not just you
know AI making teams faster, uh,
it is also forcing
organizations to mature and
create an operating model around
delivery.
Um that's that was that was
abundantly clear um across all
of my conversations.
And that was probably the
biggest thing that came out of
all those conversations was AI
is useful, AI is real, and uh,
but it's already creating that
um visibility into areas where
our uh SDLC might need
additional work uh to really
leverage it.
And I I could see some
organizations um dealing with a
lot of um tech debt in the near
future.
Speaker 4: AI is real.
AI is helping us create test
cases for our code, yeah, he's
deleting our entire mailbox.
AI is very real.
Um now, was there a difference
in the discussion on how vendors
are providing AI technology
versus customers actually using
it to ship real products?
Speaker 7: Yeah, I would say um,
you know, um I think partners
and customers, you know, they're
looking at the same shift
really, uh, real and and from
but really from different
altitudes.
Um, you know, partners were
generally very focused on what
AI can unlock.
Um, you know, and they talked a
lot about acceleration and
automation and uh reducing
cognitive load and creating more
streamlined developer
experience.
I think we see a lot of um a
lot of vendors, you know,
everybody has AI in their name.
All the tools have AI.
Um, but uh, you know, where is
it actually making marked
movement in in solving real
problems?
Um, you know, I think uh for
the customers, you know, um it
was much more grounded in
operational reality in just kind
of saying, like, hey, you know,
that's great on these features
that you're talking about and
and and everybody's talking
about them, but how do we
validate this?
Like, where does the value come
from?
How do we validate the value?
And I think that's a really,
really important piece is, and
we're seeing it in SaaS in
general.
It's like, you know, the idea
of moving from a seat-based cost
model to a um outcome-based um
model.
And I think customers are going
to be more and more um
demanding when it comes to, hey,
you've got all these features,
you've got this amazing AI tool.
Show me the outcome.
And that seemed to be pretty
consistent in from the customer
side.
And so, really, the strongest
signal for me uh was that the
gap between experimentation and
production uh is still very
real, right?
We we I don't think we've come
crossed that chasm.
Um, a lot of organizations know
they need to move to AI, but uh
I think they're still figuring
out how to do it in a way that
is governed, repeatable, and
sustainable.
Speaker 4: Yeah, and totally.
And like these organizations,
they're not in uh in a same
maturity level in terms of their
delivery as well, right?
It's like some are still in
like legacy technology, uh, some
are still like uh uh trying to
figure out like how to
modernize, and while they are
trying to do that, now there's
this AI transformation they have
to do.
So, uh did did you talk to some
customers uh uh trying to now
battle like two grounds.
One is modernizing their legacy
tech, and now they have to do
AI modernization as well.
Speaker 7: Yeah, it's
interesting.
Um, you know, I do uh in
addition to you know reInvent, I
do SDLC assessments for
organizations, and it's funny
because as fast as AI is moving,
um I don't know that there's a
solve for how we we move a
legacy system.
You know, it there's just so
much data, and there's so many
um uh learned and baked-in
processes within an organization
that um and there's a lot of um
individual knowledge that's
held by the individuals who who
do those jobs, not documented
necessarily in a system that AI
could go in and read.
And so I think it's gonna be a
combination of um someone's
gonna have to come up with a
pretty smart idea on how to do
this, but it's gonna be
challenging for those legacy
systems to move.
I think some of them will get
um could get disrupted, quite
honestly.
Um that's a that's a big
possibility in uh um being on
some of those older systems and
just not having the ability to
move to a velocity space.
Speaker 4: Yeah, and I'm pretty
sure like we'll have some more
insights from these five
interviews.
So without further ado, let's
listen to the interviews.
Speaker 7: Thank you for coming
out.
Thank you for happy to have you
in in the booth to record that
to record uh an episode with
you.
Um this show's crazy.
It is, and um, there's a lot of
I see one word or sorry, two
letters, AI is everywhere,
right?
Sure is.
It's uh it's a pretty popular
uh subject.
It's what I write about all the
time.
And so I'm I'm so interested to
hear your uh opinion on things.
I really want to understand
like what are you hearing um
that that that AI is actually
helping versus kind of hype not
actually doing much?
Right.
Well, it's not an easy question
to answer, right?
Let's go simple, yeah, yeah.
Speaker 6: Some simple ideas.
I think that um, you know,
everybody I talk to, and I talk
to a lot of CIOs, CTOs, sense of
AI, obviously, people know that
they kind of have to be getting
the companies moving in this
direction.
Um you can't go from like zero
to a hundred.
You know, you have to kind of
there's things you have to do
first.
So one of the big things is
data, right?
You have to get your data in
order, and then you can begin to
start to take advantage of this
stuff as you tune the models to
your data.
But people know that they have
to go there, not everybody is
there yet, and there's a lot of
experimentation that's going on.
Um, what I'm hearing is that
there's less like in production,
but more um, you know, we're
still doing proofs of concept,
we're still doing
experimentation.
We're trying to figure out like
how do we use AI to really like
improve efficiency to get that
return on investment.
But, you know, being a um a
company that deals with
developers, one of the areas
that seems to be the most um
mature is the code area.
So that's an area where you see
a lot of companies kind of
diving in a little deeper than
maybe some of the other areas.
Speaker 7: Yeah, I think it's
funny you mention that because I
feel like that portion of the
SDLC is accelerating quite a
bit.
Yeah.
My assumption is well, it's
actually happening because we're
seeing that deploy deploy times
are higher because there's so
much code coming in, the PRs are
accelerated, everything's going
faster.
I really wonder what's going to
happen, call it a year from
now, if we're gonna have this
tidal wave of bugs, right?
And like the the ability to fix
them, I think because maybe the
developers aren't so familiar
with the code like they were,
you know, 20 years ago when
John, who has been with the
company for 20 years and he
knows the code backwards and
forwards, if we're throwing a
bunch of code in there, you
know, it could potentially cause
additional problems.
I'm curious, the leaders that
you're talking to, are they are
they thinking about this?
Speaker 6: Or I mean, I think
people have to be thinking about
that, right?
Because one thing that AI
allows you to do, as you said,
is to go faster.
Yes.
Like going faster has its own
danger with it.
So there are two things here, I
think.
One is your experience
development team was going to be
using these tools to kind of do
things that were kind of
routine and not very fun to do,
right?
To get those things done faster
without kind of manually going
in and doing them.
But you also have this idea
that maybe the citizen developer
can start to develop.
And I I think that's where a
lot of problems could start to
prop up because people who don't
know the kinds of things that
an experienced engineer knows
can get into trouble very
quickly, and they don't know
security, they don't know
governance, and they're just
like, oh, look, I can create
this program.
Um, and I think there's part of
the problem is that the
engineering team is gonna have
to reel that in.
That's that's that's one side
of it.
But the other side of it is
like, how good is this code?
And you're putting this code
into your pipelines, right?
And like you said, you don't
necessarily know it.
I'll tell you a quick story.
I was in Miami a few weeks ago,
and I was in a coffee shop
getting coffee before my meeting
in the morning, and I see these
two guys, I overheard these two
guys talking.
One guy's like sitting there,
he's bleary-eyed, and his
buddy's like, Wow, you look
tired.
What's going on?
And he's like, Well, I had to
read 10,000 lines of code last
night.
And the guy's like, Can't you
get AI to do that?
And he's like, No, I have to
know my code.
So, what you said, like like
there's still, even though
you're creating it faster, maybe
that even makes it harder.
Because if you're somebody who
wants to still have that sense
of, I need to understand what's
going in my pipeline, then
that's not and sure, you're
efficient on one hand, but on
the other hand, you're like, now
I got all this code, I have to
be uh supervising.
Speaker 7: How do you think um
companies are redefining
engineering excellence because
of AI?
Like, what's like what how what
are you what are you seeing
there from trends?
Because I know a couple of my
guests have talked about center
of excellence uh groups and how
they're so needed in the or to
your point, creating compliance
and um making sure we have
checks and balances and all
those things.
Are the are the higher ups
thinking about this from a
perspective of I I think they
have to be, right?
Speaker 6: You know, um and like
I I spoke to um the the guy who
runs the uh the center of of
innovation here at AWS
yesterday.
Okay, so they are working you
know directly with customers to
you know kind of help them
understand everybody's kind of
learning together, right?
I think the vendors are
learning, the developers are
learning, the companies are
learning, and it's it's this
kind of chaotic mix in a way,
because everything's happening
so quickly.
And I think back like a year
ago, like there was no there was
no um you know management
layer, there was right, there
was very little security.
Yeah, um, you're starting to
see things like that be
announced um you know by AWS and
others.
Um and like when you think
about something like an agent,
it needs basic stuff, you know,
it it needs all the stuff that
you've always needed, but as you
say, you're making it
everything faster and faster.
So you have to have those
checks and balances in the
pipeline, or things are gonna
blow up when you're pretty fast,
I think.
Speaker 7: Yeah, that that
actually leads me to my next
question is like, what do you
think the skills are that that
that leaders are looking at to
really develop?
And and and I with my last
guest, we were talking about
agents.
And I want I I asked the
question are we are we now
looking for talent that is
learning specifically how to
manage agents alone?
Like, what is what is the trend
from a perspective of the type
now?
You also mentioned experienced
developers.
I've heard that trend where uh
some organizations are leaning
more towards the heavily
experienced developers versus
hiring new out-of-college
developers.
Speaker 6: Where so I think
there's two schools of thought
on that.
Some people are saying we're
hiring a lot of young people
because they get this stuff
apparently, right?
But you know, I think
ultimately you need both, right?
Yeah, you still need mentors,
you still need like even if
you're an old school developer
and you're working with a young
kid out of college, you still
need to um you know have that
mix of skills and understanding
how things get built, right?
Um in terms of skills, I think
I I wrote I wrote a commentary
um earlier this year that was it
was kind of interesting to me
because when you think back,
engineers become engineers
because they're good at math,
they're good at logic, they're
good at understanding kind of
like how code fits together.
But when you start to become AI
driven, the importance of
prompting becomes you know, a
lot becomes more important.
And yeah, that's true.
You know, so then what is what
is that skill?
That's writing, right?
It's writing and describing
what you want.
And if you're building in an
agent, like it's one thing to
prompt, you know, a chat GPT
type model and say, I need this,
and that's you know, I'm a
writer and I I find like you
have to be kind of like play
with them until you get what you
want out of it, right?
When you're an engineer and
you're creating an agent that
may be something you use
internally or something you're
selling to customers, that
prompt has to be really rich.
Right.
It has to really have a you
have to have a lot of expertise,
and you have to be able to
articulate that expertise.
So suddenly I think it's no
longer just math and logic,
although I think that still
applies.
Suddenly you need those English
skills.
Yeah, the skills, the skills
that I have suddenly become, you
know, people say, oh, you know,
with with uh these large
language models, writing becomes
less important because models.
I don't I don't agree with that
as a writer, but I also think
in some ways it becomes more
important because you have to be
able to communicate what you
want to these models.
And if you're building, you
know, an automation agent that's
going to do a lot of stuff,
yeah.
You have to first of all
understand that process inside
and out, and then you have to be
able to articulate it in a way
that you can communicate it so
that the models can carry out
these identic tasks, right?
Speaker 7: That is a very
important point, and I think
it's it's kind of scary because
my son got in trouble for using
AI for his paper report, right?
So maybe the the coming
generation isn't developing
their writing skills because of
AI when in reality we need it,
right?
Speaker 6: We absolutely need
it, and and I think that um your
son and all of all the people
in that you know in our younger
generation have to kind of learn
both, right?
Yes, I mean because like I
don't use AI to write, but I I
use AI to edit, you know.
So I'm a I'm a I run that
newsletter and blog that I have
by myself.
So I have a I have a human
editor that that checks the
pieces, but as I'm writing, I'm
like I'm checking it against
like, you know, did I make any
mistakes?
Did I, you know, did I overly
repeat words?
Did I um that kind of stuff is
really nice?
Does it thematically hold
together?
You know, is it is it logical?
You know, and you can ask the
models these things, and it's
like having this editor, but
even with that, like when I give
it to a human editor, she
always finds stuff and she'll
say, like, I don't know what you
were trying to say here.
Why does this have m dashes?
Those cursed m-dashes.
I mean, I've used m-dashes
before, but they those models,
they just love that.
Speaker 8: Oh, I don't know why.
Speaker 7: And it's an instant,
now it's an instant indicator.
Oh, it's a test written with an
AI.
So um how do you think, how do
you how do you think in
enterprises are measuring this?
How are they measuring AI?
Uh whether it be code or have
you heard any trends or anything
that's coming around from
perspective of how they're
actually going to measure this?
Speaker 6: I mean, I I I think
even in the past, you know,
sometimes they would say, like,
you're a productive engineer
because you've produced X lines
of code per day, per week, per
month, right?
Um I've heard people say that
even that is pretty flawed.
look at engineering excellence
so um but if you think about
that as a metric that becomes
less important right because if
you can tell the model what you
want and the model produces a
bunch of code for you and then
you massage that code and then
find ways to you know mix that
with other things that you're
doing that you've created then
where's the where's the
productivity measure yeah right
so um I think companies have to
start thinking about how they
they they measure uh the
production and efficiency of
engineering moving forward
because you know when you're
gonna be working with ai as a as
kind of a a worker and I kind
of I don't really like that that
metaphor yeah but I mean you
are going to be working
alongside this entity yeah
that's very true and um
companies have to find ways to
say well this person you know
knows how to use ai really well
and you know that has made them
more productive uh you know this
person maybe is doing uh more
manual coding but it's better
code you know so so I mean like
you have to kind of start to
balance all that stuff and I
don't think it's one or the
other right I think we're gonna
whether it's writing or
engineering or whatever it is
it's we're gonna find we're
gonna learn how to use this
stuff uh as a tool and I think
it is it's a tool very much so
in in your quiver that you know
um helps you do your job but it
doesn't do your job right yeah
that's a great point yeah as a
reporter and a writer with all
of this you know I mean look
every company's trying to get
attention right because they're
they're they're implementing AI
they're adding something new how
do you specifically determine
what stories you chase after I
mean you know the fundamentals
of what's news don't change you
know okay there's always a
technological trend that comes
down the bike mobile cloud you
know cloud yeah I mean the
internet going back you know
yeah I mean so uh you know I
mean and I I've been around that
long but uh you know what was
important in the early 2000s
when you went to a conference
like that was this was very
different right yeah sure um and
and I mean over the years
everything kind of shifts you
know if you came here in 2018
probably like a lot of people
talking Kubernetes and cloud
native yep um you know you came
here in 21 to 22 people talking
RPA and that that kind of
automation and now here we are
in 25 and it's just all AI all
the time.
And I would be lying if I
didn't say that had my
attention.
I mean I write about it a lot
um but everything that has AI
attached to it is not
interesting just like every
other wave that came before it
um as a journalist I have to use
my judgment which I think AI
can't do and it is my special
sauce as a human is that I can
look at something and based on
my experience and what I find
interesting say I think this is
newsworthy or this isn't and or
I've seen this 10 times and I
don't want to write about it
again.
Speaker 7: You know yeah you're
actually in a unique position
because you do get to see kind
of like early you know early
announcements or people kind of
reaching out to you hey we're
doing this we're doing that you
probably get to pick out those
eight well actually 12 people
are doing that it's not that
unique right they they may not
know that right that's very
interesting.
Speaker 6: Yeah I mean and from
whether it's a startup
perspective or an established
company um you know there are
there are always I'm always
looking for like you know have I
seen this before if I haven't
seen it before and it's solving
a problem that I hear about you
know then that's something
that's gonna kind of pique my
interest and I'm gonna want to
dig into it and write about it
and see if it's actually a trend
that's developing.
You know so um what is news is
like that's always gonna be like
okay what do I find interesting
is my own unique filter but
that doesn't change because of
AI.
Speaker 7: Sure.
Yeah that makes sense that's a
great point.
Speaker 6: Yeah you're right the
internet was a pretty big thing
pretty big deal when it came
out the web you know I remember
the web I remember when you know
in the late 90s and early 2000s
when companies were trying to
decide whether they should have
a web presence or not you know
and they didn't know what it was
they didn't know how to deal
with it.
And you know obviously that
that changed in heart.
Yeah now though everything's
operated on that now I think you
know we see all these companies
struggling with AI but
eventually it's gonna be like
SaaS right it's like you don't
look at a software company and
say oh they're SaaS now right
you know like every company is
is software.
It's what it is yeah right so I
I think with AI AI is going to
be baked into everything and it
may be not something that we
talk about it as as much as we
do now.
Right.
Speaker 7: So with your with
your you know um insights into
the business all over the place
what what is something that is
generally exciting to you um
that's coming that you see well
I mean I think that as agents
develop you know there's there's
there's this whole
infrastructure around it that
has to develop that we're only
beginning to see around security
around governance around all
the fundamentals of IT right
like whatever it is whatever
type of software it is you can
call it whatever you want today
it's agents there are certain i
and I I wrote a piece um about
this at one point where the
fundamentals still apply right
you have to be fundamentally
sound in a large organization
especially or you know it's just
not gonna work so I think we're
starting to see some of that
infrastructure develop and I
mean I was at RSA in April right
in big security conference in
San Francisco and there were
people talking like you know
these things are going to have
to go out communicate with other
agents cross systems maybe I
mean we don't know how
interesting the vision will
match the reality but as those
things happen um you know you're
gonna need a unique kind of
security right because these
things are gonna go do something
they're gonna become something
else and then maybe their their
identity and authentication
suddenly are different and
they're in their neural interest
and like it used to be that
software was pretty linear right
you know so those ones and
zeros yeah so so so so as you
went through that process it was
fairly easy to secure it once
you secured it right now it
becomes a little bit more loosey
goosey because um things are
changing so much and these
things can be you know they can
move and they have what they're
meant is that they're highly
flexible right but their their
danger is that they're not
flexible like how do you secure
something that can change and so
that that I find pretty
fascinating.
Speaker 3: It's exciting I'm I'm
telling you what um we're gonna
do a little segment ship it or
skip it okay um office pets um
ship it ship it all right I love
that uh four day work weeks um
ship it yes thank you once you
once we get to four then I'll
push for three yes with AI right
with agents we can we could get
there yeah I mean I mean if
you're gonna have AI and it
truly makes us more efficient
why not give it it's supposed to
give you time yes and you hear
some people measure it that way
you know like it can't it it
saved eight hours of development
it saved eight hours of
research whatever it is like why
not give that to us right yeah
absolutely I couldn't agree with
you more yeah well hey I really
appreciate you coming on thank
you for having thanks for uh
love it love your opinions and
uh enjoy the show thank you
thanks hey thanks for coming out
to reInvent my gosh this place
is crazy my pleasure it's I mean
my feet are killing me I mean
I've been yeah no kidding right
yeah I'm doing more than like
three 30 000 steps so yeah
that's what I love about Vegas
it's like oh it's just next door
that's like five miles away
yeah it's incredible these these
these convention centers are
absolutely massive they just
keep going forever and everyone
um I'm I'm happy to have you on
today I would love to get your
perspective on a couple of
questions um the first one is
I'm just interested to
understand from your perspective
what do you think right now is
hype and real with AI in
technology like where do you see
some good and some maybe so uh
I think AI existing AI can be
applied to technology in so many
ways to uh reduce soil and
accelerate certain things uh
where it's uh high high phase uh
really anything around uh um uh
what's it um uh AI replacing
humans completely and uh kind of
uh AGI okay that's that's high
because uh you know
fundamentally these systems are
still uh you know next token
prediction that type of thing
yeah very infant level yeah yeah
but but what they are really
good at is uh with the data it
has uh it's able to process a
lot of things very quickly uh
and and appear intelligent uh
and and deliver outcomes that
way and there's a lot of
transformation you can do i mean
even even with the existing
technology if he can productize
things I think we we easily have
more than 15 years of uh
transformation that can happen
and and deliver a significant
value for the society oh
absolutely that's great so when
we talk about platform speed and
security where do you um
especially with AI yeah where do
you draw the line because I
feel like code assist is really
speeding up velocity but maybe
causing security issues.
Yeah I I think the important
thing is uh I think doing
platform and coding right is
important to get speed uh long
term and that's where I think uh
you have uh uh security angle
also fashion sometimes.
Uh I mean I I use this uh term
uh frictionless security and uh
it often boils down to make sure
that you make it easy to do the
right thing and that way you go
a lot faster versus uh blanket
security policies that slows
things down and uh frustrates
everybody uh and and people
often looking for a workaround
because uh security is in the
way and that in fact uh lead to
poor security and slowing things
down versus uh good security at
speed.
Speaker 7: Are you seeing AI
improve that process of getting
security approvals and like
moving quicker?
Speaker 3: Definitely AI can uh
help in so many angles so things
like uh you know compliance
checks uh you know security has
a ton of uh oil work uh and
continuously testing things and
uh validating certain things I
think uh those type of things AI
could really help I feel like I
feel like security always takes
it just takes so long
especially like if we're trying
to sell some software yeah right
it takes so long what what
where do you see that where do
you see the future of it I mean
in the next let's say 18 18
months to two years the because
uh it takes so long because of
uh certain regulations and uh
things that you need to comply
and then more often uh it's
because of the approach that
people have taken why it takes
so long uh you know you usually
put a human in the mix to the
process uh and then most of the
time uh you do it because uh you
know these approval chains uh
make you feel good about it but
not necessarily sometimes uh
actually doing the right uh type
of security uh things you know
even in uh compliance you know
you have different personas that
you bring in but the exercise
itself doesn't uh I mean it's
better than nothing but that's
also fundamentally broken in
certain ways to uh have like
real security versus uh a tick
box exercise and uh kind of like
the perception of security
right what do you that that's a
great that leads me into this
next question what do you think
is the most overrated security
control that's a human and
approvals because because what
often happens is uh human and
okay no no no because I'm not
saying it's a bad thing sure we
need it uh but let me give you
two angles right uh one is uh
you put a human in the loop uh
where you think human is gonna
make uh better judgment calls
and uh do the right thing but
then you overwhelm that um uh
situation and then it becomes uh
you know a lot of things escape
and without uh understanding uh
that becomes like a uh you know
clicking exercise where the
human doesn't quite understand
the implications of everything
that's going on right and then
uh and then that's a fundamental
failure uh in the process
itself uh and then the other
cases uh when you have uh many
stakeholders and approvals you
you often uh delay things
significantly uh without having
uh kind of exact clarity on uh
what is being applied well and
what are the real implications
and how how to kind of like
proceed with it.
So yeah where do you think
security tools should live in
the platform team product team
in the I would say in the
platform team primarily uh so I
have an interesting role uh I
have uh I'm platform engineering
uh director as well as CSO for
my group now uh in in one way
you can look at empowerment and
the stake uh of the two roles uh
and the nice thing is uh you
accomplish frictionless security
by making it easy with platform
engineering to do the right
thing so more you do that you
get better security and a better
developer experience compared
to uh just uh you know uh
security for the sake of it or
security theater right do we
feel like okay so do we feel
like the platform team could
potentially not understand
context of the product teams and
there's a little bit of
friction there like so so there
is friction because uh if you
look at uh like whether it's a
you know a security organization
or a platform organization
these things are fundamentally
bottlenecks that are put into
any organization to uh they're
good bottlenecks I'm not saying
bad bottlenecks uh they're put
in to uh streamline the process
and make sure that you do things
sensibly uh so any bottleneck
you'll always have frictions if
things are not running smoothly
and competitively so this is
where I think you need to uh you
know it's more people
relationships understanding
empathy and what people like
really want along with ensuring
the process and making sure what
the the bottleneck works
efficiently and offer a
competitive offering for all the
stakeholders I think that's the
that's the key about an
effective uh bottleneck more
often what happens is in
organizations uh uh security
function uh perform engineering
functions they are they're
fundamentally broken because
this uh that the the bottleneck
is not working efficiently and
it's it it over time uh it's not
competitive enough isn't it
true that some long-term
security employees just like to
say no so like I mean uh I mean
so I'm I'm a CISO and uh you
know we have groups right like
uh there's a funny saying like
you know it's the chief uh
escaped right yeah the so so I
think you know that's a very
interesting uh uh angle
sometimes because uh your job is
on the line yeah exactly when
you say when you say yes to
things right uh and then if you
know something really bad
happens you're you're putting
your job in in the line uh so so
there is an inclination to say
no or start with no yeah uh
without necessarily appreciating
uh what the business is trying
to accomplish and uh looking at
more pragmatic parts.
But yeah I mean I think you
know the thing is you know I
don't know like personally if
you ask me uh uh that's uh not
exactly a career aspiration for
me on uh on one side like I'd
rather think more on hey uh yes
but and then try to like that
get them on uh hey let's do it
in a frictionless way do it
better these are the
implications uh yeah I mean it's
uh I like that yes and actually
not even yes but I would say
yes and you need to do XYZ
right.
Speaker 7: What is one AI
security risk that is real right
now versus one that's just not
really that so much of a worry.
Speaker 3: I think uh one that
is not real is the hype uh which
is uh I mean it's almost like
uh it's not a problem with uh AI
uh it's just people misusing AI
and just uh creating chaos and
trouble.
So I you know that there's a
lot of uh drama going on yeah
around that but uh uh but what's
some examples of real things
are I think uh uh data security
is very real so uh whenever we
look at an uh AI uh tool or
anything I think where your data
flows are and how that gets uh
exposed is uh very important to
think about because uh uh you
know with modern systems uh you
you know you you don't always
have control of your data you
have to sometimes you know trust
third parties uh they leave
certain boundaries so it's
really important to understand
those flows and how you secure
it because uh that's an that's
an area you can get into
trouble.
Another one is uh privilege
escalation.
So we've been doing a lot of uh
agent it uh and uh one pattern
that's been happening is uh when
it comes to agent tools you
give uh service account access
to uh sub agents and agents and
whereas when it comes to the
humans you have particular RBAC
scopes and and uh things around
that so mapping those uh roles
to what it can do I think that's
a big big big challenge because
uh not do getting it right
would lead to uh uh you know
accidental or intentional
privilege escalation so that's a
uh valid challenge that uh
we're seeing right now if you
had to improve developer
experience and raise your
security bar in the same quarter
what single move gets whole uh
it's doing platform engineering
right with the security uh
almost partnering with security
teams so wherever so I I mean
it's nice that I own the both
roles right now but uh in the
cases where I haven't I would uh
seek out to the security
organization partner with them
understand their requirements
and give them also good angles
around how a fundamental
platform engineering move will
improve security as well as
accelerate uh developers making
it easy to do the right thing
versus uh the wrong thing so uh
that's that's what I call
frictionless security and uh you
know yeah that's kind of your
thing yeah yeah is uh is there
anything you've seen at the show
today that is kind of like wow
yeah oh that I mean there's
there's been a lot lots lots of
stuff I mean uh I think uh you
know uh certainly agent AI it's
interesting to see how how how
much is possible and also in
some way uh some ways how much
is not realized right so it's
like uh uh that's kind of like
what's uh uh most interesting so
if I'm if I'm like uh when I'm
thinking about next year uh I
think hopefully all of these
things being possible and
actually going into production
use cases and being realized I
think uh that's the that's the
exciting so do you think agentic
is like the next big leap where
we make a change to your point
like where it becomes more
trustable more you know usable
yeah I mean uh so there's a
couple of things around agent it
one is to get around uh LLM
sort of limitations and uh uh
kind of you know say even if the
foundational models don't uh
advance agent can be leveraged
to solve a lot more complex
problems.
The other one is the data
problems we've had for decades,
right?
I mean if you look at it uh
people still don't have uh their
data platform problems and
other things sorted out and more
often than not the biggest
concerns are around data so
agent can really help where you
can have uh separation of
concerns with agents and
specific data and them
collaborating with each other so
that that's not something that
we are seeing much right now but
I think uh next year perhaps we
will see a lot of that where
you can say you know I'll I'll
give an example uh you know if I
have a personal uh health agent
I I I will trust my uh personal
health data with it but then if
if that agent is uh interacting
with uh different other agentic
system that agent could be
empowered to share what's the
program than what's not, right?
So you can you can kind of like
uh safeguard certain
information in that way.
And then if you look at like
the enterprise world and uh data
silos and all the concerns
around data, I think that's a
really significant piece that
would work in favor of agent.
Speaker 7: So do you so do you
think do you think we need to
move to a place of a talent base
that understands how to run
agents?
Like less of an like it's not I
guess it is engineering, but
like it's almost like their job
is to run agents.
Speaker 3: And also it's uh it's
a transformation right so in in
some ways uh when I when I
think about AI agent all of this
uh uh technical capabilities
there they are I think yes there
has to be a transformation but
uh that's ones and zeros and
binaries in some ways easier
right the social and the human
uh people and the process aspect
that's gonna be the more more
significant thing that needs to
be uh transformed so you know
processes need to evolve be
challenged in order to improve
things and then people they
really need to have a uh growth
mindset around how uh AI can
really help because if you start
with uh AI is gonna take my job
that's a non-starter you you
are uh I don't like you know
exactly I mean uh so it's like I
I can't see AI taking jobs uh
like you know in the story in my
lifetime I like I don't see
that right the but it's really
important leveraging AI in order
to do things better I think we
all have a lot that we can uh
kind of like really gain
absolutely okay we're gonna do a
segment ship it or skip it okay
yeah sure you're right AI code
reviews AI code reviews I think
uh skip it skip it for now for
now yeah as again you said we
just we quite don't quite trust
it yeah it just needs to get a
little bit further sorry sorry
AI oh my bad let me go let me go
AI code reviews AI code review
code reviews uh so using AI for
code reviews yes uh very very
worthwhile leveraging because AI
can uh really summarize very
complex uh code reviews it still
need to be reviewed so like the
thing like uh let's take GitOps
in platform engineering okay
GitOps PRs are a joke in most of
the teams because they are so
big and people don't understand
and like most of the time people
just click the button in
approval right so so it's like a
so using AI to help people
understand it's significantly
better.
And and and the same thing
applies to uh code uh I mean in
some teams you know hey keep it
small so people can understand
like that's been the golden rule
if you go a few years back now
with uh code being generated
there's so much that gets
generated right and and and when
you're doing uh reviews yeah if
you don't use AI it's very
difficult to uh understand
exactly what's being changed and
what are the implications so
it's like yeah that's a
bottleneck and I I think it's
important to leverage AI yeah
yeah all right ship it or skip
it yeah four day work weeks four
day work weeks careful careful
I'd say skip it I mean uh it's
like a it's an interesting thing
I think uh I mean you you have
like a so I I've I've led like
uh uh a lot of different
cultures a lot of different
teams right I think uh depending
on where you are like uh you
have a very different uh mindset
and an attitude right I think
uh to be honest like I'd say
like the time you work is
irrelevant as long as the
outcomes are easy right so it's
almost like uh I don't really
care whether people work nine to
five or uh five days an even
better answer right it's like
what I care about is the outcome
right so it's like uh I don't
want to be a bean counter hey
did you clock in clock out or
are you are you there are you in
the office for production yeah
it's uh it's a what have you
accomplished right like uh makes
sense all right no deploy
Fridays keep it that's uh that's
that's excellent because uh I
have done it like I mean it's
just you know unnecessary stress
right I mean uh you're winding
down you do something bad and
then uh you ruin the round
everybody gets everybody right
and the cost of that and then uh
the toil that creates for the
next week it's not worth it so
it's like better to uh ship uh
Monday to Thursday where you can
actually you know deal with
things uh and then uh you know
relax a little bit more and it
it actually helps you go faster
in the long run all right one
more yeah LeBron James I don't
know thank you sir thank you
appreciate it Eric man thank you
for coming out to uh reInvent
it's absolutely wild here
awesome um the craziest show
that I've ever been to right
yeah this place is just insane
bananas i i i this gets bigger
every year and it's always fun
anyway well hey look I wanted to
have you on because um I'm
interested to get your
perspective on some of the
trends that are happening now in
in the industry yeah and um I
think the AI is obviously one of
the biggest uh hot bucket
button topics that we're uh
hearing and I'm curious to kind
of understand from your
perspective like um basically
what are you what are you
feeling is kind of like hype
yeah versus actually going to
help productivity and and and
efficiency.
Speaker 1: 100% you know it's
interesting I the one of the
things that in being in this
space now I've been in the
software development space for
the last 20 years which is scary
I got gray in the hair all it's
just what it is.
But the coolest part that kind
of happened when a lot of these
model providers really started
to come out was I've got all
these different line of business
use cases that we want to try
to go build wealth management
advisors or trading platforms
and all that's happening.
And I think that there's real
value that's being derived in
the creation of those different
types of agents and use cases
with the model.
But the one that really quickly
that I think we've all seen
truly emerge that went so fast
from prompt engineering all the
way to I'm gonna build a fleet
of software engineers as AI is
in the development space.
And what has been amazing
excuse me what has been amazing
is there is a whole set of my
customer segment I manage the
global financial services
business for our developer
platform Gen AI segment.
And seeing how customers are
taking advantage of those
different services to accelerate
what the developers are able to
actually achieve and how fast
adoption has happened is unlike
anything I've ever seen in the
20 years I've been in the
software development space.
And I think that where they're
seeing the most value is how are
we able to think about an idea
of a feature that I want to go
build and begin to take you AI
to offload all of the remedial
things that I needed to do,
whether it's test creation,
whether it's pipeline
configuration, whether it's
building the right
infrastructure templating
patterns and the way we're going
to deploy it's giving
developers the chance to try to
use it for feature development,
which is critical, right?
But the area where we're seeing
the most impact is offloading
all of the things that Plat
Engine operations security were
putting back on development
where where you looked at their
typical day they might have only
been writing lines of code on a
feature for an hour or two and
they're able to get
significantly more freed up in
that space.
Speaker 7: So it is accelerating
significant.
It's accelerating okay so
that's that's a great point.
So everywhere I look around
here everybody's about AI right
okay so from your perspective
what is something that is that
demos really well or or and
actually doesn't really help?
Speaker 1: Yeah.
So I think that if if I've and
I've had a good fortune of
working with lots of different
technology providers including
our own in the space right and
that's what something that we at
Amazon are really really keen
on.
We want to make sure that
customers have choice in those
different types of options.
But the demo tends to be kind
of the same right we're gonna
prompt we're gonna try something
I need to ask a developer what
do you want to go build?
We prompt it we try it we see
what happens right I think one
of the things that's been most
impactful for the customer
segment is going back to what
can I what as a developer or an
ops team am I overburdened by on
a day-to-day basis?
What can I use AI to offload to
give myself the ability to go
back and really build the
feature segment that I want.
And where we've seen
significant success is in more
of the classic platform DevOps
domain functions that really
really require developers to go
build infrastructure, build
code, or build tooling
configurations or APIs that are
connecting to various things.
And being able to get specific
on how do we help you move away
from all of these different
consolidated developer platforms
that are being used sometimes
in a different way even though
it's the same branded logo in
you know a 15 person team that
might be a 50,000 developer
organization.
Really focusing on the areas
that we're not just going to
focus on helping you build and
write logic faster but we're
gonna build all of that
scaffolding stuff that actually
allows that to get into
production being able to show
how AI helps with that process
getting it out is actually the
most critical area where we're
seeing teams want to go latch
in.
Speaker 7: Do you think that
there are specific areas in in
in that you're saying that it's
speeding up the SDLC basically
but do you think there's
specific areas that are that are
are are being hurt yes by
development?
Like if we're like Codasys
right we're going faster with
codes is there other areas in
the SDLC that were being hurt?
Speaker 1: So that's actually
right at the heart of what I'm
saying, right?
So what's been amazing is the
explosion of different engines
that developers are using.
Because they're doing it on
their personal time or they're
actually going to the enterprise
and they're bringing in one of
these agentic IDEs or they're
actually using or building their
own developer agent.
We've seen an explosion in code
come off of those engines.
I can go faster I can build
more logic against those
features.
But in the business that I
support in global financials
there's still a lot of
regulatory risk governance
that's required to ensure that
what's being created can marry
up to the actual compliance
governance regime that I need
from that feature set.
And we go into a lot of
discussions with customers where
they're going well I had a hard
time keeping up with all this
stuff before these AI tools were
actually utilized right my
release cadence was six months,
12 months in certain capacities,
right?
So where we're seeing a really
really large set of interests
beyond just offloading or using
AI to less lessen the burden for
developers of all the ops
things they needed to do, we're
really really working with
clients and it's a why the
partnership with Horace has been
so strong helping them get
better at the domain of okay
I've used AI, how do I ensure
that what that AI and the human
built is able to actually make
its way into production.
And when we look at how we
instrument all the ways that
that comes out of the developers
kind of IDE into a CI process
and be able to move that through
ensuring that we're enforcing
policy are we actually managing
the supply chain of what is in
that release it's been an
absolutely critical aspect for
customers and it's happening
really fast.
Meaning they're using all these
technologies but they're coming
back to the table going I need
to get better at actually
getting this out and I don't
know how to make sure that my
audit team, my governance team
knows that hey we built this
quickly but it actually does
marry up the spec and that's
actually where harness has been
so important for our clients.
Speaker 7: So really adding AI
in other areas besides just
coding but also um I I have a
fear right I I feel like we're
creating this big wave or a
potential tsunami of um code
that the developers are not so
familiar with and it's going to
cause a bunch of like problems
in the back end once production
happens, right?
Speaker 1: Yeah.
Well it's a classic wave that
we're seeing everyone's super
excited about this hype of what
it can do, right?
But we are starting to really
hear from clients on I love what
this can do but I need to make
sure that what it can do we can
protect the actual logic are you
seeing AI in um like you know
financial institutions are
regular regulation and um really
you know heavily scrutinized
are you seeing other uh
solutions that are helping with
that piece of it meaning the
actual regulations around
enforcing governance and legal
and like all of those things
like because because to your
point it's like we build all
this code and then we have to
regulate it.
Speaker 7: Yeah.
Is there any is there any wave
on kind of streamlining that
process?
Speaker 1: Yeah well so we've
seen that so we've seen it
actually I mean if we really go
high level we've really been
working with a lot of different
governing bodies in the banks to
like help them understand what
secure coding actually means
what it means to actually build
a supply chain of evidence for
an auditor that says this
workload that you're deploying
on top of the the AWS hypervisor
can be blessed because we've
built to spec, right?
I think the thing that's
interesting about the question
is we're absolutely seeing the
hype wave, right?
But it actually has driven some
significant value so I don't
want to call it a hype wave,
right?
It's a value wave.
But what you are describing is
kind of what I was describing
previously there's so much new
logic being created I need a way
to make sure and maintain that
we can actually keep up it's
actually a paradigm we saw if we
go back to the example of
before these things were
introduced, these AI
capabilities were introduced we
had a lot of best of breed
tooling environments that were
built for developers.
When we have that happen the
way that we need to build the
body of evidence of the supply
chain off of how those
developers operate on a
day-to-day basis is a very heavy
lift, right?
Being able to extract the
information and then apply AI to
either enforce policy or help
those developers follow the
standard practices to ensure
that that actual governance
regime is absolutely where we
are seeing not only partners
like yourselves but areas where
you're taking your platform
forward with AI to actually
build capabilities natively to
help streamline that process.
Speaker 7: Yeah absolutely so
okay get real with me here what
is something in the tech world
business that's just a pure pet
peeve of yours like you just
can't stand it.
Speaker 1: Well you know to be
honest with you tech world
business I think that if I go
back to the hype wave right
there's a lot of just
uncertainty of where this all
goes and I'm watching clients
like change overnight the
perspective because the new tool
came out.
Right.
And developers love doing that.
We've been doing that forever
right but this one's happening
faster than I've seen in a
while.
What's cool though is so it
drives me nuts when it's like
the next day some new company
comes out and then all of a
sudden everybody only wants to
go focus on that.
It's really important though
for customers to be able to test
and try and experiment with all
those things.
So it's a little bit of a
double-edged sword to say I'm
actually frustrated by it.
Yeah that makes total sense.
But I will tell you that the
capabilities that we're seeing
and how all of these different
engines will be utilized to help
get developers more productive
and when I say more productive
focused on building the best
features they can not
infrastructure logic not APIs
for dueling not configuration
but really how are we going to
delight customers?
That's where this whole thing
is going to go and it does
require a lot of
experimentation.
Speaker 7: Yeah.
Who do you think owns the who
do you think owns the should it
be the platform team or the
products team or a combination
for AI in general?
Like who do you think should
own that?
Speaker 1: Because to your point
governance and compliance and
like all those things is that a
platform team thing and then
they're pushing it out to the
product teams or what do you
what is your create the platform
I always I I love these these
different team things that
happen because we went from like
development tooling team to
DevOps team to cloud center of
excellence to platform
engineering.
Now we're trying to drive an AI
strategy across all of that.
I think what you're gonna start
to see is there's a lot of
capability that if you're if you
don't understand how to code or
don't have a computer science
background right that these
different capabilities that AI
is bringing to the table really
democratize the barrier of entry
for people to actually go and
move into that space.
And I think that what will
start to happen is there will be
a governing body of AI across
all of the different use cases,
right?
Because it's not just
development.
We start looking at even if you
just go into like basic
financial discipline right if
we're thinking about banking
capital markets insurance the
different roles that exist
there's a lot of automation that
can be created that you're
gonna see a lot more line of
business people, like people
that are not developers and
they're already doing this,
starting to go out of building
those apps.
And what's starting to happen
now is we're hearing I need to
build more of a factory approach
to how, just like we did with
like the development teams we
need to start thinking about how
do we extend this to think of
everybody as a software
developer.
Right.
Right?
And be able to actually govern
how that actually operates and
that's where the critical
aspects of how are we building
the pipeline that's pushing
those things out into the actual
production environments that
are delighting customers.
How are we ensuring we have
governance in the fold across
that pipeline right?
So I'm seeing that start to
kind of grow out where we're
almost seeing like platform
engineering, DevOps cloud center
of excellence starting to
coalesce and then a governing
body from an AI perspective is
starting to become central to
how they're thinking about
options yeah that's 100% true
we're gonna do a segment called
Ship It or Skip It okay
non-compliant AI tools.
Speaker 7: I've used GPT for
work I bet you have what is it
at a ship or skip?
Speaker 1: You know what I think
all of them have their merits
and values I'm saying ship.
Speaker 7: I'm saying ship I
like it all right ship or skip
LeBron James Skip.
Speaker: I'm a Michael Jordan
guy all right one more full day
zoom meetings in the office full
day so I'm on zoom meetings all
day on zoom games in in the
office makes me want to lose my
mind.
Ship or skip skip 100% all
right buddy thank you man thank
you thank you for coming out to
reInvent man what a crazy show
absolutely wild it's so busy
here.
Speaker 7: Oh it's amazing the
energy is always crazy it's
crazy yeah I appreciate you
coming out look um I wanted to
have you on to kind of get your
perspective uh the tech industry
is kind of a a crazy place
right now uh AI is really taking
it by storm and uh along with
the rest of the world and I want
to get your perspective on kind
of just understanding like
what's your what are you seeing
and what's your honest opinion
like I would prefer not
marketing speak but more of a um
yes you know hey nobody's
listening right okay um but
first is are what what are you
seeing trend wise that's hype
versus actually helpful when it
comes to AI in tech?
Speaker 5: Okay so hype hype
versus helpful um I I think like
uh a lot of a lot of people
right now are are trying to make
claims that you can um you can
you can go and uh adopt some of
this new AI tooling especially
in the delivery life cycle and
you know 10x your throughput um
I do think that there's a lot of
potential to like you know hit
uh hit velocity gains in
particular like you know in in
teams uh by by uh adopting new
tools and methods at the same
time um but I think I think
there's been some overpromising
and some like you know uh what
feels like you know maybe uh
under underproducing like you
know in that on that account.
I think a healthy dose of like
sort of clear-eyed optimism
around like what it really takes
for for teams to actually fully
sort of adopt tooling yeah
adapt their processes right
actually shift in from a uh
talented people standpoint uh to
to actually get in and and uh
make the most use of that you
know is the thing that we all
need to be on on a trajectory
you know to get to before we hit
10x so I think 10x is a bit of
a hype um but but there's
there's real value and
progression there.
Speaker 7: Yeah I wonder that
because every booth I see right
there's AI in every booth.
Oh yeah 100% this is basically
an AI show right exactly and so
but that's the thing it's like
what what what what what's real
what's not right yeah so what do
you think is one of the most
expensive habits um that the
enterprise has that does not
ship product.
Speaker 5: Oh yeah um so
particularly because we're
talking about enterprises um it
seems maybe sort of crazy to say
it in 2025 but um I think a lot
of the the practices I see kind
of stem from um a lack of
really moving from a waterfall
traditional waterfall mindset
you know into uh really an agile
you know DevOps and sort of MVP
mindset.
Yeah um I I have a lot of
appreciation for where that that
comes from you know history as
well as in a lot of cases you
know um uh budgeting and you
know a financial planning you
know standpoint like you need to
understand like what an entire
solution is going to be but I
think what that often translates
to is some of the same problems
with waterfall that we we saw
where what you start out with
doesn't turn out to be you know
what you need uh and then
projects missing uh you know
timelines uh scope increasing
longer running you know cycles
and development cycles and at
the end of the day not actually
getting product out to like your
end users to uh to test out
hypotheses um actually unlock
value uh and and keep teams
moving um and we're seeing those
right in the in the the bigger
organizations that you know are
slow to move right like maybe
healthcare and pharmaceutical
these companies that have been
around a long time um I wonder
if I wonder like you know how is
AI I mean how is AI gonna
really impact that it it right
yeah so that's the really
interesting thing right um you
know one thing I've actually
been I find myself walking
around saying a bit is that
everybody's feeling the
imperative right now around AI,
right?
But you can't you can't put AI
on top of Bad, right?
Bad processes, uh, bad
technology in some cases, right?
Um, the paradigm shift with AI
in software delivery um makes it
possible to actually change
your processes very
dramatically.
Um but that takes like you know
a real evolution, uh which
means a lot in a lot of cases
breaking down some silos that
exist, like in many cases, like
in the in the enterprise, to
sort of lead to that waterfall
process around like design, you
know, develop, um, test.
Um and I think uh an awareness
and a willingness to kind of
break all that down um to
actually unlock the potential of
the new technology paradigm
shift is uh is what it takes.
Speaker 7: Yeah, I agree with
you.
Is it do you know of an AI
initiative that actually
returned cash and then one that
was just really nonsense?
Speaker 5: Oh yeah.
Um so I'll start out on the on
the nonsense.
Okay.
Well, actually, you know, so
where where my mind goes really,
I I kind of think there's
there's two buckets of sort of
nonsense, like you know, things
that that we've seen, again, as
like people have felt the AI
imperative around use cases.
The first is ideas that are
just frankly not AI ideas,
right?
Or it's like, hey, what if I
could do this?
Uh I remember um having a
conversation with uh with one
person around you know how they
could basically improve
usability you know in their
application, like using AI.
And when we really talked about
what was at the heart of like
those challenges, we realized
what they really needed to do
was make some primarily CSS
changes, like you know, to make
the system work a little bit
better.
You know, and and that comes
from like trying to kind of hit
you know whatever nail, like you
know, with an AI hammer, uh,
instead of just thinking like
what am I really trying to do
here and what's the problem.
Um, the next class is really
like areas where people just
aren't thinking through you
know, full sort of adoption, you
know, what it's really gonna
take to build you know a
reliable um you know AI solution
that people will actually
trust.
Uh so bad data is like usually
one of is often one of those.
Like one of the first use cases
people go to is like uh
everybody's looking up
information across a lot of
documents.
How do I get those into a chat
interface so people can quickly
find the answer to questions?
Um worked with somebody who was
doing that at one point, put
documents in, uh we tried it
out, we realized those documents
were not consistent.
So it turns out you're not
gonna get consistent answers
coming out of that.
Um so I've seen those things
kind of fall down a bit.
Speaker 7: You know, that
actually leads, like I think
about that, and and when we talk
about like shelfware, right?
Like it's like these projects,
we take on these projects and
then they buy a solution for it,
right?
Yeah, and then for whatever
reason, you know, it doesn't
work.
But I wonder if that's like a
lack of understanding how the
solution works and like clinging
to those old ways, right?
Or actually driving change in
an organization, right?
Speaker 5: Yeah, exactly.
I mean it's I really think it's
both.
Like you um you you need to
have a solution that in fact,
like you know, you thought out
and not gone beyond just that
happy pattern where I think a
lot of a lot of people get stuck
is they try something, they
say, hey, it worked, but they
haven't thought through all the
different like you know, like
edge cases.
And once they start getting
into what it'll actually take to
make uh a solution you know
fully work in a rounded out way
that will actually be
deployable, um, you know, they
they get stuck.
Um and then people don't they
don't see the pull through you
know in in adoption unless uh
unless that trust is there that
the solution is really gonna
work.
And then you have to also have
a uh a real you know meaningful
uh pull through like you know
change management campaign,
right?
Speaker 7: Yeah, absolutely.
What do you think execs overbuy
on when they're saying scale
engineering?
And what do you think they
underinvest in?
Speaker 5: Yeah, uh well, I
mean, classically, uh I would
just say engineering.
Um so when you say scale
engineering, the thing that the
thing that I've I've had plenty
of conversations of like, hey, I
need 20 engineers, right?
Um, you know, and and a lot of
the times um you know, we what
we see is that you know
specialization across different
disciplines and and
cross-functional teams, like you
know, really lead to you know
the best outcomes.
So a lot of those disciplines
and specialties, you know, that
really support the software
engineers, and I say that as a
leader of a software engineering
team, um, are uh are often
overlooked.
So quality engineering, uh
platform engineering and devops
that's needed to really like
help the teams uh operate
effectively, um, and all those
sort of surrounding disciplines.
So things that we often we
often see aren't aren't
initially asked for, uh, and uh
and that we're we're always
thinking about how we find the
right balance of uh to try to
really fine-tune the overall um
overall team.
Speaker 7: Do you think right
now you're seeing a trend where
the execs are saying like let's
throw AI at it?
Maybe that, right?
Speaker 5: Like well, there's
there's there's also definitely
that.
I mean, that that's the the
top-down push of you know, like
um let's let's use AI, and I'm
expecting to see like you know,
like X like you know come out of
it.
Um probably because they came
to the show and saw that 100%
10X use.
And everybody, and everybody,
everybody on a team right now
needs to figure out, you know,
the challenge is really how to
how to consume that, how to
metabolize like you know, the
the request of say, I need I
need to be using AI or I'm gonna
adopt I'm gonna suddenly uh
have this tool available to me
and really figure out how to go
make that possible using um you
know some some new techniques
and really like like it all
comes back to that people
process technology you know
triangle, right?
And the tech if those if those
are three sides of the triangle,
that technology side just
changed drastically, right?
It's the people in the process
that are still trying to figure
out how to adapt to that.
Speaker 7: Yeah, that's very
true.
So, what do you think is a
metric that we should remove
from QBRs and maybe one that
actually tells the truth?
Oh yeah.
Speaker 5: Um gosh, well, so but
talking about QBRs
specifically, maybe not even one
specific metric, but I think a
hot take, I would remove agile
project metrics from QBRs,
right?
You're the second person that
said there's something there.
I mean, whether it's it's
defect counts or story points,
those are all helpful, important
like um project metrics that
teams should use you know
internally, you know, and like
you know, with steering
committees, like you know, to
really understand what's
happening, what's the help of a
delivery.
Um, but uh those need to be
contextualized, you know, and
they're different really for
every project team.
You know, story points are
relative, they're not even like
you know, meaningful like real
measurements.
Um you know, defect counts,
like you know, like glide
various different you know,
testing strategies and
approaches.
A lot of context.
There's so much, right?
So at the end of the day,
you're you're you're looking in
a QBR setting, you're looking a
little too close at that point.
Uh but what you do you should
be looking at is you know like
how we did compared to our
initial timeline estimates, um,
and how we managed uh scope
changes along the way, you know,
and how that led to uh how that
led to actual outcomes being
there.
Speaker 7: Yeah, okay, great.
Um, what's one org change that
you can buy and it pays you back
in 90 days?
Speaker 5: So probably the
biggest thing, well, first of
all, you know, I want to I think
that siloed organization, like
this still exists, like is is
one thing, right?
If you could org change out
some of your like, you know,
your separate teams for like
those different cross-functional
capabilities and and really get
everybody working together in a
team, I think that kind of
solves what I was talking about
earlier.
But the the biggest thing I
think I would hit on right now
is the the AI engineering COE.
So creating a uh a team you
know that is um is also
representative of you know your
information security team uh to
inform and adapt your policies,
uh to uh establish responsible
usage guidelines for teams, um,
and then and then actually do
enable net for teams uh broadly
across an organization.
Speaker 7: Yeah, I couldn't
agree with that more.
How many times has somebody
bought a piece of software or
something and then just it
doesn't ever get used?
Yeah.
Not because it doesn't work,
right, but because they actually
don't know how.
Speaker 5: And we've we've seen
um we've we've seen a ton of
that right now.
A lot of people will pick it
up, they try it, uh it doesn't
immediately work, they they
discard it, they say, like, you
know, like my job's safe, I
don't need to use this.
Um and uh and then and then
they uh they they move on.
Whereas like we've been uh
seeing a lot of success with
certain sort of immersion
workshops.
Um you know, having having
people spend like several days
just learning the techniques to
actually get the most from those
tools, and then walking away
and actually feeling completely
changed.
Like, wow, not only like this
is amazing, it's gonna help me
do work so much better, uh, and
really just being excited about
that.
So there's some of that fear
you know associated, like you
know, that I think those COEs
can really help to go go get
people passed and realize that
uh this is all really helpful,
amazing technology should help
us all do do more things.
Speaker 7: So if you capped
headcount for a year, yeah, um
what practice would still raise
velocity?
Speaker 5: So um the biggest
thing, and it kind of falls to
that COE, um, I would say right
now, and we've been talking
about it here at reInvent a
bunch, is uh is context
engineering.
Oh, interesting.
So uh teams that that
appreciate that to get the most
the way I've been talking about
it is that every time you make a
call to a LLM completion API or
uh or an agent in your IDE, um
you might as well be pulling
somebody brand new off the
street that doesn't know
anything about what you're uh
you're trying to do.
Um and when we do actually pull
people in off the street, we
bring them up, you know, onboard
them to our organizations, let
them know what we're trying to
do, and we give them effectively
contacts that they can go use
to go do their job, make
decisions along the way.
Um when we when we ask an AI,
an agent to go build a feature
that like does something in an
application, we need to do the
same things, right?
And that um that kind of builds
on the concepts of prompt
engineering um by establishing
layers of context that that are
basically that need to be
maintained, you know, along with
um you know a code base, you
know, as a product is evolving.
And that's a new sort of
discipline that we're almost
seeing emerge of really kind of
how that's done and uh a hat
that needs to be worn you know
by uh by by maybe of one person
andor um multiple people on a
team to kind of take collective
ownership.
Speaker 7: That's interesting
because you're right.
If if you are prompt
engineering, it kind of is
adding context to the specific
code that you're writing,
whereas, you know, yeah, that's
that's actually great.
I love that.
Um non-compliant AI tools.
So I don't know about you, but
I uh have used Chat GPT at work,
and it may not be compliant.
Do we ship it or skip it?
Oh yeah.
100% enterprise license.
Yeah.
That's the right answer for it.
I'm still getting used.
Um four-day work weeks.
Uh skip it.
Skip it!
Speaker 5: Sorry everybody, but
we can do we can do more, we can
do more with all these tools.
Let's do it.
Let's do three-day work weeks.
Um AI code reviews.
Uh you mean using your AI to do
it.
Uh yeah, let's let's ship it.
Speaker 7: Ship it.
For sure.
100%.
All right, man.
Hey, I love it.
I appreciate you coming in.
Yeah, great to see you at the
show.
And uh, yeah, thanks for having
me.
All right, buddy.
Sweet.
Thank you for coming to
reInvent, man.
It's absolutely crazy here.
Yeah I I really have never seen
a show this crowded before.
Speaker 2: Absolutely.
Speaker 7: It's like a sporting
event.
Yeah.
Um, I wanted to have you on.
You're you're an engineering
leader and and and you're in the
in the midst of all this hectic
time right now with AI, and uh
the acceleration is just really
quite crazy.
Um I kind of wanted to
understand from you, like, if we
talk about um, you know, AI in
general, like, where are you as
an engineering leader kind of
seeing some hype versus just
actually productivity and
efficiency tools that you get
from AI in general?
Speaker 2: Yeah, so that's a
great question.
I think AI is here to help us
in every step of the way in the
whole software development
lifecycle.
I think for me, the biggest uh
areas that AI has been able to
help us is right from uh
starting with the requirements,
translation into the actual
actionable task to writing code
and to test and to deploy.
So I personally think that AI's
role is equal in every step of
the way in the whole software
development lifestyle.
Speaker 7: Is there any areas in
that that you feel like it's
not helping so much or we
haven't quite figured it out
yet?
Speaker 2: I think testing is
one area.
Speaker 7: Testing?
Speaker 2: Yeah.
Um and that's purely because uh
there are gaps that I've found
in terms of uh it being able to
understand the requirements
properly.
And if the requirement
understanding is wrong at the
first place, it might be
possible that it may come up
with a wrong set of tests or
incomplete set of tests, and
that's probably one of the gaps
that I've seen.
Speaker 7: Gotcha.
Yeah.
So what about let's say a
modernization that you that
you've gone through that worked,
but uh you know, even though
maybe you would or wouldn't
repeat it again.
Speaker 2: Yeah, so um back in
my um previous role at uh crypto
startup, uh we um went ahead
with modernizing or
decentralizing, as they say, um
the entire infrastructure, and
we spun up different AWS
accounts, different EKS clusters
that were all self-managed, and
uh we deployed all of our
applications, microservices on
those clusters, which turned out
to be uh working really well.
Um but I I wouldn't repeat that
because some of our
applications were super
lightweight and uh we could have
gone with like serverless
technologies with lesser
operational overhead, for
example AWS Lambda or uh
Fargate, even EKS on Fargate.
So some of those things I I
think that we can we could have
uh done much, uh we could we
could have gone much simpler
route.
Speaker 7: So if you cut your
platform um spin by 30%, um what
goes first and what gets the
stake?
Speaker 2: Yeah, um so again
there are nice to have things in
every platform team and there
are must-have must-have things.
Um for nice to have things,
things like uh duplicate vendors
uh that offer pretty much
redundant capabilities or
overlapping capabilities.
Um there are low-impact
environments, uh, there are uh
redundant tools, complex tools
uh that are no longer relevant
or no longer used.
But my favorite is the unused
and underused resources.
And I've been able to reduce
cost by more than 40-50% in my
previous roles, but just
focusing on cutting down or
shutting down all the underused
and underused resources and also
right sizing of some of those
resources.
So I think for me that would be
one area, uh, and what must
have is CICD and observability.
I think observability,
personally speaking, there's no
limit to it.
You can always have more
dashboards, more insights, more
information about your platform.
Some of those things you would
not even imagine or know that
you would need those, but I
think there are always some
scope and CICD equally.
Speaker 7: That's a great point.
Why do you think dashboards go
unused?
Speaker 2: Because they are
statically created.
Um I have use case one, two,
three, and I create widgets for
those one, two, three.
Or I create dashboards for
those one, but there's always
1.5 or 2.5 that are some corner
cases that we always miss.
And that's why with the um with
the AI capabilities, what we
are trying to do right now is
have agentic solutions built to
provide us all of the material,
all of the information.
Um, and that's really uh uh
becoming a conversational way of
getting to know information
that you're looking for.
Instead of statically defining
dashboards, you just simply ask
questions with your platforms,
with your observability tools
that can answer you the
questions that you're looking
for by uh you know going behind
the scenes and understanding and
uh analyzing all of the data
sets and metrics and logs and
events and so on.
So I think that's the direction
we need to go towards.
Speaker 7: Why do you think so?
This is something I I wonder
quite in depth is if you look at
an organization and they have
the finance department and the
marketing department, the sales
department, those departments
run heavily on metrics.
They live and die by metrics.
Why do you think the
engineering group has not been
able to adopt that?
Because I work with a lot of
engineering organizations and um
like adopting or getting Dora
metrics just in general, it
seems to be very difficult.
And when we do, they oftentimes
they don't want to be measured.
I'm just curious, like what
like why do you think that is?
Speaker 2: I think it comes from
the fact that a lot of
engineering organizations are
very top-down focused.
So they're given the
requirements, given the you
know, use cases that they want,
given the business problems that
they want to, or they're asked
to build the solutions, and they
kind of get narrow-sighted to
achieve those results and
provide those those outcomes.
And I think that's that's one
uh approach or mindset that's
where we miss out on a lot of
results or matrix-driven
approach.
Um, I've seen all I've also
seen some organizations working
really bottoms-up, and that's
where we define really the
ground level matrix, and we go
by that.
We try to achieve that right
from the get-go.
Uh, even when the requirements
are not very clear, uh, we we
define the results.
For example, um, in one of my
previous organizations, when I
was trying to build this
platform as a product, I defined
some of the core metrics.
Like we want to reduce the cost
by 30%, we want to increase the
utilization by 50%, um, we want
to achieve the availability SLA
by 90%.
So some of these metrics, once
we defined, then we started
building the products around
those metrics, and that's where
we were able to always backtrack
and check how well we are
progressing and what are the
gaps that we need to fill to
achieve those results.
Speaker 7: So, does it get a
little bit of tunnel vision with
metrics like again, based on
initiative?
It's kind of like the metric
becomes tunnel vision, and it's
like, okay, we have to hit this
one thing, and then maybe other
stuff goes by the wayside,
right?
Speaker 2: Yeah.
Speaker 7: Yeah.
So um what's something in
engineering that you just
absolutely think is a waste of
time and we should stop doing
it?
Speaker 2: It might be.
You can be honest.
Yeah.
Like I said, I was going to say
that I might be controversially
saying this, but uh I think uh
the process-driven software
development, um quote-unquote
agile uh methodologies, um they
are oftentimes over-engineered.
Um there are practices like
predicting the capacity,
bandwidth of the team, and uh
but my view is that engineering
is unpredictable.
That's the fun part of it,
right?
If it's so much predictable,
then it's probably not, it
should not shouldn't be called
engineering, it should be called
something else.
Speaker 7: Um so if you're not
running into problems and and
having to iterate, yeah, then
it's probably you're probably
not doing the right thing.
Speaker 2: Yeah.
For example, um back in the
days when I was building a
platform team at T-Mobile, um I
achieved like 90 story points in
one sprint and 10 story points
in another sprint.
And I loved it because the
unpredictable part was the fun
part for me.
And I think oftentimes I've
seen that uh in the pursuit of
following those strict
guidelines and processes, we
kind of lose the sight of what
we really need to deliver and
how should we innovate?
I think that's the part we
should have.
Speaker 7: Do you think then
that measuring work delivered,
or like like I guess how would
you quantify that?
Like, how would we say what
good is if you can do 90 story
points one week and 10 the next,
right?
Like how what is the what is
the end like result that we
could measure and say, oh,
regardless of number of story
points, we this X was
accomplished.
Speaker 2: Yeah, I think that it
should be measured in terms of
the user impact.
Um what that end user.
End user impact.
End user.
Speaker 7: Okay.
Speaker 2: Or even if the end
user is your own team, uh let's
say I'm building an internal
tool for my own team.
What was the impact?
Yeah, how many users actually
try to use it, how many times it
was used.
Those are the kind of things
you should always measure.
Speaker 7: If AI disappeared
tomorrow, um what's that boring
platform investment that sticks
around and is still always gonna
work?
Speaker 2: Yeah, I'll go back to
my previous answer CI CD and
observability.
CI CD.
Observability and CI CD are the
two things.
Gotcha.
Very important part, right?
Absolutely.
You just can't um get away with
that.
Speaker 7: What's the most
unpopular policy change that you
made that actually sped
delivery?
Speaker 2: That actually sped
delivery.
Umpopular change.
Speaker 7: Yeah, unpopular
change.
Okay.
Speaker 2: Um We were using a
lot of open source tools to the
extent that it was almost
breaking our internal monitoring
stack by providing us a lot of
false positive.
Something changes on the
upstream, downstream changes,
are not prepared.
So we kind of were in a
continuous loop of fixing those
problems by providing some or
like adding some one-off or
Glucode type solutions.
And that was getting out of
hands.
So I proposed a policy of stop
using open source, at least for
those specific use cases.
It was very unpopular as you
can imagine.
But we ended up building our
own in-house monitoring stack
with things like HealthWatch and
SmokeCest Suite.
And that kind of gave us much
deeper and broader visibility
into the health of our systems.
And that's not true for every
use case.
I mean, the world lives on open
source, so you not using open
source is not a great idea for
everything, but for certain
things where the open source was
not really maintained, was not
well supported by the community,
using those kind of tools was
not really a good idea.
And that kind of was the change
that we made, and we kind of
really achieved great results
with that.
Speaker 7: What's the single AI
tool that you're that you guys
are using right now that you
feel you're getting the most
value out of?
Speaker 2: I think we are using
GitHub Copilot and we are
leveraging it to the full
extent.
We use it right from the
requirements, translation, which
is in most cases very
ambiguous, and we translate
those requirements into very
specific user-actionable tasks,
and from there software
development to testing to
deployment.
Speaker 7: Are you guys using it
for document?
Like are you using AI for
documentation?
I feel like that's a really
easy one, right?
Speaker 2: It just helps we use
we use AI for um writing our
runbooks.
So any troubleshooting, any um
SRE DevOps type work, uh, we
create extensive runbooks for
different kinds of scenarios,
and we use AI to do that.
Speaker 7: What's one vendor
narrative that you think
misleads engineering leaders
today?
Speaker 2: Wow, that's a that's
a great question.
I think uh I've seen that build
versus buy is still a grey area
for a lot of engineering
leadership.
Um there's a misconception that
vendors can abstract
everything.
Um, a lot of it is true, but
there are areas where uh you
have to customize the solutions,
you have to build in-house
tools to sort of augment what
the vendors can offer and uh
support.
Um and again, there's no right
or wrong answer on when to buy
versus build.
Uh it really depends on the use
case, your skill sets, your
bandwidth, your priorities.
And um I use these parameters
as like the variables in the
equation uh to come up with the
decision whether we should build
or we should buy, with whether
we should do both.
Uh you know, buy first and then
build on top of something that
we bought.
Speaker 7: So how much do you
think um the build option is
driven by job security?
Speaker 2: Yeah.
Um again, so um job security,
the whole uh narrative about job
security has like two, three
different um verticals or uh
sort of thoughts behind it.
Uh number one is uh tribal
knowledge.
People tend to keep uh
information and you know that
gives them the job security.
Sometimes people build
unnecessarily complex systems so
that in order to maintain, they
would they would remain in the
job.
Uh I think all of those things
are very very much present even
in today's uh world.
But I personally think that AI
and automation and you know the
whole narrative of buy uh is to
not reduce the scope of job
security, but it's to actually
improve.
Um and by that I mean when you
buy and you you kind of
demonstrates that it is useful
and it can solve the problem
that would have taken you months
or weeks to build, and now you
just bought and like bought a
solution and you're able to do
it much faster.
You're also improving on your
own credentials, right?
Right.
So so I think that's a that's a
very uh overlooked point, and I
think that that's something
that people should remember when
they think like if I buy
something, I might reduce my job
security.
That's not the case.
Speaker 7: I agree.
Um okay, ship it or skip it.
AI code review, skip it.
Skip it.
Why?
You don't trust it yet?
Speaker 2: Because it I'm I want
to first trust the code written
by AI.
Okay, only then I will trust
the code review by AI.
Speaker 7: Okay, ship it or skip
it, four-day work week.
Ship it.
Ship it.
I love this guy.
Okay, ship it or skip it.
No deploy Fridays.
Speaker 2: Oh, hell yeah.
Ship it.
Speaker 7: Ship it.
Speaker 2: Yeah.
Speaker 7: I love it.
Speaker 2: Yeah.
Speaker 7: Awesome, brother,
brother.
Thank you for coming.
Speaker 2: Absolutely.
Appreciate you.
Great meeting with you and
pleasure being here.
Speaker 7: Loved having you.
Speaker 4: Those were really
insightful discussions, Thomas.
So, for our listeners, what
would be one takeaway for 2026
uh for either their engineering
excellence goal or their
modernization?
So you have assessed SDLC for a
lot of companies.
Uh, what would be one golden
rule they can follow?
Speaker 7: Yeah, I would say if
I had to boil it down, I would
say do not mistake AI
acceleration for engineering
excellence.
They are very two very
different things.
I think engineering excellence
in 2026 is going to really come
down to whether your system can
absorb speed without losing
trust.
And the trust word there is
absolutely important.
If we think about AI agents and
um implementing those, the
number one thing that will
prevent us from doing that is
trusting that the agent will
take the actions that we want it
to and not go and do something
destructive.
Um, that means that strong
platforms, embedded governance,
uh, clear ownerships, I think
those are just things that um
are of the utmost importance.
I've talked a lot about
utilizing like an internal
developer portal to be a context
layer for your agents and
things of that nature.
It's it's really going to be a
matter of trust and building in
a um a process to uh make sure
that your agents and um other AI
pieces have the ability to
check for governance and safety
guardrails in order to take
action.
Speaker 4: Yeah, I love that uh
touch on guardrails, like
guardrails not gates.
I think uh uh harness field CTO
office also repeats that uh
that uh frame that it's
guardrails not gates.
So uh you you touched on this
before.
What would be uh one make or
break factor for platform
engineering teams within the
next 12 months based on your
re-invent chats?
Speaker 7: Yeah.
I think um the one that comes
to mind for me, make or break,
in my opinion, is going to be
context and operating model
maturity.
I think that um a lot of teams
are still thinking about AI
mostly in terms of models and
tools.
Um, but one of the most
important themes that I heard uh
was that the the future is
going to be much more context
driven.
Uh and we know that you know
anybody writes a message to GPT,
uh the less context you give
it, the the worse the response
is going to be.
The more context you give it,
the better it's gonna be.
And so when we translate that
into engineering, um I think you
know the teams that win over
the next 12 months are gonna be
the teams that do two things
really well.
First, they create a strong
golden path through platform
engineering where this where
there's a secure and governed
path that's also the easiest
path.
I think that's really
important.
Uh and then the second piece is
invest in context.
So, um, how do we do that
meaningful information and
standards, workflows, uh, and
clarity that help both the human
and the AI ultimately operate
more effectively.
Speaker 4: Thomas, thank you so
much for sharing these insights.
If our listeners want to
connect with you, learn more
about your work, where can they
find you online?
Speaker 7: Yeah, absolutely.
So I'm on LinkedIn at Thomas
DocsDator.
Um, I uh I again, as I
mentioned before, for harness, I
do a lot of consulting work for
our clients in um doing uh
really SDLC assessments that are
utilizing Dora and Accelerate
and really looking at it from a
uh not from a, hey, I'm a vendor
and how can I get you to give a
product of mine, but really
from a perspective of saying
what are the people, processes,
and tools that you're using to
um operate your SDLC and uh
really helping uh organizations
zoom out, look at their whole
SDLC, and then if there's a
great harness product that could
help them, awesome.
Um that's that's the that's the
the direction that I've had a
lot of great success with our
with our current customers.
Speaker 4: So we add the links
in the chat.
That was Thomas Doc Stator,
engineering excellence at
harness.
Thanks so much for an amazing
season four.
I'll see you all in season
five.