FIRAT TEKINER: Welcome to "AI--
The Future of Us," a podcast-style video series
that explores the ways in which AI is shaping our future
and how we can prepare for the changes that lie ahead.
I'm your host, Firat Tekiner.
And today, we are joined by Forrest Brazeal,
who is Google Cloud's head of developer media.
Thank you for here-- being here today, Forrest.
FORREST BRAZEAL: Firat, it's great to be here.
FIRAT TEKINER: So we are seeing phenomenal amount
of changes in the world of AI.
And it is also impacting the DevOps community a lot as well.
So in your opinion, what are the most significant ways
that AI is currently impacting the DevOps people?
FORREST BRAZEAL: Well, AI has always--
or I should say DevOps has always been a field that's
been automation-driven.
And in a way, those of us who've been in DevOps--
I've been closely tied to the DevOps world
for more than a decade now.
And it's always been about automating yourself out
of a job and moving on to the next thing.
We did this when we were slinging physical servers
and we moved them to virtual machines.
We did this when we moved out of the data center
and into the cloud.
We did this as we moved from lift-and-shift cloud migrations
into more managed services.
We did this now as we're starting
to move more toward serverless, and Kubernetes,
and these higher and higher levels of abstraction.
But in some ways, the job has never changed in that time.
We're always seeking that next level of automation.
And we're always seeking ways that we
can continue to provide safe, resilient, quick deployments
of code to production and maintenance and security
of the code that's there.
None of those responsibilities for DevOps folks
are changing whatsoever in the world of AI.
But what we're seeing is that certain tasks that historically
have been kind of time-consuming and have taken a while--
writing that backup script, porting that code
from this language to that language,
generating this giant piece of YAML
that you're going to use to configure whatever you're
deploying-- it turns out that AI code assistants
make some of that stuff easier to do.
Now, that's not to say that it's perfect.
It's not to say that there's not responsibility on your part
to continue to look at that stuff and make sure
that it's correct.
But it is tremendously speeding up
the amount of time and effort that you
need to spend actually generating
this code from scratch.
And I think for a lot of DevOps folks,
that's exciting, because especially if you're not
a software engineer by vocation or a software developer,
you don't think of yourself as someone who just writes
a lot of code day in and day out, it could
be the case that having that scaffolding generated for you
by AI actually is really nice.
And so in that respect, I think it's
an exciting thing for DevOps folks
to look at where AI can help.
But again, there are downsides that come with that as well.
You've got to be much more careful about what's
getting generated and make sure that you actually
are comfortable signing off on that.
You've got to take professional responsibility.
FIRAT TEKINER: So what I'm hearing then--
that DevOps people are not going to be losing their jobs.
They'll be using-- to improve their productivity
and, at the same time, be really careful that the output that
is generated from these tools is really up to the standard
and will meet the requirements.
FORREST BRAZEAL: Yes.
I never want to say, oh, nobody's
going to lose their job here, because again,
going back to the very beginning of the ops world,
because the landscape changes and the level of abstraction
changes, if you get really comfortable using
a particular set of tools and you really stake
your professional identity to that,
you're probably always going to be at risk.
20 years ago, there were a lot of ops people
who were point-and-click, working in Windows
Server and this sort of thing.
And they just weren't comfortable coding.
And those have become increasingly fragile jobs
as automation has taken over.
And I think you're going to see that with AI, too.
You'll hear a lot of people saying,
well, you don't really need to learn to code now,
or you won't need to learn to code soon
because these AI tools can spit out
a lot of generated code for you.
I actually think it's quite the opposite.
I think it's going to be more important than ever
that you learn to code, that you get very comfortable reading,
writing, understanding code because, again, there's
going to be so much more code generated now.
The barrier to creating code in config
has been lowered by these services.
So it's going to increase the absolute amount of code that's
out there in the world.
And someone's going to have to debug that stuff, harden it,
maintain it.
That person could be you.
You can make an awful lot of money doing that.
But if you're not comfortable just diving
into that code space, then I think
you're going to see your opportunities increasingly
sidelined.
FIRAT TEKINER: Yes.
I totally agree with that, actually.
And where do you see the most pressing ethical considerations
surrounding AI development and use, in particular
around the DevOps as well?
FORREST BRAZEAL: That's a very interesting question
because I think as DevOps people,
we don't often think of our work as having
a strong ethical component.
We're making sure the lights stay on.
That's kind of cut and dry.
There's not a lot of nuance to it.
But first and foremost, any time you're working with these AI--
these systems that are capable of generating code,
you've got to be very careful and knowledgeable about what
you put in there.
We've seen a bunch of news stories
over the past few months about folks
who have put proprietary or sensitive things
into these services, not realizing or not
being aware that they're then being used as training data,
or possibly even being exposed directly
to folks outside of your company's control.
So any time you're looking at using one of these AI assistant
type services, make sure that you're very, very clear
and that your company is very clear about whether it's
OK for you to be putting their proprietary code in there
and what's going to happen to it once it gets there.
There are services that are cool to use, I'm sure.
Just make sure that you think very carefully about that.
You don't want to get yourself into an ethical conundrum
at your company.
And as well, I think this gets really important
when you think about how you're going to conduct yourself
professionally as it relates to bringing
new people into the industry.
I've heard a lot of people say, hey,
this AI assistant is almost like my new junior engineer.
I can give it tasks, and it'll spit back code at me.
I don't need to bring on an apprentice.
I don't need to have someone new come in
and learn beside me anymore.
I think that's a very short-sighted and, honestly,
somewhat professionally unethical way of thinking.
I think it's going to continue to be very important that you
find ways to bring in humans into that loop,
make sure that they are available to learn
and that you're not just relying on your own context
to be the only human in the loop.
Otherwise, eventually, you're going to move on.
The company-- the team's not going
to have that human expertise that they need.
So you're going to have to think about ways to bring in juniors
and find ways for them to learn and grow, even
in the presence of AI.
That's a big open problem in the industry.
I think there's a lot of value to be gained
by helping to figure that out.
But there's tremendous danger in assuming
that AI is going to eliminate the need for you
to continue to grow the next generation of engineers.
I would never assume that.
FIRAT TEKINER: Yes, I totally agree.
And I think we'll be seeing engineers will become more
and more important on the contrary,
actually, because the ones that-- who are really
competent, who can understand what the code can do, I think
will shine through as well.
FORREST BRAZEAL: I think you're absolutely right about that.
It's hard for us to intuit about leverage
in the same way it's hard for us to think
about exponential growth.
Our brains don't really work that way.
But having these AI tools at your fingertips massively
increases the amount of leverage you're
able to provide as a senior engineer.
Now, what that means, probably directly for your career,
is you want to make sure you're getting
as close to the core value prop of the business as possible.
There's a lot of DevOps people out there
that are working on internal tools that,
where you're seven layers of tools deep removed
from the customer.
And your customer is an internal team
who's a customer of an internal team, et cetera, et cetera.
And while, yes, there can be value in that,
I think now is the time to think very seriously about,
hey, is this little internal set of scripts
that I'm maintaining, is that just something that
could probably be, more or less, generated by AI
and I need to go work on what's really, really effective
and highly leveraged for the company?
Those types of roles are the things
that are going to be highly in demand
and have a lot of future as opposed
to things that just seem undifferentiated.
FIRAT TEKINER: And with that, so what
are the-- some key skills that individuals should possess
to be successful in this new era that we are entering,
from the DevOps point of view?
And that's my last question, as well, too, for us.
So-- because it's everyone's mind that--
where should I invest?
What should I want to learn in this new world?
FORREST BRAZEAL: Of course.
Now, just to be clear, there are still
lots and lots of opportunities to do things
that are a lot older than AI.
There are still a lot of enterprises out there
that are catching up to the technology world of 2007,
let alone the world of 2023.
So I'm not trying to say that everything's
going overnight, now AI is the only thing you need to know.
But if you're looking future forward
and you want to put yourself at the cutting edge,
we already talked about make sure you learn to code,
that you're confident about that, that you keep
expanding what I would call your personal mental context window.
AI systems, LLMs, they have this little context window
of a few thousand text tokens that
help them provide relevant answers to your question.
And that's all they've got.
You, as a human being, have a tremendously wide context
window that's made up of your general technical expertise
you've learned over your career, as well as
your specific knowledge about your company's constraints
and business needs.
That's unique value you bring to the table.
So continue to widen that, grow that,
practice learning and training.
Don't get mentally lazy and just try
to make AI fill in all these gaps for you.
You have a ton of unique human value to bring in to the table.
So lean into that.
We already talked about getting close to the business's value
prop, not getting sidelined, seven teams deep
in internal tools that aren't really moving the needle.
But I would say use these AI-generated tools
where they make sense.
There's going to be a lot of them.
There's going to be--
whether it's generative AI tools,
like a Bard or a ChatGPT, or whether it is just
AI being further injected into the operational services,
the managed services that you use in the cloud today.
Your-- part of your job as a professional software engineer,
a professional DevOps person, is to make
smart and cost-effective choices about when and how
to deploy, to integrate, to use those tools.
Sometimes, we call that vendor engineering.
And that's only going to get more important.
It's going to be a long time before any AI is
pulling the trigger on whether or not to buy a tool.
That's something you're going to have
to evaluate very carefully, compare these tools,
make the choice that provides the most
leverage for your business.
And then finally, one specific piece of advice I'll throw in--
Firat, I know you'll like this because you
come from the data world.
The future belongs to data scientists.
But let's be honest.
Data scientists can be kind of sloppy.
Look at those Jupyter Notebooks and all this.
There's a lot of need for data engineering prowess.
That's pipelines.
That's clusters.
Those are things that you understand as a DevOps person.
If you're looking for a tweak to put on your career
that I think will provide a ton of opportunities for you,
look into what it would take to become a data engineer.
It's your same wheelhouse of work.
But it's work that directly supports
the creation and deployment of these AI and ML systems.
It's going to be much in demand for a long time to come.
FIRAT TEKINER: Thank you much for joining us today, Forrest.
I agree with you.
It is more about, I think, playing with the bricks.
So you-- we will have lots of bricks out there.
And how we engineer that to get the optimal solution, I think,
will be the key.
We are just going to have more support doing that.
It has been incredible and insightful to hear
your thoughts on this.
And we look forward to seeing how AI will continue
to shape the world around us.
And we hope that our viewers will be inspired and advance
the potential opportunities.
Thank you.
FORREST BRAZEAL: Wonderful.
Thanks so much.