Software Engineer jobs (Mid-Senior) can NEVER be replaced by a Coding "AI" like Devin
Summary
TLDRThe video script discusses the limitations of code generation AI in replacing software engineers. It emphasizes that software engineering is more about managing people and balancing business, technical, and financial requirements than just writing code. The role of a Prompt Engineer is introduced, who would direct the AI, but the complexities of system integration and performance tuning still rely on human expertise. The video argues that unless there's a revolutionary change that makes code generation irrelevant, AI will remain a tool for software engineers rather than a replacement.
Takeaways
- π€ The core argument is that code generation AI will not replace software engineers due to the human-centric aspects of the job.
- π§ The future vision of software development includes a Prompt Engineer who communicates with AI to generate code, but this is not yet feasible.
- π The current state involves software engineers acting as the intelligence behind code generation, with product owners providing direction.
- π₯ A multitude of roles and stakeholders contribute to the software development process, not just engineers and product owners.
- π§ Software engineers often deal with technical details and integrations that product owners may not fully understand or engage with.
- π‘ The real challenge in software engineering is balancing business, technical, and financial requirements, not just writing code.
- π The role of the software engineer extends beyond coding to include handling requests from various departments and team members.
- π The complexity of software engineering lies in people management and communication, rather than technical skills alone.
- π The quality of software is influenced by the effectiveness of communication and management within the organization, not just the technology used.
- π Unless there is a revolutionary change that makes code generation irrelevant, AI will remain a tool for software engineers rather than a replacement.
Q & A
What is the main argument presented in the video script against the idea of a code generation AI replacing software engineers?
-The main argument is that the job of a software engineer is more about people, integration, and balancing various requirements than just writing code, which a code generation AI cannot effectively do.
What role does the Product Owner play in the current software development process?
-The Product Owner is the person who tells the Software Engineer what to do, focusing on what the product is supposed to be and how users will use it, rather than getting involved in technical details.
What are some of the other roles involved in a software development department?
-Other roles include stakeholders, scrum masters, integrators, DevOps folks, operations folks, UI/UX designers, subject matter experts, QA, and QC testers.
What is the role of the Prompt Engineer as described in the script?
-The Prompt Engineer is a separate role that would communicate with the AI code generator, telling it what code to generate based on the requirements provided by various stakeholders.
Why might a Prompt Engineer struggle with certain aspects of software engineering?
-A Prompt Engineer might struggle because they may not understand the technical details, such as system performance and integration with other systems, which are crucial for effective software engineering.
What is the main challenge for software engineers in balancing business, technical, and financial requirements?
-The main challenge is synthesizing all the information from various stakeholders and requirements to build a product that satisfies all those needs while dealing with the complexities of people and communication.
How does the script suggest improving the software development process?
-The script suggests teaching software engineers how to do Prompt Engineering, making AI a tool for them to use, rather than replacing them.
What are some potential revolutionary changes that could make code generation irrelevant?
-Revolutionary changes could include brain implants or uploading that allow for direct balancing of requirements without communication, or AI becoming so smart that it understands our needs better than we do ourselves.
Why does the script argue that the quality of software is not improving?
-The script argues that despite the use of agile methodologies and project management, the complexity of balancing various requirements and the inherent challenges in communication among people lead to software quality issues.
What is the ultimate conclusion the script reaches about the role of AI in software engineering?
-The script concludes that unless there is a completely revolutionary change that makes code generation irrelevant, an AI code generator will never be able to replace the nuanced and people-centric role of a software engineer.
Outlines
π€ The Role of AI in Software Engineering
This paragraph discusses the limitations of code generation AI in replacing the role of a software engineer. It explains the current state of software development, where the software engineer acts as the intelligence behind code generation, while the product owner represents the stakeholder's interests. The complexity of integrating with other teams and systems, as well as the need for human understanding in balancing business, technical, and financial requirements, are highlighted. The paragraph emphasizes that software engineering is more about managing people and their requirements than it is about writing code.
π οΈ AI as a Tool for Software Engineers
The second paragraph focuses on the idea that AI will serve as a tool for software engineers rather than replacing them. It argues that the real challenge in software engineering lies in balancing business, technical, and financial requirements, which involves understanding and managing people. The paragraph suggests that even if AI becomes more advanced, as long as human communication and understanding remain the limiting factors, AI will not be able to replace the nuanced human aspects of software engineering. It concludes by contemplating revolutionary changes that could make code generation obsolete, but asserts that until such a development occurs, AI will remain anθΎ ε© tool rather than a replacement for software engineers.
Mindmap
Keywords
π‘Code Generation AI
π‘Software Engineer
π‘Prompt Engineer
π‘Agile Roles
π‘Product Owner
π‘Integration
π‘DevOps
π‘Stakeholders
π‘Scrum Master
π‘Technical Requirements
π‘Business Requirements
Highlights
The future vision of a Prompt Engineer directing an AI to code is discussed, emphasizing the theoretical shift in software development roles.
The current state of software engineering is contrasted with the future vision, highlighting the role of the Software Engineer as the primary intelligence behind code generation.
Agile roles within a company are outlined, including the Product Owner and Scrum Master, and their importance in the software development process.
The role of integrators is explained, focusing on their responsibility for coordinating team outputs and ensuring seamless integration.
DevOps and operations teams are discussed as crucial supports for deployed software, managing cloud accounts and system performance.
UI/UX designers and subject matter experts are identified as key contributors to the software development process, though their roles may be undervalued.
QA and QC testers are acknowledged as essential in ensuring software quality, despite the complexity of their tasks.
The concept of stakeholders is expanded upon, including executives, project management, and other departments that influence software development.
The limitations of the Product Owner role are discussed, noting their focus on product vision rather than technical or integration details.
The idea of a separate Prompt Engineer is introduced, meant to communicate with the AI code generator and handle requests from various company roles.
The potential lack of understanding from a Prompt Engineer regarding system performance and integration is highlighted.
The necessity of human oversight and tuning in software development is emphasized, despite AI advancements.
The core argument is presented that software engineering is more about people and balancing requirements than code writing.
A potential future scenario is considered where AI surpasses human intelligence, which could revolutionize the need for code generation.
The conclusion is drawn that AI code generators will not replace Software Engineers, as the human element is central to the role.
The transcript suggests that the quality of software is not improving and may be deteriorating, despite various management strategies.
The necessity of teaching Software Engineers to perform Prompt Engineering is proposed, positioning AI as a tool rather than a replacement.
The idea of a revolutionary change that makes code generation irrelevant is considered, questioning the fundamental nature of software development.
Transcripts
Welcome to the Internet of Bugs.
My name's Carl and today we're
going to talk about why it is
that a code generation AI is never
going to be able
to take the job of a software
engineer.
Let's start off by talking about
the world
as they tell us it's going to be.
So they tell us there's going to be
a Prompt Engineer,
that Prompt Engineer is going to
tell the AI
what the AI is supposed to code
and then that code is going to get
spit out
and then it's going to get deployed
and it's going to be stuck on the
system
that actually the offering lives on.
Okay, before we go there,
let's talk about where we are now
and if we're actually going to get
to that world ever,
how that might happen.
What we'll do is we'll talk about
the current state
which is the Software Engineer does
what they say
the AI is going to do.
The Software Engineer is the
intelligence
that currently generates the code
that goes and gets deployed
and the Product Owner is the person
that tells the Software Engineer
what to do.
So roughly the Software Engineer is
equivalent to the AI
and the Product Owner is equivalent
to the Prompt Engineer.
Big animal pictures, just go with
it for now, okay?
Except this isn't the whole
organization, right?
You've got more stuff.
This is a very incomplete list of
agile roles, right?
You've got your stakeholders.
You've got your scrum master
'cause we have to have a scrum
master
'cause everybody has to do scrum
these days.
That's a whole other problem
but we'll talk about that in a
different video.
You've got your integrators
which basically are the people
under agile terminology.
These are the people that are
responsible
for coordinating the output of your
team
with the output of other teams
and make sure they all hook
together.
You've got the DevOps folks
that support the software once it
gets deployed.
You've got the operations folks
which are cloud accounts and all
that kind of stuff.
You've got UI designers, you've got
UX designers,
you've got your subject matter
experts
which less said about that the
better
and then you've got your QA, QC, your
testers.
And again, this is a very
incomplete list
of the roles that happen in a real
company
when you're actually in a software
development department.
This stakeholders thing is kind of
fuzzy.
So let's break that out.
So among other things,
you've got your Executive Sponsor
and really the rest of the
executives too.
You've got your Project Management
Office,
theoretically, Compliance, Legal,
Marketing, Finance, Budget.
The number of times I've heard,
"Hey, we need to change the software
so that our cloud bill stops going
up
or our cloud bill goes down.""
I don't even want to talk about
that.
You've got other tribes.
So for those of you that haven't
done the Spotify model,
basically, well, it's complicated.
You can look it up.
But it's other groups in the
company
that are doing the same kind of
thing that you do
that kind of have a say in what you're
doing
because they want it all to be the
same.
You've got your business analysts.
You've got your content strategy
folks.
And again, this is a very
incomplete list, right?
Problem is, this isn't really what
happens
because most of those folks, right?
So like the integrators, the DevOps
folks,
the operations folks,
the Product Owner doesn't
understand
any of the stuff that they're
saying.
The Product Owner is supposed to
understand
what it is the product's supposed
to be.
But they might get involved in like
UI UX maybe,
but they don't get involved in DevOps.
They don't want to know about that.
They don't want to get involved in
organizing scrum stuff.
They don't want to get involved in
how this project
integrates in a technical way
with the other projects in the
company.
All those people end up talking to
the Software Engineer,
not the Product Owner.
And then you realize that actually,
all the people in the top part of
that list
are capable of throwing
requirements in.
And sometimes those go to the
Product Owner,
but a lot of times they don't.
And you get business analysts or
content strategy people
or marketing people that just open
tickets
that you're supposed to go in and
deal with.
And that's life as a Software
Engineer.
This kind of thing happens all the
time.
And the Product Owner just isn't
the kind of person
that's going to be able to handle
all those requests
'cause that's not their job.
Their job is to focus on what the
product is supposed to be
and how users are gonna use it,
not all the technical details
of how the thing is supposed to
work.
So tell you what, what we'll do is
we'll pull the Product Owner out
and we won't make the Product Owner,
the Prompt Engineer will make the
Prompt Engineer
a separate person.
So we've got the Product Owner,
the Product Owner is gonna talk to
the Prompt Engineer,
everybody else is gonna talk to the
Prompt Engineer.
And the Prompt Engineer is gonna
talk to the AI code generator
and the AI code generator is gonna
generate the stuff
that goes into the system.
Thing is, a Prompt Engineer doesn't
know about how
the integration with the software
is gonna work
with all the other integration
stuff.
They understand how to tell
an AI what to do
is not the kind of thing where you
understand
what the performance of the system
is necessarily going to be.
Once the code gets generated, if
the code is slow
and we need to reduce the cloud
costs,
we're gonna have to go tune that by
hand.
The AI may or may not be able to do
that.
And if it does, it's gonna need
very specific instructions
about how to do that,
that a Prompt Engineer may or may
or may not understand
like the algorithms involved.
So a lot of what's happening here
is Software Engineering,
figuring out how to make the thing
run faster,
figuring out how to integrate your
technical part
of the offering with the rest of
the offering
with the other teams.
That stuff is what software
engineering really is
in a big company.
So now you've got the Software
Engineer whose job basically
is to handle all of these requests
from everybody.
You've got a Prompt Engineer that's
talking to the AI code
and you've got your offering system.
Now when you think about the fact
that the vast majority
of what's happening here is the
Software Engineering part,
right?
It's dealing with Finance.
It's dealing with the Scrum Master.
It's dealing with Operations.
It's deciding how are we gonna make
this cheaper?
How are we gonna make this faster?
How are we gonna make this scale?
That's all Software Engineering
stuff.
And so all we really need to do is
teach the Software Engineer
how to do Prompt Engineering.
And then we're back to this.
So now AI is basically just another
tool
that the Software Engineer uses.
See, the hard part of Software
Engineering
is not the code writing.
The hard part of Software
Engineering
is balancing all of the business
requirements
with all the technical requirements,
with all the finance requirements,
and figuring out what it is that we
need to build
in order to be able to get all of
those requirements satisfied.
Those are really tricky problems,
but those problems are all about
the people.
If the Product Owner was capable
of taking all of that information
and synthesizing all of it and just
telling you,
okay, this is what we need to get
built
and this is how it needs to get
built,
and you don't need to talk to
anybody else in the organization,
we wouldn't need agile
and we wouldn't need project
management
and we wouldn't need project
management offices.
But we need all of that and even
with all of that,
the quality of software is still
arguably abysmal.
It's certainly not getting any
better
and it's apparently getting worse,
or at least by some measures, it's
objectively getting worse.
So here's the thing.
It doesn't matter how much better
the AI gets.
As long as the people are the
limiting factor
and as long as the people don't get
that much smarter,
then the AI isn't gonna be able to
help.
Now, there are a couple of things
that might happen.
One is that we might get some kind
of a brain implant thing
or brain uploading thing
that lets all of those requirements
get balanced
without having to do all of the
communication,
which is the thing that takes all
the time.
It's also possible that maybe the
AI
is gonna get so smart that it's
smarter than us
and it knows what we want better
than we do.
But if either of those things
happen,
we're not gonna be bothering with
Python code anymore.
It's not gonna be the AI is gonna
spit out code
and then the code's gonna get
deployed.
The AI is just gonna do stuff.
And there's not gonna need to be
this intermediate step
of, okay, we're gonna generate this
code
and then someone's gonna have to go
stick that on a server.
So failing something completely
revolutionary
that makes code generation
irrelevant,
it's just not the case and will
never be the case
that an AI code generator is gonna
do the job
of a Software Engineer because the
job of a Software Engineer
is more about people than it is
about code.
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