A junior developer stares at their screen, fingers posed over
the keyboard, and then does absolutely nothing.
Instead, they type a prompt into AI build me a full stack SAS app
that lets users write office coffee using sentiment analysis
and a crypto wallet. Seconds later.
Poof, like code appears. It's not perfect, it's not even
going to work, but it runs, sort of.
Welcome to the age of vibe coding We're writing.
Software isn't about syntax or architecture anymore.
It's about the vibe, man. Think of it as coding by gut
feeling. Except your gut is now a chat
bot with ACPU and a questionable taste in logic.
In this episode, we'll be diving deep into what vibe coding
actually is, why it's gaining traction, You'll see it on
LinkedIn every 5 minutes, where it shines, and more importantly,
where it crashes hard. We'll explore use cases, real
life case studies, and why this trend might just be the fastest
route to technical debt. You can't refinance.
If you're a business analyst, a product owner, a developer, a
business leader, or someone just trying to stay one buzzword
ahead of your next meeting, this one's for you.
The Better Business Analysis Institute presence, the Better
Business analysis podcast with Kingsman Walsh.
Let's get into it. Here is 10 things that you need
to know about vibe coding before it's vibe puts you into
technical debt. That's right.
So number one, we really need to talk about what is this thing
called vibe coding? It sounds cool.
Vibe coding is an AI driven programming approach where
developers provide high level prompts to large language model
models. So AI, think of ChatGPT as an
example, which then generates the corresponding code.
And with things like MCP that we've talked about recently,
this could actually start building multi layer
architecture. This was coined in February
2025. So it's really new and it shifts
the developer's role from the manual Kona to guiding and
refining AI code. And while we're on this number
one point, I do this, I do vibe coding all the time because I'm
not a programmer anymore. I don't know the latest
languages and I feed in prompts to AI, more complicated prompts
and areas of code and it allows me to prototype quickly.
So we'll get on to that in a minute.
But you might be one of these developers out there today.
But this really vibe coding is when developers are doing this.
This is instead of them having to writing lines of code, they
can be, some of them can be notoriously lazy anyway.
They're actually getting AI to do the work for them #2 there's
an appeal about wide coding. It sounds cool.
It accelerates development by automating routine coding tasks
that you might have to do again, and it's boring.
It's accessible. Even individuals with limited
coding experience like myself, we can develop functional
software. And you see a lot of promotion
about this online and innovation.
It fosters rapid prototyping and experimentation.
And that's a really good point. So #3 what are the practical
applications here? Well, in start-ups, if you have
small teams, you can achieve outputs that are on scale with
larger teams by leveraging AI tools, especially for some of
the manual work, but not all of the workflow steps.
And enterprise solutions companies like Amazon utilise AI
assistance to enhance development.
They have like a a code plug in, Visual Studio has it, the GitHub
plug in for Visual Studio I've used and it helps you for
looking up classes and what not and educational tools.
So facilities can allow students to focus on problem solving
rather than syntax. So they can kind of think about
how they might program a Python script and get it about right.
And they're like, well, don't worry, you didn't get the the
syntax right. We'll move on.
And syntax, to be honest, is just the like grammar of
programming. So I use AI to fix my grammar
all the time. So programmers can use AI to
check their syntax and their syntax checkers.
So why wouldn't you use AI for that part of it #4 OK.
And there's a company called RE Pets AI generation game.
So there's a, a developer uses Repat's AI to generate a racing
game featuring actually Elon Musk and Sam Altman from Open
AI. And despite initial bugs, the
project actually demonstrated the potential of AI in a
simplified app development. It, it worked and people were
able to play it. And this gave people the idea
of, well, why don't we utilise that model for our business?
And that leads us to #5 which are the potential risks here?
Number one, it's, it's technical get.
So the architects and people that are really hot on good
effective code. I I I do not like this concept
when applied to production environments.
Rapid development without proper structure leads to maintenance
challenges and bad architectural decisions.
All the code is usually a couple of files.
It's not spread out how do you maintain it and this makes it
worse. The other issue is around
security, and this opens up security holes.
The reason we have programmers and the reason we have people
that know about security, security architects, is that AI
driven code may overlook some of these practices if you haven't
fully explained what they are. And then there's an over
reliance on AI. So developers might become
detached from the understanding of the code and then when
something breaks, they don't know when to fix it.
And so let's talk about mitigating that risk.
So number six is mitigating the technical debt problem, and this
is an argument against the technical weights Det starts and
that is that you can have code reviews to ensure that the code
quality is there. You can ask AI to generate clean
documentation and to provide it's thinking about what it's
done and include that in the code itself as comments and
testing. You can make sure that your
testing is way more robust now and it incorporates rigorous
testing to identify and fix the issues early.
So you can actually use this technique.
There is a comparative study here.
So #7 here is around Airbnb. They use this concept of
modernization, right? And the challenge was that
Airbnb had this monolithic architecture and it led to
increased dependencies and slow releases.
And we talked about that in the last episode around architecture
and the putting the bits and pieces together.
And the solution was to transition to micro services,
which is the other architecture we talked about using various, I
won't go into technical ways they did that, but they did that
to improve scalability and reduce technical debt.
And that is a kind of a better way of doing things is breaking
things up. And then maybe if you had a
small micro service, maybe you could apply AI to that micro
service to make that better, but it's kind of not applying AI and
vibe coding across the whole structure.
So get your architecture right first.
Some of the organizational considerations we need to think
about, which is number 8 is making sure that you are
training your teams effectively. You need to equip your teams
with the skills to effectively effectively the keyword here use
AI tools and not replace themselves.
There's governance. So what are the policies how we
manage AI generated code responsibly, but allow AI to be
the true copilot as opposed to the writer.
And if you I can tell you that if you're using AOI to write
code for you as a developer, then you are not going to be
needed soon enough. And integration.
So ensure these AI tools align with your existing development
workflows, checking in code, code reviews and what not.
And any of your best practices need to be built into the vibe
code ring experience, unless you're going to be using that
for prototypes, which I'm going to finish off with #9 is
strategic implementations. So if you are want to try vibe
coding and I I suggest you do, I'm not saying it's all bad.
Start with a small start scale project or a micro service to
assess the effectiveness of that in your organization and whether
or not by allowing this option, you know all the rules go out
the window. Have feedback loops, look at
feedback, provide AI prompts and outputs and look at reasoning
behind the AI prompts which are available now and look and see
if that's correct reasoning. I would say that in my
experience, the tools out there today are not that great at
programming, to be honest. I don't think they're full.
They're good at creating good structured architecture and look
at scalability. Can this AI tool that you've
just create actually be scaled? And if it can't be scaled, then
obviously it's just a peripheral concept and can be thrown away
at the end of your vibe coding session.
Before I drop into the final thoughts, the best use case for
vibe coding in my mind is you're an entrepreneur, you're working
with a developer or you're working with people who know a
bit about architecture and you want to rapidly produce a
prototype to show the market to get funding to do it properly.
OK, now that doesn't mean you don't use AI in the generation
of production materials and documentation, but you do it in
a non vibe way. You do it in a very structured,
methodical way and you use the vibe, the vibe to get the
funding and to get people on board by, you know, showing them
something cool fast. My final thoughts are vibe
coding represents a significant shift in software development,
offering both opportunities and challenging.
And I think it will evolve into just code coding as opposed to
vibe coding, apart from the use case I just mentioned which is
prototyping. And by embracing AI thoughtfully
and implementing best practices, then organizations can harness
it's benefits while mitigating the risk.
I will see you next week and I hope you know what vibe coding
is. See you later and Happy Easter.