May 13, 2026
Ep 22 - Niall Moran - Fractional CTO & Startup Advisor
In this episode of the Founders & Funding Podcast, Philip Smith speaks with Niall Moran, Fractional CTO and Startup Advisor.
Niall brings over 20 years of experience across software development, go-to-market strategy, and startup advisory, having worked with the likes of Google and Microsoft before going out on his own. He shares what it really looks like to build in the age of AI, why defensibility has completely changed for startups, and the most common mistakes he sees founders make when taking an MVP to market. They also discuss why getting sticky customers matters more than revenue or funding right now, and what technical due diligence actually means for an early stage startup.
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Hi, welcome to the Founders and
Funding podcast.
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I'm your host, Philip Smith.
On the podcast, I'll be
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interviewing founders,
investors, startup advisors on
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how to fund the journey of your
startup and some tips and advice
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they have for you along the way.
This podcast is sponsored by
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Perfect Technologies and Laden.
Enjoy the episode.
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Hi, Niall, welcome to the
Founders and Funding podcast.
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Hi.
Phil, thanks for having me.
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Well, look, thank you for taking
the time for coming on.
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I know you're very busy.
And now to kick us off, could
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you tell me a bit about yourself
as your background?
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Sure.
So I'm in the IT industry quite
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a while, 20 plus years.
I describe myself as a builder
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at heart.
So I spent most of my career
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building software applications,
building solutions for
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customers, always kind of
customer facing across multiple
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industries, every industry
that's out there from public
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sector to pharma.
So yeah, I described myself as a
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builder builder at heart.
I spent a lot of time building
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applications.
I've also spent a lot of time on
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the go to market side and
strategy side with Google and
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and Microsoft.
So I, I feel I have that kind of
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mix of, you know, technical
builder architecture, but also
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on the kind of strategy and go
to market side.
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So that's that's me, yeah.
Hmm, Well, it's a great
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combination.
You know, most people are the
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kind of leaning very heavily
into the tech side or the go to
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market side.
But I think it really is
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valuable to learn even at a
height level as much as you can
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about how the other side
operates and communicate them.
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And so well done, I suppose
working as great companies,
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having a great career gives you
the opportunity to do that and
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talk to me a bit more about what
you're working on now with the
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Sidekick CTO.
Sure.
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You know, I, last November I
left Google, I finished with
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Google and I decided I was at a,
at a, at the point of my career
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where I wanted to try and do
something myself, go out, go out
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on my own.
And again, leaning back into
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that, you know, builder
mentality, the, the kind of tech
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stuff I've done over the years
mixed with the kind of strategy
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piece.
I said, you know, this would be
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kind of a, you know, an
interesting time to, to get back
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into my startup stuff.
I was a CTO of a startup in the
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past.
I was working for an, an agent
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investment group and I really
loved startup.
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So I think the, the hustle, you
know, the, the builder mentality
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to get it done at all costs
really kind of aligned with
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where I like to work or where I
get energy.
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And I'd never really done it out
on my own as a consultant, as a
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freelance consultant.
So I decided to not give that a
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go, you know, and that's where
Sidekick CTO came from, right?
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So I spend my time either
advising startups at a very high
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level or getting really deep
into the technology for for
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some, I joined tech operators
there recently so that, you
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know, get back into the Angel
investment piece of advisory
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piece and it's going great.
I'm busy.
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I'm probably a little bit too
busy, right?
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It is a good problem to have.
I think that the scene has
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changed significantly in terms
of the AI piece.
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And I know, you know, I'd love
to talk about that as well, but
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you know, so it's a really
exciting time.
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And I think, you know, Dublin,
Ireland has always been a kind
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of a hotbed of activity for the
perspective of startups.
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And, and you know, I'm delighted
to get back into it.
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And I, and I've kind of built
some, some of my own projects as
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well, Pulse IQ dot fit as a kind
of fitness AI, kind of AI
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coaching fitness platform and
that I've built myself.
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So, you know, really excited to
get back, get back into it.
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Fantastic.
Well, it's, it's great.
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We'll see.
It's good to be, you know, I
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suppose, you know, really
unique, but great.
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You are in position where you're
on that the technical builder
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side and you can advise tariffs,
but also develop new setups, but
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also get into the investment,
uh, part of it as well.
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And see, I know you mentioned
there.
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So you were the CTO and startup
advisor for the Lucy Fund?
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That's right.
Yeah.
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Going back, going back quite a
while.
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So, you know, so that was a
great one because it was it was
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a mix of building products and
scaling products.
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And at that time, by the way, we
didn't have AI doing everything
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for us, right?
We had to, we had to do the hard
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work ourselves.
We didn't even have, you know,
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cloud hosting really AWS were
was just kind of getting going.
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And so that was a great time as
well because cloud was the big
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thing and and then, you know, as
well as building the actual
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platform, we were we were kind
of investing in other companies,
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yeah, through our kind of
technical delivery model.
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So that was really exciting.
So I, I got a really broadview
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of what's a good idea, what's a
bad idea, how to, you know, on
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the execution side, then what?
What's a good idea and what's a
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bad idea from an execution
perspective.
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So yeah, I really enjoy that
work and glad to, as I say, glad
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to be glad to be doing it again.
I'm busy.
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Yeah, absolutely.
And I suppose to put it back to
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the present a bit, what What
does an average day look like
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right now?
Right now I'm, you know, knee
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deep building a platform with a
company called better futures,
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really exciting product sets.
Of course it's AI, but it's AI
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in a very specific space where
you know, in, in the engineering
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world and mechanical engineering
world where it's very document
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centric, very regulatory
environments as well.
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So it's, it's, it's hard to kind
of, you know, replace the
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engineering knowledge that's
there.
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So in fact, it's impossible and
we shouldn't be doing that,
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right?
Because the, the, the, the kind
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of level of detail we're talking
about.
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So it's, it's, it's really about
assisting engineers, taking them
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away from paperwork and getting
them doing more of the
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innovative work that's required
from engineers across all sorts
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of products.
So better futures as a company,
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better futures at AI, uh, knee
deep building that platform and
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getting it out there.
We've got customers on boarding
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right now.
It's it's so it's a very
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exciting time and I'm loving it.
Fantastic.
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I'll be sure to check that out.
And I suppose to talk then a bit
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more about AI.
How?
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How has AI changed the way you
work?
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So it's this isn't, there's two
ways we're going to answer that
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question, right?
One, is it kind of a
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chronological view?
Because if you had asked me that
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question three months ago, it'll
probably give me a different
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answer.
Yeah, that's because it hasn't
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changed how we're working.
It's it's, you know, it's going
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to sound like a bit of a
marketing tagline, but it's
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completely transformed how we're
working.
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You know, I see right now there,
there are a lot of metrics we
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use to kind of gauge developer
success, but I think percentage
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of lines of code written with
written manually is a big one.
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And you know, how much of your
code has been written by AI.
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Is, is, is really important to
me right now, just from the
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perspective of similar to the
example I just gave of the
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Mechanical Engineers, right?
I, I want engineers focused on
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innovation and that means
solving problems in new ways.
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And I don't necessarily want
them writing code, you know, and
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they're two distinct things.
I think the world is focused
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very much on AI writing code,
which means we don't need
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engineers anymore.
And that's just not true.
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We need engineers because
they're the creativity around
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the applications and around the
products that we build.
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AI has very much transformed how
we do that and how we
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accelerate.
So what we can do now with a few
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people, you know, when I was
doing the work we talked about,
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you know, 12 years ago, it was a
team of 40 people.
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Right now it's a team of three
or four people.
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And that is a real difference.
And that means we can do more
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with that forty person team,
right?
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It means we can, we can scale
quicker, we can we can
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accelerate into new markets
quicker.
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So it's, it's definitely
transformed what we do and
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primarily in the area of, you
know, AI coding and also, you
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know, documentation.
I mentioned the documentation
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use case there earlier on.
Like a lot of the work that an
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engineer would be, you know,
doing a software engineer would
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be doing is kind of documenting
what they're doing, making sure
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everything is well documented.
And, and anybody who's been
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around for a while knows that
documentation is only as good as
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how up to date it is, right?
If it's even a few days out of
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date, it's useless because it
could send people in the wrong
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direction.
So using AI to, you know, keep
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our documentation up to date is
a really great use case for it
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as well.
And it all just points towards
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allowing developers or engineers
to focus on engineering rather
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than writing code.
Yeah.
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Yeah, I really like that.
It's like there's pros and cons.
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I absolutely.
And it's you know, like if if
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you if people are listeners or
many of them are will be are
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familiar with loverball.
Those are the types of platforms
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you can get AI to write code,
but you know, you need the you
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certainly need the the
engineers, the developers there
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to really problem solve and make
it a product and something that
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people actually want to use.
So there's always going to be a
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huge need for that and get that.
I think getting that combination
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that makes right as you
described there is, is what's
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important.
And talk to me a bit about
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defensibility.
Uh, now I know we, we've had
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past conversations about that,
you know, you know, and why is
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that important?
Well, defensibility is always
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important for a start up so that
you know, they could when when
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they launched their company.
It's not, it's not easily
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reproduced.
That's the key thing.
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And but I think what's
interesting about defensibility
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right now is, and a lot of the
rules around this are the same.
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They've always been the same.
You know, an idea is not enough.
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It's all about the execution.
For example, a lot of great
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ideas out there, but you know,
it's not a, you know, we've got
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to get them executed.
So but, but defensibility used,
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it used to be enough maybe to
build an application, you know,
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and spend time building it, a
complex application, you know,
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software solution of some sort
and get it out to market.
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And straight away you had a lead
of months to years on any
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competition that wanted to get
into that market.
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And you mentioned products like
Lovable, you know, so now that
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changes the game because people
can, CE OS can, and I'm not
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advocating for this, but CE OS
can build their own
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applications.
Certainly MVPS with Lovable and
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Repple and others, OK, it's not
just Lovable and get them out to
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market right, with, you know,
fully functioning MVPS.
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So where's the defensibility
then?
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If if I go and I try and pitch
this to somebody and they can
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reproduce it as quickly as I
built it, my defensibility has
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got to be something else.
So I think defensibility for me
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now is got to be around sticky
customers, whether it's a user
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base community or, you know,
enterprise customers.
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Are they sticky?
Do they need to use your
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product?
Because you know, they've been
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using it and then you've got the
data and you know, you're,
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you're, you know, the, the value
in them using your product is
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the fact that they're using
their product, your product,
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right?
That it grows over time and it
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learns from the customer and
that creates a sticky product.
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And so it doesn't matter that
the product could be reproduced
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relatively quickly, attracting
customers, getting them on
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boarded and making them sticky
is still the hard work.
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And that's the execution piece
that's really important.
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So yeah, ability, forget about
it.
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If you're spending, you know,
time figuring out how do I build
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a better LLM than, you know, I
don't know, Google's right then,
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or how do I build a better model
for this particular industry?
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You know, I'd be careful
because, you know, it could be
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done And let's, let's focus on,
let's get something out there.
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Let's get customers and, you
know, let's get them sticky.
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And honestly, you know, maybe
some of the venture capitalists
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might not agree with me on this,
but I think it's more important
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than revenue or funding right
now.
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You know, get something, get it
out there, get people using it,
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Figure out what works, figure
out what doesn't work.
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Figure out how your product can,
you know, adapt to those users
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so that they become sticky.
And now you've got some
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defensibility that you can go
and look for customers to pay
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you with or go look for collab
funding.
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Yeah, I know I think you're spot
on.
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You know, I do see even off for
myself, I see founders kind of
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be trying to create AI.
Suppose a defensibility or a
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thesis around why their new
application is is great and
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it'll be the one that'll solve
this problem.
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But you know, because the, you
know, large because of AI,
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because things are moving so
fast now, people can create
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products so quickly.
And it really is that product
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market fit and getting customers
and make and, and, and making
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sure that actually are retained
and sticky that, that that's
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really the, the kind of the only
defensible, I think, proposition
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now, because everything else can
be, uh, replaced it on so fast.
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And I suppose a bit broader than
looking at some of the, you
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know, from the founders you've
worked with neither there's some
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kind of common MVP challenges
you you've seen them encounter.
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Yeah, and a lot of the, a lot of
the challenges are age-old
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challenges.
So you know, there's a danger
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and getting caught up in some of
them when other things are
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moving so quickly.
And you know, there's a few
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here, but I'm going to pick the
top ones, top of mind ones.
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The first one is are you
building a product or are you
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building a solution, right?
What's the difference?
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You know, you can spend a lot of
cycles with customers building a
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solution for them, but it's not
a product you're building.
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You know, it's kind of, I always
use the analogy of, you know,
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we're building.
If I build you a tailor made kit
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car just for Philip Smith's, you
know, demands you want to the
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particular color, it was a look
a certain way, you're going to
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love it.
It's going to be great.
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But that doesn't give you
product market fit because not
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everybody wants that car.
All of a sudden you got to think
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about the production line and
what you get at the end of the
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day then isn't that nice shiny
product, you know, custom car.
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It's it's just got it right.
Or Volkswagen is going to be for
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the masses.
It's a scale issue.
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And I see a lot of customer, a
lot of startups are actually,
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you know, they're trying to get
customers and are trying to get,
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you know, which is great, but
they end up building the
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solution for that customer and
not actually a product.
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And the shift away from building
something for one customer to a
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product mindset can be quite
seismic.
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And that's, that's the biggest
mistake I see from a, you know,
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from a CTO perspective is how do
we now shift to a, to a product
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mindset?
And that's a whole different
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world.
Think production lines, you
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know, a, a, a, a kind of a, a, a
set of features that we think
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the masters will like, but maybe
one or two customers won't like.
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And that's fine, right?
The other one, I think that the
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other big one I'm saying right
now is the, you know, we talked
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about it, the AI store, fright,
AI generated apps.
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I think honestly, there's going
to be a graveyard of these
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things in time to come.
You know, there's so much more
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to building a platform or
building a product and than just
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generating an app, right?
You got to think about security,
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00:15:50,080 --> 00:15:53,120
scalability.
I literally saw on LinkedIn the
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00:15:53,120 --> 00:15:56,640
other day an article about
somebody who used who had
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launched an app that they had
built with a tool like some of
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the ones we've mentioned, and
they managed to delete their
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entire database with an agent.
And this stuff is going to
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happen, right?
It's going to happen and the,
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the, the, the question is, have
you got a disaster recovery
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plan?
Have you got a security plan?
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Have you got the prevention plan
to stop this stuff happening?
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That's what engineering a
product is about, not coding.
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And that's my fear that a lot of
startups are and look rightly so
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00:16:29,480 --> 00:16:33,400
get an MVP out there, get
customers using a get feed, but
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00:16:33,440 --> 00:16:36,600
it's an MVP.
Turning that into a product is a
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00:16:37,040 --> 00:16:38,840
is a different, a different
story.
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And then then there's a kind of
other end of the spectrum where
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I still see startups going and
offshoring an MVP development.
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And I'm like, you don't need to
do that anymore, right?
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You can do that yourself, OK?
Or you can get help from people
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like me who can build it in days
and start getting customers on
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board.
I think that the biggest piece
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of advice I've given the MVP
stuff specifically is, you know,
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00:17:04,280 --> 00:17:08,119
build it quickly, but no, it's
an MVP and no, it's a prototype.
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00:17:08,119 --> 00:17:11,800
No, that getting that to scale
and turning that into a product
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00:17:11,800 --> 00:17:16,839
with properly production, proper
production line behind it is a
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00:17:16,839 --> 00:17:19,680
different story.
And you probably need to start
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thinking about that early as
well.
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And so that you don't end up in
that graveyard that I that I
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00:17:25,800 --> 00:17:28,119
mentioned.
Yeah, I think there's a good
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00:17:28,119 --> 00:17:31,040
points like I've certainly seen
this does the, the new issues
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00:17:31,040 --> 00:17:33,840
being created by as as much as
it's solving, uh, you know,
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00:17:33,840 --> 00:17:36,120
problems is creating a lot of a
lot of problems as well.
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00:17:36,120 --> 00:17:38,320
Like I see a lot of founders
that they're adding because they
318
00:17:38,320 --> 00:17:41,680
can these add new features, you
know, and they end up having
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00:17:41,680 --> 00:17:45,000
1020 features and, you know,
without solving the core problem
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00:17:45,640 --> 00:17:47,560
just because they can.
So they're kind of going into
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00:17:47,560 --> 00:17:49,720
future overload and we're
getting too excited and they're
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00:17:49,720 --> 00:17:51,360
not just kind of pulling it
back.
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00:17:51,360 --> 00:17:53,520
We're finding and trying to
match the problem with the
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00:17:53,520 --> 00:17:55,440
solution and to limited feature
set.
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00:17:55,440 --> 00:17:58,320
But umm, yeah, it's, it's
certainly an exciting and
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00:17:58,320 --> 00:18:00,560
interesting space and changing
all the time.
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00:18:00,960 --> 00:18:03,920
Uh, before we finish, Niall,
what is one key piece of advice
328
00:18:03,920 --> 00:18:06,920
you'd have to successfully fund
a startup in terms of, you know,
329
00:18:06,920 --> 00:18:10,040
trying to help start-ups funding
that journey, uh, and how they
330
00:18:10,040 --> 00:18:13,160
can kind of keep the setup alive
to get it to that point where
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00:18:13,160 --> 00:18:15,000
they can find that product
market fit.
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00:18:16,320 --> 00:18:18,760
Yeah.
I mean, I will always go back to
333
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technical due diligence.
You know, we just discussed it,
334
00:18:22,600 --> 00:18:26,360
right?
It's, it's, it's, you know, make
335
00:18:26,360 --> 00:18:29,360
sure what you're building is
ready for scale.
336
00:18:29,360 --> 00:18:33,120
You know, I talk to, I talk to
founders, for example, who
337
00:18:33,200 --> 00:18:36,880
rightly so are being pushed by
their potential investors
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00:18:36,880 --> 00:18:42,040
because they don't have a CTO.
And I still not all VCs or not
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00:18:42,040 --> 00:18:44,800
all investors will insist on
that now because of, you know,
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00:18:44,800 --> 00:18:47,400
the products we've talked about.
But I think it's so important
341
00:18:47,760 --> 00:18:51,120
because you need to know what
you're creating is, you know,
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00:18:51,120 --> 00:18:53,920
not some sort of Frankenstein
and there's is actually fit for
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00:18:53,920 --> 00:18:55,800
purpose and, and can be
delivered.
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And then I'm firm believer that
once you get those architectural
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00:19:00,800 --> 00:19:04,240
things right and get that
technical due diligence done,
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00:19:04,800 --> 00:19:08,320
that AI will help you accelerate
your features and will help you
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00:19:08,320 --> 00:19:11,680
accelerate your product backlog
as long as the framework is in
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place to do that.
So I think the advice I'll give
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00:19:15,360 --> 00:19:19,920
you, you know, even from a
funding point of view is just
350
00:19:19,920 --> 00:19:23,720
make sure that technical due
diligence box is ticked and that
351
00:19:23,720 --> 00:19:27,280
you're not just going in with
AAI driven app that somebody
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00:19:27,280 --> 00:19:30,360
else could build for you build
and replicate really quickly.
353
00:19:30,680 --> 00:19:33,440
Even that technical due
diligence that shows you're
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00:19:33,440 --> 00:19:38,200
maturely thinking about scale
will help you with some sort of
355
00:19:38,200 --> 00:19:40,640
defensibility, right?
Because the Ethan piece is not
356
00:19:40,640 --> 00:19:43,240
easily reproduced and you're
just building defensibility as
357
00:19:43,240 --> 00:19:45,880
you go.
Yeah, I think that's great
358
00:19:45,880 --> 00:19:47,600
advice.
I think if you, if you can, if
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00:19:47,600 --> 00:19:50,200
you can follow those steps, that
instruction, I think you'll be
360
00:19:50,200 --> 00:19:53,400
able to successfully build and
fund and launch a startup and
361
00:19:53,400 --> 00:19:57,000
find that part of market fit so
many funders are striving for
362
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now.
Thanks so much for calling on
363
00:19:58,560 --> 00:19:59,920
the podcast.
It was a really nice
364
00:19:59,920 --> 00:20:01,720
conversation.
You've obviously you had a
365
00:20:01,720 --> 00:20:04,600
fantastic career and they're
still doing amazing work and
366
00:20:04,600 --> 00:20:06,640
have a lot to share.
So thank you for for coming on
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00:20:06,640 --> 00:20:09,360
this podcast to share a lot of
the insights that you've gained
368
00:20:09,360 --> 00:20:12,000
from your experience.
Thanks for having me, Philip.
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00:20:12,000 --> 00:20:13,760
Appreciate that.
Thanks, Matt.
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00:20:16,040 --> 00:20:19,080
Hi, thanks for listening to this
weeks episode of the Founders
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00:20:19,080 --> 00:20:21,560
and Funding podcast.
If you'd like to be a guest or
372
00:20:21,560 --> 00:20:23,640
have a guest suggestion, please
get in touch.
373
00:20:24,280 --> 00:20:24,720
Thank you.
00:00:01,000 --> 00:00:03,480
Hi, welcome to the Founders and
Funding podcast.
2
00:00:03,480 --> 00:00:06,080
I'm your host, Philip Smith.
On the podcast, I'll be
3
00:00:06,080 --> 00:00:09,840
interviewing founders,
investors, startup advisors on
4
00:00:09,840 --> 00:00:13,520
how to fund the journey of your
startup and some tips and advice
5
00:00:13,520 --> 00:00:17,120
they have for you along the way.
This podcast is sponsored by
6
00:00:17,120 --> 00:00:20,640
Perfect Technologies and Laden.
Enjoy the episode.
7
00:00:23,360 --> 00:00:25,760
Hi, Niall, welcome to the
Founders and Funding podcast.
8
00:00:25,760 --> 00:00:27,600
Hi.
Phil, thanks for having me.
9
00:00:28,440 --> 00:00:30,280
Well, look, thank you for taking
the time for coming on.
10
00:00:30,280 --> 00:00:33,400
I know you're very busy.
And now to kick us off, could
11
00:00:33,400 --> 00:00:35,680
you tell me a bit about yourself
as your background?
12
00:00:36,360 --> 00:00:39,880
Sure.
So I'm in the IT industry quite
13
00:00:39,880 --> 00:00:44,960
a while, 20 plus years.
I describe myself as a builder
14
00:00:44,960 --> 00:00:47,080
at heart.
So I spent most of my career
15
00:00:47,720 --> 00:00:50,600
building software applications,
building solutions for
16
00:00:50,600 --> 00:00:55,680
customers, always kind of
customer facing across multiple
17
00:00:55,680 --> 00:00:57,920
industries, every industry
that's out there from public
18
00:00:57,920 --> 00:01:02,120
sector to pharma.
So yeah, I described myself as a
19
00:01:02,120 --> 00:01:05,920
builder builder at heart.
I spent a lot of time building
20
00:01:05,920 --> 00:01:07,960
applications.
I've also spent a lot of time on
21
00:01:07,960 --> 00:01:11,440
the go to market side and
strategy side with Google and
22
00:01:11,440 --> 00:01:15,400
and Microsoft.
So I, I feel I have that kind of
23
00:01:15,400 --> 00:01:21,120
mix of, you know, technical
builder architecture, but also
24
00:01:21,120 --> 00:01:23,160
on the kind of strategy and go
to market side.
25
00:01:23,160 --> 00:01:28,200
So that's that's me, yeah.
Hmm, Well, it's a great
26
00:01:28,200 --> 00:01:29,440
combination.
You know, most people are the
27
00:01:29,440 --> 00:01:32,000
kind of leaning very heavily
into the tech side or the go to
28
00:01:32,000 --> 00:01:33,440
market side.
But I think it really is
29
00:01:33,440 --> 00:01:37,280
valuable to learn even at a
height level as much as you can
30
00:01:37,280 --> 00:01:39,880
about how the other side
operates and communicate them.
31
00:01:39,960 --> 00:01:42,960
And so well done, I suppose
working as great companies,
32
00:01:42,960 --> 00:01:46,400
having a great career gives you
the opportunity to do that and
33
00:01:46,400 --> 00:01:48,640
talk to me a bit more about what
you're working on now with the
34
00:01:48,640 --> 00:01:51,600
Sidekick CTO.
Sure.
35
00:01:52,040 --> 00:01:55,920
You know, I, last November I
left Google, I finished with
36
00:01:55,920 --> 00:01:59,800
Google and I decided I was at a,
at a, at the point of my career
37
00:02:00,120 --> 00:02:03,560
where I wanted to try and do
something myself, go out, go out
38
00:02:03,560 --> 00:02:06,760
on my own.
And again, leaning back into
39
00:02:06,760 --> 00:02:09,960
that, you know, builder
mentality, the, the kind of tech
40
00:02:09,960 --> 00:02:13,680
stuff I've done over the years
mixed with the kind of strategy
41
00:02:13,680 --> 00:02:16,840
piece.
I said, you know, this would be
42
00:02:16,840 --> 00:02:20,400
kind of a, you know, an
interesting time to, to get back
43
00:02:20,400 --> 00:02:23,040
into my startup stuff.
I was a CTO of a startup in the
44
00:02:23,040 --> 00:02:24,880
past.
I was working for an, an agent
45
00:02:24,880 --> 00:02:28,960
investment group and I really
loved startup.
46
00:02:28,960 --> 00:02:33,600
So I think the, the hustle, you
know, the, the builder mentality
47
00:02:33,600 --> 00:02:37,120
to get it done at all costs
really kind of aligned with
48
00:02:37,160 --> 00:02:39,560
where I like to work or where I
get energy.
49
00:02:40,120 --> 00:02:43,240
And I'd never really done it out
on my own as a consultant, as a
50
00:02:43,240 --> 00:02:46,040
freelance consultant.
So I decided to not give that a
51
00:02:46,040 --> 00:02:51,320
go, you know, and that's where
Sidekick CTO came from, right?
52
00:02:51,320 --> 00:02:55,440
So I spend my time either
advising startups at a very high
53
00:02:55,440 --> 00:03:00,840
level or getting really deep
into the technology for for
54
00:03:00,840 --> 00:03:06,360
some, I joined tech operators
there recently so that, you
55
00:03:06,360 --> 00:03:09,760
know, get back into the Angel
investment piece of advisory
56
00:03:09,760 --> 00:03:12,920
piece and it's going great.
I'm busy.
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00:03:12,920 --> 00:03:14,880
I'm probably a little bit too
busy, right?
58
00:03:16,040 --> 00:03:19,560
It is a good problem to have.
I think that the scene has
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00:03:19,560 --> 00:03:22,800
changed significantly in terms
of the AI piece.
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00:03:22,800 --> 00:03:25,680
And I know, you know, I'd love
to talk about that as well, but
61
00:03:27,080 --> 00:03:28,880
you know, so it's a really
exciting time.
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00:03:29,000 --> 00:03:32,640
And I think, you know, Dublin,
Ireland has always been a kind
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00:03:32,640 --> 00:03:36,680
of a hotbed of activity for the
perspective of startups.
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00:03:37,040 --> 00:03:40,680
And, and you know, I'm delighted
to get back into it.
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00:03:41,160 --> 00:03:44,600
And I, and I've kind of built
some, some of my own projects as
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00:03:44,600 --> 00:03:49,240
well, Pulse IQ dot fit as a kind
of fitness AI, kind of AI
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00:03:49,240 --> 00:03:52,800
coaching fitness platform and
that I've built myself.
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00:03:53,960 --> 00:03:56,960
So, you know, really excited to
get back, get back into it.
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Fantastic.
Well, it's, it's great.
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We'll see.
It's good to be, you know, I
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suppose, you know, really
unique, but great.
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You are in position where you're
on that the technical builder
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side and you can advise tariffs,
but also develop new setups, but
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also get into the investment,
uh, part of it as well.
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And see, I know you mentioned
there.
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00:04:13,200 --> 00:04:15,680
So you were the CTO and startup
advisor for the Lucy Fund?
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00:04:16,480 --> 00:04:17,120
That's right.
Yeah.
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00:04:17,120 --> 00:04:19,040
Going back, going back quite a
while.
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So, you know, so that was a
great one because it was it was
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a mix of building products and
scaling products.
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And at that time, by the way, we
didn't have AI doing everything
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for us, right?
We had to, we had to do the hard
83
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work ourselves.
We didn't even have, you know,
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cloud hosting really AWS were
was just kind of getting going.
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00:04:37,320 --> 00:04:40,600
And so that was a great time as
well because cloud was the big
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thing and and then, you know, as
well as building the actual
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platform, we were we were kind
of investing in other companies,
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yeah, through our kind of
technical delivery model.
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So that was really exciting.
So I, I got a really broadview
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00:04:56,160 --> 00:04:59,800
of what's a good idea, what's a
bad idea, how to, you know, on
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the execution side, then what?
What's a good idea and what's a
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bad idea from an execution
perspective.
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So yeah, I really enjoy that
work and glad to, as I say, glad
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to be glad to be doing it again.
I'm busy.
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Yeah, absolutely.
And I suppose to put it back to
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the present a bit, what What
does an average day look like
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00:05:18,680 --> 00:05:22,920
right now?
Right now I'm, you know, knee
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deep building a platform with a
company called better futures,
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really exciting product sets.
Of course it's AI, but it's AI
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in a very specific space where
you know, in, in the engineering
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world and mechanical engineering
world where it's very document
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centric, very regulatory
environments as well.
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So it's, it's, it's hard to kind
of, you know, replace the
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engineering knowledge that's
there.
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So in fact, it's impossible and
we shouldn't be doing that,
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right?
Because the, the, the, the kind
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of level of detail we're talking
about.
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So it's, it's, it's really about
assisting engineers, taking them
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away from paperwork and getting
them doing more of the
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innovative work that's required
from engineers across all sorts
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of products.
So better futures as a company,
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better futures at AI, uh, knee
deep building that platform and
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getting it out there.
We've got customers on boarding
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right now.
It's it's so it's a very
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exciting time and I'm loving it.
Fantastic.
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I'll be sure to check that out.
And I suppose to talk then a bit
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more about AI.
How?
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How has AI changed the way you
work?
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So it's this isn't, there's two
ways we're going to answer that
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question, right?
One, is it kind of a
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chronological view?
Because if you had asked me that
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question three months ago, it'll
probably give me a different
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answer.
Yeah, that's because it hasn't
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changed how we're working.
It's it's, you know, it's going
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to sound like a bit of a
marketing tagline, but it's
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completely transformed how we're
working.
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You know, I see right now there,
there are a lot of metrics we
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use to kind of gauge developer
success, but I think percentage
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of lines of code written with
written manually is a big one.
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And you know, how much of your
code has been written by AI.
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Is, is, is really important to
me right now, just from the
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perspective of similar to the
example I just gave of the
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Mechanical Engineers, right?
I, I want engineers focused on
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innovation and that means
solving problems in new ways.
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And I don't necessarily want
them writing code, you know, and
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they're two distinct things.
I think the world is focused
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very much on AI writing code,
which means we don't need
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engineers anymore.
And that's just not true.
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We need engineers because
they're the creativity around
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the applications and around the
products that we build.
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AI has very much transformed how
we do that and how we
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accelerate.
So what we can do now with a few
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people, you know, when I was
doing the work we talked about,
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you know, 12 years ago, it was a
team of 40 people.
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Right now it's a team of three
or four people.
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And that is a real difference.
And that means we can do more
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with that forty person team,
right?
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It means we can, we can scale
quicker, we can we can
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accelerate into new markets
quicker.
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So it's, it's definitely
transformed what we do and
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primarily in the area of, you
know, AI coding and also, you
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know, documentation.
I mentioned the documentation
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use case there earlier on.
Like a lot of the work that an
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engineer would be, you know,
doing a software engineer would
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be doing is kind of documenting
what they're doing, making sure
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everything is well documented.
And, and anybody who's been
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around for a while knows that
documentation is only as good as
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how up to date it is, right?
If it's even a few days out of
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date, it's useless because it
could send people in the wrong
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direction.
So using AI to, you know, keep
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our documentation up to date is
a really great use case for it
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as well.
And it all just points towards
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allowing developers or engineers
to focus on engineering rather
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than writing code.
Yeah.
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Yeah, I really like that.
It's like there's pros and cons.
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I absolutely.
And it's you know, like if if
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you if people are listeners or
many of them are will be are
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familiar with loverball.
Those are the types of platforms
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you can get AI to write code,
but you know, you need the you
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certainly need the the
engineers, the developers there
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to really problem solve and make
it a product and something that
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people actually want to use.
So there's always going to be a
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huge need for that and get that.
I think getting that combination
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that makes right as you
described there is, is what's
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important.
And talk to me a bit about
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defensibility.
Uh, now I know we, we've had
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past conversations about that,
you know, you know, and why is
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that important?
Well, defensibility is always
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important for a start up so that
you know, they could when when
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they launched their company.
It's not, it's not easily
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reproduced.
That's the key thing.
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And but I think what's
interesting about defensibility
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right now is, and a lot of the
rules around this are the same.
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They've always been the same.
You know, an idea is not enough.
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It's all about the execution.
For example, a lot of great
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ideas out there, but you know,
it's not a, you know, we've got
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to get them executed.
So but, but defensibility used,
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it used to be enough maybe to
build an application, you know,
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and spend time building it, a
complex application, you know,
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software solution of some sort
and get it out to market.
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And straight away you had a lead
of months to years on any
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competition that wanted to get
into that market.
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And you mentioned products like
Lovable, you know, so now that
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changes the game because people
can, CE OS can, and I'm not
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advocating for this, but CE OS
can build their own
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applications.
Certainly MVPS with Lovable and
197
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Repple and others, OK, it's not
just Lovable and get them out to
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00:11:00,920 --> 00:11:05,040
market right, with, you know,
fully functioning MVPS.
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So where's the defensibility
then?
200
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If if I go and I try and pitch
this to somebody and they can
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reproduce it as quickly as I
built it, my defensibility has
202
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got to be something else.
So I think defensibility for me
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now is got to be around sticky
customers, whether it's a user
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base community or, you know,
enterprise customers.
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Are they sticky?
Do they need to use your
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product?
Because you know, they've been
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using it and then you've got the
data and you know, you're,
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you're, you know, the, the value
in them using your product is
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the fact that they're using
their product, your product,
210
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right?
That it grows over time and it
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learns from the customer and
that creates a sticky product.
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And so it doesn't matter that
the product could be reproduced
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relatively quickly, attracting
customers, getting them on
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boarded and making them sticky
is still the hard work.
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And that's the execution piece
that's really important.
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So yeah, ability, forget about
it.
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00:12:01,840 --> 00:12:08,000
If you're spending, you know,
time figuring out how do I build
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a better LLM than, you know, I
don't know, Google's right then,
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or how do I build a better model
for this particular industry?
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You know, I'd be careful
because, you know, it could be
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done And let's, let's focus on,
let's get something out there.
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Let's get customers and, you
know, let's get them sticky.
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And honestly, you know, maybe
some of the venture capitalists
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might not agree with me on this,
but I think it's more important
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than revenue or funding right
now.
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You know, get something, get it
out there, get people using it,
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Figure out what works, figure
out what doesn't work.
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Figure out how your product can,
you know, adapt to those users
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so that they become sticky.
And now you've got some
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defensibility that you can go
and look for customers to pay
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you with or go look for collab
funding.
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Yeah, I know I think you're spot
on.
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You know, I do see even off for
myself, I see founders kind of
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be trying to create AI.
Suppose a defensibility or a
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thesis around why their new
application is is great and
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it'll be the one that'll solve
this problem.
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00:13:10,360 --> 00:13:14,040
But you know, because the, you
know, large because of AI,
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because things are moving so
fast now, people can create
239
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products so quickly.
And it really is that product
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market fit and getting customers
and make and, and, and making
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sure that actually are retained
and sticky that, that that's
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really the, the kind of the only
defensible, I think, proposition
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now, because everything else can
be, uh, replaced it on so fast.
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And I suppose a bit broader than
looking at some of the, you
245
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know, from the founders you've
worked with neither there's some
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kind of common MVP challenges
you you've seen them encounter.
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Yeah, and a lot of the, a lot of
the challenges are age-old
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challenges.
So you know, there's a danger
249
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and getting caught up in some of
them when other things are
250
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moving so quickly.
And you know, there's a few
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here, but I'm going to pick the
top ones, top of mind ones.
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The first one is are you
building a product or are you
253
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building a solution, right?
What's the difference?
254
00:14:05,200 --> 00:14:08,640
You know, you can spend a lot of
cycles with customers building a
255
00:14:08,640 --> 00:14:13,200
solution for them, but it's not
a product you're building.
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You know, it's kind of, I always
use the analogy of, you know,
257
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we're building.
If I build you a tailor made kit
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car just for Philip Smith's, you
know, demands you want to the
259
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particular color, it was a look
a certain way, you're going to
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love it.
It's going to be great.
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00:14:28,520 --> 00:14:30,640
But that doesn't give you
product market fit because not
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everybody wants that car.
All of a sudden you got to think
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about the production line and
what you get at the end of the
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day then isn't that nice shiny
product, you know, custom car.
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It's it's just got it right.
Or Volkswagen is going to be for
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the masses.
It's a scale issue.
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And I see a lot of customer, a
lot of startups are actually,
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you know, they're trying to get
customers and are trying to get,
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you know, which is great, but
they end up building the
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solution for that customer and
not actually a product.
271
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And the shift away from building
something for one customer to a
272
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product mindset can be quite
seismic.
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And that's, that's the biggest
mistake I see from a, you know,
274
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from a CTO perspective is how do
we now shift to a, to a product
275
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mindset?
And that's a whole different
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world.
Think production lines, you
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know, a, a, a, a kind of a, a, a
set of features that we think
278
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the masters will like, but maybe
one or two customers won't like.
279
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And that's fine, right?
The other one, I think that the
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other big one I'm saying right
now is the, you know, we talked
281
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about it, the AI store, fright,
AI generated apps.
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I think honestly, there's going
to be a graveyard of these
283
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things in time to come.
You know, there's so much more
284
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to building a platform or
building a product and than just
285
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generating an app, right?
You got to think about security,
286
00:15:50,080 --> 00:15:53,120
scalability.
I literally saw on LinkedIn the
287
00:15:53,120 --> 00:15:56,640
other day an article about
somebody who used who had
288
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launched an app that they had
built with a tool like some of
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the ones we've mentioned, and
they managed to delete their
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entire database with an agent.
And this stuff is going to
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happen, right?
It's going to happen and the,
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the, the, the question is, have
you got a disaster recovery
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plan?
Have you got a security plan?
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Have you got the prevention plan
to stop this stuff happening?
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That's what engineering a
product is about, not coding.
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And that's my fear that a lot of
startups are and look rightly so
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get an MVP out there, get
customers using a get feed, but
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it's an MVP.
Turning that into a product is a
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is a different, a different
story.
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And then then there's a kind of
other end of the spectrum where
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I still see startups going and
offshoring an MVP development.
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And I'm like, you don't need to
do that anymore, right?
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You can do that yourself, OK?
Or you can get help from people
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like me who can build it in days
and start getting customers on
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board.
I think that the biggest piece
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of advice I've given the MVP
stuff specifically is, you know,
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build it quickly, but no, it's
an MVP and no, it's a prototype.
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No, that getting that to scale
and turning that into a product
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with properly production, proper
production line behind it is a
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different story.
And you probably need to start
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thinking about that early as
well.
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And so that you don't end up in
that graveyard that I that I
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mentioned.
Yeah, I think there's a good
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points like I've certainly seen
this does the, the new issues
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being created by as as much as
it's solving, uh, you know,
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problems is creating a lot of a
lot of problems as well.
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Like I see a lot of founders
that they're adding because they
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can these add new features, you
know, and they end up having
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1020 features and, you know,
without solving the core problem
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just because they can.
So they're kind of going into
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future overload and we're
getting too excited and they're
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not just kind of pulling it
back.
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We're finding and trying to
match the problem with the
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solution and to limited feature
set.
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But umm, yeah, it's, it's
certainly an exciting and
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interesting space and changing
all the time.
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Uh, before we finish, Niall,
what is one key piece of advice
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you'd have to successfully fund
a startup in terms of, you know,
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trying to help start-ups funding
that journey, uh, and how they
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can kind of keep the setup alive
to get it to that point where
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they can find that product
market fit.
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Yeah.
I mean, I will always go back to
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technical due diligence.
You know, we just discussed it,
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right?
It's, it's, it's, you know, make
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sure what you're building is
ready for scale.
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You know, I talk to, I talk to
founders, for example, who
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rightly so are being pushed by
their potential investors
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because they don't have a CTO.
And I still not all VCs or not
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all investors will insist on
that now because of, you know,
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the products we've talked about.
But I think it's so important
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because you need to know what
you're creating is, you know,
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not some sort of Frankenstein
and there's is actually fit for
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purpose and, and can be
delivered.
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And then I'm firm believer that
once you get those architectural
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things right and get that
technical due diligence done,
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that AI will help you accelerate
your features and will help you
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accelerate your product backlog
as long as the framework is in
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place to do that.
So I think the advice I'll give
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you, you know, even from a
funding point of view is just
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make sure that technical due
diligence box is ticked and that
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you're not just going in with
AAI driven app that somebody
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else could build for you build
and replicate really quickly.
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Even that technical due
diligence that shows you're
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maturely thinking about scale
will help you with some sort of
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defensibility, right?
Because the Ethan piece is not
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easily reproduced and you're
just building defensibility as
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you go.
Yeah, I think that's great
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advice.
I think if you, if you can, if
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you can follow those steps, that
instruction, I think you'll be
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able to successfully build and
fund and launch a startup and
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find that part of market fit so
many funders are striving for
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now.
Thanks so much for calling on
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the podcast.
It was a really nice
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conversation.
You've obviously you had a
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fantastic career and they're
still doing amazing work and
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have a lot to share.
So thank you for for coming on
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this podcast to share a lot of
the insights that you've gained
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from your experience.
Thanks for having me, Philip.
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Appreciate that.
Thanks, Matt.
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Hi, thanks for listening to this
weeks episode of the Founders
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and Funding podcast.
If you'd like to be a guest or
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00:20:21,560 --> 00:20:23,640
have a guest suggestion, please
get in touch.
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Thank you.