MARTIN OMANDER: Hello.
My name is Martin Omander.
And I welcome you to "Google Root Access," the show where
startups and investors share their experiences
and lessons they've learned.
Today, I'm joined by Nicolai Wadstrom from the investor
BootstrapLabs.
Nicolai, would you tell us a bit about BootstrapLabs?
NICOLAI WADSTROM: So we are a global venture investment
company, and we try to find top talented founders
from around the world, invest in them,
and help them build global companies.
What makes us a little bit different
is that we don't really care where they're from,
if they're from Silicon Valley or outside.
And the majority of our deal flow
actually comes from outside of Silicon Valley.
But we always bring part of a team to Silicon Valley
to benefit from this fantastic ecosystem to build companies.
But they usually stay and build-- at the end,
they usually have more employees where they originally started.
So we're trying to keep that hub.
One good example of a past portfolio company
is a company called Prezi, which started in Budapest,
in Hungary, about five years ago.
So they've grown from six people back then
to 270 something, I think, today.
MARTIN OMANDER: It's a great product.
I've used it.
NICOLAI WADSTROM: Yeah.
I love it.
It's amazing.
And that's the exciting thing.
People get so excited about the product itself.
It sells itself, in a sense.
MARTIN OMANDER: Great.
So Nicolai, both you and I have a past
as programmers and chasing semicolons
and such, which is great fun.
But some people say that technology doesn't really
matter.
All that matters is the idea or the team.
Others say the technology is very
important for what platform or technology a startup picks.
When you look at investments, do you
look at the technology a startup's built on?
NICOLAI WADSTROM: Absolutely.
I think it's super important.
But the core thing is really, really,
of course, the team and the ability
to execute at the end of the day.
So because we invest early on in a post-accelerate or seed
stage, having a technology background for me
is very helpful to understand what they're building,
how competitive the opportunity is, and all these things.
But looking at technology early on and how they go about it
is really crucial for us to understand
how that team thinks, and how they
try to use that as an advantage to move
faster and quicker, figure things out.
So as a team, it's the most important.
But technology-- down the line, it
becomes really, really crucial how
they apply technology, of course.
MARTIN OMANDER: How they apply technology, got it.
Yeah, got it.
So do you still program any, by the way?
NICOLAI WADSTROM: I do.
I try to keep pretty brilliant.
If I leave my Swedish culture behind,
I'm actually damn good still.
I'm pretty good.
MARTIN OMANDER: Oh, good, great.
I try to program a little every now and then as well, just
to keep the skills sharp.
NICOLAI WADSTROM: Some real work every now and then.
MARTIN OMANDER: Some real work, there we go.
So what are your criteria for determining if a technology
that a startup has is interesting right now,
or if it isn't?
NICOLAI WADSTROM: Well, so looking at new innovation
ideas-- and yes, it's about ideas.
But we invest in technology.
So being able to apply technology
at the end of the day that has impact and great efficiencies
is really important.
Vetting criteria we look at-- let's step back a second,
so looking at areas where we go in and invest,
and deciding if whatever these founders are very
passionate about and trying to build
is relevant and interesting.
I try to look at the bigger technology landscape.
It becomes-- I build software.
It becomes like an architecture map of the value chain
from one end to another where value is being received.
And some of these pieces of technology are commoditized.
And that's an advantage, because that
means you can focus on building value on top.
Like Clayton Christensen says, when it becomes commoditized,
the value and innovation is above and below, right?
MARTIN OMANDER: Oh, right.
NICOLAI WADSTROM: So it's the same thinking.
So look at pieces.
And there's lots of things through open source software
libraries that are readily available,
and things like that, that you can use, and then
iterate much quicker on top of that.
There are services like the Google App Engine
that allows you to focus maybe on the functionality
of your app that's closer to where you add
value on your mobile phone, versus a lot of infrastructure
on the back end to scale.
And early on, this becomes really even more important.
Because when you iterate and try to find a product market where
it's really relevant or where people even care about,
the quicker you can move, the better-- how you get to build
data points and validate those assumptions.
MARTIN OMANDER: Ah, right, I see.
So that actually leads me to the next question, which
is, some people say, build quick and dirty.
You're going to throw it away anyway.
And others say, no.
You're actually going to build a business on this
and make revenue.
You need to build something that works snf scales.
I imagine the startups that you invest in,
you give them advice on this.
What kind of advice do you give them?
NICOLAI WADSTROM: Yeah, so it's a very good question.
Because somebody comes out and will say,
should I do this quick and dirty,
or should I build a stable platform?
And the CEO says, build quick and dirty.
And the CEO says, no, no, no.
It needs to be a stable platform so we
can-- at the end of the day, our advice is really,
this changes over time.
A startup goes through phases and metamorphoses between them.
Early on, it's really, really crucial
just to find what people care about.
So you build quickly, push it out,
get data points, what people really care about,
assumptions to turn into data points.
And you measure, you learn, and you iterate that way.
Once you figure those things out, a lot of mechanics
change-- how we apply technology,
lots of other pieces of the startup,
how you scale ten users.
And there's a lot of growth hacking aspects.
But if you talk about the technology, all of a sudden,
you need to be able to sustain heavy load, growth,
and all these things.
So a proper technology platform that can handle that
becomes really important really quickly.
And a lot of startups, that is a hard job.
Because from iterating quickly, you're hacking things together
to test.
And all of a sudden, it takes off.
So what do you do?
So again, I think there's a lot of ready-made commodity
solutions that you don't need to build yourself
that you can rely on to quickly transition into that.
And I like the mix of App Engine,
the platform as a service.
Because you remove a lot of complexity
on storing massive amounts of data and retrieving it quickly,
the scale out of your web servers, and all that.
But for other pieces, you might need
to really manage parts of these things.
For heavy processing and analytics and all these things,
you might need to build an infrastructure.
Using infrastructure services is what everybody
of our startups use.
They don't have their own service.
Well, there's one exception.
But everybody else used really an infrastructure
as a service, Google Compute Engine,
for example, to assemble bricks to get it quickly up and down,
and scale up when you need lots of processing,
and scale down when you don't, and things like that.
MARTIN OMANDER: So when do you advise startups to use platform
as a service, as Google App Engine,
versus infrastructure as a service,
like Google Compute Engine?
NICOLAI WADSTROM: So it comes down to that.
If most of the value add of the product they're building
and they're iterating on is on the front end in a mobile app
or such, I think the App Engine is perfect.
Because you can build an-- and I think
it's much better than things like Path
where you can't really program the back end.
But an app engine, Google App Engine,
you still will deploy your applications.
And you can still build a lot of logic and value
add on the back end, which is important, in my view,
to differentiate.
But you take away some of the scaling issues and all those,
you can be really focused on that value.
On the other hand, again, let's say
you have massive amounts of data where
you need to do customized analytics
and analyze that data.
You usually need to build some Hadoop cluster
and some processing, and customize
a lot of things that happen on top.
Then I would recommend to use the Google Compute
Engine for that.
MARTIN OMANDER: Interesting.
Well, thank you very much, Nicolai,
for your time in coming on here and telling us
about and sharing your experiences.
NICOLAI WADSTROM: Of course.
MARTIN OMANDER: We hope you found something
you can use in your startup.
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