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The NeuralPod AI Podcast
The NeuralPod is all about deep diving into technical machine learning topics and showing its real-world impact, bridging the gap between AI-first companies and adoption.
We chat with subject matter experts across reinforcement learning, recommender systems, deep learning and generative AI. As well as chatting with business leaders, VCs, operations and strategy experts.
Who are NeuralRec.ai? NeuralRec is a recruitment staffing agency. We build niche ML teams and represent some of the globe's best ML talent.
Chris: So hi.
Yes, Moenu.
Welcome to what AI means for us.
Great, great to have you on today.
How are you?
Mwenya Kawesha: I'm very well, Chris.
Uh, yeah, delighted to be here.
Thanks for having me.
Chris: You're welcome.
So yes, I've been there.
We've been chatting for a while.
And, um, yeah, your background is in
the operations, um, strategy piece.
And, um, yeah, you've been a chief of
staff, uh, for, uh, for a while into,
uh, well known startups in the UK.
And, uh, just someone
I've, I've talked to.
Grown to admire in the industry and
recruited roles, uh, chief staff roles
in the past that I can certainly put you
up there in the top echelon, I guess.
So, um, yeah, do you want to just start by
introducing your, your career up to the,
I guess, the present day and how you got
into, um, to be a chief of staff, I guess.
Mwenya Kawesha: Yeah, so I
definitely had, uh, what many would
describe as a really varied path.
Um, so I have a liberal arts
degree, as they'd say in the US.
Uh, so I studied history at Oxford
and then, um, essentially actually
moved to New York to intern
at Bill Clinton's foundation.
Um, focused on, uh, researching issues
such as climate change, education,
um, and then, uh, took a very
circuitous route and ended up moving
into the private sector and working
at Accenture in what was at the time
called their business strategy team.
So that was working with, I mean,
I think in my case, about 30 or so
different clients across all sorts
of sectors, all sorts of strategy.
Disciplines, all sorts of work
around essentially, you know, what's
an innovation strategy in a large
organization and, um, I, you know,
I was looking at so many different
types of emerging technology.
I became really interested in
going deep with one of them.
So at the time I was I was picking
between, uh, blockchain and AI, and
I, I ended up going down the AI route.
I had a former colleague from
Accenture who I, who I admired a lot,
who, uh, joined a startup and, um,
was working on basically how to use
AI to detect terrorist propaganda,
uh, for counterterrorism purposes.
So
I thought, wow, they're working on
some really, really massive challenges.
And it wasn't so much about wanting
to be in the counterterrorism space.
It was more the size of the ambition, the
way, um, they were applying AI that really
inspired me to, to get into the field.
Um, so I started out, um, actually
leading technical delivery teams who
would deliver bespoke AI solutions
for different clients, whether they
were, um, government departments
or private sector organizations.
And then really went into focusing on
how AI can address the sort of harmful
content content, how AI can address.
the harmful content, um, and
conduct that there is online
to make online spaces safer for
us all, but critically still enabling
people to be creative, have freedom of
expression, et cetera, all those things
that we would want in a liberal democracy.
So, um, and, and essentially,
um, I was working very much
with a lot of different clients.
COVID was really interesting.
And I, I got some, some really.
really big opportunities around
the strategy and operation side.
Um, so I was doing that and a lot
of client work at the same time.
So eventually went into a
business operations role.
Um, so again, kind of getting into the
chief of staff type work and then, um,
yeah, I got my first chief of staff role.
Um, Yeah.
logically, which was the second
startup that I worked at.
Chris: Nice.
And I think to me, your career
kind of seems quite purposeful in
terms of the career, the companies
that you've chosen, I guess, is
kind of a responsible tint there.
Um, you know, what, what drew you
to the ethics and strategy piece,
especially coming from somewhere like
Accenture, where you've got exposure
to lots of different projects, etc.
And you could have taken your career in,
in lots of different directions, I guess.
What, what was the draw for you?
Mwenya Kawesha: Yeah.
So I, I really love the strategic
disciplines because it's about making
really big decisions for a company
or facilitating a company to make
big decisions about how it's going
to operate in a particular market.
I like the fast growing
big markets such as AI,
um, and, and really the ethics
piece, which again, we can define
ethics in many, many different ways.
But for me, it's about the values
and the principles that you apply to
ensure that the, in this case, the
technology that you're developing
is responsible and it works for
businesses, it works for the customers,
it works for society, et cetera.
Um, so to me, There's
There's no reason for
that to, to contradict.
So what, what really has drawn me to
the companies that I've worked at is,
is this massive scale of ambition and
the level of impact that they want to
have that I feel is positive on our day
to day lives and also the way we work.
Chris: Yeah, interesting.
think today we're gonna kind of cover
some of that off around you, your strategy
and operations experience, responsible
development, and also, um, I guess you've
got the battle scars of raising funding
in the UK, which not many people can
say, but I think a big focus today is,
um, you're going to be on startups and
founders and how your experience could
potentially benefit those types of people.
But, um, yeah, I mean, I'll be honest
when I first recruited a chief of staff
last year, it was hard to define what
a What is actually the definition?
What should you look for?
And what is, what is a
chief of staff to you?
How, what does a good chief of staff do?
Mwenya Kawesha: This is a great
question for so many different reasons.
Um,
really, a chief of staff has to sit
within a context of what is best
for that organization right now,
and what is going to be best for
that organization in the future.
So thinking a little bit longer term by
longer term I mean, a couple of years.
So there are many, many,
many chiefs of staff.
I mean, so, Some Chiefs of Staff are
essentially a Chief Operating Officer,
some Chiefs of Staff, um, people may have
heard the term Founder's Associate, so
people who work really closely with a
founder who might be relatively early in
their career but have, of course, sort
of, you know, a level of potential, grit.
Those sort of qualities, um, et cetera.
I personally was a chief of staff to
a CEO, found a CEO on a leadership
team at a leadership team level.
So, so the key thing was really having
discussions about how are we shaping
and scoping and defining this role.
And you have to do that within the
context of what the leadership team.
actually is.
If the leadership team is one sole
founder and no one else, you're
essentially a co pilot and you're going
to be doing a whole lot of things.
If you've got five co founders and,
you know, a massive sort of, you
know, suite of vice presidents and
presidents and every title in between,
that role may be, by necessity
have to be a little bit narrower.
Maybe you've got a niche skill
set, whether it's, you know,
financial modeling or something that
you can drive innovation on.
So you really do need to
define it really tightly.
Um, the way I've often liked to work in
this role is thinking about the founder
complimenting them.
You know, a lot.
You know, I have an arts background.
Um, a lot of the people that I've
worked with really closely often have
had more of an engineering background.
So kind of finding someone that you
can complement um, and kind of also,
I think it can be really great when
you both like doing things that the
other person doesn't necessarily
want to do as much.
So that you've always got that
coverage and really sort of how
can I multiply the impact of this.
Principle.
Um, so it's slightly different to say, a
business operations role that is a little
bit broader because you are essentially
thinking what is the office of the CEO?
How does that interact with the
rest of the leadership team?
How can I be fair to all the executives
and think about the best interest
of the company, but while amplifying
that founder's ability to execute
so that when we bring the strategy
together, we're all sort of, you know.
working on the best things that
drive the organization forward.
There are things that
a founder can delegate.
You have to be really mindful and careful
about what exactly you're delegating.
So I think the ability to take as much off
a founder's plate as appropriate, because
sometimes it is right that, you know, the
founder's the one who delivers that big
sort of, you know, groundbreaking message
about the direction of the business.
Um, So, um, so there are
lots of books on this.
The Harvard Business Review.
I think a lot of people kind of copy and
paste what the Harvard Business Review
description is and put it in job specs.
You wouldn't do that, Chris.
I know.
Um, but, um, and yeah,
there are, you know.
fascinating books, but I think there's
one called something like second
in command or something like that.
I could be wrong.
Um, but there are all sorts of different
models, but also the other critical thing
about the role is there is a personality
and personal fit element to it much
more so than if you were doing sort
of a broader business ops or strategy
role, like you kind of have to be in the
trenches with this person all the time,
whether it's sunny, whether it's raining.
There's a thunderstorm and finding someone
where they naturally want to kind of
give that work to someone and you kind
of naturally want to work with them.
That's really critical as well.
Chris: Yeah.
So I think what, one thing I take
from that is it's okay to have like
a, a, a chief of staff really tailor
to your needs because it's a little
bit different to maybe, uh, ML Eng.
And I know you can say
that about any role.
But, um, with ML engineering, you know,
it's a little bit more, uh, you know,
everybody's kind of in one lane, so.
Um, and I guess, um, do you want?
One thing I admire about you, as I
say, is you kind of understanding
of technical markets and translating
that into the operations side.
But do you think that technical
understanding of, say, a
technology like NLP is, is, um,
essential for chief of staff?
And is it kind of a line where you
would expect, you know, a chief of staff
to have technical knowledge, I guess?
Mwenya Kawesha: Super, super
interesting question again.
Um, so, so I done some technical
delivery at a startup that was AI
focused before going into the role.
So that helps with an empathy for
how You know, technical folks may
be going about solving a particular
problem or anticipating the types of
challenges that they're going to have.
It's probably more common now that
people would have had exposure
to that type of work, but when I
started that was not common at all.
So while I do think if you If you get to
grips with the technology, if you build
relationships with the technical team,
If you also have the humility to see
it from their perspective and not your
perspective, I think you can save a lot
of time by learning their language around,
you know, how they're planning things,
why things are iterative, why you can't
necessarily get a black and white answer
about what's going to happen on Tuesday.
Um, But, you know,
if, if you're meeting someone great who
doesn't have that experience, because
to be fair, there haven't been sort of
tons and tons of those opportunities,
and if you just didn't know anybody
working in that space, maybe you
didn't go into that space, um, if
you didn't get the context around it.
Um, so, so I think the, the
critical thing really above all
else is a willingness to learn.
And a willingness to not assume like,
just because you may have sort of, you
know, a strategy background or done
some really compelling projects for high
profile clients, doesn't mean that's
automatically going to translate as is
into a more agile sort of scale up type
of environment, startup environment.
And that also means that, you know, AI,
which is such an incredibly fast moving
field, you know, it's really critical
that you're just willing to learn.
And willing to be resilient and also
that comfort level with uncertainty and
then sort of that can translate into
somebody kind of continually building
the technical awareness, I would say,
to, you know, be effective with a
really broad range of stakeholders.
But yeah, ask me this question in
10 years, and I'll probably say it's
absolutely critical because more people
probably should have that kind of.
knowledge?
Chris: Yeah, I think that's really
well put and segues nicely into the
next question around uncertainty.
And, um, are we doing enough
to, um, you know, safely develop
AI and and its future powers?
Um, you know, deep seat this week's
been the newest buzzword in in AI.
But what are your thoughts on kind of
responsible AI development at the moment?
Mwenya Kawesha: Well, well, with
responsible AI development, it's
really critical to always have your
principles in mind, whether those
principles are around, um, um, sort of
explainability of, say, data sets or a
particular model, if they're around bias.
If it's around sort of the quality of the
use cases that you're selecting if it's
around sort of you know What clients do
I choose to work with as someone who?
Lives in a liberal democracy, for example
Um, so so I think you have to be led
by certain principles and not just kind
of react all the time and go with the
wind.
But at the same time you have to look
about at the external environment and
see, you know, is there a breakthrough
that means Everything that I had
assumed has now been flipped on its
head, and then you have to really
come together and look at that.
I think when you're thinking about
responsibly applying a technology,
particularly an incredibly powerful
one like AI machine learning,
there's a broad pool of experts
who are part of that journey.
There are some Technical
things that you can do to, um, you know,
improve the safety of models, uh, improve
the performance of different models.
But at the same time, that's
operating within legal frameworks,
regulatory environment.
There might be academics
who are independent.
You might have a really robust view
and challenge on what's being built.
So not being afraid to essentially
not have all the answers and keep
these open dialogues with stakeholders
and also be proactive about, um,
looking for stakeholders who can.
Can guide and have an interesting
perspective on that before you need
them and before you have a crisis.
Um, it's really important.
So, so I am aware, you know, I
know a lot of people, a lot of
technologists who have done a lot for
a really, really long period of time.
I think the challenges.
How do we, when we've got a world filled
with, when we've got 9 billion people,
um, how, how do we make this technology
now work for the, the best, you know,
as many, many people as we possibly can?
Um, and also considering the nuances
that there are about what that means
in all sorts of different companies
as we're trying to scale globally.
Chris: Yes, and I guess that's going
to be a continuing open question.
I suppose there's no, um, there's no
right answer to that at the moment.
But moving on to you kind of
your expertise and background
around strategy and operations.
I think, um, a lot of founders
might find this useful.
As you've already said, a lot of them
can come from a technical ML engineering
background on real technical backgrounds,
and it might be the first time they're
implementing their own solutions.
I guess AI strategy.
What what's a good AI strategy
to you and how how can you ensure
proper execution of that strategy?
Mwenya Kawesha: Yeah, um, AI
strategy really sits within the
context of a business strategy.
So, It's not really AI for AI's sake
necessarily, it's what as an organization
are you trying to achieve, what problems
are you trying to solve, who are you
trying to solve them for, can you
evidence that this is a high value
problem that people actually want solving.
Um, so, so making sure that, um, AI is
essentially used, um, effectively as
a tool to solve a problem and that it
effectively engages whoever's using it.
whoever's buying it and is responsible.
Um, I think the other critical
thing, AI is so connected to the
data that these models ingest.
So, so within that, also thinking about
the quality of the data that you can
find, can you find the right volume
of data to even apply AI in this case?
Sometimes I've had people ask
me, you know, how do I use AI?
And I've said, well, Have you tried SQL?
Which will probably just solve your
problem, um, you know, have you,
we, you know, we always talk about,
you know, not wanting to use sort
of siloed Excel spreadsheets, but if
you have literally five data points.
You do not need, you do not need to
spend money on a model that's not
going to work because it's, it's
gonna, you know, be overfitting.
Um, so, so, so yeah, that just that
mindfulness and proportionality
about how AI should be
applied in the correct problems.
The data involved, um, the level
of computing power required.
Also, what are the, what are
the challenges with potentially
maintaining this model
if you need to deploy it?
So is the model going to degrade,
Um, over time, which they definitely
have a tendency to, if you're not
putting the resource behind or
the effort into model maintenance.
Um, Also, you know, those questions
around explainability, um, particularly
if you're working on the B2B side or
B2G side, that the, the clients will
inevitably require that, that you explain.
the output, what you're doing.
So, So, you need to have
a strong answer to that.
And, and also given how quickly the
field moves, um, it can be worth having
sort of deep experts in areas such as
NLP, computer vision, um, et cetera.
And just really staying ahead of the curve
and what are the latest developments,
what's the latest research in this
sector so that where possible you can
use sort of state of the art models,
state of the art techniques, um, and
really hopefully give yourself, um,
you know, a really strong advantage.
But, but the magic really is
in have you set up the problem?
To begin with, and all the
way through, are you actually
solving the right problem?
Chris: Interesting and in terms of
the next point, and we've seen a
real life example of that this week
in terms of how the markets moved
and potentially being flipped on it.
It's had a little bit.
Um, you know, in terms of refining
strategy, but then kind of
actually put to me in the past
using a data driven approach to
to refine the strategy of what?
Why is that so important
for for achieving success?
Mwenya Kawesha: So, so with a field
like, um, artificial intelligence that is
really fast moving, it's really critical
to test your assumptions, um, in the
market, in the environment So, You know,
if you have a strategy and you feel and,
and this is specifically for earlier
stage businesses, startups, scale ups,
you know, if you say in January, this
is exactly what I'm going to do for the
next 18 months, I mean, look at the last
18 months, we've had the strategy will
have just been turned upside
down lots and lots of times.
So rather than kind of.
investing a lot of time in something
that's inevitably going to change
and challenge you and you know,
wasting that money, wasting that time.
It's really about having your Vision,
your mission, your principles.
And you've got a hypothesis about what
the problem is, what your, whether it's
customers, users, clients, whoever,
what those people want and need
and how you can solve that problem.
You need to go out and test it
and see.
Does this actually have any traction?
Is there any real evidence that, you
know, this is solving a challenge?
Is this technically feasible?
Maybe it was on a plan, but actually
you're finding that, particularly if
you're working in some domains like,
you know, healthcare, whether there's
a lot of deep expertise needed,
you know, is this something where?
I can actually use AI to solve this
problem as is, or do I potentially even
need, like, a little bit of consulting
support in order to solve the particular
problem, which a lot of, a lot of,
companies do use some kind of managed
service, human, human in the loop element,
um, to address things, um, as well.
Chris: Cool.
And, um, yes, moving on
to the execution piece.
Um, you know, having the right
people is obviously key to, to
executing the right strategy.
But you've, as you said at the start,
you've kind of come from an arts
background, which, um, you know, people
might not necessarily associate with
a chief staff position or technical
role, but it's worked really well.
Um, Yeah.
What?
Why is it important to have people from
different backgrounds and attracting the
right people so critical for success?
Mwenya Kawesha: Yeah, one of the things
I love about AI is In, in the field
that we're in, when you're working
with really high impact technology
to solve really, really big problems,
or to be part of solving really,
really big problems, um, you just
need So, many different skill sets.
So, you obviously do need robust
technical skill sets And people
who are, you know, able to.
Code really, really well,
fantastic engineers of many, many
different types of engineers.
You need data security experts, privacy
experts, whether those people are formally
sort of full time in your organization
or you're working with, you know,
people on more of a consulting basis.
Um, but also, you know, we currently
do have a narrative where there
is a lot of fear around AI.
What it can do, what its potential
is, and, you know, that, that
narrative is, is evolving every day,
but You know, fear is, is really,
really quite dominant in terms of
what I'm hearing people mention.
If I had a penny for every time
I'd been asked about Killer Robots,
I would never need to work again.
Um,
So, so how do you engage people
and also tell a story around ai?
So from a commercial perspective, as
you're engaging clients and users, and
for some, some, some organizations this
may be either the first time they've
invested heavily in AI or maybe the
most significant investment they've
made, or they may, well, for the first
time have AI be a really central,
critical part of their strategy.
So.
You do need a range of different skills
to construct that story, tell that story,
even telling the story around, you know,
how certain models are performing, because
sometimes it's not always explicitly
intuitive how a model has addressed the
problem that you have, so that level
of translation, also sort of managing
projects, driving accountability, there
are all sorts of skills that are non
technical skills that are a really big
piece of the puzzle, Puzzle, particularly
when you've got, um, clients and you're
also, um, working on use cases that
are high impact that a lot of people
might intuitively have an opinion on,
even if they've not sort of been close
to that particular domain as well.
Chris: Yes, it's super interesting.
I think very well put again.
Um, okay.
In terms of, um, responsible development
against specifically related to, uh,
founders and, um, Um, You know, I think
seeing in the market around kind of US
versus EU development, it kind of, I
think the EU sometimes takes an unfair
bashing because there's, you know, lots of
great startups in London and particularly
Paris that we spoke about in the past.
But, um, you know, how do you think
an org or a particular startup
founder can balance a need to.
Develop responsibly for for I guess
our future and then, you know, not
falling behind Um or getting FOMO
in in in the startup community
Mwenya Kawesha: Yeah.
So is this about regulation, Chris?
That's kind of what I'm sensing.
Um, so, so, yeah, obviously in the EU,
there is, there is a lot of regulation.
We've had the EU AI Act.
Um, AI regulation is really interesting
because it, it cuts across many
different types of regulation.
So it's not like there's only one place
you'd go to, to learn about AI regulation.
And then in the US, Um, at a, at a
federal level, of course, there's a lot
more of a lean towards deregulation.
But then you do have certain states like
California, where of course, you know,
there's so much, um, great AI development
there that's introducing, you know,
a lot of bills around deepfakes and
creators rights and things like that.
Potentially we'll see more of that too.
So, so it's, it is, I think for
organizations, particularly smaller,
earlier stage ones, where there's only
so many people you can hire, there's
only so many consultants you can pay for.
Um, but obviously there are
some exceptions to that.
There are some very, very well
funded companies, as we know.
Um.
But, um, yeah, it's really critical
if you're operating in a regulatory
environment, you have to do the best
to understand what the regulations are,
what the, the most serious considerations
are for you, that probably are ways
that you can get some external support.
If you just
need someone to help you
understand and break down, Okay.
what are the key?
Top five things that I need to know
and, you know, embed those processes
and systems into your business.
So you can have that comfort that
you've you've covered those things.
Um, and then also that engagement.
So I think it is healthy to explain
what technology is being built.
impact it has, how it helps to
solve societal problems as well
so that as people are, you know,
defining the environment that we're in,
they're doing that with the knowledge
of how, how, you know, innovation is
actually working and also the potential
of what we can we can build as well.
Um, obviously, if You have fewer,
fewer constraints around you.
You probably can more quickly get
some technology, some very, very,
powerful technologies to market.
Um, but I think an environment where there
is, there are very very few safeguards.
I think that's not ideal either.
So, so it's about striking the right
balance and also, you know, a lot of
organizations, you know, a lot of us
companies will want to expand into Europe.
A lot of European companies would want
to expand to the U S and other markets.
So being cognizant of the full landscape
and environment that you're in and
taking into account sort of how that.
How that affects your business is really
key and it can be really complicated.
So the most important thing is, is get
help if you're not sure, but it, you know,
whatever environment you're in, you have
to, you have to do something about it.
Chris: Yeah, and I think you,
uh, raised an interesting point.
It certainly comes across
a quite complex, um.
topic, I guess.
And what, what, just taking it back
a bit, what point do you think Hiring
someone like your chief of staff would
be a good point for maybe a founder
who's, um, you know, very technical,
you know, would you advise getting
maybe fractional help initially?
And at what point and at what point
does the full time chief of staff
do you think becomes relevant?
And I think is obviously going
to be different case to case.
But in your experience, when is the
when can the biggest impact be made?
Mwenya Kawesha: Yeah,
super interesting question.
So I've seen some founders hire a
chief of staff within their first
10 hires before they'd hire, say,
a chief operating officer, if that.
There was no chief operating
officer as a co founder.
Um, I've seen some founders go and
hire chief of staff when they're
200 people in for the first time.
Um, but, um, basically hire a chief of
staff before everyone is telling you,
why don't you have a chief of staff?
That, that probably is, is the best point.
I think, um, at the point at
which, you know, you don't have
capacity to, um, think about.
You know, the business in the
way that you want to find,
you'll never have the time to do.
everything that you want to do.
But if you're really sort of
so overwhelmed with, you know,
approving everything, having to do
absolutely every action that the
board or a particular stakeholder has
requested, having to prepare every
meeting opportunity for a company.
And if you're looking back and saying,
you know, over the last three months, I've
literally, Basically done very little of
what I wanted to do, and I don't even have
the, the headspace to think about what
the next three months should look like.
Um,
that's a really critical point, whether
you do go down a chief operating
officer route or there are all sorts of
different titles you can have I think
that's the time when you really do.
Do you need somebody in?
And that can happen at all sorts of, um,
different, different, um, different times.
Um, I would say if you are having
a, are going to have a chief of
staff, I mean, before you're raising,
you're really maybe a big round.
It would be probably maybe
even the year before.
to get someone, you know,
used to the business, build
relationships with the team.
And of course, that's if you're
hiring externally, you may
well find someone internal who,
you know, you can shape a role
with, um, so that when it comes
to, you know, fundraising, critical
milestones, new market expansion, you've
got someone there to take the lift.
And you've also got someone who can
say, Hey, Why are we doing this?
Or
um, can we do this another way?
Or hang on a second, like, you know,
everyone's asking for your time.
Like, are you happy with that?
Like, how can we bring that back?
So you can also focus on a few other
things that are really critical as well.
Chris: Cool.
And, um, yeah, just final question
on, uh, responsible development.
Um, I think you put this really well to
me in the past and keen keen to share it
where responsible development in a startup
is not strictly tied to monetization
and, um, Um, At least initially, you
know, how, what steps can people take to
lay themselves by into responsible, uh,
development, but also how can you link
it to, I guess, future monetization.
Mwenya Kawesha: Yeah, so responsible
AI development is, is really critical.
In most organizations, I would expect the,
the team that's joined the organization
will have really high expectations
of, um, the standards in that startup
they've joined because they believe in
a founder, they believe in a mission,
they believe in a problem space.
So, um, and hopefully, hopefully
that space is responsible.
Um, and then also, um.
Clients, again, you
know, a lot of startups,
you know, need clients who can, who can,
um, you know, have, have the budgets to
be able to use them and to, you know, take
that, that chance on, on working with,
you know, a really innovative technology
and then scaling that long term.
So if you're trying to attract
FTSE 100 clients or, you know,
some of the most prestigious.
logos out there, they will really care
if their suppliers are responsible.
Um, and I think the, the consequences
of having scandals around not being
responsible can, can follow organizations
for an incredibly long time.
Um, and it can also impact your
potential future ability to win work.
So, you know, if you, if you've just got
your, your, uh, sort of massive, let's say
it's a, you know, uh, one of the largest
companies in the U S is your client.
That's a golden opportunity.
You want that to go well, you want,
you don't want them to kind of fire you
because You missed out on responsibility
and then you don't get that referral
going forward to, to build your pipeline.
So, so not just in terms of sort of team
engagement and morale, but really there
is, even if, if people don't see it as
short term pain, there's a long term pain
that comes with not being responsible.
Chris: Yes, it's super interesting
and I couldn't agree more with that
one in terms of long term approach.
Um, okay.
I'm just moving on to the final
part of the podcast today.
You're you're Probably, um, the
most relevant for, for any founder,
um, you know, your experience in
fundraising, super unique background
and, uh, experience in the space.
Um, yeah.
What, where should a founder start
with fundraising and, um, yeah.
How would you advise any,
anyone kind of taking that on
in today's ever changing market?
Mwenya Kawesha: Good luck.
Um, so no, um, no, nobody needs luck.
Um, so the most important thing
is what am I raising and why?
and and really being disciplined
about having that clear why the entire
time through and also having that
why once you've raised the money,
so you're actually using the funds
for what, you know, you've gone
through all that effort for, um,
some rounds can be incredibly easy.
Some rounds can be incredibly hard.
So you need to also reflect
considerably on the market timing.
If you tried to raise within a downturn,
that might just take a lot longer.
Um, or it might just be a lot higher
harder because, you know, inevitably
the scrutiny might be higher.
Um, investors, you know,
we will always say that even
in a, in a downturn for a high
performing company that they're
willing to, to still deploy capital.
And of course, that knowledge that.
Investors are partners in this.
They've got capital that they
need to deploy, so they are
looking for opportunities.
So kind of having that positive
mindset around raising, Um,
it's really critical.
Also sort of understanding
the actual process and the
language of venture capital.
There are some great books on
this, like, um, venture deals.
Um, I think by Brad, don't want
to get his last name wrong.
But venture deals, it's, it's definitely
in all quality bookstores, um,
which, which lays out, you know,
things like, you know, what
is a convertible loan node?
Um, what is it like to raise more
of The sort of, um, series A.
Chris: about
Mwenya Kawesha: sort of additional work
do you need to do to raise a series B and
beyond?
So you need sort of enhanced
legal due diligence, enhanced due
diligence at all the levels really.
Um, you know, what are the typical
terms that you can expect to see
for different types of deals?
How long do these deals typically take?
Um, so to know the
language And the process.
Chris: patient.
We
Mwenya Kawesha: you know, will really
help you sort of feel and also look
like, you know, what you're doing,
even if it's maybe the first couple of
times you've gone out, um, gone out to
raise, um, and to really, um, really
take it, take it seriously and make
sure that you're comfortable with.
The deal because the long term
implications of that are really big
to bring on sort of a new partner to
potentially, you know, give up some
equity in your business or if you've
taken on some debt, you have to,
you have to repay the interest.
So it's serious.
So I, so I think sometimes, you know,
when it comes to things like salary
negotiations, um, I don't know if you
ever saw those magazines when you were
a teenager that said, Oh, well, you
can only sort of negotiate in this
one particular area or this other
particular area, which I think is
Terrible advice just to throw that in.
Um, but when you, when you look at these
really big deals for a company, and also
if we're being really serious, a company
that could potentially generate a lot
of wealth and a lot of massive impact
in an organization, think long term.
And I'd say, Don't give everything up in
the first round and then come to regret
it all the way through the lifetime
of your company and be demotivated.
Um, and you really need to be sure,
like, am I comfortable with this deal?
Am I raising the right thing?
Should it be a convertible loan note?
Because I don't want to
price my round right now.
Like,
should it be equity?
Should it be debt?
Um, debt usually follows raising equity.
So think about the sequencing, what
you're doing, and also coming up with a
story and being crystal clear about who
is actually doing the work of raising.
It might not be everybody
in your leadership team.
It might just be you, supported by,
you know, one person on your board.
Everybody helps, but you need
ownership and who's leading this.
Chris: Yeah, I think that, yeah,
that's very clearly explained.
So thank you.
And, um, yeah, I think your, your
experience specifically raising in
the UK market and, um, yeah, your
background there, what, what do you
think some of the key challenges
would be there in terms of that you
specifically faced for fundraising,
but also if you were to translate that
to the vibe of raising in 2025, um, do
you think it would, those challenges
would hold up well, uh, to, to today?
Mwenya Kawesha: Yeah, it's interesting.
I think at the moment that
there is obviously a huge
desire for, um, AI investment.
Um, it, it's probably not unreasonable
to think that there may be some
more diversification from that.
In future years, so a lot of
organizations feel that they have to be
able to make it really clear how AI is
adding value or, you know, increasing
the efficiency of particular tools
for end users, clients, et cetera.
Um, so
AI does feel like it's still
a really, really strong theme.
um, a strong theme at the moment.
Um, one of, one of the key things, um,
in the UK is there probably is much less
of a growth at all costs, um, attitude.
So, um, having some
evidence, probably even at.
Relatively early stages that there
is some path to profitability or you
could break even if you wanted to,
you know, within a couple of years,
um, that's more critical, um, sort
of more eyes and more teased dotted,
um, and that's not to say that there
isn't probably that movement in the U.
S.
to some degree, but, um, I think you,
you will see more organizations where
the focus Sorry, more investors where
the focus really is sort of scaling
unprofitably incredibly quickly so
So that that appears to be the one
of the critical critical differences.
Chris: Cool.
And yeah, what would you say that
when you raise was actually your
biggest challenge during fundraising?
Mwenya Kawesha: Um, one of
the biggest challenges was
Fundraising is a full time job.
Being a chief of staff is a full time job.
Comms is a full time job.
There are just a lot of full time jobs.
Um, so probably time management,
prioritization, you have
to be super, super focused.
Um, you need a lot of stamina, um, and
really to be focused on all sorts of
things, whether it's financial things,
aspects of the story, really knowing
your customers in depth, also engaging
the wider leadership team on things
that, you know, a lot of investors want
to meet customers, want to meet, you
know, the person leading a particular
team and having to do that against
the backdrop of having to do other
work because the business didn't.
Stop, because it can't stop.
Some of the biggest challenges fundraising
are really the time management because
you have to Also be working with the
business and, and clients and all
sorts of things, um, at the same time.
And, um, you have to build a
really, really clear picture of
where the businesses are, where
the business is going over a
very long time horizon and have.
that tight, compelling story and, and
all the data points to, to back it up.
Um, so, so that's just a lot of work.
I know that, you know, people say
there are AI tools that can do
this, but sometimes there's a level
of nuance and sophistication that.
Yeah, I don't think I'd be comfortable
just asking, um, a tool exactly
to, you know, give me the answer.
um, um, so, um, so yeah, so that
was one of the biggest challenges,
um, in terms of the actual round.
I think, I think.
Particularly if there are lots
of different approaches you can
take, sometimes just picking what
is the right area to focus on.
I think for certain businesses,
it might be completely obvious,
um, what the right thing to do is.
I think I've worked in organizations
that had more complexity in terms
of multiple business lines, um,
operating in multiple geographies.
So, so essentially, what do you lead with?
um, um, that's, that's
a really critical thing.
Um, Also, um, uh, another key
area would be, um, yeah, how do
you communicate the progress of
the round to the wider business?
It's, it's something that
people are always fascinated by.
I think fundraising can feel
a little bit mysterious until
you start, start doing it.
Um, and it can feel very like, you know,
they're very dramatic consequences,
uh, which, which is fair, that can be.
Um, so, so yeah, how
to, how to manage that.
Um, as well, um, this was a,
my experience, I think for some
organizations, um, it may be
challenging to access the, the
network, um, in order to, you
know, help get deals over the line.
So, you know, as much as people can
do to, you know, get the support of
people who maybe in that case, maybe
you get an advisor who has raised
before, who's willing to be really
generous and, and help, help navigate.
you know, that new new process.
Chris: Yes, it's an interesting point.
And I guess if you raise him
for the first time, you'd You
don't know what you don't know.
Um, what just changing the perspective
a little bit to maybe what an
investor's Prioritizing and you know,
maybe the some of the nuance you've
picked up as you've done it twice
before Um, you know, what what do
you think investors are prioritizing?
apart from the obvious Um when they're
looking at founders and and what exactly
you you think they're looking for
Mwenya Kawesha: Yeah,
it can differ by stage.
So typically the earlier stage and
the fewer data points you can expect,
it typically is more about, um, that
founder and sort of why they would
be successful in that particular
space, why they are the best person
to solve that particular challenge.
Um, recognizing that things
can change all the time.
So, you know, things like, you know, um,
perceiving someone to have, you know, the
tenacity, the grit, et cetera, to stick
with it and be really passionate about
things when they get incredibly difficult,
which, um, as we know, most startups
scale ups, um, yeah, um, don't end up.
it.
So, so there's a really,
really high risk of that.
Um, um, the other thing, so at later
stages, once you start to get more
evidence around, you know, having, you
know, a more robust, scalable product,
having, you know, customers, users,
growth is really, really critical.
Um, Yeah, without that, I mean you, you
want to be growing much quicker than, you
know, much, much larger companies because
that really is your, your differentiator.
You want to be in a large growing market.
Um, and then you want to be gaining
traction and having evidence that you've
got good traction and then eventually
that you have product market fit.
Um, sometimes it's.
Sometimes it's, it's key that you're sort
of, you know, hash, you're not burning
too much cash because sometimes that
can be an indication that you've not
quite reached product market fit or not
kind of got the go to market strategy
in, in the optimal place just yet.
Um, but of course, you know, there
inevitably is some, some just spending
more than you're making that's needed,
um, for growth and, um, innovation
and product development, um, as well,
particularly if you're in sort of high
cost areas such as, you know, London,
Bay Area, New York, Paris, et cetera.
Um, and then, and then really,
um, as an organization is sort of
scaling beyond Series B, Series C,
it's really sort of, the
product should be there.
It's what's now the go to market
strategy to, you know, scale
incredibly quickly, but more
efficiently than you have in the past.
So, um, whether it's sort of, working with
partners who can resell software or, you
know, sometimes people want to work with
very large organizations that already
work with all the FTSE 100 companies.
And then you could just sort of sell
it through that distribution channel.
Um, so yeah, that long term potential
and traction and sort of also retaining
customers and people coming back
again and again and again for years
and years and years, having that
long lifetime value to show evidence
that essentially people really want.
What you're, what you're building.
Chris: Well put again, I think, yeah,
just showing that evidence based approach.
Um, timeless really, isn't it?
So, um, thank you so much for
when, yeah, I think you've shared
some excellent points today.
Really appreciate your, your, um, sharing
your knowledge and, and, and your time.
So thanks so much for coming on today.
Mwenya Kawesha: Yeah, that's great.
Thanks so much again for having me Chris.
Chris: You're welcome.