Essentially, it's a swarm of models, AI models that
emulate human cognition and emotion and become highly
predictive of behavior across populations. So you're
creating synthetic populations of people that are then situated
in context. Forget Personas, Jill Axline is building
synthetic populations that predict real human behavior and that changes
everything. Keep watching to learn how.
Foreign.
Hello, and welcome to Data Driven, the podcast. We explore the
exploding world of artificial intelligence, data science, and of
course, none of this would be possible without the underlying data
engineering. And with me on this road trip down the information
superhighway of the future and Buzzwords
is my most favorite data engineer in the world. How's it
going, Andy? Hey, Frank. It's going pretty good. How are you? I'm
doing all right. I'm still wearing the hipster glasses because they
were recording this about post 3 weeks since my concussion.
And as we were telling our guest in the virtual green room that
we kind of owe the show's name to a concussion.
So true, folks who, longtime listeners, know
the lore, so we won't bore them or waste any of our guests precious
time. With us, we have Jill axlein, who
is Ph.D. and is the co founder and
CEO of Mavera, which is an
interesting company and Maverick Era is what I'm told it's short for.
So welcome to the show, Jill. Hey, thanks. So happy to be here.
Yeah. So you also have three kids and. I
have three kids. Andy has three. Three plus two.
Yes, that's. I think,
I think there's a correlation between number of kids and gray hairs.
I know I have kids and five grandchildren, so there you go. But
I'm old. I'm just saying you have an age today,
you know. So
what does Mavera do and
what is brand and business meaning for? What does that mean in
high growth. Companies, brand and business.
I totally botched that. I'm sorry. I'll blame the concussion because I can do that
for another week or so. So what exactly does Mavera
do? Sure. So essentially it's a swarm of
models, AI models that emulate human
cognition and emotion and become highly predictive of behavior
across populations. So, so you're creating synthetic populations
of people that are then situated in context.
So as opposed to a model that's trained six months ago and
then is rapidly trying to iterate, it actually
pulls its synthetic database will update on a
second to second basis. So you always look at your population in
situ. Additionally, I would say
it provides a really strong pulse of what that population
looks like within the context of your business or your vertical.
Because we support a foundation with deep business context
that takes into account not just your business from the time that it
was instantiated, but it also is updating
temporally and it creates relational,
like relational connections across your business. So for instance,
if there's a marketing spend five years ago or about
the same time that you launch your flagship product or a secondary product,
it's going to show a lot of data on how the context
around that might have influenced your outcomes.
So I guess like long and short of it is you have
populations situated in context and wrapped around your business,
and you can use that pretty expeditiously to make
decisions in a much less expensive way than most market research
or, you know, strategy research, strategy based research.
It's almost like you're taking kind of like the SIMS
approach of having these individual entities, I wouldn't call them
agents because it doesn't sound like they're agents. It sounds like they're simulated entities, like
you said. Right, exactly. That's interesting. Is there like a.
That. That's an interesting approach because that does,
it probably doesn't completely insulate you from model drift, but it
probably does a good job of, well,
we're having a massive windstorm and it's like, you know, negative, whatever. Outside in your
Chicago, it's really cold. It's always sunny and it's always sunny in farmville, as
I like to tell Andy. But, but I mean, you can
insulate against a certain amount of cold, but you can't really stop it.
That's right to think about it. So you can't really stop model drift, but you
probably can prolong how, how, how long your
models are valid for this by this approach. So that's correct. In
addition to that, something that I've pushed on because I've been an
advisor with this team for well over a year. And
since I'm a ph dork and I, you know, I'm always looking at evidence.
Evidence Ev. I was the original skeptic to synthetic
populations. In my last role at Morningstar, I built our market research
team. And when I was first introduced to the idea of
synthetic populations, I was like, you know, tons of skepticism.
I think the big thing here is they've built in a level of AI
governance around things like drift, but also to
model the difference between evidence and inference. And so
they're looking for confidence scores. They'll gather first party data
around your population and then create a synthetic data layer on top
of that. And a good example would say
asset managers like ice cream. Asset managers like cold
things. They like cold, wet things, they like cold, wet, sweet things. And then a
coefficient is that assigned to each of those new synthetic data points. And so
while it makes a more robust data set in the
billions that allows it to draw inference, it's also accounting
for again, what, what's based on evidence and what's based, what is
inference of the machine. And then there's also a governor across
this swarm of models. So it's going to call on the right model
for the right facet of human thinking or
feeling that it's trying to construct. And so
I think in doing that it creates safeguards around confidence. So
we, we produce confidence scores, it will give a spread of opinion across
a population. So unlike a custom GBT or
a Persona and some pre existing platforms that are emulating
language, it's actually taking a look at
where's their entropy across emotional response and cognitive
response in this data set and what does that look like in the spread of
opinion for that audience. So it'll tell you the nature of the spread
and where that spread is happening. So now you can account for almost,
you know, sub segmentation within the population. And that might
look very different at the top of the funnel when we're looking at thought leadership
topics versus the bottom of the funnel in marketing where we're thinking of features,
functions, benefits, et cetera. And so
that allows at least marketers, but I think others,
anyone go to market to really think about what is their message for the right
audience at the right time based on, you know, where they are in their
buyer's journey. And so that to me is a little bit
different because I would say the last facet of this is
the response stability. We're also providing a level of
test retest reliability. If you go into ChatGPT
recently, someone was flaming me because I've never made
caramelized onions. And so, you know, as a joke, he kind of went in and
said how many people who are 40 something, you know, like know how to make
caramelized onions? And these percentages swung
quite significantly from the first time he queried to the second time to the
third time. Whereas we're looking at population response stability
and modeling that, projecting it into the future and looking at the trend
line from the past on how this population would continuously
answer the question. So I kind of guess like when we think about model
drift, I think that's likely inevitable. But if you're
situating and updating with minute to minute context and then you're surfacing
some of these governance factors around what the Outputs are,
we're getting to a closer place where we can actually be collaborate collaborators
with the AI and govern it and then build,
you know, a greater level of trust is the hope.
That's interesting. I'm glad you addressed the skepticism because that was going to be my
next question. Like, how do you know this is real? How do you know that
it's accurate? The other question I had, and sorry, Andy,
I had a bunch of monster energy drinks today.
You could probably run different simulations, like in
parallel, right. Assuming you had the compute. So
you can see if this happens, if that happens, right. If there's
a recession, people are going to do this, go this way. If there's a boom,
if it kind of meanders somewhere in the middle, you could probably run
only limited to what compute you have, right? I mean,
yeah, I mean, it's a credit based system. So, you know, you buy
credits like a tank of gas and it's going to, you
know, give you enough gas to, to build whatever it is you
want within limits. But I would say,
yeah, I don't think you're really, yeah, I don't think you're really
restricted in terms of what outputs look like on, on a scenario
analysis. I think obviously if the more
data we have, let's call it for a specific company, when I was working at
Morningstar, that's 40 plus years of data on the back end in
that deep business context, that makes that prediction that much easier.
And so I think it also depends on what's coming into the model and
what its power is and its ability to be predictive. I
guess I should say that's cool. Because I think this is an interesting, it seems
like it's an interesting mix of kind of predictive modeling and
LLMs. Right. Because predictive models, I mean, they're not
new. Right, but they're not. But they do. I think
they're, they're traditionally, they're
very susceptible to drift. Right. But
I think also by simulating the individual actors, because a society
and economy, a customer base is, consists of, you know,
X number of, you know, not sovereign
but unique individuals that are going to have certain
personality traits. And some of those you kind of can
guess from. Like you said, you know, asset managers. Asset
managers, everybody likes ice cream, but asset managers probably really
like luxury cars. I'm going to go out on a limb. Right,
right. And probably how much the, how many luxury cars they have and which model
of luxury car they have is probably going to determine, is probably not, not
determine how successful they are. But it's probably a correlation between
how successful they are versus like how not. You know, I don't
know. I. If you're an asset manager and you're driving around the Hyundai,
there's gotta be a good story behind that. That's right.
I agree with you. And I think again, when
you can ask the synthetic audience and pull them, you can start to get into
further nuance whether those are B2B
dimensions of, you know, like firm type, role type,
etc. AUM or it can get into that more
psychographic or it can get into start, start to break down
archetypal differences and you know, all of those
then can be mapped into attributes that are built into the channels where we
communicate with them.
Go ahead, Andy. I don't want to hog the mic. No, no, it's all good.
I'm fascinated and
kind of playing off your, your idea of model drift, Frank,
and your questions along those lines. I
mean, in one sense I would say, you know,
a synthetic audience or you know, a synthetic sample
or cohort, however you want to classify that. Is
model drift happening in that
context is probably not unheard of because
there's cultural drift. And if you're looking for
ways to effectively simulate that
and run marketing campaigns against, you know, the
synthetic cohort, it doesn't strike me
as out of the realm of possibilities that you may want
some of that you may want to even tune for, especially
if you're looking at a younger audience.
There are emerging trends that come out of
those demographics. It's just part of the nature of those
demographics. I mean, I'd love to hear your thoughts on. On that.
Yeah, I mean, I don't know that it's a function of.
I don't want to make it like a generational distinction, but I do think
that anything that's current to context is going to
impact on a minute to minute basis in some cases how
the population is going to make decisions and what level of like
arousal they have. And I don't mean that in the, you know, cheeky
sort of way, but I would say like we're working with
an index team in financial services and they asked me on the spot,
can you please model a high net worth investor in Denmark?
You know, and this was last week just to, just to say, are you thinking
about, you know, rebalancing out of blah, blah,
blah, US broad index? And you know, the
answer was not immediately, but here's my thinking on that
and here's what I would be investing in instead. So now they're trying to think
through what's their messaging around outflows in that
predominant US broad index? And then how are we
surfacing the rest of our family of indexes in its
stead? And then he asked, how does this, does
the audience, is there a large spread here? And if so,
you know, what is the nature of that? So now we can think about
discrete campaigns across this population, which
is pretty narrow of, you know, ultra high net worth investors in
Denmark. Right. So I think it's
applicable depending on what, what is that trigger, you know, that what
is that zero moment of truth for any given population that is going to be
influenced by their immediate context. And
you know, with that responsibility score, we can then tell them this is something
we think will persist over time versus this is ephemeral. And based on what's
happening in the news around tariffs today. So here's something to push out in
your channels today versus here's something to build into,
you know, a long tail campaign and how to think about product strategy in
a different sort of way. That, that's pretty fascinating.
So pivoting just a little bit, you,
you mentioned quite a few instances of
incorporating evidence into this. And I would
imagine that I'm an engineer. Okay, that's a warning.
So, so is our cto. I'm getting used to it.
I think about open and close loops all the time. It's just, you know, I
don't even have to think about thinking about it. It just happens. But
being able to, to become predictive
and have that feedback where you, you
made some, you know, you made some prediction, some predictive
analytic, and then you collect evidence on
how accurate you were and not just, you
know, percentage wise, it doesn't really apply that much, especially in
marketing type
and especially in the age of AI where you can collect information and feed it
back into the system as training data,
effectively as responses to prompts. So the
prompts themselves become part of the data
that goes in and then the outcome that was
predicted, that's very easy to see. That
part happens. But then supplying the evidence
you predicted this, the delta between the
predicted and the actual, that's evidence. And
so being able to quantify that, being able to
feed that back into the engine, I think in early
2026, as we're talking about this, we've not
had the ability to,
I'd say in, you know, in, in natural language, to provide that
sort of information with any sort of confidence that
the algorithm that we're supplying that information to, that feedback,
closing the loop on the evidence, supplying the
evidence, we just hadn't had the confidence that the
machine was going to understand what we meant. And one of the
things that sort of slipped into invisibility over the
past, gosh, what's it been, three years and a few
months since Chat GPT was released?
Is that the model mostly understands what you're
saying now. And I mean by, by mostly some number well above
90%, you know, it's going to get what you mean
and when it hallucinates, you know, it's going to be because it
misunderstands what you said, not because it went off, you
know, and started interpolating what you said and
made something completely different out of it. It's the way it was
stated, wasn't quite clear. And nowadays
I hang out mostly in Claude and Claude code.
So when I'm going back and forth with, you know, with the engine,
it's, especially in Claude code, it very often
will pause the conversation and stop and say, hey, I have this question,
you know, and here's the options. I think you're, you know, based on what you
said, I give you 1, 2, 3. And then number four is you just type
and tell me if I completely missed it. And I rarely find myself
on that bottom option. Most of the time I'm picking the, the
top option, which the one it thinks is most likely. And
so having having that sort of evidence based
feedback, number one, be so much easier
than it is before. And so I can see that limiting model
drift. I can also see it kind of making
your predictions align with
the timescale that you mentioned. So not just the population
being so, so small, which is
infinitely harder than dealing with big data, right? Dealing with a
small set of data. How do you predict in all of that? And before I
ramble anymore, I'll just stop and let you respond. How about that?
Well, it's interesting and I don't want to get over my skis
because this is really where our CTO shines.
He has the ability to create
some audiences out of what he would say he would call dark
matter. The best way for me to think that through is when I look at
a tree and I see its various branches. I'm looking at the
tree to define the tree, but there's so much more sky
and negative space around that tree that also defines it.
And so he's starting to look at data and how it affects other
data and then putting that in context and using that
kind of negative space to then define the audience that's
so small. So that is, you know, in the case
of when I was at Morningstar, Acid owners, really small group of
people, but one that Morningstar really wanted to understand a
lot better. And so that institutional audience, they're
regulated. It's hard to, you know, get permissions because they're so small.
Their time is worth a lot. So it's an expensive panel to construct.
And here he was able to build from again, like that negative
space to then recreate the audience. And, and he is
surfacing that confidence variable. And if there is a hallucination,
hallucination risk, it's tagged and it will prompt you for what sort of
data it then needs. Or it's going to say, actually have to refractor the
audience a little bit differently. There's too much entropy for me to continue and
it will go and run it again. So. And again, I don't want to get
over my skis because I'm the social scientist in the mix, but that's how it's
been described to me that I can, I can best understand it. That makes
a lot of sense actually. And like you can kind of, I think there's a
lot of inference here in terms of what you can infer. Right. So
my, my kid, my
middle kids, my two younger kids are really into and really the three
year old just likes hanging out with his big brother. They watch Dragon Ball Z,
they watch the Jujutsu Kaizen, like all the crazy anime that's
very popular now. I bet one of the things you could do, I,
I've actually gotten into it. I was never much of an anime fan, but like,
you'd say, like say Dragon's Ball Z. Right. Dragon Ball Z has been around
that I'm aware of, maybe 20, 30 years. Right. But. So you can probably,
you could probably make a solid assumption that there might be some Gen X folks
that are Dragon Ball Z fans, probably a lot of millennials, a lot of Gen
Z, Gen Alpha, whatever they're calling them now. But there's probably not a
lot of people in retirement homes, boomers and
up there are big fans of it. Is it because they wouldn't like it?
I don't know. Maybe. But it's just, it tends that since that demographic
skew is kind of small, you're probably not going to find
a lot of them that are going to be into that in the retirement. I
don't know that that's just me just firing an analogy.
I mean, my parents liked K Pop Demon Hunter when my kids made them watch
it, but I have girls, so I don't know,
they're just really cute though. That's really
cute. It's a very well done kind of cross genres, but yeah, yeah.
And K pop is very, very, very
addictive. Yeah. You know, so like it just
sticks in your head. I don't know how they did it, but
who, who are the industries? What are the industries that are really interested in this?
You obvious, you mentioned Morningstar, obviously, I would imagine financial
services. And
Morningstar is asset management. Right. Is that what it is? Or a hedge
fund or it's, I'm. Not exactly sure, data and research. So I mean, I think
primarily they're known for their research and data and how they've rated
funds over the years and they've expanded from there by way of acquisition.
So PitchBook is a part of it. DVRS is an index business. So
they, they have seven different pianos that really like traverse
financial services. At this point I
think financial services has been interested partially because I'm in financial
services and I'm literate and being able to discuss it and showcase its
benefits. Right, right. I would say this is more like
functionally, like accurate for any
place that needs human intelligence. Right. So I've worked with
private equity teams that are helping to arm their
portfolio companies with a marketing tool that doesn't
have them, then looking to boutique agencies to do this level of market
research and understand their ICP and find product market fit or message
market fit. So there for them, it's very easy to kind of get in
there, even the technical founders, and try to augment maybe a gap in
their marketing acumen. I would say marketing
agencies, creative performance, et cetera, they have taken
to it really easily because they're already wizards who
wield, you know, traditional wands on doing this kind
of work to understand a market, to understand the message that's going to
fit with that market and then to make sense of what the real results were
when the market either engaged or didn't. Right. So and
building the creative around that. So the ability to pre test all of that with
the audience gets them to the starting line before they put money behind it
or have their client put money behind it with the best possible set of
options. So I think agency has been pretty prolific there too. And then
the last. And again, I'm kind of biased because I came out of enterprise.
Enterprise marketers who are finding gaps
in the kind of the traditional products that are, have easy distribution
within the enterprise are looking to a tool like
Movera to try to get more
what decision intelligence that's human based in what they're doing
and so that's, that's where we're seeing a good amount of traction would be
like in that mid market and enterprise level marketing team,
whether that be product marketing or demand gen or market
intelligence. And I came out of brand strategy so I found great
utility for it there in corporate comms. So again I think
it's really that go to market team where human intelligence becomes so
important to decisions and current like traditional research methods
are really slow and they're quite expensive and
not everyone can do them, you know, or they think to just grab
the information from within the four walls of the firm and
anecdotes of talking to customers. Right. So this is a good
way to augment an expensive way to augment some of that decision
support. So you can like throw together like a,
what's the, like a test market simulations and you can probably,
there's probably knobs and dials you could do. So you can kind of like get
multiple answers and I, I get it. So you can kind of, you can hit
your, whatever your campaign is going to be with the running start as opposed
to it's a little bit more guided than just throwing
stuff at the. Wall and seeing what sticks. That's right. You know what to throw.
You have better idea what to throw and where to throw it. That's right. And
I mean we had, even when I was still at Morningstar, pre
tested like the first time ever they built commercials. You know, they
didn't, they don't really do brand level, you know, television commercial.
They were deploying in Chicago, New York and London. And it was
shown that in London it wasn't, whatever it was, the voiceover, the
creative itself wasn't going to resonate with that audience as well.
And so that gave us the foresight to take a look at what the voiceover
is, what channels we might use, how much money we would put behind it before
we deployed in that market. And so that, that kind of helped with channel
strategy, it helped with content strategy. It certainly helped to
evaluate that creative before any money
changed hands. And so I think that was a super helpful thing. And now it's
an award winning campaign. I'd love to feel like Movera had something to do with
it along with all the brilliant minds that worked on it.
That's cool. So you can get down to the macro, not macro, micro level of
like the voiceover may not work in this market and things like that. That's
cool. Yeah. In fact there's a. So we're in multiple modalities.
We had used, I helped to co author
the CEO's speeches for multiple years. And so we made him
pract again and again and again, and we would. We would record
them. And so the video analysis tool would look at the
substance of what he was saying, the creative that was behind him on the
deck, and then also his performance. So as it evaluated him,
it said, you know, you're not taking time to pause for emotional
resonance. And it gave all the timestamps across his speech where he
should pause and why, and potentially even for how long.
So it was looking at audience engagement and emotional connection. Then it started
to take a look at, well, your message isn't that highly differentiated. And because we
have this deep business context, we know that X, Y and
Z are also talking about the convergence of public and private markets. This
is what they're saying, here's what you should say so that it sounds uniquely
Morningstar. So it now is helping to differentiate the message.
And then when we got down to the creative, it's saying, you should do things
that are a little bit more dynamic. You should back up what you're saying here
with, you know, more data, graphs, charts, et
cetera, less imagery. And so it was giving us guidance on three
dimensions of that speech. And as we did it over time and recorded
him, we saw his scores go up and up and up. And then
it ended up being a really successful speech at
the flagship conference that spring. So, you know, I
had even said to him, like, maybe we should use this before earnings calls. You
know, you never know.
I could see the. I could see the applications and, you know, in fintech,
I could also see applications of this in political campaigns.
Yes. I was just thinking that. I'm like, you know, yeah, they
would eat this up. Yeah. So we have been in
some conversations, and I obviously can't talk about it with someone in the House
of Representatives because we also have a news digest that
will metabolize the news and give you the perspective of specific
audiences. So he wanted to look at the two counties, you
know, that he. That are part of his constituency. But then he was
also looking at the committees, you know, so he's on two
different committees and how are they responding to the news and what is it that
they're doing? So it was doing this kind of social listening and moderate, you know,
modeling of the audience. And then he could say, well, this is what my response
would be to it and get them to vet it before he, you know, would
push send on a communication. So, yeah, that was. That was
something that. It's so timely Particularly with that news
digest. Yeah, sure. And you know,
particularly in it's, you know, the sentiment
analysis angle on that's huge. And
being able to do that in near real time,
I think has, you know, applications across not just those two markets,
but a bunch of different verticals as well. Because you
almost. The perception is if you don't
respond or react, that's a response or
reaction, you know, so.
Yeah, that's right. So I, I'd say between access
to news content and then also connection with APIs. So
we have Bloomberg flowing through the platform Pitchbook. We've got it
for marketers, Ahrefs and Semrush data. If you're looking at SEO and you have
thoughts towards what does it mean to show up in answer engines, all of
this data flows and could be called through the platform so that you're
looking at real data again, we leave a receipt of like this is where we
drew this data from. You can see it. And here's where we
inferred. So now you can use your own best thought
and strategic thinking on. Okay, do I need to get that
inference score down or do I feel good about this
and I can build it into my argument in a really defensible way?
So just curious. That's cool. Yeah, I'm, I'm down
with it. I'm just curious how,
in your experience, how have the, how's
the opportunities presented themselves for someone to kind of step
out and be creative is probably a nice way to
say it. Or, and, or controversial. You know,
there's, there's value in that some of the time. I mean, from a. If you're
talking about marketing a product or service, you
definitely want the differentiation. You mentioned that earlier.
If you're talking about a campaign, whether it's a marketing
campaign or a political issues type
campaign, the opportunity to
either be portrayed as a maverick or see what I did
there or to, or to be, you
know, just portrayed as somebody kind of breaking the mold, stepping outside
the talking points. You know,
how's, you know, how's your, how's your product and service
addressing that. But also too, there might be some. I'm sorry, I
didn't mean to cut you off. No, that's trying to cut off Andy. And then
I cut you off by mistake. But also to the
inverse of that. Like maybe there's some things you people, you don't want
Mavericks, you don't. We want stability. Financial services kind of comes to mind.
So sorry, I'll shut up. Yeah. So I mean, you can
construct your own Brand identity that's going to say, you
know, typically, here's our brand standards and here's our
brand expression, which can come across creatively or tone or
what have you. So that can be constructed and put on the back end so
that everything is then scored against that and can tell you how far away from
that you're drifting. Then you can put it in front of the audience.
Typically, anyone who's working with is going to have their own framework for
understanding. You know, how do I evaluate whether this message, message can go to market
under my brand and how much risk am I willing to take? You can ask
it even to assess the risk given the audience response.
And as it splits that audience where people are having a difference of
opinion, you can isolate that and say, is this my most
likely buyer or is this the part of the audience that maybe there's a huge
population that would like this more provocative
message, but it's a, it's an audience, as it's described, that would churn.
So, like, it allows you to make a little bit like, more strategic business
decisions based on like, what. What are the attributes of that
audience that are going to resonate with that more provocative message.
The other thing I would say is just, oh, no, it's okay. This is built
on a gan. So it's an adversarial network. And I
would say, as opposed to being sycophantic, like so many models that
are like, oh, yeah, I agree with you. And then you're like, no, don't agree
with me. Be like adversarial. You know, push back. It's built
to push back. In fact, we have a Persona specifically meant to
poke holes and ask you questions and get you to question your assumptions. And
I always start there. It's called Osprey. And I, like, that's my number
one first stop on the bus is here's how I'm thinking about
this competitive analysis. Let's like sort through what.
What is wrong with that or how I can improve it. Same thing with a
market sizing exercise. It feels like that should be wrote, but as you lend
more specificity to it, I might be market sizing against not just
a product, but a specific use case that I want to build up, campaign around.
And now it becomes like a much more nuanced way of modeling
an audience. So I always, again, start with that
adversarial model to get me to think better, you know, like, really improve
my strategic critical thinking. Kind of like the
10th man in world War Zone. Okay, I don't know what that is,
but should I watch it? I'm sorry, Andy. Andy, I cut you off. Yes,
it's an interesting concept. I don't want to spoil it for you, but, like. And
it's based on a real, real army unit where
they basically become their contrarian. If nine people agree
on something, then it's. They randomly will.
If 9 out of 10 people agree on something or something like that, or 10
out of 10, they will randomly pick one to. You have to
poke holes in it. Oh,
sorry. Encountered. That's okay. I first encountered that in World War
Z. So. Yeah, that. That was where I saw
that. The. It sounds what I
was thinking as you were describing that. I guess the phrase that popped into my.
My mind was, you know, there's no such thing as bad publicity.
And if you are peaking interest, whether it's
positive or negative interest, if you're provoking some sort
of reaction in that, and I think a lot of the social media
algorithms are tuned around being able to do that very thing,
you know, to. To get a reaction, either an agreement or a
disagreement, then that can lead to
engagement. And if that's the goal, that makes perfect sense.
That's right. I. In fact, I have a book right here called Filter World.
I think that's what it's called. Yeah, Filter World. And it's really all about
how algorithms can. Can do that, feed you back things that are more
sensationalized, kind of like yellow journalism going back to Hunter S. Thompson.
Right. That are meant to create some sort of response, whether good, bad,
or ugly. So, yeah, I think that's right. But at least
you could test. Yeah, at least you can test some assumptions first
prior to taking it to market and getting slammed for it and
having unintended consequence, potentially. Yeah, Well,
I mean, if you think about it, I'm just basing this on my
experience, because I have the most experience with my experience.
I love a comeback. Right. I just. I love it. And
often the way that that comeback begins, the. The arc
starts with me first
noticing something and having a negative reaction to it.
And then as I get more information, I go, well, yeah, I could kind of
see where they're coming from and, you know, begin to identify with it and
empathize and. And then every now
and then it's rare, but when it happens, it happens huge. And I
think part of the reason is because I started so negative with it, my support
skyrockets, you know, a little. It's not a line, it's an
exponent, you know, very exponential curve of
support that Grows out of that. And like I said, I think it's powered by
stretching that rubber band in the opposite direction to start with.
Yep. Although I would say some people are built that way because my
dissertation looked at processes of empathy and processes of
perspective taking and how counter. Counterargumentation happens.
Right. What are the various factors, either in an environment or in a
message that are going to create that? But there are also some things just in
you that might have that approach to say. I would say
my 7 year old, my little guy has like, he comes from a space of
no. We always start with no. He's also like
in the 99th percentile for math. I think he has like an engineering mind. Like,
I just, I was gonna say. He sounds like an engineer before you even
mention math. Yeah, yeah, yeah. Likes to take things apart
and put it back together. So that's it. No is a good spot. Yeah. Yes.
That's funny. It reminds me
of. Here's a story from way back when. Once upon a
time, I worked for a fintech startup. We'd call it. It wasn't called
fintech then, but it was basically in early
2000s. And it was a banking portal, but it was meant to be kind
of banking for people who
weren't comfortable with finance. Right. But the,
the rationale was they wanted to make the site really friendly. And one of the
things they did was they put little cute cartoon characters
on every page, which people.
And this was in Germany. So like it was a, you know, banking
culture in the US is very conservative. Even
Germany is even more so. And
that's being kind of. Turns out
people didn't want to put their money into a website.
Which again, early 2000s. Right. Still, that was already a stretch
with cute little cartoon characters. They wanted serious, they wanted stable,
they wanted boring, they wanted, they wanted the suits, they wanted that.
And it was kind of like when I saw the website, the design rolled
out, I was like, I don't think this is gonna work. I better have my
plane ticket home just in case. And
you know, it turns out I was right. You know, trust me,
I, you know, I didn't want to be right because I would have, you know,
had dot com dreams and, you know, all that. But.
But I mean, you're right. Like sometimes it would have been helpful
if they were to test out, if they had the capacity to test out.
Hey, what if we went for a cutesy K pop kind of demon hunter thing
for a banking portal. It might fly today maybe,
but probably not.
Just depends on the audience. Again, yes, Exactly. Know your audience. Right.
That seems like a tough sell. It, you know, in Germany in the late
90s, early 2000s. I don't know. Right. It definitely was.
I think after half a billion euros
were spent, I think they acquired 120 new customers.
So, yeah, it was br. It was bad
right there. It was bad. And I might be rounding
up ratio right there. I can do that.
Yeah. So, I mean, again, I think
audience, you can't really replace, like human response to something. You have to
get something out into market and see if trust is established and people engage
and ultimately make a decision to purchase. But I think getting
to the starting line with the best set of options, with
defensible reasons behind why he went with those options,
is kind of a better spot than we were a year ago or two years
ago. Right. And so I think,
I mean, we can only go up from here, but I think, you know, I'm,
I'm optimistic that if people were to start integrating this, it doesn't have
to take them out of the job force. It just can help them do their
job a lot better, you know. No, absolutely.
Yeah.
How did you get into this? How did you get into this? Because your background
is in. Your PhD is in communications.
You're getting used to dealing with engineers. Yes.
How did you. How did you end up at a company that is largely driven
by engineers? That seems. Yeah, this is a great question.
So again, I was kind of that skeptic who was running a market research
team and always being pressed on my budget. So the budget was,
you know, in the high six figures. And it's like that's the
first place everyone wants to cut when everyone's looking at margins. But
it's also such an important place to make sure that product
strategy, message strategy, all these things are kind of coming together in the right sort
of way instead of wasting money downstream. And
so I was trying to, you know, A, look for a way to
cut cost, but B, I also really wanted to understand
what was coming with this whole, like, generative AI thing, you
know. So when I heard about let's scan LinkedIn,
LinkedIn profiles and create synthetic Personas, I
really started to pound the pavement to try to understand who's approaching this in
the right sort of way aligned to how I think about modeling human
populations, which is what I was studying. So when
the strategist I was working with kind of heard me thinking out loud about it,
he introduced me to the co founders at Marvera and,
you know, I think I asked some hard questions. They could see that I was
nerdy and skeptical and willing to try. And
so they gave me access to it for almost a full year.
I took it through the compliance process, which was helpful for them, and it was
good to see how Morningstar was thinking about this progressively
and then just hammered it and, you know, brought it into the C suite and
brought it across the firm in my presentations. And I
think through that, it really helped me to understand what the true value
of it was. And after seven years at an enterprise, I, you
know, I was definitely someone that liked to make decisions quickly, thoughtfully,
but quickly. And I was kind of looking for, you know, maybe
there's another opportunity to take my expertise and apply it in a different
way. So I had a sabbatical. It was like
a, you know, six weeks every four years. Thank you, Morningstar.
And during that time, I just spent some time with them to really understand
the technology, really understand the go to market motion
and look at their capital raise and try to get involved in that
process. And then six months later, they asked me to join
them. Oh, that's cool. Yeah, that's cool.
It was cool. I have to say, I'm drinking from the fire hose because
working with the AI engineer, Full Stack
developer and. And looking at operations and looking
corporate taxes and all these things. No, that was not really. I carried
my. You didn't wake up and you were like, I didn't want to do that.
Like, that's interesting.
The first thing that comes to mind, and I totally lost my train of thought.
So if, Andy, this is an opening for you while I kind of reboot my
blue brain blue screen. So give me a second.
Oh, now I remember. You're welcome
anytime, man. Having
you mentioned regulations, this is what kind of. True. I was very
skeptical of synthetic data, just in general, just
because, you know, you're basically feeding machines into machines. And I'm old enough
to remember when you took it like a tape cassette and you copied it and
you did that enough generations, whether it was VCR or audio cassette,
you had an issue. Right? You would get some kind of degradation. However, in
reality, I've seen synthetic data do amazing things in the AI
space, in the data space, more than it has any right to,
basically. So that's why I was not skeptical when you mentioned synthetic
crowds, because it's one of those things where it's worked better.
But one of the upshots of synthetic data is that
the reg, particularly around generating synthetic
health data and things like that, you don't quite have the same
regulatory constraints. Right? There is no PII
to speak of. And you mentioned that there were regulatory hurdles for, for this.
Like what, what were the regulatory hurdles in
this case? I'm curious. Well, how, how could
you use the outputs? Where would they be applied? If you're reconstructing
the brand voice, what are you basing that off of? Is that, you
know, is that considered for them proprietary information that would
then feed the system for other, you know, competitors or
just writ large? I think that was something that they were looking
at. They were of course looking at data privacy. So
you know, I was uploading not just our creative,
but I was looking at our business strategy across the P Ls and trying to
get it to incorporate when things are launching and where is their convergence.
So that if I'm trying to create an umbrella level message at the brand level,
it can render really strategically down to the different business units
and create continuity and coherence in the message.
So but that's, you know, that's their strategy.
So they're really worried about like, you know, at what point
can we feel like this is safe? And so, you know, in earnest,
the team approached the ISO
42001. They had a SoC2, the
ISO37000 something because I'm never great at
remembering all the numbers but like they really did like get after it in terms
of, of ensuring that enterprises specifically would feel,
you know, really like safe in this environment and
everything. It was abundance of caution. What's that? It was, I'm sorry.
Okay, that's fair. Yeah. Because one of the big selling
points I've seen is it's not real. Right. So I'm sorry to cut you off
again, but. Yeah, no, because it's not. But it's a synthetic data layer
that sits on top of proprietary data and data gathered
from like first party sources externally. So I think
once you have the mix of multiple things, they just have to ensure that
whatever's put in there is proprietary, is protected.
That makes a lot of sense. Cool. Yeah.
This is the world we live in, you know, that's cool.
So any other questions Eddie or. I didn't mean to hog them up.
I'm just fascinated by,
is fascinated by the discussion. It's, it's one of those other.
Well there's other discussions and topics where we see
the kind of the real world interacting with the
artificial and I don't say artificial in any kind of
negative way, you know, in the sense of
synthetic and, and to me it feels a lot more like art
imitating life, you know, and
as we, we find more and, and
better ways to have technology enrich
our ability to do our jobs well. I just. I find it fascinating.
So. And it's. It's cool. I can tell
that you found a real. A real fit
for your education and your skills and it sounds like your
personality and, you know, and kind of likes
and that. That's always good, you know, when. When you can do what
you are. Yeah, it's so true. I love nerding out
every day on this stuff. Plus, like, I'm. I don't. I'm
just. I can't naturally sell anything. I have no selling
ability. But I can talk about it from the perspective of, like,
a practitioner, you know, and a skeptic one at that. So
that's really where I'm coming from in any conversation is like. Like, tell me
why you don't buy it. Because I, like, I'm gonna get in your bandwagon
and not buy it with you until we can figure out how it actually, like,
works and fits into this process, you know? So.
That'S a good way to look at it. That's. That. That's really not just selling
with empathy, but selling with, like, sympathy, I guess. Right? Like, yeah,
yeah, that's cool. That's cool. Where can folks find out more about
you and Mavera? Yeah. I love talking to everyone, so please
connect with me on LinkedIn. My name is Jill Axlide.
Again, Mavera IO is where you can go and check it out.
We liked people to kick the tires, so there's a free trial for everyone for
14 days and you can connect with anyone on our team to
walk through how to use it. Cool. Awesome. And we'll let our
AI finish the show. Awesome.