00:00:00,100 --> 00:00:02,033
Yeah, it's not a little different.

00:00:02,033 --> 00:00:05,066
You know, I think even if you took out
90% of the hype,

00:00:05,766 --> 00:00:09,266
you've got a capability that is

00:00:10,266 --> 00:00:12,566
twice as big as electricity.

00:00:12,566 --> 00:00:14,500
If you believe
we can change the narrative.

00:00:14,500 --> 00:00:16,700
If you believe,
we can change our communities.

00:00:16,700 --> 00:00:20,066
If you believe we can change the outcomes,
then we can change the world.

00:00:20,800 --> 00:00:22,366
I'm Rob Richardson.

00:00:22,366 --> 00:00:24,266
Welcome to disruption. Now.

00:00:24,266 --> 00:00:25,466
Welcome to Disruption Now.

00:00:25,466 --> 00:00:27,700
I'm your host and moderator,
Rob Richardson.

00:00:27,700 --> 00:00:29,666
As always, please like subscribe.

00:00:29,666 --> 00:00:30,966
We're here at 1819.

00:00:30,966 --> 00:00:34,033
We're honored to have them as a sponsor
with me, a special guest,

00:00:34,200 --> 00:00:37,466
Pete Blackshaw,
who is the founder of Brand Rank I.

00:00:37,566 --> 00:00:40,200
Also,
he was the former leader of CentriFuze

00:00:40,200 --> 00:00:43,433
which is the central ecosystem
here in Cincinnati.

00:00:43,700 --> 00:00:45,800
And before that,
I think he did stuff with Nestle

00:00:45,800 --> 00:00:48,000
and was just doing things
in digital innovation.

00:00:48,000 --> 00:00:50,366
So he's been innovating
and disrupting for a while.

00:00:50,366 --> 00:00:52,100
Pete, great to have you shake.
It have you?

00:00:52,100 --> 00:00:53,433
Yeah, I mean.

00:00:53,433 --> 00:00:57,866
So, Pete, you know, you
this is your second startup, right?

00:00:58,966 --> 00:01:00,866
Like, what made you

00:01:00,866 --> 00:01:03,866
jump into the startup world again
at this point?

00:01:04,733 --> 00:01:07,733
In both instances,
you know, I was in a moment.

00:01:08,100 --> 00:01:08,366
Yeah.

00:01:08,366 --> 00:01:10,333
kind of an inflection point

00:01:10,333 --> 00:01:14,300
in the digital world
where you just knew that a disruption.

00:01:14,366 --> 00:01:14,900
Yeah.

00:01:14,900 --> 00:01:17,733
You know, as you would put
it, was upon us.

00:01:17,733 --> 00:01:19,933
And, you know, I struggled on the,

00:01:19,933 --> 00:01:23,866
I think, trying to convince myself
that it was hype, shiny new object.

00:01:23,900 --> 00:01:26,900
And as I

00:01:27,166 --> 00:01:30,433
use the tool myself,
which has been personally transformative,

00:01:30,700 --> 00:01:35,300
I concluded that this was the time
to take the plunge.

00:01:35,300 --> 00:01:36,333
And it's a little bit scary.

00:01:36,333 --> 00:01:39,333
You know, my age,
two kids going to college, but

00:01:39,866 --> 00:01:42,866
I concluded that
I would never forgive myself if I didn't.

00:01:43,933 --> 00:01:45,766
You know,

00:01:45,766 --> 00:01:47,266
take a shot at it. Right.

00:01:47,266 --> 00:01:51,266
There's some I'm working on an idea
that I'm particularly passionate about.

00:01:51,266 --> 00:01:54,566
So you can kind of align a historic moment
with the personal passion.

00:01:54,866 --> 00:01:56,066
You know why not.

00:01:56,066 --> 00:01:58,933
And then the other thing
I was just thinking about was

00:01:58,933 --> 00:02:02,800
I just think, you know, I
expertise is going to be sell demand.

00:02:02,833 --> 00:02:06,566
There's
no there's no downside if you jump into it

00:02:06,566 --> 00:02:09,000
because you're going to learn ten times
faster in startup mode.

00:02:09,000 --> 00:02:11,566
You may come out with no cash.
You may come out of failure.

00:02:11,566 --> 00:02:12,600
It happens technically.

00:02:12,600 --> 00:02:17,666
But there's going to be a lot
of opportunities, post that experience.

00:02:17,666 --> 00:02:19,600
And so for me, it was an acceptable risk.

00:02:19,600 --> 00:02:22,566
And but I'm really excited
about kind of shaping the space.

00:02:22,566 --> 00:02:26,066
So, hey, I've been around for a long time
like, you know, so

00:02:26,066 --> 00:02:29,900
but it was obviously generative
AI that really kind of sparked you.

00:02:30,700 --> 00:02:33,500
What do you see as this moment?

00:02:33,500 --> 00:02:36,866
Like, I know a lot of people always say
this is going to change everything.

00:02:37,133 --> 00:02:39,133
And at many moments in time.

00:02:39,133 --> 00:02:40,966
And there's been a couple
times where that's been true.

00:02:42,500 --> 00:02:43,833
but this time feels a little different.

00:02:43,833 --> 00:02:47,000
I want to get your perspective
on how you think AI

00:02:47,000 --> 00:02:50,000
is going to shape and change business
in our world in general.

00:02:50,200 --> 00:02:52,133
Yeah, it's not a little different.

00:02:52,133 --> 00:02:55,166
You know, I think even if you took out
90% of the hype,

00:02:55,866 --> 00:02:59,366
you've got a capability that is

00:03:00,366 --> 00:03:02,266
twice as big as electricity.

00:03:02,266 --> 00:03:04,466
And I often say this is as much twice.

00:03:04,466 --> 00:03:05,500
As big as electricity.

00:03:05,500 --> 00:03:07,733
Yeah, I really I really believe that.

00:03:07,733 --> 00:03:11,233
and I've often said that, you know,
I think one of the biggest historic

00:03:11,233 --> 00:03:13,700
moments was a big print
and printing press. Yeah.

00:03:13,700 --> 00:03:16,700
You know, all the knowledge was kind
of stored in the hills with the monks,

00:03:16,700 --> 00:03:19,500
you know, and that liberated everything.

00:03:19,500 --> 00:03:21,366
It was almost like social media
on steroids.

00:03:21,366 --> 00:03:22,166
Right.

00:03:22,166 --> 00:03:25,500
And, you know, this is, I mean,

00:03:25,500 --> 00:03:29,566
this is like intelligence
that's highly accessible, right?

00:03:29,700 --> 00:03:32,700
It is iterating at a scary pace.

00:03:32,700 --> 00:03:35,700
so facet policy can't keep up with it.

00:03:36,033 --> 00:03:38,666
none of us can really kind of
keep up with it.

00:03:38,666 --> 00:03:40,500
And we're not really sure
where it's going to lead.

00:03:40,500 --> 00:03:43,833
And there's always a dark side of
technology that we need to be on top of.

00:03:43,833 --> 00:03:47,733
That's why you and I are taking,
a proactive and important lead

00:03:47,733 --> 00:03:50,133
on responsible AI.

00:03:50,133 --> 00:03:52,733
but yeah, this is absolutely huge.

00:03:52,733 --> 00:03:54,433
I mean, just think about it from a,

00:03:55,433 --> 00:03:57,466
you know,
businesses are going to be transformed.

00:03:57,466 --> 00:04:02,533
there will, in fact be a lot of job
dislocation.

00:04:02,533 --> 00:04:04,700
That doesn't mean net jobs will go down.

00:04:04,700 --> 00:04:07,000
But a nice way of saying job lost.

00:04:07,000 --> 00:04:09,600
Yeah, but but but I think as leaders

00:04:09,600 --> 00:04:13,933
we have to get really in front of what
the workforce could look like.

00:04:13,933 --> 00:04:14,333
Yeah.

00:04:14,333 --> 00:04:18,533
You know, what does it mean
to copilot with this new capability?

00:04:18,533 --> 00:04:21,433
And what does it mean to say
bye bye to a certain industry?

00:04:21,433 --> 00:04:23,433
But hello to a new one.

00:04:23,433 --> 00:04:25,500
This is where universities play
a really big role.

00:04:25,500 --> 00:04:28,566
Universities can also be part of
the problem if they don't kind of get in.

00:04:28,866 --> 00:04:29,633
If they don't. Evolve.

00:04:29,633 --> 00:04:29,833
Yeah.

00:04:29,833 --> 00:04:35,033
That's why I think this kind of this
island of the future 1819 digital futures

00:04:35,033 --> 00:04:38,400
is really, really important
at this critical moment in time.

00:04:38,400 --> 00:04:41,200
But it's big.
It's it's it's going to change everything.

00:04:41,200 --> 00:04:41,366
Yeah.

00:04:41,366 --> 00:04:45,866
I, I have I'm obviously pro innovation
across the board.

00:04:45,866 --> 00:04:49,133
You know that
of course I do have concerns because,

00:04:49,133 --> 00:04:52,766
I think this I think this is different
than past technologies.

00:04:52,966 --> 00:04:55,600
So past
technologies have been sometimes industry

00:04:56,666 --> 00:04:57,500
specific.

00:04:57,500 --> 00:05:02,033
We've had we've had very little data
on, you know, general purpose technology.

00:05:02,066 --> 00:05:04,333
The internet was a general
purpose technology.

00:05:04,333 --> 00:05:06,866
so that's one example. There's not many.

00:05:06,866 --> 00:05:11,933
I think AI is a general purpose technology
where it's not getting rid of

00:05:12,466 --> 00:05:15,000
or modernizing one industry.

00:05:15,000 --> 00:05:17,433
It's not the Luddites
where it's just like across the board.

00:05:17,433 --> 00:05:18,900
It's across the board. Right?

00:05:18,900 --> 00:05:22,300
It's like we've gotten to the point
where we're think Mark Cuban said this.

00:05:22,300 --> 00:05:24,833
We're automating automation,

00:05:24,833 --> 00:05:27,266
and we don't know what that looks like
in terms of the job,

00:05:27,266 --> 00:05:30,266
in terms of the workforce,
in terms of what it looks like.

00:05:30,333 --> 00:05:33,133
It's a to your point, it's ever changing.

00:05:33,133 --> 00:05:37,566
And systems and, businesses
and everyone else have to adapt quickly.

00:05:37,833 --> 00:05:42,066
So I like to hear from you like
what is your what is your biggest concern.

00:05:42,533 --> 00:05:47,333
That business that people aren't
realizing about AI real.

00:05:47,400 --> 00:05:49,400
And then we can talk
about the biggest opportunity. Yeah.

00:05:49,400 --> 00:05:52,466
Well the the biggest concern is if
people are going to kind of miss the boat

00:05:52,466 --> 00:05:56,100
and they're and when you're not kind of in
front of it,

00:05:56,366 --> 00:06:00,066
you know, you get a little bit
brash and reckless and maybe

00:06:00,066 --> 00:06:03,866
you, pushed the wrong policies
out of fear and paranoia.

00:06:03,866 --> 00:06:06,866
And that's understandable
because there's a lot of unknown. Yep.

00:06:08,433 --> 00:06:10,766
I've been since the very day it came out.

00:06:10,766 --> 00:06:15,900
I think this can cut in a couple different
ways on the access and equity side.

00:06:15,900 --> 00:06:19,866
There's a part of me that believes that
knows that this is a revolution in access.

00:06:20,233 --> 00:06:23,233
And so therefore,
if we get ahead of the curve, we could

00:06:23,266 --> 00:06:27,033
redefine the rules of access
for entrepreneurs.

00:06:27,333 --> 00:06:31,066
I've co-led workshops
with minority small businesses where

00:06:31,566 --> 00:06:35,866
these tools have allowed these businesses,
which have not had access

00:06:35,866 --> 00:06:40,033
to a lot of resources, tools and the like
to kind of get a lot of them up front.

00:06:40,366 --> 00:06:43,366
The job issue is almost moot
at the small business level.

00:06:43,366 --> 00:06:46,766
AI gets complicated at the big level.

00:06:46,766 --> 00:06:47,100
It gets

00:06:47,100 --> 00:06:51,466
complicated at the white collar level,
it gets complicated at the service level.

00:06:51,466 --> 00:06:53,666
And that's one area
that I think is disproportionately

00:06:53,666 --> 00:06:57,933
represented by minorities and that's where
and we just got to get in front of it.

00:06:57,966 --> 00:06:59,866
Yeah. You know,
and this is where I think leadership is.

00:06:59,866 --> 00:07:01,866
I agree with that. Important I. Think.

00:07:01,866 --> 00:07:05,600
And I would say
I would give credit to the state of Ohio,

00:07:05,600 --> 00:07:07,233
kind of both sides of the aisle, I agree.

00:07:07,233 --> 00:07:10,566
Or, you know, thinking ahead again,
I think that's why you're creating.

00:07:10,566 --> 00:07:12,700
These absolutely. Innovation hubs
and the like.

00:07:12,700 --> 00:07:17,366
But we have to shape the dialog
and we need to yeah, we need to.

00:07:17,366 --> 00:07:19,533
You want a little bit of paranoia right.

00:07:19,533 --> 00:07:22,366
I think that keeps people on guard.

00:07:22,366 --> 00:07:25,066
But we also have to help people
see possibilities.

00:07:25,066 --> 00:07:26,233
I mean, what makes me

00:07:27,200 --> 00:07:27,700
different?

00:07:27,700 --> 00:07:28,400
Well, not different.

00:07:28,400 --> 00:07:30,766
I think you're I,
I probably put you in the same camp.

00:07:30,766 --> 00:07:32,633
Yeah.
You know, I'm an urgent optimist. Yeah.

00:07:32,633 --> 00:07:33,433
So I too.

00:07:33,433 --> 00:07:35,966
I see possibilities.
I'm not a total cynic.

00:07:35,966 --> 00:07:39,100
I'm not on the dark side of Armageddon,
you know? But.

00:07:40,400 --> 00:07:43,766
But I recognize that if you want
the optimism, you have to get it.

00:07:44,033 --> 00:07:45,666
But the urgency of cannot you.

00:07:45,666 --> 00:07:48,000
And then this is what we're kind of doing
right now. Yeah.

00:07:48,000 --> 00:07:50,966
What happens if we get it wrong?

00:07:50,966 --> 00:07:52,900
Listen, I think it's going to be.

00:07:52,900 --> 00:07:55,700
It'll probably change
the political environment.

00:07:55,700 --> 00:07:59,333
You know,
you've just got a lot of polar extremes

00:07:59,433 --> 00:08:02,566
in the marketplace and the political
environment on both sides.

00:08:02,566 --> 00:08:07,600
And I think where there's fear, there's
division and, where there's the unknown.

00:08:07,633 --> 00:08:10,433
and so, yeah, this could get really ugly.

00:08:10,433 --> 00:08:15,466
And, I think, I don't think we've,
you know, we've dodged

00:08:15,466 --> 00:08:19,366
a few bullets with a couple of the strikes
that have taken place.

00:08:19,400 --> 00:08:23,466
You know, I think the Hollywood thing
landed, you know, it landed.

00:08:23,466 --> 00:08:24,066
You know? Yeah.

00:08:24,066 --> 00:08:28,800
You know, and I think they kind of found
that the hybrid model, but,

00:08:29,133 --> 00:08:32,800
yeah, it could get there could be
some industries that really, create

00:08:33,233 --> 00:08:38,266
a lot of, political instability and
absolutely just got to get in front of it.

00:08:38,433 --> 00:08:42,233
And I think there's going to potentially
there's some, you know,

00:08:42,233 --> 00:08:44,100
some racial issues. We're going

00:08:44,100 --> 00:08:47,866
to have to be really sensitive,
like who is helped and who is not.

00:08:47,866 --> 00:08:48,600
And this is where we need

00:08:48,600 --> 00:08:52,333
to be really attentive to,
you know, the different sectors.

00:08:52,366 --> 00:08:57,200
I thought McKinsey did a really good job
about four months ago, looking at

00:08:57,233 --> 00:09:00,666
which groups will be impacted or not,
but they also had a

00:09:01,333 --> 00:09:02,900
they kind of same thing we said earlier.

00:09:02,900 --> 00:09:05,833
Like if we're proactive when we lead
that we can get in front of it.

00:09:05,833 --> 00:09:08,233
Yeah. so I, I agree with that completely.

00:09:08,233 --> 00:09:12,166
And I, we already have data on the issue,
like, we, we know

00:09:13,066 --> 00:09:15,400
what, what algorithms do without

00:09:15,400 --> 00:09:18,566
any type of thought process guardrails.

00:09:18,866 --> 00:09:20,666
That's what social media became.

00:09:20,666 --> 00:09:22,833
And that's the that's how we got

00:09:23,800 --> 00:09:24,533
we these are

00:09:24,533 --> 00:09:27,600
part of our human nature for on is
we have these part of our human nature.

00:09:27,600 --> 00:09:29,866
To be prejudiced against others
is part of our human nature.

00:09:29,866 --> 00:09:33,666
To, de-emphasizing
is sometimes to do things.

00:09:33,666 --> 00:09:36,666
These things it depends upon,

00:09:37,600 --> 00:09:39,466
what we want to emphasize
and unfortunately.

00:09:39,466 --> 00:09:40,166
Right.

00:09:40,166 --> 00:09:44,200
You know, a lot of the algorithms
focus on outrage and conspiracy.

00:09:44,500 --> 00:09:45,866
So those things tend to divide.

00:09:45,866 --> 00:09:47,366
And they do that
not because they're trying

00:09:47,366 --> 00:09:50,800
to focus on those things,
because their goal is singular.

00:09:51,366 --> 00:09:52,833
Is algorithms are rules.

00:09:52,833 --> 00:09:56,266
And all rules,
are written in different ways.

00:09:56,266 --> 00:09:58,966
Correct.
All rules manifest themselves in dial.

00:09:58,966 --> 00:10:02,066
That may be correct a little bit
to the right or a little bit to the left.

00:10:02,066 --> 00:10:04,500
In fact, this is kind of the heart of what
we're trying to measure.

00:10:04,500 --> 00:10:06,433
As part of my new startup.

00:10:06,433 --> 00:10:06,900
And we are

00:10:06,900 --> 00:10:11,666
when we just need to be hypersensitive
to where we think the dial needs to.

00:10:11,733 --> 00:10:15,800
Yeah, to sit in a way
that it, positively impacts,

00:10:15,833 --> 00:10:18,433
the economy, consumer.
Just have to be aware,

00:10:18,433 --> 00:10:20,866
you have to be aware
that there could be issues.

00:10:20,866 --> 00:10:21,700
Oh no question.

00:10:21,700 --> 00:10:22,033
Right.

00:10:22,033 --> 00:10:25,333
Like and that's what it gets to because
they, it's it's algorithms are rules.

00:10:25,333 --> 00:10:29,366
But you know AI is also
the maybe you're training on the data

00:10:29,800 --> 00:10:33,066
and it's it it's going to reflect
what you input to it.

00:10:33,066 --> 00:10:34,800
This is which is why it's so important,

00:10:34,800 --> 00:10:36,566
which is why it's so important
to be inclusive.

00:10:36,566 --> 00:10:41,433
So but tell us okay, but you entered into
AI wanting to solve a specific problem.

00:10:41,433 --> 00:10:43,566
What? What got you up at night?

00:10:43,566 --> 00:10:47,366
What problem are you trying to solve
within with using AI as a tool?

00:10:47,366 --> 00:10:50,366
Because I tell people it's not the
technology is the problem you're solving.

00:10:50,666 --> 00:10:52,533
So what problem are you solving?

00:10:52,533 --> 00:10:54,266
Well, let me take a step back. So I

00:10:55,666 --> 00:10:56,133
came to

00:10:56,133 --> 00:10:59,966
Cincinnati after eight years
in Switzerland, leading

00:11:00,500 --> 00:11:05,666
digital work for NASA, and had a chance
to, go all over the world and really kind

00:11:05,666 --> 00:11:09,066
of see the digital revolution
sure place in different ways.

00:11:09,066 --> 00:11:12,066
And so I came back to Cincinnati to lead

00:11:12,300 --> 00:11:15,366
this startup accelerator

00:11:16,033 --> 00:11:20,066
under funds to really kind of grow
the local and economy.

00:11:21,200 --> 00:11:25,566
And in the process
of trying to figure out, like where

00:11:26,433 --> 00:11:32,133
we could lead a big believer that internet
economies where there's a concentration

00:11:32,133 --> 00:11:36,166
of talent, technology and capital,
yeah, have kind of an outsize advantage.

00:11:36,166 --> 00:11:40,000
And so the areas that have been
not just keeping me up at night,

00:11:40,000 --> 00:11:41,766
but actually making me want to go to work.

00:11:41,766 --> 00:11:42,066
Yeah.

00:11:42,066 --> 00:11:44,700
Or areas like sustainability. Sure.

00:11:44,700 --> 00:11:47,166
Or resilience supply chains.

00:11:47,166 --> 00:11:49,833
That was a big theme
that came out of Covid.

00:11:49,833 --> 00:11:54,433
a lot of things on the health front,
but and then and then

00:11:54,766 --> 00:11:58,866
as the AI stuff started to develop,
especially in the last 18 months,

00:11:59,400 --> 00:12:02,766
thinking a lot
about the power of responsible AI,

00:12:02,766 --> 00:12:07,566
could a region that has really good
consumer credentials.

00:12:07,600 --> 00:12:10,766
Again, you got the world's largest
advertiser here, the world's third largest

00:12:10,766 --> 00:12:13,800
retailer here, and the whole ecosystem
that supports that.

00:12:14,366 --> 00:12:17,100
So could we leverage

00:12:17,100 --> 00:12:20,266
those consumer chops credentials to

00:12:21,433 --> 00:12:23,700
get in
front of these very complicated issues?

00:12:23,700 --> 00:12:27,633
And moreover,
could we cultivate a lot of entrepreneurs

00:12:27,633 --> 00:12:30,633
that would want to be the problem solvers
or the heroes in that area?

00:12:31,433 --> 00:12:35,366
And I found myself as a pretty loud
cheerleader.

00:12:35,366 --> 00:12:36,333
Let's do it.

00:12:36,333 --> 00:12:39,533
And then and then I kind of got to
that moment of truth.

00:12:39,533 --> 00:12:42,533
And I just said, I'm just going to do it
myself,

00:12:42,566 --> 00:12:45,566
you know, give up this really big
salary and,

00:12:45,666 --> 00:12:49,633
be part of the solution and is a whole
nother element of wealth creation.

00:12:49,633 --> 00:12:51,100
If you're successful.

00:12:51,100 --> 00:12:54,866
And, but and what I've been thinking
about, you know,

00:12:54,866 --> 00:12:58,000
the areas that I get excited about
is really the collision of

00:12:59,133 --> 00:13:01,200
AI and sustainability.

00:13:01,200 --> 00:13:06,133
So I actually think I'm a, I,
I do lose sleep about the world,

00:13:06,133 --> 00:13:09,133
not kind of keeping up with its,
its carbon commitments.

00:13:09,333 --> 00:13:13,866
And I think AI is a huge, opportunity
to kind of move that ahead.

00:13:13,866 --> 00:13:17,066
And some of my inspiration has come
from local companies like 80 acres,

00:13:17,066 --> 00:13:20,700
where they use 99% less water
because of the way they use

00:13:20,700 --> 00:13:24,833
artificial intelligence, you know,
and so there's some inspiration.

00:13:24,833 --> 00:13:29,466
So I think as we get smarter,
the use of AI, AI plus sensors,

00:13:29,466 --> 00:13:32,633
I mean, we could dramatically change
the green landscape.

00:13:32,633 --> 00:13:35,900
And I would like to see it cultivated,
you know, here in our backyard.

00:13:36,700 --> 00:13:36,933
Yeah.

00:13:36,933 --> 00:13:39,933
So it sounds like you were working
to be an entrepreneur,

00:13:40,166 --> 00:13:42,933
but, like,
you decided the best way to do that.

00:13:42,933 --> 00:13:45,733
You had to go. Actually,
I've always I've always kind of worked.

00:13:45,733 --> 00:13:46,666
And I was thinking about this
the other day.

00:13:46,666 --> 00:13:49,666
I've always worked in like,
five year cycles, so I was always get

00:13:49,700 --> 00:13:53,133
everybody thinks as a P&G veteran,
I was only there for like 5 or 6 years.

00:13:53,133 --> 00:13:56,766
And then I, you know,
after we got interactive Marketer the year

00:13:56,766 --> 00:14:00,066
I left to do a startup and then I did
the startup was a bit of a slog.

00:14:00,066 --> 00:14:00,666
It took us a while

00:14:00,666 --> 00:14:03,866
before we got sold to Nielsen,
but then I went to Nestlé and then I went.

00:14:03,866 --> 00:14:05,333
Startups are usually a bit of a thing.

00:14:05,333 --> 00:14:08,666
Is just kind of a hybrid
between big company and startup.

00:14:08,666 --> 00:14:11,166
There are elements of that.
We're kind of pretty big and bureaucratic.

00:14:11,166 --> 00:14:13,366
The other parts that we're kind of startup
nimble. Yeah.

00:14:13,366 --> 00:14:14,133
And now I feel.

00:14:14,133 --> 00:14:16,766
Very like I those probably sounds,
but it feels very corporate some time.

00:14:16,766 --> 00:14:17,933
But go ahead. That's my opinion.

00:14:17,933 --> 00:14:19,966
Yeah. I mean it sparks a big debate.

00:14:19,966 --> 00:14:22,000
I mean, listen,
we have a store front for services,

00:14:22,000 --> 00:14:25,300
but we also work with corporates
and we're trying to figure out how to,

00:14:26,500 --> 00:14:26,733
you know,

00:14:26,733 --> 00:14:30,600
how to unlock that startup
corporate connection.

00:14:30,600 --> 00:14:33,266
And it's a lot. Of hard challenges.

00:14:33,266 --> 00:14:34,366
Yeah I know and it's hard.

00:14:34,366 --> 00:14:35,966
Because there's different cultures.

00:14:35,966 --> 00:14:36,866
Yeah. Exactly.

00:14:36,866 --> 00:14:38,933
Yeah. Now what's different today.

00:14:38,933 --> 00:14:40,900
It's like again
you have to exploit the moment.

00:14:40,900 --> 00:14:44,133
There's always moments
where the organization need to be.

00:14:44,133 --> 00:14:45,266
It needs to be stimulated.

00:14:45,266 --> 00:14:48,433
And I think AI is a moment
where startups might have a chance

00:14:48,433 --> 00:14:49,633
to kind of get in the door,

00:14:49,633 --> 00:14:53,766
because there's a lot the big cows
don't really fully understand.

00:14:54,500 --> 00:14:56,900
And I'm hoping
that I'll be one of those folks that does

00:14:56,900 --> 00:14:58,900
get in the door,
because I'm bringing an outside

00:14:58,900 --> 00:15:02,866
in perspective on a very,
very fast evolving, world.

00:15:02,866 --> 00:15:05,700
It's just very, very hard to move fast
in an organization.

00:15:05,700 --> 00:15:08,900
And organizations are really good
at like bottling up their learning

00:15:08,900 --> 00:15:11,900
and everything about disseminating
and executing against the learning.

00:15:11,933 --> 00:15:13,733
But now in the world, generative AI,

00:15:13,733 --> 00:15:16,733
you got a whole different playbook
that's being developed. Yep.

00:15:16,800 --> 00:15:19,400
So real brand rank. yeah.

00:15:19,400 --> 00:15:20,633
Trade this. Like what?

00:15:20,633 --> 00:15:24,500
Again, I can go back to that question
like what is brand rank seeking to do?

00:15:25,333 --> 00:15:27,700
So I'll tell you all great ideas.

00:15:27,700 --> 00:15:29,733
Start with a big insight. Yes.

00:15:29,733 --> 00:15:31,466
And it's like that.

00:15:31,466 --> 00:15:34,066
Oh. Like moment.

00:15:34,066 --> 00:15:37,066
And for me, I was like

00:15:37,066 --> 00:15:40,266
you and everybody
just playing around with generative AI.

00:15:41,366 --> 00:15:42,266
And I started to do

00:15:42,266 --> 00:15:45,266
searches on sustainability.

00:15:45,366 --> 00:15:49,466
And I would ask questions
like, are campus diapers sustainable

00:15:49,933 --> 00:15:53,766
or is Unilever's Dove brand sustainable?

00:15:54,500 --> 00:15:57,500
And I was shocked how

00:15:58,166 --> 00:16:01,466
granular and size the responses were.

00:16:02,533 --> 00:16:06,433
And in some cases, some of that data
would come from the brand websites.

00:16:06,433 --> 00:16:08,600
But, you know, generative
AI is a weird way.

00:16:08,600 --> 00:16:12,233
Imagine like a blender that just kind of
takes best available knowledge,

00:16:12,233 --> 00:16:15,400
puts it in a blender, and then pours it
in a mold that you then access.

00:16:15,400 --> 00:16:16,566
Absolutely. Yeah. Okay. Yeah.

00:16:16,566 --> 00:16:19,100
And that that's a really good metaphor
doesn't really change.

00:16:19,100 --> 00:16:19,933
Now you can

00:16:19,933 --> 00:16:22,900
you can change the how you can say,
give it to me as a Jay-Z rap

00:16:22,900 --> 00:16:25,400
or give it to me as haiku
or to me as a sonnet.

00:16:25,400 --> 00:16:26,966
But the what?

00:16:26,966 --> 00:16:29,100
It's like one shot. What? Yeah.

00:16:29,100 --> 00:16:32,666
And and I thought to myself,
and it was funny because I typed in

00:16:33,300 --> 00:16:34,266
our Pampers sustainable.

00:16:34,266 --> 00:16:38,766
And it was interesting because they said
they're trying but they're not.

00:16:38,766 --> 00:16:40,966
And here's the reasons
and here are some alternatives.

00:16:40,966 --> 00:16:44,500
And I thought, wow, that's like that's
like that's like implicating for brands.

00:16:44,600 --> 00:16:46,266
Yeah. That's like a whole different level.

00:16:46,266 --> 00:16:48,766
So I just thought
so that the light bulb went off.

00:16:48,766 --> 00:16:51,833
I thought, you know, maybe
there's a business model in creating kind

00:16:51,833 --> 00:16:55,866
of a Nielsen ratings of AI search
results, helping

00:16:55,866 --> 00:16:59,866
brand builders understand
where they stand in this world.

00:17:00,233 --> 00:17:05,133
And then could you use the, I'm a really
passion about digital marketing, right?

00:17:05,133 --> 00:17:08,133
I kind of missed the role of being chief
digital officer.

00:17:08,166 --> 00:17:09,300
You know. You do that well.

00:17:09,300 --> 00:17:09,866
Yeah. Yeah.

00:17:09,866 --> 00:17:12,266
But but I think that's
at the forefront of change.

00:17:12,266 --> 00:17:13,633
And what I've noticed

00:17:13,633 --> 00:17:17,566
is that a lot of brands
have gotten really lazy about not lazy.

00:17:17,566 --> 00:17:20,966
It's just I don't think
they are even aware that they're not

00:17:21,466 --> 00:17:25,233
serving consumers at the level
that they should in this environment.

00:17:25,233 --> 00:17:27,966
So for example, like what?
What is. Stagnant?

00:17:27,966 --> 00:17:29,066
What is what is generative?

00:17:29,066 --> 00:17:32,933
I really done
it's raised the bar for giving you answers

00:17:33,600 --> 00:17:35,600
with minimal friction, correct.

00:17:35,600 --> 00:17:40,066
And with shocking levels of detail
and a willingness to continue the dialog.

00:17:40,066 --> 00:17:41,900
Yeah, right.
I mean, you can just keep going.

00:17:41,900 --> 00:17:42,466
You can.

00:17:42,466 --> 00:17:45,633
And it feels like you're talking
to a really smart best friend.

00:17:46,300 --> 00:17:48,633
And brands are really bad at that. Yeah.

00:17:48,633 --> 00:17:53,500
I mean, you can go to most brands from
you know, whether it's Unilever.

00:17:53,900 --> 00:17:56,266
I mean, there's a bunch of them.
I get names.

00:17:56,266 --> 00:17:59,633
I don't piss anybody off locally,
but but yeah, if you go into their,

00:18:00,133 --> 00:18:02,666
their FAQs or their brand sites, you know,
you're

00:18:02,666 --> 00:18:04,766
not going to get a lot of information
very, very little.

00:18:04,766 --> 00:18:08,433
So you're more likely say I'm going
to learn about Pampers on generative AI.

00:18:08,433 --> 00:18:09,266
The problem with that

00:18:09,266 --> 00:18:13,666
is that that narrative often is counter
to the brand's desired narrative.

00:18:13,700 --> 00:18:18,233
So what we're doing is we're kind of
metering it and we've run thousands

00:18:18,233 --> 00:18:24,233
of searches, to really help brands
understand, you know, how they stack up.

00:18:24,233 --> 00:18:26,833
And then we're benchmarking
it versus competition,

00:18:26,833 --> 00:18:29,300
and then we're putting a lot of consulting
on top of it.

00:18:29,300 --> 00:18:32,433
Like it's not enough to just say,
we metered your brand.

00:18:32,433 --> 00:18:34,166
It's like, here's what you need to do.

00:18:34,166 --> 00:18:36,500
And this gets tricky. Like to really win.

00:18:36,500 --> 00:18:39,500
In a world of generative AI,
you have to market to algorithms.

00:18:39,733 --> 00:18:41,733
You have to understand how.

00:18:41,733 --> 00:18:43,166
How do you market the algorithm.

00:18:43,166 --> 00:18:44,400
Well this is important.

00:18:44,400 --> 00:18:45,300
So we just put out

00:18:45,300 --> 00:18:49,200
a white paper called Brand Vulnerability
in the age of AI search.

00:18:49,200 --> 00:18:52,066
And there's something that's called
algorithmic anchors okay.

00:18:52,066 --> 00:18:55,033
And so has some similar areas
of search 1.0.

00:18:55,033 --> 00:18:58,566
But in generative AI
there are clear anchors

00:18:58,566 --> 00:19:01,966
that disproportionately influence
what is being said about you.

00:19:01,966 --> 00:19:04,100
Okay.
So I'm. Looking at Rob Richardson. Okay.

00:19:04,100 --> 00:19:04,933
It's probably going

00:19:04,933 --> 00:19:08,200
to be one of your algorithmic anchors
is probably going to be you see content.

00:19:08,766 --> 00:19:09,400
Okay. Yeah.

00:19:09,400 --> 00:19:11,500
The legacy of leadership here.

00:19:11,500 --> 00:19:13,200
It's shaped your brand.

00:19:13,200 --> 00:19:16,466
And I would say what you see
is on a scale of

00:19:16,466 --> 00:19:19,633
1 to 10, I probably give them a 7 or 8
in terms of marketing algorithms.

00:19:19,633 --> 00:19:23,633
So your content has a good chance
of getting to those algorithms.

00:19:23,633 --> 00:19:24,366
Okay.

00:19:24,366 --> 00:19:27,166
Brand websites are very,
very if they're done

00:19:27,166 --> 00:19:30,833
right, right are very important
algorithmic anchors.

00:19:30,833 --> 00:19:33,500
But it's not just the websites, it's
how you build it.

00:19:33,500 --> 00:19:35,833
So for example,
I was meeting with someone from,

00:19:36,866 --> 00:19:38,000
Kroger the other day.

00:19:38,000 --> 00:19:38,466
Okay.

00:19:38,466 --> 00:19:41,366
And Kroger's
actually doing some pretty good work on

00:19:41,366 --> 00:19:44,400
zero waste, zero hunger.

00:19:44,400 --> 00:19:47,633
I really I, I've read all their reports,
but I noticed

00:19:47,633 --> 00:19:50,733
they weren't getting enough credit
in like the AI algorithms.

00:19:50,733 --> 00:19:54,500
And I suddenly dawned on me that they put
most of their best data in PDFs.

00:19:54,933 --> 00:19:55,766
Oh, well, that'll do it.

00:19:55,766 --> 00:19:56,400
Okay.

00:19:56,400 --> 00:19:56,966
And so, you know,

00:19:56,966 --> 00:20:00,500
it's not a, you know, like a fake engine
to get it's like in a PDF.

00:20:00,500 --> 00:20:01,466
And I'm thinking, oh my gosh.

00:20:01,466 --> 00:20:04,766
They're like they're only getting
a fraction of the ROI on all that work.

00:20:04,766 --> 00:20:05,966
Think of all that work.

00:20:05,966 --> 00:20:07,500
Think of all the Rodney's speeches.

00:20:07,500 --> 00:20:09,700
Think about all those hundreds
of employees

00:20:09,700 --> 00:20:13,166
that have kind of toiled to kind of
get those statistics on the table.

00:20:13,166 --> 00:20:16,966
And then when you got to the last mile
of helping the consumer to understand it,

00:20:17,066 --> 00:20:20,333
you put it in a file
that the algorithms don't understand.

00:20:20,333 --> 00:20:21,433
And so I think,

00:20:21,433 --> 00:20:23,066
you know,
we're almost trying to figure out

00:20:23,066 --> 00:20:25,533
scorecards
that make it easy for brands to see,

00:20:25,533 --> 00:20:28,033
this is what I need to do
to get full credit. Okay.

00:20:28,033 --> 00:20:31,866
Now, in some cases, you won't get full
credit because you're not sustainable.

00:20:32,200 --> 00:20:34,100
You might be dishonest, you know?

00:20:34,100 --> 00:20:37,566
And again, the algorithms
will go to town on you and that respect.

00:20:37,566 --> 00:20:38,700
But I think generally.

00:20:38,700 --> 00:20:40,400
Speaking, how are they going to know you
if you're dishonest?

00:20:40,400 --> 00:20:41,266
Like, how would they.

00:20:41,266 --> 00:20:43,500
Oh, well, because they're looking
at a lot of sources.

00:20:43,500 --> 00:20:44,066
I mean, I'd say

00:20:44,066 --> 00:20:47,900
the brand websites are clearly the most
the biggest cheerleaders for their.

00:20:47,966 --> 00:20:49,133
Well, they're. Biased. They say, okay.

00:20:49,133 --> 00:20:50,433
Right. Yeah. I mean.

00:20:50,433 --> 00:20:52,166
It's an advertising. Vehicle. Exactly.

00:20:52,166 --> 00:20:57,266
But I mean, why is the New York Times
suing OpenAI for 20, $40 billion?

00:20:57,600 --> 00:20:57,866
Yeah.

00:20:57,866 --> 00:21:01,333
It's taken
they know that they're source of truth.

00:21:01,500 --> 00:21:03,966
This is being thrown into the blender.

00:21:03,966 --> 00:21:07,200
It's very hard for people
to see the attribution, but they know

00:21:07,800 --> 00:21:12,033
it is weaving in old New York Times
articles are pretty harsh about brand.

00:21:12,033 --> 00:21:13,900
I mean, they just kind of speak
the truth, right? Right.

00:21:13,900 --> 00:21:16,500
And there's other sites.
It could be Wikipedia.

00:21:16,500 --> 00:21:20,066
Google just spent,
what, $40 million to license read it.

00:21:20,366 --> 00:21:22,366
Yeah. So that stuff's getting weaved in.

00:21:22,366 --> 00:21:25,366
Ratings and reviews are creeping into it
as well. So.

00:21:25,466 --> 00:21:29,800
So one thing that we're, working
on, and I can't get too specific,

00:21:29,800 --> 00:21:32,766
but I will say that this institution
is helping our thinking.

00:21:32,766 --> 00:21:33,666
Right. A lot

00:21:34,633 --> 00:21:36,066
is really

00:21:36,066 --> 00:21:39,866
trying to develop some science
around those sources of attribution,

00:21:40,100 --> 00:21:43,300
like what are those anchors
and how do you get really precise?

00:21:43,300 --> 00:21:47,033
And I would love for, Greater

00:21:47,033 --> 00:21:51,266
Cincinnati to be a true hub for search
analytics, AI, search analytics.

00:21:51,266 --> 00:21:54,633
And it's going to take some work,
but I think we can figure it out.

00:21:54,633 --> 00:21:57,133
And I think the universities are
a big are a big part of that.

00:21:57,133 --> 00:21:59,666
But I think every brand needs
to understand

00:21:59,666 --> 00:22:01,566
what is disproportionately impacting.

00:22:01,566 --> 00:22:03,100
Their their search results.

00:22:03,100 --> 00:22:07,033
What would you say is your top three
advice just 1 to 1 of the top three things

00:22:07,033 --> 00:22:12,566
brands need to do in the age of algorithms
to protect their brand identity.

00:22:12,566 --> 00:22:14,233
Some of it is old fashioned guidance.

00:22:14,233 --> 00:22:17,666
I mean this you got to listen
and these are different signals.

00:22:18,266 --> 00:22:21,333
So in the same way that you would listen
to a consumer in a focus group

00:22:21,333 --> 00:22:24,666
or listen to website data
or listen to social media conversation,

00:22:24,666 --> 00:22:28,233
you got to listen
to these new search dynamics.

00:22:28,266 --> 00:22:28,566
Yeah.

00:22:28,566 --> 00:22:31,633
And that's we're kind of providing a tool
that helps them listen.

00:22:32,200 --> 00:22:34,500
And then they need to
they need to act on it.

00:22:34,500 --> 00:22:36,033
I mean they need to disrupt.

00:22:36,033 --> 00:22:36,266
Yeah.

00:22:36,266 --> 00:22:39,566
And and I mean we're trying to provide a
data set that makes it easier to disrupt.

00:22:39,900 --> 00:22:42,866
In most cases,
data leads you to incrementalism.

00:22:42,866 --> 00:22:43,266
Exactly.

00:22:43,266 --> 00:22:45,566
We're kind of saying
you've got a long way to go.

00:22:45,566 --> 00:22:47,033
So brands need to move faster.

00:22:47,033 --> 00:22:48,033
Your website sucks.

00:22:48,033 --> 00:22:50,433
It doesn't. You know, the websites
don't pay attention.

00:22:50,433 --> 00:22:54,900
The algorithms don't pay attention to it
or you know, and yeah, so we're trying to

00:22:55,266 --> 00:22:58,566
and then and then you got to follow up
and you got to be really iterative.

00:22:58,566 --> 00:23:01,966
Like like I would say, you know,
if you look at our web, our,

00:23:02,066 --> 00:23:07,066
our online site, we say today brands
need to think and act like algorithms.

00:23:07,533 --> 00:23:11,233
It doesn't mean we're all going
to become bots, but we have to understand

00:23:11,666 --> 00:23:14,933
how these our whole future
is going to get dictated by this stuff.

00:23:15,166 --> 00:23:17,333
But we can get in front of it,
we can partner with it.

00:23:17,333 --> 00:23:18,900
But we got to understand what it is.

00:23:18,900 --> 00:23:20,866
And I think that's critical for everyone.

00:23:22,066 --> 00:23:22,666
Tough question here.

00:23:22,666 --> 00:23:25,333
What happens is the algorithms know us
better than we know ourselves.

00:23:25,333 --> 00:23:28,666
This is we're going to
well they will they will I mean, you know

00:23:28,800 --> 00:23:32,666
next year you're probably going to be
interviewing yourself as a joke, right?

00:23:32,900 --> 00:23:34,766
You know two faces of Rob.

00:23:34,766 --> 00:23:36,500
That'll be interesting. Now the interest.

00:23:36,500 --> 00:23:36,700
Yeah.

00:23:36,700 --> 00:23:39,700
That might be, so

00:23:39,866 --> 00:23:40,466
I don't know.

00:23:40,466 --> 00:23:41,966
It's going to get really tricky.

00:23:41,966 --> 00:23:46,433
And we're going to have to really reboot

00:23:48,266 --> 00:23:49,033
not only brands.

00:23:49,033 --> 00:23:51,266
We got to reboot training. Yep.

00:23:51,266 --> 00:23:52,266
We're going to have to.

00:23:52,266 --> 00:23:55,366
I personally think gearing up to shake up
the universities to think differently.

00:23:55,366 --> 00:23:55,600
Okay.

00:23:55,600 --> 00:23:58,633
Through all of it, I mean, think about it
like with generative,

00:23:58,666 --> 00:24:00,566
I got Socrates in my pocket. Yeah.

00:24:00,566 --> 00:24:02,900
I mean, it's like an incredible learning
experience.

00:24:02,900 --> 00:24:04,033
It teaches math.

00:24:04,033 --> 00:24:05,800
It never forgets anything. Yeah.

00:24:05,800 --> 00:24:09,800
It's like and so the potential
for personalized education is huge,

00:24:09,800 --> 00:24:12,233
but there's gonna have to be give
and take at the university level, right?

00:24:12,233 --> 00:24:13,766
Yeah, absolutely.
I mean. It's going to be. Tricky.

00:24:13,766 --> 00:24:15,566
It isn't like how. Will Neville
Pinto lead?

00:24:15,566 --> 00:24:17,700
I mean, I you know,
I've even wrote a letter to him once.

00:24:17,700 --> 00:24:22,000
I said, dude, like this is your this will
shape your legacy more than anything.

00:24:22,000 --> 00:24:22,466
Full stop.

00:24:22,466 --> 00:24:26,400
I'd say the same thing that John Mueller
at PNG or Rodney at Kroger.

00:24:26,400 --> 00:24:29,533
This is like how you manage
this is critical I agree.

00:24:29,566 --> 00:24:30,066
You know.

00:24:30,066 --> 00:24:32,066
And if you know that there's going to be,

00:24:32,066 --> 00:24:34,600
you know, job shakeups,
how do you get in front of it?

00:24:34,600 --> 00:24:36,166
How do you start
thinking about the new jobs?

00:24:36,166 --> 00:24:38,033
How do you manage expectations?

00:24:38,033 --> 00:24:39,533
How do you turn it into a positive?

00:24:39,533 --> 00:24:42,533
And it sounds romantic,
but it's going to be tricky.

00:24:42,633 --> 00:24:43,733
It's going to be really, really tricky.

00:24:43,733 --> 00:24:46,800
It's, you know, before we go on to,
to our talk to our other points, there's

00:24:47,200 --> 00:24:51,300
one line I read from you about customer
service, about customer satisfaction.

00:24:51,300 --> 00:24:54,100
It's very important,
I think, even more with algorithms,

00:24:54,100 --> 00:24:57,566
because I think you said this is that
if you have one successful customer,

00:24:57,566 --> 00:25:01,200
they tell three people, if you have one
bad customer, they sell 3000.

00:25:01,666 --> 00:25:04,233
And now algorithm.
That was. My book, right?

00:25:04,233 --> 00:25:05,133
Right. Yes. That's your book.

00:25:05,133 --> 00:25:08,066
Yeah I see and algorithms.
Now you can add to that.

00:25:08,066 --> 00:25:10,233
You give one good customer.
Maybe you'll get six.

00:25:10,233 --> 00:25:14,500
You give one bad customer
and you get 3 million because information

00:25:14,500 --> 00:25:16,966
can really just iterate on itself
much faster.

00:25:16,966 --> 00:25:20,166
Well, I think the key Rob, is it

00:25:21,166 --> 00:25:21,633
you need to

00:25:21,633 --> 00:25:24,633
understand how to balance automation
and intimacy.

00:25:25,033 --> 00:25:25,700
oh. That's good.

00:25:25,700 --> 00:25:27,600
Okay.
What the important. So I'll say more like.

00:25:27,600 --> 00:25:31,566
So I lead,
when I was running digital at Nestlé,

00:25:31,633 --> 00:25:33,333
you know, one of my number one

00:25:33,333 --> 00:25:37,433
initiatives was implementing Service
Cloud across all the markets.

00:25:37,666 --> 00:25:42,600
You know, and the market
I most admired was in China,

00:25:43,200 --> 00:25:48,566
and it was the,
I think it was the Wyeth nutrition brand.

00:25:48,566 --> 00:25:51,333
And I see when I go to China, I would just
spend time in the call center.

00:25:51,333 --> 00:25:51,566
Yeah.

00:25:51,566 --> 00:25:55,300
And it was interesting
because they were really good at

00:25:57,000 --> 00:25:59,366
balancing Artemis,
automation and intimacy.

00:25:59,366 --> 00:25:59,666
In fact,

00:25:59,666 --> 00:26:03,766
they were very good at converting,
like, moms to e-commerce opportunities.

00:26:03,766 --> 00:26:06,833
And I use WeChat,
which is like their version of.

00:26:07,166 --> 00:26:07,966
Facebook.

00:26:07,966 --> 00:26:11,233
Instagram, and, and they would kind of

00:26:11,233 --> 00:26:14,900
do it at scale and they money
and they wanted to automate a lot of it.

00:26:14,900 --> 00:26:17,666
But other times, if they knew
there was an upsell opportunity,

00:26:17,666 --> 00:26:20,666
they would, you know,
they would have folks, you know,

00:26:20,666 --> 00:26:24,433
doing the chat and kind of work it
and then or if a mom had a complaint

00:26:24,466 --> 00:26:28,666
about their child, then
you automatically defaulted to a human.

00:26:29,100 --> 00:26:29,866
And so I think there's

00:26:29,866 --> 00:26:33,366
just a balancing act on
how do you get the benefits of automation.

00:26:33,666 --> 00:26:35,400
And I think some of
that could get really good.

00:26:35,400 --> 00:26:38,766
I mean, if you can automate
a friendly bot or a boy

00:26:38,766 --> 00:26:41,766
and they're just answering questions,
but with the

00:26:41,833 --> 00:26:44,333
with the happy face,
I think that can be a big benefit.

00:26:44,333 --> 00:26:46,133
But there's anything that's on,

00:26:46,133 --> 00:26:51,400
the negative side or the sensitive side
or the product has poison or whatever,

00:26:51,400 --> 00:26:53,033
then you want to immediately
go to a human.

00:26:53,033 --> 00:26:55,066
And I think it's going to be
really important or.

00:26:55,066 --> 00:26:57,000
Helpful for lots of other stuff. Yep.
Yeah.

00:26:57,000 --> 00:27:01,166
Like so and I so I, I don't think people
should over romanticize 100% automation.

00:27:01,166 --> 00:27:05,066
I think you just need
to find the right balance to do it right.

00:27:05,066 --> 00:27:06,533
And it's the same thing with the

00:27:06,533 --> 00:27:10,266
certain things we like on the airline,
where we can kind of get automatic alerts

00:27:10,266 --> 00:27:13,500
and other cases just get on
the damn phone, for crying out loud.

00:27:13,633 --> 00:27:14,300
And they know.

00:27:14,300 --> 00:27:15,533
And there's a calculus

00:27:15,533 --> 00:27:19,166
that says if they completely ignore Pete
when he really needs us,

00:27:19,366 --> 00:27:21,600
he's going to go to he's
going to go from Delta to American.

00:27:21,600 --> 00:27:23,366
Exactly. You know, so.

00:27:23,366 --> 00:27:27,433
Now, what would you tell young
people nowadays in this in this state,

00:27:27,433 --> 00:27:30,466
there's been more tech layoffs,
I think, than we've ever seen since the

00:27:31,133 --> 00:27:33,266
you and I went through the,
the ourselves.

00:27:33,266 --> 00:27:38,433
But the.com, burst and bubble,
there's, there's a lot of tech layoffs.

00:27:38,433 --> 00:27:40,233
It's a, it's a it's
a very shaky time for people

00:27:40,233 --> 00:27:42,766
that are in tech,
that have been in careers

00:27:42,766 --> 00:27:45,133
that have always been
thought to be untouchable.

00:27:45,133 --> 00:27:47,200
Right. Yeah. Oh big time.

00:27:47,200 --> 00:27:51,000
What do you say to, to, to those
emerging in their career at this time?

00:27:51,000 --> 00:27:53,333
Like, what advice
would you give them at this moment?

00:27:53,333 --> 00:27:55,733
I mean, number one,
just eyes wide open. Yeah.

00:27:55,733 --> 00:27:58,766
And it's funny,
my daughters, an incredible

00:27:59,166 --> 00:28:00,866
one of my daughters
is this incredible artist.

00:28:00,866 --> 00:28:02,333
And when you're talking about
she's only a sophomore

00:28:02,333 --> 00:28:06,666
and we were talking about real college
and, and I was actually rattling

00:28:06,666 --> 00:28:09,666
off a number of design schools, including,
you see, staff.

00:28:10,033 --> 00:28:12,866
And it was funny, like she was like,

00:28:12,866 --> 00:28:15,266
you know,
hey, is all that going to be real?

00:28:15,266 --> 00:28:16,300
It was very perceptive.

00:28:16,300 --> 00:28:18,766
It's sort of like,
you know, isn't that going to get kind of

00:28:18,766 --> 00:28:22,600
automated by and you know, some of it
well, but a lot of it won't.

00:28:22,600 --> 00:28:24,400
And I think we're going to have to

00:28:25,466 --> 00:28:26,433
remind our young

00:28:26,433 --> 00:28:29,433
people that we're also in a renaissance
of creative confidence.

00:28:29,733 --> 00:28:32,000
Yeah. Like my I'm
sure you feel the same thing.

00:28:32,000 --> 00:28:35,066
Like my creative confidence
quotient has gone up dramatically.

00:28:35,066 --> 00:28:37,466
Absolutely. Because I've got all these
help. Absolutely.

00:28:37,466 --> 00:28:41,466
I am you know I'm writing better
with a little bit of help.

00:28:41,766 --> 00:28:46,166
I'm taking sometimes
scatterbrained concepts and turning them

00:28:46,166 --> 00:28:49,566
into like beautiful concepts
that are kind of like and.

00:28:49,566 --> 00:28:51,100
I definitely, I think concur with that.

00:28:51,100 --> 00:28:54,633
And I think everyone's got artistic
qualities are probably severely

00:28:54,633 --> 00:28:55,666
under leveraged. Yeah.

00:28:55,666 --> 00:28:58,600
So we need to figure out like how do we

00:28:58,600 --> 00:29:01,833
almost like bring more creativity
in the world using those tools.

00:29:01,833 --> 00:29:04,133
But we're going to have to lead
that thinking.

00:29:04,133 --> 00:29:06,633
But at the same time,
we may have to tell the programmer

00:29:06,633 --> 00:29:10,500
that programing as we knew it
before is not going to be as critical.

00:29:10,633 --> 00:29:10,833
Yeah.

00:29:10,833 --> 00:29:16,133
I mean, you've got the CEO of Nvidia
basically saying,

00:29:16,133 --> 00:29:19,900
all this obsession with teaching
every kid to be a coder is nuts.

00:29:20,366 --> 00:29:22,033
And he's the head of Nvidia. Yeah.

00:29:22,033 --> 00:29:26,266
And so now prompts experimentation.

00:29:26,666 --> 00:29:28,866
How do you turn rocks over?

00:29:28,866 --> 00:29:30,000
How do you test and learn?

00:29:30,000 --> 00:29:31,866
I mean,
I think we're in a renaissance, right?

00:29:31,866 --> 00:29:35,933
I mean, I have written, I kid you not,
maybe I'm a bit of a freak,

00:29:36,333 --> 00:29:39,866
but since I discovered generative,
I probably written 100 business concepts

00:29:40,066 --> 00:29:42,366
all the way down to, like, images. Yeah.

00:29:42,366 --> 00:29:46,333
I think my last team thought I was crazy,
but I am like a real entrepreneur.

00:29:46,333 --> 00:29:47,600
And it's like.

00:29:47,600 --> 00:29:49,433
And like the things
that when I was at P&G,

00:29:49,433 --> 00:29:51,133
that would take like months to kind of

00:29:51,133 --> 00:29:53,766
get I'm like literally
like doing like overnight.

00:29:53,766 --> 00:29:54,266
Yeah.

00:29:54,266 --> 00:29:57,333
You know, I wrote a concept this morning
before I came here for a new product.

00:29:58,166 --> 00:30:03,000
and so that it's to some extent
like the ideas

649
00:30:03,000 --> 00:30:07,166
and the concepts that we have in
our mind can be formalized and laid out.

00:30:07,166 --> 00:30:09,600
And I think we just got to teach
the young people to do this.

00:30:09,600 --> 00:30:12,600
Like all the great businesses
I know, I actually.

00:30:12,866 --> 00:30:14,566
I agree, I have a of a bit of a slight.

00:30:14,566 --> 00:30:16,900
Yeah. take on that a different take.

00:30:16,900 --> 00:30:20,233
I would say
that you can do those things well

00:30:20,766 --> 00:30:24,466
because you also have some
training experience like.

00:30:25,566 --> 00:30:25,966
Right.

00:30:25,966 --> 00:30:29,066
Like say like you, you have these so like,
so I want to

00:30:29,633 --> 00:30:32,633
and I still think there are skill sets
to learning how to program,

00:30:32,933 --> 00:30:35,933
but I do
I can I completely agree with this.

00:30:35,966 --> 00:30:37,833
Creativity
has to be a part of what you do.

00:30:37,833 --> 00:30:40,900
You have to can't be one dimensional in
anything you do anymore.

00:30:41,066 --> 00:30:43,266
Like it is not. It is not going to be.

00:30:43,266 --> 00:30:46,133
So my advice would be
you don't be one dimensional, right?

00:30:46,133 --> 00:30:50,033
Can't just say people want to go in
sometimes say, I just want to do this.

00:30:50,466 --> 00:30:52,766
That world is done. Like you work.

00:30:52,766 --> 00:30:53,333
To your point.

00:30:53,333 --> 00:30:54,566
I mean, think about some of the things

00:30:54,566 --> 00:30:57,533
that you typically learning university
like critical thinking, right.

00:30:57,533 --> 00:30:58,733
How to interrogate.

00:30:58,733 --> 00:31:00,100
Now that's as relevant.

00:31:00,100 --> 00:31:04,166
It's always you're going to be constantly
interrogating the bots. Yep.

00:31:04,166 --> 00:31:08,100
You know and it's going to be a long time
before the deep fakes go away or

00:31:08,133 --> 00:31:09,400
the hallucinations.

00:31:09,400 --> 00:31:14,533
And what is real, what is not,
what is a logical sequence of dialog.

00:31:14,533 --> 00:31:18,500
And I think these, you know, we may find
that the things we always wanted

00:31:18,500 --> 00:31:23,033
kids to learn at universities
are going to be amplified in this world.

00:31:23,033 --> 00:31:23,866
Zach. Highly.

00:31:23,866 --> 00:31:28,000
I wait when I don't say
like I've got Plato in my pocket loosely.

00:31:28,000 --> 00:31:31,266
I mean, this is a highly dialectical
process, right?

00:31:31,566 --> 00:31:34,566
I mean, you're going back and forth
like 20 times more than you would.

00:31:34,566 --> 00:31:36,433
But you have to know,
but you got to know how to do that.

00:31:36,433 --> 00:31:38,766
Well, but that's a skill.
And I think that's that's a skill.

00:31:38,766 --> 00:31:40,666
You might find that
some of the universities

00:31:40,666 --> 00:31:42,566
go back to some of the basics, right.

00:31:42,566 --> 00:31:44,966
Just to kind of get. Something, but like,
oh, communication.

00:31:44,966 --> 00:31:46,500
Everybody doesn't know how to communicate

00:31:46,500 --> 00:31:48,233
more people are going to have
to really know how to communicate.

00:31:48,233 --> 00:31:49,333
It's very important.

00:31:49,333 --> 00:31:52,600
I also believe that creativity
is going to be highly important still.

00:31:52,966 --> 00:31:55,966
you know, I think we're both at a

00:31:56,366 --> 00:31:57,966
more creative moment,
but at the same time,

00:31:57,966 --> 00:32:00,966
I think we're, as a society
less creative than we've ever been.

00:32:01,100 --> 00:32:01,666
Right?

00:32:01,666 --> 00:32:03,800
I think about like musicians, for example.

00:32:03,800 --> 00:32:05,300
Like, I just like it.

00:32:05,300 --> 00:32:09,233
And this doesn't make me feel old,
but I look at the music now

00:32:09,866 --> 00:32:12,200
and there's nowhere near the,
musical input.

00:32:12,200 --> 00:32:13,433
And I think this started a while ago.

00:32:13,433 --> 00:32:13,700
Right.

00:32:13,700 --> 00:32:17,700
But, like, think about what
Michael Jackson even original rappers did.

00:32:17,800 --> 00:32:19,500
They told stories,
they put things into him.

00:32:19,500 --> 00:32:23,466
Now you can automate things,
but it's not as good quality.

00:32:23,833 --> 00:32:25,600
People still like originality.

00:32:25,600 --> 00:32:26,433
So I think

00:32:26,433 --> 00:32:30,733
the age of artificial intelligence, like,
one of my friends, Lisa Fancy

00:32:30,733 --> 00:32:33,533
Flowers, he's actually in the crypto world
and she's in the AI world.

00:32:33,533 --> 00:32:35,633
She said, in
a world of artificial intelligence,

00:32:35,633 --> 00:32:37,433
you need authentic intelligence.

00:32:37,433 --> 00:32:38,300
We are one of one.

00:32:38,300 --> 00:32:41,266
You can only fake talent
so far, right? Right.

00:32:41,266 --> 00:32:45,700
And I sometimes I felt artificially
more musical than I really am.

00:32:45,700 --> 00:32:48,900
But, you know, you still have to have
the foundation of talent.

00:32:48,933 --> 00:32:51,300
You do seem like.
They help you amplify. It's not.

00:32:51,300 --> 00:32:52,000
That's my point.

00:32:52,000 --> 00:32:55,000
Like the tool should be seen that way.

00:32:55,233 --> 00:32:57,066
And I think people are still.

00:32:57,066 --> 00:32:59,366
To your point, you mentioned earlier
when you talked about

00:32:59,366 --> 00:33:01,066
I never heard it's phrased this way,

00:33:01,066 --> 00:33:05,033
you know, making sure you had a mixture
between automation and intimacy.

00:33:05,366 --> 00:33:08,933
I look at that same way, like authenticity
and artificial Intel.

00:33:08,933 --> 00:33:11,033
You need to have authenticity. Absolutely.

00:33:11,033 --> 00:33:12,266
And that's what's going to differentiate.

00:33:12,266 --> 00:33:15,000
What I love about what
Kendra and, and Helen are doing.

00:33:15,000 --> 00:33:15,333
Absolutely.

00:33:15,333 --> 00:33:17,900
I mean, that whole human in the loop,
it's a big part of our model.

00:33:17,900 --> 00:33:19,500
Like it's interesting, like I am.

00:33:19,500 --> 00:33:21,166
I'll give you a good example. So we're,

00:33:22,500 --> 00:33:25,433
putting
out reports that are very data based,

00:33:25,433 --> 00:33:29,666
but throughout the report, you can click
a button and there's Pete in the studio

00:33:29,666 --> 00:33:33,466
explaining how to use it
with all of Pete's passion and authority.

00:33:33,466 --> 00:33:34,966
And it's really called data theater.

00:33:34,966 --> 00:33:36,866
Yeah, I've got to get myself
an avatar too.

00:33:36,866 --> 00:33:37,866
I got to do that. But, you know.

00:33:37,866 --> 00:33:38,600
But it's real.

00:33:38,600 --> 00:33:41,833
It's real me because I,
you know, data alone is not good enough.

00:33:41,833 --> 00:33:42,366
Sometimes.

00:33:42,366 --> 00:33:45,366
I'm always trying to channel my,
you know, guilt tripping

00:33:45,366 --> 00:33:49,000
Italian mother to kind of get people
to, like, act on the data.

00:33:49,000 --> 00:33:50,500
Data alone. Won't get.

00:33:50,500 --> 00:33:50,866
Hacked.

00:33:50,866 --> 00:33:53,733
And again, it's, it's that balancing act
that we need to perfect.

00:33:53,733 --> 00:33:54,600
And it's the human in.

00:33:54,600 --> 00:33:56,566
Yeah. Data is
guidance is not determinative.

00:33:56,566 --> 00:33:58,766
You have to it's something
you can use as a map.

00:33:58,766 --> 00:33:59,700
But it won't. It won't.

00:33:59,700 --> 00:34:01,433
It won't mean anything
if you don't use it.

00:34:01,433 --> 00:34:01,833
Yeah.

00:34:01,833 --> 00:34:04,833
It's not the things where we've been told
to do because rationally it's right.

00:34:04,866 --> 00:34:05,800
Yeah. Eat your peas.

00:34:05,800 --> 00:34:06,300
Yeah.

00:34:06,300 --> 00:34:08,700
It's like, you know,
it's like every study will tell you

00:34:08,700 --> 00:34:09,933
it's right that you need, like.

00:34:09,933 --> 00:34:14,100
Yeah, mom to say goddamn Jesus
or you're not, you know.

00:34:14,566 --> 00:34:16,766
Yeah. I mean, we're not
we're not rational people, so it's.

00:34:16,766 --> 00:34:17,433
We're good to go.

00:34:19,300 --> 00:34:22,900
so, I want to get to a few kind of,
like, questions that I ask people.

00:34:22,900 --> 00:34:26,533
So you have a committee of three
to advise you on

00:34:26,866 --> 00:34:28,833
life, business, whatever you want.

00:34:28,833 --> 00:34:31,833
Tell me who these three people are
and why.

00:34:34,500 --> 00:34:37,766
I really admire
John Pepper, the former CEO of P&G.

00:34:38,066 --> 00:34:41,066
I think he is just, he continues

00:34:41,166 --> 00:34:44,500
emailing him this morning on me,
and he's the type of person.

00:34:44,500 --> 00:34:47,566
If he asked me to do something,
I'd say how high or jump.

00:34:47,566 --> 00:34:50,666
And he's consistently there
and just always

00:34:50,666 --> 00:34:53,666
kind of wise, ethical,

00:34:54,500 --> 00:34:56,900
Kind of,

00:34:56,900 --> 00:34:57,700
stem.

00:34:57,700 --> 00:35:00,600
You know, there's some,

00:35:00,600 --> 00:35:01,466
you know,

00:35:01,466 --> 00:35:04,033
I want isolated to one individual,
but there's some folks

00:35:04,033 --> 00:35:05,233
from my Nestlé experience.

00:35:05,233 --> 00:35:08,233
So I just really, like to bring
to the table.

00:35:08,533 --> 00:35:09,033
And I think

00:35:10,100 --> 00:35:10,400
a lot

00:35:10,400 --> 00:35:13,400
of, you
know, family members are always there.

00:35:13,500 --> 00:35:14,933
So many respects.

00:35:14,933 --> 00:35:16,233
so you got one. You play. You know what?

00:35:16,233 --> 00:35:17,600
You know what? It used.

00:35:17,600 --> 00:35:20,200
Oh, I need to think a little bit harder
about,

00:35:20,200 --> 00:35:23,200
you know,
just because you might say I'm leaning on.

00:35:23,200 --> 00:35:26,466
I'm for certain things.
I'm fine on my kids.

00:35:26,900 --> 00:35:27,300
That's fine.

00:35:27,300 --> 00:35:30,333
And that's a good that my daughter Layla
is like a source of incredible insight.

00:35:30,333 --> 00:35:33,600
So my daughter Sophia, my son Liam, and,
you know,

00:35:33,600 --> 00:35:37,466
you have to, like,
be humble to know what you don't know.

00:35:37,466 --> 00:35:39,400
And almost
there's a certain innocence and.

00:35:39,400 --> 00:35:40,866
Yeah, world that they see.

00:35:40,866 --> 00:35:46,000
And so, but I, you know, I generally try
to, you know, listen to a lot of signals.

00:35:46,533 --> 00:35:48,500
About what you don't know. That's great.

00:35:48,500 --> 00:35:50,966
May I ask you the opposite
of that question? Yeah.

00:35:50,966 --> 00:35:52,966
What do you know
for sure? The Oprah question.

00:35:56,400 --> 00:35:57,400
And I know that I don't

00:35:57,400 --> 00:36:00,866
know, I kind of still Plato
or Aristotle, but,

00:36:01,266 --> 00:36:03,900
you know, I'm pretty humble about

00:36:03,900 --> 00:36:06,900
the fact that there's still a lot,

00:36:07,266 --> 00:36:09,900
and,

00:36:09,900 --> 00:36:13,566
and that, you know, knowledge
is, real quest.

00:36:13,566 --> 00:36:18,233
It never entirely
just kind of an end point

00:36:19,633 --> 00:36:23,666
is, I'm really fanatical about

00:36:25,166 --> 00:36:28,166
standing.

00:36:29,233 --> 00:36:30,666
Kind of guided everything I've done.

00:36:30,666 --> 00:36:33,666
And to some extent,
a lot of that is in the

00:36:33,733 --> 00:36:36,733
and, say, trust your inner.

00:36:37,300 --> 00:36:38,733
Yeah,

00:36:38,733 --> 00:36:39,566
that you're probably going

806
00:36:39,566 --> 00:36:42,566
to find all sorts
of great business ideas and the like.

00:36:42,566 --> 00:36:43,433
All right.

00:36:43,433 --> 00:36:46,533
What's an important truth you have that
very few people agree with you on?

00:36:47,066 --> 00:36:50,066
It's always a fun one for me.

00:36:50,133 --> 00:36:52,900
And I would say goes back to what I said
before, I think, you know, the

00:36:52,900 --> 00:36:55,766
trust your inner consumers.
I gave a Ted talk on it.

00:36:55,766 --> 00:36:59,900
I think, I think we does that sometimes.

00:36:59,900 --> 00:37:02,900
And I think what specifically is
that is the belief that people

00:37:03,166 --> 00:37:04,666
agree with you on any I don't understand.

00:37:04,666 --> 00:37:06,266
Tell me that something in
what do you mean?

00:37:06,266 --> 00:37:09,400
Like what's an important truth you have
that most people may disagree with.

00:37:10,200 --> 00:37:11,700
Some people may disagree with that.

00:37:11,700 --> 00:37:13,866
It's almost like
because I'm I've always believed that

00:37:13,866 --> 00:37:17,033
if you if you trust your inner consumer,
you're almost bringing more of an outside

00:37:17,033 --> 00:37:18,466
in activist perspective.

00:37:18,466 --> 00:37:20,100
Okay. I think in business sometimes.

00:37:20,100 --> 00:37:21,866
What does that mean
to trust your inner consumer?

00:37:21,866 --> 00:37:25,133
So basically means, so I've always been,

00:37:25,966 --> 00:37:28,133
I give a Ted talk
called the Work Life Advantage,

00:37:28,133 --> 00:37:31,766
and it was all about how the things we do
in our personal lives

00:37:32,233 --> 00:37:36,533
and digital really inform
how we become business leaders.

00:37:36,866 --> 00:37:39,300
So you've got to do social media yourself.

00:37:39,300 --> 00:37:41,866
It'll kind of teach you
about the power of iteration.

00:37:41,866 --> 00:37:46,333
you need to walk the talk, need to,
Yeah.

00:37:46,333 --> 00:37:50,766
You just, you know, you need to become
a good e-commerce shopper to really speak

00:37:50,766 --> 00:37:54,200
with authority when you're managing a team
about winning with Amazon.

00:37:54,200 --> 00:37:55,200
And so it's like

00:37:55,200 --> 00:37:58,500
try and you also, you know, as
consumers are pretty damn critical.

00:37:58,500 --> 00:37:58,833
Right.

00:37:58,833 --> 00:38:00,333
And sometimes we let it out.

00:38:00,333 --> 00:38:02,933
We're kind of, you know, we get
we get really angry.

00:38:02,933 --> 00:38:09,200
We tell 3000 and so but that journey is
what makes us really sharp.

00:38:09,200 --> 00:38:13,166
Now why do I feel like
I'm qualified to go on a startup

00:38:13,466 --> 00:38:15,500
if I a million startups do an I?

00:38:15,500 --> 00:38:18,866
Honestly, I've been I've been trusting
my inner consumer.

00:38:18,866 --> 00:38:21,733
I'm using this stuff like 100 times a day.

00:38:21,733 --> 00:38:25,700
I see a future that others don't
because I'm channeling my consumer side.

00:38:25,700 --> 00:38:31,166
And I think that's always going to be my
source of truth, my source of motivation.

00:38:31,600 --> 00:38:35,566
And, and I would I don't think that
that truth will ever go away.

00:38:35,900 --> 00:38:36,566
All right.

00:38:36,566 --> 00:38:37,166
Final question.

00:38:37,166 --> 00:38:40,866
What does success look like for you or
your business or yourself in five years?

00:38:41,733 --> 00:38:45,300
And I want to make a big
it's funny, I'm not I want

00:38:46,766 --> 00:38:50,100
monetary
wealth, but I'm not motivated by it.

00:38:50,100 --> 00:38:53,100
I'm very motivated by

00:38:53,366 --> 00:38:56,366
feeling that I did something

00:38:57,100 --> 00:38:59,333
in me

00:38:59,333 --> 00:39:00,533
out of positive

00:39:00,533 --> 00:39:05,166
sounds like lofty and idealistic,
but it's just where I am in my day.

00:39:05,166 --> 00:39:09,066
It's like I
if people read my epitaph at their

00:39:09,066 --> 00:39:12,366
give speech at the funeral
and they don't say that I really moved

00:39:14,100 --> 00:39:16,566
in it very thoughtfully.

00:39:16,566 --> 00:39:19,966
I'll be yeah, I'll be really disappointed
wherever I am.

00:39:20,366 --> 00:39:22,166
And, yeah.

00:39:22,166 --> 00:39:25,400
So I just, I want
I want to drive real impact.

00:39:25,900 --> 00:39:26,966
I've always been that.

00:39:26,966 --> 00:39:30,133
I mean, I, I, I grew up,
I wanted to be governor of California,

00:39:31,000 --> 00:39:33,166
and I've, I started

00:39:33,166 --> 00:39:34,600
and to some extent I've almost.

00:39:34,600 --> 00:39:36,166
You and I have that in common.

00:39:36,166 --> 00:39:36,966
Yeah. Yeah.

00:39:36,966 --> 00:39:37,766
Planted feedback.

00:39:37,766 --> 00:39:40,733
My first business was a bit of a,
stick it to the man.

00:39:40,733 --> 00:39:42,600
Yeah. It also had a business side to it.

00:39:42,600 --> 00:39:46,100
So I've always like blended
that, you know, drive impact,

00:39:46,400 --> 00:39:47,366
build a great business.

00:39:47,366 --> 00:39:48,000
And to some extent

00:39:48,000 --> 00:39:51,666
I feel like I'm doing that now
because I like the idea of accountable.

00:39:52,333 --> 00:39:55,333
Like, don't talk about sustainability
and not do it right.

00:39:55,433 --> 00:39:55,766
You know.

00:39:55,766 --> 00:39:56,900
So I'm kind of blame it on the

00:39:56,900 --> 00:40:00,600
I bought say you're lying
or you're greenwashing, but I like that.

00:40:00,600 --> 00:40:04,266
It's like, you know, because I think
it also drives drives to better outcomes.

00:40:04,266 --> 00:40:07,333
It might speed up companies
to actually meeting

00:40:07,333 --> 00:40:10,300
their green commitments or realizing
they can't go slow.

00:40:10,300 --> 00:40:12,566
Right. You know,
so those are the things I really like.

00:40:12,566 --> 00:40:14,266
Yeah. People stop being a pleasure,
man. Yeah.

00:40:14,266 --> 00:40:16,566
Thanks for all your leadership.
That's fine. Thank you. And.