July 15, 2025 • 27 mins
This week, the show takes you behind the scenes at L'Oréal’s research center in New Jersey. Malcolm Gladwell delves into the complexities of cosmetic formulation and the AI partnership with IBM. Learn how AI is poised to revolutionize the creation of beauty products, to make them even more sustainable and innovative.
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Episode Transcript
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Speaker 1 (00:00):
Welcome to Tech Stuff, a production from iHeartRadio. This season,
non Smart Talks with IBM, Malcolm Glabwell is back, and
this time he's taking the show on the road. Malcolm
is stepping outside the studio to explore how IBM clients
are using artificial intelligence to solve real world challenges and
transform the way they do business, from accelerating scientific breakthroughs
(00:23):
to reimagining education. It's a fresh look at innovation in action,
where big ideas meet cutting edge solutions. You'll hear from
industry leaders, creative thinkers, and of course, Malcolm Glabwell himself
as he guides you through each story. New episodes of
Smart Talks with IBM drop every month on the iHeartRadio app,
(00:43):
Apple Podcasts, or wherever you get your podcasts. Learn more
at IBM dot com slash smart Talks.
Speaker 2 (00:52):
To understand why the cosmetics supergiant Lorel Group is teaming
up with IBM, you must first take a closer look
at its products. Take lipstick, for example. It's one of
those things that seems straightforward, a waxy cylinder that you
rub on your lips to turn them a different color. Easy, right,
Well maybe not. As my colleague Lucy Sullivan found out
(01:14):
when I sent her an assignment to Loreel's North America
Research and Innovation Center.
Speaker 3 (01:20):
All right, I'm reporting live from the Loreal visitor's parking lot.
Malcolm told me that he would be sending me to Paris,
France for this Looreal excursion, but instead I am in Clark,
New Jersey. Pass a lot of strip malls on the
way here. But to be fair to Clark, New Jersey
(01:43):
and Lorel, this is a beautiful compound. It kind of
looks like a spa.
Speaker 2 (01:49):
Lucy went into the center and was blown away. The
facility houses about six hundred scientists and experts across skincare, makeup, fragrance,
hair care, innovative packaging, and tech. It is one of
the largest formulation lab spaces in the industry. It's the
size of six basketball courts. The reason Loreel's facility is
(02:10):
so big and has so many people is that everything
Loriel does to bring a product to market happens here,
from molecule discovery and product development to consumer testing. The
center even has its own mini factory. My conception of
lipstick that it's just a waxy stick was plain wrong.
Lipstick is a high performance product born from years of research,
(02:33):
consumer insights and precision science. Lipstick isn't simple. It's incredibly complex,
and one of the main reasons it's so complex is
just a nature of fashion trends. The kind of lipstick
consumers want is constantly changing.
Speaker 4 (02:48):
A lot of our consumer insights with Loril is like,
where are consumers going in the future.
Speaker 2 (02:52):
This is Nadine Gomez, She's vice president for Loreel's research
and innovation development.
Speaker 4 (02:58):
Team Park Chemist are working out five six years down
the line. We predicted that consumers wanted more of a
softer look on their lips as well.
Speaker 3 (03:06):
So how do you predict something like that.
Speaker 4 (03:08):
We see slow signals from fashion houses and social media
and things like that. We kind of see that trend
evolving a little bit, and then we know at five
six years it's going.
Speaker 5 (03:18):
To become big.
Speaker 2 (03:20):
Lucy talked with her about the origins of one of
their products, Mabe Lene matt Inc Liquid lipstick.
Speaker 4 (03:27):
Our competitors had two steps. The first step is a
base coat. It's super opaque. You get the color and
you get the maddy, but it's very very drying ellips.
You cannot wear that, honestly more than ten minutes. It
feels like your lips are like aching at one point,
So we had to develop a top coat, and you'll
see many of our competitors did the same thing. It's
like a bomb. You put it on top, it's super comfortable.
But we also noticed that consumers kind of get tired
(03:50):
of reapplying a bomb. So we're like, what can we
do to create this two step into one step?
Speaker 2 (03:55):
So Loriel had a challenge, how do you make a
comfortable liquid map lipstick that doesn't require consumers to reapply
a top layer of bomb. Solving this type of problem
takes a lot of resources and a lot of expertise,
and crucially, it takes time. Remember Nadine said that working
on a breakthrough product such as matt inc can take
(04:17):
years before it comes out. But can this process be accelerated,
taken further, be even more sustainable. That's what IBM and
Laurel are hoping to find out. My name is Malcolm Gladwell.
You're listening to the latest episode of Smart Talks with IBM,
where we offer our listeners a glimpse behind the curtain
(04:38):
of the world of technology. In our last episode, we
talked about how an AI assistant created with IBM Watson
X helps future teachers practice responsive teaching by simulating classroom
interactions with students. In this episode, we take you on
an even more unexpected journey into the world of cosmetics,
(05:00):
hair care, skincare, fragrance, makeup, and how a custom AI
model could help Lorel's researchers shape the future of what
we put on our faces every morning. I want to
stay on lipstick a moment longer to help illustrate what
(05:21):
goes into Loriel's product development, and let's focus on matt
inc lipstick. Loreel wanted to create something that was comfortable
and could be applied in one step.
Speaker 6 (05:32):
So to go from two step to one step, we
had to look cross functionally and try to figure out
what can we bring into the product to make it
more comfortable, And luckily we have many different types of
products at Lorel.
Speaker 2 (05:44):
That's Alex Good, a senior chemist who leads the lip
products team in North America. She says the trick to
making matt incwork was finding an elastomer, a substance they
were already using in foundation.
Speaker 6 (05:57):
We have this elastomer that can give you like more
comfortable and make it feel like there's like something on
your lips like a cushion.
Speaker 2 (06:05):
She handed Lucy two jars. The first jar contained the
former version of the product that was used in super
State twenty four. By the way, this is exactly why
I sent Lucy to the lab in my place the samples.
Speaker 6 (06:18):
And I actually have something for you to try here,
so you can try this is what was the initial product.
Speaker 3 (06:25):
Okay, So this is like it sort of looks like Okay,
it is clay. It looks like vacline that has like
a more of a color. It's kind of a beige,
looks like some skin. Okay. So this was from the
two steps this would go on after oh okay, right violet.
Speaker 6 (06:44):
Okay, So it feels like very wet as you can see,
it's kind of it's gonna absorb into your skin and
leave and then you're gonna feel the dryness.
Speaker 5 (06:53):
Of the product once it's not okay. So we're gonna move.
Speaker 6 (06:56):
From the clay product that you have on your hand
now to the last summer like you try half.
Speaker 2 (07:00):
One or This jar held the elastomer that Lareel has
spent years developing in the lab.
Speaker 3 (07:07):
This one is a clear looks like aqua for a
much player.
Speaker 6 (07:13):
And you can pay a physical layer that you're putting.
Speaker 7 (07:16):
On your aid.
Speaker 3 (07:17):
Yeah, so that's much thicker. It kind of like clumps together. Yeah,
it was more of a cloudy it's less shimmery though
that's intended.
Speaker 6 (07:26):
Yes, So this is a like a powder. This dispersed
in dimethicone and it creates like a comfort on your lips.
It feels like there's something there for a barrier to
keep the film form on. And that's like the key
ingredient that came from Foundation that we transferred into lipstick
to give us this innovative product ahead of the market. Yeah,
(07:48):
this is what gives it comfort. So the difference between
super State twenty four and matt Inc is really the comfort.
They both last a long time, but this matt Inc
you don't have to apply the bomb over over again,
so you can fly matting once for the day and
you're good.
Speaker 2 (08:03):
For Alex Good is underselling it here, once for the
day and you're good. That's a liquid lipstick revolution. Literally
millions of loreal consumers around the world have worn matt ink.
It's a blockbuster. It's also a marvel of science. The
world's first liquid lipstick was developed in the nineteen thirties
(08:24):
and it was actually just a stain for your lips,
barely counts as lipstick. Then came another wave of liquid lipstick.
When they were able to make it matt that was
a two step version. It felt heavy on your lips.
You had to keep reapplying the top coat. It was inconvenient.
Loreel tackled that challenge in the lab with chemists like
(08:45):
Alex and Nadine leaving the charge their breakthrough matt Inc.
But creating matt Inc took a long time, trial and error,
the hard work of scientific experimentation. As Nadean told Lucy,
the lipstick team had to put the new product to
extensive tests.
Speaker 4 (09:04):
We do a very robustability system here. You know, we
have color odor appearance. We monitor this in extreme conditions.
We simulate a forty five degrees celsius and that can
be something like a three year shelf life. I'm saying
we simulate your real life product. Like if you leave
your lip gloss in the car in Arizona's one hundred
and twelve degrees or three days, is it still going
to perform? Is it gonna smell? Is it gonna look granted?
(09:26):
Is it gonna change colors we.
Speaker 5 (09:28):
Do all that.
Speaker 2 (09:29):
See what I mean. Lipstick is complex. Most people would
never consider it a piece of technology, but one lip
product has millions of data points.
Speaker 4 (09:39):
So much science behind. And you can see here how
many scientists we have. You know, some of them have PhDs,
some of them have master's degrees to chemistry, biology, psychology.
Speaker 2 (09:46):
Also, when I first heard about this collaboration between LORI
L and IBM, I was surprised. I thought, these are
two very different companies. What do they really have in common?
Speaker 7 (09:57):
Pleasure, you guys, treasure. Yeah.
Speaker 2 (10:00):
To find out, I went to the IBM Research Center
outside New York City, which I have to say is
one of the coolest buildings I've ever been in, A
semi circular modernist masterpiece with a long curving wall of windows,
looks like something out of a Stanley Kubrick movie. I
was there to talk with two experts from research and
innovation at LOREL, Metheu Cassier and gabriel Bertoli. Matthew is
(10:23):
VP for Digital and Transformation, Gabrielle is a Chief Digital
Transformation Officer for Formulation. These are the people whose jobs
are to oversee big changes within the company, and Methu
told me to try on some lipstick.
Speaker 8 (10:38):
I'm gonna make you try this one.
Speaker 7 (10:40):
Okay, this is superstay vinitin final in. Yeah, so that's
a glosse. Never in my life put on lipsey. I've
no idea what I'm doing. You don't have to put it.
Speaker 8 (10:48):
You can try it virtually.
Speaker 2 (10:50):
Oh this may not be news to people who buy makeup,
but it was news to me. You can try on
loreal products virtually. They call it augmented beau.
Speaker 7 (11:01):
Oh my goodness.
Speaker 2 (11:02):
That is the strangest thing I've ever said. I look
quite fetching.
Speaker 7 (11:06):
That's amazing. And I can just.
Speaker 8 (11:09):
Hit you can choose your color absolutely.
Speaker 7 (11:12):
So I'm on a little app.
Speaker 2 (11:13):
It's looking at me and it's just showing me exactly
how I would look with different shades of lipstick. So
the odd idea of going into a store and trying
on each one, you cannot do that from home, if
you're not even at the store.
Speaker 8 (11:24):
Yeah, absolutely, that's all purpose. If you want to manage
a trend, I would go for something more like pitch.
Speaker 7 (11:30):
You think I'm a peach person.
Speaker 2 (11:32):
I don't know that looks I have to say, that
looks kind of natural. It just is enhanced. It's given
me a boys share I would not otherwise have. This
is why Loreel says it creates beauty products and beauty experiences.
Loriel is a beauty tech company. Over the last decade,
Loreel has seized the power of AI and more recently,
(11:55):
generative AI technology has become a driving force alongside science
and creativity. And while some of this digital technology is
relatively new, Matthew helped me see that IBM and Lorel
have always had a lot in common.
Speaker 8 (12:11):
So the original creator of Loyal Legentulier was a chemist
in nineteen or nine, so one hundred and sixteen years ago,
and he created this new air color type for the
market in France, and then little by little, it has
been always a very scientific company. So if you look
a little bit at key facts, we invented sun filters
in the nineteen thirties, there was a very very big
(12:34):
milestone where we also invented not only product, but a
reconstructed skin. So if you look at nineteen seventeen nine,
we've been the created this reconstructed skin that helped us
to go out of animal testing very fast, and by
the way, before the law even asked it to cosmetic companies,
and then more recently, because it's a history of innovation,
we'll launch on new molecules like one that you can
(12:56):
find in laroche pose Melabi three, which is really helping
people to find again some you know, spots they could
have on their skin. It's all about like big mountation,
how to regulate it.
Speaker 2 (13:07):
Loreel and IBM were both started in the early twentieth century,
Loreel in nineteen oh nine and IBM in nineteen eleven.
Both companies have long standing histories of innovation, of using
trial and error to improve everything they do. The two
companies have been doing that in parallel for more than
a century until recently. When does it start? When do
(13:29):
Lorel and IBM start working together?
Speaker 9 (13:32):
So we started in twenty twenty three, at the end
of the year. But you know, really the discussion is
really recent, absolutely, absolutely, it's really recent in reality. You know,
I would say the first really interaction happened at the
beginning of twenty twenty four.
Speaker 2 (13:47):
This is Gabriel Bertoli, who I spoke to alongside Matthew.
Speaker 9 (13:51):
What really played a key role here is we wanted
to bring from a logic perspective to R and D together,
which Normally, you know companies like us, you just go
to a provider. You know, it's a customer and a
supplier and new work they delivered to you. Here, the
concept was totally different.
Speaker 2 (14:11):
Mid two said that the collaboration began with simple conversations.
Speaker 8 (14:15):
So if you look at the way IBM entered into
Loreal Labs, it's started by interviewing people, what would help
you to do your job? What is your business need?
So it was, by the way, two months ago, a
long series of interviews and from all the people around
the world we have in research in Brazil, in India,
(14:35):
in China, Japan, US, France of course, So we really
want to make sure that at the end of the day,
this new model, this new tool that we will give
to people is really people c trick in the way
that it selves their daily need.
Speaker 2 (14:49):
More the point, Lorel has leveraged technology for decades and
accumulated amounted of scientific knowledge, everything from consumer aspirations and
market trends, to the results of all the experiments conducted
during product development, to which formulations melt in a hot car.
It's hard to get your head around. Loreal isn't just
(15:10):
a cosmetics company. It's a beauty data powerhouse.
Speaker 9 (15:15):
If we have sixteen thousand terabat of data coming from
consumer insights, coming from market research, coming from sales, well
with the new technology, maybe by aligning those two and
using best in class technology you can solve that problem.
Speaker 2 (15:35):
So you say you have sixteen terabytes of data. Put
that in perspective. How much data is that?
Speaker 7 (15:41):
Give me?
Speaker 9 (15:42):
This is one hundred year of Looreal data based on
the last you know, forty years of data in the systems.
So this is really I mean, we're talking about one
hundred year of data that only Lorial have. Let's take
the example of the ellipsis. I mean, you know, if
ellipsex can be between twenty and thirty year ow material,
(16:03):
each raw material will have I would say ten or
fifteen way of doing things.
Speaker 2 (16:11):
Gabrielle is talking about how things used to be done.
Researchers at Loreel needed roughly twenty five ingredients for a
new lipstick formulation, but they have to choose from a
pool of hundreds, if not thousands, of raw materials, and
even after they settle on the ones they want, they
have to figure out how much of each ingredient they
need and in what form, what molecular weight, what combination.
(16:35):
It's not just a math problem. It's a problem that
requires balancing multiple perspectives safety, performance, quality, compliance standards, sustainability,
and more. It can take years. But what if you
could simulate hundreds of cars parked in a sweltering heat.
What if you could do all those trials and errors
(16:57):
virtually over and over and over again. What if instead
of mixing materials together by hand, you could ask AI
to predict what combinations might work best and then try
those out first.
Speaker 9 (17:10):
This is ten on the power of twenty five. This
is one hundred billion of years for a human to
do a change in the formula or the possibility they have.
You can only do this by using technology, power of
technology and data that you have.
Speaker 2 (17:31):
This, Matthew says, is where IBM can come in to
help take things further. Using artificial intelligence, IBM can help
Lorio create a custom AI model that helps to crunch
those numbers, to be a companion to the researchers, to
give them superpowers.
Speaker 8 (17:48):
We don't want to replace the intuition of the sentis.
We just want to make sure that this intuition is
really augmented by some calculation poor that as Gabrielle said,
then do those ten and the poor of twenty five
solution and probably try this one, this one, this one,
it looks like a better solution and Thentimately that's really
the decision of the chemist to make it happen.
Speaker 7 (18:12):
Well.
Speaker 2 (18:13):
To make a predictive AI model that can give Lorel
researchers those superpowers, you'd need that mountain of data, years
worth of laboratory testing and all Loreal's data digitized and
AI ready. You'd need to train artificial intelligence on everything
the company has already done in order for it to
predict what it could do.
Speaker 5 (18:33):
Loriial has one hundred years of course of data, fifty
years of digitized EGDA.
Speaker 2 (18:40):
This is Mariam Ashuri, Senior director of Product Management for
IBM Watson X. Loreal has the data and part of
IBM's job is to help put that data to work,
which involves ensuring data quality. Mariam talked about the concept
of AI ready data.
Speaker 5 (18:57):
The sole purpose of this data engineering cuyper is to
clean the data, and we call them AI ready data
makes them ready to be consumed by AI. So basically
looking into biases in the data to fix the distribution,
looking into guard brains that we are putting into place
in terms of removing personal information.
Speaker 2 (19:19):
Variam that explained that a custom model like the one
IBM is creating with Lorel can be more efficient and
targeted than the larger general purpose AI models.
Speaker 5 (19:29):
You've heard about large language models. The reason that they
call them large language model is they are exposed into
really large amount of data. So the larger the model,
the more cake of all the models are, but also
the larger computed requires that translate stand increase carbon footprint
(19:50):
and energy consumption that translates stand, increase latency that's your
response time that translatestand increase costs. So we started seeing
that interprise started grabbing a much smaller model customize it
on their proprietary data that's the data their DOMAINO specific data,
or the data about their users to create something differentiated
(20:13):
that is applicable to a real word use case but
also delivers the performance that they needed for a fraction
of the costs. And that's why there's been a lot
of push around using custom models versus very large general
purpose models.
Speaker 2 (20:29):
So how is a custom model created? Miriam says, you
start with a base model. Imagine you're buying a car,
You could get a minivan or a sedan or a
sports car, and then you get to customize it. You
could add a sunroof, leather seats, or a rearview camera.
Turns out you could do the same thing with your
AI model. You pick a base and then you customize it.
(20:51):
You tune it on the data unique to your organization.
Speaker 5 (20:55):
We do believe that one model doesn't fit all use cases.
You want to truly have access to any model anywhere,
and by any model anywhere, I really mean any model anywhere,
open source, proprietary, low call out your machine wherever the
model is. You want to host it yourself, because then
(21:16):
you would be able to take advantage of the best
of the technology at any point and pick the right
model for the target use case.
Speaker 2 (21:24):
So a custom model tuned on Lorel's data would be
more targeted and efficient than a general purpose model. It
would understand the researchers world and provide transparency into its workings.
That's part of the magic. And what could a custom
AI foundation model do for a company like Lorel if.
Speaker 10 (21:44):
You accord it was just moder is contain the complexity
of the formulation.
Speaker 7 (21:50):
That's game.
Speaker 2 (21:51):
La Moline an IBM distinguished engineer and one of the
people working on the AI model.
Speaker 10 (21:57):
And to hyperli the formulate or to go not only faster,
but also I would say, be able to include more
complexity or so in the formulation, more personalization, more certain ability,
better selected ingredient. So it's really a tool to help
(22:17):
them and to also help them to unniche the creativity.
Speaker 2 (22:25):
THEOMI saying that with its custom AI model, Lorel could
improve every step of its product development pipeline, make the
process faster and more sustainable. But he's also saying that
the model could help Lorel create something that's never been
done before.
Speaker 7 (22:40):
What could that product be? So I'm mourning you with that.
All my questions are going to be really dumb.
Speaker 11 (22:50):
Okay, now, please, by all means.
Speaker 2 (22:52):
Right to find out what people at Lorel are dreaming of.
I spoke with Trisha Iyagari, global general manager at Loriel's
Abeling brand, and they asked her about her own dreams
and how technology and science could help bring those dreams
into the world. Do you have a secret wish list
of things you think that this partnership could produce, Like,
(23:13):
is there a product out there that's been technically too difficult?
That you think could be a worthy target.
Speaker 11 (23:19):
There is one that I think could be really amazing.
Speaker 7 (23:21):
What's that?
Speaker 8 (23:23):
So?
Speaker 11 (23:23):
Shine products in general are harder to create, and we're
unable to create a shiny, long wearing eyeshadow. So basically
like a shadow that could stay on your eyelids, that
won't settle into creases, that won't move all over your face,
that has a glossy effect. It's like the holy grail.
Speaker 2 (23:41):
That's the holy grail. Yeah, yeah, you may have seen
that look in fashion shows, but that look isn't real,
not for people like me and Lucy.
Speaker 11 (23:51):
Anyway, if you're walking down a runway, you see a
lot of makeup artists doing techniques where they put some
eyeshadow on, they layer vasoline over it on like slatter
vasalina somebody's eyes to create this very like glossy look.
But you know, within five minutes after they walk down
the runway, I'm sure it's all over their face or
being washed off. So the look is kind of more
(24:13):
of like a fashion look that we've been unable to create,
and real, real consumers can't wear it because it would
get it everywhere.
Speaker 2 (24:20):
Trisia had another thing on her wish list too.
Speaker 11 (24:23):
The other that we would really like is semi permanent makeup.
So we've talked a lot about really really comfortable, thin
film makeup that you could wear all over your face
and that you can sleep in, and that it will
last a couple of days basically, so whether it be
on your face, on your lashes, on your brows. So
(24:44):
anything that's like more of a semi permanent meaning lasting
for three days or more, would be amazing.
Speaker 7 (24:49):
Yeah.
Speaker 2 (24:50):
Yeah, And you say those two things have been the
whole How long have they been on the wish list
of Loreel?
Speaker 4 (24:55):
Oh?
Speaker 11 (24:56):
My gosh. I have been trying to develop this shiny
eyeshadow since I started. What did I start, like twenty ten,
And I'm sure many people had asked before me, and
we tried so many iterations of it and nobody's been
able to achieve it.
Speaker 2 (25:14):
It's clear that Loreel's experts like Tricia have a lot
of ideas. I once said what I called a magic
wand project, where I called up scientists and technologists in
as many different fields as possible and asked them what
they could create if they could just wave a magic
wand and make it real, And everyone had something they'd
(25:36):
want to create everyone. That's not the issue. The issue
is that there are a million different impediments to make
the ideas on the wish list reel. Lack of resources,
lack of time, some crucial bit of know how is lacking.
There's a gap between what we want and what we
can actually have, and one of the simplest ways to
think of the promise of AI is that it can
(25:58):
narrow that gap, not close it, of course, but do
enough that people with dreams realize there are more things
within their grasp.
Speaker 7 (26:07):
Than they could ever have imagined.
Speaker 2 (26:24):
Smart Talks with IBM is produced by Matt Romano, Amy Gaines, McQuaid,
Lucy Sullivan and Jake Harper. Were edited by Lacy Roberts.
Engineering by Nina Bird Lawrence, mastering by Sarah Brugerer. Music
by Gramoscope. Special thanks to Tatiana Lieberman and Cassidy Meyer.
Smart Talks with IBM is a production of Pushkin Industries
(26:46):
and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
listen on the iHeartRadio app, Apple Podcasts, or wherever you
get your podcasts. I'm Malcolm Gabo. This is a paid
advertised from IBM. The conversations on this podcast don't necessarily
represent IBM's positions, strategies, or opinions.
Welcome to Tech Stuff, a production from iHeartRadio. This season,
non Smart Talks with IBM, Malcolm Glabwell is back, and
this time he's taking the show on the road. Malcolm
is stepping outside the studio to explore how IBM clients
are using artificial intelligence to solve real world challenges and
transform the way they do business, from accelerating scientific breakthroughs
(00:23):
to reimagining education. It's a fresh look at innovation in action,
where big ideas meet cutting edge solutions. You'll hear from
industry leaders, creative thinkers, and of course, Malcolm Glabwell himself
as he guides you through each story. New episodes of
Smart Talks with IBM drop every month on the iHeartRadio app,
(00:43):
Apple Podcasts, or wherever you get your podcasts. Learn more
at IBM dot com slash smart Talks.
Speaker 2 (00:52):
To understand why the cosmetics supergiant Lorel Group is teaming
up with IBM, you must first take a closer look
at its products. Take lipstick, for example. It's one of
those things that seems straightforward, a waxy cylinder that you
rub on your lips to turn them a different color. Easy, right,
Well maybe not. As my colleague Lucy Sullivan found out
(01:14):
when I sent her an assignment to Loreel's North America
Research and Innovation Center.
Speaker 3 (01:20):
All right, I'm reporting live from the Loreal visitor's parking lot.
Malcolm told me that he would be sending me to Paris,
France for this Looreal excursion, but instead I am in Clark,
New Jersey. Pass a lot of strip malls on the
way here. But to be fair to Clark, New Jersey
(01:43):
and Lorel, this is a beautiful compound. It kind of
looks like a spa.
Speaker 2 (01:49):
Lucy went into the center and was blown away. The
facility houses about six hundred scientists and experts across skincare, makeup, fragrance,
hair care, innovative packaging, and tech. It is one of
the largest formulation lab spaces in the industry. It's the
size of six basketball courts. The reason Loreel's facility is
(02:10):
so big and has so many people is that everything
Loriel does to bring a product to market happens here,
from molecule discovery and product development to consumer testing. The
center even has its own mini factory. My conception of
lipstick that it's just a waxy stick was plain wrong.
Lipstick is a high performance product born from years of research,
(02:33):
consumer insights and precision science. Lipstick isn't simple. It's incredibly complex,
and one of the main reasons it's so complex is
just a nature of fashion trends. The kind of lipstick
consumers want is constantly changing.
Speaker 4 (02:48):
A lot of our consumer insights with Loril is like,
where are consumers going in the future.
Speaker 2 (02:52):
This is Nadine Gomez, She's vice president for Loreel's research
and innovation development.
Speaker 4 (02:58):
Team Park Chemist are working out five six years down
the line. We predicted that consumers wanted more of a
softer look on their lips as well.
Speaker 3 (03:06):
So how do you predict something like that.
Speaker 4 (03:08):
We see slow signals from fashion houses and social media
and things like that. We kind of see that trend
evolving a little bit, and then we know at five
six years it's going.
Speaker 5 (03:18):
To become big.
Speaker 2 (03:20):
Lucy talked with her about the origins of one of
their products, Mabe Lene matt Inc Liquid lipstick.
Speaker 4 (03:27):
Our competitors had two steps. The first step is a
base coat. It's super opaque. You get the color and
you get the maddy, but it's very very drying ellips.
You cannot wear that, honestly more than ten minutes. It
feels like your lips are like aching at one point,
So we had to develop a top coat, and you'll
see many of our competitors did the same thing. It's
like a bomb. You put it on top, it's super comfortable.
But we also noticed that consumers kind of get tired
(03:50):
of reapplying a bomb. So we're like, what can we
do to create this two step into one step?
Speaker 2 (03:55):
So Loriel had a challenge, how do you make a
comfortable liquid map lipstick that doesn't require consumers to reapply
a top layer of bomb. Solving this type of problem
takes a lot of resources and a lot of expertise,
and crucially, it takes time. Remember Nadine said that working
on a breakthrough product such as matt inc can take
(04:17):
years before it comes out. But can this process be accelerated,
taken further, be even more sustainable. That's what IBM and
Laurel are hoping to find out. My name is Malcolm Gladwell.
You're listening to the latest episode of Smart Talks with IBM,
where we offer our listeners a glimpse behind the curtain
(04:38):
of the world of technology. In our last episode, we
talked about how an AI assistant created with IBM Watson
X helps future teachers practice responsive teaching by simulating classroom
interactions with students. In this episode, we take you on
an even more unexpected journey into the world of cosmetics,
(05:00):
hair care, skincare, fragrance, makeup, and how a custom AI
model could help Lorel's researchers shape the future of what
we put on our faces every morning. I want to
stay on lipstick a moment longer to help illustrate what
(05:21):
goes into Loriel's product development, and let's focus on matt
inc lipstick. Loreel wanted to create something that was comfortable
and could be applied in one step.
Speaker 6 (05:32):
So to go from two step to one step, we
had to look cross functionally and try to figure out
what can we bring into the product to make it
more comfortable, And luckily we have many different types of
products at Lorel.
Speaker 2 (05:44):
That's Alex Good, a senior chemist who leads the lip
products team in North America. She says the trick to
making matt incwork was finding an elastomer, a substance they
were already using in foundation.
Speaker 6 (05:57):
We have this elastomer that can give you like more
comfortable and make it feel like there's like something on
your lips like a cushion.
Speaker 2 (06:05):
She handed Lucy two jars. The first jar contained the
former version of the product that was used in super
State twenty four. By the way, this is exactly why
I sent Lucy to the lab in my place the samples.
Speaker 6 (06:18):
And I actually have something for you to try here,
so you can try this is what was the initial product.
Speaker 3 (06:25):
Okay, So this is like it sort of looks like Okay,
it is clay. It looks like vacline that has like
a more of a color. It's kind of a beige,
looks like some skin. Okay. So this was from the
two steps this would go on after oh okay, right violet.
Speaker 6 (06:44):
Okay, So it feels like very wet as you can see,
it's kind of it's gonna absorb into your skin and
leave and then you're gonna feel the dryness.
Speaker 5 (06:53):
Of the product once it's not okay. So we're gonna move.
Speaker 6 (06:56):
From the clay product that you have on your hand
now to the last summer like you try half.
Speaker 2 (07:00):
One or This jar held the elastomer that Lareel has
spent years developing in the lab.
Speaker 3 (07:07):
This one is a clear looks like aqua for a
much player.
Speaker 6 (07:13):
And you can pay a physical layer that you're putting.
Speaker 7 (07:16):
On your aid.
Speaker 3 (07:17):
Yeah, so that's much thicker. It kind of like clumps together. Yeah,
it was more of a cloudy it's less shimmery though
that's intended.
Speaker 6 (07:26):
Yes, So this is a like a powder. This dispersed
in dimethicone and it creates like a comfort on your lips.
It feels like there's something there for a barrier to
keep the film form on. And that's like the key
ingredient that came from Foundation that we transferred into lipstick
to give us this innovative product ahead of the market. Yeah,
(07:48):
this is what gives it comfort. So the difference between
super State twenty four and matt Inc is really the comfort.
They both last a long time, but this matt Inc
you don't have to apply the bomb over over again,
so you can fly matting once for the day and
you're good.
Speaker 2 (08:03):
For Alex Good is underselling it here, once for the
day and you're good. That's a liquid lipstick revolution. Literally
millions of loreal consumers around the world have worn matt ink.
It's a blockbuster. It's also a marvel of science. The
world's first liquid lipstick was developed in the nineteen thirties
(08:24):
and it was actually just a stain for your lips,
barely counts as lipstick. Then came another wave of liquid lipstick.
When they were able to make it matt that was
a two step version. It felt heavy on your lips.
You had to keep reapplying the top coat. It was inconvenient.
Loreel tackled that challenge in the lab with chemists like
(08:45):
Alex and Nadine leaving the charge their breakthrough matt Inc.
But creating matt Inc took a long time, trial and error,
the hard work of scientific experimentation. As Nadean told Lucy,
the lipstick team had to put the new product to
extensive tests.
Speaker 4 (09:04):
We do a very robustability system here. You know, we
have color odor appearance. We monitor this in extreme conditions.
We simulate a forty five degrees celsius and that can
be something like a three year shelf life. I'm saying
we simulate your real life product. Like if you leave
your lip gloss in the car in Arizona's one hundred
and twelve degrees or three days, is it still going
to perform? Is it gonna smell? Is it gonna look granted?
(09:26):
Is it gonna change colors we.
Speaker 5 (09:28):
Do all that.
Speaker 2 (09:29):
See what I mean. Lipstick is complex. Most people would
never consider it a piece of technology, but one lip
product has millions of data points.
Speaker 4 (09:39):
So much science behind. And you can see here how
many scientists we have. You know, some of them have PhDs,
some of them have master's degrees to chemistry, biology, psychology.
Speaker 2 (09:46):
Also, when I first heard about this collaboration between LORI
L and IBM, I was surprised. I thought, these are
two very different companies. What do they really have in common?
Speaker 7 (09:57):
Pleasure, you guys, treasure. Yeah.
Speaker 2 (10:00):
To find out, I went to the IBM Research Center
outside New York City, which I have to say is
one of the coolest buildings I've ever been in, A
semi circular modernist masterpiece with a long curving wall of windows,
looks like something out of a Stanley Kubrick movie. I
was there to talk with two experts from research and
innovation at LOREL, Metheu Cassier and gabriel Bertoli. Matthew is
(10:23):
VP for Digital and Transformation, Gabrielle is a Chief Digital
Transformation Officer for Formulation. These are the people whose jobs
are to oversee big changes within the company, and Methu
told me to try on some lipstick.
Speaker 8 (10:38):
I'm gonna make you try this one.
Speaker 7 (10:40):
Okay, this is superstay vinitin final in. Yeah, so that's
a glosse. Never in my life put on lipsey. I've
no idea what I'm doing. You don't have to put it.
Speaker 8 (10:48):
You can try it virtually.
Speaker 2 (10:50):
Oh this may not be news to people who buy makeup,
but it was news to me. You can try on
loreal products virtually. They call it augmented beau.
Speaker 7 (11:01):
Oh my goodness.
Speaker 2 (11:02):
That is the strangest thing I've ever said. I look
quite fetching.
Speaker 7 (11:06):
That's amazing. And I can just.
Speaker 8 (11:09):
Hit you can choose your color absolutely.
Speaker 7 (11:12):
So I'm on a little app.
Speaker 2 (11:13):
It's looking at me and it's just showing me exactly
how I would look with different shades of lipstick. So
the odd idea of going into a store and trying
on each one, you cannot do that from home, if
you're not even at the store.
Speaker 8 (11:24):
Yeah, absolutely, that's all purpose. If you want to manage
a trend, I would go for something more like pitch.
Speaker 7 (11:30):
You think I'm a peach person.
Speaker 2 (11:32):
I don't know that looks I have to say, that
looks kind of natural. It just is enhanced. It's given
me a boys share I would not otherwise have. This
is why Loreel says it creates beauty products and beauty experiences.
Loriel is a beauty tech company. Over the last decade,
Loreel has seized the power of AI and more recently,
(11:55):
generative AI technology has become a driving force alongside science
and creativity. And while some of this digital technology is
relatively new, Matthew helped me see that IBM and Lorel
have always had a lot in common.
Speaker 8 (12:11):
So the original creator of Loyal Legentulier was a chemist
in nineteen or nine, so one hundred and sixteen years ago,
and he created this new air color type for the
market in France, and then little by little, it has
been always a very scientific company. So if you look
a little bit at key facts, we invented sun filters
in the nineteen thirties, there was a very very big
(12:34):
milestone where we also invented not only product, but a
reconstructed skin. So if you look at nineteen seventeen nine,
we've been the created this reconstructed skin that helped us
to go out of animal testing very fast, and by
the way, before the law even asked it to cosmetic companies,
and then more recently, because it's a history of innovation,
we'll launch on new molecules like one that you can
(12:56):
find in laroche pose Melabi three, which is really helping
people to find again some you know, spots they could
have on their skin. It's all about like big mountation,
how to regulate it.
Speaker 2 (13:07):
Loreel and IBM were both started in the early twentieth century,
Loreel in nineteen oh nine and IBM in nineteen eleven.
Both companies have long standing histories of innovation, of using
trial and error to improve everything they do. The two
companies have been doing that in parallel for more than
a century until recently. When does it start? When do
(13:29):
Lorel and IBM start working together?
Speaker 9 (13:32):
So we started in twenty twenty three, at the end
of the year. But you know, really the discussion is
really recent, absolutely, absolutely, it's really recent in reality. You know,
I would say the first really interaction happened at the
beginning of twenty twenty four.
Speaker 2 (13:47):
This is Gabriel Bertoli, who I spoke to alongside Matthew.
Speaker 9 (13:51):
What really played a key role here is we wanted
to bring from a logic perspective to R and D together,
which Normally, you know companies like us, you just go
to a provider. You know, it's a customer and a
supplier and new work they delivered to you. Here, the
concept was totally different.
Speaker 2 (14:11):
Mid two said that the collaboration began with simple conversations.
Speaker 8 (14:15):
So if you look at the way IBM entered into
Loreal Labs, it's started by interviewing people, what would help
you to do your job? What is your business need?
So it was, by the way, two months ago, a
long series of interviews and from all the people around
the world we have in research in Brazil, in India,
(14:35):
in China, Japan, US, France of course, So we really
want to make sure that at the end of the day,
this new model, this new tool that we will give
to people is really people c trick in the way
that it selves their daily need.
Speaker 2 (14:49):
More the point, Lorel has leveraged technology for decades and
accumulated amounted of scientific knowledge, everything from consumer aspirations and
market trends, to the results of all the experiments conducted
during product development, to which formulations melt in a hot car.
It's hard to get your head around. Loreal isn't just
(15:10):
a cosmetics company. It's a beauty data powerhouse.
Speaker 9 (15:15):
If we have sixteen thousand terabat of data coming from
consumer insights, coming from market research, coming from sales, well
with the new technology, maybe by aligning those two and
using best in class technology you can solve that problem.
Speaker 2 (15:35):
So you say you have sixteen terabytes of data. Put
that in perspective. How much data is that?
Speaker 7 (15:41):
Give me?
Speaker 9 (15:42):
This is one hundred year of Looreal data based on
the last you know, forty years of data in the systems.
So this is really I mean, we're talking about one
hundred year of data that only Lorial have. Let's take
the example of the ellipsis. I mean, you know, if
ellipsex can be between twenty and thirty year ow material,
(16:03):
each raw material will have I would say ten or
fifteen way of doing things.
Speaker 2 (16:11):
Gabrielle is talking about how things used to be done.
Researchers at Loreel needed roughly twenty five ingredients for a
new lipstick formulation, but they have to choose from a
pool of hundreds, if not thousands, of raw materials, and
even after they settle on the ones they want, they
have to figure out how much of each ingredient they
need and in what form, what molecular weight, what combination.
(16:35):
It's not just a math problem. It's a problem that
requires balancing multiple perspectives safety, performance, quality, compliance standards, sustainability,
and more. It can take years. But what if you
could simulate hundreds of cars parked in a sweltering heat.
What if you could do all those trials and errors
(16:57):
virtually over and over and over again. What if instead
of mixing materials together by hand, you could ask AI
to predict what combinations might work best and then try
those out first.
Speaker 9 (17:10):
This is ten on the power of twenty five. This
is one hundred billion of years for a human to
do a change in the formula or the possibility they have.
You can only do this by using technology, power of
technology and data that you have.
Speaker 2 (17:31):
This, Matthew says, is where IBM can come in to
help take things further. Using artificial intelligence, IBM can help
Lorio create a custom AI model that helps to crunch
those numbers, to be a companion to the researchers, to
give them superpowers.
Speaker 8 (17:48):
We don't want to replace the intuition of the sentis.
We just want to make sure that this intuition is
really augmented by some calculation poor that as Gabrielle said,
then do those ten and the poor of twenty five
solution and probably try this one, this one, this one,
it looks like a better solution and Thentimately that's really
the decision of the chemist to make it happen.
Speaker 7 (18:12):
Well.
Speaker 2 (18:13):
To make a predictive AI model that can give Lorel
researchers those superpowers, you'd need that mountain of data, years
worth of laboratory testing and all Loreal's data digitized and
AI ready. You'd need to train artificial intelligence on everything
the company has already done in order for it to
predict what it could do.
Speaker 5 (18:33):
Loriial has one hundred years of course of data, fifty
years of digitized EGDA.
Speaker 2 (18:40):
This is Mariam Ashuri, Senior director of Product Management for
IBM Watson X. Loreal has the data and part of
IBM's job is to help put that data to work,
which involves ensuring data quality. Mariam talked about the concept
of AI ready data.
Speaker 5 (18:57):
The sole purpose of this data engineering cuyper is to
clean the data, and we call them AI ready data
makes them ready to be consumed by AI. So basically
looking into biases in the data to fix the distribution,
looking into guard brains that we are putting into place
in terms of removing personal information.
Speaker 2 (19:19):
Variam that explained that a custom model like the one
IBM is creating with Lorel can be more efficient and
targeted than the larger general purpose AI models.
Speaker 5 (19:29):
You've heard about large language models. The reason that they
call them large language model is they are exposed into
really large amount of data. So the larger the model,
the more cake of all the models are, but also
the larger computed requires that translate stand increase carbon footprint
(19:50):
and energy consumption that translates stand, increase latency that's your
response time that translatestand increase costs. So we started seeing
that interprise started grabbing a much smaller model customize it
on their proprietary data that's the data their DOMAINO specific data,
or the data about their users to create something differentiated
(20:13):
that is applicable to a real word use case but
also delivers the performance that they needed for a fraction
of the costs. And that's why there's been a lot
of push around using custom models versus very large general
purpose models.
Speaker 2 (20:29):
So how is a custom model created? Miriam says, you
start with a base model. Imagine you're buying a car,
You could get a minivan or a sedan or a
sports car, and then you get to customize it. You
could add a sunroof, leather seats, or a rearview camera.
Turns out you could do the same thing with your
AI model. You pick a base and then you customize it.
(20:51):
You tune it on the data unique to your organization.
Speaker 5 (20:55):
We do believe that one model doesn't fit all use cases.
You want to truly have access to any model anywhere,
and by any model anywhere, I really mean any model anywhere,
open source, proprietary, low call out your machine wherever the
model is. You want to host it yourself, because then
(21:16):
you would be able to take advantage of the best
of the technology at any point and pick the right
model for the target use case.
Speaker 2 (21:24):
So a custom model tuned on Lorel's data would be
more targeted and efficient than a general purpose model. It
would understand the researchers world and provide transparency into its workings.
That's part of the magic. And what could a custom
AI foundation model do for a company like Lorel if.
Speaker 10 (21:44):
You accord it was just moder is contain the complexity
of the formulation.
Speaker 7 (21:50):
That's game.
Speaker 2 (21:51):
La Moline an IBM distinguished engineer and one of the
people working on the AI model.
Speaker 10 (21:57):
And to hyperli the formulate or to go not only faster,
but also I would say, be able to include more
complexity or so in the formulation, more personalization, more certain ability,
better selected ingredient. So it's really a tool to help
(22:17):
them and to also help them to unniche the creativity.
Speaker 2 (22:25):
THEOMI saying that with its custom AI model, Lorel could
improve every step of its product development pipeline, make the
process faster and more sustainable. But he's also saying that
the model could help Lorel create something that's never been
done before.
Speaker 7 (22:40):
What could that product be? So I'm mourning you with that.
All my questions are going to be really dumb.
Speaker 11 (22:50):
Okay, now, please, by all means.
Speaker 2 (22:52):
Right to find out what people at Lorel are dreaming of.
I spoke with Trisha Iyagari, global general manager at Loriel's
Abeling brand, and they asked her about her own dreams
and how technology and science could help bring those dreams
into the world. Do you have a secret wish list
of things you think that this partnership could produce, Like,
(23:13):
is there a product out there that's been technically too difficult?
That you think could be a worthy target.
Speaker 11 (23:19):
There is one that I think could be really amazing.
Speaker 7 (23:21):
What's that?
Speaker 8 (23:23):
So?
Speaker 11 (23:23):
Shine products in general are harder to create, and we're
unable to create a shiny, long wearing eyeshadow. So basically
like a shadow that could stay on your eyelids, that
won't settle into creases, that won't move all over your face,
that has a glossy effect. It's like the holy grail.
Speaker 2 (23:41):
That's the holy grail. Yeah, yeah, you may have seen
that look in fashion shows, but that look isn't real,
not for people like me and Lucy.
Speaker 11 (23:51):
Anyway, if you're walking down a runway, you see a
lot of makeup artists doing techniques where they put some
eyeshadow on, they layer vasoline over it on like slatter
vasalina somebody's eyes to create this very like glossy look.
But you know, within five minutes after they walk down
the runway, I'm sure it's all over their face or
being washed off. So the look is kind of more
(24:13):
of like a fashion look that we've been unable to create,
and real, real consumers can't wear it because it would
get it everywhere.
Speaker 2 (24:20):
Trisia had another thing on her wish list too.
Speaker 11 (24:23):
The other that we would really like is semi permanent makeup.
So we've talked a lot about really really comfortable, thin
film makeup that you could wear all over your face
and that you can sleep in, and that it will
last a couple of days basically, so whether it be
on your face, on your lashes, on your brows. So
(24:44):
anything that's like more of a semi permanent meaning lasting
for three days or more, would be amazing.
Speaker 7 (24:49):
Yeah.
Speaker 2 (24:50):
Yeah, And you say those two things have been the
whole How long have they been on the wish list
of Loreel?
Speaker 4 (24:55):
Oh?
Speaker 11 (24:56):
My gosh. I have been trying to develop this shiny
eyeshadow since I started. What did I start, like twenty ten,
And I'm sure many people had asked before me, and
we tried so many iterations of it and nobody's been
able to achieve it.
Speaker 2 (25:14):
It's clear that Loreel's experts like Tricia have a lot
of ideas. I once said what I called a magic
wand project, where I called up scientists and technologists in
as many different fields as possible and asked them what
they could create if they could just wave a magic
wand and make it real, And everyone had something they'd
(25:36):
want to create everyone. That's not the issue. The issue
is that there are a million different impediments to make
the ideas on the wish list reel. Lack of resources,
lack of time, some crucial bit of know how is lacking.
There's a gap between what we want and what we
can actually have, and one of the simplest ways to
think of the promise of AI is that it can
(25:58):
narrow that gap, not close it, of course, but do
enough that people with dreams realize there are more things
within their grasp.
Speaker 7 (26:07):
Than they could ever have imagined.
Speaker 2 (26:24):
Smart Talks with IBM is produced by Matt Romano, Amy Gaines, McQuaid,
Lucy Sullivan and Jake Harper. Were edited by Lacy Roberts.
Engineering by Nina Bird Lawrence, mastering by Sarah Brugerer. Music
by Gramoscope. Special thanks to Tatiana Lieberman and Cassidy Meyer.
Smart Talks with IBM is a production of Pushkin Industries
(26:46):
and Ruby Studio at iHeartMedia. To find more Pushkin podcasts,
listen on the iHeartRadio app, Apple Podcasts, or wherever you
get your podcasts. I'm Malcolm Gabo. This is a paid
advertised from IBM. The conversations on this podcast don't necessarily
represent IBM's positions, strategies, or opinions.
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