July 15, 2025 • 26 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
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
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
(00:23):
when I sent her an assignment to Lorel's North America
Research and Innovation Center.
Speaker 2 (00:29):
All right, I am reporting live from the Looreal 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
(00:51):
to Clark, New Jersey and Lorel, this is a beautiful compound.
It kind of looks like a spa.
Speaker 1 (00:59):
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
(01:19):
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,
(01:42):
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 3 (01:57):
A lot of our consumer insights with Floreal is like,
where are consumers going in the future.
Speaker 1 (02:02):
This is Nadine Gomez. She's vice president for Loreel's research
and innovation development team.
Speaker 3 (02:08):
Our chemists are working on five six years down the line.
We predicted that consumers wanted more of a softer look
on their lips as well.
Speaker 2 (02:15):
So how do you predict something like that.
Speaker 3 (02:17):
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 to become.
Speaker 1 (02:28):
Big Lucy talked with her about the origins of one
of their products, Mabelne matt Inc Liquid lipstick.
Speaker 3 (02:36):
Our competitors have two steps. The first step is a
base go 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 cot 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
(03:00):
reapplying a bomb. So we're like, what can we do
to create this two step into one step?
Speaker 1 (03:05):
So Loriel had a challenge, how do you make a
comfortable liquid matt 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
(03:27):
years before it comes out. But can this process be
accelerated taken further? Be even more sustainable. That's what IBM
and Loreel 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 of the world of technology. In our
(03:50):
last episode, we talked about how an AI assistant created
with IBM Watson X helps future teachers practice responsive teaching
by simulating class interactions with students. In this episode, we
take you on an even more unexpected journey into the
world of cosmetics, hair care, skincare, fragrance, makeup, and how
(04:13):
a custom AI model could help Loriel's researchers shape the
future of what we put on our faces every morning.
I want to say on lipstick a moment longer to
help illustrate what goes into Loriel's product development, and let's
(04:34):
focus on matt inc lipstick. Loriel wanted to create something
that was comfortable and could be applied in one step.
Speaker 4 (04:42):
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 1 (04:53):
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 4 (05:07):
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 1 (05:14):
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.
Speaker 4 (05:26):
Samples, and I actually have something for you to try here,
so you can try this is what was the initial product.
Speaker 2 (05:34):
Okay, so this is like it sort of looks like okay,
it is. It looks like vacline that has like more
of a color. It's kind of a beige, looks like
some skin.
Speaker 5 (05:44):
Okay.
Speaker 2 (05:45):
So this was from the two steps this would go
on after Oh okay, right islet Okay, So.
Speaker 4 (05:55):
It feels like very wet. As you can see, it's
kind of it's gonna absorb into your skin and leaves
and then you're gonna feel the dryness.
Speaker 2 (06:03):
Of the product. Okay, so we're gonna move.
Speaker 4 (06:05):
From the clay product that you have on your hand
now to the elastimmer or.
Speaker 6 (06:09):
You try half hour.
Speaker 1 (06:11):
This jar held the elastomer that Laurel had spent years
developing in the lab.
Speaker 2 (06:16):
This one is it clear, looks like aqua for a
much bligger and you.
Speaker 4 (06:23):
Can pay a physical layer that you're putting on your aid.
Speaker 2 (06:26):
Yeah, so that's much thicker. It kind of like clumps together. Yeah,
it's more of a cloudy. It's less shimmery though that's intended.
Speaker 4 (06:35):
Yes, so this is a like a powder, this dispersed
in dimetic code, and it creates like a comfort on
your lips, a field 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
(06:56):
the market. Yeah, 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 and over again, So you can apply matt
ink once for the day and.
Speaker 1 (07:12):
You're good, right, Alex Good is under selling 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
(07:33):
in the nineteen thirties, 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. Lorel tackled that challenge
(07:53):
in the lab, with chemists like 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 Nadine told Lucy, the lipstick team
had to put the new product to extensive tests.
Speaker 3 (08:13):
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 for three days, is it still going
to perform? Is it gonna smell? Is it gonna look granted?
(08:36):
Is it gonna change colors?
Speaker 7 (08:37):
We do all that.
Speaker 1 (08:38):
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.
Speaker 3 (08:48):
Points, 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 masters degrees chemistry, biology, psychology.
Speaker 1 (08:56):
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?
Meet you guys, Yeah. 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
(09:19):
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, Methu Cassier
and Gabriel Bertoli metthew is VP for Digital and Transformation.
Gabriel is the Chief Digital Transformation Officer for Formulation. These
(09:40):
are the people whose jobs are to oversee big changes
within the company. And Methu told me to try on
some lipstick.
Speaker 7 (09:48):
I'm gonna make you try this one.
Speaker 1 (09:50):
Okay, this is super stay viniting final inc.
Speaker 7 (09:54):
Yeah, so that's a glosse.
Speaker 1 (09:55):
Never in my life put on lipsey. You have no
idea what I'm doing.
Speaker 7 (09:57):
You don't have to put it. You can try it virtu.
Speaker 1 (10:00):
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 beauty. Oh my goodness.
That is the strangest thing I've ever said. I look
quite fetching, that's the way.
Speaker 2 (10:16):
It amazing.
Speaker 7 (10:18):
And I can just hit you can choose your color absolutely.
Speaker 1 (10:21):
So I'm on a little app 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 7 (10:33):
Yeah, absolutely, that's all purpose. If you want to manage
a trend, I would go for something more like pitch.
Speaker 1 (10:40):
You think I'm a peach person.
Speaker 4 (10:42):
I don't know.
Speaker 1 (10:43):
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, Laurel has seized
the power of AI and more recently, generative AI technology
(11:07):
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 7 (11:20):
I saw the original creator of Loyal, Jentulier, 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
(11:43):
very big 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 kin 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
story of innovation, we launch on new molecules like one
(12:05):
that you can find in laroche pose Milabi three, which
is really helping people to find against some you know,
spots that they could have on their skin. It's all
about like big mountation, how to regulate it.
Speaker 1 (12:16):
Loreel and IBM were both started in the early twentieth century,
Lorel 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
(12:39):
Lorel and IBM start working together?
Speaker 8 (12:41):
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 1 (12:56):
This is Gabriel Bertoli who I spoke to alongside too.
Speaker 8 (13:01):
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 the
supplier and your work they delivered to you. Here the
concept was totally different.
Speaker 1 (13:20):
Mid two said that the collaboration began with simple conversations.
Speaker 7 (13:24):
So if you look at the way IBM entered into
loyal 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,
(13:45):
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 centrick in the way that
it selves their daily need.
Speaker 1 (13:58):
More. The point 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. Looreal isn't just a cosmetics company.
(14:21):
It's a beauty data powerhouse.
Speaker 8 (14:24):
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 1 (14:45):
So you say you have sixteen terabytes of data, put
that in perspective. How much data is that?
Speaker 2 (14:50):
Give me?
Speaker 8 (14:52):
This is one hundred year of Floreal data based on
the last forty years of data the systems. So this
is really I mean, we're talking about one hundred years
of data that only Loreal have. Let's take the example
of the ellipsis. I mean, you know, if ellipsex can
be between twenty and thirty year ow material, each raw
(15:13):
material will have I would say ten or fifteen way
of doing things.
Speaker 1 (15:21):
Gabrielle is talking about how things used to be done.
Researchers at Lorel 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.
(15:45):
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
(16:06):
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 8 (16:20):
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 1 (16:41):
This, Matthew says, is where IBM can come in to
help take things further. Using artificial intelligence, IBM can help
Loriel create a custom AI model that helps to crunch
those numbers to be a companion to the researchers to
give them superpowers.
Speaker 7 (16:58):
We don't want to replace the intuition of sent this.
We just want to make sure that this intuition is
really augmented by some calculation poor that, as Gabrielle said,
then does all ten and the poor of twenty five
solution and se probably try this one, this one, this one,
it looks like a better solution, and then ultimately that's
really the decision of the chemist to make it happen.
Speaker 1 (17:21):
Well. To make a predictive AI model that can give
Loreal 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 need to train artificial intelligence on
everything the company has already done in order for it
to predict what it could do.
Speaker 6 (17:43):
Loriial has one hundred years a course of data, fifty
years of digitized EGGDA.
Speaker 1 (17:49):
This is Miriam Ashuri, Senior director of Product Management for
IBM Watson X. Lorel 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 6 (18:07):
The sole purpose of this data engineering pipeline 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 and the data to fix the distribution,
looking into guard brains that we are putting into place
in terms of removing personal information.
Speaker 1 (18:29):
Marriam that explained that a custom model like the one
IBM is creating with Loril can be more efficient and
targeted than the larger general purpose AI models.
Speaker 6 (18:39):
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 take of all the models are, but also
the larger computed requires that translates and increase carbon footprint
(18:59):
and entergy consumption, that translatestan increase latency that's your response
time that translatestand increased costs. So you started seeing that
enterprises 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
(19:23):
that is applicable to a real world 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 1 (19:39):
So how is a custom model created? Miriam says, you
start with the 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 cut
(20:00):
customize it. You tune it on the data unique to
your organization.
Speaker 6 (20:04):
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
(20:26):
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 1 (20:33):
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.
Speaker 9 (20:54):
Accord with more contain the complexity of the formulation.
Speaker 1 (21:00):
That's Gillomme lais Moline, an IBM distinguished engineer and one
of the people working on the AI model.
Speaker 9 (21:07):
And to hype ower the formulator to go not only
past 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 them and to also help them to unniche
(21:30):
the creativity.
Speaker 1 (21:34):
Kayomi is saying that with its custom AI model, Loreel
can 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 Loriel create something that's never
been done before. What could that product be? So I'm
(21:56):
mourning you with that. All my questions are going to
be really dumb.
Speaker 5 (21:59):
Okay, no, please by all me.
Speaker 1 (22:03):
To find out what people at Loril are dreaming of.
I spoke with Trisha Iyagari, global general manager at Loriel's
Mabeline 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,
(22:23):
is there a product out there that's been technically too
difficult that you think could be a worthy target?
Speaker 5 (22:29):
There is one that I think could be really amazing.
Speaker 1 (22:31):
What's that?
Speaker 5 (22:32):
So? 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 1 (22:51):
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 be and lucy.
Speaker 5 (23:00):
Anyway, if you're walking down a runway, you see a
lot of makeup artists doing techniques where they put some
shadow on, they layer vasoline over it, and like slather
vassaline on 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
(23:23):
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 1 (23:30):
Trisha had another thing on her wish list too.
Speaker 5 (23:32):
The other that we would really like is semi permanence 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 then it will
last a couple of days basically, so whether it be
on your face, on your lashes, on your brows. So
(23:53):
anything that's like more of a semi permanent meaning lasting
for three days or more, would be amazing.
Speaker 1 (23:59):
Yeah. Yeah, And you say those two things, they have been
the whole How long have they been on the wish
list of lorel Oh.
Speaker 5 (24:05):
My gosh. I have been trying to develop this shiny
eyeshadow since I started. What year 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 1 (24:23):
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
(24:46):
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 ga 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
(25:07):
narrow that gap, not close it, of course, but do
enough that people with dreams realize there are more things
within their grasp than they could ever have imagined. Smart
(25:34):
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 Brugaier, Music
by Gramoscope. Special thanks to Tatiana Lieberman and Cassidy Meyer.
Smart Talks with IBM is a production of Pushkin Industries
(25:56):
and Ruby's studio at iHeartMedia. To find more Pushkin podcast
listen on the iHeartRadio app, Apple Podcasts, or wherever you
get your podcasts. I'm Malcolm Laba. This is a paid
advertisement from IBM. The conversations on this podcast don't necessarily
represent IBM's positions, strategies, or opinions.
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
(00:23):
when I sent her an assignment to Lorel's North America
Research and Innovation Center.
Speaker 2 (00:29):
All right, I am reporting live from the Looreal 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
(00:51):
to Clark, New Jersey and Lorel, this is a beautiful compound.
It kind of looks like a spa.
Speaker 1 (00:59):
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
(01:19):
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,
(01:42):
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 3 (01:57):
A lot of our consumer insights with Floreal is like,
where are consumers going in the future.
Speaker 1 (02:02):
This is Nadine Gomez. She's vice president for Loreel's research
and innovation development team.
Speaker 3 (02:08):
Our chemists are working on five six years down the line.
We predicted that consumers wanted more of a softer look
on their lips as well.
Speaker 2 (02:15):
So how do you predict something like that.
Speaker 3 (02:17):
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 to become.
Speaker 1 (02:28):
Big Lucy talked with her about the origins of one
of their products, Mabelne matt Inc Liquid lipstick.
Speaker 3 (02:36):
Our competitors have two steps. The first step is a
base go 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 cot 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
(03:00):
reapplying a bomb. So we're like, what can we do
to create this two step into one step?
Speaker 1 (03:05):
So Loriel had a challenge, how do you make a
comfortable liquid matt 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
(03:27):
years before it comes out. But can this process be
accelerated taken further? Be even more sustainable. That's what IBM
and Loreel 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 of the world of technology. In our
(03:50):
last episode, we talked about how an AI assistant created
with IBM Watson X helps future teachers practice responsive teaching
by simulating class interactions with students. In this episode, we
take you on an even more unexpected journey into the
world of cosmetics, hair care, skincare, fragrance, makeup, and how
(04:13):
a custom AI model could help Loriel's researchers shape the
future of what we put on our faces every morning.
I want to say on lipstick a moment longer to
help illustrate what goes into Loriel's product development, and let's
(04:34):
focus on matt inc lipstick. Loriel wanted to create something
that was comfortable and could be applied in one step.
Speaker 4 (04:42):
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 1 (04:53):
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 4 (05:07):
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 1 (05:14):
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.
Speaker 4 (05:26):
Samples, and I actually have something for you to try here,
so you can try this is what was the initial product.
Speaker 2 (05:34):
Okay, so this is like it sort of looks like okay,
it is. It looks like vacline that has like more
of a color. It's kind of a beige, looks like
some skin.
Speaker 5 (05:44):
Okay.
Speaker 2 (05:45):
So this was from the two steps this would go
on after Oh okay, right islet Okay, So.
Speaker 4 (05:55):
It feels like very wet. As you can see, it's
kind of it's gonna absorb into your skin and leaves
and then you're gonna feel the dryness.
Speaker 2 (06:03):
Of the product. Okay, so we're gonna move.
Speaker 4 (06:05):
From the clay product that you have on your hand
now to the elastimmer or.
Speaker 6 (06:09):
You try half hour.
Speaker 1 (06:11):
This jar held the elastomer that Laurel had spent years
developing in the lab.
Speaker 2 (06:16):
This one is it clear, looks like aqua for a
much bligger and you.
Speaker 4 (06:23):
Can pay a physical layer that you're putting on your aid.
Speaker 2 (06:26):
Yeah, so that's much thicker. It kind of like clumps together. Yeah,
it's more of a cloudy. It's less shimmery though that's intended.
Speaker 4 (06:35):
Yes, so this is a like a powder, this dispersed
in dimetic code, and it creates like a comfort on
your lips, a field 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
(06:56):
the market. Yeah, 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 and over again, So you can apply matt
ink once for the day and.
Speaker 1 (07:12):
You're good, right, Alex Good is under selling 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
(07:33):
in the nineteen thirties, 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. Lorel tackled that challenge
(07:53):
in the lab, with chemists like 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 Nadine told Lucy, the lipstick team
had to put the new product to extensive tests.
Speaker 3 (08:13):
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 for three days, is it still going
to perform? Is it gonna smell? Is it gonna look granted?
(08:36):
Is it gonna change colors?
Speaker 7 (08:37):
We do all that.
Speaker 1 (08:38):
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.
Speaker 3 (08:48):
Points, 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 masters degrees chemistry, biology, psychology.
Speaker 1 (08:56):
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?
Meet you guys, Yeah. 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
(09:19):
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, Methu Cassier
and Gabriel Bertoli metthew is VP for Digital and Transformation.
Gabriel is the Chief Digital Transformation Officer for Formulation. These
(09:40):
are the people whose jobs are to oversee big changes
within the company. And Methu told me to try on
some lipstick.
Speaker 7 (09:48):
I'm gonna make you try this one.
Speaker 1 (09:50):
Okay, this is super stay viniting final inc.
Speaker 7 (09:54):
Yeah, so that's a glosse.
Speaker 1 (09:55):
Never in my life put on lipsey. You have no
idea what I'm doing.
Speaker 7 (09:57):
You don't have to put it. You can try it virtu.
Speaker 1 (10:00):
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 beauty. Oh my goodness.
That is the strangest thing I've ever said. I look
quite fetching, that's the way.
Speaker 2 (10:16):
It amazing.
Speaker 7 (10:18):
And I can just hit you can choose your color absolutely.
Speaker 1 (10:21):
So I'm on a little app 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 7 (10:33):
Yeah, absolutely, that's all purpose. If you want to manage
a trend, I would go for something more like pitch.
Speaker 1 (10:40):
You think I'm a peach person.
Speaker 4 (10:42):
I don't know.
Speaker 1 (10:43):
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, Laurel has seized
the power of AI and more recently, generative AI technology
(11:07):
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 7 (11:20):
I saw the original creator of Loyal, Jentulier, 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
(11:43):
very big 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 kin 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
story of innovation, we launch on new molecules like one
(12:05):
that you can find in laroche pose Milabi three, which
is really helping people to find against some you know,
spots that they could have on their skin. It's all
about like big mountation, how to regulate it.
Speaker 1 (12:16):
Loreel and IBM were both started in the early twentieth century,
Lorel 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
(12:39):
Lorel and IBM start working together?
Speaker 8 (12:41):
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 1 (12:56):
This is Gabriel Bertoli who I spoke to alongside too.
Speaker 8 (13:01):
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 the
supplier and your work they delivered to you. Here the
concept was totally different.
Speaker 1 (13:20):
Mid two said that the collaboration began with simple conversations.
Speaker 7 (13:24):
So if you look at the way IBM entered into
loyal 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,
(13:45):
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 centrick in the way that
it selves their daily need.
Speaker 1 (13:58):
More. The point 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. Looreal isn't just a cosmetics company.
(14:21):
It's a beauty data powerhouse.
Speaker 8 (14:24):
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 1 (14:45):
So you say you have sixteen terabytes of data, put
that in perspective. How much data is that?
Speaker 2 (14:50):
Give me?
Speaker 8 (14:52):
This is one hundred year of Floreal data based on
the last forty years of data the systems. So this
is really I mean, we're talking about one hundred years
of data that only Loreal have. Let's take the example
of the ellipsis. I mean, you know, if ellipsex can
be between twenty and thirty year ow material, each raw
(15:13):
material will have I would say ten or fifteen way
of doing things.
Speaker 1 (15:21):
Gabrielle is talking about how things used to be done.
Researchers at Lorel 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.
(15:45):
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
(16:06):
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 8 (16:20):
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 1 (16:41):
This, Matthew says, is where IBM can come in to
help take things further. Using artificial intelligence, IBM can help
Loriel create a custom AI model that helps to crunch
those numbers to be a companion to the researchers to
give them superpowers.
Speaker 7 (16:58):
We don't want to replace the intuition of sent this.
We just want to make sure that this intuition is
really augmented by some calculation poor that, as Gabrielle said,
then does all ten and the poor of twenty five
solution and se probably try this one, this one, this one,
it looks like a better solution, and then ultimately that's
really the decision of the chemist to make it happen.
Speaker 1 (17:21):
Well. To make a predictive AI model that can give
Loreal 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 need to train artificial intelligence on
everything the company has already done in order for it
to predict what it could do.
Speaker 6 (17:43):
Loriial has one hundred years a course of data, fifty
years of digitized EGGDA.
Speaker 1 (17:49):
This is Miriam Ashuri, Senior director of Product Management for
IBM Watson X. Lorel 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 6 (18:07):
The sole purpose of this data engineering pipeline 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 and the data to fix the distribution,
looking into guard brains that we are putting into place
in terms of removing personal information.
Speaker 1 (18:29):
Marriam that explained that a custom model like the one
IBM is creating with Loril can be more efficient and
targeted than the larger general purpose AI models.
Speaker 6 (18:39):
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 take of all the models are, but also
the larger computed requires that translates and increase carbon footprint
(18:59):
and entergy consumption, that translatestan increase latency that's your response
time that translatestand increased costs. So you started seeing that
enterprises 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
(19:23):
that is applicable to a real world 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 1 (19:39):
So how is a custom model created? Miriam says, you
start with the 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 cut
(20:00):
customize it. You tune it on the data unique to
your organization.
Speaker 6 (20:04):
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
(20:26):
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 1 (20:33):
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.
Speaker 9 (20:54):
Accord with more contain the complexity of the formulation.
Speaker 1 (21:00):
That's Gillomme lais Moline, an IBM distinguished engineer and one
of the people working on the AI model.
Speaker 9 (21:07):
And to hype ower the formulator to go not only
past 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 them and to also help them to unniche
(21:30):
the creativity.
Speaker 1 (21:34):
Kayomi is saying that with its custom AI model, Loreel
can 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 Loriel create something that's never
been done before. What could that product be? So I'm
(21:56):
mourning you with that. All my questions are going to
be really dumb.
Speaker 5 (21:59):
Okay, no, please by all me.
Speaker 1 (22:03):
To find out what people at Loril are dreaming of.
I spoke with Trisha Iyagari, global general manager at Loriel's
Mabeline 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,
(22:23):
is there a product out there that's been technically too
difficult that you think could be a worthy target?
Speaker 5 (22:29):
There is one that I think could be really amazing.
Speaker 1 (22:31):
What's that?
Speaker 5 (22:32):
So? 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 1 (22:51):
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 be and lucy.
Speaker 5 (23:00):
Anyway, if you're walking down a runway, you see a
lot of makeup artists doing techniques where they put some
shadow on, they layer vasoline over it, and like slather
vassaline on 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
(23:23):
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 1 (23:30):
Trisha had another thing on her wish list too.
Speaker 5 (23:32):
The other that we would really like is semi permanence 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 then it will
last a couple of days basically, so whether it be
on your face, on your lashes, on your brows. So
(23:53):
anything that's like more of a semi permanent meaning lasting
for three days or more, would be amazing.
Speaker 1 (23:59):
Yeah. Yeah, And you say those two things, they have been
the whole How long have they been on the wish
list of lorel Oh.
Speaker 5 (24:05):
My gosh. I have been trying to develop this shiny
eyeshadow since I started. What year 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 1 (24:23):
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
(24:46):
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 ga 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
(25:07):
narrow that gap, not close it, of course, but do
enough that people with dreams realize there are more things
within their grasp than they could ever have imagined. Smart
(25:34):
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 Brugaier, Music
by Gramoscope. Special thanks to Tatiana Lieberman and Cassidy Meyer.
Smart Talks with IBM is a production of Pushkin Industries
(25:56):
and Ruby's studio at iHeartMedia. To find more Pushkin podcast
listen on the iHeartRadio app, Apple Podcasts, or wherever you
get your podcasts. I'm Malcolm Laba. This is a paid
advertisement from IBM. The conversations on this podcast don't necessarily
represent IBM's positions, strategies, or opinions.
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