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- [00:02]
A
To understand why the cosmetics supergiant l' Oreal 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 when I sent her an assignment to l' Oreal's North America Research and Innovation Center.
- [00:30]
B
All right. I am reporting live from the l' Oreal visitors parking lot. Malcolm told me that he would be sending me to Paris, France for this l' Oreal excursion. But instead, I am in Clark, New Jersey. Passed a lot of strip malls on the way here. But to be fair to Clark, New Jersey and l', Oreal, this is a beautiful compound. It kind of looks like a spa.
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A
Lucy went into the center and was blown away. The facility houses about 600 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 l' Oreal's facility is so big and has so many people is that everything l' Oreal 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, 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 the nature of fashion trends. The kind of lipstick consumers want is constantly changing.
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C
A lot of our consumer insights with l' Oreal is like, where are consumers going in the future?
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A
This is Nadine Gomez. She's vice president for l' Oreal's research and innovation development team.
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C
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.
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B
How do you predict something like that?
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C
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, five, six years, it's going to become big.
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A
Lucy talked with her about the origins of one of their products, Maybelline Matte Ink Liquid Lipstick.
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C
Our competitors have two steps. The first step is a base coat. It's super opaque. You get the color and you get the matting, but it's very, very dry on your lips. You cannot wear that honestly, more than 10 minutes. It feels like your lips are like, 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 balm. You put it on top, it's super comfortable. But we also noticed that consumers kind of get tired of reapplying a balm. So we were like, what can we do to create this two step into one step?
- [03:06]
A
So l' Oreal had a challenge. How do you make a comfortable liquid matte lipstick that doesn't require consumers to reapply a top layer of balm? 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 Mat Inc. Can take years before it comes out. But can this process be accelerated, taken further, be even more sustainable? That's what IBM and l' Oreal 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 listing a glimpse behind the curtain of the world of technology. In our last episode, we talked about how an AI assistant created with IBM WatsonX 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, hair care, skincare, fragrance, makeup, and how a custom AI model could help l' Oreal'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 goes into l' Oreal's product development. And let's focus on matte ink lipstick. L' Oreal wanted to create something that was comfortable and could be applied in one step.
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D
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 l'. Oreal.
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A
That's Alex Good, a senior chemist who leads the Lip Products team in North America. She says the trick to making matte ink work was finding an elastomer, a substance they were already using in foundation.
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D
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.
- [05:15]
A
She handed Lucy two jars. The first jar contained the former version of the product that was used in Superstay 24. By the way, this is exactly why I sent Lucy to the lab in my place, the samples and I actually
- [05:29]
D
have something for you to try here, so you can try. This is what was in the initial product.
- [05:35]
B
Okay. So this is like. It's sort of looks like. Okay, it is clay. It looks like Vaseline. That has like a more of a color. It's kind of a beige. Looks like some skin. Okay, so this is from the Two step. This would go on after I would
- [05:49]
D
try it on your hand.
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B
Oh, okay. Okay.
- [05:55]
D
So it feels like very wet.
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B
Yeah.
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D
As you can see, it's kind of. It's going to absorb into your skin and leave and then you're going to feel the dryness of the product once it's gone. So we're going to move from the clay product that you have on your hand now to the elastomer.
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C
I'll let you try that one.
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A
This jar held the elastomer that l' Oreal had spent years developing in the lab.
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B
This one is a clear. Looks like Aquaphor. So much clearer.
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D
And you can feel like the physical layer that you're putting on your hands.
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B
Yeah. So that's much thicker. It kind of like clumps together. Yeah, it's more of a cloudy, less shimmery though that's intended.
- [06:36]
D
Yes. So this is a like a powder that's 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 former 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, this is what gives it comfort. So the difference between Superstay 24 and Matte Ink is really the comfort. They both last a long time. But this matte ink, you don't have to apply the balm over and over again. So you can apply matte ink once for the day and you're good.
- [07:14]
A
Alex Goode is underselling it here. Once for the day and you're good. That's a liquid lipstick revolution. Literally millions of l' Oreal consumers around the world have worn matte ink. It's a blockbuster. It's also a marvel of science. The world's first liquid lipstick was developed in the 1930s. And it was actually just a stain for your lips. It barely counts as lipstick. Then came another wave of liquid lipstick. When they were able to make it matte, that was the two step version. It felt heavy on your lips. You had to keep reapplying the top coat. It was inconvenient. L' Oreal tackled that challenge in the lab with chemists like Alex and Nadine leading the charge. Their breakthrough, matte ink. But creating matte ink 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 through extensive tests.
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C
We do a very robust stability system here. You know, we have color, odor, appearance. We monitor this in extreme conditions. We simulate in 45 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 a car In Arizona, it's 112 degrees for three days. Is it still going to perform? Is it going to smell? Is it going to look rancid? Is it going to change colors? We do all that.
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A
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.
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C
So much science behind it. You can see here how many scientists we have. You know, some of them have PhDs, some of them have master's degrees. Chemistry, biology, psychology. Also.
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A
When I first heard about this collaboration between l' Oreal and IBM, I was surprised. I thought, these are two very different companies. What do they really have in common?
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E
Pleasure to meet you guys. Pleasure, yeah.
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A
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 l', Oreal, Mathieu, Cassier and Gabriel Bertolli. Mathieu is VP for digital and Transformation. Gabriel is the chief Digital Transformation Officer for formulation. These are the people whose jobs are to oversee big changes within the company. And Mathieu told me to try on some lipstick.
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F
I'm gonna make you try this one.
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E
Okay. This is Super Stay.
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F
Vinyl ink.
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E
Vinyl ink, yeah.
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F
So that's a glossy.
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A
I've never in my life put on lipstick. I have no idea what I'm doing.
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F
You don't have to put it. You can try it virtually.
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A
Oh, this may not be news to people who buy makeup, but it was news to me. You can try on l' Oreal products virtually. They call it augmented beauty. Oh, my goodness.
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E
That is the strangest thing I've ever seen.
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A
I look quite fetching. I think it's quite amazing.
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E
And I can just hit.
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F
You can choose your color. Absolutely.
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E
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, trying on each one, you can now do that from home if you're not even at the store.
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F
Yeah, absolutely. That's the whole purpose. If you want to match a trend, I would go for something more like peach.
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E
You think I'm a peach person?
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F
I don't know.
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A
Yeah, no, that looks.
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E
I have to say, that looks kind of natural. It just enhanced. It's given me a boyish air I would not otherwise have.
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A
This is why l' Oreal says it creates beauty products and beauty experiences. L' Oreal is a beauty tech company. Over the last decade, l' Oreal has seized the power of AI and more recently, generative AI Technology has become a driving force alongside science and creativity. And while some of this digital technology is relatively new, Matthieu helped me see that IBM and l' Oreal have always had a lot in common.
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F
I saw the original creator of L', Oreal, Le Jean Schuyler, was a chemist in 1909, so 116 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 1930s. That was a very, very big milestone where we also invented not only product, but a reconstructed skin. So if you look at 1979, we've been 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 story of innovation, we launched some new molecules, like one that you can find in la Roche, Posay Melabi 3, which is really helping people to find again, some spots that they could have on their skin. It's all about pigmentation, how to regulate it.
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A
L' Oreal and IBM were both started in the early 20th century. L' Oreal in 1909 and IBM in 1911. 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?
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E
When do l' Oreal and IBM start working together?
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G
So we started in 2023, at the end of the year. But you know, really the discussion, this 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 2024.
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A
This is Gabriel Bertolli, who I spoke to alongside Mathieu.
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G
What really played a key role here is we wanted to bring, from a logic perspective, two R&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 you work, they deliver to you. Here, the concept was totally different.
- [13:21]
A
Matthieu said that the collaboration began with simple conversations.
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F
So if you look at the way IBM entered into l' Oreal Labs, it starts by interviewing people. What would help you to do your job? What is your business needs? 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, in China, Japan, us, France, 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 give to people is really people centric in the way that it serves their daily need.
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A
More to the point, l' Oreal has leveraged technology for decades and accumulated a mountain 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. L' Oreal isn't just a cosmetics company, it's a beauty data powerhouse.
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G
If we have 16,000 terabytes 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.
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E
So you say you have 16 terabytes of data. Put that in perspective. How much data is that? Give me.
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G
This is 100 year of L' Oreal data based on the last 40 years of data in the systems. So this is really, I mean, we're talking about 100 years of data that only L' Oreal have. Let's take the example of the lipsticks. I mean, you know, lipsticks can be between 20 and 30 raw material. Each raw material will have, I would say, 10 or 15 way of doing things.
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A
Gabrielle is talking about how things used to be done. Researchers at L' Oreal needed roughly 25 ingredients for a new lipstick formulation. But they have to choose from a pool of hundreds of, 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. 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 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?
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G
This is 10 on the power of 25.
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E
Yeah.
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G
This is 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.
- [16:41]
A
This, Matthieu says, is where IBM can come in to help take things further. Using artificial intelligence, IBM can help l' Oreal create a custom AI model that helps to crunch those numbers, to be a companion to the researchers, to give them superpowers.
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F
We don't want to replace the intuition of the scientists. We just want to make sure that this intuition is really augmented by some calculation power that, as Gabriel said, can do those 10 at the power of 25 solutions and say, 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.
- [17:22]
A
Well, to make a predictive AI model that can give l' Oreal researchers those superpowers, you'd need that mountain of data, years worth of laboratory testing and all l' Oreal'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.
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H
L' Oreal has hundred years worth of data, fifty years of digitized data.
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A
This is Marya Machuri, senior Director of Product management for IBM WatsonX. Loreal has the data and part of IBM's job is to help put that data to work, which involves ensuring data quality. Maryam talked about the concept of AI ready data.
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H
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 in the data to fix the distribution, looking into guardrails that we are putting into place in terms of removing personal information.
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A
Maryam then explained that a custom model like the one IBM is creating with l' Oreal can be more efficient and targeted than the larger general purpose AI models.
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H
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 capable the models are, but also the larger compute it requires. That translates to an increased carbon footprint and energy consumption. That translates to an increase latency. That's your response time. That translates to an increased cost. So we started seeing that enterprises started grabbing a much smaller model, customize it on their proprietary data, that's the data, their domain specific data, or the data about their users to create something differentiated, that is applicable to a real world use case, but also delivers the performance that they needed for a fraction of the cost. And that's why there's been a lot of push around using custom models versus very large general purpose models.
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A
So how is a custom model created? Maryam 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 rear view camera. Turns out you could do the same thing with your AI model. You pick a base and then you customize it. You tune it on the data unique to your organization.
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H
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, local, at your machine, wherever the model is, you want to host it yourself. Because then 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.
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A
So a custom model tuned on l' Oreal's data would be more targeted and efficient than a general purpose model. It would understand a researcher's 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 l'? Oreal?
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E
The goal of this model is to tame the complexity of the formulation.
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That's Guillaume Lewoir Malin, an IBM distinguished engineer and one of the people working on the AI model.
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E
And to help, I would say, the formulator to go not only faster, but also, I would say, be able to include more complexity also in their formulation. More personalization, more sustainability, better selection, the ingredient. So it's really a tool to help them and to also help them, also to unleash the creativity.
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A
Guillaume is saying that with its custom AI model, l' Oreal 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 l' Oreal create something that's never been done before. What could that product be. So I'm warning you that some of my questions are going to be really dumb.
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I
Okay? No, please, by all means.
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E
All right, all right.
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A
To find out what people at l' Oreal are dreaming of, I spoke with Tricia Iyegari, global general manager at l' Oreal's Maybelline brand. And I asked her about her own dreams and how technology and science could help bring those dreams into. Into the world. Do you have a secret wish list of things you think that this partnership could produce? Like, is there a product out there that's been technically too difficult that you think would. Could be a worthy target?
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I
There is one that I think could be really amazing.
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A
What's that?
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I
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.
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A
That's the Holy Grail?
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I
Yeah.
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E
Yeah.
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A
You may have seen that look in fashion shows, but that look isn't real. Not for people like me and Lucy, anyway.
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I
If you're walking down a Runway, you see a lot of makeup artists doing techniques where they put some eyeshadow on. They layer vaseline over it, like slather vaseline 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 of like a fashion look that we've been unable to create in real. Real consumers can't wear it because it would get it everywhere.
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A
Tricia had another thing on her wish list, too.
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I
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 then it will last a couple of days, basically. So whether it be on your face, on your lashes, on your brows. So anything that's like more of a semi permanent, meaning lasting for three days or more, would be amazing.
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A
Yeah, yeah. When you say those two things have been. How long have they been on the wish list of l'? Oreal?
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I
Oh, my gosh. I have been trying to develop this shiny eyeshadow since I started. What year did I start? Like 2010. 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.
- [24:24]
A
It's clear that l' Oreal's experts like Tricia have a lot of ideas. I once did 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 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 real. 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 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 Talks with IBM is produced by Matt Romano, Amy Gaines McQuaid, Lucy Sullivan and Jake Harper. We're edited by Lacey Roberts, engineering by Nina Bird Lawrence mastering by Sarah Bruguer Music by Grammascope. Special thanks to Tatiana Lieberman and Cassidy Meyer. Smart Talks with IBM is a production of Pushkin Industries 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 Glavo. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions. Sam.