Teaching Computers to Smell
36:16 Share

Pushkin. This is an iHeart Podcast. Behind every successful business is a vision. Bringing it to life takes more than effort. It takes the right financial foundation and support. That's where Chase for Business comes in. With convenient digital tools, helpful resources and personalised guidance, they can help your business forge ahead confidently. Learn more at chase.com backslash business. Chase for Business. Make more of what's yours.

The Chase mobile app is available for select mobile devices. Message and data rates may apply. JPMorgan Chase Bank N.A., member FDIC. Copyright 2025, JPMorgan Chase & Company. This is Justin Richmond from Broken Record. The three things I love about summer are pool days, blaring all the new summer songs that come out, and endless refreshing iced drinks from Starbucks. Even better, my favorite summer drink has returned to Starbucks. The Summer Berry Refresher is available now.

A mix of berry flavors shaken with ice and poured over raspberry-flavored pearls, it's light, vibrant, and just as refreshing as the summer fun you'll be having. So queue up your playlist and head over to Starbucks to check out their summer menu. There's something for everyone from creamy cold brews to ice-cold refreshers. Your summer berry refresher is ready at Starbucks.

Where do you see your career in 10 years? What are you doing now to help you get there? The sooner you start enhancing your skills, the sooner you'll be ready. That's why AARP has reskilling courses in a variety of categories like marketing and management to help your income live as long as you do. That's right.

AARP has a bevy of free skill-building courses for you to choose from because the steps you choose to take today will help you love what you do in the future. That's why the younger you are, the more you need AARP. Learn more at aarp.org slash skills. Tell me what we don't know about how Snell works. Oh, geez. Be shorter to tell you what we do.

This is Alex Wilczko. He's the co-founder and CEO of a company called Osmo. And despite his protests there, he did tell me some of the things nobody knows about how smell works. Why do things smell the way that they do? Why can we smell certain things and not other things?

What is the logic of how molecules are combined to create beautiful smells? Why do some smells create incredibly powerful emotional associations instantly and others seem neutral? Why do some things smell different to people? I think we have a hint in all these directions, but we have nothing like...

musical scales where we have nothing like a periodic table. We don't know any structure to why things are the way that they are. It's a ton of mystery. And that's what makes it so exciting to work on this topic is like, there's so much we don't know. And to be clear, like with light, we just know whatever. If you tell me the frequency, the wavelength, the

I can know exactly what color you're talking about. Or the same thing with a waveform of sound, right? But if I give you some random molecule and say, what does it smell like? Do you know?

So that's what I've spent a lot of my professional life working on. It's exactly that question, which is draw a structure of a molecule on a whiteboard, point at it and say, hey, what does this smell like? Wood or flowers or fruits or whatever. And so there is no way to know that for sure at all, but there's no good way even statistically to predict that without using large data sets. And at least in our hands...

You need neural networks. You need deep learning in order to do that. I'm Jacob Goldstein, and this is What's Your Problem? The show where I talk to people who are trying to make technological progress. Alex Wilczko's problem is this. Can you use AI to teach computers to smell? And once you've figured out how to do that, can you build a profitable business around it?

Osmo spun out of Google in 2023. The company recently launched a fragrance house to develop new perfumes. They've also done some work using scent to detect counterfeit shoes. And in the long run, they plan to use scent to diagnose disease. Before he started Osmo, Alex worked at Google as an AI researcher. Before that, he got a Ph.D. at Harvard studying how mice respond to scent. And he's now a professor at the University of New York.

But maybe the most important part of his bio came even earlier in his life, specifically when he was 12 years old and went off to summer camp in his home state, Texas. I was from a small town, College Station, and then most of the kids were from big towns like Houston and Dallas and Austin and San Antonio. And I hadn't really been exposed to, like, I don't know, fashion trends or, you know, what was cool or popular there.

But everybody's all lumped together in summer camp. And then there was this thing called perfume that some of the richer, frankly, richer and more popular kids had. And it was just amazing to me that these boys could spray themselves with this invisible mist, a clear mist. And then for the next four to six hours, people around them would treat them differently.

And that just blew my mind, right? Like, there's no... I can see the clothes. Yeah. I can see how they act and walk and talk and how they, you know, posture and all that. But I cannot see the fragrance. Yeah. But yet it is obviously doing something magical. It's like an Axe body spray ad. What... Yeah. What does that cause you to do? When I get home, beg my parents to buy it. And fail. I...

We shopped at TJ Maxx and I started to really look out for fragrances there. And then it just, it kind of snowballs from there, which I just realized there's like a whole lot of these things that guess what? You can just try them. And some of them are actually way better and more opinionated and more beautiful. I don't, I didn't have the vocabulary then, but like it just, it was clear to me early on that like, I

I never really thought about who made the clothes, but I started to think about who made these perfumes. Uh-huh. Because it was clear that there were choices that were being made. And like, I just remember trying, and this was years later, trying Bulgari Black, which really kind of clued me into this world. Bulgari Black is not necessarily a great fragrance, but you can experience the top, middle, and base notes in like 40 minutes, 45 minutes. It's pretty short. Yeah.

And so like a bigger fragrance like a Creed Aventus will last on your skin for a day. And so the whole fragrance unfolds. I mean, top notes will last max 15, 30 minutes, but the heart might last for several hours and the base note might last for 10 hours, right? So it smells different. You can still smell it, but it smells different, whatever, an hour after you put it on and four hours after that.

Because a great fragrance is actually many different fragrances within it. There's the first one, which peels off really quickly, burns off quickly. There's the second fragrance, which is the heart note, which will last for sometimes hours, but in this case, another 20 minutes. And then the base note, which is a third fragrance, which is what's left after those two burn off. And it's like three acts of a movie. I think it's quite beautiful. So how do we get from you being a teenager preoccupied with...

to you using AI to predict how molecules will smell. Yeah, like the computer part was always different from the fragrance part. I just, I love computers. We always had computers at home. I started programming when I was around, I don't know, eight years old. That was my life. Like my entire life was computers for a long time. Still is in a way. And fragrance was not a part of it.

I got into, you know, statistics, which became machine learning around the same time. Again, for totally independent reason, there's this thing called the Netflix Prize. It was like one of the first competitions to build great ML algorithms. I competed in that. I mean, that's basically to tell me what else I'll like on Netflix, right? That's what that contest was. Like, if I've watched whatever...

If I watch Succession and The Sopranos, what should I watch next? Then you're going to like another kind of dark but funny kind of soap opera type of a thing. Exactly. And so Netflix did a really bold thing, which is they released a data set and said, here's what good looks like. Here's how we measure it. Have at it. And they paid a million dollars to the winner, which was a combination of a few teams. But what they really did is...

They brought a particular kind of machine learning into the forefront called collaborative filtering. It really showed that this stuff worked. And by the way, other companies were already racing to use this. So like this recommender systems was a big thing. But Netflix was putting it out into the public and allowed a kid like me, and I think I was 18 or 19 years old, to actually compete and do pretty well in that. And so I just got exposed to this world through that.

And it was super fun. I mean, they gamified it. I had a blast. So that was my first exposure to machine learning. Turned out to be a good time to start working on machine learning. Totally. Because if I had started now, they wouldn't let me in because everybody's so darn smart. That was probably 10 years ago? Yeah, 10 years ago. And...

Then, you know, I was doing my undergraduate training in neuroscience and I was studying more behavior than olfaction because it actually turned out that olfaction was a hyper-specialized subfield of neuroscience. I didn't realize how niche it was. I loved smell and I was doing neuroscience and I knew I wanted to do smell neuroscience. The fancy word for that is olfactory neuroscience.

And so there's really two universities in the world that like have a critical mass of these researchers. It's Columbia and it's Harvard. And I applied to both. I went to Harvard and I realized nobody cares about this problem. Nobody cares.

about why molecules smell the way that they do, there's a much longer conversation as to why that's the case and why that's still persisting and how that's changing. Well, let me ask you this. Let me ask you this. At that time, I mean, I get it as a basic research question. I mean, I'll tell you, I was talking with the producer and editor of this show and we were getting ready for this interview and we had this interesting conversation talking about scent and what you're working on, whatever. And I went down...

And I saw my daughter and she said, what are you working on? I said, this guy who's trying to figure out scent and teach computers to smell. And she said, why? I said, I don't know. I should ask him that. So why was it compelling to you? I get it as a basic research question, but at that time, were there applications that came to your mind? Look, the steps...

of this thing that's now Osmo went through those different iterations of, you know, I started as an academic scientist and I was trained in that world. And then I left.

and I did some entrepreneurship, but I ended up in industrial research. And there, like, being curious, frankly, was enough. At Google. This is at Google. This is now a Google brain, yeah, and there's a few steps in between. But basically, you're an AI researcher at Google at this moment when you're doing industrial research, right? Exactly. And Google brain at the time, now it's Google DeepMind,

very much had like a thousand flowers bloom mentality. And so people were working on crazy stuff, including me, working on some like Bell Labs. It's basically like Bell Labs of the 21st century, right? You have it exactly. Bell Labs, Xerox PARC, that kind of vibe. It truly was. Dreamy. Sounds dreamy. It was awesome, right? And it was also a moment in time. And now I think that moment's gone, for better or for worse. The idea was pretty straightforward for Google, which was,

are the products that Google know what the world looks like and know what the world sounds like. And that's useful, right? That's information that Google's organizing. If we knew what things smelled and tasted like, that would be useful, right? Uh-huh. The original mission of Google is organize the world's information, right?

Exactly. And make it universally accessible and useful. And there was a whole slice of reality, the chemical slice of reality that was invisible, right? Not just to Google, but to computers. Yeah, yeah. And that felt really important. And we had agreement and buy-in all the way up to the executive level that like, yeah, let's go look at that. So you're doing like basic AI research at Google and you decide to see if you can basically use AI to figure out scent, to

To say, here is a molecule, what does it smell like? Right? That's the basic endeavor. How do you do that? What is it that you actually do? Yeah. So first it starts with the motivation, which is like, let's figure out smell. But it actually was a lot more natural than I think it sounds. Yeah. Which is, scent is just chemistry. Yeah. It's molecules. And we got to do AI for molecules. Right? If we're going to do AI for scent. Yeah. And the thing that had happened in between, you know, there's a five-year period between

my academic life and my industrial life. And what had happened in those five years is actually some of the people I did my PhD with, and then some of the people I ended up working with at Google Brain really cracked

machine learning or AI for molecules. But they didn't do it for scent. They did it for a few other things. They did it for drug discovery and they did it for like materials discovery. So like new materials for LEDs, right? So you happen to be doing essentially basic research at Google at this moment when there is this new way to use AI that is well suited to molecules and you say...

We can do that. Let's do it. Yeah, let's do it. Yeah, we can do it. The other pieces are great. You got the algorithm. Where's the data? Classic. That's the classic AI question, right? Exactly. Where's the data? What I did know just from being obsessed and in this world for a long time prior, that there were these collections of data sets that were honestly really more like magazine catalogs for fragrance ingredients. And so there were these catalogs basically

basically saying this is the ingredient, this is the molecular structure of this ingredient, and here's what it smells like. And by the way, the rating of what it smells like was done by a professional, by a perfumer. And so the special sauce that we added is we went and we got that data, and we fused a few data sets together, and we cleaned it very carefully. And that hadn't been done. And it's like 5,000-ish, right? It's 5,000 or so different molecules. Okay. Yeah.

Yep, exactly. And here, is this the one with the list? I love the list. Here, I have it. Is this the one, sweet, fruity, vanilla, powdery, floral, berry, fermented, nutty, ozone, buttery, musk? It's that list, right? Those are they, and there's 138 of those descriptors, I think, that we used in that data set. Sometimes we use smaller subsets, but the full set originally is about 140.

So, okay. So you have your, whatever, your 5,000 molecules labeled with 140 different scents. You train your AI model on this data set. And then you want to find out, does the model work? Does the AI work, right? If I give the model some new molecule, molecule that wasn't in the training data, will it know what that molecule smells like? And

And to test that, to answer that question, you actually do this study. So you get a bunch of people to smell these molecules that are not, that your model was not trained on, essentially, right? And say what it smells like. It's weird. Like, you don't actually care what it fundamentally smells like. You just care what everybody on average thinks it smells like. Because guess what? That's what it smells like. That's what smell is. Yeah, that's the meaning of smell. Yeah. So you ask this panel to, what do all these...

molecules smell like and then you ask the model what do they smell like and you compare the results and how does the model do?

that was really the threshold of breakthrough in my mind was like, are you worse than a person? Or are you slightly better than a person? And we got slightly better than a person, which was a breakthrough in my view. Right. And so, yes, so that paper you published in Science and you started Osmo kind of around the same time, right? You started that study at Google, is that right? And then by the time it was published, you had spun Osmo out of Google? Right. That's right.

So you have this map, you have this model that can basically, given a molecule, predict pretty well what the average person thinks that molecule will smell like.

But there is still a second problem, right, which is in the world, in the wild, you don't know what molecules are in the air. You don't know what molecule somebody's smelling. And so for that second problem, you need to try and build some kind of automated system for figuring out what molecules are in the air at a given time. That's correct. Getting...

to one molecule structure is actually not trivial. So to go from a physical thing and know all the molecular structures, like not a solve. So there's a lot of ways to do that. There's a lot of chemical sensors out there. None of them will just tell you the formula, right? So that's hard, really hard.

So there's like a chemistry problem of like isolating the molecule, basically, and deriving the chemical formula. Exactly. Taking a real smell, and it's composed of a bunch of different molecules with different structures, and there's different amounts. There's ratios. You got to get that recipe out of the air. So that's hard. That was unsolved at the time to do that in an automated way.

And by the way, if we're following this story chronologically, we hadn't done this yet at Google. Yeah. But we knew we had to do that. Yeah. Right? So we knew that, okay, if we wanted to actually digitize the world of scent and have a record of what the world smelled like and maybe even replay it, we're going to have to do this. We needed to automate that and have it be automatic. And that's what we did. So basically you can...

put any smell into the machine and it'll tell you what it's made of at this point. Oh, yeah. So you're setting out to start Osmo. Like, what are you thinking of in terms of the set of potential commercial applications? So we really had three in mind, and they're still very much present in mind. The focus has become a lot crisper, though, in terms of what we're concentrating on.

We know the fragrance industry is huge and very profitable, and it's also something I personally love. That's a thing we want to automate and understand. And then we know that dogs can detect things.

Right. And so we know dogs can detect harmful substances like drugs or bombs or things that just shouldn't be there, like produce where it shouldn't be being shipped. And then we also know that dogs, and even in some cases people, can detect health or disease states. Right. We know that Mrs. Milner, a nurse in the UK, was able to smell Parkinson's disease. Huh.

And she's since been able to teach that skill to other people, which is really amazing. And then we figured out all the chemistry of what's actually being smelled. We know that there's many, many instances where there is a scent signature to a disease or to a wellness or to a health state that hasn't yet been fully figured out. But we know that they exist. Those are the three, right? So fragrance industry, really security and supply chain, and health and wellness. And

I view them in that order because that's like the –

The order in which I think we can be useful to the world, right? So designing fragrances is something that's much more attainable technically. And frankly, it's just a great, much faster sales cycle to be business to be in. Then ultimately diagnostics, which are so hard, right? I mean, it is my North Star. It's like where I want to take the company. But I also have no illusions about how hard that is. And I've just, I've seen all the failures of the companies that have attempted it.

And I think I've learned from what hasn't worked. And so I'm incorporating those learnings into how I want to build the company, which is build a great business in fragrance, build beautiful fragrances for the world, and then strike up from that position of strength into even more ambitious frontiers. We'll be back in just a minute. This is Justin Richman from Broken Record. What's summer without new music? And what's the hottest new summer song without a refreshing iced coffee in hand?

especially the new Iced Horchata Oat Milk Shake and Espresso, available now at Starbucks. A blonde espresso combined with rich horchata syrup that delivers a wonderful hint of cinnamon, vanilla, and rice flavors. Topped with oat milk, it delivers a flavor inspired by the Mexican-style horchata for a refreshing and creamy pick-me-up.

As an LA native, I've had my fair share of horchata, and this blend is delicious. Not only does it taste like authentic horchata, but you still get a great coffee flavor. It's perfectly balanced, a little something for everyone. You can savor your coffee at the same time you kick out your summer jams this year thanks to Starbucks' new summer menu featuring everything from creamy cold brews to ice-cold refreshers. Your iced horchata oat milk shake and espresso is ready at Starbucks.

You probably think it's too soon to join AARP, right? Well, let's take a minute to talk about it. Where do you see yourself in 15 years? More specifically, your career, your health, your social life? What are you doing now to help you get there? There are tons of ways for you to start preparing today for your future with AARP.

That dream job you've dreamt about? Sign up for AARP reskilling courses to help make it a reality. How about that active lifestyle you've only spoken about from the couch? AARP has health tips and wellness tools to keep you moving for years to come. But none of these experiences are without making friends along the way. Connect with your community through AARP volunteer events. So it's safe to say it's never too soon to join AARP.

They're here to help your money, health, and happiness live as long as you do.

That's why the younger you are, the more you need AARP. Learn more at aarp.org slash wise friend. Climate disasters aren't a distant threat. They're happening now, affecting people everywhere. And just as disasters grow in frequency and intensity, wealthy nations are cutting back aid. Most disasters are predictable. So what if instead of scrambling to rebuild, we protected communities before disaster strikes?

With the human and economic costs of disaster mounting, with less aid to go around, focusing on what works has never been more urgent. Listen to Counter Crisis from the Center for Disaster Protection, available wherever you get your podcasts. When I first heard about the work Alex was doing at Osmo, I understood why it would be useful for sensing. Basically, you might be able to build automated sniffing machines that could, say, detect cancer in a person or detect a bomb in a suitcase.

But I couldn't figure out, truly, what the business case was for perfume. And in fact, Osmo has recently launched a perfume business. It's called Generation. So I asked Alex, why is using AI and fancy machines, why is that better than just designing perfume in the traditional way? We can go from the first kind of classic,

So, hey, I want to create a fragrance and here's who my brand is. Here's what I want to do. So just that description to a starting place of a fragrance,

in a minute or two. What happens at a traditional perfumer when somebody comes in with that request? Well, so let's say you're an emerging, let's say you're an emerging brand, right? So you're starting out or you have your first product and you want to add a second one, but you're small, right? You're not making a billion dollars in revenue. You're making less than that. So if you want to make a new custom fragrance, good luck.

You're not going to be able to get the attention of the big fragrance houses because they want to service business that's like millions and millions of dollars, and you're not big enough yet. So if you want a great custom fragrance that your consumers are going to love and you want to do it quickly so you're responding to trends, you aren't going to be able to get it done. Right.

So you have to make compromises, right? So if you want to move fast, you're going to have to use a regurgitated fragrance. It's also called a library fragrance, which means somebody else on the market has your smell. I'm imagining that people who sell it call it a library fragrance rather than a regurgitated fragrance. They do. They don't say regurgitated. But that's what it effectively is, right? Fair. Regurgitated does have a particular olfactory connotation. So it's a clever word for you to use. It's visceral. It sticks in the mind. What, like...

And I'm not, I just genuinely don't understand, like, why can't somebody just have a company with a bunch, like, who knows the molecules, you know, who knows what the 5,000 molecules in the book smell like? Because they've got the book and they can just use the book and be like, oh, you want this? Let's try that. Do you know what I mean? Like, I'm not trying to be difficult, but I genuinely don't understand why you need the technology to do that.

Yeah, I genuinely didn't understand this either. And there's a class of professional called a perfumer. And their job is to do what you're describing, which is, hey, I know all these ingredients and I'm going to mix them in order to create your fragrance. So they typically, there's no perfumer that knows 5,000 ingredients, but the best perfumers know 1,000 or 2,000 ingredients. Most perfumers work with

200, 100 or 200 ingredients. So already there, like we're, there's very few people in the world that can do what you're saying they can do. And then, yeah.

How, what are they going to work on, right? So it might take them weeks or months to create a fragrance. They're working on a few at a time. Why would they work on an emerging brand's fragrance when they can go work on a much larger account? So there's just a very, very limited number of people who can engage in the fragrance creation process because it is personal.

It's not so much identifying, hey, all these molecules smell this particular way, and therefore I should be able to mix them. Like, what ratios do you mix them in? Like, what are the rules? And now you're actually getting into designing a system which understands scent well enough to create new fragrance formulas as starting places. And then, of course, a perfumer finishes them. Yeah.

But you're right. It's like, huh, why shouldn't that exist? And then when you actually start to peel back the layers one by one, you realize, oh, you actually have to build what we built. It's actually hard. In order to answer that question. It's hard. So presumably now your model can not only predict what one molecule is going to smell like, but it can predict the combination of molecules. I mean, is it predicting, does it know concentration? Like, does it know? Oh, it has to. Yeah. How good is it? I mean, you have a perfumer on staff. Why? Why?

Well, I think the goal of tools is to have them in the hands of creatives.

And there's many steps to perfumery, but I think there's three that are relevant for what we're talking about. The first is a perfumer, when they're starting on a project, they have to have a starting place. They have to have a starting formula. And then they do their creative work, step two, to evolve that formula to exactly what the customer wants, to a creative expression that delights the consumer as well. That's the funnest part. Perfumers love that. That is actual creation in the creative part. Yeah.

Number three is then it has to be the right price. It has to be compliant with regulatory compliance. There cannot be allergens, all that stuff. That's more like sound engineering than it is composition or being a rock star. Steps one, the starting place. Step three, all the regulatory requirements. That's where we spend the most energy in building these tools. And then a perfumer is the person that is taking...

the formulas from starting place to creative endpoint and then handing it off for like regulatory finishing. And they're just way more effective with these tools. At least for now, right? Like that's the way I feel using an LLM. Like I feel like I have a window when me plus the LLM is better than the LLM alone. And we haven't, that window hasn't closed yet, but I'm not optimistic about my long-term prospects.

We'll see, though. Yeah, we'll see. Honest belief here, like, the tools will get better, but the drive to create will never go away. And I think people will always want to know, like, about the person behind the creation, in a way. And it's not uniform. Yeah, it's not uniform. So, like, I don't think people want to know the perfumer behind the hand soap in the gas station. Yeah. They just don't, right? But...

There, I think, will always be room for craft and creative use of tools. And the profession that uses those tools might change radically. And the industry in which those tools are used might change radically. But the tools will always be wielded by people. But the work that's being done might be unrecognizable. So, you know, we'll see how the world evolves. But like I just like AI is like an engine. It's just a technology. It's just a tool.

So what's the business model, just briefly, for Generation? Like, what's the model? The business model, really simply, is we'll design the fragrance for you, and then you'll buy that fragrance to put in your products, or we'll even actually create the full finished product. We'll put it in a bottle for you if you want. We are behind the scenes. We're an engine supporting brands. We're not a brand ourself. And we're here to make beautiful fragrance products for brands. Okay.

So what's the frontier? Like you have, you know, on the business side, the generation is kind of the central thing you're working on now. But on the more on the on the research side, like what are you trying to figure out now? What are you working on now? So there's there's our starting place, which is why does this molecule smell the way that it does? And we can never stop getting better at that.

Then there's the next question of why does this mixture of molecules smell the way that it does? And we can never stop getting better at that. And then there's, do you like it? Which is maybe the most important question from a business perspective. Or who likes it and in what context? Yeah. Exactly. Exactly. Which is, it's not just...

The formula has the input to this model, but there's also who you are. What are your experiences? Where are you from? What are the other things in your life that you like? That actually goes back to your Netflix collaborative filters. It does. If I watch Succession and The Sopranos and I'm 50 and married, then what's the cologne for me? Yeah.

Exactly. And so I was very fortunate to be able to start this company with a guy I work with at Twitter. His name is Rich Whitcomb. He's our chief technology officer. His whole professional life has been recommender system. So he was a lead on Spotify's song recommender system. So if you like your wrapped playlist or recommended playlist, like that's his code. And then he also worked on self-driving cars at NVIDIA. But he's been in this world of like,

hey, you like these things, what about this thing? Or here's the inputs that the system's getting, what do I do now? So really, really deep into that world. And we're kind of bringing that spirit, that mindset to scent and to fragrance. And then what about beyond, you know, for the parts of your work that are the next steps that you alluded to farther in the distance, the essentially sensing, right? Sensing for security, sensing for health. Like what work are you doing now toward that end?

Yeah, so we're incubating this right now. So I'll tell you two things. One is we have a partner. We've deployed sensors out in the field. We're detecting inauthentic or counterfeit goods. It's working. The second thing I'll say is we've learned something really interesting, which is the molecules that smell really good in fruits and flowers and vegetables that we have to understand to create fragrance. Yeah.

are the same molecules in counterfeit luxury goods and the same molecules in our scent. And by getting really good at understanding and designing fragrance in one domain in the fragrance industry, we're actually strengthening this platform that we're building to get really good at the next frontiers of security detection. And then ultimately what we care about is health. So that's what really surprised us is I thought that by working in fragrance,

We're making a trade-off, which is we're here to build a great business, to make ourselves resilient so that we can work on the much longer haul problems. But in reality, we're making progress on those problems by teaching our platform about what the world smells like. And it's all one, it's just scent. It's just molecules in the air. And so the more we learn about really any piece of what the world smells like, the better we get at all of it. I think, I'll tell you what I think the big like technical frontier is, is technology.

predicting emotion. Ah, that's interesting. So when you smell something, you obviously perceive something, like the first thought or first perception is whatever, fresh cut grass or grapefruit. But then there's another thing that happens almost at the same time, which is I remember or I feel a particular thing. And predicting that is something I don't think anybody's really figured out.

but is a beautiful frontier. Well, how do you get the data? You got to ask a lot of people how they feel when they smell a lot of things and they have to be able to articulate it, right? Part of the thing with scent is it's so primordial that like you might not even be able to say how you feel. So you need a brain computer interface.

You might, you might, but it turns out we have voices and faces that are effectively BCIs. There's a lot of information that leaks out of us all the time. And that was what my PhD was in is how do you interpret body language in a way that makes sense? And by the way, the body language I worked on most closely was body language driven by odors, right? Things that make sense.

I studied this in animals, but makes animals happy or sad or afraid or calm. And you can read that out. I mean, our behaviors are meant to communicate to other animals, right? We're very social. We're a social species. So I think there's more fundamentals that we have to figure out. But this is, I think there's some really fundamental stuff that's still unknown here. I heard you say in another interview that you worry sometimes that you'll hit some barrier in nature.

to your work. And you said it in passing, but I was very curious about that. What does that mean? I always think about that, which is like, what day will it be when Mother Nature says you can't figure the next hard thing out? And I just look at this from the history of science that

if somebody cared about how the planets were moving in 1200, well, good luck. You don't have the right telescopes. You don't have Tycho Brahe. There's a bunch of stuff you're going to need, right? And so in a way, it's like Mother Nature and what our society and species knows conspiring together that basically says progress will have to wait. And so I think about that. I worry about that all the time. And so my mental framework that keeps me super humble is like,

I'm just thankful for all the progress we've been able to make, that the tools were around. Yeah. Right? So I didn't invent graph neural networks. I didn't even invent the data sets. Like, we are piecing together and curating and cobbling together all these—we're standing on the shoulders of so many people. And it's just always been the case. And I don't know. It's just—it makes—maybe this is—

too philosophical, but for me, when I've been up close and personal with scientific progress, either that I've had a part in or I've observed other people do, it all feels so tenuous. It feels so lucky because once you really dig into the details, you realize, oh my gosh, they had to be right there at that time and have known about that thing. Yeah. It's amazing that anything happens when you think of how contentious everything is. It is truly amazing that anything happens. And, you know, when you really dig in, you're like, wow.

how does anything could happen at all? But nonetheless, you persist. And also, I think you can create the conditions where it's more likely than not to happen. And so that's what Osmo is. And that's why Osmo Birth Generation is like, let's create an environment where we're much more likely than not to make both the scientific progress we need to make, but also like

like really help and change the fragrance industry, which by the way, will teach us the things we need to know to get to the next thing. So I think there's so much beauty to create in the fragrance industry that I'm going to just enjoy the heck out of it and do it for the rest of my life. But I think it's going to teach us things that will allow us to do even more audacious work in the future. We'll be back in a minute with The Lightning.

This is Justin Richman from Broken Record. What's summer without new music? And what's the hottest new summer song without a refreshing iced coffee in hand? Especially the new Iced Horchata Oat Milk Shake and Espresso, available now at Starbucks. A blonde espresso combined with rich horchata syrup that delivers a wonderful hint of cinnamon, vanilla, and rice flavors. Topped with oat milk, it delivers a flavor inspired by the Mexican-style horchata for a refreshing and creamy pick-me-up.

As an LA native, I've had my fair share of horchata, and this blend is delicious. Not only does it taste like authentic horchata, but you still get a great coffee flavor. It's perfectly balanced, a little something for everyone. You can savor your coffee at the same time you kick out your summer jams this year thanks to Starbucks' new summer menu featuring everything from creamy cold brews to ice-cold refreshers. Your iced horchata oat milk shake and espresso is ready at Starbucks.

You probably think it's too soon to join AARP, right? Well, let's take a minute to talk about it. Where do you see yourself in 15 years? More specifically, your career, your health, your social life? What are you doing now to help you get there? There are tons of ways for you to start preparing today for your future with AARP.

That dream job you've dreamt about? Sign up for AARP reskilling courses to help make it a reality. How about that active lifestyle you've only spoken about from the couch? AARP has health tips and wellness tools to keep you moving for years to come. But none of these experiences are without making friends along the way. Connect with your community through AARP volunteer events. So it's safe to say it's never too soon to join AARP.

They're here to help your money, health, and happiness live as long as you do.

That's why the younger you are, the more you need AARP. Learn more at aarp.org slash wise friend. Climate disasters aren't a distant threat. They're happening now, affecting people everywhere. And just as disasters grow in frequency and intensity, wealthy nations are cutting back aid. Most disasters are predictable. So what if instead of scrambling to rebuild, we protected communities before disaster strikes?

With the human and economic costs of disaster mounting, with less aid to go around, focusing on what works has never been more urgent. Listen to Counter Crisis from the Center for Disaster Protection, available wherever you get your podcasts. Let's finish with the lightning round. I'm going to ask you a bunch of questions now. What seemingly pleasant scent do you never want to smell again? Seemingly pleasant scent I never want to smell again? Uh...

artificial cherry. It was the cough syrup that I was forced to drink as a kid. And I'm super sensitive to it. The molecules, ethylmolitol, do not like it. Are you wearing fragrance right now? And if so, what is it? I am not. I stopped wearing as soon as I started the company because I needed to smell all kinds of stuff every day. Of course. Give me a pick. Name some fragrance that you love for some reason. So I really like

this is kind of a basic choice from folks inside the industry. I love Tarte Hermes. It's like the Hermes flagship men's fragrance.

It's by a perfumer Jean-Claude Elinor. I really love his work. - Basic, is that like basic in the way of saying it's like if I asked you for a watch and you said a Rolex Submariner or something, it's just like, yeah. - Exactly, or saying like, what pop music do you like? You said Taylor Swift. People like it 'cause it's great. - Uh-huh, uh-huh. - You know, like Taylor Swift is great. A Rolex watch is a great watch. Terre de Minas is a great fragrance, but it's very popular. - What is it about it that you love?

I love its minimalism, and I just happen to like the notes, right? So it's really heavy on a molecule I like, isobisuper. I think it's a great highlight of that ingredient, and it just wears really well on my skin. So that was what I used to wear almost every day before I stopped. What's your second favorite sense?

My second favorite sense is probably going to be, it's hard between vision and hearing because I love music, but I like looking at stuff too. Like the world of beautiful. Are more expensive perfumes actually better? Sometimes, right? So I think there's just like anything, like bicycles or art, as you start to pay more, everything gets better and then it plateaus, right? What's the worst thing you ever smelled?

I have a memory. I picked a mushroom that I thought looked cool and wanted to show it to my dad when I was young, and I forgot about it, and it just turned completely gross. I had a version of that of bringing shells home from the beach that were alive, it turned out. I found out when they were dead. It's like great intentions, but didn't really have wherewithal to think that through or understand the consequences. Yeah.

Alex Wilczko is the co-founder and CEO of Osmo. Today's show was produced by Gabriel Hunter Chang. It was edited by Lydia Jean Cott and engineered by Sarah Brugier. You can email us at problem at pushkin.fm. I'm Jacob Goldstein, and we'll be back next week with another episode of What's Your Problem? What's Your Problem?

Climate disasters aren't a distant threat, they're happening now, affecting people everywhere. And just as disasters grow in frequency and intensity, wealthy nations are cutting back aid. Most disasters are predictable. So what if instead of scrambling to rebuild, we protected communities before disaster strikes?

With the human and economic costs of disaster mounting, with less aid to go around, focusing on what works has never been more urgent. Listen to Counter Crisis from the Centre for Disaster Protection, available wherever you get your podcasts. Do you know who wrote All of Me by John Legend? Or If I Were a Boy by Beyonce? Or Pickle Song Cry by Fergie? That's me, Toby Gadd.

I'm a songwriter and have a brand new podcast called Songs You Know with Grammy-winning guests like Hosea producer Jeff Giddy, Charlie XCX producer John Schaaf, Rihanna and Coldplay producer Stargate, and artists like Jessie J, Josh Groban, and Victoria Justice. We're talking about their lives, their songs, their advice, their tips and tricks, and their most embarrassing moments. So please tune in at Songs You Know podcast with Toby Gadd.

One thing about Chicago is it empowers you to want to live because it has so much love. If these walls could talk, they would say stability. They would say neighbors. They would say hope. They definitely would say love. If these walls can talk, it would say family. JPMorgan Chase Community Development Banking understands that the buildings we invest in are more than just four walls. They are you. They are us. They are Chicago.

This is an iHeart Podcast.