Speaker 1: Pushkin, tell me what we don't know about how smell works?
Speaker 1: Oh jeez, be sure to tell you what we do.
Speaker 1: This is Alex Wilscoe. He's the co founder and CEO
Speaker 1: of a company called Osmo, and despite his protests there
Speaker 1: he did tell me some of the things nobody knows
Speaker 1: about how smell works.
Speaker 2: Why do things smell the way that they do. Why
Speaker 2: can we smell certain things and not other things. What
Speaker 2: is the logic of how molecules are combined to create
Speaker 2: beautiful smells? Why do some smells create incredibly powerful emotional
Speaker 2: associations instantly and others seem neutral? Right? Why do something
Speaker 2: smell different to people? I think we have a hints
Speaker 2: in all these directions, but we have nothing like musical scale,
Speaker 2: where we have nothing like a periodic table. We don't
Speaker 2: know any structure to why things are the way that
Speaker 2: they are. It's a ton of mystery, and that's what
Speaker 2: makes it so exciting to work on this topic, is
Speaker 2: like there's so much we don't know, and to.
Speaker 1: Be clear, like with light, we just know whatever. If
Speaker 1: you tell me the frequency the wavelength, I can know
Speaker 1: exactly what color you're talking about, or the same thing
Speaker 1: with a wave form of sound, right, and so but
Speaker 1: if I give you some random molecule and say what
Speaker 1: does it smell like?
Speaker 2: Do you know? So that's what I've spent a lot
Speaker 2: of my professional life working on. It is exactly that question. Yeah,
Speaker 2: which is a draw structure of a molecule on a whiteboard,
Speaker 2: point out it and say, hey, what does this smell
Speaker 2: like wood or flowers or fruits or whatever? And so
Speaker 2: there is no way to know that for sure at all.
Speaker 2: But there's no good way even statistically to predict that
Speaker 2: without using large data sets, and at least in our hands.
Speaker 2: You need neural networks, you need deep sorting in order
Speaker 2: to do that. I'm Jacob Goldstein, and this is what's
Speaker 2: your problem?
Speaker 1: The show where I talk to people who are trying
Speaker 1: to make technological progress.
Speaker 2: Alex Wilch.
Speaker 1: Goo's problem is this, Can you use AI to teach
Speaker 1: computers to smell? And once you figured out how to
Speaker 1: do that, can you build a profitable business around it?
Speaker 1: Osmo's fun out of Google in twenty twenty three. The
Speaker 1: company recently launched a fragrance house to develop new perfumes.
Speaker 1: They've also done some work using scent to detect counterfeit shoes,
Speaker 1: and in the long run, they plan to use scent
Speaker 1: to diagnose disease. Before he started Osmo, Alex worked at
Speaker 1: Google as an AI researcher. Before that, he got a
Speaker 1: PhD at Harvard studying how mice respond to scent. But
Speaker 1: maybe the most important part of his bio came even
Speaker 1: earlier in his life, specifically when he was twelve years
Speaker 1: old and went off to summer camp in his home state, Texas.
Speaker 2: I was from a small town college station, and then
Speaker 2: most of the kids were from big towns like Houston
Speaker 2: and Dallas and Austin and San Antonio, and I hadn't
Speaker 2: really been exposed to, like I don't know fashion trends
Speaker 2: or you know, what was cool or popular. But everybody's
Speaker 2: all lumped together in summer camp. And then there is
Speaker 2: this thing called perfume that some of the richer, frankly
Speaker 2: richer and more popular kids had, And it was just
Speaker 2: amazing to me that these boys could spray themselves with
Speaker 2: this invisible mist, a clear mist, and then for the
Speaker 2: next four to six hours people around them would treat
Speaker 2: them differently, and that just blew my mind right, like
Speaker 2: there's no I can't I can see the clothes. Yeah,
Speaker 2: I can see how they act and walk and talk
Speaker 2: and how they you know, posture and all that, but
Speaker 2: I cannot see the fragrance. Yeah, but yet it is
Speaker 2: obviously doing something magical. It's like an.
Speaker 1: Axe body spray ad what what does that cause you
Speaker 1: to do?
Speaker 2: When I get home, beg my parents to buy and fail.
Speaker 2: We shopped at TJ Max and I started to really
Speaker 2: look out for fragrances there and then it just kind
Speaker 2: of snowballs from there, which I just realized there was
Speaker 2: like a whole lot of these things. I guess what,
Speaker 2: you can just try them, and some of them are
Speaker 2: actually way better and more opinionated and more beautiful. I
Speaker 2: don't I didn't have the vocabulary then, but like it
Speaker 2: just it was clear to me early on that like
Speaker 2: I never really thought about who made the clothes, but
Speaker 2: I started to think about who made these perfumes, because
Speaker 2: it was clear that there were choices that were being made.
Speaker 2: And like I just remember trying, and this is years later,
Speaker 2: trying Bulgari black, which really kind of clued me into
Speaker 2: this world. Bulgary black is not necessarily a great fragrance,
Speaker 2: but you can experience the top, middle and base notes
Speaker 2: in like forty minutes. Forty five minutes is pretty short,
Speaker 2: and so like a bigger fragrance, like a creed, events
Speaker 2: will last on your skin for a day, and so
Speaker 2: the whole fragrance unfolds. I mean, top notes will last
Speaker 2: max Fifteen thirty minutes, but the heart might last for
Speaker 2: several hours, and the base note might last for ten hours. Right,
Speaker 2: so it smells different.
Speaker 1: You can still smell it, but it smells different whatever
Speaker 1: an hour after.
Speaker 2: You put it on and four hours after that, because
Speaker 2: a great fragrance is actually many different fragrances within it. Yeah, Right,
Speaker 2: There's the first one which peels off or that quickly
Speaker 2: burns off quickly. There's the second fragrance, which is the
Speaker 2: heart note, which will last for you know, sometimes hours,
Speaker 2: but in this case, like another twenty minutes. And then
Speaker 2: the base note, which is a third fragrance, which is
Speaker 2: what's left after those two burn off. And it's like
Speaker 2: three acts of a movie, right, I think it's quite beautiful.
Speaker 2: So how do we.
Speaker 1: Get from you being a teenager preoccupied with fragrance to
Speaker 1: you using AI to predict how malls will smell?
Speaker 2: Yeah, Like the computer part was always different from the
Speaker 2: fragrance part I just I love computers. We always had
Speaker 2: computers at home. I started programming around I don't know,
Speaker 2: eight years old. It was my life, Like, my entire
Speaker 2: life was computers for a long time, still lose in
Speaker 2: a way, and fragrance was not a part of it.
Speaker 2: I got into you know, statistics, which became machine learning
Speaker 2: around the same time. Again for totally independent reason. There's
Speaker 2: this thing called the Netflix Prize. It was like one
Speaker 2: of the first competitions to build great mL algorithms. I competed.
Speaker 1: Now, that's basically to tell me what else I'll like
Speaker 1: on Netflix, right, That's what that contaest was. Like, if
Speaker 1: I've watched whatever, if I watched Succession and the Sopranos,
Speaker 1: what should I watch next?
Speaker 2: Then you're gonna like another kind of dark, but you know, funny,
Speaker 2: kind of soap opera type of a thing, exactly. And
Speaker 2: so Netflix did a really bold thing, which is they
Speaker 2: released a data set and said, here's what good looks like,
Speaker 2: here's how we measure it. Have at it. And they
Speaker 2: paid a million dollars to the Winter, which was a
Speaker 2: combination of a few teams. But what they really did
Speaker 2: is they brought a particular kind of machine learning into
Speaker 2: the forefront called collaborative filtering. Really showed that this stuff worked,
Speaker 2: and by the way, other companies were already racing to
Speaker 2: use this, So like this recommended systems was a big thing,
Speaker 2: but Netflix was putting it out into the public and
Speaker 2: allowed a kid like me I think I was eighteen
Speaker 2: or nineteen years old to actually compete and pretty well
Speaker 2: in that. And so I just got exposed to this
Speaker 2: world through that and it was super fun. I mean
Speaker 2: they gamified it and had I had a blast. So
Speaker 2: that was my first exposure to machine learning.
Speaker 1: Turned out to be a good time to start working
Speaker 1: on machine learning, Yeah.
Speaker 2: Totally, because if I had started now, they wouldn't let
Speaker 2: me in because that's probably ten years ago. Yeah, ten
Speaker 2: years ago. Yeah. And then, you know, I was doing
Speaker 2: my undergraduate training in neuroscience, and I was studying more
Speaker 2: behavior than old action because it actually turned out that
Speaker 2: olf action was a hyper specialized sub field of neuroscience.
Speaker 2: I didn't realize how niche it was. I loved smell
Speaker 2: and I was doing neuroscience, and I knew I wanted
Speaker 2: to do smell neuroscience the fancy word for nactual factory neuroscience.
Speaker 2: And so there's really two universities in the world that
Speaker 2: like have a critical mass of these researchers. It's Columbia
Speaker 2: and it's Harvard, and I applied to both. I went
Speaker 2: to Harvard, and I realized nobody cares about this problem.
Speaker 2: Nobody cares about why molecules smell the way that they do.
Speaker 2: There's a much longer conversation as so why that's the
Speaker 2: case and why that's still persistent. Now that's changing. Well,
Speaker 2: let me ask you this. Let me ask you this.
Speaker 1: At that time, I mean I get it as a
Speaker 1: basic research question.
Speaker 2: I mean, I'll tell you.
Speaker 1: We was talking with the producer and editor of this show,
Speaker 1: and we were getting ready for this interview, and we
Speaker 1: had this interesting conversation talking about scent and what you're
Speaker 1: working on whatever. And then I went down and I
Speaker 1: saw my daughter, and so, what are you organized? Said
Speaker 1: this guy who's trying to figure out scent and teach
Speaker 1: computers to smell?
Speaker 2: And she said why, I said, I don't know.
Speaker 1: I ask, so why was it compelling to I get
Speaker 1: it as a basic research question, but at that time
Speaker 1: was like, there were there applications that came to your mind.
Speaker 3: Look, the the steps of development of this thing that's
Speaker 3: now OSMO went through those different iterations.
Speaker 2: You know, I started as an academic scientist and I
Speaker 2: was trained in that world, and then I left and
Speaker 2: it did some entrepreneurship, but it ended up in industrial research,
Speaker 2: and they're like being curious, frankly was enough, and the
Speaker 2: idea this is this is Google, this is now a
Speaker 2: Google Brain. Yeah, and there's a few steps in between.
Speaker 1: But basically as you're an AI researcher at Google at.
Speaker 2: This moment when you're doing industrial research, right, yeah, exactly,
Speaker 2: and Google Brain at the time now it's Google Deep
Speaker 2: Mind very much had like a thousand flowers bloom mentality,
Speaker 2: and so people were working on crazy stuff, including me
Speaker 2: working on something Bell Labs.
Speaker 1: It's basically like Bell Labs of the twenty first century, right, you.
Speaker 2: Have it, exactly, Bell Labs, Erox Park, that kind of
Speaker 2: avid and it truly was dreamy, sounds dreamy. It was awesome, right,
Speaker 2: And it was also a moment in time, and now
Speaker 2: I think that moment's going for better or for worse.
Speaker 2: The idea was pretty straightforward for Google, which was, are
Speaker 2: the products at Google know what the world looks like
Speaker 2: and know what the world sounds like, and that's useful. Right,
Speaker 2: that's information that Google's organizing. If we knew what things
Speaker 2: smelled and tasted like, that would be useful, right. Uh huh.
Speaker 1: The original mission of Google is organized the world's information, right.
Speaker 2: Exactly, and make it universally accessible and useful. And there
Speaker 2: was a whole slice of reality, huh, the chemical slice
Speaker 2: of reality that was invisible, right, not just to Google
Speaker 2: but two computers. Yeah. Yeah, and that felt really important,
Speaker 2: and we had agreement and buying all the way up
Speaker 2: to the executive level. They're like, yeah, let's go, let's
Speaker 2: go look at that.
Speaker 1: So you're doing a like basic AI research at Google
Speaker 1: and you decide to see if you can basically use
Speaker 1: AI to figure out scent, to say, here's a molecule,
Speaker 1: what does it smell like?
Speaker 2: Right, that's the basic endeavor. How do you do that?
Speaker 2: What is it that you actually do? Yeah, so first
Speaker 2: it starts to innovation, which I was like, let's figure
Speaker 2: out smell. But it actually was a lot more natural
Speaker 2: than I think it sounds, which is scent is just chemistry.
Speaker 2: It's molecules, and we got to do AI for molecules. Right.
Speaker 2: If we're going to do AI for scent, and the
Speaker 2: thing that had happened in between. You know, there's a
Speaker 2: five year period between my academic life and my industrial life,
Speaker 2: and what had happened in those five years is actually
Speaker 2: some of the people I did my PhD with and
Speaker 2: then some of the people I ended up working with
Speaker 2: at Google Brain really cracked machine learning or AI for molecules.
Speaker 2: But they didn't do it for scent. They did it
Speaker 2: for a few other things. They did for drug discovery,
Speaker 2: and they did it for like materials discovery, so like
Speaker 2: new materials for LEDs.
Speaker 1: Right, So you happen to be doing essentially basic research
Speaker 1: at Google at this moment when there is this new
Speaker 1: way to use AI that is well suited to molecules,
Speaker 1: and you say, we can do let's do it, Yeah,
Speaker 1: let's do it.
Speaker 2: Yeah, we can do it. The other pieces are great.
Speaker 2: You got the algorithm. Where's the data? Classic? That's the
Speaker 2: classic AI question, right, like, exactly where's the data? What
Speaker 2: I did know just from being obsessed and in this
Speaker 2: world for a long time prior to that, there were
Speaker 2: these collections of data sets that were honestly really more
Speaker 2: like magazine catalogs for fragrance ingredients, and so there were
Speaker 2: these catalogs basically saying this is the ingredient, this is
Speaker 2: the molecular structure of this ingredient, and here's what it
Speaker 2: smells like. And by the way, the rating of what
Speaker 2: it smells like was done by a professional, by a perfumer.
Speaker 2: And so the special sauce that we added is we
Speaker 2: we went and we got that data and we fused
Speaker 2: a few data sets together, and we cleaned it very carefully,
Speaker 2: and that that hadn't been done.
Speaker 1: And it's something it's like five thousand dish right, it's
Speaker 1: five thousand or so different molecules.
Speaker 2: Okay, yep, exactly. And here is this the one with
Speaker 2: the list. I love the list. Here I have it.
Speaker 1: This the one sweet fruity, vanilla, powdery, fluoral, barry, fermented,
Speaker 1: nutty ozone, buttery musk.
Speaker 2: It's it's that lispright. Those are they? And there's one
Speaker 2: hundred and thirty eight of those descriptors I think that
Speaker 2: we used in that data set. Sometimes we use smaller subsets,
Speaker 2: but the full set originally is about one hundred and forty.
Speaker 1: So okay, so you have your whatever, your five thousand
Speaker 1: molecules labeled with one hundred and forty different sets. You
Speaker 1: train your AI model on this data set, and then
Speaker 1: you want to find out does the model work?
Speaker 2: Does the AI work right?
Speaker 1: If I give the model some new molecule molecule that
Speaker 1: wasn't in the training data, will it know what that
Speaker 1: molecule smells like? And to test that to answer that question,
Speaker 1: you actually.
Speaker 2: Do this study.
Speaker 1: So you get a bunch of people to smell these
Speaker 1: molecules that are not that your model was not trained on,
Speaker 1: essentially right, and say what it smells like? It's weird
Speaker 1: like what? You don't actually care what it fundamentally smells like.
Speaker 1: You just care what everybody on average thinks it smells.
Speaker 2: Like, because guess what, that's what it's what smell is? Yeah,
Speaker 2: that's what do you think of smell? Yeah? Ye?
Speaker 1: So you asked this panel to what do all these
Speaker 1: molecule smell like? And then you ask the model what
Speaker 1: do they smell like? And you compare the results and
Speaker 1: how does the model do?
Speaker 2: That was really the threshold of breakthrough in my mind
Speaker 2: was like are you worse than a person? Yeah? Or
Speaker 2: are you slightly better than a person? And we got
Speaker 2: slightly better than a person, which was a breakthrough in
Speaker 2: my view.
Speaker 1: Right, and so yes, so that paper you published in
Speaker 1: Science and you started Ozmo kind of around the same time, Right,
Speaker 1: you started that study at at Google, is that right?
Speaker 1: And then by the time it was published you had
Speaker 1: spun Osmo out of Google, right, that's right. So you
Speaker 1: have this map, you have this model that can basically
Speaker 1: given a molecule, predict pretty well what the average person
Speaker 1: thinks that molecule smell like. But there's still a second problem, right,
Speaker 1: which is, in the world, in the wild, you don't
Speaker 1: know what molecules are in the air. You don't you
Speaker 1: know what molecules somebody's smelling. And so for that second problem,
Speaker 1: you need to try and build some kind of automated
Speaker 1: system for figuring out what molecules are in the air
Speaker 1: at a given that's correct.
Speaker 2: Getting to one molecule structure is actually not trivial. So
Speaker 2: to go from a physical thing and know all the
Speaker 2: molecular structures like not a solveable. So there's a lot
Speaker 2: of ways to do that. There's a lot of chemical
Speaker 2: sensors out there, none of them will just tell you
Speaker 2: the formula, right, So that's hard, really hard.
Speaker 1: So there's like a chemistry problem of like isolating the
Speaker 1: molecule basically and deriving the chemical formula.
Speaker 2: Exactly taking a real smell and it's composed of a
Speaker 2: bunch of different molecules with different structures, and there's different amounts,
Speaker 2: there's ratios. You got to get that recipe out of
Speaker 2: the air. So that's on. That's hard. That was unsolved
Speaker 2: at the time to do it in an automated way.
Speaker 2: And by the way, if we're following this story chronologically,
Speaker 2: we hadn't done this yet, agah, but we knew we
Speaker 2: had to do that, right, So we knew that, Okay,
Speaker 2: if we wanted to actually digitize the world of scent
Speaker 2: and have a record of what the world smelled like
Speaker 2: and maybe even replay it, we're going to have to
Speaker 2: do this. We needed to automate that and have it
Speaker 2: be automatic, and that's what we did.
Speaker 1: So basically, you can put any smell into the machine
Speaker 1: and it'll tell you what it's made of. At this point,
Speaker 1: oh yeah, so you're setting out to start OSMO, Like,
Speaker 1: what are you thinking of in terms of the set
Speaker 1: of potential commercial applications.
Speaker 2: So we really had we had three in mind, and
Speaker 2: there's still very much present in mind the focuses has
Speaker 2: become a lot crisper though in terms of what we're
Speaker 2: concentrating on. We know the fragrance industry is huge and
Speaker 2: very profitable, and it's also something I personally love. That's
Speaker 2: a thing we want to automate and understand. And then
Speaker 2: we know that dogs can detect things, right, and so
Speaker 2: we know dogs can detect harmful substances like drugs or bombs,
Speaker 2: or things that just shouldn't be there, like produce where
Speaker 2: it shouldn't be being shipped. And then we also know that
Speaker 2: dogs and even in some cases people can detect health
Speaker 2: or disease states. Right. We know that missus Milner, a
Speaker 2: nurse in the UK, was able to smell Parkinson's disease
Speaker 2: and she's since been able to teach that skill to
Speaker 2: other people, which is really amazing. And then we figured
Speaker 2: out all the chemistry of what's actually being smelled. We
Speaker 2: know that there's many many instances where there is a
Speaker 2: scent signature to a disease or to a wellness or
Speaker 2: to a health state that hasn't yet been fully figured out, right,
Speaker 2: but we know that they exist. Those are the three, right,
Speaker 2: So fragrance industry really security and supply chain and health
Speaker 2: and wellness, and I view them in that order because
Speaker 2: that's like the order in which I think we can
Speaker 2: be useful to the world. Right, Designing fragrances is something
Speaker 2: that's much more attainable technically, and frankly, it's just a great,
Speaker 2: much faster sales cycle to be business to be in
Speaker 2: than ultimately diagnostics, which are so hard. Right, I mean,
Speaker 2: it is my north star. It's like where I want
Speaker 2: to take the company. But I also have no illusions
Speaker 2: about how hard that is, and I just I've seen
Speaker 2: all the failures of the companies that have attempted it,
Speaker 2: and I think I've learned from what hasn't worked, and
Speaker 2: so I'm incorporating those learnings into how I want to
Speaker 2: build the company, which is, build a great business in fragrance,
Speaker 2: build beautiful fragrances for the world, and then strike out
Speaker 2: from that position of strength and to even more ambitious frontiers.
Speaker 2: We'll be back in just a minute.
Speaker 1: When I first heard about the work Alex was doing
Speaker 1: at OSMO, I understood why it would be useful for sensing. Basically,
Speaker 1: you might be able to build automate sniffing machines that
Speaker 1: could say detect cancer in a person or detect a
Speaker 1: bomb in a suitcase. But I couldn't figure out truly
Speaker 1: what the business case was for perfume. And in fact
Speaker 1: Osmo has recently launched a perfume business. It's called Generation.
Speaker 1: So I asked Alex, why is using AI and fancy machines?
Speaker 1: Why is that better than just designing perfume in the
Speaker 1: traditional way.
Speaker 2: We can go from the first kind of client demand. So, hey,
Speaker 2: I want to create a fragrance, and here's who my
Speaker 2: brand is, here's what I want to do. So just
Speaker 2: that description to a starting place of a fragrance in
Speaker 2: a minute or two.
Speaker 1: What happens at a traditional perfumer when somebody comes in
Speaker 1: with that request.
Speaker 2: Well, so let's say you're an emerging Let's say you're
Speaker 2: an emerging brand, right, So you're starting out or you
Speaker 2: have your first product and you want to add a
Speaker 2: second one. But you're small, right, You're not making a
Speaker 2: billion dollars in revenue. You're making less than that. So
Speaker 2: if you want to make a new custom fragrance, good luck. Right,
Speaker 2: you're not going to be able to get the attention
Speaker 2: of the big fragrance houses because they want a service
Speaker 2: business that's like millions and millions of dollars and you're
Speaker 2: not big enough yet. So if you want a great
Speaker 2: custom fragrance that your consumers are going to love, and
Speaker 2: you want to do it quickly so you're responding to trends,
Speaker 2: you aren't going to be able to get it done.
Speaker 2: So you have to make compromises, right, So if you
Speaker 2: want to move fast, you're gonna have to use a
Speaker 2: regurgitated fragrance. It's also called a library fragrance, which means
Speaker 2: somebody else in the market has your spell.
Speaker 1: I'm imagining that people who sell it call it a
Speaker 1: library fragrance rather than a regurgitated library fragrance.
Speaker 2: They do, they don't say regurgitated, but that's what it
Speaker 2: effectively is, right fair.
Speaker 1: Regurgitated does have a particular old factory connotation, so it's
Speaker 1: a level.
Speaker 2: It's vic or all sticks in the mind.
Speaker 1: What like, And I'm not I just genuinely don't understand, Like,
Speaker 1: why can't somebody just have a company with a bunch
Speaker 1: like who knows the molecules? You know, who knows what
Speaker 1: the five thousand milecules in the book smell like? Because
Speaker 1: they've got the book and they can just use the
Speaker 1: book and be like, oh, you want this, let's try that.
Speaker 2: Do you know what I mean? Like, I'm not trying
Speaker 2: to be difficult, but I.
Speaker 1: Genuinely don't understand why you need the technology to do that.
Speaker 2: Yeah, I genuinely didn't understand this either. And there's there's
Speaker 2: a class of professional called a perfumer, and their job
Speaker 2: is to do what you're describing, which is, Hey, I
Speaker 2: know all these ingredients and I'm going to make them
Speaker 2: in order to create your fragrance. So they typically there's
Speaker 2: no perfumer that knows five thousand ingredients, but the best
Speaker 2: perfumers know a thousand or two thousand ingredients. Most perfumers
Speaker 2: work with two hundred, one hundred, two hundred ingredients. So
Speaker 2: already there, like where we're there is very few people
Speaker 2: in the world that can do what you're saying. Yeah,
Speaker 2: they can do, and then how what are they going
Speaker 2: to work on? Right? So it might take them weeks
Speaker 2: or months to create a fragrance. They're working on a
Speaker 2: few at a time. Why would they work on an
Speaker 2: emerging brands fragrance when they can go work on a
Speaker 2: much larger account. So there's just very very limited number
Speaker 2: of people who can engage in the fragrance creation process,
Speaker 2: because it is difficult. It's not so much identifying, Hey,
Speaker 2: all these molecules smell this particular way, and therefore I
Speaker 2: should be able to mix them, Like what ratios do
Speaker 2: you mix them in? Like? What are the rules? Right?
Speaker 2: And now you're actually getting into designing a system which
Speaker 2: understands sent well enough to create new fragrance formulas, is
Speaker 2: starting places, and then of course a perfumer finished system.
Speaker 2: But you're right, it's like, oh, why shouldn't that exist?
Speaker 2: And then when you actually start to peel back the
Speaker 2: layers one by one, you realize, oh, you actually have
Speaker 2: to build what we built. It's actually in order to
Speaker 2: answer that question.
Speaker 1: So presumably now your model cannot only predict what one
Speaker 1: molecule is going to smell like, but it can predict
Speaker 1: the combination of molecules. I mean, is it predicting? Does
Speaker 1: it know concentration? Like does it know oh yeah, yeah,
Speaker 1: how good is it?
Speaker 2: I mean you have a perfumer on staff? Why? Well,
Speaker 2: I think the goal of tools is to have them
Speaker 2: in the hands of creatives. And there's many steps to perfumery,
Speaker 2: but I think there's three that are relevant for what
Speaker 2: we're talking about. The first is a perfumer when they're
Speaker 2: when they're starting on a project, they have to have
Speaker 2: a starting place, they have a starting formula, and then
Speaker 2: they do their creative work step two to evolve that
Speaker 2: formula to exactly what the customer wants, to a creative
Speaker 2: expression that the lights the consumer as well. That's the
Speaker 2: funnest part. Perfumers love that that is actual creation in
Speaker 2: the creative part. Number three is then it has to
Speaker 2: be the right price. It has to be compliant with
Speaker 2: with a regulatory compliance. There cannot be allergens, all that
Speaker 2: stuff that's more like sound engineering than it is composition
Speaker 2: or being a rock star. Steps one, the starting place,
Speaker 2: step three, all the regulatory requirements. That's where we spend
Speaker 2: the most energy in building these tools. And then a
Speaker 2: perfumer is the person that is taking the formulas from
Speaker 2: starting place to creative endpoint and then handing it off
Speaker 2: for like regulatory finishing. And they're just way more effective
Speaker 2: with these.
Speaker 1: Tools, at least for now, right Like, that's the way
Speaker 1: I feel using an LLM, like I feel like I
Speaker 1: have a window and me Plus the LLM is better
Speaker 1: than the LLM alone and we haven't. That window hasn't
Speaker 1: closed yet, but I'm not optimistic about my long term prospects.
Speaker 2: We'll see though, I mean, but like listen, I honest
Speaker 2: belief here, like the tools will get better, but the
Speaker 2: drive to create will never go away. And I think
Speaker 2: people will always want to know about the person behind
Speaker 2: the creation in a way, and it's not uniforms. So
Speaker 2: I don't think people want to know the perfumer behind
Speaker 2: the hand soap in the gas station. They just don't,
Speaker 2: right it. But there I think will always be room
Speaker 2: for craft and creative use of tools, and the profession
Speaker 2: that uses those tools might change radically, in the industry
Speaker 2: in which those tools are used might change radically, but
Speaker 2: the tools will always be wielded by people, but the
Speaker 2: work that's being done might be unrecognizable. So you know,
Speaker 2: we'll see how the world evolves. But like I just
Speaker 2: like AI is like an engine, it's just a technologist,
Speaker 2: just a tool.
Speaker 1: So what's the what's the business model? Just briefly for generation,
Speaker 1: like how what you know?
Speaker 2: What's the model? The business model really simply is we
Speaker 2: all design the fragrance for you and then you'll buy
Speaker 2: that fragrance to put in your products or will even
Speaker 2: actually create the full finished product. We'll put in a
Speaker 2: bottle for you if you if you want. We are
Speaker 2: behind the scenes. We're an engine supporting brands. We're not
Speaker 2: a brand ourselves, and we're here to make beautiful fragrance
Speaker 2: products for for brands. So what's the frontier like you have?
Speaker 1: You know, on the business side, the generation is kind
Speaker 1: of the central thing you're working on now, but on
Speaker 1: the more on the on the research side, like what
Speaker 1: are you trying to figure out?
Speaker 2: Now? What are you working on now? So there's there's
Speaker 2: our starting place, which is why does this molecule smell
Speaker 2: the way that it does? And we can never stop
Speaker 2: getting better at that. Then there's the next question of
Speaker 2: why does this mixture of molecule smell the way that
Speaker 2: it does? And we can never stop getting better at that.
Speaker 2: And then there's do you like it? Which is maybe
Speaker 2: the most important question from a business perspective, or who
Speaker 2: likes it? And in what context? Yeah, exactly exactly, which
Speaker 2: is it's not just the formula as the input to
Speaker 2: this model, but there's also who you are, what are
Speaker 2: your experiences, where are you from, what are the other
Speaker 2: things in your life that you've got.
Speaker 1: That actually goes back to your Netflix collaborative. It gets
Speaker 1: me like if I watched Succession and the Sopranos and
Speaker 1: I'm fifty and.
Speaker 2: Then what's the cologne for me? Yeah? Exactly. And so
Speaker 2: I was very fortunate to be able to start this
Speaker 2: company with a guy work with at Twitter name. His
Speaker 2: name is Rich Witcombe. He's our chief technology officer. His
Speaker 2: whole professional life has been recommended system. So he was
Speaker 2: a lead on Spotify's song recommenders system US. So if
Speaker 2: you like your wrapped playlist or recommended playlist, like, that's
Speaker 2: his code. And then he also worked on self driving
Speaker 2: cars at Nvidia. But he's been in this world of like,
Speaker 2: hey you like these things, what about this thing? Or
Speaker 2: here's the inputs that the system is getting. What do
Speaker 2: I do nowt so really really deep into that world.
Speaker 2: Then we're kind of bringing that spirit, that mindset to
Speaker 2: sent into fragrance.
Speaker 1: And then what about beyond you know, for the parts
Speaker 1: of your work that are the next steps that you
Speaker 1: alluded to farther in the distance, the essentially sensing right,
Speaker 1: sensing for security, sensing for health, Like what work are
Speaker 1: you doing now toward that end?
Speaker 2: Yeah? So we're incubating this right now. So I'll tell
Speaker 2: you two things. So one is, we have a partner,
Speaker 2: We've deployed sensors out in the field. We're detecting inauthentic
Speaker 2: or counterfeit goods. It's working. What's really the second thing
Speaker 2: I'll say is it's we've learned something really interesting, which
Speaker 2: is the molecules that smell really good and fruits and
Speaker 2: flowers and vegetables that we have to understand to create
Speaker 2: fragrance are the same molecules in counter for luxury goods
Speaker 2: and the same molecules in our scent. And by getting
Speaker 2: really good at understanding and designing fragrance in one domain
Speaker 2: in the fragrance industry, we're actually strengthening this platform that
Speaker 2: we're building to get really good at the next frontiers
Speaker 2: of security detection. And then ultimately, what we care about
Speaker 2: is healthy. So that's what really surprised us as I
Speaker 2: thought that by working in fragrance, we're making a trade off,
Speaker 2: which is we're here to build a great business to
Speaker 2: make ourselves resilient so that we can work on the
Speaker 2: much longer haul problems. But in reality, we're making progress
Speaker 2: on those problems by teaching our platform about what the
Speaker 2: world smells like. And it's all one it's just scent,
Speaker 2: it's just molecules in the air. And so the more
Speaker 2: we learn about really any piece of what the world
Speaker 2: smells like, the better we get at all of it.
Speaker 2: I think I'll tell you what I think the big
Speaker 2: technical frontier is is predicting emotion. Ah, that's interesting. Uh huh.
Speaker 2: So when you smell something, you obviously perceive something like
Speaker 2: the first thought or first perception is whatever, fresh cut
Speaker 2: grass or grapefruit. But then there's another thing that happens
Speaker 2: almost at the same time, which is I remember or
Speaker 2: I feel a particular thing. And predicting that is something
Speaker 2: I don't think anybody's really figured out. But is a
Speaker 2: beautiful frontier. Well, how do you get the data? You
Speaker 2: got to ask a lot of people how they feel,
Speaker 2: what they smell a lot of things, and they have
Speaker 2: to be able to articulate it. Right.
Speaker 1: Part of the thing with scent is it's so primordial that, like,
Speaker 1: you might not even be able to say how you feel,
Speaker 1: so you need in the computer interface.
Speaker 2: You might you might, But turns out we have voices
Speaker 2: and faces that are effectively BCIs there's a lot of
Speaker 2: information that leaks out of us all the time. And
Speaker 2: that was what my PhD was in, is how do
Speaker 2: you interpret body language in a way that makes sense?
Speaker 2: And by the way, the body language I worked on
Speaker 2: most closely was body language driven by odors, right, things
Speaker 2: that make I studied this in animals, but makes animals
Speaker 2: happy or sad or afraid or calm, And you can
Speaker 2: read that out. I mean, our behaviors are meant to
Speaker 2: communicate to other animals. Right, We're very social, we're social species.
Speaker 2: So I think there's more fundamentals that we have to
Speaker 2: figure out. But this is I think there's some really
Speaker 2: fundamental stuff that's still unknown here.
Speaker 1: I heard you say in another interview that you worry
Speaker 1: sometimes that you'll hit some barrier in nature to your work,
Speaker 1: and you said it in passing. But I was very
Speaker 1: curious about that.
Speaker 2: What does that mean? I always think about that, which
Speaker 2: is like, what day will it be when mother nature
Speaker 2: says you can't figure the next hard thing out? And
Speaker 2: I just look at this from the history of science.
Speaker 2: You know, how if somebody cared about how the planets
Speaker 2: were moving in twelve hundred, well good luck, Like you
Speaker 2: don't have the right telescopes, you don't have tycho brahe.
Speaker 2: There's a bunch of stuff you're gonna need, right, And
Speaker 2: so in a way, it's like mother nature and what
Speaker 2: our society and species knows conspiring together that basically says
Speaker 2: progress will have to wait. And so I think about that.
Speaker 2: I worry about that all the time. And so my
Speaker 2: mental framework that keeps me super humble is like, I'm
Speaker 2: just thankful for all the progress we've been able to make.
Speaker 2: That the tools were around right. So I didn't invent
Speaker 2: graph neural networks. I didn't even invent the data sets
Speaker 2: like we are piecing together and curating, cobbling together. All
Speaker 2: these were standing on the shoulders of so many people
Speaker 2: and it's just always been the case, and I don't know,
Speaker 2: it just it makes Maybe this is too philosophical, but
Speaker 2: for me, when I've been up close and personal with
Speaker 2: scientific progress, either that I've had a part in or
Speaker 2: I've observed other people do, it all feels so tenuous.
Speaker 2: It feels so lucky because once you really dig into
Speaker 2: the details, you realize, oh my gosh, they had to
Speaker 2: be right there at that time and have known about
Speaker 2: that thing.
Speaker 1: It's amazing that anything happens, whether you think of how
Speaker 1: confut is everything.
Speaker 2: The amazing that anything happens, and you know, when you
Speaker 2: really dig in, you're like, wow, how does anything good
Speaker 2: happen at all? But nonetheless you persist. And also I
Speaker 2: think you can create the conditions where it's more likely
Speaker 2: than not to happen. And so that's what OZMO is,
Speaker 2: and that's why OSMO Birth Generation is, like, let's create
Speaker 2: an environment where we're much more likely than not to
Speaker 2: make both the scientific progress we need to make, but
Speaker 2: also like really help and change the fragrance industry, which,
Speaker 2: by the way, will teach us the things we need
Speaker 2: to know to get to the next thing. So I
Speaker 2: think there's so much beauty to create in the fragrance
Speaker 2: industry that I'm going to just enjoy the heck out
Speaker 2: of it and do it for the rest of my life.
Speaker 2: But I think it's going to teach us things that
Speaker 2: will allow us to do even more audacious work in
Speaker 2: the future.
Speaker 1: We'll be back in a minute with the lightning round.
Speaker 1: M let's finish with the lightning round. I'm gonna ask you.
Speaker 2: A bunch of questions.
Speaker 1: Now, what seemingly pleasant scent do you never want to
Speaker 1: smell again.
Speaker 2: Seemingly pleasant scent that I never want to smell again.
Speaker 2: Artificial cherry. It was the cough syrup that I was
Speaker 2: forced to drink as a kid, and I'm super sensitive
Speaker 2: to it. The molecules ethyl moltole do not like, are
Speaker 2: you wearing fragrance right now? And if so, what is it?
Speaker 2: I am not. I stopped wearing as soon as I
Speaker 2: started the company because I needed to smell, of course,
Speaker 2: but like, what's your what's your? Well, give me give
Speaker 2: me a pick. Name some fragrance that's that you love
Speaker 2: for some reason. So I really like this is kind
Speaker 2: of a basic choice from folks inside the industry. I
Speaker 2: love Terrator Maz. It's like the RMEZ flagship men's fragrance.
Speaker 2: It's by a perfumer, Jean Claude Eleena. I really love
Speaker 2: his work.
Speaker 1: Basic Is that like basic in the way of saying
Speaker 1: it's like if I asked you for a watch and
Speaker 1: you said a Rolex Submarine or something.
Speaker 2: It's just like exactly or saying, like what pop music
Speaker 2: do you like? You said Taylor Swift. People like it
Speaker 2: because it's great, Huh, Taylor Swift is great? A Rolex
Speaker 2: watch is a great watch. Terrtormnz is a great fragrance,
Speaker 2: but it's very popular. What is it about it that
Speaker 2: you love? I love it's minimalism and I just happen
Speaker 2: to like the notes, right, So it's really heavy on
Speaker 2: a molecule. I like iso be super. I think it's
Speaker 2: a great highlight of that ingredient and it just wears
Speaker 2: really well on my skin. So that was what I
Speaker 2: used to wear almost every day before I stopped. What's
Speaker 2: your second favorite sense? My second favorite sense is probably
Speaker 2: gonna be It's a hard between vision and hearing because
Speaker 2: I love music, but I like looking at stuff too,
Speaker 2: Like the World World of Beautiful are more expensive. Perfumes
Speaker 2: actually better sometimes, right, So I think there's just like
Speaker 2: anything like bicycles or art, as you start to pay more,
Speaker 2: everything gets better.
Speaker 1: And then at Plato's right, what's the worst thing you
Speaker 1: ever smelled?
Speaker 2: I have a memory. I picked a mushroom that I
Speaker 2: thought looked cool and wanted to show it to my
Speaker 2: dad when I was young, and I forgot about it
Speaker 2: and it was just turned completely gross.
Speaker 1: I had a version of that, of bringing shells home
Speaker 1: from the beach that were alive.
Speaker 2: It turned out I found out when they were dead.
Speaker 2: It's like great intentions, but didn't really have wherewithal to
Speaker 2: thick that through or understand the consequences.
Speaker 1: Alex Wiltscow is the co founder and CEO of OSMO.
Speaker 1: Today's show was produced by Gabriel Hunter Chang. It was
Speaker 1: edited by Lyddya jen Kott and engineered by Sarah Bruguer.
Speaker 1: You can email us at problem at Pushkin dot FM.
Speaker 1: I'm Jacob Goldstein, and we'll be back next week with
Speaker 1: another episode of What's Your Problem.