Alex Wiltschko - Giving Computers A Sense Of Smell - [Invest Like the Best, EP.415]
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Hello and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. If you enjoy these conversations and want to go deeper, check out Colossus Review, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus Review along with all of our podcasts at joincolossus.com.

Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum.

My guest today is Alex Wilczko.

Alex is the founder and CEO of Osmo, a science and technology company giving computers a sense of smell. He set out on a mission to digitize our sense of smell, and he describes how Osmo is teaching computers to both read and write scent.

Alex was kind enough to walk me through the laboratory, which you could watch in the video version of this interview on YouTube and Spotify, where he demonstrates the method to their madness. We discussed their first commercial application, a new brand called Generation, which is revolutionizing the fragrance industry by dramatically accelerating the typically years-long process of custom scent creation. We discuss all the potential business implications this technology unlocks, applications ranging from counterfeit detection to health monitoring. And we discuss the potential business implications of this technology on locks, applications ranging from counterfeit detection to health monitoring.

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So we smell a lot of stuff. Twice a day we run sensory panels where we just sniff stuff and label stuff. So it's just like Scale.ai has people labeling images all over the world. Turns out we couldn't just buy that service from anybody. We had to build it from the ground up. So this is where things get tested and low latency work happens. And then when we scale it, that happens elsewhere. So they're literally going through

smelling stuff. Are they like notably talented smellers? We actually have literal rankings. And so like when we need really accurate data of a certain kind, we will call on our like top dogs, you know? So in there is synthetic chemistry, just like a drug company to create a new molecule to

affect human health, we create new molecules that affect human perception. And we design those on spec for customers that like a large customer might say, all right, we're having this problem making our detergent smell this way, or the regulatory landscape is changing. We can't use this molecule. Can you please help us? So we take all those requirements. We go back into the lab and we use

but when it's applied to olfaction, we call it OI, or olfactory intelligence. So we use OI to design new molecules, which we then synthesize with our synthetic chemistry team, smell them, they work, we launch them. The smell is so remarkable, and it's like it wafts and changes. Yeah, exactly. It's so cool. So it's like a little bit maple syrupy today.

So I don't know what they're making. I guess they're going through a lot of commercial fragrances right now. So you're smelling kind of the sum total of our work. So behind you is what's called the perfumer's organ. Each bottle there is like kind of a key on a piano. And instead of 88 keys, it's about 1200 keys. So a perfumer will be able to

pull any of these ingredients together and mix them in the right ratios to recreate your scent memory. So like the smell of fresh laundry was made by a person, right? From some of these ingredients. The smell of, you know, a clean kitchen was created from some of these ingredients. So 90% of the products in your home have a fragrance and every single one of those fragrances was crafted by an individual and they were crafted by combining these ingredients together.

So, what we're doing at Osmo is teaching AI about these ingredients and how to work with them in a safe way, to do it super fast, to do it super affordable, and to be able to launch new beautiful scents that were possible. What is the first set of building blocks for doing that? So, you've got like the individual, I don't know, isolated smells or whatever. How do you create the digital...

footprint for each one of those things? This is a machine called a GCMS. This is basically a camera for the molecular world. I'll just show you kind of how it works. So this is a robotic autoloader. And so each one of these has a smell that we want to analyze at the molecular level. So this thing can run 24/7. So we load this thing up, we just let it run. What happens is you suck up a little bit of the smell as a liquid,

and you inject it, and it goes into this half of the device, which is basically an oven with a 50-meter long, very thin cable, and you're shoving the smell through that cable. And what you're trying to make the smell do is like runners in a marathon. So every molecule in that

scent is all clumped together and you experience that as one kind of unified sensation as a smell, you got to separate them to analyze them. And so what you do first is you run them through a race and the light molecules make it through the race first. And so they can be analyzed one by one here and heavy molecules come out later and later and later. So this basically separates the scent into each individual molecule that's in the smell. And then this side weighs them.

So the molecules enter the mass spectrometer after being separated, and you basically hit it with an electron gun, and it shatters the molecule into pieces, and you very carefully weigh those pieces. And then you play kind of like a Sudoku puzzle to figure out, okay, given the weights of these fragments, given how long it took to run this race, what was that molecule?

And typically, this interpretation is done part by software, part by people. And what we've done at Osmo is make that happen entirely by software. So that's a part of our OI system. So how much does the individual atomic unit of smell differ from the combinations? Like if I think about like the primary colors or something, these are primary smells is how I'm thinking about it.

Is it pretty reliable like how you can combine those things into some new set of things? Like what is the periodic table equivalent? So nobody knows, but we're teaching machines to figure that out, right? So that has been the core, core issue of why scent hasn't been digitized is because exactly what you're saying is... You don't know what maple syrup breaks down into as primary smells. Exactly. So...

People have been analyzing the molecular content of these smells for a long time. So like you can go look up in some textbook what the molecules in maple syrup are. But the ability to say, okay, I want maple syrup with a little bit more cherry or I want maple syrup, but don't use that molecule because we know it's not safe. Use this other molecule.

that requires tons of trade craft. That is what we're automating. How much will this machine change in the next five years if you're successful? Will you be building your own version of this? I see Gentech on there. It's not an Osmo machine yet. So these machines are great. And what we're

not going to do is we're not going to change the hardware because there's about a, there's 12 Nobel prizes worth of advances inside of these machines. They're fantastic. What we've done is rip out the brains and we've replaced it with our own brain. So a lot of what we've noticed is the hardware actually is already pretty good in the realm of scent and chemistry, but the software or the maps that link the different pieces of hardware has been completely missing. That's what we built. This is kind of like an inner sanctum here. So

This is where we keep every AI-designed molecule that we've made, which is probably a significant fraction of all AI-designed molecules ever. And so this is just one slice of it. So in this room is 10,000, 20,000 molecules that have all been designed by AI. And we have a digital twin of each. So if we need to go back and access it, we know it's fridge one, shelf three, row column two, four.

And the sum total of it kind of smells like a bready radish or something like that. Do you yourself have an abnormally good sense of smell? We've brought a lot of people into Osmo that have like truly world-class noses. And so I can say definitively that like I'm not world-class. So this is kind of the Rolls-Royce machine. It does the same thing as the other one, except there's two more things that are interesting. One is you don't have to inject a liquid into this. You can put like anything in.

into these vials and it will suck the smell from out of what you put in the vials and will analyze it. By turning it into a liquid first or just directly? Directly. So what it does is it pumps air into these vials with a needle syringe. So it'll get dropped in here. A needle will be pushed into it. So basically we'll suck the air and we'll concentrate it onto, you know, like Kodak film absorbs light. We have film that absorbs scent. Okay.

And they basically concentrate the smell on that thin piece of film. And then you move that needle and you inject it into the spectrometer. And it uses a flash of heat to remove all those molecules. It kind of develops the film. And then the normal machine runs. We analyze that data with AI. And then we can pull back out what the scent actually was. So this means that we can analyze flowers and vegetables and people and fruits differently.

And so what we did, the first scent that we fully teleported digitally was a fresh summer plum. So it was like kind of the purple plum, you know, like the really good ones have like a snap when you bite into it. It was like one of those fresh ones. So we sliced it, we put it into one of these vials, we analyzed the smell, and then we actually reprinted the smell on the other side of the lab, which I'll show you.

The other thing you can do with this machine, which is really cool, is you can pause the smell at any point in time and you can just sniff molecule by molecule.

So a scent will be like 30 molecules, 100 molecules all blended together, different types. You can smell them one by one by putting your nose on here. It's kind of like a debugger for software. Wow. So this is called a GCO or gas chromatograph olfactometer. But when we really want to understand the smell and kind of like build our intuition when we're building new protocols, we'll actually sit here and sniff stuff that comes off the machine. And so I understand like the strategy behind all this. So

you've got the ability to read and then you've got the ability to write. And that in so doing, that's just the first step to giving computers a sense of smell. We'll talk more later about all the applications that you could then build on top of that capability. But is that how you thought about it? That to give computers the capability in the first place

It's read and write. And write is especially important because it confirms whether or not it's being read correctly. Exactly. And if you can read and write, then you can create this virtuous cycle where you're creating data at every run of the loop. Right. And so if you actually can create new smells and then you can turn those smells into data readings of some kind, you're training AI.

Right. And then if you can tilt that process, so the next smells that you create the next day teach the system even more. That's when you're doing what's called active learning. And that's how you get AI systems to get smart really fast. And that's what we do. Can you talk about the measurement of the fidelity gap between read and write? Like if I give you a if I give you the plum and you stick it through your machine and you read it in and then and then you give me the oil essential oil on the other side, how effective?

how you measure the gap between the smell of one versus the other and how close you are. Yeah, it's like I'm simplifying, but I'm going to hand you the real and recreate it. And you're going to tell me we're good. There's more nuance to how we do that to create data that can actually be fed into a machine learning system. But that's effectively it, which is like, do these things match? And there's a few tricks that you use to

help de-bias people and get reliable data. But like, you're the arbiter, right? If it's like a smell you're familiar with and I'm trying to recreate a memory that you have, like, we either did it or we didn't. We were just with a very famous Hollywood person who said in the 80s, they tried to do this in theaters where they would have something that like puffed out smells. Aromarama. Yeah. And it just didn't work. They only had...

certain smells that were like whole seeds. And so they didn't have primary odors. They didn't have the ability to create any smell. And so here on this robot, you're obviously not putting this behind a couch cushion yet, but we're going to make this smaller. But the idea here is you need to have all the ingredients together that can be mixed on the fly to create any experience, not just like eight pre-programmed experiences. That's like a slideshow, right?

We want an actual display that can show anything. - What is the most surprising thing about primary smells? - We're kind of at the scientific frontier. And so like everything that we discover every week, every month, like pushes back what's known about smell and how to construct it. I think one thing that I've found in doing science and machine learning and combining these things is like

problems that people sometimes think are totally intractable, once you just get started, you're like, oh, we're actually making progress. And so the idea of creating scents with AI and creating those scents in partnership with people and fusing human and machine to work in this very emotional world of scent, people just don't think of it. It's like, oh, that's crazy. And then you start making progress. Then you start making progress bit by bit. And the first scents are dumb. They don't work or they don't smell right. And then you come back four weeks later, you're like,

That one was really good. Holy crap. I think it's working. And then they all start to work. And then you start to go talk to customers and they start to accept some of your sense for products. And then it's like really starts to roll. And, you know, bit by bit, you just, you know, keep going. You know what StockX is? They have a problem with fakes from Timu. I want you to hold this in your right hand and smell inside of it. And I want you to hold this in your left hand and smell inside of it.

And I want you to look at them. Can you tell the difference between these?

I mean, they're... Not really. Yeah, they're the same, right? Like they're constructed to be perfectly the same. This one smells like new shoe. That's the fake. You can't work at StockX. But it's more pungent, right? It's more pungent, right? So the difference is the counterfeiters are really good at visual identity, but the smell of the shoe is basically the fingerprint of everything that ever happened to make this. What we've been able to do with StockX is show that we can take those really big sensors that are in the lab...

cut the right corners, make them smaller. So it's actually the sensors are about the size of these two shoe boxes together. That's one right there. And what we do is we can take the thumb hole of the shoe box and basically insert it into a sniffer. And it will within 20 seconds, it'll tell you a real or fake.

And we just have to show it a few reels, a few fakes. Sometimes it's more, sometimes it's less. But then whenever a new SKU shows up, we grab some of the fakes, we grab some of the reels, we train it on the new SKU. And then now there's a device that can tell them apart. It's funny to imagine a future where there's an arms race and the counterfeiters are injecting their own. Yep. There already is an arms race. And so this, I'm sure, is going to be the next frontier because we're about to really stem the tide. God, the mind races with Apple.

The first investor prospectus that we made, the double-edged sword was like, okay, so we have this entire space to ourselves. Now the risk is, how do you focus? This is kind of like a piece of history here, Plum 1.0. This was the first scent that was teleported. So the first scent to be digitized and then reconstituted in another place. And what we've done with this, and I will show you the vial here.

is this is the essence of a fresh summer plum. And in this vial, this kind of clear liquid is probably thousands or tens of thousands of sniffs worth of this one moment of biting into a fresh summer plum.

And then what we're showing here is literally everything, the source code of that memory, of that scent experience. So I want you to smell it. So I've already dipped some in the vial. So I just put the blotter into the vial. But close your eyes and think of biting into a fresh summer plum. - Crazy.

That's wild. Right? Did we get it? Yeah. So this is, I think, a piece of history. And we only made 100 of these, but I'd like you to have one. Oh, wow. Amazing. It's important to me that what we've done. And I think, you know, it's just a plum today, but it's a lot more that we're doing in the future. So one thing that we're doing...

is we're launching a new kind of fragrance house called Generation. And the idea is to take all the technology that we've built, but also all the humanity, the people, the perfumers, the noses, and to combine that in a new way of designing scents for people who might not have been able to have access to designing a new scent or haven't been able to do it fast enough. And so that's called Generation, and that's something that we're just now launching. So we're

What we're doing with this scent is this is a scent that we custom designed for a creator. So someone who has a very large active audience on Instagram, has like a really great rapport and a brand, frankly, but what she doesn't have access to is a way of creating her own product that is resonant with her values, but also just straight up beautiful.

And so what we're able to do is take all of our technology, take our perfumery, and what we've done in very short order is design her a fragrance and we're going to help her launch it. And so part of the value proposition of generation is do you want to launch a fragrance? Do you have a place to put it in front of people but you're missing all the pieces? Because we can now automate lots of this and we can bring humanity to the rest of this,

Like, let's work together. Let's build you a fragrance. So if I wanted to go through that process and said, "I want to create my own," what are the building blocks of that process? Like, I guess I could start to describe, "I like plums, and I like fennel, and I like this." Is it that simple? It can be. So, like, let me tell you how it's done today and then how we're changing it. So the way that you get a fragrance design today, and I'm not even talking about launching the full product, like literally just the smell. There's more that you have to do to launch the full fragrance.

you submit what's called a brief. So a brief in other industries would be called a request for proposal or an RFP. And it can be anything. So it's actually very, very free form in this industry. That itself could be revolutionized. So let's say you write a Word document and you describe your brand.

You're going to describe what you want it to smell like, who you are, what you want the brand to be resonant of. And then at the bottom, you'll usually specify two pretty important things. What's the benchmark? So is there a scent that you want to beat? And usually that means I kind of want my thing to smell like this, but make these changes. And then what's the price you want to pay? So how many dollars per kilo? Which can be as low as like 10 bucks per kilo. It can go for fine fragrance, can go up into the many hundreds of dollars per kilo.

So it just depends on what you want to launch. You submit that brief to a fragrance house. And again, this is the traditional way. Somebody receives that. And in their kind of weekly meeting, they read the brief. They read the volume that you want to make and the price. And they decide whether or not they want to work on it. And here, you haven't paid the fragrance house anything.

they look at the brief and then they say, "Okay, we want to work on this." Usually, 90% of the time, they go to their library and they say, "Oh, we've already made something for somebody else. Let's send Patrick this scent that is from our library." And then you'll get that and maybe you like it, maybe you're done. But 90% of the time, sometimes more, you aren't getting a new custom scent. And by the way, that process itself, that may take weeks, months.

But let's say you push back and you say, actually, I want something that's really custom. So you know you're a sophisticated buyer. You say, don't give me a library sample. Now you're looking at like a 12 to 18 month process of going back and forth. Every time you submit notes, it might take three months for them to get back to you. Super long process. And by the way, once you get the fragrance, you still don't know if it actually works in the application that you're going to put it in, right? So you have to do application testing. So let's say you want to launch a,

a skin cream and you want it to be slightly fragranced. Well, the fragrance can't make the skin cream turn a wrong color and it can't make the scent change too much. So you have to do application testing as well. Now you've added more time. So that whole process is handled in most fragrance houses, something that looks very close to pencil and paper.

and a lot of guesswork. And so what we're doing with generation is taking each one of those pieces and applying modern methodology, and in some cases AI, in some cases just efficiencies, to make that faster and to make sure that you get something custom every time that's actually tailored to your brand. So when you submit the brief,

That should be a chat GPT interface. You should have a conversation, right, that should be available to start instantly. So we have a tool. So let's make, would you want to make a Colossus fragrance? Sure. Great. All right. So a fragrance for Colossus. What do you want it to evoke? Well, I'll describe what our mission is and maybe it'll come out of that. So our hope would be that by finding

frankly, people like you that are in pursuit of what we would call their life's work. For you, it might be giving computers a sense of smell and all the applications that are born from that. That we, by showing people these great examples of people really doggedly on the hunt to build their thing, they'll wonder what their thing is and start building it. So I would say very focused on business and investing. Those are the forms of art that I like.

But I would hope that our work encourages more people to chase their thing because we're showing them such great examples of people like you chasing theirs and really just give them permission to do so. So what do I hope it evokes? Possibility. Like I think of open air, maybe it would be like a smell that I think about. The Sequoia Parks or the Redwood Parks in San Francisco, when you get to the top, there's like a very specific like crisp smell. So what's happening behind the scenes is we're tapping into all the

tools that we've built, the olfactory intelligence that we've built over time. We will take what we've put in and we'll embed it into our map of scent. So our map is not three-dimensional like RGB, it's about 300-dimensional, which is, I think, why scent had to wait for artificial intelligence to be digitized, because it's just more complicated.

And we'll then decode that coordinate into a scent profile. We'll show you where the scent lives in the map of like the 100 top mass market hits. And then I'll show you the source code of the fragrance. And if we have something similar to it, we can actually go grab it and smell it. So...

And this is, by the way, this is a multi-month process that we're condensing down into minutes. How long do you think it will be until there's literally something sitting here that the feedback loop is more or less instantaneous? The mountain peak is something you can hold in your hand like your AirPods and your phone that can read the chemical slice of reality, capture scent moments, tell you if you need to go to the doctor, tell you what vitamins to take.

to really read scent and then another device that can recreate it. So that we can fill this room with, it can smell like walking through the redwoods, which like that forest bathing smell is one of my favorite smells of all time. So we're getting there. So you saw the big reader, you saw the one that we made smaller. We have to take that down by a factor of like four to eight in order for this to be something you would really say is portable. Right now, we can move it around, we could deploy it. I don't think you can say it's portable today.

Then you saw the scent printer which is half the size of this table. We've got a lot of work to do to make that thing smaller. But if we've known one thing about technology is making things smaller is like a thing we can do.

So let's take a look. So we've got an image. Ambition trail. Ambition trail. All right. The marketing copy is a fragrance inspired by a timeless determination in the pursuit of one's mission, capturing the essence of ambition and discovery. It seems based on all this that it's not that long from now that you will enable things that traditionally have scents, candles, detergent products. Yes.

whatever, infusers, because they're all based on the essential oil that you're delivering, that like people will be able to design scents very soon. Absolutely. So that we are rolling that out. Generation is, yeah. Generation is our business to open up scent design to more people, right? And to do that faster. If this company is going to be the thing that sort of

has your name written all over it and that you're the most proud of having created. What are the earliest seeds of that story in your life? Because, you know,

David Senra's "In Your World," he has a few phrases that really resonate, which is like, "The exit strategy is death." This is the last thing I want to do. I grew up in a town called College Station in Texas. Not too big, not too small. I got bit by the computer bug pretty early, so started programming computers when I was eight or nine. Full-on computer nerd by 12, when I started collecting perfume.

And I started collecting perfume because I noticed that people, when they put this invisible thing on them, would all of a sudden be treated differently by everybody around them. But within this little radius, right? So like it just, it was this magic potion spell combination that when you just say it like that, almost as unbelievable, which is, can you spray an invisible thing on you that is

changes how people see you and treat you, for the better or for the worse. I just couldn't understand it. I had already felt a little bit like a social outsider, and I was trying to decode that. Like, why are they popular, and I feel like I'm on the outside? And so I looked at the clothes, but it was the fragrance that really, like,

It confused me to no end at first, but then it fascinated me. And so I started looking into fragrance and I found out what these kids my age were buying. It was Polo Blue, it was Abercrombie & Fitch Fierce, both fragrances that are master perfumer that we just passed designed many years ago. So it's completely full circle now. But then I realized there's not just two or three fragrances, there's tens of thousands. And it was like,

discovering that movies exist and you can go into the movie theater and there's nobody watching the good films. Everybody's watching the popular films. And for me, this whole world opened up a fragrance where, I mean, I remember the first fragrance that really taught me that this is an art. It was Bulgari Black, which is frankly not like a very long lasting or particularly performant fragrance. It's like, it comes in a bottle shaped like a hockey puck and you spray it on, it lasts 45 minutes.

But what it does is it unfolds in three acts, right? So the first smell is like the smell of screeching tires and rubber. And then within five or 10 minutes, it cools down to like this vanilla rubbed on a leather chair. And then all of a sudden, after like another 15, 20 minutes, there's like this smoky tobacco leather chair smoking room vibe.

And I remember the first time I experienced it, whoa, this fragrance changed. Has it gone bad? And then I just sprayed it again and again and again and watched this movie play out for like an afternoon. I was like, no, no, somebody made this. And this is the whole thing. The whole fragrance unfolds over time. And that kind of was the end of it for me. I got just completely hooked. And, you know, I...

The way that my brain works, I wanted to understand where it came from, how it was made, how the brain processes it the way that it does. I think if I was born in southern France, maybe I'd be a perfumer, but I was born to two academics, and so I became a scientist. And I went to school for neuroscience at the University of Michigan and then realized that there is a subspecialty of neuroscience called olfactory neuroscience. So people who want to figure out how the brain processes smell, and the most people who don't

study that are at Harvard. So I went to Harvard and realized after many years of doing science there that like actually we don't really know how smell works at all. Like we're making progress, we're learning things, but like a simple question, like let me draw a molecule on the whiteboard like we're in chemistry class. Can you look at that molecule and tell me what it's gonna smell like? Will it smell like apple or cinnamon or anise or what?

So it turns out that's like a hundred year old problem nobody had been able to solve.

That really stuck in my craw. I'm like, why don't we know how to do this? I ended up leaving academia. I started and sold to AI companies, one in the biotech space, one in the kind of pure ML as a service space. That ML company was bought by Twitter. I helped to start their deep learning team with my co-founders and with another company that we were combined with. That's where I really learned like internet scale, artificial intelligence applications. So we

I applied AI to their ads platforms and made them a lot of money and applied AI to their data centers and saved them a lot of money. And then I was recruited away to Google Brain, which is now called Google DeepMind, which is kind of their Xerox PARC or Bell Labs. And I worked on some internal projects for a bit, but after a year or so, I said, "You know what? Let's take a crack at the smell problem again."

And it turned out that in the time between when I left academia and got into tech and entrepreneurship and when I arrived at Google Brain, some breakthroughs happened. And what happened is AI researchers figured out how to make artificial intelligence work on chemistry. And that maybe doesn't sound too crazy, but...

Up until then, AI systems really liked their inputs to be rectangles, right? Like images are like grids of pixels and text is like a long thin string of words. But molecules can have any shape. There can be any number of atoms and the bonds can be all rearranged.

And there had been a technique that had been really improved and figured out, made to work better, called graph neural networks. And that turned out to be like chocolate and peanut butter for AI and chemistry. So we didn't figure that out. But a lot of my colleagues, who I was very fortunate to work with at Brain, they figured that out for the world of drug discovery. So the intellectual arbitrage that we did was we said, let's take those techniques and let's apply it to the realm of scent.

And I had spent a long time thinking about scent and traveling in that world. And so I knew where to get the data sets, where to buy them, where to license them, how to treat them. And we fused those two things together. And I was fortunate enough to work with an incredibly talented team of folks at Google Brain. And we made this happen together. And what we're able to do is solve this 100-year-old problem. It sounds so simple, but why does this molecule with this shape smell the way that it does?

And we validated it in a really stringent way. We basically did a double-blind trial where we predicted the smell of hundreds of thousands of molecules. We picked 400 that were very different looking from anything we'd seen before. We kept our predictions secret. We bought or made the molecules. So some of these had never been made before. We sent them to our collaborator at Monell. Professor Mainland was running this.

And he trained a panel of people, and this is kind of like what we do now, but just initially it was at a smaller scale, train people to smell something and say, "Okay, this smells fruity and mineral and that's it. So I'll give it a three out of five fruit, I'll give it a one out of five mineral, the rest zeroes." And that's called rate all that apply. It's just like that's how we label data.

And then what we did is we said, OK, we have our predictions. People have their double-blind ratings. Where do our predictions fit within the people? Because the best is the average of the panel. That's how you get really high-quality data for AI.

So, were our predictions worse than the worst person or were they in the pack somehow? And it turned out that our AI predictions of what these smells were going to be were better than the average panelist. Meaning, if you're going to add one more person to this panel, you'd actually prefer to ask our software what it smells like that doesn't have access to the physical molecule than to train up another person to physically smell it, which is kind of like passing an odor-turing test.

When that happened, it was very clear that Mother Nature was not going to stand in the way of continuing on this journey of actually digitizing the sense. So if you can solve that one problem, it means you can start to ask, okay, great. Now what happens if instead of feeding this AI algorithm a pre-digitized molecule, what if I feed it a reading from a sensor, like the data off of a camera, right, if we were talking about images?

And then what if I then ask it to recreate that smell, right, with the ability to mix together different molecules to create a new scent? If you can actually round trip a smell, so take a physical smell, put it in one system, and then round trip through the reader this map that we built, this graph neural network based map,

and then write it back out again and then compare it, and it actually smells like the thing that you put in, it means that you have actually digitized a human sense. We hit all of our scientific milestones at Google and we asked ourselves, what's the right way to scale this idea? And that's where...

Josh Wolfe comes in. So we were thinking internally at Google, maybe this should be a company. And I was working with Krishna Yeshwant at GV, who's a very close friend. We'd worked together for five years. And someone at GV, another investor named Izzy Rosen, was getting lunch with Josh, who's the founding and managing partner at Lux Capital. And apparently, Josh had this 10-year-long thesis about digitizing Wolf Action, and we had never met.

And so Izzy was listening to Josh give this pitch again. He said, "Hey, have you talked to this guy, Alex? He's kind of in the smell." And then Josh and I met and it just was an instant connection. And so Josh was integral in pulling this IP out of Google Brain and building it into a completely new company. So Josh led the round, Krishna at GV co-led, and we built Osmo and we're on our way. - When you think about building Osmo the business,

How do you think about the trade-off between creating a pure play platform defined as you enable, I'll call them developers, to build any sorts of application they want on top of Osmo's root level capabilities, all the things we've talked about.

And, you know, you charge them a platform fee and, you know, lots of platforms out there that are wonderful businesses versus like, okay, we have the platform, but we're also going to create the generations, the vertical application companies on top of our own raw tech capabilities. What are the trade-offs of one approach versus the other? Are they mutually exclusive? I think in the limit, we're going to be able to explore that design space more fully and

But it really depends on what's the market you're entering with that platform capability. So a lot of successful platforms are entering markets where there's already a ton of vibrant activity and they're helping to grease business and make that happen more fluidly. There's not a lot of fragrance companies out there. So there's not that many buyers. The question is, do you become a software provider for the incumbents?

Or do you take your capabilities and do you compete in that market? And I think there's been examples on both sides of this. There's plenty where you are an input or a service provider. I think a recent example where folks decided to just enter and to compete would be like Metropolis, if you've heard of that example. They're making software for managing parking lots. The parking lot industry just weren't ready buyers of that software.

But it actually worked. It made parking lots more efficient. So they became a parking lot company. We went through a similar journey where we... I mean, if I could have sold the software here, and believe me, we've tried, like, yeah, we'd be selling software. We might not be talking. But there's not that many fragrance houses, period. And...

I think that we have the opportunity not to just sell into an industry that is like look very large, it is very secretive, but I think with this software and these tools we have the chance to really transform it for the better. Like the way that business is done here hasn't transformed for 300 years and a lot of the way that things are done should stay the same, right? Like they've stood the test of time, but like the world's getting faster. People are asking for more transparency.

People want to make sure that the fragrances that they're using are safe. And also there's a lot of people who still don't even know how to get a fragrance made, right? And like, look, every company has visual branding. They have a feeling that they're trying to create for the people that interact with the company, for the people that are in the company. But there is no modality, there's no sense that is more emotional and that has deeper ties and associations it can build than scent.

And so there's a lot of businesses out there that need to have a smell. And it's already happening, right? Like the Ritz-Carlton has a scent. - I remember the Gramercy Park Hotel so distinctly. - Right, and you walk in and what do you feel when you walk in? - Yeah, familiarity. - Yeah, familiarity, it's elegant, right? It's like, if you smell it anywhere else, you're gonna think of exactly that hotel.

And so I think one thing that we're realizing is there is an appetite to add scent to more layers of our economy, to more businesses, to more markets, to more products. It's just inaccessible. And so what we're trying to do is bring more people to scent and create more scents for people. So that's what generation is all about is

If you want to make a cent and if you want to do it quickly and if you want to do it safely, like we're here. Like we figured out how to fuse...

AI with the human aspect to create really beautiful smells. - Teach us just a little bit about smell itself. What's its history? Why is it so important? Why is it so emotional? Why are scents so memorable? - This sounds like hyperbolic, it sounds extreme, but it's the first sense, right? So if you think of us as little microbes a billion years ago, we survived by eating things. And we got better at surviving by eating things by detecting if the thing we want to eat is nearby.

that's what smell is, right? Smell is like sipping little amounts of the chemical environment around us to figure out where is there more of that thing or less of that thing. So it's a super old smell. You can even see it in the brain. So if you smell something, first of all, that is the physical world touching your brain, right? Your brain sends neurons out of your skull into the top of your nose, and your brain is literally touching the world when you smell something.

And the number of steps it takes for that information to get to your centers of memory and your centers of emotion is faster than any other sense. So we're anatomically wired to have scent project directly to our memory and to our centers of emotion. Those areas are called the hippocampus and the amygdala. So we're wired to associate smell with emotion. So it's like evolutionarily super old.

There's still a lot of mysteries that remain about smell. And there's amazing researchers that are pushing back the frontiers of what we know, figuring out why things smell the way that they do, of engineering better smells. So, like, look, it's still also the most mysterious sense. We were talking a little bit earlier. It feels weird that we haven't figured out scent, right? It's like computers can see smell.

Computers can hear, they can touch, right? We have touch screens and we have the work that formerly Control Labs is doing with these wristbands, exact haptics. But computers can't smell. And that's weird because it's such a fundamental thing. It feels like almost free or like easy for us to smell things. Like why can't we teach computers to do this?

There's this concept called Moravec's paradox. Have you heard of it? Yeah. Yeah, so like just really briefly, the idea is like the easier it is for a person to do, the harder it is for a computer. Because if it's easy for a person, it means evolution has spent a ton of time making it easy for us.

But if something is really hard, like proving a math theorem or something, turns out we've taught computers to do that stuff, right? And it was weird that those were the first problems to fall. But walking has been hard, and we're just now kind of getting good at that by making robots walk. And smelling has been extremely hard, and we're just now kind of cracking the code there. So if I think of generation as the

the creation of and manufacture of any smell that I want. - Yep. - You know, with sort of no limits on the possibilities of how I could use that scent in a product, in a space, in a showroom, in a whatever.

What are the other next two, three, four things where there's a big stack of potential utility that would be unlocked because computers can smell? I think of dogs in an airport or something like that. Totally. So that's what we're doing with StockX. Then again, our main thing is generation. We think that that's going to really have a massive impact. That's creating smell. But there's all these applications for detecting smell. So

I don't know if you've ever experienced this, but a lot of people can sometimes smell if their loved one or their partner is getting sick. Or like something's a little bit off. And then two days later, they actually get sick. That's real, right? Like what's on the inside of us gets to the outside. What's in our blood and in our organs is eventually exhausted through our breath, our sweat, whatever. And we know that dogs can pick up on that stuff.

So we've proved out that we can use the scent of a product to tell whether it's a real or a fake, basically tell its provenance. And that's something we've got deployed at StockX, and that's going quite well. We think that there's other counterfeit detection and kind of truth and safety applications for scent detection.

But I think it goes deeper than that, right? So I think we could be detecting harmful substances at the border and stopping them. Whatever sniffer dogs are doing, I think eventually a computer will be able to either help with or do entirely. But a holy grail for us is human health and wellness, right? Like I think the signal that is inside of the scent that we emit is completely untapped.

What's weird is it turns out that by getting really good at designing the scents of fruits and flowers and vegetables, you actually for free get good at these other scent problems like with human scent or with product scent because the overlap of the actual molecules that you see is actually pretty high.

So there's not an infinite number of molecules out there. There's a lot. But if you get really good at one domain of scent, it turns out to help you in other adjacent domains. Last time you and I spoke, we were looking up together on our computers the market cap of the fragrance houses. They're quite huge companies. Yeah, they're big. Maybe you can describe why they've been such good businesses, the sort of like margin profile of these things. Like what is it that makes...

fragrance, just in the publicly traded ones that you can go check out. Relatively big, good businesses. There's a few pieces here. One is they, at first brush, are recession-proof. So if people aren't buying luxury fragrances, they're buying hand soap, right? So fragrance is in 90% of the products in your household. And there's a very small number of companies that provide all of that.

And so if one category is going down, another is typically going up. So really, really great long-term profile.

The margin profiles are also very great. So fundamentally, these are manufacturing businesses with non-manufacturing margins because there's a ton of know-how that goes into producing the finished blended product. So although it's just ingredients mixed together in a jar that's then sent to a customer who then puts it in their packaging,

how you get the exact right blend of those molecules is typically a deeply held secret. And what we've done is studied the industry very, very deeply and figured out, okay, a lot of what is being provided in the industry we think can be augmented by artificial intelligence and we can do this faster. The

Other piece here is customers are typically quite sticky. So if you're running a beauty or a CPG business and you run out of stock, your first inclination is to reorder from your past supplier, not to bid out again. And so typically, if you've won the business industry,

Standards for repurchasing are well above 50%. So if you build this very wide book of business that has different parts that fluctuate based on the macro, you have a really resilient business there. And the margin profile is typically quite good. If you think about the things that could go wrong on this journey of yours. Which I always do every day. What do you think gets in the way? So I'm always paranoid when...

Mother Nature is going to show up and say, you're done. Right? Like, no, in 2025 or 2026 or 2027, this is not the year for you to peel back another mystery of how this human sense works. And so you're blocked for taking the next step.

And that could manifest in any number of places. We could fail to make these sensors small enough to be held in your hand at an appropriate price. There could be something fundamental we don't understand about the world. This is an existential risk that we can never really remove. But we continue into the darkness and into the fog regardless. What we're trying to do

is never lose sight of the mountaintop, which is we fully digitize the human sense and it's personal, it's portable, it's affordable.

And our philosophy for doing that is not to climb up the sheer face of the mountain to that single goal, but to find a route up that mountain with a shallow enough grade where at some points we can stop and build a business. The philosophy here, and I've seen other startups kind of fail to do this, is build along a responsible path that makes you harder to kill over time as opposed to makes your likelihood of success even riskier over time. This is just

Because I want to do this for my whole life, I don't want to just flip this company and sell it. I really want this to survive. It has to survive. And so we're building in our strategy a way to make that much more likely than not. One of the things that's so interesting to me about Osmo is...

It is an AI company in a very strict sense. Oh, yeah. If you look at the org chart, you're like, oh, it's an AI company that married a chemistry company. But it's quite distinctive in the sense that most AI companies, especially building, I'll call these applications,

are remixes of a lot of the same stuff. There's a lot of code, traditional code involved. There's software involved. There's putting things around the incredibly powerful reasoning models that now exist. And the whole world is kind of, I saw the other day that ChatGPT now has 400 million monthly active users, like 5% of the world's population is using ChatGPT. So people are now familiar with these language models and these generative models. But this seems like you're using the power

much differently. And I'd love you to just kind of describe some of the ins and outs of that as we think about other problems where we can apply AI that aren't just text generation, image generation, video generation, that aren't pure generative in that sense.

pure software and get into some other world. So maybe here it's chemistry and AI, but maybe describe like how you're using the tools and how you think about the growth of the capability of those tools and how it will impact what you do. Technology usually proceeds on an S curve, right? It sucks. It sucks. It's getting better. Oh my gosh, it's getting better super fast. And we're pretty much done. It levels off. And I think we're

pretty close to the right side of that S-curve with text. We kind of blew past it, but yeah, we passed the Turing test. I regularly am fooled and curious whether or not this was written by ChatGPT or not. So text works. And then Ilya Setskovor, who is really one of the progenitors of modern AI for text,

got up at the main AI conference in Europe and said, look, there's one internet and we've trained on it, right? There's no more data, right? So yes, there'll be some remaining tricks. We'll make it cheaper. We'll make it better. We'll add reasoning. But like we're out of the raw fuel that drove a ton of the innovation in text and

And I think also similarly for images, right? Like we've downloaded all the world's images and all the image models are trained on all those images. Video, we're not done yet because it's super expensive. So like we're not quite at the end of the curve. Those are just three modalities, right?

drug discovery, right? There's chemistry, design of chemistry to treat diseases. There's materials design. There's all kinds of things. And what I'm concerned with at Osmo is marching up the S-curve of a human sense, right? So we're on text, we're on vision and images. Those are handled by really brilliant people. But as far as I can tell, we're the ones who are driving AI up the S-curve for scent, right?

And we're really at the far left. So we're just starting to take off right now. And the thing that is the fuel here is data. And so...

Yes, we use specific kinds of AI models. We even use LLMs in some of the work that we do. They're super useful. Our philosophy is the right tool for the job. And so we're not going to take a dogmatic approach and try to shove everything into an LLM, although LLMs are extremely useful for this. And I think actually they will get more capable over time for even what we do. But most of what's under the waterline, most of the iceberg, as it were, is just creating the data.

Having the infrastructure for accepting that data, having the operations to create it, the physical and the digital all linked together, you have to have the data. That's the fuel that drives you up that S-curve. And so we've just realized this step by step. So we started out saying, hey, we want to digitize smell. Great. Where's the data? We tried to do some licensing deals. We were successful. The data wasn't kind of AI compatible. So we said, OK, I guess we have to create it ourselves.

And so then bit by bit, we begin to build basically the entire AI ecosystem that exists for images. We built it internal and proprietary for the sense of smell. So we have a building of people that label smells all day, every day.

We have a laboratory full of sensors that 24/7 are dissecting scent down to the molecular level. We have robots that are creating smell. We're going to get even bigger robots to create even more smells. So we've created the entire virtuous cycle here that allows us to

build the data that allows us to train the models to bring these capabilities to the world. How much of the tooling do you use? Are you tapping models from the major model providers or anyone else? And if so, how? It's suffused into everything that we do. And again, right tool for the job. So like

I don't think there's any code that we write that isn't at least touched by AI in some way. It's just like that's the new autocomplete. A ton of boilerplate stuff is just no longer relevant. It's amazing. So productivity is higher. So like, Osmos probably a smaller company as a result of all these AI tools.

There's a bunch of stuff that we just need to get up to speed on and we can just ask ChatGPT for like a reasonable 80-20 answer. Like, hey, is there any problems with this NDA? Right. Like we can just do that. And then if it's really critical, we obviously like get an informed opinion. So, yes, suffused into almost everything that we do. What excites you most about

about the frontier that you're exploring that you have to sort of hold yourself back from spending time on because you're focused on the things you are. Yeah. So I love everything that we do. And if it isn't clear, I really like smell. And I really like making smell and experiencing it and sharing it. And so that's

Generation, launching generation is kind of a dream come true. But it's not the last thing that we'll do. What's really wonderful is by building all the systems that are going to allow us to create beautiful scents for folks in generation, that platform is going to help us on our mission to understand human wellness with scent. And that is one of the next mountain peaks for us is what in the smells that we are exuding

contain information that help us make better health decisions. And there's already a link there between what we do at Generation and what we will be doing on our journey for the next R&D frontiers. Scent has powerful effects on our mood, and there's already a very deep tradition of aromatherapy. And look, the science, I think, needs to be expanded there, and I think that we'll contribute to that. But as we're turning

emotions into data, and sent into data, and then data into products for people to build better businesses, to build, launch better products. Like all of that goes into one platform that is going to help us push back the frontier. One of my favorite concepts behind technology platforms is that it's very hard to predict

what people will use them to do. And it seems like every time you digitize anything in technology history, crazy stuff happens that we can't predict. And I'm sure that's going to happen here too. That's the idea. It's like, we know what we need to do now, right? We know the markets where we can be valuable now. And so we're not going to waste any time on anything else other than building a business that makes people happier and makes other people's businesses run better, makes sense, more beautiful sense faster.

but it's really hard to predict the future, right? Computers haven't had a sense of smell. I can't see on the other side of that wall what happens when our sensors actually can fit in your pocket. What happens not just to the products that we build or the partners that we have that are building on our platform, but what happens to society, right? Society's different because that computer in your pocket can see and hear. I think it's largely better, right? There's more information flowing online.

you're remembering more things, you can hold on to moments. There's some beauty that's there that wasn't there before. And I think that there will be beauty in what comes out of what we're doing. Osmo's young, not that old. Can you describe the story of what you would define, like the defining moment so far in the company's history? I think on the journey

of asking whether or not what we hope to do is possible. Like, hey, we've got this crazy dream of actually digitizing smell. Is that possible? Like we had to get together this crazy team that had never been assembled before and then tackle this technology that had never been built before. And then when we actually teleported the plum the first time and we smelled it and it was actually a freaking plum and it was beautiful and almost like hyper real.

I just fell out of my chair. I mean, that was like a really dreamlike moment. It's like, this works, right? Like, yeah, we did it. Like computers can smell now. Like it's in the lab. We'll make it smaller and cheaper and better. But like, it's no longer zero. We've gone from zero to one here. And then there's all the stuff that

came out of that, like all these capabilities and tools that were turned into the ability to design scent even better and capture other scents, train the AI models and kind of build the entire olfactory intelligence platform. But like that moment when I smelled the plum was...

really special. I think what you're building is singular, very unique. There's no company that I've really encountered quite like this one. And what I find so cool about it is that it's taking advantage of the technology that everyone is so excited about. So thanks for letting us in today. It's been an incredible experience seeing around. I can't wait to see the next iteration of the smells and of the factory and of all the machinery. When I do these, I ask everyone the same traditional closing question. What is the kindest thing that anyone's ever done for you? There were

when people could have closed the door on me or said no thanks or I don't believe you or I don't want to take a bet on you. I really divide the people that took a bet on me into the three chapters of my professional life. First, in my academic life,

Josh Burke and then Bob Data took a huge bet on a know nothing kid to mentor and to grow as a scientist. And I'm indebted to them for taking the time and kind of taking me in a raw form and helping mold me into a scientist. And then I moved from academic science into industry and entrepreneurship.

and there i have a number of people to thank as well as brian adams first of all for taking a bet on me to co-found a company with him which we ultimately sold to twitter and then when i moved to google brain

Dee Scully, with really no reason, believed in this idea of digitizing olfaction before anybody else did at Google Brain. And then Jeff Dean saw what we were doing and said, "You know what? This weird little thing, let's just let this flower grow. Let's see how it goes." And so Jeff was instrumental in kind of giving us cover or a force field just to grow this very delicate young idea into what it's become today.

And then when we converted from an industrial research project and we decided that the right way to scale this was a company, a whole new cast of characters took a bet as well, where there was really not a lot of evidence that they should have. I'm thinking of Andy Palmer, who believed, first of all, that I could do it. And when I wasn't really sure that I could...

Krishna Yeshwant, who was there every single step of the way, both when I was inside of Google and then when it spun out the company. Josh Wolf for playing the instrumental role of, I mean, what his tagline at his fund is, is we believe before others understand. And he lived that very, very deeply with me to bring Osmo to life.

And then more recently, Colin Byrne at Two Sigma Ventures has been following along with the story and has just been an incredible cheerleader and continues to bet on the company. Look, this is my board. These are the people that bet on me and they're involved in actually growing the company. And I'm super grateful that those people are one in the same. So, you know, look, I'm leaving people out. There's so many other people to thank, but it's just these little moments where people say yes, that can make all the difference.

It's a wonderful way to put it. The little moments where people say yes. Great place to close. Alex, thanks so much for your time. Patrick, thank you so much. It's so fun to bring you here to the lab to show you what we do to kind of share this passion. And I hope we can do it again soon sometime. Thank you.

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