Alex Wiltschko - Giving Computers A Sense Of Smell - [Invest Like the Best, EP.415]

22:52But 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 skin cream and you want it to be slightly fragranced.

23:25Well, 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.

24:00So when you submit the brief, that should be a chat GPT interface. You should have a conversation that should be available to start instantly. So we have a tool. So let's make, do 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.

24:40That 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 would be like a smell that I think about. The Sequoia Parks or the Redwood Parks in San Francisco.

25:17when 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. It's 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.

25:56And 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. 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 in 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.

26:36So 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. And then you saw the sent printer, which is half the size of this table, right? We've got a lot of work to do to make that thing smaller.

27:05But 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, 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.

27:40Absolutely. So that we are rolling that out. Generation is. Generation is, yeah. Generation is our business to open up scent design to more people 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? I mean, because, you know, David Senra is in your world. He has like a few phrases that really resonate, which is like the exit strategy is death, right? Like this is the last thing I want to do. I grew up in a town called College Station in Texas.

28:21Not too big, not too small. I got bit by the computer bug pretty early. So I started programming computers when I was like 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 is unbelievable, which is, can you spray an invisible thing on you that changes how people see you and treat you for the better or for the worse?

29:07I just couldn't understand it. I had already felt a little bit like a social outsider. And I was trying to decode that. 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, you know, these kids my age were buying. It was Polo Blue, it was Abercrombie and Fitch Fierce, both fragrances that our master perfumer that we just passed designed, right, many years ago. So it's completely full circle now.

29:43But 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.

30:19But 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 of 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 watch this movie play out for like an afternoon. I was like, no, no, somebody made this. And this is the whole thing.

30:54The whole fragrance unfolds over time. And that kind of was the end of it for me. I got just completely hooked. And 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.

31:29And the most people who study that are at Harvard. So I went to Harvard and realized after many years of doing science there that actually, we don't really know how smell works at all. 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 going to smell like? Will it smell like apple or cinnamon or, you know, 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?

32:07I ended up leaving academia. I started and sold two 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 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.

32:47And 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 this 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. Like images are like grids of pixels and text is like a long, thin string of words.

33:26But 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.

34:02And 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. And it sounds so simple, but like, 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.

34:39We 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 zeros. 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, okay, we have our predictions.

35:18People 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.

35:51Which is kind of like passing an odor-touring 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?

36:30If 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 Wolf 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.

37:10We'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 olfaction, 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.

37:51And we're on our way. When you think about building Osmo the business, how do you think about the tradeoff 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 charge them a platform fee and 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?

38:33Are they mutually exclusive? I think in The Limit, we're going to be able to explore that design space more fully. 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?

39:15And 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, right? 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.

39:52We 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.

40:30And 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. 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.

41:03Right. 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 going to 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 sense for people. So that's what generation is all about is if you want to make a scent and if you want to do it quickly and if you want to do it safely, like we're here.

41:42Like 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. 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.

42:21So 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.

43:03So 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, of 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, computers can hear, they can touch, right? We have touchscreens and we have the work that formerly Control Labs is doing with these wristbands, exact haptics, but computers can't smell.

43:48and 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, It turns out we've taught computers to do that stuff, right? And it was weird that those were the first problems to fall.

44:23But like 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 creation of and manufacture of any smell that I want, 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.

45:04Totally. 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.

45:42And 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 to play 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. But I think it goes deeper than that, right? So I think, you know, 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.

46:19But 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.

46:59Last 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.

47:36And 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. 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.

48:17And 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.

49:08And the margin profile is typically quite good. If you think about the things that could go wrong on this journey of years. Which I always do every day. I'm sure you do. 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.

49:50There 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.

50:41This 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.

51:19There's a lot of traditional code involved. There's software involved. There's putting things around the incredibly powerful reasoning models that now exist. 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 and pure software and get into some other world.

52:03So maybe here it's chemistry and AI. But maybe describe 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. Like, we kind of blew past it, but yeah, we passed the Turing test, right? Like, I regularly am fooled and curious whether or not this was written by ChatGPT or not.

52:41So text works. And then Ilya Setskover, who is really one of the progenitors of modern AI for text, got up at the main AI conference, NeurIPS, 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 we're out of the raw fuel that drove a ton of the innovation in text. And I think also similarly for images. 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.

53:18So we're not quite at the end of the curve. Those are just three modalities. There's drug discovery. 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. 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. 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.

54:06We 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, right? Like having the infrastructure for accepting that data, having the operations to create it, you know, the physical and the digital all linked together, you have to have the data.

54:41Like that's the fuel that drives you up that S-curve. And so, you know, we've just realized this step by step. So we started out, say, 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, okay, guess we have to create it ourselves. And so then bit by bit, we began 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, right? We have a laboratory full of sensors that 24-7 are dissecting scent down to the molecular level.

55:20We 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 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.

55:54A ton of boilerplate stuff is just no longer relevant. It's amazing. So productivity is higher. So Osmo is 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 a reasonable 80-20 answer. Like, hey, is there any problems with this NDA? We can just do that. And then if it's really critical, we obviously get an informed opinion. So yeah, it's suffused into almost everything that we do. What excites you most 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?

56:33Yeah. 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 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, you know, what in the smells that we are exuding contain information that help us make better health decisions.

57:19And 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, you know, look, the science, I think, needs to be expanded there. And I think that we'll contribute to that. But, you know, as we're turning emotions into data and sent into data and then data into products for people to build better businesses, to launch better products, like all of that goes into one platform that is going to help us push back the frontier.

57:55One 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, make sense, more beautiful sense faster.

58:28But it's really hard to predict the future, right? Like computers haven't had a sense of smell. Like I can't see on the other side of that wall. Like what happens when our sensors actually can fit in your pocket? Like what happens not just to the products that we build or the partners that we have that are building on our platform, but like what happens to society, right? Like society is different because that computer in your pocket can see and hear. And it's like I think it's largely better, right? There's more information flowing. You're remembering more things. You can hold on to moments. Like there's some beauty that's there that wasn't there before.

59:03And 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.

59:54I 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 that moment when I smelled the plum was really special. I think what you're building is singular, very unique.

1:00:35There's no company that I 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 moments 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.

1:01:12I 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, into entrepreneurship, and there I have a number of people to thank as well. Ryan 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.

1:02:11and 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.

1:02:50And 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.

1:03:31And 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.

1:04:27Thank you.