Invest Like the Best with Patrick O'Shaughnessy
Alex Wiltschko - Giving Computers A Sense Of Smell - [Invest Like the Best, EP.415]
Tue, 18 Mar 2025
My guest today is Alex Wiltschko. 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 can watch in the video version of this interview on Youtube and Spotify, where he demonstrates their method to the madness. We discuss their first commercial application, Generation, which is revolutionizing the fragrance industry by dramatically accelerating the typically years-long process of custom scent creation. We discuss all of the potential business implications this technology unlocks, applications ranging from counterfeit detection to health monitoring, and creating a cutting-edge proprietary platform in a historically routine industry. Please enjoy my conversation with Alex Wiltschko. Subscribe to Colossus Review. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Ramp. Ramp’s mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Ramp is the fastest-growing FinTech company in history, and it’s backed by more of my favorite past guests (at least 16 of them!) than probably any other company I’m aware of. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus. – This episode is brought to you by AlphaSense. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Imagine completing your research five to ten times faster with search that delivers the most relevant results, helping you make high-conviction decisions with confidence. Invest Like the Best listeners can get a free trial now at Alpha-Sense.com/Invest and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. – This episode is brought to you by Ridgeline. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. I think this platform will become the standard for investment managers, and if you run an investing firm, I highly recommend you find time to speak with them. Head to ridgelineapps.com to learn more about the platform. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:04:25) Introduction to Plum 1.0 (00:06:20) Synthetic Chemistry & OI (00:07:09) Perfumer's Organ & Fragrance Creation (00:08:03) Launching Generation Fragrance House (00:09:15) The Fragrance Design Process (00:12:04) AI and Olfactory Intelligence (00:14:59) The GCMS Machine (00:22:08) The Scent Printer (00:26:47) The Journey of a Fragrance Enthusiast (00:30:47) The Unsolved Problem of Scent (00:32:45) Applying AI to the World of Scent (00:33:17) Validating AI Predictions with Double-Blind Trials (00:39:42) The Emotional Power of Scent (00:49:01) Challenges & Future Prospects (00:59:08) The Defining Moment: Digitizing Smell (01:00:41) The Kindest Thing Anyone Has Ever Done For Alex
Chapter 1: What is the main topic of this episode?
Something I speak about frequently on Invest Like the Best is the idea of life's work. A more fun way to think about it is that I'm looking for maniacs on a mission. This is the basis for our investment firm, Positive Sum, and it's the reason why I'm so enthusiastic about our presenting sponsor, Ramp.
Not only are the founders, Kareem and Eric, life's work-level founders, certainly maniacs on a mission, they have created a product that is effectively an unlock for founders and finance team to do more of their life's work by streamlining financial operations, saving everyone their most precious resource, time. Ramp has built a command and control system for corporate cards and expense management.
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The Ramp team is relentless, and the product continues to evolve to save you time that you would never have dreamed of getting back. To me, there is nothing more interesting than technologies that reduce friction for other entrepreneurs to be able to build the thing that they want to. So much attention has gone to cloud computing, APIs, and other ways of making life easy for founders.
What Ramp has done and is doing is build yet another set of tools in this category. To get started, go to ramp.com. Cards issued by Celtic Bank and Sutton Bank, member FDIC. Terms and conditions apply. As an investor, staying ahead of the game means having the right tools, and I want to share one that's become indispensable in my team's own research, AlphaSense.
It's the market intelligence platform trusted by 75% of the world's top hedge funds and 85% of the S&P 100 to make smarter, faster investment decisions. What sets AlphaSense apart is not just its AI-driven access to over 400 million premium sources like company filings, broker research, news, and trade journals, but also its unmatched private market insights.
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Ridgeline gets me so excited because every investment professional knows the core challenge that they solve. You love the core work of investing, but operational complexities eat up valuable time and energy. That's where Ridgeline comes in. Ridgeline is an all-in-one operating system designed specifically for investment managers, and their momentum has been incredible.
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Chapter 3: What is the fragrance design process like?
With about $350 billion now committed to the platform and a 60% increase in customers since just October, firms are flocking to Ridgeline for good reason. They've been leading the investment management tech industry in AI for over a year with 100% of their users opting into their AI capabilities, putting them light years ahead of other vendors thanks to their single source of data.
And they recently released the industry's first AI agents, digital coworkers that can operate independently. Their customers are already using this highly innovative technology and calling it mind-blowing. You don't have to put up with the juggling multiple legacy systems and spending endless quarter ends compiling reports.
Ridgeline has created a comprehensive cloud platform that handles everything in real time, from trading and portfolio management to compliance and client reporting. It's worth reaching out to Ridgeline to see what the experience can be like with a single platform. Visit RidgelineApps.com to schedule a demo. Hello and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best.
Chapter 4: How does olfactory intelligence work?
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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 creating a cutting-edge proprietary platform in a historically routine industry. Please enjoy my conversation with Alex.
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
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Chapter 5: What are the challenges in digitizing smell?
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 a
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 color like primary colors or something these are primary smells that's how i'm thinking about it is it pretty reliable like how you can combine those things into some new set of things like yeah what is the periodic table equivalent or system
So nobody knows, but we're teaching the machines to figure that out. 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 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? Like, will you be building your own version of this? I see Gentech on there. It's not, you know, an Osmo machine yet.
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Chapter 6: How can AI revolutionize the fragrance industry?
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 the 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. 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. 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.
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.
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Chapter 7: What is Generation and how does it change scent creation?
Chapter 8: How can scents be used for counterfeit detection?
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, is 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.
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