How I Built This with Guy Raz - HIBT Lab! Gro Intelligence: Sara Menker

Episode Date: December 8, 2022

Growing up in Ethiopia in the 1980s and ‘90s, Sara Menker saw the devastating effects of drought and famine firsthand. Later as a commodities trader on Wall Street, Sara realized that a major driver of food insecurity around the world was a lack of good data to predict weather events, crop yields, and food prices. That realization led Sara to found her company, Gro Intelligence, in 2014. This week on How I Built This Lab, Sara shares how Gro Intelligence uses a combination of artificial intelligence and human expertise to help private companies, nonprofits, and governments better understand agricultural markets and address global food challenges. Plus, Sara talks about building the Gro team and the importance of founders understanding all of the different jobs within their companies.  See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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It had tall windows, beautiful old details, and plenty of space for all of us. And being in that home on Airbnb, right in the middle of Vienna, walking distance from so much of the city, made it feel less like a visit and more like we were actually living there. Plus, taking a trip is the perfect time to host your space on Airbnb. Your place with all of its personal touches and its amazing location could make someone else's vacation even better. Your home might be worth more than you think. Find out how much at Airbnb.ca.com slash host. Hello and welcome to how I built this lab. I'm Guy Raz. So have you ever wondered how a drought in one part of the world could impact the cost of food at your grocery store or how extreme weather caused by climate change might. change agriculture as we know it.

Well, our global food markets are all incredibly connected, and they require vast amounts of data to understand and analyze, which is why Sarah Manker founded Grow Intelligence in 2014. Grow takes trillions of data points from a variety of sources and builds forecasts for thousands of unique agricultural products like chicken or wheat or corn or sunflower oil. Sarah's company helps clients like food suppliers, and financial institutions navigate climate risk, understand supply models, predict the demand for crops, monitor conditions for farming around the world, and more. And as of this year, Grow has raised more than $125 million to support informed decision-making in global food markets. Sarah Maker grew up

in Ethiopia, where she saw the tragic consequences of food insecurity firsthand. Yeah, I mean, it was basically during what was known as sort of the Dirk regime, in Ethiopia, and it was a communist regime. And so it was a pretty dark time in the country, but everything was, you know, run by the state and controlled by the state. So sugar, toilet paper, it was not even a rationing. With toilet paper wasn't even any rationing. It was an availability issue because most stuff was not imported.

So it was all locally produced. But, you know, fuel, we couldn't drive on Sundays. You walked on Sundays or you took the bus. And so it was just a very, it was a very, it was a very different time. It was a very dark time in sort of the country's history. You know, when you're sort of growing up in a world where it's not a world of abundance, it's a world of limited resources, like everything is limited, and it doesn't matter who you are. I think you just grow up with a sense of awareness of not taking anything for granted,

if that makes sense. Like, I don't know. I think it's impacted me a lot, especially as I've gotten older and, you know, sort of experienced a lot in life. I think it grounds you, right? It reminds you of what basic necessities and basic needs are in this life and keeps you connected to those things. You went to university in the U.S. I know you went to Mount Holyoke. And after college, you decided to kind of enter the world of finance.

You were at Morgan Stanley for a while. You focused on commodities. Tell me why you had an interest in that. that space. Yeah, I always say that, you know, I fell into finance after college. I didn't, you know, you know, there are people who grow up and you're like, I, I dreamt of going to Wall Street. I didn't even know what Wall Street was. Yeah. So, you know, a lot of friends were doing these finance internships. And I didn't think I wanted to do that. I thought I actually wanted to do a PhD in economics. And, and then I just, you know, sort of fell into the role in the sense that I met a recruiter

who convinced me to apply for a finance internship. I did that. And I realized that credit markets and credit default swaps and all these other sort of cool instruments were like the cool areas to work in. Like nobody wanted to work in commodities. Like it was always that division nobody understood. The reason I was attracted to it is that it was actually attached to some things that were so physical and necessary for life. You know, like oil markets, like things that still tied to like my academic interests, but also. tied back to the real world in some way. And so I think I just loved the idea of being part of a group that dealt with the physical world.

While you were there, you spent, I think, eight years on Wall Street, you started to think about an idea that would eventually become this business, grow intelligence. And I guess the idea really kind of began around 2008 during the financial crisis. What was going on and what led you to start to think about, agricultural forecasting and would eventually become grow intelligence. It was the 0809 financial crisis, which those of us that were there at that time will never forget. And I had a colleague who just thought the world was coming to an end in sort of a genuine way. Like he was worried and he was like, oh my gosh. And all I could think of was like, listen, like, honestly, if Morgan Stanley stock went to zero, which at that time felt like

any of that was a possibility, like Lehman had gone under and everything else, like it would suck, but the world's not come to an end. You know, like, I have seen what close to the end of the world looks like, which was sort of my upbringing and going back to the comment I made earlier, which is it keeps you grounded and it gives you perspective, right? And so his sort of way of hedging for that was buying as much gold as possible and lots of guns. And I one day said to him, like, what are you going to do, like trade a sack of potatoes for a bar of gold? Like, if you think the world's coming to an end, like, that just gold seems like the worst investment idea.

And so it was actually that that led me to look at agricultural investing. Coincidentally was also a time, though, when land in places like Ethiopia, actually, and sub-Saharan Africa more broadly, there was this really big push by governments to make arable land available for commercial farming purposes. And so I started looking at investing in agricultural land. I'm not a farmer by any means, but I thought, you know, it could be a great investment. And by the time I went through the process of assessing what seemed like a good deal, it became clear that it was actually a near impossible deal to make the economics work. And it sort of like really baffled me that it was so interesting that every time I asked questions, I got more questions or if I needed data, I got data that was like two years old. Or, you know, I'd ask about crop insurance and there was no crop insurance market.

Like all the things that seemed like basic questions that one would ask to sort of set something like this up, the infrastructure for it sort of didn't exist. And I just, and I just remember thinking, you know, gosh, like we're just not going to solve these big food security challenges. At some point it became clear to me. We were like trying to fix a system we didn't even understand. And it was just, I was just so curious about it all sort of eventually just led me. me to say that I was really good at my job and I actually really liked the people I worked with at Morgan Stanley, but I just had no passion for it. Huh. So, all right. So you, I guess you could decide to leave your job at Morgan Stanley. And we'll get to that in a moment. But help me understand a little bit more about the problems that you were starting to uncover.

I mean, essentially, I guess you realize that there just isn't enough data for investors and businesses and governments to understand what. what might happen to the price of different commodities, right? Especially in agriculture. And I guess that lack of information can make problems like food insecurity even worse. Is that more or less what you started to realize? Well, yeah. I mean, you know, when I was trading, I managed our natural gas options business.

And if you think about sort of natural gas and any commodity, like the thing that drives the price of that product is, like, how much supply is there? how much demand is it, and what's the clearing price that clears the supply and the demand. You know, hurricane season constantly disrupted production. So during hurricane season, you're using weather data and weather forecasts to sort of see the probability of a hurricane hitting some type of production. Or in the winter, how cold is it going to be because that drives how much heating demand there is. So, you know, all of those are pieces that sort of drive that the price of any commodity. And agriculture is one of them. So Grow just became this idea to do it for agriculture, and that didn't exist at the time.

All right. So you decide to leave your job at Morgan Stanley to pursue this idea, and you founded this company Grow Intelligence, I think, in 2014, right? Yeah, I ended up leaving Morgan Stanley in 2012. So I spent two years after I quit to when I sort of decided on exactly what growth, what shape, reform, grow was going to take. And so when I left in 2012, my explanation to my boss then, who I'm still very, very close to and actually was one of my very early investors, was that, listen, I love working with you guys, but this is not the place for me. I've fallen in love with this other problem. And I need to move back to Africa. And I want to do something that solves for sort of food security. And he was like, you've lost your mind. You've absolutely lost your mind. you like please don't don't leave like maybe take a sabbatical and I said no I don't want to take a sabbatical um and and he said why not and I said well if I take a sabbatical then I will want to come back or you know if something doesn't sort of fully work out um then I'll have a fallback plan I don't want to have a fallback plan like what's the worst that can happen to me I lose my life savings fine I'll get another job like I just for some reason I just

just thought that the best way to do this was to truly just sort of move on. And then I sort of embarked on this journey for two years from 2012 to 14 of just defining what the business was going to be. Let's talk a little bit about some of the challenges that you're trying to solve here. I mean, the main issue is food security and insecurity, right? Because we know, right, that the amount of food produced in the world is more than enough to feed people. And the U.S. alone, 40% of food is wasted, right? So when we talk about this idea of food insecurity, what exactly doesn't mean if there is plenty of food available and there are still people who don't have access to it?

Yeah, well, you nailed it, which is food insecurity is about access and affordability. Because you can have a lot of food, but if you can't afford it, that's also not helpful. And so what we have as a world in terms of a challenge is both an access and an affordability problem. And it looks very different depending on what part of the world you're looking at, right? So if you're looking at food insecurity in sub-Saharan Africa or in South Asia, it is an access issue in the sense that not even enough is produced locally. So there is a food insecurity that comes from the fact that domestic production is not sufficient to meet the local needs. And then on top of that, there is an affordability issue, which is when you import it. Obviously, food is more expensive.

And then if you look at food insecurity in a wealthy country like the U.S., it's very different, right? Like the U.S. this feeds most of the world, right? Not just produces enough for itself. But in the U.S., your challenge is affordability. And then to your point, you have a massive food waste problem, which could be more food, that could sort of go out to other parts of the world, right? So it's different in sort of different areas, but there's sort of that combination. So is there enough availability?

And then can you get access to it when you need it? We're going to take a short break, but when we come back more from Sarah Manker, the founder of Grow Intelligence. Stay with us. I'm Guy Raz, and you're listening. how I built this lab. Welcome back to how I built this lab. I'm Guy Raz, and my guest is Sarah Manker, founder of Grow Intelligence.

It's a company that's using artificial intelligence to create forecasts for global food supply, demand, and pricing. So, all right, so you launched Grow Intelligence in 2014, and now your clients include food suppliers, agricultural businesses, financial institutions. how do you provide forecasting models for them? What kind of data points do you provide? Yeah, so we, so taking a step back, what we did is, you know, the company's gone through sort of a journey, right, since 2014. The first set of challenges we dealt with was, can we get enough data in our system? Like, what data is out there?

Like, do we even know what's out there? It's sort of, you know, one of the things that made agriculture so much more complicated and sort of work that we did in the energy markets is that agriculture is not a single product. You know, oil is oil, natural gas is a natural gas. Agriculture is tens of thousands of different products. Every single one is sort of governed by a different set of biological rules

that govern how it grows. Supply is super fragmented. It can be in a half-acre farm or a hundred thousand acre farm, right? So our first challenge was saying, what data is even out there? and can we ingest it and come up with a technology that can take that data and standardize it and bring it in any language in any format? So we shouldn't care if it's in PDF, we shouldn't care of its images, we shouldn't care of it's in Mandarin or Portuguese or English. We should be

able to take that, automatically translate it, and also standardize the format. So what that then gives you is too much data, right? So we get data from 50,000 sources around the world. They come in all these different formats, languages, but data, on its own is not knowledge, right? It's just data. And so our second challenge then became, how do you take that and how do you develop insights and how do you develop models that tell people something they didn't know? And that's when we started building predictive models. And again, there we started simple. We started with modeling and forecasting in markets that everybody understood. So corn in the U.S., soybeans in the U.S., like really big markets before you

start to go to Russia or India or Africa where the data challenges are even bigger. So we essentially built this, what we call our modeling frameworks. And so today we have 28 modeling frameworks in the system. So think of those as yield models, climate indices, food price indices, very broadly defined, but they're essentially a combination of our data science team and our domain experts, people who actually understand these problems, design these models as templates that can then scale through sort of machine learning AI. And so today, those 28 modeling frameworks actually developed 2 million unique models by growth. So everything from forecasting the demand of pork in China to the supply of sugar in Brazil to the U.S., Africa, you name it. So what our

clients want is for us to pick and choose these models to solve very specific problems for their business needs. So for example, if you're a seed company, you want to understand how profitable farmers are because you are selling all this product to the farmers. You need to know that they can pay for that product. And so that will use a combination of our yield models, what we call our planted area models, our demand models, and price data, and combine that and actually give them a forecast for farmer profitability for every state or every district in Brazil or in China or in Argentina, right? But those same models get used by traders to just trade markets. And those same models get used by governments to look at credit risk of farmers because governments

oftentimes backstop loans. So, you know, you use these models differently and the same models get used slightly differently by a whole different set of constituents, which is what's been sort of cool and amazing about it. You know, right now there's a lot of talk about a coming grain crisis because of the Russian Ukraine conflict. And I think Ukraine provides like a significant amount of grain to particularly to countries in the developing world. What does that mean for food security or insecurity, particularly in African countries and Asian countries that rely on on countries like Ukraine for wheat? Yeah. So, you know, one of the one of the things, that we've been emphasizing is that, you know, the Russia-Ukraine war didn't start a food security

crisis, that it added fuel to an already, like, long-burning fire. And I think it's really, really important to sort of highlight that because if the war went away tomorrow, the crisis we're in doesn't go away, right? And that's really important context to sort of provide. Really, since the start of 2020, we'd been undergoing some pretty deep structural changes in agricultural markets that were sort of setting the stage for massive, massive price increases across different commodities. And what happened was in 2020, China, which has typically never been an importer of main core cereal grains. So China imports soybeans to feed its hogs and sort of pork production. But rice, wheat, corn, China's never been an importer of that stuff.

In 2020, it turned into a structural importer of grains. That means that the world, sort of largest economy went from being self-sufficient in some of these core grains to being insufficient. So that change happened irrespective of COVID and irrespective of a Russia-Ukraine war. Then you had COVID, which disrupted supply chains and drove up price changes. And they were not a simple shock that came and went when, you know, things sort of normalized in the world. They sort of persisted. So that started driving prices up. Yeah. Then you also are now having an unprecedented number of supply side shocks due to climate change around the world. So major producing regions, the U.S., Brazil, China, Australia, I mean, year after year,

for the last three years, essentially, have had one climate catastrophe or another that is heavily impacted production. And so you rarely, rarely actually have a confluence of supply and demand side shocks occurring. that was the backdrop of the start of the Russia-Ukraine war, right? And so that, when you had that sort of already happening, then what you end up with is a situation where now Russia and Ukraine over sort of the last 15 years had emerged as sort of the major growth areas of the world for agricultural production to fill sort of that demand gap that was coming from areas like sub-Saharan Africa, etc.,

where economies were growing fast, they filled that. Right? So you just had this massive disruption to, in particular, wheat, corn to some extent, and then sunflower oil. Really truly catastrophic because we're basically in year three of a crisis that existed despite this war. And this is now weighing on sort of local economies more and more. Because how long can you actually withstand a persistent shock, right?

because at some point a shock supposed to come and go, not stay. There was a speech that you gave to the UN in May of 2022, where you basically talked about some of the major challenges that are facing the global food system. And one of the things you mentioned was a lack of fertilizer. Why is there a shortage of fertilizer in the world right now? Oh, gosh, that is actually the biggest problem now. So you're touching on probably the most terrifying challenge ahead of us for the next two years is fertilizer.

So there are three types of fertilizer. There are nitrogen-based fertilizers, which are basically require natural gas. There are potash, which is mine, and then phosphate, which is mine. So let's start with the first nitrogen-based fertilizers. It's dependent on natural gas. Well, Russia, natural gas into Europe, energy crisis, right? It's all linked to the energy crisis. And 70 to 90% of the cost of producing nitrogen-based fertilizer is the cost of natural gas.

So when the cost of natural gas quadruples, guess what happens to the price of fertilizer? So you basically have a massive sort of spike in fertilizer prices. But what you've also had is a lot of capacity being shut down in Europe as a result. So a lot of the producers just can't afford it. So that sort of cascaded, though, into challenges in places like sub-Saharan Africa where people just already use little fertilizer to using none. Now, going into 2023, you have a major, major problem ahead of us because now you have an affordability and potentially an availability problem conflating the two. And we can model out what that means to global food production. And we just finished rerunning a new analysis, essentially as of November 1st.

And what we see now is that basically just the nitrogen problem alone is going to lead to a reduction of 216 trillion calories around the world. And that's just simply due to the fact that using less fertilizer is going to lead to a reduction in yield. Yeah. So that is something that is a massive, massive sort of looming 2023, 2024 problem that's even potentially bigger than it was before. So if you can forecast this now knowing what's happening with fertilizer production, what effectively could your data lead to? I mean, is it designed in theory to prompt governments to take action to subsidize fertilizer, to, I mean, literally, you know, to ship fertilizer to countries that can't afford it? It's everything from central banks around the world, looking at what that effect is. going to mean to sort of their national balance sheets because some of these are countries that are

actually typically exporters. So when you have exports go down, that means that you have a balance of payments issue that you need to manage. So you can plan in advance for like, how do I mitigate this? Like do you pre-purchase in the markets? Like how do you plan for this so that it's not absolute catastrophe while it happens? We're telling you this that as, you know, you're going into sort of 2023. It's being used by the likes of the World Food Program for emergency response. Where is it going to be the worst? Where a resource is going to be needed, right? You start to think about sort of resource allocation. It's being used by a combination of companies around the world that want to sort of think about donations for fertilizer. So you just plan for it.

You know, it just, it can either feel like all out chaos or it can feel like planning. And it's actually been amazing to sort of watch because we only launched this tool the summer. and obviously the problem has gone sort of much worse, but it's been really powerful to see, you know, really critical institutions relying on it and using it. We're going to take another short break, but we'll have more from Grow Intelligence CEO and founder, Sarah Maker, in just a moment. Stay with us. I'm Guy Raz, and you're listening to How I Built This Lab.

Welcome back to How I Built This Lab. I'm Guy Raz, and my guest is Sarah Manker, founder and CEO of Grow Intelligence. Sarah, you grew up in Ethiopia at a time when, I mean, you've talked about this, when there were images around the world of Ethiopian, you know, children in rural parts of the country dying of famine. You, of course, did not experience that living in the capital. But that was something that sort of was, there was clear image of that, right? Where we are today, right? What you're talking about, it seems like a perfect storm that could result in. A version of that in parts of the world, right?

Lack of fertilizer, obviously, climate disruption, the Ukraine War, which is disrupting grain supplies, and then just supply chain challenges that make it challenging to move things around the world. Are we looking at the possibility of, you know, significant famine in parts of the world in the next year? Absolutely. So if you look at the price change. in local currency since the start of 2020 for major foods. So if you just look at core grains and sort of your vegetable oils, like the basics, right? Like we're not looking at the fancy stuff.

Like meat as far as I'm concerned is a luxury, you know? So the basics. If you look at the basket of basic food products around the world since the start of 2020, the price of a basket of basic products in Sudan is up 1,900%. In Syria, it's up 700%. In Ethiopia, it's up 175%. In Argentina, it's up 300%. Even in the U.S., it's up 67%.

Europe, 80%. No part of the world is immune right now. And what's going to come down to is which economies have the resilience, which governments have the balance sheets to be able to deal with this kind of neat, right? And how you deal with it is going to be different. So my sort of concern is that this is so widespread,

and every country is busy fighting its own fire, right? That it actually becomes so overwhelming to think about collective action because every country is sort of focused on its own. But we've got to do something. It's just really hard. And we've been spending a lot of time bringing as much attention as possible to the problem for that reason. Sarah, let me ask you about the business side of what you do. How did you, you know, when you were,

setting up your your organization, how did you begin to gather a team? How did you find people to work with you and to help you build this? Yeah. So, you know, I always say I'm the least qualified X until I find the best qualified person to do that job, right? Which is what is like being an entrepreneur and starting a company from scratch, which is you're completely not prepared. You have no clue. You just have an idea and then you have to make it work. And so the first person I brought on, who's, is our C-O-O and my co-founder, Sweet. And, you know, she was a person that came from private equity and finance, and I had known her for a long time from New York, but she'd moved to Kenya. And she was just somebody I just trusted, you know, like, that was it. And she's

really smart. And, you know, we'd have to raise capital. And she's been on sort of the investing side, not on the operational side. And so I asked her to leave her cushy private equity job and and go on this wild ride with me, and I was lucky she agreed. So that was step one. And then with every other team member, it was sort of, and this was the benefit, I think, of taking time to sort of kick things off was, as you're learning, as I, you know, I spent those two years from 2012 to 14, like, I did so many crop tours around the world. I mean, I've done crop tours in the U.S. in South America and Malaysia and China.

I mean, I traveled the world and I learned just the agricultural industry inside out. in that process, I was like, wait, I need geospatial scientists because, like, there's all the satellite data and I know nothing about satellite data. Morgan Stanley, I had a meteorologist that told me stuff and I traded off of it, you know? And so it was always just finding the people who are the domain experts. So before actually thinking of the technologists, we thought about the domain experts. Who are people who know agriculture? We need agronomists.

We need people who traded agricultural markets. We need climate scientists. Like, we've really constructed the team. team with, I would say, a lot of intention to make sure that the team represents sort of the world we're trying to model. How does your business model work? Obviously, I'm assuming there's a subscription side to it. So companies pay a fee every year to access this data?

Correct. It's purely subscriptions. So we have different types of subscriptions people can buy. So one of the things that's always mattered to us is, sort of being able to serve really small companies as much as we serve really big companies. We work with financial institutions, again, some of the smallest and some of the world's biggest. Governments we can't afford to pay as much and those that can. And so we've developed a really flexible business model that has allowed us to essentially scale up as organizations are bigger, but also start small.

But everything is subscription-based. And what we've developed is what we call our application store. So think of it as no different from the app store on your iPhone, where you go in and the app store is built on our platform that has all these models that I mentioned. Like when you have two million models, it's too overwhelming for one person to make use of them. So each application has a very specific use case and a very specific set of targeted sort of customers. And so there's a library of applications that people choose from and say, I want to buy this application. And one application can be $10,000 per seat. Another one can be 15.

Another one's 50. Depends on the use case and then depends on our seats or whether it's an enterprise license or not. And so we've been able to sort of build out this licensing model, but one that sort of is relatively inclusive. How are you able to differentiate your data from what governments provide? Because there are government organizations that provide this information and it's available for industry. Correct. So, first of all, if you think about governments that report the sort of most extensive is the U.S. government, the USDA, namely. Everything else is just tiny, tiny, tiny, tiny order of magnitude of what the USDA does.

So to give you some perspective, even in the U.S., our models are predictive and accurate four to six months in advance of when the USDA comes out with its numbers. within like 98 to 99% accuracy. In places like India and places like Russia and places like China and places like Africa, our lead time for sort of our insights is one to two years at times. So yes, governments report, but they oftentimes report way too late. And they're also not reporting the level of depth that we report. So a lot of countries will report at the national level. We actually go down to the district level.

A lot of governments won't tell you where the crops are grown. We have our own algorithms that looks at imagery to actually identify which fields are growing what crop, because that's how you can then determine how many acres are growing, and then use that acreage to determine the yield and all the stuff. So we really have harnessed the depth of knowledge of actually public data sets, because that is necessary. Again, you need to train your models on something. You need a baseline.

You need to compare it to something. But what we offer is fundamentally different, which is why we work with the public sector as well. How did you kind of develop the technology platform? I mean, you're obviously super smart and come with incredible experience around commodities. But was there a steep learning curve for you to figure out the technology side of it? It's a steep learning curve on every single one of it because I was not a qualified agronomist. I'm not a qualified fertilizer trader. I'm not, you know, I was only a qualified energy trader.

Like, that was my qualification. Not a qualified CEO, right? Building a team is so different than trading a book. You know, everything was a steep learning curve for me. But one thing that I think has always driven me is that I love this work, right? Like, I really, really, really believe in the work that we're doing. And so that just has made me completely relentless in learning anything and everything required at that point. time to make sure that we're successful and that I'm sort of doing right by the team and that

I'm doing right by our investors, by the business, you know, just it matters a lot to me. So I think I already had a deeply sort of technical mind in the sense that, you know, I was always technical even growing up. And even at Morgan Stanley, I, you know, built my own options trading models and and built out sort of these things. But it was always by teaching myself and, And so I'm not sort of afraid to not know. I'm never afraid to, like, call people and say, like, I need to learn and I need to learn from you. So in the early days, I literally used to, like, for example, there was this scientist at USGS out in Colorado. And USGS is a government agency that produces tons of data around satellite imagery.

But I just didn't even know what these things meant. I, like, emailed him and introduced myself. And I was like, do you mind, like, teaching me? And so he used to get on the phone and like for months, I used to just get lessons from him on how to learn and understand and interpret like satellite imagery. And so then when I was recruiting the first person, I even knew what questions to ask, you know? That's the whole thing. Like you can't empathize what you're looking for unless you yourself have experienced that one thing. So pretty much every role we've had in the company, whether it's technical. Like our AWS account was set up by me. And I remember Googling. what is AWS? I didn't know what AWS was. And sort of setting it up, right? I have been involved in data science, I've been involved in design, in marketing, in sales. Like, every one of these roles I've played at some point.

It doesn't mean I was the best. In fact, I'm not. But I learned what it meant so that when we hire a team that, like, I knew exactly what we were looking for. I mean, as we're, you know, as we face a more volatile climate future, how, How will the data that you provide allow businesses and farmers to mitigate some of the effects of climate change? In other words, can this data help people work around the climate challenges to continue to produce enough food? Oh, absolutely. So one of the things that makes grow really unique is that we had to ingest tons of.

tons of environmental weather, climate data into the platform to use those as signals into sort of forecasting production, right? And in doing it, we realize this data on its own is still quite messy. Like temperature alone doesn't tell you whether you're in drought or not. You know, rainfall alone doesn't really tell you whether it's a flooding event or not. You know, so there's all these components of sort of how weather translates into a climate impact itself that's really complex. And so what we did is we developed a suite of climate indices. Now what these indices do is that they measure all of these different risks. So we have the grow drought index, the grow flood index, the tropical cyclone index, the fire index, heat index, etc. What that means is that now you can look at real-time risks of all of these different perils for every. product everywhere in the world in a consistent defined way. But the next step that we took is we developed these forward-looking models that under different climate scenarios help you understand what the trajectory is of exactly these climate events for every crop everywhere around the world.

Now, how does this translate to mitigating risk that you mentioned? Well, one of the applications that we have is called the land suitability application. So if you're a major food or ad company, think about sort of one of your biggest risks is not just procuring for today, but if you're building a new facility, where do you invest? Are the areas that grow coffee today going to be the areas that are most suitable for growing tomorrow? And where will they be most suitable five years from now, 10 years from now, 15 years from now, 20 years from now? And you'll be surprised at how much land area is going to have to shift based on these sort of different climate scenarios.

That's Sarah Manker, founder, and CEO of Grow Intelligence. Sarah, thank you so much. Thank you. Hey, thanks so much for listening to How I Built This Lab. Please do follow us on your podcast app so you always have the latest episode downloaded. If you want to follow us on Twitter, our account is at How I Built This, and mine is at Guy Raz, and on Instagram, I'm at guy.org. If you want to contact the team, our email address is H-I-B-T at ID.wondery.com.

This episode was produced by Chris Messini with editing by John Isabella. Our audio engineer was Maggie Luthor. Our music was composed by Rumpine Arableu. Our production team at How I Built This includes Alex Chung, Carla Estevez, Casey Herman, J.C. Howard, Liz Metzger, Sam Paulson, Carrie Thompson, and Elaine Coates. Our intern is Susanna Brown. Neva Grant is our supervising editor. Beth Donovan is our executive producer. I'm Guy Raz, and you've been listening to How I Built This.