Closing the Ocean Data Gap with Apeiron Labs

Rob and Ravi talk about why future conflicts may move underwater, where very little is mapped or monitored, and how the same technology that can help track submarines and mines can also watch hurricanes building in the Gulf of Mexico. They dig into how drone and satellite cost curves, along with Ravi’s rule against what he calls ​‘the endurance trap,’ made their technology possible, what better ocean data could mean for energy, shipping, insurance, and subsea cables, and what a future with pervasive ocean sensing looks like. 

Please stay tuned for important disclosures at the end of this episode.

Sanjeev Krishnan: Rob and Ravi, welcome to the podcast.

Rob Rainhart: Excited to be here.

Ravi Pappu: Thank you for having us, Sanjeev.

Sanjeev Krishnan: I’m excited because this is like six years in the making from co-founding the business with you Ravi, Applied Invention, and Mark Abbott. And then excited Rob is here to be CEO and take us to scale.

I’ll start with you, Ravi. Tell me about yourself, a little about your career and kind of what brought you to starting and co-founding the business with us.

Ravi Pappu: Yeah, great. Thank you, Sanjeev. My name is Ravi, as you know. Several years ago, I was the CTO at In-Q-Tel, which is the venture capital arm for the US intelligence community. And I ran into Mark Abbott, who ended up co-founding the business with us at Woods Hall. This is probably like 10 years ago at this point. 

And we started talking and you know, my whole background prior to that was in the Internet of Things world. I co-founded an RFID company called ThingMagic. It was a venture back startup that we sold to Trimble Navigation in 2010. And then eventually, after spending some time at Trimble, I went to InQTel. And the problem of large-scale ocean sensing kept coming up in my world. And I went and had a chat with Mark and asked him, what would it take for us to lower the cost of ocean observation by a factor of 100 or a thousand? It was sort of a hypothetical, you know, pie in the sky type of question. 

But the CubeSat revolution was in full swing at that time. And the cost of launching satellites into space was already diminishing significantly year over year. And Mark and I started thinking about what would be the analog for the ocean. And that’s how I got into this world. I had real problems from real customers in the IC, but there was no real solution that I could take to them. So it turned out to be an interesting question, something that ended up causing me to say, look, I think I should go take a shot at this. And that’s how I ended up here.

Sanjeev Krishnan: Right. We’re gonna get a lot more into that. But Rob, tell me about yourself, your background, your career, and why you decided to join as our CEO.

Rob Rainhart: Yeah, sure. I’m Rob. Most of my background, almost all my background, has been in commercial companies that are investing in products that have dual use. So both commercial and defense and intelligence applications. I started my career at Harris Corporation doing protected comms and quickly moved over to a commercial company that was doing ground stations for satellite communications out in Colorado, a company called Real Time Logic that eventually got bought by Kratos Defense. While I was there I was digging into small sats, similar story to Ravi from his time at In-Q-Tel, and started really looking at what small sats were doing to disrupt space and jumped over to HawkeEye 360 as one of the first employees there.

And I was there for nine years and helped establish that company from no satellites in orbit and a slide deck to 30 plus satellites in orbit and over a hundred million in revenue. And they recently went public.

After that I did a stint at a drone company, Auterion, that was doing software for drones, a middleware tech stack that allowed those drones to be operated at scale, with a large footprint in Ukraine and supporting Ukraine in all those activities. And that’s when Ravi called. And for me it was a little bit like going back to the model of HawkEye, but also the ability to do it in a new domain, right?

And so much of my background has been around intelligence and communications. And this was really a big natural fit for me in terms of being part of something and being part of an earlier stage company to be able to help grow it and scale it up.

Sanjeev Krishnan: Yeah. Amazing backgrounds for you both. For the audience who may not have been in the PowerPoint deck version or even the R&D version and now to the scale version, Ravi, maybe describe what the company does? 

Ravi Pappu: So our business is really simple. We want to revolutionize ocean monitoring in every ocean on the planet. The ocean is one of humanity’s largest and most important blind spots. We know so much more about the surface of Mars than we do about our oceans. And what people don’t realize is the ocean determines almost every aspect of life on Earth. For centuries we’ve been measuring the oceans, the equivalent of putting one thermometer in the state of Maine. The density at which we measure oceans has been widely under-sampled. 

And it’s always been too expensive to get the data we need from the oceans. It’s a hostile environment. We like to think of space as the easy part and ocean as the hard part. Because once you’re in the ocean, you can’t see the object and you can’t hear from it until it comes back to the surface. So essentially when you put a sensor in the ocean, you are also flying blind. So reliability is really important. So our business is to take really low cost autonomous underwater vehicles, things that cost two orders of magnitude less than the cheapest previous generation of devices, build them in large quantities, put them in every ocean on the planet and manage them

with an ocean operating system. With the end product being data that is fit-for-purpose for a variety of purposes, whether it’s national security and that could be anti-submarine warfare or mine warfare. It could be weather and ocean forecasting. So collecting data to initialize these forecast models. Or it could be for fisheries and aquaculture and offshore energy, delivering data from the ocean that has a direct impact on businesses and missions in these areas.

Sanjeev Krishnan: Maybe talk about the product name, the product spec, what’s unique about the product.

Ravi Pappu: Sure. So our first generation device, we call it a tensor. It’s just short for persistent sensor, because we deliver persistent data from the ocean. It is the smallest buoyancy control glider on the planet. That doesn’t mean much to the audience, but it is a technical feat to get such a high capability device into something that is three feet long, roughly five inches in diameter, and can be launched out of airplanes into the ocean. So one of the big problems of sensing the ocean is it’s so vast and getting to where you need the sensing has always been a challenge. And the way to solve that challenge typically has been to build bigger and bigger vehicles that are full of battery capacity or put them on ships and take them there. This has been called the tyranny of distance by the ocean community. 

We decided we’re not going to play that game and we’re going to allow our systems to be launched out of aircraft. As you particularly know, you can go around the planet in 24 hours by air. So if you want data rapidly, the only way to put ocean sensors in that region of the ocean is by air. So we design our systems to be air launchable. They go down to a quarter mile deep in the ocean. They can collect temperature, salinity, sound speeds, ocean currents, things that these fields care about, and then they come back and deliver them to the surface. We send data up via satellite into our ocean operating system. We clean it up and give the data to our customers.

Sanjeev Krishnan: Ravi, when we set up to co-found the business, we had an n of 1 thesis around the cost of data. Maybe just talk about the proof that we’ve been able to achieve on that end of the thesis?

Ravi Pappu: Yeah, so that’s a really good question. When you think about the before picture, there were not that many large deployments of ocean sensors. Everything was very bespoke. It was driven by research.

And it was driven by this need to get one shot at going and putting your sensor in the ocean because the ships are super expensive. So what you do is you design something to last for five years and live at the bottom of the ocean. And it’s basically a lab in an underwater vehicle. And the scientist is waiting for the data to come back, they wait a few years, then they analyze it, they get another grant, and they repeat this over and over.

So that’s not scalable. And so our thesis was you need really lots and lots of low cost sensors. They’re not going to do everything that the lab in a vehicle does, but you capture 80% of the problem. So that’s the core piece of the thesis. And if you can do that, you can lower the cost of the vehicle itself by two to three orders of magnitude.

Then you take advantage of all of the economies of scale from other fields: rapid manufacturing, Internet of Things, communications, computing. And then the question becomes: how do we engineer those scaling curves to give you a vehicle scaling curve? And that is how we think about the problem today, how we thought about the problem after the PowerPoint slide that you’re talking about.

And it has been an exercise in leveraging and harnessing all of the scaling curves from all these other industries to create something that’s mass market manufacturing. And we have proved that we can produce a really low-cost, high-performance device. We’ve deployed it in multiple missions. And every year or every month, we have a new mission that is longer with more units than before. And we have thousands of hours of operation in the ocean proving this thesis.

Sanjeev Krishnan: Rob, speaking of customers, you’ve been around the block as I observe your career. How would you describe the state of affairs of where we are?

Rob Rainhart: A couple of things I think about in that particular conversation. The first is I’ve always believed for the last ten or fifteen years that the world is going to evolve to a system of highly proliferated sensors that are gathering data from a number of different mediums, space, ocean, all over, right? And we’re seeing that come to fruition in a number of different ways. So that’s one part of it.

But then the other part of it is what’s driving that, right? And for me, what’s driving that is efficiencies in these operations. But one step higher than that, it’s really about decision making. The world is more volatile, right? We’ve seen it. But decision timelines are shrinking, right? So when you compound the decision timeline with the volatility, now you’re having to make decisions on a much faster basis where information and intelligence are important in that process, right? And so what we see is markets, supply chains, threats now move at machine speed because there’s this large amount of data coming in. And an under-sampled area out there is the ocean. And so that’s how I see this all folding together. 

And it matters because volatility hurts when you have no time to respond. So it’s really about that lead time to problem solving, lead time to decision.

And I think you have to go out and deploy these sensors to be able to get this data and be able to use that to inform a number of different operational pieces across a whole bunch of different markets.

Sanjeev Krishnan: I think it’s helpful in these sorts of scenarios to kind of give a mental model of where we’re sitting. I met the Applied Invention team, John O and Dave and Danny and Dan, I think 2007, 2008 time frame. And they had just started work on aerial drones and it was still pretty expensive at the time. But maybe Rob, maybe map to what has now become very mainstream in pop culture and in the media around aerial drones and where is this vis a vis the aerial drone industry?

Rob Rainhart: Yeah, I’d say it’s trailing by some number of years, maybe five years to a decade in terms of where it’s at. But I think there’s a ton of opportunity in that. So if you look at where drones are today, right, if you go look at the drone dominance program, which is the largest program in the US here, they’re buying those drones at five thousand dollars a pop, right? They fix the price, all the players bid to the same price and it drops based upon quantities going up in those subsequent rounds of the gauntlets there and the price goes down as you get to higher and higher volumes. So think about that for a minute. You have a highly accurate loitering munition that can go 20 plus kilometers in a GPS denied environment and it’s five thousand dollars. Right?

That creates a scale on the drone side on the battlefield that you just don’t see. And that’s mirroring what’s happened in Ukraine in terms of the scale there. I just saw another news article about Ukraine this week that showed the front line in Ukraine is incredibly large because there’s this no man’s land of autonomous operations going on of offense and defense, and it’s impenetrable, right?

That’s a scenario we’ve never seen before. When you look at that particular scale and then you portray that to the ocean in terms of the size and the magnitude, there’s a lot of parallels that are going to be played out in the ocean, particularly when you start thinking about having larger scale conflicts on the ocean and larger scale issues on the ocean. That’s going to force people to go under the water, right? Because the surface is going to be a place that you cannot hide anymore with these long-range drones. And so you’re going to have to be submersible under the water. That’s where this highly proliferated related piece comes in because you’re going to need to move and monitor all that. This ocean intelligence really plays into all of that from a threat perspective and monitoring. And so you’ll see parallelisms over the next five years as we scale up to be able to meet the need of delivering the data set that’s going to be needed to be able to monitor all that.

So in terms of where it’s at, in terms of the scope of the battlefield, there’s still a lot to be done to be able to scale up and meet the needs that people are going to have once the war goes under the surface.

Ravi Pappu: And just maybe to do a little thought experiment on the drone piece. Imagine the proliferation of aerial vehicles, but now turn out all the lights and make them really slow moving, right? And have them in a completely comms denied environment. That’s what we’re talking about. So they’re blind, they’re deaf, and they’re moving really slowly.

But that’s where these wars are going to be fought. I mean they’re obviously faster vehicles and things like that, but the majority of these conflicts are going to be in that kind of environment. And we have no idea how that environment behaves. That is the problem.

Sanjeev Krishnan: Yeah, I want to go beyond national security, but just one last point on national security. I imagine for military planners, Ukraine Inc. does rattle the imagination of industrial complexes or are they sort of like a one off?

Ravi Pappu: Maybe one way to think about your question, Sanjeev, is to think of it as a pyramid for aerial vehicles. At the top are the more, you know, MQ-9s, the Predator drones, which are really high (price). Then there’s all bombers and fighters and the expensive means. And then at the very bottom of the pyramid are these expendable drones. They’re still flying, but they’re cheap. And you no longer care about getting them back. 

Right now, that pyramid is reflected in the ocean world. We already see that. There’s the big expensive battleships, aircraft carriers, all of those kinds of things. Then there are the fast boats, and there’s been a tremendous amount of investment in uncrewed surface vessels. But that next level down the pyramid is just very sparsely populated and there’s no analogue to the Ukraine story underwater, but all indications are, as Rob said, when the conflict moves underwater, that layer is going to have to be filled out. We are a piece of that layer.

Rob Rainhart: Correct. Yeah, I completely agree.

Sanjeev Krishnan: Just as sort of a transition point, if you think about economic hegemony, since for four hundred years, even longer, those who control the oceans tend to have a call option on economic hegemony, and we’ve seen that. How do you see that sort of play out in your world views about your role in protecting US hegemony in the ocean sector with intelligence and other sort of diagnostics?

Rob Rainhart: Well, I go back to a little bit of what I said at the beginning, which is information becomes the framework, not an input, right? So I think that there’s a layer in there, and our role in all of this is to provide that information layer. And I definitely think there is a piece of this that as the company moves forward, there’s going to be this natural alignment between the US and its allies and the US and its interests around the world. But as we’ve seen in Ukraine and other instances, particularly for what we’re doing, right? And we saw these similarities play out at HawkEye, where low-cost, attributable, unclassified solutions at scale, brings something to that dynamic that in the past would have been very hard to do because it’s based upon very expensive systems that were protected in certain ways. So I think there’s full avenues for us to play in that scenario with being able to deliver systems to other countries, deliver data to other countries, on behalf of or in partnership with the US where it makes sense within that larger framework.

Sanjeev Krishnan: And particularly in an environment, Ravi, that is changing.

Ravi Pappu: Yeah.

Sanjeev Krishnan: Maybe talk about that change.

Ravi Pappu: Yeah, just quickly though, the technical underpinnings of economic hegemony in the oceans over the past four hundred years were shipbuilding, but also knowing where you were on the planet, not necessarily with GPS, but by looking at the stars and knowing how far you had travelled, right? And the ability to make maps. So most of that stuff doesn’t exist at scale under the ocean. We don’t know how deep the ocean is. We don’t know the terrain of the ocean to a depth greater than 100 meter resolution at best, right? And so it’s hard to keep time under the ocean. So knowing how far you’re traveling relies on expensive inertial navigation systems that are not great. So a lot of the infrastructure that created the hegemony on the surface needs to be recreated under the surface. And there’s a lot of work going on in this. You know, almost everything we know about under the ocean has been funded by navies.

Rob Rainhart: By the way, as that starts to happen, you will get pull from not just militaries, you’ll get pull from commercial companies that are investing in undersea that will then also need the same data or similar data in those areas. And that’s another way that it plays into that.

Sanjeev Krishnan: I guess where I want to go with the onion is you’re not only providing intelligence in a blind spot of a really powerful geopolitical asset, economic asset, natural ecology asset, but you’re doing it with a changing environment. 

Ravi Pappu: Yeah. So one of the things that’s unique about the ocean is its effects are not local. So if something heats up in the Pacific, the effect is felt three months later in Sudan. So knowing that something is heating up in the Pacific is a crucial element of this adaptation story. Right? This is the El Niño story. This warm pool of water flowing eastward and then creating all kinds of havoc in the climate. So as the ocean environment itself is changing rapidly, our understanding of the ocean from a static thing that we always know is going to behave the same way to a dynamic thing that we have to continuously measure has become more important.

I mean, just take the D‑Day example. I don’t know if you’re familiar with it, but there were two schools of thought for D‑Day. Things have always been the same, so we can go in,. But then there was another forecaster who said, I’ve assimilated all the data from nearby weather stations. It’s going to suck on the sixteenth. We have to delay D‑Day by a day. And that ended up being the right decision. 

So this tactical adaptation in the face of change related to the ocean and the nearby weather proved to be a very momentous, important event. But now that kind of change is happening constantly. And so it’s no longer sufficient to have last year’s picture or three months ago’s picture of the ocean. You need the picture now.

Sanjeev Krishnan: One of the things that I’ve learned, or keep learning, is that a lot of things are just trading the weather. And so going beyond defense and national security applications, the economics of the Pacific Ocean are profound, particularly like European power prices, agriculture…so I think maybe just with your lens of monetization of this data if you could potentially be that operating layer in terms of diagnostics, talk about the profound industries that we could potentially inform if they do trade the weather or have to deal with mother nature with regard to crop production et cetera, et cetera.

Ravi Pappu: Yeah, I think there’s just weather, there’s climate, which is the long term impact of it, and there’s black swan events like typhoons and hurricanes. And all of those depend on the thermohaline circulation of the ocean. 

Rob Rainhart: And just to add there, we know El Niño is happening, right? But what we’re talking about is a level of measurement now that changes the focus, right? So it’s not a generalized area of, yes, there’s a highly accurate pixel of different views that gives you an idea of how that weather is going to change or how that water is going to propagate and potentially affect other regions, right?

And nobody’s measured it with that kind of level of view and information there. So I think of it as taking it from a very low resolution image to a much more high resolution image in terms of your ability to forecast and predict there.

It affects offshore energy, wind, oil, gas, where currents and ocean conditions drive operating windows, asset risk, compliance, and reporting. It affects shipping and logistics, routing, fuel decisions, conditional forecasts of all those, insurance, reinsurance, sharper catastrophe, seasonal models, parametric products that need to be trusted and triggered on or not, right? Food systems. And then there’s also what you were mentioning, there’s the commodity traders, the ag businesses, and positioning ahead of El Niño and subsea cable operators and protecting critical infrastructure. We haven’t touched on subsea assets that are already there and monitoring the weather around them, but then also monitoring them from additional threats that are coming in, just from a business perspective. Those are all avenues of the business and users of this particular data. So when you think about it in terms of a high resolution image that nobody’s had before and then the areas in which this folds into, it scales really pretty phenomenally.

Sanjeev Krishnan: One of the things that I’m always fascinated by is systems engineering and systems thinking approaches to engineering and product. And I’m always inspired by the Tesla battery selection story where they picked laptop batteries and because that was going down in price. And maybe that CubeSat analogy is a good one. But why now can we create…and we have some smart people with you both and the Applied Invention team is second to none in terms of their track record of inventing the future. But there’s also that systems approach to engineering. What are the cost curves that we’re riding that allow us to do what we’re doing?

Ravi Pappu: Yeah, so before I answer that question, I’m just gonna say you have to pick a régime of trade-offs where you can harness those other curves. So if you want to go to a full ocean depth five-year vehicle, there’s not that many scaling curves that you can harness. So one of the crucial decisions that we have to make from a strategy perspective is how much of the ocean do we care about going in?

This is not the same discussion that we’re gonna have five years from now when we have a hundred thousand vehicles in the ocean, right? But going in, we said, let’s focus where the ocean changes the most and where we can get away with using commodity materials for the hull itself. That turns out to be the biggest part of the bill of materials. It turns out to be the crucial determinant of how the vehicle performs.

So by picking the right piece of trade-off, that is a key part of the systems engineering challenge, we were able to say once we’re in this chunk of ocean, a lot of what we think about can be harnessed from other fields. You know, lots of materials from the automotive industry, they make millions of cars every year.

A lot of materials from the drone industry, motors and things like that, they make millions a year. Obviously satellite communication and so on. So picking the régime is really important. If we went in five years ago Sanjeev and said we want to measure the whole ocean, map the ocean floor to you know 10 meter accuracy, what vehicle are we going to come up with?

We’re gonna come up with a super expensive bespoke vehicle. And I see that happening even now in many ocean companies. They start out with small compact vehicles, but they end up adding more features, which requires more energy, which makes it bigger. And once you have more energy, you can add more features. And I have drilled this into our team here. I call this the endurance trap.

You never want to be in the endurance trap. You always want to be nimble, you want to have disposable vehicles, make it distributed, and have economies of scale. It takes a lot of discipline to stick to your guns because customers always have this slightly adjacent need. And we have been successful in getting traction from customers who understand we’ll get there someday, but we can solve a good chunk, 80% of their problem today with taking advantage of all these costs.

Sanjeev Krishnan: And Rob, AI, the one bet that we’re making as a global economy in public equities and fixed income and almost every venture capital fund, it’s got me thinking about the nature of intelligence, like what is intelligence? And it’s got me thinking about what are the limitations of the current approach? And two things that I’ve noticed. One is observability, which is obviously a blind spot. And then two is error matching. And is that something you saw at HawkEye or other companies? And then it seems like that’s on steroids in ocean intelligence.

Rob Rainhart: Yeah, I think you run into that, so the answer is yes. You absolutely see it in other areas. And I think it comes back to a little bit of the framework, and you said this, but I’m gonna say it again, which is observe, orient, decide, act, right? And at which points does AI play a role there? Because AI can play a role in each of those different buckets and in different ways, right? We saw similar things like this at HawkEye in space early on.

And I think you’re going to see the same thing play out in the ocean here for us. And so a big area of capability for expansion is in that piece, which is really how do you really figure out what of the individual intelligence pieces can you add value to with additional AI processing, right? So not just, hey, a boat went by, but this specific boat went by, right? Those are two different answers. And what you find out very quickly is people get really excited when you bring new data, but they very quickly go, ​‘Wow, that’s a lot of data. I need to filter it in some way to help this downstream process there.’ And so those pre-processing pieces are all going to be part of what needs to happen as this continues to evolve.

Ravi Pappu: And there’s a related problem, Sanjeev, and we’ve seen this in atmospheric forecasting. We have weather balloons that are deployed every day, they’re expendable. We have weather satellites, we have weather stations, and all of these things have combined to produce foundation models for atmospheric weather forecasting. And when you think about what the AI is doing there, it’s really taking a single observation and extending its reach beyond the point where it was measured to a larger chunk of the atmosphere. And scientists for decades now have known that even a small number of measurements in the ocean taken consistently over time can have that same impact and be able to extend the reach of those measurements to large chunks of ocean. But we’ve never had the ability to consistently make measurements in the same place, long duration time series in the same place. We, Apeiron Labs, have the ability to do that now. And so what I expect is going to happen is even in the next year or two years, we will start having these really high-value chunks of data whose marginal utility is super important in foundation models. And the marginal cost is going to continue to go down because we’re going to have this scale. So it’s that difference, you know, you’re an economist.

Sanjeev Krishnan: Yep.

Ravi Pappu: It’s the difference between the marginal utility and the marginal cost. That is where the greatest value is going to be harnessed. And we want to be in a position to take full advantage of our unique capability there.

Sanjeev Krishnan: Okay, so how many satellites are in space? Give or take?

Ravi Pappu: On the order of like eight, nine thousand I think.

Rob Rainhart: And I was going to say between seven and a half and ten thousand, somewhere in there. 

Sanjeev Krishnan: Yeah, and it’s meant to get much higher with all all the launches. And what’s the cost per satellite?

Rob Rainhart: It ranges a lot, but let’s just say they range in price from small CubeSats to a few million on up to thirty or forty million, you know, for bigger ones.

Sanjeev Krishnan: We are a fraction of a fraction of a fraction of a fraction. I can say that publicly.

Rob Rainhart: We are a fraction of a fraction of a fraction. Yes, exactly.

Sanjeev Krishnan: And so we don’t need a ton of ubiquity, but let’s just imagine a future state five years from now where we’re rounding errors of that number and we took a blind spot and made it less blind or perhaps fully.  What are some things that you could imagine to the audience about the world we could live in in terms of predictability, commercial use cases? Let’s just assume a can opener. That’s an economics joke that I can tell over a beer. But assume a can opener, Ravi and Rob, and tell me the future.

Ravi Pappu: Yeah, so I’ll give you an actual use case, which is gonna be important to do. The Gulf of Mexico is a hot spot and many hurricanes that go through there are rapidly intensifying these days. Hurricane Michael was a big one.

We had Helene, we had all these hurricanes go through there. So I can see it being economically feasible with our technology to continuously monitor the Gulf of Mexico for a full hurricane season. 

It’s a few tens of millions per year. So it’s actually rounding errors for those kinds of things.

Rob Rainhart: And then roll that out to how that affects business operations in the Gulf for both energy and other, right? And you roll it out just from fpeople vacationing, traveling, the impact…

Sanjeev Krishnan: Ski seasons. 

Ravi Pappu: Yeah.

Sanjeev Krishnan: Gas in Europe.

Rob Rainhart: You know, it spins into a lot of different areas in terms of understanding the specifics of what’s going to happen extremely accurately, and it affects all those decisions.

Ravi Pappu: Yeah, everything in the base of Maslow’s hierarchy of needs is affected by how hot the Gulf of Mexico is in that region of the world, right?

Rob Rainhart: And then you apply that to the broader spectrum of all the different areas, and you start to extrapolate that across the globe to the different oceans, to the Mediterranean, to the Pacific Ocean down by Australia. And you get a picture that’s impacting a significant portion of economics across a lot of different…

Sanjeev Krishnan: You can trade the weather.

Rob Rainhart: You can trade the weather. Yeah, exactly. 

Ravi Pappu: Yeah. You can trade the weather. So you know the warm pool of water for El Niño doesn’t magically show up during July, August, and December. It’s happening below the surface, we just don’t know it.

Sanjeev Krishnan: All right. I’m gonna end with this. Give me your hot take on Super El Niño. What does the public not realize? You guys are in the business, give me a hot take.

Ravi Pappu: Rob, you want to go first?

Rob Rainhart: My hot take is if we had data, if we had sensors out there, we’d be providing a very accurate impact. So we wouldn’t have to speculate on what’s going to happen, right? So my hot take, which is a little bit of a cop out I guess, is like if we were monitoring that, we could tell you specifically how it’s going to impact everybody. So there would be a lot less fear. 

Ravi Pappu: So my hot take is we can know more than we know today with the technology that we have at our disposal. It’s not science fiction, it’s not the future. We are one large batch of manufacturing tensors away from being able to provide this data. And that’s what we’re going after.

Sanjeev Krishnan: Well thank you Rob for joining and leading us here to scale. Thank you Ravi for bringing us from an idea to a great business and a great company. So really excited to be partners with you both.

Ravi Pappu: Yeah. Thank you so much, Sanjeev. It was great chatting with you.

Rob Rainhart: Yeah, excited to be here. Thank you.

Tonya Bakritzes: Thank you for tuning into the S2G podcast. If you enjoyed this episode, please subscribe, leave us a review, or share it with a friend. For more information or to connect with us, check out s2gin​vest​ments​.com.

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