Jaisimha Rao Podcast Transcript
Jaisimha Rao joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to the Coruzant Technologies Home of The Digital Executive Podcast. Do you work in emerging tech, working on something innovative? Maybe an entrepreneur? Apply to be a guest at www.coruzant.com/brand.
Welcome to The Digital Executive. Today’s guest is Jaisimha Rao. Jaisimha Rao is the Founder and Chief Executive Officer of Niqo Robotics, an agricultural technology company developing AI powered robots for precision farming.
Based in Bengaluru, Niqo is the first company in Asia to commercialize green on green AI spot spraying with a footprint now spanning India and the United States, a graduate of Carnegie Mellon University in electrical and computer engineering. Jayma began his career in finance. He spent six years at BlackRock in New York where he rose to vice president and managed multi-billion dollar portfolios.
Through the 2008 financial crisis, the discipline and global perspective gained on Wall Street shaped his entrepreneurial approach to problem solving. Well, good afternoon, Jay. Welcome to the show.
Jaisimha Rao: Hey Brian, thanks for having me.
Brian Thomas: Absolutely, my friend. I really appreciate you making the time. I know you’re hailing out of Bengaluru, India.
Right now I’m in Kansas City, so traversing the globe, making calendars works sometimes is very challenging, so I really appreciate that. And Jay, if you don’t mind, we’re gonna jump into your first question. You transitioned from managing multi-billion dollar portfolios at BlackRock during the 2008 financial crisis to building AI powered farm robots. How did that experience shape your approach to risk, discipline and innovation in agriculture?
Jaisimha Rao: Yeah, I think you know, it’s obviously these two disciplines couldn’t be more further apart, but one analogy that I like to tell folks is at BlackRock.
You work, you work your day off you put in the hours and then at the end of the day you do a good job and your boss calls you in and says, Hey, nice job. Here’s your number. And the next day you start working in. And the crazy thing is we as a family, we, we grow coffee and, weirdly enough, that is the same routine.
And ironically, it is February as well, where you spend the whole year weeding, spraying, pruning your trees, all for that one day where you sell your coffee produce. Then the next day, again, you’re planning for the following year. So, bunch of similarities on, on those two fronts in terms of how do you maintain your risk on your farm throughout the year and make sure you get your payday.
And there’s always a risk reward ratio as far as playing it safe in terms of diversifying your farm in terms of what you’re growing, but also trying to maximize yield.
Brian Thomas: Thank you. I really appreciate that. And of course, you learned some hard work ethics, you know, on from the farm perspective. And then of course working really hard in the financial sector, managing multi-billion dollar portfolios.
I know that and again, it’s just rinse and repeat every day and it’s, it’s new goals, new things, and, and I know there’s a lot of hard work that goes into that, so I appreciate the backstory.
Jaisimha Rao: Yes, indeed. Yeah.
Brian Thomas: Jay, Niqo Robotics is the first company in Asia to commercialize green on green AI spot spring.
A problem so hard that even the Niqo Sense camera had to be purpose-built from scratch for those unfamiliar, what makes identifying a target crop among a sea of green, so technically demanding? And why does solving it translate up to 60% in savings on chemical costs for a cotton or a chili farmer?
Jaisimha Rao: Yeah, I think for, for your listeners maybe I have to set context here.
So, if you, India’s the largest grower of cotton. In terms of acres in the world. We have 33 million acres, and you can think of each cotton plant planted at 18 inches, one and a half feet from each other, and each row is five feet from each other as soon as you plant it. So, initially, if you come to a cotton field 15 days, 20 days after germination, you can see a sea of brown soil with dots of greens. And those are the germinated seeds. And at that stage, if you think of spraying fungicide or liquid fertilizer, which essentially means only the plants need it. And you don’t need to spray, you don’t need to spray those fertilizers on the soil because it kind of gets washed away.
That just by doing that spraying on the plant, not on the soil, that saves 60%. So. Believe it or not today the analogy again I like to draw is if you have a school teacher has 20 kids in their class and two, three kids are sick, the teacher is not giving the entire class antibiotics.
But in farming, that’s what we do. Even if there are few plants that are not doing well, you just blanket spray the entire field and with green on green technology that this smart camera allows you to move farming from an acre level decision making to a plant level decision making. So that’s the context.
Why is it hard? It’s actually quite, I asked myself the same question. My, my phone seems to be quite smart. So isn’t this an easy problem to solve? And the answer is no. And I think there’s few things that make it hard. Number one, farming conditions are extremely hard from an environment standpoint.
So you have rain, you have dust, you have ambient light. So getting a consistently good camera image, that’s a challenge. That’s a technical challenge. That’s number one. Number two these machines are moving. So when you’re moving in a harsh environment, you get things like blood. You get things like distorted images.
Now you’re running out of time. By the time the camera takes an image, processes, it runs the AI algorithm and then sends a signal of which plants to spray and which not, uh, which plants not to spray. On average, we’re dealing anywhere between 60 milliseconds to 90 milliseconds to do the entire operation.
So these two things make it extremely hard because all the compute, all the decisions happen on the farm. None of the data leaves. The farm, goes to a cloud, gets computed, comes back. So what we call, it’s on the edge algorithms that need to be done. So yeah, combination environment, technically you have limited time to take a decision.
Then finally the precision makes this all very challenging. So we had to build the camera ground up specifically for farming.
Brian Thomas: Thank you. I really appreciate that. Building something from the ground up obviously there was a need here. Of course, India is the largest cotton producer in the world. I didn’t know that.
But to really produce. This type of crop and to be more efficient at it. I like how you went into some of the technology challenges, building it from the ground up to actually accommodate and, and build a more efficient process. But absolutely the lighting, the color the camera that’s moving. And what’s, what I took away though is all that compute is actually done out on the edge, right?
Out on the farming environment. Come back to the cloud to do all that processing. It’s, it’s done right there. And I think that’s interesting. So thank you so much for that. And Jay, with the Nico Robo Weeder now running at 4.5 miles per hour in US lettuce fields, you’re entering market where weeding is one of the most labor intensive and costly farm operations.
How do you see AI powered weed? Fundamentally changing labor equation, not just replacing workers, but reshaping what farmed labor looks like and what does that mean for India where labor is still relatively abundant but increasingly unavailable when it matters.
Jaisimha Rao: Yeah. I’m glad, I’m glad you ended your question on that note because, again, the, you know, as a startup we need to go out and raise capital and when we go out and raise capital, I like to joke that if there are a hundred investors in the room and I say I’m billing an ag startup, 80 of them will leave. And then for the remaining 20, if I say I’m building a hardware ag startup in India I think the remaining 18 leave and there’s only two remaining because.
It’s a hard sell. People think India is 1.5 billion most populous country in the world. You are telling me you have a labor shortage. And the answer is yes. It’s yes, sir. It’s not that there are not enough people, it’s just the dignity of work, squatting and bending in 110 degree Fahrenheit ambient temperature and pulling weeds is not something a human really desires to do.
So we have a problem here. Obviously the problem is more acute in the US and I think your questions are on farm labor. There are, we need to, we need to address the elephant in the room. Is our vision of farming farm laborers, risking their lives with a hole in their hand, removing weeds? Or should that be done by a machine?
And I think. My answer to that is let the farmer decide. So we shouldn’t stop innovation. If the farmer has access to labor and they do have special bonds, like a lot of farmers, I have a special bond with my farm labor. Not all of them are great, but a few, and I want to replace a few of them with the machine.
We need to give the farmer that choice. So my vision of farming is. A lot of jobs need to be automated. Farming is a razor thin margin business. It’s a boom and bust business. You have one bounty year and then the rest of the year you’re trying to make ends meet. And the only way we can make put the money back in the farm and increase margins is through automation.
I don’t think we should wax romantically about having more people on farms. The future of farming is less people. That’s the way I look at it. There’s no, I’m not gonna sugarcoat it. I’m not gonna say it’s augmentation. No. I think there are certain jobs on the farm that need to be done by a machine and not by a human being.
And what happens to the existing workforce? Absolutely. Like, you, we need to train that workforce to take care of these robots. These robots are mechanical nature. You know, they have a UI similar to a phone, so. If if an operator is used to a smartphone, they’re used to operating our machine through that screen inside the cab, the, we need to upskill our workforce into operating these machines as opposed to doing the current baking work of removing weeds. And that’s where I stand.
Brian Thomas: Thank you. I appreciate that. As you talked about agriculture and, and labor, right? Those are, that’s a challenging industry to be in no matter if it’s a US or India. You talked about that. It’s just, it’s very hard on human beings and human beings prefer to work in something.
I guess the conditions would be much better than what you had mentioned about working in the, the high temperatures and that sort of thing. But at the end of the day, there is a razor thin margin in farming. And so while we wanna let the farmer decide whether they want to use human labor or machine we want to continue to innovate and give them the choice.
But again, give them those, at least those opportunities to make those decisions and be able to again, be more efficient in their farming. ’cause I know that is, is a hard business to be in. Exactly. So. Jay, the last question of the day. Niqo’s RAAs model puts spot spraying within reach at about 300 rupees per acre through village level entrepreneurs.
But precision agriculture at scale requires more than good technology. It needs financing, assisting. Dealer networks and policy infrastructure. What needs a change in global and Indian agriculture systems for AI robotics to genuinely serve smaller, smaller holder farmers and not just the large industrial farms that can afford to buy the machine outright.
Jaisimha Rao: Yeah, I think, before I answer the question, I think we are, we are quite proud of maybe the previous two, the question that we asked previously, which is I think precision ag, cutting edge AI on the AI for ag. In general, the narrative is this belongs in the domain of the west of larger farmers, wealthier farmers, larger acreages.
And at Niqo, we are really proud that you know this idea before we went to the US the machine was tested and perfected and scaled in India. So we are proud of the fact that technology can flow from the east to the west and not always trickle down from the west to the east. And I think we’ve been able to prove that.
In terms of what needs to change? I think the biggest barrier for technology adoption in especially in, in India, I think that’s the most extreme case. I think our neighbors is probably equally problematic is farm acreages. So when average Indian farmer is two and a half acres, which is probably smaller than a hobby farm in the us.
So when you have such less land or links there’s no appetite to invest in machinery and automation, et cetera. And I think the way out there is the overall headline acreage is that 120 million hectares of farmland in India, just that. How do you aggregate these acreages? I think the ownership can be divided, but how do you aggregate these acreages, uh, when it comes to pulling acres together and, uh, then enabling what we have tapped into, which is, uh, village level entrepreneurs.
And to your audience. What does village entrepreneur looks like is usually a person who, who comes from that village? On average, I would say the area of responsibility is about a thousand acres where they provide farming as a service. So they, they plow your land as a service, they spray as a service, they harvest as a service.
So these are, uh, small they entrepreneurs who are catering to small farms and their personal relationship with these farmers, and they become your main go to market. So. I think these folks exist. We’ve, we’ve sprayed close to 200,000 acres with these entrepreneurs. Out of the 50 machines that we had deployed in India all 50 machines were used.
There was zero PIL fridge, zero theft, all these other crazy, um, risks that maybe some of the investors who thankfully didn’t invest in us were, were telling us. So, it’s a high trust society. And we, for us, for act tech to scale in this part of the world, there’s no other way you gotta tap into these village level entrepreneurs from policy standpoint.
Making it easier to aggregate land holdings by not spooking the landholders. I think that can really galvanize this industry from a tech adoption standpoint.
Brian Thomas: Thank you and I appreciate that. And you know, I also heard right off, you’re proud to partner and support these smallest of farmers in the world. I think that’s a really noble cause that you’re doing. But to your point, village level entrepreneurs is where it’s at. Obviously the small business, just like here in the us small businesses, the backbone of, of business in America just like it is in India. And I appreciate you. Sharing what you’re doing to help grow that small, the smaller scale of farmer entrepreneurship. I really appreciate that.
Jaisimha Rao: Thank you.
Brian Thomas: And Jay, it was such a pleasure having you on today, and I look forward to speaking with you real soon.
Jaisimha Rao: Absolutely. Thanks for having me, Brian.
Brian Thomas: Bye for now.
Jaisimha Rao Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
