The robot revolution and our food future, with Rajat Bhageria

0:00Starting a business comes with its share of ups and downs, which is why staying true to your vision is essential. a non-negotiable for Romeo and Milka Bregali, Capital One business customers and co-owners of Ra's plant-based restaurant in New York. Romeo and Milka took a leap of faith when starting their own restaurant, gutting an empty space and building it from the ground up. Every pipe, every wall, every detail. But building from scratch came with a heavy financial burden, which is when they turned to their Capital One business card. With the flexibility of the card's no preset spending limit, they were able to spend more and earn more rewards while bringing their vision to life.

0:36Today, Raz's success is proof that with passion and the right support, it's possible to make your dreams a reality. Learn more at CapitalOne.com slash business cards. Let me set the scene for you. You work through lunch again, and your next meeting is in 15 minutes. But you don't want others to hear your grumbling stomach. You need something to eat. Fast. You open up the freezer, and there. Thankfully, you find your emergency meals. Pre-portioned, pre-cooked entrees, complete with a vegetable and protein.

1:17Okay, honestly, I stopped eating these freezer meals a few years ago. I'm trying to eat more fresh foods these days. That being said, many of us do rely on these meals. But how many of you out there actually stopped to think about them? How are those carrots such perfect cubes? How are the noodles perfectly cooked, not too mushy or underdone? The thing with food is that it's a lot more variable than might meet the eye. I'll give you an example. Like every batch of a very simple ingredient like rice is a little bit different. So if you cooked it a little bit longer, a little bit less long, you should put a little bit too much oil, less oil.

1:54It changes the stickiness, the density. And it's like it's not obvious for humans. and they often actually are very inconsistent because of that. Yeah, humans are inconsistent. But you know what's a lot more consistent than us? Robots. Rajat Bagheria runs Chef Robotics, a company making AI-powered robots for the food industry. Robots that can sort and assemble your microwave meal on the factory floor. This kind of advanced robotics is called embodied AI. and Rajat is a pioneer in the field. Just like me, he's a computer scientist turned entrepreneur. The intersection of AI-powered robots and food manufacturing is an area I see so much potential in.

2:40I'm actually an investor in Rajat's company and I'm so excited to share with you what Chef Robotics is about. In this episode, we'll get into the nitty-gritty of how robotic AI works and can solve some of the biggest labor shortage problems we have. We'll also dive into Rajat's own background as an entrepreneur and how he's building a business on the cutting edge to scale.

3:07I'm Rana El-Khalyubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

3:24Hi, Rajat. Welcome to Pioneers of AI. I am so excited to have this conversation today. Thank you, Rana. I'm super excited to have this conversation as well. So I want to set the stage here because this is our first episode on AI robotics. And not all robots are AI-driven. In fact, most robots don't have any AI in them at all. And a lot of AI today lives in the software world, right? It's like ChatGPT. It doesn't have any physical embodiment. But you live at the intersection of both AI and robotics, and we often refer to that as embodied AI or physical AI. So how would you define that? Yeah, it's a good question.

4:01I mean, I think colloquially the definition that like roboticists use is this idea of like basically you sense the world, you make decisions, and then you actuate. That's basically kind of the robotics kind of framework that most people use. And now there's this fourth idea of like communicate, which is like communicate to a user about, hey, like what's the state of the robot? Like if you go to Waymo, for example, it'll show you like why it stopped or things like that. Right. So there's quite a lot of progress, as you called out, happening in digital AI or like software AI with LLMs and VLMs and things like that.

4:30A VLM is a visual language model. Instead of being trained on large amounts of data to generate and translate texts like LLMs do, visual language models are multimodal and are trained on images, videos and texts. I think I'm very excited about the intersection of that idea to the physical world. So I think perhaps some of the biggest impact will be there. So Rajat, what does Chef Robotics actually do? The way you can kind of think about it is we essentially have two things we make. One is kind of this AI-enabled software. We call it ChefOS. It's really the software on how to manipulate food, which is to say, how do you manipulate thousands of different ingredients, no matter how you cut it and cook it, at any portion size into any tray, any compartment within the tray, things like that.

5:15So that's just kind of ChefOS. And of course, to make food, you need hardware. So on the hardware side, we mostly use actually off-the-shelf hardware that we kind of integrate into a product that we kind of scale across our customers. And then we also have a set of utensils that we have across our customers. And then we can, of course, combine the two and we have this kind of system that we can deploy at customers that will flexibly automate their production and help them basically increase production volume. Chef Robotics works with companies like Sun Basket, the healthy meal delivery company, and Amy's Kitchen, the organic food pioneer.

5:47So we're both computer scientists by background, and we actually both spent a lot of time in the computer vision world. And then we also both spent some time in the business and the investing space. I am so curious how all of this culminated into the origin story of Chef Robotics. How did you land on the food industry in particular? Yeah, it was definitely a roundabout journey, which didn't really make sense at the time. But I think the puzzle piece is kind of connected in hindsight, I guess. When I was kind of getting into like engineering, I was actually planning to be an electrical engineer.

6:16I did a bunch of research in high school with electrical engineering because my plan was to be electrical. But then basically the first week of college, there was this hackathon and I decided to enter with a few friends and we actually did really well. And I was introduced this idea of computer science and software in a more substantial way, I would say. And it was so exciting because because electrical felt slow, relatively speaking. Computer science is very fast and exciting. But also that hackathon led to the creation of my first company, ThirdEye, where we're using computer vision to recognize what's in front of you for the visually impaired.

6:48So that was my real kind of first, real kind of experience with AI and computer vision. And I think both those experiences, like of course the coursework, but also more importantly, ThirdEye, kind of got me excited about this idea that really software's biggest impact will be on the physical world. So at that point, it was like, okay, well, I wanted to do something in this idea of embodied AI, what you alluded to, but I didn't know what product or company to start. But I also had seen the graveyard of robotics and me. Like robotics, honestly, has been tough. Historically, it's been tough. I think it's gaining a lot of excitement in like the 2020s right now, but there's been a lot of robotics and me that didn't work out.

7:24So I was like, okay, I believe this idea that AI enabled robotics is going to be huge, are the biggest industry on planet Earth. But I wanted to be very practical. So I also kind of did a bunch of coursework in economics and business, and I started a small VC fund. And I think all of that kind of really allowed me to understand the economics of robotics and how economics of how these older industries work, I would say. So I think it was kind of the background from the software, computer science, third eye experience, and then kind of economics, business, venture capital. I think those experiences kind of coalesced.

7:55And I came into this idea of starting some robotics with an appreciation that I think it's just as much of an economics problem as it is a technology problem. Because if the ROI doesn't make sense, then our customers are not going to buy these robots. I think the reason the food industry happened is actually quite practical. I want to work on the biggest thing that I possibly could. So I was like, okay, well, the best proxy for that is the number of humans who do that job today from a labor perspective. So I looked at the Bureau for Labor Statistics data, and the biggest job was actually nursing and personal care aides, which I didn't feel was tractable by AI today.

8:30The next most common one was retail salespeople. Same thought. Food service was actually number three, food preparation broadly. And then, of course, we immersed ourselves in the food industry. Like we like lived literally more or less with our customers until we really understood how they think and what they care about. And that's how we learned about it. But it was a very practical decision, I would say. And then what did you find was the gap that you went on to solve? So when I had this thought about the food industry, I wasn't trying, like, I think there's been a lot of robotics assemblies that kind of put this idea of I want to do robotics on the customer.

9:03Like they kind of have leading questions. They're like, where do we put the robots? I think we had the opposite question, which is, hey, look, like we're excited broadly speaking about food. What kind of pain points do you face? Right. Not kind of setting aside AI and technology and robotics, all of that. But what was interesting is that every single customer, without exception, we talked to essentially said their biggest pain point was labor shortage and flavors of that, which is that even if they could hire the labor, it's really hard to retain the labor. I would hear stories about how people would turn over 300 to 400 percent per year.

9:34Now, by the way, food is a little bit more intense than other environments, like even worse than perhaps construction and, you know, agriculture and warehouses, mainly because it's extremely cold, like 34 degrees Fahrenheit or extremely hot, either extreme. So anyways, like that was kind of the big pain point we kind of heard over and over and over. This isn't the cooking food problem. This is the packing and like food assembly. Yeah. Yeah, so we hadn't gotten there yet when we were having these conversations. It was like, broadly speaking, what's your pain point? It was like labor, right? And they were like, it's really hard to hire because the hot room or the cold room, even if you're doing assembly or packing, it's still hot because you're next to the stove and the grill, right?

10:14So I think that that was where we were at that point. And then the next step was, like you said, what task? We learned that actually assembly is the task that all of our customers ask us to focus on, not cooking or prepping. And this is also kind of weird to us because, you know, when you cook at home or I cook at home, cooking takes the longest. And number two is prepping. And then assembly is like seconds, right? What I learned is that, you know, at even a little bit of scale, that's not true anymore. The assembly is actually 60 to 70 percent of the labor force. Basically, our customers told us you should focus there.

10:45I love that you talk about your company as a robot staffing agency. Say more about that. The big pain point that this labor shortage results in for our customers is that they're running under capacity. And one thing I should just say is that like, you know, at the moment we're selling to really food manufacturers. We do also want to sell down the line to like fast casual restaurants, prisons, hotels, stadiums. Today, our customers are food manufacturing customers. Think big food factories. Now, their biggest pain point is that because they cannot hire these people, they are running way under capacity.

11:15What that means essentially is let's say they have 10 conveyor lines. They can only staff, let's say, seven or eight of them at a time to really make the production numbers they need. So they're basically leaving revenue on the table. And hypothetically, let's just say they can staff all 10. Even if they can staff all 10, they have so much turnover that like sometimes it doesn't make sense to staff all 10 because they're just going to have to train people constantly. So basically, like what we can say is, look, like we're going to be this kind of staffing agency in the sense that we're going to charge you a recurring fee.

11:42We're not going to charge you a big CapEx up front. CapEx, as in capital expenses. These are the big upfront costs for upgrades and things like equipment. Typically, these kinds of robots are charged as a capital expense. And I think that's a notable point. A lot of automation is big CapEx purchases. We don't do that. You're going to pay us a yearly recurring fee, which is less than the cost of people. So you're paying for us just like you pay your people. And basically, you can run production now. You can go from running eight lines at a time to now running 10 lines at a time, which is a huge, huge ROI for them.

12:15You're not necessarily replacing human jobs. There is a shortage of labor in this particular application, and you're stepping in with robots to fill in this gap, which I think is a lot of people's fears and concerns around AI is about AI taking jobs. I think that's exactly right. And more generally, I would just say that, like, anytime you have automation, like automation is not a new idea. AI is a new idea, but automation is not a new idea. Like the tractor is automation, right? The dishwasher is automation. The steam engine, it's all kind of automation in some regards. It's always created a lot more jobs in aggregate than it's taken, right?

12:51Because what happens is that the cost of goods goes down, business is burgeon, and the economy kind of swells, and there's a lot more jobs in aggregate created by this than there's taken. So we've talked about why Chef Robotics came to be and how its services can help food businesses. But what does putting these robots to work actually look like? That's after the break.

13:44Thank you. Text the dots across your work so nothing gets lost. It's one AI-powered teamwork platform designed for how modern teams actually build. Learn more at Atlassian.com slash TeamChanger. That's A-T-L-A-S-S-I-A-N dot com slash TeamChanger. So set the scene for us. Take us to one of these food manufacturers. What does it look like? and how does the robot come into play? Yeah, so maybe I can give you a sense of like what a status quo is and then we can talk about how Chef fits in. So status quo is basically of these long, big assembly rooms. You might have two conveyor lines, you might have 40 conveyor lines based on the volume that the customer might be doing.

14:30On each assembly line, basically you have stations. Each station has a person and the person has a big tub of food that might be rice, carrots, dice, green beans, whatever ingredient it might be. And they're basically scooping ingredients from the source container into individual customer containers as they move down the line. The customer containers might be frozen meals. They might be fresh meals, yogurt parfaits, sandwiches, wraps, burritos, whatever kind of food the customer might be making that day. And so that's kind of status quo. And just to be clear, these are high mix customers. What I mean by that is that they're not just like, line one isn't just doing the same meal over and over again.

15:03They'll change over the line. So they might do 10 ,000 trays of Cobb salad and then they'll change over the line and they'll do a Caesar salad, for example, right? So what we do is we say, okay, look, like, you know, line eight over there is not running because of lack of people or really will find opportunities to help. Right. And we make these AI enabled robot modules, we call them. They're modules in the sense that they are modular. They're the same footprint as a human. And basically you can just slide them onto the line. They are on wheels or casters. So there's no retrofitting required. And essentially each module takes up the same footprint as a person.

15:37The only inputs we need is really compressed air and power, which every facility in the food world really has. At that point, basically what you do is you say, here's the meal I want to run. Let's say I'm running the Caesar salad. Then you might say, here's the ingredient I want to run. Let's say it's the leafy green. And then you select the portion size. Chef will query the relevant policy, the AI policy on how to manipulate that leafy green. And of course, every leafy green is a little bit different, but it'll get the relevant one. And it'll ask you to load the pans of food, the pans of leafy green.

16:06It'll also ask you to load a relevant utensil. Each policy has a associated utensil, which is kind of similar to a human utensil. So you attach a utensil and then you press play. That entire process, by the way, takes all of a couple minutes. It's very quick. And at that point, you know, Chef will detect the trays as they move down the line and then it'll place onto those trays as they move down the line. So we use computer vision to detect where the trays are and that allows us to adjust for, oh, the tray's rotated or there's no tray or there's increasing acceleration, the conveyor decreasing acceleration or the conveyor slanted or the conveyor skewed.

16:37Like there's all these variations you see in the physical world and we can be robust to all of that using that process. Hold on, I want to pause you for a second because I think this point is really important because automation in the food industry has been around forever. What is different about you guys' approach is that you're harnessing AI, and we're going to go under the hood in a second to talk about what exactly are you leveraging in terms of data and models. But for now, you're using AI and that's giving you flexibility. You don't have to have a different robot for lettuce and another one for carrots and another one for ice cream or whatever.

17:12So can you talk about that? I think this flexibility and the use of AI is really key to explain. You can think about it as a single actuator, maybe a very simple sensor like a phototransistor. It's a very simple hardware, mostly hardware system. So it's very good at doing the same thing over and over again. So an analogy might be like, you can make a dispenser work for, let's say, diced tomatoes. Great. But now if you cut your tomatoes a little bit differently, it's going to get clogged up. Right. And it can place into a single kind of tray. And if you change the kind of tray, it won't work again.

17:42It can place into, really, it's kind of fine-tuned and kind of hard-coded for one kind of conveyor. If you change the conveyor, it doesn't work. If the conveyor is a little bit farther away, it doesn't work. You know, these ideas, right? It's basically a very custom piece of automation, right? That works fine for a certain subset of customers. Like if I'm Kraft Heinz making ketchup bottles and I'm going to be making 100 million ketchup bottles, of course, I get a dedicated custom line that just runs ketchup bottles all day long and I get a custom machine just for that and it works great. for our customers, they have 300 different SKUs, 500 different SKUs, in other words, products, right?

18:19You have all these different kinds of meals that you and I might want. And because of that, they don't have custom lines for each of those products. They have flexible lines that change over. So flexibility is key. Traditional automation is not very flexible. So I think the point you made is exactly right. You could solve the high mix problem by having custom machines, essentially, for each ingredient, each portion size, each tray, each placement within the tray, each conveyor, It just goes on and on. So there's all these permutations. Our vision and what we're kind of telling the customers, like, look, and what we've productionized is a single piece of hardware.

18:52Basically an arm with like some utensils at the end. Yeah, exactly. And what's nice about that is like, you can basically pick really any ingredient at any portion size into any container, into any placement within the container, into any conveyor using purely software and different utensil inserts. So it just makes it a lot more scalable and flexible because of that. Yeah, amazing. So you gave us the example of the large scale kind of food manufacturer. What are other areas where you're applying Chef Robotics? Right now, our bread and butter is really these kind of high mix customers. And I think like the areas that we're excited about within that is really like focusing on frozen, on fresh meals, airline catering, school meals, hospital meals, things like that.

19:38Right. Where we want to expand from there is kind of two ideas. Idea number one is horizontally to other sectors in the food industry. And idea number two is vertically. So let me talk about horizontal first. Horizontal would basically be, you know, right now we're kind of working with these high volume customers, right? They're doing, let's say, 10 ,000 meals a day. And so robots are really good in those kind of environments. Where we'd like to go to next is lower and lower volume environments. Imagine like a fast casual restaurant, like let's say Cava or Sweetgreen or Chipotle. If you think about it, they basically have a lunch rush and a dinner rush, and that's like four hours, right?

20:15So to generate an ROI, you basically have to generate that ROI in four hours, which is very tough. So your robot has to be very generalized. And not only does it have to generate an ROI in four hours, within those four hours, it's got to do 90 to 100 ingredients. Like if a robot just has leafy greens for sweet green, that's not useful. It's got to do everything. So essentially the thinking for Chef is that food manufacturing is high volume. We're running the robot 16 hours a day, right? So there's a very compelling ROI and each robot at any given moment is doing a single ingredient. Now throughout the day, it might do five to six ingredients, but it's a set of ingredients.

20:52And so our thinking is that by shipping robots here, we're collecting a lot of valuable training data on how to manipulate food. And over time, we can use that training data to make a more generalized food manipulation model, which then allows us to go to lower and lower volume environments. Well, the ultimate goal of really being able to do things like fast casuals, ghost kitchens, things like that. So that's kind of how we think about scaling horizontally. So imagine robots behind the counter preparing your lunch at Chipotle and Sweetgreen. That's a goal for Chef Robotics. But Rajat doesn't just want to grow into different markets.

21:24He sees a lot of potential value in all of the data they're collecting about food production. What about the vertical integration or the vertical roadmap? app? Yeah, the vertical idea is also interesting, which is that right now what we found in the food industry is there's actually very relatively little software and data that these companies are capturing. Some of them have more than others, but it's quite a bit more manual than you might think. So I'll give an example. From a QA perspective, they'll randomly sample meals at the end of the line. And it's like they'll do it like, you know, X times per hour, which is cool.

21:56It's useful. But we are measuring the weight of every single meal, first of all. And we're doing that on the ingredient level, not the full meal level, but every single ingredient we're measuring. Okay, we're doing the same thing for placement. Like, okay, we're measuring every single metric of how the placement aesthetics look. We have a lot of metrics around throughput and basically food wastage. There's all this data we're collecting. And I think over time, what we'd like to be able to do is leverage that data and help build tools for line monitoring, placement QA, pick weight QA, because at the center of food is really these metrics on quality, right?

22:27Quality, throughput, things like that. So I think there's a lot of opportunity where we can basically over time become more of the operating system of how these companies work from a software perspective. And, you know, you can imagine also a software about like scheduling and planning, optimized for robots and humans, for example. I think that's really cool because QA or quality assurance today, as you say, isn't happening with every single meal being served, but you can do just in time QA, right? Because you've got all the data anyway. So that's really awesome. And you can interject, you know, if I don't know, I'm going to make this example up.