This Founder Raised $400 Million To Build AI-Driven Brains That Enable Robots To Operate Autonomously In Complex, Dynamic Real-World Environments Without GPS
Ali Agha is among the rare founders who have bridged the gap between the fascinating world of theoretical robotics and reliable, real-world applications. He has overcome the challenges in the promising field of robotics, which, up until now, has only been impressive in controlled spaces.
Ali’s path—from tinkering with motors as a kid to leading a company that has raised over $400M—is not just about building robots. It’s about redefining how machines think, operate, and scale in the real world. At FieldAI, Ali isn’t building robots. He’s building their brains.
The Early Spark: A Lifelong Obsession With Math and Machines
Ali’s story begins with curiosity. Growing up in a snowy region, he was drawn early to math, tools, engineering, and the simple joy of making things move.
Whether it was wiring batteries to spin motors or playing with toys that had even a hint of autonomy, the fascination was always the same: creating systems that could act on their own. That curiosity matured quickly as Ali learned to code and realized that software could perform tasks.
During his undergraduate years, Ali joined robotics teams and began building increasingly complex programs and systems—from simple line-following robots to autonomous soccer bots competing in RoboCup tournaments.
The Realization: Theory Alone Isn’t Enough
Ali did his undergraduate and master's in a program combining electrical engineering and computer science. Even before coming to the US, he had started working on more sophisticated platforms.
Sometime in 2006-2007, Ali moved from a small school on the other side of the world to Georgia, Atlanta, and won the best mobility in RoboCup competitions there. Working on the early RoboCup competitions, one insight stood out. Real-world robotics is far harder than it looks.
Ali realized how much work needed to be done on the theoretical side and in improving the algorithms for these platforms to actually deliver real-world performance.
Ali then moved on to a different league of more complex robotics systems, where they were building rescue bots. These bots had flippers and navigated unstructured environments, which truly fascinated him.
Ali began to appreciate how critical it is to have robust methodologies, advanced technologies, and theoretical frameworks for understanding the world, the robot's state, and its behavior. The robot must identify its environment, learn to make decisions safely, and consistently improve its learning.
As Ali points out, getting robots to operate reliably is beyond the theories and methodologies available in textbooks. When robots operate in the real world, they are not dealing with linear systems or Gaussian noise.
Basic assumptions that are incredibly important to learn and build on don’t directly apply. To build robots, you need to move beyond assumptions and develop solutions that actually survive off-nominal cases and the complexities of the real world.
Pursuing a PhD in Texas
The pivotal moment came when Ali was pursuing his PhD in computer science in Texas. He immersed himself in advanced mathematics, probability theory, stochastic differential equations, and risk identification and quantification.
Ali reiterates that deploying the robots in the real world, ironically, helped him appreciate the depth of theory. He wasn’t just learning existing frameworks—he was extending them, building new approaches to quantify uncertainty and risk in dynamic environments.
The result was a set of breakthroughs and seminal papers that made robots more capable, more adaptive, and more reliable in the real world.
From Academia to Impact: MIT and Qualcomm
After his PhD, Ali continued pushing the boundaries of robotics at MIT, where he expanded his work from single-robot autonomy to multi-agent systems—teams of robots coordinating in decentralized ways. For instance, coordinating flying platforms with ground platforms together to take action.
This phase also led to a pivotal relationship: meeting his future co-founder, who would go on to work at DeepMind, build models, and advance the foundations of machine learning.
From MIT, Ali moved to Qualcomm, where the focus shifted to efficiency—bringing autonomy to resource-constrained hardware. At Qualcomm, they were working on robotics, drones, and self-driving cars. Ali and his team were exploring the idea of putting algorithms on their processors.
Here, he worked on deploying perception and autonomy systems onto chips, a step that would later influence one of NASA’s most ambitious projects. Ali recalls how they organized the first CES show on the robotics and drones side—a continuous progression in his work.
That work caught NASA’s attention. At the time, the Mars helicopter was not a publicly announced mission, but NASA was in discussions with Qualcomm about using its lightweight, low-power processor on the Mars helicopter. That’s how Ali moved to NASA—a once-in-a-lifetime opportunity.
The Move to NASA
Ali enjoyed the level of intensity at NASA. His work involved two separate sections. On the management track, he was a group supervisor in aerial mobility and later also worked in the perception group.
The Mars helicopter flew on Mars, powered by a Qualcomm chip. Ali recalls how exciting it was to watch the incredible NASA team in operation. Since Earth and Mars’ relative positions cannot be altered, everything had to be ready for the launch date, which could not be changed.
At NASA’s Jet Propulsion Laboratory, Ali found himself at the center of some of the most demanding engineering challenges imaginable. From contributing to the Mars helicopter program to leading DARPA-backed initiatives, the work required not just innovation—but extreme reliability.
For Ali, working at NASA was an incredible phase of his career. Over his years of work in robotics, including his PhD and postdoctoral work, he had developed several theories for deploying robots. It was now time to apply them in the real world and test their impact.
Initiating Projects at the Jet Propulsion Laboratory
Around this time, Ali began applying for and securing grants that enabled him to initiate multiple projects at the Jet Propulsion Laboratory.
These initiatives ranged from early efforts focused on deploying legged platforms and dog robots in Mars-analog caves to search for signs of life and microbial colonies, to other projects. The legged robots would go into unknown environments with no communication in an analog setting.
Ali also led projects for DARPA challenges—competitions like the DARPA Subterranean Challenge and DARPA RACER. As he explains, these are probably among the major breakthroughs of the last decade in deploying robots in complex environments.
The task was to send a team of robots—wheeled, legged, and flying—to environments that they had never seen before—from underground to industrial facilities.
Here, the robots had to perform various tasks, from building a model of the world to detecting specific objects ahead of first responders. Ali recalls that it was a very unique experience. Teams had one hour to execute what took years to build. The margin for error was effectively zero.
This environment sharpened Ali’s understanding of what truly matters—systems that don’t just work in theory, but survive in the harshest real-world conditions. The urban phase was organized pre-COVID, and Ali’s team won the competition against some of the world's best players.
Post-COVID, although Ali and his team scrapped the project, it was a huge learning process. They had pushed the state of the art significantly on many fronts—from common wear autonomy to computer wear autonomy to multi-agent operations, all the way to a single brain operating across many different embodiments.
Post-COVID, another DARPA RACER competition was organized, focused on large vehicles traveling tens of miles into off-road environments with no GPS, no satellite imagery, and no map. They had to travel many miles to hit another flag.
Most of the team from the DARPA Subtraining Challenge moved to DARPA RACER. Ali recalls how they created a deeper, stronger bond and pushed the industry and technology much further forward. They enabled and fostered many new theoretical and technological breakthroughs there.
These competitions were behind many major innovations in robotics, and Ali was part of the key groups that enabled them. The intensity was on another level altogether.
The Breakthrough Insight: Bridging Two Worlds of AI
Talking about the inspiration behind FieldAI, Ali goes back to his time at NASA, where a critical insight began to take shape. At NASA, they were seeing the evolution of methodologies and doing things that were years ahead.
The teams were identifying the roadmap ahead and where it could go, vis-à-vis timing and hardware commoditization. This resulted in million-dollar robots and other hardware becoming available at more cost-effective levels.
Computing was also evolving quickly. This meant that robots could do much more without direct, consistent connectivity. When Ali started discussing these developments with his DeepMind cofounder and other colleagues, they concluded that there are two ends of the spectrum.
On one side of the AI spectrum were foundation models—powerful, data-driven systems capable of generalization but prone to hallucination and lacking physical grounding.
On the other side were physics-based robotics systems—highly reliable and interpretable, but difficult to scale and adapt across environments. What if you could combine both? That question became the foundation of FieldAI.
Instead of building traditional transformer-based systems, Ali and his team designed a new architecture—one that embeds physics, uncertainty, and risk awareness directly into the model from day one. The goal wasn’t just intelligence—it was deployable intelligence.
Systems that could operate safely, adapt quickly, and function in real-world environments without needing massive datasets or perfect conditions. Because that architecture was the key, FieldAI could quickly develop a go-to-market product and approach customers.
As Ali explains, the architecture enabled them to penetrate the market because it is deployable and has also helped them attract incredible talent from all the different institutions. Customers can get all the benefits from day one. And because it's safe to deploy them, the momentum has been incredible.
FieldAI: Building the Brain for Every Robot
FieldAI’s core idea is deceptively simple: create a universal “brain” that can operate across different robots, environments, and tasks. In the sense that it is a highly horizontal company.
Rather than building specialized solutions for each use case, the company is developing a general-purpose autonomy layer that can power everything from multi-ton industrial vehicles to humanoid robots.
At a high level, its business model is software or robot-as-a-service (RaaS), reflecting this flexibility. Some customers can purchase the full solution. Others can buy the brain and expand it within their ecosystem, so they just license the software.
FieldAI is deploying a range of platforms, from multi-ton, very large vehicles and wheeled platforms, to small dog robots and humanoids. These options are highly generalizable across modalities and can be deployed across different business units, including construction, the main sector.
The company focuses on what Ali calls “DDD environments”—dirty, dull, and dangerous settings where traditional automation struggles. These include construction sites, energy infrastructure, manufacturing, and urban operations such as last-mile delivery, security, and federal operations.
What sets FieldAI apart is its ability to deploy robots without pre-mapped environments, GPS, or extensive prior data. The system interprets the world in real time, much like a human worker would, and continuously improves through a data flywheel created by real-world usage.
As Ali explains, even if billions of tokens of data from that environment are unavailable, the physics and uncertainty-quantification pieces baked into these architectures kick in as soon as the robots are about to hallucinate. They adjust the platform's behavior to ensure they can be safe and deploy.
The robots can perform tasks that humans use their hands for, such as opening doors, picking up objects, and coordinating different assets in a multi-agent fashion.
Scaling Fast: From Deployment to Data Flywheel
Despite operating in a capital-intensive industry, FieldAI has maintained a relatively lean approach. Its architecture allows it to learn from smaller datasets, and early deployments have generated real revenue.
This combination—efficient learning and immediate deployment—has created strong momentum. Interest in the physical AI world, the applications of robots, the tasks they can accomplish, and the speed at which they can deliver ROI has spurred demand.
The company’s fundraising journey reflects that traction. With over $400M raised, much of the interest has been inbound, backed by investors including Bezos Expeditions, BHP Ventures, Canaan Partners, Emerson Collective, Intel Capital, Khosla Ventures, NVentures (NVIDIA’s venture capital arm), Prysm, and Temasek.
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But for Ali, capital is not the end goal—it’s an accelerator. The focus remains on expanding deployments, accelerating deployment, strengthening the data-model feedback loop, and delivering measurable ROI to customers.
The Bigger Vision: Millions of Robots, One Brain
As Ali points out, FieldAL performs across many different types of platforms, including, for instance, vehicle dynamics. The world model adapts to four-legged and two-legged robots in different environments and variations across hundreds of sites, from construction to manufacturing and more.
The market is highly expansive, and the solutions are maturing quickly. Looking ahead, Ali envisions a world where millions of machines operate autonomously across industries, safely and responsibly, thanks to physics-awareness of uncertainty and risk.
In the future, robots can be deployed in places beyond the limits of traditional and multi-agent solutions in the industrial sector.
FieldAI is building a brain that operates across a heterogeneous fleet of assets and can grow and penetrate markets that were traditionally off-limits to robotic solutions.
As Ali sees it, robots will be incredibly critical going forward, from construction to operation, to increasing productivity and increasing safety, and addressing massive labor shortages across many of the sectors in which they are being deployed.
FieldAI aims to be at the center of that transformation—providing the intelligence layer that powers these systems. And the opportunity extends far beyond Earth. From industrial applications to lunar operations, Ali sees robotics as essential infrastructure for the future.
The Defining Factor: Software, Not Hardware
While much attention in robotics focuses on hardware, Ali believes the real bottleneck—and opportunity—is software. Today’s robots are already capable of far more than we extract from them. In expert hands, they can deliver tremendous value.
Ali concedes that hardware needs a lot more improvement, and a variety of different robots will soon be launched. He foresees more robust, more cost-effective platforms coming up. The challenge is replicating that expertise through AI.
Ali asks—Can you build an AI and a solution that can do what the world champion at controlling a drone or a robot can do? The progress and speed in improving software are getting there.
The moment software reaches and surpasses human-level control across tasks, the market unlocks at an entirely new scale. That, in Ali’s view, is robotics’ “ChatGPT moment.”
A Founder’s Lesson: Obsession With Product and Customer
If there’s one lesson Ali emphasizes, it’s this: technical excellence alone is not enough. For deeply technical founders, the ability to solve complex problems can become a trap. It’s easy to build impressive solutions that lack real-world adoption.
The key is obsession—not just with the technology, but with the customer and the product’s deployment at scale. Building for millions of users requires a different way of thinking, with a focus on clear ROI, cost efficiency, seamless usability, and driving the exact value the customer wants.
Those constraints must shape both the business and the technology from day one. The focus should be on what would actually enable the solution to be deployed at scale. This aspect should be baked into the framework, on both the business and technology sides.
In Ali’s view, that would be the most critical thing for building the type of startup they are building—a deep tech startup.
Closing Thought
Ali Agha’s journey is a case study in what happens when deep technical rigor meets real-world execution. From academic breakthroughs to DARPA competitions to a fast-scaling startup, the throughline is consistent: build systems that work where it matters most.
Because in robotics, the future doesn’t belong to the smartest machines. It belongs to those who actually show up—and deliver.
Real-world robotics demands systems that can survive uncertainty, not just perform in controlled environments. · Ali Agha’s journey shows how deep theory becomes powerful when tested against real-world complexity. · FieldAI is building robot brains that combine physics, uncertainty, and AI to operate safely at scale. · The biggest opportunity in robotics may not be hardware, but the software that unlocks its full potential. · Robots that work without GPS, maps, or perfect data can transform dirty, dull, and dangerous industries. · Raising $400M gave FieldAI the fuel to accelerate deployment, data collection, and customer adoption. · For deep-tech founders, technical brilliance matters only when it translates into product value and customer ROI.
Original Version
Alejandro Cremades: Alrighty, hello everyone and welcome to the DealMakers show. So today we have an awesome founder. We're going to talking quite a bit about robotics. So I hope that you're ready. You're ready for his journey. Incredible what he's building now with this company. You know, they've raised a quite a bit, you know, 500 million plus. And we're going to be discussing the journey, you know also how he went from maybe more the academic academics to really implementing and and and having that technical depth, you know, how that looks like when you apply it really to the execution.
Alejandro Cremades: and quite the rocket ship that they're building. So again, brace yourself for this really inspiring conversation. So without further ado, let's welcome our guest today, Ali Agha. Welcome to the show.
Alejandro Cremades: So originally you were, you know, a in this snowy region in the world and essentially you had this fascination for math and from the very early days. So That fascination, where did it come from?
Ali Agha: Yeah, I mean, as far as i remember, yeah, i always loved math. I always loved tinkering with tools and engineering and put a few batteries and making motor sort of start spinning. And, you know,
Ali Agha: always loved playing with the toys that can do something a little bit on their own, right? Even if it's simple, but it was always with me. And I think over time it just grew as I learned more, oh, I can actually code and bring something to life and have these machines actually do something, have a piece of software do something and create something. just It just grew more over time more and more in me and,
Ali Agha: All of that I think is is led to sort of where I am now. I mean, i all of that passion let me join and in my undergrad a robotics team, even from the early years of my undergrad, building robots and you know programming them to
Ali Agha: from following a line at the very first ever robot I made like two decades ago, all the way to small little soccer robots that are kicking an orange a ball in RoboCup-like competitions um and and just make them you know smarter and smarter. But but that's that's how it all all started for me.
Alejandro Cremades: So talk to us too about coming to the U.S. because coming to the U.S. via Texas where you were doing your Ph.D. was quite a pivotal moment.
Ali Agha: Yeah, I mean, um like I mentioned, i in my undergrad and masters, I was doing quite a bit of robotics. So I i did my undergrad and masters in in sort of a program between electrical engineering and computer science. And um and as I mentioned,
Ali Agha: working on these RoboCup competitions started to ah instill in me how much work needs to be done on the theoretical side, how much work it needs to be done on the on improving the algorithms for these platforms to actually show performance that handle the the real world.
Ali Agha: So i in my master's, even before I come to US, now I moved to a bit more sophisticated platforms. It's still within robotic robotic competitions, RoboCop competitions, but in a different league where we were in building rescue robots. These tracked robots with flippers, very complex platforms going over step field unstructured environments. And it was a lot of fun. And i started to appreciate how critical it is to have robust methodologies to understand the world, understand
Ali Agha: the state of the the robot, where it is in this world, how it's make decision, how to do those in a safe way, how it learns over time. And we actually, from that sort of small school and on the other side of the world, we came and won the best mobility in RoboCop competitions in in Atlanta, Georgia.
Ali Agha: This is, I believe it was 2006, 2007 timeframe. And from there, as you alluded to, i that appreciation to the fact that we need better technologies, better theoretical frameworks for these robots to show of robust behavior. I moved to to Texas to do my PhD in computer science and In my PhD, I actually took, I think, more courses from math department than than any other department. And that really was, again, to go really deep in elements of the solution that I believed robots
Ali Agha: required to to operate reliably that were beyond the theories and beyond the methodologies that were in the textbooks, right? When you go to the the real world,
Ali Agha: you're not dealing with linear system or Gaussian noises or, ah ah you know, ah so a lot of assumptions that are incredibly important to learn and build your your your your basics, but they don't directly apply. You need to take them, you need to learn them, but then go beyond those to reach a point that develop a solution that actually survives.
Ali Agha: all the off-nominal cases, all the complexities of the real world. And I think that appreciation to the deployment in these competitions, that appreciation to deploying the robots in the real world, helped me to appreciate, in an ironic way, a depth of the theory. So i in my PhD, I went quite deep in in in theory, went quite deep in up probability theory, stochastic differential equations, understanding the risk, quantifying,
Ali Agha: the the uncertainty and what can go wrong and what are the delta and difference between computational models, your assumptions and what you actually face in the real world. And and we did some very interesting work, i wrote some seminal sort of papers, introduced a few new frameworks there, which made the robots smarter,
Ali Agha: made them accomplish tasks that we're were beyond what was possible in that sector that I was working on ah prior prior to those work and it was a pretty fun time.
Alejandro Cremades: Which was quite a shift too, like you know going more from the academia because then you did MIT to all of a sudden you find yourself in in places like Qualcomm or NASA. you know At what point did you you know think that perhaps having that shift um and change course of action you know in your career was the way to go?
Ali Agha: Yeah, i I don't see it really as ah as a really a shift. I think it was a natural progression. So post my PhD, I moved to to MIT. I was ah i was a postdoc there.
Ali Agha: I was working with an incredible group of people. And i was now starting to take what I had done at my PhD from a a single robot autonomy to coordinated operations across a team of robots.
Ali Agha: The moment you hit that superhuman performance across a variety of different tasks, you are unlocking a massive, massive market. All that is to say, I think software is going to be a defining factor moving forward. It’s going to change how we look at these machines, and it’s going to change how we live as physical AI gets closer and closer to its, if you will, ChatGPT-type moment.
Alejandro Cremades: Now, we've been talking about the future. I want to talk about the past, but with a lens of reflection. Let’s say I bring you back in time to that moment where you were thinking about stepping down from your position at NASA.
Alejandro Cremades: Let’s say, right before you’re pending your resignation or your notice, you’re able to show up and have a conversation with that younger Ali and give that younger Ali one piece of advice before starting Field AI. What would that be and why, given what you know now?
Ali Agha: I think one of the things we got right, and what I would recommend to the next generation who want to come into entrepreneurship and build a company, is obsession with the product and the customer.
Ali Agha: I think this served us well and best. We, as a technology team, had the risk of being able to solve any problem, which might seem good, but in the startup sector, it could be dangerous. If you're technologically very capable, you can actually solve any problem somebody throws at you, and then find yourself in a situation where you’ve solved incredible problems and gained a lot of satisfaction from the models and solutions you built.
Ali Agha: But it might be too late to realize, okay, is this actually going to be something people adopt? Because adoption across tens of solutions is very different from adoption across millions. At millions, it’s a very different game from the ROI perspective, from the cost perspective, and from understanding the exact value the customer wants.
Ali Agha: If I were to share one thing, it would be that thinking about those elements early on is extremely important, especially for very deeply technical founders. Otherwise, you risk having too much fun building a solution, and it might be too late at some point to go back and course correct.
Ali Agha: So yeah, I think the obsession with what would actually enable the solution to be deployed at millions, and baking that into your framework, both on the business side and the technology side, would be the most critical thing to build the type of startup that we are building—a deep tech startup.
Alejandro Cremades: So for the people who are listening, Ali, and would love to reach out and say hi, what is the best way for them to do so?
Ali Agha: Very easy. I think from our website, from our team, LinkedIn—many, many different ways. We would welcome people to come to the office. We have offices in different locations in the Bay Area, robots in Irvine, SoCal, Boston, and Pittsburgh.
Ali Agha: Tokyo, Singapore—yeah, we’d love to get emails from them, LinkedIn messages, and not just me, but the rest of the company as well.
Alejandro Cremades: Amazing. Well, Ali, thank you so much for being on the DealMaker Show today. It has been an absolute honor to have you with us.
