In a world where large models can "grow infinitely" from the internet, image libraries, and massive amounts of text, robots are trapped in another world—real-world data is extremely scarce, expensive, and non-reusable. Business Insider once published a seemingly simple yet highly insightful report..."AI robots are facing a data shortage, and a startup has found an unexpected solution."

The report points out that compared to the almost inexhaustible training data for language and visual models, the data required for robots to interact with the real world is far from sufficient in terms of scale, structure, and transferability. This has become a key bottleneck for the large-scale intelligence of robots. A robotics startup called FieldAI has provided its own solution to this problem.

In response to the real-world constraints of insufficient data volume and limited structure in the physical world, FieldAI has chosen a solution that differs from the mainstream perception-first approach.To build a general-purpose robot intelligence system based on physical constraints from the ground up, so as to improve the robot's generalization and autonomy in real-world environments.

Company website: https://www.fieldai.com

FieldAI's manifesto: Not just building robots, but building a general-purpose robotic brain.

At an era where most robotics companies focus on building hardware and demonstrating complex movements, FieldAI has chosen a more long-term approach, not aiming to create a specific, individual robot as its ultimate goal.Instead, it is dedicated to creating a "universal robot intelligent brain" that can be used across different types of robots and adapt to various environments.

This universal brain is called Field Foundation Models (FFMs). It is not a single piece of hardware or a single-function software, but a new type of "physics-first" foundational model built specifically for embodied intelligence.

In layman's terms,The approach of prioritizing physics differs fundamentally from the traditional AI approach of "sensing first, then controlling."From the outset, FFMs prioritize real-world physical constraints, uncertainties, and risks as the model's primary task, rather than using rules or controllers to fall back on the model's output. This enables robots to make on-site decisions and exhibit safer and more reliable intelligent behavior when facing unfamiliar environments, such as those without maps, GPS, or pre-defined routes.

FieldAI itself also emphasizes that robot intelligence is not only about the execution of behaviors, but more importantly, about forming a closed loop of real-world data. The perceptual data generated during task execution will be continuously fed back to the model for training, optimization, and iteration, thereby allowing intelligence to continuously evolve.

When explaining the company's vision, founder Agha said, "Our customers don't need precise maps or even any training; they can simply press a button and the robot can explore every corner of the environment."

In the product deployment of FieldAI,Robots are taking on essential tasks in the real world.They are focusing their attention on traditional work scenarios such as construction, logistics, energy, mining, electricity, and agriculture to achieve large-scale, industrial-grade autonomous operation.

In November 2025, FieldAI's collaboration with DPR Construction demonstrated the value created on a real construction site. Robots equipped with FieldAI's AI system could autonomously patrol the site, automatically collecting tens of thousands of photos, scanning the building interior, and creating large-scale maps. This data was then transformed into real-time information for progress tracking, risk detection, and quality analysis, saving significant time compared to manual inspections and improving on-site safety and efficiency.

NASA engineers' "realist" revolution

FieldAI’s unique technological approach is, to some extent, deeply rooted in the engineering background of its founders.

The company's founder and CEO, Ali Agha, has a well-documented career spanning seven years at NASA's Jet Propulsion Laboratory (JPL). He participated in NASA's autonomous Mars cave exploration and prototype Mars helicopter-rover collaborative autonomous project, as well as research related to DARPA RACER (an off-road autonomous vehicle).A closer look at these projects reveals a common thread: the environment is unpredictable and the cost of making mistakes is extremely high, leaving virtually no possibility of human intervention.

From an academic perspective,Ali Agha is not just a super researcher who "worked on NASA projects"; he is also a scholar who has long been personally involved in research on core robotic intelligence issues and autonomous algorithms. Based on his publicly available academic achievements, it can be concluded that he has conducted in-depth discussions on a single theme in several top robotics conferences and journals—how robots can autonomously understand their environment and make reliable decisions even in the absence of complete information.

For example, Agha and his team published a paper titled "NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge" in the Journal of Field Robotics, which systematically introduced NeBula, an autonomous decision-making framework for robots in complex and unknown environments. This framework can combine multimodal information to perform risk perception, environment mapping, and path planning when faced with incomplete perception and task uncertainty.

In addition, he has participated in several research projects published in conferences and journals such as IEEE Robotics and Automation Letters. For example, in the paper "Nonlinear MPC for Collision Avoidance and Control of UAVs With Dynamic Obstacles", he explored the problem of safety control and obstacle avoidance of unmanned systems in dynamic environments; another paper, "LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments", also demonstrated the specific technical details of stable map construction in large-scale, perception-degraded environments.

Perhaps it was within this work and academic context that Agha developed a more "low-level" understanding of robotic intelligence. Building upon this foundation,FieldAI brings together top technical experts from leading companies such as DeepMind, Tesla, SpaceX, NASA, and Amazon.The shared vision is to enable robots to work stably for extended periods in the real world and make safe and rational decisions in ever-changing environments.

The battle for a universal robotic operating system

FieldAI was officially founded in 2023, but its progress in the capital market has been far faster than the typical pace for a startup. As of August 2025,The company completed over $405 million in financing in less than two years, with a post-investment valuation of approximately $2 billion.Furthermore, the investment lineup is incredibly impressive, including Bezos's private investment office, Intel Capital, Nvidia's venture capital arm, Bill Gates' investment fund, Samsung, and others. This reflects not only the sheer scale of the investment but also the capital decisions it represents.

For these investors, betting on FieldAI is not just about choosing a specific robot product, but about betting on a more fundamental and universal direction for intelligent development.

Reuters, citing a report from F-Prime Capital, stated that "global investment in robotics will surge to $18.6 billion in 2024, an increase of 116.1 billion Tb3t from the previous year." Further data from F-Prime, released in the second half of 2025, shows that this growth momentum has not slowed, with global robotics investment expected to surpass $20.9 billion in 2025, setting a new historical record.

In addition, both general and vertical markets experienced explosive growth.Investment in General Purpose Robotics is projected to surge from $1.9 billion to $4.9 billion;Vertical robotics, designed for specific scenarios, accounts for half of the market, with its market size jumping from $8.1 billion to $13.2 billion.

In this context,FieldAI is not positioned to offer a choice between "general" and "vertical".On one hand, its full-fledged investment in the general-purpose robot intelligent brain corresponds to the fastest-growing general-purpose robot sector. Just as investors in 2025 are no longer satisfied with buying "a machine that can do work," they are vying for an "entry ticket" that enables all machines to learn how to do work. On the other hand, FieldAI focuses on the application of its products in vertical scenarios, solving real-world problems that generate commercial cash flow, such as construction sites, inspections, urban delivery, and energy. Perhaps this is why it has simultaneously attracted the combined bets of chip giants, tech founders, and long-term investors.

These years of embodied intelligence industry

If the main theme of the robotics industry over the past decade has been innovation, then the next decade will be determined by large-scale deployment. FieldAI's approach precisely targets this structural turning point. It doesn't bet on a particular robot shape or a single scenario, but rather on a scalable, reusable, and sustainably upgradeable "general-purpose robot brain + data infrastructure".

In today's booming field of embodied intelligence, we look forward to a future robot ecosystem that is as diverse and vibrant as the current smartphone ecosystem, truly serving humanity and making life more convenient.

Reference Links:
1.https://www.businessinsider.com/ai-robotics-data-problem-fieldai-surprising-fix-ali-agha-2025-9
2.https://robobdtw2025.mapyourshow.com/8_0/sessions/session-details.cfm?scheduleid=100&
3.https://spectrum.ieee.org/autonomy-unstructured-field-ai
4.https://www.reuters.com/business/robotics-startup-fieldai-raises-314-million-new-funding-sources-say-2025-08-20/
5.https://fprimecapital.com/blog/robotics-on-the-rise-the-state-of-robotics-investment-in-2025/