Ecosystem Spotlights

How AutoFlight’s Five-Tonne Matrix Could Solve the eVTOL Profitability Puzzle

AutoFlight’s five-tonne Matrix bets on heavy payloads and regional range to prove the case for electric flight

Updated

February 10, 2026 12:56 PM

A multiroter flying through a blue sky. PHOTO: UNSPLASH

The nascent industry of electric vertical takeoff and landing (eVTOL) aircraft has long been defined by a specific set of limitations: small payloads, short distances and a primary focus on urban air taxis. AutoFlight, a Chinese aviation startup, recently moved to shift that narrative by unveiling "Matrix," a five-tonne aircraft that represents a significant leap in scale for electric aviation.

In a demonstration at the company’s flight test center, the Matrix completed a full transition flight—the technically demanding process of switching from vertical lift-off to forward wing-born flight and back to a vertical landing. While small-scale drones and four-seat prototypes have become increasingly common, this marks the first time an electric aircraft of this mass has successfully executed the maneuver.

The sheer scale of the Matrix places it in a different category than the "flying cars" currently being tested for hops over city traffic. With a maximum takeoff weight of 5,700 kilograms (roughly 12,500 pounds), the aircraft has the footprint of a traditional regional turboprop, boasting a 20-meter wingspan. Its size allows for configurations that the industry has previously struggled to accommodate, including a ten-seat business class cabin or a cargo hold capable of carrying 1,500 kilograms of freight.

This increased capacity is more than just a feat of engineering; it is a direct attempt to solve the financial hurdles that have plagued the sector, specifically addressing the skepticism industry analysts have often expressed regarding the economic viability of smaller eVTOLs. These critics frequently cite the high cost of operation relative to the low passenger count as a barrier to entry.

AutoFlight’s founder and CEO, Tian Yu, suggested the Matrix is a direct response to those concerns. “Matrix is not just a rising star in the aviation industry, but also an ambitious disruptor,” Yu stated. “It will eliminate the industry perception that eVTOL = short-haul, low payload and reshape the rules of eVTOL routes. Through economies of scale, it significantly reduces transportation costs per seat-kilometer and per ton-kilometer, thus revolutionizing costs and driving profitability.”

To achieve this, the aircraft utilizes a "lift and cruise" configuration. In simple terms, this means the plane uses one set of dedicated rotors to lift it off the ground like a helicopter, but once it reaches a certain speed, it uses a separate propeller to fly forward like a traditional airplane, allowing the wings to provide the lift. This design is paired with a distinctive "triplane" layout—three layers of wings—and a six-arm structure to keep the massive frame stable.

These features allow the Matrix to serve a variety of roles. For the "low-altitude economy" being promoted by Chinese regulators, the startup is offering a pure electric model with a 250-kilometer range for regional hops, alongside a hybrid-electric version capable of traveling 1,500 kilometers. The latter version, equipped with a forward-opening door to fit standard air freight containers, targets a logistics sector still heavily reliant on carbon-intensive trucking.

However, the road to commercial flight remains a steep one. Despite the successful flight demonstration, AutoFlight faces the same formidable headwinds as its competitors, such as a complex global regulatory landscape and the rigorous demands of airworthiness certification. While the Matrix validates the company's high-power propulsion, moving from a test-center demonstration to a commercial fleet will require years of safety data.

Nevertheless, the debut of the Matrix signals a maturation of the startup’s ambitions. Having previously developed smaller models for autonomous logistics and urban mobility, AutoFlight is now betting that the future of electric flight isn't just in avoiding gridlock, but in hauling the weight of regional commerce. Whether the infrastructure and regulators are ready to accommodate a five-tonne electric disruptor remains the industry's unanswered question.

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Artificial Intelligence

New Physical AI Technology: How Atomathic’s AIDAR and AISIR Improve Machine Sensing

Redefining sensor performance with advanced physical AI and signal processing.

Updated

January 8, 2026 6:32 PM

Robot with human features, equipped with a visual sensor. PHOTO: UNSPLASH

Atomathic, the company once known as Neural Propulsion Systems, is stepping into the spotlight with a bold claim: its new AI platforms can help machines “see the invisible”. With the commercial launch of AIDAR™ and AISIR™, the company says it is opening a new chapter for physical AI, AI sensing and advanced sensor technology across automotive, aviation, defense, robotics and semiconductor manufacturing.

The idea behind these platforms is simple yet ambitious. Machines gather enormous amounts of signal data, yet they still struggle to understand the faint, fast or hidden details that matter most when making decisions. Atomathic says its software closes that gap. By applying AI signal processing directly to raw physical signals, the company aims to help sensors pick up subtle patterns that traditional systems miss, enabling faster reactions and more confident autonomous system performance.

"To realize the promise of physical AI, machines must achieve greater autonomy, precision and real-time decision-making—and Atomathic is defining that future," said Dr. Behrooz Rezvani, Founder and CEO of Atomathic. "We make the invisible visible. Our technology fuses the rigor of mathematics with the power of AI to transform how sensors and machines interact with the world—unlocking capabilities once thought to be theoretical. What can be imagined mathematically can now be realized physically."

This technical shift is powered by Atomathic’s deeper mathematical framework. The core of its approach is a method called hyperdefinition technology, which uses the Atomic Norm and fast computational techniques to map sparse physical signals. In simple terms, it pulls clarity out of chaos. This enables ultra-high-resolution signal visualization in real time—something the company claims has never been achieved at this scale in real-time sensing.

AIDAR and AISIR are already being trialled and integrated across multiple sectors and they’re designed to work with a broad range of hardware. That hardware-agnostic design is poised to matter even more as industries shift toward richer, more detailed sensing. Analysts expect the automotive sensor market to surge in the coming years, with radar imaging, next-gen ADAS systems and high-precision machine perception playing increasingly central roles.

Atomathic’s technology comes from a tight-knit team with deep roots in mathematics, machine intelligence and AI research, drawing talent from institutions such as Caltech, UCLA, Stanford and the Technical University of Munich. After seven years of development, the company is ready to show its progress publicly, starting with demonstrations at CES 2026 in Las Vegas.

Suppose the future of autonomy depends on machines perceiving the world with far greater fidelity. In that case, Atomathic is betting that the next leap forward won’t come from more hardware, but from rethinking the math behind the signal—and redefining what physical AI can do.