Fundraising: Ethernovia shines brightly in the little-known field of AI applied to physics

In a constantly evolving technological landscape, Ethernovia’s fundraising marks a significant milestone in the discrete yet crucial world of Artificial Intelligence (AI) applied to physics. As the industry explores new horizons to integrate AI directly at the heart of physical systems, this Silicon Valley-based startup recently closed an impressive funding round exceeding 90 million dollars. This unprecedented financing, led by Silicium Maverick and supported by renowned investors such as Porsche SE and Qualcomm Ventures, symbolizes a growing enthusiasm for what experts now call “physical AI.”

Driven by major technological innovation, Ethernovia stands out for its ability to design specific Ethernet processors that revolutionize how intelligent machines interact with their environment through low-latency and highly predictable networks. This often underestimated role of the network is nonetheless the backbone of advanced robotics, industrial automation, and even autonomous vehicle systems. The importance of this infrastructure, capable of orchestrating a massive flow of data in real-time, is still not well understood by the general public, but it is crucial for the proper functioning of embedded AI in our daily lives in 2026.

Ethernovia’s rise opens a new path toward unified network architectures, replacing fragmented systems inherited from the past with adaptable and scalable platforms that meet the complex demands of modern robotic and industrial applications. This development also highlights the strategic importance of solid infrastructure for AI-related scientific research, proving that innovation is not limited to algorithmic models but extends throughout the entire technological chain.

Ethernovia and the revolution of Physical Artificial Intelligence: a historic fundraising

Ethernovia’s recent fundraising, exceeding 90 million dollars, stands as a remarkable event in the technological startup sector. Unlike classic fundraisings that primarily target software or consumer applications, this one invests in an unknown yet strategic field: AI applied to physical infrastructures and networks of intelligent machines. This colossal sum was orchestrated by heavyweight players such as Silicium Maverick, but also supported by Socratic Partners, Conduit Capital, and CDIB-TEN Capital.

The renewed confidence of historic investors like Porsche SE and Qualcomm Ventures illustrates their faith in Ethernovia’s promising trajectory. This financing dynamic reflects a growing market interest in advanced technologies that combine the power of AI with complex technical systems independent of the cloud. The startup invests this capital to accelerate the commercialization of its Ethernet packet processors, which constitute the centerpiece of its technological revolution.

Concretely, these processors are designed to manage data transfer with ultra-low latency, an indispensable characteristic for applications requiring perfect synchronization between sensors, processors, and actuators in autonomous environments, such as autonomous vehicles or industrial robots. Ethernovia’s fundraising thus crystallizes the future of physical infrastructures capable of supporting reliable and efficient embedded AI—a still little-explored domain with considerable stakes.

The advanced technology behind Ethernovia: Ethernet processors and real-time network

At the heart of this innovation lies a radical network approach designed to meet the specific needs of physical AI. Ethernovia develops Ethernet packet processors that function as a central nervous system for intelligent machines, controlling the precise flow and scheduling of complex data streams. This model far surpasses traditional fragmented architectures, often built on specialized buses and point-to-point connections that limit scalability and performance.

The choice to use an innovative unified Ethernet architecture allows centralization and orchestration of real-time information flows, with predictability imposed by the very design of the hardware. This mastery of latency time reduces the risks of desynchronization between the different components of an autonomous system, which is vital for applications such as industrial robotics or automotive. Indeed, too high latency can have dramatic consequences on the safety and efficiency of automated processes.

Ethernovia’s platform also stands out for its ability to support scalable architectures such as zonal or centralized architectures, widely praised in modern automotive systems. This flexibility is a real asset for industrials, as it facilitates not only design but also subsequent updates without a complete infrastructure overhaul. This modularity relies on software tightly coupled with hardware, making the solution a reference in network integration for applied AI.

The concrete benefits of high network performance

The contribution of such technology translates into significant improvement in real-time interaction capabilities between machines and their environment. More specifically:

• Latency reduction: data transit without excessive delay, increasing the responsiveness of autonomous systems.
• Increased reliability: precise data transmission minimizes synchronization errors essential for critical tasks.
• Architectural flexibility: the ability to evolve and adapt networks in the long term meets the changing needs and growing complexity of AI systems.
• Cost optimization: by simplifying the network architecture and reducing technical layers, companies lower development and maintenance costs.

For example, in a smart factory equipped with adaptive robots, an Ethernet architecture driven by Ethernovia allows synchronizing robots with the supply chain and environmental sensors in real time. This avoids slowdowns, collisions, or production defects, thus ensuring maximum efficiency.

The impact of physical AI on robotics and industrial automation

In the field of industrial robotics, the rise of physical AI catalyzes a true revolution. Unlike traditional robots, programmed only to repeat fixed sequences, today’s intelligent machines are able to send, receive, and analyze a large amount of information simultaneously. This capability largely relies on the robustness of the network infrastructures connecting them.

The Financial Times highlights that physical AI revolutionizes human-machine collaboration, offering robots the ability to learn from unforeseen situations and adapt in real time to changing or uncertain environments. This evolution requires flawless data transmission quality to avoid any interruption or latency that could compromise safety or performance.

The successful integration of physical AI into industrial automation paves the way for more autonomous, collaborative machines capable of operating in smart factories where coordination between multiple systems is essential. The stakes are twofold: optimizing productivity while ensuring sufficient flexibility to face market fluctuations and regulatory requirements.

This transformation also implies the convergence of several techno-scientific sectors, combining principles from automotive, data centers, and traditional industrial networks. Positioned at the crossroads of these fields, Ethernovia benefits from a strategic advantage to support this transformation.

The 5 main applications of physical AI in industry

1. Adaptive robots: capable of modifying their behavior in real time.
2. Automated quality control: instant detection of anomalies through real-time data analysis.
3. Autonomous logistics systems: optimization of flows in warehouses and on industrial sites.
4. Predictive maintenance: anticipation of breakdowns based on continuous equipment monitoring.
5. Human-machine interaction: improvement of safety and collaboration in shared environments.

Technological convergence: the future of network infrastructures in embedded AI

Ethernovia’s success cannot be separated from a broader trend: the convergence of network architectures between the automotive, industrial sectors and data centers. This trend marks a break in the way infrastructures are designed, reinforcing the idea that the performance of embedded AI depends as much on the quality of networks as on the algorithmic models themselves.

Traditionally, each sector developed its own standards and systems, often incompatible, which weakened maintenance and complicated evolutions. Today, Ethernet architectures and software-defined networks allow a fusion of standards, thus facilitating the management of intelligent mixed systems over the long term.

Ethernovia exploits this evolution to offer scalable “plug and play” solutions. For example, in an autonomous car, a zonal architecture driven by their processor can be updated remotely to integrate new functions or meet enhanced safety requirements without changing the hardware. This flexibility is now an essential criterion for industrials.

Comparative table: Advantages of unified Ethernet architectures versus traditional architectures

Criterion	Traditional architectures	Unified Ethernet architectures (Ethernovia)
Complexity	Multiple buses and point-to-point connections	Centralized and programmable infrastructure
Latency	Variable, difficult to control	Predictable and low
Scalability	Limited, requires heavy modifications	Modular and adapted to upgrades
Cost	High maintenance and development costs	Optimization and cost reduction
Interoperability	Often restricted between systems	Standardized and flexible

Implications for scientific research and future development of applied AI

Beyond industrial and commercial aspects, Ethernovia’s fundraising also feeds a broader field of scientific research. Physical AI, as a discipline, involves advances in several fields such as real-time system modeling, advanced network engineering, and integration of edge learning algorithms.

The funding obtained should enable Ethernovia to strengthen its research and development teams, but also to collaborate with university laboratories and innovation centers. This synergy will foster the creation of new technological standards and the optimization of algorithms to better exploit hardware capabilities.

Researchers are notably moving toward improving data orchestration techniques and dynamic management of network priorities, ensuring that critical information is transmitted with utmost urgency. This absolute priority is a major challenge in systems where thousands of elements must interact fluidly and without latency.

Advances in these areas will have a direct impact on the emergence of new uses and on the rise of applications capable of operating in complete autonomy in complex and changing environments. Ethernovia plays a key role in this process of continuous innovation.

The strategic stakes of Ethernovia’s funding in the applied AI sector

Ethernovia’s fundraising of more than 90 million dollars illustrates how investors now consider the network dimension essential in the landscape of applied AI. This awareness changes the very nature of fundraising operations, which no longer invest solely in pure algorithmic power but also in the quality of technical infrastructures.

For industrial players, this evolution coincides with an increased demand for scalability and robustness of embedded systems. Hardware platforms are becoming more complex and the expected lifespan of equipment is lengthening, requiring solutions capable of quickly adopting new features without heavy intervention.

This dynamic is particularly critical in sectors such as robotics, autonomous transport, or sensitive industrial facilities where safety and regulatory compliance are priorities. Ethernovia’s fundraising thus opens a window of opportunities for the development of more reliable, secure, and innovative applied AI.

List of strategic benefits for investors and the industrial sector

• Early positioning on a key technology for physical AI.
• Strengthening the technological ecosystem around embedded intelligence and network infrastructures.
• Creation of new standards promoting interoperability and scalability of systems.
• Acceleration of innovation through targeted R&D funding and industrial collaboration.
• High return potential in a rapidly expanding market.

Ethernovia and the future of startups in the framework of fundraising in AI applied to physics

As the sector of Artificial Intelligence applied to physics grows, Ethernovia appears as an emblematic example of a startup successfully attracting massive funding in a technical and still emerging field. Its journey illustrates the keys to success for young technological companies seeking to establish themselves in this segment.

Ethernovia’s strength lies in its ability to address a concrete and often neglected problem in its sector: network management of critical data for physical AI. Its innovative model combining hardware and software guarantees a significant technological advance, increasing the confidence of investors and industrial clients.

Moreover, the startup benefits from a strong trend toward technological convergence, which broadens its potential markets well beyond automotive. Physical AI now extends to robotics, industrial automation, and even applications in health or energy, offering multiple growth avenues.

Fundraising in this field is multiplying, as evidenced by the recent dynamics of 2024 and 2025, confirming that innovation in AI applied to physics occupies an increasing place in the economic landscape. Ethernovia thus paves the way for a new generation of startups where scientific research and technological innovation combine to meet the challenges of the future.