As artificial intelligence moves beyond screens and enters the physical world, humanity is approaching a technological transition comparable to the emergence of the internet itself. We are beginning to see the early stages of a future in which autonomous vehicles, humanoid robots, industrial robots, service robots, household assistants, healthcare companions, and countless other intelligent machines operate continuously alongside human beings.
The software foundation of this future remains largely undefined.
Today, discussions surrounding Physical AI often focus on advances in foundation models, multimodal reasoning, robotics hardware, sensors, batteries, and autonomous decision-making. Yet a more fundamental question receives comparatively little attention:
What operating system will govern the physical AI systems that interact directly with human lives?
Just as personal computers required operating systems, smartphones required mobile operating systems, and cloud computing required standardized infrastructure software, Physical AI systems will require a common software layer that coordinates hardware resources, manages security, enforces permissions, facilitates interoperability, and establishes the fundamental relationship between humans and intelligent machines.
The decisions made during the creation of this layer may shape society for generations.
For this reason, we should begin discussing the development of an open, standardized operating system for Physical AI—one created through broad industry collaboration and designed from its inception to embody transparency, security, accountability, and human-centered values.
History provides a valuable precedent.
When the internet economy began to emerge, no single company owned the operating system of the internet. Instead, an open-source ecosystem developed around Linux and other collaborative technologies.
Linux demonstrated several principles that remain relevant today.
First, open standards accelerate innovation. When developers share a common platform, they can focus on creating differentiated applications rather than repeatedly solving the same foundational problems.
Second, transparency improves security. Contrary to the intuition that secrecy creates safety, open-source software often benefits from continuous review by thousands of engineers, researchers, and organizations worldwide.
Third, open ecosystems reduce dependency on any single vendor. Organizations gain confidence when critical infrastructure is governed by transparent processes rather than the unilateral decisions of one corporation.
Finally, collaborative governance enables long-term sustainability. Linux succeeded not merely because it was technically excellent, but because it created a framework in which competitors could cooperate on shared infrastructure while continuing to compete in products and services.
Physical AI may require a similar approach.
Some may argue that existing operating systems can simply be adapted for robots and autonomous systems.
To some extent, this is already happening. Linux-based systems are widely used in robotics. Various real-time operating systems power industrial automation. Autonomous vehicles rely on complex software stacks built on existing foundations.
However, Physical AI introduces challenges that go far beyond traditional computing.
A search engine can display an incorrect answer.
A robot can accidentally injure a person.
A software bug in a web application may affect data.
A software bug in a physical AI system may affect the real world.
Physical AI systems continuously observe their environments, collect sensory information, move through shared spaces, manipulate objects, and make decisions with direct physical consequences.
As a result, the operating system for Physical AI must address requirements that are not merely technical but deeply social and ethical.
It must manage not only computing resources but also trust.
It must define not only permissions but responsibilities.
It must establish not only interfaces between software components but boundaries between humans and machines.
One of the most important lessons from recent years is that software development itself is changing.
AI coding agents are becoming increasingly capable of reviewing code, identifying vulnerabilities, generating tests, analyzing dependencies, and continuously monitoring software quality.
This development creates a unique opportunity.
Rather than building a Physical AI operating system using traditional methods and later attempting to secure it, we can design an ecosystem in which security is embedded into the development process from the beginning.
Every code contribution could be reviewed not only by human maintainers but also by multiple independent AI agents.
Formal verification tools could continuously evaluate critical safety components.
Automated systems could search for memory vulnerabilities, privilege escalation risks, unsafe hardware interactions, and supply-chain attacks.
Machine-readable policies could ensure that core safety requirements are never violated during development.
In effect, AI could become part of the immune system of the operating system itself.
Open-source development combined with AI-assisted security review may ultimately produce software that is more resilient than many proprietary alternatives.
Without coordination, the Physical AI ecosystem risks becoming fragmented.
Different robotics manufacturers may develop incompatible architectures.
Autonomous vehicles may rely on proprietary interfaces.
Humanoid robots may implement conflicting safety frameworks.
Industrial systems may adopt different trust models.
Such fragmentation would create significant inefficiencies.
Developers would need to repeatedly adapt applications to multiple platforms.
Security researchers would struggle to audit diverse proprietary systems.
Regulators would face difficulties establishing consistent expectations.
Consumers would have limited visibility into how their robots operate.
A common operating system or common foundational platform could provide standardized interfaces for perception systems, actuation systems, safety controls, identity management, privacy controls, communication protocols, and AI model integration.
Standardization does not eliminate competition.
Instead, it creates a foundation upon which competition can flourish.
The internet itself demonstrates this principle. Companies compete intensely while relying on shared standards such as TCP/IP, HTTP, TLS, and DNS.
Physical AI deserves a similar foundation.
No single company should define the future operating system of Physical AI alone.
The stakes are too high.
The technology will influence transportation, healthcare, manufacturing, education, logistics, public safety, elder care, and domestic life.
Accordingly, the development of a Physical AI operating system should be guided by an independent consortium that includes major robotics companies, AI laboratories, automotive manufacturers, hardware providers, academic institutions, security researchers, civil society organizations, and government observers.
Competitors have collaborated successfully before.
Technology companies cooperate on internet standards.
Automobile manufacturers participate in industry safety initiatives.
Semiconductor companies contribute to shared architectures.
The same model can be applied to Physical AI.
Participation would not require companies to abandon proprietary innovation. Organizations could continue developing differentiated hardware, AI models, applications, and services while contributing to a shared infrastructure layer that benefits everyone.
The result would be reduced duplication, improved interoperability, stronger security, and increased public trust.
Perhaps the most important aspect of such a consortium would not be technical.
It would be philosophical.
Physical AI systems will increasingly occupy roles that historically belonged to trusted human assistants.
A household robot may know intimate details of daily life.
An autonomous vehicle may observe private conversations.
A healthcare companion may learn sensitive information about patients.
An elder-care robot may become a constant presence in someone's home.
In many respects, these systems will resemble historical roles such as butlers, chauffeurs, assistants, caregivers, and guardians.
Societies developed expectations for those relationships over centuries.
Confidentiality was expected.
Loyalty was expected.
Discretion was expected.
Trust was essential.
Physical AI systems should embody comparable principles.
The operating system that governs them should include mechanisms that protect confidentiality, limit unnecessary data collection, enforce transparency, and prioritize the interests of the humans they serve.
The question is not merely how robots should function.
The question is how they should behave.
And behavior is ultimately shaped by the values embedded in the systems beneath them.
Privacy should not be treated as a regulatory obstacle added after development.
Instead, privacy should become a core architectural principle.
A Physical AI operating system should provide robust mechanisms for data minimization, local processing, encrypted communication, granular permissions, user control, and transparent auditing.
Users should understand what information is collected, how it is used, where it is stored, and who can access it.
Sensitive activities occurring inside homes, vehicles, workplaces, and healthcare environments should receive especially strong protections.
Trust will become one of the most valuable assets in the Physical AI economy.
Organizations that fail to earn that trust may discover that technical capability alone is insufficient.
Physical AI systems will operate across national borders.
Robots manufactured in one country may be deployed in another.
AI models trained on global data may serve users worldwide.
As a result, a common operating system could also become a platform for international cooperation.
A globally recognized governance framework could help harmonize security expectations, privacy safeguards, interoperability requirements, and safety standards.
While legal systems will remain different, technical standards can provide a common language.
Just as internet protocols enabled global connectivity, Physical AI standards could enable global trust.
Humanity stands at the beginning of the Physical AI era.
The decisions made today will influence not only technology but also social institutions, economic structures, and human relationships for decades to come.
Rather than allowing fragmented proprietary systems to evolve independently, we have an opportunity to establish a common foundation from the start.
An open-source operating system for Physical AI—developed collaboratively by leading robotics and AI organizations, secured through continuous AI-assisted review, standardized through transparent governance, and grounded in shared human values—could become one of the most important infrastructure projects of the twenty-first century.
Its purpose would extend beyond software.
It would represent a collective agreement about how intelligent machines should coexist with humanity.
The operating system of Physical AI would not merely manage processors, sensors, and actuators.
It would help define the rules of engagement between humans and the intelligent systems that increasingly share our world.
That responsibility is too important to be left to any single company.
It is a challenge—and an opportunity—that belongs to all of us.