What is Physical AI? Why is the issue of accountability more urgent than we might think?

"Physical AI" refers to artificial intelligence systems that do more than just generate text or images on a screen; they are capable of performing physical actions in the real world—including factory robotic arms, autonomous vehicles, and surgical assistance robots. When these systems malfunction, determining liability is far more complex than with traditional software failures.

The focus of today’s AI competition has shifted. The question is no longer whether a particular model can pass a certain benchmark test, but whether autonomous systems can be deployed in factories, vehicles, or hospitals without creating risks that cannot be held accountable.

The following three realistic scenarios clearly illustrate the problem:

At 3 a.m., an AI trading bot executed an unauthorized leveraged order; in a disaster zone with no internet connection, a surgical robot performed a surgical incision; On a production line, a humanoid robot made a contact decision that resulted in a worker being injured. These three scenarios point to the same systemic failure—there is no reliable record to prove “who” made “what” decision at what time, nor is there any evidence to verify whether that decision fell within established authorization boundaries.

Why can't existing auditing tools solve this problem?

Existing auditing tools were originally designed to serve software systems operated by humans and are unable to reconstruct the complete decision-making chain of autonomous AI agents. This is not a technical limitation of the tools, but rather a fundamental architectural issue.

The deeper challenge lies in the fact that any accountability system reliant on a single company’s servers inherits that company’s own interests and risks. Logs may be lost due to system failures, altered during software updates, or even deleted under legal pressure. When insurance companies, regulators, and robot manufacturers offer conflicting accounts of the same incident, none of the parties has sufficient reason to trust the other’s server records as a basis for a neutral ruling.

AxBlade attempts to address this issue through a public blockchain: an open, permissionless execution environment where proof of behavior is anchored by cryptographic finality, rather than relying on any organizational commitments or self-declarations.

About this event: This is a working meeting, not a press conference.

AxBlade has positioned this event as a working session, rather than a product launch, a token sale, or a platform for general discussions about how "AI will change everything."

The primary objective of the agenda is to enable professionals who have already deployed autonomous systems in production environments to present and discuss real-world technical choices and business decisions—including practical challenges encountered during deployment, unresolved compliance issues, and prioritization of infrastructure development.

Expected attendees include AWS, NVIDIA, Y Combinator, Crypto.com, Roche, and Pfizer, representing a wide range of sectors such as cloud infrastructure, robotics, healthcare, and institutional finance. Participation is limited and subject to application and review.