What is Physical AI? Why is the accountability issue more urgent than imagined?

Physical AI refers to artificial intelligence systems that not only generate text or images on a screen but can also perform physical actions in the real world —— encompassing scenarios like factory robotic arms, autonomous vehicles, and surgical assistant robots. When these systems make mistakes, the complexity of assigning responsibility far exceeds that of traditional software failures.

The focus of today's AI competition has shifted. The question is no longer whether a model can pass a specific benchmark test, but whether autonomous systems can be deployed in factories, vehicles, or hospitals without creating risks that cannot be traced back to accountability.

The following three real scenarios clearly illustrate the issue at hand:

At 3 AM, an AI trading bot executed an unauthorized leveraged order; in a disconnected disaster zone, a surgical robot completed a cutting action; on the production line, a humanoid robot made a contact decision that led to an injury. These three scenarios point to the same systemic failure —— there is no credible record to prove "who" made "what" decision and when, nor is there any evidence to verify whether that decision was within the established authorization boundaries.

Why can't existing audit tools solve this problem?

Current audit tools are designed to serve software systems operated by humans and cannot restore the complete decision chain of autonomous AI agents. This is not a technological limitation of the tools, but a fundamental architectural issue.

A deeper challenge lies in: any accountability system that relies on a single company's server inherits that company's own interests and risks. Logs may be lost due to system failures, rewritten due to version updates, or may disappear under legal pressure. When insurance companies, regulators, and robot manufacturers have differing accounts of the same incident, none have sufficient reason to trust each other's server records as a neutral basis for judgment.

AxBlade attempts to respond to this issue in the form of 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-reports.

About this event: This is a working session, not a product launch

AxBlade positions this event as a working session, rather than a product launch, token event, or a platform discussing "How AI will change everything."

The core purpose of the agenda design is to allow practitioners who have already deployed autonomous systems in production environments to showcase and discuss real technology choices and business decisions —— including the actual challenges encountered during deployment, unresolved compliance issues, and prioritization decisions for infrastructure development.

Expected attendees include AWS, NVIDIA, Y Combinator, Crypto.com, Roche, and Pfizer, representing various fields such as cloud infrastructure, robotics technology, healthcare, and institutional finance. Participation is limited and requires an application review.