The Model Hardware Standard (MHS) introduced by Anthropic is designed to establish a unified, readable interaction language between AI assistants and physical hardware, thereby lowering the technical threshold for machine operation and accelerating AI deployment in high-value scenarios such as manufacturing, scientific research, and robotics. Currently available to developers as a research preview, this technology, officially named the Model Hardware Standard (MHS), is primarily targeted at the scientific, robotics, and manufacturing sectors, allowing relevant personnel to test Claude’s real-world interaction capabilities and establish necessary safety safeguards before widespread deployment.
The core function of MHS is to store structured information that AI assistants need to understand hardware operations, thus eliminating the need to consult paper manuals or rely on the tacit knowledge of a few experts. Taking a factory robotic arm as an example, suppliers can specify safety boundaries within MHS for AI operation of heavy robotic arms, including limiting movement speed or motion angles, to ensure that operations remain controllable and safe.
As AI technology continues to achieve breakthroughs, both the industry and investment community are placing increasing emphasis on its application potential in the physical world, with manufacturing, automated robotics, and scientific experimentation becoming key areas of focus. A report released by Barclays earlier this year predicts that by 2035, AI-driven robotics and autonomous systems could form a market worth trillions of dollars. At the same time, technology companies such as Google (GOOGL), OpenAI, and Nvidia (NVDA) are also actively developing AI models and software for robotics scenarios.
According to a statement released by Anthropic on Thursday, interested industry parties can now join a waitlist to trial MHS. The company plans to open-source this framework after the preview phase concludes, at which point any hardware manufacturer will be able to build their own MHS specifications. However, Anthropic has not disclosed a specific timeline for the open-source release.
Jonah Cool, one of the heads of collaboration and deployment in Anthropic’s life sciences division, stated that many scientific research endeavors are difficult to carry out precisely because equipment is either inaccessible or too demanding to operate. With MHS, Claude can serve as an assistive tool for scientists, helping them use specialized equipment at an expert level.
Previously, Anthropic had already launched and promoted a standard for software applications—the Model Context Protocol (MCP)—which enables AI assistants to interact more seamlessly with third-party applications, allowing chatbots like Claude and ChatGPT to connect with services such as Gmail, Google Calendar, and Slack. Alek Kemeny, a member of Anthropic’s technical team, pointed out that just as MCP has brought convenience to software, MHS will deliver the same value to the hardware world, with its application potential being particularly prominent in environments such as scientific laboratories, which often house dozens or even hundreds of devices.
Currently, Anthropic has partnered with multiple industry collaborators to jointly test this hardware framework, including Amazon Web Services (AWS), life sciences and diagnostics company Danaher (DHR), AI open-source community Hugging Face, and Raspberry Pi. In a promotional video released by Anthropic, a scientist from biotechnology company Genentech sends a PDF document of an experimental design to Claude, and the AI assistant then autonomously completes the execution of the experiment using hardware already configured with MHS specifications.
