AI Outside the Screen: How Anthropic Is Connecting Claude to Real Equipment
Much of a scientist’s time is not spent thinking about theories — it’s spent assembling experiments. Calibrating equipment, aligning sensors, debugging hardware and software: this stage consumes, according to Anthropic’s internal estimates, about 80% of a researcher’s time. It is essential work, but has little direct relation to the scientific question being investigated.
This bottleneck precisely motivated the creation of the Model Hardware Standard (MHS), a new protocol developed by Anthropic to enable the AI Claude to operate physical equipment directly — from laboratory microscopes to robotic arms. The idea was born from collaboration between Anthropic’s team and neuroscientist Arco Bast and is already being tested in partnership with companies such as Danaher and Genentech.
The problem: each piece of equipment speaks a different language
Arco Bast studies how memories form in the brain in real time, using a custom-built microscope with multiple lasers and components that need to be perfectly synchronized. The main challenge in assembling this type of experiment is not scientific — it’s technical: each lab device communicates via its own protocol, making it extremely laborious to have different equipment "talk" to each other.
Bast developed an approach capable of solving this problem generically, allowing any device to communicate with any other. Watching this system operate live, the Anthropic team realized that the solution was not only for that specific lab — it could become a standard to enable an AI to run any scientific experiment in the world.
How the Model Hardware Standard works in practice
From that insight, Anthropic and Bast began developing a standardized way for AI to interact with hardware. The initial tests involved the lab’s own microscope, but the protocol was soon tested on other types of devices, including a robotic arm.
One of the most important aspects demonstrated in the tests was safety: by defining a safe movement limit for the robotic arm, the system automatically rejected a command trying to move the equipment beyond the allowed area. This shows that the protocol not only executes commands but also respects predefined operational constraints — an essential requirement for AI use with expensive and sensitive equipment.
In another test, when asked to operate the robotic arm without prior guidance, the system managed to build a functional solution within minutes — something the team found impressive given no specific context about that application was provided.
Testing with real laboratory equipment
To validate the protocol beyond a controlled environment, Anthropic partnered with Danaher to connect Claude to a Leica microscope, widely used in professional labs. Since the model had never interacted with this kind of application before, the process involved trial and error: initially the system tested configurations "blindly" to capture a focused image, requiring adjustments and human guidance.
Over time, however, the behavior evolved significantly. When given the command to increase magnification, the system showed it recognized the risk of colliding the equipment against the sample — a caution that normally requires prior experience with that specific microscope.
This advancement enabled an even more ambitious test: asking the AI to adjust settings, focus the image, identify elements in the sample, and even apply false colors to highlight specific structures such as cell walls. According to the team, the result was transformative for a single day of work.
From tracking algae to accelerating PhD research
Another demonstrated experiment involved tracking moving organisms under the microscope — a task that traditionally requires a researcher to manually follow the sample for hours. The team asked the AI to write a program capable of automatically tracking the moving organism.
After some adjustments, the system not only tracked the organism but also built a visual interface to monitor the process in real time — allowing continuous observation of the tracking for several minutes without constant manual intervention.
The practical impact of this automation is significant: tasks that now require months or even years of manual system configuration for a PhD project could be reduced to weeks, freeing the researcher to focus on the scientific question itself rather than the infrastructure needed to answer it.
Applications in the pharmaceutical industry
The potential of the Model Hardware Standard is not limited to neuroscience. In partnership with Genentech, a pharmaceutical company developing medicines for serious diseases, Anthropic tested the protocol in drug discovery processes — a stage that often requires testing thousands, hundreds of thousands, or even millions of molecules until finding the effective one.
One of the tests involved identifying technical problems during automated experiments, such as the presence of bubbles in sample wells, which compromise the correct amount of liquid transferred. Operating in a closed loop — execute, evaluate the result, and adjust parameters — the AI was able to detect this type of failure and adjust the process to improve execution quality, attempt after attempt.
According to the team involved, this is a significant milestone: the first practical time that an AI interacts directly with the physical world within a real drug discovery process.
Why this matters beyond laboratories
The Model Hardware Standard reinforces a principle that applies to any sector reliant on specialized equipment, not just science: when different devices can communicate through a common protocol, artificial intelligence ceases to be just a text interface and begins to directly operate physical processes — running, monitoring, and adjusting equipment in real time.
It’s the same logic behind industrial AI applications discussed elsewhere: existing cameras, sensors, and monitoring systems can be connected to an AI capable of interpreting data and acting on it. The Model Hardware Standard takes this principle a step further, allowing the AI itself to operate the equipment, not just analyze the data it produces.
Frequently asked questions about the Model Hardware Standard
What is the Model Hardware Standard?
It is a protocol developed by Anthropic, in collaboration with neuroscientist Arco Bast, that enables Claude AI to communicate and directly operate physical equipment, such as microscopes and robotic arms, regardless of each manufacturer’s proprietary protocol.
Is this protocol safe to operate expensive and sensitive equipment?
Tests demonstrated safety mechanisms, such as predefined movement limits that the system automatically respects, rejecting commands that exceed areas or parameters considered safe for the equipment.
Which sectors can benefit from this technology?
Initial tests cover neuroscience and the pharmaceutical industry, but the concept applies to any field depending on specialized hardware, including biology, chemistry, engineering, and various industrial segments.
Conclusion
The Model Hardware Standard represents a shift in perspective on the role of artificial intelligence: instead of acting only as a conversational interface or data analysis tool, AI begins to interact directly with the physical world, operating the very instruments that generate scientific knowledge. For researchers, this could mean months of technical work compressed into weeks. For companies relying on complex physical processes, it offers a clear sign of where AI is heading: from the screen to the equipment.