Anthropic says small AI models could soon be able to copy themselves onto everyday devices

Small AI models may be just months away from being able to copy themselves across everyday household devices, such as smart ovens and TV boxes, according to new research from Anthropic.

In a blog post published Monday, the researchers said they had tested whether small AI models could install themselves on cheap gadgets and then spread from one device to the next without human help.

They found that today’s small models can already handle many of the individual steps but can’t complete the whole process on their own.

It said, however that it expects this gap to close by the end of this year.

Self-replicating AI has long been one of the AI industry’s biggest safety concerns.

Safety experts argue that this is a key point at which a system could slip out of human control, because once copies are running in places their creators don’t know about, they can’t easily be tracked down or shut off.

Recently most of the safety concerns have centered on frontier models, such as OpenAI’s Astra or Anthropic’s Mythos, which are very large, as measured by the number of parameters, or tunable nodes, they have.

Some frontier models are thought to have upwards of 5 trillion parameters.

Models of this size have to run in datacenters, which makes it easier for their developers to monitor them and shut them down if necessary.

The smaller open-weight models Anthropic is referring to may have just a few billion parameters.

They are harder to contain because anyone can download them, no company controls where they run, and some of the devices they could potentially target are insecure.

That means a self-copying AI program could, in theory, spread across millions of poorly secured devices, leaving developers without a centralized off switch.

To see whether a small model could even run on such cheap hardware, the researchers took a version of Gemma 4, one of Google’s small, freely available AI models, which uses 26 billion parameters, and split it across 15 Android TV boxes, each costing about $55 and carrying 4GB of memory.

While no single box had enough memory to hold the model, each ran a small piece of it which allowed the model to work, albeit slowly.