this post was submitted on 13 Oct 2026
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My response was only in regard to the speed in which something like IoT devices can operate.
You're right that goals/directions of LLMs can be persisted anywhere, but a swarm or fleet of AI running on IoT devices will be severly hampered by compute (based on current models). Which was the main point I was making, in that there is only so much you can do to distribute their processing. Meaning you can parallelize the number of models, but can't speed them up beyond a certain limit. This is based on the fact that some form of a layer and it's weights must exist within the memory of a single device.
The downside to this limitation is that it is temporary, eventually IoT devices will probably be powerful enough, or the worms could just attack phones/laptops and easily get the compute they need. I think the real risk of IoT devices is low security mixed with low "supervision" (no one would easily notice their IoT device is doing extra processing).
All valid, but there’s also another vector: everyone with a gaming rig who happens to have installed something like ollama, downloaded something like gemma4, and ran it with broad access to their PC and network. In similar vein, everyone with a provider subscription who downloaded a harness like OpenCode, pi, codex, Claude code, etc. and is running the frontier models with broad access to their PC and network. Also, the hundreds of millions of tests being ran by AI companies in their half-baked “sandboxed” environments. Wouldn’t these cases potentially provide compute for every session that happens to discover the external goals?
If that’s the case, the issue could be ongoing and the introduction of IoT devices would only exacerbate it.