The following is a guest post and opinion piece from Markus Levin, co-founder of XYO.
Global AI spending is expected to reach $1.5 trillion by the end of 2025, and robotics is rising along with it. Although robots now move and behave much like humans, most still break down when placed in real-world environments. Robots might carry boxes in a quiet lab or freeze in a crowded warehouse. The core problem is not the hardware, but the data, and the fact that what the machine is sensing cannot be easily verified.
Humans are constantly adjusting their perceptions. Most of the time we rely on vision, but when something doesn’t feel right we switch to balance or sound. AI models have no such instincts. Even top models experience hallucinations or make factual errors about a third of the time. They process vast amounts of information but do not evaluate it.
Robots won’t reach true autonomy unless they have a way to score, challenge, and accurately rank input internally, rather than trusting everything at face value. It starts with a network of IoT devices, sensors, and nearby robots that share what they sense. Once a robot can compare its view to dozens of other devices, it will eventually be able to ask and answer simple questions: Are other people seeing the same thing?
Robots surprise us… when we give them what they need
Connecting LLM to robots seems promising, but it is not enough. We’ve seen robots misunderstand instructions, misinterpret their environments (sometimes with disastrous results), and react with off-base reasoning when they’re unsure. They lack the grounding signals that help them understand what is real.
Robots need structures that filter bad data and lift signals that fit the environment. They need feedback loops that work just like we do, and ideally even faster.
Blockchain is eyes and brains, consensus is evaluation
This is where blockchain comes into play. Blockchain has the unique ability to create a shared record of sensor data from devices operating in the same physical space. However, unlike traditional systems, blockchain does not require processing by a central authority to reach accurate conclusions, but instead operates based on a set of shared, predetermined principles.
Blockchain is the key to autonomy. Rather than each robot relying solely on its own sensors, individual units can compare measurements from many sources. Ratings are handled by a consensus system. Score signals for consistency and relevance, and adjust scoring in real-time as conditions change.
Once perception becomes a shared system, robots will finally have the internal checks they lacked. They are able to determine what is trustworthy, discard what is not, and construct a worldview that is more vivid, more grounded, and more human, but one that is enhanced and expanded in ways that we cannot even fully imagine.
Beyond the human brain: How blockchain improves feedback loops
Humans aren’t perfect. We forget, we misjudge, we get distracted. Robots inherit these weaknesses, and their limited cognitive abilities make them even more fallible. But if we give them a layer of validation that never decays, supported by sensors around them, they gain something we don’t have. It’s not just an individual, but a memory and perspective that can grow infinitely, powered by a network of devices that all play by the same rules.
Robots use collective models built from thousands of viewpoints to create a broader and more accurate image of the world than the human nervous system can manage. True autonomy doesn’t come from a more powerful motor or a better frame. It comes from trusted data and the ability to verify it at digital speed.
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