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The following is a guest post and opinion from Markus Levin, Co-Founder of XYO.

Global AI spending is expected to hit $1.5 trillion by the end of 2025, and robotics is rising with it. Robots now move and behave in ways that almost feel human, but most still fall apart when they’re placed in real environments. A robot might carry a box in a quiet lab, then freeze in a crowded warehouse. The core problem isn’t the hardware — it’s data, and the fact that machines can’t easily verify what they’re sensing.

Humans constantly adjust our perception. We rely on sight most of the time, but we’ll switch to balance or sound when something feels off. AI models don’t have that instinct. Even top models still hallucinate or produce factual errors about a third of the time. They process huge amounts of information, but they don’t evaluate it.

Robots won’t reach real autonomy until they have a way to score, challenge, and accurately internally rank their inputs instead of trusting everything at face value. That starts with a network of IoT devices, sensors, and nearby robots that share what they’re sensing. When a robot can compare its view with dozens of other devices, it can finally ask — and answer — a simple question: do others see the same thing?

Connecting an LLM to a robot sounds promising, but it isn’t enough. We’ve seen robots misinterpret instructions, interpret their environment incorrectly (sometimes disastrously), or respond with off-topic reasoning when they’re unsure. They’re missing the grounding signals that help them understand what’s real.

Robots need a structure that filters out bad data and lifts up the signals that match the environment. They need a feedback loop that works like ours — and ideally, even more quickly.

This is where blockchain comes in. It’s uniquely capable of creating a shared record of sensor data from devices operating in the same physical space. But unlike conventional systems, blockchain does not require processing by a central authority to arrive at accurate conclusions, instead operating on a set of shared, predetermined principles.

Blockchain is the key to autonomy. Instead of each robot relying only on its own sensors, individual units can compare readings across many sources. Consensus systems handle the evaluation. They score signals for consistency and relevance, and when conditions change, the scoring adjusts in real time.

Once perception becomes a shared system, robots will finally get the internal checks they’ve been missing. They can judge what’s reliable, discard what isn’t, and build a livelier, more grounded, more human view of the world — but enhanced and expanded in ways we can’t even fully imagine.

Humans aren’t perfect. We forget, misjudge, and get distracted. Robots inherit these weaknesses and, with their limited perception, are even more fallible. But if you give them a verification layer that never decays, supported by sensors all around them, they gain something we don’t have — a memory and perspective that can grow indefinitely, fed by more than just an individual, but rather a network of devices all operating by the same rules.

With a collective model built from thousands of viewpoints, robots create a world image that’s wider and more accurate than anything a human nervous system can manage. Real autonomy won’t come from stronger motors or better frames. It will come from trustworthy data and the ability to verify it at digital speeds.

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