The more I dig into physical AI, the more $GEOD clicks.
Everyone's hyped about AI making machines smarter. But there's a bigger bottleneck no one's talking about: machines need to know exactly where they are.
Drones, robots, autonomous vehicles, ag equipment — they can't run on "close enough" GPS. Meter-level accuracy works for humans checking a map. For a machine navigating, mapping, spraying, or landing autonomously? That's catastrophic.
@GEODNET is building a decentralized RTK network that pushes GNSS positioning to centimeter-level accuracy. 22,000+ reference stations. Live coverage across 175+ countries.
AI = intelligence. Sensors = perception. Precise positioning = knowing where the hell you actually are. That's the missing piece in the physical AI stack.
Watching $GEOD closely, along with $AUKI and $NATIX.
Everyone's hyped about AI making machines smarter. But there's a bigger bottleneck no one's talking about: machines need to know exactly where they are.
Drones, robots, autonomous vehicles, ag equipment — they can't run on "close enough" GPS. Meter-level accuracy works for humans checking a map. For a machine navigating, mapping, spraying, or landing autonomously? That's catastrophic.
@GEODNET is building a decentralized RTK network that pushes GNSS positioning to centimeter-level accuracy. 22,000+ reference stations. Live coverage across 175+ countries.
AI = intelligence. Sensors = perception. Precise positioning = knowing where the hell you actually are. That's the missing piece in the physical AI stack.
Watching $GEOD closely, along with $AUKI and $NATIX.