While technically you're right about the rules of GeoGuessr as a game, I think this misses the more interesting and potentially concerning implications.
IMO, whether or not it's "cheating" at GeoGuessr is less important than what this capability represents: an AI system that can extract specific details from images, determine what's significant, and use that information to precisely locate a photo. That's impressive regardless of method.
The implications extend far beyond the game. Consider its potential use in automating OSINT tasks. An AI agent running o3 could analyze publicly posted photos (e.g., on Twitter, Instagram), identify locations from background details, and aggregate this data. Within an hour or two, this could build a detailed location profile (home, work, frequented places) for an individual - something that previously required considerable manual effort and expertise. While human analysis would likely still be needed, this significantly lowers the barrier for large-scale analysis.
This ability to extract location clues from images - whether shop names, signs, or landscapes - has significant real-world implications, particularly for automating OSINT tasks. The potential for large-scale location analysis from public photos is the more profound point, regardless of whether one specific method breaks game rules. That capability is what deserves attention.
IMO, whether or not it's "cheating" at GeoGuessr is less important than what this capability represents: an AI system that can extract specific details from images, determine what's significant, and use that information to precisely locate a photo. That's impressive regardless of method.
The implications extend far beyond the game. Consider its potential use in automating OSINT tasks. An AI agent running o3 could analyze publicly posted photos (e.g., on Twitter, Instagram), identify locations from background details, and aggregate this data. Within an hour or two, this could build a detailed location profile (home, work, frequented places) for an individual - something that previously required considerable manual effort and expertise. While human analysis would likely still be needed, this significantly lowers the barrier for large-scale analysis.
This ability to extract location clues from images - whether shop names, signs, or landscapes - has significant real-world implications, particularly for automating OSINT tasks. The potential for large-scale location analysis from public photos is the more profound point, regardless of whether one specific method breaks game rules. That capability is what deserves attention.