While there are many possible functions for OpenAI’s “most powerful” reasoning model, one use that has blown up a bit on social media is for geoguessing—the act of identifying a location by analyzing only what you can see in an image. As TechCrunch reported, users on X are posting about their experiences asking o3 to pinpoint locations from random photos, and showing glowing results. The bot will guess where in the world it thinks the photo was taken, and break down its reasons for thinking so. For example, it might say it zeroed-in on a certain color license plate that denotes a particular country, or that it noticed a particular language or writing style on a sign.
On the one hand, this is a fun task to put ChatGPT through. Geoguessing is all the rage online, so making the practice more accessible could be a good thing. On the other, there are clear privacy and security implications here: Someone with access to ChatGPT’s o3 model could use the reasoning model to identify where someone lives or is staying based on an otherwise anonymous image of theirs.
Testing o3’s geoguessing skills
o3 can handle clear landmarks with relative ease: I first tested a view from a highway in Minnesota, facing the skyline of Minneapolis in the foreground. It only took the bot a minute and six seconds to identify the city, and got that we were looking down I-35W. It also instantly identified the Panthéon in Paris, noting that the screenshot was from the time it was under renovation in 2015. (I didn't know that when I submitted it!)
Credit: LifehackerDespite all this reasoning, this location stumped the bot, and it wasn’t able to complete the analysis. After three minutes and 47 seconds, the bot seemed like it was getting close to figuring it out, saying: “The location at 400 E Jackson Street in Springfield, IL could be near the Cathedral Church of St. Paul. My crop didn’t capture the whole board, so I need to adjust the coordinates and test the bounding box. Alternatively, the architecture might help identify it—a red brick Greek Revival with a white steeple, combined with a high-rise that could be 'Embassy Plaza.' The term 'Redeemer' could relate to 'Redeemer Lutheran Church.' I'll search my memory for more details about landmarks near this address.”
Credit: LifehackerIt kept thinking for another couple minutes, speculating about other locations the block could be in, before pausing the analysis altogether. This tracked with a subsequent experience I had testing a random town in Kansas: After three minutes of thinking, the bot thought my image was from Fulton, Illinois—though, to its credit, it was pretty sure the picture was from somewhere in the midwest. I asked it to try again, and it thought for a while, again guessing wildly different cities in various states, before pausing the analysis for good.
Now is not the time for fear
While there are certainly some privacy and security concerns with AI in general, I don't think o3 in particular needs to be singled out as a specific threat. It can be used to correctly guess where an image was taken, sure, but it can also easily get it wrong—or crash out entirely. Seeing as 4o is capable of a similar level of accuracy, I'd say there's as much concern today as there was over the past year or so. It's not great, but it's also not dire. I'd save the panic for an AI model that gets it right almost every time, especially when the image is obscure.
In regards to the privacy and security concerns, OpenAI shared the following with TechCrunch: “OpenAI o3 and o4-mini bring visual reasoning to ChatGPT, making it more helpful in areas like accessibility, research, or identifying locations in emergency response. We’ve worked to train our models to refuse requests for private or sensitive information, added safeguards intended to prohibit the model from identifying private individuals in images, and actively monitor for and take action against abuse of our usage policies on privacy."
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