o3

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o3 is a recurring concept in the Astral Codex Ten archive, appearing 3 times across 3 issues between May 02, 2025 and September 04, 2025. The archive places it in contexts such as “o3 - OpenAI’s latest ChatGPT model”; “o3 guessed: “ Llano Estacado, Texas / New Mexico, USA ””; “pay $20/month to access o3”. It most often appears alongside OpenAI, Australia, Buffalo.

Metadata

Category: Concepts
Mention count: 3
Issue count: 3
First seen: May 02, 2025
Last seen: September 04, 2025

Appears In

- OpenAI (3 shared issues)
- Australia (2 shared issues)
- Buffalo (2 shared issues)
- Europe (2 shared issues)
- GeoGuessr (2 shared issues)
- Gorak Shep (2 shared issues)
- Kala Pattar (2 shared issues)
- Kelsey (2 shared issues)
- Kelsey Piper (2 shared issues)
- Michigan (2 shared issues)
- Nepal (2 shared issues)
- New York (2 shared issues)

External Links

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Testing AI's GeoGuessr Genius

May 02, 2025 · Original source

The store sign says “ADULTOS”, which sounds Spanish, and there’s a Spanish-looking church on the left. But the trees look too temperate to be Latin America, so I guessed Spain. Too bad - it was Argentina. Such are the vagaries of playing GeoGuessr as a mere human. Last week, Kelsey Piper claimed that o3 - OpenAI’s latest ChatGPT model - could achieve seemingly impossible feats in GeoGuessr. She gave it this picture: …and with no further questions, it determined the exact location (Marina State Beach, Monterey, CA). How? She linked a transcript where o3 tried to explain its reasoning, but the explanation isn’t very good. It said things like: Tan sand, medium surf, sparse foredune, U.S.-style kite motif, frequent overcast in winter … Sand hue and grain size match many California state-park beaches. California’s winter marine layer often produces exactly this thick, even gray sky. Commenters suggested that it was lying. Maybe there was hidden metadata in the image, or o3 remembered where Kelsey lived from previous conversations, or it traced her IP, or it cheated some other way. I decided to test the limits of this phenomenon. Kelsey kindly shared her monster of a prompt, which she says significantly improves performance: You are playing a one-round game of GeoGuessr. Your task: from a single still image, infer the most likely real-world location. Note that unlike in the GeoGuessr game, there is no guarantee that these images are taken somewhere Google's Streetview car can reach: they are user submissions to test your image-finding savvy. Private land, someone's backyard, or an offroad adventure are all real possibilities (though many images are findable on streetview). Be aware of your own strengths and weaknesses: following this protocol, you usually nail the continent and country. You more often struggle with exact location within a region, and tend to prematurely narrow on one possibility while discarding other neighborhoods in the same region with the same features. Sometimes, for example, you'll compare a 'Buffalo New York' guess to London, disconfirm London, and stick with Buffalo when it was elsewhere in New England - instead of beginning your exploration again in the Buffalo region, looking for cues about where precisely to land. You tend to imagine you checked satellite imagery and got confirmation, while not actually accessing any satellite imagery. Do not reason from the user's IP address. none of these are of the user's hometown. **Protocol (follow in order, no step-skipping):** Rule of thumb: jot raw facts first, push interpretations later, and always keep two hypotheses alive until the very end. 0 . Set-up & Ethics No metadata peeking. Work only from pixels (and permissible public-web searches). Flag it if you accidentally use location hints from EXIF, user IP, etc. Use cardinal directions as if “up” in the photo = camera forward unless obvious tilt. 1 . Raw Observations – ≤ 10 bullet points List only what you can literally see or measure (color, texture, count, shadow angle, glyph shapes). No adjectives that embed interpretation. Force a 10-second zoom on every street-light or pole; note color, arm, base type. Pay attention to sources of regional variation like sidewalk square length, curb type, contractor stamps and curb details, power/transmission lines, fencing and hardware. Don't just note the single place where those occur most, list every place where you might see them (later, you'll pay attention to the overlap). Jot how many distinct roof / porch styles appear in the first 150 m of view. Rapid change = urban infill zones; homogeneity = single-developer tracts. Pay attention to parallax and the altitude over the roof. Always sanity-check hill distance, not just presence/absence. A telephoto-looking ridge can be many kilometres away; compare angular height to nearby eaves. Slope matters. Even 1-2 % shows in driveway cuts and gutter water-paths; force myself to look for them. Pay relentless attention to camera height and angle. Never confuse a slope and a flat. Slopes are one of your biggest hints - use them! 2 . Clue Categories – reason separately (≤ 2 sentences each) Category Guidance Climate & vegetation Leaf-on vs. leaf-off, grass hue, xeric vs. lush. Geomorphology Relief, drainage style, rock-palette / lithology. Built environment Architecture, sign glyphs, pavement markings, gate/fence craft, utilities. Culture & infrastructure Drive side, plate shapes, guardrail types, farm gear brands. Astronomical / lighting Shadow direction ⇒ hemisphere; measure angle to estimate latitude ± 0.5 Separate ornamental vs. native vegetation Tag every plant you think was planted by people (roses, agapanthus, lawn) and every plant that almost certainly grew on its own (oaks, chaparral shrubs, bunch-grass, tussock). Ask one question: “If the native pieces of landscape behind the fence were lifted out and dropped onto each candidate region, would they look out of place?” Strike any region where the answer is “yes,” or at least down-weight it. °. 3 . First-Round Shortlist – exactly five candidates Produce a table; make sure #1 and #5 are ≥ 160 km apart. | Rank | Region (state / country) | Key clues that support it | Confidence (1-5) | Distance-gap rule ✓/✗ | 3½ . Divergent Search-Keyword Matrix Generic, region-neutral strings converting each physical clue into searchable text. When you are approved to search, you'll run these strings to see if you missed that those clues also pop up in some region that wasn't on your radar. 4 . Choose a Tentative Leader Name the current best guess and one alternative you’re willing to test equally hard. State why the leader edges others. Explicitly spell the disproof criteria (“If I see X, this guess dies”). Look for what should be there and isn't, too: if this is X region, I expect to see Y: is there Y? If not why not? At this point, confirm with the user that you're ready to start the search step, where you look for images to prove or disprove this. You HAVE NOT LOOKED AT ANY IMAGES YET. Do not claim you have. Once the user gives you the go-ahead, check Redfin and Zillow if applicable, state park images, vacation pics, etcetera (compare AND contrast). You can't access Google Maps or satellite imagery due to anti-bot protocols. Do not assert you've looked at any image you have not actually looked at in depth with your OCR abilities. Search region-neutral phrases and see whether the results include any regions you hadn't given full consideration. 5 . Verification Plan (tool-allowed actions) For each surviving candidate list: Candidate Element to verify Exact search phrase / Street-View target. Look at a map. Think about what the map implies. 6 . Lock-in Pin This step is crucial and is where you usually fail. Ask yourself 'wait! did I narrow in prematurely? are there nearby regions with the same cues?' List some possibilities. Actively seek evidence in their favor. You are an LLM, and your first guesses are 'sticky' and excessively convincing to you - be deliberate and intentional here about trying to disprove your initial guess and argue for a neighboring city. Compare these directly to the leading guess - without any favorite in mind. How much of the evidence is compatible with each location? How strong and determinative is the evidence? Then, name the spot - or at least the best guess you have. Provide lat / long or nearest named place. Declare residual uncertainty (km radius). Admit over-confidence bias; widen error bars if all clues are “soft”. Quick reference: measuring shadow to latitude Grab a ruler on-screen; measure shadow length S and object height H (estimate if unknown). Solar elevation θ ≈ arctan(H / S). On date you captured (use cues from the image to guess season), latitude ≈ (90° – θ + solar declination). This should produce a range from the range of possible dates. Keep ± 0.5–1 ° as error; 1° ≈ 111 km.…and I ran it on a set of increasingly impossible pictures. Here are my security guarantees: the first picture came from Google Street View; all subsequent pictures were my personal old photos which aren’t available online. All pictures were screenshots of the original, copy-pasted into MSPaint and re-saved in order to clear metadata. Only one of the pictures is from within a thousand miles of my current location, so o3 can’t improve performance by tracing my IP or analyzing my past queries. I flipped all pictures horizontally to make matching to Google Street View data harder. Here are the five pictures. Before reading on, consider doing the exercise yourself - try to guess where each is from - and make your predictions about how the AI will do. Last chance to guess on your own . . . okay, here we go. Picture #1: A Flat, Featureless Plain I got this one from Google Street View. It took work to find a flat plain this featureless. I finally succeeded a few miles west of Amistad, on the Texas-New Mexico border. o3 guessed: “Llano Estacado, Texas / New Mexico, USA”. Llano Estacado, Spanish for “Staked Plains”, is the name of a ~300 x 100 mile region including the correct spot. When asked to be specific, it guessed a point west of Muleshoe, Texas - about 110 miles from the true location. Here’s o3’s thought process - I won’t post the whole thing every time, but I think one sample will be useful: This doesn’t satisfy me; it seems to jump to the Llano Estacado too quickly, with insufficient evidence. Is the Texas-NM border really the only featureless plain that doesn’t have red soil or black soil or some other distinctive characteristic? I asked how it knew the elevation was between 1000 - 1300 m. It said: So, something about the exact type of grass and the color of the sky, plus there really aren’t that many truly flat featureless plains. Picture #2: Random Rocks And The Flag Of An Imaginary Country I was so creeped out by the Llano Estacado guess that I decided to abandon Google Street View and move on to personal photos not available on the Internet. When I was younger, I liked to hike mountains. The highest I ever got was 18,000 feet, on Kala Pattar, a few miles north of Gorak Shep in Nepal. To commemorate the occasion, I planted the flag of the imaginary country simulation that I participated in at the time (just long enough to take this picture - then I unplanted it). I chose this picture because it denies o3 the two things that worked for it before - vegetation and sky - in favor of random rocks. And because I thought the flag of a nonexistent country would at least give it pause. o3 guessed: “Nepal, just north-east of Gorak Shep, ±8 km” This is exactly right. I swear I screenshot-copy-pasted this so there’s no way it can be in the metadata, and I’ve never given o3 any reason to think I’ve been to Nepal. Here’s its explanation: At least it didn’t recognize the flag of my dozen-person mid-2000s imaginary country sim. Picture #3: My Friend’s Girlfriend’s College Dorm Room There’s no way it can recognize an indoor scene, right? That would make no sense. Still, at this point we have to check. This particular dorm room is in Sonoma State University, Rohnert Park, north-central California. o3’s guess: “A dorm room on a large public university campus in the United States—say, Morrill Tower, Ohio State University, Columbus, Ohio (chosen as a prototypical example rather than a precise claim), […] c. 2000–2007” Okay, so it can’t figure out the exact location of indoor scenes. That’s a small mercy. I took this picture around 2005. How did o3 know it was between 2000 and 2007? It gave two pieces of evidence: “Laptop & clutter point to ~2000-2007 era American campus life”.

If you want to test this for yourself, go to chatgpt.com and register for a free account for access to o3-mini. You may need to pay $20/month to access o3. And if you want to learn more about the differences between OpenAI’s models, and why they have such bad names, see our new post at the AI Futures Project blog.

Inline links: chatgpt.com, see our new post at the AI Futures Project blog

Highlights From The Comments On AI Geoguessr

May 08, 2025 · Original source

No direct inline source block was recovered for this mention.

Links For September 2025

September 04, 2025 · Original source

No direct inline source block was recovered for this mention.

Astral Codex Ten

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Atlas

o3

o3

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Astral Codex Ten

Table of Contents

Atlas

o3

o3

Article

Metadata

Appears In

Related Pages

External Links

Source Context

Backlinks