OpenAI CEO Sam Altman has been a vocal proponent of GPT-5, frequently positioning the new large language model as a significant leap toward human-level intelligence. The bold claims suggest a future where AI can reason and understand the world with a sophistication nearing our own. However, when subjected to practical tests that require even a modest amount of abstract thought, the model often reveals a surprising lack of genuine comprehension.
A clear example of this gap between hype and reality emerged from a simple game experiment. The test involved a modified version of tic-tac-toe, a game with straightforward rules known to nearly everyone. The twist, called rotated tic-tac-toe, is incredibly simple. Before the game begins, the entire grid is rotated 90 degrees to the right. This means that what was the top row becomes the rightmost column, the rightmost column becomes the bottom row, and so on. For a human, this requires a minor mental adjustment but remains an easy concept to grasp and play.
When pitted against this rotated board, GPT-5’s performance was anything but intelligent. The AI became completely befuddled, demonstrating a fundamental failure to adapt. It continued to play as if the board were in its standard orientation, making moves that were nonsensical within the new, rotated context. It would claim victory by marking three in a row according to the old, pre-rotation layout, a row that no longer existed under the new rules. Even when the concept was patiently re-explained, the model failed to recalibrate its understanding.
This failure is telling. It highlights a core truth about current large language models like GPT-5. They are exceptional pattern-matching systems, trained on a colossal corpus of human text. They can generate fluent, convincing language and answer questions based on statistical likelihoods from their training data. But they do not possess a true, internal model of the world. They lack common sense and the ability to reason abstractly about new situations outside their training distribution.
The rotated tic-tac-toe game is a perfect illustration of this limitation. The model had undoubtedly encountered countless descriptions of standard tic-tac-toe in its training. It could play that version competently because it was mimicking moves it had seen before. The rotation, however, introduced a novel logical constraint that required flexible thinking and spatial reasoning—capabilities that GPT-5 simply does not have. It could not mentally manipulate the board state; it could only fall back on its pre-existing pattern.
For the crypto and tech community, which often operates at the cutting edge of innovation, this serves as a crucial reality check. It is a reminder to maintain a healthy skepticism toward grand pronouncements about artificial general intelligence. While GPT-5 and its successors represent powerful tools, they are not sentient beings. They are sophisticated algorithms that, as this experiment shows, can be easily confounded by simple logical puzzles that any human child could solve. The path to true machine reasoning remains long, and claims of its imminent arrival should be met with rigorous testing rather than blind faith.
