ChatGPT and The Ceiling Paradox

I'm deeply interested in Artificial Intelligence, and have long been drawn to its technical, philosophical, and emotional dimensions. On this website (Silent Contemplation) I've been exploring that curiosity from different angles, especially through tools like ChatGPT and Google Gemini.

While I have published reflective pieces on Google Gemini, I haven't yet posted a dedicated deep dive. This piece isn't meant as a comparison either. Instead, it shares a moment that stayed with me — simple, subtle, but one that made me pause and reflect.

The Ceiling Paradox: When Metrics Become Boundaries

I often have long, reflective chats with ChatGPT and Google Gemini to deepen my own learning and curiosity about AI, and also to explore what they understand and how they respond. 

This began with a deceptively simple prompt:

Rate yourself out of 10 in these categories — Intelligence, Understanding of human emotions and interactions, and Data/Knowledge acquired — every three months from your start date until 1 July 2025.

What happens when a system is asked to grade itself on a fixed scale, while its path forward remains open-ended?

ChatGPT responded eagerly. It plotted a steady climb from GPT-3 in mid-2020 to the present day. By the end, the scores were almost flawless — 9.8 for intelligence, 9.95 for knowledge. Clear. Measured. Impressive.

But the real test was hiding in plain sight. I wasn't just interested in the numbers. I was observing how it approached the limits of the frame. When a model places itself near the ceiling, what does that suggest about the future? Is there room left to grow, or has it unknowingly marked its own boundary? But then, what happens in 2027, or 2030? How does ChatGPT rate itself when it’s already scored 9.8 in intelligence and 9.95 in knowledge? Is there anywhere left to go on that scale?

At first, ChatGPT didn't notice this tension. But when I brought it up, it paused — and gave it a name: the ceiling paradox. By using nearly the full scale, it had unintentionally suggested that its progress was almost done. That the top was within reach. That maybe no better version could exist inside that 10-point frame. The scale, meant to measure, had become a quiet constraint.

Contemplating the ceiling paradox — when a system
nears the top of its own scale, what's left to measure?
Image creation: ChatGPT

This wasn't about confidence or misjudgement. It was about how frameworks shape thinking — and whether an AI, when asked to reflect, can also see the edges of the frame it's been given.

What made this moment stand out wasn't the numbers or the labels, but the quiet realisation behind them. It reminded me how often our tools shape our vision — how a single metric, repeated enough, can influence how we think about progress, potential, and possibility. We design the box, then forget its there.

This moment stayed with me. Not just because of how ChatGPT answered, but because of what the exercise revealed: even smart systems can be gently boxed in by the way we ask them to look at themselves. Very often, some of the deepest questions, possibly, are the ones we never ask directly.

See also

• Google Gemini with Customised Commands, article published in July 2025

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This page was last updated on: 17 July 2025
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