The attention bottleneck
In AI, “attention” is a term for a software technique that determines which words in a text are most relevant to understanding each other. Those relationships map out context, and context builds meaning in language. For example, in the sentence “The bank raised interest rates,” attention helps the model establish that “bank” relates to “interest rates” in a financial context, not a riverbank context. Through attention, conceptual relationships become quantified as numbers stored in a neural network. Attention also governs how AI language models choose what information “matters most” when generating each word of their response.
Calculating context with a machine is tricky, and it wasn’t practical at scale until chips like GPUs that can calculate these relationships in parallel reached a certain level of capability. Even so, the original Transformer architecture from 2017 checked the relationship of each word in a prompt with every other word in a kind of brute force way. So if you fed 1,000 words of a prompt into the AI model, it resulted in 1,000 x 1,000 comparisons, or 1 million relationships to compute. With 10,000 words, that becomes 100 million relationships. The cost grows quadratically, which created a fundamental bottleneck for processing long conversations.
Although it’s likely that OpenAI uses some sparse attention techniques in GPT-5, long conversations still suffer performance penalties. Every time you submit a new response to ChatGPT, the AI model at its heart processes context comparisons for the entire conversation history all over again.
Of course, the researchers behind the original Transformer model designed it for machine translation with relatively short sequences (maybe a few hundred tokens, which are chunks of data that represent words), where quadratic attention was manageable. It’s when people started scaling to thousands or tens of thousands of tokens that the quadratic cost became prohibitive.
