In 2017, Google published a paper that completely discarded traditional sequential computation, using only Attention (a computation method that assigns different weights to input information) to process text—this architectural choice directly determines the commercial value of long-text processing in today's LLMs.
What this is
In the past, AI used RNNs (Recurrent Neural Networks, an old architecture that processes word-by-word sequentially and is prone to forgetting) to read text, like walking a tightrope—reaching the end meant forgetting the beginning. The attention mechanism instead teaches models to "grasp key points": calculating the relevance between the current word and all contextual words, summarizing information by weight. Its core is the QKV mechanism (Query, Key, Value, similar to search keywords, tags, and actual content), allowing AI to directly stride across to extract needed information, no longer constrained by physical distance. All current mainstream LLMs are based on this Transformer architecture, evolving AI from "goldfish memory" to digesting entire long documents.
Industry view
We note a clear divide in the industry's attitude towards the attention mechanism. Proponents argue it is the cornerstone of modern AI, solving long-range dependency issues and unlocking massive application potential; but what concerns us is that its computational cost grows quadratically with text length—for every word added, the model must calculate its association pairwise with all preceding words. Double the text length, and compute consumption quadruples. This brute-force computation is the fundamental bottleneck keeping LLM inference costs stubbornly high and context windows from expanding freely; purely stacking compute power is not a long-term solution.
Impact on regular people
For enterprise IT: When evaluating LLM long-text capabilities, stay sober—larger context windows mean steeper compute bills; do not blindly chase ultra-long texts. For individual professionals: Understanding that AI retrieves information via keyword matching logic means writing prompts with structured, highly distinct expressions to help the model reduce retrieval difficulty. For the consumer market: Hardware compute will long remain the threshold for AI experiences; smoothly running long-text processing on edge devices still requires high-end chip support.