这篇论文提出了一个叫Transformer的架构。它解决的核心问题是：AI在处理一段文字时，应该怎么"读"。
在Transformer之前，AI处理文字的方式是一个词一个词按顺序读，读到最后一个词，可能已经忘了第一个词说了什么。这就像你看一本书，只能从左到右一个字一个字地看，不能回头翻，效率极低。
Transformer改变了这一切。它让AI在处理文字时，能"同时看到"整段话里所有的内容，然后判断哪些词和哪些词之间关系最紧密。就像一个人拿到一篇文章，先扫一眼全貌，再重点看关键段落，而不是逐字逐句地硬读。
举个例子，当你说"那只猫坐在垫子上，因为它很舒服"时，人类能理解"它"指的是猫，不是垫子。Transformer让AI也具备了这种"一目十行、抓住关键"的能力。
这个看似简单的改进，打开了一扇通往新世界的大门。
从GPT到ChatGPT：大语言模型的狂飙
2018年，基于Transformer架构的第一个大语言模型GPT-1诞生了。GPT三个字母分别代表生成式（Generative）、预训练（Pre-trained）、Transformer，它开创了一种全新的AI范式：先在互联网的海量文本上"预习"语言规律，再针对具体任务做微调。
此时的大语言模型，能力有限，影响范围还仅限于学术圈和科技圈。但随后的几年，发展速度开始让人目不暇接。
2019年，GPT-2发布，能生成像样的文章段落。
2019年，Google同期发布BERT。虽然写文章的能力不如GPT，但在理解语言含义上变得更强大，搜索引擎的准确率因此大幅提升。
2020年，GPT-3发布，参数规模达到1750亿，写出来的文章已经让人分不清是人写的还是AI写的。这时候，已经有人开始讨论"AI会不会取代作家"了。
2021到2022年，国内开始跟进。 百度文心、阿里通义、讯飞星火相继布局，大模型这个赛道开始热闹起来。
然后，就是那个改变一切的时刻。
2022年11月30日，ChatGPT横空出世。
5天100万用户。2个月1亿月活用户。这个增长速度，在人类互联网历史上前所未有。
为什么是ChatGPT？ 因为它做了之前所有模型都没做到的一件事：把AI的能力包装成了一个普通人直接能用的产品。你不需要懂技术，不需要学编程，只需要像跟朋友聊天一样打字，AI就能回答问题、写文章、写代码、翻译、总结、分析。
长期以来，AI对于大众，都只是科幻电影里的一时惊艳，或者新闻里"又双叒叕取得突破"的过目即忘。ChatGPT的出现，让每个普通人，真切地感受到了AI的存在。只需要像和普通人对话一样，问AI问题、让AI写文章、叫AI写代码，然后它做出反应，而且这个反应，不再是机械刻板的，而是煞有介事得像人了。而这个"越来越像人"的趋势，也随着AI的进步，渐渐变得和人类一模一样。
这一刻，AI从专家的论文、电影的特效、新闻的热点，走进了每个人的手机上、电脑上。
这一天，被很多人称为"AI平民化元年"。
AI终于从专家的实验室里走了出来，走进了每个识字的人的日常。
百模大战与国产崛起
ChatGPT的爆火，让全球的科技公司都坐不住了。
2023年，GPT-4发布，能力再次产生质的飞跃。同年，国内大模型集体亮相，文心一言、通义千问、讯飞星火、DeepSeek，不下几十家企业和研究机构涌入赛道。这一年被称为"百模大战"的一年，模型数量之多、发布速度之快，让整个行业都眼花缭乱。
2024年，格局开始分化。 DeepSeek-V2和V3以极低的训练成本达到了接近GPT-4的能力，彻底打乱了"只有巨头玩得起"的行业共识。原来以为大模型是烧钱的游戏，DeepSeek证明了不一定。
2025年，国产模型开始真正发力。
DeepSeek V4发布，编程能力达到92分，训练成本仅557万美元，不到GPT-4的二十分之一。通义千问Qwen3全球下载量突破6亿次。智谱AI在港交所上市，成为"全球通用AI基座模型第一股"。豆包2.0发布，采用大一统多模态框架。Kimi K2.6在SWE-bench Pro测试中拿下全球第一，这是国产模型首次在该项权威评测中登顶。
2026年，行业焦点从"能不能"转向"怎么用"。 AI Agent成为年度关键词，AI从"能说会道"进化到"能说会做"，不仅能回答问题，还能帮你执行任务、操作软件、管理工作流程。Claude Opus 4.7把代码能力推到了新高，GPT-5.5把上下文窗口拉到了百万级别，Gemini 3.5 Flash在AI工具调用上做到了业界第一。
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