HackerNews中文版

从技术人员的梦想，到早期的文本生成玩具，再到颠覆世界的 ChatGPT 的发布，以及最终成为现代编程的日常工具（如 Sonnet 和 Opus），大型语言模型（LLM）的发展历程不到十年。这是一个激动人心、几乎令人难以置信的故事。让我们回顾一下我们是如何走到这一步的，以及行业目前正面临的瓶颈。 - **梦想阶段（2010-2016 年）**。在过去十年的开端（2011 年），发生了一件有趣的事情。维基百科和 Stack Overflow 这两个平台开始获得巨大的吸引力，人们在这些平台上合作，公开交流知识。回想起来，这似乎是人类更理想的、社区驱动的道路——一条我们为了今天的集中式架构而放弃的道路。 - **颠覆阶段（2016-2021 年）**。一系列看似无关的事件的完美结合为人工智能铺平了道路。到 2017 年，新程序员对 Stack Overflow 僵化的政策、主观的问题拒绝以及资深程序员的迂腐感到深感沮丧。事后看来，这些严格的版主为后来成为 Copilot 和 ChatGPT 的事物奠定了基石。如果社区不愿意在不投反对票的情况下回答初学者的提问，那么私有的 LLM 将乐于效劳。此外，谷歌在 2017 年发表的里程碑式论文《Attention Is All You Need》解锁了 Transformer 架构，以及 2020 年 COVID-19 导致的强制隔离。这一切为能够充当隔离的开发人员编程伙伴的虚拟助手创造了肥沃的土壤。 - **吸引阶段（2023-2025 年）**。ChatGPT 的发布毫无疑问地表明了“吸引”是多么容易。对于非技术人员来说，这简直是魔法。像 Copilot、Claude 和 Deepseek 这样的专业 LLM 很快就成为程序员工具箱中不可或缺的一部分。与此同时，OpenAI 仍在宣传其“非营利”的根源，普遍的共识是这纯粹是为了赋能人类。 - **终局阶段（2025 年至今/未来）**。到这个时候，人工智能公司已经误判了很多事情。他们专注于“长期”，但正如约翰·梅纳德·凯恩斯多年前所说：“从长远来看，我们都将死去。” 风险投资家今天正在失去耐心，因为尽管技术本身已经获得了巨大的普及和认可，但收入却没有那么快到来。吸引力在一定程度上奏效了，但未能完全奏效。像 Sonnet、Opus 和 GPT 5.5 这样的前沿模型仍然处于“补贴模式”。它们向用户每月收取的订阅费（每月 10/20/30 美元）与运行这些“思考……”和“沉思……”的 token 所需的所有计算和内存相比，简直是杯水车薪。为了真正实现账面盈利并摆脱补贴模式，它们必须根据输入/输出 token 的规模收费，而这似乎很困难。极少数公司能够承担这种对不可预测的硬件扩展的无限预算，最近的 Uber 事件就准确地展示了他们尝试这样做时会发生什么。前沿模型试图取代人类历史上从未成功委托或自动化过的东西——人类大脑最高级别的认知技能，如推理、演绎和逻辑。然而，努力仍在继续，目标是长期的。令人费解的是，如果他们停止补贴，吸引阶段可能会被打破——人们很有可能回归到维基百科/Stack Overflow 的旧方式，或者完全转向可以在本地硬件上运行的 Llama 和 Qwen 等开源的“干货/学术”模型。然而，他们也无法无限期地继续补贴和耗尽资金。当补贴的镜子破碎时，会发生什么？

查看原文

If you trace the timeline of how LLMs went from a technologist's dream to early text-generation toys, to the world-shifting launch of ChatGPT, and finally to the daily drivers of modern programming (Sonnet, Opus), it has taken less than a decade. It’s a thrilling, almost unbelievable tale.Let's look at how we got here, and the wall the industry is currently hitting.- The Dream Phase (2010-2016). By the dawn of the last decade (2011), an interesting thing was happening. The two platforms, Wikipedia and Stack Overflow, had started gaining tremendous traction, folks were collaborating on these platforms to openly exchange knowledge. Looking back, this feels like a more ideal, community-driven path for humanity — one we abandoned for the centralized architecture we have today.- The Disruption Phase (2016-2021). A perfect storm of unrelated events paved the way for AI. By 2017, new programmers were growing deeply frustrated by Stack Overflow's rigid policies, subjective question rejections, and senior coder pedantry. In retrospect, those strict moderators carved the first stones of what would later become Copilot and ChatGPT. If the community won't answer a beginner's question without downvoting it, a private LLM gladly will.Add to this Google's landmark 2017 paper "Attention Is All You Need" which unlocked the Transformer architecture, and the forced isolation of COVID-19 in 2020. The ground was suddenly fertile for virtual assistants that could act as isolated developers' programming partners.- The Hook Phase (2023-2025). The launch of ChatGPT left no doubt about how easy the "hook" would be. For non-technical folks, it was pure magic. It didn't take long for specialized LLMs like Copilot, Claude and Deepseek to become an indispensable part of the programmer's toolbox. Meanwhile, OpenAI was still advertising its "non-profit" roots, and the consensus was that this was purely about empowering humanity.- The Endgame Phase (2025-present/future). AI companies had miscalculated a lot of things by this time. They were optimizing for the "long-term" but as John Maynard Keynes rightly said many years ago, "In the long-term, we are all dead". The VCs are losing patience today because while the technology itself has gained massive ubiquity and appreciation, the revenues aren't coming as fast. The hook had sort of worked but failed to fully work.Most frontier models like Sonnet, Opus and GPT 5.5 are still running on 'subsidized mode'. The amount of monthly subscription they charge users (USD 10/20/30 per month) is a pittance compared to all the compute and RAM needed to run those "thinking..." and "pondering..." tokens. In order to truly show profits in the books and come out of subsidized mode, they must charge on the scaling of input/output tokens and that appears to be difficult. Very few companies might be able to sustain such unlimited budget for unpredictable hardware scaling, the recent Uber story shows exactly what happens when they try doing this.The frontier models are trying to replace something which could never be successfully delegated or automated in entire human history - the highest cognitive skills of human brain like reasoning, deduction and logic. Yet, the efforts are on and the goals are long term. The conundrum is that if they stop subsidizing, the hook phase may be undone - there is a strong possibility of folks reverting back to older ways of Wikipedia/Stack Overflow or pivot entirely to open source dry/academic models like Llama and Qwen which can run locally on their own hardware. And yet, they also can't keep subsidizing and draining the funds indefinitely.What happens when the subsidy mirror cracks?

人工智能的困境：我们正生活在一个高度受补贴、充满趣味的时代