**The half-life of AI knowledge**

> Most of what you learn about AI has a half-life of months. A few ideas have lasted decades, and they compound: most new models are built on them. The half-life of AI knowledge: what expires and what compounds Most of what you learn about AI has a half-life of months. A few ideas have lasted decades, and they compound: most new models are built on them. Zairai Oct 28, 2026 Share The weekend that expired In October 2025, OpenAI launched Agent Builder, a visual canvas for building AI agents. Eight months later it announced the shutdown. If you spent a weekend getting good at it, or at a framework’s abstractions, or at the exact phrasing that made last year’s model behave, you know the feeling. Nobody told you your weekend expired. It just did. The pace of AI isn’t the real problem. The problem is what the pace does to everything you learn: it makes it feel disposable. Why invest in anything that will be stale by spring? But not everything you learn about AI expires at the same rate. Some of it hasn’t expired since 1847. (Disclosure up front: we’re building a learning tool around these ideas. It gets one paragraph near the end.) Knowledge has a half-life A radioactive sample loses half its atoms in a fixed amount of time. You can’t say which atom goes next, but you can say how long until half are gone. The economist Fritz Machlup is usually credited with applying the idea to knowledge in 1962, asking how long the things people paid to learn stayed useful. It’s a metaphor, not a law. But it makes one thing obvious: AI knowledge isn’t one thing that decays at one rate. It’s a stack of layers, and each layer runs on its own curve. Four layers, four clocks The stack, from fastest decay to slowest. The curves are a thought experiment; the dates below them are real. Tools and products: months to a couple of years. It’s not just startup churn. LangChain 1.0 moved LLMChain , its original building block, into a legacy package. Flowise , an open-source builder for LLM apps, reached end of life in August as coding agents outgrew its drag-and-drop workflows. Different owners, different reasons, same shelf life. The tools that last, like NumPy and PyTorch, wrap stable ideas: arrays, automatic differentiation. Techniques and recipes: a few years. In 2022, “Let’s think step by step” was the prompt trick everyone copied. In 2023, a Google DeepMind paper had a model search for better phrasings, and “Take a deep breath and work on this problem step-by-step” won on grade-school math for PaLM 2: 80.2% against 71.8% for the older trick. Today, OpenAI’s guide for its reasoning models advises against chain-of-thought prompts altogether, because the models already reason internally. The phrasing expired. The idea under it, that models do better when they spend tokens reasoning, got trained into the models themselves. Architectures: five to ten years. The transformer was introduced in 2017 in “Attention Is All You Need” and still underpins nearly every frontier model nine years later. That’s a long run, not a permanent one. LSTMs, invented in 1997, carried most of language modeling from about 2014 until the transformer displaced them a few years later. Hybrids that swap most attention layers for cheaper state-space layers have been shipping since AI21’s Jamba in 2024. Notice what survives the swap, though: attention itself, letting a model weigh which inputs matter, dates to 2014 and will likely outlive the transformer. Even the hybrids keep some. Core ideas: decades. Learning from examples instead of writing rules: Frank Rosenblatt’s perceptron , 1958. Shrinking a measure of error by following its slope downhill: gradient descent, which Augustin-Louis Cauchy described in 1847 to compute orbits. Working out how much each weight contributed to the er...

**来源信息**
- **来源**：Hacker News：AI 热帖
- **分类**：tip
- **发布时间**：2026-10-28 23:19（北京时间）
- **原文**：[打开原文](https://zairai.substack.com/p/the-half-life-of-ai-knowledge-what)