This is the the quote I've been citing a lot recently.
这是我最近经常引用的那段话。
Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights: The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons: 1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing. 2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc. 3. LLM knowledge bases as an example of something that was *impossible* with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc. I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3). The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base *and* 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to... Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors.
大约一周前在 Sequoia Ascent 2026 的一场炉边谈话。几个要点：我努力推动的第一个主题是，LLM 不只是把原来已有的东西加速而已（例如 coding）。三个“新边界”的例子：1. menugen：一种可以被 LLM 完全吞没的 app，不需要任何经典代码：输入一张图像，输出一张图像，而 LLM 原生就能完成这件事。2. 用 install .md skills 替代 install .sh scripts。比如，要安装一款软件时，如果你可以把安装过程用文字写出来，再说一句“把这个直接给你的 LLM 看”，那为什么还要去写复杂的 Software 1.0 bash script 呢？LLM 是一种高级的英语解释器，能够智能地针对你的具体环境执行安装、内联调试所有问题，等等。3. LLM knowledge bases（知识库）是另一类例子：这类东西用经典代码是*不可能*实现的，因为它涉及对非结构化数据（知识）的 computation，这些数据来自任意来源、采用任意格式，包括纯文本文章等。我之所以强调这些，是因为每一次新的范式变化里，最显而易见的事情总是“把原有东西加速”或“以某种方式改进”，但这里我们看到的是一些功能：它们要么突然看起来甚至不该存在（1、2），要么在此前从根本上就不可能实现（3）。第二个（仍在持续展开的）主题，是试图解释 LLM 中那种 jaggedness（锯齿状、不均匀能力分布）的模式。为什么同一个产物可以同时 1）连贯地重构一个 10 万行的代码库，*并且* 2）告诉你走去 car wash 洗你的车。我之前写过，这种现象的来源与一个领域是否可验证（verifiability）有关；这里我进一步展开，认为它也与 economics（经济学）有关，因为 revenue/TAM 决定了 frontier labs 会在 RL 期间选择把什么内容打包进 training data distributions。你要么处在数据分布之内（跑在 RL circuits 的轨道上）一路飞驰，要么就是拿着 machete 在丛林里越野，至少相对而言是这样。我对这个解释仍然没有 100% 满意，但如果你想在实践中利用 LLM 的力量、同时避开它们的陷阱，就必须持续努力建立一个准确的 LLM 能力模型，而这也引出了……最后一个主题：agent-native economy。也就是把产品和服务分解为 sensors、actuators 和 logic（分别散落在 1.0/2.0/3.0 计算范式中），我们如何让信息对 LLM 尽可能 legible（可读、可解析），关于快速兴起的 agentic engineering 及其技能组合的一些看法、相关的招聘实践，等等，甚至还包括一些提示/梦想：未来也许 fully neural computing 能处理绝大多数 computation，而（经典）CPU coprocessors 只提供部分辅助。