Karpathy 把 LLM 的“锯齿状能力”和智能体原生经济讲透了，做 AI 产品、搞智能体开发的团队看完会对能力边界和落地方向有全新认知，值得点开细品。
Karpathy 在 Sequoia Ascent 2026 炉边谈话中分享了三个核心主题。首先，LLM 远不止是加速现有工作（如编程），他举了三个新例子：menugen 应用完全由 LLM 驱动无需传统代码、用 .md 技能替代 .sh 脚本安装软件、以及基于 LLM 的知识库处理非结构化数据。其次，他深入解释了 LLM 的“锯齿状能力”模式——为何同一个模型既能重构十万行代码，又会建议你去洗车——这源于领域可验证性和经济因素（收入/TAM 决定训练数据分布）。最后，他展望了智能体原生经济，包括产品服务分解为传感器、执行器和逻辑，以及如何让信息对 LLM 更可读，并暗示了全神经计算的未来。
Fireside chat at Sequoia Ascent 2026 from a ~week …
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.