# 红杉Ascent 2026炉边谈话：LLM的新视野、能力不均衡与智能体原生经济

- 来源：Andrej Karpathy (@karpathy)
- 发布时间：2026-05-01 01:28
- AIHOT 分数：68
- AIHOT 链接：https://aihot.virxact.com/items/cmolryiwd01xdsll9qgep9141
- 原文链接：https://x.com/karpathy/status/2049903821095354523

## AI 摘要

谈话指出LLM的核心价值在于开启全新可能，如完全由LLM驱动的应用、用自然语言描述替代脚本安装、以及处理传统代码无法应对的非结构化知识库。其次，探讨了LLM能力“锯齿状”不均衡现象，认为这与领域可验证性及经济利益影响训练数据分布有关。最后，话题转向智能体原生经济，涉及将产品服务分解为传感器、执行器和逻辑模块，使信息对LLM高度可读，并讨论了新兴的智能体工程及相关技能。谈话强调，从“氛围编程”到“智能体工程”的转变，不仅是提升效率，更是拓展能力上限，旨在智能体时代构建全新事物。

## 正文

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.

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