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Karpathy presented three concrete 'new horizons' for LLMs at Sequoia Ascent 2026…

2026-04-30 · 17:28 UTC ·@karpathy ·2 min read

Brief

Andrej Karpathy used a Sequoia Ascent 2026 fireside chat (talk published 2026-04-30) to argue that LLMs are creating qualitatively new capabilities, not just accelerating existing workflows. He gave three examples: 'menugen' (image→image apps fully handled by LLMs without classical code), replacing install .sh scripts with human-readable .md 'skills' the LLM executes and debugs, and LLM knowledge bases that perform computation over unstructured text from arbitrary sources. He also tackled LLM 'jaggedness' — why the same model can refactor a 100,000-line codebase yet hallucinate trivial actions — blaming a mix of domain verifiability and economic choices that shape RL training distributions. Finally, Karpathy sketched an 'agent-native economy' (sensors, actuators, logic), the rise of agentic engineering and hiring shifts, and the possibility of mostly neural computation aided by CPU coprocessors; Stephanie Zhan highlighted that this era raises the ceiling for what engineers can build.

Why it matters

Karpathy presented three concrete 'new horizons' for LLMs at Sequoia Ascent 2026 (talk published 2026-04-30): (1) menugen — an app wholly driven by LLMs that takes an image and outputs an image with no classical code required; (2) installing '.md' skills instead of '.sh' scripts — writing installations in English for an LLM to interpret, target, and debug for a user's setup; (3) LLM knowledge bases that enable computation over unstructured data from arbitrary sources (text/articles), a capability he says was impossible with classical code.

Key details

  • Karpathy highlighted LLM 'jaggedness': the same model can coherently refactor a 100,000-line codebase yet also produce nonsensical directions (e.g., 'walk to the car wash'). He attributes this to domain verifiability plus economics — revenue/TAM shapes what frontier labs include in training distributions and RL packaging, so models are 'on the rails' when in-distribution and 'off-roading' otherwise.
  • He framed an emerging agent-native economy: products decomposed into sensors, actuators and logic across computing paradigms, a rising 'agentic engineering' skill set and new hiring practices, and speculative moves toward mostly neural computing augmented by classical CPU coprocessors.
  • Context and reaction: the remarks were part of a fireside chat ~a week before 2026-04-30; Karpathy (@karpathy) argued agentic engineering changes what can be built, and Stephanie Zhan (@stephzhan) contrasted last year's 'vibe coding' (raising the floor) with agentic engineering (raising the ceiling) and said Karpathy feels 'more behind as a programmer.'
Source evidence

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.

Stephanie Zhan (@stephzhan)

@karpathy and I are back! At @sequoia AI Ascent 2026. And a lot has changed. Last year, he coined “vibe coding”. This year, he’s never felt more behind as a programmer.

The big shift: vibe coding raised the floor. Agentic engineering raises the ceiling.

We talk about what it means to build seriously in the agent era. Not just moving faster. Building new things, with new tools, while preserving the parts that still require human taste, judgment, and understanding.

Video

— https://nitter.net/stephzhan/status/2049518659513852109#m