The article examines why the newly AI‑powered Google Search fails at simple letter‑count questions like “how many P’s are in Google,” tracing the issue to token‑based language models, illustrating it with examples, and discussing both short‑term prompts and long‑term architectural solutions such as byte‑level models.
In a 2026 Sequoia Ascent interview, Andrej Karpathy argues that large language models are not merely speed‑up tools but a new computing paradigm that renders many legacy apps obsolete, elevates understanding as humanity’s final competitive edge, and relegates CPUs to auxiliary roles, while outlining software evolution, jagged intelligence, and the rise of agentic engineering.
In a Sequoia Ascent interview, Andrej Karpathy argues that large language models are reshaping software into a new computing paradigm, making many existing apps obsolete, emphasizing verifiable tasks as the remaining human moat, and predicting CPUs will become auxiliary to AI‑driven agents.
