At Sequoia Ascent 2026, Andrej Karpathy argued LLMs enable new horizons beyond speeding up existing tasks, like fully LLM-native apps and knowledge bases impossible with classical code. He also discussed the "jaggedness" of LLM capabilities and the emerging agent-native economy splitting products into sensors, actuators, and logic.
A Hacker News user asks why everyday apps (banks, insurance, delivery) haven't improved despite billions spent on LLM code generation, noting degraded quality in some major apps and no killer features outside AI products themselves.
A software engineer asks on Hacker News which industries are actually replacing SWEs with AI, noting that LLMs are not yet capable of independent production-grade work despite widespread claims that engineers will soon be obsolete.
A new Hacker News user proposes that the fluctuating quality of large language model (LLM) responses, particularly with Claude, may be caused by operators reducing processing depth during peak server times. The author asks the community for their thoughts on this theory.