The article argues that prompts used for AI tools like large language models are a form of technical debt, as they require ongoing maintenance, are poorly documented, and can break silently when underlying models change, leading to unpredictable costs and system fragility.
Prompts for AI coding tools are a form of technical debt that decays silently with each model upgrade, unlike code. The author advises against investing heavily in bespoke agentic setups, recommending instead using third-party tools with minimal configuration and keeping custom prompts limited to concrete project facts.
Gary Marcus scrutinizes recent claims from OpenAI and Anthropic, urging readers to examine the underlying details and math behind their headline-grabbing announcements rather than taking them at face value.
Adding assumptions to a formal property makes it logically weaker—guaranteed to work in fewer cases. Engineers add assumptions when stronger properties are impossible, too costly, or unverifiable. These assumptions often involve factors outside the program, like the operating environment or external dependencies.