Artificial intelligence has rapidly transformed software engineering. Generative AI and large language models (LLMs) can create huge volumes of code and documentation; machine-learning algorithms can monitor performance and detect security vulnerabilities. But when the task is to conceive, design, and make a complex physical system such as a jet engine, are those AI tools equally transformative' This past semester, the JARVIS Challenge (Jet-engine AI Research and Validation Intensive Sprint) set out to explore whether AI can compress the design-build-test cycle, asking MIT undergraduates to discover whether AI can help them to build faster and better. 'The JARVIS challenge showed that AI can substantially accelerate safety-critical hardware engineering, but engineering judgment remains the decisive differentiator. An AI-native engineer is not defined by using AI, but by leading it ' knowing when to trust it, when to challenge it, and how to translate AI outputs into working...
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