- The JARVIS Challenge at MIT tested if AI can accelerate the design-build-test cycle for a small gas turbine engine, with undergraduate teams building engines in four weeks.
- AI tools, accessed via the Parley platform, helped with tasks like summarizing textbooks, software tutorials, vendor sourcing, and design trade studies, but faced limitations like hallucinations and lack of physical understanding.
- Teams found that engineering judgment and expertise were crucial; while AI assisted, human oversight was needed to trust, challenge, and translate AI outputs into working hardware.
- Younger students used AI more frequently, while senior teams relied on deeper experience; the winning team was initially resistant to AI, trusting fundamentals and teamwork instead.
- Manufacturing and vendor relationships emerged as rate-limiting steps, with personal connections proving more reliable than AI-sourced vendors for tight timelines.
- The challenge demonstrated that AI can compress engineering cycles, but education and hands-on experience remain vital for developing the judgment to effectively lead AI tools.