Can AI build a jet engine? JARVIS Challenge tests role of AI copilots in tough-tech engineering | MIT News | Massachusetts Institute of Technology
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Can AI build a jet engine? JARVIS Challenge tests role of AI copilots in tough-tech engineering
Can AI build a jet engine? JARVIS Challenge tests role of AI copilots in tough-tech engineering
MIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.
Department of Aeronautics and Astronautics
Publication Date:
July 14, 2026
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Liberty
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Caption:
Faculty and TAs for the JARVIS Challenge were on hand throughout the competition to ensure safety, but students had to figure out their designs with minimal guidance. Here, Professor Zolti Spakovszky (center) helps team 811 Crew fire their jet engine.
Caption:
Team 811 Crew — (from left to right) Anhad Sawhney, Yaakov Zerykier, Elizabeth Tupaj, Zachary Bleil, and Ethan Wong — won the inaugural JARVIS Challenge with the successful test of their jet engine.
Caption:
Teams had just four weeks to design a jet engine and build and test a subscale combustor to build and to prove the safety of their designs. “The JARVIS challenge showed what’s possible when you combine AI-enabled design with motivated students and a culture of rapid experimentation,” says Professor Masha Folk. “The pace required tremendous leadership, trust, and teamwork from our instructional staff, postdocs, graduate students, and undergraduates.”
Caption:
JARVIS team members pose along with faculty, grad students, staff, and sponsors following their successful test. Rotor Technologies hosted the jet engine tests.
Caption:
JARVIS sponsor Safran hosted a group of students from the competition in May.
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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?<br>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.<br>“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 hardware. Manufacturing — not engineering design or analysis — remained the fundamental rate-limiting step,” says Professor Zolti Spakovszky, director of the MIT Gas Turbine Laboratory.<br>The teams, the tools, the task<br>The challenge gave undergraduates four weeks to design, fabricate, assemble, and test a small gas turbine aero engine, using AI as their primary engineering partner. The objective: build a “JARVIS-class” single-spool jet engine producing 50–100 pounds of thrust, running on Jet-A, and completing five 60-second runs. Teams had total freedom over design, materials, and fabrication.<br>Representing nearly every department in the School of Engineering, 31 students organized into seven teams, ranging from all first-years to senior-heavy groups. Many of the competitors initially had little experience in turbomachinery, compressible flows, or, in the case of the younger students, even thermodynamics. Many had never seen the inside of a gas turbine before signing up to build one.<br>At their disposal: MIT’s machine shops and manufacturing vendors; commercial software including Concepts NREC,...