人工智能迅速改变了软件工程。生成式 AI 和大型语言模型 (LLMs) 可以创建大量代码和文档; 机器学习算法可以监控性能并检测安全漏洞。但是，当任务是构思, 设计, 并制造复杂的物理系统（例如喷气发动机）, 那些人工智能工具同样具有变革性?
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?
上个学期, JARVIS 挑战(喷气发动机人工智能研究和验证密集冲刺) 着手探索人工智能是否可以压缩设计-构建-测试周期, 要求麻省理工学院的本科生发现人工智能是否可以帮助他们更快更好地构建。
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.
团队, 工具, 任务
The teams, the tools, the task
这项挑战给了本科生四个星期的时间来设计, 制造, 组装, 并使用人工智能作为他们的主要工程合作伙伴测试小型燃气涡轮航空发动机,。目标: 建造一台 “JARVIS 级” 单轴喷气发动机，产生 50–100 磅的推力,，在 Jet-A, 上运行并完成五次 60 秒的运行。 Teams had total freedom over design, materials, and fabrication.
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.
: MIT的 机械车间和制造供应商; 商业软件包括Concepts NREC, SolidWorks, 和ABAQUS; 以及用于表征和组装各个部件的各种测试台。
At their disposal: MIT的 machine shops and manufacturing vendors; commercial software including Concepts NREC, SolidWorks, and ABAQUS; and various test rigs for characterizing and assembling individual components.
赞助商被招募的兴趣和对人工智能如何重塑工程工作流程的真正好奇所吸引。
The sponsors were drawn by recruiting interest and genuine curiosity about how AI might reshape engineering workflows.
“We see this as the future of engineering,” Ryan (Hal) Hefron of Voyager Technologies told the students. “您’正在磨练技能，这些技能不仅令人高兴—，而且’将成为工程人员的未来基线。”
“We see this as the future of engineering,” Ryan (Hal) Hefron of Voyager Technologies told the students. “You’re honing skills that are not just nice to have — they’re going to be the future baseline in the engineering workforce.”
Vincent Garnier, managing director of Safran Tech, watched the competition unfold with excitement. “JARVIS was a genuine experiment, a learning endeavor.坦率地说，我们’不知道对学生或人工智能模型有什么期望,。让我印象深刻的是，学生们: 首先, 探索; 然后, 随着项目开发的热情, 他们都冷静地意识到人工智能可以或不能帮助他们,，然后几乎立即适应,” 他说。 “这让我相信，这一代领先的工程师可能不会轻易而短视地使用人工智能,，而是通过更多地接触实验—物理或思想实验来做到这一点。”
Vincent Garnier, managing director of Safran Tech, watched the competition unfold with excitement. “JARVIS was a genuine experiment, a learning endeavor. We frankly didn’t know what to expect, from the students or from the AI models. What struck me coming from the students was: first, the enthusiasm to explore; then, as the project developed, they all came to the cool-headed realization of what AI could or could not help them with, and then almost instantly adapted for that,” he says. “It makes me confident that this generation of leading engineers will probably not fall prey to easy and shortsighted use of AI, and will do so by keeping ever more in contact with experiments — physical or thought experiments.”
斯帕科夫斯基开发了一种谨慎的技术，可以在不给出答案或提供帮助的情况下引导团队朝正确的方向前进。团队的 演示后, 他可能会问: “你知道什么是槽口配合? 接受评论。”
Spakovszky developed a careful technique for guiding teams in the right direction without giving away answers or providing help. After a team的 presentation, he might ask: “Do you know what a rabbet fit is? Take in the comment.”
到本周末，1, 一个团队退出了竞争;，其他团队, 取得了不同程度的成功, 为其燃气轮机开发了初步设计。 Different teams used AI to summarize textbooks, teach them to use design software, source vendors, create Excel sheets, answer specific questions, find references, and create comparative analysis between design decisions.一个团队在 Parley 中创建了一个代理，并委托其担任项目经理。
By the end of week 1, one team withdrew from the competition; the others had, with varying degrees of success, developed an initial design for their gas turbines. Different teams used AI to summarize textbooks, teach them to use design software, source vendors, create Excel sheets, answer specific questions, find references, and create comparative analysis between design decisions. One team created an agent in Parley and tasked it with serving as their project manager.
By week 2, teams had to start working on detailed CAD designs, ordering parts, and prototyping their combustors.这就是团队在使用人工智能时开始遇到限制的地方。虽然 Claude 和 ChatGPT 擅长提供设计替代方案并填补知识空白, 团队发现，幻觉, 谄媚, 和缺乏物理理解已成为生成式 AI 的臭名昭著的特征，正在削弱他们的信心并减慢他们的速度。
By week 2, teams had to start working on detailed CAD designs, ordering parts, and prototyping their combustors. This is where the teams started to hit limitations in their use of AI. While Claude and ChatGPT were good at offering design alternatives and filling knowledge gaps, teams found that the hallucinations, sycophancy, and lack of physical understanding that have become notorious features of generative AI were undermining their confidence and slowing them down.
“AI 是一个有用的工具, 擅长查找信息, 帮助组织事物, 并且可以写得很好, 但它可以’t 进行设计,” 811 团队成员 Elizabeth Tupaj, 说。 “当工程师’不知道发生了什么并且人工智能负责时，设计就变得不可靠,，至少在人工智能目前的能力下是这样。”
“AI is a helpful tool, great at finding information, helping organize things, and can write well, but it can’t do design,” says Elizabeth Tupaj, a member of team 811 Crew. “The moment the engineer doesn’t know what is going on and the AI is in charge is the moment the design becomes unreliable, at least with AI at its present capabilities.”
助教约翰·张指出, “与学生亲眼目睹这一点提醒我第一印象有多么重要。如果学生’无法尽早从人工智能,中获得答案，他们很快就会感到沮丧，并形成持久的观点，阻止他们以后使用它。”
Teaching assistant John Zhang notes, “seeing this firsthand with the students reminded me how much first impressions matter. If the students couldn’t get answers from the AI early on, they quickly grew frustrated and formed a lasting opinion that precluded them from using it later.”
在最后几周,，决赛入围者遇到了人工智能无法解决的另一个障碍: 与供应商合作。 “AI 搜索发现我们与, 没有融洽关系的供应商，他们对我们紧迫的时间安排不感兴趣,” 学生报告。 “过来的供应商是与我们团队有私人关系的供应商。”
In the final weeks, the finalists hit another obstacle no AI could solve: working with vendors. “AI searches found vendors we had no rapport with, who had no interest in our tight timeline,” students reported. “The vendors who came through were the ones our team had personal relationships with.”
在三名决赛入围者中，只有, Fast 和 Fractured 实现了其微型燃烧器的首次尝试点火。该团队大量使用人工智能进行权衡研究和架构比较,，尽管他们都没有燃气轮机经验，但他们还是达成了可行的设计。
Of the three finalists, only Fast and Fractured achieved first-attempt ignition of their mini-combustor. The team had used AI heavily for trade studies and architecture comparisons, arriving at a viable design despite none of them having prior gas turbine experience.
“JARVIS 挑战赛展示了’，当您将人工智能支持的设计与积极主动的学生和快速实验文化相结合,” 航空航天查尔斯·斯塔克·德雷珀职业发展教授 Masha Folk, 说。 “最引人注目的时刻是第一个学生设计的燃烧器安装在试验台上时。它完美地点火, 升到全功率, 过渡到双燃料运行,，然后使用 100% Jet-A 燃料持续稳定燃烧。这证明我们可以极大地加快设计, 构建, 和测试的周期，同时为学生提供真正的工程挑战的实践经验。”
“The JARVIS Challenge showed what的 possible when you combine AI-enabled design with motivated students and a culture of rapid experimentation,” says Masha Folk, the Charles Stark Draper Career Development Professor of Aeronautics and Astronautics. “The moment that stood out most was when the first student-designed combustor was installed on the test stand. It ignited flawlessly, ramped to full power, transitioned to dual-fuel operation, and then sustained stable combustion on 100 percent Jet-A fuel. This was proof that we can dramatically accelerate the cycle of design, build, and test while giving students hands-on experience with a real engineering challenge.”
处于 AI 原生工程的先锋地位
At the vanguard of AI-native engineering
到 5 月底,，两个更高级的团队 – Fast and Fractured 和 811 Crew – 已完成完整的发动机测试。 Fast and Fractured, 及其 AI 辅助设计, 由于供应商的麻烦而被延迟了一周又一周,，但最终还是进入了测试。不幸的是,，当转子摩擦并卡在固定外壳上时，他们的热火被切断了。然而，811 船员, 队, 在参加比赛时更多地接触了涡轮机械和推进概念，, 最终获胜。他们的发动机从 , 启动，成功过渡到 Jet-A, 并产生净推力。
By the end of May, the two more senior teams – Fast and Fractured and 811 Crew – had completed full engine tests. Fast and Fractured, with their AI-assisted design, were delayed by vendor headaches week after week, but finally made it to test. Unfortunately, their hot fire was cut short when the rotor rubbed and seized against the stationary housing. Team 811 Crew, however, who had more exposure to turbomachinery and propulsion concepts going into the competition, emerged victorious. Their engine started, successfully transitioned to Jet-A, and generated net thrust.
“A我们站在那里，空气启动器,听到他们的发动机旋转，看着他们喷火,感觉我的心快要跳出胸膛了。有很多方法可能会出错! 这些学生在如此短的时间内所取得的成就简直令人惊叹,” 博士生 Joe Chiapperi 说。
“As we stood there with the air-starter, hearing their engines spool up and watching them spit fire, it felt like my heart was racing out of my chest. There were so many ways it could go wrong! What these students accomplished in such a short time span is nothing short of amazing,” says PhD student Joe Chiapperi.
811 团队在整个比赛中一直抵制使用人工智能,，而是信任他们的基本原理和团队合作。 “我们的员工至少对设计软件有一定程度的熟悉,机械工程师知道如何建造任何东西,还有航空航天工程师专门上过燃气涡轮发动机设计课程,”图帕杰说。
The 811 team had been resistant to using AI throughout the competition, trusting instead to their fundamentals and teamwork. “We had people who were at least somewhat familiar with the design software, mechanical engineers who knew how to build anything, and aerospace engineers who had taken classes on the design of gas turbine engines specifically,” says Tupaj.
From the start of the JARVIS Challenge, younger students used Parley more frequently and cleverly, while the juniors and seniors leveraged deeper experience.
“JARVIS 告诉我，从人工智能中获取价值需要两件事: 足够的专业知识来判断它告诉你的内容，并在它告诉你的时候抓住它 错误, 以及足够的好奇心，在它可以提供帮助的地方真正依赖它,” 安德烈亚·博布教授说。 “在冲刺中进展最快的团队经验丰富，并且严重依赖人工智能来实现这一目标。最终获胜的团队对 AI ; 更有抵抗力，他们拥有专业知识,，但这种怀疑让他们变得更慢。最佳点似乎是足够了解以继续掌控该工具,，并且足够渴望首先拿起它。对我来说, 认为 是未来的真正机会: 培训下一代工程师，让他们有判断力来指导这些人工智能工具，并有使用它们的本能。”
“JARVIS taught me that getting value from AI takes two things: enough expertise to judge what it tells you and catch it when it的 wrong, and enough curiosity to actually lean on it where it could help,” says Professor Andreea Bobu. “The team that moved fastest in the sprint was experienced and leaned heavily on AI to get there. The team that eventually won was more resistant to AI; they had the expertise, but that skepticism made them slower. The sweet spot seems to be knowing enough to stay in charge of the tool, and being eager enough to pick it up in the first place. To me, that的 the real opportunity ahead: training the next generation of engineers who have the judgment to direct these AI tools and the instinct to reach for them.”
The competition的 clearest finding: engineering experience is a multiplier, and the human factor remains a vital element.掌握基本原理和基本概念可以培养良好的工程判断力，以及在面对不完整信息时做出一系列艰难决策的能力。 And when it comes to building safety-critical physical systems, nothing can replace human hands and human accountability.
The competition的 clearest finding: engineering experience is a multiplier, and the human factor remains a vital element. Mastering the first principles and fundamental concepts breeds good engineering judgment and the ability to navigate strings of tough decisions in the face of incomplete information. And when it comes to building safety-critical physical systems, nothing can replace human hands and human accountability.
“JARVIS has shown that AI copilots can have a multiplicative effect on engineering productivity, with judgment and first-principles thinking serving as the key differentiators among teams,” adds teaching assistant Kyle Woody.
但人工智能在航空航天领域的影响是巨大的。 If small teams using well-managed AI copilots can compress design-build-test cycles from years to weeks, the consequences for workforce structure, R&D timelines, and competitive dynamics could be substantial. The students who tackled the JARVIS Challenge are among the first engineers to grapple with those stakes not as a thought experiment, but in a machine shop, with a jet engine on the test stand.
But the implications of AI in aerospace are significant. If small teams using well-managed AI copilots can compress design-build-test cycles from years to weeks, the consequences for workforce structure, R&D timelines, and competitive dynamics could be substantial. The students who tackled the JARVIS Challenge are among the first engineers to grapple with those stakes not as a thought experiment, but in a machine shop, with a jet engine on the test stand.