The rapid growth could prompt countries like the U.S. to enact new barriers to development, according to a new report.

Artificial intelligence models sort through huge amounts of satellite data to help detect leaks.

Widespread adoption of the fuel in shipping could lead to a substantial reduction in global emissions.

The sustained demand reflects a global search for alternative energy sources that has been hastened by the energy-supply crunch arising from the Middle East war.

The deal will create one of Europe's biggest integrated direct air capture companies.

Heavy monsoon rains also triggered landslides that blocked several roads and damaged homes in the Gilgit-Baltistan region.

Nature Climate Change, Published online: 15 July 2026; doi:10.1038/s41558-026-02709-7

The authors demonstrate asymmetrical impacts of forest gain and loss on evapotranspiration and precipitation: gain increases the processes more than loss reduces them. They highlight a need to better consider hydrological asymmetry in climate models and in planning of forest-based climate solutions.

One of the best bulwarks against monopoly is interoperability—that is making a new product or service work with an existing product or service. Interoperability allows users, and not the manufacturers of their devices or largest player in a market, to decide what application best serves them. Unsurprisingly, companies like Apple have worked hard to resist interoperability requirements. 

On July 8, the General Court of the  European Union (General Court) ruled against Apple in several cases the company brought against the European Commission (joint cases), affirming the company’s obligations under the Digital Markets Act (DMA). Apple argued in the cases that it should be exempted from the law’s requirements especially with regards to interoperability on multiple grounds. We applaud the General Court’s  decision, and congratulate the Free Software Foundation Europe (FSFE) as well as others who intervened in support of the Commission against Apple's attempt to shirk off its responsibilities, thus ensuring fair competition in European markets.

A Positive Development for Europeans

This is a clear and substantive win for developers and users in Europe. The stranglehold Apple exerts over its ‘walled garden’ is injurious for developers, users, and researchers alike. By confirming Apple’s obligations under the DMA, the General Court has ensured that developers will be given more choice on where they can publish their apps, and users will have more options to obtain apps which, for whatever reason, Apple dislikes. And researchers will have less roadblocks and hurdles to overcome in their studies of Apple’s OSes, particularly iOS, iPadOS, and watchOS.

Apple argues that the interoperability requirements will force it to lower the security standards that have led Apple products’ users to trust their devices. While this self-serving logic is not entirely without merit, it is far from the inevitable outcome. Especially with regards to the App Store, users can be given clear, informed choice when leaving the Apple ecosystem to obtain apps elsewhere. While we urge European courts to take Apple’s security concerns seriously, we’ve previously noted that this should not be used as a smokescreen to protect anticompetitive behavior.

Interoperability and security are not inherently at odds. When interoperable functionality is worked into the security model of a platform from the ground-up, a proper balance can be struck between two forces that are often falsely framed as naturally conflicting. While Apple OS platforms have not been built this way from the get-go, it is still possible, but takes more time to get it right. Here, the devil is in the implementation details.

Apple’s Case Arguments and the Court’s Rebuttal

Under the DMA, designation as a ‘gatekeeper’ is reserved for the biggest of Big Tech, companies that provide services deemed essential for businesses to reach end users. Apple is one of only seven companies that meet this designation, along with Alphabet, Amazon, Booking, ByteDance, Meta, and Microsoft. In its case, Apple argued that Article 6(7) of the DMA, specifying interoperability requirements for gatekeepers aimed at restoring fair competition, is unlawful in light of the Charter of Fundamental Rights of the European Union (specifically the right to property), and as such its designation as a gatekeeper subject to the requirements is unlawful and should be annulled as a result. In its ruling, the General Court rejected the argument as Article 6(7) does not form the legal basis of the designation.

Apple separately argues that the App Store fails to meet the requirements defining a core platform service (CPS), since the various stores (across iOS, iPadOS, watchOS, macOS) do not constitute a single platform. A company’s gatekeeper status relies on it providing a CPS that is an important gateway for business users to reach end users. Here, the implications of the argument are clear: remove service designation as CPSes, remove the gatekeeper status. The court rejected the argument on the basis that “irrespective of the device on which it was available, each of the App Stores was used for the same purpose, namely to intermediate between end users and business users in the distribution of applications and in-app digital content.”

Finally, the court rejected as inadmissible Apple’s argument that iMessage should not be classified as a number-independent interpersonal communication service (NIICS) constituting a CPS. This decision rested on the fact that the “classification does not, by itself, produce binding legal effects that bring about a change in Apple’s legal position” since iMessage was not listed as an “important gateway” in the designation decision and therefore was not subject to the DMA obligations.

In ruling against Apple in favor of the European Commission, the General Court has set an important precedent in ensuring competitive fairness and openness in the digital marketplace. The landmark effects of the DMA will serve to benefit all Europeans in the choice and freedom it affords them. Despite Big Tech’s legal challenges, these decisions build a strong foundation for a better digital future—a lesson which other regions should learn from and take note.

Systems using artificial intelligence to enhance forecasting, planning, and decision-making in businesses have been proliferating in recent years, but in many cases, they lack the detailed, specific information about the organization itself, limiting the usefulness of those tools. 

Devavrat Shah, a principal investigator at MIT’s Laboratory for Information and Decision Systems (LIDS), faculty member with the department of Electrical Engineering and Computer Science (EECS), and member of the Institute for Data, Systems, and Society (IDSS), has been focused on how to design methods that can handle second-by-second decision-making using limited computational resources. 

“In a sense, with a small amount of resource, you have to do a lot of heavy lifting,” he says. As a researcher, “my interest is in the ability to develop methods that can extract information from data at scale in as effective a manner as possible.”

The Andrew (1956) and Erna Viterbi Professor has been teaching at MIT since 2005. 

In 2019, he also co-founded a spinoff company called Ikigai Labs. Ikigai built a foundation model for tabular, time series data based on years of research in Shah’s lab, which was patented and licensed by MIT to the company. This model can take input from enterprise data from varied sources, continuously and at scale, so that it learns as it goes along by testing its predictions against real outcomes.

Shah explains that the system is an extension of the kind of graphical models that are used, for example, by GPS devices to convert a sparse amount of data received from satellites into an accurate model of a position on the Earth’s surface, or by communication system like that in a digital watch that communicates at high speed in an energy-efficient manner. 

“My interest was: How does one design such graphical models for generic, tabular data?” he says.

While most AI models have been taught using text and images, this system takes tabular data as its input — structured data such as the familiar kind of row-and-column format used in spreadsheets. And then it provides the kind of real-time planning, on a vastly larger scale. 

The idea for Ikigai was to provide forecasting and decision-making technology for large businesses, such as consumer goods manufacturers and pharmaceutical companies.

Shah gives the example of how a consumer electronics company might use this system. 

“Let’s say you’re making headphones and all sorts of different things. And each of the products that you manufacture has lots of small pieces that come from different parts of the world. And once the device is sold, it needs to be supported and maintained. And you have to come up with new versions of the product, you have to market them, you have to price them … So the questions you would typically ask would be: If I were to sell these next quarter or next year, how many will be sold in different places, and what would happen to demand if I change the price, or if I introduce promotion?”

He adds that all of these processes are interdependent, and at every stage of the processes decisions have to be made that have implications over time. “At some level,” he says, “digitizing these processes and being able to do predictions and constantly optimize is what leads to ultimately better business operations.”

Ikigai was recently acquired by the international firm Celonis, where Shah is now chief scientist in addition to his roles at MIT. Ultimately, he hopes the model he developed for Ikigai will help Celonis deliver tools that can integrate with a company’s own data and business processes in order to provide real-world analyses that can help make forecasts, plans, and decisions.

Shah adds that Celonis has specialized in digitizing and automating operations for more than 1,400 large companies around the world. Now that these systems are fully digitized, they provide a platform for Ikigai’s software to take the next step, reading the data from these digitized systems in order to provide detailed models to allow simulation of different options, predict optimum strategies, and forecast the results of a given set of decisions. 

“Once the digital layer of these processes exists and this information layer exists,” Shah says, “now, on top of it, we can put the Ikigai stack to enable decision-making at a much larger scale than otherwise.”

While so many companies are working on various aspects of AI, “we are very much focused on part of the domain that the rest of the world is not paying attention to,” which is the area of structured or time-domain data. By starting from such data, he says, it provides a very cost-effective version of AI. 

“A narrower focus comes with sharper technology,” he says, “but it’s broad enough that it’s very valuable.”

Shah adds, “The recent buzzword that’s become pertinent in the modern AI popular press is a ‘world model.’ In a sense, this is trying to build the enterprise process world model, so to speak.”

In an increasingly crowded, for-profit landscape for scholarly research, the health of a publishing program is often measured by the influence of its publications. This year, three MIT Press journals demonstrated their stature by earning the highest impact factors in their disciplines.

Computational Linguistics ranked first in the Linguistics category, International Security led the International Relations category, and The Review of Economics and Statistics topped the Social Sciences, Mathematical Models category in Clarivate’s 2026 journal impact factor rankings.

For the MIT Press, this achievement highlights the distinctive strength of its journals program. Although relatively small compared to other commercial and university press publishers, MIT Press journals consistently publish widely cited scholarship across a broad range of disciplines, from social science and the humanities to neuroscience and artificial intelligence. 

Clarivate’s impact factors capture the previous year’s scholarly citation activity, but the influence of MIT Press journals often extends well beyond academia. In recent months, International Security articles have been cited by Foreign Policy, Foreign Affairs, The Conversation, CBC, and Brookings. The journal has also published research with significant real-world policy relevance, including a widely discussed article by MIT political scientist Caitlin Talmadge that anticipated how a limited strike on Iran could escalate into attempts to disrupt shipping through the Strait of Hormuz, triggering a broader military and economic crisis. 

“I am proud and humbled that International Security has had the number one impact factor in International Relations for two years running,” says Jacqueline Hazelton, editor of International Security. “Thanks are due to our generous reviewers, our brilliant authors, our talented editors who handle the often-thankless work of copy editing and production, and, of course, our readers. We plan to continue leading the field in IR/security studies with rigorous scholarship that challenges the conventional wisdom, identifies new threats and opportunities, engages with policy and theory, and illuminates history.”

The MIT Press journals team is small, with under 10 people working across production, sales, and marketing; but that small team collaborates with the editorial staff of 50 disparate journals to publish around 2,500 articles annually. “Some of the joy I take in editing International Security stems from working with the people at MIT Press,” Hazelton adds. “They are helpful and patient. They know what to do, and they do it.”

“The journals division at MIT Press has undergone significant change over the past decade — from business model upheaval and rapid technological advances to the ongoing challenge of competing with commercial publishers many times our size,” says Nick Lindsay, director of journals and institutional partnerships at the MIT Press. “Through it all, the journals group has adapted and evolved to meet those challenges and remains a home for experimentation and fair and equitable publishing.”

The MIT Press’ reputation for influential publishing has attracted many prestigious partners to its journals program, including Harvard University, the American Academy of Arts and Sciences, and the University of California at Berkeley. Amid this growth and development, the program continues to launch and support new journals in emerging and interdisciplinary fields while upholding the high editorial and publishing standards that have made it what it is today.

“Computational Linguistics has long stood for depth and rigor, and in a field that moves remarkably fast, our aspiration is for it to remain a home for work that lasts — scholarship the community can keep building on for years to come,” says Wei Lu, editor of Computational Linguistics. “We are very proud of this result, which reflects both the strength of the work our authors publish and the care our reviewers and editors bring to the journal. We are grateful to MIT Press for being such a steadfast partner.”

This strong performance extended well beyond the press’ three top-ranked publications. Transactions of the Association for Computational Linguistics was ranked 2nd in the Linguistics Category out of 312 journals; Global Environmental Politics was 2nd in the International Relations category out of 173 journals; and The Review of Economics and Statistics was 17th in the Economics category among 626 journals. Other highlights include Harvard Data Science Review ranking 7th in Statistics and Probability; European Societies ranking No. 13 in Sociology; and Neurobiology of Language ranking No. 13 in Psychology, Experimental.

Overall, 13 MIT Press journals earned impact factors that place them in the top quartile of their area of publishing, including: 

• Computational Linguistics 
• European Societies
• Evolutionary Computation 
• Global Environmental Politics
• Harvard Data Science Review
• International Security
• Journal of Cold War Studies
• The Journal of Interdisciplinary History
• Linguistic Inquiry
• Neurobiology of Language
• Quantitative Science Studies
• The Review of Economics and Statistics
• Transactions of the Association for Computational Linguistics

Together, these rankings point to the strong reputation that the MIT Press has built for its journals portfolio, a relatively small program that shapes conversations across the humanities, social sciences, and STEM fields.

This year the state of New York had the dubious honor of being the first to pass a controversial provision to mandate all 3D printers come with surveillance and censorship. That means not only is there a ticking clock to protect every artist, researcher, engineer, and hobbyist in the state, but there is a real risk of other states thoughtlessly following suit—prior to the New York rules even taking effect.

We, along with many other experts, already warned about this bill buried in the state’s crowded budget process. Hundreds of our supporters and 3D printing enthusiasts in New York reached out to their representatives hoping to kill this farcical bill. While there were some welcome amendments in response to the outcry, Albany passed it anyway.

It might be well-intentioned, but bills like these sell a fantasy that can only have an untold negative impact on the privacy, free expression, and consumer rights of anyone using these general purpose devices. Behind the banner of reducing gun violence, which is nearly always committed with commercial firearms, New York lawmakers have passed draconian legislation that will let manufacturers lock in users and collect their data.

Now that the bill has passed and been signed by Governor Hochul, let’s look at two important ways the final legislation changed since we last wrote about it, and why states like California shouldn’t make the same mistake.

Reduced Risk for Lawful File Sharing 

The New York bill includes language that criminalizes access to firearm print files, a proposal correctly dropped by states like Colorado due to First Amendment concerns. While this made it through to the passed legislation, a few wins were still gained.

Originally the legislation threatened felony charges for the storing and sharing of files, potentially impacting researchers, artists, and journalists with no intention of printing a firearm component. These charges were downgraded to a Class A misdemeanor.

Two provisions criminalized file sharing. The first of the two provisions criminalizing this file sharing, which pertains to the sale or distribution of files in the state, gained an important exception for when a sender has a reasonable belief that the recipient won’t illegally print these components. However the second provision, pertaining to criminalizing file possession, complicates this. Under 2.12 of the subpart, people who possess the file with intent to share the files do not clearly get this same reasonable belief exception.

In other words, if you share one of these files the actual sharing is covered by the exception, but the law makes it ambiguous whether possessing those same files is covered when you intend to share them.

While this exception could have created some breathing room for researchers and journalists operating in good faith, this slapdash bill language leaves plenty of ambiguity and potential speech-chilling effects. However, these changes do offer a modicum of harm reduction in this unconstitutional law.

Saving Face by Preserving Online Sale

Originally the bill had a strange requirement for all 3D printers and Computer Numerical Control, or CNC, machines to be sold and delivered face-to-face, with no exception. That would have meant a major barrier to access, particularly for people in agricultural and rural areas of the state who uniquely benefit from in-home fabrication and repair. It also would have meant a major inconvenience for businesses using these devices. For everyone though, it meant fewer retailers to choose from and facing more stigma for using these devices. 

Fortunately this was dropped from the bill entirely. 

Next Step: We Find Out What Was Actually Passed

In addition to being buried in the complicated legislative process of the NY budget and avoiding proper scrutiny, this bill also kicked the can down the road in determining what exactly is being mandated. In many respects, legislators passed a vibe. We’ll see how the actual law be developed over the next year by a working group with no mandated transparency to the public. Further, they have no obligation to ensure consumer safeguards in developing this state-mandated censorware.

We are still concerned by the possibility of a biased working group acting in the interest of manufacturers or facing pressure to accept consumer harms in the standards they produce. Our remaining hope is this working group convened by the Department of State and the state university system is composed of actual experts who are aware of how unfeasible and harmful this mandate is, and prevent it from being realized.

The Fight Continues

New York is the first to go down this path of state-mandated censorship and surveillance software on 3D printers, but it’s far from the only one to entertain it. It is now more urgent that we fiercely oppose this trend in other states, like California,  as they attempt to join the bandwagon—before even seeing the real-world impacts. 

Take action

Don’t Let California Repeat NY’s Mistake

We cannot allow this to be the foundation for future restrictions on speech and design, or serve as a playbook for the state and corporations to wrest control over our tools.

The wiring and rewiring of the brain never ends. Neural pathways are constantly being reshaped as we interact with the world and learn new things. At MIT’s McGovern Institute for Brain Research and York University in Toronto, Ontario, scientists are combining detailed analysis of brain activity with computational modeling to better understand that change.

McGovern Institute postdoc Lynn Sörensen, McGovern investigator and MIT Professor James DiCarlo, and York University Assistant Professor Kohitij Kar, worked together to compare what happened when animals and an artificial neural network with brain-like architecture were trained to visually identify the same objects. As the model’s performance improved, it reorganized itself in ways that closely paralleled changes the team detected in the animal brains.

Their open-access work, reported July 8 in the journal Nature Communications, shows how changes in visual processing support animals’ ability to learn to discriminate new kinds of objects. By modeling these changes, the researchers hope to better predict how training reshapes perception, which could one day inform educational strategies for a wide range of learners.

Subtle changes

Learning about a new object calls on many parts of the brain. Visual-processing areas work together to make sense of information taken in through the eyes, then communicate with other brain areas to give the visual information meaning and guide behavior. Multiple parts of this system likely change during learning, and the research team wanted a clearer understanding of how that change is distributed.

Neuroscientists have debated how much change occurs in the brain’s visual-processing areas when an animal learns to recognize new objects. Some suspected that visual-processing pathways remain largely unchanged during learning to avoid broadly disrupting visual perception, but others have reported changes in activity within dedicated visual-processing areas with this kind of learning in humans and other primates.  

To take a closer look, the team focused on neural activity in a key component of the brain’s visual object-processing network, the inferior temporal (IT) cortex. By the time visual information reaches the IT cortex, key object features are clearly represented — so much so that it’s possible to “decode” what object the subject is seeing and even predict what errors it’s likely to make in identifying it, simply by analyzing patterns of neural activity there.

The team recorded neural activity in the IT cortex from animals as they looked at and identified images of objects. Some of the animals were untrained, so the images they saw had little meaning to them. Others had already learned to identify similar objects, so they could usually discriminate between elephants, chairs, and other select objects, even when those objects were presented at different sizes, from different angles, or against different backgrounds than the ones they had seen before.

The broad pattern of activity in the IT cortex was largely similar in trained and untrained animals, suggesting that learning had not dramatically rewritten this high-level visual representation. Still, the group found subtle but reliable differences in the way neurons in the IT cortex responded to images in animals that had learned to recognize the kinds of objects they were shown, compared to the untrained animals.

Modeling learning

The group turned to computational models to investigate how those modest changes might contribute to learning. Sörensen trained a suite of artificial neural networks whose internal components had been mapped to the IT cortex to identify the same categories of objects the animals had seen. The models were designed to learn using gradient descent, meaning they continually improved their accuracy by adjusting their parameters in response to errors.

Only some of the animal models showed learning behavior that matched that of the subjects. In those that did, the IT-like stage changed in ways that resembled the learning-related changes the researchers had observed in the IT cortex of trained animals.

While gradient descent is commonly used to train artificial intelligence, it is generally considered biologically implausible as a direct model of how the brain learns. The researchers say the strong match in learning effects between the animals and their model demonstrates that these kinds of artificial neural networks can offer insights into biological learning at a useful level of abstraction, even if the brain does not learn in the same way.

“This shows that you can actually build in silico versions of future experiments,” Sörensen says. “I think that gives us this playground of asking ‘what if’ questions — and potentially predicting new things that go beyond the experimenter’s intuition.”

Most of the changes that allowed for learning in the model occurred outside of the IT cortex. “This tells us that there is a lot between the area we recorded from and the final behavioral readout that needs to change during this process,” Kar says. He adds that the team’s model will be useful as researchers look more deeply into how downstream brain areas contribute to learning.

The researchers stress that their study allowed more granular measurements of brain activity than would be possible in humans, and because the animal brains are organized similarly to our own, their experiments have direct relevance to human learning. They say understanding the impact of plasticity in the subjects’ IT cortex could help researchers design new learning strategies for humans.

“Our prior conceptual working model of you learning new objects was that your brain makes changes to synaptic connections that are largely downstream of your visual system, so you don’t destroy your visual system,” says DiCarlo, who is also the Peter de Florez Professor of Brain and Cognitive Sciences and director of the MIT Siegel Family Quest for Intelligence. “You wouldn’t want your whole visual system to become an elephant detector [just because you’ve learned to identify an elephant]. But this study went beyond that to say actually, when you learn ‘elephant,’ your IT does change a little bit to make it a little more relevant to elephants.”

That likely has consequences for recognizing other visual features, too. Subtle changes in the IT cortex that support elephant recognition might also make you better at identifying things other than elephants, DiCarlo says. Likewise, the same changes might make it a little harder to identify something else.

These kinds of consequences may be difficult to predict intuitively, but become obvious with computational modeling. For instance, the team’s models revealed that after learning to recognize new objects, the IT cortex contained more information about objects’ locations. By providing insights like these, models could aid the design of more effective training strategies for visual tasks, including for people with altered sensory processing, who may learn from visual information in atypical ways.

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 hardware. Manufacturing — not engineering design or analysis — remained the fundamental rate-limiting step,” says Professor Zolti Spakovszky, director of the MIT Gas Turbine Laboratory.

The teams, the tools, the task

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. 

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.  

At their disposal: MIT’s machine shops and manufacturing vendors; commercial software including Concepts NREC, SolidWorks, and ABAQUS; and various test rigs for characterizing and assembling individual components.

The teams also had access to MIT Parley, a newly launched platform that aggregates frontier large language models through a single interface. Through Parley, JARVIS leads could see directly how the students were using the AI tools, including their prompts, the cost per prompt, the specific LLMs being used, and other critical information. The JARVIS leads secured early access to Parley for all participants, and with financial support from MIT Lincoln Laboratory, the Department of Mechanical Engineering, and corporate sponsors Safran, Voyager Technologies, and Beehive Industries, students had access to essentially unlimited use of AI.

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. “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. 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.”

The faculty leadership — professors Zachary Cordero, Zolti Spakovszky, Masha Folk, and Andreea Bobu of the Department of Aeronautics and Astronautics, along with Lincoln Laboratory engineers and a team of teaching assistants — were there to ensure safety. In weekly progress reviews, they would critically evaluate the student progress and assess how the students were using AI.

Spakovszky developed a careful technique for guiding teams in the right direction without giving away answers or providing help. After a team’s presentation, he might ask: “Do you know what a rabbet fit is? Take in the comment.”

Where AI helps and hurts

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. 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 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.”

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.” 

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.”

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.

“The JARVIS Challenge showed what’s 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.”

At the vanguard of AI-native engineering

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. 

“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. 

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 taught me that getting value from AI takes two things: enough expertise to judge what it tells you and catch it when it’s 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’s 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’s 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. 

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.

“JARVIS highlighted the power of AI in the design of physical systems,” says Cordero, associate director of the MIT Gas Turbine Laboratory. “But it also showed that the key to unlocking that power is education, through coursework, internships, and hands-on extracurriculars like MIT Motorsports and Rocket Team. Performance in JARVIS correlated strongly with year in school. My main takeaway is that in the AI era, education is more valuable than ever.”

This is a current list of where and when I am scheduled to speak:

• I’m speaking (virtually) at the Policy-Relevant Privacy Research Workshop in Calgary, Canada, on Monday, July 20, 2026.
• I’m speaking at Boston Leadership Exchange in Boston, Massachusetts, USA, on Wednesday, July 22, 2026.
• I’m speaking at Cognitive Security Conference in Las Vegas, Nevada, USA. The conference runs August 6-7, 2026; my speaking time is TBD.
• I’m speaking at DEF CON 34 in Las Vegas, Nevada, USA. The conventions runs August 6-9, 2026; my speaking time is TBD...

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