Beautiful cars are sometimes likened to sculptures, but French sports car brand Alpine’s latest concept really is a sculpture. The full scale exhibit previews the look of next year’s new Alpine A110 and will be the focal point of the brand’s Paris Motor Show stand this October.

The sculpture reveals the design of the 2028 A110 without revealing too much, if that makes sense. It’s as if someone has dropped a silk sheet over the coupe’s bodywork, then pulled the air out from underneath it. What you see is the shape and contours of the aluminium skin and a hint at some of the details, while still leaving some mystery.

Related: Americans Might Force Alpine To Put Gas Back In Its New Electric Sports Car

Alpine says the tension visible in the blue veil is meant to look like it was caught by the wind, a nod to lightness and aerodynamics. It even claims one of the big inspirations was Concorde, the supersonic passenger jet co-created by Britain and Frances.

But that doesn’t mean Alpine has junked everything we loved about the outgoing A110, itself heavily inspired by the classic 1960s original. The V-shaped nose and dual light setup are still there, and though the sculpture tells us the new car will be leaner and possibly lower and longer, you’re not going to mistake it for anything else.

Design Evolution, Powertrain Revolution

Unless you’ve got your eyes closed when one drives past, in which case you might assume it was a runaway washing machine. Because the A110 is going electric. At the Goodwood Festival of Speed in July Alpine demo’d the new hardware in stretched version of the current A110 (below) called the A110 Future. That hardware includes a dual-motor rear axle with active torque vectoring and clever wheel-slip control. Today’s A110 is lauded for its agility, but the EV is shaping up to be even more mobile.

It’ll also have an unusual battery arrangement. Instead of having one massive battery under the floor of the APP platform, the new A110 will have two smaller batteries placed at each end of the chassis. That keeps the floorpan low and the driving position and roof, too. It will also help Alpine achieve a 40:60 front to rear weight distribution. An 800-volt electric system will ensure fast charging, though the exact speed and range figures are still secret. As is the weight, which is expected to balloon by 660 lbs (300 kg).

Electric First, Possible ICE Option Later

Also secret are Alpine’s intentions when it comes to combustion power. The A110 was conceived as an EV when it looked like electric cars were set to make gas ones a minority in short order. But while EV sales continue to grow in Europe, where they now account for one in four new cars, enthusiast buyers have signalled their preference for petrol engines. And it sounds like Alpine is listening.

“Before you ask, yes the platform is designed to accommodate an internal combustion engine,” Alpine’s CEO Philippe Krief told British magazine Autocar in March. At Goodwood, four months later, he said a decision over whether to make a petrol model will be made by the end of 2026. If it gets the green light it could take around two years to reach production.

Could Come To America

Although Krief gave no indication of which ICE motor might find its way into the A110’s chassis, there is one obvious source. Horse Powertrain, the company created by Alpine’s parent company Renault and China’s Geely, has developed multiple hybrid engines, one of which is good for 349 hp (354 PS) and 380 lb-ft (515 Nm).

A combustion engine option would be essential if Alpine ever wanted to bring the A110 to the US – where the current car was never available, and where EV sales are very weak. Expansion to America was previously baked into Alpine’s growth plans, but they were put on hold in 2025, in part due to the introduction of tariffs by President Trump. But Krief revealed this summer that the A110 was being put through US crash tests.

Alpine

The Genesis GV90 already has a ton of interesting features to talk about. One version has no visible B-pillar. It’s available with front seats that rotate 180 degrees. There’s even one with full-on airplane-style tray tables for rear-seat occupants. If those things weren’t luxury enough, it also has a trick infotainment screen. Park the electric SUV, and a hidden portion of the display rises out of the dashboard, exposing more screen real estate for movies and streaming. Genesis’ latest look behind the dash shows how much engineering it took to make that work.

In its normal position, the OLED display presents a wide, 23.6-inch view for driving information, navigation, and media. Press a button on the center fascia while parked, and it climbs 90 mm (3.5 inches), revealing the full 24.6-inch screen. The diagonal measurement barely changes, but Genesis says the usable viewing area grows by approximately 70 percent.

More: Genesis Could Give The GV90 A Gas Engine To Do What Cadillac’s EV Won’t

The expanded panel measures 584.1 mm by 219 mm (23.0 by 8.6 inches) and has a resolution of 3840 by 1440. The display switches to a Media Home interface for streaming services and live channels, while an available 25-speaker Bang & Olufsen system handles the sound. Genesis also fitted a 25-inch head-up display to keep key driving information in the driver’s view when the vehicle is moving. In other words, there are several things to pay attention. What’s perhaps most interesting is how engineers worked around challenges a moving screen presents.

The assembly uses a brushless motor, a rack-and-pinion gear, and guides to move the panel. Genesis says the added weight raised concerns about vibration, so engineers mounted the display directly to the cowl crossbar. The company says the mechanism completed more than 100,000 test cycles, with operating noise below 40 decibels across a range of temperatures. Those are Genesis’ claims. How it feels after years on rough roads is something owners will find out. How a third- or fourth-owner will handle things when a component inevitably fails is another matter.

Also worth mentioning are the two reasons why the screen only works when the GV90 is in park. The obvious one is that Genesis doesn’t want to distract drivers. The less obvious one is that it could interfere with the front airbag. To that end, it’ll retract itself if the driver begins moving the car when the screen is fully deployed.

The GV90 is the first Genesis to use the group’s Pleos Connect infotainment platform. Its wide driving layout gives the driver and front passenger separate menu bars, while physical buttons still provide shortcuts to apps and commonly used vehicle settings. That mix all sounds sensible on paper. We can’t wait to see how owners find the experience. Hopefully, we’ll get a chance to test it ourselves soon.

Photos: Genesis

The Porsche Cayenne Electric is learning some new tricks, including some aimed squarely at drivers who find closing doors and connecting charging cables unduly exhausting. For the new model year, Porsche is adding electrically operated doors, wireless home charging, and an even more elaborate massage system. Buyers can also select from 105 exterior colors. Of those features, however, it’s the self-closing driver’s door that may attract the most attention.

The optional powered doors use servo motors to open and close automatically after a brief pull of the handle. They can also be controlled through buttons inside the cabin, the infotainment system, exterior handles, or Porsche’s Digital Key. Drivers won’t necessarily need to reach for any of those controls, as the Cayenne can close its driver’s door when the seat belt is fastened, or the brake pedal is pressed, making the start-up process feel a little more private jet than family SUV. They also feature one cool and vital trick.

More: This Cayenne V8 Convertible’s Wood Is Fake, Its $11,315 Price Is Not

Sensors monitor the surrounding area and halt the doors if they detect a person or obstacle. The system also works with the vehicle’s exit warning technology to prevent a door from opening into approaching traffic. Porsche says the powered mechanism can function on inclines, while conventional manual operation remains available.

Another new option removes the charging cable from the equation. Porsche Wireless Charging uses a weatherproof ground pad that can be installed indoors or outside. Once the Cayenne is positioned correctly, charging starts automatically at up to 11 kW. Porsche claims approximately 90 percent energy-transfer efficiency.

Motion sensors and foreign-object detection can interrupt charging if a person or animal approaches, or if a metallic object lands on the pad and begins heating up. The system is now available to order in Germany and certain European markets, with a broader rollout planned in stages. Porsche has not yet detailed US availability or pricing.

Massage Seats That Feel The Music

The front seats can now be fitted with 16 air cushions and six sound actuators apiece. Five massage programs are offered, while the actuators produce vibrations synchronized with music for what Porsche calls a “4D” audio experience. Three additional wellness modes combine massage, vibration, and curated soundscapes.

Finally, Paint to Sample adds 92 shades to the Cayenne Electric’s 13 regular colors, bringing the total to 105. Porsche hasn’t revealed what all these indulgences will cost, but expecting restraint from the options sheet would probably be optimistic.

Photos: Porsche

Jaguar Land Rover isn’t concerned about vehicles emerging from China with designs suspiciously similar to its own, noting they cannot match the heritage of Range Rover and have so far yet to impact the brand’s sales.

Of all the new SUVs being launched by Chinese brands, none have drawn more comparisons to existing Range Rover models than those from Chery-owned Jaecoo, specifically the J5 and the J7, often labeled as the ‘Temu Range Rover.’ We tested the J5 recently, though it obviously doesn’t compete in the same segment as any Range Rover. Speaking with Australian media recently, global Range Rover managing director Martin Limpert said if anything, having its designs copied is a “compliment.”

Read: They Nicknamed It The Temu Range Rover, Car Insurers Are Pricing It Like The Real One

“If other brands… copy our designs [it] is a signal that within the luxury world our design seems to be inspirational… what is important for us is to stay [as] the original – that’s why we will keep evolving,” Limpert told Car Sales. “It’s probably a bit of a badge of honor – we take it as a compliment.”

Range Rover Still Stands Out

Jaecoo 7

Limpert went on to add that people buy a Range Rover because “they want the best of the best,” and they’re willing to “pay the extra money” for it.

“We are operating from a position of strength… having that British originality, design and engineering ingenuity built over more than five decades and five generations of Range Rover has specifically brought us into a position where we are thankfully synonymous with the choice of [world] leaders.”

As impressive as some new Chinese vehicles are, they obviously lack the heritage and name cachet of some legacy brands. However, recent sales trends show that car buyers in the lower end of the market are buying Chinese cars in growing numbers, often won over by their value. Buyers in the premium and luxury segments are very loyal to the brands they like, however, and it won’t be easy for new entrants to sway shoppers out of car companies they already trust.

Moving forward, Range Rover’s global marketing boss Ryan Miller added that the company will continue to differentiate its models from the competition. One obvious example of this is the new low-slung GT, set to launch soon as the brand’s fifth model.

Earlier this year, China’s Luxeed brand made a claim it didn’t expect to have to back up, announcing that the RX, its answer to the Xiaomi YU7, had been penned by a former Ferrari designer. Ferrari’s Chinese arm read that and asked the obvious follow-up question, which was, fine, what’s his name? Luxeed has supplied one, and while it was at it, the company also put a starting price on the electric SUV.

The name came from Zhao Changjiang, executive director and executive vice president of Luxeed, in a Weibo post rounding up the brand’s latest progress. The designer is Werner Gruber, and the resume holds up. He spent time at Ferrari and worked on the LaFerrari, the FXX-K, and the Portofino, which is a strong set of credits. While the RX certainly has plenty of similarities to other performance SUVs like Ferrari’s own Purosangue and the Xiaomi YU7, there’s no denying it looks nice.

Read: China Built A 585-HP EV That Looks Like A Ferrari And Sounds Like A Lexus

Luxeed was created through a partnership between Chery and Huawei, and pre-sales of the RX kicked off in China earlier this week. Prices start at 299,800 yuan ($44,550), making it more expensive than the YU7, which starts at 253,500 yuan ($37,700), yet compared with the prices we pay for high-performance electric SUVs in the West, the RX remains relatively cheap.

Several versions are available, provided with 81.1 kWh, 100.25 kWh, and 100.42 kWh battery packs. Entry-level models feature a single rear-mounted electric motor producing 371 hp, while power-hungry buyers can also order the RX with dual motors producing 586 hp and all-wheel drive.

A Chassis From AMG’s Former Boss

It’s not just impressive performance that could win over buyers. The RX also comes equipped with 38 sensors, including four LiDARs, enabling autonomous driving between addresses. Importantly, the system officially remains at Level 2, so drivers will need to remain attentive and ready to take over control at a moment’s notice.

As it turns out, Gruber isn’t the only European industry veteran to have a hand in bringing the RX to life. In the same social media post, Zhao said that former Aston Martin chief executive and ex-AMG boss Tobias Moers tuned the chassis in a new role as chief dynamic performance advisor for the Luxeed brand, and added that former BMW engineers have joined the effort as well.

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As AI Energy Demands Grow, Improved Power Electronics Will Be Needed As AI becomes integrated into daily processes and data centers are built...

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On May 6, MIT AgeLab’s Advanced Vehicle Technology (AVT) Consortium, part of the MIT Center for Transportation and Logistics, celebrated 10 years of its global academic-industry collaboration. AVT was founded with the aim of developing new data that contribute to automotive manufacturers, suppliers, and insurers’ real-world understanding of how drivers use and respond to increasingly sophisticated vehicle technologies, such as assistive and automated driving, while accelerating the applied insight needed to advance design and development. The celebration event brought together stakeholders from across the industry for a set of keynote addresses and panel discussions on critical topics significant to the industry and its future, including artificial intelligence, automotive technology, collision repair, consumer behavior, sustainability, vehicle safety policy, and global competitiveness.

Bryan Reimer, founder and co-director of the AVT Consortium, opened the event by remarking that over the decade AVT has collected hundreds of terabytes of data, presented and discussed research with its over 25 member organizations, supported members’ strategic and policy initiatives, published select outcomes, and built AVT into a global influencer with tremendous impact in the automotive industry. He noted that current opportunities and challenges for the industry include distracted driving, a lack of consumer trust and concerns around transparency in assistive and automated driving features, and high consumer expectations for vehicle technology, safety, and affordability. How will industry respond? Major players in attendance weighed in.

In a powerful exchange on vehicle safety regulation, John Bozzella, president and CEO of the Alliance for Automotive Innovation, and Mark Rosekind, former chief safety innovation officer of Zoox, former administrator of the National Highway Traffic Safety Administration, and former member of the National Transportation Safety Board, challenged industry and government to adopt a more strategic, data-driven, and collaborative approach to safety. They asserted that regulation must evolve alongside innovation, not lag behind it by decades. Appealing to the automakers in attendance, Bozzella cited the success of voluntary commitments on automatic emergency braking as a model for future progress. “That’s a way to do something important and impactful ahead of regulation.” They advocated for shared data platforms, anonymous reporting, and a common regulatory vision that sets safety baselines while allowing room for experimentation. The 40,000 annual road fatalities demand urgency — what’s needed is a move away from tactical fixes and toward a systemic safety strategy. “Safety delayed is safety denied,” Rosekind stated. “Tell me how you’re going to improve safety. Let’s be explicit.”

Drawing inspiration from aviation’s exemplary safety record, Kathy Abbott, chief scientific and technical advisor for the Federal Aviation Administration, pointed to a culture of rigorous regulation, continuous improvement, and cross-sectoral data sharing. Aviation’s model, built on highly trained personnel and strict predictability standards, contrasts sharply with the fragmented approach in the automotive industry. The keynote emphasized that a foundation of safety culture — one that recognizes that technological ability alone isn’t justification for deployment — must guide the auto industry forward. Just as aviation doesn’t equate absence of failure with success, vehicle safety must be measured holistically and proactively.

With assistive and automated driving top of mind in the industry, Pete Bigelow of Automotive News offered a pragmatic diagnosis. With companies like Ford and Volkswagen stepping back from full autonomy projects like Argo AI, the industry is now focused on Level 2 and 3 technologies, which refer to assisted and automated driving, respectively. Tesla, GM, and Mercedes are experimenting with subscription models for driver assistance systems, yet consumer confusion remains high. JD Power reports that many drivers do not grasp the differences between L2 and L2+, or whether these technologies offer safety or convenience features. Safety benefits have yet to manifest in reduced traffic deaths, which have risen by 20 percent since 2020. The recurring challenge: L3 systems demand that human drivers take over during technical difficulties, despite driver disengagement being their primary benefit, potentially worsening outcomes. Bigelow cited a quote from Bryan Reimer as one of the best he’s received in his career: “Level 3 systems are an engineer’s dream and a plaintiff attorney’s next yacht,” highlighting the legal and design complexity of systems that demand handoffs between machine and human.

In terms of the impact of AI on the automotive industry, Mauricio Muñoz, senior research engineer at AI Sweden, underscored that despite AI’s transformative potential, the automotive industry cannot rely on general AI megatrends to solve domain-specific challenges. While landmark achievements like AlphaFold demonstrate AI’s prowess, automotive applications require domain expertise, data sovereignty, and targeted collaboration. Energy constraints, data firewalls, and the high costs of AI infrastructure all pose limitations, making it critical that companies fund purpose-driven research that can reduce costs and improve implementation fidelity. Muñoz warned that while excitement abounds — with some predicting artificial superintelligence by 2028 — real progress demands organizational alignment and a deep understanding of the automotive context, not just computational power.

Turning the focus to consumers, a collision repair panel drawing Richard Billyeald from Thatcham Research, Hami Ebrahimi from Caliber Collision, and Mike Nelson from Nelson Law explored the unintended consequences of vehicle technology advances: spiraling repair costs, labor shortages, and a lack of repairability standards. Panelists warned that even minor repairs for advanced vehicles now require costly and complex sensor recalibrations — compounded by inconsistent manufacturer guidance and no clear consumer alerts when systems are out of calibration. The panel called for greater standardization, consumer education, and repair-friendly design. As insurance premiums climb and more people forgo insurance claims, the lack of coordination between automakers, regulators, and service providers threatens consumer safety and undermines trust. The group warned that until Level 2 systems function reliably and affordably, moving toward Level 3 autonomy is premature and risky.

While the repair panel emphasized today’s urgent challenges, other speakers looked to the future. Honda’s Ryan Harty, for example, highlighted the company’s aggressive push toward sustainability and safety. Honda aims for zero environmental impact and zero traffic fatalities, with plans to be 100 percent electric by 2040 and to lead in energy storage and clean power integration. The company has developed tools to coach young drivers and is investing in charging infrastructure, grid-aware battery usage, and green hydrogen storage. “What consumers buy in the market dictates what the manufacturers make,” Harty noted, underscoring the importance of aligning product strategy with user demand and environmental responsibility. He stressed that manufacturers can only decarbonize as fast as the industry allows, and emphasized the need to shift from cost-based to life-cycle-based product strategies.

Finally, a panel involving Laura Chace of ITS America, Jon Demerly of Qualcomm, Brad Stertz of Audi/VW Group, and Anant Thaker of Aptiv covered the near-, mid-, and long-term future of vehicle technology. Panelists emphasized that consumer expectations, infrastructure investment, and regulatory modernization must evolve together. Despite record bicycle fatality rates and persistent distracted driving, features like school bus detection and stop sign alerts remain underutilized due to skepticism and cost. Panelists stressed that we must design systems for proactive safety rather than reactive response. The slow integration of digital infrastructure — sensors, edge computing, data analytics — stems not only from technical hurdles, but procurement and policy challenges as well. 

Reimer concluded the event by urging industry leaders to re-center the consumer in all conversations — from affordability to maintenance and repair. With the rising costs of ownership, growing gaps in trust in technology, and misalignment between innovation and consumer value, the future of mobility depends on rebuilding trust and reshaping industry economics. He called for global collaboration, greater standardization, and transparent innovation that consumers can understand and afford. He highlighted that global competitiveness and public safety both hang in the balance. As Reimer noted, “success will come through partnerships” — between industry, academia, and government — that work toward shared investment, cultural change, and a collective willingness to prioritize the public good.

© Photo: Kelly Davidson Studio

Across a career’s worth of pioneering product designs, Doug Field’s work has shaped the experience of anyone who’s ever used a MacBook Air, ridden a Segway, or driven a Tesla Model 3.

But his newest project is his most ambitious yet: reinventing the Ford automobile, one of the past century’s most iconic pieces of technology.

As Ford’s chief electric vehicle (EV), digital, and design officer, Field is tasked with leading the development of the company’s electric vehicles, while making new software platforms central to all Ford models.

To bring Ford Motor Co. into that digital and electric future, Field effectively has to lead a fast-moving startup inside the legacy carmaker. “It is incredibly hard, figuring out how to do ‘startups’ within large organizations,” he concedes.

If anyone can pull it off, it’s likely to be Field. Ever since his time in MIT’s Leaders for Global Operations (then known as “Leaders in Manufacturing”) program studying organizational behavior and strategy, Field has been fixated on creating the conditions that foster innovation.

“The natural state of an organization is to make it harder and harder to do those things: to innovate, to have small teams, to go against the grain,” he says. To overcome those forces, Field has become a master practitioner of the art of curating diverse, talented teams and helping them flourish inside of big, complex companies.

“It’s one thing to make a creative environment where you can come up with big ideas,” he says. “It’s another to create an execution-focused environment to crank things out. I became intrigued with, and have been for the rest of my career, this question of how can you have both work together?”

Three decades after his first stint as a development engineer at Ford Motor Co., Field now has a chance to marry the manufacturing muscle of Ford with the bold approach that helped him rethink Apple’s laptops and craft Tesla’s Model 3 sedan. His task is nothing less than rethinking how cars are made and operated, from the bottom up.

“If it’s only creative or execution, you’re not going to change the world,” he says. “If you want to have a huge impact, you need people to change the course you’re on, and you need people to build it.”

A passion for design

From a young age, Field had a fascination with automobiles. “I was definitely into cars and transportation more generally,” he says. “I thought of cars as the place where technology and art and human design came together — cars were where all my interests intersected.”

With a mother who was an artist and musician and an engineer father, Field credits his parents’ influence for his lifelong interest in both the aesthetic and technical elements of product design. “I think that’s why I’m drawn to autos — there’s very much an aesthetic aspect to the product,” he says. 

After earning a degree in mechanical engineering from Purdue University, Field took a job at Ford in 1987. The big Detroit automakers of that era excelled at mass-producing cars, but weren’t necessarily set up to encourage or reward innovative thinking. Field chafed at the “overstructured and bureaucratic” operational culture he encountered.

The experience was frustrating at times, but also valuable and clarifying. He realized that he “wanted to work with fast-moving, technology-based businesses.”

“My interest in advancing technical problem-solving didn’t have a place in the auto industry” at the time, he says. “I knew I wanted to work with passionate people and create something that didn’t exist, in an environment where talent and innovation were prized, where irreverence was an asset and not a liability. When I read about Silicon Valley, I loved the way they talked about things.”

During that time, Field took two years off to enroll in MIT’s LGO program, where he deepened his technical skills and encountered ideas about manufacturing processes and team-driven innovation that would serve him well in the years ahead.

“Some of core skill sets that I developed there were really, really important,” he says, “in the context of production lines and production processes.” He studied systems engineering and the use of Monte Carlo simulations to model complex manufacturing environments. During his internship with aerospace manufacturer Pratt & Whitney, he worked on automated design in computer-aided design (CAD) systems, long before those techniques became standard practice.

Another powerful tool he picked up was the science of probability and statistics, under the tutelage of MIT Professor Alvin Drake in his legendary course 6.041/6.431 (Probabilistic Systems Analysis). Field would go on to apply those insights not only to production processes, but also to characterizing variability in people’s aptitudes, working styles, and talents, in the service of building better, more innovative teams. And studying organizational strategy catalyzed his career-long interest in “ways to look at innovation as an outcome, rather than a random spark of genius.”

“So many things I was lucky to be exposed to at MIT,” Field says, were “all building blocks, pieces of the puzzle, that helped me navigate through difficult situations later on.”

Learning while leading

After leaving Ford in 1993, Field worked at Johnson and Johnson Medical for three years in process development. There, he met Segway inventor Dean Kamen, who was working on a project called the iBOT, a gyroscopic powered wheelchair that could climb stairs.

When Kamen spun off Segway to develop a new personal mobility device using the same technology, Field became his first hire. He spent nearly a decade as the firm’s chief technology officer.

At Segway, Field’s interests in vehicles, technology, innovation, process, and human-centered design all came together.

“When I think about working now on electric cars, it was a real gift,” he says. The problems they tackled prefigured the ones he would grapple with later at Tesla and Ford. “Segway was very much a precursor to a modern EV. Completely software controlled, with higher-voltage batteries, redundant systems, traction control, brushless DC motors — it was basically a miniature Tesla in the year 2000.”

At Segway, Field assembled an “amazing” team of engineers and designers who were as passionate as he was about pushing the envelope. “Segway was the first place I was able to hand-pick every single person I worked with, define the culture, and define the mission.”

As he grew into this leadership role, he became equally engrossed with cracking another puzzle: “How do you prize people who don’t fit in?”

“Such a fundamental part of the fabric of Silicon Valley is the love of embracing talent over a traditional organization’s ways of measuring people,” he says. “If you want to innovate, you need to learn how to manage neurodivergence and a very different set of personalities than the people you find in large corporations.”

Field still keeps the base housing of a Segway in his office, as a reminder of what those kinds of teams — along with obsessive attention to detail — can achieve.

Before joining Apple in 2008, he showed that component, with its clean lines and every minuscule part in its place in one unified package, to his prospective new colleagues. “They were like, “OK, you’re one of us,’” he recalls.

He soon became vice president of hardware development for all Mac computers, leading the teams behind the MacBook Air and MacBook Pro and eventually overseeing more than 2,000 employees. “Making things really simple and really elegant, thinking about the product as an integrated whole, that really took me into Apple.”

The challenge of giving the MacBook Air its signature sleek and light profile is an example.

“The MacBook Air was the first high-volume consumer electronic product built out of a CNC-machined enclosure,” says Field. He worked with industrial design and technology teams to devise a way to make the laptop from one solid piece of aluminum and jettison two-thirds of the parts found in the iMac. “We had material cut away so that every single screw and piece of electronics sat down into it an integrated way. That’s how we got the product so small and slim.”

“When I interviewed with Jony Ive” — Apple’s legendary chief design officer — “he said your ability to zoom out and zoom in was the number one most important ability as a leader at Apple.” That meant zooming out to think about “the entire ethos of this product, and the way it will affect the world” and zooming all the way back in to obsess over, say, the physical shape of the laptop itself and what it feels like in a user’s hands.

“That thread of attention to detail, passion for product, design plus technology rolled directly into what I was doing at Tesla,” he says. When Field joined Tesla in 2013, he was drawn to the way the brash startup upended the approach to making cars. “Tesla was integrating digital technology into cars in a way nobody else was. They said, ‘We’re not a car company in Silicon Valley, we’re a Silicon Valley company and we happen to make cars.’”

Field assembled and led the team that produced the Model 3 sedan, Tesla’s most affordable vehicle, designed to have mass-market appeal.

That experience only reinforced the importance, and power, of zooming in and out as a designer — in a way that encompasses the bigger human resources picture.

“You have to have a broad sense of what you’re trying to accomplish and help people in the organization understand what it means to them,” he says. “You have to go across and understand operations enough to glue all of those (things) together — while still being great at and focused on something very, very deeply. That’s T-shaped leadership.”

He credits his time at LGO with providing the foundation for the “T-shaped leadership” he practices.

“An education like the one I got at MIT allowed me to keep moving that ‘T’, to focus really deep, learn a ton, teach as much as I can, and after something gets more mature, pull out and bed down into other areas where the organization needs to grow or where there’s a crisis.”

The power of marrying scale to a “startup mentality”

In 2018, Field returned to Apple as a vice president for special projects. “I left Tesla after Model 3 and Y started to ramp, as there were people better than me to run high-volume manufacturing,” he says. “I went back to Apple hoping what Tesla had learned would motivate Apple to get into a different market.”

That market was his early love: cars. Field quietly led a project to develop an electric vehicle at Apple for three years.

Then Ford CEO Jim Farley came calling. He persuaded Field to return to Ford in late 2021, partly by demonstrating how much things had changed since his first stint as the carmaker.

“Two things came through loud and clear,” Field says. “One was humility. ‘Our success is not assured.’” That attitude was strikingly different from Field’s early experience in Detroit, encountering managers who were resistant to change. “The other thing was urgency. Jim and Bill Ford said the exact same thing to me: ‘We have four or five years to completely remake this company.’”

“I said, ‘OK, if the top of company really believes that, then the auto industry may be ready for what I hope to offer.’”

So far, Field is energized and encouraged by the appetite for reinvention he’s encountered this time around at Ford.

“If you can combine what Ford does really well with what a Tesla or Rivian can do well, this is something to be reckoned with,” says Field. “Skunk works have become one of the fundamental tools of my career,” he says, using an industry term that describes a project pursued by a small, autonomous group of people within a larger organization.

Ford has been developing a new, lower-cost, software-enabled EV platform — running all of the car’s sensors and components from a central digital operating system — with a “skunk works” team for the past two years. The company plans to build new sedans, SUVs, and small pickups based on this new platform.

With other legacy carmakers like Volvo racing into the electric future and fierce competition from EV leaders Tesla and Rivian, Field and his colleagues have their work cut out for them.

If he succeeds, leveraging his decades of learning and leading from LGO to Silicon Valley, then his latest chapter could transform the way we all drive — and secure a spot for Ford at the front of the electric vehicle pack in the process.

“I’ve been lucky to feel over and over that what I’m doing right now — they are going to write a book about it,” say Field. “This is a big deal, for Ford and the U.S. auto industry, and for American industry, actually.”

© Photo courtesy of the Ford Motor Co.

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.

© Credit: Courtesy of Mohamed Elrefaie

As a major contributor to global carbon dioxide (CO₂) emissions, the transportation sector has immense potential to advance decarbonization. However, a zero-emissions global supply chain requires re-imagining reliance on a heavy-duty trucking industry that emits 810,000 tons of CO₂, or 6 percent of the United States’ greenhouse gas emissions, and consumes 29 billion gallons of diesel annually in the U.S. alone.

A new study by MIT researchers, presented at the recent American Society of Mechanical Engineers 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, quantifies the impact of a zero-emission truck’s design range on its energy storage requirements and operational revenue. The multivariable model outlined in the paper allows fleet owners and operators to better understand the design choices that impact the economic feasibility of battery-electric and hydrogen fuel cell heavy-duty trucks for commercial application, equipping stakeholders to make informed fleet transition decisions.

“The whole issue [of decarbonizing trucking] is like a very big, messy pie. One of the things we can do, from an academic standpoint, is quantify some of those pieces of pie with modeling, based on information and experience we’ve learned from industry stakeholders,” says ZhiYi Liang, PhD student on the renewable hydrogen team at the MIT K. Lisa Yang Global Engineering and Research Center (GEAR) and lead author of the study. Co-authored by Bryony DuPont, visiting scholar at GEAR, and Amos Winter, the Germeshausen Professor in the MIT Department of Mechanical Engineering, the paper elucidates operational and socioeconomic factors that need to be considered in efforts to decarbonize heavy-duty vehicles (HDVs).

Operational and infrastructure challenges

The team’s model shows that a technical challenge lies in the amount of energy that needs to be stored on the truck to meet the range and towing performance needs of commercial trucking applications. Due to the high energy density and low cost of diesel, existing diesel drivetrains remain more competitive than alternative lithium battery-electric vehicle (Li-BEV) and hydrogen fuel-cell-electric vehicle (H2 FCEV) drivetrains. Although Li-BEV drivetrains have the highest energy efficiency of all three, they are limited to short-to-medium range routes (under 500 miles) with low freight capacity, due to the weight and volume of the onboard energy storage needed. In addition, the authors note that existing electric grid infrastructure will need significant upgrades to support large-scale deployment of Li-BEV HDVs.

While the hydrogen-powered drivetrain has a significant weight advantage that enables higher cargo capacity and routes over 750 miles, the current state of hydrogen fuel networks limits economic viability, especially once operational cost and projected revenue are taken into account. Deployment will most likely require government intervention in the form of incentives and subsidies to reduce the price of hydrogen by more than half, as well as continued investment by corporations to ensure a stable supply. Also, as H2-FCEVs are still a relatively new technology, the ongoing design of conformal onboard hydrogen storage systems — one of which is the subject of Liang’s PhD — is crucial to successful adoption into the HDV market.

The current efficiency of diesel systems is a result of technological developments and manufacturing processes established over many decades, a precedent that suggests similar strides can be made with alternative drivetrains. However, interactions with fleet owners, automotive manufacturers, and refueling network providers reveal another major hurdle in the way that each “slice of the pie” is interrelated — issues must be addressed simultaneously because of how they affect each other, from renewable fuel infrastructure to technological readiness and capital cost of new fleets, among other considerations. And first steps into an uncertain future, where no one sector is fully in control of potential outcomes, is inherently risky. 

“Besides infrastructure limitations, we only have prototypes [of alternative HDVs] for fleet operator use, so the cost of procuring them is high, which means there isn’t demand for automakers to build manufacturing lines up to a scale that would make them economical to produce,” says Liang, describing just one step of a vicious cycle that is difficult to disrupt, especially for industry stakeholders trying to be competitive in a free market. 

Quantifying a path to feasibility

“Folks in the industry know that some kind of energy transition needs to happen, but they may not necessarily know for certain what the most viable path forward is,” says Liang. Although there is no singular avenue to zero emissions, the new model provides a way to further quantify and assess at least one slice of pie to aid decision-making.

Other MIT-led efforts aimed at helping industry stakeholders navigate decarbonization include an interactive mapping tool developed by Danika MacDonell, Impact Fellow at the MIT Climate and Sustainability Consortium (MCSC); alongside Florian Allroggen, executive director of MITs Zero Impact Aviation Alliance; and undergraduate researchers Micah Borrero, Helena De Figueiredo Valente, and Brooke Bao. The MCSC’s Geospatial Decision Support Tool supports strategic decision-making for fleet operators by allowing them to visualize regional freight flow densities, costs, emissions, planned and available infrastructure, and relevant regulations and incentives by region.

While current limitations reveal the need for joint problem-solving across sectors, the authors believe that stakeholders are motivated and ready to tackle climate problems together. Once-competing businesses already appear to be embracing a culture shift toward collaboration, with the recent agreement between General Motors and Hyundai to explore “future collaboration across key strategic areas,” including clean energy. 

Liang believes that transitioning the transportation sector to zero emissions is just one part of an “energy revolution” that will require all sectors to work together, because “everything is connected. In order for the whole thing to make sense, we need to consider ourselves part of that pie, and the entire system needs to change,” says Liang. “You can’t make a revolution succeed by yourself.” 

The authors acknowledge the MIT Climate and Sustainability Consortium for connecting them with industry members in the HDV ecosystem; and the MIT K. Lisa Yang Global Engineering and Research Center and MIT Morningside Academy for Design for financial support.

© Photo: Bob Adams/Flickr